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

Published online by Cambridge University Press:  11 May 2023

Yuriy Baglaenko
Affiliation:
Brigham and Women’s Hospital, Boston, MA, USA
Catriona Wagner
Affiliation:
Autoimmune Association, Clinton Township, MI, USA
Vijay G. Bhoj
Affiliation:
University of Pennsylvania, Philadelphia, PA, USA
Petter Brodin
Affiliation:
Karolinska Institute, Solna, Sweden
M. Eric Gershwin
Affiliation:
University of California, Davis, Davis, CA, USA
Daniel Graham
Affiliation:
The Broad Institute of MIT and Harvard, Cambridge, MA, USA
Pietro Invernizzi
Affiliation:
Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
Kenneth K. Kidd
Affiliation:
Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
Ilya Korsunsky
Affiliation:
Brigham and Women’s Hospital, Boston, MA, USA
Michael Levy
Affiliation:
Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
Andrew L. Mammen
Affiliation:
National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, USA
Victor Nizet
Affiliation:
School of Medicine, University of California San Diego, San Diego, CA, USA
Francisco Ramirez-Valle
Affiliation:
Bristol Myers Squibb, New York, NY, USA
Edward C. Stites
Affiliation:
Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
Marc S. Williams
Affiliation:
Geisinger, Danville, PA, USA
Michael Wilson
Affiliation:
Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, CA, USA
Noel R. Rose
Affiliation:
Autoimmune Association, Clinton Township, MI, USA
Virginia Ladd
Affiliation:
Autoimmune Association, Clinton Township, MI, USA
Marina Sirota*
Affiliation:
Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA Department of Pediatrics, UCSF, San Francisco, CA, USA
*
Corresponding author: Marina Sirota; Email: Marina.Sirota@ucsf.edu
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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.

Type
Review
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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, Reference Martin-Gutierrez, Peng, Thompson, Robinson, Naja, Peckham, Wu, J’bari, Ahwireng, Waddington, Bradford, Varnier, Gandhi, Radmore, Gupta, Isenberg, Jury and Ciurtin2021), whereas patients who express human epidermal growth factor receptor-2 (HER-2) are treated with monoclonal antibodies specifically targeting HER-2 (Goutsouliak et al., Reference Goutsouliak, Veeraraghavan, Sethunath, De Angelis, Osborne, Rimawi and Schiff2020). 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., Reference Ye, Stary, Li, Gao, Kang and Xiong2018). 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, Reference Lopes-Pacheco2020).

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., Reference Humby, Durez, Buch, Lewis, Rizvi, Rivellese, Nerviani, Giorli, Mahto, Montecucco, Lauwerys, Ng, Ho, Bombardieri, Romão, Verschueren, Kelly, Sainaghi, Gendi, Dasgupta, Cauli, Reynolds, Cañete, Moots, Taylor, Edwards, Isaacs, Sasieni, Choy, Pitzalis and Celis2021), 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., Reference Boycott, Hartley, Biesecker, Gibbs, Innes, Riess, Belmont, Dunwoodie, Jojic, Lassmann, Mackay, Temple, Visel and Baynam2019), including patients with autoinflammatory diseases (Kosukcu et al., Reference Kosukcu, Taskiran, Batu, Sag, Bilginer, Alikasifoglu and Ozen2020) 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., Reference Karlson, Chibnik, Kraft, Cui, Keenan, Ding, Raychaudhuri, Klareskog, Alfredsson and Plenge2010; Dudbridge, Reference Dudbridge2013).

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., Reference Lee-Kirsch, Gong, Chowdhury, Senenko, Engel, Lee, de Silva, Bailey, Witte, Vyse, Kere, Pfeiffer, Harvey, Wong, Koskenmies, Hummel, Rohde, Schmidt, Dominiczak, Gahr, Hollis, Perrino, Lieberman and Hübner2007; Namjou et al., Reference Namjou, Kothari, Kelly, Glenn, Ojwang, Adler, Alarcón-Riquelme, Gallant, Boackle, Criswell, Kimberly, Brown, Edberg, Stevens, Jacob, Tsao, Gilkeson, Kamen, Merrill, Petri, Goldman, Vila, Anaya, Niewold, Martin, Pons-Estel, Sabio, Callejas, Vyse, Bae, Perrino, Freedman, Scofield, Moser, Gaffney, James, Langefeld, Kaufman, Harley and Atkinson2011). 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., Reference Jiang, Athanasopoulos, Ellyard, Chuah, Cappello, Cook, Prabhu, Cardenas, Gu, Stanley, Roco, Papa, Yabas, Walters, Burgio, McKeon, Byers, Burrin, Enders, Miosge, Canete, Jelusic, Tasic, Lungu, Alexander, Kitching, Fulcher, Shen, Arsov, Gatenby, Babon, Mallon, de Lucas Collantes, Stone, Wu, Field, Andrews, Cho, Pascual, Cook and Vinuesa2019). 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., Reference Brown, Cañete, Wang, Medhavy, Bones, Roco, He, Qin, Cappello, Ellyard, Bassett, Shen, Burgio, Zhang, Turnbull, Meng, Wu, Cho, Miosge, Andrews, Field, Tvorogov, Lopez, Babon, López, Gónzalez-Murillo, Garulo, Pascual, Levy, Mallack, Calame, Lotze, Lupski, Ding, Ullah, Walters, Koina, Cook, Shen, de Lucas Collantes, Corry, Gantier, Athanasopoulos and Vinuesa2022). 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., Reference Lyons, Rayner, Trivedi, Holle, Watts, Jayne, Baslund, Brenchley, Bruchfeld, Chaudhry, Cohen Tervaert, Deloukas, Feighery, Gross, Guillevin, Gunnarsson, Harper, Hrušková, Little, Martorana, Neumann, Ohlsson, Padmanabhan, Pusey, Salama, Sanders, Savage, Segelmark, Stegeman, Tesař, Vaglio, Wieczorek, Wilde, Zwerina, Rees, Clayton and Smith2012; Banchereau et al., Reference Banchereau, Hong, Cantarel, Baldwin, Baisch, Edens, Cepika, Acs, Turner, Anguiano, Vinod, Kahn, Obermoser, Blankenship, Wakeland, Nassi, Gotte, Punaro, Liu, Banchereau, Rossello-Urgell, Wright and Pascual2016; Toro-Domínguez et al., Reference Toro‐Domínguez, Martorell‐Marugán, Goldman, Petri, Carmona‐Sáez and Alarcón‐Riquelme2018; Figgett et al., Reference Figgett, Monaghan, Ng, Alhamdoosh, Maraskovsky, Wilson, Hoi, Morand and Mackay2019; Panousis et al., Reference Panousis, Bertsias, Ongen, Gergianaki, Tektonidou, Trachana, Romano-Palumbo, Bielser, Howald, Pamfil, Fanouriakis, Kosmara, Repa, Sidiropoulos, Dermitzakis and Boumpas2019; Andreoletti et al., Reference Andreoletti, Lanata, Trupin, Paranjpe, Jain, Nititham, Taylor, Combes, Maliskova, Ye, Katz, Dall’Era, Yazdany, Criswell and Sirota2021; Sandling et al., Reference Sandling, Pucholt, Hultin Rosenberg, Farias, Kozyrev, Eloranta, Alexsson, Bianchi, Padyukov, Bengtsson, Jonsson, Omdal, Lie, Massarenti, Steffensen, Jakobsen, Lillevang, Lerang, Molberg, Voss, Troldborg, Jacobsen, Syvänen, Jönsen, Gunnarsson, Svenungsson, Rantapää-Dahlqvist, Bengtsson, Sjöwall, Leonard, Lindblad-Toh and Rönnblom2021). 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., Reference Baechler, Batliwalla, Karypis, Gaffney, Ortmann, Espe, Shark, Grande, Hughes, Kapur, Gregersen and Behrens2003; Bennett et al., Reference Bennett, Palucka, Arce, Cantrell, Borvak, Banchereau and Pascual2003), 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., Reference Morand, Furie, Tanaka, Bruce, Askanase, Richez, Bae, Brohawn, Pineda, Berglind and Tummala2020). 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., Reference Khamashta, Merrill, Werth, Furie, Kalunian, Illei, Drappa, Wang and Greth2016; Furie et al., Reference Furie, Khamashta, Merrill, Werth, Kalunian, Brohawn, Illei, Drappa, Wang and Yoo2017; Morand et al., Reference Morand, Furie, Tanaka, Bruce, Askanase, Richez, Bae, Brohawn, Pineda, Berglind and Tummala2020), 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. (Reference Chiche, Jourde‐Chiche, Whalen, Presnell, Gersuk, Dang, Anguiano, Quinn, Burtey, Berland, Kaplanski, Harle, Pascual and Chaussabel2014) 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., Reference Chiche, Jourde‐Chiche, Whalen, Presnell, Gersuk, Dang, Anguiano, Quinn, Burtey, Berland, Kaplanski, Harle, Pascual and Chaussabel2014). In 2016, Banchereau et al. (Reference Banchereau, Hong, Cantarel, Baldwin, Baisch, Edens, Cepika, Acs, Turner, Anguiano, Vinod, Kahn, Obermoser, Blankenship, Wakeland, Nassi, Gotte, Punaro, Liu, Banchereau, Rossello-Urgell, Wright and Pascual2016) 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., Reference Banchereau, Hong, Cantarel, Baldwin, Baisch, Edens, Cepika, Acs, Turner, Anguiano, Vinod, Kahn, Obermoser, Blankenship, Wakeland, Nassi, Gotte, Punaro, Liu, Banchereau, Rossello-Urgell, Wright and Pascual2016). 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., Reference Banchereau, Hong, Cantarel, Baldwin, Baisch, Edens, Cepika, Acs, Turner, Anguiano, Vinod, Kahn, Obermoser, Blankenship, Wakeland, Nassi, Gotte, Punaro, Liu, Banchereau, Rossello-Urgell, Wright and Pascual2016). Using a similar approach, Toro‐Domínguez et al. (Reference Toro‐Domínguez, Martorell‐Marugán, Goldman, Petri, Carmona‐Sáez and Alarcón‐Riquelme2018, Reference Toro-Domínguez, Lopez-Domínguez, García Moreno, Villatoro-García, Martorell-Marugán, Goldman, Petri, Wojdyla, Pons-Estel, Isenberg, Morales-Montes de Oca, Trejo-Zambrano, García González, Rosetti, Gómez-Martín, Romero-Díaz, Carmona-Sáez and Alarcón-Riquelme2019) 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., Reference Papp, Gordon, Thaçi, Morita, Gooderham, Foley, Girgis, Kundu and Banerjee2022). Using single-cell RNA sequencing, Nehar-Belaid et al. (Reference Papp, Gordon, Thaçi, Morita, Gooderham, Foley, Girgis, Kundu and Banerjee2020) 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., Reference Papp, Gordon, Thaçi, Morita, Gooderham, Foley, Girgis, Kundu and Banerjee2020). In a recent study, Andreoletti et al. (Reference Andreoletti, Lanata, Trupin, Paranjpe, Jain, Nititham, Taylor, Combes, Maliskova, Ye, Katz, Dall’Era, Yazdany, Criswell and Sirota2021) 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. (Reference Guthridge, Lu, Tran, Arriens, Aberle, Kamp, Munroe, Dominguez, Gross, DeJager, Macwana, Bourn, Apel, Thanou, Chen, Chakravarty, Merrill and James2020), 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. (Reference Lanata, Paranjpe, Nititham, Taylor, Gianfrancesco, Paranjpe, Andrews, Chung, Rhead, Barcellos, Trupin, Katz, Dall’Era, Yazdany, Sirota and Criswell2019) 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, Reference Mersha and Abebe2015).

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., Reference Zhernakova, van Diemen and Wijmenga2009; Richard-Miceli and Criswell, Reference Richard-Miceli and Criswell2012). 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., Reference Barturen, Babaei, Català-Moll, Martínez-Bueno, Makowska, Martorell-Marugán, Carmona-Sáez, Toro-Domínguez, Carnero-Montoro, Teruel, Kerick, Acosta-Herrera, le Lann, Jamin, Rodríguez-Ubreva, García-Gómez, Kageyama, Buttgereit, Hayat, Mueller, Lesche, Hernandez-Fuentes, Juarez, Rowley, White, Marañón, Gomes Anjos, Varela, Aguilar-Quesada, Garrancho, López-Berrio, Rodriguez Maresca, Navarro-Linares, Almeida, Azevedo, Brandão, Campar, Faria, Farinha, Marinho, Neves, Tavares, Vasconcelos, Trombetta, Montanelli, Vigone, Alvarez-Errico, Li, Thiagaran, Blanco Alonso, Corrales Martínez, Genre, López Mejías, Gonzalez-Gay, Remuzgo, Ubilla Garcia, Cervera, Espinosa, Rodríguez-Pintó, de Langhe, Cremer, Lories, Belz, Hunzelmann, Baerlecken, Kniesch, Witte, Lehner, Stummvoll, Zauner, Aguirre-Zamorano, Barbarroja, Castro-Villegas, Collantes-Estevez, Ramon, Díaz Quintero, Escudero-Contreras, Fernández Roldán, Jiménez Gómez, Jiménez Moleón, Lopez-Pedrera, Ortega-Castro, Ortego, Raya, Artusi, Gerosa, Meroni, Schioppo, de Groof, Ducreux, Lauwerys, Maudoux, Cornec, Devauchelle-Pensec, Jousse-Joulin, Jouve, Rouvière, Saraux, Simon, Alvarez, Chizzolini, Dufour, Wynar, Balog, Bocskai, Deák, Dulic, Kádár, Kovács, Cheng, Gerl, Hiepe, Khodadadi, Thiel, Rinaldis, Rao, Benschop, Chamberlain, Dow, Ioannou, Laigle, Marovac, Wojcik, Renaudineau, Borghi, Frostegård, Martín, Beretta, Ballestar, McDonald, Pers and Alarcón-Riquelme2021). Notably, patients with different autoimmune diseases were found within each cluster (Barturen et al., Reference Barturen, Babaei, Català-Moll, Martínez-Bueno, Makowska, Martorell-Marugán, Carmona-Sáez, Toro-Domínguez, Carnero-Montoro, Teruel, Kerick, Acosta-Herrera, le Lann, Jamin, Rodríguez-Ubreva, García-Gómez, Kageyama, Buttgereit, Hayat, Mueller, Lesche, Hernandez-Fuentes, Juarez, Rowley, White, Marañón, Gomes Anjos, Varela, Aguilar-Quesada, Garrancho, López-Berrio, Rodriguez Maresca, Navarro-Linares, Almeida, Azevedo, Brandão, Campar, Faria, Farinha, Marinho, Neves, Tavares, Vasconcelos, Trombetta, Montanelli, Vigone, Alvarez-Errico, Li, Thiagaran, Blanco Alonso, Corrales Martínez, Genre, López Mejías, Gonzalez-Gay, Remuzgo, Ubilla Garcia, Cervera, Espinosa, Rodríguez-Pintó, de Langhe, Cremer, Lories, Belz, Hunzelmann, Baerlecken, Kniesch, Witte, Lehner, Stummvoll, Zauner, Aguirre-Zamorano, Barbarroja, Castro-Villegas, Collantes-Estevez, Ramon, Díaz Quintero, Escudero-Contreras, Fernández Roldán, Jiménez Gómez, Jiménez Moleón, Lopez-Pedrera, Ortega-Castro, Ortego, Raya, Artusi, Gerosa, Meroni, Schioppo, de Groof, Ducreux, Lauwerys, Maudoux, Cornec, Devauchelle-Pensec, Jousse-Joulin, Jouve, Rouvière, Saraux, Simon, Alvarez, Chizzolini, Dufour, Wynar, Balog, Bocskai, Deák, Dulic, Kádár, Kovács, Cheng, Gerl, Hiepe, Khodadadi, Thiel, Rinaldis, Rao, Benschop, Chamberlain, Dow, Ioannou, Laigle, Marovac, Wojcik, Renaudineau, Borghi, Frostegård, Martín, Beretta, Ballestar, McDonald, Pers and Alarcón-Riquelme2021). Studies using immunophenotyping (Kroef et al., Reference Kroef, Hoogen, Mertens, SLM, Haskett, Devaprasad, Carvalheiro, Chouri, Vazirpanah, Cossu, CGK, Silva-Cardoso, Affandi, CPJ, Lopes, Hillen, Bonte-Mineur, Kok, Beretta, Rossato, Mingueneau, van Roon and Radstake2020; Martin‐Gutierrez et al., Reference Martin-Gutierrez, Peng, Thompson, Robinson, Naja, Peckham, Wu, J’bari, Ahwireng, Waddington, Bradford, Varnier, Gandhi, Radmore, Gupta, Isenberg, Jury and Ciurtin2021) and soluble mediator profiling (Slight-Webb et al., Reference Slight-Webb, Guthridge, Kheir, Chen, Tran, Gross, Roberts, Khan, Peercy, Saunkeah, Guthridge and James2021) 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., Reference Liu, Shao and Fu2021), 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., Reference Zemlin, Schelonka, Bauer and Schroeder2002; Schatz and Ji, Reference Tang, Wan, Wang, Pan, Wang and Chen2011; Rechavi and Somech, Reference Rechavi and Somech2017; Nielsen and Boyd, Reference Nehar-Belaid, Hong, Marches, Chen, Bolisetty, Baisch, Walters, Punaro, Rossi, Chung, Huynh, Singh, Flynn, Tabanor-Gayle, Kuchipudi, Mejias, Collet, Lucido, Palucka, Robson, Lakshminarayanan, Ramilo, Wright, Pascual and Banchereau2019; Nielsen et al., Reference Nielsen, Roskin, Jackson, Joshi, Nejad, Lee, Wagar, Pham, Hoh, Nguyen, Tsunemoto, Patel, Tibshirani, Ley, Davis, Parsonnet and Boyd2019).

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., Reference Kurowska, Kuca-Warnawin, Radzikowska and Maśliński2017). 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, Reference Robinson2015). To accomplish this, Tan et al. (Reference Zhang, Wei, Slowikowski, Fonseka, Rao, Kelly, Goodman, Tabechian, Hughes, Salomon-Escoto, Watts, Jonsson, Rangel-Moreno, Meednu, Rozo, Apruzzese, Eisenhaure, Lieb, Boyle, Mandelin, Boyce, DiCarlo, Gravallese, Gregersen, Moreland, Firestein, Hacohen, Nusbaum, Lederer, Perlman, Pitzalis, Filer, Holers, Bykerk, Donlin, Anolik, Brenner and Raychaudhuri2014) 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., Reference Zhang, Wei, Slowikowski, Fonseka, Rao, Kelly, Goodman, Tabechian, Hughes, Salomon-Escoto, Watts, Jonsson, Rangel-Moreno, Meednu, Rozo, Apruzzese, Eisenhaure, Lieb, Boyle, Mandelin, Boyce, DiCarlo, Gravallese, Gregersen, Moreland, Firestein, Hacohen, Nusbaum, Lederer, Perlman, Pitzalis, Filer, Holers, Bykerk, Donlin, Anolik, Brenner and Raychaudhuri2014). Additional studies confirmed that ACPAs undergo affinity maturation, resulting in epitope spreading and polyreactivity with other post-translationally modified proteins (Elliott et al., Reference Elliott, Kongpachith, Lingampalli, Adamska, Cannon, Mao, Blum and Robinson2018; Titcombe et al., Reference Titcombe, Wigerblad, Sippl, Zhang, Shmagel, Sahlström, Zhang, Barsness, Ghodke-Puranik, Baharpoor, Hansson, Israelsson, Skriner, Niewold, Klareskog, Svensson, Amara, Malmström and Mueller2018; Kongpachith et al., Reference Kongpachith, Lingampalli, Ju, Blum, Lu, Elliott, Mao and Robinson2019; Steen et al., Reference Steen, Forsström, Sahlström, Odowd, Israelsson, Krishnamurthy, Badreh, Mathsson Alm, Compson, Ramsköld, Ndlovu, Rapecki, Hansson, Titcombe, Bang, Mueller, Catrina, Grönwall, Skriner, Nilsson, Lightwood, Klareskog and Malmström2019).

Repertoire analyses of plasmablasts from healthy individuals with RA-associated autoantibodies demonstrated elevated IgA responses (Kinslow et al., Reference Kinslow, Blum, Deane, Demoruelle, Okamoto, Parish, Kongpachith, Lahey, Norris, Robinson and Holers2016), 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., Reference Elliott, Kongpachith, Lingampalli, Adamska, Cannon, Mao, Blum and Robinson2018), 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., Reference Goronzy, Bartz-Bazzanella, Hu, Jendro, Walser-Kuntz and Weyand1994; Ikeda et al., Reference Ikeda, Masuko, Nakai, Kato, Hasunuma, Mizushima, Nishioka, Yamamoto and Yoshino1996; Schmidt et al., Reference Schmidt, Martens, Weyand and Goronzy1996; VanderBorght et al., Reference VanderBorght, Geusens, Vandevyver, Raus and Stinissen2000; Wagner et al., Reference Wagner, Pierer, Kaltenhäuser, Wilke, Seidel, Arnold and Häntzschel2003), including early in the disease course (Klarenbeek et al., Reference Klarenbeek, de Hair, Doorenspleet, van Schaik, Esveldt, van de Sande, Cantaert, Gerlag, Baeten, van Kampen, Baas, Tak and de Vries2012). 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., Reference Ishigaki, Shoda, Kochi, Yasui, Kadono, Tanaka, Fujio and Yamamoto2015). 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., Reference van Gaalen, van Aken, Huizinga, Schreuder, Breedveld, Zanelli, van Venrooij, Verweij, Toes and de Vries2004; Huizinga et al., Reference Huizinga, Amos, van der Helm-van Mil, Chen, van Gaalen, Jawaheer, Schreuder, Wener, Breedveld, Ahmad, Lum, de Vries, Gregersen, Toes and Criswell2005; Busch et al., Reference Busch, Kollnberger and Mellins2019). 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., Reference Rigby, Buckner, Louis Bridges, Nys, Gao, Polinsky, Ray and Bykerk2021). 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., Reference Raychaudhuri, Sandor, Stahl, Freudenberg, Lee, Jia, Alfredsson, Padyukov, Klareskog, Worthington, Siminovitch, Bae, Plenge, Gregersen and de Bakker2012), 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., Reference Delwig, Locke, Robinson and Ng2010; Law et al., Reference Law, Street, Yu, Capini, Ramnoruth, Nel, van Gorp, Hyde, Lau, Pahau, Purcell and Thomas2012; Scally et al., Reference Scally, Petersen, Law, Dudek, Nel, Loh, Wijeyewickrema, Eckle, van Heemst, Pike, McCluskey, Toes, la Gruta, Purcell, Reid, Thomas and Rossjohn2013; James et al., Reference James, Rieck, Pieper, Gebe, Yue, Tatum, Peda, Sandin, Klareskog, Malmström and Buckner2014; Gerstner et al., Reference Gerstner, Turcinov, Hensvold, Chemin, Uchtenhagen, Ramwadhdoebe, Dubnovitsky, Kozhukh, Rönnblom, Kwok, Achour, Catrina, van Baarsen and Malmström2020). In addition, T cells specific for citrullinated fibrinogen contribute to the development and progression of RA in mouse models (Hill et al., Reference Hill, Bell, Brintnell, Yue, Wehrli, Jevnikar, Lee, Hueber, Robinson and Cairns2008; Cordova et al., Reference Cordova, Willis, Haskins and Holers2013).

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. (Reference Dash, Fiore-Gartland, Hertz, Wang, Sharma, Souquette, Crawford, Clemens, Nguyen, Kedzierska, La Gruta, Bradley and Thomas2017) and Glanville et al. (Reference Glanville, Huang, Nau, Hatton, Wagar, Rubelt, Ji, Han, Krams, Pettus, Haas, CSL, Sette, Boyd, Scriba, Martinez and Davis2017) 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. (Reference Zhang, Liu, Zhang, Chen, Ye, Shukla, Qiao, Zhan, Chen, Wu, Fu and Li2020) 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., Reference Zhang, Liu, Zhang, Chen, Ye, Shukla, Qiao, Zhan, Chen, Wu, Fu and Li2020). 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., Reference Manthiram, Zhou, Aksentijevich and Kastner2017). For example, therapies targeting IL-1, such as anakinra, are approved for the inflammasomopathy cryopyrin-associated periodic fever syndrome (Hoffman, Reference Hoffman2009). 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., Reference Sohn, Barrett, van Abbema, Chang, Kohli, Kanda, Smith, Lai, Zhou, Zhang, Yang, Williams, Macleod, Hurley, Kulagowski, Lewin-Koh, Dengler, Johnson, Ghilardi, Zak, Liang, Blair, Magnuson and Wu2013; Burke et al., Reference Burke, Cheng, Gillooly, Strnad, Zupa-Fernandez, Catlett, Zhang, Heimrich, Mcintyre, Cunningham, Carman, Zhou, Banas, Chaudhry, Li, D’Arienzo, Chimalakonda, Yang, Xie, Pang, Zhao, Rose, Huang, Moslin, Wrobleski, Weinstein and Salter-Cid2019), is associated with autoimmune diseases, including psoriasis (Genetic Analysis of Psoriasis Consortium & the Wellcome Trust Case Control Consortium Reference Andreoletti, Lanata, Trupin, Paranjpe, Jain, Nititham, Taylor, Combes, Maliskova, Ye, Katz, Dall’Era, Yazdany, Criswell and Sirota2 et al., Reference Strange, Capon, Spencer, Knight, Weale, Allen, Barton, Band, Bellenguez, Bergboer, Blackwell, Bramon, Bumpstead, Casas, Cork, Corvin, Deloukas, Dilthey, Duncanson, Edkins, Estivill, Fitzgerald, Freeman, Giardina, Gray, Hofer, Hüffmeier, Hunt, Irvine, Jankowski, Kirby, Langford, Lascorz, Leman, Leslie, Mallbris, Markus, Mathew, McLean, McManus, Mössner, Moutsianas, Naluai, Nestle, Novelli, Onoufriadis, Palmer, Perricone, Pirinen, Plomin, Potter, Pujol, Rautanen, Riveira-Munoz, Ryan, Salmhofer, Samuelsson, Sawcer, Schalkwijk, Smith, Ståhle, Su, Tazi-Ahnini, Traupe, Viswanathan, Warren, Weger, Wolk, Wood, Worthington, Young, Zeeuwen, Hayday, Burden, Griffiths, Kere, Reis, McVean, Evans, Brown, Barker, Peltonen, Donnelly and Trembath2010; Ellinghaus et al., Reference Ellinghaus, Ellinghaus, Nair, Stuart, Esko, Metspalu, Debrus, Raelson, Tejasvi, Belouchi, West, Barker, Kõks, Kingo, Balschun, Palmieri, Annese, Gieger, Wichmann, Kabesch, Trembath, Mathew, Abecasis, Weidinger, Nikolaus, Schreiber, Elder, Weichenthal, Nothnagel and Franke2012; Tsoi et al., Reference Tsoi, Spain, Knight, Ellinghaus, Stuart, Capon, Ding, Li, Tejasvi, Gudjonsson, Kang, Allen, McManus, Novelli, Samuelsson, Schalkwijk, Ståhle, Burden, Smith, Cork, Estivill, Bowcock, Krueger, Weger, Worthington, Tazi-Ahnini, Nestle, Hayday, Hoffmann, Winkelmann, Wijmenga, Langford, Edkins, Andrews, Blackburn, Strange, Band, Pearson, Vukcevic, Spencer, Deloukas, Mrowietz, Schreiber, Weidinger, Koks, Kingo, Esko, Metspalu, Lim, Voorhees, Weichenthal, Wichmann, Chandran, Rosen, Rahman, Gladman, Griffiths, Reis, Kere, Nair, Franke, Barker, Abecasis, Elder and Trembath2012), psoriatic arthritis (Mease et al., Reference Morand, Furie, Tanaka, Bruce, Askanase, Richez, Bae, Brohawn, Pineda, Berglind and Tummala2022), Crohn’s disease (Franke et al., Reference Franke, McGovern, Barrett, Wang, Radford-Smith, Ahmad, Lees, Balschun, Lee, Roberts, Anderson, Bis, Bumpstead, Ellinghaus, Festen, Georges, Green, Haritunians, Jostins, Latiano, Mathew, Montgomery, Prescott, Raychaudhuri, Rotter, Schumm, Sharma, Simms, Taylor, Whiteman, Wijmenga, Baldassano, Barclay, Bayless, Brand, Büning, Cohen, Colombel, Cottone, Stronati, Denson, de Vos, D’Inca, Dubinsky, Edwards, Florin, Franchimont, Gearry, Glas, van Gossum, Guthery, Halfvarson, Verspaget, Hugot, Karban, Laukens, Lawrance, Lemann, Levine, Libioulle, Louis, Mowat, Newman, Panés, Phillips, Proctor, Regueiro, Russell, Rutgeerts, Sanderson, Sans, Seibold, Steinhart, Stokkers, Torkvist, Kullak-Ublick, Wilson, Walters, Targan, Brant, Rioux, D’Amato, Weersma, Kugathasan, Griffiths, Mansfield, Vermeire, Duerr, Silverberg, Satsangi, Schreiber, Cho, Annese, Hakonarson, Daly and Parkes2010), and SLE (Sigurdsson et al., Reference Sigurdsson, Nordmark, Göring, Lindroos, Wiman, Sturfelt, Jönsen, Rantapää-Dahlqvist, Möller, Kere, Koskenmies, Widén, Eloranta, Julkunen, Kristjansdottir, Steinsson, Alm, Rönnblom and Syvänen2005; Graham et al., Reference Graham, Morris, Bhangale, Criswell, Syvänen, Rönnblom, Behrens, Graham and Vyse2011; Tang et al., Reference Tang, Wan, Wang, Pan, Wang and Chen2015; Lee and Bae, Reference Lee and Bae2016). 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., Reference Rechavi and Somech2018), 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., Reference Morand, Furie, Tanaka, Bruce, Askanase, Richez, Bae, Brohawn, Pineda, Berglind and Tummala2022) and SLE (Morand et al., Reference Morand, Pike, Merrill, van Vollenhoven, Werth, Hobar, Delev, Shah, Sharkey, Wegman, Catlett, Banerjee and Singhal2023). 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., Reference Kim, Moon, Park and Tagkopoulos2020), to predict potential therapies is needed for drug discovery in autoimmunity. Using this approach, Cordell et al. (Reference Cordell, Fryett, Ueno, Darlay, Aiba, Hitomi, Kawashima, Nishida, Khor, Gervais, Kawai, Nagasaki, Tokunaga, Tang, Shi, Li, Juran, Atkinson, Gerussi, Carbone, Asselta, Cheung, de Andrade, Baras, Horowitz, MAR, Sun, Jones, Flack, Spicer, Mulcahy, Byun, Han, Sandford, Lazaridis, Amos, Hirschfield, Seldin, Invernizzi, Siminovitch, Ma, Nakamura and Mells2021) 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. (Reference Smillie, Biton, Ordovas-Montanes, Sullivan, Burgin, Graham, Herbst, Rogel, Slyper, Waldman, Sud, Andrews, Velonias, Haber, Jagadeesh, Vickovic, Yao, Stevens, Dionne, Nguyen, Villani, Hofree, Creasey, Huang, Rozenblatt-Rosen, Garber, Khalili, Desch, Daly, Ananthakrishnan, Shalek, Xavier and Regev2019) 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., Reference Smillie, Biton, Ordovas-Montanes, Sullivan, Burgin, Graham, Herbst, Rogel, Slyper, Waldman, Sud, Andrews, Velonias, Haber, Jagadeesh, Vickovic, Yao, Stevens, Dionne, Nguyen, Villani, Hofree, Creasey, Huang, Rozenblatt-Rosen, Garber, Khalili, Desch, Daly, Ananthakrishnan, Shalek, Xavier and Regev2019). 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., Reference Zhang, Wei, Slowikowski, Fonseka, Rao, Kelly, Goodman, Tabechian, Hughes, Salomon-Escoto, Watts, Jonsson, Rangel-Moreno, Meednu, Rozo, Apruzzese, Eisenhaure, Lieb, Boyle, Mandelin, Boyce, DiCarlo, Gravallese, Gregersen, Moreland, Firestein, Hacohen, Nusbaum, Lederer, Perlman, Pitzalis, Filer, Holers, Bykerk, Donlin, Anolik, Brenner and Raychaudhuri2019). 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., Reference Butler, Hoffman, Smibert, Papalexi and Satija2018; Haghverdi et al., Reference Haghverdi, Lun, Morgan and Marioni2018; Hie et al., Reference Hie, Bryson and Berger2019; Korsunsky et al., Reference Korsunsky, Millard, Fan, Slowikowski, Zhang, Wei, Baglaenko, Brenner, Loh and Raychaudhuri2019; Polański et al., Reference Polański, Park, Young, Miao, Meyer and Teichmann2020; Tran et al., Reference Tran, Ang, Chevrier, Zhang, Lee, Goh and Chen2020). As one example, the algorithm Harmony (Korsunsky et al., Reference Korsunsky, Millard, Fan, Slowikowski, Zhang, Wei, Baglaenko, Brenner, Loh and Raychaudhuri2019) 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., Reference Zhang, Mears, Shakib, Beynor, Shanaj, Korsunsky, Nathan, Donlin and Raychaudhuri2021), 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., Reference Phillips, Garciafigueroa, Trucco and Giannoukakis2017). 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., Reference Zubizarreta, Flórez-Grau, Vila, Cabezón, España, Andorra, Saiz, Llufriu, Sepulveda, Sola-Valls, Martinez-Lapiscina, Pulido-Valdeolivas, Casanova, Martinez Gines, Tellez, Oreja-Guevara, Español, Trias, Cid, Juan, Lozano, Blanco, Steinman, Benitez-Ribas and Villoslada2019). 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., Reference Zubizarreta, Flórez-Grau, Vila, Cabezón, España, Andorra, Saiz, Llufriu, Sepulveda, Sola-Valls, Martinez-Lapiscina, Pulido-Valdeolivas, Casanova, Martinez Gines, Tellez, Oreja-Guevara, Español, Trias, Cid, Juan, Lozano, Blanco, Steinman, Benitez-Ribas and Villoslada2019).

The therapeutic potential of polyclonal regulatory T cells has also been demonstrated in some autoimmune diseases, including MS (Kohm et al., Reference Kohm, Carpentier, Anger and Miller2002); however, the effects are mostly modest, possibly because of nonspecific regulatory T cells (Raffin et al., Reference Raffin, Vo and Bluestone2020). 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. (Reference Kim, Zhang, Yoon, Culp, Lees, Wucherpfennig and Scott2018) 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., Reference DiMasi, Hansen and Grabowski2003). 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. (Reference van Gaalen, van Aken, Huizinga, Schreuder, Breedveld, Zanelli, van Venrooij, Verweij, Toes and de Vries2011) 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., Reference Dudley, Sirota, Shenoy, Pai, Roedder, Chiang, Morgan, Sarwal, Pasricha and Butte2011).

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., Reference Brewer, Lanz, Hale, Sepich-Poore, Martino, Swafford, Carroll, Kongpachith, Blum, Elliott, Blachere, Parveen, Fak, Yao, Troyanskaya, Frank, Bloom, Jahanbani, Gomez, Iyer, Ramadoss, Sharpe, Chandrasekaran, Kelmenson, Wang, Wong, Torres, Wiesen, Graves, Deane, Holers, Knight, Darnell, Robinson and Orange2023). 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., Reference Hamada, Keum, Nishihara and Ogino2017; Hughes et al., Reference Hughes, Simons, van den Brandt, van Engeland and Weijenberg2017; Ogino et al., Reference Raffin, Vo and Bluestone2018). 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

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. https://doi.org/10.1038/s42003-021-02000-9Google Scholar
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), 26102615. https://doi.org/10.1073/pnas.0337679100Google ScholarPubMed
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), 551565. https://doi.org/10.1016/j.cell.2016.03.008Google ScholarPubMed
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), 10731085. doi:10.1002/art.41610Google ScholarPubMed
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), 711723. https://doi.org/10.1084/jem.20021553Google ScholarPubMed
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), 3237. https://doi.org/10.1016/j.cell.2019.02.040Google ScholarPubMed
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. https://doi.org/10.1126/scitranslmed.abq8476Google ScholarPubMed
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), 349356. https://doi.org/10.1038/s41586-022-04642-zGoogle ScholarPubMed
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. https://doi.org/10.1126/scitranslmed.aaw1736Google ScholarPubMed
Busch, R, Kollnberger, S and Mellins, ED 2019) HLA associations in inflammatory arthritis: Emerging mechanisms and clinical implications. Nature Reviews Rheumatology 15(6), 364381. https://doi.org/10.1038/s41584-019-0219-5Google ScholarPubMed
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), 411420. https://doi.org/10.1038/nbt.4096Google ScholarPubMed
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), 15831595. https://doi.org/10.1002/art.38628Google ScholarPubMed
Christensen, CM, Grossman, JH and Hwang, J (2009) The Innovator’s Prescription: A Disruptive Solution for Health Care. New York: McGraw-Hill.Google Scholar
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), 572581. https://doi.org/10.1016/j.jhep.2021.04.055Google ScholarPubMed
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), 14571465. https://doi.org/10.4049/jimmunol.1201517Google Scholar
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), 8993. https://doi.org/10.1038/nature22383Google ScholarPubMed
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), 143149. https://doi.org/10.1002/art.25064Google Scholar
DiMasi, JA, Hansen, RW and Grabowski, HG (2003) The price of innovation: New estimates of drug development costs. Journal of Health Economics 22(2), 151185. https://doi.org/10.1016/s0167-6296(02)00126-1Google Scholar
Dudbridge, F (2013) Power and predictive accuracy of polygenic risk scores. PLoS Genetics 9(3), e1003348. https://doi.org/10.1371/journal.pgen.1003348Google ScholarPubMed
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. https://doi.org/10.1126/scitranslmed.3002648Google ScholarPubMed
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), 636647. https://doi.org/10.1016/j.ajhg.2012.02.020Google ScholarPubMed
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), 19461958. https://doi.org/10.1002/art.40587CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1002/cti2.1093Google ScholarPubMed
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), 11181125. https://doi.org/10.1038/ng.717Google ScholarPubMed
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), 376386. https://doi.org/10.1002/art.39962CrossRefGoogle ScholarPubMed
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), 985990. https://doi.org/10.1038/ng.694Google Scholar
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. https://doi.org/10.1186/s12865-020-00357-wGoogle ScholarPubMed
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), 9498. https://doi.org/10.1038/nature22976CrossRefGoogle ScholarPubMed
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), 20682076. https://doi.org/10.1172/jci117561Google ScholarPubMed
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), 233250. https://doi.org/10.1038/s41571-019-0299-9CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1371/journal.pgen.1002341CrossRefGoogle Scholar
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. https://doi.org/10.1016/j.eclinm.2020.100291Google ScholarPubMed
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), 421427. https://doi.org/10.1038/nbt.4091Google ScholarPubMed
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), 265275. https://doi.org/10.1007/s00535-016-1272-3Google ScholarPubMed
Hie, B, Bryson, B and Berger, B (2019) Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nature Biotechnology 37(6), 685691. https://doi.org/10.1038/s41587-019-0113-3Google ScholarPubMed
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), 967979. https://doi.org/10.1084/jem.20072051Google ScholarPubMed
Hoffman, HM (2009) Therapy of autoinflammatory syndromes. Journal of Allergy and Clinical Immunology 124(6), 11291138. https://doi.org/10.1016/j.jaci.2009.11.001Google ScholarPubMed
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), 455469. https://doi.org/10.1007/s11888-017-0395-0Google ScholarPubMed
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), 34333438. https://doi.org/10.1002/art.21385Google ScholarPubMed
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), 305317. https://doi.org/10.1016/s0140-6736(20)32341-2Google ScholarPubMed
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), 446453. https://doi.org/10.1002/art.1780390312Google ScholarPubMed
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. https://doi.org/10.1038/srep12937Google ScholarPubMed
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), 17121722. https://doi.org/10.1002/art.38637CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1038/s41467-019-10242-9CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1136/ard.2009.120170Google ScholarPubMed
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), 19091916. https://doi.org/10.1136/annrheumdis-2015-208562CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1038/s41598-020-70280-yGoogle ScholarPubMed
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, 7786. https://doi.org/10.1016/j.jaut.2018.05.003CrossRefGoogle ScholarPubMed
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), 23722383. https://doi.org/10.1002/art.39771Google ScholarPubMed
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. https://doi.org/10.1136/annrheumdis-2011-200612Google ScholarPubMed
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), 47124716. https://doi.org/10.4049/jimmunol.169.9.4712Google ScholarPubMed
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), 507517. https://doi.org/10.1002/art.40760CrossRefGoogle ScholarPubMed
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), 12891296. https://doi.org/10.1038/s41592-019-0619-0Google ScholarPubMed
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), 607616. https://doi.org/10.1093/rheumatology/keaa165Google ScholarPubMed
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), 119129. https://doi.org/10.1002/eji.201948129.Google ScholarPubMed
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), 390398. https://doi.org/10.5114/ceji.2017.72807CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1038/s41467-019-11845-yGoogle Scholar
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/ar3848Google ScholarPubMed
Lee, YH and Bae, S-C (2016) Association between TYK2 polymorphisms and susceptibility to autoimmune rheumatic diseases: A meta-analysis. Lupus 25(12), 13071314. https://doi.org/10.1177/0961203316638933CrossRefGoogle ScholarPubMed
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), 10651067. doi:10.1038/ng2091Google ScholarPubMed
Liu, B, Shao, Y and Fu, R (2021) Current research status of HLA in immune‐related diseases. Immunity, Inflammation and Disease 9(2), 340350. https://doi.org/10.1002/iid3.416Google ScholarPubMed
Lopes-Pacheco, M (2020) CFTR modulators: The changing face of cystic fibrosis in the era of precision medicine. Frontiers in Pharmacology 10, 1662. https://doi.org/10.3389/fphar.2019.01662Google ScholarPubMed
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), 214223. doi:10.1056/nejmoa1108735CrossRefGoogle ScholarPubMed
Manohar, PM and Davidson, NE (2021) Updates in endocrine therapy for metastatic breast cancer. Cancer Biology and Medicine 18, 202212. https://doi.org/10.20892/j.issn.2095-3941.2021.0255Google Scholar
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), 832842. https://doi.org/10.1038/ni.3777Google ScholarPubMed
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), 16261637. doi:10.1002/art.41708Google ScholarPubMed
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), 815822. https://doi.org/10.1136/annrheumdis-2021-221664Google Scholar
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. https://doi.org/10.1186/s40246-014-0023-xGoogle Scholar
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), 242252. https://doi.org/10.1002/art.42391Google ScholarPubMed
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), 211221. https://doi.org/10.1056/nejmoa1912196Google ScholarPubMed
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), 270279. doi:10.1038/gene.2010.73CrossRefGoogle Scholar
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), 10941106. doi:10.1038/s41590-020-0743-0Google ScholarPubMed
Nielsen, SCA and Boyd, SD (2019) New technologies and applications in infant B cell immunology. Current Opinion in Immunology 57, 5357. https://doi.org/10.1016/j.coi.2018.12.005CrossRefGoogle Scholar
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.aat2004Google ScholarPubMed
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), 121. https://doi.org/10.1146/annurev-pathmechdis-012418-012818Google ScholarPubMed
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-2018-214379Google ScholarPubMed
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), 13131321. doi:10.1056/nejmoa1806382CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1126/science.abf1970CrossRefGoogle ScholarPubMed
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. https://doi.org/10.3389/fimmu.2017.01279Google ScholarPubMed
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, 964965. https://doi.org/10.1093/bioinformatics/btz625Google ScholarPubMed
Raffin, C, Vo, LT and Bluestone, JA (2020) Treg cell-based therapies: Challenges and perspectives. Nature Reviews Immunology 20(3), 158172. https://doi.org/10.1038/s41577-019-0232-6CrossRefGoogle ScholarPubMed
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), 291296. https://doi.org/10.1038/ng.1076CrossRefGoogle ScholarPubMed
Rechavi, E and Somech, R (2017) Survival of the fetus: Fetal B and T cell receptor repertoire development. Seminars in Immunopathology 39(6), 577583. https://doi.org/10.1007/s00281-017-0626-0Google Scholar
Richard-Miceli, C and Criswell, LA (2012) Emerging patterns of genetic overlap across autoimmune disorders. Genome Medicine 4(1), 66. https://doi.org/10.1186/gm305Google ScholarPubMed
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. https://doi.org/10.1186/s13075-021-02607-7Google ScholarPubMed
Robinson, WH (2015) Sequencing the functional antibody repertoire—Diagnostic and therapeutic discovery. Nature Reviews Rheumatology 11(3), 171182. https://doi.org/10.1038/nrrheum.2014.220CrossRefGoogle ScholarPubMed
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), 109117. https://doi.org/10.1136/annrheumdis-2020-218636Google ScholarPubMed
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), 25692582. doi:10.1084/jem.20131241Google ScholarPubMed
Schatz, DG and Ji, Y (2011) Recombination centres and the orchestration of V(D)J recombination. Nature Reviews Immunology 11(4), 251263. https://doi.org/10.1038/nri2941Google ScholarPubMed
Schmidt, D, Martens, PB, Weyand, CM and Goronzy, JJ (1996) The repertoire of CD4+ CD28− T cells in rheumatoid arthritis. Molecular Medicine 2(5), 608618. https://doi.org/10.1007/bf03401644CrossRefGoogle ScholarPubMed
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), 528537. doi:10.1086/428480Google ScholarPubMed
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. https://doi.org/10.1126/scitranslmed.3001318Google ScholarPubMed
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), 936946. https://doi.org/10.1002/acr.24795CrossRefGoogle ScholarPubMed
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), 714730.e22. https://doi.org/10.1016/j.cell.2019.06.029Google ScholarPubMed
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), 22052216. https://doi.org/10.4049/jimmunol.1202859Google ScholarPubMed
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), 196209. doi:10.1002/art.40699CrossRefGoogle ScholarPubMed
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), 27062715. https://doi.org/10.1002/art.38754CrossRefGoogle ScholarPubMed
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), 817824. https://doi.org/10.1007/s00011-015-0865-2CrossRefGoogle ScholarPubMed
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), 19331945. doi:10.1002/art.40590CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1038/s41598-019-51616-9CrossRefGoogle ScholarPubMed
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), 20252035. https://doi.org/10.1002/art.40653CrossRefGoogle ScholarPubMed
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/s13059-019-1850-9CrossRefGoogle ScholarPubMed
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), 13411348. https://doi.org/10.1038/ng.2467Google Scholar
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), 21132121. https://doi.org/10.1002/art.20316Google ScholarPubMed
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), 11891201. https://doi.org/10.1093/rheumatology/39.11.1189CrossRefGoogle Scholar
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), 7984. doi:10.1002/immu.200390010Google ScholarPubMed
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. https://doi.org/10.1186/s12943-018-0814-0Google ScholarPubMed
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), 265278. https://doi.org/10.1385/ir:26:1-3:265CrossRefGoogle Scholar
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. https://doi.org/10.1186/s13073-021-00881-3Google ScholarPubMed
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), 928942. https://doi.org/10.1038/s41590-019-0378-1Google ScholarPubMed
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), 13591371. doi:10.1158/1078-0432.ccr-19-3249CrossRefGoogle ScholarPubMed
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), 4355. https://doi.org/10.1038/nrg2489CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1073/pnas.1820039116Google Scholar

Author comment: Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders — R0/PR1

Comments

Dear Editor,

We are excited to submit this invited review manuscript by Baglaenko et al. entitled “Making inroads to precision medicine for the treatment of autoimmune diseases: harnessing genomic studies to better diagnose and treat complex disorders.”, which we hope will be of interest to the readers of Precision Medicine.

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., 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 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. Within that framework, improved diagnostics and targeted therapeutic approaches may advance toward precision clinical care of patients with autoimmune diseases.

We believe this study will be of great interest to clinicians, medical providers, bioinformaticians, and scientists alike, and will be informative for demonstrating approaches in leveraging precision medicine to study autoimmunity. We thank you for your time and consideration and hope for a favorable response.

Sincerely,

Marina Sirota

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

Conflict of interest statement

Reviewer declares none.

Comments

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.

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

Conflict of interest statement

Reviewer declares none.

Comments

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.

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

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

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Author comment: Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders — R1/PR6

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

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Reviewer declares none.

Comments

Comments to Author: The authors improved the paper.

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

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

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