Transcriptional Evidence for Inferred Pattern of Pollen Tube-Stigma Metabolic Coupling during Pollination

<div><p>It is difficult to derive all qualitative proteomic and metabolomic experimental data in male (pollen tube) and female (pistil) reproductive tissues during pollination because of the limited sensitivity of current technology. In this study, genome-scale enzyme correlation network models for plants (<i>Arabidopsis</i>/maize) were constructed by analyzing the enzymes and metabolic routes from a global perspective. Then, we developed a data-driven computational pipeline using the “guilt by association” principle to analyze the transcriptional coexpression profiles of enzymatic genes in the consecutive steps for metabolic routes in the fast-growing pollen tube and stigma during pollination. The analysis identified an inferred pattern of pollen tube-stigma ethanol coupling. When the pollen tube elongates in the transmitting tissue (TT) of the pistil, this elongation triggers the mobilization of energy from glycolysis in the TT cells of the pistil. Energy-rich metabolites (ethanol) are secreted that can be taken up by the pollen tube, where these metabolites are incorporated into the pollen tube's tricarboxylic acid (TCA) cycle, which leads to enhanced ATP production for facilitating pollen tube growth. In addition, our analysis also provided evidence for the cooperation of kaempferol, dTDP-alpha-L-rhamnose and cell-wall-related proteins; phosphatidic-acid-mediated Ca²⁺ oscillations and cytoskeleton; and glutamate degradation IV for γ-aminobutyric acid (GABA) signaling activation in <i>Arabidopsis</i> and maize stigmas to provide the signals and materials required for pollen tube tip growth. In particular, the “guilt by association” computational pipeline and the genome-scale enzyme correlation network models (GECN) developed in this study was initiated with experimental “omics” data, followed by data analysis and data integration to determine correlations, and could provide a new platform to assist inachieving a deeper understanding of the co-regulation and inter-regulation model in plant research.</p>