Data from: Identification of novel, clinically correlated autoantigens in the monogenic autoimmune syndrome APS1 by PhIP-Seq
Vazquez, Sara et al. (2020), Data from: Identification of novel, clinically correlated autoantigens in the monogenic autoimmune syndrome APS1 by PhIP-Seq, Dryad, Dataset, https://doi.org/10.7272/Q66H4FM2
The identification of autoantigens remains a critical challenge for understanding and treating autoimmune diseases. Autoimmune polyendocrine syndrome type 1 (APS1), a rare monogenic form of autoimmunity, 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, we performed proteome-wide programmable phage-display (PhIP-Seq) on 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, we find novel associations between autoantibodies and organ-restricted autoimmunity, including between anti-KHDC3L autoantibodies and premature ovarian insufficiency, and between anti-RFX6 autoantibodies and diarrheal-type intestinal dysfunction. Our study highlights the utility of PhIP-Seq for interrogating antigenic repertoires in human autoimmunity and the importance of antigen discovery for improved understanding of disease mechanisms.
DNA libraries were barcoded and amplified, gel purified, and subjected to Next-Generation Sequencing on an Illumina MiSeq Instrument (Illumina, San Diego, CA). Sequencing reads from raw fastq files (see: fastq.gz files) were aligned to the reference oligonucleotide library (see: reference.fasta) using bowtie2. Peptide counts were subsequently normalized by converting raw reads to percentage of total reads per sample (see: peptides.csv; for peptide sequences and gene conversion, see: peptides_seq_gene.csv). Peptide counts were also summed across all fragments mapping to the same gene for gene-level counts (see: genes.csv). Peptide and gene-level enrichments for both APS1 and non-APS1 sera were calculated by determining the fold-change of read percentage per peptide and gene in each sample over the mean read percentage per peptide and gene in a background of mock-IP.
National Institute of Allergy and Infectious Diseases (NIAID), Award: 5P01AI118688-04
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Award: 1F30DK123915-01
National Institute of Allergy and Infectious Diseases (NIAID), Award: 1ZIAAI001175-07
Chan Zuckerberg Biohub
Larry L. Hillblom Foundation (LLHF)
Parker Foundation - Helmsley Charitable Trust - JDRF