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

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

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.

COVID-19 Variant Detection with a High-Fidelity CRISPR-Cas12 Enzyme

Laboratory tests for the accurate and rapid identification of SARS-CoV-2 variants can potentially guide the treatment of COVID-19 patients and inform infection control and public health surveillance efforts. Here we present the development and validation of a rapid COVID-19 variant DETECTR(R) assay incorporating loop-mediated isothermal amplification (LAMP) followed by CRISPR-Cas12 based identification of single nucleotide polymorphism (SNP) mutations in the SARS-CoV-2 spike (S) gene. This assay targets the L452R, E484K/Q/A, and N501Y mutations that are associated with nearly all circulating viral lineages and identifies the two circulating variants of concern, Delta and Omicron. In a comparison of three different Cas12 enzymes, only the newly identified enzyme CasDx1 was able to accurately identify all targeted SNP mutations. An analysis pipeline for CRISPR-based SNP identification from 139 clinical samples yielded an overall SNP concordance of 98% and agreement with SARS-CoV-2 lineage classification of 138/139 compared to viral whole-genome sequencing. We also showed that detection of the single E484A mutation was necessary and sufficient to accurately identify Omicron from other major circulating variants in patient samples. These findings demonstrate the utility of CRISPR-based DETECTR(R) as a faster and simpler diagnostic than sequencing for SARS-CoV-2 variant identification in clinical and public health laboratories.

Predominance of antibody-resistant SARS-CoV-2 variants in vaccine breakthrough cases from the San Francisco Bay Area, California

Associations between vaccine breakthrough cases and infection by SARS coronavirus 2 (SARS-CoV-2) variants have remained largely unexplored. Here we analyzed SARS-CoV-2 whole-genome sequences and viral loads from 1,373 persons with COVID-19 from the San Francisco Bay Area from February 1 to June 30, 2021, of which 125 (9.1%) were vaccine breakthrough infections. Fully vaccinated were more likely than unvaccinated persons to be infected by variants carrying mutations associated with decreased antibody neutralization (L452R, L452Q, E484K, and/or F490S) (78% versus 48%, p = 1.96e-08), but not by those associated with increased infectivity only (N501Y) (85% versus 77%, p = 0.092). Differences in viral loads were non-significant between unvaccinated and fully vaccinated persons overall (p = 0.99) and according to lineage (p = 0.09 - 0.78). Viral loads were significantly higher in symptomatic as compared to asymptomatic vaccine breakthrough cases (p < 0.0001), and symptomatic vaccine breakthrough infections had similar viral loads to unvaccinated infections (p = 0.64). In 5 cases with available longitudinal samples for serologic analyses, vaccine breakthrough infections were found to be associated with low or undetectable neutralizing antibody levels attributable to immunocompromised state or infection by an antibody-resistant lineage. Taken together, our results suggest that vaccine breakthrough infecions are overrepresnted by circulating antibody-resistant SARS-CoV-2 variants, and that symptomatic breakthrough infections may potentially transmit COVID-19 as efficiently as unvaccinated infections, regardless of the infecting lineage.

Rapid Detection of 2019 Novel Coronavirus SARS-CoV-2 Using a CRISPR-based DETECTR Lateral Flow Assay

An outbreak of novel betacoronavirus, SARS-CoV-2 (formerly named 2019-nCoV), began in Wuhan, China in December 2019 and the COVID-19 disease associated with infection has since spread rapidly to multiple countries. Here we report the development of SARS-CoV-2 DETECTR, a rapid ([~]30 min), low-cost, and accurate CRISPR-Cas12 based lateral flow assay for detection of SARS-CoV-2 from respiratory swab RNA extracts. We validated this method using contrived reference samples and clinical samples from infected US patients and demonstrated comparable performance to the US CDC SARS-CoV-2 real-time RT-PCR assay.