Transcriptomic analysis of TGFβ-mediated fibrosis in primary human Tenon’s fibroblasts

Transcriptomic analysis of TGFβ-mediated fibrosis in primary human Tenon’s fibroblasts | 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 Transcriptomic analysis of TGFβ-mediated fibrosis in primary human Tenon’s fibroblasts Zoe Pasvanis, Antony Boynes, Roy C.K. Kong, Elsa C. Chan, Raymond C.B. Wong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4008732/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Glaucoma filtration surgery (GFS) is performed to slow down disease progression in glaucoma, a leading cause of irreversible blindness worldwide. Following surgery, pathological wound healing may lead to conjunctival fibrosis and filtering failure. Myofibroblasts are the key cells responsible for postoperative conjunctival scarring. This study aims to further understand the molecular mechanisms of conjunctival fibrosis following GFS. We utilised RNA-sequencing (RNA-seq) to delineate the TGFβ1 induced changes in the transcriptome of human Tenon’s fibroblasts (HTFs). RNA sequencing was performed on HTFs after 5 days of TGFβ1 treatment. Following quality control, 3,362 differentially expressed genes were identified, of which 1,532 were upregulated and 1,820 were downregulated. We identified signaling pathways associated with the pathogenesis of conjunctival fibrosis. The DEGs (differentially expressed genes) were enriched in pathways including myofibroblast differentiation, TGFβ-signaling, collagen and extracellular matrix organization, epithelial to mesenchymal transition, and cell cycle regulation. The results of this study identified the transition from HTF to myofibroblast is characterised by the upregulation of key genes including LDLRAD4, CDKN2B, FZD8, MYOZ1 , and the downregulation of SOD3, LTBP4 and RCAN2 . This insight into the transcriptional landscape of HTFs and myofibroblast differentiation is essential to understand the pathophysiology of conjunctival scarring and develop new therapeutic agents. Biological sciences/Genetics/Gene expression Biological sciences/Computational biology and bioinformatics/Gene ontology Biological sciences/Neuroscience/Visual system Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Glaucoma filtration surgery (GFS) is the gold-standard surgical procedure performed to lower intraocular pressure (IOP) in glaucomatous eyes. GFS allows for a gradual egression of aqueous humor from the anterior chamber of the eye into a surgically created subconjunctival space, resulting in the formation of a filtering bleb. As a result of surgical intervention, the body’s natural wound healing response is activated. In some cases, the magnitude of this response can be excessive, such that pathological wound healing is observed. This can lead to the postoperative complication of conjunctival fibrosis and surgical failure. Despite the use of adjunctive antimetabolite agents to control scarring, the operation still has a 50% failure rate over 5 years 1 . Moreover, the standard-of-care antimetabolite agents are nonspecific, cytotoxic cancer drugs such as Mitomycin C (MMC) that can result in toxicity of surrounding tissue, long-term wound breakdown and predispose the eye to severe infections that lead to blindness and even loss of the eye 2 . Therefore, there is an unmet medical need to develop novel anti-scarring strategies. One of the key effector cells responsible for postoperative conjunctival scarring is the human Tenon’s fibroblast (HTF). In an environment of injury and inflammation, as can be produced by GFS, HTFs are activated via TGFβ signalling. As a result, HTFs change functionally and phenotypically into myofibroblasts, which are specialised contractile fibroblasts that produce excess ECM components such as collagens and fibronectin 3 . Myofibroblasts are typically characterised by microfilament bundles expressing high levels of alpha smooth muscle actin (αSMA) that are organised into stress fibres; they are also vital for mediating contractile function as part of the wound healing response 4 . While myofibroblasts are a vital component of physiological wound healing, an abundant accumulation at the surgical site is one of the major causes of surgical failure 2 . Interestingly Jeon et al. have recently shown that remodelling myofibroblasts to a fibroblast-like phenotype reduces fibrosis in a feline model of established corneal scarring 5 , hence targeting myofibroblasts by manipulating phenotypic changes may well be an effective way to limit fibrosis in post-GFS scarring. Since there has been no study investigating the molecular mechanism of fibroblast-to-myofibroblast transition by HTF, the aim of the study is to establish the genomic profiles of TGFβ1-induced conversion of HTF to myofibroblasts using RNA-sequencing (RNA-seq). Results Transition of HTF to Myofibroblast Using primary HTF, we first performed time-point analysis to determine the peak expression of the marker of myofibroblasts, αSMA. We treated HTFs, derived from 3 GFS patients, with TGFβ1 for 3–6 days (Fig. 1 ). Following real time qPCR, expression of ACTA2 (which encodes αSMA) in TGFβ groups was significantly increased when compared to control at Day 4 & Day 5 (Fig. 2 a). In addition, we observed morphological change of HTFs into myofibroblasts (Fig. 2 b, Supplementary Fig. 2), where presence of αSMA (red) in collagen fibres is more pronounced, most noticeably occurs at Day 5. Collectively this data suggests that peak activity of HTF to myofibroblast transition occurs five days after treatment with TGFβ. RNA-Seq analysis of myofibroblast transition Since the peak phenotypic changes of fibroblasts to myofibroblasts occurred at day 5 following treatment with TGFβ1, we performed RNA-seq on HTFs from both control (CX2202, CX2203, CX2207) and 5-day TGFβ1 (TGF2202, TGF2203, TGF2207) treatment groups. We first explored the similarity of our samples using principal component analysis (PCA) and hierarchical clustering. Both analyses demonstrate a significant treatment effect of TGFβ1 (Fig. 3 ). PCA analysis presents the variability within the expression data set according to principal components, where we observed biological repeats of the TGF treated group or control group cluster together, supporting similarities between biological repeats (Fig. 3 a). Similarly, hierarchical clustering map displays a correlation of gene expression for all pairwise combinations of biological repeats of the treatment group. Samples having correlation values of > 0.9 suggest there is no outlying sample (Fig. 3 b). Together these plots suggest that the data are of good quality, and that it is appropriate to proceed with further differential expression analysis. Differential Gene Expression To investigate the impact of TGFβ on HTFs activation into their myofibroblast phenotype, we performed RNA-seq on primary HTFs. A total of 35691 genes were identified in all samples. Following DESeq2 analysis and hierarchical clustering, there were 3362 differentially expressed genes (DEGs) identified, of which 1532 were upregulated and 1820 were downregulated in HTF following TGFβ1 treatment (with an adjusted p value of < 0.05, Fig. 4 a). ​​ The top 20 DEGs between the two groups are visualized in both a dotplot (Fig. 4 b) and volcano plot (Fig. 4 c). The normalised dot plot visualises the differential expression of the top 20 genes in our gene set by relative position (determined by log10 normalised counts) of TGFβ treatment group sample dots (blue, pink and teal) in comparison to control group sample dots (red, yellow and green) within each column. Blue, pink and teal dots located above red, yellow and green dots indicates upregulated expression of the gene in the TGFβ treatment group, whereas the inverse indicates downregulated expression of the gene in the TGFβ treatment group (Fig. 4 c). Our results indicated that TGFβ1 treatment in HTF upregulated a number of genes including LDLRAD4, SCUBE3, TXNDC5, CDKN2B, DACT1, DYNC1I1, FZD8, FOXP4, PXDC1, MYOZ1, LANCL2, FBLN5 , while downregulated genes include IGSF10, SVIL, SECTM1, LTBP4, TNXB, RCAN2, CXCL12 and SOD3 . TGFβ Promotes Cell Cycle Arrest in HTFs TGFβ is known to cause cell senescence in many cell types but may up or downregulate cell proliferation, and cell death in others 6 . We investigated cell cycle regulatory genes to further understand their role in TGFβ-mediated HTF differentiation or activation. We identified several cell cycle related gene sets from the Molecular Signatures Database as well as our own laboratory dataset 7 . Tumour suppressor genes such as CDKN2B were highly upregulated (Figs. 4 b and 4 c) while TMPO , a cell cycle proliferator, was upregulated in the TGFβ treatment group (Fig. 5 ). Gene ontology and functional analyses Next, we performed gene ontology (GO) analysis to understand the functions of the DEG following TGFB1 treatment in HTFs. GO enrichment analysis results revealed top significant enrichment pathways were processes involved in fibrosis including extracellular matrix (ECM) organization, external encapsulating structure organization, collagen fibril organization, and actin filament bundle assembly (Fig. 6 a, 6 b, and 6 c). Interestingly our results showed TGFβ regulates several signaling pathways such as Smad phosphorylation, Wnt signaling pathway, and response to cytokines (Fig. 6 b and 6 c). These findings suggest multiple profibrotic pathways in the fibroblast to myofibroblast transition and confirm previous findings that Wnt pathway and Smad signaling promotes fibroblast to myofibroblast transition potentially through cytokines such as IL-11 8 . Regulation of the cell cycle process and regulation of the immune response were highly enriched terms suggesting TGFβ signaling controls cell proliferation and survival (Fig. 6 a). Cellular component terms reveal interesting insight into the molecular processes mediated by TGFβ. The majority of molecular functions suppressed by TGFβ were found to occur within the cell nucleus and be involved in processes including regulation of cell cycle process and mitotic cell cycle process. Conversely, the activation effects on peptidyl-proline 4-dioxygenase activity (Fig. 6 a and b) and regulation of collagen occur within the collagen network (Fig. 6 d and 6 e). These findings suggest that fibroblast to myofibroblast transition is regulated by the ECM microenvironment and TGFβ promotes cell senescence through transcriptional induction of the expression of cell-cycle inhibitors. KEGG enrichment analyses revealed top enrichment pathways included TGFβ signaling pathway, Wnt signaling pathway, cell adhesion molecules and NF-kappa B signaling pathway. BMP, LTBP1, Smad Anchor for Receptor Activation (SARA) and SMAD4 were shown to play important roles in the TGFβ-signaling pathway (Fig. 7 a). Genes from the Frizzled and Smad families were found to be involved in the Wnt-signaling pathway. SMAD3 and SMAD4 specifically were found to play a role in TGFβ, and Wnt-pathway mediated cell cycle arrest (Fig. 7 b). Cell adhesion molecules showed minor downregulation for the majority of cell types such as macrophages, T cells, and B cells. However, there was significant upregulation of myoblasts cell adhesion molecules predominantly driven by Cadherin-2 ( CDH2 ) (Fig. 7 c). Cadherin expression and cell to cell adhesion play a critical role in transition between cellular states. Here we show CDH2 as a key mediator of intercellular adhesion between myofibroblasts. KEGG network topology showed the crossover between TGFβ-signaling and Smad pathway signaling by LEFT2 , BMP6, INHBA and the interaction between TGFβ-signaling and Wnt-signaling by INHBA, INHBE , and FZD8 . As expected, genes encoding for collagen synthesis were responsible for ECM organization (Fig. 7 d). Collectively, these results suggest a gene signature for myofibroblasts following exposure to TGFβ is built upon gene expression patterns in collagen fibril organisation, ECM organisation, cell senescence, cell adhesion, TGFβ signalling, and regulation of pathway restricted Smad phosphorylation. Next, Gene set enrichment analysis (GSEA) was performed to integrate the DEGs from the TGFβ treatment group in an effort to identify top canonical pathways enriched with publicly available KEGG and GSEA-hallmark gene sets. Myofibroblast differentiation had the highest normalized enrichment score with the highest enriched genes including ALDH1B1, LOXL2, NEXN, COTL1 , and C5orf46 (Supplementary table 1)(Fig. 8 a). Genes encoding for collagen synthesis including P4HA2 and P4HA3 were enriched in the collagen fibril organization enrichment plot (Fig. 8 b) (Supplementary table 1). Tumor suppressors such as CDKN2B and oncogenes such as CCNA1 were highly enriched in cell senescence (Fig. 8 c) (Supplementary Table 1). CTGF, TGFβ1, TGFβ3 , and SMAD7 were highly enriched in the TGFβ-pathway (Fig. 8 d) (Supplementary table 1). MYOZ1 was found to be significantly enriched in wound healing organization (Supplementary Fig. 3a), and FBLN5 was significantly enriched in extracellular matrix interaction cell adhesion and within the top 20 significant DEGs (Supplementary Fig. 3b). Families of MMP and collagen genes were highly enriched in extracellular matrix organisation (Supplementary Fig. 3c). Altogether, our results identified the key genes regulating the myofibroblast transition of HTFs. Discussion This study presents the first RNA-seq study of human Tenon’s fibroblast-to-myofibroblast activation, a critical TGFβ-dependent process involved in postoperative wound healing following glaucoma filtration surgery. Myofibroblast activity drives wound healing both physiologically and pathologically. The progression of fibrotic diseases and cancers is dependent on myofibroblasts that remain pathologically activated rather than undergo apoptosis and vascular pathologies 9 . TGFβ signalling notably plays a critical role in maintenance of tissue homeostasis and is hence highly regulated at several steps in both intracellular and extracellular microenvironments 10 . As this analysis was conducted in myofibroblasts at a 5-day timepoint post-TGFβ treatment, where HTFs expressed a myofibroblast phenotype, we expected our findings to reflect the direct and indirect effects of TGFβ on activation and inhibitory signalling in myofibroblasts. Growth factors and reactive oxidants are released from damaged cells and trigger the activation and proliferation of fibroblasts 11 . CTGF is one such growth factor that has been found to play an important role in TGFβ-dependent myofibroblast differentiation and ECM production, indicated by its upregulated expression being highly enriched in the TGFβ-pathway (Fig. 8 d). As an important downstream mediator of TGFβ, CTGF has been found to influence myofibroblast differentiation in orbital fibroblasts through increasing of fibronectin and a-SMA protein expression 12 . Hence CTGF may provide a safer target for suppression therapy compared to TGFβ. ACTA2 appears to be important in myofibroblast contraction and migration. ACTA2 is involved in the regulation of multiple genes relevant to fibrosis, including COL1, GFAP, TIMP1, TGFβ , and ET1 13 . We found NOX4 , a gene encoding for reactive oxygen species NADPH oxidase 4 enzyme, was upregulated by TGFβ. NOX4-dependent generation of hydrogen peroxide is a requirement for TGFβ induced myofibroblast differentiation, as well as ECM production 14 . TXNDC5 facilitates proper protein folding in the endoplasmic reticulum and mediates redox reactions via interacting with NADPH oxidase 11 . These genes promote conjunctival fibrosis by activation of SMAD3-dependent TGFβ-signaling and lead to differentiation of HSCs into myofibroblasts, this results in considerable myofibroblast proliferation and ECM production 11 . Notably, we observed the downregulated expression of SOD3 in our TGFβ treated samples. As known ROS scavengers, SODs act to reduce oxidative stress in both extra- and intra- cellular environments, with SOD3 specifically acting in the ECM where it has been found to reduce intracellular ROS levels 15 . Hence our finding is consistent with findings of SOD3 deficiency contributing to liver fibrogenesis and TGFβ1 mediated EMT 16 . TGFβ utilizes components of the ECM such as fibronectin and integrins to communicate and control cell behaviour leading to myofibroblast differentiation. EDN1, FN1, TNC and ITGA11 were upregulated by TGFβ and have been reported in myofibroblast differentiation. EDN1 has been shown to induce resistance to apoptosis in fibroblasts and contribute to fibrogenesis by the abnormal persistence of the myofibroblast phenotype 17 . TGFβ can induce expression of fibronectin 1 extra domain A (FN1 EDA) in fibroblasts. FN1 is not expressed in healthy tissue but is expressed during wound healing, fibroblasts detect FN1 EDA and its presence is required for TGFβ-mediated myofibroblast formation 18 . TNC encodes for tenascin-C, an ECM glycoprotein that has been shown to engage integrins to elicit cell specific responses such as fibrotic responses including collagen synthesis and differentiation of myofibroblasts 19 . TNC was upregulated in the TGFβ sample and has been shown to induce myofibroblast differentiation and migration 20 . Integrins are the main cell-adhesion transmembrane receptors and bind proteins in the ECM such as fibronectin and transduce biochemical and mechanical signals 21 . ITGA11 was the most significantly upregulated integrin in our data and has been shown to co-localize with a-smooth muscle actin-positive myofibroblasts and was correlatively induced with increasing fibrogenesis in human fibrotic organs. ITGA11 knockdown has been shown to markedly reduce TGFβ-induced differentiation and fibrotic parameters 22 . Our findings are consistent with other studies that reported TGFβ upregulation of ITGA11 and CTGF predominantly binding ITGA11 23 . We suggest that ITGA11 is a key regulator of myofibroblast differentiation in HTF and this association may hold potential as a therapeutic target. Elastic fibre proteins play a pivotal role in guiding and facilitating elastogenesis involved in wound healing. The elastic fibre proteins implicated in this process include fibulins, fibronectins, fibrillins and LTBPs 24 . In our TGFβ sample, we recorded upregulated gene expression of FBLN5, FBN1, FN1 , and LTBP1 , alongside downregulated gene expression of LTBP4 and FLBN7 . Of particular interest was FBLN5 which was in the top 20 upregulated DEGs. FBLN5 encodes the matricellular protein fibulin-5, which performs a vital role in elastogenesis and dysregulated expression of FBLN5 has been reported to occur in pseudoexfoliation glaucoma 25 . These findings correlate with the observed upregulated expression of FBLN5 being highly enriched in the EMICA pathway. Typically, TGFβ is secreted in a form that is covalently bound to members of the latent TGFβ-binding protein (LTBP) family, observed as a large latent complex. It is via interactions between LTBPs, fibronectin and fibrillin that this complex can be deposited into the ECM 26 . Hence LTBPs function to regulate the bioavailability of TGFβ, by either localising latent TGFβ in the ECM, or secreting latent TGFβ from cells 27 . In Lu et al.’s 2017 study of human sclerodermal skin fibroblasts, the knockdown of LTBP4 resulted in the inhibition of collagen expression through canonical TGFβ/SMAD signalling, while also reducing extracellular levels of TGFβ 28 . However contradictorily, findings from Su et al.’s recent 2023 LTBP4 knockdown study in a murine model of renal fibrosis revealed that an LTBP4 deficiency promoted mitochondrial dysfunction, increased inflammation, oxidative stress via increased ROS production and fibrosis 29 . Hence, we suspect that the regulatory function of LTBP4 is complex and contextual, as it has been found capable of performing both anti- and pro-fibrotic functions. This makes LTBP4 a prime candidate for further research. In dermal fibroblasts, TGFβ exposure resulted in​​ increased deposition of fibrillin-1 and fibronectin into the ECM because of myofibroblast activation 30 . This associates our observed upregulation of FBN1 and FN1 in our TGFβ treated HTFs with myofibroblast activation. TGFβ mediated epithelial to mesenchymal transition is important for myofibroblast transition. Epithelial to mesenchymal transition causes several changes to cellular phenotype and cytoskeleton remodelling and increased cell migration. We identified several important genes interacting with TGFβ for this process. NREP was upregulated in the TGFβ sample and regulates the expression of TGFβ1 and has been shown to regulate myofibroblast differentiation. NREP is thought to stimulate the expression of TGFβ1 by the methylation of NREP promoter and activating TGFβ1 5’/3’ UTR 31 . Knockdown studies of SCUBE3 , which was upregulated in the TGFβ sample, have implicated its involvement with lung cancer tumorigenesis and cancer metastasis. SCUBE3 binds to TGFβ type 2 receptor and activates TGFβ signalling triggering epithelial-mesenchymal transition 32 . DACT1 was upregulated in the TGFβ sample; it has been shown to inhibit the Wnt signalling pathway. Therefore, DACT1 may release inflammatory factors that were previously suppressed by Wnt and promote local inflammation 33 . Of the Wnt signalling pathways that were highly enriched in our gene set, the Wnt/Ca 2+ pathway (Fig. 7 b) is known to play a critical role in profibrotic and proinflammatory processes including actin polymerisation, cell adhesion, cell migration and NFAT signalling 34 . A key component of this pathway involves the activation of the serine-threonine phosphatase enzyme calcineurin (CaN). RCAN2, encoded by RCAN2 , has been identified as an inhibitor of the Wnt/Ca 2+ pathway by binding CaN and subsequently inhibiting CaN’s protein phosphatase activity 35 . Findings from our GO analysis is consistent with these previous findings as the molecular function of RCAN2 was found to be involved in protein phosphatase regulator activity (Fig. 6 e, GO:0019888). Hence, we suspect that our observation that RCAN2 is downregulated in the TGFβ sample is associated with myofibroblast activation through promotion of the Wnt/Ca 2+ signalling pathway. The regulation of cell cycle process and cell senescence pathways were significantly enriched and largely driven by genes CDKN2, CCNA1, CDKN2A, CDKN2D , and CDK4 . TGFβ causes G1 phase cell cycle arrest and CDKN2B complexes with CDK4 to prevent CDK4 activation and producing cell cycle arrest 36 . These genes are known cell cycle regulators and CDKN2b/p15 has been reported to take part in multiple pathologies such as primary open angle glaucoma and cardiac fibrosis 36 . Interestingly, TGFβ treatment resulted in downregulation of the majority of cell cycle and proliferation marker genes (Fig. 5 ). The only exception to this observation was the upregulated expression of both the tumour suppressor gene CDKN2B (Figs. 4 b and 4 c) and the cell cycle proliferator gene TMPO (Fig. 5 ). Lung carcinogenesis has been linked to upregulated expression of TMPO 37 , while knockdown of TMPO in glioblastomas has been found to inhibit apoptosis-dependent cell proliferation and arrest cell cycle progression at the G2/M Phase 38 . Collectively these findings are consistent with other studies reporting the role of TGFβ as a tumour suppressor and inhibitor of cell proliferation, such that cell cycle arrest and apoptosis evasion occur 39 . CDK6 is a known cell cycle regulator for G1 to S phase transition. Dysregulated expression of CDK6 has been reported in a variety of malignancies. Palbociclib is a commercially available selective inhibitor of CDK6 which has shown significant inhibition of myeloproliferative neoplastic progenitors and cells, and amelioration of bone marrow fibrosis in myelofibrosis 40 , 41 . These findings are consistent with other studies and highlight the biological effects of TGFβ at the cellular level. TGFβ1 and TGFβ3 were upregulated in the TGFβ treatment group. The 3 TGFβ isoforms have highly homologous receptor-binding domains and similar effects on target cells but with divergent primary amino acid sequences in the latency-associated peptide (LAP) domains. Isoform-specific function may be related to different expression in cell types and different extracellular environments 42 . TGFβ1 was enriched in pathways such as TGFβ-pathway, myofibroblast differentiation, and collagen fibril organisation whereas TGFβ2 and TGFβ3 were not. This suggests that HTF induced fibrosis is largely a TGFβ1 driven process. TGFβ3 appears to play a role in regulation of epithelial to mesenchymal transition and TGFβ-pathway rather than myofibroblast differentiation which is consistent with previous reports of TGFβ3 expression being restricted to mesenchymal cells 42 . Our findings highlight the important molecular mechanisms of TGFβ-signaling, particularly positive and negative regulation of pathway-restricted Smad protein phosphorylation (Fig. 6 c; GO:0060393 & GO:0060394). The main genes upregulated in this biological process were GDF10, PMEPA1, LDLRAD4, INHBA, LEFTY2, BMP6 and SMAD7 . Canonically TGFβ signal transduction occurs via activation of downstream mediators Smad2 and Smad3, which are negatively regulated by Smad7 as part of a negative feedback loop 43 . Smad7 inhibits phosphorylation of R-Smads(2/3) by competitively binding TβR-I activated by TβR-II 44 . We speculate that the upregulated expression of SMAD7 is associated with the upregulated expression and enrichment of the SMURF1 gene as their encoded proteins regulate each other’s functions. For Smad7 to perform its inhibitory function it is required to interact with Smurf1 to be exported from the nucleus to the cytoplasm 45 . INHBA encodes inhibin beta-A, which functions as a subunit of activin A and is classified as a ligand of the TGFβ superfamily 46 . INHBA attenuation in renal fibroblasts has been found to significantly reduce their expression of profibrotic markers, inhibiting cell migration and proliferation 47 . To promote the TGFβ signalling pathway, INHBA is able to homodimerise and bind the activin type I/II receptor complex, which then can phosphorylate R-Smad proteins 48 , 49 . Hence, we suspect that upregulated INHBA expression correlates with upregulated expression of ACVR1 (encodes type I receptor, ALK2) and ACVR2B (encodes type II receptor) to promote Smad-dependent TGFβ signalling. PMEPA1, LEFTY2 , and LDLRAD4 are all negative regulators of TGFβ-signaling. PMEPA1 and LDLRAD4 are part of the PMEPAI-family genes and encode transmembrane proteins that have been found to facilitate TGFβ signalling inhibition by competitively targeting SARA through sequestration of the Smad2/3 protein complex and attenuating R-Smad complex recruitment to the TβR-1 for phosphorylation 50 , 51 . Once activated by TGFβ, LEFTY2 inhibits the phosphorylation of Smad2 and downstream heterodimerisation Smad4. Lefty also regulates the extracellular matrix components and inhibits the profibrotic effects of CTGF 52 – 54 . While the results achieved in this study advanced our understanding of the molecular mechanisms of ocular fibrosis following GFS, this study has its limitations. Although current literature supports RNA-seq analysis of HTF as a homogenous population, single cell RNA-seq would enable high resolution transcriptome analysis and potentially identify heterogeneity or subtypes within the HTF and myofibroblast population. This high-resolution transcriptome analysis could then lend to the identification of tissue biomarkers for ocular fibrosis, and together these tools could be instrumental in performing detailed clinical phenotyping. This would enable clinicians to not only identify groups of patients that are likely to scar more severely than others, but also work toward developing a tiered approach to antifibrotic therapy that is more personalised to the patient. Another limitation of the study was that specimen collection was performed at the time of GFS, from patients who were receiving medical therapy for glaucoma (Supplementary table 2). In using these glaucoma medications, most, if not all the patients were exposed to the preservative benzalkonium chloride, which is frequently associated with adverse reactions 55 . Recorded adverse side effects of benzalkonium chloride include but are not limited to trabecular meshwork cell apoptosis, conjunctival and anterior chamber inflammation, corneal cytotoxicity, tear film instability, cataract development and macular oedema 56 – 58 . Yet the effects of these medications on gene expression profile remain largely unknown. The small sample size of this study was also a limitation. Although a sample size of 3 patients is within current practice for genomic studies a larger sample size would increase the statistical power of the findings. Future knock down studies would be useful to validate the DEGs identified in this study to identify their suitability as targets for drug or gene therapy. In investigating a model of HTF transition into myofibroblasts at the transcriptomic level, we gained greater insight into the novel molecular interactions and causal pathways of ocular fibrosis. This study establishes a gene signature of myofibroblast activation from HTFs that is uniquely characterised by genes involved in regulation of myofibroblast differentiation, collagen fibril organization, cell cycle arrest, TGFβ-signaling pathways, and wound healing organization. The results of this study establish an essential milestone for the future development of effective antifibrotic therapies targeting scar tissue formation following GFS. Methods HTF Isolation Isolation of HTFs was conducted in compliance with guidelines approved by the institutional ethics committee (The Royal Victorian Eye and Ear Hospital Human Research Ethics Committee project no. 16/1294H). Following explanation of the nature of the study, informed consent was obtained from patients. HTFs were propagated from explanted subconjunctival Tenon’s capsules collected during GFS performed in three patients and handled in accordance with the tenets of the Declaration of Helsinki. Cell Culture HTFs were maintained in Dulbecco’s Modified Eagle Medium (DMEM; Sigma- Aldrich) containing Gibco Penicillin–Streptomycin– Glutamine (Thermo Fisher Scientific) and 10% fetal calf serum (FCS; Cytiva, Marlborough, MA) at 37°C with 5% CO 2 in 50 mL and 160 mL cell culture flasks (Greiner Bio-One). Human Tenon’s Fibroblasts were passaged when flasks achieved 80% confluency by dissociating monolayers with a Trypsin-EDTA solution (0.25%; Sigma-Aldrich). Fibroblasts obtained on passages 6 through 12, after initial cultures were established, were used. Phenotype verification of the cultured HTFs was achieved with the use of anti–fibroblast-specific protein 1 (S100A4) antibody (1:100; ABF32; Merck Millipore, Bayswater, VIC, Australia)(Supplementary Fig. 1). HTFs (approx. 40,000 cells/well) were seeded onto coverslips in 6-well Nunc cell culture plates (Thermo Fisher Scientific). Then HTFs were serum-starved in DMEM with 1% FCS overnight in preparation for treatment. HTFs were then assigned to either the control (medium alone) or the TGFβ1 (10 ng/mL) treatment group. Following treatment, HTFs were fixed with 4% paraformaldehyde (PFA; P6148; Sigma-Aldrich, CA, USA) over a range of time-points (from Days 3–14). Immunocytochemical/ Immunofluorescence Staining Following fixation HTFs were rinsed with Dulbecco’s Phosphate Buffered Saline (DPBS; Gibco) and permeabilised with 0.1% Triton X-100 (Sigma-Aldrich, St Louis, MO, USA) for 5–10 minutes and rinsed again with PBS. In order to prevent non-specific binding of the primary antibody, a blocking solution, UltraVision Protein Block (TA-125-PBQ; ThermoFisher Scientific, CA, USA), was applied to coverslips for 30 mins. HTFs on coverslips were then left to incubate overnight at 4°C with the primary anti-’α-SMA antibody (M0851; Dako, CA, USA). Following incubation period, HTFs on coverslips were rinsed thoroughly with PBS and were treated with a fluorophore-labelled secondary antibody, Alexa Fluor 568 goat anti-mouse IgG (H + L) (A11004; Invitrogen, OR, USA), for 1 hour in the dark at room temperature (22°C), then rinsed again with PBS. Nuclear counterstaining was conducted with the application of 4′,6-diamidino-2-phenylindole (DAPI) (1:1000; ab228549; Abcam, Cambridge, UK) for 10 mins in the dark at room temperature. Finally, coverslips were mounted onto microscope slides with DPX mounting media (20242; Labworks, Knox City Centre, VIC, Australia). All slides were observed at 20x magnification using a fluorescence microscope (Axio Scope.A1 with Axiocam 105 colour; Zeiss, Oberkochen, Germany) and analysed with corresponding software (Zen 3.1- blue edition, Zeiss, Oberkochen, Germany). Real Time qPCR RNA extraction was performed using illustra RNAspin Mini Kit (GE Healthcare Life Sciences), following manufacturer’s instructions. RNA concentration and quality were measured using SimpliNano (GE Healthcare Life Sciences). cDNA was synthesised by reverse transcription of RNA. RT-qPCR was performed using TaqMan™ Fast Advanced Master Mix (4444554; Thermo Fisher Scientific, Lithuania) and Taqman gene expression assay probe for ACTA2 (Hs00426835_g1) and the housekeeping gene 18s rRNA control mix (4318839; Applied Biosystems, Kingsland Grange, Woolston, Warrington, UK). RT-qPCR was performed on QuantStudio™ 7 Flex Real-Time PCR System (Thermo Fisher Scientific, Scoresby, VIC, Australia), following manufacturer’s instructions. The delta delta Ct (ΔΔCT) method was used to calculate and compare relative mRNA levels to control. Data were expressed as mean ± standard deviation (SD) and were analysed with two-way analysis of the variance (ANOVA) followed by post hoc Šídák's multiple comparisons test. A value of P < 0.05 was regarded as statistically significant. RNA Sequencing RNA was extracted using the Illustra RNAspin Mini Kit (GE Healthcare Life Sciences) according to manufacturer’s instructions. RNA quality was checked by bioanalyzer, followed by library construction using the TruSeq Stranded mRNA kit (Illumina) and sequenced using Novaseq 6000 (Illumina) with 100bp single-end sequencing, at a depth of ~ 20 million reads per sample (Australian Genome Research Facility). Bioinformatic analysis Following the abundance estimates of transcripts generated by Salmon v1.8, the pseudocounts were mapped to the GRCh38 genome assembly using the tximport v1.22.0 package 59 . The gene count matrix was inputted as an DESeq2Dataset object using the DESeqDataSetFromTximport function, then the DESeq2Dataset object was normalised using the counts function to make fair gene expression comparisons between samples 60 . The normalised dataset was analysed with the DESeq2 v1.34.0 R package using rlog transformation. The sample-level quality assurance and distribution bias was measured using principal component analysis while the gene-level QC was performed using hierarchical clustering. For differential expression analysis, the significant differentially expressed genes were determined using the filter function with adjusted p value of 2.0. The expression data of significant differentially expressed genes was visualised using the ggplot2 v3.3.6, pheatmap v1.0.12 and EnhancedVolcano v1.12.0 R package 61 – 63 . GO enrichment analysis was performed to investigate relationships between significantly expressed genes and their cellular component, biological process, and molecular function. Only terms with a p-value < 0.01 were considered significant. Network topology analysis was performed using Enrichr with the top 50 upregulated DEGs 64 – 66 . Gene set enrichment analysis was performed using GSEA 4.3.2 and MSigDB 2023.1 (UC San Diego and Broad Institute) 67 , 68 a false-discovery rate of q-value < 0.25 and a p-value < 0.01 were considered significant. The MSigDB gene sets genes known to be significant in the following pathways: cell cycle phase and proliferation, TGFB signaling, Wnt signaling, NFKb pathway, fibroblast markers, ECM organization, wound healing regulation, myofibroblast differentiation, collagen fibril organization, cell cycle regulation, and ocular fibroblast markers. Further GSEA and pathway analysis was conducted using msigdbr 69 and clusterProfiler packages 70 . KEGG network topology for the top 50 DEGs was performed using Enrich to obtain gene and KEGG term interactions 65 . Declarations Disclosure statement There are no conflicts of interest to disclose Data availability The transcriptome data generated in this study are available in the NCBI Gene Expression Omnibus database (GSE accession pending), including raw data and processed data. Additional interests Competing interests The authors declare no competing interests. Author Contribution Conceptual design: ECC, RCBW, JG; Conduct experiments: ZP, RK, JG; Data analysis: AB, RCBW, ECC, JG, ZP; Funding: JG, RCBW; Manuscript writing & review: ZP, AB, RCBW, ECC, JG. All authors approved the manuscript. Acknowledgement This work was supported by the Royal Victorian Eye and Ear Hospital Early Research Career Support Grant (JFG). ZP and AB are supported by an Australian Government Research Training Program Scholarship. RCBW is supported by the University of Melbourne, and the Centre for Eye Research Australia, National Health and Medical Research Council (GCT1184076) and Medical Future Research Fund (MRF2024365). The Centre for Eye Research Australia acknowledges the Victorian State Government’s Department of Innovation, Industry and Regional Development’s Operational Infrastructure Support Program. References Fan Gaskin, J. C., Nguyen, D. Q., Soon Ang, G., O’Connor, J. & Crowston, J. G. Wound Healing Modulation in Glaucoma Filtration Surgery-Conventional Practices and New Perspectives: The Role of Antifibrotic Agents (Part I). J Curr Glaucoma Pract 8, 37–45 (2014). Khaw, P. T., Bouremel, Y., Brocchini, S. & Henein, C. The control of conjunctival fibrosis as a paradigm for the prevention of ocular fibrosis-related blindness. ‘Fibrosis has many friends’. Eye 34, 2163–2174 (2020). Hinz, B., McCulloch, C. A. & Coelho, N. M. 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PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003). Bhuva D Smyth G Garnham. An ExperimentHub Package for the Molecular Signature Database (MSigDB). Bioconductor (2023) doi: 10.18129/B9.bioc.msigdb . Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012). Additional Declarations No competing interests reported. Supplementary Files supplementaryinformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4008732","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":281824084,"identity":"5c68a706-493a-49a8-9a3a-f4c0f5e7a59b","order_by":0,"name":"Zoe Pasvanis","email":"","orcid":"","institution":"Centre for Eye Research Australia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zoe","middleName":"","lastName":"Pasvanis","suffix":""},{"id":281824085,"identity":"437d220d-0eeb-44c1-aaf5-61132001aa92","order_by":1,"name":"Antony Boynes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYPCCAxDqAxCzsRNUzYzQwjgDpIWZFC3MPHABPIB/dv/Bx4Vtd+QMbjcf/mzza5s8HzMD44ePObi1SNw5zGw8s+2ZscGdY2nSuX23DduYGZglZ27DY82NZDZp3rbDiRtu5Jgx5/bcZgRqYWPmxaNFHqEl//Nny57b9gS1GCDZwiDN8ON2IkEthjeSjY15zj0zlryRZibZ23A7uY2ZsRmvX+RuJD58zFN2R47vRvLjDz/+3Lad39588MNHfN4HAUY2BgaFAyBGG5jbQEA9CPwBhkMDlDEKRsEoGAWjAB0AAN5+VOAicddBAAAAAElFTkSuQmCC","orcid":"","institution":"Centre for Eye Research Australia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Antony","middleName":"","lastName":"Boynes","suffix":""},{"id":281824086,"identity":"dd24262d-dea4-4a29-a8bc-91d729af5306","order_by":2,"name":"Roy C.K. 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(a) \u003c/strong\u003eFold change in expression of gene ACTA2 (encoding αSMA) in HTFs over 3-6 days following control and TGFβ treatment, n=3. \u003cem\u003e* Indicates significance (P\u0026lt;0.05, one-way ANOVA). \u003c/em\u003e\u003cstrong\u003e(b)\u003c/strong\u003e HTF cell morphology 3-6 days following control and TGFβ treatment. αSMA stress fibres of myofibroblasts (red) identified by immunofluorescence microscopy (magnification 20x) with use of anti-αSMA and fluorophore-labelled secondary antibody. Nuclei counterstained with DAPI (blue).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4008732/v1/d9c3011fa378fbf26bb4ddb1.png"},{"id":53185709,"identity":"cfc434a0-1ea1-4cac-b98f-963744df4a58","added_by":"auto","created_at":"2024-03-21 16:11:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssessment of TGF\u003c/strong\u003eβ \u003cstrong\u003etreatment group variability. a) \u003c/strong\u003ePrincipal Component Analysis (PCA) plot displaying 6 samples along PC1 and PC2, which describe 57% and 21% of the variability within the expression data set respectively. PCA was applied to data following rlog transformation of normalised counts using DESeq2. Treatment group specified by symbol colour, blue = untreated control HTFs, red = TGFβ1-treated HTFs. \u003cstrong\u003e(b)\u003c/strong\u003eHierarchical clustering map displaying correlation of gene expression for all pairwise combinations of samples. Hierarchical clustering was applied to data following rlog transformation of normalised counts (units) using DESeq2. CX=control and TGF=TGFβ1 (10 ng/mL for 5 days).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4008732/v1/212d8d2fc067ad796336914a.png"},{"id":53185715,"identity":"eff479bd-e2f5-4791-8742-70df5e36fc6d","added_by":"auto","created_at":"2024-03-21 16:11:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":464732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed genes (DEG) in HTF following TGFβ1 treatment. (a)\u003c/strong\u003e Hierarchical clustering DEG heatmap by sample and transcripts on all significant genes using normalized counts (\u003cstrong\u003eb)\u003c/strong\u003e Dot plot of top 20 DEGs by p adj values of 0.05 inclusive of up and down regulated genes ordered alphabetically.\u003cstrong\u003e (c\u003c/strong\u003e) Volcano plot with a default log\u003csub\u003e2 \u003c/sub\u003efold change value of \u0026gt;2 or \u0026lt;-2 and 10e\u003csup\u003e-32 \u003c/sup\u003ep value cut off. Red dots on the right side of the plot indicate genes with significantly upregulated expression, while red dots on the left side of the plot indicate genes with significantly downregulated expression.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4008732/v1/c7a29d54a4e3a3bbc05d65ed.png"},{"id":53185716,"identity":"85333453-ec45-4830-8a24-cf9411e7c916","added_by":"auto","created_at":"2024-03-21 16:11:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":172294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell cycle and proliferation gene expression in HTF following TGFβ1 treatment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell cycle and proliferation marker genes. Dot plot of normalized counts for known cell cycle and marker gene expression in control (CX2202, CX2203, CX2207) and treatment groups (TGF2202, TGF2203, TGF2207) by biological replicate with padj cut-off of 0.01. All genes were downregulated in the treatment group aside from TMPO which was upregulated (identified by dashed rectangular outline).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4008732/v1/b168e0970c7037629d8e38b5.png"},{"id":53185718,"identity":"c358076b-eae8-44e4-a06a-1c0262c00447","added_by":"auto","created_at":"2024-03-21 16:11:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":681962,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene ontology analysis of HTF following TGFβ1 treatment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO and KEGG pathway enrichment analysis TGFβ1 treatment group compared to control. \u003cstrong\u003e(a)\u003c/strong\u003e Dotplot shows the activated (upregulated) and suppressed (downregulated) GO terms of biological function associated with all DEGs. The size of the dot is based on gene count enriched in the pathway, and the colour of the dot shows the pathway enrichment significance. \u003cstrong\u003e(b)\u003c/strong\u003e Dotplot shows the activated (upregulated) and suppressed (downregulated) GO terms of molecular function associated with all DEGs. \u003cstrong\u003e(c)\u003c/strong\u003e Bar chart of top 10 enriched GO biological function for top 50 DEGs ordered by p-value. \u003cstrong\u003e(d)\u003c/strong\u003eDotplot shows the activated (upregulated) and suppressed (downregulated) GO terms of cellular component associated with all DEGs. (e) Bar chart of top 10 enriched GO molecular function for the top 50 DEGs ordered by p-value.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4008732/v1/ffc2660a5431aac6b5fcf7a8.png"},{"id":53186379,"identity":"000e5ed3-151a-420f-a210-c298adae96e3","added_by":"auto","created_at":"2024-03-21 16:19:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1164251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA using KEGG gene set in HTF following TGFβ1 treatment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG pathway map for TGF-B signaling pathway. Rectangles correspond to genes or enzymes plotted by fold changes red = increased fold change, green = decreased, and grey = not available. Colour key gives a range from -1 to 1 and values beyond that range are converted to closest extreme e.g. values \u0026gt;1 converted to 1.\u003cstrong\u003e (a)\u003c/strong\u003e KEGG pathway map for TGF-B signaling. \u003cstrong\u003e(b)\u003c/strong\u003eKEGG pathway map for Wnt signaling pathway. \u003cstrong\u003e(c)\u003c/strong\u003e KEGG pathway map for cellular adhesions molecules pathway.\u003cstrong\u003e (d)\u003c/strong\u003e Network topology of the top 200 up-regulated DE genes to KEGG pathways.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4008732/v1/a1aede6e0e6736a3db4d71eb.png"},{"id":53185710,"identity":"1d6d0593-7e1b-4819-8602-05e3e43fec56","added_by":"auto","created_at":"2024-03-21 16:11:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1054812,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA analysis in HTF following TGF-B1 treatment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene set enrichment analysis for TGF-B treatment verse control.\u003cstrong\u003e (a)\u003c/strong\u003eGSEA plot of myofibroblast differentiation with GSEA standard hallmark gene set with heatmap of gene expression red = upregulated, and blue = downregulated. The enrichment score (ES) is indicated. \u003cstrong\u003e(b)\u003c/strong\u003e GSEA plot for collagen fibril organisation using GSEA standard hallmark gene set. \u003cstrong\u003e(c)\u003c/strong\u003e GSEA plot for cell senescence using GSEA standard hallmark gene set. \u003cstrong\u003e(d)\u003c/strong\u003e GSEA plot for TGFB pathway using GSEA standard hallmark gene set.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4008732/v1/aad16c0342572736814bbbd5.png"},{"id":60880694,"identity":"9c3a63c4-0c6b-4c66-965e-4eb48e2b4613","added_by":"auto","created_at":"2024-07-23 07:04:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6466081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4008732/v1/c8b6fc8a-2531-44df-af61-37245a508f55.pdf"},{"id":53185713,"identity":"9913b4c7-11be-4769-9841-2a0b082dbc33","added_by":"auto","created_at":"2024-03-21 16:11:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2989257,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4008732/v1/e42e0e975432e5d4abee00b2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic analysis of TGFβ-mediated fibrosis in primary human Tenon’s fibroblasts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlaucoma filtration surgery (GFS) is the gold-standard surgical procedure performed to lower intraocular pressure (IOP) in glaucomatous eyes. GFS allows for a gradual egression of aqueous humor from the anterior chamber of the eye into a surgically created subconjunctival space, resulting in the formation of a filtering bleb. As a result of surgical intervention, the body\u0026rsquo;s natural wound healing response is activated. In some cases, the magnitude of this response can be excessive, such that pathological wound healing is observed. This can lead to the postoperative complication of conjunctival fibrosis and surgical failure. Despite the use of adjunctive antimetabolite agents to control scarring, the operation still has a 50% failure rate over 5 years \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Moreover, the standard-of-care antimetabolite agents are nonspecific, cytotoxic cancer drugs such as Mitomycin C (MMC) that can result in toxicity of surrounding tissue, long-term wound breakdown and predispose the eye to severe infections that lead to blindness and even loss of the eye \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Therefore, there is an unmet medical need to develop novel anti-scarring strategies.\u003c/p\u003e \u003cp\u003eOne of the key effector cells responsible for postoperative conjunctival scarring is the human Tenon\u0026rsquo;s fibroblast (HTF). In an environment of injury and inflammation, as can be produced by GFS, HTFs are activated via TGFβ signalling. As a result, HTFs change functionally and phenotypically into myofibroblasts, which are specialised contractile fibroblasts that produce excess ECM components such as collagens and fibronectin \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Myofibroblasts are typically characterised by microfilament bundles expressing high levels of alpha smooth muscle actin (αSMA) that are organised into stress fibres; they are also vital for mediating contractile function as part of the wound healing response\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. While myofibroblasts are a vital component of physiological wound healing, an abundant accumulation at the surgical site is one of the major causes of surgical failure \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Interestingly Jeon et al. have recently shown that remodelling myofibroblasts to a fibroblast-like phenotype reduces fibrosis in a feline model of established corneal scarring \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, hence targeting myofibroblasts by manipulating phenotypic changes may well be an effective way to limit fibrosis in post-GFS scarring. Since there has been no study investigating the molecular mechanism of fibroblast-to-myofibroblast transition by HTF, the aim of the study is to establish the genomic profiles of TGFβ1-induced conversion of HTF to myofibroblasts using RNA-sequencing (RNA-seq).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTransition of HTF to Myofibroblast\u003c/h2\u003e \u003cp\u003eUsing primary HTF, we first performed time-point analysis to determine the peak expression of the marker of myofibroblasts, αSMA. We treated HTFs, derived from 3 GFS patients, with TGFβ1 for 3\u0026ndash;6 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Following real time qPCR, expression of \u003cem\u003eACTA2\u003c/em\u003e (which encodes αSMA) in TGFβ groups was significantly increased when compared to control at Day 4 \u0026amp; Day 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). In addition, we observed morphological change of HTFs into myofibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplementary Fig.\u0026nbsp;2), where presence of αSMA (red) in collagen fibres is more pronounced, most noticeably occurs at Day 5. Collectively this data suggests that peak activity of HTF to myofibroblast transition occurs five days after treatment with TGFβ.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRNA-Seq analysis of myofibroblast transition\u003c/h2\u003e \u003cp\u003eSince the peak phenotypic changes of fibroblasts to myofibroblasts occurred at day 5 following treatment with TGFβ1, we performed RNA-seq on HTFs from both control (CX2202, CX2203, CX2207) and 5-day TGFβ1 (TGF2202, TGF2203, TGF2207) treatment groups. We first explored the similarity of our samples using principal component analysis (PCA) and hierarchical clustering. Both analyses demonstrate a significant treatment effect of TGFβ1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). PCA analysis presents the variability within the expression data set according to principal components, where we observed biological repeats of the TGF treated group or control group cluster together, supporting similarities between biological repeats (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Similarly, hierarchical clustering map displays a correlation of gene expression for all pairwise combinations of biological repeats of the treatment group. Samples having correlation values of \u0026gt;\u0026thinsp;0.9 suggest there is no outlying sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Together these plots suggest that the data are of good quality, and that it is appropriate to proceed with further differential expression analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Gene Expression\u003c/h2\u003e \u003cp\u003eTo investigate the impact of TGFβ on HTFs activation into their myofibroblast phenotype, we performed RNA-seq on primary HTFs. A total of 35691 genes were identified in all samples. Following DESeq2 analysis and hierarchical clustering, there were 3362 differentially expressed genes (DEGs) identified, of which 1532 were upregulated and 1820 were downregulated in HTF following TGFβ1 treatment (with an adjusted p value of \u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). ​​ The top 20 DEGs between the two groups are visualized in both a dotplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) and volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The normalised dot plot visualises the differential expression of the top 20 genes in our gene set by relative position (determined by log10 normalised counts) of TGFβ treatment group sample dots (blue, pink and teal) in comparison to control group sample dots (red, yellow and green) within each column. Blue, pink and teal dots located above red, yellow and green dots indicates upregulated expression of the gene in the TGFβ treatment group, whereas the inverse indicates downregulated expression of the gene in the TGFβ treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Our results indicated that TGFβ1 treatment in HTF upregulated a number of genes including \u003cem\u003eLDLRAD4, SCUBE3, TXNDC5, CDKN2B, DACT1, DYNC1I1, FZD8, FOXP4, PXDC1, MYOZ1, LANCL2, FBLN5\u003c/em\u003e, while downregulated genes include \u003cem\u003eIGSF10, SVIL, SECTM1, LTBP4, TNXB, RCAN2, CXCL12\u003c/em\u003e and \u003cem\u003eSOD3\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTGFβ Promotes Cell Cycle Arrest in HTFs\u003c/h2\u003e \u003cp\u003eTGFβ is known to cause cell senescence in many cell types but may up or downregulate cell proliferation, and cell death in others \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. We investigated cell cycle regulatory genes to further understand their role in TGFβ-mediated HTF differentiation or activation. We identified several cell cycle related gene sets from the Molecular Signatures Database as well as our own laboratory dataset \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Tumour suppressor genes such as \u003cem\u003eCDKN2B\u003c/em\u003e were highly upregulated (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) while \u003cem\u003eTMPO\u003c/em\u003e, a cell cycle proliferator, was upregulated in the TGFβ treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGene ontology and functional analyses\u003c/h2\u003e \u003cp\u003eNext, we performed gene ontology (GO) analysis to understand the functions of the DEG following TGFB1 treatment in HTFs. GO enrichment analysis results revealed top significant enrichment pathways were processes involved in fibrosis including extracellular matrix (ECM) organization, external encapsulating structure organization, collagen fibril organization, and actin filament bundle assembly (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Interestingly our results showed TGFβ regulates several signaling pathways such as Smad phosphorylation, Wnt signaling pathway, and response to cytokines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). These findings suggest multiple profibrotic pathways in the fibroblast to myofibroblast transition and confirm previous findings that Wnt pathway and Smad signaling promotes fibroblast to myofibroblast transition potentially through cytokines such as IL-11\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Regulation of the cell cycle process and regulation of the immune response were highly enriched terms suggesting TGFβ signaling controls cell proliferation and survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Cellular component terms reveal interesting insight into the molecular processes mediated by TGFβ. The majority of molecular functions suppressed by TGFβ were found to occur within the cell nucleus and be involved in processes including regulation of cell cycle process and mitotic cell cycle process. Conversely, the activation effects on peptidyl-proline 4-dioxygenase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and b) and regulation of collagen occur within the collagen network (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). These findings suggest that fibroblast to myofibroblast transition is regulated by the ECM microenvironment and TGFβ promotes cell senescence through transcriptional induction of the expression of cell-cycle inhibitors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKEGG enrichment analyses revealed top enrichment pathways included TGFβ signaling pathway, Wnt signaling pathway, cell adhesion molecules and NF-kappa B signaling pathway. BMP, LTBP1, Smad Anchor for Receptor Activation (SARA) and \u003cem\u003eSMAD4\u003c/em\u003e were shown to play important roles in the TGFβ-signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Genes from the Frizzled and Smad families were found to be involved in the Wnt-signaling pathway. \u003cem\u003eSMAD3\u003c/em\u003e and \u003cem\u003eSMAD4\u003c/em\u003e specifically were found to play a role in TGFβ, and Wnt-pathway mediated cell cycle arrest (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). Cell adhesion molecules showed minor downregulation for the majority of cell types such as macrophages, T cells, and B cells. However, there was significant upregulation of myoblasts cell adhesion molecules predominantly driven by Cadherin-2 (\u003cem\u003eCDH2\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). Cadherin expression and cell to cell adhesion play a critical role in transition between cellular states. Here we show \u003cem\u003eCDH2\u003c/em\u003e as a key mediator of intercellular adhesion between myofibroblasts. KEGG network topology showed the crossover between TGFβ-signaling and Smad pathway signaling by \u003cem\u003eLEFT2\u003c/em\u003e, \u003cem\u003eBMP6, INHBA\u003c/em\u003e and the interaction between TGFβ-signaling and Wnt-signaling by \u003cem\u003eINHBA, INHBE\u003c/em\u003e, and \u003cem\u003eFZD8\u003c/em\u003e. As expected, genes encoding for collagen synthesis were responsible for ECM organization (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). Collectively, these results suggest a gene signature for myofibroblasts following exposure to TGFβ is built upon gene expression patterns in collagen fibril organisation, ECM organisation, cell senescence, cell adhesion, TGFβ signalling, and regulation of pathway restricted Smad phosphorylation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, Gene set enrichment analysis (GSEA) was performed to integrate the DEGs from the TGFβ treatment group in an effort to identify top canonical pathways enriched with publicly available KEGG and GSEA-hallmark gene sets. Myofibroblast differentiation had the highest normalized enrichment score with the highest enriched genes including \u003cem\u003eALDH1B1, LOXL2, NEXN, COTL1\u003c/em\u003e, and \u003cem\u003eC5orf46\u003c/em\u003e (Supplementary table 1)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). Genes encoding for collagen synthesis including \u003cem\u003eP4HA2\u003c/em\u003e and \u003cem\u003eP4HA3\u003c/em\u003e were enriched in the collagen fibril organization enrichment plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb) (Supplementary table 1). Tumor suppressors such as \u003cem\u003eCDKN2B\u003c/em\u003e and oncogenes such as \u003cem\u003eCCNA1\u003c/em\u003e were highly enriched in cell senescence (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec) (Supplementary Table\u0026nbsp;1). \u003cem\u003eCTGF, TGFβ1, TGFβ3\u003c/em\u003e, and \u003cem\u003eSMAD7\u003c/em\u003e were highly enriched in the TGFβ-pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed) (Supplementary table 1). \u003cem\u003eMYOZ1\u003c/em\u003e was found to be significantly enriched in wound healing organization (Supplementary Fig.\u0026nbsp;3a), and \u003cem\u003eFBLN5\u003c/em\u003e was significantly enriched in extracellular matrix interaction cell adhesion and within the top 20 significant DEGs (Supplementary Fig.\u0026nbsp;3b). Families of MMP and collagen genes were highly enriched in extracellular matrix organisation (Supplementary Fig.\u0026nbsp;3c). Altogether, our results identified the key genes regulating the myofibroblast transition of HTFs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents the first RNA-seq study of human Tenon\u0026rsquo;s fibroblast-to-myofibroblast activation, a critical TGFβ-dependent process involved in postoperative wound healing following glaucoma filtration surgery. Myofibroblast activity drives wound healing both physiologically and pathologically. The progression of fibrotic diseases and cancers is dependent on myofibroblasts that remain pathologically activated rather than undergo apoptosis and vascular pathologies \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. TGFβ signalling notably plays a critical role in maintenance of tissue homeostasis and is hence highly regulated at several steps in both intracellular and extracellular microenvironments \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. As this analysis was conducted in myofibroblasts at a 5-day timepoint post-TGFβ treatment, where HTFs expressed a myofibroblast phenotype, we expected our findings to reflect the direct and indirect effects of TGFβ on activation and inhibitory signalling in myofibroblasts.\u003c/p\u003e \u003cp\u003eGrowth factors and reactive oxidants are released from damaged cells and trigger the activation and proliferation of fibroblasts \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCTGF\u003c/em\u003e is one such growth factor that has been found to play an important role in TGFβ-dependent myofibroblast differentiation and ECM production, indicated by its upregulated expression being highly enriched in the TGFβ-pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed). As an important downstream mediator of TGFβ, \u003cem\u003eCTGF\u003c/em\u003e has been found to influence myofibroblast differentiation in orbital fibroblasts through increasing of fibronectin and a-SMA protein expression\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Hence \u003cem\u003eCTGF\u003c/em\u003e may provide a safer target for suppression therapy compared to TGFβ. \u003cem\u003eACTA2\u003c/em\u003e appears to be important in myofibroblast contraction and migration. \u003cem\u003eACTA2\u003c/em\u003e is involved in the regulation of multiple genes relevant to fibrosis, including \u003cem\u003eCOL1, GFAP, TIMP1, TGFβ\u003c/em\u003e, and \u003cem\u003eET1\u003c/em\u003e \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e13\u003c/span\u003e\u003c/sup\u003e. We found \u003cem\u003eNOX4\u003c/em\u003e, a gene encoding for reactive oxygen species NADPH oxidase 4 enzyme, was upregulated by TGFβ. NOX4-dependent generation of hydrogen peroxide is a requirement for TGFβ induced myofibroblast differentiation, as well as ECM production\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eTXNDC5\u003c/em\u003e facilitates proper protein folding in the endoplasmic reticulum and mediates redox reactions via interacting with NADPH oxidase \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. These genes promote conjunctival fibrosis by activation of SMAD3-dependent TGFβ-signaling and lead to differentiation of HSCs into myofibroblasts, this results in considerable myofibroblast proliferation and ECM production \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Notably, we observed the downregulated expression of \u003cem\u003eSOD3\u003c/em\u003e in our TGFβ treated samples. As known ROS scavengers, SODs act to reduce oxidative stress in both extra- and intra- cellular environments, with \u003cem\u003eSOD3\u003c/em\u003e specifically acting in the ECM where it has been found to reduce intracellular ROS levels \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Hence our finding is consistent with findings of \u003cem\u003eSOD3\u003c/em\u003e deficiency contributing to liver fibrogenesis and TGFβ1 mediated EMT \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTGFβ utilizes components of the ECM such as fibronectin and integrins to communicate and control cell behaviour leading to myofibroblast differentiation. \u003cem\u003eEDN1, FN1, TNC\u003c/em\u003e and \u003cem\u003eITGA11\u003c/em\u003e were upregulated by TGFβ and have been reported in myofibroblast differentiation. \u003cem\u003eEDN1\u003c/em\u003e has been shown to induce resistance to apoptosis in fibroblasts and contribute to fibrogenesis by the abnormal persistence of the myofibroblast phenotype \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. TGFβ can induce expression of fibronectin 1 extra domain A (FN1 EDA) in fibroblasts. \u003cem\u003eFN1\u003c/em\u003e is not expressed in healthy tissue but is expressed during wound healing, fibroblasts detect FN1 EDA and its presence is required for TGFβ-mediated myofibroblast formation \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eTNC\u003c/em\u003e encodes for tenascin-C, an ECM glycoprotein that has been shown to engage integrins to elicit cell specific responses such as fibrotic responses including collagen synthesis and differentiation of myofibroblasts \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eTNC\u003c/em\u003e was upregulated in the TGFβ sample and has been shown to induce myofibroblast differentiation and migration \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Integrins are the main cell-adhesion transmembrane receptors and bind proteins in the ECM such as fibronectin and transduce biochemical and mechanical signals\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eITGA11\u003c/em\u003e was the most significantly upregulated integrin in our data and has been shown to co-localize with a-smooth muscle actin-positive myofibroblasts and was correlatively induced with increasing fibrogenesis in human fibrotic organs. \u003cem\u003eITGA11\u003c/em\u003e knockdown has been shown to markedly reduce TGFβ-induced differentiation and fibrotic parameters \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Our findings are consistent with other studies that reported TGFβ upregulation of \u003cem\u003eITGA11\u003c/em\u003e and \u003cem\u003eCTGF\u003c/em\u003e predominantly binding \u003cem\u003eITGA11\u003c/em\u003e\u003csup\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e23\u003c/span\u003e\u003c/sup\u003e. We suggest that \u003cem\u003eITGA11\u003c/em\u003e is a key regulator of myofibroblast differentiation in HTF and this association may hold potential as a therapeutic target.\u003c/p\u003e \u003cp\u003eElastic fibre proteins play a pivotal role in guiding and facilitating elastogenesis involved in wound healing. The elastic fibre proteins implicated in this process include fibulins, fibronectins, fibrillins and LTBPs\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. In our TGFβ sample, we recorded upregulated gene expression of \u003cem\u003eFBLN5, FBN1, FN1\u003c/em\u003e, and \u003cem\u003eLTBP1\u003c/em\u003e, alongside downregulated gene expression of \u003cem\u003eLTBP4\u003c/em\u003e and \u003cem\u003eFLBN7\u003c/em\u003e. Of particular interest was \u003cem\u003eFBLN5\u003c/em\u003e which was in the top 20 upregulated DEGs. \u003cem\u003eFBLN5\u003c/em\u003e encodes the matricellular protein fibulin-5, which performs a vital role in elastogenesis and dysregulated expression of \u003cem\u003eFBLN5\u003c/em\u003e has been reported to occur in pseudoexfoliation glaucoma\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. These findings correlate with the observed upregulated expression of \u003cem\u003eFBLN5\u003c/em\u003e being highly enriched in the EMICA pathway.\u003c/p\u003e \u003cp\u003eTypically, TGFβ is secreted in a form that is covalently bound to members of the latent TGFβ-binding protein (LTBP) family, observed as a large latent complex. It is via interactions between LTBPs, fibronectin and fibrillin that this complex can be deposited into the ECM \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Hence LTBPs function to regulate the bioavailability of TGFβ, by either localising latent TGFβ in the ECM, or secreting latent TGFβ from cells \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. In Lu et al.\u0026rsquo;s 2017 study of human sclerodermal skin fibroblasts, the knockdown of \u003cem\u003eLTBP4\u003c/em\u003e resulted in the inhibition of collagen expression through canonical TGFβ/SMAD signalling, while also reducing extracellular levels of TGFβ \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. However contradictorily, findings from Su et al.\u0026rsquo;s recent 2023 \u003cem\u003eLTBP4\u003c/em\u003e knockdown study in a murine model of renal fibrosis revealed that an \u003cem\u003eLTBP4\u003c/em\u003e deficiency promoted mitochondrial dysfunction, increased inflammation, oxidative stress via increased ROS production and fibrosis \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Hence, we suspect that the regulatory function of \u003cem\u003eLTBP4\u003c/em\u003e is complex and contextual, as it has been found capable of performing both anti- and pro-fibrotic functions. This makes \u003cem\u003eLTBP4\u003c/em\u003e a prime candidate for further research. In dermal fibroblasts, TGFβ exposure resulted in​​ increased deposition of fibrillin-1 and fibronectin into the ECM because of myofibroblast activation \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. This associates our observed upregulation of \u003cem\u003eFBN1\u003c/em\u003e and \u003cem\u003eFN1\u003c/em\u003e in our TGFβ treated HTFs with myofibroblast activation.\u003c/p\u003e \u003cp\u003eTGFβ mediated epithelial to mesenchymal transition is important for myofibroblast transition. Epithelial to mesenchymal transition causes several changes to cellular phenotype and cytoskeleton remodelling and increased cell migration. We identified several important genes interacting with TGFβ for this process. \u003cem\u003eNREP\u003c/em\u003e was upregulated in the TGFβ sample and regulates the expression of TGFβ1 and has been shown to regulate myofibroblast differentiation. \u003cem\u003eNREP\u003c/em\u003e is thought to stimulate the expression of TGFβ1 by the methylation of NREP promoter and activating TGFβ1 5\u0026rsquo;/3\u0026rsquo; UTR \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Knockdown studies of \u003cem\u003eSCUBE3\u003c/em\u003e, which was upregulated in the TGFβ sample, have implicated its involvement with lung cancer tumorigenesis and cancer metastasis. SCUBE3 binds to TGFβ type 2 receptor and activates TGFβ signalling triggering epithelial-mesenchymal transition \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eDACT1\u003c/em\u003e was upregulated in the TGFβ sample; it has been shown to inhibit the Wnt signalling pathway. Therefore, \u003cem\u003eDACT1\u003c/em\u003e may release inflammatory factors that were previously suppressed by Wnt and promote local inflammation \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Of the Wnt signalling pathways that were highly enriched in our gene set, the Wnt/Ca\u003csup\u003e2+\u003c/sup\u003e pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb) is known to play a critical role in profibrotic and proinflammatory processes including actin polymerisation, cell adhesion, cell migration and NFAT signalling \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. A key component of this pathway involves the activation of the serine-threonine phosphatase enzyme calcineurin (CaN). RCAN2, encoded by \u003cem\u003eRCAN2\u003c/em\u003e, has been identified as an inhibitor of the Wnt/Ca\u003csup\u003e2+\u003c/sup\u003e pathway by binding CaN and subsequently inhibiting CaN\u0026rsquo;s protein phosphatase activity\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Findings from our GO analysis is consistent with these previous findings as the molecular function of \u003cem\u003eRCAN2\u003c/em\u003e was found to be involved in protein phosphatase regulator activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee, GO:0019888). Hence, we suspect that our observation that \u003cem\u003eRCAN2\u003c/em\u003e is downregulated in the TGFβ sample is associated with myofibroblast activation through promotion of the Wnt/Ca\u003csup\u003e2+\u003c/sup\u003e signalling pathway.\u003c/p\u003e \u003cp\u003eThe regulation of cell cycle process and cell senescence pathways were significantly enriched and largely driven by genes \u003cem\u003eCDKN2, CCNA1, CDKN2A, CDKN2D\u003c/em\u003e, and \u003cem\u003eCDK4\u003c/em\u003e. TGFβ causes G1 phase cell cycle arrest and CDKN2B complexes with CDK4 to prevent CDK4 activation and producing cell cycle arrest \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. These genes are known cell cycle regulators and CDKN2b/p15 has been reported to take part in multiple pathologies such as primary open angle glaucoma and cardiac fibrosis\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Interestingly, TGFβ treatment resulted in downregulation of the majority of cell cycle and proliferation marker genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The only exception to this observation was the upregulated expression of both the tumour suppressor gene \u003cem\u003eCDKN2B\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) and the cell cycle proliferator gene \u003cem\u003eTMPO\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Lung carcinogenesis has been linked to upregulated expression of \u003cem\u003eTMPO\u003c/em\u003e \u003csup\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, while knockdown of \u003cem\u003eTMPO\u003c/em\u003e in glioblastomas has been found to inhibit apoptosis-dependent cell proliferation and arrest cell cycle progression at the G2/M Phase \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Collectively these findings are consistent with other studies reporting the role of TGFβ as a tumour suppressor and inhibitor of cell proliferation, such that cell cycle arrest and apoptosis evasion occur \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCDK6\u003c/em\u003e is a known cell cycle regulator for G1 to S phase transition. Dysregulated expression of \u003cem\u003eCDK6\u003c/em\u003e has been reported in a variety of malignancies. Palbociclib is a commercially available selective inhibitor of \u003cem\u003eCDK6\u003c/em\u003e which has shown significant inhibition of myeloproliferative neoplastic progenitors and cells, and amelioration of bone marrow fibrosis in myelofibrosis \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. These findings are consistent with other studies and highlight the biological effects of TGFβ at the cellular level.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTGFβ1\u003c/em\u003e and \u003cem\u003eTGFβ3\u003c/em\u003e were upregulated in the TGFβ treatment group. The 3 TGFβ isoforms have highly homologous receptor-binding domains and similar effects on target cells but with divergent primary amino acid sequences in the latency-associated peptide (LAP) domains. Isoform-specific function may be related to different expression in cell types and different extracellular environments \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eTGFβ1\u003c/em\u003e was enriched in pathways such as TGFβ-pathway, myofibroblast differentiation, and collagen fibril organisation whereas \u003cem\u003eTGFβ2\u003c/em\u003e and \u003cem\u003eTGFβ3\u003c/em\u003e were not. This suggests that HTF induced fibrosis is largely a \u003cem\u003eTGFβ1\u003c/em\u003e driven process. \u003cem\u003eTGFβ3\u003c/em\u003e appears to play a role in regulation of epithelial to mesenchymal transition and TGFβ-pathway rather than myofibroblast differentiation which is consistent with previous reports of TGFβ3 expression being restricted to mesenchymal cells\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings highlight the important molecular mechanisms of TGFβ-signaling, particularly positive and negative regulation of pathway-restricted Smad protein phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec; GO:0060393 \u0026amp; GO:0060394). The main genes upregulated in this biological process were \u003cem\u003eGDF10, PMEPA1, LDLRAD4, INHBA, LEFTY2, BMP6\u003c/em\u003e and \u003cem\u003eSMAD7\u003c/em\u003e. Canonically TGFβ signal transduction occurs via activation of downstream mediators Smad2 and Smad3, which are negatively regulated by Smad7 as part of a negative feedback loop \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Smad7 inhibits phosphorylation of R-Smads(2/3) by competitively binding TβR-I activated by TβR-II \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. We speculate that the upregulated expression of \u003cem\u003eSMAD7\u003c/em\u003e is associated with the upregulated expression and enrichment of the \u003cem\u003eSMURF1\u003c/em\u003e gene as their encoded proteins regulate each other\u0026rsquo;s functions. For Smad7 to perform its inhibitory function it is required to interact with Smurf1 to be exported from the nucleus to the cytoplasm \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eINHBA\u003c/em\u003e encodes inhibin beta-A, which functions as a subunit of activin A and is classified as a ligand of the TGFβ superfamily \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eINHBA\u003c/em\u003e attenuation in renal fibroblasts has been found to significantly reduce their expression of profibrotic markers, inhibiting cell migration and proliferation \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. To promote the TGFβ signalling pathway, \u003cem\u003eINHBA\u003c/em\u003e is able to homodimerise and bind the activin type I/II receptor complex, which then can phosphorylate R-Smad proteins \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Hence, we suspect that upregulated \u003cem\u003eINHBA\u003c/em\u003e expression correlates with upregulated expression of \u003cem\u003eACVR1\u003c/em\u003e (encodes type I receptor, ALK2) and \u003cem\u003eACVR2B\u003c/em\u003e (encodes type II receptor) to promote Smad-dependent TGFβ signalling.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePMEPA1, LEFTY2\u003c/em\u003e, and \u003cem\u003eLDLRAD4\u003c/em\u003e are all negative regulators of TGFβ-signaling. \u003cem\u003ePMEPA1\u003c/em\u003e and \u003cem\u003eLDLRAD4\u003c/em\u003e are part of the PMEPAI-family genes and encode transmembrane proteins that have been found to facilitate TGFβ signalling inhibition by competitively targeting SARA through sequestration of the Smad2/3 protein complex and attenuating R-Smad complex recruitment to the TβR-1 for phosphorylation \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Once activated by TGFβ, \u003cem\u003eLEFTY2\u003c/em\u003e inhibits the phosphorylation of Smad2 and downstream heterodimerisation Smad4. Lefty also regulates the extracellular matrix components and inhibits the profibrotic effects of CTGF \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile the results achieved in this study advanced our understanding of the molecular mechanisms of ocular fibrosis following GFS, this study has its limitations. Although current literature supports RNA-seq analysis of HTF as a homogenous population, single cell RNA-seq would enable high resolution transcriptome analysis and potentially identify heterogeneity or subtypes within the HTF and myofibroblast population. This high-resolution transcriptome analysis could then lend to the identification of tissue biomarkers for ocular fibrosis, and together these tools could be instrumental in performing detailed clinical phenotyping. This would enable clinicians to not only identify groups of patients that are likely to scar more severely than others, but also work toward developing a tiered approach to antifibrotic therapy that is more personalised to the patient. Another limitation of the study was that specimen collection was performed at the time of GFS, from patients who were receiving medical therapy for glaucoma (Supplementary table 2). In using these glaucoma medications, most, if not all the patients were exposed to the preservative benzalkonium chloride, which is frequently associated with adverse reactions \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Recorded adverse side effects of benzalkonium chloride include but are not limited to trabecular meshwork cell apoptosis, conjunctival and anterior chamber inflammation, corneal cytotoxicity, tear film instability, cataract development and macular oedema \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Yet the effects of these medications on gene expression profile remain largely unknown. The small sample size of this study was also a limitation. Although a sample size of 3 patients is within current practice for genomic studies a larger sample size would increase the statistical power of the findings. Future knock down studies would be useful to validate the DEGs identified in this study to identify their suitability as targets for drug or gene therapy.\u003c/p\u003e \u003cp\u003eIn investigating a model of HTF transition into myofibroblasts at the transcriptomic level, we gained greater insight into the novel molecular interactions and causal pathways of ocular fibrosis. This study establishes a gene signature of myofibroblast activation from HTFs that is uniquely characterised by genes involved in regulation of myofibroblast differentiation, collagen fibril organization, cell cycle arrest, TGFβ-signaling pathways, and wound healing organization. The results of this study establish an essential milestone for the future development of effective antifibrotic therapies targeting scar tissue formation following GFS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHTF Isolation\u003c/h2\u003e \u003cp\u003e Isolation of HTFs was conducted in compliance with guidelines approved by the institutional ethics committee (The Royal Victorian Eye and Ear Hospital Human Research Ethics Committee project no. 16/1294H). Following explanation of the nature of the study, informed consent was obtained from patients. HTFs were propagated from explanted subconjunctival Tenon\u0026rsquo;s capsules collected during GFS performed in three patients and handled in accordance with the tenets of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell Culture\u003c/h2\u003e \u003cp\u003eHTFs were maintained in Dulbecco\u0026rsquo;s Modified Eagle Medium (DMEM; Sigma- Aldrich) containing Gibco Penicillin\u0026ndash;Streptomycin\u0026ndash; Glutamine (Thermo Fisher Scientific) and 10% fetal calf serum (FCS; Cytiva, Marlborough, MA) at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e in 50 mL and 160 mL cell culture flasks (Greiner Bio-One). Human Tenon\u0026rsquo;s Fibroblasts were passaged when flasks achieved 80% confluency by dissociating monolayers with a Trypsin-EDTA solution (0.25%; Sigma-Aldrich). Fibroblasts obtained on passages 6 through 12, after initial cultures were established, were used. Phenotype verification of the cultured HTFs was achieved with the use of anti\u0026ndash;fibroblast-specific protein 1 (S100A4) antibody (1:100; ABF32; Merck Millipore, Bayswater, VIC, Australia)(Supplementary Fig.\u0026nbsp;1). HTFs (approx. 40,000 cells/well) were seeded onto coverslips in 6-well Nunc cell culture plates (Thermo Fisher Scientific). Then HTFs were serum-starved in DMEM with 1% FCS overnight in preparation for treatment. HTFs were then assigned to either the control (medium alone) or the TGFβ1 (10 ng/mL) treatment group. Following treatment, HTFs were fixed with 4% paraformaldehyde (PFA; P6148; Sigma-Aldrich, CA, USA) over a range of time-points (from Days 3\u0026ndash;14).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImmunocytochemical/ Immunofluorescence Staining\u003c/h2\u003e \u003cp\u003eFollowing fixation HTFs were rinsed with Dulbecco\u0026rsquo;s Phosphate Buffered Saline (DPBS; Gibco) and permeabilised with 0.1% Triton X-100 (Sigma-Aldrich, St Louis, MO, USA) for 5\u0026ndash;10 minutes and rinsed again with PBS. In order to prevent non-specific binding of the primary antibody, a blocking solution, UltraVision Protein Block (TA-125-PBQ; ThermoFisher Scientific, CA, USA), was applied to coverslips for 30 mins. HTFs on coverslips were then left to incubate overnight at 4\u0026deg;C with the primary anti-\u0026rsquo;α-SMA antibody (M0851; Dako, CA, USA). Following incubation period, HTFs on coverslips were rinsed thoroughly with PBS and were treated with a fluorophore-labelled secondary antibody, Alexa Fluor 568 goat anti-mouse IgG (H\u0026thinsp;+\u0026thinsp;L) (A11004; Invitrogen, OR, USA), for 1 hour in the dark at room temperature (22\u0026deg;C), then rinsed again with PBS. Nuclear counterstaining was conducted with the application of 4\u0026prime;,6-diamidino-2-phenylindole (DAPI) (1:1000; ab228549; Abcam, Cambridge, UK) for 10 mins in the dark at room temperature. Finally, coverslips were mounted onto microscope slides with DPX mounting media (20242; Labworks, Knox City Centre, VIC, Australia). All slides were observed at 20x magnification using a fluorescence microscope (Axio Scope.A1 with Axiocam 105 colour; Zeiss, Oberkochen, Germany) and analysed with corresponding software (Zen 3.1- blue edition, Zeiss, Oberkochen, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReal Time qPCR\u003c/h2\u003e \u003cp\u003eRNA extraction was performed using illustra RNAspin Mini Kit (GE Healthcare Life Sciences), following manufacturer\u0026rsquo;s instructions. RNA concentration and quality were measured using SimpliNano (GE Healthcare Life Sciences). cDNA was synthesised by reverse transcription of RNA. RT-qPCR was performed using TaqMan\u0026trade; Fast Advanced Master Mix (4444554; Thermo Fisher Scientific, Lithuania) and Taqman gene expression assay probe for \u003cem\u003eACTA2\u003c/em\u003e (Hs00426835_g1) and the housekeeping gene 18s rRNA control mix (4318839; Applied Biosystems, Kingsland Grange, Woolston, Warrington, UK). RT-qPCR was performed on QuantStudio\u0026trade; 7 Flex Real-Time PCR System (Thermo Fisher Scientific, Scoresby, VIC, Australia), following manufacturer\u0026rsquo;s instructions. The delta delta Ct (ΔΔCT) method was used to calculate and compare relative mRNA levels to control. Data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and were analysed with two-way analysis of the variance (ANOVA) followed by post hoc Š\u0026iacute;d\u0026aacute;k's multiple comparisons test. A value of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRNA Sequencing\u003c/h2\u003e \u003cp\u003eRNA was extracted using the Illustra RNAspin Mini Kit (GE Healthcare Life Sciences) according to manufacturer\u0026rsquo;s instructions. RNA quality was checked by bioanalyzer, followed by library construction using the TruSeq Stranded mRNA kit (Illumina) and sequenced using Novaseq 6000 (Illumina) with 100bp single-end sequencing, at a depth of ~\u0026thinsp;20\u0026nbsp;million reads per sample (Australian Genome Research Facility).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatic analysis\u003c/h2\u003e \u003cp\u003eFollowing the abundance estimates of transcripts generated by \u003cem\u003eSalmon\u003c/em\u003e v1.8, the pseudocounts were mapped to the GRCh38 genome assembly using the \u003cem\u003etximport\u003c/em\u003e v1.22.0 package\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. The gene count matrix was inputted as an DESeq2Dataset object using the \u003cem\u003eDESeqDataSetFromTximport\u003c/em\u003e function, then the DESeq2Dataset object was normalised using the \u003cem\u003ecounts\u003c/em\u003e function to make fair gene expression comparisons between samples\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. The normalised dataset was analysed with the \u003cem\u003eDESeq2\u003c/em\u003e v1.34.0 R package using rlog transformation. The sample-level quality assurance and distribution bias was measured using principal component analysis while the gene-level QC was performed using hierarchical clustering. For differential expression analysis, the significant differentially expressed genes were determined using the \u003cem\u003efilter\u003c/em\u003e function with adjusted p value of \u0026lt;\u0026thinsp;0.05 and fold change\u0026thinsp;\u0026gt;\u0026thinsp;2.0. The expression data of significant differentially expressed genes was visualised using the \u003cem\u003eggplot2\u003c/em\u003e v3.3.6, \u003cem\u003epheatmap\u003c/em\u003e v1.0.12 and \u003cem\u003eEnhancedVolcano\u003c/em\u003e v1.12.0 R package\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. GO enrichment analysis was performed to investigate relationships between significantly expressed genes and their cellular component, biological process, and molecular function. Only terms with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were considered significant. Network topology analysis was performed using Enrichr with the top 50 upregulated DEGs \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Gene set enrichment analysis was performed using GSEA 4.3.2 and MSigDB 2023.1 (UC San Diego and Broad Institute) \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e a false-discovery rate of q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were considered significant. The MSigDB gene sets genes known to be significant in the following pathways: cell cycle phase and proliferation, TGFB signaling, Wnt signaling, NFKb pathway, fibroblast markers, ECM organization, wound healing regulation, myofibroblast differentiation, collagen fibril organization, cell cycle regulation, and ocular fibroblast markers. Further GSEA and pathway analysis was conducted using msigdbr \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e and clusterProfiler packages \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. KEGG network topology for the top 50 DEGs was performed using Enrich to obtain gene and KEGG term interactions \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eDisclosure statement\u003c/u\u003e There are no conflicts of interest to disclose\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe transcriptome data generated in this study are available in the NCBI Gene Expression Omnibus database (GSE accession pending), including raw data and processed data.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003e \u003cb\u003eAdditional interests\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptual design: ECC, RCBW, JG; Conduct experiments: ZP, RK, JG; Data analysis: AB, RCBW, ECC, JG, ZP; Funding: JG, RCBW; Manuscript writing \u0026amp; review: ZP, AB, RCBW, ECC, JG. All authors approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003e This work was supported by the Royal Victorian Eye and Ear Hospital Early Research Career Support Grant (JFG). ZP and AB are supported by an Australian Government Research Training Program Scholarship. RCBW is supported by the University of Melbourne, and the Centre for Eye Research Australia, National Health and Medical Research Council (GCT1184076) and Medical Future Research Fund (MRF2024365). The Centre for Eye Research Australia acknowledges the Victorian State Government\u0026rsquo;s Department of Innovation, Industry and Regional Development\u0026rsquo;s Operational Infrastructure Support Program.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFan Gaskin, J. C., Nguyen, D. Q., Soon Ang, G., O\u0026rsquo;Connor, J. \u0026amp; Crowston, J. G. 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OMICS 16, 284\u0026ndash;287 (2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4008732/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4008732/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlaucoma filtration surgery (GFS) is performed to slow down disease progression in glaucoma, a leading cause of irreversible blindness worldwide. Following surgery, pathological wound healing may lead to conjunctival fibrosis and filtering failure. Myofibroblasts are the key cells responsible for postoperative conjunctival scarring.\u003c/p\u003e \u003cp\u003eThis study aims to further understand the molecular mechanisms of conjunctival fibrosis following GFS. We utilised RNA-sequencing (RNA-seq) to delineate the TGFβ1 induced changes in the transcriptome of human Tenon\u0026rsquo;s fibroblasts (HTFs). RNA sequencing was performed on HTFs after 5 days of TGFβ1 treatment. Following quality control, 3,362 differentially expressed genes were identified, of which 1,532 were upregulated and 1,820 were downregulated. We identified signaling pathways associated with the pathogenesis of conjunctival fibrosis. The DEGs (differentially expressed genes) were enriched in pathways including myofibroblast differentiation, TGFβ-signaling, collagen and extracellular matrix organization, epithelial to mesenchymal transition, and cell cycle regulation. The results of this study identified the transition from HTF to myofibroblast is characterised by the upregulation of key genes including \u003cem\u003eLDLRAD4, CDKN2B, FZD8, MYOZ1\u003c/em\u003e, and the downregulation of \u003cem\u003eSOD3, LTBP4\u003c/em\u003e and \u003cem\u003eRCAN2\u003c/em\u003e. This insight into the transcriptional landscape of HTFs and myofibroblast differentiation is essential to understand the pathophysiology of conjunctival scarring and develop new therapeutic agents.\u003c/p\u003e","manuscriptTitle":"Transcriptomic analysis of TGFβ-mediated fibrosis in primary human Tenon’s fibroblasts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 16:11:33","doi":"10.21203/rs.3.rs-4008732/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5661f94b-472b-4872-a7ce-8639e177610e","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29676018,"name":"Biological sciences/Genetics/Gene expression"},{"id":29676019,"name":"Biological sciences/Computational biology and bioinformatics/Gene ontology"},{"id":29676020,"name":"Biological sciences/Neuroscience/Visual system"}],"tags":[],"updatedAt":"2024-07-23T06:55:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 16:11:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4008732","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4008732","identity":"rs-4008732","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}