Experimental Design in Life Sciences

Choice of the best biomarker from the best tissue ! (biological and mechanistic issues) Dr. Sabine Langie UHasselt/VITO Genes, environment and health Exposure science Toxicokinetics Toxicodynamics Radiation toxicology Ecotoxicology Biomolecular responses Organ / system toxicology Various biological levels to study toxicity Receptor binding assays Cellular bioassays Animal models Generation studies

Exposureoutcome (e.g. ERE-CALUX) Follicle counts Sperm motility Tissue histology Gonadal weight Biomonitoring Interventions Vinken et al.(2017) Arch. Toxicol Selecting the best Biomarker(s) Langie et al. Carcinogenesis 2015 Genotoxic vs. Non-genotoxic carcinogens Genotoxic vs. Non-genotoxic carcinogens Mechanisms of NGTX compounds: - DNA methylation Histone methylation and acetylation miRNA Receptor-binding Hormonal imbalance

Cytotoxicity Chronic inflammation Oxidative stress Inhibition/induction apoptosis Disturbances in cell-cell communication Various kinds of Biomarker(s) Immune Function Abberant DNA methylation Bioavailability Antiox status External Intermediate effects BIO Bob Comet assay in salivary leukocytes for the evaluation of early biological effects of air pollution exposure in children Massimo Moretti1, Marco Verani2, Antonella De Donno3, Sara Bonetta4, Alessio Perotti5, Elisabetta Ceretti6 University of Perugia, 2University of Pisa, 3University of Salento, Lecce, 4University of Torino, 5University of Parma, 6University of Brescia (Italy).

1 12th PAMPLONA, MAPEC_LIFE: Monitoring Air Pollution Effects on Children for Supporting Public Health Policy (LIFE12 ENV/IT/000614) MAPEC: Aim of the Study Monitoring air pollution effects on children To assess relationship between exposure of children to airborne pollutants and early biological effects (health effects). Study Design: monitoring Biological Monitoring: Sampling of buccal cells from recruited children: Exfoliated epithelial cells Salivary leukocytes Micronucleus Test Comet Results: Comet Assay

Leukocytes Buccal mucosa cells Debris Results: Comet Assay Primary DNA damage in salivary leukocytes of recruited children: Conclusions The level of primary DNA damage was significantly associated with benzene, PM2.5, SO2 and NO2, when data of the first season were considered, and with ozone (O3) with whole data. Season and town of residence had some influence on the level of DNA damage as well, but for the other factors no associations were found. BIOMARKERS CAN BE TISSUE SPECIFIC! ESTIMATED DIFFERENTIAL CELL COUNTS (HOUSEMAN CORRECTION) ESTIMATED DIFFERENTIAL CELL COUNTS (HOUSEMAN CORRECTION) estimated differential cell counts (Houseman) SL MNC

CB RESULTS - FLEHS1 Differential methylation between respiratory allergy cases and controls in saliva LOOKING FOR GENE REGIONS Selection of DMRs using Comb-p software (bump hunting or probe lasso) Pedersen et al. Bioinformatics 2012 No cell correction DMR X.chrom start n_probes Padj Gene delta beta RA vs C end chr2 121497333 121498521

3 0.000002 GLI2 0.009 hyper chr13 100217961 100219013 6 0.000001 TM9SF2 -0.063 hypo 11 0.000050 ALOX12

-0.044 hypo chr17 6899084 6899577 RESULTS - FLEHS1 (Houseman et al. 2012) Differential methylation between respiratory allergy cases and controls in saliva LOOKING FOR GENE REGIONS Corrected for cell composition X.chrom DMR start end n_probes Padj Gene

delta beta RA vs C chr2 113544231 113544348 3 0.01360 IL1A -0.023 hypo chr2 121497333 121498521 3 0.00004 GLI2 0.009

hyper 4 0.00913 HTRA3 0.013 hyper 4 0.00185 FRG2 0.014 hyper 11 0.00041 SDHAP3 0.037

hyper chr4 chr4 chr5 8291936 8292328 190938632 190939230 1594281 1595048 chr7 127880931 127881440 7 0.00002 LEP 0.002 hyper chr8

96085269 96085994 5 0.00001 LOC105375650 0.008 hyper chr11 123430574 123431162 5 0.00225 GRAMD1B 0.010 hyper chr11

132662454 132662963 4 0.00063 OPCML 0.003 hyper chr12 7780735 7781431 6 0.00415 APOBEC1 -0.022 hypo chr17

6898737 6899888 15 0.00008 ALOX12 -0.044 hypo chr17 38183169 38183790 6 0.00546 MED24 -0.011 hypo

chr20 61953800 61954258 4 0.00086 COL20A1 -0.036 hypo Nanomaterials and organ toxicity Bioavailability Comet results The best biomarker from the best tissue?! I want to measure oxidative stress in human biomonitoring? The best biomarker from the best tissue?! Which group has now highest oxidative stress? The best biomarker from the best tissue?!

The best biomarker from the best tissue?! Detected in Urine Detected in DNA Integration of various biomarkers Integration of various biomarkers Integration of various biomarkers Summary Oxidative DNA lesions were increased due to Fe-exposure NER-capacity was reduced due to iron exposure Anti-oxidant supplementation prevents the inhibition of NER and reduces the formation of oxidative lesions Integration of various biomarkers Nutrition DNA methylation Genetic variations UV

Changes in Gene Expression O2- DNA Repair DNA damage Normal Cell Institute for Ageing and Health Damaged/Ageing Cell Nutritional modulation of DNA repair Langie et al. FASEB (2013) Nutritional modulation of DNA repair A) 10 Cortex 1 Hippocampus Subcortical

regions Cerebellum Cortex Hippocampus 0.1 10 C) D) 1 Low Folate - Control Control - High Fat (fold (RQ values) increase) (fold (RQincrease) values) Mutyh expression

10 (fold increase) Xrcc1 expression 0.1 Cerebellum (fold (RQincrease) values) Neil1 expression 1 Subcortical regions B) 10 Xrcc1 expression (fold (RQ increase)

values) Ogg1 expression 10 Langie et al. FASEB (2013) Low Folate - Control Low Folate - High Fat 1 Control - High Fat Low Folate - High Fat 1 0.1 0.1 Subcortical regions Cerebellum Cortex

Low Folate - Control Hippocampus 0.1 Control - High Fat Subcortical regions Cerebellum Cortex Hippocampus Low Folate - High Fat Low Folate - Control Control - High Fat Low Folate - High Fat 4 * 3

a,b 2 a b 1 0 4 4 3 2 1 Xrcc1 promoter methylation (%) Subcortical regions Mutyh promoter methylation (%) Neil1 promoter methylation (%)

A) Cortex Low folate - Control Control - High fat 3 2 1 0 Subcortical regions Cerebellum Cerebellum Control - Control Cortex Cerebellum Cortex

Hippocampus 4 Low folate - High fat 1 Subcortical regions B) Subcortical regions C) 2 0 4 Hippocampus 3 Control - Control

Langie et al. FASEB (2013) 0 Cerebellum Xrcc1 promoter methylation (%) Ogg1 promoter methylation (%) Nutritional modulation of DNA repair D) 3 Control - Control Low folate - Control Control - High fat Low folate - High fat 2 1 Hippocampus 0

Cortex Hippocampus Low Folate - Control Subcortical regions Control - High Fat Cerebellum Cortex Hippocampus Low Folate - High Fat Control - Control Low Folate - Control Control - High Fat Low Folate - High Fat Nutritional modulation of DNA repair Langie et al. FASEB (2013)

Subcortical regions Cerebellum Cortex Hippocampus Nutritional modulation of DNA repair Best biomarker From the best tissue During the best time of life Langie et al. FASEB (2013) Hypothesis Oxidative stress modulates DNA repair in the ageing brain and that epigenetic mechanisms, like DNA methylation, play a role in the age-related decrease in DNA repair capacity. Institute for Ageing and Health DNA repair process Oxidized base

OGG1 APE1 Institute for Ageing and Health Results (control group) PO2 = 20-25 mmHg 30 PO2 100 mmHg Ratio 8-oxodG/dG (E-6) 25 ROS production 20 15 10 5 0 0 5

10 15 Days after birth Log regression: R2=0.204, P=0.027 Institute for Ageing and Health 20 25 30 Results 30 Ratio 8-oxodG/dG (E-6) 25 20 15 10 5

0 0 5 10 15 20 25 30 Linear regression: R2=0.202, P=0.021 Days after birth 2.5 1.5 (mean %) APE1 promoter methylation 2.0 1.0

0.5 0.0 0 5 10 15 Days after birth Institute for Ageing and Health 20 25 30 Results 0.08 20 15 10

5 0 0 5 10 15 20 25 30 Days after birth 0.07 0.06 (normalized to 18S) Ratio 8-oxodG/dG (E-6) 25 APE1 mRNA expression levels 30

0.05 0.04 0.03 0.02 0.01 0.00 1 2.5 2 4 7 Days after birth 1.5 (mean %) APE1 promoter methylation 2.0 1.0 0.5

0.0 0 5 10 15 Days after birth Institute for Ageing and Health 20 25 30 14 28 Results 0.08 Ratio 8-oxodG/dG (E-6) 25

20 15 10 5 0.07 0.06 (normalized to 18S) APE1 mRNA expression levels 30 0.05 0.04 0.03 0.02 0.01 0 0 5 10 15

20 25 30 0.00 Days after birth 1 2 4 7 14 28 Days after birth 18 2.5 DNA incision activity

(mean %) APE1 promoter methylation 1.5 1.0 0.5 (as calculated from %DNA in tail) 16 2.0 14 12 10 8 6 4 2 0.0 0 5 10

15 Days after birth 20 25 30 0 0 5 10 15 Days after birth S.A.S. Langie et al. / DNA Repair 18 (2014) 5262 Institute for Ageing and Health 20 25 30

Summary Early life exposure to oxidative stress triggers APE1 expression and activity partly mediated via promoter methylation Increased BER activity decreased oxidative DNA damage Redox and DNA methylation seem to regulated BER genes in different ways Further studies will also focus on other DNA repair related genes Institute for Ageing and Health Study on ageing mice Long-established colony of the C57/BL(ICRFa) mouse strain, which had been selected for use in studies of intrinsic ageing because it is free from specific age-associated pathologies and thus provides a good general model of ageing (Rowlatt et al. Lab. Anim. 1976) Normal Ageing Colony (n=4/age group) 3 6 Months of age 12 Langie et al. Genes 2017 Institute for Ageing and Health 24 28

31 32 Hypothesis Ageing Brain 5m | CG 5m 5m | | CG CG CpG-rich gene promoter region ROS 5m 5m 5m 5m 5m | | | | | CG CG CG CG CG 5m 8oxo 5m 5m 8oxo || || | CG CG

CG Oxidative DNA damage DNA methylation of DNA repair genes Gene Ogg1 expression Gene Ogg1 expression 5m 5m 5m | | | CG CG CG 5m 8oxo 5m 5m | | || CG CG CG Gene expression DNA repair

DNA damage Institute for Ageing and Health Risk of Neurodegenerative diseases DNA repair process Oxidized base OGG1, NEIL1, MUTHY XRCC1 Institute for Ageing and Health Results oxidative stress Ageing Brain 5m | CG 5m 5m | | CG CG

CpG-rich gene promoter region ROS 5m 5m 5m 5m 5m | | | | | CG CG CG CG CG 5m 8oxo 5m 5m8oxo || || | CG CG CG 20 Oxidative DNA damage Ratio 8-oxodG/dG (E-6) 18 DNA methylation of DNA repair genes

Ogg1 expression Ogg1 expression 5m 5m 5m | | | CG CG CG 5m 8oxo 5m 5m | | || CG CG CG 16 14 Gene expression 12 DNA repair 10 8 DNA damage 6

4 Risk of Neurodegenerative diseases 2 0 3 6 12 Age (months) R=0.854, P=0.025 Institute for Ageing and Health 24 28 Results genome methylation ROS 0.05 Ageing Brain 5m

| CG 5m 5m | | CG CG CpG-rich gene promoter region 5m 5m 5m 5m 5m | | | | | CG CG CG CG CG 5m 8oxo 5m 5m 8oxo || || | CG CG CG 5mC/total C 0.04 Oxidative DNA damage 0.03

DNA methylation of DNA repair genes Ogg1 expression Ogg1 expression 5m 5m 5m | | | CG CG CG 5m 8oxo 5m 5m | | || CG CG CG 0.02 Gene expression Oxidation 0.01 DNA repair 0.00

3 6 12 24 DNA damage 28 Age (months) Risk of Neurodegenerative diseases 0.30 0.010 0.25 0.008 0.20 5hmC/5mC 5hmC/total C

R=0.382, P=0.008 0.012 0.006 0.004 0.002 0.15 0.10 0.05 0.000 3 6 12 Age (months) Institute for Ageing and Health 24 28 0.00 3 6

12 Age (months) 24 28 Gene promoter methylation Ageing Brain 5m | CG ROS 5m 5m | | CG CG CpG-rich gene promoter region 5m 5m 5m 5m 5m | | | | | CG CG CG CG CG 5m 8oxo 5m 5m 8oxo

|| || | CG CG CG Oxidative DNA damage DNA methylation of DNA repair genes Ogg1 expression Ogg1 expression 5m 5m 5m | | | CG CG CG 5m 8oxo 5m 5m | | || CG CG CG Gene expression

DNA repair DNA damage Risk of Neurodegenerative diseases Gene Length CGi Location # CpG sites (in TF binding sites) Ogg1 650 bp Chr=6, region 113276470-113277119 27 (14) Neil1 649 bp Chr=9, region 56995829-56996477 12 ( 9)

Mutyh 626 bp Chr=4, region 116479828-116480453 Xrcc1 810 bp Chr=7, region 25331620-25332429 Institute for Ageing and Health 3 ( 3) 25 (21) Results Gene promoter methylation Gene specific methylation (%) 5 ** 4 3 *

2 1 0 0 5 10 Ogg1 15 Age (months) Neil1 20 Mutyh 25 Xrcc1 Ogg1: R=0.416, P=0.005; **P=0.015 vs. 3 month old mice. Xrcc1: R=0.233, P=0.031); *P=0.041 vs. 3 month old mice Institute for Ageing and Health 30

Results Gene expression Ageing Brain 5m | CG ROS 5m 5m | | CG CG CpG-rich gene promoter region 5m 5m 5m 5m 5m | | | | | CG CG CG CG CG 5m 8oxo 5m 5m 8oxo || || | CG CG CG 1.5

Ogg1 Neil1 Mutyh Oxidative DNA damage Xrcc1 (Fold change) of DNA repair genes Ogg1 expression Ogg1 expression 1.0 G ene expression DNA methylation 5m 5m 5m | | | CG CG CG

5m 8oxo 5m 5m | | || CG CG CG 0.5 Gene expression 0.0 DNA repair -0.5 ** -1.0 DNA damage ** Risk of Neurodegenerative diseases -1.5 -2.0 -2.5

0 5 10 15 20 25 Age (months) Ogg1: R=0.692, P<0.001; **P<0.001 vs. 3 month old mice) Institute for Ageing and Health 30 Neil1 expression (fold change) Ogg1 expression (fold change) Results Gene expression vs methylation Ogg1 gene methylation (%) R=0.320, P=0.018

Institute for Ageing and Health Neil1 gene methylation (%) R=0.217, P=0.038 Results DNA repair Ageing Brain 5m | CG ROS BER incision activity (calculated from %DNA in tail) 6 PANOVA=0.021 5m 5m | | CG CG CpG-rich gene promoter region 5m 5m 5m 5m 5m

| | | | | CG CG CG CG CG 5m 8oxo 5m 5m 8oxo || || | CG CG CG Oxidative DNA damage 5 DNA methylation of DNA repair genes Ogg1 expression Ogg1 expression 5m 5m 5m | | | CG CG CG 5m 8oxo 5m 5m

| | || CG CG CG 4 Gene expression 3 DNA repair 2 DNA damage 1 Risk of Neurodegenerative diseases 0 3 6 12 24 28

BER incision activity Age (months) R=0.173, P=0.068 Ogg1 expression (fold change) Institute for Ageing and Health Results DNA damage Ageing Brain 5m | CG ROS 20 CpG-rich gene promoter region 5m 5m 5m 5m 5m | | | | | CG CG CG CG CG 5m 8oxo 5m 5m 8oxo || || |

CG CG CG 18 Ratio 8-oxodG/dG (E-6) 5m 5m | | CG CG Oxidative DNA damage 16 DNA methylation of DNA repair genes Ogg1 expression Ogg1 expression 14 5m 5m 5m | | |

CG CG CG 5m 8oxo 5m 5m | | || CG CG CG 12 Gene expression 10 8 DNA repair 6 DNA damage 4 2 Risk of Neurodegenerative diseases 0 3 6

12 24 28 R=0.854, P=0.025 R=0.149, P=0.156 Ratio 8-oxodG/dG (E-6) Age (months) BER incision activity Institute for Ageing and Health Conclusions Base excision repair activity in the brain decreased with ageing It seems to be related to increased methylation, especially of the Ogg1 promoter gene expression was decreased, significantly for Ogg1 correlates with the hyper-methylation of the Ogg1 and Neil1 promoter decreased BER activity increased oxidative DNA damage Increased sensitivity and risk to develop neurodegenerative diseases Institute for Ageing and Health

MOA or mode of action Pharmacotoxicology Environmental toxicology Mechanistic toxicology Delineate a hazard Define the potency of one compound and relate this to others Identify underlying mechanisms Traditional toxicity testing Gold standard: organism-level responses (e.g., hepatotoxicity, cancer, reproductive/developmental toxicity, and neurotoxicity) after high-dose testing in homogeneous groups of laboratory animals with specific approaches for extrapolation from high to lower doses and from the experimental animals to the human population. Sudin Bhattacharya et al.; PLoS One. 2011; 6(6): e20887. New approaches

Modern toxicology has incorporated techniques emerging from the field of molecular biology for assessment of modes of action and target identification. Should detect time- and hormone-specific effects Test a broad range of doses, including low-dose exposures Perform generational studies test for effects on fetus and neonate Combine in silico, in vitro and in vivo studies Sudin Bhattacharya et al.; PLoS One. 2011; 6(6): e20887. Adverse Outcome Pathways (AOPs) Vinken et al.(2017) Arch. Toxicol Adverse Outcome Pathways (AOPs) Adverse Outcome Pathways (AOPs) Adverse Outcome Pathways (AOPs) KE3 KE4 KEN AO2 omics technologies open the door to hypothesis free research

the effects of toxicants can be studied on thousands of pathway participants often reveal exciting new roles for proteins or genes in toxicant induced syndromes problem needle in a haystack use of bioinformatics tools is crucialneedle in a haystack use of bioinformatics tools is crucial Various molecular levels to study toxicity Epigenetic modifications Post-transcriptional modifications Post-translational modifications Mutations Biomarkers can be tissue dependent Effects can differ over lifespan Study various biomarkers in parallel At different molecular/cellular levels Take into account the mechanism of action Questions?

There is one thing even more vital to science than Intelligent Methods; and that is, the sincere desire to find out the Truth, whatever it may be." Charles Sanders Pierce Acknowledgments Toxicology Roger W. Godschalk Lou M. Maas Frederik J. Van Schooten Bart Tomaszewski Katie Fletcher Sofia Lisanti John Mathers Laboratory of Developmental Genetics and Imprinting Kerry Cameron Thomas von Zglinicki Wolf Reik Gabriella Ficz David Oxley Centre for Integrated Systems Biology of Ageing and Nutrition

Technical support on mice dissections Adele Kitching, Satomi Miwa, Liz Nicolson, Julie Wallace Funding: by the BBSRC & EPSRC Institute for Ageing and Health cross council initiative by the MRC, BBSRC, EPSRC and ESRC

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