Genomic analysis reveals a layered structure, and dynamic core formation in
the molecular interaction networks of yeast
Aswin Sai Narain Seshasayee1,a and M. Madan Babu2,a,*
2MRC
1Centre for Biotechnology, Anna University, Chennai 600025, India.
Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, England.
Introduction
The nature of any living organism is not only determined by the proteins that it encodes, but also by the way in which the
proteins interact with the other cellular components. Over the many years, proteins have primarily been studied in isolation and
in detail, providing us with important insights into their molecular function. But the fact that proteins rarely function in isolation,
has prompted researchers to study them using a systems approach, which will eventually help us to gain a better understanding
of how complex systems such as cells function. The set of all interactions mediated by proteins in a cell can be conceptualized
as a network, and the few available molecular interaction networks have been studied in considerable detail and common
organizational principles have emerged. However, such networks, derived from interactions of proteins with other proteins, DNA
and metabolites, referred to as the protein interaction, transcriptional interaction and metabolic interaction network, have largely
been studied only in isolation. In this work, for the first time we integrate the three different interaction networks available for
Saccharomyces cerevisiae (Fig. 1) and reveal that such biological networks form layered structures called network cores. By
integrating gene expression data and gene essentiality data, we infer novel biological insights about the organization of such
networks.
Fig 1a-d and 2: Panels 1a-d, describes the different cellular interaction networks that were used in this study. Figure 2 explains
the concept of network cores. The k-core of a network is that sub-graph that is obtained after recursive deletion of nodes with
degree less than k, and all links incident on them. Parameters to characterize network cores, the core densities, are listed by
the side.
Objectives
Even though the different types of cellular interaction networks have been studied in isolation, it is well known that these
interactions coexist in a living cell. Hence the main objective was to integrate the three different types of molecular interaction
networks and to understand if such networks form hierarchical layered structures, evident from the concept of a core (Seidman,
1983). Such layers, if present would reveal groups of proteins that are important for maintaining the network structure and the
hierarchical organization of such groups in the different networks. Next, we also ask if network-cores are dynamic structures, to
test the hypothesis that different groups of proteins assume importance during the different cellular conditions. We also
characterize the cellular states in terms of the interaction networks and also assess the biological significance of members of
high cores. Finally, we also study whether members of a high k-core interact with similar members and if such interactions could
have biological relevance.
Materials and Methods
The protein interaction network (Lee et. al, 2004), transcriptional interaction network (Luscombe et al., 2004), and metabolic
interaction network (Forster et al., 2003) were put together to obtain the integrated cellular interaction network, comprising of
5006 genes and 31365 interactions. Network cores and associated parameters, the core density values, were computed as
described in Fig. 2. For each network type, condition specific sub-networks were derived using gene expression data,
employing the procedure described earlier (Luscombe et al., 2004). Propensity measures were used to establish that different
a These
authors contributed equally to the work
* Corresponding author (email: madanm@mrc-lmb.cam.ac.uk, aswinsainarain@gmail.com)
sub-networks are dominated by different types of interaction networks. Core membership profiles for each protein was derived
and analyzed to understand the dynamics of network-core formation. The list of essential ORFs was obtained from the Yeast
Gene Deletion Project.
Results
We show that each of the three types of cellular interaction networks and the integrated network form layered structures, called
network-cores. Such cores allow us to define a hierarchical structure and groups of proteins with similar importance in such
networks. We characterized the core-based behavior of different networks using a novel measure termed “core-density”, based
on the number of nodes or number of edges belonging to a k-core. Among the three types of molecular interaction networks,
the transcriptional regulatory network has the highest normalized edge core-density value, followed by the metabolic network
and the protein-protein interaction network, suggesting a higher degree of transcriptional interaction among the regulators,
followed by higher inter-conversion of metabolites and physical interactions among proteins in the yeast cell. We note that
permeases belong to the highest cores in the integrated network and in the metabolic interaction network.
We also find that there is no correlation between essentiality and core-membership of a gene in the metabolic interaction and
transcriptional interaction network. However the protein-protein interaction network shows a very strong enrichment of essential
genes in high core layers. It might be that high core proteins in the metabolic and the transcriptional interaction network are not
essential due to the possibility of alternative pathways and regulatory modes respectively. However, physical interaction is more
selective in nature and hence loss of a single high-core protein can be disastrous.
To gain a better understanding of the dynamic nature of such molecular interaction networks, we integrated information about
gene expression across five different cellular conditions, namely cell cycle, sporulation, diauxic shift, DNA damage, and stress
response. Our analysis revealed that such layers are dynamic. This means that different groups of proteins form high cores
during the different cellular conditions. We also find that each cellular state is dominated and characterized by a different type of
interaction network. For example, cell cycle and sporulation are primarily enriched in transcriptional interactions, whereas
diauxic shift and DNA damage conditions are enriched in metabolic interactions, suggesting large scale changes in metabolite
profile in the cell. We find that high core proteins in cell-cycle are largely involved in chromosome reorganization. Small
molecule transporters appear to take control over diauxic shift. DNA damage is dominated by enzymes involved in smallmolecule biosynthesis. During sporulation, high core positions are occupied by metabolic enzymes, particularly those involved
in fatty acid beta oxidation, emphasizing the importance of energy generation during this process. Genes regulating protein
synthesis populate high cores during stress response.
Finally, we also observe that proteins of high-core interact with other high-core proteins in the same (homogeneous) or in
another (heterogeneous) component network. Such interactions among high-core proteins point to important associations
between genes, which are influential in one of the interaction networks. That homogeneous interactions in high cores are
dominant can be quantitated using a number called R, which is positive when applied to core-memberships in molecular
interaction networks. The same R value, when applied over connectivity, is negative, implying dis-assortativity. An exception to
connectivity-based dis-assortativity is our representation of the metabolic interaction network. This degree-based assortative
behavior of the metabolic interaction network is reflected in a normal-like distribution of clustering coefficients, which implies that
this network is well-connected. Hereogeneous interactions in high cores, while being only a few in number, are biologically
significant. For example YEL009C, a transcription factor that belongs to a high core in the transcriptional interaction network,
activates transcription of several amino acid biosynthesis genes that belong to high cores in the metabolic network.
Conclusions
Integration of the different molecular interaction networks is important and enables us to parse out certain key information that
are hidden when the component networks are considered individually. The concept of a network core, which has been applied
to cellular networks in this work, represent a novel way of presenting information, which reveals layers in such biological
networks. While the idea of connectivity has identified key hubs that are essential to the viability of the organism, core identifies
groups of proteins that are important at several levels, and may eventually help us to characterize the different cellular states.
References
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2. Lee, I., Date, S.V., et. al. (2004) Science, 306:1555-8.
3. Luscombe, N.M., Madan Babu, M., et. al. (2004) Nature, 431:308-12.
4. Seidman, S. (1983) Social Networks, 5:97-107.