Microarray and functional genomics Wenjing Tao University of Missouri

Microarray: high through-put whole genome approach Microarray is a tool for analyzing gene expression that consists of a small membrane or glass slide containing samples of many genes arranged in a regular pattern 48 grids, with 31k probes Each grid contain 650 probes

Microarray terminology Feature - an array element Probe - a feature corresponding to a defined sequence (immobilized on a solid surface in an ordered array) Target - a pool of nucleic acids of unknown sequence

-Find the genes and assign them functions -Predict protein structures and functions -Reconstruct metabolic, signaling, and other pathways -Reconstruct informational networks -Link genotype to phenotype -Use genotype/phenotype to predict relevant outcome -Cross- species comparisons Microarray provides the opportunities

Kinds of array features Synthetic oligonucleotides: Affymetrix genechip Long oligo array PCR products from: Cloned cDNAs Genomic DNA

cDNA & oligonucleotide arrays m spot mers Schulze and Downward, 2001 Nat Cell Biol 3, 190

Target2 Target1 RNA RT Labeling with Flouresent dye ORFs or ESTs Design long oligoes Microtiter plateMicroarray slides Hybridization Scan cDNA and long oligo array experiment

Affymetrix GeneChip

GREEN represents Reference DNA hybridized to the target DNA. RED represents Test DNA hybridized to the target DNA. YELLOW represents a combination of Test and Reference DNA hybridized equally to the target DNA. BLACK represents areas where neither the Reference nor Test DNA hybridized to the target DNA.

Fluorescent microarrays are composed of a combined two false color laser scanned images

Image file post-processing Single slide normalization – GenePix Pro 4.1 Slide-slide and dye-swap comparison – TMEV & MIDAS Cross-slides quality evaluation - GeneSpring + R script for CV filter Mixed linear model analysis of Variance to identify significant differentially expressed genes – R or SAS program Data Analysis in the Post-Genomic Era (gene annotation, ontology and pathway analysis– KOG, COG, KEGG, TAIR, Onto-Tools, GenMapp… Data validation – qPCR or Northern blot

Whole genome approaches to biological questions Gene expression Gene variation Gene function

NSF-DB , PI: Henry Nguyen Functional Genomics of Root Growth and Root Signaling under drought

Drought-stress inducible genes and their possible functions in stress tolerance and response. Yamaguchi-Shinozaki et al. JIRCAS Working Report, 2002

Dr. Henry Nguyens lab, Plant Sciences, University of Missouri Characterize the transcript profiles of apical and basal regions of the root growth zone under water deficit condition using maize long oligonucleitide arrays

To identify genes contributing to root growth maintenance under water deficit condition To determine genes responsible for progressive inhibition of root elongation under water-deficit condition To compare the differential gene expression in root region of progressive inhibition of root elongation under water stress with the normal growth deceleration in well-watered root region Objectives

WW48WS Pair-wise comparison of maize root segments using oligo array

Characterization of the maize long oligo array Maize oligo array, printed at the University of Arizona, contains 56, mer oligonucleotide probes, including >30,000 identifiable unique maize genes. 16,915 oligoes do not have any annotation. 70-mer oligonucleotides in conjunction with Operon Qiagen based on the TIGR Maize Database

WS/WW=Cy5/Cy3 WS/WW=Cy3/Cy5 Dye Swap Slides feature and dye-swap experiment

1. Channel A intensity vs. channel B intensity 2. Log channel A intensity vs. log channel B intensity 3. R-I 4. Z-score histogram 5. Box plot Two-color microarray data feature

Flip dye consistency checking - processed data count: (only slides A) - pre-filtering corr. coeff: post-filtering data count: confidence factor: dispersion factor:

Summary of the evaluation of replicates (technique & biological) ~50,000 of the 56,311 genes have intensity >200 (at least one channel). Confidence of dye-swap is > 96% 99.9% confidence limit was estimated by testing the coefficient of variance (CV) for replicates

Mixed linear model analysis of two color microarray data- producing lists of differentially expressed genes with low false discovery rates To obtain accurate and precise estimates of gene expression values between treatment and control, analyze gene effects with a simultaneous consideration of all blocking factors, a linear mixed ANOVA model is applied: There are two processes: First, global mixed model was applied: Log2(singal values) = treat + dye + treat*dye + tech_reps_effect + array_effect (within treat*dye and tech_reps_effect) Second, take residual values from the first model and then apply this model for individual gene: Residuals = treat + dye + tech_reps_effects + array(within tech_reps_effects)

POORLY CHARACTERIZED - 6% METABOLISM - 11% INFORMATION STORAGE AND PROCESSING - 4% CELLULAR PROCESSES AND SIGNALING 10% NOT ASSIGNED – 69% Gene function categorization of significantly differentially expressed genes KOG analysis

Information storage and processing

Energy production and conversion 15% Amino acid transport and metabolism 14% Nucleotide transport and metabolism 5% Carbohydrate transport and metabolism 18% Inorganic ion transport and metabolism 16% Secondary metabolites biosynthesis, transport and catabolism 14% Lipid transport and metabolism 14% Coenzyme transport and metabolism 4% Metabolites

CELLULAR PROCESSES AND SIGNALING

Summary Microarray is a high through-put tool to identify novel genes We have identified 19 hundred drought response and root growth maintenance related genes Combining functional analysis we would find drought stress tolerance related pathways and genes This knowledge will lead to novel approaches for improving drought tolerance in maize.