PTEN functions to 'prioritize' chemotactic cues and prevent 'distraction' in migrating neutrophils.

Neutrophils encounter and 'prioritize' many chemoattractants in their pursuit of bacteria. Here we tested the possibility that the phosphatase PTEN is responsible for the prioritization of chemoattractants. Neutrophils induced chemotaxis by two separate pathways, the phosphatidylinositol-3-OH kinase (PI(3)K) phosphatase and tensin homolog (PTEN) pathway, and the p38 mitogen-activated protein kinase pathway, with the p38 pathway dominating over the PI(3)K pathway. Pten(-/-) neutrophils could not prioritize chemoattractants and were 'distracted' by chemokines when moving toward bacterial chemoattractants. In opposing gradients, PTEN became distributed throughout the cell circumference, which inhibited all PI(3)K activity, thus permitting 'preferential' migration toward bacterial products via phospholipase A(2) and p38. Such prioritization was defective in Pten(-/-) neutrophils, which resulted in defective bacterial clearance in vivo. Our data identify a PTEN-dependent mechanism in neutrophils to prioritize, 'triage' and integrate responses to multiple chemotactic cues.

An inflammatory arthritis-associated metabolite biomarker pattern revealed by 1H NMR spectroscopy.

Rheumatoid arthritis, a debilitating, systemic inflammatory joint disease, is likely accompanied by alterations in circulating metabolites. Here, an 1H NMR spectroscopy-based metabolomics approach was developed to establish a metabolic 'biomarker pattern' in a model of rheumatoid arthritis, the K/BxN transgenic mouse. Sera obtained from arthritic K/BxN mice (N = 15) and a control population (N = 19) having the same genetic background, but lacking the arthritogenic T-cell receptor KRN transgene, were compared by 1H NMR spectroscopy. A unique method was developed by combining technologies such as ultrafiltration to remove proteins from serum samples, quantitative 'targeted profiling' of known metabolites, pseudo-quantitative profiling of unknown resonances, a supervised O-PLS-DA pattern recognition analysis, and a metabolic-pathway based network analysis for interpretation of results. In total, 88 spectral features were profiled (59 metabolites and 28 unknown resonances). A highly significant subset of 18 spectral features (15 known compounds and 3 unknown resonances) was identified (p = 0.00075 using MANOVA) that we term a 'metabolic bioprofile'. We identified metabolites relating to nucleic acid, amino acid, and fatty acid metabolism, as well as lipolysis, reactive oxygen species generation, and methylation. Pathway analysis suggested a shift from metabolites involved in numerous reactions (hub-metabolites) toward intermediates and metabolic endpoints associated with arthritis. The results attest to the metabolic complexity of systemic inflammation and to the power of the experimental approach for identifying a wide variety of disease-associated marker candidates. The diagnostic and prognostic implications of monitoring a spectrum of metabolic events simultaneously using serum samples is discussed with respect to the potential for individualized medicine.