Environment-coupled models of leaf metabolism

The crucial elements for lettuce (Lactuca sativa L.) growth under DMA stress and the linkage with DMA behavior: A new application of ionome

Dimethylarsinic acid (DMA) is one of the common arsenic (As) species present in soil and is more toxic to plants than others. Identifying the crucial elements for plant growth under DMA stress is essential to enhance plant tolerance to DMA. Herein, we provided for the first time an ionome-based approach to address this issue. The phenotype, As species and concentrations of 11 essential elements in lettuce tissues were monitored under exposures of 0.1, 0.5, 1, 2, 5 mg L-1 DMA in hydroponic culture for 32 days. Lettuces remained normal (no significant difference in phenotype from the control) under 0.1-2 mg L-1 DMA stress, and were inhibited with fresh weights of leaf and root under 5 mg L-1 DMA stress. Integrating the difference in ionome profiles between the two growth states (normal and inhibited) and the responses of the individual element, Mg and S were clarified as the most possible candidates for the crucial elements for lettuce growth under DMA stress. Under 5 mg L-1 DMA stress, the accumulation of Mg and S declined, yet their BCF values were significantly increased, which was consistent with the change in BCF of DMA. Based on the physiological functions of Mg and S and the toxicity of DMA, it could be inferred that the enhanced transfer of Mg and S to leaves should be induced by the potential damage caused by the increased DMA accumulation in leaves, and would result in a shortage of both elements in roots as well as the growth inhibition.

Nyctinastic movement in legumes: Developmental mechanisms, factors and biological significance

In legumes, a common phenomenon known as nyctinastic movement is observed. This movement involves the horizontal expansion of leaves during the day and relative vertical closure at night. Nyctinastic movement is driven by the pulvinus, which consists of flexor and extensor motor cells. The turgor pressure difference between these two cell types generates a driving force for the bending and deformation of the pulvinus. This review focuses on the developmental mechanisms of the pulvinus, the factors affecting nyctinastic movement, and the biological significance of this phenomenon in legumes, thus providing a reference for further research on nyctinastic movement.

Genome-scale modeling of the primary-specialized metabolism interface

Environmental challenges and development require plants to reallocate resources between primary and specialized metabolites to survive. Genome-scale metabolic models, which map carbon flux through metabolic pathways, are a valuable tool in the study of tradeoffs that arise at this interface. Due to annotation gaps, models that characterize all the enzymatic steps in individual specialized pathways and their linkages to each other and to central carbon metabolism are difficult to construct. Recent studies have successfully curated subsystems of specialized metabolism and characterized the interfaces where flux is diverted to the precursors of glucosinolates, terpenes, and anthocyanins. Although advances in metabolite profiling can help to constrain models at this interface, quantitative analysis remains challenging because of the different timescales on which specialized metabolites from constitutive and reactive pathways accumulate.

Unlocking the Changes of Phyllosphere Fungal Communities of Fishscale Bamboo (Phyllachora heterocladae) under Rhombic-Spot Disease Stressed Conditions

As an important nonwood bioresource, fishscale bamboo (Phyllachora heterocladae Oliver) is widely distributed in the subtropical region of China. Rhombic-spot disease, caused by Neostagonosporella sichuanensis, is one of the most serious diseases that threatens fishscale bamboo health. However, there is limited knowledge about how rhombic-spot disease influences the diversity and structures of phyllosphere fungal communities. In this study, we investigated the phyllosphere fungal communities from stems, branches, and leaves of fishscale bamboo during a rhombic-spot disease outbreak using 18S rRNA sequencing. We found that only the phyllosphere fungal community from stems was significantly affected by pathogen invasion in terms of community richness, diversity, and structure. FUNGuild analysis revealed that the major classifications of phyllosphere fungi based on trophic modes in stems, branches, and leaves changed from symbiotroph-pathotroph, no obvious dominant trophic mode, and symbiotroph to saprotroph, saprotroph–pathotroph–symbiotroph, and saprotroph–symbiotroph, respectively, after pathogen invasion. The fungal community composition of the three tissues displayed significant differences at the genus level between healthy and diseased plants. The associations among fungal species in diseased samples showed more complex co-occurrence network structures than those of healthy samples. Taken together, our results highlight the importance of plant pathological conditions for the assembly of phyllosphere fungal communities in different tissues.

Advances in flux balance analysis by integrating machine learning and mechanism-based models

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.