ZBP1: A Powerful Innate Immune Sensor and Double-Edged Sword in Host Immunity.

Z-conformation nucleic acid binding protein 1 (ZBP1), a powerful innate immune sensor, has been identified as the important signaling initiation factor in innate immune response and the multiple inflammatory cell death known as PANoptosis. The initiation of ZBP1 signaling requires recognition of left-handed double-helix Z-nucleic acid (includes Z-DNA and Z-RNA) and subsequent signaling transduction depends on the interaction between ZBP1 and its adapter proteins, such as TANK-binding kinase 1 (TBK1), interferon regulatory factor 3 (IRF3), receptor-interacting serine/threonine-protein kinase 1 (RIPK1), and RIPK3. ZBP1 activated innate immunity, including type-I interferon (IFN-I) response and NF-κB signaling, constitutes an important line of defense against pathogenic infection. In addition, ZBP1-mediated PANoptosis is a double-edged sword in anti-infection, auto-inflammatory diseases, and tumor immunity. ZBP1-mediated PANoptosis is beneficial for eliminating infected cells and tumor cells, but abnormal or excessive PANoptosis can lead to a strong inflammatory response that is harmful to the host. Thus, pathogens and host have each developed multiplex tactics targeting ZBP1 signaling to maintain strong virulence or immune homeostasis. In this paper, we reviewed the mechanisms of ZBP1 signaling, the effects of ZBP1 signaling on host immunity and pathogen infection, and various antagonistic strategies of host and pathogen against ZBP1. We also discuss existent gaps regarding ZBP1 signaling and forecast potential directions for future research.

Modeling and analyzing single-cell multimodal data with deep parametric inference.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

Target-Specific Machine Learning Scoring Function Improved Structure-Based Virtual Screening Performance for SARS-CoV-2 Drugs Development.

Leveraging machine learning has been shown to improve the accuracy of structure-based virtual screening. Furthermore, a tremendous amount of empirical data is publicly available, which further enhances the performance of the machine learning approach. In this proof-of-concept study, the 3CLpro enzyme of SARS-CoV-2 was used. Structure-based virtual screening relies heavily on scoring functions. It is widely accepted that target-specific scoring functions may perform more effectively than universal scoring functions in real-world drug research and development processes. It would be beneficial to drug discovery to develop a method that can effectively build target-specific scoring functions. In the current study, the bindingDB database was used to retrieve experimental data. Smina was utilized to generate protein-ligand complexes for the extraction of InteractionFingerPrint (IFP) and SimpleInteractionFingerPrint SIFP fingerprints via the open drug discovery tool (oddt). The present study found that randomforestClassifier and randomforestRegressor performed well when used with the above fingerprints along the Molecular ACCess System (MACCS), Extended Connectivity Fingerprint (ECFP4), and ECFP6. It was found that the area under the precision-recall curve was 0.80, which is considered a satisfactory level of accuracy. In addition, our enrichment factor analysis indicated that our trained scoring function ranked molecules correctly compared to smina's generic scoring function. Further molecular dynamics simulations indicated that the top-ranked molecules identified by our developed scoring function were highly stable in the active site, supporting the validity of our developed process. This research may provide a template for developing target-specific scoring functions against specific enzyme targets.

Analysis of Clinical Manifestations and Imaging of COVID-19 Patients in Intensive Care.

Objective: The study aims to summarize and analyze the clinical and CT findings of severe COVID-19 patients. Methods: From February 11 to March 31, 2020, 61 COVID-19 patients in intensive care in the E1-3 ward of Tongji Hospital were analyzed retrospectively. Results: The main clinical manifestations were cough, expectoration in 56 cases (91.8%), shortness of breath, chest tightness in 48 cases (78.7%), fever in 61 cases (100%), muscle ache and weakness in 40 cases (65.6%), diarrhea or vomiting in 8 cases (13.1%), and headache in 4 cases (6.6%). After admission, the leukocyte count was normal in 40 cases (57.7%), higher in 9 cases (15.4%), and lower in 12 cases (26.9%). The lymphocyte count decreased in 53 cases (86.9%). CRP was increased in 29 cases (47.5%); PCT was increased in 15 cases (24.6%); ESR was increased in 38 cases (62.3%); D-dimer increased in 39 cases (63.9%); ALT/AST increased in 40 cases (65.6%); CK/CK-MB increased in 8 cases (13.1%); troponin I increased in 6 cases (9.8%); NT-proBNP increased in 35 cases (57.4%); IL-1 increased in 5 cases (8.2%); IL-2 receptor increased in 28 cases (45.9%); IL-6 increased in 23 cases (37.7%); IL-8 increased in 15 cases (24.6%); IL-10 increased in 12 cases (19.7%); and NTF increased in 22 cases (36.1%). The chest CT images showed that 38 cases (65.5%) of right lung lesions were more extensive than those of left lung lesions, 20 cases (34.5%) of left lung lesions were more extensive than those of right lung lesions, 42 cases (72.5%) of lower lobe lesions were more extensive than those of upper lobe lesions, 6 cases (10.3%) of upper lobe lesions were more extensive than those of lower lobe lesions, and 10 cases (17.2%) of upper and lower part lesions were roughly the same. Ground-glass opacity (GGO) was found in 12 cases (20.7%); GGO with focal consolidation in 38 cases (65.5%); small patchy edge fuzzy density increased in 24 cases (41.4%); large consolidation in 20 cases (34.5%); reticular or fibrous cord in 54 cases (93.1%); and air bronchogram in 8 cases (13.8%). Conclusions: COVID-19 patients in intensive care have no specific clinical manifestation and CT findings. However, analysis and summary of relevant data can help us assess the severity of the disease, decide the timing of treatment, and predict prognosis.

Role of a lower cutoff of high sensitivity troponin I in identification of early cardiac damage in non-severe patients with COVID-19.

Cardiac damage in non-severe patients with coronavirus disease 2019 (COVID-19) is poorly explored. This study aimed to explore the manifestations of cardiac damage at presentation in non-severe patients with COVID-19. In this study, 113 non-severe patients with COVID-19 were grouped according to the duration from symptoms onset to hospital admission: group 1 (≤ 1 week, n = 27), group 2 (> 1 to 2 weeks, n = 28), group 3 (> 2 to 3 weeks, n = 27), group 4 (> 3 weeks, n = 31). Clinical, cardiovascular, and radiological data on hospital admission were compared across the four groups. The level of high sensitivity troponin I (hs-cTnI) in group 2 [10.25 (IQR 6.75-15.63) ng/L] was significantly higher than those in group 1 [1.90 (IQR 1.90-8.80) ng/L] and group 4 [1.90 (IQR 1.90-5.80) ng/L] (all Pbonferroni < 0.05). The proportion of patients who had a level of hs-cTnI ≥ 5 ng/L in group 2 (85.71%) was significantly higher than those in the other three groups (37.04%, 51.85%, and 25.81%, respectively) (all Pbonferroni < 0.05). Compared with patients with hs-cTnI under 5 ng/L, those with hs-cTnI ≥ 5 ng/L had lower lymphocyte count (P = 0.000) and SpO2 (P = 0.002) and higher CRP (P = 0.000). Patients with hs-cTnI ≥ 5 ng/L had a higher incidence of bilateral pneumonia (P = 0.000) and longer hospital length of stay (P = 0.000). In conclusion, non-severe patients with COVID-19 in the second week after symptoms onset were most likely to suffer cardiac damage. A detectable level of hs-cTnI ≥ 5 ng/L might be a manifestation of early cardiac damage in the patients.

SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3' tag-based RNA-seq of single cells.

Single-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. Here, we develop a stepwise computational method-called SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3' tag-based scRNA-seq. SCAPTURE detects PASs de novo in single cells with high sensitivity and accuracy, enabling detection of previously unannotated PASs. Quantified alternative PAS transcripts refine cell identity analysis beyond gene expression, enriching information extracted from scRNA-seq data. Using SCAPTURE, we show changes of PAS usage in PBMCs from infected versus healthy individuals at single-cell resolution.

Peripheral immunological features of COVID-19 patients in Taizhou, China: A retrospective study.

BACKGROUND: Abnormal peripheral immunological features are associated with the progression of coronavirus disease 2019 (COVID-19). METHODS: Clinical and laboratory data were retrieved in a cohort of 146 laboratory-confirmed COVID-19 patients. Potential risk factors for the development of severe COVID-19 were evaluated. RESULTS: On admission, lymphocytes, CD3+, CD4+ and CD8+ T cells, eosinophils, and albumin and pre-albumin were dramatically lower, whereas neutrophils, and interleukin (IL)-10, C-reactive protein (CRP), aspartate aminotransferase (AST) and gamma-glutamyltransferase (GGT) were significantly higher in severe cases. By the second week after discharge, all variables improved to normal levels. Covariate logistic regression results showed that the CD8+ cell count and CRP level were independent risk factors for severe COVID-19. CONCLUSION: Lower peripheral immune cell subsets in patients with severe disease recovered to normal levels as early as the second week after discharge. CD8+ T cell counts and CRP levels on admission are independent predictive factors for severe COVID-19.

Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants.

The recent pandemic of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has raised global health concerns. The viral 3-chymotrypsin-like cysteine protease (3CLpro) enzyme controls coronavirus replication and is essential for its life cycle. 3CLpro is a proven drug discovery target in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Recent studies revealed that the genome sequence of SARS-CoV-2 is very similar to that of SARS-CoV. Therefore, herein, we analysed the 3CLpro sequence, constructed its 3D homology model, and screened it against a medicinal plant library containing 32,297 potential anti-viral phytochemicals/traditional Chinese medicinal compounds. Our analyses revealed that the top nine hits might serve as potential anti- SARS-CoV-2 lead molecules for further optimisation and drug development process to combat COVID-19.

Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches.

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world's population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.

Reverse vaccinology assisted designing of multiepitope-based subunit vaccine against SARS-CoV-2.

BACKGROUND: Coronavirus disease 2019 (COVID-19) linked with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause severe illness and life-threatening pneumonia in humans. The current COVID-19 pandemic demands an effective vaccine to acquire protection against the infection. Therefore, the present study was aimed to design a multiepitope-based subunit vaccine (MESV) against COVID-19. METHODS: Structural proteins (Surface glycoprotein, Envelope protein, and Membrane glycoprotein) of SARS-CoV-2 are responsible for its prime functions. Sequences of proteins were downloaded from GenBank and several immunoinformatics coupled with computational approaches were employed to forecast B- and T- cell epitopes from the SARS-CoV-2 highly antigenic structural proteins to design an effective MESV. RESULTS: Predicted epitopes suggested high antigenicity, conserveness, substantial interactions with the human leukocyte antigen (HLA) binding alleles, and collective global population coverage of 88.40%. Taken together, 276 amino acids long MESV was designed by connecting 3 cytotoxic T lymphocytes (CTL), 6 helper T lymphocyte (HTL) and 4 B-cell epitopes with suitable adjuvant and linkers. The MESV construct was non-allergenic, stable, and highly antigenic. Molecular docking showed a stable and high binding affinity of MESV with human pathogenic toll-like receptors-3 (TLR3). Furthermore, in silico immune simulation revealed significant immunogenic response of MESV. Finally, MEV codons were optimized for its in silico cloning into the Escherichia coli K-12 system, to ensure its increased expression. CONCLUSION: The MESV developed in this study is capable of generating immune response against COVID-19. Therefore, if designed MESV further investigated experimentally, it would be an effective vaccine candidate against SARS-CoV-2 to control and prevent COVID-19.

Management of haemophilia patients in the COVID-19 pandemic: Experience in Wuhan and Tianjin, two differently affected cities in China.

OBJECTIVE: To identify lessons learned from haemophilia care experience in Wuhan (COVID-19 outbreak epicenter in China) and Tianjin (with relatively low COVID-19 incidence) in the pandemic. METHODS: We compared the challenges in haemophilia management attributed to local COVID-19 containment policies, healthcare resource availability, clotting factors supply, daily living restrictions and coping strategies employed. RESULTS: Wuhan was in lockdown with strict traffic controls, enforced quarantine and overwhelmed resources. Tianjin was in relatively relaxed countermeasures to COVID-19. In Wuhan, haemophilia treatment (for bleeding, prophylaxis, multidisciplinary team care, immune tolerance induction) and patient education were severely affected, while the challenges in Tianjin were less. In both cities, patients' fear for COVID-19 infection also affected their management. Coping strategy in Wuhan included channelling of clotting factors supply from hospitals to nine pharmacies; timely transfers of in-need patients to healthcare facilities by a volunteer service network jointly coordinated by the government, hospitals and the community. Although factor concentrate supply in each city was adequate, patients still worried whether there would be enough supply to last through the pandemics. Consequently, many downgraded their treatment regimens resulting in increased bleeding episodes. In both cities, telemedicine was promoted for patient care and education. CONCLUSIONS: The COVID-19 pandemic had varying adverse impacts on haemophilia care depending on the local infection incidence. Our experience suggests that haemophilia management strategies in the pandemic need to be established according to the local virus containment/mitigation policies, daily living restrictions and resource availability.

Discovery of human coronaviruses pan-papain-like protease inhibitors using computational approaches.

The papain-like protease (PLpro) is vital for the replication of coronaviruses (CoVs), as well as for escaping innate-immune responses of the host. Hence, it has emerged as an attractive antiviral drug-target. In this study, computational approaches were employed, mainly the structure-based virtual screening coupled with all-atom molecular dynamics (MD) simulations to computationally identify specific inhibitors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PLpro, which can be further developed as potential pan-PLpro based broad-spectrum antiviral drugs. The sequence, structure, and functional conserveness of most deadly human CoVs PLpro were explored, and it was revealed that functionally important catalytic triad residues are well conserved among SARS-CoV, SARS-CoV-2, and middle east respiratory syndrome coronavirus (MERS-CoV). The subsequent screening of a focused protease inhibitors database composed of ∼7,000 compounds resulted in the identification of three candidate compounds, ADM_13083841, LMG_15521745, and SYN_15517940. These three compounds established conserved interactions which were further explored through MD simulations, free energy calculations, and residual energy contribution estimated by MM-PB(GB)SA method. All these compounds showed stable conformation and interacted well with the active residues of SARS-CoV-2 PLpro, and showed consistent interaction profile with SARS-CoV PLpro and MERS-CoV PLpro as well. Conclusively, the reported SARS-CoV-2 PLpro specific compounds could serve as seeds for developing potent pan-PLpro based broad-spectrum antiviral drugs against deadly human coronaviruses. Moreover, the presented information related to binding site residual energy contribution could lead to further optimization of these compounds.