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.

MARCH8 inhibits pseudorabies virus replication by trapping the viral cell-to-cell fusion complex in the trans-Golgi network.

The membrane-associated RING-CH 8 protein (MARCH8), a member of the E3 ubiquitin ligase family, has broad-spectrum antiviral activity. However, some viruses hijack MARCH8 to promote virus replication, highlighting its dual role in the viral lifecycle. Most studies on MARCH8 have focused on RNA viruses, leaving its role in DNA viruses largely unexplored. Pseudorabies virus (PRV) is a large DNA virus that poses a potential threat to humans. In this study, we found that MARCH8 inhibited PRV replication at the cell-to-cell fusion stage. Interestingly, our findings proved that MARCH8 blocks gB cleavage by recruiting furin but this activity does not inhibit viral infection in vitro. Furthermore, we confirmed that MARCH8 inhibits cell-to-cell fusion independent of its E3 ubiquitin ligase activity but dependent on the interaction with the cell-to-cell fusion complex (gB, gD, gH, and gL). Finally, we discovered that the distribution of the cell-to-cell fusion complex is significantly altered and trapped within the trans-Golgi network. Overall, our results indicate that human MARCH8 acts as a potent antiviral host factor against PRV via trapping the cell-to-cell fusion complex in the trans-Golgi network.

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.

BJLD-CMI: a predictive circRNA-miRNA interactions model combining multi-angle feature information.

Increasing research findings suggest that circular RNA (circRNA) exerts a crucial function in the pathogenesis of complex human diseases by binding to miRNA. Identifying their potential interactions is of paramount importance for the diagnosis and treatment of diseases. However, long cycles, small scales, and time-consuming processes characterize previous biological wet experiments. Consequently, the use of an efficient computational model to forecast the interactions between circRNA and miRNA is gradually becoming mainstream. In this study, we present a new prediction model named BJLD-CMI. The model extracts circRNA sequence features and miRNA sequence features by applying Jaccard and Bert's method and organically integrates them to obtain CMI attribute features, and then uses the graph embedding method Line to extract CMI behavioral features based on the known circRNA-miRNA correlation graph information. And then we predict the potential circRNA-miRNA interactions by fusing the multi-angle feature information such as attribute and behavior through Autoencoder in Autoencoder Networks. BJLD-CMI attained 94.95% and 90.69% of the area under the ROC curve on the CMI-9589 and CMI-9905 datasets. When compared with existing models, the results indicate that BJLD-CMI exhibits the best overall competence. During the case study experiment, we conducted a PubMed literature search to confirm that out of the top 10 predicted CMIs, seven pairs did indeed exist. These results suggest that BJLD-CMI is an effective method for predicting interactions between circRNAs and miRNAs. It provides a valuable candidate for biological wet experiments and can reduce the burden of researchers.

Innate Immune Evasion of PRRSV nsp11 through Degradation of the HDAC2 by Its Endoribonuclease Activity.

Porcine reproductive and respiratory syndrome virus (PRRSV), a member of the Arteriviridae family, represents a persistent menace to the global pig industry, causing reproductive failure and respiratory disease in pigs. In this study, we delved into the role of histone deacetylases (HDAC2) during PRRSV infection. Our findings revealed that HDAC2 expression is downregulated upon PRRSV infection. Notably, suppressing HDAC2 activity through specific small interfering RNA led to an increase in virus production, whereas overexpressing HDAC2 effectively inhibited PRRSV replication by boosting the expression of IFN-regulated antiviral molecules. Furthermore, we identified the virus's nonstructural protein 11 (nsp11) as a key player in reducing HDAC2 levels. Mutagenic analyses of PRRSV nsp11 revealed that its antagonistic effect on the antiviral activity of HDAC2 is dependent on its endonuclease activity. In summary, our research uncovered a novel immune evasion mechanism employed by PRRSV, providing crucial insights into the pathogenesis of this virus and guiding the development of innovative prevention strategies against PRRSV infection.

Designing a smallpox B-cell and T-cell multi-epitope subunit vaccine using a comprehensive immunoinformatics approach.

Smallpox is a highly contagious human disease caused by the variola virus. Although the disease was eliminated in 1979 due to its highly contagious nature and historical pathogenicity, with a mortality rate of up to 30%, this virus is an important candidate for biological weapons. Currently, vaccines are the critical measures to prevent this virus infection and spread. In this study, we designed a peptide vaccine using immunoinformatics tools, which have the potential to activate human immunity against variola virus infection efficiently. The design of peptides derives from vaccine-candidate proteins showing protective potential in vaccinia WR strains. Potential non-toxic and nonallergenic T-cell and B-cell binding and cytokine-inducing epitopes were then screened through a priority prediction using special linkers to connect B-cell epitopes and T-cell epitopes, and an appropriate adjuvant was added to the vaccine construction to enhance the immunogenicity of the peptide vaccine. The 3D structure display, docking, and free energy calculation analysis indicate that the binding affinity between the vaccine peptide and Toll-like receptor 3 is high, and the vaccine receptor complex is highly stable. Notably, the vaccine we designed is obtained from the protective protein of the vaccinia and combined with preventive measures to avoid side effects. This vaccine is highly likely to produce an effective and safe immune response against the variola virus infection in the body. IMPORTANCE: In this work, we designed a vaccine with a cluster of multiple T-cell/B-cell epitopes, which should be effective in inducing systematic immune responses against variola virus infection. Besides, this work also provides a reference in vaccine design for preventing monkeypox virus infection, which is currently prevalent.

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.

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.

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.

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.

Fabrication of a biochar-doped monolithic adsorbent and its application for the extraction and determination of coumarins from Angelicae Pubescentis Radix.

A monolithic adsorbent was designed aiming to the structure of osthole and columbianadin, and fabricated using diallyl phthalate as the monomer and ethylene dimethacrylate as the crosslinker with the addition of bamboo biochar, via polymerization reaction in a stainless-steel tube. The prepared composite adsorbent packed in the tube was used as a solid-phase extraction column for the extraction and determination of two coumarins (osthole and columbianadin) in Angelicae Pubescentis Radix, combing with a C18 analytical column through an HPLC instrument, which show excellent matrix-removal ability and good selectivity to osthole and columbianadin. Furthermore, the present adsorbent shows good applicability, which was used for the extraction of osthole from Duhuo Jisheng Pill. Compared to the commercial C18 and phenyl adsorbent, the present adsorbent own better selectivity and higher resolution. These results attributed to the enhanced specific surface area (141 m2/g) and enriched interaction sites of the resulting composite adsorbent, due to the doping of bamboo biochar, which can produce hydrogen bond, dipole-dipole, π-π and hydrophobic force interactions with the osthole and columbianadin. The methodology validation indicated that the present method showed good precision and good accuracy, and the composite adsorbent showed good preparative repeatability, which can be reused for no less than 100 times with the relative standard deviation ≤4.6 % (n = 100). The present work provided a simple and efficient method for the extraction and determination osthole and columbianadin from Angelicae Pubescentis Radix.

A review on the calculation and application of lignin Hansen solubility parameters.

Hansen solubility parameters (HSPs) play a critical role in the majority of processes involving lignin depolymerization, separation, fractionation, and polymer blending, which are directly related to dissolution properties. However, the calculation of lignin HSPs is highly complicated due to the diversity of sources and the complexity of lignin structures. Despite their important role, lignin HSPs have been undervalued, attracting insufficient attention. This review summarizes the calculation methods for lignin HSPs and proposes a straightforward method based on lignin subunits. Furthermore, it highlights the crucial applications of lignin HSPs, such as identifying ideal solvents for lignin dissolution, selecting suitable solvents for lignin depolymerization and extraction, designing green solvents for lignin fractionation, and guiding the preparation of lignin-based composites. For instance, leveraging HSPs to design a series of solvents could potentially achieve sequential controllable lignin fractionation, addressing issues of low value-added applications of lignin resulting from poor homogeneity. Notably, HSPs serve as valuable tools for understanding the dissolution behavior of lignin. Consequently, we expect this review to be of great interest to researchers specializing in lignin and other macromolecules.

Novel technologies are turning a dream into reality: conditionally replicating viruses as vaccines.

Conditionally replicating viruses (CRVs) are a type of virus with one or more essential gene functions that are impaired resulting in the disruption of viral genome replication, protein synthesis, or virus particle assembly. CRVs can replicate only if the deficient essential genes are supplied. CRVs are widely used in biomedical research, particularly as vaccines. Traditionally, CRVs are generated by creating complementary cell lines that provide the impaired genes. With the development of biotechnology, novel techniques have been invented to generate CRVs, such as targeted protein degradation (TPD) technologies and premature termination codon (PTC) read-through technologies. The advantages and disadvantages of these novel technologies are discussed. Finally, we provide perspectives on what challenges need to be overcome for CRVs to reach the market.

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.

An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding.

MOTIVATION: Accumulating clinical evidence shows that circular RNA (circRNA) plays an important regulatory role in the occurrence and development of human diseases, which is expected to provide a new perspective for the diagnosis and treatment of related diseases. Using computational methods can provide high probability preselection for wet experiments to save resources. However, due to the lack of neighborhood structure in sparse biological networks, the model based on network embedding and graph embedding is difficult to achieve ideal results. RESULTS: In this paper, we propose BioDGW-CMI, which combines biological text mining and wavelet diffusion-based sparse network structure embedding to predict circRNA-miRNA interaction (CMI). In detail, BioDGW-CMI first uses the Bidirectional Encoder Representations from Transformers (BERT) for biological text mining to mine hidden features in RNA sequences, then constructs a CMI network, obtains the topological structure embedding of nodes in the network through heat wavelet diffusion patterns. Next, the Denoising autoencoder organically combines the structural features and Gaussian kernel similarity, finally, the feature is sent to lightGBM for training and prediction. BioDGW-CMI achieves the highest prediction performance in all three datasets in the field of CMI prediction. In the case study, all the 8 pairs of CMI based on circ-ITCH were successfully predicted. AVAILABILITY: The data and source code can be found at https://github.com/1axin/BioDGW-CMI-model.

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.

DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information.

Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.

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.