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1.
J Transl Med ; 19(1): 501, 2021 12 07.
Article in English | MEDLINE | ID: covidwho-1560461

ABSTRACT

BACKGROUND: Omics data, driven by rapid advances in laboratory techniques, have been generated very quickly during the COVID-19 pandemic. Our aim is to use omics data to highlight the involvement of specific pathways, as well as that of cell types and organs, in the pathophysiology of COVID-19, and to highlight their links with clinical phenotypes of SARS-CoV-2 infection. METHODS: The analysis was based on the domain model, where for domain it is intended a conceptual repository, useful to summarize multiple biological pathways involved at different levels. The relevant domains considered in the analysis were: virus, pathways and phenotypes. An interdisciplinary expert working group was defined for each domain, to carry out an independent literature scoping review. RESULTS: The analysis revealed that dysregulated pathways of innate immune responses, (i.e., complement activation, inflammatory responses, neutrophil activation and degranulation, platelet degranulation) can affect COVID-19 progression and outcomes. These results are consistent with several clinical studies. CONCLUSIONS: Multi-omics approach may help to further investigate unknown aspects of the disease. However, the disease mechanisms are too complex to be explained by a single molecular signature and it is necessary to consider an integrated approach to identify hallmarks of severity.


Subject(s)
COVID-19 , Humans , Immunity, Innate , Pandemics , SARS-CoV-2
2.
Cell Death Dis ; 12(8): 788, 2021 08 12.
Article in English | MEDLINE | ID: covidwho-1356553

ABSTRACT

In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level, but the mechanisms of interaction between host and SARS-CoV-2, determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein-protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred, applying an explorative algorithm (Random Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2. The analysis of PPI allowed to estimate the distribution of SARS-CoV-2 proteins in the host cell. Interactome built around one single viral protein allowed to define a different response, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, the network-based approach highlighted a possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm. This network-based representation of SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific clinical outcomes. We identified possible host responses induced by specific proteins of SARS-CoV-2, underlining the important role of specific viral accessory proteins in pathogenic phenotypes of severe COVID-19 patients.


Subject(s)
COVID-19/metabolism , COVID-19/virology , SARS-CoV-2/metabolism , Host Microbial Interactions , Immunity/immunology , Protein Interaction Maps/physiology , Proteome , Proteomics/methods , SARS-CoV-2/pathogenicity , Severity of Illness Index , Viral Proteins/metabolism , Viral Regulatory and Accessory Proteins/metabolism
3.
Viruses ; 13(7)2021 07 06.
Article in English | MEDLINE | ID: covidwho-1302499

ABSTRACT

Complex systems are inherently multilevel and multiscale systems. The infectious disease system is considered a complex system resulting from the interaction between three sub-systems (host, pathogen, and environment) organized into a hierarchical structure, ranging from the cellular to the macro-ecosystem level, with multiscales. Therefore, to describe infectious disease phenomena that change through time and space and at different scales, we built a model framework where infectious disease must be considered the set of biological responses of human hosts to pathogens, with biological pathways shared with other pathologies in an ecological interaction context. In this paper, we aimed to design a framework for building a disease model for COVID-19 based on current literature evidence. The model was set up by identifying the molecular pathophysiology related to the COVID-19 phenotypes, collecting the mechanistic knowledge scattered across scientific literature and bioinformatic databases, and integrating it using a logical/conceptual model systems biology. The model framework building process began from the results of a domain-based literature review regarding a multiomics approach to COVID-19. This evidence allowed us to define a framework of COVID-19 conceptual model and to report all concepts in a multilevel and multiscale structure. The same interdisciplinary working groups that carried out the scoping review were involved. The conclusive result is a conceptual method to design multiscale models of infectious diseases. The methodology, applied in this paper, is a set of partially ordered research and development activities that result in a COVID-19 multiscale model.

4.
Leukemia ; 35(7): 1864-1872, 2021 07.
Article in English | MEDLINE | ID: covidwho-1216445

ABSTRACT

Standard treatment options in classic HCL (cHCL) result in high response rates and near normal life expectancy. However, the disease itself and the recommended standard treatment are associated with profound and prolonged immunosuppression, increasing susceptibility to infections and the risk for a severe course of COVID-19. The Hairy Cell Leukemia Foundation (HCLF) has recently convened experts and discussed different clinical strategies for the management of these patients. The new recommendations adapt the 2017 consensus for the diagnosis and management with cHCL to the current COVID-19 pandemic. They underline the option of active surveillance in patients with low but stable blood counts, consider the use of targeted and non-immunosuppressive agents as first-line treatment for cHCL, and give recommendations on preventive measures against COVID-19.


Subject(s)
COVID-19/complications , Leukemia, Hairy Cell/therapy , COVID-19/epidemiology , COVID-19/pathology , COVID-19/virology , Consensus , Humans , Leukemia, Hairy Cell/complications , Pandemics , Practice Guidelines as Topic , SARS-CoV-2/isolation & purification , Severity of Illness Index
6.
J Transl Med ; 18(1): 233, 2020 06 10.
Article in English | MEDLINE | ID: covidwho-592324

ABSTRACT

BACKGROUND: Epidemiological, virological and pathogenetic characteristics of SARS-CoV-2 infection are under evaluation. A better understanding of the pathophysiology associated with COVID-19 is crucial to improve treatment modalities and to develop effective prevention strategies. Transcriptomic and proteomic data on the host response against SARS-CoV-2 still have anecdotic character; currently available data from other coronavirus infections are therefore a key source of information. METHODS: We investigated selected molecular aspects of three human coronavirus (HCoV) infections, namely SARS-CoV, MERS-CoV and HCoV-229E, through a network based-approach. A functional analysis of HCoV-host interactome was carried out in order to provide a theoretic host-pathogen interaction model for HCoV infections and in order to translate the results in prediction for SARS-CoV-2 pathogenesis. The 3D model of S-glycoprotein of SARS-CoV-2 was compared to the structure of the corresponding SARS-CoV, HCoV-229E and MERS-CoV S-glycoprotein. SARS-CoV, MERS-CoV, HCoV-229E and the host interactome were inferred through published protein-protein interactions (PPI) as well as gene co-expression, triggered by HCoV S-glycoprotein in host cells. RESULTS: Although the amino acid sequences of the S-glycoprotein were found to be different between the various HCoV, the structures showed high similarity, but the best 3D structural overlap shared by SARS-CoV and SARS-CoV-2, consistent with the shared ACE2 predicted receptor. The host interactome, linked to the S-glycoprotein of SARS-CoV and MERS-CoV, mainly highlighted innate immunity pathway components, such as Toll Like receptors, cytokines and chemokines. CONCLUSIONS: In this paper, we developed a network-based model with the aim to define molecular aspects of pathogenic phenotypes in HCoV infections. The resulting pattern may facilitate the process of structure-guided pharmaceutical and diagnostic research with the prospect to identify potential new biological targets.


Subject(s)
Betacoronavirus/physiology , Coronavirus Infections/genetics , Coronavirus Infections/virology , Gene Regulatory Networks , Host-Pathogen Interactions , Models, Biological , Pneumonia, Viral/genetics , Pneumonia, Viral/virology , Protein Interaction Mapping , COVID-19 , Humans , Membrane Glycoproteins/metabolism , Pandemics , SARS-CoV-2 , Signal Transduction/genetics , Viral Envelope Proteins
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