IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-46067-4.html
   My bibliography  Save this article

Q586B2 is a crucial virulence factor during the early stages of Trypanosoma brucei infection that is conserved amongst trypanosomatids

Author

Listed:
  • Benoit Stijlemans

    (Vrije Universiteit Brussel
    VIB Center for Inflammation Research)

  • Patrick Baetselier

    (Vrije Universiteit Brussel
    VIB Center for Inflammation Research)

  • Inge Molle

    (Vrije Universiteit Brussel
    VIB-VUB Center for Structural Biology)

  • Laurence Lecordier

    (Université Libre de Bruxelles)

  • Erika Hendrickx

    (IBMM, Université Libre de Bruxelles)

  • Ema Romão

    (Vrije Universiteit Brussel)

  • Cécile Vincke

    (Vrije Universiteit Brussel
    VIB Center for Inflammation Research)

  • Wendy Baetens

    (Vrije Universiteit Brussel
    VIB Center for Inflammation Research)

  • Steve Schoonooghe

    (Vrije Universiteit Brussel)

  • Gholamreza Hassanzadeh-Ghassabeh

    (Vrije Universiteit Brussel)

  • Hannelie Korf

    (KU Leuven)

  • Marie Wallays

    (KU Leuven)

  • Joar E. Pinto Torres

    (Vrije Universiteit Brussel)

  • David Perez-Morga

    (IBMM, Université Libre de Bruxelles
    Université Libre de Bruxelles)

  • Lea Brys

    (Vrije Universiteit Brussel
    VIB Center for Inflammation Research)

  • Oscar Campetella

    (Universidad Nacional de San Martín-CONICET)

  • María S. Leguizamón

    (Universidad Nacional de San Martín-CONICET)

  • Mathieu Claes

    (University of Antwerp)

  • Sarah Hendrickx

    (University of Antwerp)

  • Dorien Mabille

    (University of Antwerp)

  • Guy Caljon

    (University of Antwerp)

  • Han Remaut

    (Vrije Universiteit Brussel
    VIB-VUB Center for Structural Biology)

  • Kim Roelants

    (Vrije Universiteit Brussel)

  • Stefan Magez

    (Vrije Universiteit Brussel
    Ghent University Global Campus)

  • Jo A. Ginderachter

    (Vrije Universiteit Brussel
    VIB Center for Inflammation Research)

  • Carl Trez

    (Vrije Universiteit Brussel)

Abstract

Human African trypanosomiasis or sleeping sickness, caused by the protozoan parasite Trypanosoma brucei, is characterized by the manipulation of the host’s immune response to ensure parasite invasion and persistence. Uncovering key molecules that support parasite establishment is a prerequisite to interfere with this process. We identified Q586B2 as a T. brucei protein that induces IL-10 in myeloid cells, which promotes parasite infection invasiveness. Q586B2 is expressed during all T. brucei life stages and is conserved in all Trypanosomatidae. Deleting the Q586B2-encoding Tb927.6.4140 gene in T. brucei results in a decreased peak parasitemia and prolonged survival, without affecting parasite fitness in vitro, yet promoting short stumpy differentiation in vivo. Accordingly, neutralization of Q586B2 with newly generated nanobodies could hamper myeloid-derived IL-10 production and reduce parasitemia. In addition, immunization with Q586B2 delays mortality upon a challenge with various trypanosomes, including Trypanosoma cruzi. Collectively, we uncovered a conserved protein playing an important regulatory role in Trypanosomatid infection establishment.

Suggested Citation

  • Benoit Stijlemans & Patrick Baetselier & Inge Molle & Laurence Lecordier & Erika Hendrickx & Ema Romão & Cécile Vincke & Wendy Baetens & Steve Schoonooghe & Gholamreza Hassanzadeh-Ghassabeh & Hannelie, 2024. "Q586B2 is a crucial virulence factor during the early stages of Trypanosoma brucei infection that is conserved amongst trypanosomatids," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46067-4
    DOI: 10.1038/s41467-024-46067-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-46067-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-46067-4?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. Andrew W. Senior & Richard Evans & John Jumper & James Kirkpatrick & Laurent Sifre & Tim Green & Chongli Qin & Augustin Žídek & Alexander W. R. Nelson & Alex Bridgland & Hugo Penedones & Stig Petersen, 2020. "Improved protein structure prediction using potentials from deep learning," Nature, Nature, vol. 577(7792), pages 706-710, January.
    2. Guillaume Hoeffel & Guilhaume Debroas & Anais Roger & Rafaelle Rossignol & Jordi Gouilly & Caroline Laprie & Lionel Chasson & Pierre-Vincent Barbon & Anaïs Balsamo & Ana Reynders & Aziz Moqrich & Soph, 2021. "Sensory neuron-derived TAFA4 promotes macrophage tissue repair functions," Nature, Nature, vol. 594(7861), pages 94-99, June.
    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. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Zachary C. Drake & Justin T. Seffernick & Steffen Lindert, 2022. "Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    3. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2022. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Papers 2201.07168, arXiv.org.
    4. Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    5. Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.
    6. Agnese I. Curatolo & Ofer Kimchi & Carl P. Goodrich & Ryan K. Krueger & Michael P. Brenner, 2023. "A computational toolbox for the assembly yield of complex and heterogeneous structures," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    7. Noelia Ferruz & Steffen Schmidt & Birte Höcker, 2022. "ProtGPT2 is a deep unsupervised language model for protein design," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    8. Simone Vannuccini & Ekaterina Prytkova, 2021. "Artificial Intelligence’s New Clothes? From General Purpose Technology to Large Technical System," SPRU Working Paper Series 2021-02, SPRU - Science Policy Research Unit, University of Sussex Business School.
    9. Aaron Gupta & Kevin S. Kao & Rachel Yamin & Deena A. Oren & Yehuda Goldgur & Jonathan Du & Pete Lollar & Eric J. Sundberg & Jeffrey V. Ravetch, 2023. "Mechanism of glycoform specificity and in vivo protection by an anti-afucosylated IgG nanobody," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Md Tauhidul Islam & Zixia Zhou & Hongyi Ren & Masoud Badiei Khuzani & Daniel Kapp & James Zou & Lu Tian & Joseph C. Liao & Lei Xing, 2023. "Revealing hidden patterns in deep neural network feature space continuum via manifold learning," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    11. Naughtin, Claire & Hajkowicz, Stefan & Schleiger, Emma & Bratanova, Alexandra & Cameron, Alicia & Zamin, T & Dutta, A, 2022. "Our Future World: Global megatrends impacting the way we live over coming decades," MPRA Paper 113900, University Library of Munich, Germany.
    12. Lei Wang & Jiangguo Zhang & Dali Wang & Chen Song, 2022. "Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins," PLOS Computational Biology, Public Library of Science, vol. 18(3), pages 1-27, March.
    13. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    14. Zhiye Guo & Jian Liu & Jeffrey Skolnick & Jianlin Cheng, 2022. "Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    15. Nicolas Renaud & Cunliang Geng & Sonja Georgievska & Francesco Ambrosetti & Lars Ridder & Dario F. Marzella & Manon F. Réau & Alexandre M. J. J. Bonvin & Li C. Xue, 2021. "DeepRank: a deep learning framework for data mining 3D protein-protein interfaces," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    16. Willow Coyote-Maestas & David Nedrud & Antonio Suma & Yungui He & Kenneth A. Matreyek & Douglas M. Fowler & Vincenzo Carnevale & Chad L. Myers & Daniel Schmidt, 2021. "Probing ion channel functional architecture and domain recombination compatibility by massively parallel domain insertion profiling," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    17. Liu, Bokai & Wang, Yizheng & Rabczuk, Timon & Olofsson, Thomas & Lu, Weizhuo, 2024. "Multi-scale modeling in thermal conductivity of Polyurethane incorporated with Phase Change Materials using Physics-Informed Neural Networks," Renewable Energy, Elsevier, vol. 220(C).
    18. Charlotte Loh & Thomas Christensen & Rumen Dangovski & Samuel Kim & Marin Soljačić, 2022. "Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    19. Luis S. Piloto & Ari Weinstein & Peter Battaglia & Matthew Botvinick, 2022. "Intuitive physics learning in a deep-learning model inspired by developmental psychology," Nature Human Behaviour, Nature, vol. 6(9), pages 1257-1267, September.
    20. Min Jung & Michelle Dourado & James Maksymetz & Amanda Jacobson & Benjamin I. Laufer & Miriam Baca & Oded Foreman & David H. Hackos & Lorena Riol-Blanco & Joshua S. Kaminker, 2023. "Cross-species transcriptomic atlas of dorsal root ganglia reveals species-specific programs for sensory function," Nature Communications, Nature, vol. 14(1), pages 1-15, 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:15:y:2024:i:1:d:10.1038_s41467-024-46067-4. 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.