Person:

Chang, Hsiao-Han

Whole-genome sequencing of pathogens has recently been used to investigate disease outbreaks and is likely to play a growing role in real-time epidemiological studies. Methods to analyze high-resolution genomic data in this context are still lacking, and inferring transmission dynamics from such data typically requires many assumptions. While recent studies have proposed methods to infer who infected whom based on genetic distance between isolates from different individuals, the link between epidemiological relationship and genetic distance is still not well understood. In this study, we investigated the distribution of pairwise genetic distances between samples taken from infected hosts during an outbreak. We proposed an analytically tractable approximation to this distribution, which provides a framework to evaluate the likelihood of particular transmission routes. Our method accounts for the transmission of a genetically diverse inoculum, a possibility overlooked in most analyses. We demonstrated that our approximation can provide a robust estimation of the posterior probability of transmission routes in an outbreak and may be used to rule out transmission events at a particular probability threshold. We applied our method to data collected during an outbreak of methicillin-resistant Staphylococcus aureus, ruling out several potential transmission links. Our study sheds light on the accumulation of mutations in a pathogen during an epidemic and provides tools to investigate transmission dynamics, avoiding the intensive computation necessary in many existing methods.

SUMMARY: Many studies report the high prevalence of multiply drug-resistant (MDR) strains. Because MDR infections are often significantly harder and more expensive to treat, they represent a growing public health threat. However, for different pathogens, different underlying mechanisms are traditionally used to explain these observations, and it is unclear whether each bacterial taxon has its own mechanism(s) for multidrug resistance or whether there are common mechanisms between distantly related pathogens. In this review, we provide a systematic overview of the causes of the excess of MDR infections and define testable predictions made by each hypothetical mechanism, including experimental, epidemiological, population genomic, and other tests of these hypotheses. Better understanding the cause(s) of the excess of MDR is the first step to rational design of more effective interventions to prevent the origin and/or proliferation of MDR.

Background: Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most common healthcare-associated pathogens. To examine the role of inter-hospital patient sharing on MRSA transmission, a previous study collected 2,214 samples from 30 hospitals in Orange County, California and showed by spa typing that genetic differentiation decreased significantly with increased patient sharing. In the current study, we focused on the 986 samples with spa type t008 from the same population. Methods: We used genome sequencing to determine the effect of patient sharing on genetic differentiation between hospitals. Genetic differentiation was measured by between-hospital genetic diversity, FST, and the proportion of nearly identical isolates between hospitals. Results: Surprisingly, we found very similar genetic diversity within and between hospitals, and no significant association between patient sharing and genetic differentiation measured by FST. However, in contrast to FST, there was a significant association between patient sharing and the proportion of nearly identical isolates between hospitals. We propose that the proportion of nearly identical isolates is more powerful at determining transmission dynamics than traditional estimators of genetic differentiation (FST) when gene flow between populations is high, since it is more responsive to recent transmission events. Our hypothesis was supported by the results from coalescent simulations. Conclusions: Our results suggested that there was a high level of gene flow between hospitals facilitated by patient sharing, and that the proportion of nearly identical isolates is more sensitive to population structure than FST when gene flow is high. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0274-3) contains supplementary material, which is available to authorized users.

We describe sequence tag-based analysis of microbial populations (STAMP) for characterization of pathogen population dynamics during infection. STAMP analyzes the frequency changes of genetically 'barcoded' organisms to quantify population bottlenecks and infer the founding population size. Analyses of intraintestinal Vibrio cholerae revealed infection-stage and region-specific host barriers to infection and showed unexpected V. cholerae migration counter to intestinal flow. STAMP provides a robust, widely applicable analytical framework for high-confidence characterization of in vivo microbial dissemination.

We describe a new method (STAMP) for characterization of pathogen population dynamics during infection. STAMP analyzes the frequency changes of genetically “barcoded” organisms to quantify population bottlenecks and infer the founding population size. Analyses of intra-intestinal Vibrio cholerae revealed infection-stage and region-specific host barriers to infection, and unexpectedly showed V. cholerae migration counter to intestinal flow. STAMP provides a robust, widely applicable analytical framework for high confidence characterization of in vivo microbial dissemination.