Abstract
Schizophrenia is a severe mental illness with a biological basis. However, the search for reliable biomarkers suitable for clinical routine has been futile so far. Accordingly, there is a need for innovative approaches such as genomics and proteomics to achieve this goal. In the present study, we compared metabolomic and proteomic data from 26 schizophrenia patients as well as from unaffected controls carefully matched for age and gender in a multi-platform approach. The combined analysis identified many signatures with initially good biomarker characteristics. After statistical analysis and comparison of these identified serum metabolites (analysed by Gas Chromatography Mass Spectrometry) and hydrophobic serum proteins (analysed by matrix-assisted laser desorption ionisation mass spectrometry), several markers (e.g., 2-piperidinec carboxylic acid, 6-deoxy-mannofuranose, galactoseoxime and a serum peptide of m/z 3177) were determined as having the best discriminating value between the groups. Our findings represent a proof of principle indicating that metabolomic and proteomic approaches can be successfully used in psychiatric biomarker research, even though the results should be regarded as preliminary with a need for replication in larger samples.
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Acknowledgments
This work was funded by the Kuwait Cultural Office. The authors wish to acknowledge the EPSRC for allowing access to mass spectrometry equipment for the undertaking of the work and instructive support.
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K. Al Awam and I. S. Haußleiter contributed equally to this work.
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702_2014_1224_MOESM1_ESM.docx
Supplementary Figure 1. a) Overlaid average FTIR spectra of protein-depleted serum from schizophrenia patients (dotted line) and control subjects (solid line). b) PCA analysis using generated FTIR showing very little differentiation of patients and control subjects. Supplementary Figure 2. a) Comparative GCMS lipid profile from a control subject sample (upper trace) and a patient sample (lower trace). b) PCA analysis using the statistically different lipid species identified by GCMS showing some distinction between the two cohorts. (DOCX 161 kb)
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Al Awam, K., Haußleiter, I.S., Dudley, E. et al. Multiplatform metabolome and proteome profiling identifies serum metabolite and protein signatures as prospective biomarkers for schizophrenia. J Neural Transm 122 (Suppl 1), 111–122 (2015). https://doi.org/10.1007/s00702-014-1224-0
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DOI: https://doi.org/10.1007/s00702-014-1224-0