DOI: 10.7272/q6bg2m7b
title: Data from: Validation of a murine proteome-wide phage display library for identification of autoantibody specificities

description: Autoimmunity is characterized by loss of tolerance to tissue-specific as
 well as systemic antigens, resulting in complex autoantibody landscapes.
 Here, we introduce and extensively validate the performance
 characteristics of a murine proteome-wide library for phage display
 immunoprecipitation and sequencing (PhIP-seq), to profile mouse
 autoantibodies. This system and library were validated using seven genetic
 mouse models across a spectrum of autoreactivity. Mice deficient in
 antibody production (Rag2-/- and mMT) were used to model non-specific
 peptide enrichments, while cross-reactivity was evaluated using
 anti-ovalbumin B cell receptor (BCR)-restricted OB1 mice as a proof of
 principle. The PhIP-seq approach was then utilized to interrogate three
 distinct autoimmune disease models. First, serum from Lyn-/- IgD+/- mice
 with lupus-like disease was used to identify nuclear and apoptotic bleb
 reactivities, lending support to the hypothesis that apoptosis is a shared
 origin of these antigens. Second, serum from non-obese diabetic (NOD)
 mice, a polygenic model of pancreas-specific autoimmunity, enriched
 peptides derived from both insulin and predicted pancreatic proteins.
 Lastly, Aire-/- mouse sera were used to identify numerous auto-antigens,
 many of which were also observed in previous studies of humans with
 autoimmune polyendocrinopathy syndrome type 1 (APS1) carrying recessive
 mutations in AIRE. Among these were peptides derived from Perilipin-1, a
 validated autoimmune biomarker of generalized acquired lipodystrophy in
 humans. Autoreactivity to Perilipin-1 correlated with lymphocyte
 infiltration in adipose tissue and underscores the approach in revealing
 previously unknown specificities. These experiments support the use of
 murine proteome-wide PhIP-seq for antigenic profiling and autoantibody
 discovery, which may be employed to study a range of immune perturbations
 in mouse models of autoimmunity.
description: PhIP-seq library design and validation of the murine
 proteome.
description: # Mouse PhIP-seq library design and experimental files --- These files
 correspond to the publication: [
 https://insight.jci.org/articles/view/174976](https://insight.jci.org/articles/view/174976) ## Description of the data and file structure ***Design files for library:*** mouseome_design_files.zip contains: 1. mouseoligo_in_T7-packaged_ER_02212023.dna: SnapGene file of example peptide sequence cloned into T7 genome. This file has helpful annotations of the cloning site and primer sequences 2. mouse_associate_genes_no-nan.csv : a table with fasta header (peptide name), protein (gene) name, and amino acid sequence 3. mouse_T7_display_peps_alternate-header.fasta : Amino acid sequences of peptides to display (linkers trimmed) 4. mouse_T7_display_seqs_alternate-header.fasta : DNA sequences of peptides to display (includes linkers) 5. mouse_T7_display_seqs_pool_1.fasta : Pool 1 DNA sequences sent to Agilent for synthesis 6. mouse_T7_display_seqs_pool_2.fasta : Pool 2 DNA sequences sent to Agilent for synthesis ***Peptide counts of PhIP-seq experiments:*** 1. RPK_for_dryad.csv: reads per 100,000 (RPK) mapped to each peptide of the library. Columns are peptides, rows are samples. Sample names correspond to fastq filename and metadata linker column (Sequencing_ID) ***Metadata for PhIP-seq experiments:*** 1. metadata_for_dryad.csv : metadata table includes genotype information, age, and sex of mice. The first column of this CSV is identical to the name of the fasta filename and the first column of the peptide counts. ***Raw fastq of library fidelity sequencing experiments:*** library_fidelity_fastq.tar.gz contains: 1. MuG3_glycerol_R*.fastq : reads associated with sequencing the oligos ordered from Agilent 2. MuG3_growup_R*.fastq : reads associated with sequencing the final library after cloning ## Sharing/Access information All raw and processed data is available here. The remaining data is available as supplementary tables in the publication. Any other related files may be made available by contacting Joe DeRisi ## Code/Software Code used to analyze these datasets is available in the PhagePy package for python: