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Legume Nodule Biochemistry and Function
Published in Peter M. Gresshoff, Molecular Biology of Symbiotic Nitrogen Fixation, 2018
Robert B. Mellor, Dietrich Werner
Nodule cells can be considered to be bathed in a medium containing C sources provided by plant photosynthesis. Sucrose is considered to be the main energy source transported into the nodules for bacterial nitrogen fixation. N2 fixation is reduced in low light intensities for prolonged periods or increased after the addition of extra leaves by grafting or CO2 enrichment studies (see Reference 5 for review). That photosynthate supply is not the limiting factor in N2 fixation and that sucrose itself is not the form of carbon used by the bacteroids is spoken for by experiments of Werner and Krotzky,6 who showed increases in N2 fixation with raised partial pressures of O2 (also see Chapter 7). Sucrose and other carbon sources enter the plant cell into the cytoplasm. Sucrose is then broken into a catabolically usable form by sucrose synthase (catabolic). This protein in nodules is a nodulin, nodulin 100. It is a four-subunit enzyme controlling C metabolism and carbon supply to bacteroids.7
Radiation induced mutagenesis, physio-biochemical profiling and field evaluation of mutants in sugarcane cv. CoM 0265
Published in International Journal of Radiation Biology, 2022
Madhavi V. Purankar, Ashok A. Nikam, Rachayya M. Devarumath, Suprasanna Penna
Differential responses both in terms of biophysical and biochemical parameters in mutant clones were observed under saline field investigations. The mutant clones showed higher TWC and chlorophyll content than parent and control checks (Table 2). Higher TWC is known to improve chlorophyll availability (Santos et al. 2015) which in turn can improve sugarcane salt tolerance (Negi et al. 2020) and agronomic performance such as brix and sugar accumulation (Wu and Birch 2007; Zhao et al. 2016). Similarly, TWC is also reported to affect the activity of sucrose synthase (SuSy) (Du et al. 1998) and also sugarcane culm elongation (Inman-Bamber and Smith 2005).
Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation
Published in Expert Review of Proteomics, 2020
Leonardo Perez De Souza, Saleh Alseekh, Yariv Brotman, Alisdair R Fernie
Most metabolomics experiments are designed to determine the relative quantification of dozens to hundreds of metabolites in relation to general internal standards [15,16], or less frequently the absolute quantification of a considerably reduced number of metabolites [17]. Irrespective of the measurement type, common analyzes pipelines include direct application of descriptive statistics over individual metabolites comparing a treatment group against a control with common approaches such as Student’s t-test and ANOVA (Figure 1). Multivariate analysis such as PCA is also frequently applied to highlight the most relevant metabolites responsible for differentiating between the groups of individuals under study. Such analyses are invaluable to access the differences between individual metabolites, which are very often instrumental in explaining the phenotypes, and understanding the effects of environmental and genetic perturbations. However, they still look at discrete parts of the whole system, and often miss important information comprised within the complex metabolomics datasets. A great example of this limitation is the many experiments where the use of correlation networks rather than metabolite levels could unravel links between distant nodes (metabolites) representing complex interactions between different pathways and physiological functions. The correlation matrixes of S. tuberosum, for instance, show marked differences for leaves and tubers, that can partially be explained by the different roles of these organs as source and sink, respectively [18]. Even more remarkably, the differential analysis of network connectivity of a potato line exhibiting an apparently silent suppression of sucrose synthase isoform II expression reveled metabolic alterations in carbohydrate and amino acid metabolism that were not captured by differential analysis of the levels of the metabolites [19].