glucocorticoid receptor crosstalk with nf-kb in airway cells – analyzing the cistromes bios6660...
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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
• 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
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, insomniaOcular: 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 BOOPo NSIPo vasculitis
•Organ transplants•RDS of prematurity
“Off target” effects
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
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
Structural considerations
Steve Bilodeau et al. Genes Dev. 2006;20:2871-2886
-6
-4
-2
0
2
4
6
TNFAIP3
TNIP1
TNIP2
NFᴋBIA
DUSP1012345678
Chan
ge in
mRN
A le
vel (
log 2)
Pro-inflammatory
Anti-inflammatory
TNFDexDex+TNF
How do glucocorticoids work?
β-actin
HBEGF
TNFAIP3
TNFαDex
‒‒
+‒
‒+
++
Glucocorticoids “spare” the expression of negative
feedback targets of TNF (a major inflammatory signal)
How do glucocorticoids actually work?
VehTNF
Dex
Dex+TNF
0
500
1000
1500
2000
2500 pTNFAIP3I2
Rela
tive
luci
fera
se a
ctivi
ty
VehTNF
Dex
Dex+TNF
0
50
100
150
200
250pIL8
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-seqRao et al, Genome Biology, 2011
TNFAIP3
Β-actin
70 kDa
42 kDa
TNFαDex
——
+—
++
——
+—
++
siTNFAIP3siCtrl
B
TN
Fα
Dex
Dex+
TN
Fα
-2
-1
0
1
2
3
4
5 siCtrl
siTNFAIP3C
hange in IL1
a m
RN
A leve
l (l
og2)
TNFAIP3 contributes to glucocorticoid-mediated cytokine
repression in airway epithelial cells
How do glucocorticoids actually, actually work?
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 cellsSince 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…
ChIP - overview
Cross-link Chromatin shear and prep IP Purify DNA
ChIP- downstream assays
ChIP-Seq example summary dataGR PEAKS
Treatment (1 hr)
IP Antibody
control
GRdex
TNF+dex
controlNFkB-p65
TNF
TNF+dex
control
RNAP2dex
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:
Pattern 1: GR+NFkB co-occupancy & reduced RNAP2
dexGR IP
TNFp65 IP
TNFRNAP2 IPTNF+dexRNAP2 IP
TNF+dexGR IP
TNF+dexp65 IP
dexRNAP2 IP
75
125
125
50
75
125
50
IL8locus
dexGR IP
TNFp65 IP
TNFRNAP2 IPTNF+dexRNAP2 IP
TNF+dexGR IP
TNF+dexp65 IP
dexRNAP2 IP
55
80
80
45
55
80
45
CCL2locus
Pattern 1: GR+NFkB co-occupancy & reduced RNAP2
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
Pattern 2: NFkB-mediated GR occupancy & reduced RNAP2
dexGR IP
TNFp65 IP
TNFRNAP2 IPTNF+dexRNAP2 IP
TNF+dexGR IP
TNF+dexp65 IP
dexRNAP2 IP
ICAM1
locus
50
75
75
20
50
75
20
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
Pattern 3: GR+NFkB co-occupancy & enhanced RNAP2
dexGR IP
TNFp65 IP
TNFRNAP2 IPTNF+dexRNAP2 IP
TNF+dexGR IP
TNF+dexp65 IP
dexRNAP2 IP
40
10
10
165
40
10
165
SERPINA3
locus
dexGR IP
TNFp65 IP
TNFRNAP2 IPTNF+dexRNAP2 IP
TNF+dexGR IP
TNF+dexp65 IP
dexRNAP2 IP
35
35
35
60
35
35
60
TNFAIP3
locus
Pattern 3: GR+NFkB co-occupancy & maintained RNAP2
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
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
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
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 patterns1. 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
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
QUESTIONS?