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COVID-19 was the most significant infectious-agent-related cause of death in the 2020-2021 period. On average, over 60% of those admitted to ICU facilities with this disease died across the globe. In severe cases, COVID-19 leads to respiratory and systemic compromise, including pneumonia-like symptoms, acute respiratory distress syndrome, and multiorgan failure. While the upper respiratory tract and lungs are the principal sites of infection and injury, most studies on the metabolic signatures in COVID-19 patients have been carried out on serum and plasma samples. In this report we attempt to characterize the metabolome of lung parenchyma extracts from fatal COVID-19 cases and compare them with that from other respiratory diseases. Our findings indicate that the metabolomic profiles from fatal COVID-19 and non-COVID-19 cases are markedly different, with the former being the result of increased lactate and amino acid metabolism, altered energy pathways, oxidative stress, and inflammatory response. Overall, these findings provide additional insights into the pathophysiology of COVID-19 that could lead to the development of targeted therapies for the treatment of severe cases of the disease, and further highlight the potential of metabolomic approaches in COVID-19 research.

Diagnosis of pulmonary arterial hypertension (PAH) is difficult due to the lack of specific clinical symptoms and biomarkers, especially at early stages. We compared plasma metabolic fingerprints of PAH patients (n = 20) with matched healthy volunteers (n = 20) using, for the first time, untargeted multiplatform metabolomics approach consisting of high-performance liquid and gas chromatography coupled with mass spectrometry. Multivariate statistical analyses were performed to select metabolites that contribute most to groups' classification (21 from liquid in both ionization modes and 9 from gas chromatography-mass spectrometry). We found metabolites related to energy imbalance, such as glycolysis-derived metabolites, as well as metabolites involved in fatty acid, lipid and amino acid metabolism. We observed statistically significant changes in threitol and aminomalonic acid in PAH patients, which could provide new biochemical insights into the pathogenesis of the disease. The results were externally validated on independent case and control cohorts, confirming up to 16 metabolites as statistically significant in the validation study. Multiplatform metabolomics, followed by multivariate chemometric data analysis has a huge potential for explaining pathogenesis of PAH and for searching potential and new more specific and less invasive markers of the disease.

Background: Despite increasing evidence suggesting that pulmonary arterial hypertension (PAH) is a complex disease involving vasoconstriction, thrombosis, inflammation, metabolic dysregulation and vascular proliferation, all the drugs approved for PAH mainly act as vasodilating agents. Since excessive TGF-β signaling is believed to be a critical factor in pulmonary vascular remodeling, we hypothesized that blocking TGFβ-activated kinase 1 (TAK-1), alone or in combination with a vasodilator therapy (i.e., riociguat) could achieve a greater therapeutic benefit.Methods: PAH was induced in male Wistar rats by a single injection of the VEGF receptor antagonist SU5416 (20 mg/kg) followed by exposure to hypoxia (10%O2) for 21 days. Two weeks after SU5416 administration, vehicle, riociguat (3 mg/kg/day), the TAK-1 inhibitor 5Z-7-oxozeaenol (OXO, 3 mg/kg/day), or both drugs combined were administered for 7 days. Metabolic profiling of right ventricle (RV), lung tissues and PA smooth muscle cells (PASMCs) extracts were performed by magnetic resonance spectroscopy, and the differences between groups analyzed by multivariate statistical methods.Results:In vitro, riociguat induced potent vasodilator effects in isolated pulmonary arteries (PA) with negligible antiproliferative effects and metabolic changes in PASMCs. In contrast, 5Z-7-oxozeaenol effectively inhibited the proliferation of PASMCs characterized by a broad metabolic reprogramming but had no acute vasodilator effects. In vivo, treatment with riociguat partially reduced the increase in pulmonary arterial pressure (PAP), RV hypertrophy (RVH), and pulmonary vascular remodeling, attenuated the dysregulation of inosine, glucose, creatine and phosphocholine (PC) in RV and fully abolished the increase in lung IL-1β expression. By contrast, 5Z-7-oxozeaenol significantly reduced pulmonary vascular remodeling and attenuated the metabolic shifts of glucose and PC in RV but had no effects on PAP or RVH. Importantly, combined therapy had an additive effect on pulmonary vascular remodeling and induced a significant metabolic effect over taurine, amino acids, glycolysis, and TCA cycle metabolism via glycine-serine-threonine metabolism. However, it did not improve the effects induced by riociguat alone on pulmonary pressure or RV remodeling. None of the treatments attenuated pulmonary endothelial dysfunction and hyperresponsiveness to serotonin in isolated PA.Conclusion: Our results suggest that inhibition of TAK-1 induces antiproliferative effects and its addition to short-term vasodilator therapy enhances the beneficial effects on pulmonary vascular remodeling and RV metabolic reprogramming in experimental PAH.

Abstract Tuberculosis (TB) is a major cause of morbidity and mortality in children, and early diagnosis and treatment are crucial to reduce long-term morbidity and mortality. In this study, we explore whether urine nuclear magnetic resonance (NMR)-based metabolomics could be used to identify differences in the metabolic response of children with different diagnostic certainty of TB. We included 62 children with signs and symptoms of TB and 55 apparently healthy children. Six of the children with presumptive TB had bacteriologically confirmed TB, 52 children with unconfirmed TB, and 4 children with unlikely TB. Urine metabolic fingerprints were identified using high- and low-field proton NMR platforms and assessed with pattern recognition techniques such as principal components analysis and partial least squares discriminant analysis. We observed differences in the metabolic fingerprint of children with bacteriologically confirmed and unconfirmed TB compared to children with unlikely TB (p = 0.041 and p = 0.013, respectively). Moreover, children with unconfirmed TB with X-rays compatible with TB showed differences in the metabolic fingerprint compared to children with non-pathological X-rays (p = 0.009). Differences in the metabolic fingerprint in children with different diagnostic certainty of TB could contribute to a more accurate characterisation of TB in the paediatric population. The use of metabolomics could be useful to improve the prediction of TB progression and diagnosis in children.

Abstract Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics. This study aimed to identify and characterise a metabolic profile of TB in urine by high-field nuclear magnetic resonance (NMR) spectrometry and assess whether the TB metabolic profile is also detected by a low-field benchtop NMR spectrometer. We included 189 patients with tuberculosis, 42 patients with pneumococcal pneumonia, 61 individuals infected with latent tuberculosis and 40 uninfected individuals. We acquired the urine spectra from high and low-field NMR. We characterised a TB metabolic fingerprint from the Principal Component Analysis. We developed a classification model from the Partial Least Squares-Discriminant Analysis and evaluated its performance. We identified a metabolic fingerprint of 31 chemical shift regions assigned to eight metabolites (aminoadipic acid, citrate, creatine, creatinine, glucose, mannitol, phenylalanine, and hippurate). The model developed using low-field NMR urine spectra correctly classified 87.32%, 85.21% and 100% of the TB patients compared to pneumococcal pneumonia patients, LTBI and uninfected individuals, respectively. The model validation correctly classified 84.10% of the TB patients. We have identified and characterised a metabolic profile of TB in urine from a high-field NMR spectrometer and have also detected it using a low-field benchtop NMR spectrometer. The models developed from the metabolic profile of TB identified by both NMR technologies were able to discriminate TB patients from the rest of the study groups and the results were not influenced by anti-TB treatment or TB location. This provides a new approach in the search for possible biomarkers for the diagnosis of TB.

Nuclear magnetic resonance (NMR)-based metabolomics is a valuable tool for identifying biomarkers and understanding the underlying metabolic changes associated with various diseases. However, the translation of metabolomics analysis to clinical practice has been limited by the high cost and large size of traditional high-resolution NMR spectrometers. Benchtop NMR, a compact and low-cost alternative, offers the potential to overcome these limitations and facilitate the wider use of NMR-based metabolomics in clinical settings. This review summarizes the current state of benchtop NMR for clinical applications where benchtop NMR has demonstrated the ability to reproducibly detect changes in metabolite levels associated with diseases such as type 2 diabetes and tuberculosis. Benchtop NMR has been used to identify metabolic biomarkers in a range of biofluids, including urine, blood plasma and saliva. However, further research is needed to optimize the use of benchtop NMR for clinical applications and to identify additional biomarkers that can be used to monitor and manage a range of diseases. Overall, benchtop NMR has the potential to revolutionize the way metabolomics is used in clinical practice, providing a more accessible and cost-effective way to study metabolism and identify biomarkers for disease diagnosis, prognosis, and treatment.

Magnetic resonance imaging (MRI) is a valuable diagnostic tool that provides detailed information about the structure and function of tissues in the human body. In particular, measuring relaxation times, such as T1 and T2, can provide important insights into the composition and properties of different tissues. Accurate relaxation time mapping is therefore critical for clinical diagnosis and treatment planning, as it can help to identify and characterize pathological conditions, monitor disease progression, and guide interventions. However, the computation of relaxation time maps in MRI is a complex and challenging task that requires sophisticated mathematical algorithms. Thus, there is a need for robust and accurate algorithms that can reliably extract the desired information from MRI data. This article compares the performance of the Reduced Dimension Nonlinear Least Squares (RD-NLS) algorithm versus several widely used algorithms to compute relaxation times in MRI, such as Levenberg-Marquardt and Nelder-Mead. RD-NLS simplifies the search space for the optimum fit by leveraging the partial linear relationship between signal intensity and model parameters. The comparison was performed on several datasets and signal models, resulting in T1 and T2 maps. The algorithms were evaluated based on their fit error, with the RD-NLS algorithm showing a lower error than other fit-ting algorithms. The improvement was particularly notable in T1 maps, with less of a difference in T2 maps. Additionally, the average T1 values computed with different algorithms differed by up to 14 ms, indicating the importance of algorithm selection. These results suggest that the RD-NLS algorithm outperforms other commonly used algorithms for computing relaxation times in MRI.

Summary: HIF1-alpha expression defines metabolic compartments in the developing heart, promoting glycolytic program in the compact myocardium and mitochondrial enrichment in the trabeculae. Nonetheless, its role in cardiogenesis is debated. To assess the importance of HIF1-alpha during heart development and the influence of glycolysis in ventricular chamber formation, herein we generated conditional knockout models of Hif1a in Nkx2.5 cardiac progenitors and cardiomyocytes. Deletion of Hif1a impairs embryonic glycolysis without influencing cardiomyocyte proliferation and results in increased mitochondrial number and transient activation of amino acid catabolism together with HIF2α and ATF4 upregulation by E12.5. Hif1a mutants display normal fatty acid oxidation program and do not show cardiac dysfunction in the adulthood. Our results demonstrate that cardiac HIF1 signaling and glycolysis are dispensable for mouse heart development and reveal the metabolic flexibility of the embryonic myocardium to consume amino acids, raising the potential use of alternative metabolic substrates as therapeutic interventions during ischemic events.

Atrial fibrillation involves an important type of heart arrhythmia caused by a lack of control in the electrical signals that arrive in the heart, produce an irregular auricular contraction, and induce blood clotting, which finally can lead to stroke. Atrial fibrillation presents some specific characteristics, but it has been treated and prevented using conventional methods similar to those applied to other cardiovascular diseases. However, due to the influence of this pathology on the mortality caused by cerebrovascular accidents, further studies on the molecular mechanism of atrial fibrillation are required. Our aim here is provide a compressive review of the use of metabolomics on this condition, from the study of the metabolic profile of plasma to the development of animal models. In summary, most of the reported studies highlighted alterations in the energetic pathways related to the development of the condition.

Abstract Background Magnetic resonance spectroscopy (MRS) studies have identified elevated levels of the phospholipid precursor phosphocholine (PC) and phosphoethanolamine (PE) as metabolic hallmarks of cancer. Unusually, however, PC and PE levels are reduced in mutant isocitrate dehydrogenase 1 (IDHmut) gliomas that produce the oncometabolite 2-hydroxyglutarate (2-HG) relative to wild-type IDH1 (IDHwt) gliomas. The goal of this study was to determine the molecular mechanism underlying this unusual metabolic reprogramming in IDHmut gliomas. Methods Steady-state PC and PE were quantified using 31P-MRS. To quantify de novo PC and PE synthesis, we used 13C-MRS and measured flux to 13C-PC and 13C-PE in cells incubated with [1,2-13C]-choline and [1,2-13C]-ethanolamine. The activities of choline kinase (CK) and ethanolamine kinase (EK), the enzymes responsible for PC and PE synthesis, were quantified using 31P-MR-based assays. To interrogate the role of 2-HG, we examined IDHwt cells incubated with 2-HG and, conversely, IDHmut cells treated with the IDHmut inhibitor AGI-5198. To examine the role of hypoxia-inducible factor 1-α (HIF-1α), we silenced HIF-1α using RNA interference. To confirm our findings in vivo and in the clinic, we studied IDHwt and IDHmut orthotopic tumor xenografts and glioma patient biopsies. Results De novo synthesis of PC and PE was reduced in IDHmut cells relative to IDHwt. Concomitantly, CK activity and EK activity were reduced in IDHmut cells. Pharmacological manipulation of 2-HG levels established that 2-HG was responsible for reduced CK activity, EK activity, PC and PE. 2-HG has previously been reported to stabilize levels of HIF-1α, a known regulator of CK activity. Silencing HIF-1α in IDHmut cells restored CK activity, EK activity, PC and PE to IDHwt levels. Our findings were recapitulated in IDHmut orthotopic tumor xenografts and, most importantly, in IDHmut patient biopsies, validating our findings in vivo and in the clinic. Conclusions This study identifies, to our knowledge for the first time, a direct role for 2-HG in the downregulation of CK and EK activity, and thereby, PC and PE synthesis in IDHmut gliomas. These results highlight the unusual reprogramming of phospholipid metabolism in IDHmut gliomas and have implications for the identification of MRS-detectable metabolic biomarkers associated with 2-HG status.