ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis.

In plants, phosphate (Pi) homeostasis is regulated by the interaction of PHR transcription factors with stand-alone SPX proteins, which act as sensors for inositol pyrophosphates. In this study, we combined different methods to obtain a comprehensive picture of how inositol (pyro)phosphate metabolism is regulated by Pi and dependent on the inositol phosphate kinase ITPK1. We found that inositol pyrophosphates are more responsive to Pi than lower inositol phosphates, a response conserved across kingdoms. Using the capillary electrophoresis electrospray ionization mass spectrometry (CE-ESI-MS) we could separate different InsP7 isomers in Arabidopsis and rice, and identify 4/6-InsP7 and a PP-InsP4 isomer hitherto not reported in plants. We found that the inositol pyrophosphates 1/3-InsP7, 5-InsP7, and InsP8 increase several fold in shoots after Pi resupply and that tissue-specific accumulation of inositol pyrophosphates relies on ITPK1 activities and MRP5-dependent InsP6 compartmentalization. Notably, ITPK1 is critical for Pi-dependent 5-InsP7 and InsP8 synthesis in planta and its activity regulates Pi starvation responses in a PHR-dependent manner. Furthermore, we demonstrated that ITPK1-mediated conversion of InsP6 to 5-InsP7 requires high ATP concentrations and that Arabidopsis ITPK1 has an ADP phosphotransferase activity to dephosphorylate specifically 5-InsP7 under low ATP. Collectively, our study provides new insights into Pi-dependent changes in nutritional and energetic states with the synthesis of regulatory inositol pyrophosphates.

Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images.

In order to enable timely actions to prevent major losses of crops caused by lack of nutrients and, hence, increase the potential yield throughout the growing season while at the same time prevent excess fertilization with detrimental environmental consequences, early, non-invasive, and on-site detection of nutrient deficiency is required. Current non-invasive methods for assessing the nutrient status of crops deal in most cases with nitrogen (N) deficiency only and optical sensors to diagnose N deficiency, such as chlorophyll meters or canopy reflectance sensors, do not monitor N, but instead measure changes in leaf spectral properties that may or may not be caused by N deficiency. In this work, we study how well nutrient deficiency symptoms can be recognized in RGB images of sugar beets. To this end, we collected the Deep Nutrient Deficiency for Sugar Beet (DND-SB) dataset, which contains 5648 images of sugar beets growing on a long-term fertilizer experiment with nutrient deficiency plots comprising N, phosphorous (P), and potassium (K) deficiency, as well as the omission of liming (Ca), full fertilization, and no fertilization at all. We use the dataset to analyse the performance of five convolutional neural networks for recognizing nutrient deficiency symptoms and discuss their limitations.