Summary Epigenetic modifications have emerged as an important mechanism underlying plant defence against pathogens. We examined the role of JMJ14, a Jumonji (JMJ) domain‐containing H3K4 demethylase, in local and systemic plant immune responses in Arabidopsis. The function of JMJ14 in local or systemic defence response was investigated by pathogen growth assays and by analysing expression and H3K4me3 enrichments of key defence genes using qPCR and ChIP‐qPCR. Salicylic acid (SA) and pipecolic acid (Pip) levels were quantified and function of JMJ14 in SA‐ and Pip‐mediated defences was analysed in Col‐0 and jmj14 plants. jmj14 mutants were compromised in both local and systemic defences. JMJ14 positively regulates pathogen‐induced H3K4me3 enrichment and expression of defence genes involved in SA‐ and Pip‐mediated defence pathways. Consequently, loss of JMJ14 results in attenuated defence gene expression and reduced Pip accumulation during establishment of systemic acquired resistance (SAR). Exogenous Pip partially restored SAR in jmj14 plants, suggesting that JMJ14 regulated Pip biosynthesis and other downstream factors regulate SAR in jmj14 plants. JMJ14 positively modulates defence gene expressions and Pip levels in Arabidopsis.

Natural mutants of the Non-ripening (Nor) gene repress the normal ripening of tomato fruit. The molecular mechanism of fruit ripening regulation by the Nor gene is unclear. To elucidate how the Nor gene can affect ripening and fruit quality at the protein level, we used the fruits of Nor mutants and wild-type Ailsa Craig (AC) to perform iTRAQ (isobaric tags for relative and absolute quantitation) analysis. The Nor mutation altered tomato fruit ripening and affected quality in various respects, including ethylene biosynthesis by down-regulating the abundance of 1-aminocyclopropane-1-carboxylic acid oxidase (ACO), pigment biosynthesis by repressing phytoene synthase 1 (PSY1), ζ-carotene isomerase (Z-ISO), chalcone synthase 1 (CHS1) and other proteins, enhancing fruit firmness by increasing the abundance of cellulose synthase protein, while reducing those of polygalacturonase 2 (PG2) and pectate lyase (PL), altering biosynthesis of nutrients such as carbohydrates, amino acids, and anthocyanins. Conversely, Nor mutation also enhanced the fruit’s resistance to some pathogens by up-regulating the expression of several genes associated with stress and defense. Therefore, the Nor gene is involved in the regulation of fruit ripening and quality. It is useful in the future as a means to improve fruit quality in tomato.

Tomato fruit ripening is a complex process, which determines the formation of fruit quality. Many factors affect fruit ripening, including environmental conditions and genetic factors. Transcription factors (TFs) play key roles in regulating fruit ripening and quality formation. Current studies have found that the TDR4 gene is an important TF for tomato fruit ripening, but its effects on fruit metabolism and quality are less well studied. In this study, suppression of TDR4 gene expression obtained through virus-induced gene silencing (VIGS) technology resulted in an orange pericarp phenotype. Transcriptomic analysis of TDR4 -silenced fruit showed changes in the expression of genes involved in various metabolic pathways, including amino acid and flavonoid biosynthesis pathways. Metabolomic analysis showed that levels of several amino acids including phenylalanine and tyrosine, and organic acids were reduced in TDR4 -silenced fruit, while α-tomatine accumulated in TDR4 -silenced fruit. Taken together, our RNA-seq and metabolomics analyses of TDR4 -silenced fruit showed that TDR4 is involved in ripening and nutrient synthesis in tomato fruit, and is therefore an important regulator of fruit quality.

Fruit ripening is a complex biological process affecting fruit quality. In tomato the fruit ripening process is delicately regulated by transcription factors (TFs). Among these, the TOMATO AGAMOUS-LIKE 1 (TAGL1) gene plays an important role in both the development and ripening of fruit. In this study, the TAGL1 gene was successfully silenced by virus-induced gene silencing technology (VIGS), and the global gene expression and metabolites profiles of TAGL1-silenced fruits were analyzed by RNA-sequence analysis (RNA-seq) and liquid chromatography–mass spectrometry (LC-MS/MS). The TAGL1-silenced fruits phenotypically displayed an orange pericarp, which was in accordance with the results expected from the down-regulation of genes associated with carotenoid synthesis. Levels of several amino acids and organic acids were lower in the TAGL1-silenced fruits than in the wild-type fruits, whereas, α-tomatine content was greatly increased (more than 10-fold) in the TAGL1-silenced fruits compared to wild-type fruits. The findings of this study showed that TAGL1 not only regulates the ripening of tomato fruits, but also affects the synthesis and levels of nutrients in the fruit.

Machine learning has huge potential to revolutionize the field of drug discovery and is attracting increasing attention in recent years. However, lacking domain knowledge (e.g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain. To facilitate the progress of machine learning for drug discovery, we develop TorchDrug, a powerful and flexible machine learning platform for drug discovery built on top of PyTorch. TorchDrug benchmarks a variety of important tasks in drug discovery, including molecular property prediction, pretrained molecular representations, de novo molecular design and optimization, retrosynthsis prediction, and biomedical knowledge graph reasoning. State-of-the-art techniques based on geometric deep learning (or graph machine learning), deep generative models, reinforcement learning and knowledge graph reasoning are implemented for these tasks. TorchDrug features a hierarchical interface that facilitates customization from both novices and experts in this domain. Tutorials, benchmark results and documentation are available at https://torchdrug.ai. Code is released under Apache License 2.0.