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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: 11.575402
    Nicholas 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
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    In recent years, reports of non-linear regulations in age- and longevity-associated biological processes have been accumulating. Inspired by methodological advances in precision medicine involving the integrative analysis of multi-omics data, we sought to investigate the potential of multi-omics integration to identify distinct stages in the aging progression from ex vivo human skin tissue. For this we generated transcriptome and methylome profiling data from suction blister lesions of female subjects between 21 and 76 years, which were integrated using a network fusion approach. Unsupervised cluster analysis on the combined network identified four distinct subgroupings exhibiting a significant age-association. As indicated by DNAm age analysis and Hallmark of Aging enrichment signals, the stages captured the biological aging state more clearly than a mere grouping by chronological age and could further be recovered in a longitudinal validation cohort with high stability. Characterization of the biological processes driving the phases using machine learning enabled a data-driven reconstruction of the order of Hallmark of Aging manifestation. Finally, we investigated non-linearities in the mid-life aging progression captured by the aging phases and identified a far-reaching non-linear increase in transcriptional noise in the pathway landscape in the transition from mid- to late-life.

    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.



  • Review Volume 8, Issue 10 pp 2264-2289

    The role of hydrogen sulfide in aging and age-related pathologies

    Relevance score: 10.40255
    Bernard 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
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    When humans grow older, they experience inevitable and progressive loss of physiological function, ultimately leading to death. Research on aging largely focuses on the identification of mechanisms involved in the aging process. Several proposed aging theories were recently combined as the ‘hallmarks of aging’. These hallmarks describe (patho-)physiological processes that together, when disrupted, determine the aging phenotype. Sustaining evidence shows a potential role for hydrogen sulfide (H2S) in the regulation of aging. Nowadays, H2S is acknowledged as an endogenously produced signaling molecule with various (patho-) physiological effects. H2S is involved in several diseases including pathologies related to aging. In this review, the known, assumed and hypothetical effects of hydrogen sulfide on the aging process will be discussed by reviewing its actions on the hallmarks of aging and on several age-related pathologies.

    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.



  • 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: 9.471172
    Natalia 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
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    Aging interventions have progressed in recent years due to the growing curiosity about how lifestyle impacts longevity. This study assessed the effects of SRW Laboratories’ Cel System nutraceutical range on epigenetic methylation patterns, inflammation, physical performance, body composition, and epigenetic biomarkers of aging. A 1-year study was conducted with 51 individuals, collecting data at baseline, 3 months, 6 months, and 12 months. Participants were encouraged to walk 10 minutes and practice 5 minutes of mindfulness daily. Significant improvements in muscle strength, body function, and body composition metrics were observed. Epigenetic clock analysis showed a decrease in biological age with significant reductions in stem cell division rates. Immune cell subset analysis indicated significant changes, with increases in eosinophils and CD8T cells and decreases in B memory, CD4T memory, and T-regulatory cells. Predicted epigenetic biomarker proxies (EBPs) showed significant changes in retinol/TTHY, a regulator of cell growth, proliferation, and differentiation, and deoxycholic acid glucuronide levels, a metabolite of deoxycholic acid generated in the liver. Gene ontology analysis revealed significant CpG methylation changes in genes involved in critical biological processes related to aging, such as oxidative stress-induced premature senescence, pyrimidine deoxyribonucleotide metabolic process, TRAIL binding, hyaluronan biosynthetic process, neurotransmitter loading into synaptic vesicles, pore complex assembly, collagen biosynthetic process, protein phosphatase 2A binding activity, and activation of transcription factor binding. Our findings suggest that the Cel System supplement range may effectively reduce biological age and improve health metrics, warranting further investigation into its mechanistic pathways and long-term efficacy.

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