α-Synuclein interacts with the switch region of Rab8a in a Ser129 phosphorylation-dependent manner.

Alpha-synuclein (αS) misfolding is associated with Parkinson's disease (PD) but little is known about the mechanisms underlying αS toxicity. Increasing evidence suggests that defects in membrane transport play an important role in neuronal dysfunction. Here we demonstrate that the GTPase Rab8a interacts with αS in rodent brain. NMR spectroscopy reveals that the C-terminus of αS binds to the functionally important switch region as well as the C-terminal tail of Rab8a. In line with a direct Rab8a/αS interaction, Rab8a enhanced αS aggregation and reduced αS-induced cellular toxicity. In addition, Rab8 - the Drosophila ortholog of Rab8a - ameliorated αS-oligomer specific locomotor impairment and neuron loss in fruit flies. In support of the pathogenic relevance of the αS-Rab8a interaction, phosphorylation of αS at S129 enhanced binding to Rab8a, increased formation of insoluble αS aggregates and reduced cellular toxicity. Our study provides novel mechanistic insights into the interplay of the GTPase Rab8a and αS cytotoxicity, and underscores the therapeutic potential of targeting this interaction

Assessment of nitrogen diagnosis methods in sunflower

Nitrogen deficiency can severely limit sunflower (Helianthus annuus L.) grain yield and quality. Our objective was to evaluate N diagnosis methods based on: (a) pre-plant soil nitrate-nitrogen (NO3––N) test (PPSNT) and soil N mineralized in short-term anaerobic incubation (Nan), (b) Greenness index (GI) and the normalized difference vegetation index (NDVI) measured at 6 (V6) and 12 (V12) leaves, and (c) grain nitrogen concentration (Nc). Seventeen experiments were carried out between 2010 and 2019 in Argentina, evaluating nine N rates (0, 30, 40, 60, 80, 90, 120, 150, and 160 kg N ha–1). The GI, NDVI, N sufficiency index and relative normalized difference vegetation index (NDVIr) were determined at V6 and V12 growth stages. On average, yield response to N was 492 kg ha–1 and Nc response was 0.25% in 9 and 11 responsive experiments, respectively. The inclusion of Nan improved the PPSNT diagnosis method. The critical N availability (PPSNT + fertilizer N) threshold was 115 kg N ha–1 for experiments with low Nan (60 mg kg–1). The NDVIr at V12 allowed monitoring the crop N status with a 0.95 critical threshold. The Nc adequately diagnosed N deficiencies and the critical threshold was 2.26%. Also, Nc was predicted from the ratio between N availability and grain yield (R2 = .39). Our results would allow to better estimate N availability to recommend adequate N fertilizer rates for sunflower aiming to optimize grain yield and quality, and minimize the economic and environmental cost of fertilization.EEA BalcarceFil: Tovar Hernandez, Sergio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Diovisalvi, Natalia. Laboratorio de Suelos Fertilab; Argentina.Fil: Carciochi, Walter Daniel. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Izquierdo, Natalia. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Izquierdo, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Sainz Rozas, Hernán René. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.Fil: Sainz Rozas, Hernán René. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Sainz Rozas, Hernán René. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: García, Fernando. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Reussi Calvo, Nahuel Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Reussi Calvo, Nahuel Ignacio. Laboratorio de Suelos Fertilab; Argentina

Attainable yield and soil texture as drivers of maize response to nitrogen: a synthesis analysis for Argentina

The most widely used approach for prescribing fertilizer nitrogen (N) recommendations in maize (Zea Mays L.) in Argentina is based on the relationship between grain yield and the available N (kg N ha−1), calculated as the sum of pre-plant soil NO3--N at 0−60 cm depth (PPNT) plus fertilizer N (Nf). However, combining covariates related to crop N demand and soil N supply at a large national scale remains unexplored for this model. The aim of this work was to identify yield response patterns associated to yield environment (crop N demand driver) and soil texture (soil N supply driver). A database of 788 experiments (1980−2016) was gathered and analyzed combining quadratic-plateau regression models with bootstrapping to address expected values and variability on response parameters and derived quantities. The database was divided into three groups according to soil texture (fine, medium and coarse) and five groups based on the empirical distribution of maximum observed yields (from Very-Low = 13.1 Mg ha−1) resulting in fifteen groups. The best model included both, attainable yield environment and soil texture. The yield environment mainly modified the agronomic optimum available N (AONav), with an expected increase rate of ca. 21.4 kg N Mg attainable yield−1, regardless of the soil texture. In Very-Low yield environments, AONav was characterized by a high level of uncertainty, related to a poor fit of the N response model. To a lesser extent, soil texture modified the response curvature but not the AONav, mainly by modifying the response rate to N (Fine > Medium > Coarse), and the N use efficiencies. Considering hypothetical PPNT levels from 40 to 120 kg N ha−1, the expected agronomic efficiency (AENf) at the AONav varied from 7 to 31, and 9–29 kg yield response kg fertilizer N (Nf)−1, for Low and Very-High yield environments, respectively. Similarly, the expected partial factor productivity (PFPNf) at the AONav ranged from 62 to 158, and 55–99 kg yield kg Nf−1, for the same yield environments. These results highlight the importance of combining attainable yield environment and soil texture metadata for refining N fertilizer recommendations. Acknowledging the still low N fertilizer use in Argentina, space exists to safely increasing N fertilizer rates, steering the historical soil N mining profile to a more sustainable agro-environmental scenario in the Pampas.Fil: Correndo, Adrián A.. Kansas State University; Estados UnidosFil: Gutiérrez Boem, Flavio Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: García, Fernando O.. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Alvarez, Carolina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Álvarez, Cristian. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Angeli, Ariel. I+D CREA; ArgentinaFil: Barbieri, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Barraco, Mirian Raquel. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Berardo, Angel. Laboratorio de Suelo S.a.; ArgentinaFil: Boxler, Miguel. Private Consultant; ArgentinaFil: Calviño, Pablo Antonio. Private Consultant; ArgentinaFil: Capurro, Julia E.. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Carta, Héctor. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Caviglia, Octavio Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; ArgentinaFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Diaz Zorita, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Pampa. Facultad de Agronomía; ArgentinaFil: Díaz Valdéz, Santiago. Bayer Crop Science; ArgentinaFil: Echeverría, Hernán E.. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Espósito, Gabriel Pablo. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; ArgentinaFil: Ferrari, Manuel. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Ferraris, Gustavo Nestor. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Gambaudo, Sebastian Pedro. Universidad Nacional del Litoral. Facultad de Ciencias Agrarias; Argentina. Private Consultant; ArgentinaFil: Gudelj, Vicente. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Ioele, Juan P.. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Melchiori, Ricardo J. M.. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Molino, Josefina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Orcellet, Juan Manuel. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Pagani, Agustin. Clarion Inc.; ArgentinaFil: Pautasso, Juan Manuel. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Laboratorio de Suelo S.a.; ArgentinaFil: Redel, Matías. Private Consultant; ArgentinaFil: Rillo, Sergio. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Rimski-korsakov, Helena. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Sainz Rozas, Hernan Rene. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Saks, Matías. Bunge Argentina S.A; ArgentinaFil: Tellería, María Guadalupe. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Ventimiglia, Luis. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Zorzín, Jose L.. Private Consultant; ArgentinaFil: Zubillaga de Sanahuja, María de Las Mercedes. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Salvagiotti, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; Argentin