examples of functional modeling. iowa state workshop 11 june 2009
TRANSCRIPT
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:
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.
Systems Biology Workflow
Nanduri & McCarthy CAB reviews, 2008
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.
Who uses GO? http://www.ebi.ac.uk/GOA/users.html
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
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).
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
mea
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tal l
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scor
e
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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
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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L6 (R)
L7 (S)* *
* *
*IL
-4
IL-1
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IL-1
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IL-1
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IFNγ
TGFβ
GPR-8
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SMAD-7
CTLA-4
mRNA
40 –
mea
n C
t val
ueWhole tissue mRNA expression
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IL-4 IL-12 IL-18 TGFβ GPR-83 SMAD-7 CTLA-4
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mea
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t val
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mRNA
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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
Net
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ect
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Whole Tissue L6 (R)L7 (S)
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Th-1 Th-2 T-regInflammation
Phenotype
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5mm
Microscopic lesions
L6 (R)
L7 (S)
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
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