Trends in Plant Science最新文献

The Green Revolution transformed agriculture with high-yielding, stress-resistant varieties. However, the urgent need for more sustainable agricultural development presents new challenges: increasing crop yield, improving nutritional quality, and enhancing resource-use efficiency. Soil plays a vital role in crop-production systems and ecosystem services, providing water, nutrients, and physical anchorage for crop growth. Despite advancements in plant and soil sciences, our understanding of belowground plant-soil interactions, which impact both crop performance and soil health, remains limited. Here, we argue that a lack of understanding of these plant-soil interactions hinders sustainable crop production. We propose that targeted engineering of crops and soils can provide a fresh approach to achieve higher yields, more efficient sustainable crop production, and improved soil health.

Regulating gene expression in plant development and environmental responses is vital for mitigating the effects of climate change on crop growth and productivity. The eukaryotic genome largely shows the canonical B-DNA structure that is organized into nucleosomes with histone modifications shaping the epigenome. Nuclear proteins and RNA interactions influence chromatin conformations and dynamically modulate gene activity. Non-B DNA conformations and their transitions introduce novel aspects to gene expression modulation, particularly in response to environmental shifts. We explore the current understanding of non-B DNA structures in plant genomes, their interplay with epigenomics and gene expression, and advances in methods for their mapping and characterization. The exploration of so far uncharacterized non-B DNA structures remains an intriguing area in plant chromatin research and offers insights into their potential role in gene regulation.

Understanding the complex challenges that plants face from multiple stresses is key to developing climate-ready crops. We highlight the significance of the Stress Combinations and their Interactions in Plants database (SCIPdb) for studying the impact of stress combinations on plants and the importance of aligning thematic research programs to create crops aligned with achieving sustainable development goals.

Given that crop yields are strongly limited by nitrogen, engineering crop plants with self-nitrogen-fertilization capacity holds great promise for sustainable agriculture. Recently, a nitrogen-fixing organelle has been characterized in the unicellular marine microalgae Braarudosphaera bigelowii. Engineering a nitrogen-fixing organelle into the non-nitrogen-fixing crops could benefit both environmental sustainability and global food security.

Understanding molecular dynamics at the single cell level is crucial to understand plant traits. Recently, Liu et al. and Cui et al. reported multiome analysis in the same cell/nucleus to dissect the key plant traits (osmotic stress response and pod development). Their results provide novel insights into pathways and regulatory networks at a single cell resolution.

Crop productivity depends on the ability of plants to thrive across different growth environments. In nature, light conditions fluctuate due to diurnal and seasonal changes in direction, duration, intensity, and spectrum. Laboratory studies, predominantly conducted with arabidopsis (Arabidopsis thaliana), have provided valuable insights into the metabolic and regulatory strategies that plants employ to cope with varying light intensities. However, there has been less focus on how horticultural crops tolerate dynamically changing light conditions during the photoperiod. In this review we connect insights from photobiology in model plants to the application of dynamic lighting in indoor horticulture. We explore how model species respond to fluctuating light intensities and discuss how this knowledge could be translated for new lighting solutions in controlled environment agriculture.

The bottlenecks of conventional plant genome-editing methods gave an innovative rise to nanotechnology as a delivery tool to manipulate gene(s) of interest. Studies suggest a strong correlation between the physicochemical properties of nanomaterials and their efficiency in gene delivery to different plant species/tissues. In this opinion article we highlight the need for a deeper understanding of plant-nanomaterial interactions to align their full capabilities with the strategic goals of plant genome-editing. Additionally, we emphasize DNA-free plant genome-editing approaches to potentially mitigate concerns surrounding genetically modified organisms (GMOs). Lastly, we propose a strategic integration of the principles of responsible research and innovation (RRI) in R&D. We aim to initiate a dialogue on developing collaborative and socio-technical frameworks for nanotechnology and DNA-free plant genome-editing.

Plants intricately regulate the expression of protein-coding genes at multiple stages - including mRNA transcription, translation, decay, and protein degradation - to control growth, development, and responses to environmental challenges. Recent research highlights the importance of translational reprogramming as a pivotal mechanism in regulating gene expression across diverse physiological scenarios. This regulatory mechanism bears practical implications, particularly in bolstering crop productivity by manipulating RNA regulatory elements (RREs) to modulate heterologous gene expression through transgene and endogenous gene expression through gene editing. Here, we elucidate the potential of upstream open reading frames (uORFs), a prominent and stringent class of RREs, in optimizing crop performance, exemplifying the efficacy of translational control in enhancing agricultural yields.

Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotype-by-environment (G×E) interactions can offer insights into the genetic factors that affect performance in specific environments. By leveraging historical breeding data, ML streamlines strategies and automates analyses to reveal genomic patterns. In this review we examine the transformative impact of big data, including multi-trait genomics, phenomics, and environmental covariables, on genomic-enabled prediction in plant breeding. We discuss how big data and ML are revolutionizing the field by enhancing prediction accuracy, deepening our understanding of G×E interactions, and optimizing breeding strategies through the analysis of extensive and diverse datasets.

Salt contamination of soils and irrigation water is a significant environmental concern for crop production. Leaf sodium (Na⁺) exclusion is commonly proposed to be a key subtrait of salt tolerance for many crop plants. High-Affinity Potassium (K⁺) Transporter 1 (HKT1) proteins have previously been identified as major controllers of leaf Na⁺ exclusion across diverse species. However, leaf Na⁺ exclusion does not always correlate with salt tolerance. We discuss literature which shows leaf Na⁺ accumulation can, in some circumstances, be tolerated without a detrimental effect on yield when HKT1 still functions to exclude Na⁺ from reproductive tissues. We conclude that, by having an ultimate role in the protection of reproductive performance, HKT1s' role in adaptation to salinity warrants redefinition.