Metabolic Analysis Reveals Cry1C Gene Transformation Does Not Affect the Sensitivity of Rice to Rice Dwarf Virus

Abstract

Metabolomics is beginning to be used for assessing unintended changes in genetically modified (GM) crops. To investigate whether Cry1C gene transformation would induce metabolic changes in rice plants, and whether the metabolic changes would pose potential risks when Cry1C rice plants are exposed to rice dwarf virus (RDV), the metabolic profiles of Cry1C rice T1C-19 and its non-Bt parental rice MH63 under RDV-free and RDV-infected status were analyzed using gas chromatography-mass spectrometry (GC-MS). Compared to MH63 rice, slice difference was detected in T1C-19 under RDV-free conditions (less than 3%), while much more metabolites showed significant response to RDV infection in T1C-19 (15.6%) and in MH63 (5.0%). Pathway analysis showed biosynthesis of lysine, valine, leucine, and isoleucine may be affected by RDV infection in T1C-19. No significant difference in the contents of free amino acids (AAs) was found between T1C-19 and MH63 rice, and the free AA contents of the two rice plants showed similar responses to RDV infection. Furthermore, no significant differences of the RDV infection rates between T1C-19 and MH63 were detected. Our results showed the Cry1C gene transformation did not affect the sensitivity of rice to RDV, indicating Cry1C rice would not aggravate the epidemic and dispersal of RDV.

Keywords: Cry1C rice; RDV infection rates; free amino acids; metabolites; rice dwarf virus.

Figure 1

Principal component analysis (PCA) score plots of the 320 identified metabolites in rice leaves of the four treatments. MH63, green square; T1C-19, red triangle; MH63-RDV, blue circle; T1C-19-RDV, orange rhombus.

Figure 2

Hierarchical cluster analysis of the 320 identified metabolites in rice leaves of the four treatments. In the heatmap, each treatment is visualized in a single column, and each metabolite is represented by a single row. Metabolite accumulation is shown in different colors, where blue indicates high abundance and low relative expression is shown in indigo (color key scale on the right of the heat map).

Figure 3

Number of the changed metabolites in three different rice groups. Venn diagrams depicting the changed metabolites between T1C-19 and MH63 rice plants (A), between MH63-RDV and MH63 rice plants (B), and between T1C-19-RDV and T1C-19 rice plants (C). Classification of the changed metabolites and the fold changes between T1C-19 and MH63 rice plants (D), between MH63-RDV and MH63 rice plants (E), and between T1C-19-RDV and T1C-19 rice plants (F).

Figure 4

Pathway enrichment of the significantly changed metabolites. Pathway enrichment of the changed metabolites between T1C-19 and MH63 (A), between MH63-RDV and MH63 (B), and between T1C-19-RDV and T1C-19 (C). The abscissa represents the impact of the path topological analysis, and the ordinate is the negative logarithm of p-value of the pathway enrichment analysis. The bigger the bubble, the bigger the impact is; the deeper the color, the larger the value of −ln (p), indicates the more significant the enrichment.

Figure 5

The RDV infection rates between Cry1C and MH63 rice plants. The RDV infection rates were analyzed using Student’s t-test. Values are mean ± standard error (n = 3).