Bioinformatics and holobiota
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Gut Instinct

Gut Instinct | Bioinformatics and holobiota | Scoop.it

Do bacteria in the guts of African hunter-gatherers hold the key to a healthier life? An American anthropologist plans to find out.


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Amazing material! However, the natural diet of these folks is quite distant from that of the Western World, so fecal transplantation to a European might be too hard.

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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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Mismatch between Probiotic Benefits in Trials versus Food Products

Mismatch between Probiotic Benefits in Trials versus Food Products | Bioinformatics and holobiota | Scoop.it
Probiotic food products contain a variety of different bacterial strains and may offer different health effects. The objective was to document the prevalence and dosage of probiotic strains in the Canadian food supply and to review the literature investigating these strains in order to understand what health benefits these products may offer. The Food Label Information Program was used to identify probiotic-containing products in the food supply. PubMed, Web of Science, and Embase were searched for randomized controlled trials that tested the health effects of these strains in humans. There were six probiotic strains/strain combinations identified in the food supply. Thirty-one studies investigated these strains and found that they are associated with decreased diarrhea and constipation, improved digestive symptoms, glycemic control, antioxidant status, blood lipids, oral health, and infant breastfeeding outcomes, as well as enhanced immunity and support for Helicobacter pylori eradication. There were a limited number of studies investigating these strains. Many studies were funded by the food industry and tested dosages that were up to twenty-five times the dosage found in most food products. Probiotic food products could have health benefits not currently reported on their labels. However, many dosages are too low to provide the benefits demonstrated in clinical trials. Further research is needed to enable more effective use of these functional foods.
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NLRP6 Protects Il10−/− Mice from Colitis by Limiting Colonization of Akkermansia muciniphila

NLRP6 Protects Il10−/− Mice from Colitis by Limiting Colonization of Akkermansia muciniphila | Bioinformatics and holobiota | Scoop.it
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A Profile Hidden Markov Model to investigate the distribution and frequency of LanB-encoding lantibiotic modification genes in the human oral and gut microbiome

A Profile Hidden Markov Model to investigate the distribution and frequency of LanB-encoding lantibiotic modification genes in the human oral and gut microbiome | Bioinformatics and holobiota | Scoop.it
Background The human microbiota plays a key role in health and disease, and bacteriocins, which are small, bacterially produced, antimicrobial peptides, are likely to have an important function in the stability and dynamics of this community. Here we examined the density and distribution of the subclass I lantibiotic modification protein, LanB, in human oral and stool microbiome datasets using a specially constructed profile Hidden Markov Model (HMM). Methods The model was validated by correctly identifying known lanB genes in the genomes of known bacteriocin producers more effectively than other methods, while being sensitive enough to differentiate between different subclasses of lantibiotic modification proteins. This approach was compared with two existing methods to screen both genomic and metagenomic datasets obtained from the Human Microbiome Project (HMP). Results Of the methods evaluated, the new profile HMM identified the greatest number of putative LanB proteins in the stool and oral metagenome data while BlastP identified the fewest. In addition, the model identified more LanB proteins than a pre-existing Pfam lanthionine dehydratase model. Searching the gastrointestinal tract subset of the HMP reference genome database with the new HMM identified seven putative subclass I lantibiotic producers, including two members of the Coprobacillus genus. Conclusions These findings establish custom profile HMMs as a potentially powerful tool in the search for novel bioactive producers with the power to benefit human health, and reinforce the repertoire of apparent bacteriocin-encoding gene clusters that may have been overlooked by culture-dependent mining efforts to date.
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Frontiers | Evolutionary Biology Needs Wild Microbiomes | Frontiers in Microbiology

Frontiers | Evolutionary Biology Needs Wild Microbiomes | Frontiers in Microbiology | Bioinformatics and holobiota | Scoop.it
The microbiome is a vital component to the evolution of a host and much of what we know about the microbiome derives from studies on humans and captive animals. But captivity alters the microbiome and mammals have unique biological adaptations that affect their microbiomes (e.g., milk). Birds represent over 30% of known tetrapod diversity and possess their own suite of adaptations relevant to the microbiome. In a previous study, we showed that 59 species of bird displayed immense variation in their microbiomes and host (bird) taxonomy and ecology were most correlated with the gut microbiome. In this Frontiers Focused Review, I put those results in a broader context by discussing how collecting and analyzing wild microbiomes contributes to the main goals of evolutionary biology and the specific ways that birds are unique microbial hosts. Finally, I outline some of the methodological considerations for adding microbiome sampling to the research of wild animals and urge researchers to do so. To truly understand the evolution of a host, we need to understand the millions of microorganisms that inhabit it as well: evolutionary biology needs wild microbiomes.
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Gene expression profiling gut microbiota in different races of humans

Gene expression profiling gut microbiota in different races of humans | Bioinformatics and holobiota | Scoop.it
The gut microbiome is shaped and modified by the polymorphisms of microorganisms in the intestinal tract.
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A macroecological theory of microbial biodiversity

A macroecological theory of microbial biodiversity | Bioinformatics and holobiota | Scoop.it
Testing widely known biodiversity models on a dataset of >20,000 microbial community samples from a wide variety of ecosystems, the authors find that microbial abundance and diversity across scales is best predicted by a model of lognormal dynamics.
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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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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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DBH: A de Bruijn graph-based heuristic method for clustering large-scale 16S rRNA sequences into OTUs

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Frontiers | The hologenome across environments and the implications of a host-associated microbial repertoire | Microbiology

Frontiers | The hologenome across environments and the implications of a host-associated microbial repertoire | Microbiology | Bioinformatics and holobiota | Scoop.it
Our understanding of the diverse interactions between hosts and microbes has grown profoundly over the past two decades and, as a product, has revolutionized our knowledge of the life sciences. Through primarily laboratory experiments, the current framework for holobionts and their respective hologenomes aims to decipher the underpinnings and implications of symbioses between host and microbiome. However, the laboratory setting restricts the full spectrum of host-associated symbionts as compared to those found in nature; thus, limiting the potential for a holistic interpretation of the functional roles the microbiome plays in host biology. When holobionts are studied in nature, associated microbial communities vary considerably between conditions, resulting in more microbial associates as part of the ‘hologenome’ across environments than in either environment alone. We review and synthesize empirical evidence suggesting that hosts may have the ability to associate with a larger associated microbial network that, in part, corresponds to experiencing diverse environmental conditions. To conceptualize the interactions between host and microbiome in an ecological context, we suggest the ‘host-associated microbial repertoire,’ which is the sum of microbial species a host may associate with over the course of its life-history under all encountered environmental circumstances. Furthermore, using examples from both terrestrial and marine ecosystems, we discuss how this concept may be used as a framework to compare the ability of the holobiont to acclimate and adapt to environmental variation, and propose three ‘signatures’ of the concept.
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Biases in genome reconstruction from metagenomic data

Background: Technological advances in sequencing, assembly and segregation of resulting contigs into species-specific bins has enabled the reconstruction of individual genomes from environmental metagenomic data sets. Though a powerful technique, it is shadowed by an inability to truly determine whether assembly and binning techniques are accurate, specific, and sensitive due to a lack of complete reference genome sequences against which to check the data. Errors in genome reconstruction, such as missing or mis-attributed activities, can have a detrimental effect on downstream metabolic and ecological modeling, and thus it is important to assess the accuracy of the process. Methods: We compared genomes reconstructed from metagenomic data to complete genome sequences of 10 organisms isolated from the same community to identify regions not captured by typical binning techniques. The nucleotide content, as %G+C and tetranucleotide frequencies, and sequence redundancy within both the genome and across the metagenome were determined for both the captured and uncaptured regions. This direct comparison allowed us to evaluate the efficacy of nucleotide composition and coverage profiles as elements of binning protocols and look for biases in sequence characteristics and gene content in regions missing from the reconstructions. Results: We found that repeated sequences were frequently missed in the reconstruction process as were short sequences with variant nucleotide composition. Genes encoded on the missing regions were strongly biased towards ribosomal RNAs, transfer RNAs, mobile element functions and genes of unknown function. Conclusions: Our observation of increased mis-binning of short regions, especially those with variant nucleotide content, and repeated regions implies that factors which affect assembly efficiency also impact binning accuracy. To a large extent, mis-binned regions appear to derive from mobile elements. Our results support genome reconstruction as a robust process, and suggest that reconstructions determined to be >90% complete are likely to effectively represent organismal function.
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Metabolome of human gut microbiome is predictive of host dysbiosis

Metabolome of human gut microbiome is predictive of host dysbiosis | Bioinformatics and holobiota | Scoop.it
Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.
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Community structure follows simple assembly rules in microbial microcosms

Community structure follows simple assembly rules in microbial microcosms | Bioinformatics and holobiota | Scoop.it
Survival of competing microbial species pairs predicts competition outcome between a greater number of species: species that coexist with each other in pairs will survive, species that are excluded by any of the surviving species will go extinct.
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