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.