Using Variable Data-Independent Acquisition for Capillary Electrophoresis-Based Untargeted Metabolomics.

Capillary electrophoresis coupled with tandem mass spectrometry (CE-MS/MS) offers advantages in peak capacity and sensitivity for metabolic profiling owing to the electroosmotic flow-based separation. However, the utilization of data-independent MS/MS acquisition (DIA) is restricted due to the absence of an optimal procedure for analytical chemistry and its related informatics framework. We assessed the mass spectral quality using two DIA techniques, namely, all-ion fragmentation (AIF) and variable DIA (vDIA), to isolate 60-800 Da precursor ions with respect to annotation rates. Our findings indicate that vDIA, coupled with the updated MS-DIAL chromatogram deconvolution algorithm, yields higher spectral matching scores and annotation rates compared to AIF. Additionally, we evaluated a linear migration time (MT) correction method using internal standards to accurately align chromatographic peaks in a data set. Postcorrection, the data set exhibited less than 0.1 min MT drifts, a difference mostly equivalent to that of conventional reverse-phase liquid chromatography techniques. Moreover, we conducted MT prediction for metabolites recorded in mass spectral libraries and metabolite structure databases containing a total of 469,870 compounds, achieving an accuracy of less than 1.5 min root mean squares. Our platform provides a peak annotation platform utilizing MT information, accurate precursor m/z, and the MS/MS spectrum recommended by the metabolomics standards initiative. Applying this procedure, we investigated metabolic alterations in lipopolysaccharide (LPS)-induced macrophages, characterizing 170 metabolites. Furthermore, we assigned metabolite information to unannotated peaks using an in silico structure elucidation tool, MS-FINDER. The results were integrated into the nodes in the molecular spectrum network based on the MS/MS similarity score. Consequently, we identified significantly altered metabolites in the LPS-administration group, where glycinamide ribonucleotide, not present in any spectral libraries, was newly characterized. Additionally, we retrieved metabolites of false-negative hits during the initial spectral annotation procedure. Overall, our study underscores the potential of CE-MS/MS with DIA and computational mass spectrometry techniques for metabolic profiling.

Characterization of sulfur-compound metabolism underlying wax-ester fermentation in Euglena gracilis.

Euglena gracilis is a microalga, which has been used as a model organism for decades. Recent technological advances have enabled mass cultivation of this species for industrial applications such as feedstock in nutritional foods and cosmetics. E. gracilis degrades its storage polysaccharide (paramylon) under hypoxic conditions for energy acquisition by an oxygen-independent process and accumulates high amount of wax-ester as a by-product. Using this sequence of reactions referred to as wax-ester fermentation, E. gracilis is studied for its application in biofuel production. Although the wax-ester production pathway is well characterized, little is known regarding the biochemical reactions underlying the main metabolic route, especially, the existence of an unknown sulfur-compound metabolism implied by the nasty odor generation accompanying the wax-ester fermentation. In this study, we show sulfur-metabolomics of E. gracilis in aerobic and hypoxic conditions, to reveal the biochemical reactions that occur during wax-ester synthesis. Our results helped us in identifying hydrogen sulfide (H2S) as the nasty odor-producing component in wax-ester fermentation. In addition, the results indicate that glutathione and protein degrades during hypoxia, whereas cysteine, methionine, and their metabolites increase in the cells. This indicates that this shift of abundance in sulfur compounds is the cause of H2S synthesis.