Mapping the p53 gene using STRING software to study - NOPR

Indian Journal of Biotechnology Vol 12, October 2013, pp 451-461

Mapping the p53 gene using STRING software to study the alterations modulating the functioning of associated genes in leukemia Archana Jayaraman and Kaiser Jamil* Centre for Biotechnology and Bioinformatics, School of Life Sciences, Jawaharlal Nehru Institute of Advanced Studies Secunderabad 50003, India Received 31 August 2012; revised 11 December 2012; accepted 20 February 2013 Alteration in the p53 gene leads to uncontrolled cell proliferation and when these changes accumulate, it may result in carcinogenesis. A plethora of proteins have been reported that bind to the various regions of p53 in order to regulate the specificity of its activity. In the present study, our aim was to understand these connections so we have analyzed the networking role of p53 gene using ‘STRING’ software in leukemia [acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML) & chronic lymphocytic leukemia (CLL)] with emphasis on ALL, being most prevalent in children. The TP53 protein is an important tumor suppressor protein, found altered in many cancers. Using STRING tool, we successfully determined the protein-protein interaction network and studied its functional interactionpartners with which we could decipher some of the major pathways that may be deregulated in ALL. Applying the clustering algorithm, currently accessible in STRING, i.e., k-Means Clustering, we identified 8 specific non-overlapping clusters of various sizes, which emerged from this huge network of protein-interactors. Since the functionally interacting partners are closely associated with each other, alterations in one might affect the other, thus contributing to disease etiology. In conclusion, our results highlight the interaction of p53 gene network, which modulates hundreds of proteins with a trigger of MDM signaling. Investigating these modulator or trigger proteins as key signaling factors could form targets for new therapeutic intervention sites. Further, these functionally interacting partners of disease proteins could help uncover novel disease mechanisms. From a scientific point of view, our ‘STRING’ results have clearly shown the importance of p53 signaling pathways in leukemia. Keywords: ALL, AML, CLL, clusters, CML, leukemia, STRING, TP53 gene-networks

Introduction Leukemia is a type of cancer that affects the blood and bone marrow. Acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML) and chronic lymphocytic leukemia (CLL) are the most common forms of leukemia. Of the four, ALL is the most prevalent form of cancer affecting children. Although in recent years, in most of the developed countries, recovery rate has been high with almost 80% of the children surviving and nearly 20% still suffer from relapse1. All four forms of leukemia also occur in adults where the success in treatment rate has not been very high and the afflicted individuals and their families are deeply affected. In our earlier studies, we have determined the susceptibility biomarkers and risk factors in childhood leukemia2 and also identified the SNPs in drug —————— *Author for correspondence: Mobile: +91-9676872626; Telefax: +91-40-27541552 E-mail: [email protected]

metabolizing genes, such as, GST and FLT33. The frequency of alterations that occur in the chromosome and biomarkers of several hematological malignancies, including ALL, have been estimated in previous experimental studies4. Many of the genes that have been reported in leukemia are predominant in the cell cycle process and its regulation. The regulation of cell cycle is a complex process that involves several proteins/genes, such as, cyclins, cyclin dependent kinases, cyclin dependent kinase inhibitors, other protein kinases and various tumor suppressor genes5. The TP53 gene is an important tumor suppressor gene that encodes a transcription factor involved in various aspects of cell cycle, such as, cell cycle arrest, apoptosis in response to cellular stress and damage. Alteration in the p53 gene could lead to uncontrolled cell proliferation. In addition to p53, p53 related genes, such as, MDM2, CDKN2A and p19ARF, have also been implicated in the tumorigenic process through their involvement in p53 pathways6-8. The role played by interactions between p53 and genes,

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involved in various other pathways in the carcinogenesis process, indicates the need to comprehend the association of different genes and their protein products in the etiology of disease. The alteration in one biomolecule might affect the functioning of other associated biomolecules, so a better understanding of the relationship between the molecules might help in developing prognostic markers and in developing better therapeutic strategies. Protein interactions, derived through various sources, such as, physical interaction and functional association studies, are generally represented as a network graph with proteins as nodes and interactions as edges. A number of public databases, such as, BioGrid9, the Database of Interacting Proteins10, the Human Protein Reference Database (HPRD)11, I2D12, IntACT13, STRING14 and APID15, allows users to retrieve and analyze the interaction data. These databases differ with regard to the source of information used to predict the interactions. Computational predictions of protein interactions generate a more robust interactome as the interactions integrate data from varied sources and provide complete annotation information. Mapping interactions in normal and diseased state could help in understanding the pathways deregulated in disease and help in understanding the biological significance of these interactions in disease. Hence, the aim of present study was to analyze the functional interactions of TP53 and its interacting proteins to understand which pathways might be significantly altered in ALL; for which we used TP53 gene/protein as the seed in the construction of a protein-protein functional association network. Maerials and Methods In the present study, TP53 protein network was analyzed to unfold its interactions with other proteins using STRING version 9 database (Search Tool for the Retrieval of Interacting Genes, available at: http://string-db.org/)14. We selected this tool as it offers many advantages: (i) it has an extensive collection of pre-computed interaction data derived from varied sources, such as, high-throughput experimental data, literature data and computational predictions; (ii) scores the network interactions using probabilistic scoring to obtain higher confidence in the interactions; and (iii) allows grouping of interacting molecules into clusters

using MCL (Markov clustering16) and k-Means17 algorithms in the advanced mode. Therefore, we used this tool to query, retrieve and analyze the TP53 protein interaction network with the interactions restricted to those available for Homo sapiens. TP53 was entered into the query search box and an initial network was obtained, which showed the associations of TP53 along with ten top scoring predicted functional interactors. To obtain a more robust interactome, we continued growing the network to obtain an additional 200 interactors. We restricted the network to only 200 interactors to make it more pliable for analysis. The prediction methods selected for our analysis include Neighbourhood, Gene Fusion, Co-occurrence, Co-expression, Experiments, Databases and Textmining. We also filtered our interaction network to obtain only those interactions which had a confidence score greater than 0.9, representing more than 80-90% confidence in the predictions. Since the network obtained was very large, we grouped the network interactors into clusters using the k-Means clustering algorithm to obtain a better representation of the protein interactions. We selected k-Means algorithm as it is an unsupervised clustering algorithm based on adjacency matrix, which groups molecules based on pre-specified criteria. We divided the network into 8 non-overlapping clusters. The annotation information related to the protein molecule, interaction type and source of interaction were retrieved by the STRING database when we selected the particular molecule and its interaction. We selected the Evidence view display option as in this view each interaction edge is coloured differently and represents the different sources of the interaction data. The details of the protein interaction network are presented in the results. Results Using TP53 as seed, a protein-protein interaction network was constructed. A total of 211 interacting proteins with 1556 interactions were retrieved from the STRING database. The interactions based only on text mining were verified using PubMed literature database. We present the results of protein-protein interaction network in Fig. 1, which is a highly connected network of molecules. The interaction network is visualized in the form of a graph with the protein molecules forming the nodes of the graph and the interactions form the edges. Most of the proteins

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Fig. 1—Protein interaction network of TP53.

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are at the center of the network with few molecules loosely arranged at the periphery. Some of the interactors are connected to one another by multiple lines which represent interactions derived from more than one source of information. The k-Means clustering algorithm was applied to segregate the network into smaller subgroups of eight clusters (Fig. 2). Upon analysis, the following information was obtained: (i) In the first cluster, we observed 21 proteins as interactors. This cluster consists of proteins of many cyclins and cyclin dependent kinases. Most of the protein molecules in this cluster belong to the cell cycle genes, which are functionally involved in the control of cell cycle. (ii) The second cluster was found to be of a

highly connected network of 91 protein molecules. The proteins in this cluster have been reported to possess a wide range of functional attributes. TP53 is part of this cluster and was found to be highly connected, interacting with many proteins. Many TP53 binding and interacting proteins are also part of this cluster. Many tumor suppressors, apoptosis regulator proteins, transcriptional activators are part of this dense cluster. This network has been found to be associated with epidermal growth factor receptor (EGFR), death receptors and many ubiquitin proteins. (iii) The third cluster included 19 protein interactors. Many of the proteins in this cluster belong to those involved in the regulation of HIF1A proteins that act as cellular

Fig. 2—Protein interaction network after k-Means Clustering.

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oxygen sensors. (iv) The fourth cluster consisted of 11 protein molecules. Majority of them belong to the transcriptional activator group. (v) The fifth cluster contained 28 protein molecules and they function as transcriptional activators and also transcription factors. The SMAD protein found in this cluster is reported as a modulator of the TGF-β superfamily. (vi) The sixth cluster was composed of 29 protein interactors. The MYC proto-oncogene, which has been implicated in several malignancies, is part of this cluster. The Fanconi anemia family of proteins forms a major part of this cluster. This cluster also consisted of key regulators of entry into cell division, RBL1 and RBL2. Some of the other proteins play a key role in DNA repair. A few proteins are involved in ubiquitin pathways. (vii) The seventh cluster consisted of 6 molecules. Majority of them are involved in DNA mismatch repair. PCNA is involved in the control of eukaryotic DNA replication. E2F1 belongs to the transcription factor family and functioned in progression of cell cycle from G1 to S phase. It also mediates cell proliferation and p53-dependent apoptosis. (viii) The eighth cluster was composed of 6 proteins. MDM2, RB1 and ABL1, key molecules in cell cycle regulation process, are part of this cluster. Two of the molecules, RPL5 and RPL11, are involved in RNA maturation process. AKTI has been a key modulator of AKT-mTOR signaling pathway. Comparison of Interaction Partners across Different Types of Leukemia

The proteins, which have been altered in the different types of leukemia and are part of this protein-protein interaction (PPI), were identified and each cluster was analyzed to observe the disparity in the other leukemia in comparison to ALL. We found that some of the interacting proteins are common to all four types of leukemia, while some have been reported only in a particular form (Table 1). Most of the disease associated proteins are spread out across the eight clusters, but 4th and 7th clusters have very few proteins in association with disease. Among all the clusters, the most number of disease proteins were those associated with CLL. The next highest number was observed in association with ALL. The highest number of disease proteins was found in the 2nd cluster in all the four leukemia.

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Discussion The TP53 gene is localized on chromosome 17 (short arm, 17p13), a region that is frequently deleted in human cancer18,19. Although the disease mechanisms are not clearly understood, it is generally believed that many genes involved in the regulation of cell cycle process are involved as promoters of leukemogenic process. Protein-protein interactions play an essential role in various biological processes, such as, cell cycle, metabolic pathways and signal transduction20,21. Understanding these protein-protein interactions is significant because this can reveal information about the regulation of cellular activities. Information obtained from studies on protein functional interactions could be extrapolated to genes, which can further help in understanding the etiology of a particular disease. In recent years, molecular network interactions are being used to predict novel candidate genes based on the assumption that genes that lie in the neighborhood of disease causing genes in the network are likely to be associated with the same or a similar disease22. Several studies have identified novel disease genes through the use of protein-protein interaction networks and have revealed that proteins involved in cancer are extremely interconnected23,24. Studies on proteinprotein interactions in AML have helped in determining pathways that are significantly altered in the regulation of cancer stem cells25. Also, studies conducted on cancer metasignature genes have implied that proliferation of cancer may be due, largely, to the involvement of the cancer related genes in numerous pathways26. TP53 is an essential cell cycle regulatory protein. To ensure the normal functioning of TP53, several proteins, such as, ubiquitin, ligases, kinases, acetylases and transcriptional co-activators, act directly or indirectly, thus regulating the activity of this protein27. TP53 in turn regulates the activities of several dozen proteins that are involved in diverse functions, such as, cell cycle inhibition, apoptosis, genetic stability and other functions28. Any change in these interacting/regulatory molecules could affect the functioning of TP53, contributing to its role in tumorigenesis. TP53 is frequently mutated in many cancers, adversely affecting response to chemotherapy and irradiation treatments29. In ALL, TP53 mutations are more commonly seen in patients suffering from relapse30. In an earlier study using phylogenetic methods, we found that the TP53 gene appears to be

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conserved across the mammalian genome, and that the alterations of this gene are probably due to changes that accumulated during the evolutionary history of this gene31. Thus, understanding the biological networking of TP53 would be crucial in studying deregulations in TP53 pathway in leukemia. In the current study, we have applied a computational approach to retrieve and analyze the interactions of TP53 protein through the use of STRING database as it allows the feasibility of understanding the interactome using data from multiple sources. This database was used in our analysis for mining protein interactions as it displays interactions based on not only verified experimental sources but also predicts interactions based on text mining; thus providing a wider base for analyzing the protein interactome. We have used TP53 as the seed molecule to discern information regarding its interactions with other proteins and to identify other proteins that might participate in the leukemogenesis process along with this tumor suppressor. The output interaction network that we obtained from STRING is represented as a connected network of molecules where the edge represents interactions between the molecules (Fig. 1). In our analysis, the largest interaction network was centered about TP53, with TP53 itself displaying a densely connected network of interactions. Many of the proteins whose activity is regulated by p53, such as, BCL proteins and BAX, are part of this cluster. Comparison of Interaction Partners across Different Types of Leukemia

Alterations in TP53 and its pathway genes have also been reported in other hematological malignancies, namely, AML, CML and CLL, each differing in the frequency of occurrence in the patient. To understand better how different Leukemia differ in respect to TP53 and its interaction partners, we performed a comparative analysis. Fig. 3 shows a graphical representation of the disease proteins in the various clusters. A total of 41 proteins in the PPI network have been reported in ALL. These account for about 1/5th of the total number of interacting proteins and are spread across various clusters of the network. These proteins have varied functions and are part of several pathways, such as, cell cycle and its regulation, p53 signaling pathway, Wnt signaling pathways, apoptosis and JAK-STAT signaling pathway. These pathways and processes are essential for the normal functioning and development

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mechanisms of an organism. Alterations in the proteins of these pathways might lead to disruption of the pathways and thus might be significant contributors to the tumorigenesis process. The proteins involved in these pathways have been associated with altering the pathway not just in Leukemia but also in other types of cancer and several other diseases. Cluster 1

This cluster has 10 disease proteins, which have been associated with the different leukemia. Out of these 10, 6 proteins have been reported in altered state in ALL, which is more than those associated with the other leukemia. The proteins in this cluster normally play key roles in the cell cycle process. Normal cell growth and division cycle helps in maintenance of cell numbers and proper functioning of cells. The normal progression of cell cycle is in turn ensured through the various cell cycle regulatory proteins and their inhibitors. The various cyclins and cyclin dependent kinases function in ensuring the normal progression of the cell cycle into the next phase of division. An alteration in these proteins hinders the progression and contributes to the uncontrolled proliferation characteristic of cancerous cells. Cyclin D1 protein has been reported in all 4 Leukemia. It is a key cell cycle protein, involved in the transition of cell cycle from G1 to S phase. It is also thought that blast cells are transported from the bone marrow to the lymph nodes due to overexpression of the gene encoding this protein32. Since the mobilization of blast cells and their accumulation in various organs is a characteristic of leukemia, this protein might serve as an important prognostic marker. Cluster 2

This cluster has more number of disease proteins in comparison to all other clusters. Though several proteins from this cluster have been reported in CLL, AML and ALL have almost as many disease proteins. These disease proteins function normally as regulators of cellular processes, such as, proliferation, differentiation, apoptosis and DNA recombination and repair. Eight proteins from this cluster have been reported in association with all 4 leukemia. Of these, 7 proteins are involved in the regulation of apoptosis. Alterations in these proteins might significantly affect

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Fig. 3—Graph showing the disease proteins across various clusters.

their ability to ensure normal apoptotic process to be carried out, which in turn might lead to accumulation of leukemic cells and thus contribute to the apoptotic process. The other protein in this cluster, namely, ABCB1, is an important protein since it has been reported to contribute the resistance to anticancer drugs, thus hindering the therapeutic process. Apoptosis is an important mechanism, which maintains the balance between cell proliferation and cell death and ensures removal of defective cells from the system. Apoptotic pathways are very crucial for leukemia studies as defective apoptotic signaling are responsible not just for the uncontrolled proliferation of leukemic cells but have also been connected to drug resistance in experimental studies33. In lieu of this, a thorough understanding of the apoptotic proteins and their interaction with other proteins that regulate apoptotic pathways is essential so that better therapy approaches can be developed, which can overcome any resistance due to altered apoptotic pathways. Cluster 3

The proteins associated with disease in this cluster have diverse functions ranging from cell

growth to production of nitric oxide, which has essential tumoricidal and bactericidal properties. The only protein common to all 4 leukemia in this cluster is the vascular endothelial growth factor (VEGF). This protein is involved in cell growth, cell migration and inhibition of apoptosis, and regulates angiogenesis and vasculogenesis through mediation of vascular permeability. Angiogenesis is a process wherein old blood vessels are used to form new vessels. VEGFA protein plays an important role in bone marrow angiogenesis by binding to VEGF receptors and initiating cell migration and proliferation, which are essential to this process. Overexpression of VEGFA leads to increased angiogenesis and this abnormal angiogenesis process has been reported in patients suffering from leukemia and is thought to contribute to disease progression 34. VEGFA has also been reported to confer apoptosis resistance to cells, though this mechanism is still not understood clearly. Studies designed to understand this mechanism could be useful in the development of better chemotherapies and use VEGFA as a therapeutic target.

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Cluster 4

Only one disease related protein is present in this cluster. RELA is a subunit of NFKB complex. The binding of this protein to its promoters is regulated by GSK-3 protein. This binding activates target genes in B cell and contributes to survival of B cell survival in CLL and to increased accumulation of RELA in the nucleus35. This binding is being investigated by researchers for possible use as therapeutic target. Cluster 5

The 12 disease proteins in this cluster function as transcriptional activators and transcription factors, and play a role in cell growth and apoptosis, and as signal transducers. The number of disease proteins associated with ALL in this cluster is as high as 10 proteins, while the other diseases report a significantly less number of proteins from this cluster. Of the 12 proteins, FOXO3A has been observed to be altered in all 4 leukemia. This protein is an important transcriptional activator that initiates apoptosis in the absence of survival factors by regulating the expression of genes necessary for cell death. This protein is also involved in various signal transduction pathways that regulate various cellular processes, such as, aging, cell proliferation and malignancies. Altered expression of this protein has been observed in leukemia and studies have found overexpression of this protein to contribute to drug resistance in leukemic cells36. The multifunctional aspects of this protein and its mechanism to drug resistance need to be studied further to ascertain its role in leukemic pathways and also for use as a target for therapeutic interventions. Cluster 6

This cluster comprises 10 disease proteins associated with various leukemia and are functionally active in many processes; important among these being regulation of cell cycle checkpoints. The number of ALL related proteins is limited to 3, though as high as 9 CLL related proteins occur in this cluster, indicating that this cluster could be significant in studies relating to CLL. The MYC nucleoprotein is the only protein in this cluster that has been found to be altered in all 4 leukemia. The gene encoding this protein is found to be altered in various malignancies and is a key protooncogene. This protein regulates transcription of several genes that are involved in diverse functions, such as, growth, metabolism, cell cycle, differentiation,

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angiogenesis, apoptosis, etc. The expression of this protein is strongly regulated in normal cells and its improper regulation has been reported in several cancers37. Alterations in this protein affect not only its expression but also its various target genes and their pathways. MYC deregulation has been reported in ALL with low expression in CLL. In Myeloid leukemia, overexpression of the MYC protein has been found38. This data reaffirms the need to concentrate on this protein for its use an important therapeutic target in cancers, especially leukemia. Cluster 7

In this cluster, the 2 disease proteins, MSH2 and E2F1, have different functions. MSH2 is involved in DNA mismatch repair and E2F1 is an important transcription factor involved in progression of cell cycle from G1 to S phase. These proteins occur in different chronic leukemia and have not been reported in association with acute leukemia. Cluster 8

The 3 disease proteins in this cluster, viz., RB1, ABL1 and MDM2, play important roles in the regulation of cell cycle progression. They have been reported in association with ALL and CML, whereas only MDM2 is found across all the 4 leukemia. MDM2 is a negative regulator of TP53 protein. It is known that MDM2 protein regulates the stability of the p53 protein by ubiquitination and transport towards the proteasome. Abnormal accumulation of the MDM2 protein was observed in many tumours, especially sarcomas39. Inactivation of p53 has been associated with increased expression levels of this protein, affecting the tumor suppression activity of p5340. The interactions of MDM2 play a role in apoptosis and tumorigenesis process and also in the regulation of cell cycle. Alterations and overexpression of this protein can thus affect its myriad functions and lead to malignant transformations. This proto-oncogene has also been reported to contribute to resistance to therapy by binding to p53 and preventing it from initiating apoptosis41. These functions indicate the use of MDM2 as an important therapeutic target molecule. These observations signify that interacting proteins and their interactions could help understand the contribution of these proteins to disease etiology and also comprehend the extent to which alteration in one protein could affect the functions of its interacting partner. Since the proteins in each cluster are closely

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associated functionally with each other, alterations in the disease protein might also change expression of the interacting proteins and hence contribute to the pathogenesis of the disease. The comparative analysis of disease proteins carried out by this study shows that most of the disease proteins participate in common pathways, such as, those involved in developmental process and immune regulation, indicating that all the leukemia might originate from a common ancestral cell which gives rise to a malignant leukemic cell due to alterations in crucial pathways of survival and development. Comparative analysis of the proteins specific to the type of leukemia helps in understanding the differences in the dysregulated pathways unique to each type, contributing to a better understanding of the different leukemia and in turn identify critical targets for drug therapies42. Through our analysis, we observed that the disease proteins in each cluster seem to play an important role in tumorigenic sustenance process. Studies examining expression of the molecules found to interact with the disease proteins could help in identifying new pathways of disease pathogenesis. The final outcome of p53 alterations is either modulations in the cell cycle leading to uncontrolled growth or arrest and DNA repair or apoptosis. Though the mechanism leading to the choice between these fates has not yet been elucidated, it is clear from our results that the core regulation of p53 is through its interaction with several proteins that modulate its stability. Also, our study highlights the importance of computational analysis of protein interaction networks in providing a platform for understanding protein networks and their application in the study of disease etiology. In conclusion, our study dealt with observing the interactions of TP53 and its neighbor interacting proteins with the help of computationally predicted protein interactions and also looks at the differences in this network among the different leukemia. This information forms the basis for our understanding of these malignancies and this approach could be useful to identify better prognostic markers for leukemia. Further, we found that p53 is situated at the crossroads of a network of signaling pathways that are essential for cell growth regulation and apoptosis, which could contribute to its role in disease mechanisms.

Acknowledgments We are grateful to Jawaharlal Nehru Institute of Advanced Studies, Secunderabad, India for the facilities provided to the present study. References 1

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