Autoantibody discovery across monogenic, acquired, and COVID19-associated autoimmunity with scalable PhIP-Seq

Phage Immunoprecipitation-Sequencing (PhIP-Seq) allows for unbiased,
proteome-wide autoantibody discovery across a variety of disease settings,
with identification of disease-specific autoantigens providing new insight
into previously poorly understood forms of immune dysregulation. Despite
several successful implementations of PhIP-Seq for autoantigen discovery,
including our previous work (Vazquez et al. 2020), current protocols are
inherently difficult to scale to accommodate large cohorts of cases and
importantly, healthy controls. Here, we develop and validate a high
throughput extension of PhIP-seq in various etiologies of autoimmune and
inflammatory diseases, including APS1, IPEX, RAG1/2 deficiency, Kawasaki
Disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), and
finally, mild and severe forms of COVID-19. We demonstrate that 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 RAG1/2 deficiency, one of
whom 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 COVID-19, 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.