Ecology-guided prediction of cross-feeding interactions in the human gut microbiome

Abstract Understanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput. Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combined metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning. Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotation; we provide these predictions for experimentally testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.

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Ecology-guided prediction of cross-feeding interactions in the human gut microbiome

10.1101/2020.06.08.140830 ◽

2020 ◽

Author(s):

Akshit Goyal ◽

Tong Wang ◽

Veronika Dubinkina ◽

Sergei Maslov

Keyword(s):

Gut Microbiome ◽

Metabolic Profile ◽

Mechanistic Model ◽

Optimization Techniques ◽

Computational Method ◽

Large Network ◽

Human Gut ◽

Human Gut Microbiome ◽

Microbial Species ◽

Feeding Interactions

Understanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput. Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combined metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning. Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotation; we provide these predictions for experimentally testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.

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Ecology-guided prediction of cross-feeding interactions in the human gut microbiome

Nature Communications ◽

10.1038/s41467-021-21586-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Akshit Goyal ◽

Tong Wang ◽

Veronika Dubinkina ◽

Sergei Maslov

Keyword(s):

Gut Microbiome ◽

Experimental Testing ◽

Mechanistic Model ◽

Optimization Techniques ◽

Computational Method ◽

Large Network ◽

Human Gut ◽

Human Gut Microbiome ◽

Microbial Species ◽

Feeding Interactions

AbstractUnderstanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput. Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combine metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning. Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotations, which we provide for experimental testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.

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Metaproteomics as a tool for studying the protein landscape of human-gut bacterial species

10.1101/2021.09.02.458484 ◽

2021 ◽

Author(s):

Moses Stamboulian ◽

Jamie Canderan ◽

Yuzhen Ye

Keyword(s):

Human Health ◽

Gut Microbiome ◽

De Novo ◽

Bacterial Species ◽

Individual Species ◽

Human Gut ◽

Human Gut Microbiome ◽

Microbial Species ◽

Microbial Organisms

AbstractHost-microbiome interactions and the microbial community have broad impact in human health and diseases. Most microbiome based studies are performed at the genome level based on next-generation sequencing techniques, but metaproteomics is emerging as a powerful technique to study microbiome functional activity by characterizing the complex and dynamic composition of microbial proteins. We conducted a large-scale survey of human gut microbiome metaproteomic data to identify generalist species that are ubiquitously expressed across all samples and specialists that are highly expressed in a small subset of samples associated with a certain phenotype. We were able to utilize the metaproteomic mass spectrometry data to reveal the protein landscapes of these species, which enables the characterization of the expression levels of proteins of different functions and underlying regulatory mechanisms, such as operons. Finally, we were able to recover a large number of open reading frames (ORFs) with spectral support, which were missed by de novo protein-coding gene predictors. We showed that a majority of the rescued ORFs overlapped with de novo predicted proteincoding genes, but on opposite strands or on different frames. Together, these demonstrate applications of metaproteomics for the characterization of important gut bacterial species. Results are available for public access at https://omics.informatics.indiana.edu/GutBac.Author summaryMany reference genomes for studying human gut microbiome are available, but knowledge about how microbial organisms work is limited. Identification of proteins at individual species or community level provides direct insight into the functionality of microbial organisms. By analyzing more than a thousand metaproteomics datasets, we examined protein landscapes of more than two thousands of microbial species that may be important to human health and diseases. This work demonstrated new applications of metaproteomic datasets for studying individual genomes. We made the analysis results available through the GutBac website, which we believe will become a resource for studying microbial species important for human health and diseases.

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