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Diagram summarizing the EDP effects on NASH development. <span class="search-highlight">ECM</span>, <span class="search-highlight">extracellular</span>...
Published: 13 July 2018
Diagram summarizing the EDP effects on NASH development. ECM, extracellular matrix; ERC, elastin receptor complex. Diagram summarizing the EDP effects on NASH development. ECM, extracellular matrix; ERC, elastin receptor complex. More
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The adipocyte as a professional secretory cell. Many distinct categories of...
Published: 18 May 2016
Figure 4 The adipocyte as a professional secretory cell. Many distinct categories of secretory proteins have been reported to be released from adipocytes. Acrp30, adipocyte complement-related protein of 30 kDa; ASP, acylation-stimulating protein; ECM, extracellular matrix; IL-6, interleukin 6; MT1... More
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Genome-wide methylation analysis reveals insulin-induced DNA methylation in...
Published: 23 December 2016
Figure 1 Genome-wide methylation analysis reveals insulin-induced DNA methylation in isolated human skeletal muscle. Genome-wide analysis showing the 15 most significantly enriched KEGG pathways (Benjamini-Hochberg adjusted [B-H adj.] P value) of genes with significant changes in DNA methylation between basal and insulin-stimulated skeletal muscle from three healthy subjects. ECM, extracellular matrix; MAPK, mitogen-activated protein kinase. Figure 1. Genome-wide methylation analysis reveals insulin-induced DNA methylation in isolated human skeletal muscle. Genome-wide analysis showing the 15 most significantly enriched KEGG pathways (Benjamini-Hochberg adjusted [B-H adj.] P value) of genes with significant changes in DNA methylation between basal and insulin-stimulated skeletal muscle from three healthy subjects. ECM, extracellular matrix; MAPK, mitogen-activated protein kinase. More
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Identification of IR as a novel interacting protein for FBXO2. <em>A</em>...
Published: 08 December 2016
Figure 2 Identification of IR as a novel interacting protein for FBXO2. A: Venn diagram of the proteins identified from WT and MUT FBXO2 interacting proteins. B: Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the glycoproteins exclusively identified from cells overexpressing WT FBXO2. ECM, extracellular matrix; GPI, glycophosphatidylinositol. C: Gene ontology analysis of the glycoproteins exclusively identified from cells overexpressing WT FBXO2. D: Spectra counting–based quantification analysis of IR protein from WT and MUT FBXO2 interacting proteins. R1 and R2 represent two replicates. Figure 2. Identification of IR as a novel interacting protein for FBXO2. A: Venn diagram of the proteins identified from WT and MUT FBXO2 interacting proteins. B: Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the glycoproteins exclusively identified from cells overexpressing WT FBXO2. ECM, extracellular matrix; GPI, glycophosphatidylinositol. C: Gene ontology analysis of the glycoproteins exclusively identified from cells overexpressing WT FBXO2. D: Spectra counting–based quantification analysis of IR protein from WT and MUT FBXO2 interacting proteins. R1 and R2 represent two replicates. More
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A heatmap representing the pathway enrichment analysis that applied the Wik...
Published: 06 September 2019
Figure 2 A heatmap representing the pathway enrichment analysis that applied the WikiPathways curated collection of human pathways. The pathways are ranked on the basis of a standardized difference score (z score). z scores >0 indicate enrichment for significantly different genes (blue gradients), and z scores <0 indicate an absence of enrichment for significantly different genes (white). Pathways were considered significantly different when 1) z score was >1.96, 2) permuted P was <0.05, and 3) three or more significantly different genes (nominal P < 0.05) existed in the pathway. ECM, extracellular matrix; ID, inhibitor of DNA binding; NF, nuclear factor; TGF, transforming growth factor. Figure 2. A heatmap representing the pathway enrichment analysis that applied the WikiPathways curated collection of human pathways. The pathways are ranked on the basis of a standardized difference score (z score). z scores >0 indicate enrichment for significantly different genes (blue gradients), and z scores <0 indicate an absence of enrichment for significantly different genes (white). Pathways were considered significantly different when 1) z score was >1.96, 2) permuted P was <0.05, and 3) three or more significantly different genes (nominal P < 0.05) existed in the pathway. ECM, extracellular matrix; ID, inhibitor of DNA binding; NF, nuclear factor; TGF, transforming growth factor. More
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Heat map of KEGG pathway enrichment analysis. Normalized counts/pseudocount...
Published: 30 May 2017
Figure 3 Heat map of KEGG pathway enrichment analysis. Normalized counts/pseudocounts of the DE genes were subjected to GAGE analysis by using the Bioconductor package gage. Pathways with adjusted P values (Benjamini-Hochberg procedure) of <0.05 are indicated by asterisks. The Stat.mean values represent the averaged magnitude and direction of fold changes at the gene set level corresponding to the color-coded upregulated (red) and downregulated (blue) changes. KEGG pathway maps were used to perform classifications. Akt, protein kinase B; ECM, extracellular matrix; HIF-1, hypoxia-inducible factor 1; Jak, Janus kinase; MAPK, mitogen-activated kinase; PI3K, phosphatidylinositol 3-kinase; TGF, transforming growth factor; tRNA, transfer RNA. Figure 3. Heat map of KEGG pathway enrichment analysis. Normalized counts/pseudocounts of the DE genes were subjected to GAGE analysis by using the Bioconductor package gage. Pathways with adjusted P values (Benjamini-Hochberg procedure) of <0.05 are indicated by asterisks. The Stat.mean values represent the averaged magnitude and direction of fold changes at the gene set level corresponding to the color-coded upregulated (red) and downregulated (blue) changes. KEGG pathway maps were used to perform classifications. Akt, protein kinase B; ECM, extracellular matrix; HIF-1, hypoxia-inducible factor 1; Jak, Janus kinase; MAPK, mitogen-activated kinase; PI3K, phosphatidylinositol 3-kinase; TGF, transforming growth factor; tRNA, transfer RNA. More
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ROS and inflammation contribute to the pathogenesis of islet dysfunction in...
Published: 30 May 2017
Figure 7 ROS and inflammation contribute to the pathogenesis of islet dysfunction in GK rats. The illustration depicts ROS signaling flux as a core hub linking metabolic dysfunction with islet inflammation and fibrosis. The time course gene expression data for several key genes involved in the generation of ROS, antioxidants, inflammation, and fibrosis are displayed as a line chart. Gene expression line chart data are mean ± SEM of repeated experiments (n = 3 [except for WST week 4 mRNA data where n = 2]). *P < 0.01, **P < 0.001, ***P < 0.0001 by adjusted ANOVA. ECM, extracellular matrix; ER, endoplasmic reticulum; HIF-1a, hypoxia-inducible factor 1a; Mito, mitochondrion; ns, no significant difference between GK and WST. Figure 7. ROS and inflammation contribute to the pathogenesis of islet dysfunction in GK rats. The illustration depicts ROS signaling flux as a core hub linking metabolic dysfunction with islet inflammation and fibrosis. The time course gene expression data for several key genes involved in the generation of ROS, antioxidants, inflammation, and fibrosis are displayed as a line chart. Gene expression line chart data are mean ± SEM of repeated experiments (n = 3 [except for WST week 4 mRNA data where n = 2]). *P < 0.01, **P < 0.001, ***P < 0.0001 by adjusted ANOVA. ECM, extracellular matrix; ER, endoplasmic reticulum; HIF-1a, hypoxia-inducible factor 1a; Mito, mitochondrion; ns, no significant difference between GK and WST. More
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GLUT1 mediates fibronectin expression through NOX4. MCs were transfected an...
Published: 23 February 2016
Figure 7 GLUT1 mediates fibronectin expression through NOX4. MCs were transfected and exposed to LG or HG 7. Before harvesting, indicated plates were infected with adenovirus GFP (ADGFP) or adenovirus NOX4 (ADNOX4). A and B: Fibronectin (FN) expression was assessed by Western blot analysis. Actin was immunoblotted as a loading control. C: Parallel to A and B, cells were plated on cover slips. Fibronectin was examined by immunofluorescence as outlined in Research Design and Methods . D: NOX4 expression was assessed in MCs infected with adenovirus GFP or adenovirus NOX4. E: Schematic for HIF-1 inhibition blocking progression of DN through reduction of GLUT1-dependent, NOX4-derived oxidative stress in vitro and in vivo. ECM, extracellular matrix; SCR, scrambled control. Figure 7. GLUT1 mediates fibronectin expression through NOX4. MCs were transfected and exposed to LG or HG 7. Before harvesting, indicated plates were infected with adenovirus GFP (ADGFP) or adenovirus NOX4 (ADNOX4). A and B: Fibronectin (FN) expression was assessed by Western blot analysis. Actin was immunoblotted as a loading control. C: Parallel to A and B, cells were plated on cover slips. Fibronectin was examined by immunofluorescence as outlined in Research Design and Methods. D: NOX4 expression was assessed in MCs infected with adenovirus GFP or adenovirus NOX4. E: Schematic for HIF-1 inhibition blocking progression of DN through reduction of GLUT1-dependent, NOX4-derived oxidative stress in vitro and in vivo. ECM, extracellular matrix; SCR, scrambled control. More
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Proposed model of c-Kit signaling that mediates VEGF-A production and modul...
Published: 07 August 2015
Figure 8 Proposed model of c-Kit signaling that mediates VEGF-A production and modulates islet vasculature, affecting β-cell function and survival. A: c-Kit mediates VEGF-A production via the PI3K/Akt/mTOR signaling, which is essential for maintaining islet vascular formation. Proper islet vasculature facilitates the nutrient and oxygen exchange that preserves β-cell survival and function. B: Chronic constitutive c-Kit–mediated VEGF-A overproduction results in increased islet vasculature. Under long-term HFD conditions, islets become hypervascularized, with increased cytokine production, macrophage infiltration, β-cell dysfunction, and apoptosis. BM, basement membrane; ECM, extracellular matrix; VEGFR2, VEGF receptor 2. Figure 8. Proposed model of c-Kit signaling that mediates VEGF-A production and modulates islet vasculature, affecting β-cell function and survival. A: c-Kit mediates VEGF-A production via the PI3K/Akt/mTOR signaling, which is essential for maintaining islet vascular formation. Proper islet vasculature facilitates the nutrient and oxygen exchange that preserves β-cell survival and function. B: Chronic constitutive c-Kit–mediated VEGF-A overproduction results in increased islet vasculature. Under long-term HFD conditions, islets become hypervascularized, with increased cytokine production, macrophage infiltration, β-cell dysfunction, and apoptosis. BM, basement membrane; ECM, extracellular matrix; VEGFR2, VEGF receptor 2. More
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β-Cell transcriptome changes in response to excitotoxicity in <em>Abcc8</em>...
Published: 24 April 2020
Figure 2 β-Cell transcriptome changes in response to excitotoxicity in Abcc8 KO mice. A: Volcano plot showing distribution of differentially expressed genes (Log2FC over P value) in the RC-KO vs. RC-WT RNA-sequencing comparison. Top 10 differentially expressed genes are indicated by names, and total numbers of URGs and DRGs are shown (Padj < 0.05). B: Distribution of dysregulated genes by biotype. C: Functional enrichment analysis of URGs and DRGs. Select top enriched pathways are shown. D: DE levels of select top URGs (top) and DRGs (bottom), with colors indicating gene functional associations. ECM, extracellular matrix; FDR, false discovery rate; Log2FC, log2 fold change of gene expression values in normalized counts in RC-KO vs. RC-WT comparison; TCA, tricarboxylic acid. Figure 2. β-Cell transcriptome changes in response to excitotoxicity in Abcc8 KO mice. A: Volcano plot showing distribution of differentially expressed genes (Log2FC over P value) in the RC-KO vs. RC-WT RNA-sequencing comparison. Top 10 differentially expressed genes are indicated by names, and total numbers of URGs and DRGs are shown (Padj < 0.05). B: Distribution of dysregulated genes by biotype. C: Functional enrichment analysis of URGs and DRGs. Select top enriched pathways are shown. D: DE levels of select top URGs (top) and DRGs (bottom), with colors indicating gene functional associations. ECM, extracellular matrix; FDR, false discovery rate; Log2FC, log2 fold change of gene expression values in normalized counts in RC-KO vs. RC-WT comparison; TCA, tricarboxylic acid. More
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β-Cell transcriptome changes in response to overnutrition (HFD) in WT mice....
Published: 24 April 2020
Figure 3 β-Cell transcriptome changes in response to overnutrition (HFD) in WT mice. A: Volcano plot showing distribution of differentially expressed genes (Log2FC over P value) in HFD-WT vs. RC-WT RNA-sequencing comparison. Top 10 differentially expressed genes are indicated by names, and total numbers of URGs and DRGs are provided (Padj < 0.05). B: Distribution of dysregulated genes by biotype. C: Functional enrichment analysis of URGs and DRGs. Select top enriched pathways are shown. D: DE levels of select top URGs (top) and DRGs (bottom), with colors indicating gene functional associations. ECM, extracellular matrix; ERAD, ER-associated protein degradation; FDR, false discovery rate; Log2FC, log2 fold change of normalized gene expression between HFD-WT and RC-WT samples; mTOR, mammalian target of rapamycin. Figure 3. β-Cell transcriptome changes in response to overnutrition (HFD) in WT mice. A: Volcano plot showing distribution of differentially expressed genes (Log2FC over P value) in HFD-WT vs. RC-WT RNA-sequencing comparison. Top 10 differentially expressed genes are indicated by names, and total numbers of URGs and DRGs are provided (Padj < 0.05). B: Distribution of dysregulated genes by biotype. C: Functional enrichment analysis of URGs and DRGs. Select top enriched pathways are shown. D: DE levels of select top URGs (top) and DRGs (bottom), with colors indicating gene functional associations. ECM, extracellular matrix; ERAD, ER-associated protein degradation; FDR, false discovery rate; Log2FC, log2 fold change of normalized gene expression between HFD-WT and RC-WT samples; mTOR, mammalian target of rapamycin. More
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<em>Sarm1</em> gene deficiency diminishes the changes of gene expre...
Published: 22 August 2019
Figure 7 Sarm1 gene deficiency diminishes the changes of gene expression profile induced by STZ in the sciatic nerve. Scatter plot comparing the differentially expressed genes detected by high-throughput sequencing in spinal cord (A) and sciatic nerve (B) between wild-type (WT) and Sarm1−/− mice after 5-day consecutive injection with vehicle or STZ and for the subsequent 25 weeks. C and D: The percentage of genes affected by STZ treatment in spinal cord (C) and sciatic nerve (D) of WT and Sarm1−/− mice as described in A and B. E: The top 20 significantly enriched KEGG pathway of the differentially expressed genes in sciatic nerve between WT and Sarm1−/− mice treated with STZ. F: The differentially expressed genes in sciatic nerve between WT and Sarm1−/− mice treated with STZ enriched in human diseases. G: Top four abundant genes enriched in neurodegenerative diseases in F were validated by quantitative PCR. n = 6 for each group. ***P < 0.001. ECM, extracellular matrix; RPKM, read per kilobase per million mapped reads. Figure 7. Sarm1 gene deficiency diminishes the changes of gene expression profile induced by STZ in the sciatic nerve. Scatter plot comparing the differentially expressed genes detected by high-throughput sequencing in spinal cord (A) and sciatic nerve (B) between wild-type (WT) and Sarm1−/− mice after 5-day consecutive injection with vehicle or STZ and for the subsequent 25 weeks. C and D: The percentage of genes affected by STZ treatment in spinal cord (C) and sciatic nerve (D) of WT and Sarm1−/− mice as described in A and B. E: The top 20 significantly enriched KEGG pathway of the differentially expressed genes in sciatic nerve between WT and Sarm1−/− mice treated with STZ. F: The differentially expressed genes in sciatic nerve between WT and Sarm1−/− mice treated with STZ enriched in human diseases. G: Top four abundant genes enriched in neurodegenerative diseases in F were validated by quantitative PCR. n = 6 for each group. ***P < 0.001. ECM, extracellular matrix; RPKM, read per kilobase per million mapped reads. More
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Relapse reduces genes involved in fat metabolism and increases genes involv...
Published: 03 February 2021
Figure 5 Relapse reduces genes involved in fat metabolism and increases genes involved in carbohydrate metabolism. A: Venn diagram depicting KEGG pathway analysis of mCK-hLPL mice compared with WT mice during WLM and relapse. Blue portion denotes pathways that are uniquely regulated by mCK-hLPL during WLM; red portion denotes pathways that are uniquely regulated by mCK-hLPL during relapse; and purple portion denotes commonly regulated pathways by mCK-hLPL during WLM and relapse. The table lists the 14 pathways that are commonly regulated by mCK-hLPL during WLM and relapse. Pathway analysis was performed using the Generally Applicable Gene-set Enrichment (GAGE) method. B: Heat map of extracted genes involved in fat and carbohydrate metabolism. C: Diagram of the role each gene from B plays in fat and carbohydrate metabolism. Genes for B and C were extracted based on being significantly different (nonadjusted P value) between WLM and relapse WT mice or between WT and mCK-hLPL relapsing mice. ECM, extracellular matrix; P-val, P value; PPP, pentose phosphate pathway; TCA, tricarboxylic acid. Figure 5. Relapse reduces genes involved in fat metabolism and increases genes involved in carbohydrate metabolism. A: Venn diagram depicting KEGG pathway analysis of mCK-hLPL mice compared with WT mice during WLM and relapse. Blue portion denotes pathways that are uniquely regulated by mCK-hLPL during WLM; red portion denotes pathways that are uniquely regulated by mCK-hLPL during relapse; and purple portion denotes commonly regulated pathways by mCK-hLPL during WLM and relapse. The table lists the 14 pathways that are commonly regulated by mCK-hLPL during WLM and relapse. Pathway analysis was performed using the Generally Applicable Gene-set Enrichment (GAGE) method. B: Heat map of extracted genes involved in fat and carbohydrate metabolism. C: Diagram of the role each gene from B plays in fat and carbohydrate metabolism. Genes for B and C were extracted based on being significantly different (nonadjusted P value) between WLM and relapse WT mice or between WT and mCK-hLPL relapsing mice. ECM, extracellular matrix; P-val, P value; PPP, pentose phosphate pathway; TCA, tricarboxylic acid. More
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The metabolic pathway of retinoic acid. <em>A</em>: Gene ontology a...
Published: 02 March 2016
Figure 1 The metabolic pathway of retinoic acid. A: Gene ontology analysis of RNA-seq data from SC and VS adipose tissue identified retinol metabolism as the second most significant biological pathway that is differentially expressed between these two depots; this is defined by P value <0.05 after Bonferroni correction. ECM, extracellular matrix. B: Vitamin A/retinol is metabolized to atRA via two sequential enzymatic reactions. i: In the first reaction, RDH10 and DHRS3, two retinol dehydrogenases/reductases, reversibly oxidize retinol to retinal and vice versa. CRBP1 is a cellular retinol-binding protein that controls the availability of cellular retinol. ii: In the second reaction, ALDH1A1, ALDH1A2, and ALDH1A3 (retinaldehyde dehydrogenases) irreversibly oxidize retinal to atRA. iii: RA enters the nucleus and specifically binds the retinoic acid receptor family: RARs α, β, and γ or RXRs α, β, and γ. iv: atRA is metabolized to 4-hydroxy-retinoic acid by cytochrome P450 proteins (CYP26A1, CYP26B1, and CYP26C1). v: RARs are ligand-dependent transcription factors and, when bound to atRA, regulate the expression of many RA target genes through binding to the RARE. Proteins whose gene expression is upregulated in VS ASCs are highlighted in red, and those that are downregulated in VS-ASCs are blue. Figure 1. The metabolic pathway of retinoic acid. A: Gene ontology analysis of RNA-seq data from SC and VS adipose tissue identified retinol metabolism as the second most significant biological pathway that is differentially expressed between these two depots; this is defined by P value <0.05 after Bonferroni correction. ECM, extracellular matrix. B: Vitamin A/retinol is metabolized to atRA via two sequential enzymatic reactions. i: In the first reaction, RDH10 and DHRS3, two retinol dehydrogenases/reductases, reversibly oxidize retinol to retinal and vice versa. CRBP1 is a cellular retinol-binding protein that controls the availability of cellular retinol. ii: In the second reaction, ALDH1A1, ALDH1A2, and ALDH1A3 (retinaldehyde dehydrogenases) irreversibly oxidize retinal to atRA. iii: RA enters the nucleus and specifically binds the retinoic acid receptor family: RARs α, β, and γ or RXRs α, β, and γ. iv: atRA is metabolized to 4-hydroxy-retinoic acid by cytochrome P450 proteins (CYP26A1, CYP26B1, and CYP26C1). v: RARs are ligand-dependent transcription factors and, when bound to atRA, regulate the expression of many RA target genes through binding to the RARE. Proteins whose gene expression is upregulated in VS ASCs are highlighted in red, and those that are downregulated in VS-ASCs are blue. More
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Gene set enrichment analysis reveals MafA regulation of many key β-cell act...
Published: 15 May 2014
Figure 6 Gene set enrichment analysis reveals MafA regulation of many key β-cell activities. (A) The mRNA expression levels of 13 MafA microarray identified candidate genes in 3-week-old Mafa∆panc and control islets (n ≥ 3). *P < 0.05; **P < 0.01; ***P < 0.001. Gene ontology analysis showing the (B) cellular component and (C) molecular function enrichment obtained from the microarray mRNA studies performed on 3-month-old wild-type and Mafa∆panc islets. Each pie chart section is proportional to the number of genes in the category. The acinar cell signals in the Mafa∆panc microarray data are contamination, since these were not detected in the independently isolated Mafa∆panc islet preparations used for Figs. 4 and 6 . (D) Signaling pathway impact analysis illustrates those significantly altered in Mafa∆panc islets. Each dot represents a unique signaling pathway, with those labeled with an identification number most significantly perturbed. A red dot denotes a significance of P < 0.05 after Bonferroni correction and blue P < 0.05 after false discovery rate correction. ECM, extracellular matrix; GO, gene ontology; MAPK, mitogen-activated protein kinase; P NDE, probability of number of differentially expressed genes; P PERT, probability of perturbation; SPIA, signaling pathway impact analysis; TGF, transforming growth factor. Figure 6. Gene set enrichment analysis reveals MafA regulation of many key β-cell activities. (A) The mRNA expression levels of 13 MafA microarray identified candidate genes in 3-week-old Mafa∆panc and control islets (n ≥ 3). *P < 0.05; **P < 0.01; ***P < 0.001. Gene ontology analysis showing the (B) cellular component and (C) molecular function enrichment obtained from the microarray mRNA studies performed on 3-month-old wild-type and Mafa∆panc islets. Each pie chart section is proportional to the number of genes in the category. The acinar cell signals in the Mafa∆panc microarray data are contamination, since these were not detected in the independently isolated Mafa∆panc islet preparations used for Figs. 4 and 6. (D) Signaling pathway impact analysis illustrates those significantly altered in Mafa∆panc islets. Each dot represents a unique signaling pathway, with those labeled with an identification number most significantly perturbed. A red dot denotes a significance of P < 0.05 after Bonferroni correction and blue P < 0.05 after false discovery rate correction. ECM, extracellular matrix; GO, gene ontology; MAPK, mitogen-activated protein kinase; P NDE, probability of number of differentially expressed genes; P PERT, probability of perturbation; SPIA, signaling pathway impact analysis; TGF, transforming growth factor. More
Images
TF activities regulating state-specific insulin responses and gene set enri...
Published: 16 April 2021
Figure 3 TF activities regulating state-specific insulin responses and gene set enrichment analysis of insulin-responsive genes. A bioinformatic analysis of the gene sets in NO, OB, and POB groups enabled identification of the corresponding TFs underlying the observed expression responses. A: The numbers of TFs for the overrepresented (P < 0.01) TFBS sets in the different groups were subdivided using the same colors as in Fig. 2A . B: Graphs provide the Fisher exact test P values (x-axis) and odds ratios (y-axis) for the enrichment of TFBS sets from UniBind (see “Research Design and Methods”) for OB, POB, and NO groups, where individual TFs are indicated (with highest significance) and colored using the same annotation as in panel A. The highlighted TFs were common (PPARγ [encoded by PPARG], C/EBPβ [CEBPB], RELA [RELA], SREBPs [SREBF1 and SREBF2], and LXRα [NR1H3]), obesity attenuated (androgen receptor [AR], glucocorticoid receptor [NR3C1], CEBPα [CEBPA], and RUNX2 [RUNX2]), or POB enriched (EBF1 [EBF1], estrogen receptor α [ESR1], and SMAD3 [SMAD3]). C: The insulin-responding genes corresponding to TCs identified in Fig. 1 were analyzed by gene set enrichment analysis, where the corresponding pathways were grouped using the same color code as in Fig. 2A . D: Pathways enriched in the common, OB-attenuated, and POB-enriched states were grouped to reduce redundance, and three representative ranked genes are indicated. Even if Gene Ontology terms were shared, all the individual genes in the respective pathways differed among the three groups. ECM, extracellular matrix; NES, normalized enrichment score. Figure 3. TF activities regulating state-specific insulin responses and gene set enrichment analysis of insulin-responsive genes. A bioinformatic analysis of the gene sets in NO, OB, and POB groups enabled identification of the corresponding TFs underlying the observed expression responses. A: The numbers of TFs for the overrepresented (P < 0.01) TFBS sets in the different groups were subdivided using the same colors as in Fig. 2A. B: Graphs provide the Fisher exact test P values (x-axis) and odds ratios (y-axis) for the enrichment of TFBS sets from UniBind (see “Research Design and Methods”) for OB, POB, and NO groups, where individual TFs are indicated (with highest significance) and colored using the same annotation as in panel A. The highlighted TFs were common (PPARγ [encoded by PPARG], C/EBPβ [CEBPB], RELA [RELA], SREBPs [SREBF1 and SREBF2], and LXRα [NR1H3]), obesity attenuated (androgen receptor [AR], glucocorticoid receptor [NR3C1], CEBPα [CEBPA], and RUNX2 [RUNX2]), or POB enriched (EBF1 [EBF1], estrogen receptor α [ESR1], and SMAD3 [SMAD3]). C: The insulin-responding genes corresponding to TCs identified in Fig. 1 were analyzed by gene set enrichment analysis, where the corresponding pathways were grouped using the same color code as in Fig. 2A. D: Pathways enriched in the common, OB-attenuated, and POB-enriched states were grouped to reduce redundance, and three representative ranked genes are indicated. Even if Gene Ontology terms were shared, all the individual genes in the respective pathways differed among the three groups. ECM, extracellular matrix; NES, normalized enrichment score. More
Images
WLM impairs skeletal muscle lipid oxidation. <em>A</em>: Study desi...
Published: 03 February 2021
Figure 1 WLM impairs skeletal muscle lipid oxidation. A: Study design for WLM and weight regain. Obesity-prone (OP) rats were subset into three groups: an obese group that was maintained on an LFD for 42 weeks, a group that was subjected to WLM, and a group that was subjected to WLM and weight regain. Prior to WLM, obesity-prone rats were fed an HFD for 17 weeks. At the initiation of weight loss, diets were then switched to an LFD and rats were calorically restricted (CR) to reduce body weight. After 17 weeks of CR, a subset of rats was euthanized and their tissues were harvested, while another subset of rats was allowed ad libitum (AL) provisions of LFD to promote relapse back to obesity. After 8 weeks of relapse, rats were euthanized, and their tissues were collected. B: Microarrays were performed on skeletal muscle of obese and WLM rats, and downstream pathway analyses were performed. Pathways depicted are in ascending order based on the P value (P-val). C: Volcano plots illustrating downregulation of genes involved in lipid metabolism in skeletal muscle of WLM rats. D: Using a Clark electrode, state 3 and state 4 respiration in obese and WLM rats were measured. WLM rats have a lower maximal capacity to oxidize lipids. Significance was determined using a two-tailed t test (*P < 0.05). E: Microarrays were performed on skeletal muscle of obese and weight regained rats and downstream pathway analyses were performed. Pathway analysis revealed a downregulation in pathways involved in lipid breakdown and oxidation with WLM. F: Volcano plots illustrating downregulation of genes involved in lipid metabolism in skeletal muscle of WLM rats. Pathway analysis was performed using Generally Applicable Gene-set Enrichment (GAGE). P values from volcano plots were determined using Significant Analysis of Microarrays (SAM); cutoffs were P < 0.05 and are depicted in blue. ECM, extracellular matrix; Pyr, pyruvate; Palm, palmitoyl-L-carnitine; TCA, tricarboxylic acid. Figure 1. WLM impairs skeletal muscle lipid oxidation. A: Study design for WLM and weight regain. Obesity-prone (OP) rats were subset into three groups: an obese group that was maintained on an LFD for 42 weeks, a group that was subjected to WLM, and a group that was subjected to WLM and weight regain. Prior to WLM, obesity-prone rats were fed an HFD for 17 weeks. At the initiation of weight loss, diets were then switched to an LFD and rats were calorically restricted (CR) to reduce body weight. After 17 weeks of CR, a subset of rats was euthanized and their tissues were harvested, while another subset of rats was allowed ad libitum (AL) provisions of LFD to promote relapse back to obesity. After 8 weeks of relapse, rats were euthanized, and their tissues were collected. B: Microarrays were performed on skeletal muscle of obese and WLM rats, and downstream pathway analyses were performed. Pathways depicted are in ascending order based on the P value (P-val). C: Volcano plots illustrating downregulation of genes involved in lipid metabolism in skeletal muscle of WLM rats. D: Using a Clark electrode, state 3 and state 4 respiration in obese and WLM rats were measured. WLM rats have a lower maximal capacity to oxidize lipids. Significance was determined using a two-tailed t test (*P < 0.05). E: Microarrays were performed on skeletal muscle of obese and weight regained rats and downstream pathway analyses were performed. Pathway analysis revealed a downregulation in pathways involved in lipid breakdown and oxidation with WLM. F: Volcano plots illustrating downregulation of genes involved in lipid metabolism in skeletal muscle of WLM rats. Pathway analysis was performed using Generally Applicable Gene-set Enrichment (GAGE). P values from volcano plots were determined using Significant Analysis of Microarrays (SAM); cutoffs were P < 0.05 and are depicted in blue. ECM, extracellular matrix; Pyr, pyruvate; Palm, palmitoyl-L-carnitine; TCA, tricarboxylic acid. More
Journal Articles
Journal: Diabetes
Diabetes 1997;46(1):87–93
Published: 01 January 1997
... proteins in the extracellular matrix (ECM) of endothelial cells (ECs) are essential for attachment of ECs to the subintima. In this study, we investigated the effect of glycation of ECM and purified adhesive proteins on EC adhesion and spreading. ECM was incubated with the reactive sugar glucose-6...
Journal Articles
Journal: Diabetes
Diabetes 2018;67(8):1455–1456
Published: 13 July 2018
...Diagram summarizing the EDP effects on NASH development. ECM, extracellular matrix; ERC, elastin receptor complex. Diagram summarizing the EDP effects on NASH development. ECM, extracellular matrix; ERC, elastin receptor complex. ...
Images
Exendin-4 (Ex-4) decreased angiotensin II or diabetes-induced increases of ...
Published: 16 October 2012
FIG. 7. Exendin-4 (Ex-4) decreased angiotensin II or diabetes-induced increases of phospho–c-Raf (p-c-Raf)(Ser338) in the glomeruli and renal cortex. A: Immunoblots of phospho–c-Raf(Ser259), phospho–c-Raf(Ser338), and phospho-Erk1/2 (p-Erk1/2) in the glomeruli. Exendin-4 (1.0 nmol/kg) or diluents were administrated intraperitoneally to mice. After 2-h administration of exendin-4, Ang II (100 ng/kg/3 mL/min) or saline was infused through the jugular vein. After 3-h continuous infusion, glomeruli were corrected. n = 3–5. Tg, transgenic. *P < 0.05 vs. WT/Ang II/exendin-4; †P < 0.05 vs. WT/Ang II+/exendin-4; ‡P < 0.05 vs. transgenic/Ang II/exendin-4; ¶P < 0.05 vs. WT/Ang II+/exendin-4; #P < 0.05 vs. transgenic/Ang II+/exendin-4. B: Immunoblots of phospho–c-Raf(Ser259), phospho–c-Raf(Ser338), and phospho-Erk1/2 in the renal cortex. n = 6 in nondiabetic WT plus vehicle, nondiabetic WT plus exendin-4, diabetic WT plus vehicle, diabetic WT plus exendin-4, nondiabetic transgenic plus exendin-4, and diabetic transgenic plus exendin-4; n = 7 in nondiabetic transgenic plus vehicle and diabetic transgenic plus vehicle. DM, mice with STZ-induced diabetes; NDM, nondiabetic mice. *P < 0.05 vs. WT/nondiabetic/exendin-4; †P < 0.05 vs. WT/diabetic/exendin-4; ‡P < 0.05 vs. transgenic/diabetic/exendin-4. One of three independently performed experiments is shown. Comparisons were made between groups using either two-sample and paired t tests for two-way comparisons or one-way ANOVA for multiple groups to establish statistically significant differences. Results are expressed as means ± SD. C: Schematic diagram of the inhibitory effects of PKCβ2 on the protective action of GLP-1 signaling against the effects of Ang II–mediated glomerular pathology. AU, arbitrary units; ECM, extracellular matrix. FIG. 7. Exendin-4 (Ex-4) decreased angiotensin II or diabetes-induced increases of phospho–c-Raf (p-c-Raf)(Ser338) in the glomeruli and renal cortex. A: Immunoblots of phospho–c-Raf(Ser259), phospho–c-Raf(Ser338), and phospho-Erk1/2 (p-Erk1/2) in the glomeruli. Exendin-4 (1.0 nmol/kg) or diluents were administrated intraperitoneally to mice. After 2-h administration of exendin-4, Ang II (100 ng/kg/3 mL/min) or saline was infused through the jugular vein. After 3-h continuous infusion, glomeruli were corrected. n = 3–5. Tg, transgenic. *P < 0.05 vs. WT/Ang II−/exendin-4−; †P < 0.05 vs. WT/Ang II+/exendin-4−; ‡P < 0.05 vs. transgenic/Ang II−/exendin-4−; ¶P < 0.05 vs. WT/Ang II+/exendin-4−; #P < 0.05 vs. transgenic/Ang II+/exendin-4−. B: Immunoblots of phospho–c-Raf(Ser259), phospho–c-Raf(Ser338), and phospho-Erk1/2 in the renal cortex. n = 6 in nondiabetic WT plus vehicle, nondiabetic WT plus exendin-4, diabetic WT plus vehicle, diabetic WT plus exendin-4, nondiabetic transgenic plus exendin-4, and diabetic transgenic plus exendin-4; n = 7 in nondiabetic transgenic plus vehicle and diabetic transgenic plus vehicle. DM, mice with STZ-induced diabetes; NDM, nondiabetic mice. *P < 0.05 vs. WT/nondiabetic/exendin-4−; †P < 0.05 vs. WT/diabetic/exendin-4−; ‡P < 0.05 vs. transgenic/diabetic/exendin-4−. One of three independently performed experiments is shown. Comparisons were made between groups using either two-sample and paired t tests for two-way comparisons or one-way ANOVA for multiple groups to establish statistically significant differences. Results are expressed as means ± SD. C: Schematic diagram of the inhibitory effects of PKCβ2 on the protective action of GLP-1 signaling against the effects of Ang II–mediated glomerular pathology. AU, arbitrary units; ECM, extracellular matrix. More