AB1064 Exploring cerebrospinal fluid proteome in fibromyalgia
Authors: E. Ossipova, P. Emami Khoonsari, J. Lengqvist, E. Kosek, D. Kadetoff, P.-J. Jakobsson, K. Kultima, J. Lampa
Journal: Annals of the Rheumatic Diseases (2018)

<h3>Background</h3> Fibromyalgia (FM) is a heterogeneous disease of unknown etiology characterised by chronic widespread pain that affects up to 4% of population. Overlapping and heterogeneous symptoms of various chronic pain conditions complicate their diagnosis, emphasising the need for more specific biomarkers to improve the diagnosis and understand the disease mechanisms. Cerebrospinal fluid (CSF) flows in the ventricles within the brain and diffuses over the brain and spinal cord. Due to direct contact of CSF with CNS, content of CSF reflects biochemical changes in CNS making it an excellent source for biomarker discovery. <h3>Objectives</h3> In current study we aim to explore CSF proteome of FM patients utilising quantitative proteomics method based on stable isotope labelling of CSF peptides combined with multivariate data analysis (MVDA) in order to monitor the dynamics of the proteome while comparing to the CSF proteomes in patients with rheumatoid arthritis (RA) and other neurological diseases (OND) and define the potential biomarker candidates in FM. We also investigate, which protein products have been found in human CSF with respect to known "pain" genes,<sup>1</sup> human CSF proteome explored if these proteins represent any clear subgrouping of "pain proteins". <h3>Methods</h3> CSF samples from patients with FM, RA and control OND group were collected by lumbar puncture and equal aliquots were subsequently reduced, alkylated and digested by trypsin. Obtained peptides were labelled by stable isotopes and mixed prior sample fractionation. The degree of sample complexity was reduced by off-line peptide separation using HPLC instrumentation. Obtained 80 peptide fractions were combined into 10 fractions across the gradient area. Fractions were analysed by LC-MS/MS, proteins in acquired data were identified and quantified, and data was analysed using MVDA. <h3>Results</h3> Out of the 1422 proteins identified, 855 proteins were included in the quantitative data analysis. Comparing FM, RA and OND groups to each other using univariate testing we found 53 statistical significant proteins (q-value &lt;0.05). Six out of these have been reported as "pain proteins" (Complement C4-A, Prostaglandin-H2 d-isomerase, Apolipoprotein D, Granulins, Pro-cathepsin H, and BMP and activin membrane-bound inhibitor homolog). <h3>Conclusions</h3> We have employed quantitative proteomics methods combined with extensive bioinformatics data analysis to investigate differences in proteome profiles in CSFs obtained from patients with FM, and identified six differentially expressed pain proteins of various functions in CSF of FM patients. The involvement of these proteins in the disease pathogenesis as well use of the identified proteins as potential biomarkers should be investigated. <h3>Reference</h3> [1] Ultsch A, Kringel D, Kalso E, Mogil JS, Lötsch J. A data science approach to candidate gene selection of pain regarded as a process of learning and neural plasticity. Pain. 2016. <h3>Acknowledgements</h3> The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007 – 2013) under grant agreement no 6 02 919 (GLORIA), Swedish Medical Research Council, Åke Wiberg Foundation and Magnus Bergvalls Foundation and Uppsala Berzelii Technology Centre for Neurodiagnostics, with financing from the Swedish Governmental Agency for Innovation Systems. <h3>Disclosure of Interest</h3> None declared