Biology subjects

Gimelbrant, A.

Publications and source records attributed to Gimelbrant, A..

4 recordsLinked to original sources

bioRxiv2025

Methylation pseudotime analysis for label-free profiling of the temporal chromatin landscape with long-read sequencing

Faithful epigenetic inheritance across cell divisions is essential to maintaining cell identity and involves numerous epigenetic modifications, whose roles in establishing chromatin architecture are less understood. Technological approaches to temporally order epigenetic modifications throughout the cell cycle often face limitations in sequence resolution and rely on potentially damaging mitotic labeling or conversion steps. Herein, we present Methylation Pseudotime Analysis Through read-level Heterogeneity (MPATH), a label- and conversion-free method to infer post-replication DNA strand maturity from methylation patterns across single molecules. We use MPATH to temporally order hydroxymethylation throughout mitotic inheritance, revealing that CpGs within cis-regulatory elements undergo transitions between methylation states at sub-cell-cycle timescales. When applied to long reads generated by NOMe-seq, MPATH uncovered relationships between nucleosome occupancy and DNA maturity. Finally, extension of MPATH to phased reads reveals allele-specific trends in pseudotime distribution associated with X chromosome activity. Our findings suggest that when coupled with multimodal sequencing strategies, MPATH could provide valuable insights into chromatin restoration dynamics.

genomics↗

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

Maladaptive oxidative stress cascade drives type I interferon hyperactivity in TNF activated macrophages promoting necrosis in murine tuberculosis granulomas

Tuberculosis remains a critical infectious disease world-wide. The development of novel therapeutic strategies requires greater understanding of host factors that contribute to disease susceptibility. A major unknown in TB pathogenesis is the mechanism of necrosis in TB granulomas that leads to the massive lung tissue damage and cavity formation necessary for the pathogen transmission. In humans, TB progression has been linked to hyperactivity of type I IFN (IFN-I) pathway, the primary cause of which remains elusive. We studied the mechanistic drivers of pulmonary TB progression using a unique model B6J.C3-Sst1C3HeB/Fej Krmn mice that develop human-like necrotic TB granulomas and IFN-I hyperactivity. We established that IFN{beta} super-induction occurred in the susceptible macrophages in response to continuous TNF stimulation in the context of a dysregulated antioxidant defense. We observed that unresolving oxidative stress amplified the induction of IFN{beta} through JNK activation and induced the Integrated Stress Response via PKR activation as a compensatory pathway. Subsequently, PKR amplifies IFN{beta} upregulation, forming a positive feedback loop, maintaining the hyperinflammatory state in susceptible macrophages and leading to mitochondrial dysfunction. Thus, within the inflammatory milieu, a cell-intrinsic mechanism of chronic regulatory dysfunction and unresolved stress gradually weakens the macrophage and ultimately promotes the necrotization of TB granulomas. The aberrant macrophage response to TNF can be prevented by an iron chelator and inhibitor of lipid peroxidation, ferrostatin-1. Moreover, ferrostatin treatment increased macrophage survival and boosted bacterial control in the TNF-stimulated macrophages infected with virulent Mtb. These findings identify targets for host-directed therapeutics to interrupt necrotization in TB granulomas.

immunology↗

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↗