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Unveiling the autoreactome: Proteome-wide immunological fingerprints reveal the promise of plasma cell depleting therapy

Cite this dataset

Bodansky, Aaron et al. (2024). Unveiling the autoreactome: Proteome-wide immunological fingerprints reveal the promise of plasma cell depleting therapy [Dataset]. Dryad. https://doi.org/10.5061/dryad.w3r2280z6

Abstract

The prevalence and burden of autoimmune and autoantibody mediated disease continues to rise, yet the etiologies of many of these diseases remain unclear. Despite numerous new targeted immunomodulatory therapies, comprehensive approaches to apply and evaluate the effects of these treatments longitudinally are lacking. Here, we leverage advances in PhIPseq methodology to explore the modulation, or lack thereof, for autoreactive antibodies proteome-wide in both health and disease. We demonstrate that each individual, regardless of disease state, possesses a distinct set of autoreactivities constituting a unique immunological fingerprint, or "autoreactome”, that is remarkably stable over years. In addition to uncovering important new biology, the autoreactome can be used to better evaluate the relative effectiveness of various therapies in altering autoantibody repertoires. We find that therapies targeting B-Cell Maturation Antigen (BCMA) profoundly alter an individual’s autoreactome, while anti-CD19 and CD-20 therapies have minimal effects, strongly suggesting a rationale for BCMA or other plasma cell targeted therapies in autoantibody mediated diseases.

README: Individuals harbor a unique and longitudinally stable autoreactome maintained by plasma cells and altered by anti-BCMA CAR-T therapy

https://doi.org/10.5061/dryad.w3r2280z6

Attached are the complete PhIP-Seq results for the 6 cohorts analyzed in the associated manuscript. There are 7 total datasets:

-"HC_fc" is the complete gene-level fold-change over mock-IP signal for all 79 healthy controls.

-"HC_reps" is the complete gene-level raw sequencing reads for the 24 sets of duplicates among the 79 healthy controls.

-"Healthy_Longitudinal_fc" is the complete gene-level fold-change over mock-IP signal for the 35 longitudinal healthy samples from 7 individuals.

-"IVIG_fc" is the complete gene-level fold-change over mock-IP signal for the 4 sets of pre- and post-IVIG samples.

-"Ritux_fc" is the complete gene-level fold-change over mock-IP signal for the 32 samples from the 7 individuals longitudinally tacked following rituximab therapy.

-"CD19_fc" is the complete gene-level fold-change over mock-IP signal for the 14 sets of pre- and post- anti-CD19 CAR-T samples.

-"BCMA_fc" is the complete gene-level fold-change over mock-IP signal for the 9 sets of pre- and post- anti-BCMA CAR-T samples.

Also attached is "Autoreactome_complete_metadata", which links the "ID" in the PhIP-Seq data to the original source sample for the Longitudinal Healthy, Rituximab, IVIG, CD19 CAR-T and BCMA CAR-T cohorts. Other healthy control cohorts are individual samples and therefore do not need a linker file. "N/A" refers to "not applicable".

Description of the data and file structure

All results represent the average of technical replicates, except for the 79 individual healthy controls in whom only 24 of the individuals (48 samples) were performed in technical replicate (both of these data sets shown separately---see above). All human peptidome analysis was performed at the gene-level, in which all reads for all peptides mapping to the same gene were summed, and 0.5 reads were added to each gene to allow inclusion of genes with zero reads in mathematical analyses. Within each individual sample, reads were normalized by converting to the percentage of total reads. To normalize each sample against background non-specific binding, a fold-change (FC) over mock-IP was calculated by dividing the sample read percentage for each gene by the mean read-percentage of the same gene for the AG bead only controls.

Code/Software

Next generation sequencing reads from fastq files were aligned at the level of amino acids using RAPSearch2. The Pandas, Numpy, and Scipy packages were used in Python to generate the final PhIP-Seq files attached here.

Methods

All PhIP-Seq was performed similar to our previously published multichannel protocol, with minor adjustments as outlined in our new protocol: https://www.protocols.io/view/derisi-lab-phage-immunoprecipitation-sequencing-ph-czw7x7hn?step=14.1

Our human peptidome library consists of a custom-designed phage library of 731,724 unique T7 bacteriophage each presenting a different 49 amino-acid peptide on its surface. Collectively these peptides tile the entire human proteome including all known isoforms (as of 2016) with 25 amino-acid overlaps. 1 milliliter of phage library was incubated with 1 microliter of human serum overnight at 4C and immunoprecipitated with 25 microliters of 1:1 mixed protein A and protein G magnetic beads (Thermo Fisher, Waltham, MA, #10008D and #10009D). These beads were than washed, and the remaining phage-antibody complexes were eluted in 1 milliliter of E.Coli (BLT5403, EMD Millipore, Burlington, MA) at 0.5-0.7 OD and amplified by growing in 37C incubator. This new phage library was then re-incubated with the same individual’s serum and the previously described protocol repeated. DNA was then extracted from the final phage-library, barcoded, and PCR-amplified and Illumina adaptors added. Next-Generation Sequencing was then performed using an Illumina sequencer (Illumina, San Diego, CA) to a read depth of approximately 1 million per sample.

All results represent the average of technical replicates, except for the 79 individual healthy controls in whom only 24 of the individuals (48 samples) were performed in technical replicate. All human peptidome analysis was performed at the gene-level, in which all reads for all peptides mapping to the same gene were summed, and 0.5 reads were added to each gene to allow inclusion of genes with zero reads in mathematical analyses. Within each individual sample, reads were normalized by converting to the percentage of total reads. To normalize each sample against background non-specific binding, a fold-change (FC) over mock-IP was calculated by dividing the sample read percentage for each gene by the mean read-percentage of the same gene for the AG bead only controls. This FC signal was then used for side-by-side comparison between samples and cohorts, and the complete FC PhIP-Seq results are within this Dryad repository.

Funding

CZ Biohub

Eunice Kennedy Shriver National Institute of Child Health and Human Development, Award: K12-HD000850

National Institute of Allergy and Infectious Diseases, Award: R01-AI114780

National Institute on Aging, Award: R01AG032289

National Institute on Aging, Award: R01AG072475

National Cancer Institute, Award: U01CA247548