GC-MS based metabolomics analysis reveals the effects of different agitation speeds on the level of proteinogenic amino acids in Lactococcus lactis subsp. cremoris MG1363

Lactococcus lactis subsp. cremoris MG1363 is an opportunistic lactic acid bacterium (LAB) that has emerged as one of the most promising candidate cell factories. The availability of genome-level information and U.S. Federal Drug administration’s designation of ‘generally recognized as safe’ (GRAS) are two of the more important key factors for its wide-ranging applications in numerous biotechnological processes. Several studies have shown that various physiological conditions, such as temperature, salinity and pH, can influence the physiological growth of L. lactis; agitation, in particular, can increase the production of amino acids and fermentation by-products. However, the effect of different agitation speeds on the growth of L. lactis’ has rarely been examined. In the study reported here, we used a gas chromatography–mass spectrometry-based metabolomics approach to investigate the effects of different agitation speeds on the production of proteinogenic amino acids (PAAs) by L. lactis MG1363. Lactococcus lactis MG1363 was grown under four different agitation speeds (50, 100, 150 and 200 rpm) at a constant temperature of 30 °C, and the differences in the specific growth rate and levels of PAAs were determined. Approximately 15 PAAs with concentrations ranging from 0 to 50 mmol/L were detected under all conditions. Partial least squares discriminant analysis (PLS-DA) revealed a distinct difference when L. lactis was incubated at 100 and 150 rpm. Heatmap analysis showed that the levels of pyruvate-, glutamate- and aspartate-based amino acids were varied under the different agitation conditions. The time-series analysis showed an increment of lysine when L. lactis’ cells were cultured with shaking at 50, 100 and 200 rpm. Taken together, these results highlight the changes in the levels of PAAs in L. lactis cells in response to agitation. In addition, the collected dataset will be useful for optimization of ¹³C-labeling based experiments in L. lactis.