scholarly journals Gamma-Tocotrienol Modulated Gene Expression in Senescent Human Diploid Fibroblasts as Revealed by Microarray Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Suzana Makpol ◽  
Azalina Zainuddin ◽  
Kien Hui Chua ◽  
Yasmin Anum Mohd Yusof ◽  
Wan Zurinah Wan Ngah
Keyword(s):  
Gene Expression ◽  
Protein Transport ◽  
Expression Patterns ◽  
Gene List ◽  
Enrichment Analysis ◽  
Cellular Aging ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Diploid Fibroblasts ◽  
Human Diploid Fibroblasts

The effect ofγ-tocotrienol, a vitamin E isomer, in modulating gene expression in cellular aging of human diploid fibroblasts was studied. Senescent cells at passage 30 were incubated with 70 μM ofγ-tocotrienol for 24 h. Gene expression patterns were evaluated using Sentrix HumanRef-8 Expression BeadChip from Illumina, analysed using GeneSpring GX10 software, and validated using quantitative RT-PCR. A total of 100 genes were differentially expressed (P<0.001) by at least 1.5 fold in response toγ-tocotrienol treatment. Amongst the genes wereIRAK3, SelS, HSPA5, HERPUD1, DNAJB9, SEPR1, C18orf55, ARF4, RINT1, NXT1, CADPS2, COG6, andGLRX5. Significant gene list was further analysed by Gene Set Enrichment Analysis (GSEA), and the Normalized Enrichment Score (NES) showed that biological processes such as inflammation, protein transport, apoptosis, and cell redox homeostasis were modulated in senescent fibroblasts treated withγ-tocotrienol. These findings revealed thatγ-tocotrienol may prevent cellular aging of human diploid fibroblasts by modulating gene expression.

RNA profiling of neuroendocrine tumors (NETs) to define new targets and mechanisms of progression.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 302-302
Author(s):  
Namrata Vijayvergia ◽  
Suraj Peri ◽  
Karthik Devarajan ◽  
Jianming Pei ◽  
Yulan Gong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Drug Targets ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Rna Profiling ◽  
Histone Lysine Methyltransferase ◽  
Canonical Pathways ◽  
Lysine Methyltransferase

302 Background: NETs lack mutations in the “classical” signaling pathways but share mutations in regulators of gene expression (Jiao; 2011). We compared gene expression in PD & WD NETs to identify novel targets and biomarkers of differentiation. Methods: High quality RNA, extracted from paraffin blocks of deidentified NETs under an IRB-approved protocol, was profiled using a 770 gene panel (nCounter PanCancer pathway, Nanostring Technologies). The resulting data was used to identify the differentially expressed genes between PD and WD NETs using limma software (Ritchie; 2015). Gene Set Enrichment Analysis (Subramanian; 2005) identified differential pathway enrichment by calculating a Normalized Enrichment Score (NES). Results: Analysis of 16 PD and 23 WD NET samples identified 154 genes as extreme outliers ( > 2 fold up/downregulation between the subtypes). Compared to WD NETS, drug targets of interest overexpressed in PD NETs were histone lysine methyltransferase EZH2, and a cell cycle regulator CHEK1 (6.5x and 8.1x, respectively, p < 0.001). In contrast, serine/threonine protein kinase PAK 3 was upregulated in WD (10.6x, p < 0.001). These and other biomarkers will be further validated by immunolabeling of tissue sections. We also found differential enrichment of canonical pathways in PD versus WD NETs (table). Conclusions: Extreme outlier transcripts identified in PD & WD NETs support investigation of inhibitors of EZH2 (e.g. EPZ6438) and CHEK1 (e.g. LY2606368) in PD and PAK3(e.g. FRAX597) in WD NETs. Genes involved in cell cycle regulation and DNA repair in PD NETs and calcium / G protein coupled receptor signaling in WD NET account for biological differences between the 2 molecular subtypes and warrant future investigation as classifiers for NETs. Our findings provide mechanistic insights into the biology of NET and targets for therapy with direct clinical implications.[Table: see text]

scholarly journals Gene expression patterns in synchronized islet populations

2018 ◽  
Author(s):  
Nikita Mukhitov ◽  
Michael G. Roper
Keyword(s):  
Gene Expression ◽  
Environmental Changes ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Protein Translation ◽  
Gene Set Enrichment Analysis ◽  
Gene Expression Patterns ◽  
Insulin Synthesis

AbstractIn vivo levels of insulin are oscillatory with a period of ~5-10 minutes, implying that the numerous islets of Langerhans within the pancreas are synchronized. While the synchronizing factors are still under investigation, one result of this behavior is expected to be coordinated intracellular [Ca2+] ([Ca2+]i) oscillations throughout the islet population. The role that coordinated [Ca2+]i oscillations have on controlling gene expression within pancreatic islets was examined by comparing gene expression levels in islets that were synchronized using a low amplitude glucose wave and an unsynchronized population. The [Ca2+]i oscillations in the synchronized population were homogeneous and had a significantly lower drift in their oscillation period as compared to unsynchronized islets. This reduced drift in the synchronized population was verified by comparing the drift of in vivo and in vitro profiles from published reports. Microarray profiling indicated a number of Ca2+-dependent genes were differentially regulated between the two islet populations. Gene set enrichment analysis revealed that the synchronized population had reduced expression of gene sets related to protein translation, protein turnover, energy expenditure, and insulin synthesis, while those that were related to maintenance of cell morphology were increased. It is speculated that these gene expression patterns in the synchronized islets results in a more efficient utilization of intra-cellular resources and response to environmental changes.

scholarly journals Gamma-tocotrienol modulation of senescence-associated gene expression prevents cellular aging in human diploid fibroblasts

Clinics ◽  
2012 ◽  
Vol 67 (2) ◽  
pp. 135-143 ◽  
Author(s):  
S Makpol ◽  
A Zainuddin ◽  
KH Chua ◽  
YA Yusof ◽  
WZ Ngah
Keyword(s):  
Gene Expression ◽  
Cellular Aging ◽  
Diploid Fibroblasts ◽  
Human Diploid Fibroblasts ◽  
Gamma Tocotrienol

scholarly journals mCSEA: Detecting subtle differentially methylated regions

2018 ◽  
Author(s):  
Jordi Martorell-Marugán ◽  
Víctor González-Rumayor ◽  
Pedro Carmona-Sáez
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Differentially Methylated Regions ◽  
Gene Set Enrichment ◽  
Sibling Pairs ◽  
Complex Disorders ◽  
Obesity And Diabetes

AbstractMotivationThe identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders.ResultsWe present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify differentially methylated regions from Illumina 450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets, we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify differentially methylated regions not detected by other methods.AvailabilitymCSEA is freely available from the Bioconductor [email protected]

scholarly journals GSEA-InContext: Identifying novel and common patterns in expression experiments

2018 ◽  
Author(s):  
Rani K. Powers ◽  
Andrew Goodspeed ◽  
Harrison Pielke-Lombardo ◽  
Aik-Choon Tan ◽  
James C. Costello
Keyword(s):  
Expression Patterns ◽  
Null Distribution ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Specific Gene ◽  
Gene Set Enrichment ◽  
Single Experiment ◽  
Gene Set ◽  
Gene Sets

AbstractMotivationGene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment.ResultsWe compiled a compendium of 442 small molecule transcriptomic experiments and used GSEA to characterize common patterns of positively and negatively enriched gene sets. To identify experiment-specific gene set enrichment, we developed the GSEA-InContext method that accounts for gene expression patterns within a user-defined background set of experiments to identify statistically significantly enriched gene sets. We evaluated GSEA-InContext on experiments using small molecules with known targets and show that it successfully prioritizes gene sets that are specific to each experiment, thus providing valuable insights that complement standard GSEA analysis.Availability and ImplementationGSEA-InContext is implemented in Python. Code, the background expression compendium, and results are available at: https://github.com/CostelloLab/GSEA-InContext

scholarly journals Comparison of Gene Expression Between Resistant and Susceptible Families Against VPAHPND and Identification of Biomarkers Used for Resistance Evaluation in Litopenaeus vannamei

2021 ◽  
Vol 12 ◽  
Author(s):  
Qian Zhang ◽  
Yang Yu ◽  
Zheng Luo ◽  
Jianhai Xiang ◽  
Fuhua Li
Keyword(s):  
Gene Expression ◽  
Litopenaeus Vannamei ◽  
Expression Patterns ◽  
Challenge Test ◽  
Enrichment Analysis ◽  
Pacific White Shrimp ◽  
Gene Set Enrichment Analysis ◽  
White Shrimp ◽  
Pairwise Comparisons ◽  
Significant Difference

Acute hepatopancreatic necrosis disease (AHPND) has caused a heavy loss to shrimp aquaculture since its outbreak. Vibrio parahaemolyticus (VPAHPND) is regarded as one of the main pathogens that caused AHPND in the Pacific white shrimp Litopenaeus vannamei. In order to learn more about the mechanism of resistance to AHPND, the resistant and susceptible shrimp families were obtained through genetic breeding, and comparative transcriptome approach was used to analyze the gene expression patterns between resistant and susceptible families. A total of 95 families were subjected to VPAHPND challenge test, and significant variations in the resistance of these families were observed. Three pairs of resistant and susceptible families were selected for transcriptome sequencing. A total of 489 differentially expressed genes (DEGs) that presented in at least two pairwise comparisons were screened, including 196 DEGs highly expressed in the susceptible families and 293 DEGs in the resistant families. Among these DEGs, 16 genes demonstrated significant difference in all three pairwise comparisons. Gene set enrichment analysis (GSEA) of all 27,331 expressed genes indicated that some energy metabolism processes were enriched in the resistant families, while signal transduction and immune system were enriched in the susceptible families. A total of 32 DEGs were further confirmed in the offspring of the detected families, among which 19 genes were successfully verified. The identified genes in this study will be useful for clarifying the genetic mechanism of shrimp resistance against Vibrio and will further provide molecular markers for evaluating the disease resistance of shrimp in the breeding program.

scholarly journals A hypothesis-driven approach to assessing significance of differences in RNA expression levels among specific groups of genes

2017 ◽  
Author(s):  
Mingze He ◽  
Peng Liu ◽  
Carolyn J. Lawrence-Dill
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Heat Shock ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Specific Gene ◽  
Simple Method ◽  
Expression Levels ◽  
Gene Set

AbstractGenome-wide molecular gene expression studies generally compare expression values for each gene across multiple conditions followed by cluster and gene set enrichment analysis to determine whether differentially expressed genes are enriched in specific biochemical pathways, cellular components, biological processes, and/or molecular functions, etc. This approach to analyzing differences in gene expression enables discovery of gene function, but is not useful to determine whether pre-defined groups of genes share or diverge in their expression patterns in response to treatments nor to assess the correctness of pre-defined gene set groupings. Here we present a simple method that changes the dimension of comparison by treating genes as variable traits to directly assess significance of differences in expression levels among pre-defined gene groups. Because expression distributions are typically skewed (thus unfit for direct assessment using Gaussian statistical methods) our method involves transforming expression data to approximate a normal distribution followed by dividing the genes into groups, then applying Gaussian parametric methods to assess significance of observed differences. This method enables the assessment of differences in gene expression distributions within and across samples, enabling hypothesis-based comparison among groups of genes. We demonstrate this method by assessing the significance of specific gene groups’ differential response to heat stress conditions in maize.AbbreviationsGO– gene ontology HSP – heat shock proteinKEGG– Kyoto Encyclopedia of Genes and GenomesHSF TF– heat shock factor transcription factorHSBP– heat shock binding proteinRNA– ribonucleic acidTE– transposable elementTF– transcription factorTPM– transcripts per kilobase millions

scholarly journals mCSEA: detecting subtle differentially methylated regions

2019 ◽  
Vol 35 (18) ◽  
pp. 3257-3262 ◽  
Author(s):  
Jordi Martorell-Marugán ◽  
Víctor González-Rumayor ◽  
Pedro Carmona-Sáez
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Differentially Methylated Regions ◽  
Sibling Pairs ◽  
Complex Disorders ◽  
Obesity And Diabetes

Abstract Motivation The identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders. Results We present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify DMRs from Illumina450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify DMRs not detected by other methods. Availability and implementation mCSEA is freely available from the Bioconductor repository. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CSIG-03. RECONCILING TUMOR HETEROGENEITY IN GLIOBLASTOMA USING A PATHWAY-BASED APPROACH

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii28-ii28
Author(s):  
Alvaro Alvarado ◽  
Kaleab Tessema ◽  
Kunal Patel ◽  
Riki Kawaguchi ◽  
Richard Everson ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Patient Survival ◽  
Molecular Subtype ◽  
Gene List ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Molecular Architecture ◽  
Survival Pathways ◽  
Cancer Genome Atlas

Abstract Despite efforts to gain a deeper understanding of its molecular architecture, glioblastoma (GBM) remains uniformly fatal. While genome-based molecular subtyping has revealed that GBMs may be parsed into several molecularly distinct categories, this insight has yielded little progress towards extending patient survival. In particular, the great phenotypic heterogeneity of GBM – both inter and intratumorally – has hindered therapeutic efforts. To this end, we interrogated tumor samples using a pathway-based approach to resolve tumoral heterogeneity. Gene set enrichment analysis (GSEA) was applied to gene expression data and used to provide an overview of each sample that can be compared to other samples by generating sample clusters based on overall patterns of enrichment. The Cancer Genome Atlas (TCGA) samples were clustered using the canonical and oncogenic signatures and in both cases the clustering was distinct from the molecular subtype previously reported and clusters were informative of patient survival. We also analyzed single cell RNA sequencing datasets and uniformly found two clusters of cells enriched for cell cycle regulation and survival pathways. We have validated our approach by generating gene lists from common elements found in the top contributing genesets for a particular cluster and testing the top targets in appropriate gliomasphere patient-derived lines. Samples enriched for cell cycle related genesets showed a decrease in sphere formation capacity when E2F1, out top target, was silenced and when treated with fulvestrant and calcitriol, which were identified as potential drugs targeting this genelist. Conversely, no changes were observed in samples not enriched for this gene list. Finally, we interrogated spatial heterogeneity and found higher enrichment of the proliferative signature in contrast enhancing compared with non-enhancing regions. Our studies relate inter- and intratumoral heterogeneity to critical cellular pathways dysregulated in GBM, with the ultimate goal of establishing a pipeline for patient- and tumor-specific precision medicine.

scholarly journals Analysis of Transcriptional Changes in Different Brassica napus Synthetic Allopolyploids

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 82
Author(s):  
Yunxiao Wei ◽  
Guoliang Li ◽  
Shujiang Zhang ◽  
Shifan Zhang ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brassica Napus ◽  
Differentially Expressed Genes ◽  
Expression Patterns ◽  
Female Parent ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Transcriptional Changes ◽  
Aa Genome ◽  
High Degree

Allopolyploidy is an evolutionary and mechanistically intriguing process involving the reconciliation of two or more sets of diverged genomes and regulatory interactions, resulting in new phenotypes. In this study, we explored the gene expression patterns of eight F2 synthetic Brassica napus using RNA sequencing. We found that B. napus allopolyploid formation was accompanied by extensive changes in gene expression. A comparison between F2 and the parent shows a certain proportion of differentially expressed genes (DEG) and activation\silent gene, and the two genomes (female parent (AA)\male parent (CC) genomes) showed significant differences in response to whole-genome duplication (WGD); non-additively expressed genes represented a small portion, while Gene Ontology (GO) enrichment analysis showed that it played an important role in responding to WGD. Besides, genome-wide expression level dominance (ELD) was biased toward the AA genome, and the parental expression pattern of most genes showed a high degree of conservation. Moreover, gene expression showed differences among eight individuals and was consistent with the results of a cluster analysis of traits. Furthermore, the differential expression of waxy synthetic pathways and flowering pathway genes could explain the performance of traits. Collectively, gene expression of the newly formed allopolyploid changed dramatically, and this was different among the selfing offspring, which could be a prominent cause of the trait separation. Our data provide novel insights into the relationship between the expression of differentially expressed genes and trait segregation and provide clues into the evolution of allopolyploids.

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