LncRNA-Gm9866 promotes liver fibrosis by activating TGFß/Smad signaling via targeting Fam98b.

OBJECTIVE: The exact mechanism and target molecules of liver fibrosis have remained largely elusive. Here, we investigated the role of long noncoding RNA Gm9866(lncRNA-Gm9866) on liver fibrosis. METHODS: The transcription of lncRNA-Gm9866 in activated cells and mouse fibrotic livers was determined by quantitative polymerase chain reaction (qRT-PCR). The effects of lentivirus-mediated knockdown or overexpression of lncRNA-Gm9866 in liver fibrosis were examined in vitro and in vivo. Furthermore, bioinformatics analysis, cell samples validation, fluorescence in situ hybridization (FISH) co-localization, RNA binding protein immunoprecipitation (RIP), actinomycin D test and Western blot (WB) were carried out to explore the potential mechanism of lncRNA-Gm9866. RESULTS: The expression of α-smooth muscle actin (α-SMA), Collagen I (COL-1) and lncRNA-Gm9866 were significantly increased in tissues and cells. Overexpressing lncRNA-Gm9866 promoted the activation of hepatic stellate cells (HSCs). Silencing lncRNA-Gm9866 inhibited the activation of HSCs and transforming growth factor-ß1 (TGFß1) induced fibrosis. Overexpressing lncRNA-Gm9866 promoted hepatocytes (HCs) apoptosis and the expression of pro-fibrogenic genes, inhibited the proliferation and migration of HCs. Knockdown of lncRNA-Gm9866 inhibited the apoptosis of HCs, the expression of pro-fibrogenic genes, TGFß1 induced fibrosis and the occurrence of carbon tetrachloride (CCl4)-induced liver fibrosis, and promoted the proliferation and migration of HCs. Mechanistically, lncRNA-Gm9866 may directly bine with Fam98b. Silencing Fam98b in stably overexpressing lncRNA-Gm9866 cell lines reversed the increase of pro-fibrogenic genes and pro-apoptotic genes, fibrosis related pathway protein TGFß1, Smad2/3, p-Smad2/3 and Notch3 induced by overexpressing lncRNA-Gm9866. CONCLUSIONS: LncRNA-Gm9866 may regulate TGFß/Smad and Notch pathways by targeting Fam98b to regulate liver fibrosis. LncRNA-Gm9866 may be a new target for diagnosis and treatment of liver fibrosis.

Identifying causal relationships between gastroesophageal reflux and extraesophageal diseases: A Mendelian randomization study.

Traditional observational and in vivo studies have suggested an etiological link between gastroesophageal reflux disease (GERD) and the development of extraesophageal diseases (EEDs), such as noncardiac chest pain. However, evidence demonstrating potential causal relationships is lacking. This study evaluated the potential causal relationship between GERD and EEDs, including throat and chest pain, asthma, bronchitis, chronic rhinitis, nasopharyngitis and pharyngitis, gingivitis and periodontal disease, cough, using multiple Mendelian randomization (MR) methods, and sensitivity analysis was performed. The Mendelian randomization Pleiotropy RESidual Sum and Outlier and PhenoScanner tools were used to further check for heterogeneous results and remove outliers. MR with inverse-variance weighted (IVW) showed a significant causal relationship between GERD and EEDs after Bonferroni correction. IVW results indicated that GERD increased the risk of chronic rhinitis, nasopharyngitis and pharyngitis (odds ratio [OR] = 1.482, 95% confidence interval [CI] = 1.267-1.734, P < .001], gingivitis and periodontal disease (OR = 1.166, 95% CI = 1.046-1.190, P = .001), throat and chest pain (OR = 1.585, 95% CI = 1.455-1.726, P < .001), asthma (OR = 1.539, 95% CI = 1.379-1.717, P < .001), and bronchitis (OR = 1.249, 95% CI = 1.168-1.335, P < .001). Sensitivity analysis did not detect pleiotropy. Leave-one-out analysis shows that MR results were not affected by individual single nucleotide polymorphisms. The funnel plot considers the genetic instrumental variables to be almost symmetrically distributed. This MR supports a causal relationship among GERD and EEDs. Precise moderation based on causality and active promotion of collaboration among multidisciplinary physicians ensure high-quality diagnostic and treatment recommendations and maximize patient benefit.

Identification of common genes and pathways between type 2 diabetes and COVID-19.

Background: Numerous studies have reported a high incidence and risk of severe illness due to coronavirus disease 2019 (COVID-19) in patients with type 2 diabetes (T2DM). COVID-19 patients may experience elevated or decreased blood sugar levels and may even develop diabetes. However, the molecular mechanisms linking these two diseases remain unclear. This study aimed to identify the common genes and pathways between T2DM and COVID-19. Methods: Two public datasets from the Gene Expression Omnibus (GEO) database (GSE95849 and GSE164805) were analyzed to identify differentially expressed genes (DEGs) in blood between people with and without T2DM and COVID-19. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the common DEGs. A protein-protein interaction (PPI) network was constructed to identify common genes, and their diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. Validation was performed on the GSE213313 and GSE15932 datasets. A gene co-expression network was constructed using the GeneMANIA database to explore interactions among core DEGs and their co-expressed genes. Finally, a microRNA (miRNA)-transcription factor (TF)-messenger RNA (mRNA) regulatory network was constructed based on the common feature genes. Results: In the GSE95849 and GSE164805 datasets, 81 upregulated genes and 140 downregulated genes were identified. GO and KEGG enrichment analyses revealed that these DEGs were closely related to the negative regulation of phosphate metabolic processes, the positive regulation of mitotic nuclear division, T-cell co-stimulation, and lymphocyte co-stimulation. Four upregulated common genes (DHX15, USP14, COPS3, TYK2) and one downregulated common feature gene (RIOK2) were identified and showed good diagnostic accuracy for T2DM and COVID-19. The AUC values of DHX15, USP14, COPS3, TYK2, and RIOK2 in T2DM diagnosis were 0.931, 0.917, 0.986, 0.903, and 0.917, respectively. In COVID-19 diagnosis, the AUC values were 0.960, 0.860, 1.0, 0.9, and 0.90, respectively. Validation in the GSE213313 and GSE15932 datasets confirmed these results. The miRNA-TF-mRNA regulatory network showed that TYH2 was targeted by PITX1, PITX2, CRX, NFYA, SREBF1, RELB, NR1L2, and CEBP, whereas miR-124-3p regulates THK2, RIOK2, and USP14. Conclusion: We identified five common feature genes (DHX15, USP14, COPS3, TYK2, and RIOK2) and their co-regulatory pathways between T2DM and COVID-19, which may provide new insights for further molecular mechanism studies.

GDF-15 (a biomarker for metformin) and the risk of COVID-19: A two-sample Mendelian randomization study.

BACKGROUND: Regarding the impact of metformin on COVID-19, there are currently varying opinions from multiple studies. Growth differentiation factor 15 (GDF-15) is a biomarker of metformin use and dosage, and we used two-sample Mendelian randomization (MR) to assess the causal effect of GDF-15 (metformin) on COVID-19 susceptibility, hospitalization, and severe COVID-19, thereby guiding the selection of glucose-lowering agents for diabetic patients during the COVID-19 pandemic. METHODS: Two sets of genetic tools were utilized for MR analysis, derived from publicly available genetic data. The first set was GDF-15 genome-wide association study (GWAS) data from a study with 5440 participants, while the second set was COVID-19 GWAS data from the Host Genetics Initiative (HGI) GWAS meta-analysis. The primary method used to assess causal effects was random effects inverse variance weighted estimation. Complementary methods included weighted median and MR-Egger analyses. Sensitivity analysis was performed using Cochran Q tests, MR-Egger intercept tests, MR-PRESSO, leave-one-out analyses, and funnel plots. RESULTS: GDF-15 increased the risk of severe COVID-19 in patients (OR = 1.10, 95% CI 1.03-1.19; P = .006); there was no causal effect of GDF-15 on hospitalization for COVID-19 (OR = 1.02, 95% CI 0.96-1.07; P = .47) or susceptibility to COVID-19 in the general population (OR = 1.010, 95% CI 0.988-1.034; P = .354). CONCLUSIONS: Our study supports the notion that GDF-15 increases the risk of severe COVID-19 in patients. However, there is no causal relationship between GDF-15 and hospitalization or susceptibility to COVID-19.