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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.

Cancer is characterized by diverse genomic alterations that change cell state and can modify therapy responses. In some cases, the relationships between genotype and drug responses are obvious, such as the response of tumors with defined oncogenic mutations to inhibitors of those mutant proteins. However, covalent KRAS G12C inhibitors (G12Ci) induce clinical responses in less than half of patients with oncogenic KRAS G12C-driven lung cancer, illustrating that genomic alterations not obviously related to the drug mechanism can have a dramatic impact. We have developed and optimized an in vivo platform to identify pharmacogenomic interactions that dictate lung cancer responses to therapies. By coupling somatic genome editing, tumor barcoding, ultra-deep barcode sequencing, and robust statistical methods in genetically engineered mouse models of human lung cancer, we can quantify and compare candidate therapies across thousands of clonal tumors of diverse tumor suppressor genotypes. This platform provides a uniquely high-throughput and quantitative assessment of tumors that are initiated de novo within the natural immunocompetent tissue environment. To uncover genotypes that are particularly important in controlling the response of tumors to oncogenic KRAS inhibition, we applied this platform to quantify the impact of 59 tumor suppressor genes on KRASG12C-driven lung tumor responses to G12Ci. Treatment resulted in approximately three-quarters reduction in both average tumor sizes and overall tumor burden, with a clear drug and dose dependence. Approximately one-quarter of inactivated genes significantly and consistently altered tumor responses across numerous G12Ci and replicate studies. Interestingly, most of these genes are thought to be in pathways not directly related to RAS signaling, and they exhibit a striking pattern in which treatment sensitivity is positively correlated with strength of the tumor suppressor effect. This pharmacogenomic profile for G12Ci was categorically different than the profiles we have observed for chemotherapy and other therapies. Overall, these results provide direct causal evidence that certain tumor suppressor genotypes dramatically shift the effectiveness of G12Ci in vivo and generate hypotheses about patients likely to benefit from G12Ci relative to alternatives. Analysis of available human data provides early clinical support for some of these hypotheses. Ongoing efforts to define pharmacogenomic profiles of combination therapies could be of even greater importance. Platforms that can accurately predict how tumor genotype drives responses have the potential to transform precision cancer therapy, enabling more effective patient stratification and therapy combinations. Citation Format: Paul J. McMurdie, Ian P. Winters, Lily M. Blair, Lafia Sebastian, Vy Tran, Gabriel Grenot, Edwin A. Apilado, Edwin A. Apilado, Ian K. Lai, Gregory D. Wall, Dmitri A. Petrov, Monte M. Winslow, Michael J. Rosen, Joseph Juan. Tumor suppressor genotype dramatically impacts lung cancer response to KRAS G12C inhibitors in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2789.

Studies in type 2 diabetes (T2D) rodent models suggest the potential for individual microbial taxa in the human gut to improve or worsen metabolic disease via specific metabolites (signals) in hepatic or general circulation. We previously reported significant improvement in postprandial glucose control in T2D subjects following ingestion of a 5-strain novel probiotic formulation (INT) in a placebo (PBO) controlled setting. (DOI:10.1136/bmjdrc-2020-001319) Here we report measurements of fecal and fasting circulating plasma metabolites, implicated as mediators of microbiome effects, collected before and after the 12-week intervention. Consistent with the hypothesized benefits of increased colonic production of butyrate, plasma butyrate increased in INT but not PBO (within group, concentration increase = 0.15 µM, p = 0.007; between group, p = 0.11), but not acetate nor propionate, paralleling changes in subject stool and production by in vitro culture. We detected changes consistent with enhanced fatty acid oxidation in INT relative to placebo, especially a decrease in medium chain fatty acids, their carnitinylates, and a decrease in bilirubin degradation byproducts. We also detected increases in certain bile acids, especially ursodeoxycholic acid (UDCA). In vitro monoculture supernatant showed that the formulation’s C. butyricum strain specifically and efficiently synthesizes UDCA during butyrogenic growth in rich media amended with the primary bile acid chenodeoxycholic acid. Untargeted data also uncovered previously undocumented use of sulfonylurea (SFU), accounting for 6 out of 7 subjects with A1c non-response in the INT group. To our knowledge, this is the first description of an increase in circulating butyrate and UDCA due solely to a bacterial probiotic intervention in humans with T2D, and may help explain the observed improvement in postprandial glucose control. Disclosure P. Mcmurdie: Employee; Self; Pendulum Therapeutics. M. K. Stoeva: Employee; Self; Pendulum Therapeutics. M. Nemchek: Employee; Self; Pendulum Therapeutics. S. Tyagi: None. J. Gines: Employee; Self; Pendulum Therapeutics. J. Eid: Stock/Shareholder; Self; Pendulum Therapeutics. O. G. Kolterman: Board Member; Self; GlySens Incorporated, Consultant; Self; ADOCIA, Cirius Therapeutics, Nanoprecision Medical, Viacyte, Inc., Employee; Self; Pendulum Therapeutics.

IntroductionA growing body of evidence suggests that specific, naturally occurring gut bacteria are under-represented in the intestinal tracts of subjects with type 2 diabetes (T2D) and that their functions, like gut barrier stability and butyrate production, are important to glucose and insulin homeostasis. The objective of this study was to test the hypothesis that enteral exposure to microbes with these proposed functions can safely improve clinical measures of glycemic control and thereby play a role in the overall dietary management of diabetes.Research design and methodsWe evaluated whether a probiotic comprised of these anaerobic bacteria would enhance dietary management by (1) manufacturing two novel probiotic formulations containing three (WBF-010) or five (WBF-011) distinct strains in a Current Good Manufacturing Practice (cGMP) facility, (2) establishing consistent live-cell concentrations, (3) confirming safety at target concentrations dispensed in both animal and human studies and (4) conducting a 12-week parallel, double-blind, placebo-controlled, proof-of-concept study in which subjects previously diagnosed with T2D (n=76) were randomly assigned to a two times a day regimen of placebo, WBF-010 or WBF-011.ResultsNo safety or tolerability issues were observed. Compared with the placebo group, subjects administered WBF-011 (which contains inulin, Akkermansia muciniphila, Clostridium beijerinckii, Clostridium butyricum, Bifidobacterium infantis and Anaerobutyricum hallii) significantly improved in the primary outcome, glucose total area under the curve (AUC): −36.1 mg/dL/180 min, p=0.0500 and also improved in secondary outcomes, glycated hemoglobin (A1c): −0.6, glucose incremental-AUC: −28.6 mg/dL/180 min.ConclusionsTo our knowledge, this is the first randomized controlled trial to administer four of the five strains to human subjects with T2D. This proof-of-concept study (clinical trial number NCT03893422) shows that the intervention was safe and well tolerated and that supplementation with WBF-011 improves postprandial glucose control. The limited sample size and intersubject variability justifies future studies designed to confirm and expand on these observations.

Studies show that BP+MR are decreased inT2D; the impact of administering specific strains has not been reported. A 12-week, double blind, placebo-controlled study of 2 MFs prepared GMP using only GRAS constituents (MF-1: BP; MF-2: BP + MR) in T2D (drug naive + metformin ± SFU, n=76), showed improved GC. See Table for demographics and key results. The primary endpoint was change from baseline (∆) in total glucose AUC0-180 min during a Boost Plus™ (720 cal) MTT. At 12 weeks, MF-2 yielded significant ∆s in both total (T) and incremental (I) glucose AUC0-180 min, (p=0.05 and 0.007 respectively). MF-1 improved only I glucose AUC0-180 min (p=0.05). Neither MF-1 or MF-2 impacted fasting glucose, T or I insulin AUC0-180 min, weight, BMI, HOMA-IR, lipids, or c-reactive protein. No safety or tolerability concerns were observed. Pre and post fecal microbial analyses are pending. Post hoc analyses revealed 2 interesting results. 1) SFU subjects across both study groups showed minimal response while subjects not using SFUs had greater ∆s in both T glucose AUC0-180 min (-24.4%) and placebo-corrected ∆ A1c (-0.7%). 2) MF-2 showed significant reductions in ALT consistent with improved liver health. In conclusion, 1) Interventions directed at increasing BP+MR may be of benefit in T2D. 2) MF-2 appears to be a safe and effective non-pharmacologic intervention for T2D which merits further investigation. Disclosure O.G. Kolterman: Advisory Panel; Spouse/Partner; Intarcia Therapeutics, Inc. Advisory Panel; Self; Zafgen, Inc. Board Member; Self; American Diabetes Association, GlySens Incorporated, Viacyte, Inc. Consultant; Self; ADOCIA, Cirius Therapeutics Inc, DiaVacs Inc, JDRF, Nanoprecisionmedical, NuSirt Biopharma, Inc., Renova Therapeutics, Sensulin, LLC. Employee; Self; Whole Biome Inc. Stock/Shareholder; Self; Corcept Therapeutics, GI Therapeutics, Xeris Pharmaceuticals, Inc. Other Relationship; Spouse/Partner; American Diabetes Association. F. Perraudeau: Employee; Self; Whole Biome. M.S. Iyer: Employee; Self; Whole Biome Inc. J.S. Eid: Board Member; Self; Whole Biome Inc. J. Bullard: Employee; Self; Whole Biome Inc. Stock/Shareholder; Self; Whole Biome Inc. M.F. Schicklberger: Employee; Self; Whole Biome Inc. A. Cheng: None. N. Justice: Employee; Self; Whole Biome. P. McMurdie: Employee; Self; Whole Biome, Inc. B. Stoneburner: None. J. Gines: Employee; Self; Whole Biome Inc. S. Tyagi: None. C. Cutcliffe: Employee; Self; Whole Biome Inc.

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.

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.

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.

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.

ObjectiveColorectal 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.DesignRaw 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.ResultsDefinitive 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.ConclusionsDespite 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.

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 microbial 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, including both parameteric and nonparametric methods. 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, partial least squares and linear models as well as nonparametric testing using community networks and the ggnetwork package.

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.

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) × 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.

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.

Microbial communities are commonly characterized by amplifying and sequencing target genes, but errors limit the precision of amplicon sequencing. We present DADA2, a software package that models and corrects amplicon errors. DADA2 identified more real variants than other methods in Illumina-sequenced mock communities, some differing by a single nucleotide, while outputting fewer spurious sequences. DADA2 analysis of vaginal samples revealed a diversity of Lactobacillus crispatus strains undetected by OTU methods.

Summary: 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/ . Contact : mcmurdie@alumni.stanford.edu .

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., Author Summary The term microbiome refers to the ecosystem of microbes that live in a defined environment. The decreasing cost and increasing speed of DNA sequencing technology has recently provided scientists with affordable and timely access to the genes and genomes of microbiomes that inhabit our planet and even our own bodies. In these investigations many microbiome samples are sequenced at the same time on the same DNA sequencing machine, but often result in total numbers of sequences per sample that are vastly different. The common procedure for addressing this difference in sequencing effort across samples – different library sizes – is to either (1) base analyses on the proportional abundance of each species in a library, or (2) rarefy, throw away sequences from the larger libraries so that all have the same, smallest size. We show that both of these normalization methods can work when comparing obviously-different whole microbiomes, but that neither method works well when comparing the relative proportions of each bacterial species across microbiome samples. We show that alternative methods based on a statistical mixture model perform much better and can be easily adapted from a separate biological sub-discipline, called RNA-Seq analysis.

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.

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 microbial 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, including both parameteric and nonparametric methods. We provide examples of using theR packages dada2, phyloseq, DESeq2, ggplot2 and vegan to filter, visualize and test microbiome data. We also provide examples of supervised analyses using random forests, partial least squares and linear models as well as nonparametric testing using community networks and the ggnetwork package.

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.

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.

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., Author Summary Dehalococcoides are free-living sediment and subsurface bacteria with remarkably small, streamlined genomes and an unusual degree of niche specialization. These strictly anaerobic bacteria gain metabolic energy exclusively through a novel type of respiration that results in reductive elimination of chlorides from organochlorines, many of which are priority pollutants. In this article, we compare the first complete genome sequences of Dehalococcoides strains that grow via respiration of vinyl chloride (VC), a human carcinogen and abundant groundwater pollutant. Our work provides novel insights into Dehalococcoides chromosome organization and evolution, identifies specific positions in the chromosomes where new genes—like the genes responsible for growth on VC—are integrated, and generates clues how these dechlorinating bacteria adapt to anthropogenic contamination. This information sheds new light on Dehalococcoides biology and ecology, with implications for enhanced bioremediation to protect dwindling drinking water reservoirs.

Growing interest in the gut microbiome has revealed links between gut microbiome composition and many diseases including not only infections but also obesity, diabetes and inflammatory bowel disease. The microbiome composition can undergo changes as a result of interactions between gut microbiome and exogenous agents. However, the impact of commonly used pharmaceuticals on the gut microbiome has not been well characterized. In this study, we characterized the impact of 24 common pharmaceuticals on gut microbiome compositional shift in mice and in a human fecal culture system. Mice were dosed with each optimized pharmaceutical compound daily for five consecutive days. Fecal samples were collected on day -1, 0, 4 and 5 of the administration. For the human fecal culture system, human stool samples were collected and immediately stored at -80 degree C. After resuspension of the sample in reduced medium, fecal cultures were incubated with individual compounds for 24 hours in an anaerobic chamber. DNA was extracted, then the V4 region of 16S rRNA gene was amplified by PCR and sequenced using the Illumina MiSeq. A negative binomial Wald test was used to detect microbial taxa difference. Principal component analysis exhibits greatest shift in mice microbiome by antimicrobial drugs such as Azithromycin, Amoxicillin and Metronidazole as well as Type 2 diabetes drugs such as Acarbose and Metformin between day 0 and day 5. Permutational multivariate analysis of variance using Euclidean distance exhibited greatest significant microbiome shift by Acarbose, Amoxicillin and Metformin (p 0.1). These results suggest different impact of administration of antimicrobial agents (e.g. protein synthesis inhibitor) and modulation of glucose availability to the intestinal microbiome. Similar microbiome shifts were also observed in the anaerobic human fecal culture system. Understanding the impact of pharmaceuticals to the gut microbiome could ultimately be useful in improving predictions of the therapeutic intervention outcomes on variety of individuals.

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.