Research

Mass spectrometry -based identification and quantification of posttranslational modification citrullination in rheumatoid arthritis

Abstract

Post-translational modifications (PTMs) of proteins and peptides modulate their behavior and are frequently implicated to physiological functions in a normal state, and to human diseases in aberrant states. Hence, PTMs, in general, are routinely tracked as disease markers or molecular targets for developing target-specific therapies. The PTM citrullination and rheumatoid arthritis (RA) are frequently interlinked processes. Citrullination is an inflammation-dependent phenomenon, so it is not surprising that this modification underlies many autoimmune diseases. Analysis of the biological effects of these citrullinated proteins has started to reveal the true pathological roles of this particular PTM and looks promising in revolutionizing the treatment of this autoimmune disease. Various studies have revealed the presence of many citrullinated proteins in the synovial joints of the disease-active patients. Despite being citrullinated, not all the citrullinated proteins respond similarly, but on the contrary, have distinct roles to the same stimulus. However, confident identification of citrullination is quite challenging. Hence, highresolution mass spectrometry-based identification and analysis are required for sensitive and specific detection of PTMs such as citrullination. Here, this thesis study was done to develop a strategy for the identification and relative quantification of citrullination in complex biological samples from RA patients. In manuscript 1, identification and relative quantification of citrullinated peptides from fibrinogen have been performed in samples from four disease-active patients showing different degrees of inflammation and other disease characteristics. In order to gauge the feasibility to identify the citrullination sites in vivo, we also attempted to identify the corresponding citrullination sites in fibrinogen in vitro after treatment with human recombinant peptidylarginine deiminase 2 (PAD2). Generally, high disease-activity patients exhibited an increased number of identified citrullination sites and higher relative degree of citrullination. Twenty-three citrullination sites were identified in in vivo samples, of which 14 have not been reported in previous studies. Sites α84, α123, α129, α547, α573, α591, β334 and γ134 were identified in more than one disease-active patient, therefore these sites were regarded as hotspots. Overall, 48 citrullination sites were identified in this study following citrullination of fibrinogen in vitro using PAD2, of which six citrullination sites were found to be unreported before. Twenty-one out of the 23 citrullination sites identified in vivo were also detected in vitro, supporting the validity of the identifications. In manuscript 2, identification and quantification of citrullination sites were extended to the entire synovial fluid citrullinome of the same four RA patients. This study was done to find out which proteins are present in citrullinated form and which proteins were citrullinated at high levels and at which sites the citrullination was observed. Overall, 171 citrullinated proteins of which 48 were found to be reported in previous studies and 276 citrullination sites were identified in disease-active synovial fluid (SF) samples. Thirteen proteins were found common in all patient samples, which may be of immunological relevance and may behave as autoantigens in RA. Generally, the citrullination sites and citrullination occupancy was higher in patients with high disease activity, but some peptides displayed higher citrullination occupancy in the patient with the least disease activity than the patients with moderate to high disease activity. It was observed that we find higher citrulline occupancy in several sites of SF4 as compared to SF2 and SF3. SF4 displayed a low leukocyte count and the lowest disease activity. Nevertheless, our sample size was too small to determine if high ratios correlate with various disease parameters or not.

Info

Thesis PhD, 2019

UN SDG Classification
DK Main Research Area

    Science/Technology

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