Increased circulating butyrate and ursodeoxycholate during probiotic intervention in humans with type 2 diabetes.

BACKGROUND: An increasing body of evidence implicates the resident gut microbiota as playing a critical role in type 2 diabetes (T2D) pathogenesis. We previously reported significant improvement in postprandial glucose control in human participants with T2D following 12-week administration of a 5-strain novel probiotic formulation ('WBF-011') in a double-blind, randomized, placebo controlled setting (NCT03893422). While the clinical endpoints were encouraging, additional exploratory measurements were needed in order to link the motivating mechanistic hypothesis - increased short-chain fatty acids - with markers of disease. RESULTS: Here we report targeted and untargeted metabolomic measurements on fasting plasma (n = 104) collected at baseline and end of intervention. Butyrate and ursodeoxycholate increased among participants randomized to WBF-011, along with compelling trends between butyrate and glycated haemoglobin (HbA1c). In vitro monoculture experiments demonstrated that the formulation's C. butyricum strain efficiently synthesizes ursodeoxycholate from the primary bile acid chenodeoxycholate during butyrogenic growth. Untargeted metabolomics also revealed coordinated decreases in intermediates of fatty acid oxidation and bilirubin, potential secondary signatures for metabolic improvement. Finally, improvement in HbA1c was limited almost entirely to participants not using sulfonylurea drugs. We show that these drugs can inhibit growth of formulation strains in vitro. CONCLUSION: To our knowledge, this is the first description of an increase in circulating butyrate or ursodeoxycholate following a probiotic intervention in humans with T2D, adding support for the possibility of a targeted microbiome-based approach to assist in the management of T2D. The efficient synthesis of UDCA by C. butyricum is also likely of interest to investigators of its use as a probiotic in other disease settings. The potential for inhibitory interaction between sulfonylurea drugs and gut microbiota should be considered carefully in the design of future studies.

Butyrate-producing human gut symbiont, Clostridium butyricum, and its role in health and disease.

Clostridium butyricum is a butyrate-producing human gut symbiont that has been safely used as a probiotic for decades. C. butyricum strains have been investigated for potential protective or ameliorative effects in a wide range of human diseases, including gut-acquired infection, intestinal injury, irritable bowel syndrome, inflammatory bowel disease, neurodegenerative disease, metabolic disease, and colorectal cancer. In this review we summarize the studies on C. butyricum supplementation with special attention to proposed mechanisms for the associated health benefits and the supporting experimental evidence. These mechanisms center on molecular signals (especially butyrate) as well as immunological signals in the digestive system that cascade well beyond the gut to the liver, adipose tissue, brain, and more. The safety of probiotic C. butyricum strains appears well-established. We identify areas where additional human randomized controlled trials would provide valuable further data related to the strains' utility as an intervention.

Normalization of Microbiome Profiling Data.

Normalization is a term that is often used but rarely defined and poorly understood. The number of choices of normalization procedure is large-some are inappropriate or inadmissible-and all are narrowly relevant to a specific analysis that depends on both the nature of the data and the question being asked. This chapter describes key definitions of normalization as they apply in metagenomics, mainly for taxonomic profiling data; while also demonstrating specific, reproducible examples of normalization procedures in the context of analysis techniques for which they were intended. The analysis and graphics code is distributed as a supplemental companion to this chapter so that the motivated reader can re-use it on new data.

Leveraging sequence-based faecal microbial community survey data to identify a composite biomarker for colorectal cancer.

OBJECTIVE: Colorectal cancer (CRC) is the second leading cause of cancer-associated mortality in the USA. The faecal microbiome may provide non-invasive biomarkers of CRC and indicate transition in the adenoma-carcinoma sequence. Re-analysing raw sequence and metadata from several studies uniformly, we sought to identify a composite and generalisable microbial marker for CRC. DESIGN: Raw 16S rRNA gene sequence data sets from nine studies were processed with two pipelines, (1) QIIME closed reference (QIIME-CR) or (2) a strain-specific method herein termed SS-UP (Strain Select, UPARSE bioinformatics pipeline). A total of 509 samples (79 colorectal adenoma, 195 CRC and 235 controls) were analysed. Differential abundance, meta-analysis random effects regression and machine learning analyses were carried out to determine the consistency and diagnostic capabilities of potential microbial biomarkers. RESULTS: Definitive taxa, including Parvimonas micra ATCC 33270, Streptococcus anginosus and yet-to-be-cultured members of Proteobacteria, were frequently and significantly increased in stools from patients with CRC compared with controls across studies and had high discriminatory capacity in diagnostic classification. Microbiome-based CRC versus control classification produced an area under receiver operator characteristic (AUROC) curve of 76.6% in QIIME-CR and 80.3% in SS-UP. Combining clinical and microbiome markers gave a diagnostic AUROC of 83.3% for QIIME-CR and 91.3% for SS-UP. CONCLUSIONS: Despite technological differences across studies and methods, key microbial markers emerged as important in classifying CRC cases and such could be used in a universal diagnostic for the disease. The choice of bioinformatics pipeline influenced accuracy of classification. Strain-resolved microbial markers might prove crucial in providing a microbial diagnostic for CRC.

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.

Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses.

High-throughput sequencing of PCR-amplified taxonomic markers (like the 16S rRNA gene) has enabled a new level of analysis of complex bacterial communities known as microbiomes. Many tools exist to quantify and compare abundance levels or OTU composition of communities in different conditions. The sequencing reads have to be denoised and assigned to the closest taxa from a reference database. Common approaches use a notion of 97% similarity and normalize the data by subsampling to equalize library sizes. In this paper, we show that statistical models allow more accurate abundance estimates. By providing a complete workflow in R, we enable the user to do sophisticated downstream statistical analyses, whether parametric or nonparametric. We provide examples of using the R packages dada2, phyloseq, DESeq2, ggplot2 and vegan to filter, visualize and test microbiome data. We also provide examples of supervised analyses using random forests and nonparametric testing using community networks and the ggnetwork package.

DADA2: High-resolution sample inference from Illumina amplicon data.

We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.

Temporal and spatial variation of the human microbiota during pregnancy.

Despite the critical role of the human microbiota in health, our understanding of microbiota compositional dynamics during and after pregnancy is incomplete. We conducted a case-control study of 49 pregnant women, 15 of whom delivered preterm. From 40 of these women, we analyzed bacterial taxonomic composition of 3,767 specimens collected prospectively and weekly during gestation and monthly after delivery from the vagina, distal gut, saliva, and tooth/gum. Linear mixed-effects modeling, medoid-based clustering, and Markov chain modeling were used to analyze community temporal trends, community structure, and vaginal community state transitions. Microbiota community taxonomic composition and diversity remained remarkably stable at all four body sites during pregnancy (P > 0.05 for trends over time). Prevalence of a Lactobacillus-poor vaginal community state type (CST 4) was inversely correlated with gestational age at delivery (P = 0.0039). Risk for preterm birth was more pronounced for subjects with CST 4 accompanied by elevated Gardnerella or Ureaplasma abundances. This finding was validated with a set of 246 vaginal specimens from nine women (four of whom delivered preterm). Most women experienced a postdelivery disturbance in the vaginal community characterized by a decrease in Lactobacillus species and an increase in diverse anaerobes such as Peptoniphilus, Prevotella, and Anaerococcus species. This disturbance was unrelated to gestational age at delivery and persisted for up to 1 y. These findings have important implications for predicting premature labor, a major global health problem, and for understanding the potential impact of a persistent, altered postpartum microbiota on maternal health, including outcomes of pregnancies following short interpregnancy intervals.

Shiny-phyloseq: Web application for interactive microbiome analysis with provenance tracking.

UNLABELLED: We have created a Shiny-based Web application, called Shiny-phyloseq, for dynamic interaction with microbiome data that runs on any modern Web browser and requires no programming, increasing the accessibility and decreasing the entrance requirement to using phyloseq and related R tools. Along with a data- and context-aware dynamic interface for exploring the effects of parameter and method choices, Shiny-phyloseq also records the complete user input and subsequent graphical results of a user's session, allowing the user to archive, share and reproduce the sequence of steps that created their result-without writing any new code themselves. AVAILABILITY AND IMPLEMENTATION: Shiny-phyloseq is implemented entirely in the R language. It can be hosted/launched by any system with R installed, including Windows, Mac OS and most Linux distributions. Information technology administrators can also host Shiny--phyloseq from a remote server, in which case users need only have a Web browser installed. Shiny-phyloseq is provided free of charge under a GPL-3 open-source license through GitHub at http://joey711.github.io/shiny-phyloseq/.

Waste not, want not: why rarefying microbiome data is inadmissible.

Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.

Advancing our understanding of the human microbiome using QIIME.

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.

phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.

BACKGROUND: the analysis of microbial communities through dna sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. RESULTS: Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. CONCLUSIONS: The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

Comparisons of distance methods for combining covariates and abundances in microbiome studies.

This article compares different methods for combining abundance data, phylogenetic trees and clinical covariates in a nonparametric setting. In particular we study the output from the principal coordinates analysis on UNIFRAC and WEIGHTED UNIFRAC distances and the output from a double principal coordinate analyses DPCOA using distances computed on the phylogenetic tree. We also present power comparisons for some of the standard tests of phylogenetic signal between different types of samples. These methods are compared both on simulated and real data sets. Our study shows that DPCoA is less robust to outliers, and more robust to small noisy fluctuations around zero.

Phyloseq: a bioconductor package for handling and analysis of high-throughput phylogenetic sequence data.

We present a detailed description of a new Bioconductor package, phyloseq, for integrated data and analysis of taxonomically-clustered phylogenetic sequencing data in conjunction with related data types. The phyloseq package integrates abundance data, phylogenetic information and covariates so that exploratory transformations, plots, and confirmatory testing and diagnostic plots can be carried out seamlessly. The package is built following the S4 object-oriented framework of the R language so that once the data have been input the user can easily transform, plot and analyze the data. We present some examples that highlight the methods and the ease with which we can leverage existing packages.

Hydrogen production in photosynthetic microbial mats in the Elkhorn Slough estuary, Monterey Bay.

Hydrogen (H(2)) release from photosynthetic microbial mats has contributed to the chemical evolution of Earth and could potentially be a source of renewable H(2) in the future. However, the taxonomy of H(2)-producing microorganisms (hydrogenogens) in these mats has not been previously determined. With combined biogeochemical and molecular studies of microbial mats collected from Elkhorn Slough, Monterey Bay, California, we characterized the mechanisms of H(2) production and identified a dominant hydrogenogen. Net production of H(2) was observed within the upper photosynthetic layer (0-2 mm) of the mats under dark and anoxic conditions. Pyrosequencing of rRNA gene libraries generated from this layer demonstrated the presence of 64 phyla, with Bacteriodetes, Cyanobacteria and Proteobacteria dominating the sequences. Sequencing of rRNA transcripts obtained from this layer demonstrated that Cyanobacteria dominated rRNA transcript pyrotag libraries. An OTU affiliated to Microcoleus spp. was the most abundant OTU in both rRNA gene and transcript libraries. Depriving mats of sunlight resulted in an order of magnitude decrease in subsequent nighttime H(2) production, suggesting that newly fixed carbon is critical to H(2) production. Suppression of nitrogen (N(2))-fixation in the mats did not suppress H(2) production, which indicates that co-metabolic production of H(2) during N(2)-fixation is not an important contributor to H(2) production. Concomitant production of organic acids is consistent with fermentation of recently produced photosynthate as the dominant mode of H(2) production. Analysis of rRNA % transcript:% gene ratios and H(2)-evolving bidirectional [NiFe] hydrogenase % transcript:% gene ratios indicated that Microcoelus spp. are dominant hydrogenogens in the Elkhorn Slough mats.

Site-specific mobilization of vinyl chloride respiration islands by a mechanism common in Dehalococcoides.

BACKGROUND: Vinyl chloride is a widespread groundwater pollutant and Group 1 carcinogen. A previous comparative genomic analysis revealed that the vinyl chloride reductase operon, vcrABC, of Dehalococcoides sp. strain VS is embedded in a horizontally-acquired genomic island that integrated at the single-copy tmRNA gene, ssrA. RESULTS: We targeted conserved positions in available genomic islands to amplify and sequence four additional vcrABC -containing genomic islands from previously-unsequenced vinyl chloride respiring Dehalococcoides enrichments. We identified a total of 31 ssrA-specific genomic islands from Dehalococcoides genomic data, accounting for 47 reductive dehalogenase homologous genes and many other non-core genes. Sixteen of these genomic islands contain a syntenic module of integration-associated genes located adjacent to the predicted site of integration, and among these islands, eight contain vcrABC as genetic 'cargo'. These eight vcrABC -containing genomic islands are syntenic across their ~12 kbp length, but have two phylogenetically discordant segments that unambiguously differentiate the integration module from the vcrABC cargo. Using available Dehalococcoides phylogenomic data we estimate that these ssrA-specific genomic islands are at least as old as the Dehalococcoides group itself, which in turn is much older than human civilization. CONCLUSIONS: The vcrABC -containing genomic islands are a recently-acquired subset of a diverse collection of ssrA-specific mobile elements that are a major contributor to strain-level diversity in Dehalococcoides, and may have been throughout its evolution. The high similarity between vcrABC sequences is quantitatively consistent with recent horizontal acquisition driven by ~100 years of industrial pollution with chlorinated ethenes.

Localized plasticity in the streamlined genomes of vinyl chloride respiring Dehalococcoides.

Vinyl chloride (VC) is a human carcinogen and widespread priority pollutant. Here we report the first, to our knowledge, complete genome sequences of microorganisms able to respire VC, Dehalococcoides sp. strains VS and BAV1. Notably, the respective VC reductase encoding genes, vcrAB and bvcAB, were found embedded in distinct genomic islands (GEIs) with different predicted integration sites, suggesting that these genes were acquired horizontally and independently by distinct mechanisms. A comparative analysis that included two previously sequenced Dehalococcoides genomes revealed a contextually conserved core that is interrupted by two high plasticity regions (HPRs) near the Ori. These HPRs contain the majority of GEIs and strain-specific genes identified in the four Dehalococcoides genomes, an elevated number of repeated elements including insertion sequences (IS), as well as 91 of 96 rdhAB, genes that putatively encode terminal reductases in organohalide respiration. Only three core rdhA orthologous groups were identified, and only one of these groups is supported by synteny. The low number of core rdhAB, contrasted with the high rdhAB numbers per genome (up to 36 in strain VS), as well as their colocalization with GEIs and other signatures for horizontal transfer, suggests that niche adaptation via organohalide respiration is a fundamental ecological strategy in Dehalococccoides. This adaptation has been exacted through multiple mechanisms of recombination that are mainly confined within HPRs of an otherwise remarkably stable, syntenic, streamlined genome among the smallest of any free-living microorganism.

Monitoring abundance and expression of "Dehalococcoides" species chloroethene-reductive dehalogenases in a tetrachloroethene-dechlorinating flow column.

We investigated the distribution and activity of chloroethene-degrading microorganisms and associated functional genes during reductive dehalogenation of tetrachloroethene to ethene in a laboratory continuous-flow column. Using real-time PCR, we quantified "Dehalococcoides" species 16S rRNA and chloroethene-reductive dehalogenase (RDase) genes (pceA, tceA, vcrA, and bvcA) in nucleic acid extracts from different sections of the column. Dehalococcoides 16S rRNA gene copies were highest at the inflow port [(3.6 +/- 0.6) x 10(6) (mean +/- standard deviation) per gram soil] where the electron donor and acceptor were introduced into the column. The highest transcript numbers for tceA, vcrA, and bvcA were detected 5 to 10 cm from the column inflow. bvcA was the most highly expressed of all RDase genes and the only vinyl chloride reductase-encoding transcript detectable close to the column outflow. Interestingly, no expression of pceA was detected in the column, despite the presence of the genes in the microbial community throughout the column. By comparing the 16S rRNA gene copy numbers to the sum of all four RDase genes, we found that 50% of the Dehalococcoides population in the first part of the column did not contain either one of the known chloroethene RDase genes. Analysis of 16S rRNA gene clone libraries from both ends of the flow column revealed a microbial community dominated by members of Firmicutes and Actinobacteria. Higher clone sequence diversity was observed near the column outflow. The results presented have implications for our understanding of the ecophysiology of reductively dehalogenating Dehalococcoides spp. and their role in bioremediation of chloroethenes.

Unusual codon bias in vinyl chloride reductase genes of Dehalococcoides species.

Vinyl chloride reductases (VC-RDase) are the key enzymes for complete microbial reductive dehalogenation of chloroethenes, including the groundwater pollutants tetrachloroethene and trichloroethene. Analysis of the codon usage of the VC-RDase genes vcrA and bvcA showed that these genes are highly unusual and are characterized by a low G+C fraction at the third position. The third position of codons in VC-RDase genes is biased toward the nucleotide T, even though available Dehalococcoides genome sequences indicate the absence of any tRNAs matching codons that end in T. The comparatively high level of abnormality in the codon usage of VC-RDase genes suggests an evolutionary history that is different from that of most other Dehalococcoides genes.