Identification of lncRNA-miRNA-mRNA regulatory network associated with primary open angle glaucoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhou M
PROVIDER: S-EPMC7076920 | biostudies-literature | 2020 Mar
REPOSITORIES: biostudies-literature
Publications
Identification of lncRNA-miRNA-mRNA regulatory network associated with primary open angle glaucoma.
Zhou Minwen M Lu Bing B Tan Wei W Fu Mingshui M
BMC ophthalmology 20200316 1
<h4>Background</h4>Primary open angle glaucoma (POAG) is a multifactorial disorder characterized by a progressive permanent degeneration of retinal ganglion cell (RGCs) death. An increasing number of studies have suggested that long noncoding RNAs (lncRNAs) have the ability to regulate gene expression; however, thus far, the mechanisms and functions of lncRNAs in the development of POAG are still unclear.<h4>Methods</h4>Using the data from Gene Expression Omnibus (GEO), differentially expressed ...[more]
Similar Datasets
Project description:BackgroundTrabecular meshwork (TM) dysfunction-induced elevation of intraocular pressure has been identified as the main risk factor of irreversible optic nerve injury in Primary open‑angle glaucoma (POAG). Increasing evidences suggest that microRNA (miRNA) plays a vital role in the pathogenesis of POAG. This study aims to construct a miRNA-mRNA regulatory network and identify biomarkers for POAG.MethodsmiRNAs and mRNAs expression profiling of TM samples from controls and POAG patients were assessed through microarray analysis. Target genes of differentially expressed miRNAs (DEmiRNAs) were predicted by miEAA and miRNet. Then GO and KEGG pathway analysis of differentially expressed mRNAs (DEmRNAs) were performed. PPI of top 30 hub genes was identified and miRNA-mRNA network was established by STRING database and Cytoscape software. GSE27276 and GSE105269 datasets were used to verify the expression of hub genes and to predict potential biomarkers in TM and aqueous humor (AH) for POAG, respectively. Finally, GSEA analysis was conducted to estimate the main signaling pathway of POAG pathogenesis.ResultsA total of 29 up-regulated and 7 down-regulated miRNAs, 923 up-regulated and 887 down-regulated mRNAs were identified in TM of POAG compared with controls. Target genes and DEmRNAs were mainly enriched in nitric oxide biosynthetic process, vasopressin-regulated water reabsorption, and so on. Through miRNA-mRNA network construction, top 30 hub genes were regulated by 24 DEmiRNAs. 8 genes were aberrantly expressed in dataset GSE27276. 3 genes (CREB1, CAPZA2, SLC2A3) and 2 miRNAs (miR-106b-5p, miR-15a-5p) were identified as potential biomarkers for POAG in TM and AH, respectively. GSEA analysis revealed that these 3 genes modulated POAG through different pathways.ConclusionIn this study, construction of miRNA-mRNA network and identification of biomarkers provide a novel insight into the pathogenesis, early diagnosis and treatment for POAG.
| S-EPMC10634160 | biostudies-literature
Project description:MicroRNAs were associated with the development and progression of glaucoma. Our study aims to identify the potential miRNAs and target genes in human trabecular meshwork related to primary open-angle glaucoma (POAG).
2023-05-05 | GSE231760 | GEO
Project description:PurposeTo conduct an integrated bioinformatics analysis of extant aqueous humor (AH) gene expression datasets in order to identify key genes and the regulatory mechanism governing primary open-angle glaucoma (POAG) progression.MethodsTwo datasets (GSE101727 and GSE105269) were downloaded from the Gene Expression Omnibus, and the messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs) were identified between controls and POAG patients. Differentially expressed (DE) mRNAs and DElncRNAs were then subjected to pathway enrichment analyses, after which a protein-protein interaction (PPI) network was generated. This network was then expanded to establish lncRNA-miRNA-mRNA and miRNA-transcription factor (TF)-mRNA networks.ResultsThe GSE101727 dataset was used to identify 2746 DElncRNAs and 2208 DEmRNAs, while the GSE105269 dataset was used to identify 45 DEmiRNAs. We ultimately constructed a competing endogenous RNA (ceRNA) network incorporating 47 lncRNAs, six miRNAs, and 17 mRNAs. The proteins encoded by these 17 hub mRNAs were found to be significantly enriched for activities that may be linked to POAG pathogenesis. In addition, we generated a miRNA-TF-mRNA regulatory network containing two miRNAs (miR-135a-5p and miR-139-5p), five TFs (TGIF2, TCF3, FOS, and so on), and five mRNAs (SHISA7, ST6GAL2, TXNIP, and so on).ConclusionThe SHISA7, ST6GAL2, TXNIP, FOS, and DCBLD2 genes may be viable therapeutic targets for the prevention or treatment of POAG and are regulated by the TFs (TGIF2, HNF1A, TCF3, and FOS).
| S-EPMC10228979 | biostudies-literature
Project description:Construction of miRNA-mRNA Regulatory Network Indicates Potential Biomarkers for Primary Open-Angle Glaucoma
| PRJNA967639 | ENA
Project description:A substantial fraction of glaucoma has a genetic basis. About 5% of primary open angle glaucoma (POAG) is currently attributed to single-gene or Mendelian forms of glaucoma (ie glaucoma caused by mutations in myocilin or optineurin). Mutations in these genes have a high likelihood of leading to glaucoma and are rarely seen in normal subjects. Other cases of POAG have a more complex genetic basis and are caused by the combined effects of many genetic and environmental risk factors, each of which do not act alone to cause glaucoma. These factors are more frequently detected in patients with POAG, but are also commonly observed in normal subjects. Additional genes that may be important in glaucoma pathogenesis have been investigated using quantitative traits approaches. Such studies have begun to identify genes that control the magnitude of important quantitative features of glaucoma that may also be important risk factors for POAG, such as central corneal thickness. Each of these different approaches to study glaucoma genetics is providing new insights into the pathogenesis of POAG.
| S-EPMC3171270 | biostudies-literature
Project description:AimTo estimate the risk of blindness with primary angle-closure glaucoma (PACG) compared to primary open-angle glaucoma (POAG) in those population-based studies that reported blindness rates for both PACG and POAG.MethodA systematic search was performed in PubMed for articles published in English between 2000 and 2020 reporting the prevalence of POAG as well as PACG among various ethnic populations. A study was included if it was (1) population-based (2) had published prevalence and blindness rates for both PACG and POAG in the same cohort. (3) Glaucoma was defined as per the International Society for Geographical and Epidemiological Ophthalmology (ISGEO) criteria. The proportion of blindness for both POAG and PACG for each study and the cumulative proportion taking all the studies were calculated.ResultsWe included 23 studies with 78,434 participants. POAG was diagnosed in 1702 persons with 151 (8.9%) blind. There were 724 cases of PACG with 196 (27.0%) blind. The risk ratio of blindness in PACG to POAG varied from 0.73 to 10.6 among the studies. The cumulative risk ratio was 2.39 (95% confidence interval (CI); 1.99, 2.87). Risk ratios for studies including visual field restriction while defining blindness were similar to studies that did not (1.92 vs 2.64, P = 0.11). Risk ratios were also similar for studies that used greater than 2 instead of 3 or more quadrants of iridotrabecular contact to define angle closure (2.79 vs 2.25).ConclusionPrimary angle-closure disease is more likely to be associated with blindness.
| S-EPMC9582001 | biostudies-literature
Project description:Aimwe aimed to construct a bioinformatics-based co-regulatory network of mRNAs and non coding RNAs (ncRNAs), which is implicated in the pathogenesis of non-alcoholic fatty liver disease (NAFLD), followed by its validation in a NAFLD animal model.Materials and methodsThe mRNAs-miRNAs-lncRNAs regulatory network involved in NAFLD was retrieved and constructed utilizing bioinformatics tools. Then, we validated this network using an NAFLD animal model, high sucrose and high fat diet (HSHF)-fed rats. Finally, the expression level of the network players was assessed in the liver tissues using reverse transcriptase real-time polymerase chain reaction.Resultsin-silico constructed network revealed six mRNAs (YAP1, FOXA2, AMOTL2, TEAD2, SMAD4 and NF2), two miRNAs (miR-650 and miR-1205), and two lncRNAs (RPARP-AS1 and SRD5A3-AS1) that play important roles as a co-regulatory network in NAFLD pathogenesis. Moreover, the expression level of these constructed network-players was significantly different between NAFLD and normal control. Conclusion and future perspectives: this study provides new insight into the molecular mechanism of NAFLD pathogenesis and valuable clues for the potential use of the constructed RNA network in effective diagnostic or management strategies of NAFLD.
| S-EPMC8269036 | biostudies-literature
Project description:Long non-coding RNAs (lncRNAs) are ncRNA transcripts >200 nucleotides that are important genetic regulators. LncRNAs can directly regulate mRNA through a lncRNA-mRNA regulatory mode and can also regulate mRNA through competitive binding to micro (mi)RNA, which is generally known as the competitive endogenous RNA (ceRNA) network. The present study evaluated the functional roles and regulatory networks of lncRNAs in chronic glomerulonephritis (CGN). The proliferative ability of mouse glomerular mesangial cells (GMCs) induced by different concentrations of lipopolysaccharide (LPS) was assessed using the Cell Counting Kit-8 assay, and RNA sequencing (RNA-seq) was performed to identify differentially expressed lncRNAs in LPS-induced GMCs. Based on the sequencing results, six lncRNAs were selected for validation using reverse transcription-quantitative PCR (RT-qPCR). Furthermore, the lncRNA-mRNA regulatory network and the lncRNA-miRNA-mRNA ceRNA network were constructed to assess the role and mechanism of CGN-related lncRNAs. To elucidate the biological functions of lncRNAs, Gene Ontology (GO) biological process term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on all mRNAs involved in the lncRNA-mRNA regulatory network and in the ceRNA network. A total of 1,532 differentially expressed lncRNAs, including 594 upregulated lncRNAs and 938 downregulated lncRNAs, were identified using RNA-seq. The results of RT-qPCR validation were consistent with RNA-seq results. An lncRNA-mRNA regulatory network, including 236 lncRNAs and 556 mRNAs, and a ceRNA network, including 6 lncRNAs, 18 miRNAs and 419 mRNAs, were successfully constructed. The GO biological process term enrichment and KEGG pathway enrichment analyses demonstrated that those lncRNAs were often related to inflammatory response and substance metabolism. The present study identified key CGN-related lncRNAs in LPS-induced GMCs, and further demonstrated a global view of the lncRNA-mRNA regulatory network and ceRNA network involved in CGN. These results offered novel insights into the roles of lncRNAs in the pathogenesis of CGN and identified potential diagnostic biomarkers.
| S-EPMC10375445 | biostudies-literature
Project description:Stroke known as a neurological disease has significant rates of disability and mortality. Middle cerebral artery occlusion (MCAO) models in rodents is crucial in stroke research to mimic human stroke. Building the mRNA and non-conding RNA network is essential for preventing MCAO-induced ischemic stroke occurrence. Herein, genome-wide mRNA, miRNA, and lncRNA expression profiles among the MCAO group at 3 h, 6 h, and 12 h after surgery and controls using high-throughput RNA sequencing. We detected differentially expressed mRNAs (DE-mRNAs), miRNAs (DE-miRNAs), and lncRNAs (DE-lncRNAs) between the MCAO and control groups. In addition, biological functional analyses were conducted, including GO/KEGG enrichment analysis, and protein-protein interaction analysis (PPI). GO analysis indicated that the DE-mRNAs were mainly enriched in several important biological processes as lipopolysaccharide, inflammatory response, and response to biotic stimulus. The PPI network analysis revealed that the 12 DE-mRNA target proteins showed more than 30° with other proteins, and the top three proteins with the highest node degree were Alb, IL-6, and TNF. In the DE-mRNAs, we found the mRNA of Gp6 and Elane interacting with two miRNAs (novel_miR_879 and novel_miR_528) and two lncRNAs (MSTRG.348134.3 and MSTRG.258402.19). As a result of this study, a new perspective can be gained into the molecular pathophysiology leading to the formation of MCAO. The mRNA-miRNA‒lncRNA regulatory networks play an important role in MCAO-induced ischemic stroke pathogenesis and could be applied to the treatment and prevention of ischemic stroke in the future.
| S-EPMC10203218 | biostudies-literature
Project description:The total RNA from control and POAG optic nerve lamina cribrosa region was isolated and subjected to analysis on U133A 2.0 affymetrix microarray chip. The results from these two analysis was compared to determine whether PAD II RNA level is altered. Purpose of the study. This study was done to determine which RNA transcripts undergo significant changes in the optic nerve between normal and cadaver primary open-angle glaucoma donors. Using donor tissues from NDRI we have performed proteomic analyses (which will be published shortly), we intend a comparison between the proteomic and RNA changes for select proteins in our study. Sample Collection and Preparation of Labeled Copy RNA; The total RNA from optic nerve was obtained using TRIZOL (Invitrogen Inc., Carlsbad, CA) with modification of recommended protocols. The optic nerve from donor eyes were carefully excised and minced into small pieces first using a scissor and then a scalpel. Prior to use tissue was washed with DEPC water and all solutions were prepared in DEPC water. The minced tissue was placed in a glass homogenizer with 1 ml TRIZOL per 100 mg of tissue and homogenized in a glass homogenizer DUALL 20 (Kimble Kontes Glass Co, Vineland, NJ) with 10 strokes cycles each at room temperature and after freezing with liquid nitrogen for 1 min for 40 cycles. This RNA was extracted with chloroform, isoamylalcohol and precipitated with sodium citrate/sodium chloride and isopropanol. The final air-dried RNA precipitate was suspended in DEPC water and stored in â??80oC prior to use. RNA was prepared for hybridization according to the GeneChip Expression Analysis Technical Manual (Affymetrix, Santa Clara, CA). The hybridization was performed at Gene Expression Array Core Facility at Case Western Reserve University (www.geacf.net). The RNA sample was cleaned using a column from a Qiagen RNeasy kit, (part # 74104 ) in accordance with the manufacturerâ??s instructions. The volume of DEPC water used to elute the RNA from the Qiagen column was dictated by the nominal mass of the loaded RNA sample. Samples were eluted in 100ml of water, precipitated with 0.5 volume of 7.5M Ammonium Acetate and 2.5 volumes of 100% ethanol at -20oC for 1 hr and centrifuged for 10 min at room temperature. The pellet was drained and washed twice in 500µl of 70% ethanol. Supernatant was aspirated. The pellet was dried briefly (less than 1 min) in a speeedvac (Savant) and re-suspended in a small volume (~ 12â??15ml) of DEPC water. A small aliquot (2ml) of the sample was used for quantitation. Cleaned RNA samples were annotated as â??clnRNAâ?? and stored at -20oC. cDNA Synthesis; The protocol recommended by Affymetrix Inc. for cDNA synthesis was used at all times. The reaction was primed by annealing an oligo-dT primer coupled to a T7 RNA polymerase promoter to the RNA sample. Whenever possible, 8 mg of clnRNA were reverse transcribed using Superscript II reverse transcriptase in a 20ml reaction at 42oC for 1 hr. Second strand synthesis was carried out immediately in a total reaction volume of 150ml in the presence of E. coli DNA polymerase I, RNAse H and DNA ligase. The reaction mixture incubated for 2 hrs at 16oC and for a further 5 min in the presence of T4 DNA pol.. The reaction was terminated by the addition of EDTA. The sample was cleaned with a Qiagen cDNA clean-up column according to the manufacturers directions. The elution buffer volume was 14ml. The eluted samples were stored overnight at -20oC. In-Vitro Transcription (IVT) reaction; In a 0.5ml microfuge tube, complementary RNA (cRNA) was generated in an IVT reaction using a BioArray High Yield ENZO kit (Affymetrix). The 40ml reaction contained 10ml of cDNA sample and was incubated at 37oC in the heating block for 4-5hrs. Samples were mixed by flicking, pulse-centrifuged and returned to the incubator once every 40 minutes. Upon completion of the reaction, IVT samples were adjusted to a volume of 100ml with DEPC-treated water and cleaned using Qiagen RNA clean-up columns. Samples were eluted in 45ml of DEPC-treated water. A 2ml-aliquot was used for quantitation. Corrected concentrations of cRNA (minus the input clnRNA) were determined. A standard fragmentation reaction volume was 40ml in a 0.5 ml microfuge tube and used 20mg of overall IVT RNA in 1X fragmentation buffer (40mM Tris acetate, pH 8.1, 100mM KOAc, 30mM MgOAc). The reaction was incubated at 94oC for 35min in a heat block. After the 35 min incubation the fragmented samples were placed on ice. The standard hybridization cocktail volume was 300ml. 150ml of 2X hybridization buffer was added (final concentrations : 100mM MES, 1M [Na+], 20mM EDTA, 0.01% Tween 20) Herring sperm and acetylated BSA were added to a final concentration of 0.1 and 0.5mg/ml respectively. The amount of fragmentation reaction containing 15mg of cRNA was added to the cocktail and the remaining volume was made up with molecular biology grade water. A 15ml aliquot of a 20X mixture of in-vitro transcripts of bacterial genes BioB BioC BioD and cre as external controls (spikes) were added to the cocktail to give final concentrations of 1.5, 5, 25 and 100pM respectively. Control oligonucleotide (5ml) was added to a final concentration of 50pM. Array Hybridization; Sample Hyridization cocktails (300ml) were removed from storage at â??20oC and thawed in a 45oC temperature block. The samples were incubated at 45oC for 5 min, incubated at 45oC for 5 min and then centrifuged at 15,000g for 5 min. This study used commercially available Affymetrix HG U133A 2.0 arrays only. These are in situ synthesized, high density oligonucleotide arrays which can interrogate 22,777 gene entities. The part number is 900444 more detailed information may be found at www.affymetrix.com website. Meanwhile, the affymetrix chip arrays were taken from their storage at 4oC and allowed to come to room temperature. The chamber was filled with 1X hybridization buffer (final concentrations : 100mM MES, 1M [Na+], 20mM EDTA, 0.01% Tween 20). The chip was then preconditioned by incubation in the hybridization oven at 45oC for 10 min. The chip was removed and the chamber drained. Sample cocktails (200ml) were then introduced into the chamber of the chips. The ports were sealed with tough-spots stickers and placed in the hybridization oven and incubated at 45oC with rotation overnight. Next day (16hr later), the incubation was terminated and the chips removed from the oven. Samples were retrieved from the chips and stored again in a freezer at -20oC. The chips were filled with buffer A and placed in a module of Fluidics 400 wash station. The chips were subjected to the Affymetrix â??EuKws4 ver 2â?? programmed series of stringent post hybridization washes, staining and post-staining washes in accordance with Affy protocols. Upon completion of the wash series (approximately 2 hrs), the chips were removed from the Fluidics station. The chips were inspected. Chips were scanned using an Agilent Gene Array scanner 3000 driven by Affymetrix GCOS software. Generation of Expression Values; Microarray Suite, version 5 (Affymetrix), was used to generate *.cel files, and a computer program (Probe Profiler, ver. 1.3.11; Corimbia Inc., Berkeley, CA) developed specifically for the GeneChip system (Affymetrix) was used to convert intensity data into quantitative estimates of gene expression for each probe set. The software identifies informative probe pairs, and down-weights the signal contribution of probe pairs that are subject to differential cross-hybridization effects or that consistently produce no signal. The software also detects and corrects for saturation artifacts, outliers, and chip defects. A probability statistic was generated for each probe set. The probability is associated with the null hypothesis that the expression level of the probe set is equal to zero (background). Genes not significantly expressed above background in any of the samples (P > 0.05) were
2008-06-12 | E-GEOD-2387 | biostudies-arrayexpress