MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle

Abstract

BACKGROUNDThe increasing number of metagenomic and genomic sequences has dramatically improved our understanding of microbial diversity, yet our ability to infer metabolic capabilities in such datasets remains challenging.FINDINGSWe describe the Multigenomic Entropy Based Score pipeline (MEBS), a software platform designed to evaluate, compare and infer complex metabolic pathways in large ‘omic’ datasets, including entire biogeochemical cycles. MEBS is open source and available through https://github.com/eead-csic-compbio/metagenome_Pfam_score. To demonstrate its use we modeled the sulfur cycle by exhaustively curating the molecular and ecological elements involved (compounds, genes, metabolic pathways and microbial taxa). This information was reduced to a collection of 112 characteristic Pfam protein domains and a list of complete-sequenced sulfur genomes. Using the mathematical framework of relative entropy (H’), we quantitatively measured the enrichment of these domains among sulfur genomes. The entropy of each domain was used to both: build up a final score that indicates whether a (meta)genomic sample contains the metabolic machinery of interest and to propose marker domains in metagenomic sequences such as DsrC (PF04358). MEBS was benchmarked with a dataset of 2,107 non-redundant microbial genomes from RefSeq and 935 metagenomes from MG-RAST. Its performance, reproducibility, and robustness were evaluated using several approaches, including random sampling, linear regression models, Receiver Operator Characteristic plots and the Area Under the Curve metric (AUC). Our results support the broad applicability of this algorithm to accurately classify (AUC=0.985) hard to culture genomes (e.g., Candidatus Desulforudis audaxviator), previously characterized ones and metagenomic environments such as hydrothermal vents, or deep-sea sediment.CONCLUSIONSOur benchmark indicates that an entropy-based score can capture the metabolic machinery of interest and be used to efficiently classify large genomic and metagenomic datasets, including uncultivated/unexplored taxa

Large-scale protein level comparison of Deltaproteobacteria reveals cohesive metabolic groups

“…For example, cluster F has the second-highest median sulfur cycling MEBS score compared to all other clusters (Supplementary Fig. 9), and 95% of cluster F genomes contain a heterodisulfide reductase subunit (PF02662, Supplementary Table 6), a protein domain that is conserved among sulfur cycling microorganisms [65]. In contrast, cluster D has the third-highest sulfur cycling MEBS score and does not contain this conserved domain in 95% of genomes.…”

“…7). Finally, MAGs and reference genomes were analyzed using the built-in function of MEBS, which evaluates the likelihood of a given genome to perform metabolic reactions involved in major cycles (S, N, Fe, and O) based on informative entropy scores [65]. We also utilized the custom function of MEBS to search MAGs and publicly available genomes for the presence of pfam domains involved in methylamine degradation (see Supplementary Table 11).…”

Giant sponge grounds of Central Arctic seamounts are associated with extinct seep life

“…Within each sample, the resulting expression values were summed for each PFAM and divided by the overall PFAM-level arithmetic mean of 7.500434. MEBS v1.2 103 was used for completeness estimations of various metabolic pathways. Different PFAMs databases were assessed for enzymes involved in heterotrophy, including hydrolytic enzymes such as chitinases, proteinases, carbohydrate-active enzymes (CAZy), and acyl-CoA transferase CaiB and other family III transferases (PF02515), flavin-dependent oxidoreductase of the luciferase family (PF00296.20), and short-chain alcohol dehydrogenase (PF00106.25), which have previously been identified as key enzymes in microbial utilization of refractory DOM specifically by SAR202 33,61 .…”

Metagenomics of Atacama Lithobiontic Extremophile Life Unveils Highlights on Fungal Communities, Biogeochemical Cycles and Carbohydrate-Active Enzymes

“…The comparison was performed in R using Vegan library via a multidimensional scaling procedure considering taxonomic domains. Moreover, a biogeochemical cycle analysis was performed through identification of the completeness of metabolic pathways involved in carbon, methane, nitrogen, and sulfur cycling, using Multigenomic Entropy Based Score (MEBS) software [68].…”