![](https://renal.platohealth.ai/wp-content/uploads/2024/06/effect-of-cellular-senescence-on-the-response-of-human-peritoneal-mesothelial-cells-to-tgf-ceb2-scientific-reports.png)
López-Cabrera, M. Mesenchymal conversion of mesothelial cells is a key event in the pathophysiology of the peritoneum during peritoneal dialysis. Adv. Med. 2014, 473134 (2014).
Aroeira, L. S. et al. Epithelial to mesenchymal transition and peritoneal membrane failure in peritoneal dialysis patients. J. Am. Soc. Nephrol. 18, 2004–2013 (2007).
Kalluri, R. & Neilson, E. G. Epithelial-mesenchymal transition and its implications for fibrosis. J. Clin. Invest. 112, 1776–1784 (2003).
Koopmans, T. & Rinkevich, Y. Mesothelial to mesenchyme transition as a major developmental and pathological player in trunk organs and their cavities. Commun. Biol. 1, 170 (2018).
Sandoval, P. et al. Mesothelial-to-mesenchymal transition in the pathogenesis of post-surgical peritoneal adhesions. J. Pathol. 239, 48–59 (2016).
Demir, A. Y. et al. Proteome analysis of human mesothelial cells during epithelial to mesenchymal transitions induced by shed menstrual effluent. Proteomics 4, 2608–2623 (2004).
Rynne-Vidal, A. et al. Mesothelial-to-mesenchymal transition as a possible therapeutic target in peritoneal metastasis of ovarian cancer. J. Pathol. 242, 140–151 (2017).
Pascual-Antón, L. et al. Mesothelial-to-mesenchymal transition and exosomes in peritoneal metastasis of ovarian cancer. Int. J. Mol. Sci. 22, 11496 (2021).
Pascual-Antón, L. et al. Targeting carcinoma-associated mesothelial cells with antibody–drug conjugates in ovarian carcinomatosis. J. Pathol. 261, 238–251 (2023).
Yáñez-Mó, M. et al. Peritoneal dialysis and epithelial-to-mesenchymal transition of mesothelial cells. N. Engl. J. Med. 348, 403–413 (2003).
Moustakas, A. & Heldin, C. H. Induction of epithelial-mesenchymal transition by transforming growth factor β. Semin. Cancer Biol. 22, 446–454 (2012).
Strippoli, R. et al. Molecular Mechanisms underlying peritoneal EMT and fibrosis. Stem Cells Int. 2016, 3543678 (2016).
Loureiro, J. et al. Blocking TGF-beta1 protects the peritoneal membrane from dialysate-induced damage. J. Am. Soc. Nephrol. 22, 1682–1695 (2011).
Frangogiannis, N. G. Transforming growth factor–ß in tissue fibrosis. J. Exp. Med. 217, 1–16 (2020).
Massagué, J. & Sheppard, D. TGF-β signaling in health and disease. Cell 186, 4007–4037 (2023).
Tominaga, K. & Suzuki, H. I. TGF-β signaling in cellular senescence and aging-related pathology. Int. J. Mol. Sci. 20, 5002 (2019).
Debacq-Chainiaux, F. et al. Repeated exposure of human skin fibroblasts to UVB at subcytotoxic level triggers premature senescence through the TGF-β1 signaling pathway. J. Cell Sci. 118, 743–758 (2005).
Minagawa, S. et al. Accelerated epithelial cell senescence in IPF and the inhibitory role of SIRT6 in TGF-β-induced senescence of human bronchial epithelial cells. Am. J. Physiol. Lung Cell. Mol. Physiol. 300, 391–401 (2011).
Li, Z.-Y., Chen, Z.-L., Zhang, T., Wei, C. & Shi, W.-Y. Correction for: TGF-β and NF-κB signaling pathway crosstalk potentiates corneal epithelial senescence through an RNA stress response. Aging 13, 20853–20853 (2021).
Senturk, S. et al. Transforming growth factor-beta induces senescence in hepatocellular carcinoma cells and inhibits tumor growth. Hepatology https://doi.org/10.1002/hep.23769 (2010).
Burton, D. G. A. & Krizhanovsky, V. Physiological and pathological consequences of cellular senescence. Cell. Mol. Life Sci. 71, 4373–4386 (2014).
Di Micco, R., Krizhanovsky, V., Baker, D. & d’Adda di Fagagna, F. Cellular senescence in ageing: From mechanisms to therapeutic opportunities. Nat. Rev. Mol. Cell Biol. 22, 75–95 (2021).
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. Hallmarks of aging: An expanding universe. Cell 186, 243–278 (2023).
Huang, W., Hickson, L. T. J., Eirin, A., Kirkland, J. L. & Lerman, L. O. Cellular senescence: The good, the bad and the unknown. Nat. Rev. Nephrol. 18, 611–627 (2022).
Khavinson, V., Linkova, N., Dyatlova, A., Kantemirova, R. & Kozlov, K. Senescence-associated secretory phenotype of cardiovascular system cells and inflammaging: Perspectives of peptide regulation. Cells 12, 106 (2022).
Hao, X., Wang, C. & Zhang, R. Chromatin basis of the senescence-associated secretory phenotype. Trends Cell Biol. 32, 513–526 (2022).
Coppe, J. P., Desprez, P. Y., Krtolica, A. & Campisi, J. The senescence-associated secretory phenotype: The dark side of tumor suppression. Annu. Rev. Pathol. 5, 99–118 (2010).
He, S. & Sharpless, N. E. Senescence in health and disease. Cell 169, 1000–1011 (2017).
Han, S. M. et al. Network-based integrated analysis of omics data reveal novel players of TGF-β1-induced EMT in human peritoneal mesothelial cells. Sci. Rep. 9, 1–12 (2019).
Ruiz-Carpio, V. et al. Genomic reprograming analysis of the mesothelial to mesenchymal transition identifies biomarkers in peritoneal dialysis patients. Sci. Rep. 7, 44941 (2017).
Namvar, S. et al. Functional molecules in mesothelial-to-mesenchymal transition revealed by transcriptome analyses. J. Pathol. 245, 491–501 (2018).
Kawka, E. et al. Epithelial-to-mesenchymal transition and migration of human peritoneal mesothelial cells undergoing senescence. Perit. Dial. Int. 39, 35 (2019).
Strippoli, R. et al. Transition and fibrosis during peritoneal dialysis. Stem Cells Int. 7, 102–123 (2015).
Zhang, Y., Alexander, P. B. & Wang, X. F. TGF-β family signaling in the control of cell proliferation and survival. Cold Spring Harbor Perspect. Biol. 9, 1–24 (2017).
Harrington, J. S., Ryter, S. W., Plataki, M., Price, D. R. & Choi, A. M. K. Mitochondria in health, disease, and aging. Physiol. Rev. 103, 2349–2422 (2023).
Lanz, M. C. et al. Increasing cell size remodels the proteome and promotes senescence. Mol. Cell 82, 3255-3269.e8 (2022).
Lu, N. et al. The human α11 integrin promoter drives fibroblast-restricted expression in vivo and is regulated by TGF-β1 in a Smad- and Sp1-dependent manner. Matrix Biol. 29, 166–176 (2010).
Bansal, R. et al. Integrin alpha 11 in the regulation of the myofibroblast phenotype: Implications for fibrotic diseases. Exp. Mol. Med. 49, e396 (2017).
Krizhanovsky, V. et al. Senescence of activated stellate cells limits liver fibrosis. Cell https://doi.org/10.1016/j.cell.2008.06.049 (2008).
Li, Y., Wang, J. & Asahina, K. Mesothelial cells give rise to hepatic stellate cells and myofibroblasts via mesothelial–mesenchymal transition in liver injury. Proc. Natl. Acad. Sci. 110, 2324–2329 (2013).
Murphy-Ullrich, J. E. & Sage, E. H. Revisiting the matricellular concept. Matrix Biol. J. Int. Soc. Matrix Biol. 37, 1–14 (2014).
Adams, J. C. & Lawler, J. The thrombospondins. Cold Spring Harbor Perspect. Biol. 3, a009712 (2011).
Mikula-Pietrasik, J. et al. Bystander senescence in human peritoneal mesothelium and fibroblasts is related to thrombospondin-1-dependent activation of transforming growth factor-beta1. Int. J. Biochem. Cell Biol. 45, 2087–2096 (2013).
Isenberg, J. S. & Roberts, D. D. Thrombospondin-1 in maladaptive aging responses: A concept whose time has come. Am. J. Physiol. Cell Physiol. 318, C45–C63 (2020).
Murphy-Ullrich, J. E. & Suto, M. J. Thrombospondin-1 regulation of latent TGF-β activation: A therapeutic target for fibrotic disease. Matrix Biol. 68–69, 28–43 (2018).
Jiménez, B. et al. Signals leading to apoptosis-dependent inhibition of neovascularization by thrombospondin-1. Nat. Med. 6, 41–48 (2000).
Ferrari do Outeiro-Bernstein, M. A. et al. A recombinant NH(2)-terminal heparin-binding domain of the adhesive glycoprotein, thrombospondin-1, promotes endothelial tube formation and cell survival: A possible role for syndecan-4 proteoglycan. Matrix Biol. J. Int. Soc. Matrix Biol. 21, 311–324 (2002).
Catar, R. et al. The proto-oncogene C-Fos transcriptionally regulates VEGF production during peritoneal inflammation. Kidney Int. 84, 1119 (2013).
Lin, T. C. Functional roles of spink1 in cancers. Int. J. Mol. Sci. 22, 3814 (2021).
Liao, C. et al. SPINKs in tumors: Potential therapeutic targets. Front. Oncol. 12, 833741 (2022).
Campisi, J. & d’Adda di Fagagna, F. Cellular senescence: When bad things happen to good cells. Nat. Rev. Mol. Cell Biol. 8, 729–740 (2007).
Fane, M. & Weeraratna, A. T. How the ageing microenvironment influences tumour progression. Nat. Rev. Cancer 20, 89–106 (2020).
Faget, D. V., Ren, Q. & Stewart, S. A. Unmasking senescence: Context-dependent effects of SASP in cancer. Nat. Rev. Cancer 19, 439–453 (2019).
Özcan, S. et al. Unbiased analysis of senescence associated secretory phenotype (SASP) to identify common components following different genotoxic stresses. Aging 8, 1316–1329 (2016).
Fico, F. & Santamaria-Martínez, A. TGFBI modulates tumour hypoxia and promotes breast cancer metastasis. Mol. Oncol. 14, 3198–3210 (2020).
Yu, H., Wergedal, J. E., Zhao, Y. & Mohan, S. Targeted disruption of TGFBI in mice reveals its role in regulating bone mass and bone size through periosteal bone formation. Calcif. Tissue Int. 91, 81–87 (2012).
Ozawa, D. et al. TGFBI expression in cancer stromal cells is associated with poor prognosis and hematogenous recurrence in esophageal squamous cell carcinoma. Ann. Surg. Oncol. 23, 282–289 (2016).
Son, H. N., Nam, J. O., Kim, S. & Kim, I. S. Multiple FAS1 domains and the RGD motif of TGFBI act cooperatively to bind αvβ3 integrin, leading to anti-angiogenic and anti-tumor effects. Biochimica Biophys. Acta Mol. Cell Res. 1833, 2378–2388 (2013).
Kim, J.-E. et al. RGD peptides released from beta ig-h3, a TGF-beta-induced cell-adhesive molecule, mediate apoptosis. Oncogene 22, 2045–2053 (2003).
Corona, A. & Blobe, G. C. The role of the extracellular matrix protein TGFBI in cancer. Cell. Signal. 84, 110028 (2021).
Wang, Y. Q. et al. SEMA3B-AS1 suppresses colorectal carcinoma progression by inhibiting Semaphorin 3B-dependent VEGF signaling pathway activation. MedComm 4, 1–19 (2023).
Witowski, J. & Jorres, A. Angiogenic Activity of the Peritoneal Mesothelium: Implications for Peritoneal Dialysis. in Progress in Peritoneal Dialysis (ed. Krediet, R.) (Chapter 4, InTech, 2011). https://doi.org/10.5772/22084.
Lopez-Anton, M. et al. Telomere length profiles in primary human peritoneal mesothelial cells are consistent with senescence. Mech. Ageing Dev. 164, 37–40 (2017).
Blatkiewicz, M. et al. The enhanced expression of ZWILCH predicts poor survival of adrenocortical carcinoma patients. Biomedicines 11, 1233 (2023).
Szyszka, M. et al. Analysis of transcriptome, selected intracellular signaling pathways, proliferation and apoptosis of LNCaP cells exposed to high leptin concentrations. Int. J. Mol. Sci. 20, 5412 (2019).
Stelcer, E. et al. Adropin stimulates proliferation and inhibits adrenocortical steroidogenesis in the human adrenal carcinoma (HAC15) Cell Line. Front. Endocrinol. 11, 561370 (2020).
Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics (Oxford, England) 20, 307–315 (2004).
Carvalho, B. S. & Irizarry, R. A. A framework for oligonucleotide microarray preprocessing. Bioinformatics (Oxford, England) 26, 2363–2367 (2010).
Gentleman, R., Carey, V., Huber, W. & Hahne, F. genefilter: genefilter: methods for filtering genes from high-throughput experiments. (2021).
Kassambara, A. Factoextra: extract and visualize the results of multivariate data analyses. R package version 1, (2016).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Dawson, C. Ggprism: A “ggplot2” Extension Inspired by “GraphPad Prism”. R package version 1, (2021).
Wickham, H. ggplot2 (Springer, 2016). https://doi.org/10.1007/978-3-319-24277-4.
Dennis, G. J. et al. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4, P3 (2003).
Fresno, C. & Fernández, E. A. RDAVIDWebService: A versatile R interface to DAVID. Bioinformatics (Oxford, England) 29, 2810–2811 (2013).
Kanehisa, M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947–1951 (2019).
Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587–D592 (2023).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics (Oxford, England) 32, 2847–2849 (2016).
Korotkevich, G. et al. Fast gene set enrichment analysis. BioRxiv 60012 (2016).
Liberzon, A. et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Sacnun, J. M. et al. Proteome-wide differential effects of peritoneal dialysis fluid properties in an in vitro human endothelial cell model. Int. J. Mol. Sci. 23, 8010 (2022).
D’Angelo, G. et al. Statistical models for the analysis of isobaric tags multiplexed quantitative proteomics. J. Proteome Res. 16, 3124–3136 (2017).
- The Renal Warrior Project. Join Now
- Source: https://www.nature.com/articles/s41598-024-63250-1