Background
Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole-genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively.
Methods
We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared with traditional IP practice during the same period.
Results
Of 3165 isolates, there were 2752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates identified by WGS; clusters ranged from 2–14 patients. At least 1 transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real time, 25–63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192 408–$692 532.
Conclusions
EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety.
The type VI secretion system (T6SS) is used by gram-negative bacteria to translocate effectors that can antagonize other bacterial cells. Models predict the variation in collections of effector and cognate immunity genes determine competitiveness and can affect the dynamics of populations and communities of bacteria. However, the outcomes of competition cannot be entirely explained by compatibility of effector-immunity (EI) pairs. Here, we characterized the diversity of T6SS loci of plant-pathogenic Agrobacterium tumefaciens and showed that factors other than EI pairs can impact interbacterial competition. All examined strains encode T6SS active in secretion and antagonism against Escherichia coli. The spectra of EI pairs as well as compositions of gene neighborhoods are diverse. Almost 30 in-planta competitions were tested between different genotypes of A. tumefaciens. Fifteen competitions between members of different species-level groups resulted in T6SS-dependent suppression in in-planta growth of prey genotypes. In contrast, ten competitions between members within species-level groups resulted in no significant effect on the growth of prey genotypes. One strain was an exceptional case and, despite encoding a functional T6SS and toxic effector protein, could not compromise the growth of the four tested prey genotypes. The data suggest T6SS-associated EI pairs can influence the competitiveness of strains of A. tumefaciens, but genetic features have a significant role on the efficacy of interbacterial antagonism.
Highlights d Phytoplasma SAP05 proteins bind plant SPL and GATA transcription factors and RPN10 d SAP05 mediates degradation of SPLs and GATAs in a ubiquitin-independent manner d SAP05 decouples plant developmental transitions and induces witches' broom symptoms d Engineering of plant RPN10 confers resistance to SAP05 activities
Species Boundaries Among 16SrI Phytoplasmas further confirmed the rapid gains and losses of these genes, as well as the involvement of potential mobile units (PMUs) in their molecular evolution. Future improvements on the taxon sampling of phytoplasma genomes will allow further expansions of similar analysis, and thus contribute to phytoplasma taxonomy and diagnostics.
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