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

Authors

Joao Carlos Gomes-Neto, University of Nebraska-LincolnFollow
Natasha Pavlovikj, University of Nebraska-LincolnFollow
Carmen Cano, University of Nebraska-LincolnFollow
Baha Abdalhamid, University of Nebraska Medical Center
Gabriel Asad Al-Ghalith, University of Minnesota
John Dustin Loy, University of Nebraska-Lincoln
Dan Knights, University of Minnesota
Peter C. Iwen, University of Nebraska Medical Center
Byron D. Chaves, University of Nebraska-LincolnFollow
Andrew K. Benson, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

2021

Citation

Gomes-Neto JC, Pavlovikj N, Cano C, Abdalhamid B, Al-Ghalith GA, Loy JD, Knights D, Iwen PC, Chaves BD and Benson AK (2021) Heuristic and Hierarchical-Based Population Mining of Salmonella enterica Lineage I Pan-Genomes as a Platform to Enhance Food Safety. Front. Sustain. Food Syst. 5:725791. doi: 10.3389/fsufs.2021.725791

Comments

OPEN ACCESS

Abstract

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 sugEgene 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 themajor 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.