Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples

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.

In Vivo Investigation of Glucose Metabolism in Idiopathic and PRKN-Related Parkinson's Disease

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.

SiMeEx, a simplified method for metabolite extraction of adherent mammalian cells

A reliable method for metabolite extraction is central to mass spectrometry-based metabolomics. However, existing methods are lengthy, mostly due to the step of scraping cells from cell culture vessels, which restricts metabolomics in broader application such as lower cell numbers and high-throughput studies. Here, we present a simplified metabolite extraction (SiMeEx) method, to efficiently and quickly extract metabolites from adherent mammalian cells. Our method excludes the cell scraping step and therefore allows for a more efficient extraction of polar metabolites in less than 30 min per 12-well plate. We demonstrate that SiMeEx achieves the same metabolite recovery as using a standard method containing a scraping step, in various immortalized and primary cells. Omitting cell scraping does not compromise the performance of non-targeted and targeted GC-MS analysis, but enables metabolome analysis of cell culture on smaller well sizes down to 96-well plates. Therefore, SiMeEx demonstrates advantages not only on time and resources, but also on the applicability in high-throughput studies.

Plasma Metabolome Alterations Discriminate between COVID-19 and Non-COVID-19 Pneumonia

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.

Plasma Metabolome Signature Indicative of BRCA1 Germline Status Independent of Cancer Incidence

Individuals carrying a pathogenic germline variant in the breast cancer predisposition gene BRCA1 (gBRCA1+) are prone to developing breast cancer. Apart from its well-known role in DNA repair, BRCA1 has been shown to powerfully impact cellular metabolism. While, in general, metabolic reprogramming was named a hallmark of cancer, disrupted metabolism has also been suggested to drive cancer cell evolution and malignant transformation by critically altering microenvironmental tissue integrity. Systemic metabolic effects induced by germline variants in cancer predisposition genes have been demonstrated before. Whether or not systemic metabolic alterations exist in gBRCA1+ individuals independent of cancer incidence has not been investigated yet. We therefore profiled the plasma metabolome of 72 gBRCA1+ women and 72 age-matched female controls, none of whom (carriers and non-carriers) had a prior cancer diagnosis and all of whom were cancer-free during the follow-up period. We detected one single metabolite, pyruvate, and two metabolite ratios involving pyruvate, lactate, and a metabolite of yet unknown structure, significantly altered between the two cohorts. A machine learning signature of metabolite ratios was able to correctly distinguish between gBRCA1+ and controls in ~82%. The results of this study point to innate systemic metabolic differences in gBRCA1+ women independent of cancer incidence and raise the question as to whether or not constitutional alterations in energy metabolism may be involved in the etiology of BRCA1-associated breast cancer.