Functional Genomics and Bioinformatics Applied to Understanding Oxidative Stress Resistance in Plants Ruth Grene Alscher Lenwood S. Heath Naren Ramakrishnan Virginia Tech December 6, 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? Ron Sederoff Ruth Alscher Carol Loopstra Lenny Heath Naren Ramakrishnan Senior Collaborators Students Boris Chevone Jonathan Watkinson Len van Zyl Keying Ye Margaret Ellis Cecilia Vasquez Dawei Chen Logan Hanks 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 4 Genetic Regulatory Networks Revised / New Tools and Experiments 3 Test inferences with varying conditions and genotypes 2 Computational tools to infer interaction among genes, pathways 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. 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. 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. 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 Paraquat Extremes of temperature Salinity Drought Redox Regulation of Cellular Systems Environmental Stress Prooxidants (ROS) Membrane Receptors Metabolite Defense Protein kinases; phosphatases Antioxidants Transcription factors Gene Expression Defense, Repair, Apoptosis Scenarios for Effects of Abiotic Stress on Gene Expression in Plants Hypotheses • There is a group of genes whose expression confers resistance to drought stress. • Based on previous work (Alscher 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. Components of 1999 Stress Study 1999 Pine Drought Stress Experiments 2000 Expresso Prototype Select 384 Pine cDNAs Design and Print Microarrays Design Functional Hierarchy Integrate and Analyze Inductive Logic Programming (ILP) Capture Spot Intensities = water potential (bars) Imposition of Successive Cycles of Mild or Severe Drought Stress on 1-year-old Loblolly Pine Seedlings Water withheld Water withheld Water withheld Water withheld 0 -2 RNA Harvest RNA Harvest RNA Harvest RNA Harvest I II III IV -10 Water given -15 Water given Water given Water given Cycles of Mild Drought Stress = water potentional (bars) DAYS Water withheld Water withheld Water withheld 0 -2 -10 -15 Cycle I = PS (photosynthesis) RNA Harvest RNA Harvest RNA Harvest I II III Water given Cycle II DAYS Water given Cycle III Water given Cycles of Severe Drought Stress Categories within Protective and Protected Processes Gene Expression Signal Transduction Protease-associated ROS and Stress Environmental Change Protective Processes Nucleus Cell Wall Related Trafficking Phenylpropanoid Pathway Development Protected Processes Secretion Cells Cytoskeleton Tissues Plant Growth Regulation Chloroplast Associated Metabolism Carbon Metabolism Respiration and Nucleic Acids Mitochondrion Abiotic Biotic Stress Protective Processes Cell Wall Related “Isoflavone Reductases” Antioxidant Processes Phenylpropanoid Pathway Categories within “Protective Processes” Drought Dehydrins, Aquaporins Heat Non-Plant Heat shock proteins (Chaperones) Xenobiotics GSTs Chaperones NADPH/Ascorbate/ Glutathione Scavenging Pathway Sucrose Metabolism Cellulose Arabionogalactan proteins Cytosolic ascorbate peroxidase superoxide dismutase-Fe superoxide dismutase-Cu-Zn glutathione reductase Extensins and proline rich proteins Hemicellulose Pectins Xylose Other Cell Wall Proteins Lignin Biosynthesis isoflavone reductases phenylalanine ammonia-lyases S-adenosylmethionine decarboxylases glycine hydromethyltransferases 4-coumarate-CoA ligases CCoAOMTs cinnamyl-alcohol dehydrogenase Hypotheses versus Results – 1999 Expt o Among the genes responding to mild stress, there exists a population of genes whose expression confers resistance. – Candidate stress resistance genes. Genes in 69 categories ( e.g. HSP70s and 100s, but not HSP80s, aquaporins) responded positively to mild stress. Effect of severe stress was not detectable or negative. 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 to severe drought stress. Flow of a Microarray Experiment PCR Select cDNAs Replication and Randomization Robotic Printing Hypotheses Identify Spots Intensities Statistics Hybridization Test of Hypotheses Extract RNA Clustering Reverse Transcription and Fluorescent Labeling Data Mining, ILP 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) 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). 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%.” 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 NGS-Supported Work of 2001: Expresso NGS-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 NGS-Supported Work of 2001: Progress to Date • Cecilia Vasquez (plant biology graduate student): Loblolly pine seedlings were subjected to the same cycles of drought stress as in 1999, with photosynthesis, water potential measurements, and RNA isolations carried out throughout the time course of the experiment. • Jonathan Watkinson (post-doctoral associate): RNA was hybridized to an array of 2400 pine cDNAs at NCSU. Data capture. = water potential (bars) Imposition of Successive Cycles of Mild or Severe Drought Stress on 1-year-old Loblolly Pine Seedlings Water withheld Water withheld Water withheld Water withheld 0 -2 RNA Harvest RNA Harvest RNA Harvest RNA Harvest I II III IV -10 Water given -15 Water given Water given Water given Cycles of Mild Drought Stress = water potentional (bars) DAYS Water withheld Water withheld Water withheld 0 -2 -10 -15 Cycle I = PS (photosynthesis) RNA Harvest RNA Harvest RNA Harvest I II III Water given Cycle II DAYS Water given Cycle III Water given Cycles of Severe Drought Stress Differential Expression Replication Final Harvest; Control versus Mild Stress; 2001 Cy3 TIFF Image Cy5 TIFF Image 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, while HSP 80’s were unaffected, 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? Drought Stress Responses in Loblolly Pine: Proposed 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. 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. Gene Discovery in the Arabidopsis Transcriptome Drought Stress (short and long term) Postgres Database Hybridize to Arabidopsis Transcriptome Database Queries Data Mining, ILP Statistical Analysis and Clustering Scanning, Image Processing Data Capture Possible Identification of Novel Drought Responsive Genes in Arabidopsis Identification of Drought Responsive Genes and Pathways Across Provenances in Loblolly Pine Select Pine cDNAs Via Contigs Robotic Replication and Printing Hybridization Scanning, Image Processing Close The Loop Database Queries Identification of Drought Responsive Pine Genes Arabidopsis Drought Responsive genes Postgres Database Drought Stress Experiments on NC, TX Pine Data Mining, ILP Statistical Analysis and Clustering Data Capture 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. 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.