Structural insights into the molecular effects of the anthelmintics monepantel and betaine on the Caenorhabditis elegans acetylcholine receptor ACR-23.

Anthelmintics are drugs used for controlling pathogenic helminths in animals and plants. The natural compound betaine and the recently developed synthetic compound monepantel are both anthelmintics that target the acetylcholine receptor ACR-23 and its homologs in nematodes. Here, we present cryo-electron microscopy structures of ACR-23 in apo, betaine-bound, and betaine- and monepantel-bound states. We show that ACR-23 forms a homo-pentameric channel, similar to some other pentameric ligand-gated ion channels (pLGICs). While betaine molecules are bound to the classical neurotransmitter sites in the inter-subunit interfaces in the extracellular domain, monepantel molecules are bound to allosteric sites formed in the inter-subunit interfaces in the transmembrane domain of the receptor. Although the pore remains closed in betaine-bound state, monepantel binding results in an open channel by wedging into the cleft between the transmembrane domains of two neighboring subunits, which causes dilation of the ion conduction pore. By combining structural analyses with site-directed mutagenesis, electrophysiology and in vivo locomotion assays, we provide insights into the mechanism of action of the anthelmintics monepantel and betaine.

Cryo-EM structure of the polyphosphate polymerase VTC reveals coupling of polymer synthesis to membrane transit.

The eukaryotic vacuolar transporter chaperone (VTC) complex acts as a polyphosphate (polyP) polymerase that synthesizes polyP from adenosine triphosphate (ATP) and translocates polyP across the vacuolar membrane to maintain an intracellular phosphate (Pi ) homeostasis. To discover how the VTC complex performs its function, we determined a cryo-electron microscopy structure of an endogenous VTC complex (Vtc4/Vtc3/Vtc1) purified from Saccharomyces cerevisiae at 3.1 Å resolution. The structure reveals a heteropentameric architecture of one Vtc4, one Vtc3, and three Vtc1 subunits. The transmembrane region forms a polyP-selective channel, likely adopting a resting state conformation, in which a latch-like, horizontal helix of Vtc4 limits the entrance. The catalytic Vtc4 central domain is located on top of the pseudo-symmetric polyP channel, creating a strongly electropositive pathway for nascent polyP that can couple synthesis to translocation. The SPX domain of the catalytic Vtc4 subunit positively regulates polyP synthesis by the VTC complex. The noncatalytic Vtc3 regulates VTC through a phosphorylatable loop. Our findings, along with the functional data, allow us to propose a mechanism of polyP channel gating and VTC complex activation.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability.

Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Early-stage patients have a 30-50% probability of metastatic recurrence after surgical treatment. Here, we propose a new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), based on pathway hierarchy relationships to predict LUAD recurrence and explore the internal regulatory mechanisms of LUAD. IBPGNET can integrate different omics data efficiently and provide global interpretability. In addition, our experimental results show that IBPGNET outperforms other classification methods in 5-fold cross-validation. IBPGNET identified PSMC1 and PSMD11 as genes associated with LUAD recurrence, and their expression levels were significantly higher in LUAD cells than in normal cells. The knockdown of PSMC1 and PSMD11 in LUAD cells increased their sensitivity to afatinib and decreased cell migration, invasion and proliferation. In addition, the cells showed significantly lower EGFR expression, indicating that PSMC1 and PSMD11 may mediate therapeutic sensitivity through EGFR expression.

Spatially Resolved Niche and Tumor Microenvironmental Alterations in Gastric Cancer Peritoneal Metastases.

BACKGROUND & AIMS: Peritoneal metastasis (PM) in gastric cancer (GC) is associated with poor prognosis and significant morbidity. We sought to understand the genomic, transcriptomic, and tumor microenvironment (TME) features that contribute to peritoneal organotropism in GC. METHODS: We conducted a comprehensive multi-omic analysis of 548 samples from 326 patients, including primary tumors, matched normal tissues; peritoneal metastases, and adjacent-normal peritoneal tissues. We used whole exome sequencing, whole transcriptome sequencing, and digital spatial profiling to investigate molecular alterations, gene expression patterns, and TME characteristics associated with PM. RESULTS: Our analysis identified specific genomic alterations in primary tumors, including mutations in ELF3, CDH1, and PIGR, and TME signatures, such as stromal infiltration and M2 macrophage enrichment, associated with increased risk of PM. We observed distinct transcriptional programs and immune compositions in GCPM compared with liver metastases, highlighting the importance of the TME in transcoelomic metastasis. We found differential expression of therapeutic targets between primary tumors and PM, with lower CLDN18.2 and FGFR2b expression in PM. We unravel the roles of the TME in niche reprogramming within the peritoneum, and provide evidence of pre-metastatic niche conditioning even in early GC without clinical PM. These findings were further validated using a humanized mouse model, which demonstrated niche remodeling in the peritoneum during transcoelomic metastasis. CONCLUSION: Our study provides a comprehensive molecular characterization of GCPM and unveils key biological principles underlying transcoelomic metastasis. The identified predictive markers, therapeutic targets, and TME alterations offer potential avenues for targeted interventions and improved patient outcomes.

Benchmarking of computational methods for predicting circRNA-disease associations.

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.

Structure and mechanism of a neuropeptide-activated channel in the ENaC/DEG superfamily.

Phe-Met-Arg-Phe-amide (FMRFamide)-activated sodium channels (FaNaCs) are a family of channels activated by the neuropeptide FMRFamide, and, to date, the underlying ligand gating mechanism remains unknown. Here we present the high-resolution cryo-electron microscopy structures of Aplysia californica FaNaC in both apo and FMRFamide-bound states. AcFaNaC forms a chalice-shaped trimer and possesses several notable features, including two FaNaC-specific insertion regions, a distinct finger domain and non-domain-swapped transmembrane helix 2 in the transmembrane domain (TMD). One FMRFamide binds to each subunit in a cleft located in the top-most region of the extracellular domain, with participation of residues from the neighboring subunit. Bound FMRFamide adopts an extended conformation. FMRFamide binds tightly to A. californica FaNaC in an N terminus-in manner, which causes collapse of the binding cleft and induces large local conformational rearrangements. Such conformational changes are propagated downward toward the TMD via the palm domain, possibly resulting in outward movement of the TMD and dilation of the ion conduction pore.

Novel OX40 and 4-1BB derived spacers enhance CD30 CAR activity and safety in CD30 positive lymphoma models.

The chimeric antigen receptor (CAR) derived from the CD30 specific murine antibody, HRS-3, has produced promising clinical efficacy with a favorable safety profile in the treatment of relapsed or refractory CD30-positive lymphomas. However, persistence of the autologous CAR-T cells was brief, and many patients relapsed a year after treatment. The lack of persistence may be attributed to the use of a wild-type immunoglobulin (Ig)G1 spacer that can associate with Fc receptors. We first identified the cysteine-rich domain (CRD) 5 of CD30 as the primary binding epitope of HRS-3 and armed with this insight, attempted to improve the HRS-3 CAR functionality with a panel of novel spacer designs. We demonstrate that HRS-3 CARs with OX40 and 4-1BB derived spacers exhibited similar anti-tumor efficacy, circumvented interactions with Fc receptors, and secreted lower levels of cytokines in vitro than a CAR employing the IgG1 spacer. Humanization of the HRS-3 scFv coupled with the 4-1BB spacer preserved potent on-target, on-tumor efficacy, and on-target, off-tumor safety. In a lymphoma mouse model of high tumor burden, T cells expressing humanized HRS-3 CD30.CARs with the 4-1BB spacer potently killed tumors with low levels of circulating inflammatory cytokines, providing a promising candidate for future clinical development in the treatment of CD30-positive malignancies.

Single-cell RNA sequencing reveals anti-tumor potency of CD56+ NK cells and CD8+ T cells in humanized mice via PD-1 and TIGIT co-targeting.

In solid tumors, the exhaustion of natural killer (NK) cells and cytotoxic T cells in the immunosuppressive tumor microenvironment poses challenges for effective tumor control. Conventional humanized mouse models of hepatocellular carcinoma patient-derived xenografts (HCC-PDX) encounter limitations in NK cell infiltration, hindering studies on NK cell immunobiology. Here, we introduce an improved humanized mouse model with restored NK cell reconstitution and infiltration in HCC-PDX, coupled with single-cell RNA sequencing (scRNA-seq) to identify potential anti-HCC treatments. A single administration of adeno-associated virus carrying human interleukin-15 reinstated persistent NK cell reconstitution and infiltration in HCC-PDX in humanized mice. scRNA-seq revealed NK cell and T cell subpopulations with heightened PDCD1 and TIGIT levels. Notably, combination therapy with anti-PD-1 and anti-TIGIT antibodies alleviated HCC burden in humanized mice, demonstrating NK cell-dependent efficacy. Bulk-RNA sequencing analysis also revealed significant alterations in the tumor transcriptome that may contribute to further resistance after combination therapy, warranting further investigations. As an emerging strategy, ongoing clinical trials with anti-PD-1 and anti-TIGIT antibodies provide limited data. The improved humanized mouse HCC-PDX model not only sheds light on the pivotal role of NK cells but also serves as a robust platform for evaluating safety and anti-tumor efficacy of combination therapies and other potential regimens, complementing clinical insights.

IFI16-dependent STING signaling is a crucial regulator of anti-HER2 immune response in HER2+ breast cancer.

Relapse to anti-HER2 monoclonal antibody (mAb) therapies, such as trastuzumab in HER2+ breast cancer (BC), is associated with residual disease progression due to resistance to therapy. Here, we identify interferon-γ inducible protein 16 (IFI16)-dependent STING signaling as a significant determinant of trastuzumab responses in HER2+ BC. We show that down-regulation of immune-regulated genes (IRG) is specifically associated with poor survival of HER2+, but not other BC subtypes. Among IRG, IFI16 is identified as a direct target of EZH2, the underexpression of which leads to deficient STING activation and downstream CXCL10/11 expression in response to trastuzumab treatment. Dual inhibition of EZH2 and histone deacetylase (HDAC) significantly activates IFI16-dependent immune responses to trastuzumab. Notably, a combination of a novel histone methylation inhibitor with an HDAC inhibitor induces complete tumor eradication and long-term T cell memory in a HER2+ BC mouse model. Our findings demonstrate an epigenetic regulatory mechanism suppressing the expression of the IFI16-CXCL10/11 signaling pathway that provides a survival advantage to HER2+ BC to confer resistance to trastuzumab treatment.

NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network.

Drug-target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug-target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low-dimensional topology representation of drugs and targets via graph-based convolutional neural network. NGCN achieves substantial performance improvements over other state-of-the-art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction.

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks.

BACKGROUND AND OBJECTIVES: Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating a better analysis and utilization of information from multi-omics data to achieve precise classification of disease subtypes. RESULTS: The experimental results show that MOSEGCN outperforms several state-of-the-art multi-omics integrative analysis approaches on three types of omics data: mRNA expression data, microRNA expression data, and DNA methylation data, with accuracy rates of 83.0% for Alzheimer's disease and 86.7% for breast cancer subtyping. Furthermore, MOSEGCN exhibits strong generalizability on the GBM dataset, enabling the identification of important biomarkers for related diseases. CONCLUSION: MOSEGCN explores the significant relationship information among different omics and within each omics' latent features, effectively leveraging labeled and unlabeled information to further enhance the accuracy of complex disease classification. It also provides a promising approach for identifying reliable biomarkers, paving the way for personalized medicine.

KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network.

Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, many researches have shown that circRNA can be considered as the potential biomarker for clinical diagnosis and treatment of disease. Some computational methods have been proposed to predict circRNA-disease associations. However, the performance of these methods is limited as the sparsity of low-order interaction information. In this paper, we propose a new computational method (KGANCDA) to predict circRNA-disease associations based on knowledge graph attention network. The circRNA-disease knowledge graphs are constructed by collecting multiple relationship data among circRNA, disease, miRNA and lncRNA. Then, the knowledge graph attention network is designed to obtain embeddings of each entity by distinguishing the importance of information from neighbors. Besides the low-order neighbor information, it can also capture high-order neighbor information from multisource associations, which alleviates the problem of data sparsity. Finally, the multilayer perceptron is applied to predict the affinity score of circRNA-disease associations based on the embeddings of circRNA and disease. The experiment results show that KGANCDA outperforms than other state-of-the-art methods in 5-fold cross validation. Furthermore, the case study demonstrates that KGANCDA is an effective tool to predict potential circRNA-disease associations.

Endothelial-immune crosstalk contributes to vasculopathy in nonalcoholic fatty liver disease.

The top cause of mortality in patients with nonalcoholic fatty liver disease (NAFLD) is cardiovascular complications. However, mechanisms of NAFLD-associated vasculopathy remain understudied. Here, we show that blood outgrowth endothelial cells (BOECs) from NAFLD subjects exhibit global transcriptional upregulation of chemokines and human leukocyte antigens. In mouse models of diet-induced NAFLD, we confirm heightened endothelial expressions of CXCL12 in the aortas and the liver vasculatures, and increased retention of infiltrated leukocytes within the vessel walls. To elucidate endothelial-immune crosstalk, we performed immunoprofiling by single-cell analysis, uncovering T cell intensification in NAFLD patients. Functionally, treatment with a CXCL12-neutralizing antibody is effective at moderating the enhanced chemotactic effect of NAFLD BOECs in recruiting CD8+ T lymphocytes. Interference with the CXCL12-CXCR4 axis using a CXCR4 antagonist also averts the impact of immune cell transendothelial migration and restores endothelial barrier integrity. Clinically, we detect threefold more circulating damaged endothelial cells in NAFLD patients than in healthy controls. Our work provides insight into the modulation of interactions with effector immune cells to mitigate endothelial injury in NAFLD.

Structural insights into the voltage and phospholipid activation of the mammalian TPC1 channel.

The organellar two-pore channel (TPC) functions as a homodimer, in which each subunit contains two homologous Shaker-like six-transmembrane (6-TM)-domain repeats. TPCs belong to the voltage-gated ion channel superfamily and are ubiquitously expressed in animals and plants. Mammalian TPC1 and TPC2 are localized at the endolysosomal membrane, and have critical roles in regulating the physiological functions of these acidic organelles. Here we present electron cryo-microscopy structures of mouse TPC1 (MmTPC1)-a voltage-dependent, phosphatidylinositol 3,5-bisphosphate (PtdIns(3,5)P2)-activated Na+-selective channel-in both the apo closed state and ligand-bound open state. Combined with functional analysis, these structures provide comprehensive structural insights into the selectivity and gating mechanisms of mammalian TPC channels. The channel has a coin-slot-shaped ion pathway in the filter that defines the selectivity of mammalian TPCs. Only the voltage-sensing domain from the second 6-TM domain confers voltage dependence on MmTPC1. Endolysosome-specific PtdIns(3,5)P2 binds to the first 6-TM domain and activates the channel under conditions of depolarizing membrane potential. Structural comparisons between the apo and PtdIns(3,5)P2-bound structures show the interplay between voltage and ligand in channel activation. These MmTPC1 structures reveal lipid binding and regulation in a 6-TM voltage-gated channel, which is of interest in light of the emerging recognition of the importance of phosphoinositide regulation of ion channels.

Co-mutation modules capture the evolution and transmission patterns of SARS-CoV-2.

The rapid spread and huge impact of the COVID-19 pandemic caused by the emerging SARS-CoV-2 have driven large efforts for sequencing and analyzing the viral genomes. Mutation analyses have revealed that the virus keeps mutating and shows a certain degree of genetic diversity, which could result in the alteration of its infectivity and pathogenicity. Therefore, appropriate delineation of SARS-CoV-2 genetic variants enables us to understand its evolution and transmission patterns. By focusing on the nucleotides that co-substituted, we first identified 42 co-mutation modules that consist of at least two co-substituted nucleotides during the SARS-CoV-2 evolution. Then based on these co-mutation modules, we classified the SARS-CoV-2 population into 43 groups and further identified the phylogenetic relationships among groups based on the number of inconsistent co-mutation modules, which were validated with phylogenetic trees. Intuitively, we tracked tempo-spatial patterns of the 43 groups, of which 11 groups were geographic-specific. Different epidemic periods showed specific co-circulating groups, where the dominant groups existed and had multiple sub-groups of parallel evolution. Our work enables us to capture the evolution and transmission patterns of SARS-CoV-2, which can contribute to guiding the prevention and control of the COVID-19 pandemic. An interactive website for grouping SARS-CoV-2 genomes and visualizing the spatio-temporal distribution of groups is available at https://www.jianglab.tech/cmm-grouping/.

Quercetin inhibits the NOX2/ROS-mediated NF-κB/TXNIP signaling pathway to ameliorate pyroptosis of cardiomyocytes to relieve sepsis-induced cardiomyopathy.

Sepsis-induced cardiomyopathy (SIC) has high morbidity and mortality. Quercetin (QUE) has been used to treat many inflammatory diseases related to pyroptosis. However, its effect on SIC has not been reported before. We aimed to explore the therapeutic mechanism of QUE on SIC. We found that the expression levels of NOX2, markers of myocardial injury and inflammatory factors related to pyroptosis were upregulated in the serum of SIC patients. QUE improved the viability and reduced the death rate of LPS-treated H9C2 cells. It could downregulate the expression level of NOX2 and alleviate NOX2-induced mitochondrial damage to inhibit the ROS-mediated NF-κB/TXNIP pathway thus ameliorating cell pyroptosis. Overexpression of NOX2 partially attenuated the anti-pyroptotic effects of QUE on LPS-treated H9C2 cells in vitro. Besides, the results of animal experiments reported that the mitochondrial damage was reduced by QUE treatment, which subsequently inhibited the ROS-mediated NF-κB/TXNIP pathway to ameliorate cell pyroptosis to further alleviate myocardial injury in CLP-induced rats in vivo. To conclude, QUE suppressed the NOX2/ROS-mediated NF-κB/TXNIP signaling pathway to ameliorate pyroptosis of cardiomyocytes to relieve SIC.

Clinical manifestations of COVID-19 infection in dialysis patients and protective effect of COVID-19 vaccine.

BACKGROUND AND OBJECTIVE: COVID-19 infection poses a special challenge to patients with dialysis patients. The purpose of this study was to evaluate the clinical manifestations of dialysis patients with COVID-19 and the protective effect of the vaccine. METHODS: We included 41 studies based on big data, mainly analyzing the clinical symptoms of dialysis patients with COVID-19, the proportion of severe patients before and after vaccination, and the humoral reaction of vaccine in the body. RESULTS: 6.1% to 35.7% of dialysis patients with COVID-19 developed respiratory distress symptoms and needed to be admitted to an intensive care unit for mechanical ventilation. The incidence and mortality of COVID-19 in dialysis patients before vaccination were 5.5% and 1.1%, respectively, and decreased to 4.5% and 0.6% in breakthrough infected patients. There was no statistical difference in serum conversion rates between dialysis patients and healthy controls, but the neutralizing antibody titer in the control group was 1922 (IQR 533 to 3186) AU/mL, and the neutralizing antibody titer in dialysis patients significantly decreased to 367 (IQR 171 to 1650) AU/mL (P=0.046). CONCLUSIONS: Dialysis is associated with an increased risk of severe COVID-19, and generally has a poor seroconversion response to vaccines. It also confirms the protective effect of vaccines on high-risk populations such as dialysis.

DRGCNCDA: Predicting circRNA-disease interactions based on knowledge graph and disentangled relational graph convolutional network.

Emerging studies have shown that circular RNA (circRNA) plays a significant role in the diagnosis and prognosis of human disease. Some computational methods have been proposed to predict circRNA-disease associations. However, some methods only use circRNA-disease association and ignore the associations of other biological entities. In addition, these methods do not take into account the latent factors of different kinds of circRNAs and diseases. To solve these limitations of existing computational models, we propose a new computational model (DRGCNCDA) based on disentangled relational graph convolutional network. The circRNA-disease multi-relational graphs are constructed by collecting multiple relational data among circRNA, disease, miRNA and lncRNA. Then, the disentangled relational graph convolutional network is employed to obtain the feature vectors of circRNA and disease. Finally, knowledge graph model is applied to predict the affinity scores of circRNA-disease associations based on the embeddings of circRNA and disease. The 5-fold cross validation is utilized to evaluate the performance of the method. The experimental results show that the DRGCNCDA outperforms other existing models. Moreover, the case study demonstrates that the DRGCNCDA is effective to predict the circRNA-disease association and can provide reliable candidates for biological experiments.

Structures of the calcium-activated, non-selective cation channel TRPM4.

TRPM4 is a calcium-activated, phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P2) -modulated, non-selective cation channel that belongs to the family of melastatin-related transient receptor potential (TRPM) channels. Here we present the electron cryo-microscopy structures of the mouse TRPM4 channel with and without ATP. TRPM4 consists of multiple transmembrane and cytosolic domains, which assemble into a three-tiered architecture. The N-terminal nucleotide-binding domain and the C-terminal coiled-coil participate in the tetrameric assembly of the channel; ATP binds at the nucleotide-binding domain and inhibits channel activity. TRPM4 has an exceptionally wide filter but is only permeable to monovalent cations; filter residue Gln973 is essential in defining monovalent selectivity. The S1-S4 domain and the post-S6 TRP domain form the central gating apparatus that probably houses the Ca2+- and PtdIns(4,5)P2-binding sites. These structures provide an essential starting point for elucidating the complex gating mechanisms of TRPM4 and reveal the molecular architecture of the TRPM family.