DOC PREVIEW
UNC-Chapel Hill ENVR 132 - Data Integration & Systems Toxicology

This preview shows page 1-2-3-21-22-23-43-44-45 out of 45 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 45 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 45 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 45 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 45 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 45 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 45 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 45 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 45 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 45 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 45 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

Data Integration & Systems Toxicology???Toxicogenomics is the study of the response of a genome to environmental stressors and toxicants.Combines genetics, genomic-scale mRNA expression (genomics), cell and tissue-wide protein expression (proteomics), metabolite profiling (metabolomics), and bioinformatics with conventional toxicology in an effort to understand the role of gene-environment interactions in disease.ToxicogenomicsCredit: M. Waters (NIEHS)-The Chemical Effects in Biological Systems (CEBS) knowledge base aims to be a dynamic system for integrating large volumes of disparate information in a framework that serves as a continually changing heuristic engine. - CEBS will evolve both in content and capabilities to become a “system of predictive toxicology.”-http://cebs.niehs.nih.gov/A Knowledge Base DefinedA knowledge base uses data and information to carry out tasks that create new information and new understanding.Credit: M. Waters (NIEHS)Systems Biology: a complete description of how the components of a biological system work togetherDescribe a systemMeasure changes globally Perturb a systemDevelop a better model of the systemIdeker, Galitski, & Hood (2001) A new approach to decoding life: systems biology. Ann Rev Genomics Hum Genet 2: 343-372.Waters and Fostel (2004) Toxicogenomics and systems toxicology: Aims and prospects. Nat Rev Genet 5: 936-948.Credit: M. Waters (NIEHS)Toxicogenomics ObjectivesCompare toxicogenomic effects of chemicals/ stressors across species- Yielding signatures of altered gene/protein expression“Phenotypically anchor” these changes with conventional toxicology data- Classifying effects as well as disease phenotypesDelineate global changes as adaptive, pharmacologic or toxic outcomes- Defining biomarkers, sequence of key events, mechanisms of actionWhy do we need Knowledge Bases?Credit: M. Waters (NIEHS)Contemporary Toxicology Experimental DesignMicroarrayAnalysisBioinformaticsanalysisIdentification ofcandidatebiomarkersVerifyToxicologicalCharacterization• acute toxicity• pathology• clinical chemistry• chronic toxicity• carcinogenicityFractionation of targetand non-target tissues2D-PAGE / MS SELDI Candidate proteinidentification (mass spec)Analysis of serum forcandidate proteinsBiomarker Discovery, Key events,Mechanisms of actionVerifyData IntegrationProteomic AnalysisChemical exposure of animal modelsCredit: M. Waters (NIEHS)?A Framework for Systems ToxicologyWaters & Fostel Nat Rev Genet (2004)Bioinformatics challenges and biological complexityWaters & Fostel Nat Rev Genet (2004)200 10000 50.00 5.644800 4800 1.00 0.009000 300 0.03 -4.91Cy3Cy5Cy5Cy3log2Cy5Cy3        Slide courtesy of C.M. PerouWhere does the data go?NAME BC/FUMI0 BC/FUMI4 BC/FUMI4 BC601B-ABC601A-BBC/FUMI1 BC/FUMI2 BC/FUMI2 BC/FUMI1 BC/FUMI1 BC102B-BBC/FUMI2 BC/FUMI3 BC/FUMI3 BC/FUMI1 BC/FUMI1adipose differentiation-related prote0.242 1.21 -0.253 -0.841 -0.423 -0.363 -0.852 -1.383 -2.642 0.501 -0.25 -0.605 -0.636 0.229 -0.626plasminogen activator, urokinase re0.908 0.485 -0.397 -0.767 -0.886 -0.251 -0.683 0.057 -0.317 -1.2 0.125 -0.536 -0.248 -0.365plasminogen activator, urokinase re0.4635 0.3545 -0.8975 -1.23 -0.8335 0.0175 -1.002 0.1555 -0.4325 -1.008 -0.1785 -0.7445 -0.1485 0.0555 0.2055coronin, actin binding protein, 1C A0.551 0.151 -0.422 0.007 -0.638 0.087 -0.689 -0.91 -0.853 0.052 -0.492 -0.201 -0.152 -0.368 -0.741**coatomer protein complex, subun-1.061 -0.8655 -0.1235 -0.9895 0.3815 -0.4955 -0.2775 -0.1465 -1.109 -0.8635 0.2615 -0.0905 -0.3225 -0.6035 0.0195 -0.9345coactosin-like protein R78490 -0.8835 -0.4545 0.2375 -1.177 0.2155 -0.2975 -0.9385 -0.2815 -1.494 -0.5985 0.4095 -0.3465 0.2185 -0.1345 -0.2895 -0.5525folylpolyglutamate synthase R448640.686 1.583 1.313 0.048 -0.272 -0.143 -0.394 0.423 -0.445 -0.854 0.322 -0.03 -0.412 0.214 -1.098 -0.175lysozyme (renal amyloidosis) N639-0.18 1.155 1.575 -1.635 0.355 0.295 -0.805 0.135 -2.145 -0.955 0.575 0.735 -0.435 -0.855 -0.8 -1.705chemokine (C-C motif) receptor 1 AA036881 0.524 1.233 -1.459 -0.095 -0.122 -0.196 0.101 -0.942 -0.2 -0.133 -0.549 -0.763 -0.059interferon, gamma-inducible protein-0.181 -0.062 0.37 0.064 0.418 -0.33 -0.098 -0.289 -1.042 -0.332 0.907 1.056 -0.8 -0.193 -0.789 -1.25cystatin B (stefin B) H22919 -0.188 -0.489 -0.603 0.074 -0.212 -0.295 -0.54 -0.535 -0.453 -0.479 -0.021 0.291 -0.651 -0.536 -0.401 -0.511cathepsin S AA236164 -0.791 0.334 -0.316 0.723 -0.46 0.39 -0.452 -0.413 1.063 -0.849 -1.088 -0.94 -1.291small inducible cytokine A2 (monoc0.2665 0.2955 0.5315 -0.1285 0.4255 -1.099 -0.7265 -0.6035 -1.052 -1.438 0.1355 0.0365 -0.4335 0.0875 -1.218 -0.7785natural killer cell transcript 4 AA4580.483 0.348 0.575 -0.685 0.971 -0.335 -0.222 -0.116 -1.644 -0.66 -0.322 0.885 -0.08 -0.02 -0.441 -0.51superoxide dismutase 2, mitochond0.431 0.301 -0.836 0.519 -0.492 -0.834 -0.86 0.781 0.005 -1.163 -1.283 -0.969 -0.586superoxide dismutase 2, mitochondrial AA48770.3185 -0.6835 0.4865 0.6925 -0.7895 -0.6005 -0.5815 0.4995 0.0165 0.3755 -0.1225 -1.129 -1.137 -0.6935transforming growth factor, beta-ind0.0235 0.6525 -0.3785 -0.5505 -0.3675 -0.4755 -0.1105 0.3435 0.0785 -0.4735 0.7925 1.532 -0.3355 -0.0885 0.2495 -0.1985glycine dehydrogenase (decarboxy-1.122 -1.412 -1.275 -1.764 -0.611 1.259 -1.25 -0.76 -2.159 -1.72 -1.017 -0.972 -0.715 -0.543 -0.658 -0.818syndecan 2 (heparan sulfate proteo-1.828 -1.7 -1.409 -1.964 -0.975 1.516 -1.24 -1.75 -2.219 -2.477 -1.08 0.29 -1.641 -2.045 -0.315 -1.356glutathione S-transferase pi R33642-1.726 -1.892 -1.568 1.528 -1.346 -2.157 -3.114 -3.146 -0.943 0.236 -1.349 -1.674 -0.416 -1.557chitinase 3-like 2 AA668821 -0.771 -1.436 -1.454 -0.813 -1.578 0.312 -0.167 0 -0.469 0.129 -0.566 -0.489nuclear factor I/B W87528 0.464 -1.314 -0.187 -1.429 -0.189 0.551 -1.94 -1.372 -2.152 -1.825 -0.441 -0.928 0.316 -1.188ras homolog gene family, member -1.382 -0.471 -0.421 0.304 -0.448 -0.805 -0.945 -0.737 -1.222 -0.915 -0.713 -0.167 0.09 1.074 -0.393ras homolog gene family, member -1.311 -0.763 -0.61 0.198 -0.764 -0.391 -0.867 -1.469 -1.106 -0.486 -0.778 -0.579 0.812 0.348 -0.222**zinc finger, DHHC domain containing 5 AA4-0.965 -0.571 -0.304 -0.328 -0.417 -0.518 -0.473 -0.973 -0.94 -0.926 -1.153 -0.462 -0.683 0.828 0.347keratin 5 (epidermolysis bullosa sim-0.309 -0.485 -0.748 -0.909 -0.403 -0.127 -0.371 -0.778 -1.596 -1.787 -0.782 0.242 -0.559 -0.804 0.79 0.374keratin 5 (epidermolysis bullosa simplex, Dowl -0.655 -2.421 0.301 0.689 -0.38 -0.131


View Full Document

UNC-Chapel Hill ENVR 132 - Data Integration & Systems Toxicology

Documents in this Course
Load more
Download Data Integration & Systems Toxicology
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Data Integration & Systems Toxicology and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Data Integration & Systems Toxicology 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?