A delayed and unsynchronized ovary development as revealed by transcriptome of brain and pituitary of Coilia nasus

Coilia nasus is an anadromous fish that has been successfully domesticated in the last decade due to its high economic value. The fish exhibits a delayed ovary development during the reproductive season, despite breeding and selection for five to six offspring. The molecular mechanism of the delayed ovary development is still unknown, so the obstacles have not been removed in the large-scale breeding program. This study aims to investigate the key genes regulating ovarian development by comparing the transcriptomes of ovarian-stage IV and stage II brain/pituitary of Coilia nasus. Ovarian stages were validated by histological sections. A total of 75,097,641 and 66,735,592 high-quality reads were obtained from brain and pituitary transcriptomes, respectively, and alternatively spliced transcripts associated with gonadal development were detected. Compared to ovarian Ⅱ- brain, 515 differentially expressed genes (DEGs) were upregulated and 535 DEGs were downregulated in ovarian Ⅳ- brain, whereas 470 DEGs were upregulated and 483 DEGs were downregulated in ovarian Ⅳ- pituitary compared to ovarian Ⅱ- pituitary. DEGs involved in hormone synthesis and secretion and in the GnRH signaling pathway were screened. Weighted gene co-expression network analysis identified gene co-expression modules that were positively correlated with ovarian phenotypic traits. The hub genes Smad4 and TRPC4 in the modules were co-expressed with DEGs including Kiss1 receptor and JUNB, suggesting that ovarian development is controlled by a hypothalamic-pituitary-gonadal axis. Our results have provided new insights that advance our understanding of the molecular mechanism of C. nasus reproductive functions and will be useful for future breeding.

Integration of comparative transcriptomics and WGCNA characterizes the regulation of anthocyanin biosynthesis in mung bean (Vigna radiata L.)

Mung bean is a dual-use crop widely cultivated in Southeast Asia as a food and medicine resource. The development of new functional mung bean varieties demands identifying new genes regulating anthocyanidin synthesis and investigating their molecular mechanism. In this study, we used high-throughput sequencing technology to generate transcriptome sequence of leaves, petioles, and hypocotyls for investigating the anthocyanins accumulation in common mung bean variety as well as anthocyanidin rich mung bean variety, and to elucidate their molecular mechanisms. 29 kinds of anthocyanin compounds were identified. Most of the anthocyanin components contents were significantly higher in ZL23 compare with AL12. Transcriptome analysis suggested that a total of 93 structural genes encoding the anthocyanin biosynthetic pathway and 273 regulatory genes encoding the ternary complex of MYB-bHLH-WD40 were identified, of which 26 and 78 were differentially expressed in the two varieties. Weighted gene co-expression network analysis revealed that VrMYB3 and VrMYB90 might have enhanced mung bean anthocyanin content by inducing the expression of structural genes such as PAL, 4CL, F3'5'H, LDOX, and F3'H, which was consistent with qRT-PCR results. These findings are envisaged to provide a reference for studying the molecular mechanism of anthocyanin accumulation in mung beans.

Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles

Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein-protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy.

Co-expression Network Analysis Identifies Four Hub Genes Associated With Prognosis in Soft Tissue Sarcoma

Background: Soft tissue sarcomas (STS) are heterogeneous tumors derived from mesenchymal cells that differentiate into soft tissues. The prognosis of patients who present with an STS is influenced by the regulation of a complex gene network. Methods: Weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules associated with STS (Samples = 156). Results: Among the 11 modules identified, the black and blue modules were highly correlated with STS. However, using preservation analysis, the black module demonstrated low preservation, therefore the blue module was chosen as the module of interest. Furthermore, a total of 20 network hub genes were identified in the blue module, 12 of which were also hub nodes in the protein-protein interaction network of the module genes. Following additional verification, 4 of 12 genes (RRM2, BUB1B, CENPF, and KIF20A) demonstrated poorer overall survival and disease-free survival rate in the test datasets. In addition, gene set enrichment analysis (GSEA) demonstrated that samples with a high level of blue module eigengene (ME) were enriched in cell cycle and metabolism associated signaling pathways. Conclusion: In summary, co-expression network analysis identified four hub genes associated with prognosis for STS, which may diminish the prognosis by influencing cell cycle and metabolism associated signaling pathways.

Gene Expression Analysis Reveals Novel Shared Gene Signatures and Candidate Molecular Mechanisms between Pemphigus and Systemic Lupus Erythematosus in CD4+ T Cells

Pemphigus and systemic lupus erythematosus (SLE) are severe potentially life-threatening autoimmune diseases. They are classified as B-cell-mediated autoimmune diseases, both depending on autoreactive CD4⁺ T lymphocytes to modulate the autoimmune B-cell response. Despite the reported association of pemphigus and SLE, the molecular mechanisms underlying their comorbidity remain unknown. Weighted gene co-expression network analysis (WGCNA) of publicly available microarray datasets of CD4⁺ T cells was performed, to identify shared gene expression signatures and putative overlapping biological molecular mechanisms between pemphigus and SLE. Using WGCNA, we identified 3,280 genes co-expressed genes and 14 co-expressed gene clusters, from which one was significantly upregulated for both diseases. The pathways associated with this module include type-1 interferon gamma and defense response to viruses. Network-based meta-analysis identified RSAD2 to be the most highly ranked hub gene. By associating the modular genes with genome-wide association studies (GWASs) for pemphigus and SLE, we characterized IRF8 and STAT1 as key regulatory genes. Collectively, in this in silico study, we identify novel candidate genetic markers and pathways in CD4⁺ T cells that are shared between pemphigus and SLE, which in turn may facilitate the identification of novel therapeutic targets in these diseases.