Diagnostic metabolomic profiling of Parkinson's disease biospecimens

Structure characteristics and fermentation properties of konjac oligosaccharides from konjac peel prepared by one-step fermentation with Bacillus Licheniformis BJ2022

Konjac peel is one of the main wastes in the processing of konjac. More effective green recycling solutions for konjac peel need to be developed. This study explored a green method for konjac oligosaccharide extraction from konjac peel, aiming to uncover the benefits of oligosaccharide preparation by Bacillus licheniformis (BJ2022) liquid-state fermentation. The extraction rate of fermented konjac oligosaccharides (FKO) by fermenting konjac peel in a 100 L fermentation tank reached 21.78 g/L, and the purity of the extracted oligosaccharides was 74.11 %. Pentasaccharides, hexasaccharides, and heptasaccharides were purified and separated from FKO. The oligosaccharides separated from FKO were composed of glucose and mannose connected by β-1,4-glycosidic bonds. The in vitro anaerobic fermentation experiments of gut microbiota and metabolomics analysis indicated that FKO had prebiotic activity in regulating gut microbiota and promoting the production of metabolites such as short-chain fatty acids. This study indicated that Bacillus licheniformis (BJ2022) one-step fermentation was a green and efficient konjac oligosaccharides production method.

A Scoring Model Using Multi-Metabolites Based on Untargeted Metabolomics for Assessing Dyslipidemia in Korean Individuals with Obesity

BACKGROUND/OBJECTIVES

Metabolite risk score (MRS), which considers the collective effects of metabolites closely reflecting a phenotype, is a new approach for disease assessment, moving away from focusing solely on individual biomarkers. This study aimed to investigate a metabolite panel for dyslipidemia and verify the diagnostic efficacy of MRS on dyslipidemia.

METHODS

Key metabolite identification and MRS establishment were conducted in the discovery set, and MRS validation was performed in the replication set, with 50 healthy individuals and 50 dyslipidemia patients in each set. The MRS was constructed using key metabolites, identified via UPLC-MS/MS analysis, employing a weighted approach based on linear regression analysis.

RESULTS

N-acetylisoputreanine-γ-lactam and eicosapentaenoic acid were identified as key metabolites for dyslipidemia and were utilized for establishing the MRS. In addition to the MRS model, a conventional dyslipidemia diagnostic model based on lipid profiles, as well as a combined model (MRS + lipid profiles), were also established. In the discovery set, the MRS model diagnosed dyslipidemia with 85.4% accuracy. When combined with lipid profiles, accuracy improved to 91.8%. In the replication set, the MRS demonstrated diagnostic power with 76.1% accuracy, while the combined model achieved 86.0% accuracy for dyslipidemia assessment.

CONCLUSIONS

The MRS alone indicated sufficient assessment power in a real-world setting, despite a slight reduction in assessment ability when validated in the replication set. At this stage, therefore, the MRS serves as an auxiliary tool for disease diagnosis. This first attempt to apply MRS for dyslipidemia may offer a foundational concept for MRS in this disease.

Metabolomics in Parkinson's Disease and Correlation with Disease State

Changes in the level of metabolites, small molecules that are intermediates produced by metabolism or catabolism, are associated with developing diseases. Metabolite signatures in body fluids such as plasma, cerebrospinal fluid, urine, and saliva are associated with Parkinson's disease. Here, we discuss alteration of metabolites in the TCA cycle, pentose phosphate pathway, kynurenic network, and redox system. We also summarize the efforts of many research groups to differentiate between metabolite profiles that characterize PD motor progression and dyskinesia, gait and balance, and non-motor symptoms such as depression and cognitive decline. Understanding how changes in metabolites lead to progression in PD may allow for the identification of individuals at the earliest stage of the disease and the development of new therapeutic strategies.

Human blood metabolites and risk of post-traumatic stress disorder: A Mendelian randomization study

BACKGROUND

Post-traumatic stress disorder (PTSD) is a debilitating chronic mental disorder that leads to reduced quality of life and increased economic burden. Observational studies have found an association between human blood metabolites and PTSD. Nonetheless, these studies have limitations and are subject to confounding factors as well as reverse causation. Herein, we employed a two-sample Mendelian randomization (MR) approach for the systematic analysis of the blood metabolites and PTSD causal link.

METHODS

Data for the human blood metabolome, cerebrospinal fluid (CSF) metabolome, and PTSD were obtained from publicly available summary-level genome-wide association studies (GWAS), respectively. The inverse variance weighted (IVW) approach represented the main analytic method for assessing exposure-outcome causal associations, employing multiple sensitivity analyses to verify the results' stability. In addition, replication and meta-analysis, steiger test and reverse MR analysis methods were performed to clarify further that these metabolites have independent causal effects on PTSD. Finally, the results of blood and CSF metabolomics analyses were synthesized to obtain biological markers with a causal link to PTSD.

RESULTS

Conclusively, we identified potential causal associations between six blood metabolites and PTSD. The sensitivity analyses elucidated the absence of pleiotropy or heterogeneity in the MR results. The Steiger test and reverse MR analysis did not reveal reverse causal associations, proving the robustness of our results. Combined blood and CSF metabolome analyses showed the same trend for theophylline.

CONCLUSION

This study reveals a strong causal link between metabolites and PTSD, which can be used as a biomarker for clinical PTSD disease screening and prevention. This study also provides a new perspective on the mechanism of metabolite-mediated PTSD development by combining genomics and metabolomics.

Decoding aging clocks: New insights from metabolomics

Chronological age is a crucial risk factor for diseases and disabilities among older adults. However, individuals of the same chronological age often exhibit divergent biological aging states, resulting in distinct individual risk profiles. Chronological age estimators based on omics data and machine learning techniques, known as aging clocks, provide a valuable framework for interpreting molecular-level biological aging. Metabolomics is an intriguing and rapidly growing field of study, involving the comprehensive profiling of small molecules within the body and providing the ultimate genome-environment interaction readout. Consequently, leveraging metabolomics to characterize biological aging holds immense potential. The aim of this review was to provide an overview of metabolomics approaches, highlighting the establishment and interpretation of metabolomic aging clocks while emphasizing their strengths, limitations, and applications, and to discuss their underlying biological significance, which has the potential to drive innovation in longevity research and development.

Identification of 7-HOCA as a Potential Biomarker in Glioblastoma: Evidence from Genome-Wide Association Study and Clinical Validation

Purpose

Glioblastoma (GBM) is associated with metabolic disturbances, yet the relationships between metabolites with GBM have not been comprehensively explored. This study aims to fill this gap by integrating Mendelian randomization (MR) analysis with clinical validation.

Patients and Methods

Summary data from genome-wide association study (GWAS) of cerebrospinal fluid (CSF) metabolites, plasma metabolites, and GBM were obtained separately. A total of 338 CSF metabolites and 1400 plasma metabolites were utilized as exposures. Concurrently, GBM was designated as the outcome. A two-sample bidirectional MR study was conducted to investigate the potential association. The inverse variance weighted (IVW) analyses were conducted as causal estimates, accompanied by a series of sensitivity analyses to evaluate the robustness of the results. Additionally, metabolite levels in clinical plasma and CSF samples were quantified using liquid chromatography-mass spectrometry to validate the findings.

Results

MR analysis identified eight CSF metabolites and six plasma metabolites that were closely associated with GBM. Among these, elevated levels of 7-alpha-hydroxy-3-oxo-4-cholestenoate (7-HOCA) in both CSF and plasma were found to promote GBM. In terms of clinical validation, compared to the control group, 7-HOCA levels were significantly higher in both the CSF and plasma of GBM group.

Conclusion

This study provides a comprehensive analysis of the metabolic factors contributing to GBM. The identification of specific metabolites, particularly 7-HOCA, that have vital roles in GBM pathogenesis suggests new biomarkers and therapeutic targets, offering potential pathways for improved diagnosis and treatment of GBM.

Metabolomics in atrial fibrillation - A review and meta-analysis of blood, tissue and animal models

BACKGROUND

Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia associated with severe cardiovascular complications. AF presents a growing global challenge, however, current treatment strategies for AF do not address the underlying pathophysiology. To advance diagnosis and treatment of AF, a deeper understanding of AF root causes is needed. Metabolomics is a fast approach to identify, quantify and analyze metabolites in a given sample, such as human serum or atrial tissue. In the past two decades, metabolomics have enabled research on metabolite biomarkers to predict AF, metabolic features of AF, and testing metabolic mechanisms of AF in animal models. Due to the field's rapid evolution, the methods of AF metabolomics studies have not always been optimal. Metabolomics research has lacked standardization and requires expertise to face methodological challenges.

PURPOSE OF THE REVIEW

We summarize and meta-analyze metabolomics research on AF in human plasma and serum, atrial tissue, and animal models. We present the current progress on metabolic biomarkers candidates, metabolic features of clinical AF, and the translation of metabolomics findings from animal to human. We additionally discuss strengths and weaknesses of the metabolomics method and highlight opportunities for future AF metabolomics research.

Small molecule modulators of alpha-synuclein aggregation and toxicity: Pioneering an emerging arsenal against Parkinson's disease

Parkinson's disease (PD) is primarily characterized by loss of dopaminergic neurons in the substantia nigra pars compacta region of the brain and accumulation of aggregated forms of alpha-synuclein (α-Syn), an intrinsically disordered protein, in the form of Lewy Bodies and Lewy Neurites. Substantial evidences point to the aggregated/fibrillar forms of α-Syn as a central event in PD pathogenesis, underscoring the modulation of α-Syn aggregation as a promising strategy for PD treatment. Consequently, numerous anti-aggregation agents, spanning from small molecules to polymers, have been scrutinized for their potential to mitigate α-Syn aggregation and its associated toxicity. Among these, small molecule modulators like osmoprotectants, polyphenols, cellular metabolites, metals, and peptides have emerged as promising candidates with significant potential in PD management. This article offers a comprehensive overview of the effects of these small molecule modulators on the aggregation propensity and associated toxicity of α-Syn and its PD-associated mutants. It serves as a valuable resource for identifying and developing potent, non-invasive, non-toxic, and highly specific small molecule-based therapeutic arsenal for combating PD. Additionally, it raises pertinent questions aimed at guiding future research endeavours in the field of α-Syn aggregation remodelling.

Role of the Gut Bacteria-Derived Metabolite Phenylacetylglutamine in Health and Diseases

Over the past few decades, it has been well established that gut microbiota-derived metabolites can disrupt gut function, thus resulting in an array of diseases. Notably, phenylacetylglutamine (PAGln), a bacterial derived metabolite, has recently gained attention due to its role in the initiation and progression of cardiovascular and cerebrovascular diseases. This meta-organismal metabolite PAGln is a byproduct of amino acid acetylation of its precursor phenylacetic acid (PAA) from a range of dietary sources like egg, meat, dairy products, etc. The microbiota-dependent metabolism of phenylalanine produces PAA, which is a crucial intermediate that is catalyzed by diverse microbial catalytic pathways. PAA conjugates with glutamine and glycine in the liver and kidney to predominantly form phenylacetylglutamine in humans and phenylacetylglycine in rodents. PAGln is associated with thrombosis as it enhances platelet activation mediated through the GPCRs receptors α2A, α2B, and β2 ADRs, thereby aggravating the pathological conditions. Clinical evidence suggests that elevated levels of PAGln are associated with pathology of cardiovascular, cerebrovascular, and neurological diseases. This Review further consolidates the microbial/biochemical synthesis of PAGln and discusses its role in the above pathophysiologies.

Causal relationship between human blood metabolites and risk of ischemic stroke: a Mendelian randomization study

Background: Ischemic stroke (IS) is a major cause of death and disability worldwide. Previous studies have reported associations between metabolic disorders and IS. However, evidence regarding the causal relationship between blood metabolites and IS lacking. Methods: A two-sample Mendelian randomization analysis (MR) was used to assess the causal relationship between 1,400 serum metabolites and IS. The inverse variance-weighted (IVW) method was employed to estimate the causal effect between exposure and outcome. Additionally, MR-Egger regression, weighted median, simple mode, and weighted mode approaches were employed as supplementary comprehensive evaluations of the causal effects between blood metabolites and IS. Tests for pleiotropy and heterogeneity were conducted. Results: After rigorous selection, 23 known and 5 unknown metabolites were identified to be associated with IS. Among the 23 known metabolites, 13 showed significant causal effects with IS based on 2 MR methods, including 5-acetylamino-6-formylamino-3-methyluracil, 1-ribosyl-imidazoleacetate, Behenoylcarnitine (C22), N-acetyltyrosine, and N-acetylputrescine to (N (1) + N (8))-acetate,these five metabolites were positively associated with increased IS risk. Xanthurenate, Glycosyl-N-tricosanoyl-sphingadienine, Orotate, Bilirubin (E,E), Bilirubin degradation product, C17H18N2O, Bilirubin (Z,Z) to androsterone glucuronide, Bilirubin (Z,Z) to etiocholanolone glucuronide, Biliverdin, and Uridine to pseudouridine ratio were associated with decreased IS risk. Conclusion: Among 1,400 blood metabolites, this study identified 23 known metabolites that are significantly associated with IS risk, with 13 being more prominent. The integration of genomics and metabolomics provides important insights for the screening and prevention of IS.

Integrated metabolomics and proteomics analysis of plasma lipid metabolism in Parkinson's disease

INTRODUCTION

Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain.

AREAS COVERED

In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism.

EXPERT OPINION

Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

The Molecular Role of Polyamines in Age-Related Diseases: An Update

Polyamines (Pas) are short molecules that exhibit two or three amine groups that are positively charged at a physiological pH. These small molecules are present in high concentrations in a wide variety of organisms and tissues, suggesting that they play an important role in cellular physiology. Polyamines include spermine, spermidine, and putrescine, which play important roles in age-related diseases that have not been completely elucidated. Aging is a natural process, defined as the time-related deterioration of the physiological functions; it is considered a risk factor for degenerative diseases such as cardiovascular, neurodegenerative, and musculoskeletal diseases; arthritis; and even cancer. In this review, we provide a new perspective on the participation of Pas in the cellular and molecular processes related to age-related diseases, focusing our attention on important degenerative diseases such as Alzheimerߣs disease, Parkinsonߣs disease, osteoarthritis, sarcopenia, and osteoporosis. This new perspective leads us to propose that Pas function as novel biomarkers for age-related diseases, with the main purpose of achieving new molecular alternatives for healthier aging.

Peripheral metabolic alterations associated with pathological manifestations of Parkinson's disease in gut-brain axis-based mouse model

Introduction

Parkinson's disease (PD) is a representative neurodegenerative disease, and its diagnosis relies on the evaluation of clinical manifestations or brain neuroimaging in the absence of a crucial noninvasive biomarker. Here, we used non-targeted metabolomics profiling to identify metabolic alterations in the colon and plasma samples of Proteus mirabilis (P. mirabilis)-treated mice, which is a possible animal model for investigating the microbiota-gut-brain axis.

Methods

We performed gas chromatography-mass spectrometry to analyze the samples and detected metabolites that could reflect P. mirabilis-induced disease progression and pathology.

Results and discussion

Pattern, correlation and pathway enrichment analyses showed significant alterations in sugar metabolism such as galactose metabolism and fructose and mannose metabolism, which are closely associated with energy metabolism and lipid metabolism. This study indicates possible metabolic factors for P. mirabilis-induced pathological progression and provides evidence of metabolic alterations associated with P. mirabilis-mediated pathology of brain neurodegeneration.