A recombinant Mycobacterium smegmatis-based surface display system for developing the T cell-based COVID-19 vaccine.

The immune escape mutations of SARS-CoV-2 variants emerged frequently, posing a new challenge to weaken the protective efficacy of current vaccines. Thus, the development of novel SARS-CoV-2 vaccines is of great significance for future epidemic prevention and control. We herein reported constructing the attenuated Mycobacterium smegmatis (M. smegmatis) as a bacterial surface display system to carry the spike (S) and nucleocapsid (N) of SARS-CoV-2. To mimic the native localization on the surface of viral particles, the S or N antigen was fused with truncated PE_PGRS33 protein, which is a transportation component onto the cell wall of Mycobacterium tuberculosis (M.tb). The sub-cellular fraction analysis demonstrated that S or N protein was exactly expressed onto the surface (cell wall) of the recombinant M. smegmatis. After the immunization of the M. smegmatis-based COVID-19 vaccine candidate in mice, S or N antigen-specific T cell immune responses were effectively elicited, and the subsets of central memory CD4+ T cells and CD8+ T cells were significantly induced. Further analysis showed that there were some potential cross-reactive CTL epitopes between SARS-CoV-2 and M.smegmatis. Overall, our data provided insights that M. smegmatis-based bacterial surface display system could be a suitable vector for developing T cell-based vaccines against SARS-CoV-2 and other infectious diseases.

A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters.

Coronavirus disease 2019 (COVID-19) patients with liver dysfunction (LD) have a higher chance of developing severe and critical disease. The routine hepatic biochemical parameters ALT, AST, GGT, and TBIL have limitations in reflecting COVID-19-related LD. In this study, we performed proteomic analysis on 397 serum samples from 98 COVID-19 patients to identify new biomarkers for LD. We then established 19 simple machine learning models using proteomic measurements and clinical variables to predict LD in a development cohort of 74 COVID-19 patients with normal hepatic biochemical parameters. The model based on the biomarker ANGL3 and sex (AS) exhibited the best discrimination (time-dependent AUCs: 0.60-0.80), calibration, and net benefit in the development cohort, and the accuracy of this model was 69.0-73.8% in an independent cohort. The AS model exhibits great potential in supporting optimization of therapeutic strategies for COVID-19 patients with a high risk of LD. This model is publicly available at https://xixihospital-liufang.shinyapps.io/DynNomapp/.

Kynurenine-3-monooxygenase (KMO) broadly inhibits viral infections via triggering NMDAR/Ca2+ influx and CaMKII/ IRF3-mediated IFN-ß production.

Tryptophan (Trp) metabolism through the kynurenine pathway (KP) is well known to play a critical function in cancer, autoimmune and neurodegenerative diseases. However, its role in host-pathogen interactions has not been characterized yet. Herein, we identified that kynurenine-3-monooxygenase (KMO), a key rate-limiting enzyme in the KP, and quinolinic acid (QUIN), a key enzymatic product of KMO enzyme, exerted a novel antiviral function against a broad range of viruses. Mechanistically, QUIN induced the production of type I interferon (IFN-I) via activating the N-methyl-d-aspartate receptor (NMDAR) and Ca2+ influx to activate Calcium/calmodulin-dependent protein kinase II (CaMKII)/interferon regulatory factor 3 (IRF3). Importantly, QUIN treatment effectively inhibited viral infections and alleviated disease progression in mice. Furthermore, kmo-/- mice were vulnerable to pathogenic viral challenge with severe clinical symptoms. Collectively, our results demonstrated that KMO and its enzymatic product QUIN were potential therapeutics against emerging pathogenic viruses.

Strategies to Develop a Mucosa-Targeting Vaccine against Emerging Infectious Diseases.

Numerous pathogenic microbes, including viruses, bacteria, and fungi, usually infect the host through the mucosal surfaces of the respiratory tract, gastrointestinal tract, and reproductive tract. The mucosa is well known to provide the first line of host defense against pathogen entry by physical, chemical, biological, and immunological barriers, and therefore, mucosa-targeting vaccination is emerging as a promising strategy for conferring superior protection. However, there are still many challenges to be solved to develop an effective mucosal vaccine, such as poor adhesion to the mucosal surface, insufficient uptake to break through the mucus, and the difficulty in avoiding strong degradation through the gastrointestinal tract. Recently, increasing efforts to overcome these issues have been made, and we herein summarize the latest findings on these strategies to develop mucosa-targeting vaccines, including a novel needle-free mucosa-targeting route, the development of mucosa-targeting vectors, the administration of mucosal adjuvants, encapsulating vaccines into nanoparticle formulations, and antigen design to conjugate with mucosa-targeting ligands. Our work will highlight the importance of further developing mucosal vaccine technology to combat the frequent outbreaks of infectious diseases.

Weakly-Supervised Network for Detection of COVID-19 in Chest CT Scans.

Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily rely on large labeled data sets, which are difficult to acquire in this pandemic situation. Therefore, weakly-supervised approaches are in demand. In this paper, we propose an end-to-end weakly-supervised COVID-19 detection approach, ResNext+, that only requires volume level data labels and can provide slice level prediction. The proposed approach incorporates a lung segmentation mask as well as spatial and channel attention to extract spatial features. Besides, Long Short Term Memory (LSTM) is utilized to acquire the axial dependency of the slices. Moreover, a slice attention module is applied before the final fully connected layer to generate the slice level prediction without additional supervision. An ablation study is conducted to show the efficiency of the attention blocks and the segmentation mask block. Experimental results, obtained from publicly available datasets, show a precision of 81.9% and F1 score of 81.4%. The closest state-of-the-art gives 76.7% precision and 78.8% F1 score. The 5% improvement in precision and 3% in the F1 score demonstrate the effectiveness of the proposed method. It is worth noticing that, applying image enhancement approaches do not improve the performance of the proposed method, sometimes even harm the scores, although the enhanced images have better perceptual quality.

Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations.

Infection segmentation on chest CT plays an important role in the quantitative analysis of COVID-19. Developing automatic segmentation tools in a short period with limited labelled images has become an urgent need. Pseudo label-based semi-supervised method is a promising way to leverage unlabelled data to improve segmentation performance. Existing methods usually obtain pseudo labels by first training a network with limited labelled images and then inferring unlabelled images. However, these methods may generate obviously inaccurate labels and degrade the subsequent training process. To address these challenges, in this paper, an active contour regularized semi-supervised learning framework was proposed to automatically segment infections with few labelled images. The active contour regularization was realized by the region-scalable fitting (RSF) model which is embedded to the loss function of the network to regularize and refine the pseudo labels of the unlabelled images. We further designed a splitting method to separately optimize the RSF regularization term and the segmentation loss term with iterative convolution-thresholding method and stochastic gradient descent, respectively, which enable fast optimization of each term. Furthermore, we built a statistical atlas to show the infection spatial distribution. Extensive experiments on a small public dataset and a large scale dataset showed that the proposed method outperforms state-of-the-art methods with up to 5% in dice similarity coefficient and normalized surface dice, 10% in relative absolute volume difference and 8 mm in 95% Hausdorff distance. Moreover, we observed that the infections tend to occur at the dorsal subpleural lung and posterior basal segments that are not mentioned in current radiology reports and are meaningful to advance our understanding of COVID-19.