Data from: Validation of a murine proteome-wide phage display library for identification of autoantibody specificities
Data files
Feb 22, 2024 version files 13.27 GB
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library_fidelity_fastq.tar.gz
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metadata_for_dryad.csv
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mouseome_design_files.zip
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README.md
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RPK_for_dryad.csv
Abstract
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.
README: Mouse PhIP-seq library design and experimental files
These files correspond to the publication: https://insight.jci.org/articles/view/174976
Description of the data and file structure
Design files for library:
mouseome_design_files.zip contains:
- 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
- mouse_associate_genes_no-nan.csv : a table with fasta header (peptide name), protein (gene) name, and amino acid sequence
- mouse_T7_display_peps_alternate-header.fasta : Amino acid sequences of peptides to display (linkers trimmed)
- mouse_T7_display_seqs_alternate-header.fasta : DNA sequences of peptides to display (includes linkers)
- mouse_T7_display_seqs_pool_1.fasta : Pool 1 DNA sequences sent to Agilent for synthesis
- mouse_T7_display_seqs_pool_2.fasta : Pool 2 DNA sequences sent to Agilent for synthesis
Peptide counts of PhIP-seq experiments:
- 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:
- 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:
- MuG3_glycerol_R*.fastq : reads associated with sequencing the oligos ordered from Agilent
- 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 joe@derisilab.ucsf.edu
Code/Software
Code used to analyze these datasets is available in the PhagePy package for python:
https://github.com/h-s-miller/phagepy
Methods
PhIP-seq library design and validation of the murine proteome.