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Interactome Analysis for Identification of Common Drug Targets in Salmonella Species
ISSN: 2157-7420

Journal of Health & Medical Informatics
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  • Research Article   
  • J Health Med Informat 2019, Vol 10(2): 331

Interactome Analysis for Identification of Common Drug Targets in Salmonella Species

Nikita Chordia*, Priyesh Hardia and Priya Jain
Bioinformatics Sub-Center, School of Biotechnology, Takshashila Campus, Devi Ahilya University, Indore, India
*Corresponding Author: Nikita Chordia, Bioinformatics Sub-Center, School of Biotechnology, Takshashila Campus, Devi Ahilya University, Khandwa Road, Indore-452001, India, Tel: 0091-731-2470373; 0091-9685281822, Email: [email protected]

Received Date: Apr 03, 2019 / Accepted Date: May 08, 2019 / Published Date: May 20, 2019

Abstract

A wide variety of human population is infected with different species of Salmonella causing salmonellosis. At present, the major hurdle in treating infection is the development of resistance to existing antibiotics. Therefore, there is a need to identify new drug targets so that new drugs can be designed that can cure the infection efficiently. Here, we have design an interactome for all species of Salmonella using its essential and non-homologous protein to humans. So that, all types of infections of Salmonella can be treated with single antibiotic. 1399 essential proteins of Salmonella have been analyzed using interactome studies. We found 09 proteins as putative drug targets that can be used to treat all species of Salmonella.

Keywords: Drug target; Interactome; Salmonella; Bacteria; Nonhomologous; Infection; Antibiotic resistance; Treatment

Introduction

Salmonella species are gram-negative, flagellated, facultative anaerobic bacilli of the family Enterobacteriaceae [1]. Salmonella infection (salmonellosis) is a common bacterial disease that affects the intestinal tract. Clinically it ranges from the common Salmonella gastroenteritis (abdominal cramps, diarrhea and fever) to enteric fevers (typhoid) that maybe sometimes life threatening and therefore, requires prompt antibiotic therapy [2]. But, today the main hurdle in treatment of the bacterial infection is the development of resistance to existing antibiotics [3]. Therefore, there is a need to identify novel bacterial targets for drug development [4].

Salmonella species are one of the major causes of human gastroenteritis and thousands of cases were reported annually for salmonellosis [5]. Salmonella typically live in animal and human intestines. Humans become infected most frequently through contaminated water or food. The most commonly identified food sources include meat products, eggs, dairy products and raw fruits and vegetables. Faecal/intestinal contamination of carcasses is the principal source of human food-borne infections [6].

Brenner et al. reported 2,463 serotypes (serovars) of Salmonella which includes several species such as S. enterica, S. bongori, S. typhimurium, S. typhi, S. enteritidis, S. heidelberg, S. subterranean and many more [7]. Serotypes of Salmonella can be divided into two main groups-typhoidal and non-typhoidal. Typhoidal serotypes are strictly adapted to humans and include Salmonella typhi, Paratyphi A, Paratyphi B, and Paratyphi C. Nontyphoidal serotypes are more common and generally results in food poisoning. They are zoonotic i.e. they can be transferred between humans and other animals. Most infections are caused by the S. typhimurium, S. enteric or S. enteritidis [8].

Parry and Threlfall reported antimicrobial resistance in typhoidal and non-typhoidal Salmonellae. They reported variable rates of resistance in Salmonella with particular reference to quinolones and extended spectrum cephalosporins [9,10]. Increasing occurrence of drug resistance for salmonellosis is a major public health problem. To overcome drug resistance to existing antibiotic therapy for salmonellosis, there is a need to identify common drug targets for all the pathogens of Salmonella infection.

Here, in this study we have taken all the essential proteins available for Salmonella species. Undesirable proteins which are non-homologous to human are removed from the study and an interactome was created for rest of the proteins. The interactome analysis was performed and impactful proteins in the network are considered to be the common drug target for all the species of Salmonella.

Methodology

Dataset of essential proteins

Essential proteins of all Salmonella species were downloaded from DEG (Database of Essential Genes) database available online at http://tubic.org/deg_bak/ [11]. Essential proteins are absolutely necessary for the survival of an organism and are considered as foundation of life. Therefore, we have taken essential proteins of Salmonella to find the dug target.

Non-host homologous proteins

All essential proteins were subjected to similarity searching to find the non-homologous proteins with the human host. Protein are identified which were not homologous to the human host to eliminate the chances of cross reactivity with the human genome and to minimize the risk of drug toxicity. This was done using BLAST-P (Basic Local Assignment Search Tool) available online at http:// www.ncbi.nlm.gov/blast/ [12].

All essential and non-homologous human proteins were then subjected for interactome construction. Interactome is the whole set of interactions between and among proteins. This was done using STRING database available online at http://string-db.org/ [13]. Metabolic functional interaction can be determined by using the methods viz. neighborhood, gene fusion, co-occurrence, co-expression and experiment at medium confidence. Then score was calculated by using the following formula given by Kushwaha and Shakya [14].

image

Protein with score value till 0.400 was taken as the metabolically functional protein at medium confidence level.

Interactome analysis

Obtained Interactome was analyzed using Cytoscape 3.7 software [15]. Different parameters were considered namely clustering coefficient, characteristics path length, betweenness centrality and neighborhood connectivity. The values of the whole network were compared with the values of the targeted node (protein). It was done by taking the values of all the parameters of whole network then values are calculated after deleting each node one by one. Difference in values is directly related with the impact of respective protein into interactome.

Clustering coefficient is defined as the degree to which nodes in a network tend to cluster together. It generally shows greater value for the entire protein network and is decreased when node (protein) is deleted. The characteristic path length is defined as the expected distance between the nodes. Betweenness centrality is the measure of centrality, for node “x” it is calculated by summing the number of shortest paths between pairs of nodes that pass through node “x” divided by the total number of shortest paths between pairs of nodes. Neighborhood connectivity is average connectivity of neighbors of given vertex. On the basis of the above mentioned parameters, most impactful/potent protein was selected as drug targets.

Results And Discussion

A total of 1399 essential proteins of Salmonella species were downloaded from DEG database. A molecule can be drug target only if it is essential for the survival of the pathogen. Another requirement for drug target is that it must not be homologous to host. So, out of 1339, a total 1220 non-homologous proteins to human were identified using BLAST. All these 1220 proteins were submitted in STRING database for network construction. As we have submitted the sequences of different species of Salmonella only therefore, similar sequences are omitted from the interactome. Finally, we got the interactome of 927 proteins as shown in Figure 1.

health-medical-informatics-interactions

Figure 1: Interactome created by STRING. Different colour shows different interactions like gene neighbourhood, gene fusion, gene co-expression, cooccurrence and text mining.

Interactome was analyzed using cytoscape 3.7. In the first step of analysis, values of clustering coefficient, characteristics path length, betweenness centrality and neighborhood connectivity for the whole network was calculated. Then value was calculated to see the effect of every node in the network. This was done by deleting each node of the network and then values of all the parameter (clustering coefficient, characteristics path length, betweenness centrality and neighborhood connectivity) were noted. It is done for all the 927 nodes of the network. After getting all the values of the parameter for each node, difference in the value was calculated by using the values of the whole network. Difference was noted in almost all the nodes of the network that shows the impact of protein in the network. This is because we have taken all the essential proteins of the pathogen. But noticeable differences in the values of all the four parameters were observed in 9 nodes that are very important for the network. Here, we have taken the cut off value that must be fulfilled by the protein. For clustering coefficient, the difference in values can be positive and negative and any difference in the value is taken as many nodes are showing no difference. For characteristic path length, the difference of greater than 3 was taken for consideration. The cut-off of 1.0E-02 and 160.00 was taken for betweenness centrality and neighborhood connectivity respectively. Table 1 shows the results of those nodes of the network that are showing differences in all the parameters according to the mentioned criteria.

Gene Name Clustering coefficient Clustering coefficient (after deleting node) Characteristics path length Characteristics path length (after deleting node) Betweenness centrality Betweenness centrality (after deleting node) Neighborhood connectivity Neighborhood connectivity (after deleting node)
waaG 0.78 0.32 2.77 3.12 3.32 E-04 1.20E-06 95.15 80.2
rfbG 0.56 0.8 1.56 2.15 2.2 E-05 8.3E-04 101.56 97.12
thiL 0.96 0.56 3.1 2.89 4.44 E-05 7.36E-02 115.26 101.23
mukE 1 0.6 2.86 2.15 1.89E-06 1.66E-04 98.99 96.45
uppS 0.92 0.79 1.98 2.55 2.56 E-05 2.24E-04 89.46 87.56
secE 0.65 0.36 2.82 2.15 7.58E-05 8.93E-04 82.45 81.2
DD95_14630 0.82 0.59 2.95 2.65 5.28E-05 2.98E-04 109.21 101.01
Alas 0.11 0.79 2.29 2.98 2.91E-04 3.47E-04 93.85 92.88
rsgA 0.72 0.14 2.14 2.76 6.32E-05 4.78E-04 96.84 91.25

Table 1: Results of those nodes of the network that are showing differences in all the parameters according to the mentioned criteria.

Identified nine proteins from the interactome are having major influence on interaction network. Therefore, targeting any of these protein effect the pathogen metabolism and alterations in these proteins result in killing of the pathogen. The identified potential drug target was also reported by other researchers as drug target in other pathogenic diseases. This shows that our strategy in identifying drug targets from interactome is correct and can be apply for other pathogens also.

waaG codes for Glycosyltransferase and is reported as drug target by Sun [16]. Similarly, rfbG is CDP-glucose 4,6-dehydratase and found to be virulent in Candida albicans [17]. Thiamine-monophosphate kinase coded by thiL and Chromosome partition protein coded by mukE was reported to be drug target for Tuberculosis [18,19]. uppS- (Isoprenyl transferase) and secE (Protein translocase subunit SecE) was also reported as drug target [20,21]. Node DD95_14630 (Riboflavin biosynthesis protein) was reported as anti-infective drug target [22]. Alas (Aminolevulinate synthase) was also reported as drug target for malaria parasite [23]. rsgA (Small ribosomal subunit biogenesis GTPase RsgA) is also pinpointed to be drug target by Maguire [24]. All these reported targets are playing crucial role in pathogen’s biological processes that are important for the pathogen survival. Therefore, any of these targets can be selected for the disturbance of pathogen machinery. These putative targets have been predicted using interactome analysis. Drug targets using interactome was also reported for other pathogens like Listeria monocytogenes [25]. After in silico studies, further wet lab work is required. Computational combinatorial methods can be used to find the inhibitors for these targets that can serve as the drug to cure the disease. In addition to this, comparative genomics can be employed so that same medication can be used for different pathogens.

Conclusion

In this study, we identified 09 potential drug targets for Salmonella through interactome of Salmonella and are less toxic for the host organism. In addition to this, interactome analysis can be employed for any other pathogens to find new drug targets leading to fast drug discovery process. The limitation with this methodology is that it requires complete and reliable data of pathogen’s interactome.

Acknowledgement

Authors acknowledge the facilities of the Department of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi (DBT) under the Bioinformatics Sub Centre as well as M.Sc. Biotechnology program used in the present work.

Conflict of Interest

The authors confirm that they have no conflict of interest.

References

Citation: Chordia N, Hardia P, Jain P (2019) Interactome Analysis for Identification of Common Drug Targets in Salmonella Species. J Health Med Informat 10: 331.

Copyright: © 2019 Chordia N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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