Metabolomics quantitatively measures metabolites in a given biological system and facilitates the understanding of physiological and pathological activities. With the recent advancement of mass ...spectrometry (MS) technology, liquid chromatography-mass spectrometry (LC-MS) with data-independent acquisition (DIA) has been emerged as a powerful technology for untargeted metabolomics due to its capability to acquire all MS2 spectra and high quantitative accuracy. In this trend article, we first introduced the basic principles of several common DIA techniques including MS
E
, all ion fragmentation (AIF), SWATH, and MSX. Then, we summarized and compared the data analysis strategies to process DIA-based untargeted metabolomics data, including metabolite identification and quantification. We think the advantages of the DIA technique will enable its broad application in untargeted metabolomics.
The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility - mass spectrometry (IM-MS) has ...emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.
The Keap1–Nrf2–ARE ((Kelch‐like ECH‐Associating protein 1) nuclear factor erythroid 2 related factor 2‐antioxidant response element) pathway is one of the most important defense mechanisms against ...oxidative and/or electrophilic stresses, and it is closely associated with inflammatory diseases, including cancer, neurodegenerative diseases, cardiovascular diseases, and aging. In recent years, progress has been made in strategies aimed at modulating the Keap1–Nrf2–ARE pathway. The Nrf2 activator DMF (Dimethylfumarates) has been approved by the FDA as a new first‐line oral drug to treat patients with relapsing forms of multiple sclerosis, while a phase 3 study of another promising candidate, CDDO‐Me, was terminated for safety reasons. Directly inhibiting Keap1–Nrf2 protein–protein interactions as a novel Nrf2‐modulating strategy has many advantages over using electrophilic Nrf2 activators. The development of Keap1–Nrf2 protein–protein interaction inhibitors has become a topic of intense research, and potent inhibitors of this target have been identified. In addition, inhibiting Nrf2 activity has attracted an increasing amount of attention because it may provide an alternative cancer therapy. This review summarizes the molecular mechanisms and biological functions of the Keap1–Nrf2–ARE system. The main focus of this review is on recent progress in studies of agents that target the Keap1–Nrf2–ARE pathway and the therapeutic applications of such agents.
Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive ...algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.
To explore high-performance electrocatalysts, electronic regulation on active sites is essentially demanded. Herein, we propose controlled phosphorus doping to effectively modify the electronic ...configuration of nanostructured Mo2C, accomplishing a benchmark performance of noble-metal-free electrocatalysts in the hydrogen evolution reaction (HER). Employing MoOx–phytic acid–polyaniline hybrids with tunable composition as precursors, a series of hierarchical nanowires composed of phosphorus-doped Mo2C nanoparticles evenly integrated within conducting carbon (denoted as P-Mo2C@C) are successfully obtained via facile pyrolysis under inert flow. Remarkably, P-doping into Mo2C can increase the electron density around the Fermi level of Mo2C, leading to weakened Mo–H bonding toward promoted HER kinetics. Further density functional theory calculations show that the negative hydrogen-binding free energy (ΔGH*) on pristine Mo2C gradually increases with P-doping due to electron transfer and steric hindrance by P on the Mo2C surface, indicating the effectively weakened strength of Mo–H. With optimal doping, a ΔGH* approaching 0 eV suggests a good balance between the Volmer and Heyrovsky/Tafel steps in HER kinetics. As expected, the P-Mo2C@C nanowires with controlled P-doping (P: 2.9 wt%) deliver a low overpotential of 89 mV at a current density of −10 mA cm−2 and striking kinetic metrics (onset overpotential: 35 mV, Tafel slope: 42 mV dec−1) in acidic electrolytes, outperforming most of the current noble-metal-free electrocatalysts. Elucidating feasible electronic regulation and the remarkably enhanced catalysis associated with controlled P-doping, our work will pave the way for developing efficient noble-metal-free catalysts via rational surface engineering.
Objective
To systematically profile metabolic alterations and dysregulated metabolic pathways in hyperuricemia and gout, and to identify potential metabolite biomarkers to discriminate gout from ...asymptomatic hyperuricemia.
Methods
Serum samples from 330 participants, including 109 with gout, 102 with asymptomatic hyperuricemia, and 119 normouricemic controls, were analyzed by high‐resolution mass spectrometry–based metabolomics. Multivariate principal components analysis and orthogonal partial least squares discriminant analysis were performed to explore differential metabolites and pathways. A multivariate methods with Unbiased Variable selection in R (MUVR) algorithm was performed to identify potential biomarkers and build multivariate diagnostic models using 3 machine learning algorithms: random forest, support vector machine, and logistic regression.
Results
Univariate analysis demonstrated that there was a greater difference between the metabolic profiles of patients with gout and normouricemic controls than between the metabolic profiles of individuals with hyperuricemia and normouricemic controls, while gout and hyperuricemia showed clear metabolomic differences. Pathway enrichment analysis found diverse significantly dysregulated pathways in individuals with hyperuricemia and patients with gout compared to normouricemic controls, among which arginine metabolism appeared to play a critical role. The multivariate diagnostic model using MUVR found 13 metabolites as potential biomarkers to differentiate hyperuricemia and gout from normouricemia. Two‐thirds of the samples were randomly selected as a training set, and the remainder were used as a validation set. Receiver operating characteristic analysis of 7 metabolites yielded an area under the curve of 0.83–0.87 in the training set and 0.78–0.84 in the validation set for distinguishing gout from asymptomatic hyperuricemia by 3 machine learning algorithms.
Conclusion
Gout and hyperuricemia have distinct serum metabolomic signatures. This diagnostic model has the potential to improve current gout care through early detection or prediction of progression to gout from hyperuricemia.
•Noble metal catalyst surpasses metal oxides on low-temperature activity.•Low-temperature activity determines the cost of catalysis application.•Hybrid treatments with catalysis are promising ...technologies for industrial VOCs pollution.
VOCs pollution is a complex issue involving a wide variability of pollutants that threatens human health and environment. Owing to the effective and economic characters, low-temperature (293–673K) catalytic oxidation (CO) has been extensively studied for VOCs removal in research and application fields. This review examines recent progress on the VOCs catalytic oxidation with noble metal and metal oxides catalysts, and the engineering features of regenerative catalytic oxidizer (RCO), recuperative catalytic oxidizer (CO), photocatalytic oxidizer (PCO) and hybrid treatment combining adsorptive concentration/ozonation/plasma with catalytic oxidizer. The aim was to analyze the factors that relate to the low-temperature activity of catalysts, and the efficiency and economy of manifold catalytic oxidizers. It can be concluded that improving low-temperature activity of catalysts, increasing thermal recovery efficiency of oxidizers and developing hybrid treatment technologies are most effective means to control practical VOCs pollution.
Imbalanced GSH/ROS and sequential cell death Liu, Ting; Sun, Li; Zhang, Yubin ...
Journal of biochemical and molecular toxicology,
January 2022, Letnik:
36, Številka:
1
Journal Article
Recenzirano
Reactive oxygen species (ROS) are produced in cells during metabolic processes. Excessive intracellular ROS may react with large biomolecules, such as DNA, RNA, proteins, and small biomolecules, that ...is, glutathione (GSH) and unsaturated fatty acids. GSH has physiological functions, including free radical scavenging, anti‐oxidation, and electrophile elimination. The disruption of ROS/GSH balance results in the deleterious oxidation and chemical modification of biomacromolecules, which eventually leads to cell‐cycle arrest and proliferation inhibition, and even induces cell death. Imbalanced ROS/GSH may result from a direct increase of ROS, consumption of GSH, intracellular oxidoreductase interference, or thioredoxin activity reduction. Some chemicals including arsenic trioxide (ATO), pyrogallol (PG), and carbobenzoxy‐Leu‐Leu‐leucinal (MG132) could also disrupt the balance of GSH and ROS. This article reviews the occurrence and consequences of the imbalance between GSH and ROS and introduces factors responsible for the disruption of cellular ROS and GSH balance, resulting in cell death. “GSH” and “ROS” were used as keywords to search the relevant literaturess.
The rapid development of metabolomics has significantly advanced health and disease related research. However, metabolite identification remains a major analytical challenge for untargeted ...metabolomics. While the use of collision cross-section (CCS) values obtained in ion mobility-mass spectrometry (IM-MS) effectively increases identification confidence of metabolites, it is restricted by the limited number of available CCS values for metabolites. Here, we demonstrated the use of a machine-learning algorithm called support vector regression (SVR) to develop a prediction method that utilized 14 common molecular descriptors to predict CCS values for metabolites. In this work, we first experimentally measured CCS values (ΩN2) of ∼400 metabolites in nitrogen buffer gas and used these values as training data to optimize the prediction method. The high prediction precision of this method was externally validated using an independent set of metabolites with a median relative error (MRE) of ∼3%, better than conventional theoretical calculation. Using the SVR based prediction method, a large-scale predicted CCS database was generated for 35 203 metabolites in the Human Metabolome Database (HMDB). For each metabolite, five different ion adducts in positive and negative modes were predicted, accounting for 176 015 CCS values in total. Finally, improved metabolite identification accuracy was demonstrated using real biological samples. Conclusively, our results proved that the SVR based prediction method can accurately predict nitrogen CCS values (ΩN2) of metabolites from molecular descriptors and effectively improve identification accuracy and efficiency in untargeted metabolomics. The predicted CCS database, namely, MetCCS, is freely available on the Internet.
We present Mass Spectrometry-Data Independent Analysis software version 4 (MS-DIAL 4), a comprehensive lipidome atlas with retention time, collision cross-section and tandem mass spectrometry ...information. We formulated mass spectral fragmentations of lipids across 117 lipid subclasses and included ion mobility tandem mass spectrometry. Using human, murine, algal and plant biological samples, we annotated and semiquantified 8,051 lipids using MS-DIAL 4 with a 1-2% estimated false discovery rate. MS-DIAL 4 helps standardize lipidomics data and discover lipid pathways.
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