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Background and Aims Viral infections occur acutely but can also progress chronically, with the immune system having a central role in immunopathoge-nesis. The question arises whether all alterations in immune responses are reversible after viral elimination (spontaneously or by therapy). Therefore, the aim of this study is to compare soluble infammatory markers (SIM) during and after infection with SARS-CoV-2 and acute and chronic HCV-infections. Patients and Method Patients with acute HCV (n = 29), chronic HCV (n = 54), SARS-CoV-2 (n = 39) and 31 healthy-controls were included. Blood samples were tested at baseline, end of treatment/infection, and follow-up ( >= 9 months after baseline). IL-12p70, IL-1b, IL-4, IL-5, IL-6, IL-8, TNF, IFN-g, IL-10, IL-22, CXCL-10, MCP-1, MIP-1b, ITAC were quantified using the HD-SP-X Imaging and Analysis SystemTM. Results SIM profiles in patients with acute HCV were substantially elevated at baseline and the decrease during follow-up was considerably less compared to the SARS-CoV-2 cohort. In chronic HCV-patients, viral elimination by therapy resulted in a decrease in SIM, although not always to those of controls. Cirrhotic HCV patients had higher SIM levels after HCV elimination than non-cirrhotic chronic HCV-patients. In the SARS-CoV-2 cohort, most SIM returned to levels of controls 3 months after baseline. Conclusions SIM profiles and kinetics after viral elimination difer between blood-borne acute and chronic HCV- and respiratory SARS-CoV-2-infections. The immunologic imprint 9 months after cured HCV-infection (both acute and chronic) appears to be more pronounced than after SARS-CoV-2-infection. Further analysis is needed to correlate the SIM profle with the clinical pheno-type (long-HepC vs. long-COVID-19).
RESUMO
Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.