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研究开发了一种基于96孔板培养和气相色谱?质谱联用( GC?MS)技术的高通量细胞表型分析方法。该方法分别以48种物质作为唯一能源对大肠杆菌进行培养,利用GC?MS研究野生型和yfcC基因改造大肠杆菌对各物质的分解代谢情况,实现高通量的细胞表型分析。结果显示,野生型和yfcC基因过表达大肠杆菌对14种物质的代谢能力有显著差异,yfcC基因过表达大肠杆菌对甘氨酸和柠檬酸的代谢能力明显强于野生型大肠杆菌,而对其他物质的代谢能力较弱,我们推测可能是由于yfcC基因促进乙醛酸代谢,导致yfcC过表达菌株对甘氨酸的代谢能力较强;野生型和yfcC基因敲除大肠杆菌间分解有显著差异的共16种物质,其中yfcC基因敲除大肠杆菌对丙氨酸、乳糖、肌醇和柠檬酸的代谢能力较强。该方法简单、高效,可以为未知基因功能研究提供更多代谢功能相关的参考数据。

A high?throughput method for cell phenotype analysis was developed based on 96?well plate culture and gas chromatography coupled with mass spectrometry ( GC?MS ) . Forty?eight metabolites were selected as the sole energy for wild?type and its yfcC mutation E. coli ( yfcC over?expression and yfcC deletion mutants ) culture. The high?throughput phenotype analysis was achieved by investigating the catabolism difference of these metabolites among the three strains by GC?MS. The results showed that there were 14 metabolites changed significantly between wild?type and yfcC over?expression mutant E. coli. The metabolism ability of yfcC over?expression mutant E. coli for glycine and citric acid was much stronger than wild?type E. coli. For other metabolites, the wild?type strain showed stronger capabilities in metabolism. Among the 16 metabolites metabolized differentially between wild?type E. coli and its yfcC dele?tion mutant, metabolism ability of alanine, lactose, myo?inositol and citric acid in yfcC deletion mutant was much stronger. Other metabolites displayed the opposite results. Among the results acquired, we speculated that the faster metabolism of glycine in yfcC over?expression mutant E. coli might be caused by its promoted glyoxylate shunt. This high?throughput method for cell phenotype analysis was simple and efficient. It could provide more useful information about metabolism for gene study with unknown function.

参考文献

[1] Raamsdonk,LM;Teusink,B;Broadhurst,D;Zhang,N;Hayes,A;Walsh,MC;Berden,JA;Brindle,KM;Kell,DB;Rowland,JJ;Westerhoff,HV;van-Dam,K;Oliver,SG.A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations.[J].Nature biotechnology,20011(1):45-50.
[2] Wren JD.A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide.[J].Bioinformatics,200913(13):1694-1701.
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