Jian Luo

The circadian clock orchestrates photosynthetic and metabolic processes in plants, but the molecular mechanisms underlying the photoperiodic regulation of photosynthetic yield remain poorly understood. Here, we integrated computational modeling and experimental validation to investigate how the skeletal photoperiod modulates photosynthetic efficiency in celery (Apium graveolens L.). Our model revealed that endogenous circadian rhythms dynamically regulate photosynthetic gene expression (e.g., Lhcb1, psbA, RbcS1, and atpA) and photosynthetic parameters (net photosynthetic rate and stomatal conductance) through interactions between clock components (CCA1/LHY and PRR9/PRR7) and light signaling. In particular, the 3L:3D skeleton photoperiod induced the highest 24 h photosynthetic accumulation (a 32% and 22% increase in chlorophyll and nitrogen content, respectively, vs. 12L:12D), outperforming continuous light (LL) and longer photoperiods. Rhythmic peaks of photosynthetic genes aligned with circadian-driven oscillations in the photosynthetic parameters, while a strong negative correlation between the net photosynthetic rate (Pn) and intercellular CO₂ concentration (Ci) emerged under 3L:3D cycles. Model simulations demonstrated robustness in capturing phase-specific gene expression and parameter dynamics across photoperiods, highlighting the role of the circadian clock in optimizing energy use. These results demonstrate that abnormal L/D cycles, particularly 3L:3D, increase photosynthetic yield by enhancing circadian-regulated metabolic coordination, providing a low-energy, high-efficiency strategy for agricultural productivity. This work advances our understanding of photoperiodic manipulation in crop systems and provides a predictive framework for circadian-informed crop management.

The challenges posed by climate change have had a crucial impact on global food security, with crop yields negatively affected by abiotic and biotic stresses. Consequently, the identification of abiotic stress-responsive genes (SRGs) in crops is essential for augmenting their resilience. This study presents a computational model utilizing machine learning techniques to predict genes in Chinese cabbage that respond to four abiotic stresses: cold, heat, drought, and salt. To construct this model, data from relevant studies regarding responses to these abiotic stresses were compiled, and the protein sequences encoded by abiotic SRGs were converted into numerical representations for subsequent analysis. For the selected feature set, six distinct machine learning binary classification algorithms were employed. The results demonstrate that the constructed models can effectively predict SRGs associated with the four types of abiotic stresses, with the area under the receiver operating characteristic curve (auROC) for the models being 81.42%, 87.92%, 80.85%, and 88.87%, respectively. For each type of stress, a distinct number of stress-resistant genes was predicted, and the ten genes with the highest scores were selected for further analysis. To facilitate the implementation of the proposed strategy by users, an online prediction server, has been developed. This study provides new insights into computational approaches to the identification of abiotic SRGs in Chinese cabbage as well as in other plants.

In the information society, challenges posed by climate change and population growth have rendered the development of crop varieties with enhanced abiotic stress tolerance and improved nutritional value a prime objective in agricultural advancement. Chinese cabbage, recognized for its significant economic benefits, not only supplements the human body with vitamin C and vitamin E but also plays a crucial role in preventing cardiovascular diseases. Nonetheless, its growth and development are adversely affected by various abiotic stresses, including waterlogging and temperature fluctuations. Consequently, the identification of abiotic stress-responsive genes (SRGs) in Chinese cabbage is of paramount importance for enhancing its resilience. While transcriptome analysis is a reliable approach for identifying stress-related genes, it is inherently species-specific and can be time-consuming. In this study, we proposed a computational model utilizing machine learning techniques to predict genes in Chinese cabbage that respond to four specific abiotic stresses: cold, heat, drought, and salt. To construct this model, we compiled data from relevant studies regarding the response to these abiotic stresses, and the protein sequences encoded by abiotic SRGs were converted into numerical representations for subsequent analysis. For the selected feature set, we employed six distinct machine learning binary classification algorithms. The results demonstrate that various models can effectively predict SRGs associated with the four types of abiotic stresses, with the area under the receiver operating characteristic curve (auROC) for the models being 81.42%, 87.92%, 80.85%, and 88.87%, respectively. Furthermore, we have established an online prediction server, named MLAS (http://47.122.66.48:4433), designed to predict the response genes of Chinese cabbage under the aforementioned abiotic stresses. The computational model and the prediction tool developed in this study can serve as valuable resources for the identification of abiotic SRGs in Chinese cabbage.

Light is a crucial environmental factor for plant growth and development, and the light required for plant development on Earth mainly comes from solar radiation. Light not only affects the photomorphogenesis of plants as a trigger signal but also has an important influence on the whole development of plants. The study of photoperiod regulation of plant flowering is crucial in agricultural practice, as it directly affects plant variety selection, introductory adaptation, and yield stability. In this paper, we used the Bibliometrix package in R Studio, VOSviewer, and Citespace to bibliometrically and statistically analyze 3919 publications in the Web of Science core ensemble database from 2000 to 2023 and to explore the progress and trends in the study of photoperiodic regulation of plant flowering. Literature visualization techniques were used to analyze the annual quantitative distribution of the studies in the dataset, and co-occurrence analysis of collaboration between authors, co-occurrence analysis of collaboration between organizations, co-citation analysis, keyword co-occurrence analysis, and identification of topic evolution were also performed. The results show that the number of publications on photoperiodic regulation of plant flowering is increasing every year, showing a significant diversification and globalization trend in this research area. Growth, gene expression, and Arabidopsis thaliana are the most popular research areas in this field. Flowering gene networks and signaling pathways in the photoperiodic pathway, systems biology and multi-omics, environmental adaptation studies, and cross-species comparisons are current research hotspots. The thematic evolution suggests that environmental change, crop improvement and sustainable agriculture, light quality, and biological clock regulation may be foci of future research. This study reveals the research trends in photoperiodic regulation of plant flowering over the past 23 years and the areas where more research inputs are needed in the future, aiming to offer guidance for future research endeavors in the field of photoperiodic regulation of plant flowering.