BACKGROUND: The worldwide pandemic caused by the SARS-CoV-2 virus is characterized by significant and unpredictable heterogeneity in symptoms that remains poorly understood. METHODS: Transcriptome and single cell transcriptome of COVID19 lung were integrated with deeplearning analysis of MHC class I immunopeptidome against SARS-COV2 proteome. RESULTS: An analysis of the transcriptomes of lung samples from COVID-19 patients revealed that activation of MHC class I antigen presentation in these tissues was correlated with the amount of SARS-CoV-2 RNA present. Similarly, a positive relationship was detected in these samples between the level of SARS-CoV-2 and the expression of a genomic cluster located in the 6p21.32 region (40 kb long, inside the MHC-II cluster) that encodes constituents of the immunoproteasome. An analysis of single-cell transcriptomes of bronchoalveolar cells highlighted the activation of the immunoproteasome in CD68 + M1 macrophages of COVID-19 patients in addition to a PSMB8-based trajectory in these cells that featured an activation of defense response during mild cases of the disease, and an impairment of alveolar clearance mechanisms during severe COVID-19. By examining the binding affinity of the SARS-CoV-2 immunopeptidome with the most common HLA-A, -B, and -C alleles worldwide, we found higher numbers of stronger presenters in type A alleles and in Asian populations, which could shed light on why this disease is now less widespread in this part of the world. CONCLUSIONS: HLA-dependent heterogeneity in macrophage immunoproteasome activation during lung COVID-19 disease could have implications for efforts to predict the response to HLA-dependent SARS-CoV-2 vaccines in the global population.