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To realize flatness error evaluation based on coordinate measuring data, the calculation process of four evaluation methods was analyzed, and a direct calculation method of minimum zone flatness was proposed. According to the calculation process of different evaluation methods, the flatness error analysis system based on coordinate measuring data was developed, and the accuracy of the system was verified by importing the case data of related research. The practicability of the system was verified by using the developed system in the measurement task.

Triggering receptor expressed on myeloid cells 2 (TREM2) was recently highlighted as a novel immune suppressive marker in peripheral tumors. The aim of this study was to characterizeTREM2expression in gliomas and investigate its contribution in glioma progression by usingTrem2-/-mouse line. Our results showed that higherTREM2expression was correlated with poor prognosis in glioma patients. Unexpectedly, TREM2 deficiency did not have a beneficial effect in a pre-clinical model of glioma. The increasedTREM2expression in glioma was likely due to increased myeloid cell infiltration, as evidenced by our single-cell analysis showing that almost all microglia and macrophages in gliomas were TREM2+. Furthermore, we found that deficiency of TREM2 impaired tumor-myeloid phagocytosis and MHCII presentation, and significantly reduced CD4+T cells in tumor hemispheres. Our results revealed a previously unrecognized protective role of tumor-myeloid TREM2 in promoting MHCII-associated CD4+T cell response against gliomas.SUMMARYAuthors found that although higherTREM2expression is correlated with poor prognosis in glioma patients, its absence has no beneficial effect in a pre-clinical model of glioma. Deficiency of TREM2 impairs myeloid cell phagocytosis of tumor debris, leading to a reduction in MHCII-dependent CD4+anti-glioma immunity.

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

Tissue module (TM) is a spatially organized tissue region and executes specialized biological functions, recurring and varying at different tissue sites. However, the computational identification of TMs poses challenges due to their convoluted biological functions, poorly-defined molecular features, and varying spatially organized patterns. Here, we present a hypothesis-free graph Fourier transform model, SpaGFT, to represent spatially organized features using the Fourier coefficients, leading to an accurate representation of spatially variable genes and proteins and the characterization of TM at a fast computational speed. We implemented sequencing-based and imaging-based spatial transcriptomics, spatial-CITE-seq, and spatial proteomics to identify spatially variable genes and proteins, define TM identities, and infer convoluted functions among TMs in mouse brains and human lymph nodes. We collected a human tonsil sample and performed CODEX to accurately demonstrate molecular and cellular variability within the secondary follicle structure. The superior accuracy, scalability, and interpretability of SpaGFT indicate that it is an effective representation of spatially-resolved omics data and an essential tool for bringing new insights into molecular tissue biology.

Immune checkpoint blockade (ICB) has revolutionized cancer immunotherapy. However, most patients with cancer fail to respond clinically. One potential reason is the accumulation of immunosuppressive transforming growth factor β (TGFβ) in the tumor microenvironment (TME). TGFβ drives cancer immune evasion in part by inducing regulatory T cells (Tregs) and limiting CD8We investigated the role of GARP in human patients with cancer by analyzing existing large databases. In addition, we generated and humanized an anti-GARP monoclonal antibody and evaluated its antitumor efficacy and underlying mechanisms of action in murine models of cancer.We demonstrate that GARP overexpression in human cancers correlates with a tolerogenic TME and poor clinical response to ICB, suggesting GARP blockade may improve cancer immunotherapy. We report on a unique anti-human GARP antibody (named PIIO-1) that specifically binds the ligand-interacting domain of all latent TGFβ isoforms. PIIO-1 lacks recognition of GARP-TGFβ complex on platelets. Using humanGARP contributes to multiple aspects of immune resistance in cancer. Anti-human GARP antibody PIIO-1 is an efficacious and safe strategy to block GARP-mediated LTGFβ activation, enhance CD8

Sex bias exists in the development and progression of nonreproductive organ cancers, but the underlying mechanisms are enigmatic. Studies so far have focused largely on sexual dimorphisms in cancer biology and socioeconomic factors. Here, we establish a role for CD8 + T cell–dependent antitumor immunity in mediating sex differences in tumor aggressiveness, which is driven by the gonadal androgen but not sex chromosomes. A male bias exists in the frequency of intratumoral antigen-experienced Tcf7 /TCF1 + progenitor exhausted CD8 + T cells that are devoid of effector activity as a consequence of intrinsic androgen receptor (AR) function. Mechanistically, we identify a novel sex-specific regulon in progenitor exhausted CD8 + T cells and a pertinent contribution from AR as a direct transcriptional transactivator of Tcf7 /TCF1. The T cell–intrinsic function of AR in promoting CD8 + T cell exhaustion in vivo was established using multiple approaches including loss-of-function studies with CD8-specific Ar knockout mice. Moreover, ablation of the androgen-AR axis rewires the tumor microenvironment to favor effector T cell differentiation and potentiates the efficacy of anti–PD-1 immune checkpoint blockade. Collectively, our findings highlight androgen-mediated promotion of CD8 + T cell dysfunction in cancer and imply broader opportunities for therapeutic development from understanding sex disparities in health and disease.

The advent of high throughput spatial transcriptomics (HST) has allowed for unprecedented characterization of spatially distinct cell communities within a tissue sample. While a wide range of computational tools exist for detecting cell communities in HST data, none allow for characterization of community connectivity, i.e., the relative similarity of cells within and between found communities – an analysis task that can elucidate cellular dynamics in important settings such as the tumor microenvironment. To address this gap, we introduce the concept of analysis of community connectivity (ACC), which entails not only labeling distinct cell communities within a tissue sample, but understanding the relative similarity of cells within and between communities. We develop a Bayesian multi-layer network model called BANYAN for integration of spatial and gene expression information to achieve ACC. We use BANYAN to implement ACC in invasive ductal carcinoma, and uncover distinct community structure relevant to the interaction of cell types within the tumor microenvironment. Next, we show how ACC can help clarify ambiguous annotations in a human white adipose tissue sample. Finally, we demonstrate BANYAN’s ability to recover community connectivity structure via a simulation study based on real sagittal mouse brain HST data.AvailabilityAn R package banyan is available at https://github.com/carter-allen/banyan.Contactchung.911@osu.eduSupplementary informationSupplementary data are available online.Author SummaryThe proliferation of spatial transcriptomics technologies have prompted the development of numerous statistical models for characterizing the makeup of a tissue sample in terms of distinct cell sub-populations. However, existing methods regard inferred sub-populations as static entities and do not offer any ability to discover the relative similarity of cells within and between communities, thereby obfuscating the true interactive nature of cells in a tissue sample. We develop BANYAN: a statistical model for implementing analysis of community connectivity (ACC), i.e., the process of inferring the similarity of cells within and between cell sub-populations. We demonstrate the utility of ACC through the analysis of a publicly available breast cancer data set, which revealed distinct community structure between tumor suppressive and invasive cancer cell sub-populations. We then showed how ACC may help elucidate ambiguous sub-population annotations in a publicly available human white adipose tissue data set. Finally, we implement a simulation study to validate BANYAN’s ability to recover true community connectivity structure in HST data.

High throughput spatial transcriptomics (HST) technologies have allowed for identification of distinct cell sub-populations in tissue samples, i.e., tissue architecture identification. However, existing methods do not allow for simultaneous analysis of multiple HST samples. Moreover, standard tissue architecture identification approaches do not provide uncertainty measures. Finally, no existing frameworks have integrated deep learning with Bayesian statistical models for HST data analyses. To address these gaps, we developed MAPLE: a hybrid deep learning and Bayesian modeling framework for detection of spatially informed cell spot sub-populations, uncertainty quantification, and inference of group effects in multi-sample HST experiments. MAPLE includes an embedded regression model to explain cell sub-population abundance in terms of available covariates such as treatment group, disease status, or tissue region. We demonstrate the capability of MAPLE to achieve accurate, comprehensive, and interpretable tissue architecture inference through four case studies that spanned a variety of organs in both humans and animal models.AvailabilityAn R package maple is available at https://github.com/carter-allen/maple.Contactchung.911@osu.eduSupplementary informationSupplementary data are available online.

Fast-developing single-cell multimodal omics (scMulti-omics) technologies enable the measurement of multiple modalities, such as DNA methylation, chromatin accessibility, RNA expression, protein abundance, gene perturbation, and spatial information, from the same cell. scMulti-omics can comprehensively explore and identify cell characteristics, while also presenting challenges to the development of computational methods and tools for integrative analyses. Here, we review these integrative methods and summarize the existing tools for studying a variety of scMulti-omics data. The various functionalities and practical challenges in using the available tools in the public domain are explored through several case studies. Finally, we identify remaining challenges and future trends in scMulti-omics modeling and analyses.

Arterial remodeling is an important adaptive mechanism that maintains normal fluid shear stress in a variety of physiologic and pathologic conditions. Inward remodeling, a process that leads to reduction in arterial diameter, plays a critical role in progression of such common diseases as hypertension and atherosclerosis. Yet despite its pathogenic importance, molecular mechanisms controlling inward remodeling remain undefined. Mitogen-activated protein kinases (MAPKs) perform a number of functions ranging from control of proliferation to migration and cell fate transitions. While the MAPK ERK1/2 signaling pathway has been extensively examined in the endothelium, less is known about the role of the MEKK3/ERK5 pathway in vascular remodeling. To better define the role played by this signaling cascade, we studied the effect of endothelial-specific deletion of its key upstream MAP3K, MEKK3, in adult mice. The gene’s deletion resulted in a gradual inward remodeling of both pulmonary and systematic arteries leading to spontaneous hypertension in both vascular circuits and accelerated progression of atherosclerosis in hyperlipidemic mice. Molecular analysis revealed activation of TGFβ signaling both in vitro and in vivo. Endothelial-specific TGFβR1 knockout prevented inward arterial remodeling in MEKK3 endothelial knockout mice. These data point to the unexpected participation of endothelial MEKK3 in regulation of TGFβR1-Smad2/3 signaling and inward arterial remodeling in artery diseases.SignificanceInward remodeling of arteries to reduce lumen diameter is a major factor in disease progression and morbidity in multiple vascular diseases, including hypertension and atherosclerosis. However, molecular mechanisms controlling inward arterial remodeling remain largely undefined. In this study, we identify endothelial MEKK3 as an unexpected regulator of inward remodeling via inhibition of TGFβ-Smad2/3 signaling. Genetic deletion of MEKK3 in adult endothelium results in induction of TGFβ-Smad2/3 signaling, endothelial-to-mesenchymal transition and inward remodeling in both pulmonary and arterial circuits. The latter process results in pulmonary and systemic hypertension and accelerates atherosclerosis. These results provide a new basis for understanding the inward artery remodeling that leads to reduced blood flow to affected tissues and exacerbates hypertension in vascular disease.

Selective neuronal vulnerability to protein aggregation is found in many neurodegenerative diseases including Alzheimer's disease (AD). Understanding the molecular origins of this selective vulnerability is, therefore, of fundamental importance. Tau protein aggregates have been found in Wolframin (WFS1)-expressing excitatory neurons in the entorhinal cortex, one of the earliest affected regions in AD. The role of WFS1 in Tauopathies and its levels in tau pathology-associated neurodegeneration, however, is largely unknown. Here we report that WFS1 deficiency is associated with increased tau pathology and neurodegeneration, whereas overexpression of WFS1 reduces those changes. We also find that WFS1 interacts with tau protein and controls the susceptibility to tau pathology. Furthermore, chronic ER stress and autophagy-lysosome pathway (ALP)-associated genes are enriched in WFS1-high excitatory neurons in human AD at early Braak stages. The protein levels of ER stress and autophagy-lysosome pathway (ALP)-associated proteins are changed in tau transgenic mice with WFS1 deficiency, while overexpression of WFS1 reverses those changes. This work demonstrates a possible role for WFS1 in the regulation of tau pathology and neurodegeneration via chronic ER stress and the downstream ALP. Our findings provide insights into mechanisms that underpin selective neuronal vulnerability, and for developing new therapeutics to protect vulnerable neurons in AD.

We present DeepMAPS (Deep learning-based Multi-omics Analysis Platform for Single-cell data) for biological network inference from single-cell multi-omics (scMulti-omics). DeepMAPS includes both cells and genes in a heterogeneous graph to simultaneously infer cell-cell, cell-gene, and gene-gene relations. The multi-head attention mechanism in a graph transformer considers the heterogeneous relation among cells and genes within both local and global context, making DeepMAPS robust to data noise and scale. We benchmarked DeepMAPS on 18 scMulti-omics datasets for cell clustering and biological network inference, and the results showed that our method outperformed various existing tools. We further applied DeepMAPS on lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In both cases, DeepMAPS showed competitive performance in cell clustering and predicted biologically meaningful cell-cell communication pathways based on the inferred gene networks. Note that we deployed a webserver using DeepMAPS implementation equipped with multiple functions and visualizations to improve the feasibility and reproducibility of scMulti-omics data analysis. Overall, DeepMAPS represents a heterogeneous graph transformer for single-cell study and may benefit the use of scMulti-omics data in various biological systems.

Background Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) through direct lysis of infected lung epithelial cells, which releases damage-associated molecular patterns and induces a pro-inflammatory cytokine milieu causing systemic inflammation. Anti-viral and anti-inflammatory agents have shown limited therapeutic efficacy. Soluble CD24 (CD24Fc) blunts the broad inflammatory response induced by damage-associated molecular patterns via binding to extracellular high mobility group box 1 and heat shock proteins, as well as regulating the downstream Siglec10-Src homology 2 domain–containing phosphatase 1 pathway. A recent randomized phase III trial evaluating CD24Fc for patients with severe COVID-19 (SAC-COVID; NCT04317040) demonstrated encouraging clinical efficacy. Methods Using a systems analytical approach, we studied peripheral blood samples obtained from patients enrolled at a single institution in the SAC-COVID trial to discern the impact of CD24Fc treatment on immune homeostasis. We performed high dimensional spectral flow cytometry and measured the levels of a broad array of cytokines and chemokines to discern the impact of CD24Fc treatment on immune homeostasis in patients with COVID-19. Results Twenty-two patients were enrolled, and the clinical characteristics from the CD24Fc vs. placebo groups were matched. Using high-content spectral flow cytometry and network-level analysis, we found that patients with severe COVID-19 had systemic hyper-activation of multiple cellular compartments, including CD8+ T cells, CD4+ T cells, and CD56+ natural killer cells. Treatment with CD24Fc blunted this systemic inflammation, inducing a return to homeostasis in NK and T cells without compromising the anti-Spike protein antibody response. CD24Fc significantly attenuated the systemic cytokine response and diminished the cytokine coexpression and network connectivity linked with COVID-19 severity and pathogenesis. Conclusions Our data demonstrate that CD24Fc rapidly down-modulates systemic inflammation and restores immune homeostasis in SARS-CoV-2-infected individuals, supporting further development of CD24Fc as a novel therapeutic against severe COVID-19.

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations of cells within a tissue sample that may inform biological phenomena. Existing computational methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic-based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya-Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment. An R package spruce for fitting the proposed models is available through The Comprehensive R Archive Network.

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand biological functions in complex diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from the spatial transcriptomics. The embedding is then visualized by mapping as color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of sixteen 10x Genomics Visium spatial transcriptomics datasets on the human cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on a pre-defined region-or cell-type-specific genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer’s disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.

Alzheimer’s disease (AD) is pathologically characterized by amyloid beta (Aβ) plaques, neurofibrillary tangles (tau aggregates), and alterations in microglia, astrocytes and oligodendrocytes. The mesial temporal lobe is a vulnerable brain region in early AD; however, little is known about the transcriptome-scale gene expression in this region and its relation to AD pathology. Here we use the 10x Genomics Visium platform in combination with co-immunofluorescence staining of AD-associated pathological markers to define the spatial topography of gene expression in the middle temporal gyrus (MTG) from both early AD and age- and gender-matched control cases. We identify unique marker genes for six cortical layers and the adjacent white matter as well as gene expression patterns and alterations that showcase unique gene signatures and pathways associated with a range of AD pathology. Also, gene co-expression analyses of differentially expressed genes (DEGs) between AD and controls reveal four unique gene modules, which significantly change their co-expression patterns in the presence of variations of AD pathology. Furthermore, we validate the changes of key representative DEGs that are associated with AD pathology in neurons, microglia, astrocytes and oligodendrocytes using single-molecule fluorescent in situ hybridization. In summary, we provide a rich resource for the spatial transcriptomic profile of the human MTG, which will contribute to our understanding of the complex architecture and AD pathology of this vulnerable brain region.

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations within a tissue sample that reflect distinct cell types or states. Existing methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic network-based clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya–Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment.

Ganoderma lucidum immunomodulatory protein (FIP-glu) is an active ingredient with potential immunoregulatory functions. The study was conducted to explore the immunomodulatory activities of recombinant FIP-glu (rFIP-glu) and its possible mechanism in macrophage RAW264.7 cells. In vitro assays of biological activity indicated that rFIP-glu significantly activated RAW264.7 cells and possessed proinflammatory and anti-inflammatory abilities. RNA sequencing analysis and Western blot analysis showed that macrophage activation involved PI3K/Akt and MAPK pathways. Furthermore, real-time quantitative polymerase chain reaction indicated that the PI3K inhibitor LY294002 blocked the messenger RNA (mRNA) levels of MCP-1 (CCL-2), the MEK1/2 inhibitor U0126 reduced the mRNA levels of TNF-α and MCP-1 (CCL-2), and the JNK1/2/3 inhibitor SP600125 prevented the upregulation of inducible nitric oxide synthase mRNA in rFIP-glu-induced cells. rFIP-glu did not mediate these inflammatory effects through a general pathway but rather through a different pathway for a different inflammatory mediator. These data imply that rFIP-glu possessed immunomodulatory activity in macrophages, which was mediated through PI3K/Akt and MAPK pathways.

Clonal evolution drives cancer progression and therapeutic resistance1-2. Recent longitudinal analyses revealed divergent clonal dynamics in adult diffuse gliomas3–11. However, the early genomic and epigenomic factors that steer post-treatment molecular trajectories remain unknown. To track evolutionary predictors, we analyzed sequencing and clinical data of matched initial-recurrent tumor pairs from 511 adult diffuse glioma patients. Using machine learning we developed methods capable of predicting grade progression and hypermutation from tumor characteristics at diagnosis. Strikingly, MYC copy number gain in initial tumors emerged as a key factor predicting development of hypermutation under temozolomide (TMZ) treatment. The driving role of MYC in TMZ-associated hypermutagenesis has been experimentally validated in a model of TMZ-induced hypermutator using both patient-derived gliomaspheres and established glioma cell lines. Subsequent studies showed that c-Myc binding to open chromatin and transcriptionally active regions increases the vulnerability of genomic regions to TMZ-induced mutagenesis. Consequently, MYC target genes, including the key mismatch repair genes, develop loss-of-function mutations, thus triggering the hypermutation process. This study reveals MYC as an early predictor of cancer evolution and provides a machine learning platform for predicting cancer dynamics to improve patient management.

Human middle temporal gyrus (MTG) is a vulnerable brain region in early Alzheimer's disease (AD); however, little is known about the molecular mechanisms underlying this regional vulnerability. Here we use the 10x Visium platform to define the spatial topography of gene expression in the MTG from both early AD and control cases. We identify differentially expressed genes (DEGs) and enriched pathways that may contribute to the layer-specific vulnerability of AD pathology. Also, gene co-expression analyses reveal four gene modules, which significantly change their co-expression patterns in the presence of variations of AD pathology. Furthermore, we validate the changes of key representative DEGs that are associated with AD pathology using single-molecule fluorescent in situ hybridization. In summary, we provide a rich resource for the spatial transcriptomic profile of the human MTG, which will contribute to our understanding of the complex architecture and AD pathology of this vulnerable brain region.

Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell-cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. scGNN integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark scRNA-Seq datasets. In an Alzheimer’s disease study with 13,214 single nuclei from postmortem brain tissues, scGNN successfully illustrated disease-related neural development and the differential mechanism. scGNN provides an effective representation of gene expression and cell-cell relationships. It is also a novel and powerful framework that can be applied to scRNA-Seq analyses.

Objective: O6methylguanine-DNA methyltransferase (MGMT) promoter methylation is a biomarker widely used to predict the sensitivity of IDH-wildtype glioblastoma to temozolomide therapy. Given that the IDH status has critical effects on the survival and epigenetic features of glioblastoma, we aimed to assess the role of MGMT promoter methylation in IDH-mutant glioblastoma. Methods: This study included 187 IDH-mutant glioblastomas and used 173 IDH-wildtype glioblastomas for comparison. Kaplan-Meier curves and multivariate Cox regression were used to study the predictive effects. Results: Compared with IDH-wildtype glioblastomas, IDH-mutant glioblastomas showed significantly higher (P < 0.0001) MGMT promoter methylation. We demonstrated that MGMT promoter methylation status, as determined by a high cutoff value (≥30%) in pyrosequencing, could be used to significantly stratify the survival of 50 IDH-mutant glioblastomas receiving temozolomide therapy (cohort A); this result was validated in another cohort of 25 IDH-mutant glioblastomas (cohort B). The median progression-free survival and median overall survival in cohort A were 9.33 and 13.76 months for unmethylated cases, and 18.37 and 41.61 months for methylated cases, and in cohort B were 6.97 and 9.10 months for unmethylated cases, and 23.40 and 26.40 months for methylated cases. In addition, we confirmed that the MGMT promoter methylation was significantly (P = 0.0001) correlated with longer OS in IDH-mutant patients with GBM, independently of age, gender distribution, tumor type (primary or recurrent/secondary), and the extent of resection. Conclusions: MGMT promoter methylation has predictive value in IDH-mutant glioblastoma, but its cutoff value should be higher than that for IDH-wildtype glioblastoma.

Metabolic programs are crucial for T reg cell stability and function. Yu et al. report that TRAF3IP3 acts as a pivotal regulator in controlling metabolic fitness to maintain T reg cell stability and function at the lysosome., Metabolic programs are crucial for regulatory T (T reg) cell stability and function, but the underlying mechanisms that regulate T reg cell metabolism are elusive. Here, we report that lysosomal TRAF3IP3 acts as a pivotal regulator in the maintenance of T reg cell metabolic fitness. T reg–specific deletion of Traf3ip3 impairs T reg cell function, causing the development of inflammatory disorders and stronger antitumor T cell responses in mice. Excessive mechanistic target of rapamycin complex 1 (mTORC1)–mediated hyper-glycolytic metabolism is responsible for the instability of TRAF3IP3-deficient T reg cells. Mechanistically, TRAF3IP3 restricts mTORC1 signaling by recruiting the serine-threonine phosphatase catalytic subunit (PP2Ac) to the lysosome, thereby facilitating the interaction of PP2Ac with the mTORC1 component Raptor. Our results define TRAF3IP3 as a metabolic regulator in T reg cell stability and function and suggest a lysosome-specific mTORC1 signaling mechanism that regulates T reg cell metabolism., Graphical Abstract

Named data networking (NDN) is an emerging network architecture for serving content-centric applications, which are intended to support diverse services that require various quality of service (QoS) levels. In this paper, an algorithm based on ant colony optimisation—the so-called service-differentiated QoS routing algorithm (SDQR)—is proposed for service-differentiated routing of different types of services in NDN. SDQR adds a control layer on top of NDN to manipulate the underlying forwarding information base (FIB). The FIB defines an evaluation matrix to achieve differentiated updates on pheromone concentrations for different types of services. Simulation results showed that SDQR achieves service-differentiated routing for different types of services with less delay and higher throughput than several conventional approaches.

Named data networking (NDN) is a representation and implementation of an information centric network, which is considered as one of the next generation of network architectures. To the best of the authors' knowledge, very few studies have considered multiple constrained quality-of-service (QoS) routing in NDN. In this study, a particle swarm optimisation-forwarding information base (PSO-FIB) algorithm that uses the forwarding experiences of particles to maintain the forwarding probability of each entry in the FIB is proposed. Illustrating the interaction of PSO-FIB with the routing layer, the simulation results show that PSO-FIB can support multi-constrained QoS routing and achieve better performance in terms of successful delivery rate and average cost compared with random and ant colony optimisation strategies.