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.