Disease-Aging Network Reveals Significant Roles of Aging Genes in Connecting Genetic Diseases

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system–based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases

Systematic analysis of the gerontome reveals links between aging and age-related diseases

In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the ‘gerontome’. Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases

IP6K3 and IPMK variations in LOAD and longevity: evidence for a multifaceted signaling network at the crossroad between neurodegeneration and survival

Several studies reported that genetic variants predisposing to neurodegeneration were at higher frequencies in centenarians than in younger controls, suggesting they might favor also longevity. IP6K3 and IPMK regulate many crucial biological functions by mediating synthesis of inositol poly- and pyrophosphates and by acting non-enzymatically via protein–protein interactions. Our previous studies suggested they affect Late Onset Alzheimer Disease (LOAD) and longevity, respectively. Here, in the same sample groups, we investigated whether variants of IP6K3 also affect longevity, and variants of IPMK also influence LOAD susceptibility. We found that: i) a SNP of IP6K3 previously associated with increased risk of LOAD increased the chance to become long-lived, ii) SNPs of IPMK, previously associated with decreased longevity, were protective factors for LOAD, as previously observed for UCP4. SNP-SNP interaction analysis, including our previous data, highlighted phenotype-specific interactions between sets of alleles. Moreover, linkage disequilibrium and eQTL data associated to analyzed variants suggested mitochondria as crossroad of interconnected pathways crucial for susceptibility to neurodegeneration and/or longevity. Overall, data support the view that in these traits interactions may be more important than single polymorphisms. This phenomenon may contribute to the non-additive heritability of neurodegeneration and longevity and be part of the missing heritability of these traits

PAK in Alzheimer disease, Huntington disease and X-linked mental retardation.

Developmental cognitive deficits including X-linked mental retardation (XLMR) can be caused by mutations in P21-activated kinase 3 (PAK3) that disrupt actin dynamics in dendritic spines. Neurodegenerative diseases such as Alzheimer disease (AD), where both PAK1 and PAK3 are dysregulated, may share final common pathways with XLMR. Independent of familial mutation, cognitive deficits emerging with aging, notably AD, begin after decades of normal function. This prolonged prodromal period involves the buildup of amyloid-β (Aβ) extracellular plaques and intraneuronal neurofibrillary tangles (NFT). Subsequently region dependent deficits in synapses, dendritic spines and cognition coincide with dysregulation in PAK1 and PAK. Specifically proximal to decline, cytoplasmic levels of actin-regulating Rho GTPase and PAK1 kinase are decreased in moderate to severe AD, while aberrant activation and translocation of PAK1 appears around the onset of cognitive deficits. Downstream to PAK1, LIM kinase inactivates cofilin, contributing to cofilin pathology, while the activation of Rho-dependent kinase ROCK increases Aβ production. Aβ activation of fyn disrupts neuronal PAK1 and ROCK-mediated signaling, resulting in synaptic deficits. Reductions in PAK1 by the anti-amyloid compound curcumin suppress synaptotoxicity. Similarly other neurological disorders, including Huntington disease (HD) show dysregulation of PAKs. PAK1 modulates mutant huntingtin toxicity by enhancing huntingtin aggregation, and inhibition of PAK activity protects HD as well as fragile X syndrome (FXS) symptoms. Since PAK plays critical roles in learning and memory and is disrupted in many cognitive disorders, targeting PAK signaling in AD, HD and XLMR may be a novel common therapeutic target for AD, HD and XLMR

A proteomic atlas of senescence-associated secretomes for aging biomarker development.

The senescence-associated secretory phenotype (SASP) has recently emerged as a driver of and promising therapeutic target for multiple age-related conditions, ranging from neurodegeneration to cancer. The complexity of the SASP, typically assessed by a few dozen secreted proteins, has been greatly underestimated, and a small set of factors cannot explain the diverse phenotypes it produces in vivo. Here, we present the "SASP Atlas," a comprehensive proteomic database of soluble proteins and exosomal cargo SASP factors originating from multiple senescence inducers and cell types. Each profile consists of hundreds of largely distinct proteins but also includes a subset of proteins elevated in all SASPs. Our analyses identify several candidate biomarkers of cellular senescence that overlap with aging markers in human plasma, including Growth/differentiation factor 15 (GDF15), stanniocalcin 1 (STC1), and serine protease inhibitors (SERPINs), which significantly correlated with age in plasma from a human cohort, the Baltimore Longitudinal Study of Aging (BLSA). Our findings will facilitate the identification of proteins characteristic of senescence-associated phenotypes and catalog potential senescence biomarkers to assess the burden, originating stimulus, and tissue of origin of senescent cells in vivo

A Network of Genes, Genetic Disorders, and Brain Areas

The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain

Identifying aging-related genes in mouse hippocampus using gateway nodes

BACKGROUND: High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph theoretic methodology can be applied to network models to increase efficiency and speed of analysis. In this project, we propose a network model that examines temporal data from mouse hippocampus at the transcriptional level via correlation of gene expression. Using this model, we formally define the concept of “gateway” nodes, loosely defined as nodes representing genes co-expressed in multiple states. We show that the proposed network model allows us to identify target genes implicated in hippocampal aging-related processes. RESULTS: By mining gateway genes related to hippocampal aging from networks made from gene expression in young and middle-aged mice, we provide a proof-of-concept of existence and importance of gateway nodes. Additionally, these results highlight how network analysis can act as a supplement to traditional statistical analysis of differentially expressed genes. Finally, we use the gateway nodes identified by our method as well as functional databases and literature to propose new targets for study of aging in the mouse hippocampus. CONCLUSIONS: This research highlights the need for methods of temporal comparison using network models and provides a systems biology approach to extract information from correlation networks of gene expression. Our results identify a number of genes previously implicated in the aging mouse hippocampus related to synaptic plasticity and apoptosis. Additionally, this model identifies a novel set of aging genes previously uncharacterized in the hippocampus. This research can be viewed as a first-step for identifying the processes behind comparative experiments in aging that is applicable to any type of temporal multi-state network