Pharmacological Targeting of STK19 Inhibits Oncogenic NRAS-Driven Melanomagenesis.

Activating mutations in NRAS account for 20%-30% of melanoma, but despite decades of research and in contrast to BRAF, no effective anti-NRAS therapies have been forthcoming. Here, we identify a previously uncharacterized serine/threonine kinase STK19 as a novel NRAS activator. STK19 phosphorylates NRAS to enhance its binding to its downstream effectors and promotes oncogenic NRAS-mediated melanocyte malignant transformation. A recurrent D89N substitution in STK19 whose alterations were identified in 25% of human melanomas represents a gain-of-function mutation that interacts better with NRAS to enhance melanocyte transformation. STK19D89N knockin leads to skin hyperpigmentation and promotes NRASQ61R-driven melanomagenesis in vivo. Finally, we developed ZT-12-037-01 (1a) as a specific STK19-targeted inhibitor and showed that it effectively blocks oncogenic NRAS-driven melanocyte malignant transformation and melanoma growth in vitro and in vivo. Together, our findings provide a new and viable therapeutic strategy for melanomas harboring NRAS mutations.

Paneth Cell-Derived Lysozyme Defines the Composition of Mucolytic Microbiota and the Inflammatory Tone of the Intestine.

Paneth cells are the primary source of C-type lysozyme, a ß-1,4-N-acetylmuramoylhydrolase that enzymatically processes bacterial cell walls. Paneth cells are normally present in human cecum and ascending colon, but are rarely found in descending colon and rectum; Paneth cell metaplasia in this region and aberrant lysozyme production are hallmarks of inflammatory bowel disease (IBD) pathology. Here, we examined the impact of aberrant lysozyme production in colonic inflammation. Targeted disruption of Paneth cell lysozyme (Lyz1) protected mice from experimental colitis. Lyz1-deficiency diminished intestinal immune responses to bacterial molecular patterns and resulted in the expansion of lysozyme-sensitive mucolytic bacteria, including Ruminococcus gnavus, a Crohn's disease-associated pathobiont. Ectopic lysozyme production in colonic epithelium suppressed lysozyme-sensitive bacteria and exacerbated colitis. Transfer of R. gnavus into Lyz1-/- hosts elicited a type 2 immune response, causing epithelial reprograming and enhanced anti-colitogenic capacity. In contrast, in lysozyme-intact hosts, processed R. gnavus drove pro-inflammatory responses. Thus, Paneth cell lysozyme balances intestinal anti- and pro-inflammatory responses, with implications for IBD.

Infection and inflammation stimulate expansion of a CD74+ Paneth cell subset to regulate disease progression.

Paneth cells (PCs), a specialized secretory cell type in the small intestine, are increasingly recognized as having an essential role in host responses to microbiome and environmental stresses. Whether and how commensal and pathogenic microbes modify PC composition to modulate inflammation remain unclear. Using newly developed PC-reporter mice under conventional and gnotobiotic conditions, we determined PC transcriptomic heterogeneity in response to commensal and invasive microbes at single cell level. Infection expands the pool of CD74+ PCs, whose number correlates with auto or allogeneic inflammatory disease progressions in mice. Similar correlation was found in human inflammatory disease tissues. Infection-stimulated cytokines increase production of reactive oxygen species (ROS) and expression of a PC-specific mucosal pentraxin (Mptx2) in activated PCs. A PC-specific ablation of MyD88 reduced CD74+ PC population, thus ameliorating pathogen-induced systemic disease. A similar phenotype was also observed in mice lacking Mptx2. Thus, infection stimulates expansion of a PC subset that influences disease progression.

Dependency-aware deep generative models for multitasking analysis of spatial omics data.

Spatially resolved transcriptomics (SRT) technologies have significantly advanced biomedical research, but their data analysis remains challenging due to the discrete nature of the data and the high levels of noise, compounded by complex spatial dependencies. Here, we propose spaVAE, a dependency-aware, deep generative spatial variational autoencoder model that probabilistically characterizes count data while capturing spatial correlations. spaVAE introduces a hybrid embedding combining a Gaussian process prior with a Gaussian prior to explicitly capture spatial correlations among spots. It then optimizes the parameters of deep neural networks to approximate the distributions underlying the SRT data. With the approximated distributions, spaVAE can contribute to several analytical tasks that are essential for SRT data analysis, including dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, resolution enhancement and identification of spatially variable genes. Moreover, we have extended spaVAE to spaPeakVAE and spaMultiVAE to characterize spatial ATAC-seq (assay for transposase-accessible chromatin using sequencing) data and spatial multi-omics data, respectively.

Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning.

With the advances in single-cell sequencing techniques, numerous analytical methods have been developed for delineating cell development. However, most are based on Euclidean space, which would distort the complex hierarchical structure of cell differentiation. Recently, methods acting on hyperbolic space have been proposed to visualize hierarchical structures in single-cell RNA-seq (scRNA-seq) data and have been proven to be superior to methods acting on Euclidean space. However, these methods have fundamental limitations and are not optimized for the highly sparse single-cell count data. To address these limitations, we propose scDHMap, a model-based deep learning approach to visualize the complex hierarchical structures of scRNA-seq data in low-dimensional hyperbolic space. The evaluations on extensive simulation and real experiments show that scDHMap outperforms existing dimensionality-reduction methods in various common analytical tasks as needed for scRNA-seq data, including revealing trajectory branches, batch correction, and denoising the count matrix with high dropout rates. In addition, we extend scDHMap to visualize single-cell ATAC-seq data.

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization-based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.

A model-based constrained deep learning clustering approach for spatially resolved single-cell data.

Spatially resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile gene expression patterns in tissue context. However, the development of computational methods lags behind the advances in these technologies, which limits the fulfillment of their potential. In this study, we develop a deep learning approach for clustering sp-scRNA-seq data, named Deep Spatially constrained Single-cell Clustering (DSSC). In this model, we integrate the spatial information of cells into the clustering process in two steps: (1) the spatial information is encoded by using a graphical neural network model, and (2) cell-to-cell constraints are built based on the spatial expression pattern of the marker genes and added in the model to guide the clustering process. Then, a deep embedding clustering is performed on the bottleneck layer of autoencoder by Kullback-Leibler (KL) divergence along with the learning of feature representation. DSSC is the first model that can use information from both spatial coordinates and marker genes to guide cell/spot clustering. Extensive experiments on both simulated and real data sets show that DSSC boosts clustering performance significantly compared with the state-of-the-art methods. It has robust performance across different data sets with various cell type/tissue organization and/or cell type/tissue spatial dependency. We conclude that DSSC is a promising tool for clustering sp-scRNA-seq data.

Colonic healing requires Wnt produced by epithelium as well as Tagln+ and Acta2+ stromal cells.

Although Wnt signaling is clearly important for the intestinal epithelial homeostasis, the relevance of various sources of Wnt ligands themselves remains incompletely understood. Blocking the release of Wnt in distinct stromal cell types suggests obligatory functions of several stromal cell sources and yields different observations. The physiological contribution of epithelial Wnt to tissue homeostasis remains unclear. We show here that blocking epithelial Wnts affects colonic Reg4+ epithelial cell differentiation and impairs colonic epithelial regeneration after injury in mice. Single-cell RNA analysis of intestinal stroma showed that the majority of Wnt-producing cells were contained in transgelin (Tagln+) and smooth muscle actin α2 (Acta2+) expressing populations. We genetically attenuated Wnt production from these stromal cells using Tagln-Cre and Acta2-CreER drivers, and found that blockage of Wnt release from either epithelium or Tagln+ and Acta2+ stromal cells impaired colonic epithelial healing after chemical-induced injury. Aggregated blockage of Wnt release from both epithelium and Tagln+ or Acta2+ stromal cells drastically diminished epithelial repair, increasing morbidity and mortality. These results from two uncharacterized stromal populations suggested that colonic recovery from colitis-like injury depends on multiple Wnt-producing sources.

RAB11A and RAB11B control mitotic spindle function in intestinal epithelial progenitor cells.

RAB11 small GTPases and associated recycling endosome have been localized to mitotic spindles and implicated in regulating mitosis. However, the physiological significance of such regulation has not been observed in mammalian tissues. We have used newly engineered mouse models to investigate intestinal epithelial renewal in the absence of single or double isoforms of RAB11 family members: Rab11a and Rab11b. Comparing with single knockouts, mice with compound ablation demonstrate a defective cell cycle entry and robust mitotic arrest followed by apoptosis, leading to a total penetrance of lethality within 3 days of gene ablation. Upon Rab11 deletion ex vivo, enteroids show abnormal mitotic spindle formation and cell death. Untargeted proteomic profiling of Rab11a and Rab11b immunoprecipitates has uncovered a shared interactome containing mitotic spindle microtubule regulators. Disrupting Rab11 alters kinesin motor KIF11 function and impairs bipolar spindle formation and cell division. These data demonstrate that RAB11A and RAB11B redundantly control mitotic spindle function and intestinal progenitor cell division, a mechanism that may be utilized to govern the homeostasis and renewal of other mammalian tissues.

Author Correction: PAK signalling drives acquired drug resistance to MAPK inhibitors in BRAF-mutant melanomas.

In this Letter, two relevant references were omitted; see the accompanying Amendment for details. The original Letter has not been corrected.

Zbtb16 increases susceptibility of atrial fibrillation in type 2 diabetic mice via Txnip-Trx2 signaling.

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, and recent epidemiological studies suggested type 2 diabetes mellitus (T2DM) is an independent risk factor for the development of AF. Zinc finger and BTB (broad-complex, tram-track and bric-a-brac) domain containing 16 (Zbtb16) serve as transcriptional factors to regulate many biological processes. However, the potential effects of Zbtb16 in AF under T2DM condition remain unclear. Here, we reported that db/db mice displayed higher AF vulnerability and Zbtb16 was identified as the most significantly enriched gene by RNA sequencing (RNA-seq) analysis in atrium. In addition, thioredoxin interacting protein (Txnip) was distinguished as the key downstream gene of Zbtb16 by Cleavage Under Targets and Tagmentation (CUT&Tag) assay. Mechanistically, increased Txnip combined with thioredoxin 2 (Trx2) in mitochondrion induced excess reactive oxygen species (ROS) release, calcium/calmodulin-dependent protein kinase II (CaMKII) overactivation, and spontaneous Ca2+ waves (SCWs) occurrence, which could be inhibited through atrial-specific knockdown (KD) of Zbtb16 or Txnip by adeno-associated virus 9 (AAV9) or Mito-TEMPO treatment. High glucose (HG)-treated HL-1 cells were used to mimic the setting of diabetic in vitro. Zbtb16-Txnip-Trx2 signaling-induced excess ROS release and CaMKII activation were also verified in HL-1 cells under HG condition. Furthermore, atrial-specific Zbtb16 or Txnip-KD reduced incidence and duration of AF in db/db mice. Altogether, we demonstrated that interrupting Zbtb16-Txnip-Trx2 signaling in atrium could decrease AF susceptibility via reducing ROS release and CaMKII activation in the setting of T2DM.

X-Type Ligands Effect on the Operational Stability of Heavy-Metal-Free Quantum Dot Light-Emitting Diodes.

ZnSeTe quantum dots (QDs) offer an efficient avenue for realizing heavy-metal-free light-emitting diodes (LEDs) that meet the Rec.2100 blue standard. Synthetic core-shell engineering has enabled big advances in the external quantum efficiency (EQE) of ZnSeTe QD-LEDs. However, the mechanisms behind the degradation of the operational stability of ZnSeTe QD-LEDs remain relatively unexplored. In this study, we explore the impact of ligand density and composition on both material and device stability. We developed a solid-film ligand exchange utilizing an inorganic X-type ligand (zinc chloride), revealing that the substitution of inorganic ligands for organic counterparts significantly influences the stability of both materials and devices.

A safety and efficacy study of allogeneic haematopoietic stem cell transplantation for refractory and relapsed T-cell acute lymphoblastic leukaemia/lymphoblastic lymphoma patients who achieved complete remission after autologous CD7 chimeric antigen receptor T-cell therapy.

CD7-targeted chimeric antigen receptor T-cell (CAR-T) therapy has shown promising initial complete remission (CR) rates in patients with refractory or relapsed (r/r) T-cell acute lymphoblastic leukaemia and lymphoblastic lymphoma (T-ALL/LBL). To enhance the remission duration, consolidation with allogeneic haematopoietic stem cell transplantation (allo-HSCT) is considered. Our study delved into the outcomes of 34 patients with r/r T-ALL/LBL who underwent allo-HSCT after achieving CR with autologous CD7 CAR-T therapy. These were compared with 124 consecutive T-ALL/LBL patients who received allo-HSCT in CR following chemotherapy. The study revealed that both the CAR-T and chemotherapy cohorts exhibited comparable 2-year overall survival (OS) (61.9% [95% CI, 44.1-78.1] vs. 67.6% [95% CI, 57.5-76.9], p = 0.210), leukaemia-free survival (LFS) (62.3% [95% CI, 44.6-78.4] vs. 62.0% [95% CI, 51.8-71.7], p = 0.548), non-relapse mortality (NRM) rates (32.0% [95% CI, 19.0-54.0] vs. 25.3% [95% CI, 17.9-35.8], p = 0.288) and relapse incidence rates (8.8% [95% CI, 3.0-26.0] vs. 15.8% [95% CI, 9.8-25.2], p = 0.557). Patients aged ≤14 in the CD7 CAR-T group achieved high 2-year OS and LFS rates of 87.5%. Our study indicates that CD7 CAR-T therapy followed by allo-HSCT is not only effective and safe for r/r T-ALL/LBL patients but also on par with the outcomes of those achieving CR through chemotherapy, without increasing NRM.

ADAR-mediated RNA editing regulates PVR immune checkpoint in colorectal cancer.

Recent studies have revealed that tumor immunotherapy resistance is influenced by ADAR-mediated RNA editing, but its targets remain unelucidated. Our current study identified the poliovirus receptor (PVR) oncogene, which encodes an immune checkpoint in colorectal cancer (CRC), as a potential target for RNA editing. We performed transcriptome sequencing analysis and experimental validation in two Chinese CRC cohorts. PVR and ADAR expressions significantly increased in CRC tumors and showed positive correlations in both cohorts, coupled with upregulated PVR RNA editing in CRC tumors. Manipulation of ADAR expression by over-expression or knockdown substantially changed PVR expression and RNA editing in HTC116 CRC cells. Luciferase reporter and actinomycin D assays further revealed that RNA editing in PVR 3'-UTR could upregulate PVR RNA expression, probably by increasing the RNA stability. By increasing PVR expression, ADAR-mediate RNA editing might contribute to tumor- and immune-related gene functions and pathways in CRC. Moreover, a signature combining PVR RNA editing and expression showed promising predictive performance in CRC diagnosis in both Chinese CRC cohorts. Our findings thus highlight the importance of ADAR-mediated RNA editing in PVR up-regulation in CRC tumors and provide new insight into the application of PVR RNA editing as a novel diagnostic biomarker for CRC.

F11R RNA trinucleotide over-edited by ADAR in gastric and colorectal cancers: Cross-cohort validation, gene expression regulation, and diagnostic significance.

The F11 receptor (F11R) gene encoding junctional adhesion molecule A has been associated with gastric cancer (GC) and colorectal cancer (CRC), in which its role and regulation remain to be further elucidated. Recently F11R was also identified as a potential target of adenosine-to-inosine (A-to-I) mediated by the adenosine deaminases acting on RNA (ADARs). Herein, using RNA-Seq and experimental validation, our current study revealed an F11R RNA trinucleotide over-edited by ADAR, with its regulation of gene expression and clinical significance in four GC and three CRC cohorts. Our results found an over-edited AAA trinucleotide in an AluSg located in the F11R 3'-untranslated region (3'-UTR), which showed editing levels correlated with elevated ADAR expression across all GC and CRC cohorts in our study. Overexpression and knockdown of ADAR in GC and CRC cells, followed by RNA-Seq and Sanger sequencing, confirmed the ADAR-mediated F11R 3'-UTR trinucleotide editing, which potentially disrupted an RBM45 binding site identified by crosslinking immunoprecipitation sequencing (CLIP-seq) and regulated F11R expression in luciferase reporter assays. Moreover, the F11R trinucleotide editing showed promising predictive performance for diagnosing GC and CRC across GC and CRC cohorts. Our findings thus highlight both the potential biological and clinical significance of an ADAR-edited F11R trinucleotide in GC and CRC, providing new insights into its application as a novel diagnostic biomarker for both cancers.

Interfacial Dipole Engineering for Energy Level Alignment in NiOx-Based Quantum Dot Light-Emitting Diodes.

The solution-derived non-stoichiometric nickel oxide (NiOx) is a promising hole-injecting material for stable quantum dot light-emitting diodes (QLEDs). However, the carrier imbalance due to the misalignment of energy levels between the NiOx and polymeric hole-transporting layers (HTLs) curtails the device efficiency. In this study, the modification of the NiOx surface is investigated using either 3-cyanobenzoic acid (3-CN-BA) or 4-cyanobenzoic acid (4-CN-BA) in the QLED fabrication. Morphological and electrical analyses revealed that both 4-CN-BA and 3-CN-BA can enhance the work function of NiOx, reduce the oxygen vacancies on the NiOx surface, and facilitate a uniform morphology for subsequent HTL layers. Moreover, it is found that the binding configurations of dipole molecules as a function of the substitution position of the tail group significantly impact the work function of underlying layers. When integrated in QLEDs, the modification layers resulted in a significant improvement in the electroluminescent efficiency due to the enhancement of energy level alignment and charge balance within the devices. Specifically, QLEDs incorporating 4-CN-BA achieved a champion external quantum efficiency (EQE) of 20.34%, which is a 1.8X improvement in comparison with that of the devices utilizing unmodified NiOx (7.28%). Moreover, QLEDs with 4-CN-BA and 3-CN-BA modifications exhibited prolonged operational lifetimes, indicating potential for practical applications.

Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics.

MOTIVATION: The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data. RESULTS: We introduce a statistical spatial transcriptomics cell assignment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments. AVAILABILITY AND IMPLEMENTATION: https://github.com/ChengZ352/SPAN.

UV-A radiation increases biomass yield by enhancing energy flow and carbon assimilation in the edible cyanobacterium Nostoc sphaeroides.

Ultraviolet (UV) A radiation (315-400 nm) is the predominant component of solar UV radiation that reaches the Earth's surface. However, the underlying mechanisms of the positive effects of UV-A on photosynthetic organisms have not yet been elucidated. In this study, we investigated the effects of UV-A radiation on the growth, photosynthetic ability, and metabolome of the edible cyanobacterium Nostoc sphaeroides. Exposures to 5-15 W m-2 (15-46 µmol photons m-2 s-1) UV-A and 4.35 W m-2 (20 µmol photons m-2 s-1) visible light for 16 days significantly increased the growth rate and biomass production of N. sphaeroides cells by 18%-30% and 15%-56%, respectively, compared to the non-UV-A-acclimated cells. Additionally, the UV-A-acclimated cells exhibited a 1.8-fold increase in the cellular nicotinamide adenine dinucleotide phosphate (NADP) pool with an increase in photosynthetic capacity (58%), photosynthetic efficiency (24%), QA re-oxidation, photosystem I abundance, and cyclic electron flow (87%), which further led to an increase in light-induced NADPH generation (31%) and ATP content (83%). Moreover, the UV-A-acclimated cells showed a 2.3-fold increase in ribulose-1,5-bisphosphate carboxylase/oxygenase activity, indicating an increase in their carbon-fixing capacity. Gas chromatography-mass spectrometry-based metabolomics further revealed that UV-A radiation upregulated the energy-storing carbon metabolism, as evidenced by the enhanced accumulation of sugars, fatty acids, and citrate in the UV-A-acclimated cells. Therefore, our results demonstrate that UV-A radiation enhances energy flow and carbon assimilation in the cyanobacterium N. sphaeroides.IMPORTANCEUltraviolet (UV) radiation exerts harmful effects on photo-autotrophs; however, several studies demonstrated the positive effects of UV radiation, especially UV-A radiation (315-400 nm), on primary productivity. Therefore, understanding the underlying mechanisms associated with the promotive effects of UV-A radiation on primary productivity can facilitate the application of UV-A for CO2 sequestration and lead to the advancement of photobiological sciences. In this study, we used the cyanobacterium Nostoc sphaeroides, which has an over 1,700-year history of human use as food and medicine, to explore its photosynthetic acclimation response to UV-A radiation. As per our knowledge, this is the first study to demonstrate that UV-A radiation increases the biomass yield of N. sphaeroides by enhancing energy flow and carbon assimilation. Our findings provide novel insights into UV-A-mediated photosynthetic acclimation and provide a scientific basis for the application of UV-A radiation for optimizing light absorption capacity and enhancing CO2 sequestration in the frame of a future CO2 neutral, circular, and sustainable bioeconomy.

Preparation of Matrix Product States with Log-Depth Quantum Circuits.

We consider the preparation of matrix product states (MPS) on quantum devices via quantum circuits of local gates. We first prove that faithfully preparing translation-invariant normal MPS of N sites requires a circuit depth T=Ω(logN). We then introduce an algorithm based on the renormalization-group transformation to prepare normal MPS with an error ε in depth T=O[log(N/ε)], which is optimal. We also show that measurement and feedback leads to an exponential speedup of the algorithm to T=O[loglog(N/ε)]. Measurements also allow one to prepare arbitrary translation-invariant MPS, including long-range non-normal ones, in the same depth. Finally, the algorithm naturally extends to inhomogeneous MPS.

Na ions doped Fe2VO4 cathode for high performance aqueous zinc-ion batteries.

Aqueous zinc ion batteries are thought to be a new generation of secondary batteries that will replace lithium ion batteries due to their great safety and inexpensive cost. In the cathode materials of aqueous zinc ion batteries with long life and high capacity, abundant active sites and crystal structure stability play an important role. In the present work, the strategy of Na+ intercalation of Fe2VO4 (FVO) is proposed, aiming at the insertion of Na+, which not only enriches the active sites, but also sodium and iron ions act as guest species with the negatively charged VOx lattice to provide strong electrostatic attraction to stabilize the lamellar structure. In terms of electrochemical performance, the discharge specific capacity is 370 mAh g-1 at a current density of 0.1 A g-1, and when the current density is arising 5 A g-1, the specific capacity also reaches 200 mAh g-1 after cycling 2000 with a capacity retention of 99%, which is better than the electrochemical performance of Fe2VO4 (FVO) alone at 50 mAh g-1. The superior electrochemical performance proves that FVO-Na is an ideal cathode material for zinc ion batteries.