IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-35454-4.html
   My bibliography  Save this article

Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence

Author

Listed:
  • Jack S. Gisby

    (Imperial College London)

  • Norzawani B. Buang

    (Imperial College London)

  • Artemis Papadaki

    (Imperial College London)

  • Candice L. Clarke

    (Imperial College London
    Imperial College Healthcare NHS Trust)

  • Talat H. Malik

    (Imperial College London)

  • Nicholas Medjeral-Thomas

    (Imperial College London
    Imperial College Healthcare NHS Trust)

  • Damiola Pinheiro

    (Imperial College London)

  • Paige M. Mortimer

    (Imperial College London)

  • Shanice Lewis

    (Imperial College London)

  • Eleanor Sandhu

    (Imperial College London
    Imperial College Healthcare NHS Trust)

  • Stephen P. McAdoo

    (Imperial College London
    Imperial College Healthcare NHS Trust)

  • Maria F. Prendecki

    (Imperial College London
    Imperial College Healthcare NHS Trust)

  • Michelle Willicombe

    (Imperial College London
    Imperial College Healthcare NHS Trust)

  • Matthew C. Pickering

    (Imperial College London)

  • Marina Botto

    (Imperial College London)

  • David C. Thomas

    (Imperial College London
    Imperial College Healthcare NHS Trust)

  • James E. Peters

    (Imperial College London)

Abstract

Patients with end-stage kidney disease (ESKD) are at high risk of severe COVID-19. Here, we perform longitudinal blood sampling of ESKD haemodialysis patients with COVID-19, collecting samples pre-infection, serially during infection, and after clinical recovery. Using plasma proteomics, and RNA-sequencing and flow cytometry of immune cells, we identify transcriptomic and proteomic signatures of COVID-19 severity, and find distinct temporal molecular profiles in patients with severe disease. Supervised learning reveals that the plasma proteome is a superior indicator of clinical severity than the PBMC transcriptome. We show that a decreasing trajectory of plasma LRRC15, a proposed co-receptor for SARS-CoV-2, is associated with a more severe clinical course. We observe that two months after the acute infection, patients still display dysregulated gene expression related to vascular, platelet and coagulation pathways, including PF4 (platelet factor 4), which may explain the prolonged thrombotic risk following COVID-19.

Suggested Citation

  • Jack S. Gisby & Norzawani B. Buang & Artemis Papadaki & Candice L. Clarke & Talat H. Malik & Nicholas Medjeral-Thomas & Damiola Pinheiro & Paige M. Mortimer & Shanice Lewis & Eleanor Sandhu & Stephen , 2022. "Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35454-4
    DOI: 10.1038/s41467-022-35454-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-35454-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-35454-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sebastian Schafer & Sivakumar Viswanathan & Anissa A. Widjaja & Wei-Wen Lim & Aida Moreno-Moral & Daniel M. DeLaughter & Benjamin Ng & Giannino Patone & Kingsley Chow & Ester Khin & Jessie Tan & Sonia, 2017. "IL-11 is a crucial determinant of cardiovascular fibrosis," Nature, Nature, vol. 552(7683), pages 110-115, December.
    2. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    3. Elizabeth J. Williamson & Alex J. Walker & Krishnan Bhaskaran & Seb Bacon & Chris Bates & Caroline E. Morton & Helen J. Curtis & Amir Mehrkar & David Evans & Peter Inglesby & Jonathan Cockburn & Helen, 2020. "Factors associated with COVID-19-related death using OpenSAFELY," Nature, Nature, vol. 584(7821), pages 430-436, August.
    4. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    5. Denisa Bojkova & Kevin Klann & Benjamin Koch & Marek Widera & David Krause & Sandra Ciesek & Jindrich Cinatl & Christian Münch, 2020. "Proteomics of SARS-CoV-2-infected host cells reveals therapy targets," Nature, Nature, vol. 583(7816), pages 469-472, July.
    6. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    7. Yaara Finkel & Avi Gluck & Aharon Nachshon & Roni Winkler & Tal Fisher & Batsheva Rozman & Orel Mizrahi & Yoav Lubelsky & Binyamin Zuckerman & Boris Slobodin & Yfat Yahalom-Ronen & Hadas Tamir & Igor , 2021. "SARS-CoV-2 uses a multipronged strategy to impede host protein synthesis," Nature, Nature, vol. 594(7862), pages 240-245, June.
    8. Clemens Gutmann & Kaloyan Takov & Sean A. Burnap & Bhawana Singh & Hashim Ali & Konstantinos Theofilatos & Ella Reed & Maria Hasman & Adam Nabeebaccus & Matthew Fish & Mark JW. McPhail & Kevin O’Galla, 2021. "SARS-CoV-2 RNAemia and proteomic trajectories inform prognostication in COVID-19 patients admitted to intensive care," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Erik Duijvelaar & Jack Gisby & James E. Peters & Harm Jan Bogaard & Jurjan Aman, 2024. "Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joshua P White & Simon Dennis & Martin Tomko & Jessica Bell & Stephan Winter, 2021. "Paths to social licence for tracking-data analytics in university research and services," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-19, May.
    2. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    3. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    4. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    5. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    6. Jian Lu & Raheel Ahmad & Thomas Nguyen & Jeffrey Cifello & Humza Hemani & Jiangyuan Li & Jinguo Chen & Siyi Li & Jing Wang & Achouak Achour & Joseph Chen & Meagan Colie & Ana Lustig & Christopher Dunn, 2022. "Heterogeneity and transcriptome changes of human CD8+ T cells across nine decades of life," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    7. Jun Wang & Jinyong Huang & Yunlong Hu & Qianwen Guo & Shasha Zhang & Jinglin Tian & Yanqin Niu & Ling Ji & Yuzhong Xu & Peijun Tang & Yaqin He & Yuna Wang & Shuya Zhang & Hao Yang & Kang Kang & Xinchu, 2024. "Terminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    8. Hongbo Guo & Enzai Du & César Terrer & Robert B. Jackson, 2024. "Global distribution of surface soil organic carbon in urban greenspaces," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    9. Andreas Floren & Tobias Müller, 2023. "Using a Machine Learning Approach to Classify the Degree of Forest Management," Sustainability, MDPI, vol. 15(16), pages 1-14, August.
    10. Siddharth Sethi & David Zhang & Sebastian Guelfi & Zhongbo Chen & Sonia Garcia-Ruiz & Emmanuel O. Olagbaju & Mina Ryten & Harpreet Saini & Juan A. Botia, 2022. "Leveraging omic features with F3UTER enables identification of unannotated 3’UTRs for synaptic genes," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    11. Qian Wang & Tao Yan & Zhengbiao Long & Luna Yue Huang & Yang Zhu & Ying Xu & Xiaoyang Chen & Haksong Pak & Jiqiang Li & Dezhi Wu & Yang Xu & Shuijin Hua & Lixi Jiang, 2021. "Prediction of heterosis in the recent rapeseed (Brassica napus) polyploid by pairing parental nucleotide sequences," PLOS Genetics, Public Library of Science, vol. 17(11), pages 1-22, November.
    12. Michimasa Fujiogi & Yoshihiko Raita & Marcos Pérez-Losada & Robert J. Freishtat & Juan C. Celedón & Jonathan M. Mansbach & Pedro A. Piedra & Zhaozhong Zhu & Carlos A. Camargo & Kohei Hasegawa, 2022. "Integrated relationship of nasopharyngeal airway host response and microbiome associates with bronchiolitis severity," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    13. Marc Jan Bonder & Stephen J. Clark & Felix Krueger & Siyuan Luo & João Agostinho de Sousa & Aida M. Hashtroud & Thomas M. Stubbs & Anne-Katrien Stark & Steffen Rulands & Oliver Stegle & Wolf Reik & Fe, 2024. "scEpiAge: an age predictor highlighting single-cell ageing heterogeneity in mouse blood," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    14. Erik Duijvelaar & Jack Gisby & James E. Peters & Harm Jan Bogaard & Jurjan Aman, 2024. "Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    15. Paweł Teisseyre & Robert A. Kłopotek & Jan Mielniczuk, 2016. "Random Subspace Method for high-dimensional regression with the R package regRSM," Computational Statistics, Springer, vol. 31(3), pages 943-972, September.
    16. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    17. Barbara Emmenegger & Julien Massoni & Christine M. Pestalozzi & Miriam Bortfeld-Miller & Benjamin A. Maier & Julia A. Vorholt, 2023. "Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    18. Lauren P. Grant & Chris Gennings & Edmond P. Wickham & Derek Chapman & Shumei Sun & David C. Wheeler, 2018. "Modeling Pediatric Body Mass Index and Neighborhood Environment at Different Spatial Scales," IJERPH, MDPI, vol. 15(3), pages 1-19, March.
    19. María Bueno Álvez & Fredrik Edfors & Kalle Feilitzen & Martin Zwahlen & Adil Mardinoglu & Per-Henrik Edqvist & Tobias Sjöblom & Emma Lundin & Natallia Rameika & Gunilla Enblad & Henrik Lindman & Marti, 2023. "Next generation pan-cancer blood proteome profiling using proximity extension assay," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    20. Haris Babačić & Wanda Christ & José Eduardo Araújo & Georgios Mermelekas & Nidhi Sharma & Janne Tynell & Marina García & Renata Varnaite & Hilmir Asgeirsson & Hedvig Glans & Janne Lehtiö & Sara Gredma, 2023. "Comprehensive proteomics and meta-analysis of COVID-19 host response," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35454-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.