Revealing the roles of TLR7, a nucleic acid sensor for COVID-19 in pan-cancer.

Recent studies suggested that cancer was a risk factor for coronavirus disease 2019 (COVID-19). Toll-like receptor 7 (TLR7), a severe acute respiratory syndrome 2 (SARS-CoV-2) virus's nucleic acid sensor, was discovered to be aberrantly expressed in many types of cancers. However, its expression pattern across cancers and association with COVID-19 (or its causing virus SARS-CoV-2) has not been systematically studied. In this study, we proposed a computational framework to comprehensively study the roles of TLR7 in COVID-19 and pan-cancers at genetic, gene expression, protein, epigenetic, and single-cell levels. We applied the computational framework in a few databases, including The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), Human Protein Atlas (HPA), lung gene expression data of mice infected with SARS-CoV-2, and the like. As a result, TLR7 expression was found to be higher in the lung of mice infected with SARS-CoV-2 than that in the control group. The analysis in the Opentargets database also confirmed the association between TLR7 and COVID-19. There are also a few exciting findings in cancers. First, the most common type of TLR7 was "Missense" at the genomic level. Second, TLR7 mRNA expression was significantly up-regulated in 6 cancer types and down-regulated in 6 cancer types compared to normal tissues, further validated in the HPA database at the protein level. The genes significantly co-expressed with TLR7 were mainly enriched in the toll-like receptor signaling pathway, endolysosome, and signaling pattern recognition receptor activity. In addition, the abnormal TLR7 expression was associated with mismatch repair (MMR), microsatellite instability (MSI), and tumor mutational burden (TMB) in various cancers. Mined by the ESTIMATE algorithm, the expression of TLR7 was also closely linked to various immune infiltration patterns in pan-cancer, and TLR7 was mainly enriched in macrophages, as revealed by single-cell RNA sequencing. Third, abnormal expression of TLR7 could predict the survival of Brain Lower Grade Glioma (LGG), Lung adenocarcinoma (LUAD), Skin Cutaneous Melanoma (SKCM), Stomach adenocarcinoma (STAD), and Testicular Germ Cell Tumors (TGCT) patients, respectively. Finally, TLR7 expressions were very sensitive to a few targeted drugs, such as Alectinib and Imiquimod. In conclusion, TLR7 might be essential in the pathogenesis of COVID-19 and cancers.

Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction.

Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human-SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy.

Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet.

COVID-19 has caused enormous challenges to global economy and public health. The identification of patients with the COVID-19 infection by CT scan images helps prevent its pandemic. Manual screening COVID-19-related CT images spends a lot of time and resources. Artificial intelligence techniques including deep learning can effectively aid doctors and medical workers to screen the COVID-19 patients. In this study, we developed an ensemble deep learning framework, DeepDSR, by combining DenseNet, Swin transformer, and RegNet for COVID-19 image identification. First, we integrate three available COVID-19-related CT image datasets to one larger dataset. Second, we pretrain weights of DenseNet, Swin Transformer, and RegNet on the ImageNet dataset based on transformer learning. Third, we continue to train DenseNet, Swin Transformer, and RegNet on the integrated larger image dataset. Finally, the classification results are obtained by integrating results from the above three models and the soft voting approach. The proposed DeepDSR model is compared to three state-of-the-art deep learning models (EfficientNetV2, ResNet, and Vision transformer) and three individual models (DenseNet, Swin transformer, and RegNet) for binary classification and three-classification problems. The results show that DeepDSR computes the best precision of 0.9833, recall of 0.9895, accuracy of 0.9894, F1-score of 0.9864, AUC of 0.9991 and AUPR of 0.9986 under binary classification problem, and significantly outperforms other methods. Furthermore, DeepDSR obtains the best precision of 0.9740, recall of 0.9653, accuracy of 0.9737, and F1-score of 0.9695 under three-classification problem, further suggesting its powerful image identification ability. We anticipate that the proposed DeepDSR framework contributes to the diagnosis of COVID-19.

Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.

Amid the COVID-19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug-virus association entries from literature by text mining and built a human drug-virus association database. To the best of our knowledge, it is the largest publicly available drug-virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug-virus association network, the drug-drug chemical structure similarity network, and the virus-virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug-virus association is unassociated). A comparison on the curated drug-virus database shows that WRMF performs better than a few state-of-the-art methods. In addition, we selected the other two different public datasets (i.e. Cdataset and HMDD V2.0) to assess WRMF's performance. The case study also demonstrated the accuracy and reliability of WRMF to infer potential drugs for the novel virus. In summary, we offer a useful tool including a novel drug-virus association database and a powerful method WRMF to repurpose potential drugs for new viruses.

Systematic Tracing of Susceptible Animals to SARS-CoV-2 by a Bioinformatics Framework.

Since the outbreak of SARS-CoV-2 in 2019, the Chinese horseshoe bats were considered as a potential original host of SARS-CoV-2. In addition, cats, tigers, lions, mints, and ferrets were naturally or experimentally infected with SARS-CoV-2. For the surveillance and control of this highly infectious disease, it is critical to trace susceptible animals and predict the consequence of potential mutations at the binding region of viral spike protein and host ACE2 protein. This study proposed a novel bioinformatics framework to systematically trace susceptible animals to SARS-CoV-2 and predict the binding affinity between susceptible animals' mutated/un-mutated ACE2 receptors. As a result, we identified a few animals posing a potential risk of infection with SARS-CoV-2 using the docking analysis of ACE2 protein and viral spike protein. The binding affinity of some of these species is weaker than that of humans but more potent than that of Chinese horseshoe bats. We also found that a few point mutations in human ACE2 protein or viral spike protein could significantly enhance their binding affinity, posing an enormous potential threat to public health. The ancestors of the Omicron may evolve rapidly through the accumulation of mutations in infecting the host and jumped into human beings. These findings indicate that if the epidemic expands, there may be a human-animal-human transmission route, which will increase the difficulty of disease prevention and control.

Genomic variation, origin tracing, and vaccine development of SARS-CoV-2: A systematic review.

COVID-19 has spread globally to over 200 countries with more than 40 million confirmed cases and one million deaths as of November 1, 2020. The SARS-CoV-2 virus, leading to COVID-19, shows extremely high rates of infectivity and replication, and can result in pneumonia, acute respiratory distress, or even mortality. SARS-CoV-2 has been found to continue to rapidly evolve, with several genomic variants emerging in different regions throughout the world. In addition, despite intensive study of the spike protein, its origin, and molecular mechanisms in mediating host invasion are still only partially resolved. Finally, the repertoire of drugs for COVID-19 treatment is still limited, with several candidates still under clinical trial and no effective therapeutic yet reported. Although vaccines based on either DNA/mRNA or protein have been deployed, their efficacy against emerging variants requires ongoing study, with multivalent vaccines supplanting the first-generation vaccines due to their low efficacy against new strains. Here, we provide a systematic review of studies on the epidemiology, immunological pathogenesis, molecular mechanisms, and structural biology, as well as approaches for drug or vaccine development for SARS-CoV-2.

A Systematic Analysis on COVID-19 Patients in Inner Mongolia Based on Dynamic Monitoring.

COVID-19 has spread globally with over 90,000,000 incidences and 1,930,000 deaths by Jan 11, 2021, which poses a big threat to public health. It is urgent to distinguish COVID-19 from common pneumonia. In this study, we reported multiple clinical feature analyses on COVID-19 in Inner Mongolia for the first time. We dynamically monitored multiple clinical features of all 75 confirmed COVID-19 patients, 219 pneumonia patients, and 68 matched healthy people in Inner Mongolia. Then, we studied the association between COVID-19 and clinical characteristics, based on which to construct a novel logistic regression model for predicting COVID-19. As a result, among the tested clinical characteristics, WBC, hemoglobin, C-reactive protein (CRP), ALT, and Cr were significantly different between COVID-19 patients and patients in other groups. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.869 for the logistic regression model using multiple factors associated with COVID-19. Furthermore, the CRP reaction showed five different time-series patterns with one-peak and double-peak modes. In conclusion, our study identified a few clinical characteristics significantly different between COVID-19 patients and others in Inner Mongolia. The features can be used to establish a reliable logistic regression model for predicting COVID-19.

Prioritizing antiviral drugs against SARS-CoV-2 by integrating viral complete genome sequences and drug chemical structures.

The outbreak of a novel febrile respiratory disease called COVID-19, caused by a newfound coronavirus SARS-CoV-2, has brought a worldwide attention. Prioritizing approved drugs is critical for quick clinical trials against COVID-19. In this study, we first manually curated three Virus-Drug Association (VDA) datasets. By incorporating VDAs with the similarity between drugs and that between viruses, we constructed a heterogeneous Virus-Drug network. A novel Random Walk with Restart method (VDA-RWR) was then developed to identify possible VDAs related to SARS-CoV-2. We compared VDA-RWR with three state-of-the-art association prediction models based on fivefold cross-validations (CVs) on viruses, drugs and virus-drug associations on three datasets. VDA-RWR obtained the best AUCs for the three fivefold CVs, significantly outperforming other methods. We found two small molecules coming together on the three datasets, that is, remdesivir and ribavirin. These two chemical agents have higher molecular binding energies of - 7.0 kcal/mol and - 6.59 kcal/mol with the domain bound structure of the human receptor angiotensin converting enzyme 2 (ACE2) and the SARS-CoV-2 spike protein, respectively. Interestingly, for the first time, experimental results suggested that navitoclax could be potentially applied to stop SARS-CoV-2 and remains to further validation.

Dynamic blood single-cell immune responses in patients with COVID-19.

The 2019 coronavirus disease (COVID-19) outbreak caused by the SARS-CoV-2 virus is an ongoing global health emergency. However, the virus' pathogenesis remains unclear, and there is no cure for the disease. We investigated the dynamic changes of blood immune response in patients with COVID-19 at different stages by using 5' gene expression, T cell receptor (TCR), and B cell receptors (BCR) V(D)J transcriptome analysis at a single-cell resolution. We obtained single-cell mRNA sequencing (scRNA-seq) data of 341,420 peripheral blood mononuclear cells (PBMCs) and 185,430 clonotypic T cells and 28,802 clonotypic B cells from 25 samples of 16 patients with COVID-19 for dynamic studies. In addition, we used three control samples. We found expansion of dendritic cells (DCs), CD14+ monocytes, and megakaryocytes progenitor cells (MP)/platelets and a reduction of naïve CD4+ T lymphocytes in patients with COVID-19, along with a significant decrease of CD8+ T lymphocytes, and natural killer cells (NKs) in patients in critical condition. The type I interferon (IFN-I), mitogen-activated protein kinase (MAPK), and ferroptosis pathways were activated while the disease was active, and recovered gradually after patient conditions improved. Consistent with this finding, the mRNA level of IFN-I signal-induced gene IFI27 was significantly increased in patients with COVID-19 compared with that of the controls in a validation cohort that included 38 patients and 35 controls. The concentration of interferon-α (IFN-α) in the serum of patients with COVID-19 increased significantly compared with that of the controls in an additional cohort of 215 patients with COVID-19 and 106 controls, further suggesting the important role of the IFN-I pathway in the immune response of COVID-19. TCR and BCR sequences analyses indicated that patients with COVID-19 developed specific immune responses against SARS-CoV-2 antigens. Our study reveals a dynamic landscape of human blood immune responses to SARS-CoV-2 infection, providing clues for therapeutic potentials in treating COVID-19.

Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19.

A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations.

Identifying Effective Antiviral Drugs Against SARS-CoV-2 by Drug Repositioning Through Virus-Drug Association Prediction.

A new coronavirus called SARS-CoV-2 is rapidly spreading around the world. Over 16,558,289 infected cases with 656,093 deaths have been reported by July 29th, 2020, and it is urgent to identify effective antiviral treatment. In this study, potential antiviral drugs against SARS-CoV-2 were identified by drug repositioning through Virus-Drug Association (VDA) prediction. 96 VDAs between 11 types of viruses similar to SARS-CoV-2 and 78 small molecular drugs were extracted and a novel VDA identification model (VDA-RLSBN) was developed to find potential VDAs related to SARS-CoV-2. The model integrated the complete genome sequences of the viruses, the chemical structures of drugs, a regularized least squared classifier (RLS), a bipartite local model, and the neighbor association information. Compared with five state-of-the-art association prediction methods, VDA-RLSBN obtained the best AUC of 0.9085 and AUPR of 0.6630. Ribavirin was predicted to be the best small molecular drug, with a higher molecular binding energy of -6.39 kcal/mol with human angiotensin-converting enzyme 2 (ACE2), followed by remdesivir (-7.4 kcal/mol), mycophenolic acid (-5.35 kcal/mol), and chloroquine (-6.29 kcal/mol). Ribavirin, remdesivir, and chloroquine have been under clinical trials or supported by recent works. In addition, for the first time, our results suggested several antiviral drugs, such as FK506, with molecular binding energies of -11.06 and -10.1 kcal/mol with ACE2 and the spike protein, respectively, could be potentially used to prevent SARS-CoV-2 and remains to further validation. Drug repositioning through virus-drug association prediction can effectively find potential antiviral drugs against SARS-CoV-2.

Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method.

It is urgent to find an effective antiviral drug against SARS-CoV-2. In this study, 96 virus-drug associations (VDAs) from 12 viruses including SARS-CoV-2 and similar viruses and 78 small molecules are selected. Complete genomic sequence similarity of viruses and chemical structure similarity of drugs are then computed. A KATZ-based VDA prediction method (VDA-KATZ) is developed to infer possible drugs associated with SARS-CoV-2. VDA-KATZ obtained the best AUCs of 0.8803 when the walking length is 2. The predicted top 3 antiviral drugs against SARS-CoV-2 are remdesivir, oseltamivir, and zanamivir. Molecular docking is conducted between the predicted top 10 drugs and the virus spike protein/human ACE2. The results showed that the above 3 chemical agents have higher molecular binding energies with ACE2. For the first time, we found that zidovudine may be effective clues of treatment of COVID-19. We hope that our predicted drugs could help to prevent the spreading of COVID.