To fully decipher the immunogenicity of the SARS-CoV-2 Spike protein, it is essential to assess which part is highly immunogenic in a systematic way. We generate a linear epitope landscape of the Spike protein by analysing the serum IgG response of 1,051 COVID-19 patients with a peptide microarray. We reveal two regions rich in linear epitopes, i.e., CTD and a region close to the S2’ cleavage site and fusion peptide. Unexpectedly, we find that the RBD lacks linear epitope. We reveal that the number of responsive peptides is highly variable among patients and correlates with disease severity. Some peptides are moderately associated with severity and clinical outcome. By immunizing mice, we obtained linear-epitope-specific antibodies; however, no significant neutralizing activity against the authentic virus was observed for these antibodies. This landscape will facilitate our understanding of SARS-CoV-2-specific humoral responses and might be useful for vaccine refinement.

Objective: The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID-19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time. Methods: We use feature selection techniques to filter feature subsets that are highly correlated with the target. The specific deep Long Short-Term Memory (LSTM) models are employed to capture the dynamic changes of disease severity, clinical outcome, and Spike antibody level. We also propose deep LSTMs with a TA mechanism to emphasize the later blood test records because later records often attract more attention from doctors. Results: Risk factors highly correlated with COVID-19 are revealed. LSTM achieves the highest classification accuracy for disease severity prediction. Temporal Attention Long Short-Term Memory (TA-LSTM) achieves the best performance for clinical outcome prediction. For Spike antibody level prediction, LSTM achieves the best permanence. Conclusion: The experimental results demonstrate the effectiveness of the proposed models. The proposed models can provide a computer-aided medical diagnostics system by simply using time series of serological indicators.

Despite the vaccine Mycobacterium bovis Bacillus Calmette–Guérin is used worldwide, tuberculosis (TB) remains the first killer among infectious diseases. An effective vaccine is urgently required. DNA vaccine has shown prophylactic as well as therapeutic effects against TB, while its weak immunogenicity hinders the application. As a strong inducer of Th1-biased immune response, DMT, consisting of dimethyldioctadecylammonium (DDA) and two pattern recognition receptor agonists monophosphoryl lipid A and trehalose 6,6′-dibehenate (TDB), was a newly developed liposomal adjuvant. To elucidate the action mechanism of DMT and improve immunological effects induced by DNA vaccine, a new recombinant eukaryotic expression plasmid pCMFO that secretes the fusion of four multistage antigens (Rv2875, Rv3044, Rv2073c, and Rv0577) of Mycobacterium tuberculosis was constructed. pCMFO/DDA and pCMFO/DMT complexes were then prepared and their physicochemical properties were analyzed. The immunogenicity and protection against M. tuberculosis infection in vaccinated C57BL/6 mice were compared. Formulation of DNA and two agonists into the DDA liposome decreased zeta potential but increased the stability of storage, which resulted in a slower and longer-lasting release of DNA from the DNA–DMT complex than the DNA–DDA liposome. Besides Th1-biased responses, pCMFO/DMT vaccinated mice elicited more significantly CFMO-specific IL2⁺ T_CM cell responses in the spleen and provided an enhanced and persistent protection against M. tuberculosis aerosol infection, compared to pCMFO/DDA and pCMFO groups. Therefore, the adjuvant DMT can release DNA and agonists slowly, which might attribute to the improved protection of DMT adjuvanted vaccines. pCMFO/DMT, a very promising TB vaccine, warrants for further preclinical and clinical trials.