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Basis State Prediction of Cell-Cycle Transcription Factors in Saccharomyces cerevisiae Dr. Matteo...
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Basis State Prediction of Cell-Cycle Transcription Factors in Saccharomyces cerevisiae Dr. Matteo Pellegrini
Dr. Shawn Cokus
Sherri Rose
UCLAMolecular, Cell, and Developmental Biology Department
Background: Expression Analysis Microarrays measure the mRNA
concentration of genes expressed within a yeast cell.
Current statistical techniques to analyze microarray data: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Independent Component Analysis (ICA).
These techniques do not always lead to clear interpretations because they use complicated linear combinations.
Rationale: Basis State Prediction
Use biologically meaningful basis states.
Develop a technique that will describe expression data in terms of these states.
Transcription Factor Binding Basis States
pharyngula.org
The binding of 204 transcription factors to yeast genes was measured.
Expression Data Basis States
Describe expression data using basis states.
Y(1) = f(1) e(1, 1) + f(2) e(1, 2) + … + f(n) e(1, n)
Y(2) = f(1) e(2, 1) + f(2) e(2, 2) + … + f(n) e(2, n)
. . . .
. . . .
. . . .
Y(m) = f(1) e(m, 1) + f(2) e(m, 2) + … + f(n) e(m, n)
gene value in original
experiment
activity coefficient for transcription
factor n
binding of transcription factor n to
gene 2
Strategy: Basis State PredictionExpression data
Generated linear combinations of transcription factor binding basis states
Graphical representation
Analysis
Goal: Basis State Prediction of Cell-Cycle Dependence
•Predict transcription factors that are cell-cycle dependent.
•Compare the expression of a transcription factor to its activity.
Yeast Cell Cycle
http://www.tau.ac.il/
M/G1
Fourier Transform
Fourier transform was applied to identify: 1) periodic transcription factor activity 2) mRNAs expressed in a periodic manner
Data that appears to be periodic can be modeled as a sum of related sine waves.
The Fourier transform decomposes a cycle of data into its sine components.
Results I: Transcription Factors with Periodic Activity
Analysis produced a rank-ordered list of transcription factors. Some transcription factors are already known to be involved in cell cycle transcription.
Transcription Factor Periodic ActivityYOX1 0.367SWI6 0.349SWI4 0.337
YKR064W 0.332NDD1 0.327MBP1 0.321FKH1 0.320UGA3 0.310RME1 0.303HIR3 0.294SWI5 0.292
unknown protein
transcription factor associated with stress
response Not listed: ACE2
Transcription Factor Periodic Activity ExpressionYOX1 0.367 0.252SWI6 0.349 0.155SWI4 0.337 0.286
YKR064W 0.332 0.077NDD1 0.327 0.287MBP1 0.321 0.040FKH1 0.320 0.219UGA3 0.310 0.040RME1 0.303 0.023HIR3 0.294 0.065SWI5 0.292 0.143
Results I: Comparing Transcription Factor Activity and Expression
Some of the transcription factors with periodic activity do not have periodic expression levels.
Transcription Factor Periodic Activity ExpressionYOX1 0.367 0.252SWI6 0.349 0.155SWI4 0.337 0.286
YKR064W 0.332 0.077NDD1 0.327 0.287MBP1 0.321 0.040FKH1 0.320 0.219UGA3 0.310 0.040RME1 0.303 0.023HIR3 0.294 0.065SWI5 0.292 0.143
Results I: Comparing Transcription Factor Activity and Expression
Interactions Between Transcription Factors:MBP1 forms a complex with SWI6. This may explain the periodic activity of MBP1 in the cell
cycle.
MBP1 forms a complex with SWI6. This may explain
the periodic activity of MBP1 in the cell
cycle.
Interactions Between Transcription Factors
Results I: Comparing Transcription Factor
Activity and Expression
Periodic
Not Periodic
Transcription Factor Periodic Activity ExpressionYOX1 0.367 0.252SWI6 0.349 0.155SWI4 0.337 0.286
YKR064W 0.332 0.077NDD1 0.327 0.287MBP1 0.321 0.040FKH1 0.320 0.219UGA3 0.310 0.040RME1 0.303 0.023HIR3 0.294 0.065SWI5 0.292 0.143
Results I: Comparing Transcription Factor Activity and Expression
Identifying New Cell-Cycle Transcription Factors:
YKR064W a hypothetical protein. One might hypothesize that it is periodic in the cell cycle due to unknown protein interactions.
Results: Prediction of Cell-Cycle Dependence
What does this show?
– One can use this method to identify transcription factors that are cell-cycle dependent.
– One can analyze differences in expression versus activity in transcription factors.
Basis State Prediction: The Future
The ability to describe complex expression microarray data in
terms of small numbers of basis states can increase our
understanding of the data and advance attempts to construct
quantitative models of transcriptional networks.
References Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R.,
Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., and Futcher, B. 1998. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9: 3273-3297.
Harbison, C.T., Gordon, B., Lee, T.I., Rinaldi, N.J., MacIsaac, K.D., Danford, T.W., Hannett, N.M., Tagne, J.B., Reynolds, D.B., Yoo, J., Jennings, E.G., Zeitlinger, J., Pokholok, D.K., Kellis, M., Rolfe, P.A., Takusagawa, K.T., Lander, E.S., and Gifford, D.K. 2004. Transcriptional regulatory code of a eukaryotic genome. Nature 431: 99-104.