Biomarker driven drug repurposing for Fuchs’ Endothelial Corneal Dystrophy (FECD): A Computational study

doi:10.21203/rs.3.rs-4820986/v1

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Biomarker driven drug repurposing for Fuchs’ Endothelial Corneal Dystrophy (FECD): A Computational study

https://doi.org/10.21203/rs.3.rs-4820986/v1

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Fuchs’ Endothelial Corneal Dystrophy (FECD) is a slowly advancing, non-inflammatory eye condition due to gradual increase in thickness of the Descemet’s membrane and the gradual decline in corneal endothelial cells population. It can occur sporadically or be inherited in an autosomal dominant manner leading to impaired vision over time. The only available method to treat FECD is corneal transplant. This study aims to identify potential biomarkers, pathways, and identifying therapeutic targets to treat FECD. The dataset GSE171830 was retrieved from Gene Expression Ominbus (GEO) database for the purpose of investigating and identifying potential therapeutic targets for FECD. Using GEO2R tool, 2654 differentially expressed genes (DEGs) were identified, with 1763 upregulated and 881 downregulated genes, for annotating FECD pathogenesis pathways. We conducted gene enrichment analysis with the aim of identifying the pathways associated with FECD disease. Further, a total of 61 FDA approved eye care drugs were docked with the identified biomarkers. Selected DEGs functional annotations show the involvement of several FECD related biological processes and pathways. Our findings unravel potential biomarkers including HLA-DRA, CSF1R, TNFRSF11B, COL4A1, etc. through GO, KEGG analysis, and Protein Protein Interaction (PPI). Molecular docking of 61 FDA-approved eye care drugs was performed and Naphazoline, Infigratinib, Lifitegrast, Netarsudil, Verteporfin, and Hyaluronic drugs were identified as repurposable candidates. The findings from this study could offer novel insights to the molecular mechanism of FECD and the identified drugs could play a significant impact on the treatment and prevention of FECD.

FECD

Gene enrichment analysis

DEGs

Drug repurposing.

Fuchs endothelial corneal dystrophy (FECD) is a predominant condition that can cause vision problems and is described by a respective illness of the corneal endothelium. It is described by the ever-evolving loss of corneal endothelial cells and it leads to gradual increase in thickness of Descemet's layer and reason for development of an abnormal extracellular matrix (ECM) and the advancement of back central excrescences known as guttae (Ong et al. 2021). FECD was originally identified by Ernst Fuchs, an Australian ophthalmologist. Around 300 million individuals aged ≥ 30 years are affected with FECD. Studies indicate that this number is likely to increase to 415 million prior to 2050 (Aiello et al. 2022). Between January August 2010 and December 2021, a tertiary eye care centre in India performed a significant number of patients (2578) diagnosed with FECD (Das et al 2022). It was observed that females exhibited a higher susceptibility to the disease compared to males (Nanda et al. 2014). Studies conducted across various populations across the globe indicate that a higher prevalence of FECD is reported among females than males (Matthaei et al. 2019). Corneal transplantation is the major available treatment for FECD (Weiss et al. 2010). Thus, ophthalmic care faces significant challenges due to the decreasing supply of donor corneas and increasing demand. The development of safe and non-surgical methods and efficient methods to cure the disease is need of the hour.

Surgical methods comprise of various procedures like DMEK (Descemet membrane endothelial keratoplasty), which involves the inclusion of supplanting the back layer of the cornea with sound healthy endothelial cells from the donor (Storp et al. 2023). Another strategy is Descemet-stripping endothelial keratoplasty (DSEK), which involves a partial thickness cornea transplant. Additionally, there is penetrating keratoplasty (PK), which is full thickness cornea transplant (Ang et al. 2016) and transcorneal freezing joined with ROCK-inhibitor eye-drop administration (Y-27632) (Koizumi et al. 2013, Tomioka et al. 2023). Nonsurgical methods involve the targeting of cell signaling pathways, cytokine signaling, biomarker etc. which are responsible for advancing the disease and this could be a promising therapy to treat this disease.

In this study we analyse the GSE171830 microarray dataset to unravel the molecular mechanism of FECD by analyzing upregulated and downregulated genes. The high throughput microarray data from the GEO were analysed using the GEO2R to explore the potential biomarkers and pathways for the FECD. The DEGs were identified by analyzing gene expression profiles.

DEGs were further studies through Gene Ontology (GO) analysis such as Biological Processes (BP), Cellular Components (CC), and Molecular Functions (MF). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed and the DEGs were mapped in a Protein-Protein Interaction (PPI) network to determine potential candidate genes that may help in the treatment of FECD. In addition, computational drug repurposing was performed with the eye care medicines against the upregulated genes. The outcome of this study has the potential to identify the potential alternative drug targets to treat FECD and subsequent drug repurposing helps in identification of possible drug candidates for the FECD treatment.

Data collection

The dataset GSE171830 was retrieved from the GEO database and used for this study. This dataset was generated utilizing the Illumina HumanHT-12 Version 4.0 bead-based expression analysis technology (GPL10558). The Gene Bank database (GEO) hosted at http://www.ncbi.nlm.nih.gov/geo is a comprehensive open-access resource for a wide range of high-throughput sequencing data, including microarrays and various genomics data types. The GSE171830 dataset comprises a total of 12 samples that were utilized for the analysis. Among them 6 samples GSM5235406, GSM5235407, GSM5235408, GSM5235412, GSM5235413, and GSM5235414 were used as FECD model and the 6 samples GSM5235409, GSM5235410, GSM5235411, GSM5235415, GSM5235416, and GSM5235417 were used as control (unaffected).

Data Processing and Uncovering Differentially Expressed Genes

The statistical tool GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/ accessed on February 15, 2023) was employed to compute and evaluate the differential gene expression between FECD and normal samples (Ritchie et al. 2015). The T-test and the Benjamini-Hochberg methods (controlling for false discovery) were employed for the calculation of both the p-value and FDR respectively. This approach allowed us to determining the DEGs among FECD and normal (unaffected) samples (Benjamini et al 2001). We utilized a significance threshold of p-value (p < 0.05) combined with a substantial fold change (logFC ≥ 1.5 and logFC ≤ 1.5) to acquire the DEGs and statistically significant. Specifically, genes with logFC ≥ 1.5 were considered upregulated, while those with logFC ≤ 1.5 were considered downregulated (Aubert et al. 2004, Pawitan et al. 2005). DEGs were identified from the chosen data GSE171830 and a volcano plot was generated to visualize and evaluate the differential expression of both upregulated and downregulated genes among the DEGs. The top 30 genes of the DEGs were highlighted in the volcano plot, with 15 representing upregulated and 15 representing downregulated genes. Additionally, a heatmap was created utilizing SRplot (https://www.bioinformatics.com.cn/en) to visualize their expression patterns (Tang et al. 2023). The dataset comprising the identified DEGs was collected for additional investigation.

Functional analysis of DEGs

The functional enrichment analysis was conducted utilizing the DAVID web server (https://david.ncifcrf.gov, v6.8) (Database for Annotation, Visualization, and Integrated Discovery) for the analysis of DEGs. In addition, an enrichment analysis was carried out utilizing KEGG pathway database. The p-value < 0.05 was considered as statistically significant value (Jiao et al. 2012, Kanehisa et al. 2000, Kanehisa et al. 2016).

Integrating PPI network construction with module analysis

We utilized the STRING database (version v11.5, available at https://string-db.org/) to analysefunctional enrichment analysis to investigate the PPI networks among the upregulated and downregulated DEGs in FECD and unaffected (control) samples (Szklarczyk et al. 2019). The PPI pairs were identified, and a PPI network has been created using Cytoscape software (v3.10.0) (Shannon et al. 2003, Sarma et al. 2023, Mangangcha et al. 2020, Malik et al. 2019). Furthermore, we employed molecular complex detection (MCODE) algorithm to identify the most significant module within the network diagram (Bader et al. 2003, Haider et al. 2019). We applied specific criteria, including a degree cut-off of 10, an MCODE score greater than 5, a maximum depth of 100, a node score threshold cutoff of 0.2, and a k-core of 2 for our analysis (Li et al. 2017). The k-core of a graph refers to a subset of the graph in which every node has a degree of k or higher; the highest k-core corresponds to the k-core possessing the greatest k-value within the graph. The weight of v is determined as the maximum k times the density of its respective k-core. The density of a k-core C_k = (V_k, E_k) is defined as

$$\:\varvec{d}\left(\varvec{k}\right)\:=\:\frac{\left|{\varvec{E}}_{\varvec{k}}\right|}{{\left|{\varvec{V}}_{\varvec{K}}\right|}^{2}}$$

Drug repurposing

The drug repurposing approach was employed to identify the probable drugs of FECD. The three dimensional structure of four identified biomarkers namely HLA-DRA (PDB: 8EUQ), CSF1R (PDB: 6WXJ), TNFRSF11B (PDB: 3URF) and COL4A1 (PDB: 5NAY) were obtained from the RCSB Protein Data Bank (https://www.rcsb.org/). The proteins were prepared using the AutoDockTools (ADT) (Huey et al. 2008). The water molecules and ligand hetero atoms were removed from the protein using AutoDockTools and polar hydrogens and Kollman charges were added (Kumar et al. 2022, Jamir et al. 2023, Jamir et al. 2022). The grid dimensions (x,y,z) of the proteins are shown in Table 1. The structures of all four-proteins were s saved in the PDBQT format to facilitate docking studies. The 3D structure of 61 FDA-approved drugs which is used in the ocular disease treatment was retrieved from the DRUGBANK database (https://go.drugbank.com/). These 61 drugs were optimized through energy minimization using the molecular mechanic 2 (MM2) force field method using the Chem3D and converted to PDB format. The converted PDB structures of the 61 FDA-approved drugs were prepared using AutoDockTools followed by Gasteiger charges calculation and converted to PDBQT format for docking study. The best docking conformation for each drug molecule was chosen by evaluating its binding affinity (kcal/mol) and hydrogen bond interactions. PyMOL was employed to visualize the interactions within the docked complex (DeLano 2022).

Table 1

The box size and X, Y, and Z dimensions of the grid generated for the protein targets.
PDB ID	Grid size (Aͦ)	Grid dimensions (Aͦ)
PDB ID	Grid size (Aͦ)	X	Y	Z
8EUQ	126×126×126	-18.261	2.172	-6.286
6WXJ	26×28×36	11.917	25.889	2.361
3URF	50×48×50	-21.994	-29.000	26.417
5NAY	112×100×106	-7.361	-25.083	-49.000

[Table 1]

Detection and Characterization of DEGs

Gene expression study was conducted on the GSE171830 dataset from GEO, and an overview of the dataset is provided in Table 2. Overall, 31244 genes were identified from the result of GEO2R. After removing repeated genes, unassigned gene titles, empty logFC, and p-value, 20753 genes were identified for further analysis based on the specified cutoff criteria p < 0.05. The DEGs were identified using defined cutoff criteria of logFC ≥ 1.5, logFC ≤ 1.5, and a p-value of p ≤ 0.05. Out of these, 1763 genes exhibited upregulated (logFC ≥ 1.5), while 881 genes displayed downregulated (logFC ≤ 1.5) (Fig. 1A). We used a volcano plot where red colour denoted the upregulated and green colour denoted the downregulated (Fig. 1B), and heatmap (Fig. 1C) to display the DEGs (top 15 upregulated and top 15 downregulated genes).

Table 2

Details of GSE171830 dataset obtained from the GEO database.
Group	Accession	Organism	Diseases condition
Normal	GSM5235409	Homo sapiens	CornealEndothelial_Unaffected
	GSM5235410	Homo sapiens	CornealEndothelial_Unaffected
	GSM5235411	Homo sapiens	CornealEndothelial_Unaffected
	GSM5235415	Homo sapiens	CornealEndothelial_Unaffected
	GSM5235416	Homo sapiens	CornealEndothelial_Unaffected
	GSM5235417	Homo sapiens	CornealEndothelial_Unaffected
FECD	GSM5235406	Homo sapiens	CornealEndothelial_FECD
	GSM5235407	Homo sapiens	CornealEndothelial_FECD
	GSM5235408	Homo sapiens	CornealEndothelial_FECD
	GSM5235412	Homo sapiens	CornealEndothelial_FECD
	GSM5235413	Homo sapiens	CornealEndothelial_FECD
	GSM5235414	Homo sapiens	CornealEndothelial_FECD

[Fig. 1] [Table 2]

Enrichment analysis of DEGs in Functional Categories and Pathways

We utilized the DAVID database to conduct GO and KEGG pathways analysis. The genes exhibiting upregulated and downregulated were entered to the DAVID platform for analysis. We utilized the Functional Annotation Tool and selected the GO options, including GOTERM_BP_DIRECT, GOTERM_CC_DIRECT, and GOTERM_MF_DIRECT, along with KEGG_PATHWAY option. For the analysis, only GO terms and KEGG pathways with a P-value less than 0.05 were taken into consideration. The results were exported and visualized through the utilization of SRplot (http://www.bioinformatics.com.cn/srplot). Gene expression analysis of the upregulated DEGs unveiled the top 10 GO terms from the outcomes of BP, CC, and MF enrichment analysis. These results are visually displayed in Fig. 2A, B, and C respectively. GO chord plot where the genes assigned to their term are displayed in Fig. 2D. The BP of the upregulated DEGs mainly focused on Signal transduction, Inflammatory response, and Immune response (Table 3), CC and MF (Table S1 and S2). Expression analysis of the downregulated DEGs revealed the top 10 GO terms in BP, CC and MF enrichment analysis as illustrated in Fig. 3A, B, and C respectively. The BP, CC, and MF in an enriched analysis of differentially expressed genes of downregulated genes between FECD and unaffected (control) based on the top 10 according to P-Value are displayed in Table S3, S4, and S5. The KEGG pathway analysis of the upregulated DEGs, along with its GO enrichment terms, identified the genes involvement in pathways like interactions between cytokines and their receptors, Phosphoinositide 3-kinase (PI3K)-Akt signaling pathway, Th17 cell differentiation, etc. (Fig. 4A, B and Table 4). KEGG pathway analysis of the downregulated DEGs indicated their involvement in pathways such as metabolic pathways and oxidative phosphorylation (Fig. 5A, B and Table S6).

Table 3

The BP for the enriched analysis of differentially expressed of Upregulated genes between FECD and unaffected (Control) (top 10 according to P-Value).
Term	Name	Count	P-Value
GO:0007165	Signal transduction	187	6.73E-16
GO:0006954	Inflammatory response	98	7.00E-22
GO:0006955	Immune response	91	5.80E-13
GO:0010628	Positive regulation of gene expression	81	1.67E-08
GO:0070374	Positive regulation of ERK1 and ERK2 cascade	42	1.14E-06
GO:0001934	Positive regulation of protein phosphorylation	37	2.28E-05
GO:0050870	Positive regulation of T cell activation	17	2.09E-09
GO:0010718	Positive regulation of epithelial-to-mesenchymal transition	16	1.18E-05
GO:0032722	Positive regulation of chemokine production	14	4.40E-05
GO:0033674	Positive regulation of kinase activity	12	0.029343297

Table 4

The KEGG for the enriched analysis of differentially expressed of Upregulated genes between FECD and unaffected (Control) (top 10 according to P-Value).
Term	Name	Count	P-Value
hsa04640	Hematopoietic cell lineage	30	2.36E-08
hsa04668	TNF signaling pathway	30	4.48E-07
hsa04151	PI3K-Akt signaling pathway	64	7.64E-07
hsa04060	Cytokine-cytokine receptor interaction	55	1.99E-06
hsa04659	Th17 cell differentiation	28	2.34E-06
hsa04380	Osteoclast differentiation	31	2.77E-06
hsa04514	Cell adhesion molecules	33	2.89E-05
hsa04612	Antigen processing and presentation	21	3.18E-05
hsa04658	Th1 and Th2 cell differentiation	21	0.000368
hsa05322	Systemic lupus erythematosus	22	0.019226

[Fig. 2–5] [Table 3–4]

Analysis of Protein-Protein Interaction network

We employed the STRING database (Version 11.5) in combination with Cytoscape software v3.10.0 for evaluating the PPIs among the DEGs. We performed module analysis using the Cytoscape plugin MCODE v2.0.2 with a maximum depth of 100, a node score threshold cutoff of 0.2, and a k-core of for both the upregulated and downregulated genes. Among the upregulated genes, we identified the top three clusters that exhibited significance within the PPI network, as determined by MCODE scores where Cluster 1 = 23.892 (112 nodes and 1326 edges), Cluster 2 = 21.013 (76 nodes and 788 edges), and Cluster 3 = 8.110 (74 nodes and 296 edges) were preferred (Fig. 6A, B and C). We preferred the top three clusters with MCODE scores Cluster 1 (comprising 33 nodes and 454 edges), Cluster 2 (comprising 15 nodes and 44 edges), and Cluster 3 (comprising 5 nodes and 10 edges) correspond to the downregulated genes (Fig. 7A, B and C). The genes found in Cluster 1 of the PPI network, including HLA-DRA, VCAM1, CIQB, COL4A1, COL1A1, and CIQC are associated with upregulated DEGs. These genes are primarily linked to the antigen processing and presentation, cell adhesion molecules, interactions between cytokines and their receptors, hematopoietic cell lineage, Phosphoinositide 3-kinase (PI3K)-Akt signaling pathway, Th17 cell differentiation, Th1 and Th2 cell differentiation (Fig. 4B). HLA-DRA is associated with the enhancement of T-cell activation and immune responses (Fig. 2D). The genes within Cluster 2 such as CSF1R primarily linked to the interactions between cytokines and their receptors, hematopoietic cell lineage, and Phosphoinositide 3-kinase (PI3K)-Akt signaling pathway. TNFRSF11B, found within Cluster 3, is primarily associated with interactions between cytokines and their receptors.

[Fig. 6–7]

Computational Drug Repurposing

Molecular docking was performed using AutoDock Vina (v1.2.3) on a Linux OS (Eberhardt et al. 2021, Trott et al. 2010). The 61 selected FDA-approved drugs which are used in the different treatments of ocular diseases were docked in the active site of four targets (i.e. HLA-DRA, CSF1R, TNFRSF11B and COL4A1). The docking score are displayed as heatmap in Fig. 8. The top 10 drugs with their drug bank ID with the lowest binding energies are listed in the Table 5. Among the top 10 candidates, the protein-ligand interactions of the top two complexes are displayed in Fig. 9.

Table 5

The identified top 10 drugs for each target along with the binding energies.
Target	Drugbank ID	Drug name	Binding energy (kcal/mol)
HLA-DRA (PDB ID: 8EUQ)	DB00253	Medryson	-8.134
	DB00324	Fluorometholone	-8.068
	DB00620	Triamcinolone	-8.320
	DB00885	Pemirolast	-7.872
	DB06711	Naphazoline	-8.661
	DB09065	Cobicistat	-7.905
	DB11886	Infigratinib	-8.321
	DB13055	Oteseconazole	-7.934
	DB13931	Netarsudil	-8.301
	DB16628	Fosdenopterin	-8.105
CSF1R (PDB ID: 6WXJ)	DB00328	Indomethacin	-8.973
	DB00341	Cetrizine	-8.453
	DB00654	Latanoprost	-8.416
	DB00712	Flurbiprofen	-8.952
	DB00896	Rimexolone	-8.849
	DB08819	Tafluprost	-8.457
	DB11611	Lifitegrast	-9.346
	DB13055	Oteseconazole	-8.791
	DB13931	Netarsudil	-10.17
	DB14002	D-alpha-Tocopherol acetate	-8.561
TNFRSF11B (PDB ID: 3URF)	DB00253	Medrysone	-7.183
	DB00324	Fluorometholone	-7.150
	DB00328	Indomethacin	-7.114
	DB00460	Verteprofin	-7.465
	DB00896	Rimexolone	-7.299
	DB09065	Cobicstat	-7.425
	DB11611	Lifitegrast	-7.727
	DB11886	Infigratinib	-7.418
	DB13055	Oteseconazole	-7.280
	DB15071	Omidenepag isopropyl	-7.190
COL4A1 (PDB ID: 5NAY)	DB00324	Flurometholone	-9.042
	DB00328	Indomethacin	-8.821
	DB00620	Triamcinolone	-8.873
	DB00460	Verteporfin	-8.476
	DB06711	Naphazoline	-8.729
	DB06781	Difluprednate	-9.364
	DB08818	Hyaluronic acid	-9.579
	DB11611	Lifitegrast	-9.911
	DB13055	Oteseconazole	-8.689
	DB16628	Fosdenopterin	-8.586

[Fig. 8–9] [Table 5]

FECD is a disorder influenced by multiple genes, leading to intricate pathophysiology. In this study, our goal is to discover the key biomarkers and pathways from the DEGs between a group of 6 patients affected with FECD and another group of 6 unaffected (control) micro array data (Accession no. GSE171830). Microarray is one of the most frequently utilized high-throughput methods for gene expression profiling (Liang et al. 2004). The volcano plot was plotted to visualise the expression pattern of the DEGs, from both the FECD affected and unaffected (control) samples. Through our analysis, we identified HLA-DRA, CSF1R, TNFRSF11B and COL4A1 as key DEGs with significant implications in the pathogenesis or potential therapeutic relevance to FECD. These genes were identified through the analysis of GO, KEGG and PPI. Hence, these DEGs can serve as potential biomarkers with functional significance in understanding the pathogenesis and progression of FECD.

We observed significant upregulation of certain genes with specific GO terms mainly associated with signal transduction, inflammatory response, and immune response within the realm of biological processes. Some significant enrichments were observed in the cellular components, particularly cell surface, exterior surface of the plasma membrane, the essential part of the plasma membrane, endoplasmic reticulum, and ECM. The prominant molecular functions that exhibits upregulation in the DEGs includes the ECM structural constituent, integrin binding, cytokine binding & cytokine activity. KEGG pathway of the upregulation was mostly associated with antigen processing and presentation, hematopoietic cell lineage, interactions between cytokines and their receptors, Phosphoinositide 3-kinase (PI3K)-Akt signaling pathway, and tumor necrosis factor-signaling pathway.

We also observed that significant downregulation of specific GO terms which are mainly associated with cellular response to hypoxia, actin cytoskeleton organization, mitochondrion organization, retinoid metabolic process etc. in the biological process. Significant enrichments were observed in the cellular components particularly the mitochondrial inner membrane, mitochondrial matrix, mitochondrial outer membrane etc. The major molecular function downregulated in the DEGs was the identical protein binding, GTPase activity, GTP binding etc. KEGG pathway of the downregulated was mostly associated with metabolic pathways, oxidative phosphorylation etc.

Literature studies show that the PI3-Akt signaling pathway exhibits significant importance in the biological process of corneal epithelial cells (CECs) and helps in the wound healing of corneal epithelium (CE) (Guo et al. 2019, Han et al. 2014, Li et al. 2020, Bettahi et al. 2014, Chen et al. 2022). Subsequent activation of the Phosphoinositide 3-kinase (PI3K)-Akt signaling pathway promotes inflammation and initiates the secretion of inflammatory cytokines such as IL-6, IL-1β, and TNF-α. TXNIP is a glucose-sensitive gene that holds a critical role in pro- IL-1β expression and exhibits significant induction in retinal cells (Sbai et al. 2010, Koenen et al. 2011, Perrone et al. 2009). Hence, genes primarily linked to the cytokine receptor interactions and Phosphoinositide 3-kinase (PI3)-Akt signaling pathway may also be important for maintaining the integrity of the patient’s cornea and its clinical application for the new drugs.

The GO chord plot reveals that TNFRSF11B is associated with the interaction between cytokines and cytokine receptors. The expression of TNFRSF11B was confirmed by immunohistochemistry in the eyes of patients with secondary glaucoma (Ehlken et al. 2015). The presence of the COL4A1 gene is a critical component of basement membranes found in various tissues, including the vascular and corneal epithelium as well as the conjunctiva of the eyes (Guey et al. 2022). Moreover, mutations in COL4A1 damage multiorgan particularly affecting the eyes, muscles, and kidneys (Vahedi et al. 2011). Immunohistochemistry on CE-DM complexes reveals the presence of HLA-DRA that cluster around the guttae (De et al. 2017). But the expression of TNFRSF11B, COL4A1, and HLA-DRA genes particularly related to the FECD has not been reported yet. Further research is warranted to uncover the precise role of HLA-DRA, CSF1R, TNFRSF11B, and COL4A1 to establish enhanced diagnostic and therapeutic strategies for FECD.

We performed virtual screening against FDA approved drugs to identify the possible drug candidates that can be repurposed against FECD. Drugs such as Naphazoline, Infigratinib, Lifitegrast, Netarsudil, Verteporfin, and Hyaluronic drugs show good binding affinity against the identified biomarkers (Fig. 9). These findings lays-out the initial theoretical groundwork for further experimental investigations. Further, experimental studies are warranted to identify the drugs that can possibly repurposed against FECD.

In summary, a Bioinformatics analysis was conducted to identify the potential biomarkers and possible drug candidates for FECD. The gene enrichment analysis revealed that four genes have significant impact on FECD disease. These four genes are involved in multiple biological functions including antigen processing and presentation, cell adhesion molecules, interactions between cytokines and their receptors, hematopoietic cell lineage, Phosphoinositide 3-kinase (PI3K)-Akt signaling pathway, Th17 cell differentiation, Th1 and Th2 cell differentiation. In addition to this, molecular docking analysis revealed that drugs Naphazoline, Infigratinib, Lifitegrast, Netarsudil, Verteporfin, and Hyaluronic drugs may be the potential small molecule drug for the treatment of FECD. The findings from this study may help in identification of possible drug candidates that may help in FECD treatment.

Data Sharing Statement

The data used to support the findings of this study are included within the article and the supplementary information file(s).

Data Sharing Statement

The data used to support the findings of this study are included within the article and the supplementary information file(s).

Author Contributions

SDS carried out the conceptualization and study design. SDS wrote the original manuscript. SDS, PB, GG and SN performed the data curation. SDS carried out the functional analysis, pathway enrichment analysis, protein–protein interaction (PPI) analysis, molecular docking, data analysis and visualization. SDS, PB, GG and SN reviewed and edited the manuscript. PB and SN supervised the project. All authors made a significant contribution to the work reported, whether in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; agreed on the journal to which the article has been submitted; and agreed to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no conflicts of interest.

Funding

This work is supported by DBT/Wellcome Trust India Alliance (India Alliance) (GPP-0413) and CSIR-NEIST in the form of in-house project (OLP-2088).

Acknowledment

We thank Prof. G. Narahari Sastry, Professor, Indian Institute of Technology, Hyderabad for his valuable inputs and mentoring this project. This work is supported by DBT/Wellcome Trust India Alliance (India Alliance) (GPP-0413) and CSIR-NEIST in the form of an in-house project (OLP-2088). Manuscript communication number CSIR-NEIST/PUB/2024/004.

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Biomarker driven drug repurposing for Fuchs’ Endothelial Corneal Dystrophy (FECD): A Computational study

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Version 1

Abstract

Figures

Introduction

Materials and methods

Data collection

Data Processing and Uncovering Differentially Expressed Genes

Functional analysis of DEGs

Integrating PPI network construction with module analysis

Drug repurposing

[Table 1]

Results

Detection and Characterization of DEGs

[Fig. 1] [Table 2]

Enrichment analysis of DEGs in Functional Categories and Pathways

[Fig. 2–5] [Table 3–4]

Analysis of Protein-Protein Interaction network

[Fig. 6–7]

Computational Drug Repurposing

Discussion

Conclusions

Data Sharing Statement

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1