Applications of Functional Genomics and Bioinformatics

Towards an Understanding of Towards an Understanding of Oxidative Stress Resistance Oxidative Stress Resistance

in Plants: Expresso and in Plants: Expresso and ChipsChips

Overview• Environmental stress and reactive oxygen

species (ROS)• Plant responses to ROS• Stress on a chip — current results• Expresso

– Managing expression experiments– Analyzing expression data– Reaching conclusions

• Some future directions — Collaborating with CIP on resistance mechanisms in Andean root and tuber crop species

The Paradox of Aerobiosis

• Oxygen is essential, yet also potentially toxic.

• Aerobic cells maintain themselves against constant danger of production of reactive oxygen species (ROS).

• ROS can act as mutagens, they can cause lipid peroxidation and denature proteins.

Mitochondrion

Chloroplast

Nucleus

Cytosol

Cell Wall

WoundingChilling Ozone

Drought,Salinity

ExpressionGene

Antioxidant genes

Post-transcriptionalEffects

ParaquatHigh Light + Chilling

Sulfur Dioxide

,,

subcellularROS

sitesunclear

(

)

,Pathogens

Post-transcriptionalEffects

Mitochondrion

Chloroplast

Nucleus

Cytosol

Cell Wall

WoundingChilling Ozone

ExpressionGene

Antioxidant genes

Post-transcriptionalEffects

ParaquatHigh Light + Chilling

Sulfur Dioxide

Pathogens

Post-transcriptionalEffects

ROS Arise Throughout the Cell

(ROS subcellular sites unclear)

Drought Salinity

Cellular Redox Homeostasis

• Maintained enzymatically • Glutathione, Ascorbate (soluble).

– Alpha-tocopherol, Carotenoids (membrane).• Antioxidant pools increase with stress. • Protein methionine sulfoxidation is an

additional antioxidant reservoir.• Molecular chaperones (heat shock proteins) act

as repair mechanism.

ROS Arise as a Result of Exposure to:

• Ozone

• Sulfur dioxide

• High light

• Paraquat

• Extremes of temperature

• Salinity

• Drought

Plant-Environment Interactions

• Several defense systems that respond to environmental stress are known.

• Their relative importance is not known.

• Mechanistic details are not known. Redox sensing may be involved.

A Basis for Cellular Responses to ROS

Thiol Redox ControlThiol Redox Control

StressStress DefenseDefense

Redox Regulation of Gene Expression

Mem bra neRece ptors

(Oxylipins)

Protein kinases;Phosph oprotein

phosphata ses

Tra nscr iptio nfac tors (Redox-

sensitive?)

Gene expres sio n

Cellula r respons e: Defense proc esses Repai r proc esses

Prooxidant s (ROS):

O2.

H2O2

NO.

Environmenta l Stress

Antioxida nts:

Trx-(SH)2/Trx-S2

2 GSH/GSSG

Grx-(SH)2/Grx-S2

Met/MetO

Asc/ DHAsc

Cellula r DefenseRes ponse

Adaptation

ROS Scavenging in Plastids

PSII

Fe?Cu,Zn?

NADP+

NADPH

GSH

GSSG

DHA

AsA

MDHARStromalPathway

ThylakoidPathway

AsA

NAD(P)H

H2O2 H2O

Fd O2

O2.- H2O2 H2O

SOD

GR

sAPX

PSI

e-

PSII

2H2O

tAPX

Fd O2

DHAR

Fe?

Thylakoid Membrane

Thylakoid Lumen

Stroma

SOD

NAD(P)+

x2

PSI

e-

2H2O

MDA.

O2.-

MDA.

4 e- + 4 H+ + O2 4 e- + 4 H+ + O2

O2.-

Stress Resistance — Short Term “Emergency”

• Accumulated evidence suggests that successful resistance to stress imposition consists in the mobilization of cellular defense machinery.

(Short term exposures to oxidative stress conditions in a number of crop species, and cultivars within species.)

• Activation of defense genes, such as SOD, glutathione reductase

• Stimulation of antioxidant biosynthetic pathways, such as glutathione

Differential Response of Plastid SOD to Sulfur Dioxide in Two Cultivars of Pea

Exposure to sulfur dioxide in resistant (Progress) and sensitive (Nugget) cultivars of pea resulted in increases in plastid Cu-Zn SOD mRNA and protein only in the resistant cultivar. Kinetics of increase correlates with recovery of photosynthesis in cv. Progress.

Stress Resistance — Long Term Adaptation to Harsh Environmental

ConditionsLess data available than for emergency responses. But overlap with emergency processes?

Candidates include:

• Low temperatures - glutathione-associated processes, cryoprotective proteins and oligosaccharides

• High temperature- heat shock proteins

• Drought- water channel proteins (aquaporins), dehydrins

Season-Specific Isoforms of Glutathione Reductase in Spruce

Winter and summer specific isoforms of glutathione reductase exist in red spruce. The appearance of the winter specific form correlates with the onset of hardening.

Glutathione Reductase Genes (GR1)

Glutathione Reductase Genes (GR2)

Candidate Resistance Mechanisms

• In the past, candidate mechanisms were examined known gene by known gene, process by process.

• Microarray Technology

– Simultaneous examination of groups of candidate genes and associated interactions

– Possible discovery of new defense mechanisms

Spots:(Sequences affixed to slide)

1 2 3

11

2

21

3

1 2

2333

Treatment Control

Mix

1 2 3

Excitatio

n

Em

issi

on

Detection

Relative AbundanceDetection

Hybridization

Detection of gene expression effects on microarrays

Characterize gene function

Test mutant phenotypes

Genetic Regulatory Networks

Identify mutants

1

2

3

4

Iterative strategy for detection of genetic interactions using microarrays

• Precedent I: Plants adapt to adverse environmental conditions via a global cellular response involving changes in the expression patterns of numerous genes.• Precedent II: To study these changes, the Expresso team uses bioinformatics and experimental techniques.• Long term goal: To identify and improve emergency and long term adaptational stress response mechanisms in crop species.

Long Term Goal

• Integration of design and procedures

• Integration of image analysis tools and statistical analysis

• Connections to web databases and sequence alignment tools

• The software Aleph was used for inductive logic programming (ILP).

Expresso: A Problem Solving Environment for Microarray

Experiment Design and Analysis

Who’s Who

Ruth Alscher Plant Stress

Boris Chevone Plant Stress

Ron Sederoff, Ross WhettenLen van ZylY-H.SunForest Biotechnology

Plant BiologyComputer Science

Lenwood Heath (CS)Algorithms

Naren Ramakrishnan (CS)Data Mining

Problem Solving Environments

Craig Struble,Vincent Jouenne (CS)

Image Analysis

Statistics

Ina Hoeschele (DS)Statistical Genetics

Keying Ye (STAT)Bayesian Statistics

Virginia Tech

North Carolina State Univ.

Virginia Tech

Virginia Tech

Dawei Chen

Molecular Biology

Bioinformatics

Expresso People

Ross WhettenBoris Chevone

Ron Sederoff

Y-H .Sun Dawei Chen

Lenny Heath

Ruth Alscher

Vincent Jouenne

Naren Ramakrishnan

Keying Ye

Len van Zyl

Craig Struble

The 1999 Experiment: A Measure of Long Term Adaptation to

Drought Stress• Loblolly pine seedlings (two unrelated genotypes “C” and

“D”) were subjected to mild or severe drought stress for four (mild) or three (severe) cycles.

– Mild stress: needles dried down to –10 bars; little effect on growth, new flushes as in control trees.

– Severe stress: needles dried down to –17 bars; growth retardation, fewer new flushes compared to controls.

• Harvest RNA at the end of growing season, determine patterns of gene expression on DNA microarrays.

• With algorithms incorporated into Expresso, identify genes and groups of genes involved in stress responses.

Scenarios for Effects of Specific Stresses on Gene Expression

Hypotheses

• There is a group of genes whose expression confers resistance to drought stress.

• Expression of this group of genes is lower under severe than under mild stress.

• Individual members of gene families show distinct responses to drought stress.

Selection of cDNAs for Arrays

• 384 ESTs (xylem, shoot tip cDNAs of loblolly) were chosen on the basis of function and grouped into categories.

• Major emphasis was on processes known to be stress responsive.

• In cases where more than one EST had similar BLAST hits, all ESTs were used.

Categories within Protective and Protected Processes

Plant Growth Regulation

Environmental

Change

GeneExpression

SignalTransduction

ProtectiveProcesses

ProtectedProcesses

ROS and Stress

Cell Wall Related

PhenylpropanoidPathway

Development

Metabolism

Chloroplast Associated

Carbon Metabolism

Respiration and Nucleic Acids

Mitochondrion

Cells

Tissues

Cytoskeleton

Secretion

Trafficking

Nucleus

Protease-associated

A Note about Categories

Categories are not mutually exclusive; gene(s) may be assigned to more then one category. For example, heat shock proteins have been grouped under these different categories and subcategories– Abiotic stress – heat– Gene expression – post-translational

processing – chaperones– Abiotic stress - chaperones

ProtectiveProcesses

Stress

Cell Wall Related

PhenylpropanoidPathway

AbioticBiotic

Antioxidant Processes

Drought

HeatNon-Plant

Xenobiotics

NADPH/Ascorbate/GlutathioneScavenging Pathway

Cytosolicascorbateperoxidase

Dehydrins, Aquaporins

Heat shock proteins(Chaperones)

superoxidedismutase-Fe

superoxidedismutase-Cu-Zn

glutathionereductase

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-CoAligases

cinnamyl-alcoholdehydrogenase

Chaperones“IsoflavoneReductases”

GSTs

Extensins and proline rich proteinsCategorieswithin

“Protective Processes”

Quality Control

• Positive: LP-3, a loblolly gene known to respond positively to drought stress in loblloly pine, was included.

• LP-3 was positive in the moist versus mild comparison, and unchanged in the moist versus severe comparison.

• Negative: Four clones of human genes used as negative controls in the Arabidopsis Functional Genomics project were included. The clones did not respond.

ProtectiveProcesses

ROS and Stress

Cell Wall Related

PhenylpropanoidPathway

AbioticBiotic

AntioxidantProcesses

Drought

HeatNon-PlantXenobiotics

NADPH/Ascorbate/GlutathioneScavenging Pathway

Cystosolicascorbateperoxidase

Dehydrins, Aquaporins

Heat shock proteins

superoxidedismutase-Fe

superoxidedismutase-Cu-Zn

glutathionereductase

Sucrose Metabolism

Cellulose

Extensins, Arabionogalactan,and Proline Rich Proteins

Hemicellulose

Pectins

Xylose

Other Cell Wall Proteins

isoflavone reductases

phenylalanine ammonia-lyase

S-adenosylmethionine decarboxylase

glycine hydromethyltransferase

Lignin Biosynthesis CCoAOMT

4-coumarate-CoAligase

cinnamyl-alcoholdehydrogenase

Chaperones“IsoflavoneReductases”

GSTs

Categories thatcontained positives ingenotypes C and D(Control versus Mild)

Data from two slides (4 arrays)for C and two slides (4 arrays)for D were collected.

Hypotheses versus Results• Among the genes responding to mild stress, there

exists a population of genes whose expression confers resistance. – Genes in 69 categories responded positively to mild stress in

Genotypes C and D (the positive response was not observed in the severe stress condition in Genotype D).

• There is evidence for a response to drought among genes associated with other stresses.– Isoflavone reductase homologs and GSTs responded

positively to mild drought stress.

– These categories are previously documented to respond to biotic stress and xenobiotics, respectively.

Relationships among HSP Homologs

In control versus mild stress,HSP 100, 70, and 23 responded in C and D;HSP 80s did not respond in either C or D.

Candidate Categories — Long-term Adaptation to Drought Stress

• Include– Aquaporins– Dehydrins– Heat shock proteins/chaperones

• Exclude– Isoflavone reductases

• Clones on the drought-stress microarrays were replicated and randomly placed

• Experiment involved 384 archived pine ESTs

• Organized into 4 microtitre source plates after PCR

• Pipetted into 8 sets of 4 microtitre plates each

• Each set a different random arrangement of 384 ESTs

• Printed type A microarrays from first 4 sets

• Printed type B microarrays from second 4 sets

• Each array has 4 randomly placed replicates of each EST

• Each control versus stress comparison was done on 4 arrays — A and B; flip dyes; A and B

• Total of 16 replicates of each EST in each comparison

Design of Microarrays

• 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

Image Analysis

Microarray Suite:• Manual gridding• Extract two intensities for each spot• Compute ratios• Compute calibrated ratios

Our tools use the logarithm of the calibrated ratios

Computational and Statistical Analysis

• The multiple (typically 16) log calibrated ratios for a replicated clone do NOT follow a normal distribution.

• We assume a zero-centered distribution for log ratios.

• The number of positive (or negative) log ratios follows a binomial distribution with parameters 16 and 0.5.

• A clone with 12 or more positive log ratios is up-expressed with a probability of 0.96.

• We classify each EST response as one of– Up-regulated– Down-regulated– No clear change

• Provides sufficient results for the use of inductive logic programming (ILP).

Related Statistical Results• Chen et al. (J. Biomed. Optics 2, 1997, 364-374)

– Assume a normal distribution and normalize ratios

– No replicates

– Estimate a confidence interval for ratios that applies to each spot

• Lee et al. (PNAS 97, August 29, 2000, 9834-9) emphasize need for replication

• Black and Doerge (PNAS, to appear)

– Investigate distributional assumptions of log-normal and gamma distributions on intensities

– Determine the number of replicates needed for a particular confidence level under each distribution

– Assume normalization has been done and location-dependent error has been eliminated.

Further Analysis: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%.”

More Rules We Obtained• [Rule 6]

level(A,moist_vs_mild,positive) :-

category(A, transport_protein).

level(A,mild_vs_severe,negative) :-

category(A, transport_protein).• [Rule 13]

level(A,moist_vs_mild,positive) :-

category(A, heat).• [Rule 17]

level(A,moist_vs_mild,positive) :-

category(A, cellwallrelated).

ILP Subsumes Two Forms of Reasoning

• Unsupervised learning

– “Find clusters of genes that have similar/consistent expression patterns”

• Supervised learning

– “Given several patterns of gene expression for two conditions, give an equation that distinguishes the patterns for each condition ”

• Hybrid reasoning

– “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

Current Status of Expresso

• Completely automated and integrated– Statistical analysis– Data mining– Experiment capture in MEL

• Current Work: Integrating– Image processing– Querying by semi-structured views– Expresso-assisted experiment composition

Future DirectionsNext Generation Stress Chips

1. Time course, short and long term, to capture gene expression events underlying “emergency” and adaptive events following drought stress imposition.

(Use all available ESTs for candidate stress resistance genes.)

2. Generate cDNA library from stressed seedlings.

3. Initiate modeling of kinetics of drought stress responses.

Gene Expression Events Associated with Extreme Environmental Conditions

• Hypothesis 1: Specific genes that confer ability to adapt to extreme conditions are expressed in Andean potato varieties and in other root and tuber crops of the region.

• Hypothesis 2: The adaptive genes act either individually or in co-adaptive groups.

• Hypothesis 3: The adaptive genes are also expressed in temperate zone varieties or they are specific to extreme conditions.

• Proposed approach: Use of microarrays as a tool for discovery of these adaptive genes.

Successful Emergency Responses Versus Adaptation to Diurnal Variation

• Cultivar differences with respect to degree/rapidity of gene expression

• Cultivar differences with respect to rate of synthesis of antioxidant(s)

• Our question: Are the genes that respond in the short term the same ones that confer stress resistance diurnally?

Relevant Data

• Similarities among orthologous genes are sufficiently close that cross-hybridization occurs on microarrays between species (R. R. Sederoff, personal communication).

• Although the above confounds treatment responses shown by individual members of multi-gene families, it allows use of a chip based on one species in inter-specific hybridizations.

Collaborating with CIP Suggested Strategy I

• Identify patterns of gene expression in S. tuberosum associated with successful adaptation to stress (temperature extremes, with or without drought? )

• Construct two or more cDNA libraries from adapted potato

• Design “potato stress chips” including the stress cDNA library and known stress-responsive genes of S. tuberosum (SolGenes resource)

Suggested Strategy IIAn Experimental Approach

• Identify variety-specific diurnal gene expression patterns in Andean potato varieties using potato stress chip.

• Construct arrays of cDNAs from Andean varieties. Compare mRNA populations isolated during adaptation of temperate zone potatoes with RNA obtained from Andean varieties.

Detection of gene expression effects on microarrays

Characterize gene function

Test mutant phenotypes

Genetic Regulatory Networks

Identify mutants

1

2

3

4

Iterative strategy for detection of genetic interactions using microarrays

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 mutants/varying

conditions

1

2

3

4

Iterative strategy for detection of genetic interactions using microarrays

and CS expertise