Gene interaction network to unravel the role of gut bacterial species in cardiovascular diseases: E. coli O157:H7 host-bacterial interaction study

https://doi.org/10.1016/j.compbiomed.2021.104417Get rights and content

Highlights

  • 23 known Host pathogen interactions between E. coli and humans were curated.

  • 188 interactions between 40 E coli genes were collected from STRING database.

  • 204 interactions between 40 human genes were collected using STRING database.

  • FEA displayed the pathways critical for host and pathogen during infection.

  • Genes with highest node degree values can be considered as new drug targets.

Abstract

Background

Cardiovascular Disease (CVD) is one of the most common causes of mortality in humans. Presently, the role of pathogens in the initiation and progression of the CVDs is not clearly understood. Hence, it is essential to understand the molecular-level interactions between the human proteins and the microbial proteins to deduce their functional roles in the CVDs.

Method

The host-pathogen interactions (HPI) related to CVDs in the case of E. coli str. O157:H7 colonization were curated, and also the protein-protein interactions (PPI) between humans and E. coli were collected. Gene interaction network (GIN) and functional enrichment analyses (FEA) were utilized for this.

Results

The GIN revealed dense interactions between the functional partners. The FEA indicated that the essential pathways played a significant role in humans as well as in E. coli. The primary responses against most of the bacterial pathogens in humans are different from that of E. coli; Terpenoid biosynthesis and production of secondary metabolite pathways aid the survival of the E. coli inside the host. Interestingly, network analysis divulged that the E. coli genes ksgA, rpsT, ispE, rpsI, ispH, and the human genes TP53, CASP3, CYCS, EP300, RHOA communicated by significant numbers in direct interactions.

Conclusions

The results obtained from the present study will help researchers understand the molecular-level interactions in the CVDs between the human and the E. coli genes. The important genes with vital interactions can be considered as hub molecules and can be exploited for new drug discovery.

Introduction

The studies of gut microbiota and host interactions have elicited great interest worldwide because of their vital role in human health and disease. The changes in gut microbial composition lead to the disease condition called Dysbiosis, which is reported to have links with other diseases such as atherosclerosis, hypertension, chronic kidney disorders, type 2 diabetes mellitus, obesity, and heart failure. Among these, the gut microbial role has been reported in a few studies related to Cardiovascular Disease (CVD) [[1], [2], [3]]. Previous reports have disclosed the importance of the pathways such as the short-chain fatty acids pathway, tri-methylamine N-oxide (TMAO) pathway, primary and secondary bile acid pathways in CVD. These pathways help the microbiota to interact with the host and also to play an essential role in disease pathogenesis [4,5]. In addition to that, two critical pathways namely, metabolism dependent and metabolism independent pathways have been observed as potential contributors to the CVD [6,7].

The host-pathogen interaction mapping projects are drawing the attention of researchers globally. Even though, there are studies on virus-host interactions using systems biology approach, only a few reports on gene network approaches for CVDs associated with bacterial colonization are available in literature. In the current study, the Gene Interaction Network (GIN) was constructed using systems biology approach to understand the host, pathogen, and host-pathogen's molecular-level interactions during E. coli O157:H7 colonization. Gene network analysis has been widely researched for the past few years [[8], [9], [10]]. In the past decade, protein-protein interaction databases which provide experimental evidences and predict protein-protein interactions were created [11]. Systems biology approach is useful in deducing the role of these interactions at the molecular level and helps understand the functional relationship between the protein pairs. Previous reports from our laboratory have demonstrated the efficacy of gene interaction networks in identifying the antibiotic resistance patterns among the pathogenic bacteria [[12], [13], [14]].

In this study, we curated the host-bacterial interactions of E. coli O157:H7 strain and the humans (Homo sapiens) related to CVDs from the MorCVD database. The bacterium E. coli is a well-known member of human gut microbiota, and the strain E. coli O157:H7 is a Shiga toxin-producing enterohemorrhagic strain that causes hemorrhagic colitis and hemolytic uremic syndrome (HUS) [15,16]. We studied the gene interactions of the host and bacterium separately and correlated the host-pathogen interactions (HPI) based on the results obtained. The host-pathogen interaction during colonization not only helped comprehend the role of pathogen genes in the initiation and progression of CVDs but also influenced the role of host genes in the defensive mechanism against the bacterial pathogen.

Section snippets

Host pathogen interaction data curation from MorCVD

MorCVD (http://morcvd.sblab-nsit.net/About) is a data repository for the host-pathogen interactions (HPIs) between the human proteins and the microbial proteins related to CVDs. Using this, we curated the data by combining the available 23,377 human protein interactions and 432 pathogen protein interactions which were reported to have associated roles in CVDs. The database also provides hyperlinks to corresponding protein IDs, PubMed IDs, and Gene Ontology (GO) terms. It also consists of

Data curation and network construction

We observed 23 known HPI entries between the 11 genes of E. coli O157:H7 strain and the 20 genes of humans, which are listed in Table 1. All the available PPI data from the STRING database related to the humans and E. coli genes were retrieved separately. A total of 188 interactions among 40 E. coli genes with an average node degree value of 9.4 and an average local clustering coefficient value of 0.731 were collected. We observed 209 interactions between the 40 genes, with an average node

Discussion

The host-pathogen interaction studies help us to understand the mechanisms in bacterial colonization and disorders like the CVDs. The GIN employs known interacting host and bacterial genes to unravel the molecular level interactions in bacterial survival and illustrate the host responses to combat the pathogens. In this study, the host-bacterial genes that play an essential role in the CVDs were curated, and the interaction network was constructed. The genes in the network displayed dense

Conclusion

The gene network analysis revealed the essential pathways related to the host defence mechanisms against E. coli O157:H7. We have summarized the Bacterium's survival strategies to overcome the host defensive mechanism and to establish the disease successfully. In the host, the enriched pathways such as the pathways in cancers, apoptosis and p53 signaling pathways were part of host response against several bacterial pathogens, whereas in E. coli, the pathways Terpenoid biosynthesis, production

Funding information

The authors gratefully acknowledge the Indian Council of Medical Research (ICMR), the Government of India agency for the research grant (IRIS ID: 2019-0810). MSK heartfully thanks the ICMR, for the senior research fellowship grant (IRIS ID: 2020–7788).

Data availability statement

The data that support the findings of this study are available in the supplementary material of this article.

Author contributions

SR and AA designed the study; MSK collected the data and carried out the computation and generated figures; SR, AA, and MSK analyzed the data and wrote the manuscript.

Declaration of competing interest

The authors declare that there is no conflict of interest.

Acknowledgements

The authors would like to thank the management of VIT for providing the necessary facilities to carry out this research work. The authors are grateful to Prof. Srinivasan Ramachandran, School of social sciences and Languages (SSL), VIT and Prof. R. Calaivanene foreign language expert for helping us in the language check.

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