Microevolution and History of the Plague Bacillus, Yersinia pestis

Abstract

The association of historical plague pandemics with Yersinia pestis remains controversial, partly because the evolutionary history of this largely monomorphic bacterium was unknown. The microevolution of Y. pestis was therefore investigated by three different multilocus molecular methods, targeting genomewide synonymous SNPs, variation in number of tandem repeats, and insertion of IS100 insertion elements. Eight populations were recognized by the three methods, and we propose an evolutionary tree for these populations, rooted on Yersinia pseudotuberculosis. The tree invokes microevolution over millennia, during which enzootic pestoides isolates evolved. This initial phase was followed by a binary split 6,500 years ago, which led to populations that are more frequently associated with human disease. These populations do not correspond directly to classical biovars that are based on phenotypic properties. Thus, we recommend that henceforth groupings should be based on molecular signatures. The age of Y. pestis inferred here is compatible with the dates of historical pandemic plague. However, it is premature to infer an association between any modern molecular grouping and a particular pandemic wave that occurred before the 20th century.

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Document Details

Document Type
Technical Report
Publication Date
Dec 21, 2004
Accession Number
ADA429747

Entities

People

  • Giovanna Morelli
  • Ines Diehl
  • Mark Achtman
  • Peixuan Zhu
  • Thierry Wirth

Organizations

  • United States Army Medical Research Institute of Infectious Diseases

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Asia
  • Bacteria
  • Biological Sciences
  • Biology
  • Central Asia
  • Chemistry
  • Diseases And Disorders
  • Enzootic
  • Genes
  • Genetics
  • Geography
  • Human Population
  • Infectious Diseases
  • Microbiology
  • Pandemics
  • Ussr
  • Websites

Fields of Study

  • Biology

Readers

  • Immunology
  • Military History of the United States in the 20th Century.
  • Molecular and genetic basis of cancer.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms