Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells

Imran Mohammad; Kari Nousiainen; Santosh D Bhosale; Inna Starskaia; Robert Moulder; Anne Rokka; Fang Cheng; Ponnuswamy Mohanasundaram; John E Eriksson; David R Goodlett; Harri Lähdesmäki; Zhi Chen

doi:10.1371/journal.pbio.2004194

Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells

PLoS Biol. 2018 May 31;16(5):e2004194. doi: 10.1371/journal.pbio.2004194. eCollection 2018 May.

Authors

Imran Mohammad^{1

2}, Kari Nousiainen³, Santosh D Bhosale^{1

2}, Inna Starskaia^{1

2}, Robert Moulder¹, Anne Rokka¹, Fang Cheng⁴, Ponnuswamy Mohanasundaram⁴, John E Eriksson⁴, David R Goodlett⁵, Harri Lähdesmäki³, Zhi Chen¹

Affiliations

¹ Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.
² Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland.
³ Department of Computer Science, Aalto University, Espoo, Finland.
⁴ Cell Biology, Biosciences, Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland.
⁵ Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland, United States of America.

Abstract

The transcriptional network and protein regulators that govern T helper 17 (Th17) cell differentiation have been studied extensively using advanced genomic approaches. For a better understanding of these biological processes, we have moved a step forward, from gene- to protein-level characterization of Th17 cells. Mass spectrometry-based label-free quantitative (LFQ) proteomics analysis were made of in vitro differentiated murine Th17 and induced regulatory T (iTreg) cells. More than 4,000 proteins, covering almost all subcellular compartments, were detected. Quantitative comparison of the protein expression profiles resulted in the identification of proteins specifically expressed in the Th17 and iTreg cells. Importantly, our combined analysis of proteome and gene expression data revealed protein expression changes that were not associated with changes at the transcriptional level. Our dataset provides a valuable resource, with new insights into the proteomic characteristics of Th17 and iTreg cells, which may prove useful in developing treatment of autoimmune diseases and developing tumor immunotherapy.

Publication types

Comparative Study
Research Support, Non-U.S. Gov't
Validation Study

MeSH terms

Animals
Cell Differentiation
Forkhead Transcription Factors / metabolism
Mice
Proteome*
Proteomics
T-Lymphocytes, Regulatory / metabolism*
Th17 Cells / metabolism*
Transforming Growth Factor beta / metabolism
Vimentin / metabolism

Substances

Forkhead Transcription Factors
Foxp3 protein, mouse
Proteome
Transforming Growth Factor beta
Vim protein, mouse
Vimentin

Grants and funding

Academy of Finland www.aka.fi (grant number 258313 (to ZC); Centre of Excellence in Molecular Systems Immunology and Physiology Research 2012–2017 grant 250114 (to HL)). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Technology Agency of Finland (DRG was supported by Finland Distinguished Professor Programme, grant 40398). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CIMO foundation (to IS). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. University of Turku Foundation (to IM and ZC). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Finnish Cultural Foundation (to IS). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.