key: cord-1044236-pm2hp2tc authors: Das, Jayanta Kumar; Chakraborty, Subhadip; Roy, Swarup title: Impact Analysis of SARS-CoV2 on Signaling Pathways during COVID19 Pathogenesis using Codon Usage Assisted Host-Viral Protein Interactions date: 2020-07-29 journal: bioRxiv DOI: 10.1101/2020.07.29.226217 sha: aaedf36127a435e1f2d49f48feae8d0ebc440063 doc_id: 1044236 cord_uid: pm2hp2tc Understanding the molecular mechanism of COVID19 disease pathogenesis helps in the rapid development of therapeutic targets. Usually, viral protein targets host proteins in an organized fashion. The pathogen may target cell signaling pathways to disrupt the pathway genes’ regular activities, resulting in disease. Understanding the interaction mechanism of viral and host proteins involved in different signaling pathways may help decipher the attacking mechanism on the signal transmission during diseases, followed by discovering appropriate therapeutic solutions. The expression of any viral gene depends mostly on the host translational machinery. Recent studies report the great significance of codon usage biases in establishing host-viral protein-protein interactions (PPI). Exploiting the codon usage patterns between a pair of co-evolved host and viral proteins may present novel insight into the host-viral protein interactomes during disease pathogenesis. Leveraging the codon usage pattern similarity (and dissimilarity), we propose a computational scheme to recreate the hostviral protein interaction network (HVPPI). We use seventeen (17) essential signaling pathways for our current work and study the possible targeting mechanism of SARS-CoV2 viral proteins on such pathway proteins. We infer both negatively and positively interacting edges in the network. We can find a relationship where one host protein may target by more than one viral protein. Extensive analysis performed to understand the network topologically and the attacking behavior of the viral proteins. Our study reveals that viral proteins, mostly utilize codons, rare in the targeted host proteins (negatively correlated interaction). Among non-structural proteins, NSP3 and structural protein, Spike (S) protein, are the most influential proteins in interacting multiple host proteins. In ranking the most affected pathways, MAPK pathways observe to be worst affected during the COVID-19 disease. A good number of targeted proteins are highly central in host protein interaction networks. Proteins participating in multiple pathways are also highly connected in their own PPI and mostly targeted by multiple viral proteins. tigate the host-viral protein interactions is important. Knowledge gained through the understanding of the viral proteins interact with the host proteins involved in signaling pathways may translate into effective therapies 23 and vaccines. We aim to study the attacking pattern of SARS-CoV2 to-24 wards its host proteins involved in signaling pathways. We focus on reg- In this work, we try to infer a host-viral protein interaction network 67 leveraging the inherent correlation between codon usage biases between viral 68 and host proteins. To the best of our knowledge, no prior work exploiting 69 the codon usage pattern to infer host-viral PPI. We try to capture both In this section, we discuss our proposed scheme for constructing a host-78 viral PPI network using codon usage patterns of host and viral proteins. To analyze the interaction mechanism of SARS-CoV2 viral proteins in host 80 signaling pathways, we select a set of all the genes involved in few candidate 81 signaling pathways. RSCU is the ratio between the observed number of occurrences of codons and expected during uniform usage of synonymous codons and can be calculated as follows. where, X i,j is the number of occurrences of the j th codon for the i th amino 118 acid, which is encoded by n i synonymous codons. The RSCU score of a codon 119 more than 1.0 indicates excess usage (biased) of the codon, and less than 1.0 120 marks poor usage of that particular codon. We infer interaction between two proteins using codon usage similarity. Two proteins are considered to be strongly coupled if there RSCU similarity 161 bearing particular statistical significance. Given vectors of a pair of host and viral proteins respectively, proteins are strongly 164 connected if p score is less than certain threshold τ i.e. p(R v , R h ) < τ . 165 We use SciPy version 1.5.0 (sipy.stats) 3 for calculating Pearson corre-166 lation coefficient, which uses 2-tailed p-value for measuring significant rela- and R h respectively. We use SciPy version 1.5.0 4 for computing ρ. Given a set of viral proteins, V = {v 1 , v 2 , · · · v n } and host proteins H = 183 {h 1 , h 2 , · · · h n } we can create a bipartite graph in the form of adjacency ma-184 trix using above ρ and p values as follows. 185 Next, we investigate the interaction mechanism of SARS-CoV2 on human 186 signaling pathways during COVID19 disease pathogenesis. 199 produce only viral protein oriented star-like topology and unable to report 200 any host protein oriented multiple interactions. We report a list of such highly 201 connected host proteins with the viral proteins (at least 15) in Table 3 . Many that majority (82) of the host proteins are connected with only one viral node. While considering highly targeted proteins by multiple viral proteins, we see 267 fewer than 10 proteins with degree 21 (maximum degree), which is lowest 268 within the distribution. Even though our network is a bipartite graph, we 269 observe that the number of low-degree nodes is high and high degree nodes Figure 7 . We observe a nice power-law [45] like distribution 354 where the majority of proteins are participating in only one pathway and 355 fewer numbers are having high participation in multiple pathways. We list a 356 few top highly pathway central proteins and few interesting facts in Table 5 . 357 The table shows that the pathway central proteins are also highly connected 358 in their own PPI and mostly targeted by multiple viral proteins. 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Our method is generic and useful to draw a more extensive network covering The authors declare no competing interests.