Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning

Interspecies hydrogen transfer (IHT) and direct interspecies electron transfer (DIET) are two syntrophy models for methanogenesis. Their relative importance in methanogenic environments is still unclear. Our recent discovery of a novel species Candidatus Geobacter eutrophica with the genetic potential of IHT and DIET may serve as a model species to address this knowledge gap. To experimentally demonstrate its DIET ability, we performed electrochemical enrichment of Ca. G. eutrophica-dominating communities under 0 and 0.4 V vs. Ag/AgCl based on the presumption that DIET and extracellular electron transfer (EET) share similar metabolic pathways. After three batches of enrichment, Geobacter OTU650, which was phylogenetically close to Ca. G. eutrophica, was outcompeted in the control but remained abundant and active under electrochemical stimulation, indicating Ca. G. eutrophica’s EET ability. The high-quality draft genome further showed high phylogenomic similarity with Ca. G. eutrophica, and the genes encoding outer membrane cytochromes and enzymes for hydrogen metabolism were actively expressed. A Bayesian network was trained with the genes encoding enzymes for alcohol metabolism, hydrogen metabolism, EET, and methanogenesis from dominant fermentative bacteria, Geobacter, and Methanobacterium. Methane production could not be accurately predicted when the genes for IHT were in silico knocked out, inferring its more important role in methanogenesis. The genomics-enabled machine learning modeling approach can provide predictive insights into the importance of IHT and DIET.

Reshaping of bacterial molecular hydrogen metabolism contributes to the outgrowth of commensal E. coli during gut inflammation

The composition of gut-associated microbial communities changes during intestinal inflammation, including an expansion of Enterobacteriaceae populations. The mechanisms underlying microbiota changes during inflammation are incompletely understood. Here, we analyzed previously published metagenomic datasets with a focus on microbial hydrogen metabolism. The bacterial genomes in the inflamed murine gut and in patients with inflammatory bowel disease contained more genes encoding predicted hydrogen-utilizing hydrogenases compared to communities found under non-inflamed conditions. To validate these findings, we investigated hydrogen metabolism of Escherichia coli, a representative Enterobacteriaceae, in mouse models of colitis. E. coli mutants lacking hydrogenase-1 and hydrogenase-2 displayed decreased fitness during colonization of the inflamed cecum and colon. Utilization of molecular hydrogen was in part dependent on respiration of inflammation-derived electron acceptors. This work highlights the contribution of hydrogenases to alterations of the gut microbiota in the context of non-infectious colitis.

Disentangling Syntrophic Electron Transfer Mechanisms in Methanogenesis Through Electrochemical Stimulation, Omics, and Machine Learning

Background: Interspecies hydrogen transfer (IHT) and direct interspecies electron transfer (DIET) are two syntrophy models for methanogenesis. Their relative importance in methanogenic environments is still unclear. Our recent discovery of a novel species Candidatus Geobacter eutrophica with the genetic potential of IHT and DIET may serve as a model species to address this knowledge gap. Results: To experimentally demonstrate its DIET ability, we performed electrochemical enrichment of Ca. G. eutrophica-dominating communities under 0 and 0.4 V vs. Ag/AgCl based on the presumption that DIET and extracellular electron transfer (EET) share similar metabolic pathways. After three batches of enrichment, acetate accumulated in all reactors, while propionate was detected only in the electrochemical reactors. Four dominant fermentative bacteria were identified in the core population, and metatranscriptomics analysis suggested that they were responsible for the degradation of fructose and ethanol to propionate, propanol, acetate, and H2. Geobacter OTU650, which was phylogenetically close to Ca. G. eutrophica, was outcompeted in the control but remained abundant and active under electrochemical stimulation. The results thus confirmed Ca. G. eutrophica’s EET ability. The high-quality draft genome (completeness 99.4%, contamination 0.6%) further showed high phylogenomic similarity with Ca. G. eutrophica, and the genes encoding outer membrane cytochromes and enzymes for hydrogen metabolism were actively expressed. Redundancy analysis and a Bayesian network constructed with the core population predicted the importance of Ca. G. eutrophica-related OTU650 to methane production. The Bayesian network modeling approach was also applied to the genes encoding enzymes for alcohol metabolism, hydrogen metabolism, EET, and methanogenesis. Methane production could not be accurately predicted when the genes for IHT were in silico knocked out, inferring its more important role in methanogenesis.Conclusions: Ca. G. eutrophica is electroactive and simultaneously performs IHT and DIET. The results from the metatranscriptomic analysis have provided valuable information for enrichment and isolation of Ca. G. eutrophica. IHT is predicted to have a stronger impact on methane production than DIET in the electrochemical reactors. The genomics-enabled machine learning modeling approach can provide predictive insights into the importance of IHT and DIET.

Temporal dynamics and ecophysiology of thermophilic composting analyzed through metagenome-assembled genomes

Background: Thermophilic composting is a semi-engineered process carried out by diverse microbial communities. Composting is an environment friendly way of degrading biomass; its study may help uncover important biomass-degrading organisms and key enzymes. DNA sequence-based previous studies have presented a general description of the microbial-molecular features of composting, but they have lacked more specific information on the key organisms that are active during the process and their genomes. Methods: We present an analysis of metagenome-assembled genomes (MAGs) obtained from time-series samples of a thermophilic composting process in the São Paulo Zoological Park (Brazil). Our results are based on a careful analysis of MAG gene content and on metabolic modeling of their interactions. Results: We recovered 60 MAGs from sequencing datasets from two separate composting cells. Phylogenetic analysis shows that 47 of these MAGs represent novel taxa at the genus or higher levels. We have analyzed the gene repertoire of these MAGs in terms of lignocellulose degradation, secondary metabolite production, antibiotic resistance genes, denitrification genes, sulfur metabolism, hydrogen metabolism, and oxygen metabolism. For one of the composting cells we also had metatranscriptome data, which allowed a deeper analysis of 49 MAGs. This analysis showed the presence of three distinct clusters of MAGs with varying activity during the 99-day composting process. The interaction model pointed to Sphaerobacter thermophilus and Thermobispora bispora as key players in the process, as well as other bacteria that are novel. Our results also show the importance of coadjuvant bacteria and of microbial functions related to efficient bioenergetic processes during biomass conversion, such as N2O reduction and hydrogenases. A novel acidobacteria MAG encodes N2O reductase hallmark genes (nosZD). Strong metabolic dependencies predicted between MAGs revealed that cross-feeding in composting can be determined by complementary functions found in the genomes of producers and consumers, supporting the Black Queen hypothesis for co-evolutionary interactions. Conclusions: This study reveals for the first time the key bacterial players in thermophilic composting and provides a model of their dynamic metabolic interactions. These findings pave the way for more rational composting procedures and provide information that could help the development of novel biomass-degrading technologies.

Evidence for non-methanogenic metabolisms in globally distributed archaeal clades basal to the Methanomassiliicoccales

Recent discoveries of mcr and mcr-like complexes in genomes from diverse archaeal lineages suggest that methane (and more broadly alkane) metabolism is an ancient pathway with complicated evolutionary histories. The conventional view is that methanogenesis is an ancestral metabolism of the archaeal class Thermoplasmata. Through comparative genomic analysis of 12 Thermoplasmata metagenome-assembled genomes (MAGs), we show that these microorganisms do not encode the genes required for methanogenesis, which suggests that this metabolism may have been laterally acquired by an ancestor of the order Methanomassiliicoccales. These MAGs include representatives from four orders basal to the Methanomassiliicoccales, including a high-quality MAG (95% complete) that likely represents a new order, Ca. Lunaplasma lacustris ord. nov. sp. nov. These MAGs are predicted to use diverse energy conservation pathways, such as heterotrophy, sulfur and hydrogen metabolism, denitrification, and fermentation. Two of these lineages are globally widespread among anoxic, sedimentary environments, with the exception of Ca. Lunaplasma lacustris, which has thus far only been detected in alpine caves and subarctic lake sediments. These findings advance our understanding of the metabolic potential, ecology, and global distribution of the Thermoplasmata and provide new insights into the evolutionary history of methanogenesis within the Thermoplasmata.

Gut microbiome, diet and symptom interactions in irritable bowel syndrome

While several studies have documented associations between dietary habits and microbiota composition and function in healthy subjects, no study explored these associations in patients with irritable bowel syndrome (IBS), and especially in relation to symptoms. Here, we used a novel approach that combined data from 4-day food diary, integrated into a food tree, together with gut microbiota (shotgun metagenomic) for IBS patients (N=149) and healthy subjects (N=52). Paired microbiota and food-based trees allowed to detect new association between subspecies and diet. Combining co-inertia analysis and linear regression models, exhaled gas levels and symptom severity could be predicted from metagenomic and dietary data. IBS patients with severe symptoms had a diet enriched in food items of poorer quality, a high abundance of gut microbial enzymes involved in hydrogen metabolism in correlation with animal carbohydrate (mucin/meat-derived) metabolism. Our study provides unprecedented resolution of diet-microbiota-symptom interactions and ultimately paves the way for personalized nutritional recommendations.