Functional Genomics and Bioinformatics

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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
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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?

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Ruth Alscher
Carol Loopstra
Senior Collaborators
Students
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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
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Genetic Regulatory Networks
Revised / New Tools and Experiments
Computational tools to infer interaction among
genes, pathways
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2
Test inferences with varying conditions and
genotypes
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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.

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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.

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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.

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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.

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ROS Arise as a Result of Exposure to

• Ozone
• Sulfur dioxide
• High light
• Paraquat
• Extremes of temperature
• Salinity
• Drought

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Redox Regulation of Cellular Systems
Environmental Stress
Membrane Receptors
Metabolite Defense
Protein kinases phosphatases
Prooxidants (ROS)
Antioxidants
Transcription factors
Gene Expression
Defense, Repair, Apoptosis
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Scenarios for Effects of Abiotic Stress on Gene
Expression in Plants
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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.

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Components of 1999 Stress Study
1999 Pine Drought Stress Experiments
2000 Expresso Prototype
Design and Print Microarrays
Select 384 Pine cDNAs
Design Functional Hierarchy
Capture Spot Intensities
Integrate and Analyze
Inductive Logic Programming (ILP)
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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
RNA Harvest I
Cycles of Mild Drought Stress
RECOVERY
RECOVERY
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)
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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
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Categories within Protective Processes
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Hypotheses versus Results 1999 Expt

• 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.

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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.

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Flow of a Microarray Experiment
Replication and Randomization
PCR
Select cDNAs
Robotic Printing
Test of Hypotheses
Reverse Transcription and Fluorescent Labeling
Extract RNA
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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)

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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).

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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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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

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NGS-Supported Work of 2001 Expresso
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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

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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.

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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
RNA Harvest I
Cycles of Mild Drought Stress
RECOVERY
RECOVERY
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)
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Final Harvest Control versus Mild Stress 2001
Cy3 TIFF Image
Replication
Differential Expression
Cy5 TIFF Image
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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 80s were unaffected, as in 1999 results.
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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?

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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.

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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.

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Gene Discovery in the Arabidopsis Transcriptome
Drought Stress (short and long term)
Possible Identification of Novel Drought
Responsive Genes in Arabidopsis
Hybridize to Arabidopsis Transcriptome
Scanning, Image Processing
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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
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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.

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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.