1. Functional Genomics in Evolutionary Research

2. 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 bp
Oligo = 25 to 60 bp

3. Why Is Microarray Technology Important?
From NSF Program Announcement: Environmental Genomics

4. 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.
Differentially expressed Genes:
Identified statistically (e.g. t-test)
by comparing control vs experimental.

5. 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)

6. Sources of Variation in Microarray Experiments
Biological
(1) Experimental Treatments
(2) Individual variance... may or may not be good
(3) Nonspecific hybridization
Paralogs 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

7. What gene expression changes are associated with the evolution of paedomorphosis?
Example Design
Larval
Adult
Metamorphosis
R x x x x
x x x x
x 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.
Metamorphosis?
Metamorphic Life Cycle
Paedomorphic Life Cycle
(3) Quantify relative expression
of each gene across all DNA chips.
(2 life cycles x 3 tissues x 14 timepoints)
(4) Model gene expression to determine
how genes are expressed temporally within life cycle cycles for each tissue.
(6) Compare gene expression
profiles among life cycles and
tissues to identify differentially
expressed genes.
(7)Verify results by rt-PCR and
analyze candidates in thyroid
hormone-induced paedomorphs.

8. Visualization & Categorization
Can be done for genes and/or arrays... Options Include a variety of multivariate and pattern matching techniques including the methodologies listed below
Quadratic Regression
Principal Component Analysis
ClusteringHeat maps
Liu et al From the Stromberg Group
here at UK

9. 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

10. Example of a Functional Genomics Study
Molecular Ecology ,

11. 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.

12. Objective 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)

13. Organpipe vs Agria Cacti
Differ in lipids, triterpenes, and glycosides.
Differ in alcohol content.
Adh is duplicated in D. mojavensis and
The paralogs are known to play different
roles in host adaptation.

14. Mixed Model Anova Approach
Relative
hybridization
Intensity 1)
= Random Technical and Residual Variation
Yij = µ + ARRAYi + DYEj + ARRAY × DYEij + Residualij
Residual
Variation
Per Gene 2)
= Random Technical and Fixed Technical and Biological Variation
Residualijkl = µ + ARRAYi + DYEj + CACTUS + ARRAY x Spotil + Errorijkl

15. Correcting for Multiple Tests
Bonferroni correction: More conservative test where
the significance threshold is divided by the total
number of tests.
False Discovery Rate (FDR): Less conservative test
that calculates the number of false positives within
a set of significant values (P<0.05) and then
calculates a new significance threshold , q.

16. Identifying Differentially Expressed Genes
Greater Expression Organpipe
Greater Expression Agria
P value for
Each Gene
Specific Anova
-log(P)
Bonferroni (173)
False Discovery
Rate (1034)
Fold Difference Log2

17. Representation of Up-regulated
Genes Among Gene Ontology
Categories.

18. Conclusions
(i) Cactus host usage affects patterns of gene transcription.
(ii) Loci whose function involve detoxification
were differentially regulated in response to a cactus host shift.
(iii) A subset of the differentially expressed
loci may have arisen de novo in the D. mojavensis lineage.