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Examples of functional modeling. Iowa State Workshop 11 June 2009. All tools and materials from this workshop are available online at the AgBase database Educational Resources link. For continuing support and assistance please contact: [email protected]

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Examples of functional modeling

Examples of functional modeling.

Iowa State Workshop

11 June 2009


  • All tools and materials from this workshop are available online at the AgBase database Educational Resources link.

  • For continuing support and assistance please contact:

    [email protected]

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


"Today’s challenge is to realise greater online at the AgBase database Educational Resources link.knowledge and understandingfrom the data-rich opportunities provided by modern high-throughput genomic technology."Professor Andrew Cossins, Consortium for Post-Genome Science, Chairman.


Systems biology workflow
Systems Biology Workflow online at the AgBase database Educational Resources link.

Nanduri & McCarthy CAB reviews, 2008


Key points online at the AgBase database Educational Resources link.

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.


Who uses go http www ebi ac uk goa users html
Who uses GO? online at the AgBase database Educational Resources link.http://www.ebi.ac.uk/GOA/users.html


What is the gene ontology
What is the Gene Ontology? online at the AgBase database Educational Resources link.

“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


Use go for
Use GO for……. online at the AgBase database Educational Resources link.

  • Determining which classes of gene products are over-represented or under-represented.

  • Grouping gene products.

  • Relating a protein’s location to its function.

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


B-cells online at the AgBase database Educational Resources link.

Stroma

Membrane proteins grouped by GO BP:

cell cycle/cell proliferation

cell-cell signaling

cell adhesion

function unknown

cell growth

development

endocytosis

apoptosis

proteolysis and peptidolysis

immune response

ion/proton transport

signal transduction

cell migration

protein modification


LOCATION DETERMINES FUNCTION online at the AgBase database Educational Resources link.


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” coded and put into a directed acyclic graph (DAG) structure.

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.

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


Shyamesh Kumar BVSc coded and put into a directed acyclic graph (DAG) structure.


a- coded and put into a directed acyclic graph (DAG) structure.CD30 mab

a-CD8 mab

Susceptible (L72)

Resistant ( L61)

The critical time point in MD lymphomagenesis

18

16

Genotype

14

Susceptible (L72)

Resistant (L61)

12

mean total lesion score

10

Non-MHC associated resistance and susceptibility

8

6

4

2

0

0

20

40

60

80

100

days post infection

Burgess et al,Vet Pathol 38:2,2001


2008, 57: 1253-1262. coded and put into a directed acyclic graph (DAG) structure.

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).


Infection of chickens (L6 coded and put into a directed acyclic graph (DAG) structure.1 & L72), kill and post-mortem at 21dpi and sample tissues

Whole Tissue

Cryosections

Laser Capture Microdissection (LCM)

RNA extraction

RNA extraction

Duplex QPCR


Whole tissue mRNA expression coded and put into a directed acyclic graph (DAG) structure.

L6 (R)

*

*

*

L7 (S)

25

20

*

40 – mean Ct value

15

*

10

5

0

IL-4

IL-10

IFNγ

TGFβ

IL-12

IL-18

CTLA-4

GPR-83

SMAD-7

mRNA


Microscopic lesion coded and put into a directed acyclic graph (DAG) structure.mRNA expression

L6 (R)

*

25

L7 (S)

*

20

*

40 – mean Ct value

*

15

*

10

5

0

IL-4

IL-12

IL-18

TGFβ

GPR-83

SMAD-7

CTLA-4

mRNA


NAIVE CD4+ T CELL coded and put into a directed acyclic graph (DAG) structure.

APC

Th-2

T reg

Th-1

CYTOKINES AND T HELPER CELL DIFFERENTIATION


NAIVE CD4+ T CELL coded and put into a directed acyclic graph (DAG) structure.

Macrophage

APC

Th-2

T reg

Th-1

NK Cell

CTL

L6 Whole

Smad 7

L7 Whole

L7 Micro

IL 12

IL 4

Th-1, Th-2, T-reg ?

Inflammatory?

TGFβ

IL 4

IL10

IFN γ

IL 12

IL 18


Gene Ontology based hypothesis testing coded and put into a directed acyclic graph (DAG) structure.

QPCR data

Relative mRNA expression data

Gene Ontology annotation

Biological Process Modeling & Hypothesis testing


Gene product coded and put into a directed acyclic graph (DAG) structure.

Th1

Th2

Treg

Inflammation

IL-2

1

ND

1

-1

IL-4

-1

1

1

ND

IL-6

1

-1

1

IL-8

ND

ND

1

1

IL-10

-1

1

1

0

IL-12

1

-1

ND

ND

IL-13

-1

1

ND

ND

IL-18

1

1

1

1

IFN-g

1

-1

1

1

TGF-b

-1

0

1

-1

CTLA-4

-1

-1

1

-1

GPR-83

-1

-1

1

-1

SMAD-7

1

1

-1

1

ND = No data

Step III. Inclusion of quantitative data to the phenotype scoring table and calculation of net affect.

Step I. GO-based Phenotype Scoring.

Step II. Multiply by quantitative data for each gene product.


Whole Tissue coded and put into a directed acyclic graph (DAG) structure.

L6 (R)

L7 (S)

120

100

80

Net Effect

60

40

20

0

Th-1

Th-2

T-reg

Inflammation

-

20

-40


Microscopic lesions coded and put into a directed acyclic graph (DAG) structure.

L6 (R)

60

L7 (S)

50

5mm

40

Net Effect

30

20

10

0

Th-1

Th-2

T-reg

-

10

Inflammation

Phenotype

-

20


L7 Susceptible coded and put into a directed acyclic graph (DAG) structure.

L6 Resistant

L6 (R) Whole lymphoma

Pro

T-reg

Pro

Th-2

Pro

T-reg

Anti

Th-1

Pro

Th-1

Anti

Th-2

Pro CTL

Anti CTL

Pro CTL

Anti CTL


Translation to clinical research: Pig coded and put into a directed acyclic graph (DAG) structure.

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.

Bindu Nanduri


Proportional distribution of mRNA functions differentially-expressed by X- and Y-treated tissues

Treatment Y

Treatment X

immunity (primarily innate)

inflammation

Wound healing

Lipid metabolism

response to thermal injury

angiogenesis

Total differentially-expressed mRNAs: 1960

Total differentially-expressed mRNAs: 4302


Net functional distribution of differentially-expressed mRNAs: X- vs. Y-Treatment

Y

X

sensory response to pain

angiogenesis

response to thermal injury

Lipid metabolism

Wound healing

classical inflammation

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

immunity (primarily innate)

35

30

25

20

15

10

5

0

5

Relative bias


Proportional distribution of protein functions differentially-expressed by X- and Y-treated tissues

Treatment Y

Treatment X

immunity (primarily innate)

inflammation

Wound Healing

Lipid metabolism

response to Thermal Injury

Angiogenesis

hemorrhage

Total differentially-expressed proteins: 433

Total differentially-expressed proteins: 509


Net functional distribution of differentially-expressed Proteins: X- vs. Y-Treatment

hemorrhage

sensory response to pain

Treatment Y

Treatment X

angiogenesis

response to thermal injury

lipid metabolism

Wound healing

classical inflammation

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

immunity (primarily innate)

8

6

4

2

0

2

4

6

Relative bias


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