Biolinguistic graph fusion model for circRNA-miRNA association prediction.

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

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.

Genetically Encoded Lizard Color Divergence for Camouflage and Thermoregulation.

Local adaptation is critical in speciation and evolution, yet comprehensive studies on proximate and ultimate causes of local adaptation are generally scarce. Here, we integrated field ecological experiments, genome sequencing, and genetic verification to demonstrate both driving forces and molecular mechanisms governing local adaptation of body coloration in a lizard from the Qinghai-Tibet Plateau. We found dark lizards from the cold meadow population had lower spectrum reflectance but higher melanin contents than light counterparts from the warm dune population. Additionally, the colorations of both dark and light lizards facilitated the camouflage and thermoregulation in their respective microhabitat simultaneously. More importantly, by genome resequencing analysis, we detected a novel mutation in Tyrp1 that underpinned this color adaptation. The allele frequencies at the site of SNP 459# in the gene of Tyrp1 are 22.22% G/C and 77.78% C/C in dark lizards and 100% G/G in light lizards. Model-predicted structure and catalytic activity showed that this mutation increased structure flexibility and catalytic activity in enzyme TYRP1, and thereby facilitated the generation of eumelanin in dark lizards. The function of the mutation in Tyrp1 was further verified by more melanin contents and darker coloration detected in the zebrafish injected with the genotype of Tyrp1 from dark lizards. Therefore, our study demonstrates that a novel mutation of a major melanin-generating gene underpins skin color variation co-selected by camouflage and thermoregulation in a lizard. The resulting strong selection may reinforce adaptive genetic divergence and enable the persistence of adjacent populations with distinct body coloration.

A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.

MOTIVATION: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data. RESULTS: In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed. AVAILABILITY: The data and source code can be found at https://github.com/1axin/JSNDCMI.

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.

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.

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/.

A novel circRNA-miRNA association prediction model based on structural deep neural network embedding.

A large amount of clinical evidence began to mount, showing that circular ribonucleic acids (RNAs; circRNAs) perform a very important function in complex diseases by participating in transcription and translation regulation of microRNA (miRNA) target genes. However, with strict high-throughput techniques based on traditional biological experiments and the conditions and environment, the association between circRNA and miRNA can be discovered to be labor-intensive, expensive, time-consuming, and inefficient. In this paper, we proposed a novel computational model based on Word2vec, Structural Deep Network Embedding (SDNE), Convolutional Neural Network and Deep Neural Network, which predicts the potential circRNA-miRNA associations, called Word2vec, SDNE, Convolutional Neural Network and Deep Neural Network (WSCD). Specifically, the WSCD model extracts attribute feature and behaviour feature by word embedding and graph embedding algorithm, respectively, and ultimately feed them into a feature fusion model constructed by combining Convolutional Neural Network and Deep Neural Network to deduce potential circRNA-miRNA interactions. The proposed method is proved on dataset and obtained a prediction accuracy and an area under the receiver operating characteristic curve of 81.61% and 0.8898, respectively, which is shown to have much higher accuracy than the state-of-the-art models and classifier models in prediction. In addition, 23 miRNA-related circular RNAs (circRNAs) from the top 30 were confirmed in relevant experiences. In these works, all results represent that WSCD would be a helpful supplementary reliable method for predicting potential miRNA-circRNA associations compared to wet laboratory experiments.

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.

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.

Inhibition of Th17 cell differentiation by aerobic exercise improves vasodilatation in diabetic mice.

BACKGROUND: Aortic endothelial diastolic dysfunction is an early complication of diabetes and the abnormal differentiation of Th17 cells is involved in the development of diabetes. However, the exact role of exercise on regulating the Th17 cells differentiation and the underlying molecular mechanisms remain to be elucidated in diabetic mice. METHODS: db/db and db/m+ mice were randomly divided into exercise and sedentary groups. Mice in exercise group were exercised daily, 6 days/week, for 6 weeks and mice in sedentary groups were placed on a nonmoving treadmill for 6 weeks. Vascular endothelial function was measured via wire myograph and the frequencies of Th17 from peripheral blood in mice were assessed via flow cytometry. RESULTS: Our data showed that exercise improved insulin resistance and aortic endothelial diastolic function in db/db mice. In addition, the proportion of Th17 cells and IL-17A level in peripheral blood of db/db mice were significantly increased, and exercise could promote Th17 cell differentiation and reduce IL-17A level. More importantly, STAT3 or ROR-γt inhibitors could promote Th17 cell differentiation in db/db mice, while exercise significantly down-regulated p-STAT3/ROR-γt signaling in db/db mice, suggesting that exercise regulated Th17 differentiation through STAT3/ROR-γt signaling. CONCLUSIONS: This study demonstrated that exercise improved vascular endothelial function in diabetic mice via reducing Th17 cell differentiation through p-STAT3/ROR-γt pathway, suggesting exercise may be an important non-pharmacological intervention strategy for the treatment of diabetes-related vascular complications.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path.

More and more evidence suggests that circRNA plays a vital role in generating and treating diseases by interacting with miRNA. Therefore, accurate prediction of potential circRNA-miRNA interaction (CMI) has become urgent. However, traditional wet experiments are time-consuming and costly, and the results will be affected by objective factors. In this paper, we propose a computational model BCMCMI, which combines three features to predict CMI. Specifically, BCMCMI utilizes the bidirectional encoding capability of the BERT algorithm to extract sequence features from the semantic information of circRNA and miRNA. Then, a heterogeneous network is constructed based on cosine similarity and known CMI information. The Metapath2vec is employed to conduct random walks following meta-paths in the network to capture topological features, including similarity features. Finally, potential CMIs are predicted using the XGBoost classifier. BCMCMI achieves superior results compared to other state-of-the-art models on two benchmark datasets for CMI prediction. We also utilize t-SNE to visually observe the distribution of the extracted features on a randomly selected dataset. The remarkable prediction results show that BCMCMI can serve as a valuable complement to the wet experiment process.

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.

Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.

Identification of drug-target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.

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.

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.

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.

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.