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.

A metagenomic DNA sequencing assay that is robust against environmental DNA contamination (preprint)

ABSTRACT Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present contamination-free metagenomic DNA sequencing (Coffee-seq), a metagenomic sequencing assay that is robust against environmental contamination. The core idea of Coffee-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied Coffee-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of inflammatory bowel disease in blood.

Cell-Free DNA in Blood Reveals Significant Cell, Tissue and Organ Specific injury and Predicts COVID-19 Severity (preprint)

COVID-19 primarily affects the lungs, but evidence of systemic disease with multi-organ involvement is emerging. Here, we developed a blood test to broadly quantify cell, tissue, and organ specific injury due to COVID-19, using genome-wide methylation profiling of circulating cell-free DNA in plasma. We assessed the utility of this test to identify subjects with severe disease in two independent, longitudinal cohorts of hospitalized patients. Cell-free DNA profiling was performed on 104 plasma samples from 33 COVID-19 patients and compared to samples from patients with other viral infections and healthy controls. We found evidence of injury to the lung and liver and involvement of red blood cell progenitors associated with severe COVID-19. The concentration of cfDNA correlated with the WHO ordinal scale for disease progression and was significantly increased in patients requiring intubation. This study points to the utility of cell-free DNA as an analyte to monitor and study COVID-19.