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

Pan-Genome Analysis of Staphylococcus aureus Reveals Key Factors Influencing Genomic Plasticity

The massive quantities of bacterial genomic data being generated have facilitated in-depth analyses of bacteria for pan-genomic studies. However, the pan-genome compositions of one species differed significantly between different studies, so we used Staphylococcus aureus as a model organism to explore the influences driving bacterial pan-genome composition. We selected a series of diverse strains for pan-genomic analysis to explore the pan-genomic composition of S. aureus at the species level and the actual contribution of influencing factors (sequence type [ST], source of isolation, country of isolation, and date of collection) to pan-genome composition. We found that the distribution of core genes in bacterial populations restrained under different conditions differed significantly and showed "local core gene regions" in the same ST. Therefore, we propose that ST may be a key factor driving the dynamic distribution of bacterial genomes and that phylogenetic analyses using whole-genome alignment are no longer appropriate in populations containing multiple ST strains. Pan-genomic analysis showed that some of the housekeeping genes of multilocus sequence typing (MLST) are carried at less than 60% in S. aureus strains. Consequently, we propose a new set of marker genes for the classification of S. aureus, which provides a reference for finding a new set of housekeeping genes to apply to MLST. In this study, we explored the role of driving factors influencing pan-genome composition, providing new insights into the study of bacterial pan-genomes. IMPORTANCE We sought to explore the impact of driving factors influencing pan-genome composition using Staphylococcus aureus as a model organism to provide new insights for the study of bacterial pan-genomes. We believe that the sequence type (ST) of the strains under consideration plays a significant role in the dynamic distribution of bacterial genes. Our findings indicate that there are a certain number of essential genes in Staphylococcus aureus; however, the number of core genes is not as high as previously thought. The new classification method proposed herein suggests that a new set of housekeeping genes more suitable for Staphylococcus aureus must be identified to improve the current classification status of this species.

Systems-Based Approach for Optimization of Assembly-Free Bacterial MLST Mapping

Epidemiological surveillance of bacterial pathogens requires real-time data analysis with a fast turnaround, while aiming at generating two main outcomes: (1) species-level identification and (2) variant mapping at different levels of genotypic resolution for population-based tracking and surveillance, in addition to predicting traits such as antimicrobial resistance (AMR). Multi-locus sequence typing (MLST) aids this process by identifying sequence types (ST) based on seven ubiquitous genome-scattered loci. In this paper, we selected one assembly-dependent and one assembly-free method for ST mapping and applied them with the default settings and ST schemes they are distributed with, and systematically assessed their accuracy and scalability across a wide array of phylogenetically divergent Public Health-relevant bacterial pathogens with available MLST databases. Our data show that the optimal k-mer length for stringMLST is species-specific and that genome-intrinsic and -extrinsic features can affect the performance and accuracy of the program. Although suitable parameters could be identified for most organisms, there were instances where this program may not be directly deployable in its current format. Next, we integrated stringMLST into our freely available and scalable hierarchical-based population genomics platform, ProkEvo, and further demonstrated how the implementation facilitates automated, reproducible bacterial population analysis.

Heuristic and Hierarchical-Based Population Mining of Salmonella enterica Lineage I Pan-Genomes as a Platform to Enhance Food Safety

The recent incorporation of bacterial whole-genome sequencing (WGS) into Public Health laboratories has enhanced foodborne outbreak detection and source attribution. As a result, large volumes of publicly available datasets can be used to study the biology of foodborne pathogen populations at an unprecedented scale. To demonstrate the application of a heuristic and agnostic hierarchical population structure guided pan-genome enrichment analysis (PANGEA), we used populations of S. enterica lineage I to achieve two main objectives: (i) show how hierarchical population inquiry at different scales of resolution can enhance ecological and epidemiological inquiries; and (ii) identify population-specific inferable traits that could provide selective advantages in food production environments. Publicly available WGS data were obtained from NCBI database for three serovars of Salmonella enterica subsp. enterica lineage I (S. Typhimurium, S. Newport, and S. Infantis). Using the hierarchical genotypic classifications (Serovar, BAPS1, ST, cgMLST), datasets from each of the three serovars showed varying degrees of clonal structuring. When the accessory genome (PANGEA) was mapped onto these hierarchical structures, accessory loci could be linked with specific genotypes. A large heavy-metal resistance mobile element was found in the Monophasic ST34 lineage of S. Typhimurium, and laboratory testing showed that Monophasic isolates have on average a higher degree of copper resistance than the Biphasic ones. In S. Newport, an extra sugE gene copy was found among most isolates of the ST45 lineage, and laboratory testing of multiple isolates confirmed that isolates of S. Newport ST45 were on average less sensitive to the disinfectant cetylpyridimium chloride than non-ST45 isolates. Lastly, data-mining of the accessory genomic content of S. Infantis revealed two cryptic Ecotypes with distinct accessory genomic content and distinct ecological patterns. Poultry appears to be the major reservoir for Ecotype 1, and temporal analysis further suggested a recent ecological succession, with Ecotype 2 apparently being displaced by Ecotype 1. Altogether, the use of a heuristic hierarchical-based population structure analysis that includes bacterial pan-genomes (core and accessory genomes) can (1) improve genomic resolution for mapping populations and accessing epidemiological patterns; and (2) define lineage-specific informative loci that may be associated with survival in the food chain.