scholarly journals Transcriptomic analysis of differentially expressed genes in an orange-pericarp mutant and wild type in pummelo (Citrus grandis)

2015 ◽  
Vol 15 (1) ◽  
pp. 44 ◽  
Author(s):  
Fei Guo ◽  
Huiwen Yu ◽  
Qiang Xu ◽  
Xiuxin Deng
Keyword(s):  
Differentially Expressed Genes ◽  
Transcriptomic Analysis ◽  
Differentially Expressed ◽  
Wild Type

scholarly journals Transcriptomic analysis of PPARα-dependent alterations during cardiac hypertrophy

2008 ◽  
Vol 36 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Pascal J. H. Smeets ◽  
Heleen M. de Vogel-van den Bosch ◽  
Peter H. M. Willemsen ◽  
Alphons P. Stassen ◽  
Torik Ayoubi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Lipid Metabolism ◽  
Cardiac Hypertrophy ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Left Ventricular ◽  
Transcriptomic Analysis ◽  
Differentially Expressed ◽  
Wild Type

Peroxisome proliferator-activated receptor (PPAR)α regulates lipid metabolism at the transcriptional level and modulates the expression of genes involved in inflammation, cell proliferation, and differentiation. Although PPARα has been shown to mitigate cardiac hypertrophy, knowledge about underlying mechanisms and the nature of signaling pathways involved is fragmentary and incomplete. The aim of this study was to identify the processes and signaling pathways regulated by PPARα in hearts challenged by a chronic pressure overload by means of whole genome transcriptomic analysis. PPARα−/− and wild-type mice were subjected to transverse aortic constriction (TAC) for 28 days, and left ventricular gene expression profile was determined with Affymetrix GeneChip Mouse Genome 430 2.0 arrays containing >45,000 probe sets. In unchallenged hearts, the mere lack of PPARα resulted in 821 differentially expressed genes, many of which are related to lipid metabolism and immune response. TAC resulted in a more pronounced cardiac hypertrophy and more extensive changes in gene expression (1,910 and 312 differentially expressed genes, respectively) in PPARα−/− mice than in wild-type mice. Many of the hypertrophy-related genes were related to development, signal transduction, actin filament organization, and collagen synthesis. Compared with wild-type hypertrophied hearts, PPARα−/− hypertrophied hearts revealed enrichment of gene clusters related to extracellular matrix remodeling, immune response, oxidative stress, and inflammatory signaling pathways. The present study therefore demonstrates that, in addition to lipid metabolism, PPARα is an important modulator of immune and inflammatory response in cardiac muscle.

scholarly journals A red herring in zebrafish genetics: allele-specific gene expression can underlie altered transcript abundance in zebrafish mutants

2021 ◽  
Author(s):  
Richard J White ◽  
Eirinn Mackay ◽  
Stephen W Wilson ◽  
Elisabeth M Busch-Nentwich
Keyword(s):  
Differentially Expressed Genes ◽  
Differentially Expressed Gene ◽  
Transcript Abundance ◽  
Differentially Expressed ◽  
Model Organisms ◽  
Specific Gene ◽  
Wild Type ◽  
Specific Expression ◽  
Genomic Location ◽  
Allele Specific

In model organisms, RNA sequencing is frequently used to assess the effect of genetic mutations on cellular and developmental processes. Typically, animals heterozygous for a mutation are crossed to produce offspring with different genotypes. Resultant embryos are grouped by genotype to compare homozygous mutant embryos to heterozygous and wild-type siblings. Genes that are differentially expressed between the groups are assumed to reveal insights into the pathways affected by the mutation. Here we show that in zebrafish, differentially expressed genes are often overrepresented on the same chromosome as the mutation due to different levels of expression of alleles from different genetic backgrounds. Using an incross of haplotype-resolved wild-type fish, we found evidence of widespread allele-specific expression, which appears as differential expression when comparing embryos homozygous for a region of the genome to their siblings. When analysing mutant transcriptomes, this means that differentially expressed genes on the same chromosome as a mutation of interest may not be caused by that mutation. Typically, the genomic location of a differentially expressed gene is not considered when interpreting its importance with respect to the phenotype. This could lead to pathways being erroneously implicated or overlooked due to the noise of spurious differentially expressed genes on the same chromosome as the mutation. These observations have implications for the interpretation of RNA-seq experiments involving outbred animals and non-inbred model organisms.

Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

2015 ◽  
Vol 4 (4) ◽  
pp. 35-51 ◽  
Author(s):  
Bandana Barman ◽  
Anirban Mukhopadhyay
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Wild Type ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

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