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Key research themes
1. How can genome-scale metabolic modeling and multi-omics integration elucidate microbial metabolic interdependencies and community assembly?
This research area focuses on leveraging genome-scale metabolic models (GEMs), often reconstructed from genomic and metagenomic data, combined with multi-omics datasets (metagenomics, metatranscriptomics, metaproteomics, metabolomics), to predict and analyze metabolic functions, dependencies, and interactions among microbial community members. Understanding these metabolic interdependencies provides mechanistic insight into microbial community assembly, co-occurrence patterns, niche partitioning, and community stability under varying environmental constraints. The approach bridges genotype to phenotype by connecting metabolic network reconstructions with community ecology, offering predictive power for microbial interactions and metabolic exchanges in complex microbiomes.
Key finding: Using genome-scale metabolic models reconstructed from 193 bacterial genomes isolated from Arabidopsis roots, this study demonstrated that metabolic capabilities cluster quantitatively and qualitatively by phylogeny (H1), and... Read more
Key finding: Applying genome-scale metabolic modeling to 800+ natural microbial communities, including higher-order co-occurrences up to four species, this study found that metabolic dependencies, especially metabolite cross-feeding... Read more
Key finding: By integrating metagenomics, metatranscriptomics, and metaproteomics from mixed microbial communities in wastewater treatment systems, this research reconstructed community-wide metabolic networks combining abundances of... Read more
Key finding: This review highlights the application of genome-scale metabolic models (GEMs) in microbiome research, emphasizing their ability to produce mechanistic insights into metabolic interactions, nutrient preferences, and growth... Read more
Key finding: Through controlled synthetic microbial community assembly experiments and genome-scale metabolic modeling, this study established that convergent family-level community compositions reflect emergent metabolic... Read more
2. What are the methodological advances and computational frameworks for functional annotation and pathway inference in microbial metabolic diversity using omics data?
This research theme encompasses the development and refinement of computational pipelines, databases, and algorithms that enable the accurate characterization of microbial metabolic diversity from high-throughput omics data (metagenomics, metatranscriptomics, metaproteomics, metabolomics). It includes methods for genome reconstruction, gene and protein clustering, functional annotation, metabolic pathway prediction, and standardized multi-omics data integration. These advances address challenges such as data heterogeneity, incomplete references, and complex community structures, thereby supporting scalable and reproducible functional insights into microbial communities and their metabolic capabilities across environments.
Key finding: BioCyc advances provide a comprehensive, curated collection of thousands of microbial genomes linked to metabolic pathways with integrated query tools and visualization software. The workflow automates annotation,... Read more
Key finding: This comprehensive review elucidates the methodological pipeline from raw metagenomic reads to functional protein family annotation, highlighting graph-based and non-graph clustering techniques that reduce data complexity. It... Read more
Key finding: Using a large-scale multi-omics framework standardized across ~880 diverse Earth microbial community samples, this work integrated amplicon, shotgun metagenomic, and untargeted metabolomics (LC-MS/MS, GC-MS) data to map... Read more
Key finding: Applying the seed set framework to metabolic reconstructions derived from single-cell and metagenome-assembled genomes of the freshwater acI Actinobacterial lineage, this study predicted auxotrophies for essential vitamins... Read more
Key finding: This initiative paper introduces an international collaborative effort to standardize and accelerate metaproteomics methodologies in microbiome research. It emphasizes metaproteomics’ pivotal role in linking genotype to... Read more
3. How does microbial metabolic diversity correlate with environmental factors and influence community functional traits and energy production?
This area investigates how microbial metabolic diversity and functional gene repertoires relate to environmental gradients such as temperature, nutrient availability, and ecological niches. Research analyzes microbial taxa alongside metabolic and functional diversity to elucidate patterns governing energy yield, resource utilization, and metabolite profiles across habitats. Findings contribute to understanding ecosystem functioning, microbial niche specialization, and how microbial communities regulate biochemical energy flows under varying environmental constraints.
Key finding: This large-scale metagenome-assembled genome catalog comprising 52,515 genomes from diverse environments dramatically expands phylogenetic and metabolic diversity by 44%, representing 12,556 novel species-level OTUs.... Read more
Key finding: Using 16S rRNA amplicon and metagenomic profiling across geothermal samples spanning ~21.2 to 88.8°C, this study demonstrates that species diversity (SD) declines with increasing temperature in a bell-shaped manner, and... Read more
Key finding: Analyzing bacterial functional group diversity across biomes, this study observed a strong positive correlation between functional group diversity (operational taxonomic units) and estimated metabolic energy yield derived... Read more
Key finding: Through whole metagenome sequencing of cow dung anaerobic digesters grouped by methane concentration stages, this study showed that microbiome composition and diversity significantly correlate with methane yield. High-energy... Read more
Key finding: Combining 16S rRNA amplicon, metagenomics, and position-specific 13C-labeled substrate metabolic flux probing along a geothermal temperature gradient (60–95°C), this study found that despite large declines in taxonomic and... Read more