Application and integration of bioinformatic strategies towards central and peripheral proteomic profiling for diagnosis and drug discovery in schizophrenia

Proteomic profiling studies of schizophrenia have the potential to shed further light on this debilitating and poorly understood condition which affects up to 1% of the world’s population. However, recent studies suggest that the field of proteomics in general has been hindered by poor application of bioinformatic strategies, contributing to the failure of many findings to validate. In the context of schizophrenia research, there is therefore a need for a more robust application and integration of existing statistical approaches to proteomic datasets, as well as the development of new methodologies to offer solutions to current challenges. The aims of this thesis were multi-fold. Many studies have stipulated the need for new diagnostic and prognostic strategies to aid psychiatrists, particularly in predicting disease conversion from the prodromal phase. Proteomic data from serum samples was used to investigate the potential for statistical models based on biomarker panels to offer a new and clinically relevant approach. Models were trained based on either differential protein (chapter 3) or peptide (chapter 4) expression levels between schizophrenia patients and controls, as measured through multiplex immunoassay or targeted mass spectrometry technologies. In chapter 4, an SVM model based on 21 peptides was identified that is both highly sensitive and specific as a diagnostic and prognostic tool in symptomatic individuals. Furthermore, in recent years, few preclinical innovations have been made in schizophrenia research in either in vitro or in vivo studies, resulting in a standstill in the development of treatments. In chapters 5 and 6 of this thesis, proteomic information from a novel cellular model of schizophrenia was analyzed. In chapter 5, cell signalling alterations in vitro were identified which may underpin dysfunctional microglial activation in at least a subgroup of patients, thus representing new drug targets in the CNS. Subsequent analysis identified compounds which have the potential to ameliorate the observed changes. Lastly, in chapters 7 and 8, a novel systems biology methodology was developed for the functional comparison of proteomic changes in brain tissue from existing preclinical rodent models of psychiatric disorders to those in human post-mortem samples, providing a new means of overcoming some of the translational hurdles of preclinical psychiatric research. The application of different bioinformatic strategies to a range of proteomic datasets in this thesis has yielded a number of findings which have enhanced the understanding of schizophrenia pathophysiology and provide a platform for future studies towards the goal of improving outcomes for patients affected by this disorder.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2018

Author dc:creator

• Cox, David Alan

Advisor dc:contributor.advisor

• Bahn, Sabine

• Schizophrenia
• Proteomics
• Bioinformatics

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/c70b551a-9864-4cc0-8099-974acb809eb2/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

en

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.25343

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/278018