An analysis of aging-related genes derived from the Genotype-Tissue Expression project (GTEx)

Jia, Kaiwen; Cui, Chunmei; Gao, Yuanxu; Zhou, Yuan; Cui, Qinghua

doi:10.1038/s41420-018-0093-y

Cited by 35 publications

(55 citation statements)

References 55 publications

(44 reference statements)

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“…Gene expression-based methods predict a gene as aging-related if it is differentially expressed between younger age versus older age, or if its expression value either increases or decreases with age when dealing with more than two ages [12][13][14][15][16]. A limitation of gene expression-based methods is that they study genes in isolation from each other.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork

Milenković

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods for this purpose account for interactions between genes' protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an aging-specific subnetwork of the entire PPI network, obtained by integrating aging-specific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting aging-related genes from an aging-specific PPI subnetwork. We find that using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.However, cellular processes, including aging, are carried out by genes' protein products interacting with each other in complex ways [20]. So, it is essential to consider interactions between proteins.This is exactly what PPI network-based methods do. They predict a gene as aging-related according to how similar its position (i.e., node representation/embedding/feature) in the PPI network is to the network positions of known aging-related genes [7,8,17,18]. State-of-the-art approaches of this type are UniNet [8] and mBPIs [7]. UniNet's feature concatenates 14 network centrality measures (e.g., degree or betweenness), where centrality of a node captures its importance in the network. The feature of mBPIs is constructed as follows. First, m nodes with the highest degrees in the network are identified. Then, for each node v in the network, v's feature has m dimensions corresponding to the top m highest-degree nodes, where each dimension j of node v's feature indicates whether v interacts with the top m highest-degree node j. A limitation of these and other PPI network-based methods is that the current PPI network data are context-unspecific, meaning that the PPIs span different conditions (cell types, tissues, diseases, environments, patients, etc.) [21]; consequently, the current PPI network data are also static [4]. We argue that it is essential to consider aging-specific context of the entire PPI network, i.e., its aging-specific subnetwork, by integrating aging-specific gene expression data with PPI network data.Indeed, the need for at least some level of data integration in the context of aging is being recognized. There exist approaches for supervised prediction of aging-related genes that extract genes' features from each of...

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Section: Motivation and Related Workmentioning

confidence: 99%

Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork

Milenković

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…The impact of aging on gene expression and genetic regulation has been well-studied in model systems, such as yeast, fruit fly, or worm [4, 5], while much less is known about the transient nature of gene regulation and expression in humans. The majority of studies that have been performed in humans have been cross-sectional [6–15]. The few longitudinal studies have either focused on a specific disease or intervention [16–21], looked over a short time span [22], or focused on cell lines [23].…”

Section: Introductionmentioning

confidence: 99%

Genetic regulation of gene expression and splicing during a 10-year period of human aging

et al. 2019

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Background Molecular and cellular changes are intrinsic to aging and age-related diseases. Prior cross-sectional studies have investigated the combined effects of age and genetics on gene expression and alternative splicing; however, there has been no long-term, longitudinal characterization of these molecular changes, especially in older age. Results We perform RNA sequencing in whole blood from the same individuals at ages 70 and 80 to quantify how gene expression, alternative splicing, and their genetic regulation are altered during this 10-year period of advanced aging at a population and individual level. We observe that individuals are more similar to their own expression profiles later in life than profiles of other individuals their own age. We identify 1291 and 294 genes differentially expressed and alternatively spliced with age, as well as 529 genes with outlying individual trajectories. Further, we observe a strong correlation of genetic effects on expression and splicing between the two ages, with a small subset of tested genes showing a reduction in genetic associations with expression and splicing in older age. Conclusions These findings demonstrate that, although the transcriptome and its genetic regulation is mostly stable late in life, a small subset of genes is dynamic and is characterized by a reduction in genetic regulation, most likely due to increasing environmental variance with age.

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“…Ageing Related Genes We first defined and labelled genes (and or proteins) as ageing. We considered data provided from GTEx dataset that contains genes positively and negatively correlated with human age [38]. We gathered data from the GenAge dataset that derived human genes by projecting sequence orthologs in model organisms.…”

Section: Sars-cov-2 Interaction Mapmentioning

confidence: 99%

Intersection between ageing-related Genes and SARS-CoV-2 Interactome: Is it higher than expected?

Guzzi¹,

Pedace²,

Giorgi

2020

Preprint

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Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease, 2019; COVID-19) is associated with adverse outcomes in patients. It has been observed that lethality seems to be related to the age of patients. Moreover, it has been demonstrated that ageing causes some modiﬁcations at a molecular level.Objective: The study aims to investigate a possible link between the increased COVID-19 lethality and the molecular changes that occur in elderly people.Methods: We considered publicly available datasets on ageing-related genes and SARS-CoV-2 interactors. Then, for each SARS-CoV-2 protein interactor, we tested for the enrichment of ageing-related proteins. Finally, we performed a network-based analysis to identify which molecular mechanisms could play a role in the SARS-CoV-2 molecular aetiology and ultimately affect COVID-19 outcome.Results: We observed a signiﬁcant intersection between some SARS-CoV-2 interactors and ageing-related genes. Our analysis evidenced that virus infection particularly affects ageing molecular mechanisms centred around proteins EEF2, NPM1, HMGA1, HMGA2, APEX1, CHEK1, PRKDC, and GPX4.Conclusion: Our study generated a mechanistic framework aiming at explaining the correlation between COVID-19 incidence in elderly patients and molecular mechanisms of ageing. This will provide testable hypotheses for future investigation on the mechanism of action of coronaviruses and pharmacological solutions tailored on speciﬁc age ranges.

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An analysis of aging-related genes derived from the Genotype-Tissue Expression project (GTEx)

Cited by 35 publications

References 55 publications

Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork

Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork

Genetic regulation of gene expression and splicing during a 10-year period of human aging

Intersection between ageing-related Genes and SARS-CoV-2 Interactome: Is it higher than expected?

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