Metabolic Phenotyping Study of Mouse Brains Following Acute or Chronic Exposures to Ethanol

Relationship between sphingolipids-mediated neuroinflammation and alcohol use disorder

BACKGROUND

Alcohol use disorder is a chronic recurrent encephalopathy, and its pathogenesis has not been fully understood. Among possible explanations, neuroinflammation caused by the disorders of brain central immune signaling has been identified as one possible mechanism of alcohol use disorder. As the basic components of cells and important bioactive molecules, sphingolipids are essential in regulating many cellular activities. Recent studies have shown that sphingolipids-mediated neuroinflammation may be involved in the development of alcohol use disorder.

METHODS

PubMed databases were searched for literature on sphingolipids and alcohol use disorder (alcohol abuse, alcohol addiction, alcohol dependence, and alcohol misuse) including evidence of the relationship between sphingolipids-mediated neuroinflammation and alcohol use disorder (formation, withdrawal, treatment).

RESULTS

Disorders of sphingolipid metabolism, including the different types of sphingolipids and regulatory enzyme activity, have been found in patients with alcohol use disorder as well as animal models, which in turn cause neuro-inflammation in the central nervous system. Thus, these disorders may also be an important mechanism in the development of alcohol use disorder in patients. In addition, different sphingolipids may have different or even reverse effects on alcohol use disorder.

CONCLUSIONS

The sphingolipids-mediated neuroinflammation plays an important role in the development of alcohol use disorder. This review proposes a potential approach to prevent and treat alcohol use disorders by manipulating sphingolipid metabolism.

Integrated pharmacokinetics and pharmacometabolomics to reveal the synergistic mechanism of a multicomponent Chinese patent medicine, Mailuo Shutong pills against thromboangiitis obliterans

BACKGROUND

Mailuo Shutong Pills (MLST) have displayed pharmacological activity against thromboangiitis obliterans (TAO). However, the active ingredients and therapeutic mechanism of MLST against TAO remained to be further clarified.

PURPOSE

The aim of this study was to explore the active components of MLST and their synergistic mechanism against TAO by integrating pharmacokinetics (PK) and pharmacometabolomics (PM).

METHODS

TAO model rats were established by sodium laurate solution. Firstly, the efficacy of MLST was evaluated by gangrene score, blood flow velocity, and hematoxylin-eosin (H&E) staining. Secondly, PK research was conducted on bioavailable components to characterize their dynamic behaviors under TAO. Thirdly, multiple plasma and urine metabolic biomarkers for sodium laurate-induced TAO rats were found by untargeted metabolomics, and then variations in TAO-altered metabolites following MLST treatment were analyzed utilizing multivariate and bioinformatic analysis. Additionally, metabolic pathway analysis was performed using MetaboAnalyst. Finally, the dynamic link between absorbed MLST-compounds and TAO-associated endogenous metabolites was established by correlation analysis.

RESULTS

MLST significantly alleviated gangrene symptoms by improving the infiltration of inflammatory cells and blood supply in TAO rats. Significant differences in metabolic profiles were found in 17 differential metabolites in plasma and 24 in urine between Sham and TAO rats. The 10 bioavailable MLST-compounds, such as chlorogenic acid and paeoniflorin, showed positive or negative correlations with various TAO-altered metabolites related to glutamate metabolism, histidine metabolism, arachidonic acid metabolism and so on.

CONCLUSION

This study originally investigated the dynamic interaction between MLST and the biosystem, providing unique insight for disclosing the active components of MLST and their synergistic mechanisms against TAO, which also shed light on new therapeutic targets for TAO and treatment.

Metabolome and transcriptome analyses of plants grown in naturally attenuated soil after hydrogen fluoride exposure

Accidental chemical leaks and illegal chemical discharges are a global environmental issue. In 2012, a hydrogen fluoride leak in Gumi, South Korea, killed several people and contaminated the environment. This leak also led to a significant decline in crop yield, even after the soil concentration of hydrogen fluoride decreased to below the standard level following natural attenuation. To determine the cause of this decreased plant productivity, we designed direct and indirect exposure tests by evaluating the metabolome, transcriptome, and phenome of the plants. In an indirect exposure test, soil metabolomics revealed downregulation of metabolites in vitamin B6, lipopolysaccharide, osmolyte, and exopolysaccharide metabolism. Next-generation sequencing of the plants showed that ABR1 and DREB1A were overexpressed in response to stress. Plant metabolomics demonstrated upregulation of folate biosynthesis and nicotinate and nicotinamide metabolism associated with detoxification of reactive oxygen species. These results demonstrate impaired metabolism of soil microbes and plants even after natural attenuation of hydrogen fluoride in soil. The novel chemical exposure testing used in this study can be applied to identify hidden damage to organisms after natural attenuation of chemicals in soil, as well as biomarkers for explaining the decline in yield of plants grown in soil near pollutant-emitting industrial facilities.

Identifying quality markers of Mailuoshutong pill against thromboangiitis obliterans based on chinmedomics strategy

BACKGROUND

Mailuoshutong pill (MLSTP) is a traditional Chinese medicine (TCM) for the treatment of Thromboangiitis obliterans (TAO, Buerger's disease) which is a segmental non-atherosclerotic inflammatory occlusive disorder. However, the mechanism and quality standards of MLSTP have not been sufficiently studied.

PURPOSE

This work aims to investigate the potential mechanisms and quality markers (Q-markers) of MLSTP treating TAO based on the chinmedomics strategy.

METHODS

The therapeutical effect of MLSTP on TAO rats was evaluated by changes in body weight and clinical score, regional blood flow velocity and perfused blood vessel distribution, hematoxylin-eosin (H&E) staining, serum metabolic profile. Moreover, both endogenous metabolites and exogenous components were simultaneously detected in serum based on ultra-high performance liquid chromatography coupled with a Q Exactive hybrid quadrupole-orbitrap high resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS), and multivariate analysis was applied to identify the biomarkers, as well as the dynamic changes of metabolites were observed to explore the mechanism of action of MLSTP. In addition, the pharmacodynamic material basis were identified by correlation analysis between biomarkers and absorbed constituents. Finally, the Q-markers of MLSTP were determined according to the screening principles of Q-marker and validated the measurability.

RESULTS

MLSTP treatment alleviated disease severity of TAO, reduced inflammatory infiltration, and ameliorated vascular function. 26 potential biomarkers associated with glutamate metabolism, linoleic acid metabolism, arachidonic acid metabolism and so on were identified. Besides, 27 prototypical components were identified in serum, 16 of which were highly correlated with efficacy and could serve as the pharmacodynamic material basis of MLSTP against TAO. In addition, 7 compounds, namely, sweroside, chlorogenic acid, calycosin-7-glucoside, formononetin, paeoniflorin, liquiritigenin and 3-butylidenephthalide, were considered as potential Q-markers of MLSTP. Ultimately, the measurability of the seven Q-markers was validated by rapid identifcation and quantifcation.

CONCLUSION

This study successfully clarified the therapeutic effect and Q-markers of MLSTP by chinmedomics strategy, which is of great significance for the establishment of quality standards. Furthermore, it provides a certain reference for the screening of Q-markers in TCM prescriptions.

Plasma Lipidomic and Metabolomic Profiling after Birth in Neonates Born to SARS-CoV-19 Infected and Non-Infected Mothers at Delivery: Preliminary Results

Pregnant women are among the high-risk populations for COVID-19, whereas the risk of vertical transmission to the fetus is very low. Nevertheless, metabolic alternations described in COVID-19 patients may also occur in pregnant women and their offspring. We prospectively evaluated the plasma lipidomic and metabolomic profiles, soon after birth, in neonates born to infected mothers (cases, n = 10) and in the offspring of uninfected ones at delivery (controls, n = 10). All cases had two negative tests for SARS-CoV-2 (nasopharyngeal swabs) performed 72 h apart. Blood samples were obtained within the first hours after birth. Liquid chromatography-high resolution mass spectrometry (UHPLC-TOF/MS) and gas chromatography-mass spectrometry (GC-MS) were applied for the analyses. Multivariate statistical analysis was performed for data evaluation. Changes in several plasma lipid species-classes (long-chain fatty acids phosphatidylcholines, triglycerides), and amino-acids were identified that allowed for clear discrimination between the study groups. The results of this preliminary investigation suggest that neonates born to Sars-Cov-19 positive mothers, without evidence of viral infection at birth, have a distinct plasma lipidomic and metabolomic profile compared to those of uninfected mothers. Whether these findings are reflective of maternal metabolic alternations due to the virus or a metabolic response following an unidentified neonatal infection warrants further investigation.

Evaluation of Cocaine Effect on Endogenous Metabolites of HepG2 Cells Using Targeted Metabolomics

Cocaine toxicity has been a subject of study because cocaine is one of the most common and potent drugs of abuse. In the current study the effect of cocaine on human liver cancer cell line (HepG2) was assessed. Cocaine toxicity (IC50) on HepG2 cells was experimentally calculated using an XTT assay at 2.428 mM. The metabolic profile of HepG2 cells was further evaluated to investigate the cytotoxic activity of cocaine at 2 mM at three different time points. Cell medium and intracellular material samples were analyzed with a validated HILIC-MS/MS method for targeted metabolomics on an ACQUITY Amide column in gradient mode with detection on a triple quadrupole mass spectrometer in multiple reaction monitoring. About 106 hydrophilic metabolites from different metabolic pathways were monitored. Multivariate analysis clearly separated the studied groups (cocaine-treated and control samples) and revealed potential biomarkers in the extracellular and intracellular samples. A predominant effect of cocaine administration on alanine, aspartate, and glutamate metabolic pathway was observed. Moreover, taurine and hypotaurine metabolism were found to be affected in cocaine-treated cells. Targeted metabolomics managed to reveal metabolic changes upon cocaine administration, however deciphering the exact cocaine cytotoxic mechanism is still challenging.

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier

Mass spectrometry data sets from omics studies are an optimal information source for discriminating patients with disease and identifying biomarkers. Thousands of proteins or endogenous metabolites can be queried in each analysis, spanning several orders of magnitude in abundance. Machine learning tools that effectively leverage these data to accurately identify disease states are in high demand. While mass spectrometry data sets are rich with potentially useful information, using the data effectively can be challenging because of missing entries in the data sets and because the number of samples is typically much smaller than the number of features, two challenges that make machine learning difficult. To address this problem, we have modified a new supervised classification tool, the Aristotle Classifier, so that omics data sets can be better leveraged for identifying disease states. The optimized classifier, AC.2021, is benchmarked on multiple data sets against its predecessor and two leading supervised classification tools, Support Vector Machine (SVM) and XGBoost. The new classifier, AC.2021, outperformed existing tools on multiple tests using proteomics data. The underlying code for the classifier, provided herein, would be useful for researchers who desire improved classification accuracy when using their omics data sets to identify disease states.

A perspective on the standards describing mass spectrometry-based metabolic phenotyping (metabolomics/metabonomics) studies in publications

As metabolic phenotyping (metabolomics, metabonomics and also lipidomics) gains in popularity and new investigators enter the field, the need to maintain and improve standards in publication is ever more pressing. In this perspective the requirements for information that should be included in manuscripts published in the Journal of Chromatography B, to ensure that the work is both credible and repeatable, are discussed. These include aspects such as study design, ethics, quality assurance (QA), quality control (QC) and data processing. In addition, aspects such as the level of confidence required for reporting metabolite identification (to a level where they could be subsequently used to develop hypotheses) are discussed.