Fast and broad-coverage lipidomics enabled by ion mobility-mass spectrometry.

Aberrant lipid metabolism has been widely recognized as a hallmark of various diseases. However, the comprehensive analysis of distinct lipids is challenging due to the complexity of lipid molecular structures, wide concentration ranges, and numerous isobaric and isomeric lipids. Usually, liquid chromatography-mass spectrometry (LC-MS)-based lipidomics requires a long time for chromatographic separation to achieve optimal separation and selectivity. Ion mobility (IM) adds a new separation dimension to LC-MS, significantly enhancing the coverage, sensitivity, and resolving power. We took advantage of the rapid separation provided by ion mobility and optimized a fast and broad-coverage lipidomics method using the LC-IM-MS technology. The method required only 8 minutes for separation and detected over 1000 lipid molecules in a single analysis of common biological samples. The high reproducibility and accurate quantification of this high-throughput lipidomics method were systematically characterized. We then applied the method to comprehensively measure dysregulated lipid metabolism in patients with colorectal cancer (CRC). Our results revealed 115 significantly changed lipid species between preoperative and postoperative plasma of patients with CRC and also disclosed associated differences in lipid classes such as phosphatidylcholines (PC), sphingomyelins (SM), and triglycerides (TG) regarding carbon number and double bond. Together, a fast and broad-coverage lipidomics method was developed using ion mobility-mass spectrometry. This method is feasible for large-scale clinical lipidomic studies, as it effectively balances the requirements of high-throughput and broad-coverage in clinical studies.

Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease.

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.

Yersinia infection induces glucose depletion and AMPK-dependent inhibition of pyroptosis in mice.

Nutritional status and pyroptosis are important for host defence against infections. However, the molecular link that integrates nutrient sensing into pyroptosis during microbial infection is unclear. Here, using metabolic profiling, we found that Yersinia pseudotuberculosis infection results in a significant decrease in intracellular glucose levels in macrophages. This leads to activation of the glucose and energy sensor AMPK, which phosphorylates the essential kinase RIPK1 at S321 during caspase-8-mediated pyroptosis. This phosphorylation inhibits RIPK1 activation and thereby restrains pyroptosis. Boosting the AMPK-RIPK1 cascade by glucose deprivation, AMPK agonists, or RIPK1-S321E knockin suppresses pyroptosis, leading to increased susceptibility to Y. pseudotuberculosis infection in mice. Ablation of AMPK in macrophages or glucose supplementation in mice is protective against infection. Thus, we reveal a molecular link between glucose sensing and pyroptosis, and unveil a mechanism by which Y. pseudotuberculosis reduces glucose levels to impact host AMPK activation and limit host pyroptosis to facilitate infection.

Structure and mechanism of lysosome transmembrane acetylation by HGSNAT.

Lysosomal transmembrane acetylation of heparan sulfates (HS) is catalyzed by HS acetyl-CoA:α-glucosaminide N-acetyltransferase (HGSNAT), whose dysfunction leads to lysosomal storage diseases. The mechanism by which HGSNAT, the sole non-hydrolase enzyme in HS degradation, brings cytosolic acetyl-coenzyme A (Ac-CoA) and lysosomal HS together for N-acyltransferase reactions remains unclear. Here, we present cryogenic-electron microscopy structures of HGSNAT alone, complexed with Ac-CoA and with acetylated products. These structures explain that Ac-CoA binding from the cytosolic side causes dimeric HGSNAT to form a transmembrane tunnel. Within this tunnel, catalytic histidine and asparagine approach the lumen and instigate the transfer of the acetyl group from Ac-CoA to the glucosamine group of HS. Our study unveils a transmembrane acetylation mechanism that may help advance therapeutic strategies targeting lysosomal storage diseases.

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.