No abstract

Light quality affects plant growth and the functional component accumulation of fruits. However, there is little knowledge of the effects of light quality based on multiomics profiles. This study combined transcriptomic, ionomic, and metabolomic analyses to elucidate the effects of light quality on metabolism and gene expression in tomato fruit. Micro-Tom plants were grown under blue or red light-emitting diode light for 16 h daily after anthesis. White fluorescent light was used as a reference. The metabolite and element concentrations and the expression of genes markedly changed in response to blue and red light. Based on the metabolomic analysis, amino acid metabolism and secondary metabolite biosynthesis were active in blue light treatment. According to transcriptomic analysis, differentially expressed genes in blue and red light treatments were enriched in the pathways of secondary metabolite biosynthesis, carbon fixation, and glycine, serine, and threonine metabolism, supporting the results of the metabolomic analysis. Ionomic analysis indicated that the element levels in fruits were more susceptible to changes in light quality than in leaves. The concentration of some ions containing Fe in fruits increased under red light compared to under blue light. The altered expression level of genes encoding metal ion-binding proteins, metal tolerance proteins, and metal transporters in response to blue and red light in the transcriptomic analysis contributes to changes in the ionomic profiles of tomato fruit.

The pathway is a biological term that refers to a series of interactions between molecules in a cell that cause a certain product or a change in the cell [NIH20]. Pathway analysis is a powerful method for gene expression analysis [Cir17]. Through pathway maps, the lists of genes that are differently expressed across the given phenotypes are translated into various biological phenomena [Nguyen2019].Visualizing a pathway map manually is a common practice nowadays because of the limitations of existing solutions to draw complicated graphs (i.e. directed graphs, graph with edge crossings, etc).This project provides a solution to draw pathway maps automatically based on spectral graph theory and topological sort. Various methods are taken to enhance pathway maps' readability. Significant reductions in the number of edge crossings and the sum of adjacent nodes are achieved.