Genome wide association study of Escherichia coli bloodstream infection isolates identifies genetic determinants for the portal of entry but not fatal outcome

Abstract

Escherichia coli is an important cause of bloodstream infections (BSI), which is of concern given its high mortality and increasing worldwide prevalence. Finding bacterial genetic variants that might contribute to patient death is of interest to better understand infection progression and implement diagnostic methods that specifically look for those factors. E. coli samples isolated from patients with BSI are an ideal dataset to systematically search for those variants, as long as the influence of host factors such as comorbidities are taken into account. Here we performed a genome-wide association study (GWAS) using data from 912 patients with E. coli BSI from hospitals in Paris, France. We looked for associations between bacterial genetic variants and three patient outcomes (death at 28 days, septic shock and admission to intensive care unit), as well as two portals of entry (urinary and digestive tract), using various clinical variables from each patient to account for host factors. We did not find any association between genetic variants and patient outcomes, potentially confirming the strong influence of host factors in influencing the course of BSI; we however found a strong association between the papGII operon and entrance of E. coli through the urinary tract, which demonstrates the power of bacterial GWAS when applied to actual clinical data. Despite the lack of associations between E. coli genetic variants and patient outcomes, we estimate that increasing the sample size by one order of magnitude could lead to the discovery of some putative causal variants. Given the wide adoption of bacterial genome sequencing of clinical isolates, such sample sizes may be soon available.

Fig 1. Core genome phylogenetic tree of the 912 E. coli isolates used in this study.

Each ring reports the main bacterial and clinical variables of this study. The light color in the rings related to patient outcomes and portals of entry indicates the absence of the trait.

Fig 2. Narrow-sense heritability (h²) estimation for the target variables on the combined dataset.

a) Heritability estimates in the two studies combined, using a covariance matrix generated from the isolates’ phylogroup (phylogroup), a kinship matrix generated from the unitigs presence/absence matrix (variants), and the same kinship matrix conditioned with the clinical variables (variants + covariates). b) Heritability estimates in the Colibafi and c) Septicoli cohorts alone.

Fig 3. Genome-wide association analysis results on the combined dataset.

a) Number of unitigs passing the multiple testing correction p-value threshold for each target phenotype. b) Number of genes with significantly associated unitigs mapped to them for each target phenotype. c) Manhattan plots for tested unitigs mapping to E. coli IAI39 for each target phenotype; red dashed line indicates the p-value threshold used to call significant associations. d) Zoomed-in Manhattan plots for the urinary tract trait. Genes annotated with a gene name in E. coli IAI39 and with associated unitigs are indicated in the third subpanel.

Fig 4. Power simulations.

The proportion of causal variants passing the significance threshold is reported for each sample size and heritability for the simulated phenotypes.