Identification of a Proteomic Signature of Senescence in Primary Human Mammary Epithelial Cells

Abstract

Cellular senescence is the natural program by which cells enter a permanent cell cycle arrest in response to stresses including replicative exhaustion, oncogenic signaling, or DNA damage. Although senescence exerts beneficial effects by acting as a barrier against tumorigenesis, senescent cells can also drive chronic inflammation and age-related diseases through secretion of cytokines and other inflammatory proteins. Therefore, the identification of senolytic compounds that specifically eliminate senescent cells has become an area of great therapeutic promise. Here, we used mass spectrometry-based proteomics to identify senescence biomarkers in primary human mammary epithelial cells (HMECs), a model system for aging. By integrating proteomic data from replicative senescence, immortalization by telomerase reactivation, and drug-induced senescence, we identified a robust HMEC proteomic signature of senescence consisting of 57 upregulated and 29 downregulated proteins. This senescence signature identified both well-known senescence biomarkers, including downregulation of the nuclear lamina protein lamin-B1 (LMNB1), as well as novel biomarkers such as upregulation of the β-galactoside-binding protein galectin-7 (LGALS7). Then, we integrated our proteomic signature of senescence with large-scale drug screening databases to predict that EGFR inhibitors, MEK inhibitors, and dasatinib are novel senolytics in HMEC. Taken together, our results support that the combination of quantitative proteomics and public drug screening databases is a powerful approach to identify senescence biomarkers and novel senolytic compounds.

Vesicle amine transport-1 regulates hepatocellular carcinoma progression by EGF-induced STAT3 signalling

“…To verify core ndings and further explore their clinical signi cance in clinical HCC, we investigated VAT-1correlated genes globally in TCGA clinical HCC samples and performed KEGG pathways enrichment analysis from all VAT-1-correlated genes. Apart from cell cycle, ErbB signalling pathway, EGFR TKI resistance and JAK-STAT signalling pathway, we revealed many other VAT-1-associated pathways in HCC patient tumour samples such as synaptic vesicle cycle, inositol phosphate metabolism, phosphatidylinositol signalling system, axon guidance, cellular senescence and phospholipase D signalling pathway, where VAT-1 has been reported to play important roles (11,14,18,20). Remarkably, these VAT-1-associated pathways enriched from clinical HCC tissues comprise almost all pathways obtained from our mRNA-Seq transcriptome of VAT-1-downregulated cells, complementarily substantiating the results.…”

“…In prostate, VAT-1 was upregulated and contributed to the development of benign prostatic hyperplasia (19). By using liquid chromatography-mass spectrometry-based proteomics, VAT-1 was recently identi ed as one of novel senescence biomarkers in primary human mammary epithelial cells (20). VAT-1 knockdown in breast cancer cell lines (eg, MDA-MB-231 and 4T1) was showed to inhibit the cell migration by 50-80% but no effect on the cell proliferation in vitro (21).…”

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

“…A) Schematic detailing how the proteomic signature of replicative senescence in primary HMECs [39] was used to identify either senescence-inducing or senolytic drug MOAs. B) DMEA results for senescence-inducing drug MOAs.…”

“…Here, we used example data sets from the CMap web portal, including: 1) GSE32547, HUVEC cells treated with the HMGCR inhibitor pitavastatin (1 μM, 4h) or DMSO [34]; 2) GSE35230, A375 melanoma clones treated with the MEK inhibitor GSK212 (30 nM, 24 h) or DMSO [35]; 3) GSE14003, JEKO1 cells treated with the proteasome inhibitor bortezomib (10 h) or untreated [36]; 4) GSE28896, human CD34 + cells treated with the glucocorticoid agonist dexamethasone (24 h) or untreated [37]; and 5) GSE33643, A2058 cells treated with the PI3K/MTOR inhibitor BEZ235 (3 doses at 24 h) or DMSO [38]. We also used the up- and down-regulated biomarkers from a proteomic signature of senescence in primary human mammary epithelial cells (HMECs) [39]. To compare DMEA’s results to CMap’s MOA enrichment results, we used the “gsea_result.gct” file found within the “\gsea\TAG\arfs\NORM_CS” folder.…”

“…Lastly, we sought to demonstrate how DMEA can be used as a discovery tool. As an example, we analyzed our recently published proteomic signature of replicative senescence, a stress-induced irreversible growth arrest associated with aging, in primary human mammary epithelial cells (HMECs) [39]. To highlight the versatility of DMEA to either identify similar or selectively toxic drug MOA, we analyzed the same molecular signature using either the CMap L1000 Query or our WGV-based approach to rank drugs, respectively (Fig.…”