Metabolomics and Lipidomics Profiling in Asymptomatic Severe Intracranial Arterial Stenosis: Results from a Population-Based Study

Corn silk polysaccharides attenuate diabetic nephropathy through restoration of the gut microbial ecosystem and metabolic homeostasis

Introduction

The pathogenesis of diabetic nephropathy (DN) is complex, inflammation is the central link among the inducing factors in the existing research, and the gutkidney axis could scientifically explain the reasons for the accumulation of chronic low-grade inflammation. As both a medicine and food, corn silk contains abundant polysaccharides. Historical studies and modern research have both confirmed its intervention effect on diabetes and DN, but the mechanism of action is unclear.

Methods

In this study, a DN rat model was generated, and the therapeutic effect of corn silk polysaccharides (CSPs) was evaluated based on behavioral, histopathological and biochemical indicators. We attempted to fully understand the interactions between CSPs, the gut microbiota and the host at the systemic level from a gut microbiota metabolomics perspective to fundamentally elucidate the mechanisms of action that can be used to intervene in DN.

Results

Research has found that the metabolic pathways with a strong correlation with CSPs were initially identified as glycerophosphate, fatty acid, bile acid, tyrosine, tryptophan and phenylalanine metabolism and involved Firmicutes, Bacteroides, Lachnospiraceae-NK4A136- group and Dubosiella, suggesting that the effect of CSPs on improving DN is related to changes in metabolite profiles and gut microbiota characteristics.

Discussion

CSPs could be harnessed to treat the abnormal metabolism of endogenous substances such as bile acids and uremic toxins caused by changes in gut microbiota, thus alleviating kidney damage caused by inflammation. In view of its natural abundance, corn silk is safe and nontoxic and can be used for the prevention and treatment of diabetes and DN.

LC-MS/MS based metabolomic analysis of serum from patients with cerebrovascular stenosis

Cerebrovascular stenosis (CVS) is the main cause of ischemic stroke, which greatly threatens human life. Hence, it's important to perform early screenings for CVS. Metabolomics is an emerging omics approach that has great advantages in disease screening and diagnosis. Therefore, we aim to elucidate the correlation between CVS and metabolomics, which can aid in conducting CVS screening at an early stage. Patients with CVS in Beijing Hospital were included in the study. A total of 36 participants, including 18 patients diagnosed with CVS and 18 healthy individuals, were recruited at Beijing Hospital between May 2022 and October 2021. The serum samples were analyzed for liquid chromatography-tandem mass spectrometry (LC-MS/MS). Then, multivariate statistical methods, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed. Differential metabolites were obtained and demonstrated by volcano plot and heatmap. The study recruited 36 participants, including 18 patients with CVS and 18 healthy participants. A total of 150 metabolites were identified. Multivariate statistical analysis revealed significant differences between patients and healthy participants. Furthermore, 30 serum metabolites levels differed significantly between two groups. Differential metabolites were enriched in phenylalanine, tyrosine, and tryptophan biosynthesis; primary bile acid biosynthesis, and other pathways. This study identified differential metabolites in patients with CVS and elucidated the relevant metabolic pathways. Thus, these findings aid in the study of the pathogenesis of CVS and its early diagnosis. DATA AVAILABILITY STATEMENT: The datasets generated for this study are available on request to the corresponding author.

A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics

Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.

Serum metabolomics identified metabolite biomarkers and distinguished maturity-onset diabetes of the young from type 1 diabetes in the Chinese population

BACKGROUND

Maturity-onset diabetes of the young (MODY) patients have unique clinical manifestations and need individualized treatments. We identified novel serum metabolic biomarkers to distinguish MODY and explore the possible mechanism of the clinical manifestation and complications of MODY.

METHODS

Fasting serum samples were collected from MODY3 (n = 17), MODY2 (n = 33), type 1 diabetes (T1DM) (n = 34) and healthy individuals (n = 30), and were analyzed using the ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) metabolomic platform.

RESULTS

4 metabolites were found significantly fluctuated between groups, including glycerophosphocholine, LysoPC(18:2(9Z,12Z)), sphinganine and l-Phenylalanine. Glycerophosphocholine was selected as a diagnostic biomarker. The the area under the ROC curve (AUC) for distinguishing MODYs from healthy controls and differentiating MODY3 from T1DM reached 1.0. The combination of metabolites also gained good diagnostic value. The AUC of the combination of LysoPC(18:2(9Z,12Z)), sphinganine and l-Phenylalanine for discriminating MODY3 from T1DM was 0.983. Besides, the combination of clinical indices and metabolites helped to better differentiate the 2 MODY subtypes.

CONCLUSIONS

We identified the metabolic profiles of MODY2 and MODY3 and found promising biomarkers for distinguishing MODY from T1DM, which provides evidence for the pathogenesis and characteristic clinical manifestations of patients with MODY2 and MODY3.

Identification of Metabolomics Biomarkers in Extracranial Carotid Artery Stenosis

The biochemical identification of carotid artery stenosis (CAS) is still a challenge. Hence, 349 male subjects (176 normal controls and 173 stroke patients with extracranial CAS ≥ 50% diameter stenosis) were recruited. Blood samples were collected 14 days after stroke onset with no acute illness. Carotid plaque score (≥2, ≥5 and ≥8) was used to define CAS severity. Serum metabolites were analyzed using a targeted Absolute IDQ®p180 kit. Results showed hypertension, diabetes, smoking, and alcohol consumption were more common, but levels of diastolic blood pressure, HDL-C, LDL-C, and cholesterol were lower in CAS patients than controls (p < 0.05), suggesting intensive medical treatment for CAS. PCA and PLS-DA did not demonstrate clear separation between controls and CAS patients. Decision tree and random forest showed that acylcarnitine species (C4, C14:1, C18), amino acids and biogenic amines (SDMA), and glycerophospholipids (PC aa C36:6, PC ae C34:3) contributed to the prediction of CAS. Metabolite panel analysis showed high specificity (0.923 ± 0.081, 0.906 ± 0.086 and 0.881 ± 0.109) but low sensitivity (0.230 ± 0.166, 0.240 ± 0.176 and 0.271 ± 0.169) in the detection of CAS (≥2, ≥5 and ≥8, respectively). The present study suggests that metabolomics profiles could help in differentiating between controls and CAS patients and in monitoring the progression of CAS.

Association Between Intracranial Pulsatility and White Matter Hyperintensities in Asymptomatic Intracranial Arterial Stenosis: A Population-Based Study in Shandong, China

OBJECTIVES

The effects of increased intracranial pulsatility on the severity of white matter hyperintensities (WMH) in participants with asymptomatic intracranial arterial stenosis (aICAS) remain uncertain. We aimed to investigate whether an increased pulsatility index (PI) is associated with WMH volume (WMHV) in individuals with aICAS.

MATERIALS AND METHODS

All participants were recruited from the Kongcun Town aICAS Study, including a total of 103 participants with aICAS and 98 healthy controls (age- and sex-matched). PI was assessed using transcranial Doppler ultrasound. The WMHV was calculated through the lesion segmentation tool system for the Statistical Parametric Mapping package based on magnetic resonance imaging. The association between PI and lnWMHV was analyzed by linear regression models adjusting for demographics, lifestyle, and vascular risk factors.

RESULTS

The lnWMHV and PI between the aICAS and control groups showed no significant differences (P = 0.171 and 0.287, respectively). In a multivariable model, age ≥ 60 years and male sex (P = 0.000 and 0.006, respectively) were significant predictors of lnWMHV in the aICAS group. In sex-stratified analyses, there was a significant association between PI and lnWMHV in males with aICAS (P = 0.038).

CONCLUSIONS

This study suggest there might be a likely association between increased intracranial pulsatility and WMH burden in males with aICAS.

Atlas of metabolism reveals palmitic acid results in mitochondrial dysfunction and cell apoptosis by inhibiting fatty acid β-oxidation in Sertoli cells

In recent years, the impact of lipotoxicity on male fertility has received extensive attention, especially on Sertoli cells (SCs). In SCs, energy metabolism is important as disorders of energy metabolism result in infertility eventually. However, the underlying mechanism of lipotoxicity on energy metabolism in SCs remains unknown. Advances in high-throughput metabolomics and lipidomics measurement platforms provide powerful tools to gain insights into complex biological systems. Here, we aimed to explore the potential molecular mechanisms of palmitic acid (PA) regulating energy metabolism in SCs based on metabolomics and lipidomics. The results showed that glucose metabolism-related metabolites were not significantly changed, which suggested that PA treatment had little effect on glucose metabolism and may not influence the normal energy supply from SCs to germ cells. However, fatty acid β-oxidation was inhibited according to accumulation of medium- and long-chain acylcarnitines in cells. In addition, the pool of amino acids and the levels of most individual amino acids involved in the tricarboxylic acid (TCA) cycle were not changed after PA treatment in SCs. Moreover, PA treatment of SCs significantly altered the lipidome, including significant decreases in cardiolipin and glycolipids as well as remarkable increases in ceramide and lysophospholipids, which indicated that mitochondrial function was affected and apoptosis was triggered. The increased apoptosis rate of SCs was verified by elevated protein expression levels of Cleaved Caspase-3 and Bax as well as decreased Bcl-2 protein expression level. Together, these findings indicated that PA may result in mitochondrial dysfunction and increased apoptosis by inhibiting fatty acid β-oxidation of SCs.

Association between homocysteine and white matter hyperintensities in rural-dwelling Chinese people with asymptomatic intracranial arterial stenosis: A population-based study

PURPOSE

Although homocysteine (Hcy) has been proven to be associated with the incidence of white matter hyperintensities (WMH) in patients with stroke, this association remains unclear in participants with asymptomatic intracranial arterial stenosis (aICAS). This study aimed to investigate the association of Hcy with WMH in participants with aICAS.

MATERIALS AND METHODS

This was a cross-sectional study based on the Kongcun Town Study. Participants diagnosed with aICAS by magnetic resonance angiography in the Kongcun Town Study were enrolled in this study. Data on demographics, lifestyle, medical histories, and Hcy levels were collected via interviews, clinical examinations, and laboratory tests. The volume of WMH was calculated using the lesion segmentation tool system for the Statistical Parametric Mapping package based on magnetic resonance imaging. The association between Hcy and WMH volume was analyzed using linear and logistic regression analyses.

RESULTS

A total of 137 aICAS participants were enrolled in the present study. Hcy was associated with the incidence of severe WMH (4th quartile, ≥4.20 ml) after adjustment for certain covariates [Hcy as a continuous variable, odds ratio (95% confidence interval) (OR (95% CI)): 1.09 (1.00, 1.19), p = .047; as a categorical variable (Hcy ≥15 μmol/L), OR (95% CI): 3.74 (1.37, 10.19), p = .010)]. After stratification according to the degree of aICAS, this relationship remained significant only in the moderate-to-severe stenosis group (stenosis ≥50%). (Hcy as continuous variable, OR (95% CI): 1.14 (1.02, 1.27), p = .025; as a categorical variable (Hcy ≥15 μmol/L), OR (95% CI): 5.59 (1.40, 15.25), p = .015).

CONCLUSION

Serum Hcy concentration may be positively associated with the volume of WMH in rural-dwelling Chinese people with moderate-to-severe (stenosis ≥50%) aICAS.

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.

Metabonomics Analysis of Myocardial Metabolic Dysfunction in Patients with Cardiac Natriuretic Peptide Resistance

Brain natriuretic peptide (BNP) is an important biological marker and regulator of cardiac function. BNP resistance is characterized by high concentrations of less functionally effective BNP and common in heart failure (HF) patients. However, the roles and consequences of BNP resistance remain poorly understood. Investigate the effects of cardiac BNP resistance and identify potential metabolic biomarkers for screening and diagnosis. Thirty patients and thirty healthy subjects were enrolled in this study. Cardiac functions were evaluated by echocardiography. The plasma levels of cyclic guanosine monophosphate (cGMP) and BNP were measured by enzyme-linked immunosorbent assay (ELISA) and the cGMP/BNP ratio is calculated to determine cardiac natriuretic peptide resistance. Liquid chromatograph tandem mass spectrometry (LC-MS) based untargeted metabolomics analysis was applied to screen metabolic changes. The cGMP/BNP ratio was markedly lower in HF patients than controls. The cGMP/BNP ratio and ejection fraction (EF) were strongly correlated (R² = 0.676, P < 0.05). Importantly, metabolic profiles were substantially different between HF patients and healthy controls. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that the differentially expressed metabolites are involved in signaling pathways that regulate cardiac functions. In HF patients, BNP resistance develops in association with a reduction in heart function and metabolic remodeling. It suggests possible functional roles of BNP resistance in the regulation of cardiac metabolism.