We are searching data for your request:
Upon completion, a link will appear to access the found materials.
I know that simple organisms containing of just few cells have no mechanism of dying because of aging. I guess, they typically die due to being out of food or due to becoming someone else's food.
Leaving aside the numerous theories of what can be the origin of such phenomenon as aging, I'm asking a simple question: what is the most primitive organism known so far which has the feature of aging and (as a consequence) dying, even if the environment is perfect?
The adjective primitive is undefined, at least when comparing extant species. We have all gone through almost 4 billions years of evolution.
I would suspect you would consider a bacteria quite primitive though, so note that bacteria appears to age (see Do bacteria die of old age?).
Scientists discover possible building blocks of ancient genetic systems in Earth's most primitive organisms
Scientists believe that prior to the advent of DNA as Earth's primary genetic material, early forms of life used RNA to encode genetic instructions. What sort of genetic molecules did life rely on before RNA?
The answer may be AEG, a small molecule that when linked into chains forms a hypothetical backbone for peptide nucleic acids, which have been hypothesized as the first genetic molecules. Synthetic AEG has been studied by the pharmaceutical industry as a possible gene silencer to stop or slow certain genetic diseases. The only problem with the theory is that up to now, AEG has been unknown in nature.
A team of scientists from the United States and Sweden announced that they have discovered AEG within cyanobacteria which are believed to be some of the most primitive organisms on Earth. Cyanobacteria sometimes appear as mats or scums on the surface of reservoirs and lakes during hot summer months. Their tolerance for extreme habitats is remarkable, ranging from the hot springs of Yellowstone to the tundra of the Arctic.
"Our discovery of AEG in cyanobacteria was unexpected," explains Dr. Paul Alan Cox, co-author of the paper that appeared in the journal PLOS ONE. The American team members are based at the Institute for Ethnomedicine in Jackson Hole, and serve as adjunct faculty at Weber State University in Ogden, Utah.
"While we were writing our manuscript," Cox says, "we learned that our colleagues at the Stockholm University Department of Analytical Chemistry had made a similar discovery, so we asked them to join us on the paper."
To determine how widespread AEG production is among cyanobacteria, the scientists analyzed pristine cyanobacterial cultures from the Pasteur Culture Collection of Paris, France. They also collected samples of cyanobacteria from Guam, Japan, Qatar, as well as in the Gobi desert of Mongolia, the latter sample being collected by famed Wyoming naturalist Derek Craighead. All were found to produce AEG.
Professor Leopold Ilag and his student Liying Jiang at Stockholm University's Department of Analytical Chemistry analyzed the same samples and came up with identical results: cyanobacteria produce AEG. While the analysis is certain, its significance for studies of the earliest forms of life on Earth remains unclear. Does the production of AEG by cyanobacteria represent an echo of the earliest life on Earth?
"We just don't have enough data yet to draw that sort of conclusion," reports Cox. "However the pharmaceutical industry has been exploring synthetic AEG polymers for potential use in gene silencing, so I suspect we have much more to learn."
Can You Hear Me Now? Primitive Single-Celled Microbe Expert In Cellular Communication Networks
When it comes to cellular communication networks, a primitive single-celled microbe that answers to the name of Monosiga brevicollis has a leg up on animals composed of billions of cells. It commands a signaling network more elaborate and diverse than found in any multicellular organism higher up on the evolutionary tree, researchers at the Salk Institute for Biological Studies have discovered.
Their study, which will be published during the week of July 7-11 in the online edition of the Proceedings of the National Academy of Science, unearthed the remarkable count of 128 tyrosine kinase genes, 38 more than found in humans.
These kinases transmit essential signals for cell growth, stasis, and death. Though their activity is tightly regulated in normal cells, out-of-control kinases are a major cause of cancer. Many successful cancer drugs -- such as Gleevec, which is used for the treatment of leukemia, -- specifically target wayward tyrosine kinases.
This treasure trove of diverse and novel tyrosine kinases took the study's lead author Gerard Manning, who heads the Razavi-Newman Center for Bioinformatics, by surprise since it was long thought that tyrosine kinases are restricted to multicellular animals where they handle communication between cells.
"We were absolutely stunned," says Manning. "Based on past work, we had expected maybe a handful of these kinases but instead discovered that this primitive organism has a record number of them. Two other essential parts of the tyrosine kinase network - PTP and SH2 genes - are also more numerous than in any other genome, showing that it is the whole network that is elaborated here."
The 100 trillion cells in our bodies require elaborate communication systems to coordinating their activities. Tyrosine kinases, extremely well-studied enzymes that act as receivers for external cues such as a growth signals and relay their message within cells by attaching tiny phosphate groups to proteins, are a vital part or this communication system.
At first glance, Monosiga brevicollis, which belongs to the group of choanoflagellates -- microscopic, aquatic organisms that occupy the grey area between fungal and animal kingdoms -- has little in common with multicellular animals that need to co-ordinate the activities of billions of cells. But its distinctive architecture -- a collar of tentacle surrounding a whip-like tail known as flagellum -- has the same basic structure as "collar cells" that aggregate to form sponges, which are considered the most primitive multicellular organisms or metazoans.
Because of their key evolutionary position, M. brevicollis was selected as a representative choanoflagellate for whole genome sequencing. "Choanoflagellates are like 'first cousins' of animals and their genome allows us a glimpse into the evolutionary origin of animals," says Manning.
The Monosiga kinases are more divergent than anything previously seen in animals, which may help scientists understand the fundamentals of how all tyrosine kinase signaling works. Despite their extreme diversity, Monosiga kinases time and again arrive at the same solution to a problem, as do animal kinases, but using a distinct method for instance to create a sensor structure that emerges from the cell, or to target a kinase to a specific part of the cell. "This convergent evolution suggests that there are only a limited number of ways build a functional network from these components," says Manning.
With all this new information, one obvious question remains unanswered: what is a single-celled organism doing with all this communications gear? "We don't have a clue!" says Manning, "but this discovery is the first step in finding out."
Researchers who also contributed to the work include Yufeng Zhai, Ph.D. from the Salk Institute, Susan L. Young, Ph.D., in the Center for Integrative Genomics at the University of California, Berkeley and W. Todd Miller, Ph. D. in the Department of Physiology and Biophysics at Stony Brook University, Stony Brook.
The work was supported by the NIH and the Razavi Newman Center for Bioinformatics.
Materials provided by Salk Institute. Note: Content may be edited for style and length.
Three Laws of Biology
Immense scientific progress has been made in recent centuries, and the time period required to double our knowledge continues to shrink. In recent decades, the sequencing of genomes from diverse species has been a primary driving force behind the expansion of biological knowledge. It has become central to the study of molecular and organismal evolution. The technologies that, for example, enable genomics, molecular medicine, and computing to forge forward at such rapid interdependent paces, are recognized as central to our understanding of Earth’s biosphere and sustaining it for future generations.
In recent years, biology has been at the forefront of science as we satisfy our desires to understand the nature of living organisms and their evolutionary histories. The statements that follow are based on reams of evidence. Only when each statement is integrated with the others does a reasonably complete picture of life become possible. We enlist the assistance of the international scientific community to inform us of any modifications and exceptions to existing scientific dogma so that our concepts can continuously be refined. Only via this approach has it been possible to establish some basic laws of biology. The First Law of Biology: all living organisms obey the laws of thermodynamics. The Second Law of Biology: all living organisms consist of membrane-encased cells. The Third Law of Biology: all living organisms arose in an evolutionary process.
The First Law of Biology: all living organisms obey the laws of thermodynamics. This law is fundamental because the laws of the inanimate world determine the course of the universe. All organisms on all planets, including humans, must obey these laws. The laws of thermodynamics govern energy transformations and mass distributions. Cells that comprise living organisms (see The Second Law) are open systems that allow both mass and energy to cross their membranes. Cells exist in open systems so as to allow acquisition of minerals, nutrients, and novel genetic traits while extruding end products of metabolism and toxic substances. Genetic variation, which results in part from gene transfer in prokaryotes and sexual reproduction in higher organisms, allows tremendously increased phenotypic variability in a population as well as an accelerated rate of evolutionary divergence.
A corollary of the First Law is that life requires the temporary creation of order in apparent contradiction to the second law of thermodynamics. However, considering a completely closed system, including the materials and energy sources provided by the environment for the maintenance of life, living organisms affect the system strictly according to this law, by increasing randomness or chaos (entropy). Resource utilization by living organisms thus increases the entropy of the world. A second corollary of the First Law is that an organism at biochemical equilibrium is dead. When living organisms reach equilibrium with their surrounding environment, they no longer exhibit the quality of life. Life depends on interconnected biochemical pathways to allow for growth, macromolecular synthesis, and reproduction. Thus, all life forms are far from equilibrium with their environments.
The Second Law of Biology: all living organisms consist of membrane-encased cells. Enveloping membranes allow physical separation between the living and the non-living worlds. Viruses, plasmids, transposons, prions, and other selfish, biological entities are not alive. They cannot “self” reproduce. They are dependent on a living cell for this purpose. By definition, they therefore, are not alive. A corollary of the Second Law is that the cell is the only structure that can grow and divide independently of another life form. A second corollary of the Second Law is that all life is programmed by genetic instructions. Genetic instructions are required for cell division, morphogenesis, and differentiation. From single-celled prokaryotic organisms to normal or cancerous tissues in multicellular animals and plants, genetic instructions are required for the maintenance of life.
The Third Law of Biology: all living organisms arose in an evolutionary process. This law correctly predicts the relatedness of all living organisms on Earth. It explains all of their programmed similarities and differences. Natural selection occurs at organismal (phenotypic) and molecular (genotypic) levels. Organisms can live, reproduce, and die. If they die without reproducing, their genes are usually removed from the gene pool, although exceptions exist. At the molecular level, genes and their encoding proteins can evolve “selfishly,” and these can combine with other selfish genes to form selfish operons, genetic units and functional parasitic elements such as viruses.
Two corollaries of the Third Law are that (1) all living organisms contain homologous macromolecules (DNA, RNA, and proteins) that derived from a common ancestor, and (2) the genetic code is universal. These two observations provide compelling evidence for the Third Law of Biology. Because of his accurate enunciation of the Third Law, Charles Darwin is considered by many to be the greatest biologist of all time.
Although science is continually pushing back the frontiers of our knowledge, we will never know everything. In fact, we do not even know what we do not know. For example, we may never know how life arose. Although life may be sprinkled throughout the universe, life is not required for the continuity of inanimate matter that is, living organisms are not essential for the universe to function. The laws of physics continue to apply regardless of the presence of life. To the best of our knowledge, life can only arise from pre-existing life. This of course begs the question how the first living cell(s) might have arisen. Did life spontaneously arise from inanimate nature just once, or more than once? Can life be transferred between receptive planets through space travel? We simply do not know. The mechanisms that may have led to the origin of a cell capable of autonomous growth and division are a mystery. This is an area of biology that will require a tremendous amount of scientific research if evidence is ever to become available, and there are no guarantees.
The rules of biology and science cannot be broken. They are not artificial human-made laws. They are natural laws that govern all life while living organisms are evolving on our planet. In recent decades, humans have altered our common, shared biosphere with resource depletion and pollution. We know that these activities have upset the balance of Nature, causing extensive species extinction. The most significant forms of pollution can be attributed to too many humans consuming too many non-renewable resources at an ever-increasing rate. Much of this harm is driven by pleasure, greed, conflict, and the desire for power. To varying degrees, we are all to blame.
Why do so many people assault the biosphere in such a primitive manner? Some are ignorant of the outcome. They are oblivious to the consequences of their actions. They do not recognize that incorrect action can have disastrous outcomes for our biosphere and us all. They do not understand that natural selection is cruel and can cause immense suffering and death. They think only of the moment and refuse to accept that it is their offspring who will have to face calamity. Still others are fully aware of the ultimate consequences. And those of us who are aware must take action to pass on our knowledge so as to attempt to avoid or delay our self-imposed fate. Research does tell us that we are assaulting our biosphere, and that the planet cannot accommodate our huge human population. We depend on natural resources for the continuance of our existence, but we are not living-sustainable. This planet does not need more consumption and pollution. It is groaning under the weight of our ever-increasing human population. Entropy will have its way. It might help if everyone understood science and our natural world so that they would recognize what is required for survival of the human species with some reasonable quality of life and the first step in this direction is to understand the basic laws of physics, chemistry, and biology and how they govern our biosphere, which is currently under assault and in need of being rescued. However, without profound respect for Nature and compassion for life, all life, knowledge is likely to be insufficient. We must develop into more caring, sensitive, and compassionate beings.
What Is the Scientific Answer to "Why Do Living Things Die?"
Why do living things die?: originally appeared on Quora: The best answer to any question. Ask a question, get a great answer. Learn from experts and access insider knowledge. You can follow Quora on Twitter, Facebook, and Google+.
Answer by Paul King, Computational Neuroscientist
It's not that living things die it's that multicellular organisms die. But why?
Every single-celled organism alive today has been in existence since life began over 3 billion years ago. This is because individual cells do not give birth, they divide. After cell division, the two cells that result are each as old as the single cell that preceded them. The cell does not become younger by dividing. (Although this may not be exactly true, see: )
Thus every cell in your body is over 3 billion years old.
The strategy that multicellular organisms such as humans use to project themselves into the future is to create new cell colonies from a single undifferentiated cell rather than maintaining existing colonies indefinitely. The main reason is that reproduction is more flexible and robust than maintenance, and it provides a way of starting over with a "clean slate" and slightly different genes. Complex organisms accumulate billions of errors and problems over their lifetime. Most of these errors are fixed as fast as they happen, but life takes a toll and not all problems are reversible. Just as reinstalling Microsoft Windows every so often fixes accumulated system issues, so does generating a new organism every so often from a single cell.
Given that biology has selected this strategy, evolution has optimized for producing the most successful offspring. Once the individual has reproduced, its only evolutionary role is to support the success of its offspring. Aging longer is just not something evolution has had a reason to optimize. And in fact given limited environmental resources, the offspring often do better if the older generation doesn't stay around forever competing with younger generations for scarce resources.
In terms of what happens physiologically, there are two main contributors to aging.
The first is the accumulation of biological defects. Viruses and disease take a toll even after healing UV rays slowly but inevitably damage DNA and proteins, cell structure, and the neurons which hold memories all degrade over time due to thermodynamic molecular disruptions and invasions by other species.
The second is the aging process itself. The organism develops to maturity and ages in stages according to a genetically determined life plan. Muscles atrophy, bones brittle, and metabolism changes. But the life plan has never run more than 80 years until recently, and evolution only ever optimized the first 40 years or so. So humans are in new territory that is poorly understood, and which evolution has never had a reason to fine tune.
It may be possible to slow or stop some of the genetically determined aging processes. While this may not be good for an overpopulated planet, it is sure to be popular with those that can afford the medical intervention. Let's just hope the social security system holds out!
 There is evidence that even in "symmetric" cell division, one child cell may be slightly "younger" (less prone to death) than the other. See: Stewert EJ, et al (2005). Aging and death in an organism that reproduces by morphologically symmetric division. PLoS Biology.
Scientists reverse age-related vision loss, eye damage from glaucoma in mice
Harvard Medical School scientists have successfully restored vision in mice by turning back the clock on aged eye cells in the retina to recapture youthful gene function.
The team's work, described Dec. 2 in Nature, represents the first demonstration that it may be possible to safely reprogram complex tissues, such as the nerve cells of the eye, to an earlier age.
In addition to resetting the cells' aging clock, the researchers successfully reversed vision loss in animals with a condition mimicking human glaucoma, a leading cause of blindness around the world.
The achievement represents the first successful attempt to reverse glaucoma-induced vision loss, rather than merely stem its progression, the team said. If replicated through further studies, the approach could pave the way for therapies to promote tissue repair across various organs and reverse aging and age-related diseases in humans.
"Our study demonstrates that it's possible to safely reverse the age of complex tissues such as the retina and restore its youthful biological function," said senior author David Sinclair, professor of genetics in the Blavatnik Institute at Harvard Medical School, co-director of the Paul F. Glenn Center for Biology of Aging Research at HMS and an expert on aging.
Sinclair and colleagues caution that the findings remain to be replicated in further studies, including in different animal models, before any human experiments. Nonetheless, they add, the results offer a proof of concept and a pathway to designing treatments for a range of age-related human diseases.
"If affirmed through further studies, these findings could be transformative for the care of age-related vision diseases like glaucoma and to the fields of biology and medical therapeutics for disease at large," Sinclair said.
For their work, the team used an adeno-associated virus (AAV) as a vehicle to deliver into the retinas of mice three youth-restoring genes—Oct4, Sox2 and Klf4—that are normally switched on during embryonic development. The three genes, together with a fourth one, which was not used in this work, are collectively known as Yamanaka factors.
The treatment had multiple beneficial effects on the eye. First, it promoted nerve regeneration following optic-nerve injury in mice with damaged optic nerves. Second, it reversed vision loss in animals with a condition mimicking human glaucoma. And third, it reversed vision loss in aging animals without glaucoma.
The team's approach is based on a new theory about why we age. Most cells in the body contain the same DNA molecules but have widely diverse functions. To achieve this degree of specialization, these cells must read only genes specific to their type. This regulatory function is the purview of the epigenome, a system of turning genes on and off in specific patterns without altering the basic underlying DNA sequence of the gene.
This theory postulates that changes to the epigenome over time cause cells to read the wrong genes and malfunction—giving rise to diseases of aging. One of the most important changes to the epigenome is DNA methylation, a process by which methyl groups are tacked onto DNA. Patterns of DNA methylation are laid down during embryonic development to produce the various cell types. Over time, youthful patterns of DNA methylation are lost, and genes inside cells that should be switched on get turned off and vice versa, resulting in impaired cellular function. Some of these DNA methylation changes are predictable and have been used to determine the biologic age of a cell or tissue.
Yet, whether DNA methylation drives age-related changes inside cells has remained unclear. In the current study, the researchers hypothesized that if DNA methylation does, indeed, control aging, then erasing some of its footprints might reverse the age of cells inside living organisms and restore them to their earlier, more youthful state.
Past work had achieved this feat in cells grown in laboratory dishes but fell short of demonstrating the effect in living organisms.
The new findings demonstrate that the approach could be used in animals as well.
Overcoming an important hurdle
Lead study author, Yuancheng Lu, research fellow in genetics at HMS and a former doctoral student in Sinclair's lab, developed a gene therapy that could safely reverse the age of cells in a living animal.
Lu's work builds on the Nobel Prize winning discovery of Shinya Yamanaka, who identified the four transcription factors, Oct4, Sox2, Klf4, c-Myc, that could erase epigenetics markers on cells and return these cells to their primitive embryonic state from which they can develop into any other type of cell.
Subsequent studies, however, showed two important setbacks. First, when used in adult mice, the four Yamanaka factors could also induce tumor growth, rendering the approach unsafe. Second, the factors could reset the cellular state to the most primitive cell state, thus completely erasing a cell's identity.
Lu and colleagues circumvented these hurdles by slightly modifying the approach. They dropped the gene c-Myc and delivered only the remaining three Yamanaka genes, Oct4, Sox2 and Klf4. The modified approach successfully reversed cellular aging without fueling tumor growth or losing their identity.
Gene therapy applied to optic nerve regeneration
In the current study, the researchers targeted cells in the central nervous system because it is the first part of body affected by aging. After birth, the ability of the central nervous system to regenerate declines rapidly.
To test whether the regenerative capacity of young animals could be imparted to adult mice, the researchers delivered the modified three-gene combination via an AAV into retinal ganglion cells of adult mice with optic nerve injury.
For the work, Lu and Sinclair partnered with Zhigang He, HMS professor of neurology and of ophthalmology at Boston Children's Hospital, who studies optic nerve and spinal cord neuro-regeneration.
The treatment resulted in a two-fold increase in the number of surviving retinal ganglion cells after the injury and a five-fold increase in nerve regrowth.
"At the beginning of this project, many of our colleagues said our approach would fail or would be too dangerous to ever be used," said Lu. "Our results suggest this method is safe and could potentially revolutionize the treatment of the eye and many other organs affected by aging."
Reversal of glaucoma and age-related vision loss
Following the encouraging findings in mice with optic nerve injuries, the team partnered with colleagues at Schepens Eye Research Institute of Massachusetts Eye and Ear Bruce Ksander, HMS associate professor of ophthalmology, and Meredith Gregory-Ksander, HMS assistant professor of ophthalmology. They planned two sets of experiments: one to test whether the three-gene cocktail could restore vision loss due to glaucoma and another to see whether the approach could reverse vision loss stemming from normal aging.
In a mouse model of glaucoma, the treatment led to increased nerve cell electrical activity and a notable increase in visual acuity, as measured by the animals' ability to see moving vertical lines on a screen. Remarkably, it did so after the glaucoma-induced vision loss had already occurred.
"Regaining visual function after the injury occurred has rarely been demonstrated by scientists," Ksander said. "This new approach, which successfully reverses multiple causes of vision loss in mice without the need for a retinal transplant, represents a new treatment modality in regenerative medicine."
The treatment worked similarly well in elderly, 12-month-old mice with diminishing vision due to normal aging. Following treatment of the elderly mice, the gene expression patterns and electrical signals of the optic nerve cells were similar to young mice, and vision was restored. When the researchers analyzed molecular changes in treated cells, they found reversed patterns of DNA methylation—an observation suggesting that DNA methylation is not a mere marker or a bystander in the aging process, but rather an active agent driving it.
"What this tells us is the clock doesn't just represent time—it is time," said Sinclair. "If you wind the hands of the clock back, time also goes backward."
The researchers said that if their findings are confirmed in further animal work, they could initiate clinical trials within two years to test the efficacy of the approach in people with glaucoma. Thus far, the findings are encouraging, researchers said. In the current study, a one-year, whole-body treatment of mice with the three-gene approach showed no negative side effects.
Bad News For Irish Wolfhounds
An illustrative example of the aging caused by the telomere loss is the difference in longevity between giant and regular size dogs, for instance, Irish Wolfhounds and primitive breeds like Huskies. It is known that giant breeds have a significantly shorter lifespan. Scientists attribute it to the exaggerated rate of cell division (and, accordingly, loss of their telomeres) during their extremely accelerated spurt of growth during their first several months of life. Puppies are born small. In giant breeds, they have the same number of months to grow to a much larger size. If we simplify, they "spend" too many telomers during the first year of life to reach the giant size.
In 1977 the first study on coelacanth aging focused on calcified structures called macro-circuli on the scales of 12 African coelacanth museum specimens. These macro-circuli were thought to be a bit like tree rings or ice cores, marks that record intervals of time.
Based on the regular increments of macro-circuli growth on the scales, scientists at the time concluded macro-circuli were likely deposited two times a year—later revised to once a year, a result that equated to a life span of 20 years.
Curious if there was more to the story, Mahe and his team recently examined 27 African coelacanth specimens, stored at the National Museum of Natural History in Paris and caught between 1954 and 1991, including one juvenile and two embryos.
When they looked at macro-circuli with a transmitted light microscope, as the previous teams had, they found the same result as the scientists in the 1970s.
Finch CE. Evolution of the human lifespan and diseases of aging: roles of infection, inflammation, and nutrition. Proc Natl Acad Sci U S A. 2010107(suppl 1):1718–24.
Vaupel JW, Carey JR, Christensen K, Johnson TE, Yashin AI, Holm NV, et al. Biodemographic trajectories of longevity. Science. 1998280(5365):855–60.
Wilmoth JR. Demography of longevity: past, present, and future trends. Exp Gerontol. 200035(9-10):1111–29.
Oeppen J, Vaupel JW. Broken limits to life expectancy. Science. 2002296(5570):1029–31.
Vaupel JW. Biodemography of human ageing. Nature. 2010464(7288):536–42.
Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013153(6):1194–217.
Niccoli T, Partridge L. Ageing as a risk factor for disease. Curr Biol. 201222(17):R741–R52.
Crimmins EM. Lifespan and healthspan: past, present, and promise. Gerontologist. 201555(6):901–11.
Salomon JA, Wang H, Freeman MK, Vos T, Flaxman AD, Lopez AD, et al. Healthy life expectancy for 187 countries, 1990–2010: a systematic analysis for the Global Burden Disease Study 2010. Lancet. 2012380(9859):2144–62.
Global Health Observatory data repository. Life expectancy and Healthy life expectancy. Data by WHO region. http://apps.who.int/gho/data/view.main.SDG2016LEXREGv?lang=en. Accessed 18 Jul 2018.
Younis A, Goldkorn R, Goldenberg I, Geva D, Tzur B, et al. Impaired fasting glucose is the major determinant of the 20-year mortality risk associated with metabolic syndrome in nondiabetic patients with stable coronary artery disease. J Am Heart Assoc. 20176:e006609.
Carone G, Costello D, Diez Guardia N, Mourre G, Przywara B, Salomäki A. The economic impact of ageing populations in the Eu25 member states, Directorate general for economic and financial affairs European economy economic working paper no. 236.2005. https://doi.org/10.2139/ssrn.873872.
Kontis V, Bennett JE, Mathers CD, Li G, Foreman K, Ezzati M. Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble. Lancet. 2017389(10076):1323–35.
Dong X, Milholland B, Vijg J. Evidence for a limit to human lifespan. Nature. 2016538(7624):257–9.
Lenart A, Vaupel JW. Questionable evidence for a limit to human lifespan. Nature. 2017546(7660):E13–E4.
Barbi E, Lagona F, Marsili M, Vaupel JW, Wachter KW. The plateau of human mortality: demography of longevity pioneers. Science. 2018360:1459–61.
Tetzlaff J, Muschik D, Epping J, Eberhard S, Geyer S. Expansion or compression of multimorbidity? 10-year development of life years spent in multimorbidity based on health insurance claims data of Lower Saxony, Germany. Intl J Pub Health. 201762(6):679–86.
Sebastiani P, Perls T. The genetics of extreme longevity: lessons from the New England centenarian study. Front Genet. 20123:277. https://doi.org/10.3389/fgene.2012.00277.
Charlesworth B, Charlesworth D. Elements of evolutionary genetics. Greenwood Village: Roberts and Company Publishers 2010.
Rose MR. Evolutionary biology of aging. New York and Oxford: Oxford University Press 1991.
Finch CE. Longevity, senescence, and the genome. Chicago and London: The University of Chicago Press 1990.
Nussey DH, Froy H, Lemaitre JF, Gaillard JM, Austad SN. Senescence in natural populations of animals: widespread evidence and its implications for bio-gerontology. Ageing Res Rev. 201312(1):214–25.
Clutton-Brock T, Sheldon BC. Individuals and populations: the role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends Ecol Evol. 201025:562–73.
Charlesworth B. Evolutionary mechanisms of senescence. Genetica. 199391:11–9.
Baudisch A. Inevitable aging? Berlin: Springer 2008.
Munné-Bosch S. Senescence: is it universal or not? Trends Plant Sci. 201520(11):713–20.
Shefferson RP, Jones OR, Salguero-Gómez R. The evolution of senescence in the tree of life. Cambridge: Cambridge University Press 2017.
Charlesworth B. Fisher, Medawar, Hamilton and the evolution of aging. Genetics. 2000156(3):927–31.
Fisher RA. The genetical theory of natural selection. Oxford: Oxford University Press 1930.
Haldane JBS. New paths in genetics. London: Allen and Unwin 1941.
Medawar PB. Old age and natural death. Mod Quart. 19462:30–49.
Medawar PB. An unsolved problem of biology. London: H.K. Lewis 1952.
Williams GC. Pleiotropy, natural selection, and the evolution of senescence. Evolution. 195711:398–411.
Hamilton WD. Moulding of senescence by natural selection. J Theor Biol. 196612(1):12–45.
Baudisch A. Hamilton’s indicators of the force of selection. Proc Natl Acad Sci U S A. 2005102(23):8263–8.
Rose MR, Rauser CL, Benford G, Matos M, Mueller LD. Hamilton’s forces of natural selection after forty years. Evolution. 200761(6):1265–76.
Charlesworth B. Evolution in age-structured populations. 1st ed. Cambridge: Cambridge University Press 1980.
Charlesworth B. Evolution in age-structured populations. 2nd ed. Cambridge: Cambridge University Press 1994.
Partridge L, Deelen J, Slagboom PE. Facing up to the global challenges of ageing. Nature. 2018 in press
Kirkwood TBL. Evolution of ageing. Nature. 1977270:301–4.
Kirkwood TBL. The disposable soma theory of aging. In: Harrison DE, editor. Genetic effects on aging II. Caldwell: Telford Press 1990. p. 9–19.
Abrams PA, Ludwig D. Optimality theory, Gompertz’ law, and the disposable soma theory of senescence. Evolution. 199549(6):1055–66.
Charlesworth B. Patterns of age-specific means and genetic variances of mortality rates predicted by the mutation accumulation theory of aging. J Theor Biol. 2001210:47–65.
Partridge L, Barton NH. Optimality, mutation and the evolution of ageing. Nature. 1993362:305–11.
Partridge L, Barton NH. Evolution of aging - testing the theory using Drosophila. Genetica. 199391(1-3):89–98.
Partridge L, Gems D. Mechanisms of ageing: public or private? Nat Rev Genet. 20023:165–75.
Flatt T, Schmidt PS. Integrating evolutionary and molecular genetics of aging. Biochim Biophys Acta. 20091790(10):951–62.
Stearns SC, Partridge L. The genetics of aging in Drosophila. In: Masoro E, Austad S, editors. Handbook of the biology of aging. 5th ed. Cambridge: Academic Press 2001. p. 353–68.
Hughes KA, Reynolds RM. Evolutionary and mechanistic theories of aging. Annu Rev Entomol. 200550:421–45.
Flatt T. Survival costs of reproduction in Drosophila. Exp Gerontol. 201146(5):369–75.
Kenyon C. The plasticity of aging: insights from long-lived mutants. Cell. 2005120(4):449–60.
Gaillard JM, Lemaître JF. The Williams’ legacy: a critical reappraisal of his nine predictions about the evolution of senescence. Evolution. 201771(12):2768–85.
Lemaitre JF, Berger V, Bonenfant C, Douhard M, Gamelon M, Plard F, et al. Early-late life trade-offs and the evolution of ageing in the wild. Proc R Soc Lond B. 2015282(1806):20150209.
Rose M, Charlesworth B. A test of evolutionary theories of senescence. Nature. 1980287:141–2.
Rose MR. Laboratory evolution of postponed senescence in Drosophila melanogaster. Evolution. 198438(5):1004–10.
Partridge L, Prowse N, Pignatelli P. Another set of responses and correlated responses to selection on age at reproduction in Drosophila melanogaster. Proc R Soc Lond B. 1999266:255–61.
Sgro CM, Partridge L. A delayed wave of death from reproduction in Drosophila. Science. 1999286:2521–4.
Stearns SC, Ackermann M, Doebeli M, Kaiser M. Experimental evolution of aging, growth, and reproduction in fruitflies. Proc Natl Acad Sci U S A. 200097:3309–13.
Zwaan B, Bijlsma R, Hoekstra RF. Direct selection on life span in Drosophila melanogaster. Evolution. 199549(4):649–59.
Partridge L, Gems D, Withers DJ. Sex and death: what is the connection? Cell. 2005120(4):461.
Clancy DJ, Gems D, Harshman LG, Oldham S, Stocker H, Hafen E, et al. Extension of life-span by loss of CHICO, a Drosophila insulin receptor substrate protein. Science. 2001292:104–6.
Kenyon C. A conserved regulatory system for aging. Cell. 2001105(2):165–8.
Tatar M, Kopelman A, Epstein D, Tu M-P, Yin C-M, Garofalo RS. A mutant Drosophila insulin receptor homolog that extends life-span and impairs neuroendocrine function. Science. 2001292:107–10.
Tatar M, Bartke A, Antebi A. The endocrine regulation of aging by insulin-like signals. Science. 2003299(5611):1346–51.
Paaby AB, Schmidt PS. Dissecting the genetics of longevity in Drosophila melanogaster. Fly (Austin). 20093(1):29–38.
Paaby AB, Bergland AO, Behrman EL, Schmidt PS. A highly pleiotropic amino acid polymorphism in the Drosophila insulin receptor contributes to life-history adaptation. Evolution. 201468(12):3395–409.
Paaby AB, Schmidt PS. Functional significance of allelic variation at methuselah, an aging gene in Drosophila. PLoS One. 20083(4):e1987.
Charlesworth B, Hughes KA. Age-specific inbreeding depression and components of genetic variance in relation to the evolution of senescence. Proc Natl Acad Sci U S A. 199693(12):6140–5.
Shaw FH, Promislow DE, Tatar M, Hughes KA, Geyer CJ. Toward reconciling inferences concerning genetic variation in senescence in Drosophila melanogaster. Genetics. 1999152(2):553–66.
Hughes KA, Alipaz JA, Drnevich JM, Reynolds RM. A test of evolutionary theories of aging. Proc Natl Acad Sci U S A. 200299(22):14286–91.
Moorad JA, Promislow DE. What can genetic variation tell us about the evolution of senescence? Proc R Soc Lond B. 2009276(1665):2271–8.
Rodríguez JA, Marigorta UM, Hughes DA, et al. Antagonistic pleiotropy and mutation accumulation influence human senescence and disease. Nat Ecol Evol. 20171(3):55.
Wright A, Charlesworth B, Rudan I, Carothers A, Campbell H. A polygenic basis for late-onset disease. Trends Genet. 200319(2):97–106.
Charlesworth B. Evolution of senescence: Alzheimer’s disease and evolution. Curr Biol. 19966(1):20–2.
Campisi J. Aging, cellular senescence, and cancer. Annu Rev Physiol. 201375(1):685–705.
Byars SG, Huang QQ, Gray L-A, Bakshi A, Ripatti S, Abraham G, et al. Genetic loci associated with coronary artery disease harbor evidence of selection and antagonistic pleiotropy. PLoS Genet. 201713(6):e1006328.
Carter AJ, Nguyen AQ. Antagonistic pleiotropy as a widespread mechanism for the maintenance of polymorphic disease alleles. BMC Med Genet. 201112(1):1–13.
Wang X, Byars SG, Stearns SC. Genetic links between post-reproductive lifespan and family size in Framingham. Evol Med Pub Health. 20132013(1):241–53.
Kang H-J, Feng Z, Sun Y, Atwal G, Murphy ME, Rebbeck TR, et al. Single-nucleotide polymorphisms in the p53 pathway regulate fertility in humans. Proc Natl Acad Sci U S A. 2009106(24):9761–6.
Smith KR, Hanson HA, Mineau GP, Buys SS. Effects of BRCA1 and BRCA2 mutations on female fertility. Proc R Soc Lond B. 2012279(1732):1389–95.
Moorad JA, Walling CA. Measuring selection for genes that promote long life in a historical human population. Nat Ecol Evol. 20171:1773–81.
Abrams PA. Does increased mortality favor the evolution of more and rapid senescence? Evolution. 199347(3):877–87.
Williams PD, Day T. Antagonistic pleiotropy, mortality source interactions, and the evolutionary theory of senescence. Evolution. 200357(7):1478–88.
Caswell H. Extrinsic mortality and the evolution of senescence. Trends Ecol Evol. 200722(4):173–4.
Gaillard J-M, Festa-Bianchet M, Yoccoz NG, Loison A, Toïgo C. Temporal variation in fitness components and population dynamics of large herbivores. Annu Rev Ecol Syst. 200031:367–93.
Williams PD, Day T, Fletcher Q, Rowe L. The shaping of senescence in the wild. Trends Ecol Evol. 200621(8):458–63.
Reznick DN, Bryant MJ, Roff D, Ghalambor CK, Ghalambor DE. Effect of extrinsic mortality on the evolution of senescence in guppies. Nature. 2004431(7012):1095–9.
Chen H, Maklakov AA. Longer life span evolves under high rates of condition-dependent mortality. Curr Biol. 201222(22):2140–3.
Kirkwood TBL, Martin GM, Partridge L. Evolution, senescence and health in old age. In: Stearns SC, editor. Evolution in health and disease. Oxford: Oxford University Press 1999. p. 219–30.
Burke MK, King EG, Shahrestani P, Rose MR, Long AD. Genome-wide association study of extreme longevity in Drosophila melanogaster. Genome Biol Evol. 20146(1):1–11.
Highfill CA, Reeves GA, Macdonald SJ. Genetic analysis of variation in lifespan using a multiparental advanced intercross Drosophila mapping population. BMC Genet. 201617(1):113.
Ivanov DK, Escott-Price V, Ziehm M, Magwire MM, Mackay TFC, Partridge L, et al. Longevity GWAS using the Drosophila genetic reference panel. J Gerontol A Biol Sci Med Sci. 201570(12):1470–8.
Reznick DN. The genetic basis of aging: an evolutionary biologist’s perspective. Sci Aging Knowl Environ. 20052005(11):pe7.
McElwee JJ, Schuster E, Blanc E, Piper MD, Thomas JH, Patel DS, et al. Evolutionary conservation of regulated longevity assurance mechanisms. Genome Biol. 20078(7):R132.
Smith ED, Tsuchiya M, Fox LA, Dang N, Hu D, Kerr EO, et al. Quantitative evidence for conserved longevity pathways between divergent eukaryotic species. Genome Res. 200818(4):564–70.
Kenyon CJ. The genetics of ageing. Nature. 2010464(7288):504–12.
Fontana L, Partridge L, Longo VD. Extending healthy life span – from yeast to humans. Science. 2010328(5976):321–6.
Wyss-Coray T. Ageing, neurodegeneration and brain rejuvenation. Nature. 2016539:180.
Castellano JM, Mosher KI, Abbey RJ, McBride AA, James ML, Berdnik D, et al. Human umbilical cord plasma proteins revitalize hippocampal function in aged mice. Nature. 2017544:488–92.
Mair W, Dillin A. Aging and survival: the genetics of life span extension by dietary restriction. Annu Rev Biochem. 200877:727–54.
Fontana L, Partridge L. Promoting health and longevity through diet: from model organisms to humans. Cell. 2015161(1):106–18.
Anderson RM, Le Couteur DG, de Cabo R. Caloric restriction research: new perspectives on the biology of aging. J Gerontol A Biol Sci Med Sci. 201773(1):1–3.
Kapahi P, Kaeberlein M, Hansen M. Dietary restriction and lifespan: lessons from invertebrate models. Ageing Res Rev. 201739:3–14.
Gardner EM. Caloric restriction decreases survival of aged mice in response to primary influenza infection. J Gerontol A Biol Sci Med Sci. 200560(6):688–94.
Colman RJ, Anderson RM, Johnson SC, Kastman EK, Kosmatka KJ, Beasley TM, et al. Caloric restriction delays disease onset and mortality in rhesus monkeys. Science. 2009325(5937):201–4.
Mattison JA, Roth GS, Beasley TM, Tilmont EM, Handy AM, Herbert RL, et al. Impact of caloric restriction on health and survival in rhesus monkeys from the NIA study. Nature. 2012489:318–21.
Colman RJ, Beasley TM, Kemnitz JW, Johnson SC, Weindruch R, Anderson RM. Caloric restriction reduces age-related and all-cause mortality in rhesus monkeys. Nat Commun. 20145:3557.
Ingram DK, de Cabo R. Calorie restriction in rodents: caveats to consider. Ageing Res Rev. 201739:15–28.
Grandison RC, Piper MDW, Partridge L. Amino-acid imbalance explains extension of lifespan by dietary restriction in Drosophila. Nature. 2009462(7276):1061–5.
Levine ME, Suarez Jorge A, Brandhorst S, Balasubramanian P, Cheng C-W, Madia F, et al. Low protein intake is associated with a major reduction in IGF-1, cancer, and overall mortality in the 65 and younger but not older population. Cell Metab. 201419(3):407–17.
Mirzaei H, Suarez JA, Longo VD. Protein and amino acid restriction, aging and disease: from yeast to humans. Trends Endocrinol Metab. 201425(11):558–66.
Solon-Biet Samantha M, McMahon Aisling C, Ballard JWilliam O, Ruohonen K, Wu Lindsay E, Cogger Victoria C, et al. The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice. Cell Metab. 201419(3):418–30.
Simpson SJ, Le Couteur DG, Raubenheimer D, Solon-Biet SM, Cooney GJ, Cogger VC, et al. Dietary protein, aging and nutritional geometry. Ageing Res Rev. 201739:78–86.
Piper MDW, Soultoukis GA, Blanc E, Mesaros A, Herbert SL, Juricic P, et al. Matching dietary amino acid balance to the in silico-translated exome optimizes growth and reproduction without cost to lifespan. Cell Metab. 201725(3):610–21.
Cummings NE, Williams EM, Kasza I, Konon EN, Schaid MD, Schmidt BA, et al. Restoration of metabolic health by decreased consumption of branched-chain amino acids. J Physiol. 2018596(4):623–45.
Mattson MP, Allison DB, Fontana L, Harvie M, Longo VD, Malaisse WJ, et al. Meal frequency and timing in health and disease. Proc Natl Acad Sci U S A. 2014111(47):16647–53.
Brandhorst S, Choi In Y, Wei M, Cheng Chia W, Sedrakyan S, Navarrete G, et al. A periodic diet that mimics fasting promotes multi-system regeneration, enhanced cognitive performance, and healthspan. Cell Metab. 201522(1):86–99.
Longo VD, Panda S. Fasting, circadian rhythms, and time-restricted feeding in healthy lifespan. Cell Metab. 201623(6):1048–59.
Martinez-Lopez N, Tarabra E, Toledo M, Garcia-Macia M, Sahu S, Coletto L, et al. System-wide benefits of intermeal fasting by autophagy. Cell Metab. 201726(6):856–71.e5.
Moatt JP, Nakagawa S, Lagisz M, Walling CA. The effect of dietary restriction on reproduction: a meta-analytic perspective. BMC Evol Biol. 201616(1):199.
Shanley DP, Kirkwood TBL. Calorie restriction and aging: a life-history analysis. Evolution. 200054(3):740–50.
Zajitschek F, Georgolopoulos G, Vourlou A, et al. Evolution under dietary restriction decouples survival from fecundity in Drosophila melanogaster females. J Gerontol A Biol Sci Med Sci. 2018 in press
Strandin T, Babayan SA, Forbes KM. Reviewing the effects of food provisioning on wildlife immunity. Philos Trans R Soc Lond B. 2018373(1745):20170088.
Boutin S. Food supplementation experiments with terrestrial vertebrates: patterns, problems, and the future. Can J Zool. 199068:203–20.
Ruffino L, Salo P, Koivisto E, Banks PB, Korpimäki E. Reproductive responses of birds to experimental food supplementation: a meta-analysis. Front Zool. 201411:80.
Klass M, Hirsh D. Non-ageing developmental variant of Caenorhabditis elegans. Nature. 1976260:523–5.
Klass MR. A method for the isolation of longevity mutants in the nematode Caenorhabditis elegans and initial results. Mech Ageing Dev. 198322(3-4):279–86.
Johnson TE, Mitchell DH, Kline S, Kemal R, Foy J. Arresting development arrests aging in the nematode Caenorhabditis elegans. Mech Ageing Dev. 198428(1):23–40.
Johnson TE. Aging can be genetically dissected into component processes using long-lived lines of Caenorhabditis elegans. Proc Natl Acad Sci U S A. 198784(11):3777–81.
Friedman DB, Johnson TE. A mutation in the age-1 gene in Caenorhabditis elegans lengthens life and reduces hermaphrodite fertility. Genetics. 1988118(1):75–86.
Johnson TE, Lithgow GJ. The search for the genetic basis of aging: the identification of gerontogenes in the nematode Caenorhabditis elegans. J Am Geriatr Soc. 199240(9):936–45.
Kenyon C, Chang J, Gensch E, Rudner A, Tabtiang R. A C. elegans mutant that lives twice as long as wild type. Nature. 1993366(6454):461–4.
Kenyon C. The first long-lived mutants: discovery of the insulin/IGF-1 pathway for ageing. Philos Trans R Soc Lond B. 2011366(1561):9–16.
Morris J, Tissenbaum H, Ruvkun G. A phosphatidylinositol-3-OH kinase family member regulating longevity and diapause in Caenorhabditis elegans. Nature. 1996382:536–9.
Kimura K, Tissenbaum H, Liu Y, Ruvkun G. daf-2, an insulin receptor-like gene that regulates longevity and diapause in Caenorhabditis elegans. Science. 1997277:942–6.
Ogg S, Paradis S, Gottlieb S, Patterson G, Lee L, Tissenbaum H, et al. The Fork head transcription factor DAF-16 transduces insulin-like metabolic and longevity signals in C. elegans. Nature. 1997389:994–9.
Lee S, Kennedy S, Tolonen A, Ruvkun G. DAF-16 target genes that control C. elegans life-span and metabolism. Science. 2003300:644–7.
Lin K, Dorman J, Rodan A, Kenyon C. daf-16: an HNF-3/forkhead family member that can function to double the life-span of Caenorhabditis elegans. Science. 1997278:1319–22.
Murphy C, McCarroll S, Bargmann C, Fraser A, Kamath R, Ahringer J, et al. Genes that act downstream of DAF-16 to influence the lifespan of Caenorhabditis elegans. Nature. 2003424:277–84.
Hwangbo DS, Gersham B, Tu MP, Palmer M, Tatar M. Drosophila dFOXO controls lifespan and regulates insulin signalling in brain and fat body. Nature. 2004429(6991):562–6.
Giannakou ME, Goss M, Junger MA, Hafen E, Leevers SJ, Partridge L. Long-lived Drosophila with overexpressed dFOXO in adult fat body. Science. 2004305(5682):361.
Kapahi P, Zid BM, Harper T, Koslover D, Sapin V, Benzer S. Regulation of lifespan in Drosophila by modulation of genes in the TOR signaling pathway. Curr Biol. 200414(10):885–90.
Bluher M, Kahn BB, Kahn CR. Extended longevity in mice lacking the insulin receptor in adipose tissue. Science. 2003299(5606):572–4.
Selman C, Lingard S, Choudhury AI, Batterham RL, Claret M, Clements M, et al. Evidence for lifespan extension and delayed age-related biomarkers in insulin receptor substrate 1 null mice. FASEB J. 200822(3):807–18.
Holzenberger M, Dupont J, Ducos B, Leneuve P, Géloen A, Even PC, et al. IGF-1 receptor regulates lifespan and resistance to oxidative stress in mice. Nature. 2003421:182–7.
Taguchi A, Wartschow LM, White MF. Brain IRS2 signaling coordinates life span and nutrient homeostasis. Science. 2007317(5836):369–72.
Flachsbart F, Caliebe A, Kleindorp R, Blanché H, von Eller-Eberstein H, Nikolaus S, et al. Association of FOXO3A variation with human longevity confirmed in German centenarians. Proc Natl Acad Sci U S A. 2009106(8):2700–5.
Willcox BJ, Donlon TA, He Q, Chen R, Grove JS, Yano K, et al. FOXO3A genotype is strongly associated with human longevity. Proc Natl Acad Sci U S A. 2008105(37):13987–92.
Suh Y, Atzmon G, Cho MO, Hwang D, Liu B, Leahy DJ, et al. Functionally significant insulin-like growth factor I receptor mutations in centenarians. Proc Natl Acad Sci U S A. 2008105(9):3438–42.
Tazearslan C, Huang J, Barzilai N, Suh Y. Impaired IGF1R signaling in cells expressing longevity-associated human IGF1R alleles. Aging Cell. 201110(3):551–4.
Passtoors WM, Beekman M, Deelen J, van der Breggen R, Maier AB, Guigas B, et al. Gene expression analysis of mTOR pathway: association with human longevity. Aging Cell. 201312(1):24–31.
Flachsbart F, Dose J, Gentschew L, Geismann C, Caliebe A, Knecht C, et al. Identification and characterization of two functional variants in the human longevity gene FOXO3. Nat Commun. 20178(1):2063.
Bjedov I, Toivonen JM, Kerr F, Slack C, Jacobson J, Foley A, et al. Mechanisms of life span extension by rapamycin in the fruit fly Drosophila melanogaster. Cell Metab. 201011(1):35–46.
Harrison DE, Strong R, Sharp ZD, Nelson JF, Astle CM, Flurkey K, et al. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature. 2009460:392–5.
Miller RA, Harrison DE, Astle CM, Fernandez E, Flurkey K, Han M, et al. Rapamycin-mediated lifespan increase in mice is dose and sex dependent and metabolically distinct from dietary restriction. Aging Cell. 201413(3):468–77.
Johnson SC, Rabinovitch PS, Kaeberlein M. mTOR is a key modulator of ageing and age-related disease. Nature. 2013493:338–45.
Kennedy BK, Lamming DW. The mechanistic target of rapamycin: the grand conducTOR of metabolism and aging. Cell Metab. 201623(6):990–1003.
Swanson EM, Dantzer B. Insulin-like growth factor-1 is associated with life-history variation across Mammalia. Proc R Soc Lond B. 2014281(1782):20132458.
Dantzer B, Swanson EM. Mediation of vertebrate life histories via insulin-like growth factor-1. Biol Rev. 201287(2):414–29.
De Jong G, Bochdanovits Z. Latitudinal clines in Drosophila melanogaster: body size, allozyme frequencies, inversion frequencies, and the insulin-signalling pathway. J Genet. 200382(3):207–23.
Fabian DK, Kapun M, Nolte V, Kofler R, Schmidt PS, Schlötterer C, et al. Genome-wide patterns of latitudinal differentiation among populations of Drosophila melanogaster from North America. Mol Ecol. 201221(19):4748–69.
Kapun M, Fabian DK, Goudet J, Flatt T. Genomic evidence for adaptive inversion clines in Drosophila melanogaster. Mol Biol Evol. 201633(5):1317–36.
Flatt T, Amdam GV, Kirkwood TBL, Omholt SW. Life-history evolution and the polyphenic regulation of somatic maintenance and survival. Q Rev Biol. 201388(3):185–218.
Paaby AB, Blacket MJ, Hoffmann AA, Schmidt PS. Identification of a candidate adaptive polymorphism for Drosophila life history by parallel independent clines on two continents. Mol Ecol. 201019(4):760–74.
Stanley PD, Ng’oma E, O’Day S, King EG. Genetic dissection of nutrition-induced plasticity in insulin/insulin-like growth factor signaling and median life span in a Drosophila multiparent population. Genetics. 2017206(2):587–602.
Rodrigues MA, Flatt T. Endocrine uncoupling of the trade-off between reproduction and somatic maintenance in eusocial insects. Curr Opin Insect Sci. 201616:1–8.
Ament SA, Corona M, Pollock HS, Robinson GE. Insulin signaling is involved in the regulation of worker division of labor in honey bee colonies. Proc Natl Acad Sci U S A. 2008105(11):4226–31.
Corona M, Velarde RA, Remolina S, Moran-Lauter A, Wang Y, Hughes KA, et al. Vitellogenin, juvenile hormone, insulin signaling, and queen honey bee longevity. Proc Natl Acad Sci U S A. 2007104(17):7128–33.
Wang Y, Mutti NS, Ihle KE, Siegel A, Dolezal AG, Kaftanoglu O, et al. Down-regulation of honey bee IRS gene biases behavior toward food rich in protein. PLoS Genet. 20106(4):e1000896.
Mutti NS, Wang Y, Kaftanoglu O, Amdam GV. Honey bee PTEN - description, developmental knockdown, and tissue-specific expression of splice-variants correlated with alternative social phenotypes. PLoS One. 20116(7):e22195.
Wang Y, Azevedo SV, Hartfelder K, Amdam G. Insulin-like peptides (AmILP1 and AmILP2) differentially affect female caste development in the honey bee (Apis mellifera). J Exp Biol. 2013216:4347–57.
Patel A, Fondrk MK, Kaftanoglu O, Emore C, Hunt G, Frederick K, et al. The making of a queen: TOR pathway is a key player in diphenic caste development. PLoS One. 20072(6):e509.
Libbrecht R, Corona M, Wende F, Azevedo DO, Serrão JE, Keller L. Interplay between insulin signaling, juvenile hormone, and vitellogenin regulates maternal effects on polyphenism in ants. Proc Natl Acad Sci U S A. 2013110(27):11050–5.
Kirkwood TBL. Understanding the odd science of aging. Cell. 2005120(4):437–47.
Flatt T, Heyland A. Mechanisms of life history evolution - the genetics and physiology of life history traits and trade-offs. Oxford: Oxford University Press 2011.
Geiger-Thornsberry GL, Mackay TF. Quantitative trait loci affecting natural variation in Drosophila longevity. Mech Ageing Dev. 2004125(3):179–89.
Flatt T. Assessing natural variation in genes affecting Drosophila lifespan. Mech Ageing Dev. 2004125(3):155–9.
Remolina SC, Chang PL, Leips J, Nuzhdin SV, Hughes KA. Genomic basis of aging and life-history evolution in Drosophila melanogaster. Evolution. 201266(11):3390–403.
Carnes MU, Campbell T, Huang W, Butler DG, Carbone MA, Duncan LH, et al. The genomic basis of postponed senescence in Drosophila melanogaster. PLoS One. 201510(9):e0138569.
Minghui W, Qishan W, Zhen W, Qingping W, Xiangzhe Z, Yuchun P. The molecular evolutionary patterns of the insulin/FOXO signaling pathway. Evol Bioinforma. 20139:1–16.
Alvarez-Ponce D, Aguade M, Rozas J. Comparative genomics of the vertebrate insulin/TOR signal transduction pathway: a network-level analysis of selective pressures. Genome Biol Evol. 20113:87–101.
Garschall K, Flatt T. The interplay between immunity and aging in Drosophila. F1000Res. 20187:160.
Sanada F, Taniyama Y, Muratsu J, Otsu R, Shimizu H, Rakugi H, Morishita R. Source of chronic inflammation in aging. Front Cardiovasc Med. 20185:12.
Bektas A, Schurman SH, Sen R, Ferrucci L. Aging, inflammation and the environment. Exp Gerontol. 2018105:10–8.
Comfort A. The biology of senescence. London: Routledhe & Kegan Paul 1956.
Jones OR, Scheuerlein A, Salguero-Gomez R, Camarda CG, Schaible R, Casper BB, et al. Diversity of ageing across the tree of life. Nature. 2014505(7482):169–73.
Baudisch A, Salguero-Gómez R, Jones OR, Wrycza T, Mbeau-Ache C, Franco M, et al. The pace and shape of senescence in angiosperms. J Ecol. 2013101(3):596–06.
Garcia MB, Dahlgren JP, Ehrlén J. No evidence of senescence in a 300-year-old mountain herb. J Ecol. 201199(6):1424–30.
Schaible R, Scheuerlein A, Dańko MJ, Gampe J, Martínez DE, Vaupel JW. Constant mortality and fertility over age in Hydra. Proc Natl Acad Sci U S A. 2015112(51):15701–6.
Ruby JG, Smith M, Buffenstein R. Naked mole-rat mortality rates defy Gompertzian laws by not increasing with age. elife. 20187:e31157.
Lemaître J-F, Gaillard J-M. Reproductive senescence: new perspectives in the wild. Biol Rev. 201792:2182–99.
Finch CE, Austad SN. History and prospects: symposium on organisms with slow aging. Exp Gerontol. 200136(4):593–7.
Finch CE. Update on slow aging and negligible senescence - a mini-review. Gerontology. 200955(3):307–13.
Finch CE. Variations in senescence and longevity include the possibility of negligible senescence. J Gerontol A Biol Sci Med Sci. 199853(4):B235–9.
Vaupel JW, Baudisch A, Dolling M, Roach DA, Gampe J. The case for negative senescence. Theor Popul Biol. 200465(4):339–51.
Salguero-Gómez R, Shefferson RP, Hutchings MJ. Plants do not count … or do they? New perspectives on the universality of senescence. J Ecol. 2013101(3):545–54.
Peron G, Gimenez O, Charmantier A, Gaillard JM, Crochet PA. Age at the onset of senescence in birds and mammals is predicted by early-life performance. Proc R Soc Lond B. 2010277:2849–56.
Warner DA, Miller DAW, Bronikowski AM, Janzen FJ. Decades of field data reveal that turtles senesce in the wild. Proc Natl Acad Sci U S A. 2016113:6502–7.
Partridge L, Barton NH. On measuring the rate of ageing. Proc R Soc Lond B. 1996263(1375):1365–71.
Petralia RS, Mattson MP, Yao PJ. Aging and longevity in the simplest animals and the quest for immortality. Ageing Res Rev. 201416:66–82.
Bythell JC, Brown BE, Kirkwood TBL. Do reef corals age? Biol Rev. 201793(2):1192–02.
Noodén LD. Whole plant senescence. In: Thiman KV, Leopold AC, editors. Senescence and aging in plants. Cambridge: Academic Press 1988. p. 391–439.
Nelson P, Masel J. Intercellular competition and the inevitability of multicellular aging. Proc Natl Acad Sci U S A. 2017114(49):12982–7.
Bell G. Sex and death in protozoa. Cambridge: Cambridge University Press 1988.
Kirkwood TBL. Immortality of the germ-line versus disposability of the soma. In: Woodhead AD, Thompson KH, editors. Evolution of longevity in animals: a comparative approach. Boston: Springer US 1987. p. 209–18.
Jones DL. Aging and the germ line: where mortality and immortality meet. Stem Cell Rev. 20073(3):192–200.
Weissmann A. Die Kontinuität des Keimplasmas als Grundlage einer Theorie der Vererbung. Jena: Gustav Fischer 1885.
Ackermann M, Chao L, Bergstrom CT, Doebeli M. On the evolutionary origin of aging. Aging Cell. 20076(2):235–44.
Buffenstein R. Negligible senescence in the longest living rodent, the naked mole-rat: insights from a successfully aging species. J Comp Physiol B. 2008178(4):439–45.
Kim EB, Fang X, Fushan AA, Huang Z, Lobanov AV, Han L, et al. Genome sequencing reveals insights into physiology and longevity of the naked mole rat. Nature. 2011479(7372):223–7.
Valenzano DR, Aboobaker A, Seluanov A, Gorbunova V. Non-canonical aging model systems and why we need them. EMBO J. 201736(8):959–63.
Kim Y, Nam HG, Valenzano DR. The short-lived African turquoise killifish: an emerging experimental model for ageing. Dis Model Mech. 20169(2):115–29.
Valenzano Dario R, Benayoun Bérénice A, Singh Param P, Zhang E, Etter Paul D, Hu C-K, et al. The African turquoise killifish genome provides insights into evolution and genetic architecture of lifespan. Cell. 2015163(6):1539–54.
Monaghan P, Charmantier A, Nussey DH, Ricklefs RE. The evolutionary ecology of senescence. Funct Ecol. 200822(3):371–8.
Wilkinson GS, South JM. Life history, ecology and longevity in bats. Aging Cell. 20021(2):124–31.
Keller L, Genoud M. Extraordinary lifespans in ants: a test of evolutionary theories of ageing. Nature. 1997389:958–60.
Keller L, Jemielity S. Social insects as a model to study the molecular basis of ageing. Exp Gerontol. 200641:553–6.
Kuhn JMM, Korb J. Editorial overview: social insects: aging and the re-shaping of the fecundity/longevity trade-off with sociality. Curr Opin Insect Sci. 201616:vii–x.
Heinze J, Schrempf A. Aging and reproduction in social insects – a mini-review. Gerontology. 200854:160–7.
Schrempf A, Giehr J, Röhrl R, Steigleder S, Heinze J. Royal Darwinian demons: enforced changes in reproductive efforts do not affect the life expectancy of ant queens. Am Nat. 2017189(4):436–42.
von Wyschetzki K, Rueppell O, Oettler J, Heinze J. Transcriptomic signatures mirror the lack of the fecundity/longevity trade-off in ant queens. Mol Biol Evol. 201532:3173–85.
Hartmann A, Heinze J. Lay eggs, live longer: division of labor and life span in a clonal ant species. Evolution. 200357:2424–9.
Kramer BH, Schrempf A, Scheuerlein A, Heinze J. Ant colonies do not trade-off reproduction against maintenance. PLoS One. 201510:e0137969.
Blacher P, Huggins TJ, Bourke AFG. Evolution of ageing, costs of reproduction and the fecundity–longevity trade-off in eusocial insects. Proc R Soc Lond B. 2017284:20170380.
Buffenstein R, Jarvis JUM. The naked mole rat - a new record for the oldest living rodent. Sci Aging Knowl Environ. 20022002(21):pe7.
Kramer BH, van Doorn GS, Weissing FJ, Pen I. Lifespan divergence between social insect castes: challenges and opportunities for evolutionary theories of aging. Curr Opin Insect Sci. 201616:76–80.
Kramer BH, Schaible R. Life span evolution in eusocial workers - a theoretical approach to understanding the effects of extrinsic mortality in a hierarchical system. PLoS One. 20138:e61813.
Bourke AFG. Kin selection and the evolutionary theory of aging. Annu Rev Ecol Evol Syst. 200738:103–28.
Michod RE. Evolution of individuality during the transition from unicellular to multicellular life. Proc Natl Acad Sci U S A. 2007104:8613–8.
Nedelcu A, Michod RE. Molecular mechanisms of life history trade-offs and the evolution of multicellular complexity in volvocalean green algae. In: Flatt T, Heyland A, editors. Mechanisms of life history evolution – the genetics and physiology of life history traits and trade-offs. Oxford: Oxford University Press 2011. p. 271–83.
Rueffler C, Hermisson J, Wagner GP. Evolution of functional specialization and division of labor. Proc Natl Acad Sci U S A. 2012109(6):1830–1.
Partridge L. Measuring reproductive costs. Trends Ecol Evol. 19927(3):99–100.
van Noordwijk A, de Jong G. Acquisition and allocation of resources: their influence on variation in life history tactics. Am Nat. 1986128(1):137–42.
Metcalf CJE. Invisible trade-offs: Van Noordwijk and de Jong and life-history evolution. Am Nat. 2016187(4):iii–v.
Klepsatel P, Gáliková M, Maio N, Ricci S, Schlötterer C, Flatt T. Reproductive and post-reproductive life history of wild-caught Drosophila melanogaster under laboratory conditions. J Evol Biol. 201326(7):1508–20.
Charlesworth B. Optimization models, quantitative genetics, and mutation. Evolution. 199044:520–38.
Sundström J, Gulliksson G, Wirén M. Synergistic effects of blood pressure-lowering drugs and statins: systematic review and meta-analysis. BMJ Evid Based Med. 201823(2):64–9.
Blenis J. TOR, the gateway to cellular metabolism, cell growth, and disease. Cell. 2017171(1):10–3.
Mannick JB, Del Giudice G, Lattanzi M, Valiante NM, Praestgaard J, Huang B, Lonetto MA, Maecker HT, Kovarik J, Carson S, et al. mTOR inhibition improves immune function in the elderly. Sci Transl Med. 20146(268):268ra179.
Barzilai N, Crandall JP, Kritchevsky SB, Espeland MA. Metformin as a tool to target aging. Cell Metab. 201623(6):1060–5.
Johnson SC, Kaeberlein M. Rapamycin in aging and disease: maximizing efficacy while minimizing side effects. Oncotarget. 20167(29):44876–8.
Childs BG, Gluscevic M, Baker DJ, Laberge R-M, Marquess D, Dananberg J, van Deursen JM. Senescent cells: an emerging target for diseases of ageing. Nat Rev Drug Discov. 201716:718–35.
Childs BG, Durik M, Baker DJ, van Deursen JM. Cellular senescence in aging and age-related disease: from mechanisms to therapy. Nat Med. 201521:1424–35.
Rando Thomas A, Chang Howard Y. Aging, rejuvenation, and epigenetic reprogramming: resetting the aging clock. Cell. 2012148(1):46–57.
Clark RI, Walker DW. Role of gut microbiota in aging-related health decline: insights from invertebrate models. Cell Mol Life Sci. 201875(1):93–101.
Kundu P, Blacher E, Elinav E, Pettersson S. Our gut microbiome: the evolving inner self. Cell. 2017171(7):1481–93.
Schmidt TSB, Raes J, Bork P. The human gut microbiome: from association to modulation. Cell. 2018172(6):1198–215.
Smith P, Willemsen D, Popkes M, Metge F, Gandiwa E, Reichard M, Valenzano DR. Regulation of life span by the gut microbiota in the short-lived African turquoise killifish. elife. 20176:e27014.
Nielsen J, Hedeholm RB, Heinemeier J, Bushnell PG, Christiansen JS, Olsen J, Ramsey CB, Brill RW, Simon M, Steffensen KF, et al. Eye lens radiocarbon reveals centuries of longevity in the Greenland shark (Somniosus microcephalus). Science. 2016353(6300):702–4.
AnAge: The Animal Ageing and Longevity Database. Arctica islandica. http://genomics.senescence.info/species/entry.php?species=Arctica_islandica. Accessed 18 Jul 2018.
Prothero J, Jürgens KD. Scaling of lifespan in mammals. Basic Life Sci. 198742:49–74.
‘Lifespan Machine’ Probes Cause of Aging
Aging is one of the most mysterious processes in biology. We don’t know, scientifically speaking, what exactly it is. We do know for sure when it ends, but to make matters even more inscrutable, the timing of death is determined by factors that are in many cases statistically random.
Researchers in the lab of Walter Fontana, Harvard Medical School professor of systems biology, have found patterns in this randomness that provide clues into the biological basis of aging.
The research team, led by Novartis Fellow Nicholas Stroustrup, found a surprising statistical regularity in how a variety of genetic and environmental factors affect the life span of the Caenorhabditis elegans worm.
Their findings suggest that aging does not have a single discrete molecular cause but is rather a systemic process involving many components within a complex biological network. Perturb any node in the system, and you affect the whole thing.
The study, published Jan. 27 in Nature, offers an alternative to research that seeks to identify a specific master aging mechanism, such as protein homeostasis or DNA damage.
“There are many important molecular changes that occur with age, but it might not make sense to call all of them ‘causes of aging,’ per se,” said Stroustrup, first author on the paper.
Off the shelf
In order to study life span dynamics at the population level, Stroustrup constructed the Lifespan Machine, a device comprising 50 off-the-shelf flatbed scanners purchased from an office supplies store. Each scanner has been retooled to record 16 petri dishes every hour, totaling 800 dishes and 30,000 worms. The scanners capture images at 3,200 dots per inch, which is a resolution high enough to detect movements of eight micrometers, or about 12 percent of the width of an average worm.
Stroustrup subjected the worms to interventions as diverse as temperature changes, oxidative stress, changes in diet and genetic manipulations that altered, for example, insulin growth factor signaling. The Lifespan Machine recorded how long it took the worms to die under each condition. Stroustrup then aggregated the data, generated life span distribution curves for each intervention and compared results.
The life span distributions provided considerably more information than just changes in average life span. The research team measured variations arising in ostensibly identical individuals, looking at how many worms died young versus how many made it to old age under each condition. This comprehensive view was important for capturing the dynamics and randomness in the aging process.
Clear as a bell curve
In one sense, the findings were not surprising: different circumstances produced different life spans. Turning up the heat caused the worms to die quickly, and turning it up higher only increased that rate. Pictured as bell-shaped distributions, certain interventions produced a thinner, high-peaked bell, while others resulted in a more drawn-out and protracted bell.
Despite these obvious differences, the researchers found an unexpected uniformity among the curves, observing what statisticians call “temporal scaling.” Stated for the rest of us, if you were to take all of the bell-shaped curves and expand or contract them along the X-axes (which in this study represented time), they would become statistically indistinguishable. Simply compressing the protracted bell would produce a high-peaked bell, or vice versa. The two bells have, in a rigorous sense, the same shape.
The various interventions seemed to affect the duration of life in the same way across all individuals in the same population, regardless of whether chance or randomness had a short or long life in store for them. No matter which genetic process or environmental factor the researchers targeted, all molecular causes of death seemed to be affected at once and to the same extent.
“Life span is a whole-organism property,” said Fontana, “and it is profoundly difficult to study it molecularly in real time. But by discovering this kind of statistical regularity about the endpoint of aging, we have learned something about the aging process that determines that endpoint.”
Most important, said Fontana, this regularity suggests that there is profound interdependence in the physiology of an organism, and changes in one physiological aspect affect all others to determine life span.
The researchers believe that their discovery will influence how scientists study, and even define, aging—for people as well as worms. The researchers now plan to study in more detail how broad statistical regularities can emerge from the action of diverse molecular mechanisms, seeking to determine exactly how alteration of one mechanism can affect all others.
This research was funded by the National Institutes of Health and the Glenn Foundation for Medical Research.
One theory of aging assumes that the life span of a cell or organism is genetically determined—that the genes of an animal contain a “program” that determines its life span, just as eye colour is determined genetically. This theory finds support in the fact that people with parents who have lived long lives are likely to live long themselves. Also, identical twins have life spans more similar in length than do non-twin siblings.
The genetic theory of aging centres on telomeres, which are repeated segments of DNA (deoxyribonucleic acid) occurring at the ends of chromosomes. The number of repeats in a telomere determines the maximum life span of a cell, since each time a cell divides, multiple repeats are lost. Once telomeres have been reduced to a certain size, the cell reaches a crisis point and is prevented from dividing further. As a consequence, the cell dies.
Research has shown that telomeres are vulnerable to genetic factors that alter an organism’s rate of aging. In humans, variations in a gene known as TERC (telomerase RNA [ribonucleic acid] component), which encodes an RNA segment of an enzyme known as telomerase, have been associated with reduced telomere length and an increased rate of biological aging. Telomerase normally functions to prevent the overshortening of telomeres, but in the presence of TERC mutations the enzyme’s activity is altered. TERC also appears to influence the telomere length that individuals possess from the time of birth. Persons who carry TERC variations are believed to be several years older biologically compared with noncarriers of the same chronological age. This accelerated rate of biological aging is likely also influenced by exposure to environmental factors, such as smoking and obesity, which increase a carrier’s susceptibility to the onset of age-related diseases relatively early in adult life.
Mutations of genes that affect telomere length lend support to another genetic theory of aging, which assumes that cell death is the result of “errors” introduced in the formation of key proteins, such as enzymes. Slight differences induced in the transmission of information from DNA molecules of the chromosomes through RNA molecules (the “messenger” substance) to the proper assembly of the large and complex enzyme molecules could result in a molecule of the enzyme that would not “work” properly. This is precisely what happens in the instance of mutations in the TERC gene. Such mutations disrupt the normal function of the telomerase enzyme.
As cells grow and divide, a small proportion of them undergo mutation. This change in the genetic code is then reproduced when the cells again divide. The “somatic mutation” theory of aging assumes that aging is due to the gradual accumulation of mutated cells that do not perform normally.