Targeting Circadian Rhythm Disruption in Glaucoma: PTGDS Mediates Trabecular Meshwork Fibrosis and Is Therapeutically Targeted by Aprepitant

Abstract

Purpose

Trabecular meshwork (TM) fibrosis, which causes ocular hypertension (OHT), remains a key therapeutic challenge in glaucoma. Given the emerging link between circadian rhythm disruption and glaucoma, we sought to identify novel fibrotic mediators related to circadian genes, and screen for potential inhibitors to assess their therapeutic effect.

Methods

We performed an integrated analysis of human TM transcriptome data to identify key circadian rhythm-related differentially expressed genes (CRRDEGs). We confirmed the hub gene’s upregulation in an OHT mouse model. We then performed virtual screening and molecular docking to identify a potential inhibitor from existing drugs, which was subsequently tested in vitro for anti-fibrotic efficacy.

Results

PTGDS emerged as the hub gene among 15 CRRDEGs, showing significant overexpression in the TM of OHT mice. Virtual screening indicated that aprepitant as the top candidate inhibitor, showing the lowest binding affinity for PTGDS. Subsequent in vitro tests confirmed that aprepitant partially rescued human TM cell viability from TGFβ-induced fibrotic stress. It also effectively downregulated the expression of both PTGDS and established fibrosis markers.

Conclusions

PTGDS, a key circadian-related gene, is a novel mediator of TM fibrosis. Our findings demonstrate that targeting PTGDS with aprepitant can ameliorate the fibrotic phenotype in vitro. This validates PTGDS as a promising, druggable target for glaucoma therapy.

Translational Relevance

This study identifies the PTGDS-mediated fibrotic pathway as a druggable target in glaucoma. Our findings provide a preclinical rationale for evaluating the US Food and Drug Administration (FDA)-approved drug aprepitant as a repurposed therapeutic, offering a new strategy for the development of anti-fibrotic treatments.

Introduction

Glaucoma is a group of neurodegenerative optic neuropathies characterized by optic disc atrophy and progressive visual field defects, and it remains the leading cause of irreversible blindness worldwide.¹ Glaucoma is increasingly recognized as a psychosomatic disease involving interactions among psychological, environmental, and biological factors.² Numerous clinical studies have indicated that adverse lifestyle factors, such as chronic mental stress and circadian rhythm disruption (e.g., insomnia or sleep apnea), are significant risk factors for the onset and progression of glaucoma,^3,4 with one core pathophysiological link being the disruption of the body’s intrinsic circadian rhythm. For instance, under physiological conditions, intraocular pressure (IOP) peaks during the nocturnal or early morning period and is influenced by body posture.^5,6 However, in patients with primary open-angle glaucoma (POAG), this IOP circadian rhythm is disrupted, manifesting as altered peak timing and increased fluctuation amplitude,⁷ which results in increased instability that is significantly correlated with the rate of visual field deterioration.⁸ Furthermore, studies suggest that IOP circadian rhythm disruption in patients with glaucoma contributes to optic nerve damage, whereas such disruption in healthy individuals may increase the risk of developing glaucoma.⁹ Collectively, this evidence underscores the important role of circadian rhythm disruption in the pathogenesis and progression of glaucoma.

IOP is currently the only modifiable risk factor in glaucoma treatment. The trabecular meshwork (TM), being the primary site of outflow resistance, is considered central to this circadian abnormality when it is dysfunctional. We and others have confirmed that the transformation of TM cells into a myofibroblast phenotype and the excessive deposition of extracellular matrix (ECM) are key factors leading to TM fibrosis and increased aqueous humor (AH) outflow resistance.^10–15 It is noteworthy that fibrotic processes in various tissues (e.g., heart and lungs) have been shown to be precisely regulated by core clock genes, exhibiting distinct circadian fluctuations.^16,17 In the eye, fibrosis-related pathological processes, such as corneal scar healing and age-related cataract, have also been associated with differential expression of circadian rhythm-related genes.^18,19 However, the extent and mechanisms by which circadian rhythm genes regulate the fibrotic pathology of the TM, thereby contributing to impaired AH outflow in glaucoma, remains poorly understood.

Research on the circadian regulation of the TM remains in its early stages. This significant knowledge gap hinders a comprehensive understanding of glaucomatous circadian pathophysiology from the perspective of “rhythmic regulation.” However, existing literature provides a strong rationale for investigating this link. For instance, prostaglandins are known regulators of AH dynamics, and the expression of prostaglandin D2 synthase (PTGDS), a key enzyme in prostaglandin metabolism, is regulated by the circadian signaling molecule melatonin.²⁰ Existing studies have shown that PTGDS is involved in inflammatory responses and the regulation of the immune microenvironment. Furthermore, PTGDS has been demonstrated to function in fibrotic signal transduction, and its inhibition can alleviate fibrosis progression in systemic sclerosis.²¹ A recent study found upregulated PTGDS expression in the primary visual cortex (V1) of a mouse model of optic nerve injury, which was associated with neuroinflammatory signaling pathways.²²

Collectively, these findings suggest that circadian-regulated genes like PTGDS might be critical in TM pathology. However, their specific expression patterns in the glaucomatous TM and their functional impact on fibrosis remain uninvestigated. Therefore, the present study was designed to first identify and validate key circadian rhythm-related genes in the glaucomatous TM. We aimed to investigate the functional impact of the identified hub gene on TM fibrosis in vitro. Ultimately, by using virtual drug screening and molecular docking, this project sought to identify a potential therapeutic inhibitor, thereby providing a new theoretical framework and suggesting novel directions for glaucoma treatment.

Methods

Data Harmonization and Preprocessing

Gene expression data derived from human TM tissue were sourced from the Gene Expression Omnibus (GEO) database, encompassing two independent microarray datasets (GSE27276 and GSE138125). The GSE27276 dataset comprised 17 glaucomatous and 19 control samples,²³ whereas GSE138125 included 4 glaucomatous and 4 control samples.²⁴ Raw expression matrices from both datasets were integrated. To address potential technical batch effects arising from different microarray platforms, we harmonized the data using the sva package, resulting in a combined cohort of 21 glaucoma and 23 control samples. This integrated dataset was subsequently normalized and probe re-annotated using the limma package. The efficacy of the batch effect correction procedure was confirmed through principal component analysis.

Compilation of Circadian Rhythm Gene Set and Identification of Circadian Rhythm-Related Differentially Expressed Genes

A comprehensive set of circadian rhythm-related genes (CRRGs) was assembled by querying the GeneCards database and supplementing the initial list with genes curated from key review literature,²⁵ yielding a final non-redundant compendium of 1979 genes. Differential expression analysis between glaucomatous and control samples within the integrated dataset was conducted with the limma package. Genes exhibiting an absolute log2-fold change greater than 1 and a Benjamini-Hochberg adjusted P value below 0.05 were defined as differentially expressed genes (DEGs). The subset of circadian rhythm-related DEGs (CRRDEGs) was then identified as the intersection between the DEG list and the curated CRRG set. The expression profile of these CRRDEGs was visualized in a heatmap, and their genomic distribution was illustrated using a circos plot.

Functional Enrichment and Gene Set Enrichment Analysis

To explore the functional implications of the identified CRRDEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler package. In parallel, a pre-ranked Gene Set Enrichment Analysis (GSEA) was applied to the full list of genes from the integrated dataset, which were ordered by their log2-fold change values. The C2 curated gene set collection from the Molecular Signatures Database (MSigDB) served as the reference. For both enrichment strategies, terms with a nominal P value < 0.05 and a false discovery rate (FDR) <0.25 were deemed statistically significant.

Protein-Protein Interaction Network

A protein-protein interaction (PPI) network was constructed for the CRRDEGs utilizing the STRING database, applying a medium-confidence interaction score threshold of >0.400. The resulting network was imported into Cytoscape software for visualization and topological analysis. Within this network, highly interconnected modules were examined, and hub genes were selected based on their connectivity scores. The functional context and potential interaction partners for these hub genes were further investigated and visualized using the GeneMANIA prediction server.

Immunoinfiltration Analysis for Glaucoma Using the CIBERSORT Algorithm

The relative proportions of 22 immune cell types within the TM tissue samples were estimated using the CIBERSORT algorithm with the LM22 signature matrix. Samples with a CIBERSORT deconvolution P value of less than 0.05 were retained for downstream comparative analysis. Differences in immune cell composition between glaucomatous and control groups were displayed using violin plots. Pairwise Spearman correlations between the estimated fractions of all immune cell types were calculated, and significant correlations (|ρ| > 0.3, FDR < 0.05) were presented in a clustered heatmap. Furthermore, the relationships between the expression levels of the identified hub genes and the infiltration levels of specific immune cell subsets were assessed and graphically summarized in a bubble chart. Detailed information for all software and database versions is listed in Supplementary Table S3.

Animals

All animal procedures were approved by the Institutional Animal Care and Use Committee of Zhongshan Ophthalmic Center (approval number: Z2022047) and were conducted in accordance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. Adult male C57BL/6J mice, aged 6 to 8 weeks, were housed under a standard 12-hour light/dark cycle with ad libitum access to food and water.

Ocular Hypertension Animal Model and Drug Treatments

Mice were randomly allocated to control or ocular hypertension (OHT) groups. General anesthesia was induced via intraperitoneal injection of Avertin (250 mg/kg), supplemented by topical application of proparacaine hydrochloride for local ocular anesthesia. A chronic OHT model was established in the right eye by intravitreal injection of 2.5 µL of an Ad-TGFβ2^C226/228S adenoviral vector. Control animals received an intravitreal injection of an equal volume of the Ad-Null vector. Tobramycin ophthalmic ointment was applied following the injection to minimize the risk of infection.

IOP Measurement

The day of the initial intravitreal injection was designated as day 0. Longitudinal monitoring of IOP commenced on day 4 and was subsequently repeated at 4-day intervals until day 28. For each measurement session, mice were anesthetized, and IOP was measured using a TONOLAB rebound tonometer. Three independent sets of six consecutive readings were acquired per eye. The average of each set was calculated, and the final IOP value for a given eye and time point was derived from the mean of these three averages.

Cultivation and Identification of Human TM Cells

Primary human TM cells (hTMCs) were isolated from donor corneoscleral rings and cultured in Dulbecco's Modified Eagle Medium/Nutrient Mixture F-12 supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Cellular identity was verified at passage 3 by confirming the induction of myocilin expression following a 7-day treatment with 100 nM dexamethasone. All experiments utilized hTMCs between passages 3 and 6, derived from a minimum of 3 independent donors. For experimental treatments, cells were exposed to 10 ng/mL recombinant human TGFβ2, either alone or in combination with 100 nM aprepitant, for a duration of 48 hours.

Quantitative Real-Time Polymerase Chain Reaction

Total RNA was extracted from treated hTMCs using a commercial kit. Complementary DNA (cDNA) was synthesized from 1 µg of total RNA using a reverse transcription system. Quantitative real-time polymerase chain reaction (qPCR) was performed with a TB Green Premix on a real-time PCR instrument according to a standard thermal cycling protocol. The relative expression of each target gene was calculated using the 2^−ΔΔCT method, with normalization to the endogenous control β-tubulin. The sequences for all gene-specific primers are provided in Supplementary Table S1.

Docking-Based Virtual Screening

The three-dimensional crystal structure of the PTGDS protein (PDB ID: 3O22) was retrieved from the Protein Data Bank. The structure was prepared for docking by removing water molecules and adding hydrogen atoms and Gasteiger charges using AutoDock Tools. An in-house chemical library containing 3447 compounds was screened against the prepared PTGDS structure using AutoDock Vina. The docking grid was centered on the known fatty acid-binding site of the protein. For each compound, 10 independent docking runs were performed, and the conformation with the most favorable (lowest) calculated binding affinity (in kcal/mol) was retained for ranking. The two-dimensional and three-dimensional interaction models for the top-ranking compound-protein complexes were generated for visual analysis.

Cell Proliferation Assay

Cellular viability was assessed using the Cell Counting Kit-8 (CCK-8). The hTMCs were seeded into 96-well plates and subjected to the experimental treatments described above. After 48 hours, 10 µL of CCK-8 reagent was added to each well, and the plates were incubated for an additional 3 hours at 37°C. The absorbance of each well was then measured at a wavelength of 450 nm using a microplate reader. Results are expressed as a percentage relative to the absorbance measured in untreated control wells.

Western Blot

Cells were lysed in radioimmunoprecipitation assay (RIPA) buffer. Protein concentration was determined, and equal amounts of protein were separated by SDS-PAGE on 4% to 20% gradient gels, followed by electrophoretic transfer onto polyvinylidene fluoride (PVDF) membranes. After blocking, the membranes were incubated overnight at 4°C with primary antibodies directed against PTGDS, fibronectin, α-smooth muscle actin (α-SMA), or β-tubulin. Following washes, the membranes were incubated with appropriate horseradish peroxidase-conjugated secondary antibodies. Immunoreactive bands were visualized using a chemiluminescent substrate and quantified by densitometry. Protein expression levels were normalized to the loading control, β-tubulin. Detailed information for all antibodies used is listed in Supplementary Table S2.

Immunofluorescence

The hTMCs grown on coverslips or sagittal cryosections of mouse ocular tissues were fixed with 4% paraformaldehyde, permeabilized with 0.5% Triton X-100, and blocked with 10% goat serum. Samples were then incubated overnight at 4°C with primary antibodies against PTGDS, fibronectin, or α-SMA. After thorough washing, the samples were incubated with appropriate fluorophore-conjugated secondary antibodies. Cell nuclei were counterstained with DAPI. Fluorescent images were captured using a confocal laser scanning microscope.

Statistical Analysis

The sample size for each experiment is indicated in the corresponding figure legends. Statistical analysis was performed using SPSS Statistics 22.0 (IBM, USA) and Prism 9.0 (GraphPad, USA). Results are expressed as the mean ± standard deviation (SD). When comparing data across multiple groups, one-way or two-way analysis of variance (ANOVA) was applied, with post hoc analysis conducted using Tukey’s test. A probability (P) value of less than 0.05 was considered statistically significant.

Results

Identification of CRRDEGs in Glaucoma With Functional Profiling and Global Pathway Enrichment

Following batch effect removal from the combined GEO dataset (Figs. 1A, 1B), we identified 87 DEGs (26 upregulated and 61 downregulated) between glaucoma and control samples (Fig. 1C). Next, cross-referencing these DEGs with a predefined set of circadian rhythm-related genes yielded 15 CRRDEGs, which included HBB, LCN2, MAOA, DUSP1, HP, RASL11B, PTGDS, SERPINA3, CEACAM6, IGFBP2, S100A8, LGI4, GRP, SAA2, and MYH11 (Fig. 1D). Functional enrichment analysis of these 15 CRRDEGs showed that GO terms were significantly enriched for acute inflammatory response, hydrogen peroxide catabolic process, and mast cell activation in biological processes; secretory granule lumen and the haptoglobin-hemoglobin complex in cellular components; and organic acid binding and antioxidant activity in molecular functions. KEGG pathway analysis further highlighted their significant involvement in the IL-17 signaling pathway, along with phenylalanine and histidine metabolism (Fig. 1F). To obtain a global pathway-level perspective, we performed GSEA on the entire ranked gene list from the combined dataset. This analysis revealed significant enrichment of the global gene expression profile in glaucoma for pathways related to glycosaminoglycan metabolism, non-alcoholic fatty liver disease, and carbohydrate metabolism, among others (Fig. 1G).

Figure 1.

Dentification of CRRDEGs, GO, and KEGG enrichment analysis and GSEA. (A) Datasets of GEO integrated before batch removal. (B) Combined datasets of GEO data after batch processing. (C) Identification of differentially expressed genes (87 DEGs: 26 upregulated and 61 downregulated; thresholds: |logFC| > 1, adjusted P < 0.05). (D) Identification of CRRDEGs. (E) Expression levels of the 15 CRRDEGs in glaucoma and control groups. (F) Enrichment analysis results of GO and KEGG of CRRDEGs. (G) GSEA for four biological functions of combined GEO datasets.

PTGDS Emerges as a Key Circadian Rhythm-Related Hub Gene in Glaucoma From PPI Network and Immune Infiltration Analysis

PPI network analysis of the 15 CRRDEGs using STRING identified a significant interaction module among 8 genes, including HBB, LCN2, HP, PTGDS, SERPINA3, CEACAM6, S100A8, and SAA2, which were designated as hub genes (Fig. 2A). Their functional relationships were further elucidated with GeneMANIA, which constructed an interaction network encompassing the hubs and 20 related genes, revealing connections like co-expression and shared protein domains (Fig. 2B). We next assessed the hub genes’ roles in the glaucomatous immune microenvironment via immune infiltration analysis. This revealed four differentially infiltrated immune cell types (P < 0.05), such as plasma cells, monocytes, activated dendritic cells, and neutrophils (Fig. 2C). Correlation analysis among these cells showed a pronounced negative correlation between monocytes and neutrophils (r = −0.517, P < 0.05; Fig. 2D). Critically, correlating hub gene expression with immune infiltration levels identified PTGDS as having the strongest positive correlation with monocytes (r = 0.543, P < 0.05; Fig. 2E). Based on this finding and the established link between monocyte infiltration and glaucoma pathogenesis, PTGDS was selected for further investigation as a potential key gene in circadian rhythm disruption.

Figure 2.

PPI network of CRRDEGs and immune infiltration analysis in glaucoma. (A) PPI network of CRRDEGs. (B) PPI network of functionally similar genes predicted by GeneMANIA website of hub genes. (C) The proportion of immune cells in the integrated GEO dataset. (D) Correlations among the four significant immune cell types. (E) Correlation between abundance of immune cell infiltration and hub genes in integrated GEO dataset. *P < 0.05, **P < 0.01, ***P < 0.001; ns, not significant. The absolute value of the correlation coefficient (r value) below 0.3 is weak or no correlation, between 0.3 and 0.5 is weak correlation, between 0.5 and 0.8 is moderate correlation, and above 0.8 is strong correlation.

PTGDS Expression Is Elevated in the TM of OHT Animal Models and TGFβ-Induced Fibrotic hTMCs

We next validated our bioinformatic findings in vivo using a chronic OHT mouse model induced by intravitreal Ad-TGF-β2^C226/228S. Compared with Ad-Null controls, Ad-TGF-β2 injection successfully induced OHT, with IOP significantly increasing by day 4 and stabilizing by day 16 (Fig. 3B). Immunofluorescence confirmed successful viral transduction in the TM (Fig. 3A) and, critically, revealed a significant increase in PTGDS protein expression in the TM of the OHT group (Fig. 3C). Consistent with the in vivo data, in vitro stimulation of hTMCs with TGF-β2 significantly upregulated the mRNA expression of PTGDS and the fibrosis markers ACTA2 (α-SMA), CTGF, and COL4A1 (Fig. 3D). Collectively, these results validate that PTGDS is upregulated in both in vivo and in vitro models of TM fibrosis.

Figure 3.

PTGDS expression is elevated in both in vivo and in vitro models of glaucoma. (A) Confocal microscopy images showing the localization of viral autofluorescence in the anterior chamber angle of frozen sections at day 16. (B) Intraocular pressure (IOP) curves of experimental and control groups over 28 days. (C) Immunofluorescence analysis of PTGDS expression levels in the trabecular meshwork tissue. (D) The qPCR analysis of mRNA expression levels of LPGDS and fibrosis markers in TGFβ-stimulated human trabecular meshwork cells. *P < 0.05, **P < 0.01, ***P < 0.001; ns, not significant.

Virtual Screening Identifies Aprepitant as a Therapeutic Candidate for Glaucoma by Targeting PTGDS

To identify potential inhibitors of PTGDS, we performed molecular docking to analyze the binding interactions between 3447 drug candidates and the PTGDS protein. Based on the criterion that a binding affinity below −5 kcal/mol typically indicates strong binding, virtual screening identified 5 drugs with notably low binding energies under −8 kcal/mol (Fig. 4C). Among these, aprepitant, a US Food and Drug Administration (FDA)-approved drug, exhibited the lowest binding energy and was selected for further investigation (Figs. 4A, 4B). To evaluate the therapeutic potential of aprepitant against TGFβ-induced fibrosis, we measured its impact on cell viability and the transcript levels of key fibrosis-related genes. Stimulation with TGF-β2 significantly reduced the viability of hTMCs, as anticipated. However, co-treatment with 100 nM aprepitant notably restored cell viability (Fig. 4D). This protective effect was corroborated at the transcriptome level, where aprepitant significantly downregulated the mRNA expression of not only TGFβ-induced Ptgds but also the fibrosis markers Acta2 and Col4a1 (Fig. 4E). Collectively, these findings demonstrate that aprepitant is an effective inhibitor of PTGDS, capable of restoring cell viability.

Figure 4.

Virtual screening identifies aprepitant as a PTGDS inhibitor and validates its therapeutic effects. (A, B) Aprepitant exhibits the most favorable binding affinity with PTGDS in molecular docking. (C) Five candidate drugs with binding energies below −8 kcal/mol are identified through virtual screening. (D) Co-treatment with 100 nM aprepitant restores TGFβ-impaired viability of human trabecular meshwork cells (hTMCs). (E) Aprepitant downregulates the mRNA expression of TGFβ-induced PTGDS and fibrosis markers (α-SMA and COL4A1). *P < 0.05, **P < 0.01, ***P < 0.001; ns, not significant.

Aprepitant Attenuates TGFβ-Induced Fibrotic Phenotype in hTMCs via Suppression of PTGDS

To confirm these findings at the protein level, we allocated cells into 3 groups: a control group, a TGFβ-treated group, and a group co-treated with TGF-β2 and 100 nM aprepitant. Western blot analysis confirmed that aprepitant not only suppressed PTGDS transcription but also attenuated TGFβ-induced PTGDS protein overexpression. Furthermore, aprepitant administration led to a significant reduction in the expression of α-SMA, a key marker of myofibroblast transdifferentiation, and FN, a central component of the ECM (Figs. 5A, 5B). These observations were strongly corroborated by immunofluorescence analysis, which showed markedly reduced fluorescence intensity for PTGDS, α-SMA, and FN in hTMCs co-treated with aprepitant compared with those treated with TGF-β2 alone (Figs. 5C–F). Collectively, these results establish that aprepitant mitigates TGFβ-induced fibrotic phenotype in hTMCs, suppressing both PTGDS expression and subsequent myofibroblast transdifferentiation.

Figure 5.

Aprepitant suppresses TGFβ-induced fibrotic responses in hTMCs. (A, B) Western blot analysis showing that aprepitant attenuates TGFβ-induced overexpression of PTGDS, α-SMA, and FN proteins. (C–F) Immunofluorescence staining and quantification confirming the reduction in PTGDS, α-SMA, and FN fluorescence intensity in aprepitant-co-treated hTMCs compared to TGF-β2 treatment alone. *P < 0.05, **P < 0.01, ***P < 0.001; ns, not significant.

Discussion

This study identified PTGDS as a key CRRDEG implicated in the pathology of the TM in glaucoma through bioinformatic analysis, which was subsequently validated by both in vitro and in vivo experiments. Subsequently, virtual screening coupled with molecular docking nominated aprepitant as a promising inhibitor of PTGDS, and preliminary validation confirmed its efficacy in reversing the TGFβ-induced fibrotic phenotype of hTMCs. These findings establish a novel theoretical framework for elucidating the pathogenesis of circadian rhythm disruption in glaucoma and highlight a promising direction for developing new clinical therapies.

An integrated analysis of GEO datasets from glaucomatous and healthy TM tissue initially identified DEGs. The intersection of these DEGs with a predefined set of CRRGs yielded 15 CRRDEGs, and functional enrichment analysis revealed their significant enrichment in processes such as the inflammatory response and the IL-17 signaling pathway, thus providing a preliminary link between circadian rhythm disruption and the immune-inflammatory microenvironment in glaucoma. Further analyses, including PPI network and immune infiltration analysis, corroborated these findings and collectively pinpointed PTGDS as a key CRRDEG. PTGDS not only occupied a hub position within the PPI network but also exhibited the strongest positive correlation with monocyte infiltration in the glaucomatous TM. Given the established role of monocytes/macrophages in promoting TM fibrosis and increasing AH outflow resistance,^26,27 our results support the notion that PTGDS serves as a critical molecular nexus connecting circadian dysregulation, immune dysregulation, and fibrosis in the pathogenesis of glaucomatous TM pathology.

We used an adenovirus overexpressing TGF-β2 to establish a chronic OHT model. Immunofluorescence staining of frozen eye sections revealed significant PTGDS overexpression within the TM, validating its potential key role. We then screened over 3000 compounds via virtual screening and molecular docking, identifying aprepitant, a clinically approved neurokinin 1 receptor (NK1R) antagonist,^28,29 as the top candidate binding PTGDS with high efficiency, suggesting its potential for drug repurposing. Subsequent in vitro experiments demonstrated that aprepitant exerted significant protective effects, effectively reversing the TGFβ-induced decrease in hTMCs viability and suppressing TGFβ-induced PTGDS overexpression at both mRNA and protein levels. Moreover, aprepitant treatment concurrently downregulated the expression of key fibrosis markers like α-SMA and FN. These results collectively indicate that aprepitant alleviates the TGFβ-induced fibrotic phenotype in hTMCs by inhibiting PTGDS, providing a rationale for its further development as a glaucoma therapeutic.

Our study has several limitations that need to be addressed in future research. First, our identification of PTGDS as a circadian-related gene was based on bioinformatics and literature correlation; the in vitro fibrosis experiments themselves were not conducted under synchronized circadian conditions. Future studies are needed to determine if the PTGDS gene itself exhibits circadian expression in hTMCs and how this rhythm is altered by glaucoma. Second, whereas our virtual screening identified aprepitant as a high-affinity binder, and in vitro experiments showed it suppressed downstream fibrosis markers, we did not perform a direct biochemical assay to confirm that aprepitant physically binds to and directly inhibits PTGDS enzymatic function. The observed effect is therefore a strong correlation, but a direct causal link requires further validation. Finally, all our therapeutic validation was performed in vitro. While promising, these results must be replicated in in vivo animal models of glaucoma to assess aprepitant’s efficacy in lowering IOP and its potential ocular toxicity.

The significance of this study lies in its novel identification of PTGDS as a critical nexus connecting circadian rhythm disruption and TM fibrosis in glaucoma, alongside the validation of aprepitant’s therapeutic potential. This work not only deepens the understanding of circadian regulation in glaucomatous pathogenesis but also provides a solid theoretical and experimental foundation for developing novel PTGDS-targeted therapies. Given that aprepitant is an already-marketed drug with a known safety profile, its repurposing could significantly shorten the drug development timeline. Future research should focus on elucidating the precise molecular mechanisms by which PTGDS regulates TM function and evaluating the long-term efficacy of aprepitant in additional animal models to facilitate its clinical translation.