POS0241 GENOME-WIDE METHYLATION PROFILES OF PERIPHERAL WHOLE BLOOD IN NEWLY DIAGNOSED GIANT CELL ARTERITIS OR POLYMYALGIA RHEUMATICA
Authors: M. Fröhlich, M. Gernert, O. Gadeholt, H. Einsele, P. P. Strunz, T. Bley, S. E. Serfling, R. A. Werner, K. V. Guggenberger, M. Dittrich, M. Schmalzing, T. Mueller
Journal: Annals of the Rheumatic Diseases (2024)

<h3>Background:</h3> Epigenetic mechanisms are of increasing interest due to their role in regulating DNA transcription and can provide further insight into pathogenetic pathways and genes involved. Giant cell arteritis (GCA) and polymyalgia rheumatica (PMR) are considered parts of a common spectrum, called GCA PMR Spectrum Diseases (GPSD) [1]. Data on epigenetic mechanisms in GPSD are scare. <i>Coit et al.</i> have already shown that there are differences in the DNA methylation profile between GCA and healthy controls [2]. However, no data are available to date on the question of whether differences can be detected at the epigenetic level between the predominant GPSD phenotypes GCA and PMR. <h3>Objectives:</h3> Our aim was therefore to directly compare DNA methylation in GCA and PMR. In contrast to most previous studies, we did not restrict our study to specific cell subtypes when analyzing methylation, but used peripheral whole blood, due to its higher everyday practicability. <h3>Methods:</h3> Methylation profiles were obtained using MethylationEPIC v2.0 BeadChip arrays (Infinium®) from peripheral whole blood of 18 treatment-naïve patients with confirmed GCA or PMR. The total methylation level of 850,000 methylations sites (CpG) throughout the human genome were tested. For differentially methylated site analysis, we used the statistical framework R vers 4.3 and the Bioconductor packages. To estimate blood cell composition, we used the FlowSorted.Blood.EPIC R package and flow cytometry. Quality filtering and array normalization were performed using functions of the minfi R package. The differentially methylated CpG sites were identified by modelling the M values with a linear model adjusting for gender, age, and cell composition. Based on the set of significant CpGs enriched KEGG pathways were identified. Finally, we identified differentially methylated regions (DMRs), which might functionally more relevant and transcriptionally more interesting. <h3>Results:</h3> A total of 811,593 CpGs passed the quality filtering. Only CpGs that showed highly significant differential methylation were selected for further analysis (p&lt;0.001). After adjustment for gender, age, diagnosis (GCA/PMR) and several cell fractions (CD8+ T-cells, Monocytes, Granulocytes) 1,272 CpG sites remained as highly significant (p&lt;0.001). Of note, the overlap of highly significant CpGs between GCA/PMR and age or gender as potentially confounding factors was low (&lt;5%), indicating good discrimination for disease-specific methylation alterations. Finally, a genomic mapping via KEGG for the highly significant CpGs showed pathways that are significantly different between GCA and PMR such as pathways for NF-kappa B signaling or TH17 cell differentiation. <h3>Conclusion:</h3> For the first time, we were able to show that GCA and PMR show significant differences at the epigenetic level, which opens up the possibility of better stratifying and distinguishing the different phenotypes within the GPSD. Furthermore, we were able to show that peripheral whole blood is suitable for the analysis of genome-wide methylation profiles. This makes DNA methylation analysis a feasible tool in clinical practice and a potential new biomarker. <h3>REFERENCES:</h3> [1] Tomelleri, A., et al., <i>Disease stratification in GCA and PMR: state of the art and future perspectives.</i> Nat Rev Rheumatol, 2023. <b>19</b>(7): p. 446-459. [2] Coit, P., et al., <i>DNA methylation analysis of the temporal artery microenvironment in giant cell arteritis.</i> Ann Rheum Dis, 2016. <b>75</b>(6): p. 1196-202. <h3>Acknowledgements:</h3> <b>NIL.</b> <h3>Disclosure of Interests:</h3> Matthias Fröhlich: None declared, Michael Gernert: None declared, Ottar Gadeholt: None declared, Hermann Einsele: None declared, Patrick P. Strunz: None declared, Torsten Bley: None declared, Sebastian E. Serfling: None declared, Rudolf A. Werner: None declared, Konstanze V. Guggenberger: None declared, Marcus Dittrich: None declared, Marc Schmalzing BMS, Novartis, AbbVie, AstraZeneca, Chugai/Roche, Janssen-Cilag, Gilead, Boehringer/Ingelheim, Mylan, onkowissen.de, Galapagos, EUSA-Pharma, Chugai/Roche, Hexal/Sandoz, Gilead, AbbVie, Janssen-Cilag, Boehringer/Ingelheim, onkowissen.de, EUSA-Pharma, Novartis, AstraZeneca, Amgen, medac, Lilly, Galapagos, UCB, Chugai, Novartis, Tobias Mueller: None declared.