TrAC Trends in Analytical Chemistry, Journal Year: 2023, Volume and Issue: 160, P. 116985 - 116985
Published: Feb. 11, 2023
Language: Английский
Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)
Published: Nov. 4, 2022
Abstract Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, metabolite annotation is a major challenge metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), enable global from knowns unknowns The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, MS/MS similarity peak correlation network. To demonstrate principle, apply vitro enzymatic system different biological samples, with ~100–300 putative annotated each data set. Among them, >80% are corroborated silico tools. Finally, validate 5 that absent common libraries through repository mining synthesis of chemical standards. Together, enables efficient annotations, substantially advances discovery recurrent for samples model organisms, towards deciphering dark matter
Language: Английский
Citations
140
TrAC Trends in Analytical Chemistry, Journal Year: 2022, Volume and Issue: 158, P. 116903 - 116903
Published: Dec. 24, 2022
Language: Английский
Citations
54
Acta Materia Medica, Journal Year: 2023, Volume and Issue: 2(2)
Published: April 25, 2023
The rapid development of bioinformatics tools has recently broken through the bottleneck in natural products research. These advances have enabled researchers to rapidly separate and efficiently target discover previously undescribed molecules. Among these advances, tandem mass spectrometry molecular networking is a promising method for de-replicating complex mixtures, thus leading an accelerated revolution “art isolation” field. In this review we describe current networking-based metabolite analysis methods that are widely applied or implementable discovery research, metabolomics, related fields. main objective was summarize strategies can be implemented as alternative de-replication approaches efficient list examples successful applications combine with other techniques.
Language: Английский
Citations
33
Acta Pharmaceutica Sinica B, Journal Year: 2023, Volume and Issue: 13(8), P. 3238 - 3251
Published: May 23, 2023
Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value basic drug research, including disease mechanisms, identifying targets, and elucidating mode action drugs. More importantly, greatly accelerates development process by predicting pharmacokinetics, pharmacodynamics, response. In addition, facilitates exploration repurposing drug-drug interactions, as well personalized treatment strategies. Here, we briefly review recent advances technologies update our knowledge applications research development.
Language: Английский
Citations
33
BMC Bioinformatics, Journal Year: 2023, Volume and Issue: 24(1)
Published: June 15, 2023
Abstract Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide close insight into physiological states are highly volatile to genetic environmental perturbations. Variation metabolic can inform mechanisms of pathology, providing potential biomarkers diagnosis assessment the risk contracting With advancement high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis intricate essential deriving relevant robust results that be deployed real-life clinical settings. Multiple tools been developed both interpretations. In this review, we survey approaches corresponding available discovery using metabolomics.
Language: Английский
Citations
29
Molecular Diversity, Journal Year: 2023, Volume and Issue: 28(2), P. 901 - 925
Published: Jan. 21, 2023
Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area cancer therapeutics, several phytocompounds have been used till date design develop new drugs. One desired interests pharmaceutical companies researchers globally is that anti-cancer leads discovered, for which can be considered valuable source. Simultaneously, in recent years, growth computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure–activity relationship (QSAR), Absorption Distribution Metabolism Excretion Toxicity (ADMET), network biology, machine learning (ML) has gained importance efficiency, reduced time-consuming nature, cost-effectiveness. Therefore, present review amalgamates information on plant-based molecules identified lead from silico approaches. The mandate this discuss studies published last 5–6 years aim identify phytomolecules as against with help traditional well newer techniques pharmacology ML. This also lists databases webservers available public domain related harnessed discovery. It expected would useful pharmacologists, medicinal chemists, biologists, other involved development natural products (NPs) into clinically effective molecules. Reviewed niche phytomolecule-based respect current trends including learning.
Language: Английский
Citations
28
Environmental Science & Technology, Journal Year: 2023, Volume and Issue: 57(43), P. 16244 - 16254
Published: Oct. 18, 2023
Per- and polyfluoroalkyl substances (PFAS) receive significant research attention due to their potential adverse effects on human health. Evidence shows that the kidney is one of target organs PFAS. In occupational exposure scenarios, high PFAS concentrations may adversely affect metabolism, but whether this effect reflected in small metabolic molecules contained urine remains unknown. study, 72 matched serum samples from workers a fluorochemical manufactory as well 153 local residents were collected, 23 levels quantified. The Σ23PFAS 5.43 ± 1.02 μg/mL 201 46.9 ng/mL, respectively, while concentration was 6.18 0.76 ng/mL. For workers, urinary strongly correlated with (r = 0.57-0.93), indicating can be good indicator for levels. Further, nontargeted metabolomics study conducted. results association models, including Bayesian kernel machine regression, demonstrated positive correlations between key molecules. A total eight biomarkers associated identified, all them showed markers function. These findings provide first evidence serve matrix indicate health kidneys.
Language: Английский
Citations
27
Biotechnology Advances, Journal Year: 2024, Volume and Issue: 72, P. 108319 - 108319
Published: Jan. 26, 2024
Language: Английский
Citations
14
Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)
Published: July 8, 2024
Abstract 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 disturbance associated the composite individual events. Here, we conducted untargeted metabolomics analysis 333 incident matched controls. The were designated as death, myocardial infarction/stroke heart failure. A total of 23 differential metabolites majority middle long chain acylcarnitines. Distinct patterns revealed, glycerophospholipids alteration was specific Notably, addition markers significantly improved failure risk prediction. This study highlights potential significance plasma on tailed assessment events, strengthens understanding heterogenic mechanisms across different
Language: Английский
Citations
12
Food Chemistry, Journal Year: 2024, Volume and Issue: 446, P. 138906 - 138906
Published: March 9, 2024
Language: Английский
Citations
11