# Quantifying supply and demand in the pea aphid-Buchnera symbiosis reveals the metabolic Achilles' heels of this interaction

Léo Gerlin, Karen Gaget, Garance Lapetoule, Yohann Quivet, Patrice Baa-Puyoulet, Isabelle Rahioui, Mélanie Ribeiro Lopes, Pedro da Silva, Federica Calevro, Hubert Charles

<figure><img src="image_1.png" /></figure>

## To cite this version:

Léo Gerlin, Karen Gaget, Garance Lapetoule, Yohann Quivet, Patrice Baa-Puyoulet, et al. Quantifying supply and demand in the pea aphid-Buchnera symbiosis reveals the metabolic Achilles' heels of this interaction. Metabolic Engineering, 2025, 92, pp.284 - 299. 10.1016/j.ymben.2025.07.011. hal-05368482

HAL Id: hal-05368482

https://hal.science/hal-05368482v1

Submitted on 17 Nov 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

# Quantifying supply and demand in the pea aphid-*Buchnera* symbiosis reveals the metabolic Achilles' heels of this interaction

Léo Gerlin* ID, Karen Gaget ID, Garance Lapetoule ID, Yohann Quivet, Patrice Baa-Puyoulet ID, Isabelle Rahioui, Mélanie Ribeiro Lopes ID, Pedro Da Silva ID, Federica Calevro ID, Hubert Charles ** ID

INRAE, INSA Lyon, BF2I, UMR0203, Villeurbanne, France

## ARTICLE INFO

**Keywords:**

Metabolic model

Flux

Amino acids

Metabolic network

Flux balance analysis

Embryos

## ABSTRACT

Many herbivorous insects feed on unbalanced diets and rely on bacterial endosymbionts to meet all their nutritional needs. This is the case for the pea aphid (Acyrthosiphon pisum), a plant pest whose remarkable growth and reproductive capacities cannot be sustained by its sole nutritional resource, the plant phloem sap, and which relies on a symbiotic relationship maintained over millions of years with the intracellular bacterium *Buchnera aphidicola* for the biosynthesis of amino acids and vitamins. Exploiting original experimental data and metabolic reconstructions, we have built a quantitative genome-scale metabolic model of *B. aphidicola* and used it to quantify amino acid exchanges between the bacterium and its host. We found metabolites that can rewire pathways, influencing the balance between selfish (growth-focused) and mutualist (amino acid synthesis) behavior. Among the products synthesized by *Buchnera*, phenylalanine, tyrosine and leucine are the main matter sinks and consume more than 60 % of imported glucose and serine. Finally, we compared the predicted bacterial supply to the aphid demand in amino acids. We found that the pea aphid may efficiently regulate its symbiont population density depending on its metabolic requirements, but that embryos are quantitatively not self-sustaining, with embryonic bacteria supply falling short of demand by 50 %. Overall, our study highlights candidate compounds and pathways to target for destabilizing this symbiosis or predicting its resilience to environmental or nutritional perturbations.

## 1. Introduction

Many eukaryotic organisms depend on bacterial endosymbionts to meet their nutritional needs or boost their physiological capacities (Nowack and Melkonian, 2010; Muscatine and Porter, 1977; Douglas, 2009). These internalized organisms assimilate host resources and, in return, produce and export specific nutrients, such as vitamins or amino acids, to varying extents depending on the host's developmental stage or requirements (Akman et al., 2002; Nikoh et al., 2014). These symbiotic associations are observed in several herbivorous insects, as these organisms usually feed on unbalanced diets (Hansen and Moran, 2014). Aphids, like many other hemipteran insects, use plant phloem sap as their sole source of food, even though it is poor in B vitamins and most amino acids, including essential amino acids (Douglas, 1998; Lohaus, 2022), as only sugars and few predominant amino acids circulate at high

concentration (Dinant et al., 2010). The missing nutrients must be supplemented by symbiotic bacteria.

In the pea aphid *Acyrthosiphon pisum*, the bacterium *Buchnera aphidicola* has the capability to provide all essential nutrients, or precursors for their biosynthesis, that are absent from the insect's diet (Shigenobu et al., 2000). The bacteria are located in large aphid cells, the bacteriocytes, and are vertically transmitted across generations (Buchner, 1965; Baumann et al., 1995). Nutrient supply from *B. aphidicola* to *A. pisum* and *A. pisum* trophic requirements have been qualitatively well-described (Douglas, 1998; Febvay et al., 1999; Wilkinson and Ishikawa, 2000). *A. pisum* feeds on *Fabaceae*, such as alfalfa, where only asparagine, and to a lesser extent aspartate and serine, are present at high concentration in the phloem sap (Dinant et al., 2010). Other amino acids and vitamins are jointly synthesized by the aphid and its symbionts, with some reactions in a given biosynthetic pathway exclusively

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author.
E-mail addresses: leo.gerlin@inrae.fr (L. Gerlin), hubert.charles@insa-lyon.fr (H. Charles).</footnote>
carried out by the host, and others exclusively by the bacterium, forcing cooperation and metabolic exchanges between the two organisms. It is, for example, the case for the biosynthesis of phenylalanine, whose precursor phenylpyruvate is produced by *B. aphidicola*, before being converted into phenylalanine and tyrosine by *A. pisum* (Wilson et al., 2010; Simonet et al., 2016a). Similarly, isoleucine is synthesized from the conversion of *B. aphidicola*'s threonine into 2-oxobutanoate by *A. pisum*, which is then converted by *B. aphidicola* into S-3-methyl-2--oxopentanoate through three enzymatic steps before conversion into isoleucine by *A. pisum* (Wilson et al., 2010; Viñuelas et al., 2011), and pantothenate is synthesized by *B. aphidicola* from its host's beta-alanine (Blow et al., 2020a). A transcriptomic analysis corroborated this strong cooperation by revealing a coordinated expression of host and bacterial biosynthetic genes within the bacteriocyte (Hansen and Moran, 2011). More specifically, if we look at the aphid life stages, transcriptomics revealed a dynamic response that could be due to life stage-specific requirements, with: (i) a strong activation of the production and assimilation of bacteria-dependent amino acids during the early stages, followed by a reduction in later developmental stages (Pers and Hansen, 2021), (ii) an upregulation of riboflavin synthesis and protein export in embryonic bacteria compared to adult bacteria (Bermingham et al., 2009), and (iii) an upregulation of aphid tyrosine biosynthetic genes in late embryos and early nymphs (Rabatel et al., 2013). Finally, Duncan et al. (2023) demonstrated a mechanism of dynamic regulation to adjust amino acid supply to host demand: a host transporter in the symbiosomal membrane (external membrane produced by the host and surrounding each individual bacterium within the bacteriocyte) provides the non-essential amino acid glutamine to *B. aphidicola* when the pea aphid requires that the bacterium uses it as a precursor to supply arginine to the host.

While this coordinated metabolism is well-characterized, the bacteriial function of providing nutrients to the host has not been studied quantitatively. Host nutrient provision could be seen as an antagonistic function to bacterial growth, and might represent, in particular for amino acids, a massive exchange of matter. Quantifying the fluxes from the bacteria to the host would allow for a better understanding of the functioning of this nutritional symbiosis. It could help determine how bacteria allocate their resources between their own growth and the production of nutrients for the host, and how the host regulates this balance. Moreover, since the dynamics of bacteriocyte, bacteria and their gene expression are dependent on life stage and environmental conditions (Pers and Hansen, 2021; Bermingham et al., 2009; Rabatel et al., 2013; Simonet et al., 2016b, 2018; Colella et al., 2018; Ribeiro Lopes et al., 2022), it is crucial to determine if they quantitatively follow host-specific requirements or if the system is permissive.

Several metabolic models of *B. aphidicola* have been developed to explore its capacities to grow (Thomas et al., 2009; Macdonald et al., 2011, 2012; Blow et al., 2020b). These models are based on Flux Balance Analysis (FBA), a mathematical modeling framework (Orth et al., 2010). FBA formulates a linear optimization problem over the whole metabolism, maximizing or minimizing the flux of one or more metabolic reactions, and computes the flux values in the metabolism which allows these maximal or minimal target values to be achieved. FBA allowed to (i) show the fragility of the *B. aphidicola* metabolic network compared to a free-living bacterium (Thomas et al., 2009), (ii) demonstrate that host precursors can modulate the provision of amino acids (Macdonald et al., 2011), (iii) better understand nitrogen metabolism in the bacteriocyte (Macdonald et al., 2012) and (iv) predict how the system might respond to the presence of a facultative symbiotic bacterium (Blow et al., 2020b). However, all these studies remained semi-quantitative and were not able to integrate the dimension of the system, as they did not add absolute values for growth rates or uptake rates, which only allows to estimate the relative activation of reactions in the metabolic network. In addition, the previous models did not link *B. aphidicola* metabolism to the amount of amino acid required for aphid, nor took into account the variability of *Buchnera* growth during aphid lifecycle (Simonet et al., 2016b). Hence,

after decades of work on *B. aphidicola* metabolism, we still cannot rationally determine: (i) what are the most resource consuming parts of this symbiosis, between the production of each amino acid and the bacterial growth, (ii) what is more limiting and vulnerable in the system, in terms of both aphid life stages and as nutrients. This limits our ability to predict how a trophic symbiosis could be targeted at critical points to reduce the spread of a pest or its ability to cope with nutrient limitation. Indeed, a given metabolite, expected to be limiting when considering the network structure, could be overproduced when considering the system quantitatively.

We develop here the first quantitative metabolic model of *B. aphidicola*, calibrated to simulate amino acid supply to the host at different life stages of aphid life cycle. We started by curating the annotation of all metabolic pathways of *B. aphidicola*, using recent metabolic network reconstructions and experimental data, while also integrating connected parts of the aphid metabolism. We then added functions to the model to quantitatively simulate the supply of amino acids to the host, based on quantifications of aphid amino acids at different life stages. We used the model to analyze and quantify the flux distribution for two biological functions of *Buchnera*: the aforementioned amino acid supply to the host, and bacterial growth. We predicted how these functions are affected by a limitation in host-provided nutrients. Our analyses led us to reconsider some previous conclusions on *B. aphidicola* nutritional requirements and to predict how certain metabolites can regulate the two functions. Finally, we quantified bacterial biomass growth and aphid/bacteria weight ratio to compare bacterial supply to aphid demand. We revealed that bacterial population density is critical for aphid growth during early nymphal stages and for embryonic growth, providing quantitative explanations to previous observations made on nymphs (Pers and Hansen, 2021) and embryos (Bermingham et al., 2009). Altogether, our combined results highlight novel vulnerabilities of aphid nutritional symbiosis.

## 2. Results

### 2.1. Quantitative model of fluxes in *B. aphidicola* metabolism

To model *B. aphidicola* and the fluxes exchanged with its host, we started by refining and updating its metabolic model (FBA model) (Fig. 1). Our new model (iLG240) integrates information from (i) the latest metabolic network reconstruction of the bacterium, which serves as a reference (Baa-Puyoulet et al., 2025), (ii) previous metabolic models of *B. aphidicola* (Thomas et al., 2009; Macdonald et al., 2011, 2012; Blow et al., 2020b), (iii) biochemical data from publications on *B. aphidicola* (Polyakov et al., 2011; Price and Wilson, 2014; Charles et al., 2011; Huang et al., 2008) and closely related organisms (Ueda et al., 2012; Braymer et al., 2021; Haase et al., 2013; Ren et al., 2005), and (iv) other bacterial metabolic models (Nogales et al., 2020; Monk et al., 2017; Oh et al., 2007; Seif et al., 2019). Overall, more than 200 metabolic reactions (Table 1) were manually corrected or added in our new model, which was built from an initial metabolic model of 474 reactions (Blow et al., 2020b). This includes improvements of biochemical formulas and gene/protein/reaction associations, or additions of novel metabolic functions that were not integrated into previous models (Table 1, see Supplementary File 1 for a record of all the modifications). As examples, we have improved the modeling of sulfur assimilation, transport through membranes, folate metabolism, amino acid abundance in bacterial biomass, Fe-S cluster synthesis, cardiolipin synthesis, alanine racemization and methionine synthesis. Our metabolic model also integrates the aphid metabolic processes that are directly connected to *B. aphidicola* metabolism and essential for its main functions (growth and nutrient provision), such as the conversion of some amino acid precursors into amino acids (see Supplementary Text 1). The final metabolic model includes 593 metabolites, 624 reactions and 240 genes (Table 2). It is available as a spreadsheet file (Supplementary File 1) and in an XML file following SBML standards (Supplementary File 2), as well
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Pipeline to build the novel metabolic model of *B. aphidicola* (iLG240) and use it for quantitative modeling.</figcaption></figure>

The ArtSymbioCyc database provided a larger metabolic network reconstruction than metabolic models, as well as additional information on reaction biochemistry (Baa-Puyoulet et al., 2025) but required curation prior to flux modeling. Several metabolic models (FBA models) were used in the curation process: the latest *B. aphidicola* one (iAphBucHam (Blow et al., 2020b)) as a scaffold, and other models such as iJN1462 (*Pseudomonas putida* (Nogales et al., 2020)), iML1515 (*Escherichia coli* (Monk et al., 2017)), iYO844 (*Bacillus subtilis* (Oh et al., 2007)) and iYS854 (*Staphylococcus aureus* (Seif et al., 2019)) to correct or improve some reactions. Biochemistry data included specific characterizations of enzymes or transporters from *B. aphidicola* (Poliakov et al., 2011; Price and Wilson, 2014; Charles et al., 2011; Huang et al., 2008) or general information on prokaryotic metabolism (Ueda et al., 2012; Braymer et al., 2021; Haase et al., 2013; Ren et al., 2005). Physiology data integration will be described in the following parts of the study. Arrow thickness in the calibration box are symbolic of contrasted resource partitioning and are not quantitatively representative of a specific scenario.

<table><caption>**Table 1**

Summary of modifications or additions incorporated in the metabolic model iLG240 from the previous metabolic model of *Buchnera aphidicola*.</caption><thead><tr><th colspan="4">Modified reaction</th><th colspan="3">Novel reaction</th></tr><tr><th>Gene feature<sup>a</sup></th><th>Reversibility</th><th>Formula<sup>b</sup></th><th>EC number</th><th>Metabolic</th><th>Macromolecular<sup>c</sup></th><th>Transport</th></tr></thead><tbody><tr><td>56</td><td>13</td><td>13</td><td>12</td><td>32</td><td>12</td><td>255</td></tr></tbody></table>

During the curation process, the previous metabolic model of *B. aphidicola* (iAphBucHam) was confronted to literature and databases. The modifications of existing reactions and incorporations of novel ones resulting from this curation are summed up in different categories. See Supplementary File 1 for a record of all the modifications.

a Correction of the gene-protein-reaction relationships or in gene identifiers.

b Correction of the metabolites included in the reaction, or the metabolite writing itself.

c Refers to a “meta”-metabolic reaction representing a whole process, such as aphid requirement in amino acid, aphid conversions of metabolites, or biomass.

<table><caption>**Table 2**

Features of our metabolic model (iLG240) and comparison with the reference metabolic model of *Escherichia coli* K-12 (iML1515) and three bacteria (*Sodalis glossinidius*, *Bordetella pertussis* and *Xylella fastidiosa*) with reduced genome. All the simulations were performed with similar constraints for all the organisms (wide access to nutrients present in a eukaryotic cell) and can be reproduced with the code in our gitlab https://gitlab.com/bf2i/metabolicmodels/ilg240. Gene information was not available (NA) in the metabolic model of *Sodalis glossinidius* (iLF517).</caption><thead><tr><th>Organism</th><th>Model</th><th>Reactions</th><th>Metabolites</th><th>Genes</th><th>Essential genes (%)</th><th>Essential reactions (%)</th><th>Activatable reactions (%)</th></tr></thead><tbody><tr><td><em>Buchnera aphidicola</em> APS</td><td>iLG240</td><td>624</td><td>593</td><td>240</td><td>84</td><td>71</td><td>86</td></tr><tr><td><em>Sodalis glossinidius</em></td><td>iLF517</td><td>798</td><td>797</td><td>NA</td><td>NA</td><td>33</td><td>52</td></tr><tr><td><em>Bordetella pertussis</em> Tohama I</td><td>iNF792</td><td>1252</td><td>1191</td><td>796</td><td>42</td><td>29</td><td>51</td></tr><tr><td><em>Xylella fastidiosa</em> CFBP 8418</td><td>Xfm1158</td><td>1158</td><td>1107</td><td>572</td><td>47</td><td>35</td><td>57</td></tr><tr><td><em>Escherichia coli</em> K-12</td><td>iML1515</td><td>2712</td><td>1877</td><td>1515</td><td>13</td><td>10</td><td>30</td></tr></tbody></table>

as in our gitlab https://gitlab.com/bf2i/metabolicmodels/ilg240. In addition to the various reactions mentioned above, the improvement of the metabolic capabilities of our model compared to the previous *B. aphidicola* model can be valued through the increase in metabolites that are incorporated into the biomass reaction (from 56 % to 75 % of the 104 metabolites present in the biomass of the *E. coli* model,

Supplementary Fig. 1). This increase is related to the functionalization, in our model, of lipid and vitamin pathways such as unsaturated car- diolipin biosynthesis, folate conversions and lipoate biosynthesis. One auxotrophy is relaxed, as the integration of alanine racemization (Poliakov et al., 2011) allows the biosynthesis of D-alanine instead of its uptake for peptidoglycan biosynthesis.
Once the model was refined, we used Flux Balance Analysis (FBA) (Orth et al., 2010) to quantify matter fluxes in the bacterial cell and its exchanges with the host (Fig. 1). We integrated bacterial growth rates during aphid nymphal and adult phases (see Material and Methods), as well as maximal O₂ uptake rate based on O₂ measurement from Buchnera extracts (Whitehead and Douglas, 1993). This globally constrains substrate assimilation and energy generation and makes the model quantitative. Quantitative here means that the fluxes are limited by absolute values both on the uptakes (through O₂) and outputs (through growth, and in a subsequent part of this work, also through amino acid production). This allows to estimate metabolic activities and capacities in regard to the host requirements, and this differs from previous models of B. aphidicola which did not integrate absolute constraints and only used FBA to assess which metabolic reactions were taken to achieve a given objective, without linking it to host requirements. Imposed constraints are detailed in Supplementary Text 1. We analyzed essentialities and reaction activations in the model and compared it to the latest *Escherichia coli* model (Table 2). Essential genes (respectively reactions) represent the relative number of genes (reactions) in the model that will lead to null or infeasible bacterial growth when deleted. Analysis of essentialities revealed, similarly to the work of Thomas et al. (2009), a high proportion of essential genes (84 %) and reactions (70 %) in B. aphidicola (Table 2). As a comparison, the latest metabolic model of E. coli (Monk et al., 2017) only has 13 % of essential genes and 10 % of essential reactions under similar simulation conditions. Also, most of the reactions can be activated (i.e. can carry flux) when B. aphidicola achieves optimal growth (86 % of activatable reactions). In contrast, under the same growth conditions, only 30 % of E. coli reactions can be activated. We also compared B. aphidicola to less versatile bacteria with available genome-scale metabolic models: Bordetella pertussis (Fyson et al., 2017) and Xylella fastidiosa (Gerlin et al., 2020), two fastidious bacteria able to live only on very specific environments, and one insect endosymbiont, Sodalis glossinidius (Hall et al., 2020). These bacteria have similar metabolism sizes (798–1252 reactions), and close proportions of essential reactions (29–35 %) and, at least for X. fastidiosa and B. pertussis, of essential genes (respectively 47 and 42 %). Finally, the proportion of activatable reactions to achieve optimal growth are between 51 and 57 %. These comparable results on the three bacteria reveal intermediate metabolic capabilities, with a position between the versatile bacterium E. coli and the very degraded metabolism of B. aphidicola.

## 2.2. *B. aphidicola* weight during the pea aphid lifecycle

Our metabolic model had to be calibrated with numerical data to enable quantitative predictions of amino acid supply by the bacteria and its relationship with host requirements. We first needed to estimate bacterial growth (growth rate) to integrate the flux required for B. aphidicola biomass. While B. aphidicola cell density has already been estimated by qPCR (Vogel and Moran, 2011; Zhang et al., 2016; Komaki and Ishikawa, 2000; Lu et al., 2014) and more recently by flow cytometry (Simonet et al., 2016b; Ribeiro Lopes et al., 2022), we needed here to estimate growth in terms of mass and thus quantify how the weight of the B. aphidicola population evolves during aphid development and ageing. To this end, we coupled a symbiont filtration protocol (Simonet et al., 2016b) with a drying and weighing process, performed on parthenogenetic viviparous pea aphids. We also determined aphid fresh/dry weight ratio at different time points (Supplementary Fig. 2) to express the bacteria/aphid dry weight ratio at different aphid life stages (Fig. 2A) and follow aphid dry weight during development and ageing (Fig. 2B). We found that the bacteria/aphid ratios are very close for our two successive time points during nymphal growth, suggesting that aphid and B. aphidicola grow at similar rates. The Buchnera/aphid weight ratio is 30 % higher in adult aphids that have begun their reproductive period and is similar for our two successive time points at this stage. Finally, a drastic drop of the ratio is measured in senescent

<figure><img src="image_6.png" /><figcaption>Fig. 2. Measurements of aphid and *B. aphidicola* biomass during the lifecycle of parthenogenetic viviparous pea aphids (*A. pisum*).</figcaption></figure>

A. Bacteria/aphid weight ratio obtained after filtration of symbionts from aphids, drying and weighing. Each point is the mean value of five replicates, excepted the first value (day 3) which is the mean value of four replicates. Each replicate includes between 20 and 400 aphids, depending on the life stage. B. Aphid dry weight was determined by weighing five replicates of six aphids for each point and using fresh weight/dry weight ratio of aphids (Supplementary Fig. 2). C. Aphid dry weights and symbiont/aphid dry weight ratio were used to calculate the bacterial dry weight in one aphid (mean value of ratio multiplied by mean value of aphid weight). Relative growth rates (RGR) were determined by linear regression of natural logarithm of dry weight as a function of time. DW: dry weight. The squares and the bars indicate the mean values and the standard deviations, respectively.

adults. We also expressed directly *B. aphidicola* growth as weight for one aphid during aphid lifecycle (Fig. 2C). *B. aphidicola* exhibits rapid growth during the aphid nymphal stages, a slower growth during the laying phase, and a population decline between day 16 and day 23 (beginning of senescence).

## 2.3. *Nutrient sinks in amino acid metabolism*

Based on the metabolic network reconstruction of the pea aphid and B. aphidicola (Baa-Puyoulet et al., 2025), we identified twelve amino
acids that must be considered as *Buchnera*-dependent amino acids (Table 3), meaning that their supply to the host relies on a flux from *B. aphidicola*, either in the form of the amino acid itself or a precursor. In the following sections, we will refer to these amino acids as ‘*Buchnera*-dependent amino acids’ since the term ‘essential’ is ambiguous in the context of our work. In our model, we aimed at quantitatively integrating this production of amino acids according to aphid requirements. To this end, we measured by high-performance liquid chromatography (HPLC) the amount of most *Buchnera*-dependent amino acids in the pea aphid (free plus proteinogenic) at different life stages of the same line of parthenogenetic viviparous aphids than previously mentioned (Fig. 3, Supplementary Fig. 3). Only cysteine and tryptophan could not be detected, due to oxidation and hydrolysis, and their values were supplemented with data from the literature (Harper and Kaldy, 1978). Quantifications allowed us to determine the relative proportions of the other *Buchnera*-dependent amino acids, at the different stages (Fig. 3A). The significant variation in amino acid distribution across life stages (pvalue = 2.6·10⁻¹¹ for the interaction term in two-way ANOVA, see Materials and Methods and Supplementary Text 2) indicates that amino acid fluxes should be modeled in a stage-specific manner. Among the 10 measured *Buchnera*-dependent amino acids, we observed a significant

<table><caption>**Table 3**
Identification of the twelve *Buchnera*-dependent amino acids considered in the study. Information on biosynthetic capacities is from the database Art-SymbioCyc (Baa-Puyoulet et al., 2025).</caption><thead><tr><th>Amino acids</th><th>Biosynthesis</th><th>Usual definition</th><th>Status for A. pisum</th></tr></thead><tbody><tr><td>Arginine</td><td>Buchnera</td><td>Essential for insects</td><td>Buchnera-dependent</td></tr><tr><td>Cysteine</td><td>Buchnera</td><td>Non-essential</td><td>Buchnera-dependent</td></tr><tr><td>Histidine</td><td>Buchnera</td><td>Essential</td><td>Buchnera-dependent</td></tr><tr><td>Isoleucine</td><td>Shared<sup>a</sup></td><td>Essential</td><td>Buchnera-dependent</td></tr><tr><td>Leucine</td><td>Shared<sup>a</sup></td><td>Essential</td><td>Buchnera-dependent</td></tr><tr><td>Lysine</td><td>Buchnera</td><td>Essential</td><td>Buchnera-dependent</td></tr><tr><td>Methionine</td><td>Buchnera, requiring host homocysteine, itself coming from Buchnera cysteine biosynthesis</td><td>Essential</td><td>Buchnera-dependent</td></tr><tr><td>Phenylalanine</td><td>Shared<sup>a</sup></td><td>Essential</td><td>Buchnera-dependent</td></tr><tr><td>Threonine</td><td>Buchnera</td><td>Essential</td><td>Buchnera-dependent</td></tr><tr><td>Tryptophane</td><td>Buchnera</td><td>Essential</td><td>Buchnera-dependent</td></tr><tr><td>Tyrosine</td><td>Aphid, but requiring shared<sup>a</sup></td><td>Non-essential</td><td>Buchnera-dependent</td></tr><tr><td>Valine</td><td>Shared<sup>a</sup></td><td>Essential</td><td>Buchnera-dependent</td></tr><tr><td>Alanine</td><td>Aphid</td><td>Non-essential</td><td>Buchnera-independent</td></tr><tr><td>Asparagine</td><td>Aphid</td><td>Non-essential</td><td>Buchnera-independent</td></tr><tr><td>Aspartate</td><td>Aphid</td><td>Non-essential</td><td>Buchnera-independent</td></tr><tr><td>Glutamate</td><td>Aphid</td><td>Non-essential</td><td>Buchnera-independent</td></tr><tr><td>Glutamine</td><td>Aphid (requires ammonium)</td><td>Non-essential</td><td>Buchnera-independent</td></tr><tr><td>Glycine</td><td>Both, independently</td><td>Non-essential</td><td>Buchnera-independent</td></tr><tr><td>Proline</td><td>Aphid</td><td>Non-essential</td><td>Buchnera-independent</td></tr><tr><td>Serine</td><td>Aphid</td><td>Non-essential</td><td>Buchnera-independent</td></tr></tbody></table>

difference in global proportion between life stages (one way ANOVA pvalue <0.05) for seven out of 10 amino acids (except for arginine, tyrosine and valine) (see Materials and Methods and Supplementary Text 2). We further looked at individual differences between two successive life stages by running post hoc comparison tests (see Materials and Methods) to highlight where significant switches occurred. The most pronounced differences concern histidine and isoleucine, with a decrease from nymph to reproducing adult followed by an increase from reproducing adult to senescent adult, as well as leucine and lysine, which have the opposite profile: increase from nymph to reproducing adult, then decrease for senescent adult. Phenylalanine has a slightly shifted profile, with an increase during nymphal growth but a decrease from late nymph to reproducing adult, and a further decrease from reproducing adult to senescent adult (Fig. 3A, Supplementary Fig. 3).

In addition to these differences in terms of amino acid distribution, the global weight of *Buchnera*-dependent amino acids, relatively to aphid weight, also fluctuates during the pea aphid lifecycle (Fig. 3B), revealing that some life stages require more of these amino acids than others. Young nymphs (day 1) have high relative amount of amino acids (38 mg per g of fresh weight), while this amount decreases during nymphal growth (26 mg per g of fresh weight at day 5), peaks during reproduction phase (35 mg per g of fresh weight at day 16) and then decreases again during senescence (reaching 10 mg per g of fresh weight at day 35). To model this amino acid flux, we integrated three metabolic reactions of amino acid provision to the host in our model (Supplementary Table 1), one for nymph, one for reproducing adult (Fig. 3C), one for senescent adult. Each meta-metabolic reaction integrates the relative amounts of amino acids from measured abundance as stoichiometric constraints in one global reaction. This reaction is normalized to produce 1 g of a global amino acid pool at a given life stage and can be adapted to the varying global mass of amino acids required.

We then analyzed the external substrate requirements for the synthesis of each of the *Buchnera*-dependent amino acids (Table 4). Specific substrate requirements were quantified by metabolic modeling: the production of each amino acid was maximized, which provided the external uptake fluxes associated with each maximal production. The external uptake/maximal amino acid production flux ratio gave the relative amount of each substrate for 1 mol of amino acid. The three amino acids requiring the highest quantity of substrates are phenylalanine, tryptophan and tyrosine: they rely on high uptakes of glucose (respectively 7, 8.2 and 7 mol for 1 mol of amino acid) and serine (respectively 8.7, 10.1 and 8.7 mol for 1 mol of amino acid). If we consider the most abundant *Buchnera*-dependent amino acids of *A. pisum*, valine (13–11%), leucine (around 13%) and lysine (19–16%), lysine and valine have low substrate requirements (respectively 0.9 mol of glucose, 1 mol of aspartate, 1.5 mol of serine for lysine, and 1.5 mol of glucose and 2.3 mol of serine for valine), while leucine requires relatively high uptakes of glucose (2.4 mol for 1 mol of amino acid) and serine (4.9 mol for 1 mol of amino acid).

At last, we compared the relative abundance of *Buchnera*-dependent amino acids in aphids (obtained from HPLC, Fig. 3A and Table 4) with the external substrate requirements to produce each of them (Table 4). This allowed us to quantify the substrate demand necessary to meet the aphid’s nutritional needs (Fig. 3D). The major nutrient sinks are phenylalanine, leucine and tyrosine which mobilize more than 60 % of both glucose and serine usage (respectively 20–25 %, 15 % and 16–21 % of glucose, and 24–32 %, 30 % and 20–26 % of serine), while glutamine and aspartate are required at lower amounts. Comparing the different life stages, the highest requirements are for serine and glucose for phenylalanine production during nymphal stages, making it the highest sink of the pea aphid lifecycle.

## 2.4. Metabolic capacities of *B. aphidicola*

We used metabolic modeling to analyze the metabolic capacities of

<footnote><sup>a</sup> the biosynthetic pathway involves reactions from both *A. pisum* and *B. aphidicola*</footnote>
A

day post birth 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
life stage N1 N2 N3 N4 A9 A13 A16 A23 senescent adult reproducing adult nymph

<figure><img src="image_7.png" /></figure>

B

<figure><img src="image_8.png" /></figure>

C

<figure><img src="image_9.png" /></figure>

D

<figure><img src="image_10.png" /></figure>

(caption on next page)
<caption>**Fig. 3.** Quantification, modeling and substrate requirements of *Buchnera*-dependent amino acids of *Acyrthosiphon pisum*.</caption>

A., B. Amino acid (free plus proteinogenic) quantifications performed using HPLC. Quantifications allowed to determine their relative proportions at the different stages (A), and to follow the weight proportion of these amino acids over time (B). In A., * on a stage represents a significant difference between the given stage and the following (controlled pvalue <0.05 based on sequential post hoc tests, see section 4. Material and Methods). C. Representation of aphid requirements in amino acids as implemented in our metabolic model (Supplementary Table 1), here with the example of the reproducing adult stage. The size of the arrows is proportional to the size of the flux. D. External substrate requirements for the synthesis of *Buchnera*-dependent amino acids, obtained by multiplying the substrate requirements for 1 mol of each amino acid with the relative proportion of each amino acid (Table 4). Relative proportions for cysteine and tryptophan come from literature data for an adult pea aphid (Harper and Kaldy, 1978) and do not integrate the life stage variations for these amino acids.

<caption>**Table 4**</caption>

**Relative abundance and specific substrate requirements of *Buchnera*-dependent amino acids.** Abundance was measured by HPLC for all amino acids except tyrosine and cysteine, which were estimated from literature data on an adult pea aphid (Harper and Kaldy, 1978). Specific substrate requirements were quantified by metabolic modeling: the production of each amino acid was maximized, which provided the external uptake fluxes of all the organic precursors (glucose and *Buchnera*-independent amino acids) associated with each maximal production. The external uptake/maximal amino acid production flux ratio gave the relative amount of each substrate for 1 mol of amino acid.

<table><thead><tr><th>Amino acid</th><th>L-Arg</th><th>L-Cys</th><th>L-His</th><th>L-Ile</th><th>L-Leu</th><th>L-Lys</th><th>L-Met</th><th>L-Phe</th><th>L-Thr</th><th>L-Trp</th><th>L-Tyr</th><th>L-Val</th></tr></thead><tbody><tr><td><strong>Abundance (%)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Nymph (N3)</td><td>8.4</td><td>5.9</td><td>4.8</td><td>9.8</td><td>13.4</td><td>16.2</td><td>4.2</td><td>8.1</td><td>10.1</td><td>2.8</td><td>5.1</td><td>11.2</td></tr><tr><td>Reproducing adult (A13)</td><td>9.0</td><td>5.9</td><td>4.0</td><td>9.1</td><td>13.7</td><td>19.0</td><td>3.6</td><td>6.2</td><td>7.9</td><td>2.8</td><td>6.5</td><td>12.3</td></tr><tr><td>Senescent adult (A23)</td><td>8.8</td><td>5.9</td><td>4.3</td><td>9.3</td><td>13.1</td><td>18.8</td><td>3.7</td><td>6.2</td><td>8.4</td><td>2.8</td><td>6.0</td><td>12.7</td></tr><tr><td><strong>Required substrates (mol for 1 mol amino acid)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D-Glc</td><td>1.1</td><td>0.7</td><td>3.0</td><td>0.8</td><td>2.4</td><td>0.9</td><td>0.8</td><td>7.0</td><td>0.3</td><td>8.2</td><td>7.0</td><td>1.5</td></tr><tr><td>L-Gln</td><td>1</td><td>0</td><td>1.0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.2</td><td>0</td><td>0</td></tr><tr><td>L-Asp</td><td>2.3</td><td>0</td><td>4.0</td><td>1.0</td><td>0</td><td>1.0</td><td>0</td><td>0.12</td><td>1.0</td><td>1.0</td><td>0.1</td><td>0</td></tr><tr><td>L-Ser</td><td>0</td><td>1</td><td>1.0</td><td>0.3</td><td>4.9</td><td>1.5</td><td>2.0</td><td>8.7</td><td>0.0</td><td>10.1</td><td>8.7</td><td>2.3</td></tr></tbody></table>

*Buchnera aphidicola* in two different biological scenarios (Fig. 4A and B). The first scenario (Fig. 4A) mimics a bacterium using all available environmental resources for its own growth, while the second scenario (Fig. 4B) mimics a bacterium that devotes resources to providing amino acids to the aphid. This distinction is a way to rationally distinguish the metabolic capacities associated with these two biological functions, while we will later study uptake and output fluxes in a model combining the two.

When we maximize growth (i.e. biomass flux, Fig. 4A), *B. aphidicola* assimilates organic matter in the form of glucose and diverse amino acids of plant and aphid origin (alanine, glutamine, asparagine, aspartate, proline, serine, glutamine, glutamate) to synthesize all the elements required for its growth. The uptake of amino acids is even higher than that of glucose (52% of total carbon uptake for amino acids versus 37% for glucose), likely because the synthesis of *B. aphidicola*'s proteins, integrated in biomass flux, requires an important number of amino acids that cannot be synthesized from glucose only. In addition, several other uptakes are necessary at low flux to generate bacterial biomass, such as the incorporation of lipids (6%) and vitamins (5%). Among the sum of all incoming carbon, 42% can be allocated to growth, which is lower than in *E. coli* (62% in the metabolic model iML1515, consistent with experimental data (Kayser et al., 2005)), and 17% must be rejected as CO₂. This partitioning is occurring in all heterotrophic organisms (Nealson et al., 1999) but with different ratios between CO₂ and biomass (i.e. biomass yields) depending on the organism (Blombach et al., 2022). Importantly, even under this objective of biomass maximization, an important proportion of carbon, close to the amount that can be invested in biomass, is also rejected as organic matter (41%), mainly as organic acids (fumarate, alpha-ketoglutarate, acetate, succinate) and amino acid or derivatives (lysine and homocysteine). By contrast, in *E. coli*, the organic carbon assimilated can be almost fully dedicated to biomass, with only a slight proportion excreted as acetate in some conditions (less than 15% measured (Kayser et al., 2005; Gonzalez et al., 2017)).

In the second scenario (i.e. maximization of amino acid production for aphid requirements) (Fig. 4B), we observed a mixed demand for amino acids of plant or aphid origin as well as for a sugar source, similarly to the growth-only scenario. However, external uptakes are less diverse, as we do not model the lipid and vitamin requirements for bacterial biomass. We also predict that producing aphid amino acid induces the excretion of additional organic compounds (45% of the

carbon uptake), both as amino acids (glycine, methionine, valine), and, to a lesser extent, as organic acids (succinate, fumarate, alpha-ketoglutarate). In addition, 20% of the carbon is excreted as CO₂, close to the biomass scenario (Fig. 4A). Altogether, matching growth or aphid requirements is modeled as not structurally optimal for *B. aphidicola* metabolism. In each case, the metabolic routes taken induce the co-production of organic carbon in excess to optimal requirements.

## 2.5. Metabolites allowing pathway rewiring between amino acid production and growth

Our analysis of *B. aphidicola* metabolic capacities (Fig. 4A and B) revealed that bacterial growth and amino acid production require a large set of organic compounds (glucose and non-essential amino acids for both, lipids and vitamins for growth only). This implies that the host must provide *B. aphidicola* with a diverse set of compounds to ensure optimal metabolism of its symbiont. To decipher the dependencies of *B. aphidicola* on host resources, we simulated a reduction in the availability of each resource and estimated its effect on the bacterium's ability to grow and produce amino acids (Fig. 4C).

For some compounds, even severe deficiencies do not affect *B. aphidicola*. This includes diverse compounds only required in trace amounts (e.g. biotin, cobalt or octanoate) and also glutamate, because this amino acid can be alternatively assimilated as glutamine and converted back to glutamate in the cell with minimal impact on energetic requirements. Conversely, the bacterium absolutely requires a high availability of glucose and some *Buchnera*-independent amino acids (aspartate, serine, glutamine) to ensure fast growth and fast amino acid production.

Contrary to the cases mentioned above, some metabolites are specifically required for growth and not for amino acid production. This includes some *Buchnera*-independent amino acids (proline, asparagine) as well as phospholipids, which are essential for the structure of *B. aphidicola* membrane in the absence of functional lipid biosynthesis pathway in the bacterium (Baa-Puyoulet et al., 2025). Guanosine is also required for growth and not for amino acid production. We found by modeling that it is a required precursor for all nucleic acids and could thus be an interesting modulator of bacterial growth.

Further analyzing guanosine metabolism in our model, we did not
A

<figure><img src="image_11.png" /></figure>

B

<figure><img src="image_12.png" /></figure>

C

<figure><img src="image_13.png" /><figcaption>Fig. 4. Quantitative analysis of uptake/excretion fluxes (A, B) and of nutrient dependencies (C) in *Buchnera aphidicola* when the bacterium either dedicates its metabolism to its own growth (A) or to the production of amino acids for the pea aphid (B).</figcaption></figure>

Using FBA, we determined the external fluxes (arrow proportional to the relative flux intensity in % of the total mmol of carbon/g dry weight/day) obtained when the bacterium maximizes (marked with a *) its growth (A) or its production of amino acids for the host (B), highlighting a residual excretion of amino acids and organic acid in each condition. We assessed the effects of reducing the availability of required nutrients by sequentially limiting their incoming fluxes in the model (C), in the growth scenario (left panel) and amino acid production scenario (right panel). The color intensity indicates the effect either on growth or amino acid production.
predict a *de novo* synthesis of guanosine as a co-product of histidine biosynthesis. This contradicts previous metabolic models and genomic analyses of *B. aphidicola* (Thomas et al., 2009; Blow et al., 2020b; Ramsey et al., 2010). By specifically modeling the pathway, we found that co-producing guanosine through histidine biosynthesis required the supplementation of another nucleic acid such as adenosine, which is itself incorporated into AICAR (5-aminoimidazole-4-carboxamide ribonucleotide) during histidine biosynthesis (shown in Supplementary Fig. 4). To investigate nucleic acid *de novo* synthesis in *B. aphidicola*, we also modeled putrescine degradation, previously described as linked to adenine biosynthesis (Ramsey et al., 2010). We found that this pathway, instead of allowing adenine biosynthesis, requires the initial incorporation of one adenosine into S-Adenosyl-Methionine (SAM), which is later released as adenine in the pathway (shown in Supplementary Fig. 5).

Overall, this analysis reveals that a limitation (either a perturbation or a host-induced modulation) in the biosynthesis and circulation of phospholipids, asparagine, proline and guanosine rapidly affects growth capacity of *B. aphidicola*, without limiting its ability to provide *Bucknera*-dependent amino acids to the host. We found no metabolic candidate able to provoke the converse switch and affect *Bucknera*-dependent amino acid provision without impacting growth, as *Bucknera*-dependent amino acids are also essential components of *B. aphidicola* biomass.

## 2.6. Life-stage dependent supply of amino acids to the host

We aimed at estimating the maximal daily amount of amino acids that *B. aphidicola* can provide to its host during the pea aphid lifecycle. We built three *B. aphidicola* quantitative metabolic models (Fig. 5, Table 5), each adapted to one of the three major phases of the pea aphid lifecycle: nymphal growth, reproductive period and senescence. These three life stages are modeled by switching the mathematical function we maximize among our three different meta-metabolic reactions of amino acids providing to the host (Supplementary Table 1). These three meta-metabolic reactions follow the amino acid proportions measured in the different life stages (see section 2.3 Nutrient sinks in amino acid metabolism and Fig. 3). We integrated growth rate in our model from our experimental data (relative growth rate from Fig. 2C). To bring substrate utilization down to realistic values, we used a measurement of O₂ assimilation by *B. aphidicola* extracts, obtained with an oxygen electrode (Whitehead and Douglas, 1993). Constraining O₂ assimilation realistically adjusted *B. aphidicola* central metabolism, and hence also limited the assimilation of other substrates. Oxygen is abundantly available in bacteriocytes (Wilkinson and Douglas, 1998; Ishikawa, 2001), and the uptake of this substrate is probably limited by physiological constraints of the symbiotic bacteria rather than by the concentration of the substrate in the host cells. Under this oxygen constraint, glucose uptake rate

<table><caption>**Table 5**

Summary of the main exchanged fluxes in our quantitative metabolic model of *B. aphidicola* obtained by FBA. O₂ uptake and biomass are fixed values constrained in our simulations based on experimental data, and amino acids for *A. pisum* is maximized. DW: dry weight.</caption><thead><tr><th>Fluxes</th><th>Life stage<br>Nymph</th><th>Adult</th><th>Senescent<br>adult</th></tr></thead><tbody><tr><td><strong>Uptakes (mmol/gDW/day)</strong></td><td></td><td></td><td></td></tr><tr><td>O<sub>2</sub></td><td>20.18</td><td>20.18</td><td>20.18</td></tr><tr><td>Glucose</td><td>33.70</td><td>36.63</td><td>36.52</td></tr><tr><td>L-Asp</td><td>13.00</td><td>13.09</td><td>13.46</td></tr><tr><td>L-Gln</td><td>4.30</td><td>4.24</td><td>4.32</td></tr><tr><td>L-Ser</td><td>33.62</td><td>41.54</td><td>41.29</td></tr><tr><td><strong>Outputs</strong></td><td></td><td></td><td></td></tr><tr><td>Biomass (day<sup>-1</sup>)</td><td>0.441</td><td>0.022</td><td>0</td></tr><tr><td>Amino acids for <em>A. pisum</em> (g/<br>gDW/day)</td><td>3.508</td><td>3.835</td><td>3.898</td></tr></tbody></table>

was around 35 mmol/g dry weight/day, which is a mid-value for bacteria with growth rates similar to the one of *B. aphidicola* (Supplementary Table 2). Our quantitative model shows that *B. aphidicola* biomass flux satisfying experimental data is very low compared to the total assimilated carbon (0.2% of the carbon uptake is incorporated into biomass for reproducing adult), and that a large proportion of the incoming matter can be used to produce amino acids for the pea aphid (Fig. 5). This proportion between *B. aphidicola* biomass and other metabolites varies slightly among the stages, due to (i) a higher or lower substrate usage for growth, and (ii) different proportions of amino acids to provide, some of them requiring more substrate consumption (Fig. 3D).

## 2.7. Amino acid supply/demand at the aphid-B. aphidicola interface

To compare amino acid supply by *B. aphidicola* to aphid demand, we mathematically expressed amino acid supply rate ($V_{supply}$, in mass provided per day in one aphid) and amino acid demand rate ($V_{demand}$, in mass required per day in one aphid) (Fig. 6A). $V_{supply}$ is a function of amino acid production rate (determined in Table 5) and *B. aphidicola* biomass. This biomass itself depends on *B. aphidicola* initial biomass and growth rates (determined in Fig. 2C). $V_{demand}$ is a function of amino acid requirements of the aphid for its growth and reproduction. Requirements for growth depend on *Bucknera*-dependent amino acids/aphid dry weight ratio (determined in Fig. 3A and B and Supplementary Fig. 2), aphid initial biomass and growth rates (determined in Fig. 2B). Amino acid requirements for reproduction depend on: (i) daily fecundity, which has been determined by Ribeiro Lopes et al. (2022) on the same viviparous parthenogenic line as the one used in this study, (ii) young nymph (N1) weight (Fig. 2B) and young nymph requirements in

<figure><img src="image_14.png" /><figcaption>**Fig. 5.** Resource partitioning during the lifecycle of parthenogenetic viviparous pea aphids.</figcaption></figure>

External fluxes (arrows proportional to the flux intensity in mmol of carbon/g dry weight/day) computed through FBA for a maximal production of amino acids for the aphid (marked with a *). These fluxes take into account the differences in amino acid distribution between the life stages, and the bacterial growth rate (highest during nymphal development, reduced for reproducing adult and null for senescent adult).
<figure><img src="image_15.png" /><figcaption>Fig. 6. Assessment and comparison of supply and demand in *Buchnera*-dependent amino acids between *B. aphidicola* and a parthenogenetic viviparous pea aphid through its lifecycle.</figcaption></figure>

A. Schematical representation and mathematical formulas of the parameters used to determine Vsupply (supply rate, g amino acids/day) and Vdemand (demand rate, g amino acids/day). Parameters are from experimental data for YAA (aphid/amino acid ratio in g dry weight/g, see Fig. 3B), μBA and μAP (B. aphidicola and A. pisum relative growth rates in day⁻¹, see Fig. 2B and C), XBA,₀ and XAP,₀ (initial weights of B. aphidicola and A. pisum in g, see Fig. 2B and C), Fe (daily fecundity in nymph/day, from Ribeiro Lopes et al. (2022)). qAA is estimated from metabolic modeling (Table 5). B. Vsupply and Vdemand as a function of time, using ratio of bacteria (maternal/embryonic) at different stages from Simonet et al. (Simonet et al., 2016b) to express supply from maternal and embryonic bacteria. C. D. Ratio between the maximal supply (Vsupply) and the demand (Vdemand) as a function of time, considering total supply and demand (C) or only supply from embryonic bacteria (D).

amino acids (Fig. 3B). B. *aphidicola* biomass is distributed between
embryos and the mother, so we used the maternal/embryo ratio of
bacterial cells (Simonet et al., 2016b) to dissociate these two supplies.
All the numerical parameters used are detailed in Supplementary Table
3.

The maximal amino acid supply from *B. aphidicola* (Fig. 6B) follows
its growth curve, with a rapid (15-fold) increase during nymphal
development (day 1–9), a slower increase (1.2-fold) at the beginning of
the adult phase (day 9–16, when *A. pisum* laying activity is at its highest)
and then a sharp (6-fold) decrease due to the rapid decline in

B. *aphidicola* biomass, which correlates with a more limited and then
null reproduction. If we consider only the bacteria from the mother, the
supply curve follows a similar pattern, with a slightly more limited
supply at the beginning of the adult phase (75 %–87 % of the total
supply, day 9–16). A. *pisum*'s demand for amino acid increases during
nymphal stages, due to increased requirements for nymphal growth, and
peaks on the last day of nymphal development. During the adult stages,
even though the adult experiences limited weight gain (Fig. 2B), the
demand encompasses biomass production for embryos and remains
constant throughout the adult reproductive phase, at a level similar to
late nymphal growth. The requirements will progressively fall during senescence as fecundity decreases (Ribeiro Lopes et al., 2022).

Overall, comparing supply to demand (Fig. 6B and C) reveals that the maximal supply always exceeds aphid demand (supply/demand ratio between 4 and 12). However, there is one time point where supply and demand are particularly close (ratio 4), at day 9, which marks the transition between nymphal growth and the beginning of laying and adulthood. This time point involves a high demand for amino acids, both for the end of rapid nymphal growth and the beginning of laying. Before and after this step, the supply/demand ratio is more relaxed: before because high bacterial growth follows the high nymphal demand, and after because aphid demand slows down.

When we consider only embryonic bacteria, the offer/demand ratio appears to be more critical (Fig. 6B and D). Under this condition, during adult phases, the offer and demand curves almost overlap, and the supply/demand ratio oscillates with phases where supply is slightly lower than demand, reaching supply accounting for only 50 % of demand at day 8 and day 21, until we reach the period where the pea aphid's fecundity becomes null. This reveals that the intense rhythm of aphid laying, if it can be excessively covered by the total bacteria available, would be limited by nutritional symbiosis if only the activity of embryonic bacteria was considered.

### 3. Discussion

To establish a quantitative picture of the trophic interaction between the pea aphid and its primary symbiont, *Buchnera aphidicola*, our work combined flux balance analysis, which has already been successfully applied to several insect symbionts (Thomas et al., 2009; Macdonald et al., 2011, 2012; Blow et al., 2020b; Hall et al., 2020; Ankrah et al., 2017, 2018), with several quantitative data, either novel (e.g. life-stage dependent proportions and amounts of amino acids, host/symbiont weight ratios) or from previous studies (e.g. daily fecundity (Ribeiro Lopes et al., 2022)). Previous works focused on the qualitative exploration of the metabolites exchanged between symbionts or between bacteriocyte and symbionts (Thomas et al., 2009; Macdonald et al., 2011, 2012; Blow et al., 2020b; Hall et al., 2020; Ankrah et al., 2017, 2018) but did not integrate the dimensions of the system as in the present work, failing to decipher its critical points. A contribution to understand host-bacteria integrated metabolism was made in the latest metabolic models (Macdonald et al., 2012; Blow et al., 2020b) which integrated reactions of bacteriocyte metabolism inferred from transcriptomics, combined with those of *B. aphidicola*. This allowed to model the full biosynthesis of amino acids that are synthesized through pathways shared by the two symbiotic partners. This includes some host reactions and others from the bacteria. Representing these reactions require linking them to the whole aphid metabolism and will introduce incorrect flux limitations if the whole aphid metabolism is not well curated or limiting in carbon or nitrogen. As there is only one example of a manually curated metabolic model in insects, covering only very central metabolic reactions in *Drosophila melanogaster* (Schönborn et al., 2019), we favored here representing the connection between aphid metabolism and *B. aphidicola* as simplified “conversion” or “yield” reactions (i.e., *n* moles of a bacterial substrate are converted into *m* moles of product by the aphid). In addition, we integrated the weight ratio between the host and the bacteria, the amount of amino acid per gram of aphid and the variations of these two parameters during the aphid life cycle. This enabled us to model amino acids’ (or precursors’) flux from the bacteria, and between the aphid and *B. aphidicola*, then representing for the first time the requirements of the two organisms.

We first analyzed the internal metabolism of *B. aphidicola* after improvements from the previous metabolic models, and found high gene/ reaction essentialities compared to *E. coli*, and even compared to two environmentally-restricted and one endosymbiotic bacteria (Table 2). This is in agreement with Thomas et al. (2009), who highlighted the fragility of the *B. aphidicola* metabolic network, i.e. its lack of robustness

to external or internal perturbations. In addition, we quantified the proportion of reaction activations to achieve optimal growth and compared it to *E. coli* and environment-restricted or endosymbiotic bacteria. *B. aphidicola* has a strikingly high reaction activation proportion, indicating that most of its reactions are involved in an optimal metabolism, while in a versatile bacterium like *E. coli*, there is a large repository of background metabolism that is not involved in optimal growth and would only be activated for other specific adaptive functions. The three other bacteria have an intermediate number of reaction activation proportion, highlighting that their metabolism, even reduced compared to *E. coli*, still have some background functions that are lacking in *B. aphidicola*.

Specifically modeling growth (Fig. 4A) or amino acid production (Fig. 4B), we found that each scenario results in the co-production of a high amount of organic wastes (more than 30 % of the carbon uptake). Hence, the bacterium's metabolism does not seem structurally optimal for either growth or amino acid production. This can be explained by the opposing and conflicting selective pressures on a bacterium that can only undergo genome erosion (Rispe and Moran, 2000). On the one hand, optimal growth ensures bacterial fitness, while on the other hand, optimal amino acid production ensures host fitness, which is essential for bacterial survival, resulting in suboptimal metabolism in each of the scenarios.

Looking at the external substrates of *B. aphidicola* metabolism (glucose and non-essential amino acids), we combined our experimental quantifications with our metabolic model to identify which *Buchnera*-dependent amino acids were responsible for the highest matter demands in substrates (Fig. 3D). We predicted three amino acids whose biosynthesis are major matter sinks of external substrates: phenylalanine, tyrosine and leucine. Each of them necessitates an intense assimilation of serine and glucose to satisfy the requirements of aphid in those amino acids. It has been previously shown that depletion of these three amino acids in aphid diet provokes important physiological and transcriptional modifications. The bacteriocytes harboring *B. aphidicola* show intense transcriptional reprogramming (cell growth, reduced sugar metabolism, transport modulation) and physiological adaptations (more numerous and larger bacteriocytes) when phenylalanine and tyrosine are both absent from an artificial diet (Colella et al., 2018). Another study showed that, when phenylalanine or leucine are independently removed from the aphid diet, this has a drastic impact on the number and the size of bacteriocytes (Ribeiro Lopes et al., 2022). In addition, striking changes have been reported in *B. aphidicola* when leucine is removed from the diet with (i) transcriptional changes in all the genes involved in the biosynthesis of leucine and (ii) an increase in the copy number of the plasmid pLeu, which contains the operon bringing the LeuABCD operon (Viñuelas et al., 2011). The substrate burdens that phenylalanine, tyrosine and leucine were predicted in our model can explain the importance for the pea aphid to dynamically adapt bacterial density (through bacteriocyte) and activity depending on the amount of these three amino acids in its nutrition. A relationship between some of these amino acids and symbiont regulation was also analyzed experimentally in the phloem sap-feeding hemipteran whitefly, *Bemisia tabaci* (Benhamou et al., 2021). When *B. tabaci* was reared on a plant species with relatively low foliar content of phenylalanine and leucine, a highest density of several of its symbiotic bacteria, including the primary symbiont *Candidatus* Portiera aleyrodidarum, was measured (Benhamou et al., 2021).

Phenylalanine is a more intense sink during nymphal growth, while tyrosine (a phenylalanine product) is more required during adult stages, based on the combination of model prediction and our amino acid quantifications. Looking at transcriptomic data, the aphid enzyme converting phenylalanine into tyrosine (PAH, phenylalanine hydroxylase) is highly expressed during late embryonic and early nymphal stages (Simonet et al., 2016a; Rabatel et al., 2013). Its inactivation alters parthenogenetic embryo development, leading to reduced fecundity with developmental defects (Rabatel et al., 2013; Simonet et al., 2016a).
The switch from phenylalanine to tyrosine sink, from nymph to adult, could be a consequence of the high internal embryogenesis activity at the beginning of the adult stage in the parthenogenetic females, requiring increased tyrosine and derivatives.

In a subsequent part of our work, we predicted with our model the required nutrients for *B. aphidicola* growth and amino acid production (Fig. 4B and C). We still agree that, in some cases, the two organisms share a given pathway that produces a metabolite required for both (e.g. methionine, phenylalanine, isoleucine, see Table 3, Fig. 3C). However, our modeling results on nutrient requirements refute previous findings (Thomas et al., 2009; Blow et al., 2020b; Ramsey et al., 2010), which suggested that the structure of some *Buchnera* pathways imposes a flux partitioning towards different products: one linked to selfish growth (i.e. guanosine/spermidine production) and the other nutrient release for the host (i.e. histidine/adenine) (Supplementary Fig. 4; Supplementary Fig. 5).

Our model-based quantification of nutrient requirements also allowed us to propose ways to regulate *B. aphidicola* behavior between growth and nutrient release through metabolism. Amino acid production relies on only a few substrates (glucose, glutamine, serine, aspartate, oxygen, sulfate) while *B. aphidicola* is auxotrophic for a much larger number of nutrients, and selfish growth can be activated or not depending on the provision of these additional nutrients (Fig. 4B and C). *B. aphidicola* growth requires a high supply of the aforementioned amino acids as well as additional ones (alanine, asparagine, proline). It also requires a supply of diverse small molecules and ions, a few of them at high level, such as guanosine and sulfate, and the others at trace level (vitamins, such as biotin and nicotinate, or ions, such as cobalt and zinc) (Fig. 4C). An intense lipid requirement was predicted: it is well known that *B. aphidicola* is unable to synthesize its own membrane lipids (Shigenobu et al., 2000; Smith et al., 2022), and we predict here that it represents an important matter uptake from the host, which could drastically impair growth if the supply is insufficient. Aphid lipid content is known to be influenced by biotic and nutritional stresses (Jedlicka et al., 2015; XueKe et al., 2017), so their anabolism could be a critical factor in the response to both environment and symbionts. Other metabolites such as guanosine, alanine, asparagine, proline and sulfate could also be involved in a host strategy to control symbiont growth and to direct fluxes to amino acid production. Limiting the provision of one of these metabolites would drastically affect growth without affecting the amino acid synthesis capability of *B. aphidicola*. Finally, phloem sap composition could also indirectly affect *B. aphidicola* metabolism at different levels: (i) it is the supplier of inorganic content (Dinant et al., 2010; Hijaz et al., 2016), some of them being intensely required (e.g. sulfate), (ii) it complements aphid metabolism in amino acids (Dinant et al., 2010) as well as in some lipids (Madey et al., 2002) and vitamins (Teixeira et al., 2023), and could modulate the dependence of *B. aphidicola* on aphid biosynthetic pathways, or create a resource competition between host and symbionts if some resources are in limiting amounts. Some nutrients required for *Buchnera* metabolism, such as vitamins or inorganic elements, are directly dependent on phloem provision, while some others, such as *Buchnera*-independent amino acids, are under the control of aphid metabolism (Duncan et al., 2023). Ultimately, they could also eventually depend on microorganisms interacting with aphid digestive tract, even though these microorganisms are expected to be at very low amounts (Nakabachi et al., 2003). Hence, a quantitative study of phloem nutrient provision to the aphid, combined with an analysis of aphid and microflora metabolism, would allow for a better understanding of the relationship between phloem and *B. aphidicola* metabolism. In addition, although the aphid used in this study is a mono-symbiotic clone (see section 4.1.1 Aphid rearing and sampling), the presence of facultative endosymbionts in other *Acyrtosiphon pisum* clones would introduce additional nutrient sinks and bacterial supplies (Blow et al., 2020b) that could modify our representation of the system.

After the separated examination of growth and amino acid provision

functions in *B. aphidicola*, we estimated the resource partitioning between them and quantified the maximal amino acid production capacity (Fig. 5). We compared this capacity (amino acid supply) with the aphid demand deduced from our data on growth, reproduction and amino acid content at various stages (Fig. 6). Based on our predictions, supply remains higher than demand throughout the entire pea aphid life cycle. The maximal supply modeled represents an ideal scenario based on optimal metabolic activity of the bacteria, so the superiority of supply over demand was expected. The two rates (supply and demand) remain at a similar order of magnitude, validating the plausibility of our model. Analyzing nymphal growth, we estimate that the younger the nymph, the more critical the trophic symbiosis, as the maximal amount of amino acid supply gets closer to aphid demand. This is in line with recent transcriptomics data of aphid maternal bacteriocyte (Pers and Hansen, 2021), revealing a peak in the expression of aphid genes involved in amino acid assimilation during early nymphal stages, while they are predicted to be turned off in later phases of the nymphal development. This study (Pers and Hansen, 2021) provided limited predictions of adult metabolism, as only one time point was analyzed for adult, while we revealed a dynamics in adult phases, both in supply/demand (Fig. 6) and directly in amino acid composition (Fig. 3). At the very beginning of the aphid reproductive period (early adulthood), we predict another peak in the demand for amino acids that again approaches the maximal supply. We hypothesize that this results from the very high demand for embryonic growth as the aphid reaches adulthood, when it lays more than six offspring in the first days of this phase (Ribeiro Lopes et al., 2022). We compared supply and demand again, independently considering the supply from maternal and embryonic bacteria. This showed that demand, massively directed towards embryonic growth, is not totally covered by embryonic bacteria supply (Fig. 6). Quantitatively, embryonic chains are not self-sustaining with their symbiotic bacteria, and optimal embryonic growth requires significant flux inputs from maternal bacteria. This corroborates and provides quantitative explanations for previous studies that showed the high dependency of embryos on maternal amino acids (Wilkinson and Ishikawa, 2000; Wilkinson et al., 1999, 2003). This dependency could even be higher than what is estimated in our model which mimics one global physiological behavior: as embryonic bacteria are fast growing (Simonet et al., 2016b), their resource allocation to amino acid supply could be lower than estimated here. Specific compartment models (embryonic/maternal bacteriocytes) could help explore this question in more depth.

Overall, this study enabled us to decipher the most critical metabolic factors in the pea aphid/*Buchnera* trophic symbiosis. Among these Achille's heels, we determined the three *Buchnera*-dependent amino acids (leucine, phenylalanine and tyrosine) that are the most dependent on high symbiotic fluxes and high resource provisions to the bacterium. These 'nutrient sinks' are candidates for symbiosis weakening, showed by the dramatic effect of a diet deprived of leucine or phenylalanine for more than 24 h (Ribeiro Lopes et al., 2022), or as plant metabolites affecting insect reproductive success, in line with experiments on the other sap-sucking hemipteran *B. tabaci* (Benhamou et al., 2021). An in-depth analysis of the relationship between plant physiology, nutrition and the circulation of leucine, phenylalanine and tyrosine in phloem could allow to adapt agronomical practices to limit the proliferation of aphids in the field by making aphid diet even more unbalanced. In addition, among the large list of metabolites required for the bacterial metabolism, we identified a few of them that had a strong capacity of pathway rewiring between amino acid supply and selfish growth. Supplementing the diet with excesses of the 'rewiring metabolites' specifically required for bacterial growth (and accessory for amino acid production) could be a first step to unravel if they could efficiently disrupt growth/amino acid production balance, or if bacteriocyte regulation is too tightly locked. These 'rewiring metabolites' could become critical during embryonic growth, which itself is another Achilles' heel. For example, an insufficient input flux would weaken this
stage requiring fast bacterial growth and consequently lead to neonate
nymph with insufficient symbiont density. These Achilles' heels open up
more concrete perspectives for symbiosis destabilization, while also
drawing attention to the weakness of the symbiotic system when con-
fronted with a changing environmental conditions, which are both
challenging the persistence or optimality of endosymbionts (Iltis et al.,
2022; Renoz et al., 2019) and affecting plant metabolite content (Bowne
et al., 2012; Ashraf et al., 2018).

# 4. Materials and Methods

## 4.1. *Experimental procedures*

### 4.1.1. *Aphid rearing and sampling*

A long-established parthenogenetic clone (LL01) of *Acyrthosiphon pisum* (Harris) containing only the primary endosymbiont *Buchnera aphidicola* was used. The strain's symbiotic status, indicating in particular the absence of secondary facultative symbionts, is regularly verified by means of a previously described PCR-based diagnostic (Peccoud et al., 2014). Strictly parthenogenetic matrilinees were reared at 21 °C on young broad bean plants (Vicia faba, L. cv. Aquadulce) with a photo-period of 16 h light—8 h dark. To obtain synchronized apterous aphids, winged adults were allowed to reproduce for 24 h. The winged adults were then removed from the plant, and the resulting synchronized off-springs were used for our experiment. Aphid development was monitored by inspecting their molts, and we used previous works on our parthenogenetic clone (Simonet et al., 2016b, 2018; Ribeiro Lopes et al., 2022) to sample aphids at days which are representative of different developmental and adult stages.

### 4.1.2. *Measurements of aphid fresh weight/dry weight ratios*

Aphids were sampled on day 5 (third instar nymph, N3), day 16 (reproducing adult, A16) and day 35 (senescent adult, A35). For each sampling day, we used at least three biological replicates of at least 20 aphids. Each biological replicate was weighed and placed in a small glass Petri dish covered with aluminum foil (Harper and Kaldy, 1978). The Petri dishes were kept open at 70 °C in a drying oven for at least 48 h. The samples were then weighed, placed back in the drying oven for an additional period (at least 24h), and weighed again to verify that evaporation was complete.

### 4.1.3. *Aphid weighing and amino acid extraction*

Aphids were sampled on: day 1 (first instar nymph, N1), day 5 (third instar nymph, N3), day 9 (reproducing adult, A9), day 13 (reproducing adult, A13), day 16 (reproducing adult, A16), day 23 (senescent adult, A23) and day 35 (senescent adult, A35). Five replicates of six aphids were weighed for each sampling day and frozen at -80 °C prior to amino acid measurements. For adults (respectively nymphs) 20 µL (respectively 10 µL) of norvaline at 2.5 mM and 80 µL (respectively 90 µL) of H₂O were added to each replicate before crushing with beads. An amino acid standard was prepared by adding 400 µL of a commercial amino acid solution (Agilent, amino acid standard at 250 µM) to 4 µL of prepared standard containing glutamine, asparagine, tryptophane and norvaline at 25 mM. A blank standard was prepared by adding 2 µL of norvaline (25 mM) to 198 µL of H₂O, and a hydrolysis standard by adding 2 µL of norvaline (25 mM) to 200 µL of a commercial amino acid solution (Agilent, 250 µM). For hydrolysis, 25 µL of each replicate plus four samples of 25 µL for each standard were transferred to hydrolysis tubes. All the tubes were put 1 h on Thermo Fischer SpeedVac to dry under vacuum. Hydrolysis tubes were then placed into hydrolysis vials, and a hydrolysis solution was added: 5 % (w/v) phenol in HCl 6N. Air vacuum was applied until boiling, and gaseous nitrogen was then added. This step was repeated twice and a last air vacuum was applied until reaching 1 mbar. Finally, vials were put in the oven during 22 h at 115 °C. Samples were resuspended in 50 µL of ultrapure H₂O and filtered with a 0.45 µm filter.

### 4.1.4. *Amino acid measurements using HPLC*

Amino acid quantification was conducted using HPLC (Agilent 1100; Agilent Technologies, Santa Clara, CA, USA) equipped with a guard cartridge and a reverse-phase C18 column (Zorbax Eclipse AAA; 3.5 mm, 150 by 4.6 mm; Agilent Technologies, Santa Clara, CA, USA). The procedure was specifically developed for the Agilent 1100 system and subsequently adapted for quantifying amino acids in Hemiptera such as aphids and whiteflies (Rabatel et al., 2013; Benhamou et al., 2021). The derivatization process, performed at room temperature, was automated using the Agilent 1313A autosampler (Agilent Technologies, Santa Clara, CA, USA). Detection was carried out with a fluorescence detector set to excitation and emission wavelengths of 340 and 450 nm, respectively (266/305 nm for proline). Under these conditions, oxidation can result in several cysteine forms, precluding accurate detection and quantification of cysteine. Due to hydrolysis, tryptophan cannot be detected and glutamate and glutamine, as well as aspartate and asparagine, cannot be distinguished and are measured as two pools (glutamine + glutamate, asparagine + aspartate). Since glutamate, glutamine, aspartate and asparagine are not *Buchnera*-dependent amino acids, they were excluded from our analysis. For cysteine and tryptophan, we integrated known reference values for an adult aphid (A9) (Harper and Kaldy, 1978) and adjusted them according to the variation in total amino acids at the different life stages compared to A9.

### 4.1.5. *Symbiont purification and dry weighing*

Aphids were sampled on: day 3 (second instar nymph, N2), day 5 (third instar nymph, N3), day 9 (reproducing adult, A9), day 16 (reproducing adult, A16), day 23 (senescent adult, A23). For each sampling day, symbiotic bacteria were purified as previously described (Simonet et al., 2016b; Ribeiro Lopes et al., 2022). The number of aphids per replicate varied to have an optimum between a too small bacterial weight for measurement, and an excessive amount of aphid extract that would occlude the filters. This led to 400, 100, 25, 20, 30 aphids per biological replicate for respectively N1, N3, A9, A16, A23 stages, with four replicates for N1 and five for the other stages. Aphids were weighed before crushing with a Potter with 3 ml of ice-cold buffer A (0.025 M KCl, 0.01M MgCl₂, 0.25M Sucrose and 0.035M Tris-HCl, pH 7.5) and the solution was consecutively filtered through 100 µm and 30 µm filters placed over a funnel, and then 5 µm and 3 µm Merck Millipore nylon membranes (Millipore Corporation, Burlington, MA, USA) placed on a membrane support connected to a syringe. Between each step, filters, membranes and funnels were rinsed with ice-cold buffer A, and the filtrate was transferred using Pasteur pipettes. At the end, the filtrate was collected in a 50 mL centrifuge tube, and centrifuged for 5 min at 4000g and 4 °C. The supernatant was removed, and the pellet was resuspended with 600 µL of buffer A, transferred into a pre-weighted Eppendorf tube. The solution was again centrifuged for 5 min at 4000g and 4 °C, and the supernatant was removed. The tube was dried at 35 °C in a Thermo Fischer SpeedVac (Thermo Fischer Scientific, Waltham, MA, USA) until the measured weight was constant. At the end of this process, a small amount of sugars and salts from buffer A may remain in the tube, which could lead to an overestimation of the symbiont weight. However, we estimated that less than 1 µL of the buffer remained in the tube, corresponding to less than 95 µg. This represents less than five percent of the total measured weight. A correction factor integrating the expected tube loss weight during the drying phase was integrated before final estimation of bacteria/aphid weight ratio. To this end, for each experiment, 10 empty tubes underwent the same drying process, and the weight loss after drying was integrated to correct the final symbiont mass. In addition, 12.5 percent of the final mass was removed, integrating the data that around 12.5 percent of the particles obtained through this filtration process are contaminant and not bacteria (Simonet et al., 2016b).
## 4.2. In silico procedures

### 4.2.1. Metabolic reconstruction

Overall, we followed Thiele and Palsson reference protocol for generating high-quality genome-scale metabolic models (Thiele and Palsson, 2010). Our genome-scale metabolic model iLG240 of *B. aphidicola* was built using the previous genome-scale metabolic model of the bacterium as a scaffold (Blow et al., 2020b). We refined the reactions included in the model by aggregating information from older models (Thomas et al., 2009; Macdonald et al., 2011, 2012) and from the latest automatic metabolic network reconstruction (Baa-Puyoulet et al., 2025). We also integrated biochemical data from publications on *B. aphidicola* (Poliakov et al., 2011; Price and Wilson, 2014; Charles et al., 2011; Huang et al., 2008) and closely-related organisms (Ueda et al., 2012; Braymer et al., 2021; Haase et al., 2013; Ren et al., 2005) when acceptable. As examples, we changed inaccuracies in some gene-protein-reaction relationships by adjusting which gene(s) were required for a given reaction or modifying the associated Enzyme Commission (EC) number. We modified the chemical equations of some metabolic reactions based on literature and other bacterial metabolic models, mainly from the BiGG database (King et al., 2016). As other bacterial metabolic models, *Escherichia coli* latest model (iML1515) was used in most of the cases, as *Buchnera* metabolism can be mostly considered as a subset of *E. coli* metabolism (Thomas et al., 2009). In some specific cases (9 reactions, explicated in Supplementary File 1), the way metabolic reactions were written was more appropriate or rigorous in other metabolic models, even though they were more phylogenetically distant. Our model refinement also includes an overhaul of the transport reactions, that were manually corrected integrating the work by Charles et al. (2011) and of the biomass reaction (Supplementary File 3) which now takes into account protein abundance from proteomics (Poliakov et al., 2011). The biomass reaction integrates the specificities of *B. aphidicola* morphology, such as the absence of lipopolysaccharides (Shigenobu et al., 2000), the unmatured peptidoglycan (Smith et al., 2022), as well as the absence of glycogen in the cell (Wang and Wise, 2011), polyploidy and uncommon cell size (Komaki and Ishikawa, 1999) (Supplementary File 3). Phospholipids included in biomass reaction require the uptake of host phospholipids matured by *B. aphidicola*. Eight biosynthetic pathways were incomplete, as at least one reaction in was carried out by the aphid. For each of those pathways, we added an external reaction representing the conversion achieved by the aphid. A few reactions were removed from Blow et al. (2020b) model, as our manual analysis suggested that they are not present in *B. aphidicola*, or that other reactions integrated into the final model are more appropriate. All these twenty-six cases are mentioned in Supplementary Text 1.

The predicted fluxes were verified manually, pathway by pathway, for consistency and feasibility. Detailed information on the metabolic network reconstruction is available in Supplementary Text 1, and the model is available as a tabulated file (Supplementary File 1) and an XML file following SBML standards (Supplementary File 2). To generate the XML file from tabulated file, we used the java library met4j https://forg.emia.inra.fr/metexplore/met4j. The XML model is also publicly available in our gitlab (https://gitlab.com/bf2i/metabolicmodels/ilg240), in BioModels repository (Malik-Sheriff et al., 2020) under the identifier MODEL2507280001 and in the French institutional database data.gouv under the DOI 10.57745/DTMJ7I (https://doi.org/10.57745/DTMJ7I).

### 4.2.2. Metabolic modeling

Fluxes were modeled using Flux Balance Analysis (FBA) (Orth et al., 2010). Briefly, it assumes quasi-stationary state in the internal metabolism (i.e. metabolites are consumed once they are produced), simplifying mass balance in the metabolism into equation $K \cdot v = 0$, with K the stoichiometric matrix representing the reaction coefficients, and v the fluxes (mol/g dry weight/time). Linear programming is used to solve the problem, by adding upper and lower bounds to the fluxes (constraints)

and optimizing (maximizing/minimizing) one or several metabolic reaction(s) that fulfill(s) the objective function. A complete description of the constraints is available in Supplementary Text 1 and is specified in the scripts in our gitlab. Briefly, our model constrains lower bounds to zero for irreversible reactions, and $O_2$ uptake upper bound according to experimental data based on $O_2$ measurement on *Buchnera* extracts (Whitehead and Douglas, 1993). The latter globally constrains substrate assimilation and energy generation, as the bacterium metabolism is not functional without aerobic respiration. The model can also constrain growth rates during aphid nymphal and adult phases based on experimental data if biomass is not the objective function. In many bacterial metabolic models, a non-growth associated maintenance (NGAM) reaction models the energetic cost of cell maintenance through turgor pressure (Thiele and Palsson, 2010). However, here, *B. aphidicola* grows in an isotonic environment (the host cell), removing this maintenance cost (Smith et al., 2022; Otten et al., 2018). The NGAM reaction was kept in the model for further analyses under other conditions, but was unconstrained in our simulations (i.e. no mandatory flux imposed on it). To model *Buchnera*-dependent amino acid flux, we added three meta-metabolic reactions representing, through their stoichiometric coefficients, the proportion of the different amino acids necessary to generate 1 g of *Buchnera*-dependent amino acids, respectively during nymphal, reproducing adult and senescent adult life stages, based on experimental quantifications of amino acid and literature data (the reactions are shown in Supplementary Table 1). To simulate the impact of reduced host resources on growth and amino acid production, we applied a decreased maximal uptake rate to each of the nutrients required for growth or for *Buchnera*-dependent amino acid production and determined maximal growth rate and maximal amino acid production rate in each case.

Simulations were performed with Python 3.9 scripts, using COBRAPy https://cobrapy.readthedocs.io/ (Ebrahim et al., 2013) and pandas http://pandas.pydata.org libraries (both open access), and the linear programming solver CPLEX Python API, developed by IBM and free for academic institutions. All the simulations can be reproduced in our gitlab repository: https://gitlab.com/bf2i/metabolicmodels/ilg240.

### 4.2.3. Statistical analysis of amino acid quantifications

We used amino acid proportions (i.e. each amino acid quantity divided by the sum of all 18 amino acids measured) for statistical analysis. To define the production of *Buchnera*-dependent amino acids in our metabolic model, we needed to determine whether we could consider that *Buchnera*-dependent amino acid proportions remained constant during aphid lifecycle. To this end, we aimed at performing a two-way ANOVA including amino acids and aphid life stages as factors, with the null hypothesis that life stages have no effect on amino acid proportions (i.e., no significant interaction term). Data were transformed using the formula $\arcsin(\sqrt{(proportion)})$ (Draper and Smith, 1998) as Bartlett, Shapiro-Wilk and Grubbs tests were found to be significant. These three tests (conformity rules of linear model) evaluate homoscedasticity, residuals normality and absence of outliers, respectively. As the ANOVA-2 interaction term was significant, we split our dataset by amino acid and performed further analyses using one-way ANOVA on raw proportions using the same conformity tests. Data were found to be linear for all amino acids except for methionine and tyrosine for which the non-parametric Kruskal-Wallis test was performed. At last, we performed Post hoc comparison procedures with a global risk control of 5 % using the Sequen contrast from the library multcomp in R which allowed us to compare the amino acid proportions between two successive stages. For the two amino acids (methionine and tyrosine) which required non-parametric tests, we used successive Wilcoxon tests between successive stages and p-values were adjusted for multiple comparisons using the p-adjust function in R (global risk fixed at 5 %). The complete analysis (code, graphics and comments) can be found in Supplementary Text 2 and in our gitlab repository: https://gitlab.com/bf2i/metabolicmodels/ilg240. Statistical analyses were performed using
R version 4.4.2 (2024-10-31).

### CRediT authorship contribution statement

**Léo Gerlin:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Software, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Karen Gaget:** Writing – review & editing, Methodology, Investigation. **Garance Lapetoule:** Writing – review & editing, Methodology, Investigation. **Yohann Quivet:** Writing – review & editing, Investigation. **Patrice Baa-Puyoulet:** Writing – review & editing, Software, Formal analysis, Data curation. **Isabelle Rahioui:** Writing – review & editing, Methodology, Investigation. **Mélanie Ribeiro Lopes:** Writing – review & editing, Resources, Methodology. **Pedro Da Silva:** Writing – review & editing, Resources, Methodology. **Federica Calevro:** Writing – review & editing, Project administration, Methodology, Funding acquisition, Conceptualization. **Hubert Charles:** Writing – review & editing, Supervision, Software, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization.

### Acknowledgements

We thank Gabrielle Duport for her help in setting up the protocol for *B. aphidicola* purification and dry weighing. We also thank four master students: Enzo Faucher (INP Toulouse-ENSAT), Koloina Rabemanantsoa (INSA Toulouse), Mathieu Ropert (INSA Toulouse) and Bayane Saï (INSA Toulouse) for their contribution to the analysis of external substrate requirements for *Buchnera*-dependent amino acid. This work was supported by INSA Lyon (Institut National des Sciences Appliquées de Lyon), INRAE (Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement) and the INRAE-SPE DECODE program grant to Léo Gerlin.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ymben.2025.07.011.

### Data availability

All presented data are available within the main text or the supplementary materials. The modeling simulations and statistical analyses can be reproduced in our gitlab repository: https://gitlab.com/bf2i/metabolicmodels/ilg240.

### References

Akman, L., Yamashita, A., Watanabe, H., Oshima, K., Shiba, T., Hattori, M., et al., 2002. Genome sequence of the endocellular obligate symbiont of tsetse flies, *Wigglesworthia glossinidia*. Nat. Genet. 32 (3), 402-407.

Ankrah, N.Y.D., Luan, J., Douglas, A.E., 2017. Cooperative metabolism in a three-partner insect-bacterial symbiosis revealed by metabolic modeling. J. Bacteriol. 199 (15).

Ankrah, N.Y.D., Chouaia, B., Douglas, A.E., 2018. The cost of metabolic interactions in symbioses between insects and bacteria with reduced genomes. mBio 9 (5), e01433-18.

Ashraf, M.A., Iqbal, M., Rasheed, R., Hussain, I., Riaz, M., Arif, M.S., 2018. Chapter 8 - environmental stress and secondary metabolites in plants: an overview. In: Ahmad, P., Ahanger, M.A., Singh, V.P., Tripathi, D.K., Alam, P., Alyemeni, M.N. (Eds.), Plant Metabolites and Regulation under Environmental Stress. Academic Press, pp. 153-167.

Baa-Puyoulet, P., Gerlin, L., Parisot, N., Peignier, S., Renoz, F., Calevro, F., et al., 2025. ArtSymbioCyc, a metabolic network database collection dedicated to arthropod symbioses: a case study, the tripartite cooperation in *Sipha maydis*. mSystems 21, e00140, 25.

Baumann, P., Lai, C., Baumann, L., Rouhbkhsh, D., Moran, N.A., Clark, M.A., 1995. Mutualistic associations of aphids and prokaryotes: biology of the genus *Buchnera*. Appl. Environ. Microbiol. 61 (1), 1-7.

Benhamou, S., Rahioui, I., Henri, H., Charles, H., Da Silva, P., Heddi, A., et al., 2021. Cytotype affects the capability of the whitefly *Bemisia tabaci* MED species to feed and oviposit on an unfavorable host plant. mBio 12 (6), e00730, 21.

Bermingham, J., Rabatel, A., Calevro, F., Vinuelas, J., Febvay, G., Charles, H., et al., 2009. Impact of host developmental age on the transcriptome of the symbiotic bacterium *Buchnera aphidicola* in the pea aphid (*Acyrthosiphon pisum*). Appl. Environ. Microbiol. 75 (22), 7294-7297.

Blombach, B., Grünberger, A., Centler, F., Wierckx, N., Schmid, J., 2022. Exploiting unconventional prokaryotic hosts for industrial biotechnology. Trends Biotechnol. 40 (4), 385-397.

Blow, F., Bueno, E., Clark, N., Zhu, D.T., Chung, S.H., Güllert, S., et al., 2020a. B-vitamin nutrition in the pea aphid-*Buchnera* symbiosis. J. Insect Physiol. 126, 104092.

Blow, F., Ankrah, N.Y.D., Clark, N., Koo, I., Allman, E.L., Liu, Q., et al., 2020b. Impact of facultative bacteria on the metabolic function of an obligate insect-bacterial symbiosis. mBio 11 (4), e00402-e00420.

Bowne, J.B., Erwin, T.A., Juttner, J., Schnurbusch, T., Langridge, P., Bacic, A., et al., 2012. Drought responses of leaf tissues from wheat cultivars of differing drought tolerance at the metabolite level. Mol. Plant 5 (2), 418-429.

Braymer, J.J., Freibert, S.A., Rakwalska-Bange, M., Lill, R., 2021. Mechanistic concepts of iron-sulfur protein biogenesis in Biology. Biochim Biophys Acta BBA - Mol Cell Res. 1868 (1), 118863.

Buchner, P., 1965. Endosymbiosis of Animals with Plant Microorganisms. Interscience Publishers, New York, p. 909.

Charles, H., Balmand, S., Lamelas, A., Cottret, L., Pérez-Brocal, V., Burdin, B., et al., 2011. A genomic reappraisal of symbiotic function in the aphid/*Buchnera* symbiosis: reduced transporter sets and variable membrane organisations. PLoS One 6 (12), e29096.

Colella, S., Parisot, N., Simonet, P., Gaget, K., Duport, G., Baa-Puyoulet, P., et al., 2018. Bacteriocye reprogramming to cope with nutritional stress in a phloem sap feeding hemipteran, the pea aphid *Acyrthosiphon pisum*. Front. Physiol. 9, 1498.

Dinant, S., Bonnemain, J.L., Girousse, C., Kehr, J., 2010. Phloem sap intricacy and interplay with aphid feeding. C. R. Biol. 333 (6-7), 504-515.

Douglas, A.E., 1998. Nutritional interactions in insect-microbial symbioses: aphids and their symbiotic bacteria *Buchnera*. Annu. Rev. Entomol. 43 (1), 17-37.

Douglas, A.E., 2009. The microbial dimension in insect nutritional ecology. Funct. Ecol. 23 (1), 38-47.

Draper, Norman R., Smith, Harry, 1998. Applied Regression Analysis. John Wiley&Sons, Inc. New York, p. 736.

Duncan, R.P., Anderson, C.M.H., Thwaites, D.T., Luetje, C.W., Wilson, A.C.C., 2023. Co-option of a conserved host glutamine transporter facilitates aphid/*Buchnera* metabolic integration. Proc. Natl. Acad. Sci. 120 (43), e2308448120.

Ebrahim, A., Lerman, J.A., Palsson, B.O., Hyduke, D.R., 2013. COBRAPy: CConstraints-based reconstruction and analysis for Python. BMC Syst. Biol. 7 (1), 74.

Febvay, G., Rahbé, Y., Rynkiewicz, M., Guillaud, J., Bonnot, G., 1999. Fate of dietary sucrose and neosynthesis of amino acids in the pea aphid, *Acyrthosiphon pisum*, reared on different diets. J. Exp. Biol. 202 (19), 2639-2652.

Fyson, N., King, J., Belcher, T., Preston, A., Colijn, C., 2017. A curated genome-scale metabolic model of *Bordetella pertussis* metabolism. Ouzounis CA. PLoS Comput. Biol. 13 (7), e1005639.

Gerlin, L., Cottret, L., Cesbron, S., Taghouti, G., Jacques, M.A., Genin, S., et al., 2020. Genome-scale investigation of the metabolic determinants generating bacterial fastidious growth. mSystems 5 (2), e00698, 19.

Gonzalez, J.E., Long, C.P., Antoniewicz, M.R., 2017. Comprehensive analysis of glucose and xylose metabolism in *Escherichia coli* under aerobic and anaerobic conditions by 13C metabolic flux analysis. Metab. Eng. 39, 9-18.

Haase, I., Sarge, S., Illarionov, B., Laudert, D., Hohmann, H.P., Bacher, A., et al., 2013. Enzymes from the Haloacid Dehalogenase (HAD) superfamily catalyze the elusive dephosphorylation step of riboflavin biosynthesis. Chembiochem 14 (17), 2272-2275.

Hall, R.J., Thorpe, S., Thomas, G.H., Wood, A.J., 2020. Simulating the evolutionary trajectories of metabolic pathways for insect symbionts in the genus *Sodalis*. Microb Genomics 6 (7).

Hansen, A.K., Moran, N.A., 2011. Aphid genome expression reveals host-symbiont cooperation in the production of amino acids. Proc. Natl. Acad. Sci. 108 (7), 2849-2854.

Hansen, A.K., Moran, N.A., 2014. The impact of microbial symbionts on host plant utilization by herbivorous insects. Mol. Ecol. 23 (6), 1473-1496.

Harper, A.M., Kaldy, M.S., 1978. Amino acid composition and other nutritional constituents of pea aphids, *Acyrthosiphon pisum* (Hemiptera (Homoptera): aphididae) and their hosts, broad beans and alfalfa. Can. Entomol. 110 (4), 381-384.

Hijaz, F., Manthey, J.A., Van der Merwe, D., Killiny, N., 2016. Nucleotides, micro- and macro-nutrients, limonoids, flavonoids, and hydroxycinnamates composition in the phloem sap of sweet orange. Plant Signal. Behav. 11 (6), e1183084.

Huang, C.Y., Lee, C.Y., Wu, H.C., Kuo, M.H., Lai, C.Y., 2008. Interactions of chaperonin with a weakly active Anthranilate Synthase from the aphid endosymbiont *Buchnera aphidicola*. Microb. Ecol. 56 (4), 696-703.

Ilts, C., Tougeron, K., Hance, T., Louäpre, P., Foray, V., 2022. A perspective on insect-microbe holobionts facing thermal fluctuations in a climate-change context. Environ. Microbiol. 24 (1), 18-29.

Ishikawa, H., 2001. Symbiotic microorganisms in aphids (Homoptera, Insecta): a secret of one thriving insect group. Korean J Biol Sci. 5 (3), 163-177.

Jedlicka, P., Jedlicková, V., Lee, H.J., 2015. Expression of stress-related genes in the parthenogenetic forms of the pea aphid, *Acyrthosiphon pisum*. Comp. Biochem. Physiol. Mol. Integr. Physiol. 180, 32-37.

Kayser, A., Weber, J., Hecht, V., Rinas, U., 2005. Metabolic flux analysis of *Escherichia coli* in glucose-limited continuous culture. I. Growth-rate-dependent metabolic efficiency at steady state. Microbiology 151 (3), 693-706.
King, Z.A., Lu, J., Dräger, A., Miller, P., Federowicz, S., Lerman, J.A., et al., 2016. BiGG Models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 44 (D1), D515–D522.

Komaki, K., Ishikawa, H., 1999. Intracellular bacterial symbionts of aphids possess many genomic copies per bacterium. J. Mol. Evol. 48 (6), 717–722.

Komaki, K., Ishikawa, H., 2000. Genomic copy number of intracellular bacterial symbionts of aphids varies in response to developmental stage and morph of their host. Insect Biochem. Mol. Biol. 30 (3), 253–258.

Lohaus, G., 2022. Review primary and secondary metabolites in phloem sap collected with aphid styletomy. J. Plant Physiol. 271, 153645.

Lu, W.N., Chiu, M.C., Kuo, M.H., 2014. Host life stage- and temperature-dependent density of the symbiont *Buchnera aphidicola* in a subtropical pea aphid (*Acyrthosiphon pisum*) population. J. Asia Pac. Entomol. 17 (3), 537–541.

Macdonald, S.J., Thomas, G.H., Douglas, A.E., 2011. Genetic and metabolic determinants of nutritional phenotype in an insect-bacterial symbiosis. Mol. Ecol. 20 (10), 2073–2084.

Macdonald, S.J., Lin, G.G., Russell, C.W., Thomas, G.H., Douglas, A.E., 2012. The central role of the host cell in symbiotic nitrogen metabolism. Proc R Soc B Biol Sci 279 (1740), 2965–2973.

Madey, E., Nowack, L.M., Thompson, J.E., 2002. Isolation and characterization of lipid in phloem sap of canola. Planta 214 (4), 625–634.

Malik-Sheriff, R.S., Glont, M., Nguyen, T.V.N., Tiwari, K., Roberts, M.G., Xavier, A., et al., 2020. BioModels—15 years of sharing computational models in life science. Nucleic Acids Res. 48 (D1), D407–D415.

Monk, J.M., Lloyd, C.J., Brunk, E., Mih, N., Sastry, A., King, Z., et al., 2017. iML1515, a knowledgebase that computes *Escherichia coli* traits. Nat. Biotechnol. 35 (10), 904–908.

Muscatine, L., Porter, J.W., 1977. Reef corals: mutualistic symbioses adapted to nutrient-poor environments. Bioscience 27 (7), 454–460.

Nakabachi, A., Ishikawa, H., Kudo, T., 2003. Extraordinary proliferation of microorganisms in aposymbiotic pea aphids, *Acyrthosiphon pisum*. J. Invertebr. Pathol. 82 (3), 152–161.

Nealson, K.H., Conrad, P.G., 1999. Life: past, present and future. In: Zeki, S. (Ed.), Philos. Trans. R. Soc. Lond. B Biol. Sci. 354 (1392), 1923–1939.

Nikoh, N., Hosokawa, T., Moriyama, M., Oshima, K., Hattori, M., Fukatsu, T., 2014. Evolutionary origin of insect- *Wolbachia* nutritional mutualism. Proc. Natl. Acad. Sci. 111 (28), 10257–10262.

Nogales, J., Mueller, J., Gudmundsson, S., Canalejo, F.J., Duque, E., Monk, J., et al., 2020. High-quality genome-scale metabolic modeling of *Pseudomonas putida* highlights its broad metabolic capabilities. Environ. Microbiol. 22 (1), 255–269.

Nowack, E.C.M., Melkonian, M., 2010. Endosymbiotic associations within protists. Philos Trans R Soc B Biol Sci 365 (1541), 699–712.

Oh, Y.K., Palsson, B.O., Park, S.M., Schilling, C.H., Mahadevan, R., 2007. Genome-scale reconstruction of metabolic network in *Bacillus subtilis* based on high-throughput phenotyping and gene essentiality data. J. Biol. Chem. 282 (39), 28791–28799.

Orth, J.D., Thiele, I., Palsson, B.Ø., 2010. What is flux balance analysis? Nat. Biotechnol. 28 (3), 245–248.

Otten, C., Brilli, M., Vollmer, W., Viollier, P.H., Salje, J., 2018. Peptidoglycan in obligate intracellular bacteria. Mol. Microbiol. 107 (2), 142–163.

Peccoud, J., Bonhomme, J., Mahéo, F., De La Huerta, M., Cosson, O., Simon, J., 2014. Inheritance patterns of secondary symbionts during sexual reproduction of pea aphid biotypes. Insect Sci. 21 (3), 291–300.

Pers, D., Hansen, A.K., 2021. The boom and bust of the aphid's essential amino acid metabolism across nymphal development. G3 Genes Genomes Genet. 11 (9), jkab115.

Poliakov, A., Russell, C.W., Ponnala, L., Hoops, H.J., Sun, Q., Douglas, A.E., et al., 2011. Large-scale label-free quantitative proteomics of the pea aphid-*Buchnera* symbiosis. Mol. Cell. Proteomics 10 (6), 007039. M110.

Price, D.R., Wilson, A.C., 2014. A substrate ambiguous enzyme facilitates genome reduction in an intracellular symbiont. BMC Biol. 12 (1), 110.

Rabatel, A., Febvay, G., Gaget, K., Duport, G., Baa-Puyoulet, P., Sapountzis, P., et al., 2013. Tyrosine pathway regulation is host-mediated in the pea aphid symbiosis during late embryonic and early larval development. BMC Genom. 14 (1), 235.

Ramsey, J.S., MacDonald, S.J., Jander, G., Nakabachi, A., Thomas, G.H., Douglas, A.E., 2010. Genomic evidence for complementary purine metabolism in the pea aphid, *Acyrthosiphon pisum*, and its symbiotic bacterium *Buchnera aphidicola*. Insect Mol. Biol. 19 (s2), 241–248.

Ren, J., Kotaka, M., Lockyer, M., Lamb, H.K., Hawkins, A.R., Stammers, D.K., 2005. GTP Cyclohydrolase II structure and mechanism. J. Biol. Chem. 280 (44), 36912–36919.

Renzof, F., Pons, I., Hance, T., 2019. Evolutionary responses of mutualistic insect-bacterial symbioses in a world of fluctuating temperatures. Curr Opin Insect Sci 35, 20–26.

Ribeiro Lopes, M., Gaget, K., Renoz, F., Duport, G., Balmand, S., Charles, H., et al., 2022. Bacteriocyte plasticity in pea aphids facing amino acid stress or starvation during development. Front. Physiol. 13, 982920.

Rispe, C., Moran, N.A., 2000. Accumulation of deleterious mutations in endosymbionts: Muller's Ratchet with Two Levels of Selection. Am Nat 156 (4), 425–441.

Schönborn, J.W., Jahrke, L., Mettler-Altmann, T., Beller, M., 2019. FlySilico: flux balance modeling of *Drosophila* larval growth and resource allocation. Sci. Rep. 9 (1), 17156.

Seif, Y., Monk, J.M., Mih, N., Tsunemoto, H., Poudel, S., Zuniga, C., et al., 2019. A computational knowledge-base elucidates the response of *Staphylococcus aureus* to different media types. PLoS Comput. Biol. 15 (1), e1006644.

Shigenobu, S., Watanabe, H., Hattori, M., Sakaki, Y., Ishikawa, H., 2000. Genome sequence of the endocellular bacterial symbiont of aphids *Buchnera* sp. APS. Nature. 407 (6800), 81–86.

Simonet, P., Gaget, K., Parisot, N., Duport, G., Rey, M., Febvay, G., et al., 2016a. Disruption of phenylalanine hydroxylase reduces adult lifespan and fecundity, and impairs embryonic development in parthenogenetic pea aphids. Sci. Rep. 6 (1), 34321.

Simonet, P., Duport, G., Gaget, K., Weiss-Gayet, M., Colella, S., Febvay, G., et al., 2016b. Direct flow cytometry measurements reveal a fine-tuning of symbiotic cell dynamics according to the host developmental needs in aphid symbiosis. Sci. Rep. 6 (1), 19967.

Simonet, P., Gaget, K., Balmand, S., Ribeiro Lopes, M., Parisot, N., Buhler, K., et al., 2018. Bacteriocyte cell death in the pea aphid/*Buchnera* symbiotic system. Proc. Natl. Acad. Sci. 115 (8).

Smith, T.E., Li, Y., Perreau, J., Moran, N.A., 2022. Elucidation of host and symbiont contributions to peptidoglycan metabolism based on comparative genomics of eight aphid subfamilies and their *Buchnera*. PLoS Genet. 18 (5), e1010195.

Teixeira, A., Noronha, H., Frusciante, S., Diretto, G., Gerós, H., 2023. The grapevine metabolite profile of phloem sap is modified by flavescence dorée. OENO One 57 (1), 307–320.

Thiele, I., Palsson, B.Ø., 2010. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5 (1), 93–121.

Thomas, G.H., Zucker, J., Macdonald, S.J., Sorokin, A., Goryanin, I., Douglas, A.E., 2009. A fragile metabolic network adapted for cooperation in the symbiotic bacterium *Buchnera aphidicola*. BMC Syst. Biol. 3 (1), 24.

Ueda, K., Nishida, H., Beppu, T., 2012. Dispensabilities of carbonic anhydrase in proteobacteria. Int. J. Evol. Biol. 2012, 1–5.

Vinuelas, J., Febvay, G., Duport, G., Colella, S., Fayard, J., Charles, H., et al., 2011. Multimodal dynamic response of the *Buchnera aphidicola* pleu plasmid to variations in leucine demand of its host, the pea aphid *Acyrthosiphon pisum*. Mol. Microbiol. 81 (5), 1271–1285.

Vogel, K.J., Moran, N.A., 2011. Effect of host genotype on symbiont titer in the aphid-*buchnera* symbiosis. Insects 2 (3), 423–434.

Wang, L., Wise, M.J., 2011. Glycogen with short average chain length enhances bacterial durability. Naturwissenschaften 98 (9), 719–729.

Whitehead, L.F., Douglas, A.E., 1993. A metabolic study of *Buchnera*, the intracellular bacterial symbionts of the pea aphid *Acyrthosiphon pisum*. J. Gen. Microbiol. 39 (4), 821–826.

Wilkinson, T.L., Douglas, A.E., 1998. Host cell allometry and regulation of the symbiosis between pea aphids. *Acyrthosiphon pisum*, and bacteria, *Buchnera*. J. Insect Physiol. 44 (7–8), 629–635.

Wilkinson, T.L., Ishikawa, H., 2000. Injection of essential amino acids substitutes for bacterial supply in aposymbiotic pea aphids (*Acyrthosiphon pisum*). Entomol. Exp. Appl. 94 (1), 85–91.

Wilkinson, T.L., Ishikawa, H., 1999. The assimilation and allocation of nutrients by symbiotic and aposymbiotic pea aphids, *Acyrthosiphon pisum*. In: Simpson, S.J., Mordue, A.J., Hardie, J. (Eds.), Proceedings of the 10th International Symposium on Insect-Plant Relationships. Springer Netherlands, Dordrecht, pp. 195–201.

Wilkinson, T.L., Fukatsu, T., Ishikawa, H., 2003. Transmission of symbiotic bacteria *Buchnera* to parthenogenetic embryos in the aphid *Acyrthosiphon pisum* (Hemiptera: Aphidoidea). Arthropod Struct. Dev. 32 (2), 241–245.

Wilson, A.C.C., Ashton, P.D., Calevro, F., Charles, H., Colella, S., Febvay, G., et al., 2010. Genomic insight into the amino acid relations of the pea aphid, *Acyrthosiphon pisum*, with its symbiotic bacterium *Buchnera aphidicola*. Insect Mol. Biol. 19 (s2), 249–258.

XueKe, G., Shuai, Z., JunYu, L., LiMin, L., LiJuan, Z., JinJie, C., 2017. Lipidomics and RNA-seq study of lipid regulation in *Aphis gossypii* parasitized by *Lysiphlebia japonica*. Sci. Rep. 7 (1), 1364.

Zhang, Y.C., Cao, W.J., Zhong, L.R., Godfray, H.C.J., Liu, X.D., 2016. Host plant determines the population size of an obligate symbiont (*Buchnera aphidicola*) in aphids. Appl. Environ. Microbiol. 82 (8), 2336–2346.