IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-36140-9.html
   My bibliography  Save this article

Severe COVID-19 patients have impaired plasmacytoid dendritic cell-mediated control of SARS-CoV-2

Author

Listed:
  • Manon Venet

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Margarida Sa Ribeiro

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Elodie Décembre

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Alicia Bellomo

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Garima Joshi

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Célia Nuovo

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Marine Villard

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • David Cluet

    (Laboratory of Biology and Modeling of the Cell, Université de Lyon, ENS de Lyon, Université Claude Bernard, CNRS UMR 5239, Inserm)

  • Magali Perret

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Rémi Pescamona

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Helena Paidassi

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Thierry Walzer

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Omran Allatif

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Alexandre Belot

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Sophie Trouillet-Assant

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

  • Emiliano P. Ricci

    (Laboratory of Biology and Modeling of the Cell, Université de Lyon, ENS de Lyon, Université Claude Bernard, CNRS UMR 5239, Inserm)

  • Marlène Dreux

    (CIRI, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon)

Abstract

Type I and III interferons (IFN-I/λ) are important antiviral mediators against SARS-CoV-2 infection. Here, we demonstrate that plasmacytoid dendritic cells (pDC) are the predominant IFN-I/λ source following their sensing of SARS-CoV-2-infected cells. Mechanistically, this short-range sensing by pDCs requires sustained integrin-mediated cell adhesion with infected cells. In turn, pDCs restrict viral spread by an IFN-I/λ response directed toward SARS-CoV-2-infected cells. This specialized function enables pDCs to efficiently turn-off viral replication, likely via a local response at the contact site with infected cells. By exploring the pDC response in SARS-CoV-2 patients, we further demonstrate that pDC responsiveness inversely correlates with the severity of the disease. The pDC response is particularly impaired in severe COVID-19 patients. Overall, we propose that pDC activation is essential to control SARS-CoV-2-infection. Failure to develop this response could be important to understand severe cases of COVID-19.

Suggested Citation

  • Manon Venet & Margarida Sa Ribeiro & Elodie Décembre & Alicia Bellomo & Garima Joshi & Célia Nuovo & Marine Villard & David Cluet & Magali Perret & Rémi Pescamona & Helena Paidassi & Thierry Walzer & , 2023. "Severe COVID-19 patients have impaired plasmacytoid dendritic cell-mediated control of SARS-CoV-2," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36140-9
    DOI: 10.1038/s41467-023-36140-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-36140-9
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-36140-9?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. Shin-Ichiroh Saitoh & Fumiko Abe & Atsuo Kanno & Natsuko Tanimura & Yoshiko Mori Saitoh & Ryutaro Fukui & Takuma Shibata & Katsuaki Sato & Takeshi Ichinohe & Mayumi Hayashi & Kazuishi Kubota & Hiroko , 2017. "TLR7 mediated viral recognition results in focal type I interferon secretion by dendritic cells," Nature Communications, Nature, vol. 8(1), pages 1-12, December.
    2. Xiaobo Lei & Xiaojing Dong & Ruiyi Ma & Wenjing Wang & Xia Xiao & Zhongqin Tian & Conghui Wang & Ying Wang & Li Li & Lili Ren & Fei Guo & Zhendong Zhao & Zhuo Zhou & Zichun Xiang & Jianwei Wang, 2020. "Activation and evasion of type I interferon responses by SARS-CoV-2," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    3. Qian Zhang & Paul Bastard & Aurélie Cobat & Jean-Laurent Casanova, 2022. "Human genetic and immunological determinants of critical COVID-19 pneumonia," Nature, Nature, vol. 603(7902), pages 587-598, March.
    4. L. Vanderbeke & P. Van Mol & Y. Van Herck & F. De Smet & S. Humblet-Baron & K. Martinod & A. Antoranz & I. Arijs & B. Boeckx & F. M. Bosisio & M. Casaer & D. Dauwe & W. De Wever & C. Dooms & E. Dreese, 2021. "Monocyte-driven atypical cytokine storm and aberrant neutrophil activation as key mediators of COVID-19 disease severity," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    5. Alan Bénard & Anne Jacobsen & Maximilian Brunner & Christian Krautz & Bettina Klösch & Izabela Swierzy & Elisabeth Naschberger & Malgorzata J. Podolska & Dina Kouhestani & Paul David & Torsten Birkhol, 2021. "Interleukin-3 is a predictive marker for severity and outcome during SARS-CoV-2 infections," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    6. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    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. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    2. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    3. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    4. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    5. Guoli Shi & Tiansheng Li & Kin Kui Lai & Reed F. Johnson & Jonathan W. Yewdell & Alex A. Compton, 2024. "Omicron Spike confers enhanced infectivity and interferon resistance to SARS-CoV-2 in human nasal tissue," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    7. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
    8. Dursun Delen & Hamed M. Zolbanin & Durand Crosby & David Wright, 2021. "To imprison or not to imprison: an analytics model for drug courts," Annals of Operations Research, Springer, vol. 303(1), pages 101-124, August.
    9. Doruk Cengiz & Arindrajit Dube & Attila S. Lindner & David Zentler-Munro, 2021. "Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," NBER Working Papers 28399, National Bureau of Economic Research, Inc.
    10. Zhou, Jing & Li, Wei & Wang, Jiaxin & Ding, Shuai & Xia, Chengyi, 2019. "Default prediction in P2P lending from high-dimensional data based on machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    11. Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
    12. Bohdan M. Pavlyshenko, 2019. "Machine-Learning Models for Sales Time Series Forecasting," Data, MDPI, vol. 4(1), pages 1-11, January.
    13. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    14. Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.
    15. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    16. Adler, Werner & Lausen, Berthold, 2009. "Bootstrap estimated true and false positive rates and ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 718-729, January.
    17. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    18. Andrea Sciandra & Alessio Surian & Livio Finos, 2021. "Supervised Machine Learning Methods to Disclose Action and Information in “U.N. 2030 Agenda” Social Media Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 689-699, August.
    19. Mirosław Parol & Paweł Piotrowski & Piotr Kapler & Mariusz Piotrowski, 2021. "Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control," Energies, MDPI, vol. 14(5), pages 1-29, February.
    20. Tsao, Yu-Chung & Chen, Yu-Kai & Chiu, Shih-Hao & Lu, Jye-Chyi & Vu, Thuy-Linh, 2022. "An innovative demand forecasting approach for the server industry," Technovation, Elsevier, vol. 110(C).

    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:14:y:2023:i:1:d:10.1038_s41467-023-36140-9. 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.