[Frontiers in Bioscience 16, 2671-2681, June 1, 2011]

Serum biomarker of diabetic peripheral neuropathy indentified by differential proteomics

Wei Tang1, Yong-quan Shi1, Jun-jie Zou1, Xiang-fang Chen1, Jiao-yang Zheng1, Shu-wei Zhao2, Zhi-min Liu1

1Department of Endocrinology, Changzheng Hospital, Second Military Medical University, Shanghai 200003, China, 2Department of Otolaryngology, Changzheng Hospital, Second Military Medical University, Shanghai 200003, China

TABLE OF CONTENTS

1. Abstract
2. Introduction
3. Materials and methods
3.1. Patients and blood sample
3.2. Blood sample preparation
3.3. Quality control and standards detection for MALDI-TOF-MS
3.4. Mass spectrometry analysis to profiling serum proteome
3.5. Statistical methods, evaluation of diagnostic efficacy
3.6. Identification of protein markers
4. Results
4.1. System stability and experimental reproducibility were ensured through the use of standards and standard serum
4.2. Differentiation of peptides selected out between DM, DPN and CON groups
4.3. Establishment of Predicting Model
4.4. Identification of markers
5. Discussion
6. Acknowledgements
7. References

1. ABSTRACT

At least one in four diabetic patients is affected by peripheral neuropathy. In this study, the MALDI-TOF-MS mass spectra of peptides and proteins were generated following WCX CLINPROT bead fractionation of 39 diabetic peripheral neuropathy (DPN), 39 diabetes mellitus (DM), and 35 control (CON) serum samples. The spectra were analyzed statistically using flexAnalysisTM and Clin-ProtTM bioinformatics software. Identification of the selected markers was performed and affinity bead-purified plasma protein was subjected to LTQ Orbitrap XL MS/MS analysis followed by Mascot identification of the peptide sequences. 89 differentially expressed peaks of serum proteins were identified. 17, 10 and 4 most significant peaks between CON vs. DM, CON vs. DPN, DM vs. DPN, respectively, were selected out using the ClinProTool software package and used to train a Supervised Neural Network. A veracity rate of 100% was obtained for all sets. Following this analysis, a 6631-Da marker was identified as a fragment of the Apolipoprotein C-I precursor. The peptides identified may have clinical utility as surrogate markers for detection and classification of DM and DPN.