Methods for the diagnosis and treatment of autoimmune conditions

Abstract

This disclosure relates to methods of treating an autoimmune disease, e.g., rheumatoid arthritis (RA) in a subject, the methods comprising: determining a disease-associated methylation signature, identifying cell types from a biological sample with the methylation signature, and / or determining target genes that are associated with or regulated by the methylation signature; and administering to the subject a therapeutic agent targeting the methylation signature, identified cell types, and / or the target genes.

Description

[0001] METHODS FOR THE DIAGNOSIS AND TREATMENT OF AUTOIMMUNE CONDITIONS

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority to U.S. Provisional Patent Application No. 63 / 674.559 filed July 23. 2024, and U.S. Provisional Patent Application No. 63 / 702,529, filed October 2, 2024, the contents of which are incorporated herein by reference in their entirety.

[0004] FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0005] This invention was made with Government support under Grant No. AR064194. awarded by the National Institutes of Health. The Government has certain rights in the invention.

[0006] TECHNICAL FIELD

[0007] This document relates to methods for treating autoimmune conditions (e.g.. rheumatoid arthritis). For example, this document relates to methods that include administering, to a subject in need thereof, a therapeutic agent targeting genetic loci having a methylation signature, cell types with the methylation signature, or target genes regulated by the methylation signature.

[0008] BACKGROUND

[0009] Autoimmune conditions are a group of diseases in which the body’s immune system mistakenly attacks its own cells, tissues, and organs in any part of the body, weakening body function and even turning life-threatening. As many as 50 million people in the U.S. have an autoimmune disease, making it the third most prevalent disease category, surpassed only by cancer and heart disease.

[0010] Rheumatoid arthritis (RA) is one of the most prevalent autoimmune diseases, affecting about 1% of the global population. About 18 million people worldwide were living with RA. Untreated. RA can cause severe damage to the joints and their surrounding tissue. It can lead to heart, lung, or nervous system problems. The presence of anti-citrullinated protein antibodies (ACPAs), as detected by the anti- CCP3 assay, without clinical inflammatory arthritis (IA) identifies anti-CCP3+ individuals at risk for developing rheumatoid arthritis (RA). However, it remains unclear why some anti-CCP3+ individuals progress to RA, designated as Pre-RA status, while others do not. There remains a dearth of methods of identifying persons at risk of developing RA and methods of identifying helpful therapeutics.

[0011] SUMMARY

[0012] Provided herein are methods for treatment of an autoimmune condition in a subject, the method comprising (a) isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject (b) determining a methylation signature in one or more cell types from the biological sample; and (c) administering a therapeutic agent to the subject to modulate the methylation signature. In some embodiments, the treatment is a prophylactic treatment of a subject at risk of developing an autoimmune condition.

[0013] Also provided herein are methods for delaying the clinical appearance of an autoimmune condition in a subject, the method comprising (a) isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject; (b) determining a methylation signature in one or more cell types from the biological sample; and (c) administering a therapeutic agent to the subject to modulate the methylation signature.

[0014] As described herein, determining the methylation signature comprises comparing the level of methylation at a genetic locus in the biological sample with a reference level for the genetic locus. The genetic locus in the biological sample is detected using one or more probes comprising cg06609049, cgl 8531004, cg08900191, cg02331830, cgl4640588, cg06027620, cgl8937502, cgl3911068, cgl2983285, cgl7161130, cg27509052, cg08834383. cg20685674. cg02484776. cgl9337854, cg26559235, cg07073052, cg0181 1796, cgl5934191, cgl4037948, cg25539628, cgl9738121, cg23540453, cgl8437193, cg08116728, cgl6316624, cgl8649203, cg02930132, cgl7364817, cg26314066, cg02998028, cg08872313, cgl4076195, cgl9253743, cgl0511904, cg04109543, cgl6017144, cgl 1646704. cg26964117. cg04783733. cg21909090. cg09860364. cgOl 159592, cg23287902. cgl6882635, cg02500195, cgl 1444278, cg26881965, cg06086829, or cgl6528926 from a B cell; and / or cg06609049, cgl7754500, cg07219649, cgl 8824330, cgl4143519, cg09390459, cg25087589, cgl2047260, cgl5558717, cgl8411660, cgl5032490. cg06729455. cg27166921. cgl 1647944. cg04168494. cg07895523. cg03142975, cgll l23198, cg02536811, cg05658543, cg02503332, cgl4037948, cg25861229, cg24248367, cgl2367971. cg01658566. cg05355436. cg04950931. cg00135449, cg04982843, cgl6791304, cgl8645910, cgl3867670, cg22533480,cg14044930, cg07952581, cg21974923, cg00242955, cg07673127, cg26234470, cg!7724455, cg06880808, cg26352006, cg02789688, cgl6829732, cgl 1038423, cg26559235, cgl 1207385, cg03744043, or cgl0046671 from a memory’ T cell; and / or cg05949290. cg06609049. cg23540453. cg26975408. cg22262670. cg25941963. cgl 1970192, cgl8426060, cg03057712, cg0608352, cg08437936, cg03866600, cg07895523, cg05311909, cgl4037948, cg26514961, cgl6277608, cgl 1923627, cg07199354, cg27209389, cg20706691, cg02307277, cgl8522970, cg26193687, cg!8437193. cg09996455. cg09656274. cgl4217861. cg06027620. cgOl 160274. cg24774581, cgl9781641, cgl4940668, cgl9339993, cgl5420071, cg05441960, cg26559235, cgl2505146, cgl 1644057, cg04479716, cg03874965, cgl5970145, cgl7575314, cgl5307168, cgl5066777, cg23766561, cgl7686260, cg07770777, cg09697880, or cg00843561 from a naive T cell.6561, cgl7686260, cg07770777, cg09697880, or cg00843561 from a naive T cell.

[0015] In some embodiments, the genetic locus in the biological sample is detected using one or more probes comprising cg06609049, cg!8531004, cg08900191, cg02331830, cg!4640588, cg06027620, cg!8937502, cg!3911068, cgl2983285, or cgl 7161130 from a B cell; and / or cg06609049, cgl 7754500, cg07219649, cgl 8824330, cgl4143519, cg09390459, cg25087589, cgl2047260, cgl 5558717, or cgl8411660 from a memory T cell; and / or cg05949290, cg06609049, cg23540453, cg26975408, cg22262670, cg25941963, cg!1970192, cgl8426060, cg03057712, or cg06083525 from a naive T cell.

[0016] In some embodiments, the genetic locus in the biological sample is detected using one or more probes comprising cg20274267, cgl 1163388, cg25317724, cg!7878321, cg!3996186, cg22337605, cg07538039, cgl0725441, cgl3576586, cg!8001827, cg27574654, cg24779384, or cg03422094 from a B cell; and / or cg00348968. cgl 1623260. cg22196946. or cg23407507 from a memory T cell; and / or cg08320413, cg24534742, cgl4414203, cg20332756, cgl4017435, cg24392372, cg24655284, cg09865323, cgl 1745755, or cg01601841 from a naive T cell, wherein the genetic loci are associated with cytokines or cytokine signaling. In some embodiments, the level of methylation at the genetic loci is determined using methylation profiling microarray, whole genome bisulfite sequencing (WGBS), reduced-representation bisulfite sequencing (RRBS-Seq), methylated DNA immunoprecipitation (MeDIP), tet-assisted bisulfite sequencing (Tab-Seq), or hmC analy sis on a methylation array (Tab / OxBS Array).

[0017] As described herein, the therapeutic agent comprises a disease-modifying antirheumatic drug (DMARD), a biologic DMARD, and / or synthetic DMARD.

[0018] In some embodiments, the disease-modifying antirheumatic drug (DMARD) comprises methotrexate, leflunomide, sulfasalazine, hydroxychloroquine, or combinations thereof.

[0019] In some embodiments, the biologic DMARD comprises a cytokine inhibitor, a TNF inhibitor, an IL-6 inhibitor, an IL-1 inhibitor, a T cell modulator, a B cell modulator, a signal transduction inhibitor, a cell-targeting inhibitor, cell recruitment inhibitor, or combinations thereof. In some embodiments, the cytokine inhibitor comprises inhibitors of granulocyte-macrophage colony-stimulating factor (GM- CSF), IL-15, IL-17A, IL-18, IL-23, Interferon-y, lymphotoxin alpha, receptor activator of nuclear factor kappa B ligand (RANKL), or combinations thereof. In some embodiments, the TNF inhibitor comprises adalimumab, certolizumab, etanercept, golimumab, infliximab, or combinations thereof. In some embodiments, the IL-6 inhibitor comprises sarilumab, tocilizumab, or combinations thereof. In some embodiments, the IL-1 inhibitor comprises anakinra. In some embodiments, the signal transduction inhibitor comprises inhibitors of p38 MAP kinase, Brunton’s tyrosine kinase, PI3 kinase y, PI3 kinase 5, sky ty rosine kinase, or combinations thereof. In some embodiments, the cell-targeting inhibitor comprises inhibitors for cadherin-11, CD4, CD52, CD5, or combinations thereof. In some embodiments, the cell recruitment inhibitor comprises inhibitors for adhesion molecules, chemokine, and / or chemokine receptors.

[0020] In some embodiments, the synthetic DMARD comprises a JAK inhibitor, barictinib, tofacitnib. upadacitinib. or combinations thereof.

[0021] In some embodiments, the therapeutic agent comprises abatacept, rituximab, ocrelizumab, ofatumumab, epratuzumab, tabalumab, CAR-T cells, CAR-NK cells, tyrosine kinase inhibitors, TLR targeted therapy, memantine, anti-OX40 therapy, chemokine antagonists (e.g.. CX3CR1 blockers), anti-NRPl, IDO inhibitors. calcineurin antagonists (e.g., sirolimus), TGF-beta blockade (including biologies and SMAD inhibitors). PD1 agonists (e.g.. peresolimab), a TNF inhibitor, an IL-1 inhibitor, an IL-6 inhibitor, a TGFP inhibitor, one or more SMAD inhibitors, one or more inhibitors of B cell signaling and activation (e.g., BLyS or BTK), chemokine signal inhibitors (e g., PI3K), a Janus kinase inhibitor, or any combination thereof.

[0022] In some embodiments, the level of methylation detected using probe cg06609049 is reduced compared to the reference level. In some embodiments, the reference level is the methylation level detected using DNA isolated from patients diagnosed with clinical rheumatoid arthritis (RA). In some embodiments, the treatment comprises modulating the expression of the THOP1 gene.

[0023] In some embodiments, the biological sample comprises peripheral blood mononuclear cells (PMBCs), naive T cells, memory T cells, or B cells. In some embodiments, the subject is positive for anti-citrullinated protein autoantibodies (ACPA+). In some embodiments, the autoimmune condition comprises rheumatoid arthritis, systemic lupus erythematosus, scleroderma, type 1 diabetes, multiple sclerosis, Hashimoto’s thyroiditis, Graves’ disease, Sjogren’s syndrome, inflammatory bowel disease, spondyloarthritis, celiac disease, myasthenia gravis, polymyositis, dermatomyositis, or Guillain-Barre syndrome.

[0024] In some embodiments, methylation can be measured using methods comprising bisulfite sequencing, methylation-specific PCR, digital droplet PCR, methylation bead arrays, chromatin immunoprecipitation, or MALDI-TOF mass spectrometry. In some embodiments, the treatment modulating the one or more genes comprise vector-based gene therapy, small molecule activators, CRISPR-based gene editing, epigenetic modulation, transcription factor modulation, or any combinations thereof.

[0025] Provided herein are methods for selecting a therapeutic treatment for a subject with an autoimmune condition, comprising isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject; determining a methylation signature in one or more types of cell types from the biological sample; selecting a therapeutic treatment; and administering the therapeutic agent to the subject to modulate expression of one or more of target genes. In some embodiments, selecting a therapeutic treatment comprises selecting a genetic locus within the methylation signature of the biological sample and selecting a therapeutic treatment that targets the selected genetic locus. In some embodiments, the subject is positive for anti-citrullinated protein autoantibodies (ACPA+).

[0026] As described herein, selecting the genetic locus in the biological sample comprises measuring a methylation level at a genetic locus in a biological sample using one or more probes comprising cg06609049, eg 18531004, cg08900I91, cg02331830, cgl4640588, cg06027620, cgl8937502, cgl3911068, cgl2983285, cgl7161130, cg27509052, cg08834383, cg20685674, cg02484776, cgl9337854, cg26559235, cg07073052, cg01811796, cgl593419I, cg!4037948, cg25539628, cgl9738121. cg23540453. cgl8437193. cg08116728. cgl6316624. cgl8649203. cg02930132, cgl7364817, cg26314066, cg02998028, cg08872313, cgl4076195, cgl9253743, cgl0511904, cg04109543, cgl6017144, egl 1646704, cg26964117, cg04783733, cg21909090, cg09860364, cgOl 159592, cg23287902, cgl6882635, cg02500195, egl 1444278, cg26881965, cg06086829. or cgl6528926 from a B cell; and / or cg06609049, cgl7754500, cg07219649, cgl8824330, cgl4143519, cg09390459, cg25087589, cgl2047260, cgl5558717, cgl8411660, cgl5032490, cg06729455, cg27166921, cgl !647944, cg04168494, cg07895523, cg03142975, cg! 1123198, cg02536811, cg05658543, cg02503332, cg!4037948, cg25861229, cg24248367. cgl2367971. cg01658566. cg05355436. cg04950931. cg00135449. cg04982843, egl 6791304, egl 8645910, egl 3867670, cg22533480, egl 4044930, cg07952581, cg21974923, cg00242955, cg07673127, cg26234470, cgl7724455, cg06880808, cg26352006, cg02789688, cg!6829732, egl 1038423, cg26559235, egl 1207385, cg03744043, or cgl0046671 from a memory T cell; and / or cg05949290, cg06609049, cg23540453, cg26975408, cg22262670, cg25941963, egl 1970192, cgl8426060, cg03057712, cg0608352, cg08437936, cg03866600, cg07895523, cg05311909, cgl4037948, cg26514961, cgl6277608, cgl l923627, cg07199354, cg27209389, cg20706691, cg02307277, cgl8522970, cg26193687, cgl8437193. cg09996455. cg09656274. cgl4217861. cg06027620. cgOl 160274. cg24774581. cgl9781641, cgl4940668, cgl9339993, cgl5420071, cg05441960, cg26559235, cgl2505146, cgl l644057, cg04479716, cg03874965, cgl5970145, cgl7575314, cgl5307168, cgl5066777, cg23766561, cgl7686260, cg07770777, cg09697880, or cg00843561 from a naive T cell, comparing the level of methylation at the genetic locus associated with the selected probe in the biological sample with a reference level of methylation, and selecting the genetic locus based on differentiated methylation compared with a reference level.

[0027] In some embodiments, selecting the genetic locus in the biological sample comprises measuring a methylation level at a genetic locus in a biological sample using one or more probes comprising cg20274267, cgl 1163388, cg25317724, cgl7878321. cgl3996186. cg22337605. cg07538039. cgl0725441. cg!3576586. cgl 8001827, cg27574654, cg24779384, or cg03422094 from a B cell; and / or cg00348968, cgl 1623260, cg22196946, or cg23407507 from a memory T cell; and / or cg08320413, cg24534742, cgl4414203, cg20332756, cgl4017435, cg24392372, cg24655284. cg09865323. cgl 1745755. or cg01601841 from a naive T cell, comparing the level of methylation at the genetic locus associated with the selected probe in the biological sample with a reference level of methylation, and selecting the genetic locus based on differentiated methylation compared with a reference level.

[0028] In some embodiments, the autoimmune condition comprises rheumatoid arthritis, systemic lupus erythematosus, scleroderma, type 1 diabetes, multiple sclerosis, Hashimoto’s thyroiditis, Graves’ disease, Sjogren’s syndrome, inflammatory bowel disease, spondyloarthritis, celiac disease, myasthenia gravis, polymyositis, dermatomyositis, or Guillain-Barre syndrome.

[0029] In some embodiments, wherein the reference level is the methylation level detected using DNA isolated from patients diagnosed with clinical rheumatoid arthritis (RA). In some embodiments, the level of methylation detected using probe cg06609049 is reduced compared to the reference level.

[0030] DESCRIPTION OF DRAWINGS

[0031] FIGs. 1A-1C show cross-sectional analysis of patients at baseline. (FIG. 1A) PCA of all baseline samples based on the union of differentially methylated loci (DML) derived from a pairwise comparison of clinical status. (FIG. IB) Venn diagram of the DMGs identified in CCP3+ Converters and Nonconverters. (FIG. 1C) Chromatin state enrichment within DML found between Pre-RA and Nonconverter participants at baseline. Abbreviations: PCA=principal components analysis, DML=differentially methylated locus / loci, DMG=differentially methylated gene, FDR=false discovery rate. FIGs. 2A-2C show methylome remodeling in early-stage Pre-RA Patients. (FIG. 2A) DML counts of CCP3-Controls, CCP3+ Nonconverters, and CCP3+ Converters between baseline and visit 2. (FIG. 2B) Heatmaps with rows showing DML found between Pre-RA participants baseline and year 1 visits and columns showing assays from CCP3+ Nonconverters and Pre-RA at baseline and visit 2. (FIG. 2C) Chromatin state enrichment within DML found between Pre-RA participants baseline and year 1 visits. Abbreviations: DML=differentially methylated locus / loci; DMG=differentially methylated gene.

[0032] FIGs. 3A-3B show DML clusters exhibiting multiple patterns of remodeling through RA progression. (FIG. 3A) Heatmaps with rows showing DML found in paired longitudinal analysis between CCP3+ Converters baseline, at-diagnosis. and post-diagnosis visits and columns showing assays from CCP3+ Converters baseline, at-diagnosis, and post-diagnosis visits. DML clusters are based on hierarchical clustering. (FIG. 3B) Significantly enriched pathways found in comparison of individual clusters within B cells (left) and naive T cells (right) (FDR < 0.10). Abbreviations: DML=dijferentially methylated locus / loci: DMG=differentially methylated gene, FDR=false discovery rate.

[0033] FIGs. 4A-4C show a predictive model identifying key risk loci for CCP3+ con version. (FIG. 4A) Mean accuracy + / - standard deviation of predictive models from naive T cell DML and autoantibodies. Accuracies were plotted up to the optimal training accuracy for each group. (FIG. 4B) Mean importance of DML predictors in B cells, memory T cells and naive T cells. Importance metrics were calculated based on 100 iterations of data partitions and recursive feature elimination. (FIG. 4C) Methylation intensities (depicted as beta values) of the three most important DML predictors models derived from naive T cells. Abbreviations: DML=differentially methylated locus / loci; DMG=differentially methylated gene.

[0034] FIG. 5 is a table showing baseline participant information.

[0035] FIG. 6 is a table showing the number of DML / DMGs identified in cross sectional analysis among CCP3- controls, CCP3+ Nonconverter, CCP3+ Pre-RA, and Early RA in B cell, memory T cell, naive T cell, and pooled lineage (in DML / DMGs).

[0036] FIG. 7 is a table showing paired year 1 longitudinal comparison participant information. FIG. 8 is a table showing the number of DML / DMGs identified in paired longitudinal analysis among CCP3- controls, CCP3+ Nonconverter, and CCP3+ Converter, in B cell, memory T cell, and naive T cell samples (in DML / DMGs).

[0037] FIG. 9 is a table showing paired longitudinal multi-phase comparison participant information.

[0038] FIG. 10 is a table showing the number of DML / DMGs identified in paired longitudinal analysis among CCP3+ Pre-RA samples at multiple points in time, in B cell, memory T cell, and naive T cell samples (in DML / DMGs).

[0039] DETAILED DESCRIPTION

[0040] The present disclosure provides methods for treating autoimmune conditions or delaying the clinical appearance of autoimmune conditions by identifying and / or targeting disease-associated methylation signatures in one or more cell types, one or more cell types with the methylation signatures, and / or target genes with methylation signature. Specifically, the present disclosure describes methods for determining and modulating a methylation signature, downstream pathways that regulate a pro- inflammatory cell communication network, and / or multiple cell types in the network that serve as pathogenic drivers in at-risk individuals or autoimmune conditions.

[0041] Capturing this altered methylation trajectory and potentially integrating with other epigenetic data enables the prediction of the clinical onset of autoimmune conditions. This methodology can be applied to all autoimmune diseases during the at-risk period or after transition to clinical disease. Non-limiting examples of autoimmune conditions include rheumatoid arthritis (RA), systemic lupus erythematosus, scleroderma, type 1 diabetes, multiple sclerosis, Hashimoto’s thyroiditis, Graves’ disease, Sjogren’s syndrome, inflammatory bowel disease, spondyloarthntis, celiac disease, myasthenia gravis, polymyositis, dermatomyositis, or Guillain-Barre syndrome.

[0042] These cell-type-specific methylation signature pathways explain the personalized pathogenesis of autoimmune conditions and contribute to the diversify of clinical responses to targeted therapies. Furthermore, these disclosed methods provide opportunities for stratifying individuals at-risk for autoimmune conditions, and selecting therapies tailored for prevention or treatment of autoimmune conditions. Overall, the present disclosure supports a new paradigm to understand how a common clinical phenotype could arise from diverse pathogenic mechanisms and to generate mechanism-based therapeutic approach targeting case specific methylation signatures, cell types, and / or target genes.

[0043] Autoimmune conditions

[0044] A person's genes in combination with infections and other environmental exposures likely play a significant role in disease development. In autoimmune disorders, the immune system fails to distinguish between foreign invaders and the body’s own healthy cells, leading to inflammation and damage to various parts of the body. There are more than 80 types of autoimmune disorders, including rheumatoid arthritis, systemic lupus ery thematosus, scleroderma, type 1 diabetes, multiple sclerosis. Hashimoto’s thyroiditis, Graves’ disease, Sjogren’s syndrome, inflammatory bowel disease, spondyloarthritis, celiac disease, myasthenia gravis, polymyositis, dermatomyositis, or Guillain-Barre syndrome. In some embodiments, the autoimmune condition is rheumatoid arthritis.

[0045] Rheumatoid arthritis (RA) is a chronic, systemic immune-mediated disease marked by synovial inflammation, leading to swelling, pain, and joint destruction. RA can happen in most joints, but it’s most common in the small joints of the hands, wrists, and feet. Early signs and symptoms include pain, stiffness, tenderness, swelling or redness in one or more joints, usually in a symmetrical pattern (e.g., both hands and both feet). The symptoms can worsen over time and spread to more joints including the knees, elbows, or shoulders. RA can make it hard to perform daily activities like writing, holding objects with the hands, walking, and climbing stairs. People with RA often feel fatigue and general malaise (e.g., fever, poor sleep quality, loss of appetite) and may experience depressive symptoms.

[0046] The etiology of RA, as well as the timing and anatomic site at which RA- related autoimmunity' is initiated, is complex. A complex interplay of inflammatory' cells and cytokines contribute to joint inflammation, tissue damage, and the chronic nature of the disease. A model outlining the sequential immune processes in disease progression has emerged, including mucosal initiation, propagation of systemic inflammation and autoimmunity (preclinical RA / at-risk status), and the onset of clinically apparent arthritis (e.g., early clinical RA or clinical RA). Initially, mucosal inflammation and dysbiosis may trigger local autoantibody production, which under normal circumstances would be transient. However, this is followed by a systemic spread of autoimmunity, evidenced by elevated serum autoantibodies, eventually leading to the development of clinically recognizable joint inflammation.

[0047] The majority (-70-80%) of clinical RA is termed ‘seropositive’ because patients exhibit blood elevations of autoantibodies. In some embodiments, the serological tests for autoantibodies include anti-citrullinated protein antibodies (ACPAs), rheumatoid factor (RF), antibodies to modified protein antigens (AMP A), and any other autoantibodies known in art. The elevated levels of ACPAs are strongly associated with the future development of RA in up to 60% of at-risk individuals. The autoantibody elevations can be on average 3-5 years prior to the onset of clinical RA. This prolonged period of autoimmunity and inflammation prior to the onset of arthritis can be designated as preclinical RA in subjects who eventually progress to a clinical diagnosis of RA. Preclinical RA carries an at-risk status of future conversion to clinical RA.

[0048] Systemic inflammation and autoimmunity in RA begin long before the onset of detectable joint inflammation. Interventions prior to the onset of joint inflammation are very likely necessary to modify the course of the disease. Unfortunately, a major limitation to developing effective preventive strategies for RA has been a lack of ability to detect and classify individuals that are at high risk for RA, as well as limitations in the knowledge of the mechanisms of disease development, including identification of when and at what anatomic site RA begins.

[0049] Currently, no treatments are available to prevent progression to RA in these at- risk individuals. In addition, diverse pathogenic mechanisms underlying a common clinical phenotype in RA complicate therapy as no single agent is universally effective. The divergent pathogenic pathways are poorly understood, and there is need to develop reliable tests to predict benefit of targeted therapeutics for individual patients.

[0050] The methods of the disclosure show how to identify and treat a person having an autoimmune condition (e.g., rheumatoid arthritis, systemic lupus erythematosus, scleroderma, type 1 diabetes, multiple sclerosis. Hashimoto’s thyroiditis. Graves’ disease, Sjogren’s syndrome, inflammatory bowel disease, spondyloarthritis, celiac disease, myasthenia gravis, polymyositis, dermatomyositis, or Guillain-Barre syndrome), wherein the etiology of the autoimmune condition is not limited to a single pathogenic process, but rather can involve one or more methylation signatures in one or more different cell types. It is important to identify and monitor persons at risk of developing autoimmune conditions in order to provide early interventions and delay / shape the course of the disease.

[0051] Method of diagnosis

[0052] Currently, a diagnosis of RA is made when clinically apparent arthritis is present. Typically, the diagnosis is determined by a health-care provider based on the combination of signs and symptoms at typical local joints, serological tests, inflammatory markers (e.g., erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP)), and imaging of joint changes. Furthermore, classification criteria e g., 1987 ACR or 2010 ACR / EULAR classification criteria have been used to classify RA based on points assigned to the number and size of affected joints, serology (RF and ACPA), acute-phase reactants (ESR and CRP), and duration of symptoms. A score of 6 or more out of 10 suggests RA for the latter.

[0053] The present disclosure provides methods for characterizing the methylation signatures associated with preclinical RA / at-risk status or clinical RA. Diagnosing preclinical RA / at-risk status or clinical RA can promote the strategies aimed at preventing or delaying the development of RA or personalizing treatment of RA, thereby improving survival and prognosis outcomes for individuals at high risk of developing RA.

[0054] Provided herein are methods for diagnosis of an autoimmune condition (e.g., RA) in a subject, the method including (a) isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject; and (b) determining a methylation signature in one or more types of cell types from the biological sample. Determining the methylation signature can include comparing the level of methylation at genetic loci in the sample with a reference level for the genetic loci.

[0055] Also provided herein are methods for diagnosis of an autoimmune condition (e.g.. RA) in a subject, the method including (a) isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject; (b) determining a methylation signature in one or more types of cell ty pes from the biological sample; and (c) selecting one or more target genes that are related to selected genetic loci with the methylation signature of the biological sample.

[0056] In some embodiments, the subject is positive for anti-citrullinated protein autoantibodies (ACPA+). In some embodiments, the biological sample comprises peripheral blood mononuclear cells (PMBCs), naive T cells, memory T cells, or B cells.

[0057] Method of treatment

[0058] Provided herein are methods for treatment of an autoimmune condition (e.g., RA) in a subject, the method including (a) isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject; (b) determining a methylation signature in one or more types of cell types from the biological sample; and (c) administering a therapeutic agent to the subject to modulate the methylation signature. As described herein, the treatment can be a prophylactic treatment of a subject at risk of developing an autoimmune condition (e g., RA).

[0059] Also provided herein are methods for delaying the clinical appearance of an autoimmune condition (e.g., RA) in a subject, the method including (a) isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject; (b) determining a methylation signature in one or more types of cell types from the biological sample; and (c) administering a therapeutic agent to the subject to modulate the methylation signature.

[0060] Provided herein are methods for selecting a therapeutic treatment for a subject with an autoimmune condition including (a) isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject; (b) determining a methylation signature in one or more types of cell types from the biological sample; (c) selecting a therapeutic treatment; and (d) administering the therapeutic agent to the subject to modulate expression of one or more of target genes.

[0061] In some embodiments, the methods for treatment of an autoimmune condition (e.g., RA) include any combinations of therapeutic agents disclosed herein. In some embodiments, the methods for treatment of an autoimmune condition (e.g., RA), for delaying the clinical appearance of an autoimmune condition (e.g., RA), or for selecting a therapeutic treatment for a subject with an autoimmune condition, include administering any therapeutic agent disclosed herein to modulate methylation signature, target cell types that express the methylation signature, modulate selected genetic loci, modulate selected target genes, or combinations thereof.

[0062] As used in this context, to “treat” means to ameliorate at least one symptom or prevent / delay the progression of the disorder associated with an autoimmune condition (e.g.. RA). Thus, a treatment can result in preventing or delaying the progression of preclinical autoimmune conditions (e.g., preclinical RA) to clinical autoimmune conditions (e.g., clinical RA). A treatment can also result in preventing or delaying the further progression of early autoimmune symptoms. Generally, the methods include administering a therapeutic agent or any combination of therapeutic agents to a subject who is in need of such treatment.

[0063] A subject can be an individual (e.g., a human) having or suspected of having an autoimmune condition (e.g., RA). In some embodiments, the subject has a preclinical autoimmune condition (e.g., preclinical RA). In some embodiments, the subject has an at-risk status of future conversion to a clinical autoimmune condition (e.g., clinical RA). In some embodiments, the subject has an early stage of an autoimmune condition (e.g., early RA). In some embodiments, the subject is positive for anti-citrullinated protein autoantibodies (ACPA+).

[0064] Methylation signature

[0065] DNA methylation is an epigenetic modification where a methyl group is added to DNA, typically at cytosine bases in the context of CpG dinucleotides. This process can influence gene expression without altering the underlying DNA sequence.

[0066] In the context of autoimmune conditions (e.g., RA), abnormal DNA methylation patterns have been associated with the development and progression of these diseases. Hypomethylation (reduced methylation) of certain genes can lead to their overexpression, potentially triggering immune responses against the body’s own tissues. Conversely, hypermethylation (increased methylation) can silence genes that normally regulate immune responses, leading to a loss of tolerance and autoimmunity. Furthermore, persons at risk for RA without synovitis have high blood levels of rheumatoid factor or ACPAs, wherein peripheral-blood mononuclear cells are characterized by abnormal DNA methylation in immune-related genes years before the onset of clinical symptoms.

[0067] The disease-associated methylation signature for autoimmune conditions (e.g., RA) includes differentially methylated loci. In some embodiments, the disease- associated methylation signature is the elevated level of methylation at one or more genetic loci. In some embodiments, the disease-associated methylation signature is the decreased level of methylation at one or more genetic loci. In some embodiments, the disease-associated methylation signature is the combination of the elevated level of methylation at one or more genetic loci and the decreased level of methylation at one or more genetic loci.

[0068] In some embodiments, the disease-associated methylation signature for autoimmune conditions (e.g., RA) includes differentially methylated genes. In some embodiments, the disease-associated methylation signature includes an elevated level of methylation at one or more genes. In some embodiments, the disease-associated methylation signature includes a decreased level of methylation at one or more genes. In some embodiments, the disease-associated methylation signature includes the combination of an elevated level of methylation at one or more genes and a decreased level of methylation at one or more genes.

[0069] As described herein, disease-associated methylation signature for autoimmune conditions (e.g., RA) refers to the elevated or decreased level of methylation at genetic loci in the sample compared with a reference level for the genetic loci, wherein the identification of the disease-associated methylation signature is described below.

[0070] The disease-associated methylation signature includes an elevated or a decreased level of methylation at the genetic loci detected using one or more probes including, but not limited to, cg06609049, cgl8531004, cg08900191, cg02331830, cg!4640588, cg06027620, cgl8937502, cgl3911068, cgl2983285, or cgl7161130 from a B cell; and / or cg06609049, cgl7754500, cg07219649, cgl 8824330. cgl4143519. cg09390459. cg25087589. cg!2047260. cgl5558717. or cgl8411660 from a memon T cell; and / or cg05949290, cg06609049, cg23540453, cg26975408, cg22262670, cg25941963, cgl 1970192, cgl8426060, cg03057712, or cg06083525 from a naive T cell. In some embodiments, the disease-associated methylation signature includes an elevated or a decreased level of methylation at the genetic loci detected using two or more, five or more, eight or more, ten or more, twelve or more, fifteen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty -two or more, twenty -three or more, twenty -four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, twenty-nine or more, or thirty or more probes including cg06609049, cgl 8531004, cg08900191, cg02331830, cgl4640588, cg06027620, cgl8937502, cgl3911068, cgl2983285, or cgl7161130 from a B cell; and / or cg06609049, cgl7754500, cg07219649, cgl 8824330, cgl4143519, cg09390459, cg25087589, cgl2047260, cgl5558717, or cgl8411660 from a memory T cell; and / or cg05949290, cg06609049, cg23540453, cg26975408, cg22262670, cg25941963, cgl 1970192, cgl8426060, cg03057712, or cg06083525 from a naive T cell.

[0071] In some embodiments, disease-associated methylation signature includes an elevated or decreased level of methylation at the genetic loci corresponding to one or more probes including, but not limited to, cg06609049, cgl 8531004, cg08900191, cg02331830, cgl4640588, cg06027620, cgl8937502, cgl3911068, cgl2983285, cgl7161130, cg27509052, cg08834383, cg20685674, cg02484776, cgl9337854, cg26559235, cg07073052, cg01811796, cgl5934191, cgl4037948, cg25539628, cgl9738121. cg23540453. cgl8437193. cg08116728. cgl6316624. cgl8649203. cg02930132, cgl 7364817, cg26314066, cg02998028, cg08872313, cgl4076195, cgl9253743, cgl0511904, cg04109543, cgl6017144, cgl 1646704, cg26964117, cg04783733, cg21909090, cg09860364, cgOl 159592, cg23287902, cgl6882635, cg02500195, cgl 1444278, cg26881965, cg06086829. or cgl6528926 from a B cell; and / or cg06609049, cgl7754500, cg07219649, cgl8824330, cgl4143519, cg09390459, cg25087589, cgl2047260, cgl5558717, cgl8411660, cgl5032490, cg06729455, cg27166921, cgl l647944, cg04168494, cg07895523, cg03142975, cgl 1123198, cg02536811, cg05658543, cg02503332, cgl4037948, cg25861229. cg24248367. cg!2367971. cg01658566. cg05355436, cg04950931. cg00135449. cg04982843, cgl6791304, cgl8645910, cgl3867670, cg22533480, cgl4044930, cg07952581, cg21974923, cg00242955, cg07673127, cg26234470, cgl7724455, cg06880808, cg26352006, cg02789688, cgl6829732, cgl 1038423, cg26559235, cgl 1207385. cg03744043. or cgl0046671 from a memory T cell; and / or cg05949290, cg06609049, cg23540453, cg26975408, cg22262670, cg25941963, cgl 1970192, cgl 8426060, cg03057712, cg0608352, cg08437936, cg03866600, cg07895523, cg05311909, cgl4037948, cg26514961, cgl6277608, cgl 1923627, cg07199354, cg27209389, cg20706691, cg02307277, cgl8522970, cg26193687, cgl8437193, cg09996455, cg09656274, cgl4217861, cg06027620, cgOl 160274, cg24774581, cg!9781641, cg!4940668, cgl9339993, cgl5420071, cg05441960, cg26559235. cgl2505146. cgl 1644057. cg04479716. cg03874965. cgl5970145. cgl 7575314, cgl5307168, cgl5066777, cg23766561, cgl7686260, cg07770777, cg09697880, or cg00843561 from a naive T cell. In some embodiments, disease-associated methylation signature includes a reduced level of methylation detected using probe cg06609049 compared to a reference level. The genetic locus detected using probe cg06609049 is associated with THOP1 gene. In some embodiments, the reference level is the methylation level detected using DNA isolated from patients diagnosed with an autoimmune condition (e.g., clinical RA).

[0072] In some embodiments, disease-associated methylation signature includes an elevated or a decreased level of methylation at the genetic loci detected using one or more probes including, but not limited to, cg20274267, cgl 1163388, cg25317724, cg!7878321, cgl3996186, cg22337605, cg07538039, cgl0725441, cgl3576586, cgl 8001827, cg27574654, cg24779384, or cg03422094 from a B cell; and / or cg00348968. cgl 1623260. cg22196946. or cg23407507 from a memory T cell; and / or cg08320413, cg24534742, cgl4414203, cg20332756, cgl4017435, cg24392372, cg24655284, cg09865323, cgl 1745755, or cg01601841 from a naive T cell, wherein the one or more probes are associated with cytokines or cytokine signaling.

[0073] The genetic loci of hg 19 detected using the disclosed probes can be found in Tables 1-6. In some embodiments, the disease-associated methylation signature includes an elevated or a decreased level of methylation at the genetic loci detected using two or more, five or more, eight or more, ten or more, fifteen or more, twenty' or more, twenty-five or more, thirty' or more, thirty -five or more, forty or more, forty- five or more, fifty or more, sixty- or more, seventy- or more, eighty or more, ninety or more, a hundred or more, a hundred and ten or more, a hundred and twenty or more, a hundred and thirty or more, a hundred and forty- or more, a hundred and fifty or more, a hundred and sixty- or more, a hundred and seventy or more, or a hundred and seventy-seven probes, as described in Tables 1-6. Table 1. Probes and corresponding methylation positions in B cells

[0074] Table 2. Probes and corresponding methylation positions in memory T cells

[0075]

[0076] Table 3. Probes and corresponding methylation positions in naive T cells

[0077] Table 4. Probes and methylation positions corresponding to cytokines and signaling in B cells Table 5. Probes and methylation positions corresponding to cytokines and signaling in memory T cells

[0078] Table 6. Probes and methylation positions corresponding to cytokines and signaling in naive T cells

[0079] In some embodiments, the disease-associated methylation signature is a predictor for a preclinical / at risk status (e.g., preclinical RA) or an early autoimmune condition (e.g., early RA), or the progression of the autoimmune disease (e g., RA).

[0080] The disease-associated methylation signature described herein can occur in multiple cell types. For example, the cell types expressing the disease-associated methylation signature can include, but are not limited to, B cells, naive T cells, and / or memory T cells.

[0081] There are multiple cell types that can display the methylation signature, and the pattern of which cell type or cell types with the methylation signature is highly variable. Each patient can have their own combination of cell types or methylation signature components, thereby contributing to the need for individualized therapies. In some embodiments, different cell types exhibit the same disease-associated methylation signature in a subject. In some embodiments, different cell types exhibit different methylation signatures in a subject. In some embodiments, some cell types express a disease-associated methylation signature and other cell types do not express a disease-associated methylation signature in a subject. In some embodiments, the disease-associated methylation signature of a subject is different than the disease- associated methylation signature of a different subject, either because a similar methylation pattern is observed in a different cell type between the two subjects, or because each subject has a different methylation dysregulation (e.g., different disease- associated methylation pattern) in the same cell type. A specific disease-associated methylation signature can be unique to a subject either by the particular combination of differentially methylated loci and / or differentially methylated genes implicated in the disease-associated methylation signature, and / or by the particular cell type harboring the disease-associated methylation signature.

[0082] The disease-associated methylation signature in turn regulates target genes which contribute to the development or progression of an autoimmune condition (e.g., RA). In some embodiments, the target genes are a common set of genes shared among subjects having an at-risk status or an early autoimmune condition (e.g., early RA). In some embodiments, the common set of the target genes described herein is produced by different cell types among different subjects. In some embodiments, the target genes include, but not limited to. the THOP1 gene. In some embodiments, the target genes are pro-inflammatory genes including, but are not limited to, granulocytemacrophage colony-stimulating factor (GM-CSF), IL-1, IL-6, IL-15, IL-17A, IL-18, IL-23, Interferon-y, lymphotoxin alpha, receptor activator of nuclear factor kappa B ligand (RANKL), and / or TNFa.

[0083] Identifying a methylation signature

[0084] In some embodiments of the diagnosis and / or treatment methods described herein, the subject is positive for a disease-associated methylation signature in one or more cell types. In some embodiments, identifying a methylation signature includes identifying differentially methylated loci in a biological sample from a subject.

[0085] In some embodiments, measuring the level of methylation at selected genetic loci includes using one or more probes including, but not limited to, the probes listed in Tables 1-6, and comparing the level of methylation at the genetic loci associated with one or more of the probes listed in Tables 1-6 with a reference level of methylation for those genetic loci. The reference level can be the methylation level detected using DNA isolated from patients diagnosed with the autoimmune condition (e.g., clinical RA).

[0086] The level of methylation at a genetic loci can be determined using a methylation profiting microarray, whole genome bisulfite sequencing (WGBS), reduced-representation bisulfite sequencing (RRBS-Seq), methylated DNA immunoprecipitation (MeDIP), tet-assisted bisulfite sequencing (Tab-Seq), hmC analysis on a methylation array (Tab / OxBS Array), or any other methods known in the art. Methylation can be measured using methods including, but not limited to, bisulfite sequencing, methylation-specific PCR, digital droplet PCR, methylation bead arrays, chromatin immunoprecipitation, MALDI-TOF mass spectrometry, and any other methods known in the art.

[0087] Targeting a methylation signature

[0088] The disclosed methods for treatment of an autoimmune condition (e.g., RA) can include administering the subject therapeutic agents to modulate the methylation signature in one or more cell types. In some embodiments, methods of treatment of an autoimmune condition can include administering a therapeutic agent known to modulate methylation of a selected genetic loci identified by one or more probes in Tables 1-6. In some embodiments, the therapeutic agents include, but not limited to. a disease-modifying antirheumatic drug (DMARD), a biologic DMARD, and / or a synthetic DMARD.

[0089] Exemplary DMARDs include, but are not limited to, methotrexate, leflunomide, sulfasalazine, hydroxychloroquine, or combinations thereof.

[0090] Exemplary biologic DMARDs include, but are not limited to, a cytokine inhibitor, a TNF inhibitor, an IL-6 inhibitor, an IL-1 inhibitor, a T cell modulator, a B cell modulator, a signal transduction inhibitor, a cell-targeting inhibitor, a cell recruitment inhibitor, or combinations thereof. In some embodiments, the cytokine inhibitors include, but are not limited to. inhibitors of granulocyte-macrophage colony-stimulating factor (GM-CSF), IL-15, IL-17A, IL-18, IL-23, Interferon-y, lymphotoxin alpha, receptor activator of nuclear factor kappa B ligand (RANKL), or combinations thereof. In some embodiments, the TNF inhibitors include, but are not limited to, adalimumab, certolizumab. etanercept, golimumab, infliximab, or combinations thereof. In some embodiments, the IL-6 inhibitors include, but are not limited to, sarilumab, tocilizumab, or combinations thereof. In some embodiments, the IL-1 inhibitors include, but are not limited to, anakinra. In some embodiments, the signal transduction inhibitors include, but are not limited to, inhibitors of p38 MAP kinase, Brunton’s tyrosine kinase, PI3 kinase y, PI3 kinase 8, sky tyrosine kinase, or combinations thereof In some embodiments, the cell-targeting inhibitors include, but are not limited to, inhibitors of cadherin-11, CD4, CD52, CD5, or combinations thereof. In some embodiments, the cell recruitment inhibitors include, but are not limited to, inhibitors of adhesion molecules (e.g., ICAM-1). chemokine and chemokine receptors, and any cell recruitment molecule known in the art.

[0091] Exemplary synthetic DMARDs include, but are not limited to, a JAK inhibitor, barictinib, tofacitnib, upadacitinib, or combinations thereof.

[0092] As used herein, a therapeutically effective amount of the therapeutic agent is administered to a subject. In some embodiments, the dosage of the therapeutic agent administered to the subject is based on the subject’s weight. In some embodiments, the dosage of the therapeutic agent administered to the subject is independent of the subject’s weight. In some embodiments, the therapeutic agent is administered every' day. In some embodiments, the therapeutic agent is administered less than every day (e.g., every two days, every three days, twice a week, every week, every two weeks, even month, or every year). In some embodiments, the therapeutic agent is administered intravenously, intramuscularly, intradermally, subcutaneously, or intraperitoneally.

[0093] Identifying cell types

[0094] In some embodiments of the diagnosis and / or treatment methods described herein, the subject is positive for one or more cell types expressing a disease- associated methylation signature. The cell types are isolated or identified in the subject’s biological sample (e.g., peripheral blood mononuclear cells (PMBCs), naive T cells, memory T cells, or B cells). The methods used for identifying cell types can be any method known in art. including antibody detection, magnetic beads. DNA methylation pattern, fluorescence-activated cell sorting, single-cell RNA-seq, singlecell ATAC-seq, proteomics (e.g., mass spectrometry), microarrays, or any combination thereof.

[0095] In some embodiments, the one or more cell types expressing a disease- associated methylation signature include naive T cells, memory T cells, and / or B cells.

[0096] Targeting cell types expressing the methylation signature

[0097] The disclosed methods for treatment of an autoimmune condition (e.g., RA) can include administering to the subject a therapeutic agent targeting one or more cell types expressing the disease-associated methylation signature. For example, the methods may include identifying if the disease-associated methylation signature is associated with a particular cell type, and if associated with a particular cell type, administering a therapeutic agent known to target that particular cell type.

[0098] Therefore, in some embodiments of the methods, the identified cell type includes B memory cells and the therapeutic agent targets B memory cells, or the identified cell type includes naive T cells, and the therapeutic agent targets naive T cells, or the identified cell type includes memory T cells and the therapeutic agent targets memory T cells, or the identified cell type includes B cells and the therapeutic agent targets B cells.

[0099] Examples of such therapeutic agents that target a particular cell type include, but are not limited to, abatacept, rituximab, ocrelizumab, ofatumumab, epratuzumab. tabalumab, CAR-T cells, CAR-NK cells, tyrosine kinase inhibitors, TLR targeted therapy, memantine, anti-OX40 therapy, chemokine antagonists (e.g., CX3CR1 blockers), anti-NRPl, IDO inhibitors, calcineurin antagonists (e.g., sirolimus), TGF- beta blockade (including biologies and SMAD inhibitors), or PD1 agonists (e.g., peresolimab). In some embodiments, the methods for treatment of an autoimmune condition (e.g., RA) include any combination of the therapeutic agents disclosed herein. In some embodiments, the methods for treatment of an autoimmune condition (e.g., RA) include any therapeutic agent known in art that targets the identified cell types.

[0100] As used herein, a therapeutically effective amount of the therapeutic agent is administered to a subject. In some embodiments, the dosage of the therapeutic agent administered to the subject is based on the subject’s weight. In some embodiments, the dosage of the therapeutic agent administered to the subject is independent of the subject’s weight. In some embodiments, the therapeutic agent is administered every day. In some embodiments, the therapeutic agent is administered less than every day (e.g., every two days, every' three days, twice a week, every week, every7two weeks, every month, or every year). In some embodiments, the therapeutic agent is administered intravenously, intraarterially, intramuscularly, intradermally, subcutaneously, or intraperitoneally.

[0101] Selecting genetic loci or target genes

[0102] In some embodiments of the diagnosis and / or treatment methods described herein, the subject exhibits elevated and / or decreased levels of one or more genes that are associated with a disease-associated methylation signature in one or more cell types. In some embodiments identifying a methylation signature includes identifying target genes that are associated with the differentially methylated loci.

[0103] In some embodiments, the level of methylation is detected using one or more probes including, but not limited to, the probes listed in Tables 1-6. In some embodiments, the detected differentially methylated loci are associated with one or more target genes. In some embodiments, the one or more target genes are associated with one or more probes identified in Tables 1-6. In some embodiments, the one or more target genes are upregulated compared to a reference level. In some embodiments, the one or more target genes are downregulated compared to a reference level. In some embodiments, the methylation level of the one or more target genes are increased compared to a reference level. In some embodiments, the methylation level of the one or more target genes are reduced compared to a reference level.

[0104] In some embodiments, the level of methylation detected using probe cg06609049 is reduced compared to the reference level. The genetic locus detected using probe cg06609049 is associated with the THOP1 gene. The reference level is the methylation level detected using DNA isolated from patients diagnosed with the autoimmune condition (e.g., clinical RA).

[0105] The methods used for determining target genes can be any method known in art that is utilized to analyze gene expression or DNA methylation. For example, the methods include methylation profiling microarray, whole genome bisulfite sequencing (WGBS), reduced-representation bisulfite sequencing (RRBS-Seq), methylated DNA immunoprecipitation (MeDIP), tet-assisted bisulfite sequencing (Tab-Seq), hmC analysis on a methylation array (Tab / OxBS Array), any combination thereof, or any other method known in the art.

[0106] In some embodiments, the target genes are predictors for progression of a preclinical / at risk status (e.g., preclinical RA) or an early autoimmune condition (e g., early RA).

[0107] Modulating selected genetic loci or target genes

[0108] The disclosed methods for treatment of an autoimmune condition (e.g., RA) include selecting a therapeutic treatment based on the determined methylation signature and administering to the subject a therapeutic agent to modulate the expression of one or more target genes that are associated with or regulated by the disease-associated methylation signature. In some embodiments, selecting a therapeutic treatment includes selecting genetic loci within the methylation signature of the biological sample and selecting a therapeutic treatment that targets the selected genetic loci. In some embodiments, administering a therapeutic agent to modulate the expression of the target gene includes administering an inhibitor directed to the target gene.

[0109] In some embodiments, the level of methylation, detected using one or more probes listed in Tables 1-6, is reduced or increased compared to the reference level, and the detected genetic loci with reduced or increased methylation status are associated with one or more target genes. Therefore, in some embodiments of the methods, selecting a therapeutic treatment includes, but is not limited to, selecting genetic loci that are detected to exhibit a reduced or increased methylation status. In some embodiments, selecting a therapeutic treatment includes, but is not limited to, modulating the expression of selected target genes, described herein in Tables 1-6, that are associated with the detected reduced or increased methylation loci described herein.

[0110] In one non-limiting example, the level of methylation detected using probe cg06609049 is reduced compared to the reference level, and the genetic loci detected using probe cg06609049 is associated with the THOP1 gene. Therefore, in some embodiments of the methods, the treatment includes, but not limited to, modulating the expression of the THOP1 gene.

[0111] The therapeutic agent modulating the expression of the target gene can be a vector-based gene therapy, a small molecule activator, a biologic. CRISPR-based gene editing, epigenetic modulation, transcription factor modulation, or any combination thereof.

[0112] In some embodiments, the therapeutic agent modulating the expression of the target genes can be one or more of a TNF inhibitor, an IL-1 inhibitor, an IL-6 inhibitor, a TGF0 inhibitor, one or more SMAD inhibitors, one or more inhibitors of B cell signaling and activation (e.g., BLyS or BTK), chemokine signal inhibitors (e.g., PI3K), rituximab, T cell inhibitors (e.g., abatacept), or a Janus kinase inhibitor.

[0113] A therapeutically effective amount of the therapeutic agent modulating the expression of the pro-inflammatory downstream gene is administered to a subject. In some embodiments, the dosage of the therapeutic agent administered to the subject is based on the subject’s weight. In some embodiments, the dosage of the therapeutic agent administered to the subject is independent of the subject’s weight. In some embodiments, the therapeutic agent is administered every day. In some embodiments, the therapeutic agent is administered less than every day (e.g., even,' two days, every three days, twice a week, even’ week, every two weeks, every month, or every year). In some embodiments, the therapeutic agent is administered intravenously, intraarterially, intramuscularly, intradermally, subcutaneously, or intraperitoneally.

[0114] Kit for autoimmune condition testing

[0115] Provided herein are kits for use in determining whether a subject is at risk of developing and autoimmune condition (e.g., RA). The kit includes instructions and / or materials for (a) obtaining or having obtained a blood sample from the subject; (b) isolating mononuclear cells from the sample; (c) purifying DNA from the isolated mononuclear cells; (d) determining a level of methylation at one or more genetic loci; and (e) comparing the level of methylation at the one or more genetic loci with a reference level, wherein the level of methylation at the one or more genetic loci determined in step (d) that is higher or low er than the reference level indicates that the subj ect is at risk of developing RA.

[0116] As described herein, the autoimmune condition includes, but is not limited to, rheumatoid arthritis, systemic lupus erythematosus, scleroderma, type 1 diabetes, multiple sclerosis, Hashimoto’s thyroiditis, Graves’ disease, Sjogren’s syndrome, inflammatory bowel disease, spondyloarthritis, celiac disease, myasthenia gravis, polymyositis, dermatomyositis, or Guillain-Barre syndrome.

[0117] In some embodiments of the kits, the methylation level at selected genetic loci is determined using probes including, but not limited to, probe cg06609049. In some embodiments, the level of methylation detected using probe cg06609049 is reduced compared to a reference level. In some embodiments, the reference level is the methylation level detected using DNA isolated from patients diagnosed with an autoimmune condition (e.g., clinical RA). The methylation level can be determined using any methods disclosed herein. EXAMPLES

[0118] The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.

[0119] Materials and Methods

[0120] Cohort and Biosamples

[0121] The Targeting Immune Responses for Prevention of Rheumatoid Arthritis (TIP-RA) cohort was designed to prospectively study individuals at high risk for developing RA due to the presence at baseline of serum anti-CCP3 positivity in the absence of a history of IA, or IA at a baseline physical examination of 66 / 68 joints. CCP3+ and control subjects were recruited through screening of health-fair participants, first-degree relatives of patients with RA, and individuals referred for evaluation to rheumatology clinics. Individuals with RA were identified through rheumatology clinics. Inclusion required consistent anti-CCP3 test results at both the screening and baseline study visits: positivity for At-Risk and Early RA subjects, and negativity for control subjects.

[0122] Early RA subjects were additionally required to have a baseline visit within 12 months of initial identification of inflammatory arthritis judged to be RA by a rheumatologist. Prior treatment with disease-modifying anti-rheumatic drugs (DMARDs) was not permitted, except for a prednisone equivalent dose of less than 10 mg / day, to ensure enrollment of individuals with early, treatment-naive disease.

[0123] Peripheral Blood Processing and DNA Methylation Measurement

[0124] Peripheral blood mononuclear cells were obtained from participants annually, processed and stored frozen in DMSO-based medium. Viability and yield were confirmed by dye-exclusion assay. Cell subsets were isolated by sequential magnetic separation using a Miltenyi Biotec AutoMacs Pro. CD19+, CD4+ / CD45RO+ memory, CD4+ / CD45RO- naive phenotypes were used for methylation analysis. Genomic DNA was isolated from 177 patients with CCP3-. CCP3+ and early RA with multiple visits. RNA-free PBMC subset genomic DNA was prepared by affinity’ column with DNeasy Blood & Tissue Kit (Qiagen) followed by concentration with Ultra-0.5 Centrifugal Filters (Amicon) and concentration normalized (20ng / pl). The Illumina Infinium MethylationEPIC Kit chip was used to measure DNA methylation levels at -850,000 CpG loci per sample.

[0125] Beadchip Data Processing

[0126] Data generated using the Infinium MethylationEPIC Kit was processed using the minfi package vl.40.0 using a series of quality control, processing and normalization procedures. All processing and downstream analysis was performed using R version 4.2.3. Samples with a mismatch between described sex and reported sex using the minfi command getSex() were removed from analysis. Samples were then separated into female and male groups and probe detection / value analysis was performed. Samples were re-combined and methylation signal intensity, beta outlier, bisulfite conversion and beadcount quality control steps were performed.

[0127] Previous studies have described the potential bias that can arise from methylation analysis at CpG loci overlapping single nucleotide polymorphisms (SNPs), as well as probes that demonstrate cross-reactivity across multiple loci. To counter these potential sources of bias we performed further quality control procedures. Previously identified probes overlapping common SNPs were removed from the analysis based on annotation in the IlluminaHumanMethylationEPICanno.ilml0b4.hgl9 R package using the minfi dropLociWithSnps() function. Further, a targeted gap-hunting analysis was performed using the MethylToSNP package v0.99.0 to eliminate probes with evidence of potential SNP overlap specifically within the data. DML identified as key predictors in the conversion prediction model were inspected to ensure methylation distributions were not consistent with SNP overlap. Finally, potentially cross-reactive probes were removed with > 47 bp homology.

[0128] Initial data processing and between-array normalization was performed using the preprocess Illumina method. After initial processing, principal components analysis (PCA) of the data indicated the presence of a batch effect. The Harman method of correction under default parameters was used to address this artifact. Harman minimizes batch noise by correcting DNA methylation measurements for batch effect under a probabilistic constraint for overcorrection. After correction, PCA was used to evaluate the effectiveness of batch effect removal. Signal intensity values for each sample were extracted and calculated as both p and M-values. M-values were used in downstream statistical analysis and visualization due to -value heteroscedasticity in high and low methylation ranges, unless otherwise stated. Individual probe values were reported and visualized as - values.

[0129] DML, DMG & Pathway Analysis

[0130] Differentially Methylated Loci (DML) were identified using differences of mean M-value and empirical bayes-moderated t-test / i-val ue criteria. Due to the limited number of DML identified, raw -values were not adjusted for multiple testing corrections. All tests were performed using the limma R package version 3.50.3.

[0131] For this analysis, the most important loci that could potentially act as predictive biomarkers in the broadest possible population was sought to be identified. Thus, highly stringent selection parameters were applied utilizing sample from male and female participants while adjusting for important covariates (sex, age, and current smoking status). This methodology restricted the scope of the investigation to the autosome to avoid incongruent information between samples. -value < 0.05 and mean |M| > 2.0 criteria were used for cross-sectional comparisons. All longitudinal analyses utilized P-value < 0.05 and mean |M| > 3.5 as DML selection criteria and were performed using paired samples from each patient.

[0132] To compare the results against data available in public databases, a DML set was also selected based on a cross-sectional analysis using less stringent criteria, consistent with previous investigations. For this analysis DML were selected based on having P-Value < 0.05 and mean |M| > 0.5.

[0133] DML were then mapped to genes. Probe location on the GRCh37 (hgl9) assembly was determined using the IlluminaHumanMethylationEPICanno.ilml0b4.hgl9 R package. GRCh37 gene body and promoter (TSS-2,500bp to TSS+500bp) coordinates were determined using the biomaRt R package version 2.50.3. Genes with a DML found in the gene body or promoter were determined to be differentially methylated genes (DMGs).

[0134] Enriched pathways were identified using the ReactomePA R package version 1.42.0 based on Reactome Database Schema version 82. Significantly enriched pathways were selected based on having an FDR < 0. 1 (overrepresentation test, as calculated by ReactomePA), unless otherwise stated.

[0135] Chromatin State Analysis

[0136] Chromatin state analysis was performed using cell lineage specific data (E032: Primary’ B cells, E040: Primary’ T helper memory cells, and E038: Primary T helper naive cells) from the core 15-state model provided by the Roadmap Epi genomics Project. This core 15-state model was generated using ChromHMM on epigenetic data derived from 5 chromatin marks (H3K4me3, H3K4mel, H3K36me3, H3K27me3, H3K9me3) in 127 epigenomes. Enrichment analysis was performed for chromatin states in each comparison within each cell lineage using a Fisher's test based on all post-QC probes as a background. Significantly enriched (p < 0.05) chromatin states were retained and plotted.

[0137] Machine Learning Model Construction

[0138] A binary classification model was created to distinguish Pre-RA participants from Nonconverters at their baseline visit using both epigenetic markers and antibody levels. The feature set by taking the top 60 DML within each cell lineage were first selected based on p-value (empirical bayes-moderated t-test) between Pre-RA and Non-converter groups based on Visit 2 and At-Diagnosis visits, where available. A random seed was then set and Visit 1 samples were split into train and test subsets at a 70:30 ratio. The initial 60 DML feature set was ranked by importance based on the recursive feature elimination algorithm as implemented in the rfe() command in the R Caret package version 6.0.94. Only features with positive importance were retained.

[0139] Random Forest classification models were then trained on the training set with an increasing number of predictors, from most important to least, using 10-fold cross- validation and repeated 5 times. Each model was then tested on the unseen test set and evaluated based on accuracy. The train and test data split, feature importance evaluation, and model training and evaluation was repeated 100 times using different random seeds, from 1 to 100. The mean and standard deviation of the training and test accuracy was noted for each seed and each number of predictors.

[0140] This process was then repeated using only antibody / acute phase reactant data for each sample. Specifically, within this analysis anti-CCP3. C-Reactive Protein (CRP), and rheumatoid factors in IgA, IgG, and IgM isotypes were included as features. Training and test prediction performance for DML in each individual cell lineage, as well as antibody / acute phase reactant only, were then plotted up to the predictor count that achieved the maximum mean training accuracy.

[0141] Visualization

[0142] Principal components analysis (PC A) was used to visually portray the relationship between samples. This analysis was performed based on M-values using the prcompO function in R. Hierarchical clustering and heatmaps were created using the R package pheatmap version 1.0.12 using M-values with outlier (top and bottom 1%) DML corrected. For longitudinal heatmaps depicting clusters of DML, DML were first clustered using hierarchical clustering. Clusters were then identified using the cutreeQ command from R package stats version 4.2.1. While individual probe methylation values were reported and visualized as P-values, all statistics were calculated using M-values. Additionally, all other visualization analyses were performed using M-values.

[0143] Example 1. Study Design and Method Validation

[0144] Three groups were studied in TIP-RA cohorts: 172 CCP3- ‘"Controls” without inflammatory arthritis (IA), 97 CCP3+ “At-Risk” participants and 62 CCP3+ “Early RA” patients were evaluated and enrolled in the TIP-RA cohorts. The cohorts were followed for 5 years, with blood samples collected annually for each group, as well as at the time of RA conversion. A subset of these individuals was evaluated for DNA methylation in naive and memory CD4+ T cells and B cells (69 Controls. 71 At-Risk and 29 Early RA). Over the course of the project, 21 CCP3+ participants converted to classifiable RA. These individuals are referred to as “pre-RA”, while those who did not develop RA are called “non-converters”.

[0145] There were no significant differences between CCP3+ and CCP3- individuals with regard to age. sex, history of ever or current smoking, self-reported first-degree relative RA status, the presence of the HLA-DR shared epitope associated with RA, and level of high sensitivity Creactive protein (hsCRP) (FIG. 5). How ever, the CCP3+ individuals exhibited significantly higher prevalence of serum IgM, IgG and IgA rheumatoid factor at baseline (p < 0.05, BHadjusted Welch Two Sample t-test and Chi-Square test).

[0146] DNA methylation assays were performed in 3 batches, and these data were then combined. A batch effect was observed and was addressed using the Harman method of batch correction. After correction, dimension reduction based on an unbiased analysis of all filtered autosomal loci readily distinguished B cells, memory T cells and naive T cells, serving as an internal control on data reliability.

[0147] Example 2. Cross-Sectional Analysis Reveals Pre-Conversion Epigenetic Signature in CCP3+ “At-Risk” Participants at Baseline

[0148] The previous cross-sectional analysis was expanded by performing pairwise comparisons among the participant samples at baseline (see Methods). The participants were distinguished among CCP3- “Controls”, CCP3+ “Non-converters”, CCP3+ “Pre-RA”, and “Early RA” populations (FIG. 5). Baseline CCP3+ Pre-RA samples were collected at a mean of 591±458 days before conversion to RA.

[0149] A pairwise comparison based on clinical status (FIG. 6) showed the greatest methylation differences between the CCP3+ Pre-RA and RA groups in all cell lineages (360, 276, and 292 DML in CD19+ B cell, memoiy CD4+ T Cell, and naive CD4+ T cell samples, respectively). In addition, both of these groups exhibited large differences compared to all other clinical classifications. In contrast, CCP3- Controls and CCP3+ Non-converters were similar to each other (only 3, 3, and 4 DML in B cell, memoiy T Cell, and naive T cell samples, respectively). Dimension reduction plots were generated using PCA (FIG. 1A) based on the union of all DML found in comparisons of clinical status within each cell lineage. The first dimension highlights the differences between Pre-RA and Early RA while the second separates Pre-RA and Early RA samples from CCP3+ Non-converters and CCP3- Controls. Minimal differences were observed between CCP3- Control samples and CCP3+ Nonconverter samples while all other clinical classifications could be clearly distinguished from each other, consistent with the quantity of DML (FIG. 6).

[0150] A set of DML was generated using less stringent criteria in order to compare results for Early RA. The DML that were identified in T lymphocytes based on Early RA and ACPA- Controls data significantly overlapped with DNA methylome changes in Early RA (p = 3.55 * 10'92, based on union of memory and naive T cell DML by hypergeometric test).

[0151] The baseline differences between CCP3+ participants who would not convert to RA and those who would later progress to clinical RA were particularly analyzed. The greatest differences between these two groups were identified in methylation occurred in B cells, followed by naive T cells and memory T cells (202, 137, and 89 DML, respectively). B cell and naive T cell DML from this comparison were significantly enriched for Active TSS and Epigenetically Quiescent chromatin states, while memory T cells were enriched only for Active TSS chromatin (FIG. 1C).

[0152] The loci from each comparison to genes were mapped and plotted in a Venn diagram to visualize the differentially methylated genes (DMGs) identified in the comparison between CCP3+ Nonconverter and Pre-RA participants at baseline (FIG. IB). There was almost no overlap between DMGs across cell lineages. Then, it was examined whether the distribution of DMGs were suggestive of stochastic differences between Pre-RA and Non-converter participants or whether they aligned with known pathways. Pathway enrichment analysis of DMGs associated with memory T cells and naive T cells failed to identify significant pathways (FDR < 0.10). However, 17 significantly enriched pathways based on DMGs associated with B cell samples were identified. The enriched pathways were primarily related to NOTCH- 1 signaling and nucleotide excision repair, suggesting a non-random methylation trajectory that distinguishes between Pre-RA and CCP3+ Non-converter participants prior to disease onset.

[0153] Example 3. Methylome remodeling in “at risk” individuals before RA conversion

[0154] Whether there was evidence of differential methylome remodeling was evaluated from baseline over time for CCP3- controls, CCP3+ Non-converter participants and Pre-RA prior to conversion. Participants with baseline and year 1 samples in CCP3- Control. CCP3+ Nonconverter and Pre-RA groups were analyzed (FIG. 7). Pre-RA participants were limited to those who had not converted to clinical RA after 1 year (n=7). Samples from Pre-RA individuals in this comparison were collected 714 ± 191 and 374 ± 177 days before clinical RA diagnosis for the baseline visit and the year 1 visit, respectively. During this period, no significant changes were observed for anti-CCP3, hsCRP. or rheumatoid factors in IgM, IgG, and IgA isotypes for either group (p < 0.05, BH-adjusted Welch Two Sample t-test and Chi-Square test).

[0155] A paired analysis within clinical classifications was performed by comparing each participant’s baseline and year 1 samples in each cell lineage. There were minimal changes in methylation after one year for CCP3- Control and CCP3+ Nonconverter individuals. However, Pre-RA participants demonstrated significant methylome remodeling over the course of one year (162, 199. and 186 DML. for B cell, memory T cell and naive T cell samples, respectively) (FIG. 2A and FIG. 8).

[0156] Hierarchical clustering of CCP3+ Non-converter and Pre-RA samples based on identified Pre-RA DML through year 1 demonstrated clear separation of baseline samples from year 1 samples in Pre-RA patients within each cell lineage (FIG. 2B). DML were mapped to genes and DMGs were used to perform pathway enrichment analysis. No pathways from any cell lineage met the enrichment significance threshold, but the identified DML from each cell lineage were significantly enriched for Active TSS, Bivalent / Poised TSS, and Enhancer chromatin states, as well as epigenetically quiescent regions (FIG. 2C). Taken together, these results suggest that a trajectory of epigenetic modifications occur in at-risk individuals that ultimately develop RA, and that these changes occur in regions highly relevant to transcriptional regulation.

[0157] Example 4. DML Clusters Exhibit Differing Patterns of Methylome Remodeling Pre-And-Post Diagnosis

[0158] To explore methylome remodeling throughout RA progression, DNA methylation patterns were examined in Pre-RA participants as they progressed to RA conversion and then to the early RA. Pre-RA participants that had a sample taken before conversion (471 ± 159 days before diagnosis), at conversion, and after conversion (176 ± 91 days after diagnosis were selected for analysis (n=6). Within this cohort, no autoantibody or acute phase reactant levels significantly changed from baseline to diagnosis or from diagnosis to post-diagnosis visit 1 (p < 0.05, BH- adjusted Welch Tw o Sample t-test). DAS28-CRP significantly increased from baseline to diagnosis (p = 7.13 x W3, BH-adjusted Welch Tw o Sample t-test) and a decreasing trend from diagnosis to post-diagnosis visit 1 was noted (FIG. 9). Additionally, two patients began treatment between initial diagnosis and post- diagnosis visit 1, one of which received methotrexate and one received low dose prednisone.

[0159] A paired analysis of pre-diagnosis to diagnosis samples, diagnosis to postdiagnosis samples and pre-diagnosis to post-diagnosis samples was performed for each patient. DML were identified in each cell lineage. Similar numbers of DML were noted in each comparison with minimal overlap between cell lineages (FIG. 10).

[0160] Then, the degree to which DML methylation changes were consistent between comparisons was determined. Heatmaps of DML from each comparison for each cell lineage revealed multiple distinct patterns of DML remodeling (FIG. 3A). DML were then assigned to groups based on hierarchical clustering and DMGs for each group were identified. Pathway enrichment analysis was performed on the union of all DMGs across clusters for each cell type (FIG. 3A). Two pathways were identified for naive T cell samples, both related to caspase-signaling mediated apoptosis, but no significant pathways were found for B cells or memory T cells.

[0161] Because of the heterogeneity of DML methylation patterns in each cluster, the potential pathways enriched in individual DML clusters were analyzed (FIG. 3B). This analysis yielded more substantive pathway enrichment. Within B cells, cluster 1 DMGs were enriched for cell-cycle transition, deubiquitination, and adaptive immune system pathways. B cell cluster 2 DMGs were enriched for interferon signaling. Within naive T cells, cluster 4 was characterized by caspase-signaling mediated apoptosis pathways and toll-like receptor cascades.

[0162] These clusters exhibit differing patterns of methylome remodeling during their trajectory from pre-RA to RA and demonstrate the dynamic nature of these changes. For example. B cell cluster 1 DML were relatively hypomethylated at baseline, transitioned to a hypermethylated state at the time of diagnosis, and remained hypermethylated through early RA. B cell cluster 2 DML were characterized by a different pattern where loci w ere hypermethylated when transitioning between pre-to- at-diagnosis and then later reverted to baseline hypomethylation when transitioning between diagnosis to post-diagnosis. Naive T cell cluster 4 DML were split, with some loci following a monotonic trajectory while others reversed. Example 5. Epigenetic Markers Demonstrate Improved Prognostic Capability Compared to Autoantibody Data

[0163] Having noted the epigenetic differences exhibited between Pre-RA and Nonconverter samples at baseline and 1-year visits, it was next investigated whether specific epigenetic loci in the former are associated with RA conversion. To establish a performance baseline, a classifier was first built based only on autoantibody / acute phase reactant data to identify CCP3+ participants at their initial visit that would go on to convert to clinical RA. These participants were separated into training (70%) and test (30%) sets and autoantibody / acute phase reactant predictors (anti-CCP3, hsCRP. and rheumatoid factor in IgM, IgG, and IgA isotypes) were ranked by importance using recursive feature elimination. After eliminating unimportant predictors, a random forest classification model was trained using 10-fold cross- validation, which was repeated 5 times. This process was iterated 100 times using different training and test splits at each predictor count, with models evaluated for performance based on training accuracy in each iteration (FIG. 4A). Using only autoantibody / acute phase reactant data, the optimal predictor count was selected. This optimized prediction model achieved a training accuracy of 73.6% ± 4.4% and test accuracy of 71.5% ± 8.2% in predicting which baseline CCP3+ patients would go on to convert to clinical RA.

[0164] This process was then repeated using the methylation data from each cell lineage (FIG. 4B). After feature selection (see Methods), random forest classifiers were trained using increasing numbers of selected DML and selected the optimal predictor count for each cell lineage. Using B cell data, the prediction model achieved 84.2% ± 3.9% training accuracy and 73.3% ± 7.5% test accuracy using 6 predictors. The optimal classifier using memory T cell data also utilized 6 predictors and resulted in 83.8% ± 3.6% training accuracy and 73.0% ± 8.6% test accuracy. The model utilizing naive T cell data was the most accurate, achieving 86.9% ± 3.6% training accuracy and 82.6% ± 7.3% test accuracy while using 5 predictors. Models utilizing methylation markers outperformed the optimal baseline autoantibody / acute phase reactant classifier in all cell lineages, and improvement observed in the predictive model derived from naive T cells was highly significant (p = 2.68 x 10'16, Welch Two Sample t-test). Combining DML and autoantibody / acute phase reactant data resulted in slightly higher training accuracies for each lineage, but no increase in test accuracies. Additionally, combining methylation data from each cell lineage into a single integrated model similarly increased training accuracy (88.4% ± 3.1%) with no resulting increase in test accuracy over the naive T cell model (77.2% ± 7.1%). This could indicate that these datasets are not additive or that this sample set was underpowered to take advantage of the larger feature set.

[0165] The key DML that drive the predictive power of the models were evaluated. One DML. cg06609049. was in the top 10 features of all three cell lineages based on prediction importance (FIG. 4C). cg06609049 is located at position chrl9:2785107, a region flanking the active transcription start site in the promoter region of THOP1 within CD4± naive T cells. This location is significantly hypermethylated in Nonconverters compared to Controls (p = 6.66 x 10'2, p = 8.13 x 10'2. and p = 9.95 x 10'2for B cells, memory T cells and naive T cells, respectively, based on Welch Two Sample t-test) and in Pre-RA (p = 1.85 x 1 O'5, p = 6.43 x 10'5, and p = 4.78 x 10'5for B cells, memory T cells and naive T cells, respectively, based on Welch Two Sample t-test), but not compared to Early RA (FIG. 4C).

[0166] Noting the increased predictive power of models using naive T cell epigenetic markers, the most important probes contributing to the naive T cell model were examined, in addition to cg06609049 (FIGs. 4B and 4C). cg05949290 and cg23540453 were identified as the first and third most important predictors of conversion status, respectively. Neither was located in an annotated regulatory region. Further, the core 15-state model provided by the Roadmap Epigenomics Project indicates their locations are epigenetically quiescent in CD4± naive T cells. cg06609049 is significantly hypermethylated in Non-converters compared to Pre-RA participants (p = 1.85 x 10'5. p = 6.43 x 10'5, and p = 4.78 x 10'5for B cells, memory T cells and naive T cells, respectively, based on Welch Two Sample t-test). cgl42957727 is most significantly hypermethylated in naive T cells when comparing Non-converters and Pre-RA (p = 3.68 x 1 O'6), but it is also hypermethylated in B cells and memory T cells to a lesser extent (p = 1.03 x 10'2and p = 6.30 x 10'4, respectively).

[0167] OTHER EMBODIMENTS

[0168] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A method for treatment of an autoimmune condition in a subject, the method comprising:(a) isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject;(b) determining a methylation signature in one or more cell types from the biological sample; and(c) administering a therapeutic agent to the subject to modulate the methylation signature.

2. The method of claim 1, wherein the treatment is a prophylactic treatment of a subject at risk of developing an autoimmune condition.

3. A method for delaying the clinical appearance of an autoimmune condition in a subject, the method comprising:(a) isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject;(b) determining a methylation signature in one or more cell types from the biological sample; and(c) administering a therapeutic agent to the subject to modulate the methylation signature.

4. The method of any of the above claims, wherein determining the methylation signature comprises comparing the level of methylation at a genetic locus in the biological sample with a reference level for the genetic locus.

5. The method of claim 4, wherein the genetic locus in the biological sample is detected using one or more probes comprising: cg06609049, cgl8531004, cg08900191, cg02331830, cgl4640588, cg06027620, cgl8937502, cgl3911068, cgl2983285, cgl7161130, cg27509052, cg08834383, cg20685674, cg02484776, cgl9337854, cg26559235, cg07073052, cg01811796, cgl5934191. cgl4037948. cg25539628. cgl9738121. cg23540453. cgl8437193, cg08116728, cgl6316624, cgl8649203, cg02930132, cgl7364817,cg26314066, cg02998028, cgO8872313, cgl4076195, cgl9253743, cgl0511904, cg04109543, cgl6017144, cgl 1646704, cg26964117, cg04783733. cg21909090. cg09860364, cgO 1159592, cg23287902, cgl 6882635, cg02500195, cgl 1444278, cg26881965, cg06086829, or cgl6528926 from a B cell; and / or cg06609049, cgl7754500, cg07219649, cgl8824330, cgl4143519, cg09390459, cg25087589, cgl2047260, cgl5558717, cgl8411660, cgl5032490, cg06729455. cg27166921. cgl 1647944. cg04168494. cg07895523. cg03142975. cgl 1123198, cg0253681 1, cg05658543, cg02503332, cgl4037948, cg25861229, cg24248367, cgl2367971, cg01658566, cg05355436, cg04950931, cg00135449, cg04982843, cgl6791304, cgl8645910, cgl3867670, cg22533480, cgl4044930, cg07952581, cg21974923, cg00242955. cg07673127. cg26234470. cgl7724455. cg06880808, cg26352006, cg02789688, cgl6829732, cgl 1038423, cg26559235, cgl 1207385, cg03744043, or cgl0046671 from a memory T cell; and / or cg05949290, cg06609049, cg23540453, cg26975408, cg22262670, cg25941963, cgl 1970192, cgl8426060, cg03057712, cg0608352, cg08437936, cg03866600, cg07895523, cg0531 1909, cgl4037948, cg26514961, cgl6277608, cgl 1923627, cg07199354, cg27209389, cg20706691, cg02307277, cgl8522970, cg26193687, cgl8437193, cg09996455, cg09656274, cgl4217861, cg06027620, cgOl 160274, cg24774581, cgl9781641, cgl4940668, cgl9339993, cgl5420071, cg05441960, cg26559235. cgl2505146. cgl 1644057. cg04479716. cg03874965. cgl 5970145, cgl 7575314, cgl 5307168, cgl5066777, cg23766561 , cgl 7686260, cg07770777, cg09697880, or cg00843561 from a naive T cell.6561, cgl7686260, cg07770777, cg09697880, or cg00843561 from a naive T cell.

6. The method of claims 4 or 5, wherein the genetic locus in the biological sample is detected using one or more probes comprising: cg06609049, cgl8531004, cg08900191, cg02331830, cgl4640588, cg06027620, cgl8937502, cgl3911068, cgl2983285, or cgl7161130 from a B cell; and / or cg06609049, cgl7754500, cg07219649, cgl8824330, cgl4143519, cg09390459, cg25087589, cgl2047260, cgl5558717, or cgl8411660 from a memory' T cell; and / orcg05949290, cg06609049, cg23540453, cg26975408, cg22262670, cg25941963, cgl 1970192, cgl8426060, cg03057712. or cg06083525 from a naive T cell.

7. The method of claim 4, wherein the genetic locus in the biological sample is detected using one or more probes comprising: cg20274267, cgH 163388, cg25317724, cg!7878321, cg!3996186. cg22337605, cg07538039, cgl0725441, cgl3576586, cgl8001827, cg27574654, cg24779384, or cg03422094 from a B cell; and / or cg00348968, cgl 1623260, cg22196946, or cg23407507 from a memory T cell; and / or cg08320413, cg24534742, cgl4414203, cg20332756, cgl4017435, cg24392372, cg24655284, cg09865323, cgl 1745755, or cg01601841 from a naive T cell, wherein the genetic loci are associated with cytokines or cytokine signaling.

8. The method of any of the above claims, wherein the level of methylation at the genetic loci is determined using methylation profiling microarray, whole genome bisulfite sequencing (WGBS), reduced-representation bisulfite sequencing (RRBS-Seq), methylated DNA immunoprecipitation (MeDIP), tet-assisted bisulfite sequencing (Tab-Seq), or hmC analysis on a methylation array (Tab / OxBS Array).

9. The method of any of the above claims, w herein the therapeutic agent comprises a disease-modifying antirheumatic drug (DMARD), a biologic DMARD, and / or synthetic DMARD.

10. The method of claim 9, wherein the disease-modifying antirheumatic drug (DMARD) comprises methotrexate, leflunomide, sulfasalazine, hydroxychloroquine, or combinations thereof.

11. The method of claim 9, wherein the biologic DMARD comprises a cytokine inhibitor, a TNF inhibitor, an IL-6 inhibitor, an IL-1 inhibitor, a T cell modulator, a B cell modulator, a signal transduction inhibitor, a cell-targeting inhibitor, cell recruitment inhibitor, or combinations thereof.

12. The method of claim 11, wherein the cytokine inhibitor comprises inhibitors of granulocyte-macrophage colony-stimulating factor (GM-CSF). IL-15, IL-17A. IL- 18, IL-23, Interferon-y, lymphotoxin alpha, receptor activator of nuclear factor kappa B ligand (RANKL), or combinations thereof.

13. The method of claim 11, wherein the TNF inhibitor comprises adalimumab, certolizumab, etanercept, golimumab, infliximab, or combinations thereof.

14. The method of claim 11, wherein the IL-6 inhibitor comprises sarilumab, tocilizumab, or combinations thereof.

15. The method of claim 1 1, wherein the IL-1 inhibitor comprises anakinra.

16. The method of claim 11, wherein the signal transduction inhibitor comprises inhibitors ofp38 MAP kinase, Brunton's tyrosine kinase, PI3 kinase y, PI3 kinase 5, sky tyrosine kinase, or combinations thereof.

17. The method of claim 11, wherein the cell -targeting inhibitor comprises inhibitors for cadherin-11, CD4, CD52, CD5, or combinations thereof.

18. The method of claim 11, wherein the cell recruitment inhibitor comprises inhibitors for adhesion molecules, chemokine, and / or chemokine receptors.

19. The method of claim 9, wherein the synthetic DMARD comprises a JAK inhibitor, barictinib, tofacitnib, upadacitinib, or combinations thereof.

20. The method of any of claims 1-7, wherein the therapeutic agent comprises abatacept, rituximab, ocrelizumab. ofatumumab, epratuzumab, tabalumab, CAR-Tcells, CAR-NK cells, tyrosine kinase inhibitors, TLR targeted therapy, memantine, anti-OX40 therapy, chemokine antagonists (e.g., CX3CR1 blockers), anti-NRPl, IDO inhibitors, calcineurin antagonists (e.g., sirolimus), TGF-beta blockade (including biologies and SMAD inhibitors), PD1 agonists (e.g., peresolimab), a TNF inhibitor, an IL-1 inhibitor, an IL-6 inhibitor, a TGFP inhibitor, one or more SMAD inhibitors, one or more inhibitors of B cell signaling and activation (e.g., BLyS or BTK), chemokine signal inhibitors (e.g., PI3K). a Janus kinase inhibitor, or any combination thereof.

21. The method of any one of the above claims, wherein the level of methylation detected using probe cg06609049 is reduced compared to the reference level.

22. The method of any of the above claims, wherein the biological sample comprises peripheral blood mononuclear cells (PMBCs), naive T cells, memoiy T cells, or B cells.

23. The method of any one of the above claims, wherein the subject is positive for anti-citrullinated protein autoantibodies (ACPA+).

24. The method of any of the above claims, wherein the autoimmune condition comprises rheumatoid arthritis, systemic lupus erythematosus, scleroderma, type 1 diabetes, multiple sclerosis, Hashimoto’s thyroiditis, Graves’ disease, Sjogren’s syndrome, inflammatory bowel disease, spondyloarthritis. celiac disease, myasthenia gravis, polymyositis, dermatomyositis, or Guillain-Barre syndrome.

25. The method of any one of the above claims, wherein the reference level is the methylation level detected using DNA isolated from patients diagnosed with clinical rheumatoid arthritis (RA).

26. The method of any one of the above claims, wherein the treatment comprises modulating the expression of the THOP1 gene.

27. The method of any one of the above claims, wherein methylation can be measured using methods comprising bisulfite sequencing, methylation-specific PCR, digital droplet PCR, methylation bead arrays, chromatin immunoprecipitation, or MALDI-TOF mass spectrometry.

28. The method of any one of the above claims, wherein the treatment modulating the one or more genes comprise vector-based gene therapy, small molecule activators, CRISPR-based gene editing, epigenetic modulation, transcription factor modulation, or any combinations thereof.

29. A method for selecting a therapeutic treatment for a subject with an autoimmune condition, comprising:(a) isolating genomic DNA from a biological sample or having genomic DNA isolated from a biological sample obtained from the subject;(b) determining a methylation signature in one or more types of cell types from the biological sample;(c) selecting a therapeutic treatment; and(d) administering the therapeutic agent to the subject to modulate expression of one or more of target genes.

30. The method of claim 29, wherein selecting a therapeutic treatment comprises selecting a genetic locus within the methylation signature of the biological sample and selecting a therapeutic treatment that targets the selected genetic locus.

31. The method of claim 29 or 30, wherein the subject is positive for anti- citrullinated protein autoantibodies (ACPA+).

32. The method of any of claims 30-31, wherein selecting the genetic locus in the biological sample comprises measuring a methylation level at a genetic locus in a biological sample using one or more probes comprising: cg06609049, cgl8531004, cg08900191, cg02331830, cgl4640588, cg06027620, cgl8937502, cgl3911068, cgl2983285, cgl7161130, cg27509052, cg08834383, cg20685674, cg02484776. cgl9337854. cg26559235. cg07073052.cg01811796, cgl5934191, cgl4037948, cg25539628, cgl9738121, cg23540453, cg!8437193, cgO8116728, cgl6316624, cgl8649203, cg02930132. cgl7364817. cg26314066, cg02998028, cgO8872313, cgl4076195, cgl9253743, cgl0511904, cg04109543, cgl6017144, cgl 1646704, cg26964117, cg04783733, cg21909090, cg09860364, cgOl 159592, cg23287902, cgl6882635, cg02500195, cgl 1444278, cg26881965, cg06086829, or cgl6528926 from a B cell; and / or cg06609049, cg!7754500, cg07219649, cg!8824330, cg!4143519. cg09390459, cg25087589, cgl2047260, cgl5558717, cgl8411660, cgl5032490, cg06729455, cg27166921, cgl l647944, cg04168494, cg07895523, cg03142975, cgl ll23198, cg02536811, cg05658543, cg02503332, cgl4037948, cg25861229, cg24248367, cgl2367971, cg01658566. cg05355436. cg04950931. cg00135449. cg04982843, cgl6791304, cgl8645910, cgl3867670, cg22533480, cgl4044930, cg07952581, cg21974923, cg00242955, cg07673127, cg26234470, cgl7724455, cg06880808, cg26352006, cg02789688, cgl6829732, cgl 1038423, cg26559235, cgl 1207385, cg03744043, or cgl0046671 from a memory T cell; and / or cg05949290, cg06609049, cg23540453, cg26975408, cg22262670, cg25941963, cgl 1970192, cgl8426060, cg03057712, cg0608352, cg08437936, cg03866600, cg07895523, cg05311909, cgl4037948, cg26514961, cgl6277608, cgl 1923627, cg07199354, cg27209389, cg20706691, cg02307277, cgl8522970, cg26193687, cgl8437193. cg09996455. cg09656274. cgl4217861. cg06027620. cgO 1160274, cg24774581 , cgl 9781641 , cgl 4940668, cgl 9339993, cgl 5420071 , cg05441960, cg26559235, cgl2505146, cgl 1644057, cg04479716, cg03874965, cgl5970145, cgl7575314, cgl5307168, cgl5066777, cg23766561, cgl7686260, cg07770777, cg09697880, or cg00843561 from a naive T cell, comparing the level of methylation at the genetic locus associated with the selected probe in the biological sample with a reference level of methylation, and selecting the genetic locus based on differentiated methylation compared with a reference level.

33. The method of claims 30-32. wherein selecting the genetic locus in the biological sample comprises measuring a methylation level at a genetic locus in a biological sample using one or more probes comprising:cg20274267, cgl l l63388, cg25317724, cgl7878321, cgl3996186, cg22337605, cg07538039, cgl0725441, cgl3576586, cgl8001827. cg27574654. cg24779384, or cg03422094 from a B cell; and / or cg00348968, cgl 1623260, cg22196946, or cg23407507 from a memory T cell; and / or cg08320413, cg24534742, cgl4414203, cg20332756, cgl4017435, cg24392372. cg24655284. cg09865323. cgl 1745755. or cg01601841 from a naive T cell, comparing the level of methylation at the genetic locus associated with the selected probe in the biological sample with a reference level of methylation, and selecting the genetic locus based on differentiated methylation compared with a reference level.

34. The method of any of claims 29-33, wherein the autoimmune condition comprises rheumatoid arthritis, systemic lupus erythematosus, scleroderma, type 1 diabetes, multiple sclerosis, Hashimoto’s thyroiditis, Graves’ disease, Sjogren’s syndrome, inflammatory bowel disease, spondyloarthritis, celiac disease, myasthenia gravis, polymyositis, dermatomyositis, or Guillain-Barre syndrome.

35. The method of any of claims 29-34, wherein the reference level is the methylation level detected using DNA isolated from patients diagnosed with clinical rheumatoid arthritis (RA).

36. The method of any of claims 29-35, wherein the level of methylation detected using probe cg06609049 is reduced compared to the reference level.

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