Browsing by Subject "biomarker discovery"

Now showing 1 - 2 of 2
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    Association of Plasma CD163 Concentration with De Novo–Onset Chronic Graft-versus-Host Disease
    (Elsevier, 2017) Inamoto, Yoshihiro; Martin, Paul J.; Paczesny, Sophie; Tabellini, Laura; Momin, Amin A.; Mumaw, Christen L.; Flowers, Mary E. D.; Lee, Stephanie J.; Carpenter, Paul A.; Storer, Barry E.; Hanash, Samir; Hansen, John A.; Department of Pediatrics, IU School of Medicine
    Chronic graft-versus-host disease (GVHD) is the leading cause of long-term morbidity and mortality after allogeneic hematopoietic cell transplantation. To identify prognostic plasma proteins associated with de novo– or quiescent-onset chronic GVHD (cGVHD), we performed a discovery and validation proteomic study. The total study cohort included 167 consecutive patients who had no clinical evidence of GVHD under minimum glucocorticoid administration and had available plasma samples obtained at 80 ± 14 days after transplantation. We first used high-throughput mass spectrometry to screen pooled plasma using 20 cases with subsequent cGVHD and 20 controls without it, and we identified 20 candidate proteins. We then measured 12 of the 20 candidate proteins by ELISA on the same individual samples and identified 4 proteins for further verification (LGALS3BP, CD5L, CD163, and TXN for de novo onset, and LGALS3BP and CD5L for quiescent onset). The verification cohort included 127 remaining patients. The cumulative incidence of de novo–onset cGVHD was higher in patients with higher plasma soluble CD163 concentrations at day 80 than those with lower concentrations (75% versus 40%, P = .018). The cumulative incidence of de novo– or quiescent-onset cGVHD did not differ statistically according to concentrations of the 3 other proteins at day 80. CD163 is a macrophage scavenger receptor and is elevated in oxidative conditions. These results suggest that monocyte or macrophage activation or increased oxidative stress may contribute to the pathogenesis of cGVHD.
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    An integrated proteomics analysis of bone tissues in response to mechanical stimulation
    (2010-07) Li, Jillian; Zhang, Fan; Chen, Jake Yue
    Bone cells can sense physical forces and convert mechanical stimulation conditions into biochemical signals that lead to expression of mechanically sensitive genes and proteins. However, it is still poorly understood how genes and proteins in bone cells are orchestrated to respond to mechanical stimulations. In this research, we applied integrated proteomics, statistical, and network biology techniques to study proteome-level changes to bone tissue cells in response to two different conditions, normal loading and fatigue loading. We harvested ulna midshafts and isolated proteins from the control, loaded, and fatigue loaded Rats. Using a label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) experimental proteomics technique, we derived a comprehensive list of 1,058 proteins that are differentially expressed among normal loading, fatigue loading, and controls. By carefully developing protein selection filters and statistical models, we were able to identify 42 proteins representing 21 Rat genes that were significantly associated with bone cells' response to quantitative changes between normal loading and fatigue loading conditions. We further applied network biology techniques by building a fatigue loading activated protein-protein interaction subnetwork involving 9 of the human-homolog counterpart of the 21 rat genes in a large connected network component. Our study shows that the combination of decreased anti-apoptotic factor, Raf1, and increased pro-apoptotic factor, PDCD8, results in significant increase in the number of apoptotic osteocytes following fatigue loading. We believe controlling osteoblast differentiation/proliferation and osteocyte apoptosis could be promising directions for developing future therapeutic solutions for related bone diseases.