Multiple Bioinformatics Analyses of Integrated Gene Expression Profiling Data and Verification of Hub Genes Associated with Diabetic Retinopathy

Gigantol protects retinal pigment epithelial cells against high glucose-induced apoptosis, oxidative stress and inflammation by inhibiting MTDH-mediated NF-kB signaling pathway

OBJECTIVE

As a frequent complication of diabetes mellitus (DM), diabetic retinopathy (DR) is now one of the major causes of blindness. Recent reports have shown that retinal pigment epithelial cell (RPEC) damage plays an essential part in DR development and progression. This work intended to explore the potential effects of Gigantol on high glucose (HG)-stimulated RPEC damage and identify potential mechanisms.

METHODS

Cell viability, cell damage, and cell apoptosis were evaluated by CCK-8, lactate dehydrogenase (LDH) and flow cytometry assays. The levels of oxidative stress biomarkers and pro-inflammatory cytokines were assessed using corresponding commercial kits and ELISA. Additionally, the levels of MTDH and NF-kB signaling pathway-related proteins were detected by western blotting.

RESULTS

Gigantol dose-dependently enhanced cell viability and decreased apoptosis in HG-challenged ARPE-19 cells. Also, Gigantol notably relieved oxidative stress and inflammatory responses in ARPE-19 cells under HG conditions. Gigantol dose-dependently suppressed MTDH expression. In addition, MTDH restoration partially counteracted the protective effects of Gigantol on ARPE-19 cells subject to HG treatment. Mechanically, Gigantol inactivated the NF-kB signaling pathway, which was partly restored after MTDH overexpression.

CONCLUSION

Our findings suggested that Gigantol protected against HG-induced RPEC damage by inactivating the NF-kB signaling via MTDH inhibition, offering a potent therapeutic drug for DR treatment.

Probable Treatment Targets for Diabetic Retinopathy Based on an Integrated Proteomic and Genomic Analysis

Purpose

Using previously approved medications for new indications can expedite the lengthy and expensive drug development process. We describe a bioinformatics pipeline that integrates genomics and proteomics platforms to identify already-approved drugs that might be useful to treat diabetic retinopathy (DR).

Methods

Proteomics analysis of vitreous humor samples from 12 patients undergoing pars plana vitrectomy for DR and a whole genome dataset (UKBiobank TOPMed-imputed) from 1330 individuals with DR and 395,155 controls were analyzed independently to identify biological pathways associated with DR. Common biological pathways shared between both datasets were further analyzed (STRING and REACTOME analyses) to identify target proteins for probable drug modulation. Curated target proteins were subsequently analyzed by the BindingDB database to identify chemical compounds they interact with. Identified chemical compounds were further curated through the Expasy SwissSimilarity database for already-approved drugs that interact with target proteins.

Results

The pathways in each dataset (proteomics and genomics) converged in the upregulation of a previously unknown pathway involved in DR (RUNX2 signaling; constituents MMP-13 and LGALS3), with an emphasis on its role in angiogenesis and blood-retina barrier. Bioinformatics analysis identified U.S. Food and Drug Administration (FDA)-approved medications (raltitrexed, pemetrexed, glyburide, probenecid, clindamycin hydrochloride, and ticagrelor) that, in theory, may modulate this pathway.

Conclusions

The bioinformatics pipeline described here identifies FDA-approved drugs that can be used for new alternative indications. These theoretical candidate drugs should be validated with experimental studies.

Translational Relevance

Our study suggests possible drugs for DR treatment based on an integrated proteomics and genomics pipeline. This approach can potentially expedite the drug discovery process by identifying already-approved drugs that might be used for new indications.

Genome-wide characterization leading to simple sequence repeat (SSR) markers development in Shorea robusta

Tropical rainforests in Southeast Asia are enriched by multifarious biota dominated by Dipterocarpaceae. In this family, Shorea robusta is an ecologically sensitive and economically important timber species whose genomic diversity and phylogeny remain understudied due to lack of datasets on genetic resources. Smattering availability of molecular markers impedes population genetic studies indicating a necessity to develop genomic databases and species-specific markers in S. robusta. Accordingly, the present study focused on fostering de novo low-depth genome sequencing, identification of reliable microsatellites markers, and their validation in various populations of S. robusta in Uttarakhand Himalayas. With 69.88 million raw reads assembled into 1,97,489 contigs (read mapped to 93.2%) and a genome size of 357.11 Mb (29 × coverage), Illumina paired-end sequencing technology arranged a library of sequence data of ~ 10 gigabases (Gb). From 57,702 microsatellite repeats, a total of 35,049 simple sequence repeat (SSR) primer pairs were developed. Afterward, among randomly selected 60 primer pairs, 50 showed successful amplification and 24 were found as polymorphic. Out of which, nine polymorphic loci were further used for genetic analysis in 16 genotypes each from three different geographical locations of Uttarakhand (India). Prominently, the average number of alleles per locus (Na), observed heterozygosity (Ho), expected heterozygosity (He), and the polymorphism information content (PIC) were recorded as 2.44, 0.324, 0.277 and 0.252, respectively. The accessibility of sequence information and novel SSR markers potentially enriches the current knowledge of the genomic background for S. robusta and to be utilized in various genetic studies in species under tribe Shoreae.

Gene Biomarkers Related to Th17 Cells in Macular Edema of Diabetic Retinopathy: Cutting-Edge Comprehensive Bioinformatics Analysis and In Vivo Validation

Background

Previous studies have shown that T-helper 17 (Th17) cell-related cytokines are significantly increased in the vitreous of proliferative diabetic retinopathy (PDR), suggesting that Th17 cells play an important role in the inflammatory response of diabetic retinopathy (DR), but its cell infiltration and gene correlation in the retina of DR, especially in diabetic macular edema (DME), have not been studied.

Methods

The dataset GSE160306 was downloaded from the Gene Expression Omnibus (GEO) database, which contains 9 NPDR samples and 10 DME samples. ImmuCellAI algorithm was used to estimate the abundance of Th17 cells in 24 kinds of infiltrating immune cells. The differentially expressed Th17 related genes (DETh17RGs) between NPDR and DME were documented by difference analysis and correlation analysis. Through aggregate analyses such as gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis, a protein-protein interaction (PPI) network was constructed to analyze the potential function of DETh17RGs. CytoHubba plug-in algorithm, Lasso regression analysis and support vector machine recursive feature elimination (SVM-RFE) were implemented to comprehensively identify Hub DETh17RGs. The expression archetypes of Hub DETh17RGs were further verified in several other independent datasets related to DR. The Th17RG score was defined as the genetic characterization of six Hub DETh17RGs using the GSVA sample score method, which was used to distinguish early and advanced diabetic nephropathy (DN) as well as normal and diabetic nephropathy. Finally, real-time quantitative PCR (qPCR) was implemented to verify the transcription levels of Hub DETh17RGs in the STZ-induced DR model mice (C57BL/6J).

Results

238 DETh17RGs were identified, of which 212 genes were positively correlated while only 26 genes were negatively correlated. Six genes (CD44, CDC42, TIMP1, BMP7, RHOC, FLT1) were identified as Hub DETh17RGs. Because DR and DN have a strong correlation in clinical practice, the verification of multiple independent datasets related to DR and DN proved that Hub DETh17RGs can not only distinguish PDR patients from normal people, but also distinguish DN patients from normal people. It can also identify the initial and advanced stages of the two diseases (NPDR vs DME, Early DN vs Advanced DN). Except for CDC42 and TIMP1, the qPCR transcription levels and trends of other Hub DETh17RGs in STZ-induced DR model mice were consistent with the human transcriptome level in this study.

Conclusion

This study will improve our understanding of Th17 cell-related molecular mechanisms in the progression of DME. At the same time, it also provides an updated basis for the molecular mechanism of Th17 cell crosstalk in the eye and kidney in diabetes.

Long non-coding RNA MEG3 inhibits neovascularization in diabetic retinopathy by regulating microRNA miR-6720-5p and cytochrome B5 reductase 2

Diabetic retinopathy (DR) is a major cause of vision loss in working and elderly populations. long non-coding RNA (LncRNA) MEG3 is thought to have some effect on DR, but the exact mechanism remains to be clarified. The expression levels of lncRNA MEG3, miR-6720-5p, and cytochrome B5 reductase 2 (CYB5R2) in human retinal microvascular endothelial cells (hRMECs) were detected by quantitative reverse transcription polymerase chain reaction (qRT-PCR). 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), transwell migration, and tube formation assays were used to determine the cell viability, migration, and tube formation ability of hRMECs, respectively. The interaction of MEG3, miR-6720-5p, and CYB5R2 was detected and explored by a luciferase assay. The expression of MEG3 and CYB5R2 was upregulated and that of miR-6720-5p was downregulated in patients with DR and hRMECs treated with high glucose. Knocking down MEG3 or CYB5R2 promoted proliferation, migration, and neovascularization in hRMECs. The intervention of miR-6720-5p reversed the effect of MEG3 knockdown on hRMEC function, and this effect was eliminated by silencing CYB5R2. Therefore, MEG3 acted as a sponge to suppress miR-6720-5p and regulate the expression of CYB5R2, thereby inhibiting DR neovascularization.

Genome skimming-based simple sequence repeat (SSR) marker discovery and characterization in Grevillea robusta

Proteaceae, a largely southern hemisphere family consisting of 80 genera distributed in Australia and southern Africa as its centres of greatest diversity, also extends well in northern and southern America. Under this family, Grevillea robusta is a fast-growing species got popularity in farm and avenue plantations. Despite the ecological and economic importance, the species has not yet been investigated for its genetic improvement and genome-based studies. Only a few molecular markers are available for the species or its close relatives, which hinders  genomic and population genetics studies. Genetic markers have been intensively applied for the main strategies in breeding programs, especially for the economically important traits. Hence, it is of utmost priority to develop genomic database resources and species-specific markers for studying quantitative genetics in G. robusta. Given this, the present study aimed to develop de novo genome sequencing, robust microsatellites markers, sequence annotation and their validation in different stands of G. robusta in northern India. Library preparation and sequencing were carried out using Illumina paired-end sequencing technology. Approximately, ten gigabases (Gb) sequence data with 70.87 million raw reads assembled into 425,923 contigs (read mapped to 76.48%) comprising 455 Mb genome size (23 × coverage) generated through genome skimming approach. In total, 9421 simple sequence repeat (SSR) primer pairs were successfully designed from 13,335 microsatellite repeats. Afterward, a subset of 161 primer pairs was randomly selected, synthesized and validated. All the tested primers showed successful amplification but only 13 showed polymorphisms. The polymorphic SSRs were further used to estimate the measures of genetic diversity in 12 genotypes each from the states of Punjab, Haryana, Himachal Pradesh and Uttarakhand. Importantly, the average number of alleles (Na), observed heterozygosity (Ho), expected heterozygosity (He), and the polymorphism information content (PIC) were recorded as 2.69, 0.356, 0.557 and 0.388, respectively. The availability of sequence information and newly developed SSR markers could potentially be used in various genetic analyses and improvements through molecular breeding strategies for G. robusta.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-021-01035-w.