Role of the YehD fimbriae in the virulence-associated properties of enteroaggregative Escherichia coli.

The interaction of enteroaggregative Escherichia coli (EAEC) strains with the colonic gut mucosa is characterized by the ability of the bacteria to form robust biofilms, to bind mucin, and induce a local inflammatory response. These events are mediated by a repertoire of five different aggregative adherence fimbriae variants (AAF/I-V) typically encoded on virulence plasmids. In this study, we report the production in EAEC strains of a new YehD fimbriae (YDF), which is encoded by the chromosomal gene cluster yehABCD, also present in most E. coli strains. Immuno-labelling of EAEC strain 042 with anti-AAF/II and anti-YDF antibodies demonstrated the presence of both AAF/II and YDF on the bacterial surface. We investigated the role of YDF in cell adherence, biofilm formation, colonization of spinach leaves, and induction of pro-inflammatory cytokines release. To this aim, we constructed yehD deletion mutants in different EAEC backgrounds (strains 17-2, 042, 55989, C1010, 278-1, J7) each harbouring one of the five AAFs. The effect of the YDF mutation was strain dependent and AAF independent as the lack of YDF had a different impact on the phenotypes manifested by the different EAECs tested. Expression of the yehABCD operon in a E. coli K12 ORN172 showed that YDF is important for biofilm formation but not for adherence to HeLa cells. Lastly, screening of pro-inflammatory cytokines in supernatants of Caco-2 cells infected with EAEC strains 042 and J7 and their isogenic ΔyehD mutants showed that these mutants were significantly defective in release of IL-8 and TNF-α. This study contributes to the understanding of the complex and diverse mechanisms of adherence of EAEC strains and identifies a new potential target for preventive measures of gastrointestinal illness caused by EAEC and other E. coli pathogroups.

Patient-centric synthetic data generation, no reason to risk re-identification in biomedical data analysis.

While nearly all computational methods operate on pseudonymized personal data, re-identification remains a risk. With personal health data, this re-identification risk may be considered a double-crossing of patients' trust. Herein, we present a new method to generate synthetic data of individual granularity while holding on to patients' privacy. Developed for sensitive biomedical data, the method is patient-centric as it uses a local model to generate random new synthetic data, called an "avatar data", for each initial sensitive individual. This method, compared with 2 other synthetic data generation techniques (Synthpop, CT-GAN), is applied to real health data with a clinical trial and a cancer observational study to evaluate the protection it provides while retaining the original statistical information. Compared to Synthpop and CT-GAN, the Avatar method shows a similar level of signal maintenance while allowing to compute additional privacy metrics. In the light of distance-based privacy metrics, each individual produces an avatar simulation that is on average indistinguishable from 12 other generated avatar simulations for the clinical trial and 24 for the observational study. Data transformation using the Avatar method both preserves, the evaluation of the treatment's effectiveness with similar hazard ratios for the clinical trial (original HR = 0.49 [95% CI, 0.39-0.63] vs. avatar HR = 0.40 [95% CI, 0.31-0.52]) and the classification properties for the observational study (original AUC = 99.46 (s.e. 0.25) vs. avatar AUC = 99.84 (s.e. 0.12)). Once validated by privacy metrics, anonymous synthetic data enable the creation of value from sensitive pseudonymized data analyses by tackling the risk of a privacy breach.