1. PXR in zebrafish: preliminary RNA-Seq analysis

2. SRP Project 5: Developmental Toxicity of non-Dioxin-like PCBs and Chemical Mixtures John Stegeman, PI Jared Goldstone, co-Investigator

3. Rationale To identify and understand developmental effects of chemicals abundant at Superfund sites. Focus on Ortho-PCBs; implicated in neurological deficits in mammals, but still not clearly understood. Little knowledge of which congeners (non-ortho, mono-ortho or poly-ortho) interact with receptors other than AHR, and no data on CYPs induced or mechanisms of toxicity by non-DL congeners in zebrafish. Studies will be extended to populations of Fundulus to address the possibility of resistance to ortho-PCBs

5. Other organic contaminants ‘Personal care products’ Pesticides, herbicides Drugs, hormones Rotenone Carbaryl Dieldren Aldicarb Diazenon Fenthion estrogen triclosan Synthetic musks Metal chelators (from shampoo) Detergents Plastic breakdown (bisphenol A) caffeine Analgesics Anticancer drugs Antidepressants Steroid drugs Sedatives Antianxiety … DDT

6. maf AHR Arnt NRF2 Keap1 AHR Chap NRF2 CAR/PXR Chap CAR/PXR RXR Xenobiotic-metabolizing enzymes / transporters (e.g. CYP, GST, SOD, MDR) ADAPTIVE ROLES Target genes Cl C l Cl Cl O H O N Cl N Cl M e Altered cell growth & differentiation TOXIC ROLES Altered cell growth & differentiation DEVELOPMENTAL ROLES M. Hahn Gene expression and regulation: Ligand-activated transcription

7. PXR-CAR network Oxidizing Receptors, etc. Transporters Conjugating CARPXR

8. Bainy et al Aquatic Tox (2013) Zebrafish PXR 74%56% C N DBD hinge LBD 45128233

9. RXRPXR coactivators corepressors ligand PXRRE ??????{N} 2-5 ??????

10. Gene response to pregnenolone (PN) Kubota et al. (unpublished) µM Concentrations PN induces expression of PXR, CYP2AA1, 3A65, 3C1 Are these target genes for PXR?

11. Kubota et al. (unpublished) PXR is a reliable target gene for PXR: PXR knockdown and pregnenolone (PN) exposures

12. Ctl-MO DMSO PXR-MO DMSO PXR-MO PN Ctl-MO PN

13. Normal effect of PN Ctl-MO DMSO PXR-MO DMSO PXR-MO PN Ctl-MO PN Effects of PN background of reduced PXR Background : Role of PXR in normal homeostasis Effects of PN Background also changing

14. Details Standard MO injection at 1 cell stage PN treatment 48  72 hpf Sampled at 72 hpf Standard Illumina Tru-Seq mRNA selection 50 nt single end (SE), 4 samples per lane 2 batches of experiments (2 replicates/batch)

15. Zv9 Ensembl 74 + annotations 16 samples * Map to transcriptome only Tophat -T -- transcriptome-index Cuffdiff Danio_rerio.Zv9.74.gtf Ctr1.bam,Ctr2.bam,… Trt1.bam,Trt2.bam,…

16. Tophat and Cuffdiff settings Tophat (default settings) --read-mismatches = 2 --read-gap-length=2 --splice-mismatches=0 --transcriptome-only Cuffdiff (default settings) --FDR = 0.05 Pooled dispersion (4 replicates per sample, 2 conditions) per-condition dispersion possible Geometric library normalization ‘classic-fpkm’ or poisson possible

17. SampleTotal reads Mapped reads Percent mapped Ctr-MO_D1_transcriptome 27,601,435 18,641,48667.5% Ctr-MO_D2_transcriptome 26,271,248 17,823,33567.8% Ctr-MO_D3_transcriptome 23,283,511 14,804,29863.6% Ctr-MO_D4_transcriptome 31,387,740 21,612,68668.9% Ctr-MO_PN1_transcriptome 26,855,354 18,487,70868.8% Ctr-MO_PN2_transcriptome 27,393,801 18,553,57867.7% Ctr-MO_PN3_transcriptome 25,737,244 16,736,91365.0% Ctr-MO_PN4_transcriptome 27,709,251 19,132,56469.0% PXR-MO_D1_transcriptome 28,555,850 19,560,22268.5% PXR-MO_D2_transcriptome 29,387,173 20,881,06271.1% PXR-MO_D3_transcriptome 30,275,205 19,711,30465.1% PXR-MO_D4_transcriptome 23,168,627 15,749,05868.0% PXR-MO_PN1_transcriptome 26,511,140 18,071,40468.2% PXR-MO_PN2_transcriptome 22,620,334 14,313,27463.3% PXR-MO_PN3_transcriptome 25,146,055 16,202,33764.4% PXR-MO_PN4_transcriptome 39,004,311 27,451,37570.4% Average 27,556,767 18,608,288

18. Ctl-MO DMSO PXR-MO DMSO PXR-MO PN Ctl-MO PN 6↑ 18↓ PXR 0.2x control 347↑ 115↓ 4↑ 18↓ 226↑ 127↓ PXR 2.7x control PXR 0.16x control PXR not sig (2.1x control)

19. RNA-seq fragments qPCR RNA-seq and qPCR results match Kubota et al. (unpublished)

20. Control-MO; DMSO vs. PN, subset 347↑ 115↓ gene_idgeneCtlMO_DCtlMO_PN fold_ change ENSDARG00000079227plekhs10.198.01 213.5 ENSDARG00000038371cyp2k60.101.41 47.5 ENSDARG00000093640ugt5a20.221.07 10.0 ENSDARG00000051914slc14a20.442.11 9.5 ENSDARG00000027852plekhf14.9922.58 8.8 ENSDARG00000070420cyp24a12.6511.70 8.5 ENSDARG00000043587srd5a2a4.4018.51 7.9 ENSDARG00000092052GSTK1 (3 of 4)0.471.86 7.3 ENSDARG00000027088ptgdsb57.17204.38 6.3 ENSDARG00000018621slc6a19a1.304.36 5.7 ENSDARG00000019532fads25.6717.02 4.9 ENSDARG00000028396fkbp572.19213.07 4.8 ENSDARG00000033170sult2st15.9417.23 4.6 ENSDARG00000061274lss1.473.86 4.0 ENSDARG00000027529hmox11.914.41 3.3 ENSDARG00000021787abcb510.7023.94 3.2 ENSDARG00000045627cyp3a652.074.61 3.2 ENSDARG00000033160nr1d120.3443.27 3.0 ENSDARG00000038366cyp2k180.671.38 2.9 ENSDARG00000042641cyp519.4719.40 2.8 ENSDARG00000014916slc10a21.823.69 2.8 ENSDARG00000057262CYP27A1 (3 of 6)1.362.73 2.7 ENSDARG00000029766nr1i25.1510.26 2.7 ENSDARG00000028367sult2st31.893.72 2.7 ENSDARG00000089177CYP46A1 (4 of 5)1.062.08 2.7 ENSDARG00000059035POR (2 of 2)10.2919.07 2.4 ENSDARG00000045414elovl22.033.33 2.0 ENSDARG00000009852cyp19a1b1.822.94 2.0 ENSDARG00000041169hif1al107.85168.54 1.9 gene_idgeneCtlMO_DCtlMO_PN fold change ENSDARG00000062632duox1.590.140.03 ENSDARG00000094901ABCC6 (3 of 3)0.930.310.20 ENSDARG00000090403cyp2aa80.850.290.21 ENSDARG00000087120SLC5A82.651.090.28 ENSDARG00000063475abcg13.551.530.30 ENSDARG00000002981cyp2aa42.431.200.36 ENSDARG00000074635abca1a14.957.690.38 ENSDARG00000068603slc22a52.521.320.39 ENSDARG00000068910nos12.291.300.44 ENSDARG00000023537ahr1b11.567.120.50 ENSDARG00000033498rorb13.568.380.50 ENSDARG00000042533gstm42.1026.450.51

21. Ctl-MO vs PXR-MO, DMSO gene_idgeneCtlMO_DPXRMO_D log2(fold _change) ENSDARG00000034211capn2l3.07.91.38 ENSDARG00000092358si:ch211-114l13.114.110.31.31 ENSDARG00000078342zgc:1941259.020.61.19 ENSDARG00000091444TUBB8P7 (3 of 3)4.910.51.10 ENSDARG00000009890CR936442.12.34.50.97 ENSDARG00000063330mgat4a12.724.90.97 ENSDARG00000082753AC024175.17218.4137.7-0.66 ENSDARG00000086583apoa1b64.740.2-0.69 ENSDARG00000038153lgals2b84.150.4-0.74 ENSDARG00000015866apoa2110.460.0-0.88 ENSDARG00000089679UIMC1 (1 of 2)5.82.6-1.17 ENSDARG00000058476stc1l7.53.2-1.23 ENSDARG00000088436CT956064.317.56.7-1.39 ENSDARG00000081270rn7sk37.012.8-1.54 ENSDARG00000029766nr1i25.21.7-1.57 ENSDARG00000078069rrm24.71.6-1.59 ENSDARG00000096403si:dkey-153m14.12220.5717.3-1.63 ENSDARG00000091744BX296557.7102.232.5-1.65 ENSDARG00000088951AL935186.695.927.6-1.79 ENSDARG00000087345CABZ01059415.22.10.6-1.84 ENSDARG00000070212zgc:1584631279.8325.7-1.97 ENSDARG00000087732Metazoa_SRP102.224.0-2.09 ENSDARG00000044566fabp618.54.0-2.19 ENSDARG00000096145si:dkey-111b14.2155.333.9-2.20 gene_idgene Ctl- MO_PN PXR- MO_PN log2(fold _change) ENSDARG00000088891slc23a30.59.44.30 ENSDARG00000092358 si:ch211- 114l13.114.613.11.50 ENSDARG00000074210zgc:11028631.770.81.16 ENSDARG00000059529BX255936.128.348.70.78 ENSDARG00000014745epd53.131.1-0.77 ENSDARG00000036834zgc:10986870.339.1-0.85 ENSDARG00000015866apoa2131.571.4-0.88 ENSDARG00000045180acta222.011.0 ENSDARG00000088951AL935186.6164.676.7-1.10 ENSDARG00000045592tnni2a.119.39.0-1.11 ENSDARG00000021787abcb523.711.0-1.11 ENSDARG00000091744BX296557.7193.488.5-1.13 ENSDARG00000052905zgc:16542311.95.3-1.17 ENSDARG00000038153lgals2b113.449.8-1.19 ENSDARG00000096145 si:dkey- 111b14.2221.489.1-1.31 ENSDARG00000052815alad15.96.1-1.38 ENSDARG00000014916slc10a23.71.3-1.44 ENSDARG00000029766nr1i210.12.9-1.81 ENSDARG00000068181DPEP11.30.3-2.29 ENSDARG00000055539epdl213.42.5-2.44 ENSDARG00000044566fabp641.73.6-3.54 ENSDARG00000067570ctsbb1.40.1-3.63 Ctl-MO vs PXR-MO, PN

22. si:ch211-114l13.11 lincRNA lgals2b lectin, galactoside-binding, soluble, 2b (human SNP increases myocardial infarction) apoa2 apolipoprotein A2 (hdl protein, hypercholesterolemia) nr1i2 (pxr) fabp6 fatty acid binding protein 6 (also binds bile acids, bile acid pathways) si:dkey-111b14.2 lincRNA

23. Next steps Mapping to genome, not just transcriptome – So far, samples produce 91 % mapping – Alternative mapping software (STAR) that is possibly 10x faster cummeRbund plots Meme analysis of upstream region of 20-30 genes for PXR-RE discovery