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.

No abstract

Endometriosis is a chronic gynecological condition that affects 10-32% of women of reproductive age and may lead to infertility. The study of protein profiles in follicular fluid may assist in elucidating possible biomarkers related to this disease. For this, follicular fluid samples were obtained from women with tubal factor or minimal male factor infertility who had pregnancy outcomes after in vitro fertilization (IVF) treatment (control group, n = 10), women with endometriosis (endometriosis group, n = 10), along with the endometrioma from these same patients were included (endometrioma group, n = 10). For proteomic analysis, samples were pooled according to their respective groups and normalized to protein content. Proteins were analyzed by in tandem mass spectrometry (MS(E)) Spectra processing and the ProteinLynx Global Server v.2.5. was used for database searching. Data was submitted to the biological network analysis using Cytoscape 2.8.2 with ClueGO plugin. As a result, 535 proteins were identified among all groups. The control group differentially or uniquely expressed 33 (6%) proteins and equal expression of 98 (18%) proteins was observed in the control and endometriosis groups of which 41 (7%) proteins were further identified and/or quantified. Six (1%) proteins were observed in both the endometriosis and endometrioma groups, but 212 (39%) proteins were exclusively identified and/or quantified in the endometrioma group. There were 9 (1%) proteins observed in both the control and endometrioma groups and there were 139 (25%) proteins common among all three groups. Distinct differences among the protein profiles in the follicular fluid of patients included in this study were found, identifying proteins related to the disease progression and IVF success. Thus, some pathways related to endometriosis are associated with the presence of specific proteins, as well as the absence of others. This study provides a first step to the development of more sensitive diagnostic tests and treatment.

IntroductionChagas 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.MethodsTo 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.ResultsAnalyses 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.ConclusionThese 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.

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.