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: This workshop is supported by USDA CSREES grant number MISV

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

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.

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

Who uses GO?

Use GO for……. 1. Determining which classes of gene products are over-represented or under- represented. 2. Grouping gene products. 3. Relating a protein’s location to its function. 4. Focusing on particular biological pathways and functions (hypothesis-testing).

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* Quantitative analysis of Streptococcus Pneumoniae TIGR4 response to in vitro iron restriction by 2-D LC ESI MS/MS. Proteomics 8, 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. Translation to clinical research: Pig Bindu Nanduri

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

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

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

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

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

Relating a protein’s location to its function.

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

days post infection mean total lesion score Susceptible (L7 2 ) Resistant (L6 1 ) Genotype Non-MHC associated resistance and susceptibility Resistant ( L6 1 ) Burgess et al,Vet Pathol 38:2,2001 The critical time point in MD lymphomagenesis Susceptible (L7 2 )  CD30 mab  CD8 mab

Tissue CD30 lo/- hyperplastic CD30hi, Neoplastically-transformed 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

L6 (R) L7 (S) * * * * * IL-4 IL-10 IL-12 IL-18 IFNγ TGFβ GPR-83 SMAD-7 CTLA-4 mRNA 40 – mean C t value Whole tissue mRNA expression

IL-4IL-12IL-18TGFβGPR-83SMAD-7CTLA-4 * * * * 40 – mean C t value mRNA * Microscopic lesion mRNA expression L6 (R) L7 (S)

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

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

Step I. GO-based Phenotype Scoring. Gene productTh1Th2TregInflammation IL IL IL IL IL IL IL IL IFN-  0.00 TGF-  CTLA GPR SMAD Net Effect Step III. Inclusion of quantitative data to the phenotype scoring table and calculation of net affect. 111SMAD-7 1 GPR-83 1 CTLA-4 10 TGF-  111 IFN-  1111IL-18 ND 1IL-13 ND 1IL IL-10 11ND IL-8 11IL-6 ND11IL-4 1 ND 1IL-2 InflammationTregTh2Th1Gene product ND = No data Step II. Multiply by quantitative data for each gene product.

Th-1Th-2T-regInflammation Net Effect -40 Whole Tissue L6 (R) L7 (S)

Th-1Th-2T-reg Inflammation Phenotype Net Effect 5mm Microscopic lesions L6 (R) L7 (S)

Key points Modeling is subordinate to the biological questions/hypotheses. Together the 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