Immune Interaction Map of Human SARS-CoV-2 Target Genes: Implications for Therapeutic Avenues.

There exists increasing evidence that people with preceding medical conditions, such as diabetes and cancer, have a higher risk of infection with SARS-CoV-2 and are more vulnerable to severe disease. To get insights into the possible role of the immune system upon COVID-19 infection, 2811 genes of the gene ontology term "immune system process GO: 0002376" were selected for coexpression analysis of the human targets of SARS-CoV-2 (HT-SARS-CoV-2) ACE2, TMPRSS2, and FURIN in tissue samples from patients with cancer and diabetes mellitus. The network between HT-SARS-CoV-2 and immune system process genes was analyzed based on functional protein associations using STRING. In addition, STITCH was employed to determine druggable targets. DPP4 was the only immune system process gene, which was coexpressed with the three HT-SARS-CoV-2 genes, while eight other immune genes were at least coexpressed with two HT-SARS-CoV-2 genes. STRING analysis between immune and HT-SARS-CoV-2 genes plotted 19 associations of which there were eight common networking genes in mixed healthy (323) and pan-cancer (11003) tissues in addition to normal (87), cancer (90), and diabetic (128) pancreatic tissues. Using this approach, three commonly applicable druggable connections between HT-SARS-CoV-2 and immune system process genes were identified. These include positive associations of ACE2-DPP4 and TMPRSS2-SRC as well as a negative association of FURIN with ADAM17. Furthermore, 16 drugs were extracted from STITCH (score <0.8) with 32 target genes. Thus, an immunological network associated with HT-SARS-CoV-2 using bioinformatics tools was identified leading to novel therapeutic opportunities for COVID-19.

Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Cytokine Pathways Involved in COVID-19 After Rhinovirus Infection in Asthma Patients.

BACKGROUND Rhinovirus (RV) is the most common pathogen involved in asthma, and COVID-19, caused by SARS-COV-2, may be more severe in asthma patients. Here, we applied integrated bioinformatics to identify potential key genes and cytokine pathways after RV infection in asthma, and analyzed changes in angiotensin-converting enzyme 2 (ACE2), the cellular receptor of SARS-COV-2. MATERIAL AND METHODS The gene expression profile dataset GSE149273 was downloaded from NCBI-GEO, which included 90 samples of non-infected, RVA, and RVC. Differentially expressed genes (DEGs) were identified using t tests in the limma R package, and subsequently investigated by GO, KEGG, and DO analysis. Moreover, the expression of ACE2 and the proportion of immune cells were further analyzed to determine the effects of RV on cytokines. RESULTS A total of 555 DEGs of RVA and 421 of RVC were identified. There were 415 DEGs in RVA and RVC, of which 406 were upregulated and 9 were downregulated. The functional enrichment analysis showed that most DEGs were obviously enriched in cytokines, and were mainly enriched in "influenza" and "hepatitis C, chronic". In addition, the expression of ACE2 increased significantly and the proportion of immune cytokines significantly changed after RV infection. Our results suggest that RV can activate the cytokine pathway associated with COVID-19 by increasing ACE2. CONCLUSIONS The DEGs and related cytokine pathways after asthma RV infection identified using integrated bioinformatics in this study elucidate the potential link between RV and COVID-19.

Excessive Neutrophils and Neutrophil Extracellular Traps in COVID-19.

Background: Cases of excessive neutrophil counts in the blood in severe coronavirus disease (COVID-19) patients have drawn significant attention. Neutrophil infiltration was also noted on the pathological findings from autopsies. It is urgent to clarify the pathogenesis of neutrophils leading to severe pneumonia in COVID-19. Methods: A retrospective analysis was performed on 55 COVID-19 patients classified as mild (n = 22), moderate (n = 25), and severe (n = 8) according to the Guidelines released by the National Health Commission of China. Trends relating leukocyte counts and lungs examined by chest CT scan were quantified by Bayesian inference. Transcriptional signatures of host immune cells of four COVID19 patients were analyzed by RNA sequencing of lung specimens and BALF. Results: Neutrophilia occurred in 6 of 8 severe patients at 7-19 days after symptom onset, coinciding with lesion progression. Increasing neutrophil counts paralleled lesion CT values (slope: 0.8 and 0.3-1.2), reflecting neutrophilia-induced lung injury in severe patients. Transcriptome analysis revealed that neutrophil activation was correlated with 17 neutrophil extracellular trap (NET)-associated genes in COVID-19 patients, which was related to innate immunity and interacted with T/NK/B cells, as supported by a protein-protein interaction network analysis. Conclusion: Excessive neutrophils and associated NETs could explain the pathogenesis of lung injury in COVID-19 pneumonia.

Natural killer cell immunotypes related to COVID-19 disease severity.

Understanding innate immune responses in COVID-19 is important to decipher mechanisms of host responses and interpret disease pathogenesis. Natural killer (NK) cells are innate effector lymphocytes that respond to acute viral infections but might also contribute to immunopathology. Using 28-color flow cytometry, we here reveal strong NK cell activation across distinct subsets in peripheral blood of COVID-19 patients. This pattern was mirrored in scRNA-seq signatures of NK cells in bronchoalveolar lavage from COVID-19 patients. Unsupervised high-dimensional analysis of peripheral blood NK cells furthermore identified distinct NK cell immunotypes that were linked to disease severity. Hallmarks of these immunotypes were high expression of perforin, NKG2C, and Ksp37, reflecting increased presence of adaptive NK cells in circulation of patients with severe disease. Finally, arming of CD56bright NK cells was observed across COVID-19 disease states, driven by a defined protein-protein interaction network of inflammatory soluble factors. This study provides a detailed map of the NK cell activation landscape in COVID-19 disease.

CXCL10 an important chemokine associated with cytokine storm in COVID-19 infected patients.

OBJECTIVE: The specific mechanism of cytokine storm in COVID-19 infected patients is not clear. This study aims to identify the key genes that cause cytokine storm in COVID-19 infected patients. MATERIALS AND METHODS: We conducted a difference analysis on the GSE147507 data set. The analysis results are combined with immune genes to obtain immune-related genes among the differential genes. Finally, GO enrichment analysis, PPI analysis, core gene identification, and ssGSEA enrichment analysis were performed on the new gene set. RESULTS: A total of 232 differential genes were screened out. After merging with immune genes, a total of 29 immune-related genes were obtained. Further analysis revealed that the genes were enriched in 16 pathways, and the protein interaction network had a total of 29 nodes and 139 edges. After screening, the core gene was CXCL10. The ssGSEA results of CXCL10 showed that CD4 and CD8 immune-related signature were significantly enriched in high CXCL10 expression, and the samples with low CXCL10 expression were significantly enriched with monocytes and DC immune-related signature. CONCLUSIONS: CXCL10 may be a key gene related to the cytokine storm of COVID-19 infection, and it is expected to become the therapeutic target.