Muller's Ratchet As a Mechanism of Frailty and Multimorbidity
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Markers of T Cell Senescence in Humans
International Journal of Molecular Sciences Review Markers of T Cell Senescence in Humans Weili Xu 1,2 and Anis Larbi 1,2,3,4,5,* 1 Biology of Aging Program and Immunomonitoring Platform, Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Immunos Building, Biopolis, Singapore 138648, Singapore; [email protected] 2 School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore 3 Department of Microbiology, National University of Singapore, Singapore 117597, Singapore 4 Department of Geriatrics, Faculty of Medicine, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada 5 Faculty of Sciences, University ElManar, Tunis 1068, Tunisia * Correspondence: [email protected]; Tel.: +65-6407-0412 Received: 31 May 2017; Accepted: 26 July 2017; Published: 10 August 2017 Abstract: Many countries are facing the aging of their population, and many more will face a similar obstacle in the near future, which could be a burden to many healthcare systems. Increased susceptibility to infections, cardiovascular and neurodegenerative disease, cancer as well as reduced efficacy of vaccination are important matters for researchers in the field of aging. As older adults show higher prevalence for a variety of diseases, this also implies higher risk of complications, including nosocomial infections, slower recovery and sequels that may reduce the autonomy and overall quality of life of older adults. The age-related effects on the immune system termed as “immunosenescence” can be exemplified by the reported hypo-responsiveness to influenza vaccination of the elderly. T cells, which belong to the adaptive arm of the immune system, have been extensively studied and the knowledge gathered enables a better understanding of how the immune system may be affected after acute/chronic infections and how this matters in the long run. -
Moderate Stem Cell Telomere Shortening Rate Postpones Cancer Onset in Stochastic Model
Moderate stem cell telomere shortening rate postpones cancer onset in stochastic model. Simon Holbek, Kristian Moss Bendtsen, Jeppe Juul University of Copenhagen, Niels Bohr Institute, Blegdamsvej 17, DK-2100 Copenhagen, Denmark (Dated: November 3, 2018) Mammalian cells are restricted from proliferating indefinitely. Telomeres at the end of each chromosome are shortened at cell division and, when they reach a critical length, the cell will enter permanent cell cycle arrest - a state known as senescence. This mechanism is thought to be tumor suppressing, as it helps prevent precancerous cells from dividing uncontrollably. Stem cells express the enzyme telomerase, which elongates the telomeres, thereby postponing senescence. However, unlike germ cells and most types of cancer cells, stem cells only express telomerase at levels insufficient to fully maintain the length of their telomeres leading to a slow decline in proliferation potential. It is not yet fully understood how this decline influences the risk of cancer and the longevity of the organism. We here develop a stochastic model to explore the role of telomere dynamics in relation to both senescence and cancer. The model describes the accumulation of cancerous mutations in a multicel- lular organism and creates a coherent theoretical framework for interpreting the results of several recent experiments on telomerase regulation. We demonstrate that the longest average cancer free life span before cancer onset is obtained when stem cells start with relatively long telomeres that are shortened at a steady rate at cell division. Furthermore, the risk of cancer early in life can be reduced by having a short initial telomere length. Finally, our model suggests that evolution will favour a shorter than optimal average cancer free life span in order to postpone cancer onset until late in life. -
Human Cell and Organism Aging: Are There Limits? Interrupted Case Study on Cell Aging
Human Cell and Organism Aging: Are there Limits? Interrupted Case study on Cell Aging Teresa Gonya Department of Biological Sciences University of Wisconsin-Fox Valley Menasha, WI The search for the fountain of youth persists in advertising, and individuals are constantly bombarded with messages about diet, exercise, vitamins and minerals that can help prolong one’s life. The biological basis of aging is often not mentioned in the anti-aging promotions. Is it possible that a diet rich in protein, or fruits and vegetables can add years to your life? Is it possible that a vitamin or mineral ‘tonic’ can promote tissue repair and extend life? Is it possible that a vitamin drink can extend your normal life expectancy? Even clinical medicine focuses on developing new treatments that are based on preventing aging and maintaining organ longevity. We are told to exercise to keep our heart healthy and to stop smoking to prevent damage to body tissues, especially the lungs and blood vessels. The new field of regenerative medicine is based on the premise that stem cells may one day be able to repair tissue and return function to damaged organs. There are real limits to longevity that are based on genetics, lifestyle, medical history and sociology. (1) At the foundation of organism longevity is cell longevity. All organisms are composed of cells. Four different types of tissue cells form the basis of all organs that are found in any animal organism. Each type of tissue cell has a different ability to undergo mitosis and replace itself, should it become damaged or injured. -
Working at the Interface of Phylogenetics and Population
Molecular Ecology (2007) 16, 839–851 doi: 10.1111/j.1365-294X.2007.03192.x WorkingBlackwell Publishing Ltd at the interface of phylogenetics and population genetics: a biogeographical analysis of Triaenops spp. (Chiroptera: Hipposideridae) A. L. RUSSELL,* J. RANIVO,†‡ E. P. PALKOVACS,* S. M. GOODMAN‡§ and A. D. YODER¶ *Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA, †Département de Biologie Animale, Université d’Antananarivo, Antananarivo, BP 106, Madagascar, ‡Ecology Training Program, World Wildlife Fund, Antananarivo, BP 906 Madagascar, §The Field Museum of Natural History, Division of Mammals, 1400 South Lake Shore Drive, Chicago, IL 60605, USA, ¶Department of Ecology and Evolutionary Biology, PO Box 90338, Duke University, Durham, NC 27708, USA Abstract New applications of genetic data to questions of historical biogeography have revolutionized our understanding of how organisms have come to occupy their present distributions. Phylogenetic methods in combination with divergence time estimation can reveal biogeo- graphical centres of origin, differentiate between hypotheses of vicariance and dispersal, and reveal the directionality of dispersal events. Despite their power, however, phylo- genetic methods can sometimes yield patterns that are compatible with multiple, equally well-supported biogeographical hypotheses. In such cases, additional approaches must be integrated to differentiate among conflicting dispersal hypotheses. Here, we use a synthetic approach that draws upon the analytical strengths of coalescent and population genetic methods to augment phylogenetic analyses in order to assess the biogeographical history of Madagascar’s Triaenops bats (Chiroptera: Hipposideridae). Phylogenetic analyses of mitochondrial DNA sequence data for Malagasy and east African Triaenops reveal a pattern that equally supports two competing hypotheses. -
Hayflick, His Limit, and Cellular Ageing Clearly a Next Step Is Automation
PERSPECTIVES Finally, the most apparent drawback is the Whether or not nucleic acid computers 175–179 (2000). 3. Faulhammer, D., Cukras, A., Lipton, R. J. & Landweber, L. time required for each computation. ultimately prove feasible, they have already F. Molecular computation: RNA solutions to chess Whereas a simple desktop computer can contributed to multi-disciplinary science by problems. Proc. Natl Acad. Sci. USA 97, 1385–1389 (2000). solve the seven-city instance of the Travelling causing us to question the nature of comput- 4. Ouyang, Q., Kaplan, P. D., Liu, S. & Libchaber, A. DNA Salesman Problem in less than a second, ing and to forge new links between the biolog- solution of the maximal clique problem. Science 278, 1 446–449 (1997). Adleman took seven days . The use of DNA ical and computational sciences. For example, 5. Henegariu, O., Heerema, N. A., Dlouhy, S. R., Vance, G. H. chips2 or other approaches may eventually it has led us to focus on the nature of biologi- & Vogt, P. H. Multiplex PCR: Critical parameters and step- by-step protocol. Biotechniques 23, 504–511 (1997). lead to automation, which would save con- cal DNA computations, such as the assembly 6. Karp, G. Cell and Molecular Biology: Concepts and siderable amounts of time, but fundamental of modern genes from encrypted building- Experiments 2nd edn (John Wiley & Sons, New York, 1999). DNA computing technology needs to blocks in the genomes of some single-celled 7. Seife, C. RNA works out knight moves. Science 287, advance far beyond its current bounds before ciliates (FIG. 5)14. -
Telomeres and Telomerase
Oncogene (2010) 29, 1561–1565 & 2010 Macmillan Publishers Limited All rights reserved 0950-9232/10 $32.00 www.nature.com/onc GUEST EDITORIAL 2009 Nobel Prize in Physiology or Medicine: telomeres and telomerase Oncogene (2010) 29, 1561–1565; doi:10.1038/onc.2010.15 sate for the chromosomal shortening produced asso- ciated with cell division, suggesting that progressive telomere shortening may be a key factor to limit the Elizabeth H Blackburn, Carol W Greider and Jack W number of cell divisions. James D Watson (Nobel Prize Szostak were acknowledged with this year’s Nobel Prize 1962) also recognized that the unidirectional nature of in Physiology or Medicine for their discoveries on how DNA replication was a problem for the complete copy chromosomes are protected by telomeres and the of chromosomal ends (Watson, 1972). This was called enzyme telomerase. the ‘end-replication problem’. In this manner, during In the first half of the twentieth century, classic studies each cycle of cell division, a small fragment of telomeric by Hermann Mu¨ller (Nobel Prize 1945) working with DNA is lost from the end. After several rounds of the fruit fly (Drosophila melanogaster) and by Barbara division, telomeres eventually reach a critically short McClintock (Nobel Prize 1983) studying maize (Zea length, which activates the pathways for senescence and Mays) proposed the existence of a special structure at cell death (Hermann et al., 2001; Samper et al., 2001). the chromosome ends (Mu¨ller, 1938; McClintock, 1939). Uncovering the solution to the end-replication pro- This structure would have the essential role of protect- blem took several years of intense research. -
Intercellular Competition and the Inevitability of Multicellular Aging
Intercellular competition and the inevitability of multicellular aging Paul Nelsona,1 and Joanna Masela aDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721 Edited by Raghavendra Gadagkar, Indian Institute of Science, Bangalore, India, and approved October 6, 2017 (received for review November 14, 2016) Current theories attribute aging to a failure of selection, due to Aging in multicellular organisms occurs at both the cellular either pleiotropic constraints or declining strength of selection and intercellular levels (17). Multicellular organisms, by definition, after the onset of reproduction. These theories implicitly leave require a high degree of intercellular cooperation to maintain open the possibility that if senescence-causing alleles could be homeostasis. Often, cellular traits required for producing a viable identified, or if antagonistic pleiotropy could be broken, the multicellular phenotype come at a steep cost to individual cells effects of aging might be ameliorated or delayed indefinitely. (14, 18, 19). Conversely, many mutant cells that do not invest in These theories are built on models of selection between multicel- holistic organismal fitness have a selective advantage over cells lular organisms, but a full understanding of aging also requires that do. If intercellular competition occurs, such “cheater” or examining the role of somatic selection within an organism. “defector” cells may proliferate and displace “cooperating” cells, Selection between somatic cells (i.e., intercellular competition) with detrimental consequences for the multicellular organism can delay aging by purging nonfunctioning cells. However, the (20, 21). Cancer, a leading cause of death in humans at rates that fitness of a multicellular organism depends not just on how increase with age, is one obvious manifestation of cheater pro- functional its individual cells are but also on how well cells work liferation (22–24). -
Basic Population Genetics Outline
Basic Population Genetics Bruce Walsh Second Bangalore School of Population Genetics and Evolution 25 Jan- 5 Feb 2016 1 Outline • Population genetics of random mating • Population genetics of drift • Population genetics of mutation • Population genetics of selection • Interaction of forces 2 Random-mating 3 Genotypes & alleles • In a diploid individual, each locus has two alleles • The genotype is this two-allele configuration – With two alleles A and a – AA and aa are homozygotes (alleles agree) – Aa is a heterozygote (alleles are different) • With an arbitrary number of alleles A1, .., An – AiAi are homozgyotes – AiAk (for i different from k) are heterozygotes 4 Allele and Genotype Frequencies Given genotype frequencies, we can always compute allele Frequencies. For a locus with two alleles (A,a), freq(A) = freq(AA) + (1/2)freq(Aa) The converse is not true: given allele frequencies we cannot uniquely determine the genotype frequencies If we are willing to assume random mating, freq(AA) = [freq(A)]2, Hardy-Weinberg freq(Aa) = 2*freq(A)*freq(a) proportions 5 For k alleles • Suppose alleles are A1 , .. , An – Easy to go from genotype to allele frequencies – Freq(Ai) = Freq(AiAi) + (1/2) Σ freq(AiAk) • Again, assumptions required to go from allele to genotype frequencies. • With n alleles, n(n+1)/2 genotypes • Under random-mating – Freq(AiAi) = Freq(Ai)*Freq(Ai) – Freq(AiAk) =2*Freq(Ai)*Freq(Ak) 6 Hardy-Weinberg 7 Importance of HW • Under HW conditions, no – Drift (i.e., pop size is large) – Migration/mutation (i.e., no input ofvariation -
Controversy 2
Controversy 2 WHY DO OUR BODIES GROW OLD? liver Wendell Holmes (1858/1891), in his poem “The Wonderful One-Hoss Shay,” invokes a memorable image of longevity and mortality, the example of a wooden Ohorse cart or shay that was designed to be long-lasting: Have you heard of the wonderful one-hoss shay, That was built in such a logical way, It ran a hundred years to a day . ? This wonderful “one-hoss shay,” we learn, was carefully built so that every part of it “aged” at the same rate and didn’t wear out until the whole thing fell apart all at once. Exactly a century after the carriage was produced, the village parson was driving this marvelous machine down the street, when What do you think the parson found, When he got up and stared around? The poor old chaise in a heap or mound, As if it had been to the mill and ground! You see, of course, if you’re not a dunce, How it went to pieces all at once, All at once, and nothing first, Just as bubbles do when they burst. The wonderful one-horse shay is the perfect image of an optimistic hope about aging: a long, healthy existence followed by an abrupt end of life, with no decline. The one-horse shay image also suggests that life has a built-in “warranty expiration” date. But where does this limit on longevity come from? Is it possible to extend life beyond what we know? The living organism with the longest individual life span is the bristlecone pine tree found in California, more than 4,500 years old, with no end in sight. -
Cellular Aging: Theories and Technological Influence
1319 Vol.53, n. 6: pp.1319-1332, November-December 2010 BRAZILIAN ARCHIVES OF ISSN 1516-8913 Printed in Brazil BIOLOGY AND TECHNOLOGY AN INTERNATIONAL JOURNAL Cellular Aging: Theories and Technological Influence Silvia Mercado-Sáenz, Miguel J. Ruiz-Gómez *, Francisco Morales-Moreno and Manuel Martínez-Morillo Laboratory of Radiobiology; Department of Radiology and Physical Medicine; Faculty of Medicine; University of Malaga; Teatinos s/n; 29071 - Malaga - Spain ABSTRACT The aim of this article was to review the factors that influence the aging, relationship of aging with the biological rhythms and new technologies as well as the main theories to explain the aging, and to analysis the causes of aging. The theories to explain the aging could be put into two groups: those based on a program that controlled the regression of the organism and those that postulated that the deterioration was due to mutations. It was concluded that aging was a multifactorial process. Genetic factors indicated the maximum longevity of the individual and environmental factors responsible for the real longevity of the individual. It would be necessary to guarantee from early age the conservation of a natural life rhythm. Key words: Aging; longevity; biological rhythms; caloric restriction; telomerase; free radical INTRODUCTION Several theories have been proposed to explain the nature of aging. Due to the multicasual nature of this Aging is the process of becoming older. This process, it turns out improbable that only one theory traditional definition was recently challenged in the could explain all its mechanisms. It is necessary to new encyclopedia of Gerontology (Birren, 2006), bear two important points in mind: (1) the process of where aging was specifically defined as the process aging compromises a number of different genes, and of a system's deterioration through time. -
Revisiting the Hayflick Limit
bioRxiv preprint doi: https://doi.org/10.1101/2021.05.03.442497; this version posted May 4, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Revisiting the Hayflick Limit: Insights from an Integrated Analysis of Changing Transcripts, Proteins, Metabolites and Chromatin. Michelle Chan, Han Yuan*, Ilya Soifer*, Tobias M. Maile, Rebecca Y. Wang, Andrea Ireland, Jonathon O’Brien, Jérôme Goudeau, Leanne Chan, Twaritha Vijay, Adam Freund, Cynthia Kenyon, Bryson Bennett, Fiona McAllister, David R. Kelley, Margaret Roy, Robert L. Cohen, Arthur D. Levinson, David Botstein**, David G. Hendrickson** *contributed equally **Corresponding Authors Abstract Replicative senescence (RS) as a model has become the central focus of research into cellular aging in vitro. Despite decades of study, this process through which cells cease dividing is not fully understood in culture, and even much less so in vivo during development and with aging. Here, we revisit Hayflick’s original observation of RS in WI-38 human fetal lung fibroblasts equipped with a battery of high dimensional modern techniques and analytical methods to deeply profile the process of RS across each aspect of the central dogma and beyond. We applied and integrated RNA-seq, proteomics, metabolomics, and ATAC-seq to a high resolution RS time course. We found that the transcriptional changes that underlie RS manifest early, gradually increase, and correspond to a concomitant global increase in accessibility in nucleolar and lamin associated domains. During RS WI-38 fibroblast gene expression patterns acquire a striking resemblance to those of myofibroblasts in a process similar to the epithelial to mesenchymal transition (EMT). -
“Practical Course on Molecular Phylogeny and Population Genetics”
Dottorato di Ricerca “Biologia Molecolare, Cellulare ed Ambientale” Corsi dottorali per l’A.A. 2017 “Practical course on Molecular Phylogeny and Population Genetics” Emiliano Mancini, PhD 22-24 February 2017 AIMS The purpose of this practical course is to familiarize participants with the basic concepts of molecular phylogeny and population genetics and to offer a first hands-on training on data analysis. Hands-on learning activities are aimed to put concepts into practice and will be conducted using software dedicated to molecular phylogeny and population genetics analyses. COURSE PROGRAM Lessons will be held in AULA 7 (Department of Sciences, Viale G. Marconi, 446, 1st floor) . 22th February 2017 - MOLECULAR PHYLOGENY (THEORY & PRACTICE) PRINCIPLES. (9.30 - 13.00): Introduction to molecular phylogeny: aims and terminology; Alignment: finding homology among sequences; Genetic distances: modelling substitutions and measuring changes; Inferring trees: Distance, Maximum Parsimony, Likelihood and Bayesian methods; Tree accuracy: bootstrap; Molecular clocks: global and relaxed models. PRACTICE. (14.30 - 18.00): From chromatograms to dataset preparation; introduction to phylogenetic software; Practice on: i) alignment; ii) substitution models, tree building and molecular clock; iii) Bayesian tree reconstruction; iv) bootstrap and phylogenetic trees congruence. 23th February 2017 - POPULATION GENETICS (THEORY) PRINCIPLES. (9.30 - 13.00): Introduction to population genetics: aims and terminology; Hardy- Weinberg equilibrium: observed vs. expected genotype frequencies; Quantifying genetic diversity: diploid and haploid data; Genetic drift and effective population size (Ne): bottleneck and founder effects; Quantifying loss of heterozygosity: the inbreeding coefficient (FIS); Quantifying population subdivision: the fixation index (FST); Linkage disequilibrium (LD): measuring association among loci; Gene genealogies and molecular evolution: testing neutrality under the coalescent.