SWEET/MtN3/saliva genes are prevalent in cellular organisms and play diverse roles in plants. These genes are widely considered as evolutionarily conserved genes, which is inconsistent with their extensive expansion and functional diversity. In this study, SWEET genes were identified from 31 representative plant species, and exhibited remarkable expansion and diversification ranging from aquatic to land plants. Duplication detection indicated that the sharp increase in the number of SWEET genes in higher plants was largely due to tandem and segmental duplication, under purifying selection. In addition, phylogeny reconstruction of SWEET genes was performed using the maximum-likelihood (ML) method; the genes were grouped into four clades, and further classified into 10 monocot and 11 dicot subfamilies. Furthermore, selection pressure of SWEET genes in different subfamilies was investigated via different strategies (classical and Bayesian maximum likelihood (Datamonkey/PAML)). The average dN/dS for each group were lower than one, indicating purifying selection. Individual positive selection sites were detected within 4 of the 21 sub-families by both two methods, including two monocot subfamilies in Clade III, harboring five rice SWEET homologs characterized to confer resistance to rice bacterial blight disease. Finally, we traced evolutionary fate of SWEET genes in clade III for functional characterization in future.
The Sugars Will Eventually Be Exported Transporters (SWEET) gene family, is ubiquitous in plants, and plays diverse physiological and biological roles1,2,3,4,5,6,7. The first gene of SWEET family was identified as MtN3 in Medicago truncatula, which is involved in the Rhizobium-induced nodule development1. Later, a homolog of the MtN3 gene was found in Drosophila. This homolog is expressed in embryonic salivary glands and was named saliva; thus, this type of gene was initially described as a MtN3/saliva gene. Most SWEET genes encode proteins that harbour two MtN3/saliva (MtN3_slv) domains, that consist of 3 + 1 + 3 transmembrane helices. Only a few encode proteins that harbor 3 transmembrane helices that constitute one MtN3_slv domain7,8. Subsequently, members of the MtN3/saliva gene family have been predicted or characterized to be involved in various physiological processes in plants1,2,3,4,5,6,7. One of the most fascinating discoveries was that these genes can transport sucrose across the plasma membrane, and this family was finally named the SWEET gene family7,9.
Sucrose, which is the predominant type of fixed carbon transported in plants9,10, is synthesized in mesophyll cells, imported into phloem cells and subsequently transported to heterotrophic “sinks” (meristems, roots, flowers, and seeds). In this “phloem loading” process, sucrose is first effluxed from phloem parenchyma cells by SWEETs and then loaded into the sieve element-companion cell complex (SE/CC) via active proton-coupled sucrose transporters (SUTs)11,12. Sucrose translocation has critical importance in basic physiological processes such as reproductive development, senescence, and in the allocation efficiency of plants, which is closely associated with crop yield6,13. In a more recent report, ZmSWEET4c in maize and its rice ortholog OsSWEET4, which mediates hexose transportation, were shown to influence seed filling and size14. Furthermore, the SWEET genes involved in this sugar efflux system have been shown to be hijacked by pathogens5,15,16. At least three SWEET genes are involved in the resistance to various Xanthomonas oryzae pathovar oryzae (Xoo) strains, which cause one of the most devastating global rice diseases. The dominant alleles of the recessive resistant gene OsSWEET11 (xa13), OsSWEET13 (xa25) and OsSWEET14 (xa41), are induced by the various Xoo strains in the promoter region, suggesting that they supply sugar to pathogens5,16,17. In a susceptible reaction, their promoter regions are specifically targeted by bacterial type III effector genes produce four different type TAL (transcriptional activator-like) effectors. Furthermore, another two rice SWEET genes that are phylogenetically close to the three rice SWEET resistance genes have also been inferred to be Xoo virulence targets, and may be R-genes18.
The functional importance of many SWEET genes as ubiquitous transporters remains elusive. No comprehensive survey has been conducted in SWEET genes in plant taxa. To date, most investigations on SWEET genes have focused on a few species at the whole-genome scale, including Arabidopsis thaliana, rice, soybean, and tomato7,8,19,20,21. With limited data and results, SWEET genes are believed to have been extensively conserved, but this not agree with its observed functional diversity and continuous expansion and duplication8. In this study, SWEET genes were characterized in 31 plant genomes, ranging from single-celled plants to higher terrestrial plants. The distribution and duplication models of SWEET genes were also explored here. Phylogenetic reconstruction and molecular evolution analyses of SWEET genes revealed their evolutionary genetic basis.
Genome-wide identification of SWEET genes in 30 representative plant species
In this study, SWEET genes were systematically surveyed in 31 plant genomes, ranging from aquatic algae to angiosperms. A total of 636 SWEET homologs were identified among our sampled genomes (Fig. 1 and Supplementary Table S1). Interestingly, SWEET genes were detected in unicellular aquatic algae, which was indicative of its ancient origin and functional conservation. In addition, the numbers of SWEET members in land plants indicated varying degrees of expansion compared to aquatic algae. Firstly, only one, one, four and four homologs were identified in four aquatic algae O. lucimarinus, M. pusilla, C. reinhardtii and V. carteri, respectively, all of which were remarkably fewer than those found in land plants. Secondly, in the lower land plant P. patens, which is believed to be one of the earliest land lineages that diverged from aquatic plants22, and A. trichopoda, which is the single living representative of the sister lineage of all living angiosperms23, seven and nine SWEET genes were identified, respectively. Whereas, in the non-seed lycophyte S. moellendorffii, 15 SWEET homologs were found. In G. biloba, a gymnosperm species that is described as a living fossil, 17 SWEET homologs were characterized. In the seven monocot species, eight to 26 SWEET genes were identified. In eudicots, 16 to 53 SWEET genes were observed, suggesting extensive gene expansion and duplication events. Most of the SWEET genes were observed in the legume plant, G. max and the rosids plant E. grandis, which harbored 53 and 52 SWEET homologs, respectively. Although copy number variations among species were apparently complex, our data suggested that the number of SWEET homologs in each species was positively correlated with genome-wide gene numbers (r = 0.7168, P-value = 3.79e-05) (Supplementary Fig. S1). In addition, the distribution of SWEET homologs was not evenly distributed within one species or among plant lineages. For example, no SWEET genes were observed in four of the 12 rice chromosomes, whereas, roughly 57.1% of the rice homologs were detected on chromosomes 1 and chromosome 9. In the two legume genomes G. max and M. truncatula, both the copy number and distribution of SWEET homologs were distinct (Supplementary Fig. S2).
Furthermore, the characterized SWEET proteins from various species generally fell into two types. Most of these proteins harbor two MtN3_slv domains, whereas a few consist of one MtN3_slv domain7,8. Herein, a comprehensive investigation of the number of MtN3_slv domains was conducted on all 31 plants (Supplementary Fig. S3 and Table S1), and 90% of the predicted SWEET proteins contained two MtN3_slv domains, including all homologs from three unicellular plants. SWEET proteins that only harbored one MtN3_slv domain were observed in P. patens, as well as in most multicellular plants except for S. bicolor, A. thaliana, P. vulgaris and C. grandis. Interestingly, one SWEET homologs, which were characterized in E. grandis, consisted of three MtN3_slv domains.
Expansion models of SWEET genes among plant genomes
Gene expansion or duplication, which frequently occur in plant taxa, is often followed by divergence, thereby resulting in subfunctionalization, novel evolutionary materials and adaptive advantages24,25. Diverse duplication models such as whole-genome duplication (WGD) or segmental duplications (SD), local duplication (including tandem and proximal duplications) and dispersed duplication), have been hypothesized for gene duplication24,25,26,27. Each of these models is biased in regard to gene retention by either contributing to genetic redundancy or evolutionary novelty26. Hence, estimation of the duplication model of SWEET genes was performed for the surveyed genomes via MCscanX software, including two multicellular algae, the basal land species P. patens, S. moellendorffii and all angiosperms (those species were excluded due to either having a of sing-copy SWEET genes or poorly assembled genomes) (Fig. 1)28. The results revealed that the proportions of SWEET genes retained from different gene duplication models differed within or among species. Interestingly, dispersed duplication was the only duplication mode detected within all of the surveyed species. Furthermore, dispersed duplication was also the only duplication mode in SWEET genes from two algaes and P. patens. WGD/segmental duplication events involving SWEET genes were observed in each higher plant species, but not in mosses and algae, which may be related to the phenomenon that all vascular plants undergo one or more whole-genome duplication events. At least three types of duplication events in SWEET genes were detected in every surveyed angiosperm except for the aquatic moncot S. polyrhiza. In particular, SWEET genes retained from dispersed, proximal, tandem, and WGD/segmental duplication accounted for 37.2%, 4.6%, 19.4%, and 38.7% of the duplication events, respectively. The sharp increase in the number of SWEET genes in higher plants was largely due to segmental and tandem duplication compared with basal land plants. The proportion of these two types of duplication models in each species was not equal, and a species-specific duplication model preponderance was detected. For example, in monocots, WGD/segmental duplication was preferentially enriched in M. acuminata and Z. mays to a greater degree than in all of the other surveyed monocot plants. Conversely, tandem duplication mainly contributed to the expansion of SWEET genes in the two Solanaceae plants. For the only two species harboring more than 50 SWEET genes, 69.8% of genes in G. max were derived from WGD/segmental duplication events (Supplementary Fig. S3), while 52.0% of genes in E. grandis were derived from tandem duplication, which were much higher than those in the other species.
Evolutionary rate estimation of duplicated SWEET paralog genes
Considering the important role of WGD/segmental duplication and segmental duplication in SWEET gene expansion, an estimation of the evolutionary dynamics of SWEET duplicated pairs would help to understand their evolutionary process in all surveyed angiosperms including dicot and monocot lineages. The dN/dS ratio is an important parameter for estimating molecular evolutionary rates and reflects the dynamics that drive evolution. Generally, a dN/ds ratio larger than 1 indicates positive selection and a dN/dS ratio less than 1 suggests purifying selection. In the present study, the dN/dS values of most duplicated paralogous genes were lower than 1 except for three gene pairs, which strongly indicated that most of these duplicated pairs experienced purifying selection. The three gene pairs, Eucgr. F02750/Eucgr. F02751, Gorai. 001G055600/Gorai. 001G055700, and Glyma. 05G036500/Glyma. 17G090800, exhibited dN/dS values larger than 1, suggesting that they underwent positive selection pressure during their evolutionary history.
Furthermore, these results show the different evolutionary rates of WGD and TD duplicated pairs in angiosperms (Fig. 2). Comparing all of the WGD and TD duplicated pairs in angiosperms, the average dN/dS value of WGD (0.25) was less than that of the TD duplicated pairs (0.32). Comparing these two types of duplicated pairs in only monocot or dicot lineages, the average dN/dS value of WGD was less than that of the TD duplicated pairs. Smaller dN/dS values indicated WGD gene pairs evolved more slowly. Finally, both WGD and TD pairs in dicots had a higher average dN/dS value than that in monocots, reflecting the difference between the evolutionary rates of monocot and dicot duplicated SWEET pairs.
Phylogenetic analysis of SWEET genes in 30 plant species
To better explore the evolutionary history of SWEET genes in plants, complete protein sequences of SWEET genes were used to build ML trees (Figs 3 and S4). Our phylogenetic tree exhibited exactly the same topological structure described by Chen et al.7 was observed (Fig. 3 and Table 1). Thus, SWEET genes of angiosperm plants in the phylogenetic trees were also divided into four clades, and SWEET members in algae and basal land species, including three bryophyta plants, S. moellendorffi and A. trichopoda, were used as outgroups. Moreover, SWEET genes from A. thaliana were distributed among the four clades of the two phylogenetic trees, which was also consistent with the findings of the previous study7. We followed the nomenclature of Chen et al.7 and named these clades as I, II, III, and IV, in which 146, 120, 205, and 55 genes were characterized, respectively. Few large recently-duplicated subclades (gene number >5) were observed in the phylogenetic tree, except for three sub-clades in E. grandis (6, 6 and 13 genes, respectively) and one subclade in M. domestica (7 genes). These results indicated that a few extensive gene expansion events involving SWEET genes occurred in a species-specific manner; conversely, most expansion events took place before the taxonomic families or more ancient species diverged.
Interestingly, all the algal SWEET members clustered in one cluster and was apparently an outgroup, exhibiting co-orthologous relationship of all other plant SWEET genes (Figs 3 and S4). Whereas, SWEET genes in Clade II have relatively close relationship with the algal SWEET clade. Besides, each clade has nearby nested outgroups, constituted by SWEET members from all the surveyed basal land taxonomy (bryophyta plants and S. moellendorffi), indicating these four clades split as early as land plant speciation. The SWEET genes of the gymnospermous plant were also detected within all four clades. Additionally, all angiosperm plants could be found in every clade, except the aquatic moncot, S. polyrhiza. SWEET members in S. polyrhiza were absent in Clade III and IV. Finally, compared with the other three clades, clade III has the highest number of genes (205). Five rice SWEET genes in clade III have been reported to confer susceptibility to Xoo18, and may cause bacterial blight disease in rice. In the clade IV, the lowest number of genes (55) was observed.
Molecular evolutionary analysis of SWEET genes
To better estimate the evolutionary rates of the expanded SWEET family in angiosperms, especially in dicots and monocot lineages, four clades in the phylogenetic tree were classified into distinct gene families. First, the monocot-specific (M) and dicot-specific (D) gene families were defined based on the following criteria: (1) According to the species tree (Fig. 1) and the distribution of homologs in A. thaliana, the M or D gene families should consist of homologs from most monocot or dicots species (not less than half of the dicots or monocots), (2) the clades in which the M or D gene families resided should have support values for basal nodes ≥0.70 (Fig. 4 and Table 1). These SWEET gene families were preserved throughout the evolutionary history of angiosperms and are regarded as a reliable core set of SWEET genes in angiosperms. Finally, 11 D gene families and 10 M gene families were explored, and these families accounted for the majority of all SWEET homologs. In the four clades we defined above, different numbers of M and D gene family members were characterized in each clade. Three M and four D in clade I, two M and two D in clade II, three M and three D in clade III, and two M and two D family were identified, respectively.
Firstly, possible recombination events, which may play important roles in differentiation, were also determined (See in Methods). Collectively, a total of 30 breakpoints were detected, and 19 (63.33%) occurred in nine M gene families, indicating that monocot SWEET families have a high recombination rate. Additionally, the two programs, namely, MEGA5.0 and PAML, were used to calculate the average ratio of non-synonymous to synonymous (dN/dS) for the M and D gene families (Table 2). The REL method in Datamonkey and branch-site approach in PAML were applied to detect individual sites under positive selection among the subfamilies. Positive selection sites were identified in 10 out of 21 subfamilies by at least one method. Whereas, positive selection sites were only identified in five subfamilies by both methods, including M2, M3, M7 and M8. Intriguingly, M7 and M8 belonged to clade III, and harbored the most genes. To better understand how positive selection was associated with gene function, we pinpointed the sites under positive selection of M7, that harbored one positive selection sites as identified by Datamonkey and four positive selection sites as identified by PAML. According to our data, three positive sites were detected by both methods. The sequences of M6 were aligned with MEGA and analyzed with the structure of OsSWEET2b (Os01g0700100, PDB number: 5CTG) as a reference29 (Fig. 5. We found that one positive selection site were located at the L2-3 region and three were at L4-5 (Fig. 5). The potential impact of these amino acid alterations on protein structure and function remain to be clarified.
SWEET genes are ubiquitous in cellular organisms, from monocellular prokaryotes to higher eukaryotes1,2,3,4,5,6,7. The dramatic expansion of SWEET genes in plant taxa indicates their functional importance in plants7,9,13,19,20,21,30. However, to date, only a few plant species have been investigated7,8,19,20,21, and the SWEET family has been considered to be an evolutionarily conserved family7,8. The accessibility of more high quality genome sequences provides us with an unprecedented chance to analyze this multi-copy gene family in-depth. As sequencing gaps or errors occurred in almost all sequenced genomes, the prediction of a multi-copy gene family may be underestimated. In the present study, 31 well-annotated or well-assembled genome sequences were carefully selected to minimize the impact of these errors. In addition, considering assembling and sequencing errors, the incomplete of genome sequences or errors in phylogeny reconstruction, we allowed for the gene families in our analysis to be missing in up to half of the dicot or monocot genomes (see the Results). SWEET homologs were systemically surveyed in 31 representative species, ranging from unicellular aquatic algae to terrestrial higher plants, thereby demonstrating its functional importance and ancient origin. Only one to four SWEET homologs were detected in four aquatic algae and seven to 53 homologs were identified in land plants, indicating a rapid gene expansion of the SWEET gene family in higher plants (especially in angiosperms). To confirm our findings, another gene family, the HUS1 gene family, which is required for homologous recombination repair during meiosis, was also identified in 31 species. This gene family displayed a copy number conservation, evidently different than that of SWEET genes (Supplementary Fig. S5).
Family expansion is generally generated by gene duplication, which frequently occurs in plant taxa and has been considered to be a source of neo-functionalization and genetic redundancy24,25,26,27,31. Estimation of the different duplication models that led to the expansion of SWEET genes in vascular plants was also conducted, and included WGD/SD, Tandem, proximal and dispersed duplication25,31. Each duplicated model is biased for gene retention. Duplicated genes retained after different duplicated mechanisms often show opposite extremes of the spectrum, particularly in terms of their fates and divergence in expression26,27. For example, retained WGD duplicates may play a primary role as a buffer of crucial functions, thereby providing evolutionary stability. Dispersed duplications largely contribute to genetic novelty and adaptation to new environments26,27. The distinct duplication patterns observed in this study imply various functional differentiations among different species or taxa. Based on our findings, we inferred that ancestral core SWEET genes may be predominantly dispersed duplications. Subsequently, WGD/SD and tandem duplications mainly contributed to the expansion of SWEET genes in angiosperms. Further molecular evolutionary rate estimations implied that these WGD/SD and tandem duplicated correlated SWEET gene pairs underwent purifying selection.
Gene duplication and expansion are always followed by functional diversification, and functional diversification may play an important role in providing novel genes for adaptation to new environments24,25,31. Here, the expansion of SWEET genes, as well as their diverse roles in multiple processes, clearly indicates their functional diversification and evolutionary history. Together, these sugar transporters exhibited evolutionary conservation, as indicated by remarkable similarities in the phylogenetic relationships within the species tree among SWEET members in 31 species. However, these SWEET genes were diversified into four clades. Among these four clades, only clade II exhibited old, ancient member that were evolutionarily related to algae. To better trace the evolutionary history of SWEET genes, these four clades were further divided into 11 D and 10 M subfamilies. Ten of the 21 subfamilies had positive selection sites, indicating that they had important functions under positive selection. For example, M7 had two positive selection sites, and OsSWEET11 and OsSWEET15 have been shown to contribute to seed filling and size, and are important in breeding and are involved in domestication32.
Several SWEET genes acting as both transporters and R-genes, have attracted the attention of researchers5,8,17,33,34. According to our results, clade III harbored three monocot subfamilies, two of which had positive selection sites, indicating positive selection. In clade III, all five rice members were determined to have been targeted by the Xoo TAL effectors, thereby inducing pathogenic virulence18. Among these, loss-of-function alleles of 3 susceptibility loci (xa25, xa13, xa41) clustered within M7 and M8 have been identified as well-known R-genes that are utilized to combat bacterial blight disease5,17,33. We can therefore infer that families M7 and M8, or even clade III, may compose a gene pool that can be used for the identification of resistance genes from transporters in various species. Furthermore, arecently evolved hexose transporter gene in wheat (Triticum aestivum), Lr67, was found to confer partial resistance to three wheat rust pathogen species and powdery mildew; it is a member of the sugar transport proteins (STP) family34. Its ortholog in A. thaliana STP13 has also been shown to confer basal resistance to Botrytis cinerea35. Therefore, the transporters from which pathogens prey on nutrients from the host have been considered to be a genetic reservoir for R-genes. Clarifying the evolutionary fate of SWEET genes in clade III would be in favor of in-depth function and molecular mechanism analysis of SWEET genes. The Subfamilies defined in our study are believed to have been preserved throughout the evolutionary history of angiosperms and are regarded as a reliable core set of SWEET genes in angiosperms. No matter monocot or dicot species, two ‘ancestral genes’ were deduced (Fig. 6). One of these genes was duplicated into two core angiosperm gene pairs (D7, D8 and M6, M7) and the other was retained (D9 and M8). Taking the duplication modes that SWEET genes are involved in, we aimed to trace the evolutionary fate of SWEET genes in clade III, using SWEET genes in rice and maize as examples. Clear orthologous relationships were detected between these two species. Interestingly, the five rice homologs were all dispersed duplication correlated genes, while maize has more SWEET genes that originated from recently duplication, including WD and TD, and may result in functional redundancy. Different evolutionary fates may result in functional diversity or redundancy. Our results may provide a theoretical basis for further analyses of functional and molecular mechanisms of these SWEET genes. Together with our analysis, the engineering of candidate SWEET mutants with CRISPR/Cas9 system36, could be easily performed during genomic editing of TAL effector target sites, which could be a promising for the exploitation and production of multiple R-genes.
31 plant genomes and the corresponding gene models and proteomes were downloaded. Herein, annotation resources of Chlamydomonas reinhardtii, Micromonas pusilla, Ostreococcus lucimarinus, Volvox carteri, Physcomitrella patens, Sphagnum fallax, Selaginella moellendorffii, Marchantia polymorpha, Musa acuminate, Ananas comosus, Spirodela polyrhiza, Zea mays, Sorghum bicolor, Brachypodium distachyon, Oryza sativa, Solanum lycopersicum, Medicago truncatula, Phaseolus vulgaris, Glycine max, Prunus persica, Malus domestica, Populus trichocarpa, Eucalyptus grandis, Gossypium raimondii, Brassica rapa and Arabidopsis thaliana were downloaded from Phytozome (http://phytozome.jgi.doe.gov/pz/portal.html). Ginkgo biloba genome was downloaded from Spruce Genome Project database (ftp://plantgenie.org/Data/ConGenIE/Picea_abies/v1.0/). Amborella trichopoda genome and its gene models was downloaded from the Amborella Genome Database23. Capsicum annuum genome was downloaded from the Pepper Genome Database (release 2.0)37. Citrus grandis and Citrus sinensis genome were downloaded from Citrus Genome Database (https://www.citrusgenomedb.org/). Pfam_scan perl script in HMMER3.1 were applied to search all surveyed proteomes against Pfam library38. All the hits were first subjected to the Pfam database with an E-value setting of 1.039. HUS1 genes were identified from 30 surveyed species by the same method to serve as a reference gene family.
Genome Synteny and Gene duplication
MCScanX, a package developed by the Plant Genome Duplication Database (http://chibba.agtec.uga.edu/duplication/)28, was used to evaluate the whole-genome BLASTP results to compute syntenic blocks within or among species. MCScanX can efficiently classify duplicate gene origins within a gene family, including dispersed, proximal, tandem and segmental/WGD duplicates depending on their copy number and genomic distribution. We employed MCScanX to perform synteny analysis and estimate the duplication models in fine-assembled plant genomes (fine-assembled plant genomes means corresponding plant genome sequences had been assembled into pseudomolecule scales).
The ML method was used to build phylogenetic trees using the amino acid sequences of the entire CDS sequences by PhyML 3.0. All the sequences were first aligned using MAFFT with the auto strategy40. As there were too many gaps in the alignments of the entire protein sequences, trimAl v1.2 was used to delete gaps with parameter of -automated141 (Additional file 3). Then aligned sequences were further tested to select the best-fit amino acid substitution model for constructing the ML phylogenetic tree by using ProtTest 3.442. The most appropriate model estimated with ProtTest 3.4 was JTT + G + F (−lnL = 44530.08). Finally, trees were constructed with the reliability of internal nodes and evaluated by using Shimodaira-Hasegawa approximate likelihood ratio test (SH-aLRT) values43. Other criteria were set according to the results of ProtTest (gamma shape = 1.257; amino acid frequencies = observed). Obtained trees were edited with MEGA 5.0.
To decipher molecular evolutionary genetic basis of SWEET genes, their nucleotides of CDS were selected from gene model sequences of all surveyed species by a perl script. Then nucleotides of each CDS were submitted to GUIDANCE244 website and firstly translated to amino acid sequences and aligned by MAFFT. This aligned amino acid sequences were re-transferred to nucleotide sequences. Finally, unreliable alignments were masked by N with a cutoff (0.90). All the following analysis were conducted with these masked alignments. The HyPhy package with the Genetic Algorithm for Recombination Detection (GARD) method as implemented on the Data Monkey webserver (http://www.datamonkey.org/)45,46 was used to detect break point sites, which indicated points of unequal crossover.
The codon-based maximum likelihood (CodeML) method in the PAML4.0 package and MEGA 5.0 were firstly used to estimate the average dn/ds ratio of genes within each sub-families47. A branch evolutionary analysis for positive selection was conducted using CodeML for average dn/ds of the genes in the M and D sub-families with one-ration model. All masked aligned CDS in each sub-families were used to reconstruct consensus trees for molecular genetic analysis by Seqboot, Dnadist, neighbor and consense program in Phylip package48.
To identify the probabilities of sites under positive selection in each sub-families, site models (M7 vs. M8) were implemented in which ω could vary among sites49. We used estimated transition/tranversion rates and the F3×4 codon frequencies algorithm as the codon substitution models in the PAML program. Additionally, all of the positively selected sites in the site and branch-site models were identified by using Bayes Empirical Bayes (BEB) analysis with posterior probabilities ≥0.8047. Furthermore, positively selected sites were also deduced in the Datamonkey web server by the random effect likelihood (REL) method45. Candidate sites under positive selection were defined as those with Bayes factor >50 for REL45.
Availability of Data and Materials
All data employed in the present study were downloaded from public databases, which we depicted in methods and materials part of our manuscript. Genomes used for identifying SWEET genes were listed in the Supplementary Table S1. Sequence alignments used for phylogenetic tree were provided as Supplementary Dataset 2.
Gamas, P., Niebel, F. D. C., Lescure, N. & Cullimore, J. V. Use of a subtractive hybridization approach to identify new Medicago truncatula genes induced during root nodule development. Mol Plant Microbe In 9, 233–242, https://doi.org/10.1094/Mpmi-9-0233 (1996).
Artero, R. D. et al. Saliva, a new Drosophila gene expressed in the embryonic salivary glands with homologues in plants and vertebrates. Mechanisms of development 75, 159–162 (1998).
Dong, M. et al. Identification and characterisation of a homolog of an activation gene for the recombination activating gene 1 (RAG 1) in amphioxus. Fish & shellfish immunology 19, 165–174, https://doi.org/10.1016/j.fsi.2004.11.001 (2005).
Hamada, M., Wada, S., Kobayashi, K. & Satoh, N. Ci-Rga, a gene encoding an MtN3/saliva family transmembrane protein, is essential for tissue differentiation during embryogenesis of the ascidian Ciona intestinalis. Differentiation 73, 364–376, https://doi.org/10.1111/j.1432-0436.2005.00037.x (2005).
Chu, Z. et al. Targeting xa13, a recessive gene for bacterial blight resistance in rice. TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik 112, 455–461, https://doi.org/10.1007/s00122-005-0145-6 (2006).
Guan, Y. F. et al. Ruptured Pollen Grain 1, a member of the MtN3/saliva gene family, is crucial for exine pattern formation and cell integrity of microspores in arabidopsis. Plant Physiol 147, 852–863, https://doi.org/10.1104/pp.108.118026 (2008).
Chen, L. Q. et al. Sugar transporters for intercellular exchange and nutrition of pathogens. Nature 468, 527–532, https://doi.org/10.1038/nature09606 (2010).
Yuan, M. & Wang, S. P. Rice MtN3/Saliva/SWEET Family Genes and Their Homologs in Cellular Organisms. Mol Plant 6, 665–674, https://doi.org/10.1093/Mp/Sst035 (2013).
Chen, L. Q. et al. Sucrose Efflux Mediated by SWEET Proteins as a Key Step for Phloem. Transport. Science 335, 207–211, https://doi.org/10.1126/science.1213351 (2012).
Fu, Q., Cheng, L., Guo, Y. & Turgeon, R. Phloem loading strategies and water relations in trees and herbaceous plants. Plant Physiol 157, 1518–1527, https://doi.org/10.1104/pp.111.184820 (2011).
Franceschi, V. R. & Giaquinta, R. T. Specialized Cellular Arrangements in Legume Leaves in Relation to Assimilate Transport and Compartmentation - Comparison of the Paraveinal Mesophyll. Planta 159, 415–422, https://doi.org/10.1007/Bf00392077 (1983).
Ayre, B. G. Membrane-transport systems for sucrose in relation to whole-plant carbon partitioning. Mol Plant 4, 377–394, https://doi.org/10.1093/mp/ssr014 (2011).
Chen, L. Q. SWEET sugar transporters for phloem transport and pathogen nutrition. The New phytologist 201, 1150–1155 (2014).
Sosso, D. et al. Seed filling in domesticated maize and rice depends on SWEET-mediated hexose transport. Nature genetics 47, 1489–1493, https://doi.org/10.1038/ng.3422 (2015).
Sutton, P. N., Henry, M. J. & Hall, J. L. Glucose, and not sucrose, is transported from wheat to wheat powdery mildew. Planta 208, 426–430, https://doi.org/10.1007/s004250050578 (1999).
Yang, B., Sugio, A. & White, F. F. Os8N3 is a host disease-susceptibility gene for bacterial blight of rice. P Natl Acad Sci USA 103, 10503–10508, https://doi.org/10.1073/pnas.0604088103 (2006).
Hutin, M., Sabot, F., Ghesquière, A., Koebnik, R. & Szurek, B. A knowledge-based molecular screen uncovers a broad-spectrum OsSWEET14 resistance allele to bacterial blight from wild rice. The Plant Journal 84, 694–703, https://doi.org/10.1111/tpj.13042 (2015).
Streubel, J. et al. Five phylogenetically close rice SWEET genes confer TAL effector-mediated susceptibility to Xanthomonas oryzae pv. oryzae. The New phytologist 200, 808–819, https://doi.org/10.1111/nph.12411 (2013).
Yuan, M. et al. Rice MtN3/saliva/SWEET gene family: Evolution, expression profiling, and sugar transport. Journal of integrative plant biology 56, 559–570, https://doi.org/10.1111/jipb.12173 (2014).
Feng, C. Y., Han, J. X., Han, X. X. & Jiang, J. Genome-wide identification, phylogeny, and expression analysis of the SWEET gene family in tomato. Gene. https://doi.org/10.1016/j.gene.2015.07.055 (2015).
Patil, G. et al. Soybean (Glycine max) SWEET gene family: insights through comparative genomics, transcriptome profiling and whole genome re-sequence analysis. Bmc Genomics 16, https://doi.org/10.1186/S12864-015-1730-Y (2015).
Rensing, S. A. et al. The Physcomitrella genome reveals evolutionary insights into the conquest of land by plants. Science 319, 64–69, https://doi.org/10.1126/science.1150646 (2008).
Amborella Genome, P. The Amborella genome and the evolution of flowering plants. Science 342, 1241089, https://doi.org/10.1126/science.1241089 (2013).
Freeling, M. Bias in plant gene content following different sorts of duplication: tandem, whole-genome, segmental, or by transposition. Annual review of plant biology 60, 433–453, https://doi.org/10.1146/annurev.arplant.043008.092122 (2009).
Innan, H. & Kondrashov, F. The evolution of gene duplications: classifying and distinguishing between models. Nat Rev Genet 11, 97–108, https://doi.org/10.1038/nrg2689 (2010).
Wang, Y. P. et al. Modes of Gene Duplication Contribute Differently to Genetic Novelty and Redundancy, but Show Parallels across Divergent Angiosperms. Plos One 6, https://doi.org/10.1371/journal.pone.0028150 (2011).
Wang, Y., Wang, X. & Paterson, A. H. Genome and gene duplications and gene expression divergence: a view from plants. Annals of the New York Academy of Sciences 1256, 1–14, https://doi.org/10.1111/j.1749-6632.2011.06384.x (2012).
Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic acids research 40, e49, https://doi.org/10.1093/nar/gkr1293 (2012).
Tao, Y. et al. Structure of a eukaryotic SWEET transporter in a homotrimeric complex. Nature 527, 259–263, https://doi.org/10.1038/nature15391 (2015).
Xuan, Y. H. et al. Functional role of oligomerization for bacterial and plant SWEET sugar transporter family. Proc Natl Acad Sci USA 110, E3685–3694, https://doi.org/10.1073/pnas.1311244110 (2013).
Bowers, J. E., Chapman, B. A., Rong, J. & Paterson, A. H. Unravelling angiosperm genome evolution by phylogenetic analysis of chromosomal duplication events. Nature 422, 433–438, https://doi.org/10.1038/nature01521 (2003).
Yang, J. L., Luo, D. P., Yang, B., Frommer, W. B. & Eom, J. S. SWEET11 and 15 as key players in seed filling in rice. New Phytologist 218, 604–615, https://doi.org/10.1111/nph.15004 (2018).
Liu, Q. S. et al. A paralog of the MtN3/saliva family recessively confers race-specific resistance to Xanthomonas oryzae in rice. Plant Cell Environ. 34, 1958–1969, https://doi.org/10.1111/j.1365-3040.2011.02391.x (2011).
Moore, J. W. et al. A recently evolved hexose transporter variant confers resistance to multiple pathogens in wheat. Nature genetics, https://doi.org/10.1038/ng.3439 (2015).
Lemonnier, P. et al. Expression of Arabidopsis sugar transport protein STP13 differentially affects glucose transport activity and basal resistance to Botrytis cinerea. Plant molecular biology 85, 473–484, https://doi.org/10.1007/s11103-014-0198-5 (2014).
Shalem, O., Sanjana, N. E. & Zhang, F. High-throughput functional genomics using CRISPR-Cas9. Nat Rev Genet 16, 299–311, https://doi.org/10.1038/nrg3899 (2015).
Qin, C. et al. Whole-genome sequencing of cultivated and wild peppers provides insights into Capsicum domestication and specialization. P Natl Acad Sci USA 111, 5135–5140, https://doi.org/10.1073/pnas.1400975111 (2014).
Eddy, S. R. Accelerated Profile HMM Searches. PLoS computational biology 7, e1002195, https://doi.org/10.1371/journal.pcbi.1002195 (2011).
Finn, R. D. et al. Pfam: the protein families database. Nucleic acids research 42, D222–230, https://doi.org/10.1093/nar/gkt1223 (2014).
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic acids research 30, 3059–3066, https://doi.org/10.1093/Nar/Gkf436 (2002).
Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973, https://doi.org/10.1093/bioinformatics/btp348 (2009).
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics 27, 1164–1165, https://doi.org/10.1093/bioinformatics/btr088 (2011).
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Systematic biology 59, 307–321, https://doi.org/10.1093/sysbio/syq010 (2010).
Sela, I., Ashkenazy, H., Katoh, K. & Pupko, T. Guidance 2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters. Nucleic acids research 43, W7–W14, https://doi.org/10.1093/nar/gkv318 (2015).
Pond, S. L. K. & Frost, S. D. W. Datamonkey: rapid detection of selective pressure on individual sites of codon alignments. Bioinformatics 21, 2531–2533, https://doi.org/10.1093/bioinformatics/bti320 (2005).
Delport, W., Poon, A. F. Y., Frost, S. D. W. & Pond, S. L. K. Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology. Bioinformatics 26, 2455–2457, https://doi.org/10.1093/bioinformatics/btq429 (2010).
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol 24, 1586–1591, https://doi.org/10.1093/molbev/msm088 (2007).
Felsenstein, J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol 17, 368–376 (1981).
Yang, Z. & Nielsen, R. Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol 17, 32–43, https://doi.org/10.1093/oxfordjournals.molbev.a026236 (2000).
Our work was supported by National Natural Science Foundation of China (31870415 and 31571685), National key research and development plan (2016YFD0101002), Anhui natural science foundation of the colleges and universities (KJ2017A147) and Anhui science and technology research plan (15czz03119).
The authors declare no competing interests.
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Li, X., Si, W., Qin, Q. et al. Deciphering evolutionary dynamics of SWEET genes in diverse plant lineages. Sci Rep 8, 13440 (2018). https://doi.org/10.1038/s41598-018-31589-x
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