Effect of cellular senescence on the response of human peritoneal mesothelial cells to TGF-β

Effect of cellular senescence on the response of human peritoneal mesothelial cells to TGF-β | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Effect of cellular senescence on the response of human peritoneal mesothelial cells to TGF-β Edyta Kawka, Rebecca Herzog, Marcin Ruciński, Agnieszka Malińska, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3931243/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jun, 2024 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Transforming growth factor β (TGF-β) is implicated in both mesothelial-to-mesenchymal transition (MMT) and cellular senescence of human peritoneal mesothelial cells (HPMCs). We previously showed that senescent HPMCs could spontaneously acquire some phenotypic features of MMT, which in young HPMCs were induced by TGF-β. Here, we used electron microscopy, as well as global gene and protein profiling to assess in detail how exposure to TGF-β impacts on young and senescent HPMCs in vitro. We found that TGF-β induced structural changes consistent with MMT in young, but not in senescent HPMCs. Of all genes and proteins identified reliably in HPMCs across all treatments and states, 4,656 targets represented overlapping genes and proteins. Following exposure to TGF-β, 137 proteins and 46 transcripts were significantly changed in young cells, compared to 225 proteins and only 2 transcripts in senescent cells. Identified differences between young and senescent HPMCs were related predominantly to wound healing, integrin-mediated signalling, production of proteases and extracellular matrix components, and cytoskeleton structure. Thus, the response of senescent HPMCs to TGF-β differs or is less pronounced compared to young cells. As a result, the character and magnitude of the postulated contribution of HPMCs to TGF-β-induced peritoneal remodelling may change with cell senescence. Biological sciences/Cell biology/Cell signalling Biological sciences/Cell biology/Senescence Health sciences/Nephrology/Renal replacement therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION A monolayer of mesothelial cells lines the peritoneal membrane that covers the walls and internal organs of the abdominal cavity. The morphology of human peritoneal mesothelial cells (HPMCs) resembles that of classical epithelium, with a characteristic cobblestone appearance. HPMCs can also adopt a fibroblast-like phenotype by undergoing epithelial-to-mesenchymal transition. However, given the mesodermal origin of mesothelial cells, this process is usually referred to as mesothelial-to-mesenchymal transition (MMT). 1 It is associated with changes in cell signalling programmes that control the disassembly of intercellular junctions, reorganization of the cytoskeleton, loss of apical–basal polarity, and degradation of the basement membrane, allowing HPMCs to acquire myo-fibroblast properties and migrate from the monolayer to the underlying stroma. 2 The process of MMT occurs during embryonic development, 3 but also in pathologies affecting the peritoneum, including peritoneal adhesions, 4 endometriosis, 5 ovarian cancer, 6 – 8 and peritoneal dialysis (PD)-associated fibrosis. 9 Transforming growth factor-β (TGF-β) has been identified as a key driver of MMT in vitro and in vivo. 2 , 10 – 12 TGF-β has pleiotropic effects both in health and disease (see 13 , 14 for recent reviews). TGF-β exerts its functions in a manner that is highly dependent on the pathophysiological context. 15 Among others, it has been shown that TGF-β can promote cellular senescence of fibroblasts, 16 bronchial epithelial cells, 17 or some cancer cells. 18 Cellular senescence is a complex form of cell response to stress that jeopardizes genome integrity. The known triggers of senescence include telomere dysfunction, oncogene activation, reactive oxygen species, and epigenomic damage. 19 , 20 The resulting cellular senescence is characterized by irreversible growth arrest, distorted cellular morphology, resistance to apoptosis, and altered gene expression profiles. 21 , 22 Despite these changes, senescent cells remain metabolically active and produce increased quantities of various compounds – a feature known as the senescence-associated secretory phenotype (SASP). 23 , 24 SASP components include numerous cytokines, chemokines, growth factors, proteases, and matrix proteins, through which senescent cells exert a strong paracrine effect on neighbouring cells and the tissue microenvironment. 25 , 26 We have previously observed that senescent HPMCs spontaneously acquire some features of MMT, but at the same time appear less susceptible to develop a full MMT phenotype in response to TGF-β. 27 Here, we aimed to extend these observations and used electron microscopy, transcriptomics, and proteomics to identify differences between young and senescent cells in their responsiveness to TGF-β. RESULTS Effect of TGF-β on the morphology and ultrastructure of young and senescent HPMCs Consistent with previous observations, 27 senescent HPMCs had a distinctly different phenotype than young cells. After staining the cytoskeleton with phalloidin, young HPMCs showed a typical cobblestone appearance, unlike senescent cells which appeared rounded and greatly enlarged (Fig. 1 A and 1 B). Differences between young and senescent HPMCs were also seen at the ultrastructural level (Fig. 2 A-B and 2 E-F). In young HPMCs, the centrally located discoid nucleus was surrounded by mitochondria and endoplasmic reticulum, whose features did not point to intense cell activity. Further outwards, the microfilaments formed a ring around the nucleus. In senescent cells, numerous lipid vacuoles and numerous mitochondria with loosely packed short cristae were scattered throughout the cytoplasm. Microfilaments were present not only in the vicinity of the nucleus, but also in peripheral areas. After exposure to TGF-β, young HPMCs lost their cobbled morphology and acquired a spindle-shape appearance (Fig. 1 C). Electron microscopy revealed dense microfilament bundles around the nucleus, but also at one of the cell poles (Fig. 2 C and 2 G). The nucleus became irregular and rich in electron-dense heterochromatin. Senescent cells exposed to TGF-β were equally enlarged as untreated cells and even more elongated (Fig. 1 D). Electron microscopy confirmed extensive cellular hypertrophy, and general ultrastructural features were similar to those found in untreated senescent cells (Fig. 2 D and 2 H). Effect of TGF-β on the transcriptome of young and senescent HPMCs Overall, the microarray analysis identified 26,842 unique transcripts, of which 6,803 had abundance values in all samples from 4 separate donors, and which were used for further analyses ( Supplemental Table S1 ). Global gene expression profiles in TGF-β-treated cells were compared with those in untreated controls and first presented as volcano plots (Fig. 3 A). Exposure of young HPMCs to TGF-β resulted in significant up-regulation of 34 genes and down-regulation of 55 genes, whereas in senescent HPMCs only 5 genes showed significantly increased expression and 8 genes showed decreased expression (p < 0.05). The Principal Component Analysis (PCA) with top 500 regulated genes revealed differences in the datasets across experimental groups (Fig. 3 B). The datasets from TGF-β-treated and untreated young HPMCs were found to be more distinct, while the clusters from senescent cells overlapped significantly in this respect. Similarities between the groups in the expression of individual genes were then analysed, as shown in the Venn diagram (Fig. 3 C). It turned out that after TGF-β treatment, the expression of only 10 genes changed consistently in both young and senescent cells. Of these genes, 3 were found to be up-regulated and 7 down-regulated. Genes that were identified in young and senescent HPMCs as differentially expressed after TGF-β treatment (absolute fold change > 2 and p < 0.05 with 25% FDR correction) were subjected to ontology group enrichment analysis using the DAVID database. However, due to the small number of genes differentially expressed in senescent cells, significantly enriched ontological groups could be consistently identified only in young cells ( Supplementary Fig. 1 ). The regulation of ontological processes by TGF-β was assessed using Gene Set Enrichment Analysis (GSEA), and revealed that exposure to TGF-β resulted in prominent enrichment of genes involved in processes categorized broadly as skeletal development, wound healing, angiogenesis, cell motility, inflammation, and response to growth factors and response to hypoxia (Fig. 4 ). GSEA also identified down regulated groups of genes associated with microtubule-mediated cellular organization, organelle localization and cell cycle progression, as well as with metabolism of nucleotide components and organic acids. These effects were seen both in young and senescent HPMCs, however, compared to senescent cells, the absolute values of normalized enrichment score (NES) for identified processes were consistently higher in young HPMCs. Effect of TGF-β on the proteome of young and senescent HPMCs To investigate the effects of TGF-β on the HPMCs proteome, a multiplex bottom-up proteomics approach, based on isobaric labelling of digested proteins, off-line fractionation, and high-resolution mass spectrometry, was applied. After excluding single-peptide identifications and removing contaminants, 8,781 unique proteins were identified to have abundance values in all 24 samples from 6 individual donors and were used for further analysis ( Supplemental Table S2 ). Changes in the proteome are presented as volcano plots (Fig. 5 A). The analysis revealed that in young HPMCs treated with TGF-β the expression of 131 proteins was significantly changed, with 72 proteins down-regulated and 59 proteins up-regulated, whereas in senescent cells, the expression of 232 proteins was significantly altered, with 127 proteins down-regulated and 105 proteins up-regulated (p 0.5). The proteins most affected by TGF-β1 exposure in young and senescent cells are listed in Supplemental Table S3 . The PCA with the top 500 regulated proteins showed no clear separation of TGF-β-treated and untreated HPMCs in both young and senescent cells, however the sets from senescent cells appeared to be more distinct (Fig. 5 B). Protein enrichment analysis revealed potential differences between young and senescent cells in TGF-β-regulated pathways classified according to molecular function (Fig. 6 A), cellular compartment, and biological process ( Supplemental Fig. S2 A and S2B ). While all molecular functions downregulated by TGF-β in senescent cells were also downregulated in young cells, only 50% of the molecular functions enriched by TGF-β in senescent cells were up-regulated also in young cells. In addition, there was a number of molecular functions affected by TGF-β only in young cells. A canonical pathway overrepresentation analysis (p < 0.05) not only identified pathways affected by TGF-β both in young and senescent HPMCs but also assessed them in terms of their activation status (Fig. 6 B). These included the processes of wound healing (activated in both cases), cell cycle control and kinetochore assembly, and GP6 signalling for collagen binding (inhibited in both cases). Interestingly, TGF-β also affected pathways related to signalling through integrins and the Rho family of GTPases, but seemed to activate them in young cells and inhibit them in senescent cells. Integration of transcriptomic and proteomic data Of the 6,803 transcripts and 8,781 proteins over all treatments and cell states, we found 4,656 genes/proteins in both analyses, accounting for 68% of the quantified genes and 53% of the quantified proteins (Fig. 7 A). Following TGF-β exposure, 137 proteins and 46 transcripts were found to be significantly changed in young cells, while 225 proteins but only 2 transcripts were found to be changed in senescent cells following TGF-β. (Fig. 7 B). The responses to TGF-β identified at both the transcriptome and protein levels and present in both young and senescent HPMCs included TGFBI (TGF-β-induced) and ITGB3 (β3 integrin), which were upregulated, and UPK1B (uroplakin 1B) and SEMA3B (semaphorin 3B), which were downregulated ( Fig. 7 C ). Changes in response to TGF-β seen at the RNA and protein level included also VCAN (versican), ITGA11 (integrin α11), FN1 (fibronectin 1), CCNYL1 (cyclin Y-like 1) and MYOZ2 (myozenin 2), however, these were observed only in young cells. A decrease in KLK7 (kallikrein 7) gene expression after exposure to TGF-β was seen both in young and senescent HPMCs, with a decrease in KLK7 protein detected only in senescent cells. Finally, exposure to TGF-β led to an increase in THBS1 (thrombospondin 1) expression in young cells and at the protein level in senescent cells (Fig. 7 D-E). DISCUSSION In the present study, we compared how exposure to TGF-β affected the structure as well as the transcriptomic and proteomic profiles in young and senescent HPMCs. We observed some similarities, but also large differences in the direction and magnitude of alterations induced by TGF-β. Exposure of young HPMCs to TGF-β for 72 hours resulted in changes in cell phenotype consistent with MMT. 1 , 27 The acquirement of a fibroblast-like appearance by HPMCs was associated with ultrastructural re-organization of the cytoskeletal filaments. These cells also showed significant differences in the expression of 89 genes and 131 proteins. Of those, approximately 60% were found to be down-regulated. This is in line with a previous observation suggesting that TGF-β-induced MMT in vitro is largely a repression process. 28 Accordingly, enrichment analysis revealed that under-represented sets of genes and proteins in TGF-β-treated young HPMCs were those involved in cytoskeleton organization, DNA processing, cell cycle progression and nucleotide metabolism. These effects correspond with the view that in normal epithelial cells TGF-β acts as an inhibitor of cell proliferation. 29 On the other hand, sets of genes and proteins that were over-represented in TGF-β-treated young HPMCs pointed to increased activity of pathways involved in wound healing and angiogenesis. This is in keeping with the common perception of TGF-β as a master regulator of wound healing and fibrosis. 14 In contrast to distinct changes in cytomorphology induced by TGF-β in young HPMCs, the effect of TGF-β on senescent cells was rather small and more difficult to recognize. In addition, the number of genes expressed differentially upon TGF-β exposure in senescent cells was lower than in young HPMCs. Although the processes identified by GSEA as regulated by TGF-β in young cells were also detected in senescent cells, they had lower normalized enrichment scores (NES). There were only a few processes with higher NES in senescent cells and these were associated with microtubule-based movement and protein localization to membranes. Interestingly, compared to young cells, exposure to TGF-β changed the expression of almost twice as many proteins in senescent cells. One may wonder whether this is related to the increased size of senescent cells. In this respect, Lanz et al. observed that when cells grew larger, the concentrations of many individual and highly abundant proteins in the proteome increased to a greater extent than cell size. 30 Pathway analysis based on proteins enriched after TGF-β exposure indicated that the regulation of some processes (e.g. wound healing or cell cycle control) was similar in young and senescent cells. The other processes, however, seemed to be regulated differently, as exemplified by signalling through integrins, which appeared to be stimulated in young cells, but repressed in senescent ones. Interestingly, we found that expression of ITGA11, the gene encoding the subunit alpha 11 of integrins, increased in response to TGF-β significantly more in young HPMCs than in senescent cells. Integrins are key cellular receptors that mediate interactions between extracellular matrix and cytoskeleton and impact on cell phenotype and function. The α11-containing integrin α11β1 that preferentially binds to collagen I was shown to be stimulated by TGF-β in fibroblasts and involved in organ fibrosis. 31 , 32 In this respect, it has been reported that ITGA11 depletion reduced the development of myo-fibroblastic features in cells stimulated with TGF-β and alleviated liver fibrosis. 32 This finding may correspond to our previous observations suggesting that cellular senescence may limit the development of a full-blown fibroblastic phenotype in HPMCs. 27 In this respect, it has been demonstrated that senescence of hepatic stellate cells protects against excessive fibrosis following acute liver injury in mice. 33 Moreover, mesothelial cells covering the capsule of the liver were found to undergo MMT and differentiate into stellate cells in a TGF-β-dependent manner. 34 Intriguingly, we found that the protein profile of senescent HPMCs pointed to reduced activity of the liver fibrosis signalling pathway. Another gene and protein that was upregulated by TGF-β to a greater extent in young than in senescent cells was thrombospondin-1 (THBS1). THBS1 is a multi-functional protein, whose increased expression was observed in tissue injury and repair, cellular senescence and ageing-related diseases. 35 – 38 THBS1 is thought to mediate TGF-β activation by dissociating it from the latency-associated peptide. 39 Thus, the stimulation of THSB1 by TGF-β may create a fast-forward loop that promotes TGF-β functions in young cells. In contrast, reduced THBS1 expression would limit the effect of TGF-β in senescent cells. Moreover, THBS1 can exert TGFβ-independent effects that contribute to the regulation of haemostasis, cell adhesion, migration, growth factor activity, and angiogenesis. 36 For example, it has been demonstrated that while full length THBS1 inhibits angiogenesis by inducing apoptosis of endothelial cells, 40 THBS1 cleavage by kallikrein-related peptidase 7 (KLK7) creates a 28 kDa N-terminal fragment that promotes angiogenesis. 41 In this respect, we found that TGF-β decreased expression of KLK7 in both young and senescent HPMCs, which could further promote angiogenesis in addition to the previously observed stimulation of vascular endothelial growth factor (VEGF) production by HPMCs exposed to TGF-β. 42 Pathway analysis using the IPA tool pointed to cytoskeleton regulation by TGF-β in young and senescent cells by revealing the activation of actin and RhoA signalling in these cells. On the other hand, the same analysis identified activation of serine peptidase inhibitor Kazal type I (SPINK1) pancreatic cancer pathway and leukocyte extravasation signalling in senescent but not in young HPMCs. The role of SPINK1 in health and disease is of great interest because SPINK1 activity can exert both beneficial and harmful effects, with aberrant SPINK1 signalling being strongly associated with cancer progression. However, the molecular biology associated with SPINK1 signalling in a specific tissue context is not fully understood. 43 , 44 Therefore, our observation of an apparently increased activity of the SPINK1 pathway in senescent HPMCs is difficult to interpret. Nevertheless, it may be consistent with a concept of senescence being an anti-tumour mechanism, but also promoting the development of age-related malignancies. 45 , 46 The latter is largely related to the senescence-associated secretory phenotype that involves an increased production of pro-inflammatory cytokines. 21 These may drive leukocyte extravasation, tissue infiltration and chronic inflammation that alters tissue architecture and paves the way for diseases of ageing. 47 In this respect, leukocyte extravasation signalling pathway was found to be an important part of analysed senescence secretomes. 48 Targets that were identified as consistently regulated by TGF-β at transcript and protein levels in both young and senescent cells included TGFBI (up-regulated) and SEMA3B (down-regulated). TGFBI protein is secreted and deposited in extracellular matrix 49 , 50 By interacting with integrins, it mediates cell adhesion and migration 51 – 53 and thus may regulate tumour growth and angiogenesis. 54 SEMA3B is also a known regulator of angiogenesis acting to inhibit competitively VEGF signalling pathway. 55 Since the peritoneal mesothelium is a well-established source of angiogenic activity in the peritoneum 56 and TGF-β is a potent inducer of VEGF in HPMCs, 42 it remains to be determined how the alterations in TGFBI and SEMA3B expression contribute to changes in the angiogenic potential in the aging mesothelium. An important limitation of this study is that all parameters were assessed at a single time point. Because changes in gene expression, protein abundance, and cell structure most likely follow different time-dependent patterns, future experiments need to assess these parameters at various times after TGF-β stimulation. In conclusion, the profound morphological changes seen in senescent HPMCs are not significantly exacerbated by TGF-β. Likewise, changes in global gene and protein profiles in response to TGF-β appear to be less pronounced in senescent cells. Among the genes and proteins affected by TGF-β, those involved in angiogenesis and fibrosis deserve further attention as they may contribute to adverse peritoneal remodelling in disease. METHODS Ethical policy The study received approval from the Bioethics Committee of the Poznan University of Medical Sciences (# 2022 − 569) for the use of discarded omental tissue for research purposes and all patients gave their informed consent. This study was conducted in accordance with relevant guidelines and regulations. Cell culture HPMCs were obtained from pieces of omentum removed during elective abdominal surgery from consenting patients. HPMCs were isolated, identified, and cultured essentially as previously described. 42 HPMCs senescence occurred as a result of successive passages over time. 57 Cells were regarded as senescent when proliferation ceased and > 70% cells stained for senescence-associated β-galactosidase (SA-β-Gal), as determined using the SA-β-Gal Staining Kit (Cell Signaling Technology, Danvers, MA, USA). For TGF-β stimulation, paired cultures of young and senescent HPMCs were first deprived of foetal calf serum (FCS) for 24 hours and then treated for 72 hours with 1 ng/mL of human recombinant TGF-β1 (R&D Systems, Minneapolis, MN, USA) in medium containing 0.1% FCS to maintain basic cell viability. As previously shown, under these conditions, TGF-β1 can effectively induce MMT in HPMCs. 9 , 27 Immunofluorescence Cells were fixed with 3.7% paraformaldehyde, permeabilized with 0.1% Triton X-100, blocked with 1% bovine serum albumin (BSA), and incubated for 15 minutes with 330 pM Alexa Fluor®488 Phalloidin (Cell Signaling Technology, Danvers, MA, USA) in PBS containing 0.1% BSA and 0.05% Tween 20. After that cells were counterstained with 4’,6-diamidino-2-phenylindole (DAPI) (Invitrogen/ThermoFisher Scientific, Waltham, USA) and visualized on Cellinsight CX5 platform (ThermoFisher). Electron microscopy Cells were harvested with a 0.05% trypsin/0.02% EDTA solution, washed with PBS and fixed with 2.5% glutaraldehyde in PBS for 1 hour at 4°C. After that, cells were post-fixed with 1% osmium tetroxide for 30 minutes at room temperature, washed, dehydrated in a graded series of ethanol (40–100%), and infiltrated and embedded in Epon epoxy resin (Plano, Marburg, Germany). Ultrathin (40–60 nm) sections were mounted on copper grids, contrasted with 5% uranyl acetate in 50% ethanol (30 minutes) and 1% lead citrate in water (30 minutes), and analysed with a Jeol transmission electron microscope at 80 kV (Jeol, Tokyo, Japan). Microarray expression study The microarray study was conducted according to previously described procedures. 58 – 60 Total RNA from HPMCs was extracted with TRI Reagent (SigmaAlrdich, Merck Life Science, Darmstadt, Germany) purified with the Quick-RNA MiniPrep kit (Zymo Research Corp., Irvine, CA, USA) and quantified by spectrophotometry. 100 ng of total RNA was subjected to transcription in vitro, biotin labelling, and cDNA fragmentation using Affymetrix GeneChip® WT Plus Reagent Kit (Affymetrix, Santa Clara, CA, USA). The biotin-labelled cDNA fragments (5.5 µg) were hybridised with the Affymetrix® Human Gene 2.1 ST Array Strip (Affymetrix, Santa Clara, CA, USA) together with control cDNA and oligo B2. The hybridisation process was conducted with the AccuBlockTM Digital Dry Bath hybridisation oven (Labnet International, Inc., Edison, NJ, USA) at 45°C for 20 h. After hybridisation, the microarrays were washed and stained by the Imaging Station from a Gene Atlas System (Affymetrix, Santa Clara, CA, USA). Preliminary assessment of the scanned strips was conducted using Affymetrix Gene Atlas TM Operating Software (Affymetrix, Santa Clara, CA, USA). The quality of gene expression data was verified according to software-specific quality control criteria. Microarray data analysis The CEL files obtained from microarray scanning were used in further analyses using a BioConductor repository with the relevant Bioconductor libraries of the statistical R programming language (v4.1.2; R Core Team 2021). A robust multiarray average (RMA) algorithm integrated within the "Affy" library was applied to normalise and compute the expression values of examined genes 61 . The gene data table was formed by merging the annotated data table from the BioConductor "oligo" package with the normalised expression dataset. 62 Genes exhibiting low variance were removed using a variance-based filtering function from the "genefilter" library. 63 Expression data of the top 500 genes with the highest variance were utilised for principal component analysis (PCA) using the "factoextra" library. 64 Differential expression analysis and statistical assessment were performed with the "limma" library, employing linear models designed for microarray data. 65 Differentially expressed genes were determined using criteria involving an absolute fold difference exceeding 2 and a p-value of less than 0.05 with a 25% false discovery rate (FDR) correction. The general profile of transcriptome regulation was illustrated as a volcano plot, showing the total number of up-and downregulated genes. Obtained results were visualised using “ggplot2” and “ggprism” libraries. 66 , 67 Assignment of differentially expressed genes to relevant Gene Ontology (GO) Terms For each analysed comparison, up and down-regulated genes were subjected separately to functional annotation and clusterisation using the DAVID (Database for Annotation, Visualisation, and Integrated Discovery) bioinformatics tool. 68 Entrez IDs of differentially expressed genes were uploaded to DAVID by the “RDAVIDWebService” BioConductor library, 69 where DEGs were assigned to relevant GO terms, with subsequent selection of significantly enriched GO terms from GO biological process (BP), cellular component (CC) and molecular function (MF) databases. Using the same approach, an enrichment analysis was carried out for differentially expressed genes in relation to Kyoto Encyclopedia of Genes and Genomes KEGG signalling pathways. The p-values of selected GO terms were corrected using Benjamini-Hochberg correction described as adjusted p-values. Relevant GO ontological groups with adjusted p-values below 0.05 were visualised using a bubble plot. Detailed analysis of genes belonging to selected ontological groups, with their expression fold changes, are presented as heatmap using “ComplexHeatmap” library. 70 Gene Set Enrichment Analysis (GSEA) GSEA was used to determine potential enrichment or depletion in gene expression between two distinct biological cohorts. This approach employed predefined gene sets encompassing Gene Ontology (GO) terms and pathways. The method uses the Kolmogorov–Smirnov (K-S) statistical test to identify significantly enriched or depleted groups of genes. GSEA analysis was performed using the FGSEA library). 71 Normalised fold change values of all genes were log2 transformed and ordered. The enrichment of gene sets was examined in relation to the Reactome database (Molecular Signatures Database). 72 Genes belonging to the selected set were ranked according to the difference in their expression level using a signal-to-noise ratio with 10,000-time permutation. By walking down, the ranked list of genes, the enrichment score (ES) was calculated for each selected gene set. 71 These scores (ES) were normalised by their gene set size, and FDR corrected false positives. The top twenty significantly enriched and depleted ontological terms (with the highest and lowest normalised enrichment score - NES) were visualised as bar plots. Protein extraction To isolate cell proteins, HPMCs were washed extensively with ice-cold PBS, lysed with RIPA buffer with supplements (50 mM Tris, pH 7.4, 420 mM NaCl, 1% NP-40 (IGEPAL CA-630), 0.25% sodium deoxycholate, protease inhibitors (cOmplete, EDTA-free Protease Inhibitor Cocktail, Roche Diagnostics, Basel, Switzerland), 250 mM sodium fluoride, 50 mM sodium orthovanadate) and cleared by centrifugation. Total protein concentration was determined with the Bradford protein assay (Bio-Rad Protein Assay Dye Reagent, Bio-Rad Laboratories, Hercules, CA, USA) and the samples were stored at -80°C until assayed. Proteomics analysis The whole proteomics analysis was conducted as previously described. 73 In brief, 70 µg total protein of each sample and an internal pooled standard (IPS), consisting of equal parts of all samples, were used. Digestion was performed using single-pot, solid-phase enhanced sample preparation (SP3). All samples were reduced (10 mM dithiothreitol for 1h at 56°C), alkylated (55 mM 2-iodoacetamide, 30 min at RT), and proteins were bound to SP3 beads (10:1 beads:protein ratio, GE Healthcare, Chicago, IL, USA), washed with 80% ethanol and acetonitrile, and subjected to on-bead digestion with trypsin/LysC (1:25 protease:protein ratio, Promega, Madison, WI, USA) overnight at 37°C in 50 mM ammonium bicarbonate, pH 8.5 (SigmaAlrdich, Merck Life Science, Darmstadt, Germany). After elution peptides were desalted (Pierce Peptide Desalting Columns, ThermoFisher Scientific); dried in a vacuum concentrator, and reconstituted in 100 mM tetraethylammonium bromide (TEAB), pH 8.5 (SigmaAlrdich, Merck Life Science). Peptide concentration was determined according to the manufacturers’ protocol (Colorimetric Peptide Assay, ThermoFisher Scientific). For multiplexing, peptide labelling with isobaric tandem mass tags (TMTpro, ThermoFisher Scientific) was performed according the manufacturers’ protocol. TMTpro reagents were reconstituted with acetonitrile and 25 µg per sample were labelled with TMTpro. After incubation for 1h at RT the reaction was quenched by addition of 5% hydroxylamine (SigmaAlrdich, Merck Life Science) in TEAB and incubation for 15 min at RT. Labelling efficiency was determined via LC-MS. Pooled samples (2x 12 samples + IPS) were concentrated and desalted (Pierce Peptide Desalting Columns, ThermoFisher). Eluates were dried in a vacuum concentrator and reconstituted in 20 mM ammonia formate buffer, pH 10 before fractionation at basic pH. Two-dimensional liquid chromatography (LC) was performed by reverse-phase chromatography at high and low pH. In the first dimension peptides were separated on a Gemini-NX C18 (150 x 2mm, 3 µm, 110 A, Phenomenex, Torrance, USA) in 20 mM ammonia formate buffer, pH 10 and eluted over a 48 min gradient from 0–60% solvent B followed by 5 min at 100% solvent B at 50 µl/min using an Ultimate 3000 RSLC micro system (ThermoFisher Scientific) equipped with a fraction collector. Thirty-six fractions were collected in a time-based manner (every 30s from min 11.5 to 57). Organic solvent was removed in a vacuum concentrator and samples were reconstituted in 0.1% trifluoroacetic acid. Fractions were analysed on an Ultimate 3000 RSLC nano coupled directly to an Exploris 480 with FAIMSpro (all Thermo Fisher Scientific). Samples were injected onto a reversed-phase C18 column (50 cm x 75 µm i.d., packed in-house) and eluted with a gradient of 4–38% mobile phase B over 94 min by applying a flow rate of 230 nl/min. MS scans were performed in the range from m / z 375–1650 at a resolution of 120,000 (at m / z = 200). MS/MS scans were performed choosing a resolution of 30,000 with the turboTMT mode for TMTpro Reagent; normalized collision energy of 33%; isolation width of 0.7 m/z and dynamic exclusion of 90s. Two different FAIMS voltages were applied (-40V and − 60V) with a cycle time of 1.5 sec per voltage. FAIMS was operated in standard resolution mode with a static carrier gas flow of 4.6 L/min. The acquired raw MS data files were processed and analysed using Proteome Discoverer (v2.4.0.305, Thermo Fisher). SequestHT was used as search engine and following parameters were chosen: database: Homo sapiens (SwissProt, downloaded on 2021-09-24); enzyme: trypsin; max. missed cleavage sites: 2; static modifications: TMTpro (K and peptide N-terminus) and carbamidomethyl (C); dynamic modifications: oxidation (M), deamidation (N, Q), acetyl (protein N-terminus), Met-loss (M) and Met-loss + Acetyl (M); precursor mass tolerance: 10 ppm; fragment mass tolerance: 0.02 Da. For reporter ion quantification the most intense m/z in a 20 ppm window around the theoretical m/z was used. Correction of isotopic impurities for reporter ion intensities was applied. Only unique peptides were used for quantification, which was based on S/N values with an average S/N threshold of 10. Normalization was based on total peptide amount and scaling mode on controls average (internal standard). Only peptides and proteins with FDR < 0.01 are reported and single peptide identifications were excluded from the dataset. The two multiplex runs were scaled and normalized via the IPS and combined. Statistical analysis and graphical representation Biological pathway enrichment analysis and functional analysis of differentially abundant proteins was performed using the Protein Analysis Through Evolutionary Relationships (PANTHER) database version 17.0. The PANTHER analysis tool was used to performed enrichment analysis for the identification of over-represented biological pathways by a gene list. Annotation databases included in the analysis were Gene Ontology (GO) cellular component and molecular function. Data and statistical analyses and graphical representations of results were performed using R (v4.0.3; http://www.r-project.org/ ). Pathway identification by Ingenuity Pathway Analysis (IPA 7.0, Qiagen, http://www.ingenuity.com ), their respective predicted up/down regulation patterns and their affections by differentially abundant proteins were calculated for each functional pathway by a one-tailed Fisher’s exact test at an alpha level of 0.05. The IPA calculated z-score assessed the match of observed and predicted up/down regulation patterns and served as a predictor for the activation state. Differential protein abundances between conditions were analysed with linear models for microarray data (LIMMA) using the R package “limma”. 65 Limma is a combinatory statistical approach for large-scale expression studies fitting linear models for each gene/protein and utilizing Empirical Bayes and other shrinkage methods to borrow information across genes/proteins to stabilize the analysis and correct variance by shrinking it towards a pooled variance. 65 As mass spectrometry acquired proteomic data can be noisy, large, hierarchical in nature, and imbalanced due to acquisition and pre-processing methods, LIMMA, although being initially developed for microarray data, displayed superiority over conventional statistical modelling approaches (e.g. generalized linear models. 74 Correction of multiple testing was performed by using the Benjamini-Hochberg procedure. Declarations Funding This work was supported by the grant from the National Science Centre Poland (NCN, #2018/29/N/NZ3/02504) for EK and was part of the IMPROVE-PD project funded from the European Union’s Horizon 2020 Research and Innovation Programs under the Marie Sklodowska-Curie grant agreement # 812699 (RH, JMS, K(laus) K(ratochwill). RH was supported by the Austrian Science Fund (FWF) Elise Richter V907-B. The financial support by the Austrian Federal Ministry of Science, Research and Economy and the National Foundation for Research, Technology and Development is gratefully acknowledged. The authors thank the Core Facility Proteomics of the Medical University of Vienna for their support. Author contributions Conceptualization: EK, JW; Data curation: EK, RH, MR; Analysis: EK, RH, MR, AM; Investigation: EK, KK (Katarzyna Kowalska), KJ, AK; Methodology: EK, KK (Katarzyna Kowalska), KJ, AKC, MU, JMS, AW; Supervision: EK, JW; Computational analysis: RH, MR. Visualization: EK, RH, MR, Manuscript preparation, writing, editing revision: EK, RH, KK (Klaus Kratochwill), MR, and JW; All authors have read and approved the manuscript for publication. Data availability statement Mass spectrometry data have been deposited into the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with dataset identifier PXD048090. Competing interests statement RH and KK (Klaus Kratochwill) are consultants of Zytoprotec GmbH. References López-Cabrera, M. Mesenchymal Conversion of Mesothelial Cells Is a Key Event in the Pathophysiology of the Peritoneum during Peritoneal Dialysis. Advances in medicine 2014 , 473134 (2014). Aroeira, L. S. et al. Epithelial to Mesenchymal Transition and Peritoneal Membrane Failure in Peritoneal Dialysis Patients. Journal of the American Society of Nephrology 18 , 2004–2013 (2007). Koopmans, T. & Rinkevich, Y. 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International Journal of Molecular Sciences 23 , (2022). D’Angelo, G. et al. Statistical Models for the Analysis of Isobaric Tags Multiplexed Quantitative Proteomics. Journal of Proteome Research 16 , 3124–3136 (2017). Additional Declarations Competing interest reported. Rebecca Herzog and Klaus Kratochwill are consultants of Zytoprotec GmbH. All other authors do not have any competing interest to declare. Supplementary Files SupplementalTableS1.xlsx SupplementalTableS2.xlsx SupplementalTableS3.xlsx SupplementaryFigures.pdf Supplemental. Fig. S1. Bubble plot of enriched gene ontological (GO) terms by significantly up- or down-regulated genes based on: GOTERM MF FAT (A), GO CC FAT (B), GO BP DIRECT (C) and KEGG PATHWAY (D) annotations databases. The graph shows only the GO groups above the established cut-off criteria ( p with multiple testing correction <0.05). The size of each bubble reflects the number of differentially expressed genes assigned to the GO BP terms, while the intensity of the bubble’s transparency displays a p -value (more transparent indicates closer to the p =0.05 cut‐off value). The green bubble indicates stimulated GO terms, and the red bubbles - inhibited. Supplemental. Fig. S2. Bubble plot of pathway enrichment analysis of proteins significantly regulated after TGFβ stimulation. (A) Regulation of molecular compartment. (B) Regulation of biological processes. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3931243","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":274823378,"identity":"7892c6c5-cdae-4ff9-a386-873eee6b05b6","order_by":0,"name":"Edyta 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\u003cstrong\u003e(B)\u003c/strong\u003e senescent HPMCs, \u003cstrong\u003e(C)\u003c/strong\u003e young HPMCs treated with TGF-β, \u003cstrong\u003e(D)\u003c/strong\u003e senescent HPMCs treated with TGF-β; magnification 200Í, scale bar 100 μm.\u003c/p\u003e","description":"","filename":"Binder21.png","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/451df3fa4f8ada7014a32aa0.png"},{"id":51634109,"identity":"4b21b530-c139-4771-8a48-2b459dad36d9","added_by":"auto","created_at":"2024-02-26 10:31:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2660470,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectron microphotographs of HPMCs.\u003c/strong\u003e (\u003cstrong\u003eA and E\u003c/strong\u003e) Young HPMCs – centrally located disc-shaped nucleus (\u003cem\u003eN\u003c/em\u003e) with a slightly irregular heterochromatin outline (\u003cem\u003eblack arrow\u003c/em\u003e); short rough endoplasmic reticulum cisterns (\u003cem\u003ewhite arrows\u003c/em\u003e), free ribosomes (\u003cem\u003eblack arrowheads\u003c/em\u003e) and polyribosomes (\u003cem\u003ewhite arrowheads\u003c/em\u003e); mitochondria with densely packed long cristae and an electron-dense mitochondrial matrix (\u003cem\u003ewhite asterisks\u003c/em\u003e); microfilaments forming a ring around the nucleus (\u003cem\u003edouble black arrows\u003c/em\u003e). An inset (\u003cstrong\u003eE\u003c/strong\u003e) also shows cross-sections of pores in the nuclear envelope (\u003cem\u003edouble white arrows\u003c/em\u003e). Scale bars correspond to 2 μm (A) and 500 nm (E). (\u003cstrong\u003eB and F\u003c/strong\u003e) Senescent HPMCs – euchromatic cell nucleus (\u003cem\u003eN\u003c/em\u003e) with a fairly regular outline and a narrow band of heterochromatin (\u003cem\u003eblack arrow\u003c/em\u003e) close to the nuclear envelope, and with a nucleolus (\u003cem\u003en\u003c/em\u003e); mitochondria (\u003cem\u003ewhite arrows\u003c/em\u003e) with an electron-dense matrix and loosely packed short mitochondrial cristae; vimentin filaments both in the vicinity of the nucleus and in peripheral regions (\u003cem\u003edouble arrows\u003c/em\u003e); multiple lipid vacuoles (\u003cem\u003easterisks\u003c/em\u003e). Scale bars correspond to 1 μm (B) and 500 nm (F). (\u003cstrong\u003eC and G\u003c/strong\u003e) Young HPMCs after exposure to TGF-β – dense bundles of vimentin filaments (\u003cem\u003edouble black arrows\u003c/em\u003e) around the nucleus (\u003cem\u003eN\u003c/em\u003e) and at one of the cell poles (\u003cem\u003ewhite arrows\u003c/em\u003e); cell nucleus with an irregular outline and many invaginations, and abundant electron-dense heterochromatin (\u003cem\u003easterisk\u003c/em\u003e). An inset (\u003cstrong\u003eG\u003c/strong\u003e) also shows rough endoplasmic reticulum cisterns (\u003cem\u003eblack arrows\u003c/em\u003e). Scale bars correspond to 1 μm (C) and 500 nm (G). (\u003cstrong\u003eD and H\u003c/strong\u003e) Senescent HPMCs after exposure to TGF-β – cell nucleus (\u003cem\u003eN\u003c/em\u003e) rich in euchromatin with an irregular outline and narrow band of heterochromatin near the nuclear envelope (\u003cem\u003eblack arrow\u003c/em\u003e), and containing inclusion bodies (\u003cem\u003ewhite asterisk\u003c/em\u003e) and nucleoli (\u003cem\u003en\u003c/em\u003e); pleomorphic mitochondria (\u003cem\u003eblack arrowheads\u003c/em\u003e) with an electron-dense matrix; rough endoplasmic reticulum cisterns (\u003cem\u003ewhite arrows\u003c/em\u003e); vimentin filaments (\u003cem\u003edouble\u003c/em\u003e \u003cem\u003eblack arrows\u003c/em\u003e). Scale bars correspond to 5 μm (D) and 1 μm (H).\u003c/p\u003e","description":"","filename":"Binder22.png","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/7846ee5fd6b9041681c28656.png"},{"id":51633832,"identity":"749d9200-1f7a-4310-8190-111c8231277f","added_by":"auto","created_at":"2024-02-26 10:23:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1029950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptome regulation by TGF-β in young and senescent cells. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Volcano plots of gene expression profiles in young and senescent cells treated with TGF-β. Each data point corresponds to the mean normalized expression level of a specific gene from four individual donors. The orange dashed lines are placed at cut-off values based on the following parameters: |fold change| \u0026gt; 2 and \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 with 25% FDR. Genes that fulfilled the selection criteria are considered differentially expressed (DEGS) and are represented as red (down-regulated) and green (up-regulated) dots. Total number of DEGS are presented on the left and right upper corners of the graph. The five most regulated genes are marked by their gene symbols. (\u003cstrong\u003eB\u003c/strong\u003e) Principal component analysis (PCA) plots showing the first two principal components of the filtered microarray data set. (\u003cstrong\u003eC\u003c/strong\u003e) Venn diagrams illustrating the overlap and unique genes that undergo up- and down-regulation across all analysed groups compared to untreated cells. Symbols of genes common for the groups compared are shown.\u003c/p\u003e","description":"","filename":"Binder23.png","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/a6e8228dc3352b1e7101efe3.png"},{"id":51633826,"identity":"c0fcd629-61bc-47b9-aef5-b5e8c13ed287","added_by":"auto","created_at":"2024-02-26 10:23:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1326298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis\u003c/strong\u003e. The response to TGF-β was compared between young cells (green) and senescent cells (red). Bars represent twenty ontological groups with the highest (activated processes – positive values) and lowest (inhibited processes – negative values) values of normalized enrichment score (NES).\u003c/p\u003e","description":"","filename":"Binder24.png","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/ed1e4a92b37f3023b46a7d63.png"},{"id":51633835,"identity":"47b3e3cb-9dd3-44b3-92e0-a13dd74c25e4","added_by":"auto","created_at":"2024-02-26 10:23:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":836728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteome regulation by TGF-β in young and senescent cells.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Volcano plots for regulated proteins in young and senescent cells treated with TGF-β. Each data point corresponds to the mean normalized expression level of a specific protein from six individual donors. p \u0026lt; 0.05 for all points above dashed line, coloured points are p \u0026lt; 0.01 (LIMMA) after Benjamini-Hochberg (BH) correction and log2 [fold change] \u0026gt; 0.5. (\u003cstrong\u003eB\u003c/strong\u003e) PCA plots showing first two principal components of the filtered, top 500 regulated proteins of the proteomics data set.\u003c/p\u003e","description":"","filename":"Binder25.png","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/7b09b5295725266ec675cdfb.png"},{"id":51633830,"identity":"4efdba66-fe72-4300-a8ca-b5fd63f754d2","added_by":"auto","created_at":"2024-02-26 10:23:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":632235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway analysis of proteins significantly regulated by TGF-β.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Bubble plot of enriched GO molecular functions. (\u003cstrong\u003eB\u003c/strong\u003e) Activation heatmap of enriched canonical pathways from ingenuity pathway analysis (IPA, Fisher’s exact test, p\u0026lt;0.05) for the effect of TGF-β in young and senescent HPMCs.\u003c/p\u003e","description":"","filename":"Binder26.png","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/8e22e7db134651a3a494b59f.png"},{"id":51634110,"identity":"6694fa9b-b57a-43a1-a1c4-ba0e2d3b932f","added_by":"auto","created_at":"2024-02-26 10:31:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1321078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegration of transcriptomics and proteomics data.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Venn diagram illustrating the overlap of identified proteins with identified transcripts. (\u003cstrong\u003eB\u003c/strong\u003e) Upset R plot illustrating the individual overlap of significantly regulated proteins and transcripts in each condition. The stacked bars on the left (significantly regulated) display the total number of proteins and transcripts significantly regulated in each condition. The bar plot at the top shows the number of proteins or transcripts in each comparisons or condition as indicated by the matrix below each bar. (\u003cstrong\u003eC)\u003c/strong\u003e Co-regulation analysis of genes and proteins significantly regulated (p\u0026lt;0.05) in young (filled dots) and senescent (open dots) cells after treatment with TGF-β. Blue – significant (p\u0026lt;0.01) at protein level, red – significant (FDR \u0026lt;0.25) at transcriptome level, green – significant at protein and transcriptome level in either comparisons, purple – significant on protein and transcript level in both comparisons. (\u003cstrong\u003eD\u003c/strong\u003e and \u003cstrong\u003eE\u003c/strong\u003e) Co-regulation analysis of genes and proteins significantly regulated (p\u0026lt;0.05) in young (D) and senescent (E) cells after treatment with TGF-β. Blue – significant (p\u0026lt;0.01) at protein level, red – significant (FDR \u0026lt;0.25) at transcriptome level, green – significant at protein and transcriptome level in either comparisons.\u003c/p\u003e","description":"","filename":"Binder27.png","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/9adb3c5efc349f15c453d20f.png"},{"id":57653611,"identity":"5f442fce-9024-4309-b600-8d618a14dd8e","added_by":"auto","created_at":"2024-06-04 00:25:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11173218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/5f4c2ef6-5696-4846-a695-73f19ab2fcd3.pdf"},{"id":51633828,"identity":"a67a4b64-f39c-482b-beeb-207f8301c705","added_by":"auto","created_at":"2024-02-26 10:23:45","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5507259,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/3d6c81545ee9a140034bd3af.xlsx"},{"id":51633834,"identity":"6b193632-c645-41f5-9202-9ceb16fd7e80","added_by":"auto","created_at":"2024-02-26 10:23:45","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2278311,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/be35c4ee25515b4fe8a15451.xlsx"},{"id":51633827,"identity":"7049df78-f541-41cd-abd1-b652e018e3ff","added_by":"auto","created_at":"2024-02-26 10:23:45","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15050,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/4a847f9c5cbc9ab96ab2d6f5.xlsx"},{"id":51633833,"identity":"ba826a76-ccb0-431a-9b37-30d490c60e03","added_by":"auto","created_at":"2024-02-26 10:23:45","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":314891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental. Fig. S1\u003c/strong\u003e. Bubble plot of enriched gene ontological (GO) terms by significantly up- or down-regulated genes based on: GOTERM MF FAT (\u003cstrong\u003eA\u003c/strong\u003e), GO CC FAT (\u003cstrong\u003eB\u003c/strong\u003e), GO BP DIRECT (\u003cstrong\u003eC\u003c/strong\u003e) and KEGG PATHWAY (\u003cstrong\u003eD\u003c/strong\u003e) annotations databases. The graph shows only the GO groups above the established cut-off criteria (\u003cem\u003ep\u003c/em\u003e with multiple testing correction \u0026lt;0.05). The size of each bubble reflects the number of differentially expressed genes assigned to the GO BP terms, while the intensity of the bubble’s transparency displays a \u003cem\u003ep\u003c/em\u003e-value (more transparent indicates closer to the \u003cem\u003ep\u003c/em\u003e=0.05 cut‐off value). The green bubble indicates stimulated GO terms, and the red bubbles - inhibited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental. Fig. S2.\u003c/strong\u003e Bubble plot of pathway enrichment analysis of proteins significantly regulated after TGFβ stimulation. (\u003cstrong\u003eA\u003c/strong\u003e) Regulation of molecular compartment. (\u003cstrong\u003eB\u003c/strong\u003e) Regulation of biological processes.\u003c/p\u003e","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3931243/v1/7009bcc1b179c7139c64d4a6.pdf"}],"financialInterests":"Competing interest reported. Rebecca Herzog and Klaus Kratochwill are consultants of Zytoprotec GmbH. All other authors do not have any competing interest to declare.","formattedTitle":"Effect of cellular senescence on the response of human peritoneal mesothelial cells to TGF-β","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eA monolayer of mesothelial cells lines the peritoneal membrane that covers the walls and internal organs of the abdominal cavity. The morphology of human peritoneal mesothelial cells (HPMCs) resembles that of classical epithelium, with a characteristic cobblestone appearance. HPMCs can also adopt a fibroblast-like phenotype by undergoing epithelial-to-mesenchymal transition. However, given the mesodermal origin of mesothelial cells, this process is usually referred to as mesothelial-to-mesenchymal transition (MMT).\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e It is associated with changes in cell signalling programmes that control the disassembly of intercellular junctions, reorganization of the cytoskeleton, loss of apical\u0026ndash;basal polarity, and degradation of the basement membrane, allowing HPMCs to acquire myo-fibroblast properties and migrate from the monolayer to the underlying stroma.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The process of MMT occurs during embryonic development,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e but also in pathologies affecting the peritoneum, including peritoneal adhesions,\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e endometriosis,\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e ovarian cancer,\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and peritoneal dialysis (PD)-associated fibrosis.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTransforming growth factor-β (TGF-β) has been identified as a key driver of MMT in vitro and in vivo.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e TGF-β has pleiotropic effects both in health and disease (see \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e for recent reviews). TGF-β exerts its functions in a manner that is highly dependent on the pathophysiological context.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Among others, it has been shown that TGF-β can promote cellular senescence of fibroblasts,\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e bronchial epithelial cells,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e or some cancer cells.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCellular senescence is a complex form of cell response to stress that jeopardizes genome integrity. The known triggers of senescence include telomere dysfunction, oncogene activation, reactive oxygen species, and epigenomic damage.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e The resulting cellular senescence is characterized by irreversible growth arrest, distorted cellular morphology, resistance to apoptosis, and altered gene expression profiles.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Despite these changes, senescent cells remain metabolically active and produce increased quantities of various compounds \u0026ndash; a feature known as the senescence-associated secretory phenotype (SASP).\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e SASP components include numerous cytokines, chemokines, growth factors, proteases, and matrix proteins, through which senescent cells exert a strong paracrine effect on neighbouring cells and the tissue microenvironment.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe have previously observed that senescent HPMCs spontaneously acquire some features of MMT, but at the same time appear less susceptible to develop a full MMT phenotype in response to TGF-β.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Here, we aimed to extend these observations and used electron microscopy, transcriptomics, and proteomics to identify differences between young and senescent cells in their responsiveness to TGF-β.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEffect of TGF-β on the morphology and ultrastructure of young and senescent HPMCs\u003c/h2\u003e \u003cp\u003eConsistent with previous observations,\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e senescent HPMCs had a distinctly different phenotype than young cells. After staining the cytoskeleton with phalloidin, young HPMCs showed a typical cobblestone appearance, unlike senescent cells which appeared rounded and greatly enlarged (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Differences between young and senescent HPMCs were also seen at the ultrastructural level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F). In young HPMCs, the centrally located discoid nucleus was surrounded by mitochondria and endoplasmic reticulum, whose features did not point to intense cell activity. Further outwards, the microfilaments formed a ring around the nucleus. In senescent cells, numerous lipid vacuoles and numerous mitochondria with loosely packed short cristae were scattered throughout the cytoplasm. Microfilaments were present not only in the vicinity of the nucleus, but also in peripheral areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter exposure to TGF-β, young HPMCs lost their cobbled morphology and acquired a spindle-shape appearance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Electron microscopy revealed dense microfilament bundles around the nucleus, but also at one of the cell poles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). The nucleus became irregular and rich in electron-dense heterochromatin.\u003c/p\u003e \u003cp\u003eSenescent cells exposed to TGF-β were equally enlarged as untreated cells and even more elongated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Electron microscopy confirmed extensive cellular hypertrophy, and general ultrastructural features were similar to those found in untreated senescent cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEffect of TGF-β on the transcriptome of young and senescent HPMCs\u003c/h2\u003e \u003cp\u003eOverall, the microarray analysis identified 26,842 unique transcripts, of which 6,803 had abundance values in all samples from 4 separate donors, and which were used for further analyses (\u003cb\u003eSupplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Global gene expression profiles in TGF-β-treated cells were compared with those in untreated controls and first presented as volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Exposure of young HPMCs to TGF-β resulted in significant up-regulation of 34 genes and down-regulation of 55 genes, whereas in senescent HPMCs only 5 genes showed significantly increased expression and 8 genes showed decreased expression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Principal Component Analysis (PCA) with top 500 regulated genes revealed differences in the datasets across experimental groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The datasets from TGF-β-treated and untreated young HPMCs were found to be more distinct, while the clusters from senescent cells overlapped significantly in this respect.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarities between the groups in the expression of individual genes were then analysed, as shown in the Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). It turned out that after TGF-β treatment, the expression of only 10 genes changed consistently in both young and senescent cells. Of these genes, 3 were found to be up-regulated and 7 down-regulated.\u003c/p\u003e \u003cp\u003eGenes that were identified in young and senescent HPMCs as differentially expressed after TGF-β treatment (absolute fold change\u0026thinsp;\u0026gt;\u0026thinsp;2 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with 25% FDR correction) were subjected to ontology group enrichment analysis using the DAVID database. However, due to the small number of genes differentially expressed in senescent cells, significantly enriched ontological groups could be consistently identified only in young cells (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe regulation of ontological processes by TGF-β was assessed using Gene Set Enrichment Analysis (GSEA), and revealed that exposure to TGF-β resulted in prominent enrichment of genes involved in processes categorized broadly as skeletal development, wound healing, angiogenesis, cell motility, inflammation, and response to growth factors and response to hypoxia (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). GSEA also identified down regulated groups of genes associated with microtubule-mediated cellular organization, organelle localization and cell cycle progression, as well as with metabolism of nucleotide components and organic acids. These effects were seen both in young and senescent HPMCs, however, compared to senescent cells, the absolute values of normalized enrichment score (NES) for identified processes were consistently higher in young HPMCs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEffect of TGF-β on the proteome of young and senescent HPMCs\u003c/h2\u003e \u003cp\u003eTo investigate the effects of TGF-β on the HPMCs proteome, a multiplex bottom-up proteomics approach, based on isobaric labelling of digested proteins, off-line fractionation, and high-resolution mass spectrometry, was applied. After excluding single-peptide identifications and removing contaminants, 8,781 unique proteins were identified to have abundance values in all 24 samples from 6 individual donors and were used for further analysis (\u003cb\u003eSupplemental Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Changes in the proteome are presented as volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The analysis revealed that in young HPMCs treated with TGF-β the expression of 131 proteins was significantly changed, with 72 proteins down-regulated and 59 proteins up-regulated, whereas in senescent cells, the expression of 232 proteins was significantly altered, with 127 proteins down-regulated and 105 proteins up-regulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 after Benjamini-Hochberg (BH) correction and log2 [fold change]\u0026thinsp;\u0026gt;\u0026thinsp;0.5). The proteins most affected by TGF-β1 exposure in young and senescent cells are listed in \u003cb\u003eSupplemental Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e. The PCA with the top 500 regulated proteins showed no clear separation of TGF-β-treated and untreated HPMCs in both young and senescent cells, however the sets from senescent cells appeared to be more distinct (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eProtein enrichment analysis revealed potential differences between young and senescent cells in TGF-β-regulated pathways classified according to molecular function (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), cellular compartment, and biological process (\u003cb\u003eSupplemental Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA and S2B\u003c/b\u003e). While all molecular functions downregulated by TGF-β in senescent cells were also downregulated in young cells, only 50% of the molecular functions enriched by TGF-β in senescent cells were up-regulated also in young cells. In addition, there was a number of molecular functions affected by TGF-β only in young cells. A canonical pathway overrepresentation analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) not only identified pathways affected by TGF-β both in young and senescent HPMCs but also assessed them in terms of their activation status (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These included the processes of wound healing (activated in both cases), cell cycle control and kinetochore assembly, and GP6 signalling for collagen binding (inhibited in both cases). Interestingly, TGF-β also affected pathways related to signalling through integrins and the Rho family of GTPases, but seemed to activate them in young cells and inhibit them in senescent cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of transcriptomic and proteomic data\u003c/h2\u003e \u003cp\u003eOf the 6,803 transcripts and 8,781 proteins over all treatments and cell states, we found 4,656 genes/proteins in both analyses, accounting for 68% of the quantified genes and 53% of the quantified proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Following TGF-β exposure, 137 proteins and 46 transcripts were found to be significantly changed in young cells, while 225 proteins but only 2 transcripts were found to be changed in senescent cells following TGF-β. (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The responses to TGF-β identified at both the transcriptome and protein levels and present in both young and senescent HPMCs included TGFBI (TGF-β-induced) and ITGB3 (β3 integrin), which were upregulated, and UPK1B (uroplakin 1B) and SEMA3B (semaphorin 3B), which were downregulated \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC\u003cb\u003e).\u003c/b\u003e Changes in response to TGF-β seen at the RNA and protein level included also VCAN (versican), ITGA11 (integrin α11), FN1 (fibronectin 1), CCNYL1 (cyclin Y-like 1) and MYOZ2 (myozenin 2), however, these were observed only in young cells. A decrease in KLK7 (kallikrein 7) gene expression after exposure to TGF-β was seen both in young and senescent HPMCs, with a decrease in KLK7 protein detected only in senescent cells. Finally, exposure to TGF-β led to an increase in THBS1 (thrombospondin 1) expression in young cells and at the protein level in senescent cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn the present study, we compared how exposure to TGF-β affected the structure as well as the transcriptomic and proteomic profiles in young and senescent HPMCs. We observed some similarities, but also large differences in the direction and magnitude of alterations induced by TGF-β.\u003c/p\u003e \u003cp\u003eExposure of young HPMCs to TGF-β for 72 hours resulted in changes in cell phenotype consistent with MMT.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e The acquirement of a fibroblast-like appearance by HPMCs was associated with ultrastructural re-organization of the cytoskeletal filaments. These cells also showed significant differences in the expression of 89 genes and 131 proteins. Of those, approximately 60% were found to be down-regulated. This is in line with a previous observation suggesting that TGF-β-induced MMT in vitro is largely a repression process.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Accordingly, enrichment analysis revealed that under-represented sets of genes and proteins in TGF-β-treated young HPMCs were those involved in cytoskeleton organization, DNA processing, cell cycle progression and nucleotide metabolism. These effects correspond with the view that in normal epithelial cells TGF-β acts as an inhibitor of cell proliferation.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e On the other hand, sets of genes and proteins that were over-represented in TGF-β-treated young HPMCs pointed to increased activity of pathways involved in wound healing and angiogenesis. This is in keeping with the common perception of TGF-β as a master regulator of wound healing and fibrosis.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn contrast to distinct changes in cytomorphology induced by TGF-β in young HPMCs, the effect of TGF-β on senescent cells was rather small and more difficult to recognize. In addition, the number of genes expressed differentially upon TGF-β exposure in senescent cells was lower than in young HPMCs. Although the processes identified by GSEA as regulated by TGF-β in young cells were also detected in senescent cells, they had lower normalized enrichment scores (NES). There were only a few processes with higher NES in senescent cells and these were associated with microtubule-based movement and protein localization to membranes.\u003c/p\u003e \u003cp\u003eInterestingly, compared to young cells, exposure to TGF-β changed the expression of almost twice as many proteins in senescent cells. One may wonder whether this is related to the increased size of senescent cells. In this respect, Lanz et al. observed that when cells grew larger, the concentrations of many individual and highly abundant proteins in the proteome increased to a greater extent than cell size.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Pathway analysis based on proteins enriched after TGF-β exposure indicated that the regulation of some processes (e.g. wound healing or cell cycle control) was similar in young and senescent cells. The other processes, however, seemed to be regulated differently, as exemplified by signalling through integrins, which appeared to be stimulated in young cells, but repressed in senescent ones. Interestingly, we found that expression of ITGA11, the gene encoding the subunit alpha 11 of integrins, increased in response to TGF-β significantly more in young HPMCs than in senescent cells. Integrins are key cellular receptors that mediate interactions between extracellular matrix and cytoskeleton and impact on cell phenotype and function. The α11-containing integrin α11β1 that preferentially binds to collagen I was shown to be stimulated by TGF-β in fibroblasts and involved in organ fibrosis.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e In this respect, it has been reported that ITGA11 depletion reduced the development of myo-fibroblastic features in cells stimulated with TGF-β and alleviated liver fibrosis.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e This finding may correspond to our previous observations suggesting that cellular senescence may limit the development of a full-blown fibroblastic phenotype in HPMCs.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e In this respect, it has been demonstrated that senescence of hepatic stellate cells protects against excessive fibrosis following acute liver injury in mice.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Moreover, mesothelial cells covering the capsule of the liver were found to undergo MMT and differentiate into stellate cells in a TGF-β-dependent manner.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Intriguingly, we found that the protein profile of senescent HPMCs pointed to reduced activity of the liver fibrosis signalling pathway.\u003c/p\u003e \u003cp\u003eAnother gene and protein that was upregulated by TGF-β to a greater extent in young than in senescent cells was thrombospondin-1 (THBS1). THBS1 is a multi-functional protein, whose increased expression was observed in tissue injury and repair, cellular senescence and ageing-related diseases.\u003csup\u003e\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e THBS1 is thought to mediate TGF-β activation by dissociating it from the latency-associated peptide.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Thus, the stimulation of THSB1 by TGF-β may create a fast-forward loop that promotes TGF-β functions in young cells. In contrast, reduced THBS1 expression would limit the effect of TGF-β in senescent cells. Moreover, THBS1 can exert TGFβ-independent effects that contribute to the regulation of haemostasis, cell adhesion, migration, growth factor activity, and angiogenesis.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e For example, it has been demonstrated that while full length THBS1 inhibits angiogenesis by inducing apoptosis of endothelial cells,\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e THBS1 cleavage by kallikrein-related peptidase 7 (KLK7) creates a 28 kDa N-terminal fragment that promotes angiogenesis.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e In this respect, we found that TGF-β decreased expression of KLK7 in both young and senescent HPMCs, which could further promote angiogenesis in addition to the previously observed stimulation of vascular endothelial growth factor (VEGF) production by HPMCs exposed to TGF-β.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePathway analysis using the IPA tool pointed to cytoskeleton regulation by TGF-β in young and senescent cells by revealing the activation of actin and RhoA signalling in these cells. On the other hand, the same analysis identified activation of serine peptidase inhibitor Kazal type I (SPINK1) pancreatic cancer pathway and leukocyte extravasation signalling in senescent but not in young HPMCs. The role of SPINK1 in health and disease is of great interest because SPINK1 activity can exert both beneficial and harmful effects, with aberrant SPINK1 signalling being strongly associated with cancer progression. However, the molecular biology associated with SPINK1 signalling in a specific tissue context is not fully understood.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e Therefore, our observation of an apparently increased activity of the SPINK1 pathway in senescent HPMCs is difficult to interpret. Nevertheless, it may be consistent with a concept of senescence being an anti-tumour mechanism, but also promoting the development of age-related malignancies.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e The latter is largely related to the senescence-associated secretory phenotype that involves an increased production of pro-inflammatory cytokines.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e These may drive leukocyte extravasation, tissue infiltration and chronic inflammation that alters tissue architecture and paves the way for diseases of ageing.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e In this respect, leukocyte extravasation signalling pathway was found to be an important part of analysed senescence secretomes.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTargets that were identified as consistently regulated by TGF-β at transcript and protein levels in both young and senescent cells included TGFBI (up-regulated) and SEMA3B (down-regulated). TGFBI protein is secreted and deposited in extracellular matrix\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e By interacting with integrins, it mediates cell adhesion and migration\u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and thus may regulate tumour growth and angiogenesis.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e SEMA3B is also a known regulator of angiogenesis acting to inhibit competitively VEGF signalling pathway.\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e Since the peritoneal mesothelium is a well-established source of angiogenic activity in the peritoneum\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e and TGF-β is a potent inducer of VEGF in HPMCs,\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e it remains to be determined how the alterations in TGFBI and SEMA3B expression contribute to changes in the angiogenic potential in the aging mesothelium.\u003c/p\u003e \u003cp\u003eAn important limitation of this study is that all parameters were assessed at a single time point. Because changes in gene expression, protein abundance, and cell structure most likely follow different time-dependent patterns, future experiments need to assess these parameters at various times after TGF-β stimulation.\u003c/p\u003e \u003cp\u003eIn conclusion, the profound morphological changes seen in senescent HPMCs are not significantly exacerbated by TGF-β. Likewise, changes in global gene and protein profiles in response to TGF-β appear to be less pronounced in senescent cells. Among the genes and proteins affected by TGF-β, those involved in angiogenesis and fibrosis deserve further attention as they may contribute to adverse peritoneal remodelling in disease.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEthical policy\u003c/h2\u003e \u003cp\u003eThe study received approval from the Bioethics Committee of the Poznan University of Medical Sciences (# 2022\u0026thinsp;\u0026minus;\u0026thinsp;569) for the use of discarded omental tissue for research purposes and all patients gave their informed consent. This study was conducted in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eHPMCs were obtained from pieces of omentum removed during elective abdominal surgery from consenting patients. HPMCs were isolated, identified, and cultured essentially as previously described.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e HPMCs senescence occurred as a result of successive passages over time.\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e Cells were regarded as senescent when proliferation ceased and \u0026gt;\u0026thinsp;70% cells stained for senescence-associated β-galactosidase (SA-β-Gal), as determined using the SA-β-Gal Staining Kit (Cell Signaling Technology, Danvers, MA, USA). For TGF-β stimulation, paired cultures of young and senescent HPMCs were first deprived of foetal calf serum (FCS) for 24 hours and then treated for 72 hours with 1 ng/mL of human recombinant TGF-β1 (R\u0026amp;D Systems, Minneapolis, MN, USA) in medium containing 0.1% FCS to maintain basic cell viability. As previously shown, under these conditions, TGF-β1 can effectively induce MMT in HPMCs.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence\u003c/h2\u003e \u003cp\u003eCells were fixed with 3.7% paraformaldehyde, permeabilized with 0.1% Triton X-100, blocked with 1% bovine serum albumin (BSA), and incubated for 15 minutes with 330 pM Alexa Fluor\u0026reg;488 Phalloidin (Cell Signaling Technology, Danvers, MA, USA) in PBS containing 0.1% BSA and 0.05% Tween 20. After that cells were counterstained with 4\u0026rsquo;,6-diamidino-2-phenylindole (DAPI) (Invitrogen/ThermoFisher Scientific, Waltham, USA) and visualized on Cellinsight CX5 platform (ThermoFisher).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eElectron microscopy\u003c/h2\u003e \u003cp\u003eCells were harvested with a 0.05% trypsin/0.02% EDTA solution, washed with PBS and fixed with 2.5% glutaraldehyde in PBS for 1 hour at 4\u0026deg;C. After that, cells were post-fixed with 1% osmium tetroxide for 30 minutes at room temperature, washed, dehydrated in a graded series of ethanol (40\u0026ndash;100%), and infiltrated and embedded in Epon epoxy resin (Plano, Marburg, Germany). Ultrathin (40\u0026ndash;60 nm) sections were mounted on copper grids, contrasted with 5% uranyl acetate in 50% ethanol (30 minutes) and 1% lead citrate in water (30 minutes), and analysed with a Jeol transmission electron microscope at 80 kV (Jeol, Tokyo, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMicroarray expression study\u003c/h2\u003e \u003cp\u003eThe microarray study was conducted according to previously described procedures.\u003csup\u003e\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e Total RNA from HPMCs was extracted with TRI Reagent (SigmaAlrdich, Merck Life Science, Darmstadt, Germany) purified with the Quick-RNA MiniPrep kit (Zymo Research Corp., Irvine, CA, USA) and quantified by spectrophotometry. 100 ng of total RNA was subjected to transcription in vitro, biotin labelling, and cDNA fragmentation using Affymetrix GeneChip\u0026reg; WT Plus Reagent Kit (Affymetrix, Santa Clara, CA, USA). The biotin-labelled cDNA fragments (5.5 \u0026micro;g) were hybridised with the Affymetrix\u0026reg; Human Gene 2.1 ST Array Strip (Affymetrix, Santa Clara, CA, USA) together with control cDNA and oligo B2. The hybridisation process was conducted with the AccuBlockTM Digital Dry Bath hybridisation oven (Labnet International, Inc., Edison, NJ, USA) at 45\u0026deg;C for 20 h. After hybridisation, the microarrays were washed and stained by the Imaging Station from a Gene Atlas System (Affymetrix, Santa Clara, CA, USA). Preliminary assessment of the scanned strips was conducted using Affymetrix Gene Atlas TM Operating Software (Affymetrix, Santa Clara, CA, USA). The quality of gene expression data was verified according to software-specific quality control criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMicroarray data analysis\u003c/h2\u003e \u003cp\u003eThe CEL files obtained from microarray scanning were used in further analyses using a BioConductor repository with the relevant Bioconductor libraries of the statistical R programming language (v4.1.2; R Core Team 2021). A robust multiarray average (RMA) algorithm integrated within the \"Affy\" library was applied to normalise and compute the expression values of examined genes\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. The gene data table was formed by merging the annotated data table from the BioConductor \"oligo\" package with the normalised expression dataset.\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e Genes exhibiting low variance were removed using a variance-based filtering function from the \"genefilter\" library.\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e Expression data of the top 500 genes with the highest variance were utilised for principal component analysis (PCA) using the \"factoextra\" library.\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e Differential expression analysis and statistical assessment were performed with the \"limma\" library, employing linear models designed for microarray data.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e Differentially expressed genes were determined using criteria involving an absolute fold difference exceeding 2 and a p-value of less than 0.05 with a 25% false discovery rate (FDR) correction. The general profile of transcriptome regulation was illustrated as a volcano plot, showing the total number of up-and downregulated genes. Obtained results were visualised using \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;ggprism\u0026rdquo; libraries. \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssignment of differentially expressed genes to relevant Gene Ontology (GO) Terms\u003c/h2\u003e \u003cp\u003eFor each analysed comparison, up and down-regulated genes were subjected separately to functional annotation and clusterisation using the DAVID (Database for Annotation, Visualisation, and Integrated Discovery) bioinformatics tool.\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e Entrez IDs of differentially expressed genes were uploaded to DAVID by the \u0026ldquo;RDAVIDWebService\u0026rdquo; BioConductor library,\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e where DEGs were assigned to relevant GO terms, with subsequent selection of significantly enriched GO terms from GO biological process (BP), cellular component (CC) and molecular function (MF) databases. Using the same approach, an enrichment analysis was carried out for differentially expressed genes in relation to Kyoto Encyclopedia of Genes and Genomes KEGG signalling pathways. The p-values of selected GO terms were corrected using Benjamini-Hochberg correction described as adjusted p-values. Relevant GO ontological groups with adjusted p-values below 0.05 were visualised using a bubble plot. Detailed analysis of genes belonging to selected ontological groups, with their expression fold changes, are presented as heatmap using \u0026ldquo;ComplexHeatmap\u0026rdquo; library.\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Enrichment Analysis (GSEA)\u003c/h2\u003e \u003cp\u003eGSEA was used to determine potential enrichment or depletion in gene expression between two distinct biological cohorts. This approach employed predefined gene sets encompassing Gene Ontology (GO) terms and pathways. The method uses the Kolmogorov\u0026ndash;Smirnov (K-S) statistical test to identify significantly enriched or depleted groups of genes. GSEA analysis was performed using the FGSEA library).\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e Normalised fold change values of all genes were log2 transformed and ordered. The enrichment of gene sets was examined in relation to the Reactome database (Molecular Signatures Database).\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e Genes belonging to the selected set were ranked according to the difference in their expression level using a signal-to-noise ratio with 10,000-time permutation. By walking down, the ranked list of genes, the enrichment score (ES) was calculated for each selected gene set.\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e These scores (ES) were normalised by their gene set size, and FDR corrected false positives. The top twenty significantly enriched and depleted ontological terms (with the highest and lowest normalised enrichment score - NES) were visualised as bar plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eProtein extraction\u003c/h2\u003e \u003cp\u003eTo isolate cell proteins, HPMCs were washed extensively with ice-cold PBS, lysed with RIPA buffer with supplements (50 mM Tris, pH 7.4, 420 mM NaCl, 1% NP-40 (IGEPAL CA-630), 0.25% sodium deoxycholate, protease inhibitors (cOmplete, EDTA-free Protease Inhibitor Cocktail, Roche Diagnostics, Basel, Switzerland), 250 mM sodium fluoride, 50 mM sodium orthovanadate) and cleared by centrifugation. Total protein concentration was determined with the Bradford protein assay (Bio-Rad Protein Assay Dye Reagent, Bio-Rad Laboratories, Hercules, CA, USA) and the samples were stored at -80\u0026deg;C until assayed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eProteomics analysis\u003c/h2\u003e \u003cp\u003eThe whole proteomics analysis was conducted as previously described.\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e In brief, 70 \u0026micro;g total protein of each sample and an internal pooled standard (IPS), consisting of equal parts of all samples, were used. Digestion was performed using single-pot, solid-phase enhanced sample preparation (SP3). All samples were reduced (10 mM dithiothreitol for 1h at 56\u0026deg;C), alkylated (55 mM 2-iodoacetamide, 30 min at RT), and proteins were bound to SP3 beads (10:1 beads:protein ratio, GE Healthcare, Chicago, IL, USA), washed with 80% ethanol and acetonitrile, and subjected to on-bead digestion with trypsin/LysC (1:25 protease:protein ratio, Promega, Madison, WI, USA) overnight at 37\u0026deg;C in 50 mM ammonium bicarbonate, pH 8.5 (SigmaAlrdich, Merck Life Science, Darmstadt, Germany). After elution peptides were desalted (Pierce Peptide Desalting Columns, ThermoFisher Scientific); dried in a vacuum concentrator, and reconstituted in 100 mM tetraethylammonium bromide (TEAB), pH 8.5 (SigmaAlrdich, Merck Life Science). Peptide concentration was determined according to the manufacturers\u0026rsquo; protocol (Colorimetric Peptide Assay, ThermoFisher Scientific).\u003c/p\u003e \u003cp\u003eFor multiplexing, peptide labelling with isobaric tandem mass tags (TMTpro, ThermoFisher Scientific) was performed according the manufacturers\u0026rsquo; protocol. TMTpro reagents were reconstituted with acetonitrile and 25 \u0026micro;g per sample were labelled with TMTpro. After incubation for 1h at RT the reaction was quenched by addition of 5% hydroxylamine (SigmaAlrdich, Merck Life Science) in TEAB and incubation for 15 min at RT. Labelling efficiency was determined via LC-MS.\u003c/p\u003e \u003cp\u003ePooled samples (2x 12 samples\u0026thinsp;+\u0026thinsp;IPS) were concentrated and desalted (Pierce Peptide Desalting Columns, ThermoFisher). Eluates were dried in a vacuum concentrator and reconstituted in 20 mM ammonia formate buffer, pH 10 before fractionation at basic pH. Two-dimensional liquid chromatography (LC) was performed by reverse-phase chromatography at high and low pH. In the first dimension peptides were separated on a Gemini-NX C18 (150 x 2mm, 3 \u0026micro;m, 110 A, Phenomenex, Torrance, USA) in 20 mM ammonia formate buffer, pH 10 and eluted over a 48 min gradient from 0\u0026ndash;60% solvent B followed by 5 min at 100% solvent B at 50 \u0026micro;l/min using an Ultimate 3000 RSLC micro system (ThermoFisher Scientific) equipped with a fraction collector. Thirty-six fractions were collected in a time-based manner (every 30s from min 11.5 to 57). Organic solvent was removed in a vacuum concentrator and samples were reconstituted in 0.1% trifluoroacetic acid.\u003c/p\u003e \u003cp\u003eFractions were analysed on an Ultimate 3000 RSLC nano coupled directly to an Exploris 480 with FAIMSpro (all Thermo Fisher Scientific). Samples were injected onto a reversed-phase C18 column (50 cm x 75 \u0026micro;m i.d., packed in-house) and eluted with a gradient of 4\u0026ndash;38% mobile phase B over 94 min by applying a flow rate of 230 nl/min. MS scans were performed in the range from \u003cem\u003em\u003c/em\u003e/\u003cem\u003ez\u003c/em\u003e 375\u0026ndash;1650 at a resolution of 120,000 (at \u003cem\u003em\u003c/em\u003e/\u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200). MS/MS scans were performed choosing a resolution of 30,000 with the turboTMT mode for TMTpro Reagent; normalized collision energy of 33%; isolation width of 0.7 \u003cem\u003em/z\u003c/em\u003e and dynamic exclusion of 90s. Two different FAIMS voltages were applied (-40V and \u0026minus;\u0026thinsp;60V) with a cycle time of 1.5 sec per voltage. FAIMS was operated in standard resolution mode with a static carrier gas flow of 4.6 L/min.\u003c/p\u003e \u003cp\u003eThe acquired raw MS data files were processed and analysed using Proteome Discoverer (v2.4.0.305, Thermo Fisher). SequestHT was used as search engine and following parameters were chosen: database: Homo sapiens (SwissProt, downloaded on 2021-09-24); enzyme: trypsin; max. missed cleavage sites: 2; static modifications: TMTpro (K and peptide N-terminus) and carbamidomethyl (C); dynamic modifications: oxidation (M), deamidation (N, Q), acetyl (protein N-terminus), Met-loss (M) and Met-loss\u0026thinsp;+\u0026thinsp;Acetyl (M); precursor mass tolerance: 10 ppm; fragment mass tolerance: 0.02 Da. For reporter ion quantification the most intense \u003cem\u003em/z\u003c/em\u003e in a 20 ppm window around the theoretical \u003cem\u003em/z\u003c/em\u003e was used. Correction of isotopic impurities for reporter ion intensities was applied. Only unique peptides were used for quantification, which was based on S/N values with an average S/N threshold of 10. Normalization was based on total peptide amount and scaling mode on controls average (internal standard). Only peptides and proteins with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 are reported and single peptide identifications were excluded from the dataset. The two multiplex runs were scaled and normalized via the IPS and combined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and graphical representation\u003c/h2\u003e \u003cp\u003eBiological pathway enrichment analysis and functional analysis of differentially abundant proteins was performed using the Protein Analysis Through Evolutionary Relationships (PANTHER) database version 17.0. The PANTHER analysis tool was used to performed enrichment analysis for the identification of over-represented biological pathways by a gene list. Annotation databases included in the analysis were Gene Ontology (GO) cellular component and molecular function. Data and statistical analyses and graphical representations of results were performed using R (v4.0.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org/\u003c/span\u003e\u003cspan address=\"http://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pathway identification by Ingenuity Pathway Analysis (IPA 7.0, Qiagen, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ingenuity.com\u003c/span\u003e\u003cspan address=\"http://www.ingenuity.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), their respective predicted up/down regulation patterns and their affections by differentially abundant proteins were calculated for each functional pathway by a one-tailed Fisher\u0026rsquo;s exact test at an alpha level of 0.05. The IPA calculated z-score assessed the match of observed and predicted up/down regulation patterns and served as a predictor for the activation state. Differential protein abundances between conditions were analysed with linear models for microarray data (LIMMA) using the R package \u0026ldquo;limma\u0026rdquo;.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e Limma is a combinatory statistical approach for large-scale expression studies fitting linear models for each gene/protein and utilizing Empirical Bayes and other shrinkage methods to borrow information across genes/proteins to stabilize the analysis and correct variance by shrinking it towards a pooled variance.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e As mass spectrometry acquired proteomic data can be noisy, large, hierarchical in nature, and imbalanced due to acquisition and pre-processing methods, LIMMA, although being initially developed for microarray data, displayed superiority over conventional statistical modelling approaches (e.g. generalized linear models.\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e Correction of multiple testing was performed by using the Benjamini-Hochberg procedure.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the grant from the National Science Centre Poland (NCN, #2018/29/N/NZ3/02504) for EK and was part of the IMPROVE-PD project funded from the European Union’s Horizon 2020 Research and Innovation Programs under the Marie Sklodowska-Curie grant agreement # 812699 (RH, JMS, K(laus) K(ratochwill). RH was supported by the Austrian Science Fund (FWF) Elise Richter V907-B. The financial support by the Austrian Federal Ministry of Science, Research and Economy and the National Foundation for Research, Technology and Development is gratefully acknowledged. The authors thank the Core Facility Proteomics of the Medical University of Vienna for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: EK, JW; Data curation: EK, RH, MR; Analysis: EK, RH, MR, AM; Investigation: EK, KK (Katarzyna Kowalska), KJ, AK; Methodology: EK, KK (Katarzyna Kowalska), KJ, AKC, MU, JMS, AW; Supervision: EK, JW; Computational analysis: RH, MR. Visualization: EK, RH, MR, Manuscript preparation, writing, editing revision: EK, RH, KK (Klaus Kratochwill), MR, and JW; All authors have read and approved the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMass spectrometry data have been deposited into the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with dataset identifier PXD048090.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRH and KK (Klaus Kratochwill) are consultants of Zytoprotec GmbH.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eL\u0026oacute;pez-Cabrera, M. 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M. \u003cem\u003eet al.\u003c/em\u003e Proteome-Wide Differential Effects of Peritoneal Dialysis Fluid Properties in an In Vitro Human Endothelial Cell Model. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, (2022).\u003c/li\u003e\n \u003cli\u003eD\u0026rsquo;Angelo, G. \u003cem\u003eet al.\u003c/em\u003e Statistical Models for the Analysis of Isobaric Tags Multiplexed Quantitative Proteomics. \u003cem\u003eJournal of Proteome Research\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 3124\u0026ndash;3136 (2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3931243/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3931243/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTransforming growth factor β (TGF-β) is implicated in both mesothelial-to-mesenchymal transition (MMT) and cellular senescence of human peritoneal mesothelial cells (HPMCs). We previously showed that senescent HPMCs could spontaneously acquire some phenotypic features of MMT, which in young HPMCs were induced by TGF-β. Here, we used electron microscopy, as well as global gene and protein profiling to assess in detail how exposure to TGF-β impacts on young and senescent HPMCs in vitro. We found that TGF-β induced structural changes consistent with MMT in young, but not in senescent HPMCs. Of all genes and proteins identified reliably in HPMCs across all treatments and states, 4,656 targets represented overlapping genes and proteins. Following exposure to TGF-β, 137 proteins and 46 transcripts were significantly changed in young cells, compared to 225 proteins and only 2 transcripts in senescent cells. Identified differences between young and senescent HPMCs were related predominantly to wound healing, integrin-mediated signalling, production of proteases and extracellular matrix components, and cytoskeleton structure. Thus, the response of senescent HPMCs to TGF-β differs or is less pronounced compared to young cells. As a result, the character and magnitude of the postulated contribution of HPMCs to TGF-β-induced peritoneal remodelling may change with cell senescence.\u003c/p\u003e","manuscriptTitle":"Effect of cellular senescence on the response of human peritoneal mesothelial cells to TGF-β","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-26 10:23:40","doi":"10.21203/rs.3.rs-3931243/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-14T07:10:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-13T13:26:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6a1d3656-58f9-4700-b406-73d3772abd4b_SNPRID","date":"2024-03-04T08:42:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-02T11:15:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-26T06:32:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-22T12:01:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-22T11:59:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-02-05T14:33:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8e7c2124-c1bc-4cf1-9d4c-3f7686514737","owner":[],"postedDate":"February 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28959404,"name":"Biological sciences/Cell biology/Cell signalling"},{"id":28959405,"name":"Biological sciences/Cell biology/Senescence"},{"id":28959406,"name":"Health sciences/Nephrology/Renal replacement therapy"}],"tags":[],"updatedAt":"2024-06-04T00:25:25+00:00","versionOfRecord":{"articleIdentity":"rs-3931243","link":"https://doi.org/10.1038/s41598-024-63250-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-06-03 00:25:25","publishedOnDateReadable":"June 3rd, 2024"},"versionCreatedAt":"2024-02-26 10:23:40","video":"","vorDoi":"10.1038/s41598-024-63250-1","vorDoiUrl":"https://doi.org/10.1038/s41598-024-63250-1","workflowStages":[]},"version":"v1","identity":"rs-3931243","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3931243","identity":"rs-3931243","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}