PloS one 2017 09 2112(9) e0185211 doi 10.1371/journal.pone.0185211
One of the strategies for curing viral HIV-1 is a therapeutic vaccine involving the stimulation of cytotoxic CD8-positive T cells (CTL) that are Human Leucocyte Antigen (HLA)-restricted. The lack of efficiency of previous vaccination strategies may have been due to the immunogenic peptides used, which could be different from a patient’s virus epitopes and lead to a poor CTL response. To counteract this lack of specificity, conserved epitopes must be targeted. One alternative is to gather as many data as possible from a large number of patients on their HIV-1 proviral archived epitope variants, taking into account their genetic background to select the best presented CTL epitopes. In order to process big data generated by Next-Generation Sequencing (NGS) of the DNA of HIV-infected patients, we have developed a software package called TutuGenetics. This tool combines an alignment derived either from Sanger or NGS files, HLA typing, target gene and a CTL epitope list as input files. It allows automatic translation after correction of the alignment obtained between the HxB2 reference and the reads, followed by automatic calculation of the MHC IC50 value for each epitope variant and the HLA allele of the patient by using NetMHCpan 3.0, resulting in a csv file as output result. We validated this new tool by comparing Sanger and NGS (454, Roche) sequences obtained from the proviral DNA of patients at success of ART included in the Provir Latitude 45 study and showed a 90% correlation between the quantitative results of NGS and Sanger. This automated analysis combined with complementary samples should yield more data regarding the archived CTL epitopes according to the patients’ HLA alleles and will be useful for screening epitopes that in theory are presented efficiently to the HLA groove, thus constituting promising immunogenic peptides for a therapeutic vaccine.