Mapping of genetic loci that modulate differential colonization by Escherichia coli O157:H7 TUV86-2 in advanced recombinant inbred BXD mice

Abstract

BackgroundShiga toxin (Stx)-producing E. coli (STEC) are responsible for foodborne outbreaks that can result in severe human disease. During an outbreak, differential disease outcomes are observed after infection with the same STEC strain. One question of particular interest is why some infected people resolve infection after hemorrhagic colitis whereas others progress to the hemolytic uremic syndrome (HUS). Host age and infection dose have been implicated; however, these parameters do not appear to fully account for all of the observed variation in disease severity. Therefore, we hypothesized that additional host genetic factors may play a role in progression to HUS.Methods and ResultsTo mimic the genetic diversity in the human response to infection by STEC, we measured the capacity of an O157:H7 outbreak isolate to colonize mouse strains from the advanced recombinant inbred (ARI) BXD panel. We first infected the BXD parental strains C57BL/6 J (B6) and DBA/2 J (D2) with either 86–24 (Stx2a+) or TUV86-2, an Stx2a-negative isogenic mutant. Colonization levels were determined in an intact commensal flora (ICF) infection model. We found a significant difference in colonization levels between the parental B6 and D2 strains after infection with TUV86-2 but not with 86–24. This observation suggested that a host factor that may be masked by Stx2a affects O157:H7 colonization in some genetic backgrounds. We then determined the TUV86-2 colonization levels of 24 BXD strains in the ICF model. We identified several quantitative trait loci (QTL) associated with variation in colonization by correlation analyses. We found a highly significant QTL on proximal chromosome 9 (12.5–26.7 Mb) that strongly predicts variation in colonization levels and accounts for 15–20 % of variance. Linkage, polymorphism and co-citation analyses of the mapped region revealed 36 candidate genes within the QTL, and we identified five genes that are most likely responsible for the differential colonization.ConclusionsThe identification of the QTL on chromosome 9 supports our hypothesis that individual genetic makeup affects the level of colonization after infection with STEC O157:H7.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2127-7) contains supplementary material, which is available to authorized users.

A platform for experimental precision medicine: The extended BXD mouse family

“…The BXD Family Is the Largest and the Most Deeply Phenotyped Mammalian Genetic Reference Panel Data sets for the BXD encompass multiple levels-from single molecules to complex behavioral repertoires and to traits measured with environmental perturbations, including exposure to alcohol and drugs of abuse (Dickson et al, 2019;Mulligan et al, 2018;Thé berge et al, 2019;Zhou et al, 2018), infectious agents (Boon et al, 2014;Chella Krishnan et al, 2016;Russo et al, 2015), dietary modifications (Fleet et al, 2016;Jha et al, 2018bJha et al, , 2018aJones and Jellen, 2017;Reyes Fernandez et al, 2016;Rodrigues et al, 2017), stress (Diessler et al, 2018;Jung et al, 2017), and even as a function of age (Sandoval-Sierra et al, 2020;Williams et al, 2020). This phenome is linked to over 500 publications, including state-of-the-art proteome, metabolome (Williams et al, 2018(Williams et al, , 2020, epigenome (Baker et al, 2019;Sandoval-Sierra et al, 2019), and metagenome (Perez-Munoz et al, 2019) data sets available on GeneNetwork.org.…”

Pathogenicity assessment of Shiga toxin‐producing Escherichia coli (STEC) and the public health risk posed by contamination of food with STEC

“…In addition to the characteristics of the STEC strain, there are other factors that influence the occurrence and severity of clinical infection (Russo et al, 2015). The human or host factor is especially important and more than any other factor, age is associated with a higher risk of severe infection with STEC.…”

Private and sub-family specific mutations of founder haplotypes in the BXD family reveal phenotypic consequences relevant to health and disease

“…The BXDs are well suited to studying gene-by-environmental interactions (GXE) and for experimental precision medicine 11 . For almost 50 years, they have been used to study the genetic and environmental factors that underlie a diverse collection of phenotypes, including environmental toxicant exposures, alcohol and drugs of abuse [64][65][66][67][68] , infectious agents [69][70][71][72][73] , diets [74][75][76][77][78][79] , and stressors 80,81 . Beyond this, there are also extensive -omics data available for many BXD family members, including over 100 transcriptome datasets (e.g, 82,83 ), as well as more recent miRNA 84,85 , proteome [86][87][88] , metabolome 83,88,89 , epigenome 16,90 , and metagenome 91,92 profiles.…”