SARS-CoV-2 continues to accumulate mutations to evade immunity, leading to breakthrough infections after vaccination. How researchers can anticipate the evolutionary trajectory of the virus in advance in the design of next-generation vaccines requires investigation. Here, we performed a comprehensive study of 11,650,487 SARS-CoV-2 sequences, which revealed that the SARS-CoV-2 spike (S) protein evolved not randomly but into directional paths of either high infectivity plus low immune resistance or low infectivity plus high immune resistance. The viral infectivity and immune resistance of variants are generally incompatible, except for limited variants such as Beta and Kappa. The Omicron variant has the highest immune resistance but showed high infectivity in only one of the tested cell lines. To provide cross-clade immunity against variants that undergo diverse evolutionary pathways, we designed a new pan-vaccine antigen (S pan ). S pan was designed by analyzing the homology of 2675 SARS-CoV-2 S protein sequences from the NCBI database before the Delta variant emerged. The refined S pan protein harbors high-frequency residues at given positions that reflect cross-clade generality in sequence evolution. Compared with a prototype wild-type (S wt ) vaccine, which, when administered to mice, induced serum with decreased neutralization activity against emerging variants, S pan vaccination of mice elicited broad immunity to a wide range of variants, including those that emerged after our design. Moreover, vaccinating mice with a heterologous S pan booster conferred complete protection against lethal infection with the Omicron variant. Our results highlight the importance and feasibility of a universal vaccine to fight against SARS-CoV-2 antigenic drift.

SARS-CoV-2 continued to spread globally along with different variants. Here, we systemically analyzed viral infectivity and immune-resistance of SARS-CoV-2 variants to explore the underlying rationale of viral mutagenesis. We found that the Beta variant harbors both high infectivity and strong immune resistance, while the Delta variant is the most infectious with only a mild immune-escape ability. Remarkably, the Omicron variant is even more immune-resistant than the Beta variant, but its infectivity increases only in Vero E6 cells implying a probable preference for the endocytic pathway. A comprehensive analysis revealed that SARS-CoV-2 spike protein evolved into distinct evolutionary paths of either high infectivity plus low immune resistance or low infectivity plus high immune resistance, resulting in a narrow spectrum of the current single-strain vaccine. In light of these findings and the phylogenetic analysis of 2674 SARS-CoV-2 S-protein sequences, we generated a consensus antigen (S6) taking the most frequent mutations as a pan-vaccine against heterogeneous variants. As compared to the ancestry SWT vaccine with significantly declined neutralizations to emerging variants, the S6 vaccine elicits broadly neutralizing antibodies and full protections to a wide range of variants. Our work highlights the importance and feasibility of a universal vaccine strategy to fight against antigen drift of SARS-CoV-2.

The urgent necessity for precise and uninterrupted PM2.5 datasets of high spatial–temporal resolution is underscored by the significant influence of PM2.5 on weather, climate, and human health. This study leverages the AOD reconstruction method to compensate for missing values in the MAIAC AOD throughout Hubei Province. The reconstructed AOD dataset, exhibiting an R2/RMSE of 0.76/0.18, compared to AERONET AOD, was subsequently used for PM2.5 estimation. Our research breaks from traditional methodologies that solely depend on latitude and longitude information. Instead, it emphasizes the use of climate feature as an input for estimating PM2.5 concentrations. This strategic approach prevents potential spatial discontinuities triggered by geolocation information (latitude and longitude), thus ensuring the precision of the PM2.5 estimation (sample/spatial CV R2 = 0.91/0.88). Moreover, we proposed a method for identifying the absolute feature importance of machine-learning models. Contrasted with the relative feature-importance property typical of machine-learning models (a minor difference in the order of top three between geolocation-based and climate-feature-based models, and the slight difference in the top three: 0.08%/0.17%), our method provides a more comprehensive explanation of the absolute significance of features to the model (maintaining the same order and a larger difference in the top three: 0.99%/0.72%). Crucially, our findings demonstrated that AOD reconstruction can mitigate the overestimation of annual mean PM2.5 concentrations (ranging from 0.52 to 9.28 µg/m3). In addition, the seamless PM2.5 dataset contributes to reducing the bias in exposure risk assessment (ranging from −0.11 to 9.81 µg/m3).

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