Back

From Colonisation to Invasion: Genomic and Phenotypic Comparison of Faecal and Bloodstream Isolates from the same patients

Khanijau, A.; Allman, E.; Pulmones, R.; Goodman, R. N.; McGalliard, R.; Parry, C. N.; Carrol, E. D.; Roberts, A. P.

2025-07-30 microbiology
10.1101/2025.07.15.664880 bioRxiv
Show abstract

Gram-negative bloodstream infections (GNBSI) carry a significant global health burden. Escherichia coli and Klebsiella pneumoniae are the two most common causes of healthcare-associated GNBSI, which may arise from gastrointestinal tract (GIT) colonisation. Understanding genomic and phenotypic adaptations that underpin transition from GIT colonisation to invasive bloodstream infection could improve understanding of pathogenesis. This study identified linked faecal and blood isolates from children with healthcare-associated GNBSI caused by E. coli and K. pneumoniae. Linked pairs were compared for antimicrobial resistance, biofilm formation, and underwent comparative genomic analysis via whole-genome sequencing, comparative average nucleotide identity (ANI) and core genome single nucleotide polymorphism (SNP) analysis. Five isolate pairs (three E. coli, two K. pneumoniae) showed high relatedness, supporting GIT origin of bloodstream infection. Isolates within pairs had identical virulence genes whereas phenotypic assays revealed changes in antimicrobial susceptibility, with one pair undergoing changes in resistance gene profiles, and increased biofilm formation in 4/5 isolates. This study provides insight into within-host evolution from gastrointestinal colonisation to bloodstream invasion in Gram-negative pathogens. Convergence on metabolic adaptation and biofilm formation suggests these traits may be advantageous in healthcare-associated GNBSI. Further studies involving larger cohorts alongside functional validation of mutations are needed to better understand GNBSI pathogenesis.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.