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COMPLEX TIME: Adaptation, Aging, & Arrow of Time

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Hallmarks of Biological Failure/JamesDeGregori

From Complex Time

Notes by user James DeGregori (CU Denver) for Hallmarks of Biological Failure

Post-meeting Reflection

1+ paragraphs on any combination of the following:

  • Presentation highlights
  • Open questions that came up
  • How your perspective changed
  • Impact on your own work
  • e.g. the discussion on [A] that we are having reminds me of [B] conference/[C] initiative/[D] funding call-for-proposal/[E] research group

Overall thoughts: A system can take a certain # of hits. A lot of failures are linear with age on a log scale. Why?  Resiliency? Due to redundancy? What explains variance in a population? Natural selection has acted to limit physiological decline to the point that maximizes reproductive success (balanced against costs). So risk of physiological failure are not zero during reproductive years, but are minimized, certainly relative to competing risks for most of our (or other animal's) evolutionary history.

Bernie Crespi presented on how selection works stronger on the weaker components. Slow, invisible weaknesses increase over time. Can engender exponentiality of decay. Or is it that damage and mutations accumulate over time? Risk of failure is related to the degree of complexity in a system, and tightness of coupling between components. For example, power grids are tightly coupled but not hugely complex.  Aircraft are both coupled and complex. Mental disorders represent alternative attractors in a highly coupled system. For immune system, autoimmunity, chronic inflammation, cytokine storms represent alternative attractors potentiated by tight coupling. Both can have diseases associated with over-defense.  Both do not trade off well with other systems. Immune system is under greater positive selection, relative to brain system which is more stabilizing. Thoughts: I would ask how the failure rate of systems, complex or not, is impacted by environmental disconnects. For example, failures in the immune system (like autoimmunity) may be common today, but were not so common in ancestral times (re. hygiene hypothesis).

Dario Valenzano described how relaxed selection shapes the rate of aging across species. Killifish get tons of cancer late in life; almost all die with cancer. Not clear if they die of cancer, just with cancer. Some species live in areas where water is available for a few months of the year. Have drought resistant embryos. Can desiccate embryos. Hatch when rains. 5-6 rounds of spawning. Wetter areas – longer lives. Did a lot of genetic mapping. QTL of longevity. Can do crosses and single alleles can have big effects. Show heart, skeletal, and many other aging phenotypes. See evolution of life spans multiple times across killifish species. So can filter out phylogenetic relatedness. Short lived (annual) species have a larger genome mostly due to expansion of transposable elements and introns. A lot of LINE expansion. Probably due to reduced purifying selection. Much more of coding sequence is under purifying selection in non-annuals. Populations evolving in dry environments have smaller Ne - even within the same species found in dry and wet environments. See expansion of mitochondrial genomes in annuals too, with a higher mutation rate (again consistent with weakened selection). In fact, the mitochondrial polymerase is more error prone. The question that I would raise is WHY is selection weakened? Clearly, a smaller Ne would do this, but why is there a smaller Ne? Is there a bottleneck with each season where only a small fraction of dried embryos survive and reproduce again?

Shripad Tuljapurkar – As we get older, get more response to similar challenges. My thoughts - but do we? Or do we simply return to homeostasis more poorly? In fact, sometimes responses are exaggerated and prolonged (such as with inflammation) in old animals. Escape from “well” for a phenotype is dependent on depth of well, size of fluctuations, and multidimensional space. Old age involves shallowing of healthy well and deepening of sick well. Thoughts: the well may also be broader, with a greater diversity of phenotypes (cellular) for the same cell type. Relaxed purifying selection, as well as epigenetic drift, could contribute to this.

Sabrina Spencer discussed how replication stress and DNA damages induces p21 in mother cell (somatic cells in culture), leading to increased p21-high offspring which stay quiescent longer.  And these cells are stress resistant (survive better D dam). See much less pausing at early passages of foreskin fibroblasts relative to late.  Thoughts: It will be interesting to see how these dynamics contribute to tissue aging. Also, in a competitive tissue do these same cells with DNA damage get purged? There is evidence from flies and mice that that damaged cells can be pushed out by healthy neighbors. From discussions during hike, it would be great to have a method to identify cells with critically short telomeres in a population (such as by marking gH2AX colocalized with a telomere binding protein - but not feasible for living cell...).

Summary of Day 1:

Themes: 1) Basins of attraction, 2) Failure (gradual, rapid, can we predict?), 3) Underperform or misperform, 4) Tradeoffs and pleiotropies, 5) Networks, not discrete compartments. 6) complexity vs simplicity; complexity vs complicatedness, 7) Don’t make a part that consistently outlives the whole.

Day 2:

Morgan Levine – DNA methylation landscapes in aging and disease. Changes on the molecular level propagate up through networks, increasing risk of disease. Every level above can tolerate some level of failure below. Biological aging proceeds, phenotypic aging, which proceeds functional aging. Biological age can be different from chronological. Use epigenetic clocks – methylated CpGs. Measured at 353 CpGs throughout genome. R=0.98 for chronological age. Do correction for <12 year olds to account for rapid change in early life. Developed “phenotypic age” predictor of aging related mortality based on clinical measures, which can predict disease.  Had almost 10,000 individuals aged 20 and up, with up to 23 years of mortality follow up. Combine 9 clinical markers (albumin, CRP, MCV, etc).  Incredibly predictive; 1 year increase in biological age coincides with a ~10% increase in risk of CVD, cancer, diabetes and lung disease. Comparing top 10% to bottom 10% of a group shows ~2x difference in HR. Most people will fall within 5 years of their chronological age. And difference remains stable. For all cause mortality, get about a 1.05 HR (per year of change in biological age, I think). Correlations with DNA methylation clock too. Horvath clock corrects for differences between tissues (there are differences). Developed transcriptomic signatures, correlated with DNAm clock. Cellular respiration, lipid oxidation, and nucleotide metabolism correlate well with biological clock. Thoughts: how do changes in DNA methylation correlate or not with changes in DNA mutation accumulation? Some data from Sanger indicate that the increases in mutations from birth to death are linear, but epigenetic changes show a sharp increase during growth/development periods. Why? How can we take these results and make them useful for the individual? It's not enough to just tell someone they're going to die earlier than their chronological age suggests, but what could be done about it?

David Schneider – measuring resilience of hosts to disease.  Can measure tolerance to an infection. Can we predict trajectories for individuals? Not yet, but can for populations. Looking at tolerance across 150 mouse strains from crossing 8 different inbred strains. If plot NK cells by RBC, get loop. Can figure out where someone is on a “disease clock”. Can see wave of parameters, as one rises while the previous falls. IFNg is first, gamma delta T-cells last.  Kynurenine comes on when animals are sick. Orotate is late. Can look at curves for different mice strains (some die, some survive); 29 metabolites correlate with resilience (can predict who will die and who won’t).  If arginine bottoms out, the mouse will die. And arginine really is critical for surviving infection. Kynurenine pathways is also critical (as it is for aging). Parasite load correlates poorly.  If treat with anti-parasitics before d6, reverse on curve.  But not if after. Also showed that glucose can prevent deaths from infections (provides energy). Our mouse houses are at 20 C, while prefer around 30 C.  Get hypothermic upon infection instead of fever. Check out his blog:

Thoughts: how do we alter responses so that recovery is favored over failure (death)? Can we manipulate metabolites? His and perhaps other labs seem to be doing this.

Marten Scheffer – ecology of resilience. How quickly is the return to normal after perturbation? He studies lakes. Sometimes they don’t recover. Due to severity of insult or the brittleness of system. Catastrophe theory on tipping points. How to know a stability landscape? For example, a tropical rainforest can be become less stable if there is less rain. If plot tree cover with rain across Earth, not a simple correlation, but discrete clumps – attractor states (dips in landscapes). See for bacteria in human gut with age.  Basins of attraction are shallower in old age – smaller disturbances can push out to new state. Greater resilience can reduce cross talk between systems (negative cross effects). Can find early predictors of climate change, depression (mood), etc. Do we need to make perturbations in humans to predict future health? Thoughts: how do manipulate resilience? Can we increase it or at least prolong it later in life?

Wrap up: Develop frameworks for understanding biological failure.  Can we derive concepts that inform models? Bernie – can connections be made between larger theories (Antagonistic Pleiotropy and Mutation Accumulation) with molecular changes like in epigenetics. What are patterns of change? Why do they change? Feedbacks maintain homeostasis and restore it post injury.

Hmmmm. Why is so much of what bad happens to us as we age exponential?

Our charge: Frame what we agree on, what are the big questions, and what we could do to get answers (with unlimited resources) and what can we not answer?

1)     Try to come up with fundamental and important outstanding questions in term so biological failure?

2)     What are the kinds of connections that we can make between conceptual approaches that will allow us to work on these? 

Reference material notes

Some examples:

  • Here is [A] database on [B] that I pull data from to do [C] analysis that might be of interest to this group (insert link).
  • Here is a free tool for calculating [ABC] (insert link)
  • This painting/sculpture/forms of artwork is emblematic to our discussion on [X]!
  • Schwartz et al. 2017 offers a review on [ABC] migration as relate to climatic factors (add the reference as well).

Reference Materials