key: cord-0843203-y1ob8tcq
authors: Gruber, Conor; Patel, Roosheel S.; Trachtman, Rebecca; Lepow, Lauren; Amanat, Fatima; Krammer, Florian; Wilson, Karen M.; Onel, Kenan; Geanon, Daniel; Tuballes, Kevin; Patel, Manishkumar; Mouskas, Konstantinos; O'Donnell, Timothy; Merritt, Elliot; Simons, Nicole; Barcessat, Vanessa; Del Valle, Diane M.; Udondem, Samantha; Kang, Gurpawan; Gangadharan, Sandeep; Ofori-Amanfo, George; Laserson, Uri; Rahman, Adeeb; Kim-Schulze, Seunghee; Charney, Alexander; Gnjatic, Sacha; Gelb, Bruce D.; Merad, Miriam; Bogunovic, Dusan
title: Mapping Systemic Inflammation and Antibody Responses in Multisystem Inflammatory Syndrome in Children (MIS-C)
date: 2020-09-14
journal: Cell
DOI: 10.1016/j.cell.2020.09.034
sha: c2667365b875a23aa9b2961282ddddc0ed0d0b90
doc_id: 843203
cord_uid: y1ob8tcq

Initially, children were thought to be spared from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, a month into the epidemic, a novel multisystem inflammatory syndrome in children (MIS-C) emerged. Herein, we report on the immune profiles of nine MIS-C cases. All MIS-C patients had evidence of prior SARS-CoV-2 exposure, mounting an antibody response with intact neutralization capability. Cytokine profiling identified elevated signatures of inflammation (IL-18 and IL-6), lymphocytic and myeloid chemotaxis and activation (CCL3, CCL4, and CDCP1) and mucosal immune dysregulation (IL-17A, CCL20, CCL28). Immunophenotyping of peripheral blood revealed reductions of non-classical monocytes, and subsets of NK- and T- lymphocytes, suggesting extravasation to affected tissues. Finally, we profiled the auto-antigen reactivity of MIS-C plasma, which revealed both known disease-associated autoantibodies (anti-La) and novel candidates that recognize endothelial, gastrointestinal and immune-cell antigens. All patients were treated with anti-IL6R antibody and/or IVIG, which led to rapid disease resolution.

Age: 3 ~ 19 SARS-CoV-2 RNA SARS-CoV-2 Serology GI issues Cardiac abnormalities Inflammatory markers 9/9 6/9 9/9 8/9 9/9 9/9 _ + The MIS-C anti-SARS-CoV-2 antibody repertoire resembles the convalescent response.

Cytokine profiling indicates myeloid cell chemotaxis and mucosal inflammation.

Mass cytometry uncovers mDC, non-classical monocyte, and lymphocyte activation and egress to the periphery.

Auto-antibody analysis identifies auto-antibodies targeting organ systems central to MIS-C pathology.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe has led to an outbreak of life-threatening respiratory disease, termed COVID-19 (Zhou et al., 2020; Zhu et al., 2020) . While adults have suffered the highest rates of morbidity and mortality of COVID-19, children were thought to be spared (Dong et al., 2020; Ludvigsson, 2020) . Recently, cases of hyperinflammatory shock in children have been reported in regions with receding SARS-CoV-2 epidemics (Cheung et al., 2020; Jones et al., 2020; Klocperk et al., 2020; Rauf et al.; Riphagen et al., 2020; Toubiana et al., 2020; Verdoni et al., 2020; Whittaker et al., 2020) .

Initially, the syndrome was considered an atypical form of Kawasaki disease (KD), an acute systemic vasculitis in young children, given the presence of fever, rash, conjunctivitis, mucocutaneous involvement and cardiac complications (Kawasaki, 1967; Kawasaki et al., 1974) . However, it has become evident that shock, gastrointestinal symptoms, and coagulopathy, which are rarely seen in classic KD, are prominent features of this unique syndrome (Cheung et al., 2020; Jones et al., 2020; Klocperk et al., 2020; Rauf et al.; Riphagen et al., 2020; Toubiana et al., 2020; Verdoni et al., 2020; Whittaker et al., 2020) . Furthermore, Black and older children appear disproportionately affected, in contrast to the association of young children of Asian descent with KD (Holman et al., 2010; Nakamura et al., 2010) .

Recognizing these patterns, the World Health Organization (WHO) and other reporting bodies have termed the novel disease multisystem inflammatory syndrome in children (MIS-C) or pediatric inflammatory multisystem syndrome (PIMS) (ECDC, 2020; WHO, 2020) .

The concentration of this disease to regions of high local SARS-CoV-2 transmission, but with an onset weeks after the peak COVID-19 caseload, suggests MIS-C is a secondary consequence of SARS-CoV-2 infection. Indeed, over 70% of MIS-C patients test positive for serum antibodies against SARS-CoV-2 and test negative for the presence of viral RNA (Cheung et al., 2020;  J o u r n a l P r e -p r o o f 4 Jones et al., 2020; Klocperk et al., 2020; Rauf et al.; Riphagen et al., 2020; Toubiana et al., 2020; Verdoni et al., 2020; Whittaker et al., 2020) . Aside from this association, the pathophysiology of MIS-C remains largely unexplored. Here, we investigate the immune responses of MIS-C cases, profiling the innate and adaptive underpinnings of the aberrant immune activation.

We report nine children from the New York City region who presented to our institution between late-April and June 2020 with hyperinflammatory disease fulfilling standard MIS-C criteria (Table   S1 ). The median age was 12 years, and the gender distribution was approximately equal (4/9 male, 5/9 female) ( Table 1) . Patients who reported ethnicity were of Hispanic (6/8) or Black (2/8) ancestry. Two patients had a history of asthma and another, psychiatric disorders; otherwise, the children were previously healthy. All patients initially presented with fever and abdominal symptoms (pain, emesis, or diarrhea). Rash, conjunctivitis, mucocutaneous disease, and hypotension were variably present. None, however, experienced inflammatory manifestations of the extremities, as in KD. On admission, all patients demonstrated signs of coagulopathy as evidenced by elevated fibrin degradation products (D-dimer), PT, PTT and/or thrombocytopenia.

Cardiac dysfunction manifested in all patients during hospitalization. Troponin and brain natriuretic protein (BNP) were elevated in all but one patient, with variable electrocardiogram (ECG) changes in three patients. Echocardiography revealed coronary artery dilation or aneurysm in five children. Half of the patients developed respiratory complications, consisting of either reactive airway disease, pleural effusion or pneumonia, although respiratory symptoms were mild (Figure S1A-C). All patients were treated within one day of admission with intravenous immunoglobulin (IVIG), or tocilizumab (TCZ), except MIS-C 3, for whom IVIG was J o u r n a l P r e -p r o o f 5 withheld (Table 1 , Figure 1D , Figure S2A ). On investigation of SARS-CoV-2 exposure, no patient reported a recent history of upper respiratory infection. When tested during admission, 3/9 MIS-C patients were positive by polymerase chain reaction (PCR) for nasopharyngeal SARS-CoV-2 RNA. There was no evidence of other infectious agents. In one of the patients (MIS-C 4), the mother had a confirmed SARS-CoV-2 infection three weeks prior to admission.

Among the patients negative by PCR, one child (MIS-C 6) had tested positive four weeks previously when he presented with appendicitis. This last case remains the most direct evidence that SARS-CoV-2 infection can precipitate MIS-C weeks later.

Given the suspected association to prior SARS-CoV-2 infection, we performed enzyme-linked immunosorbent assay (ELISA) for the presence of serum antibodies against the SARS-CoV-2 spike protein using an FDA-approved protocol (Amanat et al., 2020a) . All MIS-C patients were seropositive regardless of PCR status ( Figure 1A ). To better understand the profile of this anti-SARS-CoV-2 response, we explored the isotypes and subclasses of the immunoglobulins specific to SARS-CoV-2 S protein in plasma collected during active MIS-C. As a comparator, we included sera from children, young adults (non-ICU patients) and adults (non-ICU patients) with acute SARS-CoV-2 infection requiring hospitalization, as well as sera from convalescent adults after mild confirmed infection. Consistent with prior SARS-CoV-2 exposure, MIS-C plasma showed elevated IgG with low levels of IgM antibody, as observed in the convalescent response. Among the IgG responses, IgG1 predominated, again resembling convalescent sera.

Uniquely, however, MIS-C patients demonstrated significantly lower levels of IgM relative to convalescent plasma. Additionally, IgA titers in MIS-C exceeded the convalescent response, approximating IgA levels of acute infection ( Figure 1A -B). When sampled weeks after discharge when symptoms resolved, two MIS-C patients (MIS-C 4 and MIS-C 7) demonstrated persistent J o u r n a l P r e -p r o o f 6 levels of IgG and IgA, with increased IgM titers against SARS-CoV-2 spike protein.

To determine whether the MIS-C serological response in the absence of clinically apparent respiratory infection was, in fact, effectively antiviral, we assayed neutralization of live SARS-CoV-2 infection by patient plasma in vitro ( Figure 1C ). All MIS-C patient plasma was capable of neutralization, with potency similar to convalescent responses in adults. In both ELISA and neutralization assays, PCR + MIS-C patients and PCR -MIS-C patients were indistinguishable ( Figure 1C -D), suggesting that the positive PCR results reflect a receding infection. Indeed, recent studies document that while PCR assays can remain positive beyond three weeks after symptom onset, infectious virus cannot be detected (La Scola et al., 2020; Wölfel et al., 2020; Zheng et al., 2020) . To estimate the average time between initial infection and MIS-C onset, we determined the temporal delay between peak COVID-19 and MIS-C admissions at our institution. Our proxy measurement revealed an approximate five-week difference ( Figure 1E ), which is consistent with the documented SARS-CoV-2 exposure of MIS-C 4 and the infection of MIS-C 6 three and four weeks prior to presentation with MIS-C, respectively.

Within a day of admission, all patients received anti-IL6R therapy and all but one received IVIG treatment (MIS-C-3). We sampled their peripheral blood either before any therapy with IVIG (MIS-C 3 and MIS-C 9) or shortly thereafter (MIS-C 1, MIS-C 3-8) ( Figure S1D ), when clinical markers of inflammation, coagulopathy and cardiac dysfunction still remained elevated ( Figure   S2A -B). Additionally, samples were collected from two patients (MIS-C 4 and MIS-C 7) weeks after recovery and discharge from the hospital. We performed high-dimensional cytokine profiling of 92 analytes using the Olink platform to define the secreted immune response in MIS-C patient plasma, and compared it to sera from age-matched healthy controls, a pediatric patient treated with IVIG with an unrelated infection (urinary tract infection (UTI)), and cases of J o u r n a l P r e -p r o o f 7 active pediatric and young adult COVID-19 infection that did not develop MIS-C. As otherwise healthy children rarely experience clinically-apparent COVID-19 infections, used samples from children with immunocompromising comorbidities (Pediatric COVID 1, 3-6, details in Table S2) , and one otherwise-healthy child (Pediatric COVID 2) who did experience clinically-apparent COVID-19 infection. Overall, the patients with MIS-C presented with striking elevations in multiple cytokine families ( Figure 2A ). This signature was consistent across patients, as all MIS-C samples grouped together by unsupervised hierarchical clustering ( Figure 2A ). IL-6 demonstrated the largest fold-change increase, although the exogenous IL-6R blockade from tocilizumab is known to contribute, at least in part, to this effect in those receiving it ( Figure 2B ) (Nishimoto et al., 2008) . Interestingly, the MIS-C circulating immune profile was marked by cytokines and chemokines that recruit NK-cells and T-cells from the circulation and modulate their function such as CCL19, CXCL10 and CDCP1 ( Figure 2C ) (Vilgelm and Richmond, 2019) .

Likewise, mediators of neutrophil and monocyte chemotaxis (CCL3 and CCL4), as well as differentiation and activity (EN-RAGE and CSF-1) were elevated in MIS-C ( Figure 2D ) (Foell et al., 2003; Maurer and Von Stebut, 2004; Stanley and Chitu, 2014; Vilgelm and Richmond, 2019) . In turn, a profile of immune exhaustion and suppression was also evident, with stark upregulation of soluble PD-L1, likely reflecting a host-driven compensatory response to the inflammation ( Figure 2E ). In concordance with the gastrointestinal disease of MIS-C, cytokines potentiating mucosal immunity were particularly prominent, both in regards to T-helper cell function (IL-17A) and mucosal chemotaxis (CCL20 and CCL28) ( Figure 2F ) (Mohan et al., 2020; Williams, 2006) .

While some of these MIS-C cytokine signatures resembled that of acute or convalescent SARS-CoV-2 infection, a unique MIS-C cytokine profile could be distinguished from that of COVID-19.

From a global analysis, MIS-C reliably clustered together by hierarchical clustering and principal component analysis (PCA) ( Figure 2G ). To more finely characterize the cytokine profile J o u r n a l P r e -p r o o f 8 differences between MIS-C patients and pediatric COVID patients, we conducted PCA analysis restricted to pediatric patients ( Figure S3A ). PC1 loading analysis identified components that separated the healthy pediatric controls from all disease patients, while PC2 resolved the profiles of pediatric COVID-19 and MIS-C ( Figure 2H ). Elevations in unique chemokines (CXCL5, CXCL11, CXCL1, CXCL6) and cytokines (including IL-17A, CD40, and IL-6) appear to distinguish MIS-C patients from pediatric COVID patients ( Figure S3B ). Importantly, nearly all of the MIS-C cytokine elevations resolved to healthy levels when sampled after hospital discharge ( Figure 2B -F; right most column).

Next, we performed CyTOF-based immunophenotyping on nine MIS-C, five age-matched healthy controls, and seven young adults with acute COVID-19 infection. Overall, while both controls and MIS-C patients had similar subset distributions in peripheral blood ( Figure 3A and Figure S4A -B), the frequencies of select immune cell types were significantly altered. The percentages of both γδ T lymphocytes, unlike αβ T lymphocytes were decreased relative to healthy donors ( Figure 3B ). Interestingly, the relative distribution of naïve, central memory, effector memory or TEMRA subsets was normal within MIS-C T-cells ( Figure 3C and D).

Likewise, B-cells were present with a largely normal frequency range and consisted of a typical distribution of naïve, memory and plasma cells in MIS-C ( Figure S4A ). These findings suggest that no active peripheral B-or T-expansion was underway at the time of sampling, and distinguish MIS-C from acute COVID-19 for which an active bias toward effector populations was readily observed ( Figure 3C and D). Among innate cells, CD56 lo NK cells were also decreased in MIS-C, but not in acute COVID-19 peripheral blood ( Figure 3E ), while nonclassical monocytes and pDCs frequencies were lower in both groups ( Figure 3F ). Weeks after J o u r n a l P r e -p r o o f 9 discharge, one MIS-C patient (MIS-C 4) was sampled again, demonstrating resolution of these cell type frequency changes ( Figure S4A -B).

Next, we carried out high-dimensional CyTOF-based phenotyping for markers of immune function, comparing MIS-C to healthy donors. Among these markers, there was robust upregulation of CD54 (ICAM1) expression on neutrophils and CD16+ monocytes in approximately half of MIS-C individuals, indicative of APC activation and trans-endothelial migration ( Figure 3G ) (Pietschmann et al., 1998; Sheikh and Jones, 2008) . Similarly, these same MIS-C patient neutrophils and CD16+ non-classical monocytes demonstrated elevated CD64 (FcR1) expression ( Figure 3H ), a common finding in autoimmune and autoinflammatory diseases (Li et al., 2009 (Li et al., , 2010 Tanaka et al., 2009a) , including KD (Hokibara et al., 2016) .

However, these cells lacked signs of active type I IFN signaling, including CD169 and STAT1 phosphorylation upregulation, suggesting other cytokines are driving this activation ( Figure S4C -D). Instead, augmented levels of phospho-STAT3 were noted in some patients, which may originate downstream of IL-6, given its robust elevation in MIS-C plasma ( Figure 3I ). Combined with tissue-homing cytokines described by Olink analysis, these data suggest extravasation of T-and NK-lymphocytes as well as activation and chemotaxis of neutrophils and nonclassical monocytes likely contribute to the underlying disease pathogenesis. Alternatively, perturbations in hematopoiesis cannot be ruled out entirely. Indeed, future studies will be needed to describe this mechanism fully.

The resolution of disease with IVIG and the delayed onset after SARS-CoV-2 infection suggest a pathological process involving adaptive immunity. Therefore, we tested the hypothesis that SARS-CoV-2 infection leads to a secondary auto-reactive humoral response. To thoroughly investigate a potential auto-reactive antibody repertoire, we assessed MIS-C (n=9) and age-J o u r n a l P r e -p r o o f matched healthy (n=4) plasma IgG and IgA reactivity against a microarray printed with over 21,000 conformationally-intact human peptides (HuProt Array). For consideration, a candidate auto-antigen had to demonstrate increased reactivity (>4-fold over healthy control) in at least 5/9 MIS-C patients. To exclude auto-reactive antibodies associated with IVIG treatment (Grüter et al., 2020; Van Der Molen et al., 2015) , we only considered antigens upregulated in at least one of the patients who did not receive IVIG at the time of sampling (MIS-C 3 and MIS-C 9)( Figure S5A We then performed an orthogonal assay, PhIP-seq, which allows for screening of the complete human proteome by phage-display of linear peptide libraries. While more expansive, this technique screens for epitopes that lack conformation and eukaryotic post-translational J o u r n a l P r e -p r o o f 11 modification. Nonetheless, PhIP-seq analysis validated 12-17% of the microarray candidates, and in doing so, identified those which are likely linear epitopes ( Figure 4E ). To confirm that conformational differences may explain the remaining discrepancies, we selected one candidate antigen (CD244) that was present in the microarray analysis and absent in the PhIP-seq, and validated the enhanced autoreactivity in MIS-C plasma by standard ELISA ( Figure 4F ).

Finally, to predict a potential function of these auto-antibodies, we queried the enrichment of identified IgG antigen set using gene-set enrichment analysis. Regulation of immune response, cell to cell adhesion and sense of smell were the most significant processes ( Figure 4G and H). Whether auto-antibody engagement with proteins in these pathways modulates such processes like activity of immune cells or immune complex formation needs to be determined. Plausibly, antibody-mediated inhibition of CD244, an immunoregulatory receptor on NK and T-Cells, could allow for breach of immune checkpoints. Future studies that specifically interrogate the function and origins of these autoantibodies will be required to understand their potential role in MIS-C pathogenesis.

Beginning on the day of admission, we monitored markers of inflammation (C-reactive protein, erythrocyte sedimentation rate, IL-6, IL-8, IL1-beta, TNF-alpha, ferritin), coagulopathy (D-dimer, prothrombin time, partial thromboplastin time, platelet count, fibrinogen) and cardiac injury (troponin and BNP). Most patients were treated within the first day of admission. All received anti-IL-6R antibody, and all but one received IVIG. Uniformly, these markers normalized rapidly (Supplemental Figure 2 and 3) with a median hospital stay of six days and favorable outcomes.

We continued to monitor these disease parameters on follow-up, noting that they continue to normalize without evident secondary consequences. In 1967, Dr. Tomisaku Kawasaki described 50 pediatric patients with a previously unrecognized febrile illness that clustered both in time and geography (Kawasaki, 1967) . Since this initial description, numerous studies have detailed the clinical features and biological manifestations of KD (Dietz et al., 2017) . However, the underlying pathophysiology remains incompletely understood. Most mechanistic explanations arise from the association with viral infections.

Namely, there is a significant increase in the incidence of PCR-positive tests for enteroviral, adenoviral, human rhinoviral and coronaviral infections in children presenting with KD relative to age-matched, healthy controls (Chang et al., 2014; Jordan-Villegas et al., 2010; Turnier et al., 2015) . Likewise, according to serologic and epidemiologic evidence, we observed that all MIS-C patients were previously exposed to SARS-CoV-2, putatively four to five weeks prior to presentation. While MIS-C has been classified as a distinct syndrome by its clinical presentation, the overlapping features are striking, suggesting that MIS-C may lie along a spectrum of KD-like pathology. These differences may arise from the introduction of a novel virus to a population with completely naïve immunity, as in SARS-CoV-2. This distinction may underlie the later age at presentation for MIS-C relative to KD, as other viruses associated with KD are common infections of early childhood. Should a different experiment of nature have occurred whereby other KD-associated viruses suddenly appeared in a naïve population, it is plausible that distinct clinical and laboratory features would also have manifested, linked to those viruses.

The extent to which genetics impacts the development of MIS-C is currently unclear. It appears that Black or Hispanic ethnicity may be a risk factor, as observed in this study and others (Cheung et al., 2020; Jones et al., 2020; Klocperk et al., 2020; Rauf et al.; Riphagen et al., J o u r n a l P r e -p r o o f 14 2020; Toubiana et al., 2020; Verdoni et al., 2020; Whittaker et al., 2020) . This enrichment diverges from KD, in which the incidence is significantly higher in children of Asian ancestry.

Several genetic variants of moderate effect size, such as ITPKC, CD40, FCGR2A and BLK have been associated with KD (Onouchi, 2018) . Interestingly, the risk among Asian children living in the United States is reduced (Uehara and Belay, 2012) , suggesting a role for environmental factors. Similarly, it is quite possible that Black and Hispanic populations are more likely to develop MIS-C due to socio-economic factors (including multi-generational households), pre-existing co-morbidities and increased occupational exposure to SARS-CoV-2 (DiMaggio et al., 2020; Vahidy et al., 2020) . This factor is especially relevant at our hospital and other metropolitan centers, which serve patients from diverse backgrounds. Only detailed genetic analyses in large cohorts will determine the relative contribution of genetic factors, which in KD also remains mostly unexplained.

Recurrence of KD is rare, and, hopefully, this will be the case for MIS-C as well. The presence of autoantibodies, as documented here, is concerning however. We postulate that these autoantibodies trigger immune complex formation and/or unleash an immune cell-driven attack against host tissues. These may arise by direct cross-reactivity between SARS-CoV-2 and selfantigens, which, if true, will pose a risk for future vaccination strategies. Although the inflammation appears transient, these autoantibodies also raise concern for recurrence or predisposition to other disorders with autoimmune features. All of these postulates need careful, methodical and well-controlled experimental dissection. Until then, MIS-C remains scientifically puzzling, but therapeutically manageable.

Due to the nature of studying this rare life-threatening syndrome in children in the midst of a world-wide pandemic, we note that our study was limited in some aspects. One, our sample size J o u r n a l P r e -p r o o f 15 was restricted to 9 children with MIS-C, for whom we were able to gain informed consent and timely process samples for. Additionally, due to the very low prevalence of healthy children presenting with active COVID-19, we chose to use samples from pediatric patients with active COVID-19 being treated for hemato-oncological malignancies, and young adult patients presenting with active COVID-19, as controls, in addition to age matched healthy controls. We note for the olink, while the main comparison group is age matched heathy controls, 5 of the 6 samples in the pediatric COVID comparator group were patients being treated for hematooncological malignancies, thus differences we see in cytokine profiles between MIS-C and pediatric COVID must be further investigated. For auto-antibody analyses, further experimental work will be required to fully assess the functional role and pathogenic potential of the identified auto-antibodies. Figure S3 and Table S2 . See also Figure S5 . Figure S1 , Figure S2 , and Table S1 . Table 1 . 

Other microbial cause Table 1 and Table S2) 

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Dusan Bogunovic (dusan.bogunovic@mssm.edu)

This study did not generate new unique reagents

Links of the raw data used to perform Olink cytokine profiling and HuProt protein microarray 

Written informed consent for all individuals in this study was provided in compliance with an institutional review board protocol. Acutely-ill and convalescent patients were recruited at the Mount Sinai Health System between April 1 st , 2020 through July 4 th , 2020. Clinical criteria detailed in Table S1 were used to recruit and classify samples as MIS-C or pediatric COVID.

Demographic and clinical data of recruited MIS-C patients and pediatric COVID patients are detailed in Table 1 and Table S2 . Samples related to young adults were obtained from the Mount Sinai Health System's COVID-19 Biobank. Criteria for young adults were as follows:

Under the age of 35, found to be SARS-COV-2 positive by PCR test and classified with severe/moderate COVID-19. Young adults were sex and ethnicity matched as close as possible to the MIS-C cohort (3 males, 3 females). Healthy volunteers were age-matched to the extent possible, including a 3 year-old female, 6 year-old female, 7 year old male, 12 year-old male,

and 19 year-old female. From each patient, blood was drawn into a Cell Preparation Tube with sodium heparin (BD Vacutainer) and serum separating tubes (SST) processed immediately.

Whole blood was fixed using Proteomic Stabilizer PROT1 (SmartTube) and frozen at -80 o C.

Peripheral blood mononuclear cells and plasma/sera was isolated by centrifugation and subsequently stored at -80 o C until use.

The development and protocol for the SARS-CoV-2 spike antigen is described elsewhere in detail (Amanat et al., 2020b) . Briefly, sera from each time point were tested in each patient using serial 4 × dilutions from 1/100 to 1/6,400 for reactivity to full-length SARS-CoV-2 recombinant protein (0.5 µg/mL). Titers were extrapolated based on a cutoff established from a pool of J o u r n a l P r e -p r o o f 27 healthy donor sera, and assays were validated using positive control sera for each antigen present on each plate. Results were considered significant if titers were ≥ 100. To assess the distribution of different immunoglobulin isotypes, assays were performed separately with antihuman IgA-AP antibody, anti-human IgM-AP antibody, anti-human IgG1 Fc-AP, anti-human IgG2 Fc-AP, anti-human IgG3 hinge-AP and anti-human IgG4 Fc-AP. Endpoint titers were calculated by the last dilution before reactivity dropped below an optical density threshold defined by the OD of a healthy donor pool.

Heat-inactivated plasma samples were serially diluted in complete media (10% 10× minimal essential medium (Gibco), 2 mM L-glutamine, 0.1% sodium bicarbonate ( along with the corresponding ninety-two oligonucleotide pairs. Next, the chip was processed through the Fluidigm BioMark qPCR reader using standard protocol provided by the supplier.

Samples were run in singlets in parallel with both blanks and inter-plate/batch controls. Details regarding assay limitations, validations, and protocols may be obtained from the Olink supplier (http://www.olink.com). Sample data quality control and normalization was done using the Olink's Normalized Protein eXpression Manager software.

Frozen stabilized blood samples were thawed according to the manufacturer's recommended 

Seromic profiling of autoantibodies was conducted as previously described (Gnjatic et al., 2010) . These assays used the CDI HuProt array. Nine MIS-C plasma samples and 4 additional age-matched pediatric controls samples were run in total (Batch 1: MIS-C 1-6, HC4, Batch 2:

MIS-C 7-9, HC 1-3). The arrays were processed according to manufacturer's instructions, at 1/500 to avoid low-titered cross-reactivity and using a robust blocking buffer to prevent unspecific binding. Each dot on the array, representing a protein printed in duplicate, was gated using Genepix software alignment and then manually QC-ed to ensure proper quantification and 

Phage immunoprecipitation sequencing was performed using a modified version of previously described PhIP-Seq methodologies Briefly, we used a phage display library consisting of 259,345 overlapping 90-aa linear peptides, corresponding to the human proteome (Xu et al., 2016) . The IgG concentration of each plasma sample was quantified using an in-house IgG ELISA consisting of capture (IgG) and detection antibodies (IgG F(ab') 2 ). Immunoprecipitation reactions were carried out in duplicate as 1 ml mixtures consisting of plasma (2 µg of IgG) and 2.6 x 10 10 plaque-forming units of the phage display library, diluted in PBS (1X). After rotating immunoprecipitation reactions overnight at 4°C, 20 µl of each protein A and protein G magnetic Dynabeads® (Invitrogen) were added to each reaction, followed by 4 hours rotating at 4°C. The beads were washed three times using a 96-well magnetic stand and resuspended in 20 µl PCR master mix containing Q5 polymerase (New England BioLabs). After 15 cycles of PCR, 2 µl of the PCR product was added to a second 20 cycle PCR for the addition of sample barcodes and Illumina P5/P7 adapters. Sequencing was performed using an Illumina NextSeq 500 system (high output, 75 bp single read) using custom sequencing primers, listed in the Key Resources Table. J o u r n a l P r e -p r o o f . Antibody end-point titres grouped as indicated in Figure   1 and plotted using the ggplot2 (v3.3.2) software in the R statistical environment (v4.0.1).

Samples for multiplex cytokine analysis were performed on samples from MIS-C patients (N=9), pediatric COVID-19 patients (N=6), active young adult COVID patients (N=4), convalescent young adult COVID (N=2), age matched healthy pediatric controls (N=4) and convalescent (recovered) MIS-C patients (N=2). Samples run in separate batches were normalized to control samples present on all plates using the Olink NPXManager software suite. Analytes with normalized protein expression values below the limit-of-detection in >75% of samples were excluded from further analysis. For the remainder of analytes, any sample over the limit of detection was assigned a value of the limit-of-detection divided by the square root of 2. The log2

fold-change over the mean healthy pediatric control (N=4) protein expression was then calculated and used for unsupervised clustering, heatmaps, box plots and principle component analysis. Statistical significance for normalized expression values between healthy controls and MIS-C patients were determined by the Benjamini-Hochberg procedure to correct for multiple testing.

Samples for mass cytometry analyses were performed on samples from age-matched healthy controls (n=5), acute COVID infection in young adults (n=7) and MIS-C patients (n=9). FCS files of acquired events were normalized and concatenated with Fluidigm acquisition software, and deconvoluted with a Matlab-based debarcoding application (Zunder et al., 2015) and resulting files were analyzed using Cytobank (Kotecha et al., 2010) . Cell events were identified as intensities between samples, a z-score standardization was applied.

Due to disparity of computational packages dedicated protein microarray analysis, we treated signal intensity data in an approach akin to RNA-chip microarray analysis. Raw signal intensity matrices were read into R statistical environment (v4.0.1) and analyzed using the limma (v3.45.9) R packages. Lowly detected probes (less than median signal intensity in more than 75% of samples) and probes exhibiting low variances (bottom 5%) were filtered out using the genefilter package (v1.71.1). To enforce equal distribution of overall array reactivity across samples, resulting matrices were normalized by cyclic loess method (method: pairs) to account for unbalanced differential signal detection. Next, a log 2 intensity values were used as "expression" data to fit a linear model to explain sample-antigen relationships. Protein microarray linear models included factors for patient and batch (identified in initial exploratory analysis). To allow for inter-patient variability in their auto-antibody response, pair-wise contrasts between single MIS-C patients versus age-matched pediatric healthy controls (N=4)

were conducted and differentially enriched antigen lists were generated on a per patient basis.

Lists were further filtered to only include antigens that exhibited a 4-fold enrichment compared to the healthy pediatric controls. Additionally to account for autoantibody presence due to the administration of IVIG, lists were further filtered to only include antigens found enriched in at least one of two IVIG treatment naïve patients (MIS-C 3, MIS-9). GSEA analyses, ranked lists were obtained for the comparison to treatment naïve MIS-C samples (N=2; MIS-C 3, MIS-C 9)

versus the healthy pediatric controls (N=4) for both IgG and IgA auto-antigens. GSEA was run on the ranked list of unique targets using the gseGO() function of the clusterProfiler R package.

Overlap of enriched samples, heatmaps and boxplots were visualized by the UpSetR (v1.4.0), ComplexHeatmap (v2.5.3) and ggplot2 (v3.3.2) packages.

PhIP-seq data analysis was performed using the phip-stat package (https://github.com/lasersonlab/phip-stat). Reads were aligned using bowtie2 to the library sequences (human90 PhIP-seq library) to generate a matrix of read counts for each peptide in each sample. Read counts matrices and associated sample meta data were read into the R statistical environment (v4.0.1) for further processing. To identify non-specific hits, counts matrices were normalized to the beads-only (IgA/IgG) controls and passing peptides were required to be enriched over the beads-only in 4 MIS-C samples. Filtered matrices were further processed using the DESeq2 (v1.29.8) package with a design model accounting for sequencing batch and patient. Similar to protein microarray analyses described above, pair-wise contrasts between single MIS-C patients versus beads only controls (N=4) were conducted and differentially enriched antigen lists were generated on a per patient basis. Lists were further filtered to only include antigens that exhibited a 1.5-fold enrichment compared to the healthy pediatric controls and were enriched in 3 or more patients. Final lists were collapsed at gene level and overlap was assessed with protein microarray analysis described above.

• The MIS-C anti-SARS-CoV-2 antibody repertoire resembles a convalescent response.

• Cytokine profiling indicates myeloid cell chemotaxis and mucosal inflammation.

• Mass cytometry uncovers immune cell activation and egress to the periphery.

• MIS-C auto-antibodies target organ systems central to MIS-C pathology.

ETOC blurb Insights into the cellular and serological immune dysfunction underlying MIS-C, a novel pediatric inflammatory syndrome associated with SARS-CoV-2 infection, reveal potential auto-antibodies that may link organ systems relevant to pathology. 

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