WIIFM: examples of functional modeling

1. WIIFM: examples of functional modeling GO Workshop 3-6 August 2010

2. 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. 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

8. Functional Understanding Canonical and other Networks Ontologies GO Cellular Component GO Biological Process GO Molecular Function BRENDA Pathway Studio 5.0 Ingenuity Pathway Analyses Cytoscape Interactome Databases

9. 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).

10. No. No. x 106 25000 18 16 20000 14 12 15000 10 8 10000 6 4 5000 2 0 0 70 75 80 85 90 95 00 05 ‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09 YEAR

11. 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

12. LOCATION DETERMINES FUNCTION

13. 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.

14. “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

15. 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. 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).

18. 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

19. 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

20. 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

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

22. 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

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

24. 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.

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

26. 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

27. 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

28. Bindu Nanduri Translation to clinical research Pig Total mRNA and protein expression was measured from quadruplicate samples of control, electroscalple and harmonic scalple-treated tissue. Differentially-expressed mRNA’s and proteins identified using Monte-Carlo resampling1. Using network and pathway analysis as well as Gene Ontology-based hypothesis testing, differences in specific phyisological processes between electroscalple and harmonic scalple-treated tissue were quantified and reported as net effects. (1) 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.

30. Proportional distribution of mRNA functions differentially-expressed by Electro and Harmonic Scalpel Electroscalpel HYPOTHESIS TERMS Harmonic Scalpel Immunity (primarily innate) Inflammation Wound healing Lipid metabolism Response to thermal injury Angiogenesis Total differentially-expressed mRNAs: 4302 Total differentially-expressed mRNAs: 1960

31. Net functional distribution of differentially-expressed mRNAs: Harmonic scalple Electro-scalple 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

32. Immunity (primarily innate) Inflammation Wound Healing Lipid metabolism Response to thermal Injury Angiogenesis Proportional distribution of protein functions differentially-expressed by Electro and Harmonic Scalpel Harmonic scalpel Electro-scalpel HYPOTHESIS TERMS Hemorrhage Total differentially-expressed proteins: 509 Total differentially-expressed proteins: 433

33. Net functional distribution of differentially-expressed proteins Harmonic Scalpel Electroscalpel Hemorrhage 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) 8 6 4 2 0 2 4 6 Relative bias

34. www.agbase.msstate.edu