The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread to nearly every continent, registering over 1,250,000 deaths worldwide. The effects of SARS-CoV-2 on host targets remains largely limited, hampering our understanding of Coronavirus Disease 2019 (COVID-19) pathogenesis and the development of therapeutic strategies. The present study used a comprehensive untargeted metabolomic and lipidomic approach to capture the host response to SARS-CoV-2 infection. We found that several circulating lipids acted as potential biomarkers, such as phosphatidylcholine 14:0_22:6 (area under the curve (AUC) = 0.96), phosphatidylcholine 16:1_22:6 (AUC = 0.97), and phosphatidylethanolamine 18:1_20:4 (AUC = 0.94). Furthermore, triglycerides and free fatty acids, especially arachidonic acid (AUC = 0.99) and oleic acid (AUC = 0.98), were well correlated to the severity of the disease. An untargeted analysis of non-critical COVID-19 patients identified a strong alteration of lipids and a perturbation of phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, aminoacyl-tRNA degradation, arachidonic acid metabolism, and the tricarboxylic acid (TCA) cycle. The severity of the disease was characterized by the activation of gluconeogenesis and the metabolism of porphyrins, which play a crucial role in the progress of the infection. In addition, our study provided further evidence for considering phospholipase A2 (PLA2) activity as a potential key factor in the pathogenesis of COVID-19 and a possible therapeutic target. To date, the present study provides the largest untargeted metabolomics and lipidomics analysis of plasma from COVID-19 patients and control groups, identifying new mechanisms associated with the host response to COVID-19, potential plasma biomarkers, and therapeutic targets.

Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics–AI systems, limitations thereof and recent tools were also discussed.

Infection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to severe respiratory tract damage and acute lung injury. Therefore, it is crucial to study breath-associated biofluids not only to investigate the breath’s biochemical changes caused by SARS-CoV-2 infection, but also to discover potential biomarkers for the development of new diagnostic tools. In the present study, we performed an untargeted metabolomics approach using a bidimensional gas chromatography mass spectrometer (GCxGC-TOFMS) on exhaled breath condensate (EBC) from COVID-19 patients and negative healthy subjects to identify new potential biomarkers for the noninvasive diagnosis and monitoring of the COVID-19 disease. The EBC analysis was further performed in patients with acute or acute-on-chronic cardiopulmonary edema (CPE) to assess the reliability of the identified biomarkers. Our findings demonstrated that an abundance of EBC fatty acids can be used to discriminate COVID-19 patients and that they may have a protective effect, thus suggesting their potential use as a preventive strategy against the infection.

Despite well-documented metabolic and hematopoietic alterations during tumor development, the mechanisms underlying this crucial immunometabolic intersection remain elusive. Of particular interest is the connection between lipid metabolism and the retinoic acid–related orphan receptor (RORC1/RORγ), whose transcriptional activity modulates cancer-related emergency myelopoiesis and is boosted by cholesterol metabolites, whereas hypercholesterolemia itself is associated with dysregulated myelopoiesis. In this study, we show that cancer and hypercholesterolemic diet independently or cooperatively activate RORγ-dependent expansion of myeloid-derived suppressor cells (MDSC) and M2-polarized tumor-associated macrophages (TAM), supporting cancer spread. Moreover, we report that tumor-induced expression of IL1β and IL6 promotes hepatic expression of proprotein convertase subtilisin/kexin type 9 in preclinical models and patients. Importantly, lowering cholesterol levels, by genetic or pharmacologic inhibition of proprotein convertase subtilisin/kexin type 9, prevents MDSC expansion, M2 TAM accumulation, and tumor progression in a RORγ-dependent manner, unleashing specific antitumor immunity. Overall, we identify RORγ as a key sensor of lipid disorders, bridging hypercholesterolemia and protumor myelopoiesis. Significance: Cancer and a hypercholesterolemic diet independently or collaboratively increase blood cholesterol, which in turn triggers RORγ-dependent expansion of suppressive monocytic MDSCs and M2-like TAMs, thus inhibiting specific antitumor immunity and facilitating disease progression.