The capacity to produce hydrogen sulfide (H2S) via cysteine degradation is ubiquitous in the human gut microbiome

As one of the three mammalian gasotransmitters, hydrogen sulfide (H2S) plays a major role in maintaining physiological homeostasis. Endogenously produced H2S plays numerous beneficial roles including mediating vasodilation and conferring neuroprotection. Due to its high membrane permeability, exogenously produced H2S originating from the gut microbiota can also influence human physiology and is implicated in reducing intestinal mucosal integrity and potentiating genotoxicity and is therefore a potential target for therapeutic interventions. Gut microbial H2S production is often attributed to dissimilatory sulfate reducers such as Desulfovibrio and Bilophila species. However, an alternative source for H2S production, cysteine degradation, is present in gut microbes, but the genes responsible for cysteine degradation have not been systematically annotated in gut microbes. To better understand the potential for H2S production via cysteine degradation by the human gut microbiome, we performed a comprehensive search for genes encoding cysteine-degrading genes in 4,644 bacterial genomes from the Unified Human Gastrointestinal Genome (UHGG) catalogue. We identified 407 gut bacterial species as putative cysteine degrading bacteria, 328 of which have not been previously implicated in H2S production. We identified the presence of at least one putative cysteine degrading bacteria in metagenomic data of 100% of 6,644 healthy subjects and the expression of cysteine-degrading genes in metatranscriptomics data of 100% of 59 samples. Additionally, putative cysteine-degrading bacteria are more abundant than sulfate reducing bacteria (p<2.2e-16). Overall, this study improves our understanding of the capacity for H2S production by the human gut microbiome and may help to inform interventions to therapeutically modulate gut microbial H2S production.

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Polyphenol utilization proteins in human gut microbiome

Applied and Environmental Microbiology ◽

10.1128/aem.01851-21 ◽

2021 ◽

Author(s):

Bo Zheng ◽

Yinchao He ◽

Pengxiang Zhang ◽

Yi-Xin Huo ◽

Yanbin Yin

Keyword(s):

Human Health ◽

Gut Microbiome ◽

Sequence Similarity ◽

Gene Clusters ◽

Metagenomic Data ◽

Human Populations ◽

Human Gut ◽

Systematic Classification ◽

Dietary Polyphenols ◽

Human Gut Microbiome

Dietary polyphenols can significantly benefit human health, but their bioavailability is metabolically controlled by human gut microbiota. To facilitate the study of polyphenol metabolism for human gut health, we have manually curated experimentally characterized polyphenol utilization proteins (PUPs) from published literature. This resulted in 60 experimentally characterized PUPs (named seeds) with various metadata, such as species and substrate. Further database search found 107,851 homologs of the seeds from UniProt and UHGP (Unified Human Gastrointestinal Protein) databases. All PUP seeds and homologs were classified into protein classes, families and subfamilies based on Enzyme Commission (EC) numbers, Pfam (protein family) domains and sequence similarity networks. By locating PUP homologs in the genomes of UHGP, we have identified 1,074 physically linked PUP gene clusters (PGCs), which are potentially involved in polyphenol metabolism in the human gut. The gut microbiome of Africans was consistently ranked the top in terms of the abundance and prevalence of PUP homologs and PGCs among all geographical continents. This reflects the fact that dietary polyphenols are more commonly consumed by African population than other populations such as Europeans and North Americans. A case study of the Hadza hunter-gatherer microbiome verified the feasibility of using dbPUP to profile metagenomic data for biologically meaningful discovery, suggesting an association between diet and PUP abundance. A Pfam domain enrichment analysis of PGCs identified a number of putatively novel PUP families. Lastly, a user-friendly web interface ( https://bcb.unl.edu/dbpup/ ) provides all the data online to facilitate the research of polyphenol metabolism for improved human health. Importance Long-term consumption of polyphenol-rich foods have been shown to lower the risk of various human diseases such as cardiovascular diseases, cancers, and metabolic diseases. Raw polyphenols are often enzymatically processed by gut microbiome, which encode various polyphenol utilization proteins (PUPs) to produce metabolites with much higher bioaccessibility to gastrointestinal cells. This study delivered dbPUP as an online database for experimentally characterized PUPs and their homologs in human gut microbiome. This work also performed a systematic classification of PUPs into enzyme classes, families, and subfamilies. The signature Pfam domains were identified for PUP families, enabling conserved domain-based PUP annotation. This standardized sequence similarity-based PUP classification system offered a guideline for the future inclusion of new experimentally characterized PUPs and the creation of new PUP families. An in-depth data analysis was further conducted on PUP homologs and physically linked PUP gene clusters (PGCs) in gut microbiomes of different human populations.

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Systematic mapping of drug metabolism by the human gut microbiome

10.1101/538215 ◽

2019 ◽

Cited By ~ 7

Author(s):

Pranatchareeya Chankhamjon ◽

Bahar Javdan ◽

Jaime Lopez ◽

Raphaella Hull ◽

Seema Chatterjee ◽

...

Keyword(s):

Drug Metabolism ◽

Small Molecules ◽

Gut Microbiome ◽

Bacterial Species ◽

Drug Bioavailability ◽

Oral Drugs ◽

Human Gut ◽

Human Gut Microbiome ◽

Comprehensive Framework

ABSTRACTThe human gut microbiome harbors hundreds of bacterial species with diverse biochemical capabilities, making it one of nature’s highest density, highest diversity bioreactors. Several drugs have been previously shown to be directly metabolized by the gut microbiome, but the extent of this phenomenon has not been systematically explored. Here, we develop a systematic screen for mapping the ability of the complex human gut microbiome to biochemically transform small molecules (MDM-Screen), and apply it to a library of 575 clinically used oral drugs. We show that 13% of the analyzed drugs, spanning 28 pharmacological classes, are metabolized by a single microbiome sample. In a proof-of-principle example, we show that microbiome-derived metabolism occursin vivo, identify the genes responsible for it, and provide a possible link between its consequences and clinically observed features of drug bioavailability and toxicity. Our findings reveal a previously underappreciated role for the gut microbiome in drug metabolism, and provide a comprehensive framework for characterizing this important class of drug-microbiome interactions.

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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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Genome Sequence of Christensenella minuta DSM 22607T

Genome Announcements ◽

10.1128/genomea.01451-16 ◽

2017 ◽

Vol 5 (2) ◽

Cited By ~ 7

Author(s):

Bruce A. Rosa ◽

Kymberlie Hallsworth-Pepin ◽

John Martin ◽

Aye Wollam ◽

Makedonka Mitreva

Keyword(s):

Genome Sequence ◽

Gut Microbiome ◽

Bacterial Species ◽

Biological Mechanism ◽

Valuable Resource ◽

Human Gut ◽

Content Type ◽

Human Gut Microbiome

ABSTRACT Obesity influences and is influenced by the human gut microbiome. Here, we present the genome of Christensenella minuta, a highly heritable bacterial species which has been found to be strongly associated with obesity through an unknown biological mechanism. This novel genome provides a valuable resource for future obesity therapeutic studies.

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Microbe-seq: high-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome

10.1101/2020.12.14.422699 ◽

2020 ◽

Author(s):

Wenshan Zheng ◽

Shijie Zhao ◽

Yehang Yin ◽

Huidan Zhang ◽

David M. Needham ◽

...

Keyword(s):

Microbial Communities ◽

High Throughput ◽

Gut Microbiome ◽

Bacterial Species ◽

Microbial Interactions ◽

Single Individual ◽

Human Gut ◽

Single Strain ◽

Human Gut Microbiome

AbstractWe present Microbe-seq, a high-throughput single-microbe method that yields strain-resolved genomes from complex microbial communities. We encapsulate individual microbes into droplets with microfluidics and liberate their DNA, which we amplify, tag with droplet-specific barcodes, and sequence. We use Microbe-seq to explore the human gut microbiome; we collect stool samples from a single individual, sequence over 20,000 microbes, and reconstruct nearly-complete genomes of almost 100 bacterial species, including several with multiple subspecies strains. We use these genomes to probe genomic signatures of microbial interactions: we reconstruct the horizontal gene transfer (HGT) network within the individual and observe far greater exchange within the same bacterial phylum than between different phyla. We probe bacteria-virus interactions; unexpectedly, we identify a significant in vivo association between crAssphage, an abundant bacteriophage, and a single strain of Bacteroides vulgatus. Microbe-seq contributes high-throughput culture-free capabilities to investigate genomic blueprints of complex microbial communities with single-microbe resolution.

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A robust statistical framework for reconstructing genomes from metagenomic data

10.1101/011460 ◽

2014 ◽

Cited By ~ 9

Author(s):

Dongwan Don Kang ◽

Jeff Froula ◽

Rob Egan ◽

Zhong Wang

Keyword(s):

Gut Microbiome ◽

Metagenomic Data ◽

Metagenomic Dataset ◽

Human Gut ◽

Sequence Composition ◽

Human Gut Microbiome ◽

Statistical Framework ◽

Large Complex ◽

Probabilistic Distances ◽

Unidentified Species

We present software that reconstructs genomes from shotgun metagenomic sequences using a reference-independent approach. This method permits the identification of OTUs in large complex communities where many species are unknown. Binning reduces the complexity of a metagenomic dataset enabling many downstream analyses previously unavailable. In this study we developed MetaBAT, a robust statistical framework that integrates probabilistic distances of genome abundance with sequence composition for automatic binning. Applying MetaBAT to a human gut microbiome dataset identified 173 highly specific genomes bins including many representing previously unidentified species.

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Mapping of the benzoate metabolism by human gut microbiome indicates food-derived metagenome evolution

Scientific Reports ◽

10.1038/s41598-021-84964-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Monika Yadav ◽

Avinash Lomash ◽

Seema Kapoor ◽

Rajesh Pandey ◽

Nar Singh Chauhan

Keyword(s):

Gut Microbiome ◽

Human Gut ◽

Food Preservatives ◽

Multiple Features ◽

Human Organ ◽

Gut Microbes ◽

Human Gut Microbiome ◽

Metabolism Analysis ◽

Aerobic And Anaerobic

AbstractSodium benzoate is one of the widely used food preservatives and its metabolism in the human body has been studied only with the host perspective. Despite the human gut microbiome being considered as a virtual human organ, its role in benzoate metabolism is yet to be elucidated. The current study uses a multi-omic approach to rationalize the role of human gut microbes in benzoate metabolism. Microbial diversity analysis with multiple features synchronously indicates the dominance of Bacteroidetes followed by Firmicutes, Actinobacteria, and Proteobacteria. Metagenomic exploration highlights the presence of benzoate catabolic protein features. These features were mapped on to the aerobic and anaerobic pathways of benzoate catabolism. Benzoate catabolism assays identified statistically significant metabolites (P < 0.05) associated with the protocatechuate branch of the beta-ketoadipate pathway of the benzoate metabolism. Analysis of the 201 human gut metagenomic datasets across diverse populations indicates the omnipresence of these features. Enrichment of the benzoate catabolic protein features in human gut microbes rationalizes their role in benzoate catabolism, as well as indicates food-derived microbiome evolution.

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Bacterial associations in the healthy human gut microbiome across populations

Scientific Reports ◽

10.1038/s41598-021-82449-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mark Loftus ◽

Sayf Al-Deen Hassouneh ◽

Shibu Yooseph

Keyword(s):

Gut Microbiome ◽

Sequence Data ◽

Bacterial Species ◽

Human Populations ◽

Human Gut ◽

Healthy Human ◽

Human Gut Microbiome ◽

Genome Shotgun Sequence ◽

Bacterial Associations ◽

And Function

AbstractIn a microbial community, associations between constituent members play an important role in determining the overall structure and function of the community. The human gut microbiome is believed to play an integral role in host health and disease. To understand the nature of bacterial associations at the species level in healthy human gut microbiomes, we analyzed previously published collections of whole-genome shotgun sequence data, totaling over 1.6 Tbp, generated from 606 fecal samples obtained from four different healthy human populations. Using a Random Forest Classifier, we identified 202 signature bacterial species that were prevalent in these populations and whose relative abundances could be used to accurately distinguish between the populations. Bacterial association networks were constructed with these signature species using an approach based on the graphical lasso. Network analysis revealed conserved bacterial associations across populations and a dominance of positive associations over negative associations, with this dominance being driven by associations between species that are closely related either taxonomically or functionally. Bacterial species that form network modules, and species that constitute hubs and bottlenecks, were also identified. Functional analysis using protein families suggests that much of the taxonomic variation across human populations does not foment substantial functional or structural differences.

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GutCyc: a Multi-Study Collection of Human Gut Microbiome Metabolic Models

10.1101/055574 ◽

2016 ◽

Cited By ~ 1

Author(s):

Aria S. Hahn ◽

Tomer Altman ◽

Kishori M. Konwar ◽

Niels W. Hanson ◽

Dongjae Kim ◽

...

Keyword(s):

Gut Microbiome ◽

Large Scale ◽

High Throughput Sequencing ◽

Human Microbiome ◽

Therapeutic Interventions ◽

Human Gut ◽

Human Gut Microbiome ◽

Disease States ◽

Health And Disease ◽

Inflammatory Bowel

AbstractAdvances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, we developed GUTCYC, a compendium of environmental pathway genome databases constructed from 418 assembled human microbiome datasets using METAPATHWAYS, enabling reproducible functional metagenomic annotation. We also generated metabolic network reconstructions for each metagenome using the PATHWAY TOOLS software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GUTCYC provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn’s disease, and type 2 diabetes. GUTCYC data products are searchable online, or may be downloaded and explored locally using METAPATHWAYS and PATHWAY TOOLS.

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MRGM: a mouse reference gut microbiome reveals a large functional discrepancy for gut bacteria of the same genus between mice and humans

10.1101/2021.10.24.465599 ◽

2021 ◽

Author(s):

Nayeon Kim ◽

Chan Yeong Kim ◽

Summo Yang ◽

Dongjin Park ◽

Sang-Jun Ha ◽

...

Keyword(s):

Gut Microbiome ◽

Bacterial Species ◽

Drug Effects ◽

Core Gene ◽

Metagenomic Sequencing ◽

Human Gut ◽

Classification Rate ◽

Human Gut Microbiome ◽

Dietary Components

The gut microbiome is associated with human diseases and interacts with dietary components and drugs. In vivo mouse models may be effective for studying diet and drug effects on the gut microbiome. We constructed a mouse reference gut microbiome (MRGM, https://www.mbiomenet.org/MRGM/) that includes newly-assembled genomes from 878 metagenomes. Leveraging samples with ultra-deep metagenomic sequencing (>130 million read pairs), we demonstrated quality improvement in assembled genomes for mouse gut microbes as sequencing depth increased. MRGM provides a catalog of 46,267 non-redundant genomes with ≥70% completeness and ≤5% contamination comprising 1,689 representative bacterial species and 15.2 million non-redundant proteins. Importantly, MRGM significantly improved the taxonomic classification rate of sequencing reads from mouse fecal samples compared to previous databases. Using MRGM, we determined that reliable low-abundance taxa profiles of the mouse gut microbiome require sequencing >10 million reads. Despite the high overall functional similarity of the mouse and human gut microbiomes, only ~10% of MRGM species are shared with the human gut microbiome. Although ~80% of MRGM genera are present in the human gut microbiome, ~70% of the shared genera have <40% of core gene content for the respective genus with human counterparts. These suggest that although metabolic processes of the human gut microbiome largely occur in the mouse gut microbiome, functional translations between them according to genus-level taxonomic commonality require caution.

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