Aging in Single-celled Organisms: from Bacteria to the Whole Tree of Life
Category: Application Area Application Area: Cellular Aging
Date/Time: February 10, 2020 - February 12, 2020
Jacopo Grilli (ICTP)
Srividya Iyer-Biswas (Purdue Univ.)
Chris Kempes (SFI)
Matteo Osella (Univ. Turin)
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This working group will bring together experimentalists and theoreticians to better understand the processes and implications of how single-celled organisms age. General principles will be drawn from high-throughput growth and division data of aging single-cells. Energetics by which they store and process information will be connected with those in larger, multicellular organisms. Some key questions this working group will consider include how the aging clock in a single-celled organism is coupled to that of a more complex organism in which it resides (and vise versa), how aging changes with growth and division, how cells with different age process information differently from the environment, and how aging differ across the tree of life.
Lin Chao (UC San Diego) - Stochasticity, immortality, and mortality in E. coli
Here we show that the bacterium Escherichia coli exhibits both lineage mortality and immortality. The outcome depends on a whether a balance is achieved between damage accumulation and the asymmetric allocation of damage from mother to daughters. At low damage rates, both old and new daughters, which are allocated respectively more and less damage, generated immortal lineages that achieved stable growth rate equilibria. At high rates, mortality ensued because while the new daughter lineage persisted, the old daughter lineage stopped dividing. The stoppage was found to result from an increase in the stochasticity of cell growth.
Sabrina Spencer (CU Boulder) - Toward a Molecular Understanding of Quiescence versus Senescence
Cellular aging is often used synonymously with cellular senescence, a state of permanent cell-cycle exit associated with DNA damage and cytokine secretion. However, senescence is easily confused with quiescence, in large part due to lack of reliable markers. We have found that the gold-standard senescence marker, senescence-associated beta-galactosidase activity, is unreliable in that it can stain strongly positive in cells that are actively dividing. We have also found that establishing a homogeneous population of senescent cells is quite difficult since many cells continue to cycle and out-proliferate senescent cells, despite the use of standard senescence-inducing treatments. Thus, the senescence field has a chicken/egg problem in that one cannot study senescence if no reliable markers exist to identify senescent cells, and one cannot develop a senescence marker without a truly senescent sample in hand. We are therefore developing a functional readout to identify cells that have not cycled in n days, where n is triggered and defined by the researcher and can be several months long. In this way, we can isolate a homogeneous senescent population that can be profiled and compared to quiescent cells to develop better markers for quiescence vs. senescence and to better study cellular aging.
Jacopo Grilli (ICTP) Link to the source page
Matteo Osella. Interesting idea of connecting laws of physiology (Hwa) with aging/senescence. Not trivial how to do that for single cells.
Lin Chao. Aging and asymmetry in E. coli. Advantage of asymmetry is portfolio diversification. Somewhat optimal level of asymmetry emerges.
Uli Steiner . Fitness as combination of fecundity and mortality. Death in the mother machine (surprisingly high): mother (early daugther) has an increased mortality rate with age, while her latest daughter has an approximately constant mortality rate. Idea: late daughter inherits the damage, while the mother was starting with minimal damage. No correlation between mother and late daughter lifespan.
Sri Iyer-Biswas. Cool data on C crescentus and collapses. Interesting observation of memory of past conditions lasting for long time.
Owen Jones. Senescence across the tree of life. Measure shape and pace (timescale)
What is aging? Requires asymmetry in division and the ability to label individual with a "time stamp". In E. coli age of the pole, in mycobacteria cell wall. Senescence is the loss of function associated to aging. The question then is what is function. We have a bias for growth rate.
It is very unclear to my whether asymmetry is adaptive or not. It is also unclear how to prove it.
The other axis is memory. Memory (information) about the environment. Unclear how that is related with aging.
Matteo Osella (Univ. Turin) Link to the source page
- General disagreement on the definitions of aging and senescence. It does not seem a purely semantic problem, but reveals the lack of universally accepted essential conditions to define the aging process.
Example: Role of asymmetry (necessary condition or not) in the cellular division process for the emergence of aging.
- Discussion on the more appropriate measure of time to observe aging of living things.... The intrinsic timescale of different organisms (for example doubling time in fast-growing conditions) and the timescale fixed by the environment (e.g. feast and famine cycles) should be taken into account.
[interesting parallel with the approximately constant lifespan of mammals if measured in heart beats shown by G.West]
- In several different systems the hazard rate (for cell division or for death) shows an exponential increase followed by a plateu (e.g. in mortality rate in E.coli shown by Uli and from Lindner group) . Different theoretical models can reproduce such a trend. Is there a way to do model selection?
- Need for measurements of metabolic rate in bacteria in order to build a complete phenomenological theory of resource partitioning and its relations with growth and mass. An extension of existing theories built on data based on balanced exponential growth probably need to include survival and the cost of maintenance...
Srividya Iyer-Biswas (Purdue Univ.) Link to the source page
This was an exciting meeting with a lot of animated discussions leading to interesting confluences and diverges of opinions.
Basic questions that came up again and again: What is aging? Given how contextual it is, what is a good operational definition? How to design and refine specific experiments that get to the interplay between asymmetry generation, aging, memory formation, rejuvenation, information processing, statistical learning, energetics, individual specific histories, intrinsic stochasticity in organismal growth, resilience to different perturbations, homeostasis, adaptation, evolvability, plasticity and tradeoffs therein.
I especially enjoyed discussions about new technologies and tools that can be co-opted and added as modules to ongoing experiments to get at some of these exciting questions.
Martin Picard (Columbia University) Link to the source page
Experimental models to study cellular aging vary dramatically.
We clearly need a consensus definition of aging, or more specific concepts. Is aging the loss of specific functions, the loss in the ability to divide, "senescence" (which itself does not have a consensus definition), the movement towards mortality, or the accumulation of "information" over time? Can there be a single definition of aging across the tree of life - from single cells to complex multicellular organisms like mammals?
If the definition is a functional one - aging is the loss in the ability to perform X function, then aging needs to be contextualized. Organisms at different scales (prokaryotes vs birds vs humans) have dramatically different "purpose" in the living world, and they carry out very different functions. Is there one type of aging that unites them all? Or qualitatively different forms of aging, or aging processes?
An interdependent challenge with the previous one is the issue of measurement. What are good measures of aging - again it depends on how it is defined.
If aging is defined as something that tells us how close to death an organism is to end of life (i.e,, mortality) or to loss of function, then it implies that aging biomarkers need to be developed prospectively. In other words, the aging marker need to predict some future behavior. One example is the DNA methylation or epigenetic clocks.
Uli Steiner (University of Southern Denmark) Link to the source page
The discrepancies what aging entails seems to be related to the difference of fields and questions tackled. To me as an evolutionary biologist aging is simply the process of senescence, where senescence is the deterioration of function, or more precisely the change of function with age. This change does not need to be a directional decline. Function should be somewhat related to fitness, which explains that survival and reproduction are first targets to quantify aging, though all functional traits could be and should be considered for understanding senescence. However if fitness is the parameter that integrates the processes, it is evident that a cell within a multi-cellular organism has a different definition of fitness than a whole organism in itself, be it unicellular or multi-cellular.
The generalities as described by Chris are highly interesting and inspirational. I gained much inspiration on how senescence is unified across cells of different level of biological organization, but where, how, and why these universal patterns fall apart is something I would love to deepen discussing.
The differences in heterogeneity and homeostasis among cells that has been shown by Sri, where I was really puzzled how similar cells are and how such similarity could be maintained, and Bree's system where the heterogeneity is large, though spatially still well structured.
What are the most prominent markers that we should focus on? How can we measure these?
Sabrina Spencer (CU Boulder) Link to the source page
What is holding back the field of aging? First is a definition of aging, which we have generally agreed represents deterioration of function. Second then, is how to measure deterioration of function. We lack good markers for most relevant items at the single-cell level: 1. Cumulative DNA damage, 2. Telomere length, 3. Oxygen consumption rate, 4. Presence of original or newly replicated DNA strand. 5. epigenetic clock measured by DNA methylation.
Of course, there are single-cell DNA damage markers, but as far as I know, they only mark current existing mismatches, damaged bases, single-strand or double-strand breaks etc, but not cumulative damage. The need for a cumulative/historical marker speaks to the idea Sri proposed that aging requires a cellular memory - something needs to be accumulated as cells age, otherwise cells would not know or have an age. While a snapshot of telomere length does contain a historical record, telomere length and oxygen consumption rate are easily measured on a population level, but not in single cells. The single-cell aspect is important because while telomere length decreases with increasing passage number, Martin Picard showed that telomere length does not correlate with when a population hits its Hayflick limit. This may well be because what cells care about is the shortest telomere in a cell, not the population telomere length. Tracking the immortal strand is technically difficult. For the epigenetic clock measured by DNA methylation, the conceptual link with deterioration of function is unclear.
Then there's the ability to return to homeostasis as a general definition of aging, but how to measure that is unclear.
The one thing that can currently be measured in single cells both in a snapshot and over time is protein aggregation. Lin Chao measures this with IBPA-GFP. Maybe measuring protein aggregates is our best bet since that is measurable and since it has been shown to cause dysfunction, particularly in neurons.
- Aging is the cost organisms pay to have offspring that are free of damage.
- Aging is a spandrel
- Aging is a consequence of the chemical reactions, energy flux, 2nd law, etc. needed for life.
- Asymmetry is inevitable.
- The parent cell will be defined here as the cell that accepts the majority of the damage.
- Which damaged components matter the most is unclear (e.g. old pole protein, mitochondria, immortal strand)
- Therefore, there will be no universal marker for aging (more granular: damage) and what we need instead is various functional definitions that are context dependent (when can we project this onto fitness of vice versa)?
- The major issue is that we don’t have the right data or know what to measure? ie We don’t know the currency of aging.
Lin Chao (UC San Diego) Link to the source page
A. What is aging? Classical definition from Medawar aging is passage of time and aging is the deterioration of function with time. Aging and senescence is nowadays used to mean equivalently the deterioration with time. We will used aging and senescence in this more contemporary context, and refer to the passage of time specifically as chronological aging. However, because in some organisms the deterioration can be reversed, we will describe those instances as reversed or positive senescence or aging (discuss?).
B. Following Medawar, we also can distinguish aging that results from wear and tear from interactions with the environment much as a automobile parked by the ocean will rust and fragment. However, because the hallmark that distinguishes physical objects such as a car and a biological organism is the latter's ability to change through evolution by natural selection, aging can be accelerate in living systems beyond physical wear and tear. The acceleration results from the production of asymmetrical daughters by dividing mother cells. While the asymmetry can result from a combination of factors, some beneficial and others deleterious, a possible cause may be damaged cellular molecules and organelles. The daughter that receives more damage ages and the other rejuvenates. The aging daughter can be viewed as the continuation of the mother, the daughter receiving less can be regarded as the new juvenile offspring. This concept can be extended to metazoans and the asymmetry
Bree Aldridge (Tufts Univ.) Link to the source page
I was stuck by the constant "laws" across species that are scale free. I am interested in how this may extend to single-cells in a populations and whether the scaling laws can be expanded from multicellular to intracellular organisms.
Aging (and the fitness objective) needs to be continuously defined and multiple forms of aging coexist in the same population.
Asymmetry is a mechanism to encode aging. Is the functional consequences of aging an evolutionary driver of asymmetry?
How do we experimentally measure aging and the effect of aging in single-cells? We are missing key experimental approaches.
Owen Jones (University of Southern Denmark) Link to the source page
This has been an inspiring meeting. Fantastic to hear diverse ways of studying and thinking about aging/senescence, and how we can measure and follow fates of cells in lab context.
Nomenclature differences among fields can cause some confusion. e.g. aging vs. senescence. In my subfield - senescence can be be positive (deterioration with age), negligible (stasis with age) or negative (improvement with age). The term "aging" is therefore just the passage of time. (Based on work of Medawar, and more recently Vaupel and colleagues).
One major goal should be to be able to generalise understanding of aging/senescence across organisms as diverse as E. coli, TB, mice, humans, plants and even colonial organisms. We will undoubtedly need to scale data for different taxa (or different environmental conditions within species) to make them comparable. In this context I was especially inspired by Chris Kempes' work and Geoffery West's talk showing striking invariants across really diverse species. How will these scaling rules be across groups e.g. single celled - metazoans - colonial.
Other questions that emerged.
- Is resilience to perturbation (homeostasis) a useful marker of senescence?
- Asymmetry in cell division - isn't asymmetry inevitable?
- Is complexity of the organism (e.g. differentiated tissues/celltypes) important to aging trajectory? e.g. does it make homeostasis harder?
- Is senescence a spandrel? Can it be adaptive?
Reference Materials by Presenting Attendees
Matteo Osella (Univ. Turin)
|Title||Author name||Source name||Year||Citation count From Scopus. Refreshed every 5 days.||Page views||Related file|
|Aging, mortality, and the fast growth trade-off of Schizosaccharomyces pombe||Hidenori Nakaoka, Yuichi Wakamoto||PLoS Biology||2017||0||0|
Uli Steiner (University of Southern Denmark)
Here are some references that explain how mortality plateaus arise.
Steinsaltz and Evans shows how mortality plateaus arise through convergence to a quasi-stationary distribution.
Steinsaltz, D., and S. N. Evans. 2004. Markov mortality models: implications of quasistationarity and varying initial distributions. Theor. Popul. Biol. 65:319–337.
Weitz and Fraser illustrate how such mortality plateaus arise from damage accumulation and purging of damage at the population level through a random walk with drift model.
Weitz, J., and H. Fraser. 2001. Explaining mortality rate plateaus. Proc. Natl. Acad. Sci. USA 98:15383–15386.
Mathematical similarities among Gamma Gompertz models and damage accumulation models (LeBras type models). We used this mathematical similarity in our Evolution paper for parameter estimation of the model.
Yashin, A. I., J. W. Vaupel, and I. A. Iachine. 1994. A duality in aging: the equivalence of mortality models based on radically different concepts. Mech. Ageing Dev. 74:1–14.
The Evolution paper that has most of the data that I presented including data on growth, division rates, cell elongation, size at division etc.
Ulrich K Steiner, Adam Lenart, Ming Ni, Peipei Chen, Xiaohu Song, François Taddei, James W Vaupel, Ariel B Lindner. 2019.Two stochastic processes shape diverse senescence patterns in a single‐cell organism'"`UNIQ--nowiki-00000017-QINU`"'https://doi.org/10.1111/evo.13708
Here an asymmetric division model that has been inspired by the early e. coli aging work:
Evans, S. N., and D. Steinsaltz. 2007. Damage segregation at fissioning may increase growth rates: a superprocessmodel. Theor. Popul.Biol. 71:473–490
Quantifying mutation in singe e. coli cells by the mismatch repair system.
Lydia Robert, Jean Ollion, Jerome Robert, Xiaohu Song, Ivan Matic, Marina Elez. Mutation dynamics and fitness effects followed in single cells.
Vol. 359, Issue 6381, pp. 1283-1286
Sabrina Spencer (CU Boulder)
Baker DJ, Childs BG, Durik M, Wijers ME, Sieben CJ, Zhong J, Saltness RA, Jeganathan KB, Verzosa GC, Pezeshki A, Khazaie K, Miller JD, van Deursen JM. Naturally occurring p16(Ink4a)-positive cells shorten healthy lifespan. Nature. 2016 Feb 11; 530 (7589):184-9 Epub 2016 Feb 03
Owen Jones (University of Southern Denmark)
Here is the database on plant population dynamics I mentioned in my talk: www.compadre-db.org  The database is called the "COMPADRE Plant Matrix Model database".
There is a sister database called "COMADRE" for animals (same web address).
|Title||Author name||Source name||Year||Citation count From Scopus. Refreshed every 5 days.||Page views||Related file|
|Diversity of ageing across the tree of life||Owen R. Jones, Alexander Scheuerlein, Roberto Salguero-Gómez, Carlo Giovanni Camarda, Ralf Schaible, Brenda B. Casper, Johan P. Dahlgren, Johan Ehrlén, María B. García, Eric S. Menges, Pedro F. Quintana-Ascencio, Hal Caswell, Annette Baudisch, James W. Vaupel||Nature||2014||0||0|