A gene expression-based diagnostic classifier for identification of severe COVID-19 and multisystem inflammatory syndrome in children (MIS-C) (preprint)

MIS-C is a severe hyperinflammatory condition with involvement of multiple organs that occurs in children who had COVID-19 infection. Accurate diagnostic tests are needed to guide management and appropriate treatment and to inform clinical trials of experimental drugs and vaccines, yet the diagnosis of MIS-C is highly challenging due to overlapping clinical features with other acute syndromes in hospitalized patients. Here we developed a gene expression-based classifier for MIS-C by RNA-Seq transcriptome profiling and machine learning based analyses of 195 whole blood RNA and 76 plasma cell-free RNA samples from 191 subjects, including 95 MIS-C patients, 66 COVID-19 infected patients with moderately severe to severe disease, and 30 uninfected controls. We divided the group into a training set (70%) and test set (30%). After selection of the top 300 differentially expressed genes in the training set, we simultaneously trained 13 classification models to distinguish patients with MIS-C and COVID-19 from controls using five-fold cross-validation and grid search hyperparameter tuning. The final optimal classifier models had 100% diagnostic accuracy for MIS-C (versus non-MIS-C) and 85% accuracy for severe COVID-19 (versus mild/asymptomatic COVID-19). Orthogonal validation of a random subset of 11 genes from the final models using quantitative RT-PCR confirmed the differential expression and ability to discriminate MIS-C and COVID-19 from controls. These results underscore the utility of a gene expression classifier for diagnosis of MIS-C and severe COVID-19 as specific and objective biomarkers for these conditions.

Nucleic acid biomarkers of immune response and cell and tissue damage in children with COVID-19 and MIS-C (preprint)

Differential host responses in coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in children (MIS-C) remain poorly characterized. Here we use next-generation sequencing to longitudinally analyze blood samples from pediatric patients with acute COVID-19 (n=70) or MIS-C (n=141) across three hospitals. Profiling of plasma cell-free nucleic acids uncovers distinct signatures of cell injury and death between these two disease states, with increased heterogeneity and multi-organ involvement in MIS-C encompassing diverse cell types such as endothelial and neuronal Schwann cells. Whole blood RNA profiling reveals upregulation of similar pro-inflammatory signaling pathways in COVID-19 and MIS-C, but also MIS-C specific downregulation of T cell-associated pathways. Profiling of plasma cell-free RNA and whole blood RNA in paired samples yields different yet complementary signatures for each disease state. Our work provides a systems-level, multi-analyte view of immune responses and tissue damage in COVID-19 and MIS-C and informs the future development of new disease biomarkers.

Early immune responses have long-term associations with clinical, virologic, and immunologic outcomes in patients with COVID-19 (preprint)

The great majority of SARS-CoV-2 infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, mild to moderate infections are an important contributor to ongoing transmission. There remains a critical need to identify host immune biomarkers predictive of clinical and virologic outcomes in SARS-CoV-2-infected patients. Leveraging longitudinal samples and data from a clinical trial of Peginterferon Lambda for treatment of SARS-CoV-2 infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients within the first 2 weeks of symptom onset. We define early immune signatures, including plasma levels of RIG-I and the CCR2 ligands (MCP1, MCP2 and MCP3), associated with control of oropharyngeal viral load, the degree of symptom severity, and immune memory (including SARS-CoV-2-specific T cell responses and spike (S) protein-binding IgG levels). We found that individuals receiving BNT162b2 (Pfizer-BioNTech) vaccine had similar early immune trajectories to those observed in this natural infection cohort, including the induction of both inflammatory cytokines (e.g. MCP1) and negative immune regulators (e.g. TWEAK). Finally, we demonstrate that machine learning models using 8-10 plasma protein markers measured early within the course of infection are able to accurately predict symptom severity, T cell memory, and the antibody response post-infection.

Mining transcriptomics and clinical data reveals ACE2 expression modulators and identifies cardiomyopathy as a risk factor for mortality in COVID-19 patients (preprint)

Angiotensin-converting enzyme 2 (ACE2) is the cell-entry receptor for SARS-CoV-2. It plays critical roles in both the transmission and the pathogenesis of the coronavirus disease 2019 (COVID-19). Comprehensive profiling of ACE2 expression patterns will help researchers to reveal risk factors of severe COVID-19 illness. While the expression of ACE2 in healthy human tissues has been well characterized, it is not known which diseases and drugs might modulate the ACE2 expression. In this study, we developed GENEVA (GENe Expression Variance Analysis), a semi-automated framework for exploring massive amounts of RNA-seq datasets. We applied GENEVA to 28,6650 publicly available RNA-seq samples to identify any previously studied experimental conditions that could directly or indirectly modulate ACE2 expression. We identified multiple drugs, genetic perturbations, and diseases that modulate the expression of ACE2, including cardiomyopathy, HNF1A overexpression, and drug treatments with RAD140 and Itraconazole. Our unbiased meta-analysis of seven datasets confirms ACE2 up-regulation in all cardiomyopathy categories. Using electronic health records data from 3936 COVID19 patients, we demonstrate that patients with pre-existing cardiomyopathy have an increased mortality risk than age-matched patients with other cardiovascular conditions. GENEVA is applicable to any genes of interest and is freely accessible at http://www.genevatool.org .

CovidCounties - an interactive, real-time tracker of the COVID-19 pandemic at the level of US counties (preprint)

Management of the COVID-19 pandemic has proven to be a significant challenge to policy makers. This is in large part due to uneven reporting and the absence of open-access visualization tools to present local trends and infer healthcare needs. Here we report the development of CovidCounties.org, an interactive web application that depicts daily disease trends at the level of US counties using time series plots and maps. This application is accompanied by a manually curated dataset that catalogs all major public policy actions made at the state-level, as well as technical validation of the primary data. Finally, the underlying code for the site is also provided as open source, enabling others to validate and learn from this work.