Discriminant biomarkers of acute respiratory distress syndrome associated to H1N1 influenza identified by metabolomics HPLC-QTOF-MS/MS platform.

Acute respiratory distress syndrome (ARDS) is a serious complication of influenza A (H1N1) virus infection. Its pathogenesis is unknown and biomarkers are lacking. Untargeted metabolomics allows the analysis of the whole metabolome in a biological compartment, identifying patterns associated with specific conditions. We hypothesized that LC-MS could help identify discriminant metabolites able to define the metabolic alterations occurring in patients with influenza A (H1N1) virus infection that developed ARDS. Serum samples from patients diagnosed with 2009 influenza A (H1N1) virus infection with (n = 25) or without (n = 32) ARDS were obtained on the day of hospital admission and analyzed by LC-MS/MS. Metabolite identification was determined by MS/MS analysis and analysis of standards. The specificity of the patterns identified was confirmed in patients without 2009 influenza A(H1N1) virus pneumonia (15 without and 17 with ARDS). Twenty-three candidate biomarkers were found to be significantly different between the two groups, including lysophospholipids and sphingolipids related to inflammation; bile acids, tryptophan metabolites, and thyroxine, related to the metabolism of the gut microflora. Confirmation results demonstrated the specificity of major alterations occurring in ARDS patients with influenza A (H1N1) virus infection.

Fingerprinting-based metabolomic approach with LC-MS to sleep apnea and hypopnea syndrome: a pilot study.

Sleep apnea and hypopnea syndrome (SAHS) is a multicomponent disorder, with associated cardiovascular and metabolic alterations, second in order of frequency among respiratory disorders. Sleep apnea is diagnosed with an overnight sleep test called a polysomnogram, which requires having the patient in hospital. In addition, a more clear classification of patients according to mild and severe presentations would be desirable. The aim of the present study was to assess the relative metabolic changes in SAHS to identify new potential biomarkers for diagnosis, able to evaluate disease severity to establish response to therapeutic interventions and outcomes. For this purpose, metabolic fingerprinting represents a valuable strategy to monitor, in a nontargeted manner, the changes that are at the base of the pathophysiological mechanism of SAHS. Plasma samples of 33 SAHS patients were collected after polysomnography and analyzed with LC coupled to MS (LC-QTOF-MS). After data treatment and statistical analysis, signals differentiating nonsevere and severe patients were detected. Putative identification of 14 statistically significant features was obtained and changes that can be related to the episodes of hypoxia/reoxygenation (inflammation) have been highlighted. Among them, the patterns of variation of platelet activating factor and lysophospholipids, together with some compounds related to differential activity of the gut microflora (bile pigments and pipecolic acid) open new lines of research that will benefit our understanding of the alterations, offering new possibilities for adequate monitoring of the stage of the disease.

Metabolomic approach with LC-QTOF to study the effect of a nutraceutical treatment on urine of diabetic rats.

The rat treated with streptozotocin has been proposed as the most appropriate model of systemic oxidative stress for studying antioxidant therapies. In that sense, rosemary extracts have long been recognized as having antioxidant properties, and folic acid may be able to improve endothelial progenitor cell function. A mixture containing both has been tested as a possible nutraceutical to improve health complications in diabetes. We have developed the methodology to evaluate metabolic changes in the urine of streptozotocin-induced diabetic rats after supplementing their diet with rosemary extract obtained with supercritical fluids (SFE) containing 10% folic acid in an acute but short-term study. It has been done with a metabolomics approach using LC-QTOF as an analytical tool. About 20 endogenous metabolites have been identified by databases and MS/MS showing statistically significant changes. Among them, several amino acids and their metabolites point to changes due to the effect of the gut microbiota. In addition, the comparison between control and streptozotocin-diabetic rats has permitted the showing of some metabolic coincidences between type 1 diabetes and other (possible) autoimmune diseases such as autism and/or Crohn's disease, and the nutraceutical intervention has succeeded in inducing changes in such biomarkers.