Volume 10 Supplement 3
MitoInteractome: Mitochondrial protein interactome database, and its application in 'aging network' analysis
- Rohit Reja†1, 2,
- AJ Venkatakrishnan†1,
- Jungwoo Lee3,
- Byoung-Chul Kim1,
- Jea-Woon Ryu1,
- Sungsam Gong4,
- Jong Bhak1, 2Email author and
- Daeui Park1Email author
© Reja et al; licensee BioMed Central Ltd. 2009
Published: 3 December 2009
Mitochondria play a vital role in the energy production and apoptotic process of eukaryotic cells. Proteins in the mitochondria are encoded by nuclear and mitochondrial genes. Owing to a large increase in the number of identified mitochondrial protein sequences and completed mitochondrial genomes, it has become necessary to provide a web-based database of mitochondrial protein information.
We present 'MitoInteractome', a consolidated web-based portal containing a wealth of information on predicted protein-protein interactions, physico-chemical properties, polymorphism, and diseases related to the mitochondrial proteome. MitoInteractome contains 6,549 protein sequences which were extracted from the following databases: SwissProt, MitoP, MitoProteome, HPRD and Gene Ontology database. The first general mitochondrial interactome has been constructed based on the concept of 'homologous interaction' using PSIMAP (Protein Structural Interactome MAP) and PEIMAP (Protein Experimental Interactome MAP). Using the above mentioned methods, protein-protein interactions were predicted for 74 species. The mitochondrial protein interaction data of humans was used to construct a network for the aging process. Analysis of the 'aging network' gave us vital insights into the interactions among proteins that influence the aging process.
MitoInteractome is a comprehensive database that would (1) aid in increasing our understanding of the molecular functions and interaction networks of mitochondrial proteins, (2) help in identifying new target proteins for experimental research using predicted protein-protein interaction information, and (3) help in identifying biomarkers for diagnosis and new molecular targets for drug development related to mitochondria. MitoInteractome is available at http://mitointeractome.kobic.kr/.
The mitochondrion, a membrane bound organelle, is often described as "the cellular power plant". It plays a critical role in energy production, metabolism, apoptosis, cellular proliferation, and other essential cellular processes. Consequently, mitochondrial dysfunctions result in disorders ranging from energy metabolism defects to neuro-degenerative diseases such as Parkinson's disease and Alzheimer's disease . The association of mitochondria to these disorders has driven experimental approaches to define the mitochondrial proteome. Recently, proteomics approaches based on 2D-gel electrophoresis and mass spectrometry were employed to estimate the size of mitochondrial proteomes of individual species. Bardel et al. separated 433 protein spots in Pisum sativum (peas) mitochondrial proteome , while Kruft et al. separated 650 different Arabidopsis thaliana (thale cress) mitochondrial proteins  and estimated that 1,500 - 2,000 nuclear genes encode mitochondrial proteins. Taylor et al. separated 615 proteins from normal human heart tissue mitochondria and estimated the total human mitochondrial proteome to be about 1,500 proteins . Owing to a steady increase in the amount of information, a comprehensive web-based information portal unifying mitochondrial proteome information has become a necessity.
In order to gain an in-depth understanding of the molecular functions and processes of mitochondria, it is essential to examine the interactions among its proteins. Here, we introduce 'MitoInteractome', a consolidated multi-species database for mitochondrial research that integrates information relevant to functions, pathways, diseases, non-synonymous SNP effects, protein-protein interactions, and physico-chemical properties. Further, we present an application of MitoInteractome in the construction and analysis of a protein interaction network for the 'aging' process.
Construction and content
The primary dataset for MitoInteractome was generated by performing a keyword search against SwissProt http://www.expasy.ch/sprot/ and including subsets from MitoP http://www.mitop.de:8080/mitop2/, MitoProteome http://www.mitoproteome.org/, HPRD http://www.hprd.org, and Gene Ontology (GO) database http://www.geneontology.org. Proteins for which GO information was unavailable, in order to filter out false positives, we used the following algorithms that predict the sub-cellular localization of a given protein: 1) Psort http://psort.ims.u-tokyo.ac.jp/, 2) TargetP http://www.cbs.dtu.dk/services/TargetP/, and 3) Predotar http://urgi.versailles.inra.fr/predotar/predotar.html. Only the proteins that were predicted to be mitochondrial by all the three algorithms were included. The refined primary dataset had 6,549 proteins spanning over 74 species. The flow-chart illustrating the process of collection of primary data and its refinement can be found in the supplement section of the MitoInteractome website http://mitointeractome.kobic.kr/supplement.php.
Protein interaction prediction
Protein-protein interactions are diverse, but all occur in a highly specific manner determined by the structural and physico-chemical properties of the interacting partners. We used two methods to predict protein interactions: PSIMAP (Protein Structural Interactome MAP) (http://psimap.com, http://psibase.kobic.re.kr) [12, 13] and PEIMAP (Protein Experimental Interaction MAP) .
PSIMAP is a structure based interaction prediction method. It predicts interactions between known PDB http://www.rcsb.org/pdb domains based on the Euclidean distance between them. Two domains are assigned as 'interacting' if they have five or more atoms within 5 Ǻ distance. To construct the mitochondrial proteomes, we first assigned known 3D structural families in SCOP  to mitochondrial protein data sets. Among 6,549 mitochondrial proteins, 2,161 (32.99%) had at least one sequence homologous to the domain(s) in SCOP, based on the BLAST sequence similarity search with the following parameters: percentage identity: 40%, sequence coverage: 70%, and e-value: 10-4. However, the same region of a protein sequence could be assigned and overlapped by more than two SCOP families, each of which falls within the same significance level. In these cases, we accepted overlaps of up to 15 amino acid residues in the alignments and regarded them as two separate domains for a given protein sequence. Using PSIMAP, we found 36,359 structural interaction pairs from the mitochondrial proteomes.
Structural interaction information tends to provide more number of predictions as many specifically different sequences share the same structure. Therefore, experiment based sequence based PPI data are usually more specific. Our usual step is to predict many partners using structural interaction information and then apply sequence based experimental (more specific incident of protein-protein interaction) PPI data to filter out the first.
PEIMAP (Protein Experimental Interaction MAP) uses experimental data available in BIND http://bond.unleashedinformatics.com, DIP http://dip.doe-mbi.ucla.edu, MINT http://mint.bio.uniroma2.it/mint, IntAct http://www.ebi.ac.uk/intact, and HPRD interaction databases as a basis for prediction. The interactions are predicted based on the concept of 'homologous interaction', which means that if two proteins in a species, are known to be interacting, then their respective orthologs in another species are also likely to interact. An illustration of the concept of homologous interaction and the required parameters for the same are available in the supplement section of MitoInteractome's website. The mitochondrial proteins were aligned with the protein sequences involved in, experimentally known, protein interactions using BLAST (cut-off sequence identity of 80%). Using PEIMAP we found 23,973 interaction pairs.
Disease related information, nsSNPs and their effects
Mitochondrial dysfunction is associated with a large number of aging-related neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, Amyotrophic lateral sclerosis, and Huntington's disease . To date, 102 heritable disorders have been attributed to defects in a quarter of the known nuclear-encoded mitochondrial proteins in humans. More than 60 disorders have been reported to be derived from mutations in the human mitochondrial genome. Many mitochondrial diseases remain unexplained, however, partly because only 40~60% of the presumed 700-1,000 proteins involved in mitochondrial function and biogenesis have been identified . Our primary source of information on diseases was OMIM http://www.ncbi.nlm.nih.gov/Omim/. The number of base substitutions sites in the mitochondrial genome related to cytopathies was 74 . Information associated with mitochondrial diseases can be accessed using the search form available at http://mitointeractome.kobic.kr/search.php.
It is widely accepted that polymorphisms in the human genome are expected to play a key role in defining the etiologic basis of phenotypic differences amongst individuals in the aspect of drug responses and common disease predisposition . Non-synonymous SNPs are known to be closely associated with inherited diseases. MitoInteractome contains 1301 non-synonymous SNPs, collected from dbSNP http://www.ncbi.nlm.nih.gov/projects/SNP/ build 129 , associated with 512 human mitochondrial proteins. We analysed the non-synonymous SNPs using PolyPhen http://genetics.bwh.harvard.edu/pph/, a tool that predicts the impact of non-synonymous SNPs on structure and function of proteins. The non-synonymous SNPs were classified based on the confidence score of their potency to be deleterious. 321 substitutions were predicted to be probably damaging and 226 were predicted as possibly damaging, while the rest were predicted as benign substitutions.
Pathway information is vital for successful quantitative modeling of biological systems. Mitochondria have been implicated in the origin of diseases, because of their crucial role in energy production, in nuclear cytoplasmic signal integration, and in the control of metabolic pathways. Mitochondrial pathway information comprising of pathways, enzymes involved, and their respective KEGG Orthology was collected from KEGG http://www.genome.jp/kegg/. The pathway information can be used to perform flux balance analyses to estimate the metabolic capabilities of the mitochondria. Enzymes were referenced to BRENDA http://www.brenda-enzymes.info/.
Utility and discussion
'Aging network' - construction and analysis
The process of 'aging' is one of the active fields of biological research, the study of the biological process aspect of which is popular as 'Biogerentology'. It is common knowledge that the onset of several neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, Leigh's syndrome etc. are of progressive nature with aging. Several studies in the past have indicated association of the aging process with mitochondria [29–31]. Since functioning of the mitochondria is dependent on the number of its constituent components, it is highly likely that a complex network of interacting proteins is involved in the aging process. Although previous studies have pointed towards individual proteins associated with the aging process, interactions among them have not been investigated.
A centrality measure - "Strength of a node" - was introduced, which is defined as the sum of the weights of other nodes it is interacting with. Strength for a weighted network is analogous to 'Connectivity' for an un-weighted network. Strength of a node in the present network would capture the size of its neighborhood, constituted by the interacting proteins, as well as the uniqueness of the neighborhood, derived from the domain conservation analysis. Proteins possessing higher strengths (e.g Gene name: GRB2, ABL1, TP53, ANDE1) were observed to be involved in at least one aging related disease, while proteins (e.g. Gene name: ACTB, HNRNPA3, EIF4H) that had low strengths were observed to be mostly house-keeping gene products.
GRB2 is known to be involved in embryonic development and malignant transformation . Due to its involvement as a signal transducer in pathways, including pathways and genes previously related to aging such as INS/IGF1 signalling and SHC1 , it is probable that GRB2 plays a vital role in ageing. GRB2 was observed to have the highest strength of 75.76.
ABL1 plays an important role in cellular proliferation and development . They are activated by oxidative stress and are related to aging and tumorigenesis. It was found that disruption of ABL1 in mice results in several defects and leads to death shortly after birth . It is present among the central proteins of the aging network with a strength of 46.16.
It has previously been reported that the p53 protein (encoded by TP53 gene) plays an important role in aging. Matheu et. al.  showed that genetically modified mice with increased levels of p53 have decreased levels of aging associated damage. In the aging network constructed the p53 protein was observed to have a high strength of 45.97. The TP53 gene acts as a tumor suppressor and also induces apoptosis depending on the physiological circumstances.
E1 is ATP dependent DNA helicase required for initiation of viral DNA replication and are involved in almost all aspect of nucleic acid metabolism including replication, repair, recombination, transcription, translation and ribosome biogenesis. Defects in helicase gene have been reported to be responsible for variety of human genetic disorders which can lead to cancer, premature aging or mental disorders. It can be inferred that E1, which has a strength of 42.05, might be significant to the aging network .
List of the proteins with high strength scores in the human 'aging network'.
Growth factor receptor-bound protein 2
SH3/SH2 adaptor activity
Cytoplasmic protein NCK1
Proto-oncogene tyrosine-protein kinase ABL1
Cellular tumour antigen p53
Replication protein E1
14-3-3 protein zeta/delta
Uncharacterized protein KIAA1377
Elongation factor 1-alpha 1
Protein unc-119 homolog A
Heat shock cognate 71 kDa protein
MitoInteractome is a comprehensive database containing the following information on proteins in mitochondria: physico-chemical properties, predicted interaction partners, polymorphism and their effects, diseases and pathways involved. The database has been cross referenced to external database where necessary. The derived information stored in MitoInteractome would serve as a precursor for the construction and analysis of protein networks.
The construction and analysis of 'aging network' presented in this work as a case-study provided us with insights into interactions among the key entities involved in the aging process. The same protocol can be extended to construct similar interaction networks and investigate other mitochondria centric processes such as Apoptosis, Oxidative Phosphorylation.
Availability and requirements
MitoInteractome can be accessed at http://mitointeractome.kobic.kr/. Java version 1.5 or later will be required to view the predicted interactions.
Other papers from the meeting have been published as part of BMC Bioinformatics Volume 10 Supplement 15, 2009: Eighth International Conference on Bioinformatics (InCoB2009): Bioinformatics, available online at http://www.biomedcentral.com/1471-2105/10?issue=S15.
List of abbreviations used
Bio molecular Interaction Network Database
Database of Interacting Proteins
Human Protein Reference Database
Kyoto Encyclopaedia of Genes and Genomes
Molecular INTeraction database
Online Mendelian Inheritance in Man
Protein Data Bank
Protein Experimental Interactome MAP
Protein Structural Interactome MAP
Structural Classification of Proteins
Single Nucleotide Polymorphism.
We thank Maryana Bhak for editing the manuscript. This work was supported by a grant from the KRIBB Research Initiative Program of Korea, by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MEST) (No. M10508040002-07N0804-00210).
This article has been published as part of BMC Genomics Volume 10 Supplement 3, 2009: Eighth International Conference on Bioinformatics (InCoB2009): Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2164/10?issue=S3.
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