Can “omic” data assist in environmental monitoring

1. Can “omic” data assist in environmental monitoring and risk assessment of chemicals and particles? Kevin Chipman The University of Birmingham, UK

2. The early days and now • Too much hype at first regarding the immediate potential of “omics”...now a rebound • Early problems around platform compatability-now largely resolved • Insufficient datapoints and complexity of early work (mainly due to costs)- now largely resolved • Many early analyses were not sufficiently objective and interpretation was flawed- informatics and pathway knowledge now starting to resolve

3. How omics may help to address the needs • Need for improved predictivity for risk assessment • Related to this we need to improve understanding of modes of action and to derive diagnostic and predictive biomarkers • The omic technologies have the ability to aid both of these areas • Contribute to “weight of evidence” in toxicity assessment • Identify possible mode(s) of action • Identify and assess impacts on susceptible populations and life stages • Improve assessments for mixtures • Dose-response assessment • Exposure assessment • Improving interspecies extrapolations

4. International Workshop to identify hurdles Vancouver 2008 : Article in press. Env. Health Perspect.2009 • Regulatory bodies are already receptive e.g. US-FDA Critical Path Initiative which encourages innovation • Reports of the National Research Council (USA) : • “Toxicology testing in the 21st century” shows the potential and the need to incorporate omics into safety assessment • Committee on application of toxicogenomic technologies and predictive toxicology and risk assessment

5. BIOINFORMATICS Transcriptomics / Proteomics /Metabolomics Networks of responses to toxicants provide a profile of response reflecting the global status of tissue Establish fingerprints characteristic and predictive of specific toxicities Identify compensatory, non-toxicity responses Define the “systems toxicology” of individuals and predict health status Derive focussed (custom/ biomarker) expression arrays, reporter gene assays etc. RISK ASSESSMENT for populations Help to understand MECHANISM of toxicity Relevant to environmental standard setting : can help to validate and monitor

6. Lack of genomic data • Microarray studies do not have to be limited to a few genetic model organisms • cDNA clones can be derived from conventional or subtracted EST libraries, eg. suppressive, subtractive hybridisation (SSH) • Automatic, practical annotation solutions for cDNA clones are available, eg. Blast2GO, Partigene • High throughput DNA sequencing (eg. 454, Solexa) can now allow swift design of oligonucleotide arrays for non-model species (e.g. Craft and Chipman Mussel programme) • Non-pollutant environmental influences and inter-individual variation. • Gene expression profiling should include laboratory exposures with the aim of identifying ‘predictive gene sets’ • Clear experimental design and sufficient replication are essential • Inter-individual variation can inform on the population structure Toxicogenomics in non-model organisms

7. Now some examples of the power of the omics Note: already successes e.g. Mamoprint in medicine e.g. Distinguishing between genotoxic and nongenotoxic carcinogens

8. Flounder cDNA Microarrays as Tools for the Identification of Expression Changes in Gene Sets Predictive of Exposure to Pollutants. Tim Williams, Steven George, Amer Diab, Margaret Brown, John Craft, Ioanna Katsiadaki, Fleur Geoghegan, Brett Lyons, Victoria Sabine, Fernando Ortega, Francesco Falciani and Kevin Chipman

9. 2-fold up 1:1 ratio Apparent Induced Genes Example scatter plot of Cd-treated flounder at day 1 vs saline. 2-fold down Apparent Repressed Genes HSP30B clones Treated fish show many changes in liver gene expression Which genes and which pathways are altered e.g. by Cd (pro-oxidant)??

10. Cadmium treatment (Williams et al EST 2006) Single intraperitoneal injection of flounder with a low dose of cadmium (0.05 mg/kg) resulted in hepatic gene expression changes related to - Chaperones Oxidative stress Protein synthesis Protein transport Protein degradation Cytoskeleton Apoptosis Cell cycle Immune Inflammation Biomarkers

11. Cu exposure of Stickleback shows similar hepatic expression changes in cholesterol biosynthesis pathway genes to Wilson’s disease, a copper accumulation disorder Stickleback Exposure to 128mg/L Cu Mouse model of Wilson’s disease (ATP7B -/-) Huster et al., 2007 JBC

12. Tyne (Heavyindustrial) Howden, Team Outer Elbe (Cuxhaven Helgoland) Alde (rural) Elbe Harbour (industrial, harbour, canal Brunsbuttel) FLOUNDER FIELD SITES Q. If fish provided “blind” could genomics identify sampling location and if so are the gene patterns reflective of pollutant exposure e.g. oxidative stress??

13. Predicting Site Membership by genetic algorithm GALGO (NERC Project) Examples of genes induced at polluted sites Phase 2 UDPGT GST Phase I Aldehyde dehydrogenase Alcohol dehydrogenase CYP1A CYP2F CYP3A CYP8B Oxidative Stress Catalase Superoxide dismutase Chaperones Calreticulin Haem biosynthesis Coproporphyrinogen oxidase Proliferation marker PCNA Protein degradation Proteasome subunits Trevino V. & Falciani F. Bioinformatics. 2006 1;22 :1154-6.

14. Could a subset of combined stress-response genes help to classify the environmental samples? Aroclor Lindane PFOA Cadmium TBHP 3 MC Time course chemical treatments Set of stress-related genes up & down regulated.

15. Use of genetic algorithm analysis using combined stress responsive genes Merge all the IDs that were selected in each of representative models for each treatment: 98 IDs NB This does not necessarily implicate these pollutants as being responsible but it helps to identify stress response differences at the sites Examples: Contig620: Retinol-binding protein II, cellular (CRBP-II) Conclude: A small number of stress response genes are predictive of site of origin ! Contig442: Glutamate carboxypeptidase (Darmin) Contig665: Ependymin

16. Modeling We are using linkage networks (Dr Francesco Falciani) to integrate gene expression and metabolomics (Dr Mark Viant) with traditional measures. Linkage shows where data are related. This simplified example was generated using ARACNE and cytoscape, employing 50 selected nodes. Interestingly traditional markers (in blue) (eg condition factor) are linked both to transcripts (purple) and to metabolites (red).

17. We can focus on particular areas to visualise which genes are linked, in terms of expression profiles. Here NF kappa B is centre of an extensive hub and linked to survivin (an anti-apoptotic gene) and vitellogenin. Survivin Vitellogenin NF kappa B

18. Using class-prediction algorithms (eg GALGO) we identified the areas of the network containing genes and metabolites most predictive of (differentially polluted) sampling sites (red) These overlap with an area of the network populated by genes related to metabolism and energy production (in green) So, starting to see connectivity between components of the network and the field

19. Application of “open” technologies to the study of nanomaterials • In ecotoxicology, genomics has a major value in assessing novel agents and also mixtures of contaminants for which we do not know appropriate end points or mechanisms. It provides a non-biased, global approach. • A highly appropriate application therefore is the assessment of the effects of nanomaterials, the products and by-products of which enter the environment as mixtures with largely unknown effects.

20. Omics, monitoring and safety assessment • Elucidate mechanisms of toxicity (e.g distinguish genotoxic vs nongenotoxic carcinogens) • Provide more informative batteries of biomarkers • Create practical assays e.g. real-time PCR, custom arrays, reporter assays, ELISAs • Focus on PROCESSES disturbed rather than single gene products • Characterise responses of sentinel species to ‘new’ pollutants • Assess the effects of mixtures • Inform on the basis of population susceptibility to toxicants • Provide detailed case studies of specific sites • A major challenge will be the ability to distinguish between adaptive vs toxic responses and the effective use of these markersin risk assessment. We need to discover patterns of change that are diagnostic and predictive

21. Challenges & Recommendations Research Needs: Linking genomic changes to adverse outcomes (AOP) Interpreting genomic information for risk assessment Training risk assessors and managers to interpret and understand genomics data in the context of a risk assessment Development of technical framework for analysis and acceptance criteria for “omic” information for scientific and regulatory purposes Adapted from Bill Benson 2008