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{
"question": [
"what genes are associated with aging?",
"Which genes are associated with aging in human ",
"What is GeneNetwork and how does it relate to aging research?"
],
"answer": [
"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.",
"The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.",
"GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity."
],
"contexts": [
[
"Following are examples of the identified genes and experimental or GWAS link between these genes and aging.On the list of the 25 top genes, NAP1L4 encodes a member of the nucleosome assembly protein (NAP) family, which interacts with both core and linker histones, and shuttles between the cytoplasm and nucleus, suggesting a role as histone chaperone.Histone protein levels decline during aging, and dramatically affect chromatin structure.Remarkably, the lifespan can be extended by manipulations that reverse the age-dependent changes to chromatin structure, indicating the pivotal role of chromatin structure in aging [32].In another example, gene expression of NAP1L4 increases with age in the skin tissue [33].Findings of GWAS link a number of the identified genes to age-related disorders, such as GAB2 and late onset Alzheimer's disease [86], and QKI and coronary heart disease/myocardial infarction [79].Interestingly, GWAS reports also link QKI to successful aging [87].Indicative biological pathways associated with the candidate aging genes",
"Examples of biological candidate genes with pleiotropic functions, which are involved in aging in general and in musculoskeletal aging in particular, are numerous: (a) in addition to the IGF-1 and vitamin D genes, estrogen metabolism pathway genes, including estrogen receptors and aromatase (CYP19), are associated with fat-free mass (Walsh et al. 2005) and BMD (Shearman et al. 2004), prostate and breast cancer (Gallicchio et al. 2006), and cardiovascular disease risk (Shearman et al. 2003).",
"In-depth analysis of the age-regulated genes revealed that multiple genes in the DNA damage response pathway were upregulated with age including those that function in non-homologous end-joining repair (mre11, rad50, Ku80 and mus308) and in translesion DNA synthesis (mus205 and DNApol-eta) [44][45][46].Genes that encoded enzymes with antioxidant properties, such as the thioredoxin reductase Trxr-1, and antioxidant genes involved in glutamate metabolism, such as GlnRS, isoQC and QC, were also upregulated with age [47][48][49][50].We also observed increased age-associated expression of chaperone genes (Cct1, Cct4, Cct5, Cct6, Hsc70-4) and the unfolded protein response transcription factor Xbp1, consistent with an induction of the unfolded protein response [51][52][53].Under stress conditions, there is a translational switch that favors production of stressrelated proteins while decreasing translation of other proteins [54].Paralogs of canonical translation factors such as NAT1 and Rack1, which were both upregulated, promote this switch to cap-independent translation [55,56].Notably, Rheb, which is downregulated with age, positively regulates ribosome production and capdependent translation by activating the mechanistic target of rapamycin (mTOR) kinase pathway [57].Thus, decreased Rheb levels during aging could decrease mTOR pathway activity, which extends lifespan and is protective against age-related pathology [58].Together, these data suggest that multiple genes are induced in aging photoreceptors to mitigate the effects of oxidative stress, protein misfolding and DNA damage.",
"Gene expression modules regulated by agingNearest-neighbor co-expression modules ranging in size from 2 to 40 genes were formed and the collective response of each module to aging across tissues was evaluated. ).The most significant 3-gene module included two proteasome subunit genes (Psmb8 and Psmb9), along with the MHC antigen H2-K1 (M = 10.0;P < 0.001; see Table 3).The three genes contained in this module exhibited highly correspondent patterns of differential expression, with decreased expression occurring in spleen with age, and an age-related up regulation of expression across 13 tissues (Additional File 11).A similar pattern was present with respect to other 3-gene co-expression modules, such as {Tyrobp, Mpeg1, Ctss} and {Sfi1, Pisd, 4933439C20Rik}, and with significant co-expression modules of larger size (Additional File 11).In each of these cases, genes belonging to the same module exhibited similar differential expression patterns in the same tissues, indicating that patterns of co-expression had considerable explanatory power in terms of age-related transcriptional effects.",
"Analysis of prior research (Online Resource 5) shows that the revealed genes can be explicitly involved in other key biological processes in an organism whose role is known to be changing with aging.Specifically, ten genes (BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, and ZKSCAN1) regulate transcription which is believed to be disrupted when an organism is getting older (Roy et al. 2002).The DBH, TPO, and LSS genes are involved in synthesis of catecholamine, thyroid, and vitamin D hormones, respectively.The GPER binds estrogen and HCRTR2 binds orexin-A and orexin-B neuropeptid hormones.Hormonal deregulation with aging is considered to be one of the major components of senescent processes in an organism (Barzilai and Gabriely 2010).Five genes (ATG2A, NEDD4L, PSMB1, UBXN4, and USP6) are involved in degradation of proteins through ubiquitin-proteasome and the lysosomal/autophagic system.Dysfunction of this system leads to accumulation of damaged proteins in an organism that is associated with aging (Koga et al. 2011).Protein degradation through ubiquitin-mediated proteolysis plays an important role in cell-cycle regulation (Reed 2003).The PSMB1, SIK1, TRIP13, and TTN genes in the revealed set coordinate cell cycle.Cell cycle is linked with the aging-related processes in humans through a gradual increase in cell division errors in all tissues in an organism (Ly et al. 2000).Five genes (EEF1A2, DBH, ITGB2, TUBB2C, and WRN) take part in regulation of apoptosis which plays an important role in the aging process and tumorigenesis (Salvioli et al. 2008).Seven genes (ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, and SOAT2) are involved in lipid metabolism which plays one of the key roles in human longevity and healthy aging (Barzilai et al. 2003).",
"In addition to testing genes known to be associated with age-related diseases and phenotypes for association with longevity, genes known to promote longevity in model organisms have been examined in human populations.Mutations in insulin or insulinlike signalling pathway genes have been shown to extend lifespan in Caenorhabditis elegans [20], Drosophila melanogaster [21,22] and mice [23,24].The insulin-signalling pathway negatively regulates the forkhead (FOXO) transcription factor [25].When insulin or insulin-like growth factor signalling is low, FOXO is activated and lifespan extension occurs [26].An overrepresentation of rare insulin-like growth factor I receptor (IGFIR) mutations has been observed in centenarians [27].These mutations are associated with reduced activity of IGFIR as measured in transformed lymphocytes [27].",
"Aging can be viewed as a lethal by-product of activities, such as reproduction and food intake, that are controlled by genes [1].Since most of these genes are evolutionarily conserved, distant species may share common pathways of aging [2].The insulin/insulin-like growth factor 1 (IGF1) signaling pathway could be one such common pathway, as it modulates aging in many species, including Caenorhabditis elegans, Drosophila, mice [3], and possibly humans [4].An elegant study carried out in C. elegans by applying microarray techniques showed that a member of the SIR2like protein family is regulated downstream of DAF-16, a FOXO-family transcription factor that affects the rate of aging in response to the insulin/IGF1 pathway [5].SIR2 proteins constitute an evolutionarily conserved family of NAD-dependent deacetylases called sirtuins [6][7][8].In model organisms the expression levels of SIR2 modulate life span [9][10][11].Since sirtuins are NAD + dependent these proteins through different routes may link energy metabolism, genome maintenance, and aging [11,12].Thus SIR2 genes may play a crucial role in conserved pathways of aging and longevity.",
"Cross-species translation of age-related processesTo identify convergent evidence across species for genes involved in aging, we integrated data from a total of 73 aging-associated gene sets (S4 Table ), derived from 31 publications across 6 species (yeast, worm, fly, rat, mouse, human), and from three web resources (GeneNetwork, GenAge [38], and GWAS Catalog (https://www.ebi.ac.uk/gwas/).Using the \"GeneSet Graph tool\" in GeneWeaver, we identified Cd63 as the most highly connected gene (i.e. it was present in the largest number of sets of genes) (Fig 3).Cd63 was present in 12 gene sets from seven publications across four species (fly, rat, mouse, and human; Table 3).The probability of finding at least one gene in a 12-way intersection, given the observed set sizes and species, is p < 0.0005 (permutations n = 2000).To validate Cd63 as an aging gene, we knocked down the C. elegans ortholog, tsp-7, by feeding RNAi and observed a 10.5% extension of mean lifespan (19.04.0,n = 312 for empty vector(RNAi) vs. 21.06.5 days, n = 317 for tsp-7(RNAi) at 25C; p = 4.8e-7 by the log-rank test) (Fig 4,S5 Table).Manipulating tsp-7 is thus sufficient to influence lifespan in at least one environmental context.",
"Genes Whose Expression Decreased with Age.Of the 26 genes that decreased expression with age in control mice, 23% are involved in DNA replication and the cell cycle (Table 2).Most of these have a negative effect on cell growth and division.Among these, the product of phosphatase and tensin homolog (Pten) gene is a tumor suppressor that induces cell-cycle arrest through inhibition of the phosphoinositide 3-kinase pathway (28).B cell translocation gene 2 (Btg2) is a tumor suppressor that increases expression in response to DNA damage (29).The murine gene product of the amino-terminal enhancer of split (Aes) is a potent corepressor of gene expression and cellular proliferation (30).Calcium-binding protein A11 (S100a10) binds to and regulates the activity of annexin II, which is involved in the transduction of calcium-related mitogenic signals (31).Insulin-like growth factor (IGF) binding protein 1 (Igfbp1) plays an important role in the negative regulation of the IGF-1 system, a stimulator of mitogenesis (32).",
"daf-16 dependent genesAmong the 52 genes that we have tested, 29 genes act almost completely in a daf-16 dependent manner, to regulate lifespan (Table 2).One of the genes identified was daf-2 (Y55D5A_391.b).This serves as a proof of principle that our screen is effective in identification of aging genes.",
"Signatures of aging in muscleFor the muscular system, six clusters of age-related genes with significant enrichment of functional annotation were identified (Fig. 2B; Supplemental Table 9).Aging in muscle was associated with an increase of transcript levels of genes (Clusters 1, 2, and 3) involved in a number of biological processes, including antimicrobial humoral response, ubiquitin-dependent protein catabolism, autophagic cell death, prosthetic group metabolism, protein membrane targeting, secretion pathway, transmembrane receptor protein tyrosine kinase signaling pathway, cell motility, and response to toxin as represented by glutathione S transferase.On the other hand, aging in muscle was found to be associated with decreased transcript levels of genes (Clusters 4-6) involved in generation of energy derived by oxidation of organic compounds as represented by succinate dehydrogenase B (SdhB), in oxidative phosphorylation as represented by ATPase coupling factor 6, in protein kinase cascade as represented by Jun-related antigen, and in metal ion transport as represented by ferritin 1 heavy chain homolog and I'm not dead yet (Indy).It has been shown that SdhB, ATP synthase, ferritin, and aconitase in C. elegans (Hamilton et al. 2005;Hansen et al. 2005) and Indy and SdhB in D. melanogaster (Rogina et al. 2000;Walker et al. 2006) modulate lifespan in these organisms, respectively.Overall, these findings suggest that a prominent feature of aging in muscle is the alteration of expression of genes involved in proteasomal and mitochondrial functions.",
"Several of the genes we identify have previously been shown to influence lifespan in experiments on model organisms.For example, knockouts of the orthologs of APOE, LDLR, CDKN2B, and RBM38 in mice shortens their lifespan [24][25][26][27] , while knockout of IGF1R has the opposite effect 28 .Similarly, overexpression of the FOXO3 orthologue in Drosophila melanogaster 29 and the SNCA orthologue in Caenorhabditis elegans 30 have shown to extend their respective lifespans.Many of our genes are also enriched for pathways previously related to ageing in eukaryotic model organisms, including genomic stability, cellular senescence, and nutrient sensing 31 .For example, FOXO3 and IGF1R are well-known players modulating survival in response to dietary restriction 32 , but we also highlight genes involved in the response to DNA damage and apoptosis, such as CDKN2B, USP28, E2F2, and BCL3.In addition to hallmarks discovered in model organisms, our results suggest that haem metabolism may play a role in human ageing.This pathway includes genes involved in processing haem and differentiation of erythroblasts 33 .Although the enrichment is largely driven by genes linked to the LDLR locus, genes linked to other loci of interest (such as FOXO3, CDKN2B, LINC02513) are involved in similar biological pathways: myeloid differentiation, erythrocyte homeostasis, and chemical homeostasis.To determine the age-related expression of the identified cisand trans-acting genes, we performed a look-up in the dataset of Peters et al. 14 .This large dataset contains the associations of genes with age in whole blood, so we limited ourselves to the cis-and trans-acting genes identified in the whole-blood datasets.We found that FOXO3 expression is increased with age in this dataset, which is in line with the life-extending variant decreasing expression (Supplementary Data 6).Moreover, one cis-(ILF3) and two trans-acting genes (E2F2 and PDZK1IP1) in the LDLR locus show a similar effect (i.e.increased or decreased expression with age combined with the life-extending variant decreasing or increasing expression, respectively).The most interesting, however, seems to be the LINC02513 locus, which showed multiple trans-acting genes to be strongly downregulated with age, while the lead life-extending variant increases expression.LEF1, CCR7, and ABLIM1 even belong to the most significantly affected genes in the whole transcriptomic dataset.This indicates that this long intergenic non-protein coding RNA may serve as a master regulator of age-related transcription in whole blood.",
"94DE MAGALHES ET AL. lar signatures of mammalian aging.Some of the genes overexpressed with age seem to be a response to aging, in that they have been previously found to have protective functions (de Magalha es et al., 2009b).As such, these genes may help organisms manage aging and could be targets for manipulation.Likewise, gene expression analysis of CR has been conducted to identify associated genes (Lee et al., 1999(Lee et al., , 2000)).A number of molecular signatures have emerged from such studies that could be useful to identify candidate processes and pathways that affect aging, biomarkers (see below), and candidate regulators (Anderson and Weindruch, 2010;Hong et al., 2010).",
"Aging-related gene prediction and putative transcriptional mechanismsGeneFriends was used to identify genes related to aging.A seed list of genes known to be consistently overexpressed with age in mammals was used [18].In total, 1119 genes were co-expressed with the aging seed list at p <10 -6 ; Table 1 shows the top 25 genes.Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30 ), Fas [20] (p = 2.70 -31 ) and growth hormone receptor Ghr [21] (p = 1.34 -19 ) also showed a significant co-expression.Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10 -25 ) as well as several S100 calcium binding proteins which have been shown to interact with annexins [22].Top 25genes co-expressed with aging related genes",
"Fig. 7 Functional relationships of genes implicated in longevity.The genes in red/blue boxes represent genes with increased/decreased mRNA expression in ageing Drosophila (color figure online)",
"The genome-wide RNAi study conducted by the Ruvkun lab, authored by Hamilton et al. [88], identified a total of 89 additional aging genes with disparate functions including cell structure, cell surface proteins, cell signaling, cellular metabolism, and protein turnover.Of the 66 genes with previously known functions, 17 corresponded to various aspects of carbon metabolism, including citric acid cycle enzymes and subunits of complexes I, IV, and V of the ETC.Researchers also speculated that protein translation might play a role in lifespan regulation, based on the identification of iff-1 (T05G5.10),a gene that has homology to the translation initiation factor eIF5A.Other hits from this screen included two genes containing PH domains known to interact with phosphatidylinositol lipids, multiple G protein-coupled receptors, protein processing and degradation genes such as proteases and ubiquitin ligases/hydrolases, and chromatin modifying factors.",
"Genetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellular processes (such as mitochondrial function, chromatin regulation, insulin signaling, transcriptional regulation, and genome stability).This indicates that aging is a complex process driven by diverse molecular pathways and biochemical events.As such, a powerful approach to study aging is to use systems biology, which allows a multitude of factors affecting aging to be analyzed in parallel.For example, DNA microarrays and gene expression chips have been used to perform a genome-wide analysis of changes in gene expres-sion in old age.Extensive studies in Caenorhabditis elegans and Drosophila melanogaster have identified hundreds of ageregulated genes (Hill et al. 2000;Zou et al. 2000;Lund et al. 2002;Pletcher et al. 2002;Murphy et al. 2003).Several studies have described age-regulated genes in the muscle and brain of mice (Lee et al. 1999(Lee et al. , 2000) ) and the retina and muscle of humans (Yoshida et al. 2002;Welle et al. 2003Welle et al. , 2004).These age-regulated genes may serve as markers of aging, enabling one to assess physiological age independently of chronological age.Analysis of the functions of these age-regulated genes has identified specific biochemical mechanisms that change toward the end of life."
],
[
"Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.",
"Background: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study.",
"Geneticlinkage studies of long-lived human families identified alongevity locus while candidate gene approaches have beenused to identify and confirm the association betweenspecific variants in the FOXO3A gene and humanlongevity [37]. Genome-wide association studies havealso been used to identify the association of APOE with life123Aging Clin Exp Resspan and have yielded insights into potential biologicalpathways and processes related to aging. Despite thesesuccesses, several problems are inherent in humanlongevity studies including potentially high degrees ofenvironmental heterogeneity, genetic diversity, and lack ofbirth matched controls, among others [8].",
"Additional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process.",
"Somatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].",
"Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.The only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained.",
"In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.",
"The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p 5 10 8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p 10 5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p 5 10 8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p 10 5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.",
"In addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations.",
"The genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.",
"Although the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhes et al ., 2005a(de Magalhes et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000).",
"Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.The only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained.",
"Most of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]."
],
[
"Our recent understanding of biological networks has led to new fields, like network medicine [29].Biological networks can be built using protein interaction and gene co-expression data.A previous paper used proteinprotein interactions to build genetic networks identifying potential longevity genes along with links between genes and aging-related diseases [30].Here, we present the network of proteins and genes co-expressed with the CellAge senescence genes.Assaying the networks, we find links between senescence and immune system functions and find genes highly connected to CellAge genes under the assumption that a guilt-by-association approach will reveal genes with similar functions [31].We next explored what information could be obtained by applying a network analysis to CellAge.From the list of CellAge genes, three networks of CS were generated: a PPI network and two co-expression networks, with the aim of identifying new senescence regulators based primarily on network centrality of the genes.We looked at the RNA-Seq co-expression network in detail, using the main connected component of 3198 genes to find highly central genes to the network as a whole, and those occupying subnetworks of interest.The RNA-Seq was a highly modular network, separated into some subnetworks of distinct functions (Fig. 4).The two largest and more central networks contained a number of known senescence genes.We expanded the analysis of these networks in particular, identifying a number of bottleneck nodes.Cluster 1 was enriched for cell cycle processes, which is not overly surprising given that senescence involves changes in cell cycle progression.However, cluster 2 comprised of enriched terms relating to immune system function.One of the aims in biogerontology is to understand and reverse the effects of aging on the immune system.Additional file 1: Table S38 highlights the genes in both clusters that are potential CS bottlenecks within the network and may warrant further study.Unweighted RNA-Seq co-expression networkWe used CellAge genes that induce and inhibit CS and their co-expressing partners to build a cellular senescence co-expression network.The network consists of a main connected network with 3198 nodes, and a number of smaller \"islands\" that are not connected to the main network (Fig. 4a).In this study, we look at the broad context of CS genes-their association with aging and aging-related diseases, functional enrichment, evolutionary conservation, and topological parameters within biological networks-to further our understanding of the impact of CS in aging and diseases.Using our networks, we generate a list of potential novel CS regulators and experimentally validate 26 genes using siRNAs, identifying 13 new senescence inhibitors.Network analysesThe CellAge genes form both protein-protein and gene co-expression networks.The formation of a proteinprotein interaction (PPI) network is significant in itself given that only ~4% of the genes in a randomly chosen gene dataset of similar size are interconnected [53].In order to have a more holistic view of CS, we were interested in the topological parameters of the networks that CS genes form.For this, several types of networks were constructed using the CellAge genes as seeds: the CS PPI network, along with two CS gene co-expression networks built using RNA-seq and microarray data.Biological networks generally have a scale-free topology in which the majority of genes (nodes) have few interactions (edges), while some have many more interactions, resulting in a power law distribution of the node degree (the number of interactions per node) [31,54].As expected, the node-degree distribution of the above networks does confirm a scale-free structure (Additional file 2: Fig. S9).Additional file 1: Table S32 presents the network summary statistics for the resulting networks.",
"Here we have curated studies from the aging literature and utilized integrative functional genomics in GeneWeaver to address four questions related to aging by analyzing these largescale, complex sets of data: 1) to identify molecular relations between cellular senescence and functional cognitive decline, 2) to examine the intersection between comorbid disease states, 3) to identify new druggable targets for longevity, and 4) to examine cross-species translation of age-related processes.GeneSet GraphTo identify the most highly connected gene within a group of gene sets related to aging, the \"GeneSet Graph\" tool was used.This tool presents a bipartite graph visualization of genes and gene sets.Genes are represented by elliptical nodes, and gene sets are represented by boxes.The least-connected genes are displayed on the left, followed by the gene sets, then the moreconnected genes in increasing order to the right.Genes and gene sets are connected by colored lines to show what genes are in which gene sets.A degree threshold is applied on the gene partite set to reduce the graph size.DiscussionThe growing number of studies and data in many fields, including ageing, requires the development of integrative and computational approaches to analyze the data for consensus and shared biological findings across conditions.Using GeneWeaver's database and analysis tools to address questions in aging research we were able to identify genes common to cellular senescence and functional cognitive decline; to examine gene products at the intersection between obesity and dementia, to identify several potential druggable targets for investigation in longevity, and to identify and validate a cross-species age-related gene from convergent evidence.Our identification of the role for CD63 in aging would not have been made without this use of this large genomic analysis tool.CD63 in C.elegans is member of the tertaspanin family of proteins [47].Tetraspanins are transmembrane scaffolding proteins involved in motility, cell adhesion, proliferation and activation.Recently we showed that knockdown of another tetraspanin in C.elegans, tsp-3, extends lifespan by >20% lifespan as well [48], suggesting that this protein family may be of broader interest in aging.",
"NIH-PA Author ManuscriptNIH-PA Author ManuscriptGeneNetwork (www.genenetwork.org), described in Chapter 6, is a suite of data sets andbioinformatics tools that stores, analyzes, and displays phenotypes as well as large geneexpression data sets for several species (human, monkey, mouse, rat, fly, barley, tomato, andArabidopsis) (Durrant et al. , 2012; Hoffman et al. , 2011; Rosen et al. , 2007). GeneNetworkusers can take advantage of a systems genetics approach (Rosen et al. , 2003, 2007).",
"Interaction network analysisThe increased accuracy and breadth of our RNA-seq data sets allowed us to generate networks of gene functional change in aging liver, above and beyond what was observed using DAVID or GOrilla.Using Ingenuity Pathway Analysis (IPA) we generated, from the differentially expressed protein-coding genes and ncRNAs, interaction networks of functional change.This resulted in multiple overlapping pro-aging networks from which we could distinguish three major molecular phenotypes: inflammation, proliferative homeostasis and lipid metabolism (Figs. 4, 5 and 6).",
"As mentioned previously, GeneNetwork(www.genenetwork.org) is a collaborative Web-based resource equipped with tools andfeatures for studying gene/gene and exploring genetic correlates to neurobehavioralphenotypes (Chesler et al. , 2003, 2004). The Web site is home to a growing collection ofgene expression and phenotypic data from a variety of species and brain regions, with a hostof links to external resources for tracing the interrelationships of a gene among multipleWeb-based resources. GeneNetwork also offers a number of correlation and mappingstrategies for assessing associations among multiple genes and QTLs.",
"The aim of this work was to construct an online tool that can be used to derive novel candidate genes for further studies in aging and complex diseases, in a quick and intuitive manner.Aging is not considered a disease, yet older individuals are more susceptible to several diseases such as Alzheimer's, Parkinson's and cancer.This is one of the reasons why research in this field is rapidly expanding and several hundreds of genes have been linked to aging [16].A major bottleneck in aging/ complex disease research is that it is difficult to determine the causality of transcriptional alterations.It is also unclear if the altered expression profile observed with aging/complex disease consists of one particular biological module or whether it consists of genes that act separately from each other.To this end, GeneFriends outputs transcription factors co-expressed with the genes supplied by the user.",
"Network analyses additionally revealed systems level relationships between age-related diseases and the aging regulators.Miller et al. [42] used a weighted gene coexpression network to identify transcriptional networks in Alzheimer's disease (AD) and found a significant association between gene expression changes during the progression of AD and those during normal aging.Wang et al. [43] constructed a human disease-aging network to study the relationships between aging genes and genetic disease genes.This study showed that disease genes located close to aging genes have central positions in the PPI network.Second, although high-throughput data on different layers of the living system Fig. (2) can now be easily obtained, it remains obscure as to how information flows or exchanges across these layers to arrive at the alternative \"old/aging\" state of the molecular network from the young state, what events cause the state transition and what are the network circuitry and epigenetic events locking the network in the aging state. [62,63].Clusters or communities in the networks were extracted by the MCL algorithm [64] and only top clusters with more than 10 genes for each network are shown, and different clusters with similar functional enrichment are merged. (A) The network based on a protein functional interaction network [65]. (B) The edges in the network represent cocitation of the two genes together in at least 2 PubMed abstracts under the context of aging, i.e. also co-cited with \"aging\", \"ageing\", \"lifespan\", \"life span\" as calculated by Cociter (http:// www.picb.ac.cn/ hanlab/cociter).In both graphs, the enriched functions within the gene clusters are coded by the colors of the nodes: green -signaling pathways, red -DNA damage response, yellow -mitochondria function and oxidative stress response, blue -ribosome and translation related genes, and purple -protein localization, transport and autophagy.Fig. (4).Network communities among known aging regulators in human and model organisms based on two different interactome datasets.Nodes include human aging regulators and human homologs of aging regulators in worm, fly and mouse from GenAge[62,63].Clusters or communities in the networks were extracted by the MCL algorithm[64] and only top clusters with more than 10 genes for each network are shown, and different clusters with similar functional enrichment are merged. (A) The network based on a protein functional interaction network[65]. (B) The edges in the network represent cocitation of the two genes together in at least 2 PubMed abstracts under the context of aging, i.e. also co-cited with \"aging\", \"ageing\", \"lifespan\", \"life span\" as calculated by Cociter (http:// www.picb.ac.cn/ hanlab/cociter).In both graphs, the enriched functions within the gene clusters are coded by the colors of the nodes: green -signaling pathways, red -DNA damage response, yellow -mitochondria function and oxidative stress response, blue -ribosome and translation related genes, and purple -protein localization, transport and autophagy.Network approaches are instrumental in discerning global properties of aging/lifespan regulators, making computational predictions and inferring the modularity and relationships of various aging regulators.However, they should be applied with great caution as to avoid bias introduced by the literature, the lack of spatial and temporal information, or the limited coverage of the network [44].",
"GeneNetwork.org also offers a powerful statistical platform foronline network analyses and mapping, enabling numerous molecular questions to be probed in one centralized location(Chesler et al. , 2003, 2005; Li et al. , 2010; Mulligan et al. , 2012,2017, 2019). Most data are from groups of animals or humanswho have been fully genotyped or even sequenced. As a result, itcan be used to model causal networks that link DNA differencesto traits such as differences in expression, cell number, volumes,and behavior using real-time computation and graphing.",
"Another use of GenAge is for researchers to associate genes already under investigation with other, little-known genes, which can lead to new experimental designs.To do this, protein-protein interactions are one possible approach, and GenAge's human data set features 673 interactions, most of which manually curated obtained from the Human Protein Reference Database (HPRD) (Peri et al ., 2003).In fact, one of our earliest applications of GenAge involved finding novel genes that may be linked to aging by way of an analysis of protein-protein interactions.The principle being that proteins not previously thought to be related to aging which interact with a large number of proteins directly linked to aging might too be involved in aging and are thus promising candidates for future studies (de Magalhes & Toussaint, 2004;Budovsky et al ., 2007).Similar works are made easy with GenAge.Protein-protein interactions with one or more genes as query can be visualized (Fig. 2), or they can be downloaded for use with more advanced biological pathway analysis software.By providing a list of candidate genes, the genes in GenAge can serve as basis for gene expression and genetic association longevity studies, including human studies, or even for clinical studies of interventions hypothesized to affect aging.In fact, recent gene expression studies have used GenAge to focus on aging-associated genes (Chen et al ., 2008;Hardman & Ashcroft, 2008).Because researchers may have disparate opinions regarding the relevance of different model systems to understand human aging, an important tool to investigate the human data set as a whole is GenAge's browser (http://genomics.senescence.info/genes/browser.php).The browser makes it possible to (among other things) retrieve only those entries that pass certain criteria related to the annotation in GenAge, such as selection process and gene function.For example, users can retrieve only genes associated with aging in non-mammalian model organisms.Several criteria can be set, all of which must be passed for genes to be retrieved.Afterwards, users can select all or a subgroup of the genes retrieved, which can then be further analysed using simple statistical tools (http://genomics.senescence.info/genes/analyse.php).Cross-links between the different sections of GenAge make it easy to navigate between the different tools and data sets."
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