Functional relevance for central cornea thickness-associated genetic variants by using integrative analyses

A comparison of endothelial cell count, central corneal thickness and intraocular pressure in different ethnic groups in the Western Cape, South Africa

PURPOSE

To compare endothelial cell counts (ECC), central corneal thickness (CCT) and intraocular pressure (IOP) in different ethnic groups.

METHODS

Between January 2019 and December 2021, we enrolled 373 patients who self-identified as native African (116), ethnically diverse (157) or of European descent (100). Mean intraocular pressure (IOP), CCT and ECC were recorded.

RESULTS

IOP was similar between the groups (African mean IOP 15.7 ± 2.2 mmHg, ethnically diverse 15.8 ± 2.2 mmHg, European 16.0 ± 2.0 mmHg, p = 0.48). There were age differences but no gender differences between ethnic groups. Mean CCT was 503.6 ± 30.0 µm (African), 516.8 ± 30.0 µm (ethnically diverse) and 539.1 ± 34.2 µm (European) (p < 0.002 for all). Mean ECC was 2775 ± 272 cells/mm2 (African), 2678 ± 233 cells/mm2 (ethnically diverse) and 2639 ± 313 cells/mm2 (European). These differences were significant between Africans and Europeans (p = 0.001) and Africans and ethnically diverse groups (p = 0.01).

CONCLUSION

ECC was highest in Africans with lowest CCT and conversely, Europeans demonstrated lowest ECC and highest CCT. Ethnically diverse participants demonstrated values between those of Africans and Europeans. We hypothesize that genomic research is required to determine if these differences have a genetic basis.

Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease

Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3' UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.

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.

Systematically Displaying the Pathogenesis of Keratoconus via Multi-Level Related Gene Enrichment-Based Review

Keratoconus (KC) is an etiologically heterogeneous corneal ectatic disorder. To systematically display the pathogenesis of keratoconus (KC), this study reviewed all the reported genes involved in KC, and performed an enrichment analysis of genes identified at the genome, transcription, and protein levels respectively. Combined analysis of multi-level results revealed their shared genes, gene ontology (GO), and pathway terms, to explore the possible pathogenesis of KC. After an initial search, 80 candidate genes, 2,933 transcriptional differential genes, and 947 differential proteins were collected. The candidate genes were significantly enriched in extracellular matrix (ECM) related terms, Wnt signaling pathway and cytokine activities. The enriched GO/pathway terms of transcription and protein levels highlight the importance of ECM, cell adhesion, and inflammatory once again. Combined analysis of multi-levels identified 13 genes, 43 GOs, and 12 pathways. The pathogenic relationships among these overlapping factors maybe as follows. The gene mutations/variants caused insufficient protein dosage or abnormal function, together with environmental stimulation, leading to the related functions and pathways changes in the corneal cells. These included response to the glucocorticoid and reactive oxygen species; regulation of various signaling (P13K-AKT, MAPK and NF-kappaB), apoptosis and aging; upregulation of cytokines and collagen-related enzymes; and downregulation of collagen and other ECM-related proteins. These undoubtedly lead to a reduction of extracellular components and induction of cell apoptosis, resulting in the loosening and thinning of corneal tissue structure. This study, in addition to providing information about the genes involved, also provides an integrated insight into the gene-based etiology and pathogenesis of KC.

Understanding the role of corneal biomechanics-associated genetic variants by bioinformatic analyses

PURPOSE

To analyze functions of corneal biomechanical properties (CBP)-related variants as corneal resistance factor (CRF) and corneal hysteresis (CH).

METHODS

Related single nucleotide polymorphisms (SNPs) and genes were identified from NHGRI-EBI GWAS catalog, GWASdb v2 and possible data in published studies. HaploReg v4.1 was used to find linkage SNPs. Functional annotations were performed by GWAVA, CADD and RegulomeDB. GTEx Portal database was used to find out expression quantitative trait locus (eQTL) association. Enrichr was used to annotate the function of GWAS gene and the associated signal pathway. STING (v11.0) database was utilized for protein interaction and network construction.

RESULTS

The integration of 302 CH-associated and 420 CRF-associated lead SNPs has produced 531 CBP-associated lead SNPs. A total of 5,324 proxy variants identified using the HaploReg v4.1 and lead SNPs were functionally annotated. Based on the threshold (CADD ≥ 10, GWAVA ≥ 0.4 and RegulomeDB < rank 3), 23 prioritized putative regulatory SNPs were identified. Eight prioritized eQTL variants (rs75203695, rs34861673, rs846766, rs11024102, rs1377416, rs3829492, rs9934438 and rs197912) were found with strong potential of CBP regulation. It was indicated that CBP-associated genes were significantly enriched in extracellular matrix receptor interaction pathway, closely related to the phenotype of corneal dystrophy and keratoconus. COL1A1, SMAD3, BMP4 and RUNX2 occupied the core position in the co-expression network.

CONCLUSIONS

Data integrative analysis can evaluate CBP variations and explore collagen and extracellular matrix pathways in CBP regulation, which is a promising tool to investigate biological process of corneal diseases.

Clinical, Demographic, and Tomographic Aspects Related to Iris Mammillations Among Patients With Keratoconus: A Cross-Sectional Study

PURPOSE

Iris mammillations (IM) were previously described in patients with keratoconus, but the clinical implications of this finding have never been studied. Our aim was to investigate demographic, tomographic, and clinical characteristics potentially associated with the presence of IM among patients with keratoconus.

METHODS

This was a cross-sectional study performed among patients with keratoconus in a public-affiliated university hospital. All patients under follow-up were considered eligible to participate in the study. Participants were evaluated by 2 trained ophthalmologists and submitted to corneal tomography (Pentacam). Selected demographic, clinical, and tomographic characteristics were assessed and compared among participants with IM (IM group) and without IM (No-IM group) using the Wilcoxon test or 2-tailed Fisher exact test, as appropriate.

RESULTS

The study population consisted of 106 subjects and 19 (17.9%) presented with IM. The median age and interquartile range were 18 years old (14-24) in the IM group and 20 years old (17-24) in the No-IM group (P = 0.135). The female proportion was 47.3% in the IM group and 52.8% in the No-IM group (P = 0.801). Median (interquartile ranges) pachymetric values of the right eyes were 498 (466-525) for the IM group and 459 (421-482) for the No-IM group (P = 0.005). For the left eyes, the values were 490 (456-523) in the IM group and 450 (418-485) in the No-IM group (P = 0.024).

CONCLUSIONS

Subjects with keratoconus presenting with IM have thicker corneas than those without IM. Follow-up studies should be performed to evaluate the clinical implications of this finding.