Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers.

BACKGROUND: Sequencing partial 16S rRNA genes is a cost effective method for quantifying the microbial composition of an environment, such as the human gut. However, downstream analysis relies on binning reads into microbial groups by either considering each unique sequence as a different microbe, querying a database to get taxonomic labels from sequences, or clustering similar sequences together. However, these approaches do not fully capture evolutionary relationships between microbes, limiting the ability to identify differentially abundant groups of microbes between a diseased and control cohort. We present sequence-based biomarkers (SBBs), an aggregation method that groups and aggregates microbes using single variants and combinations of variants within their 16S sequences. We compare SBBs against other existing aggregation methods (OTU clustering and Microphenoor DiTaxa features) in several benchmarking tasks: biomarker discovery via permutation test, biomarker discovery via linear discriminant analysis, and phenotype prediction power. We demonstrate the SBBs perform on-par or better than the state-of-the-art methods in biomarker discovery and phenotype prediction. RESULTS: On two independent datasets, SBBs identify differentially abundant groups of microbes with similar or higher statistical significance than existing methods in both a permutation-test-based analysis and using linear discriminant analysis effect size. . By grouping microbes by SBB, we can identify several differentially abundant microbial groups (FDR <.1) between children with autism and neurotypical controls in a set of 115 discordant siblings. Porphyromonadaceae, Ruminococcaceae, and an unnamed species of Blastocystis were significantly enriched in autism, while Veillonellaceae was significantly depleted. Likewise, aggregating microbes by SBB on a dataset of obese and lean twins, we find several significantly differentially abundant microbial groups (FDR<.1). We observed Megasphaera andSutterellaceae highly enriched in obesity, and Phocaeicola significantly depleted. SBBs also perform on bar with or better than existing aggregation methods as features in a phenotype prediction model, predicting the autism phenotype with an ROC-AUC score of .64 and the obesity phenotype with an ROC-AUC score of .84. CONCLUSIONS: SBBs provide a powerful method for aggregating microbes to perform differential abundance analysis as well as phenotype prediction. Our source code can be freely downloaded from http://github.com/briannachrisman/16s_biomarkers .

Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences.

BACKGROUND: Shotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters. RESULTS: To evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2. CONCLUSIONS: Our results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/.).

Correction to: Piphillin predicts metagenomic composition and dynamics from DADA2- corrected 16S rDNA sequences.

Following the publication of this article [1], the authors reported errors in Figs. 1, 2 and 5. Due to a typesetting error the asterisks denoting significance were missing from the published figures.

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.

Maternal metabolic, immune, and microbial systems in late pregnancy vary with malnutrition in mice.

Malnutrition is a global threat to pregnancy health and impacts offspring development. Establishing an optimal pregnancy environment requires the coordination of maternal metabolic and immune pathways, which converge at the gut. Diet, metabolic, and immune dysfunctions have been associated with gut dysbiosis in the nonpregnant individual. In pregnancy, these states are associated with poor pregnancy outcomes and offspring development. However, the impact of malnutrition on maternal gut microbes, and their relationships with maternal metabolic and immune status, has been largely underexplored. To determine the impact of undernutrition and overnutrition on maternal metabolic status, inflammation, and the microbiome, and whether relationships exist between these systems, pregnant mice were fed either a normal, calorically restricted (CR), or a high fat (HF) diet. In late pregnancy, maternal inflammatory and metabolic biomarkers were measured and the cecal microbiome was characterized. Microbial richness was reduced in HF mothers although they did not gain more weight than controls. First trimester weight gain was associated with differences in the microbiome. Microbial abundance was associated with altered plasma and gut inflammatory phenotypes and peripheral leptin levels. Taxa potentially protective against elevated maternal leptin, without the requirement of a CR diet, were identified. Suboptimal dietary conditions common during pregnancy adversely impact maternal metabolic and immune status and the microbiome. HF nutrition exerts the greatest pressures on maternal microbial dynamics and inflammation. Key gut bacteria may mediate local and peripheral inflammatory events in response to maternal nutrient and metabolic status, with implications for maternal and offspring health.

Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons.

Bacterial diversity among environmental samples is commonly assessed with PCR-amplified 16S rRNA gene (16S) sequences. Perceived diversity, however, can be influenced by sample preparation, primer selection, and formation of chimeric 16S amplification products. Chimeras are hybrid products between multiple parent sequences that can be falsely interpreted as novel organisms, thus inflating apparent diversity. We developed a new chimera detection tool called Chimera Slayer (CS). CS detects chimeras with greater sensitivity than previous methods, performs well on short sequences such as those produced by the 454 Life Sciences (Roche) Genome Sequencer, and can scale to large data sets. By benchmarking CS performance against sequences derived from a controlled DNA mixture of known organisms and a simulated chimera set, we provide insights into the factors that affect chimera formation such as sequence abundance, the extent of similarity between 16S genes, and PCR conditions. Chimeras were found to reproducibly form among independent amplifications and contributed to false perceptions of sample diversity and the false identification of novel taxa, with less-abundant species exhibiting chimera rates exceeding 70%. Shotgun metagenomic sequences of our mock community appear to be devoid of 16S chimeras, supporting a role for shotgun metagenomics in validating novel organisms discovered in targeted sequence surveys.

Expansion of urease- and uricase-containing, indole- and p-cresol-forming and contraction of short-chain fatty acid-producing intestinal microbiota in ESRD.

BACKGROUND: Intestinal microbiome constitutes a symbiotic ecosystem that is essential for health, and changes in its composition/function cause various illnesses. Biochemical milieu shapes the structure and function of the microbiome. Recently, we found marked differences in the abundance of numerous bacterial taxa between ESRD and healthy individuals. Influx of urea and uric acid and dietary restriction of fruits and vegetables to prevent hyperkalemia alter ESRD patients' intestinal milieu. We hypothesized that relative abundances of bacteria possessing urease, uricase, and p-cresol- and indole-producing enzymes is increased, while abundance of bacteria containing enzymes converting dietary fiber to short-chain fatty acids (SCFA) is reduced in ESRD. METHODS: Reference sets of bacteria containing genes of interest were compiled to family, and sets of intestinal bacterial families showing differential abundances between 12 healthy and 24 ESRD individuals enrolled in our original study were compiled. Overlap between sets was assessed using hypergeometric distribution tests. RESULTS: Among 19 microbial families that were dominant in ESRD patients, 12 possessed urease, 5 possessed uricase, and 4 possessed indole and p-cresol-forming enzymes. Among 4 microbial families that were diminished in ESRD patients, 2 possessed butyrate-forming enzymes. Probabilities of these overlapping distributions were <0.05. CONCLUSIONS: ESRD patients exhibited significant expansion of bacterial families possessing urease, uricase, and indole and p-cresol forming enzymes, and contraction of families possessing butyrate-forming enzymes. Given the deleterious effects of indoxyl sulfate, p-cresol sulfate, and urea-derived ammonia, and beneficial actions of SCFA, these changes in intestinal microbial metabolism contribute to uremic toxicity and inflammation.

Microbial biogeography across a full-scale wastewater treatment plant transect: evidence for immigration between coupled processes.

Wastewater treatment plants use a variety of bioreactor types and configurations to remove organic matter and nutrients. Little is known regarding the effects of different configurations and within-plant immigration on microbial community dynamics. Previously, we found that the structure of ammonia-oxidizing bacterial (AOB) communities in a full-scale dispersed growth activated sludge bioreactor correlated strongly with levels of NO2 (-) entering the reactor from an upstream trickling filter. Here, to further examine this puzzling association, we profile within-plant microbial biogeography (spatial variation) and test the hypothesis that substantial microbial immigration occurs along a transect (raw influent, trickling filter biofilm, trickling filter effluent, and activated sludge) at the same full-scale wastewater treatment plant. AOB amoA gene abundance increased >30-fold between influent and trickling filter effluent concomitant with NO2 (-) production, indicating unexpected growth and activity of AOB within the trickling filter. Nitrosomonas europaea was the dominant AOB phylotype in trickling filter biofilm and effluent, while a distinct "Nitrosomonas-like" lineage dominated in activated sludge. Prior time series indicated that this "Nitrosomonas-like" lineage was dominant when NO2 (-) levels in the trickling filter effluent (i.e., activated sludge influent) were low, while N. europaea became dominant in the activated sludge when NO2 (-) levels were high. This is consistent with the hypothesis that NO2 (-) production may cooccur with biofilm sloughing, releasing N. europaea from the trickling filter into the activated sludge bioreactor. Phylogenetic microarray (PhyloChip) analyses revealed significant spatial variation in taxonomic diversity, including a large excess of methanogens in the trickling filter relative to activated sludge and attenuation of Enterobacteriaceae across the transect, and demonstrated transport of a highly diverse microbial community via the trickling filter effluent to the activated sludge bioreactor. Our results provide compelling evidence that substantial immigration between coupled process units occurs and may exert significant influence over microbial community dynamics within staged bioreactors.

Chronic kidney disease alters intestinal microbial flora.

The population of microbes (microbiome) in the intestine is a symbiotic ecosystem conferring trophic and protective functions. Since the biochemical environment shapes the structure and function of the microbiome, we tested whether uremia and/or dietary and pharmacologic interventions in chronic kidney disease alters the microbiome. To identify different microbial populations, microbial DNA was isolated from the stools of 24 patients with end-stage renal disease (ESRD) and 12 healthy persons, and analyzed by phylogenetic microarray. There were marked differences in the abundance of 190 bacterial operational taxonomic units (OTUs) between the ESRD and control groups. OTUs from Brachybacterium, Catenibacterium, Enterobacteriaceae, Halomonadaceae, Moraxellaceae, Nesterenkonia, Polyangiaceae, Pseudomonadaceae, and Thiothrix families were markedly increased in patients with ESRD. To isolate the effect of uremia from inter-individual variations, comorbid conditions, and dietary and medicinal interventions, rats were studied 8 weeks post 5/6 nephrectomy or sham operation. This showed a significant difference in the abundance of 175 bacterial OTUs between the uremic and control animals, most notably as decreases in the Lactobacillaceae and Prevotellaceae families. Thus, uremia profoundly alters the composition of the gut microbiome. The biological impact of this phenomenon is unknown and awaits further investigation.

Adaptation of the gut holobiont to malnutrition during mouse pregnancy depends on the type of nutritional adversity.

Malnutrition can influence maternal physiology and programme offspring development. Yet, in pregnancy, little is known about how dietary challenges that influence maternal phenotype affect gut structure and function. Emerging evidence suggests that interactions between the environment, multidrug resistance (MDR) transporters and microbes may influence maternal adaptation to pregnancy and regulate fetoplacental development. We hypothesized that the gut holobiont (host and microbes) during pregnancy adapts differently to suboptimal maternal diets, evidenced by changes in the gut microenvironment, morphology, and expression of key protective MDR transporters during pregnancy. Mice were fed a control diet (CON) during pregnancy, or undernourished (UN) by 30% of control intake from gestational day (GD) 5.5-18.5, or fed 60% high fat diet (HF) for 8 weeks before and during pregnancy. At GD18.5, maternal small intestinal (SI) architecture (H&E), proliferation (Ki67), P-glycoprotein (P-gp - encoded by Abcb1a/b) and breast cancer resistance protein (BCRP/Abcg2) MDR transporter expression and levels of pro-inflammatory biomarkers were assessed. Circulating inflammatory biomarkers and maternal caecal microbiome composition (G3 PhyloChipTM) were measured. MDR transporter expression was also assessed in fetal gut. HF diet increased maternal SI crypt depth and proinflammatory load, and decreased SI expression of Abcb1a mRNA, whilst UN increased SI villi proliferation and Abcb1a, but decreased Abcg2, mRNA expression. There were significant associations between Abcb1a and Abcg2 mRNA levels with relative abundance of specific microbial taxa. Using a systems physiology approach we report that common nutritional adversities provoke adaptations in the pregnancy holobiont in mice, and reveal new mechanisms that could influence reproductive outcomes and fetal development.

StrainSelect: A novel microbiome reference database that disambiguates all bacterial strains, genome assemblies and extant cultures worldwide.

Motivation: Microbial metagenomic profiling software and databases are advancing rapidly for development of novel disease biomarkers and therapeutics yet three problems impede analyses: 1) the conflation of "genome assembly" and "strain" in reference databases; 2) difficulty connecting DNA biomarkers to a procurable strain for laboratory experimentation; and 3) absence of a comprehensive and unified strain-resolved reference database for integrating both shotgun metagenomics and 16S rRNA gene data. Results: We demarcated 681,087 strains, the largest collection of its kind, by filtering public data into a knowledge graph of vertices representing contiguous DNA sequences, genome assemblies, strain monikers and bio-resource center (BRC) catalog numbers then adding inter-vertex edges only for synonyms or direct derivatives. Surprisingly, for 10,043 important strains, we found replicate RefSeq genome assemblies obstructing interpretation of database searches. We organized each strain into eight taxonomic ranks with bootstrap confidence inversely correlated with genome assembly contamination. The StrainSelect database is suited for applications where a taxonomic, functional or procurement reference is needed for shotgun or amplicon metagenomics since 636,568 strains have at least one 16S rRNA gene, 245,005 have at least one annotated genome assembly, and 36,671 are procurable from at least one BRC. The database overcomes all three aforementioned problems since it disambiguates strains from assemblies, locates strains at BRCs, and unifies a taxonomic reference for both 16S rRNA and shotgun metagenomics. Availability: The StrainSelect database is available in igraph and tabular vertex-edge formats compatible with Neo4J. Dereplicated MinHash and fasta databases are distributed for sourmash and usearch pipelines at http://strainselect.secondgenome.com. Contact:todd.desantis@gmail.com. Supplementary information: Supplementary data are available online.

Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism.

While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed "cross-omic topics", which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.

Changes in the microbial community structure of bacteria, archaea and fungi in response to elevated CO(2) and warming in an Australian native grassland soil.

The microbial community structure of bacteria, archaea and fungi is described in an Australian native grassland soil after more than 5 years exposure to different atmospheric CO2 concentrations ([CO2]) (ambient, +550 ppm) and temperatures (ambient, + 2°C) under different plant functional types (C3 and C4 grasses) and at two soil depths (0-5 cm and 5-10 cm). Archaeal community diversity was influenced by elevated [CO2], while under warming archaeal 16S rRNA gene copy numbers increased for C4 plant Themeda triandra and decreased for the C3 plant community (P < 0.05). Fungal community diversity resulted in three groups based upon elevated [CO2], elevated [CO2] plus warming and ambient [CO2]. Overall bacterial community diversity was influenced primarily by depth. Specific bacterial taxa changed in richness and relative abundance in response to climate change factors when assessed by a high-resolution 16S rRNA microarray (PhyloChip). Operational taxonomic unit signal intensities increased under elevated [CO2] for both Firmicutes and Bacteroidetes, and increased under warming for Actinobacteria and Alphaproteobacteria. For the interaction of elevated [CO2] and warming there were 103 significant operational taxonomic units (P < 0.01) representing 15 phyla and 30 classes. The majority of these operational taxonomic units increased in abundance for elevated [CO2] plus warming plots, while abundance declined in warmed or elevated [CO2] plots. Bacterial abundance (16S rRNA gene copy number) was significantly different for the interaction of elevated [CO2] and depth (P < 0.05) with decreased abundance under elevated [CO2] at 5-10 cm, and for Firmicutes under elevated [CO2] (P < 0.05). Bacteria, archaea and fungi in soil responded differently to elevated [CO2], warming and their interaction. Taxa identified as significantly climate-responsive could show differing trends in the direction of response ('+' or '-') under elevated CO2 or warming, which could then not be used to predict their interactive effects supporting the need to investigate interactive effects for climate change. The approach of focusing on specific taxonomic groups provides greater potential for understanding complex microbial community changes in ecosystems under climate change.

Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma.

BACKGROUND: Improvement in lung function after macrolide antibiotic therapy has been attributed to reduction in bronchial infection by specific bacteria. However, the airway might be populated by a more diverse microbiota, and clinical features of asthma might be associated with characteristics of the airway microbiota present. OBJECTIVE: We sought to determine whether relationships exist between the composition of the airway bacterial microbiota and clinical features of asthma using culture-independent tools capable of detecting the presence and relative abundance of most known bacteria. METHODS: In this pilot study bronchial epithelial brushings were collected from 65 adults with suboptimally controlled asthma participating in a multicenter study of the effects of clarithromycin on asthma control and 10 healthy control subjects. A combination of high-density 16S ribosomal RNA microarray and parallel clone library-sequencing analysis was used to profile the microbiota and examine relationships with clinical measurements. RESULTS: Compared with control subjects, 16S ribosomal RNA amplicon concentrations (a proxy for bacterial burden) and bacterial diversity were significantly higher among asthmatic patients. In multivariate analyses airway microbiota composition and diversity were significantly correlated with bronchial hyperresponsiveness. Specifically, the relative abundance of particular phylotypes, including members of the Comamonadaceae, Sphingomonadaceae, Oxalobacteraceae, and other bacterial families were highly correlated with the degree of bronchial hyperresponsiveness. CONCLUSION: The composition of bronchial airway microbiota is associated with the degree of bronchial hyperresponsiveness among patients with suboptimally controlled asthma. These findings support the need for further functional studies to examine the potential contribution of members of the airway microbiota in asthma pathogenesis.

Strain level and comprehensive microbiome analysis in inflammatory bowel disease via multi-technology meta-analysis identifies key bacterial influencers of disease.

Objective: Inflammatory bowel disease (IBD) is a heterogenous disease in which the microbiome has been shown to play an important role. However, the precise homeostatic or pathological functions played by bacteria remain unclear. Most published studies report taxa-disease associations based on single-technology analysis of a single cohort, potentially biasing results to one clinical protocol, cohort, and molecular analysis technology. To begin to address this key question, precise identification of the bacteria implicated in IBD across cohorts is necessary. Methods: We sought to take advantage of the numerous and diverse studies characterizing the microbiome in IBD to develop a multi-technology meta-analysis (MTMA) as a platform for aggregation of independently generated datasets, irrespective of DNA-profiling technique, in order to uncover the consistent microbial modulators of disease. We report the largest strain-level survey of IBD, integrating microbiome profiles from 3,407 samples from 21 datasets spanning 15 cohorts, three of which are presented for the first time in the current study, characterized using three DNA-profiling technologies, mapping all nucleotide data against known, culturable strain reference data. Results: We identify several novel IBD associations with culturable strains that have so far remained elusive, including two genome-sequenced but uncharacterized Lachnospiraceae strains consistently decreased in both the gut luminal and mucosal contents of patients with IBD, and demonstrate that these strains are correlated with inflammation-related pathways that are known mechanisms targeted for treatment. Furthermore, comparative MTMA at the species versus strain level reveals that not all significant strain associations resulted in a corresponding species-level significance and conversely significant species associations are not always re-captured at the strain level. Conclusion: We propose MTMA for uncovering experimentally testable strain-disease associations that, as demonstrated here, are beneficial in discovering mechanisms underpinning microbiome impact on disease or novel targets for therapeutic interventions.

Multi-angle meta-analysis of the gut microbiome in Autism Spectrum Disorder: a step toward understanding patient subgroups.

Observational studies have shown that the composition of the human gut microbiome in children diagnosed with Autism Spectrum Disorder (ASD) differs significantly from that of their neurotypical (NT) counterparts. Thus far, reported ASD-specific microbiome signatures have been inconsistent. To uncover reproducible signatures, we compiled 10 publicly available raw amplicon and metagenomic sequencing datasets alongside new data generated from an internal cohort (the largest ASD cohort to date), unified them with standardized pre-processing methods, and conducted a comprehensive meta-analysis of all taxa and variables detected across multiple studies. By screening metadata to test associations between the microbiome and 52 variables in multiple patient subsets and across multiple datasets, we determined that differentially abundant taxa in ASD versus NT children were dependent upon age, sex, and bowel function, thus marking these variables as potential confounders in case-control ASD studies. Several taxa, including the strains Bacteroides stercoris t__190463 and Clostridium M bolteae t__180407, and the species Granulicatella elegans and Massilioclostridium coli, exhibited differential abundance in ASD compared to NT children only after subjects with bowel dysfunction were removed. Adjusting for age, sex and bowel function resulted in adding or removing significantly differentially abundant taxa in ASD-diagnosed individuals, emphasizing the importance of collecting and controlling for these metadata. We have performed the largest (n = 690) and most comprehensive systematic analysis of ASD gut microbiome data to date. Our study demonstrated the importance of accounting for confounding variables when designing statistical comparative analyses of ASD- and NT-associated gut bacterial profiles. Mitigating these confounders identified robust microbial signatures across cohorts, signifying the importance of accounting for these factors in comparative analyses of ASD and NT-associated gut profiles. Such studies will advance the understanding of different patient groups to deliver appropriate therapeutics by identifying microbiome traits germane to the specific ASD phenotype.

PyNAST: a flexible tool for aligning sequences to a template alignment.

MOTIVATION: The Nearest Alignment Space Termination (NAST) tool is commonly used in sequence-based microbial ecology community analysis, but due to the limited portability of the original implementation, it has not been as widely adopted as possible. Python Nearest Alignment Space Termination (PyNAST) is a complete reimplementation of NAST, which includes three convenient interfaces: a Mac OS X GUI, a command-line interface and a simple application programming interface (API). RESULTS: The availability of PyNAST will make the popular NAST algorithm more portable and thereby applicable to datasets orders of magnitude larger by allowing users to install PyNAST on their own hardware. Additionally because users can align to arbitrary template alignments, a feature not available via the original NAST web interface, the NAST algorithm will be readily applicable to novel tasks outside of microbial community analysis. AVAILABILITY: PyNAST is available at http://pynast.sourceforge.net.

PCR amplification-independent methods for detection of microbial communities by the high-density microarray PhyloChip.

Environmental microbial community analysis typically involves amplification by PCR, despite well-documented biases. We have developed two methods of PCR-independent microbial community analysis using the high-density microarray PhyloChip: direct hybridization of 16S rRNA (dirRNA) or rRNA converted to double-stranded cDNA (dscDNA). We compared dirRNA and dscDNA communities to PCR-amplified DNA communities using a mock community of eight taxa, as well as experiments derived from three environmental sample types: chromium-contaminated aquifer groundwater, tropical forest soil, and secondary sewage in seawater. Community profiles by both direct hybridization methods showed differences that were expected based on accompanying data but that were missing in PCR-amplified communities. Taxon richness decreased in RNA compared to that in DNA communities, suggesting a subset of 20% in soil and 60% in groundwater that is active; secondary sewage showed no difference between active and inactive populations. Direct hybridization of dscDNA and RNA is thus a viable alternative to PCR-amplified microbial community analysis, providing identification of the active populations within microbial communities that attenuate pollutants, drive global biogeochemical cycles, or proliferate disease states.

Simrank: Rapid and sensitive general-purpose k-mer search tool.

BACKGROUND: Terabyte-scale collections of string-encoded data are expected from consortia efforts such as the Human Microbiome Project http://nihroadmap.nih.gov/hmp. Intra- and inter-project data similarity searches are enabled by rapid k-mer matching strategies. Software applications for sequence database partitioning, guide tree estimation, molecular classification and alignment acceleration have benefited from embedded k-mer searches as sub-routines. However, a rapid, general-purpose, open-source, flexible, stand-alone k-mer tool has not been available. RESULTS: Here we present a stand-alone utility, Simrank, which allows users to rapidly identify database strings the most similar to query strings. Performance testing of Simrank and related tools against DNA, RNA, protein and human-languages found Simrank 10X to 928X faster depending on the dataset. CONCLUSIONS: Simrank provides molecular ecologists with a high-throughput, open source choice for comparing large sequence sets to find similarity.

Bacterial community analysis of beef cattle feedlots reveals that pen surface is distinct from feces.

The surface of beef cattle feedlot pens is commonly conceptualized as being packed uncomposted manure. Despite the important role that the feedlot pen may play in the transmission of veterinary and zoonotic pathogens, the bacterial ecology of feedlot surface material is not well understood. Our present study characterized the bacterial communities of the beef cattle feedlot pen surface material using 3647 full-length 16S rDNA sequences, and we compared the community composition of feedlot pens to the fecal source material. The feedlot surface composite was represented by members of the phylum Actinobacteria (42%), followed by Firmicutes (24%), Bacteroidetes (24%), and Proteobacteria (9%). The feedlot pen surface material bacterial communities were clearly distinct from those of the feces from animals in the same pen. Comparisons with previously published results of feces from the animals in the same pen reveal that, of 139 genera identified, only 25 were present in both habitats. These results indicate that, microbiologically, the feedlot pen surface material is separate and distinct from the fecal source material, suggesting that bacteria that originate in cattle feces face different selection pressures and survival challenges during their tenure in the feedlot pen, as compared to their residence in the gastrointestinal tract.