Lipid Peroxidation and Type I Interferon Coupling Fuels Pathogenic Macrophage Activation Causing Tuberculosis Susceptibility.

A quarter of human population is infected with Mycobacterium tuberculosis, but less than 10% of those infected develop pulmonary TB. We developed a genetically defined sst1-susceptible mouse model that uniquely reproduces a defining feature of human TB: the development of necrotic lung granulomas and determined that the sst1-susceptible phenotype was driven by the aberrant macrophage activation. This study demonstrates that the aberrant response of the sst1-susceptible macrophages to prolonged stimulation with TNF is primarily driven by conflicting Myc and antioxidant response pathways leading to a coordinated failure 1) to properly sequester intracellular iron and 2) to activate ferroptosis inhibitor enzymes.

Cell state transition analysis identifies interventions that improve control of infection by susceptible macrophages.

Understanding cell state transitions and purposefully controlling them to improve therapies is a longstanding challenge in biological research and medicine. Here, we identify a transcriptional signature that distinguishes activated macrophages from the tuberculosis (TB) susceptible and resistant mice. We then apply the cSTAR (cell state transition assessment and regulation) approach to data from screening-by-RNA sequencing to identify chemical perturbations that shift the transcriptional state of tumor necrosis factor (TNF)-activated TB-susceptible macrophages toward that of TB-resistant cells, i.

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale.

Motivation: Analysis of allele-specific expression is strongly affected by the technical noise present in RNA-seq experiments. Previously, we showed that technical replicates can be used for precise estimates of this noise, and we provided a tool for correction of technical noise in allele-specific expression analysis. This approach is very accurate but costly due to the need for two or more replicates of each library.

Cell state transition analysis identifies interventions that improve control of infection by susceptible macrophages.

Understanding cell state transitions and purposefully controlling them to improve therapies is a longstanding challenge in biological research and medicine. Here, we identify a transcriptional signature that distinguishes activated macrophages from TB-susceptible and TB-resistant mice. We then apply the cSTAR (cell State Transition Assessment and Regulation) approach to data from screening-by-RNA sequencing to identify chemical perturbations that shift the.

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale.

Motivation: Analysis of allele-specific expression is strongly affected by the technical noise present in RNA-seq experiments. Previously, we showed that technical replicates can be used for precise estimates of this noise, and we provided a tool for correction of technical noise in allele-specific expression analysis. This approach is very accurate but costly due to the need for two or more replicates of each library.

Clonal Analysis Reveals Random Monoallelic Expression in Lymphocytes That Traces Back to Hematopoietic Stem Cells.

Evaluating the epigenetic landscape in the stem cell compartment at the single-cell level is essential to assess the cells' heterogeneity and predict their fate. Here, using a genome-wide transcriptomics approach , we evaluated the allelic expression imbalance in the progeny of single hematopoietic cells (HSCs) as a read-out of epigenetic marking. After 4 months of extensive proliferation and differentiation, we found that X-chromosome inactivation (XCI) is tightly maintained in all single-HSC derived hematopoietic cells.

Medium throughput protocol for genome-based quantification of intracellular mycobacterial loads and macrophage survival during infection.

Here, we present a streamlined protocol for assessing intracellular Mycobacterium tuberculosis (Mtb) loads in macrophages. This protocol describes the simultaneous assessment of macrophage viability using automated microscopy. Further, we detail the quantification of mycobacterial loads using a rapid, inexpensive, and accurate approach for mycobacterial DNA isolation from paraformaldehyde-fixed macrophages.

RNA sequencing-based screen for reactivation of silenced alleles of autosomal genes.

In mammalian cells, maternal and paternal alleles usually have similar transcriptional activity. Epigenetic mechanisms such as X-chromosome inactivation (XCI) and imprinting were historically viewed as rare exceptions to this rule. Discovery of autosomal monoallelic autosomal expression (MAE) a decade ago revealed an additional allele-specific mode regulating thousands of mammalian genes.

Diversity of developing peripheral glia revealed by single-cell RNA sequencing.

The peripheral nervous system responds to a wide variety of sensory stimuli, a process that requires great neuronal diversity. These diverse neurons are closely associated with glial cells originating from the neural crest. However, the molecular nature and diversity among peripheral glia are not understood.

Replicate sequencing libraries are important for quantification of allelic imbalance.

A sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis.

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

Background: A 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.

KDM5 Histone Demethylase Activity Links Cellular Transcriptomic Heterogeneity to Therapeutic Resistance.

Members of the KDM5 histone H3 lysine 4 demethylase family are associated with therapeutic resistance, including endocrine resistance in breast cancer, but the underlying mechanism is poorly defined. Here we show that genetic deletion of KDM5A/B or inhibition of KDM5 activity increases sensitivity to anti-estrogens by modulating estrogen receptor (ER) signaling and by decreasing cellular transcriptomic heterogeneity. Higher KDM5B expression levels are associated with higher transcriptomic heterogeneity and poor prognosis in ER breast tumors.

Risk alleles of genes with monoallelic expression are enriched in gain-of-function variants and depleted in loss-of-function variants for neurodevelopmental disorders.

Over 3000 human genes can be expressed from a single allele in one cell, and from the other allele-or both-in neighboring cells. Little is known about the consequences of this epigenetic phenomenon, monoallelic expression (MAE). We hypothesized that MAE increases expression variability, with a potential impact on human disease.

Tumor-suppressor genes that escape from X-inactivation contribute to cancer sex bias.

There is a striking and unexplained male predominance across many cancer types. A subset of X-chromosome genes can escape X-inactivation, which would protect females from complete functional loss by a single mutation. To identify putative 'escape from X-inactivation tumor-suppressor' (EXITS) genes, we examined somatic alterations from >4,100 cancers across 21 tumor types for sex bias.

Genes with monoallelic expression contribute disproportionately to genetic diversity in humans.

An unexpectedly large number of human autosomal genes are subject to monoallelic expression (MAE). Our analysis of 4,227 such genes uncovers surprisingly high genetic variation across human populations. This increased diversity is unlikely to reflect relaxed purifying selection.

dbMAE: the database of autosomal monoallelic expression.

Recently, data on 'random' autosomal monoallelic expression has become available for the entire genome in multiple human and mouse tissues and cell types, creating a need for better access and dissemination. The database of autosomal monoallelic expression (dbMAE; https://mae.hms.

Chromatin Signature Identifies Monoallelic Gene Expression Across Mammalian Cell Types.

Monoallelic expression of autosomal genes (MAE) is a widespread epigenetic phenomenon which is poorly understood, due in part to current limitations of genome-wide approaches for assessing it. Recently, we reported that a specific histone modification signature is strongly associated with MAE and demonstrated that it can serve as a proxy of MAE in human lymphoblastoid cells. Here, we use murine cells to establish that this chromatin signature is conserved between mouse and human and is associated with MAE in multiple cell types.

Somatic Cell Fusions Reveal Extensive Heterogeneity in Basal-like Breast Cancer.

Basal-like and luminal breast tumors have distinct clinical behavior and molecular profiles, yet the underlying mechanisms are poorly defined. To interrogate processes that determine these distinct phenotypes and their inheritance pattern, we generated somatic cell fusions and performed integrated genetic and epigenetic (DNA methylation and chromatin) profiling. We found that the basal-like trait is generally dominant and is largely defined by epigenetic repression of luminal transcription factors.

Monoallelic expression of the human FOXP2 speech gene.

The recent descriptions of widespread random monoallelic expression (RMAE) of genes distributed throughout the autosomal genome indicate that there are more genes subject to RMAE on autosomes than the number of genes on the X chromosome where X-inactivation dictates RMAE of X-linked genes. Several of the autosomal genes that undergo RMAE have independently been implicated in human Mendelian disorders. Thus, parsing the relationship between allele-specific expression of these genes and disease is of interest.

(Epi)Genetic analyses of age-related macular degeneration: case-control and discordant twin studies.

Background/aims: Phenotypic discordance in monozygotic (MZ) twin pairs can have an epigenetic or genetic basis. Although age-related macular degeneration (AMD) has a strong genetic component, few studies have addressed its epigenetic basis.

Methods: Using SNP arrays, we evaluated differences in copy number variation (CNV) and allele-specific methylation (ASM) patterns (via methyl-sensitive restriction enzyme digestion of DNA) in MZ twin pairs from the US Twin Study of AMD.

Allele-specific methylation occurs at genetic variants associated with complex disease.

We hypothesize that the phenomenon of allele-specific methylation (ASM) may underlie the phenotypic effects of multiple variants identified by Genome-Wide Association studies (GWAS). We evaluate ASM in a human population and document its genome-wide patterns in an initial screen at up to 380,678 sites within the genome, or up to 5% of the total genomic CpGs. We show that while substantial inter-individual variation exists, 5% of assessed sites show evidence of ASM in at least six samples; the majority of these events (81%) are under genetic influence.

Chromatin signature of widespread monoallelic expression.

In mammals, numerous autosomal genes are subject to mitotically stable monoallelic expression (MAE), including genes that play critical roles in a variety of human diseases. Due to challenges posed by the clonal nature of MAE, very little is known about its regulation; in particular, no molecular features have been specifically linked to MAE. In this study, we report an approach that distinguishes MAE genes in human cells with great accuracy: a chromatin signature consisting of chromatin marks associated with active transcription (H3K36me3) and silencing (H3K27me3) simultaneously occurring in the gene body.

Autosomal monoallelic expression: genetics of epigenetic diversity?

In mammals, relative expression of the two parental alleles of many genes is controlled by one of three major epigenetic phenomena: X chromosome inactivation, imprinting, and mitotically stable autosomal monoallelic expression (MAE). MAE affects a large fraction of human autosomal genes and introduces enormous epigenetic heterogeneity in otherwise similar cell populations. Despite its prevalence, many functional and mechanistic aspects of MAE biology remain unknown.