WIIFM: examples of functional modeling

GO Workshop3-6 August 2010

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.

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

OntologiesCanonical and other Networks

GO Cellular Component

GO Biological Process

GO Molecular Function

BRENDA

Pathway Studio 5.0

Ingenuity Pathway Analyses

Cytoscape

Interactome Databases

Functional Understanding

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).

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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

LOCATION DETERMINES FUNCTION

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.

“GO Slim”

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

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.

Shyamesh Kumar BVSc

days post infection

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Susceptible (L72)

Resistant (L61)

Genotype

Non-MHC associated resistance and susceptibility

Resistant ( L61)

Burgess et al,Vet Pathol 38:2,2001

The critical time point in MD lymphomagenesis

Susceptible (L72)

CD30 mab CD8 mab

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).

2008, 57: 1253-1262.

Infection of chickens (L61 & L72), kill and post-mortem at 21dpi and sample tissues

Whole Tissue

RNA extraction

Laser Capture Microdissection (LCM)

Cryosections

Duplex QPCR

RNA extraction

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L7 (S)* *

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*IL

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IFNγ

TGFβ

GPR-8

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SMAD-7

CTLA-4

mRNA

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mea

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IL-4 IL-12 IL-18 TGFβ GPR-83 SMAD-7 CTLA-4

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Microscopic lesion mRNA expression

L6 (R)

L7 (S)

Th-1 Th-2

NAIVE CD4+ T CELL

CYTOKINES AND T HELPER CELL DIFFERENTIATION

APC T reg

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?

QPCR data

Gene Ontology annotation

Biological Process Modeling & Hypothesis testing

Gene Ontology based hypothesis testing

Relative mRNA expression data

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.

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Th-1 Th-2 T-reg Inflammation

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Microscopic lesions

L6 (R)

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ProT-reg Pro

Th-1Anti Th-2

Pro CTLAnti CTL

L6 (R) Whole lymphoma

L7 Susceptible

Pro CTLAnti CTL

L6 Resistant

ProT-reg Pro

Th-2AntiTh-1

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.

Translation to clinical research

(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.

Bindu Nanduri

Proportional distribution of mRNA functions differentially-expressed by Electro and 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

ElectroscalpelHarmonic ScalpelHYPOTHESIS TERMS

35 30 25 20 15 10 5 0 5

Immunity (primarily innate)

Wound healing

Lipid metabolism

Response to thermal injury

Angiogenesis

Electro-scalple Harmonic scalple

Net functional distribution of differentially-expressed mRNAs:

Relative bias

Classical inflammation(heat, redness, swelling, pain, loss of function)

Sensory response to pain

Hemorrhage

Proportional distribution of protein functions differentially-expressed by Electro and Harmonic Scalpel

Total differentially-expressed proteins: 509

Electro-scalpel

Total differentially-expressed proteins: 433

Harmonic scalpel

Immunity (primarily innate)

Inflammation

Wound Healing

Lipid metabolism

Response to thermal Injury

Angiogenesis

HYPOTHESIS TERMS

Net functional distribution of differentially-expressed proteins

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

Electroscalpel Harmonic Scalpel

www.agbase.msstate.edu