A SARS-CoV-2 host infection model network based on genomic human Transcription Factors (TFs) depletion

In December 2019 a new beta-coronavirus was isolated and characterized by sequencing samples from pneumonia patients in Wuhan, Hubei Province, China. Coronaviruses are positive-sense RNA viruses widely distributed among different animal species and humans in which they cause respiratory, enteric, liver and neurological symptomatology. Six species of coronavirus have been described (HCoV-229E, HCoV-OC43, HCoV-NL63 and HCoV-HKU1) that cause cold-like symptoms in immunocompetent or immunocompromised subjects and two strains of sometimes fatal zoonotic origin that cause severe acute respiratory syndrome (SARS-CoV and MERS-CoV). The SARS-CoV-2 strain is the emerging seventh member of the coronavirus family, which is actually determining a global emergency. In silico analysis is a promising approach for understanding biological events in complex diseases and due to serious worldwide emergency and serious threat to global health, it is extremely important to use bioinformatics methods able to study an emerging pathogen like SARS-CoV-2. Herein, we report on in silico comparative analysis between complete genome of SARS-CoV, MERS-CoV, HCoV-OC43 and SARS-CoV-2 strains, to identify the occurrence of specific conserved motifs on viral genomic sequences which should be able to bind and therefore induce a subtraction of host's Transcription Factors (TFs) which lead to a depletion, an effect comparable to haploinsufficiency (a genetic dominant condition in which a single copy of wild-type allele at a locus, in heterozygous combination with a variant allele, is insufficient to produce the correct quantity of transcript and, therefore, of protein, for a correct standard phenotypic expression). In this competitive scenario, virus versus host, the proposed in silico protocol identified the TFs same as the distribution of TFBSs (Transcription Factor Binding Sites) on analyzed viral strains, potentially able to influence genes and pathways with biological functions confirming that this approach could brings useful insights regarding SARS-CoV-2. According to our results obtained by this in silico approach it is possible to hypothesize that TF-binding motifs could be of help in the explanation of the complex and heterogeneous clinical presentation in SARS-CoV-2 and subsequently predict possible interactions regarding metabolic pathways, and drug or target relationships.