The oilseed rape developmental expression resource: a resource for the investigation of gene expression dynamics during the floral transition in oilseed rape

A transcriptomic time-series reveals differing trajectories during pre-floral development in the apex and leaf in winter and spring varieties of Brassica napus

Oilseed rape (Brassica napus) is an important global oil crop, with spring and winter varieties grown commercially. To understand the transcriptomic differences between these varieties, we collected transcriptomes from apex and leaf tissue from a spring variety, Westar, and a winter variety, Tapidor, before, during, and after vernalisation treatment, until the plants flowered. Large transcriptomic differences were noted in both varieties during the vernalisation treatment because of temperature and day length changes. Transcriptomic alignment revealed that the apex transcriptome reflects developmental state, whereas the leaf transcriptome is more closely aligned to the age of the plant. Similar numbers of copies of genes were expressed in both varieties during the time series, although key flowering time genes exhibited expression pattern differences. BnaFLC copies on A2 and A10 are the best candidates for the increased vernalisation requirement of Tapidor. Other BnaFLC copies show tissue-dependent reactivation of expression post-cold, with these dynamics suggesting some copies have retained or acquired a perennial nature. BnaSOC1 genes, also related to the vernalisation pathway, have expression profiles which suggest tissue subfunctionalisation. This understanding may help to breed varieties with more consistent or robust vernalisation responses, of special importance due to the milder winters resulting from climate change.

LsMYB15 Regulates Bolting in Leaf Lettuce (Lactuca sativa L.) Under High-Temperature Stress

High temperature is one of the primary environmental stress factors affecting the bolting of leaf lettuce. To determine the potential role of melatonin in regulating high-temperature induced bolting in leaf lettuce (Lactuca sativa L.), we conducted melatonin treatment of the bolting-sensitive cultivar "S39." The results showed that 100 μmol L^-1 melatonin treatment significantly promoted growth, and melatonin treatment delayed high-temperature-induced bolting in lettuce. RNA-seq analysis revealed that the differentially expressed genes (DEGs) involved in "plant hormone signal transduction" and "phenylpropanoid biosynthesis" were significantly enriched during high-temperature and melatonin treatment. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis suggested that the expression patterns of abscisic acid (ABA)-related genes positively correlated with stem length during leaf lettuce development. Furthermore, weighted gene co-expression network analysis (WGCNA) demonstrated that MYB15 may play an important role in melatonin-induced resistance to high temperatures. Silencing the LsMYB15 gene in leaf lettuce resulted in early bolting, and exogenous melatonin delayed early bolting in leaf lettuce at high temperatures. Our study provides valuable data for future studies of leaf lettuce quality.

Data management challenges for artificial intelligence in plant and agricultural research

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.

Data management challenges for artificial intelligence in plant and agricultural research

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.

Phytochrome-interacting factor (PIF) in rapeseed (Brassica napus L.): Genome-wide identification, evolution and expression analyses during abiotic stress, light quality and vernalization

Phytochrome-interacting factors (PIFs) are members of basic helix-loop-helix (bHLH) transcription factors and the primary partners of phytochromes (PHY) in light signaling. PIFs interact with the P_fr forms of phytochrome to play an important role in the pathways of response to light and temperature in plants. In this study, 30, 12, and 16 potential PIF genes were identified in Brassica napus, Brassica rapa, Brassica oleracea, respectively, which could be divided into three subgroups. The Br/Bo/BnaPIF genes are intron-rich and similar to the PIF genes in Arabidopsis. However, unlike the AtPIFs that exist in multiple alternative-splicing forms, the majority of Br/Bo/BnaPIF genes have no alternative-splicing forms. A total of 52 Br/Bo/BnaPIF proteins have both the conserved active PHYB binding (APB) and bHLH domains. The Ka/Ks ratio revealed that most BnaPIFs underwent purifying selection. A promoter analysis found that light-related, abscisic acid-related and MYB-binding sites were the most abundant in the promoters of BnaPIFs. BnaPIF genes displayed different spatiotemporal patterns of expression and were regulated by light quality, circadian rhythms, cold, heat, and vernalization. Our results are useful for understanding the biological functions of PIF proteins in rapeseed.