Context-specific function of the LIM homeobox 1 transcription factor in head formation of the mouse embryo

Development ◽  
2015 ◽  
Vol 142 (11) ◽  
pp. 2069-2079 ◽  
Author(s):  
N. Fossat ◽  
C. K. Ip ◽  
V. J. Jones ◽  
J. B. Studdert ◽  
P.-L. Khoo ◽  
...  
2019 ◽  
Vol 25 (1) ◽  
pp. 87-102.e9 ◽  
Author(s):  
Nicole R. Stone ◽  
Casey A. Gifford ◽  
Reuben Thomas ◽  
Karishma J.B. Pratt ◽  
Kaitlen Samse-Knapp ◽  
...  

2007 ◽  
Vol 35 (6) ◽  
pp. 1053-1067 ◽  
Author(s):  
Eric Yang ◽  
David Simcha ◽  
Richard R. Almon ◽  
Debra C. Dubois ◽  
William J. Jusko ◽  
...  

Development ◽  
2011 ◽  
Vol 138 (4) ◽  
pp. 667-676 ◽  
Author(s):  
N. Fossat ◽  
V. Jones ◽  
P.-L. Khoo ◽  
D. Bogani ◽  
A. Hardy ◽  
...  

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
C Matthew Hope ◽  
Jemma L Webber ◽  
Sherzod A Tokamov ◽  
Ilaria Rebay

During development, transcriptional complexes at enhancers regulate gene expression in complex spatiotemporal patterns. To achieve robust expression without spurious activation, the affinity and specificity of transcription factor–DNA interactions must be precisely balanced. Protein–protein interactions among transcription factors are also critical, yet how their affinities impact enhancer output is not understood. The Drosophila transcription factor Yan provides a well-suited model to address this, as its function depends on the coordinated activities of two independent and essential domains: the DNA-binding ETS domain and the self-associating SAM domain. To explore how protein–protein affinity influences Yan function, we engineered mutants that increase SAM affinity over four orders of magnitude. This produced a dramatic subcellular redistribution of Yan into punctate structures, reduced repressive output and compromised survival. Cell-type specification and genetic interaction defects suggest distinct requirements for polymerization in different regulatory decisions. We conclude that tuned protein–protein interactions enable the dynamic spectrum of complexes that are required for proper regulation.


2018 ◽  
Author(s):  
Viren Amin ◽  
Murat Can Cobanoglu

AbstractWe present EPEE (Effector and Perturbation Estimation Engine), a method for differential analysis of transcription factor (TF) activity from gene expression data. EPEE addresses two principal challenges in the field, namely incorporating context-specific TF-gene regulatory networks, and accounting for the fact that TF activity inference is intrinsically coupled for all TFs that share targets. Our validations in well-studied immune and cancer contexts show that addressing the overlap challenge and using state-of-the-art regulatory networks enable EPEE to consistently produce accurate results. (Accessible at: https://github.com/Cobanoglu-Lab/EPEE)


Oncogene ◽  
2013 ◽  
Vol 33 (10) ◽  
pp. 1207-1217 ◽  
Author(s):  
M Sarris ◽  
K Nikolaou ◽  
I Talianidis

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