The immune system uses antibodies to fight microbes that cause disease. White blood cells pump antibodies into the bloodstream, and these antibodies latch onto bacteria and viruses, targeting them for destruction. But sometimes, the immune system gets it wrong. In autoimmune diseases, white blood cells mistakenly make antibodies that target the body's own tissues. Detecting these 'autoantibodies' in the blood can help doctors to diagnose autoimmune diseases. But the identities and targets of many autoantibodies remain unknown.

In one rare disease, called autoimmune polyendocrine syndrome type 1 (APS-1), a faulty gene makes the immune system much more likely to make autoantibodies. People with this disease can develop an autoimmune response against many different healthy organs. Although APS-1 is rare, some of the autoantibodies made by individuals with the disease are the same as the ones in more common autoimmune diseases, like type 1 diabetes. Therefore, investigating the other autoantibodies produced by individuals with APS-1 could reveal the autoantibodies driving other autoimmune diseases.

Autoantibodies bind to specific regions of healthy proteins, and one way to identify them is to use hundreds of thousands of tiny viruses in a technique called proteome-wide programmable phage-display, or PhIP-Seq. Each phage carries one type of protein segment. When mixed with blood serum from a patient, the autoantibodies stick to the phages that carry the target proteins for that autoantibody. These complexes can be isolated using biochemical techniques. Sequencing the genes of these phages then reveals the identity of the autoantibodies’ targets.

Using this technique, Vazquez et al successfully pulled 23 known autoantibodies from the serum of patients with APS-1. Then, experiments to search for new targets began. This revealed many new autoantibodies, targeting proteins found only in specific tissues. They included one that targets a protein found on cells in the gut, and another that targets a protein found on egg cells in the ovaries. Matching the PhIP-Seq data to patient symptoms confirmed that these new antibodies correlate with the features of specific autoimmune diseases. For example, patients with antibodies that targeted the gut protein were more likely to have gut symptoms, while patients with antibodies that targeted the egg cell protein were more likely to have problems with their ovaries.

Further investigations using PhIP-Seq could reveal the identities of even more autoantibodies. This might pave the way for new antibody tests to diagnose autoimmune diseases and identify tissues at risk of damage. This could be useful not only for people with APS-1, but also for more common autoimmune diseases that target the same organs.

Autoimmune Polyglandular syndrome type 1 (APS1) or Autoimmune Polyglandular-Candidiasis-Ectodermal Dystrophy (APECED; OMIM #240300) is an autoimmune syndrome caused by monogenic mutations in the AIRE gene that result in defects in AIRE-dependent T cell education in the thymus (Aaltonen et al., 1997; Anderson et al., 2002; Conteduca et al., 2018; Malchow et al., 2016; Nagamine et al., 1997). As a result, people with APS1 develop autoimmunity to multiple organs, including endocrine organs, skin, gut, and lung (Ahonen et al., 1990; Ferre et al., 2016; Söderbergh et al., 2004). Although the majority of APS1 autoimmune manifestations are thought to be primarily driven by autoreactive T cells, people with APS1 also possess autoreactive B cells and corresponding high-affinity autoantibody responses (Devoss et al., 2008; Gavanescu et al., 2008; Meyer et al., 2016; Sng et al., 2019). These autoantibodies likely derive from germinal center reactions driven by self-reactive T cells, resulting in mirroring of autoantigen identities between the T and B cell compartments (Lanzavecchia, 1985; Meyer et al., 2016).

Identification of the specificity of autoantibodies in autoimmune diseases is important for understanding underlying disease pathogenesis and for identifying those at risk for disease (Rosen and Casciola-Rosen, 2014). However, despite the long-known association of autoantibodies with specific diseases in both monogenic and sporadic autoimmunity, many autoantibody specificities remain undiscovered. Challenges in antigen identification include the weak affinity of some autoantibodies for their target antigen, as well as rare or low expression of the target antigen. One approach to overcome some of these challenges is to interrogate autoimmune patient samples with particularly high affinity autoantibodies. Indeed, such an approach identified GAD65 as a major autoantigen in type 1 diabetes by using sera from people with Stiff Person Syndrome (OMIM #184850), who harbor high affinity autoantibodies (Baekkeskov et al., 1990). We reasoned that PhIP-Seq interrogation of APS1, a defined monogenic autoimmune syndrome with a broad spectrum of high affinity autoantibodies, would likely yield clinically meaningful targets – consistent with previously described APS1 autoantibody specificities that exhibit strong, clinically useful associations with their respective organ-specific diseases (Alimohammadi et al., 2008; Alimohammadi et al., 2009; Ferré et al., 2019; Landegren et al., 2015; Popler et al., 2012; Puel et al., 2010; Shum et al., 2013; Söderbergh et al., 2004; Winqvist et al., 1993).

The identification of key B cell autoantigens in APS1 has occurred most commonly through candidate-based approaches and by whole-protein microarrays. For example, lung antigen BPIFB1 autoantibodies, which are used to assess people with APS1 for risk of interstitial lung disease, were discovered first in Aire-deficient mice using a combination of targeted immunoblotting, tissue microscopy, and mass spectrometry (Shum et al., 2009; Shum et al., 2013). Recently, there have been rapid advances in large platform approaches for antibody screening; these platforms can overcome problems of antigen abundance by simultaneously screening the majority of proteins from the human genome in an unbiased fashion (Jeong et al., 2012; Larman et al., 2011; Sharon and Snyder, 2014; Zhu et al., 2001). In particular, a higher-throughput antibody target profiling approach utilizing a fixed protein microarray technology (ProtoArray) has enabled detection of a wider range of proteins targeted by autoantibodies directly from human serum (Fishman et al., 2017; Landegren et al., 2016; Meyer et al., 2016). Despite initial success of this technology in uncovering shared antigens across APS1 cohorts, it is likely that many shared antigens remain to be discovered, given that these arrays do not encompass the full coding potential of the proteome.

Here, we took an alternate approach to APS1 antigen discovery by employing Phage Immunoprecipitation-Sequencing (PhIP-Seq) based on an established proteome-wide tiled library (Larman et al., 2011; O’Donovan et al., 2018). This approach possesses many potential advantages over previous candidate-based and whole-protein fixed array approaches, including (1) expanded, proteome-wide coverage (including alternative splice forms) with 49 amino acid (AA) peptide length and 24AA resolution tiling, (2) reduced volume requirement for human serum, and (3) high-throughput, sequencing based output (Larman et al., 2011; O’Donovan et al., 2018). Of note, the PhIP-Seq investigation of autoimmune diseases of the central nervous system, including paraneoplastic disease, has yielded novel and specific biomarkers of disease (Larman et al., 2011; Mandel-Brehm et al., 2019; O’Donovan et al., 2018).

Using a PhIP-Seq autoantibody survey, we identify a collection of novel APS1 autoantigens as well as numerous known, literature-reported APS1 autoantigens. We orthogonally validate seven novel autoantigens including RFX6, KHDC3L, and ACP4, all of which exhibit tissue-restricted expression (Patel et al., 2017; Rezaei et al., 2016; Seymen et al., 2016; Smith et al., 2017; Smith et al., 2010; Zhu et al., 2015). Importantly, these novel autoantigens may carry important implications for poorly understood clinical manifestations such as intestinal dysfunction, ovarian insufficiency, and tooth enamel hypoplasia, where underlying cell-type specific antigens have remained elusive. Together, our results demonstrate the applicability of PhIP-Seq to antigen discovery, substantially expand the spectrum of known antibody targets and clinical associations in APS1, and point towards novel specificities that can be targeted in autoimmunity.

Here, we have identified a new set of autoantigens that are associated with autoimmune features in APS1 by using the broad-based antigen screening platform of PhIP-Seq. Unlike fixed protein arrays, programmable phage display possesses the advantage of being able to comprehensively cover all annotated proteins and their isoforms. The PhIP-Seq library used here is composed of over 700,000 peptides, each 49 amino acids, and corresponding to approximately 20,000 proteins and their known splicing isoforms. This is highly complementary to recently published protein arrays that cover approximately 9000 distinct proteins (Fishman et al., 2017; Landegren et al., 2016; Meyer et al., 2016). Recent protein array approaches with APS1 samples using strict cutoffs have been able to identify a number of new autoantigen targets that include PDILT and MAGEB2 (Landegren et al., 2016). Several new targets, including RFX6, KHDC3L, ACP4, NKX6-3, ASMT, and PDX1, were likely discovered here because these antigens were not present on previously published protein array platforms. Only a subset of the novel targets identified here were validated orthogonally. While none failed validation relative to non-APS1 controls, further validation work will be needed for the many additional novel targets identified by PhIP-Seq. It is also worth mentioning that the PhIP-Seq method leverages continuing declines in the cost of oligonucleotide synthesis and Next-Generation Sequencing. Both technologies benefit from economies of scale, and once constructed, a PhIP-Seq phage library may be propagated in large quantities at negligible cost. The primary disadvantage of PhIP-Seq is the fact that conformation specific antibodies are likely to be missed, unless short linear subsequences carry significant binding energy. For example, PhIP-Seq detected only limited signal towards some literature reported antigens, including GAD65 and interferon family proteins in this APS1 cohort. Given that these antigens have been reported to involve conformational epitopes, antibodies to these antigens would not be predicted to be easily detected by linear peptides (Björk et al., 1994; Meager et al., 2006; Meyer et al., 2016; Wolff et al., 2013; Ziegler et al., 1996). Nonetheless, the ability to detect anti-interferon antibodies in a subset of APS1 samples highlights the utility of PhIP-Seq for antigen discovery despite decreased sensitivity for certain epitopes (Figure 1—figure supplement 1).

People with (Anderson et al., 2002; Cheng and Anderson, 2018; Husebye et al., 2018; Malchow et al., 2016) APS1 develop autoimmune manifestations over the course of many years, and it is thought that each manifestation may be explained by autoimmune response to one or few initial protein targets. In principle, these target proteins would most likely (1) exhibit thymic AIRE-dependency and (2) be restricted to the single or narrow range of tissues associated with the corresponding autoimmune disease. For example, adrenal insufficiency, which results from autoimmune response to cells of the adrenal gland, is thought to occur due to targeting of adrenally-expressed cytochrome p450 family members (Petra et al., 2000; Winqvist et al., 1993). However, a more complete understanding of the protein target spectrum paired with clinical phenotypic associations has been lacking. This, combined with the limited applicability of murine observations to the human disease, has left the question of which clinical characteristics best associate with APS1 autoantigens a heavily debated subject (Pöntynen et al., 2006).

Testing for defined autoantibody specificities provides substantial clinical benefit for prediction and diagnosis of autoimmune disease. A primary goal of this study was to identify autoantigens with potential clinical significance; consistently, our analyses focused primarily on antigens that appeared across multiple samples, rather than autoantigens that were restricted to individual samples. Using conservative inclusion criteria, we discovered 69 novel autoantigens that were shared across a minimum of 3 APS1 samples, of which 7/7 were successfully validated at the whole protein level. Overall, we have expanded the known repertoire of common APS1 antigens, confirming that the antibody target repertoire of common antigens in APS1 is larger than previously appreciated. Interestingly, our data also suggest that the size of the commonly autoantibody-targeted repertoire of proteins is dramatically lower than the number of genes (~4000) that exhibit AIRE-dependent thymic expression.

The spectrum of different autoimmune diseases that can be observed in APS1 is extensive and has continued to expand through investigation of larger cohorts (Ahonen et al., 1990; Bruserud et al., 2016; Ferre et al., 2016). In this study, clinical metadata encompassing disease status across 24 individual disease manifestations in a total of 67 people with APS1 was leveraged to uncover (among others) an association of anti-KHDC3L antibodies and ovarian insufficiency, a disease that affects over half of all women with APS1 and manifests as abnormal menstrual cycling, reduced fertility, and early menopause. While autoreactivity to the steroidogenic granulosa cells – the cells surrounding and supporting the oocytes – has been proposed as one etiology of the clinical ovarian insufficiency, it has also been suggested that there may exist an autoimmune response to the oocyte itself (Jasti et al., 2012; Maclaren et al., 2001; Otsuka et al., 2011; Petra et al., 2000; Welt, 2008). Our finding that females with APS1-associated ovarian insufficiency exhibit autoantibodies to KHDC3L, an oocyte specific protein, supports this hypothesis. As exemplified by autoantibody presence in other autoimmune conditions, anti-KHDC3L antibodies may also have predictive value. Specifically, in our cohort, we found anti-KHDC3L antibodies to be present in many young, pre-menstrual females; these observations will require additional studies in prospective, longitudinal cohorts for further evaluation of potential predictive value. Interestingly, primary ovarian insufficiency (POI) in the absence of AIRE-deficiency is increasingly common and affects an estimated 1 in 100 women; up to half of these cases have been proposed to have autoimmune etiology (Huhtaniemi et al., 2018; Jasti et al., 2012; Nelson, 2009; Silva et al., 2014). The detection of anti-NLRP5 antibodies in a small subset of patients with non-APS1 ovarian insufficiency in a previous study further underlines the importance of evaluating the prevalence of anti-KHDC3L antibodies in women with POI (Brozzetti et al., 2015).

We noted that the majority of samples with antibodies to KHDC3L also exhibited antibodies to NLRP5, and vice versa. Remarkably, both of these proteins are critical parts of a subcortical maternal complex (SCMC) in both human and murine oocytes (Li et al., 2008; Zhu et al., 2015). Indeed, ‘multi-pronged’ targeting of the same pathway has been previously implicated in APS1, where antibodies to DDC and TPH1 – enzymes in the serotonin and melatonin synthesis pathways – have been described (Ekwall et al., 1998; Husebye et al., 1997; Kluger et al., 2015; Rorsman et al., 1995). In addition to these targets, our data revealed an additional autoantibody-targeted enzyme ASMT in the same melatonin synthesis pathway. While the earlier TPH1- and DDC-catalyzed steps occur in both the intestine and pineal gland and precede the formation of serotonin, ASMT is predominantly expressed in the pineal gland and catalyzes the last, post-serotonin step in melatonin synthesis, suggesting that targeting of this pathway occurs at multiple distinct steps. To our knowledge, this is the first reported autoantigen in APS1 whose expression is restricted to the central nervous system.

In past and ongoing investigations, some individuals with APS1 have been reported to feature histologic loss of intestinal enteroendocrine cells on biopsy (Högenauer et al., 2001; Oliva-Hemker et al., 2006; Posovszky et al., 2012; Natarajan et al., manuscript in preparation). The association of anti-RFX6 antibodies with the diarrheal type of intestinal dysfunction is consistent with published studies in murine models of Rfx6 (and enteroendocrine cell) ablation (Piccand et al., 2019; Smith et al., 2010). In addition, human enteroendocrine cell deficiency as well as mutations in enteroendocrine gene NEUROG3 have been linked to chronic diarrhea and malabsorption, and recently, intestinal enteroendocrine cells have been suggested to play a role in mediating intestinal immune tolerance (Ohsie et al., 2009; Sifuentes-Dominguez et al., 2019; Wang et al., 2006). In sum, although APS1-associated intestinal dysfunction may have multiple etiologies, including autoimmune enteritis or dysfunction of exocrine pancreas, our findings of highly prevalent anti-RFX6 antibodies provide evidence of a common, shared autoantigen involved with this disease phenotype. In addition, patients with type 1 diabetes alone (not in association with APS1) frequently exhibit intestinal dysfunction related to multiple etiologies including Celiac disease, autonomic neuropathy, and exocrine pancreatic insufficiency (Du et al., 2018); future studies will be needed to determine whether anti-RFX6 antibodies may distinguish a subset of these patients with an autoimmune enteroendocrinopathy contributing to their symptoms.

While we report many novel antigens, we also acknowledge that the relationship between autoantibody status and disease is often complicated. This concept can be illustrated by examining the well-established autoantibody specificities in autoimmune diabetes (Taplin and Barker, 2008). First, islet autoantibodies (GAD65, ZNT8, etc.) can be found within non-autoimmune sera, where they are thought to represent an increased risk of developing disease as compared to the antibody-negative population. Second, not all patients with autoimmune diabetes are autoantibody positive. In sum, while autoantibodies can be extremely useful for risk assessment as well as for diagnosis, they often lack high sensitivity and specificity; both of these caveats can result in difficulties detecting strong clinical associations. For example, anti-ACP4 antibodies are highly prevalent in our cohort, but they exhibit only a trending association with dental enamel hypoplasia despite the strong biological evidence that ACP4 dysfunction leads to enamel hypoplasia (Seymen et al., 2016; Smith et al., 2017). Our data in humans is currently insufficient to determine whether immune responses to novel antigens such as ACP4 are pathogenic, indirectly linked to risk of disease, or instead simply represent a B-cell bystander effect. To better address these questions, we propose that future studies in mouse models could elucidate whether immune response to specific proteins, including ACP4, can result in the proposed phenotypes.

As the spectrum of diseases with potential autoimmune etiology continues to expand, the characteristic multiorgan autoimmunity in APS1 provides an ideal model system to more broadly approach the question of which proteins and cell types tend to be aberrantly targeted by the immune system. The data presented here has illuminated a collection of novel human APS1 autoimmune targets, as well as a novel antibody-disease association between RFX6 and diarrheal-type intestinal dysfunction, a highly prevalent disorder in APS1 that has until now lacked clinically applicable predictive or diagnostic markers. In sum, these data have significantly expanded the known autoantigen target profile in APS1 and highlighted several new directions for exploring the mechanics and clinical consequences of this complex syndrome.

All sequencing data generated in this study are deposited on Dryad Digital Repository in conjunction with this submission (https://doi.org/10.7272/Q66H4FM2).

1. 1. Vazquez SE
   2. Ferré EMN
   3. Scheel DW
   4. Sunshine S
   5. Miao B
   6. Mandel-Brehm C
   7. Quandt Z
   8. Chan AY
   9. Cheng M
   10. German MS
   11. Lionakis MS
   12. DeRisi JL
   13. Anderson MS

   (2020) Dryad Digital Repository

   Data from: Identification of novel, clinically correlated autoantigens in the monogenic autoimmune syndrome APS1 by PhIP-Seq.

   https://doi.org/10.7272/Q66H4FM2

1. 1. Zhang Y
   2. Yan Z
   3. Qin Q
   4. Nisenblat V
   5. Chang H-M
   6. Yu Y
   7. Wang T
   8. Lu C
   9. Yang M
   10. Yang S
   11. Yao Y
   12. Zhu X
   13. Xia X
   14. Dang Y
   15. Ren Y
   16. Yuan P
   17. Li R
   18. Liu P
   19. Guo H
   20. Yan L

   (2018) NCBI Gene Expression Omnibus

   ID GSE107746. Transcriptome Landscape of Human Folliculogenesis Reveals Oocyte and Granulosa Cell Interactions.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE107746
2. 1. Pisco AO

   (2018) NCBI Gene Expression Omnibus

   ID GSE109774. Tabula Muris: Transcriptomic characterization of 20 organs and tissues from Mus musculus at single cell resolution.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109774
3. 1. Human Protein Atlas

   (2015) Human Protein Atlas

   Tissue-based map of the human proteome.

   https://www.proteinatlas.org/about/download

1. 1. Aaltonen J
   2. Björses P
   3. Perheentupa J
   4. Horelli–Kuitunen N
   5. Palotie A
   6. Peltonen L
   7. Lee YS
   8. Francis F
   9. Henning S
   10. Thiel C
   11. Leharach H
   12. Yaspo M–L
   13. Yaspo M
   14. Finnish-German APECED Consortium

   (1997) An autoimmune disease, APECED, caused by mutations in a novel gene featuring two PHD-type zinc-finger domains

   Nature Genetics 17:399–403.

   https://doi.org/10.1038/ng1297-399

   • PubMed
   • Google Scholar
2. 1. Ackermann K
   2. Bux R
   3. Rüb U
   4. Korf HW
   5. Kauert G
   6. Stehle JH

   (2006) Characterization of human melatonin synthesis using autoptic pineal tissue

   Endocrinology 147:3235–3242.

   https://doi.org/10.1210/en.2006-0043

   • PubMed
   • Google Scholar
3. 1. Adriaenssens AE
   2. Biggs EK
   3. Darwish T
   4. Tadross J
   5. Sukthankar T
   6. Girish M
   7. Polex-Wolf J
   8. Lam BY
   9. Zvetkova I
   10. Pan W
   11. Chiarugi D
   12. Yeo GSH
   13. Blouet C
   14. Gribble FM
   15. Reimann F

   (2019) Glucose-Dependent insulinotropic polypeptide Receptor-Expressing cells in the hypothalamus regulate food intake

   Cell Metabolism 30:987–996.

   https://doi.org/10.1016/j.cmet.2019.07.013

   • PubMed
   • Google Scholar
4. 1. Ahonen P
   2. Myllärniemi S
   3. Sipilä I
   4. Perheentupa J

   (1990) Clinical variation of autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED) in a series of 68 patients

   New England Journal of Medicine 322:1829–1836.

   https://doi.org/10.1056/NEJM199006283222601

   • PubMed
   • Google Scholar
5. 1. Akoury E
   2. Zhang L
   3. Ao A
   4. Slim R

   (2015) NLRP7 and KHDC3L, the two maternal-effect proteins responsible for recurrent hydatidiform moles, co-localize to the oocyte cytoskeleton

   Human Reproduction 30:159–169.

   https://doi.org/10.1093/humrep/deu291

   • PubMed
   • Google Scholar
6. 1. Alanentalo T
   2. Chatonnet F
   3. Karlen M
   4. Sulniute R
   5. Ericson J
   6. Andersson E
   7. Ahlgren U

   (2006) Cloning and analysis of Nkx6.3 during CNS and gastrointestinal development

   Gene Expression Patterns 6:162–170.

   https://doi.org/10.1016/j.modgep.2005.06.012

   • PubMed
   • Google Scholar
7. 1. Alimohammadi M
   2. Björklund P
   3. Hallgren A
   4. Pöntynen N
   5. Szinnai G
   6. Shikama N
   7. Keller MP
   8. Ekwall O
   9. Kinkel SA
   10. Husebye ES
   11. Gustafsson J
   12. Rorsman F
   13. Peltonen L
   14. Betterle C
   15. Perheentupa J
   16. Akerström G
   17. Westin G
   18. Scott HS
   19. Holländer GA
   20. Kämpe O

   (2008) Autoimmune polyendocrine syndrome type 1 and NALP5, a parathyroid autoantigen

   New England Journal of Medicine 358:1018–1028.

   https://doi.org/10.1056/NEJMoa0706487

   • PubMed
   • Google Scholar
8. 1. Alimohammadi M
   2. Dubois N
   3. Sköldberg F
   4. Hallgren A
   5. Tardivel I
   6. Hedstrand H
   7. Haavik J
   8. Husebye ES
   9. Gustafsson J
   10. Rorsman F
   11. Meloni A
   12. Janson C
   13. Vialettes B
   14. Kajosaari M
   15. Egner W
   16. Sargur R
   17. Pontén F
   18. Amoura Z
   19. Grimfeld A
   20. De Luca F
   21. Betterle C
   22. Perheentupa J
   23. Kämpe O
   24. Carel JC

   (2009) Pulmonary autoimmunity as a feature of autoimmune polyendocrine syndrome type 1 and identification of KCNRG as a bronchial autoantigen

   PNAS 106:4396–4401.

   https://doi.org/10.1073/pnas.0809986106

   • PubMed
   • Google Scholar
9. 1. Anderson MS
   2. Venanzi ES
   3. Klein L
   4. Chen Z
   5. Berzins SP
   6. Turley SJ
   7. von Boehmer H
   8. Bronson R
   9. Dierich A
   10. Benoist C
   11. Mathis D

   (2002) Projection of an immunological self shadow within the Thymus by the aire protein

   Science 298:1395–1401.

   https://doi.org/10.1126/science.1075958

   • PubMed
   • Google Scholar
10. 1. Baekkeskov S
    2. Aanstoot HJ
    3. Christgau S
    4. Reetz A
    5. Solimena M
    6. Cascalho M
    7. Folli F
    8. Richter-Olesen H
    9. De Camilli P
    10. Camilli PD

    (1990) Identification of the 64K autoantigen in insulin-dependent diabetes as the GABA-synthesizing enzyme glutamic acid decarboxylase

    Nature 347:151–156.

    https://doi.org/10.1038/347151a0

    • PubMed
    • Google Scholar
11. 1. Bebbere D
    2. Masala L
    3. Albertini DF
    4. Ledda S

    (2016) The subcortical maternal complex: multiple functions for one biological structure?

    Journal of Assisted Reproduction and Genetics 33:1431–1438.

    https://doi.org/10.1007/s10815-016-0788-z

    • PubMed
    • Google Scholar
12. 1. Berger M
    2. Scheel DW
    3. Macias H
    4. Miyatsuka T
    5. Kim H
    6. Hoang P
    7. Ku GM
    8. Honig G
    9. Liou A
    10. Tang Y
    11. Regard JB
    12. Sharifnia P
    13. Yu L
    14. Wang J
    15. Coughlin SR
    16. Conklin BR
    17. Deneris ES
    18. Tecott LH
    19. German MS

    (2015) Gαi/o-coupled receptor signaling restricts pancreatic β-cell expansion

    PNAS 112:2888–2893.

    https://doi.org/10.1073/pnas.1319378112

    • PubMed
    • Google Scholar
13. 1. Berson SA
    2. Yalow RS
    3. Bauman A
    4. Rothschild MA
    5. Newerly K

    (1956) Insulin-I131 metabolism in human subjects: demonstration of insulin binding globulin in the circulation of insulin treated subjects

    Journal of Clinical Investigation 35:170–190.

    https://doi.org/10.1172/JCI103262

    • PubMed
    • Google Scholar
14. 1. Betterle C
    2. Dal Pra C
    3. Mantero F
    4. Zanchetta R

    (2002) Autoimmune adrenal insufficiency and autoimmune polyendocrine syndromes: autoantibodies, autoantigens, and their applicability in diagnosis and disease prediction

    Endocrine Reviews 23:327–364.

    https://doi.org/10.1210/edrv.23.3.0466

    • PubMed
    • Google Scholar
15. 1. Björk E
    2. Velloso LA
    3. Kämpe O
    4. Karlsson FA

    (1994) GAD autoantibodies in IDDM, stiff-man syndrome, and autoimmune polyendocrine syndrome type I recognize different epitopes

    Diabetes 43:161–165.

    https://doi.org/10.2337/diab.43.1.161

    • PubMed
    • Google Scholar
16. 1. Brozzetti A
    2. Alimohammadi M
    3. Morelli S
    4. Minarelli V
    5. Hallgren Å
    6. Giordano R
    7. De Bellis A
    8. Perniola R
    9. Kämpe O
    10. Falorni A
    11. Italian Addison Network

    (2015) Autoantibody response against NALP5/MATER in primary ovarian insufficiency and in autoimmune Addison's disease

    The Journal of Clinical Endocrinology & Metabolism 100:1941–1948.

    https://doi.org/10.1210/jc.2014-3571

    • PubMed
    • Google Scholar
17. 1. Bruserud Ø
    2. Oftedal BE
    3. Landegren N
    4. Erichsen MM
    5. Bratland E
    6. Lima K
    7. Jørgensen AP
    8. Myhre AG
    9. Svartberg J
    10. Fougner KJ
    11. Bakke Å
    12. Nedrebø BG
    13. Mella B
    14. Breivik L
    15. Viken MK
    16. Knappskog PM
    17. Marthinussen MC
    18. Løvås K
    19. Kämpe O
    20. Wolff AB
    21. Husebye ES

    (2016) A longitudinal Follow-up of autoimmune polyendocrine syndrome type 1

    The Journal of Clinical Endocrinology and Metabolism 101:2975–2983.

    https://doi.org/10.1210/jc.2016-1821

    • PubMed
    • Google Scholar
18. 1. Cheng M
    2. Anderson MS

    (2018) Thymic tolerance as a key Brake on autoimmunity

    Nature Immunology 19:659–664.

    https://doi.org/10.1038/s41590-018-0128-9

    • PubMed
    • Google Scholar
19. 1. Choi H
    2. Kim TH
    3. Yun CY
    4. Kim JW
    5. Cho ES

    (2016) Testicular acid phosphatase induces odontoblast differentiation and mineralization

    Cell and Tissue Research 364:95–103.

    https://doi.org/10.1007/s00441-015-2310-9

    • PubMed
    • Google Scholar
20. 1. Clemente MG
    2. Obermayer-Straub P
    3. Meloni A
    4. Strassburg CP
    5. Arangino V
    6. Tukey RH
    7. De Virgiliis S
    8. Manns MP

    (1997) Cytochrome P450 1a2 is a hepatic autoantigen in autoimmune polyglandular syndrome type 1 1

    The Journal of Clinical Endocrinology and Metabolism 82:1353–1361.

    https://doi.org/10.1210/jcem.82.5.3913

    • PubMed
    • Google Scholar
21. 1. Conteduca G
    2. Indiveri F
    3. Filaci G
    4. Negrini S

    (2018) Beyond APECED: an update on the role of the autoimmune regulator gene (AIRE) in physiology and disease

    Autoimmunity Reviews 17:325–330.

    https://doi.org/10.1016/j.autrev.2017.10.017

    • PubMed
    • Google Scholar
22. 1. Devoss JJ
    2. Shum AK
    3. Johannes KP
    4. Lu W
    5. Krawisz AK
    6. Wang P
    7. Yang T
    8. Leclair NP
    9. Austin C
    10. Strauss EC
    11. Anderson MS

    (2008) Effector mechanisms of the autoimmune syndrome in the murine model of autoimmune polyglandular syndrome type 1

    The Journal of Immunology 181:4072–4079.

    https://doi.org/10.4049/jimmunol.181.6.4072

    • PubMed
    • Google Scholar
23. 1. Du YT
    2. Rayner CK
    3. Jones KL
    4. Talley NJ
    5. Horowitz M

    (2018) Gastrointestinal symptoms in diabetes: prevalence, assessment, pathogenesis, and management

    Diabetes Care 41:627–637.

    https://doi.org/10.2337/dc17-1536

    • PubMed
    • Google Scholar
24. 1. Ekwall O
    2. Hedstrand H
    3. Grimelius L
    4. Haavik J
    5. Perheentupa J
    6. Gustafsson J
    7. Husebye E
    8. Kämpe O
    9. Rorsman F

    (1998) Identification of tryptophan hydroxylase as an intestinal autoantigen

    The Lancet 352:279–283.

    https://doi.org/10.1016/S0140-6736(97)11050-9

    • Google Scholar
25. 1. Ferre EMN
    2. Rose SR
    3. Rosenzweig SD
    4. Burbelo PD
    5. Romito KR
    6. Niemela JE
    7. Rosen LB
    8. Break TJ
    9. Gu W
    10. Hunsberger S
    11. Browne SK
    12. Hsu AP
    13. Rampertaap S
    14. Swamydas M
    15. Collar AL
    16. Kong HH
    17. Lee C-CR
    18. Chascsa D
    19. Simcox T
    20. Pham A
    21. Bondici A
    22. Natarajan M
    23. Monsale J
    24. Kleiner DE
    25. Quezado M
    26. Alevizos I
    27. Moutsopoulos NM
    28. Yockey L
    29. Frein C
    30. Soldatos A
    31. Calvo KR
    32. Adjemian J
    33. Similuk MN
    34. Lang DM
    35. Stone KD
    36. Uzel G
    37. Kopp JB
    38. Bishop RJ
    39. Holland SM
    40. Olivier KN
    41. Fleisher TA
    42. Heller T
    43. Winer KK
    44. Lionakis MS

    (2016) Redefined clinical features and diagnostic criteria in autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy

    JCI Insight 1:1343.

    https://doi.org/10.1172/jci.insight.88782

    • Google Scholar
26. 1. Ferré EMN
    2. Break TJ
    3. Burbelo PD
    4. Allgäuer M
    5. Kleiner DE
    6. Jin D
    7. Xu Z
    8. Folio LR
    9. Mollura DJ
    10. Swamydas M
    11. Gu W
    12. Hunsberger S
    13. Lee CR
    14. Bondici A
    15. Hoffman KW
    16. Lim JK
    17. Dobbs K
    18. Niemela JE
    19. Fleisher TA
    20. Hsu AP
    21. Snow LN
    22. Darnell DN
    23. Ojaimi S
    24. Cooper MA
    25. Bozzola M
    26. Kleiner GI
    27. Martinez JC
    28. Deterding RR
    29. Kuhns DB
    30. Heller T
    31. Winer KK
    32. Rajan A
    33. Holland SM
    34. Notarangelo LD
    35. Fennelly KP
    36. Olivier KN
    37. Lionakis MS

    (2019) Lymphocyte-driven regional immunopathology in pneumonitis caused by impaired central immune tolerance

    Science Translational Medicine 11:eaav5597.

    https://doi.org/10.1126/scitranslmed.aav5597

    • PubMed
    • Google Scholar
27. 1. Fishman D
    2. Kisand K
    3. Hertel C
    4. Rothe M
    5. Remm A
    6. Pihlap M
    7. Adler P
    8. Vilo J
    9. Peet A
    10. Meloni A
    11. Podkrajsek KT
    12. Battelino T
    13. Bruserud Ø
    14. Wolff ASB
    15. Husebye ES
    16. Kluger N
    17. Krohn K
    18. Ranki A
    19. Peterson H
    20. Hayday A
    21. Peterson P

    (2017) Autoantibody repertoire in APECED patients targets two distinct subgroups of proteins

    Frontiers in Immunology 8:15.

    https://doi.org/10.3389/fimmu.2017.00976

    • PubMed
    • Google Scholar
28. 1. Gavanescu I
    2. Benoist C
    3. Mathis D

    (2008) B cells are required for Aire-deficient mice to develop multi-organ autoinflammation: a therapeutic approach for APECED patients

    PNAS 105:13009–13014.

    https://doi.org/10.1073/pnas.0806874105

    • PubMed
    • Google Scholar
29. 1. Gehart H
    2. van Es JH
    3. Hamer K
    4. Beumer J
    5. Kretzschmar K
    6. Dekkers JF
    7. Rios A
    8. Clevers H

    (2019) Identification of enteroendocrine regulators by Real-Time Single-Cell differentiation mapping

    Cell 176:1158–1173.

    https://doi.org/10.1016/j.cell.2018.12.029

    • PubMed
    • Google Scholar
30. 1. Goldspink DA
    2. Reimann F
    3. Gribble FM

    (2018) Models and tools for studying enteroendocrine cells

    Endocrinology 159:3874–3884.

    https://doi.org/10.1210/en.2018-00672

    • PubMed
    • Google Scholar
31. 1. Hedstrand H
    2. Ekwall O
    3. Olsson MJ
    4. Landgren E
    5. Kemp EH
    6. Weetman AP
    7. Perheentupa J
    8. Husebye E
    9. Gustafsson J
    10. Betterle C
    11. Kämpe O
    12. Rorsman F

    (2001) The transcription factors SOX9 and SOX10 are vitiligo autoantigens in autoimmune polyendocrine syndrome type I

    Journal of Biological Chemistry 276:35390–35395.

    https://doi.org/10.1074/jbc.M102391200

    • PubMed
    • Google Scholar
32. 1. Högenauer C
    2. Meyer RL
    3. Netto GJ
    4. Bell D
    5. Little KH
    6. Ferries L
    7. Santa Ana CA
    8. Porter JL
    9. Fordtran JS

    (2001) Malabsorption due to cholecystokinin deficiency in a patient with autoimmune polyglandular syndrome type I

    New England Journal of Medicine 344:270–274.

    https://doi.org/10.1056/NEJM200101253440405

    • PubMed
    • Google Scholar
33. 1. Holland AM
    2. Hale MA
    3. Kagami H
    4. Hammer RE
    5. MacDonald RJ

    (2002) Experimental control of pancreatic development and maintenance

    PNAS 99:12236–12241.

    https://doi.org/10.1073/pnas.192255099

    • PubMed
    • Google Scholar
34. 1. Huhtaniemi I
    2. Hovatta O
    3. La Marca A
    4. Livera G
    5. Monniaux D
    6. Persani L
    7. Heddar A
    8. Jarzabek K
    9. Laisk-Podar T
    10. Salumets A
    11. Tapanainen JS
    12. Veitia RA
    13. Visser JA
    14. Wieacker P
    15. Wolczynski S
    16. Misrahi M

    (2018) Advances in the molecular pathophysiology, genetics, and treatment of primary ovarian insufficiency

    Trends in Endocrinology & Metabolism 29:400–419.

    https://doi.org/10.1016/j.tem.2018.03.010

    • PubMed
    • Google Scholar
35. 1. Husebye ES
    2. Gebre-Medhin G
    3. Tuomi T
    4. Perheentupa J
    5. Landin-Olsson M
    6. Gustafsson J
    7. Rorsman F
    8. Kämpe O

    (1997) Autoantibodies against aromatic L-amino acid decarboxylase in autoimmune polyendocrine syndrome type I

    Journal of Clinical Endocrinology & Metabolism 82:147–150.

    https://doi.org/10.1210/jc.82.1.147

    • PubMed
    • Google Scholar
36. 1. Husebye ES
    2. Anderson MS
    3. Kämpe O

    (2018) Autoimmune polyendocrine syndromes

    New England Journal of Medicine 378:1132–1141.

    https://doi.org/10.1056/NEJMra1713301

    • PubMed
    • Google Scholar
37. 1. Jasti S
    2. Warren BD
    3. McGinnis LK
    4. Kinsey WH
    5. Petroff BK
    6. Petroff MG

    (2012) The autoimmune regulator prevents premature reproductive senescence in female mice

    Biology of Reproduction 86:163–169.

    https://doi.org/10.1095/biolreprod.111.097501

    • PubMed
    • Google Scholar
38. 1. Jeong JS
    2. Jiang L
    3. Albino E
    4. Marrero J
    5. Rho HS
    6. Hu J
    7. Hu S
    8. Vera C
    9. Bayron-Poueymiroy D
    10. Rivera-Pacheco ZA
    11. Ramos L
    12. Torres-Castro C
    13. Qian J
    14. Bonaventura J
    15. Boeke JD
    16. Yap WY
    17. Pino I
    18. Eichinger DJ
    19. Zhu H
    20. Blackshaw S

    (2012) Rapid identification of monospecific monoclonal antibodies using a human proteome microarray

    Molecular & Cellular Proteomics 11:O111.016253.

    https://doi.org/10.1074/mcp.O111.016253

    • PubMed
    • Google Scholar
39. 1. Jörnvall H
    2. Carlquist M
    3. Kwauk S
    4. Otte SC
    5. McIntosh CH
    6. Brown JC
    7. Mutt V

    (1981) Amino acid sequence and heterogeneity of gastric inhibitory polypeptide (GIP)

    FEBS Letters 123:205–210.

    https://doi.org/10.1016/0014-5793(81)80288-8

    • PubMed
    • Google Scholar
40. 1. Kluger N
    2. Jokinen M
    3. Lintulahti A
    4. Krohn K
    5. Ranki A

    (2015) Gastrointestinal immunity against tryptophan hydroxylase-1, aromatic L-amino-acid decarboxylase, AIE-75, Villin and paneth cells in APECED

    Clinical Immunology 158:212–220.

    https://doi.org/10.1016/j.clim.2015.03.012

    • PubMed
    • Google Scholar
41. 1. Kuroda N
    2. Mitani T
    3. Takeda N
    4. Ishimaru N
    5. Arakaki R
    6. Hayashi Y
    7. Bando Y
    8. Izumi K
    9. Takahashi T
    10. Nomura T
    11. Sakaguchi S
    12. Ueno T
    13. Takahama Y
    14. Uchida D
    15. Sun S
    16. Kajiura F
    17. Mouri Y
    18. Han H
    19. Matsushima A
    20. Yamada G
    21. Matsumoto M

    (2005) Development of autoimmunity against transcriptionally unrepressed target antigen in the Thymus of Aire-deficient mice

    The Journal of Immunology 174:1862–1870.

    https://doi.org/10.4049/jimmunol.174.4.1862

    • PubMed
    • Google Scholar
42. 1. Landegren N
    2. Sharon D
    3. Shum AK
    4. Khan IS
    5. Fasano KJ
    6. Hallgren Å
    7. Kampf C
    8. Freyhult E
    9. Ardesjö-Lundgren B
    10. Alimohammadi M
    11. Rathsman S
    12. Ludvigsson JF
    13. Lundh D
    14. Motrich R
    15. Rivero V
    16. Fong L
    17. Giwercman A
    18. Gustafsson J
    19. Perheentupa J
    20. Husebye ES
    21. Anderson MS
    22. Snyder M
    23. Kämpe O

    (2015) Transglutaminase 4 as a prostate autoantigen in male subfertility

    Science Translational Medicine 7:292ra101.

    https://doi.org/10.1126/scitranslmed.aaa9186

    • PubMed
    • Google Scholar
43. 1. Landegren N
    2. Sharon D
    3. Freyhult E
    4. Hallgren Å
    5. Eriksson D
    6. Edqvist PH
    7. Bensing S
    8. Wahlberg J
    9. Nelson LM
    10. Gustafsson J
    11. Husebye ES
    12. Anderson MS
    13. Snyder M
    14. Kämpe O

    (2016) Proteome-wide survey of the autoimmune target repertoire in autoimmune polyendocrine syndrome type 1

    Scientific Reports 6:1–11.

    https://doi.org/10.1038/srep20104

    • PubMed
    • Google Scholar
44. 1. Lanzavecchia A

    (1985) Antigen-specific interaction between T and B cells

    Nature 314:537–539.

    https://doi.org/10.1038/314537a0

    • PubMed
    • Google Scholar
45. 1. Larman HB
    2. Zhao Z
    3. Laserson U
    4. Li MZ
    5. Ciccia A
    6. Gakidis MA
    7. Church GM
    8. Kesari S
    9. Leproust EM
    10. Solimini NL
    11. Elledge SJ

    (2011) Autoantigen discovery with a synthetic human peptidome

    Nature Biotechnology 29:535–541.

    https://doi.org/10.1038/nbt.1856

    • PubMed
    • Google Scholar
46. 1. Leonard JD
    2. Gilmore DC
    3. Dileepan T
    4. Nawrocka WI
    5. Chao JL
    6. Schoenbach MH
    7. Jenkins MK
    8. Adams EJ
    9. Savage PA

    (2017) Identification of natural regulatory T cell epitopes reveals convergence on a dominant autoantigen

    Immunity 47:107–117.

    https://doi.org/10.1016/j.immuni.2017.06.015

    • PubMed
    • Google Scholar
47. 1. Li L
    2. Baibakov B
    3. Dean J

    (2008) A subcortical maternal complex essential for preimplantation mouse embryogenesis

    Developmental Cell 15:416–425.

    https://doi.org/10.1016/j.devcel.2008.07.010

    • PubMed
    • Google Scholar
48. 1. Liu C
    2. Li M
    3. Li T
    4. Zhao H
    5. Huang J
    6. Wang Y
    7. Gao Q
    8. Yu Y
    9. Shi Q

    (2016) ECAT1 is essential for human oocyte maturation and pre-implantation development of the resulting embryos

    Scientific Reports 6:38192.

    https://doi.org/10.1038/srep38192

    • PubMed
    • Google Scholar
49. 1. Maclaren N
    2. Chen QY
    3. Kukreja A
    4. Marker J
    5. Zhang CH
    6. Sun ZS

    (2001) Autoimmune hypogonadism as part of an autoimmune polyglandular syndrome

    Journal of the Society for Gynecologic Investigation 8:S52–S54.

    https://doi.org/10.1177/1071557601008001S16

    • PubMed
    • Google Scholar
50. 1. Malchow S
    2. Leventhal DS
    3. Lee V
    4. Nishi S
    5. Socci ND
    6. Savage PA

    (2016) Aire enforces immune tolerance by directing autoreactive T cells into the regulatory T cell lineage

    Immunity 44:1102–1113.

    https://doi.org/10.1016/j.immuni.2016.02.009

    • PubMed
    • Google Scholar
51. 1. Mandel-Brehm C
    2. Dubey D
    3. Kryzer TJ
    4. O'Donovan BD
    5. Tran B
    6. Vazquez SE
    7. Sample HA
    8. Zorn KC
    9. Khan LM
    10. Bledsoe IO
    11. McKeon A
    12. Pleasure SJ
    13. Lennon VA
    14. DeRisi JL
    15. Wilson MR
    16. Pittock SJ

    (2019) Kelch-like protein 11 antibodies in Seminoma-Associated paraneoplastic encephalitis

    New England Journal of Medicine 381:47–54.

    https://doi.org/10.1056/NEJMoa1816721

    • PubMed
    • Google Scholar
52. 1. Meager A
    2. Visvalingam K
    3. Peterson P
    4. Möll K
    5. Murumägi A
    6. Krohn K
    7. Eskelin P
    8. Perheentupa J
    9. Husebye E
    10. Kadota Y
    11. Willcox N

    (2006) Anti-interferon autoantibodies in autoimmune polyendocrinopathy syndrome type 1

    PLOS Medicine 3:e289.

    https://doi.org/10.1371/journal.pmed.0030289

    • PubMed
    • Google Scholar
53. 1. Meyer S
    2. Woodward M
    3. Hertel C
    4. Vlaicu P
    5. Haque Y
    6. Kärner J
    7. Macagno A
    8. Onuoha SC
    9. Fishman D
    10. Peterson H
    11. Metsküla K
    12. Uibo R
    13. Jäntti K
    14. Hokynar K
    15. Wolff ASB
    16. Krohn K
    17. Ranki A
    18. Peterson P
    19. Kisand K
    20. Hayday A
    21. Meloni A
    22. Kluger N
    23. Husebye ES
    24. Podkrajsek KT
    25. Battelino T
    26. Bratanic N
    27. Peet A

    (2016) AIRE-Deficient Patients Harbor Unique High-Affinity Disease-Ameliorating Autoantibodies

    Cell 166:582–595.

    https://doi.org/10.1016/j.cell.2016.06.024

    • Google Scholar
54. 1. Mitchell J
    2. Punthakee Z
    3. Lo B
    4. Bernard C
    5. Chong K
    6. Newman C
    7. Cartier L
    8. Desilets V
    9. Cutz E
    10. Hansen IL
    11. Riley P
    12. Polychronakos C

    (2004) Neonatal diabetes, with hypoplastic pancreas, intestinal atresia and gall bladder hypoplasia: search for the aetiology of a new autosomal recessive syndrome

    Diabetologia 47:2160–2167.

    https://doi.org/10.1007/s00125-004-1576-3

    • Google Scholar
55. 1. Moody AJ
    2. Thim L
    3. Valverde I

    (1984) The isolation and sequencing of human gastric inhibitory peptide (GIP)

    FEBS Letters 172:142–148.

    https://doi.org/10.1016/0014-5793(84)81114-X

    • Google Scholar
56. 1. Nagamine K
    2. Peterson P
    3. Scott HS
    4. Kudoh J
    5. Minoshima S
    6. Heino M
    7. Krohn KJ
    8. Lalioti MD
    9. Mullis PE
    10. Antonarakis SE
    11. Kawasaki K
    12. Asakawa S
    13. Ito F
    14. Shimizu N

    (1997) Positional cloning of the APECED gene

    Nature Genetics 17:393–398.

    https://doi.org/10.1038/ng1297-393

    • PubMed
    • Google Scholar
57. 1. Nelson LM

    (2009) Clinical practice. primary ovarian insufficiency

    The New England Journal of Medicine 360:606–614.

    https://doi.org/10.1056/NEJMcp0808697

    • PubMed
    • Google Scholar
58. 1. O'Connor DT
    2. Burton D
    3. Deftos LJ

    (1983) Chromogranin A: immunohistology reveals its universal occurrence in normal polypeptide hormone producing endocrine glands

    Life Sciences 33:1657–1663.

    https://doi.org/10.1016/0024-3205(83)90721-X

    • PubMed
    • Google Scholar
59. 1. Oftedal BE
    2. Hellesen A
    3. Erichsen MM
    4. Bratland E
    5. Vardi A
    6. Perheentupa J
    7. Kemp EH
    8. Fiskerstrand T
    9. Viken MK
    10. Weetman AP
    11. Fleishman SJ
    12. Banka S
    13. Newman WG
    14. Sewell WA
    15. Sozaeva LS
    16. Zayats T
    17. Haugarvoll K
    18. Orlova EM
    19. Haavik J
    20. Johansson S
    21. Knappskog PM
    22. Løvås K
    23. Wolff AS
    24. Abramson J
    25. Husebye ES

    (2015) Dominant mutations in the autoimmune regulator AIRE are associated with common Organ-Specific autoimmune diseases

    Immunity 42:1185–1196.

    https://doi.org/10.1016/j.immuni.2015.04.021

    • PubMed
    • Google Scholar
60. 1. Ohsie S
    2. Gerney G
    3. Gui D
    4. Kahana D
    5. Martín MG
    6. Cortina G

    (2009) A paucity of colonic enteroendocrine and/or enterochromaffin cells characterizes a subset of patients with chronic unexplained diarrhea/malabsorption

    Human Pathology 40:1006–1014.

    https://doi.org/10.1016/j.humpath.2008.12.016

    • PubMed
    • Google Scholar
61. 1. Oliva-Hemker M
    2. Berkenblit GV
    3. Anhalt GJ
    4. Yardley JH

    (2006) Pernicious Anemia and widespread absence of gastrointestinal endocrine cells in a patient with autoimmune polyglandular syndrome type I and malabsorption

    The Journal of Clinical Endocrinology & Metabolism 91:2833–2838.

    https://doi.org/10.1210/jc.2005-2506

    • PubMed
    • Google Scholar
62. 1. Otsuka N
    2. Tong Z-B
    3. Vanevski K
    4. Tu W
    5. Cheng MH
    6. Nelson LM

    (2011) Autoimmune Oophoritis with Multiple Molecular Targets Mitigated by Transgenic Expression of Mater

    Endocrinology 152:2465–2473.

    https://doi.org/10.1210/en.2011-0022

    • Google Scholar
63. Preprint

    1. O’Donovan B
    2. Mandel - Brehm C
    3. Vazquez SE

    (2018) Exploration of anti - Yo and anti - Hu paraneoplastic neurological disorders by PhIP - Seq reveals a highly restricted pattern of antibody epitopes

    bioRxiv.

    https://doi.org/10.1101/502187

    • Google Scholar
64. 1. Patel KA
    2. Kettunen J
    3. Laakso M
    4. Stančáková A
    5. Laver TW
    6. Colclough K
    7. Johnson MB
    8. Abramowicz M
    9. Groop L
    10. Miettinen PJ
    11. Shepherd MH
    12. Flanagan SE
    13. Ellard S
    14. Inagaki N
    15. Hattersley AT
    16. Tuomi T
    17. Cnop M
    18. Weedon MN

    (2017) Heterozygous RFX6 protein truncating variants are associated with MODY with reduced penetrance

    Nature Communications 8:1–8.

    https://doi.org/10.1038/s41467-017-00895-9

    • Google Scholar
65. 1. Pederson RA
    2. McIntosh CH

    (2016) Discovery of gastric inhibitory polypeptide and its subsequent fate: personal reflections

    Journal of Diabetes Investigation 7:4–7.

    https://doi.org/10.1111/jdi.12480

    • PubMed
    • Google Scholar
66. 1. Petra O-S
    2. Peer S
    3. Michael M

    (2000) Target proteins in human autoimmunity: cytochromes P450 and Udp-Glycoronosyltransferases

    Canadian Journal of Gastroenterology and Hepatology 14:429–439.

    https://doi.org/10.1155/2000/910107

    • Google Scholar
67. 1. Piccand J
    2. Vagne C
    3. Blot F
    4. Meunier A
    5. Beucher A
    6. Strasser P
    7. Lund ML
    8. Ghimire S
    9. Nivlet L
    10. Lapp C
    11. Petersen N
    12. Engelstoft MS
    13. Thibault-Carpentier C
    14. Keime C
    15. Correa SJ
    16. Schreiber V
    17. Molina N
    18. Schwartz TW
    19. De Arcangelis A
    20. Gradwohl G

    (2019) Rfx6 promotes the differentiation of peptide-secreting enteroendocrine cells while repressing genetic programs controlling serotonin production

    Molecular Metabolism 29:24–39.

    https://doi.org/10.1016/j.molmet.2019.08.007

    • PubMed
    • Google Scholar
68. 1. Pöntynen N
    2. Miettinen A
    3. Arstila TP
    4. Kämpe O
    5. Alimohammadi M
    6. Vaarala O
    7. Peltonen L
    8. Ulmanen I

    (2006) Aire deficient mice do not develop the same profile of tissue-specific autoantibodies as APECED patients

    Journal of Autoimmunity 27:96–104.

    https://doi.org/10.1016/j.jaut.2006.06.001

    • PubMed
    • Google Scholar
69. 1. Popler J
    2. Alimohammadi M
    3. Kämpe O
    4. Dalin F
    5. Dishop MK
    6. Barker JM
    7. Moriarty-Kelsey M
    8. Soep JB
    9. Deterding RR

    (2012) Autoimmune polyendocrine syndrome type 1: utility of KCNRG autoantibodies as a marker of active pulmonary disease and successful treatment with rituximab

    Pediatric Pulmonology 47:84–87.

    https://doi.org/10.1002/ppul.21520

    • PubMed
    • Google Scholar
70. 1. Posovszky C
    2. Lahr G
    3. von Schnurbein J
    4. Buderus S
    5. Findeisen A
    6. Schröder C
    7. Schütz C
    8. Schulz A
    9. Debatin KM
    10. Wabitsch M
    11. Barth TF

    (2012) Loss of enteroendocrine cells in autoimmune-polyendocrine-candidiasis-ectodermal-dystrophy (APECED) syndrome with gastrointestinal dysfunction

    The Journal of Clinical Endocrinology & Metabolism 97:E292–E300.

    https://doi.org/10.1210/jc.2011-2044

    • PubMed
    • Google Scholar
71. 1. Puel A
    2. Döffinger R
    3. Natividad A
    4. Chrabieh M
    5. Barcenas-Morales G
    6. Picard C
    7. Cobat A
    8. Ouachée-Chardin M
    9. Toulon A
    10. Bustamante J
    11. Al-Muhsen S
    12. Al-Owain M
    13. Arkwright PD
    14. Costigan C
    15. McConnell V
    16. Cant AJ
    17. Abinun M
    18. Polak M
    19. Bougnères P-F
    20. Kumararatne D
    21. Marodi L
    22. Nahum A
    23. Roifman C
    24. Blanche S
    25. Fischer A
    26. Bodemer C
    27. Abel L
    28. Lilic D
    29. Casanova J-L

    (2010) Autoantibodies against IL-17A, IL-17F, and IL-22 in patients with chronic mucocutaneous candidiasis and autoimmune polyendocrine syndrome type I

    The Journal of Experimental Medicine 207:291–297.

    https://doi.org/10.1084/jem.20091983

    • Google Scholar
72. 1. Rath MF
    2. Coon SL
    3. Amaral FG
    4. Weller JL
    5. Møller M
    6. Klein DC

    (2016) Melatonin synthesis: acetylserotonin O-Methyltransferase (ASMT) Is strongly expressed in a subpopulation of pinealocytes in the male rat pineal gland

    Endocrinology 157:2028–2040.

    https://doi.org/10.1210/en.2015-1888

    • PubMed
    • Google Scholar
73. 1. Reddy R
    2. Akoury E
    3. Phuong Nguyen NM
    4. Abdul-Rahman OA
    5. Dery C
    6. Gupta N
    7. Daley WP
    8. Ao A
    9. Landolsi H
    10. Ann Fisher R
    11. Touitou I
    12. Slim R

    (2013) Report of four new patients with protein-truncating mutations in C6orf221/KHDC3L and colocalization with NLRP7

    European Journal of Human Genetics 21:957–964.

    https://doi.org/10.1038/ejhg.2012.274

    • PubMed
    • Google Scholar
74. 1. Rezaei M
    2. Nguyen NM
    3. Foroughinia L
    4. Dash P
    5. Ahmadpour F
    6. Verma IC
    7. Slim R
    8. Fardaei M

    (2016) Two novel mutations in the KHDC3L gene in asian patients with recurrent hydatidiform mole

    Human Genome Variation 3:16027.

    https://doi.org/10.1038/hgv.2016.27

    • PubMed
    • Google Scholar
75. 1. Rorsman F
    2. Husebye ES
    3. Winqvist O
    4. Björk E
    5. Karlsson FA
    6. Kämpe O

    (1995) Aromatic-L-amino-acid decarboxylase, a pyridoxal phosphate-dependent enzyme, is a beta-cell autoantigen

    PNAS 92:8626–8629.

    https://doi.org/10.1073/pnas.92.19.8626

    • PubMed
    • Google Scholar
76. 1. Rosen A
    2. Casciola-Rosen L

    (2014) Autoantigens as partners in initiation and propagation of autoimmune rheumatic diseases

    Annual Review of Immunology 34:395–420.

    https://doi.org/10.1146/annurev-immunol-032414-112205

    • Google Scholar
77. 1. Sansom SN
    2. Shikama-Dorn N
    3. Zhanybekova S
    4. Nusspaumer G
    5. Macaulay IC
    6. Deadman ME
    7. Heger A
    8. Ponting CP
    9. Holländer GA

    (2014) Population and single-cell genomics reveal the aire dependency, relief from Polycomb silencing, and distribution of self-antigen expression in thymic epithelia

    Genome Research 24:1918–1931.

    https://doi.org/10.1101/gr.171645.113

    • PubMed
    • Google Scholar
78. 1. Schaum N
    2. Karkanias J
    3. Neff NF
    4. May AP
    5. Quake SR
    6. Wyss-Coray T
    7. Darmanis S
    8. Batson J
    9. Botvinnik O
    10. Chen MB
    11. Chen S
    12. Green F
    13. Jones RC
    14. Maynard A
    15. Penland L
    16. Pisco AO
    17. Sit RV
    18. Stanley GM
    19. Webber JT
    20. Wyss-Coray T

    (2018) Single-cell transcriptomics of 20 mouse organs creates a tabula muris

    Nature 562:1–25.

    https://doi.org/10.1038/s41586-018-0590-4

    • Google Scholar
79. 1. Seymen F
    2. Kim YJ
    3. Lee YJ
    4. Kang J
    5. Kim TH
    6. Choi H
    7. Koruyucu M
    8. Kasimoglu Y
    9. Tuna EB
    10. Gencay K
    11. Shin TJ
    12. Hyun HK
    13. Kim YJ
    14. Lee SH
    15. Lee ZH
    16. Zhang H
    17. Hu JC
    18. Simmer JP
    19. Cho ES
    20. Kim JW

    (2016) Recessive mutations in ACPT, encoding testicular acid phosphatase, cause hypoplastic amelogenesis imperfecta

    The American Journal of Human Genetics 99:1199–1205.

    https://doi.org/10.1016/j.ajhg.2016.09.018

    • PubMed
    • Google Scholar
80. 1. Sharon D
    2. Snyder M

    (2014) Serum profiling using protein microarrays to identify disease related antigens

    Methods in Molecular Biology 1176:169–178.

    https://doi.org/10.1007/978-1-4939-0992-6_14

    • PubMed
    • Google Scholar
81. 1. Shum AK
    2. DeVoss J
    3. Tan CL
    4. Hou Y
    5. Johannes K
    6. O'Gorman CS
    7. Jones KD
    8. Sochett EB
    9. Fong L
    10. Anderson MS

    (2009) Identification of an autoantigen demonstrates a link between interstitial lung disease and a defect in central tolerance

    Science Translational Medicine 1:9ra20.

    https://doi.org/10.1126/scitranslmed.3000284

    • PubMed
    • Google Scholar
82. 1. Shum AK
    2. Alimohammadi M
    3. Tan CL
    4. Cheng MH
    5. Metzger TC
    6. Law CS
    7. Lwin W
    8. Perheentupa J
    9. Bour-Jordan H
    10. Carel JC
    11. Husebye ES
    12. De Luca F
    13. Janson C
    14. Sargur R
    15. Dubois N
    16. Kajosaari M
    17. Wolters PJ
    18. Chapman HA
    19. Kämpe O
    20. Anderson MS

    (2013) BPIFB1 is a lung-specific autoantigen associated with interstitial lung disease

    Science Translational Medicine 5:206ra139.

    https://doi.org/10.1126/scitranslmed.3006998

    • PubMed
    • Google Scholar
83. 1. Sifuentes-Dominguez LF
    2. Li H
    3. Llano E
    4. Liu Z
    5. Singla A
    6. Patel AS
    7. Kathania M
    8. Khoury A
    9. Norris N
    10. Rios JJ
    11. Starokadomskyy P
    12. Park JY
    13. Gopal P
    14. Liu Q
    15. Tan S
    16. Chan L
    17. Ross T
    18. Harrison S
    19. Venuprasad K
    20. Baker LA
    21. Jia D
    22. Burstein E

    (2019) SCGN deficiency results in colitis susceptibility

    eLife 8:e49910.

    https://doi.org/10.7554/eLife.49910

    • PubMed
    • Google Scholar
84. 1. Silva CA
    2. Yamakami LY
    3. Aikawa NE
    4. Araujo DB
    5. Carvalho JF
    6. Bonfá E

    (2014) Autoimmune primary ovarian insufficiency

    Autoimmunity Reviews 13:427–430.

    https://doi.org/10.1016/j.autrev.2014.01.003

    • PubMed
    • Google Scholar
85. 1. Smith SB
    2. Qu HQ
    3. Taleb N
    4. Kishimoto NY
    5. Scheel DW
    6. Lu Y
    7. Patch AM
    8. Grabs R
    9. Wang J
    10. Lynn FC
    11. Miyatsuka T
    12. Mitchell J
    13. Seerke R
    14. Désir J
    15. Vanden Eijnden S
    16. Abramowicz M
    17. Kacet N
    18. Weill J
    19. Renard ME
    20. Gentile M
    21. Hansen I
    22. Dewar K
    23. Hattersley AT
    24. Wang R
    25. Wilson ME
    26. Johnson JD
    27. Polychronakos C
    28. German MS

    (2010) Rfx6 directs islet formation and insulin production in mice and humans

    Nature 463:775–780.

    https://doi.org/10.1038/nature08748

    • PubMed
    • Google Scholar
86. 1. Smith CE
    2. Whitehouse LL
    3. Poulter JA
    4. Brookes SJ
    5. Day PF
    6. Soldani F
    7. Kirkham J
    8. Inglehearn CF
    9. Mighell AJ

    (2017) Defects in the acid phosphatase ACPT cause recessive hypoplastic amelogenesis imperfecta

    European Journal of Human Genetics 25:1015–1019.

    https://doi.org/10.1038/ejhg.2017.79

    • PubMed
    • Google Scholar
87. 1. Sng J
    2. Ayoglu B
    3. Chen JW
    4. Schickel JN
    5. Ferre EMN
    6. Glauzy S
    7. Romberg N
    8. Hoenig M
    9. Cunningham-Rundles C
    10. Utz PJ
    11. Lionakis MS
    12. Meffre E

    (2019) AIRE expression controls the peripheral selection of autoreactive B cells

    Science Immunology 4:eaav6778.

    https://doi.org/10.1126/sciimmunol.aav6778

    • PubMed
    • Google Scholar
88. 1. Söderbergh A
    2. Myhre AG
    3. Ekwall O
    4. Gebre-Medhin G
    5. Hedstrand H
    6. Landgren E
    7. Miettinen A
    8. Eskelin P
    9. Halonen M
    10. Tuomi T
    11. Gustafsson J
    12. Husebye ES
    13. Perheentupa J
    14. Gylling M
    15. Manns MP
    16. Rorsman F
    17. Kämpe O
    18. Nilsson T

    (2004) Prevalence and clinical associations of 10 defined autoantibodies in autoimmune polyendocrine syndrome type I

    The Journal of Clinical Endocrinology & Metabolism 89:557–562.

    https://doi.org/10.1210/jc.2003-030279

    • PubMed
    • Google Scholar
89. 1. Stoffers DA
    2. Zinkin NT
    3. Stanojevic V
    4. Clarke WL
    5. Habener JF

    (1997) Pancreatic agenesis attributable to a single nucleotide deletion in the human IPF1 gene coding sequence

    Nature Genetics 15:106–110.

    https://doi.org/10.1038/ng0197-106

    • PubMed
    • Google Scholar
90. 1. Taplin CE
    2. Barker JM

    (2008) Autoantibodies in type 1 diabetes

    Autoimmunity 41:11–18.

    https://doi.org/10.1080/08916930701619169

    • PubMed
    • Google Scholar
91. 1. Uhlén M
    2. Fagerberg L
    3. Hallström BM
    4. Lindskog C
    5. Oksvold P
    6. Mardinoglu A
    7. Sivertsson Å
    8. Kampf C
    9. Sjöstedt E
    10. Asplund A
    11. Olsson I
    12. Edlund K
    13. Lundberg E
    14. Navani S
    15. Szigyarto CA
    16. Odeberg J
    17. Djureinovic D
    18. Takanen JO
    19. Hober S
    20. Alm T
    21. Edqvist PH
    22. Berling H
    23. Tegel H
    24. Mulder J
    25. Rockberg J
    26. Nilsson P
    27. Schwenk JM
    28. Hamsten M
    29. von Feilitzen K
    30. Forsberg M
    31. Persson L
    32. Johansson F
    33. Zwahlen M
    34. von Heijne G
    35. Nielsen J
    36. Pontén F

    (2015) Proteomics. Tissue-based map of the human proteome

    Science 347:1260419.

    https://doi.org/10.1126/science.1260419

    • PubMed
    • Google Scholar
92. 1. Virant-Klun I
    2. Leicht S
    3. Hughes C
    4. Krijgsveld J

    (2016) Identification of Maturation-Specific proteins by Single-Cell proteomics of human oocytes

    Molecular & Cellular Proteomics 15:2616–2627.

    https://doi.org/10.1074/mcp.M115.056887

    • PubMed
    • Google Scholar
93. 1. Wang J
    2. Cortina G
    3. Wu SV
    4. Tran R
    5. Cho JH
    6. Tsai MJ
    7. Bailey TJ
    8. Jamrich M
    9. Ament ME
    10. Treem WR
    11. Hill ID
    12. Vargas JH
    13. Gershman G
    14. Farmer DG
    15. Reyen L
    16. Martín MG

    (2006) Mutant neurogenin-3 in congenital malabsorptive diarrhea

    New England Journal of Medicine 355:270–280.

    https://doi.org/10.1056/NEJMoa054288

    • PubMed
    • Google Scholar
94. 1. Wang X
    2. Song D
    3. Mykytenko D
    4. Kuang Y
    5. Lv Q
    6. Li B
    7. Chen B
    8. Mao X
    9. Xu Y
    10. Zukin V
    11. Mazur P
    12. Mu J
    13. Yan Z
    14. Zhou Z
    15. Li Q
    16. Liu S
    17. Jin L
    18. He L
    19. Sang Q
    20. Sun Z
    21. Dong X
    22. Wang L

    (2018) Novel mutations in genes encoding subcortical maternal complex proteins may cause human embryonic developmental arrest

    Reproductive BioMedicine Online 36:698–704.

    https://doi.org/10.1016/j.rbmo.2018.03.009

    • PubMed
    • Google Scholar
95. 1. Welt CK

    (2008) Autoimmune oophoritis in the adolescent

    Annals of the New York Academy of Sciences 1135:118–122.

    https://doi.org/10.1196/annals.1429.006

    • PubMed
    • Google Scholar
96. 1. Winqvist O
    2. Gustafsson J
    3. Rorsman F
    4. Karlsson FA
    5. Kämpe O

    (1993) Two different cytochrome P450 enzymes are the adrenal antigens in autoimmune polyendocrine syndrome type I and Addison's disease

    Journal of Clinical Investigation 92:2377–2385.

    https://doi.org/10.1172/JCI116843

    • PubMed
    • Google Scholar
97. 1. Wolff AS
    2. Sarkadi AK
    3. Maródi L
    4. Kärner J
    5. Orlova E
    6. Oftedal BE
    7. Kisand K
    8. Oláh E
    9. Meloni A
    10. Myhre AG
    11. Husebye ES
    12. Motaghedi R
    13. Perheentupa J
    14. Peterson P
    15. Willcox N
    16. Meager A

    (2013) Anti-cytokine autoantibodies preceding onset of autoimmune polyendocrine syndrome type I features in early childhood

    Journal of Clinical Immunology 33:1341–1348.

    https://doi.org/10.1007/s10875-013-9938-6

    • PubMed
    • Google Scholar
98. 1. Zhang Y
    2. Yan Z
    3. Qin Q
    4. Nisenblat V
    5. Chang HM
    6. Yu Y
    7. Wang T
    8. Lu C
    9. Yang M
    10. Yang S
    11. Yao Y
    12. Zhu X
    13. Xia X
    14. Dang Y
    15. Ren Y
    16. Yuan P
    17. Li R
    18. Liu P
    19. Guo H
    20. Han J
    21. He H
    22. Zhang K
    23. Wang Y
    24. Wu Y
    25. Li M
    26. Qiao J
    27. Yan J
    28. Yan L

    (2018) Transcriptome landscape of human folliculogenesis reveals oocyte and granulosa cell interactions

    Molecular Cell 72:1021–1034.

    https://doi.org/10.1016/j.molcel.2018.10.029

    • PubMed
    • Google Scholar
99. 1. Zhang W
    2. Chen Z
    3. Zhang D
    4. Zhao B
    5. Liu L
    6. Xie Z
    7. Yao Y
    8. Zheng P

    (2019) KHDC3L mutation causes recurrent pregnancy loss by inducing genomic instability of human early embryonic cells

    PLOS Biology 17:e3000468.

    https://doi.org/10.1371/journal.pbio.3000468

    • PubMed
    • Google Scholar
100. 1. Zhu H
     2. Bilgin M
     3. Bangham R
     4. Hall D
     5. Casamayor A
     6. Bertone P
     7. Lan N
     8. Jansen R
     9. Bidlingmaier S
     10. Houfek T
     11. Mitchell T
     12. Miller P
     13. Dean RA
     14. Gerstein M
     15. Snyder M

     (2001) Global analysis of protein activities using proteome chips

     Science 293:2101–2105.

     https://doi.org/10.1126/science.1062191

     • PubMed
     • Google Scholar
101. 1. Zhu K
     2. Yan L
     3. Zhang X
     4. Lu X
     5. Wang T
     6. Yan J
     7. Liu X
     8. Qiao J
     9. Li L

     (2015) Identification of a human subcortical maternal complex

     Molecular Human Reproduction 21:320–329.

     https://doi.org/10.1093/molehr/gau116

     • PubMed
     • Google Scholar
102. 1. Ziegler B
     2. Schlosser M
     3. Lühder F
     4. Strebelow M
     5. Augstein P
     6. Northemann W
     7. Powers AC
     8. Ziegler M

     (1996) Murine monoclonal glutamic acid decarboxylase (GAD)65 antibodies recognize autoimmune-associated GAD epitope regions targeted in patients with type 1 diabetes mellitus and stiff-man syndrome

     Acta Diabetologica 33:225–231.

     https://doi.org/10.1007/BF02048548

     • PubMed
     • Google Scholar

Author details

1. Sara E Vazquez

   1. Medical Scientist Training Program, University of California, San Francisco, San Francisco, United States
   2. Tetrad Graduate Program, University of California, San Francisco, San Francisco, United States
   3. Diabetes Center, University of California, San Francisco, San Francisco, United States
   4. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   JD, MSA, and SEV have a provisional patent on clinical application of autoantigens described in this study.

   "This ORCID iD identifies the author of this article:" 0000-0002-0601-7001

2. Elise MN Ferré

   Fungal Pathogenesis Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy & Infectious Diseases, National Institutes of Health, Bethesda, United States

   Contribution

   Resources, Data curation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

3. David W Scheel

   Diabetes Center, University of California, San Francisco, San Francisco, United States

   Contribution

   Validation, Investigation, Visualization, Writing - review and editing

   Competing interests

   No competing interests declared

4. Sara Sunshine

   1. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, United States
   2. Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, United States

   Contribution

   Validation, Investigation, Visualization, Writing - review and editing

   Competing interests

   No competing interests declared

5. Brenda Miao

   Diabetes Center, University of California, San Francisco, San Francisco, United States

   Contribution

   Validation, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3393-9837

6. Caleigh Mandel-Brehm

   Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, United States

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

7. Zoe Quandt

   Department of Medicine, University of California, San Francisco, San Francisco, United States

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

8. Alice Y Chan

   Department of Pediatrics, University of California, San Francisco, San Francisco, United States

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

9. Mickie Cheng

   Diabetes Center, University of California, San Francisco, San Francisco, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

10. Michael German

    1. Diabetes Center, University of California, San Francisco, San Francisco, United States
    2. Department of Medicine, University of California, San Francisco, San Francisco, United States
    3. Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, United States

    Contribution

    Resources, Supervision, Funding acquisition, Writing - review and editing

    Competing interests

    No competing interests declared

11. Michail Lionakis

    Fungal Pathogenesis Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy & Infectious Diseases, National Institutes of Health, Bethesda, United States

    Contribution

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

    Competing interests

    No competing interests declared

12. Joseph L DeRisi

    1. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, United States
    2. Chan Zuckerberg Biohub, San Francisco, United States

    Contribution

    Conceptualization, Resources, Software, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration

    Contributed equally with

    Mark S Anderson

    For correspondence

    joe@derisilab.ucsf.edu

    Competing interests

    JD is a scientific advisory board member of Allen and Company. JD, MSA, and SEV have a provisional patent on clinical application of autoantigens described in this study.

    "This ORCID iD identifies the author of this article:" 0000-0002-4611-9205

13. Mark S Anderson

    1. Diabetes Center, University of California, San Francisco, San Francisco, United States
    2. Department of Medicine, University of California, San Francisco, San Francisco, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Writing - original draft, Project administration

    Contributed equally with

    Joseph L DeRisi

    For correspondence

    Mark.Anderson@ucsf.edu

    Competing interests

    MSA owns stock in Merck and Medtronic. JD, MSA, and SEV have a provisional patent on clinical application of autoantigens described in this study.

    "This ORCID iD identifies the author of this article:" 0000-0002-3093-4758

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