Functional Genomics and Bioinformatics Applied to Understanding Oxidative Stress Resistance in Plants

1. Functional Genomics and Bioinformatics Applied to Understanding Oxidative Stress Resistance in Plants Ruth Grene Alscher Lenwood S. Heath Virginia Tech December 14, 2001

2. Overview • Organization of our group • About environmental stress and reactive oxygen species (ROS) • Plant responses to ROS • Analysis of responses to stress on a chip -microarray technology • Expresso: management system for microarrays • Managing expression experiments • Analyzing expression data • Reaching conclusions • Where do we go from here?

3. Ron Sederoff NCSU Lenny Heath Naren Ramakrishnan, Keying Ye Jonathan Watkinson Boris Chevone Logan Hanks Margaret Ellis Len van Zyl NCSU Dawei Chen Cecilia Vasquez Ruth Alscher Carol Loopstra Texas A and M Senior Collaborators Students: VT

4. Iterative strategy for detection of stress -mediated effects on gene expression using microarrays and CS expertise Detection of stress -mediated gene expression effects on microarrays 1 Genetic Regulatory Networks Revised / New Tools and Experiments Computational tools to infer interaction among genes, pathways 4 2 Test inferences with varying conditions and genotypes 3

5. Expresso

6. Plant Response to Stress • Plants adapt to changing environmental conditions through global cellular responses involving successive changes in, and interactions among, expression patterns of numerous genes. • Our group studies these changes through a combination of bioinformatics and genomic techniques.

7. Long Term Goals • Biological: To identify molecular stress resistance mechanisms in tree and crop species. • Bioinformatic: To support iterative experimentation in plant genomics, capture and analyze experimental data, integrate biological information from diverse sources, and close the experimental loop.

8. The Paradox of Aerobiosis • Oxygen is essential, but toxic. • Aerobic cells face constant danger from reactive oxygen species (ROS). • ROS can act as mutagens, they can cause lipid peroxidation and denature proteins.

9. ROS Ariseas a Result of Exposure to: • Ozone • Sulfur dioxide • High light • Herbicides • Extremes of temperature • Salinity • Drought

10. Free Radicals

11. Responses to Environmental Signals

12. Redox Regulation of Cellular Systems Environmental Stress Membrane Receptors Metabolite Defense Prooxidants (ROS) Protein kinases; phosphatases Antioxidants Transcription factors Gene Expression Defense, Repair, Apoptosis

13. Scenarios for Effects of Abiotic Stress on Gene Expression in Plants

14. Drought Stress Responses in Loblolly Pine:Questions to be Addressed • Can a hierarchy of drought stress resistance mechanisms be identified ? • Can a clear distinction be made between rapidly responding and long term adaptational mechanisms? • Can particular subgroups within gene families be associated with drought tolerance?

15. Hypotheses • There is a group of genes whose expression confers resistance to drought stress. • Based on previous work 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. Candidate resistance genes follow this pattern of expression. • A common core of defense genes exists, which responds to several different stresses.

16. Components of Stress Study Pine Drought Stress Experiments Expresso Prototype Select Pine cDNAs 384, 2400 (1999, 2001) Design and Print Microarrays Design Functional Hierarchy Capture Spot Intensities Integrate and Analyze Inductive Logic Programming (ILP)

17. Imposition of Successive Cycles of Mild or Severe Drought Stresson 1-year-old Loblolly Pine Seedlings Water withheld Water withheld Water withheld Water withheld 0 -2 RNA Harvest II RNA Harvest III RNA Harvest IV RECOVERY RNA Harvest I RECOVERY Cycles of Mild Drought Stress RECOVERY RECOVERY DRY DOWN DRY DOWN DRY DOWN DRY DOWN  = water potential (bars) -10 Water given Water given Water given Water given -15 DAYS Water withheld Water withheld Water withheld 0 -2 RNA Harvest II RNA Harvest III RNA Harvest I Cycles of Severe Drought Stress  = water potentional (bars) RECOVERY RECOVERY RECOVERY DRY DOWN DRY DOWN DRY DOWN -10 Water given Water given Water given Cycle I Cycle II Cycle III -15 DAYS = PS (photosynthesis)

18. 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

19. Drought Dehydrins, Aquaporins Heat Heat shock proteins (Chaperones) Abiotic Non-Plant Biotic Cytosolic ascorbate peroxidase Xenobiotics GSTs “Isoflavone Reductases” Chaperones Antioxidant Processes superoxide dismutase-Fe NADPH/Ascorbate/ Glutathione Scavenging Pathway superoxide dismutase-Cu-Zn Sucrose Metabolism Stress glutathione reductase Cellulose Protective Processes Cell Wall Related Arabionogalactan proteins Extensins and proline rich proteins Phenylpropanoid Pathway Hemicellulose Pectins Xylose 4-coumarate-CoA ligases Other Cell Wall Proteins Lignin Biosynthesis CCoAOMTs isoflavone reductases cinnamyl-alcohol dehydrogenase phenylalanine ammonia-lyases S-adenosylmethionine decarboxylases glycine hydromethyltransferases Categories within “Protective Processes”

20. Hypotheses versus Results –1999 Expt • Among the genes responding positively to mild stress, there exists a population of genes whose expression is negative or unchanged under severe stress. • Candidate stress resistance genes. Genes in 69 categories ( e.g. HSP70s and 100s, aquaporins, but not HSP80s) responded positively to mild stress. Effect of severe stress was not detectable or negative.

21. Hypotheses versus Results –1999 Experiment • Genes associated with other stresses responded to drought stress • Isoflavone reductase homologs and GSTs responded positively to mild drought stress. • These categories are previously documented to respond to biotic stress and xenobiotics, respectively. • However, both isoflavone reductase homologs and GSTs responded positively also to severe drought stress. Thus, they do not fall into the category of candidate stress resistance genes.

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

23. Identify Spots Intensities Statistics Clustering Hybridization Hypotheses Data Mining, ILP Flow of a Microarray Experiment Replication and Randomization PCR Select cDNAs Robotic Printing Test of Hypotheses Reverse Transcription and Fluorescent Labeling Extract RNA

24. Design of Microarrays I --- Randomization • Selected 384 archived ESTs • Organized into four 96-well microtitre source plates after PCR • Pipetted into 8 sets of four randomized microtitre plates • Each set is a different randomized arrangement of the 384 ESTs

25. Design of Microarrays II --- Replication • Printed type A microarrays from first four sets (16 plates); printed type B microarrays from second four sets • Each array type has four replicates of each EST, randomly placed • Each comparison was performed with four different hybridizations, with dyes reversed in two • Total of 16 replicates of each EST in each comparison

26. Spot and Clone Analysis • Image Analysis: gridding, spot identification, intensity and background calculation, normalization • Statistics: • Fold or ratio estimation • Combining replicates • Higher-level Analysis: • Clustering methods • Inductive logic programming (ILP)

27. Spot Identification and Intensity Analysis • Microarray Suite: Manual grid; extract intensities for each spot; compute ratios; compute calibrated ratios • Spot Statistics: • Every calibrated ratio is divided by the mean of all the uncalibrated ratios; the result is simply that the mean of the calibrated ratios is 1.0 • Our tools use the logarithm of each calibrated ratio • Positive: expression increase • Negative: expression decrease • Zero: no change in expression

28. Analysis of Expression Data • The multiple (typically 16) log calibrated ratios for a replicated clone do NOT follow a normal distribution. • Distribution is spread relatively evenly over a large range. • Statistical analysis based on mean and standard deviation will be overly pessimistic in identifying clones that are up- or down-expressed. • From the observation of an even spread of the log ratios, we assume that a clone whose expression is not different from a probe pair will show a distribution centered at a mean log ratio of 0.0.

29. Computational Methods --- Alternate Assumptions • Our more general assumption avoids the trap of having to classify the response of each SPOT; rather, we classify the response of an EST as one of • Up-regulated • Down-regulated • No clear change • Response CLASSIFICATION rather than QUANTIFICATION allows us to develop unified relationships among genes and among treatments. • Provides sufficient results for the use of inductive logic programming (ILP).

30. Data Mining: 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).

31. 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%.”

32. 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: Information Integration • “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

33. NSF-Supported Work of 2001: Expresso Progress to Date • Margaret Ellis and Logan Hanks (computer science graduate students): • MEL: Semistructured data model for experiment capture • Parsing: Automatic parser generators to drive archival storage • Database: Loading and cataloging MEL data in a Postgres RDBMS • Pipeline: Linkages to data analysis and data mining software

34. Imposition of Successive Cycles of Mild or Severe Drought Stresson 1-year-old Loblolly Pine Seedlings Water withheld Water withheld Water withheld Water withheld 0 -2 RNA Harvest II RNA Harvest III RNA Harvest IV RECOVERY RNA Harvest I RECOVERY Cycles of Mild Drought Stress RECOVERY RECOVERY DRY DOWN DRY DOWN DRY DOWN DRY DOWN  = water potential (bars) -10 Water given Water given Water given Water given -15 DAYS Water withheld Water withheld Water withheld 0 -2 RNA Harvest II RNA Harvest III RNA Harvest I Cycles of Severe Drought Stress  = water potentional (bars) RECOVERY RECOVERY RECOVERY DRY DOWN DRY DOWN DRY DOWN -10 Water given Water given Water given Cycle I Cycle II Cycle III -15 DAYS = PS (photosynthesis)

35. Final Harvest; Control versus Mild Stress; 2001 Cy3 TIFF Image Replication Differential Expression Cy5 TIFF Image

36. Final Harvest; Control versus Mild Stress; 2001 Cy5 to Cy3 ratios. Final harvest after four drought cycles. RNA harvested 24 hours after final watering. Cy5 = treated; Cy3 = control. Aquaporins responded positively. HSP 80’s were unaffected (same as in 1999 results).

37. Drought Stress Responses in Loblolly Pine:Questions to be Addressed • Can a hierarchy of drought stress resistance mechanisms be identified ? • Can a clear distinction be made between rapidly responding and long term adaptational mechanisms? • Can particular subgroups within gene families be associated with drought tolerance?

38. Proposed Project: 2002-2005 • Plant Biology (with co-PIs: Ron Sederoff, NCSU; Carol Loopstra, TAMU) • An investigation of drought stress responses in lobolly pine in a variety of provenances. • Quantitative RT-PCR to confirm and expand results obtained with microarrays. • In situ hybridization to stressed and unstressed cell and tissue types.

39. Proposed Project: 2002-2005 • Sources of cDNAs for 2002-2005 arrays • NCSU ESTs selected on the basis of function. • Stressed cDNA libraries from roots and stems of drought tolerant families from East Texas and Lost Pines, and from the Atlantic Coastal Plain (humid conditions). • Homologs of drought-responsive Arabidopsis genes.

40. Drought Stress Responses in Loblolly Pine:Future Bioinformatics Goals • Support incorporation of biological information in the form of functional hierarchies and gene families. • Close the computational and experimental loop to support iterative experimental regimes. • Integrate information from multiple experiments involving multiple provenances, drought stresses, and EST sets.

41. Gene Discovery in the Arabidopsis Transcriptome Database Queries Data Mining, ILP Postgres Database Statistical Analysis and Clustering Data Capture Drought Stress (short and long term) Possible Identification of Novel Drought Responsive Genes in Arabidopsis Hybridize to Arabidopsis Transcriptome Scanning, Image Processing

42. Identification of Drought Responsive Genes and Pathways Across Provenances in Loblolly Pine Close The Loop Drought Stress Experiments on NC, TX Pine Database Queries Identification of Drought Responsive Pine Genes Select Pine cDNAs Via Contigs Data Mining, ILP Postgres Database Robotic Replication and Printing Arabidopsis Drought Responsive genes Statistical Analysis and Clustering Hybridization Scanning, Image Processing Data Capture

43. Proposed Project: 2002-2005 • Bioinformatics I (Alscher, Heath, Ramakrishnan) • Constraint-based selection of cDNAs, including intelligent use of contigs. • Assignment of pine ESTs to subgroups within protein families (ProDom, Pfam). • Extend information integration in ILP to include Mendel classification of gene families. • Integrating data across provenances and known degrees of drought tolerance.

44. Proposed Project: 2002-2005 • Bioinformatics II (Ramakrishnan, Heath) • Specialize ILP for particular biological information sources. • Automatic tuning of ILP parameters. • Pushing data mining functionality into the database. • Interleaving and iteration of query, data analysis, and data mining operations.