WIIFM: examples of functional modeling NMSU GO Workshop 20 May 2010

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).

1. Determining which classes of gene products are over-represented or under-represented.  most typically used functional analysis method  many, many tools that do this – see: croarray.shtml  very different visualization  will use some of these tools this afternoon

2. Grouping gene products.  high throughput data sets gives us 100s - 1,000s of gene products  can’t know everything about all gene products tendency to ‘cherry pick’ ones you recognize  instead, can group gene products by function this gives us a manageable number of categories to process enables us to see trends, patterns, etc

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

cell migration apoptosis immune response cell cycle/cell proliferation cell-cell signaling function unknown B-cellsStroma Membrane proteins grouped by GO BP:

BVDV Infection – cytopathic (CP) vs non-cytopathic (NCP) infection (comparing function between 2 different conditions) Analysis of Bovine Viral Diarrhea Viruses-infected monocytes: identification of cytopathic and non-cytopathic biotype differences. Mais Ammari, Fiona M McCarthy, Bindu Nanduri, Lesya M Pinchuk BMC Bioinformatics accepted. Biological Response Relative to CP Infection

3. Relating a protein’s location to its function.  If Gene Ontology describes gene product function, why does it include Cellular Component?  location determines function: beta-catenin (CTNNB1) – involved in cell-cell adhesion on the cell surface, translocates to the nucleus where it regulates transcription proteins in the mitochondria are involved in respiration but cause apoptosis when released into the cytoplasm

4. Hypothesis testing  high throughput data sets – ‘fishing expedition’ or hypothesis generation  but GO also serves as a repository of biological function – can be used for hypothesis testing based on these data sets  2 examples: 1. Marek’s disease resistance 2. wound healing in pig

days post infection mean total lesion score Susceptible (L7 2 ) Resistant (L6 1 ) Genotype Non-MHC associated resistance and susceptibility The critical time point in MD lymphomagenesis 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).

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

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

Pro T-reg Pro Th-1 Anti Th-2 Pro CTL Anti CTL L7 Susceptible Pro CTLAnti CTL L6 Resistant Pro T-reg Pro Th-2 Anti Th-1

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

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).  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.  There is no “right answer”: different ways of looking at your data will give you different insights.