Bhupesh Kumar Prusty

Human cytomegalovirus (HCMV) reactivation poses a substantial risk to patients receiving tranplants. Effective risk stratification and vaccine development is hampered by a lack of HCMV-derived immunogenic peptides in patients with common HLA-A∗03:01 and HLA-B∗15:01 haplotypes. This study aimed to discover novel HCMV immunogenic peptides for these haplotypes by combining ribosome sequencing (Ribo-seq) and mass spectrometry with state-of-the-art computational tools, Peptide-PRISM and Probabilistic Inference of Codon Activities by an EM Algorithm. Furthermore, using machine learning, an algorithm was developed to predict immunogenicity based on translational activity, binding affinity, and peptide localization within small open reading frames to identify the most promising peptides for in vitro validation. Immunogenicity of these peptides was subsequently tested by analyzing peptide-specific T-cell responses of HCMV-seropositive and -seronegative healthy donors as well as patients with transplants. This resulted in the direct identification of 3 canonical and 1 cryptic HLA-A∗03–restricted immunogenic peptides as well as 5 canonical and 1 cryptic HLA-B∗15–restricted immunogenic peptide, with a specific interferon gamma–positive (IFN-γ+)/CD8+ T-cell response of ≥0.02%. High T-cell responses were detected against 2 HLA-A∗03–restricted and 3 HLA-B∗15–restricted canonical peptides with frequencies of up to 8.77% IFN-γ+/CD8+ T cells in patients after allogeneic stem cell transplantation. Therefore, our comprehensive strategy establishes a framework for efficient identification of novel immunogenic peptides from both existing and novel Ribo-seq data sets.

Nucleoside analogues like 4-thiouridine (4sU) are used to metabolically label newly synthesized RNA. Chemical conversion of 4sU before sequencing induces T-to-C mismatches in reads sequenced from labelled RNA, allowing to obtain total and labelled RNA expression profiles from a single sequencing library. Cytotoxicity due to extended periods of labelling or high 4sU concentrations has been described, but the effects of extensive 4sU labelling on expression estimates from nucleotide conversion RNA-seq have not been studied. Here, we performed nucleotide conversion RNA-seq with escalating doses of 4sU with short-term labelling (1h) and over a progressive time course (up to 2h) in different cell lines. With high concentrations or at later time points, expression estimates were biased in an RNA half-life dependent manner. We show that bias arose by a combination of reduced mappability of reads carrying multiple conversions, and a global, unspecific underrepresentation of labelled RNA emerging during library preparation and potentially global reduction of RNA synthesis. We developed a computational tool to rescue unmappable reads, which performed favourably compared to previous read mappers, and a statistical method, which could fully remove remaining bias. All methods developed here are freely available as part of our GRAND-SLAM pipeline and grandR package.