Autoantibody discovery across monogenic, acquired, and COVID-19-associated autoimmunity with scalable PhIP-seq

Abstract

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 COVID19. 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 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.

Autoantigen profiling reveals a shared post-COVID signature in fully recovered and Long COVID patients

“…A two-way Kolmogorov-Smirnoff(KS) test was used for comparisons of FC PhIP-Seq data between groups of samples, except in the case of specifically looking for those genes with increased signal only in the disease-cohort in which a one-way KS test was employed. The logistic regression machine-learning classifiers were performed using our recently described methods (23). Utilizing the Scikit-learn package, logistic regression classifiers were applied to z-scored PhIP-Seq values from individuals with a designated disease category versus the designated control.…”

“…We employed a previously published proteome-wide approach using a T7 phage-display assay with immunoprecipitation and next-generation sequencing (PhiP-Seq) (20)(21)(22)(23)(24). We tested sera from 185 otherwise healthy individuals with prior SARS-CoV-2 infection in parallel to sera from 57 otherwise healthy individuals collected prior to the known existence of COVID-19 (pre-COVID).…”

Validation of a murine proteome-wide phage display library for the identification of autoantibody specificities

“…The performance of the packaged library was first benchmarked utilizing a commercial antibody with a known specificity. Previously, we have utilized commercial anti-GFAP polyclonal antibody as a positive control for human PhIP-seq libraries, due to its consistent immunoprecipitation performance (8,(11)(12)(13). The murine PhIP-seq library contains both human and mouse GFAP sequences and binding to sequences of both species was expected with the commercial antibody.…”

“…In humans, mutations in the AIRE transcription factor result in autoimmune polyendocrine syndrome type 1 (APS1), a rare monogenic disease in which patients develop multi-organ autoimmune pathologies due to autoreactive T cells and high-affinity tissue-specific B-cells (39). Using a human proteome-wide PhIP-seq library, our lab has previously characterized APS1 patient sera and identified a wide array of novel autoreactive antigens (8,9,11).…”

“…To assess proteome-wide autoreactivity in Aire -/mice, we performed PhIP-seq with sera of these mice on the NOD background and identified enriched peptides with a z-score greater than three above MBM. Around 57% of the known autoimmune targets in human APS1 (n=30 proteins) (11) were orthologous to putative autoreactive targets in Aire -/mice and around 40% of genes with known Aire-dependent thymic expression (40) were among Aire -/--specific reactivities by PhIP-seq (Fig 4A). Using the same stringent criteria as the Lyn -/-IgD +/experiment, serum from Aire -/but not control mice enriched 861 peptides, representing 698 proteins, which was significantly more than Lyn -/-IgD +/mice (861 versus 494; Supplementary Table 5; Plin1 was previously identified as an autoimmune marker of generalized lipodystrophy in both mice and humans, including at least one patient with APS1 (9,41).…”