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Meta-Analysis
doi: 10.7554/eLife.00961.

Phenotypic landscape inference reveals multiple evolutionary paths to C4 photosynthesis

Affiliations
Meta-Analysis

Phenotypic landscape inference reveals multiple evolutionary paths to C4 photosynthesis

Ben P Williams et al. Elife..

Abstract

C4 photosynthesis has independently evolved from the ancestral C3 pathway in at least 60 plant lineages, but, as with other complex traits, how it evolved is unclear. Here we show that the polyphyletic appearance of C4 photosynthesis is associated with diverse and flexible evolutionary paths that group into four major trajectories. We conducted a meta-analysis of 18 lineages containing species that use C3, C4, or intermediate C3-C4 forms of photosynthesis to parameterise a 16-dimensional phenotypic landscape. We then developed and experimentally verified a novel Bayesian approach based on a hidden Markov model that predicts how the C4 phenotype evolved. The alternative evolutionary histories underlying the appearance of C4 photosynthesis were determined by ancestral lineage and initial phenotypic alterations unrelated to photosynthesis. We conclude that the order of C4 trait acquisition is flexible and driven by non-photosynthetic drivers. This flexibility will have facilitated the convergent evolution of this complex trait. DOI:http://dx.doi.org/10.7554/eLife.00961.001.

Keywords: Bayesian model; C4 photosynthesis; Other; convergent evolution.

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Conflict of interest statement

The authors declare that no competing interests exist.

Figures

Figure 1.
Figure 1.. Evolutionary paths to C4 phenotype space modelled from a meta-analysis of C3–C4 phenotypes.
Principal component analysis (PCA) on data for the activity of fiveC4 cycle enzymes confirms the intermediacy ofC3–C4 species between C3 andC4 phenotype spaces (A). Each C4trait was considered absent in C3 species and present inC4 species, with previously studiedC3–C4 intermediate species representingsamples from across the phenotype space (B). With a datasetof 16 phenotypic traits, a 16-dimensional space was defined.(C) A 2D representation of 50 pathways across this space.The phenotypes of multiple C3–C4 specieswere used to identify pathways compatible with individual species (e.g.,Alternanthera ficoides [red nodes] andParthenium hysterophorus [blue nodes]), and pathwayscompatible with the phenotypes of multiple species (purple nodes).DOI:http://dx.doi.org/10.7554/eLife.00961.004
Figure 1—figure supplement 1.
Figure 1—figure supplement 1.. A graphical representation of key phenotypic changes distinguishing C3 and C4 leaves.
Plants using C4 photosynthesis possess a number of anatomical,cellular, and biochemical adaptations that distinguish them fromC3 ancestors. These include decreased vein spacing(A) and enlarged bundle sheath (BS) cells, which lieadjacent to veins (B). Together, these adaptations decreasethe ratio of mesophyll (M) to BS cell volume. C4 metabolism isgenerated by the increased abundance and M or BS-specific expression ofmultiple enzymes (shown in purple), which are expressed in both M and BScells of C3 leaves. Abbreviations: ME–Malic enzymes,RuBisCO—Ribulose1-5,Bisphosphate Carboxylase Oxygenase,PEPC–phosphoenolpyruvate carboxylase,PPDK–pyruvate,orthophosphate dikinase.DOI:http://dx.doi.org/10.7554/eLife.00961.006
Figure 1—figure supplement 2.
Figure 1—figure supplement 2.. Phylogenetic distribution of C4 and C3–C4 lineages across the angiosperm phylogeny.
A phylogeny of angiosperm orders is shown, based on the classification bythe Angiosperm Phylogeny Group. The phylogenetic distribution of knowntwo-celled C4 photosynthetic lineages are annotated, togetherwith the distribution of C3-C4 lineages that weused in this study. The numbers of independentC3-C4, or C4 lineages present in eachorder are shown in parentheses.DOI:http://dx.doi.org/10.7554/eLife.00961.007
Figure 1—figure supplement 3.
Figure 1—figure supplement 3.. Clustering quantitative traits by EM algorithm and hierarchical clustering.
Quantitative variables were assigned binary scores using two-dataclustering techniques. Each panel depicts the assignation of presence(red squares) and absence (blue triangles) scores by the EM algorithm.Adjacent to the right are cladograms depicting the partitioning of thesame values into clusters by hierarchical clustering. Red cladogrambranches denote values partitioned into a different group to thatassigned by EM. The variables depicted in each panel are PEPC activity(A), PPDK activity (B), C4 aciddecarboxylase activity (C), RuBisCO activity(D), MDH activity (E), vein spacing(F), number of BS chloroplasts (G), BSchloroplast size (H).DOI:http://dx.doi.org/10.7554/eLife.00961.008
Figure 1—figure supplement 4.
Figure 1—figure supplement 4.. Illustration of the principle by which evolutionary pathways emit intermediate signals.
In this illustration, the phenotype consists of three traits, yielding asimple (hyper)cubic transition network. Simulated trajectories on thisnetwork evolve according to the weights of network edges(A). Probabilities were calculated from the signals emittedby simulated trajectories at intermediate nodes (B).Ensembles of trajectories were simulated to obtain probabilities fromthese signals for every possible evolutionary transition(C).DOI:http://dx.doi.org/10.7554/eLife.00961.009
Figure 2.
Figure 2.. Verifying a novel Bayesian approach for predicting evolutionary trajectories.
(A andB) Datasets were obtained from anartificially constructed diagonal dynamic matrix (A), and adiagonal matrix with linked timing of locus acquisitions(B). The single, diagonal evolutionary trajectory wasclearly replicated in both examples, over a time-scale of 16 individualsteps, or four coarse-grained quartiles. We subjected these artificialdatasets to our inferential machinery with fully characterised artificialspecies, and with 50% of data occluded in order to replicate theproportion of missing data from our C3–C4dataset. (C) When applied to our meta-analysis ofC3–C4 data, predictions were generatedfor every trait missing from the biological dataset. We tested thispredictive machinery by generating 29 artificial datasets, each missingone data point, and comparing the presence/absence of the trait aspredicted by our approach with the experimental data from the originalstudy. (D andE) Quantitative real-time PCR(qPCR) was used to verify the predicted phenotypes of fourC3–C4 species. The abundanceRbcS (D) andMDH(E) transcripts were determined from sixFlaveria species. White bars represent phenotypesalready determined by other studies, grey bars those that were predictedby the model and asterisks denote intermediate species phenotypescorrectly predicted by our approach (Error bars indicate SEM, N =3).DOI:http://dx.doi.org/10.7554/eLife.00961.010
Figure 2—figure supplement 1.
Figure 2—figure supplement 1.. Computational prediction of C3–C4 intermediate phenotypes.
A probability for the presence of unobserved phenotypic characters wasgenerated for every characteristic not yet studied in each of theC3–C4 species included in this study. Red(upward triangles) predict a posterior mean probability of >0.75 forthe presence of a C4 trait; blue (downward triangles) predicta posterior mean probability of <0.25. Darker triangles representprobabilities whose standard deviations (SD) are lower than 0.25. Yellowblocks correspond to known data: no symbol is present for traits forwhich presence and absence have an equal probability(0.25–0.75).DOI:http://dx.doi.org/10.7554/eLife.00961.011
Figure 3.
Figure 3.. The mean ordering of phenotypic changes generating C4 photosynthesis.
EM-clustered data from C3–C4 intermediatespecies were used to generate posterior probability distributions for thetiming of the acquisition of C4 traits in sixteen evolutionarysteps (A) or four quartiles (B). Circlediameter denotes the mean posterior probability of a trait being acquiredat each step in C4 evolution (the Bayes estimator for theacquisition probability). Halos denote the standard deviation of theposterior. The 16 traits are ordered from left to right by theirprobability of being acquired early to late in C4 evolution.Abbreviations: bundle sheath (BS), glycine decarboxylase (GDC),chloroplasts (CPs), decarboxylase (Decarb.), pyruvate, orthophosphatedikinase (PPDK), malate dehydrogenase (MDH), phosphoenolpyruvatecarboxylase (PEPC).DOI:http://dx.doi.org/10.7554/eLife.00961.012
Figure 3—figure supplement 1.
Figure 3—figure supplement 1.. Results obtained using data clustered by hierarchical clustering.
Traits were also assigned presence/absence scores by hierarchicalclustering. Analysis of data partitioned by hierarchical clusteringpredicted a similar sequence of evolutionary events to that shown inFigure 3 (A).Direct comparison of posterior probabilities reveals a high degree ofsimilarity between results from the data clustered by hierarchicalclustering versus the EM algorithm (B). These resultssuggest our conclusions are not affected by the different methods ofassigning binary scores to traits.DOI:http://dx.doi.org/10.7554/eLife.00961.013
Figure 3—figure supplement 2.
Figure 3—figure supplement 2.. Adding or removing traits does not affect the predicted order of evolutionary events.
Two independent pairs of traits were randomly selected and deleted fromthe analysis. In both cases, removing two traits did not affect thepredicted timing of the remaining 14 traits in the analysis(A andB). Furthermore, including twoadditional traits associated with C4 photosynthesis also didnot alter the predicted timing of other traits (C).Together, these data suggest our results are robust to both the removaland addition of traits from the phenotype space. Abbreviations: bundlesheath (BS), glycine decarboxylase (GDC), chloroplasts (CPs),C4 acid decarboxylase (Decarb.), mitochondria (MitoC)pyruvate,orthophosphate dikinase (PPDK), malate dehydrogenase (MDH),phosphoenolpyruvate carboxylase (PEPC).DOI:http://dx.doi.org/10.7554/eLife.00961.014
Figure 3—figure supplement 3.
Figure 3—figure supplement 3.. Probabilities of C4 traits being acquired simultaneously.
The extent to which C4 traits are linked in evolution wasassessed by modelling C4 evolution from a start phenotype withone trait already acquired. Linked traits would have a high probabilityof being acquired in the next event. Artificially acquired traits arelisted on the x-axis and the probability of each additional C4trait being subsequently acquired (y-axis) is denoted in each pixel ofthe heat map. There is overall very low probability for multiple traitsbeing linked in their acquisition in the evolution of C4.DOI:http://dx.doi.org/10.7554/eLife.00961.015
Figure 4.
Figure 4.. Differences in the evolutionary events generating different C4 sub-types and distantly related taxa.
Principal component analysis (PCA) on the entire landscape of transitionprobabilities using only monocot and eudicot data (A) anddata from NADP-ME and NAD-ME sub-type lineages (B) showsbroad differences between the evolutionary pathways generatingC4 in each taxon. Monocots and eudicots differ in thepredicted timing of events generating C4 anatomy andbiochemistry (C), whereas NADP-ME and NAD-ME lineages differprimarily in the evolution of decreased vein spacing and greater numbersof chloroplasts in BS cells (D).DOI:http://dx.doi.org/10.7554/eLife.00961.016
Figure 4—figure supplement 1.
Figure 4—figure supplement 1.. Variation between lineages compared to variance of overall dataset.
PCA was performed on sampled transition networks from the sets compatiblewith the overall dataset and each of the two subsets corresponding todifferent lineages: overall/monocot/eudicot (A)overall/NAD-ME/NADP-ME (B). In (A) thevariation between monocot and eudicot lineages is observed to bepreserved when the overall transition networks are included, and on asimilar quantitative scale to the variation in the overall set, embeddedmainly on the first principal axis. In (B) the variation isof a similar scale but less distinct, correlating more with the secondprincipal axis.DOI:http://dx.doi.org/10.7554/eLife.00961.017
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