Cardiomyocyte infection by Trypanosoma cruzi promotes innate immune response and glycolysis activation.

Introduction: Chagas cardiomyopathy, a disease caused by Trypanosoma cruzi (T. cruzi) infection, is a major contributor to heart failure in Latin America. There are significant gaps in our understanding of the mechanism for infection of human cardiomyocytes, the pathways activated during the acute phase of the disease, and the molecular changes that lead to the progression of cardiomyopathy. Methods: To investigate the effects of T. cruzi on human cardiomyocytes during infection, we infected induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) with the parasite and analyzed cellular, molecular, and metabolic responses at 3 hours, 24 hours, and 48 hours post infection (hpi) using transcriptomics (RNAseq), proteomics (LC-MS), and metabolomics (GC-MS and Seahorse) analyses. Results: Analyses of multiomic data revealed that cardiomyocyte infection caused a rapid increase in genes and proteins related to activation innate and adaptive immune systems and pathways, including alpha and gamma interferons, HIF-1α signaling, and glycolysis. These responses resemble prototypic responses observed in pathogen-activated immune cells. Infection also caused an activation of glycolysis that was dependent on HIF-1α signaling. Using gene editing and pharmacological inhibitors, we found that T. cruzi uptake was mediated in part by the glucose-facilitated transporter GLUT4 and that the attenuation of glycolysis, HIF-1α activation, or GLUT4 expression decreased T. cruzi infection. In contrast, pre-activation of pro-inflammatory immune responses with LPS resulted in increased infection rates. Conclusion: These findings suggest that T. cruzi exploits a HIF-1α-dependent, cardiomyocyte-intrinsic stress-response activation of glycolysis to promote intracellular infection and replication. These chronic immuno-metabolic responses by cardiomyocytes promote dysfunction, cell death, and the emergence of cardiomyopathy.

Evaluation of Volumetric Absorptive Microsampling and Mass Spectrometry Data-Independent Acquisition of Hemoglobin-Related Clinical Markers.

Data-independent acquisition (DIA) allows comprehensive proteome coverage, while it also potentially works as a unified protocol to determine a multitude of proteins found in blood. Because of its high specificity, mass spectrometry may greatly reduce the interference observed in other assays to evaluate blood markers. Here, we combined DIA with volumetric absorptive microsampling (VAMS) and automated proteomics sample processing in a platform to assess clinical markers. As a proof of concept, we evaluated two hemoglobin-related biomarkers: the glycated hemoglobin (HbA1c) and hemoglobin (Hb) variants. HbA1c by DIA showed good correlation with the reference method, but method imprecision did not meet the quality requirement for this biomarker. We developed a strategy to identify Hb variants based on a customized database combined with a workflow for DIA data extraction and rigorous peptide evaluation. Data are available via ProteomeXchange with identifier PXD029918.

Metabolomic and lipidomic profile in men with obstructive sleep apnoea: implications for diagnosis and biomarkers of cardiovascular risk.

The use of metabolomic and lipidomic strategies for selecting potential biomarkers for obstructive sleep apnoea (OSA) has been little explored. We examined adult male patients with OSA (defined by an apnoea-hypopnoea index ≥15 events/hour), as well as age-, gender-, and fat-composition-matched volunteers without OSA. All subjects were subjected to clinical evaluation, sleep questionnaires for detecting the risk of OSA (Berlin and NoSAS score), metabolomic analysis by gas chromatography coupled to mass spectrometry and lipidomic analysis with liquid chromatography followed by detection by MALDI-MS. This study included 37 patients with OSA and 16 controls. From the 6 metabolites and 22 lipids initially selected, those with the best association with OSA were glutamic acid, deoxy sugar and arachidonic acid (metabolites), and glycerophosphoethanolamines, sphingomyelin and lyso-phosphocholines (lipids). For the questionnaires, the NoSAS score performed best with screening for OSA (area under the curve [AUC] = 0.724, p = 0.003). The combination of the NoSAS score with metabolites or lipids resulted in an AUC for detecting OSA of 0.911 and 0.951, respectively. In conclusion, metabolomic and lipidomic strategies suggested potential early biomarkers in OSA that could also be helpful in screening for this sleep disorder beyond traditional questionnaires.