Background Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling. Results We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut. Conclusion METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC.

Background Recent evidence implicates microbial sulfidogenesis as a potential trigger of colorectal cancer (CRC), highlighting the need for comprehensive knowledge of sulfur metabolism within the human gut. Microbial sulfidogenesis produces genotoxic hydrogen sulfide (H2S) in the human colon using inorganic (sulfate) and organic (taurine/cysteine/methionine) substrates; however, the majority of studies have focused on sulfate reduction using dissimilatory sulfite reductases (Dsr). Results Here, we show that genes for microbial sulfur metabolism are more abundant and diverse than previously observed and are statistically associated with CRC. Using ~ 17,000 bacterial genomes from publicly available stool metagenomes, we studied the diversity of sulfur metabolic genes in 667 participants across different health statuses: healthy, adenoma, and carcinoma. Sulfidogenic genes were harbored by 142 bacterial genera and both organic and inorganic sulfidogenic genes were associated with carcinoma. Significantly, the anaerobic sulfite reductase (asr) genes were twice as abundant as dsr, demonstrating that Asr is likely a more important contributor to sulfate reduction in the human gut than Dsr. We identified twelve potential pathways for reductive taurine metabolism and discovered novel genera harboring these pathways. Finally, the prevalence of metabolic genes for organic sulfur indicates that these understudied substrates may be the most abundant source of microbially derived H2S. Conclusions Our findings significantly expand knowledge of microbial sulfur metabolism in the human gut. We show that genes for microbial sulfur metabolism in the human gut are more prevalent than previously known, irrespective of health status (i.e., in both healthy and diseased states). Our results significantly increase the diversity of pathways and bacteria that are associated with microbial sulfur metabolism in the human gut. Overall, our results have implications for understanding the role of the human gut microbiome and its potential contributions to the pathogenesis of CRC.

Highlights d Thousands of diverse viruses encode genes that manipulate organic sulfur metabolism d Infection in the presence of sulfide increases bacteriophage production d Engineered phage T7 retains cysteine synthase (cysK) over multiple generations d These viruses can influence human gut dysbiosis, microbiomes, and biogeochemistry

Background: Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent, however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling. Results: We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric ‘MW-score’ (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~3 hours with 40 CPU threads to process ~100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~45 mins, while it takes ~5 hours to complete hmmsearch for ~3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.Conclusion: METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available at https://github.com/AnantharamanLab/METABOLIC under GPLv3.

14Summary: Microbial metabolism mediates fundamental transformations of chemistry 15 and energy that drive biogeochemical cycles on our planet. Increasingly, we can read 16 genomic blueprints of microorganisms, decipher their functional capacities and 17 activities, and reconstruct their roles in biogeochemical processes using omic-based 18 techniques such as metagenomics. Currently available tools for analyses of genomic 19 data can annotate and depict metabolic functions to some extent, but they are not 20 comprehensive. No standardized approaches are currently available for bioinformatic 21 validation of metabolic predictions and identifying contributions of microorganisms 22 36 37 Availability and implementation: METABOLIC is available on github: 38 https://github.com/AnantharamanLab/METABOLIC.39 40 Contact: karthik@bact.wisc.edu 41 42 3 131 7 3. Results 132 METABOLIC has been successfully applied on a metagenomic dataset which includes 133 98 MAGs from a deep-sea hydrothermal plume at Guaymas Basin in the Pacific Ocean, 134 and two sets of metagenomic reads (that are subsets of original reads with 10 million 135 read numbers for each pair comprising ~10% of the total reads). The total run time was 136 ~8 hours using 25 CPU threads in a Linux version 4.15.0-48-generic server (Ubuntu 137 v5.4.0). The resulting summary scheme on various biogeochemical cycling processes 138 reflects the pattern on a community scale (Figure 1) (Supplementary Data S1 contains 139 tables and figures from the METABOLIC output).