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

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?

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

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

Expresso

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. Plant Response to Stress

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. Long Term Goals

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.

ROS Arise as a Result of Exposure to: Ozone Sulfur dioxide High light Herbicides Extremes of temperature Salinity Drought

Free Radicals

Responses to Environmental Signals

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

Scenarios for Effects of Abiotic Stress on Gene Expression in Plants

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?

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.

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

Imposition of Successive Cycles of Mild or Severe Drought Stress on 1-year-old Loblolly Pine Seedlings DAYS  water potential (bars) RNA Harvest I RNA Harvest II RNA Harvest III RNA Harvest IV Cycles of Mild Drought Stress DRY DOWN = P S (photosynthesis) DAYS  water potentional (bars) Cycle I Cycle II Cycle III RNA Harvest I RNA Harvest II RNA Harvest III Cycles of Severe Drought Stress DRY DOWN Water withheld Water given Water withheld Water given Water withheld RECOVERY

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

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

Hypotheses versus Results – 1999 Expt oAmong 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.

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. Hypotheses versus Results – 1999 Experiment

Candidate Categories Include –Aquaporins –Dehydrins –Heat shock proteins/chaperones Exclude –Isoflavone reductases

Flow of a Microarray Experiment Hypotheses Select cDNAs PCR Test of Hypotheses Extract RNA Replication and Randomization Reverse Transcription and Fluorescent Labeling Robotic Printing HybridizationIdentify SpotsIntensitiesStatisticsClusteringData Mining, ILP

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 Design of Microarrays I --- Randomization

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 Design of Microarrays II --- Replication

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) Spot and Clone Analysis

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

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.

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).

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).

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%.” Rule Inference in ILP

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

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

Imposition of Successive Cycles of Mild or Severe Drought Stress on 1-year-old Loblolly Pine Seedlings DAYS  water potential (bars) RNA Harvest I RNA Harvest II RNA Harvest III RNA Harvest IV Cycles of Mild Drought Stress DRY DOWN = P S (photosynthesis) DAYS  water potentional (bars) Cycle I Cycle II Cycle III RNA Harvest I RNA Harvest II RNA Harvest III Cycles of Severe Drought Stress DRY DOWN Water withheld Water given Water withheld Water given Water withheld RECOVERY

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

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).

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?

Proposed Project: 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.

Proposed Project: Sources of cDNAs for 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.

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.

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

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

Proposed Project: 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.

Proposed Project: 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.