1. Glucocorticoid receptor crosstalk with NF-kB in airway cells – analyzing the cistromes BIOS6660 Genomic Data Analysis with R and Bioconductor Anthony Gerber MD, Ph.D. October 20, 1015

2. Transcription factors Many are ligand activated Only class of transcription factors that can be targeted by small molecules in the clinic Clinical targets include estrogen, androgen, mineralocorticoid, Vitamins D, glucocorticoid and thyroid receptors, RXR, PPAR Major interest in developing selective ligands/modulators to enable improved therapeutic windows The Nuclear Receptor family

3. Glucocorticoids in the clinic: a large footprint  10-20 million annual prescriptions for oral glucocorticoids in USA  > 50 million prescriptions for localized delivery (inhaled, topical, eye drops)  Major targets are diverse immune- mediated diseases CNS: Anxiety, insomnia Ocular: Glaucoma Muscle: Atrophy Endocrine: Diabetes, obesity Skin: Fragility Bone: Osteoporosis Cardiovascular: Hypertension Rheumatoid arthritis Inflammatory bowel disease COPD Asthma Other lung diseases o Hypersensitivity pneumonitis o BOOP o NSIP o vasculitis Organ transplants RDS of prematurity “Off target” effects

4. Balancing disease symptoms with glucocorticoid side effects in the clinic: A 50 year old female with severe, persistent asthma No oral glucocorticoid use lung function < 50% of normal short of breath after walking 2 blocks unable to go up a flight of stairs frequent coughing episodes 1-2 ER visits per quarter 2-3 hospitalizations per year Taking oral glucocorticoids lung function ~80% of normal no shortness of breath after 10 blocks able to go up 2 flights of stairs no hospitalizations or ED visits 20 pound weight gain lower extremity edema irritability high blood sugars increased risk of osteoporosis There is a major unmet need for improved glucocorticoid-based therapies

5. Background: Glucocorticoids bind to the glucocorticoid receptor (GR), causing it to regulate gene expression Image from http://brainimmune.com/the-glucocorticoid-receptor/ Glucocorticoids GR is a basic model of metazoan transcriptional regulation -> Recent example: DNA implicated as regulating GR activity through allosteric mechanisms (Hudson et al, Nat Struct Mo Bio, 2013, Meijsing et al, Science 2009) Therapeutic effects of GR activation are also intensely studied -> >10000 Pubmed citations for “asthma and glucocorticoid” “Transprepression” typically implicated in mediating therapeutic effects

6. Structural considerations Steve Bilodeau et al. Genes Dev. 2006;20:2871-2886

7. Change in mRNA level (log 2 ) Pro-inflammatory Anti-inflammatory TNF Dex Dex+TNF How do glucocorticoids work? β-actin HBEGF TNFAIP3 TNFα Dex ‒‒‒‒ +‒+‒ ‒+‒+ ++++ Glucocorticoids “spare” the expression of negative feedback targets of TNF (a major inflammatory signal)

8. How do glucocorticoids actually work? 3’ 5’ NFᴋB-BS1(CTTGGAAAGTCCAGG) NFᴋB-BS2(CTGGGGAATTCCAGA) GR-BS(CCAGAACAAAAAGTACAAT) TNFAIP3 reporter (821 bP) (+5,670 — +6,491) 2 1 TNFAIP3 Intron 2 1 2 Hela cell GR/NF-kB ChIP-seq Rao et al, Genome Biology, 2011

9. TNFAIP3 Β-actin 70 kDa 42 kDa TNFα Dex ———— +—+— ++++ ———— +—+— ++++ siTNFAIP3siCtrl B TNFAIP3 contributes to glucocorticoid- mediated cytokine repression in airway epithelial cells How do glucocorticoids actually, actually work?

10. How do glucocorticoids work and what prevents them from working in asthma? Since GR interactions with DNA define GR activity  study GR interactions with DNA in airway cells Since GR interactions with inflammatory factors are important for GC efficacy  Study DNA-based interactions between GR and NF-kB No current data on GR cistrome in airway cells…

11. ChIP - overview Cross-linkChromatin shear and prepIPPurify DNA

12. ChIP- downstream assays

13. ChIP-Seq example summary data GR PEAKS

14. Treatment (1 hr)IP Antibody control GR dex TNF+dex control NFkB-p65 TNF TNF+dex control RNAP2 dex TNF TNF+dex Cells: Beas-2B Treatments: dexamethasone (dex; 100 nM) tumor necrosis factor-α (TNF; 20 ng/ml) Sequencing: Illumina Hi-Seq; performed in biological duplicate Airway epithelial ChIP-Seq experimental design Conditions:

15. Pattern 1: GR+NFkB co-occupancy & reduced RNAP2 dex GR IP TNF p65 IP TNF RNAP2 IP TNF+dex RNAP2 IP TNF+dex GR IP TNF+dex p65 IP dex RNAP2 IP 75 125 50 75 125 50 IL8 locus

16. dex GR IP TNF p65 IP TNF RNAP2 IP TNF+dex RNAP2 IP TNF+dex GR IP TNF+dex p65 IP dex RNAP2 IP 55 80 45 55 80 45 CCL2 locus Pattern 1: GR+NFkB co-occupancy & reduced RNAP2

17. Pattern 1 summary GR binds pro-inflammatory gene in absence of TNF GR occupancy is maintained/enhanced in presence of NFkB GR+NFkB co-occupancy reduces RNAP2 recruitment NET EFFECT = repression of pro-inflammatory transcription

18. Pattern 2: NFkB-mediated GR occupancy & reduced RNAP2 dex GR IP TNF p65 IP TNF RNAP2 IP TNF+dex RNAP2 IP TNF+dex GR IP TNF+dex p65 IP dex RNAP2 IP ICAM1 locus 50 75 20 50 75 20

19. Pattern 2 summary GR binds pro-inflammatory gene only in presence of TNF Role of NFkB in GR recruitment unclear, possibly indirect GR+NFkB co-occupancy reduces RNAP2 recruitment NET EFFECT = repression of pro-inflammatory transcription

20. Pattern 3: GR+NFkB co-occupancy & enhanced RNAP2 dex GR IP TNF p65 IP TNF RNAP2 IP TNF+dex RNAP2 IP TNF+dex GR IP TNF+dex p65 IP dex RNAP2 IP 40 10 165 40 10 165 SERPINA3 locus

21. dex GR IP TNF p65 IP TNF RNAP2 IP TNF+dex RNAP2 IP TNF+dex GR IP TNF+dex p65 IP dex RNAP2 IP 35 60 35 60 TNFAIP 3 locus Pattern 3: GR+NFkB co-occupancy & maintained RNAP2

22. Pattern 3 Summary GR binds anti-inflammatory genes with dex+TNF GR+NFkB co-occupancy does not appear antagonistic GR+NFkB co-occupancy enhances or maintains RNAP2 recruitment NET EFFECT = activation (or sparing) of anti- inflammatory transcription

23. What do we want from our ChIP data? BASIC 1.Identification of peaks for GR and p65 under each condition and r values 2.Identification of differential GR binding vehicle vs. dex 3.Identification of differential binding p65 binding vehicle vs. TNF treatment

24. What do we want from our ChIP data? BASIC 1.Identification of peaks for GR and p65 under each condition and r values 2.Identification of differential GR binding vehicle vs. dex 3.Identification of differential binding p65 binding vehicle vs. TNF treatment

25. What do we want from our ChIP data? ADVANCED 1.Differential GR binding (dex vs dex + TNF) 2.Differential p65 binding (TNF vs TNF + dex) 3.RNAP2 patterns 1.Increased at TSS with dex vs vehicle 2.Closest GR peak 3.Increased at TSS with TNF and TNF + dex > TNF 4.Increased at TSS with TNF and TNF + dex < TNF 5.Closest p65 peak to TSS for patterns 3 and 4 6.Closest GR peak to TSS for patterns 3 and 4

26. What do we want from our ChIP data? Collaboration Level! 1.Compare GR binding between Gerber lab data set and paper from Rao et al (Genome Biology, 2011) 2.Compare p65 binding between Gerber lab data set and paper from Rao et al (Identification of differential GR binding vehicle vs. dex 3.Compare regulatory outcomes – i.e. correlate with RNAP2 occupancy

27. QUESTIONS ?