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Author: Moreno Medina, Sandra Carolina C Title: The molecular mechanisms of behavioural transitions in insect life-cycles

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THE MOLECULAR MECHANISMS OF BEHAVIOURAL

TRANSITIONS IN INSECT LIFE-CYCLES

Sandra C. Moreno-Medina

A dissertation submitted to the University of Bristol in accordance with the requirements for the award of the degree of Doctor of Philosophy in the Faculty of Life

Science (November 2019).

Word Count: circa 65,000

ABSTRACT

Phenotypic plasticity is one of the most important fundamental processes by which organisms adapt to changes in their environment. Alternative behavioural phenotypes emerge via the expression of shared genes from the same genome. These phenotypes are expressed at different stages of key life-history transitions, or when organisms perform distinct tasks as they shift flexibly from one behavioural state to another through their breeding cycle. The molecular mechanisms by which organisms shift between behavioural phenotypes are poorly understood. Here, I investigate the molecular mechanisms underpinning plasticity in three types of phenotypic transitions: a key life-history transition (transition from worker to queen, in the social wasp Polistes lanio), an evolutionary transition (how non-social ancestral behaviours may provide a mechanistic ground plan for the origins of division of labour and caste evolution in the social Hymenoptera, using the non-social wasp Ammophila pubescens), and a series of behavioural transitions through a nesting cycle (behavioural phases exhibited by A. pubescens during the nesting cycle). Finally, plastic phenotypes can arise when populations become isolated (e.g. due to habitat fragmentation) forcing them to exploit different resources; in my final chapter, I examine the genetic diversity and differentiation of A. pubescens across a fragmented landscape. My results show that key life-history transitions are characterised by distinct “transitional” molecular signatures, resembling those involved in cell reprogramming. Second, I found evidence that cycles of reproductive and provisioning behaviours may provide a mechanistic ground plan for the evolution of sociality. Third, I found that behavioural phases of the non-social wasp nesting cycles are defined by distinct molecular signatures during the provisioning phases, but otherwise very subtle molecular signatures for the other phases. Finally, I found little evidence of genetic differentiation in populations of A. pubescens, suggesting gene flow despite being in a fragmented habitat. In conclusion, phenotypic transitions are not only underpinned by simple shifts between molecular processes but also by elaborated changes in genomic signatures allowing organisms to adjust their behaviours responsively to their environments.

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Acknowledgements

I thank the National Council on Science and Technology of Mexico (CONACYT) and Mexico’s minister of education (SEP) for funding my PhD studies.

Thanks to the University of Bristol Alumni Foundation and the School of Biological Sciences for funding my research. Sincere gratitude to my supervisors, Dr Seirian Sumner for her advice, assistance, encouragement and patience in helping me complete this thesis. Without her help, I would not have been able to complete this work. Professor Jane Memmott (who adopted me in the middle of my PhD when my entire wasp colony (except me) migrated to UCL) for being inclusive and supervising me in my time alone in the Life Science Building.

Fieldwork would not have been possible without the collaboration of National Trust which granted me permission to study and collect wasps in Hartland Moor, Studland and Godlingston Heath. Specifically, thanks to Michelle Brown who was my direct contact with National Trust. Thanks to The Genetics Society which funded my fieldwork in 2017, and to Lydia Rowland who assisted me in the same year.

To the Sumner lab who kindly made me part of the group. Robin Southon, Adam Devenish, Patrick Kennedy, Ben Taylor and Dr Alessandro Cini. Special thanks to Dr Daisy Taylor whom I consider my bioinformatics mentor, thank you for patience and encouragement and for always having a positive attitude to life, and Dr Michael Bentley and Dr Chris Wyatts who helped me further with the bioinformatics. To the community ecology group: Edith Villa, Tom Timberlake and Nick Tew who kindly adopted me in their hive and baked the best cakes ever. I learned more than I previously knew about insects, plant communities and ecological robustness.

To the Mexican community in the School of Biological Sciences which always provided me with advice, wisdom, Mexican food and gave me asylum when I arrived in Bristol: Dora Cano, Edith Villa and Angelica Menchaca.

To my family for always supporting me and encouraging me to do what I love. Finally, this work would not have been done without the encouragement of my husband Benjamin Arana who quit his job back in Mexico and joined me on this crazy journey. Thank you for your support, your blind confidence in me, and for your endless abilities as field assistant (2016- 18), chauffeur, bioinformatician, chef, just to mention a few. Like Crazy!

I declare that the work in this dissertation was carried out in accordance with the requirements of the University’s Regulation and Code of Practice for Research Degree Programmes and that it has not been submitted for any other academic award. Except where indicated by specific reference in the text, the work is the candidate’s own work. Work done in collaboration with, or with the assistance of, other, is indicated as such. Any views expressed in the dissertation are those of the author.

SIGNED: …………………………………………… DATE: ……………………..

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Table of Contents Chapter 1: General introduction ...... 2 MOLECULAR MECHANISMS OF BEHAVIOURAL TRANSITIONS IN INSECT LIFE CYCLES ...... 3 Phenotypic plasticity ...... 3 Transitions as examples of phenotypic plasticity ...... 5 MECHANISMS OF TRANSITIONS ...... 7 TRANSITIONS IN INSECTS AS ROUTES TO UNDERSTANDING MECHANISMS OF PHENOTYPIC PLASTICITY ...... 9 Life-history transitions in plasticity in insects ...... 9 Evolutionary transitions in plasticity in insects ...... 11 Behavioural transitions in plasticity in insects ...... 13 WASPS AS MODELS TO STUDY LIFE-HISTORY AND BEHAVIOURAL TRANSITIONS ...... 15 Simple societies of Polistes wasps as models to study life-history transitions ...... 16 Ammophilini non-social wasps to study behavioural transitions ...... 17 AIMS AND QUESTIONS ADDRESSED IN THIS THESIS ...... 20 Chapter 2 – Molecular basis of a major life-history transition: the shift from worker to queen in simple societies of the social paper wasp, Polistes lanio ...... 20 Chapter 3 – Molecular basis of an evolutionary transition: testing the ovarian ground plan hypothesis in the non-social sphecid wasp, Ammophila pubescens ...... 21 Chapter 4 – Molecular basis of behavioural transitions: brain transcriptional changes during the nesting cycle of Ammophila pubescens ...... 22 Chapter 5 – Population genetics of Ammophila pubescens and implications for its conservation 22 Chapter 6 General discussion and a conceptual unified model for mechanisms of transitions – reprogramming ...... 23 Chapter 2: The molecular mechanisms underlying a key behavioural transition in the life- history of the paper wasp, Polistes lanio ...... 24 ABSTRACT ...... 25 INTRODUCTION ...... 26 MATERIAL AND METHODS ...... 29 Model species ...... 29 Behavioural determination and sample collection ...... 29 Ovarian development determination ...... 30 RNA extraction and RNA sequencing ...... 31 Pre-processing of the RNAseq data and de novo assembly ...... 31 Transcriptome functional annotation ...... 32 Transcript quantification and differential gene expression analysis ...... 32 GO enrichment analysis of differentially expressed genes (DEGs) and Network Analysis of Co- expression modules of genes ...... 33 RESULTS AND DISCUSSION ...... 33 De Novo transcriptome assembly and quality ...... 33 Result 1. Transitioning wasps are non-reproductive but transcriptomically and physiologically distinct from workers ...... 35 Result 2. Transcriptomic signatures of behavioural phenotypes (Q vs W) in stable colonies are typical of Polistes ...... 38 Result 3. Transitioning wasps arise via a non-linear reaction norm ...... 42 Result 4. Transitioning wasps are more queen-biased than worker-biased ...... 50 CONCLUSION AND FUTURE DIRECTIONS ...... 55

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Chapter 3: Testing the ovarian ground plan hypothesis for the evolution of sociality using the solitary wasp, Ammophila pubescens ...... 57 ABSTRACT ...... 58 INTRODUCTION ...... 59 MATERIAL AND METHODS ...... 65 Sample collection ...... 65 Genome sequencing and annotation ...... 65 Brain gene expression patterns analysis ...... 67 RESULTS AND DISCUSSION ...... 70 Ammophila pubescens genome characterisation...... 70 Hypothesis 1: Cycles in reproductive physiology and associated brain transcription provide a possible pre-cursor ground plan for the evolution of division of labour (test of the OGP) ...... 76 Hypothesis 2: Cycles in provisioning behaviour and associated brain transcription provide a possible pre-cursor ground plan for the evolution of division of labour (test of the PGP) ...... 84 CONCLUSION...... 93 Chapter 4: The neurogenomic signatures of behavioural transitions across the nesting cycle of the non-social wasp digger, Ammophila pubescens ...... 95 ABSTRACT ...... 96 INTRODUCTION ...... 97 METHODS ...... 104 Field sites and marking of wasps and their burrows ...... 104 Aim 1: Identification of flexible and fixed behaviours in response to prey during the nesting cycle 107 Aim 2: Molecular mechanisms underpinning the behavioural phases exhibited during the nesting cycle of A. pubescens ...... 112 RESULTS AND DISCUSSION ...... 119 Aim 1: Identification of flexible and fix behaviours in response to prey during the nesting cycle119 Aim 2: Molecular mechanisms underpinning the behavioural phases exhibited during the nesting cycle of A. pubescens ...... 122 CONCLUSION...... 140 Chapter 5: Population genetics of a non-social wasp in a fragmented habitat ...... 141 ABSTRACT ...... 142 INTRODUCTION ...... 143 METHODS ...... 146 Study sites ...... 146 Sample collection ...... 147 DNA extractions ...... 153 PCR amplification, fragment analysis and genotyping ...... 153 Data analysis ...... 155 RESULTS ...... 157 Microsatellite descriptive statistics and genetic diversity ...... 157 Hypothesis 1: The three heathlands represent genetically distinct populations ...... 159 Hypothesis 2: Males disperse more than females ...... 161 Hypothesis 3: High population viscosity within female aggregations ...... 164 DISCUSSION ...... 170 Habitat fragmentation and genetic differentiation ...... 170

High levels of genetic diversity and inbreeding ...... 171 High levels of gene flow and dispersal between heathlands ...... 171 Conservation implication and future directions ...... 172 Chapter 6: General discussion ...... 174 OVERVIEW ...... 175 SUMMARY OF MAIN FINDINGS ...... 176 Stable phenotypes in social wasps are underpinned by subtle, but distinct differences in brain gene expression ...... 176 Life history transitions from worker (non-reproductive) to queen (reproductive) are governed by non-linear behavioural reaction norms ...... 176 Mechanisms regulating provisioning cycles, as well as reproductive cycles, in non-social insects may provide the basis for the evolution of division of labour in social insects ...... 179 Behavioural transitions through a nesting cycle involve both fixed and flexible behaviour, all of which are underpinned by subtle but distinct molecular signatures ...... 181 Weak genetic differentiation of A. pubescens populations in southwest England...... 182 UNIFIED FRAMEWORK ON THE MOLECULAR BASIS OF PHENOTYPIC TRANSITIONS IN CELLS AND ORGANISMS ...... 183 THESIS CONCLUSIONS ...... 191 Chapter 7: References ...... 192 Chapter 8: Supplementary information ...... 235 Supplementary information chapter 1 ...... 236 Supplementary 1.1 ...... 236 Supplementary information chapter 2 ...... 238 Supplementary 2.1 ...... 238 Supplementary 2.2 ...... 241 Supplementary 2.3...... 242 Supplementary 2.4...... 245 Supplementary information chapter 3 ...... 250 Supplementary 3.1 ...... 250 Supplementary 3.2 ...... 252 Supplementary 3.3 ...... 257 Supplementary information chapter 4 ...... 260 Supplementary 4.1 ...... 260 Supplementary 4.2 ...... 272 Supplementary information chapter 5 ...... 273 Supplementary 5.1...... 273

Table of Figures

Figure 2.1. Reaction norm depicting a phenotypic state (state A) that switches to a new phenotype...... 27 Figure 2.2. Clear distinction between behavioural phenotypes, ...... 37 Figure 2.3. Gene expression differences between phenotypes...... 39 Figure 2.4. No clear transcriptional clustering of queen and workers in stable colonies...... 40 Figure 2.5. Clear clustering between behavioural phenotypes and a high number of DE genes...... 48 Figure 2.6. Clear distinction between phenotypes given by the most highly DE genes...... 49 Figure 2.7. Plots summarising the gene expression patterns between queen vs transitioning wasps, and the latter vs workers...... 54 Figure 3.1. Ammophila pubescens nesting sequence for a single burrow...... 64 Figure 3.2. Species rooted tree of 23 Hymenoptera bees and wasps...... 75 Figure 3.3. Changes in length of the largest oocyte through the nesting phases ...... 78 Figure 3.4. Plots summarising the gene expression patterns between queen-like (NF+EL) and worker-like (Insp+Prov) phenotypes...... 81 Figure 3.5. Boxplots of several genes reported to be involved in ovarian development in insects...... 82 Figure 3.6. Plots summarising the result from edgeR for Egg-laying (EL) vs Provisioning (Prov) wasps...... 86 Figure 3.7. Plots summarising the result from edgeR for Nest founding (NF) vs Provisioning (Prov) wasps...... 90 Figure 3.8. Plots summarising the result from edgeR for wasps after Inspection (Insp) vs Provisioning (Prov). . 91 Figure 4.1. Ammophila pubescens nesting sequence...... 103 Figure 4.2. A. pubescens aggregation in Hartland Moor (site HE)...... 106 Figure 4.3. A. pubescens females marked during the study...... 106 Figure 4.4. Bioinformatics pipeline used to analyse RNA-seq data of A. pubescens...... 117 Figure 4.5. Stacked barplot illustrating the total number of prey that were accepted (in blue) or rejected (in red) ...... 122 Figure 4.6. Principal Component Analysis (PCA) using the expression of 19,233 genes from the brain that resulted from mapping RNAseq reads of 34 A. pubescens wasps...... 125 Figure 4.7. Hierarchical clustering analysis for the 33 samples, clustered by shared patterns of gene expression patterns across 19,233 genes...... 126 Figure 4.8 Visualisation of the pink gene subnetwork or module in A. pubescens...... 127 Figure 4.9. Smear plots from edgeR displaying the log-fold changes with the DE genes (red dots) for a given behavioural phase vs the other phases...... 129 Figure 4.10. Venn diagram contrasting the list of differentially expressed genes (DEGs) in each phase relative to the other...... 130 Figure 4.11. Ontology (GO) terms that are overrepresented in genes upregulated in nest founding females . 133 Figure 4.12. Ontology (GO) terms that are overrepresented in genes upregulated in ovipositing females ...... 135 Figure 4.13. Gene Ontology (GO) enriched bar with the reduce list of GO names overrepresented for Prov contrast ...... 139 Figure 5.1.Location of the of the heathlands where A. pubescens was sampled from 2016 to 2018...... 148 Figure 5.2. Examples of some of the sites where A. pubescens wasps were sampled...... 149 Figure 5.3. Distribution of the wasps sampled within the three heathlands...... 150 Figure 5.4. STRUCTURE output representing the three heathlands in different colours...... 160 Figure 5.5. Linear regression of the relationship between geographic distance and gene flow ...... 161 Figure 5.6. STRUCTURE output of 10 simulations...... 163 Figure 5.7. Linear regression of relationship between geographic distance and gene flow (genetic distance (GD) and geographic (log(1+GGD)) among heathlands...... 164 Figure 5.8. Heatmap of the pairwise FST pairwise comparison between aggregations...... 166 Figure 5.9. PCA of the pairwise FST population values (FSTP)...... 167 Figure 5.10. Bayesian clustering A. pubescens females’ aggregations in Hartland Moor ...... 167 Figure 5.11. Linear regression of relationship between FST and geographic distance log(1+GGD)) among the aggregations...... 168 Figure 6.1. Reaction norm depicting two forms of behavioural switching ...... 185 Figure 6.2. Somatic cells (red and orange dots) ...... 189 Figure 6.3. Reaction norm of behavioural (green solid curve), physiological (black solid curve) and gene expression changes (dashed lines) ...... 190

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List of Tables

Table 1.1. Summary of the hypotheses of the evolution of sociality in Hymenoptera...... 12 Table 2.1. Summary statistics of the de novo transcriptome assembly of P. lanio, based on Trinity assembly metrics...... 34 Table 2.2. Completeness estimation of P. lanio transcriptome assembly of brain tissue...... 35 Table 2.3. Polistes paper wasps sample collection ...... 36 Table 2.4. Summary table of the total number of DE genes (FDR < 0.05) in each comparison...... 41 Table 2.5. GO ids enriched among upregulated genes in workers when contrasting with queens...... 41 Table 2.6. A subset of proteins with BLAST hits that are DE in transitioning wasps, ...... 44 Table 3.1. Contrast and pairwise comparisons between behavioural phenotypes and phases to test hypotheses 1 and 2 ...... 70 Table 3.2. Summary of functional annotation of A. pubescens genome...... 72 Table 3.3. Comparison of genome size and assembly statistics for hymenopteran wasps and bees...... 73 Table 3.4. Summary of the total number of DE genes for each pairwise comparison between the provisioning phase against the others...... 85 Table 4.1. List of sites and their coordinates in which behavioural experiments were carried out...... 105 Table 4.2. Summary of the prey experiments...... 110 Table 4.3. Classification of the actions linked to rejection and acceptance of caterpillars...... 112 Table 4.4. Summary table of transcriptomic comparisons among phases of the nesting cycle, compared using the glmLRT function...... 119 Table 4.5. Summary table of contrast with glmLRT function...... 128 Table 5.1. Geographical distribution of the aggregations where adult females and males were sampled from 2016 to 2018...... 151 Table 5.2.The total number of samples (n=176) used in this study...... 152 Table 5.3. General features of the loci used to characterise A. pubescens in southwest England...... 154 Table 5.4. Comparative metrics of the microsatellite loci used across populations...... 158 Table 5.5. Results of the AMOVA comparing allelic frequencies among 134 females of A. pubescens from three heathlands in Dorset...... 160 Table 5.6. Description of the microsatellite loci used across male populations...... 162 Table 5.7. Results of the AMOVA comparing allelic frequencies among 40 A. pubescens males from three heathlands in Dorset...... 163 Table 5.8. Pairwise comparison of FSTbetween aggregations in Hartland Moor...... 166 Table 5.9. Relatedness of A. pubescens females in seven sites of Hartland Moor ...... 168 Table 5.10. Relatedness of A. pubescens females in different years in Hartland Moor ...... 169 Table 6.1. Summary of processes related to changes in cell fate ...... 188

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CHAPTER 1

______

General introduction

MOLECULAR MECHANISMS OF BEHAVIOURAL TRANSITIONS IN INSECT

LIFE CYCLES

Phenotypic plasticity

Phenotypic plasticity is the ability of a genotype to display distinct phenotypes in response to variation in the environment (Fordyce, 2006). It is the most fundamental process by which organisms can adapt to fluctuations in their surroundings (West-Eberhard, 2003). Most, if not all, organisms exhibit some form of plasticity as it is often highly adaptive. Plasticity is expressed at multiple levels of biological organisation (genes, individuals, colonies or groups) via a diverse array of physiological mechanisms (e.g. transcription and hormonal regulation), triggering systemic and local responses. Biotic and abiotic factors can induce a plastic change in an individual’s phenotype, manifesting as changes in physiology (Navarro et al., 2014), morphology (Lombardi et al., 2015), behaviour (Wong and Candolin, 2015) and life-history (Elekonich and Roberts, 2005). Plasticity can be seasonal (Michie et al., 2011), permanent (Shine, 1999) or reversible (Elekonich and Roberts, 2005). The specificity, timing and speed of a plastic reaction are crucial to an organism’s adaptive value. The concept of phenotypic plasticity is a model to understand life on earth: it structures ecological communities (Morin et al., 2015), generates novelty (Levis, Isdaner and Pfennig, 2018), facilitates evolution (Price, Qvarnström and Irwin, 2003) and enhances fitness (Sultan, 2001). Its consequences have implications for how organisms adapt to extreme environmental fluctuations (e.g. climate changes), conquer new geographical ranges and avoid extinction.

The environment plays a key role in triggering changes in organisms that will ultimately be coordinated at the cellular and molecular level. However, the molecular mechanisms by which environmental cues are detected and cause plastic responses are mostly unknown. Identifying which changes in gene expression coordinate plastic responses would provide key insights into our understanding of plasticity and its evolution; such studies are limited to a few taxa. The development of new molecular tools and computational power in the late 1990s opened up new opportunities for research into many molecular mechanisms, including those involved in phenotypic plasticity in non-model organisms. The omics era of the 21st century now allows us to unravel the mechanisms of phenotypic plasticity in a wide diversity of species and phenotypes, using molecular methods: e.g. global gene expression patterns between individuals can be examined under different environmental conditions (Whitfield, Cziko and Robinson, 2003); candidate genes allow us to focus on small set of genes that are likely to be involved in plastic responses to environmental change (e.g. in salinity (Tomy et al., 2009), or

3 thermic stress (Hamdoun, Cheney and Cherr, 2003)). Finally, omic technologies also allow us to examine the regulatory mechanisms of gene expression, inducing the role of epigenetics (Feinberg, 2007) and hormone regulation (Lema, 2014) in plasticity.

Some of the molecular mechanisms involved in phenotypic plasticity are conserved across species. An example of a conserved molecular basis for phenotypic plasticity is the gene aromatase. This gene plays a crucial role in fish species that exhibit changes in sex (e.g. clownfish and bluehead wrasses) or shifts in dominance hierarchies (African cichlid fish), (Susan C.P. Renn, Aubin-Horth and Hofmann, 2008; Casas et al., 2016; Todd et al., 2019). By blocking the expression of aromatase in the guppies’ brain (Poecilia reticulata), courtship behaviour decreased (Hallgren et al., 2006). In birds, the activity of the same gene increases during the territorial season, which suggests that aromatase plays a role in regulating testosterone and estrogen. A second example is juvenile hormone. In insects, it regulates the expression of plasticity in horn morphology in male beetles (Onthophagus taurus), regulates gregariousness and solitary behaviour in locusts (Schistocerca gregaria) (Tawfik et al., 2000), while in social insects it regulates behavioural dominance, caste differentiation and stimulates ovarian growth (Harano et al., 2008; Tibbetts, Mettler and Donajkowski, 2013; Kelstrup et al., 2014). Further examples in social insects, include the insulin signalling pathway, the epidermal growth factor receptor and the target of rapamycin (TOR) pathway, all which appear to have a conserved role in regulating developmental caste differentiation – a form of plasticity expressed during the developmental phase of an insect (Corona, Libbrecht and Wheeler, 2016). Although phenotypic plasticity is defined as plastic variation induced by environmental cues – which does not require genetic variation - there are several examples of an evident correlation between plasticity and genetic composition (Cahan et al., 2004). This is observed in terms of the extent of plasticity in different populations, the allelic variation at specific loci and in the gene content of the genomes of species with highly plastic development (Beldade, Mateus and Keller, 2011).

Plastic responses in the life-history of organisms can be visualised in the form of reaction norms which are graphical representations of the values that a trait takes across an environmental gradient (West-Eberhard, 2003). Different phenotypes (e.g. behavioural or morphological) may have different reactions norms, indicating that they react differently to the same environmental conditions. Reaction norms can describe how hormones, gene expression, behaviour and/or physiology change with the environment (Aubin-Horth and Renn, 2009). For example, the males of the cichlid fish Astatotilapia burtoni, show two distinct

4 phenotypes, with dominant males (phenotype A) being colourful, courting and spawning females, while holding territories that they defend vigorously. By contrast, subordinate males (phenotype B) are pale, do not hold territories or reproduce, and they school with other male subordinates and females (Fitzpatrick et al., 2005). However, subordinates do remain reproductively totipotent (Kustan, Maruska and Fernald, 2012), ensuring that they can take the opportunity to mate if/when it presents itself (e.g. in the absence of dominant males). On the other hand, when a dominant male is challenged by a subordinate and the latter wins, the dominant male will reverse his phenotype (his colours will fade and his behaviour will change) to become subordinate (Burmeister, Jarvis and Fernald, 2005).

Phenotypic plasticity can shape various aspects of an organism’s life cycle and life-history including ontogeny (Sultan, 2000), response to pathogens (Roux, Gao and Bergelson, 2010), lifespan (Panowski et al., 2007), and behaviour (Sambandan et al., 2008). The life-history of any organism encompasses the events from birth to death describing maturation patterns (age or stage-specific), reproduction and survival, and how they vary between individuals, populations, species and environments (Flatt and Heyland, 2011). Life-history studies explore elements of life-cycle (taxon-specific) and life-history decisions which include patterns of sex allocation, transitions from larva to adult and alternative phenotypes. Life-history traits (e.g. morphological and behavioural) are strongly influenced by the environment; therefore, studies of life-history require a shared understanding of mechanisms of phenotypic plasticity.

Transitions as examples of phenotypic plasticity

For organisms to exhibit one of their plastic phenotypes, they need to undergo a transitioning period in which most of the physiological and/or behavioural changes take place. A transition is a process of change and can be evolutionary: from solitary to group living (Szathmáry and Maynard Smith, 1995); ontogenetic: larval to juvenile transition (Truman and Riddiford, 1999); involving life-history stages: nesting (Flatt and Heyland, 2011); or transient (and often repeatable or cyclical) changes in behaviour: response to predator. They can be seasonal (e.g. migration), permanent or reversible (e.g. transition from male to female, and from dominant to subordinate in fish, respectively) (Polidori et al., 2006; Casas et al., 2016). These transitions are all forms of phenotypic plasticity.

Ecological consequences of phenotypic plasticity range from phenology to mediating interspecific interaction and extend to the structuring of ecological communities (Miner et al., 2005), and through its ecological effects, plasticity can facilitate evolutionary changes and speciation (West-Eberhard, 2003). For instance, phenological transitions in which organisms change colouration or moult during winter/dry/wet seasons are adaptive and driven by phenotypic plasticity, being a crucial aspect of populations and communities in determining how they respond to harsh changes in the environment (Sauve, Divoky and Friesen, 2019). In polar and temperate latitude communities, organisms go through multiple physiological and life-history transitions that are essential for survival in highly seasonal conditions and harsh winters. Thus, polar birds and mammals have specialised traits such as seasonal coat colour moulting, migration and hibernation that help them to cope with environmental changes. A common example is the transition from white pigmented plumage or fur in the winter to dark in the summer; these seasonal morphological transitions are critical for maximising fitness (Zimova et al., 2018), as seasonal coat colour helps to regulate their body temperature as well as camouflage against predators.

Transitions can be observed at the behavioural level, where they allow organisms to respond to changes in their environment in a way that is beneficial to their fitness. Changes in behaviour are very often the first response to altered conditions, improving prospects for survival and reproduction (Wong and Candolin, 2015). For instance, in Polistes wasp societies, workers and queens are behaviourally different, but if the queen is lost a worker can attempt to become the new queen, changing its behavioural machinery and ultimately resulting in a change from indirect to direct fitness (Hughes, Beck and Strassmann, 1987). In desert locusts (Schistocerca gregaria), the transition from a solitary to gregarious behavioural state can take place within hours in response to changing population density (Ernst et al., 2015); this population density-dependent transition allows them to reach sexual maturity more rapidly. Members of highly social organisms can alter behaviour to benefit their society: in honeybee (Apis mellifera) societies, workers can shift their behaviour according to the hive’s needs, transitioning between food foraging and brood maintenance (Elekonich and Roberts, 2005). In some societies, failure to transition can cause rejection by nestmates: clonal raider ants (Ooceraea biroi), shift their reproductive and non-reproductive behaviours alternately; when some individuals fail to make the shift, their nestmates eventually killing them (Libbrecht, Oxley and Kronauer, 2018).

Transitions are not only important at individual, population and community levels but they are also relevant to evolution, as they contribute to the emergence of diverse biological complexity. Major evolutionary transitions are well-defined events that are characterised by a change in complexity that have marked patterns of evolution, happening by the integration of independent-free elements into a new high-level of organisation in which the former elements interact in novel ways, losing their previous autonomy (Szathmáry and Maynard Smith, 1995). Examples include the transition to multicellularity, prokaryotes to eukaryotes and solitary to social living. These evolutionary transitions allowed organisms to thrive in new environments and exploit alternative niches. One of the most outstanding evolutionary transitions is the transition from solitary to group living in which ancestrally solitary forms start to interact with conspecifics, potentially to fight against predators, until several forms of communality and cooperation evolved, some of these leading to the origin of committed animal societies with overlapping generations, reproductive specialisation, and a division of labour. Sociality has evolved multiple times in many taxonomic groups (Wilson, 1973; Burda et al., 2000; Duffy, 2003; Lubin and Bilde, 2007), but is found at its most diverse and extreme form in the Hymenoptera, which includes species that exhibit the full range of sociality, from solitary and communal living species to simple obligate group living (division of labour with behavioural castes), and complex societies or ‘superorganismality’ (reproductive division of labour with morphological castes, and the evolution of derived social traits such as within-caste differentiation and multiple mating), in which the individual group members are mutually dependent on each other, akin to the multicellular body.

MECHANISMS OF TRANSITIONS

The molecular mechanisms of phenotypic plasticity (including ecological, behavioural or evolutionary transitions) are poorly explored. One reason for this is that most studies focus on ultimate ‘end’ phenotypes (phenotype A versus phenotype B, state A versus state B), and the mechanisms that trigger the change (e.g. social environment, pheromones, mating and breading), rather than the transitional phenotype itself. One exception is work on the molecular mechanisms of the transition from male to female (hermaphroditism) in fish, which has been extensively studied in clownfish and bluehead wrasses (Casas et al., 2016; Todd et al., 2019). Similarly, the mechanisms underpinning the transition from subordinate to dominant males in African cichlid fish is well understood (Susan C.P. Renn, Aubin-Horth and Hofmann, 2008). These case studies provide some important insights into the molecular mechanisms of transitions.

Clownfish (Amphiprion bicinctus) live in pairs or social groups composed of a single large dominant female and male, together with up to four immature juveniles. When the female dies, the largest male in the group will reprogram his behaviour to become dominant and aggressive towards juveniles as part of his transition. Concurrently, the largest undifferentiated juvenile starts to differentiate into a male. The transition takes nearly 50 days, during which time the transitioning male changes his behavioural repertoire, this being accompanied by changes in brain gene expression as well as in physiology (Casas et al., 2016). A histological and gene expression time-course analysis at the gonadal and brain level respectively, revealed a gradual decrease in testicular tissue during the transition period. At the brain level, Casas et al. found an overall downregulation of genes during the transitioning stage, and ultimately very subtle differences in expression between final phenotypes (male and female), thus suggesting a limited sex bias in brain transcriptomic profiles, as found in other teleosts (Todd et al., 2019). As expected, the first changes were apparent in the brain, and later in the gonads, since behavioural change precedes physiological change. At the brain level, there were three key players: aromatase cyp19a1, which plays an important role in sex change in teleosts modulating estrogen and androgen signalling, along with sox6 and foxp4, which are both transcription factors. Sox6 regulates spermatogenesis in vertebrates, while the latter is involved in sexual development. At the gonadal level, cyp19a1 and foxl2 are pivotal for the transition from the testis to ovaries, whereas sox8, amh and dmrt1 are crucial for testis maintenance (Casas et al., 2016).

In bluehead wrasses (Thalassoma bifasciatum), the opposite transition occurs. When the dominant male protecting a territory dies, a large female will transition to become male. Like the clownfish, the first phenotypic signs of the transition are in aggression and dominance hierarchy, these taking place in only hours, whereas the maturation of testis takes up to ten days. The transition appears to be stressful as cortisol is needed to increase energy expenditure and maintain the social hierarchy. The aromatase gene involved in sex change and hierarchical dominance is repressed, whereas genes involved in processes such as innate immune response, regulation of cell proliferation, cell adhesion and protease binding seems to be mediating the transition (Todd et al., 2019).

Phenotypic transitions are not only mediated by well-known, conserved molecular pathways, but may also sometimes involve the evolution of new molecular functionality. The teneurins-2 (tenm2) is a transmembrane gene downregulated in the transition from male to female in clownfish. Genes in the same family have been shown to affect sexual maturity in chickens

(Chen et al., 2007); and gonadal maturity in bluehead wrasses (Todd et al., 2019). Meanwhile, the hepatoma-derived growth-factor-related protein 2 (hdgfrp2) is also downregulated in the transitioning stages from male to female; however, little is known about its function (Sugitani et al., 2012). On the other hand, in the transition from female to male, neofunctionalization was evident in the transition. For example, crucial components of the rspo1/Wnt/B-catenin signalling pathway regulate ovarian fate in mammals, were duplicated with one homolog showing up-regulated expression during mid to late sex transition, which suggests that the duplicates acquired novel roles associated with male sexual fate (Todd et al., 2019).

TRANSITIONS IN INSECTS AS ROUTES TO UNDERSTANDING

MECHANISMS OF PHENOTYPIC PLASTICITY

Insects are good models for studying behavioural transitions because they exhibit a wide range of transitions, including life-history, behavioural and seasonal transitions. For example, some individuals can transition to a different life-history phase when the opportunity presents (e.g. the death of a queen in a social insect colony) in order to increase their fitness (Deshpande et al., 2006). Others can do so from a solitary state to a gregarious one when their population density becomes too high (e.g. locusts (Tawfik et al., 2000)); and a few can transition from a solitary state to a social one when are transplanted from high to low-latitudes (Field et al., 2010)) . The different stages of a nesting cycle are a series of transitions (e.g. in the nesting cycling of a non-social insect, as it makes a nest, lays eggs and provisions its brood). These behavioural transitions help organisms to adapt and survive and have a direct impact on their fitness.

Life-history transitions in plasticity in insects

A key life-history transition is the shift from non-reproductive to reproductive. These are common transitions in simple societies of bees and wasps, and also ants that have secondarily lost their queen caste. A well-studied example is in paper wasps (Polistes), in which a worker will become queen replacement when the queen dies (West-Eberhard, 1969; Deshpande et al., 2006). In some societies, the highest ranked worker in the dominance hierarchy uses physical aggression to become the queen replacement (Ropalidia cyathiformis), whereas others do not engage in aggression and potentially use pheromones (Ropalidia marginata) (Deshpande et al., 2006). The transition from non-reproductive to reproductive has an important relevant to fitness, as it signifies a shift from indirect to direct reproduction. Although

9 this transition has been well studied at the behavioural and physiological level, we know little about how it takes place at the molecular level.

Ponerine ants (Harpegnathos saltator) also show behavioural transitions, having two phenotypes which are very distinct in behaviour, morphology and physiology: a “true-queen” and non-reproductive “workers”. When the queen dies, workers engage in tournaments to become “gamergates” (queen-like reproductive state) and re-establish the social hierarchy. The winners will transition to gamergates and lay eggs. This transition involves hundreds of genes, with the neuropeptide corazonin homologous to gonadotropin-releasing hormone in vertebrates seeming to be key for the transition. Indeed, when transitioning workers were injected with corazonin, the expression of vitellogenin was inhibited in the brain, triggering worker-like behaviour and suppressing egg deposition (Gospocic et al., 2017).

Reproductive transitions occur on a cyclical basis in clonal raider ants (Ooceraea biroi). These colonies are formed by genetically identical workers that alternate between a reproductive phase and a non-reproductive phase cyclically. Social cues triggered by the larvae regulate the transition between phases. When the larvae hatch, the reproductive phase ceases, and the adult members of the colony start the non-reproductive phase by suppressing their ovaries while rearing the larvae. In turn, when these brood pupate, the adults will begin to activate their ovaries again to lay eggs. Unlike H. saltator, the transition from brood care and reproduction occurs without overt competition. The cyclical transitions in behaviour are accompanied by substantial changes in brain gene expression. The transition from reproductive to non-reproductive state is underpinned by 2,043 differentially expressed genes whereas that from non-reproductive to reproductive involves 626 genes (Libbrecht, Oxley and Kronauer, 2017). The latter transition has shown to be a gradual change in gene expression, with only 7.4% of differentially expressed genes overlapping between transitions. This suggests that the molecular mechanisms underpinning such life-history transitions are not reciprocal. i.e. switching from A to B involves different processes to switching from B to A. Mating and reproductive events are important life-history transitions in insects as failure to achieve these life-history transitions naturally has an impact on fitness and leaves a molecular signature in brain transcription (Manfredini et al., 2017).

Evolutionary transitions in plasticity in insects

The evolution of sociality is the most extensively studied form of phenotypic transitions that occurs in an evolutionary context, principally because sociality has evolved across several animal taxa (e.g. spiders, shrimps, fish and mammals), multiple times. Nonetheless, Hymenoptera insects are one of the main models for studying sociality because they exhibit the whole spectrum of sociality which typify the transition from solitary living to complex, mutually dependent and obligatory social living as a superorganism. Moreover, this has evolved multiple times within this group; in all cases, lineages of social insects are thought to have evolved from a solitary ancestor. Among the mechanistic hypotheses for this evolutionary transition are the ground plan hypotheses (ovarian and diapause hypotheses), the genetic toolkit hypothesis, the heterochrony hypothesis and the novel genes hypothesis (Table 1.1), but one of the dominant hypotheses for this evolutionary transition, therefore, is “the ovarian ground plan hypothesis”. The hypothesis posits that social phenotypes (“queen” and “workers”) evolved via uncoupling of behavioural and ovarian cycles in the nesting cycle of a solitary progressive provisioning ancestor (Evans and West-Eberhard, 1973; West-Eberhard, 1987). The nesting cycle of a solitary ancestor includes behavioural phases such as nest foundation and egg-laying (which requires ovarian activation to produce a “queen-like” phenotype), and nest guarding and provisioning (ovarian quiescence, to produce a “worker- like” phenotype). Thus, in evolution, reproductive behaviours (those involved in ovarian activation) and non-reproductive behaviours (those involved ovarian quiescence) were decoupled leading to behavioural specialisation in social insects in which the queen is reproductive, and workers remain non-reproductive.

Several studies have found empirical evidence to support this hypothesis (Amdam et al., 2004; Toth et al., 2007; Graham et al., 2011; Siegel et al., 2012; Smith et al., 2013); however, these have mainly used social insects (honeybees, paper wasps and sweat bees). Most insects, however, are solitary, and so it is surprising that only two studies have been carried out on solitary species (Kapheim and Johnson, 2017b; Kelstrup et al., 2018), both of them using mainly ovarian data and behaviour. Indeed, despite the wealth of insect species available, we still lack integrative studies in solitary Hymenoptera investigating behaviour, gene expression and physiology together, to provide a better understanding of the mechanisms regulating the nesting cycle, and hence, the behavioural phases and their regulation at the molecular and physiological levels. Such analyses are required to test the ovarian ground plan hypothesis, and better understand the mechanistic basis of the major transition to sociality.

Table 1.1. Summary of the hypotheses of the evolution of sociality in Hymenoptera.

Hypotheses Description Predictions Evidence

A) Gene expression patterns of maternal care in solitary Aculeata will be similar to those in the worker caste of social Together, physiological and behavioural states displayed by a solitary ancestor * Nomia melanderi (Kapheim and Johnson, 2017b), insects. Ovarian ground were decoupled in evolution resulting in reproductive (queen-like) and non- * Synagris cornuta (Kelstrup et al., 2018) B) Behavioural phases in solitary insects are linked with plan reproductive (worker-like) social phenotypes (Evans and West-Eberhard, * Apis mellifera (Amdam et al., 2004; Graham et al., changes in ovarian states where females displaying 1973; West-Eberhard, 1996). 2011) worker-like tasks have small oocytes whereas females displaying queen-like tasks possess well developed eggs. Differences between castes are underpinned by physiological and ancestral genetic signatures that diverge into two pathways during larval development allowing individuals to adapt to seasonal environmental variations. Thus, the Gene expression patterns involved in diapause in solitary * Polistes dominula and Polistes exclamans (Hunt et Diapause first pathway (G1) produces early season small individuals (poorly fed with low insects will be similar to those present in gynes in eusocial al., 2007, 2010), ground plan fat and protein store) that will develop as workers, while the second pathway species. * Bombus impatiens (Treanore et al., 2019) (G2) produces late season large individuals showing diapause traits (well fed, high fat and protein store) that develop as gynes (Hunt and Amdam, 2005; Hunt, 2006; Hunt et al., 2010). * Transcriptional comparison between Apis mellifera, Ancestral or deeply conserved genes associated with solitary behaviour Across the social spectrum in Hymenoptera (solitary → Polistes metricus and Solenopsis Invicta (A. J. Berens, Genetic toolkit became retooled or co-opted to change their expression patterns for the eusocial), such as aggression, foraging and/or parental Hunt and Toth, 2015), * P. metricus (Toth et al., 2007, evolution of eusocial behaviour (Toth and Robinson, 2007). care behaviour will be underpinned by similar genes. 2014), * Apis mellifera and Drosophila melanogaster (Toth et al., 2010) Reproductive division of labour evolved via rearrangement of the timing of gene * Ceratina calcarata (Rehan, Berens and Toth, 2014), Maternal and sibling behaviours can be regulated by similar Maternal expression related to offspring care. This hypothesis suggests a molecular * Lasius niger (Walsh et al., 2018), gene expression patterns. heterochrony mechanism predicted to be regulating maternal and sibling care gene * Bombus terrestres (Woodard et al., 2014)

expression patterns (Linksvayer and Wade, 2005) Differentially expressed genes between castes are likely to Novel social behaviours such as worker behaviour emerged via new or * Polistes canadensis (Ferreira et al., 2013), * A. be taxa restricted with no discernible homology (shared Novel genes taxonomically restricted genes as product of rapid protein evolution (Sumner, mellifera (Johnson and Tsutsui, 2011), * Temnothorax ancestry) to those in other species or as result from rapid 2014). longispinosus (Feldmeyer, Elsner and Foitzik, 2014) gene evolution processes.

This ancestor of social insects is thought to have been a progressive provisioner, a widespread behaviour exhibited by most social bees and wasps. The ovarian ground plan hypothesis focuses on the idea that cycles of ovarian development that regulate the nesting cycle of a solitary progressive provisioner ancestor provide the mechanistic basis from which social behaviour can evolve. This hypothesis has largely focused on the reproductive cycle. However, among solitary progressive provisioners (West-Eberhard, 1987; Kelstrup et al., 2018), provisioning is as key a component of their nesting cycle as reproduction, as mothers gradually feed their larvae over time. Once the larva is fully provisioned, a female repeats the whole nesting cycle for a new egg, so the nesting cycle might be regulated by the provisioning phase. Provisioning is costly as the mothers can die when they engage in foraging for their larvae (Field et al., 2007). In addition, while the mother is away, the larva is vulnerable to parasites (flies) and predators (ants and spiders). Given the predominance of provisioning in these putative ancestors of social insects, it is surprising that no studies have explored the idea of a “provisioning ground plan”, whereby the mechanistic basis of provisioning could provide a means for the evolution of social behaviour in the major transition, alongside cycles of reproduction.

Behavioural transitions in plasticity in insects

Important transitions for most aculeates are those that occur through the nesting cycle, which consists of a pre-programmed sequence of different behavioural phases involving nest building/finding, oviposition, and caring behaviours such as incubation, cleaning and provisioning. Each phase involves a distinct set of tasks. Even some subsocial insects exhibit parental care behaviour within their nesting cycle, with many of them starting by building a single-celled nest out of mud or digging holes in wood or burrows in the ground. Nest founding behaviours are then superseded by a transition to oviposition behaviours, which may also involve provisioning and in some species a nest guarding phase (e.g. Synagris cornuta (Kelstrup et al., 2018)).

Many solitary insects do not exhibit the clear-cut distinct phenotypes found in the form of queen and worker castes of social insects, either because they show extended parental care for their larvae as they grow (Suzuki, 2010; Chen et al., 2018), or they manage several nests simultaneously. Nonetheless, females still need to switch from one behavioural phenotype to another to successfully rear their offspring: for example, many progressive provisioning solitary insects need built shelters (nest founding), lay eggs (oviposition) and to provide food to the larvae during development (provisioning). All these behaviours can be classified as

13 behavioural phenotypes, as they can be discernible from other phases by the suite of behaviours that are performed; given the evidence for a molecular basis of behaviour, each of these behavioural phases is likely to have its own distinct set of molecular machinery. For example, a solitary mud-dauber wasp will need to collect clay and mud to create a pot in which to lay an egg. Hence, she will have to collect mud multiple times, involving considerable physical effort (100 trips with mud and up to two days to complete), as well as spatial learning and memory, all of which must have a molecular basis. Next, once a mud cell is completed, she lays an egg inside, but for this to happen, the wasp needs to have a large mature oocyte in her tract, regulated by gene expression in her brain. After egg-laying, the mother remains in the cell, guarding the egg most of her time: thus, she switches from a reproductive behavioural phase to a defence and guarding phase. Finally, the provisioning phase starts when the egg hatches, requiring expression of another set of behaviours including hunting, self-defence against predators and pathogens, physical effort in carrying prey-items, and spatial navigation and memory in order that she can bring the food to the cell; she repeats this several times as the larva grows (O’Neill, 2001; Hunt, 2007b). As with queen succession in paper wasps, the behavioural mechanisms taking place in each of these phases is well studied. An outstanding question is: what are the molecular processes underpin the succession of behavioural phases? Since a solitary female repeats this several times (and often simultaneously for multiple nests) during her adult life, a plausible hypothesis is that the molecular mechanisms required for these different behavioural phases remain upregulated throughout her adult life. Alternatively, subtle changes in key regulators of gene expression may occur at each phase, responsively to the needs of parenting across many nests.

The workers of many social insect species express similar behavioural transitions to those described for non-social insects, but which are largely determined by age. A well-studied example of this is the worker honeybees, where after emerging, they behave like “nurses”, engaging in hive maintenance and brood rearing. At 2 ~ 3 weeks of age, and in response to social and pheromonal cues, nurses transition to become “foragers”, leaving the hive to collect pollen and nectar (Robinson, 1992). The transition involves circadian clock activity, changes in endocrine activity and metabolism, brain structure and chemistry, as well as gene expression (Robinson, 2002; Whitfield, Cziko and Robinson, 2003). However, this transition is reversible in accordance with the needs of the hive. The molecular basis of this transition is well studied, and involves coordinated suites of genes and being regulated by epigenetic processes (Herb et al., 2012). This suggests that there could also be a molecular basis to the subtle shifts in behaviour that occur through the nesting cycle of a non-social insect.

WASPS AS MODELS TO STUDY LIFE-HISTORY AND BEHAVIOURAL

TRANSITIONS

Wasps are a speciose group of the Hymenoptera, with over 150 thousand species, about 5000 of these being social. Wasps are less well studied compared to their cousins, the bees and ants, but they are an outstanding resource for looking into life-history behavioural and evolutionary transitions (Jandt and Toth, 2015; Rehan and Toth, 2015; Taylor, Bentley and Sumner, 2018), and the components of phenotypic plasticity therein (Gadagkar, 1997; Wilson, Mullen and Holway, 2009; Londe et al., 2015; Corona, Libbrecht and Wheeler, 2016).

Although the vast majority of wasp species are solitary, they are understudied because they are difficult to find, as they behave in a solitary manner and the window to make observations and behavioural experiments is brief. Such solitary wasps are interesting from several perspectives for understanding transitions. In terms of an evolutionary perspective, they are likely to represent the non-social ancestors of social wasps, so can offer insights into the first stages of social evolution (O’Neill, 2001; Hunt, 2007c). Specifically, the behavioural phases of solitary wasps provide a means to test the ovarian ground plan hypothesis.

From a perspective of behavioural transitions, solitary wasp nesting cycles also show a sequence of different behavioural phases, where wasps shift from task to task according to need, making them excellent models for understanding highly plastic, but also highly regulated, behaviours. Examining the molecular mechanisms regulating this non-social life can help us to understand the basis of behavioural plasticity and give us a better understanding of how behaviours accommodate responsiveness to changing environments.

Vespidae wasps have been the group of choice for examining life-history transitions. e.g. worker to queen. More specifically, Polistes wasps have been the focal group, as adult wasp workers remain totipotent, while the queen is the egg-laying female. If the opportunity is presented to a worker, she will transition to queen-like behaviour and lay eggs. This display of plasticity among adult wasps is only present in simple societies; in the more complex societies of Vespine wasps, highly eusocial bees, and most ants, phenotypic plasticity is only expressed during development in response to developmental cues (e.g. nutrition), resulting in commitment to queen and worker castes, as well as subcastes of the worker caste (Elekonich

15 and Roberts, 2005). Although, the transition from worker to queen is well studied in simple societies of social wasps at the phenotypic level, the molecular mechanisms by which this occurs remain unstudied.

This thesis exploits some of the biological diversity in wasps to examine the molecular basis of life-history, behavioural and evolutionary transitions. It uses two key model systems: Polistes social paper wasps to explore the molecular basis of a life-history transition, and Ammophila non-social digger wasps to explore the molecular basis of behavioural and evolutionary transitions.

Simple societies of Polistes wasps as models to study life-history transitions

The first part of this thesis is focused on paper wasps of the Polistes genus. These wasps have been proposed and used as models to make inferences on social evolution (Reeve, 1991; Turillazzi and West-Eberhard, 1996; Jandt, Tibbetts and Toth, 2014), as females must compete to lay eggs, whilst others help to maintain the nest and rear brood. Polistes colonies are typically initiated by a single female or more who are often related (Leadbeater et al., 2010; Miller et al., 2018; Robin J. Southon et al., 2019). During the founding phase, the foundress(es) build the cells, and provision the first brood. Although all females are potential queens, usually a single female becomes established as the sole egg layer. The emergence of the first brood – the workers – marks the start of the post-emergence phase; the workers can mate and become reproductive, but they are unlikely to do so, unless the queen dies. Instead, they help raise their siblings and perform nest maintenance activities. Temperate Polistes begin colonies in the spring and abandon them in autumn when the new sexual mates and the mated females enter diapause over the winter (Turillazzi and West-Eberhard, 1996). By contrast, neotropical Polistes continue their nesting cycle all year-round although colony activity may slow down if there is a severe dry season (West-Eberhard, 1969; Pickering, 1980).

Despite Polistes being a cosmopolitan genus, it thought to have originated in the tropics, where they lack the seasonal constraints of temperate Polistes (Santos et al., 2015); like all social lineages, Polistes is likely to have evolved from a solitary ancestor (O’Neill, 2001; Hunt, 2007b). Across the social spectrum of vespid wasps, Polistes is classified as primitively eusocial, with one of the simplest forms of social behaviour, characterised by totipotent group members who show both conflict and cooperation (Jandt and Toth, 2015). As such, they are a key model system for investigating inclusive fitness, the evolution of cooperation and

16 mechanisms for regulating a division of labour. They usually display a dominance hierarchy in which a reproductive female monopolises egg-laying; if the queen dies, one of her nestmates – usually a daughter – will replace her. In temperate Polistes, there appear to be conventions that determine the new queen, based on cues such as age (Strassmann and Meyer, 1983; Hughes and Strassmann, 1988; Strassmann et al., 2004). In tropical species, there appears to be less certainty over who the new queen will be; a contest for the new queen’s position takes place, which often involves overt aggression between competing females and can go on for several days to a week (West-Eberhard, 1969). Thereafter, aggression is reduced, and more ritualised behaviours might be displayed, and then the new queen becomes established (Jandt, Tibbetts and Toth, 2014).

The flexibility to transition from one state (worker) to another (queen) and the lack of morphological castes has positioned Polistes as a genus of choice to investigate phenotypic plasticity and caste differentiation. But, despite numerous studies on social evolution (Hunt, 2007b), phenotypic plasticity and caste differentiation (Sumner, Pereboom and Jordan, 2006; Patalano et al., 2015), physiology and genomics (Giray, Giovanetti and West-Eberhard, 2005; Cini, Meconcelli and Cervo, 2013; Ferreira et al., 2013; Patalano et al., 2015; Standage et al., 2016), no studies on Polistes have examined the molecular mechanisms during the key life- history transition that takes place during queen replacement. Several traits of Polistes make them tractable for studying social behaviour: these include their simple social behaviours, their large size makes them easy to identify and mark, colonies are relatively small and the nests lack an envelope, facilitating behavioural observations and manipulations in their natural habitat; their relative abundance and exploitation of man-made structures as nesting substrates also facilitate ease of experiments and observations. In this thesis I use the tropical species Polistes lanio (Fabricius) as a model to investigate the molecular mechanisms underpinning the transition from worker to queen.

Ammophilini non-social wasps to study behavioural transitions

Digger wasps of the genus Ammophila have caught the attention of many entomologists and ethologist over the last century; Fabre’s Souvenirs Entomologiques (Fabre, 1879), and then Baerends’ full description of Ammophila pubescens Curtis (Baerends, 1941a) provide a basis on their natural history. These wasps are part of the non-social group Apoidea which makes them phylogenetically closer to bees than to the vespid wasps (Melo, 1999; Sann et al., 2018). They are distributed in warm regions on almost all continents, except Antarctica. Non-social wasps lack cooperation and division of labour between individuals; despite this, they exhibit a

17 wide range of parental care strategies and nesting behaviour (Evans, 1959; Field, Ohl and Kennedy, 2011). For instance, some species hunt prey before founding a nest (Field, 1993), whilst others build their nests first, then hunt, and then lay an egg (Evans, 1959; O’Neill, 2001). This latter sequence requires females to remember the locations of their burrows. Some species collect all the food necessary for their eggs to develop in a single bout of foraging activity and have no further interactions with their brood - mass provisioning (Field, 1992). Other species delay provisioning, waiting until the egg hatches successfully before provisioning (Powell, 1964); a few species provision their larvae progressively over time until the larva has grown substantially and are less vulnerable to being parasitized; moreover, they attend to the needs of various burrow simultaneously – progressive provisioning (Baerends, 1941a; Field and Brace, 2004). This form of brood care is similar to the provisioning behaviour found in social wasps, where multiple brood are provisioned steadily over time, as needed (Hunt, 2007a; Wilson, 2008). However, some authors have proposed that this behaviour is a simple “progressive mass provisioning” (Tsuneki, 1968). Other parental strategies include joint provisioning (Field et al., 2007) in which multiple females provision the same larva, and facultative intraspecific parasitism, where females exploit the burrows and prey of others to lay their eggs (Field, 1989a). Previous behavioural observations (Baerends, 1941b; Evans and West-Eberhard, 1973) suggest that some of the phases in the nesting cycle of solitary wasps like Ammophila are fixed action behaviours (where they are impermeable to changes in conditions) whilst others are more flexible; there may be a molecular basis to this plasticity, and thus the nature of the behavioural transition. These peculiarities position them as useful models for comparative analyses of behaviour and ecology.

Other reasons to use Ammophila as model include their relatively large size, which makes them easy to identify and mark (Evans, 1959; Spoczynska, 1975); they are locally abundant, with populations that persist for several years and which are often in aggregations (Evans, 1959); furthermore, they are thought to be sensitive to environmental disturbance, resulting in population declines and migration, making them good models to investigate genetic drift, genetic diversity, colonisation and extinction events (Srba and Heneberg, 2012). Likewise, some of their provisioning strategies make it easy to identify individuals and their burrows. Given that they sleep in the bushes (Baerends, 1941a), manipulation of their burrows and burrow contents (brood and prey) can be conducted successfully at night (Field and Brace, 2004; Field et al., 2007; Field, Accleton and Foster, 2018).

The digger wasp Ammophila pubescens is the species I have chosen to investigate behavioural and evolutionary transitions. Unlike most Ammophila wasps, they are progressive provisioners. Adults emerge in late spring and soon after they start the nest cycle which includes digging a single-celled burrow, adding a single prey item inside on which they lay a single egg. After two days, females visit their burrows to assess the state, and if appropriate they will provision their burrows further with more prey in consecutive attempts. Burrow inspections and further provisioning are repeated again within five days after egg-laying. Finally, they seal up their burrows permanently, after bringing the last prey. The larvae will pupate and overwinter as adults, waiting until the next spring to emerge as adults and start the cycle afresh. Therefore, the generations do not overlap, and there is little/no opportunity for cooperation. Females tend to position their burrows close to each other (within 50 cm) and use areas of c40 m around the nest to hunt for prey. Males, after emerging, patrol areas of 2 x 50 m looking for receptive females to mate with. Despite being one of the best studied non- social wasps of the last century (Baerends, 1941a; Field and Brace, 2004; Field et al., 2007; Field, Accleton and Foster, 2018), we know little of their population dynamics, and their population genetics is almost unknown (Field, Accleton and Foster, 2018). Furthermore, in Central Europe, this species is of high conservation concern: in the Czech Republic and Germany (Bayern and Hessen) is has an endangered status, whereas in Poland is nearly threatened (National Red List). We lack research regarding the conservation status of A. pubescens in the UK.

One of the fascinating things about A. pubescens is that they care for several burrows at the same time. As a simultaneous progressive provisioner, they need to shift their behaviour according to the various requirements of many larvae in several different burrows at the same time. Thus, whilst they are waiting for an egg to hatch in one burrow, in another burrow the larva may be ready to be provisioned; a female must remember her burrow locations and inspect them at the right time to assess their status in order to conduct the appropriate behaviours. Their ability to respond according to conditions inside their burrows makes them excellent models to understand behavioural plasticity and behavioural transitions; e.g. what are the molecular mechanisms that underpin each phase, and what regulates their ability to process information and execute the correct behaviours. They are also good models to study the evolutionary transition from solitary to social living: specifically, could the molecular mechanisms underpinning the different stages of their nesting cycle provide the mechanistic basis for the evolution of division of labour in social wasps; we know little about molecular mechanisms underpinning these behaviours in non-social wasps, despite them being the likely ancestral state of social lineages in insects. Ammophila are Sphecids, not Eumenid wasps,

19 and thus are not part of the putative ancestral group; however, their tractability to study positions them as valuable species for understanding the mechanisms of non-social nesting behaviours and can provide insights into these key questions on evolutionary transitions.

AIMS AND QUESTIONS ADDRESSED IN THIS THESIS

The overall aims of this thesis are to characterise and determine the molecular underpinnings of behavioural, life-history and evolutionary transitions in response to the environment. To address these aims I use two species of wasps - one social species (Polistes lanio) and one non-social species (Ammophila pubescens). The thesis is organised into four data chapters and a general discussion that summarises the main findings of my work and a unified framework for the future study of the molecular basis of behavioural changes in cells and whole organisms. The aims and specific questions addressed in each chapter are summarised below.

Chapter 2 – Molecular basis of a major life-history transition: the shift from worker to queen in simple societies of the social paper wasp, Polistes lanio

Studies investigating the molecular basis of life-history and behavioural transitions to date have largely focused on the ultimate states of the transitions, e.g. differences between established subordinates and dominant males in cichlid fish, differences between established nurses and foragers in honeybees, and difference between reproductives and established replacement reproductives in ants (Whitfield, Cziko and Robinson, 2003; Maruska, 2015; Jones et al., 2017); very few have sampled what happens during the transitioning period (Casas et al., 2016; Libbrecht, Oxley and Kronauer, 2017; Todd et al., 2019; Yang et al., 2019; Glastad et al., 2020). Hence, in this chapter, I use the simple societies of P. lanio to investigate the transitioning period in which a worker becomes queen, to investigate the molecular mechanisms involved in this key life-history transition. Specifically, I investigate whether the transition is a simple switch off (or downregulation) of worker-like gene expression patterns and a switch on (or upregulation) of queen-like gene expression patterns (linear transition), or whether the transition creates a “transient” phenotype that involves an intermediate state (or phenotype) that is neither queen-like nor worker-like in transcription (non-linear transition). My analyses suggest that the transition is non-linear. The large-scale changes in gene expression patterns that occur in during the transitioning period appear to be similar to those found in other key life-history transitions like the shift from solitary to gregarious behaviours in locusts (Yang et al., 2019), the transition from female to male in bluehead wrasses (Todd et al., 2019)

20 and from non-reproductive to reproductive state in clonal raider ants (Libbrecht, Oxley and Kroauer, 2019). This transitioning state could be compared to the transition of an adult somatic cell to induced pluripotent stem cell, which ultimately is reprogrammed into a completely different cell type. A life-history transition, therefore, is defined as one that involves a shift in how fitness is achieved (e.g. indirect fitness as a worker to direct fitness as a queen), and appears to be associated with a transitionary period that involves a distinct transitional state – or phenotype, that is best described by a non-linear reaction norm, and involving genes that invoke a role for reprograming machinery. This concept is discussed further in Chapter 6.

Chapter 3 – Molecular basis of an evolutionary transition: testing the ovarian ground plan hypothesis in the non-social sphecid wasp, Ammophila pubescens

Significant progress has been made on understanding the physiological and molecular mechanisms of sociality in the Hymenoptera; but most research has focused on a relatively small number of species, most of which highly social or primitively social. There is a knowledge gap in studies on non-social insects, which are thought to represent the evolutionary ancestral basis from which social behaviours (e.g. division of labour) evolves. In this chapter, I present the first sequenced genome of a non-social wasp - Ammophila pubescens - and I use the genome and associated brain transcriptome sequence data obtained across the nesting cycle to test one of the main mechanistic hypotheses of the evolution of sociality in Hymenoptera, the ovarian ground plan (OGP) hypothesis (West-Eberhard, 1987). Although A. pubescens is not a direct ancestor of social bees and wasps (it is a Sphecid wasp, not a Eumenid), it displays the same portfolio of behaviours as the likely ancestor, and is easy to conduct the detailed behavioural observations required for this analysis. Hence, I use the brain transcriptomes of behavioural phases exhibited by individuals of this wasp during the nesting cycle to test whether the molecular basis of their queen-like and worker-like behaviours correspond to published molecular signatures of caste-specific phenotypes exhibit by social insects. I found some weak evidence for the OGP hypothesis; however, the overwhelming molecular signature came from the cycle of provisioning behaviours. I suggest that a ground plan of provisioning behaviour in non-social wasps may provide a mechanistic basis for the evolution of castes in social insects, and that this may be just as (or more) important than a cycle of reproductive mechanisms in the evolution of sociality.

Chapter 4 – Molecular basis of behavioural transitions: brain transcriptional changes during the nesting cycle of Ammophila pubescens

This chapter exploits the datasets used in Chapter 3 in more detail to investigate the molecular processes that underpin the behavioural transitions through the nesting cycle of A. pubescens. The cycle is constituted by distinct behavioural states, that occur in a strict sequence: nest founding → oviposition → inspection of burrow → provisioning; this sequence is repeated several times and in parallel simultaneously across several burrows by a single wasp, and may involve both fixed action and flexible behaviours (Baerends, 1941b; Evans and West- Eberhard, 1973). In this chapter, I first test experimentally which phases are fixed or flexible by offering a valuable prey item - a Lepidoptera caterpillar - before and after each behavioural phase. Next, I evaluate the molecular basis of each of the behavioural phases and examine whether there are any common molecular processes associated with flexible vs fixed behaviours. I found that all phases were largely flexible, with the exception of nest founding and arrival at the nest for an inspection visit. I was unable to detect any common molecular processes associated with flexible phases, but distinct genomics signatures were apparent for each behavioural phase. A small number of differentially expressed genes were found to be driving the behavioural phases, except for the provisioning phase, which was the most distinct phase and highly flexible towards prey items. The results of this chapter, therefore, reinforce the importance of provisioning behaviour as a key behavioural phase in this species.

Chapter 5 – Population genetics of Ammophila pubescens and implications for its conservation

The previous two chapters demonstrate the high levels of phenotypic plasticity expressed in A. pubescens. Phenotypic plasticity can arise even under low genetic diversity. The biology and environment of this species suggest that it may experience low genetic diversity, as a habitat specialist, the fragmented nature of its preferred habitat, and its status of conservation concern on mainland Europe. However, we lack any population-level genetic analyses of this species. The aim of this chapter was to investigate the genetic diversity and population genetics of A. pubescens in Southwest England. Although this species is not currently listed as threatened in the Red Data Book of British insects (Shirt, 1987), its habitat has been constricting over the last twenty years, making its populations potentially vulnerable to anthropogenetic changes. The results show that wasps from three different heathlands are weakly differentiated, with males dispersing farther than females. This suggests that wasps move across their fragmented habitat and that they may be relatively resilient to anthropogenic change.

Chapter 6 General discussion and a conceptual unified model for mechanisms of transitions – reprogramming

In this final chapter, I summarise the main findings, contributions and limitations of this thesis, as well as their implications in a wider context of behavioural, life-history and evolutionary transitions. Also, I propose future research and present a conceptual framework in which the molecular basis of life-history transitions in whole organisms are compared to those in transitions at the cellular level, where somatic cells can be induced pluripotent stem cells (iPSCs) and be ‘reprogrammed’ into completely different types of cells.

CHAPTER 2

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The molecular mechanisms underlying a key behavioural transition in the life- history of the paper wasp, Polistes lanio

Contributions: S. Sumner designed the study and conducted the fieldwork in Trinidad; E. Bell performed the ovarian dissections; D. Taylor conducted the RNA extractions; data analysis was carried out by S. Moreno with help from D. Taylor and C. Wyatts; and S. Moreno, C. Wyatts and S. Sumner wrote the paper.

ABSTRACT

Life-history transitions are important processes that affect the future landscape of any organism. Most life-history transition studies have focussed on the resolution of a phenotype, which has successfully completed its transition from one state to the next (e.g. unmated to mated; male to female). In contrast, we understand little about the molecular mechanisms that facilitate such transitions. This knowledge is essential for understanding the molecular basis of phenotype shifts as a key component of life-history. Here, we looked into a life-history transition that results in a shift in reproductive strategy, namely, the shift from a non- reproductive worker (who invests in indirect fitness) to a reproductive queen (who invests in direct fitness) in a social insect. We experimentally instigated the transition from worker to queen in colonies of the primitively eusocial paper wasp, Polistes lanio, and compared the behavioural and transcriptomic states of the brains in stable and transitioning wasps to determine the nature of the transition. We used the resulting data to test two specific hypotheses: are transitioning states simply a gradual shift from worker to queen? Or, are transitioning states a distinct phenotype, that involves significant phenotypic re-wiring to achieve the change? We found that transitioning wasps showed significant changes in their gene expression, relative to their stable phenotypic counterparts, such that the transitioning state appears to be characterised by significant shifts in the functional molecular machinery of the brain involving fundamental changes in gene networks. These results suggest that the transitioning phenotype is distinct from both the original stable worker phenotype as well as the ultimate queen phenotype state. We discuss how these dramatic shifts in molecular signatures found in transitioning wasps share similarities with the transcriptional shifts that typify cellular states during the reprogramming period that takes place during the development of a cell from one type (e.g. fibroblast) to another (e.g. muscle cells). Key life-history transitions may, therefore, be best described as periods of reprogramming.

INTRODUCTION

Transitions from one phenotypic state to another are key in the life-history and evolution of most organisms (Szathmáry and Maynard Smith, 1995). For example, the shift from an unmated to mated state (Kapelnikov et al., 2008), juvenile to mature, male to female (Pauly, 1984; Casas et al., 2016), and helper to reproductive (e.g. cooperative breeders like meerkats (Hodge et al., 2008). The capacity for phenotypic plasticity (where a single genotype can alter its phenotype in response to the environment) is implicit in these life-history transitions as plasticity enables individuals to exploit novel environments, fill vacated niches and increase their lifetime fitness. Transitions occur at all levels of organisation and during the lifespan of organisms (e.g. during ontogeny and life-history), and are accompanied by physiological and molecular changes (White, Nguyen and Fernald, 2002; Ernst et al., 2015; Manfredini et al., 2017). Molecular analyses have demonstrated how such transitions result in changes at the molecular level, e.g. through gene expression or methylation (Herb et al., 2012; Jones et al., 2017; Manfredini et al., 2017). However, we understand little about the molecular mechanisms that facilitate the transition itself; this knowledge is essential for understanding the molecular basis of the phenotypic plasticity that enables key life-history transitions.

Phenotypic plasticity manifests as changes in physiology, morphology and/or behaviour which is regulated by (and results in changes thereof) molecular processes (Aubin-Horth and Renn, 2009; Whitman and Agrawal, 2009). Reaction norms help explain qualitatively how the values of a trait vary in different environments (Scheiner, 1993; Kingsolver, Izem and Ragland, 2004; Kelly, Panhuis and Stoehr, 2012a). For instance, a reaction norm can be used to determine whether a phenotype changes subtly or substantially in a transitioning environment (Figure 2.1). Behavioural reaction norms (BRNs) (Dingemanse, Wolf, et al., 2010) are especially interesting, as they describe fast responses by an organism in the form of its behaviour (Smiseth, Wright and Kölliker, 2008). Environmentally triggered behavioural transitions include the transition from nurse to forager in honeybees (Whitfield, Cziko and Robinson, 2003), male to female in clownfish (W. Fricke, 1983), subordinate to dominant in male cichlid fish (Francis, Soma and Fernald, 1993), and they may result in linear or nonlinear BRNs. On the one hand, linear BRNs show gradual behavioural plasticity among different phenotypic states across an environmental gradient (e.g. orange reaction norm, Figure 2.1) (Kingsolver, Izem and Ragland, 2004; Carter, Goldizen and Heinsohn, 2012), whereas a nonlinear BRN (red reaction norm, Figure 2.1) would suggest a transitional phase that is distinct from both the original and the new stable states. Until recently, quantification of BRNs was limited to observations of behaviour, changes in morphology and/or physiology (Susan C P Renn, Aubin-Horth and

Hofmann, 2008; Casas et al., 2016), for which transitionary periods were difficult to discern. Advances in molecular biology now allow the use of molecular signatures as indicators of reaction norms, e.g. changes in gene expression when a nurse bee switches to a forager in response to a more demanding foraging environment being imposed (Herb et al., 2012). This approach offers the opportunity to examine whether key life-history transitions follow linear or non-linear BRNs, the implications of this being important as they tell us whether key transitions are akin to a linear developmental process (e.g. growth of a larva from one instar to the next) or a distinct period of reshuffling or reprogramming (e.g. as in the metamorphosis of pupation, in the transition from larva to adult in holometabolous insects (Jones, Wcislo and Robinson, 2015).

Figure 2.1. Reaction norm depicting a phenotypic state (state A) that switches to a new phenotype. (state B) due to social changes. The transition could be non-linear, with an intermediate (“transitional”) phenotype (red reaction norm) or linear, with a gradual up switching from state A to state B phenotype (orange reaction norm). During the transition, behavioural changes (green curve) typically precede physiological changes (black curve). After Aubin-Horth and Renn (2009).

We explored the transcriptional nature of the BRN in the simple societies of the paper wasp, Polistes lanio, in order to test whether such transitions were gradual changes or whether they involved a distinct transitional phenotypic state. These insects are excellent models for looking into the molecular mechanisms underpinning a key life-history transition, as a female can (and frequently does) start life off as a non-reproductive group member (worker), then later in life transitions to become a reproductive (queen) if the opportunity arises (Miyano, 1986; Strassmann et al., 2004; Deshpande et al., 2006). Moreover, behaviours are easily observed (Jandt, Tibbetts and Toth, 2014), and differential transcription is easily quantified in the brains of individuals using RNAseq (Toth et al., 2007, 2010; Ferreira et al., 2013; Ali J Berens, Hunt and Toth, 2015; Patalano et al., 2015). P. lanio lives in small-medium sized groups (5-50 females) in which a single female (queen) monopolises egg-laying, with her daughters carrying out the non-reproductive (‘worker’) tasks (e.g. foraging, brood care and nest maintenance) but retaining the ability to mate and become a fully functional reproductive. Queen turnover is common in Polistes, especially in tropical species (Strassmann et al., 2004; Deshpande et al., 2006; Robin J. Southon et al., 2019), where nests last many months and queens may die from old age (Southon et al., 2015), disease or during foraging trips for building material (Strassmann et al., 2004). If the queen dies a previously non-reproductive nestmate (often daughters) usually inherits the position of new queen, resulting in a key life-history transition in reproductive strategy where the fitness payoffs for an individual shift from indirect (helping raise relatives) to direct (as the egg-laying) reproduction.

This tractable model system allowed us to investigate the molecular processes associated with the transition from one stable life-history state (“worker“; Figure 2.1, represented by state A) to another (“queen”; Figure 2.1, represented by state B) in order to determine whether this life-history followed a linear or non-linear trajectory. Our null hypothesis is that the reaction norm of the transition is linear, as the non-reproductive state in a simple social insect colony is typified by a queue of ‘hopeful reproductives’ aiming to be an egglayer, akin to a developmental, reproductively immature state (Bang and Gadagkar, 2012). This would imply that the switch from indirect (as worker) to direct (as queen) reproduction is a simple activation (or maturation) of the reproductive system, accompanied by a gradual down-regulation of the worker-specific machinery (Alberts et al., 2015) and described as a linear reaction norm (Hypothesis 1). The alternative hypothesis is that the transition is non-linear (Hypothesis 2), involving a significant change that is untrivial to implement and which may involve a fundamental rewiring of the organism’s ground-plan, akin to the reprogramming of a cell from one type to another (Surani, 2014).

We tested these competing hypotheses by comparing brain transcriptional gene expression patterns of individuals belonging to one of three behavioural phenotypes, “queens” or “workers” (in a stable colony) and “transitioning wasps” (i.e. workers that are in the process of changing in the attempt to become queen). If Hypothesis 1(H1) is correct and the transition from state “A“ to “B” is predicted to be gradual, we expect transitioning females to lie along a linear reaction norm, with a gradual upregulation of queen-biased genes and downregulation of worker-biased genes (H1). In contrast, if Hypothesis 2 (H2) is correct and the transitioning wasps will not conform to a linear reaction norm but instead upregulate genes that are neither worker- nor queen-biased; this would suggest that the transition involves the formation of a phenotype that is distinctly different from both stable types (State A and B in Figure 2.1).

MATERIAL AND METHODS

Model species

Polistes lanio is a typical species of paper wasp in that its colony starts with one or a few foundresses who compete to become the queen and thus gain the monopoly as the sole egglayer (Robin J. Southon et al., 2019). After the first workers emerge, the queen no longer engages in nest maintenance activities, foraging or nursing (West-Eberhard, 1969). Although this species lacks morphological castes, a queen can be recognised based on her behaviour e.g. aggression toward her nestmates and continuous presence in the nest. Unlike temperate paper wasps, when the queen of a tropical species of paper wasp dies/disappears, there is overt physical competition amongst her remaining nestmates over who inherits the nest, probably because there are no seasonal constraints on the nesting cycle, so inheriting a nest is always a valuable commodity. Therefore, the absence of the queen (natural or experimental) instigates a fight amongst potential queen successors.

Behavioural determination and sample collection

P. lanio was studied in their natural habitat in Trinidad, during July 2013. Twelve post- emergence nests were observed over a period of 3 days to identify and behaviourally determine the egg-laying females (queens) and their nestmates (workers) in each nest. Colonies were relatively similar in size and in number of members which suggested that all colonies started in during the same period. To identify the queen, nests were manipulated by removing an egg (with clean forceps) and observing which female replaced the egg (usually within minutes). Once the queen was identified, her remaining nestmates were classified as

29 workers, as there is usually a single egglayer in nests of these tropical Polistes (Robin J. Southon et al., 2019). The queen (Q) and a single worker (W) who had been observed foraging, were individually removed from the nest using forceps and stored immediately in RNAlater. To do this, their heads were cut off with sterilised forceps and placed in individually labelled Eppendorf tubes containing RNAlater ® Solution (Ambion by Life technologiesTM), and kept in a cool box during the day, while their bodies were placed individually on EtOH 80% in order to examine their ovarian development.

Artificial removal of the queen is an established experimental manipulation used to stimulate queen replacement in Polistes (Hughes and Strassmann, 1988; Seppä, Queller and Strassmann, 2002; Monnin et al., 2009). In tropical Polistes during the queen replacement period, some females will behave antagonistically towards others in an attempt to become the replacement queen, the females engaging most in biting, mounting, and behaving aggressively towards their nestmates being the most likely to become the replacement queen (Sumner, S. unpublished; West-Eberhard, 1969). Based on our understanding of queen replacement in tropical Polistes, the wasps that engage in bi-directional aggression are potential candidates to become the queen, these fights continuing for at least a week, after which time, a single new egglayer is established and fighting subsides (Sumner, S. unpublished). Thus, to sample wasps that are transitioning between worker and queen phenotypes, we sampled the competing females (i.e. females displaying dominant behaviour, chewing, mounting, biting and erect postures) three days after the queen had been removed, when the overt contest is still clearly apparent. Wasps engaged in becoming the replacement queen were foragers as the workers sampled as stable phenotypes. Transitioning wasps (Tw) were sampled in the same way as the stable phenotypes (Q & W) (see above).

Ovarian development determination

A subset of females was used to determine their ovarian development to contrast between phenotypes. For these females, their abdomens were removed from their bodies and their reproductive systems extracted out onto a petri dish. For each female, the ovaries were classified as undeveloped or developed, according to the number of oocytes and their colour. The length of the longest oocyte (mm) was documented (mature eggs are 2 mm long (West- Eberhard, 1969)), while insemination status was assessed where possible by examining the insemination pocket for the presence of sperm. The characteristics of the ovary tract (see Supplementary 1.1) were also examined by way of a Multiple Correspondence Analysis (MCA) in R (R core 2019).

RNA extraction and RNA sequencing

RNA was successfully purified from the brain tissue of eighteen individual wasps, derived from six colonies (Q = 5, W = 5, Tw = 8; Table 2.3). The head capsule was dissected under RNAlater, and the brain lifted out in a complete unit for RNA purification (Patalano et al., 2015). Approximately, 100 mg of RNA was extracted using RNeasy® Plus Universal Kit (QIAGEN), following the manufacturer’s instructions. The extracted RNA was stored in RNase-free water and the quality and yield of each sample was evaluated using NanoDrop ND-8000 and an Aligent 2100 bioanalyser (Aligent Technologies). RNA sequencing libraries were prepared using the Illumina TruSeq kit, again in accordance with the manufacturer’s instructions, with sequencing conducted using an Illumina HiSeq2000 (paired-end, 100 bp reads). Five samples were pooled per lane to give > 30 million reads per sample. Library construction and sequencing were then conducted by the University of Bristol Genomics facilities.

Pre-processing of the RNAseq data and de novo assembly

The quality and visualisation of paired-end raw sequences were evaluated using FastQC V0.11.2 (Andrews, 2015). Low-quality bases (common in Illumina data) at the end of each sequence were removed. Adapters added to cDNA fragments during library preparation were also removed together with low quality reads using FASTX-Toolkit (hannonlab.cshl.edu/ fastx_toolkit). The Insecta database (18S and 20S) from SILVA (Quast et al., 2013) was used to remove ribosomal RNA via SortMeRNA (Kopylova, 2014). Approximately 15% of each sequence was removed. This all led to a de novo transcriptome assembly being created with a subset of samples representing the three phenotypes (Q, W and Tw) using Trinity v2.0.6 (Grabherr et al., 2013) (--seqType fq –JM 100G –CPU 16 −SS_lib_type FR –output Trinity_output –min_kmercov 2 –left).

The quality of the transcriptome was evaluated in three steps. Firstly, the total number of Trinity “transcripts” and Trinity “genes”, and the N50 statistics were calculated in Trinity v2.0.6. (Grabherr et al., 2013). Next, to thoroughly capture read alignments, the assembled transcripts were mapped with Bowtie2 v. 2.2.5 (Langmead and Salzberg, 2012). Finally, the completeness of the transcriptome was evaluated in BUSCO v.3 (Simão et al., 2015). To do this, the eukaryote database was utilized as a reference point to assess the extent to which Benchmarking Universal Single-Copy Orthologs (BUSCOs) was present across high taxonomic groupings.

Transcriptome functional annotation

To thoroughly annotate the transcriptome, TransDecoder v 2.0.1 (Haas et al., 2013) and Trinotate v 3.0.2 (Bryant et al., 2017) were used. TransDecoder identifies candidate coding genes within the transcriptome. The peptide assembly created with TransDecoder and the Trinity de novo assembly was then used in Trinotate. Trinotate is a suite that utilises well- known methods for functional annotation which includes homology search of the transcriptome against BLAST and/or SwissProt; protein domain identification with HMMER (http://hmmer.org/) / PFAM (Finn et al., 2014); protein signal peptides and transmembrane domains identification with SingalP v. 4.1 (Petersen et al., 2011) and tmHMM v.2.0 (Krogh et al., 2001) respectively, making use of available annotation datasets such as eggnog/GO/Kegg; and identification of rRNA homologies done using RNAMMER v. 1.2 (Lagesen et al., 2007). Given that a customised database was used to eliminate rRNA, the last step of the workflow helps to identify the potential presence of remaining rRNA. The whole workflow and suite description can be found in (https://trinotate.github.io/).

Pre-processing of the RNAseq data, the de novo assembly of the transcriptome as well as the functional annotation were automated using an in-house pipeline and processed at the High- Performance Computer facility (BlueCrystal phases 2 & 3) at the University of Bristol (bris.ac.uk/acrc/).

Transcript quantification and differential gene expression analysis

The quantification of transcript abundance was calculated using RSEM v.1.2.19 (Li and Dewey, 2011) by firstly extracting transcripts from the assembly to create a gene map; followed by the building of a reference from transcripts using bowtie2 (Langmead et al., 2009), with the quantifying of the expression for each paired-end sample completing the process. This protocol produces gene and isoform expected counts and fragment per kilobase per million (FPKM) reads. Expected counts of each sample were used for differential gene expression analyses in edgeR (Robinson, McCarthy and Smyth, 2010) which uses general linear models (GLMs) and negative binomial distribution. A multidimensional scaling analysis investigating the general trends between groups was done with edgeR, while heatmaps of pairwise comparisons were created using DESeq (Anders and Huber, 2010). Differential gene expression comparisons were carried out between Q vs W (as a reference differentiation between both behavioural castes), Q vs T and W vs Tw. Trinity genes were subsequently classified as differentially expressed when p < 0.05, and then filtered to a false discovery rate (FDR) < 0.5.

GO enrichment analysis of differentially expressed genes (DEGs) and Network Analysis of Co-expression modules of genes

Gene ontology (GO) was used to infer the functionality of the genes. To evaluate functional enrichment analysis of differentially expressed genes, we conducted a GO enrichment analysis using the transcriptome as a reference point. Functional enrichment for different GO terms was tested in Blast2GO (Conesa et al., 2005). Fisher’s exact test with FDR < 0.05 was used.

Given that genes alone do not provide a full picture of the non-independency of transcriptional dynamics, a co-expression network analysis was carried out using Weighted Gene Co- expression Network Analysis (WGCNA) which groups genes with similar gene expression patterns in modules. Such genes within modules are assumed to have a particular biological function as their expression is correlated (Langfelder and Horvath, 2008). In this case, genes within the same module display similar responses to equivalent changes in behaviour. This analysis has been shown to be powerful with regard to identifying the co-expression of genes related to cancer in humans (Naik, Gardi and Bapat, 2016; Li et al., 2018) and castes differentiation and neurogenomic signatures in insect (Mikheyev and Linksvayer, 2015; Patalano et al., 2015; Manfredini et al., 2017). The P. lanio gene co-expression network was built with WGCNA v.1.68 using the FPKM values produced from RSEM. Genes that expressed median < 10 for all wasps were discarded, and the filtered dataset was log2 transformed. These two steps were used to eliminate low expressed genes and genes with non-significant variance as they commonly represent noise (Langfelder and Horvath, 2008). The conventional automatic network construction was used, with a soft-threshold of 6, helping to minimise the number of spurious correlations based on the criterion of approximate scale-free topology (Zhang and Horvath, 2005). Once identified, modules highly correlated genes with stable phenotypes (Q and W) with the transitioning period (Tw), and the modules were visualised using VisANT (Hu et al., 2013).

RESULTS AND DISCUSSION

De Novo transcriptome assembly and quality

A total of 20 libraries were sequenced with 5 to 8 biological replicates for each of the three behavioural phenotypes of P. lanio. After filtering and trimming low quality reads and rRNA, 200 GB were recovered. The de novo transcriptome assembly had a total assembly size of

219 MB with a total of 297,741 Trinity “genes”, and a total of 389,941 transcripts ranging from 201 to 25,795 (Table 2.1) in length, with a total average length of 770.09. The number of transcripts in the transcriptome was similar to the carpenter bee (Ceratina calcarata; 358,709 transcripts) (Rehan, Berens and Toth, 2014). The assembly had a high coverage with a N50 median number, 1,294 BP, similar to that found in the bird cherry-oat aphid (Rhopalosiphum padi; N50=1,580 bp) (Duan et al., 2017). Also, the GC content of 34.15%, was similar to what has been found in the genome of several Hymenoptera species (Branstetter et al., 2018), see chapter 3, Table 3.3. However, the characteristics of an assembly may vary according to the number of libraries used as well as the quality of the samples (e.g. RNA degradation). The BUSCO assessment of the transcriptome revealed 99.3% completeness (Table 2.2) when using the eukaryota_odb9 lineage.

Table 2.1. Summary statistics of the de novo transcriptome assembly of P. lanio, based on Trinity assembly metrics.

Statistics based on Statistics based on only ALL transcript longest isoform per contigs “gene”

Contig N10 4,191 3,553

Contig N20 3,024 2,247

Contig N30 2,317 1,466

Contig N40 1,762 989

Contig N1294 1,294 717

Median contig length 418 362

Average contig 770.09 580.55

Total assembled bases 300,291,569 172,854,554

Table 2.2. Completeness estimation of P. lanio transcriptome assembly of brain tissue.

BUSCO categories Number of hits

Complete BUCOs (C) 301

Complete and single-copy BUSCOs (S) 18

Complete and duplicate BUSCOs (D9) 283

Fragmented BUSCOs (F) 1

Missing BUSCOs (M) 1

Total BUSCO groups searched 303

C:99.3% [S:5.9%, D:93.4%], F:0.3%, M:0.4%, n:303

Result 1. Transitioning wasps are non-reproductive but transcriptomically and physiologically distinct from workers

All females identified as queens had mature eggs in their ovary tracts indicating that they were indeed active egglayers (Table 2.3). Conversely, workers showed no signs of ovary development and none of them displayed mature eggs, indicating that they were non- reproductive individuals. The ovary dissections revealed that all but one of the transitioning wasps were non-reproductive and unmated, with exception of Tw402 which showed some signs of ovarian development. Mating status was difficult to assess in most samples due to the degradation of the tissue (Table 2.3). The MCA revealed differences in the ovarian characteristics between workers and transitioning wasps (Figure 2.2), which suggests that wasps involved in the transition are attempting to become reproductive.

Table 2.3. Polistes paper wasps sample collection. The sampled wasps were classified as queens (Q), worker (W) and transitioning wasps (Tw). Tw are potential replacement queens. Based on abdomen dissections, the current reproductive status of each was determined.

Egg-laying Nest ID Wasp ID Phenotype Reproductive Mated female

Q1 Queen Y W1 Worker N 2013058 Tw104 Transitioning wasp N N N

Tw105 Transitioning wasp N N N

W4 Worker N

2013061 Tw402 Transitioning wasp N Y N

Tw406 Transitioning wasp N N N

Q6 Queen Y

2013063 W6 Worker N

Tw601 Transitioning wasp N N N

Q9 Queen Y W9 Worker N 2013066 Tw902 Transitioning wasp N N N Tw903 Transitioning wasp N N N

Q10 Queen Y 2013067 Tw1005 Transitioning wasp N N N

Q15 Queen Y 2013072 W15 Worker N

Figure 2.2. Clear distinction between behavioural phenotypes, except for one W. Scatter plot of multiple components one and two from MCA of 15 descriptive categorical variables of ovarian development of transitioning wasps (Tw) and workers (W) (see Supplementary 1.1). Ellipses represent 95% confidence intervals.

Result 2. Transcriptomic signatures of behavioural phenotypes (Q vs W) in stable colonies are typical of Polistes

Pairwise comparisons of gene expression between queens and workers showed few differentially expressed (DE) genes (p < 0.05, FDR < 0.05) between both phenotypes (Figure 2.3; Table 2.4), with a total of 96 DE genes. These differences were not substantial enough to discern any clear phenotype-specific segregation in a clustering analysis (Figure 2.4), although three of the four pairs of Q & W that were sampled from the same colonies clustered by colony. Among the DE genes were those encoding proteins involved in reproduction and division of labour: kruppel-homolog 1, methyl farmesoate epoxidase, FMRFamide receptor and Tyramine β-hydroxylase (Monastirioti, 2003; Verlinden et al., 2009; Padilla et al., 2016; Cardoso-Júnior et al., 2017); aggression: Tyramine β-hydroxylase and Dopamine β- hydroxylase (Zwarts, Versteven and Callaerts, 2012). Aggression from queens towards workers is common in Polistes wasps (Jandt, Tibbetts and Toth, 2014) and is thought to function in maintaining the dominance hierarchy and the queen’s egg-laying monopoly. Other genes are putatively involved in defence: FMRFamide receptor (Zhou, Rao and Rao, 2008; Klose et al., 2010); metabolism of insect hormones and immunity: Probable cytochrome P450 6a14 and Nose resistance to fluoxetine protein 6 (Rehan, Berens and Toth, 2014; Harrison, Hammond and Mallon, 2015; Colgan et al., 2019). There were subtle signs of functional enrichment among the 63 worker-biased genes, with four enriched GO terms of functional groups putatively involved in caste and/or ‘polyphenic’ determination (Table 2.5; FDR < 0,05).

Subtle molecular differentiation among queen and worker castes in Polistes add to a growing body of similar patterns regarding the transcriptional nature of highly plastic phenotypes which permit rapid responses to changes in the environment. Firstly, studies on caste differentiation in other simple societies of social Hymenoptera found similarly, small and subtle differences in gene expression between stable phenotypes (e.g. Polistes canadensis (Ferreira et al., 2013; Patalano et al., 2015) and Polistes dominula (Standage et al., 2016); Megalopta genalis (Jones et al., 2017)). These data suggest that subtle differences in gene expression are enough to maintain hierarchies, social dominance and/ or reproductive skew at this level of sociality (Arsenault, Hunt and Rehan, 2018; Rehan et al., 2018). Moreover generally, other highly plastic phenotypes in other organisms show similarly subtle levels of molecular differentiation, e.g. the desert locust (Schistocerca gregaria) can switch from a solitary phase, depending on population density, to a swarming gregarious phase. This is an example of how alternative phenotypes can arise responsively within the lifetime of a single individual, these phenotypes differing in expression by only 214~242 genes (Chen et al., 2010; Badisco et al., 2011).

Figure 2.3. Gene expression differences between phenotypes. Log Fold-change against average count number. Black points represent genes that were not significantly different. Red points above zero represent genes that are significantly upregulated in queens but downregulated in workers. Red points below zero represent genes that are significantly downregulated in queens but upregulated in workers. The blue line indicates 2-fold change in up or down regulation.

Figure 2.4. No clear transcriptional clustering of queen and workers in stable colonies. Hierarchical clustering of gene expression data for the 50 most highly DE genes among both behavioural phenotypes. To the left of the heatmap, gene expression clustering is shown whereas phenotypic clustering is shown at the top. Gene expression levels are shown in colour shades from light green (lower expression) to dark blue (high expression). Q = queen, W = worker, the number indicates their colony.

Table 2.4. Summary table of the total number of DE genes (FDR < 0.05) in each comparison. Up and downregulated genes for the first phenotype in each comparison.

Total number Comparisons Up Down of genes DE

Queens vs Workers 96 33 63

Queens vs Transitioning wasps 712 338 374

Workers vs Transitioning wasps 5,044 2,170 2,874

Queens & Workers vs Transitioning wasps 5,636 1,785 3,851

Table 2.5. GO ids enriched among upregulated genes in workers when contrasting with queens.

Tag GO ID GO Names GO Categories FDR P-value

[OVER] GO:0048647 Polyphenic determination Biological process 0.00769 1.64215E-06

[OVER] GO:0048648 Caste determination Biological process 0.00769 1.64215E-06

Caste determination, [OVER] GO:0048650 influence by environmental Biological process 0.00769 1.64215E-06 factors

Polyphenic determination, [OVER] GO:0048651 influence by environmental Biological process 0.00769 1.64215E-06 factors

Result 3. Transitioning wasps arise via a non-linear reaction norm

Given these subtle levels of molecular differentiation between stable phenotypes (Result 1), the most parsimonious prediction for the transcriptional state of a change in phenotype is that of a linear reaction norm, leveraged by a low number of genes, and with subtle downregulation of (the few) worker biased genes and up-regulation of (the few) queen-biased genes. However, we found little support for this. Instead, the transition invokes a distinct phenotype that seems to be an entire reprogramming of their behavioural machinery. There are several lines of evidence for this.

Firstly, from a global analysis of shared patterns of gene expression across all 142,479 genes detected as expressed in all individuals, samples clustered clearly into the categories of “stable” (queens and workers) or “transitioning” wasps (Figure 2.5A). There was a single exception to this: one queen (Q10) clustered with the transitioning phenotypes and not the stable phenotypes; this possibly was a wasp that had recently transitioned just prior to behavioural observations started on the nest. The differentiation between stable and transitioning phenotypes is explained by differential expression of thousands of genes (Table 2.4; Figure 2.5B). Among the 5,656 DE genes, 3,851 genes were upregulated among transitioning wasps, and 1,785 genes were upregulated among stable phenotypes (queens and workers).

The transition from nurse to forager in honeybees (Apis mellifera) is a well-known life-history transition (Elekonich and Roberts, 2005). After emerging, worker honeybees remain in the hive engaging in brood rearing and nest maintenance (nurses). After a week, the nurses leave the hive to collect pollen or nectar (foragers). Both phenotypes differ in 2,670 genes involved in behaviour and age, as this transition is age-related (Whitfield, Cziko and Robinson, 2003). Specifically, the behavioural transition is regulated by 50 genes involved in metabolism, behavioural and neural plasticity. Contrasting with Polistes, the honeybee transition has been shown to be a gradual maturation process (Whitfield et al., 2006). Furthermore, the transition is not reproductive; hence, it does not increase the personal direct fitness of the individual switching between phenotypes, and does not involve hierarchical dominance as in the case of Polistes wasps. Clonal raider ants (Ooceraea biroi) transition from brood rearing to reproductive cyclical, but the transition from brood rearing to reproductive involved changes in 626 genes, whereas the opposite transition involved changes in 2,043 genes (Libbrecht, Oxley and Kronauer, 2018). Nonetheless, the transition to non-reproductive to brood rearing is a gradual transition of gene expression. Unlike Polistes colonies, colonies of clonal raider

42 ants are formed by morphological and genetically identical individuals and lack the queen caste. Hence, all the members transition between phenotypes without engaging in hierarchical dominance as all the members can lay eggs.

Among the DE genes, we found those coding for transcription factors (e.g. zin finger and Homeobox protein); caste differentiation (e.g. protein yellow and farnesol dehydrogenase); immune response (e.g. Hymenopteacin and defensin-1); metabolism of insect hormone (e.g. cytochrome P450); splicing and deacetylation (e.g. Pre- mRNA – splicing factor cwc22 homolog and SET domain-containing protein smyA – 8, isoform B, respectively); histone methylation (Histone-lysine N-methyltransferase EHMT1) visual learning and perception, and memory (e.g. rhodopsin and neuropilin and tolloid – like protein 1, respectively); cell fate and reprogramming (e.g. neurogenomic locus notch protein); learning and social behaviour (neuroligin-4; Y-linked); and cell cycle (Host cell factor 1). A subset of genes involved in the transitioning period is listed in Table 2.6; a large number are putative transcription factors; transcription factors have been reported to be involved in transcriptome reprogramming in the transition from the solitary phase to a swarming gregarious phase in locusts (Yang et al., 2019). In three spine sticklebacks males (Gasterosteus aculeatus), transcription factors are also involved in coordinated waves of transcription correlated with different components of behavioural plasticity, in response to territorial challenge (Bukhari et al., 2017). Furthermore, transcription factors were predicted to be associated with mate preference in guppy (Poecilia reticulata) females. Transcription factors mediate somatic cell reprogramming to pluripotency, where adult somatic cells are converted into pluripotent stem cells (Takahashi et al., 2006). All these instances suggest that transcription factors play a role in behavioural plasticity as well as in reprogramming behavioural machinery in Polistes, regulating the transition between states, and likely playing a regulatory role in the large transcriptional changes observed. The upregulation of genes involved in methylation and splicing suggests that there might also be some epigenetic processes involved (Patalano et al., 2012).

Table 2.6. A subset of proteins with BLAST hits that are DE in transitioning wasps, with descriptions of their functions in other organisms.

Protein Description Reference Reference

Transcription factor, modulates (Cubadda et al., Zink finger protein ush transcription mediated by transcription Drosophila 1997) factors of the GATA family.

Transcription factor – plays a role in Zink finger protein 521 Humans (Bond et al., 2004) neuron fate commitment.

Hoemotic protein sex Transcription factor – involved in sex (Sánchez and Drosophila combs reduced differentiation. Guerrero, 2001)

Activating Transcription factor – involved in immune Drosophila (Rynes et al., 2012) transcription factor 3 response.

Transcription factor – plays a role in (Schupbach and Protein dead ringer Drosophila oogenesis Wieschaus, 1991)

Homeobox protein Transcription factor – involved in cell Humans (Yu et al., 2006) SIX1 proliferation and apoptosis.

T-box transcription Transcription factor – involved in neuron Mouse (Song et al., 2006) factor TBx20 migration.

GATA-binding factor Transcriptional regulator – involved in (Cripps and Olson, Drosophila A dorsal cell fate 2002)

Negative elongation A component of the NELF complex that (Yamaguchi et al., Human factor E regulates the elongation of transcription 1999)

Protein lethal (2) Involved in embryogenic development as Drosphila (Vos et al., 2016) essential for life well as in response to heat.

An RNA binding factor that recruits Insulin-like growth (Holly and Perks, specific transcripts to mRNPs. Insulin-like factor 2 mRNA- Human 2006; Yan et al., growth factors are involved in epigenetic binding protein 1 2014) changes

Farnesol (Mayoral et al., Mediates juvenile hormone biosynthesis. Aedes aegypti dehydrogenase 2009)

Involved in caste differentiation and (Ferguson et al., Protein yellow Apis melifera belongs to the royal jelly protein family. 2011)

Regulates cell-fate determination. Also, it Neurogenic locus functions as a receptor for membrane- Humans/ Apis (Duncan, Hyink and Notch homolog bound ligands. It inhibits the Notch signal melifera Dearden, 2016) protein 3 pathway promoting ovarian development.

Camponotus (Ratzka et al., Defensin-1 Involved in innate immune response floridanus 2012)

(Chaimanee et al., Hymenopteacin Involved in innate immune response Apis mellifera 2012)

Fucolectin-4 Involved in innate immune response Anguilla japonica (Honda et al., 2000)

Peptidoglycan – (Werner et al., recognition protein Involved in innate immune response Drosophila 2000) SC2

G-protein coupled (Lin, Seroude and Involved in immune response and ageing Drosophila receptor Mth2 Benzer, 1998)

Cytochrome P450 Plays a role in the metabolism of insect Bemisia tabaci (Wang et al., 2011) 4C1 hormone and insecticide resistance

CytochromeP450 Plays a role in the metabolism of insect Bemisia tabaci (Wang et al., 2011) 6a13 hormone

Pre – mRNA – splicing Involved in pre-splicing Drosophila (Zhou et al., 2002) factor cwc22 homolog

Pre – mRNA – splicing factor ATP – Involved in pre – mRNA splicing and it is a Humans (Zhan et al., 2018) dependent RNA component of the spliceosome helicase PRP16

SET domain – (Thompson and containing protein Involved in histone deacetylation binding Drosophila Travers, 2008) smyA – 8, isoform B

Histone-lysine N- methyltransferase Histone acetylation Human (Chang et al., 2010) EHMT1

During myogenesis plays a role in cell (Stronach, Siegrist Muscle LIM protein differentiation and it is associated with Drosophila and Beckerle, Mlp84B muscle tissue development 1996)

(Yilmaz et al., 2016; Opsin, ultraviolet – Plays a role in visual perception Fish / Insects Bukhari et al., sensitive 2017)

Neuropilin and tolloid Camponotus Involved in visual learning and memory (Ng et al., 2009) – like protein 1 rufipes

Secondly, a hierarchical clustering analysis revealed that the effects of phenotype (transitioning or stable) were greater than the effects of colony or genetics (as found between Q and W in Result 1) (Figure 2.6). Transitioning wasps formed a clear cluster that was completely separate from the queen and worker phenotypes from stable colonies. Again, Q10 clustered with the transitioning wasps, reinforcing the possibility that this wasp might have become queen just prior to our observations and experiments. This suggests that the differential gene expression patterns between transitioning wasps and stable phenotypes reflect both caste differences (in queens and workers) and caste changes (in transitioning wasps) more than colony identity. Importantly, transitioning wasps do not cluster with their colony, previous phenotype (i.e. worker) or putative future phenotype (i.e. queen).

Thirdly, significant levels of functional enrichment were detected among the genes that were differentially expressed between the stable and transitioning wasps: A total of 242 GO IDs (from 3,851 genes) were functionally enriched in the transitioning wasps (Fisher’s exact test, FDR < 0.05). Among the GO terms for biological processes were chitin metabolic process, muscle contraction, oxide-reduction process, inner ear cell fate commitment, oviduct morphogenesis, cell differentiation in signal cord, Notch signal involved in heart development, response to oxygen levels among others (see Supplementary 2.1). These functions suggest an upregulation of processes involved in reprogramming and major molecular shifts, as well as metabolic processes in transitioning wasps, relative to stable wasps. In contrast, only 37 GO IDs (for 1,785 genes) were significantly enriched in stable phenotypes relative to transitioning wasps. Among these were functions associated with regulation of transcription from RNA polymerase II promoter, cell fate commitment and commitment of neural cell to specific neuron type in forebrain (see supplementary 2.2). These functions suggest an upregulation of cognitive processes and RNA synthesis in stable wasps, relative to transitioning wasps

Finally, there were significant differences between stable and transitioning wasps at the gene network level. The co-expression network analysis. (WGCNA) identified 57 modules of co- expression networks (power = 14, minimum module size = 50). Of these 57 modules, there was a single module (“salmon 4”) correlating significantly with colony type. That is colonies before manipulation (stable colony) and colonies after manipulation (transitioning colony); and behavioural phenotype (queen, workers and transitioning wasps). The module had 128 genes, but not any significant functional enrichment (Fisher’s exact test, FDR < 0.05). Among the highly connected genes in the network were: nipple-B-like protein B, a transcription factor

46 associated with cell migration during brain development; Neuroglian, associated with mushroom body development; Papilin associated with the development of multicellular organisms. These genes suggest cognitive processes. Another gene that was connected was venom dipeptidyl peptidase 4 involved in modulating the chemotactic activity of immune cells after an insect stings, which likely reflects the elevated levels of aggression expression in transitioning wasps (Baek et al., 2013). In fish that transition from a subordinate phase to a reproductive phase, social dominance is established by aggression towards members of their social nucleus (Burmeister, Jarvis and Fernald, 2005; Casas et al., 2016; Todd et al., 2019). Aggression, as well as stress, have been linked to changes in sexual metamorphosis in these systems. Like P. lanio, the absence of the dominant individual promotes stress as well as an opportunity to climb the social ladder.

Figure 2.5. Clear clustering between behavioural phenotypes and a high number of DE genes.A) Multidimensional scaling plot of the global gene expression patterns (142,479 genes). There are clear clusters between stable (queens = Q and worker = W) and transitioning wasps (transitioning = T); except for Q10 which clustered with transitioning colonies. B) Global gene expression pattern shows many DE genes. Log Fold-change against average count number. Black points: genes that were not significantly different. Red points above zero, represent genes upregulated in stable phenotypes but downregulated in transitioning wasps. Red points below zero, represent genes downregulated in stable phenotypes but upregulated in Workers. The blue lines indicate 2-fold up

or down.

Figure 2.6. Clear distinction between phenotypes given by the most highly DE genes. Hierarchical clustering of gene expression data for the 50 most highly DE genes between stable phenotypes (queens (Q), workers (W)) and transitioning wasps (T). The numbers followed by Q, W & T indicate colony IDs.

Result 4. Transitioning wasps are more queen-biased than worker-biased

Next, we compared the gene expression patterns between transitioning wasps and each phenotype (queens and workers) to determine whether transitioning wasps were closer to the queen- or worker- phenotype. In fact, transitioning wasps were distinct from both queens and workers (Table 2.4, Figure 2.7), but more so from workers (Figure 2.7B) than queens (Figure 2.7A), despite transitioning wasps used to be foragers three days before queen removal. Queen replacement is completed within 10 to 15 days.

Comparison of queens with transitioning wasps

A total of 712 genes were significantly differentially expressed between queens and transitioning wasps. Marginally more genes were upregulated among transitioning wasps (374 genes) relative to queens (338 genes) (Table 2.4; Figure 2.7A). Consistent with all other analyses, Q10 is an outlier, clustering with transitioning wasps rather than queens (Figure 2.7B). Some of the differentially expressed (DE) genes encoded proteins related to caste differentiation (e.g. protein yellow, farnesol dehydrogenase and major royal jelly protein 1) (Mayoral et al., 2009; Ferguson et al., 2011); transcription factors (e.g. protein dead ringer); immunity (e.g. defensin – 1 and 2 and hymenoptaecin) (Chaimanee et al., 2012); cytoskeleton and muscle development proteins (e.g. myosin heavy and light chain, tropomyosin-1; actin; twitchin, titin and collagen alpha), which have been observed regulating subdominant behaviour in rainbow trout (Oncorhynchus mykiss) (Sneddon et al., 2011), as well as being involved in adult male behaviour relating to buff-tailed bumblebees (Bombus terrestris) (Harrison, Hammond and Mallon, 2015). Transitioning wasps need to be active in the nest, aggressing other wasps to maximise their chance of climbing up the social hierarchy; this likely involves more muscular activity. Also relevant here are genes encoding sensory perception, locomotion and brain development (e.g. bestrophin-2, probable G-protein coupled receptor 52 and leishmanolysin-like peptidase, respectively) (Nishiyama et al., 2017). The Fisher’s exact (FDR < 0.05) test of the upregulated genes in transitioning wasps when compared with queens, revealed enrichment of 107 GO IDs from 374 genes. Among the enriched biological processes were fight behaviour; phototransduction, UV; behavioural response to nicotine; chitin metabolic process; defence response to bacterium; and regulation of cholesterol storage (see Supplementary 2.3.). Chitin metabolic processes have been shown to be enriched in queen bumblebees when they initiate their nests (Woodard et al., 2013), whereas the phototransduction UV involved visual perception (Manfredini et al., 2017). Transitioning wasps used to be foraging workers, which exposes them to pesticides as well as diseases. In the transition from female to male in bluehead wrasses, the transitory phenotype was shown to

50 be enriched by biological processes such as innate immunity, cholesterol and homeostasis, which suggests anticipation for reproductive development (Todd et al., 2019)

Among the upregulated genes in queens relative to transitioning wasps, there were genes encoding proteins for components of nucleosome (Histone H2A and H2B; Histone H3); neurosparin-A, inhibits the effect of the juvenile hormone (neuroparsin-A); caste differentiation (protein takeout-like); zinc finger proteins (zinc finger protein 2 homolog; 26; 585B; 83; 511); transcription factors (B-cell lymphoma/leukemia 11A) and chromatin function (HIRA- interacting protein 3). The upregulation of neuroparsin-A in queens makes sense as JH is associated with the regulation of worker behaviour (Giray, Giovanetti and West-Eberhard, 2005). There was, however, no functional enrichment for upregulated genes in queens relative to transitioning wasps. The putative functional enrichment of the transitioning phenotype relative to queens, further highlights how transitioning wasps are a distinct phenotype and not merely an intermediate pre-queen state.

Comparison of workers with transitioning wasps

Transitioning wasps and workers differed significantly in their expression by 5,144 genes (Table 2.4). This is remarkable, given that three days previously, these transitioning wasps would have been workers themselves. In Jones’ et al., (2017) study, no genes were DE in the brain between workers and replacement queens. However, 4,206 genes were DE when their abdomens were compared. Their results indicate a clear distinction between the reproductive systems. Nonetheless, these comparisons were carried out between stable phenotypes and the replacement queen. Although we did not investigate abdominal gene expression among the phenotypes, we did identify that the reproductive system between workers and transitioning wasps were distinct (Figure 2.2). A cluster that persisted across comparisons was the one formed by T04 and T05 which belong to the same colony which suggest that membership can be observed at the molecular level. Finally, other important DE genes in the transition were related to stress, reproduction, defence and immunity, brain development, memory and cell fate determination/ commitment.

Most of the DE genes in this comparison were upregulated in transitioning wasps (n=2,874) and downregulated in workers. In other words, transitioning wasps appear to activate the expression of large suites of genes as they attempt to change from worker to queen. Genes of interest that were upregulated in transitioning wasps included transcription factors (e.g. Activating transcription factor 3, akirin, homeobox protein SIX1, homeotic protein deformed, protein dead ringer and t-box transcription factor TBX20), genes encoding proteins in immune

51 response (e.g. protein toll and protein spaetzle); circadian rhythm (e.g. 14-3-3 protein epsilon and circadian clock-controlled protein); foraging behaviour (14-3-3 protein epsilon and cGMP- dependent protein kinase, isozyme 2 forms cD4/T1/T3A/T3B and protein lethal(2)essential for life); metamorphosis and transition (e.g. Krueppel homolog 1, juvenile hormone acid O- methyltransferase and Protein takeout); sensory perception and aggression (e.g. Protein takeout, opsin, ultraviolet-sensitive and frizzled-2), sensory perception only (e.g. neither inactivation nor afterpotential protein C and protein Pinocchio), and cell fate and early response (e.g. neurogenic locus Notch protein and neurogenic locus notch homolog protein 1, 2, 3 and 4; and negative elongation factor E and lachesin, respectively).

Other genes were found encoding royal jelly proteins (serine proteinase stubble (Fujita et al., 2013)), histone lysine methylation (protein msta, isoform A (Thompson and Travers, 2008)), oogenesis (protein ovo (Mahowald, 2001)) and juvenile hormone biosynthesis (farnesol dehydrogenase and methyl farnesoate epoxidase (Mayoral et al., 2009; Cameron, Duncan and Dearden, 2013)). Also, there was evidence of functional enrichment in transitioning wasps relative to workers: among the 2,374 upregulated genes in transitioning wasps, 350 GO terms were significantly enriched (FDR < 0.05). These included biological adhesion (overrepresented in late-state founding queen in bumblebees (Woodard et al., 2013)), muscle cell differentiation (enriched in the overwinter phase in the small carpenter bee (Durant et al., 2015)), melanin biosynthetic process, Rho protein signal transduction and regulation of Ras protein signal transduction (overrepresented in ants queen state (Morandin et al., 2016)) and response to external biotic stimulus (involved in aggression in Drosophila (Edwards et al., 2006)). Other interesting overrepresented biological processes were “response to other organisms”, “response to fungus”, “rhodopsin mediated signalling pathway”, “detection of light stimulus”, “behavioural response to nicotine” and “detection of external and abiotic stimulus” (see supplementary 2.4).

These results suggest that the transition from worker to queen is a complex process, at the molecular level. The upregulation of genes involved in foraging suggests that females attempting to become reproductive were foragers. Also, genes involved in metamorphosis and biological processes involved in queen behaviour suggest that, indeed, females are attempting to become reproductive. Interestingly, protein ovo is a key gene during the reprogramming phase in locusts (Yang et al., 2019). As noted above, transcription factors were also detected as upregulated. They are important during transitioning phases in other species (Yang et al., 2019). Fighting behaviour in transitioning wasps was also observed at the molecular level with the upregulation and enrichment of genes involved in aggression. In fish, the transition from subordinate to dominant (cichlid fish) (Alonso et al., 2012), from female to male (bluehead

52 wrasses) (Todd et al., 2019), and from male to female (clownfish) (Casas et al., 2016) show aggression towards similarly ranked group members.

Among upregulated genes in workers relative to transitioning wasps were genes upregulated in queens relative to transitioning wasps (e.g. Heat shock protein 83, Histone H3, Tetrasparin- 7, protein tramtrack, protein FAM60A, nucleoporin GLE1 and lutroping-choriogonadotropic hormone receptor). Also, some isoforms of genes found in transitioning wasps were found in workers as well (e.g. Dipeptidase 1, Elongation of very long chain fatty acids protein 1, Ferrochelatase, mitochondrial, and Protein toll). This suggests that transitioning wasps still have some of their previous molecular signature that had been displayed three days before the queen’s removal. In addition, biological processes overrepresented (FDR < 0.05) in workers included “commitment of neural cell to specific neuron type in forebrain”, “larval walking behaviour”, “ketarinocyte development” and “luteinizing hormone signalling pathway”.

These analyses provide some insights into the molecular signatures and processes that are likely to be important in terms of regulating the transition from worker to queen. This transitionary period involves vast transcriptional upregulation, with functional specialisation. Among the large number of genes differentially expressed, there were biological processes that have been reported as overrepresented in queen-like behaviour. The fact that we found genes involved in juvenile hormone biosynthesis in transitioning wasps relative to workers and queens, highlights that this process is also important in phenotypic plasticity. Zinc finger genes have been shown to be important in the transitioning phase in locusts (Yang et al., 2019). However, none of the zinc finger proteins found in locusts were the same in transitioning wasps. Furthermore, zinc finger proteins were also DE in workers and queens. Protein ovo, fundamental in the transition phase in locusts, were also upregulated in transitioning wasps (Yang et al., 2019). Furthermore, overrepresented biological processes related to queen behaviour in honeybees and ants suggest that wasps are reprogramming to their behavioural machinery to become reproductive.

There are a handful of other studies that capture the brain transcriptional changes that occur during the transition from worker to queen in social insects. However, in the case of clonal raider ants, the entire colony transition cycles between non-reproductive and reproductive states cyclically. Unlike tropical Polistes wasps, clonal raider ants do not engage in any competition and only behave aggressively when some individuals do not transition between phases synchronically (Libbrecht, Oxley and Kronauer, 2018). On the other hand, in the absence of the queen, ponerine worker ants engage in tournaments in which the winners (gamergates) will lay eggs. Like tropical Polistes wasps, they engage in biting and mounting

53 tin their efforts to become reproductive, but unlike Polistes, more than one female will lay eggs (Gospocic et al., 2017).

Figure 2.7. Plots summarising the gene expression patterns between queen vs transitioning wasps, and the latter vs workers. Smear plot of significantly DE genes (red dots) above and below a fold-change of 1.5 at a FDR of 5% (y axis) against average log counts per million (CPM, X axis) between queen and transitioning wasps. B) Heatmap illustrating the expression data for the 50 most highly differentially expressed genes between queens and transitioning wasps. Gene clustering, presented to the left of the heatmap, indicates expression levels: dark blue depicts high expression and light green/blue lower expression. Dendrograms depict the clustering of phenotypes with respect to shared levels of gene expression. Wasps ID are presented at the

bottom of the heatmap (Q: queen and T: transitioning wasps). C) Smear plot of significantly DE genes (red dots) above and below a fold-change of 1.5 at a FDR of 5% (Y axis) against average logCPM (X axis) between workers and transitioning wasps. D) Heatmap depicting the expression data for the 50 most highly differentially expressed genes between workers and transitioning wasps. Gene expression clustering is presented to the left of the heatmap indicate expression levels and wasps ID are presented at the bottom of the heatmap (W: worker and T: transitioning wasps).

CONCLUSION AND FUTURE DIRECTIONS

Transitioning periods in biological systems have been well studied at the cellular level, specifically in terms of how a specific adult somatic cell reverts to becoming a stem cell, before differentiating into a new type of somatic adult cell (Tosh and Slack, 2002; Graf and Enver, 2009; Buganim, Faddah and Jaenisch, 2013). At the multi-cellular organism level, there are many examples of how adult organisms with specific behavioural, morphological or physiological phenotypes perform new roles by switching their current phenotypes when the opportunity presents itself. However, we have only just started to unravel the mechanisms by which organisms undergo significant life-history transitions (Casas et al., 2016; Libbrecht, Oxley and Kronauer, 2017; Todd et al., 2019; Yang et al., 2019).

In our case study on P. lanio, the loss of the queen leads to a subset of workers competing to become the reproductive replacement wasp and this process takes at least a week, so we made the most of this extended transitionary period to determine the molecular processes that regulate it. By a combination of experimental manipulation of phenotypes in wild populations and genomic interrogation of brain transcriptomes of individual wasps, we have provided surprising insights into the molecular underpinnings of phenotypic switching. We have found that the transitioning period is not a gradual downregulation of worker molecular machinery, and gradual upregulation of the queen molecular machinery (Hypothesis 1). Instead, the transition period is characterised by a transcriptional, distinct phenotype suggesting that the process of switching from worker to queen follows a non-linear reaction norm involving a “transient” phenotype.

The loss of the queen can be a stressful period as the relatedness of nestmates to any newly laid brood will change, along with the status of the colony’s members (Strassmann et al., 2004). In addition, wasps trying to become the replacement queen can behave in a hyper aggressive manner in some tropical Polistes (Saha et al., 2018). Hence, it is possible that the

55 transition from worker to queen follows a non-linear BRN due to changes in the expression of genes involved in stress and aggression. Transitions to a dominant position in the social hierarchy in clown fish, bluehead wrasses and cichlid fish are considered stressful and aggressive behaviour is displayed by the potential dominant individual (Todd et al., 2019). Key genes involved in these transitions are aromatase and brain neurotransmitters, such as norepinephrine, arginine, vasotocin and gonadotropin-releasing hormone (clownfish and bluehead wrasses), and gene fox-4 (cichlid fish) are involved in both stress and aggression. In P. lanio, the krüppel-homolog 1 is a gene involved in caste differentiation and aggression, and immune response genes can be DE as a response to stress. However, our enrichment analysis did show enrichment of genes involved in aggression and stress. Furthermore, the experimental removal of the queen can have an effect on workers behaviour in comparison with natural queen death or turnover However, what have in common both natural queen turnover and experimental queen removal is the evident presence of a hyper aggressive wasp, and in both cases the aggressors does not lay eggs until they become the replacement queen.

As such, our results provide support for the hypothetical “reprogramming” reaction norm described in Figure 2.1 (Hypothesis 2). It is interesting to note that although just one individual will ultimately succeed in fully establishing itself as the new stable queen phenotype, all individuals involved in the contest express this transitionary phenotype: six of the transitory wasps were sampled as pairs of competing individuals from the same three nests (Table 2.3). Wasps that lose the fight will then revert back to being workers (Sumner unpublished). This suggests that although there are apparently high transitional costs and risks in competing, the payoffs (of direct fitness as queen) of succeeding outweigh these costs and risks.

This study provides a surprising insight into what was previously considered to be a simple and common life-history transition. However, additional research is required to explore this further. A combined approach that considers the molecular, physiological and behavioural changes over a fine-scale time course in a range of species which undergo such transitioning period will provide a better understanding of the mechanisms involved in reprogramming periods to identify candidate genes and pathways, and further investigate whether the same molecular processes are shared between cell reprogramming at the cellular level and phenotypic reprogramming at the multi-cellular level.

CHAPTER 3

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Testing the ovarian ground plan hypothesis for the evolution of sociality using the solitary wasp, Ammophila pubescens

Contributions: S. Sumner and S. Moreno designed the study; S. Moreno conducted the fieldwork with help from B. Arana (field assistant); S. Moreno conducted the DNA and RNA extractions; H. Himmelbauer’s team at the University of Natural Resources and Life Science in Vienna, sequenced the genome; F. Câmara’s team at the Centre for Genomic Regulation in Barcelona annotated the genome; A. pubescens genome contrast with other Hymenoptera genomes and all other analyses were carried out by S. Moreno. The manuscript was written by S. Sumner and S. Moreno.

ABSTRACT

In recent years, significant progress has been made in understanding the physiological and molecular mechanisms underpinning social behaviour in the Hymenoptera. However, to date, research has focused on a relatively small number of species, most of which are advanced eusocial species (e.g. the honeybee, clonal raider ant and ponerine ant). Studies on a more diverse set of species from across the spectrum of the evolutionary transition from solitary to social are required. In particular, we lack a comprehensive analysis of the molecular basis of behaviours in solitary species which represent the ancestral state from which sociality evolved. Here, we present the first genome sequence for a non-social non-parasitic wasp, which is likely to share similarities in behaviour with the putative solitary ancestor of social insects; we compare phenotype-specific transcriptomes in order to examine whether there are common genetic mechanisms associated with maternal care (the putative pre-worker phenotype) and reproduction (the putative pre-queen phenotype). The solitary digger wasp Ammophila pubescens is a progressive provisioner that provisions several burrows simultaneously, delaying or ceasing provisioning responsively, depending on the needs of the nest. Progressive provisioning is thought to be pre-cursor to sociality, providing a “ground plan” of cycling investment in provisioning behaviour and ovarian development that could be decoupled over evolutionary time and co-opted to produce queen (reproductive) and worker (provisioners) castes, as predicted by the Ovarian Ground Plan (OGP) hypothesis. We identified subtle but significant differences in brain gene expression among the queen-like and worker-like behavioural phases of A. pubescens nesting sequence; as predicted by the OGP hypothesis some of these genes and proteins are associated with reproductive castes in social insects. These data provide evidence that ovarian cycles of solitary insects may provide the regulatory toolbox for the evolution of castes. However, the largest predictor of transcription was provisioning behaviour, with proteins involved in sensory perception, stress and detoxification, gene expression regulation and metabolism underpinning it. Regulatory cycles of provisioning (and not just reproductive) behaviours in solitary wasps may be an important pre-adaptation for the evolution of sociality in the Hymenoptera.

INTRODUCTION

A milestone in evolutionary biology was the understanding that life on earth evolved by a series of major transitions in evolution (Szathmáry and Maynard Smith, 1995), originating new levels of biological organisation and complexity (Bourke, 2011) in which lower-level independent biological units evolved to form highly cohesive higher-levels biological units. Such transitions included the transition from prokaryotic to eukaryotic cells (Vellai and Vida, 1999); the transition from single-cell free-living organisms to multicellular organisms (Hanschen, Shelton and Michod, 2015); and the evolution of solitary to highly social group living (Kennedy et al., 2017). The latter has become a pivotal subject in evolutionary biology, given that sociality in insects has evolved multiple times (Hughes et al., 2008; Abbot and Chapman, 2017; Peters et al., 2017). Determining the mechanisms by which sociality evolves is fundamental to understanding how major transitions arise.

The transition from solitary to group living has been extensively studied in Hymenoptera (Fischman, Woodard and Robinson, 2011; Korb and Heinze, 2016; Quiñones and Pen, 2017), especially within the clades of bees and wasps which include both solitary and social species (Bloch and Grozinger, 2011; Woodard et al., 2011), in some cases, displaying the whole spectrum of sociality (Ross and Matthews, 1991; Hunt, 2007c; Jandt and Toth, 2015; Taylor, Bentley and Sumner, 2018). These insects are established as long-standing models to investigate how sociality evolved. Most stinging bees and wasps (Aculeata) live alone (solitary), but exhibit some form of parental care including provisioning behaviour (Bohart and Menke, 1976; O’Neill, 2001; Danforth et al., 2019). Of particular interest are those species that exhibit progressive provisioning behaviour (sequential feeding of brood over time) (Field and Brace, 2004; Kelstrup et al., 2018), and this has been suggested as a precursor state for evolving sociality (Wilson, 2008). Social insects evolved from a solitary ancestor but, the extent to which they express the key traits of social behaviour varies across species; e.g. some species exhibit simple societies, where there is division of reproductive labour whereby a small number of females have reproductive monopoly whilst other remain totipotent but perform non-reproductive tasks (primitively social); other species have more complex societies (‘advanced sociality’) where there is a morphological and physiological division of labour with one individual who is committed to monopolising reproduction whilst remaining group members are committed to being non-reproductive provisioners (‘superorganisms’) (Boomsma and Gawne, 2017; Kennedy et al., 2017).

One of the most prominent mechanistic hypotheses for the evolution of social behaviour from a solitary state is the idea that the different phases in the ovarian development of a solitary insect’s nesting sequence could provide the regulatory ground plan for the evolution of division of labour in the form of queen and worker castes (West-Eberhard, 1987). Changes in ovarian development are thought to regulate the reproductive (e.g. nesting and egg-laying) and non- reproductive (e.g. provisioning and defence) phases of a solitary insect’s nesting sequence. These alternating states could be a mechanistic pre-adaptation for the evolution of worker (non-social reproductive behaviour) and queen (reproductive behaviour) castes. This is known as the ovarian ground plan (OGP) hypothesis (West-Eberhard, 1996). The hypothesis was originally suggested by Evans and West-Eberhard (1973), and it makes three assumptions; 1) cycles of ovarian activation in a solitary ancestor of social hymenopterans, 2) the expression of alternate phenotypes arise from the same genotype, and 3) there are regulatory (molecular) mechanisms that switch on and off the expression of these distinct phenotypes.

Solitary bees and wasps nest, lay eggs and provision their larvae alone; some of them also defend their brood/nests. Social insects likely evolved from a solitary ancestor which carried out all the maternal tasks by itself (Hunt, 2007b). Progressive provisioners are those species which have prolonged periods of mother-larva interaction; some simultaneously provision multiple nests, simulating the provisioning behaviour that typifies all social wasps and almost all bees (exceptions are the stingless bee: Meliponini, carpenter bees: Xylocopidae, and sweat bees: Halictidae). Solitary progressive provisioners alternate the ovarian cycle with developed and undeveloped ovaries when displaying a nest sequence task (Evans, 1966). Hence, in a nest sequence that includes nest founding, egg-laying, nest guarding and provisioning phases, a cycle of ovarian development is instigated. A key element of this ovarian cycle is juvenile hormone (JH). The levels of JH have been correlated with ovarian development in insects (Nijhout and Wheeler, 1982; Agrahari and Gadagkar, 2003; Tibbetts, Mettler and Donajkowski, 2013) regulating oocyte maturation in nest founding and egg-laying phases, whilst JH mediates ovarian quiescence in nest guarding and provisioning phases (Giray, Giovanetti and West-Eberhard, 2005). This sequence of nest/ovarian cycles is repeated many times during a female’s life, resulting in repetitive cycles between developed and undeveloped ovaries. The reproductive phase of the nesting sequence resembles queen-like behaviours in social insects, while the non-reproductive phase emulates worker-like behaviours. An extension of the OGP hypothesis, the reproductive ground plan (RGP) hypothesis, postulates that gene regulatory networks and gene expression patterns underpinning foraging behaviour and reproductive cycles of the solitary ancestor’s nesting sequence may be co-opted to regulate worker and queen behaviour during evolution (Amdam et al., 2004).

The OGP idea emerged from the observations of the subsocial wasp Zethus miniatus, whereby West-Eberhard (1987) observed clear expression of alternating reproductive and provisioning behaviours. Females with mature eggs were building nests and laying eggs, whilst other females were engaged in provisioning or guarding their brood, presumably with undeveloped ovaries. Assessments and support of this hypothesis and its extension (RGP hypothesis) with genomic data have come from subsocial (Rehan, Berens and Toth, 2014; Roy-Zokan et al., 2015), primitively social (Toth et al., 2007) and advanced eusocial (Amdam et al., 2004; Rueppell, Hunggims and Tingek, 2008; Graham et al., 2011) insect taxa. Recently, Kelstrup et al., (2018) tested the hypothesis on the solitary progressive provisioning wasp Synagris cornuta, showing evidence of sequential nest/ovarian cycle when measuring the area of the largest oocytes, but no association of JH with the reproductive cycle. Interestingly, Kapheim and Johnson (2017) showed that the JH in the solitary alkali bee (Nomia melanderi) did not affect sucrose response or reproductive physiology in the short term but had a significant effect on ovary and Dufour’s gland development in the long term. These observations are compelling evidence for the OGP being a key mechanism in the evolution of sociality. However, we lack the vital experiment that compares genome-wide changes in molecular regulation across a solitary insect’s nesting sequence with the processes known to be associated with castes in social insects.

Another potentially important pre-adaptation to the division of labour found in social insects is provisioning behaviour. Together with progressive provisioning (mentioned above), simultaneous provisioning, i.e. caring for multiple larvae at the same time, may be an important trait. From this solitary adaptation, the step to adult offspring (i.e. daughters) staying home with their mothers to help in raising their siblings at the cost of her own reproductive is potentially small. An untested idea is that regulatory cycles of provisioning itself may play a role as pre-cursor for caste-biased behaviour and division of labour. Although progressive provisioning behaviour promotes brood survival and facilitates communication between the mother and the brood in solitary bees and wasps (Field and Brace, 2004), it can reduce egg production and the prolonged dependency of the larvae on the mother can also carry high costs for both mother and offspring in terms of survival (Field, 2005). Conversely, simultaneous progressive provisioning (SPP) in solitary species with overlapping generations allows a faster temporal rate of offspring production (Mitesser et al., 2017), as long as the mother provisions her offspring above the rate at which the larvae consumed the food provided. Geometric growth would typically compensate for the disadvantages of SPP when using lifetime reproductive success as a measurement of fitness instead of reproductive success. Mitesser et al. (2017) showed that the effects of geometric growth on reproduction

61 increase selection pressure for SPP in species with overlapping generations and that those benefits might emerge in seasonal eusocial species which explains why SPP is a widespread behaviour in social wasps. In social insects, provisioning behaviours are defined by clear patterns of gene expression, including a conserved role for the same set of genes across species, like foraging, vitellogenin, egr1 genes (Fitzpatrick et al., 2005; Robinson, Fernald and Clayton, 2008). We lack understanding of the molecular basis of provisioning behaviour in solitary species specifically, SPPs; these data would afford a test of the idea that cycles of provisioning behaviour may lay a (provisioning) ground plan (PGP) for the evolution of division of labour in social insects.

The lack of genomic data on solitary wasps (excluding parasitic species) might be linked to their elusive populations and behaviour, and their complex (fragmented) habitat, which makes them difficult to study. Ammophila pubescens is a solitary ground-nesting wasp (digs and provisions her own burrows alone and behaves aggressively towards conspecifics) that nests alone in loose or dense aggregations (Srba and Heneberg, 2012). Within aggregations, wasps nesting close to others are hostile towards their neighbours. A. pubescens females provision single-cell burrows progressively, visiting each nest multiple times to provision it, before sealing it up to complete development. The nesting sequence of A. pubescens consists of sequential behavioural phases which can be easily observed (Figure 3.1), switching between tasks and burrows (SPP). The sequence starts with the nest founding phase (‘NF’ in Figure 3.1), which is followed by the egg-laying or oviposition phase (‘EL’ in Figure 3.1). After two to three days, the mother comes back to perform a brief assessment (inspection phase, ‘Insp’ in Figure 3.1) of the conditions of her nest. This phase is followed by the provisioning phase (‘Prov’ in Figure 3.1), which consists of adding paralysed Lepidopteran caterpillars (3-5 prey items) in consecutive flights. Both the inspection and provisioning phases are repeated 5-7 days later when the mother will provision her brood with more caterpillars (7-12 prey items), unless the brood is parasitized or dead, in which case she will abandon it (Field and Brace, 2004). A. pubescens is a progressive provisioner that lives between 5 to 9 weeks, laying up to ten eggs in this short period, exhibiting queen-like (nesting founding and oviposition phases) and worker-like tasks (inspection and provisioning phases); they also provision multiple burrows simultaneously and so switch between phases, as do insects in simple societies where they need to be responsive to the demands of their small group. Hence, this ground- nesting wasp is a good model to test the OGP hypothesis and the more speculative PGP hypothesis for the ancestral origins of pre-social behaviour.

In this study, we present the draft genome of the ground-nesting wasp, A. pubescens; to the best of our knowledge we believe this is the first sphecid genome to be sequenced (Branstetter et al., 2018), and moreover the first non-social, aculeate wasp (Werren et al., 2010; Tvedte et al., 2019) to be sequenced. We compare this genome with those of other hymenopteran insects to identify similar and contrasting genomic traits. We then generate brain transcriptome from individual wasps exhibiting the different behavioural phases of the nesting cycle, and use these data to assess evidence for the OGP and PGP hypothesis by identifying differentially expressed genes and gene networks among females exhibiting reproductive (queen-like; red and blue in Figure 3.1) or non-reproductive (worker-like yellow and green in Figure 3.1) tasks. Following the OGP hypothesis that reproductive phases are a pre-cursor of the queen caste found in social insects (Hypothesis 1). This would predict individuals performing reproductive tasks (queen-like phenotype) will have a brain gene expression profile that is distinct from those performing non-reproductive tasks (worker-like phenotype). Secondly, we test the new idea that the provisioning phase provides a possible pre-cursor ground plan for division of labour and the evolution of the worker caste in social insects (Hypothesis 2). If the provisioning ground plan is important, we expect to find provisioning behaviour as one of the most distinct behaviours in the solitary wasp’s nesting cycle. Although Polistes queens carry out cell inspections, here "inspection behaviour" was classified as a non-reproductive behaviour given that during this period sampled wasps had three consecutive nests at the same time and not well developed ovaries. Eumenids which motivated the OGP do not inspect their nests, but they display guarding behaviour, which is alternated with provisioning, as A. pubescens which alternates inspection visits with provisioning.

Our study is a very conservative test of the OGP and the PGP since it is entirely possible that because these wasps are simultaneous progressive provisioners, there is no strong effect of reproductive or provisioning phases on gene expression since females continue to reproduce concurrently with provisioning several nests. In the event that we find no clear patterns, this would throw into question the value of any kind of behaviours and/or physiological ground plan as a pre-cursor for division of labour and social evolution. Accordingly, we simultaneously test the respective importance of cycles of reproduction and provisioning as pre-adaptations to social behaviour, in a species that is likely to have resembled the ancestral state of social insect lineages. Evidence for both would suggest that the regulation of provisioning cycles, as well as the putative regulation of reproductive cycles in the solitary ancestor of social insects, play a role in the evolution of division of labour, castes and ultimately, higher levels of social organisation.

Figure 3.1. Ammophila pubescens nesting sequence for a single burrow. It starts with a female excavating a single-cell burrow (nest founding: NF). Once completed the burrow, the owner leaves to hunt a Lepidoptera caterpillar which is brought to the burrow paralysed, and an egg is laid on it (oviposition: EL). Two days later, the owner returns to her burrow and briefly assess the content (Inspection: Insp). When she finds a hatched egg, she brings more prey items in consecutive flights (provisioning: Prov). After five to seven days Insp and Prov are repeated, and the nest is sealed up and concealed (SU). The larva overwinters as an adult and will emerge the following summer. This sequence is carried out in asynchronous parallel, across many burrows simultaneously. The bar at the bottom represents each of the phases in the nesting sequence, and the length of each segment in the bar illustrates the duration of the phase. Blue and red phases represent queen-like behaviours; yellow and green phases represent worker-like behaviours (After Baerends 1941a).

MATERIAL AND METHODS

Sample collection

A. pubescens is common on lowland heathlands in Dorset, South-west England, nesting solitarily but also forming loose or dense aggregations of nests in sandy areas. Two lowland heathlands were used as study areas, Hartland Moor heath (50°40’14.7” N, 2°04’01.9” W) and Godlingston heath (50°38’33.7” N, 1°58’42.3” W). Wasps were located along the edges of sandy paths with heterogeneous shapes and sizes, e.g. 2.0 to 2.5 m x 5.0 to 50 m.

Adult wasps were collected from mid-June to min-August in 2017 when wasps were active in performing the various nesting phases (8:30 to 18:30). Before collection, behavioural observations were carried out to identify females in each distinct phases of the nesting cycle, i.e. nest founding, egg-laying/oviposition, inspection and provisioning (Figure 3.1). Specifically, for provisioning behaviour, wasps were sampled after they added the third prey item in their nests. Wasps were collected at their burrows, as they performed the specific behaviour (describe in Figure 3.1). A small net was placed over the burrows’ entry blocking the wasps’ movements such that the wasp was caught on completion of their tasks. With RNAse and DNAse free dissection scissors, their heads were chopped off, and immediately placed in 0.5 ml Eppendorf tube full of RNAlater® Stabilization solution (Ambion ®) to halt gene expression changes. Their bodies were kept in EtOH 80% for further analysis. The tubes were labelled and stored in a cool box and later the same day placed at -20°C. After wasp collection, burrows excavated to confirm the correct assignation of the phase as well as collect any eggs in the burrow to use as a proxy of oocyte maturation. More details on wasps’ identities and their behaviours are given in Supplementary 3.1.

Genome sequencing and annotation

DNA extraction and sequencing

A total of 10 males were used to extract DNA for genome sequencing. From each male DNA was extracted from the brain, thoracic muscle and tarsus. To carried out the extraction, Qiagen® DNA extraction kit was used with some modifications at the beginning of the protocol. Each tissue was placed on 2ml tubes with a stainless-steel bead. Then, tubes were placed three hours at -80°C, and after this, tubes were placed in the TissueLyser LT at 50 Hertz

65 oscillations (1/s) for 3 min. PBS (180 µl) was added to each tube and then were placed again in the TissueLyser LT for 5 min. After a spin, the tubes were placed at 50°C overnight, and then the protocol of the manufactured was followed. The total DNA was resuspended in 100 µl of AE buffer. The DNA concentration was evaluated with Qubit 3 Fluorometer (Invitrogen ®). After this, the two samples (Ap41 and ApG03) with the highest DNA concentration in the thorax (8.44 ng/µl and 9.60 ng/µl respectively) and brain (10 ng/µl and 7.72 ng/µl, respectively) tissue were shipped to the Department of Biotechnology at Boke University Vienna for genome sequencing. All library preparation and sequencing were carried out in the Department of Biotechnology at the University of Natural Resources and Life Science in Vienna.

Genome assessment and annotation

The assembly and functional annotation of A. pubescens genome were carried out at the Centre de Regulació Genómica at Universitat Pompeu Fabra in Barcelona. The pipeline included functional annotation with InterProScan (Hunter et al., 2012), PANNZER2 (Törönen, Medlar and Holm, 2018), followed by Blast Annotator with UniProt-GOA (Huntley et al., 2015), signalP and TargetP (Petersen et al., 2011), and NCBI CDsearch (Marchler-Bauer et al., 2011) databases. Next, InterProScan v.5.32-71 (Zdobnov and Apweiler, 2001) was used to scan through all available InterPro databases, including PANTHER, Pfam, TIGRFAM, HAMAP and SUPERFAMILY, InterProScan v.5.32-71 (Zdobnov and Apweiler, 2001). To search against the NCBI non-redundant (NR) collection of protein sequences (release 2018-10) BLASTP v.2.7.1+ was used. For functional annotation of prokaryotic and eukaryotic proteins of unknown function, PANNZER2 (Protein ANNotation with Z-scoRE) was used, which is an automated service for functional annotation of prokaryotic and eukaryotic proteins of unknown function. It is designed to predict the functional description (FE) and Gene Ontology terms. The command-line remote access method for PANNZER2 was used to bypass the web-page input mode and automate the analysis as much as possible. Then, KEGG Automatic Annotation Server (KAAS) (Moriya et al., 2007) was used to assign KEGG Orthology (KO) groups, using bi-directional best hit (BBH) method against a representative gene set from 40 different species, including several Hymenoptera. KO identifiers were then used to retrieve relevant functional annotation using the KEGG (Kanehisa et al., 2012) REST-based API service (KEGG release v89.1).

Orthology

The annotation set of A. pubescens was compared to other Hymenoptera wasps and bees proteomes (n = 229) to characterise orthology. Specifically, the proteomes of the parasitoid and social wasps, as well as proteomes of solitary and social bees were retrieved from the NCBI. OrthoFinder v. 2.2.3 (Emms and Kelly, 2018) was used to define orthologous groups (orthogroups) of genes among these peptide sets. An orthogroup is a cluster of genes descended from a single gene in the last common ancestor of a cluster of species (Emms and Kelly, 2015). The resulting rooted species tree was visualised with ETE 3 (Huerta-Cepas, Serra and Bork, 2016).

Brain gene expression patterns analysis

RNA extraction and pre-processing

Using a confocal microscope, the head cavity of each wasp was opened, and the brains extracted and placed in a tube. Brains from a total of 34 A. pubescens female wasps in the nest founding (n = 10), oviposition (n= 10), inspection (n= 4) and provisioning (n = 10) were used. The RNA was extracted using Qiagen® RNeasy Plus Universal Tissue Mini kit following the manufacture’s protocol with some modification (Supplementary 3.2). For each completed the extraction, the integrity and yield of RNA were evaluated first with a Nanodrop 1000 to assess the ratios 260/280 nm and 260/230 nm, and later with Aligent 2100 Bioanalyser (Aligent Technologies) at the UCL genomics facilities (Supplementary 3.2). Then, finally, the samples were shipped to Novogene Bioinformatics Institute in China for RNA sequencing (RNA-seq). A total of 34 libraries were constructed by Novogene using NEB Next® Ultra™ RNA Library Prep Kit and RNA-sequencing was performed in NovaSeq 6000 machine.

After sequencing, low-quality reads were trimmed, and adapters were removed with TrimGalore! v 0.5.0 (www.bioinformatics.babraham.ac.uk/ projects/trim_galore/) and ribosomal RNA (rRNA) was removed with Bowtie2 v.2.3.5.1 (Langmead and Salzberg, 2012) using the the LSUParc and SSUParc database of rRNA from SILVA as reference (Quast et al., 2013). Then, the reads were mapped to the genome and quantified using the “new Tuxedo” package (Pertea et al., 2016). After transcript abundance quantification, the raw counts were filtered with a median of >1 for all samples (n = 34).

3.3.3.1 Hypothesis 1: Cycles in reproductive physiology and associated brain transcription provide a possible pre-cursor ground plan for the evolution of division of labour (test the OGP hypothesis).

Ovarian development assessment

The ovarian ground plan (OGP) hypothesis predicts that there will be a cycle in ovarian development with mature eggs during the reproductive phase (e.g. NF and EL phases) and immature eggs during the non-reproductive phase (e.g. Insp and Prov phases) during the nesting cycle of a solitary progressive provisioner. Lack of differences in ovarian development can suggest that other mechanisms different from physiology are underpinning the nesting cycle. These mechanisms can be just differences in behaviour only or driven by cues from the larvae.

Ovarian development was assessed in all the females collected throughout the nesting cycle. The dissections were conducted using a confocal microscope, and the length of the largest oocyte was measured (mm) using imageJ 1.x (Schneider, Rasband and Eliceiri, 2012). The average length was then compared between queen- and worker-like phenotypes (two- samples Wilcoxon test) and between behavioural phases (Kruskal-Wallis H test). Also, a total of 28 eggs collected from the burrows (see field methods above) were used to provide a baseline for the expected size of a mature egg. All the statistical analyses were carried out in R v.3.5.2 (R Core Team, 2018).

Gene expression pattern of queen (reproductive)- and worker-like (non-reproductive) social phenotypes

The OGP hypothesis predicts that there will be differences in gene expression between the reproductive (EL and NF) and non-reproductive (Insp and Prov) phases. On the other hand, if the wasps do not show differences in brain gene expression but they show differences in ovarian development, it would suggest that the nest cycle is underpinned by just ovarian cycles. If there are no differences at both levels (gene expression and ovarian cycles) it would mean that other processes different from those proposed by the OGP are underpinning the nest cycle; the cycle might be underpinned by provisioning behaviour which characterises Ammophilini wasps.

Differential gene expression analyses were carried out in R v.3.5.2 (R Core Team, 2018) using the filtered raw counts in the edgeR package v.3.8 (Robinson and Oshlack, 2010; Robinson, McCarthy and Smyth, 2010). A genewise negative binomial generalised linear model (glmLRT) was applied to the filtered count data to identify genes differentially expressed (DE) using a planned linear contrasts (reproductive phases (NF+EL) vs worker phases (Insp+Prov) to test hypothesis 1 (following methods in Mikheyev and Linksvayer (2015) and Manfredini et al. (2017)). The gene expression results were corrected for multiple testing using a FDR < 0.05 following the Benjamini-Hochberg method (Benjamini and Hochberg, 1995).

The lists of DE genes were then used to identify any enriched biological processes linked to caste-biased behaviour using Blast2GO (Conesa et al., 2005), Fisher exact test (FDR < 0.05). Also, we carried out a weighted gene co-expression network analysis (WGCNA) to identify co- regulatory gene modules that might be involved in queen-like and worker-like phenotypes. This analysis was carried out using the WGCNA package in R following the standard protocol and using the entire dataset of 34 samples. Specifically, this method looks for sets of co- regulated genes with similar expression patterns and clusters them into gene modules (Langfelder and Horvath, 2008), across all phenotypes, i.e. generation of a description of the species-level brain gene co-expression networks. We then examined whether any modules were significantly correlated with queen- and worker-like phenotypes using WGCNA R package and visualised these modules of interest in VisANT (Hu et al., 2013).

3.3.3.2 Hypothesis 2: Cycles in provisioning behaviour and associated brain transcription provide a possible pre-cursor ground plan for the evolution of division of labour (test of the PGP).

Differential gene expression analysis

If provisioning is important in regulating the nesting cycle, we expect to see significant effects of provisioning on brain gene expression patterns relative to the other phases.

We compared gene expression patterns between behavioural phases (Table 3.1) to determine how distinct the phases are as well as to identify to what extent the provisioning phase differs in gene expression patterns from the other phases. To test for DE genes in the pairwise analysis, an exact test for a negative binomial distribution was used. This test has strong parallels with Fisher’s exact test (edgeR user guide). The gene expression results for these

69 comparisons were also corrected for multiple testing using a FDR < 0.05 following the Benjamini-Hochberg method (Benjamini and Hochberg, 1995).

Table 3.1. Contrast and pairwise comparisons between behavioural phenotypes and phases to test hypotheses 1 and 2. The grey box illustrates the contrast between social phenotypes (Hypothesis 1), and the blue boxes show the pairwise comparison to identify the gene expression patterns of provisioning against the other phases (Hypothesis 2).

Queen-like Worker-like Phenotype

Phases NF EL Insp Prov

NF Queen-like NF+EL vs Insp+Prov EL NF vs EL

Insp NF vs Insp EL vs Insp Worker-like Prov NF vs Prov EL vs Prov Insp vs Prov

NF = nest founding, EL = egg-laying / oviposition, Insp = inspection, Prov = provisioning

RESULTS AND DISCUSSION

Ammophila pubescens genome characterisation

We generated a high-quality genome that is 515 Mbp long and predicted to be complete (BUSCO 3). Scaffolds and contigs larger than 500 bases were annotated. The pre-filtered assembly was made up of 2,285,132 scaffolds and contigs. In addition, the repeat masked version of the assembly made up of scaffolds and contigs longer than 500 bases, contains a much more manageable 11,135 scaffolds/contigs, which should contain most of the gene space. A total of 18,422 (97.24%) out of 18,944 proteins had some annotation feature (Table 3.2) derived from one of the annotation resources used here. GO terms were assigned to 12,681 (66.94%) proteins (Supplementary 3.3). Additionally, 15,809 proteins were assigned a

70 functional description. In total, 17,293 (91.28%) proteins had some type of protein signature (i.e. predictive model to classify proteins into families); 12,954 of these proteins (68.38%) were annotated with at least one signature (Supplementary 3.3), and 11,909 proteins were found to have domains. The total number of proteins varied between 6,093 and 9,753 between GO category (Supplementary 3.3.). Finally, a search for retrotransposon/transposase was carried out, and in total, 1,087 unique proteins (979 genes) were annotated as putative transposons (Supplementary 3.3).

When contrasting with other bee and wasp genomes, the genome of A. pubescens appears to be larger than most of the Hymenoptera genomes reported to date (Kapheim, M. et al., 2015; Branstetter et al., 2018) (Table 3.3), with the exception of the solitary bee Eufriesea mexicana which has a genome twice the size of A. pubescens (Table 3.3). The number of predicted genes, as well as the GC% content (see Table 3.3), were also similar to other Hymenoptera genomes (Kapheim, M. et al., 2015; Branstetter et al., 2018). Here, the genomes of several bees and wasps were used as both Apoidea wasps and bees share a common ancestor. However, Ammophila wasps are phylogenetically closer to bees than vespid wasps (Johnson et al., 2013; Peters et al., 2017). Therefore, it was more relevant to compare the Ammophila genome with bees than with the few other wasp genomes available; nonetheless, we considered it appropriate to include wasps’ genomes in our analysis. This genomic resource could be useful to understand precursors of early modules or stages in social evolution, e.g. parental care, parasitism and other behavioural innovations; specifically the tribe Ammophilini wasps display an unusual diversity of nest sequence and parental care behaviour (Field, Ohl and Kennedy, 2011).

The phylogenetic analysis positioned the parasitoid wasps at the top of the tree (Figure 3.2), being the ancestral group of Aculeata bees and wasps, with primitively social Polistes as the sister group. As expected, A. pubescens (Sphecidae) was situated as a sister group of all Apoidae bees presented in the tree, which support previous studies using morphological (Melo, 1999), genetic (Field, Ohl and Kennedy, 2011) and transcriptomic (Peters et al., 2017) data. The sister species to Ammophila was D. novaeangliae (Halictidae) which in turn is the sister group of Megachilidae and Apidae bees. Within the Apidae family, Bombus and Melipona species were clustered as sister groups, but E. mexicana a sister species of Apis (Kapheim, M. et al., 2015) was the most distinct of the Apidae bees. This might be associated with the lack of more representatives of the Apidae family and with the absence of ant species (Formicidae) to construct the phylogenetic tree.

Among the 23 species of wasps and bees used here, a total of 13,573 orthogroups were found by OrthoFinder. Within these orthogroups, 421,584 (94.8%) genes were present; only 5.2% of the genes were unassigned to an orthogroup. For A. pubescens a total of 18,944 genes were identified; from which 14, 770 (78%) were in orthogroups. Thirty-seven orthogroups were unique to A. pubescens. Only M. quadrifasciata, T. sarcophagae and T. pretiosum had a similar percentage of genes in orthogroups (72.7%, 76.2% and 88.4%, respectively). The remaining species had above 91% of genes in orthogroups. In conclusion, the A. pubescens genome resembles other sequenced Hymenoptera genomes, as fits the expectation in terms of its phylogenetic position.

Table 3.2. Summary of functional annotation of A. pubescens genome.

Genome genes proteins

Total number 16,484 18,944

Annotated 15,970 (96.88%) 18,422 (97.24%)

InterPro signatures 14,864 (90.17%) 17,293 (91.28%)

Assigned to KO groups 5,804 (35.21%) 7,073 (37.34%)

With functional description (DE) association 13,396 (81.27%) 15,809 (83.45%) (BLAST/KEGG/PANNZER2)

With GO term associations 10,407 (63.13%) 12,681 (66.94%) (blast_annotator/KEGG/PANNZER2/InterPro)

Conserved domain signatures 9,953 (51.08%) 11,909 (62.86%)

Conserved feature signatures 4,822 (29.25%) 6,012 (31.74%)

SignalP signatures 1,371 (8.32%) 1,499 (7.91%)

TargetP signatures 6,309 (38.27%) 6,929 (36.58%)

Table 3.3. Comparison of genome size and assembly statistics for hymenopteran wasps and bees.

Species Genome N50 scaffold Predicted Coverage Family Behaviour GC (%) Reference (common name) size (Mb) length (bp) genes (x)

Ammophila pubescens Sphecidae Solitary 515 2,285,132 37 16,484 6 This study (Heath sand wasp) Apis cerana Apidae Eusocial 238 1,421,626 30.02 10,651 152 (Park et al., 2015) (Asian honeybee) Apis dorsata Apidae Eusocial ~219.17 732,052 32 ~10,297 60 (Rueppell et al., 2013) (Giant honeybee*) Apis florea Apidae Eusocial ~230.4 2,863 34.5 15,810 20.5 (Qu, et al., 2012) (Red dwarf honeybee*) Apis mellifera Apidae Eusocial 234 997,192 32.7 15,314 192 (Elsik et al., 2014) (Honeybee) Bombus impatiens Apidae Primitively eusocial 248 1,399,493 37.7 15,896 108 (Sadd et al. 2015) (Common eastern bumble bee) Bombus terrestris Apidae Primitively eusocial 274 3,506,793 37.5 10,979 17.5 (Sadd et al. 2015) (Buff-tailed bumble bee) Ceratina calcarata Apidae Subsocial 364 73,643 - 11,310 37.9 (Rehan et al., 2016) (Carpenter bee) Ceratosolen solmsi Agaonidae Parasitoid 294 9,558,897 - 11,412 92.9 (Xiao et al., 2013) (Fig wasp) Diachasma alloeum (Parasitoid Braconidae Parasitoid 388.8 645,583 39.1 - - (Tvedte et al., 2019) wasp) Dufourea novaeangliae Halictidae Solitary 291 2,397,596 40 12,453 133.3 (Kapheim, M. et al., 2015) (Sweat bee) Eufriesea Mexicana Apidae Solitary 1,032 2,427 41 12,022 120.0 (Kapheim, M. et al., 2015) (Orchid bee) Fopius arisanus Braconidae Parasitoid ∼154 - 39.4 11,661 137 (Geib et al., 2017) (Parasitoid wasp)

Habropoda laboriosa Apidae Solitary 377 1,338,707 39 13,279 201.4 (Kapheim, M. et al., 2015) (Blueberry bee) Megachile rotundata Megachilidae Solitary 273 1,699,680 37 12,770 272.3 (Kapheim, M. et al., 2015) (Alfalfa leaf cutting bee) Melipona quadrifasciata Apidae Eusocial 257 1,864,352 39 15,368 126.7 (Kapheim, M. et al., 2015) (Stingless bee) Microplitis demolitor Braconidae Parasitoid 241.2 323,181 33.1 - 26 (Burke et al., 2014) (Parasitoid wasp) Nasonia vitripennis Pteromalidae Parasitoid 239.8 708,988 40.6 17,279 6 Werren et al., 2010 (Jewel wasp) Osmia bicornis Megachilidae Solitary 212.9 604,175 39.77 14,858 59.66 (Beadle et al., 2019) (Red mason bee) Polistes canadensis Vespidae Primitively eusocial 211.265 536,328 33.2 15,799 110 (Patalano et al., 2015) (Paper wasp) Polistes dominula Vespidae Primitively eusocial 208.026 1,625,592 30.77 12,153 243.3 (Standage et al., 2016) (European paper wasp) Trichogramma pretiosum Trichogrammatidae Parasitoid 195 3,706,225 - 12,928 74 (Lindsey et al., 2018) (Parasitoid wasp) Trichomalopsis sarcophagae Pteromalidae Parasitoid 242 21KB 41.6 - 74 (Martinson et al., 2017) (Parasitoid wasp)

Figure 3.2. Species rooted tree of 23 Hymenoptera bees and wasps. Support values are shown in red. A. pubescens is positioned between the primitively social wasps (Polistes) and the bees, with the solitary Halictidae sweet bee (D. novaeangliae) as its the closest apoid relative.

Hypothesis 1: Cycles in reproductive physiology and associated brain transcription provide a possible pre-cursor ground plan for the evolution of division of labour (test of the OGP)

3.4.2.1 Results for the tests of the Ovarian ground plan

Result 1. No evidence for the ovarian ground plan (OGP) at the physiological level

There was no evidence of an ovarian cycle when comparing the queen-like (NF+EL) and worker-like (Insp+Prov) phenotypes. The average length of the largest oocytes in the reproductive tracts of queen-like (n=20 wasps) and worker-like (n=14 wasps) phenotypes were 2.43 ± 0.537 mm and 2.45 ± 0.551 mm respectively. The length of the oocytes (mm) between queen- and worker-like phenotypes were not significantly different (W= 124.5, p = 0.5996), which means that both have similar length.

We sampled a total of 28 eggs soon after egg-laying wasps laid them. We use them to compare the length of oocytes in queen-like and worker-like phenotypes and investigate the difference between eggs and largest oocytes in both phenotypes. The average length of the eggs was 2.81 ± 0.27 mm, with eggs varying between 1.786- and 3.29-mm length. This is 0.2 mm smaller than eggs recorded by Baerends (1941a) for A. pubescens. There were significant differences between the length of the eggs and the length of the largest oocyte in queen-like phenotype (W = 430.5, p=0.0017). Also, there were significant differences between the length of the eggs and the length of the largest oocytes in worker-like phenotype (W = 284, p=0.01956). That is, for both comparisons, the eggs were larger than the largest oocytes in queen- and worker-like phenotypes in of A. pubescens.

Next, we contrasted the average length of oocytes in each behavioural phase of the nest cycle. we found no evidence of an ovarian cycle when comparing the average length of oocytes between nest founding (n=10, 2.68 ±0.178 mm), egg-laying (n=10, 2.18 ±0.66 mm), inspection (n=4, 2.31 ±0.665 mm) and provisioning (n=10, 2.51 ± 0.529 mm) (Kruskal-Wallis chi-squared = 4.5132, df = 3, p-value = 0.2111) (Figure 3.3), but the sample size was small.

As above, the average length of oocytes for each behavioural phase was then compared with the eggs (n=28). We found that the length of eggs and the length of oocytes in the nest founding phase were not significantly different (W = 199.5, p= 0.05047). The same pattern was found when comparing the length of the eggs and the oocytes in the provisioning phase (W = 190, p= 0.1008). On the other hand, the length of the eggs was significantly different from the largest oocytes in the egg-laying phase (W = 231, p= 0.002699), as well as in the inspection phase (W = 94, p= 0.03262). That is, eggs were larger than oocytes in the reproductive tract of A. pubescens during egg-laying and inspection phase.

The OGP hypothesis predicts that wasps starting the nesting cycle are in the reproductive phase and thus have a large mature egg in their ovaries (West-eberhard, 1987; West- Eberhard, 1996); this prediction is supported by the similarity between the length of the largest oocyte in nest founding (2.68 ±0.178 mm) and the eggs sampled from the burrows (2.81 ± 0.27) (W = 199.5, p= 0.05047). The OGP hypothesis also predicts that females after egg- laying start the non-reproductive phase. Here we sampled females soon after they had laid eggs and although there was a wide variation in the length of the largest oocytes (2.18 ±0.66, Figure 3.3) after egg-laying, the oocytes were significantly smaller than the eggs (W = 231, p= 0.002699) which supports what predicts the OGP hypothesis. Also, the inspection phase is a non-reproductive phase, and we found that the oocytes in this phase are significantly smaller than the eggs (W = 94, p= 0.03262), as is also predicted by the OGP hypothesis. However, for this phase, we had a very small sample size (n=4). The provisioning phase is the most well known non-reproductive phase in which oocytes are expected to be not well-developed (West-eberhard, 1987; West-Eberhard, 1996) which contradicts the results presented here, as the oocytes in the provisioning phase were as big as the eggs (W = 190, p= 0.1008), but after fully provisioned nests, wasps engage in start new nests (Baerends, 1941b). However, wasps in the provisioning phase were sampled when the wasps were adding the third prey item inside the nest.

The evidence presented here does not provide strong support for an ovarian cycle as predicted as the OGP hypothesis, although there is some evidence that wasps in the nest founding and provisioning phases have oocytes as big as eggs removed from the nests. The results may be reflection of the small sample size as well as the fact that A. pubescens engages in simultaneous care of multiple larvae (Baerends, 1941b; Field et al., 2007). These findings contrast with what has been reported for Synagris cornuta (Kelstrup et al., 2018) an Afrotropical solitary progressive provisioning Eumenine which has shown differential

77 oogenesis regarding the behavioural phases. However, Kelstrup et al. did not use eggs from the nests to compare their length with the length of the largest oocytes. Although they found support for differential oogenesis depending on the nest phase, in a further analysis of juvenile hormone, they did not find an association between juvenile hormone levels and the reproductive cycle. Furthermore, they did not investigate how gene expression underpins the nest phases.

Figure 3.3. Changes in length of the largest oocyte through the nesting phases in A. pubescens nesting cycle. The boxplot edges represent upper and lower quartiles, and whiskers represent a deviation of 1.5 (interquartile range) from the upper and lower quartiles. NF = nest founding (n =10), EL = oviposition/egg-laying (n = 10), Insp = inspection (n = 4) and Prov = provisioning (n=10).

Result 2. Weak evidence for an ovarian ground plan (OGP) at the brain transcriptome level

We detected few genes that were significantly differentially expressed (DE) between queen- like (NF+EL) and worker-like phenotypes (Insp+Prov).

After normalising the libraries (trimmed mean of M-values, TMM in edgeR), neither the multidimensional scaling plot (Figure 3.4A) nor the DE analysis (Figure 3.4B) revealed distinct clusters of behaviour from the patterns of gene expression in queen- and worker-like phenotypes. A total of 21 genes were significantly differentially expressed between queen-like (NF+EL) and worker-like phenotypes (Insp+Prov). Low levels of differential gene expression have been reported in some primitively eusocial paper wasps, e.g. Polistes canadensis (Patalano et al., 2015), Polistes dominula (Standage et al., 2016) and Polistes lanio (see chapter 2), all of which have simple societies with behavioural (not morphological) caste differentiation. Two out of seven genes up-regulated in queen-like phenotypes in A. pubescens had BLAST hits (protein yellow and a predicted protein – endocuticle structure glycoprotein SgAbd-8-like isoform X2). The protein yellow is part of the major royal jelly (MRJ) protein family, responsible for regulating brood development, caste differentiation, longevity and production in honeybees (Ramanathan, Nair and Sugunan, 2018) and other social Hymenoptera (Ferreira et al., 2013). The endocuticle structural glycoprotein SgAbd-8 is a component of the abdominal endocuticle in desert locust (Gryllus gregarius). Endocuticle structural glycoproteins are key in larval caste development in Bombus terrestris (Pereboom et al., 2005; Colgan et al., 2011). Also, a similar glycoprotein (Endocuticle structural glycoprotein SgAbd- 1- like mRNA) has been reported to be upregulated in the brain of mated queens compared to the brain of virgin alate queens in fire ants (Solenopsis invicta) (Calkins et al., 2018). Thus, both annotated genes have been linked to caste differentiation and reported to be in queen biased in social insects. This suggests an overlap in brain transcription between queen-like phenotype in wasps A. pubescens and the queen caste in social insects.

Among the 14 genes up-regulated in the worker-like phenotype, only four had BLAST hits. Among those were three proteins that appear to be associated with stress and detoxification: nose resistant to fluoxetine protein 6 (nrf-6), protein spaetzle, cell cycle checkpoint protein RAD17 (RAD17) and a predicted gene, acetylcholinesterase isoform X2. Nrf-6 has been reported to be upregulated in B. terrestris adult workers when they were exposed to neonicotinoid pesticides but downregulated in exposed adult queens. (Colgan et al., 2019).

Nrf-6 is also up-regulated in the gut of the moth Ostrinia nunilalis larvae in response to a bacterial toxin (Yao et al., 2014). The protein spaetzle is involved in its active form in inducing the expression of antifungal response (Lemaitre et al., 1996; Barribeau et al., 2015). In humans, RAD17 has been linked to the activation of cell-cycle checkpoints by genotoxic agents or replication stress (Tsao, Geisen and Abraham, 2004). The Acetylcholinesterase participates in the hydrolysis of acetylcholine into choline; acetylcholine is fundamental neurotransmitter of the central nervous system (Reddy, 2015). These stress-response genes may reflect the costs of worker-like behaviours in A. pubescens and exposure to toxins whilst foraging (despite being sampled in a natural reserve). During foraging, solitary insects, as well as “foragers” in social insects, are exposed to pathogens not only from their prey but also from the environment. They are exposed many times during daylight, given that their main activity is provisioning (Kelstrup et al., 2018; Figure 3.1). It has been predicted and shown that solitary and social insects with an exposed life-history will upregulate diverse type of genes and pathways involved with immunity (Imler et al., 2006; Haine et al., 2008; Barribeau et al., 2015).

No functional enrichment was detected among DE genes identified among queen-like and worker-like behaviours using gene ontology (GO) enrichment analysis in Blast2GO. However, we identified evidence of molecular signatures at the network level. In total, 63 modules of co- expressed genes were detected across the entire transcriptome dataset. However, only one of these modules was significantly correlated with queen-like and worker-like phenotypes (cor = 0.59, p = 2e-04). Evidence of network-level signature of caste differentiation among otherwise subtle differential gene expression patterns was also detected in the paper wasps P. canadensis (Patalano et al., 2015) and P. lanio (see chapter 2).

For a further test, we conducted a gene-level test of the key genes predicted by the OGP to be involved in regulating ovarian cycles in insects: this included four genes encoding vitellogenin proteins (Vitellogenin, Vitellogenin like, Vitellogenin 3 and Vitellogenin receptor isoform 3) two genes encoding JH proteins (HF epoxide hydrolase 2, Hamolymph JH binding protein) (Figure 3.5). None of this co-varied with the phases of the nesting cycle, except vitellogenin like, that showed significant differences between reproductive (EL+NF) and non- reproductive (Insp+Prov) phases (W = 217.5, p-value = 0.007042). The significant differences were between NF and Prov (p = 0.024).

Figure 3.4. Plots summarising the gene expression patterns between queen-like (NF+EL) and worker-like (Insp+Prov) phenotypes. A) The multidimensional scaling (MDS) plot does not show clear clusters between queen-like (in green) and worker-like (in purple) phenotypes. B) Volcano plot showing the genes considered significantly expressed (red dots) and those not significantly expressed (black dots). Genes that have 1.5 fold changes (FC) in gene expression are shown outside the dashed lines. Few significantly DE genes between social phenotypes.

NF+EL and Insp+Prov, W = 137, p-value = 0.9313 NF+EL and Insp+Prov; W = 217.5, p-value = 0.007042

NF+EL and Insp+Prov, W = 137, p-value = 0.9313

NF+EL and Insp+Prov, W = 166, p-value = 0.3722

NF+EL and Insp+Prov, W = 132, p-value = 0.793 NF+EL and Insp+Prov, W = 172, p-value = 0.2744

Figure 3.5. Boxplots of several genes reported to be involved in ovarian development in insects. The levels of gene expression across behavioural phases: nest founding (NF), egg- laying/oviposition (EL), inspection (Insp) and provisioning (Prov). Wilcoxon rank-sum test for each gene between queen-like (NF+EL) and worker-like (Insp+Prov) phenotypes showed no significant differences between them, except for vitellogenin like. The significant differences were between NF and Prov (p = 0.024)

3.4.2.2 Discussion on the (lack of) evidence for an Ovarian Ground Plan

The gene expression analysis between the queen-like and worker-like phenotypes showed that these behavioural repertoires, although cyclical with respect to an individual burrow, are coupled at the molecular level in A. pubescens, as very few genes were DE between these two categories. Only two of the genes upregulated in queen-like phenotype are reported as queen-biased in social insects. The same pattern was found in the worker-like phenotype as some worker-like castes genes were upregulated. However, none of those genes were precursors or mediators of the juvenile hormone (JH), a key molecular process that would otherwise support the OGP hypothesis: as JH mediates ovarian cycles in insects, the OGP hypothesis predicts that JH should also cycle with the behavioural phases of the nesting cycle. The OGP hypothesis would predict that progressive provisioners, when occupied with brood caring, would have small oocytes (and downregulation of JH), but when engaging in the nest founding and before oviposition, they would have larger/mature oocyte ready to be laid. The ovarian data presented here did not support the OGP hypothesis. However, A. pubescen behaviour and the small sample size could have influenced the results.

Support for the OGP hypothesis has been found in the solitary progressive provisioner S. cornuta, in which females experience differential oogenesis through the reproductive and non- reproductive behavioural phases (Kelstrup et al., 2018). However, in the same study, the analyses on JH and ecdysteriods did not support the OGP hypothesis. The S. cornuta reproductive cycle is between 13 to 21 days, during which time an adult female is fully engaged in caring for one larva at the time. Once the cycle has been completed in the cell, she will start a new one, and thus focus exclusively on a single burrow. By contrast, A. pubescens is a simultaneous progressive provisioner which cares for several larvae at the same time in different nests very close to each other. Therefore, A. pubescens does not guard her larvae while developing and the time possibly interacting with her larvae is brief – only while adding prey items and for inspection (Field and Brace, 2004; Field, Accleton and Foster, 2018). The ovarian patterns may be linked to the fact that they have several nests. Having multiple mature eggs ready to be laid is common in mass provisioning wasps as they can lay eggs consecutively (O’Neill, 2001).

The reproductive ground plan (RGP) hypothesis is an extension of the OGP. The RGP posits that regulatory gene networks in reproduction were co-opted and gave origin to caste differentiation (Amdam et al., 2004). The synthesis and incorporation of essential compounds like nutrients and hormones are necessary for developing oocytes (Badisco, Van Wielendaele and Broeck, 2013). IIS (insulin-like signalling) and TOR (target-of-rapamycin) pathways are involved in regulating this process. Here, we did not find evidence that either of these pathways is involved in the two social pathways. Also, we did not find overrepresented GO terms. Hence, our results do not support the RGP. Our results contrast with those found in the solitary alkali bee (Nomia melander) and honeybees (Graham et al., 2011; Kapheim and Johnson, 2017a) in which there was strong support reported for the RGP hypothesis. Outside of the Hymenoptera, a study carried out in subsocial beetles (Nicrophorus vespilloides) also showed a link between vitellogenin and its receptors associated with brood caring (Roy-Zokan et al., 2015).

In conclusion, we found only weak evidence for Hypothesis 1, the OGP, with no clear evidence of cycling patterns of ovary development through the cycle of phenotypes at the burrow and subtle molecular differences in brain transcription. Some of these subtle molecular differences, however, do involve genes that have been reported as caste biased in social insects (Ferguson et al., 2011), and overall the patterns and genes are comparable to the simple societies of paper wasps.

Hypothesis 2: Cycles in provisioning behaviour and associated brain transcription provide a possible pre-cursor ground plan for the evolution of division of labour (test of the PGP)

We next compared brain gene expression among the different phases in order to determine the extent to which the provisioning phase differs in gene expression patterns from the other phases.

3.4.3.1 Results for tests of the Provisioning Ground Plan

Result 1. Provisioning and egg-laying phases are not distinct at the molecular level.

The most obvious comparison is of egg-laying (EL) with provisioning (Prov): multidimensional scaling analysis after the library normalisation showed that individuals from these two behavioural phases were scattered along the two dimensions (Figure 3.6); however, only eight genes were differentially expressed between EL and Prov (Table 3.4). Genes upregulated by egglayers (EL) included 39S ribosomal protein L211, mitochondrial (mRpL11), the PREDICTED: endocuticle structural glycoprotein SgAbd-8-like isoform X2. mRpL11 is a structural constitute of ribosome involved in mitochondrial translation in D. melanogaster (Suzuki et al., 2001; Marygold et al., 2007); whereas the endocuticle structural glycoprotein SgAbd-8 in Schistocerca gregaria is a component of the abdominal endocuticle (Zhao et al., 2019). In Apis mellifera, endocuticle structural glycoprotein SgAbd-8 was DE between drones and workers Varroa-parasitised pupae (Surlis et al., 2018). Among the genes upregulated in provisioning females, were cytochrome b, PREDICTED: facilitated trehalose transporter Tret1-2 homolog (Tret1-2) and a hypothetical protein WN55_0043. Cytochrome b is a component of respiratory chain complex III; whereas tret1-2 is involved in trehalose transport (Kanamori et al., 2010) in insects. There was no evidence of any functional enrichment among these genes: no significant overrepresented GO terms. The small differences between both phases may be because wasps hunt for caterpillars to lay their eggs on, just before oviposition and the provisioning phase starts with the same behavioural phase.

Table 3.4. Summary of the total number of DE genes for each pairwise comparison between the provisioning phase against the others.

Pairwise Total DE Not Up* Down* comparison genes significant

EL vs. Prov 8 5 3 19247

NF vs. Prov 127 113 14 19128

Insp vs. Prov 192 130 62 19063

* Up and down-regulated genes for the first condition,

A) B)

C) D)

Figure 3.6. Plots summarising the result from edgeR for Egg-laying (EL) vs Provisioning (Prov) wasps. A) Multidimensional scaling (MDS) revelated that most EL and Prov wasps are scattered across the first two dimensions (leading log-fold-change between samples). The log- fold-change (log-FC) is the average (root-mean-square) of the largest absolute FC between samples. B) Biological Coefficient of Variation (BCV) against average log counts per million (CPM) illustrating three types of variation across the samples: Tagwise variation (gene by gene dispersion), Trend variation (takes into account groups of genes with similar expression) and Common (dispersion across the whole data set). C) Smear plot of significantly DE genes (red dots) above and below a fold-change of 1.5 (blue dashed lines) at an FDR of 5%. D) Volcano plot of the genes considered significantly expressed (red dots) and those not significantly expressed (black dots). Genes that have 1.5 fold differences in gene expression are shown outside the dashed lines.

Result 2. Provisioning and nest founding are distinct at the molecular level

In contrast to the comparison of provisioning with egg-laying (above), we found more molecular differentiation in the comparisons of provisioning (Prov) with nest founding (NF) (Table 3.4; Figure 3.7). For NF vs Prov, a total of 147 genes were differentially expressed; of these 49 were annotated and included genes involved in regulation and metabolism such as the suppressor of lurcher protein isoform X1, serine/threonine-protein kinase NLK (Nlk), Ephirin type-B receptor 1-B (ephb1-b) isoform X2, among others. Nlk regulates several transcription factors with essential roles in cell fate determination, regulation of gene expression (Gaudet et al., 2011); and Ephb1-b is a receptor tyrosine kinase and may function as a tumour suppressor in humans (Huusko et al., 2004).

A further set of differentially expressed genes are the same as those reported to be involved in caste differentiation in social insects: these include nfr-6 and PREDICTED: glycogen phosphorylase. Glycogen phosphorylase in the human brain regulates glycogen mobilisation and is an important enzyme in carbohydrate metabolism (Newgard et al., 1988; Mathieu et al., 2016). Finally, we found some evidence of functional specialisation between NF and Prov, with two overrepresented GO terms involved in biological processes: protein homooligomerization, regulation of ion transmembrane transport and potassium ion transmembrane transport.

Result 3. Provisioning and Inspection behaviours are distinct at the molecular level

The differential gene expression analysis uncovered a total of 192 DE genes between Prov and Insp phase females; 108 had BLAST hits (Table 3.4; Figure 3.8). Among these, were genes involved in sensory perception: rootletin, gamma-aminobutyric acid (GABA) receptor subunit beta, mRNA export factor, PREDICTED: general odorant-binding protein 56d (Obp56d) and odorant-binding protein 4 precursor. In D. melanogaster, rootletin is part of rootlets necessary for rootlet assembly. Rootlets are involved in sensory perception of sound and touch and neurons sensory functions (Chen et al., 2015; Styczynska-Soczka and Jarman, 2015; Jana et al., 2016). Furthermore, in Caenorhabditis elegans is necessary for a natural response to chemical stimuli and environment, as well as for natural mating behaviour (Perkins

87 et al., 1986; Ou et al., 2007; Mohan et al., 2013). GABA is a neurotransmitter that modulates neural activity facilitating neural inhibition when binding with the GABA receptor. In response to environmental stress, increase its activity (Amsalem et al., 2015). GABA receptors are also involved in learning processes in Apis melifera (Liang et al., 2014) and D. melagonaster (Liu, Krause and Davis, 2007).

Additionally, a GABA type B receptor has been related with nest-site and food-source scouting (Liang et al., 2014); and in mice GABA A receptors regulate maternal aggression (Gammie et al., 2007). The mRNA export factor is an immediate-early protein that inhibits viral splicing regulating viral gene expression (Bryant et al., 2001). Odorant-binding proteins are components of the insect’s olfactory system. Olfaction is essential for survival and reproduction, location of food sources, recognition of colony conspecifics, and determination of oviposition sites (Hekmat-Scafe et al., 2002). Specifically, Obp56d was upregulated in bumblebee mated queens (Manfredini et al., 2015).

A second set of genes that were differentially expressed between Prov and Insp were those involved in caste determination in social insects. These include: protein y, Nrf-6 isoform X2, AN1-type zinc finger protein, cGMP-dependent protein kinase (PKG), allotostain-A receptor- like, and breast cancer type 1 susceptibility protein (BRCA1).

The cGMP-dependent protein kinase (PKG) encoded by the for gene mediates the transition from defence to foraging in ant subcastes (Pheidole pallidula) (Lucas and Sokolowski 2009), strengthens polymorphism in foraging behaviour in D. melanogaster (Fitzpatrick et al., 2005). Finally, allatostatins are neuropeptides that inhibit juvenile hormone (JH) synthesis (Banerjee et al., 2008). Thus, allastostatin receptors regulate JH (Calkins et al., 2018) which is an important regulator of reproductive cycles linked with the OGP hypothesis. Specifically, allatostatin-A neurons in Drosophila, inhibit starvation-induced changes in adults feeding behaviour (Hergarden, Tayler and Anderson, 2012). Additionally, allastostatin-A receptor-like has been reported to be overexpressed among queen fire ants (Calkins et al., 2018). BRCA1 is one of the most studied genes in humans, implicated in ovarian and breast cancer. It is liked in antioxidant signalling (Gorrini et al., 2013), homologous DNA repair (Sekelsky, 2017; Zhao et al., 2017), and microRNA biosynthesis (Kawai and Amano, 2012). More recently, it has been associated with ageing and longevity in the Japanese termite, Reticulitermes speratus.

Among king termites of this species, BRCA1 was overexpressed in comparison with workers which lives shorter lives (Tasaki et al. 2018).

A third set of genes that were differentially expressed were those involved in stress and detoxification: these include cytochrome P450 4C1, cytochrome P450 6k1, Mucin-19, ejaculatory bulb-specific protein 3 and glucose dehydrogenase), Cytochrome P450 protein families are fundamental for detoxification of pesticides in insects (Evans and Wheeler, 1999). Both Cytochrome P450, as well as AN1-type zinc finger protein 1, protein y, Mucin-19, ejaculatory bulb-specific protein 3 and glucose dehydrogenase were overexpressed in bumblebee queens and workers exposed to neonicotinoid pesticides (chronic clothianidin) (Colgan et al., 2019). Contrastingly, glucose dehydrogenase in honeybees was downregulated after exposure (Christen et al., 2018).

Finally, genes reported to be involved in courtship were differentially expressed: protein spinster and the potassium voltage-gated channel protein Shaker). Protein spinster is involved in courtship and reduces female receptiveness in D. melanogaster (Sokolowski, 2001). Interestingly in the same species, mutations encoded by Shaker in the potassium channel disrupt courtship suppression (Greenspan and Ferveur, 2000). However, shaker protein has been linked to proboscis extension reflex and therefore playing a role in sensory perception of taste (Sokolowski, 2001; Ishimoto et al., 2005).

A) B)

C) D)

Figure 3.7. Plots summarising the result from edgeR for Nest founding (NF) vs Provisioning (Prov) wasps. A) Multidimensional scaling (MDS) revelated that most EL and Prov wasps are scattered across the first two dimensions (leading log-fold-change between samples). The log- fold-change (log-FC) is the average (root-mean-square) of the largest absolute FC between samples. B) Biological Coefficient of Variation (BCV) against average log counts per million (CPM) illustrating three types of variation across the samples: Tagwise variation (gene by gene dispersion), Trend variation (takes into account groups of genes with similar expression) and Common (dispersion across the whole data set). C) Smear plot of significantly DE genes (red dots) above and below a fold-change of 1.5 (blue dashed lines) at an FDR of 5%. D) Volcano plot of the genes considered significantly expressed (red dots) and those not significantly expressed (black dots). Genes that have 1.5 fold differences in gene expression are shown outside the dashed lines.

A) B)

C) D)

Figure 3.8. Plots summarising the result from edgeR for wasps after Inspection (Insp) vs Provisioning (Prov). A) Multidimensional scaling (MDS) revelated that most Prov wasps are scattered across the first two dimensions (leading log-fold-change between samples), whereas Insp were skewed to the right. The log-fold-change (log-FC) is the average (root-mean-square) of the largest absolute FC between samples. B) Biological Coefficient of Variation (BCV) against average log counts per million (CPM) illustrating three types of variation across the samples: Tagwise variation (gene by gene dispersion), Trend variation (takes into account groups of genes with similar expression) and Common (dispersion across the whole data set). C) Smear plot of significantly DE genes (red dots) above and below a fold-change of 1.5 (blue dashed lines) at an FDR of 5%. D) Volcano plot of the genes considered significantly expressed (red dots) and those not significantly expressed (black dots). Genes that have 1.5 fold differences in gene expression are shown outside the dashed lines.

3.4.3.2 Discussion on the evidence for a Provisioning Ground Plan

These results suggest that provisioning requires substantially different brain molecular processes compared to nest founding and nest inspection, but not egg-laying. Intriguingly, the near absence of differential gene expression between provisioners and egglayers contrasts with an order of magnitude more genes being differentially expressed, and evidence of molecular functional specialisation, between provisioners and nest founding and nest inspection females respectively.

The very small differences in brain molecular processes between females in the EL phase and the Prov phase maybe because the behaviours are not necessarily decoupled phenotypically; in other words, egglaying (the definitive ‘queen’ behaviour in social insects) is neither phenotypically nor transcriptomically distinct from the definitive worker-type behaviours – namely, provisioning. This suggests that it would be difficult for cycles of purely ovarian development to provide a mechanistic ground plan base for the decoupling of solitary phenotypes into the caste-specific phenotypes of a social insect.

By contrast, the behaviours involved in nest founding and inspection are non-overlapping with provisioning, with respect to a specific burrow: a wasp cannot provision until she has dug a burrow (nest founded) and she does not know whether a burrow needs more food unless she has ‘inspected’ it. In both these comparisons, there is an overall pattern of downregulating genes in provisioning females relative to nest founding (89% of DEGs are downregulated in provisioners) and Inspectors (67% of DEGs are downregulated in provisioners). During nest founding, A. pubescens females are fixated on their task, to the extent that they completely ignore the presence of a caterpillar (S. Moreno unpubl. data). These observations make sense: the molecular processes involved in the detection and capture of prey are largely shut down during nest founding. This suggests strong selection for hard-wired molecular mechanisms that maximise successful nest founding.

These results suggest that provisioning involves a distinct molecular signature, which differs from the other three main phases. If any mechanism in the solitary ancestor of social insects is available to be recruited as regulatory machinery for a division of labour in social evolution, it is the molecular machinery underpinning provisioning behaviours, and not ovarian

92 physiology. As such, we advocate further research into the possibility of a provisioning ground plan (PGP) in the evolution of sociality.

CONCLUSION

Here, we presented the first draft genome of a non-social sphecid wasp with a complex parental care behaviour. Additionally, A. pubescens is used as a model system to test the long-standing hypothesis that mechanistic elements of the solitary insect nesting cycle are decoupled in social evolution to produce queen and worker castes. Although A. pubescens does not belong to a family from which sociality evolved, this wasp is a progressive provisioner; progressive provisioning is an innovation exhibited by a small number of solitary bees and wasps, and in the case of A. pubescens, females progressively provisioning multiple brood simultaneously. Most social insects simultaneously express progressive provisioning as they rear a large number of brood, at various stages of development within their nest. The ability to progressively provision simultaneously, therefore, is thought to be an important pre- cursor in the evolution of social behaviour. As such, A. pubescens, presents an excellent (albeit conservative) test of whether cycles of behaviours present in solitary insects could form the basis of a mechanistic ‘ground plan’ for the evolution of a division of labour in the social insects.

By comparing brain transcription across four behavioural phases in Ammophila’s nesting sequence that resemble a selection of queen-like and worker-like behaviours that typically define castes in social insects, we were able to pick out clear molecular signatures for provisioning phases and more subtle signatures for division of labour, which reflect observed molecular signatures of worker and queen castes in a range of social insects. Although we found some of the elements predicted the OGP hypothesis, the evidence is subtle; more obviously, our results suggest that cycles of provisioning behaviour (not ovarian physiology) are the main drivers in gene expression during the nesting cycle of A. pubescens. Provisioning behaviour among Ammophillini wasps has been showed to be diverse (Evans, 1959) and a key component of their behaviour. Ammophila wasps spend most of the day-light hunting for caterpillars to provision their larvae and, so compared to behavioural phases like nest

93 founding, and egg-laying, provisioning is the most-consuming, energetically and cognitively expensive and risky behaviour.

Overall, our findings support the idea that the molecular regulatory machinery required for caste specialisation in social evolution is present in extant representatives of the solitary ancestors of social insects, even when there is not a clear physiological differentiation between behavioural phases. Importantly, our study suggests that social castes evolved by co-opting the regulatory mechanisms underpinning the provisioning cycles of solitary wasps, as well as the ovarian cycles. These processes are likely to be uncoupled in the emergence of queen and worker castes.

CHAPTER 4

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The neurogenomic signatures of behavioural transitions across the nesting cycle of the non-social wasp digger, Ammophila pubescens

Contributions: S. Sumner and S. Moreno designed the experiment; S. Moreno conducted field experiments with help from B. Arana (field assistant). S. Moreno carried out the data analysis, and S. Sumner and S. Moreno wrote the manuscript.

ABSTRACT

Unlike social insects, solitary hymenopteran females carry out all the tasks related to their nesting cycle alone: this includes nest founding, defence and provisioning. Most of the time, these tasks are carried out synchronously with brood development, and therefore, the mothers shift between behavioural states. The behavioural and molecular mechanisms involved in behavioural states have been studied in a few social bee, ant and wasp species, but they remain obscure in the solitary insects from which these social species evolved. Here, I evaluated the molecular mechanisms underpinning the behavioural states displayed during the nesting cycle of the non-social digger wasp Ammophila pubescens. Overall, the differences in gene expression between the behavioural phases in the nesting cycle were subtle, reflecting the fact that these insects maintain several burrows simultaneously, and thus require considerable behavioural plasticity to switch between behaviours; however, a small number of genes were clearly differentially expressed at each phase, and these may be indicative of the phase-specific behaviours, these included: insulin-like growth factor-binding protein is important in nest founding; yellow protein, cytochrome P450 4C1 and zinc finger protein Noc are important for provisioning. To the best of my knowledge, this is the first study investigating the molecular and behavioural process regulating key behavioural transitions in the life history of a non-social insect. As such, it provides the basis for future study on the molecular mechanisms that regulate behavioural plasticity in insects.

INTRODUCTION

Life-history stages are defined by distinct behavioural repertoires, physiology, and sometimes, even morphology. Life-history stages in insects can include migration (Chen et al., 2010), diapause (Amsalem et al., 2015), mating (Thornhill, 1980) and parental care in the form of provisioning (Suzuki, 2010). However, many of these stages are sequential and mutually exclusive. For example, some insects do not mate while caring for their offspring, so they must transition between distinct phases, shifting between phase-specific behavioural and physiological states (Libbrecht, Oxley and Kronauer, 2017). The transition from one state to another can be responsive to need and triggered by cues such as age (West-Eberhard, 1969), seasonality (Gobbi, Noll and Penna, 2006) or the social environment (Whitfield, Cziko and Robinson, 2003). This can be a major life-history transition that is often irreversible, such as sex change in fish (Burmeister, Jarvis and Fernald, 2005), unmated to matedness in bees (Manfredini et al., 2017) or from worker to queen (chapter 2); similarly a transition could refer to a stage in a behavioural sequence which arises in response to diverse stimuli, triggering a cascade of transitional changes and metabolic processes (Bloch et al., 2018).

Given the fitness implications of expressing the right behaviour at the right time and in the right context, many of the cues and responses for behavioural transitions are likely to be innate and hard-wired (‘programmed’) at the genetic level and inherited across generations. Programmed behaviours can be inflexible (fixed) or flexible, depending on need and context. Such transitions are likely to be regulated at the molecular level; e.g. with different gene expression patterns being associated with a particular life-history stage (Wong and Hofmann, 2010; Casas et al., 2016) or behavioural phase (Robinson, Fernald and Clayton, 2008). Foraging and nesting behaviours are examples of context-specific programmed states, which are underpinned by a molecular basis; for example, patterns of foraging behaviour in Drosophila are regulated by the for gene. The expression of any allele of this gene correlates with distinct foraging patterns in a broad range of animals (Fitzpatrick et al., 2005). The molecular basis of life-history transitions are well studied (Whitfield, Cziko and Robinson, 2003; Aubin-Horth et al., 2005; Ernst et al., 2015; Casas et al., 2016; Manfredini et al., 2017); conversely, the nature of the more plastic behavioural transitions and the molecular machinery underpinning them are less well studied, but can help us better understand decision-making and responses in animal behaviour. The nesting cycle of a solitary insect is a good example of a sequence of programmed behaviours that must be performed in a specific order to ensure survival and successful reproduction.

When a female insect is ready to reproduce, she embarks upon a sequence of behavioural phases; performance of each behavioural phase in a specific order is essential for successful reproduction. Each phase will be triggered by a different cue, and thus is likely to be regulated at the level of the genes such that distinct sets of behaviours are regulated by different molecular mechanisms through the nesting cycle. Some of these phases will be fixed in nature, such that the insect ‘programmed’ not to switch tasks until she has completed the phase. Other phases may be flexible, enabling the female to switch task if the context changes. Executing the appropriate response to a stimulus involves cognitive processes and decision-making: individuals need to classify and select the most appropriate response, and perhaps also remember the information about previous encounters (e.g. the conditions of their immature offspring). Determining the mechanisms by which these decisions are made can provide valuable insights into the regulation of behavioural transitions within the life-history of an organism.

Animals are required to make decisions frequently when they scout for feeding and breeding grounds, habitat suitability and to avoid predators. They assess the options and then choose the mode of action which maximises fitness. Many of the decisions to commence or maintain a behavioural state in response to a stimulus (environmental or physiological) are strongly influenced by the current state of the individual, at a certain time point (McComb et al., 2011; Libersat and Gal, 2013; Mazzariol et al., 2018) (e.g. reproductive state, starvation and defensive state). In the same way, the current state of the organism can be considered as driver/stimulus that results in them engaging in a subsequent behavioural task. Thus, for example, after mating, many organisms (e.g. some birds, reptiles and insects) start building nests. In birds, nests function as egg incubators providing a protected environment for chicks (Collias and Collias, 1984). Building a nest is challenging as it requires some decision-making regarding: location suitability, nest size and the availability of food sources, which requires skill, behavioural flexibility and planning capacity (Healy, Walsh and Hansell, 2008). The cognitive and molecular processes involved in nest building in both birds and insects are poorly known, but they are thought to be born with a template or a set of rules for their behaviours to happen in an appropriate sequence that leads to nest founding, and for “how to build a nest and the type of nest” which is improved by accumulative experience (through feedback) or maturation. Whether the nesting cycle requires some complex cognition skill is still unclear, but the accessibility of this behaviour in insects make them a promising system to investigate cognition in this crucial life-history state necessary for them to protect their offspring. Interestingly, in insects with nesting behaviour, it has been observed that they are

98 fully engaged in completing their nest, suggesting that this particular behaviour is fixed rather than plastic (Evans and West-Eberhard, 1973; O’Neill, 2001).

The brain connects extrinsic stimuli processing social, context-specific cues (through the genes) to an appropriate behavioural output. Although many of these behaviours are improved by experience and learning, some are fixed, exhibited without prior learning (Sokolowski and Corbin, 2012). For example, highly social insects display a complex organisation that relies mostly on the workers to follow simple rules, i.e. if there is not enough food in the nest, then go foraging. These rules, hard-wired in workers, are determined by genes (Oldroyd, 2019). Subtle changes in gene expression can result in profound differences at the phenotypic level with important fitness implications. For example, the gene expression differences between the queen and worker phenotypes in Polistes wasps are subtle, involving less than 200 genes (Patalano et al., 2015; Standage et al., 2016). Ultimately, behaviour is likely to be influenced by many genes acting together in modules, triggered by other gene networks; hence, it is unlikely that a single gene drives behavioural tasks. For example, in honeybees, a handful of transcription factors coregulate the gene networks containing thousands of genes that are differentially expressed between nurse and forager honeybees (Khamis et al., 2015). Identifying the genes that regulate behavioural states within the same individual can reveal the mechanisms by which different behaviours can be sequentially expressed.

To date, our understanding of how genes regulate behaviour has been focussed on characterising distinct phenotypes, transitioning periods and plasticity mainly in a social context (e.g. honeybees, zebra finches and rats; Robinson, Fernald and Clayton 2008 and Aubin-Horth and Renn 2009), model organisms (Sokolowski, 2001), mating, decision-making (Dickson, 2008; O’Connell and Hofmann, 2011; Bloch et al., 2018) and under social challenges (response to territory intrusion) (Rittschof et al., 2014). From such studies in insects, several candidate genes have been proposed for behaviours such as: foraging (e.g. foraging gene), ovarian regulation (e.g. vitellogenin gene), brood feeding (e.g. zinger finger C4H2 domain-containing proteins), egg-laying (e.g. THO complex subunit 6), provisioning (inositol 1,2,5, triphosphate receptor, Na channel subunit alpha and foraging genes) sensory perception and memory (e.g. olfactory/odorant receptors, rootletin, mRNA export factor and immediate early genes) (Fitzpatrick et al., 2005; Toth et al., 2010; Menzel, 2012; Woodard et al., 2013; Karpe et al., 2017).

We lack genomic information on the mechanisms regulating the behavioural states across the sequences of behaviours that occur in the nesting cycles of solitary insects. The studies that come near to this have examined the genomic basis of sequential phenotypic states at the developmental level (from egg – larva – pupa – adult) in the facultatively eusocial bee Megaloptera genalis (Jones, Wcislo and Robinson, 2015); in which each developmental level was underpinned by hundreds and thousands of genes. Other studies have investigated life- history stages from the solitary stage to group living state in bumblebees (virgin queen – diapausing queen – reproductive queen (Jedlicka et al., 2016)), as well as failure to transition (failed mated, mated, failed reproductive and reproductive (Manfredini et al., 2017)). The latter study examined the solitary phase of the bumblebee, providing some potential insights into the molecular changes that occur during this important life-history transition. For example, reproductively active queens were characterised by the upregulation of forkhead box protein O and downregulation of insulin-like peptides, whereas queen in diapause showed a high expression of Krüppel homolog 1 (Kr-h1) and a low regulation of methyl farnesoate epoxidase (MFE) and vitellogenin (Vg). But we lack studies that examine the molecular basis of plastic (and irreversible) behavioural transitions through the nesting cycle of a solitary insect.

Solitary and non-social insects exhibit diversity in parental care behaviour through various sequences of nesting and provisioning strategies. For example, one species of digger wasp (Podalonia affinis) starts the nesting sequence by digging her burrow, but some individuals start the sequence hunting caterpillars (Field, 1993). Other, like cockroach-hunters (e.g. Penepodium luteipenne), dig a burrow, then forage for cockroaches and only lay an egg on the last prey item before sealing up their burrow (Buys, 2012). Mud-nesting wasps of the genus Eumene, build their nest first (attached to plant stems), oviposit in the empty pot and then provision it before sealing it up (O’Neill, 2001). The provisioning behaviour exhibit by solitary and non-social insects is a further complexity in the nest-sequence since there are organisms that behave as mass provisioners, providing their brood with all the food they need in one foraging bout and then abandoning them, while others provision their brood progressively, several times over a period of time (progressive provisioners). The latter group is especially interesting when studying behavioural transitions, given that a single female can be provisioning multiple brood across several separate nests, and moreover, females must assess the needs of each burrow, before deciding on an action. Simultaneous progressive provisioner must execute the appropriate behaviour at any different burrow, all of which will have different and varying needs (Baerends, 1941b; Bonelli and Martinelli E. D., 1994; Field and Brace, 2004).

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The behavioural transitions required of a female, therefore, cycle in a strict order sequence at the level of burrow, but not across burrow, as burrow-specific needs are rarely synchronised (Baerends, 1941a). For example, some Sphecidae species provision three burrows simultaneously at various states of development (O’Neill, 2001; Field, Ohl and Kennedy, 2011). Hence, not only must these insects execute the right sequence of events, but they must do this iteratively, asynchronously and in parallel. The cognitive and mechanistic challenges for simultaneous progressive provisioners in regulating their nesting cycle(s), hence, are likely far outstrip those of a mass provisioner. The display of behavioural plasticity and decision- making process of simultaneous care of multiple brood, may be regulated by similar molecular mechanisms to the behavioural plasticity displayed by honeybees workers when they shift from nursing to forager and sometimes back again (Herb et al., 2012). Whilst plasticity is important for simultaneous care for multiple, unsynchronised brood development, the ability to show inflexible hard-wired (fixed) behaviours in some phases of the nesting cycle is also important. Fixed behaviours are almost unaltered by the environment and usually are triggered by a very specific stimulus (Sokolowski and Corbin, 2012).

Ammophila pubescens is a progressive provisioner with remarkable nesting biology that can be dissected into specific phases that are characterised by a suite of behaviours. Their nesting cycle, also called the nesting sequence, includes the “nest founding phase” characterised by digging a single-cell shallow burrow (Figure 4.1). At completion, the wasp hunts for a Lepidoptera caterpillar which is placed in the burrow, and an egg is laid on it– “oviposition phase”. The burrow is concealed and visited three days later for a brief assessment – “inspection visit” – whereby the wasp opens up the burrow to gain information on the condition of the nest and her larva. This phase seems to have a pivotal role during the nesting sequence as depending on the burrow’s content, the wasp may continue with the following phase, delay it or abandoned her burrow. A hatched egg instigates the “provisioning phase” – adding more paralysed caterpillars, whereas an unhatched egg will result in the delay of the provisioning phase. If the burrow is parasitized (e.g. with a fly’s eggs) the female will cease investment in that current burrow (Baerends, 1941b; Field and Brace, 2004). The provisioning phase involves foraging for caterpillars, which are brought to the burrow on consecutive flights. Provisioning visits are repeated several times over ten days, and the cycle is completed by the sealing up and concealing of the burrow’s entrance. Given that A. pubescens is a simultaneously progressive provisioner, at this point, a female can either start a new burrow or inspect another one in mid-provisioning. The fact that these wasps manage to provision, shifting between phases as required by the different burrows and remember the needs and cycle of each burrow, suggest a high level of plasticity and decision-making that has not

101 previously been explored at the behavioural and molecular level before. Specifically, the inspection phase must be highly flexible, given that it triggers different behaviours according to what they might find in the burrow.

Thus, in this chapter, I have two aims. First, I conducted field experiments to test and determine which phases of A. pubescens nesting cycle are fixed and flexible. To do this I report how wasps at the different phases of the nesting cycle respond to the offer of prey during their activity. The second aim is to determine the molecular bases of the different phases in the nesting cycle and determine any differences in gene expression among the different phases of the nesting cycle. To do this, I compare brain transcriptomes of individual wasps at different phases in the nesting cycle, collected in tandem with the behavioural experiments of aim 1. I examine global gene expression patterns and then how distinct each phase is relative to the other three. These analyses provide a detailed dissection of the molecular underpinnings of transient behaviours across the cycle of a simultaneous progressive provisioner.

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Nesting cycle

6 – 10 days

Figure 4.1. Ammophila pubescens nesting sequence. The sequence initiates with nest founding (blue), followed by oviposition (red). After two days, the owner comes back to inspect her burrow (black), and later provision it with Lepidoptera caterpillars (green). Inspection and provisioning phases are repeated later in the cycle. Provisioning visits are repeated several times during the rest of the burrow’s cycle. When the larva is fully provisioned the nest is sealed up permanently. The larva overwinters as an adult and emerges the following summer. Modified from Baerends (1941a).

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METHODS

Field sites and marking of wasps and their burrows

Fieldwork on A. pubescens was carried out during three consecutive summers from 2016 to 2018 in two lowland heathlands in Dorset (southwest England), Hartland Moor (50°40’14.7” N, 2°04’01.9” W) and some of its adjacent heaths; and Godlingston Heath (50°38’33.7” N, 1°58’42.3” W). In Hartland Moor, eleven sites (Table 4.1) were selected for the behavioural experiments. In Hartland Moor, all the sites were located on sandy footpaths used by members of the public; except site HE which separated by high vegetation. Vegetation flanking the sites consisted primarily of Dorset heather (Erica ciliaris), cross-leaved heath (Erica tetralix), dwarf gorse (Ulex minor) and purple bell heath (Erica cinerea). In Godlingston Heath, three sites were selected (Table 4.1). Site GR was located five meters from the Agglestone Rock, on one of the main paths to the rock; site GP was located next to a path which experienced less disturbance from the public. The sites were flanked by dwarf gorse (U. minor), bracken, and dried Dorset heather (E. cinerea). The study was conducted with the permission of the National Trust.

The number of wasps within aggregations varied across sites and years. The sites in Hartland with the highest number of wasps were selected to follow wasps’ behaviours and nesting activities (Table 4.1). On identification of an aggregation (Figure 4.2), wasps were caught at their burrows using insect nets and marked with a unique colour code of three dots on their thorax (Figure 4.3) with UniPosca® pens, to enable individual wasps to be recognised. The length of their right forewing (from the base of the thorax to the tip of the wing) was measured using a digital calliper (Mitutoyo 500-196-30) in mm as a reference for their body size. Wasps were then released and observed to ensure they were behaving normally (e.g. no wing damage). Burrows were marked by placing a small flag close to the entrance. The flags were painted with the same colour code as its resident wasp (Figure 4.3B); the date, the hour and the nest’s phase (see Figure 4.1) was also added to the flag. As a wasp keeps her burrows concealed when she is away, a small painted pebble (green) was placed next to the entrance to detect any activity while observing other females. The flags and the pebbles did not stop wasps from provisioning the burrows.

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Table 4.1. List of sites and their coordinates in which behavioural experiments were carried out.

Heath Site/Aggregation Coordinates

HA 50.670470 N, -2.0572060 W

HB* 50.670111 N, -2.0599660 W

HC* 50.671114 N, -2.0675862 W

HCL* 50.671559 N, -2.0681166 W

HCR* 50.671774 N, -2.0675547 W

Hartland Moor HCL-HCR centre* 50.671461 N, -2.67779 W

HD* 50.670906 N, -2.0684887 W

HE* 50.670703N, -2.0685183 W

HBH 50.667271, -2.054231 W

HH 50.666946, -2.055071 W

HAH 50.663696, -2.058859 W

GP 50.642680 N, -1.9784497 W Godlingston Heath GR 50.645009, -1.968208 W * Sites with the highest number of wasps

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Figure 4.2. A. pubescens aggregation in Hartland Moor (site HE). Each flag represents a burrow that has been provisioned.

A) B)

Figure 4.3. A. pubescens females marked during the study. A) A marked female with a unique colour code (BOG) dragging a caterpillar inside a burrow. B) A marked female opening one of her two burrows which were next to each other. The flags have the same colour code as the wasp. The date and time when the burrows were made are also written in the flag.

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Aim 1: Identification of flexible and fixed behaviours in response to prey during the nesting cycle

Experimental rationale

In A. pubescens, the fixed phases are likely to be during the nest founding phase since behavioural observations suggest that once they commit to digging a nest, only adverse weather will cause them to interrupt it. A completed burrow is the cue for a female to hunt for a prey item. Fixed behavioural patterns might help to regulate the following behavioural responses and complex decision-making; e.g. a nest prompts the wasp to lay an egg – an initiated nesting cycle will make the wasp inspect and, if needed, provision her nest. A good cue for testing whether a behaviour is fixed or not is to provide the wasps with something valuable – caterpillars seem to be valuable for A. pubescens, given that this wasp uses them to feed the larvae. During development, a single larva can consume nearly 12 Lepidoptera larvae of variable size (Strohm and Marliani, 2002). Each foraging event exposes the mother to predators and disease, and is likely to be energetically costly. If the wasp dies during foraging her part-provisioned larvae will starve to dead (Field et al., 2007). Furthermore, each event also exposes the location of the nest to predator and to inter and intraspecific parasites. Given all the potential risks of foraging, it is expected that when the wasps encounter “an easy prey” (in the form of an experimentally offered caterpillar at the burrow) she should accept it if she is able to; in other words, if she has the mechanistic plasticity to interrupt (or alter) the sequence of her nesting cycle. Based on this reasoning, I used the offer of prey at the burrow entrance during each phase of the nesting cycle, in order to test whether a female was fixed or flexible in her behaviour.

Collection of prey

A. pubescens wasps hunt prey species such as Anarta myrtilly, Ematurga atomaria, Eupithecia namata, among others. Caterpillars of these species were collected for the prey offering experiments. The caterpillars were obtained in three different ways as their abundance varied across sites and years. Theft of caterpillars is common when females nest very close to each other. Thus, recently paralysed caterpillars were taken from females bringing prey to their burrows, specifically, when they were occupied opening them. Some burrows were excavated, and the remaining viable caterpillars inside them were used for experiments. In addition, heathland vegetation was shaken from the base of the trunk to the branches, with the net’s frame facing up, collection any foliar material debris and insects present in the heath.

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Then, the content of the net was meticulously examined to find the caterpillars. The caterpillars were kept alive and fed until the day of the experiment. Caterpillars found in this way were given to a female wasp so that she could paralyse them, and thus only paralysed caterpillars were offered in the experiments.

Experiment

Prey offering was used to investigate the effect an external cue such as “the presence of a Lepidoptera caterpillar” next to a female’s burrow to test which behavioural phases are fixed or flexible. Accordingly, a paralysed caterpillar was presented to the wasps at the start and at the end of each of the key phases in the nesting cycle (Figure 4.1): nest founding, oviposition, inspection visits and provisioning events. An additional time point was used for the nest founding phase (mid-digging), to determine a time point in which females shift their behaviour.

Predictions

Prediction 1: wasps who are starting to dig their burrow will reject prey as they do not have a completed burrow (Prediction 1.1), but they will accept prey once the burrow has been completed (Prediction 1.2). Prediction 2: females arriving at their burrow to oviposit will reject a prey item as they have their own prey (Prediction 2.1). Also, they will reject prey after oviposition, as they need to wait until the egg hatches before bringing more prey items (Prediction 2.2). Prediction 3: before an inspection visit, females will reject prey as they first need to assess the content (Prediction 3.1), in order to decide on their next behaviour. After inspection, they may or may not accept prey, depending on the content of their burrows (Prediction 3.2). Prediction 4: Provisioning females will reject an extra prey item when arriving at the burrow with prey (Prediction 4.1), but they will accept it after a provisioning attempt (Prediction 4.2). Below is given a description of the experiment for each nest phase.

A) Nest founding – NF

Soil testing with tarsal and antennae is an indication that a wasp is about to start digging a burrow, as they are trying to find a suitable place to nest. When a wasp was spotted testing the soil, an observer stayed close-by, ready to present a prey item. After five minutes of digging (start of nest founding), a paralysed caterpillar was placed next to the mark of the hole (created by the wasp digging) while she was occupied removing soil. The prey item was placed close enough to be seen by the wasps. The same process was carried out after thirty minutes of

108 digging (middle of nest founding) or when half the body of the wasp was not visible from the surface; both are indicators that the wasp has been digging for a while. The prey item was placed next to the burrow’s entrance while the female was inside it. The wasp encountered the caterpillar when moving out of the burrow when throwing away soil debris. Finally, after burrow excavation was completed, and the wasp was occupied looking for pebbles to plug the burrow entrance (end of nest founding), another prey item was presented to the wasps situating it next to the burrow’s hole. The encounter took place while placing the pebbles on the burrow.

B) Oviposition – EL

After completion of a burrow, the wasp leaves to find a caterpillar. An observer remained watching the burrow waiting for the owner’s return, at which point the next phase of the sequence would take place. When the owner was approaching her burrow or when she was engaged opening it (before oviposition), a prey item was placed next to the burrow’s entrance and next to the prey item brought by the wasp. The second prey was therefore situated at the burrow entrance while the wasp was laying an egg. Thus, on her way out of the burrow (end of oviposition) she would encounter the caterpillar.

C) Inspection visit – Insp

A wasp approaching her burrow without carrying a prey item in her mandibles was an indication of an inspection visit. When this was spotted, a caterpillar was placed next to the burrow, such that on her arrival, and before opening the burrow (before inspection), the wasp would encounter the prey item. As with the oviposition phase, a second caterpillar was also presented and placed at the entrance while she was inside the burrow, such that she would encounter the caterpillar on her way out while closing the burrow (after inspection).

D) Provisioning – Prov

After an inspection visit, the burrow was watched by an observer in order to wait for the owner’s return with prey. On the wasp’s incoming to the burrow (with her own prey), a second prey item was placed at the burrow, thus she encountered it before opening it (before provisioning); the further prey item was placed at the burrow’s entrance while the owner was inside it, such that the encounter took place when the wasp was closing the burrow (after provisioning).

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A summary of the experiment is presented in

Table 4.2. The rejection or acceptance of a caterpillar presented to the wasps was used to test whether a female’s behaviour was fixed or flexible in each phase of the cycle. On presentation, the wasp’s behaviour was observed for up to forty seconds to determine whether a prey item was accepted or rejected. Rejection or acceptance was scored using a set of described behaviours (Table 4.3). When a female took the presented prey item, her burrow was also excavated by the observer to corroborate the burrow phase. I predicted that a female would show fixed action patterns at the start and middle of nest founding (prediction 1.1-2); before and after oviposition (prediction 2.1-2); and before inspection visit (prediction 3.1), and provisioning (prediction 4.1); but flexible behaviours at the end on nest founding (prediction 1.3) and after inspection visit (prediction 3.2) and provisioning (prediction 4.2). To determine whether the response of wasps towards the presence of prey is significantly different within and between phases, as well as before and after visiting a burrow, I used chi-square test in R v.3.5.2 (R Core Team, 2018).

Table 4.2. Summary of the prey experiments. Oviposition, inspection visit and provisioning phases were tested before opening the nests and after closing them. Nest founding was tested at the start, during and at the end of digging. The prediction and the interpretation behind predictions are also presented.

INTERACTION OF TIME OF PHASE FEMALES WITH THE OUTCOME PREDICTION INTERPRETATION EXPERIMENT PREY ITEM A female without a burrow will Rejection Five minutes after reject prey given that a burrow is Start Rejection digging has started necessary for oviposition and Acceptance provisioning (fixed behaviour).

30 minutes after Rejection A female without a burrow will Nest digging has started or reject prey as she does not have a founding Middle when half of the body Rejection completed burrow in which lay an (NF) of the wasp was visible Acceptance egg (fixed behaviour). on the surface

Rejection A completed burrow stimulates a The burrow chamber End Acceptance wasp to embark on a provisioning has been completed Acceptance phase (flexible behaviour).

Oviposition At the arrival of Rejection – The wasp brings prey to her Before Rejection (EL) females to their Acceptance burrow in order to lay an egg.

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burrows with a prey Hence, presenting an extra prey item item could result in either the use Acceptance of the prey or not (flexible behaviour).

Rejection A. pubescens does not add more While closing their After Rejection prey until the egg has hatched burrows Acceptance (fixed behaviour).

In this phase, the wasp responds Rejection At the arrival to the to cues inside her burrow. Hence, Before Rejection burrow with a prey item she needs to gain information from Acceptance the nest (fixed behaviour). Inspection Depending on what she finds visit inside her burrow, the wasp may or Rejection (Insp) Either may not accept prey. The wasp is While closing their After rejection or likely to accept the prey when egg burrows acceptance has hatched, or if the larva has Acceptance eaten most of the food (flexible behaviour). The wasp brings a caterpillar that At the arrival of Rejection she has hunted, to her burrow. females to their Rejection – Before Hence, presenting an extra prey burrows with a prey Acceptance item could result in either use it or item Acceptance not (flexible behaviour). Provisioning The wasp is in the “provisioning (Prov) Rejection phase” and will have to forage While closing their several times for several After Acceptance burrows caterpillars. Therefore, the wasp is Acceptance expected to take the caterpillar (fixed behaviour)

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Table 4.3. Classification of the actions linked to rejection and acceptance of caterpillars.

TIME OF PHASE ACCEPTANCE ACTIONS* REJECTION ACTIONS EXPERIMENT

Stinging and placing the prey next to Start the burrow’s entrance Nest Stinging and placing the prey next to founding Middle - Ignoring the caterpillar the burrow’s entrance (NF) Throwing the caterpillar away (10 Laying an egg on the presented End cm away) caterpillar - Stinging and throwing the Laying an egg on the presented Oviposition Before caterpillar away (10 cm away) caterpillar (EL) After Adding the caterpillar to the burrow - Stinging, followed by ignoring the

Inspection Stinging and placing the caterpillar caterpillar Before visit next to the burrow before opening it - Throwing the prey away into (Insp) After Adding the prey inside the burrow nearby bushes Stinging and placing the caterpillar Provisioning Before next to the burrow before opening it (Prov) After Adding the prey inside the burrow *Sometimes wasps lose the prey item. When this happens, females look around near the burrow and start entering backwards to the burrow. These behaviours were considered acceptance reactions.

Aim 2: Molecular mechanisms underpinning the behavioural phases exhibited during the nesting cycle of A. pubescens

Wasp collections

To investigate the molecular mechanisms associated with the different phases in the nesting cycle, females were sampled from mid-June to mid-August 2017 in both heathlands. Specifically, I sampled females at the point where they had been observed completing each particular phase of the nesting cycle, generating replicated examples of females who had completed the four key phases: nest founding, oviposition, inspection and provisioning phases.

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A) Females at the nest founding phase (diggers)

A wasp at the nest founding phase was sampled after at least forty-five minutes of observing them digging. Usually, after this period, the wasp’s body is invisible from the surface as she is digging the burrows’ chamber. Whilst a female was digging inside the burrow; a small net was placed over the burrow entrance preventing the wasp from escaping. On her way out, the wasp was caught and, with RNAse and DNAse free dissection scissors, her head was cut off, and immediately placed in 0.5 ml Eppendorf tube full of RNAlater® Stabilization solution (Ambion®am) to capture gene expression at that moment. The body was stored in EtOH 80% for further analysis. The tube was labelled and stored in a cooling box and later the same day placed at -20°C. Each burrow was excavated to confirm the correct assignation of the phase. An empty chamber (no egg or prey) confirmed that the female was indeed in the nest founding phase.

B) Females in the oviposition phase (egg-laying females)

A female sampled in the oviposition phase was monitored from the nest founding phase until she added a caterpillar to her burrow on which to lay her egg. When the wasp was occupied laying an egg (nearly 60s), the burrows’ entrance was blocked with a net. On her exit, the wasp was taken and sampled in the same manner as wasps in the nest founding phase.

C) Females at the inspection phase (inspectors)

A female arriving without prey was assigned as an inspector. On completion of her brief visit and assessment of her burrow, the wasp was captured, and her head was placed in RNAlater®, and her body in EtOH 80%, as described above. The burrow was then excavated to investigate the number of caterpillars left and the condition of the brood (hatched or unhatched egg).

D) Females in the provisioning phase (provisioners)

Provisioners were sampled after a wasp had added a prey item to her burrow. Specifically, a wasp was observed at her burrow from her inspection visit until after having provisioned with the fourth caterpillar and was engaged in closing her burrow. A net was then placed over burrows’ accesses to block and catch the owners. Once in the net, wasps were processed as

113 per the females for the other phases. A hatched larva with several caterpillars inside the excavated burrow confirmed the phase.

Samples processing

RNA extraction, library preparation and sequencing

RNA extractions were carried out using Qiagen® RNeasy Plus Universal Tissue Mini kit with some modification. Using a confocal microscope, the wasps’ heads cavities were opened to remove the brain tissue using forceps free of RNAses. Brains were placed in individual 2 ml Eppendorf tubes free from DNAses and RNAses with a stainless-steel bead. Then, tubes were placed in the TissueLyser LT operating for 3 min at 50 Hertz oscillations (1/s). After this, tubes were briefly centrifuged to keep the tissue at the bottom of them. QIAzol lysis reagent (900 µl) was added to each tube. All tubes were placed back on the TissueLyser for 5 min at 50 Hertz oscillations (1/s). After this, the tubes were briefly centrifuged. The lysates were placed in 1.5 ml Eppendorf tubes and flash freeze at -80°C. After this, the tubes were kept at room temperature for 5 min to defrost lysates. 100 µl of gDNA eliminator solution was added to each tube and vigorously shaken for 15 s. Next, 180 µl of chloroform isoamyl was added, and the tubes were shaken again for 15 s. Then, the tubes rested at room temperature for 5 min. After this, they were centrifuged at 12 000 g for 15 min at 4°C. The supernatant (upper aqueous phase) of each tube was transferred to a new 1.5 ml Eppendorf tube, and approximately 600 µl of 70% of ethanol was added to it and mixed thoroughly. Half of the mix was transferred to RNeasy mini spin columns placed in a 2ml collection tubes and centrifuged at 8 000 g for 15 s at room temperature (RT). The flow through was discarded and the second half of the mix was placed in the same column. The previous step was repeated. Then, 700 µl of buffer RWT was added to the columns which were centrifuged at 8 000 g for 15 s. The flow through was discarded and the column reused. Next, 500 µl of buffer RPE was added to the column and centrifuged at 8 000 g for 2 min. The column was placed in a new collection tube and centrifuged at full speed for 1 min. After this, the collection tube was discharged and a new 1.5 µl collection tube was put in place. Then, 40 µl of RNAse-free water was added to the column and the tubes were left at RT for 10 min. Finally, the columns were centrifuged for 8000 g for 1 min and the eluates were kept at -80°C until RNA sequencing.

RNA integrity and yield were evaluated first with Nanodrop 1000 with ratios 260/280 nm and 260/230 nm, and later with Aligent 2100 Bioanalyser (Aligent Technologies) at UCL genomics facilities. Once the quality of the samples was determined, ten samples of each behavioural

114 phase (except for inspection visit where samples were lower (n=4)) with the best RNA yield were selected for RNA sequencing (RNA-seq) at Novogene Bioinformatics Institute in China. A total of 34 libraries were constructed using NEB Next® Ultra™ RNA Library Prep Kit and RNA-sequencing was performed in NovaSeq 6000 machine. Details of RNA extractions and quality assessment are in Supplementary 3.2.

RNA sequencing (RNA-seq) data processing

RNA-seq data visualisation and pre-processing

The quality of the raw reads and basic statistics such as the total number of sequences, their length and quality, as well as GC% content were evaluated using FastQC v 0.11.8 (Andrews, 2015). To visually identify the general quality trend among all the samples a clumped summary was created with MultiQC (Ewels et al., 2016).

Errors during sequencing can impact adversely the downstream analysis of RNA-seq data. rCorrector (Song and Florea, 2015) was used to amend random sequencing errors in Illumina RNA-seq reads. The software uses De Brujin graph to efficiently denote all the trusted Kmers in the input reads and computes a local threshold at each position in a read. The output files flag reads that were corrected, as well as reads that were detected containing errors unable to correct. The latter were removed using a python script (FilterUncorrectabled PEfastq.py). Next, low-quality bases and adapters were trimmed using TrimGalore! v 0.5.0 (www.bioinformatics.babraham.ac.uk/projects/trim_galore) a wrapper for Cutadapt and FastQC that seems to completely remove adapters in reads. Ribosomal RNA (rRNA) was removed with Bowtie2 v.2.3.5.1 (Langmead and Salzberg, 2012) using as reference the LSUParc and SSUParc database of rRNA from SILVA (Quast et al., 2013). This was done by mapping trimmed treads to the SILVA database to remove undesirable rRNA. Paired-end reads that were unaligned to the reference were kept and the quality of the reads was again evaluated with FastQC to detect any overrepresented sequence.

The “new Tuxedo” package (Pertea et al., 2016) was used to align the reads to the A. pubescens genome and to quantify transcript abundance. Before mapping the reads to the genome, I built a HISAT index with HISAT2 v.2.1.0 (Kim, Langmead and Salzberg, 2015). The index is used to rapidly and accurately identify short sequences embedded in the genome, which makes the mapping faster and requires less computing memory. To create the index,

115 the A. pubescens annotation file was used to extract splice sites and exon information (Kim, Langmead and Salzberg, 2015). Next, each paired-end fasta file for all samples was mapped to the genome. The resulting SAM files were sorted and converted to BAM files with SAMtoos v.1.9.1 (Wysoker et al., 2009). After file conversion, transcripts were assembled for each sample, and later transcripts were merged with StringTie v.1.3.5 (Pertea et al., 2015). Following merging, lists of transcripts were compared to the genome annotation file using Gffcompare v0.10.1 (Pertea et al., 2016). Finally, transcripts abundance estimations were performed, and a table count was created for differential expression analysis in R v.3.5.2 (R Core Team, 2018) with edgeR (Robinson, McCarthy and Smyth, 2010; McCarthy, Chen and Smyth, 2012). The full bioinformatics pipeline performed for all samples is illustrated in Figure 4.4, and the full code of the pipeline is located in Supplementary 4.1.

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FASTQC rCorrector

FilterUncorrectablePE TrimGalore

Bowtie2 FASTQC

HISAT SAMtools

StringTIe edgeR

Figure 4.4. Bioinformatics pipeline used to analyse RNA-seq data of A. pubescens. The box at the top represents the input files used to perform the pipeline. The boxes in the diagram show the names of the programs used to process the data. The grey box shows the name of the R packaged used for differential gene expression analysis.

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Global gene expression analysis

Principal component analysis (PCA) and hierarchical clustering

Before any analyses, the raw counts (total number of reads that overlaped in a particular genomic position) were filtered with a median of >1 for all samples. After this, a PCA was carried out with the filtered raw counts frame to investigate the global trend of gene expression among the samples. In addition, a hierarchical clustering using the standard score of the filtered data was performed to identify how the samples cluster to each other. Both analyses were done in R v.3.5.2 (R Core Team, 2018) using pheatmap v.1.0.12 (Kolde, 2019) for the hierarchical clustering.

Weighted gene coexpression network analysis (WGCNA)

To assess the transcriptional organisation in wasps’ brains when carrying out a phase of the nest cycle, a weighted gene coexpression network analysis was performed (WGCNA). This analysis helps to identify sets of genes co-regulating the gene expression patterns and, clusters them in modules (Langfelder and Horvath, 2008). Thus, genes within a given module exhibit similar response to changes, in this case, in behaviour. The transcript FPKMs were filtered in the same manner as the raw counts for the analyses above. After this, the WGCNA package v.1.68 (Langfelder and Horvath, 2008) was used to construct the co-expression networks for the entire A. pubescens dataset. Then I looked for modules that correlated significantly with differences in the nesting cycle. VisANT (Hu et al., 2013) was used to visualise the modules and hub (most connected/co-regulated) genes.

Transcriptomic signature analysis of the behavioural phases in the nesting cycle

Gene expression contrast for each behavioural phase

The filtered count dataset used for hierarchical clustering and PCA was used for differential gene expression analysis. The filtered raw counts were normalised using edgeR package v.3.8 (Robinson and Oshlack, 2010; Robinson, McCarthy and Smyth, 2010) default method. A genewise negative binomial generalised linear model (glmLRT) was applied to the count data to later use a panned linear contrast to identify differentially expressed (DE) genes (Table 4.4) (Mikheyev and Linksvayer, 2015; Manfredini et al., 2017). The contrasts were used to identify DE genes in each behavioural phase with respect to the other behavioural phases. An additional contrast was conducted comparing behavioural phases involved in foraging against

118 those which do not. To identify set of genes that were common or unique to each contrast, I used jvenn (Bardou et al., 2014).

Enrichment analysis of DE genes

The lists of significantly upregulated genes in each contrast were compared with A. pubescens genome for gene ontology (GO) enrichment analysis in Blast2GO. The same gene set was used to identify overrepresented metabolic pathways. To identify a set of genes and GO terms that were common or unique to each comparison and contrast, we used jvenn (Bardou et al., 2014).

Table 4.4. Summary table of transcriptomic comparisons among phases of the nesting cycle, compared using the glmLRT function.

Phase of interest Contrast Nest founding (NF) NF vs EL+Insp+Prov

Egg-laying (EL) EL vs NF+Insp+Prov

Inspection (Insp) Insp vs NF+EL+Prov

Provisioning (Prov) Prov vs NF+EL+Insp

RESULTS AND DISCUSSION

Aim 1: Identification of flexible and fix behaviours in response to prey during the nesting cycle

A total of 277 wasps were tested in field experiments across three years - 2016-2018 (Figure 4.5): 126 for nest founding, 43 for the oviposition phase, 50 for the inspection visits and 59 for the provisioning phase. As predicted, many females responded to the presence of caterpillar by placing it inside their burrows, confirming the value of prey and that the experimental manipulation successfully mimicked a natural provisioning situation. However, there were differences among the phases as to how the wasps responded. Wasps generally accepted the extra prey item after completing a nest, egg-laying, inspection and provisioning; they

119 rejected the offered prey when digging their burrows and before inspecting their nests. Here I present the results for the phases in turn.

In the nest founding phase, wasp responded differently to the presence of a caterpillar (before

2 and after the phase, ꭙ df=3, N=120= 92.21, p < 0.001)). All females initiating a nest (beginning of nest founding phase) rejected the prey (Prediction 1.1). Once the wasps were committed to digging a burrow, the prey items went unnoticed and, even when the prey was placed over the nest entrance, the caterpillars were thrown away from the burrows like debris. This suggests that during digging wasps are hard-wired to complete nest founding successfully. However, once they had a finished burrow 30/33 females accepted the caterpillar and took it into their burrow and laid an egg on it. (Prediction 1.2). The completion of a burrow appears to trigger oviposition, at which point the wasps were able to accept prey. However, the time between taking hold of the prey and laying an egg varied between 5 to 25 minutes. It is possible that the wasps were still preparing their eggs for oviposition. A comparison of ovarian development between wasps at the end of nest founding phases and wasps arriving with prey before oviposition will be useful to investigate this assumption.

Wasps in the oviposition phase responded significantly different to the presence of prey

2 (before and after the phase, ꭙ df=1, N=42= 10.2861, p < 0.001). Before oviposition, wasps behave flexibly towards the extra prey item. The experimentally offered caterpillar and the ones caught by the provisioning wasps were selected indiscriminately (Prediction 2.1, Figure 4.5). Interestingly, when the wasps were tested after oviposition, they accepted the extra caterpillar offered. I had predicted that the prey would be rejected at this phase, showing a fixed behaviour (Prediction 2.2) as once an egg is laid, the female leaves the burrow for a few days, switching to attend to the needs of another burrow cycle (Baerends, 1941a; Field et al., 2007). Wasps adding extra prey items after egg-laying has been documented in Ammophila sabulosa (Field, 1989a). In three cases (3/13), A. sabulosa females were observed removing the caterpillars from other females’ burrows. One of the caterpillars was placed back in the burrow by the parasite and laid an egg on the prey item. When reclosing the burrow, the parasite reencountered one of the caterpillars left outside and took it back to the burrow, without laying an additional egg. Hence, it is possible that the response of A. pubescens to the extra prey items after oviposition is similar to brood parasitism observed in A. sabulosa.

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Wasps in the inspection phase responded differently towards the presence of prey (before

2 and after the phase, ꭙ df=1, N=50 = 25.9615, p < 0.001). Before the inspection visit, 22/25 wasps rejected the offered caterpillar. The caterpillars were either ignored or discarded 10 cm away from the burrows’ entrances; this indicates how hard-wired the nest inspection phase is in the A. pubescens nesting cycle (Prediction 3.1). By contrast, when caterpillars were presented after the inspection visit, 21/25 wasps took the caterpillar presented (Prediction 3.2). This is a remarkable switch from an overt rejection before they had gained information about the burrow state to a clear acceptance of prey afterwards. This is an example of a switch from a fixed action response to a flexible one and highlights the putative importance of the inspection visit in the nesting cycle of this species. For wasps to decide on the next appropriate behaviour, they need to gain information from the burrow. Once the information is acquired and the condition of the burrow is ascertained as optimal (i.e. has not been parasitized and a hatched egg with small amount of food), wasps are likely to provisioning the nest (Baerends, 1941a). This is exactly what I found after the inspection phase in most cases. The four times that caterpillars were rejected; unhatched eggs were found on excavation of the burrow.

Wasps during the provisioning phase responded differently to the presence of prey (before

2 and after the phase, ꭙ df=1, N=59 = 11.415, p < 0.001). Behaviours during the provisioning phase resembled those of the oviposition phase. On arrival at the burrow with provisioned prey, wasps were equally likely to accept (19/33) the offered caterpillar or only use the one they arrived with (Prediction 4.1); after provisioning almost all wasps took the extra prey item (25/26) (Prediction 4.2). It seems that both egg-laying and provisioning might be controlled by similar molecular processes as suggested in chapter 3. The provisioning phase is the most extensive phase among many progressive provisioners organisms (Tallamy and Wood, 1986; Gilbert and Manica, 2010; Suzuki, 2010; Gardner and Smiseth, 2011; Kelstrup et al., 2018).

We did not find statistical differences in how wasps respond to prey before oviposition and

2 provisioning (ꭙ df=1, N=45= 0.002, p = 0.963). In the same way, their responses towards prey

2 after both behaviours were not statistically different (ꭙ df=1, N=56= 0.0106, p = 0.919 The analogous response to prey items in both phases further suggests that similar mechanisms are taking place. The fact that both involve hunting for a caterpillar before the phases take place inside the burrow might suggest that wasps remain receptive to forage. Bukhari et al. (2017) have shown that some biological functions peak hours after a brief territorial challenge in male threespined sticklebacks (Gasterosteus aculeatus). Hence, a "foraging" signal may remain.

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n=50 50 n=43

40 n=33 n=33 n=30 30

n=25 n=25 n=26 No. attempts No. 20 Rejected n=12 Accepted 10

End

After After After

Before Before Before

Middle Beginning NF EL Insp Prov Behavioural phases

Figure 4.5. Stacked barplot illustrating the total number of prey that were accepted (in blue) or rejected (in red) during offering experiments carried out before and after each phase of the nesting cycle. For NF, females were tested at the beginning, middle and at the end of completing a burrow. NF = nest founding, EL = oviposition or egg-laying, Insp = inspection visit, and Prov = provisioning. The numbers at the top of each bar are the total number of experimental prey offering attempts performed for each of the behavioural phases.

Aim 2: Molecular mechanisms underpinning the behavioural phases exhibited during the nesting cycle of A. pubescens

A total of 34 individual wasp brains were successfully sequenced (see Supplementary 3.1). Specifically, ten wasps at the end of nest founding, oviposition and provisioning, and for inspection visit, only four wasps were sequenced due to the difficulty of obtaining a large sample size in the field. The total number of sequences range between 2,146,836 – 39,291,945; the GC% varied between 48-50%. After filtering low quality reads and rRNA, the

122 reads per sample varied between 2,105,389 to 37,275,039, and between 87% - 89% could be aligned to the A. pubescens genome (see Chapter 3).

4.4.2.1 Subtle differences in transcription among behavioural phases at the global gene expression level.

At the global brain transcriptomic level, there were no clear molecular signatures of the nesting cycle phases among the wasps sampled. All the samples were used to investigate the global gene expression patterns. However, the provisioner n109 appeared to be an outlier, with very distinct gene expression patterns, contributing 30% of the variation in the first two components. Hence, this sample was removed as the reasons for her being an outlier are likely to be due to something other than the behaviour of interest (e.g. parasites or other extrinsic stress). With the remaining samples (n=33), the general trends were calculated again. The principal component analysis (PCA) revealed that 60% of the variation was explained by as many as ten components (Supplementary 4.2, Figure S1). The first three components explained 38.5% of the variation. Only twelve wasps contributed 5% or more to the first two components (Supplementary 4.2, Figure S2). In general, the PCA did not show an apparent clustering among wasps displaying the same behavioural phase (Figure 4.6). However, the centroids of oviposition and provisioning phases were in the first component, whereas the nest founding phase and inspection centroids were in the second component. The clustering of the oviposition and provisioning centroids seems to support the idea that both might share molecular processes, as suggested by the results from the prey-offering experiment in aim 1. The hierarchical clustering, as well as the PCA, did not show apparent clustering of the behavioural phases. However, some females displaying the same behavioural phase did group in small clusters of three individuals (Figure 4.7).

Despite the lack of clear transcriptional clustering by phenotype, there was evidence of phenotypic differentiation at the gene network level. A total of 23,035 transcripts were used in the weighted gene co-expression network analysis (WGCNA) after removing transcripts with a median < 1 across the samples. The WGCNA detected a total of 59 modules of co- expressed genes. The average number of transcripts per module was 390 (range 53 – 3,420, SD = 564). One module (pink) with 699 transcripts showed a significant negative correlation (cor = -0.4, p-value = 0.02) with behavioural phases. Within the pink module, the gene significance and module membership were highly correlated (cor=0.4, p=4.5e-56). Given that the pink module is relatively large, the 30 most connected transcripts were used to visualise

123 the network (Figure 4.8). Among them, only three transcripts had blast hits; two of them were associated with cognition and brain function. One was Homeobox protein extradenticle isoform X4, a transcription factor involved in brain development in Drosophila (Nagao et al., 2000). Another was the mushroom body large-type Kenyon cell-specific protein 1, also a transcriptional factor located in the mushroom body, linked with memory and sensory processing in honey bees (Takeuchi et al., 2001; Park, Kunieda and Kubo, 2003). The third gene was the PREDICTED: integrator complex subunit 11, which in humans is a component of the integrator complex involved in processing the small nuclear RNAs (snRNAs) (Baillat et al., 2005).

These results suggest that, although subtle, there is a distinct molecular basis to the behavioural phases of the nesting cycle of A. pubescens. Although few genes have annotations, those that do suggest a significant role for brain function and cognition in the regulation of the behavioural sequence.

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Figure 4.6. Principal Component Analysis (PCA) using the expression of 19,233 genes from the brain that resulted from mapping RNAseq reads of 34 A. pubescens wasps. The first two principal components explained only 32% of the variation in gene expression. There is not an apparent clustering of females displaying any of the behavioural phases during the nesting sequence. Nest founding (NF), oviposition (EL), inspection (Insp) and provisioning (Prov). However, the centroids of NF and Insp were clustered in the first dimension while EL and Prov centroids were clustered in the second dimension.

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Figure 4.7. Hierarchical clustering analysis for the 33 samples, clustered by shared patterns of gene expression patterns across 19,233 genes. Small groups of females displaying the same behavioural phase clustered together. However, several wasps were scattered across the two main clusters.

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Figure 4.8 Visualisation of the pink gene subnetwork or module in A. pubescens. The pink subnetwork was significantly associated with phenotype differentiation (subnetwork trait association analysis). Nodes indicate hubs of highly connected genes. The names of these genes are in black and green lines represent the connections among these genes. The illustration shows the top 30 genes with most of the connections within the pink subnetwork.

4.4.2.2 Dissecting the behavioural phases of the nesting sequence at the brain transcriptomic level.

Despite the lack of a clear differentiation of behavioural phases at the global transcriptomic level, distinct sets of DE genes were identified for each behavioural phase. I compared each individual phase with a combination of the other three phases to identify genes that were exclusively up or downregulated in the focal phase relative to the rest of the nesting cycle. The most distinct behavioural phase was Prov with 277 DE genes (Table 4.5; Figure 4.9), followed by EL and Insp with 37 and 20 DE genes, respectively. The least distinct phase was NF with only seven DE genes relative to the other phases. The list of DE genes in each phase was visualised in a Venn diagram (Figure 4.10), to identify the genes exclusively upregulated in each behavioural phase. A few genes were shared between some phases, but none of them

127 was shared among all the phases. The genes shared between phases did not have BLAST hits.

Table 4.5. Summary table of contrast with glmLRT function. Females in the provisioning phase (Prov) had the highest number of differentially expressed genes when contrasting with the remaining phases (FDR < 0.05).

Not Phase of Contrast Genes Up Down interest significant

NF NF vs EL+Insp+Prov 7 6 1 19,248

EL EL vs NF+Insp+Prov 37 28 9 19,218

Insp Insp vs NF+EL+Prov 20 14 6 19,235

Prov Prov vs NF+EL+Insp 277 72 205 18,978

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Figure 4.9. Smear plots from edgeR displaying the log-fold changes with the DE genes (red dots) for a given behavioural phase vs the other phases. The x-axis shows the average log counts per million (logCPM) expression levels and the y-axis shows the log fold change (logFC).

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Figure 4.10. Venn diagram contrasting the list of differentially expressed genes (DEGs) in each phase relative to the other. Few DE genes were shared between lists of DEGs. NF = nest founding, EL = oviposition, Insp = inspection, and Prov = provisioning.

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Nest founding phase: no evidence of a distinct fixed action behavioural phenotype

The nest founding phase demands that wasps find and decide a suitable place to dig a burrow. The wasps have been observed testing the soil with their antennae and mandibles. Once they have identified the area, they start digging which is energetically costly, involving physical removal of soil (Tinbergen, 1974). They use their heads and legs to create a hole and throw the debris away by flying away from the nest and dropping the soil. Also, they carry out orientation flights looking for landmarks they can use to refind their nests later, which requires navigation using memory of visual landmarks (Baerends, 1941a). Furthermore, nesting behaviour is linked to reproduction in that the sole purpose of building a nest is to deposit an egg (O’Neill, 2001). Therefore, genes and or pathways related to decision-making (i.e. insect olfactory processing pathway (Barron et al., 2015)); learning and memory (i.e. transcription factors and neuropeptides (Sokolowski, 2001)); energy and muscle movement (i.e. Oxidation – reduction process (Wang et al., 2014)); and reproduction (i.e. vitellogenin and heat shock) are expected to be expressed highly in this phase.

Despite the apparent phenotypic distinctness of this phase, only seven genes were uniquely upregulated (Table 4.5). Five had BLAST hits, but none of them was associated with cognition or navigation. Two were linked to reproduction: these genes were the atrial natriuretic peptide receptor 1 (Npr1) and the insulin-like growth factor binding protein complex acid labile subunit (IGFBP-ALS), and the third one was an uncharacterised protein.

In mammals, Npr1 is a receptor for the brain natriuretic peptide NPPB/BNP which is a potent vasoactive hormone (Koller et al., 1991) that stimulates guanylate cyclases to convert GTP to cGMP which in return stimulates cGMP-dependent protein kinase which induces smooth muscle relaxation. Additionally, Npr1 is involved in signal transduction. The IGFBP-ALS is a candidate royal jelly gene that binds with the insulin-like growth factor binding proteins (GFPB) (Holly and Perks, 2006). The insulin signalling pathway plays a crucial role in growth, metabolism, age and reproduction in insects (Wu and Brown, 2006). The remaining DE genes (n=3) were uncharacterised proteins.

Despite the small number of genes upregulated by nest founding females, several gene ontology (GO) functional groups were overrepresented relative to the genome. A reduced list (created in Blast2GO) of the overrepresented GO IDs included three GO molecular functions: guanylate cyclase activity, protein kinase activity and ATP binding; one GO cellular

131 component: integral component of membrane; and three GO biological processes: cGMP biosynthesis process, intracellular signal transduction and protein phosphorylation (Figure 4.11).

Protein phosphorylation is crucial for biological regulation, through which protein function is controlled to respond to extracellular stimuli in the nervous system. Every class of neural protein is controlled by phosphorylation and is one of the major mechanisms of neural plasticity (Nestler and Grenngard, 1999). The regulation of protein phosphorylation requires protein kinases, which was a GO term overrepresented as well. The activity of guanylate cyclases is to catalyse the conversion of GTP to cGMP. The accumulation of cGMP controls complex signalling cascade through cGMP-dependent protein kinases. Both protein kinases, guanylate cyclase and cGMP, play a key role in physiological processes, such as vascular smooth muscle motility (Lucas et al., 2000). In a developmental time-course analysis of gene expression between solitary and gregarious locusts, guanylate cyclase was labelled as a gregarious gene (Chen et al., 2010). It seems that the nest founding phase requires signal transduction. The signal transduction takes place at the cellular level. In this process, a signal is transmitted triggering shifts in cellular states or activities. The signal transduction ends with the regulation of a metabolic process and transcription and is restricted within the receiving cell and events.

The prey offering experiment in the previous section showed that the nest founding phase is fixed at the start and in the middle of digging in A. pubescens; the lack of genes and pathways linked to decision-making may lend further evidence to this. However, it is important to note that the wasps used for the gene expression analysis were sampled at the end of digging (when the behaviour is more flexible), not at the start (when the behaviour is fixed action). Decision-making might take place at the start of nest founding. The presence of IGFBP-ALS seems to support the idea that reproduction is part of the nesting process. Although there was not a direct connection between Npr1 and muscle movement, it seems that Npr1 helps in the process of muscle relaxation, which might be happening once the wasps have completed their burrows.

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Figure 4.11. Ontology (GO) terms that are overrepresented in genes upregulated in nest founding females (Test set) relative to the reference dataset of the A. pubescens genome (Reference set).

Oviposition phase: Only 37 genes were differentially expressed in the oviposition phase

For the oviposition phase to take place, a wasp needs a mature oocyte to lay as well as a prey item to attach her egg. Thus, before oviposition, she will have to forage, carry the prey item to her nesting site, and remember the location of the nest that she will use. Hence, genes like vitellogenin and juvenile hormone are predicted to be upregulated in this phase, as well as genes like foraging and egr-1 for foraging and memory, respectively. Also, the prey offer experiment revealed that wasps are willing to take prey items after oviposition which could suggest that genes related to plasticity might be upregulated.

For the oviposition phase, 37 genes were DE (Table 4.5), from which 28 were up- and nine down-regulated relative to the other three phases. The low level of upregulated genes might reflect the fact that this phase is physiological rather than cognitive. The levels of gene expression are similar to those found in mated queen bumblebees (Manfredini et al., 2017).

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Among the DE genes, 21 were annotated. The annotated up-regulated genes (18/21 genes) for oviposition were the transient receptor potential cation channel subfamily V member 5 (Trpv5), homeobox protein Nkx-2.2a; REDICTED: endocuticle structure, glycoprotein SgAbd- 8-like; major royal jelly protein 1 (MRJP1); PREDICTED: bromodomain-containing protein 4- like; 26S proteosome non-ATPase regulatory subunit 9 (PSMD9); and putative S-adenosyl-L- methionine-dependent methyltransferase.

In mammals, Trpv5 plays a role in calcium ion homeostasis (Hoenderop et al., 2003). In Drosophila the subfamily A member 1 (TrpA1) is involved in calcium ion transport as well as in thermosensory behaviour (Zhong et al., 2012). Calcium ion activity connects oocytes and nurse cells in the cecropia moth, Hyalophora cecropia (Woodruff and Telfer, 1994). Homeobox protein Nkx2.2a is a transcriptional activator involved in the development of insulin-producing beta cells (Sander et al., 2000). PREDICTED: endocuticle structure glycoprotein SgAbd-8-like plays a role during larval development in Bombus terrestris (Colgan et al., 2011). MRJP1 plays a role in caste determination, influenced by environmental factors (Kamakura, 2011) and has a defence response to Gram-negative bacterium (Fontana et al., 2004). Furthermore, it promotes ovarian development, enhancing the titre of vitellogenin and juvenile hormone (Kamakura, 2011). Bromodomain-containing protein 4 is a chromatin reader that recognises and binds acetylated histones and plays a key role in the transmission of epigenetic memory across cell divisions and transcription regulation in mouse (Dey et al., 2000). In humans, PSMD9 acts as a chaperone during the assembly of the 26S proteosome (Kaneko et al., 2009). However, the subunit 11 is involved in the circadian rhythm in Drosophila melanogaster. Finally, among the down-regulated genes were Cytochrome b and two PREDICTED: uncharacterised proteins. Cytochrome b is involved in spermatogenesis in D. melanogaster (Clancy, Hime and Shirras, 2011).

There was evidence of functional enrichment in the egg-laying phase, with three GO terms overrepresented (Figure 4.12). One molecular process: ion channel activity; and two biological processes: ion transmembrane transport and polyamine biosynthetic process. The latter modifies egg-laying in crickets in which a depletion of polyamine delays oviposition. The same showed that juvenile hormone might be regulating oviposition behaviour via polyamine metabolism (Cayre et al., 1996). The evidence of upregulation of Trpv5, MRJP1 and downregulation of Cytochrome b, as well as the overrepresentation of polyamine biosynthetic process, supports the presence of genes and pathways involved in oviposition. However, the data did not reveal genes related to foraging, memory and behavioural plasticity. Nonetheless,

134 the expression of Homeobox protein Nkx2.2a (transcription factor regulator) and Bromodomain-containing protein 4 linked to epigenetic memory suggests regulation of gene expression. Epigenetic mechanisms allow organisms to adapt to fluctuations in their environment. Thus, gaining information and experience that will impact their future receptiveness towards the same stimulus (D’Urso and Brickner, 2014).

Figure 4.12. Ontology (GO) terms that are overrepresented in genes upregulated in ovipositing females (Test set) relative to the reference dataset of the A. pubescens genome (Reference set).

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Inspection phase: no evidence of cognitive processing

At the phenotypic level, the inspection visit phase is the most plastic and brief behavioural phase of the nesting cycle (Baerends, 1941b; Tinbergen, 1951), as females need to gain cues from the burrows to decide on their subsequent behaviour. The acquired cues trigger distinct fates. Frequently, wasps after inspection will be provisioning their burrows (see previous section). Nonetheless, wasps might find unhatched eggs or enough food, which makes them delay provisioning but forces them to inspect another burrow. Furthermore, a parasitized burrow (by mites or fly maggots) will result in abandonment of the burrow, avoiding any future investment in that nest (Field and Brace, 2004). Immediate early genes (IEG) are genes that respond rapidly following a neural stimuli that require immediate-early response; they can be activated and transcribed in minutes after stimulation without synthesising de novo proteins (Tischmeyer and Grimm, 1999; Sommerlandt, 2016); IEG genes are usually encoded by transcription factors linked to important biological processes such as immune system and stress; as well as neuroplasticity and decision-making (Bahrami and Drabløs, 2016; Sommerlandt et al., 2018). As such, IEG and genes associated with cognitive processing were predicted to be upregulated in the inspection phase.

We lack concrete evidence on how wasps gain information from their burrows. Specifically, we do not know whether they interact with their brood or they just asses the amount of food left. Social insects engage in mouth to mouth fluid transfer (trophallaxis) including larvae. In carpenter ants (Camponotus floridanus) is a mechanism for nestmate recognition and inter- individual communication (LeBoeuf et al., 2018). However, brood recognition in A. pubescens does not seem to affect provisioning (Field, Accleton and Foster, 2018). Either way, wasps must be stimulated by sensory perception, gaining information from the burrow itself and or from the larvae. Therefore, genes encoding potassium voltage-gated channel and protein Shaker, both involved in learning and memory, as well as in proboscis extension reflex in response to sucrose in Drosophila (Sokolowski, 2001; Ishimoto et al., 2005), are expected to be differentially expressed. Other genes encoding sensory perception of sound and touch such as rootletin (Chen et al., 2015; Styczynska-Soczka and Jarman, 2015) should be expressed in this phase as it was upregulated in inspectors when comparison with provisioner in chapter 3. Finally, as the inspection phase happens after the reproductive phases (i.e. nest founding and egg-laying), we would expect the downregulation of genes such as vitellogenin, juvenile hormone and major royal jelly protein.

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I found that among the 20 DE genes (Table 4.5), only 11 had blast hits. The upregulated genes included those encoding gag-like protein, cell cycle checkpoint protein RAD17 and PREDICTED: protein dachsous and uncharacterised proteins. Gag proteins are components of retrovirus and LTR retrotransposons (Demirov and Freed, 2004; Campillos et al., 2006); whereas the cell checkpoint protein RAD17 is involved in the cellular response to DNA damage stimulus (Tsao, Geisen and Abraham, 2004). None of them was IEG, nor are they involved in sensory perception, decision-making and neuroplasticity.

In contrast, protein yellow, protein Peter pan (ppan), and PREDICTED: uncharacterised protein, were downregulated in the inspection phase. The protein yellow in adult fruit flies controls the pigmentation of the cuticle (Wittkopp, True and Carroll, 2002); whereas in Apis mellifera, the yellow-like family may be involved in caste differentiation (Ferguson et al., 2011) and predicted to be downregulated in non-reproductive phases. In addition, ppan is required for female gamete generation, maturation of some imaginal tissue into adult structures and progression of normal oogenesis (Migeon, Garfinkel and Edgar, 1999). This support the prediction of downregulated genes involved in reproduction. The enrichment analysis did not show overrepresented GO ids for this phase.

Provisioning Phase: transcriptomically distinct from all other phases

The provisioning phase is the most remarkable phase not only among Ammophilini wasps but also among insects that gradually provide food to their larvae. This phase is one most time consuming and costly in terms of energy use and survival, as females need to add between 5 to 12 caterpillars through the nesting cycle in just one nest (Field et al., 2007). A. pubescens wasps live between 5 to 9 weeks and can provisioning up to 10 nests. Hence, the time exposed to predator, miltogrammine flies and inter and intraspecific parasites is high, given that the addition of one caterpillar exposes them to predators while hunting, and exposes the location and entrance of their burrows to potential parasites. If wasps die while provisioning, their reproductive investment will be lost as the larvae will die of starvation (Field and Brace, 2004; Field, 2005; Field et al., 2007). Among the genes expected to be DE genes are those encoding in olfaction, necessary for hunting (Casiraghi et al., 2001), memory to remember which nest to provision and metabolic processes, that may reflect high energy intake.

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I found a total of 277 genes were DE. Seventy-two genes were up and 205 downregulated (Table 4.5). A total of 98 genes had a BLAST hit. As predicted, among the upregulated genes were genes involved in sensory perception: Odorant binding proteins are required for activity of pheromone-sensitive neurons, and for olfactory behaviour in insects (Kim, Repp and Smith, 1998; Xu et al., 2005). Genes involved in immune response were also upregulated: Cytochrome P450 4C1, Hymenopteacin-like and Fas-associated death domain protein (Leulier et al., 2002; Naitza et al., 2002). Immune response genes are likely to be differentially expressed given that wasps are exposed to pathogens while hunting.

Interestingly, a gene involved in reproduction suppression was upregulated: the zinc finger protein Noc (noc). In Drosophila some noc proteins are involved in the adult ocellar structures and the development of the embryonic brain; as well as in Notch signalling pathway (Cheah et al., 1994), which mediates reproductive constraint in worker honeybees (Duncan, Hyink and Dearden, 2016). This suggests that provisioning females are actively suppressing reproduction; however, just one gene involved in this process was found.

Among the downregulated genes (n = 205), there were genes involved in aggression and mating: sex determination protein fruitless (fru) (Lee et al., 2001; Chan and Kravitz, 2007); brain development and memory: Leishmanolysin-like peptidase (McHugh et al., 2004) and Potassium voltage-gated channel protein Shaker (Sokolowski, 2001); polyadenylated RNA and spliced mRNA: Tho complex subunit 2 (Sträßer et al., 2002; Masuda et al., 2005); antioxidant signalling: Breast cancer type 1 susceptibility protein like protein (Gorrini et al., 2013); and regulation of locomotion: suppressor of lurcher protein 1, Isoform X1 (Brockie et al., 2013). The downregulation of fru and upregulation of noc further supports the idea that wasps in this phase do not express reproductive behaviour. Nonetheless, the size of the largest oocyte in the reproductive tract is similar to a mature egg (Chapter 3). The downregulation of genes related to memory suggests that the provisioning phase does not involve high functioning in memory formation or neuron activity.

The enrichment analysis showed several GO terms overrepresented. The reduced list of them included eight GO terms (Figure 4.13). Among them were six molecular functions: 3-oxo- arachidoyl-CoA synthetase activity, 3-oxo-lignoceronyl-CoA synthetase activity, 3-oxo- cerotoyl-CoA synthase activity, L-tyrosine:2-oxoglutarate aminotransferase activity, succinate dehydrogenase (ubiquinone) activity. Only one biological process was overrepresented: fatty

138 acid biosynthetic process. These GO terms are involved in providing energy. The synthesis of fatty acids is key for energy storage and are the major source of energy. Also, fatty acids are essential for oxygen transport and development of tissue (Alberts et al., 2015)which supports the prediction that the provisioning phase demands the most energy expenditure as the wasps must hunt caterpillars, sting and carry them to their burrows.

Figure 4.13. Gene Ontology (GO) enriched bar with the reduce list of GO names overrepresented for Prov contrast (Test set) using as reference A. pubescens genome annotation (Reference set).

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CONCLUSION

Here, I investigated the behavioural phases exhibited by the non-social digger wasp A. pubescens during the nesting cycle, a key life-history stage that takes place during their adult short lives. Nesting behaviour in Ammophila as in many other organisms is hardwired in their brain until they have completed the task. Nonetheless, they can display a plastic response towards eventualities when carrying out the nesting cycles, which are performed in synchrony with larval development. Hence, the mothers need to exhibit behavioural phases according to their larvae state and transition from one behavioural state to another when caring for multiple larvae at the same time. The mechanisms driving this key life-history state are relatively unknown. Furthermore, we do not know what behavioural phases exhibited in the nesting cycle triggers plastic responses of the mother.

By testing the behavioural response that A. pubescens exhibits towards the presence of prey items (a valuable resource to feed their larvae) next to their burrows, I was able to assess what behavioural phases were flexible during the nesting cycle, while others were fixed. Furthermore, by comparing brain transcriptomes across behavioural phases displayed in A. pubescens nesting cycle I was able to investigate the molecular mechanisms driving their behaviour during this key life-history state. I found that wasps are inflexible towards the presence of caterpillars while digging their burrows, but once they complete them, they are receptive and use the caterpillar to provision their larvae. Wasps after completing a phase of the nest cycle behave flexible, but before the oviposition and the provisioning phases they can take the prey item. I found discernible molecular signatures driving wasps’ behaviours during each phase of the nesting cycle. Specifically, I found set of genes involved in cognitive function and energy intake during the nest founding phase, whereas the oviposition phase was underpinned by genes involved in reproduction and foraging. I found evidence of some sensory perception driving the inspection phase while the provisioning phase was driven by processes involved in locomotion and foraging. To date, the genomic resources on solitary insects have been restricted to a small group of taxa, focusing on key behavioural phases in the life-history of organisms. Here, I provided behavioural and genomics data regarding a whole state in the life-history A. pubescens in which wasps transition from one behavioural phase to another.

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CHAPTER 5

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Population genetics of a non-social wasp in a fragmented habitat

Contributions: S. Sumner and S. Moreno designed the study; S. Moreno conducted the fieldwork with help from B. Arana and L. Rowland (field assistants); S. Moreno conducted the laboratory work, genotyping and the analyses; and S. Moreno and S. Sumner wrote the paper. National Trust granted permission to sample wasps on their land.

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ABSTRACT

Habitat fragmentation and degradation can shape the distribution and dispersal of many organisms. Fragmentation can isolate groups of individuals making them less able to disperse, reducing gene flow, and increasing the chances of inbreeding and genetic isolation; these traits make populations vulnerable to local extinctions. However, when habitat fragmentation is recent, shifts in genetic diversity and allele fixation are challenging to assess. Moreover, some mobile organisms are able to cope with fragmentation by dispersing and colonising new environments nearby, rendering the effects of fragmentation on the population negligible. Here, I investigate the effects of habitat fragmentation on populations of non-social digger wasps, in the UK heathlands. Heathland habitats harbour a unique biodiversity; although often protected, these habitats are becoming increasingly fragmented; the robustness of heathland organisms to fragmentation is poorly understood. I examine the population genetics, nesting and breeding biology of the non-social digger wasp Ammophila pubescens – a heathland specialist, which is endangered in parts of Europe – to shed light on how a fragmented landscape affects population structure and dispersal. Using sixteen highly polymorphic molecular markers, I assess the allelic diversity of this wasp in three heathland populations in Dorset, southwest England, to determine molecular differentiation within and between heathlands, and determine whether any dispersal that does occur is sex-specific. I found no significant population structuring among females across the three different heathlands; however, the pairwise comparisons reveal that wasps from the most distant heathlands were genetically distinct, with evidence of isolation by distance. On the other hand, I found that males are admixed and are not isolated by distance. Males showed to have a wider dispersal range relative to females. Furthermore, I identified that aggregations are formed by related and unrelated females. These results suggest that despite its patchy habitat, there is gene flow mediated by males and subtle differentiated subpopulations mediate by females. These findings reveal some of the mechanisms by which apparently niche-specialist insects show robustness to human modified landscapes. An understanding of population structure and dispersal mechanisms is essential for effective management of sustainable populations in rare, fragmented habitats.

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INTRODUCTION

Habitat loss, fragmentation and degradation are some of the main threats to biodiversity (Fahrig, 2003), as they trigger fluctuations in the microclimate (e.g. water flux and wind) (Saunders and Hobbs, 1991), that affect a variety of biological processes. These processes include dispersal (Coulon et al., 2010), mating (Pavlova et al., 2017), connectivity between populations (Wang et al., 2017), and shifts in patterns of microevolutionary factors which can lead to reduced gene flow, loss of genetic diversity, changes in behaviour and increased mortality for individuals/populations that are not resilient to fluctuating environments (Pini et al., 2011). Furthermore, the shift in genetic diversity and population structure can have strong effects on the survival and persistence of species in the short and long term (Matesanz et al., 2017). For example, low genetic diversity and small population size can make organisms vulnerable to disease or the prevalence of deleterious mutations (Keller and Waller, 2002; Frankham, 2010). Despite habitat fragmentation and degradation, some species/populations are resilient to shifts in their environments (Exeler, Kratochwil and Hochkirch, 2008, 2010): their natural dispersion and mating system strategies allow gene flow to persist, reducing the risk of local extinction (Fahrig and Merriam, 1994). Understanding these mechanisms of resilience is becoming an increasingly critical part of sustainable stewardship of our planet and its natural resources.

Over the last century, changes in land-use have shaped landscapes all over the globe. Moreover, in the last twenty years, nearly 40% of the land has been claimed for human use (Billen, Hilton-Taylor and Stuart, 2004). This shift in land-use has transformed natural habitats, posing a threat to biodiversity as species’ habitats are further constrained, fragmented and lost. For example, terrestrial mammals with highly fragmented habitats are prone to extinction (Crooks et al., 2017), and the number of invertebrates in fragmented habitats has been declining (Goulson, Lye and Darvill, 2008; Goulson, 2019; Seibold et al., 2019). By contrast, for other species changing landscapes present opportunities to adapt and become resilient to this shift. For example, dwarf birch (Betula nana) populations have declined and become fragmented in Britain, yet they appear to have retained sufficient genetic diversity to persist (Borrell et al., 2018). In urban environments, pollinators are less selective; hence, some plants, such as the weed Crepis sancta, have experienced relaxed selection of reproductive traits linked to attractiveness (Cheptou and Dubois, 2016). Around the world, the environmental landscape has been (and continues to be) transformed rapidly with land becoming intensively farmed for crops and grazing. In Central Europe, plagioclimax habitats like grasslands,

143 meadows and heathlands have become refugees to many plant and animal species which cannot survive in large-scale farmed landscape (Fagúndez, 2013). Heathlands are important habitats which host a unique biotic diversity; they were widespread in northern Germany, Netherlands and parts of the United Kingdom until the end of the nineteenth century. Today, this habitat is rare, fragmented and isolated, making it one of the most threatened ecosystems on the planet. In Europe, heathlands are protected by the EU Habitat Directive (EU directive 92/43/EEC); species richness in these habitats depends on careful local management of the remaining areas. More than 5,000 invertebrate species rely on heathlands; some have become heathland specialists such that they are unable to live elsewhere. Such specialisation may make them particularly vulnerable to changes in land management, such as habitat fragmentation due to the pressure of increased demand on these areas for tourism and leisure activities. The impact of low-level fragmentation is little studied for the majority of heathland species. For example, the distribution patterns of heathland plant species has been shown to be influenced by fragmentation; their extinction can be prevented by colonising adjacent patches (Piessens, Honnay and Hermy, 2005). Spider communities are strongly affected by heathland management. Intensive management of heathlands reduces niches for aerial web spinners and climbing spiders (Bell, Philip Wheater and Rod Cullen, 2001). Detrimental effects of habitat fragmentation on the genetic viability of heathland specialists are evident for a few species: e.g. the solitary Andrena bees (e.g. Andrena fuscipes and Andrena vaga) in Germany, where A. fuscipes is red-listed and exhibits low genetic differentiation and small, isolated populations (Exeler, Kratochwil and Hochkirch, 2008, 2010). We lack the broad research base on which to effectively manage viable species populations in these rare and fragmented habitats.

Here, I examine the population genetic structure of an insect habitat specialist within fragmented habitats of UK heathlands. Ammophila pubescens is non-social univoltine digger wasp with a wide distribution in central Europe. Digger wasps lay their eggs in underground burrows, which they provision with paralysed arthropod prey. Like other predatory wasps, they play an essential role in maintaining ecosystem function through regulation of arthropod (mainly Lepidoptera) populations and pollinations (Robin J Southon et al., 2019). It nests in lowland heathlands and dunes to which it is highly specialised. It is little surprise, therefore that A. pubescens is of high conservation interest in some countries in Europe: In the Czech Republic (Farkač, Král and Škorpík, 2005) and Germany (Bayern and Hessen), it has an endangered status, whereas in Poland it is nearly threatened (National Red List). In the UK it is not listed as threatened or endangered; however, given its limited distribution and its dependency on lowland heathlands (a threatened habitat), may now be under threat.

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Ammophila wasps are a poorly studied genus, but observations suggest that their populations are patchy and isolated: females build their burrows in small exposed patches of sand in close- knit aggregations (often next to land used by human activities) surrounded by heathland scrub which provides them with suitable foraging and sleeping habitats (Baerends, 1941a). Observations suggest that females are highly philopatric, whilst males patrol aggregations to find mates (Andrewes, 1969; Yeo, 1983). When most females have mated, males may patrol wider areas to find mates (Baerends, 1941a). Habitat fragmentation may, therefore, serve as a barrier to males in finding mates, and females in finding patches to reproduce in. This may result in genetic traits that are likely to prove detrimental to population viability: isolation by distance, reduced gene flow and potentially inbreeding. Moreover, areas that are grazed or frequented by humans appear to experience high rates of nest abandonment in burrowing wasp populations due to highly compacted soil (Srba and Heneberg, 2012). These observations suggest that Ammophila wasps will be particularly vulnerable to habitat loss, disturbance and fragmentation, with high population viscosity, low gene flow, and significant levels of inbreeding. However, there are no studies on the population genetic structure of these wasps, and thus, it is unclear how to manage habitats for their benefit.

Assessing the population structure, genetic diversity and dispersal strategies of specialists is essential as, by definition, most specialists have co-evolved to exploit a specific niche and are unable to survive in other habitat types. Thus, population genetic studies that reveal patterns of gene flow, preferences in mating behaviour and movement of the species, are essential for assessing the extent to which specialists are likely to be vulnerable or resilient to sudden shifts in land-use, degradation and fragmentation. To date, studies carried out on insect specialists in fragmented habitat have focused on forest ecosystems (Zimmermann et al., 2011; Freiria et al., 2012; Soro et al., 2017); by contrast, studies assessing the impact of heathland fragmentation on population structure of specialist insects are scare (Exeler, Kratochwil and Hochkirch, 2010).

Here, sixteen highly polymorphic microsatellite loci markers are used to investigate genetic diversity, population genetics, dispersion and breeding biology of A. pubescens in the Dorset area heathlands of the UK. Based on the current understanding of the biology of this genus, three specific hypotheses were tested. First, A. pubescens populations in adjacent (but fragmented) heathlands are genetically distinct with little gene flow between them and thus will exhibit significant isolation by distance (Hypothesis 1). Second, males (not females) are the main dispersers between heathlands (Hypothesis 2). Third, females are highly philopatric,

145 nesting in aggregations with related females such that within a single heathland there is distinct population structuring (Hypothesis 3). The results suggest that females from distant heathlands were genetically distinct, and I found evidence of isolated by distance. On the other hand, males across heathlands were not differentiated and seemed to be admixed which supports the hypothesis that males are the primary dispersers between heathlands. Finally, I found weak genetic differentiation between close aggregation and low levels of viscosity, but with evidence of inbreeding, which suggests that related and unrelated females form aggregations.

METHODS

Study sites

A. pubescens were sampled in Southwest England from 2016 to 2018 during the summer period (June-August) from three lowland heathlands in Dorset. Hartland Moor, (50°40’14.7” N, 2°04’01.9” W), Godlingston Heath (50°38’33.7” N, 1°58’42.3” W) and Studland Bay (50°40’08.7” N, 1°57’18.4” W) (Figure 5.1). The primary habitat of Hartland Moor is a lowland heath with valley mire and heather dominating the dryer areas (England, 2013); typical vegetation includes Dorset heath, bell heath, cross-leaved heath, western gorse, bog asphodel, with beak sedge, marsh gentian (Mitchell et al., 2000). Hartland Moor is bordered by Stoborough Heath (to the west), Arne (to the northeast) and by farmland (to the south- east). It is situated approximately 7.1 km from Godlingston Heath and 8.5 km Studland Bay, in a straight line. Godlingston Heath, a lowland heath, is adjacent to Studland Bay (to the east, separated by a road) and flanked by a golf course and farmland to the southwest and the north, respectively. Their habitats include dunes, willow cars, peat bog, alder, bell heather, cross-leaved heath and western gorse (Chapman, Clarke and Webb, 1989). The Purbeck’s area is a popular tourist destination during the summer: sandy paths score all these landscapes, providing visitors with the opportunity to explore the heathland. Godlingston Heath and Studland Bay are used heavily for activities such as walking, biking and horseback riding; Hartland Moor has fewer visitors and thus less anthropogenic disturbance.

Wasps were located by walking the sandy paths through all three heathlands. Male and female wasps were then sampled opportunistically at their aggregations, most of them located on exposed sandy paths (mainly used by trekkers); the wasps used the bushes flanking the paths for sleeping and hunting (Figure 5.2). In Hartland Moor, seven aggregations were sampled

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(aggregations HA-HE, Table 5.1; Figure 5.3). Most of the patches were connected by sandy paths, separated by 2 to 300 meters, except for site HE, which was next to HD but separated by bushes, which made this site less vulnerable to anthropogenic disturbance (Figure 5.3A). In Godlingston Heath, wasps were sampled from two aggregations separated by approximately 147 m (in a straight line) (Table 5.1; Figure 5.3B). Finally, in Studland Bay, two aggregations were selected: one sandy path surrounded by heather and protected from the wind (SH), and the dunes (SD), with less floral material and so wasps had to travel further from their nests to hunt (Table 5.1; Figure 5.3C).

Sample collection

To investigate the genetic diversity and population structure in Heathlands in the Dorset area, male and female wasps were sampled in Hartland Moor, Godlingston Heath and Studland Bay (Table 5.1). The sampling took place from 2016 to 2018 in Hartland Moor. On the other hand, sampling in Godlingston Heath took place from 2017-2018, wasps sampled from two aggregations were analysed as a single group given the small number of wasps found. Finally, wasps in Studland were sampled in 2018, as in Godlingston Heath, wasps were analysed as a single group due to the small number of wasps found. The intensive sampling in Hartland Moor was designed to test the hypothesis of genetic viscosity within aggregations. Most of the wasps were sampled within aggregations when females were occupied opening with their burrows, while males were sampled as they flew up and down the paths, presumably looking for mates. In some cases, brood were also collected to investigate if the female feeding was their mother, as intraspecific parasitism in Ammophila is common (Field, 1989b; Field et al., 2007; Field, Accleton and Foster, 2018). In order to capture genetic diversity across a heathland, some wasps were sampled opportunistically outside of aggregations, as they flew along paths in the three heathlands. Wasps were captured using sweep nets, and whole wasps were placed in 1.5 ml Eppendorf tubes with 80% of EtOH, separately. However, a few females from the Hartland Moor and Godlingston cohort were captured using a non-lethal approach (by removing the end of tarsi using clean dissection scissors) and stored in the same way as whole wasps. Given the small sample size at both aggregations and their close proximity, the wasps at these two locations were analysed as a single aggregation. Like in Godlingston, a small sample of wasps was sampled in Studland, and so wasps were analysed as a single site sample. After each field season, tubes were stored at -20°C.

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Forest

Wood

Golf course

Park

Residential area

Common and meadow

Heathland

Lake and reservoir

Farm

Brownfield site

Cemetery

Allotments

Figure 5.1.Location of the of the heathlands where A. pubescens was sampled from 2016 to 2018. In Hartland Moor seven wasp aggregations (red dots) were sampled and some wasps were sampled opportunistically across the heathlands. In Godlingston heath, three aggregations were sampled, and in Studland heath only two aggregations were sampled. Land between heathlands is characterised by farmland (light green), pine forest and grassland.

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A) B)

C) D)

E) F)

Figure 5.2. Examples of some of the sites where A. pubescens wasps were sampled. Hartland Moor sites HD (A) and HE (B), Godlingston heath: sites GG (C) and GP (D), and Studland Bay: sites SH (E) and SD (F).

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B) C)

Figure 5.3. Distribution of the wasps sampled within the three heathlands. A) Hartland Moor heath, B) Godlingston and C) Studland. The coloured dots indicate aggregations. In Hartland Moor heath seven aggregations were sampled in site HA = red, site HB = dark blue, site HC = yellow, site HCL = orange, site HCR = green, site HD = pink and site HD = light blue. In Godlingston two aggregations were samples, site GP = blue, GG = yellow. In Studland, wasps in SH = blue and SD = yellow.

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Table 5.1. Geographical distribution of the aggregations where adult females and males were sampled from 2016 to 2018. In Hartland, seven aggregations were sampled, whereas in Hartland and Godlingston, two aggregations were studied.

Heathland Aggregation Code Coordinates

A HA 50.670470N, -2.0572060

B HB 50.670111N, -2.0599660

C HC 50.671114N, -2.0675862

HARTLAND MOOR CL HCL 50.671559N, -2.0681166

CR HCR 50.671774N, -2.0675547

D HD 50.670906N, -2.0684887

E HE 50.670703N, -2.0685183

50.642680N, -1.9784497 GODLINGSTON P GP

HEATH G GG 50.642192N, -1.9790746

H SH 50.667059N, -1.9519018 STUDLAND BAY D SD 50.667403N, -1.9475600

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Table 5.2.The total number of samples (n=176) used in this study. The number of wasps sampled varied across years and sites as the density of the aggregations fluctuated. The total number of wasps sampled per year (last rows in each heathland block), per site (last column per heathland block) and per heathland (in red) consists of both females (F) and males (M).

2016 2017 2018 Total per HEATHLAND Code F/M F/M F/M site

H 2/0 8/8 0/5 23

HA 13/0 --- 0/1 14

HB 8/0 --- 3/0 11

HARTLAND HC 8/0 --- 3/0 11

MOOR HCL 18/0 1/0 14/1 34

HCR 6/0 0/1 6/0 13

HD --- 1/0 16/0 17

HE --- 14/0 14

Total per 55 19 63 137 year G --- 5/1 0/5 11 GODLINGSTO GP ------0/2 2 N HEATH GG --- 1/2 --- 3

Total per 0 9 7 16 year S ------1/2 3 STUDLAND SH ------5/5 10 BAY SD ------6/4 10

Total per 0 0 23 23 year

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DNA extractions

DNA from brood and tarsi from the non-lethally sampled wasps was extracted using the HotShot DNA extraction protocol (Truett et al., 2000) with some modifications. Prior to DNA extractions, tarsi were removed from ethanol and dried at room temperature. Tarsi, eggs and 3 mm of larvae were placed in 50 µL of HotShot alkaline solution and digested at 95°C for 2 h in an Eppendorf PCR machine. An equal amount of HotShot neutralisation buffer was added after digestion and mixed by pipetting; 35 µL of the extracted DNA was diluted in 100 µL of distilled and autoclaved water.

For whole wasps, the thoracic muscle was used to extract the DNA. For these samples, the Qiagen® DNA extraction kit was used, with some modifications. The individual’s thoracic tissue was extracted on ice and placed in 2 ml tubes with stainless-steel bead. Samples were flash frozen at -80°C and then disrupted in the TissueLyser LT at 50 Hertz oscillation (1/s) for 2 min, followed by a fast spin. 180 µL of PBS was added to each tube and frozen for an hour. After this, the sampled was placed on the TissueLyser again for 5 min. In addition to these modifications, incubation of the samples took place for at least 3 hours. The DNA was resuspended in 100 µL of AE buffer. The DNA concentration was evaluated with Qubit 3 Fluorometer (Invitrogen ®). DNA was diluted to have a final concentration of 10 ng/µL.

See supplementary 5.1 for both protocols with modifications.

PCR amplification, fragment analysis and genotyping

Sixteen of the most polymorphic loci microsatellites reported by Field et al. (2018) were selected to assess genetic diversity and population genetics. Details of the loci are listed in (Table 5.3). The PCR reactions were carried out using the QIAGEN® Multiplex PCR kit following the manufacturer’s protocols. Forward primers were labelled with fluorescent dyes. The microsatellites were split into three multiplex PCR reactions (Table 5.3) and were amplified using the following PCR profile. An initial heat activation at 95 °C for 15 min, followed by 44 amplification cycles with an initial denaturation step at 94 °C for 30 sec, an annealing step at 57 °C for 90 sec and an extension at 72 °C for 90 sec. Then, a final extension at 60 °C for 30 min. All PCRs included six positive controls, in order to standardise scoring of alleles across plates, and two negative controls, to check for contamination. The amplifications were performed in an Eppendorf Mastercycler® Nexus. The amplification of the loci were confirmed

153 by electrophoresis in 2% agarose gels. The PCR products were diluted 1:200 and sequenced at the UCL genomics facility for fragment analysis using an ABI3739xl sequencer. Alleles for each locus were scored using Geneious, Version 2019.0.3 (https://geneious.com). Fragment sizes were quantified using GeneScanTM 500 ROXTM Size Standard. Any samples with ambiguous alleles were reamplified to ensure the correct genotype.

Table 5.3. General features of the loci used to characterise A. pubescens in southwest England.

MICROSATELLITE MOTIF FLUORESCENCE

1APUA02 (GA)22 HEX 1APUA06 (CT)9 6-FAM 1APUC03 (GA)21 HEX 1APUC07 (AG)15 NED 1APUF06 (GA)21 6-FAM 2APUA12 (GA)7, (GT)6 HEX 2APUB12 (CT)13 NED 2APUC12 (TC)16 HEX 2APUE11 (TC)14 HEX 2APUH06 (CT)20 6-FAM 3APUB06 (GA)13 NED 3APUB11 (TC)13 6-FAM 3APUC08 (GA)24 NED 3APUD10 (TC)7 6-FAM 3APUH02 (AG)15 HEX 3APUC05 (AG)16 HEX

Table modified from Field et al., (Field, Accleton and Foster, 2018) 1Loci multiplexed together, 2Loci multiplexed together, 3Loci multiplexed together

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Data analysis

Microsatellite descriptive statistics and genetic diversity

The quality of the genotypes derived from the females (n=134) was investigated using Micro- Checker v.2.2.3 (Van Oosterhout et al., 2004) which assesses the presence of typographic errors, presence of stuttering, null alleles and allelic dropout. Deviations from Hardy-Weinberg equilibrium (HWE) and evidence of linkage disequilibrium were tested with GenePop v.4.2. (Raymondm and Rousset, 1995; Rousset, 2008). After this, the total number of alleles (Na), the effective number of alleles (Ne) as well as observed and expected heterozygosity (Hobs and Hexp, respectively) were estimated with GenAlEx v.6.5 (Raymondm and Rousset, 1995; Rousset, 2008). Na is an estimator of allele diversity, whereas Ne takes allele frequency into account. Small values in Ne for a given locus mean low allele frequency in a population. All these quality checking analyses were carried out with females only, as they are diploid, whereas males are haploid.

Hypothesis 1: The three heathlands represent genetically distinct populations

To investigate genetic differentiation among heathlands, the molecular variation across heathlands was examined by performing an analysis of molecular variance (AMOVA) and by calculating the genetic fixation (FST). Both were calculated in ARLEQUIN V.3.5. (Excoffier and Lischer, 2015). Genetic admixture was also examined, using STRUCTURE v. 2.3.4 (Pritchard, Stephens and Donnelly, 2000) which clusters individuals into groups according to allele frequency and the probability of cluster affiliation. The parameters set included prior sampling locations with an initial burn-in of 50 000 and then a 100 000 step Markov chain Monte Carlo (MCMC), with K = 2 – 7 (where K is the potential number of genetic populations). When a wasp’s genotype appears to fit into more than one cluster, this suggests admixture between populations (Pritchard, Stephens and Donnelly, 2000). Ten simulations were run, and the outputs were averaged and visualised on POPHELPER Structure Web App v1.0.10 (http://pophelper.com/). To examine isolation by distance, a Mantel test was carried out with the pairwise genetic distance (GD) matrix between aggregations and the log geographic distance (Log(1+GDD)) matrix in GenAlEx v.6.5 (Peakall and Smouse, 2006, 2012).

Signs of genetic bottlenecks were investigated for each of the populations using BOTTLENECK v. 1.2.02 (Piry, Luikart and Cornuet, 1999). When a recent bottleneck event has taken place, Na and Ne have small values. Heterozygosity is attributed to either the relative frequency of alleles (He) or to heterozygosity at mutation-drift equilibrium (Heq). The 155 two-phase mutation model (TPM) was assumed under 95% of single-step mutation, 5% of multiple step mutation and a variance = 12 among multiple mutation steps (Piry, Luikart and Cornuet, 1999). A Wilcoxon’s test with 1000 replications was carried out to estimate the significance of heterozygosity excess across loci.

Hypothesis 2: Males disperse more than females

Males spend most of the day patrolling patches of aggregations (e.g. 2 x 40 m) looking for females to mate with. Also, males are not limited to an aggregation. Hence, if males disperse more than females, we predict no structuring among heathlands, as well as high gene flow.

The AMOVA, as well as the Mantel test for isolation by distance, were calculated using GeneAlex v.6.5. Additional to the AMOVA, a Bayesian clustering approach to investigate population differentiation was carried out in STRUCTURE v .2.3. The parameters for the Bayesian approach included prior sampling locations with an initial burn-in of 50 000, 100 000 step MCMC and K = 1 – 5 for each of them ten simulations were run. The outputs were averaged and visualised on POPHELP Structure Web App v1.9.10.

H3: High population viscosity within females’ aggregations

A. pubescens females are likely to limit their dispersal as they need to find suitable nesting areas: choosing nesting sites in their natal aggregations is a safe option. Hence, we predict that population structure with respect to aggregations which will result in high FST, levels of relatedness that are higher than the population average and clear genetic clustering of the aggregations.

To test the prediction, data from the intensively sampled Hartland Moor, where a large number of females (n= 112) were sampled from seven different aggregations within the same heathland, was used (Table 5.2). The global value of the coefficient of genetic differentiation

(FST, for each locus separately and across all the loci) was calculated as a measure of overall population differentiation, along with the coefficient of inbreeding (FIS). Pairwise comparisons of FST between aggregations were also calculated. Low values of global FST indicate small population differentiation (Exeler, Kratochwil and Hochkirch, 2008; Černá, Straka and Munclinger, 2013). After this, we used STRUCTURE to calculate admixture among the wasps

156 across the aggregations using prior population information. A burn-in period of 50 000 and a 100 000 MCMC with prior location and a K = 2 – 10 were used. The outputs were averaged and visualised using POPHELPER. In addition, isolation by distance was tested for the aggregations in GenAlEx v.6.5 using pairwise FST population values (FSTP) and the geographic distances (Log(1+GDD)). A principal coordinate analysis (PCoA) was performed in GenAlEx v.6.5 using FST values to represent similarities between aggregations graphically.

Relatedness among dyads within and between aggregations was calculated in Coancestry v. 1.0.1.9 (Wang, 2011). Coancestry calculated the relatedness between pairs of individuals and/ or pairs of groups (e.g. populations) using seven estimators. Wang’s moment estimator (Wang, 2002) was used among the estimators as handle small sample size.

RESULTS

Microsatellite descriptive statistics and genetic diversity

A total of sixteen microsatellite loci were amplified for both females and males of A. pubescens. Overall, 132 females were successfully amplified at all 16 loci (Table 5.4). The total number of alleles per locus (Na) ranged from six to thirty-two (mean = 20.063 ± 1.51) while the effective number of alleles per locus (Ne) ranged from 3.816 to 17.573 (mean = 11.556 ± 0.956, Table 5.4). The global Hardy-Weinberg exact test to calculate heterozygosity deficit across all the loci among females revealed that eight microsatellites deviated from HWE after Bonferroni correction (Table 5.4). The site-level analysis of HWE revealed that Godlingston Heath and HA were the only populations in HWE at all loci; in Studland Bay only four loci were in HWE. The remaining population presented between one to three loci deviated from HWE. Deviation from HWE at several loci is sometimes an indicator of population substructure and inbreeding. Because these genetic traits were expected to occur in the population (see Introduction), the full set of 16 microsatellites were included in all analyses.

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Table 5.4. Comparative metrics of the microsatellite loci used across populations. A total of sixteen loci were successfully amplified; several loci deviated from Hardy-Weinberg Equilibrium (HWE p in bold).

Locus N Na Ne Hobs Hexp HWE p

APUA06 133 14 6.048 0.805 0.835 0.1437

APUF06 129 32 17.572 0.736 0.943 0.0000

APUA02 133 20 13.686 0.639 0.927 0.0000

APUC03 134 21 15.406 0.866 0.935 0.0995

APUC07 133 26 15.308 0.881 0.935 0.0192

APUH06 133 20 10.812 0.865 0.908 0.1775

APUE11 132 16 9.776 0.812 0.898 0.0022

APUC12 132 20 12.649 0.856 0.921 0.1147

APUA12 133 6 3.816 0.629 0.738 0.0229

APUB12 133 18 10.114 0.729 0.901 0.0000

APUB11 133 19 9.335 0.880 0.893 0.1757

APUD10 133 15 6.979 0.812 0.857 0.1169

APUH02 133 22 13.157 0.737 0.924 0.0000

APUC05 130 26 14.910 0.754 0.933 0.0000

APUB06 133 19 10.396 0.887 0.904 0.1603

APUC08 132 27 14.861 0.826 0.933 0.0000

Na: Allele richness, Ne: allele frequency (Ne), Hobs: Heterozygosity observed, Hexp: Heterozygosity expected, HWE p: the probability of deviation of Hardy-Weinberg equilibrium. Loci significantly deviated from HWE after Bonferroni correction are in bold.

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Hypothesis 1: The three heathlands represent genetically distinct populations

There was some evidence that wasps from the most distant heathlands were genetically distinct and isolated by distance.

There was some significant population structuring among females sampled across the three heathlands. Global FST was low and not significantly structured (0.00826, P > 0.05). Pairwise

FST comparisons found no significant differences between Hartland and Godlingston (FST =

0.00841 P > 0.05) and between Godlingston and Studland (FST = 0.01077, P > 0.05). However, there was a significant difference between Studland and Hartland (FST = 0.01320, P < 0.05) which are the most distant heathlands. Likewise, the AMOVA showed low levels of variation (1%) among heathlands (Table 5.5). The weak structure is supported by the Bayesian clustering approach, which showed that the most likely cluster count (K) was 1 (Figure 5.4). Calibration with prior population information resulted in a low degree of admixture (α after ten simulations = 1.0263 ± 0.36). The Mantel test for isolation by distance showed an effect of distance on genetic similarity among heathlands (Mantel test, Rxy = 0.0.267, P = 0.010, Figure

5.5). The global inbreeding coefficient showed significant levels of inbreeding (FIS = 0.10771,

P < 0.0001). In addition, the local inbreeding coefficient was significant for Studland (FIS =

0.374, P < 0.0001) and for Hartland (FIS = 0.08326, P < 0.0001) but not for Godlingston (FIS = 0.0425, P = 0.180), possibly due to the small sample size.

No evidence for a population bottleneck was found based on the excess of heterozygosity (He > Heq) for Studland Bay (P = 0.23187) and Hartland (P = 0.2166). Also, there was no evidence of a deficit of heterozygosity in either heathland (P = 0.7834, P = 0.79813, respectively). It was not possible to assess the bottleneck effect for Godlingston given the small sample size (n=6).

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Table 5.5. Results of the AMOVA comparing allelic frequencies among 134 females of A. pubescens from three heathlands in Dorset.

AMOVA

Sum of Variance Percentage Source of variation d.f. squares components of variation

Among heathlands 2 19.394 0.058 1%

Among individuals within heathlands 131 1022.490 0.840 11%

Within individuals 134 842.500 6.368 88%

Total 267 1884.384 7.265

Figure 5.4. STRUCTURE output representing the three heathlands in different colours. Studland (S, red), Godlingston (G, light blue) and Hartland (H, blue).

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Log(1+GGD) vs GD 70 60 y = 3.6164x + 31.204 R² = 0.0713 50 40

GD 30 Y 20 Linear (Y) 10 0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Log(1+GGD)

Figure 5.5. Linear regression of the relationship between geographic distance and gene flow (genetic distance (GD) and geographic (log(1+GGD)) among heathlands. Weak but significant isolation by distance (Mantel test, Rxy = 0.267, P = 0.010).

Hypothesis 2: Males disperse more than females

Males did not show population structure nor isolation by distance.

A total of 41 males were successfully genotypes at most of the microsatellites. However, in this sample, eleven (27%) males were diploid for at least one microsatellite loci (six males in Hartland Moor, two males in Godlingston Heath and three males in Studland Bay). Diploid males were removed from further analysis to test the hypothesis that males disperse more than females. A total of 30 males remained for further analysis (14 males in Hartland Moor, 8 in Godlingston heath and 8 in Studland Bay). The total number of alleles per locus ranged from five (APUA12) to sixteen (APUF06), whereas the values of Ne ranged from 3.840 to 11.571 (Table 5.6).

The AMOVA revealed 0% variation among males across heathlands (Table 5.7), which means that male disperse widely. Also, the Bayesian clustering approach revealed that each male’s alleles has their origin in all K=3 populations (4.14 ± 0.613) in comparable proportions (males are highly admixed) (Figure 5.6). Furthermore, the Mantel test analysis showed negative but not significant isolation (Rxy = -0.002, P = 0.5; Figure 5.7), which means they are not isolated by distance (Tonnis et al., 2005).

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Table 5.6. Description of the microsatellite loci used across male populations. N: total number of wasps that amplified for a specific locus. Na: Allele richness, Ne: allele frequency (Ne)

Locus N Na Ne

APUA06 30 10 5.921 APUF06 30 16 11.538 APUA02 27 14 11.571 APUC03 30 13 8.333 APUC07 30 15 10.976 APUH06 30 9 6.716 APUE11 30 12 8.333 APUC12 29 13 9.043 APUA12 29 5 3.840 APUB12 29 12 7.313 APUB11 28 10 6.031 APUD10 30 12 6.923 APUH02 28 13 9.561 APUC05 29 12 10.383 APUB06 29 8 5.160 APUC08 29 13 10.646

Mean 29.188 11.688 8.268 SE 0.228 0.688 0.599

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Table 5.7. Results of the AMOVA comparing allelic frequencies among 40 A. pubescens males from three heathlands in Dorset.

AMOVA Sum of Variance Percentage Source of variation d.f. squares components of variation Among heathlands 2 14.175 0.000 0%

Among individuals within heathlands 27 194.035 7.186 100%

Within individuals 29 208.209 7.186 100%

Total 58 416.419 14.372

Figure 5.6. STRUCTURE output of 10 simulations. The heathlands are presented by three colours (red = Studland Bay, light blue = Godlingston Heath and blue = Hartland Moor) and summarised by POPHELPER.

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Log(1+GGD) vs GD 18.0 16.0 14.0 12.0 10.0

GD 8.0 Y 6.0 Linear (Y) 4.0 y = -0.0062x + 14.441 R² = 3E-06 2.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Log(1+GGD)

Figure 5.7. Linear regression of relationship between geographic distance and gene flow (genetic distance (GD) and geographic (log(1+GGD)) among heathlands. A negative and non- significant isolation by distance (matel test, Rxy = -0.002, P = 0.5).

Hypothesis 3: High population viscosity within female aggregations

There was evidence of low levels of population viscosity among females nesting in aggregations within the intensively sampled heathland (Hartland Moor)

There was some significant population structuring among females sampled across seven sites in Hartland Moor. The global FST was low but not significant (no structuring) (FST = 0.00085, P

> 0.05). However, the pairwise FST comparisons between each site revealed that site HB was significantly different from the other sites (Table 5.8; Figure 5.8). Also, the PCoA of FST showed a cluster among geographically close sites (HC, HCL, HCR, HD and HE) (Figure 5.9). Site HA was shown to be distinct from HB, but closer to those in the west (Figure 5.9). The Bayesian clustering analysis showed some structure (mean α = 0.180 ± 0.130), being HB distinct from the other sites (Figure 5.10). Site HB was sampled only in 2016 due to no wasps were observed in this site the following years.

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In addition, the Mantel test revealed a positive but weak (not significant) isolation by distance

(RXY = 0.465, P = 0.128, Figure 5.11). The local coefficient of inbreeding was low and significant in sites HCL (FIS = 0.0966, P < 0.0001), HCR (FIS = 0.004752, P = 0.0047), HD

(FIS=0.0814, P < 0.0001) and HE (FIS=0.0798, P=0.0007), whereas inbreeding was not significant in HA (FIS = 0.0547, P = 0.05029), HB (FIS = 0.0109, P = 0.378) and HC (FIS = 0.0171, P = 0.301).

Related pairs of females were detected within and between aggregations, although overall, females were not significantly more closely related to others in their aggregations with the population average. Two females were detected as likely sisters at site HB (n31 and n32), and another two sisters were detected, but they were from sites HE (n196) and HC (ApG124) which are 75 m apart. Although some pairwise estimations of relatedness range from r = 0.67 to 0.20, they were minimal (72/5,565 dyads). The mean relatedness within aggregations was negative and for most sites significantly different from zero (sites: HA, HB, HCR and HCL): (

Table 5.9). When relatedness was calculated for each aggregation in different years the same pattern was found Table 5.10. Relatedness of A. pubescens females in different years in Hartland Moor

. P-values represent tests of a significance difference from a relatedness of zero (unrelated). In the first column, the aggregations are code HA, HB, HC, HCL, HCR, HD and HE followed by the years in which the populations were sampled (2016 or 2018). The number of wasps within aggregations was low in 2017 and not included. ns = non-significant.(Table 5.10). The aggregation in site HB in 2016 had the highest value or relatedness (r = 0.03602, significantly different from zero) whereas the aggregation in site HD in 2018 had the lowest value of relatedness (r = -0.00734, non-significantly different from zero).

In summary, these data suggest low differentiation between sites, with little and non-significant isolation by distance, as well as low levels of population viscosity among females, but less so than previously suggested from behavioural observation.

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Table 5.8. Pairwise comparison of FST between aggregations in Hartland Moor. Significant FST values are in bold.

HA HB HC HCL HCR HD HE HA 0 HB 0.00456 0 HC 0.00183 0.01722 0 HCL -0.00341 0.01422 0.00012 0 HCR -0.00287 0.01605 0.00037 -0.00243 0 HD 0.00663 0.01089 0.00407 0.00116 0.00098 0 HE 0.00636 0.01812 -0.00283 -0.00271 -0.00112 -0.00473 0

Figure 5.8. Heatmap of the pairwise FST pairwise comparison between aggregations.

Colour shades indicate FST values.

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Principal Coordinates (PCoA)

HE HB HCL Series1

Coord.2 HCR

HC HA

Coord. 1

Figure 5.9. PCA of the pairwise FST population values (FSTP). Sites HCR and HCL were more similar to each other compared with the other aggregations, whereas site HB was the most distinct.

Figure 5.10. Bayesian clustering A. pubescens females’ aggregations in Hartland Moor using sixteen microsatellites loci. Each colour illustrates one genetic cluster. Each vertical column depicts a female and its probability of been assigned to each of the alternative clusters.

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Log(1+GGD) vs FstP 0.020 0.018 0.016 y = 0.0298x + 0.0003 0.014 R² = 0.2159 0.012 0.010 FstP 0.008 Y 0.006 Linear (Y) 0.004 0.002 0.000 0.000 0.050 0.100 0.150 0.200 0.250 0.300 Log(1+GGD)

Figure 5.11. Linear regression of relationship between FST and geographic distance log(1+GGD)) among the aggregations. A strong but not significant isolation by distance (mantel test, Rxy = 0.889, P = 0.150).

Table 5.9. Relatedness of A. pubescens females in seven sites of Hartland Moor. P-values represent tests of a significance difference from a relatedness of zero (unrelated). ns = non- significant

Standard T-test Average Site error (p-value for relatedness* (s.e.) difference from zero) HA (n =13) -0.0158 0.007 p < 0.05 HB (n = 11) 0.0485 0.0282 p < 0.001 HC (n = 11) 0.00068 0.0125 ns HCL (n =33) -0.01386 0.0117 p < 0.05 HCR (n =12) -0.01684 0.0088 p < 0.05 HD (n = 16) -0.0003 0.01313 ns HE (n = 14) -0.0002 0.1331 ns * allele frequency in Hartland was used to investigate heathland relatedness, to later calculate pairwise relatedness between individuals within an aggregation.

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Table 5.10. Relatedness of A. pubescens females in different years in Hartland Moor. P-values represent tests of a significance difference from a relatedness of zero (unrelated). In the first column, the aggregations are code HA, HB, HC, HCL, HCR, HD and HE followed by the years in which the populations were sampled (2016 or 2018). The number of wasps within aggregations was low in 2017 and not included. ns = non-significant.

Average Standard error T-test Aggregation Sample size relatedness* (s.e.) (sig diff from zero)

HA2016 13 -0.0158 0.007 p < 0.05

HB2016 8 0.03602 0.01920 p < 0.001

HC2016 8 -0.01929 0.00830 p < 0.001

HCL2016 18 -0.01989 0.0523 ns

HCR2016 6 -0.01730 0.00354 p < 0.001

HCL2018 14 -0.04528 0.00552 p < 0.05

HCR2018 6 -0.04047 0.00353 p < 0.05

HD2018 16 -0.0003 0.01313 ns

HE2018 14 -0.0002 0.1331 ns * allele frequency in Hartland within a given year was used as reference to later calculate pairwise relatedness between individuals within an aggregation in a given year.

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DISCUSSION

Habitat loss and fragmentation are the major threats to biodiversity. One of the most affected habitats are heathlands, which have been constricted with small patches remaining in central Europe and the UK. Many species of birds, reptiles and insects depend on this habit as shelters and food resources. Furthermore, some of them have become heathland specialists, which makes them more vulnerable to extinction if the heathlands disappear. In this chapter, I investigated the population genetics of A. pubescens, a heathland specialist whose populations are understudied. To investigate population structure allows us to understand gene flow between populations as well as their genetic diversity, which can be used as a proxy of how organisms use and adapt to their habitats. I found that, although Dorset has lost nearly 86% of its heathlands and become a fragmented habitat, the populations of A. pubescens remain connected avoiding the adverse effects on its fragmented habitat.

Habitat fragmentation and genetic differentiation

Base on the biology of the species, I predicted that A. pubescens would show strong population structure, delineated by their individual heathland patches. The global coefficient of differentiation revealed no significant differences. However, the pairwise genetic differentiation showed that the most distant heathlands (Studland Bay and Hartland Moor, 6.3 km apart) were genetically distinct. Also, the genetic admixture analysis revealed weak genetic admixture. Furthermore, the mantel-test reveal isolation by distance. Similar results have been reported for the solitary bee Andrena vaga (Exeler, Kratochwil and Hochkirch, 2008), which populations showed slightly differentiation and significant isolation by distance. Also, weak genetic differentiation among populations has been reported for the heathland specialist bee Andrena fuscipes (Exeler, Kratochwil and Hochkirch, 2010), in which several populations in north-western Germany were studied. However, the distance between populations varied between 4 to 150 km. On the contrary, strong population structure has been found in other solitary and social Hymenoptera, such as the desert bee (Macrotera portalis) and the Australian small carpenter bee (Ceranita australenis) (Danforth, Shuqing and Ballard, 2003; Oppenheimer, Shell and Rehan, 2018). However, the populations studied were far apart, ranging from 17 to 343 km. Hence, the data here presented suggest some differentiation among wasps across the heathlands.

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High levels of genetic diversity and inbreeding

The allelic richness of A. pubescens was higher than that found in studies carried out in other solitary species of Hymenoptera such as the communal bee Andrena jacobi, the desert bee M. portalis and the orchid bee Eufriesea violacea (Hymenoptera, (Paxton et al., 1996; Danforth, Shuqing and Ballard, 2003; Freiria et al., 2012; Bergamaschi and del Lama, 2015; Oppenheimer, Shell and Rehan, 2018), in which used between six to eight microsatellite loci with three to eleven alleles each. High genetic variability has been associated with large populations or with widespread distribution of food supply (Exeler, Kratochwil and Hochkirch, 2010). Similar genetic diversity for A. pubescens has been reported for a smaller sample size (n=56) in the UK (Field, Accleton and Foster, 2018). The levels of genetic diversity were high, and there was no evidence of bottleneck events, which suggests that despite being in a fragmented habitat, A. pubescens’ populations remain stable.

Half of the loci showed a significant deficit of heterozygotes compared with that expected of loci in HWE, this contrast with Field et al. (2018) findings on A. pubescens in which they did not detect deviation from HW equilibrium. Also, positive and significant inbreeding was found in the most distant heathlands (Studland Bay and Hartland Moor). However, these departures from HWE are comparable to other solitary and communal Hymenoptera species (Exeler, Kratochwil and Hochkirch, 2008, 2010), as well as positive and significant values of inbreeding (Paxton et al., 1996; Zayed and Packer, 2007; Bergamaschi and del Lama, 2015). Low degree of differentiation and high gene diversity has also been documented for widespread insects such as butterflies (Vandewoestijne, Nève and Baguette, 1999; Schmitt and Hewitt, 2004). Moreover, inbreeding is associated with the nesting biology exhibit by organisms that nest gregariously such as A. vaga (Bischoff, 2003). A. pubescens wasps nest gregariously in dense or loose aggregations (Srba and Heneberg, 2012). A further line of support for inbreeding in A. pubescens’ populations was the discovery of 11 diploid males (Cowan and Stahlhut, 2004; Stahlhut and Cowan, 2004; Freiria et al., 2012) which are likely to be the result of sibling mating.

High levels of gene flow and dispersal between heathlands

The results showed that males across the three heathlands were admixed and not isolated by distance, which support the hypothesis that males are the main dispersers. Male-biased dispersal is common in other organisms (Williams and Rabenold, 2005; Roycroft, Le Port and Lavery, 2019). On the other hand, the population structure analysis of the aggregations in

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Hartland Moor showed a weak and not significant structure. However, the pairwise genetic differentiation revealed that site HB was distinct from the other; trend that was also observed in the Bayesian analysis. Although the wasps were not isolated by distance, inbreeding was detected for four aggregations which indicate that females are philopatric. Despite this, the levels of relatedness were low which suggest that related and unrelated females nest together. The communal bee A. jacobi has shown a similar trend (inbreed and low relatedness among nestmates) due to a large number of nestmates sharing the same nest (Paxton et al., 1996). A mark-recapture study in A. vaga showed that 50% of the females in the aggregations studied left or die at the beginning of the season (Bischoff, 2003). The same study suggests that females colonise new areas to nest at distance further than 200 away. Here, in 2017 one wasp nesting in one of the aggregations was observed later in the season nesting 1.5 km away. This data suggest that females disperse less than males; however, its dispersion is wider than we were expecting since many of them do not show philopatry. Finally, the analysis of relatedness within the aggregations across different years, showed low levels of relatedness. However, two dyads were recognised as sisters, which suggest a weak genetic viscosity.

Conservation implication and future directions

Dorset heathlands have experienced between 85 – 86% of habitat loss since the 1800s. To date, the remaining patches are surrounded by human construction, plantation and farmland. Although they are mostly protected and managed by local authorities and organisations like the National Trust, the encroachment of human activity on these heathlands is likely to increase. Therefore, local extinctions and loss of species which depend on these rare habitats are likely. Determining the vulnerability and resilience of heathland specialists, such as A. pubescens, to habitat disturbance, fragmentation and loss are therefore of crucial importance. The results suggest that despite the habitat disturbance that A. pubescens has experienced in Dorset, the genetic viability of its population does not appear to have been significantly affected. It is highly possible that A. pubescens mating strategies and nesting biology offer them resilience to fragmentation. Other heathland specialists like A. fuscipes show similar patterns of dispersion and resiliency: in a similar way to A. pubescens, A. fuscipes appears to have been little affected by habitat fragmentation and loss of heathland (Exeler, Kratochwil and Hochkirch, 2010).

Further population genetic analyses across a larger spatial scale, and using mark and recapture methods could shed light on the limits to dispersal in digger wasps, and provide

172 critical information that may help better conserve the populations of A. pubescens on mainland Europe, where it is red-listed as a species of concern, due to the loss of its habitat. Here, in the Dorset area, the current population seems to be genetically robust despite its restricted distribution and its threatened habitat. Evaluating the genetic diversity in areas in which this species is protected and contrast it with the current data can give us a better understanding of how this species is resilient to anthropogenic disturbance and habitat fragmentation.

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CHAPTER 6

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General discussion

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OVERVIEW

The overall aim of this thesis was to investigate the molecular bases of phenotypic plasticity, using transitions at three different levels: a life-history transition (chapter 2), an evolutionary transition from solitary to social (chapter 3), and behavioural transitions through a nesting cycle (chapter 4). To address these topics, I used two wasp models – a social wasp from the model genus Polistes, and a non-social digger wasp from the model genus Ammophila. Additionally, I explored the genetic diversity in natural populations of one of these study organisms as they are almost completely unstudied at the genetic level (chapter 5). The common underlying analytical theme through the thesis was to determine the molecular processes underpinning these transitions. I used genome and transcriptome sequencing of wasps of known behavioural repertoires to address some general questions on the molecular nature of phenotypic plasticity. These included the following questions: Do life-history transitions arise via gradual changes in gene expression, such that the genes underpinning one phenotype are gradually down-regulated, whilst those for the second phenotype up-regulated, as in a linear reaction norm? Or is the transitionary state a distinct phenotype in its own right? (chapter 2); To what extent does the molecular basis of the phenotypic diversity in the life cycle of a non- social insect provide the mechanistic basis for the evolution of social phenotypes in social insects? (chapter 3); How are strictly sequential behaviours, that are repeated in a cyclical manner through the life cycle of an insect, regulated at the molecular level, and how distinct are they at the molecular level? (chapter 4). Finally, does the behavioural plasticity of non- social insects arise irrespective of the genetic diversity in the population: to address this I examined the population genetic structure of the non-social wasp, as it was expected to show limited genetic diversity and gene flow (chapter 5). Here, I summarise the main findings of my thesis and discuss them within the context of phenotypic plasticity and the molecular processes underpinning them. In addition, I draw together the ideas from the thesis into a conceptual idea which raises the idea that we can learn a lot about the mechanisms regulating behaviour from the molecular mechanisms known to regulate cellular transitions; in particular I suggest that the processes underpinning life-history transitions may be similar to the process that occurs during cell reprogramming.

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SUMMARY OF MAIN FINDINGS

Stable phenotypes in social wasps are underpinned by subtle, but distinct differences in brain gene expression

Advances in genomics allow us to investigate and understand the molecular basis of phenotypic plasticity. In chapter 2, I investigated whether the apparent behavioural differences between castes in the tropical paper-wasp Polistes lanio are evident at the molecular level. If so, what are the mechanisms driving the differences? I found that behavioural differences are evident at the molecular level, although the differences involve only small differences in gene expression. Small differences between behavioural castes have been observed in other simple societies of Polistes wasps (Ferreira et al., 2013; Patalano et al., 2015; Standage et al., 2016) as well in a facultatively eusocial sweat bee (Megalopta genalis) (Jones et al., 2017). Some of the genes underpinning the differences have been reported to be involved in caste differentiation (Cardoso-Júnior et al., 2017), aggression (Zwarts, Versteven and Callaerts, 2012), metabolism in insect hormones and immunity (Rehan, Berens and Toth, 2014; Colgan et al., 2019). Tropical Polistes queens maintain their dominance hierarchy through aggression towards subordinate nestmates (Jandt et al., 2014) and genes related to aggression are also associated with reproductive castes (Jandt, Tibbetts and Toth, 2014). Unlike queens, workers are engaged in nest maintenance and foraging. During foraging trips, they are exposed to disease and pesticides, which highlights the importance of immune response. Finally, I identified signs of molecular functional enrichment in workers, with functional groups such as “caste determination” (GO:0048647) and “polyphonic determination” (GO:0048648).

Life history transitions from worker (non-reproductive) to queen (reproductive) are governed by non-linear behavioural reaction norms

In simple societies of P. lanio, when the queen dies, a worker becomes the replacement queen. To gain the reproductive monopoly during the queenless period, potential queen replacements behave antagonistically towards each other. Jones et al. (2017) have shown that in the facultatively eusocial bee, Megalopta genalis, the differences between queens and established replacement queens are small at the molecular level in the brain, and moreover the differences between workers and the replacement queen are absent. Until now, the molecular processes that characterise the transitionary period in the competition to become a 176 replacement queen have not been explored. Specifically, we did not know whether the transition from worker to queen is just a simple switch on of queen-like genes, as expected if the phenotype transition follows a linear reaction norm. In chapter 2, by experimentally creating queenlessness in P. lanio colonies, I found that workers attempting to become the new reproductive are a distinct phenotype, that does not resemble the queen-like phenotype or the worker-like phenotype. Transitioning wasps are not simply an intermediate phenotype between queen and worker, but instead they are a distinct and entirely different transcriptional state; this suggests that the transition follows a non-linear reaction norm. Pairwise comparisons between the three phenotypes showed that transitioning queens are more queen-biased than worker-biased which suggests that these wasps are indeed attempting to become reproductive. A massive change in gene expression (involving thousands of genes) characterises the transitioning period; although most of transitioning wasps will fail to become the replacement queen, the consequences of ‘winning’ are high in fitness terms. Therefore, despite the apparent transcriptomic costs of the transition, the potential fitness payoffs must be enough.

Despite behaving as workers prior to queen removal, after only three days, the transitioning wasps were distinct from workers, although some worker molecular signatures remained (e.g. immunity). The biological processes regulating this neuroplastic and behavioural transition were diverse. Some of the processes involved were muscle contraction, phototransduction and oviduct morphogenesis which suggest physical, sensory and physiological are changes are taking place in the transition. Moreover, transcription factors and methylation were involved in regulating behavioural reprogramming, facilitating the transition between genomic signatures.

Molecular basis of alternative phenotypes that arise as a result of a behavioural transition have been investigated in honeybee workers (Whitfield, Cziko and Robinson, 2003); however, the transition from nurse to forager is a maturation process (Whitfield et al., 2006) that remains reversible (Herb et al., 2012) and does not involve the transition from worker to queen, and so it is more like a behavioural transition than a life history transition. Life-history transitions that are comparable with queen replacement have been studied in fish: the transition from a subordinate to a dominant state have been investigated in cichlid fish (Burmeister, Jarvis and Fernald, 2005; Maruska, 2015), and from one sex to another have been investigated in clownfish and bluehead wrasses (Casas et al., 2016; Todd et al., 2019). All of these transitions are accompanied by dominance behaviour in order to be at the top of their hierarchies. In all 177 of them, transcription factors play a fundamental role during the transitioning period. The results of these life-history transitions are similar to those I found in my transitioning wasps: during the transition brain transcription is distinct, suggesting that the transition follows a non- linear reaction norm.

These transitions, in which adult organisms change from one state to another, may be considered as reprogramming periods, in a similar same way as cells undergo reprogramming. Adult somatic cells are induced pluripotent stem cells (iPSc) with the potential to become a cell with a distinct lineage (Takahashi et al. 2006). To reprogramme a cell, Takashashi et al. found that a set of transcription factors can be manipulated to induce pluripotency. Hence, compelling analogies can be made between cell reprogramming and behavioural plasticity during life-history transitions, in which organisms reprogramme their behavioural machinery to become a different phenotype.

In summary, I provided a glimpse of the molecular processes taking place during a key life- history transition, given that only a one-time point was examined during behavioural reprogramming. I propose a time-course analysis for a better understanding of the molecular, physiological and behavioural mechanisms taking place. I propose further investigation to also examine the changes suffered by the wasps that do not engage in the conflict to become reproductive. Moreover, some wasps attempting to become reproductive will fail to become the replacement queen. The molecular processes regulating the reversion from transitioning wasp to worker are unknown, but they might be similar to those involved in reversible switching in honeybees, mainly underpinned by the methylation processes (Herb et al., 2012). Nonetheless, the transition in honeybee workers does not involve social challenge. On the other hand, in cichlid fish (Astatotilapia burtoni), dominant males exposed to other dominant males or challenged by subordinates can lose their hierarchy and revert to subordinate (Burmeister, Jarvis and Fernald, 2005). Reversions in this species have been linked to transcription factors and neuroplasticity. Hence, the molecular mechanisms involved in failure to become reproductive might be driven by similar mechanisms observed in cichlid fish. Furthermore, we do not know whether the transition back to worker follows a linear downregulation of genes involved in the transitioning period.

In addition, I suggested that the transitions that follow a non-linear reaction norm are similar to cell reprogramming. Here, I presented current examples of non-linear transitions, e.g. 178 subordinate – dominant (cichlid fish), female – male (bluehead wrasses), male – female (clownfish), brood reading – reproductive (clonal raider ants) and solitary – gregarious (locusts). However, to fully understand the reprogramming period in multi-cellular organisms I propose a comparative time course study of critical life-history transitions in different taxa (e.g. insects, molluscs, fish and reptiles) investigating behavioural, physiological, molecular and epigenetic changes shared across taxa and group-specific. A unified framework to understand behavioural reprogramming is presented in section 6.3.

Mechanisms regulating provisioning cycles, as well as reproductive cycles, in non-social insects may provide the basis for the evolution of division of labour in social insects

In chapter 3, I presented the draft genome of the non-social digger wasp A. pubescens. To the best of my knowledge, this is the first genome of a non-social wasp and the first Sphecidae to be sequenced. A. pubescens belongs to the tribe Ammophilini which exhibits a wide range of provisioning behaviour. Hence, this genomic data provides an opportunity to explore the evolution of provisioning behaviour in Ammophilini wasps. Although the genome of A. pubescens is currently the second-largest genome after Eufriesea Mexicana’s genome, the characteristics of the genome were similar to those currently reported for bees and wasps. With this genomic tool, I then tested a key mechanistic hypothesis for the evolution of social behaviours: the ovarian ground plan (OGP) hypothesis. By classifying behavioural phases of A. pubescens nest sequence into queen-like (i.e. nest founding and egg-laying) and worker- like (i.e. inspection and provisioning) social phenotypes, I found some weak genomic evidence of the OGP: I found a small number of genes previously reported for the queen caste in social insects to be upregulated in the queen-like phenotype (e.g. protein yellow and endocuticle structure glycoprotein SgAbd-8-like (Calkins et al., 2018; Ramanathan, Nair and Sugunan, 2018). For the worker-like phenotype, I identified genes involved in immunity, detoxification and stress (Tsao, Geisen and Abraham, 2004; Colgan et al., 2019). The upregulation of stress response and immunity may reflect the cost of worker-like behaviours. Given A. pubescens simultaneously cares for multiple larvae in parallel, adult wasps spend most of the day time foraging and thus are likely to be exposed to pathogens and parasites. Another evindece against the OGP is the lack of a clear cycle in ovarian development. The need to maintain several burrows at the same time in A. pubescens is likely to be driving this (lack of) ovarian cycle.

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Another key prediction of the OGP is that provisioning and egg-laying behaviours are decoupled. I found little evidence for this, as these two phases differed only in eight genes. The small differences between this phase might be underpinned by the fact that both phases involve caterpillar hunting. Solitary progressive provisioners of the eumenine family are the ancestors of social wasps. Like A. pubescens, the eumenine Synagris cornuta is progressive provisioner (Kelstrup et al., 2018), but this wasp cares for one nest at a time. The nesting cycle of S. cornuta takes 13-21 days from which at least ten days are dedicated to provisioning. Hence, it is possible that similar molecular mechanisms involved in the nesting sequence of A. pubescens are underpinning the nesting sequence of S. cornuta, but we lack genomic data of solitary eumenid wasps, from which sociality evolved. But, unlike A. pubescens, the nesting sequence of S. cornuta has been found to be regulated by ovarian cycles when Kelstrup et al. compared the oocytes across behavioural phases. Despite these physiological cycles, their assessment of JH revealed no association with reproductive cycles. I anticipate that my work will encourage research on solitary vespoid wasps, in the same lineage as the social wasps, to have a better understanding of the ancestors of social wasps. Kelstrup et al. (2018) demonstrated that it is possible to work with solitary species in which sociality evolved. I suggest using the same species model (S. cornuta) to test the current hypotheses of social evolution in wasps, including the provisioning ground plan hypothesis proposed could be a very exciting route forward.

By contrast, I found support suggesting that a cycle of provisioning behaviours may provide some mechanistic patterns from which division of labour could evolve. Progressive provisioning behaviour is a novelty in non-social insects, but a key pre-adaptation for eusociality in Hymenoptera. Furthermore, simultaneous progressive provisioning is a potent pre-adaptation, given that from this adaptation, it is but one small step in evolution for adult daughters to remain with their mothers and help raise siblings instead of dispersal (Wilson, 2008). I found that provisioning requires substantially different brain molecular processes compared to nest founding and nest inspection, but not egg-laying which suggests that both behaviours are not necessarily decoupled phenotypically. I found that processes such as sensory perception, stress and detoxification, gene expression regulation and metabolism are underpinning it. Provisioning behaviour is costly not only in terms of survival, as wasps are exposed to pathogens and predators while hunting for prey items to provision, but also in terms of reproduction given that fewer eggs will be laid (Field et al., 2007).

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Ammophillini wasps are characterised by the vast diversity of provisioning behaviour exhibited, that ranges from mass to simultaneous progressive provisioning behaviour (Field, Ohl and Kennedy, 2011). The proximate mechanisms behind the transition from mass to progressive provisioning in non-social wasps are unknown, and the current molecular datasets on solitary bees phenotypes and non-social wasps are limited to mass provisioners (Johnson et al., 2013; Kocher et al., 2013; Rehan, Berens and Toth, 2014). Field (2005) has explored the circumstances under which progressive provisioning evolved in a comparative approach. My study now provides a basis to compare the molecular basis of other types of provisioning behaviours in the Ammophillini. Do the same molecular processes underpin mass provisioning? Or is there re-arrangement of gene expression and networks of conserved (or novel) genes in the transition from mass to progressive provisioning? My study shows that analyses of behaviour at the gene level can reveal insights that are not directly obvious from just phenotypic studies. Accordingly, I suggest combining phenotypic transcriptomic analyses in a comparative study across the Ammophillini wasps in order to explore the molecular mechanisms of provisioning behaviour and its evolution.

Behavioural transitions through a nesting cycle involve both fixed and flexible behaviour, all of which are underpinned by subtle but distinct molecular signatures

With the fine-scale transcriptomic data that I had generated on the different behavioural phases of the nesting cycle of A. pubescens, I further dissected the nesting cycle aiming to identify the molecular mechanisms underpinning each phase. These phases take place in strict sequence at the burrow level, but because females maintain multiple nests simultaneously, there may be little evidence of distinct molecular signatures for each phase.

As expected, I found only subtly different genomic signatures for each of the behavioural phases. The number of genes associated with each phase was minimal, except for the provisioning phase, which is the most dominant phase of the nesting cycle and further corroborates the conclusions of chapter 3, supporting the idea of a PGP driving the nesting cycle in A. pubescens. Given this, I focused on comparing how the other phases differed from provisioning, as those are phases that do not involve prey hunting. Interestingly, I then found many DE genes. One of the biological processes involved in the provisioning phase was the biosynthesis of fatty acids which supports the idea explored above, that this phase is 181 energetically costly as the wasps need to hunt, sting and transport the caterpillar by walking or flying. For the nest founding phase, I found that neuroplasticity and muscle motility are distinct in this phase, relative to provisioning. The egglaying behaviour phase is driven by processes involved in reproduction and JH regulation, as previously reported for other insects (Cayre et al., 1996). Although I was expecting cognitive processes involved in the inspection phase, this was not the case. It is possible that the small sample size in the inspection phase (4 wasps) and A. pubescens’ biological variation might have had an impact on the power my data had to detect molecular signatures here.

The simultaneous care of multiple nests exhibited by A. pubescens could explain the lack of a clear molecular signature of each nesting phase. Thus, I suggest experimentally manipulating A. pubescens to force her behave as a single-nest progressive provisioner, caring for only one nest at the time and thus only exhibiting one part of the nesting cycle at a time. Furthermore, behavioural states displayed in “nest-sequences” can be investigated in species that do not exhibit simultaneous care. Also, I only investigated the molecular changes after completing a phase in their nests, but I did not investigate whether the wasps before opening their nests are “blank-slate” ready to gain cues that will drive their future behaviour. Moreover, we do not know how wasps gain their cues inside the nests; is just an assessment of food left and the presence of parasites? Or do the wasps engage in interactions with their larvae as some social species do? Laboratory experiments using aquariums in which the content of the nests is visible through the glass will be ideal to answer these questions.

Weak genetic differentiation of A. pubescens populations in southwest England.

Plastic phenotypes can emerge when some individuals become isolated from their populations of origin. Their new environments limit their dispersal as well as gene flow, but allow them to exploit new resources if they manage to adapt. The isolated population after several generations becomes genetically distinct from its population of origin. Habit loss and fragmentation have been shown to limit dispersion as well as gene flow (Pavlova et al., 2017) which can make organisms vulnerable to extinction when they are unable to adapt to the new landscape.

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In chapter 5, I investigated the population genetics of A. pubescens; a heathland specialist that is considered to be endangered in parts of Europe given that its habitat is strongly fragmented with small patches remaining (Drees et al., 2011). I found a weak genetic differentiation between wasps sampled in three different heathlands, with higher dispersal in males relative to females. The genetic diversity found in southwest England was similar to that previously reported for Witley Common Surrey and North Norfolk, UK together (Field, Accleton and Foster, 2018), which suggests good genetic health. However, we do not know the genetic health of this wasp in countries in which is protected. Therefore, I propose a more extensive study using wasps distributed in continental Europe which will allow us a better understanding of their dispersion strategies, distribution and potential plasticity in the use of soil, prey and behaviour. Finally, the genetic and genomic data now available represent an comprehensive resource for interspecific comparative studies across Ammophila species with diverse parental care strategies.

UNIFIED FRAMEWORK ON THE MOLECULAR BASIS OF PHENOTYPIC TRANSITIONS IN CELLS AND ORGANISMS

Below, I revisit some parts of my introductory chapter as well as parts of chapter 2 to present the unified framework on the molecular basis of phenotypic transitions in cells and organisms.

The ability to switch behaviour in response to the environment enables multi-cellular organisms to maximise fitness (Maruska, 2015), e.g. by changing reproductive strategy in order to exploit new opportunities (Field, 1989b; Field et al., 2007; Cini et al., 2015), or shifting behavioural patterns in order to cope with a sudden change in environmental conditions or resources availability (Charmantier et al., 2008). For instance, in African cichlid fish (Astatopia burtoni) males can develop as dominants or as subordinates. These two states are characterised by physiological, morphological and even gene expression changes (Burmeister et al. 2005; Renn et al. 2008; White et al. 2002). Dominant males are bigger, colourful and ornamented and obtain most of the mating, whereas subordinates are smaller opaque and non-reproductive. Under the correct environmental cue (absence of dominant males), subordinates (state A) can switch phenotype to become reproductive dominants (state B). The shift involves behavioural changes initially and physiological changes subsequently: both processes are triggered by changes at the molecular level. Other examples of behavioural 183 switching are found in insects. During the first part of honeybee worker (Apis mellifera) life, they are nurses, where they stay in the hive to care for brood and perform nest maintenance tasks. Then, depending on the environmental cues (social and pheromones), nurses switch to be foragers, where they collect pollen and nectar (Robinson, 1992; Leoncini et al., 2004). In paper wasps (Polistes) a single female (foundress) starts a new nest, lays eggs and take care of brood, including foraging until the first wasps emerge. The newly emerged females then become the brood carers and maintain the nest, whilst the foundress becomes committed to being an egglayer (West-Eberhard, 1969; Reeve, 1991). These examples represent a form of behavioural reprogramming (Figure 6.1), reflect plastic (adaptive) responses of the organisms to their changing environment. The idea that behaviours are a form of reprogramming was first suggested by Tinbergen, in his seminal paper on the questions to ask when trying to understand animal behaviour (Tinbergen, 1963).

This thesis set out to explore the molecular nature of behavioural transitions and phenotypic plasticity. I adopted the concept of reaction norms, which exemplify the values that a trait takes in different environments (Kelly et al. 2012; Kingsolver et al. 2004; Scheiner 1993). For instance, a reaction norm may depict how a phenotype varies slightly, or completely in a transitioning environment (Figure 6.1). Variation in the social environment can be explained by behavioural reaction norms (BRNs) (Dingemanse, Kazem, et al., 2010) which describe fast responses by an organism in terms of its behaviour (Smiseth et al. 2008). This may also imply repeated interactions with reversible effects and temporal behavioural dynamics. The variation of an individual’s behaviour in different situations may result in linear or nonlinear behavioural reaction norms. For instance, linear BRNs show gradual behavioural plasticity among organisms only in two different environments (Carter et al. 2012; Kingsolver et al. 2004). Conversely, a nonlinear BRN would depict a behavioural shift as a consequence of a threshold response to changes in social cues. Moreover, plasticity and reaction norms have been used to describe changes in gene expression between environments, but thus far there is a lack of work investigating the molecular mechanisms leveraging different reaction norms.

I now propose to compare these patterns of reaction norms for behavioural transitions with shifts in phenotype that occur at the cellular level as and these cellular processes provide compelling phenotypic and mechanistic analogies for understanding behavioural reprogramming in whole organisms. For example, after fertilisation, totipotent zygote cells are reprogrammed to differentiate into specific cell type (Smith et al. 2016). This process of natural reprogramming has been exploited in-vitro for medical purposes, to induce reprogramming of 184 mice adult somatic cells into a pluripotent stem cell state (induced pluripotent stem cells or iPS cells) (Takahashi et al., 2006) allowing cell-types to shift to another type. Pluripotency is a term used to describe a cell which has the potential to differentiate into all cell types, but not the extraembryonic lineages. The pluripotent state that occurs in both natural and induced reprogramming cause dramatic gene expression changes including the over-expression of specific transcription factors (Hu, Qian and Zhang, 2011) (Table 6.1) that control the pluripotent state of the cell (Boulting and Eggan, 2013). This transcriptional ‘reprogramming’ signature differs from that of the pluripotent state as the target cell loses the transcriptional signature that ‘programmed’ it previously to be a specific somatic cell (David and Polo, 2014). It is then ‘reprogrammed’ to become a new type of cell with a new transcriptional state.

Figure 6.1. Reaction norm depicting two forms of behavioural switching. In cichlid fish, a subordinate (state A) can shift to dominant male (state B) in the absence of the latter, but during the process (A → B) a distinct (transitioning) phenotype is present: I define this as behavioural reprogramming. In contrast, a nurse bee (state A) transitions gradually to a forager bee (State B) depending on colony’s demand: I define this as behavioural transdifferentiation. Modified figure from chapter 2.

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However, for a cell to switch state, it does not necessarily have to revert to being pluripotent, before changing. Studies in cell therapy and regenerative medicine have shown how cells can undergo ‘transdifferentiation’ (a second type of transition) directly from one cell type into another, without going through a pluripotent state (Table 6.1) (Eguchi 1995; Jopling et al. 2011; Sisakhtnezhad and Matin 2012; Takahashi and Yamanaka 2006; Tosh and Slack 2002; De Vries et al. 2008). Transdifferentiation is highly context-dependent and occurs via the expression of specific transcription factors, which are cell/tissue dependent (Prasad et al., 2016). Importantly, the molecular signatures that occur during transdifferentiation differ from those that typify reprogramming. Specifically, transdifferentiation does not involve a pluripotent state; it undergoes a dedifferentiation (proliferation) process, followed by the activation of the natural developmental programme that allows the cell to differentiate into a new lineage (Jopling et al. 2011). It has been proposed that this switching in lineage occurs through the simultaneous downregulation of one programme and the upregulation of another (Jopling, Boue and Izpisua Belmonte, 2011). This causes an unstable intermediate state which reflects how rapidly this process occurs, with remnants of genetic programme in the form of mRNA and protein, still lingering as the new programme takes over (Yamanaka and Blau, 2010; Jopling, Boue and Izpisua Belmonte, 2011; Eguizabal et al., 2013). But, crucially, transdifferentiation is associated with a discrete change in the programme of gene expression, unlike reprogramming (Shen, Burke and Tosh, 2004).

The contrasting patterns of molecular signatures found in cell reprogramming and transdifferentiation can be explained by two types of BRNs described above (Figure 6.1 & 6.2), which provide us with testable hypotheses on the nature of behavioural changes in response to the environment. For instance, the reprogramming process undergone by iPS cell to gain totipotency has a distinct molecular signature: the somatic cell must lose its current programme, followed by physiological changes regulated by the upregulation of transcriptional factors that are associated with pluripotency (Takahashi et al., 2006) until they lose the epigenetic memory. This process is akin to a polynomial (or non-linear) BRNs wherein we observe how a stable and defined phenotype (state A) undergo a transitional intermediate state (green dotted line, Figure 6.2) which does not share similarities with the previous (state A) or future (State B) phenotype (Figure 6.3). On the other hand, transdifferentiation can be explained by a linear BRN in which a well-differentiated phenotype (state A) transitions to a new one (state B), and the process involves a gradual downregulation in the molecular signatures of state A and upregulation of state B.

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The analogy between changes in cell state and changes in behaviour is long-standing and compelling (Suzuki and Bird, 2008; Patalano et al., 2012), as it provides leverage to unfold hypotheses for a general behavioural theory of commitment and reprogramming across levels of biological organisation. A logical extension of this is to apply the concept of BRNs in environmentally induced behavioural changes to explain changes in cell state with the environment. For instance, the ability of cichlid fish to change their status within their society due to the absence of dominant males is comparable to cell reprogramming as non- reproductive subordinates exploit their totipotency in order to ‘reprogramme’ to become dominant. When a dominant male is placed in a social environment with other more dominant males, the introduced male reverts to being subordinate (Burmeister, Jarvis and Fernald, 2005). Another example of phenotype switching is in honeybees. Worker honeybees (Apis mellifera) will shift from nurse to forager life stage during their first weeks of life, this shift shows differences in gene expression between stages, and the differences are related to a gradual process during the maturation stage (Whitfield et al. 2006; Whitfield et al. 2003). This shift could be akin to transdifferentiation as to there is the same phenotype (worker bees), but they might have gone through a process of dedifferentiation and then differentiate as forager bees. Although these examples of behavioural switching seem to be like those in cell switching, it is not clear whether molecular mechanisms underpinning both are the same. These analogies allow us to pose some general hypotheses about the molecular basis of behavioural changes. Specifically, I predict that the same general patterns of molecular signatures that underpin state changes in cells may underpin changes in behavioural states.

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Table 6.1. Summary of processes related to changes in cell fate. Function, signature and reprogramming factors (markers) that allow them to change.

PROCESS REPROGRAMMING TRANSDIFERENTIATION

A stable switching of a committed state lineage to another state of a different lineage. Also, known as lineage switching Process that depicts a transition from a or lineage conversion(Graf and Enver, stable and well-differentiated state to 2009; Sisakhtnezhad and Matin, 2012). DEFINITION another pluripotent (Takahashi et al., 2006; This process also involves the loss of Yamanaka and Blau, 2010; Eguizabal et previous signature (Eguchi, 1995; Tosh al., 2013). and Slack, 2002; Sisakhtnezhad and Matin, 2012).

Does not go through a totipotency state

Regeneration of lost or damaged During fertilization to produce a totipotent tissue(Jopling, Boue and Izpisua cell(De Vries et al., 2008). Belmonte, 2011).

Conversion of somatic stem cell to an During the development of normal and induced pluripotent stem cell in physiological damage(Sisakhtnezhad and FUNCTION regenerative strategies for human Matin, 2012). medicine (Eguizabal et al., 2013). Metaplasia (Slack, 2007). During transitioning to a different Cancer (Yang and Weinberg, 2008). environment, depicting phenotypic plasticity in multicellular organisms. Phenotypic switching due to environmental changes expecting to maximize fitness.

Loss of previous programme.

Morphological and metabolic changes. Highly context-dependent Gaining of some transcription factors A dedifferentiation process take place MOLECULAR associated with pluripotency. together with cell division, and then the SIGNATURE Loss of epigenetic memory (David and new developmental programme is Polo, 2014) which can be described as a activated(Jopling, Boue and Izpisua heritable shift in behaviour or gene Belmonte, 2011; Eguizabal et al., 2013). expression that is induced by a previous stimulus (D’Urso and Brickner, 2014).

Reprogramming factors: OKSM OCT4: Homeodomain transcription factor. Cell and tissue-dependent: KLF4: Kruppel-like factor 4, a zinc-finger MAIN GENETIC transcription factor. Fibroblast to Neurons (ASCL1, BRN2 and SOX2: Transcription factor that is essential MYTL1). MARKERS for pluripotency, of undifferentiated Fibroblast to Cardiomyocytes (GATA4, embryonic stem cells. MEF2C and TBX5). MYC: proto-oncogenes that code for transcription factor.

GENE EXPRESSION Dramatic changes in gene expression Discrete changes in gene expression PATTERNS 188

My work in this thesis goes some way to providing the data needed to explore these analogies further. For example, the large scale transcriptional changes that appear to constitute the transitional state between workers and queens in Polistes lanio (chapter 2) would suggest this transition is a form of reprogramming. Conversely, the more subtle molecular differences that underpin the sequential behaviours of the digger wasp A. pubescens may be better described as transdifferentiation. Studying the molecular processes that occur during phenotypic transitions may, therefore, shed light on whether there is a distinct difference between the forms of behavioural transitions that occur across the animal kingdom. Whether a phenotypic transition is reprogramming or transdifferentiation may have important implications on selection. We have much more to learn about the cellular and molecular mechanisms underpinning phenotypic transitions, and whether these mechanisms are conserved across different taxa. Comparative studies across different taxa at different levels of biological organisations are required to test these hypotheses in order to better understand the molecular process underpinning this intriguing facet of biology.

Figure 6.2. Somatic cells (red and orange dots) can change their fate by undergoing a pluripotency state and then change their lineage (function); or they may gradually change to a transdifferentiation state without changing lineages.

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Figure 6.3. Reaction norm of behavioural (green solid curve), physiological (black solid curve) and gene expression changes (dashed lines) that characterise the transition from worker (state A) to queen (state B). The worker-like phenotype is characterised by undeveloped ovaries, worker-bias genes (red dot) are upregulated whereas queen-bias genes are downregulated (orange dot), and their behaviour is docile in comparison with the queen-like phenotype. However, during the transition from worker to queen (A → B), the potential queen replacement starts to display dominance behaviours to establish the social hierarchy (as a queen does), her ovaries will start to develop and the queen-bias genes are expected to be upregulated at the end of the transition, whereas the worker-biased signature downregulated. Modified from Aubin-Horth et al. (2009).

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THESIS CONCLUSIONS

Here I demonstrated that phenotypic plasticity in the form of life-history and behavioural transitions are not just a simple switching on and off of molecular mechanisms underpinning the transitions. I discussed how key transitions are akin to the mechanisms involved in cell reprogramming, whereas small changes in gene expression accompany the more plastic behavioural states involved in nesting cycles. Non-social insects can provide useful models to test hypotheses on the mechanistic basis of social behaviour and evolution in social Hymenoptera.

191

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Supplementary information

235

Supplementary information chapter 1

Supplementary 1.1

Summary table showing all individuals dissected for ovarian characterisation. Each row represents a single female either a worker or a transitioning wasp.

Abdomen description Wasps Ovarian description Egg description Insemination Phenotype Nest Fat bodies cuticle Fat bodies on ovaries Total ID fat Colour Thickness Abundance Colour Thickness Abundance score Developed Colour score Colour Density Laying Pocket Insem W8 W 2013065 B 2 3 B 3 3 c N Y a Y 1 N Y N 803 Tw 2013065 W 2 1 W 2 1 a N W a W 1 N Y N 805 Tw 2013065 W 2 1 Y 3 2 b N W a W 1 N Y Y 801 Tw 2013065 Y 2 2 Y 2 2 b N Y a Y 1 N Y N W6 W 2013063 Y 2 1 Y 2 2 a N C a Y 1 N N 6F1 W 2013063 W 3 2 W 1 2 a N W b W 2 N Y N 601 Tw 2013063 Y 4 3 C 2 2 b N W a W 2 N Y N 105 Tw 2013058 Y 4 3 C 3 2 b N W a W 2 N Y N 101 Tw 2013058 W 2 2 W 2 2 b Y W c W 2 N Y Y 103 Tw 2013058 Y 4 3 Y 2 2 c N Y b Y 1 N Y Y W1 W 2013058 Y 2 1 Y 1 1 a N Y a Y 0 N Y N 104 Tw 2013058 W 3 2 W 2 1 a N C a C 1 N Y N 902 Tw 2013066 Y 3 2 Y 3 3 c N W a W 0 N N 903 Tw 2013066 W 4 3 W 2 2 c N W a W 0 N Y N 901 Tw 2013066 C 3 3 W 2 1 b N W a W 1 N Y N 904 Tw 2013066 Y 4 4 Y 3 2 c N W b W 2 N Y N W9 W 2013066 C 2 1 C 2 1 a N W a W 0 N Y N 907 Tw 2013066 Y 4 2 Y 4 3 c N W a W 0 N Y N 405 Tw 2013061 Y 3 2 Y 2 2 b Y C b W 1 N Y N 401 Tw 2013061 Y 3 1 Y 2 2 a N W a W 0 N Y N 402 Tw 2013061 Y 3 1 C 2 2 a Y C c C 4 N Y N W4 W 2013061 B 2 1 B 2 1 a N B a B 0 N N 403 Tw 2013061 Y 3 2 Y 3 2 b N W a W 1 N N 236

404 Tw 2013061 Y 2 1 Y 2 2 a N W a W 1 N Y N 406 Tw 2013061 C 3 2 Y 2 2 b W W a W 1 N Y N W7 W 2013064 B 2 1 Y 2 3 b N W a C 0 N Y N

W2 W 2013059 B 1 1 Y 2 2 a N Y a Y 0 N N W13 W 2013070 B 2 2 Y 3 2 b N W a W 0 N Y N W11 W 2013068 B 1 1 Y 2 1 a N W a W 0 N Y N W10 W 2013067 B 2 1 Y 3 2 a N W a W 0 N Y Y

1005 Tw 2013067 W 3 1 C 3 2 a N W b W 1 N N 1003 Tw 2013067 W 3 1 W 2 1 a N W a W 0 N Y Y

1001 Tw 2013067 W 4 3 W 2 1 b N W a W 1 N N Wasp ID: Wasps IDs given in the field, Phenotype: W = worker, Tw = Transitioning wasp, Nest: The number assigned to a colony (year =2013 & number of the colony = e.g. 053), ABDOMEN DESCRIPTION: Fat bodies on cuticle, Colour: Fat bodies colour on cuticle (W = white, Y = yellow, B = , C = ), Thickness:Fat bodies thickness on cuticle (0 – 4), Abundance: Fat bodies abundance on cuticle (1 – 4), ABDOMEN DESCRIPTION: Fat bodies on ovaries, Colour: Fat bodies colour on ovaries, Thickness: Fat bodies thickness on ovaries, Abundance: Fat bodies abundance on ovaries, Total_fat_score: Fat bodies score on ovaries (a – c), OVARY DESCRIPTION: Developed: Ovary description (developed = yes (Y) or no (no)), Colour: Colour of the Eggs (W = white, Y = yellow, B = , C = ), Score: Score for the ovaries (a – c), EGG DESCRIPTION: Colour: Colour of the egg (W = white, Y = yellow, B = , C = ), Density: Density of the eggs ( 0 – 4), Laying: Egg laid (N = No, Y = yes), INSEMINATION: Pocket: The pocket has been inseminated (Y = yes or N = no), Insem: The wasp has been inseminated (Y = yes or N = no)

237

Supplementary information chapter 2

Supplementary 2.1

A subset of the output of REVIGO analysis showing overrepresented GO terms (Uniprot base, threshold = 0.05) clustered according to similarity in function. The table corresponds to those GO ID overrepresented among transitioning wasps. Stable phenotypes (queens and workers) versus transitioning wasps.

Term ID Description Frequency Uniqueness Dispensability

GO:0006030 chitin metabolic process 0.077 % 0.95 0

GO:0006936 Muscle contraction 0.066 % 0.59 0

GO:0007160 cell-matrix adhesion 0.051 % 0.98 0

GO:0055002 striated muscle cell development 0.037 % 0.45 0

GO:0060361 flight 0.001 % 0.99 0

GO:1905552 positive regulation of protein localization to 0.000 % 0.88 0 endoplasmic reticulum

GO:0017144 drug metabolic process 0.058 % 0.97 0.02

GO:0035336 long-chain fatty-acyl-CoA metabolic process 0.004 % 0.95 0.03

GO:0035172 hemocyte proliferation 0.002 % 0.88 0.04

GO:0042440 pigment metabolic process 0.485 % 0.89 0.06

GO:0000272 polysaccharide catabolic process 0.288 % 0.98 0.09

GO:1902769 regulation of choline O-acetyltransferase activity 0.000 % 0.93 0.1

GO:0042438 melanin biosynthetic process 0.007 % 0.86 0.11

calcium ion transport from cytosol to endoplasmic 0.000 % 0.96 0.11 GO:1903515 reticulum

GO:0031032 actomyosin structure organization 0.059 % 0.78 0.12

GO:0007602 phototransduction 0.021 % 0.78 0.13

GO:0030029 actin filament-based process 0.398 % 0.85 0.14

GO:1905243 cellular response to 3,3',5-triiodo-L-thyronine 0.000 % 0.94 0.18

GO:0055085 transmembrane transport 8.916 % 0.96 0.19

GO:0055114 oxidation-reduction process 15.060 % 0.86 0.22

GO:0035627 ceramide transport 0.002 % 0.89 0.29

238

GO:0019722 calcium-mediated signaling 0.040 % 0.83 0.31

GO:1990146 protein localization to rhabdomere 0.000 % 0.96 0.32

GO:0097435 supramolecular fiber organization 0.345 % 0.81 0.37

GO:0030198 extracellular matrix organization 0.060 % 0.82 0.37

GO:0015986 ATP synthesis coupled proton transport 0.411 % 0.74 0.41

GO:2001225 regulation of chloride transport 0.001 % 0.89 0.42

GO:0061383 trabecula morphogenesis 0.010 % 0.58 0.42

GO:2001027 negative regulation of endothelial cell chemotaxis 0.001 % 0.53 0.43

GO:0050879 multicellular organismal movement 0.010 % 0.61 0.44

GO:0006040 amino sugar metabolic process 0.244 % 0.96 0.45

GO:0035873 lactate transmembrane transport 0.017 % 0.88 0.45

GO:0060120 inner ear receptor cell fate commitment 0.001 % 0.48 0.47

GO:1904238 pericyte cell differentiation 0.001 % 0.6 0.47

GO:0099040 ceramide translocation 0.000 % 0.81 0.49

GO:0061061 muscle structure development 0.142 % 0.55 0.5

GO:0035848 oviduct morphogenesis 0.000 % 0.54 0.5

cellular component assembly involved in 0.049 % 0.48 0.51 GO:0010927 morphogenesis

GO:0006022 aminoglycan metabolic process 0.883 % 0.94 0.51

GO:0060541 respiratory system development 0.054 % 0.47 0.51

GO:0035206 regulation of hemocyte proliferation 0.002 % 0.53 0.51

GO:2000974 negative regulation of pro-B cell differentiation 0.000 % 0.48 0.52

GO:1904238 pericyte cell differentiation 0.001 % 0.6 0.47

GO:0048621 post-embryonic digestive tract morphogenesis 0.000 % 0.55 0.52

GO:0007010 cytoskeleton organization 0.786 % 0.89 0.52

GO:0060537 muscle tissue development 0.081 % 0.54 0.52

GO:0021515 cell differentiation in spinal cord 0.013 % 0.45 0.53

GO:0005976 polysaccharide metabolic process 0.906 % 0.98 0.53

GO:0060843 venous endothelial cell differentiation 0.000 % 0.48 0.53

regulation of mesenchymal cell proliferation involved 0.001 % 0.47 0.54 GO:0072199 in ureter development

239

regulation of protein localization to endoplasmic 0.000 % 0.9 0.54 GO:1905550 reticulum

GO:1905242 response to 3,3',5-triiodo-L-thyronine 0.000 % 0.96 0.55

GO:0007076 mitotic chromosome condensation 0.035 % 0.81 0.55

GO:0099039 sphingolipid translocation 0.000 % 0.81 0.56

GO:0061197 fungiform papilla morphogenesis 0.001 % 0.51 0.56

GO:1905278 positive regulation of epithelial tube formation 0.001 % 0.46 0.57

GO:0061314 Notch signaling involved in heart development 0.001 % 0.46 0.6

GO:0072141 renal interstitial fibroblast development 0.000 % 0.48 0.6

secretory columnal luminar epithelial cell differentiation involved in prostate glandular acinus 0.001 % 0.47 0.61 GO:0060528 development

GO:0010359 regulation of anion channel activity 0.002 % 0.89 0.62

anatomical structure formation involved in 0.409 % 0.52 0.63 GO:0048646 morphogenesis

GO:0009141 nucleoside triphosphate metabolic process 1.605 % 0.78 0.63

GO:0031033 myosin filament organization 0.004 % 0.81 0.63

GO:0070925 organelle assembly 0.571 % 0.88 0.64

mitochondrial electron transport, ubiquinol to 0.035 % 0.81 0.64 GO:0006122 cytochrome c

GO:0070482 response to oxygen levels 0.055 % 0.92 0.64

GO:0043282 pharyngeal muscle development 0.000 % 0.48 0.65

GO:0003241 growth involved in heart morphogenesis 0.001 % 0.48 0.66

GO:0007427 epithelial cell migration, open tracheal system 0.002 % 0.51 0.66

GO:0072359 circulatory system development 0.235 % 0.43 0.66

GO:0048845 venous blood vessel morphogenesis 0.002 % 0.48 0.66

GO:0002085 inhibition of neuroepithelial cell differentiation 0.001 % 0.46 0.66

GO:0061053 somite development 0.023 % 0.47 0.68

GO:0003012 muscle system process 0.079 % 0.6 0.69

GO:1901529 positive regulation of anion channel activity 0.001 % 0.87 0.69

GO:0007498 mesoderm development 0.031 % 0.56 0.7

GO:0035051 cardiocyte differentiation 0.026 % 0.39 0.7

240

Supplementary 2.2

Subset of the output of REVIGO analysis showing overrepresented GO terms (Uniprot base, threshold = 0.05) clustered according to similarity in function. The table corresponds to those GO ID overrepresented among the stable phenotypes (queens and workers) in contrast with transitioning wasps

Term ID Description Frequency Uniqueness Dispensability

regulation of transcription from RNA polymerase II GO:0006357 1.273 % 0.43 0 promoter

GO:0090659 walking behavior 0.008 % 0.91 0

GO:0097535 lymphoid lineage cell migration into thymus 0.001 % 0.86 0.02

GO:0031077 post-embryonic camera-type eye development 0.001 % 0.71 0.03

GO:0018130 heterocycle biosynthetic process 17.388 % 0.57 0.28

GO:1901362 organic cyclic compound biosynthetic process 17.871 % 0.58 0.45

GO:0006366 transcription from RNA polymerase II promoter 1.430 % 0.53 0.46

GO:0019438 aromatic compound biosynthetic process 16.954 % 0.57 0.48

GO:0097534 lymphoid lineage cell migration 0.001 % 0.86 0.48

GO:0007417 central nervous system development 0.191 % 0.66 0.49

GO:0033153 T cell receptor V(D)J recombination 0.002 % 0.54 0.5

GO:0003334 keratinocyte development 0.002 % 0.67 0.51

GO:0032774 RNA biosynthetic process 10.925 % 0.44 0.51

GO:0044271 cellular nitrogen compound biosynthetic process 22.502 % 0.55 0.52

GO:0045165 cell fate commitment 0.091 % 0.67 0.53

GO:0008346 larval walking behaviour 0.000 % 0.78 0.57

commitment of neuronal cell to specific neuron type in GO:0021902 0.001 % 0.65 0.59 forebrain

GO:0007418 ventral midline development 0.002 % 0.7 0.59

GO:0031326 regulation of cellular biosynthetic process 10.816 % 0.35 0.6

GO:0060429 epithelium development 0.300 % 0.71 0.64

241

Supplementary 2.3.

Subset of the output of REVIGO analysis showing overrepresented GO terms (Uniprot base, threshold = 0.05) clustered according to similarity in function. The table corresponds to those GO ID overrepresented among transitioning wasps. Transitioning wasps versus queens.

Term ID Description Frequency Uniqueness Dispensability

GO:0006030 chitin metabolic process 0.077 % 0.97 0

GO:0007522 visceral muscle development 0.001 % 0.51 0

GO:0060361 flight 0.001 % 0.94 0

positive regulation of protein localization to endoplasmic GO:1905552 0.000 % 0.81 0 reticulum

GO:0007076 mitotic chromosome condensation 0.035 % 0.79 0.04

GO:0097039 protein linear polyubiquitination 0.001 % 0.95 0.06

GO:0000272 polysaccharide catabolic process 0.288 % 0.96 0.09

GO:0007604 phototransduction, UV 0.000 % 0.8 0.11

GO:1903515 calcium ion transport from cytosol to endoplasmic reticulum 0.000 % 0.94 0.11

GO:0001952 regulation of cell-matrix adhesion 0.019 % 0.91 0.13

GO:0036335 intestinal stem cell homeostasis 0.002 % 0.9 0.13

GO:0016114 terpenoid biosynthetic process 0.245 % 0.85 0.13

GO:0018401 peptidyl-proline hydroxylation to 4-hydroxy-L-proline 0.001 % 0.87 0.21

GO:0048251 elastic fiber assembly 0.001 % 0.83 0.25

GO:0010991 negative regulation of SMAD protein complex assembly 0.001 % 0.75 0.32

GO:1990146 protein localization to rhabdomere 0.000 % 0.92 0.32

GO:1902263 apoptotic process involved in embryonic digit morphogenesis 0.001 % 0.57 0.34

GO:0045989 positive regulation of striated muscle contraction 0.002 % 0.55 0.35

GO:0015914 phospholipid transport 0.076 % 0.81 0.35

GO:0007629 flight behavior 0.001 % 0.62 0.36

GO:0007498 mesoderm development 0.031 % 0.6 0.36

GO:0036099 female germ-line stem cell population maintenance 0.002 % 0.59 0.36

GO:0072602 interleukin-4 secretion 0.001 % 0.57 0.37

GO:0034375 high-density lipoprotein particle remodeling 0.003 % 0.56 0.37

242

GO:0036309 protein localization to M-band 0.000 % 0.92 0.38

GO:1902932 positive regulation of alcohol biosynthetic process 0.004 % 0.82 0.38

GO:0007440 foregut morphogenesis 0.003 % 0.56 0.39

GO:0023035 CD40 signaling pathway 0.002 % 0.83 0.42

GO:2001225 regulation of chloride transport 0.001 % 0.84 0.42

GO:0003180 aortic valve morphogenesis 0.001 % 0.48 0.42

negative regulation of compound eye photoreceptor GO:0045316 0.000 % 0.49 0.42 development

GO:1901264 carbohydrate derivative transport 0.208 % 0.85 0.43

GO:0018107 peptidyl-threonine phosphorylation 0.032 % 0.94 0.43

GO:0007403 glial cell fate determination 0.001 % 0.56 0.43

GO:0042742 defense response to bacterium 0.097 % 0.89 0.43

GO:0043534 blood vessel endothelial cell migration 0.016 % 0.53 0.44

GO:0035206 regulation of hemocyte proliferation 0.002 % 0.56 0.47

GO:0021523 somatic motor neuron differentiation 0.002 % 0.52 0.47

GO:0009268 response to pH 0.011 % 0.91 0.47

GO:0006583 melanin biosynthetic process from tyrosine 0.000 % 0.87 0.49

GO:0042691 positive regulation of crystal cell differentiation 0.000 % 0.56 0.49

regulation of cyclin-dependent protein serine/threonine GO:0031660 0.000 % 0.82 0.51 kinase activity involved in G2/M transition of mitotic cell cycle

GO:0030708 germarium-derived female germ-line cyst encapsulation 0.000 % 0.53 0.52

GO:0035023 regulation of Rho protein signal transduction 0.125 % 0.79 0.52

GO:2000974 negative regulation of pro-B cell differentiation 0.000 % 0.5 0.53

GO:0072104 glomerular capillary formation 0.001 % 0.48 0.53

GO:0060843 venous endothelial cell differentiation 0.000 % 0.51 0.55

GO:0036306 embryonic heart tube elongation 0.000 % 0.51 0.55

negative regulation of vascular associated smooth muscle GO:1904753 0.001 % 0.75 0.56 cell migration

GO:0030241 skeletal muscle myosin thick filament assembly 0.001 % 0.51 0.56

GO:0007527 adult somatic muscle development 0.001 % 0.61 0.56

GO:0008015 blood circulation 0.097 % 0.59 0.56

GO:0060577 pulmonary vein morphogenesis 0.000 % 0.52 0.57

243

GO:0007527 adult somatic muscle development 0.001 % 0.61 0.56

GO:1903849 positive regulation of aorta morphogenesis 0.000 % 0.48 0.57

GO:0099040 ceramide translocation 0.000 % 0.78 0.58

GO:0003162 atrioventricular node development 0.001 % 0.49 0.58

GO:1901529 positive regulation of anion channel activity 0.001 % 0.82 0.6

GO:0035995 detection of muscle stretch 0.001 % 0.89 0.61

GO:0060347 heart trabecula formation 0.003 % 0.45 0.61

GO:0090103 cochlea morphogenesis 0.005 % 0.49 0.62

GO:0003344 pericardium morphogenesis 0.002 % 0.47 0.63

GO:0072141 renal interstitial fibroblast development 0.000 % 0.53 0.63

Notch signaling pathway involved in regulation of secondary GO:0003270 0.000 % 0.44 0.63 heart field cardioblast proliferation

GO:0009593 detection of chemical stimulus 0.285 % 0.89 0.64

secretory columnal luminar epithelial cell differentiation GO:0060528 0.001 % 0.51 0.64 involved in prostate glandular acinus development

GO:0060973 cell migration involved in heart development 0.004 % 0.43 0.64

GO:0008377 light-induced release of internally sequestered calcium ion 0.000 % 0.72 0.65

GO:0010885 regulation of cholesterol storage 0.003 % 0.81 0.65

GO:0030049 muscle filament sliding 0.001 % 0.58 0.67

GO:0043691 reverse cholesterol transport 0.003 % 0.84 0.68

second mitotic wave involved in compound eye GO:0016330 0.001 % 0.52 0.7 morphogenesis

244

Supplementary 2.4.

Subset of the output of REVIGO analysis showing overrepresented GO terms (Uniprot base, threshold = 0.05) clustered according to similarity in function. The table corresponds to those GO ID overrepresented among transitioning wasps. Transitioning wasps versus workers.

Term ID Description Frequency Uniqueness Dispensability

GO:0022610 biological adhesion 0.550 % 0.99 0.00

GO:0031589 cell-substrate adhesion 0.078 % 0.98 0.00

GO:0032501 multicellular organismal process 2.373 % 0.99 0.00

GO:0032502 developmental process 2.812 % 0.99 0.00

GO:0036335 intestinal stem cell homeostasis 0.002 % 0.92 0.00

GO:0040011 locomotion 0.997 % 0.99 0.00

GO:0051606 detection of stimulus 0.351 % 0.94 0.00

GO:0060361 flight 0.001 % 0.98 0.00

GO:1901615 organic hydroxy compound metabolic process 0.831 % 0.99 0.00

GO:1903515 calcium ion transport from cytosol to endoplasmic reticulum 0.000 % 0.97 0.00

GO:0017144 drug metabolic process 0.058 % 0.97 0.01

GO:0019748 secondary metabolic process 0.138 % 0.91 0.01

GO:0034367 macromolecular complex remodeling 0.007 % 0.91 0.02

GO:0006040 amino sugar metabolic process 0.244 % 0.99 0.04

GO:1901617 organic hydroxy compound biosynthetic process 0.383 % 0.97 0.04

GO:0007629 flight behavior 0.001 % 0.62 0.05

GO:1904238 pericyte cell differentiation 0.001 % 0.60 0.05

GO:0035172 hemocyte proliferation 0.002 % 0.91 0.05

GO:0005976 polysaccharide metabolic process 0.906 % 0.98 0.05

GO:0097039 protein linear polyubiquitination 0.001 % 0.97 0.07

GO:0019471 4-hydroxyproline metabolic process 0.002 % 0.89 0.09

GO:0036309 protein localization to M-band 0.000 % 0.97 0.10

GO:0030029 actin filament-based process 0.398 % 0.87 0.11

GO:0046148 pigment biosynthetic process 0.448 % 0.90 0.13

GO:0070252 actin-mediated cell contraction 0.016 % 0.82 0.13

245

GO:0042440 pigment metabolic process 0.485 % 0.91 0.15

GO:0006720 isoprenoid metabolic process 0.464 % 0.85 0.17

GO:0055114 oxidation-reduction process 15.060 % 0.88 0.22

GO:0023035 CD40 signaling pathway 0.002 % 0.84 0.22

GO:2001225 regulation of chloride transport 0.001 % 0.90 0.23

GO:0018107 peptidyl-threonine phosphorylation 0.032 % 0.97 0.24

GO:0009268 response to pH 0.011 % 0.93 0.25

GO:0043691 reverse cholesterol transport 0.003 % 0.89 0.27

GO:0019722 calcium-mediated signaling 0.040 % 0.82 0.27

GO:1990146 protein localization to rhabdomere 0.000 % 0.97 0.29

regulation of cyclin-dependent protein serine/threonine kinase GO:0031660 0.000 % 0.85 0.29 activity involved in G2/M transition of mitotic cell cycle

GO:0006323 DNA packaging 0.227 % 0.88 0.31

regulation of G-protein coupled receptor protein signaling GO:0008277 0.024 % 0.82 0.31 pathway

GO:0051707 response to other organism 0.299 % 0.91 0.32

GO:0015748 organophosphate ester transport 0.144 % 0.89 0.34

GO:0061383 trabecula morphogenesis 0.010 % 0.57 0.35

GO:0072602 interleukin-4 secretion 0.001 % 0.59 0.35

GO:0030198 extracellular matrix organization 0.060 % 0.82 0.36

GO:0043062 extracellular structure organization 0.061 % 0.83 0.36

GO:0007525 somatic muscle development 0.005 % 0.54 0.37

GO:0034368 protein-lipid complex remodeling 0.005 % 0.90 0.38

GO:0072141 renal interstitial fibroblast development 0.000 % 0.49 0.38

GO:0007403 glial cell fate determination 0.001 % 0.53 0.38

GO:0050881 musculoskeletal movement 0.009 % 0.59 0.38

GO:0036099 female germ-line stem cell population maintenance 0.002 % 0.56 0.39

GO:0071825 protein-lipid complex subunit organization 0.010 % 0.91 0.39

GO:1902903 regulation of supramolecular fiber organization 0.166 % 0.74 0.39

GO:1901071 glucosamine-containing compound metabolic process 0.132 % 0.98 0.41

GO:0007498 mesoderm development 0.031 % 0.55 0.41

246

second mitotic wave involved in compound eye GO:0016330 0.001 % 0.49 0.41 morphogenesis

positive regulation of protein localization to endoplasmic GO:1905552 0.000 % 0.87 0.42 reticulum

GO:0007440 foregut morphogenesis 0.003 % 0.52 0.42

GO:0050879 multicellular organismal movement 0.010 % 0.59 0.42

GO:0060561 apoptotic process involved in morphogenesis 0.005 % 0.57 0.43

GO:0018401 peptidyl-proline hydroxylation to 4-hydroxy-L-proline 0.001 % 0.89 0.44

GO:0061384 heart trabecula morphogenesis 0.007 % 0.58 0.44

negative regulation of vascular associated smooth muscle cell GO:1904753 0.001 % 0.81 0.44 migration

GO:0006874 cellular calcium ion homeostasis 0.091 % 0.82 0.45

GO:1901264 carbohydrate derivative transport 0.208 % 0.89 0.45

GO:0021524 visceral motor neuron differentiation 0.001 % 0.50 0.45

GO:0000768 syncytium formation by plasma membrane fusion 0.014 % 0.53 0.45

GO:0090130 tissue migration 0.061 % 0.56 0.47

GO:0009589 detection of UV 0.000 % 0.92 0.48

GO:0035922 foramen ovale closure 0.000 % 0.45 0.49

GO:0022607 cellular component assembly 2.484 % 0.87 0.50

GO:0099040 ceramide translocation 0.000 % 0.81 0.50

GO:0035206 regulation of hemocyte proliferation 0.002 % 0.52 0.51

GO:0018210 peptidyl-threonine modification 0.034 % 0.97 0.52

GO:0030708 germarium-derived female germ-line cyst encapsulation 0.000 % 0.51 0.52

GO:2000974 negative regulation of pro-B cell differentiation 0.000 % 0.47 0.52

GO:0097435 supramolecular fiber organization 0.345 % 0.81 0.52

GO:0060843 venous endothelial cell differentiation 0.000 % 0.47 0.52

GO:0035994 response to muscle stretch 0.003 % 0.92 0.53

GO:0000272 polysaccharide catabolic process 0.288 % 0.99 0.53

GO:0036306 embryonic heart tube elongation 0.000 % 0.47 0.54

GO:1905550 regulation of protein localization to endoplasmic reticulum 0.000 % 0.89 0.54

GO:0003193 pulmonary valve formation 0.000 % 0.45 0.55

247

GO:0014855 striated muscle cell proliferation 0.012 % 0.91 0.56

GO:0099039 sphingolipid translocation 0.000 % 0.81 0.56

GO:0061314 Notch signaling involved in heart development 0.001 % 0.42 0.56

GO:0046533 negative regulation of photoreceptor cell differentiation 0.002 % 0.45 0.58

GO:0060841 venous blood vessel development 0.004 % 0.44 0.58

GO:0031034 myosin filament assembly 0.003 % 0.80 0.58

GO:0031033 myosin filament organization 0.004 % 0.80 0.58

GO:0007010 cytoskeleton organization 0.786 % 0.87 0.59

GO:0003008 system process 0.660 % 0.55 0.59

GO:1903849 positive regulation of aorta morphogenesis 0.000 % 0.44 0.

GO:0048856 anatomical structure development 2.540 % 0.49 0.59

GO:0006936 muscle contraction 0.066 % 0.58 0.59

GO:0010631 epithelial cell migration 0.058 % 0.51 0.59

GO:0035265 organ growth 0.034 % 0.48 0.60

GO:0090092 regulation of transmembrane receptor protein serine/threonine 0.054 % 0.80 0.60 kinase signaling pathway

GO:0071689 muscle thin filament assembly 0.000 % 0.48 0.60

GO:1902932 positive regulation of alcohol biosynthetic process 0.004 % 0.83 0.60

GO:0007522 visceral muscle development 0.001 % 0.48 0.60

GO:0007527 adult somatic muscle development 0.001 % 0.57 0.61

GO:0072132 mesenchyme morphogenesis 0.011 % 0.44 0.61

GO:0016114 terpenoid biosynthetic process 0.245 % 0.85 0.62

GO:0009582 detection of abiotic stimulus 0.056 % 0.92 0.62

GO:0006949 syncytium formation 0.015 % 0.54 0.62

GO:0010359 regulation of anion channel activity 0.002 % 0.89 0.62

GO:0060039 pericardium development 0.005 % 0.42 0.62

GO:0048845 venous blood vessel morphogenesis 0.002 % 0.45 0.63

GO:0007604 phototransduction, UV 0.000 % 0.82 0.63

GO:0019915 lipid storage 0.032 % 0.88 0.63

GO:0007062 sister chromatid cohesion 0.098 % 0.79 0.64

GO:0030048 actin filament-based movement 0.021 % 0.82 0.64

248

secretory columnal luminar epithelial cell differentiation GO:0060528 0.001 % 0.48 0.64 involved in prostate glandular acinus development

GO:0016203 muscle attachment 0.003 % 0.45 0.64

GO:0003344 pericardium morphogenesis 0.002 % 0.43 0.64

GO:0072102 glomerulus morphogenesis 0.002 % 0.48 0.65

GO:0003266 regulation of secondary heart field cardioblast proliferation 0.002 % 0.39 0.65

GO:0060973 cell migration involved in heart development 0.004 % 0.40 0.66

GO:0048568 embryonic organ development 0.113 % 0.41 0.66

GO:0097084 vascular smooth muscle cell development 0.002 % 0.40 0.66

GO:0010927 cellular component assembly involved in morphogenesis 0.049 % 0.47 0.67

GO:0009912 auditory receptor cell fate commitment 0.001 % 0.46 0.68

GO:0070925 organelle assembly 0.571 % 0.87 0.68

GO:0061061 muscle structure development 0.142 % 0.54 0.68

GO:0046189 phenol-containing compound biosynthetic process 0.024 % 0.96 0.68

GO:0061439 kidney vasculature morphogenesis 0.002 % 0.44 0.69

GO:0003012 muscle system process 0.079 % 0.59 0.69

GO:1901529 positive regulation of anion channel activity 0.001 % 0.87 0.69

GO:0051056 regulation of small GTPase mediated signal transduction 0.218 % 0.79 0.69

GO:0003170 heart valve development 0.010 % 0.41 0.69

GO:0030261 chromosome condensation 0.108 % 0.88 0.69

GO:0014706 striated muscle tissue development 0.077 % 0.51 0.69

GO:0048769 sarcomerogenesis 0.000 % 0.48 0.69

249

Supplementary information chapter 3

Supplementary 3.1

Summary table of A. pubescens sample collection. This includes sample id (ID), length of the right forewing (mm), length of the largest oocyte in the ovarian track (mm), wasps’ location, as well as, date and time when they were sampled.

wing size Largest Behaviour during ID Location Date Time (calliper) oocyte sampling n29 9.60 2.835 Provisioning (Prov) H-CL 23/07/2017 15:17:00 n47 9.11 1.856 Provisioning (Prov) H-CR 31/07/2017 12:19:00 n48 8.82 1.106 Egg laying (EL) H-CR 31/07/2017 13:26:00 n49 10.08 2.898 Nest foundation (NF) H-CL 05/08/2017 12:25:00 n73 9.44 2.758 Egg laying (EL) H-C 22/06/2017 12:12:00 n74 9.78 1.490 Egg laying (EL) H-D 24/06/2017 14:00:00 n77 8.75 2.176 Egg laying (EL) H-CL 03/07/2017 15:23:00 n78 9.46 1.941 Egg laying (EL) H-CR 04/07/2017 16:20:00 n79 8.68 2.497 Egg laying (EL) H-CR 05/07/2017 13:14:00 n80 9.57 1.638 Egg laying (EL) H-CL 06/07/2017 16:35:00 n82 9.19 3.244 Egg laying (EL) H-HH 09/07/2017 17:00:00 n83 9.08 1.295 Provisioning (Prov) H-C 10/07/2017 11:35:00 n84 10.31 2.686 Egg laying (EL) H-C 10/07/2017 16:00:00 n88 9.15 2.397 Nest foundation (NF) H-CR 13/07/2017 13:30:00 n89 9.52 2.603 Provisioning (Prov) H-CR 13/07/2017 13:50:00 n93 8.87 2.602 Nest foundation (NF) H-CR 13/07/2017 15:15:00 n96 8.64 2.296 Egg laying (EL) H-CL 13/07/2017 16:40:00 n100 9.53 2.446 Provisioning (Prov) H-D 14/07/2017 17:55:00 n101 9.46 2.597 Nest foundation (NF) H-D 14/07/2017 18:25:00 n102 9.09 2.615 Nest foundation (NF) H-D 14/07/2017 16:40:00 n105 9.57 2.994 Nest foundation (NF) H-HAH 16/07/2017 15:00:00 n106 9.55 2.830 Nest foundation (NF) H-HAH 17/07/2017 10:38:00

250 n107 9.08 2.739 Provisioning (Prov) H-HAH 17/07/2017 10:45:00 n109 9.32 2.998 Provisioning (Prov) H-HAH 17/07/2017 11:20:00 n110 9.55 2.801 Provisioning (Prov) H-HAH 17/07/2017 11:55:00 n111 10.01 2.760 Provisioning (Prov) H-HAH 17/07/2017 12:00:00 n116 8.54 2.725 Nest foundation (NF) H-HBH 17/07/2017 14:20:00 n117 8.56 2.546 Inspection visit (Insp) H-B 17/07/2017 15:05:00 n123 9.20 2.750 Provisioning (Prov) G-GP 18/07/2017 11:49:00 n127 10.05 2.616 Nest foundation (NF) G-GP 18/07/2017 13:35:00 n140 8.88 1.326 Inspection visit (Insp) H-CL 04/08/2017 14:24:00 n142 9.04 2.778 Inspection visit (Insp) G-Goats' 11/08/2017 13:47:00 n153 9.70 2.571 Nest foundation (NF) G-GP 15/08/2017 13:27:00 n156 8.07 2.602 Inspection visit (Insp) G-Goats' 17/08/2017 16:28:00

251

Supplementary 3.2

RNA extraction protocol

Tissue: Wasps brains

Cleaning equipment

• Paper roll • 70% EtOH • 10% bleach • RNAsezap TM RNase Decontamination Solution • Nitrile gloves • Pen

RNA extraction equipment

• RNeasy Plus Universal Tissue Mini K50: • Buffer RWT – add 2 volumes 100% EtOH • Buffer RPE – add 4 volumes 100% EtOH • gDNA eliminator solution • RNase-Free Water • QIAzol lysis reagent • RNeasy Mini Spin Columns • Collection Tubes • 70% EtOH • Chloroform • 1000 µl pipette • 200 µl pipette • 1000 µl pipette tips (sterile and DNase/RNase free) • 200 µl pipette tips (sterile and DNase/RNase free) • 50 µl eppendorf tubes (sterile and DNAse/RNase free) • 1.5 ml effpendorf tubes (sterile and DNAse/RNase free) • Bench centrifuge • Refrigerated centrifuge

252

Protocol

• Before starting set the refrigerated centrifuge at 4°C • Clean bench and pipettes. First with bleach, next with RNAzap and finally with 70% EtOH

RNA lysis and homogenisation

1. Place the tubes containing the brain tissue in the TyssueLyser for 3 min at 50 Hertz oscillations (1/s) 2. Briefly centrifuge the tubes 3. Add 900 µl QIAzol lysis reagent per tube 4. Place tubes back to the TyssueLyser for 5 min at 50 Hertz oscillations (1/s) 5. Remove the tubes from the TyssueLyser and briefly centrifuge the tubes 6. Transfer the lysates into a 1.5 ml Eppendorf tube 7. Flash freeze at – 80°C

DNA elimination and RNA purification

− Allow tubes to defrost and keep them at room temperature (RT) for 5 min − Add 100 µl gDNA eliminator solution and shake the tubes vigorously for 15 s − Add 180 µl chloroform, shake vigorously for 15 s and leave the tubes at RT for 3 min − Centrifuge at 12 000 g x 15 min at 4°C − While centrifugation, set and label 1.5 ml tubes − Transfer the upper aqueous phase (~ 600 µl) to the new labelled 1.5 ml tubes − Add 1 volume (~600 µl) of 70% EtOH and mix thoroughly by pipetting − Label and set RNeasy mini spin columns − Transfer up to 700 µl of sample to RNeasy mini spin column − Centrifuge columns at 8 000 g x 15 min at RT − Carefully remove the spin column from the collection tube and discard the flow − Carefully place back together the collection tube and the column − Repeat centrifugation using the remainder of the sample − Discard the flow through

253

− Reuse the collection tube, and add 700 µl buffer RWT to the spin column − Centrifuge at 8 000 g x 15 s at RT − Carefully remove the spin column from the collection tube so it doesn’t touch − Discard the flow − Completely empty the collection tube and place back the spin column − Add 500 µl Buffer RPE to the column − Centrifuge at 8 000 g x 15s at RT − Discard the flow through − Reuse the Collection tube − Add 500 µl Buffer RPE to the column − Centrifuge at 8 000 g x 2 min. Remove the column from the collection tube so they do not touch − Place the spin column in a new collection tube and discard the old collection tube − Centrifuge at full speed for 1 min − Place the column in a new 1.5ml collection tube − Add 40 µl RNase-free water to the spin column membrane − Incubate at RT x 1 min − Centrifuge at 8 000 g x 1 min at RT − To increase RNA concentration, use the eluate and re-centrifuge at 8 000 g x 1 min − To increase volume, keep eluate and use RNase-free water to re-centrifuge at 8 000 g x 1 min − Place 2.5 µl of RNA in a 50 µl tube evaluate RNA integrity and yield by Nanodrop ND- 1000 Spectrophotometer − Place 2.5 of RNA in a 50 µl tube for a precise evaluation of the RNA using Aligent 2100 Bioanalyser − Keep the RNA at -80°C until RNA sequencing

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Brain RNA quality assessment

The initial assessment of RNA integrity and yield consists of the identification of contaminants such as proteins, salt and organic compound by the 260/280 and 260/230 ratios in the NanoDrop 1000 Spectrophotometer. Aromatic proteins have strong UV-absorbance at 280 nm while RNA and organic compounds (QIAzol, Trizol, phenol and salt, etc.) at 260 nm and 230 nm, respectively. For pure RNA, the 260/280 ratio should be around 1.8. Protein contamination is indicated by a ratio lower than 1.8 and should not be used. For 230/260 ratio, values above 2.0 are optimal while values below depict contamination.

Further analysis of RNA integrity and concentration is carried out using Bioanalyzer 2100 (Agilent Technologies). The Bioanalyzer uses an algorithm to create an RNA integrity number (RIN), RNA with a value of 10 (perfect score) reflects high quality, whereas RNA with RIN values below 7.0 is not recommended for RNA sequencing nor RNA microarrays. Bioanalyzer results include an electrophoresis gel and an electropherogram (Figure Supplementary 3.2). Good quality RNA will show a sharp and clean 18S ribosomal band in the electrophoresis gel; and a clear and sharp 18S ribosomal peak in the electropherogram. Degraded and/or contaminated RNA will have other bands and/or smearing of RNA in the electrophoresis gel; and multiple peaks in the electropherogram.

255

Figure Supplementary 3.1. Example of RNA integrity and quality assessment using Bioanalyzer. A) RNA extraction electrophoresis of sixteen samples (A5 – H6). Green boxes are the RNA integrity number (RIN) is showed at the bottom, whereas the ladder is showed in the first column. B) Electropherogram with a clear and sharp 18S ribosomal peak.

256

Supplementary 3.3

A. pubescens’ genome annotation characteristic. In total, 17,293 (91.28%) proteins had some type of protein signatures, from which 12,954 proteins (68.38%) were annotated with at least one signature coming from one of the most important InterPro databases for functional annotation (i.e. PANTHER, Pfam, TIGRFAM, HAMAP, SUPERFAMILY) (Supplementary table 3.1). After using Automatic Batch CD-server to scan a set of pre-calculated position-specific scoring matrices with our proteins. Thus, 11,909 proteins were found to have domain hits and 6,012 proteins have features data. These included active sites, inter-domain contacts, cleavage sites or proline interaction residues.

When using InterPro, KEGG, PANNZER and blast_annotator altogether, a total of 12,681 proteins had at least one GO term associated (Supplementary table 3.2). Using blast_annotator between 1-22 GO terms per protein were associated, and between 1-30 GO terms per protein with PANNZER2. The total number of proteins for each GO category varied between 6,093 and 9,753 proteins (Supplementary table 3.3). Within these signatures the PFAM domains (~80 transposase domains); GO terms GO:0006278(RNA-dependent DNA replication), GO:0015074(DNA integration), GO:0006355(regulation of transcription, DNA- templated), GO:0004803(transposase activity). Finally, a search for retrotransposon/ transposase was carried out using Blast2GO and PANNZER2. In total, 1,087 unique proteins (979 genes) were annotated as putative transposons (Supplementary table 3.4).

257

Supplementary table 3.1. The number of proteins containing a signature belonging to each specific InterPro member database.

InterPro member database Number of proteins

PANTHER 11,996

Pfam 11,458

MobiDBLite 9,927

Gene3D 9,384

SUPERFAMILY 9,247

Phobius 4,935

CDD 5,083

TMHMM 3,182

ProSiteProfiles 6,175

SMART 5,503

Coils 5,304

ProSitePatterns 3,070

PRINTS 2,331

SignalP_EUK 1,499

SignalP_GRAM_POSITIVE 817

TIGRFAM 745

PIRSF 802

SignalP_GRAM_NEGATIVE 443

Hamap 313

ProDom 153

SFLD 66

258

Supplementary table 3.2. GO terms association found by source and the number of proteins assigned by each of the sources and to the combined source.

Source Number of proteins

blast_annotator (Uniprot-GOA) 11,602(61.24%)

InterPro 9,211(48.62%)

KEGG 3,025 (15.97%)

PANNZER2 10,441(55.16%)

Combined sources 12,681 (66.94%)

Supplementary table 3.3. The total number of proteins associated with each different GO category type using blast_annotator and PANNZER.

GO category Number of proteins

Molecular function 9,753 (8,566)

Biological process 7,212 (8,187)

Cellular component 6,093 (8,546)

Supplementary table 3.4. The number of proteins associated with transposable elements.

Transposons annotated with Number of proteins

PFAM domains 146

GO terms 950 (InterPro,PANNZER2,blast_annotator,KEGG) (420;496;850;0)

BlastDef/PANNZER2 functional description (DE) 23

By three methods 0

By two methods 32

259

Supplementary information chapter 4

Supplementary 4.1

Bioinformatic pipeline script The code below describes the data processing after RNA sequencing, as well as the analysis of data to determine the genes involved in each of the behavior phases of the nest cycle of A. pubescens. Data processing was carried out using Bluecrystal phase 3 (BC3), the University of Bristol’s high-performance computing cluster. Each rectangle has the full script to submit the job in the queue system of BC3; the symbols like “#“ precede comments and/or explanations regarding the code whereas “$” precede the code for Linux command line, and “>“ precede the code for R command line.

1. Sample name

The table below show the name of each paired-end read Egg-laying female’s Diggers’ name Provisioners name Inspectors name name n49_1_fq.gz n48_1_fq.gz n29_1_fq.gz n117_1_fq.gz n49_2_fq.gz n48_2_fq.gz n29_2_fq.gz n117_2_fq.gz n88_1_fq.gz n73_1_fq.gz n47_1_fq.gz n140_1_fq.gz n88_2_fq.gz n73_2_fq.gz n47_2_fq.gz n140_2_fq.gz n93_1_fq.gz n74_1_fq.gz n83_1_fq.gz n142_1_fq.gz n93_2_fq.gz n74_2_fq.gz n83_2_fq.gz n142_2_fq.gz n101_1_fq.gz n77_1_fq.gz n89_1_fq.gz n156_1_fq.gz n101_2_fq.gz n77_2_fq.gz n89_2_fq.gz n156_2_fq.gz n102_1_fq.gz n78_1_fq.gz n100_1_fq.gz

n102_2_fq.gz n78_2_fq.gz n100_2_fq.gz n105_1_fq.gz n79_1_fq.gz n107_1_fq.gz

n105_2_fq.gz n79_2_fq.gz n107_2_fq.gz n106_1_fq.gz n80_1_fq.gz n109_1_fq.gz

n106_2_fq.gz n80_2_fq.gz n109_2_fq.gz n116_1_fq.gz n82_1_fq.gz n110_1_fq.gz

n116_2_fq.gz n82_2_fq.gz n110_2_fq.gz n127_1_fq.gz n84_1_fq.gz n111_1_fq.gz

n127_2_fq.gz n84_2_fq.gz n111_2_fq.gz n153_1_fq.gz n96_1_fq.gz n123_1_fq.gz

n153_2_fq.gz n96_2_fq.gz n123_2_fq.gz

260

2. Review of data quality #!/bin/bash # request resources: #PBS -N Fastqc #PBS -l nodes=1:ppn=8 #PBS -l walltime=020:00:00

# on compute node, change directory to 'submission directory': cd /newhome/sm15155/Ammophila_2019/Samples/

# module need it module add apps/fastqc-0.11.5

# print date date for file in /newhome/sm15155/Ammophila_2019/Samples/*_fq.gz do fastqc -t 8 -f fastq -o fastqcResults ${file} done

#Print “ending script” after it’s finished the line above #Print date echo "ending script" date

3. Correction of erroneous k-mer (paired-end reads) #Install rCorrector #Clone rCorrector from git git clone https://github.com/mourisl/Rcorrector.git

#On the main directory of rCorrector, run make

#!/bin/bash # request resources: #PBS -N kmerCorrector #PBS -l nodes=1:ppn=16 #PBS -l walltime=020:00:00

# on compute node, change directory to 'submission directory': cd $PBS_O_WORKDIR

# Add module Module add apps date perl ../k-merRemoval/rcorrector/run_rcorrector.pl -t 16 -1 *_1.fq.gz -2 *_2.fq.gz echo "the end" date

261

4. Removal of reads that were flagged by rCorrertor as “unfixable” with python #On the terminal # Clone FilterUncorrectabledPEfastq.py git clone https://github.com/harvardinformatics/TranscriptomeAssemblyTools.git

#Download FilterUncorrectablePEfastq.py and place it where #the reads are

#!/bin/bash # request resources: #PBS -N kmerRemoval #PBS -l nodes=1:ppn=16,walltime=034:00:00

# on compute node, change directory to 'submission directory': cd $PBS_O_WORKDIR date

#discard read pairs for which one of the reads is deemed unfixable python TranscriptomeAssemblyTools/FilterUncorrectabledPEfastq.py -1 ../k- merRemoval/*1.cor.fq.gz -2 ../k-merRemoval/*_2.cor.fq.gz -s n29 echo "the end" date

5. Trimming low quality reads and adapters with TrimGalore! and cutadap

#!/bin/bash # request resources: #PBS -N trimlowq #PBS -l nodes=1:ppn=16,walltime=004:00:00

# on compute node, change directory to 'submission directory': cd $PBS_O_WORKDIR

# Add module Module add languages/python-anaconda-5.0.1-2.7 module add apps/trimgalore-0.4.5 date

#Trim adapter and low quality bases from fastq files

/cm/shared/apps/TrimGalore-0.4.5/trim_galore --path_to_cutadap /cm/shared/languages/anaconda2-5.0.1/bin/cutadapt --paired --retain_unpaired -- phred33 --gzip --output_dir trimmed_reads --length 36 -q 5 --stringency 1 -e 0.1 /newhome/sm15155/Ammophila_2019/kmerDiscard/*_1.cor.fq /newhome/sm15155/Ammophila_2019/kmerDiscard/*_2.cor.fq echo "the end" date

262

6. rRNA removal SSUParc and LSUParc rRNA database were downloaded from SILVA (https://www.arb- silva.de/no_cache/download/archive/release_132/Exports/). Both SSUParc and LSUParc were concatenated and the Us were replaced by Ts. Then, the bowtie2 index for rRNA was built. #!/bin/bash # request resources: #PBS -N bowtie2Index_rRNA #PBS -q paleo #PBS -l nodes=1:ppn=16 #PBS -l walltime=025:00:00

# on compute node, change directory to 'submission directory': # cd $PBS_O_WORKDIR cd /newhome/sm15155/Ammophila_2019/rRNAremoval

# module need it module add apps/trinity-v2.8.4

# built rRNA index with bowtie2 bowtie2-build -f rRNA_SILVA.fasta rRNA_silvaIndex

echo "End" date

Next the reads are mapped #!/bin/bash # request resources: #PBS -N bowtie2Index_rRNA #PBS -q paleo #PBS -l nodes=1:ppn=16 #PBS -l walltime=025:00:00

# on compute node, change directory to 'submission directory': cd /newhome/sm15155/Ammophila_2019/rRNAremoval

# module need it module add apps/trinity-v2.8.4

date

# map the reads bowtie2 --quiet --very-sensitive-local --phred33 -x /newhome/sm15155/Ammophila_2019/rRNAremoval/rRNA_silvaIndex -1 /newhome/sm15155/Ammophila_2019/trimming/trimmed_reads/*_1.cor_val_1.fq.gz -2 /newhome/sm15155/Ammophila_2019/trimming/trimmed_reads/*_2.cor_val_2.fq.gz -- threads 12 --met-file *_bowtie2_metrics.txt --al-conc-gz blacklist_paired_aligned_*.fq.gz --un-conc-gz blacklist_paired_unaligned_*.fq.gz --al-gz blacklist_unpaired_aligned_*.fq.gz --un-gz blacklist_unpaired_unaligned_*.fq.gz

echo “the end” date 263

7. Quality assessment after filtering #!/bin/bash # request resources: #PBS -N Fastqc2 #PBS -l nodes=1:ppn=8 #PBS -l walltime=020:00:00

# on compute node, change directory to 'submission directory': cd /newhome/sm15155/Ammophila_2019/Processed/

#module need it module add apps/fastqc-0.11.5

date

for file in /newhome/sm15155/Ammophila_2019/Processed/*.gz do fastqc -t 8 -f fastq -o fastqcResults ${file} done

echo "ending script" date

Quantification and gene expression analysis

8. Conversion of the annotation file gff3 format to gtf format #!/bin/bash # request resources: #PBS -N gtf_format #PBS -l nodes=1:ppn=8 #PBS -l walltime=000:15:00

# module module add apps/cufflinks-2.2.1

gffread A.pubescens.evm.consensus.annotaiton.v1a.gff3 gff -T -o Apubescen.annotation.gtf 9. Exon and splice sites extraction from gtf file #!/bin/bash # request resources: #PBS -N ExonSplice #PBS -l nodes=1:ppn=8 #PBS -l walltime=000:15:00

# module module add apps/hisat2-2.0.3

extract_splice_sites.py newhome/sm15155/Ammophila_2019/Hisat_index/A.pubescens.annotation.gtf > A.pubesAnnotation.ss

264 extract_exons.py newhome/sm15155/Ammophila_2019/Hisat_index/A.pubescens.annotation.gtf > A.pubesAnnotation.exon

10. Building genome index #!/bin/bash # request resources: #PBS -q paleo #PBS -N Hisat_index #PBS -l nodes=1:ppn=16 #PBS -l walltime=020:00:00

# on compute node, change directory to 'submission directory': #cd $PBS_O_WORKDIR cd /newhome/sm15155/Ammophila_2019/Hisat_index

#module need it module add apps/hisat2-2.1.0

date hisat2-build --ss ApubesAnnotation.ss --exon ApubesAnnotation.exon ../A.pubescens.RM.ammophilini.fasta Apub_tran echo "ending script" date

Index inspector hisat2-inspect -s Apub_tran

# It prints a summary that includes information about index setting, as well as thename and lengths of the input sequences

11. Aligning of RNAseq Ammophila reads to the genome !/bin/bash # request resources: #PBS -N Hisat_alignment1 #PBS -l nodes=1:ppn=16 #PBS -l walltime=050:00:00

# on compute node, change directory to 'submission directory': cd $PBS_O_WORKDIR

#module need it module add apps/samtools-1.8 module add apps/hisat2-2.0.3 date hisat2 -p 16 --dta -x ../Hisat_index/Apub_tran -1 ../AM_samples_2018/n29_file6.1.gz -2 ../AM_samples_2018/n29_file6.2.gz -S n29.sam hisat2 -p 16 --dta -x ../Hisat_index/Apub_tran -1 ../AM_samples_2018/n47_file6.1.gz -2 ../AM_samples_2018/n47_file6.2.gz -S n47.sam 265 hisat2 -p 16 --dta -x ../Hisat_index/Apub_tran -1 ../AM_samples_2018/n48_file6.1.gz -2 ../AM_samples_2018/n48_file6.2.gz -S n48.sam hisat2 -p 16 --dta -x ../Hisat_index/Apub_tran -1 ../AM_samples_2018/n49_file6.1.gz -2 ../AM_samples_2018/n49_file6.2.gz -S n49.sam hisat2 -p 16 --dta -x ../Hisat_index/Apub_tran -1 ../AM_samples_2018/n73_file6.1.gz -2 ../AM_samples_2018/n73_file6.2.gz -S n73.sam hisat2 -p 16 --dta -x ../Hisat_index/Apub_tran -1 ../AM_samples_2018/n74_file6.1.gz -2 ../AM_samples_2018/n74_file6.2.gz -S n74.sam hisat2 -p 16 --dta -x ../Hisat_index/Apub_tran -1 ../AM_samples_2018/n77_file6.1.gz -2 ../AM_samples_2018/n77_file6.2.gz -S n77.sam hisat2 -p 16 --dta -x ../Hisat_index/Apub_tran -1 ../AM_samples_2018/n78_file6.1.gz -2 ../AM_samples_2018/n78_file6.2.gz -S n78.sam hisat2 -p 16 --dta -x ../Hisat_index/Apub_tran -1 ../AM_samples_2018/n79_file6.1.gz -2 ../AM_samples_2018/n79_file6.2.gz -S n79.sam hisat2 -p 16 --dta -x ../Hisat_index/Apub_tran -1 ../AM_samples_2018/n80_file6.1.gz -2 ../AM_samples_2018/n80_file6.2.gz -S n80.sam echo "the end” date

12. Sorting and conversion of SAM files to BAM files #!/bin/bash # request resources: #PBS -N Hisat_alignment1 #PBS -l nodes=1:ppn=16 #PBS -l walltime=050:00:00

# on compute node, change directory to 'submission directory': cd /newhome/sm15155/Ammophila_2019/AM_BAM_files #module need it module add apps/samtools-1.8 date samtools sort @ 12 -o n29.bam n29.sam samtools sort @ 12 -o n47.bam n47.sam samtools sort @ 12 -o n48.bam n48.sam samtools sort @ 12 -o n49.bam n49.sam samtools sort @ 12 -o n73.bam n73.sam samtools sort @ 12 -o n74.bam n74.sam samtools sort @ 12 -o n77.bam n77.sam samtools sort @ 12 -o n78.bam n78.sam samtools sort @ 12 -o n79.bam n79.sam samtools sort @ 12 -o n80.bam n80.sam samtools sort @ 12 -o n82.bam n82.sam samtools sort @ 12 -o n83.bam n83.sam samtools sort @ 12 -o n84.bam n84.sam samtools sort @ 12 -o n88.bam n88.sam samtools sort @ 12 -o n89.bam n89.sam samtools sort @ 12 -o n93.bam n93.sam samtools sort @ 12 -o n96.bam n96.sam samtools sort @ 12 -o n100.bam n100.sam samtools sort @ 12 -o n101.bam n101.sam samtools sort @ 12 -o n102.bam n102.sam samtools sort @ 12 -o n105.bam n105.sam samtools sort @ 12 -o n106.bam n106.sam

266 samtools sort @ 12 -o n107.bam n107.sam samtools sort @ 12 -o n109.bam n109.sam samtools sort @ 12 -o n110.bam n110.sam samtools sort @ 12 -o n100.bam n100.sam samtools sort @ 12 -o n101.bam n101.sam samtools sort @ 12 -o n102.bam n102.sam samtools sort @ 12 -o n105.bam n105.sam samtools sort @ 12 -o n106.bam n106.sam samtools sort @ 12 -o n107.bam n107.sam samtools sort @ 12 -o n109.bam n109.sam samtools sort @ 12 -o n110.bam n110.sam samtools sort @ 12 -o n111.bam n111.sam samtools sort @ 12 -o n116.bam n116.sam samtools sort @ 12 -o n117.bam n117.sam samtools sort @ 12 -o n123.bam n123.sam samtools sort @ 12 -o n127.bam n127.sam samtools sort @ 12 -o n140.bam n140.sam samtools sort @ 12 -o n142.bam n142.sam samtools sort @ 12 -o n153.bam n153.sam samtools sort @ 12 -o n156.bam n156.sam Echo “The end” date

13. Genes and transcripts assemble and quantification #!/bin/bash # request resources: #PBS -N Hisat_alignment1 #PBS -l nodes=1:ppn=16 #PBS -l walltime=050:00:00

# on compute node, change directory to 'submission directory': cd /newhome/sm15155/Ammophila_2019/AM_BAM_files

#module need it module add apps/stringtie.1.3.5 date stringtie -p 12 -G Apubescens.annotation.gtf -o n29.gtf -l n29 n29.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n47.gtf -l n47 n47.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n48.gtf -l n48 n48.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n49.gtf -l n49 n49.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n73.gtf -l n73 n73.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n74.gtf -l n74 n74.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n77.gtf -l n77 n77.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n78.gtf -l n78 n78.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n79.gtf -l n79 n79.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n80.gtf -l n80 n80.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n82.gtf -l n82 n82.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n83.gtf -l n83 n83.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n84.gtf -l n84 n84.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n88.gtf -l n88 n88.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n89.gtf -l n89 n89.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n93.gtf -l n93 n93.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n96.gtf -l n96 n96.bam

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stringtie -p 12 -G Apubescens.annotation.gtf -o n100.gtf -l n100 n100.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n101.gtf -l n101 n101.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n102.gtf -l n102 n102.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n105.gtf -l n105 n105.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n106.gtf -l n106 n106.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n107.gtf -l n107 n107.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n109.gtf -l n109 n109.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n110.gtf -l n110 n110.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n111.gtf -l n111 n111.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n116.gtf -l n116 n116.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n117.gtf -l n117 n117.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n123.gtf -l n123 n123.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n127.gtf -l n127 n127.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n140.gtf -l n140 n140.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n142.gtf -l n142 n142.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n153.gtf -l n153 n153.bam stringtie -p 12 -G Apubescens.annotation.gtf -o n156.gtf -l n156 n156.bam

echo “The end” date

14. Merging transcripts Create a merged-list text file and then submit the following script #!/bin/bash # request resources: #PBS -N Hisat_alignment1 #PBS -l nodes=1:ppn=16 #PBS -l walltime=050:00:00

# on compute node, change directory to 'submission directory': cd $PBS_O_WORKDIR

# module add module add apps/stringtie.1.3.5

date stringtie --merge -p 16 -G Apubescens.annotation.gtf -o stringtie_merged.gtf mergelist.txt echo “the end” date

15. Transcripts comparison with the reference annotation #Download gffcompare from here https://github.com/gpertea/gffcompare #git clone https://github.com/gpertea/gffcompare.git gffcompare -r Apubescens.annotation.gtf -G -o merged stringtie_merged.gtf

16. Transcript abundance estimation and creation of count tables for differential gene expression analysis

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#!/bin/bash # request resources: #PBS -N Hisat_alignment1 #PBS -l nodes=1:ppn=16 #PBS -l walltime=050:00:00 # on compute node, change directory to 'submission directory': cd $PBS_O_WORKDIR #module need it module add apps/stringtie.1.3.5 date stringtie -e -B -p 16 -G stringtie_merged.gtf -o n29.gtf n29.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n47.gtf n47.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n48.gtf n48.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n49.gtf n49.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n73.gtf n73.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n74.gtf n74.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n77.gtf n77.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n78.gtf n78.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n79.gtf n79.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n80.gtf n80.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n82.gtf n82.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n83.gtf n83.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n84.gtf n84.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n88.gtf n88.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n89.gtf n29.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n93.gtf n93.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n96.gtf n96.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n100.gtf n100.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n101.gtf n101.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n102.gtf n102.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n105.gtf n105.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n106.gtf n106.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n107.gtf n107.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n109.gtf n109.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n110.gtf n110.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n111.gtf n111.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n116.gtf n116.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n117.gtf n117.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n123.gtf n123.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n127.gtf n127.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n140.gtf n140.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n142.gtf n142.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n153.gtf n153.bam stringtie -e -B -p 16 -G stringtie_merged.gtf -o n156.gtf n156.bam echo “the end” date

17. Differential gene expression in R

#Install packages: ballgown, RSkittleBrewer, genefilter, dplyr, devtools #Load libraries library(ballgown) library(RSkittleBrewer) library(genefilter)

269 library(dplyr) library(devtools)

#load the phenotype data for the sample pheno_data <- read.csv("Apubes_phenotypic_data.csv")

#lets have a glimps into the phenotypic data head(pheno_data, n = 2) #only shows the first two lineas of the object "pheno_data" names(pheno_data) dim(pheno_data) str(pheno_data) #transform "year" which was passed as integer to a factor value pheno_data$year = as.factor(pheno_data$year) str(pheno_data)

#read in the expression data Apub <- ballgown(dataDir = "ballgown2", samplePattern = "n", pData = pheno_data) list.files("ballgown2") all(pheno_data$ids == list.files("ballgown2")) #You should have a TRUE if you didn't get any errow above

#filter to remove low-abundance genes Apub_fil <- subset(Apub, "rowVars(texpr(Apub)) > 1", genomesubset=TRUE)

#identify transcripts results_transcripts <- stattest(Apub_fil, feature="transcript", covariate = "phenotype", adjustvars = c("heath"), getFC = FALSE, meas = "FPKM")

#Identify genes results_genes <- stattest(Apub_fil, feature = "gene", covariate = "phenotype", adjustvars = c("heath"), getFC = FALSE, meas = "FPKM")

#Add gene names and gene IDs to the results_transcripts data frame: results_transcripts <- data.frame(geneNames = ballgown::geneNames(Apub_fil), geneIDs=ballgown::geneIDs(Apub_fil), results_transcripts)

#Sort the results from the smallest P value to the largest results_transcripts <- arrange(results_transcripts, pval) results_genes <- arrange(results_genes, pval)

#write results to a csv file

270 write.csv(results_transcripts, "Apub_transcript_results.csv", row.names = FALSE) write.csv(results_genes, "Apu_gene_results.csv", row.names = FALSE)

#Identify transcripts and genes with a q value <0.05 subset(results_transcripts, results_transcripts$qval<0.05) subset(results_genes, results_genes$qva<0.05)

#lets plot tropical = c ('darkorange', 'dodgerblue', 'hotpink', 'limegreen', 'yellow') palette(tropical)

#Show the distribution of gene abundances(measured as FPKM values) across samples, colored by sex fpkm <- texpr(Apub, meas = "FPKM") fpkm <- log2(fpkm + 1) boxplot(fpkm, col = as.numeric(pheno_data$phenotype), las = 2, ylab = 'log2(FPKM + 1)')

#Make plots of individuals transcripts across samples ballgown::transcriptNames(Apub) [12] ballgown::geneNames(Apub) [12] plot(fpkm[12,] ~ pheno_data$phenotype, border = c(1,2), main = paste(ballgown::geneNames(Apub)[12],':', ballgown::transcriptNames(Apub)[12]), pch = 19, xlab = "phenotype", ylab = 'log2(FPKM + 1)') points(fpkm[12,] ~ jitter(as.numeric(pheno_data$phenotype)), col = as.numeric(pheno_data$phenotype))

# plot the structure and epxression levels in a sample of all transcripts that share the same gene locus plotTranscripts(ballgown::geneIDs(Apub)[589], Apub, main = C('Gene #name in sample #name'), sample = c('#name'))

#plot average expression levels for all transcripts of a gene within different groups using the plotMean function plotMeans('nameofthegene', groupvar = "phenotype", legend = FALSE)

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Supplementary 4.2

Figure S1. Screen plot of eigenvalues from the largest to the smallest. The first three principal components explain 38.5% of gene expression variation.

Figure S2. Screen plot of the contribution of the sampled wasps to the first two components. The egg-laying wasp n83 contributed 15% to the first two principal component

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Supplementary information chapter 5

Supplementary 5.1.

DNA EXTRACTION PROTOCOLS

DNA extraction according to Truett et al. (2000)

Solutions

• Alkaline solution: 25mM NaOH, 0.2mM disodium EDTA without modifying the pH

• HotShot neutraliser solution: 40mM Tris-HCl without modifying the pH

Protocol

In 0.2 ml clear tubes add 50 µl of the alkaline solution.

Place the wasps or tarsus on clean lab tissue and allow them to dry at room temperature.

Place 3 mm long of any tissue inside the tubes with the alkaline solution and PCR them at 95 °C for 2 hours. After this, hold them at 4 °C until they are removed from the PCR machine.

Add 50 µl of HotShot neutraliser solution to each tube and mix by pipetting.

Once the solution is well mixed create a dilution of each sample. Add 35 µl of the extracted DNA into new tubes with 100 µl of distilled and sterile water. The dilutions can be kept at -20 °C whereas the extracted DNA at 5 °C.

DNA extraction using Qiagen® DNA extraction kit

1. On ice in a petri dish, remove thoracic muscle and place it in a clean 1.5 ml tube with a stainless-steel bead and flash freeze.

2. Place the tube in the TissueLyser for 2 min at 50 Hertz oscillations (1/s) and give a fast spin.

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3. Add 180 µl of PBS and place the tube back in the TissueLyser for 5 min. Centrifuge the tube to place the solution at the bottom of the tube.

4. Add 20 µl of Proteinase K and 200 µl of the buffer AL and mix it by vortexing.

5. Give it a fast spin to keep the solution down.

6. Place the tube at 56 °C overnight.

7. Add 200 µl of ethanol 100% and mix by vortexing.

8. Fast spin

9. Put the mixture into the DNeasy Mini spin column and centrifuge at 8 000 rpm x 1 min.

10. Place the column in a new 2 ml collection tube and add 500 µl of Buffer AW1. Centrifuge at 8 000 rpm x 1 min.

11. Remove the column and place it in a new 2ml collection tube. Add 500 µl of Buffer AW2. Centrifuge at 14 000 rpm x 3 min.

12. In a new 1.5ml tube add the column and add 100 – 150 µl of buffer AE. Incubate it for 10 minutes and centrifuge at 8 000 rpm x 1 min.

13. Take 5 µl of the elution and place it in 200 µl clear tube for DNA quantification. Keep the rest of the elution at -20 °C until preparing the dilution plate.

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