Skip to main content
PMC home page
Cambridge Prisms: Precision Medicine logoLink to Cambridge Prisms: Precision Medicine
. 2023 May 11;1:e25. doi: 10.1017/pcm.2023.14

Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders

Yuriy Baglaenko 1, Catriona Wagner 2, Vijay G Bhoj 3, Petter Brodin 4, M Eric Gershwin 5, Daniel Graham 6, Pietro Invernizzi 7,8, Kenneth K Kidd 9, Ilya Korsunsky 1, Michael Levy 10, Andrew L Mammen 11, Victor Nizet 12, Francisco Ramirez-Valle 13, Edward C Stites 9, Marc S Williams 14, Michael Wilson 15, Noel R Rose 2, Virginia Ladd 2, Marina Sirota 16,17,✉
PMCID: PMC10953750  PMID: 38550937

Abstract

Precision Medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Autoimmune diseases are those in which the body’s natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped identify patient subtypes and molecular pathways which can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity in the context of disease diagnostics and therapeutics.

Keywords: Autoimmunity, bioinformatics, data integration, diagnosis, genomics

Impact statement

Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. As defined by Christensen et al. ([2009], The Innovator’s Prescription: A Disruptive Solution for Health Care), precision medicine is provision of care for diseases that can be precisely diagnosed, whose causes are understood, and which consequently can be treated with rules-based therapies that are predictably effective. Autoimmune diseases are those in which the body’s natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. There are more than 80 types of autoimmune diseases that affect a wide range of organ systems. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped to identify patient subtypes and molecular pathways that can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity. Within that framework, improved diagnostics and targeted therapeutic approaches may advance toward precision clinical care of patients with autoimmune diseases.

Introduction

Autoimmune diseases are a diverse group of over 80 diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), multiple sclerosis (MS), ulcerative colitis (UC), and many others where the immune system attacks the body. While these diseases are primarily differentiated based on the primary target organ, they also share common features, including loss of tolerance and autoantibody production. Within each disease, there is considerable heterogeneity in clinical manifestations and disease progression, making diagnosis challenging. Furthermore, treatment options are often limited to general immunosuppressive treatments with significant toxicity and side effects with a limited number of targeted treatments. Due to a lack of predictive biomarkers, treatment decisions are primarily made empirically based on clinical symptoms and limited serological features, such as autoantibodies, resulting in substantial variation in treatment response. Therefore, new clinical strategies, rooted in precision medicine, are needed to accurately predict treatment response, identify novel therapeutic targets, reduce unexplained clinical variation in treatment, and improve clinical outcomes for autoimmune diseases.

Precision is the pursuit of being free from error. Precision medicine is, therefore, the intention to treat each person with as little error as possible using informed and carefully calibrated individually guided therapeutics. Determining the best course of action and moving toward precision medicine in autoimmune diseases entails tailoring targeted therapeutic approaches to an individual based on their underlying disease mechanisms often determined using large-scale molecular profiling and stratification. In some fields, such as oncology, this is already a reality. For example, cancer has a strong genetic component, and next-generation sequencing has led to the extensive use of precision medicine in oncology to aid diagnosis and treatment decisions. Patients with estrogen receptor-positive metastatic breast cancer, for instance, are treated with endocrine therapies (Manohar and Davidson, 2021), whereas patients who express human epidermal growth factor receptor-2 (HER-2) are treated with monoclonal antibodies specifically targeting HER-2 (Goutsouliak et al., 2020). Engineered chimeric antigen receptor (CAR) T cells that recognize specific tumor antigens have also been investigated as targeted individualized therapies for certain blood cancers (Ye et al., 2018). PD-L1 levels are used to determine patients who would benefit from PD-1 antagonists. In addition, precision medicine is used to treat monogenic diseases, such as cystic fibrosis, where affected individuals are treated according to the underlying mutations in the cystic fibrosis transmembrane conductance regulator gene (Lopes-Pacheco, 2020).

Precision medicine in autoimmune diseases has been more challenging due to the complex etiologies of these conditions, involving an interplay between genetic and environmental factors. However, recent technological and bioinformatic advances have helped reveal novel molecular pathways, and characterize disease heterogeneity, leading to the first biopsy-driven clinical trial (Humby et al., 2021), paving the way for precision medicine in autoimmunity. Inspired by the Precision Medicine: Relevance to Autoimmune Disease Colloquium, organized by the Autoimmune Association and Dr. Noel R. Rose in 2020, this review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to targeted therapeutic approaches to improve clinical care of patients with autoimmune diseases.

Resolving patient heterogeneity

Autoimmune diseases are frequently characterized by clinical features or autoantibody prevalence; however, these features are heterogeneous and often overlap between autoimmune diseases, hindering precise diagnosis, and early treatment. Therefore, moving toward molecular diagnostics, which define disease based on changes in biological molecules, may aid diagnosis, and improve clinical outcomes of autoimmune diseases. Recently, exome and genome sequencing have shown promise for identifying pathogenic genetic variants in cases of rare monogenic diseases (Boycott et al., 2019), including patients with autoinflammatory diseases (Kosukcu et al., 2020) such as hereditary fever syndromes. However, these are the rare exceptions. The genetic causes of most autoimmune diseases are complex and genetic risk is determined predominantly by the human leukocyte antigens (HLA) locus, which has the strongest association to rheumatic diseases. Outside of the HLA region, which can account for up to 50% of the genetic risk of a given complex autoimmune trait, hundreds of variants identified through genome-wide association studies (GWASs) each have small additive individual effects, making a diagnosis of autoimmune diseases based solely on genetics currently impossible. In RA, the number of RA-associated risk alleles weighted by the odds ratio correlates with disease risk; however, the predictive power of genetic risk scores is modest and not currently suitable for use in clinical practice (Karlson et al., 2010; Dudbridge, 2013).

As genetic variants identified by GWAS are common variants (generally found in 1% or more of the population – a consequence of study design) and only modestly increase the risk of autoimmune diseases, rare variants with strong effects may contribute to the missing heritability of some patients with autoimmune diseases. For example, following the discovery of mutations in the TREX1 gene causing the type I interferonopathy Aicardi–Goutières syndrome, TREX1 variants were identified in up to 0.5–2% of patients with SLE (Lee-Kirsch et al., 2007; Namjou et al., 2011). More recently, exome sequencing identified two rare variants in BLK and BANK1 in a subset of patients with SLE that increased type I interferon (IFN) activity (Jiang et al., 2019). A recently published paper illustrated the role of rare variants in TLR-7 in monogenic SLE demonstrating that with more accessible and available whole exome and genome sequencing, we will learn more about the role of rare variants in autoimmune diseases (Brown et al., 2022). Together, these studies suggest that rare variants may contribute to the genetic risk and clinical heterogeneity of autoimmune diseases. However, the extensive heterogeneity within each autoimmune disease suggests that multiple pathways may contribute to disease; therefore, identifying subgroups of patients with shared molecular signatures is the best avenue to improve the diagnosis and treatment of patients with autoimmune diseases.

As one example, multiple studies have determined subsets of patients with SLE using transcriptomic approaches (Lyons et al., 2012; Banchereau et al., 2016; Toro-Domínguez et al., 2018; Figgett et al., 2019; Panousis et al., 2019; Andreoletti et al., 2021; Sandling et al., 2021). Initial investigations found that approximately half of the patients with SLE exhibit increased peripheral blood expression of type I IFN-regulated genes, termed the “IFN signature,” associated with more severe disease (Baechler et al., 2003; Bennett et al., 2003), suggesting that a subset of patients with SLE may benefit from therapies targeting the IFN pathway. Consistent with these findings, the recently approved monoclonal antibody anifrolumab, which targets the type I IFN receptor subunit 1, is effective in about 16% of patients with SLE (Morand et al., 2020). However, there are conflicting reports regarding the effectiveness of stratifying patients based on IFN gene signatures in clinical trials of type I IFN inhibition (Khamashta et al., 2016; Furie et al., 2017; Morand et al., 2020), demonstrating the complexity of the type I IFN response in SLE and identifying the need for additional stratification approaches.

To further refine the IFN signature in SLE, Chiche et al. (2014) found that three distinct transcriptional IFN groups or modules were associated with 87% of patients with SLE and that all types of IFN, not just type I IFN, contributed to the IFN signatures. Importantly, patients with SLE could be further stratified based on the number of active IFN modules (Chiche et al., 2014). In 2016, Banchereau et al. (2016) confirmed and extended these findings in a large cohort of pediatric patients with SLE, identifying overexpression of additional transcriptional modules that correlated with disease activity and clinical parameters of SLE (Banchereau et al., 2016). In addition, patients were stratified into seven clusters based on five immune signatures correlating with disease activity, including type I IFN-, neutrophil-, and plasmablast-associated signatures (Banchereau et al., 2016). Using a similar approach, Toro‐Domínguez et al. (2018, 2019) identified three SLE patient clusters characterized by a lymphocyte or neutrophil signature that may respond differently to treatments.

Most transcriptomic studies in SLE use whole blood or bulk cell input making it difficult to discern the affected cell populations. Therefore, single-cell analyses may be necessary to identify and refine molecular clusters in disease-relevant cell state (Perez et al., 2022). Using single-cell RNA sequencing, Nehar-Belaid et al. (2020) defined the cellular subgroups that contributed to the IFN signature in pediatric SLE, including T cells, dendritic cells (DCs), monocytes, and natural killer (NK) cells. Notably, the clustering of these cell types revealed six distinct subgroups of patients associated with disease activity (Nehar-Belaid et al., 2020). In a recent study, Andreoletti et al. (2021) determined unique subgroups of patients based on the transcriptional profiles of sorted monocytes, B cells, CD4+ T cells, and NK cells that correlated with disease activity and ethnicity. In addition, multi-omic approaches may also improve patient stratification, as seen in a study by Guthridge et al. (2020), in which integration of transcriptional modules and autoantibody and soluble mediator profiles identified seven patient clusters with distinct molecular pathways but similar clinical outcomes. In another study, Lanata et al. (2019) used clinical features to define three distinct subgroups of SLE with unsupervised clustering that was supported by differential methylation patterns and ethnicity. Several of these studies explore multi-ethnic cohorts. There are known differences in SLE disease manifestations and severity across different racial and ethnic groups. When exploring biological differences across different patient groups, it’s important to note the potential inaccuracy or lack of specification between self-reported and genetic-driven subgroups which may contribute to interpretation problems as ethnicity may be more predictive of differences due to disparities than genetic background (Mersha and Abebe, 2015).

Interestingly, genomic studies have found that autoimmune diseases have shared genetic associations, suggesting that similar pathogenic mechanisms may contribute to different autoimmune diseases (Zhernakova et al., 2009; Richard-Miceli and Criswell, 2012). Indeed, transcriptome and methylome analysis of patients with seven autoimmune diseases demonstrated four patient clusters that differed in the expression of inflammatory, lymphoid, or IFN signature (Barturen et al., 2021). Notably, patients with different autoimmune diseases were found within each cluster (Barturen et al., 2021). Studies using immunophenotyping (Kroef et al., 2020; Martin‐Gutierrez et al., 2021) and soluble mediator profiling (Slight-Webb et al., 2021) also found that patients with different autoimmune diseases share similar molecular signatures. Thus, diagnosing patients based on molecular signatures in addition to clinical features may be a key step in moving toward precision medicine and targeted therapeutics.

Taken together, it becomes clear that autoimmune disorders comprise a wide spectrum of clinical manifestations. With the use of genomics, transcriptomics, and other multi-omic approaches, we can begin to examine these complex disorders under a magnifying glass to better define patient heterogeneity and identify targetable genes and pathways.

Defining the Autoantigenome

As autoimmune disorders are characterized by the body’s response to self, defining that exact “self” is critical to both treatment and diagnosis. Autoantibodies, a key component of disease that often directly contribute to outcomes, provide a window into defining these self-antigens and peptides. Of course, autoantibodies do not develop in a vacuum, and certain HLA alleles are strongly associated with autoimmune diseases (Liu et al., 2021), indicating a key role for T cell help and antigen presentation in disease pathogenesis. Understanding and defining the interaction of these three components – autoantibodies, HLA alleles, and T cell repertoire – could identify novel therapeutic targets and molecular diagnostics.

The antibody and T cell repertoires are highly diverse due to recombination of variable, diversity, and joining gene segments, followed by somatic hypermutation in B cell receptors, making the identification of antigen specificity challenging. However, recent advances in Next Generation Sequencing (NGS) and computational approaches have enabled large-scale sequencing of antibody and T Cell Receptor (TCR) repertoires in autoimmune diseases (Zemlin et al., 2002; Schatz and Ji, 2011; Rechavi and Somech, 2017; Nielsen and Boyd, 2019; Nielsen et al., 2019).

Anti-citrullinated protein antibodies (ACPAs) that recognize the posttranslational modification of the amino acid citrulline are a hallmark of RA and contribute to disease pathogenesis (Kurowska et al., 2017). Antibodies consist of two heavy- and light-chain pairs, which both contain antigen-binding domains; therefore, pairing heavy- and light-chains is necessary to determine antigen specificity (Robinson, 2015). To accomplish this, Tan et al. (2014) developed a novel DNA barcoding method to sequence heavy- and light-chain pairs from antibody-producing plasmablasts in ACPA-positive patients with RA and determined affinity-matured clonal families of antibodies. Recombinant expression of 14 antibodies identified four ACPAs with differential targeting of α-enolase, citrullinated fibrinogen, and citrullinated histone H2B (Tan et al., 2014). Additional studies confirmed that ACPAs undergo affinity maturation, resulting in epitope spreading and polyreactivity with other post-translationally modified proteins (Elliott et al., 2018; Titcombe et al., 2018; Kongpachith et al., 2019; Steen et al., 2019).

Repertoire analyses of plasmablasts from healthy individuals with RA-associated autoantibodies demonstrated elevated IgA responses (Kinslow et al., 2016), suggesting that ACPAs may originate from mucosal immune responses. Furthermore, serial analyses of patients with RA found that ACPAs that persisted over time were predominantly IgA (Elliott et al., 2018), consistent with continued mucosal antigen exposure. Therefore, identifying the specific mucosal antigens targeted by these ACPAs may help identify tolerizing therapies for patients with RA.

Early studies have identified expanded CD4+ T cell clones in the peripheral blood and synovial tissue of patients with RA (Goronzy et al., 1994; Ikeda et al., 1996; Schmidt et al., 1996; VanderBorght et al., 2000; Wagner et al., 2003), including early in the disease course (Klarenbeek et al., 2012). Phenotypic analysis combining TCR sequencing and single-cell transcriptomics revealed expanded memory CD4+ T cell clones with upregulated senescence-related transcripts, chemokine receptors, and CD5 expression, suggestive of antigen stimulation and autoreactivity (Ishigaki et al., 2015). However, the autoantigens targeted by CD4+ T cells in RA remain elusive.

The HLA-DRB1 RA susceptibility alleles contain five shared amino acids of the β1 subunit, referred to as the shared epitope, which is associated with ACPA production (van Gaalen et al., 2004; Huizinga et al., 2005; Busch et al., 2019). There is also significant clinical evidence of differential response based on mechanism in RA patients based on their ACPA/HLA epitope. HLA-DRB1 risk alleles for RA are associated with differential clinical responsiveness to abatacept and adalimumab according to the data from a head-to-head, randomized, single-blind study in autoantibody-positive early RA (Rigby et al., 2021). GWAS analysis demonstrated that an amino acid within the P4 pocket of the peptide-binding groove strongly contributed to the association of HLA-DRB1 and RA (Raychaudhuri et al., 2012), suggesting that the shared epitope may allow binding and presentation of citrullinated autoantigens. Consistent with this hypothesis, antigen discovery analyses using peptide stimulation or peptide–MHC tetramers revealed Th1 and Th17 reactivity to citrullinated antigens, including α-enolase, fibrinogen, vimentin, and aggrecan, in the peripheral blood of patients with RA (Delwig et al., 2010; Law et al., 2012; Scally et al., 2013; James et al., 2014; Gerstner et al., 2020). In addition, T cells specific for citrullinated fibrinogen contribute to the development and progression of RA in mouse models (Hill et al., 2008; Cordova et al., 2013).

Although progress has been made in the identification of autoantigens targeted in autoimmune diseases using microarrays, mass spectrometry, and phage-display assays, these approaches are limited by the need to prespecify the antigens to be studied. Therefore, due to the large number and diversity of antibodies and TCRs, computational methods are needed to predict target antigens from the TCR or antibody sequence alone. Recent progress has been made to predict TCR specificity based on the hypothesis that TCRs that recognize the same antigen share CDR3 sequence motifs. In two separate studies, Dash et al. (2017) and Glanville et al. (2017) developed different algorithms (TCRdist – https://tcrdist3.readthedocs.io/en/latest/ and GLIPH – http://50.255.35.37:8080/, respectively) that clustered TCRs dependent on CDR3 motifs and accurately defined TCR specificity based on these clusters. However, although these approaches are promising, they are limited by the availability of pre-existing knowledge of TCR specificities to make predictions, and large-scale approaches to define these interactions are required. In a recent study, Zhang et al. (2020) clustered tumor TCRs based on antigen-specificity using iSMART and identified novel antigens by integrating TCR clusters, tumor genomics, and HLA genotypes (Zhang et al., 2020). Therefore, multi-omic approaches paired with CDR3 clustering may also help define novel antigens targeted in autoimmune diseases.

In terms of precision medicine, a better understanding of the antigens, TCRs, HLAs, and BCRs driving disease offers a therapeutic window into these diverse disorders. Tolerizing therapies that target specific peptides or regulatory CAR-T cells offer a way to directly suppress autoimmune responses on a patient-by-patient basis.

The path to targeted therapeutics

Recent genomic and transcriptomic approaches have determined novel pathogenic mechanisms and begun to unravel the heterogeneity of autoimmune diseases, revealing potential therapeutic targets for precision medicine. This section will discuss current work applying knowledge obtained through genomic and transcriptomic studies toward precision medicine approaches.

Discovering novel therapeutic targets

Genetic analyses of monogenic autoinflammatory diseases have been pivotal in identifying druggable targets that are now used in clinical care (Manthiram et al., 2017). For example, therapies targeting IL-1, such as anakinra, are approved for the inflammasomopathy cryopyrin-associated periodic fever syndrome (Hoffman, 2009). Genetic studies have also revealed efficacious therapeutic targets in polygenic autoimmune disorders. Genetic variation in the Janus kinase family member tyrosine kinase 2 (TYK2), required for type 1 IFN, IL-12 and IL-23 signaling (Sohn et al., 2013; Burke et al., 2019), is associated with autoimmune diseases, including psoriasis (Genetic Analysis of Psoriasis Consortium & the Wellcome Trust Case Control Consortium 2 et al., 2010; Ellinghaus et al., 2012; Tsoi et al., 2012), psoriatic arthritis (Mease et al., 2022), Crohn’s disease (Franke et al., 2010), and SLE (Sigurdsson et al., 2005; Graham et al., 2011; Tang et al., 2015; Lee and Bae, 2016). In phase II and III clinical trials, the TYK2 inhibitor deucravacitinib (BMS-986165) was more effective compared to placebo in patients with moderate-to-severe plaque psoriasis (Papp et al., 2018), and is now approved in the US, EU, and other regions. In addition, deucravacitinib is being investigated in early trials of Crohn’s disease and SLE demonstrating efficacy in phase II trials in PsA (Mease et al., 2022) and SLE (Morand et al., 2023). However, with polygenic autoimmune diseases, not all identified gene variants may be effective drug targets. Therefore, moving beyond individual genes toward gene networks using in silico drug efficacy screening, such as drug–disease network proximity analyses (Kim et al., 2020), to predict potential therapies is needed for drug discovery in autoimmunity. Using this approach, Cordell et al. (2021) recently identified 56 genetic variants associated with primary biliary cholangitis in a genome-wide meta-analysis and predicted several candidate therapies for the disease, including approved treatments of other autoimmune diseases.

Translating genomics to cell function may also identify potentially targetable pathways. Smillie et al. (2019) created a cell atlas of UC using single-cell transcriptomics, highlighting the cells that change in proportions or gene expression compared to healthy tissues. In addition, mapping UC-associated risk alleles onto the cell atlas demonstrated enrichment of risk alleles in individual cell lineages, including M-like cells that exhibited high expression of multiple risk alleles, providing important information about disease etiology and molecular pathways (Smillie et al., 2019). There are several other large-scale efforts to generate single-cell transcriptomic and proteomic datasets in RA and SLE as well as other autoimmune diseases that are able to elucidate cell type specific disease associated genes and pathways (Zhang et al., 2019). However, comprehensive multi-disease cell atlases are needed to provide further insights, which require the integration of multiple large-scale studies and data sets that may have been collected under different conditions. To avoid confounding variables between studies, multiple computational approaches have been created to remove batch effects (Butler et al., 2018; Haghverdi et al., 2018; Hie et al., 2019; Korsunsky et al., 2019; Polański et al., 2020; Tran et al., 2020). As one example, the algorithm Harmony (Korsunsky et al., 2019) was used to integrate single-cell transcriptomic profiles from multiple disease datasets, revealing a CXCL10+CCL2+ inflammatory macrophage phenotype in the tissues of patients with RA, Crohn’s disease, UC, and COVID-19 (Consortium et al., 2021), suggesting that the same pathway may be targeted in distinct diseases.

Antigen-specific therapies

Identifying antigens targeted by antibodies and T cells in autoimmune diseases will allow for the development of antigen-specific therapies, aiming to restore immune tolerance in autoreactive lymphocytes while maintaining overall immune surveillance to infections and cancer. Tolerogenic DCs have been tested in early phase clinical trials for multiple autoimmune diseases, such as RA, Crohn’s disease, and MS (Phillips et al., 2017). In a recent phase 1b trial, autologous tolerogenic DCs loaded with myelin-derived antigens and aquaporin-4 were analyzed for efficacy in MS and neuromyelitis optica spectrum disorders (NMOSDs; Zubizarreta et al., 2019). The tolerogenic DC therapy was well-tolerated and induced IL-10 production by peptide-stimulated cells and a trend toward an increase in regulatory T cells, compatible with tolerance induction (Zubizarreta et al., 2019).

The therapeutic potential of polyclonal regulatory T cells has also been demonstrated in some autoimmune diseases, including MS (Kohm et al., 2002); however, the effects are mostly modest, possibly because of nonspecific regulatory T cells (Raffin et al., 2020). Therefore, based on knowledge acquired from T cell therapies in oncology, another approach is the generation of autologous antigen-specific regulatory T cells by transfecting TCRs or CARs for autoantigens. Kim et al. (2018) transduced human regulatory T cells with a myelin-basic protein-specific TCR isolated from an MS patient and demonstrated that the MBP-specific regulatory T cells suppressed MBP-specific effector cells in vitro and ameliorated disease in a mouse model of MS. Therefore, although still in the preclinical phase, antigen-specific regulatory T cells show promise for the treatment of MS.

Computational drug repurposing

Identifying new uses for approved drugs, or drug repurposing, will also benefit precision medicine in autoimmune diseases, as conventional drug discovery is often costly and time consuming (DiMasi et al., 2003). However, traditional drug repurposing relies on high-throughput screening technologies that can also be costly; therefore, novel methods are required to expand drug repurposing efforts. Sirota et al. (2011) developed a systematic computational approach to predict disease–drug relationships by comparing gene expression signatures of diseases with those of FDA-approved drugs. This approach identified a novel therapeutic association of an antiepileptic drug, topiramate, with inflammatory bowel disease that was efficacious in a rodent model of colitis (Dudley et al., 2011).

Caveats and conclusions

Autoimmune disorders are a highly heterogeneous class of conditions. Even within a single clinically diagnosed condition, such as SLE, the underlying causes and manifestations are highly variable. To better treat these disorders and transition toward a precision medicine framework, the last decade of research has used in-depth genetic and genomic studies to better resolve patient heterogeneity and identify the autoantigenome. The progress described in this review represents a substantial leap forward in both our understanding of these complex diseases and their potential treatments.

Although these are not the focus of the current review, there are several additional considerations that are important to note. For complex autoimmune diseases, environment, the interaction of genetics and environment as well as dietary, and lifestyle factors play an important role in affecting disease pathogenesis, progression, and treatment response. For instance, a recent study has shown that oral mucosal breaks trigger anti-citrullinated bacterial and human protein antibody responses in RA demonstrating the role of pathogens and environment in the disease (Brewer et al., 2023). Studies focusing on molecular pathological epidemiology research, which can investigate those factors in relation to molecular pathologies and clinical outcomes have been explored for other conditions such as cancer (Hamada et al., 2017; Hughes et al., 2017; Ogino et al., 2018). It is also important to note that the majority of existing studies in molecular profiling, genomics and genetics of autoimmune diseases have been carried out in patients of European background. If precision medicine is truly the goal, there is a need to explore social determinants of health in the context of disease progression and treatment response in diverse populations. More extensive studies are needed to explore the combination and interaction of the molecular, clinical, social, and environmental factors in diverse patient populations to achieve precision medicine for autoimmunity.

From using genetics to identify new gene targets, to using single-cell genomics to identify cellular and molecular subsets of disease, to computational approaches that aim to merge all this together and repurpose medicine in a targeted fashion, precision medicine in autoimmunity is an endeavor that will continue to yield enormous insights and lead to better – and more importantly – error-free therapeutics.

Open peer review

To view the open peer review materials for this article, please visit http://doi.org/10.1017/pcm.2023.14.

Author contribution

Y.B. and C.W. wrote the manuscript. M.S. edited the manuscript and oversaw the review. All authors read and approved the final manuscript.

Financial support

This work was in part supported by NIH P30 AR070155 (M.S.) and the Autoimmune Association (all authors).

Competing interest

M.S. is an advisor to Exagen. M.W. receives research funding from Genentech and Novartis; has received speaking honoraria from Genentech, Novartis, Takeda, and WebMD; and is a co-founder and is on the Board of Directors for Delve Bio. P.I. receives grants support from Intercept and is an advisory board member for Intercept, Advanz, Calliditas, Zydus, Ipsen and CymaBay. F.R.-V. is an employee and stockholder of Bristol Myers Squibb. V.B. is an inventor of patents licensed to Cabaletta Bio related to using engineered T cells for the treatment of autoimmunity.

References

  1. Andreoletti G, Lanata CM, Trupin L, Paranjpe I, Jain TS, Nititham J, Taylor KE, Combes AJ, Maliskova L, Ye CJ, Katz P, Dall’Era M, Yazdany J, Criswell LA and Sirota M (2021) Transcriptomic analysis of immune cells in a multi-ethnic cohort of systemic lupus erythematosus patients identifies ethnicity- and disease-specific expression signatures. Communications Biology 4(1), 488. 10.1038/s42003-021-02000-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ, Shark KB, Grande W, Hughes KM, Kapur V, Gregersen PK and Behrens TW (2003) Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proceedings of the National Academy of Sciences 100(5), 2610–2615. 10.1073/pnas.0337679100 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Banchereau R, Hong S, Cantarel B, Baldwin N, Baisch J, Edens M, Cepika A-M, Acs P, Turner J, Anguiano E, Vinod P, Kahn S, Obermoser G, Blankenship D, Wakeland E, Nassi L, Gotte A, Punaro M, Liu Y-J, Banchereau J, Rossello-Urgell J, Wright T and Pascual V (2016) Personalized immunomonitoring uncovers molecular networks that stratify lupus patients. Cell 165(3), 551–565. 10.1016/j.cell.2016.03.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Barturen G, Babaei S, Català-Moll F., Martínez-Bueno M, Makowska Z, Martorell-Marugán J, Carmona-Sáez P, Toro-Domínguez D, Carnero-Montoro E, Teruel M, Kerick M, Acosta-Herrera M, le Lann L, Jamin C, Rodríguez-Ubreva J, García-Gómez A, Kageyama J, Buttgereit A, Hayat S, Mueller J, Lesche R, Hernandez-Fuentes M, Juarez M, Rowley T, White I, Marañón C, Gomes Anjos T, Varela N, Aguilar-Quesada R, Garrancho FJ, López-Berrio A, Rodriguez Maresca M, Navarro-Linares H, Almeida I, Azevedo N, Brandão M, Campar A, Faria R, Farinha F, Marinho A, Neves E, Tavares A, Vasconcelos C, Trombetta E, Montanelli G, Vigone B, Alvarez-Errico D, Li T, Thiagaran D, Blanco Alonso R, Corrales Martínez A, Genre F, López Mejías R, Gonzalez-Gay MA, Remuzgo S, Ubilla Garcia B, Cervera R, Espinosa G, Rodríguez-Pintó I, de Langhe E, Cremer J, Lories R, Belz D, Hunzelmann N, Baerlecken N, Kniesch K, Witte T, Lehner M, Stummvoll G, Zauner M, Aguirre-Zamorano MA, Barbarroja N, Castro-Villegas MC, Collantes-Estevez E, Ramon E, Díaz Quintero I, Escudero-Contreras A, Fernández Roldán MC, Jiménez Gómez Y, Jiménez Moleón I, Lopez-Pedrera R, Ortega-Castro R, Ortego N, Raya E, Artusi C, Gerosa M, Meroni PL, Schioppo T, de Groof A, Ducreux J, Lauwerys B, Maudoux AL, Cornec D, Devauchelle-Pensec V, Jousse-Joulin S, Jouve PE, Rouvière B, Saraux A, Simon Q, Alvarez M, Chizzolini C, Dufour A, Wynar D, Balog A, Bocskai M, Deák M, Dulic S, Kádár G, Kovács L, Cheng Q, Gerl V, Hiepe F, Khodadadi L, Thiel S, Rinaldis E, Rao S, Benschop RJ, Chamberlain C, Dow ER, Ioannou Y, Laigle L, Marovac J, Wojcik J, Renaudineau Y, Borghi MO, Frostegård J, Martín J, Beretta L, Ballestar E, McDonald F, Pers JO and Alarcón-Riquelme ME (2021) Integrative analysis reveals a molecular stratification of systemic autoimmune diseases. Arthritis & Rheumatology 73(6), 1073–1085. doi: 10.1002/art.41610 [DOI] [PubMed] [Google Scholar]
  5. Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, Banchereau J and Pascual V (2003) Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. Journal of Experimental Medicine 197(6), 711–723. 10.1084/jem.20021553 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Boycott KM, Hartley T, Biesecker LG, Gibbs RA, Innes AM, Riess O, Belmont J, Dunwoodie SL, Jojic N, Lassmann T, Mackay D, Temple IK, Visel A and Baynam G (2019) A diagnosis for all rare genetic diseases: The horizon and the next frontiers. Cell 177(1), 32–37. 10.1016/j.cell.2019.02.040 [DOI] [PubMed] [Google Scholar]
  7. Brewer RC, Lanz TV, Hale CR, Sepich-Poore GD, Martino C, Swafford AD, Carroll TS, Kongpachith S, Blum LK, Elliott SE, Blachere NE, Parveen S, Fak J, Yao V, Troyanskaya O, Frank MO, Bloom MS, Jahanbani S, Gomez AM, Iyer R, Ramadoss NS, Sharpe O, Chandrasekaran S, Kelmenson LB, Wang Q, Wong H, Torres HL, Wiesen M, Graves DT, Deane KD, Holers VM, Knight R, Darnell RB, Robinson WH and Orange DE (2023) Oral mucosal breaks trigger anti-citrullinated bacterial and human protein antibody responses in rheumatoid arthritis. Science Translational Medicine 15(684), eabq8476. 10.1126/scitranslmed.abq8476 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Brown GJ, Cañete PF, Wang H, Medhavy A, Bones J, Roco JA, He Y, Qin Y, Cappello J, Ellyard JI, Bassett K, Shen Q, Burgio G, Zhang Y, Turnbull C, Meng X, Wu P, Cho E, Miosge LA, Andrews TD, Field MA, Tvorogov D, Lopez AF, Babon JJ, López CA, Gónzalez-Murillo Á, Garulo DC, Pascual V, Levy T, Mallack EJ, Calame DG, Lotze T, Lupski JR, Ding H, Ullah TR, Walters GD, Koina ME, Cook MC, Shen N, de Lucas Collantes C, Corry B, Gantier MP, Athanasopoulos V and Vinuesa CG (2022) TLR7 gain-of-function genetic variation causes human lupus. Nature 605(7909), 349–356. 10.1038/s41586-022-04642-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Burke JR, Cheng L, Gillooly KM, Strnad J, Zupa-Fernandez A, Catlett IM, Zhang Y, Heimrich E, Mcintyre K, Cunningham MD, Carman J, Zhou X, Banas D, Chaudhry C, Li S, D’Arienzo C, Chimalakonda A, Yang X, Xie JH, Pang J, Zhao Q, Rose SM, Huang J, Moslin RM, Wrobleski ST, Weinstein DS and Salter-Cid LM (2019) Autoimmune pathways in mice and humans are blocked by pharmacological stabilization of the TYK2 pseudokinase domain. Science Translational Medicine 11(502), eaaw1736. 10.1126/scitranslmed.aaw1736 [DOI] [PubMed] [Google Scholar]
  10. Busch R, Kollnberger S and Mellins ED (2019) HLA associations in inflammatory arthritis: Emerging mechanisms and clinical implications. Nature Reviews Rheumatology 15(6), 364–381. 10.1038/s41584-019-0219-5 [DOI] [PubMed] [Google Scholar]
  11. Butler A, Hoffman P, Smibert P, Papalexi E and Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology 36(5), 411–420. 10.1038/nbt.4096 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chiche L, Jourde‐Chiche N, Whalen E, Presnell S, Gersuk V, Dang K, Anguiano E, Quinn C, Burtey S, Berland Y, Kaplanski G, Harle J-R, Pascual V and Chaussabel D (2014) Modular transcriptional repertoire analyses of adults with systemic lupus erythematosus reveal distinct type I and type II interferon signatures. Arthritis & Rheumatology 66(6), 1583–1595. 10.1002/art.38628 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Christensen CM, Grossman JH and Hwang J (2009) The Innovator’s Prescription: A Disruptive Solution for Health Care. New York: McGraw-Hill. [Google Scholar]
  14. Cordell HJ, Fryett JJ, Ueno K, Darlay R, Aiba Y, Hitomi Y, Kawashima M, Nishida N, Khor SS, Gervais O, Kawai Y, Nagasaki M, Tokunaga K, Tang R, Shi Y, Li Z, Juran BD, Atkinson EJ, Gerussi A, Carbone M, Asselta R, Cheung A, de Andrade M, Baras A, Horowitz J, MAR Ferreira, Sun D, Jones DE, Flack S, Spicer A, Mulcahy VL, Byun J, Han Y, Sandford RN, Lazaridis KN, Amos CI, Hirschfield GM, Seldin MF, Invernizzi P, Siminovitch KA, Ma X, Nakamura M, Mells GF; PBC Consortia; Canadian PBC Consortium; Chinese PBC Consortium; Italian PBC Study Group; Japan-PBC-GWAS Consortium; US PBC Consortium and UK-PBC Consortium (2021) An international genome-wide meta-analysis of primary biliary cholangitis: Novel risk loci and candidate drugs. Journal of Hepatology 75(3), 572–581. 10.1016/j.jhep.2021.04.055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cordova KN, Willis VC, Haskins K and Holers VM (2013) A citrullinated fibrinogen-specific T cell line enhances autoimmune arthritis in a mouse model of rheumatoid arthritis. Journal of Immunology 190(4), 1457–1465. 10.4049/jimmunol.1201517 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Dash P, Fiore-Gartland AJ, Hertz T, Wang GC, Sharma S, Souquette A, Crawford JC, Clemens EB, Nguyen THO, Kedzierska K, La Gruta NL, Bradley P and Thomas PG (2017) Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547(7661), 89–93. 10.1038/nature22383 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Delwig Av, Locke J, Robinson JH and Ng W (2010) Response of Th17 cells to a citrullinated arthritogenic aggrecan peptide in patients with rheumatoid arthritis. Arthritis and Rheumatism 62(1), 143–149. 10.1002/art.25064 [DOI] [PubMed] [Google Scholar]
  18. DiMasi JA, Hansen RW and Grabowski HG (2003) The price of innovation: New estimates of drug development costs. Journal of Health Economics 22(2), 151–185. 10.1016/s0167-6296(02)00126-1 [DOI] [PubMed] [Google Scholar]
  19. Dudbridge F (2013) Power and predictive accuracy of polygenic risk scores. PLoS Genetics 9(3), e1003348. 10.1371/journal.pgen.1003348 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, Morgan AA, Sarwal MM, Pasricha PJ and Butte AJ (2011) Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Science Translational Medicine 3(96), 96ra76. 10.1126/scitranslmed.3002648 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Ellinghaus D, Ellinghaus E, Nair RP, Stuart PE, Esko T, Metspalu A, Debrus S, Raelson JV, Tejasvi T, Belouchi M, West SL, Barker JN, Kõks S, Kingo K, Balschun T, Palmieri O, Annese V, Gieger C, Wichmann HE, Kabesch M, Trembath RC, Mathew CG, Abecasis GR, Weidinger S, Nikolaus S, Schreiber S, Elder JT, Weichenthal M, Nothnagel M and Franke A (2012) Combined analysis of genome-wide association studies for Crohn disease and psoriasis identifies seven shared susceptibility loci. American Journal of Human Genetics 90(4), 636–647. 10.1016/j.ajhg.2012.02.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Elliott SE, Kongpachith S, Lingampalli N, Adamska JZ, Cannon BJ, Mao R, Blum LK and Robinson WH (2018) Affinity maturation drives epitope spreading and generation of proinflammatory anti-citrullinated protein antibodies in rheumatoid arthritis. Arthritis & Rheumatology 70(12), 1946–1958. 10.1002/art.40587 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Figgett WA, Monaghan K, Ng M, Alhamdoosh M, Maraskovsky E, Wilson NJ, Hoi AY, Morand EF and Mackay F (2019) Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus. Clinical & Translational Immunology 8(12), e01093. 10.1002/cti2.1093 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Franke A, McGovern DPB, Barrett JC, Wang K, Radford-Smith GL, Ahmad T, Lees CW, Balschun T, Lee J, Roberts R, Anderson CA, Bis JC, Bumpstead S, Ellinghaus D, Festen EM, Georges M, Green T, Haritunians T, Jostins L, Latiano A, Mathew CG, Montgomery GW, Prescott NJ, Raychaudhuri S, Rotter JI, Schumm P, Sharma Y, Simms LA, Taylor KD, Whiteman D, Wijmenga C, Baldassano RN, Barclay M, Bayless TM, Brand S, Büning C, Cohen A, Colombel JF, Cottone M, Stronati L, Denson T, de Vos M, D’Inca R, Dubinsky M, Edwards C, Florin T, Franchimont D, Gearry R, Glas J, van Gossum A, Guthery SL, Halfvarson J, Verspaget HW, Hugot JP, Karban A, Laukens D, Lawrance I, Lemann M, Levine A, Libioulle C, Louis E, Mowat C, Newman W, Panés J, Phillips A, Proctor DD, Regueiro M, Russell R, Rutgeerts P, Sanderson J, Sans M, Seibold F, Steinhart AH, Stokkers PCF, Torkvist L, Kullak-Ublick G, Wilson D, Walters T, Targan SR, Brant SR, Rioux JD, D’Amato M, Weersma RK, Kugathasan S, Griffiths AM, Mansfield JC, Vermeire S, Duerr RH, Silverberg MS, Satsangi J, Schreiber S, Cho JH, Annese V, Hakonarson H, Daly MJ and Parkes M (2010) Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nature Genetics 42(12), 1118–1125. 10.1038/ng.717 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Furie R, Khamashta M, Merrill JT, Werth VP, Kalunian K, Brohawn P, Illei GG, Drappa J, Wang L, Yoo S and CD1013 Study Investigators (2017) Anifrolumab, an anti–interferon‐α receptor monoclonal antibody, in moderate‐to‐severe systemic lupus erythematosus. Arthritis & Rheumatology 69(2), 376–386. 10.1002/art.39962 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Genetic Analysis of Psoriasis Consortium & the Wellcome Trust Case Control Consortium 2, Strange A, Capon F, Spencer CC, Knight J, Weale ME, Allen MH, Barton A, Band G, Bellenguez C, Bergboer JG, Blackwell JM, Bramon E, Bumpstead SJ, Casas JP, Cork MJ, Corvin A, Deloukas P, Dilthey A, Duncanson A, Edkins S, Estivill X, Fitzgerald O, Freeman C, Giardina E, Gray E, Hofer A, Hüffmeier U, Hunt SE, Irvine AD, Jankowski J, Kirby B, Langford C, Lascorz J, Leman J, Leslie S, Mallbris L, Markus HS, Mathew CG, McLean W, McManus R, Mössner R, Moutsianas L, Naluai AT, Nestle FO, Novelli G, Onoufriadis A, Palmer CN, Perricone C, Pirinen M, Plomin R, Potter SC, Pujol RM, Rautanen A, Riveira-Munoz E, Ryan AW, Salmhofer W, Samuelsson L, Sawcer SJ, Schalkwijk J, Smith CH, Ståhle M, Su Z, Tazi-Ahnini R, Traupe H, Viswanathan AC, Warren RB, Weger W, Wolk K, Wood N, Worthington J, Young HS, Zeeuwen PL, Hayday A, Burden AD, Griffiths CE, Kere J, Reis A, McVean G, Evans DM, Brown MA, Barker JN, Peltonen L, Donnelly P and Trembath RC (2010) A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1. Nature Genetics 42(11), 985–990. 10.1038/ng.694 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gerstner C, Turcinov S, Hensvold AH, Chemin K, Uchtenhagen H, Ramwadhdoebe TH, Dubnovitsky A, Kozhukh G, Rönnblom L, Kwok WW, Achour A, Catrina AI, van Baarsen LGM and Malmström V (2020) Multi-HLA class II tetramer analyses of citrulline-reactive T cells and early treatment response in rheumatoid arthritis. BMC Immunology 21(1), 27. 10.1186/s12865-020-00357-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Glanville J, Huang H, Nau A, Hatton O, Wagar LE, Rubelt F, Ji X, Han A, Krams SM, Pettus C, Haas N, CSL Arlehamn, Sette A, Boyd SD, Scriba TJ, Martinez OM and Davis MM (2017) Identifying specificity groups in the T cell receptor repertoire. Nature 547(7661), 94–98. 10.1038/nature22976 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Goronzy JJ, Bartz-Bazzanella P, Hu W, Jendro MC, Walser-Kuntz DR and Weyand CM (1994) Dominant clonotypes in the repertoire of peripheral CD4+ T cells in rheumatoid arthritis. Journal of Clinical Investigation 94(5), 2068–2076. 10.1172/jci117561 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Goutsouliak K, Veeraraghavan J, Sethunath V, De Angelis C, Osborne CK, Rimawi MF and Schiff R (2020) Towards personalized treatment for early stage HER2-positive breast cancer. Nature Reviews Clinical Oncology 17(4), 233–250. 10.1038/s41571-019-0299-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Graham DSC, Morris DL, Bhangale TR, Criswell LA, Syvänen AC, Rönnblom L, Behrens TW, Graham RR and Vyse TJ (2011) Association of NCF2, IKZF1, IRF8, IFIH1, and TYK2 with systemic lupus erythematosus. PLoS Genetics 7(10), e1002341. 10.1371/journal.pgen.1002341 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Guthridge JM, Lu R, Tran LT-H, Arriens C, Aberle T, Kamp S, Munroe ME, Dominguez N, Gross T, DeJager W, Macwana SR, Bourn RL, Apel S, Thanou A, Chen H, Chakravarty EF, Merrill JT and James JA (2020) Adults with systemic lupus exhibit distinct molecular phenotypes in a cross-sectional study. EClinicalMedicine 20, 100291. 10.1016/j.eclinm.2020.100291 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Haghverdi L, Lun ATL, Morgan MD and Marioni JC (2018) Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nature Biotechnology 36(5), 421–427. 10.1038/nbt.4091 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Hamada T, Keum N, Nishihara R and Ogino S (2017) Molecular pathological epidemiology: New developing frontiers of big data science to study etiologies and pathogenesis. Journal of Gastroenterology 52(3), 265–275. 10.1007/s00535-016-1272-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Hie B, Bryson B and Berger B (2019) Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nature Biotechnology 37(6), 685–691. 10.1038/s41587-019-0113-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hill JA, Bell DA, Brintnell W, Yue D, Wehrli B, Jevnikar AM, Lee DM, Hueber W, Robinson WH and Cairns E (2008) Arthritis induced by posttranslationally modified (citrullinated) fibrinogen in DR4-IE transgenic mice. Journal of Experimental Medicine 205(4), 967–979. 10.1084/jem.20072051 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Hoffman HM (2009) Therapy of autoinflammatory syndromes. Journal of Allergy and Clinical Immunology 124(6), 1129–1138. 10.1016/j.jaci.2009.11.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hughes LAE, Simons CCJM, van den Brandt PA, van Engeland M and Weijenberg MP (2017) Lifestyle, diet, and colorectal cancer risk according to (epi)genetic instability: Current evidence and future directions of molecular pathological epidemiology. Current Colorectal Cancer Reports 13(6), 455–469. 10.1007/s11888-017-0395-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Huizinga, TWJ, Amos, CI, van der Helm-van Mil AHM, Chen W, van Gaalen FA, Jawaheer D, Schreuder GMT, Wener M, Breedveld FC, Ahmad N, Lum RF, de Vries RRP, Gregersen PK, Toes REM, Criswell, LA (2005) Refining the complex rheumatoid arthritis phenotype based on specificity of the HLA–DRB1 shared epitope for antibodies to citrullinated proteins. Arthritis and Rheumatism 52(11), 3433–3438. 10.1002/art.21385 [DOI] [PubMed] [Google Scholar]
  40. Humby F, Durez P, Buch MH, Lewis MJ, Rizvi H, Rivellese F, Nerviani A, Giorli G, Mahto A, Montecucco C, Lauwerys B, Ng N, Ho P, Bombardieri M, Romão VC, Verschueren P, Kelly S, Sainaghi PP, Gendi N, Dasgupta B, Cauli A, Reynolds P, Cañete JD, Moots R, Taylor PC, Edwards CJ, Isaacs J, Sasieni P, Choy E, Pitzalis C, R4RA collaborative group and Celis R (2021) Rituximab versus tocilizumab in anti-TNF inadequate responder patients with rheumatoid arthritis (R4RA): 16-week outcomes of a stratified, biopsy-driven, multicentre, open-label, phase 4 randomised controlled trial. Lancet 397(10271), 305–317. 10.1016/s0140-6736(20)32341-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Ikeda Y, Masuko K, Nakai Y, Kato T, Hasunuma T, Mizushima Y, Nishioka K, Yamamoto K and Yoshino S (1996) High frequencies of identical T cell clonotypes in synovial tissues of rheumatoid arthritis patients suggest the occurrence of common antigen‐driven immune responses. Arthritis and Rheumatism 39(3), 446–453. 10.1002/art.1780390312 [DOI] [PubMed] [Google Scholar]
  42. Ishigaki K, Shoda H, Kochi Y, Yasui T, Kadono Y, Tanaka S, Fujio K and Yamamoto K (2015) Quantitative and qualitative characterization of expanded CD4+ T cell clones in rheumatoid arthritis patients. Scientific Reports 5(1), 12937. 10.1038/srep12937 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. James EA, Rieck M, Pieper J, Gebe JA, Yue BB, Tatum M, Peda M, Sandin C, Klareskog L, Malmström V and Buckner JH (2014) Citrulline‐specific Th1 cells are increased in rheumatoid arthritis and their frequency is influenced by disease duration and therapy. Arthritis & Rheumatology 66(7), 1712–1722. 10.1002/art.38637 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Jiang SH, Athanasopoulos V, Ellyard JI, Chuah A, Cappello J, Cook A, Prabhu SB, Cardenas J, Gu J, Stanley M, Roco JA, Papa I, Yabas M, Walters GD, Burgio G, McKeon K, Byers JM, Burrin C, Enders A, Miosge LA, Canete PF, Jelusic M, Tasic V, Lungu AC, Alexander SI, Kitching AR, Fulcher DA, Shen N, Arsov T, Gatenby PA, Babon JJ, Mallon DF, de Lucas Collantes C, Stone EA, Wu P, Field MA, Andrews TD, Cho E, Pascual V, Cook MC and Vinuesa CG (2019) Functional rare and low frequency variants in BLK and BANK1 contribute to human lupus. Nature Communications 10(1), 2201. 10.1038/s41467-019-10242-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Karlson EW, Chibnik LB, Kraft P, Cui J, Keenan BT, Ding B, Raychaudhuri S, Klareskog L, Alfredsson L and Plenge RM (2010) Cumulative association of 22 genetic variants with seropositive rheumatoid arthritis risk. Annals of the Rheumatic Diseases 69(6), 1077. 10.1136/ard.2009.120170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Khamashta M, Merrill JT, Werth VP, Furie R, Kalunian K, Illei GG, Drappa J, Wang L, Greth W and CD1067 Study Investigators (2016) Sifalimumab, an anti-interferon-α monoclonal antibody, in moderate to severe systemic lupus erythematosus: A randomised, double-blind, placebo-controlled study. Annals of the Rheumatic Diseases 75(11), 1909–1916. 10.1136/annrheumdis-2015-208562 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Kim K-J, Moon S-J, Park K-S and Tagkopoulos I (2020) Network-based modeling of drug effects on disease module in systemic sclerosis. Scientific Reports 10(1), 13393. 10.1038/s41598-020-70280-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Kim YC, Zhang A-H, Yoon J, Culp WE, Lees JR, Wucherpfennig KW and Scott DW (2018) Engineered MBP-specific human Tregs ameliorate MOG-induced EAE through IL-2-triggered inhibition of effector T cells. Journal of Autoimmunity 92, 77–86. 10.1016/j.jaut.2018.05.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Kinslow JD, Blum LK, Deane KD, Demoruelle MK, Okamoto Y, Parish MC, Kongpachith S, Lahey LJ, Norris JM, Robinson WH and Holers VM (2016) Elevated IgA plasmablast levels in subjects at risk of developing rheumatoid arthritis. Arthritis & Rheumatology 68(10), 2372–2383. 10.1002/art.39771 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Klarenbeek PL, de Hair MJH, Doorenspleet ME, van Schaik BDC, Esveldt REE, van de Sande MGH, Cantaert T, Gerlag DM, Baeten D, van Kampen AHC, Baas F, Tak PP and de Vries N (2012) Inflamed target tissue provides a specific niche for highly expanded T-cell clones in early human autoimmune disease. Annals of the Rheumatic Diseases 71(6), 1088. 10.1136/annrheumdis-2011-200612 [DOI] [PubMed] [Google Scholar]
  51. Kohm AP, Carpentier PA, Anger HA and Miller SD (2002) Cutting edge: CD4+CD25+ regulatory T cells suppress antigen-specific autoreactive immune responses and central nervous system inflammation during active experimental autoimmune encephalomyelitis. Journal of Immunology 169(9), 4712–4716. 10.4049/jimmunol.169.9.4712 [DOI] [PubMed] [Google Scholar]
  52. Kongpachith S, Lingampalli N, Ju C, Blum LK, Lu DR, Elliott SE, Mao R and Robinson WH (2019) Affinity maturation of the anti-citrullinated protein antibody paratope drives epitope spreading and polyreactivity in rheumatoid arthritis. Arthritis & Rheumatology 71(4), 507–517. 10.1002/art.40760 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh P and Raychaudhuri S (2019) Fast, sensitive and accurate integration of single-cell data with harmony. Nature Methods 16(12), 1289–1296. 10.1038/s41592-019-0619-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Kosukcu C, Taskiran EZ, Batu ED, Sag E, Bilginer Y, Alikasifoglu M and Ozen S (2021) Whole exome sequencing in unclassified autoinflammatory diseases: More monogenic diseases in the pipeline? Rheumatology 60(2), 607–616. 10.1093/rheumatology/keaa165 [DOI] [PubMed] [Google Scholar]
  55. Kroef M, Hoogen LL, Mertens JS, SLM Blokland, Haskett S, Devaprasad A, Carvalheiro T, Chouri E, Vazirpanah N, Cossu M, CGK Wichers, Silva-Cardoso SC, Affandi AJ, CPJ Bekker, Lopes AP, Hillen MR, Bonte-Mineur F, Kok MR, Beretta L, Rossato M, Mingueneau M, van Roon JAG and Radstake TRDJ (2020) Cytometry by time of flight identifies distinct signatures in patients with systemic sclerosis, systemic lupus erythematosus and Sjögrens syndrome. European Journal of Immunology 50(1), 119–129. 10.1002/eji.201948129. [DOI] [PubMed] [Google Scholar]
  56. Kurowska W, Kuca-Warnawin EH, Radzikowska A and Maśliński W (2017) The role of anti-citrullinated protein antibodies (ACPA) in the pathogenesis of rheumatoid arthritis. Central-European Journal of Immunology 42(4), 390–398. 10.5114/ceji.2017.72807 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Lanata CM, Paranjpe I, Nititham J, Taylor KE, Gianfrancesco M, Paranjpe M, Andrews S, Chung SA, Rhead B, Barcellos LF, Trupin L, Katz P, Dall’Era M, Yazdany J, Sirota M and Criswell LA (2019) A phenotypic and genomics approach in a multi-ethnic cohort to subtype systemic lupus erythematosus. Nature Communications 10(1), 3902. 10.1038/s41467-019-11845-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Law SC, Street S, Yu C-HA, Capini C, Ramnoruth S, Nel HJ, van Gorp E, Hyde C, Lau K, Pahau H, Purcell AW and Thomas R (2012) T-cell autoreactivity to citrullinated autoantigenic peptides in rheumatoid arthritis patients carrying HLA-DRB1 shared epitope alleles. Arthritis Research & Therapy 14(3), R118. doi: 10.1186/ar3848 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Lee YH and Bae S-C (2016) Association between TYK2 polymorphisms and susceptibility to autoimmune rheumatic diseases: A meta-analysis. Lupus 25(12), 1307–1314. 10.1177/0961203316638933 [DOI] [PubMed] [Google Scholar]
  60. Lee-Kirsch MA, Gong M, Chowdhury D, Senenko L, Engel K, Lee YA, de Silva U, Bailey SL, Witte T, Vyse TJ, Kere J, Pfeiffer C, Harvey S, Wong A, Koskenmies S, Hummel O, Rohde K, Schmidt RE, Dominiczak AF, Gahr M, Hollis T, Perrino FW, Lieberman J and Hübner N (2007) Mutations in the gene encoding the 3′-5′ DNA exonuclease TREX1 are associated with systemic lupus erythematosus. Nature Genetics 39(9), 1065–1067. doi: 10.1038/ng2091 [DOI] [PubMed] [Google Scholar]
  61. Liu B, Shao Y and Fu R (2021) Current research status of HLA in immune‐related diseases. Immunity, Inflammation and Disease 9(2), 340–350. 10.1002/iid3.416 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Lopes-Pacheco M (2020) CFTR modulators: The changing face of cystic fibrosis in the era of precision medicine. Frontiers in Pharmacology 10, 1662. 10.3389/fphar.2019.01662 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Lyons PA, Rayner TF, Trivedi S, Holle JU, Watts RA, Jayne DRW, Baslund B, Brenchley P, Bruchfeld A, Chaudhry AN, Cohen Tervaert JW, Deloukas P, Feighery C, Gross WL, Guillevin L, Gunnarsson I, Harper L, Hrušková Z, Little MA, Martorana D, Neumann T, Ohlsson S, Padmanabhan S, Pusey CD, Salama AD, Sanders JSF, Savage CO, Segelmark M, Stegeman CA, Tesař V, Vaglio A, Wieczorek S, Wilde B, Zwerina J, Rees AJ, Clayton DG and Smith KGC (2012) Genetically distinct subsets within ANCA-associated vasculitis. New England Journal of Medicine 367(3), 214–223. doi: 10.1056/nejmoa1108735 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Manohar PM and Davidson NE (2021) Updates in endocrine therapy for metastatic breast cancer. Cancer Biology and Medicine 18, 202–212. 10.20892/j.issn.2095-3941.2021.0255 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Manthiram K, Zhou Q, Aksentijevich I and Kastner DL (2017) The monogenic autoinflammatory diseases define new pathways in human innate immunity and inflammation. Nature Immunology 18(8), 832–842. 10.1038/ni.3777 [DOI] [PubMed] [Google Scholar]
  66. Martin-Gutierrez L, Peng J, Thompson NL, Robinson GA, Naja M, Peckham H, Wu WH, J’bari H, Ahwireng N, Waddington KE, Bradford CM, Varnier G, Gandhi A, Radmore R, Gupta V, Isenberg DA, Jury EC and Ciurtin C (2021) Stratification of patients with Sjögren’s syndrome and patients with systemic lupus erythematosus according to two shared immune cell signatures, with potential therapeutic implications. Arthritis & Rheumatology 73(9), 1626–1637. doi: 10.1002/art.41708 [DOI] [PubMed] [Google Scholar]
  67. Mease PJ, Deodhar AA, van der Heijde D, Behrens F, Kivitz AJ, Neal J, Kim J, Singhal S, Nowak M and Banerjee S (2022) Efficacy and safety of selective TYK2 inhibitor, deucravacitinib, in a phase II trial in psoriatic arthritis. Annals of the Rheumatic Diseases 81(6), 815–822. 10.1136/annrheumdis-2021-221664 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Mersha TB and Abebe T (2015) Self-reported race/ethnicity in the age of genomic research: Its potential impact on understanding health disparities. Human Genomics 9(1), 1. 10.1186/s40246-014-0023-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Morand E, Pike M, Merrill JT, van Vollenhoven R, Werth VP, Hobar C, Delev N, Shah V, Sharkey B, Wegman T, Catlett I, Banerjee S and Singhal S (2023) Deucravacitinib, a tyrosine kinase 2 inhibitor, in systemic lupus erythematosus: A phase II, randomized, double‐blind, placebo‐controlled trial. Arthritis & Rheumatology (Hoboken, N.J.) 75(2), 242–252. 10.1002/art.42391 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Morand EF, Furie R, Tanaka Y, Bruce IN, Askanase AD, Richez C, Bae SC, Brohawn PZ, Pineda L, Berglind A and Tummala R (2020) Trial of anifrolumab in active systemic lupus erythematosus. New England Journal of Medicine 382(3), 211–221. 10.1056/nejmoa1912196 [DOI] [PubMed] [Google Scholar]
  71. Namjou B, Kothari PH, Kelly JA, Glenn SB, Ojwang JO, Adler A, Alarcón-Riquelme ME, Gallant CJ, Boackle SA, Criswell LA, Kimberly RP, Brown E, Edberg J, Stevens AM, Jacob CO, Tsao BP, Gilkeson GS, Kamen DL, Merrill JT, Petri M, Goldman RR, Vila LM, Anaya JM, Niewold TB, Martin J, Pons-Estel BA, Sabio JM, Callejas JL, Vyse TJ, Bae SC, Perrino FW, Freedman BI, Scofield RH, Moser KL, Gaffney PM, James JA, Langefeld CD, Kaufman KM, Harley JB and Atkinson JP (2011) Evaluation of the TREX1 gene in a large multi-ancestral lupus cohort. Genes & Immunity 12(4), 270–279. doi: 10.1038/gene.2010.73 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Nehar-Belaid D, Hong S, Marches R, Chen G, Bolisetty M, Baisch J, Walters L, Punaro M, Rossi RJ, Chung CH, Huynh RP, Singh P, Flynn WF, Tabanor-Gayle JA, Kuchipudi N, Mejias A, Collet MA, Lucido AL, Palucka K, Robson P, Lakshminarayanan S, Ramilo O, Wright T, Pascual V and Banchereau JF (2020) Mapping systemic lupus erythematosus heterogeneity at the single-cell level. Nature Immunology 21(9), 1094–1106. doi: 10.1038/s41590-020-0743-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Nielsen SCA and Boyd SD (2019) New technologies and applications in infant B cell immunology. Current Opinion in Immunology 57, 53–57. 10.1016/j.coi.2018.12.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Nielsen SCA, Roskin KM, Jackson KJL, Joshi SA, Nejad P, Lee JY, Wagar LE, Pham TD, Hoh RA, Nguyen KD, Tsunemoto HY, Patel SB, Tibshirani R, Ley C, Davis MM, Parsonnet J and Boyd SD (2019) Shaping of infant B cell receptor repertoires by environmental factors and infectious disease. Science Translational Medicine 11(481), eaat2004. doi: 10.1126/scitranslmed.aat2004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Ogino S, Nowak JA, Hamada T, Milner DA Jr. and Nishihara R (2018) Insights into pathogenic interactions among environment, host, and tumor at the crossroads of molecular pathology and epidemiology. Annual Review of Pathology: Mechanisms of Disease 14(1), 1–21. 10.1146/annurev-pathmechdis-012418-012818 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Panousis NI, Bertsias GK, Ongen H, Gergianaki I, Tektonidou MG, Trachana M, Romano-Palumbo L, Bielser D, Howald C, Pamfil C, Fanouriakis A, Kosmara D, Repa A, Sidiropoulos P, Dermitzakis ET and Boumpas DT (2019) Combined genetic and transcriptome analysis of patients with SLE: Distinct, targetable signatures for susceptibility and severity. Annals of the Rheumatic Diseases 78(8), 1079. doi: 10.1136/annrheumdis-2018-214379 [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Papp K, Gordon K, Thaçi D, Morita A, Gooderham M, Foley P, Girgis IG, Kundu S and Banerjee S (2018) Phase 2 trial of selective tyrosine kinase 2 inhibition in psoriasis. New England Journal of Medicine 379(14), 1313–1321. doi: 10.1056/nejmoa1806382 [DOI] [PubMed] [Google Scholar]
  78. Perez RK, Gordon MG, Subramaniam M, Kim MC, Hartoularos GC, Targ S, Sun Y, Ogorodnikov A, Bueno R, Lu A, Thompson M, Rappoport N, Dahl A, Lanata CM, Matloubian M, Maliskova L, Kwek SS, Li T, Slyper M, Waldman J, Dionne D, Rozenblatt-Rosen O, Fong L, Dall’Era M, Balliu B, Regev A, Yazdany J, Criswell LA, Zaitlen N and Ye CJ (2022) Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus. Science 376(6589), eabf1970. 10.1126/science.abf1970 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Phillips BE, Garciafigueroa Y, Trucco M and Giannoukakis N (2017) Clinical tolerogenic dendritic cells: Exploring therapeutic impact on human autoimmune disease. Frontiers in Immunology 8, 1279. 10.3389/fimmu.2017.01279 [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Polański K, Park J-E, Young MD, Miao Z, Meyer KB and Teichmann SA (2020) BBKNN: Fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965. 10.1093/bioinformatics/btz625 [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Raffin C, Vo LT and Bluestone JA (2020) Treg cell-based therapies: Challenges and perspectives. Nature Reviews Immunology 20(3), 158–172. 10.1038/s41577-019-0232-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Raychaudhuri S, Sandor C, Stahl EA, Freudenberg J, Lee HS, Jia X, Alfredsson L, Padyukov L, Klareskog L, Worthington J, Siminovitch KA, Bae SC, Plenge RM, Gregersen PK and de Bakker PIW (2012) Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nature Genetics 44(3), 291–296. 10.1038/ng.1076 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Rechavi E and Somech R (2017) Survival of the fetus: Fetal B and T cell receptor repertoire development. Seminars in Immunopathology 39(6), 577–583. 10.1007/s00281-017-0626-0 [DOI] [PubMed] [Google Scholar]
  84. Richard-Miceli C and Criswell LA (2012) Emerging patterns of genetic overlap across autoimmune disorders. Genome Medicine 4(1), 6–6. 10.1186/gm305 [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Rigby W, Buckner JH, Louis Bridges S Jr, Nys M, Gao S, Polinsky M, Ray N and Bykerk V (2021) HLA-DRB1 risk alleles for RA are associated with differential clinical responsiveness to abatacept and adalimumab: Data from a head-to-head, randomized, single-blind study in autoantibody-positive early RA. Arthritis Research & Therapy 23(1), 245. 10.1186/s13075-021-02607-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Robinson WH (2015) Sequencing the functional antibody repertoire—Diagnostic and therapeutic discovery. Nature Reviews Rheumatology 11(3), 171–182. 10.1038/nrrheum.2014.220 [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Sandling JK, Pucholt P, Hultin Rosenberg L, Farias FHG, Kozyrev SV, Eloranta ML, Alexsson A, Bianchi M, Padyukov L, Bengtsson C, Jonsson R, Omdal R, Lie BA, Massarenti L, Steffensen R, Jakobsen MA, Lillevang ST, on behalf of the ImmunoArray Development Consortium and DISSECT Consortium, Lerang K, Molberg Ø, Voss A, Troldborg A, Jacobsen S, Syvänen AC, Jönsen A, Gunnarsson I, Svenungsson E, Rantapää-Dahlqvist S, Bengtsson AA, Sjöwall C, Leonard D, Lindblad-Toh K and Rönnblom L (2021) Molecular pathways in patients with systemic lupus erythematosus revealed by gene-centred DNA sequencing. Annals of the Rheumatic Diseases 80(1), 109–117. 10.1136/annrheumdis-2020-218636 [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Scally SW, Petersen J, Law SC, Dudek NL, Nel HJ, Loh KL, Wijeyewickrema LC, Eckle SBG, van Heemst J, Pike RN, McCluskey J, Toes RE, la Gruta NL, Purcell AW, Reid HH, Thomas R and Rossjohn J (2013) A molecular basis for the association of the HLA-DRB1 locus, citrullination, and rheumatoid arthritis. Journal of Experimental Medicine 210(12), 2569–2582. doi: 10.1084/jem.20131241 [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Schatz DG and Ji Y (2011) Recombination centres and the orchestration of V(D)J recombination. Nature Reviews Immunology 11(4), 251–263. 10.1038/nri2941 [DOI] [PubMed] [Google Scholar]
  90. Schmidt D, Martens PB, Weyand CM and Goronzy JJ (1996) The repertoire of CD4+ CD28− T cells in rheumatoid arthritis. Molecular Medicine 2(5), 608–618. 10.1007/bf03401644 [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Sigurdsson S, Nordmark G, Göring HHH, Lindroos K, Wiman AC, Sturfelt G, Jönsen A, Rantapää-Dahlqvist S, Möller B, Kere J, Koskenmies S, Widén E, Eloranta ML, Julkunen H, Kristjansdottir H, Steinsson K, Alm G, Rönnblom L and Syvänen A-C (2005) Polymorphisms in the tyrosine kinase 2 and interferon regulatory factor 5 genes are associated with systemic lupus erythematosus. American Journal of Human Genetics 76(3), 528–537. doi: 10.1086/428480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J and Butte AJ (2011) Discovery and preclinical validation of drug indications using compendia of public gene expression data. Science Translational Medicine 3(96), 96ra77. 10.1126/scitranslmed.3001318 [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Slight-Webb S, Guthridge CJ, Kheir J, Chen H, Tran L, Gross T, Roberts V, Khan S, Peercy M, Saunkeah B, Guthridge JM and James JA (2023) Unique serum immune phenotypes stratify Oklahoma Native American rheumatic disease patients. Arthritis Care & Research 75(4), 936–946. 10.1002/acr.24795 [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Smillie CS, Biton M, Ordovas-Montanes J, Sullivan KM, Burgin G, Graham DB, Herbst RH, Rogel N, Slyper M, Waldman J, Sud M, Andrews E, Velonias G, Haber AL, Jagadeesh K, Vickovic S, Yao J, Stevens C, Dionne D, Nguyen LT, Villani AC, Hofree M, Creasey EA, Huang H, Rozenblatt-Rosen O, Garber JJ, Khalili H, Desch AN, Daly MJ, Ananthakrishnan AN, Shalek AK, Xavier RJ and Regev A (2019) Intra- and inter-cellular rewiring of the human Colon during ulcerative colitis. Cell 178(3), 714–730.e22. 10.1016/j.cell.2019.06.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Sohn SJ, Barrett K, van Abbema A, Chang C, Kohli PB, Kanda H, Smith J, Lai Y, Zhou A, Zhang B, Yang W, Williams K, Macleod C, Hurley CA, Kulagowski JJ, Lewin-Koh N, Dengler HS, Johnson AR, Ghilardi N, Zak M, Liang J, Blair WS, Magnuson S and Wu LC (2013) A restricted role for TYK2 catalytic activity in human cytokine responses revealed by novel TYK2-selective inhibitors. Journal of Immunology 191(5), 2205–2216. 10.4049/jimmunol.1202859 [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Steen J, Forsström B, Sahlström P, Odowd V, Israelsson L, Krishnamurthy A, Badreh S, Mathsson Alm L, Compson J, Ramsköld D, Ndlovu W, Rapecki S, Hansson M, Titcombe PJ, Bang H, Mueller DL, Catrina AI, Grönwall C, Skriner K, Nilsson P, Lightwood D, Klareskog L and Malmström V (2019) Recognition of amino acid motifs, rather than specific proteins, by human plasma cell–derived monoclonal antibodies to posttranslationally modified proteins in rheumatoid arthritis. Arthritis & Rheumatology 71(2), 196–209. doi: 10.1002/art.40699 [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Tan Y, Kongpachith S, Blum LK, Ju CH, Lahey LJ, Lu DR, Cai X, Wagner CA, Lindstrom TM, Sokolove J and Robinson WH (2014) Barcode‐enabled sequencing of plasmablast antibody repertoires in rheumatoid arthritis. Arthritis & Rheumatology 66(10), 2706–2715. 10.1002/art.38754 [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Tang L, Wan P, Wang Y, Pan J, Wang Y and Chen B (2015) Genetic association and interaction between the IRF5 and TYK2 genes and systemic lupus erythematosus in the Han Chinese population. Inflammation Research 64(10), 817–824. 10.1007/s00011-015-0865-2 [DOI] [PubMed] [Google Scholar]
  99. Titcombe PJ, Wigerblad G, Sippl N, Zhang N, Shmagel AK, Sahlström P, Zhang Y, Barsness LO, Ghodke-Puranik Y, Baharpoor A, Hansson M, Israelsson L, Skriner K, Niewold TB, Klareskog L, Svensson CI, Amara K, Malmström V and Mueller DL (2018) Pathogenic citrulline‐multispecific B cell receptor clades in rheumatoid arthritis. Arthritis & Rheumatology 70(12), 1933–1945. doi: 10.1002/art.40590 [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Toro-Domínguez D, Lopez-Domínguez R, García Moreno A, Villatoro-García JA, Martorell-Marugán J, Goldman D, Petri M, Wojdyla D, Pons-Estel BA, Isenberg D, Morales-Montes de Oca G, Trejo-Zambrano MI, García González B, Rosetti F, Gómez-Martín D, Romero-Díaz J, Carmona-Sáez P and Alarcón-Riquelme ME (2019) Differential treatments based on drug-induced gene expression signatures and longitudinal systemic lupus erythematosus stratification. Scientific Reports 9(1), 15502. 10.1038/s41598-019-51616-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Toro‐Domínguez D, Martorell‐Marugán J, Goldman D, Petri M, Carmona‐Sáez P and Alarcón‐Riquelme ME (2018) Stratification of systemic lupus erythematosus patients into three groups of disease activity progression according to longitudinal gene expression. Arthritis & Rheumatology 70(12), 2025–2035. 10.1002/art.40653 [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS, Goh M and Chen J (2020) A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biology 21(1), 12. doi: 10.1186/s13059-019-1850-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Tsoi LC, Spain SL, Knight J, Ellinghaus E, Stuart PE, Capon F, Ding J, Li Y, Tejasvi T, Gudjonsson JE, Kang HM, Allen MH, McManus R, Novelli G, Samuelsson L, Schalkwijk J, Ståhle M, Burden AD, Smith CH, Cork MJ, Estivill X, Bowcock AM, Krueger GG, Weger W, Worthington J, Tazi-Ahnini R, Nestle FO, Hayday A, Hoffmann P, Winkelmann J, Wijmenga C, Langford C, Edkins S, Andrews R, Blackburn H, Strange A, Band G, Pearson RD, Vukcevic D, Spencer CCA, Deloukas P, Mrowietz U, Schreiber S, Weidinger S, Koks S, Kingo K, Esko T, Metspalu A, Lim HW, Voorhees JJ, Weichenthal M, Wichmann HE, Chandran V, Rosen CF, Rahman P, Gladman DD, Griffiths CEM, Reis A, Kere J, Collaborative Association Study of Psoriasis (CASP), Genetic Analysis of Psoriasis Consortium, Psoriasis Association Genetics Extension, Wellcome Trust Case Control Consortium 2, Nair RP, Franke A, Barker JNWN, Abecasis GR, Elder JT and Trembath RC (2012) Identification of fifteen new psoriasis susceptibility loci highlights the role of innate immunity. Nature Genetics 44(12), 1341–1348. 10.1038/ng.2467 [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. van Gaalen FA, van Aken J, Huizinga, TWJ, Schreuder GMT, Breedveld FC, Zanelli E, van Venrooij WJ, Verweij CL, Toes REM and de Vries RRP (2004) Association between HLA class II genes and autoantibodies to cyclic citrullinated peptides (CCPs) influences the severity of rheumatoid arthritis. Arthritis and Rheumatism 50(7), 2113–2121. 10.1002/art.20316 [DOI] [PubMed] [Google Scholar]
  105. VanderBorght A, Geusens P, Vandevyver C, Raus J and Stinissen P (2000) Skewed T‐cell receptor variable gene usage in the synovium of early and chronic rheumatoid arthritis patients and persistence of clonally expanded T cells in a chronic patient. Rheumatology 39(11), 1189–1201. 10.1093/rheumatology/39.11.1189 [DOI] [PubMed] [Google Scholar]
  106. Wagner U, Pierer M, Kaltenhäuser S, Wilke B, Seidel W, Arnold S and Häntzschel H (2003) Clonally expanded CD4+CD28null T cells in rheumatoid arthritis use distinct combinations of T cell receptor BV and BJ elements. European Journal of Immunology 33(1), 79–84. doi: 10.1002/immu.200390010 [DOI] [PubMed] [Google Scholar]
  107. Ye B, Stary CM, Li X, Gao Q, Kang C and Xiong X (2018) Engineering chimeric antigen receptor-T cells for cancer treatment. Molecular Cancer 17(1), 32. 10.1186/s12943-018-0814-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  108. Zemlin M, Schelonka RL, Bauer K and Schroeder HW (2002) Regulation and chance in the ontogeny of B and T cell antigen receptor repertoires. Immunologic Research 26(1–3), 265–278. 10.1385/ir:26:1-3:265 [DOI] [PubMed] [Google Scholar]
  109. Zhang F, Mears JR, Shakib L, Beynor JI, Shanaj S, Korsunsky I, Nathan A, Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium, Donlin LT and Raychaudhuri S (2021) IFN-γ and TNF-α drive a CXCL10+ CCL2+ macrophage phenotype expanded in severe COVID-19 lungs and inflammatory diseases with tissue inflammation. Genome Medicine 13(1), 64. 10.1186/s13073-021-00881-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  110. Zhang F, Wei K, Slowikowski K, Fonseka CY, Rao DA, Kelly S, Goodman SM, Tabechian D, Hughes LB, Salomon-Escoto K, Watts GFM, Jonsson AH, Rangel-Moreno J, Meednu N, Rozo C, Apruzzese W, Eisenhaure TM, Lieb DJ, Boyle DL, Mandelin AM II, Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium,Boyce BF, DiCarlo E, Gravallese EM, Gregersen PK, Moreland L, Firestein GS, Hacohen N, Nusbaum C, Lederer JA, Perlman H, Pitzalis C, Filer A, Holers VM, Bykerk VP, Donlin LT, Anolik JH, Brenner MB and Raychaudhuri S (2019) Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nature Immunology 20(7), 928–942. 10.1038/s41590-019-0378-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  111. Zhang H, Liu L, Zhang J, Chen J, Ye J, Shukla S, Qiao J, Zhan X, Chen H, Wu CJ, Fu Y-X and Li B (2020) Investigation of antigen-specific T-cell receptor clusters in human cancers. Clinical Cancer Research 26(6), 1359–1371. doi: 10.1158/1078-0432.ccr-19-3249 [DOI] [PubMed] [Google Scholar]
  112. Zhernakova A, van Diemen CC and Wijmenga C (2009) Detecting shared pathogenesis from the shared genetics of immune-related diseases. Nature Reviews Genetics 10(1), 43–55. 10.1038/nrg2489 [DOI] [PubMed] [Google Scholar]
  113. Zubizarreta I, Flórez-Grau G, Vila G, Cabezón R, España C, Andorra M, Saiz A, Llufriu S, Sepulveda M, Sola-Valls N, Martinez-Lapiscina EH, Pulido-Valdeolivas I, Casanova B, Martinez Gines M, Tellez N, Oreja-Guevara C, Español M, Trias E, Cid J, Juan M, Lozano M, Blanco Y, Steinman L, Benitez-Ribas D and Villoslada P (2019) Immune tolerance in multiple sclerosis and neuromyelitis optica with peptide-loaded tolerogenic dendritic cells in a phase 1b trial. Proceedings of the National Academy of Sciences 116(17), 8463. 10.1073/pnas.1820039116 [DOI] [PMC free article] [PubMed] [Google Scholar]
Camb Prism Precis Med. doi: 10.1017/pcm.2023.14.pr2

Review: Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders — R0/PR2

Cristina Lanata

Comments to Author: This was a very thorough and encompassing review of the recent literature on the multiple efforts to pave the way for precision medicine in autoimmune diseases.

I have a few minor comments for the author’s considerations.

- It would be relevant to include the recent published paper illustrating the role of rare variants and monogenic SLE in TLR-7 (Vinuesta et al, Nature). Although stated in the paper, with the hopefully more accessible and available WES and WGS, we will learn more of the role of rare variants in autoimmune diseases.

- The AMP has published several papers for precision medicine in RA and SLE that should be included.

- It would be also relevant to include the recent paper of transient bacteremia and ACPAs recently published in Science, highlight the role of pathogens/environment.

- There has also been interesting work in recent scientific meetings of CART cell technology in Antiphospholipid syndrome

Finally, it would be good to see a paragraph acknowledging and highlighting the lack of diversity in molecular profiling, genomics and genetics in autoimmune diseases. If precision medicine is the goal, there is a lot of work to be done in diverse populations.

Camb Prism Precis Med. doi: 10.1017/pcm.2023.14.pr3

Review: Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders — R0/PR3

Shuji Ogino 1

Comments to Author: The authors wrote a very interesting piece on precision medicine of autoimmune diseases. Autoimmune diseases are very heterogeneous. Especially cautionary aspects of precision medicine are discussed. This is, overall, of very high interest.

The authors discuss big data analyses (such as GWAS) much, but there is relative lack of discussion on environmental influences and gene-by-environment interactions. For complex diseases, actually contributions of environment seem greater than simple mendelian diseases. There are many environmental, dietary, and lifestyle factors that influence diseases, immune system, pathogenic mechanisms.

In line with all of these, for a future direction, it would be worth discussing need of research on dietary / lifestyle / environmental factors, genetics, and personalized molecular biomarkers, all of which are related to precision medicine. The authors should discuss molecular pathological epidemiology research, which can investigate those factors in relation to molecular pathologies and clinical outcomes. Molecular pathological epidemiology has been discussed in Annu Rev Pathol 2019, Curr Colorectal Cancer Rep 2017, J Gastro 2017, etc.

Camb Prism Precis Med. doi: 10.1017/pcm.2023.14.pr4

Recommendation: Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders — R0/PR4

Editor: Anneke Lucassen1

No accompanying comment.

Camb Prism Precis Med. doi: 10.1017/pcm.2023.14.pr5

Decision: Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders — R0/PR5

Editor: Anna Dominiczak1

No accompanying comment.

Camb Prism Precis Med. doi: 10.1017/pcm.2023.14.pr7

Review: Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders — R1/PR7

Shuji Ogino 1

Comments to Author: The authors improved the paper.

Camb Prism Precis Med. doi: 10.1017/pcm.2023.14.pr8

Recommendation: Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders — R1/PR8

Editor: Anneke Lucassen1

No accompanying comment.

Camb Prism Precis Med. doi: 10.1017/pcm.2023.14.pr9

Decision: Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders — R1/PR9

Editor: Anna Dominiczak1

No accompanying comment.

Articles from Cambridge Prisms: Precision Medicine are provided here courtesy of Cambridge University Press

RESOURCES