UCSF

The identification of autoantigens remains a critical challenge for understanding and treating autoimmune diseases. Phage Immunoprecipitation-Sequencing (PhIP-Seq) allows for unbiased, proteome-wide autoantigen discovery across a variety of disease settings, with identification of disease-specific autoantigens providing new insight into previously poorly understood forms of immune dysregulation with autoimmunity.The first chapter of this thesis explores autoantigen discovery in the disease Autoimmune polyendocrine syndrome type 1 (APS1), a rare monogenic form of autoimmunity that presents as widespread autoimmunity with T and B cell responses to multiple organs. Importantly, autoantibody discovery in APS1 can illuminate fundamental disease pathogenesis, and many of the antigens found in APS1 extend to common autoimmune diseases. Here, I applied PhIP-Seq to sera from an APS1 cohort and discovered multiple common antibody targets. These novel autoantigens exhibit tissue-restricted expression, including expression in enteroendocrine cells and dental enamel. Using detailed clinical phenotyping, novel associations between autoantibodies and organ-restricted autoimmunity are described, including between anti-KHDC3L autoantibodies and premature ovarian insufficiency, and between anti-RFX6 autoantibodies and diarrheal-type intestinal dysfunction. These data highlight the utility of PhIP-Seq for interrogating antigenic repertoires in human autoimmunity and the importance of antigen discovery for improved understanding of disease mechanisms. In the second chapter, I discuss and address how despite several successful implementations of PhIP-Seq for autoantigen discovery, current protocols are inherently difficult to scale to accommodate large cohorts of cases and importantly, healthy controls. I develop and validate a high throughput extension of PhIP-seq in the context of monogenic and acquired autoimmune disease, including APS1, IPEX, patients with RAG1/2 deficiency, Kawasaki Disease, multisystem inflammatory syndrome in children, and finally, mild and severe forms of COVID19. These scaled datasets enable machine-learning approaches that result in robust prediction of disease status, as well as the ability to detect both known and novel, autoantigens, such as PDYN in APS1 patients, and intestinally expressed proteins BEST4 and BTNL8 in IPEX patients. Remarkably, BEST4 antibodies were also found in 2 patients with a RAG1/2 deficiency, one of which had very early onset IBD. Scaled PhIP-Seq examination of both MIS-C and KD demonstrated rare, overlapping antigens, including CGNL1, as well as several strongly enriched putative pneumonia-associated antigens in severe COVID19, including the endosomal protein EEA1. Together, scaled PhIP-Seq provides a valuable tool for broadly assessing both rare and common autoantigen overlap between autoimmune diseases of varying origins and etiologies.