IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v9y2024i8p101-d1459709.html
   My bibliography  Save this article

Viral Targets in the Human Interactome with Comprehensive Centrality Analysis: SARS-CoV-2, a Case Study

Author

Listed:
  • Nilesh Kumar

    (Department of Biology, University of Alabama at Birmingham, 3100 East Science Hall, 902 14th Street South, Birmingham, AL 35294, USA
    IRCP—Biological Data Sciences, University of Alabama at Birmingham, Birmingham, AL 35233, USA)

  • M. Shahid Mukhtar

    (Department of Biology, University of Alabama at Birmingham, 3100 East Science Hall, 902 14th Street South, Birmingham, AL 35294, USA
    Department of Genetics & Biochemistry, Clemson University, 105 Collings St. Biosystems Research Complex, Clemson, SC 29634, USA)

Abstract

Network centrality analyses have proven to be successful in identifying important nodes in diverse host–pathogen interactomes. The current study presents a comprehensive investigation of the human interactome and SARS-CoV-2 host targets. We first constructed a comprehensive human interactome by compiling experimentally validated protein–protein interactions (PPIs) from eight distinct sources. Additionally, we compiled a comprehensive list of 1449 SARS-CoV-2 host proteins and analyzed their interactions within the human interactome, which identified enriched biological processes and pathways. Seven diverse topological features were employed to reveal the enrichment of the SARS-CoV-2 targets in the human interactome, with closeness centrality emerging as the most effective metric. Furthermore, a novel approach called CentralityCosDist was employed to predict SARS-CoV-2 targets, which proved to be effective in expanding the pool of predicted targets. Pathway enrichment analyses further elucidated the functional roles and potential mechanisms associated with predicted targets. Overall, this study provides valuable insights into the complex interplay between SARS-CoV-2 and the host’s cellular machinery, contributing to a deeper understanding of viral infection and immune response modulation.

Suggested Citation

  • Nilesh Kumar & M. Shahid Mukhtar, 2024. "Viral Targets in the Human Interactome with Comprehensive Centrality Analysis: SARS-CoV-2, a Case Study," Data, MDPI, vol. 9(8), pages 1-12, August.
    • Handle: RePEc:gam:jdataj:v:9:y:2024:i:8:p:101-:d:1459709
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/9/8/101/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/9/8/101/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Orit Rozenblatt-Rosen & Rahul C. Deo & Megha Padi & Guillaume Adelmant & Michael A. Calderwood & Thomas Rolland & Miranda Grace & Amélie Dricot & Manor Askenazi & Maria Tavares & Samuel J. Pevzner & F, 2012. "Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins," Nature, Nature, vol. 487(7408), pages 491-495, July.
    2. Edward L. Huttlin & Raphael J. Bruckner & Joao A. Paulo & Joe R. Cannon & Lily Ting & Kurt Baltier & Greg Colby & Fana Gebreab & Melanie P. Gygi & Hannah Parzen & John Szpyt & Stanley Tam & Gabriela Z, 2017. "Architecture of the human interactome defines protein communities and disease networks," Nature, Nature, vol. 545(7655), pages 505-509, May.
    3. Cuihong Wan & Blake Borgeson & Sadhna Phanse & Fan Tu & Kevin Drew & Greg Clark & Xuejian Xiong & Olga Kagan & Julian Kwan & Alexandr Bezginov & Kyle Chessman & Swati Pal & Graham Cromar & Ophelia Pap, 2015. "Panorama of ancient metazoan macromolecular complexes," Nature, Nature, vol. 525(7569), pages 339-344, September.
    4. Katja Luck & Dae-Kyum Kim & Luke Lambourne & Kerstin Spirohn & Bridget E. Begg & Wenting Bian & Ruth Brignall & Tiziana Cafarelli & Francisco J. Campos-Laborie & Benoit Charloteaux & Dongsic Choi & At, 2020. "A reference map of the human binary protein interactome," Nature, Nature, vol. 580(7803), pages 402-408, April.
    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. Michael A. Skinnider & Mopelola O. Akinlaja & Leonard J. Foster, 2023. "Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Hong-Wen Tang & Kerstin Spirohn & Yanhui Hu & Tong Hao & István A. Kovács & Yue Gao & Richard Binari & Donghui Yang-Zhou & Kenneth H. Wan & Joel S. Bader & Dawit Balcha & Wenting Bian & Benjamin W. Bo, 2023. "Next-generation large-scale binary protein interaction network for Drosophila melanogaster," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. Yesheng Fu & Lei Li & Xin Zhang & Zhikang Deng & Ying Wu & Wenzhe Chen & Yuchen Liu & Shan He & Jian Wang & Yuping Xie & Zhiwei Tu & Yadi Lyu & Yange Wei & Shujie Wang & Chun-Ping Cui & Cui Hua Liu & , 2024. "Systematic HOIP interactome profiling reveals critical roles of linear ubiquitination in tissue homeostasis," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    4. Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    5. Bai, Ling & Xiong, Long & Zhao, Na & Xia, Ke & Jiang, Xiong-Fei, 2022. "Dynamical structure of social map in ancient China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    6. Weize Wang & Ling Liang & Zonglin Dai & Peng Zuo & Shang Yu & Yishuo Lu & Dian Ding & Hongyi Chen & Hui Shan & Yan Jin & Youdong Mao & Yuxin Yin, 2024. "A conserved N-terminal motif of CUL3 contributes to assembly and E3 ligase activity of CRL3KLHL22," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    7. Gergo Gogl & Boglarka Zambo & Camille Kostmann & Alexandra Cousido-Siah & Bastien Morlet & Fabien Durbesson & Luc Negroni & Pascal Eberling & Pau Jané & Yves Nominé & Andras Zeke & Søren Østergaard & , 2022. "Quantitative fragmentomics allow affinity mapping of interactomes," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    8. Jiang, Xiongfei & Xiong, Long & Bai, Ling & Zhao, Na & Zhang, Jiu & Xia, Ke & Deng, Kai & Zheng, Bo, 2021. "Quantifying the social structure of elites in ancient China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    9. Akshat Singhal & Song Cao & Christopher Churas & Dexter Pratt & Santo Fortunato & Fan Zheng & Trey Ideker, 2020. "Multiscale community detection in Cytoscape," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-10, October.
    10. Sabrina Villar-Pazos & Laurel Thomas & Yunhan Yang & Kun Chen & Jenea B. Lyles & Bradley J. Deitch & Joseph Ochaba & Karen Ling & Berit Powers & Sebastien Gingras & Holly B. Kordasiewicz & Melanie J. , 2023. "Neural deficits in a mouse model of PACS1 syndrome are corrected with PACS1- or HDAC6-targeting therapy," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    11. Xing Chen & Chunyu Yu & Xinhua Liu & Beibei Liu & Xiaodi Wu & Jiajing Wu & Dong Yan & Lulu Han & Zifan Tang & Xinyi Yuan & Jianqiu Wang & Yue Wang & Shumeng Liu & Lin Shan & Yongfeng Shang, 2022. "Intracellular galectin-3 is a lipopolysaccharide sensor that promotes glycolysis through mTORC1 activation," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    12. Patrick Bryant & Gabriele Pozzati & Wensi Zhu & Aditi Shenoy & Petras Kundrotas & Arne Elofsson, 2022. "Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    13. Amika Singla & Daniel J. Boesch & Ho Yee Joyce Fung & Chigozie Ngoka & Avery S. Enriquez & Ran Song & Daniel A. Kramer & Yan Han & Esther Banarer & Andrew Lemoff & Puneet Juneja & Daniel D. Billadeau , 2024. "Structural basis for Retriever-SNX17 assembly and endosomal sorting," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    14. Xinxin Xu & Sheng Ma & Ziqiang Zeng, 2019. "Complex network analysis of bilateral international investment under de-globalization: Structural properties and evolution," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-16, April.
    15. Shilin Sun & Hua Tian & Runze Wang & Zehua Zhang, 2023. "Biomedical Interaction Prediction with Adaptive Line Graph Contrastive Learning," Mathematics, MDPI, vol. 11(3), pages 1-14, February.
    16. Adrià Fernández-Torras & Miquel Duran-Frigola & Martino Bertoni & Martina Locatelli & Patrick Aloy, 2022. "Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    17. Yuwan Chen & Wen Zhou & Yufei Xia & Weijie Zhang & Qun Zhao & Xinwei Li & Hang Gao & Zhen Liang & Guanghui Ma & Kaiguang Yang & Lihua Zhang & Yukui Zhang, 2023. "Targeted cross-linker delivery for the in situ mapping of protein conformations and interactions in mitochondria," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    18. Krishna B. S. Swamy & Hsin-Yi Lee & Carmina Ladra & Chien-Fu Jeff Liu & Jung-Chi Chao & Yi-Yun Chen & Jun-Yi Leu, 2022. "Proteotoxicity caused by perturbed protein complexes underlies hybrid incompatibility in yeast," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    19. Mahmoud H Elbatreek & Sepideh Sadegh & Elisa Anastasi & Emre Guney & Cristian Nogales & Tim Kacprowski & Ahmed A Hassan & Andreas Teubner & Po-Hsun Huang & Chien-Yi Hsu & Paul M H Schiffers & Ger M Ja, 2020. "NOX5-induced uncoupling of endothelial NO synthase is a causal mechanism and theragnostic target of an age-related hypertension endotype," PLOS Biology, Public Library of Science, vol. 18(11), pages 1-25, November.
    20. Florin Ratajczak & Mitchell Joblin & Marcel Hildebrandt & Martin Ringsquandl & Pascal Falter-Braun & Matthias Heinig, 2023. "Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

    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:gam:jdataj:v:9:y:2024:i:8:p:101-:d:1459709. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.