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Gene Expression Profiles of Multiple Independent Cadmium‐ and Arsenite‐ Transformed Human Urothelial Cells: Specific Gene Signatures in a Sea of Heterogeneity. Scott Garrett Department of Pathology School of Medicine and Health Sciences University of North Dakota Grand Forks ND Problems in Cancer Research • Environmental causation of cancer – President’s Cancer Panel 2008‐09, Reducing Environmental Cancer Risk, “Environmentally caused cancer are ggyrossly underestimated..” • Desperate need for diagnostic and prognostic markers of cancer. – Screening for common cancers – Prognostic markers for predicting cancer that need aggressive treatments from those that don’t • Also, need to understand carcinogenesis for prevention and treatment. Bladder Cancer Has a High Environmental Etiology • Aromatic amine dyes in bladder cancer was the first environmental etiology in cancer. • Nearly 50% of BC thought to be caused by cigarette smoking • Suscept ibility due to concentration of carcinogens in urine. Arsenite (As+3) & Cadmium (Cd+2) • Arsenite in drinking water linked to BC in South Asia • Confirmed in animal models • Arsenite, 1St in priority as a toxic substance (ATSDR). • Cadmium has also been linked to BC, albeit much weaker, but: – Cigarette smoking is a major source of cadmium Overall Experimental Approach Long‐Term Cultured Cells Transformed Cells Metal Exposure Tranf & assess Discover Induced or tumor behavior Transfect or Repressed Gene knockdown & assess cell behavior Gene X Human Tissues Assess correlation to tumor grade Mechanism of gene and clinical outcome expression change General Experimental Approach Athymic Nude Mouse Cell Culture Models In Vivo Tumor Model Biopsy Specimens (formalin fixed PET) Immunohistochemistry Attached Clinical Outcome Bladder Model • UROtsa Cells – Human urothelial cell line – Details Metal Transformation of UROtsa as a modldel of Blddladder Carcinogenesis • Culture in 1 µM Cd+2 or As+3 (several months continuous culture) • Test for anchorage independent growth Soft Agar Assay • IjInjec t s.c itinto athym icnude mice Global Gene Expression Analysis to Assess Tumors Array Hybridization SHG003‐U133_Plus_2_Unfiltered.TXT Array Analysis • Thousands of Genes • Had Keratin on our minds • KRT1666:KRT6 (6A ) Human tumors for KRT6 Heterotransplants for KRT6 Environ Health Perspect (2008) 116:434–44 Main Question • During transformation of cell lines by a carcinogen, how reproducible are the gene expression changes? – Important because of heterogeneity of cancer. • Implications to applications of cell culture based studies • Are there carcinogen based expression changes? – Give clues to mechanism of transformation – Source of biomarkers of carcinogens Experimental Setup Note on Metal Characteristics These two metals are +2 • Cd very divergent and excellent to test in this – No redox reaction system. – Binds SH groups – Accumulative toxin (especially kidney) • As+3 – Arsenic considered a metalloid – Mono & Dimethyl‐species – OidiOxidize to AVAsV (bl(resemble phhthosphate in chit)hemistry) – As+3 exists as trihydroxy neutral species at pH 7 – Relatively low in total accumulation Transformed UROtsa Isolates As+3: Five isolates Cd+2: Six Isolates Arrays ‐ three control (non‐transformed) ‐ one of each FDR and the 5% Problem • In statistical analysis, p‐value of 0.05: 5% probability of the null hypothesis being true. • But in repetitive measurements have problem • 5% of 23,000 genes in genome = 1150 false pos. • RllReally want to contltrol for 5% flfalse discovery – Thus the false discovery rate is calculated. – Requires very small p‐values 0.001 to E‐09 As+3‐Heat Map Note on Genes versus Probes • 133 Plus 2.0 is slightly redundant – 45,000 probes – Have about 23,000 genes • About 2 fold redundancy • Multiple probes measure different exons – Complexity of probe changes can also reflect alternative splicing. • Fold 2 Change and DEGs – DEG (Differentially Expressed Genes) is a statistical entity Alterations in Gene Expression in As+3‐ Transfdformed Isolates Number of probes with fold 2 change or greater Principal Component Analysis Red: Parental Controls Blue: As+3‐Transformed As+3‐ & Cd+2‐Transformants Heat Map As+3‐Cd+2 Venn Comparison Ave of leaving one Cd+2‐ Isolate out: 346 induced, 251 repressed Cd+2‐Isolates have many more repressed gene overlaps than that of As+3 As+3 & Cd+2 Principal Component Analysis Global Gene Overlap Probe Redundancy Removed p << 10E‐6 for gene overlap Overlap by random chance estimated < 20 genes Are Gene Expression Changes Random? • Single Isolate: 3871 • Overlap of two isolates: 1624 • Three isolates: 759 • Four: 414 • All five: 221 • It will probably decrease with more isolates. • Would there be any gene overlap left over with many isolates? Or will it be zero? Mathematical Modeling Gene Overlap with Increasing Number of Isolates 4 parameter Linear Model Cadmium logistic model Induced gene overlap stabilizes at about 230 genes at higher Induced numbers of isolates. Repressed gene overlap stabilizes Repressed at 198 The Linear Model • Considers random variation and statistics • Log(y‐t)=α + βx + ε – X= number of isolates – Y= number of genes – t= constant integer – α and β are pp,arameters, ε is the random error. • Y converges to t as number of isolates (x) increases. • Using y= 285 at x=6, t ranges (0 to 284) • R2 can be calculated for each line at every value t • t=233 yields best R2 value (0.993) • Minimum no of isolates: 14 to give CI <1 Mathematical Modeling Gene Overlap with Increasing Number of Isolates 4 parameter logistic model Arsenite Stabilizes at 265 Induced genes Stabilizes at Repressed 63 genes What Does This Mean? • Thousands of gene expression changes in transformed isolates. • Small but statistically significant number of gene expression changes common to all isolates. • Hypothesis: this reflects the “driver”‐ ”””passenger” mutation concept of carcinogenesis. Gene Validation using PCR‐Array • SA Biosciences • Custom primers targeted to exons near to array target sequence. • All assays were valida te d • Normalized to geometric mean of five reference genes (house‐keeping) Gene Validation using PCR‐Array • SA Biosciences • Custom primers designed to nearest exons of array target sequence • 338 genes, five house keeping genes – All on one 384 tray. – One sample per tray, 36 trays in all. Validation Heat Map 24 out of 338 failed to validate (7.1 %) KEGG Pathways As+3 Induced Repressed Pathway P‐Value Pathway P‐Value Glyco lys is / Gluconeogenes is 0040.04 Thyroid cancer 0.025 Biosynthesis of phenylpropanoids <0.001 Biosynthesis of terpenoids and steroids <0.001 Apoptosis 0.015 Biosynthesis of alkaloids derived from shikimate pathway <0.001 Endocytosis <0.001 Biosynthesis of alkaloids derived from ornithine, lysine and Acute myeloid leukemia 0.045 nicotinic acid <0.001 Antigen processing and presentation <0.001 Biosynthesis of alkaloids derived from histidine and purine 0.015 Biosynthesis of alkaloids derived from terpenoid and Type I diabetes mellitus <0.001 polyketide <0.001 Autoimmune thyroid disease <0.001 Alanine, aspartate and glutamate metabolism <0.001 Allograft rejection <0.001 Arginine and proline metabolism 0.005 Graft-versus-host disease <0.001 Nitrogen metabolism 0.035 Primary immunodeficiency <0.001 Cysteine and methionine metabolism <0.001 Fructose and mannose metabolism 0.03 Galactose metabolism 0.015 Starch and sucrose metabolism 0.015 Streptomycin biosynthesis 0 Selenoamino acid metabolism <0.001 Glyoxylate and dicarboxylate metabolism 0.015 Tropane, piperidine and pyridine alkaloid biosynthesis 0.01 D-Glutamine and D-glutamate metabolism 0.03 Glyc ine, ser ine an d threon ine me ta bo lism 0. 005 Aminophosphonate metabolism 0.025 Taurine and hypotaurine metabolism 0.02 KEGG Pathway Cd+2 Induced Repressed Pathway P‐value Pathway P‐Value Alanine, aspartate and glutamate metabolism 0.03 Jak-STAT signaling pathway <0.001 Amino sugar and nucleotide sugar metabolism 0.015 beta-Alanine metabolism 0.03 Cytokine-cytokine receptor interaction <0.001 DNA replication <0.001 Apoptosis <0.001 Fatty acid biosynthesis <0.001 Fructose and mannose metabolism 0.04 Wnt signaling pathway <0.001 Galactose metabolism 0.005 Tyrosine metabolism <0.001 Glycolysis / Gluconeogenesis 0. 045 Pentose and glucuronate interconversions <0.001 Glycosphingolipid biosynthesis - lacto and neolacto series 0.015 Cytosolic DNA-sensing pathway 0.005 Hematopoietic cell lineage 0.02 Chemokine signaling pathway 0.025 Nucleotide excision repair 0.015 Pentose phosphate pathway 0. 005 BllliBasal cell carcinoma 0. 025 Propanoate metabolism 0.03 Primary immunodeficiency 0.025 Purine metabolism 0.04 Pyrimidine metabolism 0.03 Hedgehog signaling pathway 0.025 Sphingolipid metabolism 0.04 Metabolism of xenobiotics byyy cytochrome P450 0.045 Starch and sucrose metabolism <0.001 RIG-I-like receptor signaling pathway 0.045 KEGG Pathways Both As & Cd Induced RdRepressed Pathway P‐ P‐Value Pathway P‐Value P‐Value Value Cd+2 As+3 Cd+2 As+3 Alan ine, aspartate andld glutamate Apoptosis 0. 015 <0.001 metabolism <0.001 0.03 Primary immunodeficiency <0.001 0.025 Fructose and mannose metabolism 0.03 0.04 GlGalactose metablibolism 0010.015 0000.005 Glycolysis / Gluconeogenesis 0.04 0.045 Mismatch repair 0.03 0.015 Starch and sucrose metabolism 0.015 <0.001 As+3‐Induced Specific Genes Symbol Gene Name Fold Chg FDR Validated CPA6 carboxypeptidase A6 32.14 0.0001Yes DOCK11 dedicator of cytokinesis 11 18.51 0.0137Yes C17orf96 chromosome 17 open reading frame 96 13.97 0.0252Yes HRASLS HRAS‐like suppressor 13.89
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  • Supplementary Table 1. Pain and PTSS Associated Genes (N = 604

    Supplementary Table 1. Pain and PTSS Associated Genes (N = 604

    Supplementary Table 1. Pain and PTSS associated genes (n = 604) compiled from three established pain gene databases (PainNetworks,[61] Algynomics,[52] and PainGenes[42]) and one PTSS gene database (PTSDgene[88]). These genes were used in in silico analyses aimed at identifying miRNA that are predicted to preferentially target this list genes vs. a random set of genes (of the same length). ABCC4 ACE2 ACHE ACPP ACSL1 ADAM11 ADAMTS5 ADCY5 ADCYAP1 ADCYAP1R1 ADM ADORA2A ADORA2B ADRA1A ADRA1B ADRA1D ADRA2A ADRA2C ADRB1 ADRB2 ADRB3 ADRBK1 ADRBK2 AGTR2 ALOX12 ANO1 ANO3 APOE APP AQP1 AQP4 ARL5B ARRB1 ARRB2 ASIC1 ASIC2 ATF1 ATF3 ATF6B ATP1A1 ATP1B3 ATP2B1 ATP6V1A ATP6V1B2 ATP6V1G2 AVPR1A AVPR2 BACE1 BAMBI BDKRB2 BDNF BHLHE22 BTG2 CA8 CACNA1A CACNA1B CACNA1C CACNA1E CACNA1G CACNA1H CACNA2D1 CACNA2D2 CACNA2D3 CACNB3 CACNG2 CALB1 CALCRL CALM2 CAMK2A CAMK2B CAMK4 CAT CCK CCKAR CCKBR CCL2 CCL3 CCL4 CCR1 CCR7 CD274 CD38 CD4 CD40 CDH11 CDK5 CDK5R1 CDKN1A CHRM1 CHRM2 CHRM3 CHRM5 CHRNA5 CHRNA7 CHRNB2 CHRNB4 CHUK CLCN6 CLOCK CNGA3 CNR1 COL11A2 COL9A1 COMT COQ10A CPN1 CPS1 CREB1 CRH CRHBP CRHR1 CRHR2 CRIP2 CRYAA CSF2 CSF2RB CSK CSMD1 CSNK1A1 CSNK1E CTSB CTSS CX3CL1 CXCL5 CXCR3 CXCR4 CYBB CYP19A1 CYP2D6 CYP3A4 DAB1 DAO DBH DBI DICER1 DISC1 DLG2 DLG4 DPCR1 DPP4 DRD1 DRD2 DRD3 DRD4 DRGX DTNBP1 DUSP6 ECE2 EDN1 EDNRA EDNRB EFNB1 EFNB2 EGF EGFR EGR1 EGR3 ENPP2 EPB41L2 EPHB1 EPHB2 EPHB3 EPHB4 EPHB6 EPHX2 ERBB2 ERBB4 EREG ESR1 ESR2 ETV1 EZR F2R F2RL1 F2RL2 FAAH FAM19A4 FGF2 FKBP5 FLOT1 FMR1 FOS FOSB FOSL2 FOXN1 FRMPD4 FSTL1 FYN GABARAPL1 GABBR1 GABBR2 GABRA2 GABRA4