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Quantitative Analysis of Cerebral Cortex Transcriptomes of BrapcKO Mice and their Wildtype-like Control Mice by Next-Generation Sequencing

ABSTRACT: Purpose: Transcriptome profiling (RNA-seq) was performed in this study in order to assess differential gene expressions resulting from Brap loss of function in murine cerebral cortex. Methods: Cerebral cortical mRNA profiles of 3-month-old Emx1-Cre-mediated Brap conditional knockout mice (BrapcKO) and their Cre-negative control littermates (Brap flox Cre-) were generated by next-generation sequencing, using Illumina HiSeq4000. Five BrapcKO and five control samples were analyzed. The quality of raw sequencing data was assessed using FastQC. Sequence reads that passed quality filters were analyzed by several methods, including DESeq2, edgeR, and Iimma, to discover differentially expressed genes. Results: We obtained more than 30 million sequence reads per sample that are uniquely mapped to the mouse reference genome (build mm10). After alignment of reads, expression levels of individual genes were quantified and estimated using subread::featureCounts. With the removal of lowly expressed genes using the threshold at least 10 raw read counts, 14,276 out of 21,969 annotated, valid mouse genes were retained for further analysis. Data were analyzed using several state-of-the-art tools, including DESeq2, edgeR, and Iimma, with a fold change ≥1.5 and FDR < 0.1. We collected overlapped differentially expressed mRNA genes detected with more than one method, and identified 811 differentially expressed genes between BrapcKO and control group. Conclusions: Our study represents detailed analysis of transcriptomes, with biologic replicates, generated by RNA-seq technology. The data provide insight into the neurological defects of BrapcKO mice.

ORGANISM(S): Mus musculus

PROVIDER: GSE183383 | GEO | 2021/09/05

REPOSITORIES: GEO

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Project description:Purpose: Next-generation sequencing (NGS) was used to select genes potentially associated with fiber-type distribution and cell size in porcine muscle tissue. Methods: The histological profile of the longissimus lumborum muscle was establish by method involving detection of NADPH dehydrogenase (diaphorase) and immunohistochemically detection of myosin chains. The muscle transcriptome sequencing was performed for 11 pigs using Illumina HiScan SQ in 50 single-end cycles. The quantifying transcript abundances was made using the RSEM supported by STAR aligner. The raw reads were aligned to the Sus scrofa reference genome (assembly Sscrofa10.2). Differentially expressed genes were detected by edgeR, baySeq and DESeq2. The RNA-seq results were validated using by qPCR. Results: The RNA-seq approach allows to identify 397 differentially expressed genes (DEGs) indicated as significant (FDR<0.05) using three software: DESeq2, edgeR and baySeq. Detected genes and pathways deregulated in muscle depend on tissue microstructure were associated with metabolic processes â?? 158 genes; cellular processes â?? 122; biological regulation â?? 62; localization â?? 51 and 35 genes with developmental processes. The detected DEGs were included in such pathways as: PI3K-Akt; FoxO and MAPK signaling pathways, regulation of actin cytoskeleton, lysine degradation and insulin signaling pathway as well as in mTOR and Hippo signaling. Conclusions: This results highlight the mainly metabolic pathways related with glucose metabolism and contraction processes of muscle cells. The comparison of whole gene expression profiles between muscles characterized by different fiber-type distribution showed that processes of growth and proliferation of different fiber types are determined by diverse molecular mechanism, which conditioned the unique histological structure of muscle tissue. The muscle (longissimus lumborum) transcriptome sequencing was performed for 11 pigs using Illumina HiScan SQ in 50 single-end cycles and in five technical repetitions.

Project description:Purpose: To obtain an overview of the metabolic changes associated to the absence of KpsM in Synechocystis, in particular on carbon-related metabolic pathways Methods: The trancriptomes of Synechocystis wild type and kpsM mutant were analyzed by RNA sequencing, using three biological replicates. Cultures were grown until reaching an OD730nm of 1.5. Sequencing libraries were generated using NEBNext® Ultra TM RNA Library Prep Kit for Illumina®. PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using PE Cluster Kit cBot-HS (Illumina). Raw data (raw reads) of FASTQ format were firstly processed through in-house scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter and poly-N sequences and reads with low quality from raw data. Paired-end clean reads were mapped to the reference genome using HISAT2 software.HTSeq was used to count the read numbers mapped of each gene, including known and novel genes. And then RPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. RPKM, Reads Per Kilobase of exon model per Million mapped reads, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels. Differential expression analysis between two conditions/groups (three biological replicates per condition) was performed using DESeq2 R package. DESeq2 provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P values were adjusted using the Benjamini and Hochberg’s approach for controlling the False Discovery Rate (FDR). Genes with an adjusted P value < 0.05 found by DESeq2 were assigned as differentially expressed. Results: Approximately 700 genes (out of the 3636 identified and 215 quantified, representing a coverage of 85,12%) were significantly differentially expressed (P 216 value < 0.05) in the kpsM mutant compared to the wild type. Of those, 406 were down217 regulated, while 297 were up-regulated. Conclusions: Overall, our transcriptomic data indicates alterations in the mechanisms of energy production and conversion in the kpsM mutant compared to the wild type. The approach resulted in the identification of altered levels of transcripts belonging to a variety of functional categories and highlighting a number of key metabolic processes affected by the disruption of kpsM, namely photosynthesis, oxidative phosphorylation and carbon metabolism.

Project description:Methods: We performed mRNA sequence analysis by deep sequencing, in triplicate, using Illumina HiSeq to assess the impact of PHLPP1 knockdown in regulating gene expression in RAW 264.7 cells treated with/without OxLDL. The transcriptome libraries were constructed using the NEB adapters and were sequenced on at 150 nucleotide read length using the paired-end chemistry. The raw reads were subjected to Adapter and low-quality reads removal by Trimmomatic -0.36v. The raw reads were subjected to contamination [structural RNA / low complexity sequences] removal by mapping with bowtie 2-2.2.1. The decontaminated data set was mapped to the mouse genome. Reads mapping to gene list were counted using feature count module of sub reads package. The read counts were normalized in DESeq2-3.5, and subject to differential expression analysis. Differentially expressed genes were selected based on log2-ratio change with p-value <0.05 (Student’s t-test, unpaired). Hierarchical clustering was performed with the programs Cluster (uncentered correlation; average linkage clustering) and Treeview. Results: To gain a deeper understanding of the role of PHLPP1 in lipid metabolism in macrophages, we performed mRNA Seq analysis to assess the gene expression changes in a mouse macrophage cell line. The experiment has been carried out in triplicates. Alignment of the reads showed 97% alignment to the reference mouse genome. Identification of the differentially expressed genes were then carried out by DESeq2 version 1.2.10 with stringent criteria (FDR <0.05% with 2 fold up and down regulation) which reveals in control treated vs. control (121-down, 396-Up), Test vs. Control - (326-down, 569 Up) and Test treated vs. control treated (1314-down, 1328 up) of DEG to be altered in expression respectively. Inference: RNA-seq analysis revealed that PHLPP1 knockdown parallel with OxLDL treatment resulted in diminished expression profile of cholesterol biosynthesis and lipid metabolism genes.

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Quantitative Analysis of Cerebral Cortex Transcriptomes of BrapcKO Mice and their Wildtype-like Control Mice by Next-Generation Sequencing

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