WIIFM: examples of functional modeling
NMSU GO Workshop20 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:
http://www.geneontology.org/GO.tools.microarray.shtml
very different visualization will use some of these tools this afternoon
http://david.abcc.ncifcrf.gov/home.jsp
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 transportcell migration
cell adhesioncell growthapoptosisimmune response
cell cycle/cell proliferation
cell-cell signalingfunction unknowndevelopmentendocytosisproteolysis and peptidolysis
protein modificationsignal transduction
B-cells Stroma
Membrane proteins grouped by GO BP:
cell migration
apoptosis
immune response
cell cycle/cell proliferation
cell-cell signalingfunction unknown
B-cells Stroma
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 resistance2. wound healing in pig
days post infection
mea
n to
tal l
esi
on
scor
e
0
2
4
6
8
10
12
14
16
18
0 20 40 60 80 100
Susceptible (L72)
Resistant (L61)
Genotype
Non-MHC associated resistance and susceptibility
The critical time point in MD lymphomagenesisHypothesis 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 12 IL 4
IL 4 IL10
APC
CTL
TGFβ
T regSmad 7
L6 Whole
L7 Whole
L7 Micro
Th-1, Th-2, T-reg ?
Inflammatory?
Step I. GO-based Phenotype Scoring.
Gene product Th1 Th2 Treg Inflammation
IL-2 1.58 1.58 -1.58
IL-4 0.00 0.00 0.00 0.00
IL-6 0.00 -1.20 1.20 -1.20
IL-8 0.00 0.00 1.18 1.18
IL-10 0.00 0.00 0.00 0.00
IL-12 0.00 0.00 0.00 0.00
IL-13 1.51 -1.51 0.00 0.00
IL-18 0.91 0.91 0.91 0.91
IFN- 0.00 0.00 0.00 0.00
TGF- -1.71 0.00 1.71 -1.71
CTLA-4 -1.89 -1.89 1.89 -1.89
GPR-83 -1.69 -1.69 1.69 -1.69
SMAD-7 0.00 0.00 0.00 0.00
Net Effect -1.29 -5.38 10.15 -5.98
Step III. Inclusion of quantitative data to the phenotype scoring table and calculation of net affect.
1-111SMAD-7
-11-1-1GPR-83
-11-1-1CTLA-4
-110-1TGF-
11-11IFN-
1111IL-18
NDND1-1IL-13
NDND-11IL-12
011-1IL-10
11NDNDIL-8
1-11IL-6
ND11-1IL-4
-11ND1IL-2
InflammationTregTh2Th1Gene product
ND = No data
Step II. Multiply by quantitative data for each gene product.
- 20
- 10
0
10
20
30
40
50
60
Th-1 Th-2 T-regInflammation
Phenotype
Net
Eff
ect
5mm
Microscopic lesions
L6 (R)
L7 (S)
ProT-reg Pro
Th-1Anti Th-2
Pro CTLAnti CTL
L7 Susceptible
Pro CTLAnti CTL
L6 Resistant
ProT-reg Pro
Th-2AntiTh-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*. 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: PigBindu Nanduri
Proportional distribution of mRNA functions differentially-expressed by X- and Y-treated tissues
Treatment Ximmunity (primarily innate)
inflammation
Wound healing
Lipid metabolism
response to thermal injury
angiogenesis
Total differentially-expressed mRNAs: 4302
Total differentially-expressed mRNAs: 1960
Treatment Y
35 30 25 20 15 10 5 0 5
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
8 6 4 2 0 2 4 6
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.