Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

Human MARCH1, 2, and 8 block Ebola virus envelope glycoprotein cleavage via targeting furin P domain.

Membrane-associated RING-CH (MARCH) family proteins were recently reported to inhibit viral replication through multiple modes. Previous work showed that human MARCH8 blocked Ebola virus (EBOV) glycoprotein (GP) maturation. Our study here demonstrates that human MARCH1 and MARCH2 share a similar pattern to MARCH8 in restricting EBOV GP-pseudotyped viral infection. Human MARCH1 and MARCH2 retain EBOV GP at the trans-Golgi network, reduce its cell surface display, and impair EBOV GP-pseudotyped virions infectivity. Furthermore, we uncover that the host proprotein convertase furin could interact with human MARCH1/2 and EBOV GP intracellularly. Importantly, the furin P domain is verified to be recognized by MARCH1/2/8, which is critical for their blocking activities. Besides, bovine MARCH2 and murine MARCH1 also impair EBOV GP proteolytic processing. Altogether, our findings confirm that MARCH1/2 proteins of different mammalian origins showed a relatively conserved feature in blocking EBOV GP cleavage, which could provide clues for subsequent MARCHs antiviral studies and may facilitate the development of novel strategies to antagonize enveloped virus infection.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

The emerging roles of MARCH8 in viral infections: A double-edged Sword.

The host cell membrane-associated RING-CH 8 protein (MARCH8), a member of the E3 ubiquitin ligase family, regulates intracellular turnover of many transmembrane proteins and shows potent antiviral activities. Generally, 2 antiviral modes are performed by MARCH8. On the one hand, MARCH8 catalyzes viral envelope glycoproteins (VEGs) ubiquitination and thus leads to their intracellular degradation, which is the cytoplasmic tail (CT)-dependent (CTD) mode. On the other hand, MARCH8 traps VEGs at some intracellular compartments (such as the trans-Golgi network, TGN) but without inducing their degradation, which is the cytoplasmic tail-independent (CTI) mode, by which MARCH8 hijacks furin, a cellular proprotein convertase, to block VEGs cleavage. In addition, the MARCH8 C-terminal tyrosine-based motif (TBM) 222YxxL225 also plays a key role in its CTI antiviral effects. In contrast to its antiviral potency, MARCH8 is occasionally hijacked by some viruses and bacteria to enhance their invasion, indicating a duplex role of MARCH8 in host pathogenic infections. This review summarizes MARCH8's antiviral roles and how viruses evade its restriction, shedding light on novel antiviral therapeutic avenues.

KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder.

Circular RNA (circRNA) plays an important role in the diagnosis, treatment, and prognosis of human diseases. The discovery of potential circRNA-miRNA interactions (CMI) is of guiding significance for subsequent biological experiments. Limited by the small amount of experimentally supported data and high randomness, existing models are difficult to accomplish the CMI prediction task based on real cases. In this paper, we propose KS-CMI, a novel method for effectively accomplishing CMI prediction in real cases. KS-CMI enriches the 'behavior relationships' of molecules by constructing circRNA-miRNA-cancer (CMCI) networks and extracts the behavior relationship attribute of molecules based on balance theory. Next, the denoising autoencoder (DAE) is used to enhance the feature representation of molecules. Finally, the CatBoost classifier was used for prediction. KS-CMI achieved the most reliable prediction results in real cases and achieved competitive performance in all datasets in the CMI prediction.

Host antiviral factors hijack furin to block SARS-CoV-2, ebola virus, and HIV-1†glycoproteins cleavage.

Viral envelope glycoproteins are crucial for viral infections. In the process of enveloped viruses budding and release from the producer cells, viral envelope glycoproteins are presented on the viral membrane surface as spikes, promoting the virus's next-round infection of target cells. However, the host cells evolve counteracting mechanisms in the long-term virus-host co-evolutionary processes. For instance, the host cell antiviral factors could potently suppress viral replication by targeting their envelope glycoproteins through multiple channels, including their intracellular synthesis, glycosylation modification, assembly into virions, and binding to target cell receptors. Recently, a group of studies discovered that some host antiviral proteins specifically recognized host proprotein convertase (PC) furin and blocked its cleavage of viral envelope glycoproteins, thus impairing viral infectivity. Here, in this review, we briefly summarize several such host antiviral factors and analyze their roles in reducing furin cleavage of viral envelope glycoproteins, aiming at providing insights for future antiviral studies.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes.

Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convolutional neural networks, combines node similarity to form node information and then predicts associated nodes using GCN with a distributive contribution mechanism. The model can be used not only to predict the molecular level of circRNA-miRNA interactions but also to predict circRNA-cancer and circRNA-gene associations. The AUCs of circRNA-miRNA, circRNA-disease, and circRNA-gene associations in the five-fold cross-validation experiment of SGCNCMI is 89.42%, 84.18%, and 82.44%, respectively. SGCNCMI is one of the few models in this field and achieved the best results. In addition, in our case study, six of the top ten relationship pairs with the highest prediction scores were verified in PubMed.

BioChemDDI: Predicting Drug-Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism.

During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other's mechanisms of action, correctly identifying potential drug-drug interactions (DDIs) is important to avoid a reduction in drug therapeutic activities and serious injuries to the organism. Therefore, to explore potential DDIs, we develop a computational method of integrating multi-level information. Firstly, the information of chemical sequence is fully captured by the Natural Language Processing (NLP) algorithm, and multiple biological function similarity information is fused by Similarity Network Fusion (SNF). Secondly, we extract deep network structure information through Hierarchical Representation Learning for Networks (HARP). Then, a highly representative comprehensive feature descriptor is constructed through the self-attention module that efficiently integrates biochemical and network features. Finally, a deep neural network (DNN) is employed to generate the prediction results. Contrasted with the previous supervision model, BioChemDDI innovatively introduced graph collapse for extracting a network structure and utilized the biochemical information during the pre-training process. The prediction results of the benchmark dataset indicate that BioChemDDI outperforms other existing models. Moreover, the case studies related to three cancer diseases, including breast cancer, hepatocellular carcinoma and malignancies, were analyzed using BioChemDDI. As a result, 24, 18 and 20 out of the top 30 predicted cancer-related drugs were confirmed by the databases. These experimental results demonstrate that BioChemDDI is a useful model to predict DDIs and can provide reliable candidates for biological experiments. The web server of BioChemDDI predictor is freely available to conduct further studies.

GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences.

BACKGROUND: Recent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological "haystack" merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale. RESULTS: Based on the known microbe-disease associations derived from the Human Microbe-Disease Association Database (HMDAD), the proposed model shows reliable performance with high values of the area under ROC curve (AUC) of 0.9456 and 0.8866 in leave-one-out cross validations and five-fold cross validations, respectively. In case studies of colorectal carcinoma, 80% out of the top-20 predicted microbes have been experimentally confirmed via published literatures. CONCLUSION: Based on the assumption that functionally similar microbes tend to share the similar interaction patterns with human diseases, we here propose a group based computational model of Bayesian disease-oriented ranking to prioritize the most potential microbes associating with various human diseases. Based on the sequence information of genes, two computational approaches (BLAST+ and MEGA 7) are leveraged to measure the microbe-microbe similarity from different perspectives. The disease-disease similarity is calculated by capturing the hierarchy information from the Medical Subject Headings (MeSH) data. The experimental results illustrate the accuracy and effectiveness of the proposed model. This work is expected to facilitate the characterization and identification of promising microbial biomarkers.

Blocking core fucosylation of epidermal growth factor (EGF) receptor prevents peritoneal fibrosis progression.

OBJECTIVE: Peritoneal fibrosis (PF) ultimately causes ultrafiltration failure and peritoneal dialysis (PD) termination, but there are few effective therapies for it. Core fucosylation, which is catalyzed by α1,6-fucosyltransferase (Fut8) in mammals, may play a crucial role in PF development. This study aims to assess the effects of inhibiting core fucosylation of epidermal growth factor (EGF) receptor on PF rats. METHODS: PF rats (established by 4.25% glucose dialysate) were treated with either an adenovirus-Fut8 short hairpin RNA (Fut8shRNA) or adenovirus-control. Masson's staining and net ultrafiltration were performed at week six. Fut8 level and core fucosylation of EGF receptor and collagen I in the peritoneal membrane were assessed, and EGF signaling was detected, including signal transducer and activator of transcription 3 (STAT3), nuclear factor kappa B (NF-κB) and their phosphorylation. Monocyte chemoattractant protein-1 (MCP-1) in peritoneal effluent was examined. RESULTS: Fut8 was upregulated in PF rats but decreased after Fut8shRNA treatment. EGF and EGF receptor expression was upregulated in PF rats, while core fucosylation of EGF receptor decreased after Fut8shRNA treatment. Masson's staining results showed an increase in peritoneal thickness in PF rats but a decrease after Fut8shRNA treatment. Fut8shRNA treatment increased net ultrafiltration, reduced the expression of collagen I and MCP-1 compared to PF rats. Fut8shRNA treatment suppressed phosphorylation of STAT3 and NF-κB in the peritoneal membrane of PF rats. CONCLUSIONS: Fut8shRNA treatment ameliorated the fibrotic changes in PF rats. A potential mechanism may be that Fut8shRNA treatment inactivated EGF signaling pathway by suppressing the phosphorylation of STAT3 and NF-κB.

MARCH8 Inhibits Ebola Virus Glycoprotein, Human Immunodeficiency Virus Type 1 Envelope Glycoprotein, and Avian Influenza Virus H5N1 Hemagglutinin Maturation.

Membrane-associated RING-CH-type 8 (MARCH8) strongly blocks human immunodeficiency virus type 1 (HIV-1) envelope glycoprotein (Env) incorporation into virions by downregulating its cell surface expression, but the mechanism is still unclear. We now report that MARCH8 also blocks the Ebola virus (EBOV) glycoprotein (GP) incorporation via surface downregulation. To understand how these viral fusion proteins are downregulated, we investigated the effects of MARCH8 on EBOV GP maturation and externalization via the conventional secretion pathway. MARCH8 interacted with EBOV GP and furin when detected by immunoprecipitation and retained the GP/furin complex in the Golgi when their location was tracked by a bimolecular fluorescence complementation (BiFC) assay. MARCH8 did not reduce the GP expression or affect the GP modification by high-mannose N-glycans in the endoplasmic reticulum (ER), but it inhibited the formation of complex N-glycans on the GP in the Golgi. Additionally, the GP O-glycosylation and furin-mediated proteolytic cleavage were also inhibited. Moreover, we identified a novel furin cleavage site on EBOV GP and found that only those fully glycosylated GPs were processed by furin and incorporated into virions. Furthermore, the GP shedding and secretion were all blocked by MARCH8. MARCH8 also blocked the furin-mediated cleavage of HIV-1 Env (gp160) and the highly pathogenic avian influenza virus H5N1 hemagglutinin (HA). We conclude that MARCH8 has a very broad antiviral activity by prohibiting different viral fusion proteins from glycosylation and proteolytic cleavage in the Golgi, which inhibits their transport from the Golgi to the plasma membrane and incorporation into virions.IMPORTANCE Enveloped viruses express three classes of fusion proteins that are required for their entry into host cells via mediating virus and cell membrane fusion. Class I fusion proteins are produced from influenza viruses, retroviruses, Ebola viruses, and coronaviruses. They are first synthesized as a type I transmembrane polypeptide precursor that is subsequently glycosylated and oligomerized. Most of these precursors are cleaved en route to the plasma membrane by a cellular protease furin in the late secretory pathway, generating the trimeric N-terminal receptor-binding and C-terminal fusion subunits. Here, we show that a cellular protein, MARCH8, specifically inhibits the furin-mediated cleavage of EBOV GP, HIV-1 Env, and H5N1 HA. Further analyses uncovered that MARCH8 blocked the EBOV GP glycosylation in the Golgi and inhibited its transport from the Golgi to the plasma membrane. Thus, MARCH8 has a very broad antiviral activity by specifically inactivating different viral fusion proteins.

Quinalizarin induces ROS­mediated apoptosis via the MAPK, STAT3 and NF­κB signaling pathways in human breast cancer cells.

Quinalizarin has been demonstrated to exhibit potent antitumor activities in lung cancer and gastric cancer cells, but currently, little is known regarding its anticancer mechanisms in human breast cancer cells. The aim of the present study was to investigate the apoptotic effects of quinalizarin in MCF­7 cells and to analyze its molecular mechanisms. The MTT assay was used to evaluate the viability of human breast cancer cells that had been treated with quinalizarin and 5­fluorouracil. Flow cytometric analyses and western blotting were used to investigate the effects of quinalizarin on apoptosis and cycle arrest in MCF­7 cells with focus on reactive oxygen species (ROS) production. The results demonstrated that quinalizarin exhibited significant cytotoxic effects on human breast cancer cells in a dose­dependent manner. Accompanying ROS, quinalizarin induced MCF­7 cell mitochondrial­associated apoptosis by regulating mitochondrial­associated apoptosis, and caused cell cycle arrest at the G2/M phase in a time­dependent manner. Furthermore, quinalizarin can activate p38 kinase and JNK, and inhibit the extracellular signal­regulated kinase, signal transducer and activator of transcription 3 (STAT3) and NF­κB signaling pathways. These effects were blocked by mitogen­activated protein kinase (MAPK) inhibitor and N­acetyl­L­cysteine. The results from the present study suggested that quinalizarin induced G2/M phase cell cycle arrest and apoptosis in MCF­7 cells through ROS­mediated MAPK, STAT3 and NF­κB signaling pathways. Thus, quinalizarin may be useful for human breast cancer treatment, as well as the treatment of other cancer types.

Genotyping bovine leukemia virus in dairy cattle of Heilongjiang, northeastern China.

BACKGROUND: Bovine leukemia virus (BLV) causes enzootic bovine leukosis in cattle and leads to heavy economic losses in the husbandry industry. Heilongjiang Province, China, is rich in dairy cattle. However, its current BLV epidemiology and genotypes have still not been evaluated and confirmed. In this report, we investigated the BLV epidemiology in dairy cattle in the major regions of Heilongjiang Province via the nested PCR assay. RESULTS: A total of 730 blood samples were collected from nine different farms in six regions of Heilongjiang. The results showed that the infection rate of these regions ranged from null to 31%. With a clustering analysis of 60 published BLV env sequences, genotypes 1 and 6 were confirmed to be circulating in Heilongjiang. Importantly, a new genotype, 11, and a new subgenotype, 6E, were also identified in the Harbin and Daqing regions, respectively. An epitope analysis showed that a cluster of T-X-D-X-R-XXXX-A sequences in genotype 11 gp51 neutralizing domain 2 was unique among all currently known BLV isolates and was therefore a defining feature of this new genotype. CONCLUSIONS: BLV epidemics and genotypes were initially investigated in dairy cattle of Heilongjiang. A relatively high infection rate was found in some regions of this province. A new genotype, G11, with a highly specific motif, was identified and thus added as a new member to the current BLV genotype family. This report provides an initial reference for future investigations and subsequent control of BLV transmission and spread in this region.

The retroviral accessory proteins S2, Nef, and glycoMA use similar mechanisms for antagonizing the host restriction factor SERINC5.

Serine incorporator 5 (SERINC5) is a recently identified restriction factor that blocks virus entry but is antagonized by three unrelated retroviral accessory proteins. The S2 protein from equine infectious anemia virus (EIAV) has been reported to reduce SERINC5 expression at steady-state levels likely via the endocytic pathway; however, the precise mechanism is still unclear. Here, we investigated how EIAV S2 protein down-regulates SERINC5 compared with down-regulation induced by Nef from HIV-1 and glycoMA proteins from murine leukemia virus (MLV). Using bimolecular fluorescence complementation (BiFC) assay and immunoprecipitation (IP), we detected an interaction between S2 and SERINC5. We found that this interaction relies on the S2 myristoylation site, indicating that it may occur on the plasma membrane. S2 internalized SERINC5 via receptor-mediated endocytosis and targeted it to endosomes and lysosomes, resulting in a ubiquitination-dependent decrease in SERINC5 expression at steady-state levels. Both BiFC and IP detected a glycoMA-SERINC5 interaction, but a Nef-SERINC5 interaction was detected only by BiFC. Moreover, S2 and glycoMA down-regulated SERINC5 more effectively than did Nef. We further show that unlike Nef, both S2 and glycoMA effectively down-regulate SERINC2 and also SERINC5 from Xenopus tropicalis (xSERINC5). Moreover, we detected expression of the equine SERINC5 (eSERINC5) protein and observed that its expression is much weaker than expression levels of SERINC5 from other species. Nonetheless, eSERINC5 had a strong antiviral activity that was effectively counteracted by S2. We conclude that HIV-1, EIAV, and MLV share a similar mechanism to antagonize viral restriction by host SERINC5.

Glycitein induces reactive oxygen species-dependent apoptosis and G0/G1 cell cycle arrest through the MAPK/STAT3/NF-κB pathway in human gastric cancer cells.

Glycitein is an isoflavone that reportedly inhibits the proliferation of human breast cancer and prostate cancer cells. However, its anti-cancer molecular mechanisms in human gastric cancer remain to be defined. This study evaluated the antitumor effects of glycitein on human gastric cancer cells and investigated the underlying mechanisms. We used MTT assay, flow cytometry and western blotting to investigate its molecular mechanisms with focus on reactive oxygen species (ROS) production. Our results showed that glycitein had significant cytotoxic effects on human gastric cancer cells. Glycitein markedly decreased mitochondrial transmembrane potential (ΔΨm) and increased AGS cells mitochondrial-related apoptosis, and caused G0/G1 cell cycle arrest by regulating cycle-related protein. Mechanistically, accompanying ROS, glycitein can activate mitogen-activated protein kinase (MAPK) and inhibited the signal transducer and activator of transcription 3 (STAT3) and nuclear factor-kappaB (NF-κB) signaling pathways. Furthermore, the MAPK signaling pathway regulated the expression levels of STAT3 and NF-κB upon treatment with MAPK inhibitor and N-acetyl-L-cysteine (NAC). These findings suggested that glycitein induced AGS cell apoptosis and G0/G1 phase cell cycle arrest via ROS-related MAPK/STAT3/NF-κB signaling pathways. Thus, glycitein has the potential to a novel targeted therapeutic agent for human gastric cancer.

Murine Leukemia Virus Glycosylated Gag Reduces Murine SERINC5 Protein Expression at Steady-State Levels via the Endosome/Lysosome Pathway to Counteract SERINC5 Antiretroviral Activity.

Glycosylated Gag (glycoGag) is an accessory protein expressed by most gammaretroviruses, including murine leukemia virus (MLV). MLV glycoGag not only enhances MLV replication and disease progression but also increases human immunodeficiency virus type 1 (HIV-1) infectivity as Nef does. Recently, SERINC5 (Ser5) was identified as the target for Nef, and the glycoGag Nef-like activity has been attributed to the Ser5 antagonism. Here, we investigated how glycoGag antagonizes Ser5 using MLV glycoMA and murine Ser5 proteins. We confirm previous observations that glycoMA relocalizes Ser5 from plasma membrane to perinuclear punctated compartments and the important role of its Y36XXL39 motif in this process. We find that glycoMA decreases Ser5 expression at steady-state levels and identify two other glycoGag crucial residues, P31 and R63, for the Ser5 downregulation. The glycoMA and Ser5 interaction is detected in live cells using a bimolecular fluorescence complementation assay. Ser5 is internalized via receptor-mediated endocytosis and relocalized to Rab5+ early, Rab7+ late, and Rab11+ recycling endosomes by glycoMA. Although glycoMA is not polyubiquitinated, the Ser5 downregulation requires Ser5 polyubiquitination via the K48- and K63-linkage, resulting in Ser5 destruction in lysosomes. Although P31, Y36, L39, and R63 are not required for glycoMA interaction with Ser5, they are required for Ser5 relocalization to lysosomes for destruction. In addition, although murine Ser1, Ser2, and Ser3 exhibit very poor antiviral activity, they are also targeted by glycoMA for lysosomal destruction. We conclude that glycoGag has a broad activity to downregulate SERINC proteins via the cellular endosome/lysosome pathway, which promotes viral replication.IMPORTANCE MLV glycoGag not only enhances MLV replication but also increases HIV-1 infectivity similarly as Nef. Recent studies have discovered that both glycoGag and Nef antagonize a novel host restriction factor Ser5 and promote viral replication. Compared to Nef, the glycoGag antagonism of Ser5 is still poorly understood. MLV glycoGag is a transmembrane version of the structural Gag protein with an extra 88-amino-acid leader region that determines its activity. We now show that glycoGag interacts with Ser5 in live cells and internalizes Ser5 via receptor-mediated endocytosis. Ser5 is polyubiquitinated and relocalized to endosomes and lysosomes for massive destruction. In addition to the previously identified tyrosine-based sorting signal, we find two more important residues for Ser5 relocalization and downregulation. We also find that the Ser5 sensitivity to glycoGag is conserved in the SERINC family. Together, our findings highlight the important role of endosome/lysosome pathway in the enhancement of viral replication by viral proteins.

An Efficient Ensemble Learning Approach for Predicting Protein-Protein Interactions by Integrating Protein Primary Sequence and Evolutionary Information.

Protein-protein interactions (PPIs) perform a very important function in many cellular processes, including signal transduction, post-translational modifications, apoptosis, and cell growth. Deregulation of PPIs results in many diseases, including cancer and pernicious anemia. Although many high-throughput methods have been applied to generate a large amount of PPIs data, they are generally expensive, inefficient and labor-intensive. Hence, there is an urgent need for developing a computational method to accurately and rapidly detect PPIs. In this article, we proposed a highly efficient approach to predict PPIs by integrating a new protein sequence substitution matrix feature representation and ensemble weighted sparse representation model classifier. The proposed method is demonstrated on Saccharomyces cerevisiae dataset and achieved 99.26% prediction accuracy with 98.53% sensitivity at precision of 100%, which is shown to have much higher predictive accuracy than current state-of-the-art algorithms. Extensive experiments are performed with the benchmark data set from Human and Helicobacter pylori that the proposed method achieves outstanding better success rates than other existing approaches in this problem. Experiment results illustrate that our proposed method presents an economical approach for computational building of PPI networks, which can be a helpful supplementary method for future proteomics researches.

HIV-1 Nef Antagonizes SERINC5 Restriction by Downregulation of SERINC5 via the Endosome/Lysosome System.

The primate lentiviral accessory protein Nef downregulates CD4 and major histocompatibility complex class I (MHC-I) from the cell surface via independent endosomal trafficking pathways to promote viral pathogenesis. In addition, Nef antagonizes a novel restriction factor, SERINC5 (Ser5), to increase viral infectivity. To explore the molecular mechanism of Ser5 antagonism by Nef, we determined how Nef affects Ser5 expression and intracellular trafficking in comparison to CD4 and MHC-I. We confirm that Nef excludes Ser5 from human immunodeficiency virus type 1 (HIV-1) virions by downregulating its cell surface expression via similar functional motifs required for CD4 downregulation. We find that Nef decreases both Ser5 and CD4 expression at steady-state levels, which are rescued by NH4Cl or bafilomycin A1 treatment. Nef binding to Ser5 was detected in living cells using a bimolecular fluorescence complementation assay, where Nef membrane association is required for interaction. In addition, Nef triggers rapid Ser5 internalization via receptor-mediated endocytosis and relocalizes Ser5 to Rab5+ early, Rab7+ late, and Rab11+ recycling endosomes. Manipulation of AP-2, Rab5, Rab7, and Rab11 expression levels affects the Nef-dependent Ser5 and CD4 downregulation. Moreover, although Nef does not promote Ser5 polyubiquitination, Ser5 downregulation relies on the ubiquitination pathway, and both K48- and K63-specific ubiquitin linkages are required for the downregulation. Finally, Nef promotes Ser5 colocalization with LAMP1, which is enhanced by bafilomycin A1 treatment, suggesting that Ser5 is targeted to lysosomes for destruction. We conclude that Nef uses a similar mechanism to downregulate Ser5 and CD4, which sorts Ser5 into a point-of-no-return degradative pathway to counteract its restriction.IMPORTANCE Human immunodeficiency virus (HIV) and simian immunodeficiency virus (SIV) express an accessory protein called Nef to promote viral pathogenesis. Nef drives immune escape in vivo through downregulation of CD4 and MHC-I from the host cell surface. Recently, Nef was reported to counteract a novel host restriction factor, Ser5, to increase viral infectivity. Nef downregulates cell surface Ser5, thus preventing its incorporation into virus particles, resulting in disruption of its antiviral activity. Here, we report mechanistic studies of Nef-mediated Ser5 downregulation in comparison to CD4 and MHC-I. We demonstrate that Nef binds directly to Ser5 in living cells and that Nef-Ser5 interaction requires Nef association with the plasma membrane. Subsequently, Nef internalizes Ser5 from the plasma membrane via receptor-mediated endocytosis, and targets ubiquitinated Ser5 to endosomes and lysosomes for destruction. Collectively, these results provide new insights into our ongoing understanding of the Nef-Ser5 arms race in HIV-1 infection.