Bioinformatics analysis of miRNA

Pathologies associated with Zika virus infection include partial progressive paralysis (Guillan-Barre Syndrome) and microcephaly in Brazil and Panama. We identified an 85-nucleotide stem loop structure in the 3' end of Zika virus sfRNA position (343-428) with the canonical structural and sequence characteristics of a miRNA. In comparison, the West Nile Virus has been previously demonstrated to contain a miRNA in its 3' untranslated (UTR) sfRNA region. The West Nile Virus miRNA, KUN-mir-1, was found to be 81 nucleotides in length (492-573) and 58% conserved overall as to the corresponding Zika virus stem loop nucleotide sequence. The majority on the sequence homology was in the terminal  loop (78%). The 40 nucleotide (nt) 3p arm demonstrated 60% homology, decreasing at the distal segment. The 5p arm demonstrates on 42% sequence homology.
The Zika virus stem loop in this study was identified to contain a hairpin structure in the 3' end of the Zika virus sfRNA 3' UTR segment predicted to contain a miRNA with significant homology to human hsa-mir-147a. Target analysis identified 241 human transcripts which include, but not limited to, neurofascin, synaptic vesicle glycoprotein 2A, neurofibromin 1, SAM and SH3 domain containing 1, neurogenin 2, in addition to multiple immune transcripts.
Autoantibodies to neurofascin have been reported in in Guillan-Barre Syndrome (GBS) but have not yet been reported in or after Zika virus infection. A significant 9 mer match exists between the Zika virus miRNA 3p arm and the Neurofascin 3'UTR seed region, CCACACA. In contrast to West Nile Virus, GATA4 was not predicted to be, nor was identified in the MirBase database, to be a target of the Zika virus miRNA.
These computational results indicate highly plausible mechanisms explaining the neurologic syndromes such a Guillan-Barre Syndrome and microcephaly associated with Zika virus infection and warrant further investigation.

The major aim of this study is to introduce bimolecular structures
from  a  bioinformatic  perspective  and  informs  about  the
bioinformatic softwares in this field. Each protein has a specific
chemical or structural function, strongly suggesting that each has a
unique three-dimensional structure. In this study, we investigated
α-amylases  in  Aspergillus  oryzae.  Among  starch  converting
enzymes, the α-amylases (endo-1, 4-α-D-glucan glucanohydrolase
[E.C.3.2.1.1]) are of special importance and extensively studied.
PDB  and  NCBI  databases  and  Chimera,  Predict  Protein  and
Multalign  softwares  used  for  this  research.  By  using  these
softwares, multiple analyses, including determination of residue
composition,  secondary  structures,  conserved  regions,  ligand
binding site done. Starch-hydrolyzing enzymes such as amylases,
pullulanases and glucoamylases play an important role in food,
chemical,  and  pharmaceutical  industries.  In  the  certain
applications like detergent and bakery industries the properties of
α-amylase  needed  is  quite  challenging  Using  of  bioinformatic
softwares  improves  our  understanding  about  proteins  3D
structures and their functions.

We demonstrate that the interaction between miR-450a-5p and miR-28-5p and signal
transducer and activator of transcription 1 (STAT1) mRNA correlates with the
osteoblastic differentiation of mesenchymal stem cells from human exfoliated
deciduous teeth (shed cells). STAT1 negatively regulates runx-related transcription
factor 2 (RUNX2), which is an essential transcription factor in this process. However, the
elements that trigger osteoblastic differentiation and therefore pause the inhibitory
effect of STAT1 need investigation. Usually, STAT1 can be posttranscriptionally regulated
by miRNAs. To test this, we used an in vitro model system in which shed cells were
chemically induced toward osteoblastic differentiation and temporally analyzed,
comparing undifferentiated cells with their counterparts in the early (2 days) or late (7
or 21 days) periods of induction. The definition of the entire functional genome
expression signature demonstrated that the transcriptional activity of a large set of
mRNAs and miRNAs changes during this process. Interestingly, STAT1 and RUNX2
mRNAs feature contrasting expression levels during the course of differentiation. While
undifferentiated or early differentiating cells express high levels of STAT1 mRNA, which
was gradually down-regulated, RUNX2 mRNA was up-regulated toward differentiation.
The reconstruction of miRNA-mRNA interaction networks allowed the identification of
six miRNAs (miR-17-3p, miR-28-5p, miR-29b, miR-29c-5p, miR-145-3p and miR-450a-
5p), and we predicted their respective targets, from which we focused on miR-450a-5p
and miR-28-5p STAT1 mRNA interactions, whose intracellular occurrence was validated
through the luciferase assay. Transfections of undifferentiated shed cells with miR-450a-
5p or miR-28-5p mimics or with miR-450a-5p or miR-28-5p antagonists demonstrated
that these miRNAs might play a role as posttranscriptional controllers of STAT1 mRNA
during osteoblastic differentiation.

Abstract
There was evidence that RNAs are a functionally rich class of molecules not only since the arrival of the next-generation sequencing technology. Non-coding RNAs (ncRNA) could be the key to accelerated diagnosis and enhanced prediction of disease and therapy outcomes as well as the design of advanced therapeutic strategies to overcome yet unsatisfactory approaches.In this review, we discuss the state of the art in RNA systems biology with focus on the application in the systems biomedicine field. We propose guidelines for analysing the role of microRNAs and long non-coding RNAs in human pathologies. We introduce RNA expression profiling and network approaches for the identification of stable and effective RNomics-based biomarkers, providing insights into the role of ncRNAs in disease regulation. Towards this, we discuss ways to model the dynamics of gene regulatory networks and signalling pathways that involve ncRNAs. We also describe data resources and computational methods for finding putative mechanisms of action of ncRNAs. Finally, we discuss avenues for the computer-aided design of novel RNA-based therapeutics.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes

Objective

Expression of a large number of genes during differentiation of undifferentiated pulp cells into odontoblastic cells is still unknown, hence the aim of this investigation was to compare undifferentiated pulp cells (OD-21) and odontoblast-like cells (MDPC-23) through the assessment of cell stimulation and gene expression profiling.

Design

The cells were cultured and after the experimental periods, there were evaluated cell proliferation and viability as well as alkaline phosphatase activity (ALP) and mineralization nodules. To evaluate gene expression it was used fluorescence cDNA microarray technology in addition to bioinformatics programmes such as SAM (significance analysis of microarrays). Gene expression was validated by Real Time PCR (qPCR).

Results

The results showed that viability was above 80% in both cells, cell proliferation and ALP activity was higher in MDPC-23 cells and mineralization nodules were present only in the cultures of odontoblast-like cells. There were observed genes associated to odontogenesis with similar behaviour in both cell types, such as Il10, Traf6, Lef1 and Hspa8. Regions of the heatmap showed differences in induction and repression of genes such as Jak2 and Fas.

Conclusion

OD-21 cells share many genes with similar behaviour to MDPC-23 cells, suggesting their potential to differentiate into odontoblasts.

The present work is aimed at finding variants associated with Type 1 and Type 2 diabetes mellitus (DM) that reside in functionally validated miRNAs binding sites and that can have a functional role in determining diabetes and related pathologies. Using bioinformatics analyses we obtained a database of validated polymorphic miRNA binding sites which has been intersected with genes related to DM or to variants associated and/or in linkage disequilibrium (LD) with it and is reported in genome-wide association studies (GWAS). The workflow we followed allowed us to find variants associated with DM that also reside in functional miRNA binding sites. These data have been demonstrated to have a functional role by impairing the functions of genes implicated in biological processes linked to DM. In conclusion, our work emphasized the importance of SNPs located in miRNA binding sites. The results discussed in this work may constitute the basis of further works aimed at finding functional candidates and variants affecting protein structure and function, transcription factor binding sites, and non-coding epigenetic variants, contributing to widen the knowledge about the pathogenesis of this important disease.

MicroRNAs (miRNAs) are small, noncoding RNA molecules that regulate transcriptional and posttranscriptional gene regulation of the
cell. Experimental evidence shows that miRNAs have a direct role in different cellular processes, such as immune function, apoptosis, and tumorigenesis. In a viral infection context, miRNAs have been connected with the interplay between host and pathogen, occupying a major role in pathogenesis. While numerous viral miRNAs from DNA viruses have been identified, characterization of functional RNA virus-encoded miRNAs and their potential targetsis still ongoing. Here, we used an in silico approach to analyze dengue Virus genome sequences. Pre-miRNAs were extracted through VMir software, and the identification of putative pre-miRNAs and mature miRNAs was accessed using Support Vector Machine web tools. The targets were scanned using miRanda software and functionally annotated using ClueGo. Via computational tools, eight putative miRNAs were found to hybridize with numerous targets of morphogenesis, differentiation, migration, and growth pathways that may play a major role in the interaction of the virus and its host. Future approaches will focus on experimental validation of their presence and target messenger RNA genes to further elucidate their biological functions in human and mosquito cells.

Profiling the rainbow trout hepatic miRNAome under diet-induced hyperglycemia.Carnivorous rainbow trout exhibit prolonged postprandial hyperglycemia when fed a diet exceeding 20% carbohydrate content. This poor capacity to utilize carbohydrates has led to rainbow trout being classified as "glucose-intolerant" (GI). The metabolic phenotype has spurred research to identify the underlying cellular and molecular mechanisms of glucose intolerance, largely because carbohydrate-rich diets provide economic and ecological advantages over traditionally used fish meal, considered un-sustainable for rainbow trout aquaculture operations. Evidence points to a contribution of hepatic intermediary carbohydrate and lipid metabolism, as well as upstream insulin signaling. Recently, microRNAs (miRNAs), small noncoding RNAs acting as negative posttranscrip-tional regulators affecting target mRNA stability and translation, have emerged as critical regulators of hepatic control of glucose-homeo-stasis in mammals, revealing that dysregulated hepatic miRNAs might play a role in organismal hyperglycemia in metabolic disease. To determine whether hepatic regulatory miRNA networks may contribute to GI in rainbow trout, we induced prolonged postprandial hyper-glycemia in rainbow trout by using a carbohydrate-rich diet and profiled genome-wide hepatic miRNAs in hyperglycemic rainbow trout compared with fasted trout and trout fed a diet devoid of carbohydrates. Using small RNA next-generation sequencing and real-time RT-PCR validation, we identified differentially regulated hepatic miRNAs between these groups and used an in silico approach to predict bona fide mRNA targets and enriched pathways. Diet-induced hyperglycemia resulted in differential regulation of hepatic miRNAs compared with fasted fish. Some of the identified miRNAs, such as miRNA-27b-3p and miRNA-200a-3p, are known to be responsive to hyperglycemia in the liver of hyperglycemic glucose-tolerant fish and mammals, suggesting an evolutionary conserved regulation. Using Gene Ontology term-based enrichment analysis, we identify intermediate carbohydrate and lipid metabolism and insulin signaling as potential targets of posttranscriptional regulation by hyperglyce-mia-regulated miRNAs and provide correlative expression analysis of specific predicted miRNA-target pairs. This study identifies hepatic miRNAs in rainbow trout that exhibit differential postprandial expression in response to diets with different carbohydrate content and predicts posttranscriptionally regulated target mRNAs enriched for pathways involved in glucoregulation. Together, these results provide a framework for testable hypotheses of functional involvement of specific hepatic miRNAs in GI in rainbow trout.

are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also known as COVID-19, encodes for a spike protein that is responsible for both attachment and membrane fusion, thereby being critical in the pathogenicity of this virus. Here, I report that a putative miRNA localized in the spike gene of SARS-CoV-2 matches to the forward strand of hsa-miR-8055, a miRNA expressed during T-cell response to antigen, and also binds with perfect complementarity to its seed region

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes.
v

The role of regulatory elements such as small ncRNAs and their mechanisms are poorly understood in infectious diseases. Tuberculosis is one of the oldest infectious diseases of humans and it is still a challenge to prevent and treat. Control of the infection, as well as its diagnosis, are still complex and current treatments used are linked to several side effects. This study aimed to identify possible biomarkers for tuberculosis by applying NGS techniques to obtain global miRNA expression profiles from 22 blood samples of infected patients with tuberculosis (n = 9), their respective healthy physicians (n = 6) and external healthy individuals as controls (n = 7). Samples were run through a pipeline consisting of differential expression, target genes, gene set enrichment and miRNA–gene network analyses. We observed 153 altered miRNAs, among which only three DEmiRNAs (hsa-let-7g-5p, hsa-miR-486-3p and hsa-miR-4732-5p) were found between the investigated patients and their respective p...

The role of regulatory elements such as small ncRNAs and their mechanisms are poorly understood in infectious diseases. Tuberculosis is one of the oldest infectious diseases of humans and it is still a challenge to prevent and treat. Control of the infection, as well as its diagnosis, are still complex and current treatments used are linked to several side effects. This study aimed to identify possible biomarkers for tuberculosis by applying NGS techniques to obtain global miRNA expression profiles from 22 blood samples of infected patients with tuberculosis (n = 9), their respective healthy physicians (n = 6) and external healthy individuals as controls (n = 7). Samples were run through a pipeline consisting of differential expression, target genes, gene set enrichment and miRNA–gene network analyses. We observed 153 altered miRNAs, among which only three DEmiRNAs (hsa-let-7g-5p, hsa-miR-486-3p and hsa-miR-4732-5p) were found between the investigated patients and their respective physicians. These DEmiRNAs are suggested to play an important role in granuloma regulation and their immune physiopathology. Our results indicate that miRNAs may be involved in immune modulation by regulating gene expression in cells of the immune system. Our findings encourage the application of miRNAs as potential biomarkers for tuberculosis.

Background: Esophageal, stomach, and colorectal cancers are commonly lethal gastrointestinal tract (GIT) neoplasms, causing almost
two million deaths worldwide each year. some environmental risk factors are acknowledged; however, genetic defects can significantly
contribute to predisposition to GIT cancers. Accordingly, recent works have shown that single-nucleotide polymorphisms (SNPs) within
miRNAs coding sequence (miR-SNPs) and miRNA target sites (target-SNPs) may further contribute to increased risk of developing cancer.
Objectives: In this study, we comprehensively identified miRNA-target gene pairs implicated in GIT cancers and catalogued the presence
of potentially functional miR-SNPs and target-SNPs that impair the correct functional recognition.
Materials and Methods: Using bioinformatics tools, manual literature review, and a highly accurate dataset of experimentally validated
miRNA-target gene interactions, we compiled a list of miRNA-target genes pairs related to GIT cancers and prioritized them into different
groups based on the levels of experimental support. Functional annotations (gene ontology) were applied to these pairs in each group to
gain further information.
Results: We identified 97 pairs in which both miRNAs and target genes were implicated in GIT cancers. Several pairs, denoted as highly
polymorphic pairs, had both miR-SNPs and target-SNPs. In addition, more than 5000 miRNA-target gene pairs were identified in which,
according to the previous reports, either the miRNAs or the target genes had a direct involvement in GIT cancers. More than 800 targetSNPs
are located in regulatory regions that were extracted from the ENCODE project through the RegulomeDB database. Of these, 20 were
classified as expression quantitative trait loci (eQTLs).
Conclusions: Our work provided a comprehensive source of prioritized and annotated candidate polymorphisms inside miRNAs
and their target sites in GIT cancers, which would facilitate the process of choosing right candidate miRNA-target genes and related
polymorphisms for future association or functional studies.
Keywords:MicroRNAs; Gastrointestinal Neoplasms; Colorectal Neoplasms; Gastric Neoplasms; Esophageal Neoplasms; Single
Nucleotide Polymorphisms

This paper presents our Linked Open Data (LOD) infrastructures for genomic and experimental data related to mi-croRNA biomolecules. Legacy data from two well-known mi-croRNA databases with experimental data and observations, as well as change and version information about microRNA entities, are fused and exported as LOD. Our LOD server assists biologists to explore biological entities and their evolution, and provides a SPARQL endpoint for applications and services to query historical miRNA data and track changes, their causes and effects.

In the present study, we used high-throughput sequencing, miRNA-seq, to discover and explore the expression profiles of known and novel miRNAs in TLR ligand-stimulated vis-à-vis non-stimulated (i.e. Control) peripheral blood mononuclear cells (PBMCs) isolated from blood of healthy Murrah buffaloes. Six small RNA (sRNA) libraries were multiplexed in Ion Torrent PI chip and sequenced on Ion Proton System. The reads obtained were aligned to the Bos taurus genome (UMD3.1 assembly), which is phylogenetically closest species to buffalo (Bubalus bubalis). A total of 160 bovine miRNAs were biocomputationally identified in buffalo PBMCs and 130 putatively novel miRNAs (not enlisted in the bovine mirBase) were identified. All of these 290 miRNAs identified across the six treatment and control samples represent the repertoire of novel miRNAs for the buffalo species. The expression profiles of these miRNAs across the samples have been represented by sample dendrogram and heatmap plots. The uniquely expressed miRNAs in each treatment and control groups were identified. A few miRNAs were expressed at very high levels while the majority of them were moderately expressed. The miRNAs bta-miR-103 and-191 were found to be highly abundant and expressed in all the samples. Other abundantly expressed miRNAs include bta-miR-19b,-29b,-15a,-19a,-30d,-30b-5p and members of let family (let 7a-5p, let 7g & let 7f) in LPS and CpG treated PBMCS and bta-miR-191,-103 &-19b in Poly I:C stimulated PBMCs. Only one novel miRNA (bta-miR-11039) out of 130 identified putatively novel miR-NAs, was expressed in all the six samples and differentially expressed (>2-fold) miRNAs were identified. Six of the differentially expressed miRNAs across the groups (bta-miR-421, bta-let-7i, bta-miR-138, bta-miR-21-5p, bta-miR-222 and bta-miR-27b) were subsequently confirmed by TaqMan quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, the target genes of differentially expressed miRNAs were enriched for the roles in innate immunity and TLR signaling pathways. This maiden study on profiling and cataloguing of bubaline miRNAs expressed in TLR-ligand stimulated PBMCs will

Human milk (HM) contains regulatory biomolecules including miRNAs, the origin and functional significance of which are still undetermined. We used TaqMan OpenArrays to profile 681 mature miRNAs in HM cells and fat, and compared them with maternal peripheral blood mononuclear cells (PBMCs) and plasma, and bovine and soy infant formulae. HM cells and PBMCs (292 and 345 miRNAs, respectively) had higher miRNA content than HM fat and plasma (242 and 219 miRNAs, respectively) (p < 0.05). A strong association in miRNA profiles was found between HM cells and fat, whilst PBMCs and plasma were distinctly different to HM, displaying marked inter-individual variation. Considering the dominance of epithelial cells in mature milk of healthy women, these results suggest that HM miRNAs primarily originate from the mammary epithelium, whilst the maternal circulation may have a smaller contribution. Our findings demonstrate that unlike infant formulae, which contained very few human miRNA, HM is a rich source of lactation-specific miRNA, which could be used as biomarkers of the performance and health status of the lactating mammary gland. Given the recently identified stability, uptake and functionality of food-and milk-derived miRNA in vivo, HM miRNA are likely to contribute to infant protection and development. Human milk (HM) is the optimal nutrition for term infants 1. In addition to being a food source, HM confers developmental programming to the infant and protection against infections, resulting in decreased risk of sudden infant death syndrome and reduced mortality and morbidity both in the short-and long-term 2–6. These effects are mediated by HM-specific regulatory factors including both cellular and biochemical components 7–10. In contrast , artificial infant formula cannot confer such protective and developmental functions as it lacks important HM components with bioactivity 11,12. An additional unique bioactive component of HM that has been recently discovered is miRNAs 13,14. miRNAs are small non-coding RNAs, which regulate gene expression, thus control protein synthesis at the post-transcriptional level in eukaryotic cells 15. They have been identified as key regulators of diverse biological and developmental processes in eukaryotes (cell proliferation and differentiation, apoptosis, immune system development and immune response 16,17) by targeting messenger RNA (mRNA) during its translation into protein, either degrading the mRNA or inhibiting the translation process 18. Aberrant miRNA expression has been found to be associated with pathologies, including different types of cancer, inflammation and diabetes 19. Importantly, food-derived miRNA have been recently shown to be very stable in the gastrointestinal tract and be transferred to the blood circulation of adults, influencing gene expression in different tissues 20. In addition to tissues and cells, miRNAs have been isolated from body fluids, such as plasma, urine, saliva and tears 14. Further, exosomes, small cell-derived vesicles present in body fluids and carrying proteins and molecules, have been shown to take up miRNAs mediating their protection against digestion and facilitating their regulatory functions in different tissues and organs 21. Most recently, miRNAs have been isolated in high quantities from both animal and HM, and were shown to be present both as free molecules in skim milk 13,14,22–24 and packaged in vesicles such as milk exosomes and the

Tuberous sclerosis is a genetic disease causing tumors in many organs mainly the brain, kidneys, skin, eyes, lungs and heart. This paper presents a first step towards creating a complete and comprehensive Genomic Atlas around Tuber Sclerosis. The objective of this paper is  to identify one or more cross-species genes and homologs/paralogs/orthologs with high similarity index based on gene sequence as well percent homology. The method used is through sequence alignment and phylogenic tree. For this purpose a number of available bioinformatics tools are used: PubMed to mine existing literature on Tuberous Sclerosis; GenBank & RefSeq to retrieve associated nucleotide sequences; NCBI HomoloGene to get Homologs, Blast to perform local alignment; and ClustalW2/ClustalOmega to perform sequence alignment.

Key words: Cross species, Tuber Sclerosis, Sequence alignment, Homologs.

Background / Purpose: This study aimed to obtain recombinant hepatitis C virus (HCV) structural protein as a vaccine candidate; that would produce a CD4+ T and CD8+ T cell immune responses and that could be manufactured on a large scale. In order to obtain a HCV structural protein as a vaccine component, the nucleotide sequence of C, E1, E2 (CE1E2) proteins was constructed. Based on the amino acid sequence consensus of CE1E2 protein, genotype 1b; which was derived from multiple sequence alignment of amino acid sequences retrieved from several websites. Main conclusion: Multiple sequence alignment using both Clustal X software and manual alignment chose a consensus amino acid sequence which had the GenBank accession number ABV46113.1. Consensus sequence of CE1E2 protein could represent amino acid sequences of CE1E2 protein of genotype 1b. Consensus amino acid sequence translation using CHO codon preference gave a nucleotide sequence with a codon adaptation index value, 0.72. CE1E2 pr...

microRNAs (miRNAs) play a central role in the regulation of gene expression by targeting specific mRNAs for degradation or translational repression. Each miRNA is post-transcriptionally processed into a duplex comprising two strands. One of the two miRNA strands is selectively loaded into an Argonaute protein to form the miRNA-Induced Silencing Complex (miRISC) in a process referred to as miRNA strand selection. The other strand is ejected from the complex and is subject to degradation. The target gene specificity of miRISC is determined by sequence complementarity between the Argonaute-loaded miRNA strand and target mRNA. Each strand of the miRNA duplex has the capacity to be loaded into miRISC and possesses a unique seed sequence. Therefore, miRNA strand selection plays a defining role in dictating the speci-ficity of miRISC toward its targets and provides a mechanism to alter gene expression in a switch-like fashion. Aberrant strand selection can lead to altered gene regulation by miRISC and is observed in several human diseases including cancer. Previous and emerging data shape the rules governing miRNA strand selection and shed light on how these rules can be circumvented in various physiological and pathological contexts. This article is categorized under: RNA Processing > Processing of Small RNAs Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs K E Y W O R D S arm switching, miR, miR*, miRNA, passenger, strand selection

Background

MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein–protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action.
Methods

Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G1, G2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways.
Results

A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer’s disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

Sono passati seicento anni.
Le spoglie mortali di un uomo antico, dimenticate tra rovine divorate dal verde e dal silenzio, ci parlano di tempi andati, di notti gelide al bagliore dei fuochi, di galoppate sfrenate al tramonto, di passioni e rabbia, lama e sangue.
Hai scalciato mille volte quella terra rossa che ora ti ricopre gelida, quando eri qualcuno e la gente temeva il tuo nome.
Sapevi che sarebbe successo, ma non così in fretta, non così presto.
Hai lasciato il tutto per il nulla, vuoto e buio, e nel vortice del tuo destino parli ora attraverso le nostre bocche.

Would this method for uncovering hidden objects, feature discovery, feature selection and supervised learning work? According to my understanding perfect prediction by computation optimization must be impossible as long as essential input training data features for supervised learning are still lacking. Therefore, I emphasize not to invest much effort into algorithmic computational optimization before the feature discovery process allows selecting all needed features through variation before realistic predictions can be achieved by enhancing computations and parameters. For me this simple progression from completed feature selection to optimized algorithm is essential for more rapid discoveries. That is why I am still wondering why I have seen so many papers focusing on improving computational predictions without any kind of prior considerations about having properly completed the absolutely essential exhaustive feature selection process without which no subsequent computations can lead to satisfactorily predictions, which are reasonably consistent with our experimental observations and measurements. I am afraid that I am still the only one, to whom the writing above can make sense. I had expected much more enthusiasm, excitement and optimism about very likely accelerating our discovery rate by first focusing on uncovering still hidden objects and features through diverse variations of conditions, procedures, methods, techniques and measurements, followed by the exhaustive selection for all relevant needed features, followed by designing, developing, combining and optimizing the computational steps of our machine learning algorithm until our predictions match our experimentally obtained observations. Once this major machine learning objective has been achieved we have reached its final status beyond which we cannot improve it unless we can generate conditions, which cause our previously perfectly predicting machine learning algorithm to obviously fails because it is an absolute prerequisite for discovering more relevant training reflected by more dimensions. According to my current understanding any newly discovered essential input data feature inevitably causes a rise in the dimensionality of input data components, which must be considered together but never in isolation. For example, if I train with 100 input features our input variable must consist of exactly 100 components or parts, which together form a new level of single measuring points, which tends to be much different in controlling its manipulations and their overall effects from anything, which could possibly get anticipated, when trying to add up the effects of its 100 parts to a new total. This new total tends to consist of much different dimensions and often refers to completely unrelated kind of data than when combining all 100 components consisting of exactly the same input values for every of its 100 variables but by considering all 100 components like a single indivisible unit of measurement points, which often results in completely different kinds of unrelated seeming properties/features, which are not even closely reflecting the results, which we'd obtain if we executed each dimension on its own in isolation and sequential order. For example, stopping translation prematurely by three consecutive tryptophans has a much different impact, i.e. stop, then when translating each of the tryptophan in isolation separated by other amino acids, since this causes the nascent polypeptide chain to grow. Each tRNA charged with tryptophan, which complements mRNA triplets, causes the peptide to grow by a single tryptophan, which gets added to it. So when you try 2 tryptophans the polypeptide grows by two amino acids. But when you try 3 consecutive tryptophans, then-counter-intuitively-instead of the expected growth by 3 amino acids-translation prematurely stops. Stopping translation prematurely is of a much different dimension, level, effect and data kind, then when keep adding more amino acids to the growing peptide chain. If we consider the effect of complementary binding of a tRNA to its mRNA codon our peptide grows by one amino acid any charged tRNA adds another amino acid of one of 20 categorical values. Normally no amino acid can cause the translation to step prematurely, not even two amino acids as a pair. But three amino acids as an indivisible triplet, which must be considered as a new single value requiring all three tryptophans to be sequentially to be present, like a single indivisible data block, which must not be divided only in triplets, because only triplets, but no pair or singlet, can stop translation prematurely. Another example is predicting overall cellular protein composition. It depends on how many mRNA strands coding for a particular protein are in the cytoplasm. There is proportionality between number of cytoplasmic mRNA strands and total protein. Therefore, if the cell needs to double protein abundance it could double transcription and keeps everything else the same. But a much better and less step intensive, more economic way of doubling protein concentration is to double the length of the poly-(A)-tail. Extending the length of the poly-(A)-mRNA-tail may require about 100 additional adenines whereas doubling transcription requires about at least 500-instead of only 100-new nucleotides in addition to all needed transcriptional modification steps with their elaborate synthesis machinery. If the dividing yeast must raise its lipid synthesis by more than 10-fold during the short M-phase, it could increase transcription by a factor of 10, it could make the poly-(A)-mRNA-tail 10 times longer, or it could synthesized 10 times more new ribosomes to increase the ribosomal translation by a factor of 10 simply by reducing the distance of free uncovered mRNA nucleotides between adjacent ribosomes translating running down the same mRNA strand. If more than one ribosome is translating the same mRNA strand simultaneously, it is called a poly-ribosome. Hence, having 10 times more ribosomes binding to the same mRNA strand at the same time increases translation by a factor of 10 without needing any additional transcription. Above I have given three easy examples to get 10 times more proteins. Although all 3 methods have the same final result, i.e. 10 times more proteins, their mode of action, their required essential features, their dimensions and their minimally required parts, which must be considered like a single value, are totally different.

MicroRNAs (miRNAs) are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists.

AIMS:
Recent studies have suggested that single-nucleotide polymorphisms (SNPs) in miRNA genes or their binding sites may alter an individual's susceptibility to coronary artery disease (CAD). In the present study, the association between two such SNPs (rs2910164 in miR-146a and rs12190287, which disrupts miRNA binding to TCF21) and CAD, in an Iranian population, was evaluated and in silico analyses were conducted to predict disease-related effects of miR-146a rs2910164.
METHODS:
The study population consisted of angiographically confirmed CAD patients (n = 300) and asymptomatic controls (n = 300). Genotyping was performed using the TaqMan genotyping assay.
RESULTS:
A multivariate regression analysis revealed that rs2910164 was associated with an increased CAD risk in the dominant model. In comparison to GG homozygotes, individuals who carry at least one C allele had a significantly higher risk of CAD (GC+CC vs. GG, odds ratios [OR]: 1.82, 95% confidence intervals [CI]: 1.18-2.80, p = 6.358e-3). Similarly, TCF21 rs12190287 was observed to be associated with CAD in a log-additive model (OR: 0.63, 95% CI: 0.45-0.88, p = 6.584e-3). An in silico analysis revealed that rs2910164 may modify the miR-146a-3p-mediated regulation of several biological processes that are implicated in CAD, like those that are related to the regulation of apoptosis and immune response.
CONCLUSIONS:
Our data provide the first evidence for the association of miR-146a rs2910164 and TCF21 rs12190287 with CAD in an Iranian population, encouraging further research to elucidate the disease-related effects of miR-146a rs2910164.

MicroRNAs (miRNAs) are small non-coding RNAs that play an important role in the post-transcriptional regulation of gene expression. miRNAs are involved in the regulation of many biological processes such as differentiation, apoptosis, and cell proliferation. miRNAs are expressed in embryonic, postnatal, and adult hearts, and they have a key role in the regulation of gene expression during cardiovascular development and disease. Aberrant expression of miRNAs is associated with abnormal cardiac cell differentiation and dysfunction. Tbx5 is a member of the T-box gene family, which acts as transcription factor involved in the vertebrate heart development. Alteration of Tbx5 level affects the expression of hundreds of genes. Haploinsufficiency and gene duplication of Tbx5 are at the basis of the cardiac abnormalities associated with Holt–Oram syndrome (HOS). Recent data indicate that miRNAs might be an important part of the regulatory circuit through which Tbx5 controls heart development. Using high-throughput technologies, we characterized genome-widely the miRNA and mRNA expression profiles in WT-and Tbx5-depleted zebrafish embryos at two crucial developmental time points, 24 and 48 h post fertilization (hpf). We found that several miRNAs, which are potential effectors of Tbx5, are differentially expressed; some of them are already known to be involved in cardiac development and functions, such as miR-30, miR-34, miR-190, and miR-21. We performed an integrated analysis of miRNA expression data with gene expression profiles to refine computational target prediction approaches by means of the inversely correlation of miRNA–mRNA expressions, and we highlighted targets, which have roles in cardiac contractility, cardiomyocyte proliferation/apoptosis, and morphogenesis, crucial functions regulated by Tbx5. This approach allowed to discover complex regulatory circuits involving novel miRNAs and protein coding genes not considered before in the HOS such as miR-34a and miR-30 and their targets.

The significance of miRNAs has been clarified over the last decade as thousands of these small non-coding RNAs have been found in a wide variety of species. By binding to specific target mRNAs, miRNAs act as negative regulators of gene expression in many different biological processes. Computational approaches for discovery of miRNAs in genomes usually take the form of an algorithm that scans sequences for miRNA-characteristic hairpins, followed by classification of those hairpins as miRNAs or non-miRNAs. In this study, two new approaches to genome-scale miRNA discovery are presented and evaluated. These methods, one ensemble-based and one using logistic regression, have been designed to detect miRNA candidates without relying on conservation or transcriptome data, and to achieve high-confidence predictions in reasonable computational time. GenoScan achieves high accuracy with a good balance between sensitivity and specificity. In a benchmark evaluation including 15 previously published methods, the regression-based approach in GenoScan achieved the highest classification accuracy.

A central environment and interface for microRNA (miRNA) registry and repository and a general standardized framework
for their systematic annotation was established over a decade ago. However, the numbers of experimentally and
computationally identified miRNAs are swiftly accumulating, and new aspects of miRNA-mediated gene regulation are
being revealed. Currently, it is of great significance that the annotation framework should be redefined to include newly
discovered miRNA species such as the variants of mature miRNAs (isomiRNAs), and organellar miRNAs: cipomiRNAs and
mitomiRNAs. It is also of great importance that key terminology referring to the novelty, evolutionary history or biogenesis
of miRNAs, as well as the confidence of their identification are standardized in the literature and disseminated in a central
miRNA registry. Here, we review the status of miRNA nomenclature, curation and critical points of need for a revision of
miRNA nomenclature and terminology.

miRNAs serve as crucial post-transcriptional regulators in a variety of essential cell fate decisions. However, the contribution of the mRNA-miRNA mutual regulation to bistability is not fully understood. Here, we built a set of mathematical models of mRNA-miRNA interactions and systematically analyzed the sensitivity of the response curves under various conditions. Our findings indicate that mRNA-miRNA reciprocal regulation could manifest ultrasensitivity to subserve the generation of bistability when equipped with a positive feedback loop. We also find that the region of bistability is expanded by a stronger competing endogenous mRNA (ceRNA). Interestingly, bistability can be generated without a feedback loop if multiple miRNA binding sites exist on a target mRNA. Thus, we demonstrate the importance of simple mRNA-miRNA reciprocal regulation in cell fate decisions.

Two genotypes of common bean (Phaseolus vulgaris L.) were studied to determine the structural cause of seed abortion in this species. In the non-abortive control (wild-type, cultivar BAT93), the histological analysis revealed a classical pattern of seed development and showed coordinated differentiation of the embryo proper, suspensor, endosperm tissue and seed coat. In contrast, the ethyl methanesulfonate (EMS) mutant (cultivar BAT93) showed disruption in the normal seed development leading to embryo abortion. Aborted embryos from these degenerate seeds showed abnormalities in suspensor and cotyledons at the globular, heart, torpedo and cotyledon stages. Exploring the feasibility of incorporating the available online bioinformatics databases, we identified 22 genes revealing high homology with genes involved in Arabidopsis thaliana embryo development and expressed in common bean immature seeds. The expression patterns of these genes were confirmed by RT-PCR. All genes were highly expressed in seed tissues. To study the expression profiles of isolated genes during Phaseolus embryogenesis, six selected genes were examined by quantitative RT-PCR analysis on the developing embryos of wild-type and EMS mutant plants. All selected genes were expressed differentially at different stages of embryo development. These results could help to improve understanding of the mechanism of common bean embryogenesis.

MicroRNAs control the expression of their target genes by translational repression and transcriptional cleavage. They are involved
in various biological processes including development and progression of cancer. To uncover the biological role of miRNAs
it is important to identify their target genes. The small number of experimentally validated target genes makes computer
prediction methods very important. However, state-of-the-art prediction tools result in a great number of putative targets with an
unpredictable number of false positives. In this paper, we propose and evaluate two approaches for ranking the biological relevance
of putative targets of miRNAs which are associated with breast cancer.

Background MicroRNAs (miRNAs) constitute a class of small non-coding RNAs that post-transcriptionally regulate genes involved in several key biological processes and thus are involved in various diseases, including cancer. In this study we aimed to identify a miRNA expression signature that could be used to separate between normal and malignant prostate tissues. Results Nine miRNAs were found to be differentially expressed (p <0.00001). With the exception of two samples, this expression signature could be used to separate between the normal and malignant tissues. A cross-validation procedure confirmed the generality of this expression signature. We also identified 16 miRNAs that possibly could be used as a complement to current methods for grading of prostate tumor tissues. Conclusions We found an expression signature based on nine differentially expressed miRNAs that with high accuracy (85%) could classify the normal and malignant prostate tissues in patients from the Swedish Watchful Waiting cohort. The results show that there are significant differences in miRNA expression between normal and malignant prostate tissue, indicating that these small RNA molecules might be important in the biogenesis of prostate cancer and potentially useful for clinical diagnosis of the disease.

The significance of miRNAs has been clarified over the last decade
as thousands of these small non-coding RNAs have been found in a wide variety of species. By binding to specific target mRNAs, miRNAs act as negative regulators of gene expression in many different biological processes.

Computational approaches for discovery of miRNAs in genomes usually take the form of an algorithm that scans sequences for miRNA-characteristic hairpins, followed by classification of those hairpins as miRNAs or nonmiRNAs.

In this study, two new approaches to genome-scale miRNA discovery are presented and evaluated. These methods, one ensemble-based and one using logistic regression, have been designed to detect miRNA candidates without relying on conservation or transcriptome data, and to achieve high-confidence predictions in reasonable computational time. GenoScan achieves high accuracy with a good balance between sensitivity and specificity. In a benchmark evaluation including 15 previously published methods, the regression-based approach in GenoScan achieved the highest classification accuracy.

Altered miRNAs were associated with cigarette smoking. The study aimed to examine the gene expression level of plasma let-7a among healthy smokers and compared it with the non-smokers. Forty subjects were recruited for the present study and classified into 21 smokers and 19 non-smokers, age, and sex were matched. The software that used to design functional primers was MIRprimer. Quantitative realtime PCR was employed to compare the relative expression of plasma let-7a. Results showed that the level of let-7a was down-regulated in smokers to 0.34fold (p = 0.006) that of the non-smokers. Plasma let-7a showed an area under curve (AUC) of 0.749 with sensitivity 43% and specificity 100%. In conclusion, plasma let-7a was significantly down-regulated in the smokers, and it might be considered a candidate biomarker to discriminate between smokers and non-smokers.

MicroRNAs are a newly discovered class of non-protein small RNAs with 22-24 nucleotides. They play multiple roles in biological processes including development, cell proliferation, apoptosis, stress responses and many other cell functions. In this research, several approaches were combined to make a computational prediction of potential miRNAs and their targets in Helianthus annuus (H. annuus). The already available information of the plant miRNAs present in miRBase v21 was used against expressed sequence tags (ESTs). A total of three miRNAs were detected from which one potential novel miRNA was identified following a range of strict filtering criteria. The target prediction was carried out for these three miRNAs having various targets. These targets were functionally annotated and GO terms were assigned. To study the conserved nature of the miRNAs, predicted phylogenetic analysis was carried out. These findings will significantly provide the broader picture for understanding the functions in H. annuus.

In the present study, we used high-throughput sequencing, miRNA-seq, to discover and explore the expression profiles of known and novel miRNAs in TLR ligand-stimulated vis-àvis non-stimulated (i.e. Control) peripheral blood mononuclear cells (PBMCs) isolated from blood of healthy Murrah buffaloes. Six small RNA (sRNA) libraries were multiplexed in Ion Torrent PI chip and sequenced on Ion Proton System. The reads obtained were aligned to the Bos taurus genome (UMD3.1 assembly), which is phylogenetically closest species to buffalo (Bubalus bubalis). A total of 160 bovine miRNAs were biocomputationally identified in buffalo PBMCs and 130 putatively novel miRNAs (not enlisted in the bovine mirBase) were identified. All of these 290 miRNAs identified across the six treatment and control samples represent the repertoire of novel miRNAs for the buffalo species. The expression profiles of these miRNAs across the samples have been represented by sample dendrogram and heatmap plots. The uniquely expressed miRNAs in each treatment and control groups were identified. A few miRNAs were expressed at very high levels while the majority of them were moderately expressed. The miRNAs bta-miR-103 and-191 were found to be highly abundant and expressed in all the samples. Other abundantly expressed miRNAs include bta-miR-19b,-29b,-15a,-19a,-30d,-30b-5p and members of let family (let 7a-5p, let 7g & let 7f) in LPS and CpG treated PBMCS and bta-miR-191,-103 &-19b in Poly I:C stimulated PBMCs. Only one novel miRNA (bta-miR-11039) out of 130 identified putatively novel miR-NAs, was expressed in all the six samples and differentially expressed (>2-fold) miRNAs were identified. Six of the differentially expressed miRNAs across the groups (bta-miR-421, bta-let-7i, bta-miR-138, bta-miR-21-5p, bta-miR-222 and bta-miR-27b) were subsequently confirmed by TaqMan quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, the target genes of differentially expressed miRNAs were enriched for the roles in innate immunity and TLR signaling pathways. This maiden study on profiling and cataloguing of bubaline miRNAs expressed in TLR-ligand stimulated PBMCs will

Altered miRNAs were associated with cigarette smoking. The study aimed to examine the gene expression level of plasma let-7a among healthy smokers and compared it with the non-smokers. Forty subjects were recruited for the present study and classified into 21 smokers and 19 non-smokers, age, and sex were matched. The software that used to design functional primers was MIRprimer. Quantitative realtime PCR was employed to compare the relative expression of plasma let-7a. Results showed that the level of let-7a was down-regulated in smokers to 0.34fold (p = 0.006) that of the non-smokers. Plasma let-7a showed an area under curve (AUC) of 0.749 with sensitivity 43% and specificity 100%. In conclusion, plasma let-7a was significantly down-regulated in the smokers, and it might be considered a candidate biomarker to discriminate between smokers and non-smokers.

Molecular studies regarding regulatory elements such as small ncRNAs and their mechanisms are poorly understood in infectious diseases. Tuberculosis is one of the oldest infectious diseases of humanity, and it is still a challenge to prevent and treat it. The control of the infection as well as its diagnosis are still complex, and treatments used are linked to several side effects. This study aimed to investigate miRNA’s expression profile to identify possible biomarkers for tuberculosis. We applied NGS techniques to investigate miRNA’s global expression profile from blood samples of infected patients with tuberculosis, their respective healthy physicians, and external healthy individuals as controls. Samples from 22 individuals run through a differential expression, target genes, gene set enrichment, and miRNA-gene network analysis. We observed 153 altered miRNAs, among which, only three DEmiRNAs (hsa-let-7g-5p, hsa-miR-486-3p and hsa-miR-4732-5p) were found between the investigate...

Pathologies associated with Zika virus infection include partial progressive paralysis (Guillan Barre Syndrome) and microcephaly in Brazil and Panama. We identified an 85 nucleotide stem loop structure in the 3&#39; end of Zika virus sfRNA (position (343-428) with the canonical structural and sequence characteristics of a miRNA. In comparison, the West Nile Virus has been previously demonstrated to contain a miRNA in its 3’ untranslated (UTR) sfRNA region. The West Nile Virus miRNA, KUN-mir-1, was found to be 81 nucleotides in length (492-573) and 58% conserved overall as to the corresponding Zika virus stem loop nucleotide sequence. The majority on the sequence homology was in the terminal loop (78%). The 40 nucleotide (nt) 3p arm demonstrated 60% homology, decreasing at the distal segment. The 5p arm demonstrates on 42% sequence homology. The Zika virus stem loop in this study was identified to contain a hairpin structure in the 3’ end of the Zika virus sfRNA 3’ UTR segment predic...

Background/Aim: Several links between DNA replication, pluripotency and development have been recently identified. The involvement of miRNA in the regulation of cell cycle events and pluripotency factors has also gained attention. Materials and Methods: In the present study, we used the g:Profiler platform to analyze transcription factor binding sites, miRNA networks and protein-protein interactions to identify novel links among the aforementioned processes. Results and Conclusion: A complex circuitry between retinoic acid signaling, SWI/SNF components, pluripotency factors including Oct4, Sox2 and Nanog and cell cycle regulators was identified. It is suggested that the DNA replication inhibitor geminin plays a central role in this circuitry.

MicroRNAs (miRNAs) are small non-coding RNAs that play an important role in the post-transcriptional regulation of gene expression. miRNAs are involved in the regulation of many biological processes such as differentiation, apoptosis, and cell proliferation. miRNAs are expressed in embryonic, postnatal, and adult hearts, and they have a key role in the regulation of gene expression during cardiovascular development and disease. Aberrant expression of miRNAs is associated with abnormal cardiac cell differentiation and dysfunction. Tbx5 is a member of the T-box gene family, which acts as transcription factor involved in the vertebrate heart development. Alteration of Tbx5 level affects the expression of hundreds of genes. Haploinsufficiency and gene duplication of Tbx5 are at the basis of the cardiac abnormalities associated with Holt-Oram syndrome (HOS). Recent data indicate that miRNAs might be an important part of the regulatory circuit through which Tbx5 controls heart development. Using high-throughput technologies, we characterized genome-widely the miRNA and mRNA expression profiles in WT-and Tbx5-depleted zebrafish embryos at two crucial developmental time points, 24 and 48 h post fertilization (hpf). We found that several miRNAs, which are potential effectors of Tbx5, are differentially expressed; some of them are already known to be involved in cardiac development and functions, such as miR-30, miR-34, miR-190, and miR-21. We performed an integrated analysis of miRNA expression data with gene expression profiles to refine computational target prediction approaches by means of the inversely correlation of miRNA-mRNA expressions, and we highlighted targets, which have roles in cardiac contractility, cardiomyocyte proliferation/apoptosis, and morphogenesis, crucial functions regulated by Tbx5. This approach allowed to discover complex regulatory circuits involving novel miRNAs and protein coding genes not considered before in the HOS such as miR-34a and miR-30 and their targets.

In the present study, we used high-throughput sequencing, miRNA-seq, to discover and explore the expression profiles of known and novel miRNAs in TLR ligand-stimulated vis-àvis non-stimulated (i.e. Control) peripheral blood mononuclear cells (PBMCs) isolated from blood of healthy Murrah buffaloes. Six small RNA (sRNA) libraries were multiplexed in Ion Torrent PI chip and sequenced on Ion Proton System. The reads obtained were aligned to the Bos taurus genome (UMD3.1 assembly), which is phylogenetically closest species to buffalo (Bubalus bubalis). A total of 160 bovine miRNAs were biocomputationally identified in buffalo PBMCs and 130 putatively novel miRNAs (not enlisted in the bovine mirBase) were identified. All of these 290 miRNAs identified across the six treatment and control samples represent the repertoire of novel miRNAs for the buffalo species. The expression profiles of these miRNAs across the samples have been represented by sample dendrogram and heatmap plots. The uniquely expressed miRNAs in each treatment and control groups were identified. A few miRNAs were expressed at very high levels while the majority of them were moderately expressed. The miRNAs bta-miR-103 and-191 were found to be highly abundant and expressed in all the samples. Other abundantly expressed miRNAs include bta-miR-19b,-29b,-15a,-19a,-30d,-30b-5p and members of let family (let 7a-5p, let 7g & let 7f) in LPS and CpG treated PBMCS and bta-miR-191,-103 &-19b in Poly I:C stimulated PBMCs. Only one novel miRNA (bta-miR-11039) out of 130 identified putatively novel miR-NAs, was expressed in all the six samples and differentially expressed (>2-fold) miRNAs were identified. Six of the differentially expressed miRNAs across the groups (bta-miR-421, bta-let-7i, bta-miR-138, bta-miR-21-5p, bta-miR-222 and bta-miR-27b) were subsequently confirmed by TaqMan quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, the target genes of differentially expressed miRNAs were enriched for the roles in innate immunity and TLR signaling pathways. This maiden study on profiling and cataloguing of bubaline miRNAs expressed in TLR-ligand stimulated PBMCs will

Despite the high heritability of schizophrenia (SCZ), details of its pathophysiology and etiology are still unknown. Recent findings suggest that aberrant inflammatory regulation and microRNAs (miRNAs) are involved. Here we performed a comparative analysis of the global miRNome of human induced pluripotent stem cell (iPSC)-astrocytes, derived from SCZ patients and healthy controls (CTRLs), at baseline and following inflam-matory modulation using IL-1β. We identified four differentially expressed miRNAs (miR-337-3p, miR-127-5p, miR-206, miR-1185-1-3p) in SCZ astrocytes that exhibited significantly lower baseline expression relative to CTRLs. Group-specific differential expression (DE) analyses exploring possible distinctions in the modulatory capacity of IL-1β on miRNA expression in SCZ versus CTRL astroglia revealed trends toward altered miRNA expressions. In addition, we analyzed peripheral blood samples from a large cohort of SCZ patients (n = 484) and CTRLs (n = 496) screening for the expression of specific gene targets of the four DE miRNAs that were identified in our baseline astrocyte setup. Three of these genes, LAMTOR4, IL23R, and ERBB3, had a significantly lower expression in the blood of SCZ patients compared to CTRLs after multiple testing correction. We also found nominally significant differences for ERBB2 and IRAK1, which similarly displayed lower expressions in SCZ versus CTRL. Furthermore, we found matching patterns between the expressions of identified miRNAs and their target genes when comparing our in vitro and in vivo results. The current results further our understanding of the pathobiological basis of SCZ.

A biotinidase (EC 3.5.1.12) libera a biotina da biocitina, que atua como cofator para diversas carboxilases. A deficiência de
biotinidase (DB), doença de he-rança autossômica recessiva, pode resultar em problemas neurológicos e der-matológicos quando
não tratada precocemente. O gene que codifica a biotini-dase, BTD, é composto por 4 éxons e sua região 3’UTR possui 329 pb.
Esta região é sítio de ação de reguladores pós-transcricionais que influenciam na expressão gênica, principalmente através de miRNAs. A investigação de miR-NAs com alvos em BTD podem apresentar relevância na relação genótipo-fenótipo de pacientes,
que nem sempre é clara. Objetivos: identificar miRNAs que possam estar contribuindo para os níveis da enzima biotinidase dos
paci-entes.Metodologia: Foram utilizados os softwares miRBasee miRTarBase, ba-seados em similaridade de sequência; StarBase
e TarBase, que utilizam ali-nhamento múltiplo entre espécies; TargetScanHuman, que combina status de ligações termodinâmicas
e conservação evolutiva; e o banco miRDB. Resulta-dos e discussão: Foram encontrados 42 miRNAs pelo software miRBase; 11
pelo miRTarBase; 2 pelo StarBase, 22 pelo TarBase e 1 pelo TargetScanHu-man. Estes miRNAs estão relacionados com sinalização
celular; vias de glicosilação; metabolismo de arginina, biotina, tirosina e tiamina. Na rota metabólica da Biotina, foram
identificados miRNAs em genes alvos que interagem com o BTD: SPCS1 e SPCS3, subunidades do complexo sinal da peptidase e
HLCS, um importante regulador de carboxilases dependentes de biotina. Os principais miRNAs encontrados foram hsa-miR-7-5p,
supressor de proliferação celular e indutor de apoptose,hsa-miR-34a-5p, relacionado com proliferação celular e importante
regulador da proteína P53, e hsa-miR-145, previamente identificado em recém-nascidos(Fu et. al., 2005), expresso
especificamente no fígado, onde a biotinidase é mais expressa. Vale ressaltar que os miRNAs identificados não foram comuns
entre os diferentes softwares, exigindo validação. Até o momento, não existem interações fortemente validadas entre miRNAs e
o gene BTD, o que deixa em aberto uma futura validação experimental

Background/Aim: Several links between DNA replication, pluripotency and development have been recently identified. The involvement of miRNA in the regulation of cell cycle events and pluripotency factors has also gained attention. Materials and Methods: In the present study, we used the g:Profiler platform to analyze transcription factor binding sites, miRNA networks and protein-protein interactions to identify novel links among the aforementioned processes. Results and Conclusion: A complex circuitry between retinoic acid signaling, SWI/SNF components, pluripotency factors including Oct4, Sox2 and Nanog and cell cycle regulators was identified. It is suggested that the DNA replication inhibitor geminin plays a central role in this circuitry.

Ebolavirus is one of the most virulent of human viral pathogens, killing 25-90% of patients within 6-16 days (EBOV and SUDV) resulting from complete disruption of the host immune response. Despite the relatively minor genetic differences between the species, the case fatality rate (CFR) is significantly different. The Zaire Ebolavirus (EBOV) historically is the most lethal, with its sporadic outbreaks resulting in a CFR ranging from 60% (Minkébé, Gabon 1994, Gueckedou 2014) to 90% (Kéllé, Congo 2003). This is in sharp contrast to Reston ebolavirus (RESTV) which, despite its translational similarity to EBOV and serologic evidence of human transmission, has yet to result in even minor symptoms of the disease. The viral protein, VP35, has been shown to be a multifunctional virulence factor by antagonizing anti-viral signalling pathways via its interferon inhibitory domain (IID). However, it is unlikely the VP35 pathways could fully account for the observed differential virulence between the species. Viral encoded miRNAs have already been identified in many species and shown to modulate the expression and antiviral function of interferons (IFNs). This is the first paper to summarize the computational identification of a likely miRNA, termed the Pandora Element, within the intergenic, untranslated region of the Zaire ebolavirus nucleoprotein gene that appears to target multiple human transcripts.

MicroRNAs are a newly discovered class of non-protein small RNAs with 22–24 nucleotides. They play multiple roles in biological processes including development, cell proliferation, apoptosis, stress responses and many other cell functions. In this research, several approaches were combined to make a computational prediction of potential miRNAs and their targets in Helianthus annuus (H. annuus). The already available information of the plant miRNAs present in miRBase v21 was used against expressed sequence tags (ESTs). A total of three miRNAs were detected from which one potential novel miRNA was identified following a range of strict filtering criteria. The target prediction was carried out for these three miRNAs having various targets. These targets were functionally annotated and GO terms were assigned. To study the conserved nature of the miRNAs, predicted phylogenetic analysis was carried out. These findings will significantly provide the broader picture for understanding the functions in H. annuus.