Biology subjects

Vinogradova, S.

Publications and source records attributed to Vinogradova, S..

4 recordsLinked to original sources

bioRxiv2021

Single-cell reconstitution reveals persistence of clonal heterogeneity in the murine hematopoietic system

The persistence of patterns of monoallelic expression is a controversial matter. We report a genome-wide in vivo transcriptomics approach based on allelic expression imbalance to evaluate whether the transcriptional allelic patterns of single murine hematopoietic stem cells (HSC) are still present in the respective differentiated clonal B-cell populations. For 14 genes, we show conclusive evidence for a remarkable persistence in HSC-derived B clonal cells of allele-specific autosomal transcriptional states already present in HSCs. In a striking contrast to the frequency of genes with clonal allelic expression differences in clones expanded without differentiation (up to 10%), we find that clones that have undergone multiple differentiation steps in vivo are more similar to each other. These data suggest that most of the random allele-specific stable transcriptional states on autosomal chromosomes are established de novo during cell lineage differentiation. Given that allele-specific transcriptional states are more stable in cells not undergoing extensive differentiation than in the clones we assessed after full lineage differentiation in vivo, we introduce the "Punctuated Disequilibria" model: random allelic expression biases are stable if the cells are not undergoing differentiation, but may change during differentiation between developmental stages and reach a new stable equilibrium that will only be challenged if the cell engages in further differentiation. Thus, the transcriptional allelic states may not be a stable feature of the differentiating clone, but phenotypic diversity between clones of a population at any given stage of the cell lineage is still ensured.

genetics↗

bioRxiv2020

Mechanism of monoallelic expression and allelic rheostat role of DNA methylation

In diploid cells, maternal and paternal copies of genes usually have similar transcriptional activity. Mammalian allele-specific epigenetic mechanisms such as X-chromosome inactivation (XCI) and imprinting were historically viewed as rare exceptions to this rule. The discovery of mitotically stable monoallelic autosomal expression (MAE) a decade ago revealed an additional allele-specific mode regulating thousands of mammalian genes. However, despite its prevalence, the mechanistic basis of MAE remains unknown. To uncover the mechanism of MAE maintenance, we devised a small-molecule screen for reactivation of silenced alleles across multiple loci using targeted RNA sequencing. Contrary to previous reports, we identified DNA methylation as a key mechanism of MAE mitotic maintenance. In contrast with the binary choice of the active allele in XCI, stringent transcriptome-wide analysis revealed MAE as a regulatory mode with tunable control of allele-specific expression, dependent on the extent of DNA methylation. In a subset of MAE genes, allelic imbalance was insensitive to changes in DNA methylation, implicating additional mechanisms in MAE maintenance in these loci. Our findings identify a key mechanism of MAE maintenance, reveal tunability of this mode of gene regulation, and provide the essential platform for probing the biological role of MAE in development and disease.

genomics↗

bioRxiv2020

Unexpected variability of allelic imbalance estimates from RNA sequencing

RNA sequencing and other experimental methods that produce large amounts of data are increasingly dominant in molecular biology. However, the noise properties of these techniques have not been fully understood. We assessed the reproducibility of allele-specific expression measurements by conducting replicate sequencing experiments from the same RNA sample. Surprisingly, variation in the estimates of allelic imbalance (AI) between technical replicates was up to 7-fold higher than expected from commonly applied noise models. We show that AI overdispersion varies substantially between replicates and between experimental series, appears to arise during the construction of sequencing libraries, and can be measured by comparing technical replicates. We demonstrate that compensation for AI overdispersion greatly reduces technical variation and enables reliable differential analysis of allele-specific expression across samples and across experiments. Conversely, not taking AI overdispersion into account can lead to a substantial number of false positives in analysis of allele-specific gene expression

bioinformatics↗

bioRxiv2018

MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin

SummaryA large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by allele-specific gene expression that varies between clonal cell lineages. MAE is highly cell-type specific, and mapping it in a large number of cell and tissue types can provide insight into its biological function. Its detection, however, remains challenging. We previously reported that a sequence-independent chromatin signature identifies, with high sensitivity and specificity, genes subject to MAE in multiple tissue types using readily available ChIP-seq data. Here we present an implementation of this method as a user-friendly, open-source software pipeline for monoallelic gene inference from chromatin (MaGIC).\n\nAvailability and implementationThe source code for the MaGIC pipeline and the Shiny app is available at https://github.com/gimelbrantlab/magic.\n\nContactsebastien_vigneau@dfci.harvard.edu, gimelbrant@mail.dfci.harvard.edu

bioinformatics↗