Stretching and squeezing time Nick Stroustrup Winston Anthony - - PowerPoint PPT Presentation

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Stretching and squeezing time Nick Stroustrup Winston Anthony - - PowerPoint PPT Presentation

Sep 15, 2022 519 likes •907 views

Stretching and squeezing time Nick Stroustrup Winston Anthony Walter Fontana Javier Apfeld Adam Gomez Systems Biology Vivek Gowda Harvard Medical School Isaac F. Lpez-Moyado walter@hms.harvard.edu Zachary M. Nash Bryne Ulmschneider

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SLIDE 1

Stretching and squeezing time

Walter Fontana

Systems Biology Harvard Medical School walter@hms.harvard.edu

Nick Stroustrup Winston Anthony Javier Apfeld Adam Gomez Vivek Gowda Isaac F. López-Moyado Zachary M. Nash Bryne Ulmschneider

Stroustrup, N., Anthony, W.E., Nash, Z.M., Gowda, V., Gomez, A., López-Moyado, I.F., Apfeld. J. and Fontana, W. Nature, 320: 103-107 (2016)

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SLIDE 2

“…progressive disappearance” Roman Opałka (1931 – 2011)

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SLIDE 3

Aging is often viewed as a process of “physiological decline”…

Measuring (the process of) organismic aging

What might that mean? wound healing acuity of senses physical strength endurance cellular regeneration … Measuring “capabilities”, i.e. properties such as reporting on the structure and functioning of basic physiological systems.

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SLIDE 4

The problem is that there is no characterization of the aging process to begin with.

Snag: what we wish to measure is undefined

For a specific aspect of a physiological system or a specific form of damage to qualify as an observable of the aging process, it must be shown to be related to the aging process, either as a causal factor or as a consequence faithfully tracking the process. The problem is not solved by shifting to the molecular level. It is only compounded.

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SLIDE 5

We’ve been there before

Take for example the measurement of temperature. How can we test whether the fluid in our thermometer expands regularly with increasing temperature, without a circular reliance on the temperature readings provided by the thermometer itself? How did people without thermometers learn that water boiled or ice melted always at the same temperature, so that these phenomena could be used as ‘‘fixed points’’ for calibrating thermometers?

Hasok Chang, Inventing Temperature—Measurement and Scientific Progress, OUP, 2004

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SLIDE 6

Rather than defining a process explicitly, define it implicitly by reference to its endpoint. So, let’s hang on to what we can agree on: The endpoint of organismic aging is death

The way out (no pun intended)

unobserved aging process

T = 0

prepare

T = t

  • bserve endpoint
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SLIDE 7

time fraction left

20 40 60 80 0.2 0.4 0.6 0.8 1.0 0.0

Degradation kinetics

an “elementary” thing

controlled environment

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SLIDE 8

time fraction left

risk hazard force of mortality lifespan density survival function

20 40 60 80 0.2 0.4 0.6 0.8 1.0 0.0

Death kinetics

a “complex” thing with a process inside

non-aging aging

controlled environment

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SLIDE 9

5 10 15 20 1 2 3 4 5 5 10 15 20 0.1 0.2 0.3 0.4 5 10 15 20 0.2 0.4 0.6 0.8 1.0

=

.

prob density of lifespan hazard rate survival function

Basic mortality statistics

let T, the time of death, be a random variable

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SLIDE 10

lifespan

20 40 60 80 0.02 0.04 0.06 0.08 0.10

The key is to measure the whole distribution

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SLIDE 11

lifespan

20 40 60 80 0.02 0.04 0.06 0.08 0.10

intervention

The key is to measure the whole distribution

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SLIDE 12

The general experimental frame

a “complex” thing with a process inside agnostic about proximal causes of death “intrinsic” risk agnostic about “disease” “all-cause” mortality agnostic about “causes”

controlled environment

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SLIDE 13
  • Z. F. Altun & D. H. Hall

www.wormatlas.org 0.1 mm

~ 1 mm in length ~ 70 microns in diameter 959 cells (302 neurons) 100M bp, 19000 ORFs transparent complete parts list complete developmental lineage great genetics model organism status 2 weeks average life span

  • C. elegans, the essence of life: a stomach and a gonad
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SLIDE 14

b d b a c

  • N. Stroustrup et al., "The C. elegans Lifespan Machine", Nature Methods, 10, 665-670 (2013)

acquisition of high-resolution lifespan statistics with a distributed scalable time lapse microscope based on flatbed document scanners

The Lifespan Machine

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SLIDE 15

wildtype in brightfield

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SLIDE 16

16

age1(hx586) wildtype N2

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SLIDE 17

17

age1(hx586) wildtype N2

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SLIDE 18

Age (days of adulthood) Fraction surviving By hand Machine

0.5 1.0 0.5 1.0 5 10 15 20 25

Fraction surviving

0.2 0.4 0.6 0.8 1.0 5 10 15 20 25

By hand Machine Age (days of adulthood)

484 death events

  • n a single scanner

513 death events

  • bserved manually

3,578 death events observed

  • n 10 scanners, aggregated

Precision and accuracy

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SLIDE 19

Time (days) 0.25 0.50 1.00 2.00 4.00 8.00 16.00 0.01 0.05 0.1 0.5 1.0 5.0 10.0 Hazard rate (days-1)

temperature 20.1 23.7 25.2 29.1 30 30.9 31.3 32.5 32.6

log-log scale

Temperature series

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SLIDE 20
  • r

Temporal scaling

30 60 90 0.10 0.15 0.20 0.25 0.30

Time Hazard

  • r

20 40 60 80 0.2 0.4 0.6 0.8 1.0 0.0

Fraction alive Time

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SLIDE 21

Fraction surviving Fraction surviving Residual time Time (days) 10 20 30 0.0 0.4 0.8 0.0 0.5 1.0 1.5 0.0 0.4 0.8 Fraction surviving Residual time Residual time (days) 0.1 0.5 1.0 5.0 10.0 Hazard rate (days-1) 0.6 0.7 0.8 1.0 1.2 1.4 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.2 0.4 0.6 0.8 1.0

20 25 27 33

Temperature results in residual time

20 25 27 33 25 25 33

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SLIDE 22

Time (days) Fraction surviving Fraction surviving Residual time 5 10 15 0.0 0.4 0.8 0.0 0.5 1.0 1.5 2.0 0.0 0.4 0.8

6 mM 3 mM 0 mM 1.5 mM t-butyl-hydroperoxide

O OH

  • xidative stress with

t-butyl-peroxide rescales time

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SLIDE 23

Fraction surviving Fraction surviving

Time (days)

Residual time

5 10 15 20 25 30 0.0 0.4 0.8

0.0 0.5 1.0 1.5 0.0 0.4 0.8

Diet rescales time

UV-inactivated E.coli live E.coli

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SLIDE 24

P P

DAF-2 [insulin receptor] AGE-1 [PI3K] AKT-1/AKT-2 (via PIP3 and PDK-1) DAF-16 [forkhead TF]

✗

activation activation phosphorylation translocation transcriptional modulation “stress response” activation by insulins

DAF-2/IGF signaling

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SLIDE 25

5 10 15 20 25 30 35 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0.0 0.2 0.4 0.6 0.8 1.0 5 10 15 20 25 30 0.0 0.4 0.8 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0.0 0.4 0.8 age−1(hx546) 5 10 15 20 25 0.0 0.4 0.8 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.4 0.8 5 10 15 0.0 0.4 0.8 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0.0 0.4 0.8 daf−16( mu86) wildtype daf−16( mu86) wildtype wildtype age−1(hx546) wildtype wildtype wildtype 5 10 15 20 25 30 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.4 0.8 1.2 0.0 0.2 0.4 0.6 0.8 1.0 Fraction surviving Fraction surviving Fraction surviving Fraction surviving Fraction surviving Fraction surviving Fraction surviving Fraction surviving Fraction surviving Fraction surviving Time (days) Time (days) Time (days) Time (days) Time (days) Residual time Residual time Residual time Residual time Residual time daf−2(e1368) wildtype daf−2(e1368) hif-1(ia4) wildtype wildtype hif-1 (ia4) hsf-1(sy441) wildtype hsf-1(sy441)

Mutants that rescale time

daf-2, age-1, daf-16: IGF pathway hif-1: hypoxia-inducible factor hsf-1: heat-shock factor

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SLIDE 26

5 10 15 20 25 30 35 0.0 0.4 0.8 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 5 10 15 20 25 30 35 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.4 0.8 1.2 5 10 15 20 25 30 0.0 0.4 0.8 0.0 0.5 1.0 1.5 0.0 0.4 0.8 0.50 0.75 1.00 1.25 1.50 Hazard Rate (1/Days) 0.05 0.10 0.50 1.00 5.00 10.00 50.00 0.50 0.75 1.00 1.25 1.50 Hazard Rate (1/Days) 0.05 0.10 0.50 1.00 5.00 10.00 50.00 Fraction surviving Fraction surviving Fraction surviving Residual time Time (days) Residual time Time (days) Residual time Residual time Residual time Time (days) Time (days) Time (days) eat-2(ad1116) 20 °C 22.5 °C eat-2(ad1116) 20 °C 22.5 °C nuo-6(qm200) 20 °C 25 °C eat-2 eat-2(ad1116) wild type eat-2(ad1116) wild type eat-2(ad1116) wild type eat-2(ad1116) wild type 5 10 15 20 25 30 Hazard Rate (1/Days) 0.005 0.010 0.050 0.100 0.500 1.000 5 10 15 20 25 Hazard Rate (1/Days) 0.005 0.010 0.050 0.100 0.500 1.000 5 10 15 20 25 30 0.0 0.4 0.8 0.0 0.5 1.0 1.5 0.0 0.4 0.8 nuo-6(qm200) wild type nuo-6(qm200) wild type nuo-6(qm200) wild type nuo-6(qm200) wild type nuo-6(qm200) 20 °C 25 °C 0.0 0.4 0.8 Fraction surviving Fraction surviving Fraction surviving Fraction surviving Fraction surviving Time (days) Residual time

Mutants that break scaling

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SLIDE 27

24 °C 29 °C time time runs slower here than on red lifespan distributions scale

lifespan 0.05 0.10 0.15 20 40 60 80 100

Scaling and shifting

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SLIDE 28

24 °C 29 °C time

0.05 0.10 0.15 20 40 60 80 100 lifespan

switch lifespan distributions shift

Scaling and shifting

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SLIDE 29

24 °C 26 °C 28 °C 29 °C time switch at day 3 24 °C 29 °C time day 3

The theory of the switch experiment predicts that if scaling holds In particular, if switching occurs before any deaths have occurred:

time of switch conditional lifespan expectation of the (non-switched) control population shift magnitude

Scaling and shifting

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SLIDE 30

24 °C 26 °C 28 °C 29 °C time switch at day 3 24 °C 29 °C time day 3

Shift factor Δ (days)

2.5 1.67 1.25 1.0 −0.5 0.5 1.5 2.5

slope = −3.16 ± 0.143 Scale factor 1/λ

  • 0.0

1.0 2.0 3.0 0.0 1.0 2.0 Time spent at 24 °C (days) Time shift factor (days)

  • Shifting confirms scaling
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SLIDE 31

The aging process is remarkably uniform and is ongoing long before any deaths are observed in the population.

Main observations

Interventions of distinct modalities and intensities simply rescale time in the mortality statistics of the worm.

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SLIDE 32

What does temporal scaling mean?

Scaling of mortality requires that all risk factors rescale equally (*) in response to an intervention regardless of its nature and targets.

(*) Exception: risk factors need not rescale equally, if they are Weibull, but this would be highly implausible risk determinant

death death death death competing (independent) risks model

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SLIDE 33

random scale-free

Dependency network models

4 6 3 6 5 6 8 5 5 7 4 7 6 2 9 7 4 3 8 5 7 6 4 5 4 7 2 2 10 2 3 3 6 4 4 3 2 8 3 6 10 11 10 4 13 6 8 15 10 8

Vural, D. C., Morrison, G. & Mahadevan, L. Aging in complex interdependency networks. Phys. Rev. E 89, 022811 (2014)

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SLIDE 34

5 10 15 20 25 30 35 0.0 0.2 0.4 0.6 0.8 1.0

Fraction of nodes affected 0.01 0.05 0.1 0.2 0.5 0.8 0.9 0.95 0.99 1

Survival time (arb. units) 0.5 1.0 1.5 0.0 0.2 0.4 0.6 0.8 1.0

Fraction of nodes affected 0.01 0.05 0.1 0.2 0.5 0.8 0.9 0.95 0.99 1

Survival residual time

Perturbing dependency networks

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SLIDE 35

What does temporal scaling mean?

Scaling of mortality requires that all risk factors rescale equally (*) in response to an intervention regardless of its nature and targets.

DNM suggests a state description of organismic aging independent of molecular details.

state r(t)

risk determinant

death death death death death competing (independent) risks model dependency network model

4 6 3 6 5 6 8 5 5 7 4 7 6 2 9 7 4 3 8 5 7 6 4 5 4

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SLIDE 36

What does temporal scaling mean?

state variable (“resilience” or some such)

The process of aging can be described in terms of a state variable and must be invariant to time scale transformations.

physics (of damage production) biology (of damage control) possibly

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SLIDE 37

Millions of C. elegans

Glenn Foundation

Thank you!

UNC Chapel Hill UCLA UCSD Northeastern Dana Farber UCSF WUSTL Winston Anthony Javier Apfeld Adam Gomez Vivek Gowda Isaac F. López-Moyado Zachary M. Nash Bryne Ulmschneider

Nick Stroustrup

(in nominal order)