IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-27192-w.html
   My bibliography  Save this article

Cross-species analysis of viral nucleic acid interacting proteins identifies TAOKs as innate immune regulators

Author

Listed:
  • Friederike L. Pennemann

    (Technical University of Munich, School of Medicine, Institute of Virology)

  • Assel Mussabekova

    (Université de Strasbourg, CNRS UPR9022, Institut de Biologie Moléculaire et Cellulaire)

  • Christian Urban

    (Technical University of Munich, School of Medicine, Institute of Virology)

  • Alexey Stukalov

    (Technical University of Munich, School of Medicine, Institute of Virology)

  • Line Lykke Andersen

    (Technical University of Munich, School of Medicine, Institute of Virology)

  • Vincent Grass

    (Technical University of Munich, School of Medicine, Institute of Virology)

  • Teresa Maria Lavacca

    (Technical University of Munich, School of Medicine, Institute of Virology)

  • Cathleen Holze

    (Innate Immunity Laboratory, Max-Planck Institute of Biochemistry)

  • Lila Oubraham

    (Technical University of Munich, School of Medicine, Institute of Virology)

  • Yasmine Benamrouche

    (Université de Strasbourg, CNRS UPR9022, Institut de Biologie Moléculaire et Cellulaire)

  • Enrico Girardi

    (CeMM - Center for Molecular Medicine of the Austrian Academy of Sciences)

  • Rasha E. Boulos

    (Lebanese American University)

  • Rune Hartmann

    (Aarhus University, Department of Molecular Biology and Genetics - Structural Biology)

  • Giulio Superti-Furga

    (CeMM - Center for Molecular Medicine of the Austrian Academy of Sciences
    Medical University of Vienna)

  • Matthias Habjan

    (Innate Immunity Laboratory, Max-Planck Institute of Biochemistry)

  • Jean-Luc Imler

    (Université de Strasbourg, CNRS UPR9022, Institut de Biologie Moléculaire et Cellulaire)

  • Carine Meignin

    (Université de Strasbourg, CNRS UPR9022, Institut de Biologie Moléculaire et Cellulaire)

  • Andreas Pichlmair

    (Technical University of Munich, School of Medicine, Institute of Virology
    Innate Immunity Laboratory, Max-Planck Institute of Biochemistry
    German Center for Infection Research (DZIF), Munich partner site)

Abstract

The cell intrinsic antiviral response of multicellular organisms developed over millions of years and critically relies on the ability to sense and eliminate viral nucleic acids. Here we use an affinity proteomics approach in evolutionary distant species (human, mouse and fly) to identify proteins that are conserved in their ability to associate with diverse viral nucleic acids. This approach shows a core of orthologous proteins targeting viral genetic material and species-specific interactions. Functional characterization of the influence of 181 candidates on replication of 6 distinct viruses in human cells and flies identifies 128 nucleic acid binding proteins with an impact on virus growth. We identify the family of TAO kinases (TAOK1, −2 and −3) as dsRNA-interacting antiviral proteins and show their requirement for type-I interferon induction. Depletion of TAO kinases in mammals or flies leads to an impaired response to virus infection characterized by a reduced induction of interferon stimulated genes in mammals and impaired expression of srg1 and diedel in flies. Overall, our study shows a larger set of proteins able to mediate the interaction between viral genetic material and host factors than anticipated so far, attesting to the ancestral roots of innate immunity and to the lineage-specific pressures exerted by viruses.

Suggested Citation

  • Friederike L. Pennemann & Assel Mussabekova & Christian Urban & Alexey Stukalov & Line Lykke Andersen & Vincent Grass & Teresa Maria Lavacca & Cathleen Holze & Lila Oubraham & Yasmine Benamrouche & En, 2021. "Cross-species analysis of viral nucleic acid interacting proteins identifies TAOKs as innate immune regulators," Nature Communications, Nature, vol. 12(1), pages 1-22, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27192-w
    DOI: 10.1038/s41467-021-27192-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-27192-w
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-27192-w?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. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    3. Pietro Scaturro & Alexey Stukalov & Darya A. Haas & Mirko Cortese & Kalina Draganova & Anna Płaszczyca & Ralf Bartenschlager & Magdalena Götz & Andreas Pichlmair, 2018. "An orthogonal proteomic survey uncovers novel Zika virus host factors," Nature, Nature, vol. 561(7722), pages 253-257, September.
    4. Lena Alexopoulou & Agnieszka Czopik Holt & Ruslan Medzhitov & Richard A. Flavell, 2001. "Recognition of double-stranded RNA and activation of NF-κB by Toll-like receptor 3," Nature, Nature, vol. 413(6857), pages 732-738, October.
    5. Michael D. Gordon & Marc S. Dionne & David S. Schneider & Roel Nusse, 2005. "WntD is a feedback inhibitor of Dorsal/NF-κB in Drosophila development and immunity," Nature, Nature, vol. 437(7059), pages 746-749, September.
    6. Alexey Stukalov & Virginie Girault & Vincent Grass & Ozge Karayel & Valter Bergant & Christian Urban & Darya A. Haas & Yiqi Huang & Lila Oubraham & Anqi Wang & M. Sabri Hamad & Antonio Piras & Fynn M., 2021. "Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV," Nature, Nature, vol. 594(7862), pages 246-252, June.
    7. K. L. Jønsson & A. Laustsen & C. Krapp & K. A. Skipper & K. Thavachelvam & D. Hotter & J. H. Egedal & M. Kjolby & P. Mohammadi & T. Prabakaran & L. K. Sørensen & C. Sun & S. B. Jensen & C. K. Holm & R, 2017. "IFI16 is required for DNA sensing in human macrophages by promoting production and function of cGAMP," Nature Communications, Nature, vol. 8(1), pages 1-17, April.
    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. Valter Bergant & Daniel Schnepf & Niklas Andrade Krätzig & Philipp Hubel & Christian Urban & Thomas Engleitner & Ronald Dijkman & Bernhard Ryffel & Katja Steiger & Percy A. Knolle & Georg Kochs & Rola, 2023. "mRNA 3’UTR lengthening by alternative polyadenylation attenuates inflammatory responses and correlates with virulence of Influenza A virus," Nature Communications, Nature, vol. 14(1), pages 1-17, 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. Hannaford, Naomi E. & Heaps, Sarah E. & Nye, Tom M.W. & Curtis, Thomas P. & Allen, Ben & Golightly, Andrew & Wilkinson, Darren J., 2023. "A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    2. Valter Bergant & Daniel Schnepf & Niklas Andrade Krätzig & Philipp Hubel & Christian Urban & Thomas Engleitner & Ronald Dijkman & Bernhard Ryffel & Katja Steiger & Percy A. Knolle & Georg Kochs & Rola, 2023. "mRNA 3’UTR lengthening by alternative polyadenylation attenuates inflammatory responses and correlates with virulence of Influenza A virus," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    3. Boonstra, Philip S. & Barbaro, Ryan P. & Sen, Ananda, 2019. "Default priors for the intercept parameter in logistic regressions," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 245-256.
    4. Posch, Konstantin & Truden, Christian & Hungerländer, Philipp & Pilz, Jürgen, 2022. "A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants," International Journal of Forecasting, Elsevier, vol. 38(1), pages 321-338.
    5. Solène Denolly & Alexey Stukalov & Uladzimir Barayeu & Alina N. Rosinski & Paraskevi Kritsiligkou & Sebastian Joecks & Tobias P. Dick & Andreas Pichlmair & Ralf Bartenschlager, 2023. "Zika virus remodelled ER membranes contain proviral factors involved in redox and methylation pathways," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    6. Paul A. Parker & Scott H. Holan, 2023. "A Bayesian functional data model for surveys collected under informative sampling with application to mortality estimation using NHANES," Biometrics, The International Biometric Society, vol. 79(2), pages 1397-1408, June.
    7. Niloy Biswas & Anirban Bhattacharya & Pierre E. Jacob & James E. Johndrow, 2022. "Coupling‐based convergence assessment of some Gibbs samplers for high‐dimensional Bayesian regression with shrinkage priors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 973-996, July.
    8. Anindya Bhadra & Jyotishka Datta & Yunfan Li & Nicholas Polson, 2020. "Horseshoe Regularisation for Machine Learning in Complex and Deep Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 302-320, August.
    9. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    10. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    11. Torsten Heinrich & Jangho Yang & Shuanping Dai, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," Papers 2012.14503, arXiv.org.
    12. Tamal Ghosh & Malay Ghosh & Jerry J. Maples & Xueying Tang, 2022. "Multivariate Global-Local Priors for Small Area Estimation," Stats, MDPI, vol. 5(3), pages 1-16, July.
    13. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    14. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2022. "APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1625-1658, November.
    15. Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.
    16. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    17. Martin Guth, 2022. "Predicting Default Probabilities for Stress Tests: A Comparison of Models," Papers 2202.03110, arXiv.org.
    18. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    19. David M. Phillippo & Sofia Dias & A. E. Ades & Mark Belger & Alan Brnabic & Alexander Schacht & Daniel Saure & Zbigniew Kadziola & Nicky J. Welton, 2020. "Multilevel network meta‐regression for population‐adjusted treatment comparisons," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1189-1210, June.
    20. Ander Wilson & Brian J. Reich, 2014. "Confounder selection via penalized credible regions," Biometrics, The International Biometric Society, vol. 70(4), pages 852-861, 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:12:y:2021:i:1:d:10.1038_s41467-021-27192-w. 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.