Partial convergence of the human vaginal and rectal maternal microbiota in late gestation and early post-partum

The human vaginal and fecal microbiota change during pregnancy. Because of the proximity of these perineal sites and the evolutionarily conserved maternal-to-neonatal transmission of the microbiota, we hypothesized that the microbiota of these two sites (rectal and vaginal) converge during the last gestational trimester as part of the preparation for parturition. To test this hypothesis, we analyzed 16S rRNA sequences from vaginal introitus and rectal samples in 41 women at gestational ages 6 and 8 months, and at 2 months post-partum. The results show that the human vaginal and rectal bacterial microbiota converged during the last gestational trimester and into the 2nd month after birth, with a significant decrease in Lactobacillus species in both sites, as alpha diversity progressively increased in the vagina and decreased in the rectum. The microbiota convergence of the maternal vaginal-anal sites perinatally might hold significance for the inter-generational transmission of the maternal microbiota.

Microbiomes in Neverland

Differentiating the effects of infant microbiota, developmental, and nutritional changes on immunological maturation during weaning is an ongoing challenge. In this issue of Cell Host & Microbe, Lubin and colleagues report a gnotobiotic mouse model that maintains neonatal-like microbiome composition into adulthood to help answer burning questions in this field.

Infant behavioral state and stool microbiome in infants receiving Lactocaseibacillus rhamnosus GG in formula: randomized controlled trial

Background

Our aim was to evaluate infant behavioral state, stool microbiome profile and calprotectin in infants with infantile colic receiving a partially hydrolyzed protein formula with or without added Lacticaseibacillus (formerly Lactobacillus) rhamnosus GG (LGG).

Methods

In this single-center, double-blind, controlled, parallel, prospective study, term infants (14–28 days of age) identified with colic (using modified Wessel’s criteria: cried and/or fussed ≥ 3 h/day for ≥ 3 days/week, in a one-week period) were randomized to receive one of two formulas over a three-week feeding period: marketed partially hydrolyzed cow’s milk-based infant formula (PHF, n = 35) or a similar formula with added LGG (PHF-LGG, n = 36). Parent-reported infant behavior was recorded at three time points (Study Days 2–4, 10–12, and 18–20). Duration (hours/day) of crying/fussing (averaged over each three-day period) was the primary outcome. Stool samples were collected at Baseline and Study End (Days 19–21) to determine stool LGG colonization (by qPCR) and microbial abundance (using 16S rRNA gene sequencing) and calprotectin (μg/g).

Results

Duration of crying/fussing (mean ± SE) decreased and awake/content behavior increased over time with no significant group differences over the course of the study. There were no group differences in the percentage of infants who experienced colic by study end. Colic decreased by Study End vs Baseline in both groups. Change in fecal calprotectin also was similar between groups. Comparing Study End vs Baseline, LGG abundance was greater in the PHF-LGG group (P < 0.001) whereas alpha diversity was greater in the PHF group (P = 0.022). Beta diversity was significantly different between PHF and PHF-LGG at Study End (P = 0.05). By study end, relative abundance of L. rhamnosus was higher in the PHF-LGG vs PHF group and vs Baseline.

Conclusions

In this pilot study of infants with colic, both study formulas were well tolerated. Crying/fussing decreased and awake/content behavior increased in both study groups over the course of the study. Study results demonstrate a successful introduction of the probiotic to the microbiome. The partially hydrolyzed protein formula with added LGG was associated with significant changes in the gut microbiome.

Trial registration

ClinicalTrials.gov, ClinicalTrials.gov Identifier: NCT02340143. Registered 16/01/2015.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03647-x.

Reproducible acquisition, management and meta-analysis of nucleotide sequence (meta)data using q2-fondue

MOTIVATION

The volume of public nucleotide sequence data has blossomed over the past two decades and is ripe for re- and meta-analyses to enable novel discoveries. However, reproducible re-use and management of sequence datasets and associated metadata remain critical challenges. We created the open source Python package q2-fondue to enable user-friendly acquisition, re-use and management of public sequence (meta)data while adhering to open data principles.

RESULTS

q2-fondue allows fully provenance-tracked programmatic access to and management of data from the NCBI Sequence Read Archive (SRA). Unlike other packages allowing download of sequence data from the SRA, q2-fondue enables full data provenance tracking from data download to final visualization, integrates with the QIIME 2 ecosystem, prevents data loss upon space exhaustion and allows download of (meta)data given a publication library. To highlight its manifold capabilities, we present executable demonstrations using publicly available amplicon, whole genome and metagenome datasets.

AVAILABILITY AND IMPLEMENTATION

q2-fondue is available as an open-source BSD-3-licensed Python package at https://github.com/bokulich-lab/q2-fondue. Usage tutorials are available in the same repository. All Jupyter notebooks used in this article are available under https://github.com/bokulich-lab/q2-fondue-examples.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data

QIIME 2 is a completely re‐engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open‐source web‐based platform, to re‐use available data for meta‐analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug‐ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta‐analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://qiime2.org. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://forum.qiime2.org. © 2020 The Authors.

Basic Protocol: Using QIIME 2 with microbiome data

Support Protocol: Further microbiome analyses

Beating Naive Bayes at Taxonomic Classification of 16S rRNA Gene Sequences

Naive Bayes classifiers (NBC) have dominated the field of taxonomic classification of amplicon sequences for over a decade. Apart from having runtime requirements that allow them to be trained and used on modest laptops, they have persistently provided class-topping classification accuracy. In this work we compare NBC with random forest classifiers, neural network classifiers, and a perfect classifier that can only fail when different species have identical sequences, and find that in some practical scenarios there is little scope for improving on NBC for taxonomic classification of 16S rRNA gene sequences. Further improvements in taxonomy classification are unlikely to come from novel algorithms alone, and will need to leverage other technological innovations, such as ecological frequency information.

Sources and Assembly of Microbial Communities in Vineyards as a Functional Component of Winegrowing

Microbiomes are integral to viticulture and winemaking – collectively termed winegrowing – where diverse fungi and bacteria can exert positive and negative effects on grape health and wine quality. Wine is a fermented natural product, and the vineyard serves as a key point of entry for quality-modulating microbiota, particularly in wine fermentations that are conducted without the addition of exogenous yeasts. Thus, the sources and persistence of wine-relevant microbiota in vineyards critically impact its quality. Site-specific variations in microbiota within and between vineyards may contribute to regional wine characteristics. This includes distinctions in microbiomes and microbiota at the strain level, which can contribute to wine flavor and aroma, supporting the role of microbes in the accepted notion of terroir as a biological phenomenon. Little is known about the factors driving microbial biodiversity within and between vineyards, or those that influence annual assembly of the fruit microbiome. Fruit is a seasonally ephemeral, yet annually recurrent product of vineyards, and as such, understanding the sources of microbiota in vineyards is critical to the assessment of whether or not microbial terroir persists with inter-annual stability, and is a key factor in regional wine character, as stable as the geographic distances between vineyards. This review examines the potential sources and vectors of microbiota within vineyards, general rules governing plant microbiome assembly, and how these factors combine to influence plant-microbe interactions relevant to winemaking.

Lord of the Diptera (and Moths and a Spider): Molecular Diet Analyses and Foraging Ecology of Indiana Bats in Illinois

Effective management of endangered or threatened wildlife requires an understanding of how foraging habitats are used by those populations. Molecular diet analysis of fecal samples offers a cost-effective and non-invasive method to investigate how diets of wild populations vary with respect to spatial and temporal factors. For the federally endangered Indiana bat (Myotis sodalis), documenting its preferred food sources can provide critical information to promote effective conservation of this federally endangered species. Using cytochrome oxidase I amplicon sequence data from Indiana bat guano samples collected at two roosting areas in Cypress Creek National Wildlife Refuge, we found that dipteran taxa (i.e., flies) associated with riparian habitats were the most frequently detected taxon and represented the majority of the sequence diversity among the arthropods sampled. A select few arthropods from other taxa—especially spiders—are also likely important to Indiana bat diets in this refuge. A supervised learning analysis of diet components suggest only a small fraction of the frequently detected taxa are important contributors to spatial and temporal variation. Overall, these data depict the Indiana bat as a generalist consumer whose diet includes some prey items associated with particular seasonal or spatial components, along with other taxa repeatedly consumed throughout the entire foraging season. These molecular diet analyses suggest that protecting foraging resources specifically associated with the riparian habitat of Cypress Creek National Wildlife Refuge is essential to promote effective Indiana bat conservation.

Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods

Microbiomes are integral components of diverse ecosystems, and increasingly recognized for their roles in the health of humans, animals, plants, and other hosts. Given their complexity (both in composition and function), the effective study of microbiomes (microbiomics) relies on the development, optimization, and validation of computational methods for analyzing microbial datasets, such as from marker-gene (e.g., 16S rRNA gene) and metagenome data. This review describes best practices for benchmarking and implementing computational methods (and software) for studying microbiomes, with particular focus on unique characteristics of microbiomes and microbiomics data that should be taken into account when designing and testing microbiomics methods.

An Early Pandemic Analysis of SARS-CoV-2 Population Structure and Dynamics in Arizona

In December of 2019, a novel coronavirus, SARS-CoV-2, emerged in the city of Wuhan, China, causing severe morbidity and mortality. Since then, the virus has swept across the globe, causing millions of confirmed infections and hundreds of thousands of deaths. To better understand the nature of the pandemic and the introduction and spread of the virus in Arizona, we sequenced viral genomes from clinical samples tested at the TGen North Clinical Laboratory, the Arizona Department of Health Services, and those collected as part of community surveillance projects at Arizona State University and the University of Arizona. Phylogenetic analysis of 84 genomes from across Arizona revealed a minimum of 11 distinct introductions inferred to have occurred during February and March. We show that >80% of our sequences descend from strains that were initially circulating widely in Europe but have since dominated the outbreak in the United States. In addition, we show that the first reported case of community transmission in Arizona descended from the Washington state outbreak that was discovered in late February. Notably, none of the observed transmission clusters are epidemiologically linked to the original travel-related case in the state, suggesting successful early isolation and quarantine. Finally, we use molecular clock analyses to demonstrate a lack of identifiable, widespread cryptic transmission in Arizona prior to the middle of February 2020.IMPORTANCE As the COVID-19 pandemic swept across the United States, there was great differential impact on local and regional communities. One of the earliest and hardest hit regions was in New York, while at the same time Arizona (for example) had low incidence. That situation has changed dramatically, with Arizona now having the highest rate of disease increase in the country. Understanding the roots of the pandemic during the initial months is essential as the pandemic continues and reaches new heights. Genomic analysis and phylogenetic modeling of SARS-COV-2 in Arizona can help to reconstruct population composition and predict the earliest undetected introductions. This foundational work represents the basis for future analysis and understanding as the pandemic continues.

A total crapshoot? Evaluating bioinformatic decisions in animal diet metabarcoding analyses

Metabarcoding studies provide a powerful approach to estimate the diversity and abundance of organisms in mixed communities in nature. While strategies exist for optimizing sample and sequence library preparation, best practices for bioinformatic processing of amplicon sequence data are lacking in animal diet studies. Here we evaluate how decisions made in core bioinformatic processes, including sequence filtering, database design, and classification, can influence animal metabarcoding results. We show that denoising methods have lower error rates compared to traditional clustering methods, although these differences are largely mitigated by removing low-abundance sequence variants. We also found that available reference datasets from GenBank and BOLD for the animal marker gene cytochrome oxidase I (COI) can be complementary, and we discuss methods to improve existing databases to include versioned releases. Taxonomic classification methods can dramatically affect results. For example, the commonly used Barcode of Life Database (BOLD) Classification API assigned fewer names to samples from order through species levels using both a mock community and bat guano samples compared to all other classifiers (vsearch-SINTAX and q2-feature-classifier's BLAST + LCA, VSEARCH + LCA, and Naive Bayes classifiers). The lack of consensus on bioinformatics best practices limits comparisons among studies and may introduce biases. Our work suggests that biological mock communities offer a useful standard to evaluate the myriad computational decisions impacting animal metabarcoding accuracy. Further, these comparisons highlight the need for continual evaluations as new tools are adopted to ensure that the inferences drawn reflect meaningful biology instead of digital artifacts.

Phylogenetic farming: Can evolutionary history predict crop rotation via the soil microbiome?

Agriculture has long employed phylogenetic rules whereby farmers are encouraged to rotate taxonomically unrelated plants in shared soil. Although this forms a central tenet of sustainable agriculture, strangely, this on-farm "rule of thumb" has never been rigorously tested in a scientific framework. To experimentally evaluate the relationship between phylogenetic distance and crop performance, we used a plant-soil feedback approach whereby 35 crops and weeds varying in their relatedness to tomato (Solanum lycopersicum) were tested in a two-year field experiment. We used community profiling of the bacteria and fungi to determine the extent to which soil microbes contribute to phenotypic differences in crop growth. Overall, tomato yield was ca. 15% lower in soil previously cultivated with tomato; yet, past the species level there was no effect of phylogenetic distance on crop performance. Soil microbial communities, on the other hand, were compositionally more similar between close plant relatives. Random forest regression predicted log10 phylogenetic distance to tomato with moderate accuracy (R 2 = .52), primarily driven by bacteria in the genus Sphingobium. These data indicate that, beyond avoiding conspecifics, evolutionary history contributes little to understanding plant-soil feedbacks in agricultural fields; however, microbial legacies can be predicted by species identity and relatedness.

Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2's unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2’s unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

Abstract A094: Integrative multi-omics approach reveals complex interplay between HPV, host and microbiome during cervical carcinogenesis in Hispanic and non-Hispanic women

Persistent human papillomavirus (HPV) infection is the vital factor driving cervical carcinogenesis; however, other features of the local cervicovaginal microenvironment (CVM) may play a critical role in development of precancerous cervical dysplasia and progression to invasive cervical carcinoma (ICC). Here we investigated relationships between immunoproteomic and metabolic profiles and features of the cervicovaginal microenvironment, such as HPV status, vaginal microbiota (VMB), vaginal pH and genital inflammation, to better understand the complex interplay between host, virus and bacteria. In this multicenter study we enrolled 78 women with ICC, high- and low-grade squamous intraepithelial lesions, as well as HPV-positive and healthy HPV-negative controls. Vaginal swabs and cervicovaginal lavages (CVL) were collected for HPV genotyping, microbiome, metabolome and immunoproteome analyses. The VMB compositions were determined using 16S rRNA gene sequencing. Cervicovaginal metabolic fingerprints were profiled using liquid chromatography-mass spectrometry. Levels of immune mediators and other proteins in CVL samples were evaluated using multiplex cytometric bead arrays. Abnormal vaginal pH and dysbiotic non-Lactobacillus-dominated VMB were associated with Hispanic ethnicity and severity of cervical neoplasm. We also identified microbial signatures (e.g. Sneathia spp.) to be enriched in ICC and all precancerous groups. Notably, Sneathia abundance was also increased in patients with abnormal pH and those of Hispanic origin. Analyses of 62 protein targets in CVL samples revealed elevated levels of pro-inflammatory cytokines and chemokines, growth and angiogenic factors, apoptosis-related, immune checkpoint and other proteins in ICC patients. Levels of many of these proteins depended on the VMB structure and genital inflammation. These proteomic signatures positively correlated with dysbiotic non-Lactobacillus-dominated VMB and abnormal vaginal pH, both features associated with Hispanic ethnicity. Furthermore, metabolomic analysis also revealed that VMB, together with genital inflammation, are the major drivers of metabolic profiles in the local CVM. Finally, using hierarchical clustering analyses, we identified groups of patients who significantly varied in the levels of cancer-related proteins, genital inflammation, vaginal pH and VMB composition regardless of disease severity. These microenvironmental factors may impact the HPV persistence/progression and consequently increase the risk of cervical cancer. Our study demonstrated that the racial/ethnic differences in the VMB compositions may contribute to cervical cancer disparity in Hispanic women. In the future we are planning to expand our investigation of the VMB in Native American women, which will further illuminate the relationship between race/ethnicity, the VMB, and HPV.

Citation Format: Pawel Laniewski, Zehra Esra Ilhan, Nicholas A. Bokulich, Haiyan Cui, Denise J. Roe, Dana M. Chase, J. Gregory Caporaso, Melissa M. Herbst-Kralovetz. Integrative multi-omics approach reveals complex interplay between HPV, host and microbiome during cervical carcinogenesis in Hispanic and non-Hispanic women [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr A094.

Composition of the Vaginal Microbiome Associated with High Risk HPV Infection and Increased Risk for Cervical Cancer

Persistent high-risk Human papillomavirus (HPV) is a necessary but not sufficient cause for development of cervical cancer. Changes in the vaginal microbiota including reduction of Lactobacillus and increased microbial diversity may facilitate HPV infection and persistence as well as the pathogenesis of cervical cancer. Our objective was to characterize the vaginal microbiota among women with and without persistent HPV infection with HPV 6, 11, 16, 18, 31, 33, 45, 52, 58 in a cohort of young U.S. women. Methods: This analysis used data from a longitudinal study of 1365 women followed for 12 months every two months apart from six locations across the US. HPV genotyping was performed using quantitative PCR using TaqMan probes in a customized plate (ThermoFisher Scientific). Bacterial communities were profiled by 16S rRNA gene sequences from the V3-V4 region using high-throughput pyrosequencing. Persistence was defined by examining for HPV presence in two consecutive visits. Results: Participants included 80 African-American women tested at 3 consecutive time-points. The mean age of participants was 21.4 years. About 43.7% (95% CI: 32.7%–55.3%) had persistent HPV infection, 20% (11.9%–30.4%) were able to clear the infection, and 36.2% (25.8%–47.8%) were consistently negative for HPV infection at all three time points. Atopobium and Peptoniphilus were significantly more abundant in women who were HPV negative suggesting possible protective effects. Prevotella bivia was enriched among women with persistent HPV. Conclusion: Based on these data, we can hypothesize that Prevotella richness is significantly associated with HPV persistence, suggesting a possible role in chronic HPV infection and development of cervical cancer.

Learning representations of microbe-metabolite interactions

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

Species abundance information improves sequence taxonomy classification accuracy

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 a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.

Taxonomy classification of amplicon sequences is an important step in investigating microbial communities in microbiome analysis. Here, the authors show incorporating environment-specific taxonomic abundance information can lead to improved species-level classification accuracy across common sample types.

Species abundance information improves sequence taxonomy classification accuracy

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 a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Effects of ursodeoxycholic acid on the gut microbiome and colorectal adenoma development

It has been previously reported that ursodeoxycholic acid (UDCA), a therapeutic bile acid, reduced risk for advanced colorectal adenoma in men but not women. Interactions between the gut microbiome and fecal bile acid composition as a factor in colorectal cancer neoplasia have been postulated but evidence is limited to small cohorts and animal studies. Using banked stool samples collected as part of a phase III randomized clinical trial of UDCA for the prevention of colorectal adenomatous polyps, we compared change in the microbiome composition after a 3-year intervention in a subset of participants randomized to oral UDCA at 8-10 mg/kg of body weight per day (n = 198) or placebo (n = 203). Study participants randomized to UDCA experienced compositional changes in their microbiome that were statistically more similar to other individuals in the UDCA arm than to those in the placebo arm. This reflected a UDCA-associated shift in microbial community composition (P < 0.001), independent of sex, with no evidence of a UDCA effect on microbial richness (P > 0.05). These UDCA-associated shifts in microbial community distance metrics from baseline to end-of-study were not associated with risk of any or advanced adenoma (all P > 0.05) in men or women. Separate analyses of microbial networks revealed an overrepresentation of Faecalibacterium prausnitzii in the post-UDCA arm and an inverse relationship between F prausnitzii and Ruminococcus gnavus. In men who received UDCA, the overrepresentation of F prausnitzii and underrepresentation of R gnavus were more prominent in those with no adenoma recurrence at follow-up compared to men with recurrence. This relationship was not observed in women. Daily UDCA use modestly influences the relative abundance of microbial species in stool and affects the microbial network composition with suggestive evidence for sex-specific effects of UDCA on stool microbial community composition as a modifier of colorectal adenoma risk.

q2-longitudinal: 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 interactive plotting, linear mixed-effects models, paired differences and distances, microbial interdependence testing, first differencing, longitudinal feature selection, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open-source software released under a 3-clause Berkeley Software Distribution (BSD) license and is freely available, including for commercial use. IMPORTANCE Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity. We describe q2-longitudinal, a software plugin for longitudinal analysis of microbiome data sets in QIIME 2. The availability of longitudinal statistics and visualizations in the QIIME 2 framework will make the analysis of longitudinal data more accessible to microbiome researchers.

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

BACKGROUND

Microbiota 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.

METHODS

We 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.

RESULTS

Alterations 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.

CONCLUSIONS

VLCD 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.

Loss of HDAC6 alters gut microbiota and worsens obesity

Alterations in gut microbiota are known to affect intestinal inflammation and obesity. Antibiotic treatment can affect weight gain by elimination of histone deacetylase (HDAC) inhibitor-producing microbes, which are anti-inflammatory by augmenting regulatory T (T_reg) cells. We asked whether mice that lack HDAC6 and have potent suppressive T_reg cells are protected from microbiota-induced accelerated weight gain. We crossed wild-type and HDAC6-deficient mice and subjected the offspring to perinatal penicillin, inducing weight gain via microbiota disturbance. We observed that male HDAC6-deficient mice were not protected and developed profoundly accelerated weight gain. The antibiotic-exposed HDAC6-deficient mice showed a mixed immune phenotype with increased CD4⁺ and CD8⁺ T-cell activation yet maintained the enhanced T_reg cell-suppressive function phenotype characteristic of HDAC6-deficient mice. 16S rRNA sequencing of mouse fecal samples reveals that their microbiota diverged with time, with HDAC6 deletion altering microbiome composition. On a high-fat diet, HDAC6-deficient mice were depleted in representatives of the S24-7 family and Lactobacillus but enriched with Bacteroides and Parabacteroides; these changes are associated with obesity. Our findings further our understanding of the influence of HDACs on microbiome composition and are important for the development of HDAC6 inhibitors in the treatment of human diseases.-Lieber, A. D., Beier, U. H., Xiao, H., Wilkins, B. J., Jiao, J., Li, X. S., Schugar, R. C., Strauch, C. M., Wang, Z., Brown, J. M., Hazen, S. L., Bokulich, N. A., Ruggles, K. V., Akimova, T., Hancock, W. W., Blaser, M. J. Loss of HDAC6 alters gut microbiota and worsens obesity.

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

BACKGROUND

Taxonomic classification of marker-gene sequences is an important step in microbiome analysis.

RESULTS

We 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 ).

CONCLUSIONS

Our 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.

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

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.

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

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.

A communal catalogue reveals Earth's multiscale microbial diversity

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.

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.

A new perspective on microbial landscapes within food production

High-throughput, 'next-generation' sequencing tools offer many exciting new possibilities for food research. From investigating microbial dynamics within food fermentations to the ecosystem of the food-processing built environment, amplicon sequencing, metagenomics, and transcriptomics present novel applications for exploring microbial communities in, on, and around our foods. This review discusses the many uses of these tools for food-related and food facility-related research and highlights where they may yield nuanced insight into the microbial world of food production systems.

A standardized, extensible framework for optimizing classification improves marker-gene taxonomic assignments.. [PMID: 0 DOI: 10.7287/peerj.preprints.934v2]

Background: Taxonomic classification of marker-gene (i.e., amplicon) sequences represents an important step for molecular identification of microorganisms.

Results: We present three advances in our ability to assign and interpret taxonomic classifications of short marker gene sequences: two new methods for taxonomy assignment, which reduce runtime up to two-fold and achieve high-precision genus-level assignments; an evaluation of classification methods that highlights differences in performance with different marker genes and at different levels of taxonomic resolution; and an extensible framework for evaluating and optimizing new classification methods, which we hope will serve as a model for standardized and reproducible bioinformatics methods evaluations.

Conclusions: Our new methods are accessible in QIIME 1.9.0, and our evaluation framework will support ongoing optimization of classification methods to complement rapidly evolving short-amplicon sequencing and bioinformatics technologies. Static versions of all of the analysis notebooks generated with this framework, which contain all code and analysis results, can be viewed at http://bit.ly/srta-012 .

The soil microbiome influences grapevine-associated microbiota

Grapevine is a well-studied, economically relevant crop, whose associated bacteria could influence its organoleptic properties. In this study, the spatial and temporal dynamics of the bacterial communities associated with grapevine organs (leaves, flowers, grapes, and roots) and soils were characterized over two growing seasons to determine the influence of vine cultivar, edaphic parameters, vine developmental stage (dormancy, flowering, preharvest), and vineyard. Belowground bacterial communities differed significantly from those aboveground, and yet the communities associated with leaves, flowers, and grapes shared a greater proportion of taxa with soil communities than with each other, suggesting that soil may serve as a bacterial reservoir. A subset of soil microorganisms, including root colonizers significantly enriched in plant growth-promoting bacteria and related functional genes, were selected by the grapevine. In addition to plant selective pressure, the structure of soil and root microbiota was significantly influenced by soil pH and C:N ratio, and changes in leaf- and grape-associated microbiota were correlated with soil carbon and showed interannual variation even at small spatial scales. Diazotrophic bacteria, e.g., Rhizobiaceae and Bradyrhizobium spp., were significantly more abundant in soil samples and root samples of specific vineyards. Vine-associated microbial assemblages were influenced by myriad factors that shape their composition and structure, but the majority of organ-associated taxa originated in the soil, and their distribution reflected the influence of highly localized biogeographic factors and vineyard management.

IMPORTANCE

Vine-associated bacterial communities may play specific roles in the productivity and disease resistance of their host plant. Also, the bacterial communities on grapes have the potential to influence the organoleptic properties of the wine, contributing to a regional terroir. Understanding that factors that influence these bacteria may provide insights into management practices to shape and craft individual wine properties. We show that soil serves as a key source of vine-associated bacteria and that edaphic factors and vineyard-specific properties can influence the native grapevine microbiome preharvest.

Mapping microbial ecosystems and spoilage-gene flow in breweries highlights patterns of contamination and resistance

Distinct microbial ecosystems have evolved to meet the challenges of indoor environments, shaping the microbial communities that interact most with modern human activities. Microbial transmission in food-processing facilities has an enormous impact on the qualities and healthfulness of foods, beneficially or detrimentally interacting with food products. To explore modes of microbial transmission and spoilage-gene frequency in a commercial food-production scenario, we profiled hop-resistance gene frequencies and bacterial and fungal communities in a brewery. We employed a Bayesian approach for predicting routes of contamination, revealing critical control points for microbial management. Physically mapping microbial populations over time illustrates patterns of dispersal and identifies potential contaminant reservoirs within this environment. Habitual exposure to beer is associated with increased abundance of spoilage genes, predicting greater contamination risk. Elucidating the genetic landscapes of indoor environments poses important practical implications for food-production systems and these concepts are translatable to other built environments.

DOI:http://dx.doi.org/10.7554/eLife.04634.001

Many microbes—including bacteria and fungi—can affect the food and drink we consume, for better and for worse. Some spoil food, making it less tasty or even harmful to health. However, microbes can also be important ingredients: for example, yeast ferments malted barley sugars to make the alcohol and flavor of beer.

Nowadays, many beers are made under carefully controlled conditions, where the only microbes in the beer should be the strain of yeast added to the barley sugars. A more traditional ‘coolship’ method can be used to make sour beers; the barley sugars cool in an open-topped vessel and are fermented by the yeast and bacteria found naturally on the raw ingredients and in the surrounding environment.

Relatively little was known about how microbes spread around and adapt to living inside buildings. Now, Bokulich et al. have used a range of molecular and statistical techniques to examine how bacteria and fungi are dispersed throughout a North American brewery that produces beer using both conventional and coolship brewing techniques. Most of the microbes found in the building originated from the raw ingredients used to make the beer, with different parts of the brewery containing different species. Over the course of a year, some species spread to new parts of the building; a statistical method predicted the sources of these microbes, and revealed some key areas and features of the brewery that affect microbial transfer.

Bokulich et al. also looked at the spread of genes that enable their bacterial hosts to spoil beer, including those that protect bacteria from the antimicrobial action of the hops that flavor many beers. Lactic acid bacteria are the main cause of beer spoilage and so are usually to be avoided in breweries, but are also a normal ingredient in sour beer. In the brewery Bokulich et al. investigated, beer-spoilage and hop-resistance genes were found throughout the brewery, even in areas not used to produce sour beer. However, little beer spoilage occurred.

The techniques used by Bokulich et al. to track the spread of microbes and their detrimental genes could be used in the future to understand how microbes adapt to other indoor environments. Indeed, Bokulich et al. suggest that breweries could be used as models to safely understand the factors that influence microbial movement in any food-production facility as well as other building environments.

DOI:http://dx.doi.org/10.7554/eLife.04634.002

Microbial biogeography of the transnational fermented milk matsoni

The fermented milk matsoni is a traditional, national food product of both Georgia and Armenia. Little is known about the effects of biogeography and milk type on the microbial biodiversity of matsoni or the fungal composition of matsoni fermentations. High-throughput marker-gene sequencing was used to survey the bacterial and fungal communities of matsoni from different milk types and regions throughout Armenia and Georgia. Results demonstrate that both production region and milk type influence matsoni microbiota, suggesting that the traditional production methods preserve the transfer of unique regional microbiota from batch to batch. Bacterial profiles were dominated by Lactobacillus and Streptococcus species. Yeast profiles varied dramatically, with Kluyveromyces marxianus, Candida famata, Saccharomyces cerevisiae, Lodderomyces elongisporus, and Kluyveromyces lactis being the most important species distinguishing production regions and milk types. This survey will enable more detailed capture and characterization of specific microbiota detected within these fermentations.

A bitter aftertaste: unintended effects of artificial sweeteners on the gut microbiome

Intestinal microbial communities regulate a range of host physiological functions, from energy harvest and glucose homeostasis to immune development and regulation. Suez et al. (2014) recently demonstrated that artificial sweeteners alter gut microbial communities, leading to glucose intolerance in both mice and humans.

Human milk glycomics and gut microbial genomics in infant feces show a correlation between human milk oligosaccharides and gut microbiota: a proof-of-concept study

Human milk oligosaccharides (HMOs) play a key role in shaping and maintaining a healthy infant gut microbiota. This article demonstrates the potential of combining recent advances in glycomics and genomics to correlate abundances of fecal microbes and fecal HMOs. Serial fecal specimens from two healthy breast-fed infants were analyzed by bacterial DNA sequencing to characterize the microbiota and by mass spectrometry to determine abundances of specific HMOs that passed through the intestinal tract without being consumed by the luminal bacteria. In both infants, the fecal bacterial population shifted from non-HMO-consuming microbes to HMO-consuming bacteria during the first few weeks of life. An initial rise in fecal HMOs corresponded with bacterial populations composed primarily of non-HMO-consuming Enterobacteriaceae and Staphylococcaeae. This was followed by decreases in fecal HMOs as the proportion of HMO-consuming Bacteroidaceae and Bifidobacteriaceae increased. Analysis of HMO structures with isomer differentiation revealed that HMO consumption is highly structure-specific, with unique isomers being consumed and others passing through the gut unaltered. These results represent a proof-of-concept and are consistent with the highly selective, prebiotic effect of HMOs in shaping the gut microbiota in the first weeks of life. The analysis of selective fecal bacterial substrates as a measure of alterations in the gut microbiota may be a potential marker of dysbiosis.

Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate

Wine grapes present a unique biogeography model, wherein microbial biodiversity patterns across viticultural zones not only answer questions of dispersal and community maintenance, they are also an inherent component of the quality, consumer acceptance, and economic appreciation of a culturally important food product. On their journey from the vineyard to the wine bottle, grapes are transformed to wine through microbial activity, with indisputable consequences for wine quality parameters. Wine grapes harbor a wide range of microbes originating from the surrounding environment, many of which are recognized for their role in grapevine health and wine quality. However, determinants of regional wine characteristics have not been identified, but are frequently assumed to stem from viticultural or geological factors alone. This study used a high-throughput, short-amplicon sequencing approach to demonstrate that regional, site-specific, and grape-variety factors shape the fungal and bacterial consortia inhabiting wine-grape surfaces. Furthermore, these microbial assemblages are correlated to specific climatic features, suggesting a link between vineyard environmental conditions and microbial inhabitation patterns. Taken together, these factors shape the unique microbial inputs to regional wine fermentations, posing the existence of nonrandom "microbial terroir" as a determining factor in regional variation among wine grapes.

A comparison of two probiotic strains of bifidobacteria in premature infants

OBJECTIVE

To determine the impact of 2 probiotic bifidobacteria on the fecal microbiota of premature infants fed either human milk or formula.

STUDY DESIGN

In the first of two phase 1 clinical trials, 12 premature infants receiving formula feedings were assigned randomly to receive either Bifidobacterium longum ssp infantis or Bifidobacterium animalis ssp lactis in increasing doses during a 5-week period. In the second, 9 premature infants receiving their mother's milk received each of the two bifidobacteria for 2 weeks separated by a 1-week washout period. Serial stool specimens from each infant were analyzed by terminal restriction fragment-length polymorphism and quantitative polymerase chain reaction for bacterial composition.

RESULTS

Among the formula-fed infants, there was a greater increase in fecal bifidobacteria among infants receiving B infantis (Binf) than those receiving B lactis (Blac). This difference was most marked at a dose of 1.4 × 10(9) colony-forming units twice daily (P < .05). Bacterial diversity improved over dose/time in those infants receiving Binf. Among the human milk-fed infants, greater increases in fecal bifidobacteria and decreases in γ-Proteobacteria followed the administration of Binf than Blac. The B longum group (which includes Binf but not Blac) was the dominant bifidobacteria among the human milk-fed infants, regardless of the probiotic administered.

CONCLUSIONS

Binf was more effective at colonizing the fecal microbiota than Blac in both formula-fed and human milk-fed premature infants. The combination of human milk plus Binf resulted in the greatest fecal levels of bifidobacteria.

Probiotic administration in congenital heart disease: a pilot study

OBJECTIVE

To investigate the impact of probiotic Bifidobacterium longum ssp. infantis on the fecal microbiota and plasma cytokines in neonates with congenital heart disease.

STUDY DESIGN

Sixteen infants with congenital heart disease were randomly assigned to receive either B. infantis (4.2 × 10(9) colony-forming units two times daily) or placebo for 8 weeks. Stool specimens from enrolled infants and from six term infants without heart disease were analyzed for microbial composition. Plasma cytokines were analyzed weekly in the infants with heart disease.

RESULTS

Healthy control infants had increased total bacteria, total Bacteroidetes and total bifidobacteria compared to the infants with heart disease, but there were no significant differences between the placebo and probiotic groups. Plasma interleukin (IL)10, interferon (IFN)γ and IL1β levels were transiently higher in the probiotic group.

CONCLUSION

Congenital heart disease in infants is associated with dysbiosis. Probiotic B. infantis did not significantly alter the fecal microbiota. Alterations in plasma cytokines were found to be inconsistent.

Monitoring Seasonal Changes in Winery-Resident Microbiota

During the transformation of grapes to wine, wine fermentations are exposed to a large area of specialized equipment surfaces within wineries, which may serve as important reservoirs for two-way transfer of microbes between fermentations. However, the role of winery environments in shaping the microbiota of wine fermentations and vectoring wine spoilage organisms is poorly understood at the systems level. Microbial communities inhabiting all major equipment and surfaces in a pilot-scale winery were surveyed over the course of a single harvest to track the appearance of equipment microbiota before, during, and after grape harvest. Results demonstrate that under normal cleaning conditions winery surfaces harbor seasonally fluctuating populations of bacteria and fungi. Surface microbial communities were dependent on the production context at each site, shaped by technological practices, processing stage, and season. During harvest, grape- and fermentation-associated organisms populated most winery surfaces, acting as potential reservoirs for microbial transfer between fermentations. These surfaces harbored large populations of Saccharomyces cerevisiae and other yeasts prior to harvest, potentially serving as an important vector of these yeasts in wine fermentations. However, the majority of the surface communities before and after harvest comprised organisms with no known link to wine fermentations and a near-absence of spoilage-related organisms, suggesting that winery surfaces do not overtly vector wine spoilage microbes under normal operating conditions.

The microbiology of malting and brewing

Brewing beer involves microbial activity at every stage, from raw material production and malting to stability in the package. Most of these activities are desirable, as beer is the result of a traditional food fermentation, but others represent threats to the quality of the final product and must be controlled actively through careful management, the daily task of maltsters and brewers globally. This review collates current knowledge relevant to the biology of brewing yeast, fermentation management, and the microbial ecology of beer and brewing.

Use of bifidobacterial specific terminal restriction fragment length polymorphisms to complement next generation sequence profiling of infant gut communities

Bifidobacteria are intestinal anaerobes often associated with gut health. Specific bifidobacterial species are particularly common in the gastrointestinal tract of breast-fed infants. Current short read next-generation sequencing approaches to profile fecal microbial ecologies do not discriminate bifidobacteria to the species level. Here we describe a low-cost terminal restriction fragment length polymorphism (TRFLP) procedure to distinguish between the common infant-associated bifidobacterial species. An empirical database of TRF sizes was created from both common reference strains and well-identified isolates from infant feces. Species-specific quantitative PCR validated bifidobacterial-specific TRFLP profiles from infant feces. These results indicate that bifidobacterial-specific TRFLP is a useful method to monitor intestinal bifidobacterial populations from infant fecal samples. When used alongside next generation sequencing methods that detect broader population levels at lower resolution, this high-throughput, low-cost tool can help clarify the role of bifidobacteria in health and disease.

Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing

High-throughput sequencing has revolutionized microbial ecology, but read quality remains a significant barrier to accurate taxonomy assignment and alpha diversity assessment for microbial communities. We demonstrate that high-quality read length and abundance are the primary factors differentiating correct from erroneous reads produced by Illumina GAIIx, HiSeq, and MiSeq instruments. We present guidelines for user-defined quality-filtering strategies, enabling efficient extraction of high-quality data from, and facilitating interpretation of Illumina sequencing results.

Next-generation approaches to the microbial ecology of food fermentations

Food fermentations have enhanced human health since the dawn of time and remain a prevalent means of food processing and preservation. Due to their cultural and nutritional importance, many of these foods have been studied in detail using molecular tools, leading to enhancements in quality and safety. Furthermore, recent advances in high-throughput sequencing technology are revolutionizing the study of food microbial ecology, deepening insight into complex fermentation systems. This review provides insight into novel applications of select molecular techniques, particularly next-generation sequencing technology, for analysis of microbial communities in fermented foods. We present a guideline for integrated molecular analysis of food microbial ecology and a starting point for implementing next-generation analysis of food systems.

Routine habitat change: a source of unrecognized transient alteration of intestinal microbiota in laboratory mice

The mammalian intestine harbors a vast, complex and dynamic microbial population, which has profound effects on host nutrition, intestinal function and immune response, as well as influence on physiology outside of the alimentary tract. Imbalance in the composition of the dense colonizing bacterial population can increase susceptibility to various acute and chronic diseases. Valuable insights on the association of the microbiota with disease critically depend on investigation of mouse models. Like in humans, the microbial community in the mouse intestine is relatively stable and resilient, yet can be influenced by environmental factors. An often-overlooked variable in research is basic animal husbandry, which can potentially alter mouse physiology and experimental outcomes. This study examined the effects of common husbandry practices, including food and bedding alterations, as well as facility and cage changes, on the gut microbiota over a short time course of five days using three culture-independent techniques, quantitative PCR, terminal restriction fragment length polymorphism (TRFLP) and next generation sequencing (NGS). This study detected a substantial transient alteration in microbiota after the common practice of a short cross-campus facility transfer, but found no comparable alterations in microbiota within 5 days of switches in common laboratory food or bedding, or following an isolated cage change in mice acclimated to their housing facility. Our results highlight the importance of an acclimation period following even simple transfer of mice between campus facilities, and highlights that occult changes in microbiota should be considered when imposing husbandry variables on laboratory animals.

Brewhouse-resident microbiota are responsible for multi-stage fermentation of American coolship ale

American coolship ale (ACA) is a type of spontaneously fermented beer that employs production methods similar to traditional Belgian lambic. In spite of its growing popularity in the American craft-brewing sector, the fermentation microbiology of ACA has not been previously described, and thus the interface between production methodology and microbial community structure is unexplored. Using terminal restriction fragment length polymorphism (TRFLP), barcoded amplicon sequencing (BAS), quantitative PCR (qPCR) and culture-dependent analysis, ACA fermentations were shown to follow a consistent fermentation progression, initially dominated by Enterobacteriaceae and a range of oxidative yeasts in the first month, then ceding to Saccharomyces spp. and Lactobacillales for the following year. After one year of fermentation, Brettanomyces bruxellensis was the dominant yeast population (occasionally accompanied by minor populations of Candida spp., Pichia spp., and other yeasts) and Lactobacillales remained dominant, though various aerobic bacteria became more prevalent. This work demonstrates that ACA exhibits a conserved core microbial succession in absence of inoculation, supporting the role of a resident brewhouse microbiota. These findings establish this core microbial profile of spontaneous beer fermentations as a target for production control points and quality standards for these beers.