IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-47264-x.html
   My bibliography  Save this article

Evolution of T cells in the cancer-resistant naked mole-rat

Author

Listed:
  • Tzuhua D. Lin

    (South San Francisco)

  • Nimrod D. Rubinstein

    (South San Francisco)

  • Nicole L. Fong

    (South San Francisco)

  • Megan Smith

    (South San Francisco)

  • Wendy Craft

    (South San Francisco)

  • Baby Martin-McNulty

    (South San Francisco)

  • Rebecca Perry

    (University of Illinois at Chicago)

  • Martha A. Delaney

    (University of Illinois at Urbana Champaign)

  • Margaret A. Roy

    (South San Francisco)

  • Rochelle Buffenstein

    (South San Francisco
    University of Illinois at Chicago)

Abstract

Naked mole-rats (NMRs) are best known for their extreme longevity and cancer resistance, suggesting that their immune system might have evolved to facilitate these phenotypes. Natural killer (NK) and T cells have evolved to detect and destroy cells infected with pathogens and to provide an early response to malignancies. While it is known that NMRs lack NK cells, likely lost during evolution, little is known about their T-cell subsets in terms of the evolution of the genes that regulate their function, their clonotypic diversity, and the thymus where they mature. Here we find, using single-cell transcriptomics, that NMRs have a large circulating population of γδT cells, which in mice and humans mostly reside in peripheral tissues and induce anti-cancer cytotoxicity. Using single-cell-T-cell-receptor sequencing, we find that a cytotoxic γδT-cell subset of NMRs harbors a dominant clonotype, and that their conventional CD8 αβT cells exhibit modest clonotypic diversity. Consistently, perinatal NMR thymuses are considerably smaller than those of mice yet follow similar involution progression. Our findings suggest that NMRs have evolved under a relaxed intracellular pathogenic selective pressure that may have allowed cancer resistance and longevity to become stronger targets of selection to which the immune system has responded by utilizing γδT cells.

Suggested Citation

  • Tzuhua D. Lin & Nimrod D. Rubinstein & Nicole L. Fong & Megan Smith & Wendy Craft & Baby Martin-McNulty & Rebecca Perry & Martha A. Delaney & Margaret A. Roy & Rochelle Buffenstein, 2024. "Evolution of T cells in the cancer-resistant naked mole-rat," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47264-x
    DOI: 10.1038/s41467-024-47264-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-47264-x
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-47264-x?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. Chun Chou & Xian Zhang & Chirag Krishna & Briana G. Nixon & Saida Dadi & Kristelle J. Capistrano & Emily R. Kansler & Miranda Steele & Jian Han & Amy Shyu & Jing Zhang & Efstathios G. Stamatiades & Mi, 2022. "Programme of self-reactive innate-like T cell-mediated cancer immunity," Nature, Nature, vol. 605(7908), pages 139-145, May.
    2. Martin S. Davey & Carrie R. Willcox & Stuart Hunter & Sofya A. Kasatskaya & Ester B. M. Remmerswaal & Mahboob Salim & Fiyaz Mohammed & Frederike J. Bemelman & Dmitriy M. Chudakov & Ye H. Oo & Benjamin, 2018. "The human Vδ2+ T-cell compartment comprises distinct innate-like Vγ9+ and adaptive Vγ9- subsets," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    3. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    4. Fazal Hadi & Yavuz Kulaberoglu & Kyren A. Lazarus & Karsten Bach & Rosemary Ugur & Paul Beattie & Ewan St John Smith & Walid T. Khaled, 2020. "Transformation of naked mole-rat cells," Nature, Nature, vol. 583(7814), pages 1-7, July.
    5. Alexis Vandenbon & Diego Diez, 2020. "A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    6. Xiao Tian & Jorge Azpurua & Christopher Hine & Amita Vaidya & Max Myakishev-Rempel & Julia Ablaeva & Zhiyong Mao & Eviatar Nevo & Vera Gorbunova & Andrei Seluanov, 2013. "High-molecular-mass hyaluronan mediates the cancer resistance of the naked mole rat," Nature, Nature, vol. 499(7458), pages 346-349, July.
    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. Rong Hua & Yuan-Shuo Ma & Lu Yang & Jun-Jun Hao & Qin-Yang Hua & Lu-Ye Shi & Xiao-Qing Yao & Hao-Yu Zhi & Zhen Liu, 2024. "Experimental evidence for cancer resistance in a bat species," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Guillem Sanchez Sanchez & Stephan Emmrich & Maria Georga & Ariadni Papadaki & Sofia Kossida & Andrei Seluanov & Vera Gorbunova & David Vermijlen, 2024. "Invariant γδTCR natural killer-like effector T cells in the naked mole-rat," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Lutz Bornmann & Robin Haunschild & Sven E. Hug, 2018. "Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 427-437, February.
    4. Natalya Ivanova & Ekaterina Zolotova, 2023. "Landolt Indicator Values in Modern Research: A Review," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    5. Nina Sakinah Ahmad Rofaie & Seuk Wai Phoong & Muzalwana Abdul Talib & Ainin Sulaiman, 2023. "Light-emitting diode (LED) research: A bibliometric analysis during 2003–2018," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 173-191, February.
    6. Giovanni Matteo & Pierfrancesco Nardi & Stefano Grego & Caterina Guidi, 2018. "Bibliometric analysis of Climate Change Vulnerability Assessment research," Environment Systems and Decisions, Springer, vol. 38(4), pages 508-516, December.
    7. Yi-Ming Wei & Jin-Wei Wang & Tianqi Chen & Bi-Ying Yu & Hua Liao, 2018. "Frontiers of Low-Carbon Technologies: Results from Bibliographic Coupling with Sliding Window," CEEP-BIT Working Papers 116, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    8. Loredana Canfora & Corrado Costa & Federico Pallottino & Stefano Mocali, 2021. "Trends in Soil Microbial Inoculants Research: A Science Mapping Approach to Unravel Strengths and Weaknesses of Their Application," Agriculture, MDPI, vol. 11(2), pages 1-21, February.
    9. Evi Sachini & Nikolaos Karampekios & Pierpaolo Brutti & Konstantinos Sioumalas-Christodoulou, 2020. "Should I stay or should I go? Using bibliometrics to identify the international mobility of highly educated Greek manpower," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 641-663, October.
    10. Natalya Ivanova & Ekaterina Zolotova, 2024. "Vegetation Dynamics Studies Based on Ellenberg and Landolt Indicator Values: A Review," Land, MDPI, vol. 13(10), pages 1-24, October.
    11. Vanessa Ioannoni & Tommaso Vitale & Corrado Costa & Iris Elliott, 2020. "Depicting communities of Romani studies: on the who, when and where of Roma related scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1473-1490, March.
    12. Jensen, Scott & Liu, Xiaozhong & Yu, Yingying & Milojevic, Staša, 2016. "Generation of topic evolution trees from heterogeneous bibliographic networks," Journal of Informetrics, Elsevier, vol. 10(2), pages 606-621.
    13. Chuyou Fu & Jun Wang & Ziyi Qu & Martin Skitmore & Jiaxin Yi & Zhengjie Sun & Jianli Chen, 2024. "Structural Equation Modeling in Technology Adoption and Use in the Construction Industry: A Scientometric Analysis and Qualitative Review," Sustainability, MDPI, vol. 16(9), pages 1-23, May.
    14. Collins C. Okolie & Gideon Danso-Abbeam & Okechukwu Groupson-Paul & Abiodun A. Ogundeji, 2022. "Climate-Smart Agriculture Amidst Climate Change to Enhance Agricultural Production: A Bibliometric Analysis," Land, MDPI, vol. 12(1), pages 1-23, December.
    15. Oleg E. Karpov & Elena N. Pitsik & Semen A. Kurkin & Vladimir A. Maksimenko & Alexander V. Gusev & Natali N. Shusharina & Alexander E. Hramov, 2023. "Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach," IJERPH, MDPI, vol. 20(7), pages 1-17, March.
    16. Gurzki, Hannes & Woisetschläger, David M., 2017. "Mapping the luxury research landscape: A bibliometric citation analysis," Journal of Business Research, Elsevier, vol. 77(C), pages 147-166.
    17. Zhong, Sheng & Verspagen, Bart, 2016. "The role of technological trajectories in catching-up-based development: An application to energy efficiency technologies," MERIT Working Papers 2016-013, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    18. Zamboni, Nadia Selene & Noleto Filho, Eurico Mesquita & Carvalho, Adriana Rosa, 2021. "Unfolding differences in the distribution of coastal marine ecosystem services values among developed and developing countries," Ecological Economics, Elsevier, vol. 189(C).
    19. Lovro Šubelj & Nees Jan van Eck & Ludo Waltman, 2016. "Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-23, April.
    20. Daniel Trabucchi & Laurent Muzellec & Sébastien Ronteau, 2019. "Sharing economy: seeing through the fog," Post-Print hal-03718526, HAL.

    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_s41467-024-47264-x. 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.