Institute of Information Science, Academia Sinica

Background

Transcription factor knockout microarrays (TFKMs) provide useful information about gene regulation. By using the genome-wide microarray profiling of the expression difference between mutant and wild type, the TF knockout targets of the TF being knockout can be identified by statistical methods for detection of differentially expressed genes. However, the identified TF knockout targets may contain a certain amount of false positives due to the experimental noises inherent in the high-throughput microarray technology. Even if the identified TF knockout targets are true, the molecular mechanisms of how a TF regulates its TF knockout targets remain unknown by this kind of statistical approaches.

Results

It is known that a TF regulates its direct targets by binding to their promoters and regulates its indirect targets by transcriptional regulatory chains through intermediate TFs. In this paper, we develop a method that uses this knowledge as a biological filter for extracting biologically interpretable TF knockout targets from the original TF knockout targets, which are the differentially expressed genes inferred (by statistical methods) solely from the noisy TFKMs. The details of our method are as follows. First, a TF binding network is constructed using the ChIP-chip data in the literature. Then for each original TF knockout target, it is said to be biologically interpretable if a path (in the TF binding network) from the TF being knockout to this target could be identified by graph search algorithms. The identified path explains how the TF may regulate this target either directly by binding to its promoter or indirectly by transcriptional regulatory chains through immediate TFs. We validate the biological significance of our refined TF knockout targets by assessing their functional enrichment, expression coherence, and the prevalence of protein-protein interactions. Our refined TF knockout targets outperform the original TF knockout targets across all measures.

Conclusions

By jointly analyzing the TFKM and ChIP-chip data, our method can extract the biologically interpretable TF knockout targets. The identified paths (in the TF binding network) from the TF being knockout to its knockout targets form testable hypotheses regarding the molecular mechanisms of how a TF may regulate its knockout targets. Several hypotheses predicted by our methods have been proven correct in the literature. Our work demonstrates that integrating multiple data sources is a powerful approach to study complex biological systems.