Towards predicting the geographical origin of ancient samples with metagenomic data.

Détails

ID Serval
serval:BIB_4C67F4828BA4
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Towards predicting the geographical origin of ancient samples with metagenomic data.
Périodique
Scientific reports
Auteur⸱e⸱s
Bozzi D., Neuenschwander S., Cruz Dávalos D.I., Sousa da Mota B., Schroeder H., Moreno-Mayar J.V., Allentoft M.E., Malaspinas A.S.
ISSN
2045-2322 (Electronic)
ISSN-L
2045-2322
Statut éditorial
Publié
Date de publication
18/09/2024
Peer-reviewed
Oui
Volume
14
Numéro
1
Pages
21794
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
Reconstructing the history-such as the place of birth and death-of an individual sample is a fundamental goal in ancient DNA (aDNA) studies. However, knowing the place of death can be particularly challenging when samples come from museum collections with incomplete or erroneous archives. While analyses of human DNA and isotope data can inform us about the ancestry of an individual and provide clues about where the person lived, they cannot specifically trace the place of death. Moreover, while ancient human DNA can be retrieved, a large fraction of the sequenced molecules in ancient DNA studies derive from exogenous DNA. This DNA-which is usually discarded in aDNA analyses-is constituted mostly by microbial DNA from soil-dwelling microorganisms that have colonized the buried remains post-mortem. In this study, we hypothesize that remains of individuals buried in the same or close geographic areas, exposed to similar microbial communities, could harbor more similar metagenomes. We propose to use metagenomic data from ancient samples' shotgun sequencing to locate the place of death of a given individual which can also help to solve cases of sample mislabeling. We used a k-mer-based approach to compute similarity scores between metagenomic samples from different locations and propose a method based on dimensionality reduction and logistic regression to assign a geographical origin to target samples. We apply our method to several public datasets and observe that individual samples from closer geographic locations tend to show higher similarities in their metagenomes compared to those of different origin, allowing good geographical predictions of test samples. Moreover, we observe that the genus Streptomyces commonly infiltrates ancient remains and represents a valuable biomarker to trace the samples' geographic origin. Our results provide a proof of concept and show how metagenomic data can also be used to shed light on the place of origin of ancient samples.
Mots-clé
Humans, DNA, Ancient/analysis, Metagenomics/methods, Metagenome, Geography, Microbiota/genetics
Pubmed
Open Access
Oui
Création de la notice
20/09/2024 10:22
Dernière modification de la notice
21/09/2024 6:09
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