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. 2019 Nov 25;8:e50253. doi: 10.7554/eLife.50253

Asymmetrical diversification of the receptor-ligand interaction controlling self-incompatibility in Arabidopsis

Maxime Chantreau 1, Céline Poux 1, Marc F Lensink 2, Guillaume Brysbaert 2, Xavier Vekemans 1, Vincent Castric 1,✉
Editors: Sheila McCormick3, Christian S Hardtke4
PMCID: PMC6908432  PMID: 31763979

Abstract

How two-component genetic systems accumulate evolutionary novelty and diversify in the course of evolution is a fundamental problem in evolutionary systems biology. In the Brassicaceae, self-incompatibility (SI) is a spectacular example of a diversified allelic series in which numerous highly diverged receptor-ligand combinations are segregating in natural populations. However, the evolutionary mechanisms by which new SI specificities arise have remained elusive. Using in planta ancestral protein reconstruction, we demonstrate that two allelic variants segregating as distinct receptor-ligand combinations diverged through an asymmetrical process whereby one variant has retained the same recognition specificity as their (now extinct) putative ancestor, while the other has functionally diverged and now represents a novel specificity no longer recognized by the ancestor. Examination of the structural determinants of the shift in binding specificity suggests that qualitative rather than quantitative changes of the interaction are an important source of evolutionary novelty in this highly diversified receptor-ligand system.

Research organism: A. thaliana

Introduction

A central goal of biology is to understand the evolutionary forces and molecular processes by which new traits and functions emerge in living organisms. A major challenge toward this goal is that many cellular processes rely on interactions between molecular partners (protein-protein interactions or regulatory interactions between for example transcription factors and their binding sites; Boyle et al., 2017; Courtier-Orgogozo et al., 2019) rather than on the action of individual components in isolation. At present, the overall structure of protein-protein or regulatory interaction networks is just starting to be understood at the genome level in a handful of model organisms, and a general understanding of how the diversification of these networks led to the emergence of new biological functions in distinct lineages is crucially lacking (Nooren and Thornton, 2003; Andreani and Guerois, 2014). In two-component genetic systems, specificity of the interaction can be very tight such as for example in receptor-ligand interactions (Laub and Goulian, 2007) or bacterial toxin-antitoxin systems (Aakre et al., 2015). In these systems, the emergence of novel traits involves evolutionary modification of the interacting partners, leading to potential disruption of the interaction or to detrimental cross-talk, at least transiently (Plach et al., 2017). A central aspect of the diversification process therefore concerns the functional nature of the evolutionary intermediate. Two main scenarios have been proposed (Aakre et al., 2015). The first scenario posits that a functional change results from introduction of a mutation that first modifies one of the two partners, initially disrupting the functional interaction and leading to a non-functional intermediate. If fitness of this non-functional intermediate is not impaired too drastically, it may remain in the population until a compensatory mutation hits the second partner and rescues the interaction in a modified state, resulting in the novel function. An alternative scenario though, is that the first mutation may broaden rather than inactivate specificity of one of the two interacting partners, transiently releasing constraint on the other before specificity of the first partner becomes restricted again, thus maintaining functionality of the system all along the process. Distinguishing between these two scenarios requires functional characterization of the ancestral evolutionary intermediates, which has been achieved in a very limited number of biological cases such as bacterial toxin-antitoxin systems (Aakre et al., 2015), acquisition of cortisol specificity of the mammalian glucocorticoid receptor (Bridgham et al., 2009) or mammalian retinoic acid receptors (Gutierrez-Mazariegos et al., 2016). The scenario of a promiscuous intermediate seems to have received better support, but whether this is a truly general pattern remains to be determined. A major limitation is that detailed population genetic models taking into account how natural selection is acting on variants of the two genes given their specific biological function are lacking for most genetic systems (Aakre et al., 2015; Aharoni et al., 2005; Bloom and Arnold, 2009; Bridgham et al., 2009; Matsumura and Ellington, 2001; Matton et al., 2000; Sayou et al., 2014).

Self-incompatibility (SI) in flowering plants is a prime biological system to investigate how functional diversification can proceed between two molecular partners over the course of evolution. SI has evolved as a strategy to prevent selfing and enforce outcrossing, thereby avoiding the deleterious effects of inbreeding depression (Nettancourt, 1977; Kitashiba and Nasrallah, 2014). In Brassicaceae, SI is genetically controlled by a single multiallelic non-recombining locus called the S-locus (Nettancourt, 1977). This locus encodes two highly polymorphic self-recognition determinant proteins: the female cell-surface ‘receptor’ S-LOCUS RECEPTOR KINASE (SRK) is expressed in stigmatic papillae cells (Takasaki et al., 2000) and the male ‘ligand’ S-LOCUS CYSTEIN RICH PROTEIN (SCR) is displayed at the pollen surface (Schopfer et al., 1999; Takasaki et al., 2000; Takayama et al., 2000). The SI response consists in pollen rejection by the pistil and is induced by allele-specific interaction between the SRK and SCR proteins when encoded by the same haplotype (S-haplotype, Kachroo et al., 2001; Nasrallah and Nasrallah, 1993; Takayama et al., 2001). These two interacting proteins exhibit a high degree of sequence variability and a large number of these highly diverged allelic variants segregate in natural populations of SI species (several dozens to nearly two hundred, Busch et al., 2014; Castric and Vekemans, 2004; Lawrence, 2000). This large allelic diversity of receptor-ligand combinations must have arisen through repeated diversification events, but the molecular process and evolutionary scenario involved in the diversification of an ancestral haplotype into distinct descendant specificities are still unresolved.

Three evolutionary scenarios for emergence of new self-incompatibility specificities have been proposed so far (Charlesworth et al., 2005). The ‘compensatory mutation’ scenario posits that diversification proceeds through self-compatible intermediates that is following transient disruption of the receptor-ligand interaction (Figure 1—figure supplement 1A; Gervais et al., 2011; Uyenoyama et al., 2001). Population genetic analysis confirmed that S-allele diversification is indeed possible through this scenario, but only under some combinations of model parameters (high inbreeding depression, high rate of self-pollination and low number of co-segregating S-alleles, Gervais et al., 2011). Under these restrictive conditions, the ancestral recognition specificity can be maintained in the long run along with the derived specificity, effectively resulting in allelic diversification. A second scenario was presented by Chookajorn et al. (2004), who proposed that diversification results from the progressive reinforcement of SRK/SCR recognition capacity among slight functional variants of SCR and SRK that might segregate within the population. At the limit of the very low level of intra-allelic polymorphism observed in natural populations (Castric et al., 2010) this model bears similarity to the model of Gervais et al. (2011), who also predicted the maintenance of an ancestral recognition specificity unchanged and the evolution of a new divergent specificity, except it assumes no SC intermediate. However, the progressive reinforcement process proposed by this model may rather result in the production of two descendant allelic specificities that are both functionally distinct from their ancestor (Figure 1—figure supplement 1B). A third scenario was proposed by Matton et al. (1999) and involves a dual-specificity intermediate (Figure 1—figure supplement 1C). This scenario was criticized on population genetics grounds because such a dual-specificity haplotype would recognize and reject more potential mates for reproduction than its progenitor haplotype resulting in lower reproductive success, and should therefore be disfavoured by natural selection and quickly eliminated from the populations (Charlesworth, 2000; Uyenoyama and Newbigin, 2000). A main unanswered question is whether functional specificities remain stable over time or are subject to frequent turnover. Detailed theoretical analysis of the model of Gervais et al. (2011) showed that under a large portion of the parameter space, the introduced self-compatible intermediate is predicted to exclude its functional ancestor from the population. Secondary introduction of the compensatory mutation then effectively results in turnover of recognition specificities along allelic lines rather than in diversification per se. A turnover of recognition specificities may therefore be expected along each allelic lineage, rather than their long-term maintenance over evolutionary times. A similar process could also be occurring according to the progressive reinforcement model of Chookajorn et al. (2004). Overall, while the large allelic diversity at the S-locus is one of the most striking features of this system, the evolutionary scenario and molecular route by which new S-alleles arise remain largely unknown. An essential limitation in the field is the crucial absence of direct experimental approaches.

Results

To conclusively evaluate these models we used ancestral sequence reconstruction of S-haplotypes that are currently segregating in the plant Arabidopsis halleri. Ancestral sequence reconstruction is a new approach to 'resurrect' ancestral biological systems, whereby the sequence of an ancestral gene or allele is determined through phylogenetic analyses, then synthesized de novo and its functional properties (in terms of e.g. biochemical activity, conformation or binding capacity) are compared to those of its contemporary descendants. Although most S-haplotypes in A. halleri are typically so highly diverged that even sequence alignment can be challenging, previous work identified a pair of S-haplotypes (S03 and S28) with high phylogenetic proximity based on a fragment of the SRK gene (Castric et al., 2008). We took this opportunity to reconstruct, resurrect and phenotypically characterize their putative last common ancestral receptor SRKa (‘a’ for ancestor, Figure 1) into A. thaliana, which has previously been established as a model plant for mechanistic studies of SI (Nasrallah et al., 2002; Tsuchimatsu et al., 2010).

Figure 1. Experimental approach for the ancestral resurrection experiment.

(A) The sequence of the putative last common ancestor of SRK03 and SRK28 was inferred by a phylogenetic approach using codon-based models implemented in PAML. Four different versions of SRKa were defined due to inference uncertainty at two aa positions. (B) SRK03 and SRK28 sequences were cloned from A. halleri DNA BAC clones, whereas SRKa sequences were obtained by gene synthesis. (C) Representation of the controlled cross program to decipher the specificity of SRKa.

Figure 1.

Figure 1—figure supplement 1. Three models for the emergence of new self-incompatibility specificities.

Figure 1—figure supplement 1.

Docking interaction between ligands and receptors are represented by oriented arrows. (A) In the compensatory mutation model, a non-functional self-compatible intermediate (R1/L2) segregates transiently and is rapidly compensated, creating a novel specificity (R2/L2), while the ancestral one (R1/L1) remains unchanged over time. (B) In the turnover model, slight functional variants with different affinity between them (R1’/L1’ and R1’’/L1’’) segregate in the population and give rise to new specificities (R2/L2 and R3/L3). Dotted lines correspond to weaker affinity interactions. (C) In the promiscuous model, the intermediate receptor (R1,2) has widened up its specificity spectrum enabling it to recognize another potential ligand (L2) while maintaining its capacity to recognize its original ligand (L1). Emergence of the new ligand then favours narrowing of specificity the dual-receptor (R1,2 → R2).

Figure 1—figure supplement 2. Maximum likelihood phylogenetic tree based on the 17 SRK alleles used for SRKa construction.

Figure 1—figure supplement 2.

The Bayesian reconstruction displays an identical topology. Values at the branches represent the bootstrap percentage/posterior probabilities. The node representing the reconstructed ancestral SRK allele (SRKa) is shown in red. Asterisks highlight alleles with a complete sequence available for ancestral reconstruction.

Figure 1—figure supplement 3. Schematic representation of the molecular constructs used for A. thaliana transformation.

Figure 1—figure supplement 3.

AhSRK03 and AhSRK28 constructs were cloned using a unique genomic fragment starting around −2 Kb and ending 1 Kb after the stop codon. SRKa constructs were each cloned using 3 DNA fragments (amplified promoter and kinase domain of AhSRK03 or AhSRK28 and the synthesized ancestral S-domain). The different parts of the construct were concatenated using the Multisite Gateway Technology at the positions indicated on the figure. SRK03p:GFP and SRK28p:GFP constructs were generated using the same promoter sequence as in the SRKa constructs, but were introduced into the pKGWFS7.0 destination vector.
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Expression of S03 and S28 recognition specificities in A. thaliana by genetic transformation

Because of their relatively high sequence similarity, we first ensured that the S03 and S28 recognition specificities are indeed phenotypically distinct when expressed in A. thaliana. It was previously shown that the self-compatible model plant A. thaliana can mount a functional self-incompatibility response upon introduction of the S-locus genes SCR and SRK (Boggs et al., 2009; Durand et al., 2014; Nasrallah et al., 2002) but it is unclear whether transfer is possible for all SCR and SRK variants. We first assessed the activity of AhSRK03 and AhSRK28 promoters in transformed A. thaliana lines (Figure 2—figure supplement 1), which enabled us to identify the temporal window of SRK expression (Figure 2—figure supplement 2). Then, we used A. halleri pollen carrying either the S03 or S28 male determinant to validate that a proper SI response was indeed successfully transferred in transformed A. thaliana lines for each of our SRK receptors, and that this response was strictly specific toward either of their cognate ligand (two replicate transgenic lines for SRK03 and SRK28 each, Figure 2).

Figure 2. SRK03 and SRK28 are successfully expressed in A. thaliana and represent phenotypically distinct female recognition specificities.

(A) Fluorescence microscopic observation of pistils from SRK-transformed A. thaliana plants pollinated with S03 and S28 pollen from A. halleri. A robust self-incompatibility reaction is observed between cognate alleles, whereas a compatible reaction is observed between non-cognate alleles. White arrows indicate pollen tubes that germinated in the stigma, and are specific to compatible reactions. Bar = 0.3 mm. (B) Number of germinated pollen grains per stigma after pollination. Two SRK03 and SRK28 lines were pollinated with A. halleri pollen expressing either S03 (blue) or S28 (red) specificities. The number of pollinated pistils for each pollination assay is indicated on the top of the figure. The median value for each cross is represented by a horizontal bar.

Figure 2—source data 1. Number of germinated pollen grains per stigma after pollination.
Two SRK03 and SRK28 lines were pollinated with A. halleri pollen expressing either S03 or S28 specificities.

Figure 2.

Figure 2—figure supplement 1. Localization of SRK expression by GFP florescence microscopy.

Figure 2—figure supplement 1.

(A) Flower of an untransformed plant (no SRK expression). (B,C) Flowers of two SRK03p:GFP transformed lines. (D,E) Flowers of two SRK28p:GFP transformed lines. Promoters of both SRK03 and SRK28 drive GFP expression specifically in stigmatic papillae cells (white arrows). Bar = 1 mm.
Figure 2—figure supplement 2. Pattern of proSRK03 activity along development stages of floral buds.

Figure 2—figure supplement 2.

Floral buds and pistils of SRK03p:GFP-transformed plants are observed under optical (above) and UV light (below), respectively. Developmental stages (Smyth et al., 1990) are indicated between brackets. SRK expression is located between stages 11 and 14, with a strong decrease of GFP fluorescence in mature flowers (old stage 15). Similar results were observed for the SRK28p:GFP lines (data not shown). No GFP signal was observed in any other part of the plant (inflorescence stems, leaves and non-reproductive floral organs, data not shown). Black bar = 1 mm, white bar = 0.5 mm.
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Abundant germination of pollen tubes occurred in crosses involving non-cognate SRK and SCR proteins, whereas crosses involving proteins from the same S-haplotype induced inhibition of pollen germination, which is characteristic of an incompatible response. Intensity of the incompatible response was then quantified by counting the number of germinated pollen grains at the pistil surface of two selected lines for each transgene (Figure 2A and B). Incompatible responses are characterized by less than 10 germinated pollen tubes whereas compatible crosses induced significant pollen germination, with more than 50 pollen tubes per stigma. This shows that cross-species pollinations of A. thaliana with A. halleri pollen can induce robust SI reactions, and also that S03 and S28 have distinct recognition specificities, despite their close phylogenetic proximity.

Phylogeny-based ancestral SRK reconstruction

We then used a phylogenetic approach to reconstruct the sequence of the most recent common ancestor of SRK03 and SRK28 (Figure 3—figure supplement 1). Based on a set of closely related SRK sequences from Arabidopsis and Capsella, we used the best fitting codon-based models implemented in PAML (Yang, 2007) to reconstruct the most likely ancestral amino acid sequence (SRKa). This most likely ancestral amino acid sequence had an Isoleucine (I) at position 208 and an Alanine (A) at position 305 and is therefore noted SRKaIA. For the vast majority of the 56 amino acid residues where SRK03 and SRK28 differ (Figure 3—figure supplement 1), the ancestral reconstruction had high posterior probability (>0.95), but was more uncertain at four positions (Figure 3—figure supplement 2). Two of these sites were close (208) or within (305) hyper variable regions known to be functionally important (Figure 3—figure supplement 1; Kusaba et al., 1997). To take uncertainty into account and evaluate the impact of variation at these two individual sites, three additional ancestral sequences were generated and tested: SRKaIG, SRKaMA and SRKaMG. To avoid context effects, each of these four versions of SRKa was then surrounded by a promoter and a kinase sequence corresponding either to the native SRK03 (p03_SRKa_k03) or the native SRK28 (p28_SRKa_k28) variant. For each of these constructs, we obtained several replicate transgenic lines, resulting in a total of 28 different fixed homozygote single-copy transformed Arabidopsis lines (8 SRKaIA lines; 7 SRKaIG lines; 7 SRKaMA lines and 6 SRKaMG lines). Five lines showed very low levels of SRKa transcripts in pistils (Figure 3—figure supplement 3; possibly due to positional effects as is common in transgenic constructs) and were thus excluded from further analysis, leaving 23 transgenic lines for testing.

Ligand specificity of the ancestral SRK

We then evaluated the recognition phenotype conferred by these putative ancestral SRK sequences by performing controlled pollination assays for all remaining 23 SRKa lines with pollen from A. halleri S03, A. halleri S28 and A. thaliana C24 (used as a control to validate receptivity of the acceptor pistil). The SI reaction was scored as the number of germinated pollen grains per stigma. All 23 tested SRKa expressing lines showed intense pollen germination with C24 pollen, idid not impair pistil fertility or ability to receive pollen (Figure 3 and Figure 3—figure supplement 4).

Figure 3. Incompatibility response of SRKa lines.

Each SRKa line was pollinated with A. thaliana C24 (green), A. halleri S03 (blue) and S28 (red) pollen. The number of replicate pollinated pistils is indicated above each boxplot. The horizontal bar represents the median, the box delimitates the 25% and 75% percentiles, the bottom and top whiskers both represent 25% extreme percentiles and outliers are represented by individual dots.

Figure 3—source data 1. Number of germinated pollen grains per stigma after pollination.
Each SRKa line was pollinated with A. thaliana C24, A. halleri S03 and S28 pollen.

Figure 3.

Figure 3—figure supplement 1. Comparison between the S-domain of AhSRK03 and AhSRK28 and their common ancestor.

Figure 3—figure supplement 1.

(A) Arrangement of domains in the full-length SRK protein as assigned by National Center for Biotechnology Information (upper) and protein schematic representation of the S-domain sequence of AhSRK03, AhSRK28 and their common ancestor SRKa (lower). Positions of identical amino acids between the three S-domains are not represented, positions of SRKa amino acids specific to SRK03 and SRK28 are represented by a vertical blue and red line respectively. Positions of hypervariable regions are represented by yellow rectangles. The position of the two amino acid gap presents in both AhSRKa and AhSRK03 is represented by black rectangles. Positions 208 and 305 for which the reconstruction was uncertain and positions 155 and 344 for which the aa identity is different in the two other receptors are represented in black. (B) Amino acid identity and codon sequence (in parentheses) for these four positions in the different SRK variants.
Figure 3—figure supplement 2. Histogram presenting the distribution of amino acid (aa) probability values obtained with the best fitting-model M3FMutSel.

Figure 3—figure supplement 2.

For each range of values smaller than 0.95, the nature and the position of the aa in the sequence are given. At three sites (33, 208, 305) the models used for the inferences led to different results. The variable sites between the four ancestral sequences used for A. thaliana transformation are in bold.
Figure 3—figure supplement 3. Transgene expression analysis of the different SRK-transformed lines.

Figure 3—figure supplement 3.

ΔCt (Cycle threshold) was calculated as the ratio of Ct-SRK on Ct-Ubiquitin. The number of biological replicates is indicated above each boxplot. For each biological replicate, measurements were realized in technical triplicates.
Figure 3—figure supplement 4. Quantification of incompatibility response of A. thaliana lines with SRKa transcript levels below detection threshold.

Figure 3—figure supplement 4.

Each SRKa line in S03 and S28 context were pollinated with A. thaliana C24 (green), A. halleri S03 (blue) and S28 (red) pollen. The number of pollinated pistils is indicated above each boxplot. These five lines with an undetectable level of transcripts appear compatible with the three pollen types, which is in agreement with the fact that they don’t express any SI receptor at their membrane surface.
Figure 3—figure supplement 4—source data 1. Number of germinated pollen grains per stigma after pollination of A. thaliana lines with SRKa transcript levels below detection threshold.
Each SRKa line in S03 and S28 context were pollinated with A. thaliana C24, A. halleri S03 and S28 pollen.
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With a single exception (line p03_SRKaMA_k03_2), S28 pollen induced no consistent SI rejection in any of the lines expressing the different versions of the ancestral receptor, and was indistinguishable from the compatible control (C24, Figure 3). In stark contrast, SCR03 pollen induced robust incompatibility reactions in the vast majority (12 of 14) of the lines with amino acid I at position 208 (SRKaIA and SRKaIG, Figure 3). Hence, we conclude that the S03 haplotype has retained the ancestral recognition specificity largely unaltered, while the S28 haplotype underwent substantial modification and represents a novel recognition specificity, leading to functional diversification.

The incompatibility reactions involving our different ancestral SRK S-domain sequences were largely independent of the sequence context into which we introduced them (Figure 3, Figure 1—figure supplement 3). Similarly, the amino acid present at position 305 of the reconstructed sequence had no effect on the response: lines with either A or G produced rejection reactions equally. In contrast, only four of the lines with amino acid M at position 208 (SRKaMA and SRKaMG) displayed incompatibility reactions, and these were weak (Figure 3). Hence, the single change, from the ancestral Isoleucine amino acid (I) at position 208 to Methionine (M), seems to be sufficient to almost fully inactivate the ancestral receptor's ability to recognise SCR03.

Structural determinants of SRK specificity

We next sought to identify the structural determinants of the S03 and S28 specificities. We used the structural model of the interaction complex formed by SCR and SRK (Ma et al., 2016) to compare cognate and non cognate complexes in terms of number and nature of atomic contacts and predict how the ancestral receptor is expected to interact with either SCR03 or SCR28. As previously demonstrated by Ma et al. (2016), SCR binding induces homodimerization of the extracellular domain of SRK (eSRK, Figure 3—figure supplement 1), forming eSRK:SCR heterotetramers composed of two SCR molecules bound to two SRK molecules (Ma et al., 2016). The switch in recognition specificity occurred along the branch leading to SRK28, and therefore must involve some of the 29 amino acid differences along this branch. In contrast, the 31 amino acid differences that accumulated along the branch leading to SRK03 have not fully altered the recognition specificity. Overall, we find a slightly higher number of atomic contacts between the SCR and SRK molecules in the SRK03-SCR03 cognate complex (1543 contacts over 91 and 62 distinct SRK and SCR amino acids respectively) than in the SRK28-SCR28 cognate complex (1395 contacts over 89 and 57 distinct SRK and SCR amino acids respectively, Supplementary file 1). As already noted by Ma et al. (2016) in Brassica, and in line with the distribution of positively selected sites along the sequence (Castric and Vekemans, 2007), in both cases the interaction interface was mostly concentrated around the three ‘hypervariable’ portions of the SRK protein (Figure 4).

Figure 4. Map of atomic contacts from structural modelling of S03 and S28 complexes.

Figure 4.

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The X and Y axis represent the eSRK and SCR protein respectively. Open circles and squares represent amino acid contacts between eSRK and SCR proteins in cognate S03 and S28 complexes respectively. Red symbols represent SRK amino acids that differ between SRK03 and SRK28. Full diamonds and dots correspond to amino acid contacts in non-cognate SRK03/SCR28 and SRK28/SCR03 complexes respectively. Hypervariable regions 1, 2 and 3 of eSRK are represented by vertical grey bars at position 219–225, 290–312 and 334–348 respectively (Ma et al., 2016).

Contrary to our expectation, however, the non-cognate complexes did not differ substantially from the cognate complexes on the basis of their total number of atomic contacts (Figure 4). Specifically, the SRK03/SCR28 and SRK28/SCR03 non-cognate complexes were predicted to establish a total of 1546 and 1328 contacts, respectively, between atoms of their SCR and SRK molecules, very close to the 1543 and 1395 predicted contacts for the SRK03/SCR03 and SRK28/SCR28 cognate complexes (Supplementary file 1). They were also not characterized by any obvious steric clashes that would prevent the proper docking of SCR and SRK. Hence, the overall stability of the complex does not seem to be the primary determinant of recognition specificity. Consequently, the specificity of the interaction must be a function of some qualitative features rather than of the quantitative strength of the interaction.

While the majority of amino acids retained unchanged atomic contacts in cognate vs. non-cognate interactions, using a 5% threshold we identified six amino acid positions of the eSRK protein that displayed important differences in terms of contacts they establish with SCR in the cognate vs. the non-cognate complexes (Figure 5—figure supplement 1B, C, E and F). Four of these amino acid positions also displayed very contrasted interactions in the S03 vs. the S28 complexes (residues S201, R220, R279, W296, Figure 5—figure supplement 1A and D). For instance, the R residue at position 279 of SRK establishes three times more contacts with SCR in the SRK03/SCR03 than in the SRK28/SCR28 complex (Figure 5, Figure 5—figure supplement 1A). These four residues are therefore prime candidates for the determination of binding specificity. Intriguingly, three of the six amino acids were identical between SRK03 and SRK28, and yet interacted in sharply different ways with their respective SCR ligands. For instance, while identical between the SRK03 and SRK28 sequences, residues W296 and R279 established three to four times more contacts in the S03 than in the S28 complex, and the R220 residue is involved in twice as many contacts in the S28 than in the S03 complex (Figure 5, Figure 5—figure supplement 1A). This suggests that involvement of these residues in the activity of the complex is mediated by substitutions at other positions along the protein sequence, for instance by displacing them spatially (Figure 5).

Figure 5. Four amino acid residues establish contrasted patterns of atomic contacts between SCR and SRK in the two cognate complexes.

In (A), residues R279 and W296 of eSRK establish a large number of atomic contacts with SCR in the S03 complex (upper panel), but a very low number of contacts in the S28 complex (lower panel). The situation is reversed in (B), where residues S201 and R220 of eSRK establish a large number of atomic contacts with SCR in the S28 complex (lower panel), but a very low number of contacts in the S03 complex (upper panel). SRK chains are coloured in light and dark green for SRK03 and SRK28, respectively; SCR chains are coloured in orange and red for SCR03 and SCR28, respectively. Amino acid residues are shown in stick representation, with dotted lines indicating atom pair contacts below 4 Å, excluding hydrogen atoms. Note that for clarity a more stringent threshold was used to define atomic contacts here (4 Å) than in Figure 5—figure supplement 1 and Figure 6—figure supplement 1 (where a 5 Å threshold was used for a more comprehensive analysis), but the results are qualitatively similar.

Figure 5.

Figure 5—figure supplement 1. Comparison of amino acid contacts between the different SCR/SRK complexes points to amino acids of SRK potentially involved in specificity of ligand recognition.

Figure 5—figure supplement 1.

The number of atomic contacts between amino acids of the receptor and the ligand was compared between (A) the two cognate complexes (SCR03/SRK03 vs SCR28/SRK28); (B) the cognate vs non-cognate complex established by SRK03 (SCR03/SRK03 vs SCR28/SRK03) and (C) the cognate vs non-cognate complex established by SRK28 (SCR28/SRK28 vs SCR03/SRK28). The three tables list amino acid positions who establish the most extreme differences in atomic interactions in each comparison, values in parentheses correspond to the proportion of contacts realized by each aa independently ((number of contact in the complex/number of contact for one aa) x 100). The full distributions are shown in panels (D), (E) and (F) as the difference of proportion of contacts realized by each aa in both complexes. The vertical lines represent the 5% extreme values. Positive differences correspond to aa involved in numerous contacts in complex one compare to complex two whereas negative differences correspond to aa involved in numerous contact in complex two compare to complex 1.
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We then compared the predicted binding features of the functional form of the putative ancestral receptor (SRKaIA) with both SRK03 and SRK28. When complexed with SCR03, 46 SRKaIA interacting pairs of amino acids were identical with SRK03, whereas only 24 were identical with SRK28. Similarly, when complexed with SCR28, 37 SRKaIA interacting pairs of amino acid residues were identical with SRK03, but only 18 were identical with SRK28 (Figure 6). This trend is also visible at the level of individual atomic contacts and at the level of involved amino acids both in SRK and SCR molecules (Figure 6—figure supplement 1). Hence, even though there is no quantitative difference in how strongly SRK03 and SRK28 bind SCR03 and SCR28 (Supplementary file 1), the amino acids that come into contact in the two complexes are not identical, and the contacts established by SRKaIA with SCR qualitatively resemble more those established by SRK03 than those established by SRK28.

Figure 6. Comparison of predicted binding properties of SRK03, SRK28 and SRKaIA receptors when forming a complex with either the SCR03 or the SCR28 ligand.

Residues in contact between SRKaIA and SCR are more often also in contact between SRK03 and SCR than between SRK28 and SCR. The Venn diagram indicates the number of contacts between amino acid residues that are shared or specific across variants of the SRK receptor and SCR.

Figure 6.

Figure 6—figure supplement 1. Venn diagram representations of binding features for SRK03, SRK28 and SRKaIA receptors coupled with either SCR03 or SCR28 ligand.

Figure 6—figure supplement 1.

Two features were compared: (i) Atomic contacts are defined by the nature of the amino acid in both proteins, the chain they belong to, their position and the respective atoms involved in the contact (C, N, O, CA main chain atoms or the side chain atoms Cα, Cβ, Cε, …) and (ii) The amino acid involved are defined by the nature of the amino acid, the chain they belong to and their position, independently for SRK (numbers on top) and SCR (bottom numbers). For instance, SRKaIA shared 228 atomic contacts with SRK03 and only 171 contacts with SRK28, when complexed with SCR03. This tendency is also apparent when SRKaIA was complexed with SCR28 (298 identical contact with SRK03 but only 161 with SCR28).
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Overall, asymmetrical diversification of the ancestor into the phenotypically distinct S03 and S28 specificities is therefore associated with structural changes of the SRK receptor and the way it interacts with its SCR ligand. Despite similar levels of overall sequence divergence between SRKa and either SRK03 or SRK28, the binding properties of SRK03 have remained similar to those of SRKaIA, while the way SRK28 is predicted to interact with the SCR ligand has diverged from its ancestral state, consistent with the phenotypic switch that has occurred along this lineage.

Discussion

Ancestral resurrection in a higher organism

Ancestral resurrection approaches have been used to decipher evolutionary processes in a number of biological systems including enzymes (Starr et al., 2017; Tufts et al., 2015), visual pigments (Chang et al., 2002) or transcription factors (Pougach et al., 2014). However, an essential limitation in these studies is that the ancestral protein function is typically determined in vitro, strongly limiting biological realism of the reconstructed function. To our knowledge, a single study involved a comparable in vivo ancestral resurrection approach in a higher organism, in the context of functional evolution of the alcohol dehydrogenase protein (ADH) in Drosophila lineages (Siddiq et al., 2017). Such whole organism studies are biologically most relevant, as they allow a focus on integrated phenotypes (here the self-incompatibility reaction, which has a strong and direct link with organismal fitness) rather than on highly reductionist phenotypes (such as in vitro assays of protein activity).

Asymmetrical structural and functional divergence

Functional diversification of receptor-ligand interactions is a common process that takes place over long evolutionary times. However, in most cases we remain far from a predictive understanding of how the specific evolutionary forces imposed on a given interacting pair of proteins at the molecular level translate into a given process of functional diversification. As a result, little is known about how diversification happens in nature, both in terms of whether some evolutionary pathways are preferentially followed over others and in terms of the molecular modifications involved. Here, we show that diversification of two S-alleles from their putative common ancestor proceeded through asymmetrical functional divergence, with one allele having largely retained its ancestral specificity and the other one having acquired a novel recognition specificity that was previously absent.

Overall, our results are thus compatible with the model of SI specificity diversification by compensatory mutation of Gervais et al. (2011) and one version (i.e. with maintenance of the ancestral specificity) of the progressive reinforcement model of Chookajorn et al. (2004). However, they do not allow to discriminate between these two models, that is to determine whether a transient loss of self-recognition is required (Uyenoyama et al., 2001; Gervais et al., 2011). In contrast, the model of promiscuous dual-specificity intermediates (Matton et al., 1999; Figure 1—figure supplement 1C) can be rejected, because Uyenoyama and Newbigin (2000) have shown that a dual-specificity haplotype can be maintained in the population only in the absence of the ancestral specificity, while we show here that the ancestral specificity (S03) remained present. Similarly, the progressive reinforcement model with replacement of the ancestral specificity by two functionally diverged specificities can be rejected (Figure 1—figure supplement 1B).

Long-term maintenance of SI specificity rather than rapid turnover

Models of SI evolution suggested that turnover of recognition specificities may be common over evolutionary times (Gervais et al., 2011). The observation that the ancestral SRKaIA recognition specificity was stably maintained along the SRK03 lineage demonstrates that no turnover event occurred over a substantial period of time (i.e. since S03 and S28 started to diverge from one another, ca. a few million years ago). The long-term maintenance of S-haplotype specificities was previously suggested by the comparison of certain A. halleri and A. lyrata S-lineages with their A. thaliana orthologs. Specifically, some A. thaliana accessions with haplogroup A were able to reject A. halleri pollen expressing the cognate AhSCR04 specificity (Tsuchimatsu et al., 2010), despite about 6 million years of divergence (Hohmann et al., 2015). Similarly, an overexpressed version of A. thaliana SCR haplogroup C was also able to elicit a SI reaction on pistils expressing the cognate A. lyrata AlSRK36 specificity (Dwyer et al., 2013). However, beside the fact that these were trans-specific comparisons, it is important to note that A. thaliana has become predominantly self-fertilising, and no longer has an intact SI system due to alterations of SI components (Bechsgaard et al., 2006; Shimizu and Tsuchimatsu, 2015), so our results provide the first direct test of the hypothesis that recognition specificities remain stable, rather than undergoing recurrent rapid turnover.

Despite the obvious conservation of recognition specificity between SRKaIA and SRK03, we note that the SI response elicited by the reconstructed SRKa was overall weaker and more variable across replicate lines than those elicited by the native SRK constructs. This partial response could be due to incorrect inference of the ancestral receptor sequence. Ancestral reconstruction is based on probabilistic inference, and our results show that a single amino acid modification (I vs M at position 208) can entirely compromise the SI response. It is therefore possible that the putative ancestral receptor had a partly diminished capacity to elicit the SI reaction because of imperfect reconstruction. Alternatively, this partial response could be due to parallel changes to the SCR ligand, followed by amino acid substitutions occurring between SRKa and SRK03 to fine-tune the SRK03/SCR03 interaction. Resurrection of the ancestral SCRa ligand will be needed to distinguish between these possibilities.

Structural determinants of the shift in binding specificity of a receptor-ligand interaction

Structural modelling of the cognate vs non-cognate complexes suggested that specificity of the receptor/ligand interaction depends on qualitative features rather than quantitative strength of the interaction. In other words, a ligand is predicted to bind its non-cognate just as well as its cognate receptor, but it activates only the proper one by interacting with a different set of amino acid residues at the binding interface. This may point to a mechanism of allosteric activation, whereby changes in the set (rather than number) of amino acid contacts with the ligand lead to changes in the shape of the receptor. Allosteric activation is important in the context of protein-protein interactions (Kang et al., 2015) as well as of the binding of transcription factors to their DNA binding sites (Gronemeyer and Bourguet, 2009) and may be a general feature of molecular interfaces (Ma et al., 2010). In our system, the allosteric activation model suggests that only a few amino acid residues determine recognition specificity. This can be tested by directly measuring binding affinities, following Ma et al. (2016). It will be interesting to determine experimentally whether this conclusion extends to more diverged pairs of SCR-SRK alleles, whose interactions are more common in SI systems than those between highly similar alleles studied here, because this balanced polymorphism has been maintained over a very long evolutionary time.

Materials and methods

Phylogeny-based ancestral SRK resurrection

Sequence collection

In order to reconstruct the ancestral sequence of the AhSRK28 and AhSRK03 SRK alleles, we first collected 17 full or partial sequences of the SRK first exon belonging to the haplogroup B/2. Seven allele sequences belonged to A. halleri (AhSRK03, AhSRK08, AhSRK09, AhSRK19, AhSRK23, AhSRK27, AhSRK28), six to A. lyrata (AlSRK06, AlSRK08, AlSRK14, AlSRK18, AlSRK29, AlSRK39) and four to Capsella grandiflora (CgrSRK1, CgrSRK4, CgrSRK5, CgrSRK6). For the AhSRK19, AhSRK23 and AhSRK27 alleles the SRK exon1 partial sequence was supplemented by Sanger sequencing. We added three sequences from the SRK haplogroup A3/3 as outgroups (AhSRK04, AhSRK10, AhSRK29). Accession numbers for the sequences are reported in Supplementary file 2. After preliminary analyses and in order to obtain a well-supported phylogenetic tree we removed three fast evolving sequences: CgrSRK6, AhSRK08 and AlSRK29.

Phylogenetic analysis

Sequences were aligned with MACSE v1.2 (Ranwez et al., 2011). Based on the 1338 base pairs alignment, SRK phylogenetic trees were built with both Maximum Likelihood (ML) (PHYML 3.0, Guindon et al., 2010) and Bayesian methods (MrBayes 3.2.4, Ronquist and Huelsenbeck, 2003). For the ML analysis the model used was chosen according to jModelTest 2.1.10 (Darriba et al., 2012) using the Bayesian Information Criterion (BIC) (TPM3uf+Γ). Node stability was estimated by 100 non-parametric bootstrap replicates (Felsenstein, 1985). For the Bayesian inference, a codon model with a GTR+Γ+I model of substitution was used; two runs of four Markov chains were calculated simultaneously for 400,000 generations with initially equal probabilities for all trees and a random starting tree. Trees were sampled every 10 generations, and the consensus tree with posterior probabilities was calculated after removal of a 25% burn-in period. The average standard deviation of split frequencies between the two independent runs was lower than 0.01. The resulting topology was used to infer SRKa (Figure 1—figure supplement 2).

Ancestral SRK reconstruction

The ancestral reconstruction was conducted with the codeml program of the PAML 4.8 package (Yang, 2007) under six different models: M0, M0-FMutSel0, M0-FMutSel, M3, M3-FMutSel0, M3-FMutSel. M0 refers to a one-ratio model, which assumes a single ω (dn/ds) across branches and sites (Goldman and Yang, 1994), whereas M3 allows ω to vary across sites according to a discrete distribution (with 2 or three categories depending on the models) (Yang et al., 2000). The mutation-selection models incorporate parameters for mutation bias, with (FMutSel) or without (FMutSel0) selection on synonymous rate (Yang and Nielsen, 2008). Models were compared using Likelihood Ratio Tests (LRT) and Akaike Information Criteria (AIC). The best fitting-model was found to be M3FMutSel (Supplementary file 3), therefore the ancestral amino acid sequence reconstructed with this model was used to generate transgenic lines (SRKaIA). The ancestral amino acids inferred with the three ‘M3’ models were identical except for three sites with relatively lower probability: 33 (A or V), 208 (I or M) and 305 (A or G) (Figure 3—figure supplement 2). Two of these sites were close (208) or within (305) hyper variable regions and therefore potentially functionally important. To take into account these uncertainties, three additional transgenic lines were generated with a different SRKa and tested: SRKaIG, SRKaMA and SRKaMG, in order to evaluate all four combinations of two amino acids at these two sites.

Generation and selection of A. thaliana transgenic lines

We generated 12 series of A. thaliana C24 transgenic plants (AhSRK03; AhSRK28; AhSRK03p:GFP; AhSRK28p:GFP; p03_SRKaIA_k03; p28_SRKaIA_k28; p03_SRKaIG_k03; p28_SRKaIG_k28; p03_SRKaMA_k03; p28_SRKaMA_k28; p03_SRKaMG_k03 and p28_SRKaMG_28). All DNA amplifications were performed with the primeSTAR DNA polymerase (Takara, Japan) and all constructs were validated by SANGER sequencing. We used gateway vectors (Life Technologies, USA) for expression of transgenes in A. thaliana. AhSRK03, AhSRK28, proAhSRK03 and proAhSRK28 were amplified using attB1 and attB2 containing primers (Supplementary file 4) from BAC sequences of A. halleri S03 and S28 (KJ772378-KJ772385 and KJ461475-KJ461478 respectively, Goubet et al., 2012). Amplification products were inserted by BP recombination into the pDONR 221 plasmid. Promoters proAhSRK03 and proAhSRK28 designed for p03_SRKa_k03 and p28_SRKa_k28 constructs respectively were amplified with attB4 and attB1r containing primers (Supplementary file 4). Amplification products were inserted by BP recombination into a pENTR-P4-P1R plasmid. Kinase domains of AhSRK03 and AhSRK28 were amplified with attB2r and AttB3 containing primers (Supplementary file 4). Amplification products were inserted by BP recombination into a pENTR-P2R-P3 plasmid.

Constructs expressing native SRK (AhSRK03 and AhSRK28) and GFP expressing constructs (AhSRK03p:GFP and AhSRK28p:GFP) were generated by LR reaction between entry clones and the destination vectors pK7WG or pKGWFS7.0. SRKa constructs were generated by LR triple recombination into the pK7m34GW destination vector with i) attL4 and attR1 promoters entry clones, ii) synthesized putative ancestral S-domain surrounded by attL1 and attL2 sequences and iii) attR2 and attL3 kinase domain entry clones. Details of the molecular constructs are displayed in Figure 1—figure supplement 3. A. thaliana plants were grown under 16 hr/20 °C day, 8 hr/18°C night and 70% humidity greenhouse conditions. When plants displayed approximately 20 flowers, transformation was performed by Agrobacterium tumefaciens-mediated floral deep according to Logemann et al. (2006). After seed harvesting, single insertion homozygous lines were selected via multiple rounds of antibiotic selection on selective medium as described in Zhang et al. (2006).

Microscopy

GFP expression in AhSRK03p:GFP and AhSRK28p:GFP and the results of cross pollination experiment were monitored under UV light using an Axio imager two microscope (Zeiss, Oberkochen, Germany) coupled with a HXP120 Light source (LEJ, Jena, Germany). Pictures were taken with an Axiocam 506 color camera (Zeiss, Oberkochen, Germany) and read with the Zeiss ZEN two core software package. For GFP expression, floral buds, pieces of leaves and floral hamp cross sections from AhSRK03p:GFP and AhSRK28p:GFP were observed. For pollination assays, the number of germinated pollen grains per stigma was counted after aniline blue staining up to 50 pollen tubes.

Aniline blue staining

To evaluate the SI response of transgenic lines, floral buds of developmental stage 9–11 were emasculated at day-1. At day-2, they were pollinated with frozen A. halleri pollen expressing S03 or S28 specificity. Pistils were harvested 6 hr after pollination and fixed in FAA (4% formaldehyde, 4% acetic acid, ethanol) overnight. On day-4, pollinated stigmas were washed three times in water, incubated 30 min in NaOH 4M at room temperature, washed three times in water and conserved for 30 min in the last bath. Finally, pistils were incubated overnight in aniline blue (K3PO40.15 M, 0.1% aniline blue) and mounted between microscopic slides at day-5.

Expression analysis

RNA extraction was performed onto stages 11–14 floral buds with the nucleospin RNA Plus Extraction kit (Macherey Nagel, GmbH and Co. DE). For each line, floral buds were harvested onto three different individuals. cDNA synthesis was performed using RevertAid Reverse Transcriptase (Thermo Fisher Scientific, Massachusetts, USA). 1 µg of extracted RNA was mixed with 0.2 µg of Random hexamer primer, psq H2O 12.5 µL. After incubation at 65°C, 4 µL of 5X reaction buffer; 0,5 µL of Riboblock RNase inhibitor (Thermo Fisher Scientific, Massachusetts, USA), 10 mM of each dNTP and 200 U of RevertAid Reverse Transcriptase were added and incubated firstly at 25°C and then 10 min at 42°C. cDNA quantification was performed in triplicate using LightCycler 480 Instrument II (Roche molecular systems, Inc, Pleasanton, USA). cDNA amplification of SRK fragments was performed with forward primer located on the S-domain (For: AGGAATGTGAGGAGAGGTGC) and the reverse primer located on the second exon (Rev: TCCTACTGTTGTTGTTGCCC). According to Liu et al. (2007), Ubiquitin was used as housekeeping gene (For: CTGAGCCGGACAGTCCTCTTAACTG; Rev: CGGCGAGGCGTGTATACATTTGTG).

Interacting protein modelling

All structural models were created using MODELLER (Sali and Blundell, 1993; Fiser et al., 2000), applying a template-based docking approach with the eSRK9:SCR9 X-Ray structure (Ma et al., 2016) as reference. Input sequence alignments were created using multiple sequence alignments of all relevant SRK sequences and of all relevant SCR sequences. The resulting models were subsequently optimized using the GalaxyRefineComplex web service (Heo et al., 2016).

The top models for each receptor-ligand pair were then considered and the number of atomic contacts between chains was counted using a 5 Å atom-atom cut-off (e.g. Lensink et al., 2017). Hydrogen atoms were excluded from the calculation. Visualization was done, and for clarity images were made with a 4 Å atom-atom cut-off using the PyMol molecular graphics system, version 1.7.2.1, Schrodinger LLC.

Acknowledgements

This research received support through a grant from the European Research Council (NOVEL project, grant #648321). The authors thank the French Ministère de l’Enseignement Supérieur et de la Recherche, the Hauts de France Region and the European Funds for Regional Economical Development for their financial support to this project. They also thank S Billiard, I Fobis-Loisy, E Durand, D Charlesworth, M Uyenoyama and J Nasrallah for useful discussions.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Vincent Castric, Email: vincent.castric@univ-lille.fr.

Sheila McCormick, University of California, Berkeley, United States.

Christian S Hardtke, University of Lausanne, Switzerland.

Funding Information

This paper was supported by the following grant:

  • H2020 European Research Council Novel project grant #648321 to Vincent Castric.

Additional information

Competing interests

No competing interests declared.

Author contributions

Data curation, Formal analysis, Investigation, Methodology.

Data curation, Software, Formal analysis, Investigation, Methodology.

Software, Formal analysis, Investigation, Methodology.

Software, Formal analysis, Investigation, Methodology.

Supervision.

Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Project administration.

Additional files

Supplementary file 1. Detail of the different SRK/SCR interaction protein modelling.

Following the eSRK:SCR template structure where two SCR molecules interact with two SRK proteins to form a heterotetramer (Ma et al., 2016), we indicate the two SRK molecules with their chain identifier A and B and the two SCR molecules with G and H. For each complex, the number of amino acids involved and the number of atomic contacts are defined for each protein chain interaction (AG, AH, BG and BH). Underlined numbers in the column ‘involved aa’ correspond to the number of amino acids involved in both cognate and non-cognate interactions.

elife-50253-supp1.doc (43.5KB, doc)
Supplementary file 2. Accession numbers for the sequences used in the phylogenetic reconstruction.
elife-50253-supp2.doc (37KB, doc)
Supplementary file 3. PAML ancestral analyses of the SRK protein and model comparison.

np is the number of parameters in the model; lnL is the log likelihood score; AIC (Akaike Information criterion = −2*lnL+2*np) is a measure of the goodness of fit of an estimated statistical model; ω is the nonsynonymous/synonymous substitution ratio; LR is the likelihood ratio: df is the degree of freedom in LRT (Likelihood Ratio Test); *** Highly significant (p-value<0.0001).

elife-50253-supp3.doc (35.5KB, doc)
Supplementary file 4. Gateway primers used for molecular constructs.
elife-50253-supp4.doc (31KB, doc)
Supplementary file 5. Key resources Table.
elife-50253-supp5.doc (108.5KB, doc)
Transparent reporting form

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

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Decision letter

Editor: Sheila McCormick1
Reviewed by: Sheila McCormick2, Nick H Barton3

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Self-incompatibility in the Brassicaceae is mediated by the S locus, which encodes two interacting proteins, a female-expressed S-locus Receptor Kinase and a male-expressed ligand, S-Locus Cysteine-rich Protein (SCR). Pollen is rejected when SRK and SCR are encoded by the same haplotype. Highly diverged allelic variants segregate in natural populations, but how this allelic diversity arose from an ancestral haplotype is unresolved. The authors use ancestral reconstruction to investigate how different S haplotypes might have arisen – which is interesting in itself but also as a model for the general question of how genes diversify despite constraints due to specific interactions. A main strength of this work is that four plausible ancestral alleles are reconstructed (and replicated). They then tested 3 proposed models, by introducing various constructs of A. halleri SRK into A. thaliana. Their results provide a convincing answer: favoring compensatory mutations and SC intermediates, and rejecting 2 other proposed models.

Decision letter after peer review:

Thank you for submitting your article "Asymmetrical diversification of the receptor-ligand interaction controlling self-incompatibility in Arabidopsis" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Sheila McCormick as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and Christian Hardtke as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Nick H Barton (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This impressive paper sets out to distinguish how different S haplotypes have arisen. The authors use ancestral reconstruction to investigate S locus evolution – which is interesting in itself but also, as the authors explain, a model for the general question of how genes diversify despite constraints due to specific interactions. A key issue in such studies is the reliability of the reconstruction. However, four plausible ancestral alleles are reconstructed (and replicated), which is a strength.

There are 3 ideas in the field, and to test them the authors introduce various constructs of A. halleri SRK into A. thaliana. Their results provide a convincing answer (i.e. they favor the one of compensatory mutations and SC intermediates (Gervais).

Essential revisions:

The experiments are easy to follow and the discussion is thoughtful, but could be shortened by removing unnecessary repeats. For instance, the first paragraph just repeats what has already been stated perfectly clearly. Discussion subsection “Asymmetrical structural and functional divergence” can also be shortened considerably, and these shortenings will help readers come to the most important questions that they will have in mind at this point – i.e., which of the models outlined in the Introduction are excluded. Given the value of the work, it is worthwhile to make the text as clear as possible.

This section needs slight revision, to something like the following: "Overall, our results thus unambiguously reject two previously proposed models of SI specificity diversification. The model of gradual divergence of pairwise SRK-SCR affinities along lineages (Chookajorn et al., 2004) is rejected because it predicts that both descendent alleles would have been functionally distinct from the ancestor. The model of promiscuous dual-specificity intermediates (Matton et al., 1999) is also rejected, because Uyenoyama and Newbigin, 2000, have shown that a dual-specificity haplotype can be maintained in the population only in the absence of the ancestral specificity, while we show here that the ancestral specificity (S03) remained present. In contrast, the remaining scenario (in which diversification proceeds through transient self-compatible intermediates; Gervais et al., 2011; Uyenoyama et al., 2001) is compatible with our observation of long-term maintenance of functional specificities".

Please consider stating putative ancestral throughout or "ancestral", as you only surmise that this could be the scenario. Similarly, the terms "resurrected" and "now extinct" are suppositions.

In later experiments they analyze potential amino acid contacts in the synthesized SRKs and SCRs. Using the number of contacts seems somewhat crude; are there other cases that can be cited where this sort of metric has succeeded?

Subsection “Asymmetrical structural and functional divergence”: – One cannot conclude that specificities evolve through cladogenetic change rather than anagenetic. The latter refers to the question of whether changes are associated with speciation events. Here, the observation is that function has been preserved along one lineage but not the other. Does this really address whether speciation is associated with functional divergence?

Discussion section: It may be that "the specificity of the receptor/ligand interaction depends on qualitative features rather than quantitative", but that seems more general than " allosteric activation", which refers to a change in specificity caused by a change in the shape of the protein. If the argument is that allostery is involved, it needs to be made more clearly.

Please give more information (i.e. add a figure) about the amino acid sequence differences, including the total number of differences between the SRK sequences used in the study, and their locations in relation to the recognized functional domains of the protein. Subsection “Phylogeny-based ancestral SRK reconstruction” mentions "the vast majority of amino acid residues where SRK03 and SRK28 differ", but doesn't tell us the actual number or any other information. Subsection “Ligand specificity of the ancestral SRK” says that the study examined the S-domain, but the domains have not been clearly outlined or explained. It would also have helped understanding to be told about the differences from the inferred ancestral protein sequence. Without these details, it is difficult to understand the naming of the sequences that were tested. A brief description of the phylogenetic approach used to reconstruct the sequence of SRK03 and SRK28's most recent common ancestor would also be helpful.

Can Figure 1A be revised a bit? The shapes are an over-simplification of the experiments, as there were 4 different versions tested, the figure implies only 1.

eLife. 2019 Nov 25;8:e50253. doi: 10.7554/eLife.50253.sa2

Author response

Essential revisions:

The experiments are easy to follow and the discussion is thoughtful, but could be shortened by removing unnecessary repeats. For instance, the first paragraph just repeats what has already been stated perfectly clearly.

We have removed this first paragraph.

Discussion subsection “Asymmetrical structural and functional divergence” can also be shortened considerably, and these shortenings will help readers come to the most important questions that they will have in mind at this point – i.e., which of the models outlined in the Introduction are excluded. Given the value of the work, it is worthwhile to make the text as clear as possible.

We have shortened this paragraph by one half and now come more directly to the core questions.

This section needs slight revision, to something like the following: "Overall, our results thus unambiguously reject two previously proposed models of SI specificity diversification. The model of gradual divergence of pairwise SRK-SCR affinities along lineages (Chookajorn et al., 2004) is rejected because it predicts that both descendent alleles would have been functionally distinct from the ancestor. The model of promiscuous dual-specificity intermediates (Matton et al., 1999) is also rejected, because Uyenoyama and Newbigin, 2000, have shown that a dual-specificity haplotype can be maintained in the population only in the absence of the ancestral specificity, while we show here that the ancestral specificity (S03) remained present. In contrast, the remaining scenario (in which diversification proceeds through transient self-compatible intermediates; Gervais et al., 2011; Uyenoyama et al., 2001) is compatible with our observation of long-term maintenance of functional specificities".

We have revised this paragraph (subsection “Asymmetrical structural and functional divergence”) to better clarify which models can be rejected, which led us to expand a bit the paragraph where we present the models in the Introduction. In particular we placed a stronger focus on the persistence of the ancestral specificity after diversification, which is predicted by Gervais et al., 2011 and possibly also by Chookajorn et al., 2004. We note that functional turnover is another possible outcome for both models, but the lack of formal theoretical analysis of the latter prevents more precise distinction at this stage. We feel that this more precise presentation of the models will make the implications of our results more evident to the readers.

Please consider stating putative ancestral throughout or "ancestral", as you only surmise that this could be the scenario. Similarly, the terms "resurrected" and "now extinct" are suppositions.

We have changed the wording to “putative ancestral” at most places in the text.

As requested, we have changed the term “resurrected” to “reconstructed” in the Abstract. At other places in the text we still refer to a “resurrection” experiment, as this wording is now well established in the literature.

We have retained in the Abstract the mention to the fact that the putative ancestor is “now extinct”, as this is indeed reflecting the reality and is not a supposition.

In later experiments they analyze potential amino acid contacts in the synthesized SRKs and SCRs. Using the number of contacts seems somewhat crude; are there other cases that can be cited where this sort of metric has succeeded?

Computational predictions of protein-protein interaction use several quantities to estimate the quality of the predicted model, including the fraction of native contacts. This quantity is purely based on interatomic and/or inter-residue distances criteria, as we have used here and is common practice in the field. We have added a reference to the CAPRI experiment (Lensink et al., 2017) that evaluates the performance of different metrics used to evaluate the quality of protein docking (including the number of atomic contact).

Subsection “Asymmetrical structural and functional divergence”: One cannot conclude that specificities evolve through cladogenetic change rather than anagenetic. The latter refers to the question of whether changes are associated with speciation events. Here, the observation is that function has been preserved along one lineage but not the other. Does this really address whether speciation is associated with functional divergence?

We have removed this sentence.

Discussion section: It may be that "the specificity of the receptor/ligand interaction depends on qualitative features rather than quantitative", but that seems more general than " allosteric activation", which refers to a change in specificity caused by a change in the shape of the protein. If the argument is that allostery is involved, it needs to be made more clearly.

We have modified the sentence to better explain the allosteric activation model.

Please give more information (i.e. add a figure) about the amino acid sequence differences, including the total number of differences between the SRK sequences used in the study, and their locations in relation to the recognized functional domains of the protein. Subsection “Phylogeny-based ancestral SRK reconstruction” mentions "the vast majority of amino acid residues where SRK03 and SRK28 differ", but doesn't tell us the actual number or any other information. Subsection “Ligand specificity of the ancestral SRK” says that the study examined the S-domain, but the domains have not been clearly outlined or explained. It would also have helped understanding to be told about the differences from the inferred ancestral protein sequence.

We have modified Figure 3—figure supplement 1 to make this information more apparent (number of amino acid sequence differences between SRK03 and SRK28 and with SRKa, functional domain annotation of the SRK protein). We also report these numbers directly in the text.

Without these details, it is difficult to understand the naming of the sequences that were tested. A brief description of the phylogenetic approach used to reconstruct the sequence of SRK03 and SRK28's most recent common ancestor would also be helpful.

We now explain directly the naming of the sequences: “This most likely ancestral amino acid sequence had an Isoleucine (I) at position 208 and an Alanine (A) at position 305 and is noted SRKaIA”.

Can Figure 1A be revised a bit? The shapes are an over-simplification of the experiments, as there were 4 different versions tested, the figure implies only 1.

We have improved this figure to represent the four reconstructed ancestors.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Figure 2—source data 1. Number of germinated pollen grains per stigma after pollination.

    Two SRK03 and SRK28 lines were pollinated with A. halleri pollen expressing either S03 or S28 specificities.

    elife-50253-fig2-data1.xlsx (10.8KB, xlsx)
    Figure 3—source data 1. Number of germinated pollen grains per stigma after pollination.

    Each SRKa line was pollinated with A. thaliana C24, A. halleri S03 and S28 pollen.

    elife-50253-fig3-data1.xls (56.5KB, xls)
    Figure 3—figure supplement 4—source data 1. Number of germinated pollen grains per stigma after pollination of A. thaliana lines with SRKa transcript levels below detection threshold.

    Each SRKa line in S03 and S28 context were pollinated with A. thaliana C24, A. halleri S03 and S28 pollen.

    elife-50253-fig3-figsupp4-data1.xls (31KB, xls)
    Supplementary file 1. Detail of the different SRK/SCR interaction protein modelling.

    Following the eSRK:SCR template structure where two SCR molecules interact with two SRK proteins to form a heterotetramer (Ma et al., 2016), we indicate the two SRK molecules with their chain identifier A and B and the two SCR molecules with G and H. For each complex, the number of amino acids involved and the number of atomic contacts are defined for each protein chain interaction (AG, AH, BG and BH). Underlined numbers in the column ‘involved aa’ correspond to the number of amino acids involved in both cognate and non-cognate interactions.

    elife-50253-supp1.doc (43.5KB, doc)
    Supplementary file 2. Accession numbers for the sequences used in the phylogenetic reconstruction.
    elife-50253-supp2.doc (37KB, doc)
    Supplementary file 3. PAML ancestral analyses of the SRK protein and model comparison.

    np is the number of parameters in the model; lnL is the log likelihood score; AIC (Akaike Information criterion = −2*lnL+2*np) is a measure of the goodness of fit of an estimated statistical model; ω is the nonsynonymous/synonymous substitution ratio; LR is the likelihood ratio: df is the degree of freedom in LRT (Likelihood Ratio Test); *** Highly significant (p-value<0.0001).

    elife-50253-supp3.doc (35.5KB, doc)
    Supplementary file 4. Gateway primers used for molecular constructs.
    elife-50253-supp4.doc (31KB, doc)
    Supplementary file 5. Key resources Table.
    elife-50253-supp5.doc (108.5KB, doc)
    Transparent reporting form
    elife-50253-transrepform.docx (247.1KB, docx)

    Data Availability Statement

    All data generated or analysed during this study are included in the manuscript and supporting files.

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