Myc Dysregulation in Activated Macrophages Initiates Iron-Mediated Lipid Peroxidation that Fuels Type I Interferon and Compromises TB Resistance.

A quarter of human population is infected with Mycobacterium tuberculosis, but less than 10% of those infected develop clinical, mostly pulmonary, TB. To dissect mechanisms of susceptibility in immunocompetent individuals, we developed a genetically defined sst1-susceptible mouse model that uniquely reproduces a defining feature of human TB: development of necrotic lung lesions after infection with virulent Mtb. In this study, we explored the connectivity of the sst1-regulated pathways during prolonged macrophage activation with TNF. We determined that the aberrant response of the sst1-susceptible macrophages to TNF was primarily driven by conflicting Myc and antioxidant response pathways that resulted in a coordinated failure to properly sequester intracellular iron and activate ferroptosis inhibitor enzymes. Consequently, iron-mediated lipid peroxidation fueled IFNß superinduction and sustained the Type I Interferon (IFN-I) pathway hyperactivity that locked the sst1-susceptible macrophages in a state of unresolving stress and compromised their resistance to Mtb. The accumulation of the aberrantly activated, stressed, macrophages within granuloma microenvironment led to the local failure of anti-tuberculosis immunity and tissue necrosis. Our findings suggest a novel link between metabolic dysregulation in macrophages and susceptibility to TB, offering insights into potential therapeutic targets aimed at modulating macrophage function and improving TB control.

Cell state transition analysis identifies interventions that improve control of Mycobacterium tuberculosis 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.e., prevents their aberrant activation without suppressing beneficial TNF responses. Last, we demonstrate that the compounds identified with this approach enhance the resistance of the TB-susceptible mouse macrophages to virulent Mycobacterium tuberculosis.

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. Here, we develop a spike-in approach which is highly accurate at only a small fraction of the cost. RESULTS: We show that a distinct RNA added as a spike-in before library preparation reflects technical noise of the whole library and can be used in large batches of samples. We experimentally demonstrate the effectiveness of this approach using combinations of RNA from species distinguishable by alignment, namely, mouse, human, and Caenorhabditis elegans. Our new approach, controlFreq, enables highly accurate and computationally efficient analysis of allele-specific expression in (and between) arbitrarily large studies at an overall cost increase of ∼5%. AVAILABILITY AND IMPLEMENTATION: Analysis pipeline for this approach is available at GitHub as R package controlFreq (github.com/gimelbrantlab/controlFreq).

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. Here, we develop a spike-in approach that is highly accurate at only a small fraction of the cost. Results: We show that a distinct RNA added as a spike-in before library preparation reflects technical noise of the whole library and can be used in large batches of samples. We experimentally demonstrate the effectiveness of this approach using combinations of RNA from species distinguishable by alignment, namely, mouse, human, and C.elegans . Our new approach, controlFreq , enables highly accurate and computationally efficient analysis of allele-specific expression in (and between) arbitrarily large studies at an overall cost increase of ~ 5%. Availability: Analysis pipeline for this approach is available at GitHub as R package controlFreq ( github.com/gimelbrantlab/controlFreq ). Contact: agimelbrant@altius.org.

Cell state transition analysis identifies interventions that improve control of M. tuberculosis 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. transcriptional state of the TB-susceptible macrophages towards that of TB-resistant cells. Finally, we demonstrate that the compounds identified with this approach enhance resistance of the TB-susceptible mouse macrophages to virulent M. tuberculosis .

In Vivo 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 in vivo, 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. In contrast, the vast majority of the autosomal genes did not show clonal patterns of random monoallelic expression (RME). However, a persistent allele-specific autosomal transcription in HSCs and their progeny was found in a rare number of cases, none of which has been previously reported. These data show that: 1) XCI and RME in the autosomal chromosomes are driven by different mechanisms; 2) the previously reported high frequency of genes under RME in clones expanded in vitro (up to 15%) is not found in clones undergoing multiple differentiation steps in vivo; 3) prior to differentiation, HSCs have stable patterns of autosomal RME. We propose that most RME patterns in autosomal chromosomes are erased and established de novo during cell lineage differentiation.

Medium throughput protocol for genome-based quantification of intracellular mycobacterial loads and macrophage survival during in vitro 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. Simultaneous assessment of the bacterial loads using internal standard and macrophage viability allows for precise quantification of the effects of perturbations on Mtb and host cells while accounting for technical artifacts. For complete details on the use and execution of this protocol, please refer to Chatterjee et al. (2021).

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. Despite MAE prevalence, its mechanistic basis remains unknown. Using an RNA sequencing-based screen for reactivation of silenced alleles, we identified DNA methylation as key mechanism of MAE mitotic maintenance. In contrast with the all-or-nothing allelic choice in XCI, allele-specific expression in MAE loci is tunable, with exact allelic imbalance dependent on the extent of DNA methylation. In a subset of MAE genes, allelic imbalance was insensitive to DNA demethylation, implicating additional mechanisms in MAE maintenance in these loci. Our findings identify a key mechanism of MAE maintenance and provide basis for understanding the biological role of MAE.

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. Here, we used single-cell RNA sequencing to profile developing and mature glia from somatosensory dorsal root ganglia and auditory spiral ganglia. We found that glial precursors (GPs) in these two systems differ in their transcriptional profiles. Despite their unique features, somatosensory and auditory GPs undergo convergent differentiation to generate molecularly uniform myelinating and non-myelinating Schwann cells. By contrast, somatosensory and auditory satellite glial cells retain system-specific features. Lastly, we identified a glial signature gene set, providing new insights into commonalities among glia across the nervous system. This survey of gene expression in peripheral glia constitutes a resource for understanding functions of glia across different sensory modalities.

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. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

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. RESULTS: 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). The source code for the MaGIC pipeline and the Shiny app is available at https://github.com/gimelbrantlab/magic . CONCLUSION: The pipeline can be used by researchers to map monoallelic expression in a variety of cell types using existing models and to train new models with additional sets of chromatin marks.

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. Single-cell RNA sequencing, cellular barcoding, and mathematical modeling demonstrate that endocrine resistance is due to selection for pre-existing genetically distinct cells, while KDM5 inhibitor resistance is acquired. Our findings highlight the importance of cellular phenotypic heterogeneity in therapeutic resistance and identify KDM5A/B as key regulators of this process.

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. Six of 783 non-pseudoautosomal region (PAR) X-chromosome genes (ATRX, CNKSR2, DDX3X, KDM5C, KDM6A, and MAGEC3) harbored loss-of-function mutations more frequently in males (based on a false discovery rate < 0.1), in comparison to zero of 18,055 autosomal and PAR genes (Fisher's exact P < 0.0001). Male-biased mutations in genes that escape X-inactivation were observed in combined analysis across many cancers and in several individual tumor types, suggesting a generalized phenomenon. We conclude that biallelic expression of EXITS genes in females explains a portion of the reduced cancer incidence in females as compared to males across a variety of tumor types.

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. Remarkably, MAE genes exhibit an elevated recombination rate and an increased density of hypermutable sequence contexts. However, these factors do not fully account for the increased diversity. We find that the elevated nucleotide diversity of MAE genes is also associated with greater allelic age: variants in these genes tend to be older and are enriched in polymorphisms shared by Neanderthals and chimpanzees. Both synonymous and nonsynonymous alleles of MAE genes have elevated average population frequencies. We also observed strong enrichment of the MAE signature among genes reported to evolve under balancing selection. We propose that an important biological function of widespread MAE might be the generation of cell-to-cell heterogeneity; the increased genetic variation contributes to this heterogeneity.

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.harvard.edu) incorporates data from multiple recent reports of genome-wide analyses. These include transcriptome-wide analyses of allelic imbalance in clonal cell populations based on sequence polymorphisms, as well as indirect identification, based on a specific chromatin signature present in MAE gene bodies. Currently, dbMAE contains transcriptome-wide chromatin identification calls for 8 human and 21 mouse tissues, and describes over 16 000 murine and ∼ 700 human cases of directly measured biased expression, compiled from allele-specific RNA-seq and genotyping array data. All data are manually curated. To ensure cross-publication uniformity, we performed re-analysis of transcriptome-wide RNA-seq data using the same pipeline. Data are accessed through an interface that allows for basic and advanced searches; all source references, including raw data, are clearly described and hyperlinked. This ensures the utility of the resource as an initial screening tool for those interested in investigating the role of monoallelic expression in their specific genes and tissues of interest.

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. Definition of super-enhancers highlighted a core program common in luminal cells but a high degree of heterogeneity in basal-like breast cancers that correlates with clinical outcome. We also found that protein extracts of basal-like cells are sufficient to induce a luminal-to-basal phenotypic switch, implying a trigger of basal-like autoregulatory circuits. We determined that KDM6A might be required for luminal-basal fusions, and we identified EN1, TBX18, and TCF4 as candidate transcriptional regulators of the luminal-to-basal switch. Our findings highlight the remarkable epigenetic plasticity of breast cancer cells.

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. Our analyses reveal extensive conservation in the identity of MAE genes between the two species. By analyzing MAE chromatin signature in a large number of cell and tissue types, we show that it remains consistent during terminal cell differentiation and is predominant among cell-type specific genes, suggesting a link between MAE and specification of cell identity.

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. Mutations in the human forkhead box P2 gene, FOXP2, cause developmental verbal dyspraxia with profound speech and language deficits. Here, we show that the human FOXP2 gene undergoes RMAE. Studying an individual with developmental verbal dyspraxia, we identify a deletion 3 Mb away from the FOXP2 gene, which impacts FOXP2 gene expression in cis. Together these data suggest the intriguing possibility that RMAE impacts the haploinsufficiency phenotypes observed for FOXP2 mutations.