RNA-Seq data for 35 controls using total RNA extracted using RNAZol
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ABSTRACT: Methods routinely used to analyze RNA sequencing data focus on statistical significance and the detection gene differential expression changes that meet a two-fold minimum change between groups. Due to the unique expression variability present in RNA sequencing data, this strategy may potentially overlook or obscure the detection of valuable information as a result of large expression variability in specific genes in certain samples. This paper develops tools and methods that apply variance and dispersion estimates to intra-group data in order to identify genes with expression values that diverge from the group envelope. STRING database analysis of the genes identified with this analysis characterize gene affiliations involved in physiological regulatory networks that are associated to biological variability. Samples or genes identified as divergent can be judiciously evaluated prior to any standard differential analysis. A three-step process is presented for evaluating biological variability within a group in RNA sequencing data in which gene counts were: (1) scaled to minimize heteroscedasticity; (2) rank-ordered to potentially divergent “trendlines” for every gene in the data set; and (3) tested with the STRING database to identify statistically significant pathway associations among the genes displaying marked trendline variability and dispersion. This approach was used to identify and portray the “trendline” profile of every gene in three test data sets. Control data from an in-house data set and two archived samples revealed that 65-70 % of the sequenced genes displayed trendlines with minimal variation and dispersion across the sample group after rank-ordering the samples; this is referred to as linear trendline. Nonlinear trendlines refer to all cases where the trendline is not linear. Smaller subsets of genes within the three data sets displayed markedly skewed trendlines, wide dispersion, and variability. STRING database analysis of these genes identified interferon-mediated response networks in 11-20 % of the individuals sampled at the time of blood collection. For example, in the three control data sets, 14 to 26 genes in the defense response to virus pathway were identified in 7 individuals at false discovery rates ≤ 1.92 E-15. Gene clusters involving leukocyte and neutrophil activation and degranulation pathways were also detected.
Project description:We randomly selected three serum samples each from an AS and a normal control (NC) group for high-throughput sequencing followed by using edgeR to find differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome pathway analyses and gene set enrichment analysis (GSEA)were used to comprehensively analyze the possible functions and pathways involved with these DEGs. Protein–protein interaction (PPI) networks were constructed using the STRING database and Cytoscape. The modules and hub genes of these DEGs were identified using MCODE and CytoHubba plugins. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to validate the expression levels of candidate genes in serum samples from AS patients and healthy controls.
Project description:For better understanding of radiotherapy resistance and its potential mechanism, we established radioresistance cell lines of non-small cell lung cancer (NSCLC) followed by microarray analysis. 529 differentially expressed genes (DEGs) were then screened between radiation resistant cell lines compared with the sensitive cell lines. The biological functions and enrichment pathways of the above DEGs were identified using Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses. Gene Set Enrichment Analysis (GSEA) revealed that the radiation resistance group had the most gene sets enriched in altered immune response, such as TNF signaling pathway, when compared to the radiation sensitive group. Protein-protein interaction (PPI) network was carried out from the STRING database. Following that, by means of Cytoscape software, five hub genes (CXCL10, IFIH1, DDX58, CXCL11, RSAD2) were identified, and qRT-PCR confirmed the expression of the above hub genes. ChIP-X Enrichment Analysis showed that STAT1 might be the transcription factor of the above hub genes. Our results suggested that although immune system activation occurs followed by radiation resistance, the unregulated expression of PD-L1 ultimately leads to the exhaustion of anti-tumor immunity, which may be the possible mechanisms of tumor resistance to radiotherapy.
Project description:Find the possible signaling pathways which contribute to the cell growth inhibition effect of SW-treated AGS cells Global gene expression profiling is an ideal technique to obtain useful clues for exploration of the anticancer mechanism of SW. Through comparing microarray results between solvent- and SW-treated cells, differentially expressed genes were obtained (>1.5 fold). The microarray results were validated using real-time RT-PCR. We used the KEGG database, STRING database and GO database for further ananlysis, and therefore got the possible signaling pathways underlying the anticancer effect of SW.
Project description:To find the possible signaling pathways which contribute to the anticancer effect of SW-treated HepG2 cells Global gene expression profiling is an ideal technique to obtain useful clues for exploration of the anticancer mechanism of SW. Through comparing microarray results between solvent- and SW-treated cells, differentially expressed genes were obtained (>1.5 fold). The microarray results were validated using real-time RT-PCR. We used the KEGG database, STRING database and GO database for further ananlysis, and therefore got the possible signaling pathways underlying the anticancer effect of SW.
Project description:Dilated cardiomyopathy (DCM) is characterized by left ventricular or biventricular enlargement with systolic dysfunction. To date, the underlying molecular mechanisms of dilated cardiomyopathy pathogenesis have not been fully elucidated, although some insights have been presented. In this study, we combined public database resources and a doxorubicin-induced DCM mouse model to explore the significant genes of DCM in full depth. We first retrieved six DCM-related microarray datasets from the GEO database using several keywords. Then we used the "LIMMA" (linear model for microarray data) R package to filter each microarray for differentially expressed genes (DEGs). Robust rank aggregation (RRA), an extremely robust rank aggregation method based on sequential statistics, was then used to integrate the results of the six microarray datasets to filter out the reliable differential genes. To further improve the reliability of our results, we established a doxorubicin-induced DCM model in C57BL/6N mice, using the "DESeq2" software package to identify DEGs in the sequencing data. We cross-validated the results of RRA analysis with those of animal experiments by taking intersections and identified three key differential genes (including Bex1, RGCC and VSIG4) associated with DCM as well as many important biological processes (extracellular matrix organization, extracellular structural organization, sulphur compound binding, and extracellular matrix structural components) and a signalling pathway (HIF-1 signalling pathway).
Project description:Find the possible signaling pathways which contribute to the cell growth inhibition effect of SW-treated AGS cells Global gene expression profiling is an ideal technique to obtain useful clues for exploration of the anticancer mechanism of SW. Through comparing microarray results between solvent- and SW-treated cells, differentially expressed genes were obtained (>1.5 fold). The microarray results were validated using real-time RT-PCR. We used the KEGG database, STRING database and GO database for further ananlysis, and therefore got the possible signaling pathways underlying the anticancer effect of SW. We analyzed total RNA samples of solvent- and SW-treated AGS cells using the Affymetrix Human Gene 1.0 ST platform. No techinical replicates were performed.
Project description:To find the possible signaling pathways which contribute to the anticancer effect of SW-treated HepG2 cells Global gene expression profiling is an ideal technique to obtain useful clues for exploration of the anticancer mechanism of SW. Through comparing microarray results between solvent- and SW-treated cells, differentially expressed genes were obtained (>1.5 fold). The microarray results were validated using real-time RT-PCR. We used the KEGG database, STRING database and GO database for further ananlysis, and therefore got the possible signaling pathways underlying the anticancer effect of SW. We analyzed total RNA samples of solvent- and SW-treated HepG2 cells using the Affymetrix Human Gene 1.0 ST platform. No techinical replicates were performed.
Project description:In order to investigate, at the mRNA level, the signaling pathways through which triiodothyronine (T3) and irisin (I) affects Human subcutaneous adipocytes function, human Cultured Human subcutaneous preadipocytes were differentiated into mature adipocytes, which were cultured with T3 and/or Irisin, the control group was without treatment. RNA-Seq was performed using Illumina platform, and differential gene expression was assessed with DESeq2. Among differentially expressed genes, enrichment analysis was performed for biological processes against the Gene Ontology Consortium database, using both ClusterProfiler R package and STRING. Grant 2016/03242-3, São Paulo Research Foundation (FAPESP). Grant 409438/2016, National Council for Scientific and Technological Development (CNPq).
Project description:Tistlia consotensis is a halotolerant Rhodospirillaceae that was isolated from a saline spring located in the Colombian Andes with a salt concentration close to seawater (4.5%w/vol). We cultivated this microorganism in three NaCl concentrations, i.e. optimal (0.5%), without (0.0%) and high (4.0%) salt concentration, and analyzed its cellular proteome. For assigning tandem mass spectrometry data, we first sequenced its genome and constructed a six reading frame ORF database from the draft sequence. We annotated only the genes whose products (872) were detected. We compared the quantitative proteome data sets recorded for the three different growth conditions.Peak lists were generated with the MASCOT DAEMON software (version 2.3.2) from Matrix Science using the extract_msn.exe data import filter from the Xcalibur FT package (version 2.0.7) proposed by ThermoFisher. Data import filter options were set at 400 (minimum mass), 5,000 (maximum mass), 0 (grouping tolerance), 0 (intermediate scans), and 1,000 (threshold). MS/MS spectra were searched against the home-made ORF database with the following parameters: tryptic peptides with a maximum of 2 miss cleavages during proteolytic digestion, a mass tolerance of 5 ppm on the parent ion and 0.5 Da on the MS/MS, fixed modification for carbamidomethylated Cys (+57.0215) and variable modification for oxidized Met (+15.9949). All peptide matches with a peptide score above its query threshold set at p < 0.05 with the ORF database and rank 1 were parsed using the IRMa 1.28.0 software. False-positive rate for peptide identification was estimated using a decoy database as below 0.5% with these parameters. MS/MS spectra assigned to several loci were systematically removed. A protein was considered validated when at least two different peptides were detected in the same experiment. False-positive identification of proteins was estimated using a reverse decoy database as below 0.1% with these parameters The number of MS/MS spectra per protein (spectral counts) was determined for the three replicates in each growth condition. The protein abundances were compared using the T-Fold option of the PatternLab 2.0 software. This module allows normalising the spectral count datasets, calculating the average fold changes with statistics (t-test), and estimating the resulting theoretical false discovery rate. Stringent parameters were used in this analysis: minimum fold change of 1.5, minimum p-value of 0.05 and BH-FDR Alfa of 0.15.