Preventing inappropriate immune responses is critical to the maintenance of peripheral tolerance and the prevention of autoimmunity. The risk of autoimmunity stems from a complex interplay of genetic and environmental factors (Theofilopoulos et al., 2017; Cho and Feldman, 2015). While the greatest risk factor for most autoimmune disease is HLA haplotype (Todd et al., 2007; Bonifacio et al., 2018), an individual’s total genetic risk is determined by a combination of gene variants (Sharma et al., 2018; Cotsapas et al., 2011), many of which cluster near genes responsible for regulating immune responses (Farh et al., 2015). A leading example of such an autoimmune risk variant is found in the gene protein tyrosine phosphatase non-receptor 22 (PTPN22).

While the phosphatase encoded by PTPN22 can function to modulate a range of immune signaling programs, it has been best studied in lymphocyte antigen receptor regulation (Cohen et al., 1999; Wu et al., 2006; Hill et al., 2002). An arginine to tryptophan substitution (R620W) resulting from a single nucleotide polymorphism (SNP; rs2476601) within PTPN22 is broadly associated with a large number of autoimmune diseases (Zheng et al., 2012; Tizaoui et al., 2019). PTPN22 R620W, hereafter referred to as the PTPN22 risk variant, is one of the strongest, non-HLA, genetic risk factors for autoimmune diseases, nearly doubling a carrier’s risk of type 1 diabetes, systemic lupus erythematosus, or rheumatoid arthritis (Bottini et al., 2004; Begovich et al., 2004; Kyogoku et al., 2004). As PTPN22 plays a role in multiple autoimmune diseases, it has been argued that this SNP potentially highlights a common pathway that alters immune tolerance and might serve as a potential point of intervention to prevent and/or delay disease (Rawlings et al., 2015).

In T cells, PTPN22 functions to dephosphorylate key activating tyrosine residues within the proximal tyrosine kinase effectors, LCK and ZAP70, in the TCR signaling pathway (Wu et al., 2006; Bottini et al., 2004). This function is mediated in concert with activity through PTPN22’s binding partner, C-terminal Src kinase (CSK), which also regulates LCK activity (Cloutier and Veillette, 1999). Co-association of PTPN22 and CSK results in residence of both proteins within the cytosolic plasma membrane of resting T cells through the association of CSK with another TCR inhibitory protein, PAG (Davidson et al., 2016; Simoncelli et al., 2020). The R620W substitution in PTPN22 is located within a proline rich, C-terminal domain and reduces the interaction of PTPN22 with CSK leading to altered localization of PTPN22 (Bottini et al., 2004). The functional outcome of disrupted PTPN22 protein interactions has led to significant debate as to the mechanism by which the PTPN22 risk variant contributes to altered T cell function and self-tolerance, namely whether the human PTPN22 risk variant mediates a gain- vs. a loss-of-function impact on TCR regulation.

Primary T cells isolated from carriers of the PTPN22 risk variant display significantly weaker responses to TCR engagement, as measured by calcium flux, cytokine release, and other metrics (Rieck et al., 2007; Vang et al., 2005; Arechiga et al., 2009; Vang et al., 2013). However, in murine models, expression of the risk variant leads to enhanced TCR responses, as measured by the same metrics in addition to increased T cell proliferation and survival (Zhang et al., 2011; Dai et al., 2013; Lin et al., 2016). In contrast, genetic ablation of PTPN22 in both mouse and human primary T cells results in enhanced TCR signaling relative to PTPN22 wild type cells; findings similar to murine risk model yet opposite of the phenotypes described in human carriers of the risk variant (Hasegawa et al., 2004; Anderson et al., 2019). Furthermore, genetic ablation of ptpn22 in a fixed TCR murine model has shown that loss of PTPN22 preferentially impacts T cells responses to low-avidity antigens, but not high-avidity antigens (Salmond et al., 2014); driving increased proliferation and skewing toward a Th1 phenotype in ptpn22 KO T cells. While these observations suggest an important potential mechanism for how the PTPN22 risk variant promotes autoimmunity, this concept has not been tested in primary human T cells.

Both human carriers and murine models of the PTPN22 risk variant share important traits. For instance, the risk variant is associated with an increase in memory lymphocyte populations and expanded pro-inflammatory/Th1 T cells and both exhibit increased risk of autoimmune manifestations (Vang et al., 2013; Dai et al., 2013; Sanchez-Blanco et al., 2018). Thus, it remains surprising that murine and human carriers exhibit these key similarities despite the published differences in the TCR signaling responses described human vs. mouse T cells. Notably, the murine and human T cell populations used for previous studies exhibit fundamental differences. First, most data for human PTPN22 risk variant phenotype have utilized mature T cells isolated from adult donors, while T cells from murine studies utilize donor mice with minimal environmental exposures. Studies of PTPN22 using human cord blood-derived T cells have been limited (Rawlings et al., 2015). Also, murine models of the ptpn22 risk variant are conducted in genetically homogenous, autoimmune-prone strains or models with a fixed TCR repertoire (Dai et al., 2013; Lin et al., 2016; Salmond et al., 2014; Maine et al., 2016), conditions difficult to mimic in a primary human T cell setting. Further, in vitro human T cell line models of the PTPN22 risk variant lead to discrepant results, indicating that the impact(s) of the variant are context dependent, further complicating efforts to study the variant in human T cells (Vang et al., 2005; Zikherman et al., 2009).

Recently, Crispr/Cas9 gene editing has allowed for precise manipulation of genetic loci in primary human T cells. Delivery of recombinant ribonucleoproteins (RNPs [Anderson et al., 2019; Schumann et al., 2015; Okamoto et al., 2019] containing Cas9 complexed with sequence specific guide RNA sequences) via electroporation into primary T cells efficiently mediates site-specific DNA double stranded breaks (DSBs) near a protospacer adjacent motif (PAM) site (Jinek et al., 2012). Such DNA breaks trigger a series of events driving 5’ to 3’ exonuclease activity at the broken DNA ends as the cell prepares to mend the break through potential repair pathways (Scully et al., 2019; Xue and Greene, 2021). When DSBs are generated in the presence of a single stranded oligo deoxynucleotides (ssODNs) (co-delivered with RNP), DNA repair pathways can utilize regions of homology within an ssODN as a repair template for precise genetic modification by homology directed repair (HDR) (Richardson et al., 2016; Richardson et al., 2018). This approach has previously been used to integrate a premature stop codon in the PTPN22 locus of primary human T cells with high efficiency (Anderson et al., 2019).

In this study, we utilized a combination of HDR-based gene editing and TCR co-delivery in naïve cord blood T cells to establish a platform to better understand the role of the PTPN22 risk variant in primary human T cells. Through Crispr/Cas9 gene editing we find the PTPN22 risk variant T cells mimicked PTPN22 KO T cells, with enhanced expression of surface activation markers in response to TCR stimulation. Further, using transgenic TCR stimulation in concert with gene edited cells, we found that the PTPN22 risk variant accentuates the response of low-avidity primary human T cells to antigen, implying a key role for these events in the progressive loss of T cell tolerance in human autoimmune diseases associated with this risk allele.

In this paper we utilized a robust gene editing platform in primary human cord blood CD4 T cells to efficiently alter PTPN22 non-risk T cells to express the PTPN22 R620W risk variant as well as relevant control, non-risk, or KO edited cell populations. This gene editing approach should be applicable to many cell types and broadly applicable to study the impact of candidate disease associated alleles (identified by GWAS or other sequencing strategies) within primary human cell populations. Notably, our data clearly demonstrate that PTPN22 risk variant expressing T cells exhibit a phenotype similar to PTPN22 KO T cells, leading to increased expression of TCR induced activation responses markers. Further, using transgenic TCR delivery and cognate peptide-mediated activation, in tandem with our gene editing platform, we show that PTPN22 deficiency predominantly impacts T cells activated in response to low-avidity TCR engagement. In contrast, PTPN22 function is largely dispensable in T cells receiving a high-avidity TCR signal. Together, these data suggest that the PTPN22 risk variant may contribute to autoimmune responses, at least in part, by selectively expanding the pool of lower avidity, self-reactive T cells and skewing such cells toward a pro-inflammatory phenotype. Our findings are consistent with previous work in a fixed TCR murine model that also found loss of ptpn22 to preferentially impact low-avidity TCR stimulation (Salmond et al., 2014). Importantly, our study models function of the PTPN22 regulatory pathway in a fully human gene edited primary cell setting.

Our editing platform utilizes a designer nuclease that generates a dsDNA break in close proximity to the target site for HDR editing. This combined approach allows for: (a) generation of edited cells with high rates of HDR; (b) equivalent HDR rates for alternative coding changes within the target gene via use of a series of ssODN donors; and (c) achieving editing outcomes while controlling for the overall HDR/NHEJ ratio. An additional strength is that the location and small editing footprint allows for physiological expression via use of the endogenous promoter, local enhancers, and 3’ UTR. Also, editing in this location is unlikely to disrupt the formation of natural PTPN22 splice variants. Of note, PTPN22 risk and non-risk ssODNs, while producing identical HDR rates, demonstrated reduced HDR relative to editing using the PTPN22 KO ssODN. This modest difference likely reflects the requirement to replace endogenous nucleotides immediately 5’ and 3’ to the DNA cut site to achieve risk or non-risk edits, while the KO ssODN only required insertion of new nucleotides. These minor differences aside, our editing platform should be readily adaptable for the study of multiple loci across the genome in future studies of human primary cell populations.

We demonstrate that expression of the PTPN22 risk variant can increase proliferation of primary human T cells expressing a low-avidity TCR, findings that mimic PTPN22 KO T cells. Furthermore, we show that loss of PTPN22 regulation promotes the development of a pro-inflammatory phenotype in T cells using a low-avidity TCR. These combined findings are consistent with in vivo functional impacts observed in human and mouse studies. Indeed, while previous human data and mouse models have led to contradictory conclusions regarding the impact of the risk variant on proximal TCR signaling, both humans and mice with the PTPN22 risk variant exhibit expanded memory T cell and Th1 populations (Rieck et al., 2007; Vang et al., 2013; Dai et al., 2013; Salmond et al., 2014). Importantly, PTPN22 is expressed in multiple immune and hematopoietic subsets and has been shown to have multiple functions (Rawlings et al., 2015; Stanford and Bottini, 2014; Armitage et al., 2021). Therefore, our dataset informs on a single signaling program and PTPN22 may contribute to autoimmune disease via other pathways.

While our editing design provides a useful means to address questions related to TCR engagement, this approach has limitations including assessing early intracellular signaling events. While gene edited cells exhibited proximal phosphorylation changes in response to antibody-based stimulation, biochemical changes were not detected until 24 hr post peptide-based stimulation. Future work may find a path around this technical challenge allowing better insight into early signaling events. Also, while our findings provide an important bridge between inconsistencies connecting PTPN22 to autoimmune risk in previous mouse vs. primary human cell data, it should not be viewed as a replacement for mouse models. Our study relies on ex vivo culturing of T cells, outside of optimal physiological conditions and supportive niches, features that may have key impacts on protein tyrosine phosphatase functions. Also, though our gene KO model reached 100% as measured by ddPCR, western quantification data (Figure 1E) may suggest residual expression of PTPN22, a consideration not of relevant for mouse models. Finally, as our model is restricted to peripheral mature T cells, it is inadequate to assess the role for PTPN22 in T cell development. For example, while mouse models suggest that the PTPN22 risk variant alters thymic selection (Dai et al., 2013) others have shown that PTPN22 KO has no impact on the TCR repertories (Hasegawa et al., 2004; Salmond et al., 2014; Sanchez-Blanco et al., 2018; Maine et al., 2012). Because our study shows the risk variant functions similarly in human T cells in comparison with published murine models, it suggests that returning to these models to investigate specific contexts where PTPN22 might impact selection and tolerance is likely to be informative and critical for full understanding of this variant.

While T cells expressing a lower avidity TCR were primarily impacted in our studies, we cannot eliminate potential alternative impacts of the TCRs studied in our models. The TCRs evaluated exhibited distinct differences in both EC₅₀ (Linsley et al., 2021) and triggered proliferative response to an identical IGRP derived peptide (Figure 3C; Linsley et al., 2021). However, while these data points support a working hypothesis of TCR avidity determining the functional impact, we cannot rule out the role for possible differences in TCR disassociation rates and/or overall duration of TCR engagement. Further, while our approach provides a robust system to assess the impact of the risk variant in primary human T cells, our findings are derived from a comparison of only two specific TCRs. The identification of similar pairs of ‘higher’ and ‘lower’ avidity TCRs recognizing identical self-peptides will be useful in assessing our model and its interpretation across a broader pool of TCR interactions. Notably, in this study we explored the impacts of the PTPN22 risk variant and KO specifically in cord blood CD4 T cells. CD4 T cells were chosen for this study based on well-documented use in gene editing models and likely key roles in PTPN22 associated autoimmune diseases (Zheng et al., 2012). Cord blood was used to minimize differences in environmental impact between T cell donors. Although previous work has revealed similar findings regarding the phenotype of PTPN22 KO in adult T cells (Anderson et al., 2019), it remains possible that similar risk variant editing experiments using adult peripheral blood may yield different results.

Consistent with our findings, recent work using lentiviral-based overexpression in primary human T cells also revealed hypomorphic function of the PTPN22 risk variant (Perry et al., 2021). Further, that study and others Sanchez-Blanco et al., 2018; Burn et al., 2016 have suggested that PTPN22 may modulate pathways other than the TCR including LFA-1-mediated integrin signals, stabilizing immunological synapses that promote T cell proliferation and Th1 skewing. Our data using low dose CD3 stimulation (Figure 2) support the conclusion that both the variant and PTPN22 KO can impact T cell activation through altered regulation of the TCR. Notably, our CD3 studies were performed in the absence of co-stimulation to better mimic sub-optimal TCR engagement. Thus, the relevance of our findings based on endogenous-level expression compared to lentiviral-based PTPN22 over-expression in association with CD3/CD28 co-stimulation are difficult to compare. Together, our findings that the risk variant preferentially modulates low avidity TCR engagement, with the observations of these previous studies suggests that the PTPN22 risk variant may exert its impact on T cells both via altering TCR signaling and by modulating additional signals including LFA-1 pathway regulation.

Through gene editing we demonstrate that PTPN22 risk variant expression in human T cells mediates at least a partial loss of function phenotype that mirrors findings derived from mouse models. This data, however, does not explain the marked differences in T cell responses described in mouse models vs. natural carriers of the PTPN22 risk variant. Natural carriers of the risk variant exhibit hyporesponsive T cells, with reduced TCR induced activation, and reduced cytokine responses (Rieck et al., 2007; Vang et al., 2005). In previous work, we showed that loss of function in another key negative regularity pathway in T cells leads to overexpression of functionally related proteins to maintain homeostasis. For example, KO of the IL-2 regulatory gene, PTPN2, in primary human T cells results in a transient increase in IL-2 signaling, but expansion of PTPN2 KO cells in high cytokine media leads to reduced IL-2 responses due to overexpression of the regulatory protein SOCS3 (Anderson et al., 2019). Similar compensatory events may be operable in T cells in carriers of the PTPN22 risk variant, where reduced proximal negative regulatory events might lead to expression of alternative regulatory proteins. Recent work using optogenically controlled TCR signaling has shown that T cell activation is dynamic, requiring optimal frequencies of TCR engagement relative to cessation of signal to mediate CD69 expression and subsequent activation; however, events that limit T cell responses may be more static, with expression of TIM3, for example, increasing regardless of signaling frequency (O’Donoghue et al., 2021). As loss of PTPN22 function specifically augments low-avidity TCR signaling, it is possible that tonic TCR signaling is also impacted and may drive additional events that function to limit TCR signals in carriers of the risk variant over time. Finally, it is important to highlight the environmental differences between murine models and primary human T cell donors. As opportunity for exposure to antigen increases, this may drive T cells from human carriers toward a reduced responsiveness to TCR engagement via impacts that are not operative in SPF murine models.

In summary, the data presented within this study supports a model where the PTPN22 R620W risk variant impacts human T cell function via a reduction in negative regulation of TCR signaling; findings similar to murine models, but in contrast to the T cell signaling phenotype in carriers with the risk variant. In association with previous human studies, our findings suggest that the impact of the risk variant on T cell function is likely dynamic, leading to the generation of populations of hyperresponsive T cells, but also allowing for secondary impacts on T cell gene expression that may lead to hyporesponsive T cells over time or after environmental exposures. This would help explain how PTPN22 could influence risk of diverse autoimmune diseases such as type 1 diabetes and rheumatoid arthritis, diseases with distinct ages of onset and tissue targets. Taken together, these observations suggest that PTPN22 contributes to autoimmunity by establishing temporal windows of risk for specific diseases that coincide with periods of altered T cell responsiveness.

Prism files containing the numerical data and statistical tests used to generate all figures has been provided.

1. 1. Anderson W
   2. Thorpe J
   3. Long SA
   4. Rawlings DJ

   (2019) Efficient CRISPR/cas9 disruption of autoimmune-associated genes reveals key signaling programs in primary human T cells

   Journal of Immunology 203:3166–3178.

   https://doi.org/10.4049/jimmunol.1900848

   • PubMed
   • Google Scholar
2. 1. Arechiga AF
   2. Habib T
   3. He Y
   4. Zhang X
   5. Zhang ZY
   6. Funk A
   7. Buckner JH

   (2009) Cutting edge: the PTPN22 allelic variant associated with autoimmunity impairs B cell signaling

   Journal of Immunology 182:3343–3347.

   https://doi.org/10.4049/jimmunol.0713370

   • PubMed
   • Google Scholar
3. 1. Armitage LH
   2. Wallet MA
   3. Mathews CE

   (2021) Influence of ptpn22 allotypes on innate and adaptive immune function in health and disease

   Frontiers in Immunology 12:636618.

   https://doi.org/10.3389/fimmu.2021.636618

   • PubMed
   • Google Scholar
4. 1. Begovich AB
   2. Carlton VEH
   3. Honigberg LA
   4. Schrodi SJ
   5. Chokkalingam AP
   6. Alexander HC
   7. Ardlie KG
   8. Huang Q
   9. Smith AM
   10. Spoerke JM
   11. Conn MT
   12. Chang M
   13. Chang SYP
   14. Saiki RK
   15. Catanese JJ
   16. Leong DU
   17. Garcia VE
   18. McAllister LB
   19. Jeffery DA
   20. Lee AT
   21. Batliwalla F
   22. Remmers E
   23. Criswell LA
   24. Seldin MF
   25. Kastner DL
   26. Amos CI
   27. Sninsky JJ
   28. Gregersen PK

   (2004) A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis

   American Journal of Human Genetics 75:330–337.

   https://doi.org/10.1086/422827

   • PubMed
   • Google Scholar
5. 1. Bonifacio E
   2. Beyerlein A
   3. Hippich M
   4. Winkler C
   5. Vehik K
   6. Weedon MN
   7. Laimighofer M
   8. Hattersley AT
   9. Krumsiek J
   10. Frohnert BI
   11. Steck AK
   12. Hagopian WA
   13. Krischer JP
   14. Lernmark Å
   15. Rewers MJ
   16. She J-X
   17. Toppari J
   18. Akolkar B
   19. Oram RA
   20. Rich SS
   21. Ziegler A-G
   22. TEDDY Study Group

   (2018) Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: a prospective study in children

   PLOS Medicine 15:e1002548.

   https://doi.org/10.1371/journal.pmed.1002548

   • PubMed
   • Google Scholar
6. 1. Bottini N
   2. Musumeci L
   3. Alonso A
   4. Rahmouni S
   5. Nika K
   6. Rostamkhani M
   7. MacMurray J
   8. Meloni GF
   9. Lucarelli P
   10. Pellecchia M
   11. Eisenbarth GS
   12. Comings D
   13. Mustelin T

   (2004) A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes

   Nature Genetics 36:337–338.

   https://doi.org/10.1038/ng1323

   • PubMed
   • Google Scholar
7. 1. Burn GL
   2. Cornish GH
   3. Potrzebowska K
   4. Samuelsson M
   5. Griffié J
   6. Minoughan S
   7. Yates M
   8. Ashdown G
   9. Pernodet N
   10. Morrison VL
   11. Sanchez-Blanco C
   12. Purvis H
   13. Clarke F
   14. Brownlie RJ
   15. Vyse TJ
   16. Zamoyska R
   17. Owen DM
   18. Svensson LM
   19. Cope AP

   (2016) Superresolution imaging of the cytoplasmic phosphatase PTPN22 links integrin-mediated T cell adhesion with autoimmunity

   Science Signaling 9:ra99.

   https://doi.org/10.1126/scisignal.aaf2195

   • PubMed
   • Google Scholar
8. 1. Cerosaletti K
   2. Barahmand-Pour-Whitman F
   3. Yang J
   4. DeBerg HA
   5. Dufort MJ
   6. Murray SA
   7. Israelsson E
   8. Speake C
   9. Gersuk VH
   10. Eddy JA
   11. Reijonen H
   12. Greenbaum CJ
   13. Kwok WW
   14. Wambre E
   15. Prlic M
   16. Gottardo R
   17. Nepom GT
   18. Linsley PS

   (2017) Single-cell RNA sequencing reveals expanded clones of islet antigen-reactive CD4⁺ T cells in peripheral blood of subjects with type 1 diabetes

   Journal of Immunology 199:323–335.

   https://doi.org/10.4049/jimmunol.1700172

   • PubMed
   • Google Scholar
9. 1. Cho JH
   2. Feldman M

   (2015) Heterogeneity of autoimmune diseases: pathophysiologic insights from genetics and implications for new therapies

   Nature Medicine 21:730–738.

   https://doi.org/10.1038/nm.3897

   • PubMed
   • Google Scholar
10. 1. Cloutier JF
    2. Veillette A

    (1999) Cooperative inhibition of T-cell antigen receptor signaling by a complex between a kinase and a phosphatase

    The Journal of Experimental Medicine 189:111–121.

    https://doi.org/10.1084/jem.189.1.111

    • PubMed
    • Google Scholar
11. 1. Cohen S
    2. Dadi H
    3. Shaoul E
    4. Sharfe N
    5. Roifman CM

    (1999) Cloning and characterization of a lymphoid-specific, inducible human protein tyrosine phosphatase, Lyp

    Blood 93:2013–2024.

    https://doi.org/10.1182/blood.V93.6.2013.406k25_2013_2024

    • PubMed
    • Google Scholar
12. 1. Cotsapas C
    2. Voight BF
    3. Rossin E
    4. Lage K
    5. Neale BM
    6. Wallace C
    7. Abecasis GR
    8. Barrett JC
    9. Behrens T
    10. Cho J
    11. De Jager PL
    12. Elder JT
    13. Graham RR
    14. Gregersen P
    15. Klareskog L
    16. Siminovitch KA
    17. van Heel DA
    18. Wijmenga C
    19. Worthington J
    20. Todd JA
    21. Hafler DA
    22. Rich SS
    23. Daly MJ
    24. FOCiS Network of Consortia

    (2011) Pervasive sharing of genetic effects in autoimmune disease

    PLOS Genetics 7:e1002254.

    https://doi.org/10.1371/journal.pgen.1002254

    • PubMed
    • Google Scholar
13. 1. Dai X
    2. James RG
    3. Habib T
    4. Singh S
    5. Jackson S
    6. Khim S
    7. Moon RT
    8. Liggitt D
    9. Wolf-Yadlin A
    10. Buckner JH
    11. Rawlings DJ

    (2013) A disease-associated PTPN22 variant promotes systemic autoimmunity in murine models

    The Journal of Clinical Investigation 123:2024–2036.

    https://doi.org/10.1172/JCI66963

    • PubMed
    • Google Scholar
14. 1. Davidson D
    2. Zhong MC
    3. Pandolfi PP
    4. Bolland S
    5. Xavier RJ
    6. Seed B
    7. Li X
    8. Gu H
    9. Veillette A

    (2016) The csk-associated adaptor PAG inhibits effector T cell activation in cooperation with phosphatase PTPN22 and dok adaptors

    Cell Reports 17:2776–2788.

    https://doi.org/10.1016/j.celrep.2016.11.035

    • PubMed
    • Google Scholar
15. 1. Farh KK-H
    2. Marson A
    3. Zhu J
    4. Kleinewietfeld M
    5. Housley WJ
    6. Beik S
    7. Shoresh N
    8. Whitton H
    9. Ryan RJH
    10. Shishkin AA
    11. Hatan M
    12. Carrasco-Alfonso MJ
    13. Mayer D
    14. Luckey CJ
    15. Patsopoulos NA
    16. De Jager PL
    17. Kuchroo VK
    18. Epstein CB
    19. Daly MJ
    20. Hafler DA
    21. Bernstein BE

    (2015) Genetic and epigenetic fine mapping of causal autoimmune disease variants

    Nature 518:337–343.

    https://doi.org/10.1038/nature13835

    • PubMed
    • Google Scholar
16. 1. Hasegawa K
    2. Martin F
    3. Huang G
    4. Tumas D
    5. Diehl L
    6. Chan AC

    (2004) PEST domain-enriched tyrosine phosphatase (PEP) regulation of effector/memory T cells

    Science 303:685–689.

    https://doi.org/10.1126/science.1092138

    • PubMed
    • Google Scholar
17. 1. Hill RJ
    2. Zozulya S
    3. Lu YL
    4. Ward K
    5. Gishizky M
    6. Jallal B

    (2002) The lymphoid protein tyrosine phosphatase Lyp interacts with the adaptor molecule Grb2 and functions as a negative regulator of T-cell activation

    Experimental Hematology 30:237–244.

    https://doi.org/10.1016/s0301-472x(01)00794-9

    • PubMed
    • Google Scholar
18. 1. Jinek M
    2. Chylinski K
    3. Fonfara I
    4. Hauer M
    5. Doudna JA
    6. Charpentier E

    (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity

    Science 337:816–821.

    https://doi.org/10.1126/science.1225829

    • PubMed
    • Google Scholar
19. 1. Kosicki M
    2. Tomberg K
    3. Bradley A

    (2018) Repair of double-strand breaks induced by CRISPR-Cas9 leads to large deletions and complex rearrangements

    Nature Biotechnology 36:765–771.

    https://doi.org/10.1038/nbt.4192

    • PubMed
    • Google Scholar
20. 1. Kyogoku C
    2. Langefeld CD
    3. Ortmann WA
    4. Lee A
    5. Selby S
    6. Carlton VEH
    7. Chang M
    8. Ramos P
    9. Baechler EC
    10. Batliwalla FM
    11. Novitzke J
    12. Williams AH
    13. Gillett C
    14. Rodine P
    15. Graham RR
    16. Ardlie KG
    17. Gaffney PM
    18. Moser KL
    19. Petri M
    20. Begovich AB
    21. Gregersen PK
    22. Behrens TW

    (2004) Genetic association of the R620W polymorphism of protein tyrosine phosphatase PTPN22 with human SLE

    American Journal of Human Genetics 75:504–507.

    https://doi.org/10.1086/423790

    • PubMed
    • Google Scholar
21. 1. Lin X
    2. Pelletier S
    3. Gingras S
    4. Rigaud S
    5. Maine CJ
    6. Marquardt K
    7. Dai YD
    8. Sauer K
    9. Rodriguez AR
    10. Martin G
    11. Kupriyanov S
    12. Jiang L
    13. Yu L
    14. Green DR
    15. Sherman LA

    (2016) CRISPR-cas9-mediated modification of the NOD mouse genome with ptpn22r619w mutation increases autoimmune diabetes

    Diabetes 65:2134–2138.

    https://doi.org/10.2337/db16-0061

    • PubMed
    • Google Scholar
22. 1. Linsley PS
    2. Barahmand-Pour-Whitman F
    3. Balmas E
    4. DeBerg HA
    5. Flynn KJ
    6. Hu AK
    7. Rosasco MG
    8. Chen J
    9. O’Rourke C
    10. Serti E
    11. Gersuk VH
    12. Motwani K
    13. Seay HR
    14. Brusko TM
    15. Kwok WW
    16. Speake C
    17. Greenbaum CJ
    18. Nepom GT
    19. Cerosaletti K

    (2021) Autoreactive T cell receptors with shared germline-like α chains in type 1 diabetes

    JCI Insight 6:e151349.

    https://doi.org/10.1172/jci.insight.151349

    • PubMed
    • Google Scholar
23. 1. Maine CJ
    2. Hamilton-Williams EE
    3. Cheung J
    4. Stanford SM
    5. Bottini N
    6. Wicker LS
    7. Sherman LA

    (2012) PTPN22 alters the development of regulatory T cells in the thymus

    Journal of Immunology 188:5267–5275.

    https://doi.org/10.4049/jimmunol.1200150

    • PubMed
    • Google Scholar
24. 1. Maine CJ
    2. Teijaro JR
    3. Marquardt K
    4. Sherman LA

    (2016) Ptpn22 contributes to exhaustion of T lymphocytes during chronic viral infection

    PNAS 113:E7231–E7239.

    https://doi.org/10.1073/pnas.1603738113

    • PubMed
    • Google Scholar
25. 1. Meitlis I
    2. Allenspach EJ
    3. Bauman BM
    4. Phan IQ
    5. Dabbah G
    6. Schmitt EG
    7. Camp ND
    8. Torgerson TR
    9. Nickerson DA
    10. Bamshad MJ
    11. Hagin D
    12. Luthers CR
    13. Stinson JR
    14. Gray J
    15. Lundgren I
    16. Church JA
    17. Butte MJ
    18. Jordan MB
    19. Aceves SS
    20. Schwartz DM
    21. Milner JD
    22. Schuval S
    23. Skoda-Smith S
    24. Cooper MA
    25. Starita LM
    26. Rawlings DJ
    27. Snow AL
    28. James RG

    (2020) Multiplexed functional assessment of genetic variants in CARD11

    American Journal of Human Genetics 107:1029–1043.

    https://doi.org/10.1016/j.ajhg.2020.10.015

    • PubMed
    • Google Scholar
26. 1. Mocciaro A
    2. Roth TL
    3. Bennett HM
    4. Soumillon M
    5. Shah A
    6. Hiatt J
    7. Chapman K
    8. Marson A
    9. Lavieu G

    (2018) Light-activated cell identification and sorting (LACIS) for selection of edited clones on a nanofluidic device

    Communications Biology 1:41.

    https://doi.org/10.1038/s42003-018-0034-6

    • PubMed
    • Google Scholar
27. 1. O’Donoghue GP
    2. Bugaj LJ
    3. Anderson W
    4. Daniels KG
    5. Rawlings DJ
    6. Lim WA

    (2021) T cells selectively filter oscillatory signals on the minutes timescale

    PNAS 118:e2019285118.

    https://doi.org/10.1073/pnas.2019285118

    • PubMed
    • Google Scholar
28. 1. Okamoto S
    2. Amaishi Y
    3. Maki I
    4. Enoki T
    5. Mineno J

    (2019) Highly efficient genome editing for single-base substitutions using optimized ssodns with cas9-rnps

    Scientific Reports 9:4811.

    https://doi.org/10.1038/s41598-019-41121-4

    • PubMed
    • Google Scholar
29. 1. Park SH
    2. Lee CM
    3. Dever DP
    4. Davis TH
    5. Camarena J
    6. Srifa W
    7. Zhang Y
    8. Paikari A
    9. Chang AK
    10. Porteus MH
    11. Sheehan VA
    12. Bao G

    (2019) Highly efficient editing of the β-globin gene in patient-derived hematopoietic stem and progenitor cells to treat sickle cell disease

    Nucleic Acids Research 47:7955–7972.

    https://doi.org/10.1093/nar/gkz475

    • PubMed
    • Google Scholar
30. 1. Perry DJ
    2. Peters LD
    3. Lakshmi PS
    4. Zhang L
    5. Han Z
    6. Wasserfall CH
    7. Mathews CE
    8. Atkinson MA
    9. Brusko TM

    (2021) Overexpression of the PTPN22 autoimmune risk variant LYP-620W fails to restrain human CD4+ T cell activation

    The Journal of Immunology 207:849–859.

    https://doi.org/10.4049/jimmunol.2000708

    • Google Scholar
31. 1. Rawlings DJ
    2. Dai X
    3. Buckner JH

    (2015) The role of PTPN22 risk variant in the development of autoimmunity: finding common ground between mouse and human

    Journal of Immunology 194:2977–2984.

    https://doi.org/10.4049/jimmunol.1403034

    • PubMed
    • Google Scholar
32. 1. Richardson CD
    2. Ray GJ
    3. DeWitt MA
    4. Curie GL
    5. Corn JE

    (2016) Enhancing homology-directed genome editing by catalytically active and inactive CRISPR-Cas9 using asymmetric donor DNA

    Nature Biotechnology 34:339–344.

    https://doi.org/10.1038/nbt.3481

    • PubMed
    • Google Scholar
33. 1. Richardson CD
    2. Kazane KR
    3. Feng SJ
    4. Zelin E
    5. Bray NL
    6. Schäfer AJ
    7. Floor SN
    8. Corn JE

    (2018) Crispr-Cas9 genome editing in human cells occurs via the Fanconi anemia pathway

    Nature Genetics 50:1132–1139.

    https://doi.org/10.1038/s41588-018-0174-0

    • PubMed
    • Google Scholar
34. 1. Rieck M
    2. Arechiga A
    3. Onengut-Gumuscu S
    4. Greenbaum C
    5. Concannon P
    6. Buckner JH

    (2007) Genetic variation in PTPN22 corresponds to altered function of T and B lymphocytes

    Journal of Immunology 179:4704–4710.

    https://doi.org/10.4049/jimmunol.179.7.4704

    • PubMed
    • Google Scholar
35. 1. Ronninger M
    2. Guo Y
    3. Shchetynsky K
    4. Hill A
    5. Khademi M
    6. Olsson T
    7. Reddy PS
    8. Seddighzadeh M
    9. Clark JD
    10. Lin L-L
    11. O’Toole M
    12. Padyukov L

    (2012) The balance of expression of PTPN22 splice forms is significantly different in rheumatoid arthritis patients compared with controls

    Genome Medicine 4:2.

    https://doi.org/10.1186/gm301

    • PubMed
    • Google Scholar
36. 1. Salmond RJ
    2. Brownlie RJ
    3. Morrison VL
    4. Zamoyska R

    (2014) The tyrosine phosphatase PTPN22 discriminates weak self peptides from strong agonist TCR signals

    Nature Immunology 15:875–883.

    https://doi.org/10.1038/ni.2958

    • PubMed
    • Google Scholar
37. 1. Sanchez-Blanco C
    2. Clarke F
    3. Cornish GH
    4. Depoil D
    5. Thompson SJ
    6. Dai X
    7. Rawlings DJ
    8. Dustin ML
    9. Zamoyska R
    10. Cope AP
    11. Purvis HA

    (2018) Protein tyrosine phosphatase PTPN22 regulates LFA-1 dependent Th1 responses

    Journal of Autoimmunity 94:45–55.

    https://doi.org/10.1016/j.jaut.2018.07.008

    • PubMed
    • Google Scholar
38. 1. Schumann K
    2. Lin S
    3. Boyer E
    4. Simeonov DR
    5. Subramaniam M
    6. Gate RE
    7. Haliburton GE
    8. Ye CJ
    9. Bluestone JA
    10. Doudna JA
    11. Marson A

    (2015) Generation of knock-in primary human T cells using cas9 ribonucleoproteins

    PNAS 112:10437–10442.

    https://doi.org/10.1073/pnas.1512503112

    • PubMed
    • Google Scholar
39. 1. Scully R
    2. Panday A
    3. Elango R
    4. Willis NA

    (2019) Dna double-strand break repair-pathway choice in somatic mammalian cells

    Nature Reviews. Molecular Cell Biology 20:698–714.

    https://doi.org/10.1038/s41580-019-0152-0

    • PubMed
    • Google Scholar
40. 1. Seymour BJ
    2. Singh S
    3. Certo HM
    4. Sommer K
    5. Sather BD
    6. Khim S
    7. Clough C
    8. Hale M
    9. Pangallo J
    10. Ryu BY
    11. Khan IF
    12. Adair JE
    13. Rawlings DJ

    (2021) Effective, safe, and sustained correction of murine XLA using a UCOE-BTK promoter-based lentiviral vector

    Molecular Therapy. Methods & Clinical Development 20:635–651.

    https://doi.org/10.1016/j.omtm.2021.01.007

    • PubMed
    • Google Scholar
41. 1. Sharma A
    2. Liu X
    3. Hadley D
    4. Hagopian W
    5. Chen W-M
    6. Onengut-Gumuscu S
    7. Törn C
    8. Steck AK
    9. Frohnert BI
    10. Rewers M
    11. Ziegler A-G
    12. Lernmark Å
    13. Toppari J
    14. Krischer JP
    15. Akolkar B
    16. Rich SS
    17. She J-X
    18. TEDDY Study Group

    (2018) Identification of non-HLA genes associated with development of islet autoimmunity and type 1 diabetes in the prospective TEDDY cohort

    Journal of Autoimmunity 89:90–100.

    https://doi.org/10.1016/j.jaut.2017.12.008

    • PubMed
    • Google Scholar
42. 1. Simoncelli S
    2. Griffié J
    3. Williamson DJ
    4. Bibby J
    5. Bray C
    6. Zamoyska R
    7. Cope AP
    8. Owen DM

    (2020) Multi-Color molecular visualization of signaling proteins reveals how C-terminal Src kinase nanoclusters regulate T cell receptor activation

    Cell Reports 33:108523.

    https://doi.org/10.1016/j.celrep.2020.108523

    • PubMed
    • Google Scholar
43. 1. Stanford SM
    2. Bottini N

    (2014) Ptpn22: the archetypal non-HLA autoimmunity gene

    Nature Reviews. Rheumatology 10:602–611.

    https://doi.org/10.1038/nrrheum.2014.109

    • PubMed
    • Google Scholar
44. 1. Theofilopoulos AN
    2. Kono DH
    3. Baccala R

    (2017) The multiple pathways to autoimmunity

    Nature Immunology 18:716–724.

    https://doi.org/10.1038/ni.3731

    • PubMed
    • Google Scholar
45. 1. Tizaoui K
    2. Kim SH
    3. Jeong GH
    4. Kronbichler A
    5. Lee KS
    6. Lee KH
    7. Shin JI

    (2019) Association of PTPN22 1858C/T polymorphism with autoimmune diseases: A systematic review and bayesian approach

    Journal of Clinical Medicine 8:347.

    https://doi.org/10.3390/jcm8030347

    • PubMed
    • Google Scholar
46. 1. Todd JA
    2. Walker NM
    3. Cooper JD
    4. Smyth DJ
    5. Downes K
    6. Plagnol V
    7. Bailey R
    8. Nejentsev S
    9. Field SF
    10. Payne F
    11. Lowe CE
    12. Szeszko JS
    13. Hafler JP
    14. Zeitels L
    15. Yang JHM
    16. Vella A
    17. Nutland S
    18. Stevens HE
    19. Schuilenburg H
    20. Coleman G
    21. Maisuria M
    22. Meadows W
    23. Smink LJ
    24. Healy B
    25. Burren OS
    26. Lam AAC
    27. Ovington NR
    28. Allen J
    29. Adlem E
    30. Leung H-T
    31. Wallace C
    32. Howson JMM
    33. Guja C
    34. Ionescu-Tîrgovişte C
    35. Simmonds MJ
    36. Heward JM
    37. Gough SCL
    38. Dunger DB
    39. Wicker LS
    40. Clayton DG
    41. Genetics of Type 1 Diabetes in Finland
    42. Wellcome Trust Case Control Consortium

    (2007) Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes

    Nature Genetics 39:857–864.

    https://doi.org/10.1038/ng2068

    • PubMed
    • Google Scholar
47. 1. Vang T
    2. Congia M
    3. Macis MD
    4. Musumeci L
    5. Orrú V
    6. Zavattari P
    7. Nika K
    8. Tautz L
    9. Taskén K
    10. Cucca F
    11. Mustelin T
    12. Bottini N

    (2005) Autoimmune-associated lymphoid tyrosine phosphatase is a gain-of-function variant

    Nature Genetics 37:1317–1319.

    https://doi.org/10.1038/ng1673

    • PubMed
    • Google Scholar
48. 1. Vang T.
    2. Landskron J
    3. Viken MK
    4. Oberprieler N
    5. Torgersen KM
    6. Mustelin T
    7. Tasken K
    8. Tautz L
    9. Rickert RC
    10. Lie BA

    (2013) The autoimmune-predisposing variant of lymphoid tyrosine phosphatase favors T helper 1 responses

    Human Immunology 74:574–585.

    https://doi.org/10.1016/j.humimm.2012.12.017

    • PubMed
    • Google Scholar
49. 1. Wu J
    2. Katrekar A
    3. Honigberg LA
    4. Smith AM
    5. Conn MT
    6. Tang J
    7. Jeffery D
    8. Mortara K
    9. Sampang J
    10. Williams SR
    11. Buggy J
    12. Clark JM

    (2006) Identification of substrates of human protein-tyrosine phosphatase PTPN22

    The Journal of Biological Chemistry 281:11002–11010.

    https://doi.org/10.1074/jbc.M600498200

    • PubMed
    • Google Scholar
50. 1. Xue C
    2. Greene EC

    (2021) Dna repair pathway choices in CRISPR-cas9-mediated genome editing

    Trends in Genetics 37:639–656.

    https://doi.org/10.1016/j.tig.2021.02.008

    • PubMed
    • Google Scholar
51. 1. Yang SJ
    2. Singh AK
    3. Tappen T
    4. Honaker Y
    5. Mauk K
    6. Smith J
    7. Sommer K
    8. Rawlings D
    9. Buckner JH

    (2021) Generation of islet antigen-specific engineered tregs for use in T1D therapy via homology-directed gene editing of conventional CD4+ T cells

    The Journal of Immunology 206:18.

    https://doi.org/10.4049/jimmunol.206.Supp.18.13

    • Google Scholar
52. 1. Zhang J
    2. Zahir N
    3. Jiang Q
    4. Miliotis H
    5. Heyraud S
    6. Meng X
    7. Dong B
    8. Xie G
    9. Qiu F
    10. Hao Z
    11. McCulloch CA
    12. Keystone EC
    13. Peterson AC
    14. Siminovitch KA

    (2011) The autoimmune disease-associated PTPN22 variant promotes calpain-mediated lyp/pep degradation associated with lymphocyte and dendritic cell hyperresponsiveness

    Nature Genetics 43:902–907.

    https://doi.org/10.1038/ng.904

    • PubMed
    • Google Scholar
53. 1. Zheng J
    2. Ibrahim S
    3. Petersen F
    4. Yu X

    (2012) Meta-Analysis reveals an association of PTPN22 C1858T with autoimmune diseases, which depends on the localization of the affected tissue

    Genes and Immunity 13:641–652.

    https://doi.org/10.1038/gene.2012.46

    • PubMed
    • Google Scholar
54. 1. Zikherman J
    2. Hermiston M
    3. Steiner D
    4. Hasegawa K
    5. Chan A
    6. Weiss A

    (2009) PTPN22 deficiency cooperates with the CD45 E613R allele to break tolerance on a non-autoimmune background

    Journal of Immunology 182:4093–4106.

    https://doi.org/10.4049/jimmunol.0803317

    • PubMed
    • Google Scholar

Author details

1. Warren Anderson

   Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9315-5233

2. Fariba Barahmand-pour-Whitman

   Benaroya Research Institute at Virginia Mason, Seattle, United States

   Contribution

   Conceptualization, Resources, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Peter S Linsley

   Benaroya Research Institute at Virginia Mason, Seattle, United States

   Contribution

   Conceptualization, Resources, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8960-4307

4. Karen Cerosaletti

   Benaroya Research Institute at Virginia Mason, Seattle, United States

   Contribution

   Conceptualization, Resources, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7403-6239

5. Jane H Buckner

   Benaroya Research Institute at Virginia Mason, Seattle, United States

   Contribution

   Conceptualization, Resources, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. David J Rawlings

   Department of Pediatrics and Immunology, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   For correspondence

   drawling@u.washington.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0810-1776