RESUMO
Modern health care needs preventive vaccines and therapeutic treatments with stability against pathogen mutations to cope with current and future viral infections. At the beginning of the COVID-19 pandemic, our analytic and predictive tool identified a set of eight short SARS-CoV-2 S-spike protein epitopes that had the potential to persistently avoid mutation. Here a combination of genetic, Systems Biology and protein structure analyses confirm the stability of our identified epitopes against viral mutations. Remarkably, this research spans the whole period of the pandemic, during which 93.9% of the eight peptides remained invariable in the globally predominant 43 circulating variants, including Omicron. Likewise, the selected epitopes are conserved in 97% of all 1,514 known SARS-CoV-2 lineages. Finally, experimental analyses performed with these short peptides showed their specific immunoreactivity. This work opens a new perspective on the design of next-generation vaccines and antibody therapies that will remain reliable against future pathogen mutations. HighlightsO_LIOur novel method predicts SARS-CoV-2 epitopes that are stable against future mutations C_LIO_LIGenetic analyses (performed 2 years after epitopes were identified) validate the stability of the identified peptides C_LIO_LIThese epitopes remained invariable in 97% of all 1,514 known SARS-CoV-2 lineages C_LIO_LI93.9% of such peptides were conserved in the 43 variants of most interest, including Omicron C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC="FIGDIR/small/22280980v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@a9cb10org.highwire.dtl.DTLVardef@152a16corg.highwire.dtl.DTLVardef@1e3cea5org.highwire.dtl.DTLVardef@113ec06_HPS_FORMAT_FIGEXP M_FIG C_FIG
RESUMO
Covid-19, caused by the SARS-CoV-2 virus, has reached the category of a worldwide pandemic. Even though intensive efforts, no effective treatments or a vaccine are available. Molecular characterization of the transcriptional response in Covid-19 patients could be helpful to identify therapeutic targets. In this study, RNAseq data from peripheral blood mononuclear cell samples from Covid-19 patients and healthy controls was analyzed from a functional point of view using probabilistic graphical models. Two networks were built: one based on genes differentially expressed between healthy and infected individuals and another one based on the 2,000 most variable genes in terms of expression in order to make a functional characterization. In the network based on differentially expressed genes, two inflammatory response nodes with different tendencies were identified, one related to cytokines and chemokines, and another one related to bacterial infections. In addition, differences in metabolism, which were studied in depth using Flux Balance Analysis, were identified. SARS-CoV2-infection caused alterations in glutamate, methionine and cysteine, and tetrahydrobiopterin metabolism. In the network based on 2,000 most variable genes, also two inflammatory nodes with different tendencies between healthy individuals and patients were identified. Similar to the other network, one was related to cytokines and chemokines. However, the other one, lower in Covid-19 patients, was related to allergic processes and self-regulation of the immune response. Also, we identified a decrease in T cell node activity and an increase in cell division node activity. In the current absence of treatments for these patients, functional characterization of the transcriptional response to SARS-CoV-2 infection could be helpful to define targetable processes. Therefore, these results may be relevant to propose new treatments.
