FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2022176506> ?p ?o ?g. }

• W2022176506 abstract "Background Disease is classically viewed as a disruption of healthy homeostasis. This naturally gives rise to the quest to find drugs that can efficiently transform a disease state back to a healthy one. Here we address this challenge on a genome scale for the first time. We chose to focus on aging, aiming to predict perturbations (both genetic and environmental) that can extend the organism’s lifespan. Aging forms a nice test bed to examine our approach, since it is typically accompanied by progressive changes in gene expression. Furthermore, Caloric Restriction (CR), a dietary intervention that extends lifespan and delays the onset of age-associated phenotypes, is known to reverse these expression changes [1,2]. As CR has very limited value as a therapeutic regimen, these findings strongly motivate the search for metabolic drug targets that can reverse the metabolic state of the aging to that of the young. Materials and methods Here we present a novel Metabolic Transformation Algorithm (MTA) that given a source (disease, e.g., aged) and a desired target (healthy, e.g., young) metabolic state, identifies the genetic or environmental perturbations that best enable a transformation from the source to the target state. The MTA algorithm works in the realm of metabolism and is based on Constraint-Based Modeling, an increasingly widely used computational method for studying metabolism on a genome-scale [3,4]. Results First, the prediction accuracy of MTA has been extensively validated using data from known perturbations in Escherichia coli, Saccharomyces cerevisiae and mammalian cell lines. Second, Analyzing gene expression data in aging Saccharomyces cerevisiae, seven novel lifespanextending metabolic targets predicted by MTA were further tested experimentally. Two of those were successfully validated (a 10-fold increase over their expected frequency), one of them extending lifespan markedly by about 50%. Analyzing mammalian aging muscle expression data, MTA identifies novel drug targets transforming the metabolic state to that of the young, highlighting the role of a key inflammatory pathway of Eicosanoids metabolism. These predictions are enriched with human orthologs of known lifespan-extending genes in Saccharomyces cerevisiae and Caenorhabditis elegans. Conclusions" @default.
• W2022176506 created "2016-06-24" @default.
• W2022176506 creator A5007200139 @default.
• W2022176506 creator A5014767780 @default.
• W2022176506 creator A5037867947 @default.
• W2022176506 creator A5063492133 @default.
• W2022176506 date "2012-06-01" @default.
• W2022176506 modified "2023-10-18" @default.
• W2022176506 title "Metabolic modeling predicts perturbations extending lifespan in yeast and counteracting aging in mammalian muscle" @default.
• W2022176506 cites W1583148430 @default.
• W2022176506 cites W2099787654 @default.
• W2022176506 cites W2109975763 @default.
• W2022176506 cites W2154061034 @default.
• W2022176506 doi "https://doi.org/10.1186/1753-6561-6-s3-p54" @default.
• W2022176506 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3374254" @default.
• W2022176506 hasPublicationYear "2012" @default.
• W2022176506 type Work @default.
• W2022176506 sameAs 2022176506 @default.
• W2022176506 citedByCount "0" @default.
• W2022176506 crossrefType "journal-article" @default.
• W2022176506 hasAuthorship W2022176506A5007200139 @default.
• W2022176506 hasAuthorship W2022176506A5014767780 @default.
• W2022176506 hasAuthorship W2022176506A5037867947 @default.
• W2022176506 hasAuthorship W2022176506A5063492133 @default.
• W2022176506 hasBestOaLocation W20221765061 @default.
• W2022176506 hasConcept C169760540 @default.
• W2022176506 hasConcept C2779222958 @default.
• W2022176506 hasConcept C42407357 @default.
• W2022176506 hasConcept C55493867 @default.
• W2022176506 hasConcept C60644358 @default.
• W2022176506 hasConcept C70721500 @default.
• W2022176506 hasConcept C71924100 @default.
• W2022176506 hasConcept C74909509 @default.
• W2022176506 hasConcept C86803240 @default.
• W2022176506 hasConceptScore W2022176506C169760540 @default.
• W2022176506 hasConceptScore W2022176506C2779222958 @default.
• W2022176506 hasConceptScore W2022176506C42407357 @default.
• W2022176506 hasConceptScore W2022176506C55493867 @default.
• W2022176506 hasConceptScore W2022176506C60644358 @default.
• W2022176506 hasConceptScore W2022176506C70721500 @default.
• W2022176506 hasConceptScore W2022176506C71924100 @default.
• W2022176506 hasConceptScore W2022176506C74909509 @default.
• W2022176506 hasConceptScore W2022176506C86803240 @default.
• W2022176506 hasIssue "S3" @default.
• W2022176506 hasLocation W20221765061 @default.
• W2022176506 hasLocation W20221765062 @default.
• W2022176506 hasLocation W20221765063 @default.
• W2022176506 hasLocation W20221765064 @default.
• W2022176506 hasOpenAccess W2022176506 @default.
• W2022176506 hasPrimaryLocation W20221765061 @default.
• W2022176506 hasRelatedWork W1512204921 @default.
• W2022176506 hasRelatedWork W1972582950 @default.
• W2022176506 hasRelatedWork W1987406058 @default.
• W2022176506 hasRelatedWork W2030863517 @default.
• W2022176506 hasRelatedWork W2151176490 @default.
• W2022176506 hasRelatedWork W2361939908 @default.
• W2022176506 hasRelatedWork W2621887829 @default.
• W2022176506 hasRelatedWork W4225963375 @default.
• W2022176506 hasRelatedWork W4232753137 @default.
• W2022176506 hasRelatedWork W97000671 @default.
• W2022176506 hasVolume "6" @default.
• W2022176506 isParatext "false" @default.
• W2022176506 isRetracted "false" @default.
• W2022176506 magId "2022176506" @default.
• W2022176506 workType "article" @default.