Modeling and Understanding Stress Response Mechanisms with Expresso Ruth G. Alscher Lenwood S. Heath...
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![Page 1: Modeling and Understanding Stress Response Mechanisms with Expresso Ruth G. Alscher Lenwood S. Heath Naren Ramakrishnan Virginia Tech, Blacksburg, VA 24061.](https://reader036.fdocuments.in/reader036/viewer/2022062515/56649d245503460f949fb3bd/html5/thumbnails/1.jpg)
Modeling and UnderstandingModeling and UnderstandingStress Response MechanismsStress Response Mechanisms
with with ExpressoExpresso
Ruth G. AlscherRuth G. Alscher
Lenwood S. Heath Lenwood S. Heath
Naren RamakrishnanNaren Ramakrishnan
Virginia Tech, Blacksburg, VA 24061 Virginia Tech, Blacksburg, VA 24061
NSF Site VisitNCSU Forest Biotechnology Group
July 12, 2001
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Who’s Who
Ruth Alscher Plant Stress
Boris Chevone Plant Stress
Ron Sederoff, Ross WhettenLen van ZylY-H.SunForest Biotechnology
Plant BiologyComputer Science
Lenwood Heath (CS)Algorithms
Naren Ramakrishnan (CS)Data Mining
Problem Solving Environments
Craig Struble,Vincent Jouenne (CS)
Image Analysis
Statistics
Ina Hoeschele (DS)Statistical Genetics
Keying Ye (STAT)Bayesian Statistics
Virginia Tech
North Carolina State Univ.
Virginia Tech
Virginia Tech
Dawei Chen
Molecular Biology
Bioinformatics
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People
Ross WhettenBoris Chevone
Ron Sederoff
Y-H .Sun Dawei Chen
Lenny Heath
Ruth Alscher
Vincent Jouenne
Naren Ramakrishnan
Keying Ye
Len van Zyl
Craig Struble
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Overview
• Plant responses to environmental stress• Stress on a chip• Summary of results obtained• Expresso
– Managing expression experiments– Analyzing expression data– Reaching conclusions
• Where we go from here– Modeling experiments– Modeling pathways
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Plant-Environment Interactions
• Several defense systems that respond to environmental stress are known.
• Their relative importance is not known.
• Mechanistic details are not known. Redox sensing may be involved.
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Scenarios for Effect of Abiotic Stress on Plant Gene Expression
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The 1999 Experiment: A Measure of Long Term Adaptation to
Drought Stress• Loblolly pine seedlings (two unrelated genotypes “C”
and “D”) were subjected to mild or severe drought stress for four (mild) or three (severe) cycles.– Mild stress: needles dried down to –10 bars; little
effect on growth, new flushes as in control trees.– Severe stress: needles dried down to –17 bars;
growth retardation, fewer new flushes compared to controls.
• Harvest RNA at the end of growing season, determine patterns of gene expression on DNA microarrays.
• With algorithms incorporated into Expresso, identify genes and groups of genes involved in stress responses.
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Hypotheses
• There is a group of genes whose expression confers resistance to drought stress.
• Based on previous work (RGA and others for superoxide dismutases and glutathione reductases) increased expression of defense genes is co-regulated and is correlated with resistance to oxidative stress. Failure to cope is correlated with little or no defense gene activation.
• A common core of defense genes exists, which responds to several different stresses.
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Selection of cDNAs for Arrays
• 384 ESTs (xylem, shoot tip cDNAs of loblolly) were chosen on the basis of function and grouped into categories.
• Major emphasis was on processes known to be stress responsive.
• In cases where more than one EST had similar BLAST hits, all ESTs were used.
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Categories within Protective and Protected Processes
Plant Growth Regulation
Environmental
Change
GeneExpression
SignalTransduction
ProtectiveProcesses
ProtectedProcesses
ROS and Stress
Cell Wall Related
PhenylpropanoidPathway
Development
Metabolism
Chloroplast Associated
Carbon Metabolism
Respiration and Nucleic Acids
Mitochondrion
Cells
Tissues
Cytoskeleton
Secretion
Trafficking
Nucleus
Protease-associated
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Hypotheses versus Results
• Among the genes responding to mild stress, there exists a population of genes whose expression confers resistance. – Genes in 69 categories responded positively to mild
stress in Genotypes C and D (the positive response was not observed in the severe stress condition in Genotype D).
• There is evidence for a response to drought among genes associated with other stresses.– Isoflavone reductase homologs and GSTs responded
positively to mild drought stress.– These categories are previously documented to
respond to biotic stress and xenobiotics, respectively.
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Quality Control
• Positive: LP-3, a loblolly gene known to respond positively to drought stress in loblloly pine, was included.
LP-3 was positive in the moist versus mild comparison, and unchanged in the moist versus severe comparison.
• Negative: Four clones of human genes used as negative controls in the Arabidopsis Functional Genomics project were included. The clones did not respond.
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Candidate Categories
• Include– Aquaporins– Dehydrins– Heat shock proteins/chaperones
• Exclude– Isoflavone reductases
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• Integration of design and procedures
• Integration of image analysis tools and statistical analysis
• Connections to web database and sequence alignment tools
• The software Aleph was used for inductive logic programming (ILP).
Expresso: A Problem Solving Environment (PSE) for Microarray Experiment Design and Analysis
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Expresso: A Microarray Experiment Management System
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Inductive Logic Programming
• ILP is a data mining algorithm expressly designed for inferring relationships.
• By expressing relationships as rules, it provides new information and resultant testable hypotheses.
• ILP groups related data and chooses in favor of relationships having short descriptions.
• ILP can also flexibly incorporate a priori biological knowledge (e.g., categories and alternate classifications).
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Rule Inference in ILP
• Infers rules relating gene expression levels to categories, both within a probe pair and across probe pairs, 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%.”
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More Rules we Obtained
• [Rule 6]
level(A,moist_vs_mild,positive) :-
category(A, transport_protein).
level(A,mild_vs_severe,negative) :-
category(A, transport_protein).
• [Rule 13]
level(A,moist_vs_mild,positive) :-
category(A, heat).
• [Rule 17]
level(A,moist_vs_mild,positive) :-
category(A, cellwallrelated).
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ILP subsumes two forms of reasoning
• Unsupervised learning– “Find clusters of genes that have similar/consistent
expression patterns”
• Supervised learning– “Find a relationship between a priori functional
categories and gene expression”
• Hybrid reasoning– “Is there a relationship between genes in a given
functional category and genes in a particular expression cluster?”
– ILP mines this information in a single step
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ILP in a Data Mining Context
Attribute-Value Methods
Clustering
Conceptual Clustering
SVMs SOMs
Similarity-Metric
Agglomerative Divisive(bottom-up) (top-down)
ILP combines the expressivenessof conceptual clustering withthe efficiency of attribute-valuetechniques.
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Current Status of Expresso
• Completely automated and integrated– Statistical analysis– Data mining– Experiment capture in MEL
• Current Work: Integrating– Image processing– Querying by semi-structured views– Expresso-assisted experiment composition
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Future DirectionsNext Generation Stress Chips
2. Further work on Expresso and pine cDNA microarray experiments recently funded by an NSF Next Generation Software grant.
3. Time course, short and long term, to capture gene expression events underlying “emergency” and adaptive events following drought stress imposition. (Use all currently available pine ESTs for candidate stress resistance genes.)
4. Initiate modeling of kinetics of drought stress responses.
5. Generate cDNA library from stressed seedlings.
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Future DirectionsExpresso
• An open, integrated system for design, process, analysis, data mining, data storage, and integration of information from web-based resources.
• Supports closing the experimental loop. Accumulated results influence later experiments, as well as enable construction of testable models of pathways.
• Multiple models are refined and evaluated within Expresso.
• Biologists have interactive access to models and control Expresso’s components.