Nitrogen supply and cyanide concentration influence the enrichment of nitrogen from cyanide in wheat (Triticum aestivum L.) and sorghum (Sorghum bicolor L.)

Rapid Detection of Available Nitrogen in Soil by Surface-Enhanced Raman Spectroscopy

Soil-available nitrogen is the main nitrogen source that plants can directly absorb for assimilation. It is of great significance to detect the concentration of soil-available nitrogen in a simple, rapid and reliable method, which is beneficial to guiding agricultural production activities. This study confirmed that Raman spectroscopy is one such approach, especially after surface enhancement; its spectral response is more sensitive. Here, we collected three types of soils (chernozem, loess and laterite) and purchased two kinds of nitrogen fertilizers (ammonium sulfate and sodium nitrate) to determine ammonium nitrogen (NH4-N) and nitrate nitrogen (NO3-N) in the soil. The spectral data were acquired using a portable Raman spectrometer. Unique Raman characteristic peaks of NH4-N and NO3-N in different soils were found at 978 cm−1 and 1044 cm−1, respectively. Meanwhile, it was found that the enhancement of the Raman spectra by silver nanoparticles (AgNPs) was greater than that of gold nanoparticles (AuNPs). Combined with soil characteristics and nitrogen concentrations, Raman peak data were analyzed by multiple linear regression. The coefficient of determination for the validation (Rp2) of multiple linear regression prediction models for NH4-N and NO3-N were 0.976 and 0.937, respectively, which deeply interpreted the quantitative relationship among related physical quantities. Furthermore, all spectral data in the range of 400–2000 cm−1 were used to establish the partial least squares (PLS), back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) models for quantification. After cross-validation and comparative analysis, the results showed that LSSVM optimized by particle swarm methodology had the highest accuracy and stability from an overall perspective. For all datasets of particle swarm optimization LSSVM (PSO-LSSVM), the Rp2 was above 0.99, the root mean square errors of prediction (RMSEP) were below 0.15, and the relative prediction deviation (RPD) was above 10. The ultra-portable Raman spectrometer, in combination with scatter-enhanced materials and machine learning algorithms, could be a promising solution for high-efficiency and real-time field detection of soil-available nitrogen.

Nanoparticle Size and Coating Chemistry Control Foliar Uptake Pathways, Translocation, and Leaf-to-Rhizosphere Transport in Wheat

Nanoenabled foliar-applied agrochemicals can potentially be safer and more efficient than conventional products. However, limited understanding about how nanoparticle properties influence their interactions with plant leaves, uptake, translocation through the mesophyll to the vasculature, and transport to the rest of the plant prevents rational design. This study used a combination of Au quantification and spatial analysis to investigate how size (3, 10, or 50 nm) and coating chemistry (PVP versus citrate) of gold nanoparticles (AuNPs) influence these processes. Following wheat foliar exposure to AuNPs suspensions (∼280 ng per plant), adhesion on the leaf surface was increased for smaller sizes, and PVP-AuNPs compared to citrate-AuNPs. After 2 weeks, there was incomplete uptake of citrate-AuNPs with some AuNPs remaining on the outside of the cuticle layer. However, the fraction of citrate-AuNPs that had entered the leaf was translocated efficiently to the plant vasculature. In contrast, for similar sizes, virtually all of the PVP-AuNPs crossed the cuticle layer after 2 weeks, but its transport through the mesophyll cells was lower. As a consequence of PVP-AuNP accumulation in the leaf mesophyll, wheat photosynthesis was impaired. Regardless of their coating and sizes, the majority of the transported AuNPs accumulated in younger shoots (10-30%) and in roots (10-25%), and 5-15% of the NPs <50 nm were exuded into the rhizosphere soil. A greater fraction of larger sizes AuNPs (presenting lower ζ potentials) was transported to the roots. The key hypotheses about the NPs physical-chemical and plant physiology parameters that may matter to predict leaf-to-rhizosphere transport are also discussed.

Cyanogen Metabolism in Cassava Roots: Impact on Protein Synthesis and Root Development

Cassava (Manihot esculenta Crantz), a staple crop for millions of sub-Saharan Africans, contains high levels of cyanogenic glycosides which protect it against herbivory. However, cyanogens have also been proposed to play a role in nitrogen transport from leaves to roots. Consistent with this hypothesis, analyses of the distribution and activities of enzymes involved in cyanide metabolism provides evidence for cyanide assimilation, derived from linamarin, into amino acids in cassava roots. Both β-cyanoalanine synthase (CAS) and nitrilase (NIT), two enzymes involved in cyanide assimilation to produce asparagine, were observed to have higher activities in roots compared to leaves, consistent with their proposed role in reduced nitrogen assimilation. In addition, rhodanese activity was not detected in cassava roots, indicating that this competing means for cyanide metabolism was not a factor in cyanide detoxification. In contrast, leaves had sufficient rhodanese activity to compete with cyanide assimilation into amino acids. Using transgenic low cyanogen plants, it was shown that reducing root cyanogen levels is associated with elevated root nitrate reductase activity, presumably to compensate for the loss of reduced nitrogen from cyanogens. Finally, we overexpressed Arabidopsis CAS and NIT4 genes in cassava roots to study the feasibility of enhancing root cyanide assimilation into protein. Optimal overexpression of CAS and NIT4 resulted in up to a 50% increase in root total amino acids and a 9% increase in root protein accumulation. However, plant growth and morphology was altered in plants overexpressing these enzymes, demonstrating a complex interaction between cyanide metabolism and hormonal regulation of plant growth.