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

Dec 16, 2015

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

Vistas in Student Involvement in Genomics Research

Laura L Mays Hoopes

Pomona College

2008

Page 2: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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)

Page 3: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

Is There Gene Dysregulation in Yeast Aging?

Page 4: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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.

Page 5: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

BioConductor Showed Functional Groups of Dysregulated Genes

Significant mRNA Changes in Aging

Nucleolus/ribosome Carbohydrate metabolism Transcription&Translation

Golgi & ER Mating N anabolism

Cell wall,Osmotic Transport Stress, HS, Chaperones

Methylation DNA Replication, Repair Other

Yiu et al, J Gerontol, Jan, 2008

Page 6: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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.

Page 7: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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

Page 8: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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

Page 9: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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)

Page 10: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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 strandleft telomere

Page 11: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

average mRNA compared to 1g, 7 arrays

Gene: 1g 12g 20g

SIR2 0.731 0.812 0.858

PNC1 1.13 1.47 3.58 **

SIR3 0.435 1.783 1.352

HST1 1.154 1.146 1.128

HST2 1.462 1.421 1.52

HST4 0.554 0.996 1.164

** 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.

Page 12: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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

Page 13: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

Sample ESR mRNA (HSP12) in Aging

Page 14: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

A Repressed ESR mRNA for Ribosomal Protein Rpl16A in Aging

Page 15: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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

Page 16: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

Pseudostationary Phase Features

Hexose Transporter mRNAs at 18-20g (average 2.77 fold)

0

1

2

3

4

5

6

Gene

Fo

ld C

han

ge

Co

mp

ared

to

1g avg 18-20g

•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.

Page 17: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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

Page 18: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

Yeast Diauxie Growth Curve from Joseph DeRisi, V. Iyer, P.O. Brown, Science

278:660 (1997)

Pre-Diauxie(Log phase)

Post Diauxic Shift

Page 19: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

Aging mRNA re Metabolic Changes

OLE1, lipid metabolism

COX20, electron transport

HXT15, hexose import

Page 20: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

Largest Category of Aging Expression Changes: Nucleolus/Ribosome

Vacuole

Nucleus

Rough ERRibosome

Nucleolus; rDNA is transcribed and rRNA is processed; ribosomes are assembled

Ribosomes are exported from nucleolus/nucleus to cytoplasm

Brief review: making ribosomes.

Page 21: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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.

Page 22: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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)

Page 23: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

NER Gene Induction, p <0.05

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

1g 18g

Age

Ave

rag

e F

old

Ch

ang

e at

18

g Series1

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

Page 24: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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.

Page 25: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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.

Page 26: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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.

Page 27: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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.

Page 28: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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.

Page 29: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

The 454 Sequencer

Genome 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

Page 30: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

454 DNA Template

Page 31: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

454 Data Output

Page 33: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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

Page 34: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

Yeast Transcriptome

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

deletion

Page 35: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

Time to Guess:

• What percent of DNA in yeast is not expressed?– 52%– 24%– 12%

See next slide for data!

Page 36: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

Yeast, cont.

Page 37: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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

Page 38: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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).

Page 39: Vistas in Student Involvement in Genomics Research Laura L Mays Hoopes Pomona College 2008.

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