Limits...
When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases.

Navratil V, de Chassey B, Combe CR, Lotteau V - BMC Syst Biol (2011)

Bottom Line: This in silico model was applied to comprehensively explore the molecular relationships between viruses and their associated diseases.The high centrality of targeted proteins was correlated to their essentiality for viruses' lifecycle, using functional genomic RNAi data.Networking the Human Infectome and Diseasome unravels the connectivity of viruses to a wide range of diseases and profiled molecular basis of Hepatitis C Virus-induced diseases as well as 38 new candidate genetic predisposition factors involved in type 1 diabetes mellitus.

View Article: PubMed Central - HTML - PubMed

Affiliation: Université de Lyon, IFR128 BioSciences Lyon-Gerland, Lyon 69007, France. navratil@prabi.fr

ABSTRACT

Background: Comprehensive understanding of molecular mechanisms underlying viral infection is a major challenge towards the discovery of new antiviral drugs and susceptibility factors of human diseases. New advances in the field are expected from systems-level modelling and integration of the incessant torrent of high-throughput "-omics" data.

Results: Here, we describe the Human Infectome protein interaction Network, a novel systems virology model of a virtual virus-infected human cell concerning 110 viruses. This in silico model was applied to comprehensively explore the molecular relationships between viruses and their associated diseases. This was done by merging virus-host and host-host physical protein-protein interactomes with the set of genes essential for viral replication and involved in human genetic diseases. This systems-level approach provides strong evidence that viral proteomes target a wide range of functional and inter-connected modules of proteins as well as highly central and bridging proteins within the human interactome. The high centrality of targeted proteins was correlated to their essentiality for viruses' lifecycle, using functional genomic RNAi data. A stealth-attack of viruses on proteins bridging cellular functions was demonstrated by simulation of cellular network perturbations, a property that could be essential in the molecular aetiology of some human diseases. Networking the Human Infectome and Diseasome unravels the connectivity of viruses to a wide range of diseases and profiled molecular basis of Hepatitis C Virus-induced diseases as well as 38 new candidate genetic predisposition factors involved in type 1 diabetes mellitus.

Conclusions: The Human Infectome and Diseasome Networks described here provide a unique gateway towards the comprehensive modelling and analysis of the systems level properties associated to viral infection as well as candidate genes potentially involved in the molecular aetiology of human diseases.

Show MeSH

Related in: MedlinePlus

Topological properties of the Human Infectome Network . a. Topological properties of virus-targeted proteins. The average connectivity (top), centrality (middle) and bridging centrality (bottom) properties of targeted proteins (red bars) are compared to that of HPIN proteins not targeted by any virus (blue bars). Average measures are split into low connectivity proteins (LD - with a connectivity inferior or equal to 5, the median threshold) and high-connectivity proteins (HD - with connectivity superior to 5). Differences were statistically assessed using one-tailed Wilcoxon test; P-Values associated to the test are given (NS - Non significant testing P-Value > 0.05; P-Value < 0.05 *; P-Value < 0.01 **). b. The modular landscape of the Human Infectome Network. The deconvolution of HIN using the CFinder algorithm identified interconnected modules of proteins (nodes) and modules' linkers (available in an interactive format in Additional file 1). Protein modules and linkers are coloured according to the intensity of the viral attack. Highly targeted modules or linkers are red. Poorly targeted modules or linkers are black. Biological processes and molecular functions associated to highly targeted modules are pinpointed (one-tailed Exact Fisher test; Benjamini and Hochberg multiple testing correction; P-Value < 0.05 red arrows - P-Value < 0.15 grey arrows). c. Simulation of network robustness against preferential viral attack on central and bridging proteins. The figure represents the fragmentation of the entire human protein interaction network (HPIN) according to random or preferential attack according to network properties. The fragmentation is obtained by computing the relative size of the largest connected component (S) as a function of the percentage of removed nodes (f). Nodes removal is performed either randomly (black) or as a preferential attack mode where protein nodes are eliminated from the network in decreasing order of their connectivity (blue), centrality (red) and bridging (pink) values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3037315&req=5

Figure 2: Topological properties of the Human Infectome Network . a. Topological properties of virus-targeted proteins. The average connectivity (top), centrality (middle) and bridging centrality (bottom) properties of targeted proteins (red bars) are compared to that of HPIN proteins not targeted by any virus (blue bars). Average measures are split into low connectivity proteins (LD - with a connectivity inferior or equal to 5, the median threshold) and high-connectivity proteins (HD - with connectivity superior to 5). Differences were statistically assessed using one-tailed Wilcoxon test; P-Values associated to the test are given (NS - Non significant testing P-Value > 0.05; P-Value < 0.05 *; P-Value < 0.01 **). b. The modular landscape of the Human Infectome Network. The deconvolution of HIN using the CFinder algorithm identified interconnected modules of proteins (nodes) and modules' linkers (available in an interactive format in Additional file 1). Protein modules and linkers are coloured according to the intensity of the viral attack. Highly targeted modules or linkers are red. Poorly targeted modules or linkers are black. Biological processes and molecular functions associated to highly targeted modules are pinpointed (one-tailed Exact Fisher test; Benjamini and Hochberg multiple testing correction; P-Value < 0.05 red arrows - P-Value < 0.15 grey arrows). c. Simulation of network robustness against preferential viral attack on central and bridging proteins. The figure represents the fragmentation of the entire human protein interaction network (HPIN) according to random or preferential attack according to network properties. The fragmentation is obtained by computing the relative size of the largest connected component (S) as a function of the percentage of removed nodes (f). Nodes removal is performed either randomly (black) or as a preferential attack mode where protein nodes are eliminated from the network in decreasing order of their connectivity (blue), centrality (red) and bridging (pink) values.

Mentions: Figure 2A shows that viral proteins preferentially interact with highly connected proteins (mean kh(TP) = 38 versus mean kh(NTP) = 10; one-tailed Wilcoxon test; P-value < 2 × 10-16; see Additional file 5 for the full distributions), central proteins (mean bh(TP) = 5.34 × 10-04 versus mean bh(NTP) = 8.23 × 10-05; one-tailed Wilcoxon test; P-value < 2 × 10-16; see Additional file 5 for the full distributions) and closely inter-connected cellular proteins (data not shown, mean splh(TP) = 2.91 versus mean splh(NTP) = 3.66; one-tailed Wilcoxon test; P-value < 2 × 10-16). These statistically significant trends validate, at a larger scale, previous analysis of experimental virus-host protein interaction networks [1,2]. We next applied a cross-validation procedure based on a high-quality protein-protein interactions dataset (see Additional file 3) to control the potential effect of false positive protein-protein interactions [13]. All the observed trends remained significant, highlighting the robustness of our analysis against false positive bias. This study also shows that cellular proteins targeted by multiple viruses present a distribution shift toward highly connected and central position within the cellular network as compared to proteins targeted by only one viral protein (mean degree = 57 versus mean degree = 29, one-tailed Wilcoxon test; P-value < 2.2 × 0-16). A similar shift in the distribution was observed at the taxonomic level for viral species (mean degree 62 versus 28), family (mean degree 66 versus 29) and Baltimore's group (mean degree 68 versus 30), confirming previous observations [14]. Although we cannot totally rule out the impact of potential experimental bias (i.e. inspection bias due to Y2H screening technology or false positives) until the availability of comprehensive, totally unbiased and high-quality human and virus-host interactomes, it remains that this network-based analysis provides additional support to the hypothesis that viruses have evolved convergent strategies measurable at the proteome-wide level to control highly connected and central proteins in order to subvert essential functions of the cell.


When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases.

Navratil V, de Chassey B, Combe CR, Lotteau V - BMC Syst Biol (2011)

Topological properties of the Human Infectome Network . a. Topological properties of virus-targeted proteins. The average connectivity (top), centrality (middle) and bridging centrality (bottom) properties of targeted proteins (red bars) are compared to that of HPIN proteins not targeted by any virus (blue bars). Average measures are split into low connectivity proteins (LD - with a connectivity inferior or equal to 5, the median threshold) and high-connectivity proteins (HD - with connectivity superior to 5). Differences were statistically assessed using one-tailed Wilcoxon test; P-Values associated to the test are given (NS - Non significant testing P-Value > 0.05; P-Value < 0.05 *; P-Value < 0.01 **). b. The modular landscape of the Human Infectome Network. The deconvolution of HIN using the CFinder algorithm identified interconnected modules of proteins (nodes) and modules' linkers (available in an interactive format in Additional file 1). Protein modules and linkers are coloured according to the intensity of the viral attack. Highly targeted modules or linkers are red. Poorly targeted modules or linkers are black. Biological processes and molecular functions associated to highly targeted modules are pinpointed (one-tailed Exact Fisher test; Benjamini and Hochberg multiple testing correction; P-Value < 0.05 red arrows - P-Value < 0.15 grey arrows). c. Simulation of network robustness against preferential viral attack on central and bridging proteins. The figure represents the fragmentation of the entire human protein interaction network (HPIN) according to random or preferential attack according to network properties. The fragmentation is obtained by computing the relative size of the largest connected component (S) as a function of the percentage of removed nodes (f). Nodes removal is performed either randomly (black) or as a preferential attack mode where protein nodes are eliminated from the network in decreasing order of their connectivity (blue), centrality (red) and bridging (pink) values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3037315&req=5

Figure 2: Topological properties of the Human Infectome Network . a. Topological properties of virus-targeted proteins. The average connectivity (top), centrality (middle) and bridging centrality (bottom) properties of targeted proteins (red bars) are compared to that of HPIN proteins not targeted by any virus (blue bars). Average measures are split into low connectivity proteins (LD - with a connectivity inferior or equal to 5, the median threshold) and high-connectivity proteins (HD - with connectivity superior to 5). Differences were statistically assessed using one-tailed Wilcoxon test; P-Values associated to the test are given (NS - Non significant testing P-Value > 0.05; P-Value < 0.05 *; P-Value < 0.01 **). b. The modular landscape of the Human Infectome Network. The deconvolution of HIN using the CFinder algorithm identified interconnected modules of proteins (nodes) and modules' linkers (available in an interactive format in Additional file 1). Protein modules and linkers are coloured according to the intensity of the viral attack. Highly targeted modules or linkers are red. Poorly targeted modules or linkers are black. Biological processes and molecular functions associated to highly targeted modules are pinpointed (one-tailed Exact Fisher test; Benjamini and Hochberg multiple testing correction; P-Value < 0.05 red arrows - P-Value < 0.15 grey arrows). c. Simulation of network robustness against preferential viral attack on central and bridging proteins. The figure represents the fragmentation of the entire human protein interaction network (HPIN) according to random or preferential attack according to network properties. The fragmentation is obtained by computing the relative size of the largest connected component (S) as a function of the percentage of removed nodes (f). Nodes removal is performed either randomly (black) or as a preferential attack mode where protein nodes are eliminated from the network in decreasing order of their connectivity (blue), centrality (red) and bridging (pink) values.
Mentions: Figure 2A shows that viral proteins preferentially interact with highly connected proteins (mean kh(TP) = 38 versus mean kh(NTP) = 10; one-tailed Wilcoxon test; P-value < 2 × 10-16; see Additional file 5 for the full distributions), central proteins (mean bh(TP) = 5.34 × 10-04 versus mean bh(NTP) = 8.23 × 10-05; one-tailed Wilcoxon test; P-value < 2 × 10-16; see Additional file 5 for the full distributions) and closely inter-connected cellular proteins (data not shown, mean splh(TP) = 2.91 versus mean splh(NTP) = 3.66; one-tailed Wilcoxon test; P-value < 2 × 10-16). These statistically significant trends validate, at a larger scale, previous analysis of experimental virus-host protein interaction networks [1,2]. We next applied a cross-validation procedure based on a high-quality protein-protein interactions dataset (see Additional file 3) to control the potential effect of false positive protein-protein interactions [13]. All the observed trends remained significant, highlighting the robustness of our analysis against false positive bias. This study also shows that cellular proteins targeted by multiple viruses present a distribution shift toward highly connected and central position within the cellular network as compared to proteins targeted by only one viral protein (mean degree = 57 versus mean degree = 29, one-tailed Wilcoxon test; P-value < 2.2 × 0-16). A similar shift in the distribution was observed at the taxonomic level for viral species (mean degree 62 versus 28), family (mean degree 66 versus 29) and Baltimore's group (mean degree 68 versus 30), confirming previous observations [14]. Although we cannot totally rule out the impact of potential experimental bias (i.e. inspection bias due to Y2H screening technology or false positives) until the availability of comprehensive, totally unbiased and high-quality human and virus-host interactomes, it remains that this network-based analysis provides additional support to the hypothesis that viruses have evolved convergent strategies measurable at the proteome-wide level to control highly connected and central proteins in order to subvert essential functions of the cell.

Bottom Line: This in silico model was applied to comprehensively explore the molecular relationships between viruses and their associated diseases.The high centrality of targeted proteins was correlated to their essentiality for viruses' lifecycle, using functional genomic RNAi data.Networking the Human Infectome and Diseasome unravels the connectivity of viruses to a wide range of diseases and profiled molecular basis of Hepatitis C Virus-induced diseases as well as 38 new candidate genetic predisposition factors involved in type 1 diabetes mellitus.

View Article: PubMed Central - HTML - PubMed

Affiliation: Université de Lyon, IFR128 BioSciences Lyon-Gerland, Lyon 69007, France. navratil@prabi.fr

ABSTRACT

Background: Comprehensive understanding of molecular mechanisms underlying viral infection is a major challenge towards the discovery of new antiviral drugs and susceptibility factors of human diseases. New advances in the field are expected from systems-level modelling and integration of the incessant torrent of high-throughput "-omics" data.

Results: Here, we describe the Human Infectome protein interaction Network, a novel systems virology model of a virtual virus-infected human cell concerning 110 viruses. This in silico model was applied to comprehensively explore the molecular relationships between viruses and their associated diseases. This was done by merging virus-host and host-host physical protein-protein interactomes with the set of genes essential for viral replication and involved in human genetic diseases. This systems-level approach provides strong evidence that viral proteomes target a wide range of functional and inter-connected modules of proteins as well as highly central and bridging proteins within the human interactome. The high centrality of targeted proteins was correlated to their essentiality for viruses' lifecycle, using functional genomic RNAi data. A stealth-attack of viruses on proteins bridging cellular functions was demonstrated by simulation of cellular network perturbations, a property that could be essential in the molecular aetiology of some human diseases. Networking the Human Infectome and Diseasome unravels the connectivity of viruses to a wide range of diseases and profiled molecular basis of Hepatitis C Virus-induced diseases as well as 38 new candidate genetic predisposition factors involved in type 1 diabetes mellitus.

Conclusions: The Human Infectome and Diseasome Networks described here provide a unique gateway towards the comprehensive modelling and analysis of the systems level properties associated to viral infection as well as candidate genes potentially involved in the molecular aetiology of human diseases.

Show MeSH
Related in: MedlinePlus