Modeling and Understanding Stress Response Mechanisms with Expresso

1. Modeling and UnderstandingStress Response Mechanismswith Expresso Ruth G. Alscher Lenwood S. Heath Naren Ramakrishnan Virginia Tech, Blacksburg, VA 24061 NSF Site Visit NCSU Forest Biotechnology Group July 12, 2001

2. Who’s Who Computer Science Plant Biology Virginia Tech Ruth Alscher Plant Stress Lenwood Heath (CS) Algorithms Virginia Tech Dawei Chen Molecular Biology Bioinformatics Boris Chevone Plant Stress Naren Ramakrishnan (CS) Data Mining Problem Solving Environments Ron Sederoff, Ross Whetten Len van Zyl Y-H.Sun Forest Biotechnology North Carolina State Univ. Craig Struble, Vincent Jouenne (CS) Image Analysis Ina Hoeschele (DS) Statistical Genetics Keying Ye (STAT) Bayesian Statistics Statistics Virginia Tech

3. People Ron Sederoff Craig Struble Lenny Heath Ruth Alscher Keying Ye Ross Whetten Vincent Jouenne Boris Chevone Len van Zyl Y-H .Sun Dawei Chen Naren Ramakrishnan

4. 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

5. 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.

6. Scenarios for Effect of Abiotic Stress on Plant Gene Expression

7. 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.

8. 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.

9. 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.

10. Categories within Protective and Protected Processes Gene Expression Signal Transduction Protease-associated ROS and Stress Nucleus Environmental Change Protective Processes Cell Wall Related Trafficking Phenylpropanoid Pathway Secretion Cells Cytoskeleton Development Tissues Plant Growth Regulation Protected Processes Chloroplast Associated Metabolism Carbon Metabolism Respiration and Nucleic Acids Mitochondrion

11. 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.

12. 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.

13. Candidate Categories • Include • Aquaporins • Dehydrins • Heat shock proteins/chaperones • Exclude • Isoflavone reductases

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

15. Expresso: A Microarray Experiment Management System

16. 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).

17. 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%.”

18. 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).

19. 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

20. ILP in a Data Mining Context Clustering Conceptual Clustering Attribute-Value Methods ILP combines the expressiveness of conceptual clustering with the efficiency of attribute-value techniques. Similarity-Metric SVMs SOMs Divisive Agglomerative (top-down) (bottom-up)

21. 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

22. Future Directions Next Generation Stress Chips • Further work on Expresso and pine cDNA microarray experiments recently funded by an NSF Next Generation Software grant. • 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.) • Initiate modeling of kinetics of drought stress responses. • Generate cDNA library from stressed seedlings.

23. Future Directions Expresso • 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.