A serum metabolomics analysis method based on gas chromatography-mass spectrometry(GC-MS) was used to investigate the metabolic regulation mechanism of Hericium erinaceus(H. erinaceus) polysaccharides on radiation injury. A mouse model of radiation injury was established by ~(60)Co-γ irradiation. High and low dose groups of H. erinaceus polysaccharide injection were designed, and Rubiae Radix et Rhizoma extract was set as the positive control group to investigate the therapeutic effects and metabolic reaction pathways of H. erinaceus polysaccharides on radiation injury. The metabolites of serum samples were collected by GC-MS, and principal component analysis(PCA) was conducted to establish the metabolic profiles of each group of mice. Partial least squares discriminant analysis(PLS-DA), t-test(P<0.05), and variable importance in the projection(VIP>1) were used to screen out the differential metabolite. Metabolite identification and construction of related metabolic pathways and metabolic networks were achieved by using online databases such as HMDB and METLIN. The results showed that 12 differential metabolites in the serum of mice irradiated at 6.5 Gy that were associated with the radiation injury model, including lactic acid, alanine, urea, serine, threonine, glycerol, L-5-oxoproline, L-lysine, stearic acid, stearic acid, oleic acid, and 1-monopalmitoylglucoside. Two metabolic pathways were enriched: glycerolipid metabolism and metabolism of glycine, serine, and threonine. 18 differential metabolites in the serum of mice irradiated at 8.5 Gy were associated with the radiation injury model, including lactic acid, alanine, urea, L-leucine, glycerol, nonanoic acid, serine, threonine, L-5-oxoproline, phenylalanine, L-ornithine, 1,5-dehydroorbital, L-lysine, L-tyrosine, pectic, oleic, stearic, and cholesterol. Four metabolic pathways were enriched: phenylalanine, tyrosine, and tryptophan synthesis, phenylalanine metabolism, glyceride metabolism, and glycine, serine, and threonine metabolism. It was suggested that H. erinaceus polysaccharides could intervene in radiation injury by altering amino acid and fatty acid synthesis in mice. It was assumed that H. erinaceus polysaccharides regulated the level of metabolic pathways through lipid metabolism and amino acid metabolism, thus affecting energy metabolism and amino acid metabolism and exerting its therapeutic effect on radiation damage.
Animals ; Mice ; Metabolomics/methods* ; Gas Chromatography-Mass Spectrometry/methods* ; Polysaccharides/pharmacology* ; Male ; Hericium/chemistry* ; Drugs, Chinese Herbal/administration & dosage* ; Metabolome/drug effects* ; Gamma Rays/adverse effects*

This study aims to explore the impact of host plants on the quality of Taxilli Herba and provide a theoretical basis for the quality control of Taxilli Herba. The components of Taxilli Herba from three different host plants(Morus alba, Salix babylonica, and Cinnamomum cassia) and its 3 hosts(mulberry branch, willow branch, and cinnamon branch) were detected by widely targeted metabolomics based on ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS). Principal component analysis(PCA), orthogonal partial least squares discriminant analysis(OPLS-DA), and Venn diagram were employed for analysis. A total of 717 metabolites were detected in Taxilli Herba from the three host plants and the branches of these host plants by UPLC-MS/MS. The results of PCA and OPLS-DA of Taxilli Herba from the three different host plants showed an obvious separation trend due to the different effects of host plants. The Venn diagram showed that there were 32, 8, and 26 characteristic metabolites in samples of Taxilli Herba from M. alba host, S. babylonica host, and C. cassia host, respectively. It was found by comparing the characteristic metabolites of Taxilli Herba and its hosts that each host transmits its characteristic components to Taxilli Herba, so that the Taxilli Herba contains the characteristic components of the host. The Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis showed that the differential metabolites of Taxilli Herba from the three hosts were mainly enriched in flavonoid biosynthesis, arginine and proline metabolism, and glycolysis/gluconeogenesis pathways. Furthermore, the differential metabolites enriching pathways of Taxilli Herba from the three hosts were different depending on the host. In a word, host plants have a significant impact on the metabolites of Taxilli Herba, and it may be an important factor for the quality of Taxilli Herba.
Metabolomics/methods* ; Drugs, Chinese Herbal/chemistry* ; Chromatography, High Pressure Liquid ; Tandem Mass Spectrometry ; Quality Control ; Salix/chemistry* ; Cinnamomum aromaticum/metabolism* ; Principal Component Analysis

Sjogren's syndrome (SS) is a common autoimmune disorder that primarily targets exocrine glands, leading to hallmark manifestations of xerostomia and xerophthalmia, with potential progression to multisystem involvement. The rapid advances in omics technologies-including metabolomics, proteomics, and transcriptomics-have yielded substantial insights into SS pathophysiology. This review consolidates current evidence on omics-derived biomarkers in SS. Studies consistently implicate aberrant glucose metabolism, neutrophil-derived enzyme activity, mitochondrial bioenergetic impairment, ferroptosis, and apoptotic pathways as central to SS development. These findings refine our understanding of disease mechanisms and the heterogeneity of therapeutic responses. Hydroxyproline has emerged as a candidate marker for distinguishing SS from IgG4-related disease, whereas distinct cytokine and chemokine signatures may enable earlier diagnosis. Genomic analyses demonstrate a robust association between expression of the rs11797 locus and SS-related lymphomagenesis, and several genes controlling DNA methylation represent promising therapeutic targets. Collectively, these findings lay the groundwork for personalized risk stratification and intervention in SS. The review concludes by summarizing existing progress and outlining priorities for future omics-based investigations.
Humans ; Sjogren's Syndrome/diagnosis* ; Biomarkers/analysis* ; Metabolomics/methods* ; Proteomics/methods* ; Genomics ; Multiomics

In recent years, as a new omics field, metabolomics has been proved to be of great value in the study of the mechanism of occurrence and progression, the screening of new biomarkers and the development of novel therapeutic strategies in many diseases including tumors. In this review, we briefly summarized the research methods and techniques of metabolomics, and focused on the latest research progress of metabolomics in the pathogenesis of hematological malignancies represented by leukemia, lymphoma and multiple myeloma, screening of biomarkers for diagnosis and prognosis, and development of new therapeutic strategies. This article proposes the limitations of metabolomics and future research strategies, and provides a new exploration direction for accurate diagnosis and treatment as well as prognosis evaluation of hematological malignancies.
Humans ; Metabolomics/methods* ; Hematologic Neoplasms/diagnosis* ; Biomarkers, Tumor

Lung cancer, particularly non-small cell lung cancer (NSCLC), is a leading cause of cancer-related mortality worldwide. In recent years, metabolomics has emerged as a key systems biology approach for analyzing small-molecule metabolites in cells, tissues and organisms. It provides new strategies for early diagnosis and metabolic profiling. Additionally, metabolomics plays a crucial role in studying resistance mechanisms in lung cancer. Tumor cell metabolic reprogramming is a key driving factor in the initiation and progression of lung cancer. Metabolomics studies have revealed how lung cancer cells regulate critical pathways such as energy metabolism, lipid metabolism, and amino acid metabolism to adapt to the demands of rapid proliferation and invasive metastasis. This review summarizes the latest advances in metabolomics research in lung cancer, focusing on the characteristics of metabolic reprogramming, the identification of potential metabolic biomarkers, and the prospects of metabolomics in early diagnosis and the elucidation of resistance mechanisms in lung cancer.
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Humans ; Metabolomics/methods* ; Lung Neoplasms/pathology* ; Animals ; Biomarkers, Tumor/metabolism*

OBJECTIVES: Multiple myeloma (MM) is a hematologic malignancy characterized by clonal proliferation of plasma cells and remains incurable. Patients with primary refractory multiple myeloma (PRMM) show poor response to initial induction therapy. This study aims to develop a machine learning-based model to predict treatment response in newly diagnosed multiple myeloma (NDMM) patients, in order to optimize therapeutic strategies. METHODS: NDMM and post-treatment MM patients hospitalized in the Department of Hematology, Third Xiangya Hospital, Central South University, between August 2022 and July 2023 were enrolled. Post-treatment MM patients were categorized into PRMM patients and treatment-responsive MM (TRMM) patients based on therapeutic efficacy. Serum metabolites were detected and analyzed via metabolomics. Based on the metabolomics analysis results and combined with transcriptomic sequencing data of NDMM patients from databases, differentially expressed amino acid metabolism-related genes (AAMGs) among post-treatment NDMM patients with varying therapeutic outcomes were screened. Using bioinformatics analyses and machine learning algorithms, a predictive model for treatment response in NDMM was constructed and used to identify patients at risk for PRMM. RESULTS: A total of 61 patients were included: 22 NDMM, 23 TRMM, and 16 PRMM patients. Significant differences in metabolite levels were observed among the 3 groups, with differential metabolites mainly enriched in amino acid metabolism pathways. Follow-up data were available for 16 of the 22 NDMM patients, including 12 treatment responders (ND_TR group) and 4 with PRMM (ND_PR group). A total of 23 differential metabolites were identified between these 2 groups: 6 metabolites (e.g., tryptophan) were upregulated and 17 (e.g., citric acid) were downregulated in the ND_TR group. Transcriptomic data from 108 TRMM and 77 PRMM patients were analyzed to identify differentially expressed AAMGs, which were then used to construct a prediction model. The area under the receiver operating characteristic curve (AUC) for the model exceeded 0.8, and AUC values in 3 external validation cohorts were all above 0.7. CONCLUSIONS This study delineated the metabolic alterations in MM patients with different treatment response, suggesting that dysregulated amino acid metabolism may be associated with poor treatment response in PRMM. By integrating metabolomics and transcriptomics, a machine learning-based predictive model was successfully established to forecast treatment response in NDMM patients.
Humans ; Multiple Myeloma/drug therapy* ; Machine Learning ; Male ; Female ; Metabolomics/methods* ; Middle Aged ; Aged ; Treatment Outcome ; Transcriptome ; Computational Biology ; Adult ; Multiomics

The pursuit of healthy aging has long rendered aging and senescence captivating. Age-related ailments, such as cardiovascular diseases, diabetes, and neurodegenerative disorders, pose significant threats to individuals. Recent studies have shed light on the intricate mechanisms encompassing genetics, epigenetics, transcriptomics, and metabolomics in the processes of senescence and aging, as well as the establishment of age-related pathologies. Amidst these underlying mechanisms governing aging and related pathology metabolism assumes a pivotal role that holds promise for intervention and therapeutics. The advancements in metabolomics techniques and analysis methods have significantly propelled the study of senescence and aging, particularly with the aid of multiscale metabolomics which has facilitated the discovery of metabolic markers and therapeutic potentials. This review provides an overview of senescence and aging, emphasizing the crucial role metabolism plays in the aging process as well as age-related diseases.
Humans ; Aging/metabolism* ; Metabolomics/methods* ; Neurodegenerative Diseases/metabolism* ; Cardiovascular Diseases/metabolism*

Aconitum (Ranunculaceae) has a long-standing history in traditional Chinese medicine (TCM), where it has been widely used to treat conditions such as rheumatoid arthritis (RA), myocardial infarction, and heart failure. However, the potency of Aconitum alkaloids, the primary active components of Aconitum, also confers substantial toxicity. Therefore, assessing the efficacy and toxicity of these Aconitum alkaloids is crucial for ensuring clinical effectiveness and safety. Metabolomics, a quantitative method for analyzing low-molecular-weight metabolites involved in metabolic pathways, provides a comprehensive view of the metabolic state across multiple systems in vivo. This approach has become a vital investigative tool for facilitating the evaluation of their efficacy and toxicity, identifying potential sensitive biomarkers, and offering a promising avenue for elucidating the pharmacological and toxicological mechanisms underlying TCM. This review focuses on the applications of metabolomics in pharmacological and toxicological studies of Aconitum alkaloids in recent years and highlights the significant role of metabolomics in exploring compatibility detoxification and the mechanisms of TCM processing, aiming to identify more viable methods for characterizing toxic medicinal plants.
Aconitum/metabolism* ; Metabolomics/methods* ; Alkaloids/metabolism* ; Humans ; Animals ; Drugs, Chinese Herbal/pharmacology* ; Medicine, Chinese Traditional

Aconiti Lateralis Radix Praeparata (Fuzi) represents a significant traditional Chinese medicine (TCM) that exhibits both notable pharmacological effects and toxicity. Various processing methods are implemented to reduce the toxicity of raw Fuzi by modifying its toxic and effective components, primarily diterpenoid alkaloids. To comprehensively analyze the chemical variations between different Fuzi products, ultra-high performance liquid chromatography-linear ion trap quadrupole Orbitrap mass spectrometry (UHPLC-LTQ-Orbitrap MS) was employed to systematically characterize Shengfuzi, Heishunpian and Baifupian. A total of 249 diterpenoid alkaloids present in Shengfuzi were identified, while only 111 and 61 in Heishunpian and Baifupian were detected respectively, indicating substantial differences among these products. An untargeted metabolomics approach combined with multivariate statistical analysis revealed 42 potential chemical markers. Through subsequent validation using 52 batches of commercial Heishunpian and Baifupian samples, 8 robust markers distinguishing these products were identified, including AC1-propanoic acid-3OH, HE-glucoside, HE-hydroxyvaleric acid-2OH, dihydrosphingosine, N-dodecoxycarbonylvaline and three unknown compounds. Additionally, the MS imaging (MSI) technique was utilized to visualize the spatial distribution of chemical constituents in raw Fuzi, revealing how different processing procedures affect the chemical variations between Heishunpian and Baifupian. The distribution patterns of different diterpenoid alkaloid subtypes partially explained the chemical differences among products. This research provides valuable insights into the material basis for future investigations of different Fuzi products.
Diterpenes/chemistry* ; Alkaloids/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Aconitum/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Metabolomics ; Mass Spectrometry/methods* ; Plant Roots/chemistry* ; Molecular Structure

Flavonoids are key bioactive components for evaluating the pharmacological activities of Chimonanthus praecox. Exploring the potential flavonoids and pharmacological mechanisms of C. praecox lays a foundation for the rational development and efficient utilization of this plant. This study employed ultra-performance liquid chromatography-tandem mass spectrometry-based widely targeted metabolomics to comprehensively identify the flavonoids in C. praecox. Network pharmacology was employed to explore the bioactive flavonoids and their mechanisms of action. Molecular docking was adopted to validate the predicted results. Finally, the content of bioactive flavonoids in different varieties of C. praecox was measured. The widely targeted metabolomics analysis identified 387 flavonoids in C. praecox, and the flavonoids varied among different varieties. Network pharmacology predicted 96 chemical components including 19 bioactive compounds, 181 corresponding targets and 2 504 disease targets, among which 99 targets were shared by the active components and the disease. Thirty-three core targets were predicted, involving 229 gene ontology terms and 99 pathways (P≤0.05), which indicated that the flavonoids components of C. praecox exhibited pharmacological activities including antioxidant, anti-inflammatory, antimicrobial, and antiviral activities. Topological analysis screened out five core components (salvigenin, laricitrin, isorhamnetin, quercetin, and 6-hydroxyluteolin) and five core targets (SRC, PIK3R1, AKT1, ESR1, and AKR1C3). The predicted bioactive flavonoids from C. praecox stably bound to key targets, which indicated that these flavonoids possessed potential bioactivities in their interactions with the targets. The flavonoids in C. praecox exerted pharmacological activities in a multi-component, multi-target, and multi-pathway manner. The combined application of metabolomics and network pharmacology provides a theoretical basis for in-depth studies on the pharmacological effects and mechanisms of C. praecox.
Flavonoids/metabolism* ; Network Pharmacology ; Metabolomics/methods* ; Molecular Docking Simulation ; Calycanthaceae/chemistry* ; Tandem Mass Spectrometry ; Drugs, Chinese Herbal/chemistry*