Dissecting the treatment-naive ecosystem of human melanoma brain metastasis.

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.

Lactate is an epigenetic metabolite that drives survival in model systems of glioblastoma.

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.

The IgM receptor FcµR limits tonic BCR signaling by regulating expression of the IgM BCR.

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.

Optimal fusion of genotype and drug embeddings in predicting cancer drug response.

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.

Pasa: leveraging population pangenome graph to scaffold prokaryote genome assemblies.

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.

Viral reprogramming of host transcription initiation.

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.

Stimulation of ectopically expressed muscarinic receptors induces IFN-γ but suppresses IL-2 production by inhibiting activation of pAKT pathways in primary T cells.

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.

ZAP70, too little, too much can lead to autoimmunity.

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.

Identification of complier and noncomplier average causal effects in the presence of latent missing-at-random (LMAR) outcomes: a unifying view and choices of assumptions.

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.

Human ACE2 expression, a major tropism determinant for SARS-CoV-2, is regulated by upstream and intragenic elements.

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.

Innate B Cells Tell ILC How It's Done.

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.

SpliceTools, a suite of downstream RNA splicing analysis tools to investigate mechanisms and impact of alternative splicing.

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.

AMRViz enables seamless genomics analysis and visualization of antimicrobial resistance.

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 .

Engineering the plant metabolic system by exploiting metabolic regulation.

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.

Multiple imputation for propensity score analysis with covariates missing at random: some clarity on within and across methods.

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.

The Importance of Equity Value Judgments and Estimator-Estimand Alignment in Measuring Disparity and Identifying Targets to Reduce Disparity.

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.

Effect of gibberellin on crown root development in the mutant of the rice plasmodesmal Germin-like protein OsGER4.

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.

Synergy of machine learning and density functional theory calculations for predicting experimental Lewis base affinity and Lewis polybase binding atoms.

Investigation of Lewis acid-base interactions has been conducted by ab initio calculations and machine learning (ML) models. This study aims to resolve two critical tasks that have not been quantitatively investigated. First, ML models developed from density functional theory (DFT) calculations predict experimental BF3 affinity with Pearson correlation coefficients around 0.9 and mean absolute errors around 10 kJ mol-1. The ML models are trained by DFT-calculated BF3 affinity of more than 3000 adducts, with input features readily obtained by rdkit. Second, the ML models have the capability of predicting the relative strength of Lewis base binding atoms in Lewis polybases, which is either an extremely challenging task to conduct experimentally or a computationally expensive task for ab initio methods. The study demonstrates and solidifies the potential of combining DFT calculations and ML models to predict experimental properties, especially those that are scarce and impractical to empirically acquire.

Characterization and biodistribution of under-employed gene therapy vector AAV7.

IMPORTANCE: The use of adeno-associated viruses (AAVs) as gene delivery vectors has vast potential for the treatment of many severe human diseases. Over one hundred naturally existing AAV capsid variants have been described and classified into phylogenetic clades based on their sequences. AAV8, AAV9, AAVrh.10, and other intensively studied capsids have been propelled into pre-clinical and clinical use, and more recently, marketed products; however, less-studied capsids may also have desirable properties (e.g., potency differences, tissue tropism, reduced immunogenicity, etc.) that have yet to be thoroughly described. These data will help build a broader structure-function knowledge base in the field, present capsid engineering opportunities, and enable the use of novel capsids with unique properties.

Xenografted human iPSC-derived neurons with the familial Alzheimer's disease APPV717I mutation reveal dysregulated transcriptome signatures linked to synaptic function and implicate LINGO2 as a disease signaling mediator.

Alzheimer's disease (AD) is the most common cause of dementia, and disease mechanisms are still not fully understood. Here, we explored pathological changes in human induced pluripotent stem cell (iPSC)-derived neurons carrying the familial AD APPV717I mutation after cell injection into the mouse forebrain. APPV717I mutant iPSCs and isogenic controls were differentiated into neurons revealing enhanced Aß42 production, elevated phospho-tau, and impaired neurite outgrowth in APPV717I neurons. Two months after transplantation, APPV717I and control neural cells showed robust engraftment but at 12 months post-injection, APPV717I grafts were smaller and demonstrated impaired neurite outgrowth compared to controls, while plaque and tangle pathology were not seen. Single-nucleus RNA-sequencing of micro-dissected grafts, performed 2 months after cell injection, identified significantly altered transcriptome signatures in APPV717I iPSC-derived neurons pointing towards dysregulated synaptic function and axon guidance. Interestingly, APPV717I neurons showed an increased expression of genes, many of which are also upregulated in postmortem neurons of AD patients including the transmembrane protein LINGO2. Downregulation of LINGO2 in cultured APPV717I neurons rescued neurite outgrowth deficits and reversed key AD-associated transcriptional changes related but not limited to synaptic function, apoptosis and cellular senescence. These results provide important insights into transcriptional dysregulation in xenografted APPV717I neurons linked to synaptic function, and they indicate that LINGO2 may represent a potential therapeutic target in AD.