scholarly journals Metabolic Modeling of CommonEscherichia coliStrains in Human Gut Microbiome

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yue-Dong Gao ◽  
Yuqi Zhao ◽  
Jingfei Huang
Keyword(s):  
Gut Microbiome ◽  
Metabolic Networks ◽  
High Throughput Sequencing ◽  
Carbon Sources ◽  
Microbial Interactions ◽  
Dynamic Responses ◽  
Sequencing Data ◽  
Human Gut ◽  
E Coli ◽  
Human Gut Microbiome

The recent high-throughput sequencing has enabled the composition ofEscherichia colistrains in the human microbial community to be profiled en masse. However, there are two challenges to address: (1) exploring the genetic differences betweenE. colistrains in human gut and (2) dynamic responses ofE. colito diverse stress conditions. As a result, we investigated theE. colistrains in human gut microbiome using deep sequencing data and reconstructed genome-wide metabolic networks for the three most commonE. colistrains, includingE. coliHS, UTI89, and CFT073. The metabolic models show obvious strain-specific characteristics, both in network contents and in behaviors. We predicted optimal biomass production for three models on four different carbon sources (acetate, ethanol, glucose, and succinate) and found that these stress-associated genes were involved in host-microbial interactions and increased in human obesity. Besides, it shows that the growth rates are similar among the models, but the flux distributions are different, even inE. colicore reactions. The correlations between human diabetes-associated metabolic reactions in theE. colimodels were also predicted. The study provides a systems perspective onE. colistrains in human gut microbiome and will be helpful in integrating diverse data sources in the following study.

scholarly journals Pet-Human Gut Microbiome Host Classifier Using Data from Different Studies

2020 ◽  
Vol 8 (10) ◽  
pp. 1591
Author(s):  
Nadia Bykova ◽  
Nikita Litovka ◽  
Anna Popenko ◽  
Sergey Musienko
Keyword(s):  
Gut Microbiome ◽  
Environmental Data ◽  
Domestic Cat ◽  
Sequencing Data ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Feature Importance ◽  
Importance Analysis ◽  
Using Data ◽  
Sources Of Contamination

(1) Background: microbiome host classification can be used to identify sources of contamination in environmental data. However, there is no ready-to-use host classifier. Here, we aimed to build a model that would be able to discriminate between pet and human microbiomes samples. The challenge of the study was to build a classifier using data solely from publicly available studies that normally contain sequencing data for only one type of host. (2) Results: we have developed a random forest model that distinguishes human microbiota from domestic pet microbiota (cats and dogs) with 97% accuracy. In order to prevent overfitting, samples from several (at least four) different projects were necessary. Feature importance analysis revealed that the model relied on several taxa known to be key components in domestic cat and dog microbiomes (such as Fusobacteriaceae and Peptostreptococcaeae), as well as on some taxa exclusively found in humans (as Akkermansiaceae). (3) Conclusion: we have shown that it is possible to make a reliable pet/human gut microbiome classifier on the basis of the data collected from different studies.

scholarly journals Analysis of Human Gut Microbiome: Taxonomy and Metabolic Functions in Thai Adults

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 331
Author(s):  
Nachon Raethong ◽  
Massalin Nakphaichit ◽  
Narissara Suratannon ◽  
Witida Sathitkowitchai ◽  
Wanlapa Weerapakorn ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Asian Country ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Human Gut ◽  
Thai Population ◽  
Shotgun Metagenomics ◽  
Human Gut Microbiome ◽  
Metabolic Functions

The gut microbiome plays a major role in the maintenance of human health. Characterizing the taxonomy and metabolic functions of the human gut microbiome is necessary for enhancing health. Here, we analyzed the metagenomic sequencing, assembly and construction of a meta-gene catalogue of the human gut microbiome with the overall aim of investigating the taxonomy and metabolic functions of the gut microbiome in Thai adults. As a result, the integrative analysis of 16S rRNA gene and whole metagenome shotgun (WMGS) sequencing data revealed that the dominant gut bacterial families were Lachnospiraceae and Ruminococcaceae of the Firmicutes phylum. Consistently, across 3.8 million (M) genes annotated from 163.5 gigabases (Gb) of WMGS sequencing data, a significant number of genes associated with carbohydrate metabolism of the dominant bacterial families were identified. Further identification of bacterial community-wide metabolic functions promisingly highlighted the importance of Roseburia and Faecalibacterium involvement in central carbon metabolism, sugar utilization and metabolism towards butyrate biosynthesis. This work presents an initial study of shotgun metagenomics in a Thai population-based cohort in a developing Southeast Asian country.

scholarly journals Deciphering microbial interactions in synthetic human gut microbiome communities

2018 ◽  
Vol 14 (6) ◽  
Author(s):  
Ophelia S Venturelli ◽  
Alex V Carr ◽  
Garth Fisher ◽  
Ryan H Hsu ◽  
Rebecca Lau ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Microbial Interactions ◽  
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 Microbe-seq: high-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome

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.

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

2016 ◽  
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.

scholarly journals A unique and reliable fecal DNA extraction method for 16S rRNA gene and shotgun metagenomic sequencing in the analysis of the human gut microbiome

2020 ◽  
Author(s):  
Céline Elie ◽  
Magali Perret ◽  
Karen Louis ◽  
Asmaà Fritah-Lafont ◽  
Philippe Leissner ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Dna Extraction ◽  
Gut Microbiome ◽  
High Throughput Sequencing ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Shotgun Metagenomic Sequencing

Abstract Background: The gut microbiome is widely analyzed using high-throughput sequencing, such as 16S rRNA gene amplicon sequencing and shotgun metagenomic sequencing (SMS). DNA extraction is known to have a large impact on the metagenomic analyses. The aim of this study was to select a unique and best performing DNA extraction protocol for both metagenomic sequencing methods. In that context, four commonly used DNA extraction methods were compared for the analysis of the gut microbiota. Commercial versions were evaluated against modified protocols using a stool preprocessing device (SPD, bioMérieux) in order to facilitate DNA extraction. Stool samples from nine healthy volunteers and nine patients with a Clostridium difficile infection were extracted with all protocols and sequenced with both metagenomic methods. Protocols were ranked using wet- and dry-lab criteria, including quality controls of the extracted genomic DNA, alpha-diversity, accuracy using a mock community of known composition and repeatability across technical replicates.Results: Independently of the sequencing methods used, SPD significantly improved efficiency of the four tested protocols compared with their commercial version, in terms of extracted DNA quality, accuracy of the predicted composition of the microbiota (notably for Gram-positive bacteria), sample alpha-diversity, and experimental repeatability. The best overall performance was obtained for the S-DQ protocol, SPD combined to the DNeasy PowerLyser PowerSoil protocol from QIAGEN.Conclusion: Based on this evaluation, we recommend to use the S-DQ protocol, to obtain standardized and high quality extracted DNA in the human gut microbiome studies.

scholarly journals Deciphering microbial interactions in synthetic human gut microbiome communities

2017 ◽  
Author(s):  
Ophelia S. Venturelli ◽  
Alex C. Carr ◽  
Garth Fisher ◽  
Ryan H. Hsu ◽  
Rebecca Lau ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Community Assembly ◽  
Gut Microbiome ◽  
Community Dynamics ◽  
Microbial Interactions ◽  
Model Parameters ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Pairwise Interactions ◽  
Key Species

ABSTRACTThe human gut microbiota comprises a dynamic ecological system that contributes significantly to human health and disease. The ecological forces that govern community assembly and stability in the gut microbiota remain unresolved. We developed a generalizable model-guided framework to predict higher-order consortia from time-resolved measurements of lower-order assemblages. This method was employed to decipher microbial interactions in a diverse 12-member human gut microbiome synthetic community. We show that microbial growth parameters and pairwise interactions are the major drivers of multi-species community dynamics, as opposed to context-dependent (conditional) interactions. The inferred microbial interaction network as well as a top-down approach to community assembly pinpointed both ecological driver and responsive species that were significantly modulated by microbial inter-relationships. Our model demonstrated that negative pairwise interactions could generate history-dependent responses of initial species proportions on physiological timescales that frequently does not originate from bistability. The model elucidated a topology for robust coexistence in pairwise assemblages consisting of a negative feedback loop that balances disparities in monospecies fitness levels. Bayesian statistical methods were used to evaluate the constraint of model parameters by the experimental data. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis for competitive and cooperative interactions in the community. However, these data failed to predict influential organisms shaping community assembly. In sum, these methods defined the ecological roles of key species shaping community assembly and illuminated network design principles of microbial communities.

scholarly journals Resident microbial communities inhibit growth and antibiotic resistance evolution of Escherichia coli in human gut microbiome samples

2019 ◽  
Author(s):  
Michael Baumgartner ◽  
Florian Bayer ◽  
Katia R. Pfrunder-Cardozo ◽  
Angus Buckling ◽  
Alex R. Hall
Keyword(s):  
Escherichia Coli ◽  
Antibiotic Resistance ◽  
Microbial Communities ◽  
Resistance Genes ◽  
Gut Microbiome ◽  
Individual Species ◽  
Human Gut ◽  
E Coli ◽  
Human Gut Microbiome ◽  
Resistance Evolution

AbstractCountering the rise of antibiotic resistant pathogens requires improved understanding of how resistance emerges and spreads in individual species, which are often embedded in complex microbial communities such as the human gut microbiome. Interactions with other microorganisms in such communities might suppress growth and resistance evolution of individual species (e.g. via resource competition), but could also potentially accelerate resistance evolution via horizontal transfer of resistance genes. It remains unclear how these different effects balance out, partly because it is difficult to observe them directly. Here, we used a gut microcosm approach to quantify the effect of three human gut microbiome communities on growth and resistance evolution of a focal strain of Escherichia coli. We found the resident microbial communities not only suppressed growth and colonization by focal E. coli, they also prevented it from evolving antibiotic resistance upon exposure to a beta-lactam antibiotic. With samples from all three human donors, our focal E. coli strain only evolved antibiotic resistance in the absence of the resident microbial community, even though we found resistance genes, including a highly effective resistance plasmid, in resident microbial communities. We identified physical constraints on plasmid transfer that can explain why our focal strain failed to acquire some of these beneficial resistance genes, and we found some chromosomal resistance mutations were only beneficial in the absence of the resident microbiota. This suggests, depending on in situ gene transfer dynamics, interactions with resident microbiota can inhibit antibiotic resistance evolution of individual species.

scholarly journals LightCUD: a program for diagnosing IBD based on human gut microbiome data

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Congmin Xu ◽  
Man Zhou ◽  
Zhongjie Xie ◽  
Mo Li ◽  
Xi Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gut Microbiome ◽  
High Performance ◽  
Machine Learning Algorithms ◽  
Healthy Controls ◽  
Sequencing Data ◽  
Human Gut ◽  
Data Set ◽  
16S Sequencing ◽  
Human Gut Microbiome

Abstract Background The diagnosis of inflammatory bowel disease (IBD) and discrimination between the types of IBD are clinically important. IBD is associated with marked changes in the intestinal microbiota. Advances in next-generation sequencing (NGS) technology and the improved hospital bioinformatics analysis ability motivated us to develop a diagnostic method based on the gut microbiome. Results Using a set of whole-genome sequencing (WGS) data from 349 human gut microbiota samples with two types of IBD and healthy controls, we assembled and aligned WGS short reads to obtain feature profiles of strains and genera. The genus and strain profiles were used for the 16S-based and WGS-based diagnostic modules construction respectively. We designed a novel feature selection procedure to select those case-specific features. With these features, we built discrimination models using different machine learning algorithms. The machine learning algorithm LightGBM outperformed other algorithms in this study and thus was chosen as the core algorithm. Specially, we identified two small sets of biomarkers (strains) separately for the WGS-based health vs IBD module and ulcerative colitis vs Crohn’s disease module, which contributed to the optimization of model performance during pre-training. We released LightCUD as an IBD diagnostic program built with LightGBM. The high performance has been validated through five-fold cross-validation and using an independent test data set. LightCUD was implemented in Python and packaged free for installation with customized databases. With WGS data or 16S rRNA sequencing data of gut microbiome samples as the input, LightCUD can discriminate IBD from healthy controls with high accuracy and further identify the specific type of IBD. The executable program LightCUD was released in open source with instructions at the webpage http://cqb.pku.edu.cn/ZhuLab/LightCUD/. The identified strain biomarkers could be used to study the critical factors for disease development and recommend treatments regarding changes in the gut microbial community. Conclusions As the first released human gut microbiome-based IBD diagnostic tool, LightCUD demonstrates a high-performance for both WGS and 16S sequencing data. The strains that either identify healthy controls from IBD patients or distinguish the specific type of IBD are expected to be clinically important to serve as biomarkers.

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