Kinetics of Xist-induced gene silencing can be predicted from combinations of epigenetic and genomic features

AbstractTo initiate X-chromosome inactivation (XCI), the long non-coding RNA Xist mediates chromosome-wide gene silencing of one X chromosome in female mammals to equalize gene dosage between the sexes. The efficiency of gene silencing, however is highly variable across genes, with some genes even escaping XCI in somatic cells. A genes susceptibility to Xist-mediated silencing appears to be determined by a complex interplay of epigenetic and genomic features; however, the underlying rules remain poorly understood. We have quantified chromosome-wide gene silencing kinetics at the level of the nascent transcriptome using allele-specific Precision nuclear Run-On sequencing (PRO-seq). We have developed a Random Forest machine learning model that can predict the measured silencing dynamics based on a large set of epigenetic and genomic features and tested its predictive power experimentally. While the genomic distance to the Xist locus is the prime determinant of the speed of gene silencing, we find that also pre-marking of gene promoters with polycomb complexes is associated with fast silencing. Moreover, a series of features associated with active transcription and the O-GlcNAc transferase Ogt are enriched at rapidly silenced genes. Our machine learning approach can thus uncover the complex combinatorial rules underlying gene silencing during X inactivation.

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Integrated functional genomic analyses of Klinefelter and Turner syndromes reveal global network effects of altered X chromosome dosage

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1910003117 ◽

2020 ◽

Vol 117 (9) ◽

pp. 4864-4873 ◽

Cited By ~ 6

Author(s):

Xianglong Zhang ◽

David Hong ◽

Shining Ma ◽

Thomas Ward ◽

Marcus Ho ◽

...

Keyword(s):

X Chromosome ◽

Network Effects ◽

Molecular Mechanisms ◽

Klinefelter Syndrome ◽

Gene Dosage ◽

Differentially Expressed ◽

Global Network ◽

Potential Candidate ◽

Gene Promoters ◽

Inactive Genes

In both Turner syndrome (TS) and Klinefelter syndrome (KS) copy number aberrations of the X chromosome lead to various developmental symptoms. We report a comparative analysis of TS vs. KS regarding differences at the genomic network level measured in primary samples by analyzing gene expression, DNA methylation, and chromatin conformation. X-chromosome inactivation (XCI) silences transcription from one X chromosome in female mammals, on which most genes are inactive, and some genes escape from XCI. In TS, almost all differentially expressed escape genes are down-regulated but most differentially expressed inactive genes are up-regulated. In KS, differentially expressed escape genes are up-regulated while the majority of inactive genes appear unchanged. Interestingly, 94 differentially expressed genes (DEGs) overlapped between TS and female and KS and male comparisons; and these almost uniformly display expression changes into opposite directions. DEGs on the X chromosome and the autosomes are coexpressed in both syndromes, indicating that there are molecular ripple effects of the changes in X chromosome dosage. Six potential candidate genes (RPS4X,SEPT6,NKRF,CX0rf57,NAA10, andFLNA) for KS are identified on Xq, as well as candidate central genes on Xp for TS. Only promoters of inactive genes are differentially methylated in both syndromes while escape gene promoters remain unchanged. The intrachromosomal contact map of the X chromosome in TS exhibits the structure of an active X chromosome. The discovery of shared DEGs indicates the existence of common molecular mechanisms for gene regulation in TS and KS that transmit the gene dosage changes to the transcriptome.

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X chromosome inactivation: new players in the initiation of gene silencing

F1000Research ◽

10.12688/f1000research.10707.1 ◽

2017 ◽

Vol 6 ◽

pp. 344 ◽

Cited By ~ 23

Author(s):

Ines Pinheiro ◽

Edith Heard

Keyword(s):

Gene Silencing ◽

X Chromosome ◽

Gene Dosage ◽

X Chromosome Inactivation ◽

Chromosome Inactivation ◽

Crucial Importance ◽

Compensation Process ◽

Repressive Chromatin ◽

Non Coding Rna ◽

Recent Developments

X chromosome inactivation (XCI) is a dosage compensation process that was adopted by female mammals to balance gene dosage between XX females and XY males. XCI starts with the upregulation of the non-coding RNA Xist, after which most X-linked genes are silenced and acquire a repressive chromatin state. Even though the chromatin marks of the inactive X have been fairly well described, the mechanisms responsible for the initiation of XCI remain largely unknown. In this review, we discuss recent developments that revealed unexpected factors playing a role in XCI and that might be of crucial importance to understand the mechanisms responsible for the very first steps of this chromosome-wide gene-silencing event.

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Sex-specific silencing of X-linked genes by Xist RNA

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1515971113 ◽

2016 ◽

Vol 113 (3) ◽

pp. E309-E318 ◽

Cited By ~ 16

Author(s):

Srimonta Gayen ◽

Emily Maclary ◽

Michael Hinten ◽

Sundeep Kalantry

Keyword(s):

Stem Cells ◽

Gene Silencing ◽

X Chromosome ◽

Noncoding Rna ◽

Gene Dosage ◽

Embryonic Stem ◽

Linked Gene ◽

Epiblast Stem Cells ◽

Xist Rna ◽

The Impact

X-inactive specific transcript (Xist) long noncoding RNA (lncRNA) is thought to catalyze silencing of X-linked genes in cis during X-chromosome inactivation, which equalizes X-linked gene dosage between male and female mammals. To test the impact of Xist RNA on X-linked gene silencing, we ectopically induced endogenous Xist by ablating the antisense repressor Tsix in mice. We find that ectopic Xist RNA induction and subsequent X-linked gene silencing is sex specific in embryos and in differentiating embryonic stem cells (ESCs) and epiblast stem cells (EpiSCs). A higher frequency of XΔTsixY male cells displayed ectopic Xist RNA coating compared with XΔTsixX female cells. This increase reflected the inability of XΔTsixY cells to efficiently silence X-linked genes compared with XΔTsixX cells, despite equivalent Xist RNA induction and coating. Silencing of genes on both Xs resulted in significantly reduced proliferation and increased cell death in XΔTsixX female cells relative to XΔTsixY male cells. Thus, whereas Xist RNA can inactivate the X chromosome in females it may not do so in males. We further found comparable silencing in differentiating XΔTsixY and 39,XΔTsix (XΔTsixO) ESCs, excluding the Y chromosome and instead implicating the X-chromosome dose as the source of the sex-specific differences. Because XΔTsixX female embryonic epiblast cells and EpiSCs harbor an inactivated X chromosome prior to ectopic inactivation of the active XΔTsix X chromosome, we propose that the increased expression of one or more X-inactivation escapees activates Xist and, separately, helps trigger X-linked gene silencing.

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Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

10.26434/chemrxiv.9894224.v1 ◽

2019 ◽

Author(s):

Ryther Anderson ◽

Achay Biong ◽

Diego Gómez-Gualdrón

Keyword(s):

Neural Network ◽

Machine Learning ◽

Molecular Simulation ◽

Large Scale ◽

Learning Model ◽

Operating Conditions ◽

Small Subset ◽

Screening Methods ◽

Large Set ◽

Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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Bisection of the X chromosome disrupts the initiation of chromosome silencing during meiosis in Caenorhabditis elegans

Nature Communications ◽

10.1038/s41467-021-24815-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Yisrael Rappaport ◽

Hanna Achache ◽

Roni Falk ◽

Omer Murik ◽

Oren Ram ◽

...

Keyword(s):

Caenorhabditis Elegans ◽

Rna Polymerase Ii ◽

X Chromosome ◽

Sex Chromosomes ◽

Sex Chromosome ◽

Transcription Analysis ◽

X Chromosomes ◽

Unique Character ◽

Active Transcription ◽

Heterogametic Sex

AbstractDuring meiosis, gene expression is silenced in aberrantly unsynapsed chromatin and in heterogametic sex chromosomes. Initiation of sex chromosome silencing is disrupted in meiocytes with sex chromosome-autosome translocations. To determine whether this is due to aberrant synapsis or loss of continuity of sex chromosomes, we engineered Caenorhabditis elegans nematodes with non-translocated, bisected X chromosomes. In early meiocytes of mutant males and hermaphrodites, X segments are enriched with euchromatin assembly markers and active RNA polymerase II staining, indicating active transcription. Analysis of RNA-seq data showed that genes from the X chromosome are upregulated in gonads of mutant worms. Contrary to previous models, which predicted that any unsynapsed chromatin is silenced during meiosis, our data indicate that unsynapsed X segments are transcribed. Therefore, our results suggest that sex chromosome chromatin has a unique character that facilitates its meiotic expression when its continuity is lost, regardless of whether or not it is synapsed.

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Escape from X Chromosome Inactivation and the Female Predominance in Autoimmune Diseases

International Journal of Molecular Sciences ◽

10.3390/ijms22031114 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1114

Author(s):

Ali Youness ◽

Charles-Henry Miquel ◽

Jean-Charles Guéry

Keyword(s):

Autoimmune Diseases ◽

X Chromosome ◽

Gene Dosage ◽

X Chromosome Inactivation ◽

Chromosome Inactivation ◽

X Chromosomes ◽

Female Sex Hormones ◽

Inactive X Chromosome ◽

Immune Related Genes ◽

Female Specific

Women represent 80% of people affected by autoimmune diseases. Although, many studies have demonstrated a role for sex hormone receptor signaling, particularly estrogens, in the direct regulation of innate and adaptive components of the immune system, recent data suggest that female sex hormones are not the only cause of the female predisposition to autoimmunity. Besides sex steroid hormones, growing evidence points towards the role of X-linked genetic factors. In female mammals, one of the two X chromosomes is randomly inactivated during embryonic development, resulting in a cellular mosaicism, where about one-half of the cells in a given tissue express either the maternal X chromosome or the paternal one. X chromosome inactivation (XCI) is however not complete and 15 to 23% of genes from the inactive X chromosome (Xi) escape XCI, thereby contributing to the emergence of a female-specific heterogeneous population of cells with bi-allelic expression of some X-linked genes. Although the direct contribution of this genetic mechanism in the female susceptibility to autoimmunity still remains to be established, the cellular mosaicism resulting from XCI escape is likely to create a unique functional plasticity within female immune cells. Here, we review recent findings identifying key immune related genes that escape XCI and the relationship between gene dosage imbalance and functional responsiveness in female cells.

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Detectable chromosome X mosaicism in males is rarely tolerated in peripheral leukocytes

Scientific Reports ◽

10.1038/s41598-020-80948-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Weiyin Zhou ◽

Shu-Hong Lin ◽

Sairah M. Khan ◽

Meredith Yeager ◽

Stephen J. Chanock ◽

...

Keyword(s):

X Chromosome ◽

Gene Dosage ◽

Large Population ◽

Population Based ◽

Chromosome X ◽

Genotyping Array ◽

Chromosome Mosaicism ◽

Age Related ◽

Leukocyte Function ◽

Peripheral Leukocytes

AbstractAge-related male Y and female X chromosome mosaicism is commonly observed in large population-based studies. To investigate the frequency of male X chromosome mosaicism, we scanned for deviations in chromosome X genotyping array intensity data in a population-based survey of 196,219 UK Biobank men. We detected 12 (0.006%) men with mosaic chromosome X gains ≥ 2 Mb and found no evidence for mosaic chromosome X loss, a level of detection substantially lower than for autosomes or other sex chromosomes. The rarity of chromosome X mosaicism in males relative to females reflects the importance of chromosome X gene dosage for leukocyte function.

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Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

SPE Reservoir Evaluation & Engineering ◽

10.2118/205188-pa ◽

2021 ◽

pp. 1-18

Author(s):

Gisela Vanegas ◽

John Nejedlik ◽

Pascale Neff ◽

Torsten Clemens

Keyword(s):

Machine Learning ◽

Oil Recovery ◽

Numerical Models ◽

Operating Conditions ◽

Model Parameters ◽

Large Set ◽

Model Parameter ◽

Production History ◽

Hydrocarbon Fields ◽

Parameter Distributions

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

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