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Detecting the Immune System Response of a 500 Year-Old Inca Mummy

Overview of attention for article published in PLOS ONE, July 2012
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Title
Detecting the Immune System Response of a 500 Year-Old Inca Mummy
Published in
PLOS ONE, July 2012
DOI 10.1371/journal.pone.0041244
Pubmed ID
Authors

Angelique Corthals, Antonius Koller, Dwight W. Martin, Robert Rieger, Emily I. Chen, Mario Bernaski, Gabriella Recagno, Liliana M. Dávalos

Abstract

Disease detection in historical samples currently relies on DNA extraction and amplification, or immunoassays. These techniques only establish pathogen presence rather than active disease. We report the first use of shotgun proteomics to detect the protein expression profile of buccal swabs and cloth samples from two 500-year-old Andean mummies. The profile of one of the mummies is consistent with immune system response to severe pulmonary bacterial infection at the time of death. Presence of a probably pathogenic Mycobacterium sp. in one buccal swab was confirmed by DNA amplification, sequencing, and phylogenetic analyses. Our study provides positive evidence of active pathogenic infection in an ancient sample for the first time. The protocol introduced here is less susceptible to contamination than DNA-based or immunoassay-based studies. In scarce forensic samples, shotgun proteomics narrows the range of pathogens to detect using DNA assays, reducing cost. This analytical technique can be broadly applied for detecting infection in ancient samples to answer questions on the historical ecology of specific pathogens, as well as in medico-legal cases when active pathogenic infection is suspected.

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Geographical breakdown

Country Count As %
United States 2 2%
South Africa 1 <1%
United Kingdom 1 <1%
Brazil 1 <1%
Denmark 1 <1%
Peru 1 <1%
Spain 1 <1%
Romania 1 <1%
Unknown 118 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 27%
Researcher 24 19%
Student > Master 16 13%
Student > Bachelor 9 7%
Professor > Associate Professor 8 6%
Other 23 18%
Unknown 13 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 49 39%
Biochemistry, Genetics and Molecular Biology 8 6%
Medicine and Dentistry 8 6%
Social Sciences 8 6%
Arts and Humanities 8 6%
Other 30 24%
Unknown 16 13%