heuristic-mining-hierarchical-genotypes-accessory-genome-loci

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried ...

University of Nebraska-Lincoln

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.

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

The common marmoset (Callithrix jacchus) is an omnivorous New World primate whose diet in the wild includes large amounts of fruit, seeds, flowers, and a variety of lizards and invertebrates. Marmosets also feed heavily on tree gums and exudates, and they have evolved unique morphological and anatomical characteristics to facilitate gum feeding (gummivory). In this study, we characterized the fecal microbiomes of adult and infant animals from a captive population of common marmosets at the Callitrichid Research Center at the University of Nebraska at Omaha under their normal dietary and environmental conditions. The microbiomes of adult animals were dominated by species of , , , , , and . Culturing and genomic analysis of the  populations from adult animals identified four known marmoset-associated species (, , , and ) and three unclassified taxa of  that are phylogenetically distinct. Species-specific quantitative PCR (qPCR) confirmed that these same species of  are abundant members of the microbiome throughout the lives of the animals. Genomic loci in each  species encode enzymes to support growth and major marmoset milk oligosaccharides during breastfeeding; however, metabolic islands that can support growth on complex polysaccharide substrates in the diets of captive adults (pectin, xyloglucan, and xylan), including loci in  that can support its unique ability to grow on arabinogalactan-rich tree gums, were species-specific.   species are recognized as important, beneficial microbes in the human gut microbiome, and their ability colonize individuals at different stages of life is influenced by host, dietary, environmental, and ecological factors, which is poorly understood. The common marmoset is an emerging nonhuman primate model with a short maturation period, making this model amenable to study the microbiome throughout a life history. Features of the microbiome in captive marmosets are also shared with human gut microbiomes, including abundant populations of  species. Our studies show that several species of  are dominant members of the captive marmoset microbiome throughout their life history. Metabolic capacities in genomes of the marmoset  species suggest species-specific adaptations to different components of the captive marmoset diet, including the unique capacity in  for degradation of gum arabic, suggesting that regular dietary exposure in captivity may be important for preserving gum-degrading species in the microbiome.

Captive Common Marmosets (Callithrix jacchus) Are Colonized throughout Their Lives by a Community of  Species with Species-Specific Genomic Content That Can Support Adaptation to Distinct Metabolic Niches.

Natasha Pavlovikj

Department of Computer Science and Engineering

University of Nebraska-Lincoln

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Natasha Pavlovikj

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Computer Science and Engineering

University of Nebraska-Lincoln

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Department of Computer Science and Engineering