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A metagenomic DNA sequencing assay that is robust against environmental DNA contamination

Author

Listed:
  • Omary Mzava

    (Cornell University)

  • Alexandre Pellan Cheng

    (Cornell University)

  • Adrienne Chang

    (Cornell University)

  • Sami Smalling

    (Cornell University)

  • Liz-Audrey Kounatse Djomnang

    (Cornell University)

  • Joan Sesing Lenz

    (Cornell University)

  • Randy Longman

    (Weill Cornell Medicine, Jill Roberts Center for IBD)

  • Amy Steadman

    (Global Health Labs)

  • Luis G. Gómez-Escobar

    (Weill Cornell Medicine)

  • Edward J. Schenck

    (Weill Cornell Medicine)

  • Mirella Salvatore

    (Weill Cornell Medicine)

  • Michael J. Satlin

    (Weill Cornell Medicine)

  • Manikkam Suthanthiran

    (Weill Cornell Medicine
    New York Presbyterian Hospital–Weill Cornell Medical Center)

  • John R. Lee

    (Weill Cornell Medicine)

  • Christopher E. Mason

    (Weill Cornell Medical College
    WorldQuant Initiative for Quantitative Prediction)

  • Darshana Dadhania

    (Weill Cornell Medicine
    New York Presbyterian Hospital–Weill Cornell Medical Center)

  • Iwijn De Vlaminck

    (Cornell University)

Abstract

Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present Sample-Intrinsic microbial DNA Found by Tagging and sequencing (SIFT-seq) a metagenomic sequencing assay that is robust against environmental DNA contamination introduced during sample preparation. The core idea of SIFT-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied SIFT-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of sepsis and inflammatory bowel disease in blood.

Suggested Citation

  • Omary Mzava & Alexandre Pellan Cheng & Adrienne Chang & Sami Smalling & Liz-Audrey Kounatse Djomnang & Joan Sesing Lenz & Randy Longman & Amy Steadman & Luis G. Gómez-Escobar & Edward J. Schenck & Mir, 2022. "A metagenomic DNA sequencing assay that is robust against environmental DNA contamination," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31654-0
    DOI: 10.1038/s41467-022-31654-0
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    References listed on IDEAS

    as
    1. Philip Burnham & Darshana Dadhania & Michael Heyang & Fanny Chen & Lars F. Westblade & Manikkam Suthanthiran & John Richard Lee & Iwijn De Vlaminck, 2018. "Urinary cell-free DNA is a versatile analyte for monitoring infections of the urinary tract," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    2. Peter Menzel & Kim Lee Ng & Anders Krogh, 2016. "Fast and sensitive taxonomic classification for metagenomics with Kaiju," Nature Communications, Nature, vol. 7(1), pages 1-9, September.
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    Cited by:

    1. Xian Sun & Dongshuo Yin & Fei Qin & Hongfeng Yu & Wanxuan Lu & Fanglong Yao & Qibin He & Xingliang Huang & Zhiyuan Yan & Peijin Wang & Chubo Deng & Nayu Liu & Yiran Yang & Wei Liang & Ruiping Wang & C, 2023. "Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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