Chenfei Ma

Metabolic profiling of transgenic rice with cryIAc and sck genes: An evaluation of unintended effects at metabolic level by using GC-FID and GC-MS

The cryIAc and sck genes were introduced to the rice for the purpose of improving the insect resistance. Metabolic profiles of wild and transgenic rice were compared to assess the unintended effects related to gene modification. Wild samples with different sowing dates or sites were also examined to determine the environmental effects on metabolites. The polar compounds of grains were extracted, trimethylsilylated and analyzed by gas chromatography-flame ionization detection (GC-FID). Partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA) were applied to differentiate transgenic and wild rice grains. The significantly distinguishable metabolites were picked out, and then identified by gas chromatography-mass spectrometry (GC-MS). It was found that both the environment and gene manipulation had remarkable impacts on the contents of glycerol-3-phosphate, citric acid, linoleic acid, oleic acid, hexadecanoic acid, 2,3-dihydroxypropyl ester, sucrose, 9-octadecenoic acid (Z)-, 2,3-dihydroxypropyl ester and so on. Sucrose, mannitol and glutamic acid had a significant increase in transgenic grains in contrast to those in non-genetically modified (GM) rice.

Genetic, proteomic and metabolic analysis of the regulation of energy storage in rice seedlings in response to drought

We used proteomic analysis to determine the response of rice plant seedlings to drought‐induced stress. The expression of 71 protein spots was significantly altered, and 60 spots were successfully identified. The greatest down‐regulated protein functional category was translation. Up‐regulated proteins were mainly related to protein folding and assembly. Additionally, many proteins involved in metabolism (e.g. carbohydrate metabolism) also showed differences in expression. cDNA microarray and GC‐MS analysis showed 4756 differentially expressed mRNAs and 37 differentially expressed metabolites. Once these data were integrated with the proteomic analysis, we were able to elucidate the metabolic pathways affected by drought‐induced stress. These results suggest that increased energy consumption from storage substances occurred during drought. In addition, increased expression of the enzymes involved in anabolic pathways corresponded with an increase in the content of six amino acids. We speculated that energy conversion from carbohydrates and/or fatty acids to amino acids was increased. Analysis of basic metabolism networks allowed us to understand how rice plants adjust to drought conditions.

Determination of radix ginseng volatile oils at different ages by comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry.

Comprehensive 2-D GC (GC x GC) coupled with TOF MS or flame ionization detector (FID) was employed to characterize and quantify the chemical composition of volatile oil in the radixes of Panax ginseng C. A. Mey. (ginseng) at different ages. Thirty-six terpenoids were tentatively identified based on the MS library search and retention index in a ginseng sample at the age of 3 years. An obvious group-type separation was obtained in the GC x GC-TOF MS chromatogram. The data collected by GC x GC-FID were processed using a principal component analysis (PCA) method to classify the samples at different ages. The compounds responsible for the significant differentiation among samples were defined. It was found that the relative abundances of alpha-cadinol, alpha-bisabolol, thujopsene, and n-hexadecanoic acid significantly rise with the increase in age.

Secondary Metabolic Profiling and Artemisinin Biosynthesis of Two Genotypes of Artemisia annua

Artemisinin has been proven to be an effective antimalarial compound, especially for chloroquine-resistant and cerebral malaria. However, its biosynthesis pathway is still not completely clear. In order to get new clues about artemisinin biosynthesis, metabolic profiling by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS) was applied to compare the secondary metabolites of two Artemisia annua L., genotype SP18 and 001, for some phenotypic and agricultural trait differences, including artemisinin content, existed between the two genotypes. Samples at 7 time points of three growth stages were studied. The data of profiles were subjected to multivariate analysis with partial least squares discriminant analysis (PLS-DA). The results indicated that there were clear differences in terpenoids and artemisinin metabolism between different growth stages and genotypes. Twenty-one compounds, including artemisinin and its related precursors, were selected as the marker compounds of the PLS-DA between the two genotypes. Among them, artemisinic acid, arteannuin B, borneol, beta-farnesene and an unidentified sesquiterpenoid (peak 48) were abundant in 001, while camphor, methyl artemisinic acid and lanceol accumulated mainly in SP18. The relationship between these differences and artemisinin biosynthesis in the two genotypes of A. annua were discussed.

Profiling and association mapping of grain metabolites in a subset of the core collection of Chinese rice germplasm (Oryza sativa L.).

In this study, metabolic profiles of a set of 48 rice germplasms from the Chinese core collection were obtained by gas chromatography and time-of-flight mass spectrometry (GC-TOF-MS). Forty-one metabolites were identified and relatively quantified according to the internal standard (IS). Wide ranges of variations for all metabolites were observed among rice accessions. The maximum/minimum ratios varied from 4.73 to 211.36. The metabolites were categorized into seven groups based on their chemical characteristics. Clustering analysis and a correlation network showed that most of the metabolites had variations among rice accessions in the same direction. Using 218 molecular markers, association mapping was conducted to identify the chromosomal loci influencing the concentrations of identified metabolites. Twenty markers were identified associating with the concentrations of 29 metabolites [-lg(P) > 3]. Allelic effects were investigated in detail in two markers (RM315 and RM541) as examples.