Conformation Regulation of the X Chromosome Inactivation Center: A Model

X-Chromosome Inactivation (XCI) is the process whereby one, randomly chosen X becomes transcriptionally silenced in female cells. XCI is governed by the Xic, a locus on the X encompassing an array of genes which interact with each other and with key molecular factors. The mechanism, though, establishing the fate of the X's, and the corresponding alternative modifications of the Xic architecture, is still mysterious. In this study, by use of computer simulations, we explore the scenario where chromatin conformations emerge from its interaction with diffusing molecular factors. Our aim is to understand the physical mechanisms whereby stable, non-random conformations are established on the Xic's, how complex architectural changes are reliably regulated, and how they lead to opposite structures on the two alleles. In particular, comparison against current experimental data indicates that a few key cis-regulatory regions orchestrate the organization of the Xic, and that two major molecular regulators are involved

Conditional Cooperativity in Toxin-Antitoxin Regulation Prevents Random Toxin Activation and Promotes Fast Translational Recovery

Many toxin–antitoxin (TA) loci are known to strongly repress their own transcription. This auto-inhibition is often called ‘conditional cooperativity’ as it relies on cooperative binding of TA complexes to operator DNA that occurs only when toxins are in a proper stoichiometric relationship with antitoxins. There has recently been an explosion of interest in TA systems due to their role in bacterial persistence, however the role of conditional cooperativity is still unclear. We reveal the biological function of conditional cooperativity by constructing a mathematical model of the well studied TA system, relBE of Escherichia coli. We show that the model with the in vivo and in vitro established parameters reproduces experimentally observed response to nutritional stress. We further demonstrate that conditional cooperativity stabilizes the level of antitoxin in rapidly growing cells such that random induction of relBE is minimized. At the same time it enables quick removal of free toxin when the starvation is terminated

Conditional Cooperativity of Toxin - Antitoxin Regulation Can Mediate Bistability between Growth and Dormancy

<div><p>Many toxin-antitoxin operons are regulated by the toxin/antitoxin ratio by mechanisms collectively coined “conditional cooperativity”. Toxin and antitoxin form heteromers with different stoichiometric ratios, and the complex with the intermediate ratio works best as a transcription repressor. This allows transcription at low toxin level, strong repression at intermediate toxin level, and then again transcription at high toxin level. Such regulation has two interesting features; firstly, it provides a non-monotonous response to the concentration of one of the proteins, and secondly, it opens for ultra-sensitivity mediated by the sequestration of the functioning heteromers. We explore possible functions of conditional regulation in simple feedback motifs, and show that it can provide bistability for a wide range of parameters. We then demonstrate that the conditional cooperativity in toxin-antitoxin systems combined with the growth-inhibition activity of free toxin can mediate bistability between a growing state and a dormant state.</p></div

Heterocomplex formation in a TA system.

<p>(A) Reaction scheme of the heterocomplex formations, implying that the active complex [AT] is constrained by through and with complex concentrations expressed by <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#pcbi.1003174.e039" target="_blank">eq. (2)</a>. (B) Concentration of AT heteromers for a fixed value of as a function of with . Note that it has a peak at . In the strong binding limit of with ( kept constant), for is given by for and , where always has a peak at . In this limit, for . (C) The behavior of shown in (B) is reflected in the behavior of the repression factor as a function of , calculated for fixed , and dissociation constant for AT-DNA binding .</p

Schematic summary of the role of conditional regulation in persister formation.

<p>The red curves show the toxin production rate and the blue lines give the degradation rate, both from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#pcbi.1003174.e141" target="_blank">eq. (6)</a>. Both terms depend on , and here we make approximation that is always in steady state (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#pcbi.1003174.e142" target="_blank">eq. 7</a> with ) for given , because dynamics of is much faster than due to high production and degradation rate. Since production term of and are proportional to each other and is degraded at a constant rate, resulting concentration is proportional to the production term of (red curves). The scales of curves are modified from actual functional forms so that the characteristic behaviours can be grasped easily. The ultra-sensitivity mediated by protein-protein binding combined with feedback from free toxin activity is reflected in the peak of the production rate and drop of the degradation rate, resulting in bistability of the system. This accounts for the type II persister where a cell can spontaneously switch to and out of the persister state. The non-monotonicity of the conditional regulation secures that some toxins are stored in antitoxin dominated state, helping the transition to the stress-induced activation of toxin <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#pcbi.1003174-Cataudella1" target="_blank">[6]</a>, which becomes the base for type I persister formation.</p

Conditional regulation of T with fixed A concentration.

<p>(A) Production term of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#pcbi.1003174.e064" target="_blank">eq. (4)</a> as a function of for , for (blue line), (red line), and (green line). The solid lines represent case, and the dashed lines represent case, where is the dissociation constant for the binding of AT-DNA. (B) Region in the parameter space (, ) that shows bistability for  = 1. The color of each bistable point represents the ratio between the low- fixed point and the high- fixed point.</p

Reference parameter values.

<p>Reference parameter values.</p

TA system with CR without and with feedback through free toxin activity.

<p>(A.1) Schematic representation of the genetic circuit described by <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#pcbi.1003174.e127" target="_blank">eq. (5)</a> for TA system with CR, without considering toxic activity of free T. (A.2) Null-clines for <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#pcbi.1003174.e127" target="_blank">eq. (5)</a>. Blue line represents , and red line represents . For comparable values of A and T the two null clines become parallel and does not cross, as shown in the area highlighted in grey, i.e. the system does not show bistability. The parameters used are listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#pcbi-1003174-t001" target="_blank">Table 1</a> in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#s4" target="_blank">Materials and Methods</a>. Dashed lines with arrows show the flow to the fixed point. (B.1) Schematic representation of the genetic circuit described by the model (6) and (7). (B.2) Null-clines for the system of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#pcbi.1003174.e141" target="_blank">eqs. (6)</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003174#pcbi.1003174.e142" target="_blank">(7)</a> with . Blue line , Red line . Dashed lines with arrows show the flow to the stable fixed points.</p