Identification of Molecular Integrators Shows that Nitrogen Actively Controls the Phosphate Starvation Response in Plants.

Nitrogen (N) and phosphorus (P) are key macronutrients sustaining plant growth and crop yield and ensuring food security worldwide. Understanding how plants perceive and interpret the combinatorial nature of these signals thus has important agricultural implications within the context of (1) increased food demand, (2) limited P supply, and (3) environmental pollution due to N fertilizer usage. Here, we report the discovery of an active control of P starvation response (PSR) by a combination of local and long-distance N signaling pathways in plants. We show that, in Arabidopsis (Arabidopsis thaliana), the nitrate transceptor CHLORINA1/NITRATE TRANSPORTER1.1 (CHL1/NRT1.1) is a component of this signaling crosstalk. We also demonstrate that this crosstalk is dependent on the control of the accumulation and turnover by N of the transcription factor PHOSPHATE STARVATION RESPONSE1 (PHR1), a master regulator of P sensing and signaling. We further show an important role of PHOSPHATE2 (PHO2) as an integrator of the N availability into the PSR since the effect of N on PSR is strongly affected in pho2 mutants. We finally show that PHO2 and NRT1.1 influence each other's transcript levels. These observations are summarized in a model representing a framework with several entry points where N signal influence PSR. Finally, we demonstrate that this phenomenon is conserved in rice (Oryza sativa) and wheat (Triticum aestivum), opening biotechnological perspectives in crop plants.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.