We conducted a serological study to define correlates of immunity against SARS-CoV-2. Relative to mild COVID-19 cases, individuals with severe disease exhibited elevated virus-neutralizing titers and antibodies against nucleocapsid (N) and the receptor binding domain (RBD) of spike protein. Age and sex played lesser roles. All cases, including asymptomatic individuals, seroconverted by 2 weeks post-PCR confirmation. Spike RBD and S2 and neutralizing antibodies remained detectable through 5-7 months post-onset, whereas α-N titers diminished. Testing of 5882 members of the local community revealed only 1 sample with seroreactivity to both RBD and S2 that lacked neutralizing antibodies. This fidelity could not be achieved with either RBD or S2 alone. Thus, inclusion of multiple independent assays improved the accuracy of antibody tests in low seroprevalence communities and revealed differences in antibody kinetics depending on the antigen. We conclude that neutralizing antibodies are stably produced for at least 5-7 months after SARS-CoV-2 infection.

Summary Purpose The management of epilepsy in children is particularly challenging when seizures are resistant to anti-epileptic medications, or undergo many changes in seizure type over time, or have comorbid cognitive, behavioral, or motor deficits. Despite efforts to classify such epilepsies based on clinical and electroencephalographic criteria, many children never receive a definitive etiological diagnosis. Whole exome sequencing (WES) is proving to be a highly effective method for identifying de novo variants that cause neurological disorders, especially those associated with abnormal brain development. Here we explore the utility of WES for identifying candidate causal de novo variants in a cohort of children with heterogeneous sporadic epilepsies without etiological diagnoses. Methods We performed WES (mean coverage ~40X) on 10 trios comprised of unaffected parents and a child with sporadic epilepsy characterized by difficult-to-control seizures and some combination of developmental delay, epileptic encephalopathy, autistic features, cognitive impairment, or motor deficits. Sequence processing and variant calling were performed using standard bioinformatics tools. A custom filtering system was used to prioritize de novo variants of possible functional significance for validation by Sanger sequencing. Key Findings In nine of ten probands, we identified one or more de novo variants predicted to alter protein function, for a total of 15. Four probands had de novo mutations in genes previously shown to harbor heterozygous mutations in patients with severe, early-onset epilepsies (two in SCN1A, and one each in CDKL5 and EEF1A2). In three children, the de novo variants were in genes with functional roles that are plausibly relevant to epilepsy (KCNH5, CLCN4 and ARHGEF15). The variant in KCNH5 alters one of the highly conserved arginine residues of the voltage sensor of the encoded voltage-gated potassium channel. In vitro analyses using cell-based assays revealed that the CLCN4 mutation greatly impaired ion transport by the ClC-4 2Cl−/H+-exchanger and that the mutation in ARHGEF15 reduced GEF exchange activity of the gene product, Ephexin5, by about 50%. Interestingly, these seven probands all presented with seizures within the first six months of life, and six of these have intractable seizures. Significance The finding that seven of ten children carried de novo mutations in genes of known or plausible clinical significance to neuronal excitability suggests that WES will be of use for the molecular genetic diagnosis of sporadic epilepsies in children, especially when seizures are of early onset and difficult to control.

Wastewater-based epidemiology has potential as an early-warning tool for determining the presence of COVID-19 in a community. The University of Arizona (UArizona) utilized WBE paired with clinical testing as a surveillance tool to monitor the UArizona community for SARS-CoV-2 in near real-time, as students re-entered campus in the fall. Positive detection of virus RNA in wastewater lead to selected clinical testing, identification, and isolation of three infected individuals (one symptomatic and two asymptomatic) that averted potential disease transmission. This case study demonstrated the value of WBE as a tool to efficiently utilize resources for COVID-19 prevention and response. Thus, WBE coupled with targeted clinical testing was further conducted on 13 dorms during the course of the Fall semester (Table 3). In total, 91 wastewater samples resulted in positive detection of SARS-CoV-2 RNA that successfully provided an early-warning for at least a single new reported case of infection (positive clinical test) amongst the residents living in the dorm. Overall, WBE proved to be an accurate diagnostic for new cases of COVID-19 with an 82.0% positive predictive value and an 88.9% negative predictive value. Increases in positive wastewater samples and clinical tests were noted following holiday-related activities. However, shelter-in-place policies proved to be effective in reducing the number of daily reported positive wastewater and clinical tests. This case study provides evidence for WBE paired with clinical testing and public health interventions to effectively contain potential outbreaks of COVID-19 in defined communities.

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There is an urgent need to identify cellular/molecular mechanisms responsible for severe COVID-19 progressing to mortality. We initially performed untargeted/targeted lipidomics and focused biochemistry on 127 plasma samples and found elevated metabolites associated with secreted phospholipase A2 (sPLA2) activity and mitochondrial dysfunction in severe COVID-19 patients.Deceased COVID-19 patients had higher levels of circulating, catalytically active sPLA2 Group IIA (sPLA2-IIA), with a median value 9.6-fold higher than mild patients and 5.0-fold higher than severe COVID-19 survivors. Elevated sPLA2-IIA levels paralleled several indices of COVID-19 disease severity (e.g., kidney dysfunction, hypoxia, multiple organ dysfunction). A decision tree generated by machine learning identified sPLA2-IIA levels as a central node in stratifying patients that succumbed to COVID-19. Random forest analysis and LASSO-based regression analysis additionally identified sPLA2-IIA and blood urea nitrogen (BUN) as the key variables among 80 clinical indices in predicting COVID-19 mortality. The combined PLA-BUN index performed significantly better than either alone. An independent cohort (n=154) confirmed higher plasma sPLA2-IIA levels in deceased patients vs. severe or mild COVID-19, with the PLA-BUN indexbased decision tree satisfactorily stratifying mild, severe, and deceased COVID-19 patients. With clinically tested inhibitors available, this study supports sPLA2-IIA as a therapeutic target to reduce COVID-19 mortality.