Revealing Different Systems Responses to Brown Planthopper Infestation for Pest Susceptible and Resistant Rice Plants with the Combined Metabonomic and Gene-Expression Analysis

Abstract

Brown planthopper (BPH) is a notorious pest of rice plants attacking leaf sheaths and seriously affecting global rice production. However, how rice plants respond against BPH remains to be fully understood. To understand systems metabolic responses of rice plants to BPH infestation, we analyzed BPH-induced metabolic changes in leaf sheaths of both BPH-susceptible and resistant rice varieties using NMR-based metabonomics and measured expression changes of 10 relevant genes using quantitative real-time PCR. Our results showed that rice metabonome was dominated by more than 30 metabolites including sugars, organic acids, amino acids, and choline metabolites. BPH infestation caused profound metabolic changes for both BPH-susceptible and resistant rice plants involving transamination, GABA shunt, TCA cycle, gluconeogenesis/glycolysis, pentose phosphate pathway, and secondary metabolisms. BPH infestation caused more drastic overall metabolic changes for BPH-susceptible variety and more marked up-regulations for key genes regulating GABA shunt and biosynthesis of secondary metabolites for BPH-resistant variety. Such observations indicated that activation of GABA shunt and shikimate-mediated secondary metabolisms was vital for rice plants to resist BPH infestation. These findings filled the gap of our understandings in the mechanistic aspects of BPH resistance for rice plants and demonstrated the combined metabonomic and qRT-PCR analysis as an effective approach for understanding plant-herbivore interactions.

Phloem-exudate proteome analysis of response to insect brown plant-hopper in rice

“…A beta-D-xylosidase (LOC Os04g54810) exhibited a significant difference in abundance following BPH feeding in resistant plant's phloem; a fructose-bisphosphate aldolase (LOC Os11g07020), a fructokinase (LOC Os08g02120) and a betaglucosidase (LOC Os03g53800) in susceptible plant's phloem; a malate dehydrogenase (LOC Os10g33800) in the both (Table 1), which suggests that there is a glycolytic pathway in rice phloem that is responsive to BPH feeding (Giavalisco et al, 2006). As was expected, these enzymatic proteins were less abundant at different levels in the resistant and susceptible lines, which is consistent with the finding that BPH stress causes the down-regulation of proteins involved in metabolism and induces profound metabolic changes (Liu et al, 2010;Wang et al, 2008b). Two sucrose synthases (SUSs, LOC Os03g28330 and LOC Os06g09450) which catalyze sucrose formation via a reversible reaction were identified in the rice phloem exudates.…”

“…It activates responsive pathways and plays an important role in the defense response, mainly in the phloem (Du et al, 2009). More recently, a combined metabonomic and gene expression analysis has revealed that BPH induces profound metabolic changes in both BPH-susceptible and resistant rice varieties involving transamination, the TCA cycle and glycolysis (Liu et al, 2010). It would thus seem useful to investigate the phloem sap proteins to understand the role of the phloem in defense to phloem-feeding insects.…”

Karakterisasi fenotip kultivar padi tahan dan rentan wereng coklat, Nilaparvata lugens Stí¥l. (Hemiptera: Delphacidae)

“…Meskipun demikian, dendrogram yang didasarkan atas indeks kemiripan pola pita protein total, menunjukkan adanya kemiripan yang kuat/dekat antara kultivar Pelita I/1 (rentan) dengan IR 26 (tahan biotipe 1) dan kemiripan yang Liu et al (2010) yang menyatakan bahwa tanaman padi, baik kultivar rentan maupun tahan, yang diinfestasi wereng coklat akan mengalami perubahan metabolisme yang sangat nyata. Pada kultivar rentan wereng coklat, perubahan metabolisme sangat nyata terjadi secara keseluruhan, misalnya gula, asam amino, dan beberapa hasil metabolit lain.…”

Metabolic profiling based on LC/MS to evaluate unintended effects of transgenic rice with cry1Ac and sck genes

“…Next, the retention times and mass data pairs were exported to an Excel table (Microsoft). The peak areas were then normalized to the total peak area (Liu et al 2010), and peaks that were present in less than 80% samples were discarded from the total peak list to minimize the number of missing values (Smilde et al 2005). Peaks with CV values [20% in quality control (QC) samples were deleted to ensure the reliability of the data (Zelena et al 2009;Begley et al 2009).…”