DADA2: High-resolution sample inference from Illumina amplicon data

Wild Mouse Gut Microbiota Promotes Host Fitness and Improves Disease Resistance

Laboratory mice, while paramount for understanding basic biological phenomena, are limited in modeling complex diseases of humans and other free-living mammals. Because the microbiome is a major factor in mammalian physiology, we aimed to identify a naturally evolved reference microbiome to better recapitulate physiological phenomena relevant in the natural world outside the laboratory. Among 21 distinct mouse populations worldwide, we identified a closely related wild relative to standard laboratory mouse strains. Its bacterial gut microbiome differed significantly from its laboratory mouse counterpart and was transferred to and maintained in laboratory mice over several generations. Laboratory mice reconstituted with natural microbiota exhibited reduced inflammation and increased survival following influenza virus infection and improved resistance against mutagen/inflammation-induced colorectal tumorigenesis. By demonstrating the host fitness-promoting traits of natural microbiota, our findings should enable the discovery of protective mechanisms relevant in the natural world and improve the modeling of complex diseases of free-living mammals. VIDEO ABSTRACT.

Preparing for the crewed Mars journey: microbiota dynamics in the confined Mars500 habitat during simulated Mars flight and landing

BACKGROUND

The Mars500 project was conceived as the first full duration simulation of a crewed return flight to Mars. For 520 days, six crew members lived confined in a specifically designed spacecraft mock-up. The herein described "MIcrobial ecology of Confined Habitats and humAn health" (MICHA) experiment was implemented to acquire comprehensive microbiota data from this unique, confined manned habitat, to retrieve important information on the occurring microbiota dynamics, the microbial load and diversity in the air and on various surfaces. In total, 360 samples from 20 (9 air, 11 surface) locations were taken at 18 time-points and processed by extensive cultivation, PhyloChip and next generation sequencing (NGS) of 16S rRNA gene amplicons.

RESULTS

Cultivation assays revealed a Staphylococcus and Bacillus-dominated microbial community on various surfaces, with an average microbial load that did not exceed the allowed limits for ISS in-flight requirements indicating adequate maintenance of the facility. Areas with high human activity were identified as hotspots for microbial accumulation. Despite substantial fluctuation with respect to microbial diversity and abundance throughout the experiment, the location within the facility and the confinement duration were identified as factors significantly shaping the microbial diversity and composition, with the crew representing the main source for microbial dispersal. Opportunistic pathogens, stress-tolerant or potentially mobile element-bearing microorganisms were predicted to be prevalent throughout the confinement, while the overall microbial diversity dropped significantly over time.

CONCLUSIONS

Our findings clearly indicate that under confined conditions, the community structure remains a highly dynamic system which adapts to the prevailing habitat and micro-conditions. Since a sterile environment is not achievable, these dynamics need to be monitored to avoid spreading of highly resistant or potentially pathogenic microorganisms and a potentially harmful decrease of microbial diversity. If necessary, countermeasures are required, to maintain a healthy, diverse balance of beneficial, neutral and opportunistic pathogenic microorganisms. Our results serve as an important data collection for (i) future risk estimations of crewed space flight, (ii) an optimized design and planning of a spacecraft mission and (iii) for the selection of appropriate microbial monitoring approaches and potential countermeasures, to ensure a microbiologically safe space-flight environment.

Accuracy of microbial community diversity estimated by closed- and open-reference OTUs

Next-generation sequencing of 16S ribosomal RNA is widely used to survey microbial communities. Sequences are typically assigned to Operational Taxonomic Units (OTUs). Closed- and open-reference OTU assignment matches reads to a reference database at 97% identity (closed), then clusters unmatched reads using a de novo method (open). Implementations of these methods in the QIIME package were tested on several mock community datasets with 20 strains using different sequencing technologies and primers. Richness (number of reported OTUs) was often greatly exaggerated, with hundreds or thousands of OTUs generated on Illumina datasets. Between-sample diversity was also found to be highly exaggerated in many cases, with weighted Jaccard distances between identical mock samples often close to one, indicating very low similarity. Non-overlapping hyper-variable regions in 70% of species were assigned to different OTUs. On mock communities with Illumina V4 reads, 56% to 88% of predicted genus names were false positives. Biological inferences obtained using these methods are therefore not reliable.

A clinician's guide to microbiome analysis

Microbiome analysis involves determining the composition and function of a community of microorganisms in a particular location. For the gastroenterologist, this technology opens up a rapidly evolving set of challenges and opportunities for generating novel insights into the health of patients on the basis of microbiota characterizations from intestinal, hepatic or extraintestinal samples. Alterations in gut microbiota composition correlate with intestinal and extraintestinal disease and, although only a few mechanisms are known, the microbiota are still an attractive target for developing biomarkers for disease detection and management as well as potential therapeutic applications. In this Review, we summarize the major decision points confronting new entrants to the field or for those designing new projects in microbiome research. We provide recommendations based on current technology options and our experience of sequencing platform choices. We also offer perspectives on future applications of microbiome research, which we hope convey the promise of this technology for clinical applications.

Ananke: temporal clustering reveals ecological dynamics of microbial communities

Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke.

A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome

Background

Advancements in Next Generation Sequencing (NGS) technologies regarding throughput, read length and accuracy had a major impact on microbiome research by significantly improving 16S rRNA amplicon sequencing. As rapid improvements in sequencing platforms and new data analysis pipelines are introduced, it is essential to evaluate their capabilities in specific applications. The aim of this study was to assess whether the same project-specific biological conclusions regarding microbiome composition could be reached using different sequencing platforms and bioinformatics pipelines.

Results

Chicken cecum microbiome was analyzed by 16S rRNA amplicon sequencing using Illumina MiSeq, Ion Torrent PGM, and Roche 454 GS FLX Titanium platforms, with standard and modified protocols for library preparation. We labeled the bioinformatics pipelines included in our analysis QIIME1 and QIIME2 (de novo OTU picking [not to be confused with QIIME version 2 commonly referred to as QIIME2]), QIIME3 and QIIME4 (open reference OTU picking), UPARSE1 and UPARSE2 (each pair differs only in the use of chimera depletion methods), and DADA2 (for Illumina data only). GS FLX+ yielded the longest reads and highest quality scores, while MiSeq generated the largest number of reads after quality filtering. Declines in quality scores were observed starting at bases 150–199 for GS FLX+ and bases 90–99 for MiSeq. Scores were stable for PGM-generated data. Overall microbiome compositional profiles were comparable between platforms; however, average relative abundance of specific taxa varied depending on sequencing platform, library preparation method, and bioinformatics analysis. Specifically, QIIME with de novo OTU picking yielded the highest number of unique species and alpha diversity was reduced with UPARSE and DADA2 compared to QIIME.

Conclusions

The three platforms compared in this study were capable of discriminating samples by treatment, despite differences in diversity and abundance, leading to similar biological conclusions. Our results demonstrate that while there were differences in depth of coverage and phylogenetic diversity, all workflows revealed comparable treatment effects on microbial diversity. To increase reproducibility and reliability and to retain consistency between similar studies, it is important to consider the impact on data quality and relative abundance of taxa when selecting NGS platforms and analysis tools for microbiome studies.

Electronic supplementary material

The online version of this article (10.1186/s12866-017-1101-8) contains supplementary material, which is available to authorized users.

The salivary microbiome is consistent between subjects and resistant to impacts of short-term hospitalization

In recent years, a growing amount of research has begun to focus on the oral microbiome due to its links with health and systemic disease. The oral microbiome has numerous advantages that make it particularly useful for clinical studies, including non-invasive collection, temporal stability, and lower complexity relative to other niches, such as the gut. Despite recent discoveries made in this area, it is unknown how the oral microbiome responds to short-term hospitalization. Previous studies have demonstrated that the gut microbiome is extremely sensitive to short-term hospitalization and that these changes are associated with significant morbidity and mortality. Here, we present a comprehensive pipeline for reliable bedside collection, sequencing, and analysis of the human salivary microbiome. We also develop a novel oral-specific mock community for pipeline validation. Using our methodology, we analyzed the salivary microbiomes of patients before and during hospitalization or azithromycin treatment to profile impacts on this community. Our findings indicate that azithromycin alters the diversity and taxonomic composition of the salivary microbiome; however, we also found that short-term hospitalization does not impact the richness or structure of this community, suggesting that the oral cavity may be less susceptible to dysbiosis during short-term hospitalization.

Community-like genome in single cells of the sulfur bacterium Achromatium oxaliferum

Polyploid bacteria are common, but the genetic and functional diversity resulting from polyploidy is unknown. Here we use single-cell genomics, metagenomics, single-cell amplicon sequencing, and fluorescence in situ hybridization, to show that individual cells of Achromatium oxaliferum, the world’s biggest known freshwater bacterium, harbor genetic diversity typical of whole bacterial communities. The cells contain tens of transposable elements, which likely cause the unprecedented diversity that we observe in the sequence and synteny of genes. Given the high within-cell diversity of the usually conserved 16S ribosomal RNA gene, we suggest that gene conversion occurs in multiple, separated genomic hotspots. The ribosomal RNA distribution inside the cells hints to spatially differential gene expression. We also suggest that intracellular gene transfer may lead to extensive gene reshuffling and increased diversity.

The cells of Achromatium bacteria are remarkably large and contain multiple chromosome copies. Here, Ionescu et al. show that chromosome copies within individual cells display high diversity, similar to that of bacterial communities, and contain tens of transposable elements.

Analysing Microbial Community Composition through Amplicon Sequencing: From Sampling to Hypothesis Testing

Microbial ecology as a scientific field is fundamentally driven by technological advance. The past decade's revolution in DNA sequencing cost and throughput has made it possible for most research groups to map microbial community composition in environments of interest. However, the computational and statistical methodology required to analyse this kind of data is often not part of the biologist training. In this review, we give a historical perspective on the use of sequencing data in microbial ecology and restate the current need for this method; but also highlight the major caveats with standard practices for handling these data, from sample collection and library preparation to statistical analysis. Further, we outline the main new analytical tools that have been developed in the past few years to bypass these caveats, as well as highlight the major requirements of common statistical practices and the extent to which they are applicable to microbial data. Besides delving into the meaning of select alpha- and beta-diversity measures, we give special consideration to techniques for finding the main drivers of community dissimilarity and for interaction network construction. While every project design has specific needs, this review should serve as a starting point for considering what options are available.

Replication and refinement of a vaginal microbial signature of preterm birth in two racially distinct cohorts of US women

Preterm birth (PTB) is the leading cause of neonatal morbidity and mortality. Previous studies have suggested that the maternal vaginal microbiota contributes to the pathophysiology of PTB, but conflicting results in recent years have raised doubts. We conducted a study of PTB compared with term birth in two cohorts of pregnant women: one predominantly Caucasian (n = 39) at low risk for PTB, the second predominantly African American and at high-risk (n = 96). We profiled the taxonomic composition of 2,179 vaginal swabs collected prospectively and weekly during gestation using 16S rRNA gene sequencing. Previously proposed associations between PTB and lower Lactobacillus and higher Gardnerella abundances replicated in the low-risk cohort, but not in the high-risk cohort. High-resolution bioinformatics enabled taxonomic assignment to the species and subspecies levels, revealing that Lactobacillus crispatus was associated with low risk of PTB in both cohorts, while Lactobacillus iners was not, and that a subspecies clade of Gardnerella vaginalis explained the genus association with PTB. Patterns of cooccurrence between L. crispatus and Gardnerella were highly exclusive, while Gardnerella and L. iners often coexisted at high frequencies. We argue that the vaginal microbiota is better represented by the quantitative frequencies of these key taxa than by classifying communities into five community state types. Our findings extend and corroborate the association between the vaginal microbiota and PTB, demonstrate the benefits of high-resolution statistical bioinformatics in clinical microbiome studies, and suggest that previous conflicting results may reflect the different risk profile of women of black race.

Genetic influences on the human oral microbiome

BACKGROUND

The human oral microbiome is formed early in development. Its composition is influenced by environmental factors including diet, substance use, oral health, and overall health and disease. The influence of human genes on the composition and stability of the oral microbiome is still poorly understood. We studied both environmental and genetic characteristics on the oral microbiome in a large twin sample as well as in a large cohort of unrelated individuals. We identify several significantly heritable features of the oral microbiome. The heritability persists in twins even when their cohabitation changes. The heritability of these traits correlates with the cumulative genetic contributions of over half a million single nucleotide sequence variants measured in a different population of unrelated individuals. Comparison of same-sex and opposite sex cotwins showed no significant differences. We show that two new loci on chromosomes 7 and 12 are associated with the most heritable traits.

RESULTS

An analysis of 752 twin pairs from the Colorado Twin Registry, shows that the beta-diversity of monozygotic twins is significantly lower than for dizygotic or unrelated individuals. This is independent of cohabitation status. Intraclass correlation coefficients of nearly all taxa examined were higher for MZ than DZ twin pairs. A comparison of individuals sampled over 2-7 years confirmed previous reports that the oral microbiome remains relatively more stable in individuals over that time than to unrelated people. Twin modeling shows that a number of microbiome phenotypes were more than 50% heritable consistent with the hypothesis that human genes influence microbial populations. To identify loci that could influence microbiome phenotypes, we carried out an unbiased GWAS analysis which identified one locus on chromosome 7 near the gene IMMPL2 that reached genome-wide significance after correcting for multiple testing. Another locus on chromosome 12 near the non-coding RNA gene INHBA-AS1 achieved genome-wide significance when analyzed using KGG4 that sums SNP significance across coding genes.

DISCUSSION

Using multiple methods, we have demonstrated that some aspects of the human oral microbiome are heritable and that with a relatively small sample we were able to identify two previously unidentified loci that may be involved.

Upper and lower respiratory tract microbiota in horses: bacterial communities associated with health and mild asthma (inflammatory airway disease) and effects of dexamethasone

Background

The microbial composition of the equine respiratory tract, and differences due to mild equine asthma (also called Inflammatory Airway Disease (IAD)) have not been reported. The primary treatment for control of IAD in horses are corticosteroids. The objectives were to characterize the upper and lower respiratory tract microbiota associated with respiratory health and IAD, and to investigate the effects of dexamethasone on these bacterial communities using high throughput sequencing.

Results

The respiratory microbiota of horses was dominated by four major phyla, Proteobacteria (43.85%), Actinobacteria (21.63%), Firmicutes (16.82%), and Bacteroidetes (13.24%). Fifty genera had a relative abundance > 0.1%, with Sphingomonas and Pantoea being the most abundant. The upper and lower respiratory tract microbiota differed in healthy horses, with a decrease in richness in the lower airways, and 2 OTUs that differed in abundance. There was a separation between bacterial communities in the lower respiratory tract of healthy and IAD horses; 6 OTUs in the tracheal community had different abundance with disease status, with Streptococcus being increased in IAD horses. Treatment with dexamethasone had an effect on the lower respiratory tract microbiota of both heathy and IAD horses, with 8 OTUs increasing in abundance (including Streptococcus) and 1 OTU decreasing.

Conclusions

The lower respiratory tract microbiota differed between healthy and IAD horses. Further research on the role of Streptococcus in IAD is warranted. Dexamethasone treatment affected the lower respiratory tract microbiota, which suggests that control of bacterial overgrowth in IAD horses treated with dexamethasone could be part of the treatment strategy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-017-1092-5) contains supplementary material, which is available to authorized users.

High-Resolution Microbiome Profiling for Detection and Tracking of Salmonella enterica

16S rRNA community profiling continues to be a useful tool to study microbiome composition and dynamics, in part due to advances in next generation sequencing technology that translate into reductions in cost. Reliable taxonomic identification to the species-level, however, remains difficult, especially for short-read sequencing platforms, due to incomplete coverage of the 16S rRNA gene. This is especially true for Salmonella enterica, which is often found as a low abundant member of the microbial community, and is often found in combination with several other closely related enteric species. Here, we report on the evaluation and application of Resphera Insight, an ultra-high resolution taxonomic assignment algorithm for 16S rRNA sequences to the species level. The analytical pipeline achieved 99.7% sensitivity to correctly identify S. enterica from WGS datasets extracted from the FDA GenomeTrakr Bioproject, while demonstrating 99.9% specificity over other Enterobacteriaceae members. From low-diversity and low-complexity samples, namely ice cream, the algorithm achieved 100% specificity and sensitivity for Salmonella detection. As demonstrated using cilantro and chili powder, for highly complex and diverse samples, especially those that contain closely related species, the detection threshold will likely have to be adjusted higher to account for misidentifications. We also demonstrate the utility of this approach to detect Salmonella in the clinical setting, in this case, bloodborne infections.

Secondary compound hypothesis revisited: Selected plant secondary metabolites promote bacterial degradation of cis-1,2-dichloroethylene (cDCE)

Cis-1,2-dichloroethylene (cDCE), which is a common hazardous compound, often accumulates during incomplete reductive dechlorination of higher chlorinated ethenes (CEs) at contaminated sites. Simple monoaromatics, such as toluene and phenol, have been proven to induce biotransformation of cDCE in microbial communities incapable of cDCE degradation in the absence of other carbon sources. The goal of this microcosm-based laboratory study was to discover non-toxic natural monoaromatic secondary plant metabolites (SPMEs) that could enhance cDCE degradation in a similar manner to toluene and phenol. Eight SPMEs were selected on the basis of their monoaromatic molecular structure and widespread occurrence in nature. The suitability of the SPMEs chosen to support bacterial growth and to promote cDCE degradation was evaluated in aerobic microbial cultures enriched from cDCE-contaminated soil in the presence of each SPME tested and cDCE. Significant cDCE depletions were achieved in cultures enriched on acetophenone, phenethyl alcohol, p-hydroxybenzoic acid and trans-cinnamic acid. 16S rRNA gene sequence analysis of each microbial community revealed ubiquitous enrichment of bacteria affiliated with the genera Cupriavidus, Rhodococcus, Burkholderia, Acinetobacter and Pseudomonas. Our results provide further confirmation of the previously stated secondary compound hypothesis that plant metabolites released into the rhizosphere can trigger biodegradation of environmental pollutants, including cDCE.

Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment

Our work focuses on the stability, resilience, and response to perturbation of the bacterial communities in the human gut. Informative flash flood-like disturbances that eliminate most gastrointestinal biomass can be induced using a clinically-relevant iso-osmotic agent. We designed and executed such a disturbance in human volunteers using a dense longitudinal sampling scheme extending before and after induced diarrhea. This experiment has enabled a careful multidomain analysis of a controlled perturbation of the human gut microbiota with a new level of resolution. These new longitudinal multidomain data were analyzed using recently developed statistical methods that demonstrate improvements over current practices. By imposing sparsity constraints we have enhanced the interpretability of the analyses and by employing a new adaptive generalized principal components analysis, incorporated modulated phylogenetic information and enhanced interpretation through scoring of the portions of the tree most influenced by the perturbation. Our analyses leverage the taxa-sample duality in the data to show how the gut microbiota recovers following this perturbation. Through a holistic approach that integrates phylogenetic, metagenomic and abundance information, we elucidate patterns of taxonomic and functional change that characterize the community recovery process across individuals. We provide complete code and illustrations of new sparse statistical methods for high-dimensional, longitudinal multidomain data that provide greater interpretability than existing methods.

Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course

Cyanobacterial blooms occur in lakes worldwide, producing toxins that pose a serious public health threat. Eutrophication caused by human activities and warmer temperatures both contribute to blooms, but it is still difficult to predict precisely when and where blooms will occur. One reason that prediction is so difficult is that blooms can be caused by different species or genera of cyanobacteria, which may interact with other bacteria and respond to a variety of environmental cues. Here we used a deep 16S amplicon sequencing approach to profile the bacterial community in eutrophic Lake Champlain over time, to characterise the composition and repeatability of cyanobacterial blooms, and to determine the potential for blooms to be predicted based on time course sequence data. Our analysis, based on 135 samples between 2006 and 2013, spans multiple bloom events. We found that bloom events significantly alter the bacterial community without reducing overall diversity, suggesting that a distinct microbial community-including non-cyanobacteria-prospers during the bloom. We also observed that the community changes cyclically over the course of a year, with a repeatable pattern from year to year. This suggests that, in principle, bloom events are predictable. We used probabilistic assemblages of OTUs to characterise the bloom-associated community, and to classify samples into bloom or non-bloom categories, achieving up to 92% classification accuracy (86% after excluding cyanobacterial sequences). Finally, using symbolic regression, we were able to predict the start date of a bloom with 78-92% accuracy (depending on the data used for model training), and found that sequence data was a better predictor than environmental variables.

Exact sequence variants should replace operational taxonomic units in marker-gene data analysis

Recent advances have made it possible to analyze high-throughput marker-gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently such that amplicon sequence variants (ASVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced gene region. The benefits of finer resolution are immediately apparent, and arguments for ASV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits that derive from the status of ASVs as consistent labels with intrinsic biological meaning identified independently from a reference database. Here we discuss how these features grant ASVs the combined advantages of closed-reference OTUs—including computational costs that scale linearly with study size, simple merging between independently processed data sets, and forward prediction—and of de novo OTUs—including accurate measurement of diversity and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that ASVs should replace OTUs as the standard unit of marker-gene analysis and reporting.

Eelgrass Leaf Surface Microbiomes Are Locally Variable and Highly Correlated with Epibiotic Eukaryotes

Eelgrass (Zostera marina) is a marine foundation species essential for coastal ecosystem services around the northern hemisphere. Like all macroscopic organisms, it possesses a microbiome (here defined as an associated prokaryotic community) which may play critical roles in modulating the interaction of eelgrass with its environment. For example, its leaf surface microbiome could inhibit or attract eukaryotic epibionts which may overgrow the eelgrass leading to reduced primary productivity and subsequent eelgrass meadow decline. We used amplicon sequencing of the 16S and 18S rRNA genes of prokaryotes and eukaryotes to assess the leaf surface microbiome (prokaryotes) as well as eukaryotic epibionts in- and outside lagoons on the German Baltic Sea coast. Prokaryote microbiomes varied substantially both between sites inside lagoons and between open coastal and lagoon sites. Water depth, leaf area and biofilm chlorophyll a concentration explained a large amount of variation in both prokaryotic and eukaryotic community composition. The prokaryotic microbiome and eukaryotic epibiont communities were highly correlated, and network analysis revealed disproportionate co-occurrence between a limited number of eukaryotic taxa and several bacterial taxa. This suggests that eelgrass leaf surfaces are home to a mosaic of microbiomes of several epibiotic eukaryotes, in addition to the microbiome of the eelgrass itself. Our findings thereby underline that eukaryotic diversity should be taken into account in order to explain prokaryotic microbiome assembly and dynamics in aquatic environments.

Intestinal virome changes precede autoimmunity in type I diabetes-susceptible children

Viruses have long been considered potential triggers of autoimmune diseases. Here we defined the intestinal virome from birth to the development of autoimmunity in children at risk for type 1 diabetes (T1D). A total of 220 virus-enriched preparations from serially collected fecal samples from 11 children (cases) who developed serum autoantibodies associated with T1D (of whom five developed clinical T1D) were compared with samples from controls. Intestinal viromes of case subjects were less diverse than those of controls. Among eukaryotic viruses, we identified significant enrichment of Circoviridae-related sequences in samples from controls in comparison with cases. Enterovirus, kobuvirus, parechovirus, parvovirus, and rotavirus sequences were frequently detected but were not associated with autoimmunity. For bacteriophages, we found higher Shannon diversity and richness in controls compared with cases and observed that changes in the intestinal virome over time differed between cases and controls. Using Random Forests analysis, we identified disease-associated viral bacteriophage contigs after subtraction of age-associated contigs. These disease-associated contigs were statistically linked to specific components of the bacterial microbiome. Thus, changes in the intestinal virome preceded autoimmunity in this cohort. Specific components of the virome were both directly and inversely associated with the development of human autoimmune disease.

Reconciliation between operational taxonomic units and species boundaries

The development of high-throughput sequencing technologies has revolutionised the field of microbial ecology via 16S rRNA gene amplicon sequencing approaches. Clustering those amplicon sequencing reads into operational taxonomic units (OTUs) using a fixed cut-off is a commonly used approach to estimate microbial diversity. A 97% threshold was chosen with the intended purpose that resulting OTUs could be interpreted as a proxy for bacterial species. Our results show that the robustness of such a generalised cut-off is questionable when applied to short amplicons only covering one or two variable regions of the 16S rRNA gene. It will lead to biases in diversity metrics and makes it hard to compare results obtained with amplicons derived with different primer sets. The method introduced within this work takes into account the differential evolutional rates of taxonomic lineages in order to define a dynamic and taxonomic-dependent OTU clustering cut-off score. For a taxonomic family consisting of species showing high evolutionary conservation in the amplified variable regions, the cut-off will be more stringent than 97%. By taking into consideration the amplified variable regions and the taxonomic family when defining this cut-off, such a threshold will lead to more robust results and closer correspondence between OTUs and species. This approach has been implemented in a publicly available software package called DynamiC.

The Intestinal Eukaryotic and Bacterial Biome of Spotted Hyenas: The Impact of Social Status and Age on Diversity and Composition

In mammals, two factors likely to affect the diversity and composition of intestinal bacteria (bacterial microbiome) and eukaryotes (eukaryome) are social status and age. In species in which social status determines access to resources, socially dominant animals maintain better immune processes and health status than subordinates. As high species diversity is an index of ecosystem health, the intestinal biome of healthier, socially dominant animals should be more diverse than those of subordinates. Gradual colonization of the juvenile intestine after birth predicts lower intestinal biome diversity in juveniles than adults. We tested these predictions on the effect of: (1) age (juvenile/adult) and (2) social status (low/high) on bacterial microbiome and eukaryome diversity and composition in the spotted hyena (Crocuta crocuta), a highly social, female-dominated carnivore in which social status determines access to resources. We comprehensively screened feces from 35 individually known adult females and 7 juveniles in the Serengeti ecosystem for bacteria and eukaryotes, using a set of 48 different amplicons (4 for bacterial 16S, 44 for eukaryote 18S) in a multi-amplicon sequencing approach. We compared sequence abundances to classical coprological egg or oocyst counts. For all parasite taxa detected in more than six samples, the number of sequence reads significantly predicted the number of eggs or oocysts counted, underscoring the value of an amplicon sequencing approach for quantitative measurements of parasite load. In line with our predictions, our results revealed a significantly less diverse microbiome in juveniles than adults and a significantly higher diversity of eukaryotes in high-ranking than low-ranking animals. We propose that free-ranging wildlife can provide an intriguing model system to assess the adaptive value of intestinal biome diversity for both bacteria and eukaryotes.

Long-chain fatty acid feeding frequency in anaerobic codigestion impacts syntrophic community structure and biokinetics

This study investigated the impacts of long-chain fatty acid (LCFA) feeding frequencies on microbial community structure, bioconversion kinetics, and process stability during anaerobic codigestion. Parallel laboratory-scale anaerobic codigesters fed with dairy cattle manure were either pulse-fed every two days or continuously-fed daily, respectively, with oleate (C_18:1) in incremental step increases over 200 days up to 64% of the influent chemical oxygen demand (COD). The effluent acetate concentration exceeded 3000 mg/L in the continuous-fed codigester at the highest oleate loading rate, but remained below 100 mg/L in the pulse-fed codigester at the end of its 48-hr oleate feed cycle. Maximum substrate conversion rates of oleate (q_{max, oleate}) and acetate (q_{max, acetate}) were significantly higher in the pulse-fed codigester compared to the continuous-fed codigester. 16S rRNA gene amplicon sequencing showed that Bacteria and Archaea community profiles diverged based on the codigester LCFA feeding pattern and loading rate. LCFA-degrading Syntrophomonas bacteria were significantly enriched in both LCFA codigesters relative to the control digester. The pulse-fed codigester had the highest community fraction of Syntrophomonas 16S rRNA genes by the end of the experiment with 43% of Bacteria amplicon sequences. q_{max, oleate} and q_{max, acetate} values were both significantly correlated to absolute concentrations of Syntrophomonas and Methanosaeta 16S rRNA genes, respectively. Multiple-linear regression models based on the absolute abundance of Syntrophomonas and Methanosaeta taxa provided improved predictions of oleate and acetate bioconversion kinetics, respectively. These results collectively suggest that pulse feeding rather than continuous feeding LCFA during anaerobic codigestion selected for higher microbial bioconversion kinetics and functional stability, which were related to changes in the physiological diversity and adaptive capacity of syntrophic and methanogenic communities.

A quantitative and multiplexed approach to uncover the fitness landscape of tumor suppression in vivo

Cancer growth is a multi-stage, stochastic evolutionary process. While cancer genome sequencing has been instrumental in identifying the genomic alterations that occur in human tumors, the consequences of these alterations on tumor growth remains largely unexplored. Conventional genetically engineered mouse models enable the study of tumor growth in vivo, but they are neither readily scalable nor sufficiently quantitative to unravel the magnitude and mode of action of many tumor suppressor genes. Here, we present a method that integrates tumor barcoding with ultra-deep barcode sequencing (Tuba-seq) to interrogate tumor suppressor function in mouse models of human cancer. Tuba-seq uncovers genotype-dependent distributions of tumor sizes with great precision. By combining Tuba-seq with multiplexed CRISPR/Cas9-mediated genome editing, we quantified the effects of eleven tumor-suppressor pathways that are frequently altered in human lung adenocarcinoma. With unprecedented resolution, parallelization, and precision Tuba-seq enables broad quantification of tumor suppressor gene function.

Spatial Structure of the Mormon Cricket Gut Microbiome and its Predicted Contribution to Nutrition and Immune Function

The gut microbiome of insects plays an important role in their ecology and evolution, participating in nutrient acquisition, immunity, and behavior. Microbial community structure within the gut is heavily influenced by differences among gut regions in morphology and physiology, which determine the niches available for microbes to colonize. We present a high-resolution analysis of the structure of the gut microbiome in the Mormon cricket Anabrus simplex, an insect known for its periodic outbreaks in the western United States and nutrition-dependent mating system. The Mormon cricket microbiome was dominated by 11 taxa from the Lactobacillaceae, Enterobacteriaceae, and Streptococcaceae. While most of these were represented in all gut regions, there were marked differences in their relative abundance, with lactic-acid bacteria (Lactobacillaceae) more common in the foregut and midgut and enteric (Enterobacteriaceae) bacteria more common in the hindgut. Differences in community structure were driven by variation in the relative prevalence of three groups: a Lactobacillus in the foregut, Pediococcus lactic-acid bacteria in the midgut, and Pantoea agglomerans, an enteric bacterium, in the hindgut. These taxa have been shown to have beneficial effects on their hosts in insects and other animals by improving nutrition, increasing resistance to pathogens, and modulating social behavior. Using PICRUSt to predict gene content from our 16S rRNA sequences, we found enzymes that participate in carbohydrate metabolism and pathogen defense in other orthopterans. These were predominately represented in the hindgut and midgut, the most important sites for nutrition and pathogen defense. Phylogenetic analysis of 16S rRNA sequences from cultured isolates indicated low levels of divergence from sequences derived from plants and other insects, suggesting that these bacteria are likely to be exchanged between Mormon crickets and the environment. Our study shows strong spatial variation in microbiome community structure, which influences predicted gene content and thus the potential of the microbiome to influence host function.

Aquatic environmental DNA detects seasonal fish abundance and habitat preference in an urban estuary

The difficulty of censusing marine animal populations hampers effective ocean management. Analyzing water for DNA traces shed by organisms may aid assessment. Here we tested aquatic environmental DNA (eDNA) as an indicator of fish presence in the lower Hudson River estuary. A checklist of local marine fish and their relative abundance was prepared by compiling 12 traditional surveys conducted between 1988–2015. To improve eDNA identification success, 31 specimens representing 18 marine fish species were sequenced for two mitochondrial gene regions, boosting coverage of the 12S eDNA target sequence to 80% of local taxa. We collected 76 one-liter shoreline surface water samples at two contrasting estuary locations over six months beginning in January 2016. eDNA was amplified with vertebrate-specific 12S primers. Bioinformatic analysis of amplified DNA, using a reference library of GenBank and our newly generated 12S sequences, detected most (81%) locally abundant or common species and relatively few (23%) uncommon taxa, and corresponded to seasonal presence and habitat preference as determined by traditional surveys. Approximately 2% of fish reads were commonly consumed species that are rare or absent in local waters, consistent with wastewater input. Freshwater species were rarely detected despite Hudson River inflow. These results support further exploration and suggest eDNA will facilitate fine-scale geographic and temporal mapping of marine fish populations at relatively low cost.

The Landscape Ecology and Microbiota of the Human Nose, Mouth, and Throat

Landscape ecology examines the relationships between the spatial arrangement of different landforms and the processes that give rise to spatial and temporal patterns in local community structure. The spatial ecology of the microbial communities that inhabit the human body-in particular, those of the nose, mouth, and throat-deserves greater attention. Important questions include what defines the size of a population (i.e., "patch") in a given body site, what defines the boundaries of distinct patches within a single body site, and where and over what spatial scales within a body site are gradients detected. This Review looks at the landscape ecology of the upper respiratory tract and mouth and seeks greater clarity about the physiological factors-whether immunological, chemical, or physical-that govern microbial community composition and function and the ecological traits that underlie health and disease.

Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters

Numerous ecological processes, such as bacteriophage infection and phytoplankton-bacterial interactions, often occur via strain-specific mechanisms. Therefore, studying the causes of microbial dynamics should benefit from highly resolving taxonomic characterizations. We sampled daily to weekly over 5 months following a phytoplankton bloom off Southern California and examined the extent of microdiversity, that is, significant variation within 99% sequence similarity clusters, operational taxonomic units (OTUs), of bacteria, archaea, phytoplankton chloroplasts (all via 16S or intergenic spacer (ITS) sequences) and T4-like-myoviruses (via g23 major capsid protein gene sequence). The extent of microdiversity varied between genes (ITS most, g23 least) and only temporally common taxa were highly microdiverse. Overall, 60% of taxa exhibited microdiversity; 59% of these had subtypes that changed significantly as a proportion of the parent taxon, indicating ecologically distinct taxa. Pairwise correlations between prokaryotes and myoviruses or phytoplankton (for example, highly microdiverse Chrysochromulina sp.) improved when using single-base variants. Correlations between myoviruses and SAR11 increased in number (172 vs 9, Spearman>0.65) and became stronger (0.61 vs 0.58, t-test: P<0.001) when using SAR11 ITS single-base variants vs OTUs. Whole-community correlation between SAR11 and myoviruses was much improved when using ITS single-base variants vs OTUs, with Mantel rho=0.49 vs 0.27; these results are consistent with strain-specific interactions. Mantel correlations suggested >1 μm (attached/large) prokaryotes are a major myovirus source. Consideration of microdiversity improved observation of apparent host and virus networks, and provided insights into the ecological and evolutionary factors influencing the success of lineages, with important implications to ecosystem resilience and microbial function.

Response of the rhizosphere prokaryotic community of barley (Hordeum vulgare L.) to elevated atmospheric CO2 concentration in open-top chambers

The effect of elevated atmospheric CO₂ concentration [CO₂] on the diversity and composition of the prokaryotic community inhabiting the rhizosphere of winter barley (Hordeum vulgare L.) was investigated in a field experiment, using open‐top chambers. Rhizosphere samples were collected at anthesis (flowering stage) from six chambers with ambient [CO₂] (approximately 400 ppm) and six chambers with elevated [CO₂] (700 ppm). The V4 region of the 16S rRNA gene was PCR‐amplified from the extracted DNA and sequenced on an Illumina MiSeq instrument. Above‐ground plant biomass was not affected by elevated [CO₂] at anthesis, but plants exposed to elevated [CO₂] had significantly higher grain yield. The composition of the rhizosphere prokaryotic communities was very similar under ambient and elevated [CO₂]. The dominant taxa were Bacteroidetes, Actinobacteria, Alpha‐, Gamma‐, and Betaproteobacteria. Elevated [CO₂] resulted in lower prokaryotic diversity in the rhizosphere, but did not cause a significant difference in community structure.

A ChIP-Seq Data Analysis Pipeline Based on Bioconductor Packages

Nowadays, huge volumes of chromatin immunoprecipitation-sequencing (ChIP-Seq) data are generated to increase the knowledge on DNA-protein interactions in the cell, and accordingly, many tools have been developed for ChIP-Seq analysis. Here, we provide an example of a streamlined workflow for ChIP-Seq data analysis composed of only four packages in Bioconductor: dada2, QuasR, mosaics, and ChIPseeker. 'dada2' performs trimming of the high-throughput sequencing data. 'QuasR' and 'mosaics' perform quality control and mapping of the input reads to the reference genome and peak calling, respectively. Finally, 'ChIPseeker' performs annotation and visualization of the called peaks. This workflow runs well independently of operating systems (e.g., Windows, Mac, or Linux) and processes the input fastq files into various results in one run. R code is available at github: https://github.com/ddhb/Workflow_of_Chipseq.git.

Improvement of identification methods for honeybee specific Lactic Acid Bacteria; future approaches

Honeybees face many parasites and pathogens and consequently rely on a diverse set of individual and group-level defenses to prevent disease. The crop microbiota of Apis mellifera, composed of 13 Lactic Acid Bacterial (LAB) species within the genera Lactobacillus and Bifidobacterium, form a beneficial symbiotic relationship with each other and the honeybee to protect their niche and their host. Possibly playing a vital role in honeybee health, it is important that these honeybee specific Lactic Acid Bacterial (hbs-LAB) symbionts can be correctly identified, isolated and cultured, to further investigate their health promoting properties. We have previously reported successful identification to the strain level by culture-dependent methods and we recently sequenced and annotated the genomes of the 13 hbs-LAB. However, the hitherto applied techniques are unfortunately very time consuming, expensive and not ideal when analyzing a vast quantity of samples. In addition, other researchers have constantly failed to identify the 13 hbs-LAB from honeybee samples by using inadequate media and/or molecular techniques based on 16S rRNA gene sequencing with insufficient discriminatory power. The aim of this study was to develop better and more suitable methods for the identification and cultivation of hbs-LAB. We compared currently used bacterial cultivation media and could for the first time demonstrate a significant variation in the hbs-LAB basic requirements for optimal growth. We also present a new bacterial identification approach based on amplicon sequencing of a region of the 16S rRNA gene using the Illumina platform and an error correction software that can be used to successfully differentiate and rapidly identify the 13 hbs-LAB to the strain level.

The Microbiome and Human Biology

Over the past few years, microbiome research has dramatically reshaped our understanding of human biology. New insights range from an enhanced understanding of how microbes mediate digestion and disease processes (e.g., in inflammatory bowel disease) to surprising associations with Parkinson's disease, autism, and depression. In this review, we describe how new generations of sequencing technology, analytical advances coupled to new software capabilities, and the integration of animal model data have led to these new discoveries. We also discuss the prospects for integrating studies of the microbiome, metabolome, and immune system, with the goal of elucidating mechanisms that govern their interactions. This systems-level understanding will change how we think about ourselves as organisms.

Are Oligotypes Meaningful Ecological and Phylogenetic Units? A Case Study of Microcystis in Freshwater Lakes

Oligotyping is a computational method used to increase the resolution of marker gene microbiome studies. Although oligotyping can distinguish highly similar sequence variants, the resulting units are not necessarily phylogenetically and ecologically informative due to limitations of the selected marker gene. In this perspective, we examine how oligotyping data is interpreted in recent literature, and we illustrate some of the method’s constraints with a case study of the harmful bloom-forming cyanobacterium Microcystis. We identified three Microcystis oligotypes from a western Lake Erie bacterial community 16S rRNA gene (V4 region) survey that had previously clustered into one OTU. We found the same three oligotypes and two additional sequence variants in 46 Microcystis cultures isolated from Michigan inland lakes spanning a trophic gradient. In Lake Erie, shifts in Microcystis oligotypes corresponded to spatial nutrient gradients and temporal transitions in bloom toxicity. In the cultures, Microcystis oligotypes showed preferential distributions for different trophic states, but genomic data revealed that the oligotypes identified in Lake Erie did not correspond to toxin gene presence. Thus, oligotypes could not be used for inferring toxic ecotypes. Most strikingly, Microcystis oligotypes were not monophyletic. Our study supports the utility of oligotyping for distinguishing sequence types along certain ecological features, while it stresses that 16S rRNA gene sequence types may not reflect ecologically or phylogenetically cohesive populations. Therefore, we recommend that studies employing oligotyping or related tools consider these caveats during data interpretation.

Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns

Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.

High-throughput sequencing of 16S ribosomal RNA gene amplicons has facilitated understanding of complex microbial communities, but the inherent noise in PCR and DNA sequencing limits differentiation of closely related bacteria. Although many scientific questions can be addressed with broad taxonomic profiles, clinical, food safety, and some ecological applications require higher specificity. Here we introduce a novel sub-operational-taxonomic-unit (sOTU) approach, Deblur, that uses error profiles to obtain putative error-free sequences from Illumina MiSeq and HiSeq sequencing platforms. Deblur substantially reduces computational demands relative to similar sOTU methods and does so with similar or better sensitivity and specificity. Using simulations, mock mixtures, and real data sets, we detected closely related bacterial sequences with single nucleotide differences while removing false positives and maintaining stability in detection, suggesting that Deblur is limited only by read length and diversity within the amplicon sequences. Because Deblur operates on a per-sample level, it scales to modern data sets and meta-analyses. To highlight Deblur’s ability to integrate data sets, we include an interactive exploration of its application to multiple distinct sequencing rounds of the American Gut Project. Deblur is open source under the Berkeley Software Distribution (BSD) license, easily installable, and downloadable from https://github.com/biocore/deblur.

IMPORTANCE Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.

Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns

High-throughput sequencing of 16S ribosomal RNA gene amplicons has facilitated understanding of complex microbial communities, but the inherent noise in PCR and DNA sequencing limits differentiation of closely related bacteria. Although many scientific questions can be addressed with broad taxonomic profiles, clinical, food safety, and some ecological applications require higher specificity. Here we introduce a novel sub-operational-taxonomic-unit (sOTU) approach, Deblur, that uses error profiles to obtain putative error-free sequences from Illumina MiSeq and HiSeq sequencing platforms. Deblur substantially reduces computational demands relative to similar sOTU methods and does so with similar or better sensitivity and specificity. Using simulations, mock mixtures, and real data sets, we detected closely related bacterial sequences with single nucleotide differences while removing false positives and maintaining stability in detection, suggesting that Deblur is limited only by read length and diversity within the amplicon sequences. Because Deblur operates on a per-sample level, it scales to modern data sets and meta-analyses. To highlight Deblur's ability to integrate data sets, we include an interactive exploration of its application to multiple distinct sequencing rounds of the American Gut Project. Deblur is open source under the Berkeley Software Distribution (BSD) license, easily installable, and downloadable from https://github.com/biocore/deblur. IMPORTANCE Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.

From Culture to High-Throughput Sequencing and Beyond: A Layperson's Guide to the "Omics" and Diagnostic Potential of the Microbiome

Detailed knowledge of the community of organisms in the gut has become possible in recent years because of the development of culture-independent methods. Largely based on latest DNA sequencing platforms, it is now possible to establish the composition of the microbiota and the repertoire of biochemical functions it encodes. Variations in either or both of these parameters have been linked to intestinal and extraintestinal disease. This article summarizes how these methods are applied, with special reference to gastroenterology, and describes the achievements and future potential of microbiota analysis as a diagnostic tool.

Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing

High-throughput sequencing of 16S rRNA gene amplicons (16S-seq) has become a widely deployed method for profiling complex microbial communities but technical pitfalls related to data reliability and quantification remain to be fully addressed. In this work, we have developed and implemented a set of synthetic 16S rRNA genes to serve as universal spike-in standards for 16S-seq experiments. The spike-ins represent full-length 16S rRNA genes containing artificial variable regions with negligible identity to known nucleotide sequences, permitting unambiguous identification of spike-in sequences in 16S-seq read data from any microbiome sample. Using defined mock communities and environmental microbiota, we characterized the performance of the spike-in standards and demonstrated their utility for evaluating data quality on a per-sample basis. Further, we showed that staggered spike-in mixtures added at the point of DNA extraction enable concurrent estimation of absolute microbial abundances suitable for comparative analysis. Results also underscored that template-specific Illumina sequencing artifacts may lead to biases in the perceived abundance of certain taxa. Taken together, the spike-in standards represent a novel bioanalytical tool that can substantially improve 16S-seq-based microbiome studies by enabling comprehensive quality control along with absolute quantification.

Bayesian Nonparametric Ordination for the Analysis of Microbial Communities

Human microbiome studies use sequencing technologies to measure the abundance of bacterial species or Operational Taxonomic Units (OTUs) in samples of biological material. Typically the data are organized in contingency tables with OTU counts across heterogeneous biological samples. In the microbial ecology community, ordination methods are frequently used to investigate latent factors or clusters that capture and describe variations of OTU counts across biological samples. It remains important to evaluate how uncertainty in estimates of each biological sample's microbial distribution propagates to ordination analyses, including visualization of clusters and projections of biological samples on low dimensional spaces. We propose a Bayesian analysis for dependent distributions to endow frequently used ordinations with estimates of uncertainty. A Bayesian nonparametric prior for dependent normalized random measures is constructed, which is marginally equivalent to the normalized generalized Gamma process, a well-known prior for nonparametric analyses. In our prior, the dependence and similarity between microbial distributions is represented by latent factors that concentrate in a low dimensional space. We use a shrinkage prior to tune the dimensionality of the latent factors. The resulting posterior samples of model parameters can be used to evaluate uncertainty in analyses routinely applied in microbiome studies. Specifically, by combining them with multivariate data analysis techniques we can visualize credible regions in ecological ordination plots. The characteristics of the proposed model are illustrated through a simulation study and applications in two microbiome datasets.

Effects of nasal instillation of a nitric oxide-releasing solution or parenteral administration of tilmicosin on the nasopharyngeal microbiota of beef feedlot cattle at high-risk of developing respiratory tract disease

Nitric oxide has bactericidal and virucidal properties. Nasal instillation of a nitric oxide releasing solution (NORS) on arrival at the feedlot was recently reported as inferior to a parenteral injection of tilmicosin (macrolide antibiotic) for control of bovine respiratory disease (BRD) in cattle at high-risk of developing BRD. We hypothesized that this inferiority was due to differences between treatments with regards to their effects on the nasopharyngeal microbiota. The objective was to compare nasal instillation of NORS versus parenteral administration of tilmicosin regarding their effects on the nasopharyngeal microbiota of feedlot cattle at high-risk of developing BRD. Culture-independent community profiling (16S rRNA sequencing) and culture-based methods were used to evaluate treatment effects. High-risk Angus-cross heifers (n=20) were randomly allocated to 2 treatment groups on arrival at a feedlot and received either NORS or tilmicosin for prevention of BRD. Heifers were sampled using guarded deep nasal swabs immediately prior to treatment (day 0) and on days 1, 5 and 10 after treatment. Based on culture-independent community profiling, there was a distinct shift in composition of the nasopharyngeal microbiota during the first 10 d after arrival, with 116 OTUs changing over time, but no difference between treatment groups. However, culture-based methods detected a difference between treatment groups, with more cattle culture-positive for Pasteurellaceae in the NORS group at day 5 post-treatment. This difference in ability to inhibit colonization of the nasopharynx by Pasteurellaceae may be the basis for NORS being inferior to tilmicosin for control of BRD in high-risk cattle.

Survival of Vinyl Chloride Respiring Dehalococcoides mccartyi under Long-Term Electron Donor Limitation

In anoxic groundwater aquifers, the long-term survival of Dehalococcoides mccartyi populations expressing the gene vcrA (or bvcA) encoding reductive vinyl chloride dehalogenases are important to achieve complete dechlorination of tetrachloroethene (PCE) and trichloroethene (TCE) to nonchlorinated ethene. The absence or inactivity of vcrA-containing Dehalococcoides results in the accumulation of the harmful chlorinated intermediates dichloroethene (DCE) and vinyl chloride (VC). Although vcrA-containing Dehalococcoides subpopulations depend on synergistic interaction with other organohalide-respiring populations generating their metabolic electron acceptors (DCE and VC), their survival requires successful competition for electron donor within the entire organohalide-respiring microbial community. To understand this dualism of synergy and competition under growth conditions relevant in contaminated aquifers, we investigated Dehalococcoides-level population structure when subjected to a change in the ratio of electron donor to chlorinated electron acceptor in continuously stirred tank reactors (CSTRs) operated over 7 years. When the electron donor formate was supplied in stoichiometric excess to TCE, both tceA-containing and vcrA-containing Dehalococcoides populations persisted, and near-complete dechlorination to ethene was stably maintained. When the electron donor formate was supplied at substoichiometric concentrations, the interactions between tceA-containing and vcrA-containing populations shifted toward direct competition for the same limiting catabolic electron donor substrate with subsequent niche exclusion of the vcrA-containing population. After more than 2000 days of operation under electron donor limitation, increasing the electron donor to TCE ratio facilitated a recovery of the vcrA-containing Dehalococoides population to its original frequency. We demonstrate that electron donor scarcity alone, in the absence of competing metabolic processes or inhibitory dechlorination intermediate products, is sufficient to alter the Dehalococcoides population structure. These results underscore the importance of electron donor and chloroethene stoichiometry in maintaining balanced functional performance within consortia composed of multiple D. mccartyi subpopulations, even when other competing electron acceptor processes are absent.

Profiling Living Bacteria Informs Preparation of Fecal Microbiota Transplantations

Fecal microbiota transplantation is a compelling treatment for recurrent Clostridium difficile infections, with potential applications against other diseases associated with changes in gut microbiota. But variability in fecal bacterial communities—believed to be the therapeutic agent—can complicate or undermine treatment efficacy. To understand the effects of transplant preparation methods on living fecal microbial communities, we applied a DNA-sequencing method (PMA-seq) that uses propidium monoazide (PMA) to differentiate between living and dead fecal microbes, and we created an analysis pipeline to identify individual bacteria that change in abundance between samples. We found that oxygen exposure degraded fecal bacterial communities, whereas freeze-thaw cycles and lag time between donor defecation and transplant preparation had much smaller effects. Notably, the abundance of Faecalibacterium prausnitzii—an anti-inflammatory commensal bacterium whose absence is linked to inflammatory bowel disease—decreased with oxygen exposure. Our results indicate that some current practices for preparing microbiota transplant material adversely affect living fecal microbial content and highlight PMA-seq as a valuable tool to inform best practices and evaluate the suitability of clinical fecal material.

Lactobacillus-Deficient Cervicovaginal Bacterial Communities Are Associated with Increased HIV Acquisition in Young South African Women

Elevated inflammation in the female genital tract is associated with increased HIV risk. Cervicovaginal bacteria modulate genital inflammation; however, their role in HIV susceptibility has not been elucidated. In a prospective cohort of young, healthy South African women, we found that individuals with diverse genital bacterial communities dominated by anaerobes other than Gardnerella were at over 4-fold higher risk of acquiring HIV and had increased numbers of activated mucosal CD4⁺ T cells compared to those with Lactobacillus crispatus-dominant communities. We identified specific bacterial taxa linked with reduced (L. crispatus) or elevated (Prevotella, Sneathia, and other anaerobes) inflammation and HIV infection and found that high-risk bacteria increased numbers of activated genital CD4⁺ T cells in a murine model. Our results suggest that highly prevalent genital bacteria increase HIV risk by inducing mucosal HIV target cells. These findings might be leveraged to reduce HIV acquisition in women living in sub-Saharan Africa.

Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research

As the microbiome field continues to grow, a multitude of researchers are learning how to conduct proper microbiome experiments. We outline here a streamlined and custom approach to processing samples from detailed sequencing library construction to step-by-step bioinformatic standard operating procedures. This allows for rapid and reliable microbiome analysis, allowing researchers to focus more on their experiment design and results. Our sequencing protocols, bioinformatic tutorials, and bundled software are freely available through Microbiome Helper. As the microbiome research field continues to evolve, Microbiome Helper will be updated with new protocols, scripts, and training materials.

Sequence-based approaches to study microbiomes, such as 16S rRNA gene sequencing and metagenomics, are uncovering associations between microbial taxa and a myriad of factors. A drawback of these approaches is that the necessary sequencing library preparation and bioinformatic analyses are complicated and continuously changing, which can be a barrier for researchers new to the field. We present three essential components to conducting a microbiome experiment from start to finish: first, a simplified and step-by-step custom gene sequencing protocol that requires limited lab equipment, is cost-effective, and has been thoroughly tested and utilized on various sample types; second, a series of scripts to integrate various commonly used bioinformatic tools that is available as a standalone installation or as a single downloadable virtual image; and third, a set of bioinformatic workflows and tutorials to provide step-by-step guidance and education for those new to the microbiome field. This resource will provide the foundations for those newly entering the microbiome field and will provide much-needed guidance and best practices to ensure that quality microbiome research is undertaken. All protocols, scripts, workflows, tutorials, and virtual images are freely available through the Microbiome Helper website (https://github.com/mlangill/microbiome_helper/wiki).

IMPORTANCE As the microbiome field continues to grow, a multitude of researchers are learning how to conduct proper microbiome experiments. We outline here a streamlined and custom approach to processing samples from detailed sequencing library construction to step-by-step bioinformatic standard operating procedures. This allows for rapid and reliable microbiome analysis, allowing researchers to focus more on their experiment design and results. Our sequencing protocols, bioinformatic tutorials, and bundled software are freely available through Microbiome Helper. As the microbiome research field continues to evolve, Microbiome Helper will be updated with new protocols, scripts, and training materials.

Current Knowledge and Computational Techniques for Grapevine Meta-Omics Analysis

Growing grapevine (Vitis vinifera) is a key contribution to the economy of many countries. Tools provided by genomics and bioinformatics did help researchers in obtaining biological knowledge about the different cultivars. Several genetic markers for common diseases were identified. Recently, the impact of microbiome has been proved to be of fundamental importance both in humans and in plants for its ability to confer protection or induce diseases. In this review we report current knowledge about grapevine microbiome, together with a description of the available computational methodologies for meta-omics analysis.

Resilient microorganisms in dust samples of the International Space Station-survival of the adaptation specialists

BACKGROUND

The International Space Station (ISS) represents a unique biotope for the human crew but also for introduced microorganisms. Microbes experience selective pressures such as microgravity, desiccation, poor nutrient-availability due to cleaning, and an increased radiation level. We hypothesized that the microbial community inside the ISS is modified by adapting to these stresses. For this reason, we analyzed 8-12 years old dust samples from Russian ISS modules with major focus on the long-time surviving portion of the microbial community. We consequently assessed the cultivable microbiota of these samples in order to analyze their extremotolerant potential against desiccation, heat-shock, and clinically relevant antibiotics. In addition, we studied the bacterial and archaeal communities from the stored Russian dust samples via molecular methods (next-generation sequencing, NGS) and compared our new data with previously derived information from the US American ISS dust microbiome.

RESULTS

We cultivated and identified in total 85 bacterial, non-pathogenic isolates (17 different species) and 1 fungal isolate from the 8-12 year old dust samples collected in the Russian segment of the ISS. Most of these isolates exhibited robust resistance against heat-shock and clinically relevant antibiotics. Microbial 16S rRNA gene and archaeal 16S rRNA gene targeting Next Generation Sequencing showed signatures of human-associated microorganisms (Corynebacterium, Staphylococcus, Coprococcus etc.), but also specifically adapted extremotolerant microorganisms. Besides bacteria, the detection of archaeal signatures in higher abundance was striking.

CONCLUSIONS

Our findings reveal (i) the occurrence of living, hardy microorganisms in archived Russian ISS dust samples, (ii) a profound resistance capacity of ISS microorganisms against environmental stresses, and (iii) the presence of archaeal signatures on board. In addition, we found indications that the microbial community in the Russian segment dust samples was different to recently reported US American ISS microbiota.

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

Lactobacillus iners: Friend or Foe?

The vaginal microbial community is typically characterized by abundant lactobacilli. Lactobacillus iners, a fairly recently detected species, is frequently present in the vaginal niche. However, the role of this species in vaginal health is unclear, since it can be detected in normal conditions as well as during vaginal dysbiosis, such as bacterial vaginosis, a condition characterized by an abnormal increase in bacterial diversity and lack of typical lactobacilli. Compared to other Lactobacillus species, L. iners has more complex nutritional requirements and a Gram-variable morphology. L. iners has an unusually small genome (ca. 1 Mbp), indicative of a symbiotic or parasitic lifestyle, in contrast to other lactobacilli that show niche flexibility and genomes of up to 3-4 Mbp. The presence of specific L. iners genes, such as those encoding iron-sulfur proteins and unique σ-factors, reflects a high degree of niche specification. The genome of L. iners strains also encodes inerolysin, a pore-forming toxin related to vaginolysin of Gardnerella vaginalis. Possibly, this organism may have clonal variants that in some cases promote a healthy vagina, and in other cases are associated with dysbiosis and disease. Future research should examine this friend or foe relationship with the host.

1,2-Dichloroethane Exposure Alters the Population Structure, Metabolism, and Kinetics of a Trichloroethene-Dechlorinating Dehalococcoides mccartyi Consortium

Bioremediation of groundwater contaminated with chlorinated aliphatic hydrocarbons such as perchloroethene and trichloroethene can result in the accumulation of the undesirable intermediate vinyl chloride. Such accumulation can either be due to the absence of specific vinyl chloride respiring Dehalococcoides mccartyi or to the inhibition of such strains by the metabolism of other microorganisms. The fitness of vinyl chloride respiring Dehalococcoides mccartyi subpopulations is particularly uncertain in the presence of chloroethene/chloroethane cocontaminant mixtures, which are commonly found in contaminated groundwater. Therefore, we investigated the structure of Dehalococcoides populations in a continuously fed reactor system under changing chloroethene/ethane influent conditions. We observed that increasing the influent ratio of 1,2-dichloroethane to trichloroethene was associated with ecological selection of a tceA-containing Dehalococcoides population relative to a vcrA-containing Dehalococcoides population. Although both vinyl chloride and 1,2-dichloroethane could be simultaneously transformed to ethene, prolonged exposure to 1,2-dichloroethane diminished the vinyl chloride transforming capacity of the culture. Kinetic tests revealed that dechlorination of 1,2-dichloroethane by the consortium was strongly inhibited by cis-dichloroethene but not vinyl chloride. Native polyacrylamide gel electrophoresis and mass spectrometry revealed that a trichloroethene reductive dehalogenase (TceA) homologue was the most consistently expressed of four detectable reductive dehalogenases during 1,2-dichloroethane exposure, suggesting that it catalyzes the reductive dihaloelimination of 1,2-dichloroethane to ethene.

Piphillin: Improved Prediction of Metagenomic Content by Direct Inference from Human Microbiomes

Functional analysis of a clinical microbiome facilitates the elucidation of mechanisms by which microbiome perturbation can cause a phenotypic change in the patient. The direct approach for the analysis of the functional capacity of the microbiome is via shotgun metagenomics. An inexpensive method to estimate the functional capacity of a microbial community is through collecting 16S rRNA gene profiles then indirectly inferring the abundance of functional genes. This inference approach has been implemented in the PICRUSt and Tax4Fun software tools. However, those tools have important limitations since they rely on outdated functional databases and uncertain phylogenetic trees and require very specific data pre-processing protocols. Here we introduce Piphillin, a straightforward algorithm independent of any proposed phylogenetic tree, leveraging contemporary functional databases and not obliged to any singular data pre-processing protocol. When all three inference tools were evaluated against actual shotgun metagenomics, Piphillin was superior in predicting gene composition in human clinical samples compared to both PICRUSt and Tax4Fun (p<0.01 and p<0.001, respectively) and Piphillin’s ability to predict disease associations with specific gene orthologs exhibited a 15% increase in balanced accuracy compared to PICRUSt. From laboratory animal samples, no performance advantage was observed for any one of the tools over the others and for environmental samples all produced unsatisfactory predictions. Our results demonstrate that functional inference using the direct method implemented in Piphillin is preferable for clinical biospecimens. Piphillin is publicly available for academic use at http://secondgenome.com/Piphillin.