Spatiotemporal limitations in plant biology research

Why incorporate plant architecture into trait-based ecology?

Trait-based ecology has improved our understanding of the functioning of organisms, communities, ecosystems, and beyond. However, its predictive ability remains limited as long as phenotypic integration and temporal dynamics are not considered. We highlight how the morphogenetic processes that shape the 3D development of a plant during its lifetime affect its performance. We show that the diversity of architectural traits allows us to go beyond organ-level traits in capturing the temporal and spatial dimensions of ecological niches and informing community assembly processes. Overall, we argue that consideration of multilevel topological, geometrical, and ontogenetic features provides a dynamic view of the whole-plant phenotype and a relevant framework for investigating phenotypic integration, plant adaptation and performance, and community structure and dynamics.

Achieving the impossible: prevention and eradication of invasive plants in Mediterranean-type ecosystems

An in-depth analysis of the mechanistic processes underlying the evolution and ecophysiology of typical invasive plants such as Carpobrotus spp., Acacia spp., Agave spp., and Opuntia spp. in Mediterranean-type ecosystems shows very sophisticated, complex, and efficient strategies for invasion success, particularly in fragmented habitats. Propagule pressure at both geographical and temporal scales and the establishment of long-term seed banks are determinant for invasion success. A two-sword strategy based on long-term prevention and eradication is proposed for the management of Mediterranean-type ecosystems. Eradication of invasive plants in Mediterranean-type ecosystems appears to be extremely difficult nowadays and, at least for some invaders like Carpobrotus spp., long-term approaches that ultimately culminate in the elimination of seed banks is the only path for success.

Parasitic plant-host interaction between the holoparasite Cytinus hypocistis and the shrub Cistus albidus in their natural Mediterranean habitat: local and systemic hormonal effects

Mediterranean-type ecosystems provide a unique opportunity to study parasitic plant-host interactions, such as the relationship between the dominant shrub Cistus albidus L. and the root holoparasitic plant Cytinus hypocistis L. We examined this interaction (i) locally, by measuring the hormonal profiling of the interaction zone between the holoparasitic plant and the host, and (ii) systemically, by examining the hormonal profiling and physiological status of leaves from infested and uninfested plants. Furthermore, we explored how temporal variation (seasonal effects) and geographical location influenced the systemic hormonal and physiological response of leaves. Results shed light on tissue-related variations in hormones, suggesting the parasite exerted a sink effect, mainly influenced by cytokinins. Jasmonates triggered a defense response in leaves, far from the infestation point, and both jasmonates and abscisic acid (ABA) appeared to be involved in the tolerance to holoparasitism when plants were simultaneously challenged with summer drought. Parasitism did not have any major negative impact on the host, as indicated by physiological stress markers in leaves, thus indicating a high tolerance of the shrub C. albidus to the root holoparasitic plant C. hypocistis. Rather, parasitism seemed to exert a priming-like effect and some compensatory effects were observed (increased chlorophyll contents) in the host under mild climatic conditions. We conclude that (i) cytokinins, jasmonates and ABA play a role at the local and systemic levels in the response of C. albidus to the biotic stress caused by C. hypocistis, and that (ii) seasonal changes in environmental conditions and geographical location may impact holoparasitic plant-host interactions in the field, modulating the physiological response.

Machine learning for image-based multi-omics analysis of leaf veins

Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture.

The Electrome of a Parasitic Plant in a Putative State of Attention Increases the Energy of Low Band Frequency Waves: A Comparative Study with Neural Systems

Selective attention is an important cognitive phenomenon that allows organisms to flexibly engage with certain environmental cues or activities while ignoring others, permitting optimal behaviour. It has been proposed that selective attention can be present in many different animal species and, more recently, in plants. The phenomenon of attention in plants would be reflected in its electrophysiological activity, possibly being observable through electrophytographic (EPG) techniques. Former EPG time series obtained from the parasitic plant Cuscuta racemosa in a putative state of attention towards two different potential hosts, the suitable bean (Phaseolus vulgaris) and the unsuitable wheat (Triticum aestivum), were revisited. Here, we investigated the potential existence of different band frequencies (including low, delta, theta, mu, alpha, beta, and gamma waves) using a protocol adapted from neuroscientific research. Average band power (ABP) was used to analyse the energy distribution of each band frequency in the EPG signals, and time dispersion analysis of features (TDAF) was used to explore the variations in the energy of each band. Our findings indicated that most band waves were centred in the lower frequencies. We also observed that C. racemosa invested more energy in these low-frequency waves when suitable hosts were present. However, we also noted peaks of energy investment in all the band frequencies, which may be linked to extremely low oscillatory electrical signals in the entire tissue. Overall, the presence of suitable hosts induced a higher energy power, which supports the hypothesis of attention in plants. We further discuss and compare our results with generic neural systems.

Cheap, cost-effective, and quick stress biomarkers for drought stress detection and monitoring in plants

The detection and monitoring of drought stress in plants growing in their natural habitat are essential for the study of plant stress physiology. However, with the advent of plant phenotyping and new -omics technologies, the application of simple, cheap, cost-effective, quick, and practical methods to assess drought stress in plants seems more challenging than ever, particularly in low-income countries. Here, currently available methods that do not require specialized equipment, but reliably detect and monitor drought stress in plants at low cost will be discussed. This will not only boost research on plant stress physiology in low-income countries but will also help several laboratories with very limited resources around the globe to perform high-quality research.

Differential tissue-specific accumulation and function of tocochromanols in grape berries

Grape berries have been extensively studied in terms of antioxidant characterization, specifically in anthocyanin, total phenol, and tannin accumulation. However, very little is known about vitamin E composition and contents in this fruit. Aiming to examine the function of vitamin E during grape berries ripening, tocochromanol contents and composition were evaluated in berries and leaves of grapevines (Vitis vinifera L. cv. Merlot), from just before veraison to commercial harvest. We also determined the time-course evolution of tocochromanol accumulation in various fruit tissues, including the skin, pulp, and seeds, and measured the extent of primary and secondary lipid peroxidation, as well as fruit technological maturity parameters. Vitamin E accumulated at higher levels in leaves than in fruits, although the tissue-specific evaluation of tocochromanol contents revealed that berry skin is also rich in α-tocopherol whereas tocotrienols were present in seeds only. α-Tocopherol content decreased during ripening, more specifically in the skin, and it was accompanied by an increase in the extent of lipid peroxidation. Contents and variations in the levels of α-tocopherol, but not those of the other tocochromanols, were inversely related to changes in lipid peroxidation during fruit ripening, as indicated by tissue-specific variations in malondialdehyde contents. In conclusion, α-tocopherol is more abundant in leaves than fruit, yet it apears to exert a role in the modulation of the extent of lipid peroxidation in grape berries, more specifically in the skin, where α-tocopherol depletion and malondialdehyde accumulation may be related to an adequate progression of fruit ripening.

Novel insights into maize (Zea mays) development and organogenesis for agricultural optimization

In maize, intrinsic hormone activities and sap fluxes facilitate organogenesis patterning and plant holistic development; these hormone movements should be a primary focus of developmental biology and agricultural optimization strategies. Maize (Zea mays) is an important crop plant with distinctive life history characteristics and structural features. Genetic studies have extended our knowledge of maize developmental processes, genetics, and molecular ecophysiology. In this review, the classical life cycle and life history strategies of maize are analyzed to identify spatiotemporal organogenesis properties and develop a definitive understanding of maize development. The actions of genes and hormones involved in maize organogenesis and sex determination, along with potential molecular mechanisms, are investigated, with findings suggesting central roles of auxin and cytokinins in regulating maize holistic development. Furthermore, investigation of morphological and structural characteristics of maize, particularly node ubiquity and the alternate attachment pattern of lateral organs, yields a novel regulatory model suggesting that maize organ initiation and subsequent development are derived from the stimulation and interaction of auxin and cytokinin fluxes. Propositions that hormone activities and sap flow pathways control organogenesis are thoroughly explored, and initiation and development processes of distinctive maize organs are discussed. Analysis of physiological factors driving hormone and sap movement implicates cues of whole-plant activity for hormone and sap fluxes to stimulate maize inflorescence initiation and organ identity determination. The physiological origins and biogenetic mechanisms underlying maize floral sex determination occurring at the tassel and ear spikelet are thoroughly investigated. The comprehensive outline of maize development and morphogenetic physiology developed in this review will enable farmers to optimize field management and will provide a reference for de novo crop domestication and germplasm improvement using genome editing biotechnologies, promoting agricultural optimization.

Toward kingdom-wide analyses of gene expression

Gene expression data for Archaeplastida are accumulating exponentially, with more than 300 000 RNA-sequencing (RNA-seq) experiments available for hundreds of species. The gene expression data stem from thousands of experiments that capture gene expression in various organs, tissues, cell types, (a)biotic perturbations, and genotypes. Advances in software tools make it possible to process all these data in a matter of weeks on modern office computers, giving us the possibility to study gene expression in a kingdom-wide manner for the first time. We discuss how the expression data can be accessed and processed and outline analyses that take advantage of cross-species analyses, allowing us to generate powerful and robust hypotheses about gene function and evolution.

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.