Mi, G. | bioRxiv Science

bioRxiv2024

Identifying and quantifying the contribution of maize plant traits to nitrogen uptake and use through plant modelling

Breeding for high nitrogen use efficient crops can contribute to maintaining or even increasing yield with less nitrogen. Nitrogen use is co-determined by N uptake and physiological use efficiency (PE, biomass per unit of N taken up), to which soil processes as well as plant architectural, physiological and developmental traits contribute. The relative contribution of these crop traits to N use is not well known but relevant to identify breeding targets in important crop species like maize. To quantify the contribution of component plant traits to maize N uptake and use, we used a functional-structural plant model. We evaluated the effect of varying both shoot and root traits on crop N uptake across a range of nitrogen levels. Root architectural traits were found to play a more important role in root N uptake than physiological traits. Phyllochron determined the structure of the shoot through changes in source: sink ratio over time which, in interaction with light and temperature, resulted in a significant effect on PE and N uptake. Photosynthesis traits were more relevant to biomass accumulation rather than yield, especially under high nitrogen conditions. The traits identified in this study are potential targets in maize breeding for improved crop N uptake and use. HighlightOur research provides insight into the relevance of a range of traits for maize N uptake and N use, and identifies several potential target traits based on underlying mechanisms to assist maize breeding.

plant biology↗

bioRxiv2020

Statistical classification of dynamic bacterial growth with sub-inhibitory concentrations of nanoparticles and its implications for disease treatment

Nanoparticles are promising alternatives to antibiotics since nanoparticles are easy to manufacture, non-toxic, and do not promote resistance. Nanoparticles act via physical disruption of the bacterial membrane and/or the generation of high concentrations of reactive-oxygen species locally. Potential for physical disruption of the bacterial membrane may be quantified by free energy methods, such as the extended Derjuan-Landau-Verwey-Overbeek theory, which predicts the initial surface-material interactions. The generation of reactive-oxygen species may be quantified using enthalpies of formation to predict minimum inhibitory concentrations. Neither of these two quantitative structure-activity values describes the dynamic, in situ behavioral changes in the bacterias struggle to survive. In this paper, borrowing parameters from logistic, oscillatory, and diauxic growth models, we use principal component analysis and agglomerative hierarchical clustering to classify survival modes across nanoparticle types and concentrations. We compare the growth parameters of 170 experimental interactions between nanoparticles and bacteria. The bacteria studied include Escherichia coli, Staphylococcus aureus, Methicillin-Resistant Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, and Helicobacter pylori, and were tested across multiple concentrations of liposomal drug delivery systems, amphiphilic peptide, and silver and selenium nanoparticles. Clustering reveals specific pairs of bacteria and nanoparticles where the nanoparticle induced growth dynamics could potentially spread the infection through the development of resistance and tolerance. This rapid screening also shows that bacteria generated nanoparticles do not induce growth modes indicative of the development of resistance. This methodology can be used to rapidly screen for novel therapeutics that do not induce resistance before using more robust intracellular content screening. This methodology can also be used as a quality check on batch manufactured nanoparticles.

microbiology↗