Login / Signup

Coalescence modeling of intrainfection Bacillus anthracis populations allows estimation of infection parameters in wild populations.

W Ryan EasterdayJosé Miguel PoncianoJuan Pablo GomezMatthew N Van ErtTed HadfieldKaroun BagamianJason K BlackburnNils Chr StensethWendy C Turner
Published in: Proceedings of the National Academy of Sciences of the United States of America (2020)
Bacillus anthracis, the etiological agent of anthrax, is a well-established model organism. For B. anthracis and most other infectious diseases, knowledge regarding transmission and infection parameters in natural systems, in large part, comprises data gathered from closely controlled laboratory experiments. Fatal, natural anthrax infections transmit the bacterium through new host-pathogen contacts at carcass sites, which can occur years after death of the previous host. For the period between contact and death, all of our knowledge is based upon experimental data from domestic livestock and laboratory animals. Here we use a noninvasive method to explore the dynamics of anthrax infections, by evaluating the terminal diversity of B. anthracis in anthrax carcasses. We present an application of population genetics theory, specifically, coalescence modeling, to intrainfection populations of B. anthracis to derive estimates for the duration of the acute phase of the infection and effective population size converted to the number of colony-forming units establishing infection in wild plains zebra (Equus quagga). Founding populations are small, a few colony-forming units, and infections are rapid, lasting roughly between 1 d and 3 d in the wild. Our results closely reflect experimental data, showing that small founding populations progress acutely, killing the host within days. We believe this method is amendable to other bacterial diseases from wild, domestic, and human systems.
Keyphrases
  • genetic diversity
  • electronic health record
  • healthcare
  • infectious diseases
  • big data
  • endothelial cells
  • data analysis
  • machine learning
  • bacillus subtilis
  • candida albicans
  • induced pluripotent stem cells