Study of the Transcriptome of the Prematurely Aging Yeast Mutant dna2-1 Using a New Method Allowing Comparative DNA Microarray Analysis - PowerPoint PPT Presentation

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Study of the Transcriptome of the Prematurely Aging Yeast Mutant dna2-1 Using a New Method Allowing Comparative DNA Microarray Analysis

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Study of the Transcriptome of the Prematurely Aging Yeast Mutant dna2-1 Using a New Method Allowing Comparative DNA Microarray Analysis Isabelle Lesur Thesis defense ... – PowerPoint PPT presentation

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Title: Study of the Transcriptome of the Prematurely Aging Yeast Mutant dna2-1 Using a New Method Allowing Comparative DNA Microarray Analysis


1
Study of the Transcriptome of the Prematurely
Aging Yeast Mutant dna2-1 Using a New Method
Allowing Comparative DNA Microarray Analysis
Isabelle Lesur Thesis defense 04/25/05
2
1/48
Introduction
aging
Young cells S. cerevisiae
old cells S. cerevisiae
Transcriptome study
Microarray data
Development of a large-scale automated comparison
method
3
2/48
Content
I - Experimental study of the causes of aging in
S. cerevisiae Aging in yeast The dna2-1
prematurely aging mutant Experimental
approach Experimental Results
II Comparative analysis of DNA microarray
experiments Motivation A weighted-ontology
for microarray experiments Validation
Processing of the compared datasets
III Conclusion and perspectives
4
3/48
Aging in yeast
Asymmetric division
g1
g1
g1
g2
Size increases with aging
g1
g3
g1
g4
death
gn
5
4/48
The dna2-1 prematurely aging mutant
100 80 60 40 20 0
Median life spans (generations) dna2-1
8.1 /- 2.5 DNA2 30.4 /- 5.4
Percent survival
1 11 21 31 41
generations
(Hoopes L.L.M. and J. L. Campbell. 2002 . MBC)
6
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Experimental procedure
Yeast A Yeast B
Data extraction
Total RNA extraction
RT-PCR of the mRNA
ORFs deposit
Hybridization
Reading
7
6/48
Experimental approach
1- Control (young cells vs. young cells)
After Lowest Regression Normalization
Wild-type strain

M log2(G/R)
8 10
A log2(sqrt(RG))
94.39 genes within two folds variation M ?
-1,1
After Lowest Regression Normalization
M log2(G/R)
dna2-1 mutant strain

A log2(sqrt(RG))
94.36 genes within two folds variation M ?
-1,1
Selection of genes with variation in expression
level M gt 1
8
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2- Wild-type experiments (young cells vs. old
cells)
After Lowest Regression Normalization

M log2(G/R)
A log2(sqrt(RG))
After Lowest Regression Normalization
M log2(G/R)

A log2(sqrt(RG))
627 genes upregulated in old wild-type cells
(10.22 of the genome) 387 genes downregulated in
old wild-type cells (6.30 of the genome)
9
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3- dna2-1 experiments (young cells vs. old cells)
After Lowest Regression Normalization

M log2(G/R)
A log2(sqrt(RG))
After Lowest Regression Normalization

M log2(G/R)
A log2(sqrt(RG))
898 genes upregulated in old dna2-1 cells (14.63
of the genome) 656 genes downregulated in old
dna2-1 cells (10.69 of the genome)
10
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Content
I - Experimental study of the causes of aging in
S. cerevisiae Aging in yeast The dna2-1
prematurely aging mutant Experimental
approach Experimental Results II Comparative
analysis of DNA microarray experiments
Motivation A weighted-ontology for microarray
experiments Validation Processing of the
compared datasets III Conclusion and
perspectives
11
10/48
Glucose metabolism and energy production
9 8 7 6 5 4 3
2 -1 1 -2 -3 -4 -5
Ratio of expression level old cells / young cells
Gene name
Lipid metabolism
TCA cycle
Oxidative phosphorylation
Mig1-repressed genes
Glycogen production
Glyoxylate cycle
Gluconeogenesis
Shift from glycolysis toward gluconeogenesis and
energy production associated with aging
Compared with Lin S.S., J. K. Manchester, and J.
I. Gordon. 2001. J. Biol. Chem.
12
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The Environmental Stress Response (ESR)
dna2-1 1553 genes
Compared with Gasch A. P. , P. T. Spellman, C. M.
Kao, O. Carmel-Harel, M. B. Eisen, G. Storz, D.
Botstein, and P. O. Brown. 2000. Mol. Biol. Cell.
13
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The Environmental Stress Response (ESR)
1553 genes
868 genes
dna2-1 1147 genes
ESR 462 genes
406
Compared with Gasch A. P. , P. T. Spellman, C. M.
Kao, O. Carmel-Harel, M. B. Eisen, G. Storz, D.
Botstein, and P. O. Brown. 2000. Mol. Biol. Cell.
14
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The Environmental Stress Response (ESR)
1553 genes
868 genes
ESR 352 genes
dna2-1 850 genes
321
85
110
297
Old cells react as if they were growing under
external stress conditions
Compared with Gasch A. P. , P. T. Spellman, C. M.
Kao, O. Carmel-Harel, M. B. Eisen, G. Storz, D.
Botstein, and P. O. Brown. 2000. Mol. Biol. Cell.
15
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The Telomerase Delete Response (TDR) genes common
to tlc1 and old dna2-1 cells
652 genes
1553 genes
340 genes
dna2-1 1213 genes
tlc1? 312 genes
Similarity between expression profiles associated
with aging of dna2-1 and the TDR
Compared with Nautiyal S., J. L. DeRisi, and E.
H. Blackburn. 2002. PNAS. USA Teng S. C., C.
Epstein, Y. L. Tsai, H. W. Cheng, H. L. Chen, and
J. J. Lin. 2002. Biochem Biophys Res Commun.
16
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Genes belonging to the TDR and upregulated in old
dna2-1 cells
7 6 5 4 3 2 -1 1 -2 -3
Ratio of expression level old cells / young cells
Others
ESR
Gene name
DNA synthesis
Telomere deletion signature genes
Unknown
DNA-damage signature genes
Carbohydrate metabolism
Oxydative phosphorylation
Compared with Nautiyal S., J. L. DeRisi, and E.
H. Blackburn. 2002. PNAS. USA Teng S. C., C.
Epstein, Y. L. Tsai, H. W. Cheng, H. L. Chen, and
J. J. Lin. 2002. Biochem Biophys Res Commun.
17
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Genes belonging to the TDR and downregulated in
old dna2-1 cells
5 4 3 2 -1 1 -2 -3 -4 -5
Ratio of expression level old cells / young cells
Gene name
Ribosomal genes
Histones
Compared with Nautiyal S., J. L. DeRisi, and E.
H. Blackburn. 2002. PNAS. USA Teng S. C., C.
Epstein, Y. L. Tsai, H. W. Cheng, H. L. Chen, and
J. J. Lin. 2002. Biochem Biophys Res Commun.
18
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DNA damage repair
5 4 3 2 -1 1 -2 -3
Ratio of expression level old cells / young cells
Gene name
DNA-damage signature genes
DNA-damage Checkpoint pathway
Post-replication repair pathway
Recombinational repair pathway
Mismatch DNA repair
NER pathway
Activation of numerous genes repairing DNA
19
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Result summary
Metabolic Response
Energy
ESR
20
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Result summary
Similarity to telomerase deletion mutant
Metabolic Response
Energy
ESR
TDR
21
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Result summary
Similarity to telomerase deletion mutant
Metabolic Response
Energy
ESR
TDR
Activation of the RAD52 pathway (specific WT)
DNA repair
DNA-damage signature genes
DNA-damage response
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Result summary
Similarity to telomerase deletion mutant
Metabolic Response
Energy
ESR
TDR
Activation of the RAD52 pathway (specific WT)
DNA repair
DNA-damage signature genes
DNA-damage response
23
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Content
I - Experimental study of the causes of aging in
S. cerevisiae Aging in yeast The dna2-1
prematurely aging mutant Experimental
approach Experimental Results II Comparative
analysis of DNA microarray experiments
Motivation A weighted-ontology for microarray
experiments Validation Processing of the
compared datasets III Conclusion and
perspectives
24
18/48
The need of the biologist
  • Biological study
  • ? characterization of aging in yeast
  • identification of a specific need of the
    biologist large-scale automatic comparison of
    microarray experiments
  • 3 steps required
  • Establishment of criteria of comparability
  • Development of large-scale practical methods for
    comparison of real experiments
  • requires data in a handleable format (MIAME)
  • requires a tool to structure information
    (ontology)
  • Integration of these methods in a test platform
    (not discussed here)

25
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Selection criteria in databanks
Objective reproduce the decision making process
of the biologist
26
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Formalization of the comparison process
27
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The MIAME standard
Minimum Information About a Microarray Experiment
Specification for the minimum amount of
information one needs to fully describe a
microarray experiment, interpret it and verify
the results. Objective guiding the development
of microarray databases and data management
software. Information are given by maximum use
of controlled vocabularies. The use of controlled
vocabularies is needed to enable database queries
and automated data analysis.
Brazma et al. 2001. Nature.
28
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An ontology for microarray experiments
Use of a controlled vocabulary to describe
microarray experiments. This controlled
vocabulary is structured as a tree vertices
classes edges relation is-a between each
pair of classes
29
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MGED ontology for microarray experiments
(Microarray Gene Expression Data)
Microarray experiment
Experimental design author, experimental
dimensions,
Array design array description, quality
controls,
Samples Extraction, preparation, labeling,
hybridization Procedures, parameters,
Measurements Images, quantification, specificatio
ns,
Normalization types, values algorithm
http//www.mged.sourceforge.net/ontologies/MGEDont
ology.php
30
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Our ontology for microarray experiments
69 classes
Microarray experiment
Biological attributes Statistical attributes
Comparison of values
31
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Our ontology for microarray experiments
69 classes
Microarray experiment
Biological attributes Statistical attributes
filtering normalization
Upper threshold, Lower threshold,
Global normalization, Intensity-dependent
normalization,
Minimal cost conversion
32

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Cost-model for datasets comparison
Most arbitrary pairs of classes can not be
reasonably compared.
- Each edge between two classes is associated
with an edge cost 0,1 cost 0 100
penalty cost 1 0 penalty
  • Each path between two classes of the ontology
    (i.e. two values of an attribute) is unique (we
    consider the shortest path) and associated with a
    local cost
  • The costs along a path are multiplicative
    symmetry and transitivity
  • cost (A ? B) cost (B ? A)
  • cost (A ? C) cost (A ? B) cost (B ? C)

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Relevant attribute
1- Only relevant attributes describing a
microarray experiment are taken into
consideration to decide whether or not to
compared two experiments 2- Each relevant
attribute does not have the same influence on the
decision making process
34
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Global comparison cost
The global comparison cost C associated with the
comparison of two microarray experiments is
computed as follows
C
0 C 1
C 0 ? No comparison between the two
experiments is allowed
C 1 ? A quantitative comparison between the
two experiments is allowed
0 lt C lt 1 ? A qualitative comparison between the
two experiments is allowed
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4 steps in the comparison process
- Biologist experiment - Ontology - Repository
Algo 1 identification of comparable microarray
experiments
List of experiments from Repository comparable to
the biologists experiment (sorted from the more
comparable to the less comparable)
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4 steps in the comparison process
Biologists experiment, Ontology, Repository
Algo 1
List of experiments from Repository comparable to
the biologists experiment (sorted from the more
comparable to the less comparable)
One experiment comparable to the biologists
experiment
Algo 2 statistical conversion of two comparable
datasets
Filtering thresholds Fmin, Fmax Normalization
techniques Nb, Nc
37
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4 steps in the comparison process
Biologists experiment, Ontology, Repository,
Bank of orthologous genes
Algo 1
List of experiments from Repository comparable to
the biologists experiment (sorted from the more
comparable to the less comparable)
One experiment comparable to the biologists
experiment
Algo 2
Filtering thresholds Fmin, Fmax Normalization
techniques Nb, Nc
Algo 3 quantitative comparison of two datasets
ecomb
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4 steps in the comparison process
Biologists experiment, Ontology, Repository,
Bank of orthologous genes
Algo 1
List of experiments from Repository comparable to
the biologists experiment (sorted from the more
comparable to the less comparable)
One experiment comparable to the biologists
experiment
Algo 2
Filtering thresholds Fmin, Fmax Normalization
techniques Nb, Nc
Bank of orthologous genes
Algo 4 qualitative comparison of two datasets
Algo 3
equal
ecomb
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Search for comparable experiments
40
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Validation of the method
Objective assessing the validity of our criteria
E Lund et al. 2002 Murphy et
al. 2003 Nautiyal et al. 2002 Teng
et al. 2002 Gasch et al. 2000
Gasch et al. 2001 Spellman et al. 1998
Lin et al. 2001 Shepard et al. 2003
Ren et al. 2000
Eb Lesur et al. 2004
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Validation of the method
Objective assessing the validity of our criteria
Global comparison cost
E Lund et al. 2002 0.775 Murphy
et al. 2003 0.7 Nautiyal et al. 2002 1
Teng et al. 2002 1 Gasch et al.
2000 1 Gasch et al. 2001 1 Spellman
et al. 1998 0.925 Lin et al. 2001 0.925
Shepard et al. 2003 0 Ren et al.
2000 0
Eb Lesur et al. 2004
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Validation of the method
Objective assessing the validity of our criteria
Global comparison cost
E Lund et al. 2002 0.775 Murphy
et al. 2003 0.7 Nautiyal et al. 2002 1
Teng et al. 2002 1 Gasch et al.
2000 1 Gasch et al. 2001 1 Spellman
et al. 1998 0.925 Lin et al. 2001 0.925
Shepard et al. 2003 0 Ren et al.
2000 0
Eb Lesur et al. 2004
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Validation with a test coverage
7 relevant attributes ? classes of
equivalence 288 representative biological feature
sheets
  • Each pair of biological feature sheet is
    associated with two costs
  • One cost is computed by our algorithm
  • One cost is manually estimated using the rules
    defined in our method

100 identity between the two costs Correct
identification of the pairs of experiments not
comparable
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Distribution of the comparison costs
non-comparable experiments comparable
experiments
12800 cost ? 0
45
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Validation on real data
2 public repositories used - ArrayExpress
(http//www.ebi.ac.uk/arrayexpress) - Stanford
Microarray Database (SMD) (http//genome-www5.stan
ford.edu) ArrayExpress 268 experiments (268
datasets) SMD 21 experiments (70 datasets) Our
repository content 289 experiments (338 datasets)
46
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Distribution of the comparison costs
No comparison allowed
Qualitative comparison
83521 comparison costs No ArrayExpress experiment
usable Correct identification of experiments
manually compared to our aging experiment
Quantitative comparison
47
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Comparison of our aging experiment with the
experiments stored in our repository
No comparison allowed
Qualitative comparison
Quantitative comparison
- 11 experiments (65 datasets) identified as
comparable with our experiment on aging - 6
experiments correctly combined with our
experiment - 3 experiments correctly
qualitatively compared with our experiment
48
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Identification of new experiments comparable with
our aging experiment
  • 2 newly identified experiments as being
    qualitatively comparable to our experiment
  • 1. Trinklein et al. MBC. 2004.
  • Study of the transcriptional response to heat
    shock in fibroblasts from wild-type mice and mice
    lacking the heat shock transcription factor 1
    gene (HSF1).
  • 2. Arbeitman et al. Science. 2002.
  • Gene expression patterns during the life cycle of
    Drosophila melanogaster.

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Processing of the compared datasets
50
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Combined dataset
ecomb scomb , Dcomb
scomb sc1 U sb
D1 g,rg1 g ? GD1 D2 g,rg2 g ? GD2
51
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Combined dataset
ecomb scomb , Dcomb
52
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Qualitatively compared datasets
equal squal, Dqual
squal sc2 U sb
Pi i r1,, rn i, ri Ob , Oc2 organisms
53
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Qualitatively compared datasets
equal squal, Dqual
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Why create a global dataset ?
? Global analysis of the compared data
D1 D2 Pi 12
D1 D2 Pi 1 Pi 2 Pi 12
Ex clustering
55
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Conclusion and perspectives
1- The biological issue Identification of three
major pathways affected during aging in
Saccharomyces cerevisiae metabolism, stress
response, and genetic stability ? shift from
glycolysis toward energy storage ? similarity
with the ESR ? induction of many DNA repair
genes ? specific activation of the
recombinational repair pathway in the old
wild-type cells ? similarity of the
transcriptional profile of the old dna2-1 cells
with the response of cells senescing after
deletion of telomerase RNA We introduced a novel
connection to telomeric senescence.
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Conclusion and perspectives
  • More biological questions
  • Does the induction of the DNA damage genes
    during aging depends on MEC1?
  • Does DNA2 control the transcriptional response
    or recombinational genes?
  • Is the damage that induces repair genes in old
    dna2-1 cells the same as in tlc1? ?

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Conclusion and perspectives
2- The informatic issue Development of a method
to automatically compare microarray
experiments ? creation of a weighted
ontology ? development of the method ?
validation of the method This system allows the
biologist to automatically identify published
experiments comparable to his own work. It
qualitatively or quantitatively compares the
selected microarray experiments and perform a
pre-processing of the data.
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Conclusion and perspectives
  • Possible improvements
  • Generalization of the use of the MIAME format in
    public repositories
  • Establishment of a consensus of relevant
    attributes describing microarray experiments
  • Refinement of the edge costs of the ontology
  • Comparison of experimental protocols across
    different organisms

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DNA-damage signature genes
6 5 4 3 2 -1 1 -2
Ratio of expression level old cells / young cells
Gene name
Wild-type cells
dna2-1
Compared with Gasch A. P. , M. Huang, S.
Metzner, D. Botstein, S. J. Elledge, and P. O.
Brown. 2001. Mol. Biol. Cell.
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Rad52 recombinational repair pathway
5 4 3 2 -1 1 -2 -3
Ratio of expression level old cells / young cells
Gene name
Wild-type cells
dna2-1
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Does Mec1 control the response to DNA damage ?
DNA damage
Mec1p Tel1p
Rad53p Chk1p
Dun1p
Cell cycle arrest
Cell cycle-regulated genes
Msn4p ? Crt1p
ESR DNA damage
signature
(Gasch A. P. , M. Huang, S. Metzner, D. Botstein,
S. J. Elledge, and P. O. Brown. 2001. Mol. Biol.
Cell.)
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Does Mec1 control the response to DNA damage ?
DNA damage
Mec1p Tel1p
Rad53p Chk1p
Ratio of expression level old cells / young cells
Dun1p
Cell cycle arrest
Gene name
Cell cycle-regulated genes
Msn4p ? Crt1p
DNA-damage signature genes
ESR DNA damage signature
(Gasch A. P. , M. Huang, S. Metzner, D. Botstein,
S. J. Elledge, and P. O. Brown. 2001. Mol. Biol.
Cell.)
64
Similarity calculation
Pop. A (ex dna2-1) Pop. B (ex tlc1?)
N/X
M/X
P/X
Observed /- 2x in pop. B M/X 100
Observed /- 2x in pop. A N/X 100
Observed /- 2x common in pop. A and pop. B
P/X 100 Random similarity between pop. A and
pop. B N/X M/X R If P/X 100 gt R
the similarity observed is significant.
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