Seasonal fluctuations of native available N and soil management implications

Effect of land use and soil depth on the distribution of phyto-available nutrients and SOC pools of Vertisols in Central India

A proper understanding of soil parameters under different production systems of the world is necessary for efficient soil management. We, therefore, carried out the present study to assess the status of some selected soil properties (soil pH and electrical conductivity (EC)), phyto-available nutrients (available nitrogen (AN), available potassium (AK), exchangeable calcium (Ex. Ca), exchangeable magnesium (Ex. Mg), available sulfur (AS), and soil organic carbon (SOC) pools (SOC, total organic carbon (TOC), very labile C, labile C, less labile C, and non-labile C) and to establish relationships among the measured soil parameters at different depths of Vertisols of India under various land uses. A total of 150 composite soil samples (from 25 plots including nine from agricultural land, nine from horticultural land, three from forest land, and four from grassland) were collected from 6 soil depths viz, 0-10, 10-20, 20-40, 40-60, 60-80, and 80-100 cm under agriculture, horticulture, forest, and grassland land uses present in Central India and analyzed. The values of soil pH, EC, AN, AK, Ex. Ca, Ex. Mg, and AS in various soil depths under different land uses varied widely. The values of SOC (0.19 to 1.00%), TOC (0.58 to 2.42%), very labile C (0.14 to 0.83%), labile C (0.05 to 0.25%), less labile C (0.05 to 0.26%) and non-labile C (0.23 to 1.42%) in various soil depths under different land uses also varied significantly. Forest and grassland land uses had higher levels of SOC, TOC, very labile, and non-labile C content in all the soil depths in comparison to SOC, TOC, very labile, and non-labile C content in different soil depths under agriculture and horticulture land use. The levels of SOC, TOC, very labile, and non-labile C content under all the land uses decreased with increasing soil depths. SOC was positively and significantly correlated with AN, AK, AS, and estimated SOC pools in surface soil layers. Principal component analysis (PCA) of soil parameters in different soil depths resulted in 5 principal components (PCs) with > 1 eigenvalue and accounting for > 75% variability. This information could be used for managing SOC status and phyto-available nutrients in Vertisols under different land uses.

Improving nitrogen use efficiency by manipulating nitrate remobilization in plants

Increasing nitrogen use efficiency (NUE) is critical to improve crop yield, reduce N fertilizer demand and alleviate environmental pollution. N remobilization is a key component of NUE. The nitrate transporter NRT1.7 is responsible for loading excess nitrate stored in source leaves into phloem and facilitates nitrate allocation to sink leaves. Under N starvation, the nrt1.7 mutant exhibits growth retardation, indicating that NRT1.7-mediated source-to-sink remobilization of stored nitrate is important for sustaining growth in plants. To energize NRT1.7-mediated nitrate recycling, we introduced a hyperactive chimeric nitrate transporter NC4N driven by the NRT1.7 promoter into the nrt1.7 mutant. NRT1.7p::NC4N::3' transgenic plants accumulated more nitrate in younger leaves, and ¹⁵NO₃^- tracing analysis revealed that more ¹⁵N was remobilized into sink tissues. Consistently, transgenic Arabidopsis, tobacco and rice plants showed improved growth or yield. Our study suggests that enhancing source-to-sink nitrate remobilization represents a new strategy for enhancing NUE and crop production.

Structural control of metabolic flux

Organisms have to continuously adapt to changing environmental conditions or undergo developmental transitions. To meet the accompanying change in metabolic demands, the molecular mechanisms of adaptation involve concerted interactions which ultimately induce a modification of the metabolic state, which is characterized by reaction fluxes and metabolite concentrations. These state transitions are the effect of simultaneously manipulating fluxes through several reactions. While metabolic control analysis has provided a powerful framework for elucidating the principles governing this orchestrated action to understand metabolic control, its applications are restricted by the limited availability of kinetic information. Here, we introduce structural metabolic control as a framework to examine individual reactions' potential to control metabolic functions, such as biomass production, based on structural modeling. The capability to carry out a metabolic function is determined using flux balance analysis (FBA). We examine structural metabolic control on the example of the central carbon metabolism of Escherichia coli by the recently introduced framework of functional centrality (FC). This framework is based on the Shapley value from cooperative game theory and FBA, and we demonstrate its superior ability to assign “share of control” to individual reactions with respect to metabolic functions and environmental conditions. A comparative analysis of various scenarios illustrates the usefulness of FC and its relations to other structural approaches pertaining to metabolic control. We propose a Monte Carlo algorithm to estimate FCs for large networks, based on the enumeration of elementary flux modes. We further give detailed biological interpretation of FCs for production of lactate and ATP under various respiratory conditions.

Insight into the functioning of metabolic control to meet changing demands is a first step in rational engineering of biological systems towards a desired behavior. Metabolic control analysis provides the means to examine the impact of change of reaction fluxes on a specific target flux based on kinetic modeling, but suffers from limitations of the kinetic approach. Here, we introduce and analyze structural metabolic control as a framework to overcome these limitations. We utilize functional centrality, a framework based on the Shapley value from cooperative game theory and flux balance analysis, to determine the contribution of individual reactions to the functions accomplished by a metabolic network. These contributions, in turn, depend on the control exerted on the remaining network. Functional centrality provides the mathematical means to gain further understanding of metabolic control. The potential applications range from facilitating strategies of rational strain design to drug target identification.