Outsourcing of energetically costly amino acids at the origin of animals

Domain: neuroscience
Published: 2026-01-22
Editorial outcome: minor_revision

Abstract
Animals are generally capable of synthesizing eleven amino acids, while the remaining nine—often referred to as essential—must be acquired through diet. This metabolic trait profoundly shapes animal ecology and evolutionary trajectories, and recent phylogenomic studies indicate that it arose from gene losses at the root of the animal tree. However, it remains unclear which selective forces, if any, directed this far-reaching metabolic simplification. Here, we show that essential amino acids are energetically far more expensive to synthesize than non-essential ones, particularly under high respiratory conditions—a hallmark of the animal lifestyle. Using a probabilistic selection test, we find support for the hypothesis that energy-related selection, counterbalanced by pleiotropic effects, favored the outsourcing of essential amino acid production under well-oxygenated and nutrient-rich conditions. Remarkably, we also find that extant animals use expensive amino acids more frequently than their closest unicellular relatives, suggesting that constraints on their usage were relaxed once their synthesis was externalized, thereby enabling animal genes to evolve more freely across protein sequence space. In this light, we propose that the origin of animals was tightly linked to energy-related adaptations rather than to unpredictable stochastic events, as recently suggested.

Introduction
Proteins are made of 20 proteinogenic amino acids (AAs). The biosynthesis of these AAs is an indispensable biochemical process on which all life depends. Many organisms, such as plants, fungi, various bacteria, and many unicellular eukaryotes are capable of synthesizing all 20 AAs1–4. However, other lineages lost the capability to synthesize some AAs, which mean that they have to harvest these essential amino acids (EAAs) from their environment1,2. A prominent example is animals (Metazoa), which lost the capability to synthesize nine EAAs1,3,5. Recent phylogenomic studies showed that this simplification occurred at the root of the animal tree, after the split from choanoflagellates6,7. This metabolic loss is part of a broader simplification event that preceded metazoan radiation and included a wide range of metabolic reductions6–8.

However, which selective forces, or random processes, were driving this simultaneous loss of EAA biosynthetic pathways in metazoans remains a great mystery3. The biosynthesis of AAs is generally energetically costly9, but there is also considerable variability among them in the energy required for their production, with the cheapest and most expensive AAs differing by almost an order of magnitude9–12. On the other hand, free energy is a critical and limited resource that sustains biological systems, meaning that its harnessing and efficient usage are under continuous selective pressure13,14. For instance, energetically expensive AAs sparingly occur in protein sequences, especially in highly expressed genes9,15–17. It has been shown that this selectively favored bias leads to a net reduction in energy costs related to AA synthesis in cells13,14.

Inspired by these findings, we reasoned that the massive loss of EAA biosynthesis capability at the root of the animal tree could also be connected to energy management. A plausible hypothesis is that the biosynthesis of EAAs, as a group, incurs higher energy costs than the biosynthesis of non-essential AAs (NEAAs). By outsourcing the production of energetically costly AAs, an animal ancestor could have freed a part of its energy budget, which could then be reallocated to other functional needs. We further assumed that the outsourcing of expensive EAAs would enable animals to use them more freely in their proteomes—compared to their unicellular holozoan ancestors—as the energetic constraints on their usage would be reduced. Surprisingly, these possibilities have never been tested, even though estimates of AA biosynthesis energy costs have been available for a long time9–11 and AA biosynthetic capabilities have been studied from various perspectives1–5,11. It is also puzzling that no effort has yet been made to conceptually link AA synthesis costs with their essentiality status, nor has any study considered the potential role of selection in shaping AA auxotrophies in animals.

In this work, we identify the potential selective forces and ecophysiological conditions that led to the essential/non-essential AA dichotomy. Although alternative explanations cannot be definitively ruled out, our analyses support the hypothesis that energy management and pleiotropic effects exert antagonistic selective pressures that—together with ecophysiological factors such as respiration mode and AA availability in the environment—played a significant role in driving the outsourcing of EAAs synthesis in the metazoan lineage. Moreover, we find that animal proteomes contain a higher proportion of energetically costly AAs than those of their closest unicellular relatives, indicating that EAAs were more freely integrated into proteomes once their synthesis was outsourced. Accordingly, we argue that the emergence of animals was driven by energy-management adaptations rather than stochastic events.

Results
Essential amino acids are expensive
We first retrieved the most recent estimates12 on the required number of energy-bearing metabolites (ATP, NADH, and NADPH) for AA biosynthesis (Supplementary Data 1). These most recent estimates corrected some inadvertent errors that had propagated in previous studies using these values to calculate AA biosynthesis energy costs (see “Methods”). With these corrected values, we then calculated the direct cost of AA biosynthesis, representing the number of high-energy phosphate bonds (~P) required for synthesizing an AA from its metabolic precursor (Supplementary Data 1 and Supplementary Table 1).

It is important to note that direct costs quantify the energy used for AA production from metabolic precursors without considering how this metabolic mode impacts the overall energy balance of the cell. To address this, we also calculated the opportunity cost (see “Methods”), defined as the total energy that would have been produced if the precursors had been metabolized, plus the energy lost during the synthesis of AAs (Supplementary Data 1 and Supplementary Table 1). This alternative cost measure, which has been extensively used in previous work9–11,18, quantifies the impact of the choice to synthesize an AA on the overall energy balance of the cell by accounting for the unrealized energy gain from precursors (lost opportunity). In essence, it shows how much energy is given up by an organism when producing AAs18.

However, actual values of both opportunity costs and direct costs are directly dependent on the respiratory mode of a cell, which can differ within the life cycle of an organism as well as between different species10,19–21. Surprisingly, the fundamental importance of respiration mode for AA energy cost estimation is rarely recognized10,12. To account for this effect, we calculated here both direct and opportunity costs for three respiratory modes: fermentation, low respiration, and high respiration (see “Methods”, Supplementary Data 1 and Supplementary Table 1). This range of respiration modes encompasses all major metabolic states across the tree of life, enabling us to identify the respiratory conditions that most strongly favor the outsourcing of AAs.

For comparative purposes, we first sorted the 20 AAs in increasing order based on the opportunity cost calculated for high respiratory conditions, which correspond to the metabolic lifestyle of Metazoans (Fig. 1a). To illustrate how the dispensability status of AAs in animals is distributed in this representation, we marked the nine EAAs and eleven NEAAs1,3,5 in different colors (Fig. 1a). Remarkably, the AAs are arranged almost perfectly into two groups: (1) energetically cheaper NEAAs and (2) energetically more expensive EAAs (Fig. 1a).

The only exceptions are cysteine (C) and tyrosine (Y), which have relatively high energy costs and are usually considered NEAAs. However, in animals they are synthesized directly from other EAAs, which are secured from the environment; i.e., cysteine (C) from methionine (M) and tyrosine (Y) from phenylalanine (F)4. This suggests that cysteine (C) and tyrosine (Y) should be considered partially essential, as most of their synthesis costs are externally covered. In this context, it is evident that nearly all energy costs related to the top 55% (11 out of 20) most expensive AAs are outsourced (Fig. 1a). The analyses of opportunity costs under low respiration and fermentation conditions revealed very similar patterns, although overall values were lower and the difference between the cheapest and most expensive AAs was reduced (Supplementary Figs. 1 and 2).

While opportunity cost has been the prevailing energy cost measure in previous studies9–11,18, we also considered direct cost here. In contrast to opportunity costs, which account for the alternative option of respiring AA precursors, direct costs strictly reflect the expenses of AA synthesis. This measure is therefore particularly useful in situations where cells have a surplus of energy, making alternative metabolic options less relevant. A simple visual inspection of the direct cost chart (Fig. 1b) revealed that the direct costs of NEAAs are typically lower than those of EAAs; however, the difference between the two groups, although evident, is not as clear-cut as it is for opportunity costs (Fig. 1b and Supplementary Figs. 1b and 2b). Thus, to assess these differences more quantitatively, we statistically compared average direct costs as well as opportunity costs between NEAAs and EAAs using a non-parametric test (Fig. 2; Supplementary Figs. 3 and 4). We considered AA dispensability in two versions; in the first classical one, we grouped standard 9 EAAs (Fig. 2a, c), whereas in the second one, we extended this set to 11 members by treating cysteine (C) and tyrosine (Y) as EAAs (Fig. 2b, d).

Regardless of which cost measure is considered, under high respiration conditions the average energy costs of EAAs are significantly higher than the average energy costs of NEAAs with mostly large effect sizes (r > 0.5) (Fig. 2). Interestingly, although average EAA costs are always higher than average NEAA costs, this difference becomes less apparent and significant under low respiration, and even less so under a fermentation lifestyle (Supplementary Figs. 3 and 4). To better present this trend, we plotted the sum of AA opportunity costs as well as direct costs across three respiratory conditions (Fig. 3). This chart revealed a clear pattern in which the differences in total AA synthesis costs between NEAAs and EAAs are the lowest under fermentation, increase under low respiration, and are the highest under high respiration (Fig. 3). This trend is obvious for both direct and opportunity costs but is much stronger for opportunity costs, showing that more efficient respiration disproportionally increases the energy costs of EAAs in contrast to NEAAs. If we assume that energy consumption related to AA synthesis influences fitness, the observed differences between NEAAs and EAAs should be most strongly exposed to selection under high respiration. Subsequently, this selective pressure could lead to the eventual loss of EAA biosynthesis pathways, as observed at the root of Metazoa.

To further evaluate the hypothesis that selective forces related to energy management contributed to the outsourcing of EAAs under high respiration conditions, we developed the combinatorial phenotype selection (CPS) test, which probabilistically assesses whether a quantitative factor (e.g., biosynthetic cost) exerts directional selection on a combinatorial phenotypic trait (e.g., the auxotrophic/prototrophic status of amino acids), using neutral evolution as the null hypothesis (see “Methods”). We first generated all possible permutations of AA dispensability status under the assumption that 9 (or 11) out of 20 AAs are essential and then calculated the average opportunity (direct) cost of EAAs for every permutation. Based on the obtained distribution of average opportunity (direct) costs of EAAs, we calculated the matching empirical probability mass function (PMF) which was then used to retrieve the probability that the real-life set of EAAs in animals appeared randomly (Fig. 4). A low p-value would indicate that selection related to the AA synthesis costs shaped the observed set of EAAs in animals more strongly than random processes.

All performed CPS tests showed that the mean cost of the observed EAAs in animals falls at the right tail of the empirical distribution (Fig. 4 and Supplementary Data 2). In fact, in some analyses, the observed EAA combination in animals has the highest average opportunity cost in comparison to 167,960 possible permutations (Fig. 4b). This suggests that the observed EAA combination is highly unlikely to have arisen by chance alone and that our results are consistent with the hypothesis that AA production costs shaped the evolution of the EAA set in animals. Although animal lifestyle is universally linked to high respiration, it is interesting to test if selection could shape the EAA set observed in animals under low respiration and fermentation conditions. Similar to high respiration data, the CPS test of low respiration and fermentation data returned quite low p-values, which suggests that even in these metabolic situations, it is unlikely that random processes were a major force in shaping the EAA set. However, these p-values are less significant compared to those obtained for cost measures under high respiration (Supplementary Figs. 5 and 6), which highlights that the selective loss of EAAs is most probable under high-respiration conditions.

Selective trade-off between AA energetics and pleiotropy
If one assumes that AA energy management is an important selective force driving the loss of EAA production, the question arises why only half of the most energetically expensive AAs were outsourced in animals. Theoretically, if AA energetics exert strong selective pressure, all AAs could potentially be outsourced. A likely explanation is that AA pleiotropy counteracts AA energetics, leading to a set of EAAs shaped by a selective trade-off between these two factors. For example, the biosynthesis of arginine is part of the urea cycle, which is essential for the excretion of excess nitrogen in ureotelic animals (primarily amphibians and mammals)22. Despite its relatively high energetic cost, an animal lacking an alternative means of nitrogen excretion cannot afford to lose the ability to synthesize arginine.

To test the role of pleiotropy, we applied the same battery of tests used in the analyses of AA energy costs. To visually inspect the data, we first mapped the number of KEGG pathways and biochemical reactions on individual AAs distributed according to their increasing opportunity costs (Fig. 5a). It can be seen that most of the cheapest AAs, and especially glutamate, take part in a large number of reactions and pathways. However, two conditionally essential AAs (cysteine and tyrosine) and half of the essential AAs also display relatively high involvement in various cellular processes (Fig. 5a).

The comparison using non-parametric tests showed that EAAs have on average lower pleiotropy than NEAAs, however with borderline significance (Supplementary Fig. 7). To test if these differences could be governed by selection as opposed to random processes, we again applied CPS test where the tested factor, instead of energy costs, was pleiotropy measured by the number of KEGG pathways and biochemical reactions in which AAs participate. This test showed that the observed EAA pleiotropies fall within the left tails of the empirical PMF, with p-values fluctuating around the 0.05 threshold (Fig. 5b–e). These results indicate that pleiotropy may have imposed selective pressure to maintain the biosynthetic capabilities of certain amino acids. In turn, this implies that pleiotropy counteracts energy-related selection to some extent, although it appears to be the weaker force in this interaction.

AA dispensability in animals
Nine AAs are canonically considered essential in animals, although there may be exceptions to this rule, often related to further losses in biosynthetic capabilities5,23,24. However, the information on the AA dispensability status in animals and their allies is rather cursory and scattered over methodologically disparate studies5,6,23–28. To get a coherent overview of AAs dispensability in animals and their closest relatives (non-animal holozoans), we explored the completeness score (CS) of AA synthesis pathways on a large holozoan tree comprising 167 species using reciprocal best hit as well as clustering approaches at different significance cutoffs (see Methods). We also note that the completeness score of AA synthesis pathways is only a proxy of AA dispensability because it is possible that some taxa evolved or co-opted alternative pathways. Nevertheless, regardless of the approach and parameters used, we obtained highly consistent results (Fig. 6, Supplementary Figs. 8–13).

Most notably, we recovered an almost universal trend of reduction of pathways involved in the biosynthesis of the 9 canonically essential AAs in animals. This trend is not apparent in the non-animal holozoans where pathway completeness does not follow a clear pattern (Fig. 6, Supplementary Figs 8–13). Since the choanoflagellate group Acanthoecida has in our analysis high completeness of biochemical pathways, and the same is true for Teretosporea and Capsaspora, we conclude that the ability to synthesize all 20 AAs is likely a plesiomorphic feature of holozoans, which agrees with previous work6.

To statistically evaluate whether non-animal holozoans are significantly more prototrophic than animals, we applied a non-parametric test to compare the average AA auxotrophy index (AIavg) calculated for each species29. Briefly, the AIavg quantifies the overall extent of incompleteness in AA biosynthesis pathways in a given species, with values ranging from 0 (fully prototrophic) to 1 (fully auxotrophic) (see “Methods”). Indeed, regardless of the method used to detect the presence of AA biosynthesis enzymes, animals consistently appear significantly more auxotrophic than other holozoans (Fig. 6, Supplementary Figs. 8–13).

In our analysis, we detected a few additional AA biosynthesis pathways with low completeness in animals, beyond the canonical nine. The most notable example is the arginine pathway, which is partially complete in many non-vertebrate taxa (Fig. 6, Supplementary Figs. 8–13). This result aligns well with previous studies that report arginine auxotrophy in some insects and tunicates5,30. This volatility in auxotrophic/prototrophic status within animals is intriguing, given that arginine is the most expensive NEAA; that is, it lies at the boundary between NEAAs and EEAs when ranked by opportunity costs (Fig. 1a). Its dispensability status in animals is most likely dependent on the sensitive interplay between taxon-dependent differences in its environmental availability, the energy burden it poses on the metabolism and its pleiotropic effects5,30.

The analysis of AA pathway completeness allowed us to estimate AA auxotrophies for each metazoan species individually (Supplementary Data 4). Based on the completeness score estimates (Fig. 6), we assigned auxotrophy status to any AA whose biosynthesis pathway was incomplete. This enabled us to compare the opportunity costs between the EAA and NEAA sets and perform the CPS test in each metazoan species independently (Supplementary Data 2 and 4). We found that, across species, EAAs consistently exhibited higher opportunity costs than NEAAs, supporting the hypothesis that AA outsourcing was driven by energy-related selection (Supplementary Data 2 and 4).

Although the significance of these patterns gradually declines at higher levels of auxotrophy, it remains statistically significant in all species, with only two showing p-values slightly above 0.05. However, even in those two cases (Choloepus hoffmanni and Mnemiopsis leidyi), the trend of EAAs being more energetically costly than NEAAs is still evident (Supplementary Data 2 and 4). This is an expected effect, as the canonical (and most prevalent) EAA set (Fig. 1) represents one of the most energetically costly combinations. Adding more auxotrophic AAs to this canonical set naturally lowers the average EAA opportunity cost. However, such extensions of the canonical EAA set are relatively uncommon—only a little over 6% of species in our metazoan dataset exhibit more than 11 auxotrophies (Supplementary Data 2 and 4).

Animal proteomes are expensive
The metabolic simplification through the loss of EAA synthesis capabilities were only possible under the condition that the last common ancestor of animals (animal LCA) was able to secure these costly EAAs in surplus from the environment. If we assume that the unicellular ancestor of animals was a prototroph for AAs6 (Fig. 6), then an important shift in feeding ecology occurred in the animal LCA that allowed this massive metabolic outsourcing. This might be a switch from a predominantly bacteria-eating lifestyle, as seen in present-day choanoflagellates31,32, to more efficient multicellular-style suspension/filter feeding, or possibly to some sort of grazing behavior or macrophagous predation33,34.

Irrespective of which of these feeding adaptations occurred, it should have allowed animals to use EAAs more freely in their proteomes, since the energy for their production was largely outsourced. Previous studies have consistently shown that AA synthesis cost to negatively correlate with the AA frequency in the proteome11,15. Consistent with these findings, we observed the same trend in our data (Supplementary Data 3). However, no study has compared the AA frequencies between the proteomes of prototrophic and auxotrophic organisms. To test this, we compared the usage of EEAs between non-animal holozoans and Metazoa (Fig. 7, Supplementary Figs. 14 and 15). This comparison showed that animals use on average more EAAs in their proteins than non-animal holozoans (Fig. 7a, b; Supplementary Figs. 14 and 15). Similarly, we found that the energetic cost of an average AA in the proteome (OCproteome and DCproteome; see Eqs. 1 and 2) is higher in metazoans than in non-animal holozoans (Fig. 7c, d).

However, when all genes within a genome are considered, these observed trends could depend on the internal evolutionary dynamics of a particular lineage. To account for this, we reduced the dataset to include only the conserved genes shared between animals and non-animal holozoans (see “Methods”). The patterns obtained for these conserved genes are congruent with those obtained using all genes within a genome (Supplementary Fig. 14), suggesting that, in animals, the restriction on the usage of expensive AAs is also relaxed for conserved genes. Together, these patterns indicate that the usage of expensive AAs in animal proteomes generally increases once their production is outsourced. All these results are consistent with our prediction that the reduction of selective constraints on energetically expensive AAs allowed animals to more freely explore protein sequence space35–37.

Methods
AA cost estimates and pleiotropy
We retrieved data on the number of ATP and NAD(P)H used in the biosynthesis of AAs from Kaleta et al.12. Previous studies primarily relied on estimates from Craig and Weber18, which contain some inaccuracies. For instance, ATP values for asparagine and serine were erroneously transcribed from the original source57. Additionally, minor updates were made to the ATP and NAD(P)H values for arginine, cysteine, histidine, methionine, and tryptophan based on the most recent biochemical findings12. We also retrieved the number of ATP and NAD(P)H utilized in the metabolism of AA precursors58. To approximate ATP generation under different respiratory conditions, we converted the reducing equivalents to ATP as follows: (i) “high respiration,” representing fully functional oxidative phosphorylation: 1 NAD(P)H = 2 FADH2 = 2 ATP12; (ii) “low respiration,” representing oxidative phosphorylation without proton pumping at complex I: 1 NAD(P)H = 2 FADH2 = 1 ATP, which corresponds, for instance, to the metabolism of S. cerevisiae and some E. coli strains21,59; (iii) “fermentation,” representing anaerobic conditions without conversion of reducing equivalents to ATP.

We calculated the costs of AA production in two ways: direct cost and opportunity cost. The direct cost was calculated by summing ATP and ATP equivalents of energy-bearing metabolites (NADH and NADPH) used in the production of an AA, starting from the precursor molecules (Supplementary Table 1 and Supplementary Data 1). The opportunity cost was calculated by summing the energy lost in the synthesis of AAs (direct cost) and the energy that would have been produced if a cell catabolized precursors instead of making AAs (Supplementary Table 1 and Supplementary Data 1). The energy gained from the catabolism of precursors was calculated by summing produced ATP and ATP equivalents of energy-bearing metabolites (NADH, NADPH, and FADH2) (Supplementary Table 1 and Supplementary Data 1). The opportunity cost measure, which has been extensively used in previous work9–11,18, quantifies how a decision to synthesize an AA impacts the overall energetic balance of a cell by taking into account an unrealized energy gain from precursors (lost opportunity). In essence, opportunity cost shows how much energy is given up by an organism when producing AAs18, and is analogous to the “opportunity cost” concept in economics60.

Data on pleiotropy were collected from the KEGG database4 and are represented by two metrics: (i) the number of KEGG reactions, and (ii) the number of KEGG pathways in which each amino acid (AA) is involved.

Combinatorial phenotype selection (CPS) test
We define the CPS test as a statistical method used to evaluate whether a quantitative factor (e.g., synthesis cost or pleiotropy) exerts directional selection on a combinatorial phenotypic trait (e.g., the auxotrophic/prototrophic status of amino acids), using neutral evolution as the null hypothesis. The CPS test estimates the probability (p-value) that an observed combinatorial phenotypic state arose by chance with respect to the tested factor, based on an empirically derived PMF obtained through permutations. In other words, the permutations represent all possible alternative phenotypes that could have arisen during the evolution of the trait, allowing us to compare these potential outcomes with the phenotype observed in nature. In this way, we explored the entire combinatorial space of possible phenotypic states.

A similar statistical approach was previously used to assess the positional evolutionary bias of enzymes on the endoplasmic reticulum membrane56. The ability to synthesize AAs is a unique combinatorial phenotype because 20 biogenic AAs make a relatively large set of biochemically similar molecules that all forms of life use as building blocks for proteins. A binary state of prototrophy/auxotrophy can be easily assigned to the AA set, allowing for the straightforward exploration of all possible combinations through permutations.

Although the CPS test could theoretically be applied to other similar data, it was specifically designed for this particular phenotype.

The CPS tests (Figs. 4 and 5; Supplementary Figs. 5 and 6) were performed by calculating the average value of each factor (direct cost, opportunity cost, number of pathways, number of reactions) across all possible permutations for a given number of EAAs. For instance, in the case of nine or eleven EAAs, there are 167,960 possible permutations. Because the number of distinct average values is finite, we grouped them into bins containing identical values. This produced a distribution of averages for each factor, conditional on the EAA group size. We then calculated the proportion of permutations falling into each bin by dividing the number of elements in the bin by the total number of permutations. The resulting distribution forms an empirical PMF for a given factor and EAA group size, which was used to estimate the probability that the observed EAA set could have arisen randomly. These probabilities (p-values) were calculated by summing the proportions of permutations from the observed real-world value to the most extreme value at the nearest tail of the distribution.

The central idea of the CPS test is to estimate the likelihood that a combinatorial phenotypic state (e.g., the EAA set) could have arisen by chance with respect to a given quantitative factor. If the factor had no influence on the composition of the observed group, the group’s average value would be expected to fall near the center of the distribution—yielding a high p-value. Conversely, if the tested factor contributed to shaping the group’s composition, the average value would likely fall near one of the distribution’s tails, resulting in a low p-value. Importantly, a low p-value for one factor does not exclude the possibility that other factors, acting in concert, also exerted selective pressure on the group’s composition. The code used to perform the CPS test is available in a GitHub repository (see “Code availability”).

Analyses of AA frequencies in proteomes
The proteomes of non-animal holozoans (27 species) and metazoans (140 species) were taken from our previous study (Supplementary Data 4)7. Using this dataset, we calculated the frequency of each of the 20 AAs in each proteome. By summing frequencies, we calculated the cumulative frequency of nine AAs, which are generally considered to be essential in Metazoa (Thr, Val, Leu, Lys, Ile, Met, His, Phe, Trp). We repeated this analysis by adding two additional AAs that could be considered conditionally essential (Cys, Tyr) (Fig. 7a, b; Supplementary Fig. 15).

Using AA frequencies, we also calculated the opportunity cost and the direct cost of an average AA in a proteome (OCproteome and DCproteome) (Fig. 7c, d). This value represents a weighted mean of AA biosynthesis energy costs where the frequencies of twenty AAs in a proteome are acting as weights (Supplementary Data 4), using the following equations29,41:

OCproteome = (sum(OCi * Ni)) / (sum(Ni)) = sum(OCi * Fi) (1)
DCproteome = (sum(DCi * Ni)) / (sum(Ni)) = sum(DCi * Fi) (2)

In these equations, OCi represents the opportunity cost of amino acid i, DCi represents the direct cost of amino acid i, Ni denotes the total number of occurrences of this AA in the entire proteome, and Fi represents the frequency of the AA in the proteome.

To control for the presence of lineage-specific genes, we repeated this procedure using only genes conserved between animals and non-animal holozoans (Supplementary Figs. 14 and 15). We first clustered all 167 proteomes using the following MMSeqs2 (version 14-7e284) parameters: -e 0.001 -c 0.8 --max-seqs 400 --cluster-mode 1 --cov-mode 0. By using a cutoff of c = 0.8, we obtained clusters of proteins with highly similar architectures7. We then extracted only clusters that contained both metazoan and non-metazoan holozoan proteins and repeated the frequency and energy cost analyses. The initial dataset contained 3,514,971 proteins, while the dataset with only conserved genes contained 1,460,551 proteins (42%).

To examine correlations between AA costs and AA frequencies (Supplementary Data 3), we calculated average AA frequencies and compared them with AA opportunity costs. We calculated correlations between AA costs and AA frequencies for each species individually (Supplementary Data 3) using the Pearson correlation coefficient.

AA biosynthesis pathway completeness
To construct the heatmaps depicting the completeness of AA biosynthetic pathways (Fig. 6, Supplementary Figs. 8–13), we first assembled a list of all enzymes involved in these pathways from the MetaCyc61 and KEGG4 databases. For AAs that can be synthesized via multiple alternative pathways, we treated each pathway separately, even when they shared some enzymes. Using this collection of enzyme codes associated with AA biosynthesis, we retrieved protein sequences from the KEGG database for 1200 available eukaryotic organisms resulting in a total of 645,363 protein sequences (Supplementary Data 4).

We used this collection of AA biosynthesis enzyme sequences as a query to perform a reciprocal best hit search, as implemented in MMSeqs262, against our database of 167 holozoan proteomes7. The search was conducted using the “mmseqs easy-rbh” option with default parameters. To test the robustness of our results, we considered the reciprocal best hit results at three e-value cutoffs: 10−10, 10−30, and 10−60 (Supplementary Figs. 8–10). In line with earlier study, which has shown that functional orthologs always have e-values lower than 10−605, we presented in the main text the results at this e-value cutoff (Fig. 6).

To further verify results obtained by reciprocal best hit search, we performed MMSeqs2 clustering for each species separately. For every species, we created an MMSeqs2 database consisting of a proteome of that single species and our collection of AA biosynthesis enzyme sequences. In the next step we preformed MMSeqs2 clustering using the following parameters: cluster -c 0.8, --cov-mode 0, --cluster-reassign (Supplementary Figs. 11–13). By using the -c 0.8 parameter, we generated clusters of proteins with highly similar architectures with at least 80% shared alignment length7,29,41. Similar to reciprocal best hit search, to test the robustness of our findings, we repeated clustering at three e-value cutoffs: 10−10, 10−30, and 10−60 (Supplementary Figs. 11–13).

Finally, for each AA and species, we divided the number of detected enzymes in an AA synthesis pathway i (DEi) by the expected total number of enzymes present in that pathway (Ei), resulting in the completeness score of the AA synthesis pathway i (CSi) ranging from 0 to 1. If alternative AA biosynthetic pathways were present in a species, we selected the most complete one. The CSi was calculated using the following equation:

CSi = DEi / Ei (3)

For all considered holozoan species (27 non-animal holozoans, 140 metazoans), we computed the average auxotrophy index (AIavg) as described in Kasalo et al.29,41. For each species, we first calculated the AA auxotrophy index of each amino acid i (AIi), where i denotes one of 20 AAs, by subtracting the completeness score (CSi) from 1. We then averaged AIi values across all amino acids in a given species to obtain AIavg, a measure of the species’ overall auxotrophy for AA biosynthesis pathways. In theory, AIavg values range from 0 (fully prototrophic) to 1 (fully auxotrophic) and are calculated using the following equation:

AIavg = (sum(AIi)) / 20 = (sum(1 - CSi)) / 20 (4)

We calculated AIavg values for all six versions of our AA biosynthesis completeness search and used the Mann-Whitney U test to determine if there is a statistically significant difference in AIavg values between metazoans and non-animal holozoans (Fig. 6, Supplementary Figs. 8–13). The code used to detect AA biosynthesis pathway completeness is available in a GitHub repository (see “Code availability”).

AA auxotrophy in individual metazoan species
Building on the pathway completeness analysis, we next examined AA auxotrophy in each metazoan species individually. For every species, we classified an AA as auxotrophic (i.e., part of the EAA set) if its completeness score was below 1, as determined by the reciprocal best hit protocol with an e-value cutoff of 10−60 (Fig. 6 and Supplementary Fig. 8). Using these species-specific EAA sets, we compared the opportunity costs of EAAs and NEAAs with the Mann-Whitney U test. We also estimated the probability that energy-related selection was implicated in amino acid outsourcing using the CPS test (Supplementary Data 2 and 4).

Statistical analyses and tools
The significance of differences in the energy costs between EAAs and NEAAs, differences in the frequency of EAA usage, differences in the energy cost of an average AA, and differences in the number of reactions and pathways was tested by the two-tailed Mann-Whitney U with continuity correction. For each plot, we report the Mann-Whitney statistic (W), the p-value, the effect size (r), and the 95% non-parametric confidence intervals. The Mann-Whitney U tests were conducted in the R environment using the package rcompanion (version 2.4.36).

To calculate Pearson correlations, we used the cor.test() function in the R stats package (version 3.6.2). Permutations were calculated using the RcppAlgos package (version 2.9.3). Phylogeny mapping was performed using the ggtree package (version 3.10.1)63.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Kasalo et al. present here a quite theoretical study on the relation between the energy costs of producing AA to the loss of the pathways needed to synthesise essential AA in Metazoa. The study is mainly theoretical, presenting several interesting hypotheses based on statistics taken from different papers and a broad scale protein presence / absence analysis. I found the paper interesting but I have several points I’d like to address:\nMy main issue with the paper is the use of the permutation test to justify that AA pathways were lost because of high energy costs. In Figure 1A it is already shown that the 9 EAA are the ones with a higher opportunity cost, therefore the permutation analysis in the particular case just shows that the many other combinations of AA have a lower energy cost. No need to perform the permutation analysis. When the same is applied to direct costs, then the conclusion is different, there’s a small percentage of combinations of AA that have a higher direct cost than the one formed by the EAA, therefore other combinations of AA could have become essential according to the direct cost measure.\nIt may have been interesting to measure such permutations with different numbers of AA and then average the energy costs by number of AA considered to keep it comparable. Would a combination of 10 AA or 12 AA have a higher direct energy cost? Would that match to the more sparse AA losses observed in some Metazoa species?\nAlso, is the permutation test really a proxy for detecting selection? The paper shows two different reasons as to why these specific AA may have been outsourced (energy cost and pleiotropy), but that certainly was not enough for non-Metazoan species to lose AA pathways given that many other species across the tree of life have kept theirs. Therefore, despite the high cost of producing certain AA, there needs to be a much more complex evolutionary process that would create the mechanisms present in the ancient Metazoan ancestor for such losses to not be deleterious and for them to offer fitness advantages. Mechanisms that have little to do with energy cost given that other species are still able to synthesise the most expensive AA despite their cost.\nAnother point of interest is that different AA have a different usage depending on the species. A very energy expensive EAA may not have a big impact on the average cost of all AA when its use in a given proteome is sparse. What would the energy cost graphs / permutation tests look like if the number of times an AA appears in the proteome was considered?\nAre there estimates of the different usages of EAA per different animal species? Is there a large variation? How does that compare to non-EAA?\nWhile opportunity cost does indeed follow the trend of being higher for EAA, the direct cost does not follow the same trend. How do the authors explain this observation?\nFigure 1A should be split in two plots, one for opportunity costs and a second one for Direct cost with the AA sorted accordingly, else the representation coupled with the colouring of the bars can mislead the readers. Figure 2 could then be plotted with additional barplots\nFigure 1 B, C, D, E → There should be dots on the exact positions of the values and the letter of the AA above / below it. Letters of NEAA can not be seen properly given the similarity in colours between the color of the boxplot and the letters.\nLine 313: “We also found that the energy cost of an average AA is higher in metazoa”. I’m not sure I follow this, do you refer the cost of an average AA based on its usage in the complete proteome of a given species?\nFigure 7: The legend should be re-writen. It is not clear on first sight what the numbered i), ii) iii) and iv) refer to in the graph", "reviewer_2": "I have read with interest the manuscript by Kasalo and colleagues. The manuscript focuses on animal-specific essential amino acids (AA), which due to gene losses early in their evolution made them reliant on obtaining these AA from their diet. The study argues that this was driven by the high energy cost of producing these amino acids under the oxygen-rich, high-respiration conditions typical of animal life; this outsourcing allowed greater protein evolution and points to energy management as the driver of this change.\nI had reviewed a previous version of this manuscript, and I believe it has gone through significant changes since the last time I read it. Many issues raised about how to calculate the energy costs, the incorporation of respiratory modes, and the use of focal species have been dealt with. I’d like to congratulate the authors for their efforts. I still have some concerns I’d like to share with the authors.\nThe first one is that the manuscript states a few times that the analyses support that selective forces drove the AA outsourcing. However, my understanding of their analyses is that by using permutation tests, the correlation between the AA that are essential and their energy costs is statistically significant and it is not random. However, I can’t identify any clear indication that this is caused by natural selection, the lack of randomness could be caused by something else; for example, a very fantastical option is that these AA were floating around in excess in the environment and animals lost the capacity to synthesize them, not because they were expensive but because they were overabundant. While I agree that this is the most likely explanation and it should be mentioned as a possible cause in the Discussion section, I don’t think the analyses (permutation tests) and results reveal anything about the driving force behind this AA outsourcing, just that it is not random. But I may be wrong, in which case I would appreciate to be pointed towards the relevant part of the manuscript.\nThe second issue is the homologs search which is the basis of Figure 5. I understand the authors used a one-way DIAMOND similarity search, and they used a 40% similarity cutoff which is not that astringent. If this is the case, this is below the standards of the field. Two-way or reciprocal search is the bare minimum, and for the last 10-15 years analyses complement similarity search with HMM profile search (HMMER) and/or clustering approaches (OrthoFinder).\nMinor points:\n1) Line 24, this sentence has a teleological whiff, I’d suggest rephrasing it.\n2) Line 26 and later, could the authors elaborate why they think this is result of “stabilizing selection”? Wouldn’t expect the “cheapest” AA to be also outsourced under stabilizing selection? Do they maybe mean directional selection towards keeping cheaper AA/remove most expensive ones?\n3) Line 71, selection is mentioned here again, but I don’t believe that selection has been proven here.\n4) Line 83, “new” I guess it refers to previous versions of this manuscript? Or does it mean “recent”?\n5) Line 95, “decision” is another finalistic statement, please rephrase.\n6) Line 225, maybe an example of AA pleiotropy would help with this concept.\n7) Line 314, please clarify what the manuscript refers to with “all genes”.\n8) Line 432, I believe the criticism on the terms “essential” and “non-essential” is unjustified, it is clear that these terms refer to their essentiality in the diet, not in the biology of animals."}, "author_response": "Reviewer #1 (Remarks to the Author):\nComments 1: Kasalo et al. present here a quite theoretical study on the relation between the\nenergy costs of producing AA to the loss of the pathways needed to synthesise essential AA in\nMetazoa. The study is mainly theoretical, presenting several interesting hypotheses based on\nstatistics taken from different papers and a broad scale protein presence / absence analysis. I\nfound the paper interesting but I have several points I'd like to address:\nResponse 1: We thank the reviewer for finding our study interesting and for very insightful\ncomments that helped us significantly improve the manuscript.\nComments 2: My main issue with the paper is the use of the permutation test to justify that\nAA pathways were lost because of high energy costs. In Figure 1A it is already shown that the\nparticular case just shows that the many other combinations of AA have a lower energy cost.\nNo need to perform the permutation analysis. When the same is applied to direct costs, then\nthe conclusion is different, there's a small percentage of combinations of AA that have a higher\ndirect cost than the one formed by the EAA, therefore other combinations of AA could have\nbecome essential according to the direct cost measure.\nResponse 2: We thank the reviewer for this comment. In response to previous reviewers'\nrequests, we developed a statistical approach to test whether selection on energy costs, rather\nthan random processes, led to the outsourcing of the observed EAAs in animals. Initially\nreferred to as a \"permutation analysis,\" we now use the term combinatorial phenotype selection\n(CPS) test and provide its explicit definition for clarity (Lines 182-187, 573-608).\nIn short, we define the CPS test as a statistical method for evaluating whether a quantitative\nfactor (e.g., synthesis costs) exerts directional selection on a complex phenotypic trait suitable\nfor combinatorial analysis (e.g., the auxotrophic/prototrophic status of amino acids), using\nneutral evolution as the null hypothesis. The test estimates the probability (p-value) that the\nobserved combinatorial state of the phenotypic trait arose by chance, based on an empirically\nderived probability mass function (PMF) obtained through permutations.\nWhile the descriptive statistics (Fig. 1A) show that the 11 EAAs represent an energetically\nextreme combination, such descriptive statistics alone cannot assess the likelihood of this\noutcome occurring by chance. The CPS test addresses this by providing p-values that quantify\nthe probability under neutrality. This is especially important when considering direct costs (Fig.\n1B), where some possible combinations may appear more extreme, but the actually observed\nEAA set still yields low p-values (Fig. 4C, D)—supporting the role of energy-related selection.\nWe have revised the Methods and the Results to more clearly explain the purpose of the CPS\ntest and the interpretation of its results. We have also provided the code used to run the CPS\ntest.\nComments 3: It may have been interesting to measure such permutations with different\nnumbers of AA and then average the energy costs by number of AA considered to keep it\ncomparable. Would a combination of 10 AA or 12 AA have a higher direct energy cost? Would\nthat match to the more sparse AA losses observed in some Metazoa species?\ncompleteness (Fig. 6), we have now computationally estimated AA auxotrophies, compared\nenergy costs, and performed CPS tests for all 140 metazoan species individually\n(Supplementary Data 2, Supplementary Data 4). Based on the completeness score estimates\n(Fig. 6), we assigned auxotrophy status to any AA whose biosynthesis pathway was incomplete.\nRegardless of the recovered number of auxotrophic AAs per species (ranging from 9 to 15), we\nfound the EAA set to be significantly more expensive than the NEAA set and detected a\nsignificant impact of energy-related selection on its composition in all species—with two\nexceptions, where we observed borderline significance. This indicates that the imprint of\nenergy-related selection is consistent across metazoan species. These analyses have now been\nincluded in the Results and Methods (Lines 299-315, 648-691).\nComments 4: Also, is the permutation test really a proxy for detecting selection? The paper\nshows two different reasons as to why these specific AA may have been outsourced (energy\ncost and pleiotropy), but that certainly was not enough for non-Metazoan species to lose AA\npathways given that many other species across the tree of life have kept theirs. Therefore,\ndespite the high cost of producing certain AA, there needs to be a much more complex\nevolutionary process that would create the mechanisms present in the ancient Metazoan\nancestor for such losses to not be deleterious and for them to offer fitness advantages.\nMechanisms that have little to do with energy cost given that other species are still able to\nsynthesise the most expensive AA despite their cost.\nResponse 4: As explained above, the \"permutation test\"—now referred to as the CPS test—\nestimates the probability that the observed AA grouping arose by chance with respect to a given\nquantitative factor (e.g., energetic cost or pleiotropy). A low p-value suggests directional\nselection, but does not imply that the tested factor is the sole driver of the observed AA\nphenotype. Other factors may also contribute, potentially acting in concert. In fact, we\ndemonstrate through a similar set of tests that energy costs and pleiotropy represent\ncounteracting selective forces. We have now explicitly acknowledged the possibility of\nadditional contributing factors in the revised manuscript (Lines 477-488).\nWe agree with the reviewer that a unique constellation of factors in early metazoans likely\nenabled the outsourcing of energetically expensive EAAs while minimizing deleterious effects\nand conferring fitness advantages. Our model proposes that environmental amino acid\navailability and respiration mode were key conditions that set the stage for this evolutionary\nshift. We have expanded the Discussion to more explicitly recognize the complex interplay\nbetween various factors that could have influenced the evolution of Metazoa, as well as the\nlikely contribution of stochastic events in this process (Lines 477-488).\nHowever, when selective pressures are comparable, similar EAA patterns are expected to\nemerge independently. Indeed, we recently demonstrated that aerobic predatory bacteria\n(Bdellovibrionota) evolved an EAA set strikingly similar to that of animals\n(https://doi.org/10.3390/ijms26073024). This cross-domain convergence strongly supports the\nidea that amino acid synthesis costs represent an important selective pressure influencing EAA\ncomposition. Our work on predatory bacteria further suggests that high respiration rates\ntogether with obligate predation—ecological features common to both Bdellovibrionota and\nanimals—create favorable conditions for outsourcing costly EAAs (Lines 443-447).\nlines of evidence point to energy-related selection as an important factor in the evolution of AA\nusage in animals. However, we also acknowledge that a specific constellation of evolutionary\npreconditions must be met before energy-related selection on AA auxotrophies becomes\noperational, as evidenced by the fact that many organisms remain prototrophic (Lines 477-\n488).\nComments 5: Another point of interest is that different AA have a different usage depending\non the species. A very energy expensive EAA may not have a big impact on the average cost\nof all AA when its use in a given proteome is sparse. What would the energy cost graphs /\npermutation tests look like if the number of times an AA appears in the proteome was\nconsidered?\nResponse 5: As the reviewer noted, AA usage inevitably fluctuates between the proteomes of\ndifferent species. However, despite these variations, previous studies have consistently shown\na species-independent trend: AA synthesis cost tends to negatively correlate with AA frequency\nin the proteome. This has been interpreted as a signature of pervasive energy-related selection\nthat limits the use of energetically expensive AAs. Consistent with these findings, we have now\nconfirmed this trend in our data (see Supplementary Data 3).\nCombining AA frequency and energy cost into a single metric would be difficult to interpret,\nas it conflates cause and effect. For this reason, such a measure is not suitable for box plot\ncomparisons or for statistical testing using the CPS test. However, we have already analyzed\nAA frequencies in Figure 7A, B and Supplementary Figures 14A, B and 15, where we showed\nthat the frequency of expensive EAAs is higher in metazoans.\nThis suggests that the selective pressure against the usage of expensive AAs is weakened once\ntheir production is outsourced. We have now expanded on these points in the Results (Lines\n360-364).\nComments 6: Are there estimates of the different usages of EAA per different animal species?\nIs there a large variation? How does that compare to non-EAA?\nResponse 6: Estimates of EAA and NEAA usage (frequencies) for all species are now provided\nin Supplementary Data 4 (sheet Proteome energetics) and as correlations in Supplementary\nData 3. Statistical comparisons of EAA frequencies between metazoans and other holozoans\nare shown in Fig. 7 and Supplementary Fig. 14. As the reviewer suggested, we have added a\nnew Supplementary Fig. 15 that directly compares the frequencies of EAAs and NEAAs in\nmetazoans and other holozoans.\nAs expected, NEAAs generally show higher average frequencies than EAAs across proteomes\n(Supplementary Fig. 15). However, this difference is reduced in metazoans, likely due to\nrelaxed negative selection on the use of expensive AAs made possible by EAA outsourcing\n(Lines 346–358).\nComments 7: While opportunity cost does indeed follow the trend of being higher for EAA,\nthe direct cost does not follow the same trend. How do the authors explain this observation?\nthe same trend, with EAAs consistently showing significantly higher values than NEAAs.\nHowever, this trend is more pronounced for opportunity costs (Fig. 3). This difference likely\narises because opportunity costs more accurately reflect the biological phenotype under\nselection, accounting not only for the direct energy required for synthesis but also for the\nenergetic trade-offs involved in diverting metabolic precursors toward AA production.\nWhile direct costs may be useful when cells have a continuous surplus of energy—making\nalternative metabolic options less relevant—this scenario is probably unrealistic in natural\necosystems. Therefore, we suggest that opportunity costs generally provide a better measure\nfor testing ecologically relevant scenarios than direct costs. We have now expanded along these\nlines in the Discussion section (Lines 412-421).\nComments 8: Figure 1A should be split in two plots, one for opportunity costs and a second\none for Direct cost with the AA sorted accordingly, else the representation coupled with the\ncolouring of the bars can mislead the readers. Figure 2 could then be plotted with additional\nbarplots\nResponse 8: We have reorganized the figures as suggested.\nComments 9: Figure 1 B, C, D, E → There should be dots on the exact positions of the values\nand the letter of the AA above / below it. Letters of NEAA can not be seen properly given the\nsimilarity in colours between the color of the boxplot and the letters.\nResponse 9: We have improved the labeling of the figures as suggested.\nComments 10: Line 313: \"We also found that the energy cost of an average AA is higher in\nmetazoa\". I'm not sure I follow this, do you refer the cost of an average AA based on its usage\nin the complete proteome of a given species?\nResponse 10: Thank you for noting this. We have revised the sentence to clarify that it refers\nto the energetic cost of an average AA in the proteome (Lines 360–364). We have also added\nthe corresponding Equations 1 and 2 (OCₚᵣₒₜₑₒₘₑ and DCₚᵣₒₜₑₒₘₑ) in the Methods section (Lines\n618–638).\nComments 11: Figure 7: The legend should be re-writen. It is not clear on first sight what the\nnumbered i), ii) iii) and iv) refer to in the graph\nResponse 11: We have removed this confusing labeling from the caption.\nReviewer #2 (Remarks to the Author):\nComments 1: I have read with interest the manuscript by Kasalo and colleagues. The\nmanuscript focuses on animal-specific essential amino acids (AA), which due to gene losses\nearly in their evolution made them reliant on obtaining these AA from their diet. The study\nargues that this was driven by the high energy cost of producing these amino acids under the\noxygen-rich, high-respiration conditions typical of animal life; this outsourcing allowed greater\nprotein evolution and points to energy management as the driver of this change.\nsignificant changes since the last time I read it. Many issues raised about how to calculate the\nenergy costs, the incorporation of respiratory modes, and the use of focal species have been\ndealt with. I'd like to congratulate the authors for their efforts. I still have some concerns I'd\nlike to share with the authors.\nResponse 1: We thank the reviewer for constructive comments on both this and the previous\nversion of the manuscript, which have helped us significantly improve the quality and clarity\nof our work.\nComments 2: The first one is that the manuscript states a few times that the analyses support\nthat selective forces drove the AA outsourcing. However, my understanding of their analyses\nis that by using permutation tests, the correlation between the AA that are essential and their\nenergy costs is statistically significant and it is not random. However, I can't identify any clear\nindication that this is caused by natural selection, the lack of randomness could be caused by\nsomething else; for example, a very fantastical option is that these AA were floating around in\nexcess in the environment and animals lost the capacity to synthesize them, not because they\nwere expensive but because they were overabundant. While I agree that this is the most likely\nexplanation and it should be mentioned as a possible cause in the Discussion section, I don't\nthink the analyses (permutation tests) and results reveal anything about the driving force behind\nthis AA outsourcing, just that it is not random. But I may be wrong, in which case I would\nappreciate to be pointed towards the relevant part of the manuscript.\nResponse 2: Thank you for this insightful question, which led us to refine both the\ninterpretation of our tests and the terminology used. Instead of “permutation test,” we now use\nthe term combinatorial phenotype selection (CPS) test and provide an explicit definition for\nclarity (Lines 182–187, 573–608). Please also see our response to Reviewer #1, Comment 4.\nBriefly, the CPS test assesses whether a quantitative factor (e.g., biosynthesis cost) exerts\ndirectional selection on a combinatorial phenotypic trait (e.g., auxotrophic/prototrophic status\nof amino acids), under neutral evolution as the null hypothesis. The permutation procedure\nestimates an empirical probability mass function (PMF) for each variable (e.g., opportunity\ncost, pleiotropy), which is then used to calculate the probability (p-value) that the observed\ntrait combination (e.g., EAAs) arose by chance. We have also included the code used to run the\nCPS test.\nThe PMF is computed independently for each factor and is contingent on trait group size. Thus,\nEAAs may appear random with respect to one factor but non-random with respect to another.\nIn our case, both tested factors—opportunity costs and pleiotropy—act as deterministic\n(selective) forces.\nOther factors could be tested if specific values can be assigned to individual AAs. The reviewer\nsuggested environmental AA abundance, but this would require accurate paleoenvironmental\nconcentration data and the assumption of long-term stability, which are not available. If one\nassumes ancient AA availability mirrored present-day genomic frequencies, cheaper AAs\nwould be expected to be outsourced and expensive AAs retained—yet we observe the opposite:\nexpensive AAs are outsourced.\nenergy-related selection was likely the primary driver of AA auxotrophy patterns in animals.\nWe now include this explanation and note that other, untested factors may also contribute\n(Lines 412–433, 477–488).\nComments 3: The second issue is the homologs search which is the basis of Figure 5. I\nunderstand the authors used a one-way DIAMOND similarity search, and they used a 40%\nsimilarity cutoff which is not that astringent. If this is the case, this is below the standards of\nthe field. Two-way or reciprocal search is the bare minimum, and for the last 10-15 years\nanalyses complement similarity search with HMM profile search (HMMER) and/or clustering\napproaches (OrthoFinder).\nResponse 3: We agree that more effective homology search methods are available. Since\nsubmission, we have reworked our analysis using an enriched reference database of AA\nsynthesis enzymes, MMSeqs2 reciprocal best hit search, and MMSeqs2 clustering. We also\ntested robustness by repeating searches across multiple e-value thresholds (Fig. 6;\nSupplementary Figs. 8–13; Lines 270–288, 648–684).\nAll methods yielded consistent results: animals have higher auxotrophy levels than non-animal\nholozoans. This was confirmed by statistical comparison of the average AA auxotrophy index\n(AI ), which summarizes overall auxotrophy from completeness scores (Kasalo et al. 2025b;\navg\nLines 281-288, 669-682).\nMinor points:\nComments 4: 1) Line 24, this sentence has a teleological whiff, I'd suggest rephrasing it.\nResponse 4: The sentence has been rewritten (Lines 23-24).\nComments 5: 2) Line 26 and later, could the authors elaborate why they think this is result of\n\"stabilizing selection\"? Wouldn't expect the \"cheapest\" AA to be also outsourced under\nstabilizing selection? Do they maybe mean directional selection towards keeping cheaper\nAA/remove most expensive ones?\nResponse 5: Thank you; we see the reviewer’s point. Indeed, there appears to be no specific\nterm for a situation in which two factors impose directional selection on the same trait in\nopposite directions. In our case, pleiotropy pushes the AA synthesis phenotype toward full\nbiosynthetic capacity, whereas energy-related selection drives it toward full auxotrophy. The\nobserved auxotrophy levels fall somewhere between these extremes.\nWe therefore agree that “stabilizing selection” is probably not the best term here, as it typically\nrefers to a single selective factor. Accordingly, we have rephrased the text to use the terms\ndirectional selection and selective trade-off for greater accuracy (Lines 23–28).\nComments 6: 3) Line 71, selection is mentioned here again, but I don't believe that selection\nhas been proven here.\n64). In the entire text, we have now emphasized the probabilistic nature of our approach to\nassessing possible selective pressures.\nComments 7: 4) Line 83, \"new\" I guess it refers to previous versions of this manuscript? Or\ndoes it mean \"recent\"?\nResponse 7: We have corrected the sentence using the term \"recent\" (Line 75).\nComments 8: 5) Line 95, \"decision\" is another finalistic statement, please rephrase.\nResponse 8: We have rephrased the sentence using the term \"mode\" (Line 81).\nComments 9: 6) Line 225, maybe an example of AA pleiotropy would help with this concept.\nResponse 9: We have now provided an example involving arginine, whose biosynthesis is\nlinked to nitrogen excretion and is maintained despite arginine’s relatively high cost in cases\nwhere the organism lacks alternative pathways for eliminating excess nitrogen (Lines 226–\n231).\nComments 10: 7) Line 314, please clarify what the manuscript refers to with \"all genes\".\nResponse 10: We have clarified that we are referring to analyses conducted on all genes within\na genome (Lines 353, 357).\nComments 11: 8) Line 432, I believe the criticism on the terms \"essential\" and \"non-essential\"\nis unjustified, it is clear that these terms refer to their essentiality in the diet, not in the biology\nof animals.\nResponse 11: We thank the reviewer for this comment. The usefulness of the terms “essential”\nand “non-essential” AAs is also debated within dietary science, as some “non-essential” amino\nacids—although synthesized de novo within an organism—still need to be supplemented\nthrough the diet to ensure optimal health and fitness (doi.org/10.1177/1535370215587913;\ndoi.org/10.1007/s00726-012-1444-2). It seems that these terms are already vague in the field\nof dietary science, so we improved this part of the text by citing these additional references\n(Lines 513-515).\n7"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adressed my concerns", "reviewer_2": "I'd like to congratulate the authors for their efforts to improve the manuscript; the significant changes are clearly noticeable. I appreciate especially the use of new approaches to define gene homology as well as all the text edits and\nI also appreciate the explanation the Combinatorial Phenotype Selection (CPS) test. However, I must confess that, while I\ndon't yet claim to fully understand the nuances of the new approach, my core reservation remains: while the test clearly\ndemonstrates that the observed correlation between \"AA essentiality\" and \"energy cost\" is statistically significant, I am still\nnot entirely convinced of its capacity to definitively point to selection as the main force producing this, as it still presents\nstrongly as a randomization-based approach."}, "author_response": "RESPONSE TO REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nComments 1: The authors have adressed my concerns.\nResponse 1: We thank the reviewer for the comments and are glad that our revisions have\naddressed the concerns.\nReviewer #2 (Remarks to the Author):\nComments 1: I'd like to congratulate the authors for their efforts to improve the manuscript;\nthe significant changes are clearly noticeable. I appreciate especially the use of new approaches\nto define gene homology as well as all the text edits and I also appreciate the explanation the\nCombinatorial Phenotype Selection (CPS) test. However, I must confess that, while I don't yet\nclaim to fully understand the nuances of the new approach, my core reservation remains: while\nthe test clearly demonstrates that the observed correlation between \"AA essentiality\" and\n\"energy cost\" is statistically significant, I am still not entirely convinced of its capacity to\ndefinitively point to selection as the main force producing this, as it still presents strongly as a\nrandomization-based approach.\nResponse 1: We thank the reviewer for the positive comments and for recognizing the\nimprovements to the manuscript. We also appreciate the reviewer’s careful consideration of the\nlimitations of our approach.\nIn response to this comment and the Editor’s suggestion, we have moderated our language\nthroughout the manuscript to clarify that, while the CPS test provides strong statistical support\n1\nto the hypothesis that the correlation between AA essentiality and energy cost is driven by\nenergy-related selection, it does not definitively demonstrate that this selection is the sole\ndriving force.\nWe now explicitly acknowledge that alternative factors and neutral processes may also have\ncontributed (lines 489–491) and further clarify the role of permutations in the CPS test, which\nallow us to explore the entire combinatorial space of possible phenotypic outcomes (lines 583–\n586).\n2"}]

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