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Use of -omics technologies in population studies for the discovery of biomarkers of environmental health:
Overview and experience from the EnviroGenomarkers project
Soterios A. Kyrtopoulos
National Hellenic Research Foundation,
Institute of Biological Research and Biotechnology,
Athens, Greece
–omics markers in molecular epidemiology: Potential advantages
Genomics Transcriptomics Metabonomics Proteomics Epigenomics
global, untargeted searches - no prior hypothesis
continually evolving genomic profiles reflecting changing state of
tissues during disease pathogenesis
exposure clinical
disease
internal dose biologically
effective dose
altered
structure/
function
early
biological effects
chemicals /
metabolites /
in body fluids or
tissues
protein adducts /
DNA adducts
gene or chromosome
mutations
mutation spectra
in tumours or
pre-cancerous cells
evolving
omic profiles
biomarkers of
exposure intermediate
biomarkers biomarkers of
disease
biomarkers reflecting
discrete states of cell
exposure biomarkers
of exposure
intermediate
biomarkers
disease
“Meet-in-the-middle” approach (Vineis & Perera, CEBP 2007)
biomarkers of risk
biomarkers of exposure
biomarkers linking exposure to disease
Methylation status methylated unmethylated All OR (95% CI’s)
asthma (% yes)* 11/28 (39%) 4/28 (14%) 15/56 (27%) 3.9 (1.1-14.3)
median PAH exposure# 3.39 ng/m3 1.7 ng/m3 2.26 ng/m3
(min, max) (1.11, 34.48) (0.49, 3.33) (0.49, 34.48)
* p=0.03; # p<0.001
ACSL3 methylation is significantly associated with maternal airborne PAH exposure and with risk of asthma
prospective study, 58 mother-child pairs; umbilical cord white blood cell DNA unbiased genome-wide CpG island methylation profiling (methylation-sensitive restriction fingerprinting) >30 DNA sequences with methylation status dependent on maternal PAH exposure Acyl-CoA synthetase long-chain family member 3 (ACSL3) showed highest concordance between CpG island methylation and expression level in matched placental tissues
PLoS ONE 4(2): e4488 (2009)
ACSL3
CGI methylation status methylated unmethylated p OR (95% CI’s)
asthma 11/28 (39%) 4/28 (14%) 0.03 3.9 (1.1-14.3)
median PAH exposure 3.39 ng/m3 1.7 ng/m3 <0.001
Through the unbiased, genome-wide screening of cord blood DNA, methylation of the CpG island of ACSL3 was discovered as an intermediate biomarker which is significantly associated with both higher maternal airborne PAH exposure AND with an increased risk of asthma in children
McHale et
al., 2010
Gene expression profiling of people exposed to low doses of
benzene (leukemogen):
16 genes upregulated at all levels of exposure
Wild CP, Cancer Epidemiol Biomarkers Prev. 14 (2005):1847-50
Wild CP, Int J Epidemiol. 2012 Jan 31. PubMed PMID: 22296988
Rappaport SM & Smith MT, Science 330 (2010):460-1
The totality of exposures from ALL sources (air, water,
food, lifestyle, behaviour, metabolism, inflammation,
oxidative stress, psychological stress etc) during ALL
stages of life starting from conception
The concept of the Exposome
Omics-based investigations on exposed human populations (McHale et al., 2010) Exposure Reference Transcriptomics
Air Pollution Van Leeuwen et al. 2006; 2008 Arsenic Lu et al. 2001; Wu et al. 2003; Argos et al. 2006; Fry et al. 2007;
Andrew et al. 2008 Benzene Forrest et al. 2005; McHale et al. 2009
Cigarette smoke Lampe et al. 2004; Van Leeuwen et al. 2007; Beane et al. 2007
Metal fumes Wang et al. 2005 TCDD McHale et al. 2007
Welding fumes Rim et al. 2007 Proteomics
Arsenic Hegedus et al. 2008; Harezlak et al. 2008 Benzene Joo et al. 2004; Vermeulen et al. 2005
Epigenomics - miRNA
Cigarette smoke Schembri et al. 2009 - DNA methylation
Benzene Zhang et al. 2009 Cigarette smoke Christensen et al. 2009; Breitling et al. 2011
Even with relatively small studies, -omics profiles or distinct signals, which have biological meaning, have been identified, implying the potential to serve as biomarkers of toxic exposure or disease risk
Issues: validation: between-study reproducibility / intra-individual variability / sample handling / … appropriateness of blood as a surrogate tissue suitability of samples collected in previous decades, prior to advent of omics technologies, and stored in biobanks (more than 1.5 million blood-derived biosamples are stored in European biobanks)
prospective study
EnviroGenomarkers: Genomics biomarkers of environmental health
www.envirogenomarkers.net
Objective: Evaluation of potential of –omics technologies for application in large-scale population studies using biosamples in long-term storage in existing biobanks
diseased (cases)
healthy (controls)
exposure
biomarkers of exposure
intermediate –omics
biomarkers
disease
Diseases and exposures
EPIC Italy and NSHDS breast cancer (600 case/control pairs) vs PCBs PAHs cadmium B-cell lymphoma (300 case/control pairs) vs PCBs Rhea mother-child cohort (Crete) 600 children with chronic diseases of the nervous and immune system & allergies vs early life exposure to endocrine disruptors (PCBs, PAHs, phthalates, polybrominated diphenyl ethers)
Biomarkers of exposure serum PCBs & PBDEs (Univ. Kuopio) erythrocyte Cd (Univ. Lund) urine phthalates (Univ. Crete) leukocyte PAH-DNA adducts (NHRF, Athens)
Intermediate –omics biomarkers
transcriptomics - Agilent 44K microarrays (Univ. of Maastricht) epigenomics - Illumina Infinium HumanMethylation450K CpG island microarrays (HRF, Athens) LC/MS/MS metabonomics (Imperial College, London)
wide-target proteomics - Luminex multiplex platform (Univ. Utrecht)
Biomarkers
Pilot phase: Technically validate applicability of –omics technologies to samples collected in the context of population studies, with a focus on samples long-term storage in existing biobanks
Discovery phase: Analyse using high-density, –omics technologies approx. 30% of all samples; select small number of targets for each –omics technology Validation phase: Analyse selected targets in all samples using low-density, high-throughput methods
Biomarker analyses
Pilot study
Collect fresh blood, mimic treatment of biobank samples at the time of their collection
Optimise methods for sample processing
Identify sources of variability and establish cut-off criteria for applicability of –omics technologies
Test on real biobank samples
3-4 persons, buffy coats
anticoagulants: collected in heparin, EDTA, citrate
bench-time: 0, 2, 4, 8, 24h at room
storage temp.: -80oC, liq. N2
analysed 2-20 weeks after collection
Main conclusions of EnviroGenomarkers pilot study – Cut-off criteria for selecting samples for omic analyses
1. Blood samples should be processed for separation of buffy coat,
erythrocytes & serum as soon as possible and not later than 4 hours after collection
2. Blood samples collected using different anticoagulants, or stored frozen
at different temperatures, cannot be used for pooled analyses of omic data
3. RNA of microarray quality can be isolated from buffy coats collected
in the absence of RNA preservative and cold-stored in long-term biobanks
Epigenomics
5-meC in CpG dinucleotides (mainly CpG islands) histone modifications microRNAs
targeted: 5-meC in specific sequences global: 5-meC in repetitive sequences (LINE-1, Alu; ~30% of human genome; > 1/3 of DNA methylation is in repetitive elements) methyl acceptor assay (MAA) untargeted: microarrays
Question Epigenetics is a tissue- and cell-specific phenomenon;
relevance of blood cell epigenetics to disease?
exposure / lifestyle factor
genome-wide gene-specific effects
methylation up methylation down no effect yes no
benzene LINE-1; Alu p16; p15; MAGE-1 p15
POPs LINE-1; Alu LINE-1
lead LINE-1; Alu Alu
air pollution LINE-1; Alu Alu NOS2
arsenic MAA p16; p53
tobacco 5-meC; MSRD; LINE-1; Alu F2RL3 COMT; MAOA
prenatal tobacco LINE-1 Alu MAA IGF2; BDNF
alcohol Alu LINE-1; Alu MSRD HERP
low folate total 5-meC; LINE-1; Alu
physical activity LINE-1
age MAA; LINE-1; Alu total 5-meC; LINE-1 PKDIP2; GUK1; CALCA; MGMT
H19; IGF2
gender (f vs m) 5-meC; LINE-1; Alu
5-meC; LINE-1; Alu MSRD; Alu MAOA; CALCA; MTHFR; MGMT; DRD4; SERT; F8
H19; IGF2
WBC DNA methylation and exposures / lifestyle factors
Blood DNA methylation profiles
respond to environmental exposures,
diet and lifestyle factors
Terry et al., Epigenetics 6:7, 828-837; July 2011
WBC DNA methylation in case-control studies of cancer
Terry et al., Epigenetics 6:7, 828-837; July 2011
Gene-specific methylation and cancer risk
Blood DNA methylation can serve as a marker
of disease or risk in cancer
Genome-wide methylation analysis
Platform: 450K DNA Methylation array (Infinium HumanMethylation450 BeadChip)
485,764 cytosine positions of the human genome
Sandoval et. al Epigenetics 6 (2011) 692-702
EnviroGenomarkers project: current state
Pilot study completed, criteria for selection of biobank samples for
analysis by omic technologies identified
Omics analysis of 100 case/control pairs for breast cancer and B-cell
lymphoma completed
For transcriptomics, metabonomics & proteomics, signals associating
with the targeted exposures or disease risks are few and statistically weak,
suggesting the need larger sample size
Omics analyses extended to larger numbers of samples
In general, Italy presents higher exposure levels than Sweden
A. Exposure levels of POPs and heavy metals in Italy and in Sweden
DNA methylation vs exposure
POPs: HCB, DDE, dioxin-
like PCBs (118, 156), non
dioxin-like PCBs (153, 138,
170, 180)
Heavy metals: cadmium &
lead
N=300 per cohort
Genome-wide analysis of CpG methylation: Associations with exposure - Italy (N=200)
exposure no. of sign. CpG sites
HCB 25
DDE 58
dioxin-like PCBs 18
non-dioxin-like PCBs 17
Cd 140
Pb 48
Compare methylation levels for each site in case/control pairs 31 CpG sites come up as significantly different
Genome-wide analysis of CpG methylation: Associations with breast cancer risk, Italy
Conclusions
High-density (omics) technologies provide new opportunities for the discovery of novel biomarkers of exposure to toxic agents enriched with mechanistic information
In the context of human biomonitoring, omics-based biomarkers, in combination with chemical-specific biomarkers of exposure and disease incidence data, can support the establishment of biologically-plausible links with disease causation
Acknowledgements
Epigenomics: National Hellenic Res. Found. Panagiotis Georgiadis Margarita Bekyrou Aristotelis Hadjiioannou Giannis Valavanis Christina Papadopoulou Transcriptomics: Univ. Maastricht Dennie Hebbels Theo de Kok Jos Kleinjans Biobanks EPIC Italy NSHDS study Domenico Palli Ingvar Bergdahl Goran Hallmans
Funded by the European Union FP7, Theme: Environment (including climate change)
(Grant no. 226756)
Exposure biomarkers serum PCBs: THL, Kuopio Hannu Kiviranta erythrocyte Cd/Pb: Univ. Lund B. Jönsson, T. Lundh
Epigenomics: National Hellenic Res. Found. Panagiotis Georgiadis Margarita Bekyrou Aristotelis Hadjiioannou Giannis Valavanis Christina Papadopoulou Transcriptomics: Univ. Maastricht Dennie Hebbels Theo de Kok Jos Kleinjans Biobanks EPIC Italy NSHDS study Domenico Palli Ingvar Bergdahl Goran Hallmans
Exposure biomarkers serum PCBs: THL, Kuopio Hannu Kiviranta erythrocyte Cd/Pb: Univ. Lund B. Jönsson, T. Lundh
The EnviroGenomarkers consortium
Partner PI
National Hellenic Research Foundation S.A. Kyrtopoulos
Univ. Maastricht J. Kleinjans
Imperial College, London P. Vineis
Univ. Umeå I. Bergdahl
Istituto per lo Studio e la Prevenzione Oncologica (ISPO), Florence
D. Palli
Univ. Crete E. Stephanou
Univ. Utrecht R. Vermeulen
Istituto Superiore di Sanita P. Comba
National Public Health Institute (THL), Kuopio H. Kiviranta
Univ. Leeds M. Gilthorpe
Univ. Lund B. Jönsson
National Taiwan University K.-L. Chien