Fatty acid desaturation and lipoxygenase pathways support trained immunity.

Infections and vaccines can induce enhanced long-term responses in innate immune cells, establishing an innate immunological memory termed trained immunity. Here, we show that monocytes with a trained immunity phenotype, due to exposure to the Bacillus Calmette-Guérin (BCG) vaccine, are characterized by an increased biosynthesis of different lipid mediators (LM) derived from long-chain polyunsaturated fatty acids (PUFA). Pharmacological and genetic approaches show that long-chain PUFA synthesis and lipoxygenase-derived LM are essential for the BCG-induced trained immunity responses of human monocytes. Furthermore, products of 12-lipoxygenase activity increase in monocytes of healthy individuals after BCG vaccination. Grasping the underscoring lipid metabolic pathways contributes to our understanding of trained immunity and may help to identify therapeutic tools and targets for the modulation of innate immune responses.

Identification of Mycobacterium abscessus Subspecies by MALDI-TOF Mass Spectrometry and Machine Learning.

Mycobacterium abscessus is one of the most common and pathogenic nontuberculous mycobacteria (NTM) isolated in clinical laboratories. It consists of three subspecies: M. abscessus subsp. abscessus, M. abscessus subsp. bolletii, and M. abscessus subsp. massiliense. Due to their different antibiotic susceptibility pattern, a rapid and accurate identification method is necessary for their differentiation. Although matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has proven useful for NTM identification, the differentiation of M. abscessus subspecies is challenging. In this study, a collection of 325 clinical isolates of M. abscessus was used for MALDI-TOF MS analysis and for the development of machine learning predictive models based on MALDI-TOF MS protein spectra. Overall, using a random forest model with several confidence criteria (samples by triplicate and similarity values >60%), a total of 96.5% of isolates were correctly identified at the subspecies level. Moreover, an improved model with Spanish isolates was able to identify 88.9% of strains collected in other countries. In addition, differences in culture media, colony morphology, and geographic origin of the strains were evaluated, showing that the latter had an impact on the protein spectra. Finally, after studying all protein peaks previously reported for this species, two novel peaks with potential for subspecies differentiation were found. Therefore, machine learning methodology has proven to be a promising approach for rapid and accurate identification of subspecies of M. abscessus using MALDI-TOF MS.