Metabolomics最新文献

Background: Cancer cells exhibit remarkable metabolic plasticity, enabling them to adapt to fluctuating nutrient conditions. This study investigates the impact of a combination of low glucose levels and inhibition of stearoyl-CoA desaturase 1 (SCD1) using A939572 on cancer metabolic plasticity and growth.

Methods: A comprehensive metabolomic and lipidomic analysis was conducted to unravel the intricate changes in cellular metabolites and lipids. MCF-7 cells were subjected to low glucose conditions, and SCD1 was inhibited using A939572. The resulting alterations in metabolic pathways and lipid profiles were explored to elucidate the synergistic effects on cancer cell physiology.

Results: The combination of low glucose and A939572-induced SCD1 inhibition significantly impaired cancer cell metabolic plasticity. Metabolomic analysis highlighted shifts in key glycolytic and amino acid pathways, indicating the cells' struggle to adapt to restricted glucose availability. Lipidomic profiling revealed alterations in lipid composition, implying disruptions in membrane integrity and signaling cascades.

Conclusion: Our findings underscore the critical roles of glucose availability and SCD1 activity in sustaining cancer metabolic plasticity and growth. Simultaneously targeting these pathways emerges as a promising strategy to impede cancer progression. The comprehensive metabolomic and lipidomic analysis provides a detailed roadmap of molecular alterations induced by this combination treatment, that may help identify potential therapeutic targets.

Background: Sepsis is defined as a dysfunctional host response to infection. The diverse clinical presentations of sepsis pose diagnostic challenges and there is a demand for enhanced diagnostic markers for sepsis as well as an understanding of the underlying pathological mechanisms involved in sepsis. From this perspective, metabolomics has emerged as a potentially valuable tool for aiding in the early identification of sepsis that could highlight key metabolic pathways and underlying pathological mechanisms.

Objective: The aim of this investigation is to explore the early metabolomic and lipidomic profiles in a prospective cohort where plasma samples (n = 138) were obtained during ambulance transport among patients with infection according to clinical judgement who subsequently developed sepsis, patients who developed non-septic infection, and symptomatic controls without an infection.

Methods: Multiplatform metabolomics and lipidomics were performed using UHPLC-MS/MS and UHPLC-QTOFMS. Uni- and multivariable analysis were used to identify metabolite profiles in sepsis vs symptomatic control and sepsis vs non-septic infection.

Results: Univariable analysis disclosed that out of the 457 annotated metabolites measured across three different platforms, 23 polar, 27 semipolar metabolites and 133 molecular lipids exhibited significant differences between patients who developed sepsis and symptomatic controls following correction for multiple testing. Furthermore, 84 metabolites remained significantly different between sepsis and symptomatic controls following adjustment for age, sex, and Charlson comorbidity score. Notably, no significant differences were identified in metabolites levels when comparing patients with sepsis and non-septic infection in univariable and multivariable analyses.

Conclusion: Overall, we found that the metabolome, including the lipidome, was decreased in patients experiencing infection and sepsis, with no significant differences between the two conditions. This finding indicates that the observed metabolic profiles are shared between both infection and sepsis, rather than being exclusive to sepsis alone.

Introduction: Trimethylamine N-oxide (TMAO) is a gut bacteria-dependent metabolite associated with poor cardiovascular health. Exercise is a known cardioprotective activity but the impact of an acute bout of exercise on TMAO production is unknown.

Objectives/methods: This study assessed choline-derived production of TMAO following a single bout of intermittent exercise in a young, healthy cohort.

Results: Choline supplemented after either exercise or a time-matched resting period demonstrated a similar increase in circulating TMAO across an 8-hour period.

Conclusion: This suggests that a single bout of intermittent exercise does not alter gut microbial metabolic behaviour and thus does not provide additional cardioprotective benefits related to blood levels of TMAO.

Background: Cognitive impairments are a hallmark symptom of schizophrenia (SCZ). Phosphatidylethanolamine (PE) is the second most abundant phospholipid in mammalian cells, yet its role in cognitive deficits remains unexplored. The aim of this study was to investigate the association between plasma LysoPE and cognitive improvements following olanzapine monotherapy in drug-naïve first-episode (DNFE) SCZ patients.

Methods: Twenty-five female DNFE SCZ patients were treated with olanzapine for four weeks, and cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) at baseline and after the 4-week follow-up. Utilizing an untargeted ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabolomics approach, we measured LysoPE concentrations.

Results: Significant improvements in immediate and delayed memory domains were observed post-treatment. We identified nine differential LysoPE species after olanzapine monotherapy, with increased concentrations for all LysoPE except LysoPE (22:6). Elevated LysoPE (22:1) concentration positively correlated with cognitive improvement in patients. Baseline LysoPE (16:1) emerged as a predictive factor for cognitive improvement following olanzapine monotherapy.

Conclusions: This study offers preliminary evidence for the involvement of LysoPE in cognitive improvements observed in drug-naïve first-episode SCZ patients after olanzapine treatment.

Introduction: This study focuses on metabolic profiling of a robust marine green algal strain Picochlorum sp. MCC39 that exhibits resilient growth under diverse outdoor open pond conditions. Given its potential for producing high-value chemicals through metabolic engineering, understanding its metabolic dynamics is crucial for pathway modification.

Objectives: This study primarily aimed to investigate the metabolic response of Picochlorum sp. to environmental changes. Unlike heterotrophs, algae are subject to diurnal light and temperature, which affect their growth rates and metabolism. Using an environmental photobioreactor (ePBR), we explored how the algal strain adapts to fluctuations in light intensities and temperature within a simulated pond environment.

Methods: We performed a reverse phase ion pairing-LC/MS-MS based metabolome profiling of the MCC39 strain cultivated in simulated pond conditions in ePBR. The experimental setup included diurnal and bi-seasonal variations in light intensities and temperature.

Results: The metabolome profile revealed significant differences in 85 metabolites, including amino acids, carboxylic acids, sugar phosphates, purines, pyrimidines, and dipeptides, which exhibited up to 25-fold change in relative concentration with diurnal variations. Seasonal variations also influenced the production of storage molecules, revealing a discernible pattern. The accumulation pattern of metabolites involved in cellular wall formation and energy generation indicated a well-coordinated initiation of photosynthesis and the Calvin cycle with the onset of light.

Conclusion: The results contribute to a deeper understanding of the adaptability and metabolic response of Picochlorum sp., laying the groundwork for future advancements in algal strain modification.

Background: Metabolomics, the systematic analysis of small molecules in a given biological system, emerged as a powerful tool for different research questions. Newer, better, and faster methods have increased the coverage of metabolites that can be detected and identified in a shorter amount of time, generating highly dense datasets. While technology for metabolomics is still advancing, another rapidly growing field is metabolomics data analysis including metabolite identification. Within the next years, there will be a high demand for bioinformaticians and data scientists capable of analyzing metabolomics data as well as chemists capable of using in-silico tools for metabolite identification. However, metabolomics is often not included in bioinformatics curricula, nor does analytical chemistry address the challenges associated with advanced in-silico tools.

Aim of review: In this educational review, we briefly summarize some key concepts and pitfalls we have encountered in a collaboration between a bioinformatician (originally not trained for metabolomics) and an analytical chemist. We identified that many misunderstandings arise from differences in knowledge about metabolite annotation and identification, and the proper use of bioinformatics approaches for these tasks. We hope that this article helps other bioinformaticians (as well as other scientists) entering the field of metabolomics bioinformatics, especially for metabolite identification, to quickly learn the necessary concepts for a successful collaboration with analytical chemists.

Key scientific concepts of review: We summarize important concepts related to LC-MS/MS based non-targeted metabolomics and compare them with other data types bioinformaticians are potentially familiar with. Drawing these parallels will help foster the learning of key aspects of metabolomics.

Introduction: Bisphenol A (BPA), an organic compound used to produce polycarbonate plastics and epoxy resins, has become a ubiquitous contaminant due to its high-volume production and constant release to the environment. Plant metabolomics can trace the stress effects induced by environmental contaminants to the variation of specific metabolites, making it an alternative way to study pollutants toxicity to plants. Nevertheless, there is an important knowledge gap in metabolomics applications in this area.

Objective: Evaluate the influence of BPA in French lettuce (Lactuca Sativa L. var capitata) leaves metabolic profile by gas chromatography coupled to mass spectrometry (GC-MS) using a hydroponic system.

Methods: Lettuces were cultivated in the laboratory to minimize biological variation and were analyzed 55 days after sowing (considered the plant's adult stage). Hexanoic and methanolic extracts with and without derivatization were prepared for each sample and analyzed by GC-MS.

Results: The highest number of metabolites was obtained from the hexanoic extract, followed by the derivatized methanolic extract. Although no physical differences were observed between control and contaminated lettuce leaves, the multivariate analysis determined a statistically significant difference between their metabolic profiles. Pathway analysis of the most affected metabolites showed that galactose metabolism, starch and fructose metabolism and steroid biosynthesis were significantly affected by BPA exposure.

Conclusions: The preparation of different extracts from the same sample permitted the determination of metabolites with different physicochemical properties. BPA alters the leaves energy and membrane metabolism, plant growth could be affected at higher concentrations and exposition times.

Background & objective: The progression of dengue fever to severe dengue (SD) is a major public health concern that impairs the capacity of the medical system to predict and treat dengue patients. Hence, the present study used a metabolomic approach integrated with machine models to identify differentially expressed metabolites in patients with SD compared to nonsevere patients and healthy controls.

Methods: Comprehensively, the plasma was collected at different clinical phases during dengue without warning signs (DWOW, N = 10), dengue with warning signs (DWW, N = 10), and SD (N = 10) at different stages [i.e., day of admission (DOA), day of defervescence (DOD), and day of convalescent (DOC)] in comparison to healthy control (HC). The samples were subjected to LC‒ESI‒MS/MS to identify metabolites. Statistical and machine learning analyses were performed using R and Python language. Further, biomarker, pathway and correlation analysis was performed to identify potential predictors of dengue.

Results & conclusion: A total of 423 metabolites were identified in all the study groups. Paired and unpaired t-tests revealed 14 highly differentially expressed metabolites between and across the dengue groups, with four metabolites (shikimic acid, ureidosuccinic acid, propionyl carnitine, and alpha-tocopherol) showing significant differences compared to HC. Furthermore, biomarker (ROC) analysis revealed 11 potential molecules with a significant AUC value of 1 that could serve as potential biomarkers for identifying different dengue clinical stages that are beneficial for predicting dengue disease outcomes. The logistic regression model revealed that S-adenosylhomocysteine, hypotaurine, and shikimic acid metabolites could be beneficial indicators for predicting severe dengue, with an accuracy and AUC of 0.75. The data showed that dengue infection is related to lipid metabolism, oxidative stress, inflammation, metabolomic adaptation, and virus manipulation. Moreover, the biomarkers had a significant correlation with biochemical parameters like platelet count, and hematocrit. These results shed some light on host-derived small-molecule biomarkers that are associated with dengue severity and novel insights into metabolomics mechanisms interlinked with disease severity.

Introduction/objectives: Changes in the stool metabolome have been poorly studied in the metabolic syndrome (MetS). Moreover, few studies have explored the relationship of stool metabolites with circulating metabolites. Here, we investigated the associations between stool and blood metabolites, the MetS and systemic inflammation.

Methods: We analyzed data from 1,370 participants of the KORA FF4 study (Germany). Metabolites were measured by Metabolon, Inc. (untargeted) in stool, and using the AbsoluteIDQ^® p180 kit (targeted) in blood. Multiple linear regression models, adjusted for dietary pattern, age, sex, physical activity, smoking status and alcohol intake, were used to estimate the associations of metabolites with the MetS, its components and high-sensitivity C-reactive protein (hsCRP) levels. Partial correlation and Multi-Omics Factor Analysis (MOFA) were used to investigate the relationship between stool and blood metabolites.

Results: The MetS was significantly associated with 170 stool and 82 blood metabolites. The MetS components with the highest number of associations were triglyceride levels (stool) and HDL levels (blood). Additionally, 107 and 27 MetS-associated metabolites (in stool and blood, respectively) showed significant associations with hsCRP levels. We found low partial correlation coefficients between stool and blood metabolites. MOFA did not detect shared variation across the two datasets.

Conclusions: The MetS, particularly dyslipidemia, is associated with multiple stool and blood metabolites that are also associated with systemic inflammation. Further studies are necessary to validate our findings and to characterize metabolic alterations in the MetS. Although our analyses point to weak correlations between stool and blood metabolites, additional studies using integrative approaches are warranted.

Introduction: Volatile organic compounds (VOCs) can arise from underlying metabolism and are detectable in exhaled breath, therefore offer a promising route to non-invasive diagnostics. Robust, precise, and repeatable breath measurement platforms able to identify VOCs in breath distinguishable from background contaminants are needed for the confident discovery of breath-based biomarkers.

Objectives: To build a reliable breath collection and analysis method that can produce a comprehensive list of known VOCs in the breath of a heterogeneous human population.

Methods: The analysis cohort consisted of 90 pairs of breath and background samples collected from a heterogenous population. Owlstone Medical's Breath Biopsy^® OMNI^® platform, consisting of sample collection, TD-GC-MS analysis and feature extraction was utilized. VOCs were determined to be "on-breath" if they met at least one of three pre-defined metrics compared to paired background samples. On-breath VOCs were identified via comparison against purified chemical standards, using retention indexing and high-resolution accurate mass spectral matching.

Results: 1471 VOCs were present in > 80% of samples (breath and background), and 585 were on-breath by at least one metric. Of these, 148 have been identified covering a broad range of chemical classes.

Conclusions: A robust breath collection and relative-quantitative analysis method has been developed, producing a list of 148 on-breath VOCs, identified using purified chemical standards in a heterogenous population. Providing confirmed VOC identities that are genuinely breath-borne will facilitate future biomarker discovery and subsequent biomarker validation in clinical studies. Additionally, this list of VOCs can be used to facilitate cross-study data comparisons for improved standardization.