IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0153674.html
   My bibliography  Save this article

Respiratory Mucosal Proteome Quantification in Human Influenza Infections

Author

Listed:
  • Tony Marion
  • Husni Elbahesh
  • Paul G Thomas
  • John P DeVincenzo
  • Richard Webby
  • Klaus Schughart

Abstract

Respiratory influenza virus infections represent a serious threat to human health. Underlying medical conditions and genetic make-up predispose some influenza patients to more severe forms of disease. To date, only a few studies have been performed in patients to correlate a selected group of cytokines and chemokines with influenza infection. Therefore, we evaluated the potential of a novel multiplex micro-proteomics technology, SOMAscan, to quantify proteins in the respiratory mucosa of influenza A and B infected individuals. The analysis included but was not limited to quantification of cytokines and chemokines detected in previous studies. SOMAscan quantified more than 1,000 secreted proteins in small nasal wash volumes from infected and healthy individuals. Our results illustrate the utility of micro-proteomic technology for analysis of proteins in small volumes of respiratory mucosal samples. Furthermore, when we compared nasal wash samples from influenza-infected patients with viral load ≥ 28 and increased IL-6 and CXCL10 to healthy controls, we identified 162 differentially-expressed proteins between the two groups. This number greatly exceeds the number of DEPs identified in previous studies in human influenza patients. Most of the identified proteins were associated with the host immune response to infection, and changes in protein levels of 151 of the DEPs were significantly correlated with viral load. Most important, SOMAscan identified differentially expressed proteins heretofore not associated with respiratory influenza infection in humans. Our study is the first report for the use of SOMAscan to screen nasal secretions. It establishes a precedent for micro-proteomic quantification of proteins that reflect ongoing response to respiratory infection.

Suggested Citation

  • Tony Marion & Husni Elbahesh & Paul G Thomas & John P DeVincenzo & Richard Webby & Klaus Schughart, 2016. "Respiratory Mucosal Proteome Quantification in Human Influenza Infections," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-16, April.
  • Handle: RePEc:plo:pone00:0153674
    DOI: 10.1371/journal.pone.0153674
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0153674
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0153674&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0153674?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. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    2. Gabriele Neumann & Takeshi Noda & Yoshihiro Kawaoka, 2009. "Emergence and pandemic potential of swine-origin H1N1 influenza virus," Nature, Nature, vol. 459(7249), pages 931-939, June.
    3. Sabine Vettorazzi & Constantin Bode & Lien Dejager & Lucien Frappart & Ekaterina Shelest & Carina Klaßen & Alpaslan Tasdogan & Holger M. Reichardt & Claude Libert & Marion Schneider & Falk Weih & N. H, 2015. "Glucocorticoids limit acute lung inflammation in concert with inflammatory stimuli by induction of SphK1," Nature Communications, Nature, vol. 6(1), pages 1-12, November.
    4. Rikinari Hanayama & Masato Tanaka & Keiko Miwa & Azusa Shinohara & Akihiro Iwamatsu & Shigekazu Nagata, 2002. "Identification of a factor that links apoptotic cells to phagocytes," Nature, Nature, vol. 417(6885), pages 182-187, May.
    5. Avijit Dutta & Ching-Tai Huang & Tse-Ching Chen & Chun-Yen Lin & Cheng-Hsun Chiu & Yung-Chang Lin & Chia-Shiang Chang & Yueh-Chia He, 2015. "IL-10 inhibits neuraminidase-activated TGF-β and facilitates Th1 phenotype during early phase of infection," Nature Communications, Nature, vol. 6(1), pages 1-11, May.
    6. Tokiko Watanabe & Maki Kiso & Satoshi Fukuyama & Noriko Nakajima & Masaki Imai & Shinya Yamada & Shin Murakami & Seiya Yamayoshi & Kiyoko Iwatsuki-Horimoto & Yoshihiro Sakoda & Emi Takashita & Ryan Mc, 2013. "Characterization of H7N9 influenza A viruses isolated from humans," Nature, Nature, vol. 501(7468), pages 551-555, September.
    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. Aaron C Ericsson & J Wade Davis & William Spollen & Nathan Bivens & Scott Givan & Catherine E Hagan & Mark McIntosh & Craig L Franklin, 2015. "Effects of Vendor and Genetic Background on the Composition of the Fecal Microbiota of Inbred Mice," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    2. Hossain, Ahmed & Beyene, Joseph & Willan, Andrew R. & Hu, Pingzhao, 2009. "A flexible approximate likelihood ratio test for detecting differential expression in microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3685-3695, August.
    3. Xiaohong Li & Guy N Brock & Eric C Rouchka & Nigel G F Cooper & Dongfeng Wu & Timothy E O’Toole & Ryan S Gill & Abdallah M Eteleeb & Liz O’Brien & Shesh N Rai, 2017. "A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-22, May.
    4. Kerr Kathleen F., 2012. "Optimality Criteria for the Design of 2-Color Microarray Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-9, January.
    5. Ambroise Jérôme & Bearzatto Bertrand & Robert Annie & Macq Benoit & Gala Jean-Luc, 2012. "Combining Multiple Laser Scans of Spotted Microarrays by Means of a Two-Way ANOVA Model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-20, February.
    6. J. McClatchy & R. Strogantsev & E. Wolfe & H. Y. Lin & M. Mohammadhosseini & B. A. Davis & C. Eden & D. Goldman & W. H. Fleming & P. Conley & G. Wu & L. Cimmino & H. Mohammed & A. Agarwal, 2023. "Clonal hematopoiesis related TET2 loss-of-function impedes IL1β-mediated epigenetic reprogramming in hematopoietic stem and progenitor cells," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    7. Alexandra Gyurdieva & Stefan Zajic & Ya-Fang Chang & E. Andres Houseman & Shan Zhong & Jaegil Kim & Michael Nathenson & Thomas Faitg & Mary Woessner & David C. Turner & Aisha N. Hasan & John Glod & Ro, 2022. "Biomarker correlates with response to NY-ESO-1 TCR T cells in patients with synovial sarcoma," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    8. Sora Yoon & Seon-Young Kim & Dougu Nam, 2016. "Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-16, November.
    9. Yu Lianbo & Gulati Parul & Fernandez Soledad & Pennell Michael & Kirschner Lawrence & Jarjoura David, 2011. "Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-22, September.
    10. Ricardo Aguas & Neil M Ferguson, 2013. "Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-10, October.
    11. Sinan Xiong & Jianbiao Zhou & Tze King Tan & Tae-Hoon Chung & Tuan Zea Tan & Sabrina Hui-Min Toh & Nicole Xin Ning Tang & Yunlu Jia & Yi Xiang See & Melissa Jane Fullwood & Takaomi Sanda & Wee-Joo Chn, 2024. "Super enhancer acquisition drives expression of oncogenic PPP1R15B that regulates protein homeostasis in multiple myeloma," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    12. Michael George Roberts & Hiroshi Nishiura, 2011. "Early Estimation of the Reproduction Number in the Presence of Imported Cases: Pandemic Influenza H1N1-2009 in New Zealand," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-9, May.
    13. Christian Sieben & Erdinc Sezgin & Christian Eggeling & Suliana Manley, 2020. "Influenza A viruses use multivalent sialic acid clusters for cell binding and receptor activation," PLOS Pathogens, Public Library of Science, vol. 16(7), pages 1-27, July.
    14. Chaofeng Yuan & Wensheng Zhu & Xuming He & Jianhua Guo, 2019. "A mixture factor model with applications to microarray data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 60-76, March.
    15. Nan Li & Matthew N. McCall & Zhijin Wu, 2017. "Establishing Informative Prior for Gene Expression Variance from Public Databases," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 160-177, June.
    16. Brian Caffo & Liu Dongmei & Giovanni Parmigiani, 2004. "Power Conjugate Multilevel Models with Applications to Genomics," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1062, Berkeley Electronic Press.
    17. Nott, David J. & Yu, Zeming & Chan, Eva & Cotsapas, Chris & Cowley, Mark J. & Pulvers, Jeremy & Williams, Rohan & Little, Peter, 2007. "Hierarchical Bayes variable selection and microarray experiments," Journal of Multivariate Analysis, Elsevier, vol. 98(4), pages 852-872, April.
    18. Santu Ghosh & Alan M. Polansky, 2022. "Large-Scale Simultaneous Testing Using Kernel Density Estimation," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 808-843, August.
    19. Qianxing Mo & Faming Liang, 2010. "Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model," Biometrics, The International Biometric Society, vol. 66(4), pages 1284-1294, December.
    20. Ahmed Hossain & Hafiz T.A. Khan, 2016. "Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2538-2549, October.

    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:plo:pone00:0153674. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.