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

Area of Science:

• Molecular Biology
• Genomics
• Systems Biology

Background:

• The cell cycle is a key model for studying genome-wide transcriptional regulation.
• Existing methods struggle to identify cell cycle regulated transcription factors (CCRTFs) because their activity, not just expression, is periodically regulated.
• Large-scale ChIP-chip data, crucial for inferring TF activity, is often unavailable for many species.

Purpose of the Study:

• To develop and validate a novel computational method for identifying CCRTFs.
• To infer transcription factor (TF) activities across the cell cycle by integrating gene expression and TF-binding data.
• To overcome limitations posed by the lack of extensive ChIP-chip data in many organisms.

Main Methods:

• A two-step computational approach integrating microarray cell cycle data with ChIP-chip or motif discovery data.
• Inference of TF activities across the cell cycle using combined expression and binding information.
• Utilizing in-silico motif discovery as an alternative to ChIP-chip data for species lacking it.

Main Results:

• Identification of 42 CCRTFs in S. cerevisiae, with 23 experimentally validated.
• Predicted TF activities and cell cycle phase behaviors align with established knowledge.
• Discovery of 8 cell cycle associated regulatory motifs, 7 linked to known cell cycle TFs.
• Observation that cell synchronization treatments can perturb periodical TF activity.

Conclusions:

• The proposed method effectively identifies CCRTFs by integrating microarray data with TF-gene binding information.
• In-silico motif discovery offers a viable alternative to ChIP-chip data for identifying CCRTFs in species lacking extensive experimental binding data.
• The method is adaptable for analyzing microarray cell cycle datasets across diverse species.