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      A universal transcriptomic signature of age reveals the temporal scaling of Caenorhabditis elegans aging trajectories

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          Abstract

          We collected 60 age-dependent transcriptomes for C. elegans strains including four exceptionally long-lived mutants (mean adult lifespan extended 2.2- to 9.4-fold) and three examples of lifespan-increasing RNAi treatments. Principal Component Analysis (PCA) reveals aging as a transcriptomic drift along a single direction, consistent across the vastly diverse biological conditions and coinciding with the first principal component, a hallmark of the criticality of the underlying gene regulatory network. We therefore expected that the organism’s aging state could be characterized by a single number closely related to vitality deficit or biological age. The “aging trajectory”, i.e. the dependence of the biological age on chronological age, is then a universal stochastic function modulated by the network stiffness; a macroscopic parameter reflecting the network topology and associated with the rate of aging. To corroborate this view, we used publicly available datasets to define a transcriptomic biomarker of age and observed that the rescaling of age by lifespan simultaneously brings together aging trajectories of transcription and survival curves. In accordance with the theoretical prediction, the limiting mortality value at the plateau agrees closely with the mortality rate doubling exponent estimated at the cross-over age near the average lifespan. Finally, we used the transcriptomic signature of age to identify possible life-extending drug compounds and successfully tested a handful of the top-ranking molecules in C. elegans survival assays and achieved up to a +30% extension of mean lifespan.

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          miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database

          MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nucleotides, which negatively regulate the gene expression at the post-transcriptional level. This study describes an update of the miRTarBase (http://miRTarBase.mbc.nctu.edu.tw/) that provides information about experimentally validated miRNA-target interactions (MTIs). The latest update of the miRTarBase expanded it to identify systematically Argonaute-miRNA-RNA interactions from 138 crosslinking and immunoprecipitation sequencing (CLIP-seq) data sets that were generated by 21 independent studies. The database contains 4966 articles, 7439 strongly validated MTIs (using reporter assays or western blots) and 348 007 MTIs from CLIP-seq. The number of MTIs in the miRTarBase has increased around 7-fold since the 2014 miRTarBase update. The miRNA and gene expression profiles from The Cancer Genome Atlas (TCGA) are integrated to provide an effective overview of this exponential growth in the miRNA experimental data. These improvements make the miRTarBase one of the more comprehensively annotated, experimentally validated miRNA-target interactions databases and motivate additional miRNA research efforts.
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            DNA methylation age of blood predicts all-cause mortality in later life

            Background DNA methylation levels change with age. Recent studies have identified biomarkers of chronological age based on DNA methylation levels. It is not yet known whether DNA methylation age captures aspects of biological age. Results Here we test whether differences between people’s chronological ages and estimated ages, DNA methylation age, predict all-cause mortality in later life. The difference between DNA methylation age and chronological age (Δage) was calculated in four longitudinal cohorts of older people. Meta-analysis of proportional hazards models from the four cohorts was used to determine the association between Δage and mortality. A 5-year higher Δage is associated with a 21% higher mortality risk, adjusting for age and sex. After further adjustments for childhood IQ, education, social class, hypertension, diabetes, cardiovascular disease, and APOE e4 status, there is a 16% increased mortality risk for those with a 5-year higher Δage. A pedigree-based heritability analysis of Δage was conducted in a separate cohort. The heritability of Δage was 0.43. Conclusions DNA methylation-derived measures of accelerated aging are heritable traits that predict mortality independently of health status, lifestyle factors, and known genetic factors. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0584-6) contains supplementary material, which is available to authorized users.
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              Genetics: influence of TOR kinase on lifespan in C. elegans.

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                Author and article information

                Contributors
                andrey.tarkhov@gero.ai
                peter.fedichev@gero.ai
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 May 2019
                14 May 2019
                2019
                : 9
                : 7368
                Affiliations
                [1 ]Gero LLC, Nizhny Susalny per. 5/4, Moscow, 105064 Russia
                [2 ]Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Bolshoy Boulevard 30, bld. 1, Moscow, 121205 Russia
                [3 ]ISNI 0000 0004 0419 1545, GRID grid.413916.8, Central Arkansas Veterans Healthcare System, Research Service, ; Little Rock, Arkansas USA
                [4 ]ISNI 0000 0004 4687 1637, GRID grid.241054.6, Department of Geriatrics, Reynolds Institute on Aging, , University of Arkansas for Medical Sciences, ; Little Rock, Arkansas USA
                [5 ]ISNI 0000 0000 8607 342X, GRID grid.418846.7, Institute of Biomedical Chemistry, ; 119121 Moscow, Russia
                [6 ]ISNI 0000000406204151, GRID grid.18919.38, National Research Center “Kurchatov Institute”, ; 1, Akademika Kurchatova pl., Moscow, 123182 Russia
                [7 ]Bioinformatics Program, University of Arkansas for Medical Sciences, and University of Arkansas at Little Rock, Little Rock, Arkansas USA
                [8 ]ISNI 0000000092721542, GRID grid.18763.3b, Moscow Institute of Physics and Technology, ; 141700, Institutskii per. 9, Dolgoprudny, Moscow Region, Russia
                Author information
                http://orcid.org/0000-0003-3350-4785
                Article
                43075
                10.1038/s41598-019-43075-z
                6517414
                31089188
                bf35c107-5f47-42bf-98b5-a97a403a6395
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 July 2018
                : 15 April 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: P01 AG012411-17A1
                Award ID: P01 AG012411-17A1
                Award ID: P01 AG012411-17A1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000738, U.S. Department of Veterans Affairs (Department of Veterans Affairs);
                Award ID: I01 BX001655
                Award ID: I01 BX001655
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                gene regulatory networks,regulatory networks
                Uncategorized
                gene regulatory networks, regulatory networks

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