Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents.

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.

AllCCS2: Curation of Ion Mobility Collision Cross-Section Atlas for Small Molecules Using Comprehensive Molecular Representations.

The development of ion mobility-mass spectrometry (IM-MS) has revolutionized the analysis of small molecules, such as metabolomics, lipidomics, and exposome studies. The curation of comprehensive reference collision cross-section (CCS) databases plays a pivotal role in the successful application of IM-MS for small-molecule analysis. In this study, we presented AllCCS2, an enhanced version of AllCCS, designed for the universal prediction of the ion mobility CCS values of small molecules. AllCCS2 incorporated newly available experimental CCS data, including 10,384 records and 7713 unified values, as training data. By leveraging a neural network trained on diverse molecular representations encompassing mass spectrometry features, molecular descriptors, and graph features extracted using a graph convolutional network, AllCCS2 achieved exceptional prediction accuracy. AllCCS2 achieved median relative error (MedRE) values of 0.31, 0.72, and 1.64% in the training, validation, and testing sets, respectively, surpassing existing CCS prediction tools in terms of accuracy and coverage. Furthermore, AllCCS2 exhibited excellent compatibility with different instrument platforms (DTIMS, TWIMS, and TIMS). The prediction uncertainties in AllCCS2 from the training data and the prediction model were comprehensively investigated by using representative structure similarity and model prediction variation. Notably, small molecules with high structural similarities to the training set and lower model prediction variation exhibited improved accuracy and lower relative errors. In summary, AllCCS2 serves as a valuable resource to support applications of IM-MS technologies. The AllCCS2 database and tools are freely accessible at http://allccs.zhulab.cn/.

Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer.

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). Therapeutic efficacy of nCRT is significantly affected by treatment-induced diarrhea and hematologic toxicities. Metabolic alternations in cancer therapy are key determinants to therapeutic toxicities and responses, but exploration in large-scale clinical studies remains limited. Here, we analyze 743 serum samples from 165 LARC patients recruited in a phase III clinical study using untargeted metabolomics and identify responsive metabolic traits over the course of nCRT. Pre-therapeutic serum metabolites successfully predict the chances of diarrhea and hematologic toxicities during nCRT. Particularly, levels of acyl carnitines are linked to sex disparity in nCRT-induced diarrhea. Finally, we show that differences in phenylalanine metabolism and essential amino acid metabolism may underlie distinct therapeutic responses of nCRT. This study illustrates the metabolic dynamics over the course of nCRT and provides potential to guide personalized nCRT treatment using responsive metabolic traits.

Screening of lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry.

Coronary heart disease (CHD) has a high mortality worldwide. This study aimed to screen lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry. Extraction and reconstitution solvents, liquid chromatographic and mass spectrometry conditions were optimized to detect more plasma lipid metabolites. In this study, the chromatographic and mass spectra characteristics of lipid metabolites were summarized. A total of 316 lipid metabolites were annotated via diagnostic fragment ion filtration, nitrogen rule filtration, and neutral loss filtration. Glycerophospholipid metabolism and sphingolipid metabolism were revealed as the main lipid disorders of CHD. This study provides a novel insight for high-throughput detection of lipid metabolites in plasma and provides a further understanding of the occurrence of CHD, which can provide valuable suggestions for the prevention of CHD.

Uncovering the antioxidant characteristics of black tea by coupling in vitro free radical scavenging assay with UHPLC-HRMS analysis.

Black tea (BT) is rich in dietary antioxidants, but its antioxidant composition has not been fully understood. To identify the true antioxidants occurring in BT, we established an approach based on 2,2-diphenyl-1-picrylhydrazyl radical scavenging assay coupled with ultra-high performance liquid chromatography-high resolution mass spectrometry (DPPH-UHPLC-HRMS). The employment of HRMS enable us to detect trace antioxidants, resolve co-eluted antioxidants, and characterize chemical structures of unknown antioxidants. In total, 56 phenolic compounds were screened as potential antioxidants from 106 compounds identified in BT. Catechol and pyrogallol were revealed as the key substructures in enhancing the antioxidant abilities of phenolic compounds. During BT brewing, high temperature with extended time promote antioxidant leaching but may induce the degradation of esterified and glycosylated compounds such as theaflavin-3-gallate and rutin. In conclusion, this work identified the true antioxidant constituents of BT, their structural characteristics, and their dynamic changes under various brewing conditions.