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Dietary suppression of MHC class II expression in intestinal epithelial cells enhances intestinal tumorigenesis
No full textLittle is known about how interactions of diet, intestinal stem cells (ISCs), and immune cells affect early-stage intestinal tumorigenesis. We show that a high-fat diet (HFD) reduces the expression of the major histocompatibility complex class II (MHC class II) genes in intestinal epithelial cells, including ISCs. This decline in epithelial MHC class II expression in a HFD correlates with reduced intestinal microbiome diversity. Microbial community transfer experiments suggest that epithelial MHC class II expression is regulated by intestinal flora. Mechanistically, pattern recognition receptor (PRR) and interferon-gamma (IFNγ) signaling regulates epithelial MHC class II expression. MHC class II-negative (MHC-II−) ISCs exhibit greater tumor-initiating capacity than their MHC class II-positive (MHC-II+) counterparts upon loss of the tumor suppressor Apc coupled with a HFD, suggesting a role for epithelial MHC class II-mediated immune surveillance in suppressing tumorigenesis. ISC-specific genetic ablation of MHC class II increases tumor burden cell autonomously. Thus, HFD perturbs a microbiome-stem cell-immune cell interaction that contributes to tumor initiation in the intestine
A Meta-Analytic Single-Cell Atlas of Mouse Bone Marrow Hematopoietic Development
Get PDFThe clinical importance of the hematopoietic system makes it one of the most heavily studied lineages in all of biology. A clear understanding of the cell types and functional programs during hematopoietic development is central to research in aging, cancer, and infectious diseases. Known cell types are traditionally identified by the expression of proteins on the surface of the cells. Stem and progenitor cells defined based on these markers are assigned functions based on their lineage potential. The rapid growth of single cell RNA sequencing technologies (scRNAseq) provides a new modality for evaluating the cellular and functional landscape of hematopoietic stem and progenitor cells. The popularity of this technology among hematopoiesis researchers enables us to conduct a robust meta-analysis of mouse bone marrow scRNAseq data. Using over 300,000 cells across 12 datasets, we evaluate the classification and function of cell types based on discrete clustering, in silico FACS sorting, and a continuous trajectory. We identify replicable signatures that define cell types based on genes and known cellular functions. Additionally, we evaluate the conservation of signatures associated with erythroid and monocyte lineage development across species using co-expression networks. The co-expression networks predict the effectiveness of the signature at identifying erythroid and monocyte cells in zebrafish and human scRNAseq data. Together, this analysis provides a robust reference, particularly marker genes and functional annotations, for future experiments in hematopoietic development
ACE: a probabilistic model for characterizing gene-level essentiality in CRISPR screens.
Get PDFHigh-throughput CRISPR-Cas9 knockout screens are widely used to evaluate gene essentiality in cancer research. Here we introduce a probabilistic modeling framework, Analysis of CRISPR-based Essentiality (ACE), that accounts for multiple sources of variation in CRISPR-Cas9 screens and enables new statistical tests for essentiality. We show using simulations that ACE is effective at predicting both absolute and differential essentiality. When applied to publicly available data, ACE identifies known and novel candidates for genotype-specific essentiality, including RNA m6-A methyltransferases that exhibit enhanced essentiality in the presence of inactivating TP53 mutations. ACE provides a robust framework for identifying genes responsive to subtype-specific therapeutic targeting
Branch-pipe: Improving graph skeletonization around branch points in 3D point clouds
Get PDFModern plant phenotyping requires tools that are robust to noise and missing data, while being able to efficiently process large numbers of plants. Here, we studied the skeletonization of plant architectures from 3D point clouds, which is critical for many downstream tasks, including analyses of plant shape, morphology, and branching angles. Specifically, we developed an algorithm to improve skeletonization at branch points (forks) by leveraging the geometric properties of cylinders around branch points. We tested this algorithm on a diverse set of high-resolution 3D point clouds of tomato and tobacco plants, grown in five environments and across multiple developmental timepoints. Compared to existing methods for 3D skeletonization, our method efficiently and more accurately estimated branching angles even in areas with noisy, missing, or non-uniformly sampled data. Our method is also applicable to inorganic datasets, such as scans of industrial pipes or urban scenes containing networks of complex cylindrical shapes
Genetic dissection of the glutamatergic neuron system in cerebral cortex.
Get PDFDiverse types of glutamatergic pyramidal neurons mediate the myriad processing streams and output channels of the cerebral cortex1,2, yet all derive from neural progenitors of the embryonic dorsal telencephalon3,4. Here we establish genetic strategies and tools for dissecting and fate-mapping subpopulations of pyramidal neurons on the basis of their developmental and molecular programs. We leverage key transcription factors and effector genes to systematically target temporal patterning programs in progenitors and differentiation programs in postmitotic neurons. We generated over a dozen temporally inducible mouse Cre and Flp knock-in driver lines to enable the combinatorial targeting of major progenitor types and projection classes. Combinatorial strategies confer viral access to subsets of pyramidal neurons defined by developmental origin, marker expression, anatomical location and projection targets. These strategies establish an experimental framework for understanding the hierarchical organization and developmental trajectory of subpopulations of pyramidal neurons that assemble cortical processing networks and output channels
Replication and single-cycle delivery of SARS-CoV-2 replicons
Get PDFMolecular virology tools are critical for basic studies of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and for developing new therapeutics. There remains a need for experimental systems that do not rely on viruses capable of spread that could potentially be used in lower containment settings. Here, we develop spike-deleted SARS-CoV-2 self-replicating RNAs using a yeast-based reverse genetics system. These non-infectious self-replicating RNAs, or replicons, can be trans-complemented with viral glycoproteins to generate Replicon Delivery Particles (RDPs) for single-cycle delivery into a range of cell types. This SARS-CoV-2 replicon system represents a convenient and versatile platform for antiviral drug screening, neutralization assays, host factor validation, and characterizing viral variants
Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity.
Get PDFBACKGROUND: Antivirals are needed to combat the COVID-19 pandemic, which is caused by SARS-CoV-2. The clinically-proven protease inhibitor Camostat mesylate inhibits SARS-CoV-2 infection by blocking the virus-activating host cell protease TMPRSS2. However, antiviral activity of Camostat mesylate metabolites and potential viral resistance have not been analyzed. Moreover, antiviral activity of Camostat mesylate in human lung tissue remains to be demonstrated. METHODS: We used recombinant TMPRSS2, reporter particles bearing the spike protein of SARS-CoV-2 or authentic SARS-CoV-2 to assess inhibition of TMPRSS2 and viral entry, respectively, by Camostat mesylate and its metabolite GBPA. FINDINGS: We show that several TMPRSS2-related proteases activate SARS-CoV-2 and that two, TMPRSS11D and TMPRSS13, are robustly expressed in the upper respiratory tract. However, entry mediated by these proteases was blocked by Camostat mesylate. The Camostat metabolite GBPA inhibited recombinant TMPRSS2 with reduced efficiency as compared to Camostat mesylate. In contrast, both inhibitors exhibited similar antiviral activity and this correlated with the rapid conversion of Camostat mesylate into GBPA in the presence of serum. Finally, Camostat mesylate and GBPA blocked SARS-CoV-2 spread in human lung tissue ex vivo and the related protease inhibitor Nafamostat mesylate exerted augmented antiviral activity. INTERPRETATION: Our results suggest that SARS-CoV-2 can use TMPRSS2 and closely related proteases for spread in the upper respiratory tract and that spread in the human lung can be blocked by Camostat mesylate and its metabolite GBPA. FUNDING: NIH, Damon Runyon Foundation, ACS, NYCT, DFG, EU, Berlin Mathematics center MATH+, BMBF, Lower Saxony, Lundbeck Foundation, Novo Nordisk Foundation
T CELL IMMUNOTHERAPIES TRIGGER NEUTROPHIL ACTIVATION TO ELIMINATE TUMOR ANTIGEN ESCAPE VARIANTS
No full textBackgroundTargeted immune-based therapies such as adoptive T cell transfer (ACT) are often ineffective because tumors evolve over time and under selective pressure display antigen loss variant clones. A classic example in melanoma is de-differentiation and loss of expression of antigenic proteins. Therapies that activate multiple branches of the immune system may eliminate such escape variantsMethodsHere we show that melanoma-specific CD4+ ACT therapy in combination with OX40 co-stimulation or CTLA-4 blockade can eradicate large melanoma tumors with clonal escape variants.ResultsEarly on-target recognition of melanoma antigens by adoptively transferred tumor-specific CD4+ T cells was required. Surprisingly, however, complete tumor eradication was partially dependent on neutrophils. Supporting these findings, extensive neutrophil activation and neutrophil extracellular traps were found in mouse tumors and in biopsies of melanoma patients treated with immune checkpoint blockade.ConclusionsOur findings uncover a novel interplay between T cells mediating the initial tumor- and tissue-specific immune response, and neutrophils mediating tumor destruction of antigen loss variants.Ethics ApprovalAll tissues were collected at MSKCC following study protocol approval by the MSKCC Institutional Review Board. All mouse procedures were performed in accordance with institutional protocol guidelines at Memorial Sloan-Kettering Cancer Center (MSKCC) under an approved protocol.</jats:sec
SIA: Selection Inference Using the Ancestral Recombination Graph
Get PDFDetecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity