Title: Functional Genomics and Bioinformatics
1Functional Genomics and Bioinformatics Applied to
Understanding Oxidative Stress Resistance in
Plants
Ruth Grene Alscher Lenwood S. Heath Naren
Ramakrishnan Virginia Tech December 6, 2001
2Overview
- 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 Ruth Alscher
Carol Loopstra
Senior Collaborators
Students
4Iterative 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
5Proposed 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.
6Plant 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.
7Long 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.
8The 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.
9ROS Arise as a Result of Exposure to
- Ozone
- Sulfur dioxide
- High light
- Paraquat
- Extremes of temperature
- Salinity
- Drought
10Redox Regulation of Cellular Systems
Environmental Stress
Membrane Receptors
Metabolite Defense
Protein kinases phosphatases
Prooxidants (ROS)
Antioxidants
Transcription factors
Gene Expression
Defense, Repair, Apoptosis
11Scenarios for Effects of Abiotic Stress on Gene
Expression in Plants
12Hypotheses
- 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.
13Components 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)
14Imposition 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)
15Categories 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
16Categories within Protective Processes
17Hypotheses 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.
18Hypotheses 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.
19Flow of a Microarray Experiment
Replication and Randomization
PCR
Select cDNAs
Robotic Printing
Test of Hypotheses
Reverse Transcription and Fluorescent Labeling
Extract RNA
20Spot 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)
21Data 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).
22Rule 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.
23ILP 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
-
-
24NGS-Supported Work of 2001 Expresso
25NGS-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
26NGS-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.
27Imposition 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)
28Final Harvest Control versus Mild Stress 2001
Cy3 TIFF Image
Replication
Differential Expression
Cy5 TIFF Image
29Final 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.
30Drought 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?
31Drought 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.
32Proposed 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.
33Gene 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
34Identification 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
35Proposed 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.
36Proposed 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.