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Differential gene expression (DGE) analysis has been widely employed to identify genes expressed differentially with respect to a trait of interest using RNA sequencing (RNA-Seq) data. Recent RNA-Seq data with large samples pose challenges to existing DGE methods, which were mainly developed for dichotomous traits and small sample sizes. Especially, existing DGE methods are likely to result in inflated false positive rates. To address this gap, we employed a linear mixed model (LMM) that has been widely used in genetic association studies for DGE analysis of quantitative traits. We first applied the LMM method to the discovery RNA-Seq data of dorsolateral prefrontal cortex (DLPFC) tissue (n = 632) with four continuous measures of Alzheimer's Disease (AD) cognitive and neuropathologic traits. The quantile–quantile plots of p-values showed that false positive rates were well calibrated by LMM, whereas other methods not accounting for sample-specific mixed effects led to serious inflation. LMM identified 37 potentially significant genes with differential expression in DLPFC for at least one of the AD traits, 17 of which were replicated in the additional RNA-Seq data of DLPFC, supplemental motor area, spinal cord, and muscle tissues. This application study showed not only well calibrated DGE results by LMM, but also possibly shared gene regulatory mechanisms of AD traits across different relevant tissues. [ABSTRACT FROM AUTHOR]

As novel master regulators of initiation and progression, long non-coding RNAs (lncRNAs) can response to therapy in a wide variety of malignances. This study aimed to explore the regulatory mechanisms in non-small cell lung cancer (NSCLC) by constructing lncRNA-miRNA-mRNA networks. The whole transcriptome sequence was performed in five NSCLC tissues and their corresponding paired adjacent normal tissues. MicroRNAs (miRNAs) and mRNAs expression profiles were obtained from TCGA database. "EdgeR" R package was used for gene expression comparisons and the genes with | log2FC |> 2 and false discovery rate (FDR) adjusted P<0.05 were considered significant. Totally, 559 differentially expressed lncRNAs (DELs) (235 up-regulated, 324 down-regulated) were identified via whole transcriptome sequence analysis. Subsequently, 14 up-regulated lncRNAs were obtained in the intersection of our RNA-seq data and TCGA dataset. The dysregulations of the selected lncRNAs were evaluated through GEPIA. Three candidate lncRNAs (LINC01426, TYMSOS, VPS9D1-AS1) were finally selected for further study. Through bioinformatics prediction, with the three lnRNAs and their associated miRNAs (n=14) and mRNAs (n=218), a lncRNA-miRNA-mRNA network was constructed. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway associated with the targeted genes were proceeded. In addition, the protein–protein interaction (PPI) network construction was performed by STRING and lncRNA-miRNA-mRNA regulatory network was visualized via Cytoscape. The top ten significant hub-genes in the PPI network are identified based on their node degrees. Moreover, the lncRNA-miRNA and miRNA-mRNA interactions were predicted using the StarBase and survival analyses were performed in Kaplan-Meier plotter website. We concluded that TYMSOS/hsa-miR-101-3p/CEP55 and TYMSOS/hsa-miR-195-5p/CHEK1 ceRNA regulatory aixs may play vital roles in the occurrence and development of NSCLC, and may provide novel therapeutic targets in the future. [ABSTRACT FROM AUTHOR]

With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we develop easyMF (https://github.com/cma2015/easyMF), a web platform that can facilitate functional gene discovery from large-scale transcriptome data using matrix factorization (MF) algorithms. Compared with existing MF-based software packages, easyMF exhibits several promising features, such as greater functionality, flexibility and ease of use. The easyMF platform is equipped using the Big-Data-supported Galaxy system with user-friendly graphic user interfaces, allowing users with little programming experience to streamline transcriptome analysis from raw reads to gene expression, carry out multiple-scenario MF analysis, and perform multiple-way MF-based gene discovery. easyMF is also powered with the advanced packing technology to enhance ease of use under different operating systems and computational environments. We illustrated the application of easyMF for seed gene discovery from temporal, spatial, and integrated RNA-Seq datasets of maize (Zea mays L.), resulting in the identification of 3,167 seed stage-specific, 1,849 seed compartment-specific, and 774 seed-specific genes, respectively. The present results also indicated that easyMF can prioritize seed-related genes with superior prediction performance over the state-of-art network-based gene prioritization system MaizeNet. As a modular, containerized and open-source platform, easyMF can be further customized to satisfy users' specific demands of functional gene discovery and deployed as a web service for broad applications. [ABSTRACT FROM AUTHOR]

Golden snub-nosed monkeys are endangered animals in China, and their cells have been demonstrated to be important as genetic resources and in applications for advancing biological research. Moreover, in primary research, basic fibroblast growth factor (bFGF) is used to promote the proliferation of fibroblasts to create abundant cells for cryopreservation. To further investigate the effect of bFGF on the efficiency of preservation of fibroblasts obtained from an endangered species, a fibroblast cell line was isolated from a dead golden snub-nosed monkey. Cell viability and mitochondrial membrane potential were assessed using CCK8 and JC-1 assay kits. The karyotype was analyzed by chromosomal microarray analysis, while RNA sequencing and gene expression analyses were performed to assess molecular changes in response to bFGF. Flow cytometry was used to characterize changes in cell surface markers in response to bFGF treatment. The results showed that cells maintained typical fibroblast morphology, while cell viability and mitochondrial membrane potential were not significantly affected between three and eight passages (p > 0.05). We also observed that the addition of bFGF promoted fibroblast proliferation and increased mitochondrial membrane potential. In addition, the bFGF treatment did not alter the normal karyotype of cells, downregulating fibroblast-associated genes and upregulating those associated with cell regulation, including those of the WNT, PI3K and MAPK pathways. The addition of bFGF also increased CD29, CD90, CD105, CD34 and CD44 expression while decreasing that of CD14 and HLA-DR at the protein level. Taken together, these results demonstrate that bFGF may upregulate the WNT, PI3K and MAPK pathways to promote cell proliferation while also increasing the expression of genes and surface markers associated with mesenchymal and hematopoietic cell linages. [ABSTRACT FROM AUTHOR]