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Systematic identification of cell cycle regulated transcription factors from microarray time series data.

Cheng C, Li LM - BMC Genomics (2008)

Bottom Line: For most species, however, large-scale ChIP-chip data are still not available.Moreover, by integrating expression data with in-silico motif discovery data, we identify 8 cell cycle associated regulatory motifs, among which 7 are binding sites for well-known cell cycle related TFs.In S. cerevisiae, the TF-gene binding information is provided by the systematic ChIP-chip experiments.

View Article: PubMed Central - HTML - PubMed

Affiliation: Molecular and Computational biology program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. chaochen@usc.edu

ABSTRACT

Background: The cell cycle has long been an important model to study the genome-wide transcriptional regulation. Although several methods have been introduced to identify cell cycle regulated genes from microarray data, they can not be directly used to investigate cell cycle regulated transcription factors (CCRTFs), because for many transcription factors (TFs) it is their activities instead of expressions that are periodically regulated across the cell cycle. To overcome this problem, it is useful to infer TF activities across the cell cycle by integrating microarray expression data with ChIP-chip data, and then examine the periodicity of the inferred activities. For most species, however, large-scale ChIP-chip data are still not available.

Results: We propose a two-step method to identify the CCRTFs by integrating microarray cell cycle data with ChIP-chip data or motif discovery data. In S. cerevisiae, we identify 42 CCRTFs, among which 23 have been verified experimentally. The cell cycle related behaviors (e.g. at which cell cycle phase a TF achieves the highest activity) predicted by our method are consistent with the well established knowledge about them. We also find that the periodical activity fluctuation of some TFs can be perturbed by the cell synchronization treatment. Moreover, by integrating expression data with in-silico motif discovery data, we identify 8 cell cycle associated regulatory motifs, among which 7 are binding sites for well-known cell cycle related TFs.

Conclusion: Our method is effective to identify CCRTFs by integrating microarray cell cycle data with TF-gene binding information. In S. cerevisiae, the TF-gene binding information is provided by the systematic ChIP-chip experiments. In other species where systematic ChIP-chip data is not available, in-silico motif discovery and analysis provide us with an alternative method. Therefore, our method is ready to be implemented to the microarray cell cycle data sets from different species. The C++ program for AC score calculation is available for download from URL http://leili-lab.cmb.usc.edu/yeastaging/projects/project-base/.

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AC score profiles of four TFs which may be perturbed in activity by α-factor synchronizing treatment.
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Figure 4: AC score profiles of four TFs which may be perturbed in activity by α-factor synchronizing treatment.

Mentions: In order to measure gene expression during the cell cycle, the yeast cells must be synchronized using certain techniques, such as α-factor arrest and temperature arrest. However, these synchronization techniques may perturb the cell status and result in activity modification of TFs [30]. As a consequence, the periodic activity fluctuation of some CCRTFs may be perturbed and can not be detected. Figure 4 shows the effect of the α-factor to the activity of four different TFs. As shown in Figure 4A–C, Dig1, Ste12 and Tec1 exhibit extraordinary high activities at the initiation of the time series after releasing from the α-factor arrest (AC scores are 22.3, 31.9 and 16.1, respectively). For Ace2, although its activity is only moderately up-regulated at the initiation of the time series (AC scores is 6.8), the periodicity of its activity profiles is perturbed by the α-factor treatment. As shown in Figure 4D, the activity profile of Ace2 exhibits quite different patterns in the two consecutive cell cycles. In fact, Dig1, Ste12 and Tec1 are transcription factors that are activated by the MAP kinase signaling cascade and involved in the regulation of genes in mating or pseudohyphal/invasive growth pathways [31-33]. α-factor pheromone is the activator of the MAP kinase pathway [34], so it is not surprising to see the up-regulation of Dig1, Ste12, and Tec1 by the α-factor treatment.


Systematic identification of cell cycle regulated transcription factors from microarray time series data.

Cheng C, Li LM - BMC Genomics (2008)

AC score profiles of four TFs which may be perturbed in activity by α-factor synchronizing treatment.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2315658&req=5

Figure 4: AC score profiles of four TFs which may be perturbed in activity by α-factor synchronizing treatment.
Mentions: In order to measure gene expression during the cell cycle, the yeast cells must be synchronized using certain techniques, such as α-factor arrest and temperature arrest. However, these synchronization techniques may perturb the cell status and result in activity modification of TFs [30]. As a consequence, the periodic activity fluctuation of some CCRTFs may be perturbed and can not be detected. Figure 4 shows the effect of the α-factor to the activity of four different TFs. As shown in Figure 4A–C, Dig1, Ste12 and Tec1 exhibit extraordinary high activities at the initiation of the time series after releasing from the α-factor arrest (AC scores are 22.3, 31.9 and 16.1, respectively). For Ace2, although its activity is only moderately up-regulated at the initiation of the time series (AC scores is 6.8), the periodicity of its activity profiles is perturbed by the α-factor treatment. As shown in Figure 4D, the activity profile of Ace2 exhibits quite different patterns in the two consecutive cell cycles. In fact, Dig1, Ste12 and Tec1 are transcription factors that are activated by the MAP kinase signaling cascade and involved in the regulation of genes in mating or pseudohyphal/invasive growth pathways [31-33]. α-factor pheromone is the activator of the MAP kinase pathway [34], so it is not surprising to see the up-regulation of Dig1, Ste12, and Tec1 by the α-factor treatment.

Bottom Line: For most species, however, large-scale ChIP-chip data are still not available.Moreover, by integrating expression data with in-silico motif discovery data, we identify 8 cell cycle associated regulatory motifs, among which 7 are binding sites for well-known cell cycle related TFs.In S. cerevisiae, the TF-gene binding information is provided by the systematic ChIP-chip experiments.

View Article: PubMed Central - HTML - PubMed

Affiliation: Molecular and Computational biology program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. chaochen@usc.edu

ABSTRACT

Background: The cell cycle has long been an important model to study the genome-wide transcriptional regulation. Although several methods have been introduced to identify cell cycle regulated genes from microarray data, they can not be directly used to investigate cell cycle regulated transcription factors (CCRTFs), because for many transcription factors (TFs) it is their activities instead of expressions that are periodically regulated across the cell cycle. To overcome this problem, it is useful to infer TF activities across the cell cycle by integrating microarray expression data with ChIP-chip data, and then examine the periodicity of the inferred activities. For most species, however, large-scale ChIP-chip data are still not available.

Results: We propose a two-step method to identify the CCRTFs by integrating microarray cell cycle data with ChIP-chip data or motif discovery data. In S. cerevisiae, we identify 42 CCRTFs, among which 23 have been verified experimentally. The cell cycle related behaviors (e.g. at which cell cycle phase a TF achieves the highest activity) predicted by our method are consistent with the well established knowledge about them. We also find that the periodical activity fluctuation of some TFs can be perturbed by the cell synchronization treatment. Moreover, by integrating expression data with in-silico motif discovery data, we identify 8 cell cycle associated regulatory motifs, among which 7 are binding sites for well-known cell cycle related TFs.

Conclusion: Our method is effective to identify CCRTFs by integrating microarray cell cycle data with TF-gene binding information. In S. cerevisiae, the TF-gene binding information is provided by the systematic ChIP-chip experiments. In other species where systematic ChIP-chip data is not available, in-silico motif discovery and analysis provide us with an alternative method. Therefore, our method is ready to be implemented to the microarray cell cycle data sets from different species. The C++ program for AC score calculation is available for download from URL http://leili-lab.cmb.usc.edu/yeastaging/projects/project-base/.

Show MeSH
Related in: MedlinePlus