Background
Materials and methods
Conceptual domains identification
Scoping review
Results
Molecular characterisation of the virus and of the entry phase into the host cells
A. SARS-CoV-2 characterization | ||
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Investigation field | Viral genomics evidence | Viral proteomics evidence |
Genome evolution and geographical distribution | Evolutionary history of SARS-CoV-2 reconstructed by a phylogenetic approach among the 5 subgenera of Betacoronaviruses [TE01-TE03] At the beginning of pandemic SARS-CoV-2 genomes were classified into 5 main clades: S84, V251, I378, D392, and G61 (the most frequent ancestral type) [TE04-TE05] | |
Genomic hotspots for mutation, drivers of evolution and correlation with pathogenesis | In SARS-CoV-2 genomes: 10 hyper-variable genomic hotspots [TE14] Genomic regions encoding nsps, except nsp11, had values of dN/dS ratio < 1. Among the structural genes, only S and M displayed dN/dS < 1. Deletions in ORF7b and ORF8 of SARS-CoV-2 genome confer lower odds of developing hypoxia in infected hosts [TE09; TE12] | |
Intra-host genomic variability | Small- and large-scale intra-host variations [TE19-TE20] Spatial–temporal redistribution of variants in respiratory and gastro-intestinal tract [TE19-TE21] | |
Single viral proteins | Two mutations in nsp6 and in a region near ORF10 confer lower stability to S, N, M, E proteins, linked to autophagy. [TE24-TE25] Non-conservative substitutions in functional regions of the S, nsp1 and nsp3 may contribute to separate SARS-CoV and SARS-CoV-2 in spread and virulence [TE27] | |
Whole viral proteome | Dynamicome study, based on Viral Integrated Structural Dynamic Database (VIStEDD), among 273 virus/host PP interactions highlighted 6 major viral nodes influencing the activity of 166 host nodes involved in various cellular processes [TE28-TE29] | |
Immune proteomics | Viral proteomics was used to design multi-epitope vaccines and to find possible host–pathogen molecular mimicry [TE31] |
B. SARS-CoV-2—host interactome | |
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Investigation field | Multi-omics |
Viral RNA and protein interactions | 9 potential silencer RNA (siRNA) targets, conserved among all the studied SARS-CoV-2 genomes. [TE32] 3 SARS-CoV small viral (sV) RNAs involved in lung pathology of mice. [TE33] In Vero E6 and to Huh-7 cells infected by SARS-CoV-2, 163 and 229 host protein bind SARS-CoV-2 RNA. [TE37] GO enrichment analysis revealed that most of the proteins were protective from virus-induced cell death, regulating SARS-CoV-2 pathogenicity. [TE38] Functional analysis discovered novel proviral genes and pathways, including chromatin remodelling complexes [TE33-TE39] |
Virus–host protein–protein interactions | 1311 PPIs were used to build a large coronavirus-host interactome. Relevant small protein complexes: EIF4E2-GIGYF2 dimer, involved in proteins translation repression and the MAT2A-MAT2B complex [TE42]; DNA-PK kinase contributing to interferon induction [TE42]; Mitochondrial proteins PHB, PHB2 and STOML2, regulating mitophagy. [TE42] Host interactome linked to S of SARS-CoV and MERS-CoV: innate immunity involved. [TE43] The International Molecular Exchange (IMEx) Consortium cured a dataset of PPI, contained interactions of SARS-CoV-2 and SARS-CoV, with human proteins [TE44] |
Multilayer virus–host interactions | Multilayer analysis, in few cases may predict different SARS-CoV-2 disease phenotypes: Immune regulation appears to be linked to gene TMPRSS2, involved in SARS-CoV-2 virus entry [TE52] SARS-CoV-2 transcripts detectable only in BAL from severe COVID-19 patients. [TE53] SARS-CoV-2 transcripts strongly enriched in ciliated and epithelial progenitor cell population and in the SPP1 + macrophage population. [TE53] Master Regulator Analysis on multiple datasets showed that SARS-CoV-2 mainly affected: Apoptotic and mitochondrial mechanisms [TE49] ACE2 protein receptor regulation [TE49] COVID-19 Disease Map, an open-access repository containing ordered molecular interaction diagrams, implicated in the disease. It is available on website [TE50] |
Virus–host receptor interaction | Interactome of 45 proteins connected to four cell surface seed proteins (ATP6V1A, AP3B1, STOM, and ZDHHC5) with physical affinity to viral S,E and M proteins. [TE73] 7 miRNA (miR-124-3p, let-7 g-5p, miR-133a-3p, miR-133b, miR-218-5p, miR-22-3p, and miR-506-3p) interconnect with proteins involved in viral entry and replication process. [TE73] A probabilistic modelling using iDREM (interactive Dynamic Regulatory Events Miner) revealed: 63 significant regulators expressed in SARS-CoV-2 infected Calu-3 cells (14 also identified analysing the transcriptome of PBMC and Broncho-alveolar cells) An interactome involved in viral entry, including prohibition (PHB) as alternative receptor or co-receptor [TE48] |
Genome evolution and geographical distribution
Genomic hotspots for mutation, drivers of evolution and correlation with COVID-19 pathogenesis
Intra-host genomic variability
Single viral protein and whole viral proteome studies
Immune proteomics
Viral RNA and host protein interactions
Virus-host protein–protein interactions (PPIs)
Multilayer analysis of virus-host interactions (transcriptomics, proteomics)
Viral entry
Pathways
A. Lung and other tissues | |||
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Pathway | Omics | Body district(s) | Host signature |
Inflammatory cytokines | Proteomics | Lung | DEP* up in fatal COVID-19 [TE96, TE110] |
IFN response IL6 signaling Complement cascade | BULK RNAseq | Nasopharyngeal (NP) swabs BAL | DEG** up in COVID-19[TE99, TE108] |
Monocyte and neutrophil recruitment | BULK RNAseq | Nasopharyngeal (NP) swabs BAL | DEG** up and down in COVID-19[TE99, TE108] |
Morphogenesis and migration of immune cells | BULK RNAseq | BAL | DEG** down in COVID-19[TE108] |
Neutrophils extracellular traps TGF-beta response Extracellular traps | BULK RNAseq | Nasopharyngeal (NP) swabs Lung colon | DEG** up in fatal cases and correlated with SARS-CoV-2 viral load in NP[TE119] |
Anti-inflammatory pathways | scRNAseq/CyTOFF | BAL | DEG** down in CD14 + /CD16 + cells of severe cases[TE99] |
Immune cell activation | scRNAseq/CyTOFF | Colon | DEG** down in fatal COVID-19 cases[TE110] |
B. Peripheral blood | |||
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Pathways | Omics | ||
Proteomics | Bulk RNAseq | Metabolomics | |
Inflammation and immune response Neutrophil activation Acute phase response Platelet degranulation Antimicrobial response Tissue damage Coagulation and complement activation | Soluble mediators DEP* up in COVID-19 [TE87,TE88,TE89] | ||
Platelet degranulation Coagulation and complement activation | Soluble mediators DEP* down in COVID-19 [TE89,TE94, TE95] | ||
Inflammation and immune response Neutrophil activation Acute phase response Tissue damage | Soluble mediators DEP* up in severe COVID-19 vs mild [TE89, TE90, TE94]; | ||
Inflammation and immune response Lipid metabolism Coagulation Tissue damage | Soluble mediators DEP* down in severe COVID-19 vs mild [TE89, TE90, TE94]; | ||
Acute phase response Coagulation and complement activation Inflammation and immune response | Soluble mediators DEP* up in severe COVID-19 vs mild DEP* up in fatal COVID-19 DEP* up in poor prognosis for COVID-19 DEP* up in COVID-19 vs influenza [TE88, TE96]; | ||
Inflammatory cytokines and chemokines IFN response | DEG* up and down in COVID-19 [TE90] | ||
Inflammation Immune response Lipid hormones Lipid metabolism Tissue damage | Metabolites up and down in COVID-19 [TE129] | ||
Amino acid metabolism Inflammation and Immune response Lipid metabolism Oxidative Pathways of Cellular Energy Production REDOX homeostasis Tissue damage Urea cycle Xenobiotic metabolism | Metabolites up and down in severe COVID-19 vs. mild [TE101, TE102, TE103, TE104] | ||
Inflammatory response Neutrophil activation generation of NETs Negative regulators of innate immune signaling TCR signaling | DEG** up in severe COVID-19 vs. mild [TE97] | ||
Negative regulators of innate immune signaling TCR signaling | DEG** down in severe COVID-19 vs. mild [TE89] | ||
IFN response | DEG** up in mild COVID-19 [TE89] | ||
Inflammatory response | DEG** up in fatal COVID-19 vs. mild [TE89] | ||
IFN response IL1 IL6, IL10 signaling Complement/coagulation cascade | DEG** up and down in moderate COVID-19 vs coronaviruses, influenza or bacterial pneumonia [TE98] |
C. Specific cell types among blood immune cells (CyTOF/scRNAseq) | |||
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Innate cell compartment | Adaptive cell compartment | ||
Pathways | Modulation | Pathways | Modulation |
Dysregulation of innate cells | DEG** up and down in COVID-19 [TE96, TE112] | Gene expression related to T cell apoptosis T cells activation, Th1, Th2 and Th17 | DEG** up and down in COVID-19 [TE89, TE110, TE112, TE144] |
Classical and non-classical Monocytes activation and recruitment Suppressive low density neutrophils NETs formation Dendritic cells function | DEG** up and down in severe COVID-19 vs. mild [TE89, TE105, TE112, TE113] | Negative T cell signaling T cell activation Antibodies production T cell exhaustion Cytotoxic effector cells CD8 polyfunctionality | DEG** up and down in mild, moderate, survivor COVID-19, severe vs mild, survivors vs fatal, COVID-19 vs seasonal coronavirus, influenza or bacterial pneumonia). [TE98, TE105, TE111] |
Host signatures
Systemic profile of soluble mediators
Proteomic studies
Metabolomic studies
Transcriptomics/CyTOF studies
Immune response in peripheral blood (BULK RNAseq)
Innate immune cell compartment (scRNAseq/CyTOF)
Adaptive immune cell compartment (scRNAseq/CyTOF)
Immune response in lung and other tissues
Phenotypes
A.SARS-CoV-2—host interactions in the lung | ||
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Phenotypes | Pathways | Reactome code |
Severe | Interferon type I signalling pathways ISGs and ACE2: gene highly expressed TMPRSS2 [TE107] | R-HSA-909733 |
Metabolism of proteins Significant increase of antigens processing [TE108] | R-HSA-392499 | |
Cytokine Signalling in Immune system Upregulation of proinflammatory cytokine and chemokine genes [TE116] | R-HSA-128021 | |
Neutrophil degranulation Genes involved in neutrophil extracellular traps generation (NETs) [TE117] | R-HSA-6798695 | |
Disorders of transmembrane transporters Expression of the Lipopolysaccharide (LPS) sensors [TE114] | R-HSA-5619115 | |
Mild/asymptomatic | Cytokine Signalling in Immune system Increasing of CCL2 chemokine [TE88] | R-HSA-1280215 |
Other infections | Interferon type I signalling pathways Increasing of five cytokines (IFNG, IL6, CXCL8, CXCL10 and CCL2) in in mild and severe COVID-19 patients than influenza [TE96] | R-HSA-909733 |
B. DEG and DEP analysis in other organs and tissues | ||
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Phenotypes | Pathways | Reactome code |
Severe | TMPRSS2 Mediated SARS-CoV-1 Spike Protein Cleavage and Endocytosis; ACE2 overexpression [TE121] | R-HSA-2022344 |
Attachment and Entry Altered expression of ACE2 in intestinal tissue, as effect of SARS-CoV-2 ACE2 exhibits the highest co-expression correlation with TMPRSS2, SLC6A20C [TE121, TE123] | R-HSA-9678110 | |
Transport of small molecules Decrease of brain-enhanced proteins that regulate neurotransmitter synthesis (GLS, OGDH, DLD, etc.), neurotransmitter transport (GLUL, GLUD2, GLUD1), neurotransmitter receptors (HTRA3) [TE96] | R-HSA-382551 | |
Mild/asymptomatic | Diseases of haemostasis Upregulated serum proteins, such as S100A8, S100A9, serum amyloid A1 (SAA1) [TE94] | R-HSA-9671793 |
C. Hub genes and pathways of innate immune response | ||
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Phenotypes | Pathways | Reactome code |
Severe | ISG15 antiviral mechanism IFN deficiency in the blood [TE130, TE89] | R-HSA-1169408 |
Cytokine Signalling in Immune system Robust levels of chemokines, including CCL2, CCL8, and CCL11. Significant increase in circulating IL6, IL1RA levels along with CXCL9 and CXCL16, CCL8 and CCL2 [TE87] | R-HSA-1280215 | |
Clathrin-mediated endocytosis Clathrin-mediated endocytosis signalling, actin cytoskeleton signalling, mechanisms of viral exit from host cells [TE136] | R-HSA-8856828 | |
Toll-like Receptor Cascades Significantly upregulated NETs [TE137] | R-HSA-168898 | |
Mild/asymptomatic | Interferon-α/β signaling Early, transient type I IFN production in the lung, that induces ISGs in the peripheral blood [TE89] | R-HSA-909733 |
Programmed Cell Death T cell apoptosis [TE111] | R-HSA-5357801 |
D. Comorbidities COVID19 associated not sharing COVID19 pathogenesis | ||
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Phenotypes | Pathways | Reactome code |
Severe | Histone Modifications (Post-translational protein modification) Several genes related to histone modifications (HAT1, HDAC2, KDM5B) were identified in severe COVID19 patients with comorbidities. [TE119] | R-HSA-597592 |
TMPRSS2 Mediated SARS-CoV-1 Spike Protein Cleavage and Endocytosis Several genes positively associated with ACE2 are regulated by KDM5B, and by specific histone acetylation. [TE119] | R-HSA-2022344 |
E. Comorbidities associated and related to COVID-19 pathway | ||
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Phenotype | Pathways | Reactome code |
Severe | Attachment and Entry Increased circulating furin levels that could cleave the spike protein and increase SARS-CoV-2 infectivity [TE121] | R-HSA-9678110 |
Glycerophospholipid biosynthesis Lipids quantified in the negative mode (arachidonic acid, oleic acid, glycerophosphoethanolamines, and glycerophosphoethanolamines); Two significant pathways: fat digestion and adsorption and glycerophospholipid metabolism. [TE145] | R-HSA-1483206 | |
Mild/asymptomatic | Glycerophospholipid biosynthesis Non-critical patients are characterized by strong alteration of lipids, including acylcarnitines [TE90] | R-HSA-1483206 |
Tryptophan catabolism Metabolic alteration in mild/asymptomatic COVID19 patients include: altered tryptophan metabolism into the kynurenine pathway, regulating inflammation and immunity [TE146] | R-HSA-71240 | |
Bacterial infection Lipid rafts are associated with SARS-CoV-2 virulence [TE143] | R-HSA-9664407 |
Discussion
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Stage 1: mild systemic inflammation and coagulopathy, with patients having mild symptoms and no need for respiratory support;
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Stage 2: progressive pulmonary inflammation, coagulopathy in pulmonary alveoli and microthrombosis, with patients developing more severe symptoms and often requiring additional oxygen supply;
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Stage 3: strong pro-inflammatory reaction, development of local and systemic coagulopathy, characterized by high D-dimer and fibrinogen concentration, prolonged prothrombin time, reduced platelet counts, and high incidence of deep vein thrombosis (DVT) or pulmonary embolism (PE); patients’ conditions deteriorate, requiring organ support, in particular mechanical ventilation, including extracorporeal membrane oxygenation (ECMO) [TE108].