Rice-Arabidopsis FOX line screening with FT-NIR-based fingerprinting for GC-TOF/MS-based metabolite profiling

Abstract

The full-length cDNA over-expressing (FOX) gene hunting system is useful for genome-wide gain-of-function analysis. The screening of FOX lines requires a high-throughput metabolomic method that can detect a wide range of metabolites. Fourier transform-near-infrared (FT-NIR) spectroscopy in combination with the chemometric approach has been used to analyze metabolite fingerprints. Since FT-NIR spectroscopy can be used to analyze a solid sample without destructive extraction, this technique enables untargeted analysis and high-throughput screening focusing on the alteration of metabolite composition. We performed non-destructive FT-NIR-based fingerprinting to screen seed samples of 3000 rice-Arabidopsis FOX lines; the samples were obtained from transgenic Arabidopsis thaliana lines that overexpressed rice full-length cDNA. Subsequently, the candidate lines exhibiting alteration in their metabolite fingerprints were analyzed by gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) in order to assess their metabolite profiles. Finally, multivariate regression using orthogonal projections to latent structures (O2PLS) was used to elucidate the predictive metabolites obtained in FT-NIR analysis by integration of the datasets obtained from FT-NIR and GC-TOF/MS analyses. FT-NIR-based fingerprinting is a technically efficient method in that it facilitates non-destructive analysis in a high-throughput manner. Furthermore, with the integrated analysis used here, we were able to discover unique metabotypes in rice-Arabidopsis FOX lines; thus, this approach is beneficial for investigating the function of rice genes related to metabolism.