SNV-FEAST: microbial source tracking with single nucleotide variants.

Elucidating the sources of a microbiome can provide insight into the ecological dynamics responsible for the formation of these communities. Source tracking approaches to date leverage species abundance information; however, single nucleotide variants (SNVs) may be more informative because of their high specificity to certain sources. To overcome the computational burden of utilizing all SNVs for a given sample, we introduce a novel method to identify signature SNVs for source tracking. Signature SNVs used as input into a previously designed source tracking algorithm, FEAST, can more accurately estimate contributions than species and provide novel insights, demonstrated in three case studies.

Evaluating supervised and unsupervised background noise correction in human gut microbiome data.

The ability to predict human phenotypes and identify biomarkers of disease from metagenomic data is crucial for the development of therapeutics for microbiome-associated diseases. However, metagenomic data is commonly affected by technical variables unrelated to the phenotype of interest, such as sequencing protocol, which can make it difficult to predict phenotype and find biomarkers of disease. Supervised methods to correct for background noise, originally designed for gene expression and RNA-seq data, are commonly applied to microbiome data but may be limited because they cannot account for unmeasured sources of variation. Unsupervised approaches address this issue, but current methods are limited because they are ill-equipped to deal with the unique aspects of microbiome data, which is compositional, highly skewed, and sparse. We perform a comparative analysis of the ability of different denoising transformations in combination with supervised correction methods as well as an unsupervised principal component correction approach that is presently used in other domains but has not been applied to microbiome data to date. We find that the unsupervised principal component correction approach has comparable ability in reducing false discovery of biomarkers as the supervised approaches, with the added benefit of not needing to know the sources of variation apriori. However, in prediction tasks, it appears to only improve prediction when technical variables contribute to the majority of variance in the data. As new and larger metagenomic datasets become increasingly available, background noise correction will become essential for generating reproducible microbiome analyses.

The effect of gastric acid suppression on probiotic colonization in a double blinded randomized clinical trial.

BACKGROUND & AIMS: Probiotics contain living microorganisms consumed for their putative benefits on the intestinal microbiota and general health and a concept is emerging to use probiotic as a therapeutic intervention to reduce proton pump inhibitors (PPIs) negative effects, but data is lacking. The use of PPIs can result in disordered gut microbiota, leading to a risk of enteric infections. PPIs are frequently prescribed in the general practice setting for gastroesophageal reflux disease (GERD), peptic ulcer disease, and related conditions. Despite the availability and widespread use of probiotics and acid-suppressing medications, the effect of PPIs-induced gastric acid suppression on the survival and colonization of probiotics bacterial species is currently unclear. We hypothesized that gastric acid suppression may improve intestinal colonization of probiotics bacterial species and probiotic intervention may have a potential role in mitigating untoward effects of PPI. METHODS: In a randomized, double-blind, placebo-controlled study, healthy subjects were given either proton pump inhibitor (PPI, n = 15) or placebo (n = 15) over 6 weeks. All subjects then consumed multi-strain probiotics from weeks 2-6. Thirty participants (10 males, 20 females, age range: 18-56 years) were enrolled in the study. Shotgun metagenomic sequencing and untargeted metabolomics analyses were performed on stool samples collected at week 0, 2, and 6. RESULTS: Short term PPI treatment increased the microbial abundance of Streptococcaceae (p = 0.004), Leuconostacaceae (p = 0.001), and Pasteurellaceae (p = 0.020) at family level and corresponding genus levels. The metabolomic analysis of the stools revealed a change in 10 metabolites where Gly Arg Val and phenylacetic acid were consistently increased compared to the baseline. Probiotic intervention inhibited PPI-induced microbial changes such as a decrease in Leuconostacaceae family (p = 0.01) and led to an increase in metabolite 1H-Indole-4-carbaldehyde. Notably, PPI enhanced the colonization of certain probiotic bacterial species like Streptococcus thermophilus (p < 0.05) along with other species present in the multi-strain probiotic. CONCLUSION: Acid suppression enhanced certain probiotic associated bacterial colonization and probiotics in turn suppressed PPI-mediated intestinal microbial alterations. Thus, probiotics in combination with PPI might be a beneficial strategy that allows probiotic colonization and suppress PPI-induced microbial perturbations. CLINICAL TRIALS. GOV, NUMBER: NCT03327051.

Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis.

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.

FEAST: fast expectation-maximization for microbial source tracking.

A major challenge of analyzing the compositional structure of microbiome data is identifying its potential origins. Here, we introduce fast expectation-maximization microbial source tracking (FEAST), a ready-to-use scalable framework that can simultaneously estimate the contribution of thousands of potential source environments in a timely manner, thereby helping unravel the origins of complex microbial communities ( https://github.com/cozygene/FEAST ). The information gained from FEAST may provide insight into quantifying contamination, tracking the formation of developing microbial communities, as well as distinguishing and characterizing bacteria-related health conditions.

Modeling the temporal dynamics of the gut microbial community in adults and infants.

Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.

Whole Genome Analyses Suggests that Burkholderia sensu lato Contains Two Additional Novel Genera (Mycetohabitans gen. nov., and Trinickia gen. nov.): Implications for the Evolution of Diazotrophy and Nodulation in the Burkholderiaceae.

Burkholderia sensu lato is a large and complex group, containing pathogenic, phytopathogenic, symbiotic and non-symbiotic strains from a very wide range of environmental (soil, water, plants, fungi) and clinical (animal, human) habitats. Its taxonomy has been evaluated several times through the analysis of 16S rRNA sequences, concantenated 4⁻7 housekeeping gene sequences, and lately by genome sequences. Currently, the division of this group into Burkholderia, Caballeronia, Paraburkholderia, and Robbsia is strongly supported by genome analysis. These new genera broadly correspond to the various habitats/lifestyles of Burkholderia s.l., e.g., all the plant beneficial and environmental (PBE) strains are included in Paraburkholderia (which also includes all the N2-fixing legume symbionts) and Caballeronia, while most of the human and animal pathogens are retained in Burkholderia sensu stricto. However, none of these genera can accommodate two important groups of species. One of these includes the closely related Paraburkholderia rhizoxinica and Paraburkholderia endofungorum, which are both symbionts of the fungal phytopathogen Rhizopus microsporus. The second group comprises the Mimosa-nodulating bacterium Paraburkholderia symbiotica, the phytopathogen Paraburkholderia caryophylli, and the soil bacteria Burkholderia dabaoshanensis and Paraburkholderia soli. In order to clarify their positions within Burkholderia sensu lato, a phylogenomic approach based on a maximum likelihood analysis of conserved genes from more than 100 Burkholderia sensu lato species was carried out. Additionally, the average nucleotide identity (ANI) and amino acid identity (AAI) were calculated. The data strongly supported the existence of two distinct and unique clades, which in fact sustain the description of two novel genera Mycetohabitans gen. nov. and Trinickia gen. nov. The newly proposed combinations are Mycetohabitans endofungorum comb. nov., Mycetohabitansrhizoxinica comb. nov., Trinickia caryophylli comb. nov., Trinickiadabaoshanensis comb. nov., Trinickia soli comb. nov., and Trinickiasymbiotica comb. nov. Given that the division between the genera that comprise Burkholderia s.l. in terms of their lifestyles is often complex, differential characteristics of the genomes of these new combinations were investigated. In addition, two important lifestyle-determining traits-diazotrophy and/or symbiotic nodulation, and pathogenesis-were analyzed in depth i.e., the phylogenetic positions of nitrogen fixation and nodulation genes in Trinickia via-à-vis other Burkholderiaceae were determined, and the possibility of pathogenesis in Mycetohabitans and Trinickia was tested by performing infection experiments on plants and the nematode Caenorhabditis elegans. It is concluded that (1) T. symbiotica nif and nod genes fit within the wider Mimosa-nodulating Burkholderiaceae but appear in separate clades and that T. caryophyllinif genes are basal to the free-living Burkholderia s.l. strains, while with regard to pathogenesis (2) none of the Mycetohabitans and Trinickia strains tested are likely to be pathogenic, except for the known phytopathogen T. caryophylli.

SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles.

BACKGROUND: Molecular signatures are collections of genes characteristic of a particular cell type, tissue, disease, or perturbation. Signatures can also be used to interpret expression profiles generated from heterogeneous samples. Large collections of gene signatures have been previously developed and catalogued in the MSigDB database. In addition, several consortia and large-scale projects have systematically profiled broad collections of purified primary cells, molecular perturbations of cell types, and tissues from specific diseases, and the specificity and breadth of these datasets can be leveraged to create additional molecular signatures. However, to date there are few tools that allow the visualization of individual signatures across large numbers of expression profiles. Signature visualization of individual samples allows, for example, the identification of patient subcategories a priori on the basis of well-defined molecular signatures. RESULT: Here, we generate and compile 10,985 signatures (636 newly-generated and 10,349 previously available from MSigDB) and provide a web-based Signature Visualization Tool (SaVanT; http://newpathways.mcdb.ucla.edu/savant ), to visualize these signatures in user-generated expression data. We show that using SaVanT, immune activation signatures can distinguish patients with different types of acute infections (influenza A and bacterial pneumonia). Furthermore, SaVanT is able to identify the prominent signatures within each patient group, and identify the primary cell types underlying different leukemias (acute myeloid and acute lymphoblastic) and skin disorders. CONCLUSIONS: The development of SaVanT facilitates large-scale analysis of gene expression profiles on a patient-level basis to identify patient subphenotypes, or potential therapeutic target pathways.

Symbiotic Burkholderia Species Show Diverse Arrangements of nif/fix and nod Genes and Lack Typical High-Affinity Cytochrome cbb3 Oxidase Genes.

Genome analysis of fourteen mimosoid and four papilionoid beta-rhizobia together with fourteen reference alpha-rhizobia for both nodulation (nod) and nitrogen-fixing (nif/fix) genes has shown phylogenetic congruence between 16S rRNA/MLSA (combined 16S rRNA gene sequencing and multilocus sequence analysis) and nif/fix genes, indicating a free-living diazotrophic ancestry of the beta-rhizobia. However, deeper genomic analysis revealed a complex symbiosis acquisition history in the beta-rhizobia that clearly separates the mimosoid and papilionoid nodulating groups. Mimosoid-nodulating beta-rhizobia have nod genes tightly clustered in the nodBCIJHASU operon, whereas papilionoid-nodulating Burkholderia have nodUSDABC and nodIJ genes, although their arrangement is not canonical because the nod genes are subdivided by the insertion of nif and other genes. Furthermore, the papilionoid Burkholderia spp. contain duplications of several nod and nif genes. The Burkholderia nifHDKEN and fixABC genes are very closely related to those found in free-living diazotrophs. In contrast, nifA is highly divergent between both groups, but the papilionoid species nifA is more similar to alpha-rhizobia nifA than to other groups. Surprisingly, for all Burkholderia, the fixNOQP and fixGHIS genes required for cbb3 cytochrome oxidase production and assembly are missing. In contrast, symbiotic Cupriavidus strains have fixNOQPGHIS genes, revealing a divergence in the evolution of two distinct electron transport chains required for nitrogen fixation within the beta-rhizobia.