DECOMPOSITION OF GENE REGULATORY NETWORKS INTO FUNCTIONAL PATHS AND THEIR MATCHING WITH MICROARRAY GENE EXPRESSION PROFILES

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1 DECOMPOSITION OF GENE REGULATORY NETWORKS INTO FUNCTIONAL PATHS AND THEIR MATCHING WITH MICROARRAY GENE EXPRESSION PROFILES A. Kanterakis*, D. Kafetzopoulos**, V. Moustakis*** and G. Potamias* * FORTH/Institute of Computer Science (ICS), Heraklion, Greece * FORTH/Institute of Molecular Biology & Biotechnology, Heraklion (IMBB), Greece *** TUC/Department of Production Engineering and Management, Chania, Greece {kantale,moustaki,potamias}@ics.forth.gr, kafetzo@imbb.forth.gr Abstract: Gene Regulatory Networks (GRNs) and DNA Microarrays (MAs) present two of the most prominent and heavily researched concepts in contemporary molecular biology and bioinformatics. GRNs model the interfering relations among gene products during the regulation of the cell function. MAs measure the simultaneous expression profile of thousands of genes. In an effort to combine these two sources of biological information, concurrent studies try either to 'build from scratch' or, focus on the expression profile of genes regulated in a specific GRN. Our work aims to discover the functional parts of GRNs by identifying consistencies and inconsistencies with respective MA gene-expression data. Specifically, GRNs are decomposed into all possible functional paths. Functional GRN paths from the repository are then matched against microarray gene-expression profiles of samples (with different clinical phenotype). In this way the underlying mechanism the highly matched and phenotype-differentiating functional paths that guide to specific gene-expression profiles are uncovered. The discovery guides the finer re-classification of samples with profound diagnostic and prognostic potential, and/or triggers further molecular biology research. Preliminary results on applying our methodology on a real-world microarray study and targeting the Apoptosis gene-regulatory network are encouraging and demonstrate the suitability, efficiency and reliability of the approach. 1. Introduction Current post-genomics bioinformatics research seeks for methods that not only combine the information from dispersed and heterogeneous data sources but distil the knowledge and provide a systematic, genome-scale view of biology [1]. The advantage of this approach is that it can identify emergent properties of the underlying molecular system as a whole an endeavour of limited success if targeted genes, reactions or even molecular pathways are studied in isolation [2]. Individuals show different phenotypes for the same disease they respond differently to drugs and sometimes the effects are unpredictable. Many of the genes examined in early clincio-genomic studies were linked to single-gene traits, but future advances engage the elucidation of multi-gene determinants of drug response. Differences in the individuals background DNA code but mainly, differences in the underlying gene regulation mechanisms alter the expression or function of proteins being targeted by drugs, and contribute significantly to variation in the responses of individuals. The challenge is to accelerate our understanding of the molecular mechanisms of these variations and to produce targeted individualized therapies. Two of the most promising and advanced technologies and notions of contemporary bioinformatics research are Microarrays (MA) and Gene Regulatory Networks (GRNs). Combining knowledge from both knowledge sources will accelerate our understanding of the molecular mechanisms of genome variations and will guide the discovery of targeted individualized therapies. Faced with such a challenge we devised and present an integrated methodology that amalgamates knowledge and data from both GRNs and MA geneexpression sources. The methodology aims to uncover potential gene-regulatory fingerprints and mechanisms that govern the genomic profiles of diseases. A preliminary implementation of our methodology is made in a system called MinePath. 2. MAs, GRNs and their Combination Microarrays. With the recent advances in MA technology [3, 4], the potential for molecular diagnostic and prognostic tools seem to come in reality. The last years, microarray-chips have been devised and manufactured in order to measure the expression profile of thousands of genes. The aim is to add molecular characteristics to the classification of diseases so that diagnostic procedures are enhanced and prognostic predictions are improved. These studies demonstrate that gene-expression profiling has great potential in identifying and predicting various targets and prognostic factors of diseases. By measuring transcription levels of genes in an organism under various conditions, in different tissue samples, we can build up gene expression profiles, which characterize the dynamic 1

2 functioning of each gene in the genome. The microarray data are represented in a matrix with rows representing genes, columns representing samples (e.g. various tissues, developmental stages and treatments), and each cell containing a number characterizing the expression level of the particular gene in the particular sample - gene expression matrix. Gene Regulatory Networks. Gene regulatory networks (GRNs) are network structures that depict the interaction of DNA segments during the transcription of the genes into mrna. The prominent and vital role of GRNs in the study of various biology processes is a major sector in contemporary biology research, where numerous thorough studies have been made [5, 6]. Each network has inputs, usually genes/proteins or transcription factors that initiate the network function, and outputs that usually is a certain gene. The network by itself acts as a mechanism that determines cellular behaviour where the nodes are genes and edges are functions that represent the molecular reactions between the nodes. These functions can be perceived as Boolean functions, where nodes have only two possible states ( on and off ), and the whole network represented as a simple directed graph [7]. It is indicative that most of the relations in known and established GRNs have been derived from laborious and extensive laboratory experiments and careful study of the existing biochemical literature. Thus GRNs are far from complete. Current efforts focus on the reconstruction of GRNs by exploring gene-expression data. Specifically, Babu in [8] identified that network topologies extracted from gene co-expression events could discover motifs and regulatory hubs that can characterize the entire cellular states and guide further the pharmaceutical research. Combining MAs and GRNs: The Needs. MA experiments involve more variables (genes) than samples (patients). This fact, leads to results with poor biological significance. To remedy this there is an open debate whether we should concentrate on gathering more data or on building new algorithms. Simon et al., in [9] published a very strict criticism on common pitfalls on microarray data mining while in [10] commented about the bias in the gene selection processes. Another significant aspect is the noisy content of the experiment. Appropriate statistical analysis of noisy data is very important in order to obtain meaningful biological information [11, 12]. Evidence on this is given by the fact that different methods produce gene lists (i.e., gene-markers and molecular signatures) that are strikingly different. Very few methods of gene regulatory inference are considered superior to the others mainly because of the intrinsically noisy property of the data, the curse of dimensionality, and the unknown true underlying networks. The study of the function, structure and evolution of GRNs in combination with microarray gene-expression profiles and data is essential for contemporary biology research. Cell, tissues, organs, organisms or any other biological systems defined by evolution are essentially complex physicochemical systems. They consist of numerous dynamic networks of biochemical reactions and signalling interactions between active cellular components. This cellular complexity has made it difficult to build a complete understanding of cellular machinery to achieve a specific purpose [13]. To circumvent this complexity, microarrays, biology knowledge and biology networks can be combined in order to document and support the detected and predicted interactions [14]. The advances and tools that each discipline carries can be integrated in a holistic and generic perspective so that the chaotic complexity of biology networks can be traced down. 3. Decomposition of GRNs Existing GRNs databases provide us with widely utilized networks of proved molecular validity. The most known are network that describe important cellular processes such as cell-cycle, apoptosis, signaling, and regulation of important growth factors. Online public repositories contain a variety of information that includes not only the network per se but links to respective nodes (genes) and edge (regulation) metadata and annotation. Currently MinePath utilizes the KEGG pathways repository (Kyoto Encyclopedia of Genes and Genomes; KEGG provides a standardized format representation operationalised by its own markup description language, the KGML (KEGG Markup Language; genome.jp/kegg/xml/). Figure 1. Path decomposition. Top: A target part of the KEGG cell-cycle GRN; Bottom: The five decomposed paths for the tergated path part - all possible functional routes taking place during network regulaion machinery. Our methodology relies on a novel approach for GRN processing that takes into account all possible interpretations of the network. The different GRN interpretations correspond to the different functional paths that can be followed during the regulation of a target gene. Different GRNs are downloaded from the KEGG repository. With an XML parser we obtain all the internal network semantics (see next sub-section). In a subsequent step, all possible and functional network 2

3 paths are extracted as exemplified in Figure 1 above. Each functional path is annotated with the possible valid values according to Kauffman s principles that follow a binary setting: each gene in a functional path can be either ON or OFF. According to Kauffman [7], the following functional gene-regulatory semantics apply: (a) the network is a directed graph with genes (inputs and outputs) being the graph nodes and the edges between them representing the casual (regulatory) links between them; (b) each node can be in one of the two states: on or off ; (c) for a gene, on corresponds to the gene being expressed (i.e., the respective substance being present); and (d) time is viewed as proceeding in discrete steps - at each step, the new state of a node is a Boolean function of the prior states of the nodes with arrows pointing towards it. KEGG encompasses and models a variety of regulation links (edges). Figure 2 shows these links accompanied with their underlying notation and semantics. Figure 2. Different functional relationships between genes (or group of genes) Since the regulation edge connecting two genes defines explicitly the possible values of each gene, we can set all possible state-values that a gene may take in a path. Thus, each extracted path contains not only the relevant sub-graph but the state-values ( ON or OFF ) of each gene as well. The only requirement concerns the assumption that for a path being functional, the path should be active during the GRN regulation process. In other words we assume that all genes in a path are functionally active. For example assume the functional path A B ( is an activation/expression regulatory relation). If gene A is on an OFF state then, gene B is not allowed to be on an ON state - B could become ON only and only if it is activated/expressed by another gene in a different functional path, e.g., C B). If we had allowed non-functional genes to have arbitrary values then the significant paths would be more likely to be noisy rather than of biological importance. The extracted and annotated paths are stored in a database that acts as a repository for future reference. Through this repository we can query paths being parts of targeted GRN, contain specific genes or, involve a specific regulatory relation. Moreover, the stored paths can be combined and analyzed in the view of specific microarray experiments and respective gene-expression sample profiles. Furthermore, as the database repository contain and retrieves functional paths from a variety of different GRNs (e.g., cell-cycle, apoptosis etc), we may combine knowledge (i.e., the functional paths) from different molecular pathways and networks a major need for molecular biology and a big challenge for contemporary bioinformatics research. 3. Matching GRN Functional-Paths with MA Gene-Expression Profiles The next step is to locate microarray experiments and respective gene-expression data where we expect the targeted GRN to play an important role. For example the cell-cycle and apoptosis GRNs play an important role in cancer studies and respective experiments dealing with tumor progression. With a geneexpression/functional-path matching operation, the valid and most prominent GRN functional paths are identified. In order to combine and match GRN paths with microarray data the respective gene-expression values should be transformed into two (binary) states - on and off. Microarray gene-expression discretization is a popular method to indicate vigorously the expressed (up-regulated/ on ) and non-expressed (downregulated/ off ) genes. Currently MinePath encompasses an information-theoretic process for the binary transformation of gene-expression values. The process pre-supposes the categorization of the input microarray samples into two classes and is based on an information-theoretic setting. A detailed presentation of the discretization process may be found in [15]. GRN and MA gene-expression data matching aims to differentiate GRN functional paths and identify the most prominent of them with respect to the gene-expression profiles of the input samples. In other words, the quest is for those functional paths that exhibit high matching scores for one of class and low matching scores for the other. This is a paradigm shift from mining for genes with differential expression, to mining for subparts of GRN with differential function. The procedure for differential path identification is inherently simple. Assume five input samples {S 1, S 2, S 3 } and {S 4, S 5 } assigned to (phenotypic) class POS and NEG, respectively, and engages five genes {g 1, g 2, g 3, g4, g 5 }. Furthermore assume that we are about to explore (target) an artificial GRN being decomposed into four functional-paths {P 1, P 2, P 3, P 4 } (see Figure 3). For matching a sample with a specific path we consider just the genes engaged by this path. Our notion of matching is realized as a consistency relation between functional GRN paths and gene-expression profiles. In other words, we care just for those GRN parts which represent causal putative molecular mechanisms that govern the expression status of genes for specific samples. For example, sample S 1 matches perfectly path P 1 even if this path engages just two of the total five genes. In contrast, S 5 does not match perfectly path P 4 even if both share the same ( on ) state for genes g 1 and g 4, they differ in the state of gene g 5 ( on in path P 4 and off in sample S 5 ). 3

4 Figure 3. Matching GRN functional paths with samples geneexpression profiles; red and light-green coloring represents on and off states of genes, respectively, and represent activation/expression and inhibition relations between genes, respectively.). In general, for each path we compute the number of samples that is consistent for each class. Suppose that there are S 1 and S 2 samples belonging to the first and second class, respectively. Assume that path P i is consistent with S i;1 and S i;2 samples form the first and second class, respectively. The formula below, computes the differential power of the specific path with respect to the two classes. dp(p i ) = ( Si; 1 / S 1) ( Si;2 / S 2) Ranking of paths according to the above formula, provides the most differentiating and prominent GRN functional paths for the respective phenotypic classes. These paths uncover putative molecular mechanisms that govern specific phenotypes (disease, disease states, drug responses etc). For the example of figure 3 the following matching path/samples matching and differentiating power matrix is computed. Examining the matrix we may identify a path, path P 2 (shaded), that differentiates perfectly between the two classes it is a path that matches just class POS samples and none from class NEG. So, a direct conclusion is that a putative molecular mechanism governing class 'POS' is represented by functional path P 2. The introduced dp formula can be enriched so that longer consistent paths acquire stronger power. It can also be relaxed so that consistent presents a continuous, rather than a Boolean indicator. Finally we may introduce unknown values for missing and erroneous gene expression values. 4. Preliminary Experiments and Results We applied the presented methodology on a real-world, and widely utilized, cancer-related microarray study. The study concerns the gene-expression profiling of breast-cancer (BRCA) patients. The targeted phenotypic classes concern the good and bad prognosis of the patients; good and bad modelled as >5 - and 5 - years free-of-relapse, respectively. The study concerns 34 and 44 gene-expression profiles for RELAPSE and NON-RELAPSE patients, respectively. For a detailed description of this clinicogenomic study please refer to [Veer]. In the aforementioned study publication, the researchers were able to identify a molecular signature of 70 differentially expressed genes the expression profiles of which was capable of adequately distinguish between the two phenotypic classes. But there was no evidential reporting on the underlying gene-regulatory mechanisms governing the expression status of the identified molecular signature. In an effort to identify such mechanisms we applied the introduced methodology on the respective BRCA microarray dataset, targeting the Apoptosis regulatory network ( hsa04210.html). Our conjecture is: for RELAPSE patients the gene-regulating molecular mechanisms guiding to apoptosis should be blocked, with the inverse for the NON-RELAPSE patients. A total of 406 consistent matching paths were identified, each one matching with a different degree the different samples. Moreover, each of these paths exhibit different differential power with respect to the two classes. Based on the computed (by the introduced dp formula) differential power of paths, we were able to identify: (i) six paths that match only NON-RELAPSE samples and none of the RELAPSE ones; and (ii) just one path for the inverse case; see Figure 5. In the figure, the on and off genes are represented with red and light-green colour, respectively. The parts of the Apoptosis GRN where the six identified paths are active for the NON- RELAPSE, and the one being active for the RELAPSE cases are enclosed in the blue and red rectangles, respectively. RELAPSE path: CASP87 (caspase7, apoptosisrelated cysteine peptidase) expresses/activates DFFA (DNA fragmentation factor) which indirectly guides to Degradation effects, and which also dissociates (represented with ) DFFB (DNA fragmentation factor) making it inactive. As DFFB guides to Apoptosis we may conclude that apoptosis is blocked. So, the specific path presents an anti-apoptotic molecular behavior which is consistent with cancerrelapse cases. NON-RELAPSE path: This part of the Apoptosis GRN permits apoptotic events. In the upper-part of the blue rectangle notice the on (red) genes being active (their regulation is indicated by the blue arrows). A putative mechanism guiding to apoptosis is identified by the bold-shaded arrows that engage the Cleavage of Caspase Substrate cascade which indirectly guides to apoptosis. Following the down part of the 4

5 rectangle, and because of the activation of MAP3K14 (mitogen-activated protein kinase kinase kinase 14) which in turn phosphorylates IKK (CHUK - conserved helix-loop-helix ubiquitous kinase) a whole GRN sub-network should be activated (shaded elliptic area). This sub-network guides to degradation and anti-survival events. From this, we may conclude that the MAP3K phosphorylation - --> IKK is probably not active. This finding may present a promising hint for further molecular lab experimentation. paths, and the matching of these paths with samples gene-expression profiles. An initial implementation of the whole methodology is made in a system called MinePath. MinePath was applied in a real-world and widely utilised gene-expression breast-cancer with encouraging results that demonstrate the suitability and reliability of the whole approach. Among others, our on-going and immediate research on the field include: (a) further experimentation with various real-world microarray studies and different GRN targets (accompanied with the evaluation of results form molecular biology experts); (b) extension of pathdecomposition to multiple GRNs; (c) elaboration on more sophisticated path/gene-expression sample matching formulas and operations; (d) incorporation of different gene nomenclatures in order to cope with microarray experiments from different platforms and encodings; and (e) porting of the whole methodology in a Web-Services and workflow environment. References Figure 5. The Apoptosis -related RELAPSE vs. NON- RELAPSE putative molecular mechanisms. From the above findings it should be evident that there are two different putative molecular regulatory mechanisms that differentiate between RELAPSE and NON-RELAPSE behaviours in BRCA cases. The discovered and identified differentiated paths may be of high value for deciding treatment plans and potential therapeutic targets in drug design processes. Of course, a more complete picture could be achieved when more regulatory networks take part in the analysis and more differentiating paths are identified, e.g., cell-cycle, p53 (tumour suppressor gene) signalling pathways etc. Moreover, all the identified functional paths share a number of common genes that exhibit the same ( on or off ) for both classes. So, with a standard gene-selection approach these genes could not be highly ranked and selected as potential gene-markers. It is the power of their regulation and not the state of genes themselves that makes the difference! This is a realization of the already mentioned paradigm shift: from mining for genes with differential expression, to mining for subparts of GRN with differential function. 4. Conclusions We have presented an integrated methodology for the combined mining of both GRNs and MA geneexpression profiles. In the heart of the methodology is the decomposition of GRNs into all possible functional [1] T. Ideker, T. Galitski and L. Hood, A new approach to decoding life: systems biology, Annu Rev Genomics Hum Genet, 2, (2001). [2] F.S. Collins, E.D. Green, A. E. Guttmacher and M. S. Guyer, A Vision for the Future of Genomics Research, Nature, 422(6934), (2003). [3] H.F. Friend, How DNA microarrays and expression profiling will affect clinical practice, Br Med J., 319, 1-2 (1999). [4] D.E. Bassett, M.B. Eisen, and M.S. Boguski, Gene expression informatics: it s all in your mine, Nature Genetics, 21(Supplement 1), (1999). [5] J. M. Bower and H.Bolouri, Computational Modeling of Genetic and Biochemical Networks, Computational Molecular Biology Series, MIT Press, [6] B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, and P. Walter, Molecular Biology of the Cell, Garland Science, New York, [7] S. A. Kauffman, The Origins of Order: Self- Organization and Selection in Evolution, Oxford Univ. Press, New York, [8] N.M. Babu, N.M. Luscombe, L. Aravind, M. Gerstein and S.A. Teichmann, Structure and evolution of transcriptional regulatory networks, Curr. Opin. Struct. Biol., 14, (2004). [9] R. Simon, M. D. Radmacher, K. Dobbin and L. M. McShane, Pitfalls in the Use of DNA Microarray Data for Diagnostic Classification, Journal of the National Cancer Institute, 95(1), 14-18, (2003). [10] Ambroise and G. J. McLachlan, Selection bias in gene extraction on the basis of microarray gene-expression data, PNAS, 99(10), , (2002). [11] D.K. Slonim, From pattern to pathways: gene expression data analysis comes of age, Nature Genetics, 32, (2002). [12] J. Quackenbush, Computational Analysis of Microarray Data, Nature Reviews Genetics, 2, (2001). [13] H. Kitano, Systems biology: a brief overview, Science, 295(5560), (2002). [14] K. Kwoh and P. Y. Ng, Network analysis approach for biology, Cell. Mol. Life Sci., 64, (2007). [15] G. Potamias, L. Koumakis and V. Moustakis, Gene Selection via Discretized Gene-Expression Profiles and Greedy Feature-Elimination, LNAI, 3025, (2004). 5