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A theme that I see across the talks and in the discussion is the issue of complexity and how integrity of complex systems is lost with age and how it might be retained to impinge on health and resilience.
The idea of the adaptive landscape is very useful as is the idea of tipping point - i particularly like the idea of aging as a series of transitions where the path taken dictates the possibilities open for the future
Hochberg: Concepts that caught my attention, as a function of age is loss of resilience equally felt through the lifespan ie young v old? Also Diverse/idiosynchratic networks – how should models be informed. The idea of hierarchies of regulatory or adaptive nodes is interesting but I wonder do we know that there are grades of nodes in the first place, if there are how do we find them?
Crespi : Biological Risk Matrix… coupling versus complexity. I had difficulty with this idea because the systems were assigned importance but it wasn't clear to me what the basis for those assignations was. I have viewed the organ systems as different but inseparable pieces of the organism as a whole. I do like the idea of viewing the aging of specific processes in terms of trade-offs - I wonder about the inbuilt redundancy of systems and think we could consider the possibility that age-induced adaptions might just as well be beneficial - ie tailored to the prevailing internal environment or the current disposition of regulatory nodes.
Valenzano: The fact that ecology predicts genome size was super interesting and that the expansion is explained by transposons! I also loved the idea that the long-lived species had more emphasis on positive/purifying selection and the short-lived species had more evidence of the relaxed selection. These are an amazingly useful species for the interactions of genetics, environment - the exposome!
Di Gregorio: Among cancers age dependence in risk is shared despite differences in etiology and mechanisms of tumorigenesis and differences in the stem cell pools that these cancers arise from. All map to a common cancer curve – incidence as a function of age all lie right on top of each other. This nicely captures the idea that aging creates a ubiquitous risk increase for cancer incidence. Metastasis – moving from the environment where the tumorigenesis initiated – giant hurdle for success but likely to be huge number of cells that slough off. Idea that youth is associated with “Healthy Neighbors” proximal to the initiating cancer cells. Aging is not just accumulation of mutation: idea that the tissue changes create the promiscuous setting. Essentially: the behavior of a single mutation is not equivalent in young and old environments
Tuljapurkar: Response to challenge changes with age – makes the case that the amplitude of the response in young is muted and un-muted and over-amplified in aged, although I would think of it as a disconnection in the response whether that be under or over reactionary. I was very taken with the idea that response to a challenge could push you out of the equilibrium space and into a different state altogether & that you would need to consider the following: Depth of well; curvature of the well; size of the fluctuations.
Spencer: very interesting model for thinking about non-genetic sources of heterogeneity looking at individual cells through the lens of Proliferating v quiescence as a cell state. Spontaneous heterogeneity in asynchronously cycling cells used to identify key nodes in dictating the pace of cell cycle - really nice cell biology and time lapse imaging to uncover CDK2, p21, and stalled forks in the mechanisms. Interesting observation that Mothers pass damage on to the daughters so that the intent to enter quiescence already established in G2 of the mother. Genetic approaches to manipulate CDK2/p21 show that the slow cycling cells have higher stress tolerance – If you force CDK2 in the pausers have a fitness deficit - I wonder though if this is just because cells not ready for division were forced into it creating a vulnerability to stress rather than exposing a beneficial role for the pause.
Natterson-Horowitz: Using examples from different species the relative balance of sympathetic/parasympahtetic v vagal response to stress was explored. High-adrenergic events: eg sudden cardiac, death cardiomyopathy refelctive of a dysregulation of autonomic balanceTonic immobility in response to attach – seen in many different species. Alarm bradycardia is primarily a juvenile response. Not called syncope (because not people) but it sure looks like it. As the animals transition to adulthood they swap over to the sympathetic/parasympathetic response. I do wonder how this is coordinated and communicated with maturation - could there be a signal to indicate that a critical physical threshold had been reached for example, like myokines even? I like the idea that with age there may be a disconnect where there is a loss of the ability to toggle between sympathetic/parasympathetic and vagal responses☂Open questions are whether early experiences inform future balance in terms of response to trauma, Can we build predictive models?
OPEN DISCUSSION: Identified 3 themes: 1. Vulnerability with age, 2. Variance among individuals, 3. Complexity
Some of the ideas that caught my ear include the following: a) The theme of return to homeostasis; b)Changing landscapes with age – and changing landscapes with exposure, the Idea of tipping points applies here too, are there sets of perturbations that would allow you to track their responses and use that to predict the transitions among wells. Expectation that the well tracks with fitness may not be realistic – items roam around the ridges, c) idea that using “Layers” might be a useful way to parse the different components, can we use layers to frame the aging landscape – feedback loops among layers. This concept is likely important for understanding resilience, d) I really liked the analogy to collapse of ancient civilizations: ebbs and flows – tendency toward senescence – more complex, more overheads, more fragility. Is there a way to articulate loss of integrity or resilience and how it might be conceptually related to ecosystems or larger scale entities? so you might think of system failure as an emergent property, e) Nailing down the semantics – framing the language by context would be helpful for developing strategies to move these ideas forward.
Levine: Changes on the molecular level propagate up through the system. I really liked the concept that some degree of failure is tolerated up to a point. Important goal to provide an endophenotype that allows evaluation of intervention efficacy, provide insight into aging - the strategy is to work with risk identification – biomarkers of vulnerability. The methylation "clock" has been developed independently by several groups. Want it to predict more than chronological age so the goal now is to capture the true residual. There is some fascinating biology during development: under age 12y get a boost in methylation that has steeper trajectory from about 15y on the slope is consistent up to 60y. Interestingly, clocks with highest association to chronological age do not have the best predictor ability for disease when age is used as a covariate ☂. Looking now for gene networks – weighted gene correlation networks and then establish associations among networks and the various clocks – amazingly mitochondrial function OxPhos is a primary network ☂. Great next steps will be to take any given module and ask how will they are conserved across the clock – see congruent and discordant depending on what is being looked at for a given context- or in other words, which piece of the clock is driving the associations at different age group and diseases – building “disease-ome” maps.
Schneider: In traditional strategies to determine the resilience of hosts you infect and look at the dynamics of load and symptoms make a regression – generate a tolerance curve. Works at the population level but not at the individual level. To get around this employ a strategy where you generate phase plots – hysteretic curve- this nicely demonstrates that at each point in the infection you ae in a different space- curved around arrow. There was a wonderful theme centered on how to best visualize complex challenge and recovery data - so creative! One idea was to explore Resiliance/Tolerance/Robustness versus Frailty. Taking this a step further you can add new dimensions – generating a disease manifold. Superimposing genetic diversity on this framework revealed the beuatiful underlying biology: Cytokines, metabolome: orotate, parasitic density, granulocytes, reticulocytes. See basically the same changes among the different mouse strains but occurring at temporally distinct phases but always tracking with the course of infection and recovery. In a higher dimension see a wave move through the system – like ripples on a pond. As the shape of the wave changes that indicates where you are in disease course, if the order is perturbed then that might indicate morbidity. This is perfectly set up for exploring aging and comorbidity in the loss of implementation of the infection response and recovery. Some very interesting metabolism is at the heart of this response. Parasite load doesn’t correlate well with outcomes across lines but metabolic status going into the infection does.
Scheffer: Resilience: capacity to recover from challenge, or how quickly homeostasis is recovered after perturbation. Idea of brittleness. System state v conditions (and their interactions)– tipping points where you have two basins in unstable equilibrium – loose resilience when basin of attraction becomes small ☂. An interesting example is abundance of particular bacterial group in the gut, the landscape changes with age and there are apparently tipping elements in the human intestinal ecosystem. As you age more likely to have a particular population distribution. A really fascinating idea is that of Universal rules that govern critical points – true for all dynamic systems where critical slowing down occurs at mathematical bifurcations. The idea is that there may be generic early warning signals for tipping points that could potentially be used for determining indicators of resilience.Using this concept, you might investigate natural fluctuations – the uniformity and small scale of resilient systems becomes increased in variance in the less resilient and so the fluctuation change serves as a dynamic indicator of resilience. The emphasis is on patterns of micro-recovery that inform of the dynamics of the system rather than on the state of the system. If the system as a whole reduces you then get more cross-correlations between subsystems. Failure in one system is communicated to other connected systems. This is a really clever idea and a terrific match for understanding the slippage of system integrity with age.