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A population-level strain genotyping method to study pathogen strain dynamics in human infections
Sarah J. Morgan, … , Matthew C. Radey, Pradeep K. Singh
Sarah J. Morgan, … , Matthew C. Radey, Pradeep K. Singh
Published December 22, 2021
Citation Information: JCI Insight. 2021;6(24):e152472. https://doi.org/10.1172/jci.insight.152472.
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Resource and Technical Advance Infectious disease Microbiology

A population-level strain genotyping method to study pathogen strain dynamics in human infections

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Abstract

A hallmark of chronic bacterial infections is the long-term persistence of 1 or more pathogen species at the compromised site. Repeated detection of the same bacterial species can suggest that a single strain or lineage is continually present. However, infection with multiple strains of a given species, strain acquisition and loss, and changes in strain relative abundance can occur. Detecting strain-level changes and their effects on disease is challenging because most methods require labor-intensive isolate-by-isolate analyses, and thus, only a few cells from large infecting populations can be examined. Here, we present a population-level method for enumerating and measuring the relative abundance of strains called population multi-locus sequence typing (PopMLST). The method exploits PCR amplification of strain-identifying polymorphic loci, next-generation sequencing to measure allelic variants, and informatic methods to determine whether variants arise from sequencing errors or low-abundance strains. These features enable PopMLST to simultaneously interrogate hundreds of bacterial cells that are cultured en masse from patient samples or are present in DNA directly extracted from clinical specimens without ex vivo culture. This method could be used to detect epidemic or super-infecting strains, facilitate understanding of strain dynamics during chronic infections, and enable studies that link strain changes to clinical outcomes.

Authors

Sarah J. Morgan, Samantha L. Durfey, Sumedha Ravishankar, Peter Jorth, Wendy Ni, Duncan T. Skerrett, Moira L. Aitken, Edward F. McKone, Stephen J. Salipante, Matthew C. Radey, Pradeep K. Singh

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