L'Épigéné)que-etles-maladies-auto5immunes- inflammatoires ...€¦ · SSc-Mortality-Denmark UK...
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L'Épigéné)que et les maladies auto-‐immunes inflammatoires : En quête du Graal
Marie Hudson, MD MPH Rhumatologue Hôpital Général Juif, Institut Lady Davis Université McGill
Disclosures
• Unrestricted grants from Roche, Janssen, Grifols, Baxter, CSL Behring
• Advisory board for Novar)s, Sanofi
SSc Mortality Denmark
UK Sweden 1 Sweden 2
Spain Canada (Montreal, 1980s)
Italy Spain
Canada (Ontario, 1970s)
Pooled SMR = 3.53 (95% CI 3.03, 4.11) Elhai et al. Rheumatology 2012
Canadian Scleroderma Research Group (2005-2011)
SMR = 4.7 (95% CI 3.6, 5.7) Khananian et al. CRA 2014
Objec)ves 1. Understand epigene)c mechanisms and
their role in systemic autoimmune inflammatory rheuma)c diseases
2. Review preliminary data in SARDs
Gene)cs – your DNA is your des)ny
I am the beneficiary of a lucky break in the genetic sweepstake. Isaac Asimov (1920-1992)
The genomics revolu)on
• Human genome project -‐ 2003 • Over 2000 loci have now been
iden)fied to be significantly and robustly associated with one or more complex traits.
• However, the propor)on of phenotypic varia)on explained by GWAS findings remains rela)vely low for most complex traits (typically less than 10-‐15%) – suscep)bility genes are
neither necessary nor sufficient
Disease is not only about DNA
Brooks et al. J Autoimmunity 2010.
Discordance of A-I phenotype in identical twins
January 2010
The “genomics” revolu)on
Overview of the basics
Gene expression
Main components of epigene)c code
Methyl marks can repress gene activity
Histone tails can modulate gene activity
Non-coding RNAs can regulate gene expression
DNA methylation is the addition of a methyl group to the 5’ end of a cytosine in a cytosine-guanine dinucleotide
DNA methyla)on
Cau)on!
Agou) sisters
Jirtle and Waterland, Duke University
Agouti gene – important in determining colour of coat, propensity for obesity, diabetes, and cancer
Agou) gene
Methylated agouti gene – off
Un-methylated agouti gene - on
Epigene)cs and and environmental exposures
SAME GENOME, DIFFERENT EPIGENOME Variability in CpG methylation determines
phenotype
Molec Cell Biol 2003.
Pregnant agouti mice were fed diets varying in B12 and folate…
Those with more B12 and folate had brown, thin pups with less diabetes. Jirtle and Waterland
Epigene)cs and environmental exposures
BPA - a hormone-disrupting chemical found, for instance, in some plastics and the linings of some canned goods. Mice that were exposed to diets high in BPA prenatally were more likely to be yellow, become obese and develop diabetes and cancers compared to genetically identical mice without the exposure (Dolinoy et al. 2007). Offsprings of female
mice exposed to BPA (bisphenol-A) during pregnancy.
Jirtle PNAS 2007.
It’s complicated…
• Epigene)c influences can cancel each other out – Eg. high methyl diet and BPA in agou) mice
– R. Jirtle – hgp://videocast.nih.gov/summary.asp?live=10520&bhcp=1, minute 28.04
• Mice are not humans and these mice were exposed to huge doses.
Back to humans…
Yellow shows where the twins have epigenetic tags in the same place. Green and red show where the twins have epigenetic tags in different places.
Epigene)cs – where genes meet the environment
Epigene)cs in health
• Epigene)c signals help to organize genomic informa)on in a cell.
• Epigene)c processes are fundamental to normal development and cell differen)a)on. – Responsible for regula)ng gene expression and in controlling specific cellular func)ons.
– All cells have the same DNA but can differen)ate into cell types via epigene)c signals.
Epigene)cs in disease • Epigene)c marks sensi)ve to the environment • Epigene)c abnormali)es increasingly recognized in human disease
– Cancer -‐ Epigene)c abnormali)es might explain 80-‐90% of cancers • Eg. Tumour suppressor genes are inac)vated by smoking
• Importantly, epigene)c marks are poten)ally reversible by drug treatments.
– Several inhibitors of chroma)n-‐modifying enzymes, including histone deacetylase (HDAC) inhibitors and DNA methyltransferase (DNMT) inhibitors, have now been FDA and EU approved and are being used in clinical prac)ce with good prognosis for tumor regression.
– Eg. VIDAZA (azaci)dine; DNA demethyla)on) for MDS
– Drug development targe)ng epigene)c marks is in now in full swing.
Epigene)cs in autoimmune diseases
• Autoimmune diseases are thought to arise from an interplay of “gene)c predisposi)on” and “environmental triggers”
• Epigene)cs could be the link between these two elements
Drug-‐induced lupus • Procainamide and
hydralazine – Both drugs shown to be
associated with DNA hypomethyla)on
– T cell methyla)on defect traced to low DNMT1 levels resul)ng from abnormal ERK pathway signaling.
– Gene)c predisposi)on may explain why some but not all pa)ents exposed to these drugs develop lupus-‐like disease
Cornacchia E, Golbus J, Maybaum J, Strahler J, Hanash S, Richardson B. Hydralazine and procainamide inhibit T cell DNA methylation and induce autoreactivity. J Immunol. 1988;140:2197–2200.
Other poten)al environmental triggers
Somers, Richardson. Lupus 2014.
The SARD Hypothesis
South African daisies (Gorteria diffusa) are unique flowers that appear in 16 different shapes and colours (ie. genotype-‐phenotype discordance).
Ellis, Stephens. Am J Botany 2008.
The SARD Hypothesis
Familial poly-autoimmunity supports shared SARD signature.
Hudson et al. J Autoimmunity 2008.
Kuo et al. JAMA Int Med 2015.
Addi)onal evidence for SARD hypothesis
• Clinical -‐ share demographic (women > men) and clinical (inflammatory arthri)s, Raynaud’s phenomenon, inters))al lung disease) manifesta)ons
• Immunological -‐ share complex abnormali)es in CD4+ T lymphocyte func)on, in par)cular Treg cell subset
• Gene.c – share HLA and non-‐HLA gene.c suscep.bili.es – Cotsapas et al. PLOS gene)cs 2011 – Cho and Gregersen. NEJM 2011
• Environment – eg. smoking is common risk factor
The SARD Hypothesis
We hypothesize that the DNA methylomes of SARD patients have common and unique ‘SARD specific’ DNA methylation signatures. Environmental exposures modulate the risk of having those signatures.
Ellis, Stephens. Am J Botany 2008.
Genome-‐wide DNA methyla)on in SARD
Main limitations
– confounding by cell type, established disease, medication exposures
- low resolution approaches
Epi-‐SARD study -‐ Methods • Incident, treatment naïve subjects • Isolated CD4 + T cells • Genome-‐wide DNA methyla)on of CD4+ T cells was assessed using the Illumina Infinium HumanMethyla)on450 BeadChip array
• Genome-‐wide gene expression was carried out using Illumina TruSeq stranded RNA-‐seq
CLiPP 01/2013-01/2015
Cell isola)on
Integra)ve analysis • First, iden)fied significantly differen)ally methylated (DM) sites between SARD cases and controls (ie. no significant differences between disease (p-‐value >0.2) but differen)ally methylated between SARD and controls). • Second, iden)fied significantly differen)ally expressed (DE) RNAseq transcripts between SARD cases and controls. • Third, iden)fied genes that were both significantly DM and DE. • Adjusted for mul)ple tes)ng.
SARD signature
Log fold-change Ave. expression t P value Adjusted p B CD1C 1.878176186 -1.571708727 3.480974367 0.001103574 0.009594854 -0.875142348 AIPL1 1.180516781 -3.71637427 3.472447414 0.001131673 0.009774192 -0.921683609 CD36 1.071191215 -0.390132877 2.716164032 0.009265551 0.047078545 -2.687077009 CALHM2 0.896048994 0.74309745 2.787199521 0.0076947 0.040897653 -2.637585631 SYNPO2 0.891739201 -0.795858834 2.831603948 0.006841965 0.037306678 -2.41327067 DPYSL2 0.841284915 3.497565036 4.320155386 8.22E-05 0.00144616 1.256675619 SCD 0.839306682 0.750065481 3.668618319 0.000630144 0.006457008 -0.413870911 SLFN12L 0.80749901 4.102152265 5.385864606 2.37E-06 0.00010394 4.610626079 LIMA1 0.693959117 2.92256851 4.637757041 2.92E-05 0.000672132 2.276814437 CEP97 0.667110577 3.619971987 5.179085323 4.78E-06 0.00017278 3.956808949 TNRC6B 0.638464317 7.313600826 6.322637212 9.39E-08 1.03E-05 7.630432549 BCL2 0.621165505 7.31856972 3.989997394 0.000234452 0.003130261 0.042201908 ACTR3 0.513120975 6.982132479 5.054392222 7.28E-06 0.000235728 3.396416081 PCCA 0.43961969 2.018788673 3.948954273 0.000266507 0.003409277 0.255757429 MRPL48 0.434716098 2.259246861 3.373764885 0.001510666 0.012197772 -1.378318591 ZNF407 0.426504893 5.225624043 3.596960434 0.000781727 0.007521222 -1.013604888 TFDP1 0.343909917 3.558235673 3.377760897 0.001493214 0.012101897 -1.507046395 EIF2C1 0.337982277 4.361111941 3.874960676 0.00033532 0.004028302 -0.166530369 KIF13B 0.317276523 4.463419278 3.745123833 0.000499576 0.005408581 -0.551064288 NPEPPS 0.303402848 4.455457817 4.36023599 7.22E-05 0.001323894 1.287257248 ZMYM4 0.261843777 5.209462487 3.033239699 0.003964164 0.02497668 -2.533809017 CUL4A 0.245138862 4.993450196 3.490056435 0.001074379 0.009433317 -1.306978327 YWHAG -0.313231679 5.098025859 -3.274939384 0.002009299 0.014985822 -1.916324232 WIPI2 -0.314485056 4.580701632 -2.787930555 0.007679899 0.040849003 -3.124779224 ZNF552 -0.3269609 2.470099055 -3.342544169 0.001653851 0.013010132 -1.537143507 ATP5G2 -0.431385337 5.217949044 -4.029482884 0.000207157 0.00287371 0.219288047 RNMTL1 -0.462209923 3.28863435 -4.587305332 3.44E-05 0.000766109 2.039352774 CCDC40 -1.21616542 -1.930467602 -4.596073798 3.35E-05 0.000750982 2.203933966
Top “28”
Gene by gene analysis • CD1C
– cluster of differen)a)on 1C – over-‐expressed > 6-‐fold – gene expresses an MHC-‐I –like
molecule that presents lipid an)gens to T cells and helps bridge the temporal gap between the onset of innate immunity and the adap)ve responses of MHC-‐restricted T cells.
– Role for CD1c in M. Tb and other microbial infec)ons
– Very ligle known for role of CD1c in autoimmune diseases.
Gene ontology • GO is a bioinforma)cs approach used to describe gene products in terms of their biological processes, cellular components and molecular func)ons.
• Eg. Ingenuity Pathway Analysis
Canonical pathways P value Mitochondrial L-carnitine shuttle pathway 3.24E-03
Signaling by Rho family GTPases 6.56E-03
Virus entry via endocytic pathways 1.02E-02
Mitochondrial dysfunction 1.09E-02
RhoGDI signaling 1.13E-02
IPA analysis
Canonical pathways P value Mitochondrial L-carnitine shuttle pathway 3.24E-03
Signaling by Rho family GTPases 6.56E-03
Virus entry via endocytic pathways 1.02E-02
Mitochondrial dysfunction 1.09E-02
RhoGDI signaling 1.13E-02
IPA analysis
Jan 2016
Canonical pathways P value Mitochondrial L-carnitine shuttle pathway 3.24E-03
Signaling by Rho family GTPases 6.56E-03
Virus entry via endocytic pathways 1.02E-02
Mitochondrial dysfunction 1.09E-02
RhoGDI signaling 1.13E-02
IPA analysis
Jan 2016
Of course, all of this needs to be reproduced!!!
La quête du Graal?
Conclusion • Epigenomics offers great opportuni)es for understanding auto-‐immune diseases (and iden)fying novel biomarkers)
• Success predicated on high-‐quality data
• Cri)cal need for collabora)ons because of rela)ve rarity of diseases and complexity of science Gourley M, Miller FW. Nat Clin Pract Rheumatol (2007).
Acknowledgements JGH Murray Baron
LDI Celia Greenwood Kathleen Klein Greg Voisin Aurélie Labbe
MUHC Ines Colmegna Sasha Bernatsky
McGill GQIC Tomi Pas)nen
McGill MDTC Joaquim Madrenas Corinne Maurice
Poten)al for discovery
• Many genes and pathways iden)fied were related to other biological func)ons – cellular cytoskeleton, including SYNOP2, LIMA1, KIF13B and ZMYM4 – cell trafficking, including CEP97 (over-‐expressed) and CCDC40 (under-‐
expressed).
• This is in contrast to GWAS studies that have iden)fied genes of relevance predominantly for immune func)on only and provides novel cellular targets of interest.
Human trait variation.(A) Differences in a human trait (such as height) are partly due to the combined effects of genetic variants that alter the expression of multiple genes.
Terrence S. Furey, and Praveen Sethupathy Science 2013;342:705-706
Published by AAAS
C57BL/6 mice – lupus resistant
C57BL/6 x SJL mice – lupus susceptible
Inbred ERK signalling defect
Methyl-supplemented diet
Slight Increase in dsDNA
Significant increase in dsDNA
Kidney disease on standard and MR but not MS diet
Standard diet Kidney disease only on MR but not standard and MS diet
Methyl-restricted diet
DNA methyla)on and diet
Strickland, Richardson. A and R 2013.