Life continuously creates genetic innovation, e.g., genes coding for proteins with novel functions. Most new genes emerge by modification of existing genes rather than de novo (Long et al., 2013). The first who connected the generation of new functions with the accumulation of mutations and duplication of pre-existing genes was Muller, 1936. Later on, different theories arose discussing the point at which divergence takes place (Ohno, 1970; Jensen, 1976; Lynch and Force, 2000; Bergthorsson et al., 2007). Modern evolutionary models assume an ancestral gene to acquire a novel function while retaining its original function (Bergthorsson et al., 2007; Hughes, 1994; Aharoni et al., 2005; Des Marais and Rausher, 2008; Näsvall et al., 2012). If a novel function is beneficial for the organism, the gene variant is subjected to positive selection. Consecutive duplication of the gene allows the establishment of the new function in the population. This model of divergence prior to gene duplication allows for the accumulation and selection of mutations with adaptive potential while the original gene function is maintained, thus creating a ‘generalist’ gene with multiple functions (Soskine and Tawfik, 2010).

When considering the evolution of novel gene functions, the substrate range of ‘classical’, soluble enzymes is arguably the most archetypal field of study, important for the rapid evolution of antibiotic resistance, degradation of artificial xenobiotics, or use of new nutrients (Aharoni et al., 2005; Copley et al., 2023). The substrate spectrum of an enzyme is often a key determinant of the fitness of an organism. Generally, enzymes are thought of as being highly specific for a certain substrate or a group of chemically similar substrates. Nonetheless, many enzymes studied in isolation show side activities toward other substrates (Copley et al., 2023; Khersonsky and Tawfik, 2010; Andorfer et al., 2017). These side activities are often obscured in the complex setting of a biological cell, and as such contribute little to the fitness of the organism. However, these side activities are thought of as a major source for genetic innovation: proteins can evolve the ability to use new substrates by improving an already existing, but low, side activity (Janssen et al., 2005; Jørgensen et al., 2017; Caligiore et al., 2022; Zheng et al., 2020).

Membrane transporters form a special class of enzymes, as they generally do not break or make covalent bonds. Hence, they experience fewer constraints of the chemistry of their substrates. In principle, any small molecule can be transported through a lipid membrane. Thus, it is not surprising that life has evolved transport systems for virtually every metabolite, ion, and xenobiotic (Saier et al., 2014; Saier et al., 2021). In many branches of the tree of life, transporter genes underwent processes of repeated duplication and divergence, leading to extended gene families; many genomes have an array of transporter paralogs (Copley et al., 2023; Saier et al., 2021; Jack et al., 2000; Perland and Fredriksson, 2017). Typically, each paralog displays a distinct substrate specificity profile, and thus a distinctly evolved function. Some notable examples of gene family expansion are found within the APC (amino acid-polyamine-organocation) superfamily of transporters, which include: the neurotransmitter transporter family in animals (20 paralogs in human, TC 2.A.22), the AAAP family in plants (58 paralogs in rice, TC 2.A.18), the AAT family in Proteobacteria (11 paralogs in Escherichia coli, TC 2.A.3.1), and the yeast amino acid transporter (YAT) family in fungi (18 paralogs in budding yeast, TC 2.A.3.10). The APC superfamily is the second-largest group of membrane transport proteins (Vastermark et al., 2014).

Most APC superfamily members are transporters for amino acids, their analogs, or related amines. Some transporters are generalists, accepting a wide range of substrates; others are considered specialists, recognizing one or a few related substrates (Jack et al., 2000; Bianchi et al., 2019). How do such patterns arise? Does the evolution of specialists move through a generalist stage? Or do new specialists evolve through switching from one specific substrate to another? For classical enzymes, many lines of evidence point to the former (Aharoni et al., 2005; Copley et al., 2023; Khersonsky and Tawfik, 2010; Risso et al., 2013; Mascotti et al., 2018). However, the evolution of new traits in the special case of transporters is understudied, partly because of a lack of appropriate experimental evolution methods (Bali et al., 2018).

Here, we propose that APC-type transporters can evolve novel functions through generalist intermediates, similar to classical enzymes. We showcase the presence of so far unknown substrates of five out of seven studied YAT and develop a growth-based selection platform. Utilizing in vivo experimental evolution, under purifying selection for the original function and simultaneous selection for a new function, we evolve the substrate spectrum of two of these transporters. We show that the acquired mutations result either in improving an existing weak activity or in establishing activity for a new substrate. At the same time, each mutation has a distinct impact on the fitness of the organism for each of the transporter’s original substrates.

The budding yeast, Saccharomyces cerevisiae, typically encodes 18 members of the YAT family, each with a different substrate profile (Bianchi et al., 2019). This array of transporters enables yeast to use 20 different l-amino acids as the sole nitrogen source in minimal media. These include 17 of the 20 proteinogenic amino acids (the exceptions are Cys, His, and Lys) and 3 non-proteinogenic ones (γ-amino butyric acid [GABA], L-ornithine [Orn], and L-citrulline [Cit]). Here, we use an engineered yeast strain (Δ10AA) that lacks 10 membrane transporters for amino acids (Besnard et al., 2016). This strain is thus severely deficient in the uptake of amino acids, preventing it from using most of them as the nitrogen source. When an amino acid transporter is genetically reintroduced, its substrate profile directly determines which amino acids can support growth. In the following text, the gene names are used in place of the protein names for consistency (e.g. AGP1 instead of Agp1). The Δ10AA yeast strain expressing different transporter genes was used in all growth and transport assays.

Substrate spectrum of seven YAT

We investigated the substrate profile of seven YAT, by measuring the growth rates of Δ10AA strains overexpressing each transporter gene from pADHXC3GH, in the presence of 2 mM of each amino acid that served as nitrogen source. We determined growth rates in order to study each transporter’s substrate range separately. For five of the seven transporters tested, we found substrates that were not previously reported in literature. These substrates were identified by significantly increased growth rates of Δ10AA expressing the respective transporter in comparison to the strain carrying the empty plasmid (vector control) (Table 1). Because of high background growth of the vector control on Arg and Orn (probably due to the activity of the VBA5 transporter Shimazu et al., 2012), these two amino acids were excluded from further analysis. Additionally, some growth of the empty vector control was observed on Gly, Trp, Leu, Met, and Gln, which complicates the interpretation of the data. The observed growth could indicate an unknown substrate specificity of an endogenous transporter. We observed that some of the newly characterized substrates support growth rates in similar ranges as the transporter’s substrates already reported in literature, while other substrates support very slow growth rates, which we call weak or promiscuous activities (Figure 1 and Figure 1—figure supplement 1).

Table 1

Transporter gene	Uniprot accession number (UniProt Consortium, 2023)	Substrates described in literature (reviewed in Bianchi et al., 2019)	Substrates found in the present study
AGP1	P25376	Ala, Asn, Asp, Cys*, Gln, Glu, Gly, His*, Ile, Leu, Met, Phe, Pro, Ser, Thr, Trp, Tyr, Val, GABA	Ala, Asn, Asp, GABA, Gln, Glu, Ile, Leu, Met, Phe, Pro, Ser, Thr, Trp, Tyr, Val, (Cit)
BAP2	P38084	Ala, Cys*, Ile, Leu, Met, Phe, Tyr, Val	Ala, Asn, Asp, Cit, Gln, Glu, Gly, Ile, Leu, Met, Phe, Pro, Ser, Thr, Tyr, Val
CAN1	P04817	Arg, His*, Lys*, Orn, Ser	–
HIP1	P06775	His*	–
LYP1	P32487	Lys*, Met	Ala, Asn, Met, Phe, Ser, Val
MMP1	Q12372	S-Methylmethionine*	Val
PUT4	P15380	Ala, Gly, Pro, GABA	Ala, GABA, Pro, Ser, Val

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Figure 1—source data 1

Growth rates of yeast amino acid transporter (YAT) expressing yeast.

Growth rate values of Δ10AA expressing either one of the seven different wild-type YAT genes (AGP1, BAP2, CAN1, HIP1, LYP1, MMP1, PUT4) from pADHXC3GH and the empty vector control on 2 mM of each amino acid. The growth rates were calculated based on Figure 1—figure supplement 1.

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For example, BAP2, previously reported to transport seven amino acids, was found to support growth on 16 amino acids (specific growth rate µ=0.07–0.31 hr^–1), making it a broad-range transporter. The differences on the observed growth rates on each amino acid can be explained by differences in the activity of the overexpressed transporter for these substrates or changes in the expression level of the endogenous transporter. Specifically, the twofold higher growth rate on Leu compared to Pro could be caused by a higher transcription rate of the endogenous transporter in the presence of Leu (Didion et al., 1996; Magasanik and Kaiser, 2002). Another transporter which showed a broader substrate range than previously reported is PUT4. The PUT4 transporter was found to support fast growth on Ser (µ=0.21 hr^–1) and slow growth on Val (µ=0.03 hr^–1), in addition to the already reported transport of Ala, Gly, Pro, and GABA. AGP1, an already known broad-range transporter, additionally supports very slow growth (µ=0.03 hr^–1) on Cit (l-citrulline), which is lower than the growth rate on any of the previously known substrates (µ=0.11–0.31 hr^–1). As a comparison, growth on ammonium, which does not depend on amino acid transporters, yielded the highest measured growth rate of µ=0.36 hr^–1 (vector control shown in Figure 3—figure supplement 1).

Experimental evolution of membrane transporter specificity

We tested whether the substrate range of amino acid transporters could be changed by experimental evolution, either by improving promiscuous activities or by evolving toward completely new substrates. For the former, we chose to study AGP1 under a selective pressure for Cit uptake. For the latter, we studied PUT4 under selective pressures for uptake of Asp and Glu. For the in vivo evolution, the OrthoRep system was used, which allows for random mutagenesis of a gene while it is actively expressed in yeast (Ravikumar et al., 2014; Ravikumar et al., 2018). Each of the two transporter genes was encoded on this system and introduced into the transport-deficient Δ10 strain, with additional Δura3 and Δhis3 deletions needed for selection.

To select for mutants with changed substrate specificity, we employed a dual-selection scheme: The cultures were grown in a minimal medium containing a nitrogen-limiting mixture of natively transported amino acids (1 mM final concentration) to ensure low-level growth of the culture. The mixture of amino acids was composed of equimolar amounts of 17 original substrates for AGP1 and 2 original substrates for PUT4. A concentration of 3 mM of the target substrate was added in the selection for evolved mutants. We expect that in this setup nonfunctional transporters are purged from the evolving population, as these do not support uptake, neither of the original substrates nor of the target amino acid. Transporters with the original substrate range are kept in low numbers, and transporter variants with novel substrates can increase to high numbers as they support growth on the target substrate present at threefold higher concentration than the original substrates.

The cultures of AGP1 and PUT4 evolution strains were passaged for multiple generations in the selective media. Colonies that supported growth on the target amino acid were isolated, and the transporter gene was sequenced. The gene was cloned to the pADHXC3GH expression vector and reintroduced into Δ10AA cells. Three AGP1 variants from Cit selection and three PUT4 variants each from Asp and Glu selection (Table 2) conferred the ability to grow on the respective amino acids (Figure 2—figure supplement 1 and Figure 2—figure supplement 2).

Table 2

Evolved variant	Nucleotide substitutions (nonsynonymous in bold)	Amino acid substitutions	Abbreviated names of site-directed mutants
AGP1-Cit1	T117C T1001A T1191A A1234G T1329C C1461G T1899C	I334N (=N) I412V (=V)	AGP1-N AGP1-V AGP1-NV AGP1-G AGP1-T
AGP1-Cit3	T117C A308G T915C T1001A C1451G C1461G T1684C	D103G I334N (=N) A484G (=G) F562L
AGP1-Cit11	T111C T121C G1450A A1609G	S41P A484T (=T) I537V
PUT4-Asp1	T620C T1474C	L207S (=S) F492L	PUT4-S
PUT4-Asp2	T620C T1415C T1474C	L207S (=S) V472A F492L
PUT4-Asp3	T191C T620C C734T G956A	I64T L207S (=S) S245F G319D
PUT4-Glu1	G225A T620C	L207S (=S)
PUT4-Glu2	T191C T620C G956A	I64T L207S (=S) G319D
PUT4-Glu3	T191C T620C	I64T L207S (=S)

Each evolved AGP1 variant had multiple nonsynonymous mutations, most of which occurred in the predicted transmembrane part of the protein (Figure 2 and Figure 2—figure supplement 3). In two of the three variants, the I334N (transmembrane helix TM6) mutation was found. The I334 position is located at the permeation pathway of the protein as predicted from AlphaFold (Jumper et al., 2021). A mutation in position A484 (TM10) was found in two variants (A484G and A484T). Additionally, mutations were found at the intracellular N-terminus (S41P, D103G) and the predicted transmembrane helices further away from the permeation pathway (I537V in TM11, F562L in TM12).

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Most PUT4 variants had multiple nonsynonymous mutations, again mostly in the predicted transmembrane part of the protein (Figure 2 and Figure 2—figure supplement 3). Interestingly, each variant from both Asp and Glu selections had the same L207S mutation (TM3). Overlay of homologous and structurally similar proteins indicates that this position is part of the substrate-binding site of the transporter (Figure 2—figure supplement 4; Gibrat et al., 1996). A change from a large hydrophobic residue to a small hydrophilic residue is thus expected to considerably change the properties of the substrate-binding site. Furthermore, in two of the three variants from Asp selection, the F492L mutation (TM10) was found. Mutations were also found at the intracellular N-terminus (I64T), between TM4 and TM5 (S245F), in TM6 (G319D), and in TM9 (V472A).

Single mutations are sufficient for altered substrate specificity

We wondered whether the altered substrate specificities could be conferred by single mutations. We made site-directed mutations in the wild-type transporter genes and tested the variants for uptake of Cit (AGP1) and Asp/Glu (PUT4). Generally, only mutations in the transmembrane domains were considered. The shorthand names for each site-directed mutant can be found in Table 2.

For the AGP1 gene, mutations in positions that occurred multiple times were investigated (i.e. I334N, A484G, and A484T; respective abbreviations AGP1-N, AGP1-G, and AGP1-T). Additionally, the I412V mutation was included in the study (shorthand AGP1-V), along with a double mutant combining I334N and I412V (abbreviation AGP1-NV, recreating the genotype observed in AGP1-Cit1). To compare the growth of the mutants on different media, the growth rate of Δ10AA expressing each variant from the plasmid pADHXC3GH was measured. The surface expression of the transporters was estimated by analyzing the fluorescence at the cell envelope (Figure 3—figure supplement 1A) and was similar for the wild-type and the evolved proteins (Figure 3—figure supplement 1B). Furthermore, there was no statistically significant difference between the growth rate of the strains under conditions where the nitrogen source was not dependent on amino acid uptake (Figure 3—figure supplement 1C). Strikingly, when grown in media with Cit as the sole nitrogen source, each of the variants showed a large increase in growth rate relative to the wild-type (Figure 3A). Specifically, the growth rate increased 1.9-fold for AGP1-N, 2.4-fold for AGP1-V, 3.2-fold for AGP1-G, and 2.4-fold for AGP1-T. The double mutant AGP1-NV showed a growth rate increase that is higher than each of its single mutants (2.8-fold). In separate uptake assays with radiolabeled Cit, we found a similar general trend of higher uptake rate of Cit by the mutants (Figure 3B and Figure 3—figure supplement 3A). We thus conclude that each of the tested single mutations is adaptive for growth on Cit as the nitrogen source.

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Regarding the PUT4 gene, we focused on the L207S single mutation since it occurred in all evolved variants (mutant abbreviation PUT4-S). The growth rates of Δ10AA expressing either the wild-type or the PUT4-S variant from pADHXC3GH were measured. The growth rate of the strains under conditions where the nitrogen source was not dependent on amino acid uptake was not significantly different (Figure 3—figure supplement 2C). While the wild-type transporter conferred no growth in the presence of Asp and Glu, the PUT4-S variant allowed growth on both of these substrates, showing that the single mutation is sufficient to change the specificity range of the transporter (Figure 3C). To confirm that the observed growth is due to transport of a novel substrate by PUT4-S, we conducted a whole-cell uptake assay with radiolabeled Glu. Indeed, Glu was only taken up by the mutant, whereas the wild-type PUT4 behaved like the vector control (Figure 3D and Figure 3—figure supplement 3B). Even though the surface expression of PUT4-S was higher than that of the wild-type (Figure 3—figure supplement 2B), this difference is unlikely to reason for the observed gain of function.

Single mutations have distinct impacts on the fitness for the original substrates

Above, we established that amino acid transporters can be evolved toward new specificity or increased activity of a substrate by just one mutation. Next, we wanted to know how these adaptive mutations affect the original substrate range and specificity of the transporters. Are the original substrates still transported by the variants? And if so, how does the transport compare to the wild-type? For that, we compared the fitness effects of the AGP1 and PUT4 variants for their respective substrates. The relative fitness of each mutant in the studied media was calculated from the growth rate of yeast expressing that mutant, normalized to the growth rate of yeast expressing the wild-type transporter. Additionally, we measured the uptake rates of a set of original substrates in whole cells to investigate if the growth fitness effects were due to changed transport activity.

For the AGP1 variants, we measured growth rates for 17 substrates (Figure 4 and Figure 4—figure supplement 1) and found that none of the single and double mutants had lost the capacity to grow on any of the original substrates. As a general trend, the AGP1 variants had a lower fitness than the wild-type, although some had a higher fitness for a given substrate.

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The AGP1 variants had different growth rates on the original substrates (Table 3 and Figure 4B). The amino acid substitutions I334N and I412V had a different effect on the relative fitness depending on whether they were analyzed individually or together in the double mutant. Specifically, the AGP1-V variant showed significantly increased relative fitness on GABA as nitrogen source, while showing no significant differences in any of the other substrates. The AGP1-N variant showed decreased relative fitness on the negatively charged amino acids Asp and Glu, as well as on Ala. The double mutant AGP1-NV showed decreased relative fitness on the majority of the tested amino acids (a total of 12 out of 17), including the ones in which the single mutant AGP1-N also exhibited decreased relative fitness. Interestingly, the AGP1-NV did not show significantly increased relative fitness in GABA, in contrast to AGP1-V. Despite the higher relative fitness of the double mutant on Cit (2.8) compared to that of the single mutants, the variant’s low relative fitness on the rest of the amino acids has a greater cost on the overall fitness of that strain (0.78). Also, the observed mistargeting of the transporters to internal membranes in the cases of AGP1-N and AGP1-NV could indicate misfolding of a fraction of the proteins, which could impact the relative fitness (Figure 5A).

Table 3

AGP1 variant	Substrates with significantly increased relative fitness	Substrates with significantly decreased relative fitness	Overall relative fitness (mean of 17 original substrates)	Relative fitness on Cit
AGP1-N		Ala, Asp, Glu	0.92	1.9
AGP1-V	GABA		0.98	2.4
AGP1-NV		Ala, Asn, Asp, Glu, Ile, Leu, Met, Phe, Pro, Ser, Thr, Tyr	0.78	2.8
AGP1-G	GABA		1.0	3.2
AGP1-T		Asp, Gln, Glu, Ile, Leu, Met, Phe, Thr, Val	0.86	2.4

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The fitness effects were drastically different between AGP1-G and -T, both having A484 substituted. The AGP1-G variant showed significantly increased relative fitness on GABA and a trend of overall increased relative fitness on small aliphatic amino acids of intermediate hydrophilicity (Pro, Gly). Simultaneously, its relative fitness on Cit was higher than any of the other mutants. AGP1-T showed mainly negative relative fitness effects, with decreased relative fitness for charged amino acids and the majority of non-polar amino acids (a total of 9 out of 17).

The idea that the observed fitness effects are due to changed transport activity and/or affinity constant for the substrate was investigated by measuring the uptake rates of radiolabeled Phe and Glu. The uptake was measured at two different amino acid concentrations, namely 0.1 mM and 2 mM, which are respectively lower and higher than the K_m of the wild-type protein for amino acids (K_m = 0.2–0.79 mM [reviewed in Bianchi et al., 2019]; Figure 5 and Figure 5—figure supplement 1). For Glu it was found that the uptake rates at both concentrations (Figure 5C and D) correlate well with the relative fitness. An almost identical pattern was observed when the uptake and the growth rates of the variants were compared at 2 mM Glu (Figure 5B and D). The fact that AGP1-N, AGP1-NV, and AGP1-T showed a pattern of both reduced growth rate and reduced uptake rate compared to the wild-type indicates that the relative fitness indeed is linked to the activity of the transporter (Figure 5—figure supplement 2B). Equivalently, in the case of Phe as nitrogen source, the relative fitness and uptake rate of the AGP1 variants followed similar trends. At 2 mM Phe, the effects on uptake were almost congruent to the growth rates (Figure 5E and G, Figure 5—figure supplement 2A). The reduced uptake and growth rate of AGP1-NV compared to the wild-type indicates that the lower fitness of the AGP1-NV on Phe possibly stems from its reduced transport rate, but an effect of the internalization of the transporter cannot be excluded (Figure 5A).

The growth rates of Δ10AA expressing PUT4 and PUT4-S showed that the mutation considerably affects the relative fitness of the strain in three of the original substrates (Figure 6B). Specifically, PUT4-S was associated with a significant fitness loss during growth on 2 mM Ala and GABA, and a fitness gain in Val. To investigate the phenotype further, we followed the uptake of radiolabeled Ala and GABA at a concentration of 10 μM, well below the reported K_m (reviewed in Bianchi et al., 2019; Figure 6 and Figure 6—figure supplement 1). As with AGP1 variants, the relative fitness and uptake rate trends coincided, indicating that the slower amino acid uptake may cause the slower growth of PUT4-S on both Ala and GABA (Figure 6C and D). Additionally, we investigated the uptake of Gly, another known substrate of PUT4. Due to high background growth of the Δ10AA strain on Gly, there was no significant difference in the growth rates between PUT4-expressing and vector control cultures. However, the uptake assays showed a clear transport activity of the wild-type PUT4 strain, which decreased dramatically in the case of PUT4-S (Figure 6E). There is thus a clear cost of the mutation on the uptake of Gly.

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The wild-type transporter supported very slow growth on Val (µ=0.03 hr^–1) and we therefore consider its transport a promiscuous activity. This coincides with the larger size and hydrophobicity of the Val molecule relative to Ala, GABA, Gly, Pro, and Ser. For Val, the fitness of PUT4-S was dramatically increased compared to wild-type (2.4-fold), which could be explained with the predicted size increase of the substrate-binding site of the transporter (see above). Thus, the gain-of-function mutation L207S affected the transport of at least four original substrates, three of which negatively, and one positively.

Single mutations can significantly broaden the substrate range of amino acid transporters

All variants with increased fitness for one substrate can still transport the wild-type’s original substrates. For AGP1, each of the four tested single mutants as well as the double mutant retained the ability to confer growth on all amino acids that we tested.

Similarly, for PUT4, the L207S mutation added transport of Glu and Asp, while still retaining the ability to transport the original substrates Ala, GABA, Gly, Pro, Ser, and Val. Since we established that one single evolutionary mutation extended the strain’s growth by two additional amino acids, we wondered whether this mutation also impacted the overall substrate range of the transporter. Therefore, growth assays for the PUT4-S strain were conducted with 18 amino acids (Figure 7A). Remarkably, the transporter variant conferred growth on 15 of the studied substrates. Specifically, the evolved variant carrying the L207S mutation transports Asp and Glu (the amino acids used for the initial selection), as well as Asn, Cit, Gln, Thr, Ile, Leu, and Met in addition to the six substrates of the wild-type transporter PUT4. Regarding the growth on Gln, we note that although the calculated growth rates of PUT4-S and PUT4 were not significantly different, the shape of the growth curve implies that Gln is indeed taken up (Figure 4—figure supplement 1). In the case of growth on Phe and Trp, the results were not conclusive due to the high background growth of the vector control strain.

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Upon grouping the amino acids according to their size and hydropathy index (Kyte and Doolittle, 1982; Zamyatnin, 1972), a clear trend emerged for the novel substrates of PUT4-S. In addition to the small substrates of low and intermediate hydrophilicity, the evolved variant facilitates the uptake of larger hydrophobic amino acids (Ile, Leu, Met) as well as hydrophilic amino acids (Thr, Asp, Asn, Gln, Glu, Cit) (Figure 7B, colored areas 2 and 1, respectively). Thus, we showcase that a single adaptive mutation is sufficient to evolve a narrow-range transporter (PUT4: 6 substrates) to a broad-range transporter (PUT4-S: 15 substrates).

Different theoretical models have been developed to explain the origin of new functions in proteins. Many favor the idea that new functions are acquired prior to gene duplication while the original functions are maintained (Soskine and Tawfik, 2010). While these models have been used to describe the evolution of functions in soluble enzymes, we showcase that the theory also applies to membrane transporters. To simulate natural transporter evolution, we leveraged in vivo evolution of two YAT, AGP1 and PUT4, with selective pressure for transport of novel substrates.

We show that the substrate spectrum of five wild-type YAT is substantially broader than previously described. The use of a growth-based screening process enabled us to identify so far unknown substrates (up to nine in the case of BAP2). The transporter genes were expressed from a multicopy plasmid (2µ origin of replication), where the number of plasmid copies that each daughter cell receives is stochastic (Chan et al., 2013). Cells with different copy numbers could potentially have an advantage in our growth rate-based measurements of transporter activity. Thus, the mean copy number of the culture is expected to change during the 3-day cultivation period. The growth rate is therefore a measure of the fitness of the strain when the transporter gene copy number is optimized, which is probably the reason why the growth-based assay is so sensitive for promiscuous transporter activities that have not been described before.

It is apparent that the relative fitness of each strain expressing a transporter depends on the amino acid provided. For the strain expressing AGP1, Cit supports very low growth rates indicative of a promiscuous uptake activity by AGP1 that has not been reported previously. We show that upon the presence of selective pressure for the uptake of Cit, this weak transport activity of AGP1 is increased by adaptive mutations. Each of the five studied mutants resulted in a gain of relative fitness for growth on Cit. The differential Cit uptake rates of the single and double mutants showcase how in vivo experimentally generated single mutations can alter the substrate spectrum of the YAT without the loss of ancestral functions. Our observations are in line with studies of substrate promiscuity being the result of adaptive mutations in different membrane transporters (Regenberg and Kielland-Brandt, 2001; Tutulan-Cunita et al., 2005; Adler and Bibi, 2005; Gros and Schuldiner, 2010; Ghaddar et al., 2014; Young et al., 2014; Meier et al., 2023). The altered specificity for a substrate, which is mirrored in our case in the altered fitness, could be a result of a different affinity constant for the amino acid and/or maximal transport rate.

We also established that through applying evolutionary pressure upon the PUT4 membrane transporter, single mutations can emerge in the population which provide a gain of function. The newly obtained function is essential for the survival of the organism in the new environment. Interestingly, the same mutation (L207S) in the PUT4 gene was found when evolutionary pressure was applied for the growth on media supplemented with either Asp or Glu as the sole nitrogen source. The L207 residue is predicted to be part of the substrate-binding site of the transporter. Structurally equivalent sites on other APC superfamily transporters have been shown to affect the function of the studied proteins (Edwards et al., 2018; Ghaddar et al., 2014). Specifically, the non-conserved V104 in Aquifex aeolicus LeuT shapes the binding pocket and influences the substrate specificity (Singh et al., 2008; Yamashita et al., 2005), while T180 in CAN1 is expected to contribute to the transporter’s interaction with its arginine substrate (Ghaddar et al., 2014; Gournas et al., 2015). In the case of PUT4-S, the replacement of the large hydrophobic leucine residue with a small hydrophilic serine residue is likely to increase both the size and the hydrophilicity of the binding pocket. This could be the reason that the evolved transporter now facilitates the uptake of hydrophilic as well as large hydrophobic amino acids.

The evolved mutations in AGP1 are also positioned in the transmembrane helices that create the permeation pathway of LeuT-fold transporters (Kazmier et al., 2017). I334 in TM6 of AGP1 is conserved in some members of the YAT family (DIP5, CAN1, LYP1, ALP1, TAT3). I412 in TM8 is part of the conserved sequence [IV]-L-[ITAVL] and thus in AGP1-V and AGP1-NV, Ile is changed to the only other common residue, Val, which is found in DIP5, LYP1, and ALP1 (Gournas et al., 2015). Reportedly, single site substitutions in conserved regions of CAN1 and GNP1 have been shown to broaden the substrate range of these YAT and accommodate for the uptake of Cit (Regenberg and Kielland-Brandt, 2001). Substitutions in non-conserved regions can also have an effect on a transporter’s substrate spectrum. The non-conserved T456 in CAN1 is important for the substrate specificity of the protein. Single site substitution of T456 can broaden the substrate spectrum of CAN1 by one amino acid, while S176N/T456S abolishes the uptake of arginine, a high affinity substrate of CAN1, and supports high affinity for the uptake of lysine similar to that of LYP1 (Ghaddar et al., 2014). Interestingly, T456 lies two residues away from N454 in CAN1, whose equivalent position is A484 in AGP1 (Gournas et al., 2015).

In both AGP1 and PUT4, the evolved variants retain their original substrate spectrum albeit with altered specificities. Our results showcase that specific adaptive mutations for a novel substrate often decrease the fitness for the original substrates, creating a situation of trade-off between new and old function. However, in some cases, the fitness for single original substrates actually increases (e.g. AGP1-G and AGP1-V for GABA, or PUT4-S for Val). Depending on the environment, a mutation can thus be adaptive for multiple substrates at the same time, including ones that were not selected for. For evolution on Cit, the mutant with the highest fitness gain (AGP1-G) showed no loss of relative fitness for the original substrates. Thus, this mutation could potentially occur in the population even without the presence of Cit as selective pressure or even outcompete others in a natural setting where the selective pressure is applied. On the contrary, the combination of two adaptive mutations (AGP1-NV) had a higher fitness cost for original substrates than the separate mutations, but also a higher fitness gain for the novel substrate, which can be viewed as the first step toward specialization. Thus, in a natural setting, such a variant would only be viable if the novel substrate gives a very high selective advantage as compared to the original ones. Alternatively, gene duplication could lead to two distinct transporter genes, one specializing in the novel substrate, while the other (re-)specializes for the original substrates (Soskine and Tawfik, 2010). To generalize our findings to natural evolution, it is important to keep in mind that our selection process was intentionally designed to emulate a newly encountered substrate, for which no suitable transporter is yet available. This led to evolution toward substrate range expansion and produced broad-spectrum transporter variants. In wild-type organisms, broad specificities are not always favorable. Each organism typically possesses multiple nutrient transporters with partial redundancy in their substrates, making it possible to finely tune-up and down-regulate specific transport activities according to the nutrient availability (Bianchi et al., 2019). Therefore, our experimentally derived transporter variants would probably not fare well in natural settings, as they come from an organism that already has evolved a full suite of amino acid transporters. They do, however, give information about evolvability of transporters when faced with a new solute, whose transport leads to increased fitness of the organism. An example of a transporter with a very special substrate can be found in the cystine transporter CYN1, which is distantly related to LYP1. Only a fraction of all yeast species has orthologs of this transporter and the respective cystine uptake activity (Yadav and Bachhawat, 2011). Seemingly, not all yeasts profit from the ability to assimilate cystine from the medium, and therefore do not possess a suitable transporter. At one point in time, however, an ancestor of LYP1 evolved toward transport of cystine and ultimately turned into the highly selective CYN1 found today. These processes take place over long periods of time and are hard to observe in the field. Our experiments emulate the first steps in such an evolutionary scenario and make them observable within laboratory time scale.

Taken together, our findings show that transporters can have promiscuous weak activities, which are often hidden in the natural context of the cell. Furthermore, transporters can evolve novel substrates through generalist intermediates, either by increasing a weak activity or by establishing a new one. This coincides with what has been shown for soluble enzymes, and thus we show that membrane transporters can follow the same evolutionary logic (Bergthorsson et al., 2007; Hughes, 1994; Aharoni et al., 2005; Des Marais and Rausher, 2008; Näsvall et al., 2012). Looking at single adaptive mutations, we quantify the fitness trade-off between novel and original substrates, indicating that each mutation impacts the original substrates differently. Some of the identified adaptive mutations are almost neutral toward the original substrates, indicating that these mutations could become present in a large population, even before a new substrate is encountered by this population. This ‘standing genetic variation’ is a hallmark of contemporary theories of genetic innovation, and can explain how new functionalities arise while the protein is still under purifying selection for its original function (Soskine and Tawfik, 2010). Furthermore, such standing variation primes organisms for rapid adaptation (Barrett and Schluter, 2008). Along the same vein, we show that five wild-type amino acid transporters have inherent side reactivities. In the light of evolution, this is a feature rather than a bug, as it can prime these transporters to enhance the side reactivities either by mutation or duplication, as observed for soluble enzymes (Copley et al., 2023; Khersonsky and Tawfik, 2010). Overall, we show how transporter evolution could help organisms to explore ecological niches where novel transport activities give a fitness benefit, while still retaining the ability to thrive in the old environment.

Code and raw data are available on Zenodo (https://zenodo.org/records/10928101).

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Author details

1. Foteini Karapanagioti

   Department of Biochemistry, University of Groningen, Groningen, Netherlands

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0007-1700-4867

2. Úlfur Águst Atlason

   Department of Biochemistry, University of Groningen, Groningen, Netherlands

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

3. Dirk J Slotboom

   Department of Biochemistry, University of Groningen, Groningen, Netherlands

   Contribution

   Supervision, Writing – original draft, Writing – review and editing

   For correspondence

   d.j.slotboom@rug.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5804-9689

4. Bert Poolman

   Department of Biochemistry, University of Groningen, Groningen, Netherlands

   Contribution

   Resources, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   b.poolman@rug.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1455-531X

5. Sebastian Obermaier

   Department of Biochemistry, University of Groningen, Groningen, Netherlands

   Contribution

   Conceptualization, Data curation, Supervision, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   s.obermaier@protonmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1238-4502

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