Ecological and evolutionary drivers of hemoplasma infection and bacterial genotype sharing in a Neotropical bat community

Most emerging pathogens can infect multiple species, underlining the importance of understanding the ecological and evolutionary factors that allow some hosts to harbour greater infection prevalence and share pathogens with other species. However, our understanding of pathogen jumps is based primarily around viruses, despite bacteria accounting for the greatest proportion of zoonoses. Because bacterial pathogens in bats (order Chiroptera) can have conservation and human health consequences, studies that examine the ecological and evolutionary drivers of bacterial prevalence and barriers to pathogen sharing are crucially needed. Here were studied haemotropic Mycoplasma spp. (i.e., haemoplasmas) across a speciesâ€rich bat community in Belize over two years. Across 469 bats spanning 33 species, half of individuals and twoâ€thirds of species were haemoplasma positive. Infection prevalence was higher for males and for species with larger body mass and colony sizes. Haemoplasmas displayed high genetic diversity (21 novel genotypes) and strong host specificity. Evolutionary patterns supported codivergence of bats and bacterial genotypes alongside phylogenetically constrained host shifts. Bat species centrality to the network of shared haemoplasma genotypes was phylogenetically clustered and unrelated to prevalence, further suggesting rare—but detectable—bacterial sharing between species. Our study highlights the importance of using fine phylogenetic scales when assessing host specificity and suggests phylogenetic similarity may play a key role in host shifts not only for viruses but also for bacteria. Such work more broadly contributes to increasing efforts to understand crossâ€species transmission and the epidemiological consequences of bacterial pathogens

Efficient oligonucleotide probe selection for pan-genomic tiling arrays

Background: Array comparative genomic hybridization is a fast and cost-effective method for detecting, genotyping, and comparing the genomic sequence of unknown bacterial isolates. This method, as with all microarray applications, requires adequate coverage of probes targeting the regions of interest. An unbiased tiling of probes across the entire length of the genome is the most flexible design approach. However, such a whole-genome tiling requires that the genome sequence is known in advance. For the accurate analysis of uncharacterized bacteria, an array must query a fully representative set of sequences from the species' pan-genome. Prior microarrays have included only a single strain per array or the conserved sequences of gene families. These arrays omit potentially important genes and sequence variants from the pan-genome. Results: This paper presents a new probe selection algorithm (PanArray) that can tile multiple whole genomes using a minimal number of probes. Unlike arrays built on clustered gene families, PanArray uses an unbiased, probe-centric approach that does not rely on annotations, gene clustering, or multi-alignments. Instead, probes are evenly tiled across all sequences of the pangenome at a consistent level of coverage. To minimize the required number of probes, probes conserved across multiple strains in the pan-genome are selected first, and additional probes are used only where necessary to span polymorphic regions of the genome. The viability of the algorithm is demonstrated by array designs for seven different bacterial pan-genomes and, in particular, the design of a 385,000 probe array that fully tiles the genomes of 20 different Listeria monocytogenes strains with overlapping probes at greater than twofold coverage. Conclusion: PanArray is an oligonucleotide probe selection algorithm for tiling multiple genome sequences using a minimal number of probes. It is capable of fully tiling all genomes of a species on a single microarray chip. These unique pan-genome tiling arrays provide maximum flexibility for the analysis of both known and uncharacterized strains.https://doi.org/10.1186/1471-2105-10-29

An FDA bioinformatics tool for microbial genomics research on molecular characterization of bacterial foodborne pathogens using microarrays

<p>Abstract</p> <p>Background</p> <p>Advances in microbial genomics and bioinformatics are offering greater insights into the emergence and spread of foodborne pathogens in outbreak scenarios. The Food and Drug Administration (FDA) has developed a genomics tool, ArrayTrack^TM, which provides extensive functionalities to manage, analyze, and interpret genomic data for mammalian species. ArrayTrack^TM has been widely adopted by the research community and used for pharmacogenomics data review in the FDA’s Voluntary Genomics Data Submission program. </p> <p>Results</p> <p>ArrayTrack^TM has been extended to manage and analyze genomics data from bacterial pathogens of human, animal, and food origin. It was populated with bioinformatics data from public databases such as NCBI, Swiss-Prot, KEGG Pathway, and Gene Ontology to facilitate pathogen detection and characterization. ArrayTrack^TM’s data processing and visualization tools were enhanced with analysis capabilities designed specifically for microbial genomics including flag-based hierarchical clustering analysis (HCA), flag concordance heat maps, and mixed scatter plots. These specific functionalities were evaluated on data generated from a custom Affymetrix array (FDA-ECSG) previously developed within the FDA. The FDA-ECSG array represents 32 complete genomes of <it>Escherichia coli</it> and<it> Shigella.</it> The new functions were also used to analyze microarray data focusing on antimicrobial resistance genes from <it>Salmonella</it> isolates in a poultry production environment using a universal antimicrobial resistance microarray developed by the United States Department of Agriculture (USDA).</p> <p>Conclusion</p> <p>The application of ArrayTrack^TM to different microarray platforms demonstrates its utility in microbial genomics research, and thus will improve the capabilities of the FDA to rapidly identify foodborne bacteria and their genetic traits (e.g., antimicrobial resistance, virulence, etc.) during outbreak investigations. ArrayTrack^TM is free to use and available to public, private, and academic researchers at <url>http://www.fda.gov/ArrayTrack</url>. </p

Comparative genomics of the bacterial genus Listeria: Genome evolution is characterized by limited gene acquisition and limited gene loss

<p>Abstract</p> <p>Background</p> <p>The bacterial genus <it>Listeria </it>contains pathogenic and non-pathogenic species, including the pathogens <it>L. monocytogenes </it>and <it>L. ivanovii</it>, both of which carry homologous virulence gene clusters such as the <it>prfA </it>cluster and clusters of internalin genes. Initial evidence for multiple deletions of the <it>prfA </it>cluster during the evolution of <it>Listeria </it>indicates that this genus provides an interesting model for studying the evolution of virulence and also presents practical challenges with regard to definition of pathogenic strains.</p> <p>Results</p> <p>To better understand genome evolution and evolution of virulence characteristics in <it>Listeria</it>, we used a next generation sequencing approach to generate draft genomes for seven strains representing <it>Listeria </it>species or clades for which genome sequences were not available. Comparative analyses of these draft genomes and six publicly available genomes, which together represent the main <it>Listeria </it>species, showed evidence for (i) a pangenome with 2,032 core and 2,918 accessory genes identified to date, (ii) a critical role of gene loss events in transition of <it>Listeria </it>species from facultative pathogen to saprotroph, even though a consistent pattern of gene loss seemed to be absent, and a number of isolates representing non-pathogenic species still carried some virulence associated genes, and (iii) divergence of modern pathogenic and non-pathogenic <it>Listeria </it>species and strains, most likely circa 47 million years ago, from a pathogenic common ancestor that contained key virulence genes.</p> <p>Conclusions</p> <p>Genome evolution in <it>Listeria </it>involved limited gene loss and acquisition as supported by (i) a relatively high coverage of the predicted pan-genome by the observed pan-genome, (ii) conserved genome size (between 2.8 and 3.2 Mb), and (iii) a highly syntenic genome. Limited gene loss in <it>Listeria </it>did include loss of virulence associated genes, likely associated with multiple transitions to a saprotrophic lifestyle. The genus <it>Listeria </it>thus provides an example of a group of bacteria that appears to evolve through a loss of virulence rather than acquisition of virulence characteristics. While <it>Listeria </it>includes a number of species-like clades, many of these putative species include clades or strains with atypical virulence associated characteristics. This information will allow for the development of genetic and genomic criteria for pathogenic strains, including development of assays that specifically detect pathogenic <it>Listeria </it>strains.</p

High Density Microarray Analysis Reveals New Insights into Genetic Footprints of Listeria monocytogenes Strains Involved in Listeriosis Outbreaks

Listeria monocytogenes, a foodborne bacterial pathogen, causes invasive and febrile gastroenteritis forms of listeriosis in humans. Both invasive and febrile gastroenteritis listeriosis is caused mostly by serotypes 1/2a, 1/2b and 4b strains. The outbreak strains of serotype 1/2a and 4b could be further classified into several epidemic clones but the genetic bases for the diverse pathophysiology have been unsuccessful. DNA microarray provides an important tool to scan the entire genome for genetic signatures that may distinguish the L. monocytogenes strains belonging to different outbreaks. We have designed a pan-genomic microarray chip (Listeria GeneChip) containing sequences from 24 L. monocytogenes strains. The chip was designed to identify the presence/absence of genomic sequences, analyze transcription profiles and identify SNPs. Analysis of the genomic profiles of 38 outbreak strains representing 1/2a, 1/2b and 4b serotypes, revealed that the strains formed distinct genetic clusters adhering to their serotypes and epidemic clone types. Although serologically 1/2a and 1/b strains share common antigenic markers microarray analysis revealed that 1/2a strains are further apart from the closely related 1/2b and 4b strains. Within any given serotype and epidemic clone type the febrile gastroenteritis and invasive strains can be further distinguished based on several genetic markers including large numbers of phage genome, and intergenic sequences. Our results showed that the microarray-based data can be an important tool in characterization of L. monocytogenes strains involved in both invasive and gastroenteritis outbreaks. The results for the first time showed that the serotypes and epidemic clones are based on extensive pan-genomic variability and the 1/2b and 4bstrains are more closely related to each other than the 1/2a strains. The data also supported the hypothesis that the strains causing these two diverse outbreaks are genotypically different and this finding might be important in understanding the pathophysiology of this organism

Advancing understanding of affect labeling with dynamic causal modeling

Mechanistic understandings of forms of incidental emotion regulation have implications for basic and translational research in the affective sciences. In this study we applied Dynamic Causal Modeling (DCM) for fMRI to a common paradigm of labeling facial affect to elucidate prefrontal to subcortical influences. Four brain regions were used to model affect labeling, including right ventrolateral prefrontal cortex (vlPFC), amygdala and Broca’s area. 64 models were compared, for each of 45 healthy subjects. Family level inference split the model space to a likely driving input and Bayesian Model Selection within the winning family of 32 models revealed a strong pattern of endogenous network connectivity. Modulatory effects of labeling were most prominently observed following Bayesian Model Averaging, with the dampening influence on amygdala originating from Broca’s area but much more strongly from right vlPFC. These results solidify and extend previous correlation and regression-based estimations of negative corticolimbic coupling

Discovery of Natural Atypical Nonhemolytic Listeria seeligeri Isolates

We found seven Listeria isolates, initially identified as isolates with the Xyl(+) Rha(−) biotype of Listeria welshimeri by phenotypic tests, which exhibited discrepant genotypic properties in a well-validated Listeria species identification oligonucleotide microarray. The microarray gives results of these seven isolates being atypical hly-negative L. seeligeri isolates, not L. welshimeri isolates. The aberrant L. seeligeri isolates were d-xylose fermentation positive, l-rhamnose fermentation negative (Xyl(+) Rha(−)), and nonhemolytic on blood agar and in the CAMP test with both Staphylococcus aureus (S(−) reaction) and Rhodococcus equi (R(−) reaction). All genes of the prfA cluster of L. seeligeri, located in the prs-ldh region, including the orfA2, orfD, prfA, orfE, plcA, hly, orfK, mpl, actA, dplcB, plcB, orfH, orfX, orfI, orfP, orfB, and orfA genes, were checked by PCR and direct sequencing for evidence of their presence in the atypical isolates. The prs-prfA cluster-ldh region of the L. seeligeri isolates was approximately threefold shorter due to the loss of orfD, prfA, orfE, plcA, hly, orfK, mpl, actA, dplcB, plcB, orfH, orfX, and orfI. The genetic map order of the cluster genes of all the atypical L. seeligeri isolates was prs-orfA2-orfP-orfB-orfA-ldh, which was comparable to the similar region in L. welshimeri, with the exception of the presence of orfA2. DNA sequencing and phylogenetic analysis of 17 housekeeping genes indicated an L. seeligeri genomic background in all seven of the atypical hly-negative L. seeligeri isolates. Thus, the novel biotype of Xyl(+) Rha(−) Hly(−) L. seeligeri strains can only be distinguished from Xyl(+) Rha(−) L. welshimeri strains genotypically, not phenotypically. In contrast, the Rha(+) Xyl(+) biotype of L. welshimeri would not present an identification issue