Longevity Network: Construction and Implications

Mechanisms of Ageing and Development 128 (2007) 117–124 www.elsevier.com/locate/mechagedev

Longevity network: Construction and implications Arie Budovsky a, Amir Abramovich a, Raphael Cohen b, Vered Chalifa-Caspi b, Vadim Fraifeld a,* a Department of Microbiology and Immunology, Faculty of Health Sciences, Center for Multidisciplinary Research in Aging, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105, Israel b Bioinformatics Support Unit, National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105, Israel

Available online 20 November 2006

Abstract The vast majority of studies on longevity have focused on individual genes/proteins, without adequately addressing the possible role of interactions between them. This study is the first attempt towards constructing a ‘‘longevity network’’ via analysis of human protein–protein interactions (PPIs). For this purpose, we (i) compiled a complete list of established longevity genes from different species, including those that most probably affect the longevity in humans, (ii) defined the human orthologs of the longevity genes, and (iii) determined whether the encoded proteins could be organized as a network. The longevity gene-encoded proteins together with their interacting proteins form a continuous network, which fits the criteria for a scale-free network with an extremely high contribution of hubs to the network connectivity. Most of them have never been annotated before in connection with longevity. Remarkably, almost all of the hubs of the ‘‘longevity network’’ were reported to be involved in at least one age-related disease (ARD), with many being involved in several ARDs. This may be one of the ways by which the proteins with multiple interactions affect the longevity. The hubs offer the potential of being primary targets for longevity-promoting interventions. # 2006 Elsevier Ireland Ltd. All rights reserved.

Keywords: Longevity genes and proteins; Protein–protein interactions; Longevity network; Age-related diseases

1. Introduction: from longevity genes to a ‘‘longevity that, until now, the vast majority of biogerontological studies network’’ have focused on individual genes/proteins, without considering the possible role of interactions between them. Given that the Genetic manipulations in model organisms (S. cerevisae, LAGs act in a cooperative manner, it seems unrealistic to C .elegans, D. melanogaster, M. musculus) and the studies of examine experimentally the effect of various gene combina- genetic polymorphisms in human populations revealed a tions on aging/longevity. Ironically, the time required for such number of longevity-associated genes (LAGs) and pathways an examination might, to some extent, be comparable to that of (Guarente and Kenyon, 2000; Jazwinski, 2000; Finch and evolution. Thus, a more efﬁcient strategy is required to focus Ruvkun, 2001a; Hekimi and Guarente, 2003; Khalyavkin and the studies on the basic mechanisms of aging and longevity. Yashin, 2003a, b; Atzmon et al., 2005, 2006; de Magalhaes, One of the principles of such a strategy could be based on the 2005; Franceschi et al., 2005; Kenyon, 2005; Vijg and Suh, idea of an interactome or, more speciﬁcally, a network. 2005; Warner, 2005; Christensen et al., 2006; Gami and During the last decade, it has become more and more Wolkow, 2006). However, our knowledge of the key obvious that biological systems function as complex networks. determinants of aging and/or longevity is still limited. Indeed, Thus, the properties of a system should not be reduced to the in most cases, the increase in the maximum life span of mutants properties of its components (though they are also important) versus wild type did not exceed 20–40%. One of the reasons is but rather that the network’s topology determines the system’s behavior (Barabasi and Albert, 1999; Albert and Barabasi, 2002). The important point is that the formation and the * Corresponding author. Tel.: +972 8 6477292; fax: +972 8 6477626. properties of various complex networks (biological, technical, E-mail addresses: [email protected], social) are governed by universal principles (Albert and [email protected] (V. Fraifeld). Barabasi, 2002; Barabasi and Oltvai, 2004).

Despite of existing limitations (for example, incomplete and Table 1 partially imperfect data; for recent review see Siegal et al., Distribution of established LAGs according to the species of origin 2007), the network-based approach has been successfully Species Number of LAGs applied for analysis of the protein–protein networks in yeast, Yeast, S. cerevisae 62 (14.5%) worms, and flies (Jeong et al., 2001; Yook et al., 2004; Hahn Worm, C. elegans 252 (58.9%) and Kern, 2005), the protein–protein network for degeneration Fly, D. melanogaster 45 (10.5%) of Purkinje cells and human inherited ataxias (Lim et al., 2006), Mouse, M. musculus 49 (11.4%) the neurogenic network in Drosophila (Meir et al., 2002), and Human, H. sapiens 19 (4.7%) Other, P. anserine 1 (0.2%) the metabolic network in E. coli (Ravasz et al., 2002). Most biological networks examined thus far (protein–protein net- Percentage of total number of LAGs (n = 428) is presented in parentheses. works, in particular) are scale-free, which follow a power-law distribution of connectivity: P(k) kÀg, where P(k) is the number of C. elegans LAGs were recently identified by means probability that a selected node has exactly k connections of RNAi screens on the genome wide scale (Lee et al., 2003; (degrees) with other nodes (e.g., proteins); g is the degree Hamilton et al., 2005; Hansen et al., 2005). This powerful exponent, a characteristic value for a given network which approach has not as yet been used widely for identification of determines many properties of the system. The smaller the g LAGs in other model organisms. value, the more important is the role of the nodes with a high During the last decade more than 120 human genes were connectivity in the network (Barabasi and Oltvai, 2004). studied in relation to exceptional longevity (Atzmon et al., Possible relationships between LAGs or their products 2005; Franceschi et al., 2005). However, only a few of them (LAPs, longevity-associated proteins) are only beginning to be were confirmed by independent studies in different human defined. Thus far, the interactions between them have mainly populations (Christensen et al., 2006). been considered in view of their connectivity within entire or With this in mind, we selected 13 genes with the most limited (e.g., nucleus, cytoplasm) protein interactomes of S. probable longevity-predisposing polymorphisms. Also, six cerevisae (Promislow, 2004; Ferrarini et al., 2005), C. elegans, others were included in the present analysis since they were and D. melanogaster (Ferrarini et al., 2005). In general, an shown to be involved in human progeroid syndromes. importance for considering the role of interactions in aging, in Next, we determined the human orthologs for the LAGs particular between proteins, has been proposed (Kirkwood and established in model organisms. These data were mostly Kowald, 1997; Promislow and Pletcher, 2002). extracted from the ‘‘InParanoid’’ database – Eukaryotic The present study is the first attempt towards constructing a Ortholog Groups (http://inparanoid.cgb.ki.se, O’Brien et al., ‘‘longevity network’’ via analysis of human protein–protein 2005) and partially from NCBI HomoloGene (http:// interactions. For this purpose, we (i) compiled a full list of www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=homologene) established LAGs, including those that most probably affect the and Wormbase (http://www.wormbase.org). A common longevity in humans, (ii) defined the human orthologs of the method for the determination of orthologs is described LAGs established in model organisms, and (iii) determined elsewhere (O’Brien et al., 2005) and is based on pair-wise whether the encoded proteins could be organized as a network. similarity scores which are by default calculated with the NCBI The rationale behind the construction of the ‘‘longevity BLAST program (best-best hits between sequences from two network’’ on the basis of human protein–protein interactions different species). (PPIs) was grounded on the following: (a) many LAGs and The majority of LAGs reported for the model organisms had longevity-associated pathways are evolutionary conserved, human orthologs (Fig. 1), indicating their high evolutionary from yeast to humans (reviewed by Warner, 2005); (b) the large number of annotated human PPIs are available from the BioGRID database; (c) the possibility to connect the data to age-related diseases that have been studied extensively in humans.

2. General characterization of longevity-associated genes and proteins

Initially, we compiled a full list of LAGs established thus far. A comprehensive analysis of scientific literature revealed 428 LAGs, 409 in model organisms and 19 in humans (Table 1). Their partial or full loss-of-function mutations, RNAi-induced gene silencing, over-expression, or genetic polymorphisms were reported to promote longevity or cause premature aging. Fig. 1. Proportion of human orthologs for LAGs and for all genes of the model organisms, extracted from the ‘‘InParanoid’’ database (http://inparanoid.cgb.- More than a half of the LAGs identified originate from the ki.se). The Fisher’s exact test (one-sided) was highly significant for each pairs studies in C. elegans, the most investigated model organism in (confidence level of 0.95): p = 6.1 Â 10À5, S. cerevisae; p < 2.2 Â 10À16, the genetics of aging and longevity. Of note, a considerable C. elegans; p = 0.00031, D. melanogaster; p = 4.6 Â 10À7, M. musculus. A. Budovsky et al. / Mechanisms of Ageing and Development 128 (2007) 117–124 119

Fig. 2. Distribution of longevity-associated genes/proteins by function and cell location. The basic function of the human orthologs (n = 310) for the collected LAGs together with selected human LAGs (n = 19) was extracted from the literature (left diagram). The primary cell location of the encoded proteins (right diagram) was determined according to annotations of the HPRD – Human Protein Reference Database (http://www.hprd.org). conservation. Notably, the proportion of human orthologs for Remarkably, only the mitochondrial protein fraction was LAGs considerably exceeds that estimated for the entire significantly enriched with LAPs ( p < 2.2 Â 10À16): 16% of animals’ genomes (Fig. 1; the differences are highly all LAPs versus 4% of the entire proteome according to significant). In further analysis, we used these orthologs annotations of the HPRD database (http://www.hprd.org). This (310, without replicates) and selected human LAGs (19; total much larger than expected proportion of mitochondrial proteins 329). Such an approach provides a common platform for the among LAPs, further supports the suggested essential role of construction of the longevity network based on PPIs data in mitochondria in aging and longevity (Harman, 1972; Linnane humans (see the next section). Apart from a large number of et al., 1989; Chinnery et al., 2002; Lehmann et al., 2006). One PPIs annotated in the BioGRID database (http://www.thebio- could expect, however, that a high representation of LAPs in the grid.org, Stark et al., 2006), the human PPIs-based analysis mitochondria might be due to research bias, where researchers allows for the linking of the data obtained to age-related focused disproportionately on mitochondria. In this regard, it diseases, where our knowledge is much more advanced in should be noted that almost half of the mitochondrial LAPs comparison to that for model organisms. Furthermore, the considered in our study (22 out of 47) were found by wide overlap between human, fly and worm ortholog datasets of PPIs (unbiased) RNAi screens that focused on identification of is relatively small despite a large number of annotated LAGs in the whole genome and not of those with particular interactions (Ghandhi et al., 2006). That is, the conservation mitochondrial function (Lee et al., 2003; Hamilton et al., 2005; of orthologs in evolution, including LAGs, would not Hansen et al., 2005). In these studies, an over-presentation of necessarily be accompanied by the conservation of the LAGs important for mitochondrial function was demonstrated. interactions between them (Wagner, 2001). Thus, this may help to elucidate unique PPIs that affect aging/longevity in 3. Construction of the longevity network humans. The human orthologs and LAGs display a marked diversity One could suggest that LAPs may act independently of each in their basic function and primary cellular location of the other or in a cooperative manner. The latter suggests interactions encoded proteins. As seen in Fig. 2 (left diagram), many of between them, including direct (physical) interactions as a them are related to signal transduction, metabolism and DNA possibility. With this in mind, we first determined all their PPIs maintenance, which is in accordance with previous observa- annotated in the BioGRID database (http://www.thebiogrid. tions. In particular, it was noted that many LAGs are involved in org, Stark et al., 2006) using the Osprey program (http:// signaling pathways, including the extracellular ligands, plasma biodata.mshri.on.ca/osprey/servlet/Index; Breitkreutz et al., membrane receptors, adaptor proteins, intermediate signaling 2003). Those data are currently available for 211 out of 329 molecules, and ultimately transcription factors (Warner, 2005; selected LAPs. Many of these LAPs interact with each other Sagi et al., 2005; Gami and Wolkow, 2006). Not surprisingly, a (Fig. 3) and exhibit multiple PPIs. Remarkably, a significant relatively high number of LAGs are involved in DNA repair, fraction of the interacting LAPs (75 out of 211 proteins; 35.5%) DNA replication and maintenance of genomic stability. These form a continuous network between themselves (further denoted genes are suggested to play one of the central roles in both as a ‘‘LAPs’ network’’; Table 2). Randomly selected proteins ‘‘physiological’’ and premature aging in humans (reviewed by with annotated PPIs in BioGRID (a special Perl script written by Kyng and Bohr, 2005). R.C.), however, do not form such a network. Indeed, in 100 The comparison of primary cellular location of the LAG- random samples of the same size, the percent of continuously encoded proteins (LAPs; Fig. 2, right diagram) with all proteins connected proteins ranged from one percent (two proteins) to annotated in the HPRD – Human Protein Reference Database 14%, with a median value of 3%. The percent of LAPs with (http://www.hprd.org), did not reveal a significant difference annotated interactions included in the LAPs’ network was ( p < 0.1) in their distribution in most cell components. significantly much higher than expected by chance (Wilcoxon 120 A. Budovsky et al. / Mechanisms of Ageing and Development 128 (2007) 117–124

Table 2 Characteristics of the human longevity protein–protein networks Characteristics LAPs’ network CLN ELN Number of nodes (N) 75 1486 3422 Number of LAPs 75 153 193 Number of interactions (L) 98 2064 8474 Average connectivity 2.623 2.778 4.953 Degree exponent (g) 1.346 1.386 1.639 Number of hubs 4 17 82 An average connectivity was calculated as 2L/N. The degree exponent (g) was calculated from the log–log plot of P(k) against k, where P(k), the degree distribution, is a probability that randomly selected node has exactly k edges observed (described in details elsewhere, for example, Barabasi and Oltvai, 2004). The hubs were deﬁned as nodes with the lowest P(k) values. This corresponds to k 7 for the LAPs’ network (P(k) < 10À2)ortok 35 for the core (CLN) and extended (ELN) longevity networks (P(k) < 10À3).

Fig. 3. Graphical output of the direct interactions between 211 human LAPs. Drawn by using the Osprey program (http://biodata.mshri.on.ca/osprey/servlet/ The smaller the g value, the more important is the role of hubs Index; Breitkreutz et al., 2003). in the network. When g < 2, it means that the largest hubs are in contact with most of the nodes in the network. Thus, the CLN is signed rank test with continuity correction (one-sided): a scale-free network with an extremely high contribution of p < 2.2 Â 10À16). Thus, the results indicate that at least part hubs to the network connectivity. It should be stressed that 15 of of the LAPs may cooperate directly in affecting aging/longevity. 17 hubs in the CLN are LAPs. Moreover, these LAPs are organized as a network which ﬁts the Of special interest is that among the non-LAPs in CLN are criteria for a scale-free network (Barabasi and Oltvai, 2004). those that interact with several LAPs. In particular, 33 non- It is reasonable to suggest that LAPs could interact indirectly LAPs have connections with at least 5 LAPs or more. When the through common partners other than LAPs established so far CLN was extended by including all the PPIs for these 33 (‘‘non-LAPs’’). To test this possibility, we constructed the common nodes, an additional 40 LAPs that did not appear in the network (further denoted as a ‘‘core longevity network’’, CLN) CLN, became included in the ‘‘extended longevity network’’ based on the entire set of PPIs available for 211 LAPs (Fig. 4, (ELN; Fig. 5, Table 2). Thus, most of LAPs with reported PPIs Table 2). Of them, 153 LAPs became included in the CLN, (193 out of 211) are presented in the ELN, further highlighting highlighting the role of indirect interactions between the LAPs. the importance of common ‘‘non-LAPs’’ in connecting the The CLN is characterized by the presence of highly LAPs within the network. interconnecting nodes (hubs), and an unusually low g value As already mentioned, most of the genes examined for (1.38). According to the Barabasi–Albert model (Barabasi and association of their polymorphisms with exceptional longevity Oltvai, 2004), scale-free networks have typically 2 < g < 3. in humans, were not included in the present analysis because of

Fig. 4. Graphical output of the core longevity network (left) and the log–log plot of P(k) against k (right) illustrating its scale-free topology (for details, see the text and Table 2). The network was constructed using the Osprey program (http://biodata.mshri.on.ca/osprey/servlet/Index; Breitkreutz et al., 2003) which pulls physical PPIs data determined in vitro and in vivo from the BioGRID database (http://www.thebiogrid.org, Stark et al., 2006) and draws a graphical output of the network. A. Budovsky et al. / Mechanisms of Ageing and Development 128 (2007) 117–124 121

Fig. 5. Graphical output of the extended longevity network (left) and the log–log plot of P(k) aganst k (right) illustrating its scale-free topology. For explanatory notes, see Fig. 4. insufficient evidence for such an association. Interestingly, Some pathologies, such as osteoporosis, sarcopenia, presbyopia some of them (for example, APOB, IGF2, IL2) appear as ‘‘non- are so widely presented in old age that they are hardly LAP’’ nodes in the CLN and ELN (11 and 27, respectively). distinguishable from ‘‘normal’’ aging and contribute greatly to Because of still incomplete data on PPIs, it is difficult to the overall age-related decline in functional ability. Generally, exclude possible research biases that may to some extent affect an elderly person suffers from more than one ARD, in an the results of a network analysis. In particular, one could expect evident or pre-morbid form. The role of ARDs in determination that the PPIs between aging/longevity-related proteins attracted of longevity in humans gained further support from the studies a special attention of researchers and, thus, were studied more on centenarians that have clearly shown that ‘‘the older you get, intensively than PPIs for other proteins. It seems, however, that the healthier you have been’’ (Hitt et al., 1999). This is the latter is not the case. For example, searching throughout especially true for species with ‘‘negligible senescence’’, or PubMed, we found that the number of ‘‘immunoprecipitation ‘‘slow aging’’ (Finch and Austad, 2001b). We believe that and aging’’ records is only 0.65% of that for ‘‘immunopre- ARDs not only are direct consequences of aging but actually cipitation’’, whereas the number of annotated LAPs with represent its diverse manifestations, being an essential part of known protein interactions in BioGRID is 3.3%, i.e., more than the ‘‘normal’’ aging process. The rationale for this assumption fivefold higher. The higher connectivity of aging/longevity- is that common molecular mechanisms may stand behind both associated proteins was also reported by Promislow (2004) and aging and ARDs (Budovsky et al., 2006). From this point of Ferrarini et al. (2005). view, it could be expected that genes/proteins involved in The vast majority of CLN/ELN proteins and most of the ARDs, would also have an impact on longevity. For example, it hubs in the ELN (67 out of 82) have never been connected to seems that the lack of ARDs-predisposing APOE4 rather than longevity. The question therefore arises as to their relevance to the presence of longevity-associated allele APOE2, allows longevity. Could these proteins, especially with multiple humans to reach extreme old age (Perls, 2002). interactions, be considered the potential LAPs just because The analysis of relationships between ELN hubs and ARDs they belong to the longevity network? If so, what are the revealed that almost all of them are involved in at least one possible ways by which these proteins may extend or reduce the ARD, with many being involved in several ARDs (Table 3). life span, particularly in humans? Thus, the involvement in ARDs may be one of the ways by which the proteins with multiple interactions affect the 4. Hubs and age-related diseases: implications for longevity. Supporting the role of hubs in aging are the longevity-promoting interventions studies on the yeast protein networks, demonstrating a higher average connectivity among the proteins associated with The major factor limiting the life span in protected human senescence (Promislow, 2004; Ferrarini et al., 2005). populations is ARDs including atherosclerosis, cancer, Altogether, this implies that the hubs offer the potential of Alzheimer’s disease, and diabetes type II, and predisposing being the primary targets for longevity-promoting interven- to them, obesity (Olshansky et al., 2005). The frequency of tions. Yet, one could realize that such an approach may these degenerative pathologies increases progressively with encounter a number of difficulties deriving from the topology advanced age, and are the main causes of death in the elderly. of a scale-free network. 122 A. Budovsky et al. / Mechanisms of Ageing and Development 128 (2007) 117–124

Table 3 Examples of the hubs in the human longevity protein–protein interaction network (ELN) that are involved in multiple age-related diseases Gene Common name Primary function Number of PPIs BCL2 B-cell CLL/lymphoma 2 Apoptosis 55 CTNNB1 Catenin-beta 1 Signal transduction 81 ESR1 Estrogen receptor 1 Transcription 81 MYC v-myc myelocytomatosis vira oncogene homolog Transcription 45 NFKB1 Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (p105) Transcription 50 PCNA Proliferating cell nuclear antigen Signal transduction 59 PLSCR1 Phospholipid scramblase 1 Signal transduction 68 PXN Paxillin Cell maintenance 48 STAT3 Signal transducer and activator of transcription 3 (acute-phase response factor) Transcription 49 SYK Spleen tyrosine kinase Signal transduction 40 UBE2I Ubiquitin-conjugating enzyme E2I (UBC9 homolog, yeast) Protein metabolism 75

Gross interventions, such as removal of hubs can lead to a et al., 2004; Zahn et al., 2006). Thus, the dynamic significant decline in the robustness of the network or even to its characteristics of the longevity network appear to be an degradation (Albert and Barabasi, 2002; Kitano, 2002, 2004; important point of future investigation. Ferrarini et al., 2005). Indeed, in most cases, the knockout of One could expect more PPIs are discovered the more LAGs hubs results in a lethal phenotype or a dramatic reduction of life will be included in the longevity network. For example, Klotho, span. For example, Jeong et al. (2001) showed that, in the an evolutionary conserved LAG, mutations of which exerted a S. cerevisiae protein–protein interactome, proteins with more clear effect on the life span in mice (Kurosu et al., 2005), was than 15 links (referred by authors as to hubs) constitute only not included in the longevity networks because of the absence 0.7% of the total proteome, yet for 62% of these, the deletion is of reported PPIs for its human ortholog KL in the BioGRID lethal. Similar to these results are the data on mice with database. However, the specific FGF receptors (FGF1-4) for the knocked out hubs (available for 55 of 82 hubs in the ELN) secreted form of the Klotho protein have recently been collected by us from the NCBI OMIM database: the lethal identified (Kurosu et al., 2006). Testing the protein interactions phenotype was observed for 31 of 55 cases (56%); in other 24 of FGF receptors revealed that they, and therefore KL, belong to cases, the knockout mice had serious health problems from the the ELN. early stages of life. These observations indicate that the The constructed longevity protein–protein network is majority of ELN hubs are vitally essential for normal growth actually a sub-network, being a part of the entire interactome. and development. However, later in life they may be involved in Other interactions, such as metabolic and regulatory ones multiple age-related pathologies, i.e., exert adverse effects. To should be also taken into account in order to build more realistic some extent, this may be interpreted in terms of antagonistic longevity network models. Yet, the CLN/ELN provide a useful pleiotropy (Williams, 1957), supporting the hypothesis of framework for analysis of the possible longevity-associated Promislow (2004) that the higher the connectivity of proteins pathways and for defining the potential LAGs/LAPs and the key the more pleiotropic they tend to be. Thus, an important targets for longevity-promoting interventions. implication is that the hubs-based longevity-promoting interventions could and should be initiated in adult age. Another point of consideration is that the ARDs-associated Acknowledgements hubs may interconnect diverse genes and pathways. Therefore, the overall effect of gross changes in hub expression or activity This study was supported by grant from the Center for may be difficult to predict and, thus, should be avoided. Given Multidisciplinary Research in Aging, Ben-Gurion University of the role of hubs in the network, it appears that manipulation of the Negev (to V.F.). We thank Dr. Irving Listowsky and Dr. Aviv the hubs should be directed mainly towards modulation of their Bergman (both from the Albert Einstein College of Medicine, expression and/or their mode of action. The latter may imply Bronx, NY) for critical reading of the manuscript. We also the maintenance of PPIs with pro-longevity partners and appreciate the assistance of Mrs. Reut Mali (Ben-Gurion inhibition of those with the opposite effect. University of the Negev) in preparation of the manuscript. The It is important to stress that networks are dynamic not static comments of anonymous referees were extremely helpful for entities. Despite the fact that biological networks can display a improving the paper. We were pleased to find out that 10 out of surprising degree of tolerance for errors, they still experience 10 newly reported longevity-associated genes with annotated gradual aging as nodes lose their connections (Albert et al., PPIs in BioGRID were already present in the constructed 2000; Albert and Barabasi, 2002). More specifically, the longevity networks (CLN and ELN). longevity network may undergo age-related changes in protein profiles (e.g., substitution of isoforms) or expression, post- References translational modifications, and alterations in protein–protein interactions. Yet, besides the common patterns, some of these Albert, R., Barabasi, A.L., 2002. Statistical mechanics of complex networks. age-related changes are cell-type- and tissue-specific (Rodwell Rev. Mod. Phys. 74, 47–97. A. Budovsky et al. / Mechanisms of Ageing and Development 128 (2007) 117–124 123

Albert, R., Jeong, H., Barabasi, A.L., 2000. Error and attack tolerance of Kenyon, C., 2005. The plasticity of aging: insights from long-lived mutants. complex networks. Nature 406, 378–382. Cell 120, 449–460. Atzmon, G., Rincon, M., Rabizadeh, P., Barzilai, N., 2005. Biological Khalyavkin, A.V., Yashin, A.I., 2003a. How the analysis of genetic mutations evidence for inheritance of exceptional longevity. Mech. Ageing Dev. can help us to solve basic problems in gerontology? I. Life extending 126, 341–345. genetic modifications in round worm C. elegans. Adv. Gerontol. 11, Atzmon, G., Rincon, M., Schechter, C.B., Shuldiner, A.R., Lipton, R.B., 34–42. Bergman, A., Barzilai, N., 2006. Lipoprotein genotype and conserved Khalyavkin, A.V., Yashin, A.I., 2003b. How the analysis of genetic pathway for exceptional longevity in humans. PLoS Biol. 4, e113. mutations can help us to solve basic problems in gerontology? II. Life Barabasi, A.L., Albert, R., 1999. Emergence of scaling in random networks. extending genetic modifications in budding yeast S. cereviseae,fruitflyD. Science 286, 509–512. melanogaster and laboratory mice M. musculus. Adv. Gerontol. 12, Barabasi, A.L., Oltvai, Z.N., 2004. Network biology: understanding the cell’s 46–56. functional organization. Nat. Rev. Genet. 5, 101–113. Kirkwood, T.B., Kowald, A., 1997. Network theory of aging. Exp. Gerontol. 32, Breitkreutz, B.J., Stark, C., Tyers, M., 2003. Osprey: a network visualization 395–399. system. Genome Biol. 4, R22. Kitano, H., 2002. Computational systems biology. Nature 420, 206–210. Budovsky, A., Muradian, K.K., Fraifeld, V.E., 2006. From disease-oriented to Kitano, H., 2004. Biological robustness. Nat. Rev. Genet. 5, 826–837. aging/longevity-oriented studies. Rejuvenation Res. 9, 207–210. Kurosu, H., Yamamoto, M., Clark, J.D., Pastor, J.V., Nandi, A., Gurnani, P., Christensen, K., Johnson, T.E., Vaupel, J.W., 2006. The quest for genetic McGuinness, O.P., Chikuda, H., Yamaguchi, M., Kawaguchi, H., Shimo- determinants of human longevity: challenges and insights. Nat. Rev. Genet. mura, I., Takayama, Y., Herz, J., Kahn, C.R., Rosenblatt, K.P., Kuro-o, M., 7, 436–448. 2005. Suppression of aging in mice by the hormone Klotho. Science 309, Chinnery, P.F., Samuels, D.C., Elson, J., Turnbull, D.M., 2002. Accumulation of 1829–1833. mitochondrial DNA mutations in ageing, cancer, and mitochondrial disease: Kurosu, H., Ogawa, Y., Miyoshi, M., Yamamoto, M., Nandi, A., Rosenblatt, is there a common mechanism? Lancet 360, 1323–1325. K.P., Baum, M.G., Schiavi, S., Hu, M.C., Moe, O.W., Kuro-o, M., 2006. de Magalhaes, J.P., 2005. Open-minded scepticism: inferring the causal Regulation of fibroblast growth factor-23 signaling by klotho. J. Biol. Chem. mechanisms of human ageing from genetic perturbations. Ageing Res. 281, 6120–6123. Rev. 4, 1–22. Kyng, K.J., Bohr, V.A., 2005. Gene expression and DNA repair in progeroid Ferrarini, L., Bertelli, L., Feala, J., McCulloch, A.D., Paternostro, G., 2005. A syndromes and human aging. Ageing Res. Rev. 4, 579–602. more efficient search strategy for aging genes based on connectivity. Lehmann, G., Budovsky, A., Muradian, K.K., Fraifeld, V.E., 2006. Mitochon- Bioinformatics 21, 338–348. drial genome anatomy and species-specific lifespan. Rejuvenation Res. 9, Finch, C.E., Ruvkun, G., 2001a. The genetics of aging. Annu. Rev. Genomics 223–226. Hum. Genet. 2, 435–462. Linnane, A.W., Marzuki, S., Ozawa, T., Tanaka, M., 1989. Mitochondrial DNA Finch, C.E., Austad, S.N., 2001b. History and prospects: symposium on mutations as an important contributor to ageing and degenerative diseases. organisms with slow aging. Exp. Gerontol. 36, 593–597. Lancet 333, 642–645. Franceschi, C., Olivieri, F., Marchegiani, F., Cardelli, M., Cavallone, L., Capri, Lee, S.S., Lee, R.Y.,Fraser, A.G., Kamath, R.S., Ahringer, J., Ruvkun, G., 2003. M., Salvioli, S., Valensin, S., De Benedictis, G., Di Iorio, A., Caruso, C., A systematic RNAi screen identifies a critical role for mitochondria in C. Paolisso, G., Monti, D., 2005. Genes involved in immune response/inflam- elegans longevity. Nat. Genet. 33, 40–48. mation, IGF1/insulin pathway and response to oxidative stress play a major Lim, J., Hao, T., Shaw, C., Patel, A.J., Szabo, G., Rual, J.F., Fisk, C.J., Li, N., role in the genetics of human longevity: the lesson of centenarians. Mech. Smolyar, A., Hill, D.E., Barabasi, A.L., Vidal, M., Zoghbi, H.Y., 2006. A Ageing Dev. 126, 351–361. protein–protein interaction network for human inherited ataxias and dis- Gami, M.S., Wolkow, C.A., 2006. Studies of Caenorhabditis elegans DAF-2/ orders of Purkinje cell degeneration. Cell 125, 801–814. insulin signaling reveal targets for pharmacological manipulation of life- Meir, E., von Dassow, G., Munro, E., Odell, G.M., 2002. Robustness, flexibility, span. Aging Cell 5, 31–37. and the role of lateral inhibition in the neurogenic network. Curr. Biol. 12, Ghandhi, T.K., Zhong, J., Mathivanan, S., Karthick, L., Chandrika, K.N., 778–786. Mohan, S.S., Sharma, S., Pinkert, S., Nagaraju, S., Periaswamy, B., Mishra, O’Brien, K.P., Remm, M., Sonnhammer, E.L., 2005. Inparanoid: a compre- G., Nandakumar, K., Shen, B., Deshpande, N., Nayak, R., Sarker, M., hensive database of eukaryotic orthologs. Nucleic Acids Res. 33 (Database Boeke, J.D., Parmigiani, G., Schultz, J., Bader, J.S., Pandey, A., 2006. issue), D476–D480. Analysis of the human protein interactome and comparison with yeast, Olshansky, S.J., Passaro, D.J., Hershow, R.C., Layden, J., Carnes, B.A., Brody, worm and fly interaction datasets. Nat. Genet. 38, 285–293. J., Hayflick, L., Butler, R.N., Allison, D.B., Ludwig, D.S., 2005. A potential Guarente, L., Kenyon, C., 2000. Genetic pathways that regulate ageing in model decline in life expectancy in the United States in the 21st century. N. Engl. J. organisms. Nature 408, 255–262. Med. 352, 1138–1145. Hahn, M.W., Kern, A.D., 2005. Comparative genomics of centrality and Perls, T., 2002. Genetic and environmental influences on exceptional longevity essentiality in three eukaryotic protein interaction networks. Mol. Biol. and the AGE nomogram. Ann. N. Y. Acad. Sci. 959, 1–13. Evol. 22, 803–806. Promislow, D.E., Pletcher, S.D., 2002. Advice to an aging scientist. Mech. Hamilton, B., Dong, Y., Shindo, M., Liu, W., Odell, I., Ruvkun, G., Lee, S.S., Ageing Dev. 123, 841–850. 2005. A systematic RNAi screen for longevity genes in C. elegans. Genes Promislow, D.E., 2004. Protein networks, pleiotropy and the evolution of Dev. 19, 1544–1555. senescence. Proc. Biol. Sci. 271, 1225–1234. Hansen, M., Hsu, A.L., Dillin, A., Kenyon, C., 2005. New genes tied to Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabasi, A.L., 2002. endocrine, metabolic, and dietary regulation of lifespan from a Caenor- Hierarchical organization of modularity in metabolic networks. Science habditis elegans genomic RNAi screen. PLoS Genet. 1, 119–128. 297, 1551–1555. Harman, D., 1972. The biologic clock: the mitochondria? J. Am. Geriatr. Soc. Rodwell, G.E., Sonu, R., Zahn, J.M., Lund, J., Wilhelmy, J., Wang, L., Xiao, W., 20, 145–147. Mindrinos, M., Crane, E., Segal, E., Myers, B.D., Brooks, J.D., Davis, R.W., Hekimi, S., Guarente, L., 2003. Genetics and the specificity of the aging Higgins, J., Owen, A.B., Kim, S.K., 2004. A transcriptional profile of aging process. Science 299, 1351–1354. in the human kidney. PLoS Biol. 2, e427. Hitt, R., Young-Xu, Y., Silver, M., Perls, T., 1999. Centenarians: the older you Sagi, O., Wolfson, M., Utko, N., Muradian, K., Fraifeld, V., 2005. p66ShcA and get, the healthier you have been. Lancet 354, 652. ageing: modulation by longevity-promoting agent aurintricarboxylic acid. Jazwinski, S.M., 2000. Aging and longevity genes. Acta Biochim. Pol. 47, 269– Mech. Ageing Dev. 126, 249–254. 279. Siegal, M.L., Promislow, D.E., Bergman, A., 2007. Functional and evolutionary Jeong, H., Mason, S.P., Barabasi, A.L., Oltvai, Z.N., 2001. Lethality and inference in gene networks: does topology matter? Genetica 129, centrality in protein networks. Nature 411, 41–42. 83–103. 124 A. Budovsky et al. / Mechanisms of Ageing and Development 128 (2007) 117–124

Stark, C., Breitkreutz, B.J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M., Williams, G.C., 1957. Pleiotropy, natural selection, and the evolution of 2006. BioGRID: a general repository for interaction datasets. Nucleic Acids senescence. Evolution 11, 398–411. Res. 34 (Database issue), 535–539. Yook, S.H., Oltvai, Z.N., Barabasi, A.L., 2004. Functional and topological Vijg, J., Suh, Y., 2005. Genetics of longevity and aging. Annu. Rev. Med. 56, characterization of protein interaction networks. Proteomics 4, 928– 193–212. 942. Wagner, A., 2001. The yeast protein interaction network evolves rapidly and Zahn, J.M., Sonu, R., Vogel, H., Crane, E., Mazan-Mamczarz, K., Rabkin, R., contains few redundant duplicate genes. Mol. Biol. Evol. 18, 1283–1292. Davis, R.W., Becker, K.G., Owen, A.B., Kim, S.K., 2006. Transcriptional Warner, H.R., 2005. Longevity genes: from primitive organisms to humans. proﬁling of aging in human muscle reveals a common aging signature. PLoS Mech. Ageing Dev. 126, 235–242. Genet. 2, e115.