OP0254 CHROMATIN CONFORMATION SIGNATURE ANALYSIS IN EARLY VS LATE SCLERODERMA PHENOTYPES
Authors: M. Galloway, E. Hunter, A. Akoulitchev, S. Vigneswaran, B. Abdi, C. Denton, D. Abraham, R. Stratton
Journal: Annals of the Rheumatic Diseases (2020)

Background: Systemic sclerosis (scleroderma, SSc) is a heterogeneous disease in which clinical outcomes vary widely. Predicting outcomes on an individual basis remains challenging despite progress made through autoantibody analysis and gene expression profiling. Effective targeted therapies are evolving and accurately predicting outcomes is important to enable patient stratification for therapy. Chromatin Conformation Signature (CCS) profiling of peripheral blood for systemic epigenetic deregulations could be used for such a purpose. The EpiSwitch platform offering high throughput and resolution chromosome conformation (3C) capture detects significant regulatory changes in 3D genome architecture and maps long range interaction between distant genomic locations. This then reveals the spatial disposition and physical properties of the chromosome, such as chromatin loops and inter-chromosomal connections, which have a role in network organization and genetic epistasis controlling gene expression. EpiSwitch automated platform has been successfully utilised in patient stratification in RA, MS and other indications. This methodology could be applied to patients with SSc to identify CCS associated with different phenotypes and may ultimately be used to stratify and identify patients into pathogenic subtypes. Objectives: We aimed to determine significant CCSs associated with early and late phenotypes of SSc. Methods: The EpiSwitch-based chromosome conformation capture (3C) method was applied to blood samples from early phenotype, and late phenotype SSc patients. Intact nuclei were isolated from peripheral blood mononuclear cells and subjected to formaldehyde fixation resulting in crosslinking between physically touching segments of the genome via contacts between their DNA bound proteins. For quantification of cross-linking frequencies, the cross linked DNA was digested and then subjected to ligation. Cross-linking was then reversed and individual ligation products detected and quantified by EpiSwitch custom oligo array annotated across the whole genome to the anchoring sites of 3D genome architecture. Results: 7 significant CCSs were found over the HLA-C, HLA-B and TNF regions on Chromosome 6 in the early phenotype. The top 8 pathways for genetic locations associated to the CCSs are shown in Table 1. Table 1. Top 8 pathways for genetic locations associated to significant CCS for the early phenotype. GeneSet 1 Natural Killer cell mediated cytotoxicity 2 Immunoregulatory interations between a lymphoid cell and a non-lymphoid cell 3 Antigen Processing &amp; presentation 4 Phagosome 5 Graft versus host disease 6 Type 1 diabetes mellitus 7 Osteoclast differentiation 8 Class 1 MHC mediated antigen processing &amp; presentation 2 significant CCSs were found centred around the IFNG region of chromosome 12 in the late phenotype. The top 8 pathways for genetic locations associated to significant CCSs are shown in Table 2. Table 2. Top 8 pathways for genetic locations associated to significant CCS for the late phenotype. GeneSet 1 Surfactant metabolism 2 IL12 signalling mediated by STAT4 3 Protein digestion &amp; absorption 4 Calcineruin regulated NFAT dependent transcription in lymphocytes 5 Transcriptional misregulation in cancer 6 Kaposi’s sarcoma associated herpes virus infection 7 IL2 mediated signalling events 8 Inflammatory bowel disease Conclusion: Significant CCSs, as part of 3D genomic regulatory control, and their associated pathways for the genetic locations, were identified in both late and early phenotypes. There were distinct CCSs in the early phenotype compared to the late suggesting the CCSs change as the disease progresses and varies between phenotypes. If CCSs could be linked to each clinically defined subgroup across a SSc cohort they could be used as a biomarker tool to predict outcome and progression in patients. Disclosure of Interests: Megan Galloway: None declared, Ewan Hunter: None declared, Alexandre Akoulitchev: None declared, Shivanee Vigneswaran: None declared, Bahja Abdi: None declared, Christopher Denton Grant/research support from: GlaxoSmithKline, Inventiva, CSF Behring, Consultant of: Roche-Genentech, Actelion, GlaxoSmithKline, Sanofi Aventis, Inventiva, CSL Behring, Boehringer Ingelheim, Bayer, David Abraham: None declared, Richard Stratton: None declared