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. 2010 Jun 7;4:80.
doi: 10.1186/1752-0509-4-80.

The Biological Context of HIV-1 Host Interactions Reveals Subtle Insights Into a System Hijack

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Free PMC article

The Biological Context of HIV-1 Host Interactions Reveals Subtle Insights Into a System Hijack

Jonathan E Dickerson et al. BMC Syst Biol. .
Free PMC article

Abstract

Background: In order to replicate, HIV, like all viruses, needs to invade a host cell and hijack it for its own use, a process that involves multiple protein interactions between virus and host. The HIV-1, Human Protein Interaction Database available at NCBI's website captures this information from the primary literature, containing over 2,500 unique interactions. We investigate the general properties and biological context of these interactions and, thus, explore the molecular specificity of the HIV-host perturbation. In particular, we investigate (i) whether HIV preferentially interacts with highly connected and 'central' proteins, (ii) known phenotypic properties of host proteins inferred from essentiality and disease-association data, and (iii) biological context (molecular function, processes and location) of the host proteins to identify attributes most strongly associated with specific HIV interactions.

Results: After correcting for ascertainment bias in the literature, we demonstrate a significantly greater propensity for HIV to interact with highly connected and central host proteins. Unexpectedly, we find there are no associations between HIV interaction and inferred essentiality. Similarly, we find a tendency for HIV not to interact with proteins encoded by genes associated with disease. Crucially, we find that functional categories over-represented in HIV-host interactions are innately enriched for highly connected and central proteins in the host system.

Conclusions: Our results imply that HIV's propensity to interact with highly connected and central proteins is a consequence of interactions with particular cellular functions, rather than being a direct effect of network topological properties. The lack of a propensity for interactions with phenotypically essential proteins suggests a selective pressure to minimise virulence in retroviral evolution. Thus, the specificity of HIV-host interactions is complex, and only superficially explained by network properties.

Figures

Figure 1
Figure 1
Subset of the HIV-host interaction network. Red circles and grey squares correspond to HIV-1 (n = 19) and human proteins, respectively (n = 3,118). Human proteins that are hubs (degree ≥23) or bottlenecks (betweenness ≥2.43 × 10-04) are shown as green triangles (n = 69) and yellow triangles (n = 45), respectively. Human proteins that are hubs and bottlenecks are shown as blue triangles (n = 261). Pink edges correspond to interactions between HIV-1 and human proteins (n = 2588) whilst blue edges correspond to human-human interactions (n = 3800), where one of the proteins interacts with HIV-1. HIV-1 proteins are labelled.
Figure 2
Figure 2
A) Degree enrichment amongst HIV and randomised data sets. Distributions of enrichment scores (ES), from gene set enrichment analysis (GSEA), for the degree of 10,000 random samples taken from the population of protein-coding genes, rand(pop) (grey) and 10,000 randomised samples taken to match the publication count distribution of the HIV proteins rand(lit) (purple). The average ES amongst the rand(pop) sample is 0.69 (p-value of 8.90 × 10-48) whilst that of rand(lit) is 0.80 (p-value of 6.63 × 10-15). B) Betweenness enrichment amongst HIV and randomised data sets. Distributions of ES for the betweenness centrality of rand(pop) (grey) and rand(lit) (orange). The ES(betweenness) of the HIV-interacting proteins (red arrow) is 0.90 and the average amongst the rand(pop) sample is 0.84 (p-value of 1.98 × 10-21) whilst that of rand(lit) is 0.88 (p-value of 4.36 × 10-8).
Figure 3
Figure 3
Rejection sampling versus random sampling. Average publication distributions for 10,000 random samples taken from the population of protein-coding genes, rand(pop) (grey) and 10,000 randomised samples taken to match the publication count distribution of the HIV sample, rand(lit) (blue). The rand(lit) samples match the HIV publication distribution with a p-value of 0.43 (chi-squared).
Figure 4
Figure 4
Essentiality and topological properties of the human network. (A) Relationship between protein degree and essentiality. Protein degree and the percentage of essential genes (blue) demonstrate a positive linear relationship (p-value of 2.52 × 10-6, r2 = 0.92). Excluding overlapping disease-associated genes from the essential set (purple) does not alter the relationship (p-value of 9.87 × 10-6 r2 = 0.89). (B) Relationship between protein betweenness centrality and essentiality. As for panel A, but for betweenness centrality. The percentage of essential genes (blue) demonstrate a positive linear relationship with betweenness (p-value of 5.86 × 10-5, r2 = 0.90). Similarly excluding overlapping disease-associated genes from the essential set (orange) does not alter the relationship, (p-value of 6.14 × 10-5, r2 = 0.88). (C) Relationship between publication count and essentiality. Essential genes (green) have an average greater publication count than non-essential genes (grey): 64 to 14 publications, respectively.
Figure 5
Figure 5
Essentiality and HIV-interacting proteins. (A) Visualisation of essential proteins amongst HIV-interacting proteins. Green squares correspond to human proteins identified as essential from mouse knockout data. Black squares and red circles correspond to human and HIV proteins, respectively. Pink edges correspond to interactions between HIV-1 and human proteins, as shown in Figure 4. HIV proteins are labelled accordingly. (B) Number of essential proteins amongst HIV and randomised data sets. Without correcting for bias, rand(pop)contained an average 143 (9.99%) essential proteins, compared to 376 (26.28%) essential proteins in the HIV set (p-value of 3.86 × 10-95). When the bias is corrected for, rand(lit)contains an average 399 (27.88%) essential genes, whilst being under-represented, the result is not significant (p-value of 0.0574) and is hence similar to the HIV-1 interacting sample.
Figure 6
Figure 6
Disease association and topological properties of the human network. (A) Relationship between protein degree and disease-association. Protein degree and the percentage of disease-associated genes (blue) demonstrate a slight positive linear relationship (p-value of 0.02). Excluding overlapping essential genes from the disease-associated set (purple) removes the relationship (p-value of 0.29). (B) Relationship between protein betweenness centrality and disease-association. As for panel A, but for betweenness centrality. The percentage of disease-associated genes (blue) demonstrate a positive linear relationship with betweenness, (p-value of 0.01). Similarly excluding overlapping essential genes from the disease-associated set (orange) removes the relationships, (p-value of 0.45). (C) Relationship between publication count and disease-association. Disease-associated genes (green) have an average greater publication count than non-disease-associated genes (grey), 67 to 16 publications, respectively.
Figure 7
Figure 7
Disease association and HIV-interacting proteins. (A) Visualisation of disease-associated genes amongst HIV-interacting proteins. Blue squares correspond to human proteins identified in OMIM as being disease-associated. Black squares and red circles correspond to human and HIV proteins, respectively. Pink edges correspond to interactions between HIV-1 and human proteins, as shown in Figure 4. HIV proteins are labelled accordingly. (B) Number of disease associated genes amongst HIV and randomised data sets. Without correcting for bias, rand(pop)contained an average 120 (8.39%) disease-associated genes, compared to 244 (17.05%) disease-associated genes in the HIV set (p-value of 8.58 × 10-32). When the bias is corrected for, rand(lit)contains an average 336 (23.48%) disease-associated genes, which is different to the HIV-1 interacting sample (p-value of 3.48 × 10-12).
Figure 8
Figure 8
Protein degree (A) and betweenness centrality (B) for proteins involved in key cellular processes. The degree and betweenness centrality for proteins involved in the key over-represented biological processes GO terms from [4]. Grey dashed lines correspond to the average degree (2.63) and betweenness centrality (2.33 × 10-5) amongst the human-human protein interaction network. The functionally over-represented proteins have a mean degree and betweenness of 7.27 and 7.40 × 10-5, respectively. P-values (from Wilcoxon rank-sum test) < 0.05 are indicated by an asterisk (*) above the data-points, suggesting the degree/betweenness distribution for each GO term is significantly different than that for all GO terms.
Figure 9
Figure 9
Directionality of HIV-host interactions by functional category. The frequency of interaction types classified as positive (green), negative (red) or neutral (blue) according to functional categories over-represented for HIV interactions; see Results for further details.

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