Profiling SARS-CoV-2 HLA-I peptidome reveals T cell epitopes from out-of-frame ORFs.

T cell-mediated immunity plays an important role in controlling SARS-CoV-2 infection, but the repertoire of naturally processed and presented viral epitopes on class I human leukocyte antigen (HLA-I) remains uncharacterized. Here, we report the first HLA-I immunopeptidome of SARS-CoV-2 in two cell lines at different times post infection using mass spectrometry. We found HLA-I peptides derived not only from canonical open reading frames (ORFs) but also from internal out-of-frame ORFs in spike and nucleocapsid not captured by current vaccines. Some peptides from out-of-frame ORFs elicited T cell responses in a humanized mouse model and individuals with COVID-19 that exceeded responses to canonical peptides, including some of the strongest epitopes reported to date. Whole-proteome analysis of infected cells revealed that early expressed viral proteins contribute more to HLA-I presentation and immunogenicity. These biological insights, as well as the discovery of out-of-frame ORF epitopes, will facilitate selection of peptides for immune monitoring and vaccine development.

Genome annotation improvements from cross-phyla proteogenomics and time-of-day differences in malaria mosquito proteins using untargeted quantitative proteomics.

The malaria mosquito, Anopheles stephensi, and other mosquitoes modulate their biology to match the time-of-day. In the present work, we used a non-hypothesis driven approach (untargeted proteomics) to identify proteins in mosquito tissue, and then quantified the relative abundance of the identified proteins from An. stephensi bodies. Using these quantified protein levels, we then analyzed the data for proteins that were only detectable at certain times-of-the day, highlighting the need to consider time-of-day in experimental design. Further, we extended our time-of-day analysis to look for proteins which cycle in a rhythmic 24-hour ("circadian") manner, identifying 31 rhythmic proteins. Finally, to maximize the utility of our data, we performed a proteogenomic analysis to improve the genome annotation of An. stephensi. We compare peptides that were detected using mass spectrometry but are 'missing' from the An. stephensi predicted proteome, to reference proteomes from 38 other primarily human disease vector species. We found 239 such peptide matches and reveal that genome annotation can be improved using proteogenomic analysis from taxonomically diverse reference proteomes. Examination of 'missing' peptides revealed reading frame errors, errors in gene-calling, overlapping gene models, and suspected gaps in the genome assembly.

Mucosal Gene Expression in Pediatric and Adult Patients With Ulcerative Colitis Permits Modeling of Ideal Biopsy Collection Strategy for Transcriptomic Analysis.

Background: Transcriptional profiling has been performed on biopsies from ulcerative colitis patients. Limitations in prior studies include the variability introduced by inflammation, anatomic site of biopsy, extent of disease, and medications. We sought to more globally understand the variability of gene expression from patients with ulcerative colitis to advance our understanding of its pathogenesis and to guide clinical study design. Methods: We performed transcriptional profiling on 13 subjects, including pediatric and adult patients from 2 hospital sites. For each patient, we collected 6 biopsies from macroscopically inflamed tissue and 4 biopsies from macroscopically healthy-appearing tissue. Isolated RNA was used for microarray gene expression analysis utilizing Affymetrix Human Primeview microarrays. Ingenuity pathway analysis was used to assess over-representation of gene ontology and biological pathways. RNAseq was also performed, and differential analysis was assessed to compare affected vs unaffected samples. Finally, we modeled the minimum number of biopsies required to reliably detect gene expression across different subject numbers. Results: Transcriptional profiles co-clustered independently of the hospital collection site, patient age, sex, and colonic location, which parallels prior gene expression findings. A small set of genes not previously described was identified. Our modeling analysis reveals the number of biopsies and patients per cohort to yield reliable results in clinical studies. Conclusions: Key findings include concordance, including some expansion, of previously published gene expression studies and similarity among different age groups. We also established a reliable statistical model for biopsy collection for future clinical studies.