Integrative transcriptomics analysis and experimental validation reveal immunomodulatory patterns in keratoconus

MicroRNAs in Corneal Diseases: Emerging Roles as Biomarkers, Regulators, and Therapeutics

MicroRNAs (miRNAs) are conserved, short, non-coding RNAs that play a crucial role in regulating gene expression. Emerging evidence suggests that miRNAs are closely involved in the pathophysiology of various corneal diseases, particularly in regulating corneal wound healing, inflammation and neovascularization. In this review, we summarized the recent progress of miRNAs in corneal diseases, especially focused on their application as diagnostic biomarkers, regulators of cell biology, and therapeutic targets. Recent advances in miRNA detection technology have made it possible to analyze minimal miRNAs in samples such as tears or exosomes, further enhancing the ability to identify disease-specific miRNA profiles and providing potential objective indicators for the early diagnosis of disease. Meanwhile, we summarized the mechanisms and pathways of multiple miRNAs in regulating various biological processes of corneal cells, as well as the advantages of studying miRNA compared to proteins or genes. Furthermore, we explore the potential of miRNAs-based therapies, especially introduce various miRNA delivery systems and challenges associated with clinical translation. This review highlights the need for further research to harness the full potential of miRNAs in treating various corneal diseases.

Unraveling the Link Between Obesity and Keratoconus Risk Based on Genetic Evidence

Purpose

Keratoconus (KCN) is a progressive corneal disorder leading to vision impairment. While genetic and environmental factors contribute to its development, the role of obesity in KCN risk remains unclear. This study aimed to estimate the causal effect between obesity, measured by body mass index (BMI) and waist-to-hip ratio (WHR), and the risk of KCN.

Methods

This two-sample Mendelian randomization (MR) case-control study used genome-wide association study data from GIANT, MRC-IEU UK Biobank, and FinnGen. BMI and WHR were used to estimate general and central obesity, respectively. Data from 311 KCN cases and 209,287 controls were analyzed. Causal effect estimates of BMI, WHR, and obesity-related chronic diseases on KCN risk were calculated.

Results

Genetically predicted higher BMI was significantly associated with increased KCN risk (odds ratio [OR] = 2.003; 95% confidence interval [CI], 1.203-3.335; P = 0.008), as determined using European genetic databases. Consistent results were observed with the weighted median, MR-Egger, and MR-pleiotropy residual sum and outlier methods. No significant causal effect was found between WHR and KCN risk (OR = 0.578; 95% CI, 0.196-1.705; P = 0.321). Sensitivity analyses showed no evidence of pleiotropy, and no significant causal effect was observed between obesity-related chronic diseases and KCN risk.

Conclusions

Using European genetic databases, general obesity was identified as a strong, independent causal effect contributor to KCN, while central obesity showed no association. These findings provide new insights into obesity's role in KCN development and may inform future preventive strategies.

Translational Relevance

This study suggests that general obesity is a causal risk factor for keratoconus, suggesting that obesity management could help prevent or mitigate KCN progression.

Omics in Keratoconus: From Molecular to Clinical Practice

Keratoconus (KC) is a progressive ocular disorder marked by structural and functional alterations of the cornea, leading to significant visual impairment. Recent studies indicate that these corneal changes are linked to molecular and cellular mechanisms that disrupt and degrade the extracellular matrix. This degradation is influenced by proteinases that contribute to a loss of homeostasis and an imbalance in the antioxidant/oxidative state within the cornea, fostering oxidative stress, inflammation, and apoptosis. Although these biological processes have been identified primarily through molecular biology research, omics technologies have significantly advanced our understanding of the physiological and pathological phenomena associated with KC. Omics studies encompassing genomics, transcriptomics, proteomics, epigenomics, and metabolomics, have emerged as critical tools in elucidating the complex biological landscape of various diseases, including ocular conditions. The integrative application of these studies has demonstrated their potential in personalizing medicine across diverse fields such as oncology, neurology, and ophthalmology. This review aims to describe findings from omics research applied to keratoconus, highlighting the genomic, transcriptomic, proteomic, epigenomic, and metabolomic aspects derived from ocular and other biological samples. Notably, the molecular insights gained from these studies hold promise for identifying biomarkers of keratoconus, which could enhance diagnostic accuracy and therapeutic strategies. The exploration of these biomarkers may facilitate improved management and treatment options for patients, contributing to personalized care in keratoconus management.

Constructing a disease-specific ceRNA coregulatory network for keratoconus diagnosis and landscape of the immune environment

PURPOSE

The early diagnosis of keratoconus (KC) is crucial for making treatment decisions. Therefore, the purpose of this study was to determine the potential disease-specific gene biomarker and landscape the immune environment in KC.

METHODS

The transcriptome data of KC was obtained from Gene Expression Omnibus (GEO) and ArrayExpress databases for next analysis. The differently expressed mRNAs, microRNAs and lncRNAs between KC and control groups were firstly identified and the disease-specific protein-protein interaction (PPI) network as well as competing endogenous RNA (ceRNA) coregulatory network were constructed to explore the underlying molecular mechanism of KC. Besides, ElasticNet algorithm was used to develop a diagnostic model and associated nomograms to improve diagnosis of KC. Finally, multiple deconvolution methodologies were applied to decode the immune environment of KC patients.

RESULTS

In brief, we constructed the disease-specific PPI and ceRNA networks in KC through integrative analyses. The pathway enrichment manifested that these networks were significantly associated with lipopolysaccharide, chemokine and inflammatory related pathways. Based on the ceRNA network, we constructed a diagnostic model and associated nomogram which manifested a good performance for diagnosis of KC. Moreover, contrasted to control groups, we obviously observed that a distinct immune microenvironment existed in KC patients. Via single-cell sequencing analysis, we found that immune cells (Monocytes, Macrophages, and T cells) were strongly connected with corneal cells in KC patients.

CONCLUSIONS

In sum, we systematically constructed a diagnostic model and associated nomogram which provided novel biomarkers for the early detection of KC. Besides, our study comprehensively displayed the immune microenvironment of KC which provided new insights for understanding the molecular mechanism of KC.

Mechanical Strain of Corneal Epithelium Influences the Expression of Genes Implicated in Keratoconus

Purpose

Although mechanical injury to the cornea (e.g. chronic eye rubbing) is a known risk factor for keratoconus progression, how it contributes to loss of corneal integrity is not known. Here, we set out to determine how eye rubbing can influence keratoconus progression by exploring the expression of known disease markers in mechanically stressed corneal epithelial cells.

Methods

To explore the effects of mechanical stress on the expression of genes implicated in keratoconus (e.g. WNT10A, COL12A1, and TGFB1), we measured their expression using an in vitro model that simulates eye rubbing by cyclic stretching of an immortalized human corneal epithelial cell line (hTCEpi) for 16 hours. We further examined the influence of WNT10A expression in hTCEpi cells using loss-of-function approaches.

Results

Mechanical strain led to a marked reduction in WNT10A mRNA and protein expression, as well as decreased collagen XII mRNA and protein expression, in hTCEpi cells. Reduced expression of WNT10A protein in WNT10A knockdown cells resulted in reduced protein expression of collagens I and XII, and reduced mRNA expression of MMP9 and TGFB1. Conversely, primary keratocytes treated with recombinant WNT10A protein increased TGFB1 mRNA expression.

Conclusions

We provide a molecular explanation for how mechanical strain results in reduced expression of WNT10A in the corneal epithelium, which, in turn, leads to depletion of collagen type I and XII, and TGFβ1 expression. These results provide a molecular link among mechanical strain, WNT10A expression, and the biomechanical failure of the keratoconus cornea.

Transcriptomic analysis of keratoconus in Han Chinese patients: Insights into differential gene expression and ethnic-specific patterns

Keratoconus (KC) is a progressive corneal ectatic disorder with a high prevalence among Asians. This study aimed to explore the differential gene expression patterns in Han Chinese patients with KC, focusing on mRNAs and long noncoding RNAs (lncRNAs), to provide insights into the pathogenesis of the disease. Corneal tissues from KC patients and healthy controls were collected, and RNA sequencing was performed to profile mRNA and lncRNA expression. A total of 1973 differentially expressed mRNAs (DEGs) and 386 differentially expressed lncRNAs (DELs) were identified in KC-affected corneas. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed significant enrichment in pathways related to ECM modulation, PI3K-Akt pathway and calcium signaling pathway. Furthermore, protein-protein interaction (PPI) network highlighted hub genes involved in ECM remodeling and inflammatory responses. Co-expression analysis of lncRNAs and mRNAs further prioritized 13 DELs linked to these hub genes. RT-qPCR validation confirmed the differential expression of select candidates. A meta-analysis integrating seven datasets from diverse ethnic backgrounds was performed and it suggested ethnic-specific differences in gene expression patterns. This study sheds new light on the molecular mechanisms underlying KC in the Han Chinese population, pinpointing potential therapeutic targets. It also emphasizes the critical role of ethnic-specific gene expression patterns in KC research, highlighting a need for tailored approaches in disease management and treatment.

Multi-dataset identification of innovative feature genes and molecular mechanisms in keratoconus

This study aimed to identify feature genes and explore the molecular mechanisms of keratoconus (KC). We downloaded data files from NCBI GEO public database. The Limma package was used for differential expression analysis of gene profiles. Lasso regression was used to identify the feature genes. The CIBERSORT algorithm was used to infer the proportion of immune-infiltrating cells and analyse the correlation between gene expression levels and immune cells. Related transcription factors and miRNAs of key genes were predicted using the Cistrome DB and Mircode databases. Analysis of expression differences in disease genes was based on the GeneCards database. The CMap was used to analyse targeted therapeutic drugs. IHC was performed to verify the expression levels of ATOH7 and MYRF in corneas. Exactly 593 upregulated and 473 downregulated genes were identified. Lasso regression analysis identified ATOH7, DBNDD1, RNF217-AS1, ARL11, MYRF and SNORA74B as feature genes for KC. All key genes were correlated with immune infiltration and the levels of activated memory CD4+ T cells and plasma cells were significantly increased. miRNA, IRF and STAT families were correlated to feature genes. The expression levels of key genes were significantly correlated to KC-related genes. Entinostat, ochratoxin-a, diphencyprone and GSK-3-inhibitor-II were predicted as potential KC medications. The expression of MYRF was significantly higher in the KC samples, contrary to the expression of ATOH7. KC is related to both immune infiltration and genetic factors. MYRF and ATOH7 were newly identified and verified feature genes of KC.

The candidate proteins associated with keratoconus: A meta-analysis and bioinformatic analysis

PURPOSE

Keratoconus (KC) is a multifactorial disorder. This study aimed to conduct a systematic meta-analysis to exclusively explore the candidate proteins associated with KC pathogenesis.

METHODS

Relevant literature published in the last ten years in Pubmed, Web of Science, Cochrane, and Embase databases were searched. Protein expression data were presented as the standard mean difference (SMD) and 95% confidence intervals (CI). The meta-analysis is registered on PROSPERO, registration number CRD42022332442 and was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement (PRISMA). GO and KEGG enrichment analysis were performed, as well as the miRNAs and chemicals targeting the candidate proteins were predicted. PPI was analyzed to screen the hub proteins, and their expression was verified by RT-qPCR.

RESULTS

A total of 21 studies were included in the meta-analysis, involving 346 normal eyes and 493 KC eyes. 18 deregulated proteins with significant SMD values were subjected to further analysis. In which, 7 proteins were up-regulated in KC compared with normal controls, including IL6 (SMD 1.54, 95%CI [0.85, 2.24]), IL1B (SMD 2.07, 95%CI [0.98, 3.16]), TNF (SMD 2.1, 95%CI [0.24, 3.96]), and MMP9 (SMD 1.96, 95%CI [0.68, 3.24]). While 11 proteins were down-regulated in KC including LOX (SMD 2.54, 95%CI [-4.51, -0.57]). GO and KEGG analysis showed that the deregulated proteins were involved in inflammation, extracellular matrix (ECM) remodeling, and apoptosis. MMP9, IL6, LOX, TNF, and IL1B were regarded as hub proteins according to the PPI analysis, and their transcription changes in stromal fibroblasts of KC were consistent with the results of the meta-analysis. Moreover, 10 miRNAs and two natural polyphenols interacting with hub proteins were identified.

CONCLUSION

This study obtained 18 candidate proteins and demonstrated altered cytokine profiles, ECM remodeling, and apoptosis in KC patients through meta-analysis and bioinformatic analysis. It will provide biomarkers for further understanding of KC pathogenesis, and potential therapeutic targets for the drug treatment of KC.

Identification of the immune-associated characteristics and predictive biomarkers of keratoconus based on single-cell RNA-sequencing and bulk RNA-sequencing

Background

Whether keratoconus (KC) is an inflammatory disease is currently debated. Hence, we aimed to investigate the immune-related features of KC based on single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data.

Methods

scRNA-seq data were obtained from the Genome Sequence Archive (GSA), bulk RNA-seq data were obtained from the Gene Expression Omnibus (GEO), and immune-associated genes(IAGs) were obtained from the ImmPort database. Cell clusters of KC were annotated, and different cell clusters were then selected. The IAG score of each cell was calculated using the AUCell package. Three bulk RNA-seq datasets were merged and used to identify the differentially expressed genes (DEGs), biological functions, and immune characteristics. Weighted gene coexpression network analysis (WGCNA) was used to select the IAG score-related hub genes. Based on scRNA-seq and bulk RNA-seq analyses, three machine learning algorithms, including random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) regression analysis, were used to identify potential prognostic markers for KC. A predictive nomogram was developed based on prognostic markers.

Results

Six cell clusters were identified in KC, and decreased corneal stromal cell-5 (CSC-5) and increased CSC-6 were found in KC. CSC and immune cell clusters had the highest IAG scores. The bulk RNA-seq analysis identified 1362 DEGs (553 upregulated and 809 downregulated) in KC. We found different immune cell populations and differentially expressed cytokines in KC. More than three key IAG score-related modules and 367 genes were identified. By integrating the scRNA-seq and bulk RNA-seq analyses, 250 IAGs were selected and then incorporated into three machine learning models, and 10 IAGs (CEP112, FYN, IFITM1, IGFBP5, LPIN2, MAP1B, RNASE1, RUNX3, SMIM10, and SRGN) were identified as potential prognostic genes that were significantly associated with cytokine and matrix metalloproteinase(MMP)1-14 expression. Finally, a predictive nomogram was constructed and validated.

Conclusion

Taken together, our results identified CSCs and immune cell clusters that may play a key role during KC progression by regulating immunological features and maintaining cell stability.