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. 2016 Oct 4:148:126-38.
doi: 10.1016/j.jprot.2016.07.014. Epub 2016 Jul 25.

Integrative proteomic analysis reveals reprograming tumor necrosis factor signaling in epithelial mesenchymal transition

Yingxin Zhao  1 , Bing Tian  2 , Rovshan G Sadygov  3 , Yueqing Zhang  4 , Allan R Brasier  5
Affiliations

Integrative proteomic analysis reveals reprograming tumor necrosis factor signaling in epithelial mesenchymal transition

Yingxin Zhao et al. J Proteomics. .

Abstract

The airway epithelium is a semi-impermeable barrier whose disruption by growth factor reprogramming is associated with chronic airway diseases of humans. Transforming growth factor beta (TGFβ)-induced epithelial mesenchymal transition (EMT) plays important roles in airway remodeling characteristic of idiopathic lung fibrosis, asthma and chronic obstructive pulmonary disease (COPD). Inflammation of the airways leads to airway injury and tumor necrosis factor alpha (TNFα) plays an important pro-inflammatory role. Little systematic information about the effects of EMT on TNFα signaling is available. Using an in vitro model of TGFβ-induced EMT in primary human small airway epithelial cells (hSAECs), we applied quantitative proteomics and phosphoprotein profiling to understand the molecular mechanism of EMT and the impact of EMT on innate inflammatory responses. We quantified 7925 proteins and 1348 phosphorylation sites by stable isotope labeling with iTRAQ technology. We found that cellular response to TNFα is cell state dependent and the relative TNFα response in mesenchymal state is highly compressed. Combined bioinformatics analyses of proteome and phosphoproteome indicate that the EMT state is associated with reprogramming of kinome, signaling cascade of upstream transcription regulators, phosphor-networks, and NF-κB dependent cell signaling.

Biological significance: Epithelial mesenchymal transition and inflammation have important implications for clinical and physiologic manifestations of chronic airway diseases such as severe asthma, COPD, and lung fibrosis. Little systematic information on the interplay between EMT and innate inflammation is available. This study combined quantitative proteomics and phosphorproteomics approach to obtain systems-level insight into the upstream transcription regulators involved in the TGFβ-induced EMT in primary human small airway epithelial cells and to elucidate how EMT impacts on the TNFα signaling pathways. The proteomics and phosphoproteomics analysis indicates that many signaling pathways involved in TGFβ-induced EMT and EMT has profound reprogramming effects on innate inflammation response.

Keywords: Epithelial mesenchymal transition; Innate immune response; Mass spectrometry; Phosphoproteomics; Proteomics.

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Conflict of interest statement

Additional information The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. TGFβ induce ETM in hSAECs. (A) Imaging to show the cell phenotype change after EMT
The fixed hSAECs (upper panel) and EMT-hSAECs (low-panel) were stained with phalloidin-FITC for F-actin and DAPI for nuclei (magnify x122). (B) Gene expression level of EMT transcription factors (SNAI1, ZEB1 and TWIST1) by RT-qPCR.
Figure 2
Figure 2. Experimental workflow for systems-level proteomic profiling of TGF-β and TNFα signaling
hSAECs cells were induced to EMT with TGFβ stimulation for 15 days. The hSAECs and EMT-hSAECs cells were stimulated with TNFα. After stimulation, cells were lysed, digested with Lys-C/trypsin and labeled with 8-plex iTRAQ reagents. Samples were mixed and separated via SCX. 90% of total peptides in each SCX fraction were further enriched for phosphopeptides with IMAC, whereas 10% of total peptides were used for quantitative proteomic profiling. All samples were analyzed by Q Exactive Orbitrap mass spectrometer. The proteins and phosphoproteins were identified and quantified with Proteome Discoverer.
Figure 3
Figure 3. Protein expression of EMT markers and proteins related to cytoskeletal architecture and cell motility
(A) Expression level of EMT markers, quantified by iTRAQ based stable isotope tagging displayed as a heatmap of log2 expression values relative to untreated hSAECs. Abbreviations: Col, collagen; Ctrl, control; VIM, vimentin. (B) SID-SRM-MS validation of iTRAQ quantification of protein and phosphoprotein profiles. Shown are the relative changes of keratin (KRT)19, CDH1 , S100A9, MMP2, SPARC, and FN1 in response to EMT and or TNFα treatment. Data are expressed as the mean ± SD of the ratio of measured protein relative to stable isotope standard (native/SIS). Data represent biological replicates n=2 assayed in duplicate.
Figure 4
Figure 4. Protein expression profiles in response to TNFα in the absence or presence of TGFβ-induced EMT
(A) Principle component analysis of the 4,398 proteins with abundance that are significantly different in at least one experiment group; from two biological replicates of hSAECs control cells (black open circle), hSAECs treated with TNFα (blue square), EMT-hSAECs control cells (red closed circle), and EMT-hSAECs treated with TNFa (purple diamond) are shown. (B) Heatmap of z score- and averaged log2-transformed ratios of the abundance of the 4,398 proteins with abundance that is significantly different in at least one experiment group (ANOVA, FDR 1%). Proteins were grouped using unsupervised hierarchical clustering. Examples of significantly enriched functional annotations for each cluster are shown (Fisher’s exact test, P<0.05, FDR <2%).
Figure 5
Figure 5. Upstream regulator analysis of transcription regulators of TGFβ-induced EMT and TNFα signaling in the absence or presence of EMT
Heatmap of the activation Z scores for unstream transcription regulators predicted to be activated (red) or inhibited (blue) by stimulation of TNFα in absence or presence of TGFβ-induced EMT.
Figure 6
Figure 6. Expression profiles of protein kinases
Protein kinases were mapped in the dendrogram of the human kinome. Kinome dendrogram of hSAECs with TNFα treatment (A), EMT-hSAECs control cells (B), and EMT-hSAECs with TNFα treatment (C) are shown. Kinases quantified in the proteomic study are marked with circles, where red circles (upregulated) and green circles (downregulated) are the kinases which abundance are significantly differences in at least one experiment group, while the grey circles are kinases which expression levels remain unchanged cross the four experimental groups; and large circles indicate higher degree of change in expression level as indicated in the figure legend.
Figure 7
Figure 7. Expression profiles of phosphorylation evets in response to TNFα in the absence or presence of TGFβ-induced EMT
(A) SID-SRM-MS validation of iTRAQ quantification of protein and phosphoprotein profiles. Shown are the relative changes of IRS2-phospho-Ser (pS) 1100, HSPB1 pS82 and pS15 in response to EMT and or TNFα treatment. Data are expressed as the mean ± SD of the ratio of measured protein relative to stable isotope standard (native/SIS). Data represent biological replicates n=2 assayed in duplicate. (B) Principle component analysis of the 781 phosphorylation sites with abundance that was significantly different in at least one experiment group; from two biological replicates of hSAECs control cells (black circle), hSAECs treated with TNFα (blue square), EMT-hSAECs control cells (red circle), and EMT-hSAECs treated with TNFa (purple diamond) are shown. (C) Heatmap of z score- and log2-transformed ratios of the average abundance of the 781phosphorylation sites with abundance that is significantly different in at least one experiment group. Phosphorylation sites were grouped using unsupervised hierarchical clustering. Examples of significantly enriched functional annotations for each cluster are shown. (Fisher’s exact test, P<0.05, FDR <2%). (D) Box-plots of all phosphorylation events within kinase substrate motifs identified as significantly altered TNFα or TNFα-induced EMT. (E) Sequence logo graphs of examples of significantly enriched kinase substrate motif for individual clusters of phosphorylation sites shown in (D).
Figure 8
Figure 8. String analysis of differentially expression protein kinases and phosphoproteins
STRING networks for kinases and phosphoproteins that are up-regulated upon TNFα stimulation (A, B), and STRING networks for down-regulated kinases and phosphoproteins in response to TNFα stimulation (C,D). The confidence level of the predicted interactions was set as high (confidence score > 0.7). The nodes that did not interact with any other protein evaluated or networks that have less than three connections are not shown. (E) Quantification of phosphorylation of RelA S536, RSP6KA1 Thr 359/Ser 363, and Ser 221. RelA S536 and RSP6KA1 Thr 359/Ser 363 were quantified with ELISA; RSP6KA1 Ser 221 was quantified with mass spectrometry and iTRAQ stable isotope labeling.
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