Evolução Experimental da Forma da Asa de Drosophila ...€¦ · Drosophila melanogaster Integração morfológica, plasticidade fenotípica, bases celulares e expressão gênica

Universidade Federal do Rio de Janeiro Instituto de Biologia Pós-Graduação em Biodiversidade e Biologia Evolutiva

Evolução Experimental da Forma da Asa de Drosophila melanogaster

Integração morfológica, plasticidade fenotípica, bases celulares e expressão gênica

Daniel de Mattos Corrêa

Orientação: Blanche Christine Pires de Bitner-Mathé Leal

Rio de Janeiro 2015

I

Evolução Experimental da Forma da Asa de Drosophila melanogaster


Tese de Doutorado apresentada ao Programa de Pós-graduação em Biodiversidade e Biologia Evolutiva, Instituto de Biologia, Universidade Federal do Rio de Janeiro, como parte dos requisitos necessários à obtenção do título de Doutor em Ciências Biológicas (Biodiversidade e Biologia Evolutiva).


Rio de Janeiro

Abril de 2015

II

FICHA CATALOGRÁFICA:

Corrêa, Daniel de Mattos Evolução Experimental da Forma da Asa de Drosophila melanogaster/Daniel de Mattos Corrêa. Rio de Janeiro: UFRJ / IB, 2015. XV, 140 p. Orientadora: Blanche Christine Pires de Bitner-Mathé Leal Tese (Doutorado) – Universidade Federal do Rio de Janeiro, Insituto de Biologia, Programa de Pós-graduação em Biodiversidade e Biologia Evolutiva, 2015. Referências bibliográficas: f. 128-140 1. Seleção artificial. 2. Expressão Gênica 3. Drosophila melanogaster – Tese. I. Bitner-Mathé, B.C. II. Universidade Federal do Rio de Janeiro, Instituto de Biologia. III. Evolução Experimental da Forma da Asa de Drosophila melanogaster.

III

Evolução Experimental da Forma da Asa de Drosophila



Tese de Doutorado apresentada ao Programa de Pós-Graduação em Biodiversidade e Biologia Evolutiva, Instituto de Biologia, Universidade Federal do Rio de Janeiro, como parte dos requisitos necessários à obtenção do título de Doutor em Ciências Biológicas (Biodiversidade e Biologia Evolutiva).

Data: 28 de Abril de 2015 Aprovada por:

Prof. Dr. Antônio Solé-Cava (Departamento de Genética, IB - UFRJ)

Profª. Dra. Leila Maria Pessoa (Departamento de Zoologia, IB – UFRJ)

Profª. Dra. Helena Marcolla Araujo (Departamento de Histologia e Embriologia - UFRJ)

Prof. Dr. Paulo Cesar de Paiva (Departamento de Zoologia, IB – UFRJ)

Prof. Dr. Régis Lopes Corrêa (Departamento de Genética. IB - UFRJ)

Profª. Dra. Cássia Mônica Sakuragui (Instituto de Biologia – UFRJ. Suplente Interno)

IV

Profª. Dra. Katia Carneiro de Paula (Instituto de Ciências Biomédicas – UFRJ. Suplente Externo)

AGRADECIMENTOS

À Blanche, minha querida orientadora, por mais de uma década de ensinamentos, muito além

das técnicas em drosófila ou genética, mas de como ser professor, de como ter paixão por

ciência e de como viver uma vida laboratorial agradável!

Aos meus pais, Gerson e Izabel, pelo apoio irrestrito aos meus projetos pessoais e de trabalho.

Sem a semente e a rega de vocês, nada teria acontecido. Obrigado por acreditarem em mim

e por me darem uma rede de suporte sem a qual tudo seria muito mais difícil! À Lia e ao Celso

por expandirem minha definição de família e por trilharem ao meu lado essa jornada de todos

nós.

Ao André, pelo carinho, pelo companheirismo, pelas alegrias e por compreender e me ajudar

a atravessar de forma mais leve pelos estresses que vieram no último ano da elaboração dessa

tese. Obrigado!

Ao meu irmão Eduardo, pelas cervejas, pelos papos, pelas discussões políticas e pelo carinho.

À minha querida Raquel, pelas overdoses de cafeína e pelos litros de cerveja, compartilhando

medos, alegrias e as angústias dessa vida, sobretudo da vida acadêmica, mas também do resto

dela!

À Mariana, Renata e Clarice por aguentarem as incontáveis horas de prosa analítica,

escrutinando cada detalhe da vida. Por suportarem minhas paixões por discussões, minhas

relações intensas de prazer e angústia com os mínimos detalhes dessa empreitada de se saber

o que se puder saber.

À Fe Nunes por ser esquisita que nem eu e entender junto comigo o que parece ser uma

linguagem só nossa.

V

À Fe Braga, por todo o carinho, pelos papos e por ter dado vida ao Pedro e ao Luquinha; essas

duas crianças incríveis!

À Pat, Lucia, Dri, Aline e todos os amigos que têm feito esses 12 anos desde que entramos na

Biologia tão alegres e tão divertidos!

Aos amigos da vida, da praça e dos papos, Thaís, Tadeu, Mari Santana e todos os demais que

contribuem para dar sentido ao desenrolar dos dias.

À família Paiva por me ensinar que amigos são família também e que há poucos prazeres

maiores do que uma mesa com comida, vinho e amigos ao redor.

Ao meu fígado, fiel escudeiro, por aguentar firme, forte e jovial apesar de eu maltratá-lo

algumas vezes.

À todos os professores da banca, não somente pelas discussões que serão levantadas durante

a sabatina dessa tese, mas por terem contribuído com a minha formação durante esses 12

anos de Biologia e por terem sido inspiração para a construção do professor que eu gostaria

de um dia vir a ser.

Ao Programa de Pós-graduação em Biodiversidade e Biologia Evolutiva da UFRJ da qual tive o

prazer de fazer parte da primeira turma de ingressantes. Vida longa e próspera ao programa.

Às drosófilas, sem as quais esse trabalho jamais seria possível.

Este trabalho foi desenvolvido com recursos da Coordenação de Aperfeiçoamento Pessoal de

Ensino Superior (CAPES), incluindo a Bolsa de Doutorado pela qual sou grato.

VI

RESUMO

Fenótipos complexos são aqueles cuja variação não pode ser explicada por relações

mendelianas simples e suas bases genéticas apresentam alta pleiotropia com grande parte da

variação residindo nas interações entre os elementos ontogenéticos. Forma e tamanho de

estruturas biológicas, assim como o autismo e o câncer, são exemplos de traços complexos. O

ambiente também interfere no desenvolvimento desse tipo de caráter, processo conhecido

por plasticidade fenotípica. A identificação dos elementos que contribuem para a

determinação fenotípica torna-se igualmente complexa. A asa de Drosophila é um modelo

amplamente estudado que oferece uma grande cobertura de conhecimentos sobre sua

morfologia, desenvolvimento, genética e evolução, tornando-se, portanto, um alvo ideal para

um melhor entendimento da variação quantitativa de fenótipos complexos. Neste trabalho,

utilizamos linhagens de D. melanogaster selecionadas para formas extremas da asa a fim de

investigarmos diversos aspectos envolvidos na variação quantitativa de forma e tamanho.

Primeiramente, apresentamos um estudo descritivo da morfologia da asa, assim como

estimativas de integração genética e fenotípica de traços correlacionados com o intuito de

testarmos a previsibilidade de trajetórias evolutivas através de matrizes de correlação

genética da população inicial, antes do processo de seleção artificial ocorrer. No segundo

capítulo apresentamos um estudo sobre a plasticidade fenotípica relacionada à temperatura,

descrevendo as normas de reação em 10 temperaturas, cobrindo quase todo o espectro

possível para a espécie. Mostramos também que a média fenotípica influencia a norma de

reação, sugerindo que parte dos genes devem ser compartilhados entre as duas, cenário

chamado de sensitividade alélica. O terceiro capítulo aborda as bases celulares, mostrando

que o número de células da asa, mas não o tamanho delas, desempenham um importante

papel na resposta de forma à seleção, com um aumento no número de células em linhagens

com asa alongada. Por fim, investigamos os genes candidatos ao controle da forma da asa

através de um ensaio de microarranjo, real time qPCR e validações morfológicas pelo

silenciamento gênico tecido-específico mediado por RNAi. A natureza poligênica fica evidente,

sugerindo um modelo genético infinitesimal com múltiplos genes de efeitos pequenos. Ainda

assim, o desenho experimental permitiu a identificação de 11 genes fortemente candidatos a

terem uma grande contribuição relativa.

VII

ABSTRACT

Complex phenotypic traits are those whose variation cannot be explained by simple

mendelian inheritance and its genetic bases exhibit high pleiotropy, with a great amount of

variation residing on the interaction of ontogenetic elements. Shape and size of biological

structures, as well as autism and cancer, are classic examples of complex traits. Environment

also plays an important role in these traits through a process called phenotypic plasticity.

Identification of contributory elements to phenotypic determination is equally complex. The

Drosophila wing is a widely studied model, offering a broad coverture of published knowledge

on its morphology, development, genetics and evolution, thus making it an ideal target for a

better comprehension of the quantitative variation of complex traits. In the present work, we

use D. melanogaster strains artificially selected for extreme wing shapes in order to

investigate multiple layers involved in the quantitative variation of shape and size. First, we

present a descriptive study on wing morphology, as well as genetic and phenotypic integration

estimates of correlated traits. We tested the predictability of evolutionary trajectories

imposed by genetic correlation matrices of the initial population, prior to selection. On the

second chapter, we present a study on thermal-related plasticity that describes the reaction

norms on a gradient of 10 developmental temperatures, covering almost the entire spectrum

of livable temperatures for the species. We show that phenotypic mean influences its reaction

norm, suggesting that some genes might be shared, favoring a scenario called allelic

sensitivity. The third chapter addresses the cellular bases, showing that cell number, but not

their size, plays an important role on shape response to selection. Finally, we investigated

candidate genes for controlling wing shape through a microarray assay, real time qPCR and

morphological validation through tissue-specific gene silencing mediated by RNAi. The

polygenic nature becomes evident, suggesting an infinitesimal genetic model, with multiple

genes of small effects. Still, experimental design allowed the identification of 11 strong

candidate genes with a high relative contribution.

VIII

LISTA DE ILUSTRAÇÕES

INTRODUÇÃO

Figura 1: “INFINITAS FORMAS DE GRANDE BELEZA” ....................................................... 17

Figura 2: ASAS FOSSILIZADAS ........................................................................................... 21

Figura 3: DISCOS IMAGINAIS DE DROSOPHILA ................................................................. 23

Figura 4: DISCO IMAGINAL DE ASA ................................................................................... 24

Figura 5: EVERSÃO DO DISCO IMAGINAL .......................................................................... 24

Figura 6: EXPANSÃO DA LÂMINA DA ASA ......................................................................... 25

Figura 7: MODIFICAÇÕES NO FORMATO CELULAR ........................................................... 25

Figura 8: MODULARIZAÇÃO NO DESENVOLVIMENTO DO DISCO IMAGINAL ................... 27

Figura 9: ASAS DE SELEÇÃO COM FORMAS EXTREMAS .................................................... 30

Figura 10: MEDIDAS DE FORMA E TAMANHO DA ASA EM DROSOPHILA ......................... 30

CAPÍTULO I: MORFOLOGIA

Figura 1: THE ELLIPSE METHOD ......................................................................................... 37

Figura 2: HISTOGRAMS OF SIZE AND SHAPE ..................................................................... 41

Figura 3: ILLUSTRATIVE WINGS FROM L AND R STRAINS .................................................. 42

Figura 4: HISTOGRAMS OF WING LENGTH AND WIDTH ................................................... 43

Figura 5: PLOT OF WING LANDMARKS ............................................................................. 45

Figura 6: GRAPHICAL REPRESENTATION OF GENETIC AND PHENOTYPIC CORRELATIONS47

CAPÍTULO II: PLASTICIDADE FENOTÍPICA

Figura 1: HISTOGRAMS OF WING SHAPE AND SIZE .......................................................... 58

Figura 2: TEMPERURE REACTION NORMS FOR WING SHAPE AND SIZE ........................... 59

IX

Figura 3: RELATIONSHIP OF b_WSH AND PHENOTYPIC MEAN .......................................... 61

Figura 4: PROPORTIONAL VARIATION OF WW AND WL ..................................................... 62

CAPÍTULO III: BASES CELULARES

Figura 1: MORPHOMETRIC METHODS FOR INTERVEIN VARIATION ................................ 84

Figura 2: MEAN VALUES AND STANDARD ERRORS OF WING TRAITS ............................... 87

Figura 3: MEAN VALUES AND STANDARD ERROS PER INTERVEIN REGION ...................... 90

Figura 4: QUADRATIC CURVES FITTED TO AVERAGE CELL AREA ....................................... 91

Figura S1: ELLIPSE ADJUSTMENT ..................................................................................... 100

CAPÍTULO IV: GENES CANDIDATOS

Figura 1: EXPRESSION PATTERN OF GAL4 ....................................................................... 109

Figura 2: GAL4 / UAS SILENCING SYSTEM ....................................................................... 109

Figura 3: PIE CHART SUMMARIZING MICROARRAY ASSAY RESULTS .............................. 111

Figura 4: BOXPLOT OF WSH VARIATION ........................................................................... 112

Figura 5: HEATMAPS OF EXPRESSION RATIOS ................................................................ 113

Figura 6: VISUAL REPRESENTATION OF EXPRESSION LEVELS.......................................... 114

Figura 7: EFFECTS OF RNAi-MEDIATED SILENCING OF dp ............................................... 117

Figura 8: MALFORMATIONS ON THE WING OF D. melanogaster ................................... 117

CONCLUSÕES E PERSPECTIVAS FUTURAS

Figura 1: PADRÕES DE INTERFERÊNCIA NA ASA DE DROSOPHILA .................................. 126

X

LISTA DE TABELAS

CAPÍTULO I: MORFOLOGIA

Tabela 1: PHENOTYPIC AND GENETIC CORRELATION MATRICES FOR PRE-SELECTION .... 40

Tabela 2: ANCOVA FOR WSH AND WSI. ............................................................................... 42

Tabela 3: MANCOVA FOR ANGULAR MOVIMENTS OF LANDMARKS ................................ 44

Tabela 4: PHENOTYPIC CORRELATION FOR POST-SELECTION .......................................... 46

CAPÍTULO II: PLASTICIDADE FENOTÍPICA

Tabela 1: ANOVA FOR WSH AND WSI. ................................................................................. 58

Tabela 2: ANOVA FOR REACTION NORM PARAMETERS ................................................... 60

Tabela S1: MEAN AND STANDARD ERROR OF EXPERIMENTAL GROUPS. ......................... 71

Tabela S2: LINEAR AND QUADRATIC COEFFICIENTS ......................................................... 74

Tabela S3: PAIRWISE COMPARISON FOR THE SEL EFFECT OF ANOVA (TABLE2). ............. 76

CAPÍTULO III: BASES CELULARES

Tabela 1: ANOVA OF MORPHOLOGICAL AND CELLULAR TRAITS. ..................................... 89

Tabela S1: MEAN AND STANDARD ERROR FOR SELECTION STRAINS. ............................ 101

Tabela S2: MEAN AND STANDARD ERROR FOR BASELINE POPULATION... ..................... 102

Tabela S3: QUADRATIC REGRESSIONS OF AVERAGE CELL AREA. .................................... 103

CAPÍTULO IV: GENES CANDIDATOS

Tabela 1: F-TESTS FOR THE EFFECTS OS SEL . .................................................................. 112

Tabela 2: F-TESTS FOR RNAi STRAINS ............................................................................. 116

XI

LISTA DE ABREVIATURAS E SIGLAS

1C, 2C, 5C e 6C linhagens controle do processo de seleção

1L, 2L, 5L e 6L linhagens selecionadas para forma alongada da asa

1R, 2R, 5R e 6R linhagens selecionadas para forma alongada da asa

a raio maior da elipse

ANCOVA análise de covariância

ANOVA análise de variância

APF após formação da pupa

b raio menor da elipse

BIOREP réplicas biológicas do processo de seleção

b _WSH parâmetro da inclinação da reta que descreve a NR de WSH

BED biologia evolutiva do desenvolvimento

cDNA ácido desoxirribonucleico complementar

c _W_ parâmetro da curvatura da NR do traço analisado

CA área da célula da asa

CAaverage área média das células da asa

CD diâmetro celular

CN número de células

CNtotal número de células totais da asa

CNWW número de células na largura da asa

CNWL número de células no comprimento da asa

CNWW número de células no comprimento da asa

CNWW número de células no comprimento da asa

DE genes diferencialmente expressos

DNA ácido desoxirribonucleico

DV eixo do desenvolvimento dorsoventral

DT temperatura de desenvolvimento

EP erro padrão

G64 geração 64 das linhagens de seleção

G100 geração 100 das linhagens de seleção

XII

G1_TD16 1ª geração nascida em laboratório mantida a 16°C

h2 herdabilidade

IVR região interveia

IVRA-E região interveia especificada

L linhagens de seleção para asas longas

MANCOVA análise de variância multivariada

NER razão da expressão normalizada

NR norma de reação

PD eixo do desenvolvimento proximodistal

PF plasticidade fenotípica

qPCR reação em cadeia da polimerase quantificada em tempo real

QTL lócus de traço quantitativo

R linhagens de seleção para asa arredondada

rG correlação genética

RN norma de reação

RNAi ácido ribonucleico de interferência

RT transcrição reversa

SD dimorfismo sexual

SEL efeitos da seleção

TD temperatura de desenvolvimento

WA asa da área

WSH índice de forma da asa de Drosophila

WSHmean média de WSH

WSI índice de tamanho da asa de Drosophila

WL índice de comprimento da asa de Drosophila

WW índice de largura da asa de Drosophila

WWpredicted medida esperada de WW

ângulo que estima o posicionamento de veias

XIII

SUMÁRIO

INTRODUÇÃO ......................................................................................................................... 16

VARIAÇÃO FENOTÍPICA E EVOLUÇÃO BIOLÓGICA ............................................................ 16

A ASA DE DROSOPHILA ..................................................................................................... 19

1 ASPECTOS MACROEVOLUTIVOS .......................................................................... 19

2 A ASA ENQUANTO MODELO ............................................................................... 21

3 DESENVOLVIMENTO ............................................................................................ 22

4 MODULARIZAÇÃO E GENES CANDIDATOS AO CONTROLE DA VARIAÇÃO

QUANTITATIVA ....................................................................................................................... 25

5 PLASTICIDADE FENOTÍPICA ................................................................................. 28

6 SELEÇÃO ARTIFICIAL DA ASA DE D. MELANOGASTER ......................................... 29

OBJETIVOS E ORGANIZAÇÃO DA TESE .................................................................................. 31

CAPÍTULO I: MORFOLOGIA .................................................................................................... 32

Phenotypic and Genetic Integration in The Drosophila Wing

ABSTRACT .......................................................................................................................... 33

INTRODUCTION ................................................................................................................. 34

MATERIALS AND METHODS .............................................................................................. 35

ARTIFICIAL SELECTION PROGRAM .............................................................................. 35

PRE-SELECTION GENERATION HERITABILITY, PHENOTYPIC AND GENETIC

CORRELATION ESTIMATES .......................................................................................... 36

POST SELECTION GENERATION: MORPHOLOGICAL INTEGRATED RESPONSE AND

REACTION NORMS ...................................................................................................... 36

WING MORPHOLOGY MEASUREMENTS .................................................................... 36

STATISTICS .................................................................................................................. 37

RESULTS ............................................................................................................................. 38

DISCUSSION ....................................................................................................................... 47

REFERENCES ...................................................................................................................... 49

XIV

CAPÍTULO II: PLASTICIDADE FENOTÍPICA ............................................................................. 52

Thermal Plasticity Evolution in Strains of Drosophila melanogaster Selected for Divergent Wing Shape

ABSTRACT .......................................................................................................................... 53 INTRODUÇÃO ..................................................................................................................... 55

MATERIAL AND METHODS ................................................................................................ 55

STRAINS ...................................................................................................................... 55

REACTION NORMS ...................................................................................................... 55

WING SHAPE AND SIZE DESCRIPTORS ........................................................................ 56

RESULTS ............................................................................................................................. 57

DISCUSSION ....................................................................................................................... 63

ACKNOWLEDGEMENTS ..................................................................................................... 66

REFERENCES ...................................................................................................................... 66

SUPPORTING INFORMATION ............................................................................................. 71

CAPÍTULO III: BASES CELULARES ........................................................................................... 77

Cellular Basis of Morphological Variation and Temperature-related Plasticity in Drosophila melanogaster Strains with Divergent Wing Shapes

ABSTRACT .......................................................................................................................... 78

INTRODUÇÃO ..................................................................................................................... 81

MATERIALS AND METHODS .............................................................................................. 81

FLIES (STRAINS WITH ARTIFICIALLY SELECTED WING SHAPES) .................................. 81

EXPERIMENTAL DESIGN .............................................................................................. 82

WING MORPHOMETRICS ........................................................................................... 82

CELL SIZE (AREA) AND CELL NUMBER ........................................................................ 84

STATISTICAL ANALYSES AND DISTRIBUTION OF CELL AREA ACROSS INTERVEIN

REGIONS ................................................................................................................... 85

RESULTS ............................................................................................................................. 85

DISCUSSION ....................................................................................................................... 92

ACKNOWLEDGMENTS ....................................................................................................... 96

REFERENCES ...................................................................................................................... 96

SUPPORTING INFORMATION ........................................................................................... 100

XV

CAPÍTULO IV: GENES CANDIDATOS ..................................................................................... 104

Gene Expression Profile and Candidate Genes in Strains of Drosophila melanogaster Selected for Divergent Wing Shapes

ABSTRACT ........................................................................................................................ 105

INTRODUCTION ............................................................................................................... 106

MATERIAL AND METHODS .............................................................................................. 107

MICROARRAY ON 1L AND 1R STRAINS ..................................................................... 107

BIOLOGICAL REPLICATE STRAINS ............................................................................. 107

WING IMAGINAL DISC RNA EXTRACTION AND REAL TIME qPCR ............................. 107

MORPHOLOGICAL VALIDATION ............................................................................... 108

RESULTS ........................................................................................................................... 110

EXPRESSION PATTERNS ............................................................................................ 110

MORPHOLOGICAL VALIDATION ............................................................................... 115

DISCUSSION ..................................................................................................................... 118

ACKNOWLEDGMENT ....................................................................................................... 120

REFERENCES .................................................................................................................... 121

CONCLUSÕES E PERSPECTIVAS FUTURAS ........................................................................... 123

REFERÊNCIAS BIBLIOGRÁFICAS ........................................................................................... 125

I N T R O D U Ç Ã O

16

VARIAÇÃO FENOTÍPICA E EVOLUÇÃO BIOLÓGICA

Este planeta é uma vastidão de “infinitas formas de grande beleza” (figura 1) como

rapidamente percebeu Charles Darwin em sua longa jornada pelas florestas tropicais da

América do Sul. Sob a regência das leis básicas da física, fervilha aqui uma miríade de formas,

cores, sons, texturas e estratégias, “enquanto este planeta segue sua órbita, seguindo a lei

fixa da gravidade” (DARWIN, 1859). Essa vasta rede interconectada pela história evolutiva

compartilhada e pela ecologia se mostra imponente, compelindo muitos de nós a buscarmos

uma melhor compreensão sobre seus meandros e mecanismos.

Desde a formulação feita por Darwin e Wallace (WALLACE, 1858), a teoria da evolução

vem sofrendo transformações, corroborando os nortes básicos originais, mas agregando

novas informações e perspectivas para uma melhor compreensão do processo evolutivo. São

as “infinitas” variações entre indivíduos o substrato para a seleção natural, que irá atuar sobre

os componentes herdáveis que promovem as diferenças e não sobre aqueles que não podem

ser reproduzidos nas gerações subsequentes (RIDLEY, 2004). Por essa razão, os modelos

clássicos da biologia evolutiva confiaram, predominantemente, a variação fenotípica às

variações alélicas, em especial àquelas com efeitos aditivos (DAWKINS, 1976). Na realidade o

que se viu foi um cenário bem mais complexo do que o esperado com diversos fenótipos

observáveis não sendo facilmente explicados por diferenças em alelos. A dificuldade em se

explicar esses fenótipos ou associá-los à variantes genéticas, tem aberto novas frentes de

pesquisa. Essa dificuldade tem inclusive apontado para teorizações que recapitulam processos

antes descartados e apresentam perspectivas neo-lamarckistas, em especial para explicar a

evolução humana e seus aspectos culturais (JABLONKA; LAMB, 2005).

Esse tipo de variação é predominante na natureza e esses traços são considerados

fenótipos complexos, ou seja, aqueles cuja variação fenotípica não pode ser explicada por

relações mendelianas simples, demandando uma visão sistêmica para o seu estudo (SHAO et

al., 2008). Em geral são determinados por um grande número de genes com efeitos

pleiotrópicos e uma grande parte da variação reside nas interações entre genes, dificultando

o estabelecimento de relações causais entre variantes genéticas e as variações fenotípicas. O

tamanho e forma de estruturas, respostas comportamentais, doenças como o câncer, autismo

e esquizofrenia, são exemplos de caracteres complexos (AYROLES et al., 2009; PEARLSON;

FOLLEY, 2008; SHINJI IJICHI, NAOMI IJICHI, YUKINA IJICHI, 2011).


17

Figura 1. “Infinitas formas de grande beleza”. Fotografias: Daniel Mattos.


18

No que se refere aos elementos envolvidos nessa classe de determinação fenotípica,

variações nas sequências de nucleotídeos em regiões codificantes e em regiões promotoras

ou acentuadoras podem gerar fenótipos diferenciados. Por outro lado, fatores epigenéticos

como mudanças de estados da cromatina, padrões de metilação e acetilação de regiões

codificantes e reguladoras também promovem variação em fenótipos complexos (CARROLL,

2005; FALCONER; MACKAY, 1996; PIGLIUCCI, 2001). A metilação de regiões do DNA é um dos

mais estudados mecanismos para o controle dos níveis de expressão gênica e estudos têm

indicado que o padrão de metilação pode ser transmitido às gerações subsequentes e,

portanto, ter impactos nas trajetórias evolutivas dos caracteres. Por exemplo, Waterland e

Jirtle (2003) demonstraram que diferenças nas concentrações do radical metil na dieta de

camundongos alteram radicalmente a coloração dos pelos e a predisposição à obesidade, com

impactos fenotípicos nas gerações subsequentes, mesmo estas não tendo sido expostas ao

estímulo nutricional.

Finalmente, condições ambientais também são capazes de alterar trajetórias do

desenvolvimento, gerando fenótipos variados, processo chamado de plasticidade fenotípica

(PIGLIUCCI, 2001). Essa propriedade vem sendo estudada há mais de um século quando

Woltereck cunhou o termo norma de reação para descrever a função matemática das

variações morfológicas observadas em Dapnhia sp. em resposta à presença de predadores

(SARKAR, 1999; WOLTERECK, 1909). Ao longo do século XX, diversas pesquisas mostraram

que a plasticidade é um processo comum a praticamente todos os traços fenotípicos (WEST-

EBERHARD, 2003). A busca pelos elementos que promovem as “infinitas formas de grande

beleza” continua mais acirrada do que nunca.

Com esse intuito, a biologia evolutiva do desenvolvimento (BED) tem reunido os

conhecimentos de áreas que permaneceram por muito tempo separadas. Diversos estudos

têm relacionado a variação fenotípica aos reguladores da expressão gênica, em oposição à

visão clássica de que a variabilidade estaria contida majoritariamente em regiões codificantes

dos genes. A BED tem encontrado evidências de grandes mudanças evolutivas relacionadas às

sequências reguladoras que funcionariam como interruptores da expressão gênica em função

da localização e do tempo de desenvolvimento. Quanto ao plano básico e forma dos

organismos, há evidências de que poucos genes, com elevada conservação entre espécies

filogeneticamente distantes, seriam responsáveis pela determinação desses fenótipos, sendo


19

reutilizados em diversas estruturas durante o desenvolvimento. Mudanças nas regiões

reguladoras desses genes seriam responsáveis por modificações fenotípicas de grande

importância evolutiva, possibilitando a enorme diversidade do grupo (CARROLL, 2006). A

expressão gênica pode ser regulada por diversos fatores como mudanças nas sequências

reguladoras, estados metilacionais, disponibilidade de fatores de transcrição, além de

reguladores pós-transcricionais como, por exemplo, o RNA de interferência (CARROLL, 2005;

CHENG et al., 2005; KING; WILSON, 1975). Entretanto, a hipótese de que modificações das

taxas de expressão gênica podem explicar parte das variações entre populações e espécies é

difícil de ser testada (LI; SAUNDERS, 2005) e há ainda grande demanda de estudos de

expressão gênica entre populações e espécies que possam justificar parte das divergências

morfológicas observadas, em especial em caracteres complexos.

Em sistemas como o desenvolvimento, a compreensão dos inúmeros fatores que

participam do processo é extremamente difícil. Contudo, é possível a identificação de

elementos cuja variação é capaz de alterar trajetórias do desenvolvimento, levando à

produção de fenótipos muito distintos. Nesse sentido, a asa de Drosophila oferece um modelo

único para o entendimento da evolução de caracteres complexos devido à enorme quantidade

de informação disponível e a possibilidade de se formular desenhos experimentais e analisá-

los sob uma gigantesca base de dados.

A ASA DE DROSOPHILA

1 ASPECTOS MACROEVOLUTIVOS

Drosófilas são insetos da ordem Diptera e possuem somente um par de asas. O

segundo par de asas foi perdido durante a evolução do grupo, tendo se reduzido a um par de

halteres, estruturas vestigiais que auxiliam o equilíbrio durante o voo. Por se tratarem de

estruturas muito finas e delicadas, as asas de dípteros não são facilmente encontradas no

registro fóssil, mas os mais antigos conhecidos datam do Permiano superior, há 250ma

(YEATES; WIEGMANN, 1999). Nesse período, a Terra presenciava também a diversificação dos

amniotas que, mais tarde, dariam origem aos mamíferos e répteis. Toda a massa continental

estava agrupada no supercontinente Pangeia e um clima seco e desértico predominava no


20

ambiente terrestre. A radiação do gênero Drosophila é datada em 50ma, no início do Eoceno,

que viu o surgimento dos dois subgêneros: Drosophila e Sophophora na região tropical do

Velho Mundo. A espécie Drosophila (Sophophora) melanogaster é ainda mais recente,

surgindo há aproximadamente 2,3ma, no início do Pleistoceno, também no Velho Mundo

(RUSSO; TAKEZAKI; NEI, 1995). Acredita-se que a divisão entre grupos do Velho Mundo e

Neotropicais (há aproximadamente 40ma, no fim do Eoceno) tenha ocorrido pela invasão de

espécies pelo estreito de Bering e não pela separação entre América do Sul e África que já

estava ocorrendo no Cretáceo Superior (POWELL, 1997). Ainda não foi encontrado registro

fóssil de indivíduos pertences à espécie D. melanogaster, embora fósseis de grupos próximos

já tenham sido identificados. A figura 2 apresenta um díptero preservado em âmbar (resina

vegetal) datado em 42ma, no Eoceno, onde é possível verificar que a padronização de veias

das asas já era muito similar à encontrada em grupos atuais. Ainda hoje, há uma grande

conservação nos padrões de venação entre espécies do grupo melanogaster (CORRÊA, 2009)

e mesmo espécies que divergiram há milhões de anos apresentam variação quantitativa, e

não qualitativa, na forma da asa (OBBARD et al., 2012).


21

2 A ASA ENQUANTO MODELO

A asa de Drosophila é um excelente modelo para estudos da evolução de traços

complexos já que é tradicionalmente utilizada em estudos de Biologia Evolutiva

(GIDASZEWSKI; BAYLAC; KLINGENBERG, 2009; KATAYAMA et al., 2014; POWELL, 1997;

TSUJINO; TAKAHASHI, 2014; YEH; TRUE, 2014) e Biologia do Desenvolvimento (AVANESOV et

al., 2012; BLAIR, 2007; BUCHMANN; ALBER; ZARTMAN, 2014; DE CELIS, 2003; LECUIT,

THOMAS; LE GOFF, 2007; NETO-SILVA; WELLS; JOHNSTON, 2009; STRIGINI; COHEN, 1999).

Soma-se a isso a vantagem dos genomas de diversas espécies desse gênero estarem

sequenciados (CONSORTIUM et al., 2007). Além disso, a similaridade na variação quantitativa

Figura 2. Fotomicrografia de Pareuthychaeta em âmbar do Eoceno. (A) P. electrica em âmbar Báltico e (B) P.

calpinei em âmbar Báltico. (C) Asa atual de D. melanogaster de linhagem controle do processo seletivo descrito

na seção Seleção Artificial da Asa de D. melanogaster desta tese. Figura modificada de GRIMALDI e SINGH (2012).


22

da forma da asa entre populações de D. melanogaster e entre espécies filogeneticamente

distantes oferece uma oportunidade ímpar já que, possivelmente, os elementos envolvidos

na variação populacional podem ser extrapolados para a variação interespecífica (ALEXIS;

ISAAC; DAVID, 2015) .

3 DESENVOLVIMENTO

Por ser um modelo amplamente utilizado, o desenvolvimento da asa de Drosophila é

muito bem descrito. Os apêndices de Drosophila são formados a partir de estruturas

precursoras chamadas discos imaginais (figura 3; MORATA, 2001). O disco imaginal de asa se

forma a partir de um grupo de células precursoras que invaginam do ectoderma embrionário

(BATE; ARIAS, 1991). Os processos morfogenéticos que ocorrem no disco têm impacto direto

no órgão adulto (DOLEZAL et al., 2010). Portanto são nas mudanças das trajetórias de

desenvolvimento do disco imaginal e da fase pupal que residem grande parte das variações

quantitativas de tamanho e forma da asa.

O disco imaginal de larvas de 3° estadio é composto de uma monocamada com 20 a

50 células de tecido epitelial e sua parte mais central, de formato circular, irá, durante a

metamorfose, se diferenciar em uma coluna tridimensional de células que dará origem à asa

e à parte dorsal do tórax. Nesse momento, já há uma divisão clara de compartimentos

definidos pelos eixos dorsoventral (DV) e anteroposterior (AP), com a participação de

morfógenos clássicos como Wingless (Wg; especificação de DV) e Decapentaplegic (Dpp;

especificação de AP). Os genes scalloped e vestigial (vg) também participam da especificação

desses eixos (BRAY, 1999). Nessas etapas, genes como Engrailed, Hedehog, knot, EGF-R,

Notch, Hairless, entre outros, estão especificando os territórios de veias e diversos métodos

descritores de forma da asa são baseados na localização das veias, logo esses eventos estão

diretamente ligados às variações de forma (BIER, 2000; CROZATIER; GLISE; VINCENT, 2004;

CROZATIER et al., 2003; JOHANNES; PREISS, 2002). Em estágios mais tardios, genes da via de

Bmp interagem com integrinas e também alteram a posição de veias (ARAUJO, 2003). Ao final

do 3° estadio, o disco já é uma estrutura organizada que apresenta entre 30.000 e 50.000

células. Já na fase pupal, ocorre a eversão da parte mais central do disco, formando uma

bicamada celular, já com os compartimentos ventral e dorsal justapostos. A figura 4 ilustra e


23

resume os processos envolvidos no desenvolvimento inicial do disco (ALEXIS; ISAAC; DAVID,

2015). Há então um alongamento pronunciado do tecido que envolve eventos mitóticos,

rearranjos celulares e modificações no formato celular (KANCA et al., 2014; TAYLOR, J.; ADLER,

2008). Por fim, a porção mais proximal da asa, próxima à articulação, se contrai, promovendo

a distensão do tecido ao longo do eixo proximodistal ao mesmo tempo em que o eixo

anteroposterior é comprimido (figura 5), com modificações na forma da asa (figura 6; AIGOUY

et al., 2010). Esse processo é mediado por divisões e rearranjos celulares que interferem na

forma da asa, enquanto as células se tornam mais hexagonais (figura 7; SUGIMURA; ISHIHARA,

2013).

Figura 3. Discos imaginais precursores dos apêndices do inseto adulto. Figura

adaptada de V. Hartenstein (http://flybase.bio.indiana.edu).

http://flybase.bio.indiana.edu/

http://flybase.bio.indiana.edu/


24

Figura 4. Disco imaginal de asa. (a) disco em larvas de 3° estadio; compartimentos anterior e posterior

definidos pelo gradiente de Dpp e compartimentos dorsal e ventral definidos pelo gradiente de Wg. (b)

Disco em fase pupal, durante a metamorfose; eversão tridimensional do disco na região que irá formar a

asa e a porção dorsal do tórax. Note que em ambas as etapas, os territórios de veias já estão sendo

definidos. Figura modificada de ALEXIS; ISAAC; DAVID (2015).

Figura 5. Eversão do disco imaginal da asa. (a) Vista lateral do disco com os principais genes que determinam os

compartimentos dorsal e ventral entre parênteses. (b e c) Dobra do tecido do disco na região mais central de

aspecto circular, chamada de “bolsa do disco” (do inglês, disc pouch). (d) Pressão da hemolinfa contribui para a

eversão do disco. (e) Asa evertida. Nesse momento, ocorre grande extensão da área da asa. (f) No início do

processo, as células têm um formato colunar e ao fim adquirem formato cuboide, contribuindo para o aumento

de área. (g) Intercalação celular orientada contribui para o alongamento da lâmina da asa ao longo do eixo

proximodistal. Figura modificada de ALEXIS; ISAAC e DAVID (2015).


25

4 MODULARIZAÇÃO E GENES CANDIDATOS AO CONTROLE DA VARIAÇÃO

QUANTITATIVA

O resumo dos principais eventos envolvidos na formação da asa evidencia uma

gigantesca complexidade no seu desenvolvimento com diversos processos capazes de

promover alterações quantitativas na estrutura adulta. O disco imaginal, assim como a asa

Figura 7. Modificações no formato celular em asas de pupa. As células são coloridas conforme o seu número de

lados (indicado em cima e à esquerda). Ao longo do desenvolvimento pupal, há um aumento na densidade de

células hexagonais (cinza). As modificações no formato celular contribuem para a determinação da morfologia

adulta. Figura modificada de (AIGOUY et al. (2010). APF refere-se a após formação da pupa.

Figura 6. (A-C) Expansão da lâmina da asa (vermelho) pela contração da articulação (azul). (D-F) Fluxo celular

durante a eversão. Migração celular ocorre a uma velocidade de 50m/h. Figura adaptada de AIGOUY et

al. (2010).


26

adulta, apresenta indícios de modularização no seu desenvolvimento (BLAIR, 2003; GARCIA-

BELLIDO; RIPOLL; MORATA, 1973), apesar de a asa também responder de maneira bastante

integrada (KLINGENBERG, 2009). Diversos estudos mostraram ser possível a criação de

linhagens mutantes apresentando discos imaginais com taxas de crescimento heterogêneas

entre porções do disco, com grandes impactos na morfologia da asa adulta (ROGULJA;

RAUSKOLB; IRVINE, 2008; SCHWANK et al., 2011). Baena-lópez, Baonza e García-bellido (2005)

demonstraram que na parte mais central do disco, chamada de bolsa do disco (do inglês, disc

pouch), as divisões celulares são orientadas radialmente, com origem mais central, dividindo-

se em direção à todas as extremidades. Já nas regiões periféricas, as divisões são orientadas

tangencialmente à bolsa, evidenciando o caráter modular do desenvolvimento da estrutura

(figura 8). A orientação radial no centro parece ser coordenada pelo sistema Fat, Dachsous,

Four-jointed e Dachs que apresentam um gradiente também radial (AMBEGAONKAR et al.,

2012; HALE et al., 2015).

Diferenças não-sinônimas nas sequências dos genes envolvidos no desenvolvimento

podem gerar variações quantitativas, mas também variações nas sequências promotoras,

acentuadoras em cis ou trans, e em outros elementos capazes de alterar os padrões de

expressão gênica. Ao nível de tecidos, variações nos domínios de expressão, ainda que

pequenas, podem gerar variações morfológicas significativas. Variações ambientais também

interagem com o desenvolvimento e, por transdução, interferir nos padrões de expressão

(GILBERT; EPEL, 2008). Carreira e outros (2011) investigaram as bases genéticas da

morfogênese da asa, com ênfase em dimorfismo sexual e nos efeitos não-alométricos das

variações de forma e demonstraram que diversas mutações induzidas através da inserção de

elementos móveis causaram variações não-alométricas com efeitos compartimentalizados, ou

seja, restritos à uma região ou outra da asa.

Na seção anterior, diversos genes que participam da formação da asa foram descritos.

Fica evidente que há um grande número de genes participando do seu desenvolvimento. De

fato, 50% de todos os genes codificantes de proteínas apresentam variações nos níveis de

expressão durante a formação da asa, chegado a quase 80% o número de genes com

transcritos já detectados (O’KEEFE et al., 2012). Com relação às variações quantitativas da

forma, Matta e Bitner-Mathé (2010) identificaram diversos QTLs (loci de traço quantitativo,

do inglês quantitative trait loci) associados ao tamanho e forma da asa. Com o mesmo intuito,


27

WEBER e outros (2008) identificaram centenas de genes candidatos ao controle da variação

de forma, evidenciando a grande dificuldade em se demonstrar quais seriam, de fato,

relevantes para a variação quantitativa. Fica evidente a necessidade de mais estudos sobre as

bases genéticas desse tipo de variação, com desenhos experimentais que permitam a

identificação de um número menor, porém mais relevante, de genes candidatos.

Faz-se necessária uma distinção. Os genes que participam diretamente no

desenvolvimento de estruturas não são obrigatoriamente os mesmos envolvidos na variação

quantitativa observada entre indivíduos ou populações. As técnicas para identificação de

genes participantes de vias do desenvolvimento (knockout, mutantes funcionais, etc.), em

geral, inferem na participação de um gene em particular pela malformação da estrutura

quando o gene alvo é perturbado. Porém, genes envolvidos com a variação quantitativa

podem atuar epistaticamente em relação às vias de desenvolvimento e, portanto, não serem

facilmente identificados pelas técnicas tradicionais.

Figura 8. Modularização no desenvolvimento do disco. (a) Esquema geral da

expressão periférica de Dachsous (roxo) e gradiente radial de Dachs (setas verdes).

Wg demarcando o eixo dorsoventral. (b) Proteína Dachsous corada em roxo na

periferia do disco. (c) Divisão celular orientada radialmente na porção central do

disco e tangencialmente à bolsa do disco na periferia. Figura adaptada de ALEXIS;

ISAAC; DAVID, 2015 e AMBEGAONKAR et al., 2012.


28

5 PLASTICIDADE FENOTÍPICA

Plasticidade fenotípica é definida como a capacidade de um mesmo genótipo produzir

diferentes fenótipos em ambientes distintos e é usualmente descrita em termos da norma de

reação (NR) que é a representação gráfica das variações fenotípicas em função do gradiente

experimental (PIGLIUCCI; MURREN; SCHLICHTING, 2006). Mudanças nas condições ambientais

também são capazes de promover variações quantitativas na forma e tamanho da asa. A

temperatura é a principal condição ambiental testada em estudos de plasticidade da asa e,

impressionantemente, todos os estudos apontam para a mesma NR de tamanho, com moscas

desenvolvendo asas cada vez menores conforme a temperatura aumenta. Esse padrão é

observado, inclusive, em espécies filogeneticamente distantes e asas menores são

correlacionadas à diminuição corporal (BITNER-MATHÉ; KLACZKO, 1999b; DAVID et al., 1997,

2011; DE MOED; DE JONG; SCHARLOO, 1997; DEBAT; DEBELLE; DWORKIN, 2009; DEBAT et al.,

2003, 2008; KARAN et al., 2000; LOH et al., 2008; TROTTA et al., 2010; TROTTA et al., 2006).

Por outro lado, quanto às NR da forma da asa em relação à temperatura, também

analisadas em muitos dos estudos citados acima, elas apresentam variações mais erráticas e

de difícil comparação entre os diferentes trabalhos. Isso acontece por que os métodos

descritores de forma variam muito, dificultando associações. Muitos trabalhos utilizam a

morfometria geométrica como, por exemplo, o método Procrustes (KLINGENBERG, 2002) que

extrai informações de forma através de transformações matemáticas sobre coordenadas

tomadas em landmarks da asa com o intuito de se extrair influências alométricas. Essas

medidas, muitas vezes, tornam-se biologicamente abstratas, com pouca recapitulação de um

fenótipo compreensível.

Além da temperatura, a forma e tamanho da asa também respondem a outros

estímulos ambientais como latitudes (AZEVEDO et al., 1998; COLLINGE; HOFFMANN;

MCKECHNIE, 2006; GIBERT et al., 2004; LIEFTING; HOFFMANN; ELLERS, 2009), altitude

(BITNER-MATHÉ; PEIXOTO; KLACZKO, 1995; PITCHERS; POOL; DWORKIN, 2013),

endocruzamento (SCHOU; KRISTENSEN; LOESCHCKE, 2015; TROTTA, VINCENZO et al., 2011) e

nutrição (SOTO et al., 2008; VIJENDRAVARMA; NARASIMHA; KAWECKI, 2011).

Fica evidente que a asa de Drosophila tem servido como modelo para estudos de

plasticidade, abrindo possibilidades para diversos desenhos experimentais. Ainda assim,

muitas questões carecem de investigações. As bases genéticas da plasticidade, quer sejam


29

elas da asa ou de outros traços fenotípicos, são pouco conhecidas. Sabe-se que a plasticidade

tem bases genéticas e evolui (SCHLICHTING, 1986), contudo, pouco sabe-se sobre quais genes

estariam envolvidos com a transdução de sinais ambientais para as vias de desenvolvimento,

com trabalhos apontando para a participação de chaperonas conhecidas como heat shock

proteins (Hsp), mas sem nenhuma indicação clara de que a plasticidade seria mediada por elas

(DEBAT et al., 2006). Novos estudos sobre as bases genéticas da plasticidade são necessários

para uma melhor compreensão das variações quantitativas causadas por modificações

ambientais.

6 SELEÇÃO ARTIFICIAL DA ASA DE D. MELANOGASTER

Em nosso laboratório foram estabelecidas, por um extensivo programa de evolução

experimental por seleção artificial, oito linhagens independentes de Drosophila melanogaster

divergentes para a forma das asas (MENEZES, 2007; TESSEROLI, 2005). 135 linhagens

isofêmeas forneceram um casal cada, dando início à uma linhagem massal. Todos as fêmeas

foram medidas e as 10 com asa mais arredondada foram cruzadas com dez machos

aleatoriamente determinados. As 10 com asa mais alongada também foram cruzadas com dez

machos. Dessa maneira, quatro linhagens foram formadas com as asas mais alongadas, sendo

chamadas de linhagens Longas e quatro com forma mais arredonda, sendo chamadas de

linhagens Redondas (figura 9). Cada linhagem estabelecida sofreu o processo de seleção

initerruptamente durante 21 gerações, quando o processo passou a ser intermitente. Os

fenótipos gerados neste processo extrapolam, inclusive, variações entre espécies (figura 10;

CORRÊA, 2009). Outros estudos também obtiveram uma grande resposta morfológica através

da seleção artificial na asa de Drosophila indicando que, apesar da conservação interespecífica

de forma, há variação genética aditiva (CARTER; HOULE, 2011; HOULE et al., 2003; LE ROUZIC;

HOULE; HANSEN, 2011; WEBER, 1990).

As linhagens de seleção com fenótipos extremos aliadas a todo o conhecimento já

publicado sobre morfologia, evolução, desenvolvimento e genética da asa de Drosophila

permitem a formulação de desenhos experimentais que possam responder a diversas

questões ainda a serem investigadas nessas áreas.


30

Figura 10. Medidas de forma e tamanho da asa de espécies de Drosophila. Note que as linhagens selecionadas para formas extremas da asa (Redonda – círculo vermelho e Longa – círculo azul) extrapolam quase toda a variação de forma. Geração 67. Figura adaptada de CORRÊA (2009).

YAKUBAWILLISTONI

SIMULANSREDONDAMOJAVENSISMELANOGASTERLONGA

ERECTACONTROLEANANASSAE

ESPECIES$

0.3 0.4 0.5 0.6

SHAPE

200

300

400

500

600

SIZ

E

D. ananassae

Controle - D. melanogaster D. erecta

Longa - D. melanogaster D. melanogaster Selvagem

D. mojavensis Redonda - D. melanogaster D. simulans D. willistoni D. yakuba

WSI

– t

am

an

ho

da

asa

WSH

– forma da asa

Espécies

Figura 9. Asas de linhagens selecionadas para formas extremas. Linhagem Redonda (esquerda) e Longa (direita). Linhagens controle (não exibida) apresentam fenótipo intermediário. Geração 123.

O B J E T I V O S E O R G A N I Z A Ç Ã O D A T E S E

31

OBJETIVO GERAL

Investigar a complexidade de promotores de variação quantitativa da forma e

tamanho da asa de Drosophila melanogaster em diferentes níveis de variações biológica: as

correlações genéticas e fenotípicas, as respostas às variações ambientais, as bases celulares e

variações na expressão gênica.

OBJETIVOS ESPECÍFICOS E ORGANIZAÇÃO DA TESE

A presente tese é composta de 4 capítulos que abordam níveis diferentes da variação

biológica subjacentes à variação quantitativa de forma e tamanho da asa. No primeiro,

apresentamos uma descrição da morfologia da asa. No segundo, o ambiente enquanto

promotor de variação é estudado através de estimativas de plasticidade fenotípica. O terceiro

capítulo abarca as bases celulares e o último promove uma busca pelos genes candidatos ao

controle da variação de forma.

I. MORFOLOGIA

Analisar os padrões de integração morfológica na asa de Drosophila melanogaster e testar a

capacidade de previsibilidade da matriz de correlação genética sobre a trajetória evolutiva de

caracteres correlacionados.

II. PLASTICIDADE FENOTÍPICA (submetido ao Journal of Evolutionary Biology)

Avaliar os efeitos da seleção sobre a resposta plástica de tamanho e forma e averiguar a

influência da mudança na média fenotípica de forma sobre as normas de reação.

III. BASES CELULARES (publicado no periódico Genetica)

Avaliar as bases celulares (número e tamanho de células) subjacentes à variação de forma e

tamanho da asa de Drosophila melanogaster.

IV. GENES CANDIDATOS

Identificar genes candidatos ao controle da variação quantitativa de forma da asa através de um

ensaio de microarranjo, de análises dos níveis de expressão gênica em réplicas biológicas do

processo de seleção artificial através de PCR quantitativos em tempo real e por validações

morfológicas, através do silenciamento gênico tecido-específico e análise das consequências

morfológicas.

C A P Í T U L O I : M O R F O L O G I A

32

Phenotypic and Genetic Integration in the Drosophila wing

Daniel Mattos and Blanche Christine Bitner-Mathé*.

Manuscript under current refinement for publication.

Authors’ affiliations: Laboratório de Evolução de Caracteres Complexos – Drosophila,

Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro –

Brasil

Running title: Drosophila wing correlated traits


33

ABSTRACT

Phenotypic integrations compromise the response of traits to selective pressures.

Although it is considered in most morphological evolutionary models, phenotypic integration

as a facilitator or a constraint to changes is hard to assess since most studies are hindsight,

departing from the established morphological divergence to infer the previous integration

patterns. Here we use artificially selected strains for divergent wing shapes in Drosophila

melanogaster to test predictions based on the genetic correlation matrix of the population

prior to selection. Based on Pre-Selection generation, we present heritability estimates,

phenotypic and genetic correlation matrices for wing shape, size and venation landmarks and

found that most of the correlated responses of wing traits followed predictions by genetic

parameters.

Keywords: Morphology ; wing shape; artificial selection.


34

INTRODUCTION

Organ development, as many complex phenotypic traits, is multifactorial, influenced

by many genes, many environmental conditions and their interaction. Organ development is

mediated by modularization of its parts, with a relative independence of each modular group

of traits. Modules are defined as “structural units that are internally integrated by

developmental interactions” (KLINGENBERG, 2002, 2014). Traits within modules are then

expected to be more phenotypically integrated than those in adjacent modules by sharing

genetic variation or by responding to common environmental cues. Evolutionary implications

of modules and the evolvability of the modules themselves are not straightforward and

require more studies (WAGNER; PAVLICEV; CHEVERUD, 2007). These phenotypic integration

(PI) by means of genetic correlation can be summarized by the genetic variance-covariance

matrix, which can change the evolutionary trajectory of individual traits (PHILLIPS; WHITLOCK;

FOWLER, 2001; STEPPAN; PHILLIPS; HOULE, 2002) e or constraining evolution. Some authors

defend that developmental modules evolve aligned with functional modules, thus facilitating

the organ evolution (CHEVERUD, 1984; WAGNER; ALTENBERG, 1996) while others emphasize

modularity as a constraint (ARTHUR, 2001). The two viewpoints are not, however, exclusive

and modularity will be a constraint or a facilitator depending on the selective pressures

imposed to the structure. Although it is acknowledged in morphological evolutionary models,

it is hard to target questions on the integration states and its relations to evolution since most

analyses have a hindsight perspective. Artificial selection programs are time and resource

consuming, but they offer a way to pose questions on phenotypic integration and modularity

on a forward perspective since one can assess the previous integration profile and the

consequences after a morphological divergence is established.

For such studies, the wing of Drosophila is an ideal target since both genetic and

phenotypic aspects have been largely described (BITNER-MATHÉ; KLACZKO, 1999a, b; DAVID

et al., 2003; MATTA; BITNER-MATHÉ, 2004, 2010; MATTA; BITNER-MATHÉ; ALVES-FERREIRA,

2011; TORQUATO et al., 2014). Five longitudinal veins run along the proximodistal axis and

two transversal veins run along the anteroposterior axis. This venation pattern is widely

conserved across Drosophila species. While the expression domains of some genes encompass

the entire wing imaginal disc, others are only expressed in one compartment or along the

developing veins (BLAIR, 2007; KOLZER, 2003; LECUIT; LENNE, 2007). This mosaic of different


35

genes being expressed locally or widespread and at a particular time may account for part of

the phenotypic integrations of wing features.

On the morphological aspect, no evidence of modularization of wing compartments

was found (KLINGENBERG, 2009). However, there is evidence of genetic correlation among

wing traits (MATTA; BITNER-MATHÉ, 2004), but wether genetic correlation determines the

trajectory of correlated traits is still unknown. Lastly, the reaction norms of each of these traits

can be more or less coupled with the genetic architecture underlying wing shape. Here we

analyze Drosophila melanogaster strains that were submitted to an extensive artificial

selection program targeting wing overall shape at the generation prior to the program (Pre-

Selection) and the 64th generation after selection (Post-Selection). We describe morphological

variation of wing features, phenotypic integration patterns, genetic parameters such as

heritability estimates and the genetic correlation matrix for Pre-Selection population and

conclude that most of the traits trajectories were determined by the genetic correlation

matrix, although unpredicted trajectories were also observed.

MATERIAL AND METHODS

ARTIFICIAL SELECTION PROGRAM

The strains used in this work were previously established by B. C. Bitner-Mathé, D.

Tesseroli & B. F. Menezes. The artificial selection program is explained in details in Menezes

et al (2013). Briefly, 135 isofemale lines founded the initial baseline population from which

strains were established by decreasing or increasing a shape index based on width-to-length

ratio. Selection was applied for 21 consecutive generations and intermittently after that. Here

we use the most divergent strains selected for elongated wings (named L strain) and for

rounded shaped wings (R strain) at the 64th generation after selection started (hereafter

referred to as Post-Selection). Strains are assumed fairly homozygous.


36

PRE-SELECTION GENERATION HERITABILITY, PHENOTYPIC AND GENETIC CORRELATION

ESTIMATES

From the offspring of each isofemale line, two virgin females were taken and allowed

to mate with one male collected in the field. Females were then separated and put to oviposit

in separate vials at 16° for rearing. Wings were measured for two female offspring and the

mother. A regression of the offspring on the mother was performed to assess heritability

estimates, which equal two times the regression slope (FALCONER; MACKAY, 1996).

Heritability, genetic and phenotypic correlations from Pre-Selection flies are used to predict

the morphological consequences on the correlated traits and compared to the actual

trajectory of the Post-Selection flies.

POST SELECTION GENERATION: MORPHOLOGICAL INTEGRATED RESPONSE AND

REACTION NORMS

At the 64th generation, 10 couples of flies with virgin females from each strain were

transferred to two different temperatures (16°C or 25°C) for mating and oviposition in a

standard Drosophila medium. Every two days, adults were transferred to a different bottle for

oviposition, hence creating replicates. Measurements were taken for approximately 15 left

wings (up to 5 in each replicate) of females per strain submitted to each selection program

and reared in one of the two developmental temperatures.

WING MORPHOLOGY MEASUREMENTS

Since selection was applied to females only (with males being randomly chosen at each

generation), all analyses in this paper refer to female individuals. All left wings were mounted

on slides and photographed with a digital camera attached to a stereoscope microscope. The

program ImageJ (http://rsbweb.nih.gov/ij/) was used to take the coordinates of 20 semi-

landmarks around the wing border and the position of 11 landmarks at the intersection of

veins or at their extremities, ensuring homology (Fig. 1). Shape and size estimates were

assessed by the Ellipse Method (Klaczko and Bitner-Mathé, 1990; Klaczko, 2006)) which

estimates the best-fitted ellipse to the given coordinates taken around wing contour. Wing

shape (WSH) is defined as the ratio between the ellipse smallest and largest radius (WW/WL)


37

and size (WSI) is the square root of the product of those measures (√𝑊𝐿 ∗ 𝑊𝑊). The Ellipse

method also provides a positional description of venation pattern by Polar Coordinates, with

each landmark being characterized by the angle formed between WL and the radius

connecting the landmark to the center of the ellipse. Since radius lengths are highly correlated

to size, we analyzed landmark variation by the angular component only which will be here

forth addressed as , followed by the letter corresponding to the respective landmark.

Statistical analyses were carried out using SYSTAT© v.13.0 (SPSS Inc.).

STATISTICS

Homogeneity of distributions was tested by an analysis of covariance (ANCOVA) for

univariate traits and a multivariate analysis of covariance (MANCOVA) for the group of

landmarks. Both analyses were carried out using the directions of selection (SEL: L x R strains)

as a fixed effect while developmental temperature (DT: 16ºC and 25ºC) was not considered a

fixed effect. Replicates were nested within the interaction term between those factors.

Figure 1. (a) Wing semi-landmarks (in red) to estimate wing contour best-fitted ellipse (red ellipse). Landmarks describing wing venation in purple. Landmark H was not included in statistical analyses due to a method’s limitation. (b) Estimated ellipse and determination of wing largest radius (WL), smallest radius (WW) and

illustrative description of landmark B by B.


38

Phenotypic correlation matrices were estimated by the Pearson’s product-moment

correlation. The total genetic correlation (rG) was calculated as the arithmetic mean of two

reciprocal between-trait daughter–mother covariances divided by the geometric mean of the

within-trait daughter–mother covariances.

rG=(COVX1Z2+COVX2Z1)/(2√(COVX1Z1COVX2Z2)), where COVX1Z2 is the covariance between

trait 1 of the parents and trait 2 of the offspring, COVX2Z1 is the covariance between trait 2 of

the parents and trait 1 of the offspring, COVX1Z1 and COVX2Z2 are the offspring–parent

covariances of traits 1 and 2, respectively (Becker, 1992; Falconer; Mackay, 1996; as explained

in Matta; Bitner-Mathé 2004).

RESULTS

Heritability estimates, phenotypic and genetic correlations for wing traits of the Pre-

Selection baseline population are exhibited on Table 1. WSH has high heritability (h2=0.59),

suggesting high additive genetic variance to respond to the selective pressure. For WSI,

heritability is low and not significant, indicating a high environmental regulation with little

genetic variance contributing to total phenotypic variance. Most landmark angles also

presented high heritability.

The Pre-Selection population was then submitted to a bidirectional artificial selection

that stretched the variation of WSH by selecting flies with elongated wings (low WSH values, L

strains) or rounder wings ((high WSH values, R strains). The effects of this program on the 64th

generation can be appreciated in Fig. 2 that presents the histograms of Pre-Selection

population reared at 16ºC and the selected strains reared at 16ºC and 25ºC. Both L and R

strains responded to the selection applied, with a slightly stronger response in the R Strain. It

is noteworthy that despite the great divergence in WSH, distribution of WSI remains overlapped

showing that selecting for wing shape did not impose a change in wing size. Differences

between the distribution of WSI from Post-Selection flies reared at 16ºC and 25ºC shows a high

temperature-related plasticity of this trait. Fig. 3 shows two illustrative wings from L and R

strains with the mean estimated ellipses superposed. To test homogeneity of these

distributions, we performed an Analysis of Covariance (ANCOVA) on the Post-Selection flies,

presented on Table 2. As expected by the high heritability of WSH, we detected a strong

response effect of direction of selection (SEL: L x R strains) holding 88% of WSH variance. We


39

detected no change in WSH due to developmental temperature (DT). Difference is detected in

WSI between L and R strains. WSI was highly influenced by DT and this result is in accordance

with the low heritability found for this trait. Moreover, we detected a significant interaction

SEL*DT for WSH, indicating a change in the reaction norm of L and R strains due to selection.

Predictions of evolutionary trajectories after selection are based on the genetic

correlations of the Pre-Selection population. According to the genetic correlations with WSH,

an increase in this value (i.e. selecting for a rounder wing), would impose a reduction in WSI

(rG = -0.31), and increase in A (rG=0.45) and so forth. OnlyD and I do not exhibit significant

rG with WSH. Since angular movements are harder to visualize, these correlations can be best

viewed in Fig. 5, which graphically summarizes these variations.


40

WSH WSI A B C D E F G I J K O

WSH 0.59 -0.309 0.446 -0.454 -0.186 0.137 0.348 0.414 0.320 -0.145 0.705 0.463 0.495

WSI -0.269 0.27 -0.139 0.392 -0.066 -0.078 -0.261 0.125 -0.035 -0.353 -0.167 -0.121 -0.317

A 0.092 0.250 0.42 -0.024 -0.253 -0.527 -0.750 0.678 0.593 -0.639 0.771 0.368 0.541

B -0.526 0.391 0.291 0.37 0.467 -0.191 -0.404 0.039 -0.033 -0.250 -0.497 -0.316 -0.440

C 0.002 -0.102 0.053 0.143 0.57 0.560 -0.041 -0.089 -0.350 0.104 -0.523 -0.794 -0.685

D 0.284 -0.230 -0.120 -0.271 0.556 0.78 0.584 -0.221 -0.419 0.127 -0.478 -0.674 -0.588

E 0.339 -0.279 -0.528 -0.514 0.154 0.623 0.71 -0.427 -0.521 0.465 -0.379 -0.384 -0.414

F 0.020 0.247 0.796 0.384 -0.057 -0.184 -0.501 0.57 0.872 -0.504 0.771 0.590 0.785

G -0.095 0.147 0.696 0.381 -0.131 -0.249 -0.524 0.900 0.42 -0.408 0.819 0.780 0.959

I 0.131 -0.258 -0.455 -0.409 0.103 0.361 0.691 -0.461 -0.466 0.62 -0.556 -0.457 -0.581

J 0.027 0.253 0.863 0.293 -0.048 -0.147 -0.463 0.900 0.843 -0.426 0.29 0.759 1.013

K 0.005 0.075 0.688 0.172 -0.166 -0.204 -0.344 0.774 0.793 -0.317 0.885 0.39 0.980

O -0.023 0.179 0.786 0.237 -0.144 -0.203 -0.424 0.855 0.834 -0.365 0.943 0.942 0.49

Table 1. Phenotypic (lower diagonal) and genetic (upper) correlation matrices in the Pre-Selection generation. Heritability estimates in blue.

Pearson’s product-moment correlations between wing traits, bold values are significant (P < 0.05).


41

Figure 2. Histograms of wing size (WSI; left) and shape (WSH; right) from D. melanogaster unselected females

(Pre-Selection) that later originated the artificially selected strains (Post-Selection from the 64th generation

after selection initiated reared at 16°C and 25°C). Note that WSI distributions overlap while WSH exhibit great

divergence between L and R, with R strain presenting a slightly stronger response to selection when

compared to Pre-Selection distribution.


42

Effects D.F WSH WSI

Direction of Selection (SEL) 2 0.876 *** 0.010 Developmental Temperature (DT) 1 0.004 0.971 *** SEL x DT 2 0.104 *** 0.008 Replicate (SEL x DT) 9 0.009 0.007 Residuals 63 *** p < 0.001

The estimates of WSH and WSI are composed by the measures of the wing length (WL)

and width (WW) as described in the Material and Methods section. The selection program

focused on the ratio WW/WL to isolate the targeted phenotype. Fig. 4 shows the histograms of

these measures. Since WL and WW present temperature-related phenotypic plasticity, the

effects of selection compared to Pre-Selection flies should be restrained to flies reared at 16ºC

only. Regarding WL, we found a more intense response in the R strain while for WW, both R

and L strains equally responded. This scenario suggests that reducing the biological axes might

Figure 3. Illustrative wings from L Strains (left) and R Strains (right). Superimposition of ellipses drawn by the

parameters estimated by the Ellipse method from mean value of 1L and 1R strains.

Table 2: Results of the ANCOVA for wing shape (WSH) and size (WSI) testing the homogeneity

of distribution between directions of selection (L x R strains) and developmental

temperature (16ºC and 25ºC). Reaction norms changes are assessed by the interaction term

(SEL*DT). Percentage of variance explained by each effect.


43

be easier than stretching. Similar variation haS been observed in other experiments with these

strains (unpublished data).

Figure 4: Histograms of wing length (WL; upper four graphs) and width (WW; lowest four

graphs) from D. melanogaster unselected females (Pre-Selection) that later originated the

artificially selected strains (Post-Selection from the 64th generation after selection initiated

reared at 16°C and 25°C). Since wing length and width are size-sensitive to developmental

temperature, artificial selection effects should be noticed on the left graphs since all flies were

reared at 16°C. Note that WL exhibits a more pronounced divergence in the R Strains while

for WW, L and R Strains diverged from the Pre-Selection distribution.


44

We then analyzed the integration pattern in Post-Selection flies. Polar coordinates

provided by the Ellipse Method were transformed into Cartesian coordinates following the

equation x= R*cos and y= R*sin, where R is the radius and the angle of each landmark

(Fig. 5). Landmarks A, C, D, E, all along the wing border, are highly divergent between L and R

strains. Proximal landmarks F, G, J, K, O and distal landmarks C, D and E are very divergent,

suggesting that selection affected the whole wing blade and was not constrained to some of

its compartments. To test homogeneity of variation of landmarks, we performed a

multivariate analysis of variance (MANCOVA) presented on Table 3. We detect a significant

effect on landmarks of SEL (L x R) and of developmental temperature (16ºC x 25ºC) and ad

interaction between those factors indicating a change in the reaction norms. These results

indicate a high degree of integration between wing traits, with a change in wing overall shape

imposing a change in most of its components. Venation pattern also exhibits temperature-

related phenotypic plasticity. Phenotypic correlation matrices are exhibited on Table 4 and

summarized in Fig. 6. The increase in WSH (R Strains) is correlated with an increase in A, C,

D, F, G, JG, G, O and a decrease in I. Comparing the predicted movements from Pre-

Selection genetic correlation with the actual results in Post-Selection, we observe that the first

successfully predicted the direction of movements for A, D, F, G, I, J, K, O, but not

for WSI, B, C and E.

Ellipse

Effects Wilk’s F-ratio D.F. p

Direction of Selection (SEL) 0.289 30.621 11, 137 0.000

Developmental Temperature (DT) 0.420 17.196 11, 137 0.000

SEL x DT 0.841 2.357 11, 137 0.011

Strain (SEL) 0.161 7.311 44, 526 0.000

N = 155

Table 3: Summary of the results of MANCOVA for variation in angular movements of

landmarks (Ellipse ).


45

- 320 - 240 - 160 - 80 0 80 160 240 320 400

- 300

- 250

- 200

- 150

- 100

- 50

0

50

100

150

200

A B

C

D

E

F

G H

I

O

JK

16°C

- 320 - 240 - 160 - 80 0 80 160 240 320 400

A x

- 300

- 250

- 200

- 150

- 100

- 50

0

50

100

150

200

Ay

AB

C

D

E

F

GH

I

O

JK

A

B

C

D

E

F

G H

I

O

JK

16°C

25°C

Pre

-Sele

cti

on

Po

st-

Se

lecti

on

- 320 - 240 - 160 - 80 0 80 160 240 320 400

- 300

- 250

- 200

- 150

- 100

- 50

0

50

100

150

200

Figure 5. Plot of landmarks from Pre-Selection (black dots – upper graph) and from L Strain

(blue dots) and R Strain (red dots) reared in two developmental temperatures (16°C and

25°C). Polar coordinates extracted from the Ellipse method and transformed to Cartesian

coordinates. Upper wings on the right indicate orientation of the wing with landmarks.

Letters A-O indicate the landmark represented by the nearest 95% confidence ellipse.

Landmark H excluded from statistical analyses.


46

WSH WSI A B C D E F G I J K O WSH 1

WSI 0.347 1

A 0.926 0.225 1

a B 0.168 0.068 0.183 1

C 0.783 0.138 0.712 0.415 1

D 0.850 0.271 0.775 0.307 0.851 1

E -0.377 -0.140 -0.403 -0.204 -0.136 -0.046 1

F 0.868 0.224 0.953 0.255 0.669 0.725 -0.489 1

G 0.768 0.054 0.884 0.351 0.641 0.648 -0.552 0.955 1

I -0.812 -0.505 -0.716 -0.203 -0.528 -0.645 0.498 -0.706 -0.627 1

J 0.878 0.153 0.957 0.256 0.696 0.732 -0.473 0.981 0.947 -0.702 1

K 0.731 0.022 0.880 0.275 0.561 0.558 -0.523 0.933 0.947 -0.573 0.954 1

O 0.811 0.112 0.939 0.248 0.652 0.675 -0.452 0.965 0.929 -0.615 0.976 0.968 1

WSH WSI A B C D E F G I J K O WSH 1

WSI -0.045 1

A 0.935 -0.006 1 b

B 0.562 0.228 0.658 1

C 0.811 0.064 0.815 0.690 1

D 0.872 -0.004 0.867 0.636 0.919 1

E -0.434 -0.100 -0.564 -0.586 -0.505 -0.349 1

F 0.918 -0.027 0.945 0.626 0.834 0.861 -0.576 1

G 0.822 0.050 0.889 0.608 0.819 0.810 -0.599 0.928 1

I -0.711 -0.157 -0.769 -0.601 -0.734 -0.674 0.759 -0.787 -0.767 1

J 0.933 -0.058 0.955 0.607 0.816 0.851 -0.583 0.981 0.930 -0.786 1

K 0.880 -0.060 0.928 0.606 0.780 0.842 -0.554 0.969 0.927 -0.726 0.967 1

O 0.921 -0.053 0.955 0.629 0.816 0.866 -0.566 0.976 0.935 -0.761 0.979 0.974 1

Table 4. Phenotypic correlation matrices for Post-Selection generation reared at 16ºC (a) and 25ºC (b). N=33 (a) and 38 (b). p<0.05 in bold.

C A P Í T U L O I – M O R F O L O G I A

47

DISCUSSION

Heritability found for wing shape was high and significant (h2=0.59; p<0.05) while for

size was non-significant, a pattern similar to what has been reported for D. simulans, D.

serrata, D. gouveai, D. mercatorum, D. paranaensis, Z. indianus and other D. melanogaster

populations, indicating the conservation in the amount of additive genetic variance for these

traits (GILCHRIST; HUEY; SERRA, 2001; HOFFMANN; SHIRRIFFS, 2002; LEIBOWITZ; SANTOS;

FONTDEVILA, 1995; LOH; BITNER-MATHÉ, 2005; MATTA; BITNER-MATHÉ, 2004; MORAES et

al., 2004; MORAES; SENE, 2004; SZTEPANACZ; BLOWS, 2015). The high value found for wing

shape is also in accordance with the large morphological divergence achieved by the selection

program in Post-Selection strains. The low and non-significant h2 observed for wing size may

also explain how Post-Selection strains kept an unchanged size, retaining temperature-related

plastic response with little additive genetic variance relative to the total phenotypic variance.

The contrasting results for wing shape and size suggest the first is under a tighter genetic

control while size is more responsive to environmental variations such as temperature. The

high heritability for wing shape and the fact that shapes achieved by artificial selection are

hardly seen in nature suggest that wing shape is under selective pressure, although the

Figure 6. Graphical representation of Genetic and Phenotypic Correlations (see tables 1 and 3) from Pre (left) and

Post-Selection (right) reared at 16ºC. Arrow size reflects intensity of correlation with WSH. The angular variation

of any given trait tends to be followed by the variation shown for all others. Orientation of arrows shows only

one direction of the correlations and all can be inverted once the sign of WSH is also inverted.

.


48

adaptive character of wing shape has not yet been satisfactorily demonstrated (BIRDSALL et

al., 2000; WEBER et al., 1999; ZIMMERMAN; PALSSON; GIBSON, 2000).

Regarding the integration patterns of the wing in Pre-Selection flies, the only traits with

a significant phenotypic correlation to wing shape are wing size, D, B, D and E, but

almost all exhibit high genetic correlations, i.e., covariation in these traits in the offspring are

partially explained by covariation in those of the mother. As wings depart from the

intermediate shape by means of the selection program, trajectories of other wing traits are

expected to be influenced by the genetic correlation matrix. As seen on figure 5, for most of

the wing traits, genetic estimates were successfully able to predict the new locations of

landmarks in R and L strains. In fact, differences between predicted and observed are roughly

in intensity of the displacement of landmarks, rather than direction, with few exceptions as

B, C, E and I. All these landmarks are located on the distal portion of the wing with

most being along the wing border.

Two morphological compartments with an apparent high genetic modularization can

be observed. On the most proximal portion of the wings, encompassing landmarks A, F,

G, J, K and O, there appears to be a consistent developmental module with all

landmarks responding to the same direction and with equal intensities. The most distal

portion of the wing also appears as a module, with landmarks C and D responding similarly.

These results are in contradiction with those obtained by Klingenberg (2009) and by

Klingenberg and Aklan (2000) through a different approach for module recognition. They did

not find evidence of morphological modularization on the wing. Also interestingly, wing size

responded in the opposite direction predicted by the genetic correlation estimates, slightly

increasing in R strains, although this difference was not significant, as seen on Table 2.

The effects of selection on shape variation can be appreciated on figure 1. The

response to selection of R strains was more pronounced than in L strains. Furthermore,

selection affected both axes (length and width) in R strains, whereas in L strains only the width

exhibited a pronounced divergence (figure 3, considering only flies reared at 16°C, since

comparisons with 25°C cannot depict allometric variations). However, small random

fluctuations in shape and size are expected when different generations are analyzed, and this

might account for these effects.


49

Despite the great divergence in shape, strains did not present any significant size

difference, although all strains had their size reduced compared to the Pre-Selection

population. This is surprising considering that both size and shape variables are calculated by

the same ellipse axes provided by the adjustment of the fittest ellipse to the set of

coordinates. The selection program also did not alter wing size thermal plasticity and the

reaction norm found was similar to those reported in the literature, with wings becoming

smaller at higher temperatures (DAVID et al., 2011; DEBAT; DEBELLE; DWORKIN, 2009). Clinal

studies have repeatedly found smaller wings at higher latitudes, a pattern usually explained

by temperature-related selection (COYNE; BEECHAM, 1987; GOCKEL et al., 2001; HOFFMANN;

SHIRRIFFS, 2002). Other studies have reported a certain freedom between the variation of

these two traits (DEBAT; DEBELLE; DWORKIN, 2009; DEBAT et al., 2003; GILCHRIST; HUEY;

SERRA, 2001). Nonetheless, these results do not imply a total independency of the traits and

it is more likely that the variance in one trait will interfere, at least partially, with the other,

especially when considering its evolution, since the wing is perceived by selection as a whole

structure and, in nature, a trade-off between these two features of the wing might occur to

regulate the outcome phenotype.

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C A P Í T U L O I I : P L A S T I C I D A D E F E N O T Í P I C A

52

Thermal Plasticity Evolution in Strains of Drosophila melanogaster Selected for Divergent

Wing Shape

Daniel Mattos1, Felipe Rocha2, Louis Bernard Klaczko2 and Blanche Christine Bitner-Mathé1*.

Submitted to the Journal of Evolutionary Biology on 03/12/2014 and resubmitted (the

current revised version) on 08/03/2015.

Authors’ affiliations:

1. Universidade Federal do Rio de Janeiro, Departamento de Genética, Instituto de

Biologia, Brasil

2. Universidade Estadual de Campinas, Departamento de Genética e Evolução, Instituto

de Biologia, Brasil

Running title: influence of wing shape mean on reaction norm


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ABSTRACT

Phenotypic plasticity is the property of genotypes that allows an increase in phenotypic

variance when organisms develop in different environmental conditions by adjustment of

developmental pathways, residing on the frontier of eco-evo-devo studies. Few empirical

studies have addressed the long debate on the relationship between the mean phenotypic

value of a trait and its plastic. Here we used strains of Drosophila melanogaster artificially

selected for divergence of wing shape that retain remarkably similar wing sizes to investigate

the independency between mean value and reaction norm. Flies from elongated, rounded and

unselected control strains, with three distinct phenotypic mean values, developed at

a thermal gradient ranging from 14°C to 30°C. Wing size was highly responsive to

developmental temperature and sex, but similar amongst all strains. On the other hand,

reaction norms of wing shape were altered by the selection program and the phenotypic mean

value of wing shape explained 36% of the variation amongst reaction norms. The contrasting

responses of wing shape and size is intriguing, especially since both are estimated by the same

measures of wing length and width. Wing length and width exhibited a proportional response,

allowing size to vary without imposing great disturbance on wing shape variance. Results

indicate that wing shape and its reaction norm cannot be assumed independent while wing

size and shape can have independent responses.

keywords: artificial selection – ecological genetics - insects - morphometrics – reaction

norm – wing size


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INTRODUCTION

Phenotypic plasticity is a central issue in eco-evo-devo studies since it allows variation

by changes in developmental programs when organisms are exposed to different

environmental conditions producing different phenotypes (WEST-EBERHARD, 2003). Plasticity

has been demonstrated to evolve and thus has a genetic basis (DEBAT; DEBELLE; DWORKIN,

2009; PIGLIUCCI, 2005). Plastic variation is often represented by the reaction norm (RN) that

plots the phenotypic values across an environmental gradient. Two possibly overlapping

genetic mechanisms might underlie RN shapes. RN might be influenced by allelic sensitivity,

where some alleles responsible for trait establishment have varying effects across

environments, or by regulatory sensitive genes regulating the expression of downstream

genes (VIA et al., 1995). Allelic sensitivity implies a higher dependency between the RN and

the trait mean, since evolution of the trait mean would have a direct impact on the genetic

basis of its plasticity. The relative contribution of allelic sensitivity or regulatory genes and

hence the independency between the trait mean and plasticity has long been debated (DAVID

et al., 2005; SCHEINER, 1993; VIA, 1993). Rocha et al. (2009) addressed this issue and found a

high correlation between the trait mean and the RN showing that, for some traits,

independency should not be assumed. Trait mean and RN are likely to show variable degrees

of independence for different traits, and more empirical data is necessary to provide a better

framework for understanding of the genetic basis and evolution of phenotypic plasticity. Since

plasticity is a property hard to manipulate, one can address this issue by imposing variation

on the population mean in order to investigate the side effects on the plastic response.

Phenotypic plasticity has long been studied on the wing of Drosophila, mostly because

the wing a virtually two-dimensional organ and much of its genetics and development is

largely described, thus making it an interesting model for such studies (BITNER-MATHÉ;

KLACZKO, 1999a, b; BLAIR, 2007; DAVID, JEAN et al., 2011; GIRALDEZ; COHEN, 2003; KARAN;

MORETEAU; DAVID, 1999; NEUFELD et al., 1998). Wing size is sensitive to rearing temperature,

with flies exhibiting bigger wings at lower temperatures (DAVID et al., 1994). The extent to

which wing plasticity is independent from the population mean has never been addressed and

whether the wing shape RN depends on the phenotypic value remains unclear. Furthermore,

most studies focusing wing plasticity use a narrow range temperature gradient with only two

or three temperatures, thus leaving those RN still to be more fully described.


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Here we used strains of Drosophila melanogaster artificially selected for extreme

values of wing shape and control unselected strains with intermediate values to investigate

the selection effects on the wing plasticity and to test the independency between wing shape

mean value and its reaction norm. The strains were founded from the same baseline

population and the artificial selection program focused on the wing shape described by width-

to-length ratio. Our results indicate that wing shape temperature-related RN is influenced by

the phenotypic mean value, with rounded and unselected control strains exhibiting opposite

slope signs while L strains developed an upward parabolic RN. Divergently selected strains

exhibit very similar size, showing independency between wing size and shape variation.

Furthermore, no change in size RN was detected. Lastly we show that a proportional variation

between the wing length and width, which compose both our measures of shape and size,

explain how these two features can respond differently to the analyzed factors.


STRAINS

We used flies from strains previously established by B. C. Bitner-Mathé, D. Tesseroli

and B. F. Menezes (MENEZES et al., 2013). Briefly, a baseline population founded from 135

isofemale lines was used to found independent strains artificially selected for decreasing or

increasing a shape index based on a width-to-length ratio. Selection was applied for 21

consecutive generations and intermittently applied after that with no generation overlap.

Here we use 3 biological replicate strains, independently selected for elongated wings (named

L strains), 3 for rounded shaped wings (R strains) and 2 unselected controls (C strains) from

the 123rd generation. Due to the prolonged and strong selection program, strains are assumed

to be fairly homozygous.

REACTION NORMS

Strains were reared in a thermal gradient (modifed from Fogleman 1978) ranging from

14°C to 30°C with two degrees interval. Five randomly chosen couples were kept in each vial

at 23°C for two days for mating and oviposition in a standard Drosophila medium, after which


56

adults were removed and vials placed in the thermal gradient. Emerged adults aging 13-19

days were stored in 80% ethanol and their wings were mounted on a microscope slide and

photographed with a digital camera (Optronics DEI-750, 1.3 megapixels, software Axion Vision

3.0) attached to a stereoscope microscope (Zeiss Stemi SV11). Left wing measurements were

taken for up to 43 females and 40 males per strain and temperature, for a total of 1136 wings.

Due to viability issues of these strains, balanced analyses were not possible. All emerged flies

with undamaged wing were included.

Reaction norms were described both by linear (f(T)= a + bT) and quadratic (f(T) a + bT

+ cT2) models, where f is the trait of interest (wing shape or size) and T is the temperature.

Quadratic was considered the best-fit model when most of the reaction norms curvatures (c)

were significantly different from zero (ROCHA, F.; MEDEIROS; KLACZKO, 2009; ZAR 2010;

ROCHA; KLACZKO 2012 for a better description of the procedure). For traits with c not

significantly different from zero, a linear regression on developmental temperature was

performed and the regression slope (b) used as the reaction norm parameter. b can be

interpreted as a plasticity index, where a value approaching zero means canalization while

either negative and positive values indicate an increase in plasticity with opposite phenotypic

response to the environmental condition. Traits for which the curvature (c) was significantly

different from zero were then described by a quadratic regression with c used as the reaction

norm descriptor. A c approaching zero means that RN is essentially linear, whereas a positive

c means an upward parabola and a negative a downward one.

WING SHAPE AND SIZE DESCRIPTORS

Wing length (WL) was measured as the distance between the alula opening and the

distal edge of the 3rd longitudinal vein (L3) and wing width (WW) as the line from the 5th

longitudinal vein (L5) to the top of the wing, approximately perpendicular to WL. Shape and

size estimates followed the Ellipse method where wing shape (WSH) wass calculated as the

ratio WW/WL and wing size (WSI) estimated as √(Ww/2) (WL/2 ) (Klaczko & Bitner-Mathé,

1990; Klaczko, 2006). See supporting material Fig. S1 for the fitting of the estimated ellipse

drawn by these parameters onto illustrative wings. Measurements of WW and WL were taken

on the program ImageJ 1.47v (http://rsbweb.nih.gov/ij/). The artificial selection program was


57

based on the WW/WL ratio and in these strains more variation is expected to be found along

these axes.

All statistical analyses were conducted on SYSTAT© v.13.0 (SPSS Inc.).

RESULTS

Fig. 1 shows wing shape (WSH) and size (WSI) variation between flies from artificially

selected strains (L and R) in the 123rd generation. The high divergence for shape and the

similarity for size between the selected strains are evidenced by the histograms, which

includes all data, regardless of sex or developmental temperature. Mean values, standard

errors and group sample sizes by strain, sex and temperature can be found at supporting

information Table S1. The effects of direction of selection (L × C × R), sex, temperature, their

interactions and biological replicates nested within direction of selection were tested by an

analysis of variance (ANOVA) on Table 1. Direction of selection (SEL) is the major source of

variation of WSH, encompassing 78% of the total variation of WSH and 8% among biological

replicates within each SEL. For wing size (WSI), the main factor of variation was Temperature

(59%), followed by Sex (24%). No significant difference of WSI was detected among SEL,

showing that despite the large divergence in WSH, wing size was similar among directions of

selections.

Linear and quadratic models were used to describe the reaction norms along the

temperature gradient of the artificially selected strains. Linear (b) and quadratic coefficients

(c) with standard errors are exhibited on Table S2. For WSH, c is not significantly different from

zero in 10 out of the 16 groups (8 strains with 2 sexes), indicating that WSH reaction norm is

predominantly linear; except for L strains, for which the quadratic model best describes WSH

variation. For WSI, c is significant for most of the RNs; hence, the quadratic model was used.


58

%SS

Source df WSH WSI

SEL 2 77.51 ** 1.36 Temperature (T) 8 0.45 59.08 * Sex 1 2.43 23.79 ** SEL x T 16 2.03 1.35 SEL x Sex 2 0.13 0.08 Sex x T 8 0.13 0.07 SEL x Sex x T 16 0.25 0.24 Replicates (SEL) 5 7.96 *** 3.78 *** Error 1077 9.11 10.25

Level of significance: *P<0.05; **P<0.0l; ***P<0.00l. df: degrees of freedom.

Figure 1. Histograms of wing shape (WSH; left) and size (WSI; right), including males and females from the artificially selected strains at all temperatures, showing great shape variation while size distributions overlap. Illustrative wings of Elongated strains (L) and Round Strains (R) exhibited. Control unselected strains not included for clarity.

Table 1: ANOVA for wing shape (WSH) and size (WSI) testing the effect of directions of selections (SEL: L x C x R), sex, temperatures, their interactions and the effect of biological replicates nested within direction of selection (Replicates (SEL)). F value is estimated using the Replicates (SEL) effect as the error term. The table shows the percentage of the total variance explained by each effect.


59

Fig. 2 shows the adjustment of the best-fit model for the wing traits by direction of selection and sex along the temperature gradient; for WSH in L strain, both adjustments are presented. Wings from R strains become more elongated as temperature increases; conversely, C strains become rounder and, for L strains, the linear regression suggests a WSH homogeneity (with a b not significantly different from zero), but the quadratic adjustment shows a slight increase for this trait at extreme temperatures. Concerning WSI, the influence of temperature variation is more evident, with values decreasing as the temperature increases forming a downward parabola.

Therefore, we used the linear regression coefficient to describe the reaction norm of

WSH (b_WSH) and the quadratic coefficient to describe the nonlinear reaction norms of WSI,

(c_WSI). The extent of the effect of direction of selection (SEL) on the reaction norms and was

assessed by the ANOVA on Table 2. The effects of SEL, Sex, their interactions and biological

replicates nested within SEL were tested. SEL is responsible for 81% of the variation of b_WSH,

showing that divergence in wing shape among SEL was accompanied by modification in the

plastic response of WSH. Differences among biological replicates was assessed by the nested

Figure 2. Temperature reaction norms of wing shape (WSH) and wing size (WSI) for artificially selected strains (Round – R; Elongated – L; unselected control – C). Symbols of sex and letters representing the strains highlight important sources of variation within the data.


60

effect of Replicates(SEL), which was nonsignificant for b_WSH, indicating the homogeneity of

reaction norms among strains selected for the same direction (nonetheless, F values were still

calculated using the Replicates(SEL) as the error term for a more conservative analysis). A

post-hoc pairwise comparison for b_WSH (Tukey’s Honestly-Significant-Difference Test) was

then performed for the SEL effect. Directions of selection were significantly different from

each other (L × R, L × C and R × C: p<0.000002 – Table S3). No effect for Sex or for SEL × Sex

interaction was found.

The relationship between the phenotypic value of wing shape and its reaction norm

(b_WSH) is shown in Fig. 3. A quadratic regression was performed since data is nonlinear, with

intermediate phenotypes (C Strains) exhibiting extreme values of b_WSH. WSHmean explained

36% of the variation of b_WSH, thus corroborating the above results, in that selecting for

different wing shapes systematically changed the wing shape reaction norm.

%SS

Source df b_WSH c_WSI

SEL 2 80.6 ** 23.2

Sex 1 1.3 17.8 SEL x Sex 2 1.0 0.7 Replicates (SEL) 5 13.0 55.6 ** Error 5 4.0 2.7 Level of significance: *P<0.05; **P<0.0l; ***P<0.00l. df: degrees of freedom.

Table 2: ANOVA for wing traits temperature-reaction norms parameters, testing the effect of directions of selections (SEL), sex, their interactions and biological replicates nested within direction of selection (Replicates (SEL)). Reaction norms are described by the angular coefficient of wing shape (b_WSH) and by the curvature of the quadratic polynomials of wing size (c_WSI). F value is estimated using the Replicates (SEL) as the error term. The table shows the percentage of the total variance explained by each effect.


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Differences of wing size reaction norms were nonsignificant (effect of SEL on c_WSI).

Effects of Sex and SEL × Sex interaction were also not significant. Biological replicates held

most of the variation of c_WSI (56%). Overall, our results reveal a high independence pattern

of variation between WSI and WSH (and their reaction norm parameters). This contrasting

response to the experimental factors is surprising because both variables were extracted from

the same biological axes (WW and WL) and one might expect more interference between them.

To analyze how these axes responded to size-imposing sources of variation once the shape

values had been established by selection. In order to allow variance in size while attempting

to maintain a somewhat stable shape, a compensatory pattern between WL and WW is

expected and values of the latter were predicted following the equation (Wwpredicted=

(MeanWSH)(WL)). Wwpredicted represents the expected values in case the mean shape of each

strain were to be kept unchanged despite variation of size in WL. Fig. 4 shows the line

representing the relations between values of Wwpredicted and real WL. WW is scattered around

Figure 3. Relationship of b_WSH (reaction norm parameter for wing shape) on the total phenotypic mean of each strain and sex (total mean calculated on the pool of individuals from all temperatures by strains and sex). The adjusted quadratic regression explains 36% of reaction norms variation.


62

the prediction line, showing that real values tend to follow the prediction equation. These

results indicate that once wing shape is established for a particular strain, variation in size is

proportional and affects both biological axes with the same intensity. This proportional

variation explains how wings exhibit great variance in size across temperatures while not

imposing variation of the same order of magnitude to wing shape.

Figure 4. Proportional variation of WW and WL. Thick line represents the predicted values of wing width (WWpredicted) in a function of the real values of wing length (WL) that allows variation of size while preserving the mean WSH of each direction of selection. WWpredicted = (MeanWSH)*WL. Note that real values of WW are distributed around the prediction line (thick line) showing a proportional growth between axes that allows variance in wing size while maintaining a stable WSH. Mean WSH for L=0.431; C=0.474 and R=0.517. Females on the left and males on the right. Thin long line represents the linear regression of WW on WL.


63

DISCUSSION

The artificially selected strains used in this work exhibit great divergence in wing shape,

while their size is remarkably similar. When these strains were submitted to a large

temperature gradient, each direction of selection exhibited a different wing shape reaction

norm and a similar one for size. Control unselected strains exhibit a shape RN where wings

become rounder as temperature increased, a pattern also found in closely related species

(LOH et al., 2008; MATTA; BITNER-MATHÉ, 2004). However, the intense selection program led

rounded winged flies (R strains) to develop an opposite trend, with wings getting more

elongated as temperature increases. Elongated winged flies (L strains), despite their apparent

strong canalization when analyzed by the linear model (i.e. they exhibit a b=0), evolved a

quadratic RN, with flies reared at the extreme temperatures developing rounder wings. This

change in RN is consistent within the independently selected strains within each direction of

selection. On the other hand, for wing size reaction norms, curvature of the RNs was not

affected by the selection program and are similar to size-related RNs described for many

different populations of Drosophila species (DAVID et al., 1994; MORIN et al., 1999; PÉTAVY

et al., 1997).

The association between the mean value of a trait and its plastic response has long

been debated, with a tendency to assume their independence (see Rocha et al. 2009 for a

brief review on this issue). The discussion gained new insights from an empirical study

demonstrating that reaction norms of abdominal pigmentation in D. mediopunctata are highly

dependent on the phenotypic mean (ROCHA, F.; MEDEIROS; KLACZKO, 2009) adding up to a

few plant studies pointing in the same direction (ELBERSE et al., 2004; KLIEBENSTEIN; FIGUTH;

MITCHELL-OLDS, 2002; STINCHCOMBE; DORN; SCHMITT, 2004). Our results also empirically

show evidence supporting the dependency point-of-view, presenting evidence that wing

shape mean value and its plasticity are, at least partially, dependent, with 36% of the variation

of shape RN explained by the phenotypic mean of wing shape. Part of the genetic basis

associated with wing shape seems to be tightly coupled with the trait capability to present

variation at different developmental temperatures, favoring an allelic sensitivity scenario in

opposition to exclusive plasticity regulatory genes. Genetic linkage is unlikely to explain these

results since wing shape is a complex trait influenced by several genes and one would not


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expect the consistency found here among reaction norms of independently selected strains

within each phenotypic group.

The slope (b) of linear reaction norms is an index of plasticity, with b=0 meaning

canalization and values departing from zero, an increase in plasticity (ROCHA; KLACZKO,

2012,2014). Small changes in reaction norms have profound impacts on phenotypes and on

trait evolution. The average function provided by the linear regressions (from which slopes b

were extracted and used in the ANOVA) for R strains was f(T)=-0.0008(T)+0.54, hence, the

predicted phenotype for R strains at 30ºC is WSH=0.516 (which is close to the real mean of WSH

at 30ºC). For C strains was f(T)=0.0016(T)+0.44. We then computed what would be the

phenotype of R strains if the reaction norm slope had not deviated from the C strains. Hence,

we computed f(30)=0.0016(30)+0.54=0.588 (C strain slope and R strain constant) and found a

difference of almost 14% in wing shape had the slopes of R strains been the same as C strains.

Since b is not commonly presented in papers, comparison with other reaction norms is not

straightforward. Therefore, we visually estimated b from published shape reaction norms. Loh

et al. (2008) analyzed natural populations of Z. indianus, using a similar wing shape descriptor.

The rough estimate was b=0.0003, close to the order of magnitude observed here (Table S2).

Even when we analyzed wing shape RN that used different shape descriptors, we found b with

similar magnitudes. For (AZEVEDO et al., 1998), we estimated a b=0.002 and in (TROTTA et al.,

2010), using the Procrustes generalized least square procedure on a set of landmarks taken

on the wing, we estimated b=-0.001 for the most plastic strain. The order of magnitude of

wing shape RN seems to be similar and small, despite the wing descriptor. On the other hand,

although wing size RN is similar amongst all strains regardless of the direction of selection

applied, it displays more plasticity than shape. For size, the average b=-5.7, an order of

magnitude 4 times higher than for shape RN. This is compelling evidence that wing shape

variation is less plastic than size, presumably because an optimum shape must be more

regulated than size.

Less plasticity of wing shape in contrast to size was also reported in D. mediopunctata

(BITNER‐MATHÉ; KLACZKO 1999c) and by Torquato et al.(2014) using the strains produced by

the same artificial selection program in the 64th generation. (DEBAT; DEBELLE; DWORKIN,

2009) proposed that conservation of wing shape in Drosophila genus is likely to be associated

with stabilizing selection since many studies (HOULE et al., 2003; PÉLABON et al., 2006;


65

WEBER, 1992,1990), including ours, have shown an intense response to shape selection;

therefore it is not the lack of genetic variation nor any constraint in development that prevents

more divergent shapes in nature. The association between shape RN and the phenotypic mean

might contribute to the stabilization of wing shape at intermediate optimum temperatures.

Because of the intense selection program, flies still exhibit great divergence in wing shape, but

smaller genetic divergences might be optimized by different RN associated to them, stabilizing

the phenotype at optimum temperatures.

The contrasting response of wing size and shape to temperature is especially

impressive because both WSH and WSI are composed by the same biological axes (WL, proxy of

proximodistal, and WW, proxy of anteroposterior, developmental axes) and one might expect

more interferences between them. In any biological structure, variation of size and shape

must be orchestrated by developmental axes. The predominance of anteroposterior,

dorsoventral and proximodistal axes specifying tissue organization during development

(NIEHRS, 2010) is an indication that morphological variation should be, whenever possible,

described in terms of those axes because those variation can then be comprehended in terms

of its ontogeny. In the wing of Drosophila, the developmental genetics underlying the

establishment of biological axes have been well described (CIFUENTES; GARCÍA-BELLIDO,

1997; STRIGINI; COHEN, 1999). But it remains unclear how these axes cope with shape and

size variation to produce a phenotype with a size that usually correlates with total body size

and yet preserving a functional shape.

The strains in this work were obtained by an artificial selection program focused on the

ratio WW/WL to generate changes in the shape of the wing, unintentionally preserving overall

wing size. Once the divergent shape was established among C, L and R strains, size-imposing

sources of variation, such as developmental temperatures, affected both axes with similar

intensity and direction, promoting a proportional response that allowed massive size variation

across the temperatures while not imposing great disturbance on wing shape. This

coordinated pattern suggests that genes involved in variation of size are more likely to be

those whose expression domain is on the entire pouch of the wing imaginal disc, rather than

those with restricted territories. A broad expression might explain the proportional response

of the biological axes, both in the composition of size and shape.


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Evolution of plasticity is a property hard to manipulate and study. The large

temperature gradient used here provided a thorough description of wing shape and size

reaction norms. Two main properties previously observed remained unaltered despite great

shape divergence: low plasticity of wing shape and a high plasticity of wing size. On the other

hand, divergent wing shape led to different reaction norms, revealing that genetic variation

for wing shape plasticity is influenced by the mean value.

ACKNOWLEDGEMENTS

We thank Danielle Tesseroli and Bianca Menezes for the establishment and maintenance of

the artificial selection strains, and Dulcinea da Rocha for technical assistance. We also thank

Professor Jean David for helpful comments on an early draft of the manuscript and valuable

suggestions from the two anonymous reviewers. This paper is part of the D. Sc. requirements

of Daniel Mattos at the Biodiversity and Evolutionary Biology Graduate Program of the Federal

University of Rio de Janeiro and was supported by Coordenação de Aperfeiçoamento de

Pessoal de Nível Superior (CAPES; graduate scholarship of D. Mattos), Conselho Nacional de

Desenvolvimento Científico e Tecnológico (CNPq; B.C.B-M.: #485332/2007-8; L.B.K:

#312292/2009-0 and #312066/2014-7), Fundação de Amparo à Pesquisa do Rio de Janeiro

(FAPERJ; B.C.B-M.: #E26/171.314/2008) and Fundação de Amparo à Pesquisa de São Paulo

(FAPESP; L.B.K.: #2012/03144-0 and #2013/04980-0).

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71

SUPPORTING INFORMATION

All Temperatures 14°C 16°C 18°C

Fem

ales

R

WSH 0.511 ± 0.001 ( 201 ) 0.509 ± 0.006 ( 14 ) 0.526 ± 0.003 ( 15 ) 0.520 ± 0.004 ( 12 )

WSI 404.5 ± 2.256 ( 201 ) 438.5 ± 4.351 ( 14 ) 450.1 ± 2.585 ( 15 ) 444.2 ± 5.538 ( 12 )

WL 1132 ± 6.234 ( 201 ) 1231 ± 17.33 ( 14 ) 1241 ± 8.322 ( 15 ) 1232 ± 18.33 ( 12 )

WW 579 ± 3.456 ( 201 ) 625 ± 4.610 ( 14 ) 653 ± 3.923 ( 15 ) 641 ± 7.172 ( 12 )

L

WSH 0.420 ± 0.001 ( 201 ) 0.428 ± 0.003 ( 14 ) 0.419 ± 0.003 ( 30 ) 0.420 ± 0.002 ( 22 )

WSI 402.5 ± 2.007 ( 201 ) 420.9 ± 2.394 ( 14 ) 428.1 ± 2.180 ( 30 ) 433.8 ± 1.996 ( 22 )

WL 1242 ± 6.438 ( 201 ) 1287 ± 8.390 ( 14 ) 1323 ± 6.643 ( 30 ) 1339 ± 6.714 ( 22 )

WW 522 ± 2.646 ( 201 ) 551 ± 3.661 ( 14 ) 554 ± 3.781 ( 30 ) 562 ± 3.164 ( 22 )

C

WSH 0.467 ± 0.001 ( 162 ) 0.457 ± 0.002 ( 21 ) 0.455 ± 0.003 ( 19 ) 0.466 ± 0.004 ( 15 )

WSI 410.6 ± 2.978 ( 162 ) 445.9 ± 2.763 ( 21 ) 456.2 ± 2.524 ( 19 ) 432.9 ± 8.075 ( 15 )

WL 1203 ± 9.371 ( 162 ) 1320 ± 9.683 ( 21 ) 1352 ± 6.975 ( 19 ) 1268 ± 21.93 ( 15 )

WW 561 ± 3.886 ( 162 ) 603 ± 3.555 ( 21 ) 616 ± 4.645 ( 19 ) 591 ± 12.33 ( 15 )

Mal

es

R

WSH 0.523 ± 0.001 ( 201 ) 0.527 ± 0.006 ( 12 ) 0.532 ± 0.004 ( 11 ) 0.531 ± 0.006 ( 13 )

WSI 360.1 ± 2.085 ( 201 ) 395.4 ± 3.072 ( 12 ) 403.4 ± 2.974 ( 11 ) 396.8 ± 3.199 ( 13 )

WL 996 ± 5.631 ( 201 ) 1090 ± 12.40 ( 12 ) 1106 ± 8.305 ( 11 ) 1089 ± 11.53 ( 13 )

WW 521 ± 3.237 ( 201 ) 574 ± 4.289 ( 12 ) 588 ± 5.373 ( 11 ) 578 ± 5.024 ( 13 )

L

WSH 0.440 ± 0.001 ( 205 ) 0.444 ± 0.002 ( 24 ) 0.440 ± 0.003 ( 29 ) 0.436 ± 0.004 ( 24 )

WSI 362.7 ± 2.145 ( 205 ) 390.1 ± 2.730 ( 24 ) 391.1 ± 1.902 ( 29 ) 389.0 ± 1.918 ( 24 )

WL 1094 ± 6.631 ( 205 ) 1171 ± 7.061 ( 24 ) 1179 ± 5.836 ( 29 ) 1179 ± 6.940 ( 24 )

WW 481 ± 2.883 ( 205 ) 520 ± 4.512 ( 24 ) 519 ± 3.250 ( 29 ) 514 ± 3.551 ( 24 )

C

WSH 0.480 ± 0.001 ( 166 ) 0.464 ± 0.003 ( 9 ) 0.479 ± 0.003 ( 22 ) 0.483 ± 0.005 ( 10 )

WSI 367.4 ± 2.494 ( 166 ) 398.1 ± 1.520 ( 9 ) 409.4 ± 3.263 ( 22 ) 395.5 ± 5.683 ( 10 )

WL 1062 ± 7.573 ( 166 ) 1169 ± 7.097 ( 9 ) 1183 ± 9.236 ( 22 ) 1139 ± 20.37 ( 10 )

WW 509 ± 3.399 ( 166 ) 542 ± 2.318 ( 9 ) 567 ± 5.236 ( 22 ) 549 ± 6.603 ( 10 )

Table S1: Mean ± Standard error estimated using the pool of all individuals by direction of selection, sex and temperature (sample size in parenthesis). Round strains (R), Elongated

Strains (L) and Control unselected Strains (C). Wing shape (WSH), wing size (WSI), wing length (WL) and wing width (WW). Units of WSI, WL and WW are pixels (px). 1px=0,0017mm. WSH is a

dimensionless ratio.


72

20°C 22°C 24°C 26°C

Fem

ales

R

WSH 0.506 ± 0.004 ( 24 ) 0.515 ± 0.004 ( 27 ) 0.509 ± 0.004 ( 34 ) 0.509 ± 0.004 ( 26 )

WSI 427.4 ± 2.438 ( 24 ) 408.9 ± 2.437 ( 27 ) 406.7 ± 2.902 ( 34 ) 389.7 ± 1.896 ( 26 )

WL 1202 ± 7.603 ( 24 ) 1140 ± 7.505 ( 27 ) 1141 ± 8.042 ( 34 ) 1093 ± 6.111 ( 26 )

WW 608 ± 4.417 ( 24 ) 587 ± 4.247 ( 27 ) 580 ± 5.211 ( 34 ) 556 ± 3.711 ( 26 )

L

WSH 0.420 ± 0.002 ( 26 ) 0.415 ± 0.002 ( 23 ) 0.415 ± 0.003 ( 43 ) 0.419 ± 0.005 ( 13 )

WSI 417.4 ± 3.146 ( 26 ) 409.6 ± 1.362 ( 23 ) 388.3 ± 1.844 ( 43 ) 381.8 ± 3.421 ( 13 )

WL 1288 ± 7.817 ( 26 ) 1272 ± 5.119 ( 23 ) 1206 ± 7.642 ( 43 ) 1179 ± 8.538 ( 13 )

WW 541 ± 5.110 ( 26 ) 528 ± 2.471 ( 23 ) 500 ± 2.453 ( 43 ) 495 ± 6.416 ( 13 )

C

WSH 0.465 ± 0.002 ( 28 ) 0.467 ± 0.003 ( 9 ) 0.484 ± 0.004 ( 18 ) 0.471 ± 0.002 ( 18 )

WSI 427.4 ± 4.961 ( 28 ) 420.8 ± 5.919 ( 9 ) 376.1 ± 3.483 ( 18 ) 395.0 ± 1.746 ( 18 )

WL 1253 ± 14.76 ( 28 ) 1232 ± 19.52 ( 9 ) 1082 ± 12.77 ( 18 ) 1151 ± 5.200 ( 18 )

WW 583 ± 6.870 ( 28 ) 575 ± 7.374 ( 9 ) 523 ± 4.426 ( 18 ) 542 ± 2.654 ( 18 )

Mal

es

R

WSH 0.517 ± 0.003 ( 24 ) 0.523 ± 0.003 ( 33 ) 0.528 ± 0.003 ( 35 ) 0.516 ± 0.004 ( 29 )

WSI 381.5 ± 2.446 ( 24 ) 364.9 ± 2.172 ( 33 ) 364.1 ± 2.409 ( 35 ) 345.2 ± 2.526 ( 29 )

WL 1062 ± 6.265 ( 24 ) 1010 ± 6.094 ( 33 ) 1003 ± 7.070 ( 35 ) 961 ± 6.139 ( 29 )

WW 549 ± 4.487 ( 24 ) 528 ± 3.944 ( 33 ) 529 ± 3.996 ( 35 ) 496 ± 4.815 ( 29 )

L

WSH 0.436 ± 0.002 ( 33 ) 0.438 ± 0.003 ( 14 ) 0.437 ± 0.003 ( 37 ) 0.449 ± 0.004 ( 12 )

WSI 372.8 ± 1.863 ( 33 ) 368.6 ± 3.382 ( 14 ) 346.6 ± 1.894 ( 37 ) 341.6 ± 2.309 ( 12 )

WL 1129 ± 5.237 ( 33 ) 1114 ± 11.58 ( 14 ) 1050 ± 6.993 ( 37 ) 1020 ± 7.445 ( 12 )

WW 492 ± 3.103 ( 33 ) 488 ± 4.350 ( 14 ) 458 ± 2.820 ( 37 ) 458 ± 3.987 ( 12 )

C

WSH 0.477 ± 0.002 ( 40 ) 0.504 ± 0.008 ( 2 ) 0.481 ± 0.003 ( 36 ) 0.486 ± 0.004 ( 19 )

WSI 381.9 ± 2.623 ( 40 ) 379.6 ± 4.843 ( 2 ) 351.3 ± 3.269 ( 36 ) 355.8 ± 1.754 ( 19 )

WL 1106 ± 8.397 ( 40 ) 1069 ± 5.245 ( 2 ) 1014 ± 11.24 ( 36 ) 1021 ± 4.652 ( 19 )

WW 527 ± 3.615 ( 40 ) 539 ± 11.11 ( 2 ) 487 ± 4.082 ( 36 ) 496 ± 3.738 ( 19 )


73

28°C 30°C

Fem

ales

R

WSH 0.512 ± 0.004 ( 27 ) 0.504 ± 0.005 ( 22 )

WSI 367.1 ± 2.043 ( 27 ) 360.1 ± 3.858 ( 22 )

WL 1026 ± 7.37 ( 27 ) 1014 ± 8.85 ( 22 )

WW 525 ± 3.60 ( 27 ) 512 ± 7.14 ( 22 )

L

WSH 0.430 ± 0.003 ( 21 ) 0.429 ± 0.006 ( 9 )

WSI 363.1 ± 2.950 ( 21 ) 339.7 ± 2.728 ( 9 )

WL 1108 ± 10.65 ( 21 ) 1038 ± 8.92 ( 9 )

WW 476 ± 3.87 ( 21 ) 445 ± 5.41 ( 9 )

C

WSH 0.455 ± 0.005 ( 16 ) 0.483 ± 0.004 ( 18 )

WSI 363.3 ± 2.348 ( 16 ) 363.2 ± 1.752 ( 18 )

WL 1077 ± 7.08 ( 16 ) 1045 ± 5.74 ( 18 )

WW 490 ± 4.75 ( 16 ) 505 ± 3.49 ( 18 )

Mal

es

R

WSH 0.526 ± 0.004 ( 29 ) 0.512 ± 0.005 ( 15 )

WSI 323.6 ± 2.235 ( 29 ) 313.6 ± 4.124 ( 15 )

WL 893 ± 5.90 ( 29 ) 877 ± 10.46 ( 15 )

WW 470 ± 4.28 ( 29 ) 449 ± 7.08 ( 15 )

L

WSH 0.447 ± 0.002 ( 23 ) 0.441 ± 0.005 ( 9 )

WSI 313.9 ± 3.290 ( 23 ) 301.4 ± 4.095 ( 9 )

WL 939 ± 10.17 ( 23 ) 908 ± 10.31 ( 9 )

WW 420 ± 4.53 ( 23 ) 400 ± 6.93 ( 9 )

C

WSH 0.471 ± 0.004 ( 14 ) 0.492 ± 0.004 ( 14 )

WSI 323.6 ± 1.782 ( 14 ) 319.9 ± 3.588 ( 14 )

WL 943 ± 4.73 ( 14 ) 913 ± 10.30 ( 14 )

WW 444 ± 3.58 ( 14 ) 449 ± 5.56 ( 14 )


74

Trait Direction

of Selection

Strain Sex Linear Regression Quadratic Regression

b SE c SE

Win

g Sh

ape

(WSH

)

R Strains

1R F -0.0009 0.0003 ** -0.0001 0.0001

Negative Linear RN

1R M -0.0007 0.0003 * -0.0002 0.0001 *

5R F -0.0006 0.0003 * 0.0000 0.0001

5R M -0.0004 0.0004 0.0001 0.0001

6R F -0.0009 0.0004 * 0.0000 0.0001

6R M -0.0012 0.0003 *** 0.0000 0.0001

C Strains

6C F 0.0011 0.0002 *** 0.0000 0.0001

Positive Linear RN

6C M 0.0009 0.0003 ** 0.0001 0.0001

1C F 0.0026 0.0007 *** 0.0006 0.0002 **

1C M 0.0017 0.0006 ** 0.0003 0.0002

L Strains

1L F -0.0001 0.0003 0.0002 0.0001 *

Quadratic RN

1L M -0.0004 0.0003 0.0001 0.0001

2L F -0.0003 0.0003 0.0002 0.0001 **

2L M 0.0001 0.0004 0.0004 0.0001 ***

5L F 0.0011 0.0002 *** 0.0001 0.0001 *

5L M 0.0001 0.0002 0.0001 0.0001

Table S2: Linear (b) and quadratic (c) coefficients with standard error (SE) from the respective regressions for each

artificially selected strains used as the reaction norm parameter in all analyses. Note that RN of both R and C

Strains are predominantly linear with inverse signs for the linear coefficient while L Strains exhibit quadratic RN.

Females (F) and males (M). 10/16 reaction norms of WSH are significantly linear


75

Table S2 continuation.

Trait Direction

of Selection

Strain Sex Linear Regression Quadratic Regression

b SE

SE

Win

g Si

ze (

WSI

)

R Strains

1R F -5.1903 0.4209 *** -0.3704 0.0797 ***

1R M -5.9272 0.3268 *** -0.3151 0.0584 ***

5R F -4.5799 0.4170 *** -0.1674 0.0777 *

5R M -3.3777 0.4996 -0.1053 0.0935

6R F -6.8319 0.2932 *** -0.2159 0.0560 ***

6R M -6.7955 0.2427 *** -0.1327 0.0513 *

C Strains

6C F -6.3435 0.2393 *** -0.4030 0.0473 ***

6C M -6.2545 0.2687 *** -0.3456 0.0528 ***

1C F -6.0621 0.8873 -0.3062 0.2691

1C M -7.2668 0.6247 -0.2335 0.2019

L Strains

1L F -5.0341 0.4237 *** -0.3158 0.0768 ***

1L M -6.4387 0.3979 * -0.1857 0.0778 *

2L F -4.5394 0.3637 *** -0.2823 0.0775 ***

2L M -3.7554 0.3139 *** -0.2507 0.0662 ***

5L F -6.5762 0.3451 *** -0.4120 0.0798 ***

5L M -6.8940 0.2659 *** -0.2834 0.0639 ***


76

Selection Selection Difference p-Value 95% Confidence Interval

Lower Upper

C L 0.0457 < 10 -5 0.04338 0.04802

C R -0.0452 < 10 -5 -0.04756 -0.0429

L R -0.0909 < 10 -5 -0.09313 -0.0887

Table S3: Results of the Pairwise comparison of the Selection effect of ANOVA

presented on Table 2. Tukey’s Honestly-Significance-Difference Test.

C A P Í T U L O I I I : B A S E S C E L U L A R E S

77

Cellular basis of morphological variation and temperature-related plasticity in Drosophila

melanogaster strains with divergent wing shapes

Libéria Souza Torquato; Daniel Mattos; Bruna Palma Matta; Blanche Christine Bitner-Mathé

Published in Genetica (2014) 142:495-505 DOI 10.1007/s10709-014-9795-0

Authors’ affiliations:

Laboratório de Evolução de Caracteres Complexos – Drosophila, Departamento de Genética,

Instituto de Biologia, Universidade Federal do Rio de Janeiro – Brasil

Running title: cellular basis of wing shape and size variation


78

ABSTRACT

Organ shape evolves through cross-generational changes in developmental patterns at

cellular and/or tissue levels that ultimately alter tissue dimensions and final adult proportions.

Here, we investigated the cellular basis of an artificially selected divergence in the outline

shape of Drosophila melanogaster wings, by comparing flies with elongated or rounded wing

shapes but with remarkably similar wing sizes. We also tested whether cellular plasticity in

response to developmental temperature was altered by such selection. Results show that

variation in cellular traits is associated with wing shape differences, and that cell number may

play an important role in wing shape response to selection. Regarding the effects of

developmental temperature, a size-related plastic response was observed, in that flies reared

at 16ºC developed larger wings with larger and more numerous cells across all intervein

regions relative to flies reared at 25ºC. Nevertheless, no conclusive indication of altered

phenotypic plasticity was found between selection strains for any wing or cellular trait. We

also described how cell area is distributed across different intervein regions. It follows that

cell area tends to decrease along the anterior wing compartment and increase along the

posterior one. Remarkably, such pattern was observed not only in the selected strains but also

in the natural baseline population, suggesting that it might be canalized during development

and was not altered by the intense program of artificial selection for divergent wing shapes.

Keywords: artificial selection; morphological evolution; phenotypic plasticity; size; wing

development


79

INTRODUCTION

For organ shape to evolve, phenotypic variation in tissue dimensions and final adult

proportions must be altered across generations. Natural selection can promote such changes,

by acting on the heritable counterpart of phenotypic variation for the target trait, which may

reflect the existence of heritable variation in subordinate traits at lower levels of biological

organization (GARLAND; KELLY, 2006). For instance, when variation in organ shape is selected

for, effective response to selection should be expected to occur through evolutionary changes

in cellular parameters during tissue development, such as cell size, shape, number and/or

organization (LECUIT; LE GOFF, 2007). Another predicted outcome of natural or artificial

selection, not often discussed in the literature (GARLAND; KELLY, 2006), is that average

phenotypic plasticity for the trait under selection (or for subordinate traits) could also be

affected, in a correlated response to selection on the target trait. Therefore, investigating the

cellular basis of evolution and plasticity in organ morphology is crucial to the understanding

of which cellular traits and developmental processes are able to respond to selective and

environmental factors that affect organ development.

Describing and isolating size and shape components of organ morphology is one

essential step in studies of morphological evolution. The Drosophila wing has been widely

used as a model for such studies, given some experimental advantages to other organs like:

adult wing is essentially bidimensional, which facilitates the characterization and

interpretation of its morphological variation (KLACZKO; BITNER-MATHÉ, 1990; KLINGENBERG,

2002), and wing development is relatively well known (BLAIR, 2007; GARCIA-BELLIDO; DE

CELIS, 1992; NETO-SILVA; WELLS; JOHNSTON, 2009). And since each wing cell produces a

single trichome (DOBZHANSKY, 1929), estimation of cell number and cell area can be

performed through trichome density in regions of adult wing. In natural populations, genetic

basis for latitudinal and altitudinal variation in wing shape traits has been reported; the

geographical variation being generally non-linear (BITNER-MATHÉ; PEIXOTO; KLACZKO, 1995;

GILCHRIST et al., 2000; HOFFMANN; SHIRRIFFS, 2002; IMASHEVA et al., 1995; LOH; BITNER-

MATHÉ, 2005; PITCHERS; POOL; DWORKIN, 2013). But the adaptive nature of wing shape is

difficult to be evidenced. One successful demonstration was made by Menezes et al. (2013),

using replicate strains of D. melanogaster that were artificially selected for increasing the

quantitative divergence in the outline shape of the wings. These authors observed higher


80

mating success for males from strains with elongated wings and found no consistent evidence

that general size (wing or thorax size) is related to such success. In fact, a large amount of

additive genetic variation for the outline shape of the wing has been observed, even in species

from different Drosophila subgenera (BITNER-MATHÉ; KLACZKO, 1999a; MATTA; BITNER-

MATHÉ, 2004), over which natural and artificial might act. So, overall, wing shape should be

considered as a meaningful component of reproductive fitness in Drosophila and might be a

direct target for selection in nature.

Despite the different methods used to estimate morphological variation, the adaptive

nature of wing size has been extensively reported, at least as a proxy for body size (ARENDT,

2007; KLEPSATEL et al., 2014; NIJHOUT, 2003). Ectotherms generally follow Bergmann’s rule,

in that animals from higher latitudes tend to have larger bodies and body parts than those

from lower latitudes. This clinal variation has been commonly considered as an adaptive

response to the latitudinal variation in temperature, but contradicting results indicate that

other selective pressures should also be acting. In a thorough review, Arendt (2007) discusses

the patterns of cellular alterations that underlie wing size variation, especially in D.

melanogaster. As reported by the author, plastic change of wing size in response to rearing

temperature is consistently due to changes in cell size: flies that develop at lower

temperatures have larger wings as a result of increased cell size, with little or no contribution

of cell number. A similar pattern is observed in thermal selection experiments. When reared

at different temperatures for extended periods of time, cold-adapted flies show an evolved

(genetic) response to temperature that is mostly due to larger cells in their (larger) wings. On

the other hand, the evolved response to natural latitudinal variation generally follows an

opposite trend: when flies from different populations are reared under similar conditions,

high-latitude (larger) wings show a consistent increase in cell number, with little or no

contribution of cell size, relative to low-latitude (smaller) wings (ARENDT, 2007; KLEPSATEL et

al., 2014). In this context, therefore, the means to be larger depends on which environmental

and/or evolutionary factors are acting on the correlated subordinate mechanisms that affect

cellular alterations during development.

However, it remains unclear which cellular processes are involved with evolution of

wing shape. One way to find associations between variation in cellular traits and differences

in organ shape would be through comparison of populations or strains that significantly


81

diverge in organ shape but have similar organ sizes, so that confounding effects of size

components could be largely minimized without the need of any scaling transformation. The

D. melanogaster strains used by Menezes et al. (2013) present such features. These strains

were previously established in our laboratory, by B. C. Bitner-Mathé, D. Tesseroli and B. F.

Menezes (unpublished data), through artificial selection for increasing or decreasing the

values of a wing shape index, which resulted in flies with elongated (L strains) or rounded (R

strains) wings. It is noteworthy that variances in wing outline shape of L and R strains do not

show any overlap after selection (MENEZES et al., 2013). Moreover, despite the shape

divergence, these strains have evolved similar wing sizes (MATTA, BRUNA P; BITNER-MATHÉ;

ALVES-FERREIRA, 2011; MENEZES et al., 2013). So, here we investigated the cellular basis

involved in wing morphological variation in these strains, as well as a possible plastic response

to developmental temperature of wing features and cellular traits. Moreover, we tested for

any experimentally evolved change in temperature-related plasticity, which could have arisen

as a byproduct of selection on wing outline shape.

MATERIALS AND METHODS

FLIES (STRAINS WITH ARTIFICIALLY SELECTED WING SHAPES)

Detailed information on the program of bidirectional artificial selection that generated

the fly strains can be found in Menezes et al. (2013). Briefly, wild-caught D. melanogaster

females were collected (Rio de Janeiro: 22º95’52”S / 43º19’68”W) and were individually set

in vials with cornmeal sucrose medium at 16ºC, resulting in 135 isofemale lines. After eclosion,

males and virgin-females were used as the baseline laboratory population for the selection

program, which was henceforth performed at 22ºC. Using the width-to-length ratio as an

index of wing shape (WSH – see next section), bidirectional selection was simultaneously

applied to four independent biological replicates. As a result, four strains with elongated (L)

wings were established through selection for decreasing WSH (L strains), while four strains with

rounded (R) wings were set by selection for increasing WSH (R strains). Selection was

performed at every generation until the 21st. Subsequent selection was applied intermittently,

but extra care was always taken to not overlap any generation.


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EXPERIMENTAL DESIGN

This work was performed at generation 64 (G64), for which the last preceding selection

was made at generation 62. For comparison, we also used the wild-caught females (Wild) and

their laboratory daughters (G1) reared at 16ºC. All flies were fixed in ethanol 70%.

Given the labor intensive requirements of trichome counting, only females from two

biological replicates were analyzed in each direction of selection: 1L and 5L (L strains), 1R and

5R (R strains); a sample of each was transferred to 25ºC two generations prior the following

experiment. For each selection strain, ten random pairs of G64 flies were placed into bottles

containing 40mL of cornmeal sucrose medium for oviposition. Egg density was controlled by

transferring parental flies to new bottles, daily, during six days. To allow oviposition at

different temperatures of development (TD), bottles from days 1, 3 and 5 were maintained at

25°C while bottles from days 2, 4 and 6 were maintained at 16°C. Hence, three batches of

siblings raised in each TD were obtained for each selection strain (1L, 5L, 1R and 5R). Up to

five females from each batch had their left wings mounted on microscope slides.

WING MORPHOMETRICS

Wing length (WL) was estimated as the distance between the alula opening (AO) and

the distal edge of the 3rd longitudinal vein (L3), while wing width (WW) was estimated as a

straight line approximately perpendicular to WL, using the posterior edge of the 5th

longitudinal vein (L5) as a landmark (Fig. 1a).

Klaczko and Bitner-Mathé (1990) demonstrated that the outline shape of Drosophila

wing can be geometrically described by the fitting of an ellipse to semi-landmarks taken on

the wing contour, thereby allowing an estimation of its outline shape (named SH) through the

ratio between minor and major ellipse radii. Wing size (named SI) is then estimated by the

geometric mean between both ellipse radii, which corresponds to the radius of a circle with

the same ellipse area. Because no scaling transformation is applied (see also Klaczko 2006),

any information obtained with the ellipse adjustment can be traced back to the original

landmarks taken from the wing, making biological inferences straightforward. Here, a proxy

of such ellipse indexes were estimated: wing shape (named WSH) is given by the width-to-

length ratio (WW/WL); while wing size (named WSI) is given by the geometric mean between

WW/2 and WL/2, and has the same measurement unit of WW and WL. By superposing the


83

estimated ellipse over the original wing image (see Fig. S1), we show that the ellipse proxy can

be considered a good approximation to the ellipse adjustment, and that both of these

methods capture most of the outline shape and size of each original wing. So wings with bigger

SH or WSH ratios are rounder because they have larger width and/or smaller length; the

contrary is true for more elongated wings.

We acknowledged that methods of geometric morphometrics are broadly used and

have remarkable statistical power for estimating size and shape components of wing

morphology, especially non-allometric components in different aspects of wing shape (see

KLINGENBERG, CHRISTIAN PETER, 2002; PITCHERS; POOL; DWORKIN, 2013). In this work,

however, we have used the WSH proxy mainly because: (1) it produces a straightforward

description of wing outline shape without the need of any scaling transformation (as shown

in Fig. S1); (2) the fly strains used here were generated through selection on WSH ratios, and

so should vary the most along WW and WL measurements.

Regarding internal aspects of the wing, five intervein regions (IVR) named A-E were

analyzed by estimating their sizes through polygonal surface areas, with no scaling applied.

More specifically, each IVR area was estimated as the area of a polygon delimited by

landmarks at intersections of wing veins, as well as by 5 to 10 semi-landmarks on curved

regions; see the respective traced areas in Fig. 1a.

Whole wing images were taken at 36X magnification using digital camera (Optronics

DEI-750, 1.3 megapixels, software Axion Vision 3.0) attached to a stereoscope microscope

(Zeiss Stemi SV11). WW and WL were measured using ImageJ v.1.46r

(http://rsbweb.nih.gov/ij/) and were scaled to millimeters. The polygonal surface area of each

IVR (in mm²) was estimated using ImageJ’s polygon tool.

Raw data will be available from Dryad Digital Repository (http://datadryad.org/).


84

CELL SIZE (AREA) AND CELL NUMBER

Images of fixed-size rectangles within each intervein region (IVR) at dorsal wing surface

(Fig. 1b) were captured with above mentioned digital camera (and software) on a optic

microscope (Zeiss Stemi Axioskop 2) and used for trichome counting (1000X magnification;

fixed rectangle area of 0.00906 mm²). Trichome counting was performed using the cell

counter plugin v.2010 on ImageJ v.1.46r; all visible trichomes were counted. Given that cell

density might not be homogeneous throughout the wing, we aimed at taking each rectangle

image in equivalent positions for all analyzed wings. A proxy for average cell area (CA) in each

IVR was estimated through the ratio between the fixed surface area of rectangle image

(0.00906 mm2) and the respective number of counted trichomes (Fig. 1b). We also

investigated how variation in cell area is distributed across the wing surface and whether this

distribution was affected by response to artificial selection and/or temperature variation

during the development (details in next section). In turn, cell number (CN) in each IVR was

given by the ratio between the estimated IVR area (traced areas in Fig. 1a) and the respective

CA. We note that a thorough estimation of CA and CN across the wing blade was performed

by analyzing five different intervein regions, instead of using only one or two regions as

Figure 1. Morphometric methods presented in a wild-type female wing of Drosophila melanogaster. (a) Wing showing wing length (WL), wing width (WW) and the area of each intervein region (IVR) A-E (traced lines). Within each IVR, rectangles indicate approximate locations where all trichomes were counted. (b) Magnification used for image taking and trichome counting at each rectangle region shown in (a); each wing cell has a single characteristic trichome (Colour figure online)


85

commonly performed in the literature. Total cell number at dorsal wing surface (CNTotal)

was calculated through the ratio between the estimates of whole wing area (π * WL/2

* WW/2; a proxy to the ellipse area) and average cell area across all intervein regions ( ).

STATISTICAL ANALYSES AND DISTRIBUTION OF CELL AREA ACROSS INTERVEIN REGIONS

Two-way nested ANOVAs were performed to test the homogeneity between the

following fixed factors: direction of selection (SEL: R×L strains), temperature of development

(TD: 16ºC×25oC), interactions between these factors (SEL×TD), plus the nested effects of

biological replicates within such interaction (SEL×TD{REP}), in order to enhance statistical

power. Whenever the SEL×TD{REP} effect was significant, it was used as the error term for the

remaining effects (for details on nested ANOVA, see SOKAL; ROHLF, 1981). Almost all traits

(including WSH ratios) were normally distributed (Kolmogorov-Smirnov test: all P > 0.00625;

alfa from Bonferroni correction), except for CAD in 1R strain TD 16ºC (P = 0.00260); so no data

transformation was applied.

The distribution of cell area across the intervein regions of each wing was estimated

through the following polynomial regression: Y = g0 + g1X + g2X²; where Y is CA (dependent

variable), X is the rank order of intervein region (independent variable), and coefficients are

g0 (intercept), g1 (slope) and g2 (quadratic). To do so, a rank order was attributed to each

intervein region according to their proximity from the most anterior region of the wing: IVRA

was ranked as 1, IVRB as 2 and so forth. The following polynomial parameter and characteristic

values were then used as variables that describe cell area distribution: the shape of the curve

(g2), minimum value (MV) and intervein region of minimum value (IVR-MV); for details see

David et al. (1997).

All statistical analyses were performed using SYSTAT© v.13.0 (SPSS Inc.).

RESULTS

Mean values of wing traits (WSH, WSI, WW, WL), plus total cell number (CNTotal) and

average cell area (CAAverage) at dorsal wing surface are shown in Fig. 2, for the artificially

selected strains with rounded (R) or elongated (L) wings (values per biological replicates are

presented in Table S1). ANOVA results for each trait are reported in Table 1. Regarding overall

CA


86

shape divergence, significant effects of direction of selection (SEL: R×L strains) were found for

WSH, WW and WL. It follows that wings from R flies are rounder (bigger WSH ratios) than wings

from L flies, and this difference in outline shape is accounted by significant differences in both

width and length: R flies have wider (bigger WW) and shorter (smaller WL) wings than L flies.

No difference in wing size associated to direction of selection was found (non-significant SEL

effect for WSI). This result suggests that SEL effects can be considered a shape-related source

of variation, with no detectable influence on wing size. As for the cellular traits in whole dorsal

surface, we found that wings from R flies have significantly more cells (increased CNTotal) than

wings from L flies (Fig. 2 and Table 1), but no significant SEL effect was detected for average

cell area (CAAverage).

The effect of temperature of development (TD: 16ºC×25oC) was significant for all size-

related traits, but not for WSH (Table 1). Wings are on average bigger and present larger width

and length when flies are raised at 16ºC (Fig. 2 and Table S1), a pattern repeatedly described

in the literature. Both cell number and cell area account for such temperature-related

variation in size: bigger wings result from bigger and more numerous cells when flies are

reared at 16ºC. Moreover, we did not detect any significant SEL×TD effect. Therefore, we have

no indication of variation in the plasticity of any trait between the strains selected for

divergent wing shapes.


87

For comparison purposes, mean values of wild-caught females (Wild) and their

daughters (G1) are also presented in Fig. 2 (and detailed in Table S2). We note that, due to

logistics in our laboratory at the time of the experiment, G1 flies had to be maintained at 16oC,

but all subsequent generations of the selection program were maintained at 22oC. It is clear

that average WSH values have diverged from Wild and G1 flies, increasing in R strains and

decreasing in L strains. Differences in size-related (WSI, WW and WL) and cellular traits (CNTotal

and CAAverage) can also be observed between baseline population (Wild and G1) and selection

strains from G64. Nevertheless, it is interesting to note that, when reared at similar conditions

(16oC), mean values of size-related and cellular traits in R and L strains tend to approach those

of G1 16oC-reared flies; a pattern not observed for WSH.

Figure 2. Mean values and standard-errors of wing traits according to temperature of development (16oC or 25oC) in females from L strains (dashed line) and R strains (dotted line), sampled at the 64th generation of artificial selection. Mean values and standard-errors for wild-caught females (Wild) and their laboratory daughters (G1), which founded the selection strains, are also presented. Abbreviations: wing outline shape (WSH), wing size (WSI, in mm), wing width (WW, in mm) and length (WL, in mm), plus total cell number (CNTotal) and average cell area (CAAverage, in mm2 × 10-4) at dorsal wing blade


88

To further explore the cellular basis of temperature-related plasticity subjacent to the

morphological variation between R and L strains, intervein regions (A-E) were individually

analyzed. Fig. 3 presents mean and standard error for each intervein area (IVR), cell area (CA)

and cell number (CN) (detailed in Table S1 and S2). ANOVA in Table 1 shows that IVRA was

significantly different between selection strains. It follows that flies from R strains have bigger

IVRA than flies from L strains. This morphological variation seem to result from increased cell

number: CNA is significantly bigger in R strains relative to L strains, but no significant change

between R and L strains was detected for cell area at intervein region A (CAA). In addition,

significant but opposite SEL differences were found for CAE and CNE, which might explain the

non-significant morphological difference in the area of IVRE. That is, cells at region E of R wings

are on average smaller but more numerous relative to the same region in L wings, so that the

area on intervein region E does not significantly differ between R and L wings. No other

significant SEL difference was found for any IVR, CA or CN estimates. Regarding the effects of

temperature, all intervein regions were significantly bigger at 16ºC with larger and more

numerous cells, relative to flies reared at 25oC (except for CNB; see TD effects in Table 1).

Table 1: ANOVA of each morphological and cellular trait in D. melanogaster strains with divergent wing shapes.


89

ANOVA – Mean Squares

Trait SEL TD SEL×TD SEL×TD{REP} error

WSH 17.511** 0.014 0.121 0.324*** 0.008

WSI 0.403 12.423*** 0.078 0.117* 0.041

WW 26.487** 23.214** 0.965 0.400** 0.090

WL 47.423* 108.701** 0.173 2.862*** 0.368

IVRA 1.096*** 2.333*** 0.033 0.043 0.027

IVRB 0.796 6.316** 0.112 0.139** 0.036

IVRC 0.121 10.968*** 0.028 0.041 0.048

IVRD 0.016 8.067*** 0.051 0.077* 0.023

IVRE 0.071 18.215*** 0.003 0.033 0.075

CAA 0.021 86.694*** 10.629 1.642 3.143

CAB 0.659 190.978** 6.487 8.291** 2.295

CAC 0.984 37.632** 0.085 7.466 3.488

CAD 1.610 98.665*** 4.652 1.655 1.936

CAE 39.184** 149.136*** 8.767 1.799 5.091

CAAverage 3.593 105.720*** 4.522 1.959 1.149

CNA 253,995.430*** 158,029.631*** 2,716.003 3,756.846 9,969.001

CNB 298,286.299 92,097.881 4,536.593 78,961.420** 17,081.196

CNC 2,111.319 1,610,532.739*** 20,533.579 39,795.842 33,675.012

CND 29,304.266 765,368.141*** 3,711.323 15,841.698 10,978.311

CNE 816,289.880** 850,056.972** 131,497.232 60,167.240 82,163.968

CNTotal 5,629,535.408*** 14,228,061.620*** 110,600.228 551,419.905 278,865.760

Wing traits: outline shape (WSH), size (WSI), width (WW), length (WL), and the area of intervein regions A-E (IVRA-

E). Cellular traits: average cell area (CA) or cell number (CN) at intervein regions A-E (subscripts), plus the average cell area across all intervein regions (CAAverage) and total cell number (CNTotal) at dorsal wing blade. Units: WSH is a ratio; WSI, WW and WL are in millimeters; and all CA estimates are in mm2 ×10-4. ANOVA model: direction of selection (SEL: L strains × R strains; df=1), temperature of development (TD: 16oC × 25oC; df=1), SEL×TD interaction (df=1), nested effects of biological replicates (SEL×TD{REP}; df=4), and model error (df=72); whenever SEL×TD{REP} effect was significant, it was used as error term for the remaining effects. N=10 for each group (1L, 5L, 1R and 5R strains reared at each TD). Mean squares (MS) are presented ×10-2, except for cell number estimates. ***P ≤ 0.001; **P ≤ 0.01; *P ≤ 0.05.


90

Figure 3 Mean values and standard-errors of wing traits per intervein region (A-E) according to temperature of development (16oC or 25oC), in females from L strains (dashed line) and R strains (dotted line) that were sampled at the 64th generation of artificial selection. Mean values and standard errors for wild-caught females (Wild) and their laboratory daughters (G1), which founded the selection strains, are also presented. Abbreviations: area of each intervein region (IRVA-E, in mm2), cell number (CNA-E) and cell area (CAA-E, in mm2 × 10-4)


91

To investigate how variations in cell area are distributed across the wing surface, each

CA mean value was plotted against the respective IVR rank order (Fig. 4). It is clear that average

cell area tend to decrease from intervein regions A-C and to increase from regions D-E, despite

the direction of selection or temperature at which flies were raised. Interestingly, a similar

pattern was observed in wings of Wild and G1 flies, which were never subjected to artificial

selection. This result demonstrates that such distribution was already present in wings from

the natural baseline population and was not altered by the intense program of artificial

selection. Adjustment of the quadratic regression was significant for all experimental groups

and explains from 18% to 38% of cell area variation across the wing blade. No differences

between L and R strains, or between developmental temperatures, were found for the

parameters that describe each polynomial regression (Table S3), except for minimum value of

16oC × 25oC-reared flies. But this is an expected result, since wings of 16ºC-reared flies tend

to have larger cells across the whole wing surface, regardless the selection strain.


92

DISCUSSION

In this study, we investigated the cellular basis of an artificially selected divergence on

the outline shape of D. melanogaster wings, as well as of temperature-related plasticity in

these selected strains. Confounding effects of wing size on wing shape estimates are

commonly observed in natural populations and do not allow a direct association between

cellular variation and wing shape differences. One advantage of the selection strains used in

our work is that, despite their wide divergence in shape, wing size is remarkably similar. So, in

this specific case, most of the phenotypic variation in the wing contour is directly related to

variation in shape, even without scaling transformations. This is particularly interesting since,

when no scaling is applied, cellular variation can be directly related to the morphological

variation that is being captured. We were thus able to isolate cellular variation involved with

shape-related effects of artificial selection on wing outline shape from cellular variation

associated with size-related effects of plastic response to developmental temperature; with

minimum or no interaction between such effects.

Figure 4. Quadratic curves fitted to average cell area (CAAverage, in mm2 × 10-4) across the ranked intervein regions for the complete data set of each experimental group; rank order was attributed respecting their proximity from the most anterior region of the wing: IVRA was ranked as 1, IVRB as 2 and so forth. Legend of experimental groups also presents the percentage of variation explained (R2) by the respective polynomial adjustment (Colour figure online)


93

In respect to the cellular basis of artificially selected divergence in outline shape, our

results show a significant increase in total cell number of rounded wings relative to elongated

wings. It seems that cell number may play an important role in wing shape response to

selection. Regarding the relative contribution of intervein regions, a significant shape-related

effect of direction of selection was detected only for IRVA and CNA. Although not statistically

significant, intervein region B also seems to exhibit a small divergence between R and L strains,

regarding both IVR area (IRVB) and cell number CNB (see Fig. 3). Hence, we found that response

to selection on wing shape was primarily associated to changes in the anterior region of the

wing (A and possibly B). Such result is not in agreement with previous studies that identified

a more prominent response of posterior and distal regions in relation to different aspects of

wing shape variation in Drosophila (Gilchrist et al. 2000, Pezzoli et al. 1997). This discrepancy

might be related to the fact that, here, wing outline shape was the direct target of selection;

while in natural populations it is not straightforward to isolate the amount of wing shape

variation that actually results from direct selection on wing shape (especially from optimizing

selection; see Gilchrist et al. 2000). But we note that the variation in the outline shape of the

wing might not be completely independent from variation in the positioning of some wing

veins. For instance, genetic correlation between outline shape and the positioning of second,

fourth and fifth longitudinal veins has already been found, even in different Drosophila species

(BITNER-MATHÉ; KLACZKO, 1999; MATTA; BITNER-MATHÉ, 2004). Given that IRVA and IRVB are

both associated to the second longitudinal vein, it is possible that the positioning of such wing

vein have also changed as a correlated response to selection on outline shape. But a thorough

description of correlated responses from different size and shape traits to the selection on

outline shape still needs to be performed.

Regardless the wing compartments, studies on the cellular basis of natural adaptation

and artificial selection associated with Drosophila wing morphology have also found a primary

role of cell number, for both shape and size-related traits. In experiments of artificial selection

for body size (reviewed in ARENDT, 2007), flies selected to be larger tend to have larger wings

with increased number of cells than control flies (NOACH; DE JONG; SCHARLOO, 1997;

PARTRIDGE et al., 1999). The contrary was found for flies selected to be smaller, although a

large effect of (reduced) cell size was also observed (GUERRA et al., 1997; PARTRIDGE et al.,

1999). When comparing natural populations along latitudinal clines, several studies have


94

reported that flies from higher latitudes tend to have larger wings than those from lower

latitudes, and this wing size difference is mainly explained by an increase in cell number, with

small or no contribution of cell size (ARENDT, 2007; KLEPSATEL et al. 2014). Although it is

commonly assumed that adaptation to temperature is the most important factor for the

establishment of latitudinal clines, thermal selection experiments have shown a contradicting

result: the wing size increase observed in laboratory cold-adapted flies largely result from an

increase in cell size rather than cell number (ARENDT, 2007). Therefore, it is reasonable to

assume that other selective pressures (apart from developmental temperature) should be

acting in latitudinal clines of wing size, such as balancing selection for optimal wing shape

(GILCHRIST et al., 2000). Regarding the cellular basis of wing shape adaptation in nature,

Pezzoli et al. (1997) examined temperate and subtropical populations of D. melanogaster and

found that significant differences in components of wing shape were mainly explained by

variation in cell number. But given the differences in wing size, the exact contribution of cell

number and cell area to such differences in wing shape components could not be estimated.

Our results add to this discussion, since we showed that morphological responses to a direct

selection on wing outline shape were achieved mainly through changes in cell number. So it

appears that, when caused by direct selection on wing morphology (for shape or size-related

traits) or by selective pressures in latitudinal clines other than developmental temperature,

evolutionary changes in wing size and shape might be essentially achieved through

subordinate changes in cell number.

As for the size-related plastic response to developmental temperature, we observed

that flies reared at the lowest temperature developed larger wings with bigger and more

numerous cells across the whole wing surface. Moreover, WSI was high and similarly

correlated with both cell size and number (Pearson’s correlation using whole dataset: r =

0.753, P < 0.0001 for WSI×CNTotal; and r = 0.724, P < 0.0001 for WSI×CAAverage). Remarkably, we

did not detect a correlation between CNtotal and CAaverage (r = 0.093; P = 1.000). This suggest

that developmental processes involved in establishment of final organ size might be capable

of regulating both cell number and size but with some independency between them. In fact,

it has been repeatedly demonstrated that cell size has a major role in the plasticity of

Drosophila wing size to developmental temperature (reviewed in ARENDT, 2007). But the

importance of variation in cell number to such temperature-related plasticity is not clear, and


95

has only been reported as a conditional sex-effect (ARENDT, 2007; NOACH et al. 1997). So

further investigation is needed in order to improve the validity of such findings; ideally using

a greater number of biological replicates, different selection populations and gradients of

developmental temperature.

Regarding the distribution of cell area across the different intervein regions, we also

observed an interesting counterclockwise-like pattern: cell area tends to decrease along the

anterior compartment of the wing (from IVRA to IVRC) and increase along the posterior wing

compartment (from IVRD to IVRE), despite the direction of selection or temperature in which

flies were raised. Remarkably, this pattern of distribution was observed not only for L and R

strains, but also for the wild-caught baseline population and its G1 daughters reared under

laboratory conditions, which were never subjected to artificial selection. This result indicates

that the quadratic pattern of cell area distribution might be canalized during development and

did not change in face of developmental temperature or artificial selection for rounded or

elongated wings (Fig. 4). We note that this is the first time in which such pattern is thoroughly

analyzed, but evidence for its existence in natural populations can be obtained by plotting

results from Pezzoli et al. (1997). So what developmental processes could generate such cell

area distribution across the wing surface? In wing imaginal discs, the location of presumptive

vein and intervein regions along the anteroposterior axis are determined during late third

instar wing disc development (BIER, 2000; BLAIR, 2007; RESTREPO; ZARTMAN; BASLER, 2014).

From this stage on, presumptive intervein regions are recognized as developmental subunits

that largely restrict cell proliferation within their boundaries (GARCIA-BELLIDO; MERRIAM,

1971; GONZÁLEZ-GAITÁN; CAPDEVILA; GARCIA-BELLIDO, 1994). So it is tempting to argue that

genes activated or repressed downstream of the signaling pathways that pattern presumptive

vein and intervein regions (such as Decapentaplegic signaling pathway) could also be

responsible for the final definition of cell area at each intervein region. It is also possible that

such pattern might be achieved or refined during hinge contraction along the proximodistal

axis, in the development of Drosophila pupal wing (OLGUIN; MLODZIK, 2010).

One major goal of experiments that perform artificial selection is gaining insight into

evolutionary patterns that might also occur in nature, not only for the focal trait, but also for

subordinate or correlated traits (FULLER; BAER; TRAVIS, 2005). Expected outcomes of natural

selection, like possible changes in average phenotypic plasticity due to directional selection


96

(GARLAND; KELLY, 2006), can also be tested in such experiments. In our study, no conclusive

indication of altered phenotypic plasticity due to wing shape selection was found, given that

no significant SEL×TD interaction was observed for any wing or cellular trait. Nevertheless,

reaction norms can be quite complex and linearity cannot be assumed (DAVID et al., 1997;

ROCHA; KLACZKO, 2012). So, possible changes in phenotypic plasticity due to the intense

artificial selection are yet to be thoroughly tested, particularly on a wider and more refined

gradient of developmental temperature.

Investigating the cellular basis of wing shape divergence and associated changes in

plasticity might be a difficult task, especially because of confounding wing size effects. But this

is a worthwhile quest, since the evolution of organ shape is a fascinating subject in

evolutionary developmental biology. In this study, we were able to directly relate cellular

variation to wing shape differences and show that cell number may have an important role in

response to selection on wing shape.

ACKNOWLEDGMENTS

We thank Danielle Tesseroli and Bianca Menezes for the establishment and

maintenance of the artificial selection strains, and Dulcinea da Rocha for technical assistance.

We also thank Louis Bernard Klaczko and reviewers for helpful comments. This study is part

of the thesis research of L.M.S.T. in pursuit of her Ph.D. in Genetics at the Genetics Department

of Universidade Federal do Rio de Janeiro (UFRJ), and was supported by Conselho Nacional de

Desenvolvimento Científico e Tecnológico – CNPq (B.C.B.-M. research project: 485332/2007-

8; L.S.T. graduate scholarship: 140993/2010-0) and by Coordenação de Aperfeiçoamento de

Pessoal de Nível Superior – CAPES (B.P.M. research project and postgraduate scholarship:

23038.0080/2010-97 AUXPE-PNPD 2799/2010; D.M. graduate scholarship).

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SUPPORTING INFORMATION

Figure S1. Original wings images superposed with respective estimated ellipses, using actual data from ellipse adjustment to semi-landmarks (traced blue lines) and its proxy via wing width (WW) and length (WL) measurements (red solid lines). Presented wings were randomly chosen: first specimen in alphabetical order for each D. melanogaster selection strain (1L, 1R, 5L, 5R). Ellipse adjustment was performed using a least square procedure to solve the ellipse equation for Cartesian coordinates of 20 semi-landmarks taken on the wing contour (details in Klaczko and Bitner-Mathé 1990; and Klaczko 2006; see main text). In all four cases, correlation between x-observed and their y-expected ellipse values was greater than 99%. Given that no scaling transformation is applied, the estimated ellipses were reconstructed from values of minor (b) and major (a) ellipse radii (in mm), each multiplied by 2. In turn, ellipses for the proxy of such adjustment were reconstructed using WW and WL values (in mm). On average, WW estimates 94.5% of the respective 2b value, while WL estimates 98.7% of the respective 2a value; similar values were found in data from other studies (not shown).


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Table S1: Mean ± SE for wing morphology and cellular variations at intervein regions (Fig. 1a) by individual strain and temperature of development (TD).

TD 25°C (mean ± SE) TD 16°C (mean ± SE) TD 25°C (mean ± SE) TD 16°C (mean ± SE)

Trait 1L (n=10) 1R (n=10) 1L (n=10) 1R (n=10) 5L (n=10) 5R (n=10) 5L (n=10) 5R (n=10)

WSH 0.445 ± 0.002 0.542 ± 0.001 0.438 ± 0.003 0.564 ± 0.003 0.453 ± 0.002 0.528 ± 0.004 0.439 ± 0.002 0.516 ± 0.004

WSI 0.678 ± 0.003 0.666 ± 0.007 0.745 ± 0.006 0.760 ± 0.005 0.656 ± 0.003 0.684 ± 0.006 0.734 ± 0.011 0.760 ± 0.006

WW 0.904 ± 0.005 0.980 ± 0.010 0.986 ± 0.011 1.141 ± 0.009 0.883 ± 0.005 0.994 ± 0.008 0.973 ± 0.015 1.092 ± 0.009

WL 2.033 ± 0.007 1.809 ± 0.020 2.251 ± 0.017 2.023 ± 0.011 1.949 ± 0.011 1.885 ± 0.021 2.217 ± 0.035 2.118 ± 0.017

IVRA 0.129 ± 0.004 0.140 ± 0.003 0.162 ± 0.008 0.184 ± 0.004 0.128 ± 0.002 0.156 ± 0.003 0.154 ± 0.007 0.188 ± 0.007

IVRB 0.250 ± 0.004 0.240 ± 0.005 0.289 ± 0.008 0.317 ± 0.004 0.227 ± 0.004 0.262 ± 0.004 0.285 ± 0.009 0.312 ± 0.007

IVRC 0.280 ± 0.004 0.259 ± 0.006 0.348 ± 0.008 0.348 ± 0.005 0.274 ± 0.004 0.273 ± 0.006 0.347 ± 0.012 0.338 ± 0.005

IVRD 0.225 ± 0.003 0.210 ± 0.005 0.280 ± 0.006 0.280 ± 0.003 0.216 ± 0.004 0.227 ± 0.005 0.280 ± 0.009 0.296 ± 0.004

IVRE 0.371 ± 0.004 0.373 ± 0.009 0.468 ± 0.011 0.473 ± 0.008 0.359 ± 0.003 0.371 ± 0.007 0.461 ± 0.018 0.465 ± 0.007

CAA 2.084 ± 0.049 1.939 ± 0.073 2.207 ± 0.044 2.234 ± 0.059 2.010 ± 0.036 2.002 ± 0.049 2.158 ± 0.063 2.270 ± 0.067

CAB 1.796 ± 0.035 1.687 ± 0.048 1.901 ± 0.053 2.085 ± 0.079 1.751 ± 0.031 1.710 ± 0.027 2.150 ± 0.046 2.044 ± 0.043

CAC 1.735 ± 0.043 1.630 ± 0.071 1.851 ± 0.036 1.846 ± 0.058 1.773 ± 0.036 1.848 ± 0.058 1.945 ± 0.071 1.892 ± 0.081

CAD 1.772 ± 0.042 1.618 ± 0.046 1.901 ± 0.067 1.941 ± 0.045 1.690 ± 0.025 1.691 ± 0.038 1.909 ± 0.035 1.908 ± 0.043

CAE 1.869 ± 0.080 1.609 ± 0.054 2.020 ± 0.081 1.969 ± 0.092 1.838 ± 0.049 1.685 ± 0.054 2.101 ± 0.073 2.004 ± 0.075

CAAverage 1.851 ± 0.025 1.697 ± 0.046 1.976 ± 0.037 2.015 ± 0.045 1.813 ± 0.021 1.787 ± 0.032 2.053 ± 0.025 2.024 ± 0.030

CNA 623.5 ± 28.1 728.7 ± 30.7 736.3 ± 33.1 827.8 ± 28.7 636.7 ± 15.0 780.2 ± 16.4 725.0 ± 48.1 835.5 ± 39.0

CNB 1,393.9 ± 16.6 1,427.1 ± 40.3 1,529.5 ± 47.3 1,541.3 ± 61.1 1,296.1 ± 23.8 1,537.3 ± 42.5 1,326.3 ± 42.8 1,528.6 ± 40.0

CNC 1,621.1 ± 45.4 1,603.8 ± 54.1 1,882.2 ± 51.1 1,899.5 ± 47.0 1,553.5 ± 38.7 1,486.2 ± 49.3 1,795.9 ± 67.0 1,822.1 ± 93.3

CND 1,258.6 ± 25.5 1,300.9 ± 29.7 1,488.4 ± 48.0 1,456.5 ± 26.8 1,280.8 ± 24.1 1,344.4 ± 33.2 1,478.1 ± 34.6 1,560.3 ± 38.6

CNE 2,024.1 ± 105.9 2,352.6 ± 97.7 2,341.5 ± 94.3 2,481.4 ± 115.4 1,963.2 ± 54.8 2,217.3 ± 57.8 2,237.0 ± 124.3 2,346.0 ± 87.9

CNTotal 7,811.0 ± 102.7 8,243.3 ± 194.4 8,841.1 ± 166.9 9,029.2 ± 163.9 7,467.8 ± 89.8 8,245.4 ± 127.3 8,273.4 ± 238.9 8,997.6 ± 196.6

Wing traits: outline shape (WSH), size (WSI), width (WW), length (WL), and the area of intervein regions A-E (IVRA-E). Cellular traits: average cell area (CA) or cell number (CN) at intervein regions A-E (subscripts), plus the average cell area across all intervein regions (CAAverage) and total cell number in dorsal wing blade (CNTotal). Units: WSH is a ratio (WW/WL); WSI, WW and WL are in millimeters, IVR areas are in mm2; and all CA estimates are in mm2 ×10-4.


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Table S2: Mean ± SE for wing morphology and cellular variations at intervein regions in baseline population.

Baseline population

Trait Wild (n=23) G1 (n=20)

WSH 0.485 ± 0.002 0.471 ± 0.003

WSI 0.632 ± 0.008 0.782 ± 0.006

WW 0.881 ± 0.012 1.073 ± 0.010

WL 1.815 ± 0.025 2.281 ± 0.018

IVRA 0.125 ± 0.004 0.184 ± 0.004

IVRB 0.226 ± 0.006 0.329 ± 0.006

IVRC 0.251 ± 0.006 0.385 ± 0.006

IVRD 0.197 ± 0.005 0.309 ± 0.005

IVRE 0.322 ± 0.010 0.505 ± 0.010

CAA 1.989 ± 0.027 2.349 ± 0.050

CAB 1.797 ± 0.035 2.303 ± 0.055

CAC 1.685 ± 0.044 2.068 ± 0.035

CAD 1.700 ± 0.032 2.087 ± 0.031

CAE 1.816 ± 0.039 2.358 ± 0.062

CAAverage 1.798 ± 0.025 2.233 ± 0.024

CNA 631.4 ± 19.8 790.3 ± 22.0

CNB 1,260.6 ± 32.0 1,436.0 ± 33.3

CNC 1,506.8 ± 43.8 1,873.5 ± 46.4

CND 1,166.3 ± 31.8 1,489.7 ± 34.1

CNE 1,777.9 ± 46.6 2,173.2 ± 72.8

CNTotal 7,008.0 ± 148.1 8,638.8 ± 164.9

Abbreviations are the same presented in Table S1. G1 flies were reared at

16°C.


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TABLE S3: Quadratic regressions of average cell area across rank order of all five intervein regions fitted to complete data set of each experimental group, and F-tests of group differences from quadratic regressions adjusted to each individual wing.

Abbreviations: coefficient that describes the shape of the curve (g2) and characteristic estimates of minimum value (MV) and intervein region that holds the minimum value (IVR-MV). A rank order was attributed to each intervein region according to their proximity from the most anterior region of the wing: IVRA was ranked as 1, IVRB as 2 and so forth. See Fig. 4 for visual representation of the quadratic curves fitted to complete data set of each experimental group. ANOVA (F-ratio) of group differences was performed in data derived from quadratic regressions adjusted to each individual wing: group differences (df=5), model error: df=112. aTukeys’s a posteriori pairwise comparisons for the significant group differences in MV; six outlier cases were excluded: (Wild = R_TD25 = L_TD25) ≠ (G1 = L_TD16 = R_TD16); α = 0.05. G1 flies were reared at 16°C. ***P ≤ 0.001.

Experimental

quadratic regression fitted to complete data set of each group

Mean ± SE from quadratic regressions adjusted to each individual wing

group n g2 MV IRV-MV F-ratio R2 g2 MV IRV-MV

WILD 23 0.053 1.682 3.417 23.167*** 28.0% 0.053 ± 0.009 1.669 ± 0.035 3.588 ± 0.257

G1 20 0.063 2.105 3.156 11.992*** 18.2% 0.063 ± 0.013 2.092 ± 0.040 2.944 ± 0.222

L_TD16 20 0.054 1.900 3.337 14.876*** 21.9% 0.054 ± 0.011 1.934 ± 0.059 2.556 ± 0.713

R_TD16 20 0.053 1.891 3.627 20.260*** 28.0% 0.053 ± 0.015 1.942 ± 0.053 2.708 ± 0.354

L_TD25 20 0.056 1.711 3.382 30.752*** 37.5% 0.056 ± 0.010 1.742 ± 0.063 2.261 ± 1.420

R_TD25 20 0.029 1.643 4.192 18.451*** 26.1% 0.029 ± 0.009 1.680 ± 0.042 3.018 ± 0.455

F-ratio of group

differencesa

1.067 12.289*** 0.459

C A P Í T U L O I V : G E N E S C A N D I D A T O S

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Gene Expression Profile and Candidate Genes in Strains of Drosophila melanogaster

Selected for Divergent Wing Shape

Daniel Mattos, Bruna Palma da Matta, Márcio Alves and Blanche Christine Bitner-Mathé.

Manuscript under current refinement for publication.

Running title: gene expression of Drosophila wing shape


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ABSTRACT

Mapping the elements involved in the phenotypic determination of complex traits has

been a challenge. The Drosophila wing is a great model for such studies because much of its

developmental biology, genetics, and environmental responses are well known. However,

models still fail to satisfactorily map genetic determinants controlling quantitative wing shape

variation. Here we investigated candidate genes through an extensive search in the expression

profile; both by a microarray assay and by real time quantitative PCR on biological replicates.

Results indicate that genetics underlying wing shape follows an infinitesimal genetic model,

with a great number of genes with small effects, aligned with results obtained for other shape

indexes in the literature. We also identified strong candidate genes (lft, Trl, Idgf4, sgg,

CG17919, ems, GstD3, dp, CG10208, rho and deltaTry) that should be tested on interspecific

variation due to their consistent response in all biological replicates. Furthermore, we discuss

that a large amount of shape variation seems to lie within the interaction of nuclear elements

rather than deterministic elements with major effects alone.

Keywords: microarray – real time qPCR – evolution - complex traits


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INTRODUCTION

Since the rediscovery of Mendel’s work in the beginning of the 20th century, geneticists

worldwide have tried to map the underlying genetics of perceivable phenotypes. More than

a century later, astounding breakthroughs have been accomplished, including the discovery

of the DNA and part of the intricate code within it. Many phenotypes have been successfully

mapped, with huge impact on many scientific fields. However, many others refuse to fit into

simplistic models and fall into a class of complex traits. In common, these traits share a

multidimensional phenotypic space (e.g. shape variation or medical syndromes with multiple

symptoms) and a great number of epistatic and pleiotropic genes. The infinitesimal genetic

model is usually assumed with many genes of small effect underlying phenotypic variation.

Moreover, these traits are usually influenced by environmental conditions that blur the effects

of single genetic variants. Expression patterns, regulation and epigenetics then became the

focus of recent studies in an attempt to break the genetic codes of such phenotypes. Still,

there is a large amount of variation not accounted for by such processes. Baranzini et al. (2010)

made an extensive research on genetic and epigenetic elements underlying multiple sclerosis

and found no single variant underlying the disease. However, the study points to many

elements that together contribute to the syndrome. Similar inferences about genetic

establishment and evolutionary persistence were made for the causes of schizophrenia

(NETTLE; CLEGG, 2006; PEARLSON; FOLLEY, 2008; WILKINS, 2011).

Drosophila wing offers great opportunities for the understanding of the evolution of

complex traits. Developmental pathways are well described (ALEXIS; ISAAC; DAVID, 2015;

BIER, 2000; LEGOFF; ROUAULT; LECUIT, 2013) and many studies on shape variation are

available (CARREIRA et al., 2011; DEBAT; DEBELLE; DWORKIN, 2009; DEBAT et al., 2003;

MENEZES et al., 2013; TORQUATO et al., 2014). In addition, the wing is formed from a

modularized structure in the larva, the wing imaginal disc, isolating the genetic framework

from the development of other structures.

Here we use strains submitted to an intense selection program on wing shape that

generated strains with divergent phenotypes to (1) identify wing shape candidate genes and

(2) test the replicability of expression profiles in biological replicates. Finally, we tested

whether strong candidate genes could alone account for shape variation through tissue-

specific RNAi-mediated silencing. We found support for an infinitesimal genetic model, with


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many genes with small effects behind wing shape. Furthermore, results suggest that biological

replicates achieved similar shapes through alternate genetic pathways.


MICROARRAY ON 1L AND 1R STRAINS

Experimental conditions for Microarray assays were previously described by Matta

(2010). Briefly, 1L (elongated winged strain) and 1R (rounded winged strain) from the 67th

generation were analyzed, with four biological replicates. 100 imaginal disc were collected

from each strain and replicate. The microarray chip produced by Drosophila Genomics

Resource Center (DGRC-2 Ologonucleotide Array; https://dgrc.cgb.indiana.edu/), was read on

VersArray ChipReaderTM 3m form BioRad. Details on the microarray essay can be found in

Matta (2010).

BIOLOGICAL REPLICATE STRAINS

Three round strains (1R, 2R and 5R), three elongated strains (1L, 2L and 5L) and

unselected controls (1C, 2C and 5C) were analyzed. For each of the nine strains, two biological

replicate crosses (named A and B) were done. Eight couples from generations 120 and 121

were randomly put in vials containing standard Drosophila medium with blue bromophenol

for third instar larvae identification (ASHBURNER, 1989). Adults were allowed to mate and

oviposit for three days after which they were transferred to a new vial, hence generating six

technical replicates. On the 4th day after initial contact, third instar larvae were sexed and had

wing imaginal discs collected. Remaining larvae were allowed to fully develop and adults were

stored in 95% alcohol.

WING IMAGINAL DISC RNA EXTRACTION AND REAL TIME qPCR

For each biological replicate (A and B) of L and R strains, 60 imaginal discs were

collected, half of each sex and stored in 500µL of RNA Later at -20°C. For unselected controls,

40 discs were collected. In total, 1280 imaginal discs were extracted. RNA was extracted by

RNeasy mini kit (QIAGEN), following manufacturer’s instructions. RNA was quantified on

https://dgrc.cgb.indiana.edu/


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Nanodrop (Thermo Fisher) and Bioanalyzer (Agilent Technologies); all samples presented good

quality and quantity. cDNA was then synthetized and used on RT-qPCR for expression level

quantification, through the incorporation of SybrGreen during qPCR. 22 genes were tested.

Reference genes were established by Matta (2010).

MORPHOLOGICAL VALIDATION

Candidate genes had their expression silenced by enhancer_GAL4 / UAS_RNAi

silencing system and morphological consequences evaluated. Briefly, a Drosophila strain

produced with an element consisting of a specific genomic enhancer (here we used vestigial

and nubbin enhancers due to their known expression on the wing disc pouch; CIFUENTES;

GARCÍA-BELLIDO, 1997) attached to the yeast transcription factor GAL4. When genomic

enhancer is activated (Fig. 1), GAL4 is produced and binds to the UAS promoter associated to

the silencing target gene (Fig. 2). Nubbin_Gal4 strain also expresses the protein Dicer (Dcr)

which intensifies gene silencing by degrading the silencing complex formed. Each tested gene

was crossed with vg_Gal4 and with nubbinDcr_GAL4. Control strains (GD-60.000 and KK-

60.100) with no element inserted were also crossed to both GAL4 strains. Reciprocal crosses

and (A and B) were performed with five couples at each cross. Strains were acquired at

Bloomington stock center (http://flystocks.bio.indiana.edu/) and Vienna stock center

(http://stockcenter.vdrc.at/control/main).

Cross 1A: 5♀ UAS-RNAi X 5♂ GAL4-nub-Dcr

Cross 1B: 5♂ UAS-RNAi X 5♀ GAL4-nub-Dcr

Cross 2A: 5♀ UAS-RNAi X 5♂ GAL4-vg

Cross 2B: 5♂ UAS-RNAi X 5♀ GAL4-vg

Controls

Cross 1A: 5♀ Control 60.000 X 5♂ GAL4-nub-Dcr

Cross 1B: 5♂ Control 60.000 X 5♀ GAL4-nub-Dcr

Cross 2A: 5♀ Control 60.000 X 5♂ GAL4-vg

Cross 2B: 5♂ Control 60.000 X 5♀ GAL4-vg


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Figure 1. Expression pattern of protein GAL4 on the wing imaginal disc. nubbin_GAL4 element (a and b) showing

GAL4 in the nubbin expression domain and vestigial_GAL4 (c and d). Images taken on a fluorescence microscope.

For both elements, GAL4 is expressed in the imaginal disc pouch, which originates adult wing blade. GAL4 will

then drive the expression of the silencing element, hence silencing the target gene.

Figure 2. GAL4 / UAS silencing system. When genomic enhancer is activated, GAL4 is expressed and binds to the UAS promoter, driving the expression of silencing gene X. Figure

modified from ST Johnston (2002).


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RESULTS

EXPRESSION PATTERNS

The microarray performed on the 1L/1R67thGen strains indicated 150 differentially

expressed genes (DE). Fig. 3 groups DE genes by biological function; enzymes are the most

represented group. Ten transcription factors and 11 signaling transduction constitute good

candidates genes due to their ability to regulate downstream genes and change

developmental pathways.

From these candidate genes, 22 were tested through qPCR on three biological

replicates from 120th and 121st generations, forming pairs with the highest phenotypic

divergence. Fig. 4 shows the morphological variation between pairs and Table 1 shows the F-

tests. Genes were chosen according the following criteria: (1) the 5 genes with highest ranks,

taking into account both FC and significance; (2) highly ranked genes with known participation

on wing imaginal disc developmental pathways and (3) highly ranked transcription factors due

to their regulatory function. Fig. 5 exhibits the expression profile by heatmap. Differential

expression has low consistency among biological replicates for most genes. In fact, only three

genes (lft, sgg and Idgf4) have consistent and significant variation in all three replicates and

eight genes are consistent in at least two (CG10208, lft, Trl, sgg, ems, Idgf4, GstD3 and

CG17919). These genes represent the strongest candidates for wing shape variation that

emerged from this analysis. The large number of DE genes in the microarray and the low

consistency among biological replicates are evidence supporting the infinitesimal genetic

model for shape variation.

Comparing L/R Strains expressions provides information on DE genes, but is

uninformative regarding the direction of the response related to the original state prior to

selection. Therefore, for the eight strong candidates, we quantified the expression for an

unselected control strains (1C Strain) in order to find if divergence in expression was

bidirectional (i.e., both L and R strains shifted expression levels) or unidirectional. Fig. 6 shows

the plot of the normalized expression ratios (NER) for L, C and R Strains. C Strain is exhibited

in the center and each is connected by lines to the others representing the expression shift.

To our knowledge, this form of presentation is original in such studies and can provide an easy-

to-visualize comparison with the treatments of interest when the control state is available.


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Figure 3. Pie chart summarizing results of the Microarray, grouped by biological function of significantly regulated genes. Note the prevalence of genes with unknown biological functions and an enrichment of transcription factors.


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1L / 1R 2L / 5R 5L / 2R

SEL Error SEL Error SEL Error

DF 1 48 1 48 1 48

MS 0.1713 0.0001 0.1715 0.0001 0.051 0.000 p 0.0000 0.0000 0.000

Table 1. F-Tests for the effects of direction of selection ((SEL: L x R Strains) for each pair of biological replicates in the 123rd generation analyzed by qPCR.

Figure 4. Boxplot of WSH variation in the 123rd generation with the pairs of strains analyzed by the RT-qPCR. L Strains in blue and R Strains in red. Pairs were formed aiming highest morphological divergence.


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Figure 5. Heatmaps of expression ratios of candidate genes by the microarray of the 67th generation (array) and by real time qPCR testing replicability of three biological replicates of the 121st and 122nd generations. Green refers to genes upregulated on the L Strains and red refers to those downregulated. Significantly DE genes marked by *.


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Figure 6. Visual representation of the variations of expression in L (Log L) and R (Log R) Strains compared to unselected control (Log C). Log C represents expression levels of intermediate phenotype. Normalized expression ratio (NER) used on the Y axe of the graphs as the expression quantifier. Only 1L/1R (contiguous line) and 2L/5R (traced line) are shown because morphological divergence is greater. This graph allows a rapid visualization of which direction of selection had its expression shifted from the control dosage. E.g., for sgg, L strains diverged from the control while R strains remained unaltered. Blue line indicates significant expression differences.


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MORPHOLOGICAL VALIDATION

RNAi-mediated gene silencing targeting transcripts present on the imaginal disc pouch

of third instar larvae were performed for all 8 candidate genes and to 12 others due to their

rank in the microarray, consistency among biological replicates or to known biological

functions during wing development. Table 2 shows mean and standard error for wing shape,

size, length and width of control and expression-silenced strains with the respective F-tests,

exhibiting only results containing the enhancer element (nubbinDcr_GAL4 or vg-GAL4) that

provided information on quantitative variation; i.e. crosses that generated no progeny or

malformed wing are not exhibited. Only 4 silenced strains had their wing shape significantly

altered, two of them from the list of 8 candidates (Idgf4, sgg, dp and deltaTry). The effect of

silencing dp was rather impressive, with silenced strains exhibiting a very round wing, mainly

explained by a shortening in wing length (figure 7). However, others were able to significantly

alter at least one of the biological axes (length or width) that compose our index of wing shape

(lft, CG17919, rho and Trl) and are also good candidates for controlling wing shape

quantitative variation. Regarding experimental designs with RNAi-mediated silencing strains,

the element nubbinDcr_GAL4 had a higher frequency of malformed wings or unviable

progeny, probably due to its intense silencing or to unspecific effects of the higher dosage of

the Dicer protein during wing development. Figure 8 shows four examples of malformed wings

with nubbinDcr_GAL4 (for which quantitative variation was analyzed by the vg_GAL4 cross).


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Table 2. RNAi strains wing shape phenotypic mean and standard error. F-test between silenced strain (gene) and its

respective control (60.000 or 60.100). Control strains were tested against an unspecific control strain with Y

chromosome genes known to be expressed in the testis (Ppr-y and KL-5) silencing element. Wing shape (WSH), size

(WSI), length (WL) and width (WW).

WSH WSI WL WW

Gene Gal4 Control Mean SE Mean SE Mean SE Mean SE

60.000 KL-5 0.460 0.002 807.313 12.625 *** 1191.104 19.815 * 547.216 8.098

60.100 Ppr-y 0.457 0.003 797.106 11.704 1180.391 20.310 538.397 6.787

Idgf4 nubDcr 60.100 0.484 0.003 * 721.462 11.411 1037.643 17.027 501.691 7.881

lft nubDcr 60.100 0.482 0.002 738.585 13.415 *** 1064.032 20.019 *** 512.734 9.118

CG15353 nubDcr 60.100 0.462 0.002 797.402 13.201 1174.301 20.678 541.535 8.567

CG17919 nubDcr 60.000 0.449 0.003 762.009 12.600 1137.431 19.819 510.601 8.327 *

chit nubDcr 60.100 0.458 0.002 771.613 13.299 1140.803 20.920 521.963 8.576

elB nubDcr 60.100 0.463 0.003 797.713 13.870 1172.702 20.399 542.707 9.642

rho nubDcr 60.100 0.479 0.002 690.550 11.284 998.544 17.328 * 477.598 7.435

CG10208 nubDcr 60.000 0.459 0.002 760.961 12.251 1124.050 18.965 515.204 8.035

CG32373 nubDcr 60.100 0.481 0.002 784.600 11.148 1131.316 17.347 544.203 7.348

deltaTry nubDcr 60.000 0.465 0.002 *** 788.495 12.014 1157.202 18.769 537.331 7.874

Jon65Aiii nubDcr 60.100 0.459 0.003 801.682 12.585 1183.793 21.138 543.000 7.505

Vang nubDcr 60.100 0.486 0.003 770.979 11.778 1106.388 19.415 537.335 7.101

Dr / Drop nubDcr 60.100 0.474 0.003 797.599 14.480 1159.625 24.141 548.694 8.570

60.000 KL-5 0.477 0.003 795.497 12.735 1151.763 19.623 549.513 8.460

60.100 Ppr-y 0.468 0.003 810.065 12.203 1185.707 21.029 553.536 6.983

ems Vg 60.000 0.479 0.004 805.076 13.730 * 1163.889 22.637 557.013 8.540

bun Vg 60.000 0.470 0.003 680.738 16.119 994.106 25.725 466.224 10.151

sgg Vg 60.100 0.454 0.005 * 713.893 15.268 *** 1060.719 25.872 480.682 9.381 ***

dp Vg 60.000 0.754 0.008 *** 532.707 9.548 *** 614.414 12.462 *** 462.097 7.842 *

Trl Vg 60.100 0.471 0.003 732.421 14.910 *** 1067.171 21.779 * 502.756 10.449 ***


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WSH WSI

WL

WW

Gene Gal4 Control Mean SE Mean SE Mean SE Mean SE

GstD3 Vg 60.100 0.468 0.003 786.074 13.141 1150.010 22.098 537.427 7.840

vito Vg 60.100 0.475 0.005 778.101 10.834 1130.682 18.813 535.764 7.017

* P < 0.05 ; *** P < 0.01

Figure 7. Effects of RNAi-mediated silencing of dp on the wing imaginal disc of 3rd instar larvae of D. melanogaster. Control wing (left) and dp silenced (right). Note that most of the shape variation is due to a shortening on the wing length.

Figure 8. Malformations on the wing of D. melanogaster due to RNAi-mediated gene silencing by the nubbinDcr_GAL4 element.


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DISCUSSION

Microarray indicated 150 DE genes underlying wing shape quantitative variation.

Taking under consideration all analyses, we identified as strong candidates the following: lft,

Trl, Idgf4, sgg, CG17919, ems, GstD3, dp, CG10208, rho and deltaTry. Testing those genes in

interspecific variation by real time qPCR is the next step towards identifying major quantitative

wing shape genes. However, consistency of expression ratios across biological replicates is

usually low and even when expression analysis was repeated for the 1L/1R pair in a later

generation through real time qPCR, some genes were no longer significantly regulated. Nine

out of the twenty tested genes by RNAi-mediated silencing were able to alter the phenotype

apart from the control; four with detectable shape differences and the others interfering with

wing length or width. All tested silenced genes, with the exception of dp, had a very small

phenotypic impact, suggesting that it is not the variation of expression in one or a few genes

that promotes quantitative variation but, rather, the cumulative effects of multiple genes

interacting, supporting the infinitesimal genetic model for Drosophila wing shape. Similar

conclusions were reached for other wing shape index (WEBER et al., 2008). However, when

lower levels of phenotypic variance are analyzed, identification of genes with major effects

seems more efficient (or viable). Umetsu; Dunst; Dahmann (2014) identified only the action

of Eph receptor in an RNAi screening targeting almost 3000 genes required for shaping the

anteroposterior compartment boundary of the wing imaginal disc.

Weber et al. (2008) used a microarray analysis to identify wing shape candidate genes.

Their shape index is different from ours and captures variations mostly along the

anteroposterior axis. Still, they also found a very large number of genes (198-534 depending

on the lines compared on the microarrays) and interpreted results as confirming “the highly

polygenic and degenerate basis of wing shape”, although accounting some genes to “fixation

of noncontributory alleles” in lines selected from larger population sizes. LaayounI et al.

(2007), analyzing expression profile of thermal tolerance in D. suboscura, also found a large

number of genes and recognized the same issue. The number of DE genes indicated by the

microarray on our strains indicated a smaller number of genes (150), however still large. Our

selection strains were founded from 135 isofemale lines and the same effect might explain

the still large number of genes we found here and the low replicability among replicates.


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Besides the identification of noncontributory alleles, another issue common to studies

with expression ratios is that genes with higher FC are considered the best candidates,

although analyses try to correct this assumption by ranking genes based on the replicability as

well as the FC. Technical replicates might reduce experimental errors due to differential RNA

extraction or treatment conditions but they still do not account for the replicability of the

effects of such a dosage ratio. Only biological replicates might reduce (but not extinguish) the

biological relevance of FC intensity. A gene 100x more expressed might have little or no impact

on any phenotype depending on the genetic and epigenetic background of the particular cell

while an increase of 0.5x in the expression of another might have profound impacts by

regulating downstream genes. Wilkins (2011) discusses the nonlinearity of the effects of gene

expressions shifts, but argues in favor of qualitative conclusions that can be addressed under

the linearity assumption. Comparison of the expression levels of the selected strains (L and R)

and the unselected control (C) indicates a nonlinear effect of those genes on wing shape.

Taking these issues in consideration, morphological validation is extremely important, but

challenging when complex traits are being analyzed since, as seen in this work, in which most

of the individual gene silencing did not recapitulate the expected phenotypes.

From the list of genes assessed by the Microarray, many do not participate in known

wing developmental pathways. In fact, many genes are involved with general metabolic

processes that are unlikely to be directly related to organ morphogenesis. This suggests that

wing development and wing quantitative shape variation might be regulated by different

genes, with those involved in the quantitative variation epistatically interacting with the

known developmental pathways.

Finding robust candidate genes, epigenetics or any nuclear causal element for

quantitative traits has proven to be challenging. The interactions of all these elements seem

to underlie the quantitative phenotypes observed rather than the allelic or even the dosage

variation of expression of few genes. Epistatic interactions between genes, genetic and

epigenetic markers might have such a huge effect on phenotypes that isolating each variant

will account for a small part of the observed phenotypic variance. Assessing the variance

associated with the interactions is harder but may be crucial for a better comprehension of

phenotypes, including for areas like medicine. For instance, when heart diseases are

considered, isolated genetic variants and individual habits (such as smoking or obesity) are


120

associated with higher risks, but neither alone is able to account for a major amount of the

associated risk. Hence, it is likely that the increase in risk is correlated with the interaction of

those and other factors. This seems to be true for most complex traits (BARANZINI et al.,

2010).

Considering the effects of the artificial selection program on the strains, it remains

unclear which elements actually responded. Differences in expressions levels can be explained

by changes in cis or trans regulatory genetic regions and by epigenetic shifts. From the list of

strong candidate genes, analyses of the cis-regulatory regions as well as epigenetic markers

might elucidate the participation of both types of nuclear variation contributing to short-term

phenotypic evolution. Wittkopp; Haerum; Clark (2008), analyzing gene expression divergence

and the contribution of cis and trans regulatory regions between D. melanogaster and D.

simulans, found that “cis-regulatory changes seem to accumulate preferentially over time.”

The same might not be true for short-term evolution and comparing both would bring

important insights.

Li and Saunders (2005) argue that testing the hypothesis that gene expression shifts

might account for a significant amount of the observed phenotypic variance among

populations and species is hard, but crucial. The genetics of complex traits keeps challenging

the most current techniques of biological exploration. Understanding the relative contribution

and the interaction of the components underlying variation in such traits is imperative both

for our understanding of evolution and for medical research.

ACKNOWLEDGMENT

We thank Danielle Tesseroli and Bianca Menezes for the establishment and

maintenance of the artificial selection strains and Dulcinea da Rocha for technical assistance.

This paper is part of the D. Sc. requirements of Daniel Mattos at the Biodiversity and

Evolutionary Biology Graduate Program of the Federal University of Rio de Janeiro and was

supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES.


121

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C O N C L U S Õ E S E P E R S P E C T I V A S F U T U R A S

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CONCLUSÕES

Os estudos apresentados nesta tese evidenciam o caráter complexo da variação

quantitativa da forma e tamanho da asa. O programa de evolução experimental da asa

forneceu linhagens com fenótipos extremos que permitem a investigação de diversos níveis

de variação biológica.

O programa de seleção artificial gerou linhagens com asas alongadas e linhagens com

asas arredondadas. Asas redondas foram alcançadas por um aumento da largura de mesma

intensidade da diminuição do comprimento. Já asas alongadas parecem ser fruto

primariamente da redução da largura. Dados não apresentados do início da seleção indicam

que linhagens redondas apresentaram uma resposta mais acentuada diminuindo o

comprimento e só após algumas gerações de seleção houve resposta da largura.

Aparentemente, a redução de medidas é uma resposta morfológica mais facilmente

recrutada. Além disso, a resposta à seleção ocorreu através de modificações na quantidade

de células da asa, sem mudanças de tamanho celular. Dois compartimentos foram

identificados, em especial, pela uniformidade de intensidade e direção de landmarks

localizados na porção mais proximal da asa. Além disso, o padrão de correlação genética da

população prévia ao processo seletivo foi capaz de prever parte das respostas de traços

correlacionados indicando que as matrizes de correlação genética têm grande impacto nas

trajetórias evolutivas.

O estudo da plasticidade fenotípica (PF) trouxe importantes insights nos anos recentes.

Em especial, a área tem despertado interesse devido à possibilidade de se incorporar essas

informações aos modelos de previsões de impacto das mudanças climáticas e suas

consequências sobre a diversidade no planeta. Por exemplo, um estudo recente mostrou que,

na Europa, insetos de coloração mais escura são favorecidos em climas mais amenos

enquanto os de coloração mais clara são favorecidos em ambientes mais quentes (ZEUSS et

al., 2014). Outro trabalho avaliou a influência da pigmentação abdominal em Drosophila sobre

o dessecamento e resistência a raios ultravioleta (MATUTE; HARRIS, 2013), cujas normas de

reação são bem conhecidas (ROCHA; KLACZKO, 2009). A reunião dessas informações pode

facilitar previsões locais de perdas de biodiversidade de insetos. A importância da PF para a

sobrevivência de espécies torna-se cada vez mais evidente. Ramp e outros (2015)


124

argumentam que a PF pode explicar, parcialmente, como baleias de barbatana puderam

sobreviver a mudanças nas temperaturas oceânicas nos últimos milhões de anos. Outro

estudo recente mostrou que a PF do mimetismo de peixes da espécie Pseudochromis fuscus é

capaz de conferir diversos ganhos em fitness. A PF retornou ao centro das atenções com tanta

força que novas formas de se analisar as normas de reação estão sendo constantemente

propostas (PERTOLDI et al., 2014; ROCHA; KLACZKO, 2012, 2014), fornecendo novos olhares

sobre problemas tão antigos.

No presente estudo, mostramos a influência da média fenotípica da forma da asa

sobre a inclinação da norma de reação, invertendo a orientação da variação termal da forma

da asa em linhagens com asas arredondadas. A dependência entre média fenotípica e norma

de reação para a forma da asa tem consequências importantes para o inseto, uma vez que

possíveis aumentos de temperatura que selecionem populações com diferentes formatos

terão impacto sobre a capacidade dessas populações em responderem à própria variação

termal.

Entretanto, apesar do alto número de publicações descrevendo as normas de reação

de tamanho e forma da asa de Drosophila, pouco se sabe sobre as bases genéticas que

permitem a plasticidade. A dependência entre média fenotípica e plasticidade evidenciada

aqui sugere que, para a forma da asa, a PF é em parte explicada por sensitividade alélica, onde

alguns dos genes envolvidos na determinação do fenótipo responderiam de maneiras

diferentes às mudanças ambientais. No capítulo de expressão gênica, apontamos também

alguns dos genes envolvidos na determinação fenotípica e uma busca aprofundada dentre os

candidatos poderia apontar novas frentes de estudo para identificação de genes também

envolvidos na resposta plástica. Um estudo recente em insetos da infraordem Fulgoromorpha

(Ordem: Hemiptera) identificou dois receptores de insulina envolvidos na determinação

plástica de dois morfotipos de asa, mostrando ser possível a identificação de genes capazes

de apresentar respostas às variações ambientais. Nesse modelo de estudo, trata-se de um

polifenismo com apenas dois morfotipos e alternativas binárias nas vias de desenvolvimento,

facilitando a identificação de genes reguladores das vias.

Por fim, o estudo apontou 11 genes candidatos ao controle da variação quantitativa

da forma da asa (lft, Trl, Idgf4, sgg, CG17919, ems, GstD3, dp, CG10208, rho and deltaTry).


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Alguns desses genes têm participação conhecida na morfogênese da asa (lft, Trl, Idgf4, rho;

flybase.org/). Os genes Trl e ems se associam ao promotor da RNA polimerase II, regulando a

expressão de diversos genes (CHOPRA et al., 2008; TAYLOR, H. S., 1998) e, portanto, têm

grande capacidade de promover modificações globais nos padrões de expressão das células

da asa. dp está associado com a aposição das lâminas dorsal e ventral do disco imaginal

(PROUT et al., 1997). Há pouca ou nenhuma informação conhecida sobre os genes CG17919,

CG10208, GstD3 (glutationa) e deltaTry (peptidase) e nenhuma informação fenotípica; em

especial, nada foi reportado sobre o envolvimento na formação da asa. Já o gene rho é

conhecido por especificar a diferenciação celular e, portanto, também consiste em um

importante candidato (MARENDA et al., 2006).

Um cenário bastante interessante está sendo construído para um melhor

entendimento do desenvolvimento de asa em insetos. Aproveitando o largo conhecimento já

publicado sobre genes envolvidos na formação da asa de Drosophila, Linz e Tomoyasu (2015)

promoveram uma extensiva busca pelos genes candidatos em Tribolium castaneum através

de silenciamento por RNAi. Seus resultados mostram uma alta conservação nos genes de

desenvolvimento, sem ter encontrado um único gene exclusivo de T. castaneum. Avançar os

conhecimentos sobre a asa de Drosophila permitirá, cada vez mais, um melhor entendimento

sobre a genética, o desenvolvimento e a evolução de asas de insetos em geral.

PERSPECTIVAS FUTURAS

Uma resposta, em geral, abre novas perguntas. Essa tese arranha a superfície de um

problema cujo núcleo ainda está distante. Um melhor entendimento das variações

quantitativas em traços ditos complexos é premente, seja para o avanço da nossa

compreensão dos processos evolutivos, seja para novas soluções em medicina. A asa de

Drosophila tem se mostrado um promissor modelo para estudo desse tipo de variação e novos

insights são obtidos a todo tempo. Ano passado, um novo olhar foi lançando sobre as asas,

revelando que há seleção sexual em padrões de coloração iridescentes por interferência

luminosa; um efeito ignorado durante um século de estudos com Drosophila, mostrando que

apesar de todo o conhecimento sobre elas, as asas ainda guardam surpresas (figura 1,

KATAYAMA et al., 2014). As linhagens de seleção geradas por Danielle Tesseroli e Bianca


126

Menezes ainda prometem ser generosas no estudo das variações quantitativas de forma e

tamanho. Experimentos de seleção artificial permitem uma ampla gama de desenhos

experimentais, porém há uma grande demanda de tempo, espaço e recursos humanos para o

processo seletivo. Trabalhos em genética quantitativa também exigem tamanhos amostrais

que demandam altos recursos. Entretanto, um novo sistema automatiza a determinação

sexual, medição de peso, tamanho corporal e caracteres morfométricos, incluindo forma e

tamanho da asa em moscas vivas para experimentos de seleção artificial em larga escala

(MEDICI et al., 2015). Tal ferramenta promete abrir caminhos para perguntas ainda mais

instigantes, com tamanhos amostrais antes limitantes.

Dos elementos envolvidos na variação de forma da asa de Drosophila, algumas

perguntas se impõem. Qual a participação de outros eventos celulares, como migração e

mudanças no formato celular, no estabelecimento da forma da asa? Qual a contribuição de

diferenças alélicas em sequências codificantes de genes envolvidos na determinação

fenotípica? Quais elementos contribuem para a variação observada nos níveis de expressão

dos genes candidatos; seriam predominantemente diferenças em sequências reguladoras ou

elementos epigenéticos capazes de alterar os níveis de expressão? Alguns dos genes

Figura 1. Padrões de interferência da asa de Drosophila melanogaster. Machos com colorações mais vívidas são mais sexualmente atraentes para fêmeas. Figura adaptada de Katayama et al. (2014)


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candidatos ao controle da variação de forma também controlam a variação por plasticidade

fenotípica? Os genes candidatos também estão envolvidos na diferença morfológica

interespecífica? Mudanças evolutivas de curto prazo são mediadas por elementos

qualitativamente diferentes daqueles envolvidos em variações a longo prazo; i.e. há uma

maior participação de mudanças na regulação gênica por fatores epigenéticos na primeira e

por mudanças nas sequências reguladoras entre espécies?

Com sorte e trabalho, novos estudos, novas colaborações, novos alunos devem tentar

dar resposta a essas e tantas outras questões.

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