Nicholas A. Bokulich

Antibiotics, birth mode, and diet shape microbiome maturation during early life

A longitudinal study of intestinal microbiota in children and their mothers shows that antibiotics, cesarean section, and infant formula alter patterns of microbial acquisition and succession during the first 2 years of childhood. Snapshots of the developing infant gut microbiota The intestinal “microbiota,” that is, the community of microbes inhabiting the human intestinal tract, undergoes many changes during the first 2 years of life. Bokulich et al. now show that this pattern of development is altered in children who are delivered by cesarean section, fed formula, or treated with antibiotics, compared to those babies who were born vaginally, breast-fed, or unexposed to antibiotics. Future studies will determine whether these disturbances influence the health of these babies. Early childhood is a critical stage for the foundation and development of both the microbiome and host. Early-life antibiotic exposures, cesarean section, and formula feeding could disrupt microbiome establishment and adversely affect health later in life. We profiled microbial development during the first 2 years of life in a cohort of 43 U.S. infants and identified multiple disturbances associated with antibiotic exposures, cesarean section, and formula feeding. These exposures contributed to altered establishment of maternal bacteria, delayed microbiome development, and altered α-diversity. These findings illustrate the complexity of early-life microbiome development and its sensitivity to perturbation.

Metabolic and metagenomic outcomes from early-life pulsed antibiotic treatment

Mammalian species have co-evolved with intestinal microbial communities that can shape development and adapt to environmental changes, including antibiotic perturbation or nutrient flux. In humans, especially children, microbiota disruption is common, yet the dynamic microbiome recovery from early-life antibiotics is still uncharacterized. Here we use a mouse model mimicking paediatric antibiotic use and find that therapeutic-dose pulsed antibiotic treatment (PAT) with a beta-lactam or macrolide alters both host and microbiota development. Early-life PAT accelerates total mass and bone growth, and causes progressive changes in gut microbiome diversity, population structure and metagenomic content, with microbiome effects dependent on the number of courses and class of antibiotic. Whereas control microbiota rapidly adapts to a change in diet, PAT slows the ecological progression, with delays lasting several months with previous macrolide exposure. This study identifies key markers of disturbance and recovery, which may help provide therapeutic targets for microbiota restoration following antibiotic treatment.

Antibiotic perturbation of the murine gut microbiome enhances the adiposity, insulin resistance, and liver disease associated with high-fat diet

Background Obesity, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD) are serious health concerns, especially in Western populations. Antibiotic exposure and high-fat diet (HFD) are important and modifiable factors that may contribute to these diseases. Methods To investigate the relationship of antibiotic exposure with microbiome perturbations in a murine model of growth promotion, C57BL/6 mice received lifelong sub-therapeutic antibiotic treatment (STAT), or not (control), and were fed HFD starting at 13 weeks. To characterize microbiota changes caused by STAT, the V4 region of the 16S rRNA gene was examined from collected fecal samples and analyzed. Results In this model, which included HFD, STAT mice developed increased weight and fat mass compared to controls. Although results in males and females were not identical, insulin resistance and NAFLD were more severe in the STAT mice. Fecal microbiota from STAT mice were distinct from controls. Compared with controls, STAT exposure led to early conserved diet-independent microbiota changes indicative of an immature microbial community. Key taxa were identified as STAT-specific and several were found to be predictive of disease. Inferred network models showed topological shifts concurrent with growth promotion and suggest the presence of keystone species. Conclusions These studies form the basis for new models of type 2 diabetes and NAFLD that involve microbiome perturbation. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0297-9) contains supplementary material, which is available to authorized users.

mockrobiota: a public resource for microbiome bioinformatics benchmarking

The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community. ABSTRACT Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at http://caporaso-lab.github.io/mockrobiota/ . The materials contained in mockrobiota include data set and sample metadata, expected composition data (taxonomy or gene annotations or reference sequences for mock community members), and links to raw data (e.g., raw sequence data) for each mock community data set. mockrobiota does not supply physical sample materials directly, but the data set metadata included for each mock community indicate whether physical sample materials are available. At the time of this writing, mockrobiota contains 11 mock community data sets with known species compositions, including bacterial, archaeal, and eukaryotic mock communities, analyzed by high-throughput marker gene sequencing. IMPORTANCE The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community.

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

BackgroundTaxonomic classification of marker-gene sequences is an important step in microbiome analysis.ResultsWe present q2-feature-classifier (https://github.com/qiime2/q2-feature-classifier), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated “novel” marker-gene sequences, are available in our extensible benchmarking framework, tax-credit (https://github.com/caporaso-lab/tax-credit-data).ConclusionsOur results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

Impact of the [GAR+] Prion on Fermentation and Bacterial Community Composition with Saccharomyces cerevisiae UCD932

The efficiency and efficacy of alcoholic fermentation by yeast is crucial for the winemaking process. Sluggish or arrested fermentations can negatively affect winery operations and wine quality. Here, we present a novel mechanism by which problem fermentations can arise. Yeast can induce a prion known as [GAR+] that allows the cell to circumvent glucose repression of alternative carbon substrates. We have confirmed that Saccharomyces cerevisiae strain UCD932 can spontaneously generate the [GAR+] phenotype and that this phenotype exhibits the genetic traits of a prion. Differences were observed in the fermentative behavior of UCD932 wild-type [gar−] versus [GAR+] yeasts in laboratory-scale model juice fermentations. To further understand these differences, fermentations were performed in Chardonnay juice to study the interaction of the [GAR+] prion and presence of sulfur dioxide (SO2) on fermentation kinetics, bacterial community composition, and volatile compound production. Cells harboring the [GAR+] prion displayed reduced fermentation capacity, which was especially evident in the absence of SO2. Presence of SO2 and fermentation time had the most significant effects on the types of bacteria present in the fermentation. However, [GAR+] yeasts without added SO2 were especially sensitive to bacterial competition. This difference was also reflected in the bacterial and volatile profiles of the finished wine. We hypothesize that the bacterial induction of the [GAR+] prion by yeast during fermentation is another possible mechanism by which stuck or sluggish fermentations may become established.

q2-longitudinal: a QIIME 2 plugin for longitudinal and paired-sample analyses of microbiome data

Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre/post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including paired differences and distances, linear mixed effects models, microbial interdependence test, first differencing, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open source software released under a BSD-3-Clause license and is freely available, including for commercial use.

Species-level microbial sequence classification is improved by source-environment information

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate that species-level resolution is attainable.

Fecal microbiota and bile acid interactions with systemic and adipose tissue metabolism in diet-induced weight loss of obese postmenopausal women

BackgroundMicrobiota and bile acids in the gastrointestinal tract profoundly alter systemic metabolic processes. In obese subjects, gradual weight loss ameliorates adipose tissue inflammation and related systemic changes. We assessed how rapid weight loss due to a very low calorie diet (VLCD) affects the fecal microbiome and fecal bile acid composition, and their interactions with the plasma metabolome and subcutaneous adipose tissue inflammation in obesity.MethodsWe performed a prospective cohort study of VLCD-induced weight loss of 10% in ten grades 2–3 obese postmenopausal women in a metabolic unit. Baseline and post weight loss evaluation included fasting plasma analyzed by mass spectrometry, adipose tissue transcription by RNA sequencing, stool 16S rRNA sequencing for fecal microbiota, fecal bile acids by mass spectrometry, and urinary metabolic phenotyping by 1H-NMR spectroscopy. Outcome measures included mixed model correlations between changes in fecal microbiota and bile acid composition with changes in plasma metabolite and adipose tissue gene expression pathways.ResultsAlterations in the urinary metabolic phenotype following VLCD-induced weight loss were consistent with starvation ketosis, protein sparing, and disruptions to the functional status of the gut microbiota. We show that the core microbiome was preserved during VLCD-induced weight loss, but with changes in several groups of bacterial taxa with functional implications. UniFrac analysis showed overall parallel shifts in community structure, corresponding to reduced abundance of the genus Roseburia and increased Christensenellaceae;g__ (unknown genus). Imputed microbial functions showed changes in fat and carbohydrate metabolism. A significant fall in fecal total bile acid concentration and reduced deconjugation and 7-α-dihydroxylation were accompanied by significant changes in several bacterial taxa. Individual bile acids in feces correlated with amino acid, purine, and lipid metabolic pathways in plasma. Furthermore, several fecal bile acids and bacterial species correlated with altered gene expression pathways in adipose tissue.ConclusionsVLCD dietary intervention in obese women changed the composition of several fecal microbial populations while preserving the core fecal microbiome. Changes in individual microbial taxa and their functions correlated with variations in the plasma metabolome, fecal bile acid composition, and adipose tissue transcriptome.Trial Registration ClinicalTrials.gov NCT01699906, 4-Oct-2012, Retrospectively registered. URL-https://clinicaltrials.gov/ct2/show/NCT01699906

q2-sample-classifier: machine-learning tools for microbiome classification and regression

Summary q2-sample-classifier is a plugin for the QIIME 2 microbiome bioinformatics platform that facilitates access, reproducibility, and interpretation of supervised learning (SL) methods for a broad audience of non-bioinformatics specialists.