Phenotypic plasticity of body pigmentation in Drosophila: correlated variations between segments

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Phenotypic plasticity of body pigmentation in

Drosophila: correlated variations between segments

Patricia Gibert, Brigitte Moreteau, Samuel M. Scheiner, Jean R. David

To cite this version:

Original

article

Phenotypic

plasticity

of

_body

pigmentation

in

_Drosophila:

correlated variations

between

_segments

Patricia Gibert

_Brigitte

Moreteau

Samuel

M.

Scheiner

Jean

R.

David

a

Laboratoire

_{populations, génétique}

et

_évolution,

Centre national de la recherche

scientifique,

91198 Gif-sur-Yvette

cedex,

France

b

Department

of Life

Sciences,

Arizona State

_{University West,}

P.O. Box

_37100,

Phoenix AZ

85069,

USA

(Received

8

_{September 1997 ; accepted}

22

_January

₁₉₉₈₎

Abstract -

_{Phenotypic plasticity of body pigmentation}

_(the

last three abdominal _segments and the

_mesothorax)

was

_investigated

as a function of

_growth

_temperature in

_Drosophila

melanogaster

and

Drosophila

simulans. Two

_populations

of each

_species

were

_analysed,

from two French localities with different climatic conditions. For each

population,

ten isofemale lines were reared at temperatures

ranging

from 14 to 31 °C. Two methods

were used and

_compared

to estimate

genetic

correlations

_(rg)

between segments, a

_simple

method

using directly

the

family

mean values

_(r

_m

₎

and a

_{theoretically}

better method

correcting

variances and covariances for

_family

size

_(r

_c

_).

Both methods

_produced

_very similar data but the first one

_(rI")

was

_preferred

because it

_allowed

an estimate of

rg in all cases. Genetic and

_phenotypic

correlations decreased

_regularly

with distance

between

_body

_segments,

_revealing

an

_{antero-posterior gradient: the}

extension of dark

pigmentation

is determined

_{by increasingly}

different

_genetic

systems in more distant

segments. Genetic correlations were

_{substantially larger}

than

_{phenotypic correlations,}

in

opposition

to Cheverud’s

_{conjecture, although}

the two sets of values were

_highly

correlated.

©

Inra/Elsevier,

Paris

D. _melanogaster

_/

D. simulans

_/

_genetic correlation

_/

Cheverud’s _conjecture

_/

isofemale

lines

*

Correspondence

and

_reprints

E-mail:

_{gibert@pge.cnrs-gif.fr}

Résumé - Plasticité _{phénotypique} de la _{pigmentation corporelle} chez _{Drosophila :} variations corrélées entre segments. La

_{plasticité phénotypique}

de la

_pigmentation

des conditions

climatiques

différentes. Pour

_{chaque population,}

dix

_lignées

isofemelles ont été élevées à des

températures comprises

entre 14 et 31 °C. Deux méthodes ont

été utilisées et

comparées

pour estimer les corrélations

génétiques

(rg)

entre segments,

une méthode

simple

utilisant directement les valeurs moyennes des familles

(r

m

)

et une

méthode

_{théoriquement meilleure, corrigeant}

les variances et les covariances par la taille de la famille

_(r

_c

_).

Les deux méthodes ont

_produit

des données similaires mais la

_première

(r

m

)

a été

_préférée,

car elle _permet une estimation dans tous les cas. Les corrélations

génétiques

et

_{phénotypiques}

diminuent

_quand

on _compare des _segments

_{plus distants,}

révélant un

_{gradient antéro-postérieur.}

L’extension de la

_pigmentation

noire est déterminée

par des

systèmes génétiques

de

_plus

en

_plus

différents dans des _segments

_plus

éloignés.

Les

corrélations

_génétiques

sont

_{significativement supérieures}

aux corrélations

_{phénotypiques,}

ce

_qui

est en

_opposition

avec la

_conjecture

de

Cheverud,

bien _que les deux valeurs soient

hautement corrélées.

_©

_{Inra/Elsevier,}

Paris

D. _melanogaster

_/

D. simulans

_/

corrélation génétique

/

conjecture de Cheverud

_/

lignées isofemelles

1. INTRODUCTION

Phenotypic

plasticity,

the

_capacity

of a

_single

_genotype

to

_produce

different

phenotypes

in different

_{environments,}

is a

_general

_property

of

_living

_organisms.

In many cases, for

example enzymatic adaptation

in bacteria or the

production

of

wingless

or

_winged

forms in

_aphids,

the

_{adaptive significance}

of the

_polymorphism

is obvious

_[27,

_{32, 36,}

_39!.

The

_{interpretation is, however,}

far more difficult when a

continuous environmental

_gradient

results in continuous variation of the

_phenotype,

the _response curve

_being

called the norm of reaction. Numerous cases have been

investigated

in

_plants

and

_{animals, unravelling}

two

_major,

but unsolved

_questions.

Are there

_specific

_genes

_acting

on the

shape

of the _norms,

_{independently}

of the mean trait value? Is the

_shape

of the norms acted _upon

_by

natural

_selection,

thus

exhibiting

a

specific adaptive

value?

Body pigmentation

in numerous ectotherm

_species

is known to exhibit broad

variation,

resulting

either from

_genetic

_polymorphism

or

_{phenotypic plasticity.}

In

the latter case, darker individuals are

_generally

observed at low

temperatures

[1,

8, 9, 15, 16, 18, 19, 22, 25,

40].

Increased melanization at lower

_temperatures

is

generally

thought

to be

_adaptive

for

_{thermoregulation: being}

darker will favor the

_absorption

of

_light

radiation and thus

_improve

metabolic

_activity

at low

temperature;

the reverse

_being

true at

_high

_temperature.

In

Drosophila

melanogaster

and related

_species,

_{phenotypic plasticity}

of

pigmen-tation can be

_quantified

on the mesothorax

(a

dark

_pattern

with a trident

shape)

[2, 8]

and on the

_tergites

of female abdomen

_segments

_{[9, 13!.}

The occurrence of

latitudinal clines for the thoracic trident is a

_major

_argument

in favor of the

adap-tive

_significance

of

_pigmentation

variations

_[2,

_8,

_24].

For abdomen

_{pigmentation,}

it was also shown that the

_shapes

of reaction norms were different in two French localities

_{differing by}

their climatic conditions

_!13!.

_Reactivity

to low

developmen-tal

_temperature

was

_stronger

in

_{populations living}

in the

_place

with more marked

seasonal variation and colder winters. The fact that similar results were observed

in two

_sibling

_species

_(D.

_melanogaster

and D.

_simulans)

enhanced the likelihood

that this variation in reaction norm

_shape

was

_adaptive.

In these two

_species,

the

suggests

an interaction between

developmental

genes which

specify

the formation of

adult

_segments

_{and genes which} react to

_temperature

and determine the extension of the black

_pigment.

The

_procedure

of isofemale lines is not the best method for

investigating

the

genetic

architecture of

_quantitative

traits. This

procedure

has

_proved

however to be

extremely

useful in

_ecological

_genetics

for the

_description

of

_quantitative

characters of natural

_populations

_[14,

23,

26].

Once isofemale lines have been

analysed,

we need to extract the maximum information from

_experimental

observations. With

_respect

to

_{heritability,}

the coefficient of intraclass correlation is

_generally

used as an

_{approximation}

_providing

an ’isofemale

heritability’

[3, 17!.

Concerning

genetic

correlation,

the situation is more

_ambiguous.

In various _papers

[17]

_genetic

correlations have been estimated

_using

a software

package,

but the exact

_procedure

was not described. On the other

_hand,

the

_problem

was

_{clearly analysed}

_by

Via

(37!,

but several solutions were considered.

In the

_present

paper, we

_accept

the inconveniences of isofemale

lines, adopt

a

pragmatic

approach,

and address three main issues.

_a)

What is the best _way for

estimating genetic

correlations from isofemale lines data?

_b)

To what extent are

different genes involved in

determining

the extension of black

pigment

in different

body segments?

c)

We tested Cheverud’s

_conjecture

_[5,

_20,

_29]

that

_phenotypic

cor-relations are similar to

_genetic

correlations. In other

words,

phenotypic

correlations,

which are easier to _measure, could be convenient

_predictors

of

_genetic

correlations.

2. MATERIALS AND METHODS

2.1.

_Populations

and

_{experimental procedures}

Sympatric

French

_populations

of D.

_melanogaster

and D. simulans were collected in a

_city

_garden

in Villeurbanne near

_Lyon

and a

_vineyard

in Grande Ferrade near

Bordeaux. Wild

_living

females were isolated in culture vials to establish isofemale

lines. After

_offspring

emergence, ten lines of each

species

and

_locality

were

_randomly

taken,

and from each line ten adult

_pairs

were used as

_parents

of the studied flies.

This

_procedure

is _necessary to obtain a sufficient _progeny number

(23!.

_Investigated

flies were thus the second

_laboratory

_generation.

These

_parental

flies were allowed to

_oviposit

for a few hours in successive vials

_containing

a killed

yeast

medium

_(6!.

Vials with eggs were then transferred at one of six

_experimental

_temperatures

(14,

17, 21, 25,

28 and 31

_°C)

chosen to cover almost the full _{thermal range}

_compatible

with sufficient

_viability

_(7!.

With this

_procedure,

larval

_density

was

kept

around 150 per

vial,

and the use of a

_high

nutrient food reduced

_crowding

effects. On _emergence, adults were transferred to fresh food and examined a few

days

later. From each line at each

_temperature,

ten females were

_randomly

taken and measured.

2.2.

_Pigmentation

scores

We

_analysed

_pigmentation

on the mesonotum and on the last three abdominal

segments.

Thoracic

_pigmentation

_(a

dark area with a trident

_{shape, designated}

as

trid)

can be

analysed

in males and females

[8]

while abdomen

_pigmentation

extension of black

_pigment

on the last three abdominal

_tergites

(segments

_5,

6

and

₇₎

was estimated

_visually;

11

_phenotypic

classes were used

(see

David et al.

_[9]

for

_details),

_ranging

from 0

_(completely

_yellow)

to 10

_{(completely dark).}

For the thoracic

_{trident, only}

four

_phenotypic

classes were

used, ranging

from 0

(no

visible

trident)

to 3

_{(dark trident)}

_(see

David et al.

_[8]

for

_details).

For

_comparing

thorax and abdominal

pigmentation,

we standardized their

_possible

_range of variation. The

trident

_pigmentation

score was thus

_{multiplied by}

_3.33,

so that

_{variability ranged}

between 0 and 10.

Pigmentation

variation on either thorax or abdomen is continuous and the

establishment of

_phenotypic

classes introduces the

_possibility

of some bias

according

to observer. We have

_always

been careful about this

_problem,

and verified that identical average scores would be obtained either when the same observer is

looking

twice at the same flies or when two observers compare their data

([8]

and

_unpublished

_{observations).}

The same conclusion arises from the

_high

_genetic

repeatability

which is found when successive

_generations

of the same isofemale lines are

_investigated

(14!.

2.3. Data

_analysis

Analyses

were

performed

with Statistica

[33]

and

SAS

O

[31]

software.

With available data

_(four

_segments)

we calculated six

possible pairwise

corre-lations _among

_segments.

For each

_pair

of

_characters,

each

_temperature

and each

segment,

we calculated three different correlations: the

total,

phenotypic

correla-tion

_(rp;

100

_flies);

the correlation between

_family

means

(r

m

;

10

families)

and the

within

_family

correlation

_(r

_w

_;

10 flies _per

_family).

It was

suggested

[37]

that in many cases rm could be used as an

_{approximation}

of

the

_genetic

correlation _rg.

_By

simulation,

Roff and Preziosi

_[30]

showed however that

equating

rm to _rg would be biased unless

_family

size n would be

_{sufficiently large}

(>

20).

Since in our case

family

size was less

(n

=

10),

_we calculated _a corrected value

_(r.)

between

_family

means, for a better estimate of the

_{genetic correlation,}

according

to the

_expression:

where

_X,

Y

_designate

the covariates

_{(two segments),}

COVrn and covw are the covariances between

_family

means and within

families,

and varm and varw the

corresponding

variances of each variable.

In the

_present

_work,

numerous correlation values were available for

_example

between the ten females of each line grown at each

_temperature.

These values were

then submitted to

_ANOVA,

after z transformation. Results

_helped

us to decide how to

_pool

the data for a more

_synthetic

_{presentation.}

For

_example,

in absence of a

significant

_temperature

effect,

we calculated a

single

value for each isofemale

line,

as carried out in table IIL Values of the ten lines could then be

_averaged

and

a standard error

_{provided. Comparisons}

of distributions were made with a

non-parametric

Mann-Whitney

test. Since the

_objective

of this paper was focused on

3. RESULTS

3.1. An

_{antero-posterior gradient}

Darker flies are obtained at lower

_growth

_temperatures

but the various

_segments

do not react in the same _way and exhibit different levels of

_plasticity.

This

inter-action between

temperature

and

body

segments

is illustrated

_figure

1 for the two

species.

In D.

_simulans,

a

_regular

_{antero-posterior}

_gradient

of

_increasing

darkness is

observed at low

temperatures

(14-21

°C).

At

_higher

_temperatures

an

irregularity

is

observed since

_segment

6 is darker than

_segment

7. In D.

_{melanogaster,}

the

antero-posterior

gradient

is not found for two reasons. One is that the thoracic trident is

darker than in D. simulans. The second is that

segment

6 is darker than

segment

7 at all

_{temperatures.}

Phenotypic

correlations between any

pair

of

_segments

were calculated for each

temperature

and

_population

₍₁₀₀

flies in each

_sample,

_{table 1)}

and submitted to an ANOVA

_(not

_shown)

after a z transformation.

_Higher

correlations were ob-served between

_adjacent

_segments

while lower values were found when more

dis-tant

_body

_segments

are considered

(F(

5

,

67

)

=

66.49,

P _<

0.001).

Average

rp

val-ues decrease from about 0.50 when

_adjacent

_segments

were correlated

(5-6

and

6-7)

down to a

_{non-significant}

0.07 when

correlating

segment

7 with trident

(figure 2).

There are

_higher

correlations in D.

_melanogaster

than in its

_sibling

(F(

1

,

67

)

=

42.24,

P _<

0.001),

higher

values in Villeurbanne than in Bordeaux

es-pecially

in D.

_melanogaster

_(F(

₁

_.

₆₇

₎

=

9.96,

P _<

0.01),

and

higher

correlations at low

_temperatures

_(F(

₅

_.

₆₇

₎

=

5.97,

P _<

0.001).

Two other correlations _were calcu-lated and

_analysed:

the within-line correlation

_{(r w)}

_(table

_III)

and the correlation between mean values of the isofemale lines

_(r

_n

_{,) (table II).}

All values are

_{significantly}

superior

to zero with the

_exception

of

_{thorax-segment}

7

(interval

_6,

all

correlations),

and the

_{thorax-segment}

6

_{(interval 5)}

for the within-line correlations. In each case

significant

variations were also observed

( figure 2)

_{corresponding}

to

_decreasing

cor-relations with

_increasing

_segmental

_distance,

thus

_confirming

the

_{antero-posterior}

gradient.

3.2. Genetic correlations

As stated in the method

_section,

the correlation between

_family

means

_(r

_m

₎

should be corrected when the

_family

size is less than 20. We calculated these corrected values 7’c, and

compared

them to rn,.

Average

values were found to be very similar and not

_{statistically}

different

_(D.

_{melanogaster.}

rc = 0.547 !

_0.0383,

r n

, = 0.539 t

_0.0396,

_n =

64;

D. simulans: _cr = 0.441 t

0.071,

_rI&dquo; = 0.408 !

0.0634,

n =

40).

Moreover,

variations of _r! and _rm were themselves

_highly

correlated

(figure 3).

Finally,

numerous r could not be estimated because of either null or

negative

variances,

or of values

_greater

than 1. Over the whole data set 126 rm

were available but

_only

104 fc- We thus decided to use rm as a better

_general

estimate of _rg

_{(table IB}

and submitted them to an ANOVA

(not shown)

after a z

transformation.

These values decreased

_along

the

_{antero-posterior}

_gradient

_{(figure 2)}

_(F<

₅

_,

₆₇

_{> =}

than in D. simulans

_(0.54

versus

0.33)

(F!l,s7)

=

37.35,

P _<

0.001)

and

higher

in Villeurbanne than in Bordeaux in D.

_melanogaster

_(0.65

versus

_0.44)

_(F!l,s7)

=

8.18,

P < 0.001).

3.3.

_Phenotypic

correlations and Cheverud’s

_conjecture

correlation. The correlations between

_segments

for the ten females reared in the

same vial were calculated for each

population,

line and

_temperature.

In _many cases

these correlations could not be calculated because either one _{average value}

_(mainly

the trident of D.

_simulans)

or a variance

(at

extreme

_{temperatures)}

was null. For

each

line,

correlations were

_averaged

over

_{temperatures,}

and mean values are

_given

in table III.

_Average

within-line correlations were

_highly

variable between

_segments,

illustrating

again

the

_{antero-posterior}

_gradient

_(table

III,

figure

2).

Cheverud’s

conjecture

[5]

states that

_phenotypic

correlations

might

be

conve-nient

_{approximations}

of

_genetic

correlations. This

_requires

₁₎

that the two sets of

across

species,

populations

and

_temperatures

(Spearman

rank correlation rs =

0.87,

n =

126,

P _<

0.0001).

Phenotypic

correlations _were biased

downwards,

however. The _average difference between the two correlations was 0.14

_{(s.e. 0.02).}

In

_general,

genetic

correlations were

_greater

than

_{phenotypic correlations,}

except

when all

correlations tend to zero.

4. DISCUSSION AND CONCLUSIONS 4.1. An

_{antero-posterior gradient}

There was a

_progressive

decrease of all correlations

_(phenotypic,

_genetic

and

environmental)

as

_body

_segments

became more distant

_(figure

_!).

For

_example,

the

pigmentation

of the mesothorax was

_positively

correlated with that of abdominal

segment

5 in more cases than with that of abdominal

_segment

7. In

previous

work

[9, 13]

it was

argued

that different

shapes

of reaction norms in different

_segments

demonstrated that their

_genetic

bases were not

_exactly

the same. The

_regular

decrease in the

_genetic

correlations with

_segmental

distance is further

_proof

of that

conclusion.

_{Investigating}

correlations between values of the same trait in different

environments is central in

_{understanding}

the

_genetics

of

_{phenotypic plasticity}

_[10,

37,

38].

Falconer

_[11]

proposed

that the same trait measured in two environments

David et al.

_[9]

showed that when

_pigmentation

scores of the same lines at different

temperatures

were

_compared,

the correlation decreased

_regularly

when more distant

temperatures

were considered. This

_phenomenon

was also observed in the

_present

Pigmentation

genes are

_expressed

_differently

across environments and

_body

segments.

The fact that

_{pigmentation expression}

varies

_according

both to

_body

segment

and

temperature

indicates the existence of a

_complex

interaction between internal and external environments. But this interaction has

_{regularities.}

The

embryonic

determinism of

_body

_segments

and the role of homeotic _genes are now

well known in

_Drosophila

_(21!.

Our

_knowledge

on the

_development

of the

_epidermis

of adult

_segments

_is,

_however,

much less

_[12]

so that _any

_precise

_hypothesis

on the nature of

_interacting

_genes seems

_premature.

4.2.

_Phenotypic

correlations and Cheverud’s

_conjecture

For the

_analysis

of natural

_populations,

various fitness-related traits are often

measured on different

individuals,

providing

an estimate of

_phenotypic

correlation.

Evolutionary theory

has

_emphasised

the

_major

_significance

of trade-offs between characters for

_explaining

the

_stability

of life

_history

_strategies

_[4,

_{28, 34,}

_35].

Since evolution is

_dealing

with

_{genetic variations, estimating}

heritabilities and

genetic

correlations in natural

_populations

appears as a

_major

issue in

_evolutionary

biology.

In various

_{investigations,}

both

_phenotypic

and

_genetic

correlations have

been

measured,

and in _many cases similar values have been observed

_{[5, 29!.}

This

led Cheverud to _propose his

_conjecture

_according

to which

_phenotypic

correlations could be used in many cases as a crude estimate of

genetic

correlations.

The isofemale line

technique provides

an estimate of the

genetic

variability

of _any

trait

_{[3, 17!.}

When two traits are measured on the same

individual,

the between-line

covariance also allows an estimate of the

_genetic

correlation.

_Except

when

_family

size is

_{sufficiently large}

_(n

>

₂₀₎

the variance and covariance between families should

be corrected

_by

the within

_family

terms. In the

_present

_work,

a total of 144 values of

rg could be

calculated,

and it was thus

possible

to test

empirically

the

efficiency

of

the correction.

_{Quite surprisingly,}

both methods

_provided

very similar coefficients

of correlation.

_{Additionally,}

in about 17

%

of _cases, the corrected values could not

be calculated.

_Practically,

and at least when

_family

number is

quite small,

it seems

better to estimate the

_genetic

correlation

_{by simply}

_using

the

_family

_means, as

already suggested

by

Via

_(37!.

We obtained confirmation but also invalidation of Cheverud’s

_[5]

_conjecture.

On

the one

_hand,

we found that the two sets of correlations were

_highly

correlated and varied in a

_parallel

_way. On the other

_hand,

the

_genetic

correlations were

regularly

and

_consistently

greater

that the

_phenotypic

ones. In our

experimental

protocol,

we

_kept

the environmental variance and covariance

(as

estimated

_by

the

within-line

_values)

as small as

_possible

_through

the control of the

_experimental

conditions: constant

_temperature

and

_good

larval

_feeding.

This

_{might explain}

the low value of the within-line correlations. It

_is,

_thus,

unclear to what extent that

Cheverud’s

_conjecture

_might

better

_apply

to natural

_{populations living}

in a variable

environment,

or to endothermic

_species.

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