scholarly journals A meta-analysis study of gene expression datasets in mouse liver under PPARα knockout

2013 ◽  
Vol 95 (2-3) ◽  
pp. 78-88 ◽  
Author(s):  
KAN HE ◽  
ZHEN WANG ◽  
QISHAN WANG ◽  
YUCHUN PAN
Keyword(s):  
Gene Expression ◽  
Mouse Liver ◽  
Meta Analysis ◽  
Expression Patterns ◽  
Hepatic Tissue ◽  
Marker Genes ◽  
Specific Marker ◽  
Peroxisome Proliferator ◽  
Peroxisome Proliferator Activated Receptor ◽  
Microarray Datasets

SummaryGene expression profiling of peroxisome-proliferator-activated receptor α (PPARα) has been used in several studies, but there were no consistent results on gene expression patterns involved in PPARα activation in genome-wide due to different sample sizes or platforms. Here, we employed two published microarray datasets both PPARα dependent in mouse liver and applied meta-analysis on them to increase the power of the identification of differentially expressed genes and significantly enriched pathways. As a result, we have improved the concordance in identifying many biological mechanisms involved in PPARα activation. We suggest that our analysis not only leads to more identified genes by combining datasets from different resources together, but also provides some novel hepatic tissue-specific marker genes related to PPARα according to our re-analysis.

Abstract 290: Proliferation of Cardiac Fibroblasts Defines Early Stages of Genetic Dilated Cardiomyopathy and Precedes Myocardial Metabolic Derangement

2014 ◽  
Vol 115 (suppl_1) ◽  
Author(s):  
Michael A Burke ◽  
Stephen Chang ◽  
Danos C Christodoulou ◽  
Joshua M Gorham ◽  
Hiroko Wakimoto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cardiac Fibroblasts ◽  
Expression Patterns ◽  
Molecular Networks ◽  
Peroxisome Proliferator ◽  
Metabolic Derangement ◽  
Peroxisome Proliferator Activated Receptor ◽  
Cell Remodeling ◽  
Ppar Signaling ◽  
Myocyte Proliferation

The complex molecular networks underpinning DCM remain poorly understood. To study distinct pathways and networks in the longitudinal development of DCM we performed RNAseq on LV tissue from mice carrying a human DCM mutation in phospholamban (PLN R9C/+ ) before phenotype onset (pre-DCM), with DCM, and during overt heart failure (HF), and also on isolated myocytes and non-myocytes from DCM hearts. PLN R9C/+ mice show progressive fibrosis (20% vs. 1% control, p=6x10 −33 ; n=3) associated with proliferation of cardiac non-myocytes (33% increase over control, p=6x10 −4 ; n=3). Consistent with this, cardiac non-myocytes have upregulated gene expression and pathways, while these are generally downregulated in myocytes. Non-myocytes were enriched in fibrosis, inflammation, and cell remodeling pathways, from pre-DCM to HF. In contrast, myocytes were enriched for metabolic pathways only with overt DCM and HF. Myocytes showed profound derangement of oxidative phosphorylation with DCM (p=2.5x10 −41 ; 44% (53/120) of pathway genes downregulated), suggesting mitochondrial dysfunction. Additionally, we detected probable inhibition of peroxisome proliferator-activated receptor (PPAR) signaling by diminished expression of pathway genes (Figure). DCM and hypertrophic remodeling was compared using RNAseq of a mouse model of HCM; similar patterns of fibrosis with myocyte metabolic dysregulation were evident despite unique differential gene expression patterns between models. DCM caused by PLN R9C/+ is associated with early non-myocyte proliferation and later myocyte metabolic derangement possibly governed by altered PPAR signaling, and is common to DCM and HCM.

scholarly journals Peroxisome proliferator–activated receptor γ (PPARγ) induces the gene expression of integrin αVβ5 to promote macrophage M2 polarization

2018 ◽  
Vol 293 (43) ◽  
pp. 16572-16582 ◽  
Author(s):  
Qinyu Yao ◽  
Jia Liu ◽  
Zihui Zhang ◽  
Fan Li ◽  
Chao Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Foam Cell ◽  
Human Peripheral Blood ◽  
Luciferase Reporter ◽  
Foam Cell Formation ◽  
Marker Genes ◽  
Peroxisome Proliferator ◽  
Blood Monocytes ◽  
Peroxisome Proliferator Activated Receptor ◽  
M2 Polarization

Peroxisome proliferator–activated receptor γ (PPARγ) is a member of the nuclear receptor superfamily and polarizes the macrophages into an anti-inflammatory M2 state. Integrins are transmembrane receptors that drive various cellular functions, including monocyte adhesion and foam cell formation. In this study, we first reported that the expression of integrins αV and β5 was up-regulated by PPARγ activation in RAW264.7 cells and human peripheral blood monocytes. Luciferase reporter and ChIP assay revealed that PPARγ directly bound to the potential PPAR-responsive elements sites in the 5′-flanking regions of both murine and human integrin αV and β5 genes, respectively. In addition, we showed that PPARγ augmented the ligation of integrins αV and β5. Knockdown of integrin αVβ5 by siRNA strategy or treatment with cilengitide, a potent inhibitor of integrin αVβ5, attenuated PPARγ-induced expression of Ym1 (chitinase-like protein 3), Arg1 (Arginase1), Fizz1 (resistin-like molecule RELMα), and other M2 marker genes, suggesting that the heterodimers of integrin αVβ5 were involved in PPARγ-induced M2 polarization. In conclusion, these results provided novel evidence that PPARγ-mediated gene expression and the ensuing ligation of integrins αV and β5 are implicated in macrophage M2 polarization.

scholarly journals PAX8 and peroxisome proliferator-activated receptor gamma 1 gene expression status in benign and malignant thyroid tissues

2004 ◽  
pp. 367-374 ◽  
Author(s):  
L Lacroix ◽  
C Mian ◽  
T Barrier ◽  
M Talbot ◽  
B Caillou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Neoplasms ◽  
Genetic Alterations ◽  
Specific Marker ◽  
Peroxisome Proliferator ◽  
Rt Pcr ◽  
Peroxisome Proliferator Activated Receptor ◽  
Normal Tissues ◽  
Pax8 Gene ◽  
Follicular Tumors

OBJECTIVE: Genetic alterations involving the thyroid transcription factor PAX8 and the peroxisome proliferator-activated receptor gamma 1 (PPARgamma1) genes have been described in thyroid neoplasms. We investigated in a series of thyroid samples, including 14 normal, 13 hyperfunctioning tissues, 26 follicular adenomas, 21 follicular and 41 papillary carcinomas, both the frequency of the PAX8-PPARgamma1 rearrangement and the expression of the PAX8 and PPARgamma transcripts. METHODS: Using RT-PCR followed by sequencing PCR products, PAX8-PPARgamma1 translocation was not detected in benign tissues nor in papillary carcinomas and was detected in 4 (19%) of 21 follicular carcinomas and in one (4%) of 26 follicular adenomas. RESULTS: Specific real-time quantitative RT-PCR (Q RT-PCR) methods detected high levels of PPARgamma transcripts in follicular carcinomas presenting the rearrangement. Interestingly, the level of PPARgamma transcripts was significantly decreased in papillary carcinomas in comparison with those found in benign adenomas and follicular carcinomas. Finally, PAX8 gene expression was decreased in both papillary and follicular thyroid carcinomas, and in these tumors to the same extent in the presence or absence of the rearrangement. These alterations in both PPARgamma and PAX8 gene expression may explain the poorly differentiated histotype of follicular carcinomas harboring the translocation.Immunohistochemistry showed that nuclear PPARgamma staining was weak in normal tissues, adenomas, papillary carcinomas and in some follicular carcinomas, and strong in the follicular carcinomas positive for the PAX8-PPARgamma1 translocation, but also in some follicular tumors in which no translocation could be evidenced. CONCLUSION: These observations confirm that the PAX8-PPARgamma1 translocation characterizes a subset of thyroid follicular carcinomas but is not a specific marker of carcinoma and that its frequency is lower than that initially reported. Finally, immunohistochemistry is not a reliable method for the specific detection of the translocation, that can be specifically evidenced by Q RT-PCR.

scholarly journals scClustViz – Single-cell RNAseq cluster assessment and visualization

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1522 ◽  
Author(s):  
Brendan T. Innes ◽  
Gary D. Bader
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Clustering Algorithms ◽  
Expression Patterns ◽  
Software Tool ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Single Experiment

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

scholarly journals Regulation of Proteome Maintenance Gene Expression by Activators of Peroxisome Proliferator-Activated Receptor α

PPAR Research ◽  
2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Hongzu Ren ◽  
Beena Vallanat ◽  
Holly M. Brown-Borg ◽  
Richard Currie ◽  
J. Christopher Corton
Keyword(s):  
Gene Expression ◽  
Mouse Liver ◽  
Peroxisome Proliferator ◽  
Peroxisome Proliferator Activated Receptor ◽  
Expression Of Genes ◽  
Affymetrix Microarrays ◽  
Proteasomal Genes ◽  
Transcriptional Changes ◽  
Rats And Mice ◽  
Ethylhexyl Phthalate

The nuclear receptor peroxisome proliferator-activated receptor α (PPARα) is activated by a large number of xenobiotic and hypolipidemic compounds called peroxisome proliferator chemicals (PPCs). One agonist of PPARα (WY-14,643) regulates responses in the mouse liver to chemical stress in part by altering expression of genes involved in proteome maintenance (PM) including protein chaperones in the heat shock protein (Hsp) family and proteasomal genes (Psm) involved in proteolysis. We hypothesized that other PPARα activators including diverse hypolipidemic and xenobiotic compounds also regulate PM genes in the rat and mouse liver. We examined the expression of PM genes in rat and mouse liver after exposure to 7 different PPCs (WY-14,643, clofibrate, fenofibrate, valproic acid, di-(2-ethylhexyl) phthalate, perfluorooctanoic acid, and perfluorooctane sulfonate) using Affymetrix microarrays. In rats and mice, 174 or 380 PM genes, respectively, were regulated by at least one PPC. The transcriptional changes were, for the most part, dependent on PPARα, as most changes were not observed in similarly treated PPARα-null mice and the changes were not consistently observed in rats treated with activators of the nuclear receptors CAR or PXR. In rats and mice, PM gene expression exhibited differences compared to typical direct targets of PPARα (e.g.,Cyp4afamily members). PM gene expression was usually delayed and in some cases, it was transient. Dose-response characterization of protein expression showed that Hsp86 and Hsp110 proteins were induced only at higher doses. These studies demonstrate that PPARα, activated by diverse PPC, regulates the expression of a large number of genes involved in protein folding and degradation and support an expanded role for PPARα in the regulation of genes that protect the proteome.

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