AraPathogen2.0: An Improved Prediction of Plant-Pathogen Protein-Protein Interactions Empowered by the Natural Language Processing Technique.

Plant-pathogen protein-protein interactions (PPIs) play crucial roles in the arm race between plants and pathogens. Therefore, the identification of these interspecies PPIs is very important for the mechanistic understanding of pathogen infection and plant immunity. Computational prediction methods can complement experimental efforts, but their predictive performance still needs to be improved. Motivated by the rapid development of natural language processing and its successful applications in the field of protein bioinformatics, here we present an improved XGBoost-based plant-pathogen PPI predictor (i.e., AraPathogen2.0), in which sequence encodings from the pretrained protein language model ESM2 and Arabidopsis PPI network-related node representations from the graph embedding technique struc2vec are used as input. Stringent benchmark experiments showed that AraPathogen2.0 could achieve a better performance than its precedent version, especially for processing the test data set with novel proteins unseen in the training data.

Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana.

Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.

Chimeric antigen receptor-based immunotherapy in breast cancer: Recent progress in China.

Breast cancer (BC) is the fourth most prevalent cancer in China. Despite conventional treatment strategies, BC patients often have poor therapeutic outcomes, leading to significant global cancer mortality rates. Chimeric antigen receptor (CAR)-based immunotherapy is a promising and innovative approach for cancer treatment that redirects immune cells to attack tumor cells expressing selected tumor antigens (TAs). T cells, natural killer (NK) cells, and macrophages, key components of the immune system, are used in CAR-based immunotherapies. Although remarkable progress has been made with CAR-T cells in hematologic malignancies, the application of CAR-based immunotherapy to BC has lagged. This is partly due to obstacles such as tumor heterogeneity, which is further associated with the TA and BC subtypes, and the immunosuppressive tumor microenvironment (TME). Several combinatorial approaches, including the use of immune checkpoint inhibitors, oncolytic viruses, and antitumor drugs, have been proposed to overcome these obstacles in BC treatment. Furthermore, several CAR-based immunotherapies for BC have been translated into clinical trials. This review provides an overview of the recent progress in CAR-based immunotherapy for BC treatment, including targeting of TAs, consideration of BC subtypes, assessment of the TME, and exploration of combinatorial therapies. The authors focused on preclinical studies and clinical trials of CAR-T cells, CAR-NK cells, and CAR-macrophages especially conducted in China, followed by an internal comparison and discussion of current limits. In conclusion, this review elucidates China's contribution to CAR-based immunotherapies for BC and provides inspiration for further research. PLAIN LANGUAGE SUMMARY: Despite conventional treatment strategies, breast cancer (BC) patients in China often have poor therapeutic outcomes. Chimeric antigen receptor (CAR)-based immunotherapy, a promising approach, can redirect immune cells to kill tumor cells expressing selected tumor antigens (TAs). However, obstacles such as TA selection, BC subtypes, and immunosuppressive tumor microenvironment still exist. Therefore, various combinatorial approaches have been proposed. This article elucidates several Chinese CAR-based preclinical and clinical studies in BC treatment with comparisons of foreign research, and CAR-immune cells are analyzed, providing inspiration for further research.

Comparative Toxicity of 3-5 Ringed Polycyclic Aromatic Hydrocarbons to Skeletal Development in Zebrafish Embryos and the Possible Reason.

Polycyclic aromatic hydrocarbons (PAHs) are pervasive pollutants in the environment. To compare the developmental toxicity of PAHs with different ring numbers to fish embryos, benzo(a)pyrene (BaP), pyrene (Pyr) and phenanthrene (Phe) were selected as the representatives of 3, 4 and 5-ringed PAHs, and fertilized embryos of zebrafish (Danio rerio) were exposed to 5 nM PAHs for 72 h. The PAH-treated embryos showed defects in craniofacial cartilage. The order of toxicity to the development of craniofacial cartilage was Phe > Pyr > BaP. The transcription of genes related to the development of craniofacial cartilage was downregulated. The GC-MS/MS detection showed that bioaccumulation of BaP in the exposed embryos was two orders of magnitude lower than that of Phe and Pyr. It is suggested that the more uptake and accumulation of Phe and Pyr could be one of the reasons for their greater toxicity to development in early stage embryos.

Prognostic value of EIF5A2 in solid tumors: A meta-analysis and bioinformatics analysis.

Aims: In cancer biology, the aberrant overexpression of eukaryotic translation initiation factor 5A2 (EIF5A2) has been correlative with an ominous prognosis, thereby underscoring its pivotal role in fostering metastatic progression. Consequently, EIF5A2 has garnered significant attention as a compelling prognostic biomarker for various malignancies. Our research endeavors were thus aimed at elucidating the utility and significance of EIF5A2 as a robust indicator of cancer outcome prediction. Method: An exhaustive search of the PubMed, EMBASE, and Web of Science databases found relevant studies. The link between EIF5A2 and survival prognosis was examined using hazard ratios and 95% confidence intervals. Subsequently, The Cancer Genome Atlas (TCGA) and the Gene Expression Profiling Interactive Analysis (GEPIA) databases were employed to validate EIF5A2 expression across various cancer types. Results: Through pooled analysis, we found that increased EIF5A2 expression was significantly associated with decreased overall survival (OS) and disease-free survival/progression-free survival/relapse-free survival (DFS/PFS/RFS). Moreover, TCGA analysis revealed that EIF5A2 was significantly upregulated in 27 types of cancer, with overexpression being linked to shorter OS in three, worse DFS in two, and worse PFS in six types of cancer. GEPIA showed that patients with EIF5A2 overexpression had reduced OS and DFS. Conclusions: In solid tumors, EIF5A2 emerges as a reliable prognostic marker. Our meta-analysis comprehensively analyzed the prognostic value of EIF5A2 in solid tumors and assessed its efficacy as a predictive marker.

Using PlaPPISite to Predict and Analyze Plant Protein-Protein Interaction Sites.

Proteome-wide characterization of protein-protein interactions (PPIs) is crucial to understand the functional roles of protein machinery within cells systematically. With the accumulation of PPI data in different plants, the interaction details of binary PPIs, such as the three-dimensional (3D) structural contexts of interaction sites/interfaces, are urgently demanded. To meet this requirement, we have developed a comprehensive and easy-to-use database called PlaPPISite ( http://zzdlab.com/plappisite/index.php ) to present interaction details for 13 plant interactomes. Here, we provide a clear guide on how to search and view protein interaction details through the PlaPPISite database. Firstly, the running environment of our database is introduced. Secondly, the input file format is briefly introduced. Moreover, we discussed which information related to interaction sites can be achieved through several examples. In addition, some notes about PlaPPISite are also provided. More importantly, we would like to emphasize the importance of interaction site information in plant systems biology through this user guide of PlaPPISite. In particular, the easily accessible 3D structures of PPIs in the coming post-AlphaFold2 era will definitely boost the application of plant interactome to decipher the molecular mechanisms of many fundamental biological issues.

Pre-trained protein language model sheds new light on the prediction of Arabidopsis protein-protein interactions.

BACKGROUND: Protein-protein interactions (PPIs) are heavily involved in many biological processes. Consequently, the identification of PPIs in the model plant Arabidopsis is of great significance to deeply understand plant growth and development, and then to promote the basic research of crop improvement. Although many experimental Arabidopsis PPIs have been determined currently, the known interactomic data of Arabidopsis is far from complete. In this context, developing effective machine learning models from existing PPI data to predict unknown Arabidopsis PPIs conveniently and rapidly is still urgently needed. RESULTS: We used a large-scale pre-trained protein language model (pLM) called ESM-1b to convert protein sequences into high-dimensional vectors and then used them as the input of multilayer perceptron (MLP). To avoid the performance overestimation frequently occurring in PPI prediction, we employed stringent datasets to train and evaluate the predictive model. The results showed that the combination of ESM-1b and MLP (i.e., ESMAraPPI) achieved more accurate performance than the predictive models inferred from other pLMs or baseline sequence encoding schemes. In particular, the proposed ESMAraPPI yielded an AUPR value of 0.810 when tested on an independent test set where both proteins in each protein pair are unseen in the training dataset, suggesting its strong generalization and extrapolating ability. Moreover, the proposed ESMAraPPI model performed better than several state-of-the-art generic or plant-specific PPI predictors. CONCLUSION: Protein sequence embeddings from the pre-trained model ESM-1b contain rich protein semantic information. By combining with the MLP algorithm, ESM-1b revealed excellent performance in predicting Arabidopsis PPIs. We anticipate that the proposed predictive model (ESMAraPPI) can serve as a very competitive tool to accelerate the identification of Arabidopsis interactome.

Exploring the crosstalk between endothelial cells, immune cells, and immune checkpoints in the tumor microenvironment: new insights and therapeutic implications.

The tumor microenvironment (TME) is a highly intricate milieu, comprising a multitude of components, including immune cells and stromal cells, that exert a profound influence on tumor initiation and progression. Within the TME, angiogenesis is predominantly orchestrated by endothelial cells (ECs), which foster the proliferation and metastasis of malignant cells. The interplay between tumor and immune cells with ECs is complex and can either bolster or hinder the immune system. Thus, a comprehensive understanding of the intricate crosstalk between ECs and immune cells is essential to advance the development of immunotherapeutic interventions. Despite recent progress, the underlying molecular mechanisms that govern the interplay between ECs and immune cells remain elusive. Nevertheless, the immunomodulatory function of ECs has emerged as a pivotal determinant of the immune response. In light of this, the study of the relationship between ECs and immune checkpoints has garnered considerable attention in the field of immunotherapy. By targeting specific molecular pathways and signaling molecules associated with ECs in the TME, novel immunotherapeutic strategies may be devised to enhance the efficacy of current treatments. In this vein, we sought to elucidate the relationship between ECs, immune cells, and immune checkpoints in the TME, with the ultimate goal of identifying novel therapeutic targets and charting new avenues for immunotherapy.

Bacterial cellulose flakes loaded with Bi2MoO6 nanoparticles and quantum dots for the photodegradation of antibiotic and dye pollutants.

Effective strategies to improve charge separation in semiconductor particles are critical for improving the photodegradation of organic pollutants at levels sufficient for environmental applications. Herein, Bi2MoO6 (BMOMOF), comprising both nanoparticles (NPs) and quantum dots (QDs), was synthesized from a bismuth-based metal-organic framework (Bi-MOF) precursor. Surface defects on BMOMOF, the combination of NPs and QDs, and modified energy band edges improved photogenerated charge separation and facilitated redox reactions. When compared to BMO derived from uncoordinated Bi, the BMOMOF photocatalyst (PC) was more efficient at photodegrading tetracycline hydrochloride (TCH) and ciprofloxacin (CIP), two widely-used antibiotics ubiquitous in wastewater, as well as the carcinogenic pollutant rhodamine B (RhB). BMOMOF was then loaded on the biopolymer bacterial cellulose (BC) to further enhance photocatalytic performance and facilitate the recovery of the PC after water treatment processes. The novel BMOMOF/BC photocatalytic flakes were significantly larger than pure BMOMOF, and thus easier to recuperate. Furthermore, anchoring BMOMOF on BC flakes augmented significantly the photodegradation of TCH, CIP, and RhB, mainly because hydroxyl groups in BC act as hole traps facilitating photogenerated electron-hole separation. Results obtained with BMOMOF/BC highlight promising approaches to develop optimal PCs for aqueous pollutants degradation.

Recent Advances in the Aging Microenvironment of Breast Cancer.

Aging is one of the risk factors for advanced breast cancer. With the increasing trend toward population aging, it is important to study the effects of aging on breast cancer in depth. Cellular senescence and changes in the aging microenvironment in vivo are the basis for body aging and death. In this review, we focus on the influence of the aging microenvironment on breast cancer. Increased breast extracellular matrix stiffness in the aging breast extracellular matrix can promote the invasion of breast cancer cells. The role of senescence-associated secretory phenotypes (SASPs) such as interleukin-6 (IL-6), IL-8, and matrix metalloproteases (MMPs), in breast cancer cell proliferation, invasion, and metastasis is worthy of exploration. Furthermore, the impact of senescent fibroblasts, adipocytes, and endothelial cells on the mammary matrix is discussed in detail. We also list potential targets for senotherapeutics and senescence-inducing agents in the aging microenvironment of breast cancer. In conclusion, this review offers an overview of the influence of the aging microenvironment on breast cancer initiation and progression, with the aim of providing some directions for future research on the aging microenvironment in breast cancer.

Computer simulation of different modes of ACE based on the dynamic complexation model.

Several modes of the often used ACE processes are simulated based on the principle of dynamic complexation of interacting species in a capillary column. The model is built on the mass transfer equation, to provide insight into the detailed analyte migration and interaction processes in CE. Normal ACE, Hummel-Dreyer method, vacancy affinity CE, vacancy peak method, and CE frontal analysis are simulated based on typical ACE conditions, and the results are compared with the detector responses of real CE processes using BSA and warfarin as a model system. Remarkable resemblance between the simulated results and the experimental observations was demonstrated for well-buffered ACE systems.