Altered Regulation of mRNA and miRNA Expression in Epithelial and Stromal Tissue of Keratoconus Corneas

Stachon, Tanja; Nastaranpour, Mahsa; Seitz, Berthold; Meese, Eckart; Latta, Lorenz; Taneri, Suphi; Ardjomand, Navid; Szentmáry, Nóra; Ludwig, Nicole

doi:10.1167/iovs.63.8.7

Cited by 10 publications

(6 citation statements)

References 37 publications

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“…SFRP1 accumulation may induce apoptosis in corneal epithelial cells [ 55 , 56 ]. In a study of differentially expressed genes (DEGs) in the epithelium and stroma of eight healthy and eight KC corneas, the most upregulated gene in the epithelium was SFRP1 [ 18 ]. In the present study, we also found up-regulation of HSPB1 and SFRP1, with a down-regulation of LTF, confirming the elevated levels of oxidative stress and cell apoptosis in KC.…”

Section: Discussionmentioning

confidence: 99%

“…Post-transcriptional regulation by miRNAs has been proven to be involved in many ocular diseases [ 66 ]. The role of miRNAs in KC has been explored recently [ 18 , 67 , 68 ]. Thirteen miRNAs in our study showed up-regulation expression in conical corneas, and their targeted mRNAs were lowly expressed in KC corneas.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, gene expression profiles using the whole cornea tissue, epithelium, or stroma layer from individuals with KC have been applied to identify KC-related proteins and pathways using microarray, next-generation sequencing (NGS), and other high-throughput technologies. Dysregulated gene expression has been reported in KC corneal tissue and stromal cells [ 15 – 18 ]. Single-cell RNA sequencing (scRNA-Seq) analysis was performed on 39,214 cells from the central corneas of patients with KC and healthy individuals, confirming the central role of dysregulation of collagen in corneal stromal cells in KC [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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

Song,

et al. 2024

PLoS ONE

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Song,

et al. 2024

PLoS ONE

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“…In KC, abnormal differentiation signals are observed in corneal superficial cells. A recent study observed a reduced pool of limbal suprabasal cells and found that signalling pathways that affect metabolic changes and cell contact in epithelial and stromal cells are affected in KC ( 54 ). Further research is needed to determine whether these genes and their encoded proteins have an impact on other corneal cells.…”

Section: Discussionmentioning

confidence: 99%

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

Niu,

Xu,

Zhu

et al. 2023

Front. Immunol.

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BackgroundWhether 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.MethodsscRNA-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.ResultsSix 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.ConclusionTaken 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.

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“…Then nuclear RNase III enzyme Drosha and its cofactor process pri-miRNAs into 60 nt length precursor miRNAs (pre-miRNAs) and further processing by RNase III enzyme Dicer leads to creation of mature miRNAs ( Ghorai and Ghosh, 2014 ; Fuchs Wightman et al, 2018 ). Previously, miRNAs have been studied in KTCN mainly with the focus on miR-184 that was reported to be related to this disease ( Hughes et al, 2011 ; Lechner et al, 2013 ; Bykhovskaya et al, 2015 ; Farzadfard et al, 2016 ; Stachon et al, 2022 ; Zhang et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Differentially expressed microRNAs targeting genes in key pathways in keratoconus

Nowak-Malczewska,

Swierkowska,

Gajecka

2024

Front. Genet.

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Introduction: Keratoconus (KTCN) is a corneal ectasia, characterized by a progressive thinning and protrusion of the cornea, with a complex etiology involving genetic, behavioral, lifestyle, and environmental factors. Previous studies indicated that microRNAs (miRNAs) could be involved in KTCN pathogenesis. This in silico study aimed to identify precursor microRNAs (pre-miRNAs) differentially expressed in KTCN corneas and to characterize mature miRNAs and their target genes.Materials and methods: Expression levels of pre-miRNAs were retrieved from our previously obtained RNA sequencing data of 25 KTCN and 25 non-KTCN human corneas (PMID:28145428, PMID:30994860). Differential expression with FDR ≤0.01 and ≥1.5-fold changes were considered significant. Lists of target genes (target score ≥90) of mature miRNAs were obtained from miRDB. Revealed up-/downregulated miRNAs and their target genes were assessed in databases and literature. Enrichment analyses were completed applying the DAVID database.Results: From a total of 47 pre-miRNAs, six were remarkably upregulated (MIR184, MIR548I1, MIR200A, MIR6728, MIR429, MIR1299) and four downregulated (MIR6081, MIR27B, MIR23B, MIR23A) in KTCN corneas. Out of the 1,409 target genes, 220 genes with decreased and 57 genes with increased expression levels in KTCN samples vs non-KTCN samples were found. The extracellular matrix (ECM) organization, response to mechanical stimulus, regulation of cell shape, and signal transduction processes/pathways were identified as distinctive in enrichment analyses. Also, processes associated with the regulation of transcription and DNA binding were listed.Conclusion: Indicated miRNAs and their target genes might be involved in KTCN pathogenesis via disruption of crucial molecular processes, including ECM organization and signal transduction.

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Altered Regulation of mRNA and miRNA Expression in Epithelial and Stromal Tissue of Keratoconus Corneas

Cited by 10 publications

References 37 publications

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

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

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

Differentially expressed microRNAs targeting genes in key pathways in keratoconus

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