Metabolomics最新文献

Introduction: Extracranial and intracranial tumors are a diverse group of malignant and benign neoplasms, influenced by multiple factors. Given the complex nature of these tumors and usually late or accidental diagnosis, minimally invasive, rapid, early, and accurate diagnostic methods are urgently required. Metabolomics offers promising insights into central nervous system tumors by uncovering distinctive metabolic changes linked to tumor development.

Objectives: This study aimed to elucidate the role of altered metabolites and the associated biological pathways implicated in the development of gliomas and meningiomas.

Methods: The study was conducted on 95 patients with gliomas, 68 patients with meningiomas, and 71 subjects as a control group. The metabolic profiling of gliomas and meningiomas achieved by integrating untargeted metabolomic analysis based on GC-MS and targeted analysis performed using LC-MS/MS represents the first comprehensive study. Three comparisons (gliomas or meningiomas vs. controls as well as gliomas vs. meningiomas) were performed to reveal statistically significant metabolites.

Results: Comparative analysis revealed 97, 56, and 27 significant metabolites for gliomas vs. controls, meningiomas vs. controls and gliomas vs. meningiomas comparison, respectively. Moreover, among above mentioned comparisons unique metabolites involved in arginine biosynthesis and metabolism, the Krebs cycle, and lysine degradation pathways were found. Notably, 2-aminoadipic acid has been identified as a metabolite that can be used in distinguishing two tumor types.

Conclusions: Our results provide a deeper understanding of the metabolic changes associated with brain tumor development and progression.

Morels are edible fungi growing naturally in the wild and cultivated for food and medicines worldwide. They have been collected and consumed by people since ancient times. In the present study, fruiting bodies of Morchella elata were collected from the field during the years 2020-22 through consecutive field visits. Identification was carried out through a morpho-anatomical and phylogenetic study that confirmed the collected morel species as Morchella elata. The metabolic analysis was conducted using Ultra High-Performance Liquid Chromatography/Mass Spectrometry (UHPLC/MS) and FTICR/orbitrap techniques. The study revealed the presence of 159 organic compounds and 435 peptide sequences in the ascocarp. Different bioactive and significant compounds have been identified in the fruiting bodies of M. elata. This mushroom is highly nutritious, and the presence of these bioactive compounds contributes to its health benefits, making it a potential functional food in nutraceuticals. From the current study, it is concluded that M. elata is an edible, highly nutritive fungus and contains many bioactive contents. It could be used in the screening of bioactive substances useful in the preparation of anticancer drugs.

Background: Despite the insights that metabolite analysis can provide into the onset, development, and progression of diseases-thus offering new concepts and methodologies for prevention, diagnosis, and treatment-traditional wet lab experiments are often time-consuming and labor-intensive. Consequently, this study aimed to develop a machine learning model named COM-RAN, which is based on a knowledge graph and random forest algorithm, to identify potential associations between metabolites and diseases.

Methods: Firstly, we integrated the known associations between diseases and metabolites. Secondly, we provided a synthesis of the extant data regarding diseases and metabolites, accompanied by supplementary information pertinent to these entities. Thirdly, knowledge graph-based embedded features were used to characterize disease-metabolite associations. Finally, a random forest algorithm was employed to construct a model for identifying potential disease-metabolite associations.

Results: The experimental results demonstrated that the proposed model achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.968 in 5-fold cross-validations, while the Area Under the Precision-Recall Curve (AUPR) was 0.901, outperforming the vast majority of existing prediction methods. The case studies corroborated the majority of the novel associations identified by COM-RAN, thereby further demonstrating the reliability of the current method in predicting the potential relationship between metabolites and diseases.

Conclusion: The COM-RAN model demonstrated promise in predicting associations between diseases and metabolites, suggesting that integrating knowledge graphs with machine learning methodologies can significantly improve the accuracy and reliability of predictions related to disease-associated metabolites.

Introduction: Fecal metabolomics studies have garnered interest in recent years due to the potential for these samples to provide unique information about an individual. Stool is a dynamic mixture of human excrement, microbiota, and enzymes that yields a constantly changing metabolite profile. The main challenge in a fecal metabolomics study is ensuring that the metabolite profile changes as little as possible between sample collection and sample processing/analysis.

Objectives: This study aimed to evaluate the efficacy of five solutions in preserving human fecal metabolites over a seven-day storage period at ambient temperature, enabling at-home collection, cost-effective ambient transport and sample storage.

Method: Five solutions with varying chemical compositions were evaluated for their ability to stabilize fecal metabolites. Samples were stored at ambient temperature for seven days, and metabolites were analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The stabilizing efficacy of the solutions was assessed using total useful peak area (TUPA), absolute relative change (ARC) and compound class-based analyses, comparing the initial, stabilized, and unstabilized samples.

Results: Different solutions demonstrated varied efficiencies for different compound classes. Overall, the results indicated that the use of stabilization solutions significantly minimized changes in the fecal metabolite profile compared to unstabilized samples left at room temperature for one week.

Conclusion: This study demonstrates that stabilization solutions are effective in preserving fecal metabolites during storage at ambient temperature, supporting the feasibility of at-home sample collection.

Introduction: Maternal tobacco smoking in the perinatal period increases the risk for adverse outcomes in offspring.

Objective: To better understand the biological pathways through which maternal tobacco use may have long-term impacts on child metabolism, we performed a high-resolution metabolomics (HRM) analysis in newborns, following an untargeted metabolome-wide association study workflow.

Methods: The study population included 899 children without cancer diagnosis before age 6 and born between 1983 and 2011 in California. Newborn dried blood spots were collected by the California Genetic Disease Screening Program between 12 and 48 h after birth and stored for later research use. Based on HRM, we considered mothers to be active smokers if they were self- or provider-reported smokers on birth certificates or if we detected any cotinine or high hydroxycotinine intensities in newborn blood. We used partial least squares discriminant analysis and Mummichog pathway analysis to identify metabolites and metabolic pathways associated with maternal tobacco smoking.

Results: A total of 26,183 features were detected with HRM, including 1003 that were found to be associated with maternal smoking late in pregnancy and early postpartum (Variable Importance in Projection (VIP) scores > = 2). Smoking affected metabolites and metabolic pathways in neonatal blood including vitamin A (retinol) metabolism, the kynurenine pathway, and tryptophan and arachidonic acid metabolism.

Conclusion: The smoking-associated metabolites and pathway perturbations that we identified suggested inflammatory responses and have also been implicated in chronic diseases of the central nervous system and the lung. Our results suggest that infant metabolism in the early postnatal period reflects smoking specific physiologic responses to maternal smoking with strong biologic plausibility.

Introduction: The identification of lipids is a cornerstone of lipidomics, and due to the specific characteristics of lipids, it requires dedicated analysis workflows. Identifying novel lipids and lipid species for which no reference spectra are available is tedious and often involves a lot of manual work. Integrating high-resolution mass spectrometry with enhancements from chromatographic and ion mobility separation enables the in-depth investigation of intact lipids.

Objectives: We investigated phosphorylated glycosphingolipids from the nematode Caenorhabditis elegans, a biomedical model organism, and aimed to identify different species from this class of lipids, which have been described in one particular publication only. We checked if these lipids can be detected in lipid extracts of C. elegans.

Methods: We used UHPLC-UHR-TOF-MS and UHPLC-TIMS-TOF-MS in combination with dedicated data analysis to check for the presence of phosphorylated glycosphingolipids. Specifically, candidate features were identified in two datasets using Mass Spec Query Language (MassQL) to search fragmentation data. The additional use of retention time (RT) and collisional cross section (CCS) information allowed to filter false positive annotations.

Results: As a result, we detected all previously described phosphorylated glycosphingolipids and novel species as well as their biosynthetic precursors in two different lipidomics datasets. MassQL significantly speeds up the process by saving time that would otherwise be spent on manual data investigations. In total over 20 sphingolipids could be described.

Conclusion: MassQL allowed us to search for phosphorylated glycosphingolipids and their potential biosynthetic precursors systematically. Using orthogonal information such as RT and CCS helped filter false positive results. With the detection in two different datasets, we demonstrate that these sphingolipids are a general part of the C. elegans lipidome.

Introduction: Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as a transformative technology in agricultural research, offering powerful analytical capabilities for field crop improvement. With global challenges such as food security and climate change intensifying, there is an urgent need for innovative methodologies to enhance our understanding of plant health, metabolic pathways, and crop-environment interactions. NMR's ability to provide nondestructive, real-time analysis of plant metabolites and soil chemistry positions it as a critical tool for addressing these pressing concerns.

Objective: This review aims to elucidate the potential of NMR spectroscopy in advancing field crop improvement by highlighting its applications, challenges, and future perspectives in agricultural methodologies. The focus is on the evolution and application of NMR in agricultural research, particularly in metabolomics, phenotyping, and quality assessment.

Method: A comprehensive literature review was conducted to analyze recent advancements in NMR applications in agriculture. Particular emphasis was given to high-resolution magic angle spinning (HR-MAS) and time-domain NMR techniques, which have been instrumental in elucidating plant metabolites and soil chemistry. Studies showcasing the integration of NMR with complementary technologies for enhanced metabolic profiling and genetic marker identification were reviewed.

Results: Findings indicate that NMR spectroscopy is an indispensable tool in agriculture due to its ability to identify biomarkers indicative of crop resilience, monitor soil composition, and contribute to food safety and quality assessments. The integration of NMR with other technologies has accelerated metabolic profiling, aiding in the breeding of high-yielding and stress-resistant crop varieties. However, challenges such as sensitivity limitations and the need for standardization remain.

Conclusion: NMR spectroscopy holds immense potential for revolutionizing agricultural research and crop improvement. Overcoming existing challenges, such as sensitivity and standardization, is crucial for its broader application in practical agricultural settings. Collaborative efforts among researchers, agronomists, and policymakers will be essential for leveraging NMR technology to address global food security challenges and promote sustainable agricultural practices.

Neoadjuvant therapy is a standard treatment for breast cancer, but its effectiveness varies among patients. This highlights the importance of developing accurate predictive models. Our study uses metabolomics and machine learning to predict the response to neoadjuvant therapy in breast cancer patients.

Objective: To develop and validate predictive models using machine learning and circulating metabolites for forecasting responses to neoadjuvant therapy among breast cancer patients, enhancing personalized treatment strategies.

Methods: Based on pathological analysis after neoadjuvant chemotherapy and surgery, this retrospective study analyzed 30 young women breast cancer patients from a single institution, categorized as responders or non-responders. Utilizing liquid chromatography-tandem mass spectrometry, we investigated the plasma metabolome, explicitly targeting 40 metabolites, to identify relevant biomarkers linked to therapy response, using machine learning to generate a predictive model and validate the results.

Results: Eighteen significant biomarkers were identified, including specific acylcarnitines and amino acids. The most effective predictive model demonstrated a remarkable accuracy of 90.7% and an Area Under the Curve (AUC) of 0.999 at 95% confidence, illustrating its potential utility as a web-based application for future patient management. This model's reliability underscores the significant role of circulating metabolites in predicting therapy outcomes.

Conclusion: Our study's findings highlight the crucial role of metabolomics in advancing personalized medicine for breast cancer treatment by effectively identifying metabolite biomarkers correlated with neoadjuvant therapy response. This approach signifies a critical step towards tailoring treatment plans based on individual metabolic profiles, ultimately improving patient outcomes in breast cancer care.

Introduction: Despite considerable advances in cancer research, the increasing prevalence and high mortality rate of clear cell renal cell carcinoma (ccRCC) remain a significant challenge. A more detailed comprehension of the distinctive metabolic characteristics of ccRCC is vital to enhance diagnostic, prognostic, and therapeutic strategies.

Objectives: This study aimed to investigate the metabolic signatures of ccRCC tumours and, for the first time, their correlation with the urinary metabolome of the same patients.

Methods: We applied a gas chromatography-mass spectrometry (GC-MS)-based metabolomic approach to analyse matched tissue and urine samples from a cohort of 18 ccRCC patients and urine samples from 18 cancer-free controls. Multivariate and univariate statistical methods, as well as pathway and correlation analyses, were performed to assess metabolic dysregulations and correlations between tissue and urine.

Results: The results showed a ccRCC metabolic signature characterized by reprogramming in amino acid, energy, and sugar and inositol phosphate metabolisms. Our study identified, for the first time, significantly decreased levels of asparagine, proline, gluconate, 3-aminoisobutanoate, 4-aminobutanoate and urea in ccRCC tumours, highlighting the involvement of arginine biosynthesis, β-alanine metabolism and purine and pyrimidine metabolism in ccRCC. The correlations between tissue and urine metabolomes provide evidence for the potential usefulness of urinary metabolites in understanding systemic metabolic changes driven by RCC tumours.

Conclusions: These findings significantly advance our understanding of metabolic reprogramming in ccRCC and the systemic metabolic changes associated with the disease. Future research is needed to validate these findings in larger cohorts and to determine their potential implications for diagnosis and targeted therapies.

Background: Chronic periodontitis is a condition impacting approximately 50% of the world's population. As chronic periodontitis progresses, the bacteria in the oral cavity change resulting in new microbial interactions which in turn influence metabolite production. Chronic periodontitis manifests with inflammation of the periodontal tissues, which is progressively developed due to bacterial infection and prolonged bacterial interaction with the host immune response. The bi-directional relationship between periodontitis and systemic diseases has been reported in many previous studies. Traditional diagnostic methods for chronic periodontitis and systemic diseases such as chronic kidney diseases (CKD) have limitations due to their invasiveness, requiring practised individuals for sample collection, frequent blood collection, and long waiting times for the results. More rapid methods are required to detect such systemic diseases, however, the metabolic profiles of the oral cavity first need to be determined.

Aim of review: In this review, we explored metabolomics studies that have investigated salivary metabolic profiles associated with chronic periodontitis and systemic illnesses including CKD, oral cancer, Alzheimer's disease, Parkinsons's disease, and diabetes to highlight the most recent methodologies that have been applied in this field.

Key scientific concepts of the review: Of the rapid, high throughput techniques for metabolite profiling, Nuclear magnetic resonance (NMR) spectroscopy was the most applied technique, followed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Furthermore, Raman spectroscopy was the most used vibrational spectroscopic technique for comparison of the saliva from periodontitis patients to healthy individuals, whilst Fourier Transform Infra-Red Spectroscopy (FT-IR) was not utilised as much in this field. A recommendation for cultivating periodontal bacteria in a synthetic medium designed to replicate the conditions and composition of saliva in the oral environment is suggested to facilitate the identification of their metabolites. This approach is instrumental in assessing the potential of these metabolites as biomarkers for systemic illnesses.