Biological function through network topology: a survey of the human diseasome

INTRODUCTION

Molecular causes of diseases are explored through many different techniques, such as through the examination of their causal genes, the disruption of related pathways, analysis of age factors and various other external influences. Currently, not much is known about the interconnectivity of all these different causes and elucidating the relationship between the malfunctioning of a system and its genomic data would provide insights into disease and set directions for future research. A scientific area that is attempting to address this unification is that of biological networks. It brings together the concepts of the ‘human diseasome’—a combined set of all known disorders and their implicated genetic mutations—and all systems-level molecular data.

Networks, also called ‘graphs’, are defined as sets of nodes (also called vertices) and edges (also called links), where nodes are singular entities and edges represent relations between them. This seemingly simple mathematical concept is a powerful approach for modelling real-world phenomena across various disciplines, including biological data, such as physical interactions between proteins, or synthesis of metabolic compounds. Also, graphs have been used to model relationships between diseases and they are a key part of the rising field of network medicine, which aims to decipher the complex wirings that govern human diseases [1–3]. An important part of this complex cellular wiring is the network of protein–protein interactions (PPIs) [4]. Since proteins interact, a single gene mutation is not confined within the actions of its gene products, but can propagate throughout the system, influencing other gene products that otherwise contain no aberrations. Hence, the final phenotypic effect is a result of a combination of the initial defect along with the influence that it has on other parts of the networked system.

This review is organized into four segments. The first section describes biological data and models used in their network representations. It illustrates the process of reconstructing the human metabolome and the human diseaseome. The second part presents basic methods of linking biological concepts within these networks by virtue of their common features. The third section focuses on more complex graph-theoretical approaches to data analysis and describes key biological insights that such analyses provide. In addition, the section covers scientific controversies that arose in this field and how they led the development of more sophisticated methods and tools. The final section gives an overview of future directions that yield as results of most recent developments in this area.

GUILT BY ASSOCIATION

In this section we outline some commonly used approaches that are based on shared features for extracting disease information out of networks.

Shared microRNAs

microRNAs (miRNAs) are responsible for post-transcriptional regulation of protein-coding genes by means of inhibiting, destabilizing or degrading target mRNAs. A single miRNA down-regulates hundreds of target mRNA, thus having a key role in cellular functions such as development, differentiation, proliferation, apoptosis and metabolism. Recently, miRNA-based network reconstruction was implemented by Lu et al. [81], where they connected disease pairs whose associated genes are targeted by one or more shared miRNAs. A network constructed in such a way had clusters associated with diseases, such as cancer or cardiovascular diseases. Also, a negative correlation was found between tissue-specificity of a miRNA and the number of diseases associated with it. Another study has shedded additional light on the biological meaning of miRNA-based network reconstruction by strengthening the fact that a set of mRNA targets which are regulated by a single miRNA generally consist of functionally-associated molecules in human cells, rather than a random set of functionally-independent genes [82].

EXAMINING NETWORK TOPOLOGY

Regardless of data representation, networks of interconnected biomolecules in the cell and the concept of the human diseasome offer an opportunity for data analyses that aim to increase our understanding of why particular diseases co-manifest their phenotypes more often than others. The hope is that variants of network representation of these data and the new tools for their analyses will lead to novel approaches for disease diagnosis and treatment and aid drug discovery [83]. Even though only ∼10% of human genes have a known association to diseases [84], such limited knowledge about disease genes has still yielded insight into the link between network topology around a gene and its involvement in disease.

Since proteins are the main workhorses of the cell and they aggregate to perform a function, PPI networks have been analysed to elucidate cellular processes and disease. Early studies tried to use very simple methods to analyse PPI networks and link their topology to biological function [85–92]. For example, it was noticed that essential genes in early PPI networks of baker’s yeast obtained by Y2H methods tend to code for hub proteins [85], which was later refuted on more recent PPI data [93]. Also, it was postulated that there is a negative correlation between the connectivity of a gene and its rate of evolution and concluded that hub-coding genes are older and tend to evolve more slowly than non-hub-coding genes [86–88]. The number of phenotypic outcomes upon the removal of hub-coding genes has also been examined [55,89]. Similarly, 346 cancer-related proteins have been shown to have, on average, two times more direct interacting partners than non-cancer-related ones [91].

However, using such simple analysis methods on noisy data that are obtained by biased data collection and sampling may lead to questionable conclusions [46–55].

Hence, more sophisticated methods that give consistent results even in the presence of noise in the data have been designed.

Rather than looking at the network as a whole on a global, ‘macroscopic’ level, the local, ‘microscopic’ topology of networks has been examined. ‘Network motifs’, small sub-graphs that occur in real networks much more often than is expected at random, have been introduced to examine the structure of complex networks [94]. They enabled systematic detection of repeated appearances of topological substructures in Escherichia coli transcriptional regulation network and related them to specific biological responses to external signals [95]. However, since by definition motifs need to be over-represented in the data when compared to random graph models, the question is what models are the best fitting to real biological networks. This problem cannot be answered exactly due to NP-completeness (i.e. provable computational intractability) of the underlying sub-graph isomorphism problem that test whether one network exist as a copy in another network [56]. Hence, various methods for approximately comparing [96–98] and aligning [99–107] networks have been proposed.

‘Graphlets’ have been designed to further strengthen the bond between network topology and biological function and disease: they are small sub-graphs of large networks that do not need to be over-represented in the data (Figure 2) [96,98]. The statistics of frequencies of appearance of graphlets in entire networks, or around nodes in network have been used to classify networks into models [96,98], as well as to link the topology around a node in a network with the node’s biological function and involvement in disease: proteins with similar wiring up to a four-deep neighbourhood (Figure 3) were shown to belong to the same protein complexes, perform the same biological functions, are localized in the same sub-cellular compartments, and are co-expressed in tissues [108–113]. Furthermore, clustering of nodes based solely on graphlet-based topology of human PPI networks was used to successfully predict RNAi targets as novel components of melanogenesis regulatory pathways that could not have been identified by other existing approaches [111]. Similarly, involvement of genes in cancer was successfully predicted and validated both through literature curation and experimentally, thus providing evidence that topological wiring around cancer genes differs from wiring around non-cancer genes [113].

The role of ‘topologically central’ proteins has also been examined. Several measures of topological centrality have been proposed, including degree centrality (i.e. hub nodes described above), betweenness centrality, closeness centrality, sub-graph centrality and graphlet centrality [114]. A general consensus is that topologically central proteins in PPI networks are involved in disease and aging. The concept of a dominating set (DS), that is, the set of nodes in a network such that all nodes are either in it or adjacent to it, has also been explored. Finding a DS of minimum size in a network is another computationally intractable problem, so heuristics are being sought. It was shown that proteins in a DS of the human PPI network constitute the ‘spine’ of the PPI network: this DS is statistically significantly enriched with disease, aging, and proteins participating in signalling pathways [114].

Predicting new disease proteins has also been based on examining disease gene DNA neighbourhoods and cellular co-localization of proteins [112,115–121]. Despite their simplicity, these approaches provided new insight: for example, Oti et al. [121] searched 432 loci for candidate disease genes achieving a 10-fold enrichment in success rate of valid disease–gene predictions. In the same study, additional consideration of cellular co-localization led to 1000-fold enrichment. Other methods base their predictions on overlaying network topology with additional information, such as the knowledge of functional or disease modules in the network [122–124]. They do this by, for instance, deriving a phenotype similarity score to identify new protein complexes associated with disease.

Recently, ‘driver’ genes have been proposed to be those whose mutations trigger genetic instability and cancer formation [125–128]. Also, genetic interactions, that is, pairs of genes whose joint mutation produces a distinct phenotype, have been indicated as potential targets for therapeutic intervention [125]. A very small number of driver genes are currently known and it is believed that the complete set of driver genes is not very large [125]. Thus far, there does not exist a standard method for finding driver genes. Hence, a new method for analysing PPI network topology has been proposed aiming to give insight into network parts that are rich in driver genes [129]. This method, which effectively captures a large portion of known driver and other disease-related genes, is based on an iterative pruning of the human PPI network: it uses k-core decomposition, a method that first removes nodes of degree one (k = 1), then from the remaining network it removes nodes of degree at most two (k ≤ 2) etc., until it reaches the value of k where removing nodes would result in an empty graph [130–133]. The largest value of k that leaves the graph in a non-empty state is called k_max. In this way, the part of the human PPI network that remains after k_max-core decomposition is obtained and examined for structural (i.e. topological) and functional uniqueness: this central, tightly-knit sub-network of the human PPI network is statistically significantly enriched with disease genes, driver genes and genetic interactions currently targeted by many drugs. Hence, it is termed ‘The Core Diseasome’ [129].

FUTURE DIRECTIONS

Despite noise and incompleteness in PPI and other systems-level biological network data, the structural properties of these networks have already given insight into biological function and involvement in disease of individual proteins. As we gather more network data and as the network data matures and becomes more reliable, we need to ensure that our models keep representing the data well and that our methods can cope with increased data complexity. Also, systems-level biological networks are currently only static representations of all interactions that we have ever observed under any condition and in any tissue, while cells are in fact dynamic, time- and condition-dependent systems. Hence, our data and methods should be extended to capture this systems-wide dynamics of biological processes [134–139]. Analysing the Human Diseasome in such a systems-level dynamic network framework has a potential to fully explain molecular, and environmental causes for onset and progression of disease and substantially change therapeutic practices.

Putting into the context of biological network data other approaches for analysing molecular causes of disease may to lead further insight. For example, it has been demonstrated that a significant number of diseases with early-life onset result from defective enzyme-coding genes, whereas adulthood onset diseases are caused by alterations in receptors and modifiers of protein function [140]. Also, it has been indicated that age-related diseases are a consequence of accumulation of mitochondrial dysfunction over the life of an individual [141,142]. Mitochondria perform oxidative phosphorylation that produces adenosine triphosphate (ATP) by utilizing energy released from oxidation of nutrients. In this process, toxic side-products, reactive oxygen species (ROS), are generated, which are molecules such as oxygen ions and peroxides. Increased ROS levels may lead to significant aberration of cell structure, specifically to DNA damage. Hence, many studies examine the role of energy and in particular, energy deficiency in human disease [141–143]. A direct link between mitochondrial dysfunction and disease was established and it was shown that mutations in mitochondrial DNA (mtDNA) alone are sufficient to generate major clinical phenotypes [144]. In particular, mtDNA is present in thousands of copies in a cell and it mediates effects of the environment onto genes by accumulating somatic mutations in post-mitotic tissue and resulting in delayed-onset of age-related diseases [141]. Analysing these processes in the context of systems-level biological networks may yield further insight. Another way in which the disruption of one gene was shown to trigger the onset of a seemingly unrelated disease is through what is known as the neighbouring gene effect (NGE) [145], which is also termed the ‘uncertainty principle of genetics’ [146]; It posits that the deletion of a genomic locus may affect the function of one or more neighbouring loci [146], effectively disrupting events downstream of the unintentionally affected loci. Hence, NGE may lead to erroneous gene annotation: it is estimated that NGE erroneously affects the annotation of 10% of the human interactome [147]. Due to these effects, global changes in the currently available interaction maps may soon be necessary.

Funding

The work was supported by ERC Starting Independent Researcher Grant 278212, NSF CDI OIA–1028394 grant, and the Serbian Ministry of Education and Science Project III44006.