Examination of Mechanisms of Hepatotoxicity of Anti-diabetic PPARγ Agonists Using Applied Biosystems Rat Whole Genome Microarrays

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Examination of Mechanisms of Hepatotoxicity of Anti-diabetic PPARγ Agonists Using Applied Biosystems Rat Whole Genome Microarrays Lu Zhang b, Lei Guo a, Leming Shi a, Weida Tong a, Yongming Sun b, Gary P. Schroth b, Eugene Herman a and Yvonne Dragan a Collaboration study between FDA/NCTR a and Applied Biosystems b

Type 2 Diabetes and PPARγ Type 2 Diabetes: Major health problem in the US and worldwide Insulin resistance PPARγ: (Peroxisome Proliferation Activated Receptor γ) Ligand-activated transcription factor Major target for diabetic drugs Three approved by FDA: Troglitazone (Rezulin) Rosiglitazone (Avandia) Pioglitazone (Actos) Insulin sensitizing

Cellular Mechanism of Action of PPARγ Agonists PPARγ agonists Glucose Uptake and Utilization PPARγ RXR Transcription of downstream genes Lipid Synthesis Adipogenesis

PPARγ agonist: Thiazolidinedione (TZDs) Troglitazone (Rezulin): Marketed by Pfizer in 1997, withdrawn in 2000 because of severe liver toxicity with unknown mechanism Rosiglitazone (Avandia) Marketed by GSK in 1999 Pioglitazone (Actos) Marketed by Takeda and Eli Lilly in 1999 Ciglitazone Drug candidate, Failed in Phase III trials

Rat Whole Genome Survey Array 27088 genes represented More than 10,000 novel genes

Rat array target source 27,088 genes 26,857 total probes 26,847 current probes Celera and public 10,887 (40.2%) Celera only 15,990 (59.0%) Public only 211 (0.8%) RR1B1

Experimental design I Samples from FDA: 36 total RNA samples from Rat hepatocyte primary cultures Treatment group: Four glitazones: Troglitazone (Tro) Rosiglitazone (Rosi) Pioglitazone (Pio) Ciglitazone (Cig) Two controls: JTT-501: Dual agonist for both PPARγ and PPARα DMSO Two time points: 6 and 10 hours One dosage: 30 um Three biological replicates

DMSO Experimental design II Tro Cig Rosi Pio JTT Primary Hepatocyte Animal A, B, C total RNA total RNA total RNA total RNA total RNA total RNA RT-IVT RT-IVT crna1 crna2 crna1 crna2 RT-IVT RT-IVT RT-IVT RT-IVT -crna pooled -crna pooled -crna -crna -crna -crna Within each animal and time group

Data Analysis for Differential Gene Expression 2-way ANOVA Analysis Treatment vs. Time Testing mixed effect of treatment and time* Fold change and Welch t test Each compound vs DMSO gene signatures Biological Interpretation of Results

2 Way ANOVA Analysis Data preparation Quantile-Quantile Normalization across arrays Per gene normalized to median P value < 0.05 with Benjamini-Hochberg FDR 2 way ANOVA (treatment vs. time) 46 Arrays: DMSO 5, Tro 6, Cig 3, Pio 3, Rosi 3, JTT 3 for both time points

Number of Differentially Expressed Genes by 2 way ANOVA ANOVA Gene Numbers Treatment 300 Time* 3258 Cutoff: P value < 0.05 with BH FDR * Time series have huge numbers of gene changed, apoptotic effects of cultured time for primary hepatocytes ** Gene Numbers for mixed effect of time vs. treatment is zero because of the big change by time alone

Hierarchical Clustering based on 2 Way ANOVA 300 Gene list Cig Tro Cig DMSO JTT Pio/Rosi

Compound Specific Clusters: Tro and Cig upregulated for both time points Cig Tro Cig DMSO JTT Pio/Rosi

Compound Specific Clusters: Tro and Cig up-regulated for 6 hour Cig Tro Cig DMSOJTT Pio/Rosi

Compound Specific clusters: Tro and Cig down-regulated Cig TroCig DMSO JTT Pio/Rosi

Compound Specific clusters: Pio/Rosi, JTT Cig TroCig DMSO JTT Pio/Rosi

Biological Interpretation Pathway Analysis Jubilant Pathway Genes in the pathway Overlapping genes Overlapping p-value Insulin Signaling Pathway 45 5 0.000127 NGF Signaling Pathway 18 3 0.000926 CDK5 Mediated Pathway 8 2 0.00317 PPAR Mediated Pathway 15 2 0.0113 TNF Signaling Pathway 44 3 0.0122 LDL Signaling Pathway 16 2 0.0128 PPARγ Mediated Pathway 25 2 0.0301 Jubilant Physiology or Disease Genes in the group Overlapping genes Overlapping p-value Diabetes Type II 103 5 0.00546 Obesity 48 3 0.0154 Use 2 way ANOVA 300 gene list, p value cutoff <0.05

Insulin signaling pathway 5/300

LDL and TNF signaling pathway 3/300

PPAR signaling pathway 2/300

KEGG MAPK signaling pathway 9/300

KEGG MAPK signaling pathway 9/300 Tro and Cig up regulated, while the other three remains unchanged

KEGG Fatty Acid Metabolism 4/300 JTT and/or Pio, Rosi up regulated, while Tro and Cig Remain the same

Fold Change and Welch t test Data preparation Cross arrays: QQ Normalization for 51 arrays DMSO 5, Tro 6, Cig 4, Pio 4, Rosi 4, JTT 4 for 6 hr, DMSO 6, Tro 6, Cig 3, Pio 3, Rosi 3, JTT 3 for 10hr Cross genes: Normalized to median P value cutoff: 0.001-0.05 Fold change calculated using raw data, cutoff 1.7-2.0

Number of Differentially Expressed Genes by t test and fold changes Compound vs. P value cutoff Fold Changes P < 0.002& DMSO (6 hr) 0.05 0.01 0.002 0.001 1.7 2 FC > or < 1.7 Troglitazone 2272 671 170 96 2712 1322 47 Ciglitazone 1152 278 52 28 3638 2021 32 Pioglitazone 767 134 18 8 2383 1490 10 Rosiglitazone 1214 222 45 27 3193 1672 27 JTT-501 1209 221 54 30 2998 1544 23 Total list 135 Total number of genes used in clustering: 135 P < 0.002 and Fold change ><1.7

Hierarchical Clustering based on t test and Fold change 135 Gene list 6 hour only Cig Tro JTT DMSO Pio Rosi

Potential Compound Signatures Cig Signature Tro Signature Rosi Signature

Summary Microarray results indicate that troglitazone and ciglitazone can be distinguished from pioglitazone and rosiglitazone based upon the expression profiles. Genes involved in the PPARγ and Insulin metabolism pathways are identified in these profiles. These profiles can be used to better understand the mechanism of liver toxicity in these compounds. Compound specific signatures have been identified for all the treatments. These signatures may be useful for predicting the efficacy of other TZD compounds.

Induction of Cytotoxicity in Primary Rat Hepatocytes and Human HepG2 Cells by Selected PPARγ Agonists SOT Poster session: #1912 Liver II, March 9, 2005 from 1:30 pm to 4:30 pm Presenter: Dr. Lei Guo, FDA/NCTR

Acknowledgement FDA/NCTR group: Dr. Lei Guo a Dr. Leming Shi b Dr. Weida Tong b Dr. Eugene Herman c Dr. Yvonne Dragan a a Center for Hepatotoxicity b Center for Toxicoinformatics, Division of Systems Toxicology, National Center for Toxicological Research (NCTR) US Food and Drug Administration Jefferson, AR 72079 c Center for Drug Evaluation and Review Division Applied Pharmacology Research US Food and Drug Administration Silver Springs, MD 20993 Applied Biosystems: Dr. Lu Zhang Dr. Gary Schroth & Arrays Product Development Group Dr. Yongming Sun & Data Analysis Group Dr. Jack Zhai & Marketing Group Applied Biosystems Foster City, CA 94404

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