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

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.

scholarly journals Systematic characterization of human gut microbiome-secreted molecules by integrated multi-omics

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Bianca De Saedeleer ◽  
Antoine Malabirade ◽  
Javier Ramiro-Garcia ◽  
Janine Habier ◽  
Jean-Pierre Trezzi ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Complex Mixture ◽  
Host Cells ◽  
Methodological Framework ◽  
Secretion Systems ◽  
Human Gut ◽  
Healthy Human ◽  
Human Gut Microbiome ◽  
Health And Disease

AbstractThe human gut microbiome produces a complex mixture of biomolecules that interact with human physiology and play essential roles in health and disease. Crosstalk between micro-organisms and host cells is enabled by different direct contacts, but also by the export of molecules through secretion systems and extracellular vesicles. The resulting molecular network, comprised of various biomolecular moieties, has so far eluded systematic study. Here we present a methodological framework, optimized for the extraction of the microbiome-derived, extracellular biomolecular complement, including nucleic acids, (poly)peptides, and metabolites, from flash-frozen stool samples of healthy human individuals. Our method allows simultaneous isolation of individual biomolecular fractions from the same original stool sample, followed by specialized omic analyses. The resulting multi-omics data enable coherent data integration for the systematic characterization of this molecular complex. Our results demonstrate the distinctiveness of the different extracellular biomolecular fractions, both in terms of their taxonomic and functional composition. This highlights the challenge of inferring the extracellular biomolecular complement of the gut microbiome based on single-omic data. The developed methodological framework provides the foundation for systematically investigating mechanistic links between microbiome-secreted molecules, including those that are typically vesicle-associated, and their impact on host physiology in health and disease.

scholarly journals Polyphenol utilization proteins in human gut microbiome

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.

scholarly journals Characterization of the SOS meta-regulon in the human gut microbiome

2014 ◽  
Vol 30 (9) ◽  
pp. 1193-1197 ◽  
Author(s):  
Joseph P. Cornish ◽  
Neus Sanchez-Alberola ◽  
Patrick K. O’Neill ◽  
Ronald O'Keefe ◽  
Jameel Gheba ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Human Gut ◽  
Human Gut Microbiome

scholarly journals Reconstruction of ancient microbial genomes from the human gut

Nature ◽  
2021 ◽  
Author(s):  
Marsha C. Wibowo ◽  
Zhen Yang ◽  
Maxime Borry ◽  
Alexander Hübner ◽  
Kun D. Huang ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Large Scale ◽  
De Novo ◽  
Human Gut ◽  
Microbial Genomes ◽  
Functional Profiling ◽  
History Of ◽  
Southwestern Usa ◽  
Gut Microorganisms

AbstractLoss of gut microbial diversity1–6 in industrial populations is associated with chronic diseases7, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000–2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont Methanobrevibacter smithii. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces.

scholarly journals Systematic mapping of drug metabolism by the human gut microbiome

2019 ◽  
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.

scholarly journals Discovery and Characterization of FMN-Binding β-Glucuronidases in the Human Gut Microbiome

2019 ◽  
Vol 431 (5) ◽  
pp. 970-980 ◽  
Author(s):  
Samuel J. Pellock ◽  
William G. Walton ◽  
Samantha M. Ervin ◽  
Dariana Torres-Rivera ◽  
Benjamin C. Creekmore ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Human Gut ◽  
Human Gut Microbiome

scholarly journals Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference

2019 ◽  
Vol 244 (6) ◽  
pp. 445-458 ◽  
Author(s):  
Anders B Dohlman ◽  
Xiling Shen
Keyword(s):  
Human Health ◽  
Gut Microbiome ◽  
Network Inference ◽  
Therapeutic Strategies ◽  
Microbial Interactions ◽  
Interaction Networks ◽  
Human Gut ◽  
Statistical Tools ◽  
Human Gut Microbiome ◽  
Network Analyses

Advances in high-throughput sequencing have ushered in a new era of research into the gut microbiome and its role in human health and disease. However, due to the unique characteristics of microbiome survey data, their use for the detection of ecological interaction networks remains a considerable challenge, and a field of active methodological development. In this review, we discuss the landscape of existing statistical and experimental methods for detecting and characterizing microbial interactions, as well as the role that host and environmental metabolic signals play in mediating the behavior of these networks. Numerous statistical tools for microbiome network inference have been developed. Yet due to tool-specific biases, the networks identified by these methods are often discordant, motivating a need for the development of more general tools, the use of ensemble approaches, and the incorporation of prior knowledge into prediction. By elucidating the complex dynamics of the microbial interactome, we will enhance our understanding of the microbiome’s role in disease, more precisely predict the microbiome’s response to perturbation, and inform the development of future therapeutic strategies for microbiome-related disease. Impact statement This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome’s role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.

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

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.

scholarly journals Genome Sequence of Christensenella minuta DSM 22607T

2017 ◽  
Vol 5 (2) ◽  
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.

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

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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