Metabolomics最新文献

Purpose: Fish aquaculture faces sustainable production challenges. Among them are the pathogenic outbreaks that can compromise the health of the stocks from various perspectives, including broodstock reproduction. This study focused on identifying the metabolite alterations produced after a bacterial infection by Vibrio anguillarum in the gonads of European seabass (Dicentrarchus labrax). Sex-related response to the infection challenge was studied using a metabolomics approach.

Method: The metabolome of testes and ovaries of adult fish were extracted and analyzed after 48 h of bacterial exposure by ultra-high-performance liquid chromatography-mass spectrometer using negative-mode electrospray ionization (ESI) (UHPLC-MS, Vanquish Horizon UHPLC coupled to a Thermo Fisher Scientific Q-Exactive HF). To further decipher the molecular events, metabolomic and transcriptomic data were interconnected.

Results: In total, 97 metabolites were identified. In the ovary, uric acid, O-phosphoethanolamine, allantoin, and acetoacetic acid were more represented. By contrast, nine metabolites were altered after the infection in testes, including uridine, N-acetylglucosamine-6-Phosphate, and Gamma-aminobutyric acid (GABA). The most abundant metabolic cascades triggered by infection in ovaries were related to glyoxylate and dicarboxylate metabolism, nitrogen metabolism, and purine metabolism, while in testes, we observed changes in glycerolipid metabolism, glycerophospholipid metabolism, and galactose metabolism.

Conclusion: The present results demonstrate, for the first time in fish, that changes in metabolic pathways induced following infection are sex-dependent. The findings will help develop sex-specific immune therapies, identify resistant phenotypes, and improve aquaculture infection protocols.

Objective: Metabolomic risk factors for dementia are under studied, especially in Latinos. We examined the relationship between plasma metabolomic profiles and a Magnetic-Resonance Imaging (MRI)-based markers of brain aging in a cohort of older adult Puerto Ricans residing in the greater Boston area.

Methods: We used multiple linear regression, adjusted for covariates, to examine the association between metabolite concentration and MRI-derived brain age deviation. Metabolites were measured at baseline with untargeted metabolomic profiling (Metabolon, Inc). Brain age deviation was calculated at wave 4 (~ 9 years from Boston Puerto Rican Health Study (BPRHS) baseline) as chronologic age, minus MRI-estimated brain age, representing the rate of biological brain aging relative to chronologic age. We also examined if metabolites associated with brain age deviation were similarly associated with hippocampal volume and global cognitive function.

Results: Several metabolites, including isobutyrylcarnitine, propionylcarnitine, phenylacetylglutamine, phenylacetylcarnitine (acetylated peptides), p-cresol-glucuronide, phenylacetylglutamate, and trimethylamine N-oxide (TMAO) were associated with worse brain aging. Taurocholate sulfate, a bile salt, was marginally associated with better brain aging. Most metabolites with negative associations with brain age deviation also were inversely, although not significantly, associated with hippocampal volume and cognitive function.

Conclusion: The metabolites associated with brain aging in this study are generally consistent with prior literature and highlight the potential role of TMAO, BCAA and other microbially derived metabolites in dementia.

Background: The dairy cow is an integral part of the global agricultural economy and plays a vital role in human nutrition. Compromised health in dairy cows leads to substantial economic losses and adverse environmental impacts. Understanding cow physiology and the etiology of common diseases is essential for developing effective strategies to improve animal health and mitigate negative consequences. Over the past two decades, metabolomics has emerged as a powerful approach not only for assessing and monitoring the health status of dairy cows but also for predicting and diagnosing diseases.

Aim of review: To review current metabolomics research aimed at improving the understanding of cow health status and metabolic changes associated with common diseases in dairy cows.

Key scientific concept of review: This review discusses findings from nearly 100 studies that report metabolic changes linked to health status and common diseases in dairy cows. It focuses on disease-specific metabolite alterations across different biofluids in conditions such as mastitis, lameness, acidosis, ketosis, and hypocalcemia, highlighting their relevance to pathological significance. The review also discusses how metabolomics can support early diagnosis and examines metabolic shifts related to physiological factors such as pregnancy, lactation, transition period, and parity.

Introduction: Robust evidence endorsed by the International Agency for Research on Cancer demonstrates that excess body fat represents a risk for the development of at least 13 types of cancer.

Objective: We investigated the serum of individuals with obesity who had no history of cancer (either personal or familial) to identify biomarkers.

Methods: 45 individuals were included in this study; they did not show significant differences regarding age, sex, or physical activity. A quality-of-life questionnaire was administered to all participants, revealing that the obese group self-reported difficulties in functional capacity and a greater association with comorbidities, notably hypertension. FTIR, metabolomic, and lipidomic analyses were performed to identify spectral peaks, metabolic, and lipids differentiating between the NO (non-obese) and OB (obese) groups.

Results: The identified peaks are predominantly associated with profiles of lipids, carbohydrates, and nitrogenous compounds. A total of six metabolites and lipids were identified at different levels in the serum of the OB group that have a direct relationship with the development or metabolism of cancer cells (three metabolites and three lipids). Among these, some suggest a reduced likelihood of cancer development, while others indicate an increased potential for cancer development. In this context, we can highlight two substances: the metabolite 4-Hydroxyphenylglyoxylate, which is reduced in the serum of individuals with obesity, and the lipid Glycidyl palmitate, which is elevated in individuals with obesity.

Conclusion: 4-Hydroxyphenylglyoxylate and Glycidyl palmitate can be used as biomarkers for cancer and obesity correlation. New experiments manipulating these substances may contribute to a better understanding of their interactions with cancer cells.

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) (formerly Non-Alcoholic Fatty Liver Disease, NAFLD) encompasses a spectrum of metabolic disorders ranging from isolated steatosis to Metabolic Dysfunction-Associated Steatohepatitis (MASH), potentially progressing to cirrhosis and hepatocellular carcinoma (HCC). In this study, we investigated metabolic changes in MASLD by analysing plasma lipidomics and metabolomics profiles from 315 biopsy-characterized patients. We observed significant alterations in alanine/serine (ala/ser) ratio, 1-deoxysphingolipids, alanine aminotransferase (ALT), and waist/hip ratio (whr) between patients with and without MASH. These findings highlight a close interplay between amino acid metabolism and sphingolipid biosynthesis in MASLD progression. The shift in ala/ser ratio particularly distinguished non-MASH from borderline MASH patients, suggesting that early metabolic disruptions precede overt liver damage. Additionally, elevated branched-chain and aromatic amino acids correlated with steatosis severity, reinforcing the central role of amino acid dysregulation in MASLD. While a simple model combining ALT, ala/ser ratio, and whr showed some potential to support risk stratification, the primary significance of our findings lies in the mechanistic insights into metabolic dysfunction. In conclusion, this study emphasizes the importance of metabolic network alterations in MASLD and points toward future opportunities for both mechanistic research and the development of supportive diagnostic strategies.

Background: Algal nutraceuticals have emerged as the valuable bioresources due to their various chemical compositions and potential health benefits. Algae contain many bioactive compounds, including polyphenols, polysaccharides, omega-3 fatty acids, pigments, and vitamins, which are vital for the various biological processes in the human body. Understanding these complex metabolites is essential for their application in functional foods, dietary supplements, and pharmaceuticals. In this context, metabolomics provides a comprehensive approach for analyzing algal metabolic profiles and their nutritional and medicinal values.

Aim of review: This review explores the role of metabolomics in the evaluation and development of algal nutraceuticals. It focuses particularly on the identification and characterization of small-molecule metabolites in algae, offering insights into their functional properties and bioactivities. This review also discusses the integration of metabolomics with other omics technologies to achieve a holistic understanding of the metabolism of algae.

Key scientific concepts of review: Metabolomic studies have successfully explored a wide range of bioactive compounds in algae with antioxidant, anti-inflammatory, anticancer, antibacterial, and neuroprotective activities. Techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have advanced the detection and quantification of metabolites with high sensitivity and resolution, respectively. Additionally, metabolomics aids to determine the quality biomarkers and the assessment of algal nutritional content. Integrating metabolomics with genomics, proteomics, and transcriptomics will further elucidate the metabolic pathways and regulatory networks in algae. This review highlights the critical role of metabolomics in maximizing the utilization of algae for health benefits.

Aim: Diabetic kidney disease (DKD) is a severe complication of diabetes, with early detection crucial for preventing irreversible kidney damage. Despite numerous DKD metabolite profiling studies, results remain inconsistent. This meta-analysis aims to identify consensus dysregulated metabolites as potential biomarkers for Type 2 diabetes (T2D)-induced DKD.

Materials and methods: Following PRISMA guidelines, a systematic review from 2014 to 2024 included human studies of T2D and DKD. Quality assessment employed the Newcastle-Ottawa Scale (NOS). Bubble plots determined predominant metabolite classes. MetaboAnalyst 5.0 facilitated pathway and enrichment analysis, while RevMan v5.4 performed meta-analysis.

Results: Amino acids were the most studied metabolite class in both T2D and DKD. Enrichment analysis highlighted glycine and serine metabolism; phenylalanine and tyrosine metabolism; and methionine metabolism as dominant pathways. Meta-analysis revealed low ornithine (-0.50 [-0.91, -0.10], p = 0.01) and high isoleucine (0.76[0.50, 1.03], p < 0.00001) concentrations associated with T2D. Conversely, lower methionine (-0.32 [-0.57, -0.08), p = 0.01), tyrosine (-0.73 [-1.28, -0.17], p = 0.01), and valine (-2.32 [-2.99, -1.66], p = 0.009) levels were associated with DKD. Correlation analysis revealed associations between phenylalanine, tyrosine, and serine with albumin and creatinine levels in T2D but not in DKD.

Conclusions: These identified metabolites hold potential as early markers for T2D-induced DKD. However, the use of these metabolites for clinical purposes requires experimental validation and clinical trials.