1. Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008

2. Step 1: Expression Microarrays GCAT support for materials, colleagues for consultations Student-originated experiments with predictions and data analysis –Yeast replicative aging (Yiu, G* Alejandra McCord*, Laty Cahoon, Alison Wise*, Rishi Jindal*, Jennifer Hardee*, Allen Kuo*, Michelle Yuen Shimogawa*, Michelle Wu, John Kloke, Johanna Hardin, and Laura L. Mays Hoopes. Gene Expression During Replicative Aging in Yeast. J Gerontology :Biological Sciences 63A (1):21-34 (2008.) –Todd Eckdahl, Adam Brown, Steven Hart, Kelly Malloy, Laurie Heyer, Martha Shott, Laura L. Mays Hoopes, Gloria Yiu*, Laurie Heyer. Microarray analysis of the in vivo sequence preferences of a minor groove binding drug BMC Genomics. (2008), 9:32. –Stress-related TFs in yeast aging (Cameron et al, ms in preparation) –Gene expression during meiotic aging clock resetting (Zhao et al, ms in preparation)

3. Is There Gene Dysregulation in Yeast Aging?

4. Clustering of Genes Significantly Changed in Expression at 12g and 18-20g mRNAs increased in aging mRNAs decreased in aging 1g 12g 20g Data: Gloria Yiu, Alejandra McCord, Rishi Jindal, Jennifer Hardee, Allen Kuo, Michele Yuen, Laty Cahoon, Michele Wu.

5. BioConductor Showed Functional Groups of Dysregulated Genes Yiu et al, J Gerontol, Jan, 2008

6. Important Molecule in Yeast Aging: Sir2 Protein Sir2 is a NAD+-dependent histone deacetylase that compacts chromatin Sir2 turns off gene expression Sir2 moves from the telomeres to the ribosomal RNA genes during aging in yeast.

7. How Do We Know Sir2 Is Important in Aging? Deletions of sir2 have ~30% shorter life spans than wild type Strains with one extra copy of SIR2 gene have life spans extended ~30%. Homologs in animals sometimes affect life span

8. Predictions Related to SIR2 in Aging Sir2 might increase in aging and/or an enzyme that produces NAD+, its coenzyme, could increase and activate it more Sir2/Sir3/Sir4 proteins start at telomeres in young cells; move to rDNA during aging, thus telomeric genes could turn on as yeast get older

9. Expression Patterns of Yeast Chromosomes with Age Yellow: Y=O, Red: O>>Y (More mRNA, Green O<<Y (Less mRNA) 1g (yellow) 12g (some red/green) 20g (more red/green)

10. Closeup of Left Telomeric Regions of Chromosomes 1-7 at 20 g Conclusion: While some ‘red’ or induced genes are telomeric, there’s no special concentration of up- regulated genes there. green chromosome axis genes on Watson strand genes on Crick strand left telomere

11. average mRNA compared to 1g, 7 arrays Gene: 1g 12g 20g SIR20.7310.8120.858 PNC11.131.473.58 ** SIR30.4351.7831.352 HST11.1541.1461.128 HST21.4621.4211.52 HST40.5540.9961.164 ** Statistically significant at p < 0.05 NB HST3 and SIR4 data rejected (excessive variation) Sir2 Related Aging Gene Expression In agreement with Sinclair’s data on Pnc1, its mRNA increased and the NAD+ produced by the enzyme could be activating Sir2.

12. Environmental Stress Response Gasch et al. (Mo Bio Cell, 2000, 11:4241) found about 900 genes are affected similarly in expression by different environmental stresses Gene groups include ribosomal genes, stress response genes, a few DNA repair genes Some ESR genes are induced by stresses and others are repressed

13. Sample ESR mRNA (HSP12) in Aging

14. A Repressed ESR mRNA for Ribosomal Protein Rpl16A in Aging

15. Genes with STRE –containing promoters such as CTT1(4.27x), SIP18 (4.74x), GRE1(5.97x), GRE2 (2.14x) Positive Regulation Negative Regulation C source limitation Reg1(3.099x)-Glc7 Phosphatase moves Msn2,4 to nucleus Other regulatory factors: Sds22 (5.42x), Glc8 (3.607x), Shp1 (2.035x), Reg2 (7.9x)), GCN1 (4.9x) Msn2 (1.33x) and Msn4 (3.756x), TFs Ras Protein kinase A, cAMP (BCY1, TPK1, 2, 3) Phosphate sends Msn2, 4 to cytoplasm Sip2 2.41x Snf1Snf4Sip1 protein kinase Protein Phosphatase/Kinase Stress Response Cascades Affected by Aging 18-20g ratio to 1g expression given in parentheses

16. Pseudostationary Phase Features HXT induction Glycogen gene induction SNZ or snooze gene induction Diauxic response gene induction/repression Shift from ethanolic anaerobic fermentation to aerobic respiration HOWEVER: KEPT IN LOG PHASE, NOT IN STATIONARY PHASE!! Why increase glucose import? Glucose is not all gone! Hypothesis: it’s because of big sizes of elderly cells so it’s hard to diffuse glucose within cells.

17. Pseudostationary Phase Component: Diauxie Yeast begin using glucose through glycolysis with ethanol as the end product When they near stationary phase glucose in the medium is almost exhausted, they switch metabolism During the switch, they begin to metabolize ethanol aerobically via the TCA cycle, electron transport, and oxidative phosphorylation During the switch, they also induce/repress some of the environmental stress response genes, for example ribosome synthesis is switched off in stress and in diauxie

18. Yeast Diauxie Growth Curve from Joseph DeRisi, V. Iyer, P.O. Brown, Science 278:660 (1997) Pre- Diauxie (Log phase) Post Diauxic Shift

19. Aging mRNA re Metabolic Changes OLE1, lipid metabolism COX20, electron transport HXT15, hexose import

20. Largest Category of Aging Expression Changes: Nucleolus/Ribosome Vacuole Nucleus Rough ER Ribosome Nucleolus; rDNA is transcribed and rRNA is processed; ribosomes are assembled Ribosomes are exported from nucleolus/nucleus to cytoplasm Brief review: making ribosomes.

21. Nucleolus/Ribosome Potential Regulons in Aging Yeast Cells Nucleolar “RBB” and Ribosomal Protein (RP) Gene regulons. Numbers are the number of genes in each group. 83 104 19 Overlap in Aging and RBB sets 14 107 74 Overlap in Aging and RP sets RBB overlaps but probably isn’t the aging regulon, lots of RBB genes unchanged in aging(83) RP Tentative aging regulon, only 14 RP genes that aren’t changed in aging.

22. Could DNA Damage Contribute to Yeast Aging? Overall, DNA repair mRNAs are unchanged from in young cells Gene from one DNA repair pathway are significantly overexpressed for many genes in the pathway: NER The overexpression level of NER is low (only about 2 fold) at 18-20g. (Yiu et al, 2008)

23. Genes with changes significant at p < 0.05 are RAD2, RAD3, RAD4, RAD7, RAD10, RAD14, and RAD28. NER genes without significant differences were RAD1, 16, 23, and 23. mRNAs Increased for Nucleotide Excision Repair Pathway Yiu et al, 2008

24. Overall Summary of Gene Expression Changes in Aging Yeast Environmental Stress Response turned on (1/2 of the ~900 genes) Protein Phosphatase1 subunits and stress response up- regulated Metabolism switched: Pseudostationary phase –Respiration up, fermentation and fat metabolism down –Anabolism down-regulated Nucleolar/Ribosome functions down-regulated (RP, some ribosome assembly functions) DNA Repair: NER up-regulated; rest unchanged Methylation: down-regulated Cell Wall functions: up-regulated Mating/Sporulation functions: down-regulated Data of Yiu, Cameron, Cahoon, McCord, Jindal, Hardee, Yuen, Wu, Wise, Hardin, and Hoopes J Gerontology, January, 2008.

25. What’s Good About Microarray Student Research? Students can see the mRNAs from the entire genome, not just the mRNAs predicted to change. Holistic/discovery approach makes them see the whole organism better. Whole pathway changes in expression are robust and repeatable, while single gene changes can be false positive/false negative.

26. What’s Frustrating about Microarray Research? There is too much data. For example our published study had 27 datasets with ~6000 pieces of data each. You need a good statistical collaborator if possible. Good data, passing the scanner’s quality control, may not be “real.” An independent method should be used to confirm important findings, such as qPCR.

27. Step 2: Beyond Expression Arrays CGH…comparative genomic hybridization, enables you to search for deletions or insertions of major regions. ChIP on chip…Chromatin Immuno Precipitation isolates DNA where a protein is bound; DNA is isolated and hybridized to identify targets in vivo. Nucleosomal placement…cut chromatin with Micrococcal nuclease, see which parts of the DNA are still there to hybridize with the array. Need genomic DNA arrays, not just ORFs.

28. Step 3: Beyond Arrays… Massively parallel sequencing. New generation of sequencers can be used to examine mRNAs of a cell (Nagatakshmi et al, Science 320:1344, 2008). More next slide. Single molecule sequencing. Anticipated generation after next sequencers, which have been demostrated in principle, can sequence individual molecules for 1500 or so nucleotides in massively parallel sequencers.

29. The 454 Sequencer Genome sequencing in microfabricated high-density picolitre reactorsGenome sequencing in microfabricated high-density picolitre reactors Margulies, M. Eghold, M. et al. Nature. 2005 Sep 15; 437(7057):326- 7 454's ground breaking Nature paper describing the 454 Sequencing technology 1. Fluidic Assembly 2. Flow chamber with fiber optic slide 3. CDC camera 4. Computer

30. 454 DNA Template

31. 454 Data Output

32. First 454 Model’s Statistics Genome sequencing in microfabricated high-density picolitre reactorsGenome sequencing in microfabricated high-density picolitre reactors Margulies, M. Eghold, M. et al. Nature. 2005 Sep 15; 437(7057):326-7

33. Advantages of Parallel Sequencing over Microarrays for Expression Replication not of just a few standards as on our WU slides but of every mRNA sequenced Can see direct evidence for alternative splice variants and assess prevalence Can detect overlapping genes easily Can find genes not predicted by gene- calling software

34. Yeast Transcriptome Nagalakshmi et al. Science 320:1344 (2008) The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing. deletion

35. Time to Guess: What percent of DNA in yeast is not expressed? –52% –24% –12% See next slide for data!

36. Yeast, cont.

37. Yeast, cont 2: Good confirmation of expression data via sequencing

38. Yeast, cont 4: Discovery of new gene by sequencing Study found a transcribed gene in this region that was not previously annotated (khaki bar; see transcription on upper graph).

39. Genomics Vistas with Students Students are capable of doing excellent genomics The new methods coming forth are no harder than expression microarrays, at which our students have succeeded GCAT has a bright future