Metabolites of L-ARG in Exhaled Breath Condensate and Serum Are Not Biomarkers of Bronchial Asthma in Children

(1) Background: L-arginine (L-ARG) and its metabolites are involved in some aspects of asthma pathogenesis (airway inflammation, oxidative stress, bronchial responsiveness, collagen deposition). Published data indicate that lungs are a critical organ for the regulation of L-ARG metabolism and that alterations in L-ARG metabolism may be significant for asthma. The aim of this study was to assess the levels of L-ARG and its metabolites in pediatric patients with asthma in serum and exhaled breath condensate (EBC) by mass spectrometric analysis and compare them with non-asthmatic children. (2) Methods: Sixty-five children (37 pediatric patients with bronchial asthma and 28 healthy control subjects) aged 6–17 participated in the study. All participants underwent a clinical visit, lung tests, allergy tests with common aeroallergens, and serum and EBC collection. The levels of biomarkers were determined in both serum and EBC. Analytical chromatography was conducted using an Acquity UPLC system equipped with a cooled autosampler and an Acquity HSS T3 column. Mass spectrometric analysis was conducted using the Xevo G2 QTOF MS with electrospray ionization (ESI) in positive ion mode. (3) Results: Asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA) levels in serum and EBC did not differ significantly in asthmatic children and healthy control subjects. We found no correlation between forced expiratory volume in one second (FEV1) and L-ARG and its metabolites, as well as between interleukin-4 (IL-4) serum level and L-ARG and its metabolites. Concentrations of ADMA, SDMA, citrulline (CIT), and ornithine (ORN) were higher in serum than EBC in asthmatics and non-asthmatics. By contrast, concentrations of dimethylarginine (DMA) were higher in EBC than serum. ADMA/L-ARG, SDMA/L-ARG, and DMA/L-ARG ratios were significantly higher in EBC than in serum in asthmatics and in non-asthmatics. (4) Conclusions: Serum and EBC concentrations of L-ARG and its metabolites were not an indicator of pediatric bronchial asthma in our study.

Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics

Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril-forming protein, and its accurate identification is essential to the choice of treatment. Mass spectrometry-based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective interpretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model-assisted method for the unbiased identification of amyloid-containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid-containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid-containing biopsies and 78 Congo Red negative biopsies to identify novel “amyloid signature” proteins that included clusterin, fibulin-1, vitronectin complement component C9 and also three collagen proteins, as well as the well-known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid-negative control biopsies. The trained algorithm performed superior in the discrimination of amyloid-containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected amyloid-containing biopsies. Moreover, our method successfully classified amyloidosis patients according to the subtype in 102 out of 103 blinded cases. Collectively, our model-assisted approach identified novel amyloid-associated proteins and demonstrated the use of mass spectrometry-based data in clinical diagnostics of disease by the unbiased and reliable model-assisted classification of amyloid deposits and of the specific amyloid subtype.