Spatial orchestration of the genome: topological reorganisation during X-chromosome inactivation

Genomes are organised through hierarchical structures, ranging from local kilobase-scale cis-regulatory contacts to large chromosome territories. Most notably, (sub)-compartments partition chromosomes according to transcriptional activity, while topologically associating domains (TADs) define cis-regulatory landscapes. The inactive X chromosome in mammals has provided unique insights into the regulation and function of the three-dimensional (3D) genome. Concurrent with silencing of the majority of genes and major alterations of its chromatin state, the X chromosome undergoes profound spatial rearrangements at multiple scales. These include the emergence of megadomains, alterations of the compartment structure and loss of the majority of TADs. Moreover, the Xist locus, which orchestrates X-chromosome inactivation, has provided key insights into regulation and function of regulatory domains. This review provides an overview of recent insights into the control of these structural rearrangements and contextualises them within a broader understanding of 3D genome organisation

Short-term memory in gene induction reveals the regulatory principle behind stochastic IL-4 expression

Combining experiments on primary T cells and mathematical modeling, we characterized the stochastic expression of the interleukin-4 cytokine gene in its physiologic context, showing that a two-step model of transcriptional regulation acting on chromatin rearrangement and RNA polymerase recruitment accounts for the level, kinetics, and population variability of expression.A rate-limiting step upstream of transcription initiation, but occurring at the level of an individual allele, controls whether the interleukin-4 gene is expressed during antigenic stimulation, suggesting that the observed stochasticity of expression is linked to the dynamics of chromatin rearrangement.The computational analysis predicts that the probability to re-express an interleukin-4 gene that has been expressed once is transiently increased. In support, we experimentally demonstrate a short-term memory for interleukin-4 expression at the predicted time scale of several days.The model provides a unifying framework that accounts for both graded and binary modes of gene regulation. Graded changes in expression level can be achieved by controlling transcription initiation, whereas binary regulation acts at the level of chromatin rearrangement and is targeted during the differentiation of T cells that specialize in interleukin-4 production

Rnf12—A Jack of All Trades in X Inactivation?

International audiencePlacental mammals compensate the dosage imbalance of X-linked genes between males (XY) and females (XX) by silencing one randomly chosen X chromosome in females. This process is initiated during early embryonic development and can be recapitulated during differentiation of murine embryonic stem cells (mESCs). X chromosome inactivation (XCI) is initiated by up-regulation of a non-coding RNA on the future inactive X chromosome, named Xist, which lies within a large complex locus, called the X inactivation center (Xic). Subsequently, Xist RNA induces silencing of the entire chromosome in cis. Although central to the XCI process, the molecular mechanisms underlying Xist's regulation still remain to be deciphered. In particular, it is unclear (1) how the up-regulation of Xist is triggered at the onset of differentiation, (2) why this is restricted to female cells, and (3) why one allele and not the other is affected? Although each aspect could in principle be controlled by distinct factors and sequence elements, one protein has recently been proposed to regulate Xist at all three levels: the E3 ubiquitin ligase Rnf12/Rlim. The X-linked Rnf12 gene acts as a dose-dependent activator of Xist, which is expressed at elevated levels in female relative to male cells and is up-regulated during differentiation. Two recent studies shed further light on the precise role of Rnf12 in XCI

Retrieval, alignment, and clustering of computational models based on semantic annotations

As the number of computational systems biology models increases, new methods are needed to explore their content and build connections with experimental data. In this Perspective article, the authors propose a flexible semantic framework that can help achieve these aims

Propagating semantic information in biochemical network models

<p>Abstract</p> <p>Background</p> <p>To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation.</p> <p>Results</p> <p>A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements.</p> <p>Conclusions</p> <p>Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at <url>http://www.semanticsbml.org</url>.</p

Evaluation of presumably disease causing SCN1A variants in a cohort of common epilepsy syndromes

Objective: The SCN1A gene, coding for the voltage-gated Na+ channel alpha subunit NaV1.1, is the clinically most relevant epilepsy gene. With the advent of high-throughput next-generation sequencing, clinical laboratories are generating an ever-increasing catalogue of SCN1A variants. Variants are more likely to be classified as pathogenic if they have already been identified previously in a patient with epilepsy. Here, we critically re-evaluate the pathogenicity of this class of variants in a cohort of patients with common epilepsy syndromes and subsequently ask whether a significant fraction of benign variants have been misclassified as pathogenic. Methods: We screened a discovery cohort of 448 patients with a broad range of common genetic epilepsies and 734 controls for previously reported SCN1A mutations that were assumed to be disease causing. We re-evaluated the evidence for pathogenicity of the identified variants using in silico predictions, segregation, original reports, available functional data and assessment of allele frequencies in healthy individuals as well as in a follow up cohort of 777 patients. Results and Interpretation: We identified 8 known missense mutations, previously reported as path

CasTuner is a degron and CRISPR/Cas-based toolkit for analog tuning of endogenous gene expression

Abstract Certain cellular processes are dose-dependent, requiring specific quantities or stoichiometries of gene products, as exemplified by haploinsufficiency and sex-chromosome dosage compensation. Understanding dosage-sensitive processes requires tools to quantitatively modulate protein abundance. Here we present CasTuner, a CRISPR-based toolkit for analog tuning of endogenous gene expression. The system exploits Cas-derived repressors that are quantitatively tuned by ligand titration through a FKBP12F36V degron domain. CasTuner can be applied at the transcriptional or post-transcriptional level using a histone deacetylase (hHDAC4) fused to dCas9, or the RNA-targeting CasRx, respectively. We demonstrate analog tuning of gene expression homogeneously across cells in mouse and human cells, as opposed to KRAB-dependent CRISPR-interference systems, which exhibit digital repression. Finally, we quantify the system’s dynamics and use it to measure dose-response relationships of NANOG and OCT4 with their target genes and with the cellular phenotype. CasTuner thus provides an easy-to-implement tool to study dose-responsive processes in their physiological context