Functional Genomics in Evolutionary Research

What Is Microarray Technology?

High throughput method for measuring simultaneously, mRNA abundances for thousands of genes.

Thousands of probes or features adhered to a solid substrate at known x,y coordinates.

Probes:Spotted cDNA ~ 200 bpOligo = 25 to 60 bp

Why Is Microarray Technology Important?

From NSF Program Announcement: Environmental Genomics

How Do Microarrays Work?

Hybridization Technique

- RNA targets isolated from a cell line or tissue of interest are labeled and hybridized to the probes.

- Label intensity at a given location on the substrate correlates with the amount of target for a given mRNA (gene) present in the sample.

Identified statistically (e.g. t-test) by comparing control vs experimental.

Differentially expressed Genes:

The Burden of Multiple Testing

A given microarray may have over 40,000 probes!!!

This means that you may run as many as 40,000 statistical tests.

If you reject a null hypothesis when P < 0.05, then 5% of the time you are rejecting true null hypotheses.

If you run 40,000 tests, then by chance alone you will reject ~ 40,000 x 0.05 = 2000 true null hypotheses (i.e., you will have ~ 2000 false positives)

Sources of Variation in Microarray Experiments

Biological

(1) Experimental Treatments

(2) Individual variance... may or may not be good

(3) Nonspecific hybridizationParalogs of gene families

Technical (Bad)

(1) RNA quality

(2) Dye biases

(3) Stochasticity during scanning, image processing

(5) Errors during probe synthesis or deposition

(6) Stochasticity in labeling targets

Larval AdultMetamorphosis

R x x x x x x x xx x x x x x

(1) Sample tissue from 15 time points (x), including an early reference (R) time point.

(2) Compare expression for each time point and the reference on a DNA chip. Larval Adult

R x x x x x x x xx x x x x x

Metamorphosis?

Metamorphic Life Cycle

Paedomorphic Life Cycle

(3) Quantify relative expressionof each gene across all DNA chips.(2 life cycles x 3 tissues x 14

timepoints)

(4) Model gene expression to determinehow genes are expressed temporally within life cycle cycles for each tissue.

(6) Compare gene expression profiles among life cycles and tissues to identify differentiallyexpressed genes.

(7)Verify results by rt-PCR andanalyze candidates in thyroidhormone-induced paedomorphs.

What gene expression changes are associated with the evolution of paedomorphosis?

Example Design

Visualization & Categorization

ClusteringHeat maps

Principal Component Analysis

Quadratic Regression

Liu et al. 2005... From the Stromberg Group

here at UK

Can be done for genes and/or arrays... Options Include a variety of multivariate and pattern matching techniques including the methodologies listed below

Gene Ontology & Biological Relevance

• Microarray datasets can be overwhelming because they contain

A LOT of information• Even experts on a system can be overwhelmed by the number of

genes that are differentially regulated in some experiments• Having a standardized nomenclature that places a gene into one

or more biological contexts can be invaluable for functional grouping (previous grouping techniques were irrespective of biological information)

Gene Ontology is a standardized hierarchical nomenclature that classifies genes under three broad categories

Example of a Functional Genomics Study

Molecular Ecology 2006 15, 4635-4643

Drosophila

• Most species are very poor ecological model organisms.

• D. mojavensis is cactophilic: it uses 4 different kinds of cactus host in the Sonoran Desert.

• Oviposits in necrotic tissues, exposing larvae to varied toxic chemicals.

Identify gene expression differences of 3rd instar larvae reared between two chemically distinct cactus hosts:

•Agria (Stenocereus gummosus), native host•Organpipe (Stenocereus thurberi), alternative host

Used a custom microarray (6520 anonymous cDNA fragments that were pinned robotically to glass slides)

Objective

Organpipe vs Agria Cacti

• Differ in lipids, triterpenes, and glycosides.

•Differ in alcohol content.

•Adh is duplicated in D. mojavensis andThe paralogs are known to play different roles in host adaptation.

Yij = µ + ARRAYi + DYEj + ARRAY × DYEij + Residualij

Relative hybridizationIntensity

Residualijkl = µ + ARRAYi + DYEj + CACTUS + ARRAY x Spotil + Errorijkl

= Random Technical and Residual Variation

Mixed Model Anova Approach

1)

ResidualVariationPer Gene

= Random Technical and Fixed Technical and Biological Variation

2)

Correcting for Multiple Tests

Bonferroni correction: More conservative test wherethe significance threshold is divided by the totalnumber of tests.

False Discovery Rate (FDR): Less conservative testthat calculates the number of false positives withina set of significant values (P<0.05) and then calculates a new significance threshold , q.

Greater Expression AgriaGreater Expression Organpipe

P value forEach Gene

Specific Anova-log(P)

Fold Difference Log2

Bonferroni (173)

False DiscoveryRate (1034)

Identifying Differentially Expressed Genes

Representation of Up-regulated Genes Among Gene OntologyCategories.

Conclusions

(i) Cactus host usage affects patterns of gene transcription.

(ii) Loci whose function involve detoxificationwere differentially regulated in response to a cactus host shift.

(iii) A subset of the differentially expressedloci may have arisen de novo in the D. mojavensis lineage.