SARS-CoV-2 infected cells present HLA-I peptides from canonical and out-of-frame ORFs

Weingarten-Gabbay, Shira; Klaeger, Susan; Sarkizova, Siranush; Pearlman, Leah R.; Chen, Da Yuan; Bauer, Matthew R.; Taylor, Hannah; Conway, Hasahn L; Tomkins-Tinch, Christopher H.; Finkel, Yaara; Nachshon, Aharon; Gentili, Matteo; Rivera, Keith; Keskin, Derin B.; Rice, Charles M.; Clauser, Karl R.; Hacohen, Nir; Carr, Steven A.; Abelin, Jennifer G.; Saeed, Mohsan; Sabeti, Pardis C.

doi:10.1101/2020.10.02.324145

Cited by 23 publications

(30 citation statements)

References 63 publications

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“…As SARS-CoV-2 shows high genomic similarity to the SARS-CoV-1 virus 1 , we elected to analyze both SARS-CoV-1/2 mRNA sequences and extract CAMAP scores for all their potential MAPs. We first confirm that naturally processed SARS-CoV-1 specific MAPs (identified since the pandemic of 2002-2004), and mass spectrometry identified SARS-CoV-2 MAPs 23 are indeed associated with high CAMAP scores. We then used both CAMAP scores and predicted MHC-I binding affinity to define a set of 15 very common HLA class I alleles in order to identify a combination of 48 high potential SARS-CoV-2-specific MAPs.…”

Section: Anothersupporting

confidence: 58%

“…In a recent study, Daouda We also showed that mass-spectrometry identified SARS-CoV-2 MAPs were also associated with higher CAMAP scores. Of note, in Weingarten-Gabbay et al study, the 2 proteins that gave rise to the highest number of MAPs were ORF1ab (11/30, 37%) and the S proteins (7/30, 23%), while only 1 peptide originated from each of the M and N proteins 23 . This result is very surprising, as the mRNA expression levels of the ORF1a and ORF1ab is 10 to 1000-fold lower than that of structural ORFs 30 .…”

Section: Discussionmentioning

confidence: 89%

“…Furthermore, 3 out of 48 peptides that we identified as SARS-CoV-2 targets MAPs (see Table 1 ) had >65% identity (i.e. overlapping amino acids) by mass spectrometry of SARS-CoV-2 infected cells 23 ( Supplementary Table S3 ).…”

Section: Design Of a Sars-cov-2 Peptide Vaccinementioning

confidence: 99%

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Codon arrangement modulates MHC-I peptides presentation: implications for a SARS-CoV-2 peptide-based vaccine

Daouda

Dumont-Lagacé

Feghaly

et al. 2021

Preprint

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Among various vaccination strategies, peptide-based vaccines appear as excellent candidates because they are cheap to produce, are highly stable and harbor low toxicity. However, predicting which MHC-I Associated Peptide (MAP) will ultimately reach cell surface remains challenging, due to high false discovery rates. Previously, we demonstrated that synonymous codon arrangement (usage and placement) is predictive of, and modulates MAP presentation. Here, we apply CAMAP (Codon Arrangement MAP Predictor), the artificial neural network we used to unveil the role of codon arrangement in MAP presentation, to predict SARS-CoV MAPs. We report that experimentally identified SARS-CoV-1 and SARS-CoV-2 MAPs are associated with significantly higher CAMAP scores. Based on CAMAP scores and binding affinity, we identified 48 non-overlapping MAP candidates for a peptide-based vaccine, ensuring coverage for a high proportion of HLA haplotypes in the US population (>78%) and SARS-CoV-2 strains (detected in >98% of SARS-CoV-2 strains present in the GISAID database). Finally, we built an interactive web portal (https://www.epitopes.world) where researchers can freely explore CAMAP predictions for SARS-CoV-1/2 viruses. Collectively, we present an analysis framework that can be generalizable to empower the rapid identification of virus-specific MAPs, including in the context of an emergent virus, to help accelerate target identification for peptide-based vaccine designs that could be critical in safely attaining group immunity in the context of a global pandemic.

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Section: Anothersupporting

confidence: 58%

Section: Discussionmentioning

confidence: 89%

See 1 more Smart Citation

Codon arrangement modulates MHC-I peptides presentation: implications for a SARS-CoV-2 peptide-based vaccine

Daouda

Dumont-Lagacé

Feghaly

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…St-Germain et al [36] proteomics yes yes no Stukalov et al [116] proteomics no no no Sun et al [117] glycoproteomics no no NA Supekar et al [118] glycoproteomics yes partial NA Watanabe et al [133] glycoproteomics no no NA Weingarten-Gabbay et al [134] proteomics no no no Wendt et al [135] proteomics no no no Yang, Du, and Kaltashov [141] proteomics no no NA Zecha et al [142] proteomics yes partial no Zhao et al [144] glycoproteomics yes partial no…”

Section: Referencementioning

confidence: 99%

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

et al. 2021

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The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein-protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus-host interactome dataset to detect protein-protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

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“…For the bulk RNA-sequencing (RNA-seq) datasets whose read count data is available at the time of analysis, we performed differential expression (DE) analysis comparing the SARS-CoV-2-infected or positive samples to the non-infected control or negative samples with DESeq2 ( Love et al 2014 ). For Weingarten-Gabby et al 2020 , a nested design for DE was needed and DESeq2 failed to run properly, and limma-voom ( Law et al 2014 ) was used. For Butler et al 2020 and Xiong et al 2020 , we obtained the DE results provided from the supplementary materials of the respective publication, as we were not able to obtain their gene-level expression data at the time of analysis.…”

Section: Methodsmentioning

confidence: 99%

Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets

Cheng

Martin-Sancho

Pal

et al. 2021

Preprint

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Tremendous progress has been made to control the COVID-19 pandemic, including the development and approval of vaccines as well as the drug remdesivir, which inhibits the SARS-CoV-2 virus that causes COVID-19. However, remdesivir confers only mild benefits to a subset of patients, and additional effective therapeutic options are needed. Drug repurposing and drug combinations may represent practical strategies to address these urgent unmet medical needs. Viruses, including coronaviruses, are known to hijack the host metabolism to facilitate their own proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM). We find that SARS-CoV-2 infection can induce recurrent and complicated metabolic reprogramming spanning a wide range of metabolic pathways. We next applied the GEM-based metabolic transformation algorithm (MTA) to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. These predictions are enriched for validated targets from various published experimental drug and genetic screens. Further analyzing the RNA-sequencing data of remdesivir-treated Vero E6 cell samples that we generated, we predicted metabolic targets that act in combination with remdesivir. These predictions are enriched for previously reported synergistic drugs with remdesivir. Since our predictions are based in part on human patient data, they are likely to be clinically relevant. We provide our top high-confidence candidate targets for their evaluation in further studies, demonstrating host metabolism-targeting as a promising antiviral strategy.

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SARS-CoV-2 infected cells present HLA-I peptides from canonical and out-of-frame ORFs

Cited by 23 publications

References 63 publications

Codon arrangement modulates MHC-I peptides presentation: implications for a SARS-CoV-2 peptide-based vaccine

Codon arrangement modulates MHC-I peptides presentation: implications for a SARS-CoV-2 peptide-based vaccine

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets

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