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Unlocking the potential of personalized medicine in point-of-care settings requires a new generation of biomarker and proteomic assays. Ideally, assays could inexpensively perform hundreds of quantitative protein measurements in parallel at the bedsides of patients. This goal greatly exceeds current capabilities. Furthermore, biomarker assays are often challenging to translate from benchtop to clinic due to difficulties achieving and assessing the necessary selectivity, sensitivity, and reproducibility. To address these challenges, we developed an efficient (70 %) immunoassay method. Specifically, the immunoblot membrane is dotted with the sample and then developed in a vortex fluidic device (VFD) reactor. All assay steps-blocking, binding, and washing-leverage the unique thin-film microfluidics of the VFD. The approach can accelerate direct, indirect, and sandwich immunoblot assays. The applications demonstrated include assays relevant to both the laboratory and the clinic.

A previous report demonstrated the strong association between the presence of antibodies binding to an epitope region from SARS-CoV-2 nucleocapsid, termed Ep9, and COVID-19 disease severity. Patients with anti-Ep9 antibodies (Abs) had hallmarks of antigenic interference (AIN), including early IgG upregulation and cytokine-associated injury. Thus, the immunological memory of a prior infection was hypothesized to drive formation of suboptimal anti-Ep9 Abs in severe COVID-19 infections. This study identifies a putative primary antigen capable of stimulating production of cross-reactive, anti-Ep9 Abs. Binding assays with patient blood samples directly show cross-reactivity between Abs binding to Ep9 and only one bioinformatics-derived, homologous putative antigen, a sequence derived from the neuraminidase protein of H3N2 influenza A virus. This cross-reactive binding is highly influenza strain specific and sensitive to even single amino acid changes in epitope sequence. The neuraminidase protein is not present in the influenza vaccine, and the anti-Ep9 Abs likely resulted from the widespread influenza infection in 2014. Therefore, AIN from a previous infection could underlie some cases of COVID-19 disease severity.

A previous report demonstrated the strong association between the presence of antibodies binding to an epitope region from SARS-CoV-2 nucleocapsid, termed Ep9, and COVID-19 disease severity. Patients with anti-Ep9 antibodies (Abs) had hallmarks of antigenic imprinting (AIM), including early IgG upregulation and cytokine-associated injury. Thus, the immunological memory of a previous infection was hypothesized to drive formation of suboptimal anti-Ep9 Abs in severe COVID-19 infections. This study identifies a putative primary antigen capable of stimulating production of cross-reactive, anti-Ep9 Abs. Binding assays with patient blood samples directly show cross-reactivity between Abs binding to Ep9 and only one bioinformatics-derived, homologous potential antigen, a sequence derived from the neuraminidase protein of H3N2 Influenza A virus. This cross-reactive binding is highly influenza strain specific and sensitive to even single amino acid changes in epitope sequence. The neuraminidase protein is not present in the influenza vaccine, and the anti-Ep9 Abs likely resulted from the widespread influenza infection in 2014. Therefore, AIM from a previous infection could underlie some cases of COVID-19 disease severity.ImportanceInfections with SARS-COV-2 result in diverse disease outcomes, ranging from asymptomatic to fatal. The mechanisms underlying different disease outcomes remain largely unexplained. Previously, our laboratory identified a strong association between the presence of an antibody and increased disease severity in a subset of COVID-19 patients. Here, we report that this severity-associated antibody cross-reacts with viral proteins from an influenza A viral strain from 2014. Therefore, we speculate that antibodies generated against previous infections, like the 2014 influenza A, play a significant role in directing some peoples’ immune responses against SARS-COV-2. Such understanding of the sources and drivers of COVID-19 disease severity can help early identification and pre-emptive treatment.

The COVID-19 pandemic has resulted in over two million deaths worldwide. Despite efforts to fight the virus, the disease continues to overwhelm hospitals with severely ill patients., Effective methods for predicting COVID-19 disease trajectories are urgently needed. Here, enzyme-linked immunosorbent assay (ELISA) and coronavirus antigen microarray (COVAM) analysis mapped antibody epitopes in the plasma of COVID-19 patients (n = 86) experiencing a wide range of disease states. The experiments identified antibodies to a 21-residue epitope from nucleocapsid (termed Ep9) associated with severe disease, including admission to the intensive care unit (ICU), requirement for ventilators, or death. Importantly, anti-Ep9 antibodies can be detected within 6 days post-symptom onset and sometimes within 1 day. Furthermore, anti-Ep9 antibodies correlate with various comorbidities and hallmarks of immune hyperactivity. We introduce a simple-to-calculate, disease risk factor score to quantitate each patient’s comorbidities and age. For patients with anti-Ep9 antibodies, scores above 3.0 predict more severe disease outcomes with a 13.42 likelihood ratio (96.7% specificity). The results lay the groundwork for a new type of COVID-19 prognostic to allow early identification and triage of high-risk patients. Such information could guide more effective therapeutic intervention. IMPORTANCE The COVID-19 pandemic has resulted in over two million deaths worldwide. Despite efforts to fight the virus, the disease continues to overwhelm hospitals with severely ill patients. Diagnosis of COVID-19 is readily accomplished through a multitude of reliable testing platforms; however, prognostic prediction remains elusive. To this end, we identified a short epitope from the SARS-CoV-2 nucleocapsid protein and also a disease risk factor score based upon comorbidities and age. The presence of antibodies specifically binding to this epitope plus a score cutoff can predict severe COVID-19 outcomes with 96.7% specificity.

DJ-1, a 20.7 kDa protein, is overexpressed in people who have bladder cancer (BC). Its elevated concentration in urine allows it to serve as a marker for BC. But no biosensor for the detection of DJ-1 has been demonstrated. Here, we describe a virus bioresistor (VBR) capable of detecting DJ-1 in urine at a concentration of 10 pM in one minute. The VBR consists of a pair of millimeter-scale gold electrodes that measure the electrical impedance of an ultra-thin (≈150–200 nm), two-layer polymeric channel. The top layer of this channel (90–105 nm in thickness) consists of an electrodeposited virus-PEDOT (PEDOT is poly(3,4-ethylenedioxythiophene)) composite containing embedded M13 virus particles that are engineered to recognize and bind to the target protein of interest, DJ-1. The bottom layer consists of spin-coated PEDOT-PSS (poly(styrene sulfonate)). Together, these two layers constitute a current divider. We demonstrate here that reducing the thickness of the bottom PEDOT-PSS layer increases its resistance, and concentrates the resistance drop of the channel in the top virus-PEDOT layer, thereby increasing the sensitivity of the VBR and enabling the detection of DJ-1. Large signal amplitudes coupled with the inherent simplicity of the VBR sensor design results in high signal-to-noise (S/N > 100) and excellent sensor-to-sensor reproducibility characterized by coefficients of variation in the range of 3–7% across the DJ-1 binding curve down to a concentration of 30 pM, near the 10 pM limit of detection (LOD), encompassing four orders of magnitude in concentration.