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Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities.

ABSTRACT: Multi-omics data integration via mechanistic models of metabolism is a scalable and flexible framework for exploring biological hypotheses in microbial systems. However, although most microorganisms are unculturable, such multi-omics modeling is limited to isolate microbes or simple synthetic communities. Here, we developed an approach for modeling microbial activity and interactions that leverages the reconstruction of metagenome-assembled genomes and associated genome-centric metatranscriptomes. At its core, we designed a method for condition-specific metabolic modeling of microbial communities through the integration of metatranscriptomic data. Using this approach, we explored the behavior of anaerobic digestion consortia driven by hydrogen availability and human gut microbiota dysbiosis associated with Crohn's disease, identifying condition-dependent amino acid requirements in archaeal species and a reduced short-chain fatty acid exchange network associated with disease, respectively. Our approach can be applied to complex microbial communities, allowing a mechanistic contextualization of multi-omics data on a metagenome scale.

SUBMITTER: Zampieri G

PROVIDER: S-EPMC9939383 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities.

Zampieri Guido G Campanaro Stefano S Angione Claudio C Treu Laura L

Cell reports methods 20230106 1

Multi-omics data integration via mechanistic models of metabolism is a scalable and flexible framework for exploring biological hypotheses in microbial systems. However, although most microorganisms are unculturable, such multi-omics modeling is limited to isolate microbes or simple synthetic communities. Here, we developed an approach for modeling microbial activity and interactions that leverages the reconstruction of metagenome-assembled genomes and associated genome-centric metatranscriptome ...[more]

PMID: 36814842

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Project description:BackgroundDespite rapid advances in genomic-resolved metagenomics and remarkable explosion of metagenome-assembled genomes (MAGs), the function of uncultivated anaerobic lineages and their interactions in carbon mineralization remain largely uncertain, which has profound implications in biotechnology and biogeochemistry.ResultsIn this study, we combined long-read sequencing and metatranscriptomics-guided metabolic reconstruction to provide a genome-wide perspective of carbon mineralization flow from polymers to methane in an anaerobic bioreactor. Our results showed that incorporating long reads resulted in a substantial improvement in the quality of metagenomic assemblies, enabling the effective recovery of 132 high-quality genomes meeting stringent criteria of minimum information about a metagenome-assembled genome (MIMAG). In addition, hybrid assembly obtained 51% more prokaryotic genes in comparison to the short-read-only assembly. Metatranscriptomics-guided metabolic reconstruction unveiled the remarkable metabolic flexibility of several novel Bacteroidales-affiliated bacteria and populations from Mesotoga sp. in scavenging amino acids and sugars. In addition to recovering two circular genomes of previously known but fragmented syntrophic bacteria, two newly identified bacteria within Syntrophales were found to be highly engaged in fatty acid oxidation through syntrophic relationships with dominant methanogens Methanoregulaceae bin.74 and Methanothrix sp. bin.206. The activity of bin.206 preferring acetate as substrate exceeded that of bin.74 with increasing loading, reinforcing the substrate determinantal role.ConclusionOverall, our study uncovered some key active anaerobic lineages and their metabolic functions in this complex anaerobic ecosystem, offering a framework for understanding carbon transformations in anaerobic digestion. These findings advance the understanding of metabolic activities and trophic interactions between anaerobic guilds, providing foundational insights into carbon flux within both engineered and natural ecosystems. Video Abstract.

Project description:Genome-scale metabolic modeling is a powerful framework for predicting metabolic phenotypes of any organism with an annotated genome. For two decades, this framework has been used for the rational design of microbial cell factories. In the last decade, the range of applications has exploded, and new frontiers have emerged, including the study of the gut microbiome and its health implications and the role of microbial communities in global ecosystems. However, all the critical steps in this framework, from model construction to simulation, require the use of powerful linear optimization solvers, with the choice often relying on commercial solvers for their well-known computational efficiency. In this work, I benchmark a total of six solvers (two commercial and four open source) and measure their performance to solve linear and mixed-integer linear problems of increasing complexity. Although commercial solvers are still the fastest, at least two open-source solvers show comparable performance. These results show that genome-scale metabolic modeling does not need to be hindered by commercial licensing schemes and can become a truly open science framework for solving urgent societal challenges.IMPORTANCEModeling the metabolism of organisms and communities allows for computational exploration of their metabolic capabilities and testing their response to genetic and environmental perturbations. This holds the potential to address multiple societal issues related to human health and the environment. One of the current limitations is the use of commercial optimization solvers with restrictive licenses for academic and non-academic use. This work compares the performance of several commercial and open-source solvers to solve some of the most complex problems in the field. Benchmarking results show that, although commercial solvers are indeed faster, some of the open-source options can also efficiently tackle the hardest problems, showing great promise for the development of open science applications.

Project description:BackgroundThe role of methane in global warming has become paramount to the environment and the human society, especially in the past few decades. Methane cycling microbial communities play an important role in the global methane cycle, which is why the characterization of these communities is critical to understand and manipulate their behavior. Methanotrophs are a major player in these communities and are able to oxidize methane as their primary carbon source.ResultsLake Washington is a freshwater lake characterized by a methane-oxygen countergradient that contains a methane cycling microbial community. Methanotrophs are a major part of this community involved in assimilating methane from lake water. Two significant methanotrophic species in this community are Methylobacter and Methylomonas. In this work, these methanotrophs are computationally studied via developing highly curated genome-scale metabolic models. Each model was then integrated to form a community model with a multi-level optimization framework. The competitive and mutualistic metabolic interactions among Methylobacter and Methylomonas were also characterized. The community model was next tested under carbon, oxygen, and nitrogen limited conditions in addition to a nutrient-rich condition to observe the systematic shifts in the internal metabolic pathways and extracellular metabolite exchanges. Each condition showed variations in the methane oxidation pathway, pyruvate metabolism, and the TCA cycle as well as the excretion of formaldehyde and carbon di-oxide in the community. Finally, the community model was simulated under fixed ratios of these two members to reflect the opposing behavior in the two-member synthetic community and in sediment-incubated communities. The community simulations predicted a noticeable switch in intracellular carbon metabolism and formaldehyde transfer between community members in sediment-incubated vs. synthetic condition.ConclusionIn this work, we attempted to predict the response of a simplified methane cycling microbial community from Lake Washington to varying environments and also provide an insight into the difference of dynamics in sediment-incubated microcosm community and synthetic co-cultures. Overall, this study lays the ground for in silico systems-level studies of freshwater lake ecosystems, which can drive future efforts of understanding, engineering, and modifying these communities for dealing with global warming issues.

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Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities.

Publications

Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities.

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