Serum Proteome in a Sporadic Amyotrophic Lateral Sclerosis Geographical Cluster

Stefano De Benedetti, Elisabetta Gianazza, Cristina Banfi, Christian Lunetta, Alessandro Marocchi, Silvana Penco, Francesco Bonomi, Stefania Iametti

Research output: Contribution to journalArticle

Abstract

This study is meant to characterize the serum proteome in a small geographical cluster of sporadic ALS subjects originating from a restricted geographical area and sharing the same environmental exposure, in a broader context of evaluating the relevance of environmental factors to disease onset, status, and progression. An Artificial Neural Network based software is used to compare the relative abundance of proteins identified as different (by means of bi-dimensional electrophoresis and mass spectrometry) in the serum proteome of patients and age-matched healthy controls. The patient's group is characterized by altered levels of acute phase reactants and of proteins involved in lipid homeostasis, along with over-representation of the APOE*4 allele. Characterization of the serum proteome in a small cluster of sporadic ALS patients, originating from a geographically restricted area with a high prevalence of the disease and evaluation of the results with software based on artificial neural networks, highlights the association of the relative abundance of some proteins (most notably, acute phase reactants and lipid homeostasis proteins) with the disease presence and status.

Original languageEnglish
Article number1700043
JournalProteomics - Clinical Applications
Volume11
Issue number11-12
DOIs
Publication statusPublished - Dec 1 2017

Keywords

  • Artificial neural network
  • Geographical cluster
  • Serum proteomics
  • Sporadic amyotrophic lateral sclerosis

ASJC Scopus subject areas

  • Clinical Biochemistry

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    De Benedetti, S., Gianazza, E., Banfi, C., Lunetta, C., Marocchi, A., Penco, S., Bonomi, F., & Iametti, S. (2017). Serum Proteome in a Sporadic Amyotrophic Lateral Sclerosis Geographical Cluster. Proteomics - Clinical Applications, 11(11-12), [1700043]. https://doi.org/10.1002/prca.201700043