Difference between revisions of "Hallmarks of Biological Failure/SabrinaSpencer"
|(One intermediate revision by the same user not shown)|
|Line 1:||Line 1:|
|Post-meeting summary=<u>'''Day 1.'''</u>
|Post-meeting summary=<u>'''Day 1.'''</u>
Latest revision as of 03:10, April 10, 2019
Notes by user Sabrina Spencer (CU Boulder) for Hallmarks of Biological Failure
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
Accidents (failure) are inevitable (normal). Challenger disaster, Max 8 jet. Risk of failure depends on complexity of the system. Tight coupling vs modular. Tight coupling – butterfly flaps wings causes a storm elsewhere.
Plot of tightness of coupling vs complexity. Brain and immune system go in top right corner. Is biological failure mediated predominantly by the brain and immune system?
Can make this plot at top right corner at the cellular level too.
SLS: how to test resilience of cells? Can we come up with a set of perturbations and appropriate responses? A stress test for humans before a surgery, or for populations of single cells?
Disease and senescence anti correlate with intelligence. Correlation between intelligence and lifespan is mostly genetic. A result of having more ‘good’ genes. Peter Visscher lab 2016.
Mental disorders represent alternative attractors due to tight coupling. Schizo, depression, autism.
Neurons have to be well-defended bc they are long lived and aren’t replaced.
Neuronal stress: age, apoe4, infection, energetics, insulin resistance, sleep deprivation.
Bodily senescence is mediated by over-defense and inflammation.
Failure due to bad tradeoffs.
Is senescence due mainly to defense against death? The defense ends up killing you.
The immune system is what is keeping people alive in the face of infectious disease.
Loss of coordination among organ systems causes health risk. They are networks. brain is made up of many different kinds of cells including immune cells.
Relaxed selection shapes the rate of aging across species. How can we reverse time and intervene in aging?
Killifish live 4 months.
Some strains live 15 weeks, others 30 weeks. Cross them and get genetic maps controlling lifespan.
Why do we get more cancer late in life? Not the accumulation of oncogenic mutations. Loss of tumor suppressors actually reduces renewal of stem cells. but past authors focused on increase in cancer.
Investment in tissue maintenance during youth.
New model of multi stage carcinogenesis where a mutation has a negative impact early in life and a benefit later in life. Bc a stem cell is not well adapted to an old lung and thus will evolve. The same mutation can be maladaptive early in life but adaptive late in life.
Bad cells are poorly adapted to their environment and are pushed out.
It’s not the cell, it’s the mismatch between cell and environment. Adaptation is more likely bc the system is not working.
Senescent cells are a huge part of the environment.
Adding another dimension to somatic evolution.
IL37 transgene is anti-inflammatory. *
Inducing a genetic translocation with crispr in young mice did not cause tumors. But it did if you induce in old mice. But not if you suppress the immune system.
Quality control goes down late in life.
Lawrence Loeb showed that increasing mutation frequency by mutating DNA Pol delta’s proofreading function does not result in increased cancer.
We are loaded with oncogenic mutations. We are not avoiding cancer by avoiding mutations.
Can capture the effects of caloric restriction by increasing autophagy.
Caloric restriction (in the absence of malnutrition).
RNA processing is different in the caloric restriction group.
Push pull of metabolism vs growth… gets disconnected with age.
Increase expression of PGC1alpha in mitochondria by 1.5x to mimic caloric restriction.
Wound healing is slower in CR animals. They would not be vigorous in the wild. They do things more slowly. The animals are smaller so there’s a growth effect of CR.
Constant vs periodic caloric restriction? Intermittent fasting. Fasting and resilience research is growing.
SLS: How well can you capture the benefits of continuous caloric restriction by doing 12hr fasting?
The periodic ketogenic period is important. Can now look for panels of molecules and patterns of change.
Back and forth between evolutionary timescales and one person’s lifetime.
Assume we are the same today as the Romans were.
When we are young we can handle challenges (like missing the bus). We have a muted response to challenges when we are young. Older people worry about things and have higher amplitude responses.
People get less homeostatic as they age.
Recovery is harder as you get older.
Menopause has to do with the number of reproductive follicles that you are born with. (?)
Grandmothers; learning and wisdom. Theory of transfers. Can transfer care, knowledge from old to young.
How much added longevity can we explain from transfers? 15 years. 50yrs to 65yrs old.
Questions I got from the audience after my talk:
· Is high-p21 CDK2-low state a trap? Bc p21 goes up and up, suppresses CDK2 more and more, which makes it harder and harder for a cell to escape and re-enter the cell cycle.
· Would every cell go through the CDK2low state at some point? Can you develop a sensor that would turn on once a cell goes into that state once. (sc)RNA-seq to identify features of CDK2low cells that would be long-lived or permanently on.
· Does % CDK2low cells increase as cells become senescent? Use primary cells at increasing passages.
· Have you normalized p21 and 53bp1 curves by CDK2inc? Set CDK2inc trace to 0 and look at points of convergence with the other 3 subpopulations.
· Similarity to yeast aging and asymmetry of mothers-daughter division?
Barbara Natterson Horowitz.
High adrenergic cardiovascular events = stressful events.
Vasovagal syncope = Fainting. An external event (like getting blood drawn) triggers slowing of heart, vasaodilation, underperfusion of the brain.
Alarm bradycardia. Loss of tone and fainting is a life-saving event when animals are being hunted. Playing dead. It is primarily in juveniles.
In adults, stressful events lead to fight or flight, not fainting.
A developmental response. Until you’re able to run away successfully, there’s a parasympathetic response. 13 years old is age of highest fainting
High adrenergic events cause heart attacks. Spike during Northridge earthquake.
Heart rate variability.
Restore dynamic properties of autonomic nervous system with exercise or mindfulness meditation.
Trapped shorebirds – 10% have heart attacks (and die?) after being trapped with a net. Some species fare worse than others – can you use the differences to predict which patients will have heart failure at the next stress.
Themes that have come up:
Potential wells. Hysteresis. A to B is not B to A. light stress vs strong stress.
Resilience / homeostasis.
Can you see how frail a person is before a surgery? Don’t protect the elderly from all stress, give them some slight challenges and some exercise.
Measures of potential wells. Cell-to-cell variability. Maintenance of a proper phenotype.
How can we compute/quantify potential wells?
Define health: Physiological, psychological, social.
What is surviving? The ecosystem.
Scales: Genes, molecules, cells, tissues, individuals, populations, ecosystems, civilizations
Why do cars age and collapse?
What is aging? Relationship to immortality?
20190409 Day 2.
Themes that came up again and again:
1. Basins of attraction. Wide, narrow, deep, shallow. For sick and healthy.
2. Failure. Gradual, rapid, can we predict?
3. What is failure? Underperform, misperform
4. Tradeoffs. Investment in critical functions could be associated with aging.
5. Everything is connected as a network (eg organs)
6. Complexity (many knots) (vs complicated – many folds)
Let’s define aging:
Programmed development. As well as non-programmed stochastic decay.
Process of aging vs pathology of aging. Separate disease-related aging.
Chronological age vs biological age
How does body protect itself and make itself new again?
It’s an open system so you just age.
Lack of rejuvenation and regeneration.
But a 40 year old woman can make eggs that produce a 0 year old baby.
Aging is entropy
Aging vs longevity
A series of repair mechanisms for wear and tear.
Cataracts and fibrosis are pathologies of aging. These are repair mechanisms that allow longevity.
Aging is suboptimal adaptation to environmental stressors
Aging is an active response, not passive.
Don’t forget fitness.
Geroscience. Failure starts at molecular level, each level up can tolerate some issues.
People age at different rates. Can we quantify biological age.
Epigenetic clocks. CpG methylation. Usually turns down transcription if it’s in the promoter.
Horvath clock. 53 different tissues. Many post-mortem. Measure 353 CpGs across the genome and get a strong age predictor.
Hannum clock was trained on whole blood.
Uses machine learning to minimize the residual of the fit to a line. Want some residuals since an r=1 gives no information (you wouldn’t be predicting their age, it would just be their age).
It’s exponentially changing under 15 yrs old, then linear.
Get phenotypic age, then try to infer biological age.
10k people. Take out accidental death and HIV.
Q: Why focus on all-cause mortality? And not likelihood of a specific disease? Bc age increases your risk of all diseases.
We have multiple biological ages across different tissues.
Most people fall within 5 years of their biological age.
People are stable. Age at one time point is predictive of age 9 years later. People age 1 year per year on average. People don’t reverse.
Levine clock. Can predict age at menopause.
Most of the CpGs do not overlap across the 3 clocks.
Want to cluster the CpGs into module to figure out what they represent.
SLS: do you do any longitudinal studies? Do take your own tissues over time and predict age?
SLS: have you compared these clocks to telomere lengths?
Measuring the resilience of hosts to infections by mapping disease space.
How sick will you get with a given load of pathogen?
Tolerance to microbes is a population-level thing. Can’t do with one individual.
Health is a stable state, death is a stable state, but sickness is a set of many transient states.
Resilience: 1. How stretchy the system is. 2. How far you can go before you break.
With age, the phase diagram might change such that a state that was survivable when you are young is no longer survivable when you are old.
Measure cytokines, parasite density, NK cells, RBCs, etc over the course of the infection.
Could potentially predict where in the trajectory a kid is who comes to the clinic with malaria.
IFNG is first, gamma-delta-Tcells are last.
29 metabolites before infection predict which mice will survive.
Looking for critical transitions.
Q: Do animals have to always go all the way around the curve? No, if you treat them with drugs before day 6, the curve reverses, but not after.
Can rescue ketogenic mice from death by providing glucose.
Now want to do it all in humans.
Q: have you done this in old vs young mice? Yes, in 70 week-old mice
Resilience. How quickly things go back to normal.
In ecosystems, sometimes they don’t recover from a perturbation. They become brittle.
Landscapes are a useful metaphor.
Tipping points – 2 alternative basins of attraction.
How do you know where you are in a landscape?
Look over every square kilometer at tree cover vs amt of rain. Scheffer Science 2011.
Forest is stable, savannah is stable.
Abundance of a particular gut flora increases with age.
Systemic resilience of humans and other animals. Scheffer PNAS 2018.
Can we infer fragility? Climate collapse, societal collapse?
Generic early warning signals = indicators of resilience
Critical slowing down happens at mathematical bifurcations. If you push a ball and the slope is shallow, it takes a long time to return to normal. Fluctuations are longer, are more correlated over time.
Resilience in mood if a bird shits on your head. One hour later, are you happy again or still down?
All systems, human, earth, are continually being perturbed.
SLS: Can we make use of low-level fluctuations in single cells and compute autocorrelation to calculate resilience? Can we use this to measure cellular age? **
Measure resilience by measuring small fluctuations over time. Systems close to tipping point show:
1. Larger fluctuations
2. Stronger autocorrelation
What the other working group talked about: Measure balance on a balance plate (elderly do worse); Measure blood pressure during rapid standing from sitting (blood pressure slower to return to normal in elderly). Once you are frail, it’s already too late.
How can you predict from autocorrelation how far you are from tipping point? You cannot measure the distance to the tipping point. Bc it depends on chance.
Reference material notes
- 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).
- Baker et al. Nature, 2011: Paper from Jan van Deursen's lab on delaying (reversing?) aging by clearing senescent cells in a mouse.
- Zhang et al. Cell Syst., 2016: 3 different models for aging in c elegans with evidence for 2 of the models, from Zach Pincus's lab
|Title||Author name||Source name||Year||Citation count From Scopus. Refreshed every 5 days.||Page views||Related file|
|Extended Twilight among Isogenic C. elegans Causes a Disproportionate Scaling between Lifespan and Health||Cell Systems||2016||0||2|