DOI: 10.7272/q6sj1hvh
title: Evidence of molecular mimicry in multisystem inflammatory syndrome in children (MIS-C)

description: Multisystem inflammatory syndrome in children (MIS-C) is a severe,
 post-infectious sequela of SARS-CoV-2 infection, yet the
 pathophysiological mechanism connecting the infection to the broad
 inflammatory syndrome remains unknown. Here we leveraged a large set of
 MIS-C patient samples (n=199) to identify a distinct set of host proteins
 that are differentially targeted by patient autoantibodies relative to
 matched controls. We identified an autoreactive epitope within SNX8, a
 protein expressed primarily in immune cells that regulates an antiviral
 pathway associated with MIS-C pathogenesis. In parallel, we also probed
 the SARS-CoV-2 proteome-wide MIS-C patient antibody response and found it
 to be differentially reactive to a distinct domain of the SARS-CoV-2
 nucleocapsid (N) protein relative to controls. This viral N region and the
 mapped SNX8 epitope bear remarkable biochemical similarity. 
 Furthermore, we find that many children with anti-SNX8 autoantibodies also
 have T cells cross-reactive to both SNX8 and this distinct region of the
 SARS-CoV-2 N protein. Together, these findings suggest that MIS-C patients
 develop a characteristic immune response against the SARS-CoV-2 N protein
 that is associated with cross-reactivity to the self-protein SNX8,
 demonstrating a mechanistic link from the infection to the inflammatory
 syndrome with implications for better understanding a range of
 post-infectious autoinflammatory diseases.
description: <strong>Patients</strong> Patients
 were recruited through the prospectively enrolling multicenter Overcoming
 COVID-19 and Taking on COVID-19 Together study in the United States. The
 study was approved by the central Boston Children’s Hospital Institutional
 Review Board (IRB) and reviewed by IRBs of participating sites with CDC
 IRB reliance. A total of 292 patients were enrolled into 1 of the
 following independent cohorts between June 1, 2020 and September 9, 2021:
 223 patients hospitalized with MIS-C (199 in the primary discovery cohort,
 24 in a separate subsequent validation cohort), 29 patients hospitalized
 for COVID-19 in either an intensive care or step-down unit (referred to as
 severe acute COVID-19 in this study), and 45 outpatients (referred to as
 “at-risk controls” in this study) post-SARS-CoV-2 infections associated
 with mild or no symptoms. The demographic and clinical data are summarized
 in Table I, Extended Data Table 1, and Extended Data Table 2. The 2020 US
 Centers for Disease Control and Prevention case definition was used to
 define MIS-C(<em>1</em>). All patients with MIS-C had positive
 SARS-CoV-2 serology results and/or positive SARS-CoV-2 test results by
 reverse transcriptase quantitative PCR. All patients with severe COVID-19
 or outpatient SARS-CoV-2 infections had a positive antigen test or nucleic
 acid amplification test for SARS-CoV-2. For outpatients, samples were
 collected from 36 to 190 days after the positive test (median, 70 days
 after the positive test; interquartile range, 56-81 days). For use as
 controls in the SARS-CoV-2 specific PhIP-Seq, plasma from 48 healthy,
 pre-COVID-19 controls was obtained as deidentified samples from the New
 York Blood Center. These samples were part of retention tubes collected at
 the time of blood donations from volunteer donors who provided informed
 consent for their samples to be used for research.
 <strong>Human proteome PhIP-Seq</strong>
 Human Proteome PhIP-Seq was performed following our previously
 published vacuum-based PhIP-Seq protocol (<em>2</em>)
 (https://www.protocols.io/view/scaled-high-throughput-vacuum-phip-protocol-ewov1459kvr2/v1). 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 then 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 was repeated. DNA was then extracted from the final phage library, barcoded, and PCR-amplified, and Illumina adaptors were 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. <strong>Human proteome PhIP-Seq analysis</strong> All human peptidome analysis (except when specifically stated otherwise) 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 the 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. FC values were also used to calculate z-scores for each MIS-C patient relative to controls and for each control sample by using all remaining controls. These z-scores were used for the logistic regression feature weighting. In instances of peptide-level analysis, raw reads were normalized by calculating the number of reads per 100,000 reads. <strong>SARS-CoV-2 proteome PhIP-Seq</strong> SARS-CoV-2 Proteome PhIP-Seq was performed as previously described(<em>3</em>). Briefly, 38 amino acid fragments tiling all open reading frames from SARS-CoV-2, SARS-CoV-1, and 7 other CoVs were expressed on T7 bacteriophage with 19 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). Beads were washed 5 times on a magnetic plate using a P1000 multichannel pipette. 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 a 37°C incubator. This new phage library was then re-incubated with the same individual’s serum and the previously described protocol was repeated for a total of 3 rounds of immunoprecipitations. DNA was then extracted from the final phage library, barcoded, and PCR-amplified, and Illumina adaptors were 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. <strong>Coronavirus proteome PhIP-Seq analysis</strong> To account for differing read depths between samples, the total number of reads for each peptide fragment was converted to the number of reads per 100k (RPK). To calculate normalized enrichment relative to pre-COVID controls (FC &gt; Pre-COVID), the RPK for each peptide fragment within each sample was divided by the mean RPK of each peptide fragment among all pre-COVID controls. These FC &gt; Pre-COVID values were used for all subsequent analyses as described in the text and figures. HAN Archive - 00432 (2021). https://emergency.cdc.gov/han/2020/han00432.asp. S. E. Vazquez, S. A. Mann, A. Bodansky, A. F. Kung, Z. Quandt, E. M. N. Ferré, N. Landegren, D. Eriksson, P. Bastard, S.-Y. Zhang, J. Liu, A. Mitchell, I. Proekt, D. Yu, C. Mandel-Brehm, C.-Y. Wang, B. Miao, G. Sowa, K. Zorn, A. Y. Chan, V. M. Tagi, C. Shimizu, A. Tremoulet, K. Lynch, M. R. Wilson, O. Kämpe, K. Dobbs, O. M. Delmonte, R. Bacchetta, L. D. Notarangelo, J. C. Burns, J.-L. Casanova, M. S. Lionakis, T. R. Torgerson, M. S. Anderson, J. L. DeRisi, Autoantibody discovery across monogenic, acquired, and COVID-19-associated autoimmunity with scalable PhIP-seq. <em>Elife</em> <strong>11</strong> (2022). C. R. Zamecnik, J. V. Rajan, K. A. Yamauchi, S. A. Mann, R. P. Loudermilk, G. M. Sowa, K. C. Zorn, B. D. Alvarenga, C. Gaebler, M. Caskey, M. Stone, P. J. Norris, W. Gu, C. Y. Chiu, D. Ng, J. R. Byrnes, X. X. Zhou, J. A. Wells, D. F. Robbiani, M. C. Nussenzweig, J. L. DeRisi, M. R. Wilson, ReScan, a Multiplex Diagnostic Pipeline, Pans Human Sera for SARS-CoV-2 Antigens. <em>Cell Rep Med</em> <strong>1</strong>, 100123 (2020).
description: # Evidence of molecular mimicry in multisystem inflammatory syndrome in
 children (MIS-C) ## Description of the data and file structure This Dryad
 submission contains PhIP-Seq results for two distinct T7 bacteriophage
 libraries, one for identifying autoantibodies targeting human proteins,
 and one for identifying antibodies targeting SARS-CoV-2 proteins. Each
 library was run on MIS-C patients (n=199) and at-risk pediatric controls
 (n=45). For human proteome results, all data is formatted as a fold-change
 over mock-IP. For all SARS-CoV-2 results, all data is formatted as a
 fold-change over pre-COVID. There are therefore 4 separate CSV files: *
 Human Proteome MIS-C patients * Human Proteome at-risk controls *
 SARS-CoV-2 Proteome MIS-C patients * SARS-CoV-2 Proteome at-risk controls
 Each individual is a column. For the Human Proteome library, each row
 represents the summed values from each peptide corresponding to a given
 gene. For the SARS-CoV-2 Proteome library, each row represents the signal
 to a unique peptide derived from a coronavirus. Therefore a FASTA file
 **(covid19lib_pepts_.fa)** with the annotations for the coronavirus phage
 display library is also provided. ## Code/Software Next generation
 sequencing reads from fastq files were aligned at the level of amino acids
 using RAPSearch2. The Pandas and Numpy packages were used in Python to
 generate the final PhIP-Seq files attached here.