Examples of functional modeling.

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 understandingfrom 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……. • 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).

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

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” 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. In contrast, we need to use the deep granular information rich data suitable for hypothesis-testing

17. Shyamesh Kumar BVSc

18. 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

19. 2008, 57: 1253-1262. 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).

20. Infection of chickens (L61 & L72), kill and post-mortem at 21dpi and sample tissues Whole Tissue Cryosections Laser Capture Microdissection (LCM) RNA extraction RNA extraction Duplex QPCR

21. 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

22. 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

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

24. 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

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

27. 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.

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

29. 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

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

31. 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

32. 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

33. 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

34. 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

35. 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