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.

Comparative proteomic study of pig muscle proteins during growth and development of an animal

The production of high-quality pork is closely related to the growth and development of muscle tissue. The present article provides a comparative proteomic research of l. dorsi, b. femoris, m. brachiocephalicus during the pigs’ growth and development (at age of 60 days and 180 days). This work was supported by data of electrophoretic methods: one-dimensional electrophoresis according to Laemmli with densitometric assessment in the ImageJ software and two-dimensional electrophoresis according to O’Farrell method with its further processing on the software ImageMaster. The mass spectrometric identification was conducted with the help of the high-performance liquid chromatography (HPLC) system connected to a mass spectrometer; further the data were interpreted by search algorithm Andromeda. When comparing frequency diagrams of one-dimensional electrophoregrams of all three muscle tissues of weaned pigs, the greatest difference was observed for the muscle sample l. dorsi. Comparison of diagrams of muscle tissue samples taken for mature pigs showed a great similarity of all three studied muscles samples. Within the framework of the research, the Fold indicator was calculated. The exceeding its value by more than 2 units is generally considered to be a statistically significant difference. When analyzing two-dimensional electrophoretograms of weaned pigs’ muscles, 18 protein fractions were revealed with Fold > 2. When examining the muscle tissue of mature pigs, 15 of those proteins were found; the differences were mostly detected in the minor protein fractions. The mass spectrometric analysis of the cut bands with well-pronounced differences from the onedimensional electrophoretogram revealed 214 proteins involved to a greater extent in cellular and metabolic processes, physical activity and localization. Growth and development protein — semaphorin‑6B (96.78 kDa) — was revealed in muscle tissue of l. dorsi, a. Also in l. dorsi and b. femoris the growth and development proteins were found: cadherin‑13 (78.23 kDa), cadherin‑7 (87.01 kDa), the F‑actin-cap protein beta subunit (30.66 kDa), and two uncharacterized proteins at 65.60 kDa and 63.88 kDa.

Dihydrobenzofurans and Propynylthiophenes From the Roots of Eupatorium heterophyllum

Seven new dihydrobenzofurans and 2 new propynyl thiophenes were isolated from the roots of Eupatorium heterophyllum together with 13 known compounds. The compounds were characterized using spectroscopic methods including 2D NMR, infrared, and mass spectrometric techniques. Aerial parts of this plant have been known to contain various sesquiterpenoids and displayed high chemical diversity (several compounds isolated and/or identified) among their chemical constituents depending on the collection site. Nevertheless, we found that the chemical diversity in the roots was lower than in the aerial parts.

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.