Hubs with Network Motifs Organize Modularity Dynamically in the Protein-Protein Interaction Network of Yeast

Abstract

Background

It has been recognized that modular organization pervades biological complexity. Based on network analysis, ‘party hubs’ and ‘date hubs’ were proposed to understand the basic principle of module organization of biomolecular networks. However, recent study on hubs has suggested that there is no clear evidence for coexistence of ‘party hubs’ and ‘date hubs’. Thus, an open question has been raised as to whether or not ‘party hubs’ and ‘date hubs’ truly exist in yeast interactome.

Methodology

In contrast to previous studies focusing on the partners of a hub or the individual proteins around the hub, our work aims to study the network motifs of a hub or interactions among individual proteins including the hub and its neighbors. Depending on the relationship between a hub's network motifs and protein complexes, we define two new types of hubs, ‘motif party hubs’ and ‘motif date hubs’, which have the same characteristics as the original ‘party hubs’ and ‘date hubs’ respectively. The network motifs of these two types of hubs display significantly different features in spatial distribution (or cellular localizations), co-expression in microarray data, controlling topological structure of network, and organizing modularity.

Conclusion

By virtue of network motifs, we basically solved the open question about ‘party hubs’ and ‘date hubs’ which was raised by previous studies. Specifically, at the level of network motifs instead of individual proteins, we found two types of hubs, motif party hubs (mPHs) and motif date hubs (mDHs), whose network motifs display distinct characteristics on biological functions. In addition, in this paper we studied network motifs from a different viewpoint. That is, we show that a network motif should not be merely considered as an interaction pattern but be considered as an essential function unit in organizing modules of networks.

About the Authors:

Guangxu Jin

Affiliations Academy of Mathematics and Systems Science, Chinese Academy of Science, Beijing, China, Graduate University of Chinese Academy of Sciences, Beijing, China

Shihua Zhang

Affiliations Academy of Mathematics and Systems Science, Chinese Academy of Science, Beijing, China, Graduate University of Chinese Academy of Sciences, Beijing, China

Xiang-Sun Zhang

*To whom correspondence should be addressed. E-mail: [email protected] (XZ); [email protected] (LC)

Affiliation: Academy of Mathematics and Systems Science, Chinese Academy of Science, Beijing, China

Luonan Chen

*To whom correspondence should be addressed. E-mail: [email protected] (XZ); [email protected] (LC)

Affiliations Institute of Systems Biology, Shanghai University, Shanghai, China, Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka, Japan, Exploratory Research for Advanced Technology (ERATO) Aihara Complexity Modelling Project, Japan Science and Technology Corporation (JST), Tokyo, Japan, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

Introduction

Many types of molecular networks display scale-free topologies which are characterized by the power-law degree distribution [1]–[5]. In spite of some negative remarks [6]–[9] on the studies of network structures, a small fraction of proteins generally interacting with many partners, i.e. so-called hubs, have attracted great interests [10]–[15] from the communities of both engineering and biology. To identify whether hubs vary their biological roles with the timing and location of the interactions, Han et al. proposed two types of hubs, i.e. ‘party hubs’ and ‘date hubs’, based on whether or not the hubs are co-expressed with their partners by using yeast microarray data [10]. The two distinct types of hubs not only display diverse spatial distribution for their partners but also organize the modules in different manners, where a module is referred as a group of physically or functionally linked molecules that work together to achieve a relatively distinct function [10], [16].

It should be noticed that the result of Han et al. on ‘party hub’ and ‘date hub’ was drawn from a filtered yeast interactome data (FYI). Recently, Batada et al. derived different results, in contrast to those of Han et al., based on another filtered yeast interactome data (HCfyi) manually curated from online publications [11] (see Materials and Methods). Due to the topological difference between FYI and HCfyi, Batada et al. found that there is no evidence for coexistence of party hubs and date hubs, and the results about ‘party hubs’ and ‘date hubs’ are totally not correct. Thus, the most striking question raised by them is whether or not the ‘party hubs’ and ‘date hubs’ truly exist in the networks.

In this paper, we aim to solve the contradiction between the two previous works. In virtue of network motifs, we define two new types of hubs in HCfyi, i.e. ‘motif party hub’ and ‘motif date hub’, which have the same characteristics as ‘party hub’ and ‘date hub’ respectively. Network motifs are the subgraphs that occur significantly more frequently in original network than random ones [17]–[22]. They have been revealed as the functional building blocks of biology networks [17], [18] and the spandrels of cellular complexity [23]. Similar to a previous research work on the role of network motifs in information processing [24], we focus on their important roles in acting as functional units in organizing modules. Moreover, in contrast to the previous studies on hubs, our work emphasizes on interactions of hubs and network motifs instead of individual proteins around hubs.

Specifically, we divide hubs into ‘motif party hubs’ (mPHs) and ‘motif date hubs’ (mDHs) based on the relationship between a hub's network motifs and protein complexes. In this paper, we demonstrate that the network motifs of an mPH (i.e. network motifs take the mPH as one of their nodes) are more likely to stay inside a protein complex with their mPH, control the local topological structure, locate in the same cellular localizations as the mPH, and co-express in microarray data. On the other hand, we reveal that the network motifs of an mDH tend to spread into different complexes, and act as the connectors among signal pathways, control the global topological structure, locate in different cellular localizations, and express differently in microarray data.

Results

Motif party hub (mPH) and motif date hub (mDH)

About 20% proteins, i.e. 197 proteins, in HCfyi were defined as hubs whose partners are not less than 12. There are 196 motif hubs with at least one network motif and only one hub without any network motif (see Methods and Materials and Table S1). Based on the relationship between a hub's network motifs and protein complexes, we divided the 196 motif hubs into 98 mPHs and 98 mDHs (see Methods and Materials). The quantitative criterion Complexratio-same was defined to identify the relationship between a hub's network motifs and protein complexes. A relatively high Complexratio-same implies that more network motifs of a hub (a network motif takes a hub as one of its nodes, and a hub may be used by multiple network motifs) belong to the same protein complex as the hub, e.g. the four proteins or three proteins in such a network motif are more likely to be in just one protein complex. Otherwise, it indicates that less network motifs of the hub belong to the same protein complex, e.g. the four proteins or three proteins in such a network motif are more likely to be parts of different protein complexes. From the definition of mPHs and mDHs, we can see that the network motifs of an mPH more likely stay together in the same protein complex as the mPH while those of an mDH spread outside the protein complex of the mDH.

In this paper, mPHs and mDHs defined by network motifs and protein complexes were introduced to study hubs at the level of network motifs instead of individual proteins (or nodes), so as to solve the open question whether or not HCfyi contains date hubs and party hubs. Due to the topological distinction between HCfyi and FYI (HCfyi looks like stratus while FYI looks like altocumulus [11]), 103 hubs were found in the overlap between 199 hubs in FYI and 197 hubs in HCfyi. We found that more than 60% of ‘party hubs’ and ‘date hubs’ defined by Han et al. have been correctly divided into mPHs and mDHs respectively by our method among the overlapped 103 hubs between FYI (proposed by Han et al.) and HCfyi (proposed by Batada et al.) (see Figure 1). Furthermore, we can see that the motifs of mPHs and mDHs have the same characteristics as the partners of party hubs and date hubs respectively in cellular localization or spatial distribution, controlling topological structure, linking with signal pathways and co-expression in microarray data. Therefore, we call them as motif ‘party’ and motif ‘date’ hubs due to the facts that they are similar to ‘party’ and ‘date’ hubs mentioned in Han et al. [10].

[Figure omitted. See PDF.]

Figure 1. The hub overlap of FYI and HCfyi and the correctly divided mPHs and mDHs in the overlap.

https://doi.org/10.1371/journal.pone.0001207.g001

Distinct cellular localizations for mPHs and mDHs

One of main distinctions between party hubs and date hubs is their different spatial distributions (partners of date hubs are significantly more diverse in spatial distribution than those of party hubs) [10]. In virtue of another criterion, i.e. Localizationratio-same (see Materials and Methods), we found that Localizationratio-sames of mPHs are relatively high and those of mDHs are relatively low. The criterion Localizationratio-same shows localization relationship of network motifs of a hub. A higher one implies that three proteins or four proteins in each of network motifs of a hub are likely to locate in the same cellular localization as the hub. On the other hand, a lower one shows that the proteins in each of network motifs of a hub more likely locate in different cellular localizations. In Figure 2, mPHs have significantly higher Localizationratio-sames than mDHs (mean of mPHs: 0.7926; mean of mDHs: 0.4826; P<10−11 for Mann-Whitney U test.). Thus we can say that the network motifs of mPHs and mDHs have significantly different spatial distributions.

[Figure omitted. See PDF.]

Figure 2. The spatial distribution of hubs.

https://doi.org/10.1371/journal.pone.0001207.g002

In our analysis, ‘nucleus’ and ‘cytoplasm’ were not excluded from the cellular localization data (see Materials and Methods). In this respect, our method is also different from one of Han et al. who excluded the ‘nucleus’ and ‘cytoplasm’ from the cellular localization data. Moreover, we found that mPHs and mDHs have a significant difference in these two cellular localizations. Most mPHs (about 65%) are located in the nucleus. However, most mDHs (about 63%) are localized in subcellular compartments other than the nucleus (see Figure 3). It is clear that there is a statistically significant localization difference between mPHs and mDHs (x2 = 15.69, P≤0.001 for chi-square test). Therefore, mPHs prefer to ‘nucleus’ of a cell while mDHs are likely outside ‘nucleus’.

[Figure omitted. See PDF.]

Figure 3. The localizations for mDHs and mPHs.

(A) Cellular localizations of mDHs. (B) Cellular localizations of mPHs.

https://doi.org/10.1371/journal.pone.0001207.g003

The cellular localization distribution of hubs and their network motifs implies that, mPHs with their network motifs tend to locate in nucleus while mDHs are more likely to locate outside nucleus, and their network motifs have a scattered spatial distribution.

mPHs and mDHs control network architecture differently

In early work, it has been shown that the HCfyi network is tolerant to hubs' deletion, which means that the key components of the HCfyi network still remain after removal of date hubs or party hubs, or even all the hubs [11]. One of direct reasons is that the protein-protein interactions in the network are too dense to be broken into fragments by only removing ‘date hubs’ or ‘party hubs’. Therefore, hubs rarely have effect on the structure of the network in such a case. However, hubs may affect the network topological structure in a different manner. In this paper, a new approach for breaking down both the hubs and their motifs from the network was introduced based on mPHs and mDHs. In Figure 4A, it appears that deleting the mPHs and their motifs has little influence on the main network structure, whereas deleting the mDHs and their motifs makes the network broken into many fragments in Figure 4B. In other words, mDHs and their motifs clearly have a global effect on the network structure. In addition, we also evaluated the p-values for the cases by removing mPHs with their motifs and by removing mDHs with their motifs in Figure 4D ( both of them are less than 0.001). Moreover, in Figure 4C, about 50% of proteins are still connected in the largest component after removal of mPHs (Figure 4A) and their network motifs while only less than 10% of proteins are connected in the largest component after removal of mDHs and their network motifs (Figure 4B). In Table 1, we can see that the largest component after deletion of mPHs and their network motifs (Figure 4A) contains 533 protein-protein interactions while it contains only 47 protein-protein interactions after deletion of mDHs and their network motifs (Figure 4B). Clearly, those results demonstrate that the mPHs mainly control the local structure by their motifs while the mDHs control the global structure by their motifs.

[Figure omitted. See PDF.]

Figure 4. Deleting mPHs with their motifs and mDHs with their motifs respectively.

(A) The case of deleting mPHs and their motifs. In this case, the main components remain (the points in the same color are in the same component). (B) The case of deleting mDHs and their network motifs. Deleting the mDHs and their motif proteins causes the main components to disappear (the points in the same color are in the same component). (C) Deleting hubs one by one. The HCfyi network is tolerant for the deletion of mPHs with their motifs. However, it is not tolerant for the deletion of mDHs with their motifs. (D) Deleting a randomly chosen set of 98 hubs with their motifs. We repeat the removal of 98 hubs with their motifs randomly for 1000 times. The sizes of the largest remaining components are all less than 555 that are the size of the largest component after removing the mPHs with their motifs. The sizes of the largest remaining components are all larger than 47 that are the size of the largest component after removing the mDHs with their motifs. Empirical P values are both less than 10−3. Biolayout [53] has been used to produce the figures in (A) and (B).

https://doi.org/10.1371/journal.pone.0001207.g004

[Figure omitted. See PDF.]

Table 1. Sizes and Numbers of the network components after removal of mPHs and mDHs respectively

https://doi.org/10.1371/journal.pone.0001207.t001

mPHs and mDHs link with signal pathways in different ways

The definitions of mPHs and mDHs imply that most network motifs of an mPH stay together with the mPH in a protein complex while the network motifs of an mDH are not restricted in a protein complex. Furthermore, we built up a network composed of hubs and signal pathways shown in Figure 5, by which we found that mDHs are more likely to be the connectors among signal pathways. If at least one of a hub's network motifs takes one or more proteins in some signal pathway as its node, there is one link between the hub and the signal pathway in the network as shown in Figure 5. It is not difficult to see that mDHs link with more signal pathways than mPHs (see Table 2, x2 = 5.02, P<0.05 for Chi-square test). In addition, each mDH links with several signal pathways, i.e. the degrees of about 90% (86/98) of mDHs are all larger than 2, and their mean value is 4.3 (see Figure S1). As a result, mPHs and their network motifs mostly stay inside protein complexes, whereas mDHs and their motifs act as connectors among signal pathways.

[Figure omitted. See PDF.]

Figure 5. Network motifs linking up pathways with mDHs and mPHs.

In the middle of the panel, the red circles are abstract representations of pathways, where 14 smaller ones only connect with 6 mDHs (circles in light blue), and 26 larger ones connect with not only mDHs in blue circles (except the light blue one at the top of the panel) but also mPHs in orange circles. If more than one motif proteins of an mDH or an mPH are involved in one pathway, we connect the mDH or mPH with the pathway by an edge. Biolayout [53] has been used to produce the figure.

https://doi.org/10.1371/journal.pone.0001207.g005

[Figure omitted. See PDF.]

Table 2. Linking with signal pathways by network motifs

https://doi.org/10.1371/journal.pone.0001207.t002

Network motifs of mPHs are more co-expressed than those of mDHs

Another of main distinctions between party hubs and date hubs is whether or not the hubs are co-expressed (or are expressed simultaneously) with their partners [10]. Han et al. took the average PCC (or APCC) of the hubs as a measure to distinguish party hubs (with relatively high APCC) from date hubs (with relatively low APCC), where PCC is a Pearson correlation coefficient between a hub and one of its partners in microarray data. In this paper, however, we take another criterion to measure whether or not the network motifs of hubs are co-expressed. Specifically, we considered the standard deviation of average motif correlations (SAMC) of a hub (see Materials and Methods), in which average motif correlation (AMC) of a network motif is the average value of Pearson correlation coefficients between two proteins connected in the network motif. If SAMC is relatively low, the network motifs of the hub are more likely expressed at the same time (or say, the expression difference among the hub's network motifs is small). Moreover, besides the difference between APCC and SAMC in considering individual partners and network motifs respectively, it seems that SAMC is not equivalent to APCC while another one, i.e. mean of average motif correlations (MAMC), is similar to APCC. Such facts were actually confirmed by the numerical experiments. That is, by numerical experiments, we found that the MAMCs of mPHs have no significant difference from those of mDHs but the SAMCs of mPHs are significantly lower than those of mDHs (see Table 3). Therefore, in this paper, if there is no significantly difference in the average expressions of network motifs (MAMCs) between mPHs and mDHs, it is more confident for us to conclude that the network motifs of mPHs are more likely to be co-expressed than those of mDHs depending on the significant SAMC difference between mPHs and mDHs.

[Figure omitted. See PDF.]

Table 3. Statistic significance for the differences of SAMCs between mDHs and mPHs in Microarray data

https://doi.org/10.1371/journal.pone.0001207.t003

Discussion

At the level of network motifs instead of individual proteins, we found two types of hubs, motif party hubs (mPHs) and motif date hubs (mDHs), whose network motifs display distinct characteristics in organizing modules, cellular localizations, controlling network architecture, and co-expression in microarray data. More importantly, such a result answered the open question whether or not HCfyi contains ‘date hubs’ and ‘party hubs’, i.e. the contradiction on ‘date hubs’ and ‘party hubs’ between works of Han et al. [10] and Batada et al. [11], at the level of network motifs. Moreover, more results on degree and cluster coefficient differences (see Figure S2) and GO function difference between mPHs and mDHs (see Figure S3) were also found (see Text S1).

Although our results support the observation on ‘party hub’ and ‘date hub’ conducted by Han et al. [10], our analysis methods are totally different from theirs. That is, their study focus remains at the level of individual proteins (or nodes), however, our work is at the level of network motifs. The main procedures of the proposed method based on network motifs can be summarized as follows:

1. First, dividing hubs by considering how many network motifs stay together with their hubs in protein complexes;

2. Second, constructing spatial distribution of hubs by considering how many hubs' network motifs locate in the same cellular localizations as the hubs;

3. Third, controlling the network architecture of hubs by considering the important roles of network motifs in breaking down network topological structure;

4. Fourth, organizing the modules by considering the roles of network motifs in linking hubs and signal pathways;

5. Lastly, analyzing co-expression in microarray data of hubs by considering the difference among network motifs' expressions.

Why were network motifs adopted to distinguish ‘party hubs’ from ‘date hubs’ in HCfyi in this work? One of main reasons is that we surprisingly found that all network motifs in HCfyi occupy about 74% proteins and involve 85% interactions of HCfyi (see Figure 6). In other words, from the viewpoint of network motifs, the main structure of HCfyi is composed of network motifs rather than individual proteins. Thus, it is natural that we adopt network motifs as main elements while studying hubs. Another reason is that the appropriate size of the chosen network motifs, i.e. 3 or 4, determines their important role in characterizing both small size elements, i.e. molecules, at the ‘low level’ of a network, and large size elements, i.e. modules such as protein complexes [25]–[28] and signal pathways [29], [30], at the ‘high level’ of a network. In our analysis, we did study the network from different levels [31]. For example, in our analysis, mPHs' network motifs control a local topological structures and stay together inside protein complexes, which represents a ‘lower level’ of the network. On the other hand, mDHs' network motifs control the global topological structure and act as the connectors among signal pathways, which represent a ‘high level’ of the network. At either ‘low level’ or ‘high level’ of the network, the network motif is a suitable and essential building block or functional unit to characterize both relatively small elements, i.e. molecules, at a ‘low level’, and relatively large ones, i.e. modules, at a ‘high level’.

[Figure omitted. See PDF.]

Figure 6. The proteins and interactions in HCfyi and in all network motifs of HCfyi respectively.

https://doi.org/10.1371/journal.pone.0001207.g006

We studied the biological network of yeast from the viewpoint of network motifs in the paper, in particular stressing on their biological roles in biological networks rather than the topological structures of network motifs. Both theoretical and numerical analysis show that network motifs should not be merely considered as a connection pattern from topological structures but be considered as essential function units in organizing the modules from biological processes[24].

Materials and Methods

Protein interaction data

The FYI dataset of 2491 interactions among 1375 proteins was obtained from Han et al. [10]. The HCfyi dataset of 3976 interactions among 1291 proteins was obtained from Batada et al. [11]. The methodologies to construct FYI and HCfyi are similar. They were both based on an intersection method in which only the interactions observed at least twice are retained from various datasets. Datasets of the FYI were derived from HTP [25], [26], [32]–[34], APT [34]–[36], in silico-predicted dataset [37]–[39] and MIPS [40]. Datasets of the HCfyi were derived from all extent protein interaction datasets, which include all LC interaction data (BioGRID [41], BIND [36], DIP [42], MINT [43], and MIPS [40]), and all HTP interaction data [25]–[28], [34], [35], [42]. Especially, the LC data were manually curated from over 31,793 abstracts and online publications [41], and there is no interaction derived from standard large-scale experiments in both FYI and HCfyi.

Protein complex, signal pathway and cellular localization data

The protein complex data were derived from MIPS [40] in September of 2006 and the signal pathway data were derived from KEGG [44] in November of 2006. Cellular localization data were derived from Huh et al. [45].

Gene expression data

The 6 microarray datasets [10], [11], [46] (Stress response [47], cell cycle [48], pheromone treatment [49], unfolded protein response [50], sporulation [51] and compendium [46]) were normalized with Z score normalization [52] (i.e. the expression measurement for each gene was adjusted to have a mean of 0 and a standard deviation of 1) using the original log2 fold change values. Compendium gene expression data are an expression-profiling compendium of 315 data points for most yeast genes across other five different experimental conditions. The PCC (Pearson correlation coefficient) of motifs were calculated for the five conditions and the combined set of all conditions (compendium).

Hub

We selected about top 20% proteins with relatively more partners, i.e. 197 proteins, in the HCfyi network, which are defined as hubs. All of their partners are not less than 12. There are 103 hubs in the overlap of hubs of HCfyi and FYI (199 hubs).

Network Motifs detected by mfinder1.2

In consideration that the protein-protein interaction networks are undirected, the network motifs appearing in these undirected networks are undoubtedly undirected. For three-node substructures, only one network motif, i.e. triangle or ID: 238, has been found, whose Z-score is 317.43. For four-node substructures, one network motif, i.e. square or ID: 13260, has been found and been chosen as the representative four-node network motif in our study, whose Z-score is 12.90. Indeed, for four-node network motifs, others have also been found. The reason why we chose the square four-node network motif is that the other four-node network motifs can be composed of the triangle and the square. The network motifs were found by mfinder1.2 [17], [18].

Division of hubs into mPHs and mDHs

We propose a quantitative criterion to divide hubs into mPHs and mDHs in this paper. According to the quantitative criterion Complexratio-same, we can have a partition of hubs based on the relationship between a hub's network motifs and protein complexes. If a protein H is a hub in FYI, and M is a set of the hub's network motifs that are composed of M1, M2,…, M|M| (for every network motif Mi, i∈{1,2,…,|M|}, H∈Mi must be satisfied) , then we havewhere set Msame is composed of those network motifs whose three proteins or four proteins all belong to just the same protein complex. In other words, for the protein Pj in some network motif Mk, j∈{1,2,…,|Mk|}, the protein complex set of Pj is Complexj that is composed of those protein complexes containing the protein Pj. If , then Mk∈Msame. |•| is the number of elements in some set. Thus, 98 hubs with relatively high Complexratio-same (larger than or equal to 0.5) are called mPHs, and 98 hubs with relatively low Complexratio-same (less than 0.5) are called mDHs (see Table S1).

A measure for cellular localizations

According to the quantitative criterion Localizationratio-same proposed in this paper, we can measure the spatial distribution of a hub's network motifs. If a protein H is a hub in FYI, and M is a set of the hub's network motifs that are composed of M1, M2,…, M|M|, then we have where set Msame is composed of those network motifs whose three proteins or four proteins all locate in just the same cellular localization. In other words, for the protein Pj in some network motif Mk, j∈{1,2,…,|Mk|}, the cellular localization set of Pj is Localizationj that is composed of those cellular localizations of the protein Pj. If , then Mk∈Msame. |•| is the number of elements in some set.

Standard deviation of Average Motif Correlation (SAMC)

To analyze co-expression or study the difference in gene expression of a hub's network motifs, we define Standard deviation of Average Motif Correlation (SAMC). Average Motif Correlation (AMC) for one of a hub's network motifs is to measure the average gene expression level for the network motif. If a protein H is a hub in FYI, and M is a set of the hub's network motifs that are composed of M1, M2,…, M|M|. For each network motif Mi (i∈{1,2,…,|M|}) that contains three proteins or four proteins as mentioned in the section about network motifs, then we have where |Mi| is the number of interactions in network motif Mi, i.e. 3 or 4, and Pj, Pk are any two proteins in Mi. PCC(Pj, Pk) is the Pearson correlation coefficient between proteins Pj and Pk. I is a function defined as equal to 1 if Pj, Pk are linked in the network motif Mi, and equal to 0 if Pj, Pk are not linked in the network motif Mi.

Thus, SAMC is the Standard deviation of all AMCs for all network motifs of a hub.

Mean of Average Motif Correlation (MAMC)

Mean of Average Motif Correlation (MAMC) is the Mean of all AMCs for all network motifs of a hub.

Supporting Information

[Figure omitted. See PDF.]

Text S1.

Additional results on mPHs and mDHs.

https://doi.org/10.1371/journal.pone.0001207.s001

(0.11 MB PDF)

Table S1.

mPHs and mDHs.

https://doi.org/10.1371/journal.pone.0001207.s002

(0.02 MB PDF)

Figure S1.

The degrees of mDHs in Figure 5.

https://doi.org/10.1371/journal.pone.0001207.s003

(0.08 MB TIF)

Figure S2.

The degree and cluster coefficient differences between mPHs and mDHs.

https://doi.org/10.1371/journal.pone.0001207.s004

(0.07 MB TIF)

Figure S3.

Function_ratio-sames of mDHs is significantly different from those of mPHs

https://doi.org/10.1371/journal.pone.0001207.s005

(0.04 MB TIF)

Acknowledgments

We are very grateful to Jing-Dong J. Han and Nizar N Batada for helpful suggestions on collecting Microarray data, and Nizar N Batada for providing us the data of HCfyi network.

Author Contributions

Conceived and designed the experiments: GJ. Performed the experiments: GJ. Analyzed the data: SZ GJ. Contributed reagents/materials/analysis tools: SZ GJ. Wrote the paper: XZ SZ LC GJ. Other: Supervised whole project: LC XZ.

Citation: Jin G, Zhang S, Zhang X-S, Chen L (2007) Hubs with Network Motifs Organize Modularity Dynamically in the Protein-Protein Interaction Network of Yeast. PLoS ONE2(11): e1207. https://doi.org/10.1371/journal.pone.0001207

References

1. Jeong H, Mason SP, Barabasi AL, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411: 41–42.H. JeongSP MasonAL BarabasiZN Oltvai2001Lethality and centrality in protein networks.Nature4114142

2. Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 406: 378–382.R. AlbertH. JeongAL Barabasi2000Error and attack tolerance of complex networks.Nature406378382

3. Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286: 509.AL BarabasiR. Albert1999Emergence of scaling in random networks.Science286509

4. Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet 5(2): 101–13.AL BarabasiZN Oltvai2004Network biology: understanding the cell's functional organization.Nat Rev Genet5(2)10113

5. Barabasi AL (2002) Linked: the new science of networks. Cambridge Mass: Perseus Pub. AL Barabasi2002Linked: the new science of networks.Cambridge MassPerseus Pub

6. Przulj N, Wigle DA, Jurisica I (2004) Functional topology in a network of protein interactions. Bioinformatics. 12;20(3): 340–8.N. PrzuljDA WigleI. Jurisica2004Functional topology in a network of protein interactions.Bioinformatics.12;20(3)3408

7. Stumpf MP, Wiuf C, May RM (2005) Subnets of scale-free networks are not scale-free: sampling properties of networks. Proc Natl Acad Sci U S A. 22;102(12): 4221–4.MP StumpfC. WiufRM May2005Subnets of scale-free networks are not scale-free: sampling properties of networks.Proc Natl Acad Sci U S A.22;102(12)42214

8. Thomas A, Cannings R, Monk NA, Cannings C (2003) On the structure of protein-protein interaction networks. Biochem Soc Trans. 31(Pt 6): 1491–6.A. ThomasR. CanningsNA MonkC. Cannings2003On the structure of protein-protein interaction networks.Biochem Soc Trans.31(Pt 6)14916

9. Han JD, Dupuy D, Bertin N, Cusick ME, Vidal M (2005) Effect of sampling on topology predictions of protein-protein interaction networks. Nat Biotechnol. 23(7): 839–44.JD HanD. DupuyN. BertinME CusickM. Vidal2005Effect of sampling on topology predictions of protein-protein interaction networks.Nat Biotechnol.23(7)83944

10. Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, et al. (2004) Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430: 88–93.JD HanN. BertinT. HaoDS GoldbergGF Berriz2004Evidence for dynamically organized modularity in the yeast protein-protein interaction network.Nature4308893

11. Batada NN, Reguly T, Breitkreutz A, Boucher L, Breitkreutz BJ, et al. (2006) Stratus not altocumulus: A new view of the yeast protein interaction network. PLoS Biol 4(10): e317.NN BatadaT. RegulyA. BreitkreutzL. BoucherBJ Breitkreutz2006Stratus not altocumulus: A new view of the yeast protein interaction network.PLoS Biol4(10)e317

12. Ekman D, Light S, Bjorklund AK, Elofsson A (2006) What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae? Genome Biol 7: R45.D. EkmanS. LightAK BjorklundA. Elofsson2006What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae?Genome Biol7R45

13. He X, Zhang J (2006) Why do hubs tend to be essential in protein networks? PLoS Genet 2(6): 0868–0834.X. HeJ. Zhang2006Why do hubs tend to be essential in protein networks?PLoS Genet2(6)08680834

14. Batada NN, Hurst LD, Tyers M (2006) Evolutionary and physiological importance of hub proteins. PLoS Comput Biol 2(7): e88.NN BatadaLD HurstM. Tyers2006Evolutionary and physiological importance of hub proteins.PLoS Comput Biol2(7)e88

15. Maslov S, Sneppen K (2002) Specificity and stability in topology of protein networks. Science 296: 910–913.S. MaslovK. Sneppen2002Specificity and stability in topology of protein networks.Science296910913

16. Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402: C47–C52.LH HartwellJJ HopfieldS. LeiblerAW Murray1999From molecular to modular cell biology.Nature402C47C52

17. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, et al. (2002) Network Motifs: Simple Building Blocks of Complex Networks. Science 298: 824–827.R. MiloS. Shen-OrrS. ItzkovitzN. KashtanD. Chklovskii2002Network Motifs: Simple Building Blocks of Complex Networks.Science298824827

18. Milo R, Itzkovitz S, Kashtan N, Levitt R, Shen-Orr S, et al. (2004) Superfamilies of designed and evolved networks. Science 303: 1538–42.R. MiloS. ItzkovitzN. KashtanR. LevittS. Shen-Orr2004Superfamilies of designed and evolved networks.Science303153842

19. Shen-Orr S, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31: 64–68.S. Shen-OrrR. MiloS. ManganU. Alon2002Network motifs in the transcriptional regulation network of Escherichia coli.Nat Genet316468

20. Mangan S, Itzkovitz S, Zaslaver A, Alon U (2006) The Incoherent Feed-forward Loop Accelerates the Response-time of the gal System of Escherichia coli. JMB 356: 1073–81.S. ManganS. ItzkovitzA. ZaslaverU. Alon2006The Incoherent Feed-forward Loop Accelerates the Response-time of the gal System of Escherichia coli.JMB356107381

21. Mangan S, Zaslaver A, Alon U (2003) The Coherent Feedforward Loop Serves as a Sign-sensitive Delay Element in Transcription Networks. JMB 334/ 2: 197–204.S. ManganA. ZaslaverU. Alon2003The Coherent Feedforward Loop Serves as a Sign-sensitive Delay Element in Transcription Networks.JMB 334/2197204

22. Li C, Chen L, Aihara K (2007) A systems biology perspective on signal processing in genetic motifs. IEEE Signal Proc Mag, 24: 136–147.C. LiL. ChenK. Aihara2007A systems biology perspective on signal processing in genetic motifs.IEEE Signal Proc Mag,24136147

23. Sole RV, Valverde S (2006) Are network motifs the spandrels of cellular complexity? Trends Ecol Evol 21: 419–422.RV SoleS. Valverde2006Are network motifs the spandrels of cellular complexity?Trends Ecol Evol21419422

24. Balazsi G, Barabasi AL, Oltvai ZN (2005) Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli. Proc Natl Acad Sci U S A. 31;102(22): 7841–6.G. BalazsiAL BarabasiZN Oltvai2005Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli.Proc Natl Acad Sci U S A.31;102(22)78416

25. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, et al. (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci U S A 98: 4569–4574.T. ItoT. ChibaR. OzawaM. YoshidaM. Hattori2001A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc Natl Acad Sci U S A9845694574

26. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, et al. (2000) A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403: 623–627.P. UetzL. GiotG. CagneyTA MansfieldRS Judson2000A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.Nature403623627

27. Gavin AC, Aloy P, Grandi P, Krause R, Boesche M, et al. (2006) Proteome survey reveals modularity of the yeast cell machinery. Nature 440: 631–637.AC GavinP. AloyP. GrandiR. KrauseM. Boesche2006Proteome survey reveals modularity of the yeast cell machinery.Nature440631637

28. Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, et al. (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440: 637–644.NJ KroganG. CagneyH. YuG. ZhongX. Guo2006Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.Nature440637644

29. Gustin MC, Albertyn J, Alexander M, Davenport K (1998) MAP kinase pathways in the yeast Saccharomyces cerevisiae. Microbiol Mol Biol Rev 62(4): 1264–300.MC GustinJ. AlbertynM. AlexanderK. Davenport1998MAP kinase pathways in the yeast Saccharomyces cerevisiae.Microbiol Mol Biol Rev62(4)1264300

30. Herskowitz I (1995) MAP kinase pathways in yeast: for mating and more. Cell 80(2): 187–97.I. Herskowitz1995MAP kinase pathways in yeast: for mating and more.Cell80(2)18797

31. Ideker T, Lauffenburger D (2003) Building with a scaffold: emerging strategies for high- to low-level cellular modeling. Trends Biotechnol 21: 255–262.T. IdekerD. Lauffenburger2003Building with a scaffold: emerging strategies for high- to low-level cellular modeling.Trends Biotechnol21255262

32. Fromont-Racine M, Mayes AE, Brunet-Simon A, Rain JC, Colley A, et al. (2000) Genome-wide protein interaction screens reveal functional networks involving Sm-like proteins. Yeast 17: 95–110.M. Fromont-RacineAE MayesA. Brunet-SimonJC RainA. Colley2000Genome-wide protein interaction screens reveal functional networks involving Sm-like proteins.Yeast1795110

33. Fromont-Racine M, Rain JC, Legrain P (1997) Toward a functional analysis of the yeast genome through exhaustive two-hybrid screens. Nature Genet 16: 277–282.M. Fromont-RacineJC RainP. Legrain1997Toward a functional analysis of the yeast genome through exhaustive two-hybrid screens.Nature Genet16277282

34. Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, et al. (2002) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415: 180–183.Y. HoA. GruhlerA. HeilbutGD BaderL. Moore2002Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.Nature415180183

35. Gavin AC, Bosche M, Krause R, Grandi P, Marzioch M, et al. (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415: 141–147.AC GavinM. BoscheR. KrauseP. GrandiM. Marzioch2002Functional organization of the yeast proteome by systematic analysis of protein complexes.Nature415141147

36. Bader GD, Betel D, Hogue CW (2003) BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res 31: 248–250.GD BaderD. BetelCW Hogue2003BIND: the Biomolecular Interaction Network Database.Nucleic Acids Res31248250

37. von Mering C, Krause R, Snel B, Cornell M, Oliver SG, et al. (2002) Comparative assessment of large-scale data sets of proteinCprotein interactions. Nature 417: 399–403.C. von MeringR. KrauseB. SnelM. CornellSG Oliver2002Comparative assessment of large-scale data sets of proteinCprotein interactions.Nature417399403

38. Marcotte EM, Pellegrini M, Thompson MJ, Yeates TO, Eisenberg D (1999) A combined algorithm for genome-wide prediction of protein function. Nature 402: 83–86.EM MarcotteM. PellegriniMJ ThompsonTO YeatesD. Eisenberg1999A combined algorithm for genome-wide prediction of protein function.Nature4028386

39. Dandekar T, Snel B, Huynen M, Bork P (1998) Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem Sci 23: 324–328.T. DandekarB. SnelM. HuynenP. Bork1998Conservation of gene order: a fingerprint of proteins that physically interact.Trends Biochem Sci23324328

40. Mewes HW, Frishman D, Guldener U, Mannhaupt G, Mayer K, et al. (2002) MIPS: a database for genomes and protein sequences. Nucleic Acids Res 30: 31–34.HW MewesD. FrishmanU. GuldenerG. MannhauptK. Mayer2002MIPS: a database for genomes and protein sequences.Nucleic Acids Res303134

41. Reguly T, Breitkreutz A, Boucher L, Breitkreutz BJ, Hon GC, et al. (2006) Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae. J Biol 5: 11.T. RegulyA. BreitkreutzL. BoucherBJ BreitkreutzGC Hon2006Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae.J Biol511

42. Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, et al. (2000) DIP: The database of interacting proteins. Nucleic Acids Res 28: 289–291.I. XenariosDW RiceL. SalwinskiMK BaronEM Marcotte2000DIP: The database of interacting proteins.Nucleic Acids Res28289291

43. Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, et al. (2002) MINT: a Molecular INTeraction database. FEBS Lett. 20;513(1): 135–40.A. ZanzoniL. Montecchi-PalazziM. QuondamG. AusielloM. Helmer-Citterich2002MINT: a Molecular INTeraction database.FEBS Lett.20;513(1)13540

44. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, et al. (2006) From genomics to chemical genomics: new devel-opments in KEGG. Nucleic Acids Res 34: D354–7.M. KanehisaS. GotoM. HattoriKF Aoki-KinoshitaM. Itoh2006From genomics to chemical genomics: new devel-opments in KEGG.Nucleic Acids Res34D3547

45. Huh WK, Falvo JV, Gerke LC, Carroll AS, Howson RW, et al. (2003) Global analysis of protein localization in budding yeast. Nature 425: 686–691.WK HuhJV FalvoLC GerkeAS CarrollRW Howson2003Global analysis of protein localization in budding yeast.Nature425686691

46. Kemmeren P, van Berkum NL, Vilo J, Bijma T, Donders R, et al. (2002) Protein interaction verification and functional annotation by integrated analysis of genome-scale data. Mol Cell 9: 1133–1143.P. KemmerenNL van BerkumJ. ViloT. BijmaR. Donders2002Protein interaction verification and functional annotation by integrated analysis of genome-scale data.Mol Cell911331143

47. Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, et al. (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Evol 11: 4241–4257.AP GaschPT SpellmanCM KaoO. Carmel-HarelMB Eisen2000Genomic expression programs in the response of yeast cells to environmental changes.Mol Biol Evol1142414257

48. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, et al. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Evol 9: 3273–3297.PT SpellmanG. SherlockMQ ZhangVR IyerK. Anders1998Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.Mol Biol Evol932733297

49. Roberts CJ, Nelson B, Marton MJ, Stoughton R, Meyer MR, et al. (2000) Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. Science 287: 873–880.CJ RobertsB. NelsonMJ MartonR. StoughtonMR Meyer2000Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles.Science287873880

50. Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, et al. (1998) The transcriptional program of sporulation in budding yeast. Science 282: 699–705.S. ChuJ. DeRisiM. EisenJ. MulhollandD. Botstein1998The transcriptional program of sporulation in budding yeast.Science282699705

51. Travers KJ, Patil CK, Wodicka L, Lockhart DJ, Weissman JS, et al. (2000) Functional and genomic analyses reveal an essential coordination between the unfolded protein response and ERassociated degradation. Cell 101: 249–258.KJ TraversCK PatilL. WodickaDJ LockhartJS Weissman2000Functional and genomic analyses reveal an essential coordination between the unfolded protein response and ERassociated degradation.Cell101249258

52. Quackenbush J (2002) Microarray data normalization and transformation. Nat Genet 32: Suppl496–501.J. Quackenbush2002Microarray data normalization and transformation.Nat Genet32Suppl496501

53. Enright AJ, Ouzounis CA (2001) BioLayout-an automatic graph layout algorithm for similarity visualization. Bioinformatics 17(9): 853–854.AJ EnrightCA Ouzounis2001BioLayout-an automatic graph layout algorithm for similarity visualization.Bioinformatics17(9)853854

54. Bernard R (1986) Fundamentals of Biostatistic. Boston Mass: Duxbury Press. R. Bernard1986Fundamentals of Biostatistic.Boston MassDuxbury Press