Expresso and Chips Studying Drought Stress in Plants with cDNA Microarrays Lenwood S. Heath Department of Computer Science Virginia Tech, VA 24061

Expresso and ChipsStudying Drought Stress in Plants

with cDNA Microarrays

Lenwood S. Heath

Department of Computer Science

Virginia Tech, VA 24061

Expresso and Chips Fordham University May 6, 2003

Outline

• Expresso Team• Drought Stress in Plants• Microarray Technology• Expresso System• Biological Results• Networks in Biology• Future


EXPRESSO TEAM

VT

Ron Sederoff

Lenny Heath

Naren Ramakrishnan

Layne Watson

Cecilia Vasquez-RobinetShrinivasrao Mane Allan SiosonMaulik ShuklaHarsha Rajasimha

Jonathan Watkinson

Boris Chevone Ruth Grene

Andrew McElrone

Catarina Moura

Duke

NCSU


Outline



Grand GoalDevelop explanatory and predictive

models of phenomena occurring within plant cells in response to

drought and other oxidative stresses


Questions Currently Addressed in the Grene Lab

1. Big picture: What makes a plant successfully acclimate to drought stress?

2. Specifically: Which changes in gene expression are associated with physiological acclimation to drought stress?

3. Goal: Using Expresso and the smarts of several computer scientists, can we construct, or amend, pathways depicting the perception of drought stress, and successive events which culminate in acclimation?

4. Future Work: Which changes in the metabolite population are associated with acclimation?


Long term objective of drought experiments in Expresso

Develop explanatory and predictive models of phenomena occurring within plant cells in response to drought using cDNA microarrays and metabolomics.

Gene Expression

Stress perceptionMetabolic acclimatory responses

Protective responses - LEAs, antioxidants


Responses to Environmental Signals


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= Needles harvest


Outline



How the microarray process works(courtesy J.M. Trent)


Flow of Procedures

Hypotheses

Select cDNAs

PCR

Extract RNA

Replication and Randomization

Reverse Transcription and

Fluorescent Labeling

Robotic Printing

Hybridization

Identify Spots

Intensities

Statistics

Clustering

Data Mining, ILP

CS and Biologists

Biologists

CS

Confirm with RT-PCR

Experiment

PS, water pot.


Key Steps in cDNA Microarrays

• Probe generation and microarray design– What to put on the chip?– How to amplify desired genetic material?– Where should selected probes be placed?

• Target preparation and hybridization– How to isolate samples from control and treated

tissues?– How to ensure suitable conditions for hybridization?

• Data generation and analysis– What methods are available for image processing?– How to accommodate errors in downstream analysis?– How to validate results from microarray studies?


Outline



• Integration of design and procedures

• Integration of image analysis tools and statistical analysis

• Data mining using inductive logic programming (ILP)

• Closing the loop

• Integrating models

Expresso: A Problem Solving Environment (PSE) for Microarray Experiment Design and Analysis


Probe Selection

• Biologists provide keywords • Keywords used to search Arabidopsis database at

TIGR• Arabidopsis proteins used to BLAST against pine EST

database– Cut-off value of 10e-4– Select ESTs close to 3’ end of Arabidopsis protein

(without compromising match)


Example of cDNA Selection: bZIPs

At1g58110 bZIP family transcription factor BI397695 NXPV_104_B12_FAt2g21230 bZIP family transcription factor AW290027 NXNV009G09FAt3g51960 bZIP family transcription factor BF049843 NXCI_111_F10_FAt3g56660 bZIP family transcription factor BF778575 NXSI_088_C07_FAt3g60320 bZIP protein AW042749 ST24H11At3g60320 bZIP protein BG318985 NXPV_022_C01_FAt1g52320 bZIP protein, putative BQ198053 NXLV124_H06_FAt2g31370 bZIP transcription factor (POSF21) BG833004 NXPV_084_G10_FAt2g12900 bZIP transcription factor family protein BM133964 NXLV_014_G03_FAt5g06960 bZIP transcription factor, OBF5 BM428294 NXRV_012_A06_FAt1g02110 bZIP-like protein AW697487 ST61B05

• At3g60320 e-210• At2g21230 e-190• At3g51960 e-190• At3g56660 e-185• At3g60320 e-150• At1g58110 e-134• At5g06960 e-100

• At3g60320 e-200• At1g23600 12• At3g43920 34

• At3g60320 e-1• At2g37640 15• At4g28990 28

Arabidopsis gene At3g60320

Best hit pine contig

Pine ESTs


Elements of Array Design• Precise tracking of clones from NCSU archive to

deposition on the slide

• Spiking controls:

– Orient layout of spots

– Generate standard curves

– Normalize laser focus and intensity between channels

• Replication of deposits

• Printing by more than one pin

• Placement at different positions on the slide


cDNA libraries at NCSUJuvenile and normal wood

96 Well Archive Plates (VT)

Addition of blanks, and spiking controls

96 Well PCR Plates

96 Well Storage Plates

Cleaning

384 Well Printing PlatesTransfer 4 to 1


12 x 24 Subarray of deposits

1 4

13 16

Slide

Mic

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PrintingPlates


• Reciprocal labelings• Modified loop design

(Kerr and Churchill, 2001)

Hybridization

C3

T1

C1

T3

C2

T2


Image Capture and Analysis• Image capture on ScanArray 5000

– Model laser and photomultiplier tube

– Model inconsistencies in slide and spot

• Image analysis– Currently using ScanArray Express

– Incorporate into Expresso


Wolfinger Statistical Approach

• Assumption: Biological phenomena are in terms of multiplicative effects [Kerr, Churchill, 2001]

• Two Stage Analysis Method [Wolfinger, et al., 2001]– Normalization Step

• ANOVA Mixed Model as the Normalization Model• Removes the Global Effects from Array, Dye, Pin,

Treatment, etc.– Gene Treatment Interaction Estimation

• ANOVA Mixed Model as the Gene Model• Multiple Comparisons per Gene


Analysis: The Wolfinger Model• Two-phase analysis to remove global effects and estimate

the interaction between gene and treatment

APPDATy .

G GT GA GD GS(A) .

– y is the log of intensity value of a specific spot on a specific array accounts for the overall mean of values in a specific comparison– T, A, D, P and AP are constant and represent variation in different factors

accounts for residual from the ANOVA model– G is the overall mean of the residual for each gene in a comparison and GT is the

overall mean of the residual in treatment or control– A t-test is used to test whether the GT between treated and control is different or equal

ANOVA normalization model

Gene model


Log of

2 fold1.4 fold


Analysis: Data mining by redescription (ILP)

• Based on a collection of 15 relational databases implemented using Postgres– Experimental conditions– cDNA details– Physiological measurements– Gene expression levels


Inductive Logic Programming

• A more expressive way to mine patterns than attribute-based clustering

• Traditional clustering (SOMs, agglomerative etc.)

• Clusters are difficult to interpret

• Clusters may not correspond to biological knowledge

• Difficult to incorporate a priori information

• ILP

• Mines only clusters that are “describable” in terms of prior knowledge, e.g., functional categories


How ILP is used in Expresso• Infers rules relating gene expression levels to

categories, or to other expression levels, without explicit direction

• Example Rule:

[Rule 142] [Pos cover = 69 Neg cover = 3]

level(A,moist_vs_severe,not positive) :- level(A,moist_vs_mild,positive).

• Interpretation:

“If the moist versus mild stress comparison was positive for some clone named A, it was negative or unchanged in the moist versus severe comparison for A, with a confidence of 95.8%.”


Another example• This one relates expression level to functional categories

level(A,moist_vs_mild,positive) :-

category(A, transport_protein).

• What ILP needs as input

• Training data

• Genes placed in functional categories (can be a many-many relationship)

• Expression levels, physiological data (can be multi-dimensional)

• What ILP produces as output

• Rules “redescribing” sets of genes defined using one facet in terms of another – (it finds sets automatically!)


How ILP works• Searches through every possible subset of genes that

can be redescribed, from one facet to another

• Uses clever pruning strategies to pick out the best redescriptions (rules)

• Evaluates promising rules in terms of

• Support: how many genes are being considered in the rule?

• Confidence: of the genes that satisfy the body, how many also satisfy the head?

• Arranges rules in terms of support, confidence, or other metric


Current Work on Models

• Populate library of models for various stages– biophysics (PCR, hybridization)– molecular biology (sequence selection)– robotics (pipetting and transfers)– statistics (error propagation and assessment of treatment effects)– surrogate models (all stages)

• Configure suitable sequences of models– “run” or “optimize”

• Example scenarios– “perform end-to-end validation of gene expression”– “design a chip that hybridizes to cDNAs from closely related

species”– “where should I sample next for improving data mining results?”


Why is this problem difficult?• Model-based optimization of compositional codes

– sequential refinement and optimization infeasible!– models are of various fidelities– errors compound further into the design cycle!

• Current approaches– “hand tuning” or “word of mouth” protocols– lack of understanding of functional relationships– do not harness existing biological knowledge

• Need to judiciously– configure virtual experiments to give realistic estimates– minimize cost of additional data collection– maximize information content per experiment


An example of Expresso modeling• Capture PCR reaction dynamics

– to model gene quantification computationally– e.g., a Markov model

• Factors– reaction temperature– rate coefficients– number of reaction cycles– activation energy for nucleotide addition

• Optimize PCR model to pose– “how many RNA molecules were there in the start of the

system?”– leads to full-scale physics-based validation of microarray

experiments


Closing-the-loop in Data Mining

Redesign probe set to clarify functional patterns– discrete optimization problem

• minimizing cross-hybridization• maximizing specificity


Outline



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Net Photosynthesis (mol CO2 m-2 s-1)

Significant Gene Expression

Condition Cycle Control Stressed Positive Negative

Mild 1 4.28 2.48 133 94

2 3.54 3.82 213 159

3 4.75 3.28 62 90

Severe 1 3.67 0.88 145 144

2 3.00 0.19 162 156

3 2.90 0.77 135 53


A.

B.

Positive Change in Expression

Negative Change in Expression



Clone ID Annotation 1 2 3 1 2 3NXCI_047_C05 DAHP synthase + - -NXCI_071_C01 3-dehydroquinate synthase + + -NXCI_117_D08 3-dehydroquinate dehydrataseNXNV_185_H02 Shikimate dehydrogenaseNXCI_034_B01 Shikimate kinase

EPSP SynthaseNXCI_163_G07 Chorismate synthase + - +NXSI_051_F10 Chorismate synthaseNXCI_016_F11 Chorismate mutase +

Prephenate aminotransferaseArogenate dehydrataseArogenate dehydrogenase

NXCI_093_H05 PAL -NXSI_118_A03 Cinnimate 4 hydroxylaseNXCI_087_F07 Cinnimate 4 hydroxylaseNXCI_045_B07 Cinnimate 4 hydroxylase + +

12 E05 Caffeoyl O methyl transferase +NXSI_055_H08 Caffeoyl O methyl transferaseNXSI_130_F05 Caffeoyl O methyl transferase02 B03 Cinnamyl alcohol dehydrogenase -NXNV_162_F07 Cinnamyl alcohol dehydrogenase -NXCI_165_H04 Cinnamoyl CoA reductase -34 F04 Cinnamoyl CoA reductaseNXNV_044_G05 Laccase -NXSI_127_C02 Laccase + -NXNV_136_F10 Laccase - + +NXCI_005_C10 Laccase -NXCI_018_F10 Pinoresinol reductase

Chalcone synthaseNXCI_098_F10 Chalcone/Flavone isomerase + +07 H08 Chalcone/Flavone isomerase + + +NXNV_127_E04 Isoflavone reductaseNXNV_127_F01 Isoflavone reductaseNXCI_002_E07 Isoflavone reductase + + -NXSI_063_D01 Naringenin-2-oxo dioxygenase + + +28 B11 Naringenin-2-oxo dioxygenase + +13 H06 Leucoanthocyanidin reductase +

Mild Severe

Flavonoids

Aromatic Amino Acid

Phenyl-propanoid

Lignin




Outline



Glycolytic Pathway, Citric Acid Cycle, and Related Metabolic Processes


Carbon Metabolism


Responses to Environmental Signals


ROS Response


Network of Munnik and Meijer


Network of Shinozaki and Yamaguchi-Shinozaki


• Partial differential equations

• Boolean networks

• Bayesian networks

• Logic programs

• Neural networks

• Petri nets

• Fuzzy cognitive maps

• Weak or none (ad hoc)

Mathematical Models for Biological Networks


• Chemical Reaction

• Molecules: proteins (enzymes and others), DNA, RNA, organic molecules, water, etc.

• Cellular components: membranes, chromosomes, nucleus, ribosomes, etc.

• Processes: metabolism, environmental sensing

• Environmental Condition

• Time or Stage

What a Node Might Represent


• Transformation in a Chemical Reaction: Substrate to product

• Catalytic Relationship: Enzyme to substrate or reaction

• Protein/Protein Interaction

• Signal Transduction

• Regulation of Transcription

• Regulation of Translation

• Activation and Deactivation

What an Edge Might Represent


Outline

• Expresso Team• Drought Stress in Plants• Microarray Technology• Expresso System• Networks in Biology• Future


Ongoing Expresso Work

• Increase model library coverage– New biophysics models of hybridization and spotting

• A heterologous chip– Pinus taeda (Loblolly Pine)– Picea abies (Norway Spruce)

• Multimodal networks– Represent and manipulate biological networks– Incorporate into Expresso and biologists’ work


• Missing biological data is a fact of life

• As a consequence, a network can be lacking in some details, biologically wrong, or even self-contradictory

• Ability to reason computationally with uncertainty and with probabilities is essential

• Uncertainty can suggest hypotheses that can be tested experimentally to refine a network

Uncertainty in Networks


Reconciling Networks


• Nodes and edges have flexible semantics to represent:

- Time

- Uncertainty

- Cellular decision making; process regulation

- Cell topology and compartmentalization

- Rate constants, etc.

• Hierarchical

Multimodal Networks


• Help biologists find new biological knowledge

• Visualize and explore

• Generating hypotheses and experiments

• Predict regulatory phenomena

• Predict responses to stress

• Incorporate into Expresso as part of closing the loop

Using Multimodal Networks


Supported by:Next Generation Software

Information Technology Research

NSF

Documents

Expresso and Chips Studying Drought Stress in Plants with cDNA Microarrays Lenwood S. Heath Department of Computer Science Virginia Tech, VA 24061