Comparative analysis of blood protein fractions in two mediterranean farmed fish: Dicentrarchus labrax and Sparus aurata.

Total protein levels in fish are widely used in health and welfare studies, providing a simple and accessible measure. However, the multifaceted role of blood proteins makes it sometimes challenging to link total protein content to specific health issues, while specific protein fractions may offer more precise insights into fish biology and health, particularly in farmed fish species where such data is often lacking. Data were gathered from two experiments involving Dicentrarchus labrax and Sparus aurata, key species in European marine aquaculture. The aim was (1) to assess how different globulin fractions contribute to total protein content in blood and (2) how these contributions vary across different sampling times in healthy animals. In D. labrax, the beta1 globulin fraction emerged as the major contributor (34.16%), followed by albumin and alpha2 globulins (18.24% and 16.41%, respectively). In contrast, pre-albumins and alpha1 fractions had the least contribution (5.49% and 7.71%). S. aurata exhibited albumin as the primary contributor (23.39%), followed by beta1 and alpha2 globulins (19.71% and 19.15%, respectively), with gamma and alpha1 fractions contributing the least (5.34% and 8.63%). Notably, the study revealed relatively stable contributions of globulin fractions to total proteins within both species, albeit with minor variations over time, potentially linked to environmental and individual factors. Furthermore, larger fish displayed higher total protein levels. This research underscores the need for further investigation into the diverse factors influencing globulin contributions to total proteins, ultimately enhancing health and welfare monitoring for farmed fish species.

Assessing direct analysis in real-time mass spectrometry for the identification and serotyping of Legionella pneumophila.

AIMS: The efficacy of ambient mass spectrometry to identify and serotype Legionella pneumophila was assessed. To this aim, isolated waterborne colonies were submitted to a rapid extraction method and analysed by direct analysis in real-time mass spectrometry (DART-HRMS). METHODS AND RESULTS: The DART-HRMS profiles, coupled with partial least squares discriminant analysis (PLS-DA), were first evaluated for their ability to differentiate Legionella spp. from other bacteria. The resultant classification model achieved an accuracy of 98.1% on validation. Capitalising on these encouraging results, DART-HRMS profiling was explored as an alternative approach for the identification of L. pneumophila sg. 1, L. pneumophila sg. 2-15 and L. non-pneumophila; therefore, a different PLS-DA classifier was built. When tested on a validation set, this second classifier reached an overall accuracy of 95.93%. It identified the harmful L. pneumophila sg. 1 with an impressive specificity (100%) and slightly lower sensitivity (91.7%), and similar performances were reached in the classification of L. pneumophila sg. 2-15 and L. non-pneumophila. CONCLUSIONS: The results of this study show the DART-HMRS method has good accuracy, and it is an effective method for Legionella serogroup profiling. SIGNIFICANCE AND IMPACT OF THE STUDY: These preliminary findings could open a new avenue for the rapid identification and quick epidemiologic tracing of L. pneumophila, with a consequent improvement to risk assessment.