In contrast to the conventional spermiogram, metabolomics approaches give insights into the molecular composition of semen and may provide more detailed information on the fertility status of the respective donor. Given the intra-individual variability of spermiogram parameters between two donations, this study sought to elucidate the biological variability of the seminal plasma metabolome over an average period of 8 weeks. Two time-shifted semen samples from 15 healthy donors were compared by a targeted metabolomics approach utilizing the Biocrates AbsoluteIDQ p180 kit. Next to intraclass correlation coefficients (ICC), which represent a measure of reliability, coefficients of variation within individuals (CVW) and coefficients of variation between individuals (CVB) were calculated for each metabolite to demonstrate its stability. Furthermore, men were divided into two cohorts, a similar sperm concentration (SSC) and a differing sperm concentration (DSC) cohort, based on the observed variance in sperm concentration between the two semen donations. The ICC was higher in the SSC compared to the DSC cohort. The levels of 18 metabolites, primarily acylcarnitines, varied between the initial and subsequent donations. After subdivision into subgroups, only ornithine and phosphatidylcholine 40:5 exhibited differential levels between the two donations in the SSC group, compared to 14 metabolites in the DSC group. CVB was higher than CVW but both differed between the metabolite subclasses. Biogenic amines were identified as the least reliable analytes over time, exhibiting the highest CVW, compared to sphingomyelins, which demonstrated the highest reliability with the lowest variation. CVB was the highest for ether-bound glycerophosphatidylcholines and the lowest for amino acids.
Humans ; Male ; Semen/metabolism* ; Metabolome ; Adult ; Sperm Count ; Carnitine/metabolism* ; Metabolomics ; Ornithine/metabolism* ; Semen Analysis ; Phosphatidylcholines/metabolism*

Varicocele (VC) is a common cause of male infertility, yet there is a lack of molecular information for VC-associated male infertility. This study investigated alterations in the seminal plasma metabolomic and lipidomic profiles of infertile male VC patients. Twenty infertile males with VC and twenty-three age-matched healthy controls (HCs) were recruited from Peking Union Medical College Hospital (Beijing, China) between October 2019 and April 2021. Untargeted metabolite and lipid profiles from seminal plasma were analyzed using mass spectrometry. Four hundred and seventy-six metabolites and seventeen lipids were significantly different in infertile male VC patients compared to HCs. The top enriched pathways among these significantly different metabolites are protein digestion and absorption, aminoacyl-transfer RNA (tRNA) biosynthesis, and biosynthesis of amino acids. Different key lipid species, including triglyceride (TG), diacylglycerol (DG), ceramides (Cer), and phosphatidylserine (PS), varied between VC and HC groups. The distinct metabolites and lipids were moderately correlated. DL-3-phenyllactic acid is a potential diagnostic biomarker for VC-related male infertility (area under the curve [AUC] = 0.893), positively correlating with sperm count, concentration, and motility. Furthermore, DL-3-phenyllactic acid is the only metabolite shared by all four comparisons (VC vs HC, VC-induced oligoasthenospermia [OAS] vs VC-induced asthenospermia [AS], OAS vs HC, and AS vs HC). DL-3-phenyllactic acid significantly decreased in OAS than AS. Metabolite-targeting gene analysis revealed carbonic anhydrase 9 (CA9) might be the strongest candidate associated with the onset and severity of VC. The seminal plasma metabolite and lipid profiles of infertile males with VC differ significantly from those of HCs. DL-3-phenyllactic acid could be a promising biomarker.
Humans ; Male ; Varicocele/complications* ; Infertility, Male/etiology* ; Semen/metabolism* ; Lipidomics ; Adult ; Metabolomics ; Case-Control Studies ; Biomarkers/metabolism*

OBJECTIVES: To investigate the targets and mechanisms of 7-hydroxyethyl chrysin (7-HEC) in prevention and treatment of high-altitude cerebral edema (HACE) in rats. METHODS: Fifty-four male Wistar rats were randomly divided into normal control group, HACE model group, and 7-HEC-treated group (18 rats in each group). Except for the normal control group, rats in the two other groups were exposed to a hypobaric hypoxic chamber simulating a 7000 m altitude for 72 h to establish the HACE model. The 7-HEC-treated group was intraperitoneally injected with 7-HEC (150 mg·kg^－¹·d^－¹) for 3 consecutive days before modeling, while the model group received equivalent isotonic sodium chloride solution. Tandem Mass Tag (TMT) proteomics technology was used to detect differentially expressed proteins (DEPs) with screening criteria set at a fold change >1.2 and P<0.05. Western blotting was used to verify the expression levels of target proteins. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed. RESULTS: Compared with the normal control group, 256 DEPs were identified in the HACE model group. Compared with the HACE model group, 87 DEPs were identified in the 7-HEC-treated group. Among them, 19 DEPs that were dysregulated in the HACE model group were restored after 7-HEC intervention, of which seven (HSPA4, Arhgap20, SERT, HACL1, CCDC43, POLR3A, and PCBD1) were confirmed by Western blotting. GO enrichment analysis of the DEPs between the HACE model and 7-HEC-treated groups revealed their involvement in 13 biological processes, five cellular components, and two molecular functions. KEGG pathway analysis indicated associations with the mRNA surveillance pathway, Th17 cell differentiation, serotonergic synapse, RNA polymerase, protein processing in the endoplasmic reticulum, peroxisome, neuroactive ligand-receptor interaction, folate biosynthesis. PPI network analysis demonstrated that HSPA4, POLR3A, and HACL1, which were validated by Western blotting, interacted with multiple signaling pathways and ranked among the top 20 hub proteins by degree value, suggesting their potential role as core regulatory factors. Arhgap20, SERT and PCBD1 also exhibited interactions with several proteins, suggesting their potential as key regulatory proteins, whereas no interactions for CCDC43 were identified. CONCLUSIONS This study applied TMT proteomics to identify seven potential therapeutic targets of 7-HEC for the prevention and treatment of HACE. These targets may be involved in the pathogenesis of HACE through multiple pathways, including maintaining cellular homeostasis, ameliorating oxidative stress, regulating energy metabolism, and reducing vascular permeability.
Animals ; Male ; Proteomics/methods* ; Rats, Wistar ; Flavonoids/therapeutic use* ; Rats ; Brain Edema/etiology* ; Altitude Sickness/metabolism* ; Protein Interaction Maps

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

OBJECTIVE: To investigate the susceptibility of venetoclax-resistant acute myeloid leukemia (AML) cell lines to ferroptosis and to uncover the underlying molecular mechanisms using transcriptomic and metabolomic analysis methods. METHODS: Venetoclax-resistant AML cell lines were constructed using a low-dose concentration escalation method. The sensitivity of cells to chemotherapeutic drugs was detected by CCK-8 assay. The susceptibility of drug-resistant cell lines to ferroptosis was assessed using transcriptomic and metabolomic analysis methods. The expression of cellular GPX4 and SLC7A11 protein was detected by Western blot, and cell death and lipid peroxidation levels were measured by flow cytometry. Depmap database and TCGA cohort were applied to explore the effect of ferroptosis-related genes expression on prognosis. RESULTS: Venetoclax-resistant cell lines exhibited sensitivity to ferroptosis inducers RSL3, APR246, and sorafenib. The ferroptosis inhibitor Fer-1 partially inhibited cell death induced by these inducers. Compared with the parental cells, significant changes in metabolites and gene expression levels related to ferroptosis were observed in the resistant cell lines. In particular, deregulated expression of SLC7A11 and GPX4 may play critical role in ferroptosis susceptibility. Besides, GPX4 was identified as more important for AML cell survival and higher GPX4 expression may predict shortened overall survival, NPM1 mutant and IDH1 R132 mutation positive patients may prone to possess higher GPX4 expression. CONCLUSION Venetoclax-resistant AML cell lines remain susceptible to ferroptosis, higher GPX4 expression maybe a critical marker for poor prognosis. Regulating the expression of ferroptosis-related genes and metabolites may enhance the efficacy of venetoclax and provide new treatment options for AML patients.
Humans ; Ferroptosis ; Leukemia, Myeloid, Acute/metabolism* ; Sulfonamides/pharmacology* ; Bridged Bicyclo Compounds, Heterocyclic/pharmacology* ; Drug Resistance, Neoplasm ; Metabolomics ; Cell Line, Tumor ; Phospholipid Hydroperoxide Glutathione Peroxidase ; Amino Acid Transport System y+/metabolism* ; Transcriptome

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*

Saliva is an important body fluid in the oral cavity containing lots of biomarkers, whose inherent micro-ecosystem holds significant value for early diagnosis and monitoring of oral diseases. Simultaneously, saliva has particular advantages, such as ease of sampling, painless and non-invasive collection, and suitability for repeated sampling, making it highly appropriate for surveillance and follow-up of diseases. In a series of studies conducted by the research group for preventive dentistry in Peking University School and Hospital of Stomatology, we compared different segments of saliva and those samples collected via different sampling methods using proteomic/peptidomic and microbiomic technologies to explore the stability of saliva samples. Besides, the significance of applying representative salivary biomarkers in early prevention and control of representative oral diseases (e.g. dental caries, periodontal diseases) and systemic conditions (e.g. type 2 diabetes mellitus, chronic kidney disease) was confirmed as well.
Humans ; Saliva/chemistry* ; Dental Caries/diagnosis* ; Biomarkers/analysis* ; Periodontal Diseases/diagnosis* ; Mouth Diseases/diagnosis* ; Proteomics/methods* ; Diabetes Mellitus, Type 2/diagnosis* ; Microbiota ; Renal Insufficiency, Chronic/prevention & control*

OBJECTIVE: To explore the mechanism of Dihuang Yinzi (DHYZ) in the treatment of Alzheimer's disease (AD) by integrating metabolomics and experimental verification. METHODS: Forty-eight male APP/PS1 mice were divided into model, high- (DHYZ-H), medium- (DHYZ-M), and low-dose DHYZ (DHYZ-L) groups (12 mice per group) according to a random number table. Mice in DHYZ groups were gavaged with DHYZ 6.34, 12.68, and 25.35 g/(kg·d), respectively. Twelve C57BL/6 mice were gavaged with distilled water as the blank group. Metabolomics was used to analyze differential metabolites in the brains of mice. Morris water maze test was used to detect the memory abilities of mice. The hematoxylin-eosin staining and transmission electron microscopy were used to observe the general morphology and ultrastructure of neurons. The enzyme-linked immunosorbent assay was used to detect the levels of superoxide dismutase (SOD), reactive oxygen species (ROS), and amyloid β -protein 1-42 (A β_1-42). The real-time quantitative polymerase chain reaction was used to detect the mRNA expressions of density-regulated protein 1 (DRP1), fission 1 (FIS1), mitofusin-1 (MFN1), and optic atrophy protein 1 (OPA1). Western blot was used to detect the protein expressions of cyclic adenosine monophosphate (cAMP), protein kinase A (PKA), cAMP response binding protein (CREB), brain-derived neurotrophic factor (BDNF), synapsin 1 (SYN1), synaptophysin (SYP), and postsynaptic density protein 95 (PSD95). RESULTS: A total of 82 differential metabolites were identified in the brains of APP/PS1 mice, among which 7 differential metabolites could be regulated by DHYZ. After DHYZ intervention, the memory abilities of mice significantly increased (P<0.05 or P<0.01), the number of synapses and neurons in the hippocampus increased, and the mitochondrial morphology and structure were relatively intact. The DHYZ groups exhibited a significant reduction in hippocampal ROS and A β_1-42 levels, along with a significant elevation in SOD level (P<0.05 or P<0.01). The mRNA expressions of DRP1 and FIS1 were reduced, while the mRNA expressions of MFN1 and OPA1 were increased after DHYZ treatment (P<0.05 or P<0.01). The cAMP/PKA/CREB-BDNF pathway was activated, and the expressions of SYN1, SYP and PSD95 proteins were significantly increased in the DHYZ-H group (P<0.05 or P<0.01). CONCLUSIONS DHYZ could improve mitochondrial dynamics and synaptic plasticity in APP/PS1 mice, inhibit oxidative stress, and thereby enhancing learning and memory abilities in APP/PS1 mice. Its mechanism might be related to activation of the cAMP/PKA/CREB-BDNF signaling pathway.
Animals ; Brain-Derived Neurotrophic Factor/metabolism* ; Male ; Cyclic AMP Response Element-Binding Protein/metabolism* ; Brain/drug effects* ; Metabolomics ; Mice, Inbred C57BL ; Neuronal Plasticity/drug effects* ; Drugs, Chinese Herbal/therapeutic use* ; Cyclic AMP-Dependent Protein Kinases/metabolism* ; Cyclic AMP/metabolism* ; Reactive Oxygen Species/metabolism* ; Amyloid beta-Protein Precursor/metabolism* ; Mice, Transgenic ; Mice ; Amyloid beta-Peptides/metabolism* ; Signal Transduction/drug effects* ; Alzheimer Disease/drug therapy* ; Superoxide Dismutase/metabolism*

OBJECTIVES: Hepatocellular carcinoma is one of the most common primary malignant tumors with the third highest mortality rate worldwide. This study aims to identify key genes associated with hepatocellular carcinoma prognosis using the Gene Expression Omnibus (GEO) database and provide a theoretical basis for discovering novel prognostic biomarkers for hepatocellular carcinoma. METHODS: Hepatocellular carcinoma-related datasets were retrieved from the GEO database. Differentially expressed genes (DEGs) were identified using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and key genes were identified using Cytoscape software. The University of Alabama at Birmingham Cancer Data Analysis Resource (UALCAN) was used to analyze the expression levels of key genes in normal and hepatocellular carcinoma tissues, as well as their associations with pathological grade, clinical stage, and patient survival. The Human Protein Atlas (THPA) was used to further validate the impact of key genes on overall survival. Expression levels of key genes in the blood of hepatocellular carcinoma patients were evaluated using the expression atlas of blood-based biomarkers in the early diagnosis of cancers (BBCancer). RESULTS: A total of 78 DEGs were identified from the GEO database. GO and KEGG analyses indicated that these genes may contribute to hepatocellular carcinoma progression by promoting cell division and regulating protein kinase activity. Sixteen key genes were screened via Cytoscape and validated using UALCAN and THPA. These genes were overexpressed in hepatocellular carcinoma tissues and were associated with disease progression and poor prognosis. Finally, BBCancer analysis showed that ASPM and NCAPG were also elevated in the blood of hepatocellular carcinoma patients. CONCLUSIONS This study identified 16 key genes as potential prognostic biomarkers for hepatocellular carcinoma, among which ASPM and NCAPG may serve as promising blood-based markers for hepatocellular carcinoma.
Humans ; Carcinoma, Hepatocellular/mortality* ; Liver Neoplasms/pathology* ; Prognosis ; Computational Biology/methods* ; Protein Interaction Maps/genetics* ; Biomarkers, Tumor/genetics* ; Gene Expression Regulation, Neoplastic ; Gene Expression Profiling ; Gene Ontology ; Databases, Genetic

OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks