Search

Importance: Feasibility of home urogenital microbiome specimen collection is unknown., Objectives: This study aimed to evaluate successful sample collection rates from home and clinical research centers., Study Design: Adult women participants enrolled in a multicentered cohort study were recruited to an in-person research center evaluation, including self-collected urogenital samples. A nested feasibility substudy evaluated home biospecimen collection prior to the scheduled in-person evaluation using a home collection kit with written instructions, sample collection supplies, and a Peezy™ urine collection device. Participants self-collected samples at home and shipped them to a central laboratory 1 day prior to and the day of the in-person evaluation. We defined successful collection as receipt of at least one urine specimen that was visibly viable for sequencing., Results: Of 156 participants invited to the feasibility substudy, 134 were enrolled and sent collection kits with 89% (119/134) returning at least 1 home urine specimen; the laboratory determined that 79% (106/134) of these urine samples were visually viable for analysis. The laboratory received self-collected urine from the research center visit in 97% (115/119); 76% (91/119) were visually viable for sequencing. Among 401 women who did not participate in the feasibility home collection substudy, 98% (394/401) self-collected urine at the research center with 80% (321/401) returned and visibly viable for sequencing., Conclusions: Home collection of urogenital microbiome samples for research is feasible, with comparable success to clinical research center collection. Sample size adjustment should plan for technical and logistical difficulties, regardless of specimen collection site., Competing Interests: Emily S. Lukacz is a scientific advisory board member and consultant for Pathnostics (2021 to present), receives royalties from UpToDate (2015 to present), is an advisory board member for Emmi Solutions (patient education material, 2023 to present), and works for Tegus.com (2023 to present). Furthermore, she is a consultant (study section) for NIDDK and received research funding from NIDDK, NICHD, and PCORI. Cynthia S. Fok received author royalty from UptoDate and is a consultant for UroCure and Medtronic. Melanie R. Meister is an advisory board member for AbbVie Inc. Margaret G. Mueller provides expert testimony for Butler Snow and received educational honoraria from Axonics and NIH funding (NIDDK and NICHD). Ariana L. Smith received research funding from Convatec and PA Department of State, is an associate editor for NAU, and is on the board of directors and a treasurer for SUFU. Ann Stapleton is a consultant for GSK. Daniel McDonald is a consultant for, and has equity in, BiomeSense, Inc. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. Rob Knight is a scientific advisory board member and consultant for BiomeSense, Inc., has equity, and receives income. He is a scientific advisory board member and has equity in GenCirq. He is a consultant and scientific advisory board member for DayTwo and receives income. He has equity in and acts as a consultant for Cybele. He is a cofounder of Biota, Inc. and has equity. He is a cofounder of Micronoma, has equity, and is a scientific advisory board member. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. Linda Brubaker received editorial stipends from JAMA and UptoDate. The rest of the authors reported no disclosures., (Copyright © 2024 American Urogynecologic Society. All rights reserved.)

Large-scale studies are essential to answer questions about complex microbial communities that can be extremely dynamic across hosts, environments, and time points. However, managing acquisition, processing, and analysis of large numbers of samples poses many challenges, with cross-contamination being the biggest obstacle. Contamination complicates analysis and results in sample loss, leading to higher costs and constraints on mixed sample type study designs. While many researchers opt for 96-well plates for their workflows, these plates present a significant issue: the shared seal and weak separation between wells leads to well-to-well contamination. To address this concern, we propose an innovative high-throughput approach, termed as the Matrix method, which employs barcoded Matrix Tubes for sample acquisition. This method is complemented by a paired nucleic acid and metabolite extraction, utilizing 95% (vol/vol) ethanol to stabilize microbial communities and as a solvent for extracting metabolites. Comparative analysis between conventional 96-well plate extractions and the Matrix method, measuring 16S rRNA gene levels via quantitative polymerase chain reaction, demonstrates a notable decrease in well-to-well contamination with the Matrix method. Metagenomics, 16S rRNA gene amplicon sequencing (16S), and untargeted metabolomics analysis via liquid chromatography-tandem mass spectrometry (LC-MS/MS) confirmed that the Matrix method recovers reproducible microbial and metabolite compositions that can distinguish between subjects. This advancement is critical for large-scale study design as it minimizes well-to-well contamination and technical variation, shortens processing times, and integrates with automated infrastructure for enhancing sample randomization and metadata generation., Importance: Understanding dynamic microbial communities typically requires large-scale studies. However, handling large numbers of samples introduces many challenges, with cross-contamination being a major issue. It not only complicates analysis but also leads to sample loss and increased costs and restricts diverse study designs. The prevalent use of 96-well plates for nucleic acid and metabolite extractions exacerbates this problem due to their wells having little separation and being connected by a single plate seal. To address this, we propose a new strategy using barcoded Matrix Tubes, showing a significant reduction in cross-contamination compared to conventional plate-based approaches. Additionally, this method facilitates the extraction of both nucleic acids and metabolites from a single tubed sample, eliminating the need to collect separate aliquots for each extraction. This innovation improves large-scale study design by shortening processing times, simplifying analysis, facilitating metadata curation, and producing more reliable results., Competing Interests: R.K. is a scientific advisory board member and consultant for BiomeSense, Inc., has equity, and receives income. He is a scientific advisory board member and has equity in GenCirq. He is a consultant and scientific advisory board member for DayTwo and receives income. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a cofounder of Micronoma, has equity, and is a scientific advisory board member. D.M. is a consultant for, and has stock in, BiomeSense, Inc. P.C.D. is an advisor and holds equity in Cybele, Sirenas, and BileOmix and is a scientific co-founder and advisor for and holds equity in Ometa, Enveda, and Arome with prior approval by University of California San Diego. P.C.D. also consulted for DSM animal health in 2023. The terms of these arrangements have been reviewed and approved by the University of California San Diego, in accordance with its conflict of interest policies.

High levels of social connectivity among group-living animals have been hypothesized to benefit individuals by creating opportunities to rapidly reseed the microbiome and maintain stability against disruption. We tested this hypothesis by perturbing the microbiome of a wild population of Grant's gazelles with an antibiotic and asking whether microbiome recovery differs between individuals with high versus low levels of social connectivity. We found that after treatment, individuals with high social connectivity experienced a faster increase in microbiome richness than less socially connected individuals. Unexpectedly, the rapid increase in microbiome richness of highly connected individuals that received treatment led to their microbiomes becoming more distinct relative to the background population. Our results suggest that the microbiome of individuals with high social connectivity can be rapidly recolonized after a perturbation event, but this leads to a microbiome that is more distinct from, rather than more similar to the unperturbed state. This work provides new insight into the role of social interactions in shaping the microbiome.

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology., (© 2024. The Author(s).)

Importance: The composition of the human vaginal microbiome has been linked to a variety of medical conditions including yeast infection, bacterial vaginosis, and sexually transmitted infection. The vaginal microbiome is becoming increasingly acknowledged as a key factor in personal health, and it is essential to establish methods to collect and process accurate samples with self-collection techniques to allow large, population-based studies. In this study, we investigate if using AssayAssure Genelock, a nucleic acid preservative, introduces microbial biases in self-collected vaginal samples. To our knowledge, we also contribute some of the first evidence regarding the impacts of multiple swabs taken at one time point. Vaginal samples have relatively low biomass, so the ability to collect multiple swabs from a unique participant at a single time would greatly improve the replicability and data available for future studies. This will hopefully lay the groundwork to gain a more complete and accurate understanding of the vaginal microbiome., Competing Interests: E.S.L. is a consultant and advisory board member for Pathnostics. L.B. receives editorial stipends from JAMA, Urogynecology, and Up to Date. R.K. owns stock in and is a scientific advisory board (SAB) member for Gencirq, is a consultant and SAB member for DayTwo, owns stock in and is a consultant for Cybele, owns stock in and is a consultant and SAB member for Biomesense, owns stock in and is an SAB member and co-founder of Micronoma, and owns stock in and is co-founder of Biota. D.M. owns stock in and is a consultant for Biomesense. All other authors declare no conflict of interest.

Replicability is a well-established challenge in microbiome research with a variety of contributing factors at all stages, from sample collection to code execution. Here, we focus on voided urine sample storage conditions for urogenital microbiome analysis. Using urine samples collected from 10 adult females, we investigated the microbiome preservation efficacy of AssayAssure Genelock (Genelock), compared with no preservative, under different temperature conditions. We varied temperature over 48 h in order to examine the impact of conditions samples may experience with home voided urine collection and shipping to a central biorepository. The following common lab and shipping conditions were investigated: -20°C, ambient temperature, 4°C, freeze-thaw cycle, and heat cycle. At 48 h, all samples were stored at -80°C until processing. After generating 16S rRNA gene amplicon sequencing data using the highly sensitive KatharoSeq protocol, we observed individual variation in both alpha and beta diversity metrics below interhuman differences, corroborating reports of individual microbiome variability in other specimen types. While there was no significant difference in beta diversity when comparing Genelock versus no preservative, we did observe a higher concordance with Genelock samples shipped at colder temperatures (-20°C and 4°C) when compared with the samples shipped at -20°C without preservative. Our results indicate that Genelock does not introduce a significant amount of microbial bias when used on a range of temperatures and is most effective at colder temperatures. IMPORTANCE The urogenital microbiome is an understudied yet important human microbiome niche. Research has been stimulated by the relatively recent discovery that urine is not sterile; urinary tract microbes have been linked to health problems, including urinary infections, incontinence, and cancer. The quality of life and economic impact of UTIs and urgency incontinence alone are enormous, with $3.5 billion and $82.6 billion, respectively, spent in the United States. annually. Given the low biomass of urine, novelty of the field, and limited reproducibility evidence, it is critical to study urine sample storage conditions to optimize scientific rigor. Efficient and reliable preservation methods inform methods for home self-sample collection and shipping, increasing the potential use in larger-scale studies. Here, we examined both buffer and temperature variation effects on 16S rRNA gene amplicon sequencing results from urogenital samples, providing data on the consequences of common storage methods on urogenital microbiome results.

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment., (© 2022. The Author(s).)

Assigning taxonomy remains a challenging topic in microbiome studies, due largely to ambiguity of reads which overlap multiple reference genomes. With the Web of Life (WoL) reference database hosting 10,575 reference genomes and growing, the percentage of ambiguous reads will only increase. The resulting artifacts create both the illusion of co-occurrence and a long tail end of extraneous reference hits that confound interpretation. We introduce genome cover, the fraction of reference genome overlapped by reads, to distinguish these artifacts. We show how to dynamically predict genome cover by read count and examine our model in Staphylococcus aureus monoculture. Our modeling cleanly separates both S. aureus and true contaminants from the false artifacts of reference overlap. We next introduce saturated genome cover, the true fraction of a reference genome overlapped by sample contents. Genome cover may not saturate for low abundance or low prevalence bacteria. We assuage this worry with examination of a large human fecal data set. By compositing the metric across like samples, genome cover saturates even for rare species. We note that it is a threshold on saturated genome cover, not genome cover itself, which indicates a spurious reference hit or distant relative. We present Zebra, a method to compute and threshold the genome cover metric across like samples, a recurrence to estimate genome cover and confirm saturation, and provide guidance for choosing cover thresholds in real world scenarios. Standalone genome cover and integration into Woltka are available: https://github.com/biocore/zebra_filter, https://github.com/qiyunzhu/woltka. IMPORTANCE Taxonomic assignment, assigning sequences to specific taxonomic units, is a crucial processing step in microbiome analyses. Issues in taxonomic assignment affect interpretation of what microbes are present in each sample and may be associated with specific environmental or clinical conditions. Assigning importance to a particular taxon relies strongly on independence of assigned counts. The false inclusion of thousands of correlated taxa makes interpretation ambiguous, leading to underconstrained results which cannot be reproduced. The importance sometimes attached to implausible artifacts such as anthrax or bubonic plague is especially problematic. We show that the Zebra filter retrieves only the nearest relatives of sample contents enabling more reproducible and biologically plausible interpretation of metagenomic data.

The first week after birth is a critical time for the establishment of microbial communities for infants. Preterm infants face unique environmental impacts on their newly acquired microbiomes, including increased incidence of cesarean section delivery and exposure to antibiotics as well as delayed enteral feeding and reduced human interaction during their intensive care unit stay. Using contextualized paired metabolomics and 16S sequencing data, the development of the gut, skin, and oral microbiomes of infants is profiled daily for the first week after birth, and it is found that the skin microbiome appears robust to early life perturbation, while direct exposure of infants to antibiotics, rather than presumed maternal transmission, delays microbiome development and prevents the early differentiation based on body site regardless of delivery mode. Metabolomic analyses identify the development of all gut metabolomes of preterm infants toward full-term infant profiles, but a significant increase of primary bile acid metabolism only in the non-antibiotic treated vaginally birthed late preterm infants. This study provides a framework for future multi-omic, multibody site analyses on these high-risk preterm infant populations and suggests opportunities for monitoring and intervention, with infant antibiotic exposure as the primary driver of delays in microbiome development., (© 2022 Wiley-VCH GmbH.)

Microbiome data have several specific characteristics (sparsity and compositionality) that introduce challenges in data analysis. The integration of prior information regarding the data structure, such as phylogenetic structure and repeated-measure study designs, into analysis, is an effective approach for revealing robust patterns in microbiome data. Past methods have addressed some but not all of these challenges and features: for example, robust principal-component analysis (RPCA) addresses sparsity and compositionality; compositional tensor factorization (CTF) addresses sparsity, compositionality, and repeated measure study designs; and UniFrac incorporates phylogenetic information. Here we introduce a strategy of incorporating phylogenetic information into RPCA and CTF. The resulting methods, phylo-RPCA, and phylo-CTF, provide substantial improvements over state-of-the-art methods in terms of discriminatory power of underlying clustering ranging from the mode of delivery to adult human lifestyle. We demonstrate quantitatively that the addition of phylogenetic information improves effect size and classification accuracy in both data-driven simulated data and real microbiome data. IMPORTANCE Microbiome data analysis can be difficult because of particular data features, some unavoidable and some due to technical limitations of DNA sequencing instruments. The first step in many analyses that ultimately reveals patterns of similarities and differences among sets of samples (e.g., separating samples from sick and healthy people or samples from seawater versus soil) is calculating the difference between each pair of samples. We introduce two new methods to calculate these differences that combine features of past methods, specifically being able to take into account the principles that most types of microbes are not in most samples (sparsity), that abundances are relative rather than absolute (compositionality), and that all microbes have a shared evolutionary history (phylogeny). We show using simulated and real data that our new methods provide improved classification accuracy of ordinal sample clusters and increased effect size between sample groups on beta-diversity distances.

Preterm infants are at a greater risk for the development of asthma and atopic disease, which can lead to lifelong negative health consequences. This may be due, in part, to alterations that occur in the gut microbiome and metabolome during their stay in the Neonatal Intensive Care Unit (NICU). To explore the differential roles of family history (i.e., predisposition due to maternal asthma diagnosis) and hospital-related environmental and clinical factors that alter microbial exposures early in life, we considered a unique cohort of preterm infants born ≤ 34 weeks gestational age from two local level III NICUs, as part of the MAP (Microbiome, Atopic disease, and Prematurity) Study. From MAP participants, we chose a sub-cohort of infants whose mothers had a history of asthma and matched gestational age and sex to infants of mothers without a history of asthma diagnosis (control). We performed a prospective, paired metagenomic and metabolomic analysis of stool and milk feed samples collected at birth, 2 weeks, and 6 weeks postnatal age. Although there were clinical factors associated with shifts in the diversity and composition of stool-associated bacterial communities, maternal asthma diagnosis did not play an observable role in shaping the infant gut microbiome during the study period. There were significant differences, however, in the metabolite profile between the maternal asthma and control groups at 6 weeks postnatal age. The most notable changes occurred in the linoleic acid spectral network, which plays a role in inflammatory and immune pathways, suggesting early metabolomic changes in the gut of preterm infants born to mothers with a history of asthma. Our pilot study suggests that a history of maternal asthma alters a preterm infants' metabolomic pathways in the gut, as early as the first 6 weeks of life., (© 2022. The Author(s).)

Background: Early microbiota perturbations are associated with disorders that involve immunological underpinnings. Cesarean section (CS)-born babies show altered microbiota development in relation to babies born vaginally. Here we present the first statistically powered longitudinal study to determine the effect of restoring exposure to maternal vaginal fluids after CS birth., Methods: Using 16S rRNA gene sequencing, we followed the microbial trajectories of multiple body sites in 177 babies over the first year of life; 98 were born vaginally, and 79 were born by CS, of whom 30 were swabbed with a maternal vaginal gauze right after birth., Findings: Compositional tensor factorization analysis confirmed that microbiota trajectories of exposed CS-born babies aligned more closely with that of vaginally born babies. Interestingly, the majority of amplicon sequence variants from maternal vaginal microbiomes on the day of birth were shared with other maternal sites, in contrast to non-pregnant women from the Human Microbiome Project (HMP) study., Conclusions: The results of this observational study prompt urgent randomized clinical trials to test whether microbial restoration reduces the increased disease risk associated with CS birth and the underlying mechanisms. It also provides evidence of the pluripotential nature of maternal vaginal fluids to provide pioneer bacterial colonizers for the newborn body sites. This is the first study showing long-term naturalization of the microbiota of CS-born infants by restoring microbial exposure at birth., Funding: C&D, Emch Fund, CIFAR, Chilean CONICYT and SOCHIPE, Norwegian Institute of Public Health, Emerald Foundation, NIH, National Institute of Justice, Janssen., Competing Interests: Declaration of interests New York University has a U.S. patent (10357521) on behalf of M.G.D.-B., related to methods for restoring the microbiota of newborns., (Copyright © 2021. Published by Elsevier Inc.)

Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions 1-10 , we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas 11 (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia-IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration.

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.

Microbes can be used effectively as trace evidence, at least in research settings. However, it is unknown whether skin microbiomes change prior to autopsy and, if so, whether these changes interfere with linking objects to decedents. The current study included microbiomes from 16 scenes of death in the City and County of Honolulu and tested whether objects at the scenes can be linked to individual decedents. Postmortem skin microbiomes were stable during repeated sampling up to 60 h postmortem and were similar to microbiomes of an antemortem population. Objects could be traced to decedents approximately 75% of the time, with smoking pipes and medical devices being especially accurate (100% match), house and car keys being poor (0%), and other objects like phones intermediate (~80%). These results show that microbes from objects at death scenes can be matched to individual decedents, opening up a new method of establishing associations and identifications., (© 2018 American Academy of Forensic Sciences.)

Hypothesis-driven research has led to many scientific advances, but hypotheses cannot be tested in isolation: rather, they require a framework of aggregated scientific knowledge to allow questions to be posed meaningfully. This framework is largely still lacking in microbiome studies, and the only way to create it is by discovery-driven, tool-driven, and standards-driven research projects. Here we illustrate these issues using several such non-hypothesis-driven projects from our own laboratories, including spatial mapping, the American Gut Project, the Earth Microbiome Project (which is an umbrella project integrating many smaller hypothesis-driven projects), and the knowledgebase-driven tools GNPS and Qiita. We argue that an investment of community resources in infrastructure tasks, and in the controls and standards that underpin them, will greatly enhance the investment in hypothesis-driven research programs., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The trillions of microbes living in the gut-the gut microbiota-play an important role in human biology and disease. While much has been done to explore its diversity, a full understanding of our microbiomes demands an evolutionary perspective. In this review, we compare microbiomes from human populations, placing them in the context of microbes from humanity's near and distant animal relatives. We discuss potential mechanisms to generate host-specific microbiome configurations and the consequences of disrupting those configurations. Finally, we propose that this broader phylogenetic perspective is useful for understanding the mechanisms underlying human-microbiome interactions.

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.

As pollinators, bees are cornerstones for terrestrial ecosystem stability and key components in agricultural productivity. All animals, including bees, are associated with a diverse community of microbes, commonly referred to as the microbiome. The bee microbiome is likely to be a crucial factor affecting host health. However, with the exception of a few pathogens, the impacts of most members of the bee microbiome on host health are poorly understood. Further, the evolutionary and ecological forces that shape and change the microbiome are unclear. Here, we discuss recent progress in our understanding of the bee microbiome, and we present challenges associated with its investigation. We conclude that global coordination of research efforts is needed to fully understand the complex and highly dynamic nature of the interplay between the bee microbiome, its host, and the environment. High-throughput sequencing technologies are ideal for exploring complex biological systems, including host-microbe interactions. To maximize their value and to improve assessment of the factors affecting bee health, sequence data should be archived, curated, and analyzed in ways that promote the synthesis of different studies. To this end, the BeeBiome consortium aims to develop an online database which would provide reference sequences, archive metadata, and host analytical resources. The goal would be to support applied and fundamental research on bees and their associated microbes and to provide a collaborative framework for sharing primary data from different research programs, thus furthering our understanding of the bee microbiome and its impact on pollinator health., (Copyright © 2016 Engel et al.)

Exposure of newborns to the maternal vaginal microbiota is interrupted with cesarean birthing. Babies delivered by cesarean section (C-section) acquire a microbiota that differs from that of vaginally delivered infants, and C-section delivery has been associated with increased risk for immune and metabolic disorders. Here we conducted a pilot study in which infants delivered by C-section were exposed to maternal vaginal fluids at birth. Similarly to vaginally delivered babies, the gut, oral and skin bacterial communities of these newborns during the first 30 d of life was enriched in vaginal bacteria--which were underrepresented in unexposed C-section-delivered infants--and the microbiome similarity to those of vaginally delivered infants was greater in oral and skin samples than in anal samples. Although the long-term health consequences of restoring the microbiota of C-section-delivered infants remain unclear, our results demonstrate that vaginal microbes can be partially restored at birth in C-section-delivered babies.

Westernization has propelled changes in urbanization and architecture, altering our exposure to the outdoor environment from that experienced during most of human evolution. These changes might affect the developmental exposure of infants to bacteria, immune development, and human microbiome diversity. Contemporary urban humans spend most of their time indoors, and little is known about the microbes associated with different designs of the built environment and their interaction with the human immune system. This study addresses the associations between architectural design and the microbial biogeography of households across a gradient of urbanization in South America. Urbanization was associated with households' increased isolation from outdoor environments, with additional indoor space isolation by walls. Microbes from house walls and floors segregate by location, and urban indoor walls contain human bacterial markers of space use. Urbanized spaces uniquely increase the content of human-associated microbes-which could increase transmission of potential pathogens-and decrease exposure to the environmental microbes with which humans have coevolved.

A recent report warns that DNA extraction kits and other laboratory reagents are considerable sources of contamination in microbiome experiments. The issue of contamination is particularly problematic for samples of low biomass.

Inflammatory bowel diseases (IBDs), including Crohn's disease (CD), are genetically linked to host pathways that implicate an underlying role for aberrant immune responses to intestinal microbiota. However, patterns of gut microbiome dysbiosis in IBD patients are inconsistent among published studies. Using samples from multiple gastrointestinal locations collected prior to treatment in new-onset cases, we studied the microbiome in the largest pediatric CD cohort to date. An axis defined by an increased abundance in bacteria which include Enterobacteriaceae, Pasteurellacaea, Veillonellaceae, and Fusobacteriaceae, and decreased abundance in Erysipelotrichales, Bacteroidales, and Clostridiales, correlates strongly with disease status. Microbiome comparison between CD patients with and without antibiotic exposure indicates that antibiotic use amplifies the microbial dysbiosis associated with CD. Comparing the microbial signatures between the ileum, the rectum, and fecal samples indicates that at this early stage of disease, assessing the rectal mucosal-associated microbiome offers unique potential for convenient and early diagnosis of CD., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Mammals have diversified into many dietary niches. Specialized myrmecophagous (ant- and termite-eating) placental mammals represent a textbook example of evolutionary convergence driven by extreme diet specialization. Armadillos, anteaters, aardvarks, pangolins and aardwolves thus provide a model system for understanding the potential role of gut microbiota in the convergent adaptation to myrmecophagy. Here, we expand upon previous mammalian gut microbiome studies by using high-throughput barcoded Illumina sequencing of the 16S rRNA gene to characterize the composition of gut microbiota in 15 species representing all placental myrmecophagous lineages and their close relatives from zoo- and field-collected samples. We confirm that both diet and phylogeny drive the evolution of mammalian gut microbiota, with cases of convergence in global composition, but also examples of phylogenetic inertia. Our results reveal specialized placental myrmecophages as a spectacular case of large-scale convergence in gut microbiome composition. Indeed, neighbour-net networks and beta-diversity plots based on UniFrac distances show significant clustering of myrmecophagous species (anteaters, aardvarks and aardwolves), even though they belong to phylogenetically distant lineages representing different orders. The aardwolf, which diverged from carnivorous hyenas only in the last 10 million years, experienced a convergent shift in the composition of its gut microbiome to become more similar to other myrmecophages. These results confirm diet adaptation to be a major driving factor of convergence in gut microbiome composition over evolutionary timescales. This study sets the scene for future metagenomic studies aiming at evaluating potential convergence in functional gene content in the microbiomes of specialized mammalian myrmecophages., (© 2013 John Wiley & Sons Ltd.)

Human-associated microbial communities vary across individuals: possible contributing factors include (genetic) relatedness, diet, and age. However, our surroundings, including individuals with whom we interact, also likely shape our microbial communities. To quantify this microbial exchange, we surveyed fecal, oral, and skin microbiota from 60 families (spousal units with children, dogs, both, or neither). Household members, particularly couples, shared more of their microbiota than individuals from different households, with stronger effects of co-habitation on skin than oral or fecal microbiota. Dog ownership significantly increased the shared skin microbiota in cohabiting adults, and dog-owning adults shared more 'skin' microbiota with their own dogs than with other dogs. Although the degree to which these shared microbes have a true niche on the human body, vs transient detection after direct contact, is unknown, these results suggest that direct and frequent contact with our cohabitants may significantly shape the composition of our microbial communities. DOI:http://dx.doi.org/10.7554/eLife.00458.001.

High-throughput DNA sequencing technologies, coupled with advanced bioinformatics tools, have enabled rapid advances in microbial ecology and our understanding of the human microbiome. QIIME (Quantitative Insights Into Microbial Ecology) is an open-source bioinformatics software package designed for microbial community analysis based on DNA sequence data, which provides a single analysis framework for analysis of raw sequence data through publication-quality statistical analyses and interactive visualizations. In this chapter, we demonstrate the use of the QIIME pipeline to analyze microbial communities obtained from several sites on the bodies of transgenic and wild-type mice, as assessed using 16S rRNA gene sequences generated on the Illumina MiSeq platform. We present our recommended pipeline for performing microbial community analysis and provide guidelines for making critical choices in the process. We present examples of some of the types of analyses that are enabled by QIIME and discuss how other tools, such as phyloseq and R, can be applied to expand upon these analyses., (© 2013 Elsevier Inc. All rights reserved.)

High levels of social connectivity among group-living animals have been hypothesized to benefit individuals by creating opportunities to rapidly reseed the microbiome and maintain stability against disruption. We tested this hypothesis by perturbing the microbiome of a wild population of Grants gazelles with an antibiotic and asking whether microbiome recovery differs between individuals with high versus low levels of social connectivity. We found that after treatment, individuals with high social connectivity experienced a faster increase in microbiome richness than less socially connected individuals. Unexpectedly, the rapid increase in microbiome richness of highly connected individuals that received treatment led to their microbiomes becoming more distinct relative to the background population. Our results suggest that the microbiome of individuals with high social connectivity can be rapidly recolonized after a perturbation event, but this leads to a microbiome that is more distinct from, rather than more similar to the unperturbed state. This work provides new insight into the role of social interactions in shaping the microbiome.

Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions1-10, we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas11 (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia-IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration.

Urbanization represents a profound shift in human behaviour, and has considerable cultural and health-associated consequences1,2. Here, we investigate chemical and microbial characteristics of houses and their human occupants across an urbanization gradient in the Amazon rainforest, from a remote Peruvian Amerindian village to the Brazilian city of Manaus. Urbanization was found to be associated with reduced microbial outdoor exposure, increased contact with housing materials, antimicrobials and cleaning products, and increased exposure to chemical diversity. The degree of urbanization correlated with changes in the composition of house bacterial and microeukaryotic communities, increased house and skin fungal diversity, and an increase in the relative abundance of human skin-associated fungi and bacteria in houses. Overall, our results indicate that urbanization has large-scale effects on chemical and microbial exposures and on the human microbiota.