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. 2022 May 13;7(1):157.
doi: 10.1038/s41392-022-00959-3.

Gefitinib and fostamatinib target EGFR and SYK to attenuate silicosis: a multi-omics study with drug exploration

Mingyao Wang #  1   2 , Zhe Zhang #  3   4   5 , Jiangfeng Liu #  6 , Meiyue Song #  1   7 , Tiantian Zhang #  1 , Yiling Chen  1   8 , Huiyuan Hu  1   8 , Peiran Yang  1 , Bolun Li  1 , Xiaomin Song  1 , Junling Pang  1 , Yanjiang Xing  1 , Zhujie Cao  1 , Wenjun Guo  1 , Hao Yang  9 , Jing Wang  10 , Juntao Yang  11 , Chen Wang  1
Affiliations

Gefitinib and fostamatinib target EGFR and SYK to attenuate silicosis: a multi-omics study with drug exploration

Mingyao Wang et al. Signal Transduct Target Ther. .

Abstract

Silicosis is the most prevalent and fatal occupational disease with no effective therapeutics, and currently used drugs cannot reverse the disease progress. Worse still, there are still challenges to be addressed to fully decipher the intricated pathogenesis. Thus, specifying the essential mechanisms and targets in silicosis progression then exploring anti-silicosis pharmacuticals are desperately needed. In this work, multi-omics atlas was constructed to depict the pivotal abnormalities of silicosis and develop targeted agents. By utilizing an unbiased and time-resolved analysis of the transcriptome, proteome and phosphoproteome of a silicosis mouse model, we have verified the significant differences in transcript, protein, kinase activity and signaling pathway level during silicosis progression, in which the importance of essential biological processes such as macrophage activation, chemotaxis, immune cell recruitment and chronic inflammation were emphasized. Notably, the phosphorylation of EGFR (p-EGFR) and SYK (p-SYK) were identified as potential therapeutic targets in the progression of silicosis. To inhibit and validate these targets, we tested fostamatinib (targeting SYK) and Gefitinib (targeting EGFR), and both drugs effectively ameliorated pulmonary dysfunction and inhibited the progression of inflammation and fibrosis. Overall, our drug discovery with multi-omics approach provides novel and viable therapeutic strategies for the treatment of silicosis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Multi-omics profiling of the silicosis mouse lungs. a The flow chart depicted the experimental procedures and data analysis workflow. b Venn diagrams showed the overlapped features including 4,745 mRNA, 11,294 proteins and 10,968 phosphosites among samples collected from silicosis model in different stages. c Principle component analysis (PCA) scatter plots were performed with all identified features to explore the largest sources of variance within each omics dataset. d Heatmaps of differentially expressed features in transcriptome, proteome, and phosphoproteome. 2W, 2 weeks; 4W, 4 weeks; 6W, 6 weeks, 10W, 10 weeks. The color bars in the heatmaps right represented the gene expression level (blue, downregulation and red, upregulation)
Fig. 2
Fig. 2
Trend analysis of silicosis progression in five groups (PBS, 2W, 4W, 6W, 10W). a 4340 mRNA, b 4482 proteins, and c 5052 phospho sites were clustered with fuzzy c-means clustering into 6 discrete expression clusters respectively to illustrate the relative expression changes of the transcriptomic, proteomic and phosphoproteomic data along the progression of the disease in mouse model, and the top 3 pathways enriched for each cluster in metacore were provided alongside the clusters. Key pathways highlighted in red were proposed to be consistently upregulated in silicosis among multi-omics datasets, while essential pathways highlighted in blue proposed to be consistently downregulated in silicosis among multi-omics datasets. 2W, 2 weeks; 4W, 4 weeks; 6W, 6 weeks, 10W, 10 weeks
Fig. 3
Fig. 3
Visualization of meta-analysis results based on transcriptome, proteome, and phosphoproteome features. a Circos plot showed the overlap features and the overlap biological processes based on Gene Ontology between three lists (up-regulated feature listed in three omics). Dark red lines linked the same features that were shared by multiple feature lists. Light red lines linked the features which were annotated in same functional terms. b Circos plot showed the overlap genes and functional annotations between three lists (downregulated feature lists in three omics). Dark blue lines linked the same features that were shared by multiple feature lists. Light blue lines linked the features which were annotated in same functional terms. Enrichment network visualization for results from c three upregulated feature lists and d three downregulated feature lists, where nodes were represented by circles colored by their biological processes. The ellipses in different colors represented the proteins in different signaling pathways
Fig. 4
Fig. 4
Identification of drug target in upregulated pathways with metacore. a Shared pathways of the upregulated modules above were obtained from metacore enrichment, with the cutoff adjusted p value < 0.05. b Bubble plot of the top 10 shared pathways in (a), the color represented adjusted p-value and bubble size represented counts of genes in the top 10 enriched pathways. c Illustration of the proposed underlying mechanisms involved in silicosis progression based on evidence from the multi-omics enrichment analysis. Key pathways and features highlighted in red were proposed to be important in silicosis. d Diagram of multi-omics data analysis and therapeutic target screening. D, differentially expressed features; C, consistently upregulated clusters; P, overlapped pathways; F, features in top 10 pathways; f, features significantly changed in phosphorylation; T, available targets
Fig. 5
Fig. 5
Gefitinib and fostamatinib treatment suppressed pulmonary inflammation and fibrosis in silicosis mice. Western blot analysis of a EGFR and b SYK expression and phosphorylation with silicosis and control mouse lung tissues. c Schematic diagram of gefitinib and fostamatinib treatment in silicosis mice. The relative mRNA levels of d Il-1β, Il-6, Tnf-α, e Col-1 and Fn-1 in lung tissues. Representative images of f Van Gieson, and the scale bar indicates 50 μm. Quantification of pulmonary inflammation and fibrosis in (f). All quantitative results were presented as mean ± SD. All the experimental groups in panel d-g were compared by a two-way ANOVA followed by Bonferroni’ s multiple comparisons test, ns: no significance, PBS group: n = 9 each group; Silica-exposed group: n = 9 each group. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. PBS phosphate-buffered saline, G gefitinib, F fostamatinib. 2W, 2 weeks; 4W, 4 weeks
Fig. 6
Fig. 6
Gefitinib inhibited the profibrotic activity of lung fibroblasts while fostamatinib inhibited the pro-inflammatory activity of lung macrophages in silicosis. a EGFR and fibroblast marker expression in mouse lung tissue, fibroblasts were immunolabeled with anti-EGFR (green), anti-PDGFRA (red), and nuclei were immunolabeled with DAPI (blue). b Western blot analysis of EGFR expression and phosphorylation with control or EGF or TGF-β-treated ibroblasts cell line Mlg2908 lysates. c The relative mRNA levels of Fn-1 in control and treated fibroblasts. d SYK and macrophage marker expression in mouse lung tissue, macrophages were immunolabeled with anti-CD68 (red), nuclei were immunolabeled with DAPI (blue) and SYK was immunolabeled with anti-SYK (green). e Western blot analysis of SYK expression and phosphorylation with treated and control cell lysates. f The relative mRNA levels of Il-1β in control and treated macrophages. g Graphical abstract of treatment process in cell experiments. All quantitative results were presented as mean ± SD. All experiment groups in panels c and f were compared by a two-way ANOVA followed by Bonferroni’s multiple comparisons test, n = 3 each group. ns: no significance; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. PBS phosphate-buffered saline, G gefitinib, F fostamatinib
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