IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-28639-4.html
   My bibliography  Save this article

Prognostic tools and candidate drugs based on plasma proteomics of patients with severe COVID-19 complications

Author

Listed:
  • Maryam A. Y. Al-Nesf

    (Hamad General Hospital, Hamad Medical Corporation
    Royal Free Campus, University College London)

  • Houari B. Abdesselem

    (Hamad Bin Khalifa University, Qatar Foundation)

  • Ilham Bensmail

    (Hamad Bin Khalifa University, Qatar Foundation)

  • Shahd Ibrahim

    (Hamad General Hospital, Hamad Medical Corporation)

  • Walaa A. H. Saeed

    (Hamad General Hospital, Hamad Medical Corporation)

  • Sara S. I. Mohammed

    (Hamad General Hospital, Hamad Medical Corporation)

  • Almurtada Razok

    (Hamad General Hospital, Hamad Medical Corporation)

  • Hashim Alhussain

    (Qatar University)

  • Reham M. A. Aly

    (Qatar University)

  • Muna Al Maslamani

    (Hamad Medical Corporation)

  • Khalid Ouararhni

    (Hamad Bin Khalifa University, Qatar Foundation)

  • Mohamad Y. Khatib

    (Hezm Mebairek General Hospital, Hamad Medical Corporation)

  • Ali Ait Hssain

    (Hamad General Hospital, Hamad Medical Corporation)

  • Ali S. Omrani

    (Hamad Medical Corporation)

  • Saad Al-Kaabi

    (Hamad General Hospital, Hamad Medical Corporation)

  • Abdullatif Al Khal

    (Hamad Medical Corporation)

  • Asmaa A. Al-Thani

    (Qatar University)

  • Waseem Samsam

    (Anti-Doping Laboratory Qatar)

  • Abdulaziz Farooq

    (Aspetar Hospital, Orthopaedic and Sports Medicine Hospital, FIFA Medical Centre of Excellence)

  • Jassim Al-Suwaidi

    (Hamad General Hospital, Hamad Medical Corporation)

  • Mohammed Al-Maadheed

    (Royal Free Campus, University College London
    Anti-Doping Laboratory Qatar)

  • Heba H. Al-Siddiqi

    (Hamad Bin Khalifa University, Qatar Foundation)

  • Alexandra E. Butler

    (Hamad Bin Khalifa University, Qatar Foundation
    Royal College of Surgeons of Ireland in Bahrain)

  • Julie V. Decock

    (Hamad Bin Khalifa University, Qatar Foundation
    Hamad Bin Khalifa University, Qatar Foundation)

  • Vidya Mohamed-Ali

    (Royal Free Campus, University College London
    Anti-Doping Laboratory Qatar)

  • Fares Al-Ejeh

    (Hamad Bin Khalifa University, Qatar Foundation
    Hamad Bin Khalifa University, Qatar Foundation
    University of Queensland)

Abstract

COVID-19 complications still present a huge burden on healthcare systems and warrant predictive risk models to triage patients and inform early intervention. Here, we profile 893 plasma proteins from 50 severe and 50 mild-moderate COVID-19 patients, and 50 healthy controls, and show that 375 proteins are differentially expressed in the plasma of severe COVID-19 patients. These differentially expressed plasma proteins are implicated in the pathogenesis of COVID-19 and present targets for candidate drugs to prevent or treat severe complications. Based on the plasma proteomics and clinical lab tests, we also report a 12-plasma protein signature and a model of seven routine clinical tests that validate in an independent cohort as early risk predictors of COVID-19 severity and patient survival. The risk predictors and candidate drugs described in our study can be used and developed for personalized management of SARS-CoV-2 infected patients.

Suggested Citation

  • Maryam A. Y. Al-Nesf & Houari B. Abdesselem & Ilham Bensmail & Shahd Ibrahim & Walaa A. H. Saeed & Sara S. I. Mohammed & Almurtada Razok & Hashim Alhussain & Reham M. A. Aly & Muna Al Maslamani & Khal, 2022. "Prognostic tools and candidate drugs based on plasma proteomics of patients with severe COVID-19 complications," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28639-4
    DOI: 10.1038/s41467-022-28639-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-28639-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-28639-4?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. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    Full references (including those not matched with items on IDEAS)

    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. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).
    2. Asma Shaheen & Javed Iqbal, 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    3. Ramón Ferri-García & María del Mar Rueda, 2022. "Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys," Statistical Papers, Springer, vol. 63(6), pages 1829-1881, December.
    4. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    5. Sangjin Kim & Jong-Min Kim, 2019. "Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data," Mathematics, MDPI, vol. 7(6), pages 1-16, May.
    6. Baihan Wang & Alfred Pozarickij & Mohsen Mazidi & Neil Wright & Pang Yao & Saredo Said & Andri Iona & Christiana Kartsonaki & Hannah Fry & Kuang Lin & Yiping Chen & Huaidong Du & Daniel Avery & Dan Sc, 2025. "Comparative studies of 2168 plasma proteins measured by two affinity-based platforms in 4000 Chinese adults," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    7. Foutzopoulos, Giorgos & Pandis, Nikolaos & Tsagris, Michail, 2024. "Predicting full retirement attainment of NBA players," MPRA Paper 121540, University Library of Munich, Germany.
    8. Zhao-Yue Chen & Hervé Petetin & Raúl Fernando Méndez Turrubiates & Hicham Achebak & Carlos Pérez García-Pando & Joan Ballester, 2024. "Population exposure to multiple air pollutants and its compound episodes in Europe," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    9. Schrader, Silja & Graham, Sonia & Campbell, Rebecca & Height, Kaitlyn & Hawkes, Gina, 2024. "Grower attitudes and practices toward area-wide management of cropping weeds in Australia," Land Use Policy, Elsevier, vol. 137(C).
    10. Piotr Pomorski & Denise Gorse, 2023. "Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes," Papers 2310.04536, arXiv.org.
    11. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
    12. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    13. Abolfazl Mollalo & Kiara M. Rivera & Behzad Vahedi, 2020. "Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    14. Chunyang Huang & Shaoliang Zhang, 2023. "Explainable artificial intelligence model for identifying Market Value in Professional Soccer Players," Papers 2311.04599, arXiv.org, revised Nov 2023.
    15. Faisal Alsayegh & Moh A Alkhamis & Fatima Ali & Sreeja Attur & Nicholas M Fountain-Jones & Mohammad Zubaid, 2022. "Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-15, January.
    16. Basso, Franco & Cox, Tomás & Pezoa, Raúl & Maldonado, Tomás & Varas, Mauricio, 2024. "Characterizing last-mile freight transportation using mobile phone data: The case of Santiago, Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 186(C).
    17. Andrea Albergoni & Florentina J. Hettinga & Wim Stut & Francesco Sartor, 2020. "Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence," IJERPH, MDPI, vol. 17(17), pages 1-18, August.
    18. Franck Ramaharo & Fitiavana Randriamifidy, 2023. "Determinants of renewable energy consumption in Madagascar: Evidence from feature selection algorithms," Papers 2401.13671, arXiv.org.
    19. Yu, Yang & Yu, Qinghua & Luo, RunSen & Chen, Sheng & Yang, Jiebo & Yan, Fuwu, 2024. "Degradation and polarization curve prediction of proton exchange membrane fuel cells: An interpretable model perspective," Applied Energy, Elsevier, vol. 365(C).
    20. Weichun Zhang & Yunyi Zhang & Xin Zhang & Wei Wu & Hongbin Liu, 2024. "The Spatiotemporal Variability of Soil Available Phosphorus and Potassium in Karst Region: The Crucial Role of Socio-Geographical Factors," Land, MDPI, vol. 13(6), pages 1-21, June.

    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_s41467-022-28639-4. 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.