The SEB1741 Aptamer Is an Efficient Tool for Blocking CD4+ T Cell Activation Induced by Staphylococcal Enterotoxin B.

Staphylococcal enterotoxin B (SEB) is a protein produced by Staphylococcus aureus, which is toxic to humans. It is well known for its ability to stimulate the exacerbated activation of proinflammatory CD4+ T cells (Th1 profile), and in vitro studies have been conducted to understand its mechanism of action and its potential use as an immune therapy. However, the efficiency of the SEB1741 aptamer in blocking SEB has not been experimentally demonstrated. METHODS: Enrichment CD4+ T cells were stimulated with SEB, and as a blocker, we used the SEB1741 aptamer, which was previously synthesised by an "in silico" analysis, showing high affinity and specificity to SEB. The efficiency of the SEB1741 aptamer in blocking CD4+ T cell activation was compared with that of an anti-SEB monoclonal antibody. Flow cytometry and Bio-Plex were used to evaluate the T-cell function. RESULTS: In vitro, SEB induced the activation of CD4+ T cells and favoured a Th1 profile; however, the SEB1741 aptamer was highly efficient in decreasing the frequency of CD4+ T cells positive to ki-67 and CD69 cells, this means that proliferation and activation of CD4+ T cells was decreased. Moreover, the production of interleukin 2 (IL-2) and interferon-gamma (IFN-γ) was affected, suggesting that the Th1 profile is not present when the SEB1441 aptamer is used. Thus, the SEB1741 function was similar to that of anti-SEB. CONCLUSIONS: The SEB1741 aptamer is a valuable tool for blocking CD4+ T cell activation and the subsequent release of proinflammatory cytokines by SEB stimulation.

Severe COVID-19 Patients Show an Increase in Soluble TNFR1 and ADAM17, with a Relationship to Mortality.

Overproduction of inflammatory cytokines is a keystone event in COVID-19 pathogenesis; TNF and its receptors (TNFR1 and TNFR2) are critical pro-inflammatory molecules. ADAM17 releases the soluble (sol) forms of TNF, TNFR1, and TNFR2. This study evaluated TNF, TNFRs, and ADAM17 at the protein, transcriptional, and gene levels in COVID-19 patients with different levels of disease severity. In total, 102 patients were divided into mild, moderate, and severe condition groups. A group of healthy donors (HD; n = 25) was included. Our data showed that solTNFR1 and solTNFR2 were elevated among the COVID-19 patients (p < 0.0001), without increasing the transcriptional level. Only solTNFR1 was higher in the severe group as compared to the mildly ill (p < 0.01), and the level was higher in COVID-19 patients who died than those that survived (p < 0.0001). The solTNFR1 level had a discrete negative correlation with C-reactive protein (p = 0.006, Rho = -0.33). The solADAM17 level was higher in severe as compared to mild disease conditions (p < 0.01), as well as in COVID-19 patients who died as compared to those that survived (p < 0.001). Additionally, a potential association between polymorphism TNFRSF1A:rs767455 and a severe degree of disease was suggested. These data suggest that solTNFR1 and solADAM17 are increased in severe conditions. solTNFR1 should be considered a potential target in the development of new therapeutic options.

In silico selection of aptamers for bacterial toxins detection.

The most commonly used toxins in biological warfare are staphylococcal enterotoxin B (3SEB), cholera toxin (1XTC), and botulinum toxin (3BTA). Uncovering novel strategies for identifying these toxins is paramount; therefore, aptamers are used for this purpose. Aptamers are single-stranded DNA or RNA oligonucleotides selected via Systematic Evolution of Ligands by Exponential Enrichment (SELEX) with high binding affinity and specificity against target molecules. However, SELEX in vitro is tedious; hence, adopting alternative in silico molecular docking approaches is necessary. We aimed to conduct molecular docking with accessible tools and obtain RNA aptamers. First, 4,820,095 sequences obtained from an initial library of 9.5 × 109 Python script sequences were used. The GraphClust program was used to create representative groups or clusters, and the DoGSiteScorer (https://proteins.plus/) was used to conduct binding site detection of the proteins: 5DO4 (thrombin), 3SEB, 1XTC, and 3BTA. rDock, HDock, and PatchDock were adopted, combining different docking program results (consensus scoring), to improve receptor-ligand prediction. An analysis of the poses and root mean square deviation (RMSD) was performed, and 468 structurally different aptamers were obtained. The DoGSiteScorer program predicted the binding site of each protein to direct the interaction with the aptamer. Candidate aptamers for 3SEB, 1XTC, and 3BTA were selected according to the pose value considering the closeness of the interaction with a lower mean of 45.923 Å, 45.854 Å, and 72.490 Å, respectively.Communicated by Ramaswamy H. Sarma.