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Examples of functional modeling.

Examples of functional modeling. NCSU GO Workshop 29 October 2009. Tools and materials from this workshop will be available online at the AgBase database Educational Resources link. For continuing support and assistance please contact: agbase@cse.msstate.edu.

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Examples of functional modeling.

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  1. Examples of functional modeling. NCSU GO Workshop 29 October 2009

  2. Tools and materials from this workshop will be available online at the AgBase database Educational Resources link. • For continuing support and assistance please contact: agbase@cse.msstate.edu This workshop is supported by USDA CSREES grant number MISV-329140.

  3. "Today’s challenge is to realise greater knowledge and understandingfrom the data-rich opportunities provided by modern high-throughput genomic technology."Professor Andrew Cossins, Consortium for Post-Genome Science, Chairman.

  4. Bio-ontologies • Bio-ontologies are used to capture biological information in a way that can be read by both humans and computers. • necessary for high-throughput “omics” datasets • allows data sharing across databases • Objects in an ontology (eg. genes, cell types, tissue types, stages of development) are well defined. • The ontology shows how the objects relate to each other.

  5. What is the Gene Ontology? “a controlled vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing” the de facto standard for functional annotation assign functions to gene products at different levels, depending on how much is known about a gene product is used for a diverse range of species structured to be queried at different levels, eg: find all the chicken gene products in the genome that are involved in signal transduction zoom in on all the receptor tyrosine kinases human readable GO function has a digital tag to allow computational analysis of large datasets COMPUTATIONALLY AMENABLE ENCYCLOPEDIA OF GENE FUNCTIONS AND THEIR RELATIONSHIPS

  6. Who uses GO? http://www.ebi.ac.uk/GOA/users.html

  7. Use GO for……. • Determining which classes of gene products are over-represented or under-represented. • Grouping gene products. • Relating a protein’s location to its function. • Focusing on particular biological pathways and functions (hypothesis-testing).

  8. Translation to clinical research: Pig Global mRNA and protein expression was measured from quadruplicate samples of control, X- and Y-treated tissue. Differentially-expressed mRNA’s and proteins identified from Affymetrix microarray data and DDF shotgun proteomics using Monte-Carlo resampling*. * Nanduri, B.,P. Shah, M. Ramkumar, E. A. Allen, E. Swaitlo,S. C. Burgess*,and M. L. Lawrence*. 2008. Quantitative analysis of Streptococcus Pneumoniae TIGR4 response to in vitro iron restriction by 2-D LC ESI MS/MS. Proteomics 8, 2104-14. Using network and pathway analysis as well as Gene Ontology-based hypothesis testing, differences in specific phyisological processes between X- and Y-treated were quantified and reported as net effects. Bindu Nanduri

  9. Proportional distribution of mRNA functions differentially-expressed by X- and Y-treated tissues Treatment Y Treatment X immunity (primarily innate) inflammation Wound healing Lipid metabolism response to thermal injury angiogenesis Total differentially-expressed mRNAs: 1960 Total differentially-expressed mRNAs: 4302

  10. Net functional distribution of differentially-expressed mRNAs: X- vs. Y-Treatment Y X sensory response to pain angiogenesis response to thermal injury Lipid metabolism Wound healing classical inflammation (heat, redness, swelling, pain, loss of function) immunity (primarily innate) 35 30 25 20 15 10 5 0 5 Relative bias

  11. Proportional distribution of protein functions differentially-expressed by X- and Y-treated tissues Treatment Y Treatment X immunity (primarily innate) inflammation Wound Healing Lipid metabolism response to Thermal Injury Angiogenesis hemorrhage Total differentially-expressed proteins: 433 Total differentially-expressed proteins: 509

  12. Net functional distribution of differentially-expressed Proteins: X- vs. Y-Treatment hemorrhage sensory response to pain Treatment Y Treatment X angiogenesis response to thermal injury lipid metabolism Wound healing classical inflammation (heat, redness, swelling, pain, loss of function) immunity (primarily innate) 8 6 4 2 0 2 4 6 Relative bias

  13. B-cells Stroma apoptosis immune response cell-cell signaling (Looking at function, not gene.)

  14. Relating a protein’s location to its function.

  15. Focusing on particular biological pathways and functions (hypothesis-testing). Shyamesh Kumar BVSc

  16. a-CD30 mab a-CD8 mab Susceptible (L72) Resistant ( L61) The critical time point in MD lymphomagenesis 18 16 Genotype 14 Susceptible (L72) Resistant (L61) 12 mean total lesion score 10 Non-MHC associated resistance and susceptibility 8 6 4 2 0 0 20 40 60 80 100 days post infection Burgess et al,Vet Pathol 38:2,2001

  17. Tissue CD30hi, Neoplastically-transformed CD30 lo/- hyperplastic Marek’s Disease Lymphoma Model : Chicken The neoplastically-transformed (CD30hi) cells in Marek’s disease lymphoma cell phenotype most closely resembles T-regulatory cells. LA Shack, T. Buza, SC Burgess. Cancer Immunology and Immunotherapy, 2008

  18. Whole tissue mRNA expression L6 (R) * * * L7 (S) 25 20 * 40 – mean Ct value 15 * 10 5 0 IL-4 IL-10 IFNγ TGFβ IL-12 IL-18 CTLA-4 GPR-83 SMAD-7 mRNA

  19. Microscopic lesionmRNA expression L6 (R) * 25 L7 (S) * 20 * 40 – mean Ct value * 15 * 10 5 0 IL-4 IL-12 IL-18 TGFβ GPR-83 SMAD-7 CTLA-4 mRNA

  20. NAIVE CD4+ T CELL APC Th-2 T reg Th-1 CYTOKINES AND T HELPER CELL DIFFERENTIATION

  21. NAIVE CD4+ T CELL Macrophage APC Th-2 T reg Th-1 NK Cell CTL L6 Whole Smad 7 L7 Whole L7 Micro IL 12 IL 4 Th-1, Th-2, T-reg ? Inflammatory? TGFβ IL 4 IL10 IFN γ IL 12 IL 18

  22. Gene product Th1 Th2 Treg Inflammation IL-2 1 ND 1 -1 IL-4 -1 1 1 ND IL-6 1 -1 1 IL-8 ND ND 1 1 IL-10 -1 1 1 0 IL-12 1 -1 ND ND IL-13 -1 1 ND ND IL-18 1 1 1 1 IFN-g 1 -1 1 1 TGF-b -1 0 1 -1 CTLA-4 -1 -1 1 -1 GPR-83 -1 -1 1 -1 SMAD-7 1 1 -1 1 ND = No data Step III. Inclusion of quantitative data to the phenotype scoring table and calculation of net affect. Step I. GO-based Phenotype Scoring. Step II. Multiply by quantitative data for each gene product.

  23. Whole Tissue L6 (R) L7 (S) 120 100 80 Net Effect 60 40 20 0 Th-1 Th-2 T-reg Inflammation - 20 -40

  24. Microscopic lesions L6 (R) 60 L7 (S) 50 5mm 40 Net Effect 30 20 10 0 Th-1 Th-2 T-reg - 10 Inflammation Phenotype - 20

  25. Key points • Modeling is subordinate to the biological questions/hypotheses. • Togetherthe Gene Ontology and canonical genetic networks/pathways provide the central and complementary foundation for modeling functional genomics data. • The strategy you use to model your data will depend upon • what information is readily available for your species of interest • what biological system you are studying

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