Genome-wide identification of post-translational modulators of transcription factor activity in human B cells

Abstract

The ability of a transcription factor to regulate its targets is modulated by a variety of genetic and epigenetic mechanisms, resulting in highly context-dependent regulatory networks. However, high-throughput methods for the identification of proteins that affect transcription factor activity are still largely unavailable. Here we introduce a systems biology framework, MINDy (Modulator Inference by Network Dynamics), for the genome-wide identification of post-translational modulators of transcription factor activity within a specific cellular context. When used to dissect the regulation of MYC activity in human B lymphocytes, the approach inferred novel modulators of MYC function, which act by distinct mechanisms, including protein turn-over, transcriptional complex formation, and selective enzyme recruitment. MINDy is generally applicable to study the post-translational modulation of mammalian transcription factors in any cellular context. As such it provides a useful resource to dissect context-specific signaling pathways and combinatorial transcriptional regulation.

Hippo signalling in the G2/M cell cycle phase: Lessons learned from the yeast MEN and SIN pathways

“…By analysing the cell cycle activation of NDR1/2 via kinase assays and phosphorylation specific antibodies, Cornils et al (2011) defined human NDR1/2 as cell cycle regulated kinases that are mainly activated upon the G1/S cell cycle transition [76]. They further showed that NDR1/2 are essential for the G1/S cell cycle transition by regulating p21 and c-myc levels [76][77][78]. Significantly, NDR1/2 kinases require only MST3 kinase activity for this cell cycle role, but not MST1/2 [76,79].…”

Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

“…modulators of transcription factor activity and the effect on expression of their target genes (11,(13)(14)(15)(16).…”

“…Many studies rely on knowledge of TF-target interactions to decipher the transcriptional network (27)(28)(29)(30). However, few algorithms are able to identify modulators of TF-target connectivity at a network level (14)(15)(16). These typically derive information from statistical relationships across conditions between the mRNA abundance of gene triplets: the TF of interest, a candidate modulator, and a target of the TF.…”

“…Considering genome-wide mRNA abundance along with information on genotypic variation represents the combined effect of the latter on the regulatory state of the cell (3-7) and can yield better prediction of phenotype (8,9), the combined effect of allelic variation on transcript abundance (8,10), the effect of genetic variation on transcription factor activity (11,12), or transcript stability (13). However, there are few methods that systematically identify the effect of genetic modifiers on the strength of the connection ("connectivity") between a transcription factor and its target genes (14)(15)(16).…”

From blastocyst to gastrula: gene regulatory networks of embryonic stem cells and early mouse embryogenesis

“…Approaches such as Algorithm for the Reconstruction of Accurate Cellular Networks have been developed with this issue in mind, as it can eliminate indirect transcriptional interactions from inferred networks [147]. Furthermore, methods such as Modulator Inference by Network Dynamics can detect three-way interactions between gene products (such as protein kinases), downstream regulators and their targets [148].…”