Literature DB >> 12620064

A sigmoidal transcriptional response: cooperativity, synergy and dosage effects.

Reiner A Veitia1.   

Abstract

A sigmoidal transcriptional response (STR) is thought to act as a molecular switch to control gene expression. This nonlinear behaviour arises as a result of the cooperative recognition of a promoter/enhancer by transcription factors (TFs) and/or their synergy to attract the basal transcriptional machinery (BTM). Although this cooperation between TFs is additive in terms of energy, it leads to an exponential increase in affinity between the BTM and the pre-initiation complexes. This exponential increase in the strength of interactions is the principle that governs synergistic systems. Here, I propose a minimalist quasi-equilibrium model to explore qualitatively the STR taking into account cooperative recognition of the promoter/enhancer and synergy. Although the focus is on the effect of activators, a similar treatment can be applied to inhibitors. One of the main insights obtained from the model is that generation of a sigmoidal threshold is possible even in the absence of cooperative DNA binding provided the TFs synergistically interact with the BTM. On the contrary, when there is cooperative binding, the impact of synergy diminishes. It will also be shown that a sigmoidal response to a morphogenetic gradient can be used to generate a nested gradient of another morphogen. Previously, I had proposed that halving the amounts of TFs involved in sigmoidal transcriptional switches could account for the abnormal dominant phenotypes associated with some of these genes. This phenomenon, called haploinsufficiency (HI), has been recognised as the basis of many human diseases. Although a formal proof linking HI and a sigmoidal response is lacking, it is tempting to explore the model from the perspective of dosage effects.

Entities:  

Mesh:

Year:  2003        PMID: 12620064     DOI: 10.1017/s1464793102006036

Source DB:  PubMed          Journal:  Biol Rev Camb Philos Soc        ISSN: 0006-3231


  42 in total

Review 1.  A differential dosage hypothesis for parental effects in seed development.

Authors:  Brian P Dilkes; Luca Comai
Journal:  Plant Cell       Date:  2004-12       Impact factor: 11.277

Review 2.  Paralogs in polyploids: one for all and all for one?

Authors:  Reiner A Veitia
Journal:  Plant Cell       Date:  2005-01       Impact factor: 11.277

3.  Statistical epistasis is a generic feature of gene regulatory networks.

Authors:  Arne B Gjuvsland; Ben J Hayes; Stig W Omholt; Orjan Carlborg
Journal:  Genetics       Date:  2006-10-08       Impact factor: 4.562

Review 4.  Exploring the molecular etiology of dominant-negative mutations.

Authors:  Reiner A Veitia
Journal:  Plant Cell       Date:  2007-12-14       Impact factor: 11.277

Review 5.  Computational methods to dissect cis-regulatory transcriptional networks.

Authors:  Vibha Rani
Journal:  J Biosci       Date:  2007-12       Impact factor: 1.826

6.  Combinatorial Gene Regulation through Kinetic Control of the Transcription Cycle.

Authors:  Clarissa Scholes; Angela H DePace; Álvaro Sánchez
Journal:  Cell Syst       Date:  2016-12-29       Impact factor: 10.304

Review 7.  Gene and genome duplications: the impact of dosage-sensitivity on the fate of nuclear genes.

Authors:  Patrick P Edger; J Chris Pires
Journal:  Chromosome Res       Date:  2009       Impact factor: 5.239

8.  Dosage effects in morphogenetic gradients of transcription factors: insights from a simple mathematical model.

Authors:  Reiner A Veitia
Journal:  J Genet       Date:  2018-06       Impact factor: 1.166

9.  Avoiding transcription factor competition at promoter level increases the chances of obtaining oscillation.

Authors:  Andreea Munteanu; Marco Constante; Mark Isalan; Ricard V Solé
Journal:  BMC Syst Biol       Date:  2010-05-17

10.  Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks.

Authors:  Martin T Swain; Johannes J Mandel; Werner Dubitzky
Journal:  BMC Bioinformatics       Date:  2010-09-14       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.