Host genetic architecture and the landscape of microbiome composition: humans weigh in

Comparative analyses of the control of mammalian microbiomes by host genetic architecture reveal striking conserved features that have implications for the evolution of host–microbiome interactions

Host genetic architecture and the landscape of microbiome composition: humans weigh in

Comparative analyses of the control of mammalian microbiomes by host genetic architecture reveal striking conserved features that have implications for the evolution of host–microbiome interactions

Coagglutination and Enzyme Capture Tests for Detection of \u3ci\u3eEscherichia coli\u3c/i\u3e β-Galactosidase, β-Glucuronidase, and Glutamate Decarboxylase

Polyclonal antibodies to Escherichia coli β-galactosidase, β-glucuronidase, and glutamate decarboxylase were used in coagglutination tests for identification of these three enzymes in cell lysates. Enzyme capture assays were also developed for the detection of E. coli β-galactosidase and β-glucuronidase. The enzymes were released by using a gentle lysis procedure that did not interfere with antibody-enzyme interactions. All three enzymes were detected in 93% (51 of 55) of the E. coli strains tested by coagglutination; two of the three enzymes were identified in the remaining 7%. Of 42 non-E. coli tested by coagglutination, only four nonspecifically agglutinated either two or three of the anti-enzyme conjugates. Thirty-two (76%) non-E. coli isolates were negative by coagglutination for all three enzymes. The enzyme capture assay detected the presence of β-galactosidase in seven of eight and β-glucuronidase in all eight strains of E. coli tested. Some strains of β-galactosidase-positive Citrobacterfreundii and Enterobacter cloacae were also positive by the enzyme capture assay, indicating that the antibodies were not entirely specific for E. coli β-galactosidase; however, five other gas-positive non-E. coli isolates were negative by the enzyme capture assay. The coagglutination tests and enzyme capture assays were rapid and sensitive methods for the detection of E. coli ,β-galactosidase, β-glucuronidase, and glutamate decarboxylase

Coagglutination and Enzyme Capture Tests for Detection of \u3ci\u3eEscherichia coli\u3c/i\u3e β-Galactosidase, β-Glucuronidase, and Glutamate Decarboxylase

Polyclonal antibodies to Escherichia coli β-galactosidase, β-glucuronidase, and glutamate decarboxylase were used in coagglutination tests for identification of these three enzymes in cell lysates. Enzyme capture assays were also developed for the detection of E. coli β-galactosidase and β-glucuronidase. The enzymes were released by using a gentle lysis procedure that did not interfere with antibody-enzyme interactions. All three enzymes were detected in 93% (51 of 55) of the E. coli strains tested by coagglutination; two of the three enzymes were identified in the remaining 7%. Of 42 non-E. coli tested by coagglutination, only four nonspecifically agglutinated either two or three of the anti-enzyme conjugates. Thirty-two (76%) non-E. coli isolates were negative by coagglutination for all three enzymes. The enzyme capture assay detected the presence of β-galactosidase in seven of eight and β-glucuronidase in all eight strains of E. coli tested. Some strains of β-galactosidase-positive Citrobacterfreundii and Enterobacter cloacae were also positive by the enzyme capture assay, indicating that the antibodies were not entirely specific for E. coli β-galactosidase; however, five other gas-positive non-E. coli isolates were negative by the enzyme capture assay. The coagglutination tests and enzyme capture assays were rapid and sensitive methods for the detection of E. coli ,β-galactosidase, β-glucuronidase, and glutamate decarboxylase

ProkEvo: an automated, reproducible, and scalable framework for high-throughput bacterial population genomics analyses

Whole Genome Sequence (WGS) data from bacterial species is used for a variety of applications ranging from basic microbiological research, diagnostics, and epidemiological surveillance. The availability of WGS data from hundreds of thousands of individual isolates of individual microbial species poses a tremendous opportunity for discovery and hypothesis-generating research into ecology and evolution of these microorganisms. Flexibility, scalability, and user-friendliness of existing pipelines for population-scale inquiry, however, limit applications of systematic, population-scale approaches. Here, we present ProkEvo, an automated, scalable, reproducible, and open-source framework for bacterial population genomics analyses using WGS data. ProkEvo was specifically developed to achieve the following goals: (1) Automation and scaling of complex combinations of computational analyses for many thousands of bacterial genomes from inputs of raw Illumina paired-end sequence reads; (2) Use of workflow management systems (WMS) such as Pegasus WMS to ensure reproducibility, scalability, modularity, fault-tolerance, and robust file management throughout the process; (3) Use of high-performance and high-throughput computational platforms; (4) Generation of hierarchical-based population structure analysis based on combinations of multi-locus and Bayesian statistical approaches for classification for ecological and epidemiological inquiries; (5) Association of antimicrobial resistance (AMR) genes, putative virulence factors, and plasmids from curated databases with the hierarchically-related genotypic classifications; and (6) Production of pan-genome annotations and data compilation that can be utilized for downstream analysis such as identification of population-specific genomic signatures. The scalability of ProkEvo was measured with two datasets comprising significantly different numbers of input genomes (one with ~2,400 genomes, and the second with ~23,000 genomes). Depending on the dataset and the computational platform used, the running time of ProkEvo varied from ~3-26 days. ProkEvo can be used with virtually any bacterial species, and the Pegasus WMS uniquely facilitates addition or removal of programs from the workflow or modification of options within them. To demonstrate versatility of the ProkEvo platform, we performed a hierarchical-based population structure analyses from available genomes of three distinct pathogenic bacterial species as individual case studies. The specific case studies illustrate how hierarchical analyses of population structures, genotype frequencies, and distribution of specific gene functions can be integrated into an analysis. Collectively, our study shows that ProkEvo presents a practical viable option for scalable, automated analyses of bacterial populations with direct applications for basic microbiology research, clinical microbiological diagnostics, and epidemiological surveillance

Role of σ\u3csup\u3eB\u3c/sup\u3e in Adaptation of \u3ci\u3eListeria monocytogenes\u3c/i\u3e to Growth at Low Temperature

The activity of σB in Listeria monocytogenes is stimulated by high osmolarity and is necessary for efficient uptake of osmoprotectants. Here we demonstrate that, during cold shock, σB contributes to adaptation in a growth phase-dependent manner and is necessary for efficient accumulation of betaine and carnitine as cryoprotectants

Shared mechanisms among probiotic taxa: implications for general probiotic claims

Strain-specificity of probiotic effects has been a cornerstone principle of probiotic science for decades. Certainly, some important mechanisms are present in only a few probiotic strains. But scientific advances now reveal commonalities among members of certain taxonomic groups of probiotic microbes. Some clinical benefits likely derive from these shared mechanisms, suggesting that sub-species-specific, species specific or genus-specific probiotic effects exist. Human trials are necessary to confirm specific health benefits. However, a strain that has not been tested in human efficacy trials may meet the minimum definition of the term ‘probiotic’ if it is a member of a well-studied probiotic species expressing underlying core mechanisms and it is delivered at an effective dose

Shared mechanisms among probiotic taxa: implications for general probiotic claims

Strain-specificity of probiotic effects has been a cornerstone principle of probiotic science for decades. Certainly, some important mechanisms are present in only a few probiotic strains. But scientific advances now reveal commonalities among members of certain taxonomic groups of probiotic microbes. Some clinical benefits likely derive from these shared mechanisms, suggesting that sub-species-specific, speciesspecific or genus-specific probiotic effects exist. Human trials are necessary to confirm specific health benefits. However, a strain that has not been tested in human efficacy trials may meet the minimum definition of the term ‘probiotic’ if it is a member of a well-studied probiotic species expressing underlying core mechanisms and it is delivered at an effective dose

Identification of \u3ci\u3eEscherichia coli\u3c/i\u3e O157:H7 Genomic Regions Conserved in Strains with a Genotype Associated with Human Infection

Beta-glucuronidase-negative, sorbitol-nonfermenting isolates of Shiga toxin-producing Escherichia coli O157 comprise part of a clone complex of related enterohemorrhagic E. coli isolates. High-resolution genotyping shows that the O157 populations have diverged into two different lineages that appear to have different ecologies. To identify genomic regions unique to the most common human-associated genotype, suppression subtractive hybridization was used to identify DNA sequences present in two clinical strains representing the human lineage I O157:H7 strains but absent from two bovine-derived lineage II strains. PCR assays were then used to test for the presence of these regions in 10 lineage I strains and 20 lineage II strains. Twelve conserved regions of genomic difference for lineage I (CRDI) were identified that were each present in at least seven of the lineage I strains but absent in most of the lineage II strains tested. The boundaries of the lineage I conserved regions were further delimited by PCR. Eleven of these CRDI were associated with E. coli Sakai S-loops 14, 16, 69, 72, 78, 82, 83, 91 to 93, 153, and 286, and the final CRDI was located on the pO157 virulence plasmid. Several potential virulence factors were identified within these regions, including a putative hemolysin-activating protein, an iron transport system, and several possible regulatory genes. Cluster analysis based on lineage I conserved regions showed that the presence/absence of these regions was congruent with the inferred phylogeny of the strains

Negative binomial mixed models for analyzing microbiome count data

Background: Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. Results: In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. Conclusions: We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM), providing a useful tool for analyzing microbiome data