Examples of functional modeling.

NCSU GO Workshop

29 October 2009

Tools and materials from this workshop will be available online at the AgBase database Educational Resources link.

For continuing support and assistance please contact:

agbase@cse.msstate.edu

This workshop is supported by USDA CSREES grant number MISV-329140.

"Today’s challenge is to realise greater knowledge and understanding from the data-rich opportunities provided by modern high-throughput genomic technology."

Professor Andrew Cossins,

Consortium for Post-Genome Science, Chairman.

Bio-ontologies Bio-ontologies are used to capture biological

information in a way that can be read by both humans and computers.necessary for high-throughput “omics” datasetsallows data sharing across databases

Objects in an ontology (eg. genes, cell types, tissue types, stages of development) are well defined.

The ontology shows how the objects relate to each other.

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

Who uses GO? http://www.ebi.ac.uk/GOA/users.html

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).

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: 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

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

B-cells Stroma

immune response

apoptosis

cell-cell signaling

(Looking at function, not gene.)

Relating a protein’s location to its function.

Shyamesh Kumar BVSc

Focusing on particular biological pathways and functions (hypothesis-testing).

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

Resistant ( L61)

Burgess et al,Vet Pathol 38:2,2001

The critical time point in MD lymphomagenesis

Susceptible (L72)

CD30 mab CD8 mab

Tissue

CD30 lo/- hyperplastic

CD30hi, Neoplastically-transformed

Marek’s Disease Lymphoma Model : Chicken

The neoplastically-transformed (CD30hi) cells in Marek’s disease lymphoma cell phenotype most closely resembles T-regulatory cells. LA Shack, T. Buza, SC Burgess. Cancer Immunology and Immunotherapy, 2008

0

5

10

15

20

25

L6 (R)

L7 (S)* *

* *

*IL

-4

IL-1

0

IL-1

2

IL-1

8

IFNγ

TGFβ

GPR-8

3

SMAD-7

CTLA-4

mRNA

40 –

mea

n C

t val

ueWhole tissue mRNA expression

0

5

10

15

20

25

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

**

**

40 –

mea

n C

t val

ue

mRNA

*

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?

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

0

20

40

60

80

100

120

Th-1 Th-2 T-reg Inflammation

Net

Eff

ect

-40

Whole Tissue L6 (R)L7 (S)

- 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)

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.

• The strategy you use to model your data will depend upon

• what information is readily available for your species of interest• what biological system you are studying