Metagenomic Analysis Reveals Presence of Treponema denticola in a Tissue Biopsy of the Iceman
Author(s) -
Frank Maixner,
Anton Thomma,
Giovanna Cipollini,
Stefanie Widder,
Thomas Rattei,
Albert Zink
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0099994
Subject(s) - metagenomics , treponema denticola , biology , microbiome , human microbiome project , ribosomal rna , genome , oral microbiome , treponema , computational biology , genetics , evolutionary biology , gene , bacteria , virology , porphyromonas gingivalis , syphilis , human immunodeficiency virus (hiv)
Ancient hominoid genome studies can be regarded by definition as metagenomic analyses since they represent a mixture of both hominoid and microbial sequences in an environment. Here, we report the molecular detection of the oral spirochete Treponema denticola in ancient human tissue biopsies of the Iceman, a 5,300-year-old Copper Age natural ice mummy. Initially, the metagenomic data of the Iceman’s genomic survey was screened for bacterial ribosomal RNA (rRNA) specific reads. Through ranking the reads by abundance a relatively high number of rRNA reads most similar to T. denticola was detected. Mapping of the metagenome sequences against the T. denticola genome revealed additional reads most similar to this opportunistic pathogen. The DNA damage pattern of specifically mapped reads suggests an ancient origin of these sequences. The haematogenous spread of bacteria of the oral microbiome often reported in the recent literature could already explain the presence of metagenomic reads specific for T. denticola in the Iceman’s bone biopsy. We extended, however, our survey to an Iceman gingival tissue sample and a mouth swab sample and could thereby detect T. denticola and Porphyrimonas gingivalis , another important member of the human commensal oral microflora. Taken together, this study clearly underlines the opportunity to detect disease-associated microorganisms when applying metagenomics- enabled approaches on datasets of ancient human remains.
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