Inhibitory effect and mechanism of algicidal bacteria on Chaetomorpha valida.

Chaetomorpha valida, filamentous green tide algae, poses a significant threat to both aquatic ecosystems and aquaculture. Vibrio alginolyticus Y20 is a new algicidal bacterium with an algicidal effect on C. valida. This study aimed to investigate the physiological and molecular responses of C. valida to exposure to V. alginolyticus Y20. The inhibitory effect of V. alginolyticus Y20 on C. valida was content dependent, with the lowest inhibitory content being 3 × 105 CFU mL-1. The microscopic results revealed that C. valida experienced severe morphological damage under the influence of V. alginolyticus Y20, with a dispersion of intracellular pigments. V. alginolyticus Y20 caused the decrease in chlorophyll-a content and Fv/Fm in C. valida. At the molecular level, V. alginolyticus Y20 downregulated the expression of genes related to photosynthetic pigment synthesis, light capture, and electron transport. Furthermore, V. alginolyticus Y20 induced oxidative damage to algal cells. The production of reactive oxygen species significantly increased after 11 days of exposure. Malondialdehyde content significantly increased, and the cell membranes were severely damaged by lipid peroxidation. The content of superoxide dismutase and peroxidase also markedly increased, whereas catalase content decreased significantly. Additionally, peroxisomes were inhibited due to the downregulation of PEX expression, leading to irreversible oxidative damage to algal cells. Our findings provided a new theoretical basis for exploring the interaction between algicidal bacteria and green tide algae at the molecular level.

Aggregation-Induced-Emission Photosensitizer-Loaded Nano-Superartificial Dendritic Cells with Directly Presenting Tumor Antigens and Reversed Immunosuppression for Photodynamically Boosted Immunotherapy.

The success of tumor immunotherapy highlights the potential of harnessing immune system to fight cancer. Activating both native T cells and exhausted T cells is a critical step for generating effective antitumor immunity, which is determined based on the efficient presentation of tumor antigens and co-stimulatory signals by antigen-presenting cells, as well as immunosuppressive reversal. However, strategies for achieving an efficient antigen presentation process and improving the immunosuppressive microenvironment remain unresolved. Here, aggregation-induced-emission (AIE) photosensitizer-loaded nano-superartificial dendritic cells (saDC@Fs-NPs) are developed by coating superartificial dendritic cells membranes from genetically engineered 4T1 tumor cells onto nanoaggregates of AIE photosensitizers. The outer cell membranes of saDC@Fs-NPs are derived from recombinant lentivirus-infected 4T1 tumor cells in which peptide-major histocompatibility complex class I, CD86, and anti-LAG3 antibody are simultaneously anchored. These saDC@Fs-NPs could directly stimulate T-cell activation and reverse T-cell exhaustion for cancer immunotherapy. The inner AIE-active photosensitizers induce immunogenic cell death to activate dendritic cells and enhance T lymphocyte infiltration by photodynamic therapy, promoting the transformation of "cold tumors" into "hot tumors," which further boosts immunotherapy efficiency. This work presents a powerful photoactive and artificial antigen-presenting platform for activating both native T cells and exhausted T cells, as well as facilitating tumor photodynamic immunotherapy.

Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment.

Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein⁻protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein⁻protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.

Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting.

Identification of hot spots, a small portion of protein-protein interface residues that contribute the majority of the binding free energy, can provide crucial information for understanding the function of proteins and studying their interactions. Based on our previous method (PredHS), we propose a new computational approach, PredHS2, that can further improve the accuracy of predicting hot spots at protein-protein interfaces. Firstly we build a new training dataset of 313 alanine-mutated interface residues extracted from 34 protein complexes. Then we generate a wide variety of 600 sequence, structure, exposure and energy features, together with Euclidean and Voronoi neighborhood properties. To remove redundant and irrelevant information, we select a set of 26 optimal features utilizing a two-step feature selection method, which consist of a minimum Redundancy Maximum Relevance (mRMR) procedure and a sequential forward selection process. Based on the selected 26 features, we use Extreme Gradient Boosting (XGBoost) to build our prediction model. Performance of our PredHS2 approach outperforms other machine learning algorithms and other state-of-the-art hot spot prediction methods on the training dataset and the independent test set (BID) respectively. Several novel features, such as solvent exposure characteristics, second structure features and disorder scores, are found to be more effective in discriminating hot spots. Moreover, the update of the training dataset and the new feature selection and classification algorithms play a vital role in improving the prediction quality.

An Effective Delay Reduction Approach through a Portion of Nodes with a Larger Duty Cycle for Industrial WSNs.

For Industrial Wireless Sensor Networks (IWSNs), sending data with timely style to the stink (or control center, CC) that is monitored by sensor nodes is a challenging issue. However, in order to save energy, wireless sensor networks based on a duty cycle are widely used in the industrial field, which can bring great delay to data transmission. We observe that if the duty cycle of a small number of nodes in the network is set to 1, the sleep delay caused by the duty cycle can be effectively reduced. Thus, in this paper, a novel Portion of Nodes with Larger Duty Cycle (PNLDC) scheme is proposed to reduce delay and optimize energy efficiency for IWSNs. In the PNLDC scheme, a portion of nodes are selected to set their duty cycle to 1, and the proportion of nodes with the duty cycle of 1 is determined according to the energy abundance of the area in which the node is located. The more the residual energy in the region, the greater the proportion of the selected nodes. Because there are a certain proportion of nodes with the duty cycle of 1 in the network, the PNLDC scheme can effectively reduce delay in IWSNs. The performance analysis and experimental results show that the proposed scheme significantly reduces the delay for forwarding data by 8.9~26.4% and delay for detection by 2.1~24.6% without reducing the network lifetime when compared with the fixed duty cycle method. Meanwhile, compared with the dynamic duty cycle strategy, the proposed scheme has certain advantages in terms of energy utilization and delay reduction.

Probing the functions of long non-coding RNAs by exploiting the topology of global association and interaction network.

Long non-coding RNAs (lncRNAs) are involved in many biological processes, such as immune response, development, differentiation and gene imprinting and are associated with diseases and cancers. But the functions of the vast majority of lncRNAs are still unknown. Predicting the biological functions of lncRNAs is one of the key challenges in the post-genomic era. In our work, We first build a global network including a lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network according to the expressions and interactions, then extract the topological feature vectors of the global network. Using these features, we present an SVM-based machine learning approach, PLNRGO, to annotate human lncRNAs. In PLNRGO, we construct a training data set according to the proteins with GO annotations and train a binary classifier for each GO term. We assess the performance of PLNRGO on our manually annotated lncRNA benchmark and a protein-coding gene benchmark with known functional annotations. As a result, the performance of our method is significantly better than that of other state-of-the-art methods in terms of maximum F-measure and coverage.

PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.

BACKGROUND: Identifying specific residues for protein-DNA interactions are of considerable importance to better recognize the binding mechanism of protein-DNA complexes. Despite the fact that many computational DNA-binding residue prediction approaches have been developed, there is still significant room for improvement concerning overall performance and availability. RESULTS: Here, we present an efficient approach termed PDRLGB that uses a light gradient boosting machine (LightGBM) to predict binding residues in protein-DNA complexes. Initially, we extract a wide variety of 913 sequence and structure features with a sliding window of 11. Then, we apply the random forest algorithm to sort the features in descending order of importance and obtain the optimal subset of features using incremental feature selection. Based on the selected feature set, we use a light gradient boosting machine to build the prediction model for DNA-binding residues. Our PDRLGB method shows better overall predictive accuracy and relatively less training time than other widely used machine learning (ML) methods such as random forest (RF), Adaboost and support vector machine (SVM). We further compare PDRLGB with various existing approaches on the independent test datasets and show improvement in results over the existing state-of-the-art approaches. CONCLUSIONS: PDRLGB is an efficient approach to predict specific residues for protein-DNA interactions.