Bioinformatics and holobiota
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What is a ‘Healthy’ Microbiome? — NOVA Next | PBS

What is a ‘Healthy’ Microbiome? — NOVA Next | PBS | Bioinformatics and holobiota | Scoop.it

In psychology and other social sciences, the participants in most experiments are college students from so-called WEIRD countries—that is, Western, educated, industrialized, rich, and democratic. Such nations include just 12% of the world’s population, and the behaviour of their students—an even narrower stratum—is distinctly unrepresentative of humanity at large.

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On Amazonian hunter-gatherers as potential healthy microbiota carriers, unrepresentativeness of existing global metagenomic datasets and more 

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Diet, Microbiota, and Metabolic Health: Trade-Off Between Saccharolytic and Proteolytic Fermentation. - PubMed - NCBI

Diet, Microbiota, and Metabolic Health: Trade-Off Between Saccharolytic and Proteolytic Fermentation. - PubMed - NCBI | Bioinformatics and holobiota | Scoop.it
Annu Rev Food Sci Technol. 2018 Mar 25;9:65-84. doi: 10.1146/annurev-food-030117-012830. Epub 2018 Jan 3.
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On the Road to Strain-Resolved Comparative Metagenomics

On the Road to Strain-Resolved Comparative Metagenomics | Bioinformatics and holobiota | Scoop.it
Metagenomics has transformed microbiology, but its potential has not been fully expressed yet. From computational methods for digging deeper into metagenomes to study designs for addressing specific hypotheses, the Segata Lab is pursuing an integrative metagenomic approach to describe and model human-associated microbial communities as collections of strains. Linking strain variants to host phenotypes and performing cultivation-free population genomics require large cohorts and meta-analysis strategies to synthesize available cohorts but can revolutionize our understanding of the personalized host-microbiome interface which is at the base of human health.

mSystems® vol. 3, no. 2, is a special issue sponsored by Janssen Human Microbiome Institute (JHMI) .
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Strain-level dissection of the contribution of the gut microbiome to human metabolic disease

Strain-level dissection of the contribution of the gut microbiome to human metabolic disease | Bioinformatics and holobiota | Scoop.it
The gut microbiota has been linked with metabolic diseases in humans, but demonstration of causality remains a challenge. The gut microbiota, as a complex microbial ecosystem, consists of hundreds of individual bacterial species, each of which contains many strains with high genetic diversity. Recent advances in genomic and metabolomic technologies are facilitating strain-level dissection of the contribution of the gut microbiome to metabolic diseases. Interventional studies and correlation analysis between variations in the microbiome and metabolome, captured by longitudinal sampling, can lead to the identification of specific bacterial strains that may contribute to human metabolic diseases via the production of bioactive metabolites. For example, high-quality draft genomes of prevalent gut bacterial strains can be assembled directly from metagenomic datasets using a canopy-based algorithm. Specific metabolites associated with a disease phenotype can be identified by nuclear magnetic resonance-based metabolomics of urine and other samples. Such multi-omics approaches can be employed to identify specific gut bacterial genomes that are not only correlated with detected metabolites but also encode the genes required for producing the precursors of those metabolites in the gut. Here, we argue that if a causative role can be demonstrated in follow-up mechanistic studies—for example, using gnotobiotic models—such functional strains have the potential to become biomarkers for diagnostics and targets for therapeutics.
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The Gut Microbiota, Food Science, and Human Nutrition: A Timely Marriage

The Gut Microbiota, Food Science, and Human Nutrition: A Timely Marriage | Bioinformatics and holobiota | Scoop.it
The Gut Microbiota, Food Science, and Human Nutrition: A Timely Marriage
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Personalised models for human – gut microbiota interaction

It is now becoming feasible to determine the composition of an individual gut microbiota (gut microflora), as well as the individual genome. In addition, whole genome scale metabolic models (GEMs) exist for a range of bacteria, and also for human. In principle this enables us to build models for gut microbiota by aggregating strain-specific models and also place this within the human context, and to make predictions on a personalised basis of the influence of gut microbiota on human metabolism, and how the interactions between these microbiota and also the human may evolve. Such aggregation, however, raises several challenges, which we discuss in this paper. Furthermore, we present techniques and supporting tools which permit the development of personlised models for human – gut microbiota interaction. The construction of such models is supported by a suite of modelling and analysis tools which permit the exploration of the dynamic behaviour of the very large metabolic models, comprising Snoopy, Charlie, Prolog, MC2, and Marcie. Our tools could be applied to populations of models in the context of human - gut microbiota in- teractions. Our approach that we have developed permits the description of the dynamic behavioural interaction between different bacterial strains and their human host on a personalised level within one aggregated model represented as a coloured Petri net. We use simulative model checking techniques over coloured traces to analyse the huge amounts of data generated by the dynamic simulation of these very large and hierarchically structured models.
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Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania

Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania | Bioinformatics and holobiota | Scoop.it
Among the Hadza of western Tanzania, a few hundred people still live in small groups as hunter-gatherers, reliant solely on the wild environment for food. Smits et al. found that the microbiota of these people reflects the seasonal availability of different types of food (see the Perspective by Peddada). Between seasons, striking differences were observed in their gut microbial communities, with some taxa apparently disappearing, only to reappear when the seasons turned. Further comparison of the Hadza microbiota with that of diverse urbanized peoples revealed distinctly different patterns of microbial community composition.

Science , this issue p. [802][1]; see also p. [754][2]

[1]: /lookup/doi/10.1126/science.aan4834
[2]: /lookup/doi/10.1126/science.aao2997
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Human gut microbes impact host serum metabolome and insulin sensitivity : Nature : Nature Research

Human gut microbes impact host serum metabolome and insulin sensitivity : Nature : Nature Research | Bioinformatics and holobiota | Scoop.it
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Influence of diet on the gut microbiome and implications for human health

Influence of diet on the gut microbiome and implications for human health | Bioinformatics and holobiota | Scoop.it
Recent studies have suggested that the intestinal microbiome plays an important role in modulating risk of several chronic diseases, including inflammatory bowel disease, obesity, type 2 diabetes, cardiovascular disease, and cancer. At the same time, it is now understood that diet plays a significant role in shaping the microbiome, with experiments showing that dietary alterations can induce large, temporary microbial shifts within 24 h. Given this association, there may be significant therapeutic utility in altering microbial composition through diet. This review systematically evaluates current data regarding the effects of several common dietary components on intestinal microbiota. We show that consumption of particular types of food produces predictable shifts in existing host bacterial genera. Furthermore, the identity of these bacteria affects host immune and metabolic parameters, with broad implications for human health. Familiarity with these associations will be of tremendous use to the practitioner as well as the patient.
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Assessing the impact of protein extraction methods for human gut metaproteomics - ScienceDirect

Assessing the impact of protein extraction methods for human gut metaproteomics - ScienceDirect | Bioinformatics and holobiota | Scoop.it
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Potential and active functions in the gut microbiota of a healthy human cohort

Potential and active functions in the gut microbiota of a healthy human cohort | Bioinformatics and holobiota | Scoop.it
metabolo
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metaSNV: A tool for metagenomic strain level analysis

metaSNV: A tool for metagenomic strain level analysis | Bioinformatics and holobiota | Scoop.it
We present metaSNV, a tool for single nucleotide variant (SNV) analysis in metagenomic samples, capable of comparing populations of thousands of bacterial and archaeal species. The tool uses as input nucleotide sequence alignments to reference genomes in standard SAM/BAM format, performs SNV calling for individual samples and across the whole data set, and generates various statistics for individual species including allele frequencies and nucleotide diversity per sample as well as distances and fixation indices across samples. Using published data from 676 metagenomic samples of different sites in the oral cavity, we show that the results of metaSNV are comparable to those of MIDAS, an alternative implementation for metagenomic SNV analysis, while data processing is faster and has a smaller storage footprint. Moreover, we implement a set of distance measures that allow the comparison of genomic variation across metagenomic samples and delineate sample-specific variants to enable the tracking of specific strain populations over time. The implementation of metaSNV is available at: http://metasnv.embl.de/.
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DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data

bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
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Two distinct metacommunities characterize the gut microbiota in Crohn's disease patients | GigaScience | Oxford Academic

Two distinct metacommunities characterize the gut microbiota in Crohn's disease patients | GigaScience | Oxford Academic | Bioinformatics and holobiota | Scoop.it
The inflammatory intestinal disorder Crohn's disease (CD) has become a health challenge worldwide. The gut microbiota closely interacts with the host immune system, but its functional impact in CD is unclear. Except for studies on a small number of CD patients, analyses of the gut microbiota in CD have used 16S rDNA amplicon sequencing. Here we employed metagenomic shotgun sequencing to provide a detailed characterization of the compositional and functional features of the CD microbiota, comprising also unannotated bacteria, and investigated its modulation by exclusive enteral nutrition. Based on signature taxa, CD microbiotas clustered into 2 distinct metacommunities, indicating individual variability in CD microbiome structure. Metacommunity-specific functional shifts in CD showed enrichment in producers of the pro-inflammatory hexa-acylated lipopolysaccharide variant and a reduction in the potential to synthesize short-chain fatty acids. Disruption of ecological networks was evident in CD, coupled with reduction in growth rates of many bacterial species. Short-term exclusive enteral nutrition elicited limited impact on the overall composition of the CD microbiota, although functional changes occurred following treatment. The microbiotas in CD patients can be stratified into 2 distinct metacommunities, with the most severely perturbed metacommunity exhibiting functional potentials that deviate markedly from that of the healthy individuals, with possible implication in relation to CD pathogenesis.
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Data and Statistical Methods To Analyze the Human Microbiome

Data and Statistical Methods To Analyze the Human Microbiome | Bioinformatics and holobiota | Scoop.it
The Waldron lab for computational biostatistics bridges the areas of cancer genomics and microbiome studies for public health, developing methods to exploit publicly available data resources and to integrate -omics studies.

mSystems® vol. 3, no. 2, is a special issue sponsored by Janssen Human Microbiome Institute (JHMI) .
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Gastric acid suppression promotes alcoholic liver disease by inducing overgrowth of intestinal Enterococcus

Gastric acid suppression promotes alcoholic liver disease by inducing overgrowth of intestinal Enterococcus | Bioinformatics and holobiota | Scoop.it
Article
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Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota

Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota | Bioinformatics and holobiota | Scoop.it
Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota
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Cross-species comparisons of host genetic associations with the microbiome

Recent studies in human populations and mouse models reveal notable congruences in gut microbial taxa whose abundances are partly regulated by host genotype. Host genes associating with these taxa are related to diet sensing, metabolism, and immunity
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Oxalobacter formigenes -associated host features and microbial community structures examined using the American Gut Project

Oxalobacter formigenes -associated host features and microbial community structures examined using the American Gut Project | Bioinformatics and holobiota | Scoop.it
Increasing evidence shows the importance of the commensal microbe Oxalobacter formigenes in regulating host oxalate homeostasis, with effects against calcium oxalate kidney stone formation, and other oxalate-associated pathological conditions. However, limited understanding of O. formigenes in humans poses difficulties for designing targeted experiments to assess its definitive effects and sustainable interventions in clinical settings. We exploited the large-scale dataset from the American Gut Project (AGP) to study O. formigenes colonization in the human gastrointestinal (GI) tract and to explore O. formigenes-associated ecology and the underlying host–microbe relationships. In >8000 AGP samples, we detected two dominant, co-colonizing O. formigenes operational taxonomic units (OTUs) in fecal specimens. Multivariate analysis suggested that O. formigenes abundance was associated with particular host demographic and clinical features, including age, sex, race, geographical location, BMI, and antibiotic history. Furthermore, we found that O. formigenes presence was an indicator of altered host gut microbiota structure, including higher community diversity, global network connectivity, and stronger resilience to simulated disturbances. Through this study, we identified O. formigenes colonizing patterns in the human GI tract, potential underlying host–microbe relationships, and associated microbial community structures. These insights suggest hypotheses to be tested in future experiments. Additionally, we proposed a systematic framework to study any bacterial taxa of interest to computational biologists, using large-scale public data to yield novel biological insights.
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Large-scale comparative metagenomics of Blastocystis, a common member of the human gut microbiome. - PubMed - NCBI

Large-scale comparative metagenomics of Blastocystis, a common member of the human gut microbiome. - PubMed - NCBI | Bioinformatics and holobiota | Scoop.it
ISME J. 2017 Aug 22. doi: 10.1038/ismej.2017.139. [Epub ahead of print]
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The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level

The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level | Bioinformatics and holobiota | Scoop.it
Objective It is evident that the gut microbiota and factors that influence its composition and activity effect human metabolic, immunological and developmental processes. We previously reported that extreme physical activity with associated dietary adaptations, such as that pursued by professional athletes, is associated with changes in faecal microbial diversity and composition relative to that of individuals with a more sedentary lifestyle. Here we address the impact of these factors on the functionality/metabolic activity of the microbiota which reveals even greater separation between exercise and a more sedentary state.

Design Metabolic phenotyping and functional metagenomic analysis of the gut microbiome of professional international rugby union players (n=40) and controls (n=46) was carried out and results were correlated with lifestyle parameters and clinical measurements (eg, dietary habit and serum creatine kinase, respectively).

Results Athletes had relative increases in pathways (eg, amino acid and antibiotic biosynthesis and carbohydrate metabolism) and faecal metabolites (eg, microbial produced short-chain fatty acids (SCFAs) acetate, propionate and butyrate) associated with enhanced muscle turnover (fitness) and overall health when compared with control groups.

Conclusions Differences in faecal microbiota between athletes and sedentary controls show even greater separation at the metagenomic and metabolomic than at compositional levels and provide added insight into the diet–exercise–gut microbiota paradigm.
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Analysis of large 16S rRNA Illumina data sets: Impact of singleton read filtering on microbial community description

Analysis of large 16S rRNA Illumina data sets: Impact of singleton read filtering on microbial community description | Bioinformatics and holobiota | Scoop.it
Next‐generation sequencing technologies give access to large sets of data, which are extremely useful in the study of microbial diversity based on 16S rRNA gene. However, the production of such larg
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Abundance estimation and differential testing on strain level in metagenomics data | Bioinformatics | Oxford Academic

Abundance estimation and differential testing on strain level in metagenomics data | Bioinformatics | Oxford Academic | Bioinformatics and holobiota | Scoop.it
Motivation: Current metagenomics approaches allow analyzing the composition of microbial communities at high resolution. Important changes to the composition are known to even occur on strain level and to go hand in hand with changes in disease or ecological state. However, specific challenges arise for strain level analysis due to highly similar genome sequences present. Only a limited number of tools approach taxa abundance estimation beyond species level and there is a strong need for dedicated tools for strain resolution and differential abundance testing.Methods: We present DiTASiC (Differential Taxa Abundance including Similarity Correction) as a novel approach for quantification and differential assessment of individual taxa in metagenomics samples. We introduce a generalized linear model for the resolution of shared read counts which cause a significant bias on strain level. Further, we capture abundance estimation uncertainties, which play a crucial role in differential abundance analysis. A novel statistical framework is built, which integrates the abundance variance and infers abundance distributions for differential testing sensitive to strain level.Results: As a result, we obtain highly accurate abundance estimates down to sub-strain level and enable fine-grained resolution of strain clusters. We demonstrate the relevance of read ambiguity resolution and integration of abundance uncertainties for differential analysis. Accurate detections of even small changes are achieved and false-positives are significantly reduced. Superior performance is shown on latest benchmark sets of various complexities and in comparison to existing methods.Availability and Implementation: DiTASiC code is freely available from https://rki_bioinformatics.gitlab.io/ditasic.Contact:renardB@rki.deSupplementary information:Supplementary data are available at Bioinformatics online.
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An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography

An international, peer-reviewed genome sciences journal featuring outstanding original research that offers novel insights into the biology of all organisms
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Abundance estimation and differential testing on strain level in metagenomics data | Bioinformatics | Oxford Academic

Abundance estimation and differential testing on strain level in metagenomics data | Bioinformatics | Oxford Academic | Bioinformatics and holobiota | Scoop.it
Motivation: Current metagenomics approaches allow analyzing the composition of microbial communities at high resolution. Important changes to the composition are known to even occur on strain level and to go hand in hand with changes in disease or ecological state. However, specific challenges arise for strain level analysis due to highly similar genome sequences present. Only a limited number of tools approach taxa abundance estimation beyond species level and there is a strong need for dedicated tools for strain resolution and differential abundance testing.Methods: We present DiTASiC (Differential Taxa Abundance including Similarity Correction) as a novel approach for quantification and differential assessment of individual taxa in metagenomics samples. We introduce a generalized linear model for the resolution of shared read counts which cause a significant bias on strain level. Further, we capture abundance estimation uncertainties, which play a crucial role in differential abundance analysis. A novel statistical framework is built, which integrates the abundance variance and infers abundance distributions for differential testing sensitive to strain level.Results: As a result, we obtain highly accurate abundance estimates down to sub-strain level and enable fine-grained resolution of strain clusters. We demonstrate the relevance of read ambiguity resolution and integration of abundance uncertainties for differential analysis. Accurate detections of even small changes are achieved and false-positives are significantly reduced. Superior performance is shown on latest benchmark sets of various complexities and in comparison to existing methods.Availability and Implementation: DiTASiC code is freely available from https://rki_bioinformatics.gitlab.io/ditasic.Contact:renardB@rki.deSupplementary information:Supplementary data are available at Bioinformatics online.
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