Diffuse intrinsic pontine gliomas (DIPGs) are aggressive pediatric brain tumors for which there is currently no effective treatment. Some of these tumors combine gain-of-function mutations in ACVR1, ...PIK3CA, and histone H3-encoding genes. The oncogenic mechanisms of action of ACVR1 mutations are currently unknown. Using mouse models, we demonstrate that Acvr1G328V arrests the differentiation of oligodendroglial lineage cells, and cooperates with Hist1h3bK27M and Pik3caH1047R to generate high-grade diffuse gliomas. Mechanistically, Acvr1G328V upregulates transcription factors which control differentiation and DIPG cell fitness. Furthermore, we characterize E6201 as a dual inhibitor of ACVR1 and MEK1/2, and demonstrate its efficacy toward tumor cells in vivo. Collectively, our results describe an oncogenic mechanism of action for ACVR1 mutations, and suggest therapeutic strategies for DIPGs.
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•Mouse model of the pediatric high-grade diffuse glioma-driving Acvr1G328V mutation•Acvr1G328V causes oligodendroglial lineage differentiation arrest•Combining Acvr1G328V, Hist1h3bK27M, and Pik3caH1047R causes high-grade diffuse gliomas•E6201 is a dual inhibitor of MEK and ACVR1, and shows anti-tumor activity
Fortin et al. find that Acvr1G328V upregulates transcription factors to block oligodendroglial cell differentiation. Acvr1G328V cooperates with Hist1h3bK27M and Pik3caH1047R to induce diffuse gliomas in mice. E6201, a covalent MEK1/2 inhibitor, can inhibit ACVR1 and reduce growth of ACVR1 mutant glioma xenografts.
Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the ...correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low-especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities' signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset.
Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were ...developed in the microarray era, before high-throughput sequencing-with its unique statistical properties-became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca.
Myeloid cells contribute to tumor progression, but how the constellation of receptors they express regulates their functions within the tumor microenvironment (TME) is unclear. We demonstrate that ...Fcmr (Toso), the putative receptor for soluble IgM, modulates myeloid cell responses to cancer. In a syngeneic melanoma model, Fcmr ablation in myeloid cells suppressed tumor growth and extended mouse survival. Fcmr deficiency increased myeloid cell population density in this malignancy and enhanced anti-tumor immunity. Single-cell RNA sequencing of Fcmr-deficient tumor-associated mononuclear phagocytes revealed a unique subset with enhanced antigen processing/presenting properties. Conversely, Fcmr activity negatively regulated the activation and migratory capacity of myeloid cells in vivo, and T cell activation by bone marrow-derived dendritic cells in vitro. Therapeutic targeting of Fcmr during oncogenesis decreased tumor growth when used as a single agent or in combination with anti-PD-1. Thus, Fcmr regulates myeloid cell activation within the TME and may be a potential therapeutic target.
Reliable estimation of the mean fragment length for next-generation short-read sequencing data is an important step in next-generation sequencing analysis pipelines, most notably because of its ...impact on the accuracy of the enriched regions identified by peak-calling algorithms. Although many peak-calling algorithms include a fragment-length estimation subroutine, the problem has not been adequately solved, as demonstrated by the variability of the estimates returned by different algorithms.
In this article, we investigate the use of strand cross-correlation to estimate mean fragment length of single-end data and show that traditional estimation approaches have mixed reliability. We observe that the mappability of different parts of the genome can introduce an artificial bias into cross-correlation computations, resulting in incorrect fragment-length estimates. We propose a new approach, called mappability-sensitive cross-correlation (MaSC), which removes this bias and allows for accurate and reliable fragment-length estimation. We analyze the computational complexity of this approach, and evaluate its performance on a test suite of NGS datasets, demonstrating its superiority to traditional cross-correlation analysis.
An open-source Perl implementation of our approach is available at http://www.perkinslab.ca/Software.html.
Resident macrophages orchestrate homeostatic, inflammatory, and reparative activities. It is appreciated that different tissues instruct specialized macrophage functions. However, individual tissues ...contain heterogeneous subpopulations, and how these subpopulations are related is unclear. We asked whether common transcriptional and functional elements could reveal an underlying framework across tissues. Using single-cell RNA sequencing and random forest modeling, we observed that four genes could predict three macrophage subsets that were present in murine heart, liver, lung, kidney, and brain. Parabiotic and genetic fate mapping studies revealed that these core markers predicted three unique life cycles across 17 tissues. TLF
(expressing TIMD4 and/or LYVE1 and/or FOLR2) macrophages were maintained through self-renewal with minimal monocyte input; CCR2
(TIMD4
LYVE1
FOLR2
) macrophages were almost entirely replaced by monocytes, and MHC-II
macrophages (TIMD4
LYVE1
FOLR2
CCR2
), while receiving modest monocyte contribution, were not continually replaced. Rather, monocyte-derived macrophages contributed to the resident macrophage population until they reached a defined upper limit after which they did not outcompete pre-existing resident macrophages. Developmentally, TLF
macrophages were first to emerge in the yolk sac and early fetal organs. Fate mapping studies in the mouse and human single-cell RNA sequencing indicated that TLF
macrophages originated from both yolk sac and fetal monocyte precursors. Furthermore, TLF
macrophages were the most transcriptionally conserved subset across mouse tissues and between mice and humans, despite organ- and species-specific transcriptional differences. Here, we define the existence of three murine macrophage subpopulations based on common life cycle properties and core gene signatures and provide a common starting point to understand tissue macrophage heterogeneity.
PirB is an inhibitory cell surface receptor particularly prominent on myeloid cells. PirB curtails the phenotypes of activated macrophages during inflammation or tumorigenesis, but its functions in ...macrophage homeostasis are obscure. To elucidate PirB-related functions in macrophages at steady-state, we generated and compared single-cell RNA-sequencing (scRNAseq) datasets obtained from myeloid cell subsets of wild type (WT) and PirB-deficient knockout (PirB KO) mice. To facilitate this analysis, we developed a novel approach to clustering parameter optimization called "Cluster Similarity Scoring and Distinction Index" (CaSSiDI). We demonstrate that CaSSiDI is an adaptable computational framework that facilitates tandem analysis of two scRNAseq datasets by optimizing clustering parameters. We further show that CaSSiDI offers more advantages than a standard Seurat analysis because it allows direct comparison of two or more independently clustered datasets, thereby alleviating the need for batch-correction while identifying the most similar and different clusters. Using CaSSiDI, we found that PirB is a novel regulator of Cebpb expression that controls the generation of Ly6C
patrolling monocytes and the expansion properties of peritoneal macrophages. PirB's effect on Cebpb is tissue-specific since it was not observed in splenic red pulp macrophages (RPMs). However, CaSSiDI revealed a segregation of the WT RPM population into a CD68
Irf8
"neuronal-primed" subset and an CD68
Ftl1
"iron-loaded" subset. Our results establish the utility of CaSSiDI for single-cell assay analyses and the determination of optimal clustering parameters. Our application of CaSSiDI in this study has revealed previously unknown roles for PirB in myeloid cell populations. In particular, we have discovered homeostatic functions for PirB that are related to Cebpb expression in distinct macrophage subsets.
Experimental autoimmune encephalomyelitis (EAE) is a mouse model of multiple sclerosis (MS) in which Th17 cells have a crucial but unclear function. Here we show that choline acetyltransferase ...(ChAT), which synthesizes acetylcholine (ACh), is a critical driver of pathogenicity in EAE. Mice with ChAT-deficient Th17 cells resist disease progression and show reduced brain-infiltrating immune cells. ChAT expression in Th17 cells is linked to strong TCR signaling, expression of the transcription factor Bhlhe40, and increased Il2, Il17, Il22, and Il23r mRNA levels. ChAT expression in Th17 cells is independent of IL21r signaling but dampened by TGFβ, implicating ChAT in controlling the dichotomous nature of Th17 cells. Our study establishes a cholinergic program in which ACh signaling primes chronic activation of Th17 cells, and thereby constitutes a pathogenic determinant of EAE. Our work may point to novel targets for therapeutic immunomodulation in MS.
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