Fruit setting rewires central metabolism via gibberellin cascades

Abstract

Fruit set is the process whereby ovaries develop into fruits after pollination and fertilization. The process is induced by the phytohormone gibberellin (GA) in tomatoes, as determined by the constitutive GA response mutant procera. However, the role of GA on the metabolic behavior in fruit-setting ovaries remains largely unknown. This study explored the biochemical mechanisms of fruit set using a network analysis of integrated transcriptome, proteome, metabolome, and enzyme activity data. Our results revealed that fruit set involves the activation of central carbon metabolism, with increased hexoses, hexose phosphates, and downstream metabolites, including intermediates and derivatives of glycolysis, the tricarboxylic acid cycle, and associated organic and amino acids. The network analysis also identified the transcriptional hub gene SlHB15A, that coordinated metabolic activation. Furthermore, a kinetic model of sucrose metabolism predicted that the sucrose cycle had high activity levels in unpollinated ovaries, whereas it was shut down when sugars rapidly accumulated in vacuoles in fruit-setting ovaries, in a time-dependent manner via tonoplastic sugar carriers. Moreover, fruit set at least partly required the activity of fructokinase, which may pull fructose out of the vacuole, and this could feed the downstream pathways. Collectively, our results indicate that GA cascades enhance sink capacities, by up-regulating central metabolic enzyme capacities at both transcriptional and posttranscriptional levels. This leads to increased sucrose uptake and carbon fluxes for the production of the constituents of biomass and energy that are essential for rapid ovary growth during the initiation of fruit set.

The miR166–SlHB15A regulatory module controls ovule development and parthenocarpic fruit set under adverse temperatures in tomato

“…During the submission of this work, Shinozaki and colleagues reported on the role of a tomato DELLA homolog called PROCERA and its loss-of-function mutant, procera (pro), in parthenocarpic fruit set (Shinozaki et al, 2020). Interestingly SlHB15A was found to be downregulated in the parthenocarpic pro mutant.…”

High-Oleic Palm Oil (HOPO) Production from Parthenocarpic Fruits in Oil Palm Interspecific Hybrids Using Naphthalene Acetic Acid

“…Then, the auxin concentration drops to allow the action of other hormones such as gibberellins [31]. For example, in tomato, auxin levels are low two days before anthesis and start increasing after anthesis, reaching the maximum value four days after anthesis (DAA) and dropping rapidly after that [32,33]. In African oil palm, applied auxins failed to induce oil-producing parthenocarpic fruits [21], even though parthenocarpy was achieved when the hormone was used very closed to anthesis [34].…”

Gene-Metabolite Network Analysis Revealed Tissue-Specific Accumulation of Therapeutic Metabolites in Mallotus japonicus

“…It further narrows down candidate genes for functional characterization and to study the biosynthetic machinery of specialized metabolites in medicinal plants [53]. To understand the biosynthetic network of specialized metabolites in M. japonicus, we performed a Pearson correlation analysis between the identified metabolites and transcripts modules using the average value of expression or accumulation of transcripts or metabolites assigned to a given module, respectively, as described previously [54] (Table S12). The highest correlation was obtained for four transcript-metabolite module pairs, namely TransM15-MetM6, TransM4-MetM7, TransM16-MetM4, and TransM12-MetM2, with correlation values of 0.986, 0.986, 0.98, and 0.916, respectively (Figure 6a).…”

“…We, therefore, hypothesized that the transcript modules correlated with the MetM7 metabolite module would include functional genes most likely associated with the biosynthesis of rutin and associated metabolites. Several studies have highlighted the importance of integrating transcriptome and metabolome datasets in identifying functional genes [29,38,[54][55][56][57][58]. Therefore, using a similar strategy in M. japonicus, we performed a correlation analysis between metabolite and transcript modules and identified the transcript modules TransM9 and TransM4, sharing a high correlation (R 2 > 0.7) with the MetM7 metabolite module.…”