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ABSTRACT The rapid worldwide spread of SARS-CoV-2 has accelerated research and development for controlling the COVID-19 pandemic. A multi-coronavirus protein microarray was created containing full-length proteins, overlapping protein fragments of various lengths, and peptide libraries from SARS-CoV-2 and four other human coronaviruses. Sera from confirmed COVID-19 patients as well as unexposed individuals were applied to multicoronavirus arrays to identify specific antibody reactivity. High-level IgG, IgM, and IgA reactivity to structural proteins S, M, and N of SARS-CoV-2, as well as accessory proteins such as ORF3a and ORF7a, were observed that were specific to COVID-19 patients. Antibody reactivity against overlapping 100-, 50-, and 30-amino acid fragments of SARS-CoV-2 proteins was used to identify antigenic regions. Numerous proteins of SARS-CoV, Middle East respiratory syndrome coronavirus (MERS-CoV), and the endemic human coronaviruses HCoV-NL63 and HCoV-OC43 were also more reactive with IgG, IgM, and IgA in COVID-19 patient sera than in unexposed control sera, providing further evidence of immunologic cross-reactivity between these viruses. Whereas unexposed individuals had minimal reactivity against SARS-CoV-2 proteins that poorly correlated with reactivity against HCoV-NL63 and HCoV-OC43 S2 and N proteins, COVID-19 patient sera had higher correlation between SARS-CoV-2 and HCoV responses, suggesting that de novo antibodies against SARS-CoV-2 cross-react with HCoV epitopes. Array responses were compared with validated spike protein-specific IgG enzyme-linked immunosorbent assays (ELISAs), showing agreement between orthologous methods. SARS-CoV-2 microneutralization titers were low in the COVID-19 patient sera but correlated with array responses against S and N proteins. The multi-coronavirus protein microarray is a useful tool for mapping antibody reactivity in COVID-19 patients. IMPORTANCE With novel mutant SARS-CoV-2 variants of concern on the rise, knowledge of immune specificities against SARS-CoV-2 proteins is increasingly important for understanding the impact of structural changes in antibody-reactive protein epitopes on naturally acquired and vaccine-induced immunity, as well as broader topics of cross-reactivity and viral evolution. A multi-coronavirus protein microarray used to map the binding of COVID-19 patient antibodies to SARS-CoV-2 proteins and protein fragments as well as to the proteins of four other coronaviruses that infect humans has shown specific regions of SARS-CoV-2 proteins that are highly reactive with patient antibodies and revealed cross-reactivity of these antibodies with other human coronaviruses. These data and the multi-coronavirus protein microarray tool will help guide further studies of the antibody response to COVID-19 and to vaccination against this worldwide pandemic.

As numerous viral variants continue to emerge in the coronavirus disease 2019 (COVID-19) pandemic, determining antibody reactivity to SARS-CoV-2 epitopes becomes essential in discerning changes in the immune response to infection over time. This study enabled us to identify specific areas of antigenicity within the SARS-CoV-2 proteome, allowing us to detect correlations of epitopes with clinical metadata and immunological signals to gain holistic insight into SARS-CoV-2 infection.

Stop codon recognition is a crucial event during translation termination and is performed by class I release factors (RF1 and RF2 in bacterial cells). Recent crystal structures showed that stop codon recognition is achieved mainly through a network of hydrogen bonds and stacking interactions between the stop codon and conserved residues in domain II of RF1/RF2. Additionally, previous studies suggested that recognition of stop codons is coupled to proper positioning of RF1 on the ribosome, which is essential for triggering peptide release. In this study we mutated four conserved residues in Escherichia coli RF1 (Gln185, Arg186, Thr190, and Thr198) that are proposed to be critical for discriminating stop codons from sense codons. Our thermodynamic and kinetic analysis of these RF1 mutants showed that the mutations inhibited the binding of RF1 to the ribosome. However, the mutations in RF1 did not affect the rate of peptide release, showing that imperfect recognition of the stop codon does not affect the proper positioning of RF1 on the ribosome.

ABSTRACT Shigella spp. are a major cause of diarrhea and dysentery in children under 5 years old in the developing world. The development of an effective vaccine remains a public health priority, necessitating improved understanding of immune responses to Shigella and identification of protective antigens. We report the development of a core Shigella proteome microarray consisting of 2,133 antigen targets common to all Shigella species. We evaluated the microarray with serum samples from volunteers immunized with either an inactivated whole-cell S. flexneri serotype 2a (Sf2aWC) vaccine or a live attenuated S. flexneri 2a vaccine strain (CVD 1204) or challenged with wild-type S. flexneri 2a (Sf2a challenge). Baseline reactivities to most antigens were detected postintervention in all three groups. Similar immune profiles were observed after CVD 1204 vaccination and Sf2a challenge. Antigens with the largest increases in mean reactivity postintervention were members of the type three secretion system (T3SS), some of which are regarded as promising vaccine targets: these are the invasion plasmid antigens (Ipas) IpaB, IpaC, and IpaD. In addition, new immunogenic targets (IpaA, IpaH, and SepA) were identified. Importantly, immunoreactivities to antigens in the microarray correlated well with antibody titers determined by enzyme-linked immunosorbent assay (ELISA), validating the use of the microarray platform. Finally, our analysis uncovered an immune signature consisting of three conserved proteins (IpaA, IpaB, and IpaC) that was predictive of protection against shigellosis. In conclusion, the Shigella proteome microarray is a robust platform for interrogating serological reactivity to multiple antigens at once and identifying novel targets for the development of broadly protective vaccines. IMPORTANCE Each year, more than 180 million cases of severe diarrhea caused by Shigella occur globally. Those affected (mostly children in poor regions) experience long-term sequelae that severely impair quality of life. Without a licensed vaccine, the burden of disease represents a daunting challenge. An improved understanding of immune responses to Shigella is necessary to support ongoing efforts to identify a safe and effective vaccine. We developed a microarray containing >2,000 proteins common to all Shigella species. Using sera from human adults who received a killed whole-cell or live attenuated vaccine or were experimentally challenged with virulent organisms, we identified new immune-reactive antigens and defined a T3SS protein signature associated with clinical protection.

Der Einfluss gestiegener Einkommensungleichheit auf die „Great Depression“ und die „Great Recession“ wurde mehrfach postuliert (Galbraith 1954/2009; Eccles 1951; Rajan 2010; Stiglitz 2012; Piketty 2014). Konkrete empirische Arbeiten zum Zusammenhang zwischen Einkommensverteilung und dem Entstehen von Wirtschaftskrisen gibt es aber bislang wenige. Kumhof/Ranciere (2010) überprüften die von Rajan (2010) aufgestellte Hypothese, die einen entsprechenden Zusammenhang postuliert, mittels Modellrechnung. Bordo/Meissner (2012) und darauf aufbauend Gu/Huang (2014) verwendeten unterschiedliche Regressionsmodelle in Bezug auf einen entsprechenden Zusammenhang, ohne jedoch eindeutige Ergebnisse zu liefern. Die vorliegende Arbeit schließt an diese Arbeiten an, beschränkt die Untersuchung allerdings auf Staaten, für die Daten für die letzten hundert Jahre verfügbar sind, und untersucht zudem explizit die Zeiträume um die beiden größten Krisen der letzten hundert Jahre, die „Great Depression“ und die „Great Recession“. Die Auswertungen zeigen, dass die personelle Einkommensverteilung ein guter Prädiktor für die Kriseneintrittswahrscheinlichkeit ist.