1. Functional Genomics in Non-Model Organisms

2. What is Functional Genomics?
Functional genomics refers to the development and application of global (genome-wide or system-wide) experimental approaches to assess gene function by making use of the information and reagents provided by structural genomics. It is characterized by high-throughput or large-scale experimental methodologies combined with statistical or computational analysis of the results (Hieter and Boguski 1997)
Functional genomics as a means of assessing phenotype differs from more classical approaches primarily with respect to the scale and automation of biological investigations. A classical investigation of gene expression might examine how the expression of a single gene varies with the development of an organism in vivo. Modern functional genomics approaches, however, would examine how 1,000 to 10,000 genes are expressed as a function of development. (UCDavis Genome Center)

3. Functional Genomics Hunt & Livesey (eds.)
Subtracted cDNA Libraries
Differential Display
Representational Difference Analysis
Suppression Subtractive Hybridization
cDNA Microarrays
Serial Analysis of Gene Expression
2-D Gel Electrophoresis

4. My View of Functional Genomics
Differential Gene expression
SAGE/MPSS
RDA/SSH
*Open systems*
Identifying the Function of Genes
Functional Complementation
RNA interference/RNA silencing

5. Disclaimer Relevant primarily to eukaryotes
Most common systems (literature/class)
Personal experience with them
I like them

6. Why We Need Functional Genomics
Organism
# genes
% of genes with inferred function
Completion date of genome
E. coli
4288 60
1997
yeast
6,600 40
1996
C. elegans
19,000
1998
Drosophila
12-14K 25
1999
Arabadopsis
25,000
2000
mouse
~30,000?
10-20
2002
human

7. My Two Cents (as expressed by Hieter & Boguski 97)
Functional genomics will not replace the time-honored use of genetics, biochemistry, cell biology and structural studies in gaining a detailed understanding of biological mechanisms.
The extent to which any functional genomics approach actually defines the function of a particular protein (or set of proteins) will vary depending on the method and gene involved.

8. mRNA abundance classes (Okamuro & Goldberg)
Superabundant
15-90% of mRNA mass
<10 structural gene transcripts
>5000 molecules per cell per sequence
Abundant
50-75% of mRNA mass
~ structural gene transcripts (5% of diversity)
molecules per cell per sequence
Rare/complex
<25% of mRNA mass; individual seqs <0.01%
95% of mRNA diversity
1-10 molecules per cell per sequence

9. SAGE & MPSS Serial Analysis of Gene Expression
Massively Parallel Signature Sequencing
Start from mRNA (euks)
Generate a short sequence tag (9-21 nt) for each mRNA ‘species’ in a cell

10. Generate cDNA primed with biotin-oligo(dT)
Restriction digest double-stranded cDNA with a 4-base cutter “anchoring enzyme”; bind to streptavidin coated beads
AAAA
TTTT
AAAA
TTTT
GTAC
AAAA
TTTT
AAAA
TTTT
GTAC
Divide pool in half & ligate to different linkers (1 or 2),
both of which have a restriction site for the “tagging enzyme”
CATG
GTAC
AAAA
TTTT
CATG
GTAC
AAAA
TTTT 1 2
Restriction digest with a Type IIS restriction enzyme, which recognizes the linker sequences and cuts downstream in a sequence independent fashion; fill-in 5’ overhang to blunt ends.
GGATGCATGXXXXXXXXXX
CCTACGTACXXXXXXXXXX
GGATGCATGOOOOOOOOOO
CCTACGTACOOOOOOOOOO 1 2
Blunt end ligate pool 1 to pool 2, and PCR amplify with primers specific to linker sequences 1 and 2
Tag 1
Tag 2 1
GGATGCATGXXXXXXXXXXOOOOOOOOOOCATGCATCC
CCTACGTACXXXXXXXXXXOOOOOOOOOOGTACGTAGG 2
Ditag
Restriction digest with same anchoring enzyme (above); concatenate ditags and ligate to cloning/sequencing vector
Ditag
Ditag
-----CATGXXXXXXXXXXOOOOOOOOOOCATGXXXXXXXXXXOOOOOOOOOOCATG----
----GTACXXXXXXXXXXOOOOOOOOOOGTACXXXXXXXXXXOOOOOOOOOOGTAC----
Tag 1
Tag 2
Tag 3
Tag 4

13. SAGE Described by Velculescu et al. (1995)
Originally 9 bp tags, now LongSAGE 21 bp
10-50 tags in a clone
Only requires a sequencer (and some time)

14. MPSS Proprietary technology; published 2000
Generates 17 nt “signature sequence”
Collects >1,000,000 signatures per sample
Requires 2 µg of mRNA and $$

15. What is significantly different. Ruijter et al. 2002. Physiol
What is significantly different? Ruijter et al Physiol. Genomics 11:37-44.

16. What is significantly different?

17. Planning SAGE experiments…

18. How many tags need to be sequenced?

19. Comparing 2 libraries…

20. MPSS - Alexandrium fundyense
39931 unique tags; 3172 different at p<0.001

21. Not every tag is a unique sequence Not every sequence has a unique tag
Alternative splicing, >1 tag per gene
No restriction site, no tags per gene
Sequencing error (random, 0.7% for SAGE, Velculescu et al. 1995)
Antisense transcripts

22. Tag Abundance Distribution

23. Expression Ratio

25. RDA Initially used for DNA comparisons (Lisitsyn et al. 1993)
Later modified for cDNA to reduce complexity (Hubank and Schatz 1994)
May need >1 enzyme to cover all genes
Should pick up transcript present at <=0.005%
Time-intensive + a LOT of manipulation

26. Success with RDA DNA markers in ginbuna (Murakami et al. 2002)
mRNA induced under hypoxia in tiger salamander (McKean et al. 2002)
Rice & date palm 2002; oak 2001; tobacco 2000; pea & maize 1998; earliest 1996
No more recent refs

27. MPSS - Alexandrium fundyense
39931 unique tags; 3172 different at p<0.001

28. all components denatured
Tester cDNA with Adaptor 1
Driver cDNA (in excess)
Tester cDNA with Adaptor 2
first hybridization
all components denatured a b c { d
second hyb: mix, add freshly denatured driver; anneal
a,b,c,d + e
fill in the ends
add primers; PCR amplify a
no amplification b
no amplification c
linear amplification d
no amplification
exponential amplification e

29. Efficacy of SSH… Ji et al. 2002 BMC Genomics 3:12
Diatchenko et al. 1996; could detect as little as 0.001% target
Critical factor is relative concentration of target in tester and driver populations
Effective enrichment when:
Target present at >= 0.01%
Concentration ratio>= 5-fold

30. What this looks like
208 signatures at >=0.01%, >= 5-fold induction

31. Success with SSH Armbrust 1999, diatoms Lots of biomedical refs 2003
Xylella, Aspergillus, Dunaliella

33. Post-translational gene silencing
Fungi
Neurospora
quelling
transgenes
Plants
Petunia, Nicotiana, Arabadopsis, rice, tomato, potato, etc.
PTGS
Co-suppression
viruses
Animals:
Invertebrates
C. elegans
Drosophila
Paramecium
Planaria
Hydra
T. brucei
RNAi
RNAI
dsRNA
Animals: Vertebrates
Zebrafish
mouse

34. Kamath et al. 2003 16,757 strains = 86% of predicted ORFs
Looked for sterility or lethality(Nonv), slow growth (Gro) or defects (Vpep)
1,722 strains (10.3% had such phenotypes)

35. Genes involved in basic metabolism & cell maintenance are enriched for Nonv phenotype Genes involved in more complex ‘metazoan’ processes (signal transduction, transcriptional regulation) are enriched for Vpep phenotype Nonv phenotypes highly underrepresented on the X chromosome X chromosome is enriched for Vpep phenotypes

36. Basal functions of eukaryotes are shared: - lethal (Nonv) genes tended to be of ancient origin - ‘animal-specific’ genes tended to be non-lethal (Vpep) - almost no ‘worm-specific’ genes were lethal

37. Genes producing a defective phenotype are clustered: Nonv clustered in central regions, except: on the X chromosome, which is underenriched for Nonv phenotypes

38. Functional Complementation
Often yeast, E. coli
The goal of the SGDP is to generate as complete a set as possible of yeast deletion strains with the overall goal of assigning function to the ORFs through phenotypic analysis of the mutants.
As of 01/03, 95% of the approx ORFs have been deleted; more than 20,000 strains are available from Research Genetics, Open Biosystems and the ATCC.

39. Functional Complementation
Intramembrane cleaving proteases: Drosophila rhomboid complements the aarA of Providencia stuartii and vice versa (Gallio et al. 2002)
Cyclophilin-RNA interacting proteins in Paramecium, conserved from yeast to humans (Krzywicka et al. 2001)