Severity Predictors Integrating salivary Transcriptomics and proteomics with Multi neural network Intelligence in SARS-CoV2 infection in Children (SPITS MISC)

AbstractChildren have been disproportionately less impacted by the Corona Virus Disease 2019 (COVID-19) causedby the Severe Acute Respiratory Syndrome Corona Virus 2 (SAR-CoV-2) compared to adults. However,severe illnesses including Multisystem Inflammatory Syndrome (MIS-C) and respiratory failure have occurredin a small proportion of children with SARS-CoV-2 infection. Nearly 80% of children with MIS-C are critically illwith a 2-4% mortality rate. Currently there are no modalities to characterize the spectrum of disease severityand predict which child with SARS-CoV-2 exposure will likely develop severe illness including MIS-C. Thusthere is an urgent need to develop a diagnostic modality to distinguish the varying phenotypes of disease andrisk stratify disease. The epigenetic changes in microRNA (miRNA) profiles that occur due to an infection canimpact disease severity by altering immune response and cytokine regulation which may be detected in bodyfluids including saliva. Our long-term goal is to improve outcomes of children with SARS-CoV-2 by earlyidentification and treatment of those at risk for severe illness. Our central hypothesis is that a model thatintegrates salivary biomarkers with social and clinical determinants of health will predict disease severity inchildren with SARS-CoV-2 infection. The central hypothesis will be pursued through phased four specific aims.The first two aims will be pursued during the R61 phase and include: 1) Define and compare the salivarymolecular host response in children with varying phenotypes (severe and non severe) SARS-CoV-2 infectionsand 2) Develop and validate a sensitive and specific model to predict severe SARS-CoV-2 illness in children.During the R33 phase we will pursue the following two aims: 3) Develop a portable, rapid device that quantifiessalivary miRNAs with comparable accuracy to predicate technology (qRT-PCR), and 4) Develop an artificialintelligence (AI) assisted cloud and mobile system for early recognition of severe SARS-CoV-2 infection inchildren. We will pursue the above aims using an innovative combination of salivaomics and bioinformatics,analytic techniques of AI and clinical informatics. The proposed research is significant because development ofa sensitive model to risk stratify disease is expected to improve outcomes of children with severe SARS-CoV-2infection via early recognition and timely intervention. The proximate expected outcome of this proposal isbetter understanding of the epigenetic regulation of host immune response to the viral infection which weexpect to lead to personalized therapy in the future. The results will have a positive impact immediately as itwill lead to the creation of patient profiles based on individual risk factors which can enable early identificationof severe disease and appropriate resource allocation during the pandemic.