METHODS: Raw and cooked extracts of the giant freshwater prawn were prepared. The IgE reactivity pattern was identified by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and immunoblotting technique with the sera of 20 skin prick test (SPT) positive patients. The major allergen identified was then characterized using the proteomics approach involving a combination of two-dimensional (2-DE) electrophoresis, mass spectrometry and bioinformatics tools.
RESULTS: SDS-PAGE of the raw extract showed 23 protein bands (15-250 kDa) but those ranging from 40 to 100 kDa were not found in the cooked extract. From immunoblotting experiments, raw and cooked extracts demonstrated 11 and 5 IgE-binding proteins, respectively, with a molecular mass ranging from 15 to 155 kDa. A heat-resistant 36 kDa protein was identified as the major allergen of both extracts. In addition, a 42 kDa heat-sensitive protein was shown to be a major allergen of the raw extract. The 2-DE gel fractionated the prawn proteins to more than 50 different protein spots. Of these, 10 spots showed specific IgE reactivity with patients' sera. Matrix assisted laser desorption/ionization-time of flight (MALDI-TOF) analysis led to identification of 2 important allergens, tropomyosin and arginine kinase.
CONCLUSIONS: It can be concluded that the availability of such allergens would help in component-based diagnosis and therapy of prawn allergies.
METHODS: Data from the Malaysian Epidemiological Investigation of Rheumatoid Arthritis population-based case-control study involving 910 female early RA cases and 910 female age-matched controls were analysed. Self-reported information on ever/never occupationally exposed to textile dust was used to estimate the risk of developing anti-citrullinated protein antibody (ACPA)-positive and ACPA-negative RA. Interaction between textile dust and the human leucocyte antigen DR β-1 (HLA-DRB1) shared epitope (SE) was evaluated by calculating the attributable proportion due to interaction (AP), with 95% CI.
RESULTS: Occupational exposure to textile dust was significantly associated with an increased risk of developing RA in the Malaysian female population (OR 2.8, 95% CI 1.6 to 5.2). The association between occupational exposure to textile dust and risk of RA was uniformly observed for the ACPA-positive RA (OR 2.5, 95% CI 1.3 to 4.8) and ACPA-negative RA (OR 3.5, 95% CI 1.7 to 7.0) subsets, respectively. We observed a significant interaction between exposure to occupational textile dust and HLA-DRB1 SE alleles regarding the risk of ACPA-positive RA (OR for double exposed: 39.1, 95% CI 5.1 to 297.5; AP: 0.8, 95% CI 0.5 to 1.2).
CONCLUSIONS: This is the first study demonstrating that textile dust exposure is associated with an increased risk for RA. In addition, a gene-environment interaction between HLA-DRB1 SE and textile dust exposure provides a high risk for ACPA-positive RA.
METHODS: A total of 160 serum samples (Discovery, n = 60 and Validation, n = 100) of obese and lean individuals with stable Body Mass Index (BMI) values were retrieved from The Malaysian Cohort biobank. Metabolic profiles were obtained using LC-MS/Q-TOF in dual-polarity mode. Metabolites were identified using a molecular feature and chemical formula algorithm, followed by a differential analysis using MetaboAnalyst 5.0. Validation of potential metabolites was conducted by assessing their presence through collision-induced dissociation (CID) using a targeted tandem MS approach.
RESULTS: A total of 85 significantly differentially expressed metabolites (p-value <0.05; -1.5 < FC > 1.5) were identified between the lean and the obese individuals, with the lipid class being the most prominent. A stepwise logistic regression revealed three metabolites associated with increased risk of obesity (14-methylheptadecanoic acid, 4'-apo-beta,psi-caroten-4'al and 6E,9E-octadecadienoic acid), and three with lower risk of obesity (19:0(11Me), 7,8-Dihydro-3b,6a-dihydroxy-alpha-ionol 9-[apiosyl-(1->6)-glucoside] and 4Z-Decenyl acetate). The model exhibited outstanding performance with an AUC value of 0.95. The predictive model underwent evaluation across four machine learning algorithms consistently demonstrated the highest predictive accuracy of 0.821, aligning with the findings from the classical logistic regression statistical model. Notably, the presence of 4'-apo-beta,psi-caroten-4'-al showed a statistically significant difference between the lean and obese individuals among the metabolites included in the model.
CONCLUSIONS: Our findings highlight the significance of lipids in obesity-related metabolic alterations, providing insights into the pathophysiological mechanisms contributing to obesity. This underscores their potential as biomarkers for metabolic dysregulation associated with obesity.