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Successful microbial colonization of the gastrointestinal (GI) tract hinges on an organism's ability to overcome the intense competition for nutrients in the gut between the host and the resident gut microbiome. Enteric pathogens can exploit ethanolamine (EA) in the gut to bypass nutrient competition. However, Klebsiella pneumoniae (K. pneumoniae) is an asymptomatic gut colonizer and, unlike well-studied enteric pathogens, harbors two genetically distinct ethanolamine utilization (eut) loci. Our investigation uncovered unique roles for each eut locus depending on EA utilization as a carbon or nitrogen source. Murine gut colonization studies demonstrated the necessity of both eut loci in the presence of intact gut microbiota for robust GI colonization by K. pneumoniae. Additionally, while some Escherichia coli gut isolates could metabolize EA, other commensals were incapable, suggesting that EA metabolism likely provides K. pneumoniae a selective advantage in gut colonization. Molecular and bioinformatic analyses unveiled the conservation of two eut loci among K. pneumoniae and a subset of the related taxa in the K. pneumoniae species complex, with the NtrC-RpoN regulatory cascade playing a pivotal role in regulation. These findings identify EA metabolism as a critical driver of K. pneumoniae niche establishment in the gut and propose microbial metabolism as a potential therapeutic avenue to combat K. pneumoniae infections. Author summary: Klebsiella pneumoniae (K. pneumoniae) is a leading cause of nosocomial infections but is considered a silent gut colonizer. A molecular understanding of how K. pneumoniae colonizes the GI tract and overcomes host- and resident gut microbiota-mediated colonization resistance requires further investigation. A direct mechanism of colonization resistance is through competition for the same nutrient resources. K. pneumoniae can sidestep nutrient competition by metabolizing ethanolamine as an alternative nutrient source, thus overcoming microbiota-mediated colonization resistance. The unique ethanolamine utilization loci of K. pneumoniae are both required for robust gut colonization and are conserved among K. pneumoniae, suggesting that ethanolamine metabolism is an important mediator of gut colonization. [ABSTRACT FROM AUTHOR]

Reverse epidemiology is a mathematical modelling tool used to ascertain information about the source of a pathogen, given the spatial and temporal distribution of cases, hospitalisations and deaths. In the context of a deliberately released pathogen, such as Bacillus anthracis (the disease-causing organism of anthrax), this can allow responders to quickly identify the location and timing of the release, as well as other factors such as the strength of the release, and the realized wind speed and direction at release. These estimates can then be used to parameterise a predictive mechanistic model, allowing for estimation of the potential scale of the release, and to optimise the distribution of prophylaxis. In this paper we present two novel approaches to reverse epidemiology, and demonstrate their utility in responding to a simulated deliberate release of B. anthracis in ten locations in the UK and compare these to the standard grid-search approach. The two methods—a modified MCMC and a Recurrent Convolutional Neural Network—are able to identify the source location and timing of the release with significantly better accuracy compared to the grid-search approach. Further, the neural network method is able to do inference on new data significantly quicker than either the grid-search or novel MCMC methods, allowing for rapid deployment in time-sensitive outbreaks. Author summary: In this paper we demonstrate three methods for estimating the source location and timing of a deliberate release of Bacillus anthracis based on the temporal and spatial distribution of cases. Two of our proposed methods, a modified MCMC approach and a neural network based approach, provide significant improvements over previous methods by directly addressing the problematic parameter-likelihood surface, and, in the case of the neural network approach, addressing the slow deployment speeds of existing methods. Our results represent a major step forward in the accuracy and speed of epidemiological back-calculation. [ABSTRACT FROM AUTHOR]