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San Roman, J. A.

Publications and source records attributed to San Roman, J. A..

2 recordsLinked to original sources

Glycolysis and hexosamine biosynthesis pathways are key for inflammatory protein maturation and leukocyte adhesion to human aortic valve cells

Inflammation and metabolism reprogramming are hallmarks of calcific aortic valve disease (CAVD). Recent studies link inflammation to hyperglycolysis and calcification in valve interstitial cells (VICs). The metabolism of valve endothelial cells (VECs) has received less attention despite both resident valve cells are exposed to alike inflammatory clues involved in the biosynthesis of pathologically relevant glycoproteins during the early stages of CAVD. On this basis, we investigated the outcomes of glucose metabolism rewiring on glycoprotein maturation and immune cell adhesion in human resident valve cells. Real-time metabolic analysis revealed that basal VECs are more glycolytic than VICs. Also, VECs and VICs exposed to inflammatory stimuli exhibited a distinct rewiring, with VECs shifting to a more energetic metabolism, despite a similar upregulation of glycolytic genes. Blunting glucose metabolism in VICs and VECs inhibited inflammatory routes canonically associated with glycolysis, and the expression of proteins associated to the inflammatory response like interleukin-6 and cyclooxigenase-2. Moreover, Western blot and adhesion assays revealed that glycolysis is necessary for the expression and post-translational modifications of intercellular adhesion molecule-1 and vascular cell adhesion molecule-1, and the ensuing process of monocyte-VECs adhesion. Notably, inhibition of the hexosamine biosynthetic pathway using DON and of N-glycosylation by tunicamycin, further disrupted adhesion molecule maturation and monocyte-VECs adhesion. In conclusion, glycolysis and its side-branch route the hexosamine biosynthesis pathway are necessary for nutrient-driven post-translational modifications of inflammatory proteins in inflamed valve cells and the subsequent process of monocyte-VECs adhesion that plays a key role in the initial stages of CAVD pathogenesis. NEW & NOTEWORTHYThe study uncovers a relevant role of glycolysis and its side-branch route the hexosamine biosynthesis pathway in sugar-driven post-translational modifications that are critical for the proper function of leukocyte adhesion molecules and other relevant proinflammatory molecules in aortic valve cells. These events are essential for the recruitment of cells of the monocytic lineage to aortic valve leaflets in the initial stages of calcific aortic valve disease. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC="FIGDIR/small/693904v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1211b22org.highwire.dtl.DTLVardef@7c5df7org.highwire.dtl.DTLVardef@fd0da2org.highwire.dtl.DTLVardef@18a40a1_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

Exploratory and prospective model of stent restenosis after Percutaneous Coronary Intervention using MultivariateGaussian Subspatial Regression

A new statistical analysis methodology, "Multivariate Gaussian Subspatial Regression" (MGSR), has been applied to randomized clinical trial data collected from percutaneous coronary intervention (PCI) patients, which combines the descriptive quality of Factorial Techniques and the predictive power of Gaussian Processes. This model has been built from 3 different quantitative coronary angiographic core-lab measures of the same lesion from 2 separate angiograms (at baseline before PCI, at baseline immediately after PCI and at 12 months follow-up). Measurements of the pre-PCI variables of a patient are mapped to the factorial plane and predictions are visualized as regions of interest in this plane. MGSR makes it possible to detect patients at risk of coronary stent restenosis or patients in whom ruling out the disease, in a graphical way; avoiding unnecessary, costly and possibly risky treatments for patients with no complications predicted; and advising to closely follow patients at risk. In addition, the model recovers missing values regardless of the variables, and once fitted, it corrects itself when more dependent variables are included. MGSR software is freely available online at https://github.com/victorvicpal/MGSR.

bioinformatics↗