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 nonCOVID-19 pneumonia

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 Signature Indicative of 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