Metabolomics最新文献

Introduction: Large variations in fatty and amino acid natural ²H/¹H ratios in reference with solvent water point to the active involvement of compartmental, inter- and intramolecular deuterium disequilibrium in adaptive biology. Yet, the human deutenome is an untapped area of energy metabolism and health in humans.

Objectives: The purpose of this scoping review is to examine health effects through deuterium homeostasis using deuterium-depleted water and/or a deuterium-depleted diet. We also aim to reveal health effects of nutritional, metabolic and exercise ketosis, i.e. complete mitochondrial fatty acid oxidation with the production of deuterium depleted (deupleted) metabolic water.

Methods: A protocol process approach was used to retrieve current research in deuterium depletion according to the preferred reporting items protocol for systematic reviews and meta-analyses, extension for scoping reviews with checklist (PRISMA-ScR).

Results: Fifteen research articles were used. All retrieved articles were heterogenous in nature and additional themes did not evolve. Deuterium depletion was found to have beneficial health effects in the following conditions: cancer prevention, cancer treatment, depression, diabetes, long-term memory, anti-aging, and sports performance. Deutenomics is actively pursued in drug research and there are biomarker roles attributed to large natural variations with adaptive significance in biology.

Conclusion: Even with limited data, consistent deuterium depletion can be seen across all conditions reviewed. More randomized control trials are recommended to confirm cause and effect for translationally and clinically informed integrative nutrition-based medical interventions.

Background: Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids.

Aim of review: This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted.

Key scientific concepts of review: We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

Introduction: Biliary atresia (BA) is a rare progressive neonatal cholangiopathy with unknown pathophysiology and time of onset. Newborn Screening (NBS) in Germany is routinely performed in the first days of life to identify rare congenital diseases utilizing dried blood spot (DBS) card analyses. Infants with biliary atresia (BA) are known to have altered amino acid profiles (AAP) at the time point of diagnosis, but it is unclear whether these alterations are present at the time point of NBS.

Objectives: We aimed to analyze amino acid profiles in NBS-DBS of infants with Biliary Atresia.

Methods: Original NBS-DBS cards of 41 infants who were later on diagnosed with BA were retrospectively obtained. NBS-DBS cards from healthy newborns (n = 40) served as controls. In some BA infants (n = 14) a second DBS card was obtained at time of Kasai surgery. AAP in DBS cards were analyzed by targeted metabolomics.

Results: DBS metabolomics in the NBS of at that time point seemingly healthy infants later diagnosed with BA revealed significantly higher levels of Methionine (14.6 ± 8.6 μmol/l), Histidine (23.5 ± 50.3 μmol/l), Threonine (123.9 ± 72.8 μmol/l) and Arginine (14.1 ± 11.8 μmol/l) compared to healthy controls (Met: 8.1 ± 2.6 μmol/l, His: 18.6 ± 10.1 μmol/l, Thr: 98.1 ± 34.3 μmol/l, Arg: 9.3 ± 6.6 μmol/l). Methionine, Arginine and Histidine showed a further increase at time point of Kasai procedure. No correlation between amino acid levels and clinical course was observed.

Conclusion: Our data demonstrate that BA patients exhibit an altered AAP within 72 h after birth, long before the infants become symptomatic. This supports the theory of a prenatal onset of the disease and, thus, the possibility of developing a sensitive and specific NBS. Methionine might be particularly relevant due to its involvement in glutathione metabolism. Further investigation of AAP in BA may help in understanding the underlying pathophysiology.

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.

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.

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.