BackgroundUnderstanding of immune response mechanisms of pathogen-infected host requires multi-scale analysis of genome-wide data. Data integration methods have proved useful to the study of biological processes in model organisms, but their systematic application to the study of host immune system response to a pathogen and human disease is still in the initial stage.ResultsTo study host-pathogen interaction on the systems biology level, an extension to the previously described BiologicalNetworks system is proposed. The developed methods and data integration and querying tools allow simplifying and streamlining the process of integration of diverse experimental data types, including molecular interactions and phylogenetic classifications, genomic sequences and protein structure information, gene expression and virulence data for pathogen-related studies. The data can be integrated from the databases and user's files for both public and private use.ConclusionsThe developed system can be used for the systems-level analysis of host-pathogen interactions, including host molecular pathways that are induced/repressed during the infections, co-expressed genes, and conserved transcription factor binding sites. Previously unknown to be associated with the influenza infection genes were identified and suggested for further investigation as potential drug targets. Developed methods and data are available through the Java application (from BiologicalNetworks program at http://www.biologicalnetworks.org) and web interface (at http://flu.sdsc.edu).

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.

It is a single-stranded RNA genome consists of 30 kb nucleotides, which transcript 4 main structural proteins: spike (S), membrane (M), envelope (E), and nucleocapsid (N) proteins. The virus characteristic appearance stems from, the S protein which is club shape glycoprotein radiating in a crown like configuration. 6 Genome researches have shown that interlocking between the receptor-binding domain of S protein and the angiotensin-converting enzyme 2 (ACE2) facilitates SARS-CoV-2 entry into the host cells. 7 The similarity of the receptor-binding sites between SARS-CoV-2 and SARS-CoV explains their shared pathogenicity and biological traits. Moreover, both covid-19 and SARS share common clinical (fever, cough, body aches, and dyspnea) and typical radiological manifestations (multifocal ground-glass opacities (GGOs) and subsegmental areas of consolidation). 8 Nevertheless, both viruses are highly contagious with incubation period range from several days to two weeks.