Fusion Step Research Articles

Fusing multiplex heterogeneous networks using graph attention-aware fusion networks

Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. Popular GNN-based architectures operate on networks of single node and edge type. However, a large number of real-world networks include multiple types of nodes and edges. Enabling these architectures to work on networks with multiple node and edge types brings additional challenges due to the heterogeneity of the networks and the multiplicity of the existing associations. In this study, we present a framework, named GRAF (Graph Attention-aware Fusion Networks), to convert multiplex heterogeneous networks to homogeneous networks to make them more suitable for graph representation learning. Using attention-based neighborhood aggregation, GRAF learns the importance of each neighbor per node (called node-level attention) followed by the importance of each network layer (called network layer-level attention). Then, GRAF processes a network fusion step weighing each edge according to the learned attentions. After an edge elimination step based on edge weights, GRAF utilizes Graph Convolutional Networks (GCN) on the fused network and incorporates node features on graph-structured data for a node classification or a similar downstream task. To demonstrate GRAF’s generalizability, we applied it to four datasets from different domains and observed that GRAF outperformed or was on par with the baselines and state-of-the-art (SOTA) methods. We were able to interpret GRAF’s findings utilizing the attention weights. Source code for GRAF is publicly available at https://github.com/bozdaglab/GRAF.

Formation of a giant unilocular vacuole via macropinocytosis-like process confers anoikis resistance

Cell survival in metazoans depends on cell attachment to the extracellular matrix (ECM) or to neighboring cells. Loss of such attachment triggers a type of programmed cell death known as anoikis, the acquisition of resistance to which is a key step in cancer development. The mechanisms underlying anoikis resistance remain unclear, however. The intracellular F-actin cytoskeleton plays a key role in sensing the loss of cell–ECM attachment, but how its disruption affects cell fate during such stress is not well understood. Here, we reveal a cell survival strategy characterized by the formation of a giant unilocular vacuole (GUVac) in the cytoplasm of the cells whose actin cytoskeleton is disrupted during loss of matrix attachment. Time-lapse imaging and electron microscopy showed that large vacuoles with a diameter of >500 nm accumulated early after inhibition of actin polymerization in cells in suspension culture, and that these vacuoles subsequently coalesced to form a GUVac. GUVac formation was found to result from a variation of a macropinocytosis-like process, characterized by the presence of inwardly curved membrane invaginations. This phenomenon relies on both F-actin depolymerization and the recruitment of septin proteins for micron-sized plasma membrane invagination. The vacuole fusion step during GUVac formation requires PI(3)P produced by VPS34 and PI3K-C2α on the surface of vacuoles. Furthermore, its induction after loss of matrix attachment conferred anoikis resistance. Our results thus show that the formation of a previously unrecognized organelle promotes cell survival in the face of altered actin and matrix environments.

Formation of a giant unilocular vacuole via macropinocytosis-like process confers anoikis resistance.

Cell survival in metazoans depends on cell attachment to the extracellular matrix (ECM) or to neighboring cells. Loss of such attachment triggers a type of programmed cell death known as anoikis, the acquisition of resistance to which is a key step in cancer development. The mechanisms underlying anoikis resistance remain unclear, however. The intracellular F-actin cytoskeleton plays a key role in sensing the loss of cell-ECM attachment, but how its disruption affects cell fate during such stress is not well understood. Here, we reveal a cell survival strategy characterized by the formation of a giant unilocular vacuole (GUVac) in the cytoplasm of the cells whose actin cytoskeleton is disrupted during loss of matrix attachment. Time-lapse imaging and electron microscopy showed that large vacuoles with a diameter of >500 nm accumulated early after inhibition of actin polymerization in cells in suspension culture, and that these vacuoles subsequently coalesced to form a GUVac. GUVac formation was found to result from a variation of a macropinocytosis-like process, characterized by the presence of inwardly curved membrane invaginations. This phenomenon relies on both F-actin depolymerization and the recruitment of septin proteins for micron-sized plasma membrane invagination. The vacuole fusion step during GUVac formation requires PI(3)P produced by VPS34 and PI3K-C2α on the surface of vacuoles. Furthermore, its induction after loss of matrix attachment conferred anoikis resistance. Our results thus show that the formation of a previously unrecognized organelle promotes cell survival in the face of altered actin and matrix environments.

Regulation of autophagy by Rab27B in colorectal cancer

Autophagy is a cellular recycling process that is associated with tumor growth, anti-tumor immune responses, and therapy resistance in colorectal cancer (CRC). In this report, we identify the small GTPase Rab27B to control the autophagy process in CRC. Depletion of Rab27B showed an abnormal accumulation of autophagy vesicles and increased autophagy markers in CRC cells, indicating autophagy dysregulation. Image analysis indicated that autophagy flux is blocked at the autophagosome/lysosome fusion step when Rab27B is lost. While Rab27B deficient cells are proficient at growth under 2D in vitro conditions, cell growth was significantly impacted in both in vitro 3D growth and in vivo tumorigenesis studies. Together, these results demonstrate a new role of Rab27B in the autophagy trafficking process in CRC and identify Rab27B as a potential therapeutic target for CRC.

Role of Complexin 2 in the regulation of hormone secretion from the islet of Langerhans.

Regulated secretion of insulin from β-cells, glucagon from α-cells, and somatostatin from δ-cells is necessary for the maintenance of glucose homeostasis. The release of these hormones from pancreatic islet cells requires the assembly and disassembly of the SNARE protein complex to control vesicle fusion and exocytosis. Complexin 2 (Cplx 2) is a small soluble synaptic protein that participates in the priming and release steps of vesicle fusion. It plays a dual role as a molecular switch that first clamps and prevents fusion pore opening, and subsequently undergoes a conformational change upon Ca 2+ binding to synaptotagmin to facilitate exocytosis. Using a Cplx 2 knockout (KO) mouse model, we show a direct inhibitory role of Cplx 2 for glucagon and somatostatin secretion, along with an indirect role in the paracrine inhibition of insulin secretion by somatostatin. Deletion of Cplx 2 increases glucagon and somatostatin secretion from intact mouse islets, while there is no difference in insulin secretion between WT and Cplx 2 KO islets. The normal paracrine inhibition of insulin secretion by somatostatin is disrupted in Cplx 2 KO islets. On the contrary, deletion of Cplx 2 did not affect the known role of somatostatin in the paracrine inhibition of glucagon at elevated glucose levels, since the paracrine inhibition of glucagon secretion by somatostatin is similar for both WT and Cplx 2 KO islets. In both β- and α-cells, the secretion profiles are parallel to Ca 2+ activity changes following somatostatin treatment of WT and Cplx 2 KO islets. The loss of paracrine inhibition of insulin secretion is substantiated by direct measurements of insulin vesicle fusion events in Cplx 2 KO islets. Together, these data show a differential role for Cplx 2 in regulating hormone secretion from pancreatic islets.

Conserved residues of the immunosuppressive domain of MLV are essential for regulating the fusion-critical SU-TM disulfide bond.

The Env protein of murine leukemia virus (MLV) is the prototype of a large clade of retroviral fusogens, collectively known as gamma-type Envs. Gamma-type Envs are found in retroviruses and endogenous retroviruses (ERVs) representing a broad range of vertebrate hosts. All gamma-type Envs contain a highly conserved stretch of 26-residues in the transmembrane subunit (TM) comprising two motifs, a putative immunosuppressive domain (ISD) and a CX6CC motif. Extraordinary conservation of the ISD and its invariant association with the CX6CC suggests a fundamental contribution to Env function. To investigate ISD function, we characterized several mutants with single amino acid substitutions at conserved positions in the MLV ISD. A majority abolished infectivity, although we did not observe a corresponding loss in intrinsic ability to mediate membrane fusion. Ratios of the surface subunit (SU) to capsid protein (CA) in virions were diminished for a majority of the ISD mutants, while TM:CA ratios were similar to wild type. Specific loss of SU reflected premature isomerization of the labile disulfide bond that links SU and TM prior to fusion. Indeed, all non-infectious mutants displayed significantly lower disulfide stability than wild-type Env. These results reveal a role for ISD positions 2, 3, 4, 7, and 10 in regulating a late step in entry after fusion peptide insertion but prior to creation of the fusion pore. This implies that the ISD is part of a larger domain, comprising the ISD and CX6CC motifs, that is critical for the formation and regulation of the metastable, intersubunit disulfide bond.IMPORTANCEThe gamma-type Env is a prevalent viral fusogen, found within retroviruses and endogenous retroviruses across vertebrate species and in filoviruses such as Ebolavirus. The fusion mechanism of gamma-type Envs is unique from other Class I fusogens such as those of influenza A virus and HIV-1. Gamma-type Envs contain a hallmark feature known as the immunosuppressive domain (ISD) that has been the subject of some controversy in the literature surrounding its putative immunosuppressive effects. Despite the distinctive conservation of the ISD, little has been done to investigate the role of this region for the function of this widespread fusogen. Our work demonstrates the importance of the ISD for the function of gamma-type Envs in infection, particularly in regulating the intermediate steps of membrane fusion. Understanding the fusion mechanism of gamma-type Envs has broad implications for understanding the entry of extant viruses and aspects of host biology connected to co-opted endogenous gamma-type Envs.

Developing a workflow for the isolation of hybridoma cells producing fully human antigen-specific antibodies using a surface IgG detection method

The antigen-mediated B cell isolation method, based on the detection of surface IgG (sIgG), has increased the efficiency of therapeutic antibody (Ab) discovery. However, the reduction in sIgG expression on B cells during plasma cell differentiation presents challenges as it enables Ab production from only a small subset of B cells (e.g., memory B cells). The present study aimed to addressed this problem by developing a workflow to isolate human-IgG-secreting hybridoma cells produced by cell fusion, the majority of which express sIgG. We showed that our sIgG-based antigen-coated bead separation method efficiently enriched hybridoma cells expressing antigen-specific Abs with a yield of 83.5% (from the cell fusion pool) and a positive rate of 73.2%. Furthermore, because the separation could be performed after only a short (1–2-day) culture period following cell fusion, diverse hybridoma clones could be obtained, minimizing clonal selection and the incidence of duplicates. Given that the expression of membrane-bound IgG and sIgG are regulated by different splicing mechanisms, we speculate that the cell fusion step potentially attenuated the suppression of human sIgG expression. Overall, our proposed method is expected to markedly improve the efficiency of therapeutic Ab candidate production, which will have important clinical implications.

Gut symbiont-derived sphingosine modulates vector competence in Aedes mosquitoes

The main vectors of Zika virus (ZIKV) and dengue virus (DENV) are Aedes aegypti and Ae. albopictus, with Ae. aegypti being more competent. However, the underlying mechanisms remain unclear. Here, we find Ae. albopictus shows comparable vector competence to ZIKV/DENV with Ae. aegypti by blood-feeding after antibiotic treatment or intrathoracic injection. This suggests that midgut microbiota can influence vector competence. Enterobacter hormaechei_B17 (Eh_B17) is isolated from field-collected Ae. albopictus and conferred resistance to ZIKV/DENV infection in Ae. aegypti after gut-transplantation. Sphingosine, a metabolite secreted by Eh_B17, effectively suppresses ZIKV infection in both Ae. aegypti and cell cultures by blocking viral entry during the fusion step, with an IC50 of approximately 10 μM. A field survey reveals that Eh_B17 preferentially colonizes Ae. albopictus compared to Ae. aegypti. And field Ae. albopictus positive for Eh_B17 are more resistant to ZIKV infection. These findings underscore the potential of gut symbiotic bacteria, such as Eh_B17, to modulate the arbovirus vector competence of Aedes mosquitoes. As a natural antiviral agent, Eh_B17 holds promise as a potential candidate for blocking ZIKV/DENV transmission.

Toxoplasma FER1 is a versatile and dynamic mediator of differential microneme trafficking and microneme exocytosis

Toxoplasma gondii is a polarized cell concentrating several secretory organelles at the apical pole. The secretory micronemes come in two sub-populations differentiated by dependence on Rab5A/C in their biogenesis. Calcium-dependent exocytosis of micronemes occurs at the very apical tip and is critical for parasite egress from its host cell, adhesion and invasion of the next cell. Ferlins represent a protein family with roles in exocytosis containing multiple Ca2+-sensing C2 domains. We determined that T. gondii’s ferlin 1 (FER1) localized dynamically to the parasite’s secretory pathway. FER1 function was dissected by dominant negative overexpression strategies. We demonstrated that FER1 traffics microneme organelles along the following trajectories: (1) Along the cortex to the apical end; (2) To the apical tip for fusion with the plasma membrane; (3) Differential microneme sub-population traffic, and that FER1 could putatively be responsible for microneme protein trafficking. (4) From the trans-Golgi-endosomal network to the subpellicular cortex; (5) Retrograde transport allowing microneme recycling from mother to daughter. Finally, FER1 overexpression triggers a microneme exocytosis burst, supporting the notion that the radially organized micronemes at the apical tip comprise a readily-releasable microneme pool. In summary, FER1 is pivotal for dynamic microneme trafficking, acts differently on the two microneme subpopulations, and acts on the plasma membrane fusion step during microneme exocytosis.

Multimodal registration network with multi-scale feature-crossing.

A critical piece of information for prostate intervention and cancer treatment is provided by the complementary medical imaging modalities of ultrasound (US) and magnetic resonance imaging (MRI). Therefore, MRI-US image fusion is often required during prostate examination to provide contrast-enhanced TRUS, in which image registration is a key step in multimodal image fusion. We propose a novel multi-scale feature-crossing network for the prostate MRI-US image registration task. We designed a feature-crossing module to enhance information flow in the hidden layer by integrating intermediate features between adjacent scales. Additionally, an attention block utilizing three-dimensional convolution interacts information between channels, improving the correlation between different modal features. We used 100 cases randomly selected from The Cancer Imaging Archive (TCIA) for our experiments. A fivefold cross-validation method was applied, dividing the dataset into five subsets. Four subsets were used for training, and one for testing, repeating this process five times to ensure each subset served as the test set once. We test and evaluate our technique using fivefold cross-validation. The cross-validation trials result in a median target registration error of 2.20 mm on landmark centroids and a median Dice of 0.87 on prostate glands, both of which were better than the baseline model. In addition, the standard deviation of the dice similarity coefficient is 0.06, which suggests that the model is stable. We propose a novel multi-scale feature-crossing network for the prostate MRI-US image registration task. A random selection of 100 cases from The Cancer Imaging Archive (TCIA) was used to test and evaluate our approach using fivefold cross-validation. The experimental results showed that our method improves the registration accuracy. After registration, MRI and TURS images were more similar in structure and morphology, and the location and morphology of cancer were more accurately reflected in the images.

Lipid Selectivity of Membrane Action of the Fragments of Fusion Peptides of Marburg and Ebola Viruses.

The life cycle of Ebola and Marburg viruses includes a step of the virion envelope fusion with the cell membrane. Here, we analyzed whether the fusion of liposome membranes under the action of fragments of fusion peptides of Ebola and Marburg viruses depends on the composition of lipid vesicles. A fluorescence assay and electron microscopy were used to quantify the fusogenic activity of the virus fusion peptides and to identify the lipid determinants affecting membrane merging. Differential scanning calorimetry of lipid phase transitions revealed alterations in the physical properties of the lipid matrix produced by virus fusion peptides. Additionally, we found that plant polyphenols, quercetin, and myricetin inhibited vesicle fusion induced by the Marburg virus fusion peptide.

Endochondral Ossification for Spinal Fusion: A Novel Perspective from Biological Mechanisms to Clinical Applications.

Degenerative scoliosis (DS), encompassing conditions like spondylolisthesis and spinal stenosis, is a common type of spinal deformity. Lumbar interbody fusion (LIF) stands as a conventional surgical intervention for this ailment, aiming at decompression, restoration of intervertebral height, and stabilization of motion segments. Despite its widespread use, the precise mechanism underlying spinal fusion remains elusive. In this review, our focus lies on endochondral ossification for spinal fusion, a process involving vertebral development and bone healing. Endochondral ossification is the key step for the successful vertebral fusion. Endochondral ossification can persist in hypoxic conditions and promote the parallel development of angiogenesis and osteogenesis, which corresponds to the fusion process of new bone formation in the hypoxic region between the vertebrae. The ideal material for interbody fusion cages should have the following characteristics: (1) Good biocompatibility; (2) Stable chemical properties; (3) Biomechanical properties similar to bone tissue; (4) Promotion of bone fusion; (5) Favorable for imaging observation; (6) Biodegradability. Utilizing cartilage-derived bone-like constructs holds promise in promoting bony fusion post-operation, thus warranting exploration in the context of spinal fusion procedures.

Structural transition of GP64 triggered by a pH-sensitive multi-histidine switch

The fusion of viruses with cellular membranes is a critical step in the life cycle of enveloped viruses. This process is facilitated by viral fusion proteins, many of which are conformationally pH-sensitive. The specifics of how changes in pH initiate this fusion have remained largely elusive. This study presents the cryo-electron microscopy (cryo-EM) structures of a prototype class III fusion protein, GP64, in its prefusion and early intermediate states, revealing the structural intermediates accompanying the membrane fusion process. The structures identify the involvement of a pH-sensitive switch, comprising H23, H245, and H304, in sensing the low pH that triggers the initial step of membrane fusion. The pH sensing role of this switch is corroborated by assays of cell-cell syncytium formation and dual dye-labeling. The findings demonstrate that coordination between multiple histidine residues acts as a pH sensor and activator. The involvement of a multi-histidine switch in viral fusion is applicable to fusogens of human-infecting thogotoviruses and other viruses, which could lead to strategies for developing anti-viral therapies and vaccines.

A self-supervised entity alignment framework via attribute correction

Entity alignment (EA), aiming to match entities with the same meaning across different knowledge graphs (KGs), is a critical step in knowledge fusion. Existing EA methods usually encode the multi-aspect features of entities as embeddings and learn to align the embeddings with supervised learning. Although these methods have achieved remarkable results, two issues have not been well addressed. Firstly, these methods require pre-aligned entity pairs to perform EA tasks, limiting their applicability in practice. Secondly, these methods overlook the unique contribution of digital attributes to EA tasks when utilising attribute information to enhance entity features. In this paper, we propose a self-supervised entity alignment framework via attribute correction. Specifically, we first design a highly effective seed pair generator based on multi-aspect features of entities to solve the labour-intensive problem of obtaining pre-aligned entity pairs. Then, a novel alignment mechanism via attribute correction is proposed to address the problem that different types of attributes have different contributions to the EA task. Extensive experiments on real-world datasets with semantic features demonstrate that our framework outperforms state-of-the-art (SOTA) EA tasks.

Hybrid Long Short-Term Memory Fused Convolution Neural Network for Weather Forecasting

Weather forecasting is an effort by meteorologists to predict whether states at a few prospective times and the conditions of weather that might be estimated. With new modern technology, present weather forecasting methods are highly precise. To achieve high accuracy, the methods developed for weather forecasting were much more complicated owing to many factors. Here, the usage of time series data for weather forecasting is done by the devised Long-Short Term Memory fused Convolutional neural network (LSTMFCNN). At first, the acquisition of input time series data from the specific dataset is done. In The feature extraction technical features are done by considering the input time series data. Then, the feature extraction is done utilizing the Rider Optimization Algorithm-Based Neural Network (RideNN) with the Soergel metric. RideNN is the integration of the Rider Optimization Algorithm (ROA) with the Neural Network (NN) classifier. Thus, the feature fusion step reduces the complexity and improves the accuracy. Thereafter, the oversampling technique is utilized for the data augmentation (DA) process. Finally, weather forecasting is done utilizing the newly designed LSTMFCNN and is obtained by the integration of Convolutional Neural Network (CNN) and Deep Long-Short Term Memory (DLSTM).

Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features

BackgroundCancer pathology shows disease development and associated molecular features. It provides extensive phenotypic information that is cancer-predictive and has potential implications for planning treatment. Based on the exceptional performance of computational approaches in the field of digital pathogenic, the use of rich phenotypic information in digital pathology images has enabled us to identify low-level gliomas (LGG) from high-grade gliomas (HGG). Because the differences between the textures are so slight, utilizing just one feature or a small number of features produces poor categorization results.MethodsIn this work, multiple feature extraction methods that can extract distinct features from the texture of histopathology image data are used to compare the classification outcomes. The successful feature extraction algorithms GLCM, LBP, multi-LBGLCM, GLRLM, color moment features, and RSHD have been chosen in this paper. LBP and GLCM algorithms are combined to create LBGLCM. The LBGLCM feature extraction approach is extended in this study to multiple scales using an image pyramid, which is defined by sampling the image both in space and scale. The preprocessing stage is first used to enhance the contrast of the images and remove noise and illumination effects. The feature extraction stage is then carried out to extract several important features (texture and color) from histopathology images. Third, the feature fusion and reduction step is put into practice to decrease the number of features that are processed, reducing the computation time of the suggested system. The classification stage is created at the end to categorize various brain cancer grades. We performed our analysis on the 821 whole-slide pathology images from glioma patients in the Cancer Genome Atlas (TCGA) dataset. Two types of brain cancer are included in the dataset: GBM and LGG (grades II and III). 506 GBM images and 315 LGG images are included in our analysis, guaranteeing representation of various tumor grades and histopathological features.ResultsThe fusion of textural and color characteristics was validated in the glioma patients using the 10-fold cross-validation technique with an accuracy equals to 95.8%, sensitivity equals to 96.4%, DSC equals to 96.7%, and specificity equals to 97.1%. The combination of the color and texture characteristics produced significantly better accuracy, which supported their synergistic significance in the predictive model. The result indicates that the textural characteristics can be an objective, accurate, and comprehensive glioma prediction when paired with conventional imagery.ConclusionThe results outperform current approaches for identifying LGG from HGG and provide competitive performance in classifying four categories of glioma in the literature. The proposed model can help stratify patients in clinical studies, choose patients for targeted therapy, and customize specific treatment schedules.

TS-align: A temporal similarity-aware entity alignment model for temporal knowledge graphs

Entity Alignment (EA) is a crucial step in knowledge graph fusion, aiming to match equivalent entity pairs across different knowledge graphs (KGs). In recent years, Temporal Knowledge Graphs (TKGs) have extended static KGs by introducing timestamps, providing a new perspective for EA. However, while temporal information is critical in TKGs, the EA approach to existing TKG does not fully utilize this critical resource. To solve this problem, we propose a temporal similarity-aware EA model for TKGs (TS-Align). The model innovatively uses the overlap degree of multiple temporal points and temporal intervals of entities to calculate the temporal similarity matrix, and deeply digs and integrates the temporal and relational information in TKGs through a well-designed encoder-decoder architecture. To verify the effectiveness of TS-Align, we conducted experiments on four standard datasets and compared it to other advanced methods. The experimental results show that the accuracy of TS-Align on EA tasks is significantly improved compared with other methods. Specifically, TS-Align is about 0.4 –1.8 % higher on the Hit@1 metric compared to the most advanced time-aware models. This is about 7.4 –17.6 % higher than the most advanced GCN-based models. This is about 20.8 –42.3 % higher than the most advanced translation-based models. In addition, through ablation experiments, we verify that the proposed temporal similarity matrix, relational embedding, and entity embedding contribute to the significant improvement in performance.

X-ray pneumonia detection using angular and radial local binary patterns fusion

Detecting pneumonia via X-ray images is a crucial phase in determining any probable bacterial or viral disease. The automation of this stage is critical since it speeds up the diagnosing process. LBPs have excelled in investigating local conditions within a neighborhood, where they aggregate and analyze local statistical results. In this paper, we present a novel family of LBP descriptors based on local accumulative pixel disparities. We construct the new descriptors using a circular neighborhood acquired from the initial square filter. These descriptors consider angular and radial transitions that occur in both the microstructures and macrostructures of image textures. Eventually, a more accurate and comprehensive visual representation is obtained. Furthermore, we offer a data fusion step based on a voting mechanism to integrate the retrieved data efficiently. We show many types of analyses that demonstrate LBP's and the suggested extensions' capability to extract discriminant features from X-ray images. The suggested method is tested on two different datasets with large diversities that include images from various demographics and regions. Several measures, including accuracy rate, precision, sensitivity, F-measure, and specificity, are used to evaluate the system's efficacy. According to the testing results, the proposed system provides a best successful recognition rate of 82.7 %.

MARCH1 and MARCH2 inhibit pseudorabies virus replication by trapping the viral cell-to-cell fusion complex in trans-Golgi network

The membrane-associated RING-CH (MARCH) family of proteins are members of the E3 ubiquitin ligase family and are essential for a variety of biological functions. Currently, MARCH proteins are discovered to execute antiviral functions by directly triggering viral protein degradation or blocking the furin cleavage of viral class I fusion proteins. Here, we report a novel antiviral mechanism of MARCH1 and MARCH2 (MARCH1/2) in the replication of Pseudorabies virus (PRV), a member of the Herpesviridae family. We discovered MARCH1/2 restrict PRV replication at the cell-to-cell fusion step. Furthermore, MARCH1/2 block gB cleavage, and this is dependent on their E3 ligase activity. Interestingly, the blocking of gB cleavage by MARCH1/2 does not contribute to their antiviral activity in vitro. We discovered that MARCH1/2 are associated with the cell-to-cell fusion complex of gB, gD, gH, and gL and trap these viral proteins in the trans-Golgi network (TGN) rather than degrading them. Overall, we conclude that MARCH1/2 inhibit PRV by trapping the viral cell-to-cell fusion complex in TGN.

Structure of the endosomal CORVET tethering complex

Cells depend on their endolysosomal system for nutrient uptake and downregulation of plasma membrane proteins. These processes rely on endosomal maturation, which requires multiple membrane fusion steps. Early endosome fusion is promoted by the Rab5 GTPase and its effector, the hexameric CORVET tethering complex, which is homologous to the lysosomal HOPS. How these related complexes recognize their specific target membranes remains entirely elusive. Here, we solve the structure of CORVET by cryo-electron microscopy and revealed its minimal requirements for membrane tethering. As expected, the core of CORVET and HOPS resembles each other. However, the function-defining subunits show marked structural differences. Notably, we discover that unlike HOPS, CORVET depends not only on Rab5 but also on phosphatidylinositol-3-phosphate (PI3P) and membrane lipid packing defects for tethering, implying that an organelle-specific membrane code enables fusion. Our data suggest that both shape and membrane interactions of CORVET and HOPS are conserved in metazoans, thus providing a paradigm how tethering complexes function.