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

courtesy MT Smith, Univ. California

Omic

Serum albumin adductomics of smokers and non-smokers

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

Project cohorts & biobanks

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