COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic and has claimed over 2 million lives worldwide. Although the genetic sequences of SARS-CoV and SARS-CoV-2 have high homology, the clinical and pathological characteristics of COVID-19 differ significantly from those of SARS. How and whether SARS-CoV-2 evades (cellular) immune surveillance requires further elucidation. In this study, we show that SARS-CoV-2 infection leads to major histocompability complex class Ι (MHC-Ι) down-regulation both in vitro and in vivo. The viral protein encoded by open reading frame 8 (ORF8) of SARS-CoV-2, which shares the least homology with SARS-CoV among all viral proteins, directly interacts with MHC-Ι molecules and mediates their down-regulation. In ORF8-expressing cells, MHC-Ι molecules are selectively targeted for lysosomal degradation via autophagy. Thus, SARS-CoV-2–infected cells are much less sensitive to lysis by cytotoxic T lymphocytes. Because ORF8 protein impairs the antigen presentation system, inhibition of ORF8 could be a strategy to improve immune surveillance.

Highlights d RBD and HR nanoparticle vaccines induce potent neutralizing antibody responses d Nanoparticle vaccines protect against SARS-CoV-2 infection in mice d HR antigens elicit both humoral and cellular immune responses d HR antigens within nanoparticles contribute to crossprotective immunity

Attention-based learning for fine-grained image recognition remains a challenging task, where most of the existing methods treat each object part in isolation, while neglecting the correlations among them. In addition, the multi-stage or multi-scale mechanisms involved make the existing methods less efficient and hard to be trained end-to-end. In this paper, we propose a novel attention-based convolutional neural network (CNN) which regulates multiple object parts among different input images. Our method first learns multiple attention region features of each input image through the one-squeeze multi-excitation (OSME) module, and then apply the multi-attention multi-class constraint (MAMC) in a metric learning framework. For each anchor feature, the MAMC functions by pulling same-attention same-class features closer, while pushing different-attention or different-class features away. Our method can be easily trained end-to-end, and is highly efficient which requires only one training stage. Moreover, we introduce Dogs-in-the-Wild, a comprehensive dog species dataset that surpasses similar existing datasets by category coverage, data volume and annotation quality. This dataset will be released upon acceptance to facilitate the research of fine-grained image recognition. Extensive experiments are conducted to show the substantial improvements of our method on four benchmark datasets.

SARS-CoV-2 infection have caused global pandemic and claimed over 5,000,000 tolls [1][2][3][4] . Although the genetic sequences of their etiologic viruses are of high homology, the clinical and pathological characteristics of COVID-19 significantly differ from SARS 5,6 . Especially, it seems that SARS-CoV-2 undergoes vast replication in vivo without being effectively monitored by anti-viral immunity 7 . Here, we show that the viral protein encoded from open reading frame 8 (ORF8) of SARS-CoV-2, which shares the least homology with SARS-CoV among all the viral proteins, can directly interact with MHC-I molecules and significantly down-regulates their surface expression on various cell types. In contrast, ORF8a and ORF8b of SARS-CoV do not exert this function. In the ORF8-expressing cells, MHC-I molecules are selectively target for lysosomal degradation by an autophagy-dependent mechanism.As a result, CTLs inefficiently eliminate the ORF8-expressing cells. Our results demonstrate that ORF8 protein disrupts antigen presentation and reduces the recognition and the elimination of virus-infected cells by CTLs 8 . Therefore, we suggest that the inhibition of ORF8 function could be a strategy to improve the special immune surveillance and accelerate the eradication of SARS-CoV-2 in vivo.