HighlightLack of chloroplastic FBPase induces a dramatic reduction in plant development, while loss of the cytosolic enzyme increases the starch content without affecting the phenotype. Inactivation of both enzymes causes a wide range of metabolite changes.

MicroRNAs (miRNAs) play a pivotal role in regulating gene expression during plant development. Although a substantial fraction of plant miRNAs has proven responsive to pathogen infection, their role in disease resistance remains largely unknown, especially during fungal infections. In this study, we screened Arabidopsis thaliana lines in which miRNA activity has been reduced using artificial miRNA target mimics (MIM lines) for their response to fungal pathogens. Reduced activity of miR396 (MIM396 plants) was found to confer broad resistance to necrotrophic and hemibiotrophic fungal pathogens. MiR396 levels gradually decreased during fungal infection, thus, enabling its GRF (GROWTH-REGULATING FACTOR) transcription factor target genes to trigger host reprogramming. Pathogen resistance in MIM396 plants is based on a superactivation of defense responses consistent with a priming event during pathogen infection. Notably, low levels of miR396 are not translated in developmental defects in absence of pathogen challenge. Our findings support a role of miR396 in regulating plant immunity, and broaden our knowledge about the molecular players and processes that sustain defense priming. That miR396 modulates innate immunity without growth costs also suggests fine-tuning of miR396 levels as an effective biotechnological means for protection against pathogen infection.

This work presents quantitative prediction of severity of the disease caused by Phytophthora infestans in potato crops using machine learning algorithms such as multilayer perceptron, deep learning convolutional neural networks, support vector regression, and random forests. The machine learning algorithms are trained using datasets extracted from multispectral data captured at the canopy level with an unmanned aerial vehicle, carrying an inexpensive digital camera. The results indicate that deep learning convolutional neural networks, random forests and multilayer perceptron using band differences can predict the level of Phytophthora infestans affectation on potato crops with acceptable accuracy.

Microarray analysis was used to follow changes in gene expression coinciding with seasonal changes in the dormancy status of crown buds of field-grown leafy spurge. Known cold-regulated genes were induced, and numerous gibberellic acid–responsive genes were down-regulated during the transition from paradormancy to endodormancy. Genes involved in photomorphogenesis were induced during endodormancy. Also, ethylene signaling responses were observed during ecodormancy rather than endodormancy. These results provide additional insights into the signals regulating expression of several genes previously associated with transition from paradormancy to growth in root buds.