Gut Microbiome Samples Research Articles

Microbial community composition interacts with local abiotic conditions to drive colonization resistance in human gut microbiome samples.

Biological invasions can alter ecosystem stability and function, and predicting what happens when a new species or strain arrives remains a major challenge in ecology. In the mammalian gastrointestinal tract, susceptibility of the resident microbial community to invasion by pathogens has important implications for host health. However, at the community level, it is unclear whether susceptibility to invasion depends mostly on resident community composition (which microbes are present), or also on local abiotic conditions (such as nutrient status). Here, we used a gut microcosm system to disentangle some of the drivers of susceptibility to invasion in microbial communities sampled from humans. We found resident microbial communities inhibited an invading Escherichia coli strain, compared to community-free control treatments, sometimes excluding the invader completely (colonization resistance). These effects were stronger at later time points, when we also detected altered community composition and nutrient availability. By separating these two components (microbial community and abiotic environment), we found taxonomic composition played a crucial role in suppressing invasion, but this depended critically on local abiotic conditions (adapted communities were more suppressive in nutrient-depleted conditions). This helps predict when resident communities will be most susceptible to invasion, with implications for optimizing treatments based on microbiota management.

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

BackgroundThe 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.ResultsUsing 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.ConclusionsAs 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.

Improved metagenome binning and assembly using deep variational autoencoders.

Despite recent advances in metagenomic binning, reconstruction of microbial species from metagenomics data remains challenging. Here we develop variational autoencoders for metagenomic binning (VAMB), a program that uses deep variational autoencoders to encode sequence coabundance and k-mer distribution information before clustering. We show that a variational autoencoder is able to integrate these two distinct data types without any previous knowledge of the datasets. VAMB outperforms existing state-of-the-art binners, reconstructing 29-98% and 45% more near-complete (NC) genomes on simulated and real data, respectively. Furthermore, VAMB is able to separate closely related strains up to 99.5% average nucleotide identity (ANI), and reconstructed 255 and 91 NC Bacteroides vulgatus and Bacteroides dorei sample-specific genomes as two distinct clusters from a dataset of 1,000 human gut microbiome samples. We use 2,606 NC bins from this dataset to show that species of the human gut microbiome have different geographical distribution patterns. VAMB can be run on standard hardware and is freely available at https://github.com/RasmussenLab/vamb .

An integrative understanding of the large metabolic shifts induced by antibiotics in critical illness

ABSTRACT Antibiotics are commonly used in the Intensive Care Unit (ICU); however, several studies showed that the impact of antibiotics to prevent infection, multi-organ failure, and death in the ICU is less clear than their benefit on course of infection in the absence of organ dysfunction. We characterized here the compositional and metabolic changes of the gut microbiome induced by critical illness and antibiotics in a cohort of 75 individuals in conjunction with 2,180 gut microbiome samples representing 16 different diseases. We revealed an “infection-vulnerable” gut microbiome environment present only in critically ill treated with antibiotics (ICU+). Feeding of Caenorhabditis elegans with Bifidobacterium animalis and Lactobacillus crispatus, species that expanded in ICU+ patients, revealed a significant negative impact of these microbes on host viability and developmental homeostasis. These results suggest that antibiotic administration can dramatically impact essential functional activities in the gut related to immune responses more than critical illness itself, which might explain in part untoward effects of antibiotics in the critically ill.

Engaging Adolescent and Young Adults in Microbiome Sample Self-Collection: Strategies for Success.

Human microbiome research provides rich opportunities to elucidate factors influencing health, uncover novel biomarkers, and expand disease treatment options. A well-conducted microbiome study depends not only on a rigorous design but also on successfully engaging participants in collecting quality samples. In this paper, we aim to describe (1) strategies our team used to engage adolescents and young adults in vaginal and gut microbiome sample self-collection and (2) their effectiveness. In our prospective, longitudinal, feasibility study of 20 female adolescents and young adults, research participants self-collected vaginal and gut microbiome samples at home. Using a participatory and iterative process, we developed strategies to engage participants in sample self-collection, including (1) providing clear instructions to ensure comprehension and buy-in, (2) providing a user-friendly take-home package, (3) minimizing disgust/embarrassment associated with sample collection, and (4) follow-up communications to facilitate sample collections and return. With these strategies, we achieved 100% participant retention and 100% sample return rates. All samples (n = 80, 100%) were usable for downstream 16s rRNA gene sequencing and analysis. All participants rated the study procedures as acceptable, and qualitative data showed that strategies were well received by participants. This study suggests that carefully planning and implementing strategies to engage participants in sample self-collection can result in high degrees of participant compliance, sample quality, and participant satisfaction in microbiome research.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data.

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

Data-Driven Modeling for Species-Level Taxonomic Assignment From 16S rRNA: Application to Human Microbiomes.

With the emergence of next-generation sequencing (NGS) technology, there have been a large number of metagenomic studies that estimated the bacterial composition via 16S ribosomal RNA (16S rRNA) amplicon sequencing. In particular, subsets of the hypervariable regions in 16S rRNA, such as V1–V2 and V3–V4, are targeted using high-throughput sequencing. The sequences from different taxa are assigned to a specific taxon based on the sequence homology. Since such sequences are highly homologous or identical between species in the same genus, it is challenging to determine the exact species using 16S rRNA sequences only. Therefore, in this study, homologous species groups were defined to obtain maximum resolution related with species using 16S rRNA. For the taxonomic assignment using 16S rRNA, three major 16S rRNA databases are independently used since the lineage of certain bacteria is not consistent among these databases. On the basis of the NCBI taxonomy classification, we re-annotated inconsistent lineage information in three major 16S rRNA databases. For each species, we constructed a consensus sequence model for each hypervariable region and determined homologous species groups that consist of indistinguishable species in terms of sequence homology. Using a k-nearest neighbor method and the species consensus sequence models, the species-level taxonomy was determined. If the species determined is a member of homologous species groups, the species group is assigned instead of a specific species. Notably, the results of the evaluation on our method using simulated and mock datasets showed a high correlation with the real bacterial composition. Furthermore, in the analysis of real microbiome samples, such as salivary and gut microbiome samples, our method successfully performed species-level profiling and identified differences in the bacterial composition between different phenotypic groups.

Cutaneous Bacteria in the Gut Microbiome as Biomarkers of Systemic Malodor and People Are Allergic to Me (PATM) Conditions: Insights From a Virtually Conducted Clinical Trial

Background The skin is a dynamic ecosystem of microbes and the source of many chemical compounds that affect human health. Skin-microbiome interactions can cause persistent, psychosocially devastating body smell despite good hygiene. Since odor production is often transient, malodors may not be perceptible during medical examinations. Therefore, having odor complaints can be diagnosed as body dysmorphic disorder and referred for psychological evaluations. Development of simple at-home tests and virtual care programs could improve the diagnosis and management of socially debilitating malodor conditions. Objective The aim of this study was to assess potential effectiveness of at-home gut microbiome testing in the diagnosis and management of idiopathic body and breath odor and in people are allergic to me (PATM) syndrome. Methods We contacted participants of prior metabolic body odor (MEBO) and PATM studies and online support groups by email or social media. Individuals who consented to participate were mailed test kits for at-home collection of gut microbiome samples. Participants completed an online survey (specially developed for this study) addressing their symptoms and other quality-of-life indicators at baseline and after sampling. Participants collected stool samples after flare-ups or symptom improvements and mailed them to the laboratory to be processed and analyzed. We evaluated between-group differences in symptom severity, as well as symptom improvement observations for the same individuals. For differential abundance testing of microbial taxa, we performed nonparametric statistical analyses using Mann-Whitney U tests for unpaired samples and Wilcoxon signed rank test for paired samples. Results A total of 112 individuals from 21 countries consented to participate. About half the participants had been tested for the metabolic disorder trimethylaminuria, and about half of those tested were diagnosed with the disorder. The levels of bacteria previously associated with cutaneous body odor were significantly elevated in gut samples. For the combination of species from Anaerococcus, Corynebacterium, Campylobacter, and Propionibacterium genera, the differences were P=.002 for active (73 participants, 182 samples) versus regression or remission groups (30 participants, 51 samples); P=.01 for those experiencing symptoms most or all of the time (46 participants, 88 samples) versus those who had symptoms sometimes, rarely, or never (25 participants, 74 samples); and P<.001 for improvement of symptoms in the same individuals (22 participants, 43 sets of matched samples). Changes in microbial diversity were significant for between- but not within-participant comparisons. Conclusions Changes in the gut microbiome composition affect MEBO and PATM severity. In particular, an increase in intestinal bacteria producing odor when in skin flexures was associated with increased intensity of self-reported symptoms. The changes were consistent in the within-group and between-group analyses. Our findings support the feasibility of remote and decentralized clinical studies of malodor conditions. Supplementary sample collection procedures may help to meet established research quality standards. Trial Registration ClinicalTrials.gov NCT03582826; http://clinicaltrials.gov/ct2/show/NCT03582826 International Registered Report Identifier (IRRID) RR2-10.1101/2020.08.21.20179242

Using data science for medical decision making case: role of gut microbiome in multiple sclerosis

BackgroundA decade ago, the advancements in the microbiome data sequencing techniques initiated the development of research of the microbiome and its relationship with the host organism. The development of sophisticated bioinformatics and data science tools for the analysis of large amounts of data followed. Since then, the analyzed gut microbiome data, where microbiome is defined as a network of microorganisms inhabiting the human intestinal system, has been associated with several conditions such as irritable bowel syndrome - IBS, colorectal cancer, diabetes, obesity, and metabolic syndrome, and lately in the study of Parkinson’s and Alzheimer’s diseases as well. This paper aims to provide an understanding of differences between microbial data of individuals who have been diagnosed with multiple sclerosis and those who were not by exploiting data science techniques on publicly available data.MethodsThis study examines the relationship between multiple sclerosis (MS), an autoimmune central nervous system disease, and gut microbial community composition, using the samples acquired by 16s rRNA sequencing technique. We have used three different sets of MS samples sequenced during three independent studies (Jangi et al, Nat Commun 7:1–11, 2016), (Miyake et al, PLoS ONE 10:0137429, 2015), (McDonald et al, Msystems 3:00031–18, 2018) and this approach strengthens our results. Analyzed sequences were from healthy control and MS groups of sequences. The extracted set of statistically significant bacteria from the (Jangi et al, Nat Commun 7:1–11, 2016) dataset samples and their statistically significant predictive functions were used to develop a Random Forest classifier. In total, 8 models based on two criteria: bacteria abundance (at six taxonomic levels) and predictive functions (at two levels), were constructed and evaluated. These include using taxa abundances at different taxonomy levels as well as predictive function analysis at different hierarchical levels of KEGG pathways.ResultsThe highest accuracy of the classification model was obtained at the genus level of taxonomy (76.82%) and the third hierarchical level of KEGG pathways (70.95%). The second dataset’s 18 MS samples (Miyake et al, PLoS ONE 10:0137429, 2015) and 18 self-reported healthy samples from the (McDonald et al, Msystems 3:00031–18, 2018) dataset were used to validate the developed classification model. The significance of this step is to show that the model is not overtrained for a specific dataset but can also be used on other independent datasets. Again, the highest classification model accuracy for both validating datasets combined was obtained at the genus level of taxonomy (70.98%) and third hierarchical level of KEGG pathways (67.24%). The accuracy of the independent set remained very relevant.ConclusionsOur results demonstrate that the developed classification model provides a good tool that can be used to suggest the presence or absence of MS condition by collecting and analyzing gut microbiome samples. The accuracy of the model can be further increased by using sequencing methods that allow higher taxa resolution (i.e. shotgun metagenomic sequencing).

Simple Analysis of Primary and Secondary Bile Salt Hydrolysis in Mouse and Human Gut Microbiome Samples by Using Fluorogenic Substrates

Animals produce bile to act as an antibacterial agent and to maximize the absorption of lipophilic nutrients in the gut. The physical properties of bile are largely dictated by amphipathic bile salt molecules, which also participate in signaling pathways by modulating physiological processes upon binding host receptors. Upon excretion of bile salts from the gall bladder into the intestine, the gut microbiota can create metabolites with modified signaling capabilities. The category and magnitude of bile salt metabolism can have positive or negative effects on the host. A key modification is bile salt hydrolysis, which is a prerequisite for all additional microbial transformations. We have synthesized five different fluorogenic bile salts for simple and continuous reporting of hydrolysis in both murine and human fecal samples. Our data demonstrate that most gut microbiomes have the highest capacity for hydrolysis of host-produced primary bile salts, but some microbially modified secondary bile salts also display significant turnover.

Analysis of the infant gut microbiome reveals metabolic functional roles associated with healthy infants and infants with atopic dermatitis using metaproteomics.

The infant gut microbiome consists of a complex and diverse microbial community. Comprehensive taxonomic and metabolic functional knowledge about microbial communities supports medical and biological applications, such as fecal diagnostics. Among the omics approaches available for the investigation of microbial communities, metaproteomics-based analysis is a very powerful approach; under this method, the activity of microbial communities is explored by investigating protein expression within a sample. Through use of metaproteomics, this study aimed to investigate the microbial community composition of the infant gut to identify different key proteins playing metabolic functional roles in the microbiome of healthy infants and infants with atopic dermatitis in a Thai population-based birth cohort. Here, 18 fecal samples were analyzed by liquid chromatography-tandem mass spectrometry to conduct taxonomic, functional, and pathway-based protein annotation. Accordingly, 49,973 annotated proteins out of 68,232 total proteins were investigated in gut microbiome samples and compared between the healthy and atopic dermatitis groups. Through differentially expressed proteins (DEPs) analysis, 130 significant DEPs were identified between the healthy and atopic dermatitis groups. Among these DEPs, eight significant proteins were uniquely expressed in the atopic dermatitis group. For instance, triosephosphate isomerase (TPI) in Bifidobacteriaceae in the genus Alloscardovia and demethylmenaquinone methyltransferase (DMM) in Bacteroides were shown to potentially play metabolic functional roles related to disease. PPI network analysis revealed seven reporter proteins showing metabolic alterations between the healthy and disease groups associated with the biosynthesis of ubiquinone and other quinones as well as the energy supply. This study serves as a scaffold for microbial community-wide metabolic functional studies of the infant gut microbiome in relation to allergic disease.

Effects of a Low-Fat Vegan Diet on Gut Microbiota in Overweight Individuals and Relationships with Body Weight, Body Composition, and Insulin Sensitivity. A Randomized Clinical Trial.

Diet modulates gut microbiota and plays an important role in human health. The aim of this study was to test the effect of a low-fat vegan diet on gut microbiota and its association with weight, body composition, and insulin resistance in overweight men and women. We enrolled 168 participants and randomly assigned them to a vegan (n = 84) or a control group (n = 84) for 16 weeks. Of these, 115 returned all gut microbiome samples. Gut microbiota composition was assessed using uBiome Explorer™ kits. Body composition was measured using dual energy X-ray absorptiometry. Insulin sensitivity was quantified with the predicted clamp-derived insulin sensitivity index from a standard meal test. Repeated measure ANOVA was used for statistical analysis. Body weight decreased in the vegan group (treatment effect −5.9 kg [95% CI, −7.0 to −4.9 kg]; p < 0.001), mainly due to a reduction in fat mass (−3.9 kg [95% CI, −4.6 to −3.1 kg]; p < 0.001) and in visceral fat (−240 cm3 [95% CI, −345 to −135 kg]; p < 0.001). PREDIcted M, insulin sensitivity index (PREDIM) increased in the vegan group (treatment effect +0.83 [95% CI, +0.48 to +1.2]; p < 0.001). The relative abundance of Faecalibacterium prausnitzii increased in the vegan group (+5.1% [95% CI, +2.4 to +7.9%]; p < 0.001) and correlated negatively with changes in weight (r = −0.24; p = 0.01), fat mass (r = −0.22; p = 0.02), and visceral fat (r = −0.20; p = 0.03). The relative abundance of Bacteroides fragilis decreased in both groups, but less in the vegan group, making the treatment effect positive (+18.9% [95% CI, +14.2 to +23.7%]; p < 0.001), which correlated negatively with changes in weight (r = −0.44; p < 0.001), fat mass (r = −0.43; p < 0.001), and visceral fat (r = −0.28; p = 0.003) and positively with PREDIM (r = 0.36; p < 0.001), so a smaller reduction in Bacteroides fragilis was associated with a greater loss of body weight, fat mass, visceral fat, and a greater increase in insulin sensitivity. A low-fat vegan diet induced significant changes in gut microbiota, which were related to changes in weight, body composition, and insulin sensitivity in overweight adults, suggesting a potential use in clinical practice.

Widespread protein lysine acetylation in gut microbiome and its alterations in patients with Crohn\u2019s disease

Lysine acetylation (Kac), an abundant post-translational modification (PTM) in prokaryotes, regulates various microbial metabolic pathways. However, no studies have examined protein Kac at the microbiome level, and it remains unknown whether Kac level is altered in patient microbiomes. Herein, we use a peptide immuno-affinity enrichment strategy coupled with mass spectrometry to characterize protein Kac in the microbiome, which successfully identifies 35,200 Kac peptides from microbial or human proteins in gut microbiome samples. We demonstrate that Kac is widely distributed in gut microbial metabolic pathways, including anaerobic fermentation to generate short-chain fatty acids. Applying to the analyses of microbiomes of patients with Crohn’s disease identifies 52 host and 136 microbial protein Kac sites that are differentially abundant in disease versus controls. This microbiome-wide acetylomic approach aids in advancing functional microbiome research.

Cluster Analysis of Microbiome Data by Using Mixtures of Dirichlet–Multinomial Regression Models

SummaryThe human gut microbiome is one of the fundamental components of our physiology, and exploring the relationship between biological and environmental covariates and the resulting taxonomic composition of a given microbial community is an active area of research. Previously, a Dirichlet–multinomial regression framework has been suggested to model this relationship, but it did not account for any underlying latent group structure. An underlying group structure of guts (such as enterotypes) has been observed across gut microbiome samples in which guts in the same group share similar biota compositions. In the paper, a finite mixture of Dirichlet–multinomial regression models is proposed that accounts for this underlying group structure and to allow for a probabilistic investigation of the relationship between bacterial abundance and biological and/or environmental covariates within each inferred group. Furthermore, finite mixtures of regression models which incorporate the concomitant effect of the covariates on the resulting mixing proportions are also proposed and examined within the Dirichlet–multinomial framework. We utilize the proposed mixture model to gain insight on underlying subgroups in a microbiome data set comprising tumour and healthy samples and the relationships between covariates and microbial abundance in those subgroups.

History of breastfeeding but not mode of delivery shapes the gut microbiome in childhood.

The naïve neonatal gut is sensitive to early life experiences. Events during this critical developmental window may have life-long impacts on the gut microbiota. Two experiences that have been associated with variation in the gut microbiome in infancy are mode of delivery and feeding practices (eg, breastfeeding). It remains unclear whether these early experiences are responsible for microbial differences beyond toddlerhood. Our study examined whether mode of delivery and infant feeding practices are associated with differences in the child and adolescent microbiome. We used an adoption-sibling design to compare genetically related siblings who were reared together or apart. Gut microbiome samples were collected from 73 children (M = 11 years, SD = 3 years, range = 3-18 years). Parents reported on child breastfeeding history, age, sex, height, and weight. Mode of delivery was collected through medical records and phone interviews. Negative binomial mixed effects models were used to identify whether mode of delivery and feeding practices were related to differences in phylum and genus-level abundance of bacteria found in the gut of child participants. Covariates included age, sex, and body mass index. Genetic relatedness and rearing environment were accounted for as random effects. We observed a significant association between lack of breastfeeding during infancy and a greater number of the genus Bacteroides in stool in childhood and adolescence. The absence of breastfeeding may impart lasting effects on the gut microbiome well into childhood.

PepFunk: a tool for peptide-centric functional analysis of metaproteomic human gut microbiome studies.

Enzymatic digestion of proteins before mass spectrometry analysis is a key process in metaproteomic workflows. Canonical metaproteomic data processing pipelines typically involve matching spectra produced by the mass spectrometer to a theoretical spectra database, followed by matching the identified peptides back to parent-proteins. However, the nature of enzymatic digestion produces peptides that can be found in multiple proteins due to conservation or chance, presenting difficulties with protein and functional assignment. To combat this challenge, we developed pepFunk, a peptide-centric metaproteomic workflow focused on the analysis of human gut microbiome samples. Our workflow includes a curated peptide database annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG) terms and a gene set variation analysis-inspired pathway enrichment adapted for peptide-level data. Analysis using our peptide-centric workflow is fast and highly correlated to a protein-centric analysis, and can identify more enriched KEGG pathways than analysis using protein-level data. Our workflow is open source and available as a web application or source code to be run locally. pepFunk is available online as a web application at https://shiny.imetalab.ca/pepFunk/ with open-source code available from https://github.com/northomics/pepFunk. dfigeys@uottawa.ca. Supplementary data are available at Bioinformatics online.

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

Countering 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 colonisation by focal E. coli but 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.

Neotropical termite microbiomes as sources of novel plant cell wall degrading enzymes

In this study, we used shotgun metagenomic sequencing to characterise the microbial metabolic potential for lignocellulose transformation in the gut of two colonies of Argentine higher termite species with different feeding habits, Cortaritermes fulviceps and Nasutitermes aquilinus. Our goal was to assess the microbial community compositions and metabolic capacity, and to identify genes involved in lignocellulose degradation. Individuals from both termite species contained the same five dominant bacterial phyla (Spirochaetes, Firmicutes, Proteobacteria, Fibrobacteres and Bacteroidetes) although with different relative abundances. However, detected functional capacity varied, with C. fulviceps (a grass-wood-feeder) gut microbiome samples containing more genes related to amino acid metabolism, whereas N. aquilinus (a wood-feeder) gut microbiome samples were enriched in genes involved in carbohydrate metabolism and cellulose degradation. The C. fulviceps gut microbiome was enriched specifically in genes coding for debranching- and oligosaccharide-degrading enzymes. These findings suggest an association between the primary food source and the predicted categories of the enzymes present in the gut microbiomes of each species. To further investigate the termite microbiomes as sources of biotechnologically relevant glycosyl hydrolases, a putative GH10 endo-β-1,4-xylanase, Xyl10E, was cloned and expressed in Escherichia coli. Functional analysis of the recombinant metagenome-derived enzyme showed high specificity towards beechwood xylan (288.1 IU/mg), with the optimum activity at 50 °C and a pH-activity range from 5 to 10. These characteristics suggest that Xy110E may be a promising candidate for further development in lignocellulose deconstruction applications.

Developing a Reproducible Microbiome Data Analysis Pipeline Using the Amazon Web Services Cloud for a Cancer Research Group: Proof-of-Concept Study.

BackgroundCloud computing for microbiome data sets can significantly increase working efficiencies and expedite the translation of research findings into clinical practice. The Amazon Web Services (AWS) cloud provides an invaluable option for microbiome data storage, computation, and analysis.ObjectiveThe goals of this study were to develop a microbiome data analysis pipeline by using AWS cloud and to conduct a proof-of-concept test for microbiome data storage, processing, and analysis.MethodsA multidisciplinary team was formed to develop and test a reproducible microbiome data analysis pipeline with multiple AWS cloud services that could be used for storage, computation, and data analysis. The microbiome data analysis pipeline developed in AWS was tested by using two data sets: 19 vaginal microbiome samples and 50 gut microbiome samples.ResultsUsing AWS features, we developed a microbiome data analysis pipeline that included Amazon Simple Storage Service for microbiome sequence storage, Linux Elastic Compute Cloud (EC2) instances (ie, servers) for data computation and analysis, and security keys to create and manage the use of encryption for the pipeline. Bioinformatics and statistical tools (ie, Quantitative Insights Into Microbial Ecology 2 and RStudio) were installed within the Linux EC2 instances to run microbiome statistical analysis. The microbiome data analysis pipeline was performed through command-line interfaces within the Linux operating system or in the Mac operating system. Using this new pipeline, we were able to successfully process and analyze 50 gut microbiome samples within 4 hours at a very low cost (a c4.4xlarge EC2 instance costs $0.80 per hour). Gut microbiome findings regarding diversity, taxonomy, and abundance analyses were easily shared within our research team.ConclusionsBuilding a microbiome data analysis pipeline with AWS cloud is feasible. This pipeline is highly reliable, computationally powerful, and cost effective. Our AWS-based microbiome analysis pipeline provides an efficient tool to conduct microbiome data analysis.

A Bayesian framework for identifying consistent patterns of microbial abundance between body sites.

Recent studies have found that the microbiome in both gut and mouth are associated with diseases of the gut, including cancer. If resident microbes could be found to exhibit consistent patterns between the mouth and gut, disease status could potentially be assessed non-invasively through profiling of oral samples. Currently, there exists no generally applicable method to test for such associations. Here we present a Bayesian framework to identify microbes that exhibit consistent patterns between body sites, with respect to a phenotypic variable. For a given operational taxonomic unit (OTU), a Bayesian regression model is used to obtain Markov-Chain Monte Carlo estimates of abundance among strata, calculate a correlation statistic, and conduct a formal test based on its posterior distribution. Extensive simulation studies demonstrate overall viability of the approach, and provide information on what factors affect its performance. Applying our method to a dataset containing oral and gut microbiome samples from 77 pancreatic cancer patients revealed several OTUs exhibiting consistent patterns between gut and mouth with respect to disease subtype. Our method is well powered for modest sample sizes and moderate strength of association and can be flexibly extended to other research settings using any currently established Bayesian analysis programs.