The plant short-chain dehydrogenase (SDR) superfamily: genome-wide inventory and diversification patterns

Abstract

Background: Short-chain dehydrogenases/reductases (SDRs) form one of the largest and oldest NAD(P)(H) dependent oxidoreductase families. Despite a conserved 'Rossmann-fold' structure, members of the SDR superfamily exhibit low sequence similarities, which constituted a bottleneck in terms of identification. Recent classification methods, relying on hidden-Markov models (HMMs), improved identification and enabled the construction of a nomenclature. However, functional annotations of plant SDRs remain scarce.

Results: Wide-scale analyses were performed on ten plant genomes. The combination of hidden Markov model (HMM) based analyses and similarity searches led to the construction of an exhaustive inventory of plant SDR. With 68 to 315 members found in each analysed genome, the inventory confirmed the over-representation of SDRs in plants compared to animals, fungi and prokaryotes. The plant SDRs were first classified into three major types - 'classical', 'extended' and 'divergent' - but a minority (10% of the predicted SDRs) could not be classified into these general types ('unknown' or 'atypical' types). In a second step, we could categorize the vast majority of land plant SDRs into a set of 49 families. Out of these 49 families, 35 appeared early during evolution since they are commonly found through all the Green Lineage. Yet, some SDR families - tropinone reductase-like proteins (SDR65C), 'ABA2-like'-NAD dehydrogenase (SDR110C), 'salutaridine/menthone-reductase-like' proteins (SDR114C), 'dihydroflavonol 4-reductase'-like proteins (SDR108E) and 'isoflavone-reductase-like' (SDR460A) proteins - have undergone significant functional diversification within vascular plants since they diverged from Bryophytes. Interestingly, these diversified families are either involved in the secondary metabolism routes (terpenoids, alkaloids, phenolics) or participate in developmental processes (hormone biosynthesis or catabolism, flower development), in opposition to SDR families involved in primary metabolism which are poorly diversified.

Conclusion: The application of HMMs to plant genomes enabled us to identify 49 families that encompass all Angiosperms ('higher plants') SDRs, each family being sufficiently conserved to enable simpler analyses based only on overall sequence similarity. The multiplicity of SDRs in plant kingdom is mainly explained by the diversification of large families involved in different secondary metabolism pathways, suggesting that the chemical diversification that accompanied the emergence of vascular plants acted as a driving force for SDR evolution.

Figure 1

Decision rules used to make an inventory of plant SDRs using three sets of HMM. All the HMM sets were run independently on the 10 predicted proteomes. The complete inventory and the ambiguous predictions are included as supplementary material (Additional file 2: Table S2 and Additional file 1: Table S1).

Figure 2

Distribution of the SDR families in the analyzed plant genomes represented as a heat map. The heat map was built on a distribution matrix deduced from the inventory classification shown in Additional file 2: Table S2. The blue to red color gradient reflects the number of SDR listed in each family in the different genomes; the absence of family is indicated with a white square. The names of the families were deduced from the ‘SDR Nomenclature Initiative’ HMMs or by a representative gene accession for orphan families not recognized by a specific HMM.

Figure 3

Phylogenetic tree of the SDR108E and SDR115E families. The blue arrow indicates the node at the origin of the ‘AnR, 4-DFR and SDR115E’ branch. Amino acid sequences recognized by the SDR108E and SDR115E HMMs were aligned with ClustalW algorithm. The evolutionary history was inferred using the Neighbor-Joining method. The percentages of replicate trees in which the associated taxa clustered together in the bootstrap test (500 replicates) are shown next to the branches. Full references of sequences compressed in different clusters are provided as supplemental data (Additional file 3: figure 1A). Consistent trees were obtained using the Maximum Likelihood method or rooting the tree with other SDR families (SDR1E, SDR6E, SDR31E) as outgroups.

Figure 4

Diversification patterns of plant SDR families deduced from Principal Component Analysis. PCA was calculated on the distribution matrix shown in Figure 2. A) Scatter plot deduced from the two first components: the first and second axes respectively participate for 79% and 9% of the diversity. B) Contribution of different genomes (expressed as vectors) in the first and second axes values. Angiosperms genomes follow the order (anticlockwise): G. max, Z. mays, A. thaliana, S. bicolor, P. trichocarpa, O. sativa, V. vinifera .

Figure 5

Phylogenetic tree of the SDR110C family. Amino acids sequences recognized by the SDR110C HMM were aligned with ClustalW algorithm. The evolutionary history and the bootstrap test (500 replicates) were computed as described for SDR108E (Figure 3). Full references of sequences compressed in different clusters are provided as supplemental data (Additional file 3: Figure S1B).

Figure 6

Phylogenetic trees of the chlorophyllase b (NYC1/NOL) family. Amino acids sequences from the AT4G13250 family were aligned with ClustalW algorithm. The evolutionary history and the bootstrap test (500 replicates) were computed as described for SDR108E (Figure 3).

Figure 7

Phylogenetic trees of three families involved in lipid primary metabolism. (A) SDR152C-FasII-β-keto-reductase (β-KR); (B): SDR87D-FasII-Enoyl-ACP-reductase (ENR); (C) SDR52E UDP-sulfoquinovose synthase (SQD1). Amino acids sequences recognized by the SDR152C, SDR87D and SDR52E HMMs were aligned with ClustalW algorithm. The evolutionary history and the bootstrap test (500 replicates) were computed as described for SDR108E (Figure 3).