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EXPERIMENTAL EVOLUTION

Laboratory Selection Experiments and Field Validations

P CALOSI CeMEB Course 2014

Charles Darwin

Is evolu3on slow?

Laboratory Selection Experiments Escherichia coli

end 1980’s-‐1190’s

Albert F. BenneI

Richard Lenski

Benne. et al. 1990 Rapid evolu3on in response to high-‐temperature selec3on. Nature 346, 6279, pp.79-‐81. <<Temperature is an important environmental factor affec9ng all organisms, and there is ample evidence from compara3ve physiology that species and even conspecific popula3ons can adapt gene3cally to different temperature regimes. But the effect of these adapta9ons on fitness and the rapidity of their evolu9on is unknown, as is the extent to which they depend on pre-‐exis3ng gene3c varia3on rather than new muta3ons. We have begun a study of the evolu9onary adapta9on of Escherichia coli to different temperature regimes, taking advantage of the large popula9on sizes and short genera9on 9mes in experiments on this bacterial species. We report significant improvement in temperature-‐specific fitness of lines maintained at 42 °C for 200 genera3ons (about one month). These changes in fitness are due to selec9on on de novo muta9ons and show that some biological systems can evolve rapidly in response to changes in environmental factors such as temperature.>>

Laboratory Selection Experiments

Evolution of Evolution Evolution: Past, Present and Future

Scientific knowledge may permit humans to guide future evolution

Richard Lenski <<Because bacteria reproduce so quickly, we use them in experiments to test evolu3onary hypotheses. For over 20 years and 45.000 bacterial genera9ons, my students and I have maintained twelve popula3ons of E. coli in small flasks of sugar water. We measure the process that Darwin discovered – adapta9on by natural selec9on – by compe3ng ‘modern’ bacteria against their ancestors, which we store frozen and then revive for the tests. Imagine if we could bring Homo erectus back to life, and challenge them to games of football and chess! In our flasks, the modern bacteria outscore their ancestors in the struggle for existence.>>.

hIps://www.nsf.gov/news/special_reports/darwin/textonly/bio_essay1.jsp hIp://www.nsf.gov/news/news_summ.jsp?cntn_id=125492 hIp://www.nsf.gov/discoveries/disc_summ.jsp?cntn_id=128414

Evolution of Evolution

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Artificial Selection !

Laboratory Selection The Ultimate Tool to Study Evolution?

The experimenter can:

-‐  define the driver(s) of selec9on (e.g. temperature, pH, salinity, oxygen), -‐  define (to a very fine level) the direc9on and intensity of

selec9on (i.e. can have a rigorous control of the experimental condi3ons), -‐  define the traits he/she wants to select for (i.e. produc3vity, taste,

coloura3on, ac3vity levels), -‐  set the level of replica9on (i.e. how many lines/families are employed), -‐  develop protocols (assays) repeatable by other laboratories, -‐  repeat the of selec9on exercise.

Laboratory Selection

Laboratory Selection Types of Selections

Ar3ficial Selec3on: 1)  Laboratory Ar9ficial Selec9on experiments: using this approach the

experimenter select individuals with specific traits (e.g. running or swimming or flying speed, resistance to a toxicant or a parasite). Also synonym of Selec3ve Breeding.

2)  Culling Selec9on experiments: the process of Culling is the systema3c removal of individuals (carrying specific undesired traits) from a breeding popula3on. In experimental terms we may also apply a stressful condi3on un3l a threshold of survival is reached (e.g. 50% of our breeding popula3on is lek). Probably the closest to what happen in nature, where mortality levels are oken extremely high. Effec3vely here the selec3on is based on a bo;leneck.

3)  Laboratory Natural Selec9on (LNS) experiments: using this approach the experimenter does not select individuals with specific traits but fix the environmental driver(s) causing selec3on an observe the outcome of this process: i.e. breeding is not selec3ve


2. Ar3ficial selec3on

Breeder’s equa3on: R = S x h2

h2 = R / S

from Sam’s lecture #3-‐4

Laboratory Selection Types of Selections

Ar3ficial Selec3on: 1)  Laboratory Ar9ficial Selec9on experiments: using this approach the

experimenter select individuals with specific traits (e.g. running or swimming or flying speed, resistance to a toxicant or a parasite). Also synonym of Selec3ve Breeding.

2)  Culling Selec9on experiments: the process of Culling is the systema3c removal of individuals (carrying specific undesired traits) from a breeding popula3on. In experimental terms we may also apply a stressful condi3on un3l a threshold of survival is reached (e.g. 50% of our breeding popula3on is lek). Probably the closest to what happen in nature, where mortality levels are oken extremely high. Effec3vely here the selec3on is based on a bo;leneck.

3)  Laboratory Natural Selec9on (LNS) experiments: using this approach the experimenter does not select individuals with specific traits but fix the environmental driver(s) causing selec3on an observe the outcome of this process: i.e. breeding is not selec3ve


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Selec3on is something we have used for millennia:

•  domes9ca9on of animals and plants, •  selec3on of plants’ cul0var resistant to pests, •  selec3on of animals’ breeds able to convert low

energy food into high value proteins, •  selec3on of strains of fish and shellfish be.er

suited for farming.

Is Selection something new?

Illinois Corn Experiment (1886) to improve oil content

The selection of corn

Examples of Artificial Selection plants domestication and programmes of amelioration

built in 1907 to improve the culture of citrus plants in California&

Citrus Experiment Station of Riverside &

Agricultural Experiment Station selection for resistance to pests and longer growing season&

!

modern cows aurochs

modern chickens

red jungle fowl

Examples of Artificial Selection animals domestication

Holstein Friesian

1950’s

Modern ideal

These belong to the same species?

Examples of Artificial Selection selection for “weird” forms in pets and breed amelioration

Darwin used ar9ficial selec9on as an analogy for natural selec9on.

Darwin’s Pigeons

Rev. Dr. William Henry Dallinger FRS

W. Dallinger (1887) The President's Address. Jl. R. Microscp. Soc. 184-‐199.

The first Laboratory Selection Experiment

Major outcomes after over a century of selection experiments 1.  (Physiological) Evolu9on is rela9vely quick under

laboratory condi9ons

2.  Pa.erns of evolu9on are not always predictable

3.  Physiology and behaviour co-‐evolve and interact

Major outcomes after over a century of selection experiments

Laboratory Selection Evolutionary Novelty (?)

hIp://www.biology.ucr.edu/people/faculty/Garland/SelPubs.html

Garland et al. 2002 Evolu3on 56, 6,pp.1267–1275

Evolu3on of a small-‐muscle polymorphism in lines of house mice selected for high ac3vity levels


<<My lab also uses experimental evolu9on to study how physiological systems func3on and evolve under stress. A current project involves finding genes that affect obesity in starva3on-‐resistant fruit flies.>>

Allen Gibbs (University of Nevada Las Vegas)

Laboratory Selection Medical Applications

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How relevant are these model organisms for our understanding of the evolution of marine organismal physiology?!

!

EVERYBODY KNOWS THAT SNAKE IS THE

WORD!!

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Marine Examples – heritability studies !

Litopenaeus vannamei Pascual et al. 2004 Aquaculture 230, pp.405–416

They looked a the effect of a size-‐based selec3on program on blood metabolites and immune response of juveniles of the shrimp Litopenaeus vannamei that were fed different dietary carbohydrate levels.

Compared physiological responses in wild and cul9vated juveniles (7 genera3ons) reared with a high (44%) and low (3%) carbohydrate diet.

In wild individuals there is a direct rela9onship between the type of carbohydrate diet and levels of lactate, protein and haemocyte. They used carbohydrate to synthesis proteins via transamina3on pathways.

In cul9vated individuals metabolites levels were propor9onal inverse to dietary carbohydrate levels, as capacity to synthesize protein from dietary carbohydrate was repressed. Hence, size-‐based breeding programs caused the selec9on for individuals unable to use dietary carbohydrate.

Marine Examples - aquaculture

Fleming et al. 2002. Effects of domes3ca3on on growth physiology and endocrinology of Atlan3c salmon (Salmo salar) Can. J. Fish. Aquat. Sci. 59, 8 , pp.1323-‐1330 -‐  selec9on programs for fish frequently target growth rate as a breeding goal, yet

surprisingly liIle is known about which mechanisms underlying the growth process are being targeted.

-‐  the ar9ficial selec9on of Atlan9c salmon (Salmo salar) has resulted in higher growth rate

-‐  is this selec3on process resulted in changes of the growth hormone (GH) � insulin-‐like growth factor I (IGF-‐I) axis of endocrine growth regula3on.

-‐  tested comparing reared seventh-‐genera9on farm salmon with wild salmon .

-‐  the domes9cated fish outgrew their wild counterparts.

-‐  pituitary GH content was posi9vely correlated with growth rate and correspondingly was significantly higher in the faster growing domes9cated fish than in the wild fish. Plasma GH levels were also significantly higher in the domes3cated fish, whereas IGF-‐I levels did not differ.

Marine Examples - aquaculture

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Sinead Collins (Edinburgh University)

hIp://www.smallbutmighty.bio.ed.ac.uk/research/evolu3onary-‐responses-‐to-‐high-‐co2.html

Collins and Bell 2004. Phenotypic consequences of 1000 genera3ons of selec3on at elevated CO2 in a green alga. Nature 431:566-‐569.

Marine Examples – where did we start with OA?

Published empirical evidence for the actual capacity for rapid adapta9on to global change drivers in marine systems

exist only for unicellular organisms

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differences between transplant and original target populations are shown by vertical lines

with point ends.

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EXPERIMENT 1: MULTIGENERATIONAL EXPOSURE We carried out a mul3-‐genera3onal experiment (6 genera3ons) to determine the fitness consequences of the pre-‐experimental condi3ons (on F1) and the effects of the mul3-‐genera3onal exposure to low pCO2 condi3ons (F2-‐F6). E X P E R I M E N T 2 : R E C I P R O C A L TRANSPLANT Reciprocal transplants were performed between pCO2 treatments with F7 individuals to determine if an adap3ve response to low pCO2 levels had occurred.

Rodríguez-‐Romero A., Jarrold M.D., Massamba-‐N’Siala G., Spicer J.I., Calosi P. Rapid evolu9onary adapta9on to changes in environmental pCO2 in a marine polychaete is facilitated by trans-‐genera9onal plas9city. Proceedings of the Royal Society, under submission.

pCO2: 1000 μatm (E)

F1 F

2 F3 F

4 F5

F6

pCO2: 400 μatm (L)

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2 F3 F

4 F5

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Conclusions

Our results suggest that some marine metazoans …. •  possess sufficient plas9city to cope with rapid changes in

pCO2 over one genera9on

•  may have the capacity to rapidly adapt to changes in pCO2

•  Is plas9city the mechanism for rapid adapta9on to occur?

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Laboratory Selection Experiments and Experimental Evolution can help selecting for organisms resilient to ongoing Global

Climatic Change Drivers, and thus prevent potential local and global extinction.

So why do not we use it all the time?

Disadvantage and Limita3ons:

-‐  require species with rela3ve short genera3on 3mes, -‐  require laboratory hardy species (non necessarily representa3ve of extant biodiversity) -‐  you must guarantee a rigorous controls of experimental condi3ons, -‐  you must maintain healthy popula3ons for mul3ple genera3ons, -‐  you must maintain an unselected line (i.e. control popula3ons), -‐  you must start from very large founding popula3ons (to avoid gene3c boIlenecks), -‐  simple experiments can give rise to complicated paIerns: example laboratory

selec3on exp. on the desicca3on-‐tolerant of Drosophila seems rela3vely straight-‐forward. However, we can have developmental-‐stage-‐specific, gender-‐specific differences in the heritability of traits, which complicate this ‘simple picture’.

Are Laboratory Selection experiments really the ultimate tool to study physiological evolution?

Huey and Rosenzweig (2009) chapter 22 Experimental Evolu3on by Garland and Rose

Experimenters have to eventually: -‐  verify that the pa.erns observed

under laboratory condi3ons do actually occur in nature,

-‐ provide evidence that individuals selected are s9ll ‘compe99ve’ in their natural environment/communi3es.

Laboratory Evolution Meets Catch-22

Field Validations

What?

Where?

How?

Field Validations

Field Validations

What do you need ?

“natural analogies”

Field Validations

Field Validations

Where do you find “natural analogies”?

wherever there is an environmental gradient

Field Validations

Field Validations

How do you go about working along this gradients?

Characterising phenotypes (genetic, biochemical, physiological, life history, behavioural) along/at the

extreme of a gradient

Field Validations

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Validations using Natural Systems

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Both acclima9za9on and adapta9on enable persistance of worm species in CO2 vents

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Laboratory Evolution Meets Catch-22

CONCLUSION Although we have focused on problems that can plague LNS experiments as emula9ons of natural selec9on in the wild, we do not mean to imply that LNS experiments are without u3lity. Quite the contrary. There are many ways to study evolu9on, some descrip9ve, some experimental. As has been noted repeatedly (Huey et al. 1991; Huey and Kingsolver 1993; Rose et al. 1996; Gibbs 1999; Garland 2003; Swallow and Garland 2005; Futuyma and BenneI this volume; Rose and Garland this volume), each method has its advantages, and each has its limita9ons. Moreover, an awareness of limita9ons can open opportuni9es for novel studies (e.g., chronic vs. nonchronic selec3on). In any case, a complete understanding of evolu9on will require the applica9on of mul9ple integrated approaches. We see LNS as an essen3al tool for tes3ng field-‐derived hypotheses, but one that must be handled though�ully, used along with other tools, and interpreted with care. No ma.er how hard we work, no experiment or study will ever be perfect. We need to do away with the “Myth of Defini9ve Results” (Underwood 1998) and recognize that our view of evolu9on is deeper if we look at it through different and complementary glasses, not just though LNS ones. And we should try to improve the validity of each approach, learning as we go. As Underwood (1998, 345) noted, “The hallmark of progressive ideas is that they progress. Given that there is a good chance we are wrong quite oken, we should be prepared to discover how wrong as fast as possible.”

Huey and Rosenzweig (2009) chapter 22 Experimental Evolu3on by Garland and Rose

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