Autoantibody discovery across monogenic, acquired, and COVID19-associated autoimmunity with scalable PhIP-Seq

Phage Immunoprecipitation-Sequencing (PhIP-Seq) allows for unbiased, proteome-wide autoantibody discovery across a variety of disease settings, with identification of disease-specific autoantigens providing new insight into previously poorly understood forms of immune dysregulation. Despite several successful implementations of PhIP-Seq for autoantigen discovery, including our previous work (Vazquez et al. 2020), current protocols are inherently difficult to scale to accommodate large cohorts of cases and importantly, healthy controls. Here, we develop and validate a high throughput extension of PhIP-seq in various etiologies of autoimmune and inflammatory diseases, including APS1, IPEX, RAG1/2 deficiency, Kawasaki Disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), and finally, mild and severe forms of COVID19. We demonstrate that these scaled datasets enable machine-learning approaches that result in robust prediction of disease status, as well as the ability to detect both known and novel autoantigens, such as PDYN in APS1 patients, and intestinally expressed proteins BEST4 and BTNL8 in IPEX patients. Remarkably, BEST4 antibodies were also found in 2 patients with RAG1/2 deficiency, one of whom had very early onset IBD. Scaled PhIP-Seq examination of both MIS-C and KD demonstrated rare, overlapping antigens, including CGNL1, as well as several strongly enriched putative pneumonia-associated antigens in severe COVID19, including the endosomal protein EEA1. Together, scaled PhIP-Seq provides a valuable tool for broadly assessing both rare and common autoantigen overlap between autoimmune diseases of varying origins and etiologies.

Multicenter Validation of a Machine Learning Algorithm for Diagnosing Pediatric Patients with Multisystem Inflammatory Syndrome and Kawasaki Disease

BackgroundMultisystem inflammatory syndrome in children (MIS-C) is a novel disease identified during the COVID-19 pandemic characterized by systemic inflammation following SARS-CoV-2 infection. Delays in diagnosing MIS-C may lead to more severe disease with cardiac dysfunction or death. Most pediatric patients recover fully with anti-inflammatory treatments, but early detection of MIS-C remains a challenge given its clinical similarities to Kawasaki disease (KD) and other acute childhood illnesses. MethodsWe developed KIDMATCH (KawasakI Disease vs Multisystem InflAmmaTory syndrome in CHildren), a deep learning algorithm for screening patients for MIS-C, KD, or other febrile illness, using age, the five classical clinical KD signs, and 17 laboratory measurements prospectively collected within 24 hours of admission to the emergency department from 1448 patients diagnosed with KD or other febrile illness between January 1, 2009 and December 31, 2019 at Rady Childrens Hospital. For MIS-C patients, the same data was collected from 131 patients between May 14, 2020 to June 18, 2021 at Rady Childrens Hospital, Connecticut Childrens Hospital, and Childrens Hospital Los Angeles. We trained a two-stage model consisting of feedforward neural networks to distinguish between MIS-C and non MIS-C patients and then KD and other febrile illness. After internally validating the algorithm using 10-fold cross validation, we incorporated a conformal prediction framework to tag patients with erroneous data or distribution shifts, enhancing the model generalizability and confidence by flagging unfamiliar cases as indeterminate instead of making spurious predictions. We externally validated KIDMATCH on 175 MIS-C patients from 16 hospitals across the United States. FindingsKIDMATCH achieved a high median area under the curve in the 10-fold cross validation of 0.988 [IQR: 0.98-0.993] in the first stage and 0.96 [IQR: 0.956-0.972] in the second stage using thresholds set at 95% sensitivity to detect positive MIS-C and KD cases respectively during training. External validation of KIDMATCH on MIS-C patients correctly classified 76/83 (2 rejected) patients from the CHARMS consortium, 47/50 (1 rejected) patients from Boston Childrens Hospital, and 36/42 (2 rejected) patients from Childrens National Hospital. InterpretationKIDMATCH has the potential to aid frontline clinicians with distinguishing between MIS-C, KD, and similar febrile illnesses in a timely manner to allow prompt treatment and prevent severe complications. FundingEunice Kennedy Shriver National Institute of Child Health and Human Development, National Heart, Lung, and Blood Institute, Patient-Centered Outcomes Research Institute, National Library of Medicine

Autoantibodies Detected in MIS-C Patients due to Administration of Intravenous Immunoglobulin

The autoantibody profile associated with known autoimmune diseases in patients with COVID-19 or multisystem inflammatory syndrome in children (MIS-C) remains poorly defined. Here we show that adults with COVID-19 had a moderate prevalence of autoantibodies against the lung antigen KCNRG, and SLE-associated Smith autoantigen. Children with COVID-19 rarely had autoantibodies; one of 59 children had GAD65 autoantibodies associated with acute insulin-dependent diabetes. While autoantibodies associated with SLE/Sjogrens syndrome (Ro52, Ro60, and La) and/or autoimmune gastritis (gastric ATPase) were detected in 74% (40/54) of MIS-C patients, further analysis of these patients and of children with Kawasaki disease (KD), showed that the administration of intravenous immunoglobulin (IVIG) was largely responsible for detection of these autoantibodies in both groups of patients. Monitoring in vivo decay of the autoantibodies in MIS-C children showed that the IVIG-derived Ro52, Ro60, and La autoantibodies declined to undetectable levels by 45-60 days, but gastric ATPase autoantibodies declined more slowly requiring >100 days until undetectable. Together these findings demonstrate that administration of high-dose IVIG is responsible for the detection of several autoantibodies in MIS-C and KD. Further studies are needed to investigate autoantibody production in MIS-C patients, independently from IVIG administration.

An AI-guided invariant signature places MIS-C with Kawasaki disease in a continuum of host immune responses

A significant surge in cases of multisystem inflammatory syndrome in children (MIS-C, also called Pediatric Inflammatory Multisystem Syndrome - PIMS) has been observed amidst the COVID-19 pandemic. MIS-C shares many clinical features with Kawasaki disease (KD), although clinical course and outcomes are divergent. We analyzed whole blood RNA sequences, serum cytokines, and formalin fixed heart tissues from these patients using a computational toolbox of two gene signatures, i.e., the 166-gene viral pandemic (ViP) signature, and its 20-gene severe (s)ViP subset that were developed in the context of SARS-CoV-2 infection and a 13-transcript signature previously demonstrated to be diagnostic for KD. Our analyses revealed that KD and MIS-C are on the same continuum of the host immune response as COVID-19. While both the pediatric syndromes converge upon an IL15/IL15RA-centric cytokine storm, suggestive of shared proximal pathways of immunopathogenesis, they diverge in other laboratory parameters and cardiac phenotypes. The ViP signatures also revealed unique targetable cytokine pathways in MIS-C, place MIS-C farther along in the spectrum in severity compared to KD and pinpoint key clinical (reduced cardiac function) and laboratory (thrombocytopenia and eosinopenia) parameters that can be useful to monitor severity.