A Metabolomics-Based Strategy for Identifying Endogenous Inhibitors of IAPP Aggregation

Unravelling the phytochemical complexity of neuroprotective plants: a metabolomics approach to Alzheimer’s disease

Integrating Network Toxicology and Untargeted Metabolomics to Identify Candidate Mechanisms and Metabolic Markers of α-Amanitin-Induced Hepatorenal Injury in Mice

Highlights What are the main findings? Metabolomic pathway analysis of plasma from α-amanitin-poisoned mice preliminarily reveals that plasma metabolites follow a temporally dynamic progression across seven different groups. PTGS2 emerges as a potential candidate target; propionylcarnitine and 2-arachidonoylglycerol (2-AG) are the key associated metabolites. What are the implications of the main findings? Similar to the clinical symptomatology, α-amanitin-induced liver and kidney injury follows a temporally dynamic evolutionary process, which may inform reference criteria for poisoning treatment. By integrating untargeted metabolomics with network toxicology, this study couples the phenotypic endpoints represented by differential metabolites with the regulatory networks mediated by potential candidate targets. This approach enables a multi-level elucidation of the mechanisms underlying α-amanitin-induced hepatorenal injury, which might provide a reference basis for early toxicity warning and precision intervention. Abstract Background/Objectives: Mushroom poisoning is a leading cause of death from foodborne illness, and α-amanitin is its most potent toxin. However, the mechanisms and biomarkers of α-amanitin-induced hepatorenal injury remain inadequately understood. Methods: First, the potential candidate targets and metabolic pathways were identified using network toxicology. Subsequently, untargeted metabolomics was employed to screen for potential metabolic markers of poisoning in α-amanitin-treated mice across eight time groups: 0 h, 3 h, 8 h, 12 h, 24 h, 2 d, 4 d, and 7 d. Following this, a compound–reaction–enzyme–gene network was constructed based on the differential metabolites to identify relevant genes, which were integrated with the core candidate target genes derived from network toxicology. The findings were further validated by molecular docking and immunohistochemistry. Results: Network toxicology identified eight candidate targets associated with α-amanitin-induced combined hepatic and renal injury. Metabolomic analysis revealed that α-amanitin-induced hepatorenal injury followed a temporally dynamic progression, and that propionylcarnitine was the common differential metabolite across all time points. Integration of the network toxicology and untargeted metabolomics results indicated that PTGS2 served as a potential candidate target and 2-arachidonoylglycerol (2-AG) was selected as a potential differential metabolite in the combined hepatic and renal injury caused by α-amanitin. Conclusions: By integrating network toxicology and untargeted metabolomics, this study preliminarily reveals that α-amanitin-induced hepatorenal injury follows a temporally dynamic progression, which is consistent with the toxicological injury–response–adaptation model. PTGS2 is tentatively annotated as the potential candidate target, while propionylcarnitine and 2-AG are the potential candidate metabolites. These findings may provide valuable guidance for poisoning treatment and identification in cases of mushroom poisoning caused by Amanita species.

Transcriptomics-based identification of shared biomarkers across type 2 diabetes, mild cognitive impairment, and uric acid metabolism

Three candidate biomarkers co-dysregulated across T2DM and MCI transcriptomes and associated with uric acid metabolism are identified and considered hypothesis-generating and require validation in larger independent cohorts.

A metabolomic signatures in hyperuricemia: a systematic review

Hyperuricemia (HUA) is traditionally viewed as a disorder of purine metabolism. However, its broader metabolic alterations remain incompletely understood. Metabolomics provides a useful approach for exploring metabolite changes associated with HUA, but a comprehensive synthesis of existing findings is still lacking. This systematic review and meta-analysis aimed to characterize the systemic metabolic signature of HUA beyond purine pathways. By synthesizing data from 27 metabolomics studies involving 12,335 participants, the study sought to identify consistent metabolite biomarkers and key dysregulated pathways to provide new insights for diagnosis and therapeutic targeting. This review included 27 metabolomics studies involving 12,335 participants and identified 1,187 metabolites reported in association with HUA. Qualitative synthesis showed 54 consistently elevated and 20 consistently decreased blood metabolites, mainly involving amino acids, lipid-related metabolites, energy-related compounds, vitamins and their derivatives, and purine nucleoside metabolites. The meta-analysis was limited to two eligible studies, with one study contributing most of the statistical weight; it suggested higher levels of Alanine, Leucine, Phenylalanine, and Tyrosine and lower Histidine levels in HUA. Pathway enrichment analysis highlighted “One carbon pool by folate,” “Arginine biosynthesis,” “Glutathione metabolism,” and related amino acid and energy metabolism pathways. Overall, these findings suggest that HUA may be associated with metabolic perturbations beyond purine metabolism alone, but the candidate metabolites and pathways require further validation in longitudinal, standardized, and mechanistic studies.

Amino acid- and lipid-related metabolic remodeling in PTZ-kindled mice reveals candidate plasma signatures of chronic epilepsy

Metabolic dysfunction is increasingly implicated in epilepsy, but the systemic metabolic alterations associated with chronic seizures remain incompletely characterized. This study aimed to define peripheral metabolic signatures of chronic epilepsy in a pentylenetetrazol (PTZ)-kindled mouse model and to examine whether the altered pathways were supported by human epilepsy transcriptomic data. Male C57BL/6 mice were subjected to repeated PTZ injections to establish chronic epilepsy, while control mice received saline. Plasma samples were analyzed using ultra-high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics. Differential metabolites were identified through multivariate and univariate analyses, followed by KEGG pathway enrichment. Least absolute shrinkage and selection operator (LASSO) regression was used as an exploratory feature-selection method. To provide complementary human hippocampal transcriptomic context, the hippocampal subset of GSE256068 was reanalyzed using differential expression analysis, and KEGG pathway enrichment analysis. PTZ-kindled mice displayed a distinct plasma metabolic profile compared with controls. A total of 349 differential metabolites were identified, mainly enriched in amino acid metabolism, tryptophan metabolism, and central carbon metabolism. LASSO regression identified six candidate metabolic features, all of which are reported in the supplementary material. Among them, indolelactic acid (ILA) and DG (20:3n6/0:0/20:3n6) were prioritized for biological interpretation because of their relevance to tryptophan-related and lipid-related metabolism. Reanalysis of the human hippocampal transcriptomic dataset revealed pathway-level alterations related to GABAergic synaptic signaling, glycosphingolipid biosynthesis, inflammatory regulation of TRP channels, and inflammatory response, providing complementary human hippocampal context for the neurotransmitter-, lipid-, and inflammatory--related metabolic alterations observed in PTZ-kindled mice. PTZ-induced kindling was associated with marked plasma metabolic alterations involving amino acid-, tryptophan-, and lipid-related pathways. ILA and DG (20:3n6/0:0/20:3n6) emerged as candidate plasma metabolic features associated with the PTZ-kindled seizure phenotype. Complementary analysis of human temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) hippocampal transcriptomic data identified related alterations in neurotransmission-, lipid-, and inflammatory-associated pathways. This pathway-level convergence supports the broader relevance of metabolic dysregulation to epilepsy, while the mechanistic roles and biomarker potential of individual metabolites require independent validation.

Progressive Metabolic Dysregulation from Prediabetes to Diabetes Revealed by Untargeted Metabolomics

Type 2 diabetes (T2D) and prediabetes represent a progressive glycemic continuum associated with multi-pathway metabolic deterioration that often precedes clinical diagnosis. Early identification of molecular alterations underlying this transition is critical for prevention strategies. Untargeted gas chromatography–mass spectrometry (GC–MS) metabolomics combined with multivariate statistical analysis (PCA) was applied to serum samples from 188 participants stratified into Control (n = 48), Prediabetes (n = 113), and Diabetes (n = 27) groups according to ADA/WHO diagnostic criteria. Progressive metabolic alterations were observed across the glycemic continuum. Several amino acid-, lipid-, and organic acid-related features showed nominal differences between the study groups. However, none of the detected metabolomic features remained statistically significant after false discovery rate (FDR) correction, indicating that these findings should be considered exploratory. Diabetes was associated with widespread downregulation of amino acid-related features, long-chain and complex lipid species, and small organic acids relative to both control and prediabetes groups. PCA (PC1 = 35.9%) showed progressive metabolic stratification primarily driven by hyperglycemia, dyslipidemia, and blood pressure elevation. These exploratory findings suggest that the transition from prediabetes to diabetes may be accompanied by alterations in amino acid, lipid, and energy metabolism. The identified metabolomic features represent candidate metabolites that require validation in larger independent cohorts using targeted metabolomics.