bioRxiv Science⌕ Search

bioRxiv · 10.1101/2024.05.08.593128

In vitro assays for clinical isolates of sequence type 131 Escherichia coli do not recapitulate in vivo infectivity using a murine model of urinary tract infection

Abstract

Sequence Type 131 isolates are a major cause of cystitis and pyelonephritis. Many studies rely solely on in vitro assays to screen for bacterial virulence factors associated with the pathogenicity of clinical isolates of E. coli. Few studies have compared in vitro findings to in vivo infectivity of clinical isolates. The purpose of this study was to evaluate the correlation between in vitro assays with the ability to cause cystitis and pyelonephritis in a murine model of urinary tract infection. In vitro assays were conducted according to published protocols and included: motility assays, biofilm formation, epithelial cell adhesion and invasion, and curli production. Twenty-one UPEC isolates of E. coli ST131 and non-ST131 were used for both in vivo and in vitro studies. Six mice per isolate were inoculated via urethral catheterization. CFUs were determined from bladder and kidneys. In vitro and in vivo correlations were evaluated by multiple linear regression analysis. Pairwise linear regressions showed trendlines with weak positive correlations for motility, adhesion, and invasion and weak negative correlations for hemagglutination, biofilm and curli production. The ability of E. coli ST131 and non-ST131 clinical isolates to cause cystitis and pyelonephritis varies among strains. The R2 Pearson Correlation value was less than {+/-}0.5 for any pair, indicating little to no statistical association between in vitro and in vivo findings. These data show in vitro data are not predictive of the ability of ST131 E. coli to infect and/or cause disease in a mouse model. Author summaryUrinary tract infections affect 150 million people annually and E. coli ST131 have become the pandemic strain responsible for a majority of UTI, cystitis, and pyelonephritis cases. How ST131 E. coli have become such prolific strain still remains to be elucidated. When evaluating bacterial pathogenicity, it is customary practice to use in vitro assays to predict isolate virulence and mechanisms of fitness, due to the lower cost, and relative ease of experimentation compared to more costly and complicated in vivo models. It is also common to use model organisms like pathogenic E. coli CFT073 or non-pathogenic lab strains such as BW25113 as representatives for the entire species. However, our research has shown that not only are model organisms substantially different from clinical isolates of ST131 E. coli, but in vitro assays are poor predictors of clinical isolates ability to cause infection in a murine model of UTI. As such, research into the mechanisms of fitness for ST131 infectivity need to veer away from studying only model organisms and focus on utilizing pathogenic clinical isolates in conditions that more closely recapitulate urinary tract environmental niches.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rudick, C. P., Cox, R., Bourret, T. J., Hanson, N. D.. 2024-05-10. In vitro assays for clinical isolates of sequence type 131 Escherichia coli do not recapitulate in vivo infectivity using a murine model of urinary tract infection. https://doi.org/10.1101/2024.05.08.593128

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

A population-scale landscape of the subgingival microbiome reveals divergent routes to periodontal dysbiosis

Periodontitis is an archetypical mucosal inflammatory disease in which microbiome dysbiosis at the tooth-epithelial interface interacts with host genetic and behavioral risk factors to drive immune-mediated tissue destruction. Although subgingival microbiome compositional shifts are thought to parallel disease severity, microbiome variation at the population-level and its relationship to periodontal clinical phenotypes and disease-modifying factors remain poorly defined. Here, we use unsupervised manifold learning to map the compositional landscape of the subgingival microbiome in 1,355 adults spanning periodontal health to severe periodontitis. We identified eight latent microbiome states organized along a branching continuum from eubiosis to dysbiosis. An intermediate microbial configuration marked ecological destabilization and bifurcation into two distinct periodontitis-associated dysbiotic trajectories, distinguished by links to gingival inflammation and smoking. Although the microbiome trajectories broadly tracked periodontal destruction, a minority of individuals showed discordant microbiome-clinical phenotypes, with some individuals with periodontitis retaining otherwise eubiotic microbiomes enriched for low-abundance pathobionts, while some cases of health or mild disease had highly dysbiotic communities, suggesting distinct host susceptibility. Together, these findings define a population-scale ecological landscape of the subgingival microbiome, reveal divergent trajectories to periodontal dysbiosis, and highlight heterogeneity in the relationship between microbial community structure and clinical disease expression.

microbiology↗

Rapid and largely reversible shifts in the canine fecal metabolome during dietary change

Diet can rapidly change the fecal metabolome, but less is known about recovery after the original diet is restored. We used untargeted UPLC-MS metabolomics to analyze 72 fecal samples from nine Pumi dogs during an owner-managed switch from dry food to raw food and back to dry food. Diet phase accounted for a large proportion of variation in both ionization modes. More than 13,000 LC-MS features changed at the first sampling point after the switch to raw food, with a similarly large response after return to dry food. Among features significant in both comparisons, more than 99% changed in opposite directions. At the final sampling point, no positive-mode (ESI+) features and only 13 negative-mode (ESI-) features differed from the second dry-food baseline under the same threshold. BARF-associated patterns persisted in analyses excluding individual dogs and in pedigree-adjusted candidate models, although individual feature effects depended on normalization. Putative metabolites from several biochemical classes differed in their response and recovery. The fecal metabolome therefore changed rapidly and returned largely toward baseline, with differences among dogs.

microbiology↗

Taxonomic and functional concordance between full-length ONT 16S and ONT shotgun metagenomics in the canine gut microbiome

Background: Full-length Oxford Nanopore Technologies (ONT) 16S rRNA sequencing provides a scalable view of microbial community composition and can support phylogeny-based functional prediction, but it is not equivalent to shotgun metagenomics. We asked which biological conclusions are preserved when the same canine fecal specimens are profiled by full-length ONT 16S and ONT whole-genome shotgun (WGS) sequencing, and how their agreement depends on analytical scale, reference representation and classifier. Methods: Ninety-seven fecal specimens from 51 dogs were profiled with both assays from the same DNA extract. Functional profiles predicted from NanoASV/NanoPredict with PICRUSt2 were compared with WGS-supported KEGG Ortholog (KO) profiles generated by Kadath. Taxonomy was benchmarked in a source-genome-matched RefSeq universe and in a host-specific DogMAG universe using minitax and Kraken2. Agreement was evaluated at whole-profile, feature-abundance, detection, between-sample structure and biological-inference scales. Age-associated transfer was assessed with dog-aware continuous mixed models, grouped signed-score analyses and paired/dog-blocked PERMANOVA. Results: Functional whole-profile concordance was high: median within-sample CLR Spearman correlations ranged from 0.781 to 0.860 across developmental strata, while between-sample functional structure remained significant by Mantel (rho=0.543) and Procrustes (r=0.693; both p=0.001). Feature-wise transfer was substantially weaker (median KO-wise CLR Spearman=0.318). Continuous age-associated KO slopes showed substantial cross-assay concordance (Spearman=0.727; signed-score Spearman=0.753; direction agreement=77.9%), although 1,290/5,258 eligible KOs retained significant assay-by-age interactions. Taxonomically, exact genus/species abundance agreement was much lower than agreement in between-sample ecological structure. Host-specific DogMAG improved species-level median Spearman from 0.261 to 0.656 for minitax SpeciesEstimate and from 0.181 to 0.512 for Kraken2. The classifier effect was independent of reference choice: under both RefSeq and DogMAG, minitax yielded stronger 16S-WGS concordance than Kraken2, with all eight prespecified RefSeq paired genus/species endpoints and all 10 DogMAG primary paired endpoints significant after BH correction. The same ordering extended to developmental inference, with DogMAG genus/species age-slope concordance of 0.795/0.799 for SpeciesEstimate versus 0.693/0.702 for Kraken2. Taxonomic Aitchison PERMANOVA detected age-associated structure in every assay/reference/classifier/rank combination, whereas age-by-assay interactions were consistently significant but small (R2 approximately 1.1 to 2.2%). Stricter NanoASV identity thresholds removed substantial 16S abundance without improving species-level agreement. Conclusions: The extent of cross-assay agreement depends on the level of analysis. Full-length ONT 16S preserves broad functional organization, ecological structure and much of the direction of age-associated change, but exact fine-rank composition, individual-feature abundance and effect magnitude remain assay dependent. Host-specific reference representation substantially narrows the taxonomic gap, and classifier choice exerts an additional independent effect: within the same matched reference set, minitax consistently yields stronger 16S-WGS concordance than Kraken2 across abundance, detection, ecological-distance and developmental-inference endpoints. Full-length ONT 16S is therefore well suited to broad ecological screening and hypothesis generation, whereas WGS remains preferable when conclusions depend on quantitative fine-rank composition, directly supported gene content or precise feature-level effect estimates.

microbiology↗