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Implementation of the Methyl-Seq
platform to identify targets of stress
and glucocorticoid exposure in
rodents and humans.
Richard S. Lee, Ph.D.
The Johns Hopkins Mood Disorders Center
“Agilent products are For Research Use Only. Not for use in diagnostic procedures”
Table of Contents
• Stress and its role in psychiatric illnesses • Glucocorticoid signaling • Epigenetics and tools • FKBP5 as a candidate gene for epigenetic study • Justification for Methyl-Seq • Mouse Methyl-Seq and glucocorticoids • Rat Methyl-Seq and Stress
– Design – Validation – Chronic stress exposure
• Human Methyl-Seq and Stress • Current and Future Directions using the Methyl-Seq platform
The Stress Response
NE
NE
NE
NE
NE
NE
ACTH
CRH
heart rate
blood pressure
GI function
reproductive function
immune function
inflammation
energy mobilization
energy storage
growth
epinephrine release
glucocorticoid release
(cortisol or corticosterone)
respiration
ACh
NE
McEwen et al., 2015
Chronic Stress and Disease
NE
NE
NE
NE
NE
NE
ACh
NE
Cardiovascular disease
Hypertension
Irritable bowel syndrome
Diarrhea
Cancer metastasis
Obesity
Diabetes
Loss of libido
Sexual dysfunction
Amenorrhea
Impaired fertility
Infection susceptibility
Depression
Anxiety disorders
Post-traumatic stress disorder
ACTH
CRH
McEwen et al., 2015
Stress and stress hormone
• Stress exposure is a robust risk factor for anxiety and mood disorders, as well as neurological diseases such as AD
• Dysregulation of stress response intimately tied to mental illnesses
• Stress hormone (and response) can be measured in plasma
Role of glucocorticoids in psychiatry
• Hypercortisolemia and glucocorticoid resistance often comorbid with depression
• Glucocorticoids are one of the few molecules that can trigger mania and depression in BP
• Cushing’s Disease – 60~90% of Cushing’s patients develop depression
• Depressive symptoms alleviated with resolution of hypercortisolemia
• Stress in the 21st century – Black Death
Fardet et al., 2012 American Journal of Psychiatry
“HPA-axis genes”
Lee/Sawa
Neuroendocrinology 2014;100:278–287DOI: 10.1159/000369585
280
dexamethasone suppression test. Remarkably, depression is comorbid in 60–90% of these Cushing’s patients [28–30] , and resolution of both hypercortisolemia and depressive symptoms by surgical removal of the adenomas suggests a causal role for hypercortisolemia and HPA axis dysregula-tion in the mood of these patients [29, 31] . Similarly, a landmark epidemiological study that examined hundreds of thousands of patients who were prescribed glucocorti-coids (i.e., iatrogenic Cushing’s syndrome) for nonpsychi-atric disorders found a significant increase in cases of de-pression, suicide, mania, and anxiety associated with glu-cocorticoid therapy [32] . Taken together, these studies highlight the importance of stress and its principal in vivo agent cortisol in mood regulation and necessitate a closer examination of the processes that govern this relationship.
Epigenetics and the HPA Axis: Case Studies
The following studies using animal models and epi-genetic tools have reported associations between behav-ioral changes relevant to mental illness and genes that are
either targets of or directly regulate HPA axis function (HPA axis genes; table 1 ).
Influences of Poor Maternal Care on Neurodevelopment Stress-mediated epigenetic modifications may be
more pronounced during the stress-vulnerable, early-life period where regions implicated in emotionality and stress reactivity such as the hippocampus, amygdala, and the prefrontal cortex are undergoing rapid changes in dendritic density, myelination, and synaptic plasticity [33, 34] . Weaver et al. [35] have reported that good ma-ternal nursing behavior (vs. neglect) is required for prop-er postnatal epigenetic programming of HPA axis func-tion in adulthood. They examined exon I 7 promoter re-gion of Nr3c1 (glucocorticoid receptor, Gr ) and found that CpG dinucleotides that reside within the binding sites for the nerve growth factor-inducible protein NGFI-A are heavily methylated in the pups that experienced poor nursing behavior. This differential methylation state was associated with decreased binding to NGFI-A, de-creased Nr3c1 expression, elevated plasma corticosterone
Table 1. Representative stress-/HPA axis-associated genes
Location Symbol Name Function Significance
Extracellularand cell surface
CRH (or CRF) Corticotropin-releasing hormone CRH signaling Trauma, depression, suicide, and animal models of stress [77 – 80]CRHR1 CRH receptor, Type 1 CRH signaling
CRHR2 CRH receptor, Type 2 CRH signaling
CRHBP CRH binding protein CRH signaling
Cytoplasmic FKBP4 FK506 binding protein 4 GC sensitivity Animal model of stress [39, 81]
FKBP5 FK506 binding protein 5 GC sensitivity Depression, PTSD, suicide, bipolar disorder [40, 41, 43, 82]
HSP70/HSPA1 Heat-shock protein, 70 kDa, 1A,1B, and 1L paralogs
GC sensitivity Stress and schizophrenia [83 – 86]
HSP90AA1 Heat-shock protein, 90 kDa GC sensitivity
GR Glucocorticoid receptor GC sensitivity Suicide and depression [87 – 89]
Nuclear BAG1 BCL2-associated athanogene 1 GC sensitivity Manic and depressive symptoms in mice [90, 91]
BDNF Brain-derived neurotrophic factor Target gene Anxiety and depressive phenotypes [64, 92, 93]
PER1 Period homolog 1 Target gene Circadian rhythm in bipolar disorder [94 – 96]
SGK1 Serum/GC-regulated kinase 1 Target gene Animal model of stress [97, 98]
TH Tyrosine hydroxylase Target gene Neurotransmission and psychosis [49, 99 – 101]
GC = Glucocorticoid.
Dow
nlo
aded
by:
JH
U J
ohn
Hopkin
s U
niv
ers
ity
162.1
29.2
51.6
9 -
5/1
9/2
016
9:3
4:0
6 P
M
Lee and Sawa, 2014
Homeostatic Cortisol
Dynamics
HPA-axis genes
Stress or Glucocorticoids
Disease: e.g. BDNF and TH
Epigenetic alterations: DNA methylation and Histone modifications
Changes in HPA-axis function: e.g. CRH, FKBP5, and GR
HPA-axis
Stress and glucocorticoid epigenetics
Lee and Sawa, 2014
Mechanism of action of cortisol
Wochnik et al, 2004
What is epigenetics?
• Study of heritable changes in gene function or phenotype, caused by mechanisms other than changes in underlying DNA sequence
• Study and measurement of transcriptional context, potential, and inertia
Tools of epigenetics
• Detection of DNA methylation
• Detection of Histone modifications
• Detection of gene-modifying RNA species
• Candidate assays and genome-wide approaches using arrays or next-generation sequencing
Bisulfite conversion of DNA
ACCTCGAGTTACCGTA ATTTCGAGTTATTGTA
Determination of % Methylation
50% 75% 25% 0%
45.7% 79.2% 25.5% 4.2%
.
.
.
X109~12?
Colony sequencing Pyrosequencing
Pyrosequencing
Biotage, 2005
FKBP5 in literature
• SNPs in FKBP5 associated with increased recurrence of depressive
episodes and response to antidepressant treatment (Binder et al.,
2004)
• Family-based association of FKBP5 to BP (Willour et al., 2009).
• Interaction of FKBP5 with childhood adversity on risk for PTSD ( Xie
et al., 2010)
• DNA sequence variants of the FKBP5 gene are associated with
unipolar depression (Zobel et al., 2010)
• Interaction of FKBP5, a stress-related gene, with childhood trauma
increases the risk for attempting suicide (Roy et al., 2010)
• SNPs in FKBP5 modulate the Trier response (Mahon and Wand,
2014)
FKBP5
FKBP5
FKBP5 FKBP5
FKBP5 FKBP5
Mechanism of action of FKBP5
X
Genomic organization of Fkbp5
CpG islands
118 kb
Ex11 AltEx1
Chr17: 28,536,042 bp
Ex1
28,654,469 bp
Intron 1 Intron 5 Promoter
a.
40
50
60
70
80
90
0 2 4 6 8
% D
NA
m
Weeks treated with CORT CpG-1
40
50
60
70
80
90
0 2 4 6 8
% D
NA
m
Weeks treated with CORT CpG-2
0
0.5
1
1.5
2
2.5
3
0 2 4 6 8
Rea
l-ti
me
exp
ress
ion
Weeks treated with CORT
b. c. d.
Control CORT
* *
* * *
* * * * *
* *
*
*
CORT CORT CORT RECOVERY RECOVERY RECOVERY
50
60
70
80
90
100
CpG-1 CpG-2
% D
NA
m
Hippocampus: Fkbp5 intron 1
0
10
20
30
40
50
60
70
CpG-1 CpG-2 CpG-3 CpG-4
% D
NA
m
Hippocampus: Fkbp5 intron 5
0
10
20
30
40
50
60
70
CpG-1 CpG-2 CpG-3 CpG-4
% D
NA
m
Hypothalamus: Fkbp5 intron 5
a. b.
c. d.
*
* *
*
*
* *
* *
* *
50
60
70
80
90
100
CpG-1 CpG-2
% D
NA
m
Hypothalamus: Fkbp5 intron 1
*
DNA methylation patterns in the brain
Control CORT Control Recovery CORT Recovery
60
65
70
75
80
85
90
95
100
0 2 4 6 8 10 12 14
% D
NA
m
Days treated with CORT: HT22 Fkbp5 intron 1 CpG-2
c.
60
65
70
75
80
85
90
95
100
0 2 4 6 8 10 12 14
% D
NA
m
Days treated with CORT: HT22 Fkbp5 intron 5 CpG-3
b.
0
0.5
1
1.5
2
2.5
3
3.5
4
0 2 4 6 8 10 12 14
Rel
ativ
e re
al-t
ime
exp
ress
ion
Days treated with CORT: HT22 Fkbp5
a.
CORT RECOVERY
CORT RECOVERY
* * *
* *
*
* *
*
*
*
CORT RECOVERY
Fkbp5 in the cell line
Control
CORT
DNA methylation vs. Plasma CORT levels
A P = 2.3 x 10-9
r² = 0.69
20
40
60
80
100
0 200 400 600 800
% D
NA
m a
t P
os
.1
Plasma CORT (ng/mL)
P = 0.00036 r² = 0.34
0
1
2
3
4
5
6
0 200 400 600 800
Rela
tive e
xp
res
sio
n o
f F
kb
p5
Plasma CORT (ng/mL)
P = 2.8 x 10-5 r² = 0.56
0.0
2.0
4.0
6.0
8.0
20 40 60 80 100
Sp
lee
n w
eig
ht
(mg
/g B
W)
% DNAm at Pos.1
P = 9.0 x 10-5 r² = 0.51
5
10
15
20
25
30
20 40 60 80 100
% v
isc
era
l fa
t
% DNAm at Pos.1
C D
B
Blood DNA methylation vs. brain DNA methylation and expression of Fkbp5
a) b)
30
40
50
60
70
80
90
20 30 40 50% D
NA
Meth
yla
tio
n in
Blo
od
% DNA Methylation in Hippocampus
Blood Intron 1 CpG-2 DNAm vs. Hippocampal Intron 5 CpG-4 DNAm
30
40
50
60
70
80
90
0.8 1 1.2 1.4 1.6 1.8% D
NA
Meth
yla
tio
n in
Blo
od
Relative Expression in Hippocampus (a.u.)
Blood Intron 1 CpG-2 DNAm vs. Hippocampal mRNA
R2 = 0.43
P = 3.5x10-5
0.8
1
1.2
1.4
1.6
1.8
2
20 25 30 35 40 45 50
Rela
tive e
xp
ressio
n (
a.u
.)
% DNA Methylation in Hippocampus
Hippocampal mRNA vs. Hippocampal Intron 5 CpG-4 DNAm
c)
R2 = 0.49
P = 2.7x10-5
R2 = 0.29
P = 0.003
Genome-wide investigation
• What other targets are epigenetically altered by glucocorticoids?
• What can a genome-wide assay tell us about glucocorticoid biology?
Sequence Capture Platform (Methyl-Seq) followed by bisulfite conversion and next generation sequencing.
Table 1. Mouse Methylome Target Capture Design. There was approximately 10 Mb of target overlap between data repositories resulting in a final hybridization capture array targeting 99 Mb across the murine genome. TFBS; transcription factor binding sites.
Hing et al., 2015
Analysis step Quality control Alignment Duplicate removal Methylation calling
Data type Raw paired-end sequences Quality-controlled sequences Quality-trimmed sequences Adapter-trimmed Sequences
Output Per sequence base quality Per tile sequence quality Per sequence quality scores Per base sequence content Per sequence GC content Per base N content Sequence length distribution Sequence duplication levels Overrepresented sequences Adapter and kmer content Uniquely mapped paired-end aligned sequences Deduplicated uniquely mapped paired-end aligned sequences Methylation calls
Optional Steps Alignment quality control
Tools FASTQC v0.11.3 Trim Galore v0.3.7 Bismark v0.13.0 Bowtie 2 v2.1.0 Samtools v0.1.19 BamUtil v1.0.12
HIPPOCAMPUS
VEHICLE VEHICLE CORT CORT
Sample_HPC1 Sample_HPC3 Sample_HPC2 Sample_HPC4
Total paired end (pe) reads 42,346,826 41,741,546 40,059,427 35,260,700
Total uniquely mapped pe reads 29,107,648 32,733,678 31,775,425 26,470,496
Mapping efficiency (Total uniquely mapped pe
reads / Total pe reads) 68.70 78.40 79.30 75.10
Duplicate reads (% of Total uniquely mapped
reads) 27.51 10.68 12.56 21.91
Total deduplicated uniquely mapped pe reads 21,101,491 29,237,995 27,784,971 20,670,502
CpGs (1.45M) coverage with at least 10 reads 48x 39x 40x 39x
“On Target”
Total uniquely pe reads 18,498,282 25,755,305 23,909,147 18,075,210
% of Deduplicated total uniquely mapped
reads 87.66 88.09 86.05 87.44
Average read depth 26x 35x 31x 24x
Number of bases mapped 147Mb 149Mb 152Mb 148Mb
Percent of mapped target bases covered by:
at least 10 reads 55.11 66.04 62.8 57.55
DMR#97: Snx18
b)
a)
Mouse MethylSeq DMR List Output and Bsmooth Plot
Me
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chr13: 113,613,597 − 113,614,253 (width = 657, extended = 5,000)
Smoothing provides corrected methylation percentage estimates based on Correlation between CpGs and also provdies T-stat
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chr8: 8,621,381 − 8,622,128 (width = 748, extended = 5,000)M
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chr8: 8,621,381 − 8,622,128 (width = 748, extended = 5,000)
0
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VEHICLE
CORT
** **
** ** **
Hippocampus: EfnB2
Chr8: 8,621,381-8,622,128
a) b)
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chr13: 52,975,989 − 52,977,245 (width = 1,257, extended = 5,000)
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chr8: 8,621,381 − 8,622,128 (width = 748, extended = 5,000)
Genome-wide DNA methylation:
Mouse MethylSeq Results and Pyrosequencing of DMRs in HPC and Blood
Blood: Nfil3
c)
Chr13: 52,975,989-52,977,245
0
20
40
60
80
100
CpG-1 CpG-2 CpG-3 CpG-4 CpG-5
VEHICLE
CORT
d)
*** ***
*** ***
***
**p<0.01
***p<0.001
10.1%
12.8%
54.7%
35.6%
Blood DMRs
8.9%
14.9%
52.5%
36.8%
Hippocampal DMRs
Promoter
Exon
Intron
Intergenic
0.8%
22.1%
77.6%
Blood DMRs
1.9%
19.0%
79.8%
Hippocampal DMRs
CpG Island
Shores
Other
Loss of DNAm 72%
Gain of DNAm 28%
BLOOD
Loss of DNAm 69%
Gain of DNAm 31%
HIPPOCAMPUS
Ratio of directions of DNA methylation (DNAm) change
How do these numbers compare to the number of genes
that are up- or down-regulated?
Methylation values for each CpG in Vehicle and CORT treated blood DNA
0
200
400
600
800
1000
1200
0 0.1 0.5 1 2 5 10 15 20 25 50 50+
Nu
mb
er o
f D
MR
s
Distance between Common DMRs (X1,000 Basepairs)
Absolute Distance by Kilobases
0
200
400
600
800
1000
0 10 20 30 40 50 60 70 80 90 100 100+
Nu
mb
er o
f D
MR
s
Distance Between Common DMRs Normalized by Gene Length (%)
Distance Normalized by Gene Length
Rat Methyl-Seq
• Platform Design
• Validation
• Implementation on a rat model of stress
Source Site Classification Number of
Targets
Total Bases Covered
(bp)
rat.Nov.2004.m4.refGene Promoters (-/+1kb) 17,620 17,620,000
rat.Nov.2004.m4.cpgIslandGgfAndyM
asked CpG Islands 88,554 42,699,212
rat.Nov.2004.m4.cpgIslandGgfAndyM
asked
Shores (CpG Islands -
/+1kb) 88,554 177,108,000
Rat CHARM GC-rich sequences 32,889 79,820,886
PubMed GR-Targets 1,183 274,007
Total 228,800 317,522,105
Total non-redundant bases covered (after merging overlapping regions from above and
trimming i.e. selecting 500 base pairs region for every 1000 base pairs skipped):
111,038,241 bp; ~2.3 million on-target CpGs; and average region size = 594 bp.
Genomic regions targeted by the SureSelect Rat Methyl-Seq Platform
0
10
20
30
40
50
60
70
80
CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6
Cd52: Blood vs. Liver
Blood
Liver
0
10
20
30
40
50
60
70
80
90
100
Shank2: Hippo vs. Liver
Hippo
Liver
30
40
50
60
70
80
90
100
CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6 CpG-7
Cit: Hippo vs. Hypo
Hippo
Hypo
0
20
40
60
80
100
CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6 CpG-7
Grap2: Hippo vs. Blood
Hippo
Blood
Tissue-specific DMRs
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0
20
40
60
80
100
CpG-1 CpG-2 CpG-3 CpG-4
Chr X: Bcor
Hippo Male
Hippo Female
0
20
40
60
80
100
CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6
Chr X: Tsix
Male
Female
0
20
40
60
80
100
Chr X: Xist
0
20
40
60
80
100
CpG-1 CpG-2 CpG-3 CpG-4 CpG-5
Chr. X: Pgk1
X-chromosomal DMRs
** **
**
** ** *
* **
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**
**
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**
**
** ** **
** **
** ** **
**
0
10
20
30
40
50
60
70
80
90
CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6
Chr 13: Lrrn2
Hippo Male
Hippo Female
Autosomal DMRs M
eth
yla
tion
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0.5
0.8 ●
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chr13: 45,731,124 − 45,731,287 (width = 164, extended = 1,000)
**
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* *
Me
thyla
tion
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0.5
0.8
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chr18: 63,921,357 − 63,921,561 (width = 205, extended = 1,000)
0
20
40
60
80
100
CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6 CpG-7 CpG-8 CpG-9
Chr 18: Tubb6
Hippo Male
Hippo Female
* *** ***
** ** ***
*
Chronic variable stress in the rat: stress hormone levels
0
20
40
60
80
100
120
140
160
180
0 5 10 15 20 25
Pla
sm
a C
OR
T u
g/m
L
Time in Days
Plasma Corticosterone Levels During CVS
STRESS CONTROL
120
160
200
240
280
320
0 50 100 150 200
An
xie
ty-l
ike b
eh
avio
r
Plasma CORT ug/mL
R2=0.17
P=0.077
Chronic variable stress in the rat: behavior
chr start end gene
symbol
distance from
gene areaStat
Mean
Diff stress control direction
chr20 1,644,246 1,644,390 RT1-M4 in_gene 93.03 0.22 0.33 0.11 Gain
chr5 160,361,352 160,361,564 LOC690911 in_gene -70.75 -0.19 0.72 0.91 Loss
chr3 61,138,281 61,138,330 RGD1564319 265569 61.79 0.21 0.94 0.72 Gain
chr2 143,064,811 143,065,010 Ufm1 8569 -59.48 -0.11 0.13 0.24 Loss
chr7 30,764,111 30,764,284 Ntn4 in_gene 57.04 0.21 0.94 0.73 Gain
chr17 12,469,112 12,469,218 Idnk 41996 -50.91 -0.13 0.74 0.88 Loss
chr7 47,101,725 47,101,930 Pawr in_gene -50.54 -0.12 0.64 0.76 Loss
chr5 76,111,248 76,111,822 Txndc8 151703 -50.38 -0.11 0.85 0.96 Loss
chr11 80,640,132 80,640,356 Dgkg in_gene -50.07 -0.16 0.73 0.89 Loss
chr8 71,759,248 71,759,411 Mir190 210226 -47.84 -0.17 0.58 0.75 Loss
Chronic variable stress in the rat: List of DMRs
Gene Networks and Pathways
Me
thyla
tion
0.2
0.5
0.8
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chr20: 1,644,246 − 1,644,390 (width = 145, extended = 1,000)
a)
0
10
20
30
40
50
60
CpG-1 CpG-2 CpG-3 CpG-4 CpG-5 CpG-6 CpG-7 CpG-8 CpG-9 CpG-10 CpG-11 CpG-12
% D
NA
Meth
yla
tio
n
RT1M4
Control
Stress
b)
* *
*
*
*
*
* *
*
*
p<0.05
Result: Validation of a candidate DMR in the rat
R2=0.54
P=0.001
20
30
40
50
60
0 20 40 60 80 100 120 140 160 180
% D
NA
Meth
yla
tio
n
Plasma CORT Levels ug/mL
RT1M4: DNA Methylation vs. Plasma CORT
Result: Identification of a candidate stress DMR in the rat
Human 30-day Stress Study
Emotional stress high-low: DMR: chr15: 27138591-27140153 (GABRA5, GABRB3)
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
% D
NA
Meth
yla
tion
CpG Position
Emotional Stress Levels and DNA Methylation of GABRA5
Low
High*
**
*
* * **
Current and Future Directions using the Methyl-Seq platform
• Rat stress brain methylation
• Human Methyl-Seq correlation to 30-day saliva cortisol
• Human postmortem bipolar brain study (custom-made Methyl-Seq platform)
Acknowledgements
Mood Disorders Group
Tim Moran
Kellie Tamashiro
Peter Zandi
Fayaz Seifuddin
Olivia Cox
Wand Lab
Gary Wand
Xiaoju Yang
Agilent
Jonathan Levine
Josh Wang
Brigette Kipphut
Mary Napier
Support NARSAD NIMH Rales Foundation