The use of a plant enzyme for rapid and sensitive analysis of naturally-occurring folates in food by liquid chromatography-tandem mass spectrometry.

A rapid, sensitive and reproducible method for analysis of naturally-occurring folates and folic acid in food has been developed and validated. A single-enzyme extraction step, in which a pure recombinant enzyme of plant origin (Arabidopsis thaliana) was used, enabled fast and reproducible deglutamylation during folate extraction within the incubation time of 1 h. Six commonly occurring folate forms (tetrahydrofolate, 5,10-methenyltetrahydrofolate, 10-formylfolic acid, 5-formyltetrahydrofolate, folic acid and 5-methyltetrahydrofolate) were detected and quantified in 9 min using liquid chromatography-tandem mass spectrometry (LC-MS/MS). 13C5-labeled 5-formyltetrahydrofolate, 13C5-labeled folic acid and 13C5-labeled 5-methyltetrahydrofolate were used as internal standards for the quantification. The method is described by a calibration curve (R2>0.99 and trueness 85-115%), a limit of quantification at 0.1 µg/100 g, trueness at 80-120% in spiked samples and certified reference materials, and a precision <10%. However, the precision in quantification of tetrahydrofolate was not within the acceptable limits due to the lack of use of the corresponding internal standard. An interconversion study of unstable formyl forms was performed which showed that 50% of 5,10-methenyltetrahydrofolate is converted to 5-formyltetrahydrofolate during the analysis. The developed LC-MS/MS method is a candidate for a future standard method for folate analysis in food.

Metabolic engineering of folate and its precursors in Mexican common bean (Phaseolus vulgaris L.).

Folate (vitamin B9) deficiency causes several health problems globally. However, folate biofortification of major staple crops is one alternative that can be used to improve vitamin intakes in populations at risk. We increased the folate levels in common bean by engineering the pteridine branch required for their biosynthesis. GTP cyclohydrolase I from Arabidopsis (AtGchI) was stably introduced into three common bean Pinto cultivars by particle bombardment. Seed-specific overexpression of AtGCHI caused significant increases of up to 150-fold in biosynthetic pteridines in the transformed lines. The pteridine boost enhanced folate levels in raw desiccated seeds by up to threefold (325 µg in a 100 g portion), which would represent 81% of the adult recommended daily allowance. Unexpectedly, the engineering also triggered a general increase in PABA levels, the other folate precursor. This was not observed in previous engineering studies and was probably caused by a feedforward mechanism that remains to be elucidated. Results from this work also show that common bean grains accumulate considerable amounts of oxidized pteridines that might represent products of folate degradation in desiccating seeds. Our study uncovers a probable different regulation of folate homoeostasis in these legume grains than that observed in other engineering works. Legumes are good sources of folates, and this work shows that they can be engineered to accumulate even greater amounts of folate that, when consumed, can improve folate status. Biofortification of common bean with folates and other micronutrients represents a promising strategy to improve the nutritional status of populations around the world.

GridMass: a fast two-dimensional feature detection method for LC/MS.

One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community.