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Research Paper Volume 12, Issue 12 pp 12393-12409
Multi-omics network analysis reveals distinct stages in the human aging progression in epidermal tissue
Relevance score: 11.575402Nicholas Holzscheck, Jörn Söhle, Boris Kristof, Elke Grönniger, Stefan Gallinat, Horst Wenck, Marc Winnefeld, Cassandra Falckenhayn, Lars Kaderali
Keywords: multi-omics, biological age, aging phases, hallmarks of aging, transcriptional noise
Published in Aging on June 18, 2020
Study and analysis setup. Workflow diagram depicting the two-stage longitudinal study setup and the main steps of multi-omics data generation, integration and analysis.
Multi-omics similarity between subjects in the integrated network. (A) Heatmap visualization showing similarities between subjects in the fused multi-omics similarity network generated from gene expression and methylation data, with subjects ordered by increasing chronological age. (B) Same heatmap visualization of multi-omics similarity as in (A), with subjects ordered by increasing DNAm age. (C) Same heatmap visualization of multi-omics similarity as in (A) and (B), with the subjects ordered by the identified aging phases.
Biological age validation of the identified phases. (A) Boxplot showing chronological age distributions among the four identified aging phases. (B) Chronological age outliers among the aging phases, denoted as “old-like” for subjects that appeared to prematurely cluster into a higher aging phase, and “young-like” for subjects that were classified into a lower aging phase relative to their chronological age. (C) Boxplot showing the deviation of DNAm from chronological age based on aging phase outlier status, revealing a divergence in DNAm aging rate for aging phase outliers. Statistical significance determined using pairwise T-tests. (D) Hallmark of Aging signal strengths in gene expression data, comparing chronological age groups to the biological aging phases. Shown are the adjusted p-values from Anova comparisons, testing the segregation of the groupings among gene set enrichment scores. Figure adapted from the original Hallmark of Aging publication [15]. (E) Longitudinal validation after three-year period. The chord diagram shows aging phase classification of re-invited subjects at both time points, with phase transitions highlighted in red.
Characterization of Hallmark of Aging predictivity within the aging phases. (A) Hierarchical clustering of the nine Hallmarks of Aging based on their gene set predictivity analysis along the four aging phases. Predictivity was determined using cross-validated random forest classifiers, trained to distinguish each of the aging phases from the others. (B) Predictivity of the Hallmark of Aging gene sets along the four aging phases, grouped into primary, secondary and integrative hallmarks. Statistical testing was performed using one-sided Wilcoxon tests. All predictivity scores were derived from 100 permutations.
Global loss in pathway predictivity in the transition from mid- to late-life. (A) Heatmap showing the changes in pathway predictivity along the identified aging phases. The predictivities shown are the average predictivities calculated from 100 permutations for every pathway. (B) Scatterplots visualizing the changes in predictivity along the aging phases for selected pathways, several of which show distinctly non-linear patterns. (C) Overall loss in pathway predictivity observed in the transition from aging phase 3 to phase 4 is also detectable using gene set enrichment analysis. (D) Pairwise Pearson correlation between all subjects based on transcriptional and DNA methylation patterns.
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Review Volume 8, Issue 10 pp 2264-2289
The role of hydrogen sulfide in aging and age-related pathologies
Relevance score: 10.40255Bernard W. Perridon, Henri G.D. Leuvenink, Jan-Luuk Hillebrands, Harry van Goor, Eelke M. Bos
Keywords: hydrogen sulfide, H2S, aging, hallmarks of aging, gasotransmitters
Published in Aging on September 27, 2016
Overview of the Hallmarks of Aging and their functional interactions. The proposed nine hallmarks of aging are categorized based on common characteristics and their contribution to aging. Left panel: The primary hallmarks of aging are the hallmarks regarded as the primary cause of cellular damage. Middle panel: The antagonistic hallmarks of aging are those hallmarks considered to be part of compensatory or antagonistic responses to damage. These hallmarks initially mitigate the damage, but eventually can become deleterious themselves. Right panel: The integrative hallmarks of aging are the end result of the two previously described categories and are ultimately responsible for the functional decline associated with aging. The interactions between the categories are indicated at the top of the panels.
Overview of the endogenous and exogenous H2S production in the mammalian body. Left panel: The endogenous production of H2S in mammalian cells. Several important enzymes are mentioned along the arrows. Right panel: The exogenous production of H2S in the gastrointestinal tract by the intestinal microbiota and sulfate-reducing bacteria, for which the H2S production is endogenous. The dashed lines between the left and the right panel indicate the transport of molecules between the compartments.
Overview of the effects of physiological levels of H2S on the Hallmarks of Aging. Hydrogen sulfide affects at least one pathway involved in almost all hallmarks of aging. A direct effect of H2S on pathways involved in telomere attrition was not shown, however the effects of H2S on genome stability might also be beneficial for telomere maintenance, by protecting the integrity of the genome. This is indicated by the interrupted line between H2S and telomere attrition. Physiological levels of H2S were shown to prevent the dysregulation of the pathways associated with aging.
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Research Paper pp undefined-undefined
Effects of a natural ingredients-based intervention targeting the nine hallmarks of aging on epigenetic clocks, physical function, and body composition: A single-arm clinical trial
Relevance score: 9.471172Natalia Carreras-Gallo, Rita Dargham, Shealee P. Thorpe, Steve Warren, Tavis L. Mendez, Ryan Smith, Greg Macpherson, Varun B. Dwaraka
Keywords: epigenetic age change, physiological age change, epigenetic biomarker proxies, hallmarks of aging, aging, nutraceutical longevity interventions
Published in Aging on Invalid Date