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Plant hormones play central roles in plant growth, developmental processes, and plant response to biotic and abiotic stresses. On the one hand, plant hormones may allocate limited resources to the most serious stresses; on the other hand, the crosstalks among multiple plant hormone signaling regulate the balance between plant growth and defense. Many studies have reported the mechanism of crosstalks between jasmonic acid (JA) and other plant hormones in plant growth and stress responses. Based on these studies, this paper mainly reviews the crosstalks between JA and other plant hormone signaling in regulating the balance between plant growth and defense response. The suppressor proteins JASMONATE ZIM DOMAIN PROTEIN (JAZ) and MYC2 as the key components in the crosstalks are also highlighted in the review. We conclude that JA interacts with other hormone signaling pathways [such as auxin, ethylene (ET), abscisic acid (ABA), salicylic acid (SA), brassinosteroids (BRs), and gibberellin (GA)] to regulate plant growth, abiotic stress tolerance, and defense resistance against hemibiotrophic pathogens such as Magnaporthe oryzae and Pseudomonas syringae . Notably, JA may act as a core signal in the phytohormone signaling network.

Identifying differentially methylated regions (DMRs) is a basic task in DNA methylation analysis. However, due to the different strategies adopted, different DMR sets will be predicted on the same dataset, which poses a challenge in selecting a reliable and comprehensive DMR set for downstream analysis. Here, we develop DMRIntTk, a toolkit for integrating DMR sets predicted by different methods on a same dataset. In DMRIntTk, the genome is segmented into bins, and the reliability of each DMR set at different methylation thresholds is evaluated. Then, the bins are weighted based on the covered DMR sets and integrated into final DMRs using a density peak clustering algorithm. To demonstrate the practicality of DMRIntTk, it was applied to different scenarios, including tissues with relatively large methylation differences, cancer tissues versus normal tissues with medium methylation differences, and disease tissues versus normal tissues with subtle methylation differences. Our results show that DMRIntTk can effectively trim regions with small methylation differences from the original DMR sets and thereby enriching the proportion of DMRs with larger methylation differences. In addition, the overlap analysis suggests that the integrated DMR sets are quite comprehensive, and functional analyses indicate the integrated disease-related DMRs are significantly enriched in biological pathways associated with the pathological mechanisms of the diseases. A comparative analysis of the integrated DMR set versus each original DMR set further highlights the superiority of DMRIntTk, demonstrating the unique biological insights it can provide. Conclusively, DMRIntTk can help researchers obtain a reliable and comprehensive DMR set from many prediction methods.

Rice blast disease is one of the most common rice diseases worldwide. It is essential to improve disease resistance through environment-friendly methods, while maintaining yield and quality parameters. In this study, jasmonic acid (JA), a plant hormone with anti-fungal activity, was obtained, at both low (100 μmol/L) and high (400 μmol/L) concentrations in rice leaves, before, during, and after infection, respectively. JA could inhibit germination and appressorium formation of rice blast spores in a dose-dependent manner. A total of 400-μmol/L JA treatment significantly enhanced cell viability and endogenous JA level in rice leaves. Furthermore, rice leaves inoculated with Magnaporthe oryzae and sprayed with JA 72 h post-inoculation showed the maximum symptom relief and the highest endogenous JA production among all treatment approaches. The expressions of defense-related genes, OsPR10a and OsAOS2, were highly up-regulated in response to JA, whereas OsEDS1 was down-regulated. Hence, we revealed that exogenous JA could activate JA signaling to effectively control the symptoms of rice blast.

Background A drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs. Result In this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation. Conlusion In 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.

Key messageMoSDT1, a rice blast fungus transcription factor, is as an inducer to activate defense response through mainly mediating phosphorylated proteins in rice.Pathogen effector proteins play a dual role in infecting the host or triggering a defense response. Our previous research found a Magnaporthe oryzae effector, MoSDT1, which could activate the rice defense response when it was overexpressed in rice. However, we still know little about the mechanisms on how MoSDT1 in vivo or in vitro influences the resistance ability of rice. Our results showed that decreased ROS and increased lignin contents appeared along with significant upregulation of defense-related genes, raffinose synthesis gene, and phenylalanine ammonialyase gene. Moreover, we revealed that the contents of lignin were increased, which was in accordance with the upregulation of its precursor phenylalanine gene despite the fact that the glutamate-/thiamine-responsive genes were inhibited in MoSDT1 transgenic rice, and these indicated that MoSDT1 triggered the defense system of rice in vivo. Interestingly, in vitro studies, we further found that MoSDT1 induced the defense system by ROS synthesis, callose deposition, PR gene expression and SA/JA synthesis/signal genes using the purified prokaryotic expression system in rice plants. In addition, this defense response was confirmed to be activated by the zinc finger domain of MoSDT1 via prokaryotic expression of MoSDT1 truncated mutants in rice plants. To elucidate the regulative effects of MoSDT1 on protein phosphorylation in rice, phosphoproteome analysis was performed in both MoSDT1-transgenic and wild type rice. We found that MoSDT1 specifically up-regulated the expression levels of a few phosphorylated proteins, which were involved in multiple functions, such as biotic/abiotic stress and growth. In addition, the motifs in these specific proteins ranked the top among the top-five conserved motifs in the MoSDT1-transgenic rice. MoSDT1 played a crucial role in enhancing rice resistance by modulating several genes and signaling pathways.

Background Some of the pathogenic effector proteins play an active role in stimulating the plant defense system to strengthen plant resistance. Results In this study, ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS) was implemented to identify altered metabolites in transgenic rice containing over-expressed M. oryzae Systemic Defense Trigger 1 (MoSDT1) that was infected at three-time points. The characterized dominating metabolites were organic acids and their derivatives, organic oxygen compounds, lipids, and lipid-like molecules. Among the identified metabolites, shikimate, galactinol, trehalose, D-mannose, linolenic acid, dopamine, tyramine, and L-glutamine are precursors for the synthesis of many secondary defense metabolites Carbohydrate, as well as amino acid metabolic, pathways were revealed to be involved in plant defense responses and resistance strengthening. Conclusion The increasing salicylic acid (SA) and jasmonic acid (JA) content enhanced interactions between JA synthesis/signaling gene, SA synthesis/receptor gene, raffinose/fructose/sucrose synthase gene, and cell wall-related genes all contribute to defense response in rice. The symptoms of rice after M. oryzae infection were significantly alleviated when treated with six identified metabolites, i.e., galactol, tyramine, L-glutamine, L-tryptophan, α-terpinene, and dopamine for 72 h exogenously. Therefore, these metabolites could be utilized as an optimal metabolic marker for M. oryzae defense.

The complexity of cancer frequently results in diverse therapeutic responses among patients with the same cancer type undergoing identical treatments. Additionally, the development of anti-cancer drugs faces significant challenges due to extended timelines and high attrition rates during the process. Advances in machine learning, combined with the availability of extensive drug research databases, have facilitated the emergence of computational approaches designed to predict drug responses more accurately. However, the noise and uncertainty introduced by incomplete records across different biological data sources pose challenges, ultimately constraining the predictive capabilities of these models. To overcome these limitations, this study introduces a novel multi-modal learning-based drug response prediction framework, DSGPred. By constructing dynamic sample graphs, DSGPred accounts for missing modality types and quantities within individual samples, enabling a more granular understanding of data completeness. The framework integrates multi-modal heterogeneous graph convolutional networks with advanced fusion modules to deeply capture and synthesize diverse drug and cell line features. Additionally, DSGPred employs an interaction-focused feature extraction module to learn dual interaction modes, thereby enhancing the richness of drug-cell line interaction embeddings. Experimental evaluations on the benchmark and independent datasets indicate that DSGPred consistently surpasses existing methods in predictive performance. Furthermore, tests involving previously unseen drugs and cell lines validate DSGPred’s generalization ability. Practical applicability is further underscored through case studies, highlighting its utility in real-world scenarios, and offering robust predictions and insights for drug response prediction and personalized therapy design. The codes for DSGPred are available at https://github.com/zhc940702/DSGPred/.

Background MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs. Result In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine. Conclusion The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods.

Background Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited. Result In this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF). Conlusion The experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.

Cisplatin‐based chemotherapy resistance is a common issue for cancer clinical efficacy. Metformin is being studied for its possible anticancer effect. The present study aimed to investigate whether metformin affects the chemosensitivity of gastric cancer to cisplatin and reveal the molecular mechanism. In this study, the effects of combination therapy with metformin and cisplatin on cell viability, cell apoptosis, malondialdehyde, superoxide dismutase, reactive oxygen species level, glucose uptake, lactate production, protein level, and xenograft tumor formation were analyzed in gastric cancer cells. Immunohistochemical staining was performed to detect Ki67 expression in matched tumor samples. The results showed that NCI‐N87 and SNU‐16 cells were most resistant and sensitive to cisplatin, respectively. Metformin treatment increased the cisplatin sensitivity of gastric cancer by inhibiting cell viability and metabolic reprogramming and promoting cell apoptosis and oxidative stress. Furthermore, overexpression of nuclear factor erythroid 2‐related factor 2 (Nrf2) reversed the effects of metformin in the cisplatin sensitivity of gastric cancer by inhibiting cell viability and metabolic reprogramming and promoting cell apoptosis and oxidative stress. Metformin activated p53 and AMPK pathways in cisplatin‐induced NCI‐N87 cells, which were reversed by upregulating Nrf2. BAY‐3827 (AMPK inhibitor) or p‐nitro‐Pifithrin‐α (p53 inhibitor) treatments also reversed the effects of metformin increased the cisplatin sensitivity of gastric cancer by inhibiting cell viability and metabolic reprogramming and promoting cell apoptosis and oxidative stress. These results suggest that metformin significantly increases chemosensitivity of gastric cancer to cisplatin by inhibiting Nrf2 expression and metabolic reprogramming and activating oxidative stress and the pathway of p53 and AMPK.