Search

Abstract 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.

Abstract 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

Abstract 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.

Abstract 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 (R2 = .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.

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.

Nucleotide sequence and taxonomy reference databases are critical resources for widespread applications including marker-gene and metagenome sequencing for microbiome analysis, diet metabarcoding, and environmental DNA (eDNA) surveys. Reproducibly generating, managing, using, and evaluating nucleotide sequence and taxonomy reference databases creates a significant bottleneck for researchers aiming to generate custom sequence databases. Furthermore, database composition drastically influences results, and lack of standardization limits cross-study comparisons. To address these challenges, we developed RESCRIPt, a Python 3 software package and QIIME 2 plugin for reproducible generation and management of reference sequence taxonomy databases, including dedicated functions that streamline creating databases from popular sources, and functions for evaluating, comparing, and interactively exploring qualitative and quantitative characteristics across reference databases. To highlight the breadth and capabilities of RESCRIPt, we provide several examples for working with popular databases for microbiome profiling (SILVA, Greengenes, NCBI-RefSeq, GTDB), eDNA and diet metabarcoding surveys (BOLD, GenBank), as well as for genome comparison. We show that bigger is not always better, and reference databases with standardized taxonomies and those that focus on type strains have quantitative advantages, though may not be appropriate for all use cases. Most databases appear to benefit from some curation (quality filtering), though sequence clustering appears detrimental to database quality. Finally, we demonstrate the breadth and extensibility of RESCRIPt for reproducible workflows with a comparison of global hepatitis genomes. RESCRIPt provides tools to democratize the process of reference database acquisition and management, enabling researchers to reproducibly and transparently create reference materials for diverse research applications. RESCRIPt is released under a permissive BSD-3 license at https://github.com/bokulich-lab/RESCRIPt. Author summary Generating and managing sequence and taxonomy reference data presents a bottleneck to many researchers, whether they are generating custom databases or attempting to format existing, curated reference databases for use with standard sequence analysis tools. Evaluating database quality and choosing the “best” database can be an equally formidable challenge. We developed RESCRIPt to alleviate this bottleneck, supporting reproducible, streamlined generation, curation, and evaluation of reference sequence databases. RESCRIPt uses QIIME 2 artifact file formats, which store all processing steps as data provenance within each file, allowing researchers to retrace the computational steps used to generate any given file. We used RESCRIPt to benchmark several commonly used marker-gene sequence databases for 16S rRNA genes, ITS, and COI sequences, demonstrating both the utility of RESCRIPt to streamline use of these databases, but also to evaluate several qualitative and quantitative characteristics of each database. We show that larger databases are not always best, and curation steps to reduce redundancy and filter out noisy sequences may be beneficial for some applications. We anticipate that RESCRIPt will streamline the use, management, and evaluation/selection of reference database materials for microbiomics, diet metabarcoding, eDNA, and other diverse applications.

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.

Abstract 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 0.05). 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.

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.

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.

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.

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 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.

Abstract 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

Abstract 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.

ABSTRACT Studies of the intestinal microbiome commonly utilize stool samples to measure the microbial composition in the distal gut. However, collection of stool can be difficult from some subjects under certain experimental conditions. Sampling of fecal material using sterile swabs can streamline sample collection, handling, and processing. In this study, we validate the use of swabs of fecal matter to approximate measurements of microbiota in stool using 16S rRNA gene Illumina amplicon sequencing and evaluate the effects of shipping time at ambient temperatures on accuracy. The results indicate that swab samples reliably replicate the stool microbiota bacterial composition, alpha diversity, and beta diversity when swabs are processed quickly (≤2 days) but that sample quality quickly degrades after this period and is accompanied by increased abundances of Enterobacteriaceae. Fresh swabs appear to be a viable alternative to stool sampling when standard collection methods are challenging, but extended exposure to ambient temperatures prior to processing threatens sample integrity. IMPORTANCE Collection of fecal swab samples simplifies handling, processing, and archiving compared to collection of stool. This study confirms that fecal swabs reliably replicate the bacterial composition and diversity of stool samples, provided that the swabs are processed shortly after collection. These findings support the use of fecal swabs, when shipping and handling are done properly, to streamline measurements of intestinal microbiota.

ABSTRACT 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.

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.

ABSTRACT Regionally distinct wine characteristics (terroir) are an important aspect of wine production and consumer appreciation. Microbial activity is an integral part of wine production, and grape and wine microbiota present regionally defined patterns associated with vineyard and climatic conditions, but the degree to which these microbial patterns associate with the chemical composition of wine is unclear. Through a longitudinal survey of over 200 commercial wine fermentations, we demonstrate that both grape microbiota and wine metabolite profiles distinguish viticultural area designations and individual vineyards within Napa and Sonoma Counties, California. Associations among wine microbiota and fermentation characteristics suggest new links between microbiota, fermentation performance, and wine properties. The bacterial and fungal consortia of wine fermentations, composed from vineyard and winery sources, correlate with the chemical composition of the finished wines and predict metabolite abundances in finished wines using machine learning models. The use of postharvest microbiota as an early predictor of wine chemical composition is unprecedented and potentially poses a new paradigm for quality control of agricultural products. These findings add further evidence that microbial activity is associated with wine terroir. IMPORTANCE Wine production is a multi-billion-dollar global industry for which microbial control and wine chemical composition are crucial aspects of quality. Terroir is an important feature of consumer appreciation and wine culture, but the many factors that contribute to terroir are nebulous. We show that grape and wine microbiota exhibit regional patterns that correlate with wine chemical composition, suggesting that the grape microbiome may influence terroir. In addition to enriching our understanding of how growing region and wine properties interact, this may provide further economic incentive for agricultural and enological practices that maintain regional microbial biodiversity.

ABSTRACT 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.

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.

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.