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Fig 3
a Network shapes. Figure legend for the IPA network. https://analysis.ingenuity.com/pa/info/help/help.htm#legend.htm . b Relationships. Figure legend for the molecular relationships in the IPA network. https://analysis.ingenuity.com/pa/info/help/help.htm#legend.htm
Figures 2 and 3a shows one of the largest protein clusters that were generated by the pathway analysis of the protein ratios between N/DMN.
by Overgaard, Anne Julie; Thingholm, Tine E.; Larsen, Martin R.; Tarnow, Lise; Rossing, Peter; McGuire, James N.; Pociot, FlemmingJournal: Clinical Proteomics Vol. 6 Issue 4DOI: 10.1007/s12014-010-9053-0Published: 2010-11-02Institution(s): Hagedorn Research Institute, Novo Nordisk A/S, University of Southern Denmark, Steno Diabetes Center, University of Lund
Abstract
As part of a clinical proteomics programme focused on diabetes and its complications, it was our goal to investigate the proteome of plasma in order to find improved candidate biomarkers to predict diabetic nephropathy.
Proteins derived from plasma from a cross-sectional cohort of 123 type 1 diabetic patients previously diagnosed as normoalbuminuric, microalbuminuric or macroalbuminuric were enriched with hexapeptide library beads and subsequently pooled within three groups. Proteins from the three groups were compared by online liquid chromatography and tandem mass spectrometry in three identical repetitions using isobaric mass tags (iTRAQ). The results were further analysed with ingenuity pathway analysis. Levels of apolipoprotein A1, A2, B, C3, E and J were analysed and validated by a multiplex immunoassay in 20 type 1 diabetic patients with macroalbuminuria and 10 with normoalbuminuria.
A total of 112 proteins were identified in at least two out of three replicates. The global protein ratios were further evaluated by ingenuity pathway analysis, resulting in the recognition of apolipoprotein A2, B, C3, D and E as key nodes in the top-rated network. The multiplex immunoassay confirmed the overall protein expression patterns observed by the iTRAQ analysis.
The candidate biomarkers discovered in this cross-sectional cohort may turn out to be progression biomarkers and might have several clinical applications in the treatment and monitoring of diabetic nephropathy; however, they need to be confirmed in a longitudinal cohort.
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