The autoimmune signature of hyperinflammatory multisystem inflammatory syndrome in children

Rebecca A. Porritt1,*, Aleksandra Binek2,*, Lisa Paschold3,*, Magali Noval Rivas1, Angela Mc Ardle2, Lael M. Yonker4,5, Galit Alter4,5,6, Harsha Chandnani7, Merrick Lopez7, Alessio Fasano4,5, Jennifer E. Van Eyk2,8,†, Mascha Binder3,† and Moshe Arditi1,9,†

1Departments of Pediatrics, Division of Infectious Diseases and Immunology, and Infectious and Immunologic Diseases Research Center (IIDRC), Department of Biomedical Sciences, Cedars- Sinai Medical Center, Los Angeles, CA, USA. 2Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 3Department of Internal Medicine IV, Oncology/Hematology, Martin-Luther-University Halle- Wittenberg, 06120 Halle (Saale), Germany 4Massachusetts General Hospital, Mucosal Immunology and Biology Research Center and Department of Pediatrics, Boston, MA, USA. 5Harvard Medical School, Boston, MA, USA. 6Ragon Institute of MIT, MGH and Harvard, Cambridge, MA, USA. 7Department of Pediatrics, Loma Linda University Hospital, CA, USA. 8Barbra Streisand Women's Heart Center, Cedars-Sinai Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 9Cedars-Sinai Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

* these authors contributed equally † these senior authors contributed equally

Corresponding author: Moshe Arditi, MD 8700 Beverly Blvd. Davis Building, Rooms D4024, 4025, 4027 Los Angeles, CA 90048 Phone:310-423-4471 Email: [email protected]

Conflict of Interest The authors have declared that no conflict of interest exists. Abstract

Multisystem Inflammatory Syndrome in Children (MIS-C) manifests as a severe and uncontrolled inflammatory response with multiorgan involvement, occurring weeks after SARS-

CoV-2 infection. Here we utilized proteomics, RNA sequencing, autoantibody arrays and B-cell receptor (BCR) repertoire analysis to characterize MIS-C immunopathogenesis and identify factors contributing to severe manifestations and intensive care unit admission. Inflammation markers, humoral immune responses, neutrophil activation, complement and coagulation pathways were highly enriched in MIS-C patient serum, with a more hyperinflammatory profile in severe than in mild MIS-C cases. We identified a strong autoimmune signature in MIS-C, with autoantibodies targeted to both ubiquitously expressed and tissue-specific antigens, suggesting autoantigen release and excessive antigenic drive may result from systemic tissue damage. We further identified a cluster of patients with enhanced neutrophil responses as well as high anti- spike IgG and autoantibody titers. BCR sequencing of these patients identified a strong imprint of antigenic drive with substantial BCR sequence connectivity and usage of autoimmunity- associated immunoglobulin heavy chain variable region (IGHV) genes. This cluster was linked to a TRBV11-2 expanded T cell receptor (TCR) repertoire, consistent with previous studies indicating a superantigen-driven pathogenic process. Overall, we identify a combination of pathogenic pathways that culminate in MIS-C and may inform treatment.

Introduction

While cases of severe Coronavirus Disease 2019 (COVID-19) occur relatively less frequently in children than adults (1, 2), a small proportion of SARS-CoV-2 infected children develop a novel pediatric febrile entity called multisystem inflammatory syndrome in children

(MIS-C) within 2 to 5 weeks of initial SARS-CoV-2 exposure (3-9). MIS-C disproportionately affects male children and children of Hispanic or Black descent. It manifests as a severe and uncontrolled inflammatory response with multiorgan involvement (3-10). A hyperinflammatory state is evidenced by clinical makers including high serum concentrations of C-reactive protein

(CRP), ferritin, and D-dimers, and increased levels of pro-inflammatory cytokines (3, 6, 9, 11, 12).

Children often present with persistent fever, severe gastrointestinal symptoms, cardiovascular manifestations, respiratory and neurological symptoms (3, 4, 6-10). Cardiovascular manifestations include hypotension, shock, cardiac dysfunction, myocarditis and pericardial effusion (5). In the USA, admission to the intensive care unit occurs in approximately 58% of cases (5).

The immunological features of MIS-C involve perturbations in both innate and adaptive immune responses. MIS-C patients have a highly activated myeloid cell compartment (13-15) and elevated levels of cytokines and chemokines that mediate neutrophil and monocyte chemotaxis, differentiation and activation (14). MIS-C is associated with severe T cell lymphopenia, high levels of T cell activation and expansion of vascular patrolling CX3CR1+ CD8+ T cells (13-16). We recently identified HLA class I alleles associated with expansion of TRBV11-2 T cells in severe

MIS-C (17). Interestingly, in these cases TRBV11-2 expansion did not follow the typical CDR3- dependent clonal selection observed with conventional antigens (17). Instead, CDR3 regions of expanded TRBV11-2 T cells were diverse, similar to observations following superantigen-driven

T cell expansion (17). This expansion correlated with disease severity and cytokine storm, suggesting TRBV11-2 T cell expansion may be an important mechanism driving severe MIS-C (17). B cell responses may also contribute to MIS-C pathogenesis, as plasmablast frequencies have been reported to be elevated during MIS-C (13, 18). In addition, most children with MIS-C have IgG and IgA anti-SARS-CoV-2-specific antibodies (14, 15, 19), including highly inflammatory anti-SARS-CoV-2 IgG-specific antibodies involved in monocyte activation and Fc binding (19). A number of autoantibodies against immunomodulatory mediators and antigens from endothelial, cardiac and gastrointestinal tissues have been identified and may play a role in driving MIS-C pathogenesis (14, 16, 18).

Here, we sought to further our understanding of MIS-C immunopathogenesis through proteomics, RNA-sequencing, BCR-sequencing and autoantibody analysis of a unique cohort of

MIS-C patients with severe and mild clinical courses. We identify a highly inflammatory MIS-C subset of patients that show both enhanced neutrophil activation and imprints of harmful adaptive immune responses. In addition to superantigenic T cell interactions, these patients show strong selection of autoreactive B cells which may be the cellular counterpart of the high autoantibody levels found in these patients. RESULTS

Severe MIS-C patients exhibit hyperinflammation and cytokine storm.

Our MIS-C cohort was composed of 7 patients with mild MIS-C and 20 patients with severe

MIS-C (Table 1). MIS-C diagnosis was performed according to CDC guidelines, and patients who required treatment in the pediatric intensive care unit (ICU) were defined as severe MIS-C patients. We first compared circulating biomarkers of inflammation and heart failure, and cytokine profiles of severe and mild MIS-C patients. Both mild and severe MIS-C patients showed elevated levels of C-reactive protein (CRP), ferritin, fibrinogen, pro-BNP, AST, ALT, D-dimers and creatine compared to normal reference ranges (Figure S1A). Severe MIS-C cases showed significantly higher levels of ferritin, pro-BNP, D-dimers and creatine, and a trend for increased levels of CRP,

ALT and fibrinogen compared with mild MIS-C patients (Figure S1A).

We next assessed and compared the circulating cytokine profiles of healthy controls, mild

MIS-C and severe MIS-C patients. MIS-C patients showed increased levels of IFN-γ, TNF-α, IL-

6, IL-8, IL-10 and IL-1β with severely ill patients showing stronger dysregulation than those with milder courses (Figure S1B). Since some of the clinical symptoms of MIS-C, such as erythematous rashes, conjunctivitis, and inflammatory changes in the oral mucosa are suggestive of Kawasaki disease (KD), we also characterized the cytokine profiles of 7 KD patients that were recruited before the COVID-19 pandemic (Figure S1B). KD diagnosis was performed according to the established American Heart Association guidelines (20). Except for IL-8, which appeared less dysregulated in KD, the overall cytokine pattern was similar in MIS-C and KD (Figure S1B).

Proteomic analysis identifies a highly inflammatory proteomics profile in MIS-C

To assist in the elucidation of the pathogenesis of MIS-C and to identify proteins associated with the severe form of disease, we performed proteomics analysis of serum or plasma samples from our study cohort (Figure 1A). We collected serum from healthy children (n= 20), mild MIS-C patients (non-ICU, n=5), and severe MIS-C patients who required ICU treatment (n =

20). We also included analysis of plasma samples from KD patients which were collected prior to the pandemic (n=7) (Table 1). Healthy adult serum (n = 4) was used for reference range quality control. To obtain a high resolution of protein expression we performed discovery proteomics analysis on native and depleted (top 14 most abundant proteins) serum or plasma samples (21)

(Figure 1B and S2A). Both datasets were integrated at the transition, peptide and protein level.

As plasma was used for KD samples, clotting factors (Figure S2B) were removed from the dataset for any downstream analysis involving the KD group. Principal component analysis

(Figure 1B) and hierarchical clustering (Figure 1C) showed that the MIS-C and KD proteomes clustered separately from healthy controls. Similar to our cytokine analysis, MIS-C and KD showed similar protein profiles, indicating shared pathological pathways likely exist between the diseases (Figure 1B-C). The major proteins contributing to dimension 1 of the PCA plot, which separates disease samples (MIS-C and KD) from the healthy controls, included inflammatory markers and alarmins such as SERPINA3, CRP, haptoglobin (HP)/Zonulin, LBP, CD14, S100A8 and S100A9 (Figures 1B and S2C).

Protein Interaction Network Extractor (PINE) (22) analysis of differentially expressed proteins between the groups revealed an enrichment of protein networks involved in multiple inflammatory processes and pathways, including neutrophil degranulation, platelet activation, complement and coagulation cascades, phagocytosis, angiogenesis, acute phase responses, oxidative stress, metabolism, and cell migration and adhesion (Figure S2D).

Hierarchical clustering analysis led to the identification of three main clusters of disease samples, sample clusters S1, S2 and S3 (Figure 1C). A large set of proteins (protein cluster 1,

C1) were upregulated in both KD and MIS-C, most strikingly in sample clusters S1 and S2, compared with healthy controls (Figure 1C). Functional annotation analysis revealed that this cluster was enriched with proteins involved in leukocyte-mediated immunity, neutrophil-mediated immunity, humoral immune responses and extracellular matrix (Figures 1D and S3A). Functional annotation analyses revealed that a second set of proteins (C2) enriched in sample cluster S2 included proteins involved in platelet activation and aggregation, myofibrils and smooth muscle cell contraction (Figures 1D and S3B). Proteins in C3 were upregulated exclusively in sample cluster S1, mostly severe MIS-C patients, and included heavy and light chain immunoglobins

(Igs), as well as components of the classical complement cascade, C1QA, C1QB, C1QC (Figures

1D and S3C).

Proteomic characterization reveals biomarkers that differentiate severe MIS-C from mild disease and from KD

We next sought to identify proteins and associated pathways upregulated or downregulated in severe MIS-C and mild MIS-C patients compared to healthy controls (Figures

2, S4). We identified 244 proteins increased and 135 proteins decreased in quantity in severe

MIS-C compared to healthy controls (top 25 modulated proteins, Figure 2A and B). Network and pathway analysis of significantly increased (Figure 2C-D) or decreased (Figure 2E-F) proteins revealed that humoral immune responses and complement pathways were highly enriched in severe MIS-C including various Igs and C1q proteins, C1QA, C1QB and C1QC (Figure S5A-B).

The expression of proteins associated with platelet activation and coagulation pathways, including

VWF, F5, F9, F11, Fibrinogen α and β chains (FGA and FGB), and SERPINF2, was also increased in severe MIS-C compared with healthy controls (Figure S5A-B). Fc receptor signaling, neutrophil mediated responses and phagocytosis pathways were also enriched in severe MIS-C.

These include FCGR3A (CD16a), IgGFc-binding protein (FCGBP), calprotectin (S100A8 and

S100A9), tissue inhibitor of metalloproteinases 1 (TIMP1), SERPINA1, SERPINA3 and acute- phase reactant leucine-rich alpha-2-glycoprotein 1 (LRG1) (Figures S6A-B and S2C). Proteins involved in VEGF signaling and smooth muscle cell contraction were increased in severe MIS-C including ICAM1, tropomyosin 4 (TPM4), p21 activated kinase (PAK2), FGA and FGB and filamin

B (FLNB) (Figures S6C and S5B). As these proteins are highly expressed in vascular smooth muscle cells, their presence in the serum may reflect release from damaged blood vessels.

Overall, the proteins and pathways increased in severe MIS-C indicate Ig-mediated inflammatory responses and endothelial dysfunction.

Networks of proteins reduced in severe MIS-C revealed pathways involved in regulation of lipid transport, lipid metabolic processes and lipoprotein clearance including APOA1, APOA2,

APOA4, APOC1 and APOM (Figure 2E-F and S6D). Some components of clotting and coagulation pathways were also downregulated in severe MIS-C compared to healthy controls

(Figure 2E-F and S5A). Furthermore, there was a downregulation of proteins involved in the regulation of body fluids and relaxation of cardiac muscle including CAMK2D, which aligns with the increase in proBNP and the cardiovascular manifestations and shock observed in MIS-C

(Figures 2E-F and S6E).

To determine factors contributing to severe disease, we compared severe MIS-C with mild

MIS-C (Figure 3). We found the expression of 75 proteins were significantly enhanced in severe

MIS-C, while 61 proteins were significantly reduced when compared to mild MIS-C. Selected proteins differentially expressed between MIS-C severity groups are presented in Figure 3A.

Compared to mild MIS-C, severe MIS-C patients had increased levels of proteins involved in pathways including proteolysis, classical complement cascade, coagulation, acute phase response and inflammation (Figure 3A-B and S7). These included CRP, S100A9, SAA1, SAA2,

STAT3, FCGR3A, LBP, CD163, ORM1, SERPINA1, SERPINA3, TIMP1, TLN-1, VWF, various

Igs and components of the C1 complex of the complement system (C1QA, C1QB, C1QC)

(Figures S2C, S5B, S6B S7). In contrast, severe MIS-C patients had reduced expression of proteins in pathways including negative regulators of peptidase activity, ECM proteoglycans, complement and coagulation cascades, and high-density lipid protein remodeling (Figure S8A-

B).

Next, we aimed to determine which proteins distinguished severe MIS-C from mild MIS-C and KD (Figure 3C-E), excluding clotting factors. Amongst proteins of interest, ferritin light chain

(FTL) was highly expressed in severe MIS-C, as were proteins involved in Ig-mediated immune activation, including FCGR3A and components of the classical complement cascade C1QA,

C1QB, C1QC (Figure 3E). Proteins that have been associated with heart failure were also identified as enhanced in severe MIS-C, including Tenascin C (TNC) (23) and QSOX1 (24).

(Figure 3D-E). Proteins with reduced expression in severe MIS-C were also identified and included histidine-rich glycoprotein (HRG), sex hormone binding globulin (SHBG) and complement component 7 (C7) (Figure 3D-E). Overall, the proteomic profiles of MIS-C and KD were similar, indicating shared pathogenic pathways. However, distinguishing proteins indicate

MIS-C may be mediated more so by immune complexes, and have greater heart muscle involvement than KD. These proteins have potential to act as biomarkers to distinguish severe

MIS-C from mild MIS-C or KD.

RNA-sequencing reveals a subgroup of hyperinflammatory MIS-C patients with enhanced myeloid responses, TRBV11-2 expansion and SARS-CoV-2 specific antibodies.

We performed RNA-sequencing (RNA-seq) analysis using RNA isolated from whole blood of febrile controls (n = 13), mild MIS-C (n = 4) and severe MIS-C patients (n = 8) (Figure 4A).

Hierarchical clustering and PCA demonstrated two subsets of MIS-C patients (Figure 4B and

Figure S9A). The first subset of MIS-C patients clustered separately (cluster 1) from febrile controls, while the other overlapped with febrile controls (cluster 2) (Figure 4B). Cluster 1 consisted predominantly of severe MIS-C patients (5 severe and 1 mild), while cluster 2 contained an equal number of severe and mild MIS-C patients (3 severe and 3 mild) (Figure 4B). Analyses revealed a large set of genes differentially expressed between the two MIS-C clusters (2895 genes upregulated and 2921 genes downregulated in cluster 1, FDR <0.05, FC > 2). The top 20 genes up- and downregulated in cluster 1 are presented in Figure 4C. Functional annotation analysis revealed that genes with increased expression in cluster 1 were involved in macrophage activation, neutrophil chemotaxis, innate signaling pathways, T cell activation, cytokine signaling, complement pathways, response to wounding and apoptosis (Figure 4D and Figure S9B). Cell deconvolution analysis identified increased relative abundance of neutrophils in cluster 1 MIS-C samples (Figure 4E). Genes with reduced expression in cluster 1 were involved in adaptive immune responses, as well as ribonucleoprotein complexes and RNA processing (Figure 4C-D and Figure S9C). In line with these findings, cell deconvolution analysis revealed a reduction in adaptive immune cells in cluster 1, most strikingly a reduction in naïve B cells, which may reflect lymphopenia that is observed in MIS-C (Figure 4E).

We next compared the proteomes of cluster 1 with cluster 2 patients (Figure 4F-G and

Figure S9D-E). Cluster 1, which was primarily severe MIS-C cases, was characterized by significantly enhanced expression of inflammatory markers including CRP, SAA1 and SAA2, as well as proteins associated with neutrophil activation, including myeloperoxidase (MPO),

Lipocalin 2 (LCN2), Cathepsin B (CATB), ICAM1, granulin (GRN) and LPS-binding protein (LBP)

(Figure 4F). In line with this, pathway analysis of protein expression in cluster 1 identified an enrichment of neutrophil-mediated responses (Figure 4G). Analysis of proteins and pathways downregulated in cluster 1 identified lipoprotein particle proteins including APOA1 and APOA4

(Figure S9D-E), which were also observed as downregulated in severe MIS-C compared to mild

MIS-C or healthy controls (Figure S6D). Interestingly, we found a reduction in complement and coagulation cascade protein pathways in cluster 1 compared with cluster 2 (Figure S9D-E). Since complement pathways were identified as increased in cluster 1 by transcriptomics (Figure 4D), we analyzed the correlation between the direction of protein expression and gene expression changes when comparing cluster 1 with cluster 2 (Figure S10A). We did not find a significant correlation between protein and gene expression changes, likely because for a subset, the gene expression and protein expression changes occurred in opposite directions (increased by gene expression yet decreased by protein expression in cluster 1). Functional annotation analysis showed that these genes/proteins, including C5, C3, C4BP, HP, F12, F5, and PF4, were enriched in complement and coagulation cascades (Figure S10B). The increased gene expression but decreased protein expression of this subset may indicate excessive activation and consumption of these molecules. Interestingly, a reduction in C3 protein has also been observed in COVID-19 non-survivors compared to survivors (25).

We previously identified TRBV11-2 skewing in MIS-C patients which correlated with disease severity and cytokine storm (17). As this study utilizes the same patient samples, we compared TRBV11-2 usage between the two MIS-C clusters identified by RNA-seq analysis

(Figure 4B). The patients with TRBV11-2 expansion were restricted to cluster 1, which contained primarily severe MIS-C patients (Figure 4H). We also examined antibody titers to Spike protein between the two groups and found that cluster 1 MIS-C patients had higher levels of anti-Spike

IgG antibodies than patients in cluster 2 (Figure 4I). This is similar to observations in adult COVID-

19 patients, in which increased antibody titers against SARS-CoV-2 are associated with disease severity (26). Overall, these data indicate that the patients in MIS-C cluster 1 exhibited increased inflammatory makers, increased neutrophil responses, reduced lymphocytes, increased SARS-

CoV-2 antibodies and TRBV11-2 T cell expansion.

MIS-C autoantibodies are targeted to a diverse set of intracellular autoantigens and are enhanced in MIS-C cluster 1 patients

We next sought to characterize the levels of autoantibodies in our patient cohort and determine how these relate to the hyperinflammatory cluster 1 identified by RNA-seq analysis. Autoantibody analysis was performed using the HuProt array (CDI labs) with serum from febrile controls (n=5) and MIS-C patients (n=11, 3 mild and 8 severe). The MIS-C patient group included

6 samples identified by RNA-seq as belonging to cluster 1, and 5 MIS-C samples from cluster 2

(Figure 5A). Candidate autoantibody targets were identified (p-value < 0.05, FC > 2) based on differential expression analysis of MIS-C samples, or RNA clusters, compared to febrile controls

(Figure 5B-C).

While the majority of IgG autoantibodies significantly increased in MIS-C compared to febrile controls were targeted to ubiquitously expressed antigens, we identified a number of tissue-specific antigens from the gastrointestinal (GI) tract, cardiovascular, skeletal muscle and brain tissues, reflecting the systemic nature of MIS-C and the involvement of specific organs in clinical presentation of disease (Figure 5D). GI tract autoantigens included ATPase H+/K+ transporting alpha subunit (ATP4A), SRY-box 6 (SOX6), Family with sequence similarity 84 member A (FAM84A) and RAB11 family interacting protein 1 (RAB11FIP1) (Figure 5D).

Cardiovascular autoantigens included PDZ and LIM domain 5 (PDLIM5) and Eukaryotic translation initiation factor 1A, Y linked (EIF1AY) (Figure 5D). Skeletal muscle autoantigens included RNA binding motif protein 38 (RBM38) and skeletal Troponin C2 (TNNC2) (Figure 5D).

Brain autoantigens included Microtubule associated protein 9 (MAP9) and NSF attachment protein beta (NAPB). Interestingly, several antigens highly expressed in neutrophils were identified, including Endothelin converting enzyme 1 (ECE1), SOX6 and RAB11FIP1 (Figure 5D).

Autoantibodies were predominantly targeted to intracellular antigens, suggesting they may result from a secondary immune response to cell damage. We identified three IgA autoantibodies that were significantly increased in MIS-C compared with febrile controls, namely FAM84A, which is highly expressed in GI tissues, TNNC2, which, as mentioned above is highly expressed in skeletal muscle, and Guanylate binding protein family member 6 (GBP6) (Figure 5E). FAM84A and TNNC2 were significantly increased in RNA cluster 1, but not RNA cluster 2, compared with febrile controls.

We next examined how the two RNA clusters differed in autoantibody responses (Figure

5F-G). Overall, patients in RNA cluster 1 had greater autoantibody responses than those in RNA cluster 2 (Figure 5B-C), with largest differences identified in IgG autoantibodies against ATP4A,

UBE3A, FOXK2, SATB1 and MAOA (Figure 5F), and in IgA autoantibodies against FAM84A

(Figure 5G). Overall, our data suggest systemic tissue damage and cell death may contribute to excessive antigenic drive against a diverse set of tissue-specific and ubiquitously expressed antigens. The enhanced levels of autoantibodies in RNA cluster 1 link autoantibody development to hyperinflammation, myeloid cell activation, lymphopenia, increased SARS-CoV-2 antibodies and TRBV11-2 T cell expansion.

Patients belonging to RNA cluster 1 show BCR repertoires with highly connected networks of CDR3 sequences

To further study B cell repertoire metrics and antigenic selection in our cohort, we performed B cell receptor (BCR) sequencing on extracted RNA from blood samples of patients with mild (n=4) or severe (n=8) MIS-C, and age-matched febrile control patients (n=15). We found a trend towards higher richness and a lower fraction of antigen-experienced B cells with somatic hypermutation in MIS-C patients than in febrile control patients. However, repertoire richness was distributed quite heterogeneously across patients, and the high richness pattern appeared to apply more to the small group of individuals with mild MIS-C without reaching statistical significance due to the small group size (Figure S11). These distinct immune metrics were observable in all Ig chains—heavy chain (IGH) as well as kappa (IGK) and lambda (IGL) light chains—arguing in favor of the specificity of this finding. These findings were also consistent with the previously reported increased richness in T cell receptor repertoires in patients with mild MIS- C (17). Since our RNA sequencing revealed two clusters of MIS-C patients, with cluster 1 correlated with high levels of SARS-CoV-2 antibodies and autoantibodies as well as TRBV11-2 T cell expansion, we subdivided our MIS-C cohort into these clusters for the following analyses of

BCR repertoires. Our aim was to determine imprints of (auto)antigenic selection or other B cell repertoire features that discriminate cluster 1 from cluster 2. MIS-C patients from cluster 1 showed lower B cell richness than febrile control patients and cluster 2 (Figure 6A), consistent with the contracted B cell compartment suggested by the transcriptome analysis of this cluster.

Interestingly, although no differences in the level of somatic hypermutation were detectable between MIS-C patients of RNA cluster 1 and 2, the BCRs of all cluster 1 patients converged towards networks of highly similar CDR3 amino acid sequences (Figure 6B). The degree of connected sequences was significantly enriched in cluster 1 repertoires and comprised up to 99% of BCR clones when Levenshtein distances of 1-3 were used for network construction (Figure

6B). Thus, MIS-C patients in cluster 1 showed strong imprints of antigenic selection in their B cell repertoires.

Higher frequency of autoantibody-associated IGHV genes IGHV4-34 and IGHV4-39 in MIS-

C

The higher autoantibody levels in RNA cluster 1 patients prompted us to investigate if specific IGHV sequences known to be involved in the formation of autoantibodies are overrepresented in this cluster. To globally investigate IGHV-J gene usage in RNA cluster 1 versus cluster 2, we studied the repertoires by PCA. This revealed a significant skewing of IGHV-

J gene usage between cluster 1 and cluster 2 (Figure 6C). Amongst the genes preferentially used in B cell repertoires of patients from RNA cluster 1 was IGHV4-39, which has been previously reported to be used by autoreactive lymphocytes in multiple sclerosis (27, 28). Moreover, IGHV4-

34, a gene extensively studied for its usage in autoreactive B cells (29), was preferentially used in B cells from MIS-C patients in general, with a numerically higher expansion in cluster 1 repertoires. Furthermore, IGHV1-69 which is preferentially used in autoreactive B cells (30, 31), was also overrepresented in cluster 1 (Figure 6C).

We next asked which factors drive B lineage repertoires in MIS-C patients towards autoreactivity. The majority of patients from RNA cluster 1, where imprints of antigenic selection beyond SARS-CoV-2 reactivity were most obvious, showed superantigenic T cell interactions, which could be one driver promoting autoreactive B lymphocytes. The RNA transcriptomics data pointed to increased BAFF expression in RNA cluster 1, and cytokine analysis pointed to increased IL-6 and IL-10 levels in the serum (Figure 6D-E). This indicated that B cell dysregulation in MIS-C patients from RNA cluster 1 may not only be driven by superantigenic T cell interactions, but also by soluble factors derived from the pronounced myeloid/innate cell compartment in these patients.

DISCUSSION

MIS-C is a hyperinflammatory disease with multi-organ involvement that is triggered by

SARS-CoV-2 infection. Here, we sought to further understand the mechanisms driving MIS-C through a combination of discovery proteomics, transcriptomics, BCR repertoire analysis and autoantibody arrays. Overall, our data point to an autoimmune phenotype in MIS-C characterized by dysregulated B cell responses, autoantibody production, and complement and myeloid cell- mediated inflammation.

Our data highlight a central role for neutrophils and complement pathways in MIS-C, both of which are implicated in multiple systemic, autoimmune and vasculitic diseases including ANCA- associated vasculitis, Behcet’s disease, SLE and KD (32-34). Similar mechanisms of neutrophil activation via FcγR and complement pathways as well as neutrophil-mediated pathogenesis through NETosis, cytokine and chemokine expression, and tissue damage via ROS and proteases are likely to occur in MIS-C (32, 34). Cytotoxic complement pathways, activated by antigen-autoantibody immune complexes as well as by alternative pathways may also cause direct cytotoxic tissue injury leading to multi-organ tissue damage (35, 36). Notably, IVIG is proposed to function through neutralization of FcγR proteins as well as inhibition of complement pathways (37), which may explain the success of IVIG in treating MIS-C (3, 6-8).

We found enhanced levels of multiple components of the complement pathway in MIS-C, although notably there were differences in complement components between mild and severe forms of the disease. Severe MIS-C patients expressed high levels of classical complement activation components C1QA, C1QB and C1QC. The enhanced levels of C1q proteins and Igs in severe MIS-C may reflect an abundance of autoantibody immune complexes and an important role for immune-complex mediated pathology in MIS-C. The classical complement pathway through C1q plays a role in driving a number of autoimmune diseases through immune complex interactions (35). However, the role of C1q proteins in autoimmune disease is complex, as C1q deficiency can also enhance susceptibility to SLE, likely by reducing clearance of apoptotic cells

(38). Interestingly, we found that severe MIS-C patients had reduced levels of C6 and C7 compared with mild MIS-C patients, components of the membrane attack complex. Furthermore, we found a reduction in complement protein levels C3 and C5 in serum of the hyperinflammatory

RNA cluster 1 despite enhanced gene expression. These expression patterns may reflect excessive activation and consumption of complement components and a failure to clear autoantibody immune complexes.

We identified enhanced autoantibody levels in MIS-C patients, consistent with two previous reports (14, 16). Three autoantigens identified in our array were previously identified by

Gruber et al. (14) as associated with MIS-C (UBE3A, ECE1 and RBM38). Another eight IgG autoantigen candidates identified in our cohort have been previously reported as autoantigens in other diseases, including ATP4A (type I diabetes and corpus atrophic gastritis) (39, 40), TROVE2 (Ro60, SSA2; systemic lupus erythematosus and Sjogren’s syndrome) (41), KLHL12 (primary biliary cirrhosis and Sjogren’s syndrome) (42, 43), FAM84A (inflammatory bowel disease) (44),

ANXA11 (prostate cancer) (45), HK1 (Primary Biliary Cirrhosis) (42), MAOA (Alzheimer’s disease)

(46) and CTDP1 (Behcet’s disease) (47). While we did not confirm findings by Consiglio et al. (16) of autoantibodies targeting endoglin, this may be due to differences in assays and cohorts used.

Most autoantigens identified in our array were intracellular and have diverse cell expression profiles indicating systemic tissue damage may drive autoantibody production in MIS-C. However, we also observed tissue-specific antigens from organs such as gut, heart, endothelial and skeletal muscle reflecting the severe symptoms associated with these organs. Increased autoantibodies are common in various autoimmune and inflammatory diseases, and in response to some viral infections (48, 49). A number of factors can lead to autoantibody production, including abnormal survival of autoreactive lymphocytes, breakdown in B and T cell activation threshold, and excessive antigenic drive as a result of auto-antigen release from damaged tissues and dying cells (49). Our data point to a combination of these events in MIS-C.

MIS-C patients belonging to RNA cluster 1 represented an interesting subset. These patients had high levels of inflammatory markers, activated myeloid function, and some showed superantigen-like T cell expansion. Moreover, this subset exhibited high levels of SARS-CoV-2 antibodies as well as autoantibodies. When looking in depth into the B cell immune repertoires of these patients, we found prominent B cell networks indicative of pronounced ongoing antigenic B cell selection. Immunogenetic repertoire biases with enrichments of genes previously described to be used in autoreactive lymphocytes (27-31) reflected the plasma autoantibody profiles of these patients. While the promotion of autoreactive B cells could well be driven by superantigen-like T cell expansions in this cluster, it may also be favored by cytokines that were enriched in the cluster. One such candidate is BAFF, which may contribute to human autoimmune diseases not only by breaking tolerance during B cell development, but also by enhancing plasmablast survival. BAFF was strongly increased in patients from cluster 1, aligning with the evidence that these patients have activated neutrophil and innate cell compartments, which may give rise to elevated

BAFF levels. Indeed, neutrophils can modulate B cell function through BAFF expression (50, 51) and promote autoimmune B cell responses (52). Positive feedback mechanisms between neutrophils/monocytes and B cell responses are likely important mechanisms driving MIS-C pathogenesis.

The association of TRBV11-2 T cell expansion with the hyperinflammatory RNA cluster 1 highlights the potential for TRBV11-2 expansion to act as a biomarker for severe disease.

TRBV11-2 T cell activation and cytokine production may participate in the promotion of autoimmune responses through a number of mechanisms. Given the finding that HLA class I is associated with TRBV11-2 expansion (17), engagement of cytotoxic T cells with HLA class I molecule on endothelial cells may drive tissue damage and autoantigen release. Cytokine storm mediated by activated T cells may also contribute to tissue damage and modulation of immune cell functions that promote autoreactivity. IL-6, IL-10 and TNFα expression correlate with

TRBV11-2 expansion (17), all of which can drive B cell proliferation. However, whether TRBV11-

2 T cell activation is the precipitating event that leads to development of MIS-C, or is an additional mechanism exacerbating the inflammatory autoimmune responses remains to be determined.

Gut dysfunction plays a role in driving autoimmunity, including autoreactive T cell and antibody responses (53). Severe GI symptoms are a common feature of MIS-C (5). We identified autoantibodies targeting gut specific antigens in MIS-C patients which may drive some of these symptoms and lead to gut dysfunction. We identified increased serum levels of LBP and CD14 in

MIS-C, which are indirect markers of gut dysfunction and are induced by gut microbial translocation (54, 55). Furthermore, a recent study identified persistent SARS-CoV-2 RNA in the stool of MIS-C patients, weeks after initial infection, and found sustained levels of shed Spike subunit 1 (S1) in the blood of MIS-C patients which correlated with high antibody titers against Spike and TRBV11-2 skewing (56). These data suggest that leakage of Spike and other gut antigens including LPS may occur across the gut barrier to promote a systemic immune response.

This may include the TRBV11-2 expansion observed in MIS-C through a superantigen like-motif at the C-terminal of the Spike S1 subunit (17, 57).

Many immunological features of severe MIS-C are highly similar to those of severe

COVID-19 in adults, including lymphopenia, CDR3-independent T cell expansion, reduced BCR diversity, high levels of autoantibodies, neutrophilia and complement activation. Our data further demonstrate similarities between severe MIS-C and severe COVID-19 proteomic profiles, with common upregulated pathways including neutrophil degranulation, platelet degranulation, coagulation cascades, macrophage activation and acute phase response (58, 59). Furthermore, a downregulation of proteins involved in plasma lipid regulation is evident for both diseases, including a downregulation of protein constituents of HDL (58). Moreover, the hyperinflammatory cluster of MIS-C patients we identified (RNA cluster 1) shares many similarities with severe

COVID-19 patients, including enhanced monocyte and neutrophil responses, increased SARS-

CoV-2 antibody titers, high levels of autoantibodies and an association with CDR3 independent

TCR Vβ expansion. These similarities indicate that common mechanisms may be driving both diseases.

Overall, our study furthers our understanding of the immunopathogenesis of MIS-C, particularly the autoimmune aspects of disease, and highlights pathogenic pathways that may act as targets for more directed therapeutic interventions. We identify B cell receptor profiles that are associated with hyperinflammation and autoimmune responses. We confirm our previous finding that TRBV11-2 T cell expansion is linked to a more severe disease phenotype and demonstrate a link between TRBV11-2 expansion and a cluster of hyperinflammatory MIS-C patients with autoimmune signatures. Furthermore, we identify serum proteins with potential to act as biomarkers to predict severity of MIS-C. Limitations of this study

Our study had several limitations due to sample availability. For proteomics, we could only obtain serum from control and MIS-C patients, and plasma from KD patients. Therefore, differences in coagulation pathways between MIS-C and KD could not be compared, and we removed clotting factors for any comparisons involving KD. Given that MIS-C is a new and rare disease, our RNA-seq, BCR-seq and autoantibody analyses were limited by sample size.

Validation in a larger cohort of patients is needed in future studies. METHODS

Study Design

This is a descriptive study that aimed to characterize and identify pathogenic pathways, proteins and signatures associated with MIS-C by multi-omics analysis, including proteomics,

RNA-sequencing, autoantibody arrays and BCR-sequencing. Samples consisted of mild MIS-C

(n=7), severe MIS-C (n=20), KD (n=7), healthy control (n=20) and febrile control (n=15) patients.

MIS-C severity classification was based on admission to Pediatric ICU requiring vasopressor use

(severe) or no ICU admission (mild). 92% of MIS-C serum samples and 100% of KD plasma samples were collected prior to IVIG administration. Patient data is presented in Table 1. The samples from the KD group were collected prior to the COVID-19 pandemic. As MIS-C is a new and rare syndrome, sample size was based on availability.

Case definitions

MIS-C was defined as per the Centers for Disease Control and Prevention (CDC) definition, which includes an individual age <21 years, a persistent fever of >38ºC for more than

24 hours, laboratory evidence of inflammation, two or more organs involved, no alternative plausible diagnosis and positivity or recent SARS-CoV-2 infection demonstrated by either real time quantitative PCR (RTqPCR) on nasopharyngeal swab, serology or antigen testing, or exposure to an individual with COVID-19 within 4 weeks prior to the onset of symptoms (60). The

American Heart Association definition was used for KD diagnosis (20).

Proteomics

Detailed proteomics methods can be found in Supplementary Material and outlined in

McArdle et al. (21). Depletion: Plasma/serum samples were either analyzed as naïve sample or underwent depleted of the 14 most abundant plasma proteins albumin, Immunoglobulins A, E G and M (kappa and lambda light chains), alpha-1-acidglycoprotein, alpha-1-antitrypsin, alpha-2- macroglobulin, apolipoprotein A1, fibrinogen, haptoglobin, and transferrin using High select Top

14 Abundant Protein Depletion Resin comprised of anti-camel antibodies (Thermo Fisher

Scientific. Digestion: Plasma/serum and depleted plasma/serum underwent trypsin digestions and desalting were performed using an automated workstation (Beckman i7) which is programed to perform reactions at a controlled temperature with uniform mixing as previously described (61) using standardized sample processing workflow (21). LC-MS/MS: Data independent analysis

(DIA) analysis was performed on an Orbitrap Exploris 480 (Thermo) instrument interfaced with a flex source coupled to an Ultimate 3000 ultra high-pressure chromatography system with mobile phase A 0.1% formic acid in water and mobile phase B 0.1 % formic acid in acetonitrile. Peptides were separated on a linear gradient on a C18 column (15 cm, 3 µm) over the course of total

60mins at a flow rate of 9.5 ul/min. Fragmented ions were detected across 50 DIA non-overlapping precursor windows of 12Da size. Bioinformatic Data analysis: DIA MS raw files were converted to mzML and the raw intensity data for peptide fragments was extracted from DIA files using the open source OpenSWATH workflow (62, 63) against the Human Twin population plasma peptide assay library (64). The total ion current (TIC) normalized transition-level data was scored (65), aligned (66) and processed using the mapDIA software (67) to perform pairwise comparisons between groups at the peptide and protein level. Clustering Analysis and Network analysis:

Principal component analysis was performed using the Factoextra package in R. Hierarchical clustering was performed using the pheatmap package in R. Protein network analysis was performed with PINE (22). The mass spectrometry proteomics data have been deposited to the

ProteomeXchange Consortium via the PRIDE (68) partner repository with the dataset identifier

PXD025462. Data can be accessed at ProteomeXchange (69)

(http://www.proteomexchange.org).

Cytokine Array Cytokine analysis of serum or plasma was performed using the V-PLEX Proinflammatory

Panel 1 Human Kit (MSD) No detection of cytokine was assigned a value of 0.

RNA-sequencing

RNA-seq was performed using RNA isolated from whole blood. Library construction was performed using the SMARTer Stranded Total RNA-Seq Kit v3 – Pico Input Mammalian kit

(Takara Bio USA, Inc.). Total RNA samples were assessed for concentration using a Qubit fluorometer (ThermoFisher Scientific), and for quality using the 2100 Bioanalyzer (Agilent

Technologies). Up to 10 ng of total RNA per sample was used for fragmentation, followed by reverse transcription. Indexing PCR added Illumina sequencing adapters with barcodes. Next, ribosomal cDNA was depleted, and the resulting library was PCR amplified. Sequencing was performed on the NovaSeq 6000 (Illumina) using 2x50 bp sequencing. Normalization and analysis of gene expression data was performed in R using edgeR and Limma-voom. Genes were considered differentially expressed with an adjusted p value of less than 0.05 and a fold change of greater than 2. Differentially expressed genes were analyzed using PINE (22). Cell deconvolution analysis was performed with CIBERSORT (70) (Cell-type Identification By

Estimating Relative Subsets Of RNA Transcripts) using the cell signature file LM22. The RNA- seq data have been deposited in the NCBI’s Gene Expression Omnibus (71) under accession number GSE179992.

SARS-CoV-2 Spike RBD antibody assay

SARS-CoV-2 serological Simoa assays for IgG against Spike RBD were prepared and performed as previously described (15, 72).

Autoantibody Analysis Autoantibody analysis was performed by CDI labs using native HuProt™ arrays for IgG and IgA profiling of serum samples. Briefly, the arrays were blocked and probed with the samples at a 1:1,000 dilution and incubated at room temperature for 1 hour. Then the arrays were washed and probed with Alexa-647-anti-human-IgG(Fc) and Cy3-anti-human-IgA secondary antibodies for signal detection. All samples were analyzed on the same day to avoid batch variations. Utilizing

CDI software, quantile normalization of the raw signal intensities (F635 median for IgG; F532 median for IgA) was performed on all arrays. T test was used to compare the different groups and candidates were identified using the following criteria; the fold change of average signal intensity is greater than 2 between the 2 groups, p value less than 0.05, and the signal intensity of the candidates is at least 5 standard deviations above the mean signal intensity in one group. Data are represented as Log2 of the fold change relative to the mean of febrile controls. Tissue expression and cellular compartment analysis was performed with Human Protein Atlas(73)

(http://www.proteinatlas.org).

BCR Sequencing

Library construction was performed using the SMARTer Human BCR IgG IgM H/K/L

Profiling Sequencing Kit (Takara Bio Inc.). Total RNA samples were assessed for concentration using a Qubit fluorometer (ThermoFisher Scientific), and for quality using the 2100 Bioanalyzer

(Agilent Technologies). Up to 50 ng of total RNA per sample was used for reverse transcription, followed by four separate PCR amplification reactions for IgG, IgM, IgK, and IgL. A second round of PCR amplified the entire BCR variable region and a small portion of the constant region. After size selection, quantification and fragment analysis of the individual libraries was performed.

Individual chains were then pooled and sequenced on the MiSeq (Illumina) using 2x300 bp sequencing. Fastq raw data have been deposited in the European Nucleotide Archive (ENA) under accession number PRJEB44566.

BCR Analysis

Raw fastq files were converted to fasta sequences using seqtk tool

(https://github.com/lh3/seqtk). Fasta sequences were submitted to IMGT/HighV-QUEST (74) for annotation of immunoglobulin (IG) rearrangements. All analyses and data plotting were performed using R version 3.5.1. Repertoires for heavy (IGH), kappa (IGK) and lambda light chains (IGL) were extracted and normalized to the lowest read count per chain by subsampling of 20685,

137544 or 126509 productive IGH, IGK or IGL reads, respectively. Richness was calculated as the number of unique CDR3 nucleotide sequences per repertoire. The percentage of antigen experienced cells refers to cells which underwent somatic hypermutation. We calculated the percentage of sequence identity to germline alleles by dividing the number of identical nucleotides within the V gene by the length of the V gene per sequence. Sequences with < 98% sequence identity to germline were counted as antigen experienced cells. Dot plots for richness and somatic hypermutation were generated with GraphPad Prism 8.0.2 (GraphPad Software,). The networks of connected sequences were constructed for a subset of the 1,000 most frequent different CDR3 amino acid sequences per repertoire using Imnet (75). Networks were constructed for

Levenshtein distances 1 and 3. Petri dish plots and percentage of connected sequences were obtained from Imnet output files via R package igraph (76) (https://igraph.org). Principal component analysis and V gene usage box plots were visualized with R package ggplot2.

Statistical Analysis

For direct comparison of laboratory data between groups, Mann-Whitney t-test was used.

For multiple comparisons of cytokine data between groups, Kruskal-Wallis one way ANOVA with

Dunn’s multiple comparison test was used. Differences were considered statistically significant with a p value < 0.05. For proteomics data, differences between groups were identified by pair- wise comparisons with multiple testing correction and proteins were considered significantly different with an FDR < 0.05. For RNA-seq data, differences between clusters were identified by t-test with Benjamini and Hochberg's multiple testing correction and genes were considered significantly different with an FDR < 0.05. For autoantibody array, t-test was used to compare the different groups and candidates were identified using the following criteria; the fold change of average signal intensity is greater than 2 between the 2 groups, p value < 0.05, and the signal intensity of the candidates is at least 5 standard deviations above the mean signal intensity in one group. For BCR data, ordinary one-way ANOVA was used for global analysis, unpaired student’s t-test was used for paired comparison and Pillai–Bartlett test of MANOVA of principal components was used. Data was considered statistically different with a p value < 0.05.

Study Approval

Biobanked, deidentified patient blood remnant samples as well as control samples were obtained from Massachusetts General Hospital (Boston), Cedars Sinai Medical Center (Los Angeles) and

Loma Linda University Hospital (Loma Linda) under Ethics committee approval and informed consent.

Author contributions

Conceptualization: R.A.P., A.B., L.P., M.N.R., J.V.E., M.B. and M.A. Investigation: R.A.P., A.B.,

A.M., L.P. and G.A. Resources: L.M.Y., H.C., M.L. and A.F. Data Analysis: R.A.P., A.B., L.P.,

M.N.R., J.V.E., M.B. and M.A. Writing: R.A.P., A.B., L.P., M.N.R., M.B. and M.A.

Acknowledgements

We thank the Applied Genomics, Computation & Translational core at Cedars Sinai and acknowledge BioRender.

Funding Cedars-Sinai Precision Health award (R.A.P.). NIH awards R01 AI072726 (M.A.),

3RO1AI072726-10S1 (M.A.) and R01HL11136 (J.V.E.). Erika Glazer Covid Fund (J.V.E.).

Advanced Clinical Biosystems Research Institute (J.V.E.).

REFERENCES

1. Stokes EK ZL, Anderson KN, et al. Coronavirus Disease 2019 Case Surveillance — United States, January 22–May 30, 2020. MMWR Morb Mortal Wkly Rep 2020;69:759– 65. 2. Götzinger F, Santiago-García B, Noguera-Julián A, Lanaspa M, Lancella L, Calò Carducci FI, et al. COVID-19 in children and adolescents in Europe: a multinational, multicentre cohort study. The Lancet Child & Adolescent Health. 2020;4(9):653-61. 3. Verdoni L, Mazza A, Gervasoni A, Martelli L, Ruggeri M, Ciuffreda M, et al. An outbreak of severe Kawasaki-like disease at the Italian epicentre of the SARS-CoV-2 epidemic: an observational cohort study. Lancet. 2020;395(10239):1771-8. 4. Toubiana J, Poirault C, Corsia A, Bajolle F, Fourgeaud J, Angoulvant F, et al. Kawasaki- like multisystem inflammatory syndrome in children during the covid-19 pandemic in Paris, France: prospective observational study. Bmj. 2020;369:m2094. 5. Belay ED, Abrams J, Oster ME, Giovanni J, Pierce T, Meng L, et al. Trends in Geographic and Temporal Distribution of US Children With Multisystem Inflammatory Syndrome During the COVID-19 Pandemic. JAMA Pediatr. 2021;175(8):837-45. 6. Riphagen S, Gomez X, Gonzalez-Martinez C, Wilkinson N, and Theocharis P. Hyperinflammatory shock in children during COVID-19 pandemic. Lancet. 2020;395(10237):1607-8. 7. Whittaker E, Bamford A, Kenny J, Kaforou M, Jones CE, Shah P, et al. Clinical Characteristics of 58 Children With a Pediatric Inflammatory Multisystem Syndrome Temporally Associated With SARS-CoV-2. Jama. 2020;324(3):259-69. 8. Cheung EW, Zachariah P, Gorelik M, Boneparth A, Kernie SG, Orange JS, et al. Multisystem Inflammatory Syndrome Related to COVID-19 in Previously Healthy Children and Adolescents in New York City. Jama. 2020;324(3):294-6. 9. Belhadjer Z, Méot M, Bajolle F, Khraiche D, Legendre A, Abakka S, et al. Acute Heart Failure in Multisystem Inflammatory Syndrome in Children in the Context of Global SARS-CoV-2 Pandemic. Circulation. 2020;142(5):429-36. 10. McMurray JC, May JW, Cunningham MW, and Jones OY. Multisystem Inflammatory Syndrome in Children (MIS-C), a Post-viral Myocarditis and Systemic Vasculitis-A Critical Review of Its Pathogenesis and Treatment. Front Pediatr. 2020;8:626182. 11. Cook A, Janse S, Watson J, and Erdem G. Manifestations of Toxic Shock Syndrome in Children, Columbus, Ohio, USA, 2010–2017. Emerg Infect Dis. 2020;26(6):1077-83. 12. Pierce CA, Preston-Hurlburt P, Dai Y, Aschner CB, Cheshenko N, Galen B, et al. Immune responses to SARS-CoV-2 infection in hospitalized pediatric and adult patients. Sci Transl Med. 2020;12(564). 13. Vella LA, Giles JR, Baxter AE, Oldridge DA, Diorio C, Kuri-Cervantes L, et al. Deep immune profiling of MIS-C demonstrates marked but transient immune activation compared to adult and pediatric COVID-19. Sci Immunol. 2021;6(57). 14. Gruber CN, Patel RS, Trachtman R, Lepow L, Amanat F, Krammer F, et al. Mapping Systemic Inflammation and Antibody Responses in Multisystem Inflammatory Syndrome in Children (MIS-C). Cell. 2020;183(4):982-95.e14. 15. Carter MJ, Fish M, Jennings A, Doores KJ, Wellman P, Seow J, et al. Peripheral immunophenotypes in children with multisystem inflammatory syndrome associated with SARS-CoV-2 infection. Nat Med. 2020;26(11):1701-7. 16. Consiglio CR, Cotugno N, Sardh F, Pou C, Amodio D, Rodriguez L, et al. The Immunology of Multisystem Inflammatory Syndrome in Children with COVID-19. Cell. 2020;183(4):968-81.e7. 17. Porritt RA, Paschold L, Rivas MN, Cheng MH, Yonker LM, Chandnani H, et al. HLA class I-associated expansion of TRBV11-2 T cells in multisystem inflammatory syndrome in children. J Clin Invest. 2021;131(10). 18. Ramaswamy A, Brodsky NN, Sumida TS, Comi M, Asashima H, Hoehn KB, et al. Immune dysregulation and autoreactivity correlate with disease severity in SARS-CoV-2- associated multisystem inflammatory syndrome in children. Immunity. 2021;54(5):1083- 95.e7. 19. Bartsch YC, Wang C, Zohar T, Fischinger S, Atyeo C, Burke JS, et al. Humoral signatures of protective and pathological SARS-CoV-2 infection in children. Nat Med. 2021;27(3):454-62. 20. McCrindle BW, Rowley AH, Newburger JW, Burns JC, Bolger AF, Gewitz M, et al. Diagnosis, Treatment, and Long-Term Management of Kawasaki Disease: A Scientific Statement for Health Professionals From the American Heart Association. Circulation. 2017;135(17):e927-e99. 21. Ardle AM, Binek A, Moradian A, Orgel BC, Rivas A, Washington KE, et al. Standardized workflow for precise mid- and high-throughput proteomics of blood biofluids. bioRxiv. 2021:2021.03.26.437268. 22. Sundararaman N, Go J, Robinson AE, Mato JM, Lu SC, Van Eyk JE, et al. PINE: An Automation Tool to Extract and Visualize Protein-Centric Functional Networks. J Am Soc Mass Spectrom. 2020;31(7):1410-21. 23. Terasaki F, Okamoto H, Onishi K, Sato A, Shimomura H, Tsukada B, et al. Higher serum tenascin-C levels reflect the severity of heart failure, left ventricular dysfunction and remodeling in patients with dilated cardiomyopathy. Circ J. 2007;71(3):327-30. 24. Vanhaverbeke M, Vausort M, Veltman D, Zhang L, Wu M, Laenen G, et al. Peripheral Blood RNA Levels of QSOX1 and PLBD1 Are New Independent Predictors of Left Ventricular Dysfunction After Acute Myocardial Infarction. Circ Genom Precis Med. 2019;12(12):e002656. 25. Fang S, Wang H, Lu L, Jia Y, and Xia Z. Decreased complement C3 levels are associated with poor prognosis in patients with COVID-19: A retrospective cohort study. Int Immunopharmacol. 2020;89(Pt A):107070. 26. Garcia-Beltran WF, Lam EC, Astudillo MG, Yang D, Miller TE, Feldman J, et al. COVID- 19-neutralizing antibodies predict disease severity and survival. Cell. 2021;184(2):476- 88 e11. 27. von Budingen HC, Kuo TC, Sirota M, van Belle CJ, Apeltsin L, Glanville J, et al. B cell exchange across the blood-brain barrier in multiple sclerosis. J Clin Invest. 2012;122(12):4533-43. 28. Baranzini SE, Jeong MC, Butunoi C, Murray RS, Bernard CC, and Oksenberg JR. B cell repertoire diversity and clonal expansion in multiple sclerosis brain lesions. J Immunol. 1999;163(9):5133-44. 29. Potter KN, Hobby P, Klijn S, Stevenson FK, and Sutton BJ. Evidence for involvement of a hydrophobic patch in framework region 1 of human V4-34-encoded Igs in recognition of the red blood cell I antigen. J Immunol. 2002;169(7):3777-82. 30. Avnir Y, Prachanronarong KL, Zhang Z, Hou S, Peterson EC, Sui J, et al. Structural Determination of the Broadly Reactive Anti-IGHV1-69 Anti-idiotypic Antibody G6 and Its Idiotope. Cell Rep. 2017;21(11):3243-55. 31. Hwang KK, Trama AM, Kozink DM, Chen X, Wiehe K, Cooper AJ, et al. IGHV1-69 B cell chronic lymphocytic leukemia antibodies cross-react with HIV-1 and hepatitis C virus antigens as well as intestinal commensal bacteria. PLoS One. 2014;9(3):e90725. 32. Thieblemont N, Wright HL, Edwards SW, and Witko-Sarsat V. Human neutrophils in auto-immunity. Semin Immunol. 2016;28(2):159-73. 33. Biezeveld MH, van Mierlo G, Lutter R, Kuipers IM, Dekker T, Hack CE, et al. Sustained activation of neutrophils in the course of Kawasaki disease: an association with matrix metalloproteinases. Clin Exp Immunol. 2005;141(1):183-8. 34. Gupta S, and Kaplan MJ. The role of neutrophils and NETosis in autoimmune and renal diseases. Nat Rev Nephrol. 2016;12(7):402-13. 35. Chen M, Daha MR, and Kallenberg CG. The complement system in systemic autoimmune disease. J Autoimmun. 2010;34(3):J276-86. 36. Walport MJ. Complement. Second of two parts. N Engl J Med. 2001;344(15):1140-4. 37. Galeotti C, Kaveri SV, and Bayry J. IVIG-mediated effector functions in autoimmune and inflammatory diseases. Int Immunol. 2017;29(11):491-8. 38. Botto M, and Walport MJ. C1q, autoimmunity and apoptosis. Immunobiology. 2002;205(4-5):395-406. 39. Lahner E, Marzinotto I, Lampasona V, Dottori L, Bazzigaluppi E, Brigatti C, et al. Autoantibodies Toward ATP4A and ATP4B Subunits of Gastric Proton Pump H+,K+- ATPase Are Reliable Serological Pre-endoscopic Markers of Corpus Atrophic Gastritis. Clin Transl Gastroenterol. 2020;11(10):e00240. 40. Wenzlau JM, Fain PR, Gardner TJ, Frisch LM, Annibale B, and Hutton JC. ATPase4A Autoreactivity and Its Association With Autoimmune Phenotypes in the Type 1 Diabetes Genetics Consortium Study. Diabetes Care. 2015;38 Suppl 2:S29-36. 41. Chen X, and Wolin SL. The Ro 60 kDa autoantigen: insights into cellular function and role in autoimmunity. J Mol Med (Berl). 2004;82(4):232-9. 42. Norman GL, Yang CY, Ostendorff HP, Shums Z, Lim MJ, Wang J, et al. Anti-kelch-like 12 and anti-hexokinase 1: novel autoantibodies in primary biliary cirrhosis. Liver Int. 2015;35(2):642-51. 43. Uchida K, Akita Y, Matsuo K, Fujiwara S, Nakagawa A, Kazaoka Y, et al. Identification of specific autoantigens in Sjogren's syndrome by SEREX. Immunology. 2005;116(1):53- 63. 44. Vermeulen N, de Beeck KO, Vermeire S, Van Steen K, Michiels G, Ballet V, et al. Identification of a novel autoantigen in inflammatory bowel disease by protein microarray. Inflamm Bowel Dis. 2011;17(6):1291-300. 45. Ummanni R, Duscharla D, Barett C, Venz S, Schlomm T, Heinzer H, et al. Prostate cancer-associated autoantibodies in serum against tumor-associated antigens as potential new biomarkers. J Proteomics. 2015;119:218-29. 46. de Oliveira-Junior LC, Araujo Santos Fde A, Goulart LR, and Ueira-Vieira C. Epitope Fingerprinting for Recognition of the Polyclonal Serum Autoantibodies of Alzheimer's Disease. Biomed Res Int. 2015;2015:267989. 47. Hu CJ, Pan JB, Song G, Wen XT, Wu ZY, Chen S, et al. Identification of Novel Biomarkers for Behcet Disease Diagnosis Using Human Proteome Microarray Approach. Mol Cell Proteomics. 2017;16(2):147-56. 48. Suurmond J, and Diamond B. Autoantibodies in systemic autoimmune diseases: specificity and pathogenicity. J Clin Invest. 2015;125(6):2194-202. 49. Elkon K, and Casali P. Nature and functions of autoantibodies. Nat Clin Pract Rheumatol. 2008;4(9):491-8. 50. Scapini P, Bazzoni F, and Cassatella MA. Regulation of B-cell-activating factor (BAFF)/B lymphocyte stimulator (BLyS) expression in human neutrophils. Immunol Lett. 2008;116(1):1-6. 51. Scapini P, Nardelli B, Nadali G, Calzetti F, Pizzolo G, Montecucco C, et al. G-CSF- stimulated neutrophils are a prominent source of functional BLyS. J Exp Med. 2003;197(3):297-302. 52. Costa S, Bevilacqua D, Cassatella MA, and Scapini P. Recent advances on the crosstalk between neutrophils and B or T lymphocytes. Immunology. 2019;156(1):23-32. 53. Ruff WE, and Kriegel MA. Autoimmune host-microbiota interactions at barrier sites and beyond. Trends Mol Med. 2015;21(4):233-44. 54. Stehle JR, Jr., Leng X, Kitzman DW, Nicklas BJ, Kritchevsky SB, and High KP. Lipopolysaccharide-binding protein, a surrogate marker of microbial translocation, is associated with physical function in healthy older adults. J Gerontol A Biol Sci Med Sci. 2012;67(11):1212-8. 55. Younas M, Psomas C, Reynes C, Cezar R, Kundura L, Portales P, et al. Microbial Translocation Is Linked to a Specific Immune Activation Profile in HIV-1-Infected Adults With Suppressed Viremia. Front Immunol. 2019;10:2185. 56. Yonker LM, Gilboa T, Ogata AF, Senussi Y, Lazarovits R, Boribong BP, et al. Multisystem inflammatory syndrome in children is driven by zonulin-dependent loss of gut mucosal barrier. J Clin Invest. 2021;131(14). 57. Cheng MH, Zhang S, Porritt RA, Noval Rivas M, Paschold L, Willscher E, et al. Superantigenic character of an insert unique to SARS-CoV-2 spike supported by skewed TCR repertoire in patients with hyperinflammation. Proc Natl Acad Sci U S A. 2020;117(41):25254-62. 58. Overmyer KA, Shishkova E, Miller IJ, Balnis J, Bernstein MN, Peters-Clarke TM, et al. Large-Scale Multi-omic Analysis of COVID-19 Severity. Cell Syst. 2021;12(1):23-40 e7. 59. Shen B, Yi X, Sun Y, Bi X, Du J, Zhang C, et al. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera. Cell. 2020;182(1):59-72 e15. 60. CDC. Information for Healthcare Providers about Multisystem Inflammatory Syndrome in Children (MIS-C). https://wwwcdcgov/mis-c/hcp/. 2020. 61. Fu Q, Johnson CW, Wijayawardena BK, Kowalski MP, Kheradmand M, and Van Eyk JE. A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation. J Vis Exp. 2020(158). 62. Rost HL, Aebersold R, and Schubert OT. Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms. Methods Mol Biol. 2017;1550:289-307. 63. Rost HL, Rosenberger G, Navarro P, Gillet L, Miladinovic SM, Schubert OT, et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol. 2014;32(3):219-23. 64. Liu Y, Buil A, Collins BC, Gillet LC, Blum LC, Cheng LY, et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol Syst Biol. 2015;11(1):786. 65. Reiter L, Rinner O, Picotti P, Huttenhain R, Beck M, Brusniak MY, et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods. 2011;8(5):430-5. 66. Rost HL, Liu Y, D'Agostino G, Zanella M, Navarro P, Rosenberger G, et al. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nat Methods. 2016;13(9):777-83. 67. Teo G, Kim S, Tsou CC, Collins B, Gingras AC, Nesvizhskii AI, et al. mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. J Proteomics. 2015;129:108-20. 68. Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47(D1):D442-D50. 69. Deutsch EW, Bandeira N, Sharma V, Perez-Riverol Y, Carver JJ, Kundu DJ, et al. The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics. Nucleic Acids Res. 2020;48(D1):D1145-D52. 70. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453-7. 71. Edgar R, Domrachev M, and Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207- 10. 72. Norman M, Gilboa T, Ogata AF, Maley AM, Cohen L, Busch EL, et al. Ultrasensitive high-resolution profiling of early seroconversion in patients with COVID-19. Nat Biomed Eng. 2020;4(12):1180-7. 73. Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419. 74. Li S, Lefranc MP, Miles JJ, Alamyar E, Giudicelli V, Duroux P, et al. IMGT/HighV QUEST paradigm for T cell receptor IMGT clonotype diversity and next generation repertoire immunoprofiling. Nat Commun. 2013;4:2333. 75. Miho E, Roskar R, Greiff V, and Reddy ST. Large-scale network analysis reveals the sequence space architecture of antibody repertoires. Nat Commun. 2019;10(1):1321. 76. Csardi G, and Nepusz T. The igraph software package for complex network research. InterJournal, Complex Systems. 2006;1695(5):1-9.

Figure 1

Individuals − PCA 20 Healthy Kawasaki Mild Severe Mass A Spectrometry Control Disease MIS-C MIS -C Individuals − PCA Serum or Native plasma Combined Differential expression analysis 20 data set Pathway and network analysis 10 Depleted n = 20 n = 7 n = 5 Groupn = 20

Adult_HC HC 10 B C Groups Group KD

Dim2 (9%) 0 AdultAdult_HC HC MildMild_MISC MIS-C Type HCHC SevereSevere_MISC MIS-C Type 6 Individuals − PCAKD HC KD

Dim2 (9%) 20 4 0 20 Mild_MISC Mild_MISC Severe_MISC 2 Severe_MISC Adult_HC −10 0

−2 1010 −10 0 10 20 Group −10 −4 Dim1 (22.8%) Adult_HC HC KD

−10 0 10 Dim2 (9%) 0 20 Mild_MISC Dim1 (22.8%) Severe_MISC C1 Dimension 2 (9%) Dimension

-−1010

-−1010 0 1010 2020 vascular endothelialvascular growth endothelial factor receptor growth signaling factor receptorpathway signaling pathway Dim1 (22.8%) specific granule specific granule Dimension 1 (22.8%) C2 renin−angiotensinrenin system−angiotensin system protein targeting toprotein membrane targeting to membrane positive regulationpositive of neuron regulation death of neuron death C3 Dimension 1 Loadings phagosome phagosome 1.0 neutrophil mediatedneutrophil immunity mediated immunity modification by hostmodification of symbiont by morphology host of symbiont or physiology morphology or physiology macrophage markersmacrophage markers leukocyte mediatedleukocyte immunity mediated immunity C1 0.5 C1 hyaluronan metabolichyaluronan process metabolic process humoral immune responsehumoral immune response Adult_3 Adult_4 Adult_1 Adult_2 HC_5 HC_13 HC_19 HC_16 HC_18 HC_8 HC_20 HC_6 HC_9 HC_4 HC_15 HC_12 HC_2 glycosaminoglycanHC_3 HC_11 HC_10 HC_17 HC_1 HC_7 MISC_2 MISC_24 MISC_4 MISC_19 glycosaminoglycanHC_14 bindingMISC_5 MISC_11 MISC_13 MISC_7 MISC_14 KD_5 MISC_17 KD_3 KD_1 MISC_6 bindingMISC_23 MISC_25 MISC_3 MISC_16 KD_2 MISC_18 KD_6 MISC_22 KD_4 MISC_20 MISC_21 KD_7 MISC_15 MISC_10 MISC_8 MISC_12 MISC_1 MISC_9

0.0 Severe_MISC Mild_MISC KD HC focal adhesion focal adhesion

C9 HP C5 Type CFB CRPLBP ficolin−1−rich granuleficolin lumen−1−rich granule lumen GDI1 4 2 ORM1 PYGB LRG1 CD14 CALR CCT3 ORM2PRCPENPL LCP1 CAPN1 GALK1 − − 0 2 4 6 MAN1A1 S100A9S100A8 CAVIN2 extracellular matrixextracellular matrix proteins proteins SERPINA3SERPINF2 SERPINA1 SERPING1

Type Sample Sample Sample

MISC_9 exopeptidase activityexopeptidase activity

MISC_1 20 20

MISC_12 -4 -2 0 2 4 6 Cluster S1 Cluster S2 Cluster S3

MISC_8 endocytic vesicle lumenendocytic vesicle lumen

MISC_10 40 40

MISC_15 KD_7 degradation of thedegradation extracellular of matrix the extracellular matrix

GO terms GO terms

MISC_21 60 60

MISC_20 cellular detoxificationcellular detoxification

KD_4

D MISC_22 Protein cluster functional annotation terms

KD_6 azurophil granule azurophilmembrane granule membrane

MISC_18

KD_2

MISC_16

MISC_3

vascular endothelial growth factor receptorMISC_25 signaling pathway smooth muscle contractionsmooth muscle contraction

MISC_23

MISC_6specific granule KD_1 platelet activation,platelet signaling activation, and aggregation signaling and aggregation

KD_3

renin−angiotensinMISC_17 system platelet activation platelet activation KD_5

MISC_14

protein targetingMISC_7 to membrane pathogenic Escherichiapathogenic coli infection Escherichia coli infection

MISC_13

MISC_11 C2 C2

positive regulationMISC_5 of neuron death myofibril myofibril

HC_14

MISC_19 phagosome MISC_4 interleukin−12−mediatedinterleukin signaling−12−mediated pathway signaling pathway

MISC_24

neutrophil mediatedMISC_2 immunity glycolysis and gluconeogenesisglycolysis and gluconeogenesis

HC_7

HC_1

modification by host of symbiont morphologyHC_17 or physiology ficolin−1−rich granuleficolin lumen−1−rich granule lumen

HC_10

macrophageHC_11 markers HC_3 association betweenassociation physico− betweenchemical physicofeatures− chemicaland toxicity features and toxicity

HC_2

leukocyte mediatedHC_12 immunity antioxidant activityantioxidant activity HC_15 C1

HC_4

hyaluronanHC_9 metabolic process HC_6

C3 C3

HC_20 complement cascadecomplement cascade

humoralHC_8 immune response

HC_18

HC_16 complement and coagulationcomplement cascades and coagulation cascades

glycosaminoglycanHC_19 binding HC_13

HC_5 focal adhesion 10 20 3010 4020 30 40 ficolin−1−rich granule lumen −log10(FDR)-log10−FDRlog10(FDR) extracellular matrix proteins exopeptidase activity proteins20 endocytic vesicle lumen 2040 degradation of the extracellular matrix GO terms 4060 cellular detoxification azurophil granule membrane 60 10 20 30 40 smooth muscle contraction -log10FDR platelet activation, signaling and aggregation platelet activation pathogenic Escherichia coli infection

myofibril C2 interleukin−12−mediated signaling pathway glycolysis and gluconeogenesis ficolin−1−rich granule lumen association between physico−chemical features and toxicity antioxidant activity

complement cascade C3 complement and coagulation cascades 10 20 30 40 −log10(FDR) Figure 1: Proteomic profiling of MIS-C cases. A. Experimental design of native and depleted serum proteomics profiling of healthy controls (n = 20), mild MIS-C (n = 5), severe MIS-C (n = 20) and Kawasaki Disease (n = 7) patients. B. PCA analysis of proteomics data and top proteins contributing to dimension 1 of the PCA plot. C. Heatmap and hierarchical clustering of proteomics expression data revealed three protein sets (C1, C2 and C3) driving separation between three clades of MIS-C and KD patients (sample clusters S1, S2 and S3). D. ClueGO ontology analysis via PINE (Protein Interaction Network Extractor) for visualization of pathways and functional categories significantly enriched within each of the three protein sets (C1, C2 and C3) revealed by hierarchical clustering analysis in panel C. X axis denotes negative decimal logarithm of the

FDR of enrichment (term p value corrected with Bonferroni). Size of the node denotes number of proteins within each term.

Figure 2

A B 8 Top upregulated proteins Top downregulated proteins wound healing Severe MIS-C vs. Healthy Control (FDR < 0.05) very−low−densitySevere lipoprotein MIS- Cparticle vs. Healthy Control (FDR < 0.05) serine−type endopeptidase inhibitor activity 6 secretory granule lumen regulation of relaxation of cardiac muscle regulation of plasma lipoprotein particle levels regulation of IGF transport-2 and uptake by IGF binding proteins FC

2 4 regulation of body fluid levels positive regulation of lipid metabolic process log Log2FC FC platelet degranulation 2 platelet- 1 activation, signaling and aggregation downregulated 2 log plasma lipoprotein particle organization

-Log2FC plasma lipoprotein particle clearance negative regulation of lipid metabolic process negative regulation of immune effector process 0 0 negative regulation of fibrinolysis monosaccharide biosynthetic process HP megakaryocyte differentiation CRP LBP AQR HBB FGB TTR BGN SAA1 PSIP1PYGBLRG1 ORM1 RPS7 SND1 ACP1HBA1 low−SHBGdensity lipoprotein particle CCT7clearanceRBP4 GATM DDX6 TPP1 CAPN1 CDC5LS100A9 S100A8RAB21 GAPDH IGFBP3GPLD1 LAMC1RPL15IGFALSlipid transportPCMT1 SHMT1VDAC1 GLOD4 APOL2 APOA4APOA1 CCDC58 HLA-DRA AKR1A1 TALDO1 HIST1H4ASERPINA3 highSERPINA5−density lipoproteinSERPINA4 particle PGLYRP2 hemostasis formation of fibrin clot (clotting cascade) extracellular matrix C Upregulated - Severe MIS-C vs. Healthy Control D Upregulatedeukaryotic - Severe translation MIS initiation-C vs. Healthy Control cytoplasmic ribosomal proteins Functional annotation networks Functional annotationblood termscoagulation arachidonic acid metabolism proteins VEGFA−VEGFR2 signaling pathway wound healingsmooth muscle contraction 25 very−low−density lipoprotein particlesignaling by Interleukins complement serine−typeglycolysis endopeptidasewound healing inhibitorserine activity−type endopeptidase activity 50 immunoglobulins very−low−density lipoproteinresponse particle to elevated platelet cytosolic Ca2+

activation, GO terms secretory granule lumen serine−type endopeptidaseregulation of relaxation inhibitor activityof cardiac muscleregulation of lipid localization 75 response to endocytosis secretorywound healing granule lumen receptor−mediated endocytosis very−low−regulationdensity lipoprotein of plasma particle lipoprotein particle levels metal ion regulation regulationof IGF transport of relaxation and uptake of cardiac by IGFreactive muscle binding oxygen proteins species metabolic process serineregulation−type endopeptidase of plasma lipoproteininhibitor activity particle levels programmed cell death secretory granuleregulation lumen of bodypositive fluid levels regulation of angiogenesis regulation of regulationIGF transport of relaxation andpositive uptake of regulation cardiacby IGF musclebinding of lipid proteins metabolic processplatelet degranulation neutrophil regulation of plasma lipoproteinregulation particle of body levelsplatelet fluidplatelet levels degranulation activation, signaling and aggregation proteolysis downregulated degranulationregulation of IGF transportpositive and uptake regulationplatelet by IGF activation,of binding lipid metabolic proteins signaling process and aggregation phagocytosis plasmaplatelet lipoprotein degranulation particle organization neutrophil mediated immunity coagulation, upregulated

regulation of body fluid levels downregulated positive plateletregulation activation, of lipid metabolic signalingplasma lipoprotein processand aggregation particle clearanceneutrophil degranulation reactive response to plasmanegative lipoprotein regulation particle of organizationlipid metabolicnegative process regulation of proteolysis platelet degranulation membrane attack complex wound plasmanegative lipoprotein regulation particle of immune clearance effector process downregulated oxygen platelet activation, signaling and aggregation lymphocyte mediated immunity plasmanegative lipoprotein regulation particle of lipidnegative organization metabolic regulation process of fibrinolysis species negative regulation of immune effector process leukocyte migration plasma lipoprotein monosaccharideparticle clearance biosynthetic processinnate immune response negative regulation ofnegative lipid metabolic regulationmegakaryocyte process of fibrinolysisimmunoglobulin differentiation mediated immune response negative regulationmonosaccharide of immunelow−density effector biosynthetic lipoprotein process process particle clearancehumoral immune response negative regulationmegakaryocyte of fibrinolysis differentiationlipid transport hemostasis phagosome monosaccharidelow−density biosyntheticlipoproteinhigh particle process−density clearance lipoprotein particleficolin−1−rich granule lumen megakaryocyte differentiationlipid transporthemostasisFc receptor signaling pathway low−density lipoproteinhighformation− particledensity ofclearancelipoprotein fibrin clot particle(clotting cascade) extracellular matrix lipid transporthemostasisextracellular matrix defense response highformation−density of fibrinlipoprotein cloteukaryotic (clotting particle cascade) translationcytokine initiation signaling in Immune system acute phase cytoplasmicextracellular ribosomal complementmatrix proteins activation, classical pathway hemostasis complement activation, alternative pathway formation of fibrin cloteukaryotic (clotting translation cascade) initiationblood coagulation blood coagulation cytoplasmicextracellular ribosomalarachidonic matrix proteins acid metabolism azurophil granule proteins cytokineeukaryotic translation bloodinitiation coagulation antioxidant activity signalingcytoplasmicarachidonic ribosomalVEGFA− proteinsVEGFR2acid metabolism signaling pathwayacute inflammatory response smooth muscle contraction proteins 25 blood coagulation 0 10 20 30 40 arachidonicVEGFA−VEGFR2 acid metabolism signalingsignaling pathway by Interleukins smoothserine muscle−type endopeptidasecontraction activity proteins−log10(FDR)-log10FDR 25 50 VEGFA−VEGFR2response signalingsignaling to elevated pathway by plateletInterleukins cytosolic Ca2+ GO terms smoothserine− muscletype endopeptidase contractionregulation ofactivity lipid localization Proteins 25 50 75 response tosignaling elevated by platelet receptorInterleukins cytosolic−mediated Ca2+ endocytosis GO terms serine−typereactive endopeptidaseregulation oxygen of activitylipidspecies localization metabolic process 50 75 response to elevated receptorplatelet cytosolic−mediated Ca2+ programmedendocytosis cell death E Downregulated - SevereGO terms MIS-C vs. Healthy Controlreactiveregulation oxygen ofspecies lipidpositive localization metabolic regulationF process ofDownregulated angiogenesis - Severe MIS-C vs. Healthy Control75 receptor−mediatedprogrammed endocytosisplatelet cell deathwound degranulation healing Functional annotation networks reactive oxygen speciespositiveplatelet metabolicvery regulationactivation,−low− process densityof signaling angiogenesis lipoprotein andFunctional aggregation particle annotation terms serineprogrammed−typeplatelet endopeptidase cellwound deathdegranulation healing inhibitorphagocytosis activity positivevery regulation−low−density of angiogenesis lipoproteinneutrophil particlemediated immunity platelet activation, signaling andsecretory aggregation granule lumen upregulated serine−type endopeptidaseregulationplatelet degranulation of relaxation inhibitorneutrophilphagocytosis activityof cardiac degranulation muscle wound healing wound healing platelet activation, signalingneutrophil secretoryandwoundnegative aggregation mediated healing granule regulation immunity lumen ofvery proteolysis−low−density lipoproteinvery−low particle−density lipoprotein particle

regulation of plasma lipoprotein particle levels upregulated very−low−density lipoproteinphagocytosis particlemembraneserine attack−type complexendopeptidaseserine− inhibitortype endopeptidase activity inhibitor activity stress serineregulation−type regulationof endopeptidase IGF transport of relaxation andinhibitorneutrophil uptake of activity cardiac degranulationby IGF muscle binding proteins regulationneutrophil of plasmanegative mediated lipoprotein regulationlymphocyte immunity particle of proteolysis mediated levels immunitysecretory granule lumensecretory granule lumen fiber secretory granuleregulation lumen of regulationbody fluid of levels relaxationregulation of cardiac of relaxationmuscle of cardiacupregulated muscle regulation of IGF transport andpositiveneutrophil uptake membraneregulation by degranulation IGF binding ofattack lipid proteinsleukocytecomplexmetabolic migrationprocess regulationnegative of relaxation regulation of cardiac of proteolysis muscleinnateregulation immune of plasmaresponse regulationlipoprotein of particle plasma levels lipoprotein particle levels regulation of plasma lipoproteinlymphocyteregulationregulation particle ofmediated body oflevels IGFplatelet fluid transport immunity regulationlevels degranulation and ofuptake IGF transport by IGF binding and uptake proteins by IGF binding proteins membraneimmunoglobulin attack complexmediated immune response downregulated regulation of IGF transportpositive and uptake regulationplatelet by IGF activation,of binding lipid leukocytemetabolic proteins signaling migrationprocess and aggregationregulation of body fluidregulation levels of body fluid levels lymphocyteplasma mediatedinnateplatelet lipoprotein immuneimmunityhumoral degranulation particle responsepositive immune organizationregulation response of positivelipid metabolic regulation process of lipid metabolic process

regulation of body fluid levels downregulated plateletimmunoglobulin activation,Platelet leukocytemediated signalingplasma lipoprotein migrationimmune and aggregation response particlehemostasis clearance platelet degranulationplatelet degranulation downregulated positive regulation of lipid metabolic process downregulated plasmanegativeinnate lipoprotein humoralimmune regulation particle responseimmuneficolin of organizationlipid−1 plateletresponse− metabolicrich granule activation, process lumen signalingplatelet andactivation, aggregation signaling and aggregation plateletactivation, degranulation plasma lipoprotein particleplasma organization lipoprotein particle organization immunoglobulinplasma negativemediated lipoprotein regulation immune Fc particleresponse of receptor immunehemostasis clearance signaling effector processpathway downregulated platelet activation, signalinghumoral immune and aggregation response extracellularplasma lipoproteinmatrix particleplasma clearance lipoprotein particle clearance ribosomes extracellular plasmanegative lipoprotein regulation degranulationparticleficolin of lipidnegative −organization1− metabolicrich granule regulation process lumen of fibrinolysis negative regulationFc ofmonosaccharide receptor immunehemostasis signaling effector biosyntheticnegative processpathwaydefense regulation responseprocess ofnegative lipid metabolic regulation process of lipid metabolic process matrix complementplasma lipoproteinficolin −1− particlerichcytokine granule clearance signaling lumennegative in Immune regulation system ofnegative immune regulation effector process of immune effector process negative regulation ofnegative lipid metabolic regulationmegakaryocyteextracellular process of fibrinolysis matrix differentiationnegative regulation ofnegative fibrinolysis regulation of fibrinolysis coagulationmonosaccharideFc receptorlowcomplement− densitysignaling biosynthetic lipoprotein defenseactivation,pathway responseprocess particle classical clearance pathway negative regulation of immuneextracellular effector process matrix monosaccharide biosyntheticmonosaccharide process biosynthetic process negativecytokine regulationcomplementmegakaryocyte signaling of fibrinolysis activation, in Immune differentiation alternative systemlipid transport pathwaymegakaryocyte differentiationmegakaryocyte differentiation lowcomplement−density lipoprotein activation,defensehigh particleresponse −classicaldensity clearance lipoproteinpathwaybloodlow−density coagulation particle lipoproteinlow particle−density clearance lipoprotein particle clearance monosaccharidecytokine signaling biosynthetic in Immune process system complementmegakaryocyte activation, differentiation alternativelipid transport pathwayazurophilhemostasis granule lipid transport lipid transport complement activation,highformation−density classical oflipoprotein bloodfibrinpathway clotcoagulation particle(clottingantioxidant cascade)high activity−density lipoproteinhigh particle−density lipoprotein particle complementlow−density activation,lipoprotein alternative particle clearancepeptidase pathway lipid transportacuteazurophil hemostasisinflammatoryextracellular granule response matrix hemostasis hemostasis formation of fibrinblood clot coagulationeukaryotic (clottingantioxidant cascade) translation activityformation initiation of fibrin clotformation (clotting ofcascade) fibrin clot (clotting cascade) high−density lipoproteinazurophil particle granule 0extracellular10 matrix20extracellular30 matrix40 transport HDL acute inflammatoryhemostasiscytoplasmicextracellular response ribosomal matrix proteins eukaryoticantioxidant translation activity initiationblood coagulationeukaryotic translationeukaryotic initiation−log10(FDR) translation initiation lipid transportformation ofacute fibrin inflammatoryclot (clotting cascade) response 0cytoplasmic10 ribosomal20cytoplasmic proteins30 ribosomal40 proteins cytoplasmicextracellularclotting ribosomalarachidonic matrix proteins acid metabolism blood coagulation blood coagulation granule eukaryotic translation bloodinitiation coagulation0 10 20 −log10(FDR)30 40 proteins VEGFA−VEGFR2 signaling pathwayarachidonic acid metabolismarachidonic acid metabolism proteins proteins cytoplasmicarachidonic ribosomal proteinsacid metabolism 0 10 20 blood coagulationsmooth muscleVEGFA contraction−VEGFR2−log10(FDR) signalingVEGFA pathway−VEGFR2 signaling pathway proteins 25 arachidonicVEGFA−VEGFR2 acid metabolism signalingsignaling pathway by Interleukinssmooth muscle contractionsmooth muscle contraction -log10FDR 25 25 smoothserine muscle−type endopeptidasecontraction activitysignaling by Interleukinssignaling by Interleukinsproteins 25 50 VEGFA−VEGFR2response signalingsignaling to elevated pathway by plateletInterleukins cytosolicserine Ca2+−type endopeptidaseserine− activitytype endopeptidase activity 50 50 GO terms regulationresponse of lipid localization to elevatedresponse platelet cytosolic to elevated Ca2+ platelet cytosolic Ca2+ GO terms smoothserine− muscletype endopeptidase contraction GO terms activity Proteins 25 50 75 response tosignaling elevated by platelet receptorInterleukins cytosolic−mediated Ca2+ endocytosisregulation of lipid localizationregulation of lipid localization 75 75 GO terms reactive oxygen species metabolic processreceptor−mediated endocytosisreceptor−mediated endocytosis serine−type endopeptidaseregulation of activitylipid localizationreactive oxygen speciesreactive metabolic oxygen process species metabolic process50 75 response to elevated receptorplatelet cytosolic−mediated Ca2+ programmedendocytosis cell death

GO terms programmed cell deathprogrammed cell death reactiveregulation oxygen ofspecies lipidpositive localization metabolic regulation process of angiogenesispositive regulation of positiveangiogenesis regulation of angiogenesis75 receptor−mediatedprogrammed endocytosisplatelet cell death degranulation platelet degranulationplatelet degranulation reactive oxygen speciespositiveplatelet metabolic regulationactivation, process of signaling angiogenesisplatelet and activation,aggregation signalingplatelet andactivation, aggregation signaling and aggregation programmedplatelet cell deathdegranulationphagocytosis phagocytosis phagocytosis neutrophil mediated immunityneutrophil mediated immunityneutrophil mediated immunity upregulated positive regulation of angiogenesis upregulated platelet activation, signaling and aggregation upregulated platelet degranulationneutrophilphagocytosis degranulationneutrophil degranulationneutrophil degranulation platelet activation, signalingneutrophil andnegative aggregation mediated regulation immunity of proteolysisnegative regulation ofnegative proteolysis regulation of proteolysis upregulated neutrophilphagocytosismembrane degranulation attack complexmembrane attack complexmembrane attack complex neutrophilnegative mediated regulationlymphocyte immunity of proteolysis mediated immunitylymphocyte mediatedlymphocyte immunity mediated immunity leukocyte migration leukocyteupregulated migration neutrophilmembrane degranulation attack leukocytecomplex migrationinnate immune responseinnate immune response negative regulationlymphocyte of proteolysis mediatedinnate immunityimmunoglobulin immune response mediatedimmunoglobulin immune response mediated immune response membraneimmunoglobulin attackleukocyte complexmediated migration immune responsehumoral immune responsehumoral immune response lymphocyte mediatedinnate immuneimmunityhumoral response immune response hemostasis hemostasis immunoglobulin leukocytemediated migrationimmune responsehemostasisficolin−1−rich granuleficolin lumen−1−rich granule lumen innate humoralimmune responseimmuneficolin−1 response−rich granule Fclumen receptor signalingFc pathway receptor signaling pathway immunoglobulin mediated immuneFc response receptorhemostasis signaling pathway extracellular matrix extracellular matrix humoralficolin immune−1− richresponse granuleextracellular lumen matrix defense response defense response Fc receptorhemostasis signaling pathwaydefensecytokine response signaling in cytokineImmune signalingsystem in Immune system ficolin−1−richcytokine granule signaling lumen incomplement Immune system activation,complement classical pathway activation, classical pathway extracellularcomplement matrix activation,complement alternative activation, pathway alternative pathway Fc receptorcomplement signaling defenseactivation,pathway response classical pathway blood coagulation blood coagulation cytokinecomplement signalingextracellular activation, in Immune matrix alternative system pathway azurophil granule azurophil granule complement activation,defense response classical pathwayblood coagulation antioxidant activity antioxidant activity cytokinecomplement signaling activation, in Immune alternative system pathwayazurophil granuleacute inflammatory responseacute inflammatory response complement activation, classical pathway antioxidant activity blood coagulation 0 10 20 0 3010 4020 30 40 complement activation, alternativeacute azurophilpathway inflammatory granule response blood coagulation antioxidant activity 0 10 20 30 −log10(FDR)40 −log10(FDR) acuteazurophil inflammatory granule response antioxidant activity −log10(FDR) acute inflammatory response 0 10 20 30 40 0 10 20 −log10(FDR)30 40 −log10(FDR) Figure 2. Characterization of severe MIS-C. Protein expression was compared between severe

MIS-C (n = 20) and healthy controls (n = 20). Proteins were considered significantly changed when FDR was less than 0.05 as determined by mapDIA statistical software for protein differential expression using MS/MS fragment-level quantitative data. A. Top proteins enhanced in severe

MIS-C, ranked by fold change. B. Top proteins reduced in severe MIS-C, ranked by fold change.

C. ClueGO Ontology analysis via PINE visualized a network of pathways and functional annotation terms enriched in a set of proteins significantly increased in severe MIS-C population when compared to healthy controls. D. Selected pathways and functional annotation terms from

PINE analysis of proteins increased in severe MIS-C when compared to healthy controls. E.

Network plots visualized via PINE analysis of proteins reduced in severe MIS-C group when compared to healthy controls. F. Selected pathways and functional annotation terms from protein functional enrichment analysis of proteins reduced in severe MIS-C group when compared to healthy controls.

secretory IgA immunoglobulin complex regulation of immune effector process platelet degranulation platelet aggregation (plug formation) phagocytosis upregulated secretory IgA immunoglobulinmacrophage complex chemotaxis regulation of immuneinitial effectortriggering process of complement immune response−activating cell surfaceplatelet receptor degranulation signaling pathway proteins cross−presentationplatelet of soluble aggregation exogenous (plug antigens formation) (endosomes) complementFigure and 3 phagocytosiscoagulation cascades 5 macrophagecomplement chemotaxis activation upregulated classical antibodyinitial triggering−mediated of complement complement activation 10 immune response−activatingbinding cell and surface uptake receptor of ligands signaling by scavenger pathwayTop regulated receptors proteins A B proteins 15 Functional annotation terms cross−presentation of soluble exogenous antigensacute inflammatory(endosomes)Severe MIS response-C vs. Mild MIS-C Proteins Severe MIS-C vs. Mild MIS-C

GO terms activation of NF−kappaB in B cells complement and coagulation cascades 55 2020 complement activation Enhanced in Severe regulation of plasmaReduced lipoprotein in Severe particle levels secretory IgA immunoglobulin complex classical antibody−mediated complement activation 1010 2525 regulation of IGF transport and uptake by IGF binding proteins (excluding Ig’s) downregulated regulation of immune effector process binding and uptake of ligands by scavenger receptors regulation of blood coagulationSAA1 platelet degranulation acuteCOL6A1 inflammatory response 1515 AKR1A1 platelet degranulationSERPINB3 platelet aggregation (plug formation) GO terms activation of NF−kappaB in B cells negativeLAMC1 regulation of peptidase activityTIMP1 20 phagocytosis FTL upregulated regulation of plasma lipoproteinhighSHBG−density particle lipoprotein levels particle macrophage chemotaxis PI16 PSMA1 25 ECM proteoglycans downregulated initial triggering of complement regulation of IGF transport and uptake byTNXB IGF binding proteins RSP11 complement and coagulation cascades immune response−activating cell surface receptor signaling pathway regulationMUCB of blood coagulation FCGR3A proteins plateletF12 basal degranulation plasma membrane PCCA cross−presentation of soluble exogenous antigens (endosomes) negative regulationAPMAP of peptidase activity FASTKD5 10 20 30 complement and coagulation cascades 5 high−densityPTGDS lipoprotein particle CD163 complement activation GATM TNC −log10(FDR) ECM proteoglycans classical antibody−mediated complement activation 10 APOA4 CRP complement and coagulation cascades binding and uptake of ligands by scavenger receptors IVL Individuals − PCAEXOSC1 15 basalAOC3 plasma membrane VWF acute inflammatory response

20 COL1A1 GO terms activation of NF−kappaB in B cells FETUB 10 20 30 20 IndividualsGGH − PCA CAPN1 GPLD1 APOE −log10(FDR) regulation of plasma lipoprotein particle levels 25 ERAP2 downregulated 20 CTSD regulation of IGF transport and uptake by IGF binding proteins APOM PYGB regulation of blood coagulation GOT2 EIF4H platelet degranulation APOC4 C7 negative regulation of peptidase activity DDX6 CSF1R LCAT LBP high−density lipoprotein particle CYB5B10 GRN ECM proteoglycans SRRM2 FCN2 Groupcomplement and coagulation cascades basal plasma membrane 0 10 -2 -1 0.0 0.5 1.0 1.5 2.0 Adult_HC 10 20 30 log2FC log2FC Group HC Severe_MISC Mild_MISC KD HC −log10(FDR)-log10FDR

Adult_HC KD Type 2 1 Dim2 (9%) C 0 D HCHC MildMild_MISC MIS-C -2− -1− 00 11 22 33 44 55 KDKD SevereSevere_MISC MIS-C

Dim2 (9%) 0 Mild_MISC Severe MIS-C vs. Mild Mild MIS-C vs. KD Severe_MISC MIS-C (129, FDR < 0.05) (119, FDR < 0.05) 5 5 Type

C7 ECM1 SHBG SERPINA6 FETUB A2M HRG GPX3 QSOX1 PRSS3 FCN2 RPS11 SERPINB3 TNC FCGR3A TypeFTL C1QC C1QB C1QA AZGP1 Type Type Type Type HC_10 AZGP1 4 HC

−10 AZGP1 AZGP1C1QA

HC_7 4 HC 4 HC

C1QB KD HC_1 C1QA C1QA 3

KD C1QC Mild_MISCKD HC_19 C1QB C1QB

48 3 FTL 3 34 38 HC_8 C1QC C1QCFCGR3A 2 Severe_MISC

−10 Mild_MISC TNC Mild_MISC HC_16 FTL FTL

SERPINB3 1

−10 0 10 20 HC_18 FCGR3A 2 Severe_MISCRPS11FCGR3A 2 Severe_MISC 10 HC_17 TNC TNCFCN2

Dim1 (22.8%) PRSS3 0

37 23 HC_9 SERPINB3 1 QSOX1SERPINB3 1

HC_20 RPS11 RPS11GPX3 −1

−10 0 10 20 HC_3 FCN2 HRGFCN2

Dim1 (22.8%) HC_15 PRSS3 0 PRSS3A2M 0−2

44 HC_2 QSOX1 QSOX1FETUB HC_11 SERPINA6

GPX3 GPX3SHBG HC_12 −1 −1

ECM1

HC_4 HRG HRGC7 MISC_23 MISC_25 MISC_1 MISC_9 MISC_3 MISC_16 MISC_20 MISC_22 MISC_7 MISC_18 MISC_2 MISC_4 MISC_24 MISC_5 MISC_14 MISC_13 MISC_19 MISC_21 MISC_15 KD_4 MISC_8 MISC_10 MISC_12 KD_2 KD_7 MISC_11 KD_3 KD_6 MISC_17 KD_1 KD_5 HC_14 MISC_6 HC_6 HC_5 HC_13 HC_4 HC_12 HC_11 HC_2 HC_15 HC_3 HC_20 HC_9 HC_17 HC_18 HC_16 HC_8 HC_19 HC_1 HC_7 HC_10

HC_13 A2M −2 A2M −2

HC_5 FETUB FETUB

Severe MIS-C vs. KD HC_6 SERPINA6 SERPINA6

(114, FDR < 0.05) MISC_6 SHBG SHBG

HC_14 ECM1 ECM1

KD_5 C7 C7 MISC_23 MISC_25 MISC_1 MISC_9 MISC_3 MISC_16 MISC_20 MISC_22 MISC_7 MISC_18 MISC_2 MISC_4 MISC_24 MISC_5 MISC_14 MISC_13 MISC_19 MISC_21 MISC_15 KD_4 MISC_8 MISC_10 MISC_12 KD_2 KD_7 MISC_11 KD_3 KD_6 MISC_17 KD_1 KD_5 HC_14 KD_1MISC_6 HC_6 HC_5 HC_13 HC_4 HC_12 HC_11 HC_2 HC_15 HC_3 HC_20 HC_9 HC_17 HC_18 HC_16 HC_8 HC_19 HC_1 HC_7 HC_10 MISC_23 MISC_25 MISC_1 MISC_9 MISC_3 MISC_16 MISC_20 MISC_22 MISC_7 MISC_18 MISC_2 MISC_4 MISC_24 MISC_5 MISC_14 MISC_13 MISC_19 MISC_21 MISC_15 KD_4 MISC_8 MISC_10 MISC_12 KD_2 KD_7 MISC_11 KD_3 KD_6 MISC_17 KD_1 KD_5 HC_14 MISC_6 HC_6 HC_5 HC_13 HC_4 HC_12 HC_11 HC_2 HC_15 HC_3 HC_20 HC_9 HC_17 HC_18 HC_16 HC_8 HC_19 HC_1 HC_7 HC_10

MISC_17

KD_6

KD_3

MISC_11

KD_7

KD_2

E MISC_12

FTLFTL FCGR3AFCGR3A C1QA C1QB C1QCC1QC MISC_10 TNCTNC QSOX1QSOX1 GPX3GPX3

MISC_8

66 *** 66 *** *** *** *** KD_466 *** *** ***

ns ns ns ns MISC_15 ns ns ns 44 44 44 ** MISC_21 44 44

** *** *** *** *** MISC_19 *** *** ***

4 4 4

4 4 MISC_134

22 22 22 MISC_14 22

MISC_5 22

MISC_24

22 22 MISC_42

2 0

00 00 00 MISC_2 0

MISC_18 00

MISC_7

00 00 -−22 −2 −2 MISC_2200 -−22

-2 -2 MISC_20

MISC_16 -−22

MISC_3 HC Mild Severe KD HC Mild Severe KD HC Mild Severe KD HC Mild Severe KD HC Mild Severe KD HC Mild Severe KD MISC_9HC Mild Severe KD HC Mild Severe KD HC KD MISC_1 HC KD

Mild HC Mild KD HC Mild KD HC Mild KD HC Mild KD HC Mild KD HC Mild KD Mild Severe Severe Severe Severe Severe MISC_25 Severe Severe Severe

PRSS3PRSS3 AZGP1AZGP1 SERPINB3SERPINB3 RPS11RPS11 A2MA2M MISC_23 HRGHRG SHBGSHBG C7 44 *** *** *** *** 44 *** *** *** *** ns ns ns ns ns ns ns ns 44 44 44 44 44 44 *** *** *** *** *** *** *** *** 22 22 22 22 22 22 22 22

00 00 00 00 00 00 00 00

-−22 -−22 -−22 -−22 -−22 -−22 HC Mild Severe KD HC Mild Severe KD HC Mild Severe KD HC Mild Severe KD HC Mild Severe KD HC Mild Severe KD HC Mild Severe KD HC Mild Severe KD HC KD HC KD HC KD HC KD KD KD KD Mild Mild Mild Mild HC Mild KD HC Mild HC Mild HC Mild Severe Severe Severe Severe Severe Severe Severe Severe

Figure 3. Proteins distinguishing severe MIS-C from mild disease and KD. Protein differential expression analysis was performed between severe MIS-C (n = 20) and mild MIS-C (n = 5) groups. Proteins were considered significantly changed when FDR was less than 0.05 as calculated by mapDIA statistical software. A. Bar graphs show top increased and top decreased proteins in severe MIS-C when compared to mild, ranked by fold change and excluding Ig’s. B.

Selected pathways and functional annotation terms from protein functional enrichment analysis facilitated by PINE software using proteins increased (top panel) and decreased (bottom panel) in severe MIS-C compared with mild MIS-C. C. Venn diagram of proteins differentially regulated between severe MIS-C, mild MIS-C and KD. D. Heatmap of selected proteins distinguishing severe MIS-C from mild MIS-C and KD. E. Box plots of selected proteins found increased in severe MIS-C compared with mild MIS-C and KD. For the improved visualization purposes boxplots are showing scaled protein expression values. Scaling was performed by mean- centering and division by standard deviation of each protein variable. For box plots: the bounds of the boxes represent IQR (Q1 to Q3) and the whiskers represent the nonoutlier minimum and maximum values 1.5 × IQR. The median values are marked with a horizontal line in the boxes, and outliers are marked with a black centered point outside the whisker. Statistical analysis was calculated by mapDIA statistical software for protein differential expression using MS/MS fragment-level quantitative data. **p<0.01, ***p<0.001, ns = not significant. Figure 4

A Febrile Mild Severe Control MIS-C MIS-C Clustering RNA-seq Cell deconvolution whole blood RNA Differential expression analysis Pathway and network analysis n = 13 n = 4 n = 8 Top genes reduced in Top genes enhanced in B C Cluster 1 (FDR < 0.05) Cluster 1 (FDR < 0.05)

CD248 IDO1 ICOSLG C4BPA CD27 ETV7 CD8B BATF2 CARD17 CD8A MIS-C SERPING1 CD79A Cluster 1 APOL4 CXCR3 GBP6 CD3G Dimension2 CASP5 CD5 Leading logFC dim 2 dim logFC Leading CD274 CD28 IL1R2 MIS-C LCK P2RY14 Cluster 2 CD79B CXCL10 CD3E 12SOCS1 -2.0 -1.0 0.0 0.5 1.0 1.5 CD96 TGFA -2 -1 0 1 CD247 IL27 Dimension 1 CD22 BCL6 Leading logFC dim 1 B_cells_naive Febrile Control CD40LG SOCS3 CD4 CXCL8 B_cells_memory Mild MIS-C CD19 S100A8 Log2FCPlasma_cells Severe MIS-C T_cells_CD8 -3 -2 -1 0 0 1 2 3 4T_cells_CD4_naive5 6 7 log2FC log2FCT_cells_CD4_memory_resting T_cells_CD4_memory_activated Functional annotation terms - Cluster 1 vs. Cluster 2 T_cells_follicular_helper D E Cell deconvolution 12T_cells_regulatory_(Tregs)12 T cell activation Downregulated Fraction T_cells_gamma_delta z score ribonucleoprotein complex biogenesis NK_cells_resting 0.6 lymphocyte activation NK_cells_activated 0.4 hematopoietic cell lineage B_cells_naive Monocytes B_cells_naive 0.2 coreceptor activity B_cells_memory Macrophages_M0B_cells_memory 0 Plasma_cells B Cell Receptor Signaling Pathway Plasma_cells Macrophages_M1 T_cells_CD8 T_cells_CD8 adaptive immune system Macrophages_M2 T_cells_CD4_naive Dendritic_cells_restingT_cells_CD4_naive T_cells_CD4_memory_resting T_cells_CD4_memory_resting VEGFA−VEGFR2 signaling pathway Dendritic_cells_activated T_cells_CD4_memory_activated Mast_cells_restingT_cells_CD4_memory_activated type I interferon production T_cells_follicular_helper T_cells_follicular_helper Mast_cells_activated T cell activationTNF signaling pathway Downregulated genesT_cells_regulatory_(Tregs) T_cells_regulatory_(Tregs) Eosinophils ribonucleoprotein complex biogenesis T cell activation T_cells_gamma_delta T_cells_gamma_delta z score z score z 50 Neutrophils lymphocyte activationresponse to wounding 0.6 NK_cells_resting NK_cells_resting 0.6 0.4 hematopoieticresponse cell tolineage external biotic stimulus 0.4 NK_cells_activated 100 NK_cells_activated

coreceptor activity 0.2 Upregulated Monocytes Monocytes 0.2 GO terms reactive oxygen species metabolic process 0 Macrophages_M0 150 Macrophages_M0 0 B Cell Receptorpositive regulation Signaling ofPathway interleukin−8 production adaptive immune system Macrophages_M1 Macrophages_M1 positive regulation of immune effector process Pedi_I_255 Pedi_I_282 Pedi_I_287 Pedi_I_294 Macrophages_M2 Pedi_I_328 Pedi_I_279 Pedi_I_325 Pedi_I_206 Pedi_I_243 Pedi_I_276 Pedi_I_274 Pedi_I_239 Macrophages_M2 VEGFA−VEGFR2NOD signaling−like receptorpathway signaling pathway Dendritic_cells_resting Dendritic_cells_resting type I interferonNF production−kappa B signaling pathway Dendritic_cells_activated Dendritic_cells_activated TNF signaling pathwayMAPK signaling pathway genes Mast_cells_resting Mast_cells_resting Mast_cells_activated Mast_cells_activated T cell activationmacrophage activation Eosinophils human complement system 50 Eosinophils response to wounding Neutrophils Neutrophils response to external biotic stimulus FCGR activation 100 MISC_4 MISC_8 MISC_9 MISC_5 MISC_1 MISC_26 MISC_10 MISC_27 MISC_13 MISC_12 MISC_11 MISC_7 Upregulated GO terms reactive oxygen species metabolic processcytokine receptor binding 150 positive regulation of interleukinapoptosis−8 production modulation and signaling positive regulation of immune effector process 5 10 15 20 Pedi_I_239 Pedi_I_274 Pedi_I_276 Pedi_I_243 Pedi_I_206 Pedi_I_325 Pedi_I_279 Pedi_I_328 Pedi_I_294 Pedi_I_287 Pedi_I_282 Pedi_I_255 NOD−like receptor signaling pathway −log10(FDR)-log10FDR Pedi_I_255 Pedi_I_282 MISPedi_I_287 -CPedi_I_294 Pedi_I_328 Pedi_I_279 Pedi_I_325 Pedi_I_206 MISPedi_I_243 -CPedi_I_276 Pedi_I_274 Pedi_I_239 regulationFNF−kappa of Bimmune signalingTopregulation effector pathwayproteins process of immune enhanced effector in Cluster process 1 Cluster 1 Cluster 2 regulation of IGF transportregulation and of uptake MAPKIGF4 transport by signaling IGF binding and pathway uptake proteins(FDR by IGF < binding0.05) proteins platelet activation,macrophage signalingplatelet andactivation, activation aggregation signaling and aggregation 3 downregulated humanplasma complement lipoprotein system particleplasma lipoprotein particle downregulated FC negative2 2 regulationFCGR activation ofnegative proteolysis regulation of proteolysis H I

log TRBV11-2 skewing IgG to Spike RBD humoral immune responsehumoral mediated immune cytokine byresponse1 circulating receptor mediated immunoglobulin binding by circulating immunoglobulin TRBV11-2 Skewing IgG to Spike RBD apoptosiscomplement modulation and coagulationcomplement and signaling cascades and coagulation cascades 0 30 p*=0.0277 35 p*=0.0171 regulation of immunecomplement effector process activationcomplement5 activation10 15 20 collagen−containing collagenextracellular−containing matrix LTFextracellular matrix proteins proteins 30 regulation of IGF transport and uptake by IGF bindingMPO proteinsCRP PKM−log10(FDR) GRN LBP SAA2SAA1 LCN2 CTSB ICAM1 VNN1 LTA4H cellular response to heatcellular stressIGFBP2 response to heat stress platelet activation, signaling and aggregation FAM76B FCGR3A 10 10 25 blood coagulation blood coagulation downregulated plasma lipoprotein particle 20 acute inflammatory responseacute inflammatory response negativeG regulationFunctional of proteolysis annotation terms of proteins enhanced in Cluster 1 20 20 20 GO terms GO terms humoral immune response mediated by circulatingvesicle− immunoglobulinmediated transportvesicle−mediated transport 30 30 complement and coagulation cascades 15 response to stress response to stress IgG (ug/ml) upregulated complement activation upregulated 10 pyruvate metabolismpyruvate metabolism 10 collagen−containing extracellular matrix Proteinsproteins

neutrophil degranulationneutrophil degranulation TRBV11-2 Usage (%) cellular response to heat stress neutrophil activation neutrophilinvolved in activation immune response involved in immune response 10 5 blood coagulation leukocyte mediated immunityleukocyte mediated immunity acute inflammatory response 0 0 innate immune systeminnate immune system 20 GO terms vesicleimmune−mediated effector transport processimmune effector process 30 responsecellular localizationto stress cellular localization Cluster 1 Cluster 2 Cluster 1 Cluster 2 pyruvate metabolism 0 10 20 0 30 10 40 upregulated 20 30 40 neutrophil degranulation −log10(FDR)-log10FDR−log10(FDR) neutrophil activation involved in immune response leukocyte mediated immunity innate immune system immune effector process cellular localization 0 10 20 30 40 −log10(FDR) Figure 4. RNA-seq analysis of MIS-C. RNA-seq was performed using whole blood RNA isolated from febrile controls (n=13), mild MIS-C (n=4) and severe MIS-C (n=8) patients. A. Experimental design of RNA-seq analysis and patient groups. B. Principal co-ordinate analysis of RNA-seq profiles. C. Genes up or downregulated in cluster 1 vs. cluster 2 MIS-C patients (FDR < 0.05). D.

Selected pathways and functional annotation terms from gene functional enrichment analysis performed with PINE software using significantly up and down regulated (FDR<0.01, log2FC>1.5 and < -1.25) genes in cluster 1 vs. cluster 2 MIS-C patients. E. Cell deconvolution analysis of

RNA-seq data by CIBERSORT. F. Top proteins increased in cluster 1 vs. cluster 2, based on proteomics data. G. Enriched pathways and functional annotation terms based on protein expression changes significantly (FDR<0.05) upregulated in cluster 1 with respect to cluster 2. H.

TRBV11-2 expansion of RNA-seq samples (17). Data is presented as mean ± SEM. Statistical analysis: Mann-Whitney test. I. IgG titers against Spike protein receptor binding domain (RBD).

Data is presented as mean ± SEM. Statistical analysis: Mann-Whitney test. *p<0.05.

2 FC Log

8 7 6 5 4 3 2 1 0 -1

USP10

ZNF254

TROVE2

SOX6

STAT5A

PSME3

KLHL12

MAP9

RBM38

PDLIM5

CLN5

CCDC102B

SET

COMMD8

NAPB

ECE1

EIF1AY

TNNC2

Figure 5 ZBED1

ANXA11 A B C RGS19

MIS-C (n = 11) Candidate IgG MIS-C vs. Febrile Candidate IgA MIS-C vs. Febrile autoantibody Control autoantibody Control DYDC1

Febrile RNA RNA targets 14 targets 1

Control Cluster 1 Cluster 2 HK1

13 7 2 0 NCB2

2 0 FAM84A

RNA Cluster 1 13 0 9 RNA Cluster 2 RNA Cluster 1 3 0 1 RNA Cluster 2 EIF4B vs. Febrile vs. Febrile vs. Febrile vs. Febrile RAB11FIP1

Control Control Control Control

n = 5 n = 6 n = 5 WT1-AS

CTDP1

D IgG autoantibody targets (MISC vs. Febrile Control) F IgG autoantibody targets (MIS-C vs. Febrile Control) C11orf68

MAOA

2

FC

ATP4A * Cluster 2 Log ATP4A SATB1

UBE3A Cluster 1

TROVE2 * FOXK2

8 7 6 5 4 3 2 1 0 FOXK2 -1

UBE3A *

UBE3A

SATB1 1 Cluster

MAP9 *

Cluster 1 USP10

MAOA ATP4A

DYDC1 * 2 Cluster C11orf68 Cluster 2 ZNF254 RBM38 *

CTDP1 TNNC2 * TROVE2

ZNF254 WT1-AS *

ZBED1 RAB11FIP1 * SOX6 COMMD8 EIF4B * STAT5A

FAM84A PSME3 * PSME3

SOX6 NCB2 *

KLHL12 HK1 KLHL12 NCB2 DYDC1 MAP9

RGS19 FAM84A RBM38

PDLIM5 ANXA11 *

FOXK2 ZBED1 PDLIM5 ANXA11 TNNC2 * CLN5

EIF1AY C11orf68 CCDC102B

ECE1 ECE1 * Cluster 2 SET EIF1AY NAPB *

* Cluster 1 HK1 COMMD8 * COMMD8

SET Increased in Cluster 1 SET NAPB

NAPB CCDC102B * * ECE1

EIF4B CLN5 * STAT5A PDLIM5 EIF1AY

SATB1 RBM38

MAOA MAP9 TNNC2 RGS19 KLHL12 * ZBED1

* CCDC102B PSME3 ANXA11

WT1-AS STAT5A

RAB11FIP1 SOX6 RGS19

CTDP1 TROVE2 DYDC1 CLN5 ZNF254 HK1

USP10

USP10 * NCB2 C

- -2 -1 0 1 2 3 4 5 6 7 8 -1 0 1 2 3 4 5 6 7 8 FAM84A

log2FC log2FC Tissue Log FC Log FC EIF4B (relative to mean of2 Febrile Control) (relative to mean of2 Febrile Control) AnOther

AnMIS

- - A RAB11FIP1 A

Compartment

Tissue Autoantigen (A-An) Compartment WT1-AS

Cardiovascular Brain Previously reported Plasma Membrane CTDP1

Skeletal Muscle Immune Cell Not reported Intracellular C11orf68

GI tract Diverse/other MAOA

SATB1

E IgA autoantibody targets (MIS-C vs. Febrile Control) G IgA autoantibody targets (MIS-C vs. Febrile Control) FOXK2 UBE3A

FAM84A FAM84A 1 * Cluster Cluster 1 ATP4A

TNNC2 TNNC2 * 2 Cluster Cluster 2 GBP6 GBP6 -2 -1 0 1 2 3 4 -2 -1 0 1 2 3 4 log FC log FC 2 Log2 FC (relative to meanLog of2FC Febrile Control) (relative to mean of2 Febrile Control)

Figure 5. Autoantibody analysis of MIS-C. A. Autoantibody analysis was performed on serum from febrile controls (n=5) and MIS-C patients (n=11) using HuProt array. MIS-C samples correspond to RNA cluster 1 (n=6) and RNA cluster 2 (n=5) identified in Figure 4. B. Venn diagram of IgG A-Ab candidates in MIS-C and RNA clusters (p < 0.05, FC > 2). C. Venn diagram of IgA A-

Ab candidates in MIS-C and RNA clusters (p < 0.05, FC > 2). D. IgG autoantibody targets identified in MIS-C (n = 11) compared with febrile controls (n=5). The bar represents the log2FC.

Each dot represents one MIS-C patient presented as log2FC above the mean of febrile controls.

E. IgA autoantibody targets identified in MIS-C (n = 11) compared with febrile controls (n=5). The bar represents the log2FC. Each dot represents one MIS-C patient presented as log2FC above the mean of febrile controls. F. IgG autoantibody targets separated based on RNA cluster 1 (n=6) and RNA cluster 2 (n=5). Data is presented as log2FC above the mean of febrile controls. G. IgA autoantibody targets separated based on RNA cluster 1 (n=6) and RNA cluster 2 (n=5). Data is presented as log2FC above the mean of febrile controls. For box plots; the bounds of the boxes represent the interquartile range (IQR, Q1 to Q3) and the whiskers represent the minimum and maximum values. The median values are marked with a horizontal line within the box. * FDR <

0.05 compared to febrile controls.

Figure 6

A Somatic hyper- B A RRichnessichness (IGH) (IGH) Somatic hyper- B mutationmutation (IGH) p**p**=0.014 = 0.0 0(ANOVA)14 (ANOVA) p=0.0809p = 0.0 (ANOVA)809 (ANOVA) Cluster 1 10000 p**p**=0.0075=0.00 (t -(t-test)test) 100 MIS-C_4 MIS-C_8 d

8000 e 80 c s e en p i r y

t 6000 60 pe x ono e l c n

f 4000 40 e o g i . t o N

2000 An 20

% MIS-C_9 MIS-C_26 0 0 l l o 1 2 o 1 2 tr r r tr r r n te te n te te o s s o s s C lu lu C lu lu C C C C C C

10 p* = 0.0171 Cluster 2

Cluster 1 MIS-C_7 MIS-C_10 MIS-C_11

PC2 0 Cluster 2 Control

0 10 PC1

p* = 0.0120 MIS-C_12 MIS-C_13 MIS-C_27 10 Cluster 1 Cluster 2 PC2 0

0 10 PC1

p**=0.0014 p**=0.0020 IGHV4-39

pertoire 100 e

IGHV4-38 r r e

IGHV2 Control p Cluster 1 s e

IGHV3-66 Cluster 1 c Cluster 2 n IGHV3-74 e Cluster 2 u q 0 e

IGHV4-4 s d e

IGHV4-34 t c e

IGHV3-49 n n IGHV1-2 o C 0 IGHV1-69-2 % Levenshtein Levenshtein 0 0.1 0.2 distance 1 distance 3 Frequency

D TNFSF13B (BAFF) E IL-6 IL-10 ** ** 10000 ** 120 250

200 80 150 1000 100

IL-6 (pg/ml) 40 IL-10 (pg/ml) 50 Normalized Counts

100 0 0

Cluster 1 Cluster 2 Cluster 1 Cluster 2 Cluster 1 Cluster 2

Figure 6. B cell repertoire metrics, connectivity characteristics and skewing of IGHV/J usage of MIS-C patients RNA cluster 1 and 2. A. Richness and somatic hypermutation of productive IGH repertoires of MIS-C patients of RNA cluster 1 (n=5) and RNA cluster 2 (n=6) compared to age-matched febrile control patients (n=15). Bars indicate mean ± SD. Statistical analysis: Ordinary one-way ANOVA for global analysis and unpaired student’s t-test for paired comparison. B. Petri dish plots of IGH repertoire networks of MIS-C patients of RNA cluster 1 and

2. A sample of 1000 unique CDR3 amino acid clones per repertoire were subjected to imNet network analysis (75). Petri dish plots are shown for Levenshtein distance 1. Percentages of connected sequences of MIS-C patients of RNA cluster 1 and 2 obtained from networks with

Levenshtein distance 1 and 3 are shown as bar plot. Bars indicate mean ± SD. Statistical analysis:

Unpaired student’s t-test. C. Principal component analysis (PCA) of differential IGHVJ gene usage in MIS-C patients of RNA cluster 1 (n=5) versus cluster 2 (n=6) versus age-matched febrile controls (n=15). Statistical analysis: Pillai–Bartlett test of MANOVA of all principal components.

Frequencies per repertoire of the ten most skewed IGHV genes in MIS-C and febrile control patients are shown as boxplots. The boxes extend from the 25th to 75th percentiles, whiskers from min to max, the line within the box indicates the median. D. BAFF expression in MIS-C cluster 1 and cluster 2, using RNA-sequencing data in Figure 5. Data is presented as mean ±

SEM. Statistical analysis: Mann-Whitney test. E. IL-6 and IL-10 levels in serum of MIS-C cluster

1 and cluster 2 patients, using cytokine data from Figure S1. Data is presented as mean ± SEM.

Statistical analysis: Mann-Whitney test. **p<0.01.

Table 1: Patient Demographics

Healthy Febrile Mild MIS-C Severe MIS-C MIS-C All KD

Control (n=20) Control (n=15) (n=7) (n=20) (n=27) (n=7) Median Age, years (IQR) 15.5 (11.5-17) 12 (5.5-18.5) 3 (1.1-9.3) 10 (6.6-15) 8 (3-13.1) 3 (2-3.5)

Sex Male 11 (55%) 11 (73.3%) 3 (43%) 15 (75%) 18 (66.7%) 5 (71%) Female 9 (9%) 4 (26.6%) 4 (57%) 5 (25%%) 9 (33.3%) 2 (29%)

Race and Ethnicity

White/Non-Hispanic 16 (80%) 5 (33.3%) 4 (66.7%) 2 (10%) 6 (22.2%) Hispanic 4 (20%) 6 (40%) 0 14 (70%) 14 (51.8%)

Black 0 1 (6.7%) 0 1 (5%) 1 (3.7%)

Black/Hispanic 0 0 0 1 (5%) 1 (3.7%) Asian 0 1 (6.7%) 1 (16.7%) 1 (5%) 2 (7.4%) ND 0 2 (13.3%) 2 (33.3%) 1 (5%) 2 (7.4%) 7

SARS-CoV-2 serology + 0/15 0/15 6/7 (1 ND) 19/20 25/27 (1 ND) NA

SARS-CoV-2 PCR + 0/15 0/15 1/7 (1 ND) 7/20 8/27 (1 ND) NA (at time of admission)

Serum/plasma samples NA NA 5/5 17/20 22/25 7/7 collected pre-IVIG

RNA samples collected NA NA 4/4 5/8 9/12 NA pre-IVIG

ND=not determined, unknown value; NA=not appropriate