vhfRNAi: a web-platform for analysis of host genes involved in viral infections discovered by genome wide RNAi screens

Anamika Thakur , Abid Qureshi and Manoj Kumar *
Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research, Sector 39-A, Chandigarh-160036, India. E-mail: manojk@imtech.res.in; Fax: +91-172-2690585; Fax: +91-172-2690632; Tel: +91-172-6665453

Received 13th December 2016 , Accepted 18th May 2017

First published on 19th May 2017

Knockdown of host genes using high-throughput genome-wide RNA interference screens has identified numerous host factors that affect viral infections, which would be helpful in understanding host–virus interactions. We have developed a vhfRNAi web resource based on genome-wide RNAi experiments for viruses. It contains experimental details of 12[thin space (1/6-em)]249 entries (host factors + restriction factors) for 18 viruses. Simultaneously, this resource encompasses analysis of overlapping genes, genome wide association studies, gene ontology (GO), pathogen interacting proteins, interaction networks and pathway enrichment. Using overlap analysis, it was found that Influenza A virus shared overlapping host genes with the majority of viruses including Hepatitis C virus and Dengue virus 2. In the genome wide association studies analysis, 429 diseases/traits were mapped, of which obesity-related traits were the most common. GO analysis revealed that the major categories belonged to metabolic processes, molecule transport, signal transduction, proteolysis, etc. In the pathogen interacting protein analysis, protein interaction data from different resources can be explored for further understanding of host–virus biology. By pathway enrichment analysis, a total of 8955 genes were mapped on 303 pathways with most of the hits coming from metabolic pathways. We have found 491 genes that are not essential for the host but essential for the virus and can be targeted to inhibit the virus. These may be explored as potential candidates for drug targets. The resource is freely accessible at http://bioinfo.imtech.res.in/manojk/vhfrnai and will be useful in understanding host–virus biology as well as identification of targets for the development of antiviral therapeutics.


Viral diseases are a global health threat.1–3 There is a need to understand the interaction between the host and virus to spot suitable targets which will help in curbing viral infections.4 Traditionally different types of techniques such as affinity chromatography, co-immunoprecipitation, the two hybrid method, phage display, fluorescence systems etc. are used in the characterization of these interactions.5 Lately genome-wide RNA interference screens have accelerated the identification of host factors critical for viral propagation.6,7

Fire et al. described the first report on RNAi demonstrating a strong gene silencing result by introducing double stranded (ds) RNA into Caenorhabditis elegans roundworm.8,9 In the RNAi pathway, dsRNA is chopped by the dicer protein to ds siRNAs which are smaller (19–21 nt) in length and have 3′ overhangs at their termini.10 The siRNA is further unwound by the Argonaute protein followed by inclusion of the antisense (guide) strand into the RNA Induced Silencing Complex (RISC). Employing the guide strand, the RISC targets the matching mRNA causing its degradation.11

There are many general interaction databases reported in the literature such as MINT – the Molecular INTeraction database,12 IntAct, which is an open source resource for molecular interaction data,13 and MatrixDB, which is an extracellular matrix interaction database.14 In addition there are a few databases containing information on host–virus interactions like VirusMentha, which is a resource for virus–host protein interactions,15 VirHostNet, which is a knowledge base for the management and the analysis of proteome-wide virus–host interaction networks,16 the HIV-1 Human Protein Interactions Database,17 HCVpro, which is a Hepatitis C Virus Protein Interaction Database18etc. During the past decade, genome-wide RNAi screens for many important viruses were developed.19–21 Although there are a few RNAi screen based resources such as the Genome RNAi database,22 the Drosophila RNAi screening center,23 DEG, which is a database of essential genes,24 the EHFPI-database of essential host factors for pathogenic infection25etc., there is no committed virus specific RNAi screen resource.

In this study we collected the information about different viral RNAi screens that deal with the categorization of host factors implicated in virus survival and presented the data in the form of an integrated and freely available online resource (Fig. 1). vhfRNAi has exhaustive and up to date information on RNAi screen analysis of viral host and restriction factors. vhfRNAi also provides tools such as overlap, network, enrichment and interaction analysis to augment the perception of virus–host biology.

image file: c6mb00841k-f1.tif
Fig. 1 Overview of vhfRNAi analysis.

Materials and methods

Data source

To fetch relevant articles from the scientific literature, we used the advanced search option in PubMed. We searched the database with the following text mining script using abbreviations and synonyms to retrieve the maximum number of papers: (((((genomewide) OR genome-wide) OR *genome*wide*)) AND ((((RNA interference) OR RNAi) OR RNA*interference*) OR RNAi*)) AND ((virus) OR viral). The initial search returned 175 papers, however, only limited papers have the relevant data. Finally, data were extracted from 32 articles published from June 2007 to January 2016.

Data organization

The vhfRNAi database provides comprehensive information about essential viral host genes and includes the following fields extracted from the literature – (i) Gene Symbol, (ii) Gene Description, (iii) Ensembl Gene ID, (iv) UniProt ID, (v) Virus, (vi) Entrez Gene ID, (vii) GO, (viii) Pathway, (ix) Organism, (x) Virus name, (xi) Taxonomy, (xii) Phenotype, (xiii) Hits Number or replicates that are beyond 2–3 standard deviation values from the mean, (xiv) Confirmed Hits Number, (xv) Assay Type, (xvi) BioModel Description, (xvii) Confirmatory Screen Description, (xviii) Primary Screen Description or a strong signal/background ratio with low variation in the assay, (xix) Screen Note and (xx) PubMed ID. The vhfRNAi database also provides external links to other resources like Uniprot, Ensembl, Entrez, Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) terms, Taxonomy browser, and PubMed for each record.

Data analysis

The vhfRNAi database also provides different types of analyses to facilitate in depth understanding of host and restriction factors implicated in viral pathogenesis. They are: (i) overlap analysis, (ii) interaction analysis, (iii) gene ontology analysis, (iv) genome wide association studies (GWAS) analysis, (v) virus interacting proteins analysis and (vi) pathway enrichment analysis.
(i) Overlap analysis. In the overlap analysis, common genes from different viruses were extracted using the Perl script and then displayed in the form of clickable heat maps as well as chord diagrams using Circos26 representing viruses, their families and groups.
(ii) Interaction analysis. Interaction analysis is a tool to show interaction between host genes (host factors + restriction factors) and the virus. There are two types of analysis performed in vhfRNAi. They are – “Network analysis” and “vhfRNAi interaction with virus”. Network analysis is performed using cytoscape which is an open source software for visualization of bimolecular interaction networks.27 All the host factors (HF) along with restriction factors (RF) for the user-selected virus are shown in graphical view. “vhfRNAi interaction with virus” analysis also displays the interaction of the gene with the virus. It was developed using D3 (Data-Driven Documents), which is a JavaScript library for visualizing data with HTML, CSS and SVG.
(iii) Gene ontology (GO) analysis. GO analysis is done using the BiNGO plugin28 in cytoscape. Using host genes of different viruses, this tool is used to determine the GO categories that are significantly overrepresented and map the predominant functional themes.
(iv) Genome wide association studies (GWAS) analysis. GWAS analysis identifies genes that are associated with different diseases/traits mapped from the NHGRI GWAS Catalog.29
(v) Virus interacting proteins analysis. This analysis contains a list of pathogen interacting proteins (PIP) collected from VirHostNet16 and VirusMINT4 which would be helpful in understanding host–virus biology.
(vi) Pathway enrichment analysis. This provides the functional annotation of the host genes involved in cellular pathways and was performed using KEGG Mapper.30


Database statistics

vhfRNAi is a manually curated, open source resource of viral host factors experimentally validated using high throughput RNAi screen technology. Currently vhfRNAi provides information on 12[thin space (1/6-em)]249 host genes belonging to 18 different human viruses (Table 1). These host genes are divided into three groups: (i) “host factors (HFs)” that increase viral infection, (ii) “restriction factors (RFs)”, which decrease the viral infection and (iii) “neutral factors” that do not alter the viral infection. There were 5441 and 6782 cumulative entries of HFs and RFs. Moreover, about 26 entries were either significantly inhibited or enhanced and hence, their status as “HF/RF” was not confirmed. The maximum number of unique host factors belong to poliovirus with 3340 entries followed by Human immunodeficiency virus 1 (HIV-1), Adeno-associated virus (AAV) and so on as displayed in the ESI Table S1. These host factors have been confirmed on different cell lines like HEp-2C, Vero, HeLa, HEK293 etc. Of these, HEp-2C and Vero cells correspond to a maximum of4375 entries followed by HeLa and HEK293 with 3344 and 1835 entries respectively. Similarly techniques like ELISA, RT-PCR, immunofluorescence, luciferase reporter assay etc. were used for confirmatory screening. Amongst them, ELISA has a maximum of 4375 entries whereas, RT-PCR, immunofluorescence and luciferase reporter assays have 3087, 1459 and 402 entries correspondingly (Fig. 2).
Table 1 Number of entries (HF + RF) for 18 viruses in vhfRNAi
S. no. Virus Entries Host factor (HF) Restriction factor (RF)
1 Adeno-associated virus (AAV) 1459 501 958
2 Avian influenza virus (AIV) 11 0 11
3 Borna disease virus (BDV) 34 0 34
4 Dengue virus 2 (DEN-2) 40 0 40
5 Hepatitis C virus (HCV) 466 25 441
6 Human herpesvirus 1 (HSV-1) 72 0 72
7 Human immunodeficiency virus 1 (HIV-1) 2322 335 1961
8 Human parainfluenza virus 3 (HPIV-3) 27 0 27
9 Influenza A virus (IAV) 1356 4 1352
10 Lymphocytic choriomeningitis virus (LCMV) 54 0 54
11 Marburgvirus (MARV) 11 0 11
12 Poliovirus (PV) 4375 4375 0
13 Rotavirus (RV) 522 0 522
14 Sendai virus (SeV) 59 46 13
15 Sindbis virus (SiV) 111 54 57
16 Vaccinia virus (VACV) 793 29 764
17 Vesicular stomatitis virus (VSV) 87 0 87
18 West Nile virus (WNV) 450 72 378
Total 12[thin space (1/6-em)]249 5441 6782

image file: c6mb00841k-f2.tif
Fig. 2 vhfRNAi statistics: pie-charts signifying statistical distribution of (a) cell lines and (b) experimental methods.

Data retrieval

The desired information on viral RNAi screens can be obtained using browse and search options provided in the online resource.
Search. Using the search option, users can insert the query in the box and can select the given option or can use the default ‘all’ option that explores all fields in the database by using either an exact match or a match containing the query. The output includes information on various components like vhfRNAi Accession, gene symbol, gene description, organism name, uniprot ID, PubMed IDs, phenotype, group name etc. Individual outputs can be viewed by clicking on the vhfRNAi ID. Users can also sort the results by clicking on the headers of the columns or by entering keyword in the boxes given below the headers (Fig. 3).
image file: c6mb00841k-f3.tif
Fig. 3 Screenshot of vhfRNAi search output including gene symbol, description, UniProt ID, phenotype and PubMed ID.
Browse. The browse option includes the list of viruses with their entries for each virus e.g. HIV-1 has 2322 entries. Information on each virus can be viewed by clicking on the virus name (Fig. 3).
Genes targeted. The gene target option has the list of genes used in different articles. Kits used in these studies were from Dharmacon, Qiagen, Ambion etc. Users can view up- and down-regulated genes as well as see the list of total genes targeted along with genes unaffected, i.e. neutral genes.


vhfRNAi also offers useful analysis such as overlap, network, enrichment, interactions etc. to help in comprehensive understanding of host–virus biology.

Overlap analysis identifies HFs as well as RFs involved in multiple viral infection pathways. The analysis graphically depicts the connection between viruses and host genes using clickable heat maps. The maximum number of overlapping host genes was found between HIV-1 and Influenza A virus (IAV) with 202 common host genes. IAV has the maximum number of overlapping host genes with HIV-1, Vaccinia virus (VACV) and Hepatitis C virus (HCV) having 202, 129 and 109 entries respectively. Meanwhile, HIV-1 shared the highest number of overlapping host genes with IAV, AAV, VACV and HCV that have 202, 148, 108 and 82 entries correspondingly. These overlapping host genes can also be viewed family-wise and group-wise (Fig. 4). Retroviridae shared the highest number of overlapping host genes with picornaviridae, orthomyxoviridae, parvoviridae, flaviviridae and poxviridae with 472, 202, 148, 128 and 108 entries respectively. In the case of group-wise overlapping, Group IV ((+) ssRNA) has the highest frequency of overlapping host genes with Group VI (ssRNA-RT), Group V ((−) ssRNA), Group II (ssDNA) and Group I (dsDNA) with 617, 430, 389 and 156 entries, respectively. Circos plots and interactive chord diagrams of the overlapping genes representing the viruses and their families and groups are also generated and displayed on the vhfRNAi website.

image file: c6mb00841k-f4.tif
Fig. 4 Heatmap showing overlapping host genes in vhfRNAi in terms of virus (a), family (b) and group (c).

We found that RF ARCN1 is the only gene that was found in seven viruses (HCV, Human parainfluenza virus 3 (HPIV-3), IAV, Lymphocytic choriomeningitis virus (LCMV), Rotavirus (RV), Sindbis virus (SiV) and Vesicular stomatitis virus (VSV)). For six viruses, seven RFs were common out of which five, COPA, COPB1, COPB2, COPG1 and COPZ1, belong to the coatomer subunit while FAU and MAT2A were ATP-binding. Table 2 displays the number of genes (HF + RF) common in four, five, six and seven viruses where HFs are denoted with ↑ symbol and RFs are represented with ↓ symbol. The numbers of shared genes (HF + RF) that are found in two and three viruses are given in the ESI Table S2. Table 3 shows the individual virus gene (HF + RF) entries that are overlapping among different viruses. The IAV virus has the maximum number of genes that are the same in the six viruses. Also, HCV, RV, LCMV and VSV individually share host factors between the six viruses.

Table 2 Table representing genes (HF + RF) common among different viruses
S. no. Number of viruses Gene name Total number of genes
↑ denotes host factor and ↓ denotes restriction factor.
1 4 COASY↓↑, COX17↓↑, EIF3C, H1FOO↓↑, HAVR1↓↑, MAVS↓↑, MED17↓↑, MED4↓↑, NAA15↓↑, NUP88↓↑, PI4KA↓↑, PIGA↓↑, RAB34↓↑, SIAH2↓↑, TBCD↓↑, TRPM8↓↑, AURKB↓↑, CDK1↓↑, CIB2↓↑, EGFR, HNRNPK, PELI1, RELA↓↑, SMAD5↓↑, STX2↓↑, XBP1↓↑, ABCE1↓↑, ARF1, EIF3H↓↑, KIF17, XAB2, XPO1, DCD↓↑, UTP11L↓↑, ATP6V0D1, NDST1, PSMD2↓↑, RAB5C↓↑, SYVN1↓↑ 39
2 5 IRF3↓↑, MED14↓↑, TAF5↓↑, ATP6V0B, NOP56, WDR46, ATP6V0C, ERN2, EIF3A, EIF3G, EIF3I, NHP2L1↓↑ 12
4 7 ARCN1 1

Table 3 List of viruses sharing overlapping genes (HF + RF)
S. no. Virus Number of overlapping genes
2 3 4 5 6 7
1 Adeno-associated virus (AAV) 437 78 15 3 0 0
2 Avian influenza virus (AIV) 7 3 1 0 0 0
3 Borna disease virus (BDV) 15 4 1 0 0 0
4 Dengue virus 2 (DEN-2) 9 3 0 1 0 0
5 Hepatitis C virus (HCV) 140 50 13 3 6 1
6 Human immunodeficiency virus 1 (HIV-1) 688 145 20 3 1 0
7 Human parainfluenza virus 3 (HPIV-3) 0 10 3 6 7 1
8 Human herpesvirus 1 (HSV-1) 24 10 3 1 0 0
9 Influenza A virus (IAV) 356 123 18 8 7 1
10 Lymphocytic choriomeningitis virus (LCMV) 14 16 8 8 7 1
11 Marburgvirus (MARV) 7 3 1 0 0 0
12 Poliovirus (PV) 881 130 22 2 0 0
13 Rotavirus (RV) 124 35 9 3 3 1
14 Sendai virus (SeV) 13 7 5 2 0 0
15 Sindbis virus (SiV) 28 9 1 2 0 1
16 Vaccinia virus (VACV) 204 66 18 4 4 0
17 Vesicular stomatitis virus (VSV) 30 23 9 8 7 1
18 West Nile virus (WNV) 93 29 9 6 0 0

We have also provided a virus-wise list of overlapping genes that are not essential for the host as per the database of essential genes (DEG)24 but essential for the virus as per VirusMentha15 that can be targeted to inhibit the virus. Initially, 6469 essential genes (HF + RF) out of 12[thin space (1/6-em)]249 were removed using DEG and we were left with 5780 non-essential host genes. The latter were further reduced to 603 host genes with 491 unique host genes that are essential for the virus but not the host using VirusMentha. Out of the 491 host genes, we found that 79 genes are shared with two viruses. The IRF3 gene (HF + RF) was found to be common in five viruses, whereas, seven and two genes are common in three and four viruses respectively (Table 4).

Table 4 List of overlapping host genes (HF + RF) non-essential for the host but essential for the virus
S. no. Number of viruses Gene-name Number of genes
↑ denotes host factor (HF) and ↓ denotes restriction factor (RF).
1 2 2AAB↓↑, AAPK2↓↑, ABCB6↓↑, AN13A, APOA1, APOBEC3G, ATD3A↓↑, C8AP2↓↑, CALCOCO1, CHIN↓↑, CLIC1, CLU, DECR, DNAJB1, DRS7B, E2F5↓↑, ELF1↓↑, EST1, FLOT1↓↑, FMR1, GABPB2, GAS6↓↑, GLIP1, GOPC↓↑, GORS2, HSPB8, IRF1↓↑, ITM2C↓↑, LMO3↓↑, LZTS2↓↑, MSH6↓↑, MT2A, MTX1, MYOF, NAGK↓↑, NCEH1↓↑, NUMBL, OPTN, PCCB, PDIA6↓↑, PGAM4↓↑, PIEZO1, PIP↓↑, PNMA1↓↑, PSME1↓↑, PSME2↓↑, RAB6A, RIPK3↓↑, RN125, RNF10↓↑, RRBP1↓↑, SDCG3↓↑, SDF4, SFXN3↓↑, SIDT2↓↑, SNX9↓↑, SP100↓↑, SPP2B↓↑, SRSF5↓↑, SYNE2, T200A↓↑, TAP1, TBB6↓↑, THAS↓↑, TM245, TMEM140, TOPK↓↑, TRAP1↓↑, TRHDE↓↑, TRPV2, TXND5↓↑, TYK2, UNG↓↑, UTRO, VKORL↓↑, WDR6, ZAP70↓↑, CBX3↓↑, CD4 79
2 3 VSIG4↓↑, IFITM3↓↑, CCL2, FAS↓↑, PARP4↓↑, SMAD3, PAK1↓↑ 7
3 4 RAB34↓↑, RAB5C↓↑ 2
4 5 IRF3↓↑ 1

The enrichment analysis gives a functional annotation of the host genes in cellular pathways using KEGG Mapper.30 8955 genes were mapped on 303 pathways with most of the hits coming from metabolic pathways, pathways in cancer, the PI3K-Akt signaling pathway, olfactory transduction, HTLV-I infection, cytokine-cytokine receptor interaction, neuroactive ligand-receptor interaction, viral carcinogenesis, endocytosis and the MAPK signaling pathway (Fig. 5a). In addition, GO analysis of the host genes of different viruses was done using BiNGO to determine which GO categories are significantly overrepresented and map the predominant functional themes (Fig. 5b). In the bubble diagram, the nodes that are uncolored are not overrepresented, yellow nodes are overrepresented at a specific significance level. While, the nodes become orange in color when the p-value becomes more significant. The major categories belonged to metabolic processes, biological regulation, molecule transport, signal transduction, proteolysis, phosphorylation, membrane organization and the cell cycle.

image file: c6mb00841k-f5.tif
Fig. 5 (a) Pathway enrichment analysis of genes in vhfRNAi. (b) Gene Ontology (GO) analysis of HPIV-3 RF.

Interaction analysis shows the protein–protein interactions between the host gene and virus for further analysis. All the interaction partners or host genes (HF/RF) for the user-selected virus are shown graphically. The HFs and RFs are shown in blue and the main virus is displayed in green color. Whereas, viruses sharing the same gene with the main virus are depicted in yellow. In Fig. 6, DEN-2 is the main virus (shown in green color) that contains RFs and shares them with ten viruses viz. HCV, PV, AAV, VACV, HIV-1, IAV, VSV, LCMV, WNV and HSV-1. This analysis was performed using cytoscape. We also found that Poliovirus (PV) and IAV had the maximum number of interaction partners and shared common genes with all the viruses. While, the HIV-1 and AAV genes are common with fifteen and sixteen viruses. Marburgvirus (MARV), Avian influenza virus (AIV) and Borna disease virus (BDV) had the least amongst the 18 viruses in our database.

image file: c6mb00841k-f6.tif
Fig. 6 Schematic representation of interaction analysis for DEN-2 virus where green color denotes the main virus, blue color represents the restriction factor (RF) and yellow color representing those viruses that share RFs with the main virus.

In the pathogen interacting proteins (PIP) analysis, protein interaction data from VirHostNet16 and VirusMINT4 can be analyzed for further understanding of host–virus biology. The majority of interactions were reported in HCV, Human papillomavirus (HPV), HIV-1, AAV and Human herpesvirus (HSV-1) (Fig. 7).

image file: c6mb00841k-f7.tif
Fig. 7 Pie chart representing statistical representation of pathogen interacting proteins of the top five viruses.

In the genome wide association studies analysis users can analyze candidate disease host genes identified from genome wide association studies (GWAS). A total of 405 diseases/traits were mapped for 702 genes of which obesity-related traits, height, multiple sclerosis, platelet counts, inflammatory bowel disease, Crohn's disease, HDL cholesterol, Type 2 diabetes, breast cancer, and systemic lupus erythematosus were the majority in number (Fig. 8). IAV has the maximum number of gene entries with the majority of diseases/traits followed by HIV-1, PV, VACV, and RV as given in the ESI Table S3.

image file: c6mb00841k-f8.tif
Fig. 8 Bar graph showing the number of the top ten diseases/traits identified for candidate host genes from genome wide association studies.


vhfRNAi is developed using the open source LAMP solution stack with MySQL and Apache in the back-end and the front-end of the web interface is programmed using PHP, HTML and JavaScript. The resource is accessible at http://bioinfo.imtech.res.in/manojk/vhfrnai.


We developed an online resource, vhfRNAi, for analysis of HFs plus RFs involved in viral infection identified by genome wide RNAi screens that comprises 12[thin space (1/6-em)]249 host genes for 18 different viruses. The data was curated from different RNAi screen publications and different types of analysis were performed. The data include the list of genes that either significantly inhibited (down-regulate) or enhanced (up-regulate) the viral replication with 6782 and 5441 entries respectively. The genes that do not show any effect (neutral genes) are also provided on the web server. This resource provides easy to use functions such as browse, search, sort, filter etc. to retrieve the desired data. The interaction analysis graphically displays the interactions between host factors and the selected virus. Using overlap analysis, one can find the common overlapping genes between different viruses. Maximum overlapping was found for the ARCN1 gene that was common in seven viruses including HCV, SiV, HPIV-3, IAV, LCMV, RV and VSV. ARCN1 is the human archain gene also known as Coatomer Protein Complex Subunit Delta (COPD) that binds to dilysine motifs. It is involved in vesicle structure and in protein transport from the endoplasmic reticulum (ER) to the golgi complex.31 In as many as six viruses, seven host genes were found to be common, out of which COPA, COPB1, COPB2 and FAU belong to HCV, HPIV-3, IAV, LCMV, RV and VSV. COPG1 and COPZ1 are found in HCV, HPIV-3, IAV, LCMV, VACV and VSV. Furthermore, the MAT2A protein is found in IAV, LCMV, VACV, HIV-1, HPIV-3 and VSV. The genes COPA, COPB1, COPB2, COPG1 and COPZ1 belong to the coatomer protein complex (COPI) subunit. The FAU gene encodes ubiquitin like protein FUBI,32 which fuses to the ribosomal S30 protein, while the MAT2A gene codes for methionine adenosyl transferase, which is an essential enzyme responsible for S-adenosyl methionine (SAMe) biosynthesis.33 The genes IRF3, MED14, TAF5, ATP6V0B, NOP56, WDR46, ATP6V0C, ERN2, EIF3A, EIF3G, EIF3I and NHP2L1 were found to be common among five viruses. The IRF3 gene is considered non-essential for the host but essential for the virus as per VirusMentha15 and is shared by AAV, HCV, IAV, Sendai virus (SeV) and West Nile virus (WNV). IRF3 is an interferon regulatory factor that contains several domains like DNA-binding, nuclear export signal and various phosphorylation sites. The genes RAB34 and RAB5C were found to be shared by four viruses and are also considered non-essential host genes (Table 4). RAB34 and RAB5C belong to the RAS protein family and are small GTPases involved in protein transport. These non-essential genes may act as apposite drug targets for therapeutics development against the viruses.

The enrichment analysis provides functional annotation of the host factors in cellular pathways. In the pathogen interacting protein analysis, protein interaction data from different resources can be explored for further understanding of host–virus biology. The genome wide association studies (GWAS) analysis enables the user to explore candidate disease host genes identified from genome wide association studies. In the GWAS analysis, diseases or traits such as obesity-related traits, Type 2 diabetes, height, red blood cell traits, Crohn's disease, HDL cholesterol, prostate cancer, inflammatory bowel disease, multiple sclerosis, and platelet counts topped the list. Viral infections have been linked with obesity in both humans as well as animals.34 The viruses activate transcription factors and enzymes, which results in build-up of triglycerides and differentiation of adipocytes.35 Adenovirus 36 is the most extensively researched virus associated with obesity.34,36 Many viral infections like those of HCV and Herpes Simplex Virus-1 have been associated with Type 2 diabetes mellitus. The viruses can damage and cause apoptosis in β-cells of the pancreas.37,38 Viruses like HIV-1 have been reported to cause a decrease in traits like height and weight in infected children.39,40 Patients suffering from AIDS and hepatitis are reported to have a high subpopulation of hyperchromic RBCs due to their effects on the immune system and liver function.41,42 Crohn's disease, which is an inflammatory disorder of the gastrointestinal tract, has also been associated with viral infections from epidemiological studies.43,44 Viral infections also lead to considerable changes in lipid metabolism and lipoprotein composition. Triglyceride and VLDL cholesterol levels elevate, while HDL cholesterol and LDL cholesterol levels are decreased.35,45 Proteins linked to regulation of cholesterol bioavailability in lipid rafts have been reported to help the virus to enter the cells.46 Similarly, the BK Polyomavirus has been suggested as a factor in prostate cancer.47 Prostate cancer patients have also been detected with viral reads for JC polyomavirus, Merkel cell polyomavirus and Human Papillomavirus types 89 or 40.48 Likewise many studies have correlated Epstein-Barr virus (EBV) infection with inflammatory bowel disease.49,50 There has been increasing evidence that in multiple sclerosis, an autoimmune disease, viruses such as EBV and human herpesvirus 6 act as environmental triggers in susceptible persons.51,52 Viral infections such as those of respiratory virus have been shown to influence platelet count and functions.53–55

Even though there are a few existing RNAi screen databases such as the GenomeRNAi database,22 Drosophila RNAi screening center,23 database of essential genes (DEG),24 database of essential host factors for pathogenic infection (EHFPI)25etc., none of them is exclusive for viral host factors. The EHFPI is a general host–pathogen interaction database having details on 6718 essential host factors (5106 unique) pertaining to bacteria, fungi and viruses. It contains information on around 4890 viral host factors (3896 unique) whereas vhfRNAi comprises the latest data on 12[thin space (1/6-em)]249 host genes (9751 unique) along with useful insights like gene overlap analysis, interaction networks, pathway enrichment etc. The resource furnishes information regarding the host gene, its pathway, ontology, virus taxonomy, phenotype, hits, assay, biomodel and screen description along with links to related databases such as Uniprot, KEGG, Taxonomy browser, PubMed etc. To sum up, the vhfRNAi resource will be helpful to boost understanding of host–virus biology.


We have developed an integrated resource, vhfRNAi, dedicated to information and analysis of host genes involved in viral infection identified by genome wide RNAi screens. The web server provides up to date information on RNAi screen data of 18 important human viruses along with analysis and visualization tools. vhfRNAi will help in comprehending host–virus biology and host regulatory networks and also to identify potential drug targets.

Conflict of interest

The authors declare that they have no conflict of interest.

Author contributions

MK conceived this idea. AT/AQ collected the data and manually curated it. AQ/AT designed the web server. MK/AT/AQ prepared the manuscript. All authors approved and read the final manuscript.


The Council of Scientific and Industrial Research, India (GENESIS-BSC0121) and Department of Biotechnology, Government of India (GAP0001). Funding for open access charge: CSIR-Institute of Microbial Technology, Chandigarh, India.


  1. S. T. Nichol, J. Arikawa and Y. Kawaoka, Proc. Natl. Acad. Sci. U. S. A., 2000, 97, 12411–12412 CrossRef CAS PubMed.
  2. A. Qureshi, N. Thakur and M. Kumar, J. Transl. Med., 2013, 11, 305 CrossRef PubMed.
  3. B. E. Pickett, D. S. Greer, Y. Zhang, L. Stewart, L. Zhou, G. Sun, Z. Gu, S. Kumar, S. Zaremba, C. N. Larsen, W. Jen, E. B. Klem and R. H. Scheuermann, Viruses, 2012, 4, 3209–3226 CrossRef PubMed.
  4. A. Chatr-aryamontri, A. Ceol, D. Peluso, A. Nardozza, S. Panni, F. Sacco, M. Tinti, A. Smolyar, L. Castagnoli, M. Vidal, M. E. Cusick and G. Cesareni, Nucleic Acids Res., 2009, 37, D669–D673 CrossRef CAS PubMed.
  5. E. M. Phizicky and S. Fields, Microbiol. Rev., 1995, 59, 94–123 CAS.
  6. D. Panda, A. Das, P. X. Dinh, S. Subramaniam, D. Nayak, N. J. Barrows, J. L. Pearson, J. Thompson, D. L. Kelly, I. Ladunga and A. K. Pattnaik, Proc. Natl. Acad. Sci. U. S. A., 2011, 108, 19036–19041 CrossRef CAS PubMed.
  7. H. Cheng, K. Koning, A. O'Hearn, M. Wang, E. Rumschlag-Booms, E. Varhegyi and L. Rong, Virol. J., 2015, 12, 194 CrossRef PubMed.
  8. A. Fire, S. Xu, M. K. Montgomery, S. A. Kostas, S. E. Driver and C. C. Mello, Nature, 1998, 391, 806–811 CrossRef CAS PubMed.
  9. A. Qureshi, N. Thakur, I. Monga, A. Thakur and M. Kumar, Database, 2014, 2014, 1–10 CrossRef PubMed.
  10. W. Woessmann, C. Damm-Welk, U. Fuchs and A. Borkhardt, Rev. Clin. Exp. Hematol., 2003, 7, 270–291 CAS.
  11. W. Filipowicz, Cell, 2005, 122, 17–20 CrossRef CAS PubMed.
  12. L. Licata, L. Briganti, D. Peluso, L. Perfetto, M. Iannuccelli, E. Galeota, F. Sacco, A. Palma, A. P. Nardozza, E. Santonico, L. Castagnoli and G. Cesareni, Nucleic Acids Res., 2012, 40, D857–D861 CrossRef CAS PubMed.
  13. S. Kerrien, Y. Alam-Faruque, B. Aranda, I. Bancarz, A. Bridge, C. Derow, E. Dimmer, M. Feuermann, A. Friedrichsen, R. Huntley, C. Kohler, J. Khadake, C. Leroy, A. Liban, C. Lieftink, L. Montecchi-Palazzi, S. Orchard, J. Risse, K. Robbe, B. Roechert, D. Thorneycroft, Y. Zhang, R. Apweiler and H. Hermjakob, Nucleic Acids Res., 2007, 35, D561–D565 CrossRef CAS PubMed.
  14. G. Launay, R. Salza, D. Multedo, N. Thierry-Mieg and S. Ricard-Blum, Nucleic Acids Res., 2015, 43, D321–D327 CrossRef CAS PubMed.
  15. A. Calderone, L. Licata and G. Cesareni, Nucleic Acids Res., 2015, 43, D588–D592 CrossRef PubMed.
  16. T. Guirimand, S. Delmotte and V. Navratil, Nucleic Acids Res., 2015, 43, D583–D587 CrossRef PubMed.
  17. D. Ako-Adjei, W. Fu, C. Wallin, K. S. Katz, G. Song, D. Darji, J. R. Brister, R. G. Ptak and K. D. Pruitt, Nucleic Acids Res., 2015, 43, D566–D570 CrossRef PubMed.
  18. S. K. Kwofie, U. Schaefer, V. S. Sundararajan, V. B. Bajic and A. Christoffels, Infect., Genet. Evol., 2011, 11, 1971–1977 CrossRef CAS PubMed.
  19. H. Zhou, M. Xu, Q. Huang, A. T. Gates, X. D. Zhang, J. C. Castle, E. Stec, M. Ferrer, B. Strulovici, D. J. Hazuda and A. S. Espeseth, Cell Host Microbe, 2008, 4, 495–504 CAS.
  20. D. Silva-Ayala, T. Lopez, M. Gutierrez, N. Perrimon, S. Lopez and C. F. Arias, Proc. Natl. Acad. Sci. U. S. A., 2013, 110, 10270–10275 CrossRef CAS PubMed.
  21. A. Yasunaga, S. L. Hanna, J. Li, H. Cho, P. P. Rose, A. Spiridigliozzi, B. Gold, M. S. Diamond and S. Cherry, PLoS Pathog., 2014, 10, e1003914 Search PubMed.
  22. E. E. Schmidt, O. Pelz, S. Buhlmann, G. Kerr, T. Horn and M. Boutros, Nucleic Acids Res., 2013, 41, D1021–D1026 CrossRef CAS PubMed.
  23. I. T. Flockhart, M. Booker, Y. Hu, B. McElvany, Q. Gilly, B. Mathey-Prevot, N. Perrimon and S. E. Mohr, Nucleic Acids Res., 2012, 40, D715–D719 CrossRef CAS PubMed.
  24. H. Luo, Y. Lin, F. Gao, C. T. Zhang and R. Zhang, Nucleic Acids Res., 2014, 42, D574–D580 CrossRef CAS PubMed.
  25. Y. Liu, D. Xie, L. Han, H. Bai, F. Li, S. Wang and X. Bo, Nucleic Acids Res., 2015, 43, D946–D955 CrossRef PubMed.
  26. M. Krzywinski, J. Schein, I. Birol, J. Connors, R. Gascoyne, D. Horsman, S. J. Jones and M. A. Marra, Genome Res., 2009, 19, 1639–1645 CrossRef CAS PubMed.
  27. P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski and T. Ideker, Genome Res., 2003, 13, 2498–2504 CrossRef CAS PubMed.
  28. S. Maere, K. Heymans and M. Kuiper, Bioinformatics, 2005, 21, 3448–3449 CrossRef CAS PubMed.
  29. D. Welter, J. MacArthur, J. Morales, T. Burdett, P. Hall, H. Junkins, A. Klemm, P. Flicek, T. Manolio, L. Hindorff and H. Parkinson, Nucleic Acids Res., 2014, 42, D1001–D1006 CrossRef CAS PubMed.
  30. M. Kanehisa, S. Goto, Y. Sato, M. Furumichi and M. Tanabe, Nucleic Acids Res., 2012, 40, D109–D114 CrossRef CAS PubMed.
  31. H. Chen, B. Sun, Y. Zhao, X. Song, W. Fan, K. Zhou, L. Zhou, Y. Mao and D. Lu, PLoS One, 2012, 7, e52864 CAS.
  32. T. G. Rossman, M. A. Visalli and E. V. Komissarova, Oncogene, 2003, 22, 1817–1821 CrossRef CAS PubMed.
  33. H. Chen, M. Xia, M. Lin, H. Yang, J. Kuhlenkamp, T. Li, N. M. Sodir, Y. H. Chen, H. Josef-Lenz, P. W. Laird, S. Clarke, J. M. Mato and S. C. Lu, Gastroenterology, 2007, 133, 207–218 CrossRef CAS PubMed.
  34. R. L. Atkinson, Mayo Clin. Proc., 2007, 82, 1192–1198 CrossRef PubMed.
  35. Y. S. Aulchenko, S. Ripatti, I. Lindqvist, D. Boomsma, I. M. Heid, P. P. Pramstaller, B. W. Penninx, A. C. Janssens, J. F. Wilson, T. Spector, N. G. Martin, N. L. Pedersen, K. O. Kyvik, J. Kaprio, A. Hofman, N. B. Freimer, M. R. Jarvelin, U. Gyllensten, H. Campbell, I. Rudan, A. Johansson, F. Marroni, C. Hayward, V. Vitart, I. Jonasson, C. Pattaro, A. Wright, N. Hastie, I. Pichler, A. A. Hicks, M. Falchi, G. Willemsen, J. J. Hottenga, E. J. de Geus, G. W. Montgomery, J. Whitfield, P. Magnusson, J. Saharinen, M. Perola, K. Silander, A. Isaacs, E. J. Sijbrands, A. G. Uitterlinden, J. C. Witteman, B. A. Oostra, P. Elliott, A. Ruokonen, C. Sabatti, C. Gieger, T. Meitinger, F. Kronenberg, A. Doring, H. E. Wichmann, J. H. Smit, M. I. McCarthy, C. M. van Duijn, L. Peltonen and E. Consortium, Nat. Genet., 2009, 41, 47–55 CrossRef CAS PubMed.
  36. E. Ponterio and L. Gnessi, Viruses, 2015, 7, 3719–3740 CrossRef CAS PubMed.
  37. S. Karim, Z. Mirza, M. A. Kamal, A. M. Abuzenadah, E. I. Azhar, M. H. Al-Qahtani and S. S. Sohrab, CNS Neurol. Disord.: Drug Targets, 2014, 13, 429–439 CAS.
  38. L. Galleri, G. Sebastiani, F. Vendrame, F. A. Grieco, I. Spagnuolo and F. Dotta, Adv. Exp. Med. Biol., 2012, 771, 252–271 Search PubMed.
  39. S. A. Nachman, J. C. Lindsey, J. Moye, K. E. Stanley, G. M. Johnson, P. A. Krogstad and A. A. Wiznia, Pediatr. Infect. Dis. J., 2005, 24, 352–357 CrossRef PubMed.
  40. M. L. Newell, M. C. Borja and C. Peckham, Pediatrics, 2003, 111, e52–e60 CrossRef PubMed.
  41. J. W. Deuel, H. U. Lutz, B. Misselwitz and J. S. Goede, Ann. Hematol., 2012, 91, 1427–1434 CrossRef PubMed.
  42. P. Gao, P. Xiao, Y. L. Yang, Q. F. Chen, X. R. Mao, Z. B. Zhao, L. Shi, L. Z. Yang and W. Zhou, Beijing Da Xue Xue Bao, 2014, 46, 941–944 CAS.
  43. V. M. Hubbard and K. Cadwell, Viruses, 2011, 3, 1281–1311 CrossRef PubMed.
  44. M. S. Smith and A. J. Wakefield, Ann. Med., 1993, 25, 557–561 CAS.
  45. C. Global Lipids Genetics, C. J. Willer, E. M. Schmidt, S. Sengupta, G. M. Peloso, S. Gustafsson, S. Kanoni, A. Ganna, J. Chen, M. L. Buchkovich, S. Mora, J. S. Beckmann, J. L. Bragg-Gresham, H. Y. Chang, A. Demirkan, H. M. Den Hertog, R. Do, L. A. Donnelly, G. B. Ehret, T. Esko, M. F. Feitosa, T. Ferreira, K. Fischer, P. Fontanillas, R. M. Fraser, D. F. Freitag, D. Gurdasani, K. Heikkila, E. Hypponen, A. Isaacs, A. U. Jackson, A. Johansson, T. Johnson, M. Kaakinen, J. Kettunen, M. E. Kleber, X. Li, J. Luan, L. P. Lyytikainen, P. K. Magnusson, M. Mangino, E. Mihailov, M. E. Montasser, M. Muller-Nurasyid, I. M. Nolte, J. R. O'Connell, C. D. Palmer, M. Perola, A. K. Petersen, S. Sanna, R. Saxena, S. K. Service, S. Shah, D. Shungin, C. Sidore, C. Song, R. J. Strawbridge, I. Surakka, T. Tanaka, T. M. Teslovich, G. Thorleifsson, E. G. Van den Herik, B. F. Voight, K. A. Volcik, L. L. Waite, A. Wong, Y. Wu, W. Zhang, D. Absher, G. Asiki, I. Barroso, L. F. Been, J. L. Bolton, L. L. Bonnycastle, P. Brambilla, M. S. Burnett, G. Cesana, M. Dimitriou, A. S. Doney, A. Doring, P. Elliott, S. E. Epstein, G. I. Eyjolfsson, B. Gigante, M. O. Goodarzi, H. Grallert, M. L. Gravito, C. J. Groves, G. Hallmans, A. L. Hartikainen, C. Hayward, D. Hernandez, A. A. Hicks, H. Holm, Y. J. Hung, T. Illig, M. R. Jones, P. Kaleebu, J. J. Kastelein, K. T. Khaw, E. Kim, N. Klopp, P. Komulainen, M. Kumari, C. Langenberg, T. Lehtimaki, S. Y. Lin, J. Lindstrom, R. J. Loos, F. Mach, W. L. McArdle, C. Meisinger, B. D. Mitchell, G. Muller, R. Nagaraja, N. Narisu, T. V. Nieminen, R. N. Nsubuga, I. Olafsson, K. K. Ong, A. Palotie, T. Papamarkou, C. Pomilla, A. Pouta, D. J. Rader, M. P. Reilly, P. M. Ridker, F. Rivadeneira, I. Rudan, A. Ruokonen, N. Samani, H. Scharnagl, J. Seeley, K. Silander, A. Stancakova, K. Stirrups, A. J. Swift, L. Tiret, A. G. Uitterlinden, L. J. van Pelt, S. Vedantam, N. Wainwright, C. Wijmenga, S. H. Wild, G. Willemsen, T. Wilsgaard, J. F. Wilson, E. H. Young, J. H. Zhao, L. S. Adair, D. Arveiler, T. L. Assimes, S. Bandinelli, F. Bennett, M. Bochud, B. O. Boehm, D. I. Boomsma, I. B. Borecki, S. R. Bornstein, P. Bovet, M. Burnier, H. Campbell, A. Chakravarti, J. C. Chambers, Y. D. Chen, F. S. Collins, R. S. Cooper, J. Danesh, G. Dedoussis, U. de Faire, A. B. Feranil, J. Ferrieres, L. Ferrucci, N. B. Freimer, C. Gieger, L. C. Groop, V. Gudnason, U. Gyllensten, A. Hamsten, T. B. Harris, A. Hingorani, J. N. Hirschhorn, A. Hofman, G. K. Hovingh, C. A. Hsiung, S. E. Humphries, S. C. Hunt, K. Hveem, C. Iribarren, M. R. Jarvelin, A. Jula, M. Kahonen, J. Kaprio, A. Kesaniemi, M. Kivimaki, J. S. Kooner, P. J. Koudstaal, R. M. Krauss, D. Kuh, J. Kuusisto, K. O. Kyvik, M. Laakso, T. A. Lakka, L. Lind, C. M. Lindgren, N. G. Martin, W. Marz, M. I. McCarthy, C. A. McKenzie, P. Meneton, A. Metspalu, L. Moilanen, A. D. Morris, P. B. Munroe, I. Njolstad, N. L. Pedersen, C. Power, P. P. Pramstaller, J. F. Price, B. M. Psaty, T. Quertermous, R. Rauramaa, D. Saleheen, V. Salomaa, D. K. Sanghera, J. Saramies, P. E. Schwarz, W. H. Sheu, A. R. Shuldiner, A. Siegbahn, T. D. Spector, K. Stefansson, D. P. Strachan, B. O. Tayo, E. Tremoli, J. Tuomilehto, M. Uusitupa, C. M. van Duijn, P. Vollenweider, L. Wallentin, N. J. Wareham, J. B. Whitfield, B. H. Wolffenbuttel, J. M. Ordovas, E. Boerwinkle, C. N. Palmer, U. Thorsteinsdottir, D. I. Chasman, J. I. Rotter, P. W. Franks, S. Ripatti, L. A. Cupples, M. S. Sandhu, S. S. Rich, M. Boehnke, P. Deloukas, S. Kathiresan, K. L. Mohlke, E. Ingelsson and G. R. Abecasis, Nat. Genet., 2013, 45, 1274–1283 CrossRef PubMed.
  46. A. Pirillo, A. L. Catapano and G. D. Norata, Handb. Exp. Pharmacol., 2015, 224, 483–508 CAS.
  47. V. Smelov, D. Bzhalava, L. S. Arroyo Muhr, C. Eklund, B. Komyakov, A. Gorelov, J. Dillner and E. Hultin, Sci. Rep., 2016, 6, 25235 CrossRef CAS PubMed.
  48. S. Delbue, P. Ferrante and M. Provenzano, Oncoscience, 2014, 1, 296–303 Search PubMed.
  49. E. Dimitroulia, V. C. Pitiriga, E. T. Piperaki, N. E. Spanakis and A. Tsakris, Dis. Colon Rectum, 2013, 56, 322–327 CrossRef PubMed.
  50. T. Ali, L. Yun, D. Shapiro, M. F. Madhoun and M. Bronze, Am. J. Med. Sci., 2012, 343, 227–232 CrossRef PubMed.
  51. H. L. Lipton, Z. Liang, S. Hertzler and K. N. Son, Ann. Neurol., 2007, 61, 514–523 CrossRef CAS PubMed.
  52. J. O. Virtanen and S. Jacobson, CNS Neurol. Disord.: Drug Targets, 2012, 11, 528–544 CAS.
  53. A. Chabert, H. Hamzeh-Cognasse, B. Pozzetto, F. Cognasse, M. Schattner, R. M. Gomez and O. Garraud, BMC Immunol., 2015, 16, 26 CrossRef PubMed.
  54. J. K. Kim, J. S. Jeon, J. W. Kim and G. Y. Kim, J. Clin. Lab. Anal., 2016, 30, 185–189 CrossRef PubMed.
  55. A. Assinger, Front. Immunol., 2014, 5, 649 Search PubMed.


Electronic supplementary information (ESI) available. See DOI: 10.1039/c6mb00841k
These authors contributed equally to this work.

This journal is © The Royal Society of Chemistry 2017