Strategies in data integration to predict fish susceptibility to toxicants

Transcription

1 Strategies in data integration to predict fish susceptibility to toxicants Fernando Ortega and Francesco Falciani School of Biosciences The University of Birmingham

2 NERC PGP Project Kevin Chipman Mark Viant Francesco Falciani Ioanna Katsiadakis Brett Lyons Charles Tyler Identifying and defining the bases of individual and population susceptibility and adaptation to environmental pollutants in fish: An integrated 'omic' approach Steve George Mike Leaver John Taggart John Craft Colin Moffat

3 General Problem Continuous value data acquired from highthroughput experiments Small scale experiments Computational predictions Databases Problems Missing data Systematic biases in each type of data set and THE INTEGROME a long etc.

4 What makes fish populations resistant or vulnerable to environmental change and impact? Acute Exposures metal PAH estrogen Long term exposures Field Sampling Depuration Reproductive Fitness Hepatocytes Flounder Stickleback Bioinformatics Individual Biomarkers Microarrays 2D-PAGE proteomics Metabolomics Microsatellites SNPs Chemistry Data Dissemination Biomarker Development

5 Genomics - Flounder Microarrays All arrays were printed at the Birmingham University Functional Genomics Laboratory STAGE 1 Gene Picking Cloning Classical Stress Response Genes STAGE 2 Suppressive Subtractive Hybridisation (SSH) New Responders NERC STAGE 3 Amplification of random flounder clones from an induced liver cdna library Environmental SSH Tyne vs Alde Laboratory SSH Benzo(a)pyrene Cadmium 12,830 clone array Complete 160 gene array Complete 500 gene array Complete NERC PG+P

6 Type of Experiments Fish exposed to different environments Transcriptomics Multivariate statistics Analysis + GA (GALGO) Gene ontology terms + GeneSpring Gene expressions Set of genes selected Tool to classified fishes List of genes that identify each environment Toxic & detoxification pathways (Literature specific + associate measurements) Environmental chemical profiles

7 Type of Experiments Non polluted adult fishes Cu 2+, DBA, EE 2 Temporal experiments 1, 2, 4, 8 and 16 days Transcriptomics Different gene profile expressions GO terms Temporal behaviour Different pollutants Set of significant profile expressions (fingerprint)

8 Chemical treatments a) Diagnostic fingerprints of chemical effects in the environment b) Linkage of genotype & phenotype Environmental Exposures Two strategies of data integration we will follow 1. Statistical Metaanalysis strategy (combine p values from k different sets) 2. Integrated Modelling strategy

9 1. Statistical Metaanalysis strategy. Multiple data sets (different size and types) e.g. transcriptomic, proteomics and metabolomics. Minimization of the number of false positive and false negative. Different methods to combine p values from k different sets. E.g. k Fisher s weighted = 2 w i ln( p i ) i = 1

10 Test Network Environmental input

11 2. Integrated Modelling strategy. Combine Multiple data sets e.g. transcriptomic, proteomics, metabolomics, SNP, microsatellites. It is based on statistical classification tree models that evaluate the contribution of multiple for of data. If expression gen 1 > x & metabolite 37 level > y If nº SNP 14 > N & metabolite 45 level > z

12 Test Network Environmental input polluted non-polluted Simulated data: * Gene expression and * Protein levels Add Nonsense randomize data Mathematical model (ODE) Normal Mutated Ouput 1 Ouput 2 e.g. SNP Continuous variables Noise is Introduced at kinetic and conc. levels Discrete variables (presence/absence SNP)

13 ~ GEP ~ 500 metabolites ~ 20 SNP If expression gen 1 > x & metabolite 37 level > y

14 a) We consider appropriate to store the different type of data sets in spreadsheets. b) Flow diagram representation Time experiment GE Proteom Metab SNP treatment