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Development and Application of Human Coronavirus Protein Microarray for Specificity Analysis.

ABSTRACT: Coronavirus is an enveloped RNA virus that causes mild to severe respiratory diseases in humans, including HKU1-CoV, 229E-CoV, NL63-CoV, OC43-CoV, SARS-CoV, MERS-CoV, and SARS-CoV-2. Due to the outbreak of SARS-CoV-2, it is important to identify the patients and investigate their immune responses. Protein microarray is one of the best platforms to profile the antibodies in the blood because of its fast, multiplexed, and sensitive nature. To fully understand the immune responses and biological specificities, this study developed a human coronavirus (HCoV) protein microarray and included all seven human coronaviruses and three influenza viruses. Each protein was printed in triplicate and formed 14 identical blocks per array. The HCoV protein microarray showed high reproducibility and sensitivity to the monoclonal antibodies against spike and nucleocapsid protein with detection limits of 10-200 pg. The HCoV proteins that were immobilized on the array were properly folded and functional by showing interactions with a known human receptor, e.g., ACE2. By profiling the serum IgG and IgA from 32 COVID-19 patients and 36 healthy patients, the HCoV protein microarray demonstrated 97% sensitivity and 97% specificity with two biomarkers. The results also showed the cross-reactivity of IgG and IgA in COVID-19 patients to spike proteins from various coronaviruses, including that from SARS-CoV, HKU1-CoV, and OC43-CoV. Finally, an innate immune protein named surfactant protein D showed broad affinities to spike proteins in all human coronaviruses. Overall, the HCoV protein microarray is multiplexed, sensitive, and specific, which is useful in diagnosis, immune assessment, biological development, and drug screening.

SUBMITTER: Du PX

PROVIDER: S-EPMC8146142 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundmRNA profiling has become an important tool for developing and validating prognostic assays predictive of disease treatment response and outcome. Archives of annotated formalin-fixed paraffin-embedded tissues (FFPET) are available as a potential source for retrospective studies. Methods are needed to profile these FFPET samples that are linked to clinical outcomes to generate hypotheses that could lead to classifiers for clinical applications.MethodsWe developed a two-color microarray-based profiling platform by optimizing target amplification, experimental design, quality control, and microarray content and applied it to the profiling of FFPET samples. We profiled a set of 50 fresh frozen (FF) breast cancer samples and assigned class labels according to the signature and method by van 't Veer et al 1 and then profiled 50 matched FFPET samples to test how well the FFPET data predicted the class labels. We also compared the sorting power of classifiers derived from FFPET sample data with classifiers derived from data from matched FF samples.ResultsWhen a classifier developed with matched FF samples was applied to FFPET data to assign samples to either "good" or "poor" outcome class labels, the classifier was able to assign the FFPET samples to the correct class label with an average error rate = 12% to 16%, respectively, with an Odds Ratio = 36.4 to 60.4, respectively. A classifier derived from FFPET data was able to predict the class label in FFPET samples (leave-one-out cross validation) with an error rate of approximately 14% (p-value = 3.7 x 10(-7)). When applied to the matched FF samples, the FFPET-derived classifier was able to assign FF samples to the correct class labels with 96% accuracy. The single misclassification was attributed to poor sample quality, as measured by qPCR on total RNA, which emphasizes the need for sample quality control before profiling.ConclusionWe have optimized a platform for expression analyses and have shown that our profiling platform is able to accurately sort FFPET samples into class labels derived from FF classifiers. Furthermore, using this platform, a classifier derived from FFPET samples can reliably provide the same sorting power as a classifier derived from matched FF samples. We anticipate that these techniques could be used to generate hypotheses from archives of FFPET samples, and thus may lead to prognostic and predictive classifiers that could be used, for example, to segregate patients for clinical trial enrollment or to guide patient treatment.

Project description:Current seasonal influenza virus vaccines engender antibody-mediated protection that is hemagglutinin (HA) subtype specific and relatively short-lived. Coverage for other subtypes or even variants within a subtype could be improved from a better understanding of the factors that promote HA-specific antibody cross-reactivity. Current assays to evaluate cross-reactivity, such as the ELISA, require a separate test for each antigen and are neither high-throughput nor sample-sparing. To address this need, we produced an array of 283 purified HA proteins from influenza A virus subtypes H1 to H16 and H18 and influenza B virus. To evaluate performance, arrays were probed with sera from individuals before and after a booster dose of inactivated heterologous H5N1 vaccine and naturally infected cases at presentation and follow-up during the 2010 to 2011 influenza season, when H3N2 was prevalent. The response to the H5 vaccine boost was IgG only and confined to H5 variants. The response to natural H3N2 infection consisted of IgG and IgA and was reactive with all H3 variants displayed, as well as against other group 2 HA subtypes. In both groups, responses to HA1 proteins were subtype specific. In contrast, baseline signals were higher, and responses broader, against full-length HA proteins (HA1+HA2) compared to HA1 alone. We propose that these elevated baseline signals and breadth come from the recognition of conserved epitopes in the stalk domain by cross-reactive antibodies accumulated from previous exposure(s) to seasonal influenza virus. This array is a valuable high-throughput alternative to the ELISA for monitoring specificity and cross-reactivity of HA antibodies and has many applications in vaccine development.IMPORTANCE Seasonal influenza is a serious public health problem because the viral infection spreads easily from person to person and because of antigenic drift in neutralizing epitopes. Influenza vaccination is the most effective way to prevent the disease, although challenging because of the constant evolution of influenza virus subtypes. Our high-throughput protein microarrays allow for interrogation of subunit-specific IgG and IgA responses to 283 different HA proteins comprised of HA1 and HA2 domains as well as full-length HA proteins. This provides a tool that allows for novel insights into the response to exposure to influenza virus antigens. Data generated with our technology will enhance our understanding of the factors that improve the strength, breadth, and durability of vaccine-mediated immune responses and develop more effective vaccines.

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Development and Application of Human Coronavirus Protein Microarray for Specificity Analysis.

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