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

Metabolomics analysis reveals novel serum metabolite alterations in cancer cachexia

BackgroundCachexia is a body wasting syndrome that significantly affects well-being and prognosis of cancer patients, without effective treatment. Serum metabolites take part in pathophysiological processes of cancer cachexia, but apart from altered levels of select serum metabolites, little is known on the global changes of the overall serum metabolome, which represents a functional readout of the whole-body metabolic state. Here, we aimed to comprehensively characterize serum metabolite alterations and analyze associated pathways in cachectic cancer patients to gain new insights that could help instruct strategies for novel interventions of greater clinical benefit.MethodsSerum was sampled from 120 metastatic cancer patients (stage UICC IV). Patients were grouped as cachectic or non-cachectic according to the criteria for cancer cachexia agreed upon international consensus (main criterium: weight loss adjusted to body mass index). Samples were pooled by cachexia phenotype and assayed using non-targeted gas chromatography-mass spectrometry (GC-MS). Normalized metabolite levels were compared using t-test (p < 0.05, adjusted for false discovery rate) and partial least squares discriminant analysis (PLS-DA). Machine-learning models were applied to identify metabolite signatures for separating cachexia states. Significant metabolites underwent MetaboAnalyst 5.0 pathway analysis.ResultsComparative analyses included 78 cachectic and 42 non-cachectic patients. Cachectic patients exhibited 19 annotable, significantly elevated (including glucose and fructose) or decreased (mostly amino acids) metabolites associating with aminoacyl-tRNA, glutathione and amino acid metabolism pathways. PLS-DA showed distinct clusters (accuracy: 85.6%), and machine-learning models identified metabolic signatures for separating cachectic states (accuracy: 83.2%; area under ROC: 88.0%). We newly identified altered blood levels of erythronic acid and glucuronic acid in human cancer cachexia, potentially linked to pentose-phosphate and detoxification pathways.ConclusionWe found both known and yet unknown serum metabolite and metabolic pathway alterations in cachectic cancer patients that collectively support a whole-body metabolic state with impaired detoxification capability, altered glucose and fructose metabolism, and substrate supply for increased and/or distinct metabolic needs of cachexia-associated tumors. These findings together imply vulnerabilities, dependencies and targets for novel interventions that have potential to make a significant impact on future research in an important field of cancer patient care

Pyruvate dehydrogenase fuels a critical citrate pool that is essential for Th17 cell effector functions

Pyruvate dehydrogenase (PDH) is the central enzyme connecting glycolysis and the tricarboxylic acid (TCA) cycle. The importance of PDH function in T helper 17 (Th17) cells still remains to be studied. Here, we show that PDH is essential for the generation of a glucose-derived citrate pool needed for Th17 cell proliferation, survival, and effector function. In vivo, mice harboring a T cell-specific deletion of PDH are less susceptible to developing experimental autoimmune encephalomyelitis. Mechanistically, the absence of PDH in Th17 cells increases glutaminolysis, glycolysis, and lipid uptake in a mammalian target of rapamycin (mTOR)-dependent manner. However, cellular citrate remains critically low in mutant Th17 cells, which interferes with oxidative phosphorylation (OXPHOS), lipid synthesis, and histone acetylation, crucial for transcription of Th17 signature genes. Increasing cellular citrate in PDH-deficient Th17 cells restores their metabolism and function, identifying a metabolic feedback loop within the central carbon metabolism that may offer possibilities for therapeutically targeting Th17 cell-driven autoimmunity

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

A non-invasive approach to explore the discriminatory potential of the urinary volatilome of invasive ductal carcinoma of the breast

Worldwide, breast invasive ductal carcinoma (IDC) accounts for the majority of the reported cases of this form of cancer. IDC effective management, as for any form of cancer, would greatly benefit from early diagnosis. This, however, due to various socio-economic reasons, is very far for the reality in developing countries like India, where cancer diagnosis is often carried out at late stages when disease management is troublesome. With the present work, we aim to evaluate a simple analytical methodology to identify a set of volatile organic compounds (VOCs) in urine samples, as a biosignature for IDC. Using solid-phase microextraction followed by gas chromatography/mass spectrometry, a panel of 14 urinary VOCs was found to discriminate IDC (n = 65) from a healthy control (HC) group (n = 70) through multivariate statistical treatments. Furthermore, metabolic pathway analysis revealed various dysregulated pathways involved in IDC patients hinting that their detailed investigations could lead to novel mechanistic insights into the disease pathophysiology. In addition, we validated the expression pattern of five of these VOCs namely 2-ethyl-1-hexanol, isolongifolenone, furan, dodecanoic acid, 2-methoxy-phenol in another external cohort of 59 urinary samples (IDC = 32 and HC = 27) and found their expression pattern to be consistent with the primary sample set. To our knowledge, this is the first study exploring breast IDC volatome alterations in Indian patients.info:eu-repo/semantics/publishedVersio

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