Regulatory interdependence of myeloid transcription factors revealed by Matrix RNAi analysis

With the move towards systems biology, we need sensitive and reliable ways to determine the relationships between transcription factors and their target genes. In this paper we analyze the regulatory relationships between 78 myeloid transcription factors and their coding genes by using the matrix RNAi system in which a set of transcription factor genes are individually knocked down and the resultant expression perturbation is quantified.

By considering whether the targets of a given transcription factor are naturally up- or downregulated during phorbol 12-myristate 13-acetate-induced differentiation, we could classify these edges as pro-differentiative (229), anti-differentiative (76) or neither (571) using expression profiling data obtained in the FANTOM4 study. This classification analysis suggested that several factors could be involved in monocytic differentiation, while others such as MYB and the leukemogenic fusion MLL-MLLT3 could help to maintain the initial undifferentiated state by repressing the expression of pro-differentiative factors or maintaining expression of anti-differentiative factors.

Using small interfering RNAs we knocked down the 78 transcription factor genes in monocytic THP-1 cells and monitored the perturbation of the expression of the same 78 transcription factors and 13 other transcription factor genes as well as 5 non-transcription factor genes by quantitative real-time RT-PCR, thereby building a 78 x 96 matrix of perturbation and measurement. This approach identified 876 cases where knockdown of one transcription factor significantly affected the expression of another (from a potential 7,488 combinations). Our study also revealed cell-type-specific transcriptional regulatory networks in two different cell types. Conclusions: By considering whether the targets of a given transcription factor are naturally up- or downregulated during phorbol 12-myristate 13-acetate-induced differentiation, we could classify these edges as pro-differentiative (229), anti-differentiative (76) or neither (571) using expression profiling data obtained in the FANTOM4 study. This classification analysis suggested that several factors could be involved in monocytic differentiation, while others such as MYB and the leukemogenic fusion MLL-MLLT3 could help to maintain the initial undifferentiated state by repressing the expression of pro-differentiative factors or maintaining expression of anti-differentiative factors.