1. Examples of functional modeling. Iowa State Workshop 11 June 2009

2. All tools and materials from this workshop are 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 understanding from the data-rich opportunities provided by modern high-throughput genomic technology." Professor Andrew Cossins, Consortium for Post-Genome Science, Chairman.

4. Systems Biology Workflow Nanduri & McCarthy CAB reviews, 2008

5. 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. Annotation follows information and information changes daily: STEP 1 in analyzing functional genomics data is re-annotating your dataset. Examples of how we do functional modeling of genomics datasets.

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

10. 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

12. 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).

13. ion/proton transport cell migration cell adhesion cell growth apoptosis immune response cell cycle/cell proliferationcell-cell signaling function unknown development endocytosis proteolysis and peptidolysis protein modification signal transduction B-cellsStroma Membrane proteins grouped by GO BP:

14. LOCATION DETERMINES FUNCTION

15. GO is the “encyclopedia” of gene functions captured, coded and put into a directed acyclic graph (DAG) structure. In other words, by collecting all of the known data about gene product biological processes, molecular functions and cell locations, GO has become the master “cheat-sheet” for our total knowledge of the genetic basis of phenotype. Because every GO annotation term has a unique digital code, we can use computers to mine the GO DAGs for granular functional information. Instead of having to plough through thousands of papers at the library and make notes and then decide what the differential gene expression from your microarray experiment means as a net affect, the aim is for GO to have all the biological information captured and then retrieve it and compile it with your quantitative gene product expression data and provide a net affect.

16. “GO Slim” In contrast, we need to use the deep granular information rich data suitable for hypothesis-testing Many people use “GO Slims” which capture only high-level terms which are more often then not extremely poorly informative and not suitable for hypothesis-testing.

17. Shyamesh Kumar BVSc

18. days post infection mean total lesion score 0 2 4 6 8 10 12 14 16 18 020406080100 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

19. Hypothesis At the critical time point of 21 dpi, MD-resistant genotypes have a T-helper (Th)-1 microenvironment (consistent with CTL activity), but MD-susceptible genotypes have a T-reg or Th-2 microenvironment (antagonistic to CTL). 2008, 57: 1253-1262.

20. Infection of chickens (L6 1 & L7 2 ), kill and post-mortem at 21dpi and sample tissues Whole Tissue RNA extraction Laser Capture Microdissection (LCM) Cryosections Duplex QPCR RNA extraction

21. 0 5 10 15 20 25 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

22. 0 5 10 15 20 25 IL-4IL-12IL-18TGFβGPR-83SMAD-7CTLA-4 * * * * 40 – mean C t value mRNA * Microscopic lesion mRNA expression L6 (R) L7 (S)

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

24. 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?

25. QPCR data Gene Ontology annotation Biological Process Modeling & Hypothesis testing Gene Ontology based hypothesis testing Relative mRNA expression data

27. Step I. GO-based Phenotype Scoring. Gene productTh1Th2TregInflammation IL-21.58 -1.58 IL-40.00 IL-60.00-1.201.20-1.20 IL-80.00 1.18 IL-100.00 IL-120.00 IL-131.51-1.510.00 IL-180.91 IFN-  0.00 TGF-  -1.710.001.71-1.71 CTLA-4-1.89 1.89-1.89 GPR-83-1.69 1.69-1.69 SMAD-70.00 Net Effect-1.29-5.3810.15-5.98 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-12 011IL-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.

28. -20 0 40 60 80 100 120 Th-1Th-2T-regInflammation Net Effect -40 Whole Tissue L6 (R) L7 (S)

29. - 20 - 10 0 20 30 40 50 60 Th-1Th-2T-reg Inflammation Phenotype Net Effect 5mm Microscopic lesions L6 (R) L7 (S)

30. Pro T-reg Pro Th-1 Anti Th-2 Pro CTL Anti CTL L6 (R) Whole lymphoma L7 Susceptible Pro CTL Anti CTL L6 Resistant Pro T-reg Pro Th-2 Anti Th-1

31. 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. Translation to clinical research: Pig Bindu Nanduri

32. 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

33. 353025201510505 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

34. 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

35. 86420246 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