Overexpression of a Cytosolic 6-Phosphogluconate Dehydrogenase Gene Enhances the Resistance of Rice to Nilaparvata lugens

Exploring the Potential Role of Ribosomal Proteins to Enhance Potato Resilience in the Face of Changing Climatic Conditions

Potatoes have emerged as a key non-grain crop for food security worldwide. However, the looming threat of climate change poses significant risks to this vital food source, particularly through the projected reduction in crop yields under warmer temperatures. To mitigate potential crises, the development of potato varieties through genome editing holds great promise. In this study, we performed a comprehensive transcriptomic analysis to investigate microtuber development and identified several differentially expressed genes, with a particular focus on ribosomal proteins-RPL11, RPL29, RPL40 and RPL17. Our results reveal, by protein-protein interaction (PPI) network analyses, performed with the highest confidence in the STRING database platform (v11.5), the critical involvement of these ribosomal proteins in microtuber development, and highlighted their interaction with PEBP family members as potential microtuber activators. The elucidation of the molecular biological mechanisms governing ribosomal proteins will help improve the resilience of potato crops in the face of today's changing climatic conditions.

Engineering plant immune circuit: walking to the bright future with a novel toolbox

Plant pathogens destroy crops and cause severe yield losses, leading to an insufficient food supply to sustain the human population. Apart from relying on natural plant immune systems to combat biological agents or waiting for the appropriate evolutionary steps to occur over time, researchers are currently seeking new breakthrough methods to boost disease resistance in plants through genetic engineering. Here, we summarize the past two decades of research in disease resistance engineering against an assortment of pathogens through modifying the plant immune components (internal and external) with several biotechnological techniques. We also discuss potential strategies and provide perspectives on engineering plant immune systems for enhanced pathogen resistance and plant fitness.

Fatty acyl-CoA reductase influences wax biosynthesis in the cotton mealybug, Phenacoccus solenopsis Tinsley

Mealybugs are highly aggressive to a diversity of plants. The waxy layer covering the outermost part of the integument is an important protective defense of these pests. However, the molecular mechanisms underlying wax biosynthesis in mealybugs remain largely unknown. Here, we analyzed multi-omics data on wax biosynthesis by the cotton mealybug, Phenacoccus solenopsis Tinsley, and found that a fatty acyl-CoA reductase (PsFAR) gene, which was highly expressed in the fat bodies of female mealybugs, contributed to wax biosynthesis by regulating the production of the dominant chemical components of wax, cuticular hydrocarbons (CHCs). RNA interference (RNAi) against PsFAR by dsRNA microinjection and allowing mealybugs to feed on transgenic tobacco expressing target dsRNA resulted in a reduction of CHC contents in the waxy layer, and an increase in mealybug mortality under desiccation and deltamethrin treatments. In conclusion, PsFAR plays crucial roles in the wax biosynthesis of mealybugs, thereby contributing to their adaptation to water loss and insecticide stress.

The role of a fatty acyl-CoA reductase (PsFAR) in wax biosynthesis of cotton mealybug is investigated, RNAi against PsFAR resulted in insects with lower generation of waxy filaments and higher mortality under desiccation and deltamethrin treatments.

Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava

The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation.