The Gene Ontology (GO) database and informatics resource

PLOS Computational Biology, 2009

In recent years, ontologies have become a mainstream topic in biomedical research. When biological entities are described using a common schema, such as an ontology, they can be compared by means of their annotations. This type of comparison is called semantic similarity, since it assesses the degree of relatedness between two entities by the similarity in meaning of their annotations. The application of semantic similarity to biomedical ontologies is recent; nevertheless, several studies have been published in the last few years describing and evaluating diverse approaches. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization. We review semantic similarity measures applied to biomedical ontologies and propose their classification according to the strategies they employ: node-based versus edge-based and pairwise versus groupwise. We also present comparative assessment studies and discuss the implications of their results. We survey the existing implementations of semantic similarity measures, and we describe examples of applications to biomedical research. This will clarify how biomedical researchers can benefit from semantic similarity measures and help them choose the approach most suitable for their studies. Biomedical ontologies are evolving toward increased coverage, formality, and integration, and their use for annotation is increasingly becoming a focus of both effort by biomedical experts and application of automated annotation procedures to create corpora of higher quality and completeness than are currently available. Given that semantic similarity measures are directly dependent on these evolutions, we can expect to see them gaining more relevance and even becoming as essential as sequence similarity is today in biomedical research.

Weed Science, 2007

Genomics programs in the weed science community have not developed as rapidly as that of other crop, horticultural, forestry, and model plant systems. Development of genomic resources for selected model weeds are expected to enhance our understanding of weed biology, just as they have in other plant systems. In this report, we describe the development, characteristics, and information gained from an expressed sequence tag (EST) database for the perennial weed leafy spurge. ESTs were obtained using a normalized cDNA library prepared from a comprehensive collection of tissues. During the EST characterization process, redundancy was minimized by periodic subtractions of the normalized cDNA library. A sequencing success rate of 88% yielded 45,314 ESTs with an average read length of 671 nucleotides. Using bioinformatic analysis, the leafy spurge EST database was assembled into 23,472 unique sequences representing 19,015 unigenes (10,293 clusters and 8,722 singletons). Blast similarity searches to the GenBank nonredundant protein database identified 18,186 total matches, of which 14,205 were nonredundant. These data indicate that 77.4% of the 23,472 unique sequences and 74.7% of the 19,015 unigenes are similar to other known proteins. Further bioinformatics analysis indicated that 2,950, or 15.5%, of the unigenes have previously not been identified suggesting that some may be novel to leafy spurge. Functional classifications assigned to leafy spurge unique sequences using Munich Information Center for Protein or Gene Ontology were proportional to functional classifications for genes of arabidopsis, with the exception of unclassified or unknowns and transposable elements which were significantly reduced in leafy spurge. Although these EST resources have been developed for the purpose of constructing high-density leafy spurge microarrays, they are already providing valuable information related to sugar metabolism, cell cycle regulation, dormancy, terpenoid secondary metabolism, and flowering. Nomenclature: Leafy spurge, Euphorbia esula L. EPHES; arabidopsis, Arabidopsis thaliana (L.) Heynh.

BMC Systems Biology, 2014

The oral cavity is a complex ecosystem where human chemical compounds coexist with a particular microbiota. However, shifts in the normal composition of this microbiota may result in the onset of oral ailments, such as periodontitis and dental caries. In addition, it is known that the microbial colonization of the oral cavity is mediated by protein-protein interactions (PPIs) between the host and microorganisms. Nevertheless, this kind of PPIs is still largely undisclosed. To elucidate these interactions, we have created a computational prediction method that allows us to obtain a first model of the Human-Microbial oral interactome.

Science Signaling, 2005

Text mining for metabolic pathways, signaling cascades, and protein networks. Sci. STKE 2005, pe21 (2005).

PloS one, 2014

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of...

Frontiers in microbiology, 2014

Dramatic increases in research in the area of microbial biofuel production coupled with high-throughput data generation on bioenergy-related microbes has led to a deluge of information in the scientific literature and in databases. Consolidating this information and making it easily accessible requires a unified vocabulary. The Gene Ontology (GO) fulfills that requirement, as it is a well-developed structured vocabulary that describes the activities and locations of gene products in a consistent manner across all kingdoms of life. The Microbial ENergy processes Gene Ontology () project is extending the GO to include new terms to describe microbial processes of interest to bioenergy production. Our effort has added over 600 bioenergy related terms to the Gene Ontology. These terms will aid in the comprehensive annotation of gene products from diverse energy-related microbial genomes. An area of microbial energy research that has received a lot of attention is microbial production of ...

PLoS ONE, 2012

Background: The basally divergent phylogenetic position of amphioxus (Cephalochordata), as well as its conserved morphology, development and genetics, make it the best proxy for the chordate ancestor. Particularly, studies using the amphioxus model help our understanding of vertebrate evolution and development. Thus, interest for the amphioxus model led to the characterization of both the transcriptome and complete genome sequence of the American species, Branchiostoma floridae. However, recent technical improvements allowing induction of spawning in the laboratory during the breeding season on a daily basis with the Mediterranean species Branchiostoma lanceolatum have encouraged European Evo-Devo researchers to adopt this species as a model even though no genomic or transcriptomic data have been available. To fill this need we used the pyrosequencing method to characterize the B. lanceolatum transcriptome and then compared our results with the published transcriptome of B. floridae.

Physiological Genomics, 2008

In whole genome microarray studies major gene expression changes are easily identified, but it is a challenge to capture small, but biologically important, changes. Pathway based programs can capture small effects, but may have the disadvantage to be restricted to functionally annotated genes. A structured approach towards the identification of major and small changes for interpretation of biological effects is needed. 45

PROTEOMICS, 2009

While Listeria monocytogenes is responsible for listeriosis, it is also a saprophytic species with exceptional survival aptitudes. Secreted proteins are one of the main tools used by bacteria to interact with their environment. In order to take into account the biodiversity of L. monocytogenes species, exoproteomic analysis was carried out on 12 representative strains. Following 2-DE and MALDI-TOF MS, a total of 151 spots were identified and corresponded to 60 non-orthologous proteins. To categorize and analyze these proteomic data, a rational bioinformatic approach predicting final subcellular localization was carried out. Fifty-two out of the 60 proteins identified (86.7%) were indeed predicted as localized in the extracellular milieu (gene ontology (GO): 0005576). Most of them (65.4%) were actually predicted as secreted via the Sec translocon. Comparative analysis allowed for proteins found in all or only in a subset of L. monocytogenes strains to be defined. While the core exoproteome included most proteins related to bacterial virulence, cell wall biogenesis, as well as proteins secreted by unknown pathways, a slight variation in the protein members of these categories were observed and constituted the variant exoproteome. This investigation resulted in the first definition of the core and variant exoproteomes of L. monocytogenes where corollaries on bacterial physiology are further discussed.

Microbial Cell Factories, 2013

Background: Industrial fermentations can generally be described as dynamic biotransformation processes in which microorganisms convert energy rich substrates into a desired product. The knowledge of active physiological pathways, reflected by corresponding gene activities, allows the identification of beneficial or disadvantageous performances of the microbial host. Whole transcriptome RNA-Seq is a powerful tool to accomplish in-depth quantification of these gene activities, since the low background noise and the absence of an upper limit of quantification allow the detection of transcripts with high dynamic ranges. Such data enable the identification of potential bottlenecks and futile energetic cycles, which in turn can lead to targets for rational approaches to productivity improvement. Here we present an overview of the dynamics of gene activity during an industrial-oriented fermentation process with Bacillus licheniformis, an important industrial enzyme producer. Thereby, valuable insights which help to understand the complex interactions during such processes are provided. Results: Whole transcriptome RNA-Seq has been performed to study the gene expression at five selected growth stages of an industrial-oriented protease production process employing a germination deficient derivative of B. licheniformis DSM13. Since a significant amount of genes in Bacillus strains are regulated posttranscriptionally, the generated data have been confirmed by 2D gel-based proteomics. Regulatory events affecting the coordinated activity of hundreds of genes have been analyzed. The data enabled the identification of genes involved in the adaptations to changing environmental conditions during the fermentation process. A special focus of the analyses was on genes contributing to central carbon metabolism, amino acid transport and metabolism, starvation and stress responses and protein secretion. Genes contributing to lantibiotics production and Tat-dependent protein secretion have been pointed out as potential optimization targets.

PloS one, 2008

Alternative splicing generates an enormous amount of functional and proteomic diversity in metazoan organisms. This process is probably central to the macromolecular and cellular complexity of higher eukaryotes. While most studies have focused on the molecular mechanism triggering and controlling alternative splicing, as well as on its incidence in different species, its maintenance and evolution within populations has been little investigated. Here, we propose to address these questions by comparing the structural characteristics as well as the functional and transcriptional profiles of genes with monomorphic or polymorphic splicing, referred to as MS and PS genes, respectively. We find that MS and PS genes differ particularly in the number of tissues and cell types where they are expressed.We find a striking deficit of PS genes on the sex chromosomes, particularly on the Y chromosome where it is shown not to be due to the observed lower breadth of expression of genes on that chrom...

BMC Bioinformatics, 2007

Background: To date more than 2,1 million gene products from more than 100000 different species have been described specifying their function, the processes they are involved in and their cellular localization using a very well defined and structured vocabulary, the gene ontology (GO). Such vast, well defined knowledge opens the possibility of compare gene products at the level of functionality, finding gene products which have a similar function or are involved in similar biological processes without relying on the conventional sequence similarity approach. Comparisons within such a large space of knowledge are highly data and computing intensive. For this reason this project was based upon the use of the computational GRID, a technology offering large computing and storage resources.

BMC Genomics, 2012

Background: Elucidating the genomic basis of adaptation and speciation is a major challenge in natural systems with large quantities of environmental and phenotypic data, mostly because of the scarcity of genomic resources for non-model organisms. The Atlantic molly (Poecilia mexicana, Poeciliidae) is a small livebearing fish that has been extensively studied in evolutionary ecology research, particularly because this species has repeatedly colonized extreme environments in the form of caves and toxic hydrogen sulfide containing springs. In such extreme environments, populations show strong patterns of adaptive trait divergence and the emergence of reproductive isolation. Here, we used RNA-sequencing to assemble and annotate the first transcriptome of P. mexicana to facilitate ecological genomics studies in the future and aid the identification of genes underlying adaptation and speciation in the system. Description: We provide the first annotated reference transcriptome of P. mexicana. Our transcriptome shows high congruence with other published fish transcriptomes, including that of the guppy, medaka, zebrafish, and stickleback. Transcriptome annotation uncovered the presence of candidate genes relevant in the study of adaptation to extreme environments. We describe general and oxidative stress response genes as well as genes involved in pathways induced by hypoxia or involved in sulfide metabolism. To facilitate future comparative analyses, we also conducted quantitative comparisons between P. mexicana from different river drainages. 106,524 single nucleotide polymorphisms were detected in our dataset, including potential markers that are putatively fixed across drainages. Furthermore, specimens from different drainages exhibited some consistent differences in gene regulation.

Protein Science, 2006

We describe a method to assign a protein structure to a functional family using family-specific fingerprints. Fingerprints represent amino acid packing patterns that occur in most members of a family but are rare in the background, a nonredundant subset of PDB; their information is additional to sequence alignments, sequence patterns, structural superposition, and active-site templates. Fingerprints were derived for 120 families in SCOP using Frequent Subgraph Mining. For a new structure, all occurrences of these family-specific fingerprints may be found by a fast algorithm for subgraph isomorphism; the structure can then be assigned to a family with a confidence value derived from the number of fingerprints found and their distribution in background proteins. In validation experiments, we infer the function of new members added to SCOP families and we discriminate between structurally similar, but functionally divergent TIM barrel families. We then apply our method to predict function for several structural genomics proteins, including orphan structures. Some predictions have been corroborated by other computational methods and some validated by subsequent functional characterization.

Journal of Computer-Aided Molecular Design

Protein function prediction is one of the central problems in computational biology. We present a novel automated protein structure-based function prediction method using libraries of local residue packing patterns that are common to most proteins in a known functional family. Critical to this approach is the representation of a protein structure as a graph where residue vertices (residue name used as a vertex label) are connected by geometrical proximity edges. The approach employs two steps. First, it uses a fast subgraph mining algorithm to find all occurrences of family-specific labeled subgraphs for all well characterized protein structural and functional families. Second, it queries a new structure for occurrences of a set of motifs characteristic of a known family, using a graph index to speed up Ullman's subgraph isomorphism algorithm. The confidence of function inference from structure depends on the number of family-specific motifs found in the query structure compared...

Proceedings of the National Academy of Sciences, 2006

Much evidence from studies in humans and animals supports the hypothesis that alcohol addiction is a complex disease with both hereditary and environmental influences. Molecular determinants of excessive alcohol consumption are difficult to study in humans. However, several rodent models show a high or low degree of alcohol preference, which provides a unique opportunity to approach the molecular complexities underlying the genetic predisposition to drink alcohol. Microarray analyses of brain gene expression in three selected lines, and six isogenic strains of mice known to differ markedly in voluntary alcohol consumption provided >4.5 million data points for a meta-analysis. A total of 107 arrays were obtained and arranged into six experimental data sets, allowing the identification of 3,800 unique genes significantly and consistently changed between all models of high or low amounts of alcohol consumption. Several functional groups, including mitogen-activated protein kinase signaling and transcription regulation pathways, were found to be significantly overrepresented and may play an important role in establishing a high level of voluntary alcohol drinking in these mouse models. Data from the general meta-analysis was further filtered by a congenic strain microarray set, from which cis-regulated candidate genes for an alcohol preference quantitative trait locus on chromosome 9 were identified: Arhgef12, and Zfp291 and four ESTs. The present study demonstrates the use of (i) a microarray meta-analysis to analyze a behavioral phenotype (in this case, alcohol preference) and (ii) a congenic strain for identification of cis regulation.

Proceedings of the Royal Society B: Biological Sciences, 2013

Evolutionary and acclimatory responses require functional variability, but in contrast with mRNA and protein abundance data, most physiological measurements cannot be obtained in a high-throughput manner. Consequently, one must either rely on high-throughput transcriptomic or proteomic data with only predicted functional information, or accept the limitation that most physiological measurements can give fewer data than those provided by transcriptomics or proteomics. We evaluated how transcriptional and redox enzyme activity data agreed with regard to population differentiation (i.e. a system in steady state in which any time lag between transcription, translation and post-translational effects would be irrelevant) and in response to an acute 68C increase in temperature (i.e. a disequilibrium state wherein translation could not have caught up with transcription) in the three-spined stickleback (Gasterosteus aculeatus). Transcriptional and enzyme activity data corresponded well with regard to population differentiation, but less so with regard to acute temperature increase. The data thus suggest that transcriptional and functional measurements can lead to similar conclusions when a biological system is in a steady state. The responses to acute changes must, as has been demonstrated earlier, be based on changes in cellular conditions or properties of existing proteins without significant de novo synthesis of new gene products.

Science Translational Medicine, 2013

FEBS Letters, 2005

DNA Microarrays are used to simultaneously measure the levels of thousands of mRNAs in a sample. We illustrate here that a collection of such measurements in different cell types and states is a sound source of functional predictions, provided the microarray experiments are analogous and the cell samples are appropriately diverse. We have used this approach to study stem cells, whose identity and mechanisms of control are not well understood, generating Affymetrix microarray data from more than 200 samples, including stem cells and their derivatives, from human and mouse. The data can be accessed online (StemBase;

Proceedings of the National Academy of Sciences, 2008

Gene expression profiles were assessed in the hippocampus, entorhinal cortex, superior-frontal gyrus, and postcentral gyrus across the lifespan of 55 cognitively intact individuals aged 20 -99 years. Perspectives on global gene changes that are associated with brain aging emerged, revealing two overarching concepts. First, different regions of the forebrain exhibited substantially different gene profile changes with age. For example, comparing equally powered groups, 5,029 probe sets were significantly altered with age in the superiorfrontal gyrus, compared with 1,110 in the entorhinal cortex. Prominent change occurred in the sixth to seventh decades across cortical regions, suggesting that this period is a critical transition point in brain aging, particularly in males. Second, clear gender differences in brain aging were evident, suggesting that the brain undergoes sexually dimorphic changes in gene expression not only in development but also in later life. Globally across all brain regions, males showed more gene change than females. Further, Gene Ontology analysis revealed that different categories of genes were predominantly affected in males vs. females. Notably, the male brain was characterized by global decreased catabolic and anabolic capacity with aging, with down-regulated genes heavily enriched in energy production and protein synthesis/transport categories. Increased immune activation was a prominent feature of aging in both sexes, with proportionally greater activation in the female brain. These data open opportunities to explore age-dependent changes in gene expression that set the balance between neurodegeneration and compensatory mechanisms in the brain and suggest that this balance is set differently in males and females, an intriguing idea.

The American Journal of Human Genetics, 2006

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.

BMC Genomics, 2012

The classical view on eukaryotic gene expression proposes the scheme of a forward flow for which fluctuations in mRNA levels upon a stimulus contribute to determine variations in mRNA availability for translation. Here we address this issue by simultaneously profiling with microarrays the total mRNAs (the transcriptome) and the polysome-associated mRNAs (the translatome) after EGF treatment of human cells, and extending the analysis to other 19 different transcriptome/translatome comparisons in mammalian cells following different stimuli or undergoing cell programs.

Wiley interdisciplinary reviews. Systems biology and medicine

Contemporary views of human disease are based on simple correlation between clinical syndromes and pathological analysis dating from the late 19th century. Although this approach to disease diagnosis, prognosis, and treatment has served the medical establishment and society well for many years, it has serious shortcomings for the modern era of the genomic medicine that stem from its reliance on reductionist principles of experimentation and analysis. Quantitative, holistic systems biology applied to human disease offers a unique approach for diagnosing established disease, defining disease predilection, and developing individualized (personalized) treatment strategies that can take full advantage of modern molecular pathobiology and the comprehensive data sets that are rapidly becoming available for populations and individuals. In this way, systems pathobiology offers the promise of redefining our approach to disease and the field of medicine.

BMC Research Notes, 2010

Background: The commercial production of Atlantic halibut (Hippoglossus hippoglossus L.) suffers from a major bottleneck due to the low success of producing juveniles for on-growing. Atlantic halibut females are routinely handstripped and incorrect timing of stripping can result in low quality eggs due to post-ovulatory aging. Post-ovulatory aging leads to compositional changes in eggs that include maternally provided proteins and RNAs. There have been few studies of the maternally provided mRNA transcripts that control early development in commercially important fish species. The present study aimed to study maternal gene expression in Atlantic halibut and its relation to egg quality parameters including blastomere symmetry and hatching success.

Lecture Notes in Computer Science, 2005

Micro-arrays can identify co-expressed genes at large scale. The gene expression analysis does however not show functional relationships between co-expressed genes. To address this problem, we link gene expression data to protein interaction data. For the gene products of coexpressed genes, we identify structural domains by sequence alignment and threading. Next, we use the protein structure interaction PSIMAP to find structurally interacting domains. Finally, we generate structural and sequence alignments of the original gene products and the identified structures and check conservation of the relevant interaction interfaces. From this analysis, we derive potentially relevant protein interactions for the gene expression data. We applied this method to co-expressed genes in pancreatic ductal carcinoma. Our method reveals among others a number of functional clusters related to the proteasome, signalling, ubiquitinisation, serine proteases, immunoglobulin and kinases. We investigate the kinase cluster in detail and reveal an interaction between the cell division control protein CDC2 and the cyclin-dependent kinase inhibitor CDKN3, which is also confirmed by literature. Furthermore, our method reveals new interactions between CDKN3 and the cell division protein kinase CDK7 and between CDKN3 and the serine/threonine-protein kinase CDC2L1.

Proceedings of the National Academy of Sciences, 2004

Large-scale analysis of the GC-content distribution at the gene level reveals both common features and basic differences in genomes of different groups of species. Sharp changes in GC content are detected at the transcription boundaries for all species analyzed, including human, mouse, rat, chicken, fruit fly, and worm. However, two substantially distinct groups of GC-content profiles can be recognized: warm-blooded vertebrates including human, mouse, rat, and chicken, and invertebrates including fruit fly and worm. In vertebrates, sharp positive and negative spikes of GC content are observed at the transcription start and stop sites, respectively, and there is also a progressive decrease in GC content from the 5 untranslated region to the 3 untranslated region along the gene. In invertebrates, the positive and negative GC-content spikes at the transcription start and stop sites are preceded by spikes of opposite value, and the highest GC content is found in the coding regions of the genes. Cross-correlation analysis indicates high frequencies of GC-content spikes at transcription start and stop sites. The strong conservation of this genomic feature seen in comparisons of the human͞mouse and human͞rat orthologs, and the clustering of genes with GC-content spikes on chromosomes imply a biological function. The GC-content spikes at transcription boundaries may reflect a general principle of genomic punctuation. Our analysis also provides means for identifying these GC-content spikes in individual genomic sequences.

PloS one, 2011

Members of the genus Xenorhabdus are entomopathogenic bacteria that associate with nematodes. The nematode-bacteria pair infects and kills insects, with both partners contributing to insect pathogenesis and the bacteria providing nutrition to the nematode from available insect-derived nutrients. The nematode provides the bacteria with protection from predators, access to nutrients, and a mechanism of dispersal. Members of the bacterial genus Photorhabdus also associate with nematodes to kill insects, and both genera of bacteria provide similar services to their different nematode hosts through unique physiological and metabolic mechanisms. We posited that these differences would be reflected in their respective genomes. To test this, we sequenced to completion the genomes of Xenorhabdus nematophila ATCC 19061 and Xenorhabdus bovienii SS-2004. As expected, both Xenorhabdus genomes encode many anti-insecticidal compounds, commensurate with their entomopathogenic lifestyle. Despite the...

BMC Systems Biology, 2008

The study of gene mutants and their interactions is fundamental to understanding gene function and backup mechanisms within the cell. The recent availability of large scale genetic interaction networks in yeast and worm allows the investigation of the biological mechanisms underlying these interactions at a global scale. To date, less than 2% of the known genetic interactions in yeast or worm can be accounted for by sequence similarity.

PloS one, 2013

We present an enzyme protein function identification algorithm, Catalytic Site Identification (CatSId), based on identification of catalytic residues. The method is optimized for highly accurate template identification across a diverse template library and is also very efficient in regards to time and scalability of comparisons. The algorithm matches three-dimensional residue arrangements in a query protein to a library of manually annotated, catalytic residues--The Catalytic Site Atlas (CSA). Two main processes are involved. The first process is a rapid protein-to-template matching algorithm that scales quadratically with target protein size and linearly with template size. The second process incorporates a number of physical descriptors, including binding site predictions, in a logistic scoring procedure to re-score matches found in Process 1. This approach shows very good performance overall, with a Receiver-Operator-Characteristic Area Under Curve (AUC) of 0.971 for the training...

Molecular & Cellular Proteomics, 2005

A comprehensive analysis of plasma membrane proteins is essential to in-depth understanding of brain development, function, and diseases. Proteomics offers the potential to perform such a comprehensive analysis, yet it requires efficient protocols for the purification of the plasma membrane compartment. Here, we present a novel and efficient protocol for the separation and enrichment of brain plasma membrane proteins. It lasts only 4 h and is easy to perform. It highly enriches plasma membrane proteins and can be applied to small amounts of brain tissue, such as the cerebellum of a single rat, which was used in the present study. The protocol is based on affinity partitioning of microsomes in an aqueous twophase system. Marker enzyme assays demonstrated a more than 12-fold enrichment of plasma membranes and a strong reduction of other compartments, such as mitochondria and the endoplasmic reticulum. 506 different proteins were identified when the enriched proteins underwent LC-MS/MS analysis subsequent to protein separation by SDS-PAGE. Using gene ontology, 146 proteins were assigned to a subcellular compartment. Ninetythree of those (64%) were membrane proteins, and 49 (34%) were plasma membrane proteins. A combined literature and database search for all 506 identified proteins revealed subcellular information on 472 proteins, of which 197 (42%) were plasma membrane proteins. These comprised numerous transporters, channels, and neurotransmitter receptors, e.g. the inward rectifying potassium channel Kir7.1 and the cerebellum-specific ␥-aminobutyric acid receptor GABRA6. Surface proteins involved in cell-cell contact and disease-related proteins were also identified. Six of the 146 assigned proteins were derived from mitochondrial membranes and 5 from membranes of the endoplasmic reticulum. Taken together, our protocol represents a simple, rapid, and reproducible tool for the proteomic characterization of brain plasma membranes. Because it conserves membrane structure and protein interactions, it is also suitable to enrich multimeric protein complexes from the plasma membrane for subsequent analysis. Molecular & Cellular Proteomics 5:390 -400, 2006.

BMC Bioinformatics, 2009

Background: Time series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult.

BMC Genomics, 2008

Background: Leishmania parasites cause a diverse spectrum of diseases in humans ranging from spontaneously healing skin lesions (e.g., L. major) to life-threatening visceral diseases (e.g., L. infantum). The high conservation in gene content and genome organization between Leishmania major and Leishmania infantum contrasts their distinct pathophysiologies, suggesting that highly regulated hierarchical and temporal changes in gene expression may be involved.

Proceedings of the National Academy of Sciences, 2006

The question of how duplicate genes are retained in a population remains controversial. The duplication-degeneration-complementation model, which involves no positive selection, stipulates a higher retention rate of duplicate genes in a small population than in a large one. This model has been accepted by many evolutionists. However, we found considerably more retentions and fewer losses of duplicate genes in the mouse genome than in the human genome, although the population size of rodents is in general larger than that of primates. Indeed, in nearly every interval of synonymous divergence between duplicate genes, the number of gene retentions in mouse is larger than that in human. Our findings suggest a more important role of positive selection in duplicate retention than duplication-degeneration-complementation. In addition, certain functional categories show a higher tendency of lineage-specific expansion than expected, suggesting lineagespecific selection or functional bias in retained duplicates.

Evolutionary applications, 2014

Genome sequencing projects were long confined to biomedical model organisms and required the concerted effort of large consortia. Rapid progress in high-throughput sequencing technology and the simultaneous development of bioinformatic tools have democratized the field. It is now within reach for individual research groups in the eco-evolutionary and conservation community to generate de novo draft genome sequences for any organism of choice. Because of the cost and considerable effort involved in such an endeavour, the important first step is to thoroughly consider whether a genome sequence is necessary for addressing the biological question at hand. Once this decision is taken, a genome project requires careful planning with respect to the organism involved and the intended quality of the genome draft. Here, we briefly review the state of the art within this field and provide a step-by-step introduction to the workflow involved in genome sequencing, assembly and annotation with pa...

PLoS ONE, 2009

Background: In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).

Proceedings of the National Academy of Sciences, 2004

The dissection of complex biological systems is a challenging task, made difficult by the size of the underlying molecular network and the heterogeneous nature of the control mechanisms involved. Novel high-throughput techniques are generating massive data sets on various aspects of such systems. Here, we perform analysis of a highly diverse collection of genomewide data sets, including gene expression, protein interactions, growth phenotype data, and transcription factor binding, to reveal the modular organization of the yeast system. By integrating experimental data of heterogeneous sources and types, we are able to perform analysis on a much broader scope than previous studies. At the core of our methodology is the ability to identify modules, namely, groups of genes with statistically significant correlated behavior across diverse data sources. Numerous biological processes are revealed through these modules, which also obey global hierarchical organization. We use the identified modules to study the yeast transcriptional network and predict the function of >800 uncharacterized genes. Our analysis framework, SAMBA (Statistical-Algorithmic Method for Bicluster Analysis), enables the processing of current and future sources of biological information and is readily extendable to experimental techniques and higher organisms.

PLoS ONE, 2010

Single nucleotide polymorphisms (SNPs) constitute an important mode of genetic variations observed in the human genome. A small fraction of SNPs, about four thousand out of the ten million, has been associated with genetic disorders and complex diseases. The present study focuses on SNPs that fall on protein domains, 3D structures that facilitate connectivity of proteins in cell signaling and metabolic pathways. We scanned the human proteome using the PROSITE web tool and identified proteins with SNP containing domains. We showed that SNPs that fall on protein domains are highly statistically enriched among SNPs linked to hereditary disorders and complex diseases. Proteins whose domains are dramatically altered by the presence of an SNP are even more likely to be present among proteins linked to hereditary disorders. Proteins with domain-altering SNPs comprise highly connected nodes in cellular pathways such as the focal adhesion, the axon guidance pathway and the autoimmune disease pathways. Statistical enrichment of domain/motif signatures in interacting protein pairs indicates extensive loss of connectivity of cell signaling pathways due to domainaltering SNPs, potentially leading to hereditary disorders.

Multiscale Modeling & Simulation, 2006

In this paper we present a method for the multi-resolution comparison of biomolecular electrostatic potentials without the need for global structural alignment of the biomolecules. The underlying computational geometry algorithm uses multi-resolution attributed contour trees (MACTs) to compare the topological features of volumetric scalar fields. We apply the MACTs to compute electrostatic similarity metrics for a large set of protein chains with varying degrees of sequence, structure, and function similarity. For calibration, we also compute similarity metrics for these chains by a more traditional approach based upon 3D structural alignment and analysis of Carbo similarity indices. Moreover, because the MACT approach does not rely upon pairwise structural alignment, its accuracy and efficiency may make it particularly well-suited to large-scale, structural genomics-type efforts. The MACT method discriminates between protein chains at a level comparable to the Carbo similarity index method; i.e., it is able to accurately cluster proteins into functionally-relevant groups which demonstrate strong dependence on ligand binding sitesw. The results of the analyses are available from the linked web databases

BMC Genomics, 2008

Background: Duplication, followed by fixation or random loss of novel genes, contributes to genome evolution. Particular outcomes of duplication events are possibly associated with pathogenic life histories in fungi. To date, differential gene gain and loss have not been studied at genomic scales in fungal pathogens, despite this phenomenon's known importance in virulence in bacteria and viruses.

Plant Molecular Biology, 2010

NPR1 is a central regulator of salicylic-acid (SA)-mediated defense signaling in Arabidopsis. Here, we report the characterization of OsNPR1, an Oryzae sativa (rice) ortholog of NPR1, focusing on its role in blast disease resistance and identification of OsNPR1-regulated genes. Blast resistance tests using OsNPR1 knockdown and overexpressing rice lines demonstrated the essential role of OsNPR1 in benzothiadiazole (BTH)-induced blast resistance. Genome-wide transcript profiling using OsNPR1knockdown lines revealed that 358 genes out of 1,228 BTH-upregulated genes and 724 genes out of 1,069 BTHdownregulated genes were OsNPR1-dependent with respect to BTH responsiveness, thereby indicating that OsNPR1 plays a more vital role in gene downregulation. The OsNPR1-dependently downregulated genes included many of those involved in photosynthesis and in chloroplast translation and transcription. Reduction of photosynthetic activity after BTH treatment and its negation by OsNPR1 knockdown were indeed reflected in the changes in Fv/Fm values in leaves. These results imply the role of OsNPR1 in the reallocation of energy and resources during defense responses. We also examined the OsNPR1-dependence of SA-mediated suppression of ABA-induced genes.

BMC Bioinformatics, 2011

Background: The accurate determination of orthology and inparalogy relationships is essential for comparative sequence analysis, functional gene annotation and evolutionary studies. Various methods have been developed based on either simple blast all-versus-all pairwise comparisons and/or time-consuming phylogenetic tree analyses.

Journal of Virology, 2015

Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections in infants. Despite over 50 years of research, to date no safe and efficacious RSV vaccine has been licensed. Many experimental vaccination strategies failed to induce balanced T-helper (Th) responses and were associated with adverse effects such as hypersensitivity and immunopathology upon challenge. In this study we explored the well-established recombinant vaccinia virus (rVV) RSV F/G vaccination-challenge mouse model to study phenotypically distinct vaccine mediated host immune responses at the proteome level. In this model, rVV-G priming and not rVV-F priming results in the induction of Th2 skewed host responses upon RSV challenge. Mass spectrometry-based spectral count comparisons enabled us to identify seven host proteins for which expression in lung tissue is associated with an aberrant Th2 skewed response characterized by the influx of eosinophils and neutrophils. These proteins are involved in processes related to the influx of eosinophils directly (Epx), chemotaxis and extravasion processes (Chil3) as well as eosinophil and neutrophil homing signals to the lung (Itgam). In addition, the increased protein levels of Arg1 and Chil3 point at a functional and regulatory role for alternatively activated macrophages and type 2 innate lymphoid cells in Th2 cytokine driven RSV vaccine-mediated enhanced disease. RSV alone is responsible for 80% of acute bronchiolitis cases in infants worldwide and causes substantial mortality in developing countries. Clinical trials with formalin-inactivated RSV vaccine preparations in the 1960s failed to induce protection upon natural RSV infection, and even predisposed for enhanced disease. Despite the clinical need, to date no safe and efficacious RSV vaccine has been licensed. Since RSV vaccines have a tendency to prime for unbalanced responses associated with an exuberant influx of inflammatory cells and enhanced disease, detailed characterization of primed host responses has become a crucial element in RSV vaccine research. We investigated the lung proteome of mice challenged with RSV upon priming with vaccine preparations known to induce phenotypically distinct host responses. Seven host proteins have been identified which expression levels associated with vaccine mediated enhanced disease. The identified protein biomarkers support the development, as well as detailed evaluation of next generation RSV vaccines.

Molecular & Cellular Proteomics, 2006

Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community.

Proceedings of the National Academy of Sciences, 2007

To systematically identify genes that maintain genome structure, yeast knockout mutants were examined by using three assays that followed marker inheritance in different chromosomal contexts. These screens identified 130 null mutant strains exhibiting chromosome instability (CIN) phenotypes. Differences in both phenotype severity and assay specificity were observed. The results demonstrate the advantages of using complementary assays to comprehensively identify genome maintenance determinants. Genome structure was important in determining the spectrum of gene and pathway mutations causing a chromosome instability phenotype. Protein similarity identified homologues in other species, including human genes with relevance to cancer. This extensive genome instability catalog can be combined with emerging genetic interaction data from yeast to support the identification of candidate targets for therapeutic elimination of chromosomally unstable cancer cells by selective cell killing.

Journal of Biological Chemistry, 2013

The nuclei of most vertebrate cells contain members of the high mobility group N (HMGN) protein family, which bind specifically to nucleosome core particles and affect chromatin structure and function, including transcription. Here, we study the biological role of this protein family by systematic analysis of phenotypes and tissue transcription profiles in mice lacking functional HMGN variants. Phenotypic analysis of Hmgn1(tm1/tm1), Hmgn3(tm1/tm1), and Hmgn5(tm1/tm1) mice and their wild type littermates with a battery of standardized tests uncovered variant-specific abnormalities. Gene expression analysis of four different tissues in each of the Hmgn(tm1/tm1) lines reveals very little overlap between genes affected by specific variants in different tissues. Pathway analysis reveals that loss of an HMGN variant subtly affects expression of numerous genes in specific biological processes. We conclude that within the biological framework of an entire organism, HMGNs modulate the fidelity of the cellular transcriptional profile in a tissue- and HMGN variant-specific manner.

Critical Reviews in Biotechnology, 2009

PLoS Genetics, 2011

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein-and transcriptmapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

Biological Chemistry, 2000

We recently identified a group of proteins which are dysregulated in renal cell carcinoma (RCC). In this study, we performed bioinformatics and pathway analysis of these proteins. Proteins were mapped to gene ontology biological processes. The upregulated proteins tend to cluster in processes, such as cancer initiation and progression. In addition, we identified a number of pathways that are significantly enriched in RCC. Some of these are 'common' pathways which are dysregulated in many cancers, but we also identified a number of pathways which were not previously linked to RCC. In addition to their potential prognostic values, many of these pathways have a potential as therapeutic targets for RCC. To verify our findings, we compared our proteins to a pool of datasets from published reports. Although there were only a minimal number of common proteins, there was a significant overlap between the identified pathways in the two groups. Moreover, out of 16 individually discovered genes identified by a literature search, 10 were found to be related to our dysregulated pathways. We also verified the upregulation of the mammalian target of rapamycin signaling pathway in RCC by immunohistochemistry. Finally, we highlight the potential clinical applications of pathway analysis in kidney cancer.

PLoS ONE, 2013

Intrinsically Disordered Proteins/Regions (IDPs/IDRs) are currently recognized as a widespread phenomenon having key cellular functions. Still, many aspects of the function of these proteins need to be unveiled. IDPs conformational flexibility allows them to recognize and interact with multiple partners, and confers them larger interaction surfaces that may increase interaction speed. For this reason, molecular interactions mediated by IDPs/IDRs are particularly abundant in certain types of protein interactions, such as those of signaling and cell cycle control. We present the first large-scale study of IDPs in Arabidopsis thaliana, the most widely used model organism in plant biology, in order to get insight into the biological roles of these proteins in plants. The work includes a comparative analysis with the human proteome to highlight the differential use of disorder in both species. Results show that while human proteins are in general more disordered, certain functional classes, mainly related to environmental response, are significantly more enriched in disorder in Arabidopsis. We propose that because plants cannot escape from environmental conditions as animals do, they use disorder as a simple and fast mechanism, independent of transcriptional control, for introducing versatility in the interaction networks underlying these biological processes so that they can quickly adapt and respond to challenging environmental conditions.

International Journal on Semantic Web and Information Systems, 2000

With the emergence of tools for collaborative ontology engineering, more and more data about the creation process behind collaborative construction of ontologies is becoming available. Today, collaborative ontology engineering tools such as Collaborative Protégé offer rich and structured logs of changes, thereby opening up new challenges and opportunities to study and analyze the creation of collaboratively constructed ontologies. While there exists a plethora of visualization tools for ontologies, they have primarily been built to visualize aspects of the final product (the ontology) and not the collaborative processes behind construction (e.g. the changes made by contributors over time). To the best of our knowledge, there exists no ontology visualization tool today that focuses primarily on visualizing the history behind collaboratively constructed ontologies. Since the ontology engineering processes can influence the quality of the final ontology, we believe that visualizing process data represents an important stepping-stone towards better understanding of managing the collaborative construction of ontologies in the future. In this application paper, we present a tool -PragmatiX -which taps into structured change logs provided by tools such as Collaborative Protégé to visualize various pragmatic aspects of collaborative ontology engineering. The tool is aimed at managers and leaders of collaborative ontology engineering projects to help them in monitoring progress, in exploring issues and problems, and in tracking quality-related issues such as overrides and coordination among contributors. The paper makes the following contributions: (i) we present PragmatiX, a tool for visualizing the creation process behind collaboratively constructed ontologies (ii) we illustrate the functionality and generality of the tool by applying it to structured logs of changes of two large collaborative ontology-engineering projects and (iii) we conduct a heuristic evaluation of the tool with domain experts to uncover early design challenges and opportunities for improvement. Finally, we hope that this work sparks a new line of research on visualization tools for collaborative ontology engineering projects. represent an integral part of ontology engineering processes, it is only recently that disciplined tools and infrastructure for collaborative ontology engineering have emerged. Tools such as Collaborative Protégé (Tudorache, Noy, Tu, & Musen, 2008) not only provide an infrastructure for collaboration and coordination, but also provide a structured log of all ontological changes, which users have made via the tool. These logs can, for example, include records of concepts added, properties changed, or relationships qualified. In aggregation, such logs can essentially capture the entire evolution of an ontology from its inception to its final stages on a very fine-grained level. At the same time, the availability of fine-grained logs poses new challenges and opportunities for studying and analyzing the history of collaborative ontology engineering projects. While there exists a plethora of visualization tools for ontologies, they have primarily been built to visualize aspects of the final product (the ontology) and not the collaborative processes behind construction (e.g. the changes made by contributors over time). To the best of our knowledge, there exists no ontology visualization tool today that focuses primarily on visualizing the creation processes behind collaboratively constructed ontologies. This application paper sets out to present a visualization tool that primarily focuses on visualizing pragmatic aspects of collaborative ontology engineering, i.e. the social processes that yield collaboratively constructed ontologies. We present a tool -PragmatiX -that taps into structured log of changes provided by tools such as Collaborative Protégé and visualizes them via network-based and other kinds of visualizations. The tool is aimed at managers and leaders of collaborative ontology engineering projects to help them in monitoring progress, exploring issues and problems, and tracking quality-related issues such as overrides and coordination among contributors.

Tree Genetics & Genomes, 2014

Embryogenesis is the initial stage of plant life, when the basics of body plan and the post-embryonic development are laid down. Epigenetic memory formed in the Norway spruce embryos permanently affect the timing of bud burst and bud set in progenies, vitally important adaptive traits in this long-lived forest species. The epigenetic memory marks are established in response to the temperature conditions prevailing during zygotic and somatic embryogenesis; the epitype is fixed by the time the embryo is fully developed and is mitotically propagated throughout the tree's life span. Somatic embryogenesis closely mimics the natural zygotic embryo formation and results in epigenetically different plants in a predictable temperature-dependent manner with respect to altered phenology. Using Illumina-based Massive Analysis of cDNA Ends, the transcriptome changes were monitored in somatic embryos during morphogenesis stage under two different temperatures (18 vs. 30°C). We found distinct differences in transcriptomes between the genetically identical embryogenic tissues grown under the two epitype-inducing temperatures suggesting temperature-dependent canalizing of gene expression during embryo formation, putatively based on chromatin modifications. From 448 transcripts of genes coding for proteins involved in epigenetic machinery, we found 35 of these to be differentially expressed at high level under the epitype-inducing conditions. Therefore, temperature conditions during embryogenesis significantly alter transcriptional profiles including numerous orthologs of transcriptional regulators, epigenetic-related genes, and large sets of unknown and uncharacterized transcripts.

BMC Plant Biology, 2012

Background: Maize is a major crop plant, grown for human and animal nutrition, as well as a renewable resource for bioenergy. When looking at the problems of limited fossil fuels, the growth of the world's population or the world's climate change, it is important to find ways to increase the yield and biomass of maize and to study how it reacts to specific abiotic and biotic stress situations. Within the OPTIMAS systems biology project maize plants were grown under a large set of controlled stress conditions, phenotypically characterised and plant material was harvested to analyse the effect of specific environmental conditions or developmental stages. Transcriptomic, metabolomic, ionomic and proteomic parameters were measured from the same plant material allowing the comparison of results across different omics domains. A data warehouse was developed to store experimental data as well as analysis results of the performed experiments.

PLoS Neglected Tropical Diseases, 2011

Zaire ebolavirus (ZEBOV) infections are associated with high lethality in primates. ZEBOV primarily targets mononuclear phagocytes, which are activated upon infection and secrete mediators believed to trigger initial stages of pathogenesis. The characterization of the responses of target cells to ZEBOV infection may therefore not only further understanding of pathogenesis but also suggest possible points of therapeutic intervention. Gene expression profiles of primary human macrophages exposed to ZEBOV were determined using DNA microarrays and quantitative PCR to gain insight into the cellular response immediately after cell entry. Significant changes in mRNA concentrations encoding for 88 cellular proteins were observed. Most of these proteins have not yet been implicated in ZEBOV infection. Some, however, are inflammatory mediators known to be elevated during the acute phase of disease in the blood of ZEBOV-infected humans. Interestingly, the cellular response occurred within the first hour of Ebola virion exposure, i.e. prior to virus gene expression. This observation supports the hypothesis that virion binding or entry mediated by the spike glycoprotein (GP 1,2 ) is the primary stimulus for an initial response. Indeed, ZEBOV virions, LPS, and virus-like particles consisting of only the ZEBOV matrix protein VP40 and GP 1,2 (VLP VP40-GP ) triggered comparable responses in macrophages, including pro-inflammatory and proapoptotic signals. In contrast, VLP VP40 (particles lacking GP 1,2 ) caused an aberrant response. This suggests that GP 1,2 binding to macrophages plays an important role in the immediate cellular response.

Human Mutation, 2006

We experimentally investigated more than 1,200 entries in dbSNP that would change amino-acids (nsSNPs), using various subsets of DNA samples drawn from 18 global populations (1,000 subjects in total). First, we mined the data for any SNP features that correlated with a high validation rate. Useful predictors of valid SNPs included multiple submissions to dbSNP, having a dbSNP validation statement, and being present in a low number of ESTs. Together, these features improved validation rates by almost 10-fold. Higher-abundance SNPs (e.g., T/C variants) also validated more frequently. Second, we considered derived alleles and noted a considerably (10%) increased average derived allele frequency (DAF) in Europeans vs. Africans, plus a further increase in some other populations. This was not primarily due to an SNP ascertainment bias, nor to the effects of natural selection. Instead, it can be explained as a drift-based, progressive increase in DAF that occurs over many generations and becomes exaggerated during population bottlenecks. This observation could be used as the basis for novel DAF-based tests for comparing demographic histories. Finally, we considered individual marker patterns and identified 37 SNPs with allele frequency variance or F ST values consistent with the effects of population-specific natural selection. Four particularly striking clusters of these markers were apparent, and three of these coincide with genes/regions from among only several dozen such domains previously suggested by others to carry signatures of selection. Hum Mutat 27(2), 173-186, 2006. r r 2006 Wiley-Liss, Inc.

Cancer Medicine, 2014

a These authors contributed equally to this work.

New Phytologist, 2012

To enable easy access and interpretation of heterogenous and scattered data, we have developed a user-friendly tool for data mining and integration in Arabidopsis, named CORNET. This tool allows the browsing of microarray data, the construction of coexpression and protein-protein interaction (PPI) networks and the exploration of diverse functional annotations. Here, we present the new functionalities of CORNET 2.0 for data integration in plants.

PLoS ONE, 2007

Background. Clostridium botulinum and related clostridial species express extremely potent neurotoxins known as botulinum neurotoxins (BoNTs) that cause long-lasting, potentially fatal intoxications in humans and other mammals. The amino acid variation within the BoNT is used to categorize the species into seven immunologically distinct BoNT serotypes (A-G) which are further divided into subtypes. The BoNTs are located within two generally conserved gene arrangements known as botulinum progenitor complexes which encode toxin-associated proteins involved in toxin stability and expression. Methodology/ Principal Findings. Because serotype A and B strains are responsible for the vast majority of human botulism cases worldwide, the location, arrangement and sequences of genes from eight different toxin complexes representing four different BoNT/A subtypes (BoNT/A1-Ba4) and one BoNT/B1 strain were examined. The bivalent Ba4 strain contained both the BoNT/A4 and BoNT/bvB toxin clusters. The arrangements of the BoNT/A3 and BoNT/A4 subtypes differed from the BoNT/A1 strains and were similar to those of BoNT/A2. However, unlike the BoNT/A2 subtype, the toxin complex genes of BoNT/A3 and BoNT/A4 were found within large plasmids and not within the chromosome. In the Ba4 strain, both BoNT toxin clusters (A4 and bivalent B) were located within the same 270 kb plasmid, separated by 97 kb. Complete genomic sequencing of the BoNT/B1 strain also revealed that its toxin complex genes were located within a 149 kb plasmid and the BoNT/A3 complex is within a 267 kb plasmid. Conclusions/Significance. Despite their size differences and the BoNT genes they contain, the three plasmids containing these toxin cluster genes share significant sequence identity. The presence of partial insertion sequence (IS) elements, evidence of recombination/gene duplication events, and the discovery of the BoNT/A3, BoNT/Ba4 and BoNT/B1 toxin complex genes within plasmids illustrate the different mechanisms by which these genes move among diverse genetic backgrounds of C. botulinum. Citation: Smith TJ, Hill KK, Foley BT, Detter JC, Munk AC, et al (2007) Analysis of the Neurotoxin Complex Genes in Clostridium botulinum A1-A4 and B1 Strains: BoNT/A3, /Ba4 and /B1 Clusters Are Located within Plasmids. PLoS ONE 2(12): e1271.

Transplant International, 2006

Microarray technology holds a distinct advantage over traditional genomic methods, with the unique capability to rapidly generate multiple global gene expression profiles in parallel. This technology is quickly gaining widespread use in many areas of science and medicine because it can be easily adapted to study many experimental questions, particularly relating to disease heterogeneity. Microarray experiments have begun to advance our understanding of the underlying molecular processes in solid organ transplantation; however, several obstacles must be overcome before this technology is ready for application in the clinical setting. This article will review the current applications of microarray technology in the field of transplantation, and discuss the potential impact of this technology on monitoring of solid organ transplant recipients.

BMC bioinformatics, 2006

With the completion of the genome sequences of human, mouse, and other species and the advent of high throughput functional genomic research technologies such as biomicroarray chips, more and more genes and their products have been discovered and their functions have begun to be understood. Increasing amounts of data about genes, gene products and their functions have been stored in databases. To facilitate selection of candidate genes for gene-disease research, genetic association studies, biomarker and drug target selection, and animal models of human diseases, it is essential to have search engines that can retrieve genes by their functions from proteome databases. In recent years, the development of Gene Ontology (GO) has established structured, controlled vocabularies describing gene functions, which makes it possible to develop novel tools to search genes by functional similarity. By using a statistical model to measure the functional similarity of genes based on the Gene Onto...

PLoS ONE, 2011

Background: Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species.

Journal of Biomedicine and Biotechnology, 2010

Background. A comparative microarray investigation was done using detection call methodology (DCM) and differential expression analyses. The goal was to identify genes found in specific cell populations that were eliminated by differential expression analysis due to the nature of differential expression methods. Laser capture microdissection (LCM) was used to isolate nearly homogeneous populations of plant root cells. Results. The analyses identified the presence of 13,291 transcripts between the 4 different sample types. The transcripts filtered down into a total of 6,267 that were detected as being present in one or more sample types. A comparative analysis of DCM and differential expression methods showed a group of genes that were not differentially expressed, but were expressed at detectable amounts within specific cell types. Conclusion. The DCM has identified patterns of gene expression not shown by differential expression analyses. DCM has identified genes that are possibly cell-type specific and/or involved in important aspects of plant nematode interactions during the resistance response, revealing the uniqueness of a particular cell population at a particular point during its differentiation process.

Plant Molecular Biology, 2011

Glycine max L. Merr. (soybean) resistance to Heterodera glycines Ichinohe occurs at the site of infection, a nurse cell known as the syncytium. Resistance is classified into two cytologically-defined responses, the G. max [Peking] -and G. max [PI 88788] -types. Each type represents a cohort of G. max genotypes. Resistance in G. max [Peking] occurs by a potent and rapid localized response, affecting parasitic second stage juveniles (p-J2). In contrast, resistance occurs by a potent but more prolonged reaction in the genotype G. max [PI 88788] that affects nematode development at the J3 and J4 stages. Microarray analyses comparing these cytologically and developmentally distinct resistant reactions reveal differences in gene expression in pericycle and surrounding cells even before infection. The differences include higher relative levels of the differentially expressed in response to arachidonic acid 1 gene (DEA1 [Gm-DEA1]) (?224.19-fold) and a protease inhibitor (?68.28-fold) in G. max [Peking/PI 548402] as compared to G. max [PI 88788] . Gene pathway analyses compare the two genotypes (1) before, (2) at various times during, (3) constitutively throughout the resistant reaction and (4) at all time points prior to and during the resistant reaction. The amplified levels of transcriptional activity of defense genes may explain the rapid and potent reaction in G. max [Peking/PI 548402] as compared to G. max [PI 88788] . In contrast, the shared differential expression levels of genes in G. max [Peking/PI 548402] and G. max [PI 88788] may indicate a conserved genomic program underlying the G. max resistance on which the genotype-specific gene expression programs are built off.

Epigenetics, 2013

histone deacetylases (hDacs) have emerged as important targets for cancer treatment. hDac-inhibitors (hDacis) are well tolerated in patients and have been approved for the treatment of patients with cutaneous T-cell lymphoma (cTcL). To improve the clinical benefit of hDacis in solid tumors, combination strategies with hDacis could be employed. In this study, we applied analysis of Functional annotation (aFa) to provide a comprehensive list of genes and pathways affected upon hDaci-treatment in prostate cancer cells. This approach provides an unbiased and objective approach to high throughput data mining. By performing aFa on gene expression data from prostate cancer cell lines DU-145 (an hDaci-sensitive cell line) and pc3 (a relatively hDaci-resistant cell line) treated with hDacis valproic acid or vorinostat, we identified biological processes that are affected by hDacis and are therefore potential treatment targets for combination therapy. Our analysis revealed that hDac-inhibition resulted among others in upregulation of major histocompatibility complex (Mhc) genes and deregulation of the mitotic spindle checkpoint by downregulation of genes involved in mitosis. These findings were confirmed by aFa on publicly available data sets from hDaci-treated prostate cancer cells. In total, we analyzed 375 microarrays with hDaci treated and non-treated (control) prostate cancer cells. all results from this extensive analysis are provided as an online research source (available at the journal's website and at http:// luigimarchionni.org/hDacIs.html). By publishing this data, we aim to enhance our understanding of the cellular changes after hDac-inhibition, and to identify novel potential combination strategies with hDacis for the treatment of prostate cancer patients.

PLoS ONE, 2012

More than 400 cancer genes have been identified in the human genome. The list is not yet complete. Statistical models predicting cancer genes may help with identification of novel cancer gene candidates. We used known prostate cancer (PCa) genes (identified through KnowledgeNet) as a training set to build a binary logistic regression model identifying PCa genes. Internal and external validation of the model was conducted using a validation set (also from KnowledgeNet), permutations, and external data on genes with recurrent prostate tumor mutations. We evaluated a set of 33 gene characteristics as predictors. Sixteen of the original 33 predictors were significant in the model. We found that a typical PCa gene is a prostate-specific transcription factor, kinase, or phosphatase with high interindividual variance of the expression level in adjacent normal prostate tissue and differential expression between normal prostate tissue and primary tumor. PCa genes are likely to have an antiapoptotic effect and to play a role in cell proliferation, angiogenesis, and cell adhesion. Their proteins are likely to be ubiquitinated or sumoylated but not acetylated. A number of novel PCa candidates have been proposed. Functional annotations of novel candidates identified antiapoptosis, regulation of cell proliferation, positive regulation of kinase activity, positive regulation of transferase activity, angiogenesis, positive regulation of cell division, and cell adhesion as top functions. We provide the list of the top 200 predicted PCa genes, which can be used as candidates for experimental validation. The model may be modified to predict genes for other cancer sites.

Clinical & Experimental Ophthalmology, 2011

Background: To profile the pattern of gene expression in diabetic rat retinas with or without intravitreal injection of erythropoietin.

BMC Bioinformatics, 2009

The Comparative Toxicogenomics Database (CTD) is a publicly available resource that promotes understanding about the etiology of environmental diseases. It provides manually curated chemical-gene/protein interactions and chemical-and gene-disease relationships from the peer-reviewed, published literature. The goals of the research reported here were to establish a baseline analysis of current CTD curation, develop a text-mining prototype from readily available open source components, and evaluate its potential value in augmenting curation efficiency and increasing data coverage.

Nature, 2011

PloS one, 2014

Mussels are useful indicator species of environmental stress and degradation, and the global decline in freshwater mussel diversity and abundance is of conservation concern. Elliptio complanata is a common freshwater mussel of eastern North America that can serve both as an indicator and as an experimental model for understanding mussel physiology and genetics. To support genetic components of these research goals, we assembled transcriptome contigs from Illumina paired-end reads. Despite efforts to collapse similar contigs, the final assembly was in excess of 136,000 contigs with an N50 of 982 bp. Even so, comparisons to the CEGMA database of conserved eukaryotic genes indicated that ∼ 20% of genes remain unrepresented. However, numerous candidate stress-response genes were present, and we identified lineage-specific patterns of diversification among molluscs for cytochrome P450 detoxification genes and two saccharide-modifying enzymes: 1,3 beta-galactosyltransferase and fucosyltra...

PLoS ONE, 2013

The genus Gossypium is a globally important crop that is used to produce textiles, oil and protein. However, gossypol, which is found in cultivated cottonseed, is toxic to humans and non-ruminant animals. Efforts have been made to breed improved cultivated cotton with lower gossypol content. The delayed gland morphogenesis trait possessed by some Australian wild cotton species may enable the widespread, direct usage of cottonseed. However, the mechanisms about the delayed gland morphogenesis are still unknown. Here, we sequenced the first Australian wild cotton species (Gossypium australe) and a diploid cotton species (Gossypium arboreum) using the Illumina Hiseq 2000 RNA-seq platform to help elucidate the mechanisms underlying gossypol synthesis and gland development. Paired-end Illumina short reads were de novo assembled into 226,184, 213,257 and 275,434 transcripts, clustering into 61,048, 47,908 and 72,985 individual clusters with N50 lengths of 1,710 bp, 1544 BP and 1,743 bp, respectively. The clustered Unigenes were searched against three public protein databases (TrEMBL, SwissProt and RefSeq) and the nucleotide and protein sequences of Gossypium raimondii using BLASTx and BLASTn. A total of 21,987, 17,209 and 25,325 Unigenes were annotated. Of these, 18,766 (85.4%), 14,552 (84.6%) and 21,374 (84.4%) Unigenes could be assigned to GO-term classifications. We identified and analyzed 13,884 differentially expressed Unigenes by clustering and functional enrichment. Terpenoid-related biosynthesis pathways showed differentially regulated expression patterns between the two cotton species. Phylogenetic analysis of the terpene synthases family was also carried out to clarify the classifications of TPSs. RNA-seq data from two distinct cotton species provide comprehensive transcriptome annotation resources and global gene expression profiles during seed germination and gland and gossypol formation. These data may be used to further elucidate various mechanisms and help promote the usage of cottonseed.

PLoS ONE, 2013

Mesenchymal stem cells (MSCs) are promising tools for the treatment of diseases such as infarcted myocardia and strokes because of their ability to promote endogenous angiogenesis and neurogenesis via a variety of secreted factors. MSCs found in the Wharton's jelly of the human umbilical cord are easily obtained and are capable of transplantation without rejection. We isolated MSCs from Wharton's jelly and bone marrow (WJ-MSCs and BM-MSCs, respectively) and compared their secretomes. It was found that WJ-MSCs expressed more genes, especially secreted factors, involved in angiogenesis and neurogenesis. Functional validation showed that WJ-MSCs induced better neural differentiation and neural cell migration via a paracrine mechanism. Moreover, WJ-MSCs afforded better neuroprotection efficacy because they preferentially enhanced neuronal growth and reduced cell apoptotic death of primary cortical cells in an oxygen-glucose deprivation (OGD) culture model that mimics the acute ischemic stroke situation in humans. In terms of angiogenesis, WJ-MSCs induced better microvasculature formation and cell migration on co-cultured endothelial cells. Our results suggest that WJ-MSC, because of a unique secretome, is a better MSC source to promote in vivo neurorestoration and endothelium repair. This study provides a basis for the development of cell-based therapy and carrying out of follow-up mechanistic studies related to MSC biology.

Physiological Genomics, 2005

expression profiling demonstrates that TGF-␤1 signals exclusively through receptor complexes involving Alk5 and identifies targets of TGF-␤ signaling. Transforming growth factor-␤1 (TGF-␤) regulates cellular functions like proliferation, differentiation, and apoptosis. On the cell surface, TGF-␤ binds to receptor complexes consisting of TGF-␤ receptor type II (T␤RII) and activin-like kinase receptor-5 (Alk5), and the downstream signaling is transduced by Smad and MAPK proteins. Recent data have shown that alternative receptor combinations aside from the classical pairing of T␤RII/Alk5 can be relevant for TGF-␤ signaling. We have screened for alternative receptors for TGF-␤ and also for gene targets of TGF-␤ signaling, by performing functional assays and microarray analysis in murine embryonic fibroblast (MEF) cell lines lacking Alk5. Data from TGF-␤stimulated Alk5 Ϫ/Ϫ cells show them to be completely unaffected by TGF-␤. Additionally, 465 downstream targets of Alk5 signaling were identified when comparing Alk5 Ϫ/Ϫ or TGF-␤-stimulated Alk5 ϩ/ϩ MEFs with unstimulated Alk5 ϩ/ϩ cells. Our results demonstrate that, in MEFs, TGF-␤ signals exclusively through complexes involving Alk5, and give insight to its downstream effector genes.

BMC Bioinformatics, 2013

High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed.

Current Protocols in Bioinformatics, 2002

EURASIP Journal on Bioinformatics and Systems Biology, 2008

Structured information provided by manual annotation of proteins with Gene Ontology concepts represents a high-quality reliable data source for the research community. However, a limited scope of proteins is annotated due to the amount of human resources required to fully annotate each individual gene product from the literature. We introduce a novel method for automatic identification of GO terms in natural language text. The method takes into consideration several features: (1) the evidence for a GO term given by the words occurring in text, (2) the proximity between the words, and (3) the specificity of the GO terms based on their information content. The method has been evaluated on the BioCreAtIvE corpus and has been compared to current state of the art methods. The precision reached 0.34 at a recall of 0.34 for the identified terms at rank 1. In our analysis, we observe that the identification of GO terms in the "cellular component" subbranch of GO is more accurate than for terms from the other two subbranches. This observation is explained by the average number of words forming the terminology over the different subbranches.

Planta, 2013

The high chromosome numbers, polyploid genomes, and long juvenile phases of most ornamental orchid species render functional genomics difficult and limit the discovery of genes influencing horticultural traits. The orchid Erycina pusilla has a low chromosome number (2n = 12) and flowers in vitro within 1 year, making it a standout candidate for use as a model orchid. However, transcriptomic and genomic information from E. pusilla remains limited. In this study, next-generation sequencing (NGS) technology was used to identify 90,668 unigenes by de novo assembly. These unigenes were annotated functionally and analyzed with regard to their gene ontology (GO), clusters of orthologous groups (COG), and KEGG pathways. To validate the discovery methods, a homolog of CONSTANS (CO), one of the key genes in the flowering pathway, was further analyzed. The Arabidopsis CO-Like (COL) amino acid sequences were used to screen for homologs in the E. pusilla transcriptome database. Specific primers to the homologous unigenes were then used to isolate BAC clones, which were sequenced to identify 12 E. pusilla CO-like (EpCOL) full-length genes. Based on sequence homology, domain structure, and phylogenetic analysis, these EpCOL genes were divided into four groups. Four EpCOLs fused with GFP were localized in the nucleus. Some EpCOL genes were regulated by light. These results demonstrate that nascent E. pusilla resources (transcriptome and BAC library) can be used to investigate the E. pusilla photoperiod-dependent flowering genes. In future, this strategy can be applied to other biological processes, marketable traits, and molecular breeding in this model orchid.

Genome biology, 2005

The mouse is an important model of human genetic disease. Describing phenotypes of mutant mice in a standard, structured manner that will facilitate data mining is a major challenge for bioinformatics. Here we describe a novel, compositional approach to this problem which combines core ontologies from a variety of sources. This produces a framework with greater flexibility, power and economy than previous approaches. We discuss some of the issues this approach raises.

BioMed Research International, 2013

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization.

PLoS ONE, 2013

Identifying diagnostic biomarkers based on genomic features for an accurate disease classification is a problem of great importance for both, basic medical research and clinical practice. In this paper, we introduce quantitative network measures as structural biomarkers and investigate their ability for classifying disease states inferred from gene expression data from prostate cancer. We demonstrate the utility of our approach by using eigenvalue and entropy-based graph invariants and compare the results with a conventional biomarker analysis of the underlying gene expression data.

BMC Bioinformatics, 2011

Background: Several large-scale gene co-expression networks have been constructed successfully for predicting gene functional modules and cis-regulatory elements in Arabidopsis (Arabidopsis thaliana). However, these networks are usually constructed and analyzed in an ad hoc manner. In this study, we propose a completely parameter-free and systematic method for constructing gene co-expression networks and predicting functional modules as well as cis-regulatory elements. Results: Our novel method consists of an automated network construction algorithm, a parameter-free procedure to predict functional modules, and a strategy for finding known cis-regulatory elements that is suitable for consensus scanning without prior knowledge of the allowed extent of degeneracy of the motif. We apply the method to study a large collection of gene expression microarray data in Arabidopsis. We estimate that our coexpression network has~94% of accuracy, and has topological properties similar to other biological networks, such as being scale-free and having a high clustering coefficient. Remarkably, among the~300 predicted modules whose sizes are at least 20, 88% have at least one significantly enriched functions, including a few extremely significant ones (ribosome, p < 1E-300, photosynthetic membrane, p < 1.3E-137, proteasome complex, p < 5.9E-126). In addition, we are able to predict cis-regulatory elements for 66.7% of the modules, and the association between the enriched cis-regulatory elements and the enriched functional terms can often be confirmed by the literature. Overall, our results are much more significant than those reported by several previous studies on similar data sets. Finally, we utilize the co-expression network to dissect the promoters of 19 Arabidopsis genes involved in the metabolism and signaling of the important plant hormone gibberellin, and achieved promising results that reveal interesting insight into the biosynthesis and signaling of gibberellin. Conclusions: The results show that our method is highly effective in finding functional modules from real microarray data. Our application on Arabidopsis leads to the discovery of the largest number of annotated Arabidopsis functional modules in the literature. Given the high statistical significance of functional enrichment and the agreement between cis-regulatory and functional annotations, we believe our Arabidopsis gene modules can be used to predict the functions of unknown genes in Arabidopsis, and to understand the regulatory mechanisms of many genes.

BMC Bioinformatics, 2010

Background: Systems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses. Results: We present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/ proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system.

BMC genomics, 2005

The Marine Genomics project is a functional genomics initiative developed to provide a pipeline for the curation of Expressed Sequence Tags (ESTs) and gene expression microarray data for marine organisms. It provides a unique clearing-house for marine specific EST and microarray data and is currently available at http://www.marinegenomics.org. The Marine Genomics pipeline automates the processing, maintenance, storage and analysis of EST and microarray data for an increasing number of marine species. It currently contains 19 species databases (over 46,000 EST sequences) that are maintained by registered users from local and remote locations in Europe and South America in addition to the USA. A collection of analysis tools are implemented. These include a pipeline upload tool for EST FASTA file, sequence trace file and microarray data, an annotative text search, automated sequence trimming, sequence quality control (QA/QC) editing, sequence BLAST capabilities and a tool for interacti...

BMC bioinformatics, 2016

Biomedical information and knowledge, structural and non-structural, stored in different repositories can be semantically connected to form a hybrid knowledge network. How to compute relatedness between concepts and discover valuable but implicit information or knowledge from it effectively and efficiently is of paramount importance for precision medicine, and a major challenge facing the biomedical research community. In this study, a hybrid biomedical knowledge network is constructed by linking concepts across multiple biomedical ontologies as well as non-structural biomedical knowledge sources. To discover implicit relatedness between concepts in ontologies for which potentially valuable relationships (implicit knowledge) may exist, we developed a Multi-Ontology Relatedness Model (MORM) within the knowledge network, for which a relatedness network (RN) is defined and computed across multiple ontologies using a formal inference mechanism of set-theoretic operations. Semantic const...

BMC Biology

Background: Cassava is an important food crop in tropical and sub-tropical regions worldwide. In Africa, cassava production is widely affected by cassava mosaic disease (CMD), which is caused by the African cassava mosaic geminivirus that is transmitted by whiteflies. Cassava breeders often use a single locus, CMD2, for introducing CMD resistance into susceptible cultivars. The CMD2 locus has been genetically mapped to a 10-Mbp region, but its organization and genes as well as their functions are unknown. Results: We report haplotype-resolved de novo assemblies and annotations of the genomes for the African cassava cultivar TME (tropical Manihot esculenta), which is the origin of CMD2, and the CMD-susceptible cultivar 60444. The assemblies provide phased haplotype information for over 80% of the genomes. Haplotype comparison identified novel features previously hidden in collapsed and fragmented cassava genomes, including thousands of allelic variants, inter-haplotype diversity in coding regions, and patterns of diversification through allele-specific expression. Reconstruction of the CMD2 locus revealed a highly complex region with nearly identical gene sets but limited microsynteny between the two cultivars.

BMC genomics, 2017

Organisms typically face infection by diverse pathogens, and hosts are thought to have developed specific responses to each type of pathogen they encounter. The advent of transcriptomics now makes it possible to test this hypothesis and compare host gene expression responses to multiple pathogens at a genome-wide scale. Here, we performed a meta-analysis of multiple published and new transcriptomes using a newly developed bioinformatics approach that filters genes based on their expression profile across datasets. Thereby, we identified common and unique molecular responses of a model host species, the honey bee (Apis mellifera), to its major pathogens and parasites: the Microsporidia Nosema apis and Nosema ceranae, RNA viruses, and the ectoparasitic mite Varroa destructor, which transmits viruses. We identified a common suite of genes and conserved molecular pathways that respond to all investigated pathogens, a result that suggests a commonality in response mechanisms to diverse p...

Systems Biology, 2010

The relentless advance of biochemistry has enabled us to take apart biological systems with ever more fine-grained and precise instruments. The fruits of this dissection are millions of measurements of base pairs and biochemical concentrations. Yet to make sense of these numbers, we need to reverse our dissection by putting the system back together on the computer. This first step in this process is reconstructing molecular anatomy through static modeling, the determination of which pieces (DNA, RNA, protein, and metabolite) is present, and how they are related (e.g., regulator, target, inhibitor, cofactor). Given this broad outline of component connectivity, we may then attempt to reconstruct molecular physiology via dynamic modeling, computer simulations that model when cellular events occur (ODE), where they occur (PDE), and how frequently they recur (SDE). In this review we discuss techniques for both of these modeling paradigms, illustrating each by reference to important recent papers.

Standards in genomic sciences, 2017

Crude oil contamination of soils and waters is a worldwide problem, which has been actively addressed in recent years. Sequencing genomes of microorganisms involved in the degradation of hydrocarbons have allowed the identification of several promoters, genes, and degradation pathways of these contaminants. This knowledge allows a better understanding of the functional dynamics of microbial degradation. Here, we report a first draft of the 44.2 Mbp genome assembly of an environmental strain of the fungus Scedosporium apiospermum. The assembly consisted of 178 high-quality DNA scaffolds with 1.93% of sequence repeats identified. A total of 11,195 protein-coding genes were predicted including a diverse group of gene families involved in hydrocarbon degradation pathways like dioxygenases and cytochrome P450. The metabolic pathways identified in the genome can potentially degrade hydrocarbons like chloroalkane/alkene, chorocyclohexane, and chlorobenzene, benzoate, aminobenzoate, fluorob...

Bioinformatics, 2009

Motivation: We noted that the sumoylation site in C/EBP homologues is conserved beyond the canonical consensus sequence for sumoylation. Therefore, we investigated whether this pattern might define a more general protein motif. Results: We undertook a survey of the human proteome using a regular expression based on the C/EBP motif. This revealed significant enrichment of the motif using different Gene Ontology terms (e.g. 'transcription') that pertain to the nucleus. When considering requirements for the motif to be functional (evolutionary conservation, structural accessibility of the motif and proper cell localization of the protein), more than 130 human proteins were retrieved from the UniProt/Swiss-Prot database. These candidates were particularly enriched in transcription factors, including FOS, JUN, Hif-1α, MLL2 and members of the KLF, MAF and NFATC families; chromatin modifiers like CHD-8, HDAC4 and DNA Top1; and the transcriptional regulatory kinases HIPK1 and HIPK2. The KEPE motif appears to be restricted to the metazoan lineage and has three length variants-short, medium and long-which do not appear to interchange.

PLOS ONE, 2016

Bean (Phaseolus vulgaris) anthracnose caused by the hemi-biotrophic pathogen Colletotrichum lindemuthianum is a major factor limiting production worldwide. Although sources of resistance have been identified and characterized, the early molecular events in the hostpathogen interface have not been investigated. In the current study, we conducted a comprehensive transcriptome analysis using Illumina sequencing of two near isogenic lines (NILs) differing for the presence of the Co-1 gene on chromosome Pv01 during a time course following infection with race 73 of C. lindemuthianum. From this, we identified 3,250 significantly differentially expressed genes (DEGs) within and between the NILs over the time course of infection. During the biotrophic phase the majority of DEGs were up regulated in the susceptible NIL, whereas more DEGs were up-regulated in the resistant NIL during the necrotrophic phase. Various defense related genes, such as those encoding PR proteins, peroxidases, lipoxygenases were up regulated in the resistant NIL. Conversely, genes encoding sugar transporters were up-regulated in the susceptible NIL during the later stages of infection. Additionally, numerous transcription factors (TFs) and candidate genes within the vicinity of the Co-1 locus were differentially expressed, suggesting a global reprogramming of gene expression in and around the Co-1 locus. Through this analysis, we reduced the previous number of candidate genes reported at the Co-1 locus from eight to three. These results suggest the dynamic nature of P. vulgaris-C. lindemuthianum interaction at the transcriptomic level and reflect the role of both pathogen and effector triggered immunity on changes in plant gene expression.

Bioinformatics, 2008

The R package HCGene (Hierarchical Classification of Genes) implements methods to process and analyze the Gene Ontology and the FunCat taxonomy in order to support the functional classification of genes. HCGene allows the extraction of subgraphs and subtrees related to specific biological problems, the labelling of genes and gene products with multiple and hierarchical functional classes, and the association of different types of bio-molecular data to genes for learning to predict their functions.

PLOS ONE

Acute hepatopancreatic necrosis disease (AHPND), caused by marine bacteria Vibrio Parahaemolyticus, is a huge problem in shrimp farms. The V. parahaemolyticus infecting material is contained in a plasmid which encodes for the lethal toxins PirAB Vp , whose primary target tissue is the hepatopancreas, causing sloughing of epithelial cells, necrosis, and massive hemocyte infiltration. To get a better understanding of the hepatopancreas response during AHPND, juvenile shrimp Litopenaeus vannamei were infected by immersion with V. parahaemolyticus. We performed transcriptomic mRNA sequencing of infected shrimp hepatopancreas, at 24 hours post-infection, to identify novel differentially expressed genes a total of 174,098 transcripts were examined of which 915 transcripts were found differentially expressed after comparative transcriptomic analysis: 442 up-regulated and 473 downregulated transcripts. Gene Ontology term enrichment analysis for up-regulated transcripts includes metabolic process, regulation of programmed cell death, carbohydrate metabolic process, and biological adhesion, whereas for down-regulated transcripts include, microtubule-based process, cell activation, and chitin metabolic process. The analysis of proteinprotein network between up and down-regulated genes indicates that the first gene interactions are connected to oxidation-processes and sarcomere organization. Additionally, protein-protein networks analysis identified 20-top highly connected hub nodes. Based on their immunological or metabolic function, ten candidate transcripts were selected to measure their mRNA relative expression levels in AHPND infected shrimp hepatopancreas by RT-qPCR. Our results indicate a close connection between the immune and metabolism PLOS ONE |

Entropy

Selection of biologically relevant genes from high-dimensional expression data is a key research problem in gene expression genomics. Most of the available gene selection methods are either based on relevancy or redundancy measure, which are usually adjudged through post selection classification accuracy. Through these methods the ranking of genes was conducted on a single high-dimensional expression data, which led to the selection of spuriously associated and redundant genes. Hence, we developed a statistical approach through combining a support vector machine with Maximum Relevance and Minimum Redundancy under a sound statistical setup for the selection of biologically relevant genes. Here, the genes were selected through statistical significance values and computed using a nonparametric test statistic under a bootstrap-based subject sampling model. Further, a systematic and rigorous evaluation of the proposed approach with nine existing competitive methods was carried on six dif...

The autophagy-lysosomal pathway is impaired in many neurodegenerative diseases characterized by protein aggregation, but the link between aggregation and lysosomal dysfunction remains poorly understood. Here, we use artificial amyloid-like β-sheet proteins (β proteins) to investigate the gain-of-function effects of protein aggregation in primary neurons. We show that β proteins form fibrillar aggregates and cause neurotoxicity. Cryo-electron tomography reveals lysosomal alterations reminiscent of lysosomal storage disorders. Mass spectrometry-based analysis of the β protein interactome shows that β proteins sequester AP-3μ1, a subunit of the AP-3 adaptor complex involved in protein trafficking to lysosomal organelles. Importantly, restoring AP-3μ1 expression ameliorates neurotoxicity caused by β proteins. Our results point to lysosomes as particularly vulnerable organelles in neurodegenerative diseases, and emphasize the role of toxic gain-of-function of protein aggregates in lysoso...

Frontiers in Molecular Biosciences

Wound healing involves an orchestrated response that engages multiple processes, such as hemostasis, cellular migration, extracellular matrix synthesis, and in particular, inflammation. Using a murine model of cutaneous wound repair, the transcriptome was mapped from 12 h to 8 days post-injury, and in response to a multicomponent, multi-target natural product, Tr14. Using single-molecule RNA sequencing (RNA-seq), there were clear temporal changes in known transcripts related to wound healing pathways, and additional novel transcripts of both coding and non-coding genes. Tr14 treatment modulated >100 transcripts related to key wound repair pathways, such as response to wounding, wound contraction, and cytokine response. The results provide the most precise and comprehensive characterization to date of the transcriptome's response to skin damage, repair, and multicomponent natural product therapy. By understanding the wound repair process, and the effects of natural products, it should be possible to intervene more effectively in diseases involving aberrant repair.

BMC genomics, 2015

There is an increasing interest in berries, especially blackberries in the diet, because of recent reports of their health benefits due to their high content of flavonoids. A broad range of genomic tools are available for other Rosaceae species but these tools are still lacking in the Rubus genus, thus limiting gene discovery and the breeding of improved varieties. De novo RNA-seq of ripe blackberries grown under field conditions was performed using Illumina Hiseq 2000. Almost 9 billion nucleotide bases were sequenced in total. Following assembly, 42,062 consensus sequences were detected. For functional annotation, 33,040 (NR), 32,762 (NT), 21,932 (Swiss-Prot), 20,134 (KEGG), 13,676 (COG), 24,168 (GO) consensus sequences were annotated using different databases; in total 34,552 annotated sequences were identified. For protein prediction analysis, the number of coding DNA sequences (CDS) that mapped to the protein database was 32,540. Non redundant (NR), annotation showed that 25,418...

eLife

How cells adjust nutrient transport across their membranes is incompletely understood. Previously, we have shown that S. cerevisiae broadly re-configures the nutrient transporters at the plasma membrane in response to amino acid availability, through endocytosis of sugar- and amino acid transporters (AATs) (Müller et al., 2015). A genome-wide screen now revealed that the selective endocytosis of four AATs during starvation required the α-arrestin family protein Art2/Ecm21, an adaptor for the ubiquitin ligase Rsp5, and its induction through the general amino acid control pathway. Art2 uses a basic patch to recognize C-terminal acidic sorting motifs in AATs and thereby instructs Rsp5 to ubiquitinate proximal lysine residues. When amino acids are in excess, Rsp5 instead uses TORC1-activated Art1 to detect N-terminal acidic sorting motifs within the same AATs, which initiates exclusive substrate-induced endocytosis. Thus, amino acid excess or starvation activate complementary α-arrestin...

PloS one, 2015

Hibernation is one type of torpor, a hypometabolic state in heterothermic mammals, which can be used as an energy-conservation strategy in response to harsh environments, e.g. limited food resource. The liver, in particular, plays a crucial role in adaptive metabolic adjustment during hibernation. Studies on ground squirrels and bears reveal that many genes involved in metabolism are differentially expressed during hibernation. Especially, the genes involved in carbohydrate catabolism are down-regulated during hibernation, while genes responsible for lipid β-oxidation are up-regulated. However, there is little transcriptional evidence to suggest physiological changes to the liver during hibernation in the greater horseshoe bat, a representative heterothermic bat. In this study, we explored the transcriptional changes in the livers of active and torpid greater horseshoe bats using the Illumina HiSeq 2000 platform. A total of 1358 genes were identified as differentially expressed duri...

BMC Bioinformatics, 2011

Background Genomics and proteomics experiments produce a large amount of data that are awaiting functional elucidation. An important step in analyzing such data is to identify functional units, which consist of proteins that play coherent roles to carry out the function. Importantly, functional coherence is not identical with functional similarity. For example, proteins in the same pathway may not share the same Gene Ontology (GO) terms, but they work in a coordinated fashion so that the aimed function can be performed. Thus, simply applying existing functional similarity measures might not be the best solution to identify functional units in omics data. Results We have designed two scores for quantifying the functional coherence by considering association of GO terms observed in two biological contexts, co-occurrences in protein annotations and co-mentions in literature in the PubMed database. The counted co-occurrences of GO terms were normalized in a similar fashion as the statis...

Journal of Structural and Functional Genomics, 2012

The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recently developments of local structure-based methods and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, PFP and ESG, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.

PLOS Computational Biology

Mechanism is a widely used concept in biology. In 2017, more than 10% of PubMed abstracts used the term. Therefore, searching for and reasoning about mechanisms is fundamental to much of biomedical research, but until now there has been almost no computational infrastructure for this purpose. Recent work in the philosophy of science has explored the central role that the search for mechanistic accounts of biological phenomena plays in biomedical research, providing a conceptual basis for representing and analyzing biological mechanism. The foundational categories for components of mechanisms-entities and activities-guide the development of general, abstract types of biological mechanism parts. Building on that analysis, we have developed a formal framework for describing and representing biological mechanism, MecCog, and applied it to describing mechanisms underlying human genetic disease. Mechanisms are depicted using a graphical notation. Key features are assignment of mechanism components to stages of biological organization and classes; visual representation of uncertainty, ignorance, and ambiguity; and tight integration with literature sources. The MecCog framework facilitates analysis of many aspects of disease mechanism, including the prioritization of future experiments, probing of gene−drug and gene−environment interactions, identification of possible new drug targets, personalized drug choice, analysis of nonlinear interactions between relevant genetic loci, and classification of diseases based on mechanism.

Journal of Clinical Medicine

Despite a major interest in understanding how the endothelial cell phenotype is established, the underlying molecular basis of this process is not yet fully understood. We have previously reported the generation of induced pluripotent stem cells (iPS) from human umbilical vein endothelial cells and differentiation of the resulting HiPS back to endothelial cells (Ec-Diff), as well as neural (Nn-Diff) cell lineage that contained both neurons and astrocytes. Furthermore, the identities of these cell lineages were established by gene array analysis. Here, we explored the same arrays to gain insight into the gene alteration processes that accompany the establishment of endothelial vs. non-endothelial neural cell phenotypes. We compared the expression of genes that code for transcription factors and epigenetic regulators when HiPS is differentiated into these endothelial and non-endothelial lineages. Our in silico analyses have identified cohorts of genes that are similarly up- or downreg...

PLOS Biology

Cell-cycle checkpoints and DNA repair processes protect organisms from potentially lethal mutational damage. Compared to other budding yeasts in the subphylum Saccharomycotina, we noticed that a lineage in the genus Hanseniaspora exhibited very high evolutionary rates, low Guanine-Cytosine (GC) content, small genome sizes, and lower gene numbers. To better understand Hanseniaspora evolution, we analyzed 25 genomes, including 11 newly sequenced, representing 18/21 known species in the genus. Our phylogenomic analyses identify two Hanseniaspora lineages, a faster-evolving lineage (FEL), which began diversifying approximately 87 million years ago (mya), and a slower-evolving lineage (SEL), which began diversifying approximately 54 mya. Remarkably, both lineages lost genes associated with the cell cycle and genome integrity, but these losses were greater in the FEL. E.g., all species lost the cell-cycle regulator WHIskey 5 (WHI5), and the FEL lost components of the spindle checkpoint pathway (e.g., Mitotic Arrest-Deficient 1 [MAD1], Mitotic Arrest-Deficient 2 [MAD2]) and DNA-damage-checkpoint pathway (e.g., Mitosis Entry Checkpoint 3 [MEC3], RADiation sensitive 9 [RAD9]). Similarly, both lineages lost genes involved in DNA repair pathways, including the DNA glycosylase gene 3-MethylAdenine DNA Glycosylase 1 (MAG1), which is part of the base-excision repair pathway, and the DNA photolyase gene PHotoreactivation Repair deficient 1 (PHR1), which is involved in pyrimidine dimer repair. Strikingly, the FEL lost 33 additional genes, including polymerases

BMC Systems Biology, 2012

Background Control of growth rate is mediated by tight regulation mechanisms in all free-living organisms since long-term survival depends on adaptation to diverse environmental conditions. The yeast, Saccharomyces cerevisiae, when growing under nutrient-limited conditions, controls its growth rate via both nutrient-specific and nutrient-independent gene sets. At slow growth rates, at least, it has been found that the expression of the genes that exert significant control over growth rate (high flux control or HFC genes) is not necessarily regulated by growth rate itself. It has not been determined whether the set of HFC genes is the same at all growth rates or whether it is the same in conditions of nutrient limitation or excess. Results HFC genes were identified in competition experiments in which a population of hemizygous diploid yeast deletants were grown at, or close to, the maximum specific growth rate in either nutrient-limiting or nutrient-sufficient conditions. A hemizygou...

Molecular Genetics and Genomics

Bursaphelenchus xylophilus is an emerging pathogenic nematode that is responsible for a devastating epidemic of pine wilt disease worldwide, causing severe ecological damage and economic losses to forestry. Two forms of this nematode have been reported, i.e., with strong and weak virulence, commonly referred as virulent and avirulent strains. However, the pathogenicity-related genes of B. xylophilus are not sufficiently characterized. In this study, to find pathogenesis related genes we re-sequenced and compared genomes of two virulent and two avirulent populations. We identified genes affected by genomic variation, and functional annotation of those genes indicated that some of them might play potential roles in pathogenesis. The performed analysis showed that both avirulent populations differed from the virulent ones by 1576 genes with high impact variants. Demonstration of genetic differences between virulent and avirulent strains will provide effective methods to distinguish the...

Nucleic acids research, 2018

Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target selection for drug discovery. Here, we present Target-Pathogen database (http://target.sbg.qb.fcen.uba.ar/patho), designed and developed as an online resource that allows the integration and weighting of protein information such as: function, metabolic role, off-targeting, structural properties including druggability, essentiality and omic experiments, to facilitate the identification and prioritization of candidate drug targets in pathogens. We include in the database 10 genomes of some of the m...

BioMed research international, 2014

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biologic...

Acta Pharmaceutica

Recently, an outbreak of a fatal coronavirus, SARS-CoV-2, has emerged from China and is rapidly spreading worldwide. Possible interaction of SARS-CoV-2 with DPP4 peptidase may partly contribute to the viral pathogenesis. An integrative bioinformatics approach starting with mining the biomedical literature for high confidence DPP4-protein/gene associations followed by functional analysis using network analysis and pathway enrichment was adopted. The results indicate that the identified DPP4 networks are highly enriched in viral processes required for viral entry and infection, and as a result, we propose DPP4 as an important putative target for the treatment of COVID-19. Additionally, our protein-chemical interaction networks identified important interactions between DPP4 and sitagliptin. We conclude that sitagliptin may be beneficial for the treatment of COVID-19 disease, either as monotherapy or in combination with other therapies, especially for diabetic patients and patients with...

BMC Bioinformatics

Background With the rapid advancement of genomic sequencing techniques, massive production of gene expression data is becoming possible, which prompts the development of precision medicine. Deep learning is a promising approach for phenotype prediction (clinical diagnosis, prognosis, and drug response) based on gene expression profile. Existing deep learning models are usually considered as black-boxes that provide accurate predictions but are not interpretable. However, accuracy and interpretation are both essential for precision medicine. In addition, most models do not integrate the knowledge of the domain. Hence, making deep learning models interpretable for medical applications using prior biological knowledge is the main focus of this paper. Results In this paper, we propose a new self-explainable deep learning model, called Deep GONet, integrating the Gene Ontology into the hierarchical architecture of the neural network. This model is based on a fully-connected architecture ...

PLoS ONE, 2008

Background: The effect of dietary fats on human health and disease are likely mediated by changes in gene expression. Several transcription factors have been shown to respond to fatty acids, including SREBP-1c, NF-kB, RXRs, LXRs, FXR, HNF4a, and PPARs. However, it is unclear to what extent these transcription factors play a role in gene regulation by dietary fatty acids in vivo. Methodology/Principal Findings: Here, we take advantage of a unique experimental design using synthetic triglycerides composed of one single fatty acid in combination with gene expression profiling to examine the effects of various individual dietary fatty acids on hepatic gene expression in mice. We observed that the number of significantly changed genes and the fold-induction of genes increased with increasing fatty acid chain length and degree of unsaturation. Importantly, almost every single gene regulated by dietary unsaturated fatty acids remained unaltered in mice lacking PPARa. In addition, the majority of genes regulated by unsaturated fatty acids, especially docosahexaenoic acid, were also regulated by the specific PPARa agonist WY14643. Excellent agreement was found between the effects of unsaturated fatty acids on mouse liver versus cultured rat hepatoma cells. Interestingly, using Nuclear Receptor PamChipH Arrays, fatty acid-and WY14643induced interactions between PPARa and coregulators were found to be highly similar, although several PPARa-coactivator interactions specific for WY14643 were identified. Conclusions/Significance: We conclude that the effects of dietary unsaturated fatty acids on hepatic gene expression are almost entirely mediated by PPARa and mimic those of synthetic PPARa agonists in terms of regulation of target genes and molecular mechanism. Use of synthetic dietary triglycerides may provide a novel paradigm for nutrigenomics research.

ABSTRACTEnTAP (Eukaryotic Non-Model Transcriptome Annotation Pipeline) was designed to improve the accuracy, speed, and flexibility of functional gene annotation for de novo assembled transcriptomes in non-model eukaryotes. This software package addresses the fragmentation and related assembly issues that result in inflated transcript estimates and poor annotation rates. Following filters applied through assessment of true expression and frame selection, open-source tools are leveraged to functionally annotate the translated proteins. Downstream features include fast similarity search across three repositories, protein domain assignment, orthologous gene family assessment, and Gene Ontology term assignment. The final annotation integrates across multiple databases and selects an optimal assignment from a combination of weighted metrics describing similarity search score, taxonomic relationship, and informativeness. Researchers have the option to include additional filters to identif...

BMC Genomics, 2006

Background: DNA duplex sequences that can be targets for triplex formation are highly overrepresented in the human genome, especially in regulatory regions. Results: Here we studied using bioinformatics tools several properties of triplex target sequences in an attempt to determine those that make these sequences so special in the genome. Conclusion: Our results strongly suggest that the unique physical properties of these sequences make them particularly suitable as "separators" between protein-recognition sites in the promoter region.

Molecular Ecology

The fire ant Solenopsis invicta and its close relatives are highly invasive. Enhanced social cooperation may facilitate invasiveness in these and other invasive ant species. We investigated whether invasiveness in Solenopsis fire ants was accompanied by positive selection on sociobiological traits by applying a phylogenomics approach to infer ancient selection, and a population genomics approach to infer recent and ongoing selection in both native and introduced S. invicta populations. A combination of whole-genome sequencing of 40 haploid males and reduced-representation genomic sequencing of 112 diploid workers identified 1,758,116 and 169,682 polymorphic markers, respectively. The resulting high-resolution maps of genomic polymorphism provide high inference power to test for positive selection. Our analyses provide evidence of positive selection on putative ion channel genes, which are implicated in neurological functions, and on vitellogenin, which is a key regulator of development and caste determination. Furthermore, molecular functions implicated in pheromonal signalling have experienced recent positive selection. Genes with signatures of positive selection were significantly more often those overexpressed in workers compared with queens and males, suggesting that worker traits are under stronger selection than queen and male traits. These results provide insights into selection pressures and ongoing adaptation in an invasive social insect and support the hypothesis that sociobiological traits are under more positive selection than nonsocial traits in such invasive species.

Genetic Epidemiology, 2009

Genome-wide association studies (GWAS) continue to gain in popularity. To utilize the wealth of data created more effectively, a variety of methods have recently been proposed to include a priori information (e.g., biologically interpretable sets of genes, candidate gene information, or gene expression) in GWAS analysis. Six contributions to Genetic Analysis Workshop 16 Group 11 applied novel or recently proposed methods to GWAS of rheumatoid arthritis and heart disease related phenotypes. The results of these analyses were a variety of novel candidate genes and sets of genes, in addition to the validation of well known genotype-phenotype associations. However, because many methods are relatively new, they would benefit from further methodological research to ensure that they maintain type I error rates while increasing power to find additional associations. When methods have been adapted from other study types (e.g., gene expression data analysis or linkage analysis) the lessons learned there should be used to guide implementation of techniques. Lastly, many open research questions exist concerning the logistic details of the origin of the a priori information and the way to incorporate it. Overall, our group has demonstrated a strong potential for identifying novel genotype-phenotype relationships by including a priori data in the analysis of GWAS, while also uncovering a series of questions requiring further research.

Glycobiology, 2006

Lectins, a class of carbohydrate-binding proteins, are now widely recognized to play a range of crucial roles in many cell-cell recognition events triggering several important cellular processes. They encompass different members that are diverse in their sequences, structures, binding site architectures, quaternary structures, carbohydrate affinities, and specificities as well as their larger biological roles and potential applications. It is not surprising, therefore, that the vast amount of experimental data on lectins available in the literature is so diverse, that it becomes difficult and time consuming, if not impossible to comprehend the advances in various areas and obtain the maximum benefit. To achieve an effective use of all the data toward understanding the function and their possible applications, an organization of these seemingly independent data into a common framework is essential. An integrated knowledge base (Lectindb, http://nscdb.bic.physics.iisc.ernet.in) together with appropriate analytical tools has therefore been developed initially for plant lectins by collating and integrating diverse data. The database has been implemented using MySQL on a Linux platform and web-enabled using PERL-CGI and Java tools. Data for each lectin pertain to taxonomic, biochemical, domain architecture, molecular sequence, and structural details as well as carbohydrate and hence blood group specificities. Extensive links have also been provided for relevant bioinformatics resources and analytical tools. Availability of diverse data integrated into a common framework is expected to be of high value not only for basic studies in lectin biology but also for basic studies in pursuing several applications in biotechnology, immunology, and clinical practice, using these molecules.

Geoscience Communication

International Journal of Molecular Sciences

Both agonist studies and loss-of-function models indicate that PPARγ plays an important role in cutaneous biology. Since PPARγ has a high level of basal activity, we hypothesized that epidermal PPARγ would regulate normal homeostatic processes within the epidermis. In this current study, we performed mRNA sequencing and differential expression analysis of epidermal scrapings from knockout mice and wildtype littermates. Pparg-/-epi mice exhibited a 1.5-fold or greater change in the expression of 11.8% of 14,482 identified transcripts. Up-regulated transcripts included those for a large number of cytokines/chemokines and their receptors, as well as genes associated with inflammasome activation and keratinization. Several of the most dramatically up-regulated pro-inflammatory genes in Pparg-/-epi mouse skin included Igfl3, 2610528A11Rik, and Il1f6. RT-PCR was performed from RNA obtained from non-lesional full-thickness skin and verified a marked increase in these transcripts, as well a...

Cancers

Therapeutic blockade of PD-1/PD-L1 shows promising results in Hodgkin’s lymphoma (HL) and in some diffuse large B-cell lymphoma (DLBCL) patients, but biomarkers predicting such responses are still lacking. To this end, we recently developed a transcriptional scoring of immune escape (IE) in cancer biopsies. Using this method in DLBCL, we identified four stages of IE correlated with overall survival, but whether Hodgkin’s lymphomas (HL) also display this partition was unknown. Thus, we explored the transcriptomic profiles of ~1000 HL and DLBCL using a comparative meta-analysis of their bulk microarrays. Relative to DLBCL, the HL co-clustered at the advanced stage of immune escape, displaying significant enrichment of both IE and T-cell activation genes. Analyses via transcriptome deconvolution and immunohistochemistry showed more CD3+ and CD4+ tumor-infiltrating lymphocytes (TILs) in HL than DLBCL. Both HL and non-GCB DLBCL shared a high abundance of infiltrating CD8+ T-cells, but HL...

Journal of biomedical semantics, 2017

High-throughput technologies produce huge amounts of heterogeneous biological data at all cellular levels. Structuring these data together with biological knowledge is a critical issue in biology and requires integrative tools and methods such as bio-ontologies to extract and share valuable information. In parallel, the development of recent whole-cell models using a systemic cell description opened alternatives for data integration. Integrating a systemic cell description within a bio-ontology would help to progress in whole-cell data integration and modeling synergistically. We present BiPON, an ontology integrating a multi-scale systemic representation of bacterial cellular processes. BiPON consists in of two sub-ontologies, bioBiPON and modelBiPON. bioBiPON organizes the systemic description of biological information while modelBiPON describes the mathematical models (including parameters) associated with biological processes. bioBiPON and modelBiPON are related using bridge rul...

The Plant Cell, 2013

Plant behaviors across levels of cellular organization, from biochemical components to tissues and organs, relate and reflect growth habitats. Quantification of the relationship between behaviors captured in various phenotypic characteristics and growth habitats can help reveal molecular mechanisms of plant adaptation. The aim of this article is to introduce the power of using statistics originally developed in the field of geographic variability analysis together with prominent network models in elucidating principles of biological organization. We provide a critical systematic review of the existing statistical and network-based approaches that can be employed to determine patterns of covariation from both uni- and multivariate phenotypic characteristics in plants. We demonstrate that parameter-independent network-based approaches result in robust insights about phenotypic covariation. These insights can be quantified and tested by applying well-established statistics combining th...

Nucleic Acids Research, 2012

The Gene Ontology Normal Usage Tracking System (GONUTS) is a community-based browser and usage guide for Gene Ontology (GO) terms and a community system for general GO annotation of proteins. GONUTS uses wiki technology to allow registered users to share and edit notes on the use of each term in GO, and to contribute annotations for specific genes of interest. By providing a site for generation of third-party documentation at the granularity of individual terms, GONUTS complements the official documentation of the Gene Ontology Consortium. To provide examples for community users, GONUTS displays the complete GO annotations from seven model organisms: Saccharomyces cerevisiae, Dictyostelium discoideum, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Mus musculus and Arabidopsis thaliana. To support community annotation, GONUTS allows automated creation of gene pages for gene products in UniProt. GONUTS will improve the consistency of annotation efforts across genome projects, and should be useful in training new annotators and consumers in the production of GO annotations and the use of GO terms. GONUTS can be accessed at http://gowiki.tamu .edu. The source code for generating the content of GONUTS is available upon request.

Natural rubber is an indispensable commodity used in approximately 40,000 products and is fundamental to the tire industry. Among the species that produce latex, the rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell-Arg.], a species native to the Amazon rainforest, is the major producer of latex used worldwide. The Amazon Basin presents optimal conditions for rubber tree growth, but the occurrence of South American leaf blight, which is caused by the fungus Microcyclus ulei (P. Henn) v. Arx, limits rubber tree production. Currently, rubber tree plantations are located in scape regions that exhibit suboptimal conditions such as high winds and cold temperatures. Rubber tree breeding programs aim to identify clones that are adapted to these stress conditions. However, rubber tree breeding is time-consuming, taking more than 20 years to develop a new variety. It is also expensive and requires large field areas. Thus, genetic studies could optimize field evaluations, thereb...

BMC Bioinformatics

Background: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes. Results: We evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes. Conclusions: The results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods.

Communications Physics

Protein–protein interaction networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID–19 and providing ground for applications, such as drug repurposing. Characterizing molecular (dis)similarities between SARS-CoV-2 and other viral agents allows one to exploit existing information about the alteration of key biological processes due to known viruses for predicting the potential effects of this new virus. Here, we compare the novel coronavirus network against 92 known viruses, from the perspective of statistical physics and computational biology. We show that regulatory spreading patterns, physical features and enriched biological pathways in targeted proteins lead, overall, to meaningful clusters of viruses which, across scales, provide complementary perspectives to better characterize SARS-CoV-2 and its effects on humans. Our results indicate that the virus responsible for COVID–19 ex...

Big Data and Cognitive Computing

In the era of Big Data and IoT, successful systems have to be designed to discover, store, process, learn, analyse, and predict from a massive amount of data—in short, they have to behave intelligently. Despite the success of non-symbolic techniques such as deep learning, symbolic approaches to machine intelligence still have a role to play in order to achieve key properties such as observability, explainability, and accountability. In this paper we focus on logic programming (LP), and advocate its role as a provider of symbolic reasoning capabilities in IoT scenarios, suitably complementing non-symbolic ones. In particular, we show how its re-interpretation in terms of LPaaS (Logic Programming as a Service) can work as an enabling technology for distributed situated intelligence. A possible example of hybrid reasoning—where symbolic and non-symbolic techniques fruitfully combine to produce intelligent behaviour—is presented, demonstrating how LPaaS could work in a smart energy grid...

Proceedings of the National Academy of Sciences, 2007

Scientific Reports

Molecular mechanisms associated with biochar-elicited suppression of soilborne plant diseases and improved plant performance are not well understood. A stem base inoculation approach was used to explore the ability of biochar to induce systemic resistance in tomato plants against crown rot caused by a soilborne pathogen, Fusarium oxysporum f. sp. radicis lycopersici. RNA-seq transcriptome profiling of tomato, and experiments with jasmonic and salycilic acid deficient tomato mutants, were performed to elucidate the in planta molecular mechanisms involved in induced resistance. Biochar (produced from greenhouse plant wastes) was found to mediate systemic resistance against Fusarium crown rot and to simultaneously improve tomato plant growth and physiological parameters by up to 63%. Transcriptomic analysis (RNA-seq) of tomato demonstrated that biochar had a priming effect on gene expression and upregulated the pathways and genes associated with plant defense and growth such as jasmoni...

Background: One of the most challenging issue in the variant calling process is handling the resulting data, and filtering the genes retaining only the ones strictly related to the topic of interest. Several tools permit to gather annotations at different levels of complexity for the detected genes and to group them according to the pathways and/or processes they belong to. However, it might be a time consuming and frustrating task. This is partly due to the size of the file, that might contain many thousands of genes, and to the search of associated variants that requires a gene-by-gene investigation and annotation approach. As a consequence, the initial gene list is often reduced exploiting the knowledge of variants effect, novelty and genotype, with the potential risk of losing meaningful pieces of information. Results: Here we present Var2GO, a new web-based tool to support the annotation and filtering of variants and genes coming from variant calling of high-throughput sequencing data. Var2GO permits to upload either the unprocessed Variant Calling Format file or a table containing the annotated variants. The raw data undergo a preliminary step of variants annotation, using the SnpEff tool, and are converted to a table format. The table is then uploaded into an on the fly generated database. Genes associated to the variants are automatically annotated with the corresponding Gene Ontology terms covering the three GO domains. Using the web interface it is then possible to filter and extract, from the whole list, genes having annotations in the domain of interest, by simply specifying filtering parameters and one or more keywords. The relevance of this tool is demonstrated on exome sequencing data. Conclusions: Var2GO is a novel tool that implements a topic-based approach, expressly designed to help biologists in narrowing the search of relevant genes coming from variant calling analysis. Its main purpose is to support non-bioinformaticians in handling and processing raw variant calling data through an intuitive web interface. Furthermore, Var2GO offers a complete pipeline that, starting from the raw VCF file, allows to annotate both variants and associated genes and supports the extraction of relevant biological knowledge.

Applied Network Science

The study of co-expression programs in the context of cancer can help to elucidate the genetic mechanisms that are altered and lead to the disease. The identification of gene co-expression patterns, unique to healthy profiles (and absent in cancer) is an important step in this direction. Networks are a good tool for achieving this as they allow to model local and global structural properties of the gene co-expression program. This is the case of gene co-expression networks (GCNs), where nodes or vertices represent genes and an edge between two nodes exists if the corresponding genes are co-expressed. Single threshold co-expression networks are often used for this purpose. However, important interactions in a broader co-expression space needed to unravel such mechanisms may be overlooked. In this work, we use a multilayer network approach that allows us to study co-expression as a discrete object, starting at weak levels of co-expression building itself upward towards the top co-expr...

Scientific Reports

Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is popular for addressing the challenges of hidden population stratification and polygenic effects. However, most of these methods including LMM are sensitive to phenotypic outliers that may lead the misleading results. To overcome this problem, in this paper, we proposed a way to robustify the LMM approach for reducing the influence of outlying observations using the β-divergence method. The performance of the proposed method was investigated using both synthetic and real data analysis. Simulation results showed that the proposed method performs better than both linear regression model (LRM) and LMM approaches in terms of powers and false discovery rates in presence of phenotypic outliers. On the other hand, the proposed method perfor...

Lecture Notes in Computer Science

Thyroid cancer is one of the most prevalent cancers which affects a large population all over the world. To find effective therapeutic measures against thyroid cancer, it is necessary to identify potential genes which lead to this disease. In this paper, we consider an ensemble of structural, semantic and post translational modification (PTM) similarities based clustering of human genes using known thyroid cancer genes as seeds. Our purpose is to identify potential genes which may be responsible for thyroid cancer from the clusters.

Gut pathogens, 2013

The yeast Saccharomyces boulardii is used worldwide as a probiotic to alleviate the effects of several gastrointestinal diseases and control antibiotics-associated diarrhea. While many studies report the probiotic effects of S. boulardii, no genome information for this yeast is currently available in the public domain. We report the 11.4 Mbp draft genome of this probiotic yeast. The draft genome was obtained by assembling Roche 454 FLX + shotgun data into 194 contigs with an N50 of 251 Kbp. We compare our draft genome with all other Saccharomyces cerevisiae genomes. Our analysis confirms the close similarity of S. boulardii to S. cerevisiae strains and provides a framework to understand the probiotic effects of this yeast, which exhibits unique physiological and metabolic properties.

ABSTRACTEfficient integration and visualization of heterogeneous biomedical information in a single view is a key challenge. In this study, we present Arena3Dweb, the first, fully interactive and dependency-free, web application which allows the visualization of multilayered graphs in 3D space. With Arena3Dweb, users can integrate multiple networks in a single view along with their intra- and inter-layer connections. For clearer and more informative views, users can choose between a plethora of layout algorithms and apply them on a set of selected layers either individually or in combination. Users can align networks and highlight node topological features, whereas each layer as well as the whole scene can be translated, rotated and scaled in 3D space. User-selected edge colors can be used to highlight important paths, while node positioning, coloring and resizing can be adjusted on-the-fly. In its current version, Arena3Dweb supports weighted and unweighted undirected graphs and is...

Biology

Functional enrichment is a widely used method for interpreting experimental results by identifying classes of proteins/genes associated with certain biological functions, pathways, diseases, or phenotypes. Despite the variety of existing tools, most of them can process a single list per time, thus making a more combinatorial analysis more complicated and prone to errors. In this article, we present FLAME, a web tool for combining multiple lists prior to enrichment analysis. Users can upload several lists and use interactive UpSet plots, as an alternative to Venn diagrams, to handle unions or intersections among the given input files. Functional and literature enrichment, along with gene conversions, are offered by g:Profiler and aGOtool applications for 197 organisms. FLAME can analyze genes/proteins for related articles, Gene Ontologies, pathways, annotations, regulatory motifs, domains, diseases, and phenotypes, and can also generate protein–protein interactions derived from STRIN...

Horticulture Research

Lithospermum erythrorhizon (red gromwell; zicao) is a medicinal and economically valuable plant belonging to the Boraginaceae family. Roots from L. erythrorhizon have been used for centuries based on the antiviral and wound-healing properties produced from the bioactive compound shikonin and its derivatives. More recently, shikonin, its enantiomer alkannin, and several other shikonin/alkannin derivatives have collectively emerged as valuable natural colorants and as novel drug scaffolds. Despite several transcriptomes and proteomes having been generated from L. erythrorhizon, a reference genome is still unavailable. This has limited investigations into elucidating the shikonin/alkannin pathway and understanding its evolutionary and ecological significance. In this study, we obtained a de novo genome assembly for L. erythrorhizon using a combination of Oxford Nanopore long-read and Illumina short-read sequencing technologies. The resulting genome is ∼367.41 Mb long, with a contig N50...

Scientific Reports

Muscular atrophy or muscle loss is a multifactorial clinical condition during many critical illnesses like cancer, cardiovascular diseases, diabetes, pulmonary diseases etc. leading to fatigue and weakness and contributes towards a decreased quality of life. The proportion of older adults (&gt;65 y) in the overall population is also growing and aging is another important factor causing muscle loss. Some muscle miRNAs (myomiRs) and their target genes have even been proposed as potential diagnostic, therapeutic and predictive markers for muscular atrophy. MyomirDB (http://www.myomirdb.in/) is a unique resource that provides a comprehensive, curated, user- friendly and detailed compilation of various miRNA bio-molecular interactions; miRNA-Transcription Factor-Target Gene co-regulatory networks and ~8000 tripartite regulons associated with 247 myomiRs which have been experimentally validated to be associated with various muscular atrophy conditions. For each database entry, MyomirDB co...

Genes

Alzheimer’s disease (AD) is a neurodegenerative disorder, and represents the most common cause of dementia. In this study, we performed several different analyses to detect loci involved in development of the late onset AD in the Russian population. DNA samples from 472 unrelated subjects were genotyped for 63 SNPs using iPLEX Assay and real-time PCR. We identified five genetic loci that were significantly associated with LOAD risk for the Russian population (TOMM40 rs2075650, APOE rs429358 and rs769449, NECTIN rs6857, APOE ε4). The results of the analysis based on comparison of the haplotype frequencies showed two risk haplotypes and one protective haplotype. The GMDR analysis demonstrated three significant models as a result: a one-factor, a two-factor and a three-factor model. A protein–protein interaction network with three subnetworks was formed for the 24 proteins. Eight proteins with a large number of interactions are identified: APOE, SORL1, APOC1, CD33, CLU, TOMM40, CNTNAP2...

International Journal of Molecular Sciences

Autoinducer 2 (or AI-2) is one of the molecules used by bacteria to trigger the Quorum Sensing (QS) response, which activates expression of genes involved in a series of alternative mechanisms, when cells reach high population densities (including bioluminescence, motility, biofilm formation, stress resistance, and production of public goods, or pathogenicity factors, among others). Contrary to most autoinducers, AI-2 can induce QS responses in both Gram-negative and Gram-positive bacteria, and has been suggested to constitute a trans-specific system of bacterial communication, capable of affecting even bacteria that cannot produce this autoinducer. In this work, we demonstrate that the ethanologenic Gram-negative bacterium Zymomonas mobilis (a non-AI-2 producer) responds to exogenous AI-2 by modulating expression of genes involved in mechanisms typically associated with QS in other bacteria, such as motility, DNA repair, and nitrogen fixation. Interestingly, the metabolism of AI-2-...

INDIN '05. 2005 3rd IEEE International Conference on Industrial Informatics, 2005., 2005

BMC Bioinformatics

Background: In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement. Methods: Most of the existing computational prediction methods are essentially based on the assumption that similar drugs are inclined to share the same side-effects, which has given rise to remarkable performance. It is also rational to assume an inverse proposition that dissimilar drugs are less likely to share the same side-effects. Based on this inverse similarity hypothesis, we proposed a novel method to select highly-reliable negative samples for side-effect prediction. The first step of our method is to build a drug similarity integration framework to measure the similarity between drugs from different perspectives. This step integrates drug chemical structures, drug target proteins, drug substituents, and drug therapeutic information as features into a unified framework. Then, a similarity score between each candidate negative drug and validated positive drugs is calculated using the similarity integration framework. Those candidate negative drugs with lower similarity scores are preferentially selected as negative samples. Finally, both the validated positive drugs and the selected highly-reliable negative samples are used for predictions. Results: The performance of the proposed method was evaluated on simulative side-effect prediction of 917 DrugBank drugs, comparing with four machine-learning algorithms. Extensive experiments show that the drug similarity integration framework has superior capability in capturing drug features, achieving much better performance than those based on a single type of drug property. Besides, the four machine-learning algorithms achieved significant improvement in macro-averaging F1-score (e.g., SVM from 0.655 to 0.898), macro-averaging precision (e.g., RBF from 0.592 to 0.828) and macro-averaging recall (e.g., KNN from 0.651 to 0.772) complimentarily attributed to the highly-reliable negative samples selected by the proposed method. Conclusions: The results suggest that the inverse similarity hypothesis and the integration of different drug properties are valuable for side-effect prediction. The selection of highly-reliable negative samples can also make significant contributions to the performance improvement.

PloS one, 2017

The origin and natural history of molecular functions hold the key to the emergence of cellular organization and modern biochemistry. Here we use a genomic census of Gene Ontology (GO) terms to reconstruct phylogenies at the three highest (1, 2 and 3) and the lowest (terminal) levels of the hierarchy of molecular functions, which reflect the broadest and the most specific GO definitions, respectively. These phylogenies define evolutionary timelines of functional innovation. We analyzed 249 free-living organisms comprising the three superkingdoms of life, Archaea, Bacteria, and Eukarya. Phylogenies indicate catalytic, binding and transport functions were the oldest, suggesting a &#39;metabolism-first&#39; origin scenario for biochemistry. Metabolism made use of increasingly complicated organic chemistry. Primordial features of ancient molecular functions and functional recruitments were further distilled by studying the oldest child terms of the oldest level 1 GO definitions. Network...

BMC Genomics, 2009

Background It has been recognized cancer cells acquire characters reminiscent of those of normal stem cells, and the degree of stem cell gene expression correlates with patient prognosis. Lgr5(+) or CD133(+) epithelial stem cells (EpiSCs) have recently been identified and these cells are susceptible to neoplastic transformation. It is unclear, however, whether genes enriched in EpiSCs also contribute in tumor malignancy. Endometrial endometrioid carcinoma (EEC) is a dominant type of the endometrial cancers and is still among the most common female cancers. Clinically endometrial carcinoma is classified into 4 FIGO stages by the degree of tumor invasion and metastasis, and the survival rate is low in patients with higher stages of tumors. Identifying genes shared between advanced tumors and stem cells will not only unmask the mechanisms of tumor malignancy but also provide novel therapeutic targets. Results To identify EpiSC genes in late (stages III-IV) EECs, a molecular signature d...

Malaria Journal, 2021

Background The emergence and spread of malaria drug resistance have resulted in the need to understand disease mechanisms and importantly identify essential targets and potential drug candidates. Malaria infection involves the complex interaction between the host and pathogen, thus, functional interactions between human and Plasmodium falciparum is essential to obtain a holistic view of the genetic architecture of malaria. Several functional interaction studies have extended the understanding of malaria disease and integrating such datasets would provide further insights towards understanding drug resistance and/or genetic resistance/susceptibility, disease pathogenesis, and drug discovery. Methods This study curated and analysed data including pathogen and host selective genes, host and pathogen protein sequence data, protein–protein interaction datasets, and drug data from literature and databases to perform human-host and P. falciparum network-based analysis. An integrative compu...

Scientific Reports

One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer’s (AD) and Parkinson’s Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important st...

2021

Departamento de Genética, Universidad de Granada, Granada, Spain, 2 Research Unit Modeling Nature, Universidad de Granada, Granada, Spain, Departamento de Ecología, Universidad de Granada, Granada, Spain, Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States, 5 Section of Plant Biology and the L.H. Bailey Hortorium, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States

Cancers, 2021

Simple Summary Most epithelial ovarian cancer (EOC) patients, although initially responsive to standard treatment with platinum-based chemotherapy, develop platinum resistance over the clinical course and succumb due to drug-resistant metastases. It has long been hypothesized that resistance to platinum develops as a result of epigenetic changes within tumor cells evolving over time. In this study, we investigated epigenomic changes in EOC patient samples, as well as in cell lines, and showed that profound changes at enhancers result in a platinum-resistant phenotype. Through correlation of the epigenomic alterations with changes in the transcriptome, we could identify potential novel prognostic biomarkers for early patient stratification. Furthermore, we applied a combinatorial RNAi screening approach to identify suitable targets that prevent the enhancer remodeling process. Our results advance the molecular understanding of epigenetic mechanisms in EOC and therapy resistance, whic...

Pathogens

Cucumber mosaic virus (CMV), with extremely broad host range including both monocots and dicots around the world, belongs to most important viral crop threats. Either natural or genetically constructed sources of resistance are being intensively investigated; for this purpose, exhaustive knowledge of molecular virus-host interaction during compatible and incompatible infection is required. New technologies and computer-based “omics” on various levels contribute markedly to this topic. In this work, two cucumber cultivars with different response to CMV challenge were tested, i.e., sensitive cv. Vanda and resistant cv. Heliana. The transcriptomes were prepared from both cultivars at 18 days after CMV or mock inoculation. Subsequently, four independent comparative analyses of obtained data were performed, viz. mock- and CMV-inoculated samples within each cultivar, samples from mock-inoculated cultivars to each other and samples from virus-inoculated cultivars to each other. A detailed ...

Frontiers in Bioengineering and Biotechnology

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer-and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

Molecular & Cellular Proteomics, 2006

Lipid storage droplets are universal organelles essential for the cellular and organismal lipometabolism including energy homeostasis. Despite their apparently simple design they are proposed to participate in a growing number of cellular processes, raising the question to what extent the functional multifariousness is reflected by a complex organellar proteome composition. Here we present 248 proteins identified in a subproteome analysis using lipid storage droplets of Drosophila melanogaster fat body tissue. In addition to previously known lipid droplet-associated PAT (Perilipin, ADRP, and TIP47) domain proteins and homologues of several mammalian lipid droplet proteins, this study identified a number of proteins of diverse biological function, including intracellular trafficking supportive of the dynamic and multifaceted character of these organelles. We performed intracellular localization studies on selected newly identified subproteome members both in tissue culture cells and in fat body cells directly. The results suggest that the lipid droplets of fat body cells are of combinatorial protein composition. We propose that subsets of lipid droplets within single cells are characterized by a protein "zip code," which reflects functional differences or specific metabolic states.

Scientific reports, 2018

Anacardic acid (AnAc), a potential dietary agent for preventing and treating breast cancer, inhibited the proliferation of estrogen receptor α (ERα) positive MCF-7 and MDA-MB-231 triple negative breast cancer cells. To characterize potential regulators of AnAc action, MCF-7 and MDA-MB-231 cells were treated for 6 h with purified AnAc 24:1n5 congener followed by next generation transcriptomic sequencing (RNA-seq) and network analysis. We reported that AnAc-differentially regulated miRNA transcriptomes in each cell line and now identify AnAc-regulated changes in mRNA and lncRNA transcript expression. In MCF-7 cells, 80 AnAc-responsive genes were identified, including lncRNA MIR22HG. More AnAc-responsive genes (886) were identified in MDA-MB-231 cells. Only six genes were commonly altered by AnAc in both cell lines: SCD, INSIG1, and TGM2 were decreased and PDK4, GPR176, and ZBT20 were increased. Modeling of AnAc-induced gene changes suggests that AnAc inhibits monounsaturated fatty aci...

PLoS Computational Biology, 2013

Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires 'ground-truth' functional annotations, which are lacking at the isoform level. To address this challenge, we developed a generic framework that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms. For a specific function, our algorithm identifies the 'responsible' isoform(s) of a gene and generates classifying models at the isoform level instead of at the gene level. Through cross-validation, we demonstrated that our algorithm is effective in assigning functions to genes, especially the ones with multiple isoforms, and robust to gene expression levels and removal of homologous gene pairs. We identified genes in the mouse whose isoforms are predicted to have disparate functionalities and experimentally validated the 'responsible' isoforms using data from mammary tissue. With protein structure modeling and experimental evidence, we further validated the predicted isoform functional differences for the genes Cdkn2a and Anxa6. Our generic framework is the first to predict and differentiate functions for alternatively spliced isoforms, instead of genes, using genomic data. It is extendable to any base machine learner and other species with alternatively spliced isoforms, and shifts the current genecentered function prediction to isoform-level predictions.

Tree Genetics & Genomes, 2013

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BMC Genomics

Background: The quantitative relations between RNA and protein are fundamental to biology and are still not fully understood. Across taxa, it was demonstrated that the protein-to-mRNA ratio in steady state varies in a direction that lessens the change in protein levels as a result of changes in the transcript abundance. Evidence for this behavior in tissues is sparse. We tested this phenomenon in new data that we produced for the mouse auditory system, and in previously published tissue datasets. A joint analysis of the transcriptome and proteome was performed across four datasets: inner-ear mouse tissues, mouse organ tissues, lymphoblastoid primate samples and human cancer cell lines. Results: We show that the protein levels are more conserved than the mRNA levels in all datasets, and that changes in transcription are associated with translational changes that exert opposite effects on the final protein level, in all tissues except cancer. Finally, we observe that some functions are enriched in the inner ear on the mRNA level but not in protein. Conclusions: We suggest that partial buffering between transcription and translation ensures that proteins can be made rapidly in response to a stimulus. Accounting for the buffering can improve the prediction of protein levels from mRNA levels.

International Journal of Molecular Sciences

Although understanding of the biomedical basis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is growing, the underlying pathological mechanisms remain uncertain. We recently reported a reduction in the proportion of basal oxygen consumption due to ATP synthesis by Complex V in ME/CFS patient-derived lymphoblast cell lines, suggesting mitochondrial respiratory inefficiency. This was accompanied by elevated respiratory capacity, elevated mammalian target of rapamycin complex 1 (mTORC1) signaling activity and elevated expression of enzymes involved in the TCA cycle, fatty acid β-oxidation and mitochondrial transport. These and other observations led us to hypothesise the dysregulation of pathways providing the mitochondria with oxidisable substrates. In our current study, we aimed to revisit this hypothesis by applying a combination of whole-cell transcriptomics, proteomics and energy stress signaling activity measures using subsets of up to 34 ME/CFS and 31 healthy co...

PLoS ONE, 2012

MicroRNAs (miRNAs) have emerged as fundamental regulators that silence gene expression at the post-transcriptional and translational levels. The identification of their targets is a major challenge to elucidate the regulated biological processes. The overall effect of miRNA is reflected on target mRNA expression, suggesting the design of new investigative methods based on high-throughput experimental data such as miRNA and transcriptome profiles. We propose a novel statistical measure of non-linear dependence between miRNA and mRNA expression, in order to infer miRNA-target interactions. This approach, which we name antagonism pattern detection, is based on the statistical recognition of a triangular-shaped pattern in miRNA-target expression profiles. This pattern is observed in miRNA-target expression measurements since their simultaneously elevated expression is statistically under-represented in the case of miRNA silencing effect. The proposed method enables miRNA target prediction to strongly rely on cellular context and physiological conditions reflected by expression data. The procedure has been assessed on synthetic datasets and tested on a set of real positive controls. Then it has been applied to analyze expression data from Ewing's sarcoma patients. The antagonism relationship is evaluated as a good indicator of real miRNA-target biological interaction. The predicted targets are consistently enriched for miRNA binding site motifs in their 39UTR. Moreover, we reveal sets of predicted targets for each miRNA sharing important biological function. The procedure allows us to infer crucial miRNA regulators and their potential targets in Ewing's sarcoma disease. It can be considered as a valid statistical approach to discover new insights in the miRNA regulatory mechanisms.

Communications Biology

Revealing the mechanisms underlying the reversible physiology of hibernation could have applications to both human and animal health as hibernation is often associated with disease-like states. The present study uses RNA-sequencing to reveal the tissue and seasonal transcriptional changes occurring in grizzly bears (Ursus arctos horribilis). Comparing hibernation to other seasons, bear adipose has a greater number of differentially expressed genes than liver and skeletal muscle. During hyperphagia, adipose has more than 900 differentially expressed genes compared to active season. Hibernation is characterized by reduced expression of genes associated with insulin signaling, muscle protein degradation, and urea production, and increased expression within muscle protein anabolic pathways. Across all three tissues we find a subset of shared differentially expressed genes, some of which are uncharacterized, that together may reflect a common regulatory mechanism. The identified gene fam...

eLife

Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as &#39;decoherence&#39;. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of &#39;correlation quantitative trait loci (QTLs)&#39;. Together, this work furthers our understanding of how and why coordinated biolog...

ASN Neuro, 2019

Zika virus (ZIKV) is an arthropod-borne virus (arbovirus) member of the Flaviviridae family, which has been associated with the development of the congenital Zika syndrome (CZS). RNA viruses, such as flaviviruses, have been reported to exert a profound impact on host microRNAs (miRNAs). Cellular miRNAs modulated by ZIKV may help identify cellular pathways of relevance to pathogenesis. Here, we screened 754 human cellular miRNAs modulated by ZIKV infection (Brazilian PE strain) in a neuroblastoma cell line. Seven miRNAs (miR-99a*, miR-126*, miR-190b, miR-361-3p, miR-522-3p, miR-299-5p, and miR-1267) were downregulated during ZIKV infection, while miR-145 was upregulated. Furthermore, 11 miRNAs were exclusively expressed in ZIKV-infected (miR-148a, miR-342-5p, miR-598, and miR-708-3p) or mock cells (miR-208, miR-329, miR-432-5p, miR-488, miR-518b, miR-520g, and miR-767-5p). Furthermore, in silico analysis indicated that some central nervous system, cellular migration, and adhesion function-related biological processes were overrepresented in the list of target genes of the miRNAs regulated in ZIKV-infected cells, especially for miR-145 and miR-148a. The induction of miR-145 and miR-148a was confirmed in postmortem brain samples from stillborn with severe CZS. Finally, we determined the expression regulation of microcephaly related genes through RNA interference pathway caused by ZIKV directly on neuron cells.

BMC Medical Genomics, 2022

Background Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy associated with a poor prognosis. High-throughput disease-related-gene expression data provide valuable information on gene interaction, which consequently lead to deeper insight about pathogenesis. The co-expression analysis is a common approach that is used to investigate gene interaction. However, such an approach solely is inadequate to reveal the complexity of the gene interaction. The three-way interaction model is known as a novel approach applied to decode the complex relationship between genes. Methods In the current study, the liquid association method was used to capture the statistically significant triplets involved in the PDAC pathogenesis. Subsequently, gene set enrichment and gene regulatory network analyses were performed to trace the biological relevance of the statistically significant triplets. Results The results of the current study suggest that “response to estradiol” and “Regulation of ...

Clinical & Experimental Immunology

Summary Repository corticotrophin injection (RCI, H.P Acthar® gel) has been approved for use in the management of multiple autoimmune and inflammatory diseases for more than a half-century, but its mechanism of action is not well understood. We used RNA-Seq methods to define RCI-regulated mRNAs in cultured human B cells under conditions of activation by interleukin (IL)-4 and CD40 ligand. Following IL-4/CD40L activation and RCI treatment we found up-regulation of 115 unique mRNA transcripts and down-regulation of 80 unique mRNAs. The effect on these RNA levels was dose-dependent for RCI and was distinct from changes in mRNA expression induced by treatment with a potent synthetic glucocorticoid. RCI down-regulated mRNAs were observed to include a significant over-representation of genes critical for B cell proliferation under activating conditions. These data confirm that RCI exerts direct effects on human B cells to modulate mRNA expression in specific pathways of importance to B ce...

Environmental Science and Pollution Research

Air pollution is a serious threat to our health and has become one of the major causes of many diseases including cardiovascular disease, respiratory disease, and cancer. The association between air pollution and various diseases has long been a topic of research interest. However, it remains unclear how air pollution actually impacts health by modulating several important cellular functions. Recently, some evidence has emerged about air pollution-induced epigenetic changes, which are linked with the etiology of various human diseases. Among several epigenetic modifications, DNA methylation represents the most prominent epigenetic alteration underlying the air pollution-induced pathogenic mechanism. Several other types of epigenetic changes, such as histone modifications, miRNA, and non-coding RNA expression, have also been found to have been linked with air pollution. Hypersensitivity pneumonitis (HP), one of the most prevalent forms of interstitial lung diseases (ILDs), is triggered by the inhalation of certain organic and inorganic substances. HP is characterized by inflammation in the tissues around the lungs' airways and may lead to irreversible lung scarring over time. This review, in addition to other diseases, attempts to understand whether certain pollutants influence HP development through such epigenetic modifications.

PLoS ONE, 2014

Genome-wide association studies (GWAS) have successfully identified several risk loci for Alzheimer's disease (AD). Nonetheless, these loci do not explain the entire susceptibility of the disease, suggesting that other genetic contributions remain to be identified. Here, we performed a meta-analysis combining data of 4,569 individuals (2,540 cases and 2,029 healthy controls) derived from three publicly available GWAS in AD and replicated a broad genomic region (.248,000 bp) associated with the disease near the APOE/TOMM40 locus in chromosome 19. To detect minor effect size contributions that could help to explain the remaining genetic risk, we conducted network-based pathway analyses either by extracting genewise p-values (GW), defined as the single strongest association signal within a gene, or calculated a more stringent genebased association p-value using the extended Simes (GATES) procedure. Comparison of these strategies revealed that ontological sub-networks (SNs) involved in glutamate signaling were significantly overrepresented in AD (p,2.7610 211 , p, 1.9610 211 ; GW and GATES, respectively). Notably, glutamate signaling SNs were also found to be significantly overrepresented (p,5.1610 28 ) in the Alzheimer's disease Neuroimaging Initiative (ADNI) study, which was used as a targeted replication sample. Interestingly, components of the glutamate signaling SNs are coordinately expressed in disease-related tissues, which are tightly related to known pathological hallmarks of AD. Our findings suggest that genetic variation within glutamate signaling contributes to the remaining genetic risk of AD and support the notion that functional biological networks should be targeted in future therapies aimed to prevent or treat this devastating neurological disorder.

Neural Plasticity, 2016

Wnt/β-catenin signaling modulates brain development and function and its deregulation underlies pathological changes occurring in neurodegenerative and neurodevelopmental disorders. Since one of the main effects of Wnt/β-catenin signaling is the modulation of target genes, in the present work we examined global transcriptional changes induced by short-term Wnt3a treatment (4 h) in primary cultures of rat hippocampal neurons. RNAseq experiments allowed the identification of 170 differentially expressed genes, including known Wnt/β-catenin target genes such as Notum, Axin2, and Lef1, as well as novel potential candidates Fam84a, Stk32a, and Itga9. Main biological processes enriched with differentially expressed genes included neural precursor (GO:0061364,p-adjusted = 2.5 × 10−7), forebrain development (GO:0030900,p-adjusted = 7.3 × 10−7), and stem cell differentiation (GO:0048863p-adjusted = 7.3 × 10−7). Likewise, following activation of the signaling cascade, the expression of a sign...

Frontiers in Cellular and Infection Microbiology, 2020

Extracellular vesicles (EVs), are considered as vehicles of cellular communication. Parasites usually release EVs in their excretory-secretory products to modulate host environment. However, little is known about the secretion of EVs by ticks. In this study, we show for the first time that the tick Haemaphysalis longicornis secretes EVs in saliva that resembles exosomes. EVs were purified from pilocarpine induced saliva of partially engorged H. longicornis ticks. Electron microscopy analysis revealed the presence of exosome-like vesicles with a size of 100 nm. Proteomic analysis by LC-MS/MS identified a total of 356 proteins in tick-derived EVs. Proteome data of tick-derived EVs was validated by Western blot analysis. Immunodetection of Hsp70 and GAPDH proteins indicated that the proteomics data of tick-derived EVs were highly reliable. Bioinformatics analysis (Gene Ontology) indicated association of certain biological and molecular functions with proteins which may be helpful during tick development. Likewise, KEGG database revealed involvement of vesicular proteins in proton transport, detoxification, ECM-receptor interaction, ribosome, RNA transport, ABC transporters, and oxidative phosphorylation. The results of this study provide evidence that EVs are being secreted in tick saliva and suggest that tick saliva-derived EVs could play important roles in host-parasite relationships. Moreover, EVs could be a useful tool in development of vaccines or therapeutics against ticks.

2022

BackgroundThe vaginal microbiome is an underexplored environment which may yield insights about endometrial cancer (EC) pathophysiology and serve as an early marker of disease. Our objective was to evaluate the preoperative vaginal microbiome in women undergoing hysterectomy for benign conditions and EC to identify patterns which could segregate benign disease from EC, and to assess if the microbiome distinguishes low-grade (LG) from high-grade (HG) histologies.MethodsVaginal microbiome samples were prospectively collected at the time of hysterectomy, and clinicopathologic data collected. Extracted DNA underwent shotgun metagenomics sequencing. Microbial diversity was calculated using the Shannon Index (α-diversity) and PERMANOVA (β-diversity). Hierarchical clustering was used to describe community state types (CST), which were then compared by microbial diversity and grade. Machine learning was utilized to assess the predictive value of grade and histology based on relative bacteri...

Membranes, 2022

Acetylation is the most important post-translation modification (PTM) in eukaryotes; it has manifold effects on the level of protein that transform an acetyl group from an acetyl coenzyme to a specific site on a polypeptide chain. Acetylation sites play many important roles, including regulating membrane protein functions and strongly affecting the membrane interaction of proteins and membrane remodeling. Because of these properties, its correct identification is essential to understand its mechanism in biological systems. As such, some traditional methods, such as mass spectrometry and site-directed mutagenesis, are used, but they are tedious and time-consuming. To overcome such limitations, many computer models are being developed to correctly identify their sequences from non-acetyl sequences, but they have poor efficiency in terms of accuracy, sensitivity, and specificity. This work proposes an efficient and accurate computational model for predicting Acetylation using machine l...

Pathogens, 2020

Vibrio harveyi is a Gram-negative marine bacterium that causes major disease outbreaks and economic losses in aquaculture. Phage therapy has been considered as a potential alternative to antibiotics however, candidate bacteriophages require comprehensive characterization for a safe and practical phage therapy. In this work, a lytic novel jumbo bacteriophage, vB_VhaM_pir03 belonging to the Myoviridae family was isolated and characterized against V. harveyi type strain DSM19623. It had broad host lytic activity against 31 antibiotic-resistant strains of V. harveyi, V. alginolyticus, V. campbellii and V. owensii. Adsorption time of vB_VhaM_pir03 was determined at 6 min while the latent-phase was at 40 min and burst-size at 75 pfu/mL. vB_VhaM_pir03 was able to lyse several host strains at multiplicity-of-infections (MOI) 0.1 to 10. The genome of vB_VhaM_pir03 consists of 286,284 base pairs with 334 predicted open reading frames (ORFs). No virulence, antibiotic resistance, integrase enco...

Frontiers in Bioengineering and Biotechnology, 2020

International journal of molecular sciences, 2017

Dianthus spiculifolius, a perennial herbaceous flower and a member of the Caryophyllaceae family, has strong resistance to cold and drought stresses. To explore the transcriptional responses of D. spiculifolius to individual and combined stresses, we performed transcriptome sequencing of seedlings under normal conditions or subjected to cold treatment (CT), simulated drought treatment (DT), or their combination (CTDT). After de novo assembly of the obtained reads, 112,015 unigenes were generated. Analysis of differentially expressed genes (DEGs) showed that 2026, 940, and 2346 genes were up-regulated and 1468, 707, and 1759 were down-regulated in CT, DT, and CTDT samples, respectively. Among all the DEGs, 182 up-regulated and 116 down-regulated genes were identified in all the treatment groups. Analysis of metabolic pathways and regulatory networks associated with the DEGs revealed overlaps and cross-talk between cold and drought stress response pathways. The expression profiles of ...

GigaScience, 2019

Background Achatina fulica, the giant African snail, is the largest terrestrial mollusk species. Owing to its voracious appetite, wide environmental adaptability, high growth rate, and reproductive capacity, it has become an invasive species across the world, mainly in Southeast Asia, Japan, the western Pacific islands, and China. This pest can damage agricultural crops and is an intermediate host of many parasites that can threaten human health. However, genomic information of A. fulica remains limited, hindering genetic and genomic studies for invasion control and management of the species. Findings Using a k-mer–based method, we estimated the A. fulica genome size to be 2.12 Gb, with a high repeat content up to 71%. Roughly 101.6 Gb genomic long-read data of A. fulica were generated from the Pacific Biosciences sequencing platform and assembled to produce a first A. fulica genome of 1.85 Gb with a contig N50 length of 726 kb. Using contact information from the Hi-C sequencing dat...

BMC Genomics, 2021

Background Miscanthus sinensis Andersson is a perennial grass that exhibits remarkable lignocellulose characteristics suitable for sustainable bioenergy production. However, knowledge of the genetic resources of this species is relatively limited, which considerably hampers further work on its biology and genetic improvement. Results In this study, through analyzing the transcriptome of mixed samples of leaves and stems using the latest PacBio Iso-Seq sequencing technology combined with Illumina HiSeq, we report the first full-length transcriptome dataset of M. sinensis with a total of 58.21 Gb clean data. An average of 15.75 Gb clean reads of each sample were obtained from the PacBio Iso-Seq system, which doubled the data size (6.68 Gb) obtained from the Illumina HiSeq platform. The integrated analyses of PacBio- and Illumina-based transcriptomic data uncovered 408,801 non-redundant transcripts with an average length of 1,685 bp. Of those, 189,406 transcripts were commonly identifi...

BMC Genomics, 2016

Background: Gene duplication is a genetic mutation that creates functionally redundant gene copies that are initially relieved from selective pressures and may adapt themselves to new functions with time. The levels of gene duplication may vary from small-scale duplication (SSD) to whole genome duplication (WGD). Studies with yeast revealed ample differences between these duplicates: Yeast WGD pairs were functionally more similar, less divergent in subcellular localization and contained a lesser proportion of essential genes. In this study, we explored the differences in evolutionary genomic properties of human SSD and WGD genes, with the identifiable human duplicates coming from the two rounds of whole genome duplication occurred early in vertebrate evolution. Results: We observed that these two groups of duplicates were also dissimilar in terms of their evolutionary and genomic properties. But interestingly, this is not like the same observed in yeast. The human WGDs were found to be functionally less similar, diverge more in subcellular level and contain a higher proportion of essential genes than the SSDs, all of which are opposite from yeast. Additionally, we explored that human WGDs were more divergent in their gene expression profile, have higher multifunctionality and are more often associated with disease, and are evolutionarily more conserved than human SSDs. Conclusions: Our study suggests that human WGD duplicates are more divergent and entails the adaptation of WGDs to novel and important functions that consequently lead to their evolutionary conservation in the course of evolution.

Scientific Reports, 2021

Host–pathogen interaction is one of the most powerful determinants involved in coevolutionary processes covering a broad range of biological phenomena at molecular, cellular, organismal and/or population level. The present study explored host–pathogen interaction from the perspective of human–bacteria protein–protein interaction based on large-scale interspecific and intraspecific interactome data for human and three pathogenic bacterial species, Bacillus anthracis, Francisella tularensis and Yersinia pestis. The network features revealed a preferential enrichment of intraspecific hubs and bottlenecks for both human and bacterial pathogens in the interspecific human–bacteria interaction. Analyses unveiled that these bacterial pathogens interact mostly with human party-hubs that may enable them to affect desired functional modules, leading to pathogenesis. Structural features of pathogen-interacting human proteins indicated an abundance of protein domains, providing opportunities for...

2017

Solenodons are insectivores living on the Caribbean islands, with few surviving related taxa. The genus occupies one of the most ancient branches among the placental mammals. The history, unique biology and adaptations of these enigmatic venomous species, can be greatly advanced given the availability of genome data, but the whole genome assembly for solenodons has never been previously performed, partially due to the difficulty in obtaining samples from the field. Island isolation has likely resulted in extreme homozygosity within the Hispaniolan solenodon (Solenodon paradoxus), thus we tested the performance of several assembly strategies for performance with genetically impoverished species’ genomes. The string-graph based assembly strategy seems a better choice compared to the conventional de Brujn graph approach, due to the high levels of homozygosity, which is often a hallmark of endemic or endangered species. A consensus reference genome was assembled from sequences of five i...

Frontiers in Immunology, 2021

Radiotherapy, the most frequent treatment of oral squamous cell carcinomas (OSCC) besides surgery is employed to kill tumor cells but, radiotherapy may also promote tumor relapse where the immune-suppressive tumor microenvironment (TME) could be instrumental. We established a novel syngeneic grafting model from a carcinogen-induced tongue tumor, OSCC13, to address the impact of radiotherapy on OSCC. This model revealed similarities with human OSCC, recapitulating carcinogen-induced mutations found in smoking associated human tongue tumors, abundant tumor infiltrating leukocytes (TIL) and, spontaneous tumor cell dissemination to the local lymph nodes. Cultured OSCC13 cells and OSCC13-derived tongue tumors were sensitive to irradiation. At the chosen dose of 2 Gy mimicking treatment of human OSCC patients not all tumor cells were killed allowing to investigate effects on the TME. By investigating expression of the extracellular matrix molecule tenascin-C (TNC), an indicator of an immu...

Evolutionary Bioinformatics, 2020

Computational prediction of gene-gene associations is one of the productive directions in the study of bioinformatics. Many tools are developed to infer the relation between genes using different biological data sources. The association of a pair of genes deduced from the analysis of biological data becomes meaningful when it reflects the directionality and the type of reaction between genes. In this work, we follow another method to construct a causal gene co-expression network while identifying transcription factors in each pair of genes using microarray expression data. We adopt a machine learning technique based on a logistic regression model to tackle the sparsity of the network and to improve the quality of the prediction accuracy. The proposed system classifies each pair of genes into either connected or nonconnected class using the data of the correlation between these genes in the whole Saccharomyces cerevisiae genome. The accuracy of the classification model in predicting ...

BMC Genomics, 2021

Background Despite proven therapeutic effects in inflammatory conditions, the specific mechanisms of phytochemical therapies are not well understood. The transcriptome effects of Traumeel (Tr14), a multicomponent natural product, and diclofenac, a non-selective cyclooxygenase (COX) inhibitor, were compared in a mouse cutaneous wound healing model to identify both known and novel pathways for the anti-inflammatory effect of plant-derived natural products. Methods Skin samples from abraded mice were analyzed by single-molecule, amplification-free RNAseq transcript profiling at 7 points between 12 and 192 h after injury. Immediately after injury, the wounds were treated with either diclofenac, Tr14, or placebo control (n = 7 per group/time). RNAseq levels were compared between treatment and control at each time point using a systems biology approach. Results At early time points (12–36 h), both control and Tr14-treated wounds showed marked increase in the inducible COX2 enzyme mRNA, wh...

Frontiers in cell and developmental biology, 2018

The zebrafish inner ear organs and lateral line neuromasts are comprised of a variety of cell types, including mechanosensitive hair cells. Zebrafish hair cells are evolutionarily homologous to mammalian hair cells, and have been particularly useful for studying normal hair cell development and function. However, the relative scarcity of hair cells within these complex organs, as well as the difficulty of fine dissection at early developmental time points, makes hair cell-specific gene expression profiling technically challenging. Cell sorting methods, as well as single-cell RNA-Seq, have proved to be very informative in studying hair cell-specific gene expression. However, these methods require that tissues are dissociated, the processing for which can lead to changes in gene expression prior to RNA extraction. To bypass this problem, we have developed a transgenic zebrafish model to evaluate the translatome of the inner ear and lateral line hair cells in their native tissue enviro...

PLOS ONE, 2022

Cells have developed diverse mechanisms to monitor changes in their surroundings. This allows them to establish effective responses to cope with adverse environments. Some of these mechanisms have been well characterized in the budding yeast Saccharomyces cerevisiae, an excellent experimental model to explore and elucidate some of the strategies selected in eukaryotic organisms to adjust their growth and development in stressful conditions. The relevance of structural disorder in proteins and the impact on their functions has been uncovered for proteins participating in different processes. This is the case of some transcription factors (TFs) and other signaling hub proteins, where intrinsically disordered regions (IDRs) play a critical role in their function. In this work, we present a comprehensive bioinformatic analysis to evaluate the significance of structural disorder in those TFs (170) recognized in S. cerevisiae. Our findings show that 85.2% of these TFs contain at least one...

IET Systems Biology, 2020

Computational analysis of microarray data is crucial for understanding the gene behaviours and deriving meaningful results. Clustering and biclustering of gene expression microarray data in the unsupervised domain are extremely important as their outcomes directly dominate healthcare research in many aspects. However, these approaches fail when the time factor is added as the third dimension to the microarray datasets. This three-dimensional data set can be analysed using triclustering that discovers similar gene sets that pursue identical behaviour under a subset of conditions at a specific time point. A novel triclustering algorithm (TriRNSC) is proposed in this manuscript to discover meaningful triclusters in gene expression profiles. TriRNSC is based on restricted neighbourhood search clustering (RNSC), a popular graph-based clustering approach considering the genes, the experimental conditions and the time points at an instance. The performance of the proposed algorithm is evaluated in terms of volume and some performance measures. Gene Ontology and KEGG pathway analysis are used to validate the TriRNSC results biologically. The efficiency of TriRNSC indicates its capability and reliability and also demonstrates its usability over other state-of-art schemes. The proposed framework initiates the application of the RNSC algorithm in the triclustering of gene expression profiles.

PLOS ONE, 2015

Introduction Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis.

BMC genomics, 2017

Pistachio (Pistacia vera L.) is one of the most important commercial nut crops worldwide. It is a salt-tolerant and long-lived tree, with the largest cultivation area in Iran. Climate change and subsequent increased soil salt content have adversely affected the pistachio yield in recent years. However, the lack of genomic/global transcriptomic sequences on P. vera impedes comprehensive researches at the molecular level. Hence, whole transcriptome sequencing is required to gain insight into functional genes and pathways in response to salt stress. RNA sequencing of a pooled sample representing 24 different tissues of two pistachio cultivars with contrasting salinity tolerance under control and salt treatment by Illumina Hiseq 2000 platform resulted in 368,953,262 clean 100 bp paired-ends reads (90 Gb). Following creating several assemblies and assessing their quality from multiple perspectives, we found that using the annotation-based metrics together with the length-based parameters...

The ISME Journal, 2018

Lucinidae clams harbor gammaproteobacterial thioautotrophic gill endosymbionts that are environmentally acquired. Thioautotrophic lucinid symbionts are related to metabolically similar symbionts associated with diverse marine host taxa and fall into three distinct phylogenetic clades. Most studies on the lucinid-bacteria chemosymbiosis have been done with seagrass-dwelling hosts, whose symbionts belong to the largest phylogenetic clade. In this study, we examined the taxonomy and functional repertoire of bacterial endosymbionts at an unprecedented resolution from Phacoides pectinatus retrieved from mangrove-lined coastal sediments, which are underrepresented in chemosymbiosis studies. The P. pectinatus thioautotrophic endosymbiont expressed metabolic gene variants for thioautotrophy, respiration, and nitrogen assimilation distinct from previously characterized lucinid thioautotrophic symbionts and other marine symbionts. At least two other bacterial species with different metabolisms were also consistently identified in the P. pectinatus gill microbiome, including a Kistimonas-like species and a Spirochaeta-like species. Bacterial transcripts involved in adhesion, growth, and virulence and mixotrophy were highly expressed, as were host-related hemoglobin and lysozyme transcripts indicative of sulfide/ oxygen/CO 2 transport and bactericidal activity. This study suggests the potential roles of P. pectinatus and its gill microbiome species in mangrove sediment biogeochemistry and offers insights into host and microbe metabolisms in the habitat.

Biomedicines

Background: Patients with chronic obstructive pulmonary disease (COPD) are commonly treated with inhaled corticosteroid/long-acting ß2-agonist combination therapy. While previous studies have investigated the host–microbiome interactions in COPD, the effects of specific steroid formulations on this complex cross-talk remain obscure. Methods: We collected and evaluated data from the Study to Investigate the Differential Effects of Inhaled Symbicort and Advair on Lung Microbiota (DISARM), a randomized controlled trial. Bronchoscopy was performed on COPD patients before and after treatment with salmeterol/fluticasone, formoterol/budesonide or formoterol-only. Bronchial brush samples were processed for microbial 16S rRNA gene sequencing and host mRNA sequencing. Longitudinal changes in the microbiome at a community, phylum and genus level were correlated with changes in host gene expression using a Spearman’s rank correlation test. Findings: In COPD patients treated with salmeterol/flut...

BMC evolutionary biology, 2017

Despite extensive study on hemoglobins and hemocyanins, little is known about hemerythrin (Hr) evolutionary history. Four subgroups of Hrs have been documented, including: circulating Hr (cHr), myohemerythrin (myoHr), ovohemerythrin (ovoHr), and neurohemerythrin (nHr). Annelids have the greatest diversity of oxygen carrying proteins among animals and are the only phylum in which all Hr subgroups have been documented. To examine Hr diversity in annelids and to further understand evolution of Hrs, we employed approaches to survey annelid transcriptomes in silico. Sequences of 214 putative Hr genes were identified from 44 annelid species in 40 different families and Bayesian inference revealed two major clades with strong statistical support. Notably, the topology of the Hr gene tree did not mirror the phylogeny of Annelida as presently understood, and we found evidence of extensive Hr gene duplication and loss in annelids. Gene tree topology supported monophyly of cHrs and a myoHr cla...

The Biological Bulletin, 2018

Among animals, two major groups of oxygen-binding proteins are found: proteins that use iron to bind oxygen (hemoglobins and hemerythrins) and two non-homologous hemocyanins that use copper. Although arthropod and mollusc hemocyanins (herein designated HcA and HcM, respectively) bind oxygen in the same manner, they are distinct in their molecular structures. In order to better understand the range of natural variation in Hcs, we searched for Hcs in a diverse array of metazoan transcriptomes using bioinformatics tools to examine Hc evolutionary history and consequently revive the discussion about whether all metazoan Hcs shared a common origin with frequent losses, or, originated separately after the divergence of Lophotrochozoa and Ecdysozoa. We confirm that the distribution of Hc-like genes is more widespread than previously reported, including five putative novel HcM genes in two annelid species from Chaetopteridae. For HcA, 16 putative novel genes were retained, and the presence of HcA in 11 annelid species represent novel observations. Interestingly, Annelida is the lineage that presents the greatest repertoire of oxygen transport proteins reported to date, possessing all the main superfamily proteins, which could be explained partially by the immense variability of life styles and habitats. Work presented here contradicts the canonical view that hemocyanins are restricted to molluscs and arthropods, suggesting the occurrence of copper-based blood pigments in metazoans has been underestimated. Our results also support the idea of the presence of oxygen carrier Hcs being widespread across metazoans with an evolutionary history characterized by frequent losses.

Genome Biology and Evolution, 2020

Animals depend on the sequential oxidation of organic molecules to survive; thus, oxygen-carrying/transporting proteins play a fundamental role in aerobic metabolism. Globins are the most common and widespread group of respiratory proteins. They can be divided into three types: circulating intracellular, noncirculating intracellular, and extracellular, all of which have been reported in annelids. The diversity of oxygen transport proteins has been underestimated across metazoans. We probed 250 annelid transcriptomes in search of globin diversity in order to elucidate the evolutionary history of this gene family within this phylum. We report two new globin types in annelids, namely androglobins and cytoglobins. Although cytoglobins and myoglobins from vertebrates and from invertebrates are referred to by the same name, our data show they are not genuine orthologs. Our phylogenetic analyses show that extracellular globins from annelids are more closely related to extracellular globins...

Scientific Reports, 2020

Bacillus thuringiensis serovar israelensis (Bti) is used to control insect vectors of human and animal diseases. In the present study, the toxicity of four strains of Bti, named T0124, T0131, T0137, and T0139, toward Aedes aegypti and Culex quinquefasciatus larvae was analyzed. The T0131 strain showed the highest larvicidal activity against A. aegypti (LC50 = 0.015 µg/ml) and C. quinquefasciatus larvae (LC50 = 0.035 µg/ml) when compared to the other strains. Furthermore, the genomic sequences of the four strains were obtained and compared. These Bti strains had chromosomes sizes of approximately 5.4 Mb with GC contents of ~35% and 5472–5477 putative coding regions. Three small plasmids (5.4, 6.8, and 7.6 kb) and three large plasmids (127, 235, and 359 kb) were found in the extrachromosomal content of all four strains. The SNP-based phylogeny revealed close relationship among isolates from this study and other Bti isolates, and SNPs analysis of the plasmids 127 kb did not reveal any ...

PLoS ONE, 2013

American foulbrood disease of honey bees is caused by the bacterium Paenibacillus larvae. Infection occurs per os in larvae and systemic infection requires a breaching of the host peritrophic matrix and midgut epithelium. Genetic variation exists for both bacterial virulence and host resistance, and a general immunity is achieved by larvae as they age, the basis of which has not been identified. To quickly identify a pool of candidate genes responsive to P. larvae infection, we sequenced transcripts from larvae inoculated with P. larvae at 12 hours post-emergence and incubated for 72 hours, and compared expression levels to a control cohort. We identified 75 genes with significantly higher expression and six genes with significantly lower expression. In addition to several antimicrobial peptides, two genes encoding peritrophic-matrix domains were also up-regulated. Extracellular matrix proteins, proteases/protease inhibitors, and members of the Osiris gene family were prevalent among differentially regulated genes. However, analysis of Drosophila homologs of differentially expressed genes revealed spatial and temporal patterns consistent with developmental asynchrony as a likely confounder of our results. We therefore used qPCR to measure the consistency of gene expression changes for a subset of differentially expressed genes. A replicate experiment sampled at both 48 and 72 hours post infection allowed further discrimination of genes likely to be involved in host response. The consistently responsive genes in our test set included a hymenopteranspecific protein tyrosine kinase, a hymenopteran specific serine endopeptidase, a cytochrome P450 (CYP9Q1), and a homolog of trynity, a zona pellucida domain protein. Of the known honey bee antimicrobial peptides, apidaecin was responsive at both time-points studied whereas hymenoptaecin was more consistent in its level of change between biological replicates and had the greatest increase in expression by RNA-seq analysis.

Biomolecules

Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network diffusion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19....

Journal of Personalized Medicine

The coronavirus disease 2019 (COVID-19) pandemic causes many morbidity and mortality cases. Despite several developed vaccines and antiviral therapies, some patients experience severe conditions that need intensive care units (ICU); therefore, precision medicine is necessary to predict and treat these patients using novel biomarkers and targeted drugs. In this study, we proposed a multi-level biological network analysis framework to identify key genes via protein–protein interaction (PPI) network analysis as well as survival analysis based on differentially expressed genes (DEGs) in leukocyte transcriptomic profiles, discover novel biomarkers using microRNAs (miRNA) from regulatory network analysis, and provide candidate drugs targeting the key genes using drug–gene interaction network and structural analysis. The results show that upregulated DEGs were mainly enriched in cell division, cell cycle, and innate immune signaling pathways. Downregulated DEGs were primarily concentrated ...

PeerJ, 2021

Background Spodoptera frugiperda (J. E. Smith), commonly known as fall armyworm (FAW), is one of the most destructive agricultural pests in the world and has posed a great threat to crops. The improper use of insecticides has led to rapid development of resistance. However, the genetic data available for uncovering the insecticide resistance mechanisms are scarce. Methods In this study, we used PacBio single-molecule real-time (SMRT) sequencing aimed at revealing the full-length transcriptome profiling of the FAW larval brain to obtain detoxification genes. Results A total of 18,642 high-quality transcripts were obtained with an average length of 2,371 bp, and 11,230 of which were successfully annotated in six public databases. Among these, 5,692 alternative splicing events were identified.

Cognitive Computation, 2017

Plagiarism takes place when we use any person's work without giving due acknowledgment. There are several fields where the text similarity is involved like web document retrieval, information mining, and searching related articles. Several approaches have been introduced for detecting plagiarism in the text documents based on the syntactic structure of the text, string similarity, fingerprinting, semantic meaning underlying the text, etc. The basic limitation of plagiarism detection systems these days is that they fail to detect tough cases of plagiarism. The proposed plagiarism detection approach is the hybrid of semantic and syntactic similarity between the text documents. This novel approach exploits linguistic information sources non-linearly using the lexical database for finding the relatedness between text documents. The proposed approach uses semantic knowledge to perform cognitive-inspired computing. The framework is capable of detecting intelligent plagiarism cases like a verbatim copy, paraphrasing, rewording in a sentence, and sentence transformation. The approach has been evaluated on the standard PAN-PC-11 dataset. The experiments show that our technique has outperformed other strong baseline techniques in terms of precision, recall, F-measure, and plagiarism detection (PlagDet) score.

Advances in Candida albicans, 2021

The growing evidences of Candida albicans (C. albicans) infections are slowly becoming a threat to public health. Moreover, prevalence of antifungal resistant strains of C. albicans has emphasized the need for identification of potent targets for rational drug designing. In this aspect, traditional methods for target identification with validation have been found to be expensive and time-consuming. To overcome the concern, genome scale metabolic model construction provides a promising platform that allows novel target identification in combination with subtractive genome analysis. Thus, the chapter details current advancement in model construction, target identification and validation. In brief, it elucidates the overall strategies of C. albicans metabolome draft preparation, gap filling, curation of model, simulation followed by model validation, target identification and host pathogen interaction analysis. Finally, several examples of successful metabolic model construction and th...

Journal of Lasers in Medical Sciences, 2021

Introduction: There are many documents about the significant role of low-level laser therapy (LLLT) in different processes such as regenerator medicine and bone formation. The aim of this study is to assess the role of LLLT in blood hemostasis in rats via bioinformatic investigation. Methods: The differentially expressed plasma proteins of treated rats via LLLT from the literature and the added 50 first neighbors were investigated via network analysis to find the critical dysregulated proteins and biological processes by using Cytoscape software, the STRING database, and ClueGO. Results: A scale-free network including 55 nodes was constructed from queried and added first neighbor proteins. Fibrinogen gamma, fibrinogen alpha, and plasminogen were highlighted as the central genes of the analyzed network. Fibrinolysis was determined as the main group of biological processes that were affected by LLLT. Conclusion: Findings indicate that LLLT affects blood hemostasis which is an importan...

Frontiers in Genetics, 2017

Background: Dyslipidemia is one of the major forms of lipid disorder, characterized by increased triglycerides (TGs), increased low-density lipoprotein-cholesterol (LDL-C), and decreased high-density lipoprotein-cholesterol (HDL-C) levels in blood. Recently, MicroRNAs (miRNAs) have been reported to involve in various biological processes; their potential usage being a biomarkers and in diagnosis of various diseases. Computational approaches including text mining have been used recently to analyze abstracts from the public databases to observe the relationships/associations between the biological molecules, miRNAs, and disease phenotypes. Materials and Methods: In the present study, significance of text mined extracted pair associations (miRNA-lipid disease) were estimated by one-sided Fisher's exact test. The top 20 significant miRNA-disease associations were visualized on Cytoscape. The CyTargetLinker plug-in tool on Cytoscape was used to extend the network and predicts new miRNA target genes. The Biological Networks Gene Ontology (BiNGO) plug-in tool on Cytoscape was used to retrieve gene ontology (GO) annotations for the targeted genes. Results: We retrieved 227 miRNA-lipid disease associations including 148 miRNAs. The top 20 significant miRNAs analysis on CyTargetLinker provides defined, predicted and validated gene targets, further targeted genes analyzed by BiNGO showed targeted genes were significantly associated with lipid, cholesterol, apolipoprotein, and fatty acids GO terms. Conclusion: We are the first to provide a reliable miRNA-lipid disease association network based on text mining. This could help future experimental studies that aim to validate predicted gene targets.

BioMed Research International

Host-pathogen molecular cross-talks are critical in determining the pathophysiology of a specific infection. Most of these cross-talks are mediated via protein-protein interactions between the host and the pathogen (HP-PPI). Thus, it is essential to know how some pathogens interact with their hosts to understand the mechanism of infections. Malaria is a life-threatening disease caused by an obligate intracellular parasite belonging to the Plasmodium genus, of which P. falciparum is the most prevalent. Several previous studies predicted human-plasmodium protein-protein interactions using computational methods have demonstrated their utility, accuracy, and efficiency to identify the interacting partners and therefore complementing experimental efforts to characterize host-pathogen interaction networks. To predict potential putative HP-PPIs, we use an integrative computational approach based on the combination of multiple OMICS-based methods including human red blood cells (RBC) and Pl...

PLOS ONE, 2020

Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.

Scientific Reports, 2020

To explore the proper prognostic markers for the likelihood of metastasis in CRC patients. Seventy-seven fresh CRC samples were collected to evaluate the mRNA level of the selected marker using Real-time PCR. Moreover, 648 formalin-fixed paraffin-embedded CRC tissues were gathered to evaluate protein expression by immunohistochemistry (IHC) on tissue microarrays. The results of Real-Time PCR showed that low expression of Talin1 was significantly associated with advanced TNM stage (p = 0.034) as well as gender (p = 0.029) in mRNA levels. Similarly, IHC results indicated that a low level of cytoplasmic expression of Talin1 was significantly associated with advanced TNM stage (p = 0.028) as well as gender (p = 0.009) in CRC patients. Moreover, decreased expression of cytoplasmic Talin1 protein was found to be a significant predictor of worse disease-specific survival (DSS) (p = 0.011) in the univariate analysis. In addition, a significant difference was achieved (p = 0.039) in 5-year s...

Interdisciplinary Sciences: Computational Life Sciences, 2017

report notes and highlights directly on images, thus offering, among the others, to query and compare similar clinical cases. This contribution is the result of a framework aimed to support oncologists in managing DICOM images and clinical data from different departments. Data integration is performed using a here-proposed XML-based module also utilized to trace temporal changes in image annotations. Keywords Bioimages • Data integration • CDA-XML • DICOM image analysis 1 Introduction Recently, the use of bioimages has gained an important role in clinical practice supporting the diagnostic definition and the development of individual treatments [1]. Imaging

Genes, 2018

Magnolias are widely cultivated for their beautiful flowers, but despite their popularity, the molecular mechanisms regulating flower bud differentiation have not been elucidated. Here, we used paraffin sections and RNA-seq to study the process of flower bud differentiation in . Flower bud development occurred between 28 April and 30 May 2017 and was divided into five stages: undifferentiated, early flower bud differentiation, petal primordium differentiation, stamen primordium differentiation, and pistil primordium differentiation. A total of 52,441 expressed genes were identified, of which 11,592 were significantly differentially expressed in the five bud development stages. Of these, 82 genes were involved in the flowering. In addition, MADS-box and family genes play critical roles in the formation of flower organs and 20 differentially expressed genes associated with flower bud differentiation were identified in . A qRT-PCR analysis verified that the MADS-box and family genes we...

Biomolecules

Finding, exploring and filtering frequent sentence-based associations between a disease and a biomedical entity, co-mentioned in disease-related PubMed literature, is a challenge, as the volume of publications increases. Darling is a web application, which utilizes Name Entity Recognition to identify human-related biomedical terms in PubMed articles, mentioned in OMIM, DisGeNET and Human Phenotype Ontology (HPO) disease records, and generates an interactive biomedical entity association network. Nodes in this network represent genes, proteins, chemicals, functions, tissues, diseases, environments and phenotypes. Users can search by identifiers, terms/entities or free text and explore the relevant abstracts in an annotated format.

2019

ABSTRACTBACKGROUND & AIMSDysregulated immune responses are the cause of inflammatory bowel diseases. Studies in both mice and humans suggest a central role of IL-23 producing mononuclear phagocytes in disease pathogenesis. Mechanistic insights into the regulation of IL-23 are prerequisite for select IL-23 targeting therapies as part of personalized medicine.METHODSWe performed transcriptomic analysis to investigate IL-23 expression in human mononuclear phagocytes and peripheral blood mononuclear cells. We investigated the regulation of IL-23 expression and used single-cell RNA-sequencing to derive a transcriptomic signature of hyper-inflammatory monocytes. Using gene network correlation analysis, we deconvolve this signature into components associated with homeostasis and inflammation in patient biopsy samples.RESULTSWe characterized monocyte subsets of healthy individuals and patients with inflammatory bowel disease that express IL-23. We identified auto- and paracrine sensing of I...

Frontiers in Medicine, 2022

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is ke...

Scientific Reports, 2021

Monitoring and early detection of emerging infectious diseases in wild animals is of crucial global importance, yet reliable ways to measure immune status and responses are lacking for animals in the wild. Here we assess the usefulness of bio-loggers for detecting disease outbreaks in free-living birds and confirm detailed responses using leukocyte composition and large-scale transcriptomics. We simulated natural infections by viral and bacterial pathogens in captive mallards (Anas platyrhynchos), an important natural vector for avian influenza virus. We show that body temperature, heart rate and leukocyte composition change reliably during an acute phase immune response. Using genome-wide gene expression profiling of whole blood across time points we confirm that immunostimulants activate pathogen-specific gene regulatory networks. By reporting immune response related changes in physiological and behavioural traits that can be studied in free-ranging populations, we provide baselin...

Bioscience, Biotechnology, and Biochemistry, 2009

We have developed WeGAS, a Web based microbial Genome Annotation System, which provides features that include gene prediction, homology search, promoter/motif analysis, genome browsing, gene ontology analysis based on the COGs and GO, and metabolic pathway analysis with web-based interfaces. Most raw data and intermediate data from genome projects can be managed with the WeGAS database system, and analysis results, including information on each gene and final genome maps, are provided by its visualization modules. Especially, a pie-view browser displaying circular maps of contigs and a COG-GO combination browser are very helpful for an overview of projects. Major public microbial genome databases can be imported, searched, and browsed through the WeGAS modules. WeGAS is freely accessible via web site http: //ns.smallsoft.co.kr:8051.

Heliyon, 2021

The oomycete genus Phytophthora includes devastating plant pathogens that are found in almost all ecosystems. We sequenced the genomes of two quarantined Phytophthora species-P. fragariae and P. rubi. Comparing these Phytophthora species and related genera allowed reconstruction of the phylogenetic relationships within the genus Phytophthora and revealed Phytophthora genomic features associated with infection and pathogenicity. We found that several hundred Phytophthora genes are putatively inherited from red algae, but Phytophthora does not have vestigial plastids originating from phototrophs. The horizontally-transferred Phytophthora genes are abundant transposons that "transmit" exogenous gene to Phytophthora species thus bring about the gene recombination possibility. Several expansion events of Phytophthora gene families associated with cell wall biogenesis can be used as mutational targets to elucidate gene function in pathogenic interactions with host plants. This work enhanced the understanding of Phytophthora evolution and will also be helpful for the design of phytopathological control strategies.

Frontiers in Genetics, 2018

Genomics and bioinformatics are increasingly contributing to our understanding of infectious diseases caused by bacterial pathogens such as Mycobacterium tuberculosis and parasites such as Plasmodium falciparum. This ranges from investigations of disease outbreaks and pathogenesis, host and pathogen genomic variation, and host immune evasion mechanisms to identification of potential diagnostic markers and vaccine targets. High throughput genomics data generated from pathogens and animal models can be combined with host genomics and patients' health records to give advice on treatment options as well as potential drug and vaccine interactions. However, despite accounting for the highest burden of infectious diseases, Africa has the lowest research output on infectious disease genomics. Here we review the contributions of genomics and bioinformatics to the management of infectious diseases of serious public health concern in Africa including tuberculosis (TB), dengue fever, malaria and filariasis. Furthermore, we discuss how genomics and bioinformatics can be applied to identify drug and vaccine targets. We conclude by identifying challenges to genomics research in Africa and highlighting how these can be overcome where possible.

Scientific Reports, 2020

Halobacteria, a class of Euryarchaeota are extremely halophilic archaea that can adapt to a wide range of salt concentration generally from 10% NaCl to saturated salt concentration of 32% NaCl. It consists of the orders: Halobacteriales, Haloferaciales and Natriabales. Pan-genome analysis of class Halobacteria was done to explore the core (300) and variable components (Softcore: 998, Cloud:36531, Shell:11784). The core component revealed genes of replication, transcription, translation and repair, whereas the variable component had a major portion of environmental information processing. The pan-gene matrix was mapped onto the core-gene tree to find the ancestral (44.8%) and derived genes (55.1%) of the Last Common Ancestor of Halobacteria. A High percentage of derived genes along with presence of transformation and conjugation genes indicate the occurrence of horizontal gene transfer during the evolution of Halobacteria. A Core and pan-gene tree were also constructed to infer a phy...

Horticulturae, 2021

Ogura cytoplasmic male sterility (Ogura CMS), originally identified in wild radish (Raphanus sativus), has enabled complete pollen sterility in Brassica plants, but the underlying mechanism in Ogura CMS Chinese cabbage (Brassica rapa ssp. pekinensis) remains unclear. In this study cytological analysis showed that during microsporogenesis the meiosis occurred normally, and the uninucleated pollens subsequently formed, but the development of both binucleated and trinucleated pollens was obviously disrupted due to defects of pollen mitosis in the Ogura CMS line (Tyms) compared with the corresponding maintainer line (231–330). In transcriptome profiling a total of 8052 differentially expressed genes (DEGs) were identified, among which 3890 were up-regulated and 4162 were down-regulated at the pollen abortion stages in an Ogura CMS line. KOG cluster analysis demonstrated that a large number of DEGs were related to the cytoskeleton’s dynamics, which may account for the failure of pollen m...

Toxins, 2018

Transcriptomic and genomic analyses have illuminated the diversity of venoms in three of the four venomous arachnid orders (scorpions, spiders, and ticks). To date, no venom gland transcriptome analysis has been available for pseudoscorpions, the fourth venomous arachnid lineage. To redress this gap, we sequenced an mRNA library generated from the venom glands of the species (Garypidae). High-throughput sequencing by the Illumina protocol, followed by de novo assembly, resulted in a total of 238,331 transcripts. From those, we annotated 131 transcripts, which code for putative peptides/proteins with similar sequences to previously reported venom components available from different arachnid species in protein databases. Transcripts putatively coding for enzymes showed the richest diversity, followed by other venom components such as peptidase inhibitors, cysteine-rich peptides, and thyroglobulin 1-like peptides. Only 11 transcripts were found that code for putatively low molecular ma...

Journal of Biomedical Informatics, 2020

Identifying the symptom clusters (two or more related symptoms) with shared underlying molecular mechanisms has been a vital analysis task to promote the symptom science and precision health. Related studies have applied the clustering algorithms (e.g. k-means, latent class model) to detect the symptom clusters mostly from various kinds of clinical data. In addition, they focused on identifying the symptom clusters (SCs) for a specific disease, which also mainly concerned with the clinical regularities for symptom management. Here, we utilized a network-based clustering algorithm (i.e., BigCLAM) to obtain 208 typical SCs across disease conditions on a large-scale symptom network derived from integrated high-quality disease-symptom associations. Furthermore, we evaluated the underlying shared molecular mechanisms for SCs, i.e., shared genes, protein-protein interaction (PPI) and gene functional annotations using integrated networks and similarity measures. We found that the symptoms in the same SCs tend to share a higher degree of genes, PPIs and have higher functional homogeneities. In addition, we found that most SCs have related symptoms with shared underlying molecular mechanisms (e.g. enriched pathways) across different disease conditions. Our work demonstrated that the integrated network analysis method could be used for identifying robust SCs and investigate the molecular mechanisms of these SCs, which would be valuable for symptom science and precision health.

ACM Transactions on Asian Language Information Processing, 2006

Expressions of negation in the biomedical literature often encode information of contrast as a means for explaining significant differences between the objects that are so contrasted. We show that such information gives additional insights into the nature of the structures and/or biological functions of these objects, leading to valuable knowledge for subcategorization of protein families by the properties that the involved proteins do not have in common. Based on the observation that the expressions of negation employ mostly predictable syntactic structures that can be characterized by subclausal coordination and by clause-level parallelism, we present a system that extracts such contrastive information by identifying those syntactic structures with natural language processing techniques and with additional linguistic resources for semantics. The implemented system shows the performance of 85.7% precision and 61.5% recall, including 7.7% partial recall, or an F score of 76.6. We ap...

PloS one, 2017

Common bean (Phaseolus vulgaris L.) fixes atmospheric nitrogen (N2) through symbiotic nitrogen fixation (SNF) at levels lower than other grain legume crops. An understanding of the genes and molecular mechanisms underlying SNF will enable more effective strategies for the genetic improvement of SNF traits in common bean. In this study, transcriptome profiling was used to identify genes and molecular mechanisms underlying SNF differences between two common bean recombinant inbred lines that differed in their N-fixing abilities. Differential gene expression and functional enrichment analyses were performed on leaves, nodules and roots of the two lines when grown under N-fixing and non-fixing conditions. Receptor kinases, transmembrane transporters, and transcription factors were among the differentially expressed genes identified under N-fixing conditions, but not under non-fixing conditions. Genes up-regulated in the stronger nitrogen fixer, SA36, included those involved in molecular...

Semantic Web, 2019

Data Analytics Solution (DAS) engineering often involves multiple tasks from data exploration to result presentation which are applied in various contexts and on different datasets. Semantic modeling based on the open world assumption supports flexible modeling of linked knowledge. The objective of this paper is to review existing techniques that leverage semantic web technologies to tackle challenges such as heterogeneity and changing requirements in DAS engineering. We explore the application scope of those techniques, the different types of semantic concepts they use and the role these concepts play during DAS development process. To gather evidence for the study we performed a systematic mapping study by identifying and reviewing 82 papers that incorporate semantic models in engineering DASs. One of the paper's findings is that existing models can be classified within four types of knowledge spheres: domain knowledge, analytics knowledge, services and user intentions. Another finding is to show how this knowledge is used in literature to enhance different tasks within the analytics process. We conclude our study by discussing the limitations of the existing body of research, showcasing the potential of semantic modeling to enhance DASs and presenting the possibility of leveraging ontologies for effective end-to-end DAS engineering.

Biomolecules, 2019

Coronary artery disease (CAD) is a major cause of end-stage cardiac disease. Although profound efforts have been made to illuminate the pathogenesis, the molecular mechanisms of CAD remain to be analyzed. To identify the candidate genes in the advancement of CAD, microarray dataset GSE23766 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and pathway and gene ontology (GO) enrichment analyses were performed. The protein-protein interaction network was constructed and the module analysis was performed using the Biological General Repository for Interaction Datasets (BioGRID) and Cytoscape. Additionally, target genes-miRNA regulatory network and target genes-TF regulatory network were constructed and analyzed. There were 894 DEGs between male human CAD samples and female human CAD samples, including 456 up regulated genes and 438 down regulated genes. Pathway enrichment analyses revealed that DEGs (up and down regulat...

PLoS ONE, 2012

To measure the strength of natural selection that acts upon single nucleotide variants (SNVs) in a set of human genes, we calculate the ratio between nonsynonymous SNVs (nsSNVs) per nonsynonymous site and synonymous SNVs (sSNVs) per synonymous site. We transform this ratio with a respective factor f that corrects for the bias of synonymous sites towards transitions in the genetic code and different mutation rates for transitions and transversions. This method approximates the relative density of nsSNVs (rdnsv) in comparison with the neutral expectation as inferred from the density of sSNVs. Using SNVs from a diploid genome and 200 exomes, we apply our method to immune system genes (ISGs), nervous system genes (NSGs), randomly sampled genes (RSGs), and gene ontology annotated genes. The estimate of rdnsv in an individual exome is around 20% for NSGs and 30-40% for ISGs and RSGs. This smaller rdnsv of NSGs indicates overall stronger purifying selection. To quantify the relative shift of nsSNVs towards rare variants, we next fit a linear regression model to the estimates of rdnsv over different SNV allele frequency bins. The obtained regression models show a negative slope for NSGs, ISGs and RSGs, supporting an influence of purifying selection on the frequency spectrum of segregating nsSNVs. The y-intercept of the model predicts rdnsv for an allele frequency close to 0. This parameter can be interpreted as the proportion of nonsynonymous sites where mutations are tolerated to segregate with an allele frequency notably greater than 0 in the population, given the performed normalization of the observed nsSNV to sSNV ratio. A smaller y-intercept is displayed by NSGs, indicating more nonsynonymous sites under strong negative selection. This predicts more monogenically inherited or de-novo mutation diseases that affect the nervous system.

Genes, 2021

Epigenetics is a mechanism underlying cardiovascular disease. It is unknown whether DNA hydroxymethylation is prospectively associated with the risk for cardiovascular death independent of germline and common environment. Male twin pairs middle-aged in 1969–1973 and discordant for cardiovascular death through December 31, 2014, were included. Hydroxymethylation was quantified in buffy coat DNA collected in 1986–1987. The 1893 differentially hydroxymethylated regions (DhMRs) were identified after controlling for blood leukocyte subtypes and age among 12 monozygotic (MZ) pairs (Benjamini–Hochberg False Discovery Rate &lt; 0.01), of which the 102 DhMRs were confirmed with directionally consistent log2-fold changes and p &lt; 0.01 among additional 7 MZ pairs. These signature 102 DhMRs, independent of the germline, were located on all chromosomes except for chromosome 21 and the Y chromosome, mainly within/overlapped with intergenic regions and introns, and predominantly hyper-hydroxymet...

BMC Bioinformatics

Background Due to the ever-expanding gap between the number of proteins being discovered and their functional characterization, protein function inference remains a fundamental challenge in computational biology. Currently, known protein annotations are organized in human-curated ontologies, however, all possible protein functions may not be organized accurately. Meanwhile, recent advancements in natural language processing and machine learning have developed models which embed amino acid sequences as vectors in n-dimensional space. So far, these embeddings have primarily been used to classify protein sequences using manually constructed protein classification schemes. Results In this work, we describe the use of amino acid sequence embeddings as a systematic framework for studying protein ontologies. Using a sequence embedding, we show that the bacterial carbohydrate metabolism class within the SEED annotation system contains 48 clusters of embedded sequences despite this class con...

Supportive Care in Cancer, 2017

Purpose Fatigue is the most common symptom associated with cancer and its treatment. Investigation of molecular mechanisms associated with fatigue in oncology patients may identify new therapeutic targets. The objectives of this study were to evaluate the relationships between gene expression and perturbations in biological pathways and evening fatigue severity in oncology patients who received chemotherapy (CTX). Methods The Lee Fatigue Scale (LFS) and latent class analysis were used to identify evening fatigue phenotypes. We measured 47,214 ribonucleic acid transcripts from whole blood collected prior to a cycle of CTX. Perturbations in biological pathways associated with differential gene expression were identified from public data sets (i.e., Kyoto Encyclopedia Gene and Genomes, BioCarta). Results Patients were classified into Moderate (n = 65, mean LFS score 3.1) or Very High (n = 195, mean LFS score 6.4) evening fatigue groups. Compared to patients with Moderate fatigue, patients with Very High fatigue exhibited differential expression of 29 genes. A number of the perturbed pathways identified validated prior mechanistic hypotheses for fatigue, including alterations in immune function, inflammation, neurotransmission, energy metabolism, and circadian rhythms. Based on our findings, energy metabolism was further divided into alterations in carbohydrate metabolism and skeletal muscle energy. Alterations in renal function-related pathways were identified as a potential new mechanism. Conclusions This study identified differential gene expression and perturbed biological pathways that provide new insights into the multiple and likely interrelated mechanisms associated with evening fatigue in oncology patients.

Royal Society Open Science, 2019

Soil detritivores such as Collembola impact plant growth, tissue nutrient concentration and gene expression. Using a model system with pedunculate oak ( Quercus robur ) microcuttings that display a typical endogenous rhythmic growth with alternating shoot (SF) and root flushes (RF), we investigated the transcriptomic response of oak with and without mycorrhiza ( Piloderma croceum ) to the presence of Collembola ( Protaphorura armata ), and linked it to changes in resource allocation by pulse labelling the plants with 13 C and 15 N. Collembola impacted Gene Ontology (GO) terms as well as plant morphology and elemental ratios with the effects varying markedly with developmental phases. During SF Collembola increased GO terms related to primary growth and this was mirrored in increased 13 C and 15 N excess in aboveground plant compartments. During RF, Collembola increased GO terms related to plant secondary metabolism and physical fortification. Further, Collembola presence resulted in...

PLoS ONE, 2014

The availability of many complete, annotated proteomes enables the systematic study of the relationships between protein conservation and functionality. We explore this question based solely on the presence or absence of protein homologues (a.k.a. conservation profiles). We study 18 metazoans, from two distinct points of view: the human's and the fly's. Using the GOrilla gene ontology (GO) analysis tool, we explore functional enrichment of the ''universal proteins'', those with homologues in all 17 other species, and of the ''non-universal proteins''. A large number of GO terms are strongly enriched in both human and fly universal proteins. Most of these functions are known to be essential. A smaller number of GO terms, exhibiting markedly different properties, are enriched in both human and fly non-universal proteins. We further explore the non-universal proteins, whose conservation profiles are consistent with the ''tree of life'' (TOL consistent), as well as the TOL inconsistent proteins. Finally, we applied Quantum Clustering to the conservation profiles of the TOL consistent proteins. Each cluster is strongly associated with one or a small number of specific monophyletic clades in the tree of life. The proteins in many of these clusters exhibit strong functional enrichment associated with the ''life style'' of the related clades. Most previous approaches for studying function and conservation are ''bottom up'', studying protein families one by one, and separately assessing the conservation of each. By way of contrast, our approach is ''top down''. We globally partition the set of all proteins hierarchically, as described above, and then identify protein families enriched within different subdivisions. While supporting previous findings, our approach also provides a tool for discovering novel relations between protein conservation profiles, functionality, and evolutionary history as represented by the tree of life.

International Journal of Cancer, 2010

Benzo[a]pyrene (BaP) is a prototypical polycyclic aromatic hydrocarbon (PAH) found in combustion processes. Cytochrome P450 1A1 and 1B1 enzymes (CYP1A1, CYP1B1) can both

Plant Disease

Proceedings of the National Academy of Sciences, 2008

The chromalveolates form a highly diverse and fascinating assemblage of organisms, ranging from obligatory parasites such as Plasmodium to free-living ciliates and algae such as kelps, diatoms, and dinoflagellates. Many of the species in this monophyletic grouping are of major medical, ecological, and economical importance. Nevertheless, their genome evolution is much less well studied than that of higher plants, animals, or fungi. In the current study, we have analyzed and compared 12 chromalveolate species for which whole-sequence information is available and provide a detailed picture on gene loss and gene gain in the different lineages. As expected, many gene loss and gain events can be directly correlated with the lifestyle and specific adaptations of the organisms studied. For instance, in the obligate intracellular Apicomplexa we observed massive loss of genes that play a role in general basic processes such as amino acid, carbohydrate, and lipid metabolism, reflecting the tr...

Signal Transduction and Targeted Therapy

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby lead...

2020

How cells adjust transport across their membranes is incompletely understood. Previously, we have shown thatS.cerevisiaebroadly re-configures the nutrient transporters at the plasma membrane in response to amino acid availability, through selective endocytosis of sugar- and amino acid transporters (AATs) (Müller et al., 2015). A genome-wide screen now revealed that Art2/Ecm21, a member of the α-arrestin family of Rsp5 ubiquitin ligase adaptors, is required for the simultaneous endocytosis of four AATs and induced during starvation by the general amino acid control pathway. Art2 uses a basic patch to recognize C-terminal acidic sorting motifs in these AATs and instructs Rsp5 to ubiquitinate proximal lysine residues. In response to amino acid excess, Rsp5 instead uses TORC1-activated Art1 to detect N-terminal acidic sorting motifs within the same AATs, which initiates exclusive substrate-induced endocytosis of individual AATs. Thus, amino acid availability activates complementary α-ar...

Genomics, Proteomics &amp; Bioinformatics, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

2020

NORMA is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations (e.g. Gene Ontology/Pathway enrichment or clustering results) can be uploaded and visualized in a network either as colored pie-chart nodes or as color-filled convex hulls in a Venn-diagram-like style. In the case where no annotation exists, algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefly, with NORMA, users can encode three types of information simultaneously. These are: i) the network, ii) the communities or annotations and iii) node categories or expression values. Finally, NORMA offers basic topol...

Briefings in Bioinformatics, 2017

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

Human Genetics, 2016

disease association were investigated using publicly accessible databases (EntrezGene, UniProt, OMIM). The Gene Ontology terms of the biological processes mediated by the candidate genes were used to cluster them using the GOTermMapper (Lewis-Sigler Institute, Princeton University), speculating on six super-clusters: (a) anatomical development, (b) cell division, growth and motility, (c) cell metabolism and catabolism, (d) cell transport, (e) cell structure organization and (f) organ/system-specific processes. This review aims to increase the knowledge on the mechanisms underlying the co-occurrence of tooth agenesis and orofacial clefts, to pave the way for improving targeted (prenatal) molecular diagnosis and finally to reflect on therapeutic or ultimately preventive strategies for these disabling conditions in the future.

Comparative and functional genomics, 2011

Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proved incorrect, and applying uniform weight in the pathway analysis may not be an appropriate approach for the tasks like molecular classification of diseases, as genes in a functional group may have different predicting power. Hence, we propose to use different weights to genes in pathway-based analysis and devise four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Among all schemes, random weighting scheme, which generates random weights and selects optimal weights minimizing an objective function, performs best in terms of P value or error rate reduction. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed u...

BMC Bioinformatics, 2008

Background Alternative RNA splicing greatly increases proteome diversity and thereby contribute to species- or tissue-specific functions. The possibility to study alternative splicing (AS) events on a genomic scale using splicing-sensitive microarrays, including the Affymetrix GeneChip Exon 1.0 ST microarray (exon array), has appeared very recently. However, the application of this new technology is hindered by the lack of free and user-friendly software devoted to these novel platforms. Results In this study we present a Java-based freeware, easyExon http://microarray.ym.edu.tw/easyexon, to process, filtrate and visualize exon array data with an analysis pipeline. This tool implements the most commonly used probeset summarization methods as well as AS-orientated filtration algorithms, e.g. MIDAS and PAC, for the detection of alternative splicing events. We include a biological filtration function according to GO terms, and provide a module to visualize and interpret the selected ex...

Vaccines

Chagas disease (CD) is endemic in large parts of Central and South America, as well as in Texas and the southern regions of the United States. Successful parasites, such as the causative agent of CD, Trypanosoma cruzi have adapted to specific hosts during their phylogenesis. In this work, we have assembled an interactive network of the complex relations that occur between molecules within T. cruzi. An expert curation strategy was combined with a text-mining approach to screen 10,234 full-length research articles and over 200,000 abstracts relevant to T. cruzi. We obtained a scale-free network consisting of 1055 nodes and 874 edges, and composed of 838 proteins, 43 genes, 20 complexes, 9 RNAs, 36 simple molecules, 81 phenotypes, and 37 known pharmaceuticals. Further, we deployed an automated docking pipeline to conduct large-scale docking studies involving several thousand drugs and potential targets to identify network-based binding propensities. These experiments have revealed that...

Life Science Alliance, 2021

The autophagy-lysosomal pathway is impaired in many neurodegenerative diseases characterized by protein aggregation, but the link between aggregation and lysosomal dysfunction remains poorly understood. Here, we combine cryo-electron tomography, proteomics, and cell biology studies to investigate the effects of protein aggregates in primary neurons. We use artificial amyloid-like β-sheet proteins (β proteins) to focus on the gain-of-function aspect of aggregation. These proteins form fibrillar aggregates and cause neurotoxicity. We show that late stages of autophagy are impaired by the aggregates, resulting in lysosomal alterations reminiscent of lysosomal storage disorders. Mechanistically, β proteins interact with and sequester AP-3 μ1, a subunit of the AP-3 adaptor complex involved in protein trafficking to lysosomal organelles. This leads to destabilization of the AP-3 complex, missorting of AP-3 cargo, and lysosomal defects. Restoring AP-3μ1 expression ameliorates neurotoxicity...

Applied Sciences

The transformation of virtual product development to Digital Engineering (DE) requires the successful integration of Digital Engineering or data-driven methods into existing product development processes. Those methods allow for the analysis and usage of existing data. However, missing knowledge about these methods, as well as their performance or limitations, is a major burden for their application, especially in small and medium-sized enterprises. In order to close this gap, this paper proposes the AI4PD ontology, linking product development processes (PD) and Digital Engineering methods (AI). This knowledge representation gives companies an overview of the available methods to support them in selecting a suitable solution for their problems. The representation of AI4PD is performed in Protégé using the W3C standard OWL syntax. The opportunities of AI4PD are shown by a use case of identifying a DE-Method for predicting manufacturing possibilities based on test data and CAD files. ...

2020

ABSTRACTDespite the high burden of HIV-1 in Botswana, the population of Botswana is significantly underrepresentation in host genetics studies of HIV-1. Furthermore, the bulk of previous genomics studies evaluated common human genetic variations, however, there is increasing evidence of the influence of rare variants in the outcome of diseases which may be uncovered by comprehensive complete and deep genome sequencing. This research aimed to evaluate the role of rare-variants in susceptibility to HIV-1 and progression through whole genome sequencing. Whole genome sequences (WGS) of 265 HIV-1 positive and 125 were HIV-1 negative unrelated individuals from Botswana were mapped to the human reference genome GRCh38. Population joint variant calling was performed using Genome Analysis Tool Kit (GATK) and BCFTools. Cumulative effects of rare variant sets on susceptibility to HIV-1 and progression (CD4+ T-cell decline) were determined with optimized Sequence Kernel Association Test (SKAT-O...

Scientific Reports

SIN3/HDAC is a multi-protein complex that acts as a regulatory unit and functions as a co-repressor/co-activator and a general transcription factor. SIN3 acts as a scaffold in the complex, binding directly to HDAC1/2 and other proteins and plays crucial roles in regulating apoptosis, differentiation, cell proliferation, development, and cell cycle. However, its exact mechanism of action remains elusive. Using the Caenorhabditis elegans (C. elegans) model, we can surpass the challenges posed by the functional redundancy of SIN3 isoforms. In this regard, we have previously demonstrated the role of SIN-3 in uncoupling autophagy and longevity in C. elegans. In order to understand the mechanism of action of SIN3 in these processes, we carried out a comparative analysis of the SIN3 protein interactome from model organisms of different phyla. We identified conserved, expanded, and contracted gene classes. The C. elegans SIN-3 interactome -revealed the presence of well-known proteins, such...

Memórias do Instituto Oswaldo Cruz, 2010

In this paper we review the impact that the availability of the Schistosoma mansoni genome sequence and annotation has had on schistosomiasis research. Easy access to the genomic information is important and several types of data are currently being integrated, such as proteomics, microarray and polymorphic loci. Access to the genome annotation and powerful means of extracting information are major resources to the research community.

Ecology and Evolution, 2021

This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Scientific reports, 2016

Several studies have been conducted to check the prevalence of methicillin-resistant strains of Staphylococcus aureus (MRSA) in animals and animal-derived food products but limited data are available regarding their virulence and associated gene expression profile. In the present study, antibiotic resistance and virulence of MRSA and methicillin-sensitive S. aureus animal isolates were determined in vitro by agar dilution, biofilm formation, adhesion, invasion and intracellular survivability assays. In addition, the pathogenicity of these isolates was examined in a murine model of S. aureus sepsis. MRSA1679a, a strain isolated from chicken, was observed to be highly virulent, in cell culture and in mouse model, and exhibited extensive resistant profile. Comparative gene expression profile of MRSA1679a and the reference human MRSA strain (ATCC 29213) was performed using Illumina-based transcriptome and RT-qPCR analyses. Several virulence elements including 22 toxin genes were detecte...

Scientific reports, 2017

Numerous studies have examined the prevalence of pathogenic Escherichia coli in poultry and poultry products; however, limited data are available regarding their resistance- and virulence-associated gene expression profiles. This study was designed to examine the resistance and virulence of poultry E. coli strains in vitro and in vivo via antibiotic susceptibility, biofilm formation and adhesion, and invasion and intracellular survivability assays in Caco-2 and Raw 264.7 cell lines as well as the determination of the median lethal dose in two-day old chickens. A clinical pathogenic multidrug-resistant isolate, E. coli 381, isolated from broilers, was found to be highly virulent in cell culture and 1000-fold more virulent in a chicken model than other strains; accordingly, the isolate was subsequently selected for transcriptome analysis. The comparative gene expression profile of MDR E. coli 381 and the reference human strain E. coli ATCC 25922 was completed with Illumina HiSeq. 2500...

Frontiers in Plant Science, 2021

Ultraviolet radiation (UVR) is a major environmental stressor for terrestrial plants. Here we investigated genetic responses to acute broadband UVR exposure in the highly desiccation-tolerant mosses Syntrichia caninervis and Syntrichia ruralis, using a comparative transcriptomics approach. We explored whether UVR protection is physiologically plastic and induced by UVR exposure, addressing the following questions: (1) What is the timeline of changes in the transcriptome with acute UVR exposure in these two species? (2) What genes are involved in the UVR response? and (3) How do the two species differ in their transcriptomic response to UVR? There were remarkable differences between the two species after 10 and 30 min of UVR exposure, including no overlap in significantly differentially abundant transcripts (DATs) after 10 min of UVR exposure and more than twice as many DATs for S. caninervis as there were for S. ruralis. Photosynthesis-related transcripts were involved in the respon...

Human Genomics, 2023

Background Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. Results We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. Conclusions This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.

Heliyon

Scientific Reports

In silico profiling is used in identification of active compounds and guide rational use of traditional medicines. Previous studies on Ethiopian indigenous aloes focused on documentation of phytochemical compositions and traditional uses. In this study, ADMET and drug-likeness properties of phytochemicals from Ethiopian indigenous aloes were evaluated, and pharmacophore-based profiling was done using Discovery Studio to predict therapeutic targets. The targets were examined using KEGG pathway, gene ontology and network analysis. Using random-walk with restart algorithm, network propagation was performed in CODA network to find diseases associated with the targets. As a result, 82 human targets were predicted and found to be involved in several molecular functions and biological processes. The targets also were linked to various cancers and diseases of immune system, metabolism, neurological system, musculoskeletal system, digestive system, hematologic, infectious, mouth and dental, ...

International journal of genomics, 2016

Mya arenaria is a bivalve mollusk of commercial and economic importance, currently impacted by ocean warming, acidification, and invasive species. In order to inform studies on the growth of M. arenaria, we selected and inbred a population of soft-shell clams for a fast-growth phenotype. This population displayed significantly faster growth (p &lt; 0.0001), as measured by 35.4% greater shell size. To assess the biological basis of this growth heterosis, we characterized the complete transcriptomes of six individuals and identified differentially expressed genes by RNAseq. Pathways differentially expressed included structural gene pathways. Also differentially expressed was the nucleotide-binding oligomerization domain 2 (NOD2) receptor pathway that contributes to determination of growth, immunity, apoptosis, and proliferation. NOD2 pathway members that were upregulated included a subset of isoforms of RIPK2 (mean 3.3-fold increase in expression), ERK/MAPK14 (3.8-fold), JNK/MAPK8 (4....

Mol. BioSyst., 2016

LMA: A novel model to predict target of pluripotency transcriptional factors in human embryonic stem cell.

PLOS ONE, 2019

Rafflesia possesses unique biological features and known primarily for producing the world's largest and existing as a single flower. However, to date, little is known about key regulators participating in Rafflesia flower development. In order to further understand the molecular mechanism that regulates Rafflesia cantleyi flower development, RNA-seq data from three developmental stages of floral bud, representing the floral organ primordia initiation, floral organ differentiation, and floral bud outgrowth, were analysed. A total of 89,890 transcripts were assembled of which up to 35% could be annotated based on homology search. Advanced transcriptome analysis using K-mean clustering on the differentially expressed genes (DEGs) was able to identify 12 expression clusters that reflect major trends and key transitional states, which correlate to specific developmental stages. Through this, comparative gene expression analysis of different floral bud stages identified various transcription factors related to flower development. The members of WRKY, NAC, bHLH, and MYB families are the most represented among the DEGs, suggesting their important function in flower development. Furthermore, pathway enrichment analysis also revealed DEGs that are involved in various phytohormone signal transduction events such as auxin and auxin transport, cytokinin and gibberellin biosynthesis. Results of this study imply that transcription factors and phytohormone signalling pathways play major role in Rafflesia floral bud development. This study provides an invaluable resource for molecular studies of the flower development process in Rafflesia and other plant species.

Blood cancer journal, 2011

The use of selective inhibitors targeting Bcr-Abl kinase is now established as a standard protocol in the treatment of chronic myelogenous leukemia; however, the acquisition of drug resistance is a major obstacle limiting the treatment efficacy. To elucidate the molecular mechanism of drug resistance, we established K562 cell line models resistant to nilotinib and imatinib. Microarray-based transcriptome profiling of resistant cells revealed that nilotinib- and imatinib-resistant cells showed the upregulation of kinase-encoding genes (AURKC, FYN, SYK, BTK and YES1). Among them, the upregulation of AURKC and FYN was observed both in nilotinib- and imatinib-resistant cells irrespective of exposure doses, while SYK, BTK and YES1 showed dose-dependent upregulation of expression. Upregulation of EGF and JAG1 oncogenes as well as genes encoding ATP-dependent drug efflux pump proteins such as ABCB1 was also observed in the resistant cells, which may confer alternative survival benefits. Fu...

COVID

The nasal microenvironment plays a crucial role in the transmission, modulation, and clinical progression of COVID-19; however, the immune responses at the site of viral entry remain poorly understood. We deciphered the link between nasopharyngeal (NP) immune and inflammatory response that triggers cytokine/chemokine storms in the nasal route of COVID-19-positive patients. We used RT-PCR, multiplex ELISA, flow cytometry, and LC-MS/MS to decipher nasopharyngeal immune perturbations associated with severe COVID-19. In addition, we performed in vitro assays using cultured human monocytes-derived macrophages trained both in the presence and absence of SARS-CoV-2 trimeric spike protein(s) and co-cultured with and without autologous human peripheral blood mononuclear cells (hPBMCs)/total T-cells/CD8 T-cells. In vitro immune perturbations were examined by flow cytometry and LC-MS/MS assays. Our findings confirm that macrophages orchestrate NP immune inflammatory responses and highlight the...

Microorganisms

Whole-genome sequencing (WGS) of bacterial pathogens is widely conducted in microbiological, medical, and clinical research to explore genetic insights that could impact clinical treatment and molecular epidemiology. However, analyzing WGS data of bacteria can pose challenges for microbiologists, clinicians, and researchers, as it requires the application of several bioinformatics pipelines to extract genetic information from raw data. In this paper, we present BacSeq, an automated bioinformatic pipeline for the analysis of next-generation sequencing data of bacterial genomes. BacSeq enables the assembly, annotation, and identification of crucial genes responsible for multidrug resistance, virulence factors, and plasmids. Additionally, the pipeline integrates comparative analysis among isolates, offering phylogenetic tree analysis and identification of single-nucleotide polymorphisms (SNPs). To facilitate easy analysis in a single step and support the processing of multiple isolates...

International Journal of Environmental Research and Public Health, 2019

Algorithms for measuring semantic similarity between Gene Ontology (GO) terms has become a popular area of research in bioinformatics as it can help to detect functional associations between genes and potential impact to the health and well-being of humans, animals, and plants. While the focus of the research is on the design and improvement of GO semantic similarity algorithms, there is still a need for implementation of such algorithms before they can be used to solve actual biological problems. This can be challenging given that the potential users usually come from a biology background and they are not programmers. A number of implementations exist for some well-established algorithms but these implementations are not generic enough to support any algorithm other than the ones they are designed for. The aim of this paper is to shift the focus away from implementation, allowing researchers to focus on algorithm’s design and execution rather than implementation. This is achieved b...

Molecular Ecology Resources, 2020

Elephant grass (2n = 4x = 28; Cenchrus purpureus Schumach.), also known as Napier grass, is an important forage grass and potential energy crop in tropical and subtropical regions of Asia, Africa and America. However, no study has yet reported a genome assembly for elephant grass at the chromosome scale. Here, we report a high-quality chromosome-scale genome of elephant grass with a total size of 1.97 Gb and a 1.5% heterozygosity rate, obtained using short-read sequencing, single-molecule long-read sequencing and Hi-C chromosome conformation capture. Evolutionary analysis showed that subgenome A' of elephant grass and pearl millet may have originated from a common ancestor more than 3.22 million years ago (MYA). Further, allotetraploid formation occurred at approximately 6.61 MYA. Syntenic analyses within elephant grass and with other grass species indicated that elephant grass has experienced chromosomal rearrangements. We found that some key enzyme-encoding gene families related to the biosynthesis of anthocyanidins and flavonoids were expanded and highly expressed in leaves, which probably drives the production of these major anthocyanidin compounds and explains why this elephant grass cultivar has a high anthocyanidin content. In addition, we found a high copy number and transcript levels of genes involved in C 4 photosynthesis and hormone signal transduction pathways that may contribute to the fast growth of elephant grass. The availability of elephant grass genome data advances our knowledge of the genetic evolution of elephant grass and will contribute to further biological research and breeding as well as for other polyploid plants in the genus Cenchrus.

2021

ABSTRACTBackground and AimsHybridization is a common and important force in plant evolution. One of its outcomes is introgression - the transfer of small genomic regions from one taxon to another by hybridization and repeated backcrossing. This process is believed to be common in glacial refugia, where range expansions and contractions can lead to cycles of sympatry and isolation, creating conditions for extensive hybridization and introgression. Polyploidization is another genome-wide process with a major influence on plant evolution. Both hybridization and polyploidization can have complex effects on plant evolution. However, these effects are often difficult to understand in recently evolved species complexes.MethodsWe combined flow cytometry, transcriptomic and genomic analyses, and pollen-tube growth assays to investigate the consequences of polyploidization, hybridization, and introgression on the recent evolution of several Erysimum (Brassicaceae) species from the South of th...

International Journal of Molecular Sciences, 2021

Porcine heart xenotransplantation is a potential treatment for patients with end-stage heart failure. To understand molecular mechanisms of graft rejection after heart transplantation, we transplanted a 31-day-old alpha-1,3-galactosyltransferase knockout (GTKO) porcine heart to a five-year-old cynomolgus monkey. Histological and transcriptome analyses were conducted on xenografted cardiac tissue at rejection (nine days after transplantation). The recipient monkey’s blood parameters were analyzed on days −7, −3, 1, 4, and 7. Validation was conducted by quantitative real-time PCR (qPCR) with selected genes. A non-transplanted GTKO porcine heart from an age-matched litter was used as a control. The recipient monkey showed systemic inflammatory responses, and the rejected cardiac graft indicated myocardial infarction and cardiac fibrosis. The transplanted heart exhibited a total of 3748 differentially expressed genes compared to the non-transplanted heart transcriptome, with 2443 upregu...

Experimental Eye Research, 2021

Purpose: Retinal bipolar cells survive even in the later stages of inherited retinal degenerations (IRDs) and so are attractive targets for optogenetic approach es to vision restoration. However, it is not known to wh at extent the remodelling that these cells un dergo du ring degeneration affects their func tion. Specifically, it is un clear if they are free from me tabolic stress, receptive to adeno-associated viral vectors, suitable for opsinbased optogenetic tools and able to propagate signals by releasing neurotransmitter. Methods: Fluorescenc e activated cell sorting (FACS) was perform ed to isolate labe lled bipolar cells from dissociated retinae of litter-ma tes with or without the IRD mu tation Pd e6b rd1/rd1 selectively expressing an enhanc ed yellow fluorescent protein (EYFP) as a ma rker in ON-bipolar cells. Subsequent mRNA extraction allowed Illumina® microarray comparison of gene expression in bipolar cells from degenerate to those of wildtype retinae. Changes in four candidate genes were further investigated at the protein level using retinal immu nohistoch em istry over the course of degeneration. Results: A total of sixty differentially expressed transcripts reached statistical significanc e: these did not includ e any genes directly associated with native prima ry bipolar cell signalling, nor ch anges consistent with me tabolic stress. Four significantly altered genes (Srm 2, Slf2, An xa7 & Cntn1) , implicated in synaptic remodelling, neurotransmitter release and viral vector entry had immu nohistoch em ical staining colocalising with ON-bipolar cell ma rkers and varying over the course of degeneration. Conclusion: Our find ings suggest relatively few gene expression ch anges in the context of degeneration: that despite remodelling, bipolar cells are likely to rema in viable targets for optogenetic vision restoration. In addition, several genes wh ere ch anges were seen could provide a basis for investigations to enhanc e the efficacy of optogenetic therapies.

2020

The medicinal leech,Hirudo verbana, is a powerful model organism for investigating fundamental neurobehavioral processes. The well-documented arrangement and properties ofH. verbana’s nervous system allows changes at the level of specific neurons or synapses to be linked to physiological and behavioral phenomena. Juxtaposed to the extensive knowledge ofH. verbana’snervous system is a limited, but recently expanding, portfolio of molecular and multi-omics tools. Together, the advancement of genetic databases forH. verbanawill complement existing pharmacological and electrophysiological data by affording targeted manipulation and analysis of gene expression in neural pathways of interest. Here, we present the first draft genome assembly forH. verbana, which is approximately 250 Mbp in size and consists of 61,282 contigs. Whole genome sequencing was conducted using an Illumina sequencing platform followed by genome assembly with CLC-Bio Genomics Workbench and subsequent functional anno...

Frontiers in Plant Science, 2020

Apricot (Prunus armeniaca L.) is a valuable worldwide agronomical crop, with a delicious fruit highlighted as a functional food with both nutritional and bioactive properties, remarkably beneficial to human health. Apricot fruit ripening is a coordinated developmental process which requires change in the expression of hundreds to thousands of genes to modify many biochemical and physiological processes arising from quality characteristics in ripe fruit. In addition, enhancing fruit and nutraceutical quality is one of the central objectives to be improved in the new varieties developed by breeding programs. In this study we analyzed the contents of main metabolites linked to the nutraceutical value of apricot fruits, together with the most important pomological characteristics and biochemical contents of fruit during the ripening process in two contrasted apricot genotypes. Additionally, the gene expression changes were analyzed using RNA-Seq and real time qPCR. Results showed that genes with differential expression in the biosynthetic pathways, such as phenylpropanoids, flavonoids, starch and sucrose and carotenoid metabolism, could be possible candidates as molecular markers of fruit quality characteristics for fruit color and soluble solid content. The gene involves in carotenoid metabolism carotenoid cleavage dioxygenase 4, and the gene sucrose synthase in starch and sucrose metabolism were identified as candidate genes in the ripening process for white skin ground color and flesh color and high soluble sugar content. The application of these candidate genes on marker-assisted selection in apricot breeding programs may contribute to the early selection of high-quality fruit genotypes with suitable nutraceutical values.

BioMed Research International, 2018

Endometriosis is a disease characterized by the development of endometrial tissue outside the uterus, but its cause remains largely unknown. Numerous genes have been studied and proposed to help explain its pathogenesis. However, the large number of these candidate genes has made functional validation through experimental methodologies nearly impossible. Computational methods could provide a useful alternative for prioritizing those most likely to be susceptibility genes. Using artificial intelligence applied to text mining, this study analyzed the genes involved in the pathogenesis, development, and progression of endometriosis. The data extraction by text mining of the endometriosis-related genes in the PubMed database was based on natural language processing, and the data were filtered to remove false positives. Using data from the text mining and gene network information as input for the web-based tool, 15,207 endometriosis-related genes were ranked according to their score in t...

Molecular ecology resources, 2018

Ramie, Boehmeria nivea (L.) Gaudich, family Urticaceae, is a plant native to eastern Asia, and one of the world&#39;s oldest fiber crops. It is also used as animal feed and for the phytoremediation of heavy metal-contaminated farmlands. Thus, the genome sequence of ramie was determined to explore the molecular basis of its fiber quality, protein content, and phytoremediation. For further understanding ramie genome, different paired-end and mate-pair libraries were combined to generate 134.31 Gb of raw DNA sequences using the Illumina whole-genome shotgun sequencing approach. The highly heterozygous B. nivea genome was assembled using the Platanus Genome Assembler, which is an effective tool for the assembly of highly heterozygous genome sequences. The final length of the draft genome of this species was approximately 341.9 Mb (contig N50 = 22.62 kb, scaffold N50 = 1,126.36 kb). Based on ramie genome annotations, 30,237 protein-coding genes were predicted, and the repetitive element ...

Frontiers in Molecular Biosciences, 2021

Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein–protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machin...

PLOS ONE, 2012

Background: Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism's metabolic network.

PLoS ONE, 2008

Olivier (2008) Probing host pathogen cross-talk by transcriptional profiling of both Mycobacterium tuberculosis and infected human dendritic cells and macrophages. PLoS ONE, 3 (1). e1403.

Proceedings of the National Academy of Sciences of the United States of America, 2005

International Journal of Molecular Sciences, 2023

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

2022

ABSTRACTIn diverse cells from bacterial to mammalian species, inorganic phosphate is stored in long chains called polyphosphates (polyP). These near universal polymers, ranging from 3 to thousands of phosphate moieties in length, are associated with molecular functions including energy homeostasis, protein folding, and cell signaling. In many cell types, polyphosphate is concentrated in subcellular compartments or organelles. In the budding yeast S. cerevisiae, polyP synthesis by the membrane-bound VTC complex is coupled to its translocation into the lumen of the vacuole, a lysosome-related organelle, where it is stored at high concentrations. In contrast, ectopic expression of bacterial polyphosphate kinase, PPK, results in the toxic accumulation of polyP outside of the vacuole. In this study, we used label-free mass spectrometry to investigate the mechanisms underlying this toxicity. We find that PPK expression results in the activation of a stress response mediated in part by the...

IMA Fungus

During the construction and assembly of the Mars 2020 mission components at two different NASA cleanrooms, several fungal strains were isolated. Based on their colony morphology, two strains that showed yeast-like appearance were further characterized for their phylogenetic position. The species-level classification of these two novel strains, using traditional colony and cell morphology methods combined with the phylogenetic reconstructions using multi-locus sequence analysis (MLSA) based on several gene loci (ITS, LSU, SSU, RPB1, RPB2, CYTB and TEF1), and whole genome sequencing (WGS) was carried out. This polyphasic taxonomic approach supported the conclusion that the two basidiomycetous yeasts belong to hitherto undescribed species. The strain FJI-L2-BK-P3T, isolated from the Jet Propulsion Laboratory Spacecraft Assembly Facility, was placed in the Naganishia albida clade (Filobasidiales, Tremellomycetes), but is genetically and physiologically different from other members of th...

Microbiology Resource Announcements

The draft genomes of five Naganishia strains were sequenced using MinION and annotated using Funannotate pipeline. Phylogenetic and genomic analyses were performed to provide their genetic relationships, diversity, and potential functional capabilities. This approach will aid in understanding their potential to survive under microgravity and their resilience to extreme environments.

Frontiers in Ecology and Evolution, 2023

Introduction: The Mepraia genus is composed of three endemic species in Chile, namely Mepraia spinolai, Mepraia gajardoi, and Mepraia parapatrica, all capable of transmitting the causative agent of Chagas disease. Although species divergence within this genus is supported by morphological, molecular, and geological evidence, it has never been validated by genome-wide data.

Frontiers in Genetics, 2021

Scientific research consistently demonstrates that diseases may be delayed, treated, or even prevented and, thereby, health may be maintained with health-promoting functional food ingredients (FFIs). Consumers are increasingly demanding sound information about food, nutrition, nutrients, and their associated health benefits. Consequently, a nutrition industry is being formed around natural foods and FFIs, the economic growth of which is increasingly driven by consumer decisions. Information technology, in particular artificial intelligence (AI), is primed to vastly expand the pool of characterised and annotated FFIs available to consumers, by systematically discovering and characterising natural, efficacious, and safe bioactive ingredients (bioactives) that address specific health needs. However, FFIproducing companies are lagging in adopting AI technology for their ingredient development pipelines for several reasons, resulting in a lack of efficient means for large-scale and highthroughput molecular and functional ingredient characterisation. The arrival of the AI-led technological revolution allows for the comprehensive characterisation and understanding of the universe of FFI molecules, enabling the mining of the food and natural product space in an unprecedented manner. In turn, this expansion of bioactives dramatically increases the repertoire of FFIs available to the consumer, ultimately resulting in bioactives being specifically developed to target unmet health needs.

bioRxiv (Cold Spring Harbor Laboratory), 2022

Deciphering of BTH-induced response of tomato (Solanum lycopersicum L.) and its effect on plant virus infection through the multi-omics approach Frąckowiak Patryk https://orcid.

Pharmaceutics

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of tran...

Frontiers in Genetics, 2020

In this study, we aimed to develop novel genic simple sequence repeat (eSSR) markers and to study phylogenetic relationship among Pistacia species. Transcriptome sequencing was performed in different tissues of Siirt and Atlı cultivars of pistachio (Pistacia vera). A total of 37.5-Gb data were used in the assembly. The number of total contigs and unigenes was calculated as 98,831, and the length of N50 was 1,333 bp after assembly. A total of 14,308 dinucleotide, trinucleotide, tetranucleotide, pentanucleotide, and hexanucleotide SSR motifs (4-17) were detected, and the most abundant SSR repeat types were trinucleotide (29.54%), dinucleotide (24.06%), hexanucleotide (20.67%), pentanucleotide (18.88%), and tetranucleotide (6.85%), respectively. Overall 250 primer pairs were designed randomly and tested in eight Pistacia species for amplification. Of them, 233 were generated polymerase chain reaction products in at least one of the Pistacia species. A total of 55 primer pairs that had amplifications in all tested Pistacia species were used to characterize 11 P. vera cultivars and 78 wild Pistacia genotypes belonging to nine Pistacia species (P. khinjuk, P. eurycarpa, P. atlantica, P. mutica, P. integerrima, P. chinensis, P. terebinthus, P. palaestina, and P. lentiscus). A total of 434 alleles were generated from 55 polymorphic eSSR loci with an average of 7.89 alleles per locus. The mean number of effective allele was 3.40 per locus. Polymorphism information content was 0.61, whereas observed (Ho) and expected heterozygosity (He) values were 0.39 and 0.65, respectively. UPGMA (unweighted pair-group method with arithmetic averages) and STRUCTURE analysis divided 89 Pistacia genotypes into seven populations. The closest species to P. vera was P. khinjuk. P. eurycarpa was closer P. atlantica than P. khinjuk. P. atlantica-P. mutica and P. terebinthus-P. palaestina pairs of species were not clearly separated from each other, and they were suggested as the same species. The present study demonstrated that eSSR markers can be used in the characterization and phylogenetic analysis of Pistacia species and cultivars, as well as genetic linkage mapping and QTL (quantitative trait locus) analysis.

Multimedia Tools and Applications

Text plagiarism has greatly spread in the recent years, it becomes a common problem in several fields such as research manuscripts, textbooks, patents, academic circles, etc. There are many sentence similarity features were used to detect plagiarism, but each of them is not discriminative to differentiate the similarity cases. This causes the discovery of lexical, syntactic and semantic text plagiarism types to be a challenging problem. Therefore, a new plagiarism detection system is proposed to extract the most effective sentence similarity features and construct hyperplane equation of the selected features to distinguish the similarity cases with the highest accuracy. It consists of three phases; the first phase is used to preprocess the documents. The second phase is depended on two paths, the first path is based on traditional paragraph level comparison, and the second path is based on the computed hyperplane equation using Support Vector Machine (SVM) and Chi-square techniques....

Scientific Reports, 2019

Coffee production is a global industry valued at approximately 173 billion US dollars. One of the main challenges facing coffee production is the management of the coffee berry borer (CBB), Hypothenemus hampei, which is considered the primary arthropod pest of coffee worldwide. Current control strategies are inefficient for CBB management. Although biotechnological alternatives, including RNA interference (RNAi), have been proposed in recent years to control insect pests, characterizing the genetics of the target pest is essential for the successful application of these emerging technologies. In this study, we employed RNA-seq to obtain the transcriptome of three developmental stages of the CBB (larva, female and male) to increase our understanding of the CBB life cycle in relation to molecular features. The CBB transcriptome was sequenced using Illumina Hiseq and assembled de novo. Differential gene expression analysis was performed across the developmental stages. The final assembly produced 29,434 unigenes, of which 4,664 transcripts were differentially expressed. Genes linked to crucial physiological functions, such as digestion and detoxification, were determined to be tightly regulated between the reproductive and nonreproductive stages of CBB. The data obtained in this study help to elucidate the critical roles that several genes play as regulatory elements in CBB development. Coffee (Coffea spp.) is one of the most traded commodities in the world and is the second-most consumed beverage, with more than 9 million tons being consumed annually 1. Currently cultivated in over 70 countries, the coffee industry is valued at approximately 173 billion US dollars, representing an important source of employment around the world 2. Crop yield is reduced by more than 30 species of insect pests 3. Among these pests, the coffee berry borer (CBB), Hypothenemus hampei (Ferrari, 1867) (Coleoptera: Curculionidae), is considered the most damaging pest for the coffee industry, causing annual global losses in excess of 500 million US dollars 4. The CBB life cycle occurs within the coffee seed, with the adult female laying eggs in galleries formed throughout the endosperm. After hatching, the larvae feed on the coffee seed, reducing grain quality and increasing seed susceptibility to pathogen attack 5. The endophytic behavior of CBB limits the use of traditional management methods due to poor cost-effectiveness, biosafety concerns and the development of insect resistance 4. Biological

Scientific Reports

The Tazy is a breed of sighthound common in Kazakhstan. The identification of runs of homozygosity (ROH) is an informative approach to assessing the history and possible patterns of directional selection pressure. To our knowledge, the present study is the first to provide an overview of the ROH pattern in the Tazy dogs from a genome-wide perspective. The ROH of the Tazy was found to be mainly composed of shorter segments (1–2 Mb), accounting for approximately 67% of the total ROH. The estimated ROH-based inbreeding coefficients (FROH) ranged from 0.028 to 0.058 with a mean of 0.057. Five genomic regions under positive selection were identified on chromosomes 18, 22, and 25. The regions on chromosomes 18 and 22 may be breed specific, while the region on chromosome 22 overlaps with regions of hunting traits in other hunting dog breeds. Among the 12 candidate genes located in these regions, the gene CAB39L may be a candidate that affects running speed and endurance of the Tazy dog. Ei...

Journal of Molecular Medicine, 2007

Barrett's esophagus is characterized by the replacement of squamous epithelium with specialized intestinal metaplastic mucosa. The exact mechanisms of initiation and development of Barrett's metaplasia remain unknown, but a hypothesis of "successful adaptation" against noxious reflux components has been proposed. To search for the repertoire of adaptation mechanisms of Barrett's metaplasia, we employed high-throughput functional genomic and proteomic methods that defined the molecular background of metaplastic mucosa resistance to reflux. Transcriptional profiling was established for 23 pairs of esophageal squamous epithelium and Barrett's metaplasia tissue samples using Affymetrix U133A 2.0 GeneChips and validated by quantitative real-time polymerase chain reaction. Differences in protein composition were assessed by electrophoretic and mass-spectrometry-based methods. Among 2,822 genes differentially expressed between Barrett's metaplasia and squamous epithelium, we observed significantly overexpressed metaplastic mucosa genes that encode cytokines and growth factors, constituents of extracellular matrix, basement membrane and tight junctions, and proteins involved in prostaglandin and phosphoinositol metabolism, nitric oxide production, and bioenergetics. Their expression likely reflects defense and repair responses of metaplastic mucosa, whereas overexpression of genes encoding heat shock proteins and several protein kinases in squamous epithelium

G3: Genes, Genomes, Genetics, 2023

The Retinoid-related orphan receptor beta (RORβ) gene encodes a developmental transcription factor and has 2 predominant isoforms created through alternative first exon usage; one specific to the retina and another present more broadly in the central nervous system, particularly regions involved in sensory processing. RORβ belongs to the nuclear receptor family and plays important roles in cell fate specification in the retina and cortical layer formation. In mice, loss of RORβ causes disorganized retina layers, postnatal degeneration, and production of immature cone photoreceptors. Hyperflexion or "high-stepping" of rear limbs caused by reduced presynaptic inhibition by Rorb-expressing inhibitory interneurons of the spinal cord is evident in RORβ-deficient mice. RORβ variants in patients are associated with susceptibility to various neurodevelopmental conditions, primarily generalized epilepsies, but including intellectual disability, bipolar, and autism spectrum disorders. The mechanisms by which RORβ variants confer susceptibility to these neurodevelopmental disorders are unknown but may involve aberrant neural circuit formation and hyperexcitability during development. Here we report an allelic series in 5 strains of spontaneous Rorb mutant mice with a high-stepping gait phenotype. We show retinal abnormalities in a subset of these mutants and demonstrate significant differences in various behavioral phenotypes related to cognition. Gene expression analyses in all 5 mutants reveal a shared over-representation of the unfolded protein response and pathways related to endoplasmic reticulum stress, suggesting a possible mechanism of susceptibility relevant to patients.

Communications biology, 2022

Candida species are a dominant constituent of the human mycobiome and associated with the development of several diseases. Understanding the Candida species metabolism could provide key insights into their ability to cause pathogenesis. Here, we have developed the BioFung database, providing an efficient annotation of protein-encoding genes. Along, with BioFung, using carbohydrate-active enzyme (CAZymes) analysis, we have uncovered core and accessory features across Candida species demonstrating plasticity, adaption to the environment and acquired features. We show a greater importance of amino acid metabolism, as functional analysis revealed that all Candida species can employ amino acid metabolism. However, metabolomics revealed that only a specific cluster of species (AGAu species-C. albicans, C. glabrata and C. auris) utilised amino acid metabolism including arginine, cysteine, and methionine metabolism potentially improving their competitive fitness in pathogenesis. We further identified critical metabolic pathways in the AGAu cluster with biomarkers and anti-fungal target potential in the CAZyme profile, polyamine, choline and fatty acid biosynthesis pathways. This study, combining genomic analysis, and validation with gene expression and metabolomics, highlights the metabolic diversity with AGAu species that underlies their remarkable ability to dominate they mycobiome and cause disease.

Infection, Genetics and Evolution, 2021

Apicomplexans are alveolate parasites which include Plasmodium falciparum, the main cause of malaria, one of the world’s biggest killers from infectious disease. Apicomplexans are characterized by a reduction in proteome size, which appears to result from metabolic and functional simplification, commensurate with their parasitic lifestyle. However, other factors may also help to explain gene loss such as population bottlenecks experienced during transmission, and the effect of reducing the overall genomic information content. The latter constitutes an ‘informational constraint’, which is proposed to exert a selective pressure to evolve and maintain genes involved in informational fidelity and error correction, proportional to the quantity of information in the genome (which approximates to proteome size). The dynamics of gene loss was examined in 41 Apicomplexan genomes using orthogroup analysis. We show that loss of genes involved in amino acid metabolism and steroid biosynthesis can be explained by metabolic redundancy with the host. We also show that there is a marked tendency to lose DNA repair genes as proteome size is reduced. This may be explained by a reduction in size of the informational constraint and can help to explain elevated mutation rates in pathogens with reduced genome size. Multiple Sequentially Markovian Coalescent (MSMC) analysis indicates a recent bottleneck, consistent with predictions generated using allele-based population genetics approaches, implying that relaxed selection pressure due to reduced population size might have contributed to gene loss. However, the non-randomness of pathways that are lost challenges this scenario. Lastly, we identify unique orthogroups in malaria-causing Plasmodium species that infect humans, with a high proportion of membrane associated proteins. Thus, orthogroup analysis appears useful for identifying novel candidate pathogenic factors in parasites, when there is a wide sample of genomes available.

Genome Biology and Evolution, 2021

The spotted scat, Scatophagus argus is a member of the family Scatophagidae found in Indo-Pacific coastal waters. It is an emerging commercial aquaculture species, particularly in East and Southeast Asia. In this study, the first chromosome-level genome of S. argus was constructed using PacBio and Hi-C sequencing technologies. The genome is 572.42 Mb, with a scaffold N50 of 24.67 Mb. Using Hi-C data, 563.28 Mb (98.67% of the genome) sequences were anchored and oriented in 24 chromosomes, ranging from 12.57 Mb to 30.38 Mb. The assembly is of high integrity, containing 94.26% conserved single-copy orthologues, based on BUSCO analysis. A total of 24,256 protein-coding genes were predicted in the genome, and 96.30% of the predicted genes were functionally annotated. Evolutionary analysis showed that S. argus diverged from the common ancestor of Japanese puffer (Takifugu rubripes) approximately 114.8 Ma. The chromosomes of S. argus showed significant correlation to T. rubripes chromosome...

Functional &amp; Integrative Genomics

Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by poor prognosis and limited treatment options. Oleuropein and oleocanthal are bioactive chemicals found in extra-virgin olive oil; they have been shown to have anti-cancer potential. In this study, we examined the inhibitory effects of these two natural compounds, on MDA-MB-231 and MDA-MB-468 TNBC cell lines. The human TNBC MDA-MB-231 and MDA-MB-468 cell lines were treated with oleuropein or oleocanthal at ranging concentrations for 48 h. After determining the optimum concentration to reach IC50, using the sulforhodamine B assay, total RNA was extracted after 12, 24, and 48 h from treated and untreated cells. Poly(A)-RNA selection was conducted, followed by library construction and RNA sequencing. Differential gene expression (DEG) analysis was performed to identify DEGs between treated and untreated cells. Pathway analysis was carried out using the KEGG and GO databases. Oleuropein and oleocanthal co...

Frontiers in Genetics, 2022

International journal of molecular sciences, 2018

Variations in substrate chemistry and the micro-structure were shown to have a significant effect on the biology of human mesenchymal stromal cells (hMSCs). This occurs when differences in the surface properties indirectly modulate pathways within numerous signaling networks that control cell fate. To understand how the surface features affect hMSC gene expression, we performed RNA-sequencing analysis of bone marrow-derived hMSCs cultured on tissue culture-treated polystyrene (TCP) and poly(l-lactide) (PLLA) based substrates of differing topography (Fl: flat and Fs: fibrous) and chemistry (Pr: pristine and Am: aminated). Whilst 80% of gene expression remained similar for cells cultured on test substrates, the analysis of differentially expressed genes (DEGs) revealed that surface topography significantly altered gene expression more than surface chemistry. The Fl and Fs topologies introduced opposite directional alternations in gene expression when compared to TCP control. In additi...

Complex Networks & Their Applications IX, 2021

to the high chance of missing essential references and relevant literature. In particular, in research areas like biology or bio-medicine, thanks to fast-track publication journals, the number of published papers increases significantly fast. On the other hand, network analysis has become a critical enabling technology to understand mechanisms of life, living organisms, and in general, uncover the underlying fundamental biological processes. Examples of applications include: (i) analyzing disease networks for identifying disease-causing genes and pathways (Barabási et al. 2010); (ii) discovering the functional interdependence among molecular mechanisms through network inference and construction Szklarczyk et al. 2016; (iii) releasing Network-based inference models with application on drug re-purposing (Himmelstein et al. 2017). In the last few years, thanks to the availability of sizeable open-access article repositories such as PubMed Central (Beck 2010), arxiv (https:// arxiv. org) bioarxiv (https:// www. biorx iv. org/) as well as ontology databases which hold entities and their relations (Lambrix et al. 2007), the research community has focused on text mining tools and machine learning algorithms to digest these corpora and extract valuable semantic knowledge from them. Text mining (Cohen 2005), and Natural Language

Mediators of Inflammation, 2017

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting the motor nervous system. Despite the mechanism underlying motor neuron death is not yet clarified, multiple pathogenic processes have been proposed to account for ALS. Among these, inflammatory/immune responses have recently gained particular interest, although there are conflicting reports on the role of these processes in ALS pathogenesis and treatment. This apparent discrepancy may be due to the absence of an effective stratification of ALS patients into subgroups with markedly different clinical, biological, and molecular features. Our research group recently described genome-wide characterization of motor cortex samples from sporadic ALS (SALS) patients, revealing the existence of molecular and functional heterogeneity in SALS. Here, we reexamine data coming from our previous work, focusing on transcriptomic changes of inflammatory-related genes, in order to investigate their potential contribution in ALS. A total of 1573 inflammatory genes were identified as differentially expressed between SALS patients and controls, characterizing distinct topological pathways and networks, suggestive of specific inflammatory molecular signatures for different patient subgroups. Besides providing promising insights into the intricate relationship between inflammation and ALS, this paper represents a starting point for the rationale design and development of novel and more effective diagnostic and therapeutic applications.

Biomedical engineering, 2022

BMC Bioinformatics, 2019

Background: Although a few comparison methods based on the biological meaning of gene lists have been developed, the goProfiles approach is one of the few that are being used for that purpose. It consists of projecting lists of genes into predefined levels of the Gene Ontology, in such a way that a multinomial model can be used for estimation and testing. Of particular interest is the fact that it may be used for proving equivalence (in the sense of "enough similarity") between two lists, instead of proving differences between them, which seems conceptually better suited to the end goal of establishing similarity among gene lists. An equivalence method has been derived that uses a distance-based approach and the confidence interval inclusion principle. Equivalence is declared if the upper limit of a one-sided confidence interval for the distance between two profiles is below a pre-established equivalence limit. Results: In this work, this method is extended to establish the equivalence of any number of gene lists. Additionally, an algorithm to obtain the smallest equivalence limit that would allow equivalence between two or more lists to be declared is presented. This algorithm is at the base of an iterative method of graphic visualization to represent the most to least equivalent gene lists. These methods deal adequately with the problem of adjusting for multiple testing. The applicability of these techniques is illustrated in two typical situations: (i) a collection of cancer-related gene lists, suggesting which of them are more reasonable to combine -as claimed by the authors-and (ii) a collection of pathogenesis-based transcript sets, showing which of these are more closely related. The methods developed are available in the goProfiles Bioconductor package. The method provides a simple yet powerful and statistically well-grounded way to classify a set of genes or other feature lists by establishing their equivalence at a given equivalence threshold. The classification results can be viewed using standard visualization methods. This may be applied to a variety of problems, from deciding whether a series of datasets generating the lists can be combined to the simplification of groups of lists.

Springer eBooks, 2016

The plant Golgi apparatus has a central role in the secretory pathway and is the principal site within the cell for the assembly and processing of macromolecules. The stacked membrane structures of the Golgi apparatus along with its interactions with the cytoskeleton and endoplasmic reticulum have historically made the isolation and purification of this organelle difficult. Density centrifugation has typically been used to enrich Golgi membranes from plant microsomal preparations, and aside from minor adaptations, the approach is still widely employed. Here we outline the enrichment of Golgi membranes from an Arabidopsis cell suspension culture that can be used to investigate the proteome of this organelle. We also provide a useful workflow for the examination of proteomic data as the result of multiple analyses. Finally, we highlight a simple technique to validate the subcellular localization of proteins by fluorescent tags after their identification by tandem mass spectrometry.

bioRxiv (Cold Spring Harbor Laboratory), 2023

Despite large omics datasets, the establishment of a reliable gene annotation is still challenging for eukaryotic genomes. Here, we used the reference genome of the major fungal wheat pathogen Zymoseptoria tritici (isolate IPO323) as a case study to develop methods to improve eukaryotic gene prediction. Four previous IPO323 annotations identified 10,933 to 13,260 gene models, but only one third of these coding sequences (CDS) have identical structures. To resolve these discrepancies and improve gene models, we generated full-length transcripts using long-read sequencing. This dataset was used together with other evidence (RNA-Seq transcripts and protein sequences) to generate novel ab initio gene models. The selection of the best structure among novel and existing gene models was performed according to transcript and protein evidence using InGenAnnot, a novel bioinformatics suite. Overall, 13,414 re-annotated gene models (RGMs) were predicted, including 671 new genes among which 53 encoded effector candidates. This process corrected many of the errors (15%) observed in previous gene models (coding sequence fusions, false introns, missing exons). While fungal genomes have poor annotations of untranslated regions (UTRs), our Iso-Seq long-read sequences outlined 5' and 3'UTRs for 73% of the RGMs. Alternative transcripts were identified for 13% of RGMs, mostly due to intron retention (75%), likely corresponding to unprocessed pre-mRNAs. A total of 353 genes displayed alternative transcripts with combinations of previously predicted or novel exons. Long non-coding transcripts (lncRNAs) and double-stranded RNAs from two fungal viruses were also identified. Most lncRNAs corresponded to antisense transcripts of genes (52%). lncRNAs that were up or down regulated during infection were enriched in antisense transcripts (70%), suggesting their involvement in the control of gene expression. Our results showed that combining different ab initio gene predictions and evidence-driven curation using InGenAnnot improved the quality of gene annotations of a compact eukaryotic genome. Our analysis also provided new insights into the transcriptional landscape of Z. tritici, helping develop an increasingly complex picture of its biology.

2020

The study of human genome variations can contribute towards understanding population diversity and the genetic aetiology of health-related traits. We sought to characterise human genomic variations of Botswana in order to assess diversity and elucidate mutation burden in the population using whole genome sequencing. Whole genome sequences of 390 unrelated individuals from Botswana were available for computational analysis. The sequences were mapped to the human reference genome GRCh38. Population joint variant calling was performed using Genome Analysis Tool Kit (GATK) and BCFTools. Variant characterisation was achieved by annotating the variants with a suite of databases in ANNOVAR and snpEFF. The genomic architecture of Botswana was delineated through principal component analysis, structure analysis and FST. We identified a total of 27.7 million unique variants. Variant prioritisation revealed 24 damaging variants with the most damaging variants being ACTRT2 rs3795263, HOXD12 rs200302685, ABCB5 rs111647033, ATP8B4 rs77004004 and ABCC12

PLoS ONE, 2012

With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.

bioRxiv (Cold Spring Harbor Laboratory), 2022

COVID-19 develops certain neurological symptoms, the molecular pathophysiology of which is obscure. In the present study, two networks were constructed and their hubbottleneck and driver nodes were evaluated to consider them as 'target genes' followed by identifying 'candidate genes' and their associations with neurological phenotypes of COVID-19. A tripartite network was first constructed using literature-based neurological symptoms of COVID-19 as input. The target genes evaluated therefrom were then used as query genes to identify the co-expressed genes from the RNA-sequence data of the frontal cortex of COVID-19 patients using pair-wise mutual information to genes. A 'combined gene network' (CGN) was constructed with 189 genes selected from TN and 225 genes co-expressed in COVID-19. Total 44 'target genes' evaluated from both networks and their connecting genes in respective networks were analyzed functionally by measuring pair-wise 'semantic similarity scores' (SSS) and finding Enrichr annotation terms against a set of genes. A new integrated 'weighted harmonic mean score' was formulated using SSS and STRING-based 'combined score' to select 21 gene-pairs among 'target genes' that provided 21 'candidate genes' with their properties as 'indispensable driver nodes' of CGN. Finally, six pairs providing seven prevalent candidate genes (ADAM10,

PLoS ONE, 2013

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.

Fishes

The Antarctic continent is one of the most pristine environments on planet Earth, yet one of the most fragile and susceptible to the effects of the ongoing climate change. The overwhelming majority of the components of Antarctic marine trophic chain are stenotherm organisms, highly adapted to the extreme, but extremely stable, freezing temperatures of the Antarctic ocean, which have not changed significantly during the past fifteen million years. Notothenioid fishes are the most abundant representatives of ichthyofauna at these latitudes, being ubiquitously found in coastal areas across the entire continent. While different Antarctic fish species have been previously subjected to studies aimed at defining their range of thermal tolerance, or at studying the response to acute thermal stress, just a handful of authors have investigated the effects of the exposure to a moderate increase of temperature, falling within the expected forecasts for the next few decades in some areas of the ...

npj biofilms and microbiomes, 2022

While deprivation of dietary fiber has been associated with adverse health outcomes, investigations concerning the effect of dietary fiber on the gut microbiome have been largely limited to compositional sequence-based analyses or utilize a defined microbiota not native to the host. To extend understanding of the microbiome's functional response to dietary fiber deprivation beyond correlative evidence from sequence-based analyses, approaches capable of measuring functional enzymatic activity are needed. In this study, we use an activity-based protein profiling (ABPP) approach to identify sugar metabolizing and transport proteins in native mouse gut microbiomes that respond with differential activity to the deprivation or supplementation of the soluble dietary fibers inulin and pectin. We found that the microbiome of mice subjected to a high fiber diet high in soluble fiber had increased functional activity of multiple proteins, including glycoside hydrolases, polysaccharide lyases, and sugar transport proteins from diverse taxa. The results point to an increase in activity of the Bifidobacterium shunt metabolic pathway in the microbiome of mice fed high fiber diets. In those subjected to a low fiber diet, we identified a shift from the degradation of dietary fibers to that of gut mucins, in particular by the recently isolated taxon "Musculibacterium intestinale", which experienced dramatic growth in response to fiber deprivation. When combined with metabolomics and shotgun metagenomics analyses, our findings provide a functional investigation of dietary fiber metabolism in the gut microbiome and demonstrates the power of a combined ABPP-multiomics approach for characterizing the response of the gut microbiome to perturbations.

2019

Cell cycle checkpoints and DNA repair processes protect organisms from potentially lethal mutational damage. Compared to other budding yeasts in the subphylum Saccharomycotina, we noticed that a lineage in the genusHanseniasporaexhibited very high evolutionary rates, low GC content, small genome sizes, and lower gene numbers. To better understandHanseniasporaevolution, we analyzed 25 genomes, including 11 newly sequenced, representing 18 / 21 known species in the genus. Our phylogenomic analyses identify twoHanseniasporalineages, the fast-evolving lineage (FEL), which began diversifying ∼87 million years ago (mya), and the slow-evolving lineage (SEL), which began diversifying ∼54 mya. Remarkably, both lineages lost genes associated with the cell cycle and genome integrity, but these losses were greater in the FEL. For example, all species lost the cell cycle regulatorWHI5, and the FEL lost components of the spindle checkpoint pathway (e.g.,MAD1, MAD2) and DNA damage checkpoint pathw...

Science of The Total Environment, 2020

The Anniston Community Health Survey (ACHS-I) was initially conducted from 2005-2007 to assess polychlorinated biphenyl (PCB) exposures in Anniston, Alabama residents. In 2014, a follow-up study (ACHS-II) was conducted to measure the same PCBs as in ACHS-I and additional compounds e.g., polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and dioxin-like non-ortho (cPCBs) substituted PCBs. In this epigenome-wide association study (EWAS), we examined the associations between PCDD, PCDF, and PCB exposures and DNA methylation. Whole blood DNA methylation was measured using Illumina EPIC arrays (n=292). We modeled lipid-adjusted toxic equivalencies (TEQs) for: ΣDioxins (sum of 28 PCDDs, PCDFs, cPCBs, and mPCBs), PCDDs, PCDFs, cPCBs, and mPCBs using robust multivariable linear regression adjusting for age, race, sex, smoking, bisulfite conversion batch, and estimated percentages of six blood cell types. Among all exposures we identified 10 genomewide (Bonferroni p≤6.74E-08) and 116 FDR (p≤5.00E-02) significant associations representing 10 and 113 unique CpGs, respectively. Of the 10 genome-wide associations, seven (70%) occurred in *

BioData mining, 2016

Redundant hierarchical relations refer to such patterns as two paths from one concept to another, one with length one (direct) and the other with length greater than one (indirect). Each redundant relation represents a possibly unintended defect that needs to be corrected in the ontology quality assurance process. Detecting and eliminating redundant relations would help improve the results of all methods relying on the relevant ontological systems as knowledge source, such as the computation of semantic distance between concepts and for ontology matching and alignment. This paper introduces a novel and scalable approach, called FEDRR - Fast, Exhaustive Detection of Redundant Relations - for quality assurance work during ontological evolution. FEDRR combines the algorithm ideas of Dynamic Programming with Topological Sort, for exhaustive mining of all redundant hierarchical relations in ontological hierarchies, in O(c·|V|+|E|) time, where |V| is the number of concepts, |E| is the num...

BMC Plant Biology, 2012

Background Plant microRNAs (miRNAs) have been revealed to play important roles in developmental control, hormone secretion, cell differentiation and proliferation, and response to environmental stresses. However, our knowledge about the regulatory mechanisms and functions of miRNAs remains very limited. The main difficulties lie in two aspects. On one hand, the number of experimentally validated miRNA targets is very limited and the predicted targets often include many false positives, which constrains us to reveal the functions of miRNAs. On the other hand, the regulation of miRNAs is known to be spatio-temporally specific, which increases the difficulty for us to understand the regulatory mechanisms of miRNAs. Description In this paper we present miRFANs, an online database for Arabidopsis thaliana miRNA function annotations. We integrated various type of datasets, including miRNA-target interactions, transcription factor (TF) and their targets, expression profiles, genomic annota...

bioRxiv (Cold Spring Harbor Laboratory), 2023

Aging and the age-associated decline of the proteome is determined in part through neuronal control of evolutionarily conserved transcriptional effectors, which safeguard homeostasis under fluctuating metabolic and stress conditions by regulating an expansive proteostatic network. We have discovered the Caenorhabditis elegans homeodomaininteracting protein kinase (HPK-1) acts as a key transcriptional effector to preserve neuronal integrity, function, and proteostasis during aging. Loss of hpk-1 results in drastic dysregulation in expression of neuronal genes, including genes associated with neuronal aging. During normal aging hpk-1 expression increases throughout the nervous system more broadly than any other kinase. Within the aging nervous system, hpk-1 induction overlaps with key longevity transcription factors, which suggests hpk-1 expression mitigates natural age-associated physiological decline. Consistently, pan-neuronal overexpression of hpk-1 extends longevity, preserves proteostasis both within and outside of the nervous system, and improves stress resistance. Neuronal HPK-1 improves proteostasis through kinase activity. HPK-1 functions cell non-autonomously within serotonergic and GABAergic neurons to improve proteostasis in distal tissues by specifically regulating distinct components of the proteostatic network. Increased serotonergic HPK-1 enhances the heat shock response and survival to acute stress. In contrast, GABAergic HPK-1 induces basal autophagy and extends longevity, which requires mxl-2 (MLX), hlh-30 (TFEB), and daf-16 (FOXO). Our work establishes hpk-1 as a key neuronal transcriptional regulator critical for preservation of neuronal function during aging. Further, these data provide novel insight as to how the nervous system partitions acute and chronic adaptive response pathways to delay aging by maintaining organismal homeostasis.

BMC Genomics, 2005

We use an approach based on Factor Analysis to analyze datasets generated for transcriptional profiling. The method groups samples into biologically relevant categories, and enables the identification of genes and pathways most significantly associated to each phenotypic group, while allowing for the participation of a given gene in more than one cluster. Genes assigned to each cluster are used for the detection of pathways predominantly activated in that cluster by finding statistically significant associated GO terms. We tested the approach with a published dataset of microarray experiments in yeast. Upon validation with the yeast dataset, we applied the technique to a prostate cancer dataset. Results: Two major pathways are shown to be activated in organ-confined, non-metastatic prostate cancer: those regulated by the androgen receptor and by receptor tyrosine kinases. A number of gene markers (HER3, IQGAP2 and POR1) highlighted by the software and related to the later pathway have been validated experimentally a posteriori on independent samples. Using a new microarray analysis tool followed by a posteriori experimental validation of the results, we have confirmed several putative markers of malignancy associated with peptide growth factor signalling in prostate cancer and revealed others, most notably ERRB3 (HER3). Our study suggest that, in primary prostate cancer, HER3, together or not with HER4, rather than in receptor complexes involving HER2, could play an important role in the biology of these tumors. These results provide new evidence for the role of receptor tyrosine kinases in the establishment and progression of prostate cancer.

NeuroImage

Structural magnetic resonance imaging studies have shown that brain anatomical abnormalities are associated with cognitive deficits in preterm infants. Brain maturation and geometric features can be used with machine learning models for predicting later neurodevelopmental deficits. However, traditional machine learning models would suffer from a large feature-to-instance ratio (i.e., a large number of features but a small number of instances/samples). Ensemble learning is a paradigm that strategically generates and integrates a library of machine learning classifiers and has been successfully used on a wide variety of predictive modeling problems to boost model performance. Attribute (i.e., feature) bagging method is the most commonly used feature partitioning scheme, which randomly and repeatedly draws feature subsets from the entire feature set. Although attribute bagging method can effectively reduce feature dimensionality to handle the large feature-to-instance ratio, it lacks consideration of domain knowledge and latent relationship among features. In this study, we proposed a novel Ontology-guided Attribute Partitioning (OAP) method to better draw feature subsets by considering domain-specific relationship among features. With the better partitioned feature subsets, we developed an ensemble learning framework, which is referred to as OAP-Ensemble Learning (OAP-EL). We applied the OAP-EL to predict cognitive deficits at 2 year of age using quantitative brain maturation and geometric features obtained at term equivalent age in very preterm infants. We demonstrated that the proposed OAP-EL approach significantly outperformed the peer ensemble learning and traditional machine learning approaches.