Herrmann, J.-L. | Search bioRxiv

bioRxiv2018

Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings

Antibiotic-resistance of hospital-acquired infections is a major public health issue. The worldwide emergence and diffusion of extended-spectrum {beta}-lactamase (ESBL)-producing Enterobacteriaceae, including Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP), is of particular concern. Preventing their nosocomial spread requires understanding their transmission. Using Close Proximity Interactions (CPIs), measured by wearable sensors, and weekly ESBL-EC- and ESBL-KP-carriage data, we traced their possible transmission paths among 329 patients in a 200-bed long-term care facility over 4 months. Based on phenotypically defined resistance profiles to 12 antibiotics, new bacterial acquisitions were tracked. Extending a previously proposed statistical method, the CPI networks ability to support observed incident colonization episodes of ESBL-EC and ESBL-KP was tested. Finally, mathematical modeling based on our findings assessed the effect of several infection-control measures. A potential infector was identified in the CPI network for 80% (16/20) of ESBL-KP acquisition episodes. The lengths of CPI paths between ESBL-KP incident cases and their potential infectors were shorter than predicted by chance (P = 0.02), indicating that CPI-network relationships were consistent with dissemination. Potential ESBL-EC infectors were identified for 54% (19/35) of the acquisitions, with longer-than-expected lengths of CPI paths. These contrasting results yielded differing impacts of infection control scenarios, with contact reduction interventions proving less effective for ESBL-EC than for ESBL-KP. These results highlight the widely variable transmission patterns among ESBL-producing Enterobacteriaceae species CPI networks supported ESBL-KP, but not ESBL-EC spread. These outcomes could help design more specific surveillance and control strategies to prevent in-hospital Enterobacteriaceae dissemination.\n\nAuthor summaryTracing extended-spectrum {beta}-lactamase (ESBL) dissemination in hospitals is an important step in the fight against the spread of multi-drug resistant bacteria. Indeed, understanding ESBL spreading dynamics will help identify efficient control interventions. In the i-Bird study, patients and hospital staff from a French long-term care facility in France carried a wearable sensor to capture their interactions at less than 1.5 meters, every 30 seconds over a 4-month period. Every week, patients were also swabbed to detect carriage of ESBL-producing Enterobacteriaceae. Based on the analysis of these longitudinal data, this study shows that ESBL-producing Klebsiella pneumoniae (ESBL-KP) mostly spreads during close-proximity interactions between individuals, while this is not the case for ESBL-producing Escherichia coli (ESBL-EC), suggesting that ESBL-KP but not ESBL-EC may be controlled by contact reduction interventions.

epidemiology↗

bioRxiv2017

Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells

Tuberculosis (TB) is a deadly infectious disease, which kills millions of people every year. The causative pathogen, Mycobac-terium tuberculosis (MTB), is estimated to have infected up to a third of the worlds population; however, only approximately 10% of infected healthy individuals progress to active TB. Despite evidence for heritability, it is not currently possible to predict who may develop TB. To explore approaches to classify susceptibility to TB, we infected with MTB dendritic cells (DCs) from putatively resistant individuals diagnosed with latent TB, and from susceptible individuals that had recovered from active TB. We measured gene expression levels in infected and non-infected cells and found hundreds of differentially expressed genes between susceptible and resistant individuals in the non-infected cells. We further found that genetic polymorphisms nearby the differentially expressed genes between susceptible and resistant individuals are more likely to be associated with TB susceptibility in published GWAS data. Lastly, we trained a classifier based on the gene expression levels in the non-infected cells, and demonstrated decent performance on our data and an independent data set. Overall, our promising results from this small study suggest that training a classifier on a larger cohort may enable us to accurately predict TB susceptibility.

genomics↗