Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic level less than 4 months after the first official cases. Hence, the search for drugs and vaccines that could prevent or treat infections by SARS-CoV-2 began, intending to reduce a possible collapse of health systems. After 2 years, efforts to find therapies to treat COVID-19 continue. However, there is still much to be understood about the virus' pathology. Tools such as transcriptomics have been used to understand the impact of SARS-CoV-2 on different cells isolated from various tissues, leaving datasets in the databases that integrate genes and differentially expressed pathways during SARS-CoV-2 infection. After retrieving transcriptome datasets from different human cells infected with SARS-CoV-2 available in the database, we performed an integrative analysis associated with deep learning algorithms to determine differentially expressed targets mainly after infection. The targets found represented a fructose transporter (GLUT5) and a component of proteasome 26s. These targets were then molecularly modeled, followed by molecular docking that identified potential inhibitors for both structures. Once the inhibition of structures that have the expression increased by the virus can represent a strategy for reducing the viral replication by selecting infected cells, associating these bioinformatics tools, therefore, can be helpful in the screening of molecules being tested for new uses, saving financial resources, time, and making a personalized screening for each infectious disease.

Whole Transcriptome Analysis of Breast Cancer Tumors during Neoadjuvant Chemotherapy: Association with Hematogenous Metastasis.

The assessment of molecular genetic landscape changes during NAC and the relationship between molecular signatures in residual tumors are promising approaches for identifying effective markers of outcome in breast cancer. The majority of the data in the literature present the relationship between the molecular genetic landscape and the response to NAC or are simply descriptive. The present study aimed to determine changes in expression profiles during NAC and assess the relationship between gene expression and the outcome of patients with luminal B HER2 breast cancer depending on distant hematogenous metastasis. The study included 39 patients with luminal B HER2-BC. The patients received 6-8 courses of NAC, and paired samples consisting of biopsy and surgical materials were analyzed. A full transcriptome microarray analysis was performed using the human Clariom™ S Assay platform (Affymetrix, 3450 Central Expy, Santa Clara, CA, 95051, USA). A comparison of the expression profiles of patients with breast cancer before and after NAC, depending on the status of hematogenous metastasis, was conducted. It was shown that the amount of DEGs in the tumor was reduced by more than six times after NAC. The top 10 signaling pathways were also found, the activity of which varied depending on the status of hematogenous metastasis before and after NAC. In addition, the association of DEGs with hematogenous metastasis in patients with breast cancer was evaluated: MFS was assessed depending on the expression level of 21 genes. It was shown that MFS was significantly associated with the expression level and pattern of nine genes. The expression levels of nine DEGs in the tumors of patients with breast cancer after NAC were significantly correlated with MFS when the status of hematogenous metastasis was taken into account.

Humoral immunity and transcriptome differences of COVID-19 inactivated vacciane and protein subunit vaccine as third booster dose in human.

Under the background of the severe human health and world economic burden caused by COVID-19, the attenuation of vaccine protection efficacy, and the prevalence and immune escape of emerging variants of concern (VOCs), the third dose of booster immunization has been put on the agenda. Systems biology approaches can help us gain new perspectives on the characterization of immune responses and the identification of factors underlying vaccine-induced immune efficacy. We analyzed the antibody signature and transcriptional responses of participants vaccinated with COVID-19 inactivated vaccine and protein subunit vaccine as a third booster dose. The results from the antibody indicated that the third booster dose was effective, and that heterologous vaccination with the protein subunit vaccine as a booster dose induced stronger humoral immune responses than the homologous vaccination with inactivated vaccine, and might be more effective against VOCs. In transcriptomic analysis, protein subunit vaccine induced more differentially expressed genes that were significantly associated with many important innate immune pathways. Both the homologous and heterologous boosters could increase the effectiveness against COVID-19, and compared with the inactivated vaccine, the protein subunit vaccine, mediated a stronger humoral immune response and had a more significant correlation with the innate immune function module, which provided certain data support for the third booster immunization strategy.

Interaction-based transcriptome analysis via differential network inference.

Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.

Discovering Common Pathogenic Mechanisms of COVID-19 and Parkinson Disease: An Integrated Bioinformatics Analysis.

Coronavirus disease 2019 (COVID-19) has emerged since December 2019 and was later characterized as a pandemic by WHO, imposing a major public health threat globally. Our study aimed to identify common signatures from different biological levels to enlighten the current unclear association between COVID-19 and Parkinson's disease (PD) as a number of possible links, and hypotheses were reported in the literature. We have analyzed transcriptome data from peripheral blood mononuclear cells (PBMCs) of both COVID-19 and PD patients, resulting in a total of 81 common differentially expressed genes (DEGs). The functional enrichment analysis of common DEGs are mostly involved in the complement system, type II interferon gamma (IFNG) signaling pathway, oxidative damage, microglia pathogen phagocytosis pathway, and GABAergic synapse. The protein-protein interaction network (PPIN) construction was carried out followed by hub detection, revealing 10 hub genes (MX1, IFI27, C1QC, C1QA, IFI6, NFIX, C1S, XAF1, IFI35, and ELANE). Some of the hub genes were associated with molecular mechanisms such as Lewy bodies-induced inflammation, microglia activation, and cytokine storm. We investigated regulatory elements of hub genes at transcription factor and miRNA levels. The major transcription factors regulating hub genes are SOX2, XAF1, RUNX1, MITF, and SPI1. We propose that these events may have important roles in the onset or progression of PD. To sum up, our analysis describes possible mechanisms linking COVID-19 and PD, elucidating some unknown clues in between.

Transcriptome Analysis of Peripheral Blood Mononuclear Cells in SARS-CoV-2 Naïve and Recovered Individuals Vaccinated With Inactivated Vaccine.

The urgent approval of the use of the inactivated COVID-19 vaccine is essential to reduce the threat and burden of the epidemic on global public health, however, our current understanding of the host immune response to inactivated vaccine remains limited. Herein, we performed serum IgG antibody detection and transcriptomics analysis on 20 SARS-CoV-2 naïve individuals who received multiple doses of inactivated vaccine and 5 SARS-CoV-2 recovered individuals who received single dose of inactivated vaccine. Our research revealed the important role of many innate immune pathways after vaccination, identified a significant correlation with the third dose of booster vaccine and proteasome-related genes, and found that SARS-CoV-2 recovered individuals can produces a strong immune response to a single dose of inactivated vaccine. These results help us understand the reaction mechanism of the host's molecular immune system to the inactivated vaccine, and provide a basis for the choice of vaccination strategy.

Infectious Bronchitis Virus Infection Increases Pathogenicity of H9N2 Avian Influenza Virus by Inducing Severe Inflammatory Response.

Infectious bronchitis virus (IBV) and H9N2 avian influenza virus (AIV) are frequently identified in chickens with respiratory disease. However, the role and mechanism of IBV and H9N2 AIV co-infection remain largely unknown. Specific-pathogen-free (SPF) chickens were inoculated with IBV 2 days before H9N2 virus inoculation (IBV/H9N2); with IBV and H9N2 virus simultaneously (IBV+H9N2); with H9N2 virus 2 days before IBV inoculation (H9N2/IBV); or with either IBV or H9N2 virus alone. Severe respiratory signs, pathological damage, and higher morbidity and mortality were observed in the co-infection groups compared with the IBV and H9N2 groups. In general, a higher virus load and a more intense inflammatory response were observed in the three co-infection groups, especially in the IBV/H9N2 group. The same results were observed in the transcriptome analysis of the trachea of the SPF chickens. Therefore, IBV might play a major role in the development of respiratory disease in chickens, and secondary infection with H9N2 virus further enhances the pathogenicity by inducing a severe inflammatory response. These findings may provide a reference for the prevention and control of IBV and H9N2 AIV in the poultry industry and provide insight into the molecular mechanisms of IBV and H9N2 AIV co-infection in chickens.

Gene Expression Profiling Reveals the Shared and Distinct Transcriptional Signatures in Human Lung Epithelial Cells Infected With SARS-CoV-2, MERS-CoV, or SARS-CoV: Potential Implications in Cardiovascular Complications of COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative virus for the current global pandemic known as coronavirus disease 2019 (COVID-19). SARS-CoV-2 belongs to the family of single-stranded RNA viruses known as coronaviruses, including the MERS-CoV and SARS-CoV that cause Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS), respectively. These coronaviruses are associated in the way that they cause mild to severe upper respiratory tract illness. This study has used an unbiased analysis of publicly available gene expression datasets from Gene Expression Omnibus to understand the shared and unique transcriptional signatures of human lung epithelial cells infected with SARS-CoV-2 relative to MERS-CoV or SARS-CoV. A major goal was to discover unique cellular responses to SARS-CoV-2 among these three coronaviruses. Analyzing differentially expressed genes (DEGs) shared by the three datasets led to a set of 17 genes, suggesting the lower expression of genes related to acute inflammatory response (TNF, IL32, IL1A, CXCL1, and CXCL3) in SARS-CoV-2. This subdued transcriptional response to SARS-CoV-2 may cause prolonged viral replication, leading to severe lung damage. Downstream analysis of unique DEGs of SARS-CoV-2 infection revealed changes in genes related to apoptosis (NRP1, FOXO1, TP53INP1, CSF2, and NLRP1), coagulation (F3, PROS1, ITGB3, and TFPI2), and vascular function (VAV3, TYMP, TCF4, and NR2F2), which may contribute to more systemic cardiovascular complications of COVID-19 than MERS and SARS. The study has uncovered a novel set of transcriptomic signatures unique to SARS-CoV-2 infection and shared by three coronaviruses, which may guide the initial efforts in the development of prognostic or therapeutic tools for COVID-19.

Single-Cell Sequencing of Glioblastoma Reveals Central Nervous System Susceptibility to SARS-CoV-2.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the recent global COVID-19 outbreak, which led to a public health emergency. Entry of SARS-CoV-2 into human cells is dependent on the SARS-CoV receptor, angiotensin converting enzyme 2 (ACE2) receptor, and cathepsin. Cathepsin degrades the spike protein (S protein), which results in the entry of viral nucleic acid into the human host cell. METHODS: We explored the susceptibility of the central nervous system (CNS) to SARS-CoV-2 infection using single-cell transcriptome analysis of glioblastoma. RESULTS: The results showed that ACE2 expression is relatively high in endothelial cells (ECs), bone marrow mesenchymal stem cells (BMSCs), and neural precursor cells (NPCs). Cathepsin B (Cat B) and cathepsin (Cat L) were also strongly expressed in various cell clusters within the glioblastoma microenvironment. Immunofluorescence staining of glioma and normal brain tissue chips further confirmed that ACE2 expression co-localized with CD31, CD73, and nestin, which confirmed the susceptibility to SARS-CoV-2 of nervous system cells, including ECs, BMSCs, and NPCs, from clinical specimens. CONCLUSIONS: These findings reveal the mechanism of SARS-CoV-2 neural invasion and suggest that special attention should be paid to SARS-CoV-2-infected patients with neural symptoms, especially those who suffered a glioma.