TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome

Crosstalk between the gut microbiome and the host plays an important role in animal development and health. Small compounds are key mediators in this host–gut microbiome dialogue. For instance, tryptophan metabolites, generated by biotransformation of tryptophan through complex host–microbiome co-metabolism can trigger immune, metabolic, and neuronal effects at local and distant sites. However, the origin of tryptophan metabolites and the underlying tryptophan metabolic pathway(s) are not well characterized in the current literature. A large number of the microbial contributors of tryptophan metabolism remain unknown, and there is a growing interest in predicting tryptophan metabolites for a given microbiome. Here, we introduce TrpNet, a comprehensive database and analytics platform dedicated to tryptophan metabolism within the context of host (human and mouse) and gut microbiome interactions. TrpNet contains data on tryptophan metabolism involving 130 reactions, 108 metabolites and 91 enzymes across 1246 human gut bacterial species and 88 mouse gut bacterial species. Users can browse, search, and highlight the tryptophan metabolic pathway, as well as predict tryptophan metabolites on the basis of a given taxonomy profile using a Bayesian logistic regression model. We validated our approach using two gut microbiome metabolomics studies and demonstrated that TrpNet was able to better predict alterations in in indole derivatives compared to other established methods.

Download Full-text

A Tree of Human Gut Bacterial Species and its Applications to Metagenomics and Metaproteomics Data Analysis

10.1101/2020.09.24.311720 ◽

2020 ◽

Author(s):

Moses Stamboulian ◽

Thomas G. Doak ◽

Yuzhen Ye

Keyword(s):

Gut Microbiome ◽

Phylogenetic Trees ◽

Bacterial Species ◽

Taxonomic Composition ◽

Marker Genes ◽

Missing Information ◽

Human Gut ◽

Taxonomic Profiling ◽

Tree Building ◽

The Impact

Abstract1BackgroundRecent advances in genome and metagenome sequencing have dramatically enriched the collection of genomes of bacterial species related to human health and diseases. In metagenomic studies phylogenetic trees are commonly used to depict, describe, and compare the bacterial members of the community under study. The most accurate tree-building algorithms now use large sets of marker genes taken from across genomes. However, many of the current bacterial genomes were assembled from metagenomic datasets (i.e., metagenome assembled genomes, MAGs), and often contain missing information. It is therefore important to study how well the phylogeny approach performs on such genomes. Further, phylogeny methods are not perfect and it is important to know how reliable an inferred tree is.ResultsHere we examined the impact of incompleteness of the genomes on the tree reconstruction, and we showed that phylogeny approaches including RAxML (which handles missing data explicitly) and FastTree generally performed well on simulated collection of 400 genomes with missing information. As RAxML is computationally prohibitive for the much larger collections of gut genomes, we chose FastTree to build a unified tree of human-gut associated bacterial species (referred to as gut tree), including more than 3000 genomes, most of which are incomplete. We developed two downstream applications of the gut tree: peptide-centric analysis of metaproteomics datasets; and taxonomic characterization of metagenomic sequences. In both applications, the gut tree provided the basis for quantification of species composition at various taxonomic resolutions.ConclusionsThe gut tree presented in this study provides a useful framework for taxonomic profiling of human gut microbiome. Including MAGs in the tree provides more comprehensive representation of microbial species diversity associated with human gut, important for studying the taxonomic composition of gut microbiome.Availability and ImplementationThe tree construction pipeline and downstream applications of the gut tree are freely available at https://github.com/mgtools/guttree.

Download Full-text

Human gut microbiome composition and tryptophan metabolites were changed differently by fast food and Mediterranean diet in 4 days: a pilot study

Nutrition Research ◽

10.1016/j.nutres.2020.03.005 ◽

2020 ◽

Vol 77 ◽

pp. 62-72 ◽

Cited By ~ 3

Author(s):

Chenghao Zhu ◽

Lisa Sawrey-Kubicek ◽

Elizabeth Beals ◽

Chris H. Rhodes ◽

Hannah Eve Houts ◽

...

Keyword(s):

Pilot Study ◽

Mediterranean Diet ◽

Gut Microbiome ◽

Fast Food ◽

Human Gut ◽

Microbiome Composition ◽

Human Gut Microbiome ◽

Tryptophan Metabolites

Download Full-text

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.

Download Full-text

Phylogenetic barriers to horizontal transfer of antimicrobial peptide resistance genes in the human gut microbiota

10.1101/385831 ◽

2018 ◽

Author(s):

Bálint Kintses ◽

Orsolya Méhi ◽

Eszter Ari ◽

Mónika Számel ◽

Ádám Györkei ◽

...

Keyword(s):

Antibiotic Resistance ◽

Gut Microbiota ◽

Resistance Genes ◽

Gut Microbiome ◽

Horizontal Transfer ◽

Bacterial Species ◽

Antibiotic Resistance Genes ◽

Genetic Exchange ◽

Human Gut ◽

Human Gut Microbiota

AbstractThe human gut microbiota has adapted to the presence of antimicrobial peptides (AMPs) that are ancient components of immune defence. Despite important medical relevance, it has remained unclear whether AMP resistance genes in the gut microbiome are available for genetic exchange between bacterial species. Here we show that AMP- and antibiotic-resistance genes differ in their mobilization patterns and functional compatibilities with new bacterial hosts. First, whereas AMP resistance genes are widespread in the gut microbiome, their rate of horizontal transfer is lower than that of antibiotic resistance genes. Second, gut microbiota culturing and functional metagenomics revealed that AMP resistance genes originating from phylogenetically distant bacteria only have a limited potential to confer resistance inEscherichia coli, an intrinsically susceptible species. Third, the phenotypic impact of acquired AMP resistance genes heavily depends on the genetic background of the recipient bacteria. Taken together, functional compatibility with the new bacterial host emerges as a key factor limiting the genetic exchange of AMP resistance genes. Finally, our results suggest that AMPs induce highly specific changes in the composition of the human microbiota with implications for disease risks.

Download Full-text

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.

Download Full-text

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.

Download Full-text

Rice Bran and Quercetin Produce a Positive Synergistic Effect on Human Gut Microbiota, Elevate the Level of Propionate, and Reduce the Population of Enterobacteriaceae family when Determined using a Bioreactor Model

10.1101/2020.02.13.947515 ◽

2020 ◽

Cited By ~ 1

Author(s):

Sudeep Ghimire ◽

Supapit Wongkuna ◽

Ranjini Sankaranarayanan ◽

Elizabeth P. Ryan ◽

G. Jayarama Bhat ◽

...

Keyword(s):

Synergistic Effect ◽

Gut Microbiota ◽

Rice Bran ◽

Gut Microbiome ◽

Bacterial Species ◽

Human Gut ◽

Food Ingredients ◽

Microbiome Composition ◽

Human Gut Microbiota ◽

Host Health

AbstractDiet is one of the prominent determinants of gut microbiota composition significantly impacting human health. Recent studies with dietary supplements such as rice bran and quercetin have been shown to provide a beneficial impact on the host by positively influencing the gut microbiota. However, the specific bacterial species impacted when rice bran or quercetin is present in the diet is not well understood. Therefore, in this study, we used a minibioreactor array system as a model to determine the effect of quercetin and rice bran individually, as well as in combination, on gut microbiota without the confounding host factors. We found that rice bran exerts higher shift in gut microbiome composition when compared to quercetin. At the species level, Acidaminococcus intestini was the only significantly enriched taxa when quercetin was supplemented, while 15 species were enriched in rice bran supplementation and 13 were enriched when quercetin and rice bran were supplemented in combination. When comparing the short chain fatty acid production, quercetin supplementation significantly enriched isobutyrate production while propionate dominated the quercetin and rice bran combined group. Higher levels of propionate were highly correlated to the lower abundance of the potentially pathogenic Enterobacteriaceae family. These findings suggest that the combination of rice bran and quercetin serve to enrich beneficial bacteria and reduce potential opportunistic pathogens. However, further in vivo studies are necessary to determine the synergistic effect of rice bran and quercetin on host health and immunity.ImportanceRice bran and quercetin are dietary components that shape host health by interacting with the gut microbiome. Both these substrates have been reported to provide nutritional and immunological benefits individually. However, considering the complexity of the human diet, it is useful to determine how the combination of food ingredients such as rice bran and quercetin influences the human gut microbiota. Our study provides insights into how these ingredients influence microbiome composition alone and in combination in vitro. This will allow us to identify which species in the gut microbiome are responsible for biotransformation of these dietary ingredients.. Such information is helpful for the development of synbiotics to improve gut health and immunity.

Download Full-text

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.

Download Full-text

MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

mSystems ◽

10.1128/msystems.00606-19 ◽

2020 ◽

Vol 5 (1) ◽

Cited By ~ 14

Author(s):

Christian Diener ◽

Sean M. Gibbons ◽

Osbaldo Resendis-Antonio

Keyword(s):

Gut Microbiota ◽

Growth Rates ◽

Gut Microbiome ◽

Bacterial Species ◽

Microbial Community Composition ◽

Healthy Population ◽

Optimization Approach ◽

Human Gut ◽

Modeling Framework ◽

Metabolic Interactions

ABSTRACT Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micom. IMPORTANCE The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people.

Download Full-text

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.

Download Full-text