NMR-based metabolomics has become a practical and analytical methodology for discovering novel genes, biomarkers, metabolic phenotypes, and dynamic cell behaviors in organisms. Recent developments in NMR-based metabolomics, however, have not concentrated on improvements of comprehensiveness in terms of simultaneous large-scale metabolite detections. To resolve this, we have devised and implemented a statistical index, the SpinAssign p-value, in NMR-based metabolomics for large-scale metabolite annotation and publicized this information. It enables simultaneous annotation of more than 200 candidate metabolites from the single (13)C-HSQC (heteronuclear single quantum coherence) NMR spectrum of a single sample of cell extract.

Succinate is a building block compound that the U.S. Department of Energy (DOE) has declared as important in biorefineries, and it is widely used as a commodity chemical. Here, we identified the two genes increasing succinate production of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Succinate was excreted under dark, anaerobic conditions, and its production level increased by knocking out ackA, which encodes an acetate kinase, and by overexpressing sigE, which encodes an RNA polymerase sigma factor. Glycogen catabolism and organic acid biosynthesis were enhanced in the mutant lacking ackA and overexpressing sigE, leading to an increase in succinate production reaching five times of the wild-type levels. Our genetic and metabolomic analyses thus demonstrated the effect of genetic manipulation of a metabolic enzyme and a transcriptional regulator on succinate excretion from this cyanobacterium with the data based on metabolomic technique.