Supervised machine learning with feature selection for prioritization of targets related to time-based cellular dysfunction in aging (original) (raw)
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An evidence-based approach to identify aging-related genes in Caenorhabditis elegans
BMC Bioinformatics, 2015
Background: Extensive studies have been carried out on Caenorhabditis elegans as a model organism to elucidate mechanisms of aging and the effects of perturbing known aging-related genes on lifespan and behavior. This research has generated large amounts of experimental data that is increasingly difficult to integrate and analyze with existing databases and domain knowledge. To address this challenge, we demonstrate a scalable and effective approach for automatic evidence gathering and evaluation that leverages existing experimental data and literature-curated facts to identify genes involved in aging and lifespan regulation in C. elegans. Results: We developed a semantic knowledge base for aging by integrating data about C. elegans genes from WormBase with data about 2005 human and model organism genes from GenAge and 149 genes from GenDR, and with the Bio2RDF network of linked data for the life sciences. Using HyQue (a Semantic Web tool for hypothesis-based querying and evaluation) to interrogate this knowledge base, we examined 48,231 C. elegans genes for their role in modulating lifespan and aging. HyQue identified 24 novel but well-supported candidate aging-related genes for further experimental validation. Conclusions: We use semantic technologies to discover candidate aging genes whose effects on lifespan are not yet well understood. Our customized HyQue system, the aging research knowledge base it operates over, and HyQue evaluations of all C. elegans genes are freely available at http://hyque.semanticscience.org.
Network analysis in aged C. elegans reveals candidate regulatory genes of ageing
2021
Ageing is a biological process guided by genetic and environmental factors that ultimately lead to adverse outcomes for organismal lifespan and healthspan. Determination of molecular pathways that are affected with age and increase disease susceptibility is crucial. The gene expression profile of the ideal ageing model, namely the nematode Caenorhabditis elegans mapped with the microarray technology initially led to the identification of age-dependent gene expression alterations that characterize the nematode's ageing process. The list of differentially expressed genes was then utilized to construct a network of molecular interactions with their first neighbors/interactors using the interactions listed in the WormBase database. The subsequent network analysis resulted in the unbiased selection of 110 candidate genes, among which well-known ageing regulators appeared. More importantly, our approach revealed candidates that have never been linked to ageing before, thus suggesting ...
Deep Proteome Analysis Identifies Age-Related Processes in C. elegans
Cell Systems, 2016
Highlights d Near doubling in C. elegans proteome coverage with agedependent protein measurements d Putative sub-cellular localization assignments for >6,000 nematode proteins d Peroxisome protein import impaired during aging d Most age-variant proteins do not scale with biological age in insulin/IGF-1 mutants
Healthy aging: what can we learn from Caenorhabditis elegans?
Zeitschrift für Gerontologie und Geriatrie, 2013
Animal models represent an invaluable tool to gain insight into molecular mechanisms regulating different biological processes, including aging [1]. Most genetic, pharmacological and environmental factors that modulate the aging process were first identified in Caenorhabditis elegans (C. elegans) and are involved in the pathogenesis of age-associated diseases and possibly in the regulation of human longevity . Thanks to the evolutionarily conservation of genes, signaling pathways and age-associated changes between nematodes and humans [5], every intervention which extends C. elegans lifespan may reveal relevant strategies to positively impact on healthy human longevity (. ).
Quantitative transcriptional analysis of aging C. elegans
My analysis uses methods developed for data mining microarray experiments, adapted for aging research. Methods bridge knowledge of statistical mechanics with data mining methods developed in statistical mathematics. Analyses can reveal how the transcriptional regulation of genes might coincide, thereby implicating proteins as parts of networks acting together towards a common biological function. Such experiments are most useful for complex biological traits that result from the presumed functioning of several molecular pathways. Aging is one such biological phenomenon that apparently incorporates numerous molecular mechanisms underlying environmental stimulus sensing, metabolic regulation, stress responses, reproductive signaling, hibernation, and transcriptional regulation.
Age-related behaviors have distinct transcriptional profiles in Caenorhabditis elegans
Aging Cell, 2008
There has been a great deal of interest in identifying potential biomarkers of aging (Butler et al. 2004). Biomarkers of aging would be useful to predict potential vulnerabilities in an individual that may arise well before they are chronologically expected, due to idiosyncratic aging rates that occur between individuals. Prior attempts to identify biomarkers of aging have often relied on the comparisons of long-lived animals to a wild-type control (Dhahbi et al. 2004). However, the effect of interventions in model systems that prolong lifespan (such as single gene mutations, or caloric restriction) can sometimes be difficult to interpret due to the manipulation itself having multiple unforeseen consequences on physiology, unrelated to aging itself (Gems et al. 2002; Partridge & Gems 2006). The search for predictive biomarkers of aging therefore is problematic, and the identification of metrics that can be used to predict either physiological or chronological age would be of great value (Butler et al. 2004). One methodology which has been used to identify biomarkers for numerous pathologies is gene expression profiling. Here, we report whole-genome expression profiles of individual wild-type Caenorhabditis elegans covering the entire wild-type nematode life span. Individual nematodes were scored for either age-related behavioral phenotypes, or survival, and then subsequently associated with their respective gene expression profiles. This facilitated the identification of transcriptional profiles that were highly associated with either physiological or chronological age. Overall, our approach serves as a paradigm for identifying potential biomarkers of aging in higher organisms that can be repeatedly sampled throughout their lifespan.
Aging cell, 2017
We report a systematic RNAi longevity screen of 82 Caenorhabditis elegans genes selected based on orthology to human genes differentially expressed with age. We find substantial enrichment in genes for which knockdown increased lifespan. This enrichment is markedly higher than published genomewide longevity screens in C. elegans and similar to screens that preselected candidates based on longevity-correlated metrics (e.g., stress resistance). Of the 50 genes that affected lifespan, 46 were previously unreported. The five genes with the greatest impact on lifespan (>20% extension) encode the enzyme kynureninase (kynu-1), a neuronal leucine-rich repeat protein (iglr-1), a tetraspanin (tsp-3), a regulator of calcineurin (rcan-1), and a voltage-gated calcium channel subunit (unc-36). Knockdown of each gene extended healthspan without impairing reproduction. kynu-1(RNAi) alone delayed pathology in C. elegans models of Alzheimer's disease and Huntington's disease. Each gene dis...
The journals of gerontology. Series A, Biological sciences and medical sciences, 2011
Two nonsense mutants of age-1, the Caenorhabditis elegans gene encoding phosphoinositide 3-kinase, live nearly 10-fold longer than wild-type controls and are exceptionally resistant to several stresses. Genome-wide expression analyses implicated downregulation of many more genes than were upregulated in second-generation age-1 homozygotes. Functional-annotation analysis, based on Gene Ontology terms, suggested that novel mechanisms may mediate the stronger phenotypes observed for these worms than with milder age-1 disruption. For the current study, the same microarray data were reanalyzed using novel meta-analytic procedures that we developed recently. First, gene p values were corrected for systematic biases based on the observed distribution for nonexpressed genes; these values were then combined to derive an aggregate p value for each functional-annotation term while adjusting for intergene covariance. This resulted in much better coverage of relevant gene categories, including m...
Predicting Aging/Longevity-Related Genes in the Nematode Caenorhabditis elegans 1
We present a novel mathematical/computational strategy for predicting genes/proteins associated with aging/longevity. The novelty of our method arises from the topological analysis of an organismal longevity gene/protein network (LGPN), which extends the existing cellular networks. The LGPN nodes represent both genes and corresponding proteins. Links stand for all known interactions between the nodes. The LGPN of C. elegans incorporated 362 genes/proteins, 160 connecting and 202 age-related ones, from a list of 321 with known impact on aging/longevity. A longevity core of 129 directly interacting genes or proteins was identified. This core may shed light on the large-scale mechanisms of aging. Predictions were made, based upon the finding that LGPN hubs and centrally located nodes have higher likelihoods of being associated with aging/longevity than do randomly selected nodes. Analysis singled-out 15 potential aging/longevity-related genes for further examination: mpk-1,