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

Stretching and squeezing time Nick Stroustrup Winston Anthony Walter Fontana Javier Apfeld Adam Gomez Systems Biology Vivek Gowda Harvard Medical School Isaac F. López-Moyado walter@hms.harvard.edu 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)

“…progressive disappearance” Roman Opa ł ka (1931 – 2011)

Measuring (the process of) organismic aging Aging is often viewed as a process of “physiological decline”… What might that mean? Measuring “capabilities”, i.e. properties such as wound healing acuity of senses physical strength endurance cellular regeneration … reporting on the structure and functioning of basic physiological systems.

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 that there is no characterization of the aging process to begin with. The problem is not solved by shifting to the molecular level. It is only compounded.

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

The way out (no pun intended) 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 unobserved aging T = 0 process prepare observe endpoint T = t

Degradation kinetics an “elementary” thing 1.0 0.8 fraction left 0.6 0.4 0.2 controlled environment 0.0 0 20 40 60 80 time

Death kinetics a “complex” thing with a process inside lifespan density risk survival function hazard force of mortality 1.0 aging non-aging 0.8 fraction left 0.6 0.4 0.2 controlled environment 0.0 0 20 40 60 80 time

Basic mortality statistics let T , the time of death, be a random variable prob density of lifespan hazard rate survival function 0.4 5 1.0 4 0.8 0.3 . 3 0.6 = 0.2 2 0.4 0.1 1 0.2 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20

The key is to measure the whole distribution 0.10 0.08 0.06 0.04 0.02 0 0 20 40 60 80 lifespan

The key is to measure the whole distribution 0.10 0.08 0.06 0.04 0.02 0 0 20 40 60 80 lifespan intervention

The general experimental frame a “complex” thing with a process inside “all-cause” mortality agnostic about proximal causes of death agnostic about “disease” agnostic about “causes” “intrinsic” risk controlled environment

C. elegans, the essence of life: a stomach and a gonad ~ 1 mm in length ~ 70 microns in diameter 0.1 mm 959 cells (302 neurons) 100M bp, 19000 ORFs 2 weeks average life span transparent complete parts list complete developmental lineage great genetics model organism status Z. F. Altun & D. H. Hall www.wormatlas.org

The Lifespan Machine acquisition of high-resolution lifespan statistics with a distributed scalable time lapse microscope based on flatbed document scanners a b b c d N. Stroustrup et al., "The C. elegans Lifespan Machine", Nature Methods , 10 , 665-670 (2013)

wildtype in brightfield

wildtype N2 age1(hx586) 16

wildtype N2 age1(hx586) 17

Precision and accuracy 484 death events on a single scanner 1.0 1.0 Machine Machine By hand 0.8 Fraction surviving Fraction surviving 0.5 0.6 0 1.0 By hand 0.4 0.5 0.2 0 0 0 5 10 15 20 25 0 5 10 15 20 25 Age (days of adulthood) Age (days of adulthood) 3,578 death events observed 513 death events on 10 scanners, aggregated observed manually

Temperature series 10.0 5.0 Hazard rate (days -1 ) 1.0 0.5 0.1 0.05 0.01 0.25 0.50 1.00 2.00 4.00 8.00 16.00 log-log scale Time (days) temperature 20.1 23.7 25.2 29.1 30 30.9 31.3 32.5 32.6

Temporal scaling or or 0.30 1.0 0.25 0.20 0.8 0.15 Fraction alive 0.6 Hazard 0.10 0.4 0.2 0.0 0 20 40 60 80 30 60 90 Time Time

Temperature results in residual time 0.8 0.8 Fraction surviving Fraction surviving 27 25 20 0.4 0.4 33 27 25 20 0.0 0.0 0 10 20 30 0.0 0.5 1.0 1.5 Time (days) Residual time 25 1.0 10.0 Fraction surviving Hazard rate (days -1 ) 0.8 33 5.0 0.6 33 25 1.0 0.4 0.5 0.2 0.1 0.0 0.6 0.7 0.8 1.0 1.2 1.4 0.0 0.5 1.0 1.5 2.0 2.5 Residual time (days) Residual time

t-butyl-peroxide rescales time oxidative stress with OH O Fraction surviving 0.8 Fraction surviving 0.8 0.4 0.4 0.0 0.0 0 5 10 15 0.0 0.5 1.0 1.5 2.0 Time (days) Residual time 6 mM 3 mM 1.5 mM 0 mM t-butyl-hydroperoxide

Diet rescales time UV-inactivated E.coli Fraction surviving Fraction surviving 0.8 0.8 live E.coli 0.4 0.4 0.0 0.0 0.0 0.5 1.0 1.5 0 5 10 15 20 25 30 Time (days) Residual time

DAF-2/IGF signaling DAF-2 [insulin receptor] activation by insulins activation “stress response” AGE-1 [PI3K] activation (via PIP3 and PDK-1) P ✗ P phosphorylation translocation transcriptional modulation AKT-1/AKT-2 DAF-16 [forkhead TF]

Mutants that rescale time 1.0 1.0 Fraction surviving Fraction surviving Fraction surviving Fraction surviving 0.8 0.8 0.8 0.8 0.6 0.6 0.4 0.4 0.4 0.4 0.2 daf−2(e1368) 0.2 hif-1(ia4) hif-1 (ia4) daf−2(e1368) wildtype wildtype wildtype 0.0 wildtype 0.0 0.0 0.0 0 5 10 15 20 25 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0 5 10 15 20 25 30 35 Time (days) Residual time Time (days) Residual time 0.0 0.2 0.4 0.6 0.8 1.0 1.0 Fraction surviving Fraction surviving 0.8 0.8 Fraction surviving Fraction surviving 0.8 0.6 0.4 0.4 0.4 age−1(hx546) age−1(hx546) 0.2 hsf-1(sy441) hsf-1(sy441) wildtype wildtype 0.0 0.0 wildtype wildtype 0.0 0 5 10 15 20 25 30 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0.0 0.4 0.8 1.2 0 5 10 15 20 25 30 Time (days) Residual time Time (days) Residual time 0.8 0.8 Fraction surviving Fraction surviving daf-2, age-1, daf-16: IGF pathway 0.4 0.4 hif-1: hypoxia-inducible factor daf−16( m u86) daf−16( m u86) wildtype wildtype 0.0 0.0 hsf-1: heat-shock factor 0 5 10 15 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Time (days) Residual time

Mutants that break scaling Hazard Rate (1/Days) 50.00 1.000 Hazard Rate (1/Days) 0.500 Fraction surviving 0.8 Fraction surviving 10.00 0.8 5.00 0.100 1.00 0.4 0.050 0.4 0.50 nuo-6(qm200) nuo-6(qm200) nuo-6(qm200) nuo-6(qm200) 0.010 0.10 wild type wild type wild type wild type 0.05 0.005 0.0 0.0 0.50 0.75 1.00 1.25 1.50 5 10 15 20 25 Time (days) Residual time 0 5 10 15 20 25 30 35 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Time (days) Residual time Hazard Rate (1/Days) Hazard Rate (1/Days) 1.000 50.00 0.500 0.8 0.8 Fraction surviving Fraction surviving eat-2 10.00 5.00 0.100 0.050 1.00 0.4 0.4 0.50 eat-2(ad1116) eat-2(ad1116) eat-2(ad1116) eat-2(ad1116) 0.010 wild type wild type wild type wild type 0.10 0.0 0.0 0.005 0.05 0.50 0.75 1.00 1.25 1.50 5 10 15 20 25 30 0 5 10 15 20 25 30 0.0 0.5 1.0 1.5 Time (days) Residual time Time (days) Residual time 1.0 1.0 Fraction surviving 0.8 0.8 Fraction surviving Fraction surviving Fraction surviving 0.8 0.8 0.6 0.6 0.4 0.4 0.4 0.4 nuo-6(qm200) nuo-6(qm200) eat-2(ad1116) eat-2(ad1116) 0.2 0.2 20 °C 20 °C 20 °C 20 °C 25 °C 25 °C 22.5 °C 22.5 °C 0.0 0.0 0.0 0.0 0 5 10 15 20 25 30 0.0 0.5 1.0 1.5 0 5 10 15 20 25 30 35 0.0 0.4 0.8 1.2 Time (days) Residual time Time (days) Residual time

Scaling and shifting lifespan distributions scale 0.15 29 °C 0.10 0.05 time runs slower here than on red 24 °C 0 0 20 40 60 80 100 lifespan time

Scaling and shifting lifespan distributions shift 0.15 29 °C 0.10 0.05 24 °C 0 0 20 40 60 80 100 lifespan time switch

Scaling and shifting The theory of the switch experiment predicts that if scaling holds time of switch shift magnitude conditional lifespan expectation of the (non-switched) control population In particular, if switching occurs before any deaths have occurred: 29 °C 29 °C 28 °C 26 °C 24 °C 24 °C time time day 3 switch at day 3

Shifting confirms scaling slope = −3.16 ± 0.143 ● Time shift factor (days) Shift factor Δ (days) 2.0 2.5 ● 1.5 ● 1.0 0.5 −0.5 0.0 ● 2.5 1.67 1.25 1.0 0.0 1.0 2.0 3.0 Scale factor 1/λ Time spent at 24 °C (days) 29 °C 29 °C 28 °C 26 °C 24 °C 24 °C time time day 3 switch at day 3