Establishment of urinary proteome and functional analysis of urinary differential proteins in patients with metabolic syndrome / 中华临床营养杂志

ObjectiveTo establish the urinary proteome profile of the metabolic syndrome ( MetS ) patients,compare the different urinary proteins between the MetS patients and the normal individuals,and analyze the function of the different proteins,so as to explore the pathogenesis of MetS.MethodsOvernight urine were collected from normal controls (n =6) and MetS patients ( n =6).Acetone precipitation method was used to precipitate proteins of urine.Intra-group proteins were mixed together,identified by reversed phase liquid chromatography-mass spectrometry/mass spectrometry and quantified relatively using spectral counting method.The functions of differential proteins were analyzed using Panther.ResultsA total of 807 and 630 proteins were identified respectively in normal controls and MetS patients.Comparing MetS patients with normal controls,sixty different proteins were found,of which 23 proteins were up-regulated and 37 proteins were down-regulated in MetS patients.In the up-regulated proteins,plasminogen was involved in the plasminogen activation cascade and isoform of alphaenolase,phosphoglycerate kinase 1 and fructose-bisphosphate aldolase B down-regulated in MetS patients were involved in the process of glycolysis and fructose metabolism.ConclusionsThe urinary proteome profile of patients with MetS was established by reversed phase liquid chromatography-mass spectrometry/mass spectrometry.Different proteins between MetS patients and normal people were identified.The plasminogen activation cascade,glycolysis and fructose metabolism play key roles in the pathogenesis of MetS.

Identifying differences in protein expression levels by spectral counting and feature selection

Spectral counting is a strategy to quantify relative protein concentrations in pre-digested protein mixtures analyzed by liquid chromatography online with tandem mass spectrometry. In the present study, we used combinations of normalization and statistical (feature selection) methods on spectral counting data to verify whether we could pinpoint which and how many proteins were differentially expressed when comparing complex protein mixtures. These combinations were evaluated on real, but controlled, experiments (yeast lysates were spiked with protein markers at different concentrations to simulate differences), which were therefore verifiable. The following normalization methods were applied: total signal, Z-normalization, hybrid normalization, and log preprocessing. The feature selection methods were: the Golub index, the Student t-test, a strategy based on the weighting used in a forward-support vector machine (SVM-F) model, and SVM recursive feature elimination. The results showed that Z-normalization combined with SVM-F correctly identified which and how many protein markers were added to the yeast lysates for all different concentrations. The software we used is available at http://pcarvalho.com/patternlab.