Here, we describe that the SIRT1/mTOR axis regulates metabolic rewiring, inflammasome activation, and autophagy in macrophages, in which SIRT1 overexpression actively contributes to aggravate cholestatic liver disease progression in mice.
Pneumonia is a common cause of morbidity and mortality and is most often caused by bacterial pathogens. COVID-19 is characterized by lung infection with potential progressive organ failure. The systemic consequences of both disease on the systemic blood metabolome are not fully understood. The aim of this study was to compare the blood metabolome of both diseases and we hypothesize that plasma metabolomics may help to identify the systemic effects of these diseases. Therefore, we profiled the plasma metabolome of 43 cases of COVID-19 pneumonia, 23 cases of non-COVID-19 pneumonia, and 26 controls using a non-targeted approach. Metabolic alterations differentiating the three groups were detected, with specific metabolic changes distinguishing the two types of pneumonia groups. A comparison of venous and arterial blood plasma samples from the same subjects revealed the distinct metabolic effects of pulmonary pneumonia. In addition, a machine learning signature of four metabolites was predictive of the disease outcome of COVID-19 subjects with an area under the curve (AUC) of 86 ± 10 %. Overall, the results of this study uncover systemic metabolic changes that could be linked to the etiology of COVID-19 pneumonia and non-COVID-19 pneumonia.
Background Alterations in mitochondrial dysfunction have been implicated in the pathogenesis of Parkinson's disease (PD). Mitochondrial energy production is linked to glucose metabolism, and diabetes is associated with PD. However, studies investigating glucose metabolism in vivo in genetically stratified PD patients and controls have yet to be performed. Objectives The objectives of this study were to explore glucose production, gluconeogenesis, and the contribution of gluconeogenesis to glucose production in idiopathic and PRKN PD compared with healthy controls with state‐of‐the‐art biochemical methods. Methods We applied a dried‐blood sampling/gas chromatography/mass spectrometry approach to monitor fluxes in the Cori cycle in vivo. Results The contribution of gluconeogenesis to total glucose production is increased in idiopathic PD patients (n = 33), but not in biallelic PRKN mutation carriers (n = 5) compared with healthy controls (n = 13). Conclusions We provide first‐time in vivo evidence for alterations in glucose metabolism in idiopathic PD, in keeping with the epidemiological evidence for an association between PD and diabetes. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.
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