Protein Interaction Networks: Computational Analysis


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Introduction

The chronic inflammatory activity within the CNS is the main giant component of this network has proteins. The component whereby axons and neurons are lost through differences in the average degree and the betweenness dis- unknown processes in the late chronic stages of the dis- tribution between the seed-proteins and their neighbors ease. Several lines of evidence suggest that the degenera- are shown in Table 2. The seed-proteins of the MS-blood tion of demyelinated axons is the most important factor network have a lower average degree and betweenness in MS neurodegeneration [12].

We rial disease in which many immune system and CNS path- assessed whether some of the functional pathways iden- ways are involved [13]. Current therapies partially tified by Gene Ontology GO were overrepresented in ameliorate the inflammatory process, but more effective the gene set corresponding to MS seed-proteins. We found therapeutic approaches are required to stop disease pro- that 36 GO terms were overrepresented in seed proteins gression and prevent neurodegeneration.

The AD network from blood tissue AD-blood contains 20 out of seed-proteins seed-proteins had no links and 76 neighbors. Thus the network has 96 nodes and its giant component has 82 proteins Fig. The seed-proteins of the AD-blood network have a lower aver- age degree than their neighbor proteins Table 2 and we found no GO terms overrepresented in seed proteins when compared to their neighbors after FDR correction [see additional file 3].

Brief Introduction of Protein-Protein Interactions (PPIs)

The AD network from brain tissue AD-brain contains 25 out of 35 seed-proteins 10 seed-proteins had no links and neighbors. Thus the network has nodes and its giant component has 84 proteins Fig. The seed- proteins of the AD-brain network have a lower average degree and betweenness than their neighbor proteins Table 2.

We found 18 GO terms that were overrepre- sented in seed proteins after FDR correction [see addi- tional file 4], terms that were involved in CNS development, oxygen transport or complement activa- tion, among others. As indicated in Tables 1 and 2, we found seed-proteins displayed a lower average degree with respect to the degree of their PPI neighbors in both diseases and in both Figure 1 and representation of each disease network Retrieval tissues.

In addition, direct interactions between seed pro- Retrieval and representation of each disease net- teins were very low: MS-blood: 1 total links: ; MS- work.

Special order items

There were not big differ- array studies. The network in which such proteins certain homogeneity in the architecture of the PPI subnet- were embedded was built by retrieving the first neighbor of works analyzed in this study Table 1. With regards the each protein in the protein-protein interaction database centrality of seed proteins, our study shows a low corre- available at the STRING database. Such pathways not only included terms related neck regions.

Network theory provides a useful tool to study the com- plexity of neurodegenerative diseases. Here we report a The MS network from brain tissue MS-brain contains 38 novel approach to study PPI networks at the meso-scale out of 99 seed-proteins 61 seed-proteins had no links based on the products of genes differentially expressed in and 96 neighbors.

Thus the network has nodes and its MS and AD. Our approach was to analyze PPI networks giant component has proteins Fig. The seed-pro- based on seed-protein neighborhoods from the genes that teins of the MS-brain network have a lower average degree were differentially expressed in DNA array studies. The than the neighbor proteins Table 2 , and we found 67 method for growing networks from seed-proteins is criti- GO terms overrepresented in seed proteins after FDR cor- cal for determining their topological properties [19].

For rection [see additional file 2]. Again, overrepresented this reason, the network growth in our study was carried pathways not only included components of the immune out by expanding it through experimentally validated pro- tein interactions. Purple nodes indicate the seed-proteins with their name. Orange nodes indicate neighboring proteins belonging to the giant component. Green nodes indicate neighbors that do not belong to the giant component. The graphs were built using Pajek software and the network files are available as.

In terms of iden- the topology of the map, i.

For example, net- neurodegenerative disorders, very interesting results were works with a scale-free topology are resistant to random obtained by carrying out a topological analysis. There failure but they are vulnerable to targeted attack, specifi- Table 1: Network measurements for the four disease networks. Non-zero degree and betweenness point, since we analyzed whether degree was any different were calculated after excluding the non-connected non-zero nodes.

Accordingly, we found that the degree of seed-proteins was lower than were multiple pathways affected by proteins with a low that of the PPI neighbors, situating seed proteins in degree, and half the time with high betweenness. According to our results of the GO analysis, such peripheral regions are distributed During the last decade, network studies have been applied among several pathways that could be involved in disease.

Orange nodes indicate neighbors proteins belonging to the giant component. Green nodes indicate neighbors that are not included in the giant component. Therefore our results support the applica- expressed but not necessarily correlated have a particu- tion of strategies other than those previously applied, lar distribution with regards their neighbors neighbors whereby only hubs that might compromise the robust- found in a database that includes structural and experi- ness of networks were generally searched [25,26].

Original Research ARTICLE

The fact that we obtained similar results with regards the We can consider complex diseases as an evolutionary low average degree of seed proteins in two diseases and stage in which the pathogenesis process hijacks the two different tissues suggests that this might be a common robustness of the biological pathways.

Such an event may property in complex diseases, more relevant than the be followed by a cascade of failures in these pathways issues associated with the techniques applied such as DNA [8,27]. In this sense and from a therapeutic point of view, array technology. Although it The aim of this therapy would be to drive those pathways is more difficult to relate gene expression data from hubs to a non-pathological state or at least, to a less deleterious with that of other genes, this would not bias our analysis state. The topological implications of the observed scale-free work [29,30].

Another reason why hubs might not be properties in biological networks would indicate that the good therapeutic targets is because their critical role in the best therapeutic targets to modify network behavior network modules might prevent them from fluctuating would be the genes or proteins corresponding to the substantially. For the same reason, we can speculate that hubs in the network.

However, our findings suggest that networks would poorly tolerate modifications in hub less extensively connected proteins might be more appro- behavior without spreading such changes across the net- priate therapeutic targets than hyper-connected ones, at work and thereby, inducing significant side effects. The fact that in both diseases MS and AD and in two different tissues ana- The results we present here indicate that both neurode- lyzed blood and cerebral tissue , seed-proteins are generative diseases MS and AD share common charac- weakly connected nodes taking part in many different teristics, such as the low degree of seed-proteins and in pathways, strengthens the concept of the multifactorial two of the four disease networks, a high degree of pathogenesis of neurodegenerative diseases.

Thus, our betweenness. These findings mainly situate seed-proteins results suggest that to modify the disease course we need in peripheral regions of the PPI map in terms of degree , to target many genes or proteins in several pathways. In a involved in different pathways as indicated by the associ- previous network analysis in MS we demonstrated that ated GO terms and the direct interactions, and integrated therapies act on different regions of the gene network that into subnetworks of the complete Human proteome net- control T-cell activation, suggesting that a pleiotropic work.

It is important to note that we expressed in DNA array studies focused on the specific dis- did not consider neighbors as newly proposed proteins ease and on a particular tissue. In this study, the diseases implicated in the disease but rather, they were taken sim- considered are Multiple Sclerosis MS and Alzheimer ply to capture the network context in which seed-proteins Disease AD and the tissues are blood and brain.

Only seed-pro- Network modeling teins linked to neighboring proteins were included in the Starting from seed-proteins involved in either MS or AD, network analysis isolated seed-proteins were not we obtained a PPI network through the interaction of included in the analysis shown in Table 1. A general scheme of the approach adopted here is presented in Fig- -MS-brain seed-proteins: proteins whose genes were differ- ure 1. This configura- from seed-proteins.

A teins, MS-neighbors and their interactions. We did not consider either the direction of -AD-blood seed-proteins: proteins whose genes were differ- each protein interaction or self-interactions.

Computational analysis of protein-protein interactions: From sequences to networks

In this study, it represents entially expressed in DNA array studies of brain tissue the number of experimentally validated interactions from AD patients [34]. Table s2 ms-brain. Additional file 3 Table s3 ad-blood. Additional file 4 Table s4 ad-brain. A FDR multiple hypothesis test adjustment was further carried out using the Benjamini- Additional file 6 Hochberg BH procedure [37] and taking the total msbrain. JG, PV. New York , W. Freeman and Company; Villoslada P, Oksenberg J: Neuroinformatics in clinical practice: Kitano H: Biological robustness.

Nat Rev Genet , are computers going to help neurological patients and their 5 11 Future Neurology , 1 2 Ann Rev Biochem , 73 :. Cell , :. Rhodes DR, Chinnaiyan AM: Integrative analysis of the cancer interaction network for human inherited ataxias and disor- transcriptome. Nat Genet , 37 Suppl :SS Cell , 4 Bioinformatics , molecular machines involved in Caenorhabditis elegans 23 16 Several lines of evidence suggest that the degeneration of demyelinated axons is the most important factor in MS neurodegeneration [ 12 ].

Computational Analysis of the Chaperone Interaction Networks. - Abstract - Europe PMC

Thus, MS is a multifactorial disease in which many immune system and CNS pathways are involved [ 13 ]. Current therapies partially ameliorate the inflammatory process, but more effective therapeutic approaches are required to stop disease progression and prevent neurodegeneration. Alzheimer's Disease AD is the most common neurodegenerative disease and it represents one of the biggest unmet needs in modern medicine.

AD is characterized by the loss of neurons in conjunction with the presence of oxidative stress, axonal dystrophy, mature senile plaques and neurofibrillary tangles [ 14 ]. A set of gene mutations involved in the amyloid beta and tau pathways have been associated with hereditary AD and, in conjunction with neuropathological findings, it has been demonstrated that amyloid and tau are involved in the pathogenesis of AD. However, current evidence suggests that sporadic AD is a multifactorial disease in which many pathways are involved [ 15 , 16 ]. Indeed, recent studies have also identified molecular abnormalities in the blood of patients with AD [ 17 ].

Because the AD therapies available are symptomatic, and considering the epidemic proportions of this disease in western countries, the development of new therapies to stop its progress is an important health priority. To better understand the basis of neurodegenerative diseases, we set out to study the centrality related features of proteins whose genes were differentially expressed seed proteins in MS and AD with respect to their protein neighbors.

The main features examined were the degree and the betweenness of these seed proteins and its comparison to their neighbors. Retrieval and representation of each disease network. The corresponding protein seed-protein for each differentially expressed gene was identified in public databases STRING. The network in which such proteins were embedded was built by retrieving the first neighbor of each protein in the protein-protein interaction database available at the STRING database.

The MS network from blood tissue MS-blood contains 28 out of the 42 seed-proteins and neighbors were derived. The 14 seed-proteins that had no links i. The giant component of this network has proteins. Accordingly, we studied the measurements listed in Table 1 in a network with nodes Fig. The differences in the average degree and the betweenness distribution between the seed-proteins and their neighbors are shown in Table 2. We assessed whether some of the functional pathways identified by Gene Ontology GO were overrepresented in the gene set corresponding to MS seed-proteins.

We found that 36 GO terms were overrepresented in seed proteins after false discovery rate FDR correction [see additional file 1 ]. Such pathways not only included terms related with the activity of the immune system but also with many other cellular process, such as metabolic process, protein degradation and the response to stress. MS-blood network. Purple nodes indicate the seed-proteins with their name. Orange nodes indicate neighboring proteins belonging to the giant component. Green nodes indicate neighbors that do not belong to the giant component.

The graphs were built using Pajek software and the network files are available as. The MS network from brain tissue MS-brain contains 38 out of 99 seed-proteins 61 seed-proteins had no links and 96 neighbors. Thus the network has nodes and its giant component has proteins Fig. The seed-proteins of the MS-brain network have a lower average degree than the neighbor proteins Table 2 , and we found 67 GO terms overrepresented in seed proteins after FDR correction [see additional file 2 ]. Again, overrepresented pathways not only included components of the immune response but also those involved in synaptic transmission, neurogenesis and neuron differentiation, among others.

MS-brain network. Orange nodes indicate neighbors proteins belonging to the giant component. Green nodes indicate neighbors that are not included in the giant component. The AD network from blood tissue AD-blood contains 20 out of seed-proteins seed-proteins had no links and 76 neighbors. Thus the network has 96 nodes and its giant component has 82 proteins Fig. The seed-proteins of the AD-blood network have a lower average degree than their neighbor proteins Table 2 and we found no GO terms overrepresented in seed proteins when compared to their neighbors after FDR correction [see additional file 3 ].

AD-blood network. The AD network from brain tissue AD-brain contains 25 out of 35 seed-proteins 10 seed-proteins had no links and neighbors. Thus the network has nodes and its giant component has 84 proteins Fig. The seed-proteins of the AD-brain network have a lower average degree and betweenness than their neighbor proteins Table 2. We found 18 GO terms that were overrepresented in seed proteins after FDR correction [see additional file 4 ], terms that were involved in CNS development, oxygen transport or complement activation, among others.

AD-brain network. As indicated in Tables 1 and 2 , we found seed-proteins displayed a lower average degree with respect to the degree of their PPI neighbors in both diseases and in both tissues. In addition, direct interactions between seed proteins were very low: MS-blood: 1 total links: ; MS-brain: 4 total links: ; AD-blood: 2 total links: ; AD-brain: 2 total links: With regards the centrality of seed proteins, our study shows a low correspondence between their degree and betweenness Table 2 , indicating that critical proteins in disease pathogenesis are not highly connected, but tend to be located in bottleneck regions.

Network theory provides a useful tool to study the complexity of neurodegenerative diseases. Here we report a novel approach to study PPI networks at the meso-scale based on the products of genes differentially expressed in MS and AD. Our approach was to analyze PPI networks based on seed-protein neighborhoods from the genes that were differentially expressed in DNA array studies. The method for growing networks from seed-proteins is critical for determining their topological properties [ 19 ]. For this reason, the network growth in our study was carried out by expanding it through experimentally validated protein interactions.

The stability, dynamics and functioning of networks are generally characterized by determining the topology of the map, i. For example, networks with a scale-free topology are resistant to random failure but they are vulnerable to targeted attack, specifically against the most connected nodes. In terms of identifying common properties among the genes involved in neurodegenerative disorders, very interesting results were obtained by carrying out a topological analysis. There were multiple pathways affected by proteins with a low degree, and half the time with high betweenness.

During the last decade, network studies have been applied to biological data bearing in mind that the degree of connectivity is a key property of any network, as demonstrated in yeast [ 21 ]. The most common approach to identify key nodes consists of obtaining networks from high throughput data and having obtained the network, searching for the most connected nodes hubs. The underlying assumption was that these hubs could be critical to explain the pathogenesis of diseases.

However, betweenness is another key indicator of centrality that demonstrates how nodes with a low degree of centrality may be relevant in a network i. Our study was performed from a novel viewpoint, since we analyzed whether degree was any different respect to the PPI neighbors starting from critical nodes in terms of differentially expressed genes. Accordingly, we found that the degree of seed-proteins was lower than that of the PPI neighbors, situating seed proteins in peripheral regions of the network. According to our results of the GO analysis, such peripheral regions are distributed among several pathways that could be involved in disease.

Indeed, our results are in agreement with a recent study in asthma showing that hubs exhibit small changes in gene expression [ 24 ]. Therefore our results support the application of strategies other than those previously applied, whereby only hubs that might compromise the robustness of networks were generally searched [ 25 , 26 ].

The fact that we obtained similar results with regards the low average degree of seed proteins in two diseases and two different tissues suggests that this might be a common property in complex diseases, more relevant than the issues associated with the techniques applied such as DNA array technology. Although it is more difficult to relate gene expression data from hubs with that of other genes, this would not bias our analysis since we focused on whether genes that are differentially expressed but not necessarily correlated have a particular distribution with regards their neighbors neighbors found in a database that includes structural and experimental evidence and not correlation profiles.

We can consider complex diseases as an evolutionary stage in which the pathogenesis process hijacks the robustness of the biological pathways. Such an event may be followed by a cascade of failures in these pathways [ 8 , 27 ]. In this sense and from a therapeutic point of view, it may be necessary to target many of the pathways involved following a systems biology rationale, and based on the dynamics and topology of the networks involved. The aim of this therapy would be to drive those pathways to a non-pathological state or at least, to a less deleterious state.

The topological implications of the observed scale-free properties in biological networks would indicate that the best therapeutic targets to modify network behavior would be the genes or proteins corresponding to the hubs in the network. However, our findings suggest that less extensively connected proteins might be more appropriate therapeutic targets than hyper-connected ones, at least in neurodegenerative diseases.

The fact that in both diseases MS and AD and in two different tissues analyzed blood and cerebral tissue , seed-proteins are weakly connected nodes taking part in many different pathways, strengthens the concept of the multifactorial pathogenesis of neurodegenerative diseases. Thus, our results suggest that to modify the disease course we need to target many genes or proteins in several pathways.

In a previous network analysis in MS we demonstrated that therapies act on different regions of the gene network that control T-cell activation, suggesting that a pleiotropic activity is required in order to modulate the immune response [ 28 ]. In addition, recent network studies in neurodegenerative diseases suggest that several common pathways are involved in their pathogenesis, reinforcing the need to interact with several regions of the PPI network [ 29 , 30 ]. Another reason why hubs might not be good therapeutic targets is because their critical role in the network modules might prevent them from fluctuating substantially.

For the same reason, we can speculate that networks would poorly tolerate modifications in hub behavior without spreading such changes across the network and thereby, inducing significant side effects. The results we present here indicate that both neurodegenerative diseases MS and AD share common characteristics, such as the low degree of seed-proteins and in two of the four disease networks, a high degree of betweenness.

These findings mainly situate seed-proteins in peripheral regions of the PPI map in terms of degree , involved in different pathways as indicated by the associated GO terms and the direct interactions, and integrated into subnetworks of the complete Human proteome network. Only seed-proteins linked to neighboring proteins were included in the network analysis isolated seed-proteins were not included in the analysis shown in Table 1.

It is important to note that we did not consider neighbors as newly proposed proteins implicated in the disease but rather, they were taken simply to capture the network context in which seed-proteins are located. For the construction and analysis of the MS and AD networks, we selected seed proteins from previously published studies in blood [ 31 , 33 ] and brain [ 32 , 34 ]. Starting from seed-proteins involved in either MS or AD, we obtained a PPI network through the interaction of these proteins with their direct neighbors.


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A general scheme of the approach adopted here is presented in Figure 1. This configuration implies that only the experimental evidence of interactions with a high level of confidence were extracted from the database as valid links for each PPI network. A detailed description of each parameter can be found elsewhere [ 18 ]. We did not consider either the direction of each protein interaction or self-interactions. Network files in Pajek format. In this study, it represents the number of experimentally validated interactions links that connect one protein node to its neighbors.

When combined with the degree, it is a key measure to assess the relevance of the location of nodes within a network vertices within a graph.

Introduction

Gene symbol identities corresponding to the four different lists of seed-proteins were loaded into the ExPlainTm 2. A FDR multiple hypothesis test adjustment was further carried out using the Benjamini-Hochberg BH procedure [ 37 ] and taking the total number of GO-BP as those in which at least one protein of the seed-protein list is included. We used the Kolmogorov-Smirnov test to compare the distributions of degree and betweenness between seed-proteins and neighbors for each disease. Freeman and Company, 4th. Villoslada P, Oksenberg J: Neuroinformatics in clinical practice: are computers going to help neurological patients and their physicians?

Protein Interaction Networks: Computational Analysis Protein Interaction Networks: Computational Analysis
Protein Interaction Networks: Computational Analysis Protein Interaction Networks: Computational Analysis
Protein Interaction Networks: Computational Analysis Protein Interaction Networks: Computational Analysis
Protein Interaction Networks: Computational Analysis Protein Interaction Networks: Computational Analysis
Protein Interaction Networks: Computational Analysis Protein Interaction Networks: Computational Analysis
Protein Interaction Networks: Computational Analysis Protein Interaction Networks: Computational Analysis
Protein Interaction Networks: Computational Analysis Protein Interaction Networks: Computational Analysis
Protein Interaction Networks: Computational Analysis Protein Interaction Networks: Computational Analysis

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