Functional Genomics in Non-Model Organisms

1. Functional Genomicsin 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 GenomicsHunt & 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

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 • ~200-1000 structural gene transcripts (5% of diversity) • 500-2500 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. -----CATGXXXXXXXXXXOOOOOOOOOOCATGXXXXXXXXXXOOOOOOOOOOCATG---------CATGXXXXXXXXXXOOOOOOOOOOCATGXXXXXXXXXXOOOOOOOOOOCATG---- ----GTACXXXXXXXXXXOOOOOOOOOOGTACXXXXXXXXXXOOOOOOOOOOGTAC---- 1 2 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 2 1 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. GGATGCATGOOOOOOOOOO CCTACGTACOOOOOOOOOO GGATGCATGXXXXXXXXXX CCTACGTACXXXXXXXXXX 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 GGATGCATGXXXXXXXXXXOOOOOOOOOOCATGCATCC CCTACGTACXXXXXXXXXXOOOOOOOOOOGTACGTAGG Ditag Restriction digest with same anchoring enzyme (above); concatenate ditags and ligate to cloning/sequencing vector Ditag Ditag 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. 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 sequenceNot 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. Tester cDNA with Adaptor 2 Tester cDNA with Adaptor 1 Driver cDNA (in excess) 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 no amplification a no amplification b linear amplification c no amplification d 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

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 phenotypeGenes involved in more complex ‘metazoan’ processes (signal transduction, transcriptional regulation) are enriched for Vpep phenotypeNonv phenotypes highly underrepresented on the X chromosomeX 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. 6200 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: Drosophilarhomboid 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)