The role of annexin A1 in Candida albicans and Candida auris infections in murine neutrophils.

Annexin A1 (AnxA1) is an anti-inflammatory protein expressed in various cell types, especially macrophages and neutrophils. Because neutrophils play important roles in infections and inflammatory processes and the relationship between AnxA1 and Candida spp. infections is not well-understood, our study examined whether AnxA1 can serve as a target protein for the regulation of the immune response during fungal infections. C57BL/6 wild-type (WT) and AnxA1 knockout (AnxA1-/-) peritoneal neutrophils were coinfected with Candida albicans or Candida auris for 4 h. AnxA1-/- neutrophils exhibited a marked increase in cyclooxygenase 2 (COX-2), phosphorylated extracellular signal-related kinase (ERK), p-38, and c-Jun N-terminal kinase (JNK) levels after coinfection with both Candida spp. A lipidomics approach showed that AnxA1 deficiency produced marked differences in the supernatant lipid profiles of both control neutrophils and neutrophils coinfected with Candida spp. compared with WT cells, especially the levels of glycerophospholipids and glycerolipids. Our results showed that endogenous AnxA1 regulates the neutrophil response under fungal infection conditions, altering lipid membrane organization and metabolism.

Gas chromatography-mass spectrometry untargeted profiling of non-Hodgkin's lymphoma urinary metabolite markers.

Non-Hodgkin's lymphoma (NHL) is a cancer of the lymphatic system where the lymphoid and hematopoietic tissues are infiltrated by malignant neoplasms of B, T, and natural killer lymphocytes. Effective and less invasive methods for NHL screening are urgently needed. Herein, we report an untargeted gas chromatography-mass spectrometry (GC-MS) method to investigate metabolic changes in non-volatile derivatized compounds from urine samples of NHL patients (N = 15) and compare them to healthy controls (N = 34). Uni- and multivariate data analysis showed 18 endogenous metabolites, including amino acids and their metabolites, sugars, small organic acids, and vitamins, as statistically significant for group differentiation. A receiver operating characteristic curve (ROC) generated from a support vector machine (SVM) algorithm-based model achieved 0.998 of predictive accuracy, displaying the potential and relevance of GC-MS-detected urinary non-volatile compounds for predictive purposes. Furthermore, a specific panel of key metabolites was also evaluated, showing similar results. All in all, our results indicate that this robust GC-MS method is an effective screening tool for NHL diagnosis and it is able to highlight different pathways of the disease. Graphical Abstract.