METABOLIC: High-Throughput Profiling of Microbial Genomes for Functional Traits, Metabolism, Biogeochemistry, and Community-scale Functional Networks

Abstract

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.

Evolutionary ecology of microbial populations inhabiting deep sea sediments associated with cold seeps

“…METABOLIC-G, an implementation of METABOLIC (v4.0) 67 , was used to predict metabolic and biogeochemical functional trait profiles of MAGs. For phylogenetic analysis of functional genes related to methane and sulfate metabolism, amino acid sequences were aligned using the MUSCLE algorithm 68 included in the software package MEGA X 69 .…”

The sulfur cycle connects microbiomes and biogeochemistry in deep-sea hydrothermal plumes

“…Building on our findings from both thermodynamic modeling and omics-based biogeochemical estimations which indicated the importance of sulfur-based metabolisms, we studied microbial metabolic interactions associated with sulfur cycling in all plumes. We recently developed a metric, metabolic weight score (MW-score) 35 to measure the contribution of metabolic/biogeochemical steps, and their metabolic connectivity in a microbial community. More frequently shared functions and their higher abundances in a microbial community lead to higher MW-scores 35 .…”

“…We recently developed a metric, metabolic weight score (MW-score) 35 to measure the contribution of metabolic/biogeochemical steps, and their metabolic connectivity in a microbial community. More frequently shared functions and their higher abundances in a microbial community lead to higher MW-scores 35 . Both metagenomics and metatranscriptomic data showed elemental sulfur oxidation to be the key reaction in the sulfur cycle (Fig.…”

The majority of microorganisms in gas hydrate-bearing subseafloor sediments ferment macromolecules

“…For individual MAGs, metabolic genes were identified by METABOLIC v4.0 [89]. Genomes were also annotated using DRAM with default parameters [92] against KOfam, MEROPS and dbCAN databases to identify CAZymes, peptidases, lipases, nucleases, transporters, and other proteins of interest.…”

“…For all contigs assembled from 13 metagenomic samples, the functional annotation was undertaken with METABOLIC v4.0 [89]. All predicted coding sequences were pooled and clustered at 95% nucleotide sequence similarity using CD-HIT v4.8.1 [90] (parameters: -c 0.95 -T 0 -M 0 -G 0 -aS 0.9 -g 1 -r 1 -d 0).…”