Bioinformatics Analysis and High-Throughput Sequencing to Identify Differentially Expressed Genes in Nebulin Gene (NEB) Mutations Mice

Bioinformatics and functional experiments reveal that MRC2 inhibits atrial fibrillation via the PPAR signaling pathway

Background

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia that requires improved clinical markers to increase diagnostic accuracy and provide insight into its pathogenesis. Although some biomarkers are available, new ones need to be discovered to better capture the complex physiology of AF. However, their limitations are still not fully addressed. Bioinformatics and functional studies can help find new clinical markers and improve the understanding of AF, meeting the need for early diagnosis and individualized treatment.

Methods

To identify AF-related differentially expressed genes (DEGs), We applied the messenger RNA (mRNA) expression profile retrieved in Series Matrix File format from the GSE143924 microarray dataset obtained from the Gene Expression Omnibus (GEO) database, and then used weighted gene co-expression network analysis (WGCNA) to identify the overlapping genes. These genes were analyzed by enrichment analysis, expression analysis and others to obtain the hub gene. Additionally, the potential signaling pathway of hub gene in AF was explored and verified by functional experiments, like quantitative real-time polymerase chain reaction (qRT-PCR), cell counting kit-8 (CCK-8), flow cytometry, and Western blotting (WB) assay.

Results

From the GSE143924 data (410 DEGs) and tan module (57 genes), 10 overlapping genes were identified. A central down-regulated gene in AF, MRC2, was identified through bioinformatics analysis. based on these results, it was hypothesized that the PPAR signaling pathway is related to the mechanism of action of MRC2 in AF. Moreover, over-MRC2 markedly reduced the growth speed of angiotensin II (Ang II)-induced human cardiac fibroblasts (HCFs) and increased apoptosis. Conversely, knockdown of MRC2 promoted HCFs cell proliferation number. Additionally, MRC2 over-expression increased the protein expression level of PPARα, PPARγ, CPT-1, and SIRT3 in Ang II-induced HCFs.

Conclusions

While meeting the need for new biomarkers in the diagnosis and prognosis of AF, this study reveals the inherent limitations of current biomarkers. We identified MRC2 as a key player as an inhibitory gene in AF, highlighting its role in suppressing AF progression through the PPAR signaling pathway. MRC2 may not only serve as a diagnostic indicator, but also as a promising therapeutic target for patients with AF, which is expected to be applied in clinical practice and open up new avenues for individualized interventions.

Identification of the hub and prognostic genes in liver hepatocellular carcinoma via bioinformatics analysis

Hepatocellular carcinoma (HCC) is a common malignancy. However, the molecular mechanisms of the progression and prognosis of HCC remain unclear. In the current study, we merged three Gene Expression Omnibus (GEO) datasets and combined them with The Cancer Genome Atlas (TCGA) dataset to screen differentially expressed genes. Furthermore, protein‒protein interaction (PPI) and weighted gene coexpression network analysis (WGCNA) were used to identify key gene modules in the progression of HCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that the terms were associated with the cell cycle and DNA replication. Then, four hub genes were identified (AURKA, CCNB1, DLGAP5, and NCAPG) and validated via the expression of proteins and transcripts using online databases. In addition, we established a prognostic model using univariate Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) regression. Eight genes were identified as prognostic genes, and four genes (FLVCR1, HMMR, NEB, and UBE2S) were detrimental gens. The areas under the curves (AUCs) at 1, 3 and 5 years were 0.622, 0.69, and 0.684 in the test dataset, respectively. The effective of prognostic model was also validated using International Cancer Genome Consortium (ICGC) dataset. Moreover, we performed multivariate independent prognostic analysis using multivariate Cox proportional hazards regression. The results showed that the risk score was an independent risk factor. Finally, we found that all prognostic genes had a strong positive correlation with immune infiltration. In conclusion, this study identified the key hub genes in the development and progression of HCC and prognostic genes in the prognosis of HCC, which was significant for the future diagnosis and prognosis of HCC.

Identification and Comparative Analysis of Long Non-coding RNAs in High- and Low-Fecundity Goat Ovaries During Estrus

The ovary is the most important reproductive organ in goats and directly affects the fecundity. Long non-coding RNAs (lncRNAs) are involved in the biological process of oocyte maturation. However, in the context of reproduction in goats, few studies have explored the regulation of lncRNAs. Therefore, we herein used the ovaries of high and low fecundity Leizhou black goats to identify differentially expressed lncRNAs (DElncRNAs) by high-throughput RNA sequencing; moreover, we analyzed the target genes of lncRNAs by functional annotation to explore the role of DElncRNAs in ovarian development. Twenty DElncRNAs were identified, of which six were significantly upregulated and 14 were significantly downregulated in high fecundity goats. Gene Ontology analyses suggested that MSTRG.3782 positively influences the expression of the corresponding gene API5, exerting regulative effects on the development of follicles, through which litter size might show variations. The target gene KRR1 of ENSCHIT00000001883 is significantly enriched in cell components, and ENSCHIT00000001883 may regulate cell growth and thus affect follicular development. Further, as per Kyoto Encyclopedia of Genes and Genomes pathway analyses, MSTRG.2938 was found to be significantly enriched, and we speculate that MSTRG.2938 could regulate ribosomal biogenesis in the pre-snoRNP complex as well as cell transformation in eukaryotes. Quantitative real-time PCR results were consistent with sequencing data. To conclude, our research results indicate that some lncRNAs play a key role in regulating follicle development and cell growth during goat’ s ovarian development.