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Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma; yet, our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naive MBMs and 10 extracranial melanoma metastases (ECMs) and matched spatial single-cell transcriptomics and T cell receptor (TCR)-seq. Cancer cells from MBM were more chromosomally unstable, adopted a neuronal-like cell state, and enriched for spatially variably expressed metabolic pathways. Key observations were validated in independent patient cohorts, patient-derived MBM/ECM xenograft models, RNA/ATAC-seq, proteomics, and multiplexed imaging. Integrated spatial analyses revealed distinct geography of putative cancer immune evasion and evidence for more abundant intra-tumoral B to plasma cell differentiation in lymphoid aggregates in MBM. MBM harbored larger fractions of monocyte-derived macrophages and dysfunctional TOX+CD8+ T cells with distinct expression of immune checkpoints. This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration.
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Neoplasias Encefálicas , Melanoma , Neoplasias Encefálicas/tratamiento farmacológico , Neoplasias Encefálicas/secundario , Linfocitos T CD8-positivos/patología , Ecosistema , Humanos , RNA-SeqRESUMEN
Lactate accumulates to a significant amount in glioblastomas (GBMs), the most common primary malignant brain tumor with an unfavorable prognosis. However, it remains unclear whether lactate is metabolized by GBMs. Here, we demonstrated that lactate rescued patient-derived xenograft (PDX) GBM cells from nutrient-deprivation-mediated cell death. Transcriptome analysis, ATAC-seq, and ChIP-seq showed that lactate entertained a signature of oxidative energy metabolism. LC/MS analysis demonstrated that U-13C-lactate elicited substantial labeling of TCA-cycle metabolites, acetyl-CoA, and histone protein acetyl-residues in GBM cells. Lactate enhanced chromatin accessibility and histone acetylation in a manner dependent on oxidative energy metabolism and the ATP-citrate lyase (ACLY). Utilizing orthotopic PDX models of GBM, a combined tracer experiment unraveled that lactate carbons were substantially labeling the TCA-cycle metabolites. Finally, pharmacological blockage of oxidative energy metabolism extended overall survival in two orthotopic PDX models in mice. These results establish lactate metabolism as a novel druggable pathway for GBM.
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Glioblastoma , Acetilación , Animales , Línea Celular Tumoral , Epigénesis Genética , Glioblastoma/genética , Glioblastoma/patología , Histonas/metabolismo , Humanos , Ácido Láctico/metabolismo , RatonesRESUMEN
The FcµR receptor for the crystallizable fragment (Fc) of immunoglobulin M (IgM) can function as a cell-surface receptor for secreted IgM on a variety of cell types. We found here that FcµR was also expressed in the trans-Golgi network of developing B cells, where it constrained transport of the IgM-isotype BCR (IgM-BCR) but not of the IgD-isotype BCR (IgD-BCR). In the absence of FcµR, the surface expression of IgM-BCR was increased, which resulted in enhanced tonic BCR signaling. B-cell-specific deficiency in FcµR enhanced the spontaneous differentiation of B-1 cells, which resulted in increased serum concentrations of natural IgM and dysregulated homeostasis of B-2 cells; this caused the spontaneous formation of germinal centers, increased titers of serum autoantibodies and excessive accumulation of B cells. Thus, FcµR serves as a critical regulator of B cell biology by constraining the transport and cell-surface expression of IgM-BCR.
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Linfocitos B/fisiología , Inmunoglobulina M/metabolismo , Células Precursoras de Linfocitos B/fisiología , Receptores de Antígenos de Linfocitos B/metabolismo , Receptores Fc/metabolismo , Animales , Diferenciación Celular , Células Cultivadas , Citocinas/metabolismo , Femenino , Regulación de la Expresión Génica , Inmunoglobulina M/genética , Masculino , Ratones , Ratones Endogámicos C57BL , Ratones Noqueados , Receptores de Antígenos de Linfocitos B/genética , Transducción de Señal , Células TH1/inmunología , Células Th2/inmunologíaRESUMEN
Glioma cells hijack developmental programs to control cell state. Here, we uncover a glioma cell state-specific metabolic liability that can be therapeutically targeted. To model cell conditions at brain tumor inception, we generated genetically engineered murine gliomas, with deletion of p53 alone (p53) or with constitutively active Notch signaling (N1IC), a pathway critical in controlling astrocyte differentiation during brain development. N1IC tumors harbored quiescent astrocyte-like transformed cell populations while p53 tumors were predominantly comprised of proliferating progenitor-like cell states. Further, N1IC transformed cells exhibited increased mitochondrial lipid peroxidation, high ROS production and depletion of reduced glutathione. This altered mitochondrial phenotype rendered the astrocyte-like, quiescent populations more sensitive to pharmacologic or genetic inhibition of the lipid hydroperoxidase GPX4 and induction of ferroptosis. Treatment of patient-derived early-passage cell lines and glioma slice cultures generated from surgical samples with a GPX4 inhibitor induced selective depletion of quiescent astrocyte-like glioma cell populations with similar metabolic profiles. Collectively, these findings reveal a specific therapeutic vulnerability to ferroptosis linked to mitochondrial redox imbalance in a subpopulation of quiescent astrocyte-like glioma cells resistant to standard forms of treatment.
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Predicting cancer drug response using both genomics and drug features has shown some success compared to using genomics features alone. However, there has been limited research done on how best to combine or fuse the two types of features. Using a visible neural network with two deep learning branches for genes and drug features as the base architecture, we experimented with different fusion functions and fusion points. Our experiments show that injecting multiplicative relationships between gene and drug latent features into the original concatenation-based architecture DrugCell significantly improved the overall predictive performance and outperformed other baseline models. We also show that different fusion methods respond differently to different fusion points, indicating that the relationship between drug features and different hierarchical biological level of gene features is optimally captured using different methods. Considering both predictive performance and runtime speed, tensor product partial is the best-performing fusion function to combine late-stage representations of drug and gene features to predict cancer drug response.
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Antineoplásicos , Genotipo , Neoplasias , Redes Neurales de la Computación , Humanos , Neoplasias/genética , Neoplasias/tratamiento farmacológico , Antineoplásicos/uso terapéutico , Antineoplásicos/farmacología , Aprendizaje Profundo , Genómica/métodos , Biología Computacional/métodosRESUMEN
Whole genome sequencing has increasingly become the essential method for studying the genetic mechanisms of antimicrobial resistance and for surveillance of drug-resistant bacterial pathogens. The majority of bacterial genomes sequenced to date have been sequenced with Illumina sequencing technology, owing to its high-throughput, excellent sequence accuracy, and low cost. However, because of the short-read nature of the technology, these assemblies are fragmented into large numbers of contigs, hindering the obtaining of full information of the genome. We develop Pasa, a graph-based algorithm that utilizes the pangenome graph and the assembly graph information to improve scaffolding quality. By leveraging the population information of the bacteria species, Pasa is able to utilize the linkage information of the gene families of the species to resolve the contig graph of the assembly. We show that our method outperforms the current state of the arts in terms of accuracy, and at the same time, is computationally efficient to be applied to a large number of existing draft assemblies.
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Algoritmos , Bacterias , Genoma Bacteriano , Bacterias/clasificación , Bacterias/genética , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Análisis de Secuencia de ADN/métodosRESUMEN
Viruses are master remodelers of the host cell environment in support of infection and virus production. For example, viruses typically regulate cell gene expression through modulating canonical cell promoter activity. Here, we show that Epstein Barr virus (EBV) replication causes 'de novo' transcription initiation at 29674 new transcription start sites throughout the cell genome. De novo transcription initiation is facilitated in part by the unique properties of the viral pre-initiation complex (vPIC) that binds a TATT[T/A]AA, TATA box-like sequence and activates transcription with minimal support by additional transcription factors. Other de novo promoters are driven by the viral transcription factors, Zta and Rta and are influenced by directional proximity to existing canonical cell promoters, a configuration that fosters transcription through existing promoters and transcriptional interference. These studies reveal a new way that viruses interact with the host transcriptome to inhibit host gene expression and they shed light on primal features driving eukaryotic promoter function.
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Infecciones por Virus de Epstein-Barr , Herpesvirus Humano 4 , Iniciación de la Transcripción Genética , Replicación Viral , Humanos , Herpesvirus Humano 4/fisiología , Interacciones Huésped-Patógeno , Regiones Promotoras Genéticas , TATA Box , Factores de Transcripción/metabolismo , Sitio de Iniciación de la Transcripción , Transcripción Genética , Proteínas Virales/metabolismo , Proteínas Virales/genética , Infecciones por Virus de Epstein-Barr/metabolismo , Infecciones por Virus de Epstein-Barr/virologíaRESUMEN
T cell antigen receptor stimulation induces tyrosine phosphorylation of downstream signaling molecules and the phosphatidylinositol, Ras, MAPK, and PI3 kinase pathways, leading to T cell activation. Previously, we reported that the G-protein-coupled human muscarinic receptor could bypass tyrosine kinases to activate the phosphatidylinositol pathway and induce interleukin-2 production in Jurkat leukemic T cells. Here, we demonstrate that stimulating G-protein-coupled muscarinic receptors (M1 and synthetic hM3Dq) can activate primary mouse T cells if PLCß1 is coexpressed. Resting peripheral hM3Dq+PLCß1 (hM3Dq/ß1) T cells did not respond to clozapine, an hM3Dq agonist, unless they were preactivated by TCR and CD28 stimulation which increased hM3Dq and PLCß1 expression. This permitted large calcium and phosphorylated ERK responses to clozapine. Clozapine treatment induced high IFN-γ, CD69, and CD25 expression, but surprisingly did not induce substantial IL-2 in hM3Dq/ß1 T cells. Importantly, costimulation of both muscarinic receptors plus the TCR even led to reduced IL-2 expression, suggesting a selective inhibitory effect of muscarinic receptor costimulation. Stimulation of muscarinic receptors induced strong nuclear translocation of NFAT and NFκB and activated AP-1. However, stimulation of hM3Dq led to reduced IL-2 mRNA stability which correlated with an effect on the IL-2 3'UTR activity. Interestingly, stimulation of hM3Dq resulted in reduced pAKT and its downstream pathway. This may explain the inhibitory impact on IL-2 production in hM3Dq/ß1T cells. Moreover, an inhibitor of PI3K reduced IL-2 production in TCR-stimulated hM3Dq/ß1 CD4 T cells, suggesting that activating the pAKT pathway is critical for IL-2 production in T cells.
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Clozapina , Interleucina-2 , Humanos , Animales , Ratones , Receptores Muscarínicos , Interferón gamma , Proteínas de Unión al GTP , TirosinaRESUMEN
Establishing both central and peripheral tolerance requires the appropriate TCR signaling strength to discriminate self- from agonist-peptide bound to self MHC molecules. ZAP70, a cytoplasmic tyrosine kinase, directly interacts with the TCR complex and plays a central and requisite role in TCR signaling in both thymocytes and peripheral T cells. By studying ZAP70 hypomorphic mutations in mice and humans with a spectrum of hypoactive or hyperactive activities, we have gained insights into mechanisms of central and peripheral tolerance. Interestingly, both hypoactive and hyperactive ZAP70 can lead to the development of autoimmune diseases, albeit through distinct mechanisms. Immature thymocytes and mature T cells rely on normal ZAP70 function to complete their development in the thymus and to modulate T cell responses in the periphery. Hypoactive ZAP70 function compromises key developmental checkpoints required to establish central tolerance, allowing thymocytes with potentially self-reactive TCRs a greater chance to escape negative selection. Such 'forbidden clones' may escape into the periphery and may pose a greater risk for autoimmune disease development since they may not engage negative regulatory mechanisms as effectively. Hyperactive ZAP70 enhances thymic negative selection but some thymocytes will, nonetheless, escape negative selection and have greater sensitivity to weak and self-ligands. Such cells must be controlled by mechanisms involved in anergy, expansion of Tregs, and upregulation of inhibitory receptors or signaling molecules. However, such potentially autoreactive cells may still be able to escape control by peripheral negative regulatory constraints. Consistent with findings in Zap70 mutants, the signaling defects in at least one ZAP70 substrate, LAT, can also lead to autoimmune disease. By dissecting the similarities and differences among mouse models of patient disease or mutations in ZAP70 that affect TCR signaling strength, we have gained insights into how perturbed ZAP70 function can lead to autoimmunity. Because of our work and that of others on ZAP70, it is likely that perturbations in other molecules affecting TCR signaling strength will be identified that also overcome tolerance mechanisms and cause autoimmunity. Delineating these molecular pathways could lead to the development of much needed new therapeutic targets in these complex diseases.
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Enfermedades Autoinmunes , Autoinmunidad , Proteínas Tirosina Quinasas/metabolismo , Animales , Humanos , Tolerancia Inmunológica , Ratones , Receptores de Antígenos de Linfocitos T/metabolismo , Timocitos , TimoRESUMEN
Recent years have witnessed the success of αß T cells engineered to express chimeric antigen receptors (CARs) in treating haematological cancers. CARs combine the tumour antigen binding capability of antibodies with the signalling functions of the T-cell receptor (TCR) ζ chain and co-stimulatory receptors. Despite the success, αß CAR T cells face limitations. Possible solutions would be the use of γδ T cells and new chimeric receptors, such as TCR fusion constructs (TRuCs). Notably, γδ CAR T cells are gaining traction in pre-clinical and clinical studies, demonstrating a promising safety profile in several pilot studies. This review delves into the current understanding of γδ CAR and TCR fusion construct T cells, exploring the opportunities and challenges they present for cancer treatment.
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The study of treatment effects is often complicated by noncompliance and missing data. In the one-sided noncompliance setting where of interest are the complier and noncomplier average causal effects, we address outcome missingness of the latent missing at random type (LMAR, also known as latent ignorability). That is, conditional on covariates and treatment assigned, the missingness may depend on compliance type. Within the instrumental variable (IV) approach to noncompliance, methods have been proposed for handling LMAR outcome that additionally invoke an exclusion restriction-type assumption on missingness, but no solution has been proposed for when a non-IV approach is used. This article focuses on effect identification in the presence of LMAR outcomes, with a view to flexibly accommodate different principal identification approaches. We show that under treatment assignment ignorability and LMAR only, effect nonidentifiability boils down to a set of two connected mixture equations involving unidentified stratum-specific response probabilities and outcome means. This clarifies that (except for a special case) effect identification generally requires two additional assumptions: a specific missingness mechanism assumption and a principal identification assumption. This provides a template for identifying effects based on separate choices of these assumptions. We consider a range of specific missingness assumptions, including those that have appeared in the literature and some new ones. Incidentally, we find an issue in the existing assumptions, and propose a modification of the assumptions to avoid the issue. Results under different assumptions are illustrated using data from the Baltimore Experience Corps Trial.
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Angiotensin-converting enzyme 2 (ACE2), part of the renin-angiotensin system (RAS), serves as an entry point for SARS-CoV-2, leading to viral proliferation in permissive cell types. Using mouse lines in which the Ace2 locus has been humanized by syntenic replacement, we show that regulation of basal and interferon induced ACE2 expression, relative expression levels of different ACE2 transcripts, and sexual dimorphism in ACE2 expression are unique to each species, differ between tissues, and are determined by both intragenic and upstream promoter elements. Our results indicate that the higher levels of expression of ACE2 observed in the lungs of mice relative to humans may reflect the fact that the mouse promoter drives expression of ACE2 in populous airway club cells while the human promoter drives expression in alveolar type 2 (AT2) cells. In contrast to transgenic mice in which human ACE2 is expressed in ciliated cells under the control of the human FOXJ1 promoter, mice expressing ACE2 in club cells under the control of the endogenous Ace2 promoter show a robust immune response after infection with SARS-CoV-2, leading to rapid clearance of the virus. This supports a model in which differential expression of ACE2 determines which cell types in the lung are infected, and this in turn modulates the host response and outcome of COVID-19.
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Enzima Convertidora de Angiotensina 2 , COVID-19 , Receptores Virales , Animales , Humanos , Ratones , Enzima Convertidora de Angiotensina 2/genética , COVID-19/genética , Ratones Transgénicos , Receptores Virales/genética , SARS-CoV-2 , Tropismo ViralRESUMEN
Innate lymphoid cells (ILCs) are known as first responders to infections and as instructors of subsequent CD4(+) T cell cytokine profiles. In this issue of Immunity, Fan and colleagues now demonstrate that even earlier responding innate-like B cells (NKB) induce these protective ILC responses.
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Linfocitos B , Inmunidad Innata/inmunología , Citocinas/inmunología , Humanos , Linfocitos/inmunología , Linfocitos TRESUMEN
As a fundamental aspect of normal cell signaling and disease states, there is great interest in determining alternative splicing (AS) changes in physiologic, pathologic, and pharmacologic settings. High throughput RNA sequencing and specialized software to detect AS has greatly enhanced our ability to determine transcriptome-wide splicing changes. Despite the richness of this data, deriving meaning from sometimes thousands of AS events is a substantial bottleneck for most investigators. We present SpliceTools, a suite of data processing modules that arms investigators with the ability to quickly produce summary statistics, mechanistic insights, and functional significance of AS changes through command line or through an online user interface. Utilizing RNA-seq datasets for 186 RNA binding protein knockdowns, nonsense mediated RNA decay inhibition, and pharmacologic splicing inhibition, we illustrate the utility of SpliceTools to distinguish splicing disruption from regulated transcript isoform changes, we show the broad transcriptome footprint of the pharmacologic splicing inhibitor, indisulam, we illustrate the utility in uncovering mechanistic underpinnings of splicing inhibition, we identify predicted neo-epitopes in pharmacologic splicing inhibition, and we show the impact of splicing alterations induced by indisulam on cell cycle progression. Together, SpliceTools puts rapid and easy downstream analysis at the fingertips of any investigator studying AS.
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Empalme Alternativo , Empalme del ARN , Empalme Alternativo/genética , Sulfonamidas , Transcriptoma/genética , Análisis de Secuencia de ARN/métodosRESUMEN
We have developed AMRViz, a toolkit for analyzing, visualizing, and managing bacterial genomics samples. The toolkit is bundled with the current best practice analysis pipeline allowing researchers to perform comprehensive analysis of a collection of samples directly from raw sequencing data with a single command line. The analysis results in a report showing the genome structure, genome annotations, antibiotic resistance and virulence profile for each sample. The pan-genome of all samples of the collection is analyzed to identify core- and accessory-genes. Phylogenies of the whole genome as well as all gene clusters are also generated. The toolkit provides a web-based visualization dashboard allowing researchers to interactively examine various aspects of the analysis results. Availability: AMRViz is implemented in Python and NodeJS, and is publicly available under open source MIT license at https://github.com/amromics/amrviz .
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Genoma Bacteriano , Genómica , Programas Informáticos , Genómica/métodos , Farmacorresistencia Bacteriana/genética , Filogenia , Bacterias/genética , Bacterias/efectos de los fármacos , Antibacterianos/farmacologíaRESUMEN
Plants are the most sophisticated biofactories and sources of food and biofuels present in nature. By engineering plant metabolism, the production of desired compounds can be increased and the nutritional or commercial value of the plant species can be improved. However, this can be challenging because of the complexity of the regulation of multiple genes and the involvement of different protein interactions. To improve metabolic engineering (ME) capabilities, different tools and strategies for rerouting the metabolic pathways have been developed, including genome editing and transcriptional regulation approaches. In addition, cutting-edge technologies have provided new methods for understanding uncharacterized biosynthetic pathways, protein degradation mechanisms, protein-protein interactions, or allosteric feedback, enabling the design of novel ME approaches.
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Sistemas CRISPR-Cas , Plantas , Plantas/genética , Plantas/metabolismo , Edición Génica/métodos , Ingeniería Metabólica/métodos , Redes y Vías Metabólicas , Genoma de PlantaRESUMEN
Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, an optimized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license.
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Genoma Bacteriano , Genómica , Programas Informáticos , Flujo de Trabajo , Genómica/métodos , Biología Computacional/métodos , Bacterias/genética , Genoma Microbiano , Farmacorresistencia Bacteriana/genéticaRESUMEN
In epidemiology and social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness. However, how to appropriately use multiple imputation for propensity score analysis is not completely clear. This paper aims to bring clarity on the consistency (or lack thereof) of methods that have been proposed, focusing on the within approach (where the effect is estimated separately in each imputed dataset and then the multiple estimates are combined) and the across approach (where typically propensity scores are averaged across imputed datasets before being used for effect estimation). We show that the within method is valid and can be used with any causal effect estimator that is consistent in the full-data setting. Existing across methods are inconsistent, but a different across method that averages the inverse probability weights across imputed datasets is consistent for propensity score weighting. We also comment on methods that rely on imputing a function of the missing covariate rather than the covariate itself, including imputation of the propensity score and of the probability weight. Based on consistency results and practical flexibility, we recommend generally using the standard within method. Throughout, we provide intuition to make the results meaningful to the broad audience of applied researchers.
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The choice of which covariates to adjust for (so-called allowability designation (AD)) in health disparity measurements reflects value judgments about inequitable versus equitable sources of health differences, which is paramount for making inferences about disparity. Yet, many off-the-shelf estimators used in health disparity research are not designed with equity considerations in mind, and they imply different ADs. We demonstrated the practical importance of incorporating equity concerns in disparity measurements through simulations, motivated by the example of reducing racial disparities in hypertension control via interventions on disparities in treatment intensification. Seven causal decomposition estimators, each with a particular AD (with respect to disparities in hypertension control and treatment intensification), were considered to estimate the observed outcome disparity and the reduced/residual disparity under the intervention. We explored the implications for bias of the mismatch between equity concerns and the AD in the estimator under various causal structures (through altering racial differences in covariates or the confounding mechanism). The estimator that correctly reflects equity concerns performed well under all scenarios considered, whereas the other estimators were shown to have the risk of yielding large biases in certain scenarios, depending on the interaction between their ADs and the specific causal structure.
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Hipertensión , Juicio , Humanos , Grupos RacialesRESUMEN
Rice is an essential but highly stress-susceptible crop, whose root system plays an important role in plant development and stress adaptation. The rice root system architecture is controlled by gene regulatory networks involving different phytohormones including auxin, jasmonate, and gibberellin. Gibberellin is generally known as a molecular clock that interacts with different pathways to regulate root meristem development. The exogenous treatment of rice plantlets with Gibberellin reduced the number of crown roots, whilst the exogenous jasmonic acid treatment enhanced them by involving a Germin-like protein OsGER4. Due to those opposite effects, this study aims to investigate the effect of Gibberellin on crown root development in the rice mutant of the plasmodesmal Germin-like protein OsGER4. Under exogenous gibberellin treatment, the number of crown roots significantly increased in osger4 mutant lines and decreased in the OsGER4 overexpressed lines. GUS staining showed that OsGER4 was strongly expressed in rice root systems, particularly crown and lateral roots under GA3 application. Specifically, OsGER4 was strongly expressed from the exodermis, epidermis, sclerenchyma to the endodermis layers of the crown root, along the vascular bundle and throughout LR primordia. The plasmodesmal protein OsGER4 is suggested to be involved in crown root development by maintaining hormone homeostasis, including Gibberillin.