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Evaluation of an adapted semi-automated DNA extraction for human salivary shotgun metagenomics

Output Details

Published October 10, 2023

Recent attention has highlighted the importance of oral microbiota in human health and disease, e.g., in Parkinson’s disease, notably using shotgun metagenomics. One key aspect for efficient shotgun metagenomic analysis relies on optimal microbial sampling and DNA extraction, generally implementing commercial solutions developed to improve sample collection and preservation, and provide high DNA quality and quantity for downstream analysis. As metagenomic studies are today performed on a large number of samples, the next evolution to increase study throughput is with DNA extraction automation. In this study, we proposed a semi-automated DNA extraction protocol for human salivary samples collected with a commercial kit, and compared the outcomes with the DNA extraction recommended by the manufacturer. While similar DNA yields were observed between the protocols, our semi-automated DNA protocol generated significantly higher DNA fragment sizes. Moreover, we showed that the oral microbiome composition was equivalent between DNA extraction methods, even at the species level. This study demonstrates that our semi-automated protocol is suitable for shotgun metagenomic analysis, while allowing for improved sample treatment logistics with reduced technical variability and without compromising the structure of the oral microbiome.
Identifier (DOI)
10.3390/biom13101505
Tags
  • DNA isolation
  • Metagenome
  • Original Research

Meet the Authors

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    Victoria Meslier

    External Collaborator

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    Elisa Menozzi

    External Collaborator

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    Aymeric David

    External Collaborator

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    Christian Morabito

    External Collaborator

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    Sara Lucas Del Pozo

    External Collaborator

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    Alexandre Famechon

    External Collaborator

  • User avatar fallback logo

    Janet North

    External Collaborator

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    Benoit Quinquis

    External Collaborator

  • Sofia Koletsi, MSc

    Project Manager: Team Schapira

    University College London

  • User avatar fallback logo

    Jane Macnaughtan

    External Collaborator

  • User avatar fallback logo

    Roxana Mezabrovschi

    External Collaborator

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    Stanislav Ehrlich

    External Collaborator

  • Anthony Schapira, PhD

    Lead PI (Core Leadership): Team Schapira

    University College London

  • Mathieu Almeida, PhD

    Co-PI (Core Leadership): Team Schapira

    INRAE (National Research Institute for Agriculture, Food and the Environment)

Aligning Science Across Parkinson's
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