Understanding of Zaire ebolavirus–human protein interaction for drug repurposing

Abstract

The Ebola virus is a human aggressive pathogen causes Ebola virus disease that threatens public health, for which there is no Food Drug Administration approved medication. Drug repurposing is an alternative method to find the novel indications of known drugs to treat the disease effectively at low cost. The present work focused on understanding the host–virus interaction as well as host virus drug interaction to identify the disease pathways and host-directed drug targets. Thus, existing direct physical Ebola–human protein–protein interaction (PPI) was collected from various publicly available databases and also literature through manual curation. Further, the functional and pathway enrichment analysis for the proteins were performed using database for annotation, visualization, and integrated discovery and the enriched gene ontology biological process terms includes chromatin assembly or disassembly, nucleosome organization, nucleosome assembly. Also, the enriched Kyoto Encyclopedia of Genes and Genome pathway terms includes systemic lupus erythematosus, alcoholism, and viral carcinogenesis. From the PPI network, important large histone clusters and tubulin were observed. Further, the host–virus and host–virus–drug interaction network has been generated and found that 182 drugs are associated with 45 host genes. The obtained drugs and their interacting targets could be considered for Ebola treatment.

Electronic supplementary material

The online version of this article (10.1007/s13337-020-00570-6) contains supplementary material, which is available to authorized users.

Introduction

Ebola virus is a non-segmented negative sense and single-stranded RNA virus belongs to the Filoviridae family which causes Ebola virus disease (EVD). It’s a contagious disease through contact with body fluids, blood or consumption of infected animal’s meat. The virus enters through the mucous membrane via mouth, eyes, ears, open wounds either by direct or indirect contact of the infected source [13]. Moreover, it is reported that Ebola hemorrhagic fever resulted in mortality rate up to 90% in human as well as non-human primates (NHPs) [28, 43]. The virus family has been classified into five species named after the origin of the outbreaks, such as Zaire ebolavirus (ZEBOV), Sudan ebolavirus (SUDV), Tai Forest ebolavirus (TAFV), Bundibugyo ebolavirus (BDBV) and Reston ebolavirus (RESTV). Recent studies report the outbreak of the Ebola in Western Africa and is deadly destructive [3]. Among all the Ebola species, only RESTV does not cause disease in humans [7]. The genome size of the virus is approximately 19 kb and encodes seven structural proteins such as, envelop glycoprotein (GP), membrane-associated virion protein (VP24), transcription activator (VP30), polymerase cofactor (VP35), matrix protein (VP40), nucleoprotein (NP) and RNA dependent RNA polymerase (L) [49]. Even though Ebola has been reported in 1976, still the pathogenesis of the virus remains unclear [45].

Also, the requirement of Biosafety Level 4 (BSL4) facility for conducting the preclinical studies [38] make the process of designing a drug for Ebola becomes tedious. Further, the conventional drug discovery process takes more than 15 years with an approximate amount of $1.5 billion to bring out an effective drug successfully. Identifying and developing new drugs is an effort and time-consuming process at a higher cost. In a conventional drug designing procedure, only 35% of new drugs are approved by regulatory agencies [10]. Such limitations led the researchers to focus on drug repurposing which may have new disease indications for other treatment. However, no recent research has been reported on the availability of approved therapeutic and licensed drugs for the treatment of EVD. Although, ZMapp and GS-5734 have been encouraged to treat Ebola in the ongoing clinical trials [2].

Drug repurposing is the process to find the new function of existing drugs with an advantage of ready to use with already satisfied basic absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. Thus, the main challenge of drug repurposing is to study the effectiveness of established approved drugs [13]. Host-directed therapies (HDT) is an emerging method targeting the host terrain which interferes the host cell factors and vital for pathogen survival in the host framework. In spite of the fact that HDTs incorporating interferon’s are entrenched for the treatment of chronic viral hepatitis, hence novel method need to proceed to cure viral infections and the improvement of expansive range antivirals is essential against the rising infections [20].

Therefore, in the current study, 182 drugs associated with 45 host genes were found to have direct interaction with viral proteins. In addition, the obtained drugs are classified as microtubule inhibitors, estrogen receptors, antihistamines and pump/channels.

Materials and methods

Data collection

The EVD has continuously been re-emerging for almost 50 years in the African continent to cause contagious and highly fateful hemorrhagic fever. Significant challenges ahead to govern, control and managing future epidemics and patient care, particularly in remote areas where typically Ebola reemerges [24]. For the current study, the proteins/genes involved in Ebola human interactions were collected from experimentally reported literature and databases and also abstracts of original publications describing an experimental proof of the association of human host proteins with Zaire Ebola virus. The term “Ebola” or specific Ebola protein along with the keywords like Ebola interaction, Ebola virus infection, an inhibitor of ebola, Ebola viral protein, viral receptor and human host with “Yeast two hybridization and ebola”, “co-immune precipitation” were used as a search query. The proteins were also collected from various databases such as VirusMentha [4], VirHostNet [16] and IntAct [17]. The overall workflow of the study is shown in Supplementary Fig. 1.

Host proteins overlapping analysis

To remove duplicates, the collected data are subjected to overlapping analysis using Venny v2.0 [33], an interactive tool for comparison (http://bioinfogp.cnb.csic.es/tools/venny/index.html).

Enrichment analysis

Database for annotation, visualization, and integrated discovery (DAVID) tool [9] incorporated the Kyoto Encyclopedia of Genes and Genome (KEGG) database [19] was utilized to carry out the functional and pathway enrichment analysis of the interacted proteins/genes in the protein–protein interaction network [26]. Where, functional annotation analysis includes a cellular component (CC), molecular function (MF) and biological process (BP). p < 0.05 was considered as statistical significance.

Protein–protein interaction analysis

To understand the interaction of host proteins; a network is generated by The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database [42]. Where, the STRING database gives the experimental and predicted information on protein–protein interaction (PPI), which is based on neighborhood, gene fusion, and co-occurrence, and co-expression, experimental evidence from various other databases such as IntAct [21], BioGrid [6], HPRD [34], and MINT [5].

Module and enrichment analysis

To study the protein complexes and its functional modules among the Ebola interacted human proteins [42], the top three modules were identified using MCODE Cytoscape plugin. The default parameters such as degree cutoff 2, node score cut off = 0.2, K-core = 2 and max. Depths = 100 are used for top three cluster generation. Further, these clusters were analyzed for their functional and pathway annotation using ClueGO plugin version 2.3.5 [1] in Cytoscape. It integrates GO terms as well as KEGG/Biocarta pathways and creates functionally enriched network. It can take one or two list of genes and analyze and comprehensively visualize the gene enrichment as network and chart. Enrichment analysis was calculated by right-sided hypergeometric with Benjamini–Hochberg correction and kappa score of 0.3.

Host–virus interaction network

To understand the Ebola-protein interaction with humans during infection, a host–virus interaction network was constructed by interrogating host factors using Cytoscape v3.4.0 [39]. The dataset imported by excel files which contain host and viral genes.

Host–virus–drug interaction network

Further, host–virus–drug interaction network is generated using CyTargetLinker, a Cytoscape plug-in, and an open source which provides a user-friendly interface to study biological regulatory networks such as microRNA-target, transcription factor-target analysis, and drug-target.

Results

Host gene overlapping analysis

The collected data are preprocessed in order to remove the duplicates. In total, 270 genes are obtained out of 157 from databases and 104 by manual curation as shown in Fig. 1.

Fig. 1.

List of curated host factors affected during EVD infection. An overlap of 10 genes and self-overlapping genes on both side of the Venn diagram found but interactions are with different targets

Pathway enrichment analysis

From the network, the pathways enriched are, systemic lupus erythematosus, alcoholism, viral carcinogenesis, influenza A, hepatitis C, protein processing in the endoplasmic reticulum, spliceosome, pathogenic Escherichia coli infection, measles, hepatitis B tabulated as Supplementary Table 1. Gene ontology terms are associated with, Chromatin assembly or disassembly, nucleosome organization, nucleosome assembly, chromatin assembly, protein–DNA complex assembly, DNA packaging, cellular macromolecular complex assembly, cellular macromolecular complex subunit organization tabulated in Table 1.

Table 1.

Top 10 enriched Gene Ontology (biological process) of host–Zaire Ebola virus infection

Term	Category	Description	Count	p value
GO:0006333	BP	Chromatin assembly or disassembly	35	1.78 × 10−33
GO:0034728	BP	Nucleosome organization	31	2.39 × 10−32
GO:0006334	BP	Nucleosome assembly	30	2.73 × 10−32
GO:0031497	BP	Chromatin assembly	30	9.03 × 10−32
GO:0065004	BP	Protein–DNA complex assembly	30	4.10 × 10−31
GO:0006323	BP	DNA packaging	32	2.08 × 10−30
GO:0034622	BP	Cellular macromolecular complex assembly	44	5.13 × 10−29
GO:0034621	BP	Cellular macromolecular complex subunit organization	45	5.70 × 10−28
GO:0006325	BP	Chromatin organization	38	4.79 × 10−20
GO:0065003	BP	Macromolecular complex assembly	48	1.73 × 10−19

Protein–protein interaction analysis

PPI network is generated for Ebola interacted human proteins for understanding virus-associated protein functional complexes with a confidence score of 0.70 (high confidence) consists of 246 nodes and 1518 edges and top three sub modules generated by MCODE are shown in Fig. 2.

Fig. 2.

Construction of PPI network using experimental data for the interaction of host protein with Ebola on the basis of protein–protein interaction. The top three sub clusters are generated by as represented as yellow diamond (color figure online)

Identification of modules and their enrichment

The top thee modules are selected with MCODE score for cluster 1 (score = 31.438, nodes = 33, edges = 503), cluster 2 (score = 18.211, nodes = 20, edges = 173), cluster 3 (score = 15, nodes = 15, edges = 105). Next, gene ontology for these modules were further analyzed using ClueGO (Fig. 3). The enriched terms are IRE-1 mediated unfold protein response, ribosome assembly, innate immune response, spliceosome snRNA assembly, negative regulation of gene expression epigenetic, SRP dependent cotranslational protein targeting to membrane, chromatin assembly and disassembly, mRNA stabilization, RNA splicing via transesterification reactions with bulged adenosine as nucleophile, chromatin silencing.

Fig. 3.

The sub clusters generated by MCODE are visualized in CluGO for gene enrichment, cluster 1 represented by green color, cluster 2 represented by green blue, cluster 3 represented by red color (color figure online)

Host–virus interaction network

Out of collecting host factors interacted with ZEBOV, 270 host genes are obtained by combining 163 host genes identified from databases and 107 from the experimentally reported literatures. Finally, the proteins/genes were inputted in Cytoscape and the network was constructed which consists of 257 nodes and 270 edges. From 270 host factors, 18 are found to interact with Glycoprotein (GP), 103 interact with VP24, 2 with VP30, 78 with VP35, 7 with VP40 and 62 host factors with L (RNA dependent RNA polymerase) viral protein as shown in Table 2 and Fig. 4.

Table 2.

Total number of human proteins involved in direct interactions with Ebola proteins

S. no.	Virus protein	Number of interacting human genes
1.	GP	18
2.	VP24	103
3.	VP30	02
4.	VP35	78
5.	VP40	07
6.	L	62

Fig. 4.

Direct interaction network of host protein-virus using Cytoscape v3.5.1 Red hexagons represent Ebola proteins and yellow represents host proteins human genes (color figure online)

Host–virus–drug interaction

The constructed host–virus interaction network is considered to generate the drug-host interaction network. The network is generated to find the alternative drugs which interact with a host may inhibit ZEBOV replication and infection. The drugs interacted with the set of 270 host factors are queried using DGIdb [8] and CyTargetlinker, a Cytoscape based plug-in as shown in Supplementary Table 2 and Fig. 5. EBOV-host interaction associated repurposed drugs listed are based on the multi-targeted host–virus–drug interaction (Table 3 and Fig. 6) (Supplementary Table 3). In total, 270 Zaire Ebola viruses associated host genes are collected out of where 163 genes are derived from databases and 107 from the literature survey. From the host–virus–drug network, it is noticed these 182 drugs in teract with 45 host gene targets.

Fig. 5.

The network of direct physical interactions of virus–host–drug using Cytoscape v3.5.1 red hexagons represent Zaire Ebola viral proteins, yellow represents human genes and green diamond represents drugs (color figure online)

Table 3.

List of repurposed drugs

S. No.	Drugs name	Human gene name	Viral gene name	Description of the drug
1.	Paclitaxel	TUBA1C TUBB	VP24	Paclitaxel is an FDA approved cancer drug, the main function of this drug is to stabilize microtubule and induce the mitotic arrest which causes cell death [48]. It initiates the apoptosis by binding specifically interacting tubulin [40]
		TUBA1A	VP35
		TUBA1B TUBB6 TOP2A	L
2.	Vincristine sulfate	TUBA1C TUBB	VP24	The mechanism of vincristine sulfate inhibits the function of mitotic spindle formation which leads to the arrest of cells in the metaphase stage [11]
		TUBA1A	VP35
		TUBA1B TUBB6	L
3.	Vinblastine sulfate	TUBA1C TUBB	VP24	Vinblastin is tubulin binding molecules used for anti-tumor drugs [47]
		TUBA1A	VP35
		TUBA1B TUBB6	L
4.	Eribulin mesylate	TUBA1C TUBB	VP24	Eribulin mesylate is the novel inhibitor that interferes with the microtubule dynamics [15]
		TUBA1A	VP35
		TUBA1B TUBB6	L
5.	Docetaxel hydrate	TUBA1C TUBB	VP24	Docetaxel interferes with the normal function of microtubule dynamics [29]
		TUBA1A	VP35
		TUBA1B TUBB6	L
6.	Tretinoin	HSPA8, SMARCA4	VP24	Used in acne treatment and involved in opsonization of the immune system [37]
		FUS	VP35
7.	Cyclosporine	AFM1 HSPA8	VP24	Interact with heat shock protein [30]
		ILF3	VP35
8.	Enoxacin	ATP1A1	VP24	Enoxacin interact with topoisomerase II [22]
		TOP2A	L
9.	Hydrogen peroxide	HIST4H4	VP24	HSV-1 shown sensitive by treatment of Hydrogen Peroxide in the presence of catalase inhibitor [32]
		PRDX1	L
10.	Teniposide	HIST3H2A	VP35	Teniposide interacting with topoisomerase II, which leads to inhibition of DNA synthesis. It interferes with the cell cycle at S or early G2 phase, this prevents the cells from entering mitosis [46]
		TOP2A	L
11.	Caffeine	PRKDC	VP24	Caffeine is efficient suppresses the DNA. Repair and DNA damage-activated checkpoints. This inhibits retroviral transduction [31]
		TOP1	L
12.	Podofilox	TUBB	VP24	Interfere with the function of topoisomerase [18]
		TOP2A	L

Fig. 6.

The repurposed drugs has connection more than one connection, red hexagons represent Zaire Ebola viral proteins, yellow represents human genes and green diamond represents drugs (color figure online)

Out of 45 targets, which have more than one connection are selected with criteria the targets which have more than one. Paclitaxel, vincristine sulfate vinblastine, sulfate, eribulin mesylate, docetaxel hydrate, tretinoin, cyclosporine, enoxacin, hydrogen peroxide, teniposide, caffeine, podofilox drug have more interactions among 182 drugs and may have the property to inhibit the of ebola. Most of these drugs are microtubule inhibitors used for the cancer treatment, and remaining classes of drugs are used for acne, rheumatoid arthritis, urinary tract infection, fatigue, and genital warts.

Discussion

Ebola virus is a life threatening virus, where there is no approved medication and they depend on host cell machinery to promote their replications, thus it is necessary to understand the mechanism of pathogenesis. In the present study, the data consisting of existing direct physical Ebola–human protein–protein interaction (PPI) were collected to build the host–virus network and drug host virus interaction to predict the therapeutic targets. Also, gene ontology results indicate that the genes are mainly enriched in chromatin assembly or disassembly, Nucleosome organization, Nucleosome assembly, Protein–DNA complex assembly, DNA packing, cellular macromolecular complex assembly, cellular molecular complex subunit organization, and macromolecular complex assembly.

The host–virus networks are constructed in order to understand the interaction and to find the therapeutic targets. These findings of the current research are also supported by recents reported studies, where the epigenetic modification of virus affects the normal chromatin dynamics [25]. Kuznetsova et al. reported that 53 active compounds blocks HDT Ebola virus-like particle (VLP) through drug repurposing and classified as microtubule inhibitors, anticancer/antibiotics, estrogen receptor modulators, antihistamines, antipsychotics, and pump/channel antagonists [24]. Also, it is understood that the interplay between virus-chromatin interactions paves a way to understand the role of chromatin in viral infection [23] and Madrid et al. [27] assembled and screened the efficacy of 1012 FDA approved drugs for various bacterial and viral pathogens by in vitro cell culture assays.

Microtubules play a vital role as a part of the infection process. Ebola matrix protein VP40 co-localized with microtubules and enhances the tubulin polymerization without any cellular mediators. Microtubules may play an important role in Ebolavirus life cycle and potentially act as a novel therapeutic target [35]. It also participates in early and late events of assembly, budding, shuttling intermediate viral products, important transcription regulator of several negative-strand RNA viruses [35, 36, 41]. Microtubules inhibitors are most potent to inhibit the entry of ebola, but with the wild type of viruses, it is not confirmed with drug activity [24]. Structural bioinformatics analysis of the interactions may further lead to identify effective drugs. Cellular protein tubulin beta chain has a higher probability of interaction with VP24 [14]. Ebola GP protein has a receptor binding region (RBR) which helps the virus to enter into the host and consistently its transport to the cell surface which is controlled by microtubules [12].

The functional analysis of host–virus–drug interaction is necessary to find the potential host-oriented anti-viral drugs. As host-oriented drug target identification has many benefits than the virus due to its lower mutation rate in human than viral genes and also host targets are better to solve the problem of drug resistance than viruses [44].

In summary, the conventional drug discovery process is much costlier, lengthy and time-consuming for approval where drug repurposing is an alternative method to identify the new action of old therapeutics. In this work, the experimentally reported Ebola and human interacting genes are collected followed by the enrichment analysis to understand host–virus interactions. Then, the host–virus–drug network is generated to predict the insights into the development of new antiviral.

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Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests.