Accepted Manuscript Quantitative mass spectrometry-based proteomics reveals the dynamic protein landscape during initiation of human Th17 cell polarization Subhash K. Tripathi, Tommi Välikangas, Ankitha Shetty, Mohd Moin Khan, Robert Moulder, Santosh D. Bhosale, Elina Komsi, Verna Salo, Rafael Sales De Albuquerque, Omid Rasool, Sanjeev Galande, Laura L. Elo, Riitta Lahesmaa PII:
S2589-0042(18)30250-5
DOI:
https://doi.org/10.1016/j.isci.2018.12.020
Reference:
ISCI 249
To appear in:
ISCIENCE
Received Date: 23 February 2018 Revised Date:
8 August 2018
Accepted Date: 20 December 2018
Please cite this article as: Tripathi, S.K., Välikangas, T., Shetty, A., Khan, M.M., Moulder, R., Bhosale, S.D, Komsi, E., Salo, V., De Albuquerque, R.S., Rasool, O., Galande, S., Elo, L.L, Lahesmaa, R., Quantitative mass spectrometry-based proteomics reveals the dynamic protein landscape during initiation of human Th17 cell polarization, ISCIENCE (2019), doi: https://doi.org/10.1016/ j.isci.2018.12.020. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Proteomics Expression changes between Th17 cells and Th0 cells show high degree of correlation in proteomics and transcriptomics
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Title: Quantitative mass spectrometry-based proteomics reveals the dynamic
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protein landscape during initiation of human Th17 cell polarization
3 Authors: Subhash K. Tripathi1±, Tommi Välikangas1,2±, Ankitha Shetty1,4±, Mohd Moin
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Khan1,3, Robert Moulder1, Santosh D Bhosale1,3, Elina Komsi1, Verna Salo1,3, Rafael Sales
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De Albuquerque1, Omid Rasool1, Sanjeev Galande4, Laura L Elo*1± and Riitta
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Lahesmaa*1±
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8 Affiliations:
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1
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Tykistökatu 6, FI-20520 Turku, Finland
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Turku, University Hill, FI-20014, Turku, Finland
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Tykistökatu 6, FI-20520 Turku, Finland
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Education and Research (IISER), Pune, India 411008
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± Equal contribution
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*Correspondence
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Lead Contact - Professor Riitta Lahesmaa (
[email protected]),
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Dr. Laura Elo (
[email protected])
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Turku Centre for Biotechnology, University of Turku and Åbo Akademi University,
Doctoral Programme in Mathematics and Computer Sciences (MATTI), University of
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Turku Doctoral Programme of Molecular Medicine (TuDMM), University of Turku,
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Centre of Excellence in Epigenetics, Department of Biology, Indian Institute of Science
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Running title: Proteome analysis of early human Th17 cell differentiation
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Key words: human, Th17 cell differentiation, Proteomics, Mass spectrometry, SATB1
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Summary:
27 Th17 cells contribute to pathogenesis of inflammatory and autoimmune diseases and
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cancer. To reveal the Th17 cell-specific proteomic signature regulating Th17 cell
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differentiation and function in human we used a label-free mass spectrometry-based
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approach. Further, a comprehensive analysis of the proteome and transcriptome of cells
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during human Th17 differentiation revealed a high degree of overlap between the
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datasets. However, when compared to a corresponding published mouse data, we found
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very limited overlap between the proteins differentially regulated in response to Th17
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differentiation. Validations were made for a panel of selected proteins with known and
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unknown functions. Finally, using RNA interference (RNAi), we showed that SATB1
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negatively regulates human Th17 cell differentiation. Overall, the current study illustrates a
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comprehensive picture of the global protein landscape during early human Th17 cell
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differentiation. Poor overlap with mouse data underlines the importance of human studies
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for translational research.
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Introduction
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The CD4+ T cells are key players of the adaptive immune system. Upon antigenic
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stimulation, the naive CD4+ T (T helper precursor; Thp) cells polarize into distinct T helper
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(Th, i.e. Th1, Th2, Th9, and Th17) and regulatory T (Treg) cells (O’Shea and Paul, 2010).
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Although their primary function is to provide protective immunity against various
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intracellular and extracellular pathogens, they can also exhibit inappropriate responses in
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inflammatory and autoimmune diseases (McKinstry, Strutt and Swain, 2010; Ghoreschi et
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al., 2011; Cosmi et al., 2014). Th17 cells are a subset of Th cells that are characterized by
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the expression of their key transcription factors, STAT3 and RORC, chemokine receptor
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CCR6 and the secretion of signature cytokines IL-17A and IL-17F. They are critical in
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combating fungal infections and contribute to the pathogenesis of several inflammatory
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and autoimmune diseases and various cancers (Hernández-Santos and Gaffen, 2012;
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Bailey et al., 2014; Burkett, Zu Horste and Kuchroo, 2015). The characterization of the
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molecular mechanisms regulating the differentiation and function of the Th17 cells is
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therefore of great interest for research into the aetiology and treatment of these diseases.
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Until now, our understanding of the
proteins
that are most important in Th17
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differentiation and function in both human and mouse has originated from the
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transcriptional profiling analyses (Ciofani et al., 2012; Tuomela et al., 2012; Yosef et al.,
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2013; Gaublomme et al., 2015). These analyses have identified a number of genes that
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now serve as key markers for the Th17 cells. However, this approach is limited as some
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transcriptional changes are not necessarily reflected at the proteome level (Vogel and
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Marcotte, 2012). For example, several post-transcriptional and post-translational
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mechanisms modulate the stability and activity of many of the proteins involved (Liu et al.,
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ACCEPTED MANUSCRIPT 2016). Characterization of the cellular proteome of Th17-cells enables the discovery of
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unique protein signatures and regulated cellular pathways that drive Th17-cell
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development and function. A better understanding of the proteome during the
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differentiation process facilitates the rational design of drugs targeting Th17-mediated
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inflammatory and autoimmune diseases.
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Mass spectrometry-based proteomic analysis is a powerful tool for comprehensively
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profiling the proteome in different cellular systems, including T cells (Cox and Mann, 2011;
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Howden et al., 2013). Previously, mass-spectrometry-based proteomic analysis of
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differentiating T cells has focused mainly on classical Th1 and Th2 cells using in vitro
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differentiation systems (Loyet et al., 2005; Rautajoki et al., 2007). In addition, addressing
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disease related traits, the proteomic profiles were compared for in vivo differentiated Th1
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and Th1/Th17 cell clones isolated from biopsies of gut samples from patients with Crohn’s
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disease (Riaz et al., 2016). Recently, a number of studies identified distinct set of
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differentially regulated proteins when comparing the proteomes of CD4+CD25+ Foxp3
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expressing natural Treg cells (nTreg) and induced Treg (iTreg) with CD4+ conventional T
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cells both in human and mouse (Kubach et al., 2007; Duguet et al., 2017; Cuadrado, van
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den Biggelaar, de Kivit, Y.-Y. Chen, et al., 2018; Schmidt et al., 2018). Most recently, a
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study reported Th17 proteome profiles in mouse (Mohammad I et al., 2018). Whilst studies
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of the molecular profiles and mechanisms governing different Th and Treg cell
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differentiation have been mostly performed in mouse, previous reports that have compared
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the transcriptomics profiles of human and mouse have revealed significant differences
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between the two species (Schwanhüusser et al., 2011; Vogel and Marcotte, 2012). Since
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the findings from studies based on mouse disease models often cannot be replicated in
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ACCEPTED MANUSCRIPT human, studies in humans are critical (Mestas and Hughes, 2004; Mak, Evaniew and
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Ghert, 2014).
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In the current study, we utilized a label-free mass spectrometry-based approach to build a
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quantitative data set on the cellular proteome of naïve CD4+ human T cells, CD3/CD28
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activated T (Th0) cells and Th17 cells at 24h and 72h after the initiation of polarization.
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Statistical analysis of the data revealed a Th17 cell-specific proteome signature with a
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number of proteins regulated during Th17 cell differentiation already at the early stage of
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the differentiation process. Moreover, selected proteins with previously known and
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unknown Th17-related functions were validated in additional samples by distinct methods
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to confirm the results. Further, the proteomics and transcriptomics data generated in this
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study were compared to determine the degree of concordance between these two.
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Notably, comparison of our human Th17 regulated proteome with the mouse Th17
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proteome demonstrated poor overlap between the two species. Lastly, using the RNA
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interference (RNAi) approach, we demonstrated SATB1 as negative regulator of the
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human Th17 cell differentiation process in contrast to mouse Th17 cell differentiation
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where it positively regulates Th17 cell differentiation (Ciofani et al., 2012). This study
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illustrates the global protein landscape and mRNA-protein associations during early
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human Th17 cell differentiation. This dataset provides a valuable resource of candidate
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proteins potentially regulating the differentiation and functions of Th17 cells in humans.
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Further investigation on these candidate proteins might lead to the rational design of
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targets with therapeutic potential for modulating Th17-mediated immune responses in
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human.
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Results
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Quantitative proteomic analysis during initiation of human Th17 differentiation
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ACCEPTED MANUSCRIPT We investigated the quantitative changes in the cellular proteome of human naive T helper
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(Th) cells during the early stages of human Th17 cell differentiation using shotgun label-
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free quantitative (LFQ) proteomics. Naive CD4+ T cells isolated from the human umbilical
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cord blood were either activated by T-cell receptor (TCR) crosslinking with CD3 and CD28
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antibodies (Th0 cells) or polarized with a cytokine cocktail of IL-1β, TGF-β and IL-6 in
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combination with TCR/CD28 crosslinking to initiate Th17 cell differentiation. Polarization
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was monitored by the expression of Th17 cell marker genes, including CCR6, IL-17A and
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IL-17F, and the master transcription factor RORC (Figures S1 A-D). The Thp cell sample,
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and Th0 and Th17 cell samples at 24h and 72h time points post initiation of the
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polarization, were collected from five individual donors. To rule out the possibility of
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polarization towards IFN-y expressing pathogenic Th17 cells, expression of the IFN-y
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cytokine was monitored in three separate Th17 cultures prepared in a similar manner
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(Supplementary Figure S1E-F). The samples were prepared using a Filter Aided Sample
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Preparation protocol and analysed in triplicate by LC-MS/MS (Figure S1G).
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Using the label-free mass spectrometry approach, we identified more than 5600 unique
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proteins among the Thp, Th0 and Th17 cell subsets (Table S1). Samples in the normalized
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data clustered according to the biological replicates and cell-lineages indicating successful
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normalization, good reproducibility and general good quality of the data (Figure S2). The
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proportion of missing values in the samples was low in general (
