Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Systems identification and characterization of β-glucuronosyltransferase genes involved in arabinogalactan-protein biosynthesis in plant genomes

Abstract

Utilizing plant biomass for bioethanol production requires an understanding of the molecular mechanisms involved in plant cell wall assembly. Arabinogalactan-proteins (AGPs) are glycoproteins that interact with other cell wall polymers to influence plant growth and developmental processes. Glucuronic acid, which is transferred to the AGP glycan by β-glucuronosyltransferases (GLCATs), is the only acidic sugar in AGPs with the ability to bind calcium. We carried out a comprehensive genome-wide analysis of a putative GLCAT gene family involved in AGP biosynthesis by examining its sequence diversity, genetic architecture, phylogenetic and motif characteristics, selection pressure and gene expression in plants. We report the identification of 161 putative GLCAT genes distributed across 14 plant genomes and a widely conserved GLCAT catalytic domain. We discovered a phylogenetic clade shared between bryophytes and higher land plants of monocot grass and dicot lineages and identified positively selected sites that do not result in functional divergence of GLCATs. RNA-seq and microarray data analyses of the putative GLCAT genes revealed gene expression signatures that likely influence the assembly of plant cell wall polymers which is critical to the overall growth and development of edible and bioenergy crops.

Introduction

Plants have continually evolved adaptive features after moving to land about 400 million years ago in order to be physiologically and functionally suited for their terrestrial habitats1,2. The evolution of gene families can facilitate gene family expansion or contraction and is an important mechanism that drives natural variation for adaptation or speciation in green plants3. Important evolutionary mechanisms such as gene duplication and whole genome duplication events (polyploidy) and segmental gene deletion has altered gene family sizes across lineages and advanced phenotypic diversity across the plant kingdom4. Following gene duplication and loss, adaptation and speciation appear to proceed through a combination of both structural and cis-regulatory changes in one or more paralogous genes5. Recent advances in sequencing technology have enabled researchers to make significant progress in understanding gene evolution. This provides insights into the underlying connections between the expansion of gene families and evolution of new gene functions6.

Arabinogalactan-proteins (AGPs) are plant cell wall glycoproteins with structurally complex, large-branched polysaccharides attached to hydroxyproline (Hyp) residues. They are ubiquitous in bryophytes and higher plants7,8 and are implicated in virtually all aspects of plant growth and development9,10,11. AGPs contain approximately 10% protein and 90% sugar, which forms the interactive molecular surface of AGPs which are essential to their biological functions12,13,14. Type II arabinogalactan (AG) polysaccharides of AGPs possesses structural characteristics mainly a β-(1 → 3)-galactan backbone with β-(1 → 6)-linked galactan side chains which are further decorated with galactopyranosyl (Galp) and arabinofuranosyl (Araf) residues as well as with other less abundant residues including rhamnosyl (Rha), fucosyl (Fuc), and glucuronosyl (GlcA; with or without 4-O-methylation) residues15,16. The formation and assembly of AG sugar moieties are controlled by a glycosylation process catalyzed by glycosyltransferases (GTs). GTs regulate the sequence and length of the AG chains and act in a regio- and stereo-specific manner17. It is speculated that at least ten functionally distinct GTs are required for the biosynthesis of type II AG11 and many of these GTs await functional discovery and characterization.

β-glucuronosyltransferases (GLCATs) are involved in the transfer of glucuronic acid (GlcA) to AGPs. Out of the eleven putative β-glucuronosyltransferases (GLCATs) previously discovered in Arabidopsis thaliana and assigned to GT14 in the Carbohydrate Active Enzyme (CAZy; https://www.cazy.org/) database, only three GLCATs, namely, AT5G39990 (ATGLCAT14A), AT5G15050 (ATGLCAT14B) and AT2G37585 (ATGLCAT14C) have demonstrated GLCAT catalytic activity based on an in-vitro assay11 and contain the conserved Branch domain PF02485 (referred to as GLCAT domain henceforth) present in GT14 gene family. The number and genetic features of putative GLCATs in other plant species remain to be verified despite their fully sequenced genomes. Previous work using nuclear magnetic resonance and molecular dynamics simulations demonstrated that the terminal carboxyl groups of GlcA residues can act as potential intramolecular Ca2+-binding sites18. Apart from the fact that GlcA residues are the only acidic sugars in the AG chain, their importance in the binding and release of extracellular calcium have been demonstrated18. Intuitively, GLCATs may play an essential role in global Ca2+ signaling processes in plants and could be a potential target for improving AGP-based products and increasing biomass for biofuel production from bioenergy crops.

The advent of high-throughput technologies in the post-genomic era has generated enormous amounts of data that can yield important biological information for researchers through data mining of publicly available databases19. A bioinformatics approach can be an effective tool in understanding gene family expansion, identifying paralogs and orthologs using sequence features, identifying sites under selection pressure and elucidating gene expression dynamics among gene family members. Examination of such data can yield valuable insights that can facilitate and guide further research in the field. Such is the case here, where the availability of whole genome sequences along with microarray, proteome and transcriptome data can enable large-scale investigations into identifying and characterizing gene family members in multiple plant genomes using bioinformatics approaches.

The present study is focused on mining plant genomes for evolutionary footprints of functional importance among putative GLCATs. We carried out a comprehensive analysis aimed at identifying and characterizing the family of enzymes (GLCAT) that transfer GlcA to AG glycan belonging to GT14 family using Arabidopsis thaliana GLCAT domain of functionally characterized GLCATs as queries. Our specific goal is to utilize phylogenetic analysis, physical mapping of genes, motif identification, synteny and gene expression analyses to gain insight into the evolution, sequence diversification, functional similarity/divergence, and tissue-specific transcriptional profiling among putative GLCAT gene family members in 14 plant genomes.

Results

Sequence retrieval, identification and multiple sequence alignments of putative GLCAT gene family members in plant lineages

A total of 161 putative GLCAT genes were found distributed across 14 plant genomes. Eleven genes were identified as members of the GLCAT genes in Arabidopsis thaliana, Arabidopsis lyrata, Brachipodium distachyon, Oryza sativa (rice), Amborella trichopoda and Sorghum bicolor (sorghum). Thirteen GLCAT genes were identified in Citrus sinensis (orange) and Populus trichocarpa (poplar), 22 in Glycine max (soybean), 15 in Solanum lycopersicum (tomato) and Gossypium raimondi (cotton), 6 in Physcomitrella patens, 2 in Selaginella moellendorffii, 9 in Vitis vinifera (grape) and 10 in Zea mays (corn) (Supplementary Table S1). Selaginella xylosyltransferase (XYLT) genes appear to be misclassified as it contains sequences that are closer to putative GLCATs than XYLTs. In a blastp analyses, Selaginella sequences had 60.2% sequence identity and 98.8% sequence coverage with ATGLCAT14A compared to 28% sequence identity and 69% coverage with mammalian XYLTs. Also, only the GLCAT domain is present in Selaginella and lacks the XYLT domain present in mammalian GT14 sequences20. Each of these genes were confirmed for the presence of the GLCAT domain while excluding sequences having a DUF 266 domain21. Multiple alignment of the putative GLCAT protein sequences of representative members of the investigated species identified a widely conserved GLCAT domain which is the catalytic region with less conserved N terminal and C terminal ends following protein sequence alignments (Fig. 1). The DXD motif is a metal ion binding motif present in many glycosyltransferases and this motif is essential to their catalytic functions20. There are some representative sequences that appear to have a highly conserved DWD motif with the first D residue less conserved than the second D residue of the motif. In addition, the second D residue of the X/DWD/X motif is substituted with either asparagine (N) or threonine (T) for some dicot species and serine (S) for some monocot grass species (Fig. 2b,c). Surprisingly, we discovered a highly conserved, uncharacterized tryptophan (W) residue in the X/DWD/X motif shared among plant and animal species (Supplementary Fig. S1).

Figure 1
figure 1

Protein alignment of representative GLCATs from various plant species. Twenty-six proteins representing the diversity of putative GLCATs in the investigated genomes were aligned using ClustalW and illustrated in Jalview33. Conserved GLCAT domains are indicated with black arrow lines. The X/DWD/X motif is indicated by the red box region.

Figure 2
figure 2

Phylogenetic analyses of putative GLCATs in plant genomes using maximum likelihood method. Figure showed the phylogenetic analyses in 14 plant species investigated (a), dicots (b), monocot grasses (c) and AtGLCAT14A orthologs in 14 plant species (d). In (a), the phylogenetic tree was created with the full-length GLCAT protein sequences from 14 plant species based on the maximum likelihood method. Ten phylogenetic classes (classes aj) were identified. The blue X and red X represent dicot and monocot grass specific clades respectively, corresponding to the different species. The red oval indicates a phylogenetic clade specific to lower land plants while the black oval indicates a clade shared by lower (bryophytes) and higher land plants (monocot grass and dicots). In (b), eight phylogenetic clades (classes 1a, 1b and 2af) were identified. The red and blue asterisks indicated amino acid changes in the second D residue from D → N and D →  T in the DWD consensus motif found in plants respectively. In (c), ten phylogenetic clades (clade aj) were identified and clades were assigned if it contained minimum of 3 out of the 4 monocot grasses investigated. The red asterisks indicated amino acid changes D → S in the X/DWD/X motif respectively. Arabidopsis GLCAT sequences were included for efficient classification. Genes with no asterisk possess no substitution. In (d), the phylogenetic tree was created with the full-length protein sequences of AtGLCAT14A (AT5G39990) and its orthologs in the 14 plant species by the Bayesian Inference using the Bayesian Markov Chain Monte Carlo method as implemented in BEAST software v1.5.434. Support values/posterior probabilities and branch lengths are indicated while I, II and III corresponds to labels for foreground and background in branch and branch-site analyses.

Phylogenetic analyses of GLCAT genes in the investigated plant genomes

The maximum likelihood (ML) method was used to examine the phylogenetic relationships among all 14 plant species (Fig. 2a), including eight dicot species (Fig. 2b) and four monocot grass species (Fig. 2c), which were also examined separately. Similarly, Bayesian Inference (BI) was used to assess the phylogenetic relationships among AtGLCAT14A orthologs and results were compared with those obtained using ML (Fig. 2d). Results obtained from the phylogenetic tree for all species identified a phylogenetic clade (clade E) that is representative of lower and higher land plants (Fig. 2a). Results obtained from the phylogenetic tree for dicot species identified two major clades, namely clades 1 and 2. Clade 1 was subdivided into groups A and B and clade 2 was subdivided into groups A–F (Fig. 2b). Two Arabidopsis thaliana genes, At3g24040 (group 1A) and At1g71070 (group 1B) belong to clade 1 while the remaining nine Arabidopsis thaliana genes are present in clade 2. All eight dicot species have orthologs represented in each group (1A-B, 2A-F) except for group 2D which lacks GLCAT genes from Arabidopsis thaliana and Arabidopsis lyrata. No putative GLCAT gene members from a species were clustered into a single group except in Amborella trichopoda, whose putative GLCATs occupy distinct positions in the phylogeny analysis (Supplementary Fig. S6). Clades A, B, G, H, I and J contain all species representative of monocot grass species while clades C, D and E clustered with putative Arabidopsis thaliana GLCAT genes (Fig. 2c). In class D, the LOC_Os03g16890.1, Bradi1g66497.1 and Sobic. 001g418800.1 genes exhibited D to S residue changes in the second D residue of the DWD motif; this residue change is absent in its closest Arabidopsis homolog AtGLCAT14C (At2g37585) (Fig. 2c). Results of the BI analysis of AtGLCAT14A and its orthologs showed a similar clustering pattern that separates monocot grasses from dicots (Fig. 2d).

Physical mapping, gene structure analysis and synteny analysis

Physical mapping of putative GLCAT genes in the 14 plant genomes

The physical maps of putative GLCAT genes were distributed on all 5 chromosomes in Arabidopsis thaliana and seven of the eight chromosomes in Arabidopsis lyrata. The largest numbers were observed on chromosome 1 and 3 for Arabidopsis thaliana and chromosome 3 for Arabidopsis lyrata. Solanum lycopersicum and Gossypium raimondi have the same number of GLCAT genes distributed across 8 chromosomes in Solanum lycopersicum and 9 chromosomes in Gossypium raimondi. The largest number of genes were found on chromosomes 12 and 7 for Solanum lycopersicum and Gossypium raimondi respectively. Similarly, Vitis vinifera and Glycine max GLCAT genes were distributed over 7 and 12 chromosomes respectively, with the largest number of genes found on chromosomes 1 and 8 for Vitis vinifera and chromosomes 6, 12 and 13 for Glycine max. Also, putative GLCAT genes were distributed over 2 and 6 chromosomes in Selaginella moellendorffii and Physcomitrella patens respectively (Supplementary Table S1, Supplementary Fig. S2).

Gene structure and synteny analyses of putative GLCAT genes in the 14 plant genomes

Gene structure diversification was studied to gain insights into the evolution of putative GLCAT genes in the investigated genomes. Generally, all of the GLCAT genes including those of the Amborella trichopoda have 4 exons and 3 introns with variable lengths in the 5′ and 3′ UTR regions, except for some representative species of Glycine max, Gossypium raimondi, Physcomitrella patens and Citrus sinensis (Fig. 3, Supplementary Fig. S3). Comparative synteny relationship maps displayed a high degree of sequence similarity between Arabidopsis thaliana GLCAT gene family members and their respective homologs in other species (Supplementary Fig. S4). Surprisingly, At2g37585 displayed weak sequence similarity with its closest homolog in Arabidopsis lyrata (Al4g34150) (Fig. 4).

Figure 3
figure 3

Gene structure display of representative GLCATs from various plant species. Twenty-six proteins representing the diversity of putative GLCATs in the plant kingdom were used for the gene structure analysis as illustrated by using the GSDS 2.0 server37. ‘83,932 = putative GLCAT sequence in Sellaginella moellendorffii; AL = Arabidopsis lyrata; AT = Arabidopsis thaliana; GRMZM = Zea mays; GSVIV = Vitis vinifera; Glyma = Glycine max; Gorai = Gossypium raimondii; LOC = Oryza sativa; Potri = Populus trichocarpa; Pp = Physcomitrella patens; Sobic = Sorghum bicolor; Solyc = Solanum lycopersicum; orange = Citrus sinensis.

Figure 4
figure 4

Comparative analyses of ATGLCAT14C (AT2G37585) and AL4G34150 genes. (a) Synteny of Arabidopsis thaliana and Arabidopsis lyrata GLCAT genes. Weak sequence similarity (green ribbon) between ATGLCAT14C and its ortholog AL4G34150 in A. lyrata. (b) Pairwise comparison between ATGLCAT14C and its ortholog AL4G34150 displaying conserved regions (highlighted in dark blue and yellow blocks) as illustrated by Jalview33. The first 167 amino acid residues in ATGLCAT14C are conspicuously absent in AL4G34150.

Distribution of amino acid motifs/blocks in putative GLCAT gene family in plant genomes

A total of 161 protein sequences from 14 plant genomes was used to generate a MEME-driven search for conserved amino acid motifs named "blocks". The MEME analysis generated 50 sequence blocks with various lengths ranging from six (blocks 24 and 40) to 41amino acids (blocks 2, 4 and 37) (Supplementary Table S2). Sequence blocks 1–6, 8 and 9 are found in more than 95% of the sequences analyzed (Supplementary Fig. S5). We identified sequence blocks 1, 2, 3, 4, 5, 6, 7, 11 and 12 to be localized to the GLCAT domain. Some sequence blocks are unique to monocot grasses and dicots lineages while some are shared across certain plant lineages (Supplementary Table S2).

Positively selected sites in GLCAT14A orthologs and their putative biological significance

The site-specific, branch and branch-site models were used to detect sites under selective pressure among AtGLCAT14A orthologs. After removing the gaps, all the amino acid sequences were analyzed using the CodeML program. Results showed that neither the M0 versus M3 or M2a versus M1a models identified positively selected sites. Only the M8 versus M7 model identified several sites with ω values significantly greater than 1 (M8 vs. M7, 2ΔL = 261.53, p < 0.001). Eighty-four amino acid sites were identified under positive selection by the M8 alternative model, including 29 amino acid sites that have posterior probability (PP) > 0.95 and 55 sites with PP > 0.99 (Supplementary Table S3). The branch model identified no significant differences between the free ratio model and the one ratio model in all the three branches investigated (Fig. 2d; Supplementary Table S3). According to the likelihood ratio tests (LRT) for the branch site model, comparing model A versus model A null for the branches I, II and III, LRT were significantly different (2ΔlnL = 53.43, p = 0.00004 for branch I , 2ΔlnL = 17.92711 , p = 0.00002 for branch II; 2ΔlnL = 7.614822, p = 0.006 for branch III). In the branch-site model, branch sites had PP < 0.95 based on the Bayes empirical Bayes computation while using each of the branches as foreground and the remaining as backgrounds (Supplementary Table S3).

Digital expression analysis of a putative GLCAT gene family

The expression pattern among putative GLCAT gene family members in Arabidopsis thaliana showed differential gene expression across 32 anatomical parts with the highest expression observed in the radical elongation zone (Fig. 5a). Across developmental stages, all Arabidopsis GLCAT gene family members had low to moderate expression except in the senescence stage. Interestingly, two genes, At4g27480 and At1g53100 have very high expression levels in the senescence stage compared to other stages of development (Fig. 5b). For the expression of GLCAT genes during germination in Arabidopsis, microarray data showed that AtGLCAT14A (At5g39990) was significantly upregulated and highly expressed (fold change > 2.5) at 1, 6, 12, 24 and 48 h of germination (Fig. 6a). Similarly, significant upregulation (fold change > 2.5) was observed for AtGLCAT14B (At5g15050), At3g15350, At3g24040 and At1g71070 at 6, 12, 24 and 48hrs of germination (Fig. 6a). Expression profile of GLCAT gene family members in Oryza sativa identified LOC_Os12g44240 as expressed in all 20 anatomical parts with differential expression patterns observed among rice GLCAT gene members (Fig. 5c). Low to medium expression was observed among the genes; however, two genes, LOC_Os3g48560 and LOC_Os1g56570 were highly expressed in the maturation zone of rice root seedlings (Fig. 5c). Differential gene expression was observed across all developmental stages (Fig. 5d). For GLCAT gene family members in soybean across 27 anatomical parts, medium expression was observed for most soybean GLCAT genes except for Glyma.03g083000 whose expression was not detected (Fig. 5e). Higher expression during the seedling stage was observed in root hairs for Glyma.12G155800, while Glyma.06G240700 was expressed predominantly in the elongation zone of seedlings (Fig. 5e). Across developmental stages, low to medium expression was observed across GLCAT gene family members except for Glyma.03G083000 which had no detectable expression (Fig. 5f). For soybean GLCAT gene family, most of the genes were significantly upregulated (fold change > 2.5) especially at 12 h and 24 h of germination (Fig. 6b). Notably, Glyma.13g065900, Glyma.19g018800, Glyma.12g107700, Glyma.10g264600, Glyma.09g119700, Glyma.10g127100 were highly upregulated (fold change > 2.5) at 3 h, 6 h, 12 h and 24 h of germination (Fig. 6b).

Figure 5
figure 5figure 5

Expression pattern of putative GLCAT genes across anatomical reference points and developmental stages in Arabidopsis, rice and soybean. Figure showed the digital expression pattern in the anatomical parts and across developmental stages of Arabidopsis (a, b), rice (c, d) and soybean (e, f). The red boxed region corresponds to the functionally characterized AtGLCAT14A (AT5g39990) gene (a) and its orthologs in rice (c) and soybean (e)11.

Figure 6
figure 6

Digital expression pattern of putative GLCATs in Arabidopsis and soybean during germination. (a) Expression pattern of GLCAT genes in Arabidopsis thaliana during germination. (b) Expression pattern of GLCAT genes in soybean during germination. The red boxed regions indicated the AtGLCAT14A (AT5g39990) gene and its ortholog in soybean.

Digital expression of putative GLCAT genes under abiotic stress in Arabidopsis thaliana and Oryza sativa

The modification of plant cell walls is one of the key processes that drives the adaptability of crop species to abiotic stressors. In heat, anoxia and hypoxia studies in Arabidopsis thaliana, all the GLCAT gene family members either showed no change in expression or were significantly down-regulated (Fig. 7a). Interestingly, AtGLCAT14A and At1g71070 were significantly down-regulated (fold change < 1.5) in all the stressors examined (Fig. 7a). In rice, abiotic expression data was only available for cold stress in specific rice genotypes (Cold tolerance imbred line K354 and its recurrent parent C418, which possesses a cold sensitive phenotype; IR29 and LTH, both chilling-tolerant and chilling-sensitive lines, respectively). For the responses of rice GLCAT gene family members to cold stress, LOC_Os12g44240 was down-regulated (fold change < 2.5) following exposure to cold stress (4 °C) for 24 h and 48 h in shoot and leaf tissues but up-regulated when re-exposed to 29 °C for 24 h only in IR29 (indica) and LTH (japonica) genotypes (Fig. 7b). Surprisingly, LOC_Os03g16890 was significantly up-regulated (fold change > 1.5) under cold stress but down-regulated during the recovery phase (at 29 °C) in both IR29 and LTH genotypes. Also, LOC_Os04g23580 was highly up-regulated during the recovery phase (at 29 °C) in IR29 and LTH genotypes while LOC_Os12g44240 was up-regulated only in IR29 but not in the LTH genotype (Fig. 7b).

Figure 7
figure 7

Expression pattern of putative GLCATs genes in Arabidopsis and rice in response to abiotic stress. (a) Expression pattern of GLCAT genes in Arabidopsis thaliana during anoxia, heat and hypoxia (b) Expression pattern of GLCAT genes in rice during cold stress. Note the differential gene expression in all GLCATs in Arabidopsis and rice in response to different stressors. The red boxed region indicates AtGlcAT14A (AT5g39990) and its ortholog in rice, both of which are relatively down-regulated in response to stressors.

Discussion

AGPs are a complex family of macromolecules with heterogenous sugar chains decorating the protein backbones. AGPs are plant cell wall glycoprotein components with great potential for industrial and food applications, which builds upon their well-known emulsifying, adhesive, and water-holding properties. We considered the plant GT14 gene family as putative GLCATs for the following reasons: (1) The glucuronic acid substitution of xylan (GUX) enzymes have been demonstrated to attach α-GlcA to xylan and the loss of function of GUX genes resulted in the complete loss of GlcA in xylan22; as such, we speculate that a different set of enzymes may be responsible for the AGP-specific GlcAT activity. (2) The investigated genes share similar domain characteristics with functionally characterized GLCATs (ATGLCAT14A, ATGLCAT14B and ATGLCAT14C) whose function primarily involves the transfer of β-GlcA to AGPs11. (3) The glcat14aglcat14bglcat14c genetic knock-out mutants resulted in only 40% reduction in GlcA content in AGP isolates23. Unlike the complete loss of GlcA in gux1/2/3 triple mutant, the 40% loss of GlcA in glcat14aglcat14bglcat14c triple mutant may suggest the possible involvement of other putative GLCAT genes in the plant GT14 family in AGP GLCAT activity. In this study, we focused on a family of enzymes called GLCATs that are involved in the transfer of GlcA to AGPs for two reasons. First, GlcA is the only acidic sugar decorating the large arabinogalactan (AG) sugar chains and is known to bind to calcium in a pH-dependent manner with functional ramifications18. Given the important role of calcium in plant growth and developmental processes, the understanding of the evolution of this gene family will provide key insights that could be exploited for use in increasing plant biomass in bioenergy crops. Second, previous work showed that the addition of extra amounts of GlcA to commercially available gum arabic improved its emulsification properties24. This offers the opportunity of developing new gum arabic variants useful in the design of novel AGP-based products. To this end, we identified and characterized putative GLCAT gene family members in 14 plant genomes to gain insight into their evolution, sequence diversification, functional similarities/differences, and patterns of gene expression.

Gene families include genes that share similar cellular functions and commonly arise as a result of gene or genome duplication events. Gene duplication and deletion are generally considered important evolutionary mechanisms that give rise to phenotypic diversity6. The number of putative GLCAT gene family members in the investigated land plant species are comparable to an earlier report21 and ranged from two GLCAT genes in Selaginella moellendorffii to 22 GLCAT genes in soybean (Supplementary Table S1). This may be an indication of the extent of gene family expansion of GLCATs across evolutionary time, conceivably driven by forces of natural selection, coupled with the demand to adapt to a life on land.

Conserved regions among gene family members across species can identify functionally important motifs. Many GTs require divalent metal ions, commonly Mn2+ or Mg2+, for catalytic activity17 which bind to DXD motifs. In Arabidopsis, ATGLCAT14A showed full activity in the absence of Mn2+ or Mg2+; however, the addition of EDTA (> 5 mM) inhibited its activity. Apparently, this suggests that other divalent metal ions may be involved in enzyme activity11. In this study, we identified a moderately conserved motif, X/DWD/X among putative GLCAT gene family members with 71 out of 161 sequences having the DWD motif, including a bryophyte and a lycophyte. Representative sequences of the mammalian O-β-xylosyltransferases (XT-I, XT-II), belonging to the GT14 family, possesses a highly conserved and functionally characterized DXD motif20 that is absent in plant GLCATs and an uncharacterized X/DWD motif common to plants and mammals (Supplementary Fig. S1). Gotting et al.20 argued that the DWD motif is unique in the sense that no GT families investigated so far have an aromatic amino acid, tryptophan (W), at the central position of the consensus motif, and this observation has been reported earlier25. Interestingly, an investigation into the role of the tryptophan residue demonstrated that alterations of the tryptophan (W) residue in the DWD motif in the human XT-1 protein to either a neutral, basic, or acidic amino acid resulted in a > 60% reduction in enzyme activity20. Although, this functionally important DWD motif in human XT-1 does not align with the Arabidopsis GLCAT sequences in this study (Supplementary Fig. S1), we hypothesize that the highly conserved W residue in the X/DWD motif shared by all species may have been evolutionarily selected to play either a functional or structural role that promotes GLCAT catalytic activity in plants especially when this is the only X/DWD/X motif present among plant GT14 family members. Site-directed mutagenesis studies aimed at discovering the role of this conserved W and the flanking aspartate residues in the DWD motif could address this hypothesis.

Phylogenetic analysis provides a method for assessing homology and inferring relationships among genes. Ten clades (A-J) were identified in a phylogenetic analysis of all putative GLCAT gene family members investigated across plant species (Fig. 2a). Surprisingly, we discovered that clade E family members share a common ancestor before the divergence between higher and lower land plants. It is conceivable that based on the phylogenetic analysis, Pp3c10_2430v3.10 and Pp3c14_2510v3 found in clade E played key roles in expansion of the GLCAT genes into dicots and monocot grasses (Fig. 2a). Dicot specific phylogenetic analysis showed two distinct phylogenetic clades (1A and B; 2A–F), each having species representatives in each subclade (Fig. 2b). With the exception of the putative GLCAT genes in Amborella trichopoda (Supplementary Fig. S6), no single clade was found to consist of GLCAT genes from a single species of monocot grasses and dicots, suggesting that the putative GLCATs in Amborella trichopoda evolved prior to the emergence of putative GLCATs in angiosperms (Dicot and grasses).

GLCAT gene structure was highly conserved with most species having 4 exons and 3 introns in 97% of the sequences which includes lower and higher land plants. Similarly, the average number of amino acids in each species ranged from 360–450 amino acids (Supplementary Table 1). The above observations showed that the genetic architecture of GLCAT gene family members in lower and higher land plants appears to be evolutionarily conserved. In addition, we observed that the same gene architecture (4 exons, 3 introns) is present in Amborella trichopoda and the lower land plants examined (a bryophyte and a lycophyte). It is therefore conceivable that this gene structure originated from the shared common ancestor between the lower and higher land plants, possibly prior to the movement of plants from aquatic to terrestrial life.

Plant genomes contains a variety of structured patterns that are conserved and can be used to discover putative functions of gene family members. We identified 9 sequence blocks (blocks 1–7, 11 and 12, hence referred to as GLCAT motifs) localized in the GLCAT domain. Eight sequence blocks (blocks 1–6, 8 and 9) are present in 95% of the GLCAT sequences; however, blocks 8 and 9 are in the N terminal and C terminal regions flanking the GLCAT domains respectively (Supplementary Fig. S5). Surprisingly, block 2 which contains the highly conserved W residue of the DWD consensus motif is present in all the sequences except in Arabidopsis lyrata (AL4G34150), which after further investigation demonstrated weak sequence similarity with its closest homolog AtGLCAT14C (Fig. 4a). Despite the fact that other Arabidopsis thaliana GLCAT gene family members showed high sequence similarity with Arabidopsis lyrata gene family members as revealed by phylogenetic analyses (Fig. 2b), AL4G34150 and AtGLCAT14C share two things in common: (1) they both have their exon–intron structure conserved and reside in the same phylogenetic clade (clade E) with P. patens (Fig. 2a) and (2) they both lack some blocks found in the GLCAT domain (AtGLCAT14C lacks blocks 11 and 12, while AL4G34150 lacks blocks 2, 4, 5, 7 and 11). Although, AtGLCAT14C (At2g37585) has in-vitro GLCAT activity24, it is unknown whether its closest homolog in Arabidopsis lyrata has such GLCAT activity. This raises some interesting questions. For example, since ATGLCAT14C lacks blocks 11 and 12 in the GLCAT domain, could it be that sequence blocks 11 and 12 are not critical to GLCAT activity in vivo? Does its closest homolog AL4G34150, which lacks several blocks in the GLCAT domain and lacks the highly conserved W residue in the DWD consensus motif possess GLCAT activity? We speculate that generating and expressing constructs that lack one or more of these motifs in the GLCAT domain and testing them in an in-vitro assay will provide answers to these questions.

Previous research reported that positively selected genes are more likely to interact with each other than genes not under positive selection26. Using the site model of BI analysis, ω values identified amino acid residues under positive selection (p < 0.05). The branch model did not identify a branch under positive selection, while the branch-site model identified some sites but with significance values greater than 0.05 (Supplementary Table S3). The inability to detect positively selected sites (p > 0.05) in the branch-site model is indicative of functional similarity among AtGLCAT14A orthologs in both lower and higher land plants. Similarity in the genetic architecture, the conserved GLCAT domain and the shared phylogenetic clades of GLCAT genes may be an evolutionary pattern that is necessary for GLCAT activity in-vivo. It is well documented that the cis‐regulatory elements are seen as the most likely target for the evolution of gene regulation since the modular nature of cis‐regulatory elements largely frees protein coding regions from deleterious pleiotropic effects27. We are, however, not certain that this is the case in GLCATs since we did not investigate the role of cis-regulatory sites and their contribution to gene function in the investigated genomes.

The availability of high throughput techniques has led to the release of large amounts of data that could be mined to understand gene expression of candidate genes of major crops. Variations in cell wall composition are linked to differential gene expression patterns in different plant tissues28,29. In the species investigated, varying degrees of gene expression of GLCAT gene family members were observed across developmental stages and anatomical parts. Similarly, gene expression profiles in the anatomical parts of Arabidopsis, rice and soybean showed that most of the putative GLCAT gene family members have moderate to high expression values during the seedling stage, primarily in the elongation and maturation zones of the radicle (Fig. 5a,c,e). This might be reflective of the extensive cell wall modifications necessary for radicle growth. We investigated the gene expression pattern of all GLCAT gene family members and found AtGLCAT14A (AT5G39990) homologs in rice (LOC_Os12g44240.1) and soybean (Glyma. 13G065900) to be consistently expressed across all anatomical parts and may reflect conserved gene regulatory processes. In contrast, varying degrees of gene expression were observed across developmental stages and may suggest some variation of GlcA abundance in their polysaccharide chains at specific stages of development24.

Radicle emergence from the seed is a highly regulated process that involves discrete and coordinated changes in plant cell wall extensibility and rearrangements of its components30. The available data in GENEVESTIGATOR allows us to investigate the gene expression pattern of Arabidopsis and soybean during germination. While there are differential expression patterns across putative GLCAT gene family members in both species, there were some parallels in their temporal expression patterns. Notably, some genes in Arabidopsis and soybean were significantly up-regulated (fold change > 2) across all time points (Fig. 6a,b). Some GLCATs genes have low or no expression across all or some of the time points in Arabidopsis and soybean. These two observations underscore the importance of certain GLCAT gene family members in germination as growing cells need to produce tailored polysaccharide-rich cell walls essential to determining overall plant growth and biomass.

As sessile organisms, plants must cope with the onslaught of abiotic stresses, such as cold, hypoxia, and extreme temperatures. As most of the GLCAT genes were significantly up-regulated during germination, some of the GLCAT gene family members were significantly down-regulated in response to abiotic stress. We observed that the expression of most, if not all of the GLCAT genes in Arabidopsis were either unchanged or severely down-regulated (fold change < 2) in response to anoxia, hypoxia and heat stress (Fig. 7a). This might be as a result of the need to divert resources from growth to the expression of genes involved in adaptation and survival. In all the rice genotypes examined, we observed a differential response to cold stress; while most GLCAT genes were down-regulated, LOC_Os03g16890 gene was consistently up-regulated in response to cold (4 °C) but down-regulated as the temperature increased to 29 °C in the LTH and IR29 rice genotypes. Notably, LOC_Os12g44240 (an AtGLCAT14A homolog) was consistently down-regulated in response to cold, but up-regulated as the temperature increased to 29 °C in the LTH and IR29 rice genotypes (Fig. 7b). Taken together, there appear to be differences in GLCAT gene expression in response to abiotic stress, with concomitant effects on plant growth and biomass production.

Materials and methods

Sequence retrieval and identification of GLCAT gene family in plant lineages

Database searching was conducted with the Phytozome database version v12.1.6 (https://phytozome.jgi.doe.gov/pz/portal.html) for 14 plant genomes including Amborella trichopoda using the Arabidopsis thaliana GLCAT gene family as queries in a BLAST search. The database search was conducted for the following investigated genomes belonging to the following species: Selaginella moellendorffii, Physcomitrella patens, Arabidopsis thaliana, Arabidopsis lyrata, Brachipodium distachyon, Citrus sinensis, Glycine max, Gossypium raimondii, Oryza sativa, Populus trichocarpa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, and Zea mays. For the identification and analysis of GLCAT genes in the plant genomes, genomic sequences, coding sequences (cds) and peptide sequences were downloaded from the Phytozome version v12.1.6 database31 after a BLASTp 2.2.28 + search. BLASTp was performed by using Arabidopsis GLCAT gene members as queries to retrieve GLCAT gene family members of the remaining 13 investigated genomes with an E-value of 10−5. Unique GLCAT genes were filtered by excluding partial and redundant sequences. All identified putative GLCAT proteins were further confirmed for the presence of family specific conserved domains using NCBI’s Conserved Domain Database (CDD); (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi), Branch domain (PF02485) and EMBL InterProScan (https://www.ebi.ac.uk/interpro/). After confirming the presence of the conserved Branch domains (PF02485) for all putative GLCAT proteins, we further obtained gene IDs, functional annotations, chromosome locations, chromosome numbers, genomic coordinates and peptide sizes from the Phytozome database. Protein molecular weights and pI-values for the identified proteins were calculated using the ExPASy online tool32.

Phylogeny, gene structure analysis, physical mapping and synteny analysis

The GLCAT protein sequences of the investigated genomes were aligned in ClustalW and illustrated using Jalview33. PhyML and Bayesian inference (BI) were used to construct respective phylogenetic trees. PhyML was constructed using maximum likelihood with a bootstrap value of 1000 iterations, and all positions containing gaps and missing data were excluded in order to achieve phylogenetic trees. For the BI, phylogenetic reconstruction was carried out using Bayesian Markov Chain Monte Carlo (MCMC) as implemented in BEAST software v1.5.434. The analysis was carried out with the following parameters: relaxed molecular clock with an uncorrected log-normal distribution model for rate of variation, the HKY substitution model, four gamma categories and a Yule model of speciation. Three independent runs were carried out, each with 1 million MCMC generations and sampled every 1000th generation. Finally, the trees were visualized and managed in iTOL35.

A physical map of GLCAT gene members was constructed using the chromosome numbers and genomic coordinates in Mapchart 2.3036. Gene Structure Display Server37 was used to determine the number of introns and exons. Also, syntenic regions were identified between A thaliana and its orthologs in the investigated genomes using Circoletto38. The color represented the extent of similarity, blue for the lowest similarity, followed by green, orange and red, showing the increasing extent of similarity with increasing bit score. Specifically, blue for the first (i.e. worst) 25% of the maximum bitscore, green for the next 25%, orange for the third, and red for the top (i.e., best) bitscores of between 75 and 100% of the maximum bitscore38.

Motif identification in GLCAT sequences in plant genomes

For motif identification, conserved motifs among GLCATs were identified using the MEME program39 with the following parameters: number of repetitions = zero or one, maximum number of motifs = 6, and optimum motif width constrained between 6 and 50 residues.

Positively selected sites in GLCATs and their putative biological significance

For the identification of positively selected sites, we considered ATGLCAT14A (AT5G39990) orthologs since this protein is the only GLCAT that has been extensively characterized11 and would be a more reliable estimates of selection pressure. We performed a strict statistical analysis using the CodeML program in the EasycodeML software40 using branch model, site model, and branch-site model41 in a run based on the non-synonymous (dN) and synonymous (dS) nucleotide substitution rate ratio (dN/dS) or ω. If ω > 1, then there was a positive selection on some branches or sites; ω < 1 suggests a purifying selection (selective constraints); and ω = 1 indicates neutral evolution. The parameter estimates (ω) and likelihood scores42 were calculated for three pairs of models. These were M0 (one-ratio) versus M3 (discrete), M1a (nearly-neutral) versus M2a (positive-selection), and M7 (beta) versus M8 (beta&ω)43. The LRT44 was used to compare the fit to the data of two nested models, which measured the statistical significance of each pair of nested models based on the estimated p values45. A significantly higher likelihood of the alternative model compared to the null model suggests positive selection (ω > 1). Similarly, we also implemented the branch model to select the statistically significant “foreground branch” presumed to be under positive selection while all other branches in the tree were “background” branches (for example, making branch I foreground while branch II and III are background, Fig. 2d). The background branches share the same distribution of ω values among sites, whereas different values can apply to the foreground branch. Then, the branch-site model was applied, which further estimated the different dN/dS values among the significant branches detected by the branch model and among sites46. Finally, a Bayes empirical Bayes (BEB) approach was then used to calculate the posterior probabilities that a site comes from the site class with ω > 1, which when implemented in EasycodeML software, were used to identify sites under positive selection or purifying selection in the foreground group with significant LRTs44. Each branch group was labeled as a foreground group while other branch groups were considered background.

Digital expression analysis of plant genomes

Publicly available mRNA-seq and Affymetrix microarray data were used to determine the expression of the GLCAT gene family members in Oryza sativa, Arabidopsis thaliana and Glycine max. Complete GLCAT gene expression data were only available for these three species. We used the GENEVESTIGATOR software (https://genevestigator.com/gv/) because the expression dynamics for GLCAT genes are useful for species comparisons under different conditions. Specifically, mRNA-seq data were used to evaluate the expression of GLCAT gene family members across developmental stages and anatomical parts, while the Affymetrix microarray data were used to investigate the expression of the GLCAT gene family members during germination and abiotic conditions. Both mRNA-seq and Affymetrix data were derived from GENEVESTIGATOR (https://genevestigator.com/gv/) and species expression values for the GLCAT gene members were displayed as heatmaps by GENEVESTIGATOR.

Data availability

All data generated or analyzed during this study are included in this published article (and its Supplementary Information files).

References

  1. Albert, V. A. et al. The amborella genome and the evolution of flowering plants. Science 342, 1241089. https://doi.org/10.1126/science.1241089 (2013).

    CAS  Article  Google Scholar 

  2. Zhu, J. K. Abiotic stress signaling and responses in plants. Cell 167, 313–324. https://doi.org/10.1016/j.cell.2016.08.029 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Lynch, M. & Conery, J. S. The evolutionary fate and consequences of duplicate genes. Science 290(5494), 1151–1155 (2000).

    ADS  CAS  Article  Google Scholar 

  4. Tautz, D. & Domazet-Loso, T. The evolutionary origin of orphan genes. Nat. Rev. Genet. 12(10), 692–702 (2011).

    CAS  Article  Google Scholar 

  5. Coyne, J. A. & Hoekstra, H. E. The locus of evolution: Evo devo and the genetics of adaptation. Evol. Int. J. Organ. Evol. 61(5), 995–1016 (2007).

    Article  Google Scholar 

  6. Harris, M. R. & Hofmann, H. A. Seeing is believing: Dynamic evolution of gene families. Proc. Nat. Acad. Sci. 112(5), 1252–1253 (2015).

    ADS  CAS  Article  Google Scholar 

  7. Seifert, G. J. & Roberts, K. The biology of arabinogalactan proteins. Annu Rev. Plant Biol. 58, 137–161 (2007).

    CAS  Article  Google Scholar 

  8. Ellis, M., Egelund, J., Schultz, C. J. & Bacic, A. Arabinogalactan proteins: Key regulators at the cell surface?. Plant Physiol. 153, 403–419 (2010).

    CAS  Article  Google Scholar 

  9. Van Hengel, A. J. & Roberts, K. Fucosylated arabinogalactan-proteins are required for full root cell elongation in Arabidopsis. Plant J. 32, 105–113 (2002).

    Article  Google Scholar 

  10. Cheung, A. Y., Wang, H. & Wu, H. M. A floral transmitting tissue-specific glycoprotein attracts pollen tubes and stimulates their growth. Cell 82, 383–393 (1995).

    CAS  Article  Google Scholar 

  11. Knoch, E., Dilokpimol, A. & Geshi, N. Arabinogalactan proteins: Focus on carbohydrate active enzymes. Front. Plant Sci. 5, 198 (2014).

    Article  Google Scholar 

  12. Kieliszewski, M. J. & Lamport, D. T. A. Extensin: Repetitive motifs, functional sites, post-translational codes, and phylogeny. Plant J. 5, 157–172 (1994).

    CAS  Article  Google Scholar 

  13. Borner, G. H. H., Lilley, K. S., Stevens, T. J. & Dupree, P. Identification of glycosylphosphatidylinositol-anchored proteins in Arabidopsis: A proteomic and genomic analysis. Plant Physiol. 132, 568–577 (2003).

    CAS  Article  Google Scholar 

  14. Xu, J., Tan, L., Lamport, D. T. A., Showalter, A. M. & Kieliszewski, M. J. The O-Hyp glycosylation code in tobacco and Arabidopsis and a proposed role of Hyp-glycans in secretion. Phytochemistry 69, 1631–1640 (2008).

    CAS  Article  Google Scholar 

  15. Tan, L. et al. Plant O-hydroxyproline arabinogalactans are composed of repeating trigalactosyl subunits with short bifurcated side chains. J. Biol. Chem. 285, 24575–24583 (2010).

    CAS  Article  Google Scholar 

  16. Tryfona, et al. Carbohydrate structural analysis of wheat flour arabinogalactan protein. Carbohydr. Res. 345, 2648–2656 (2010).

    CAS  Article  Google Scholar 

  17. Lairson, L. L., Henrissat, B., Davies, G. J. & Withers, S. G. Glycosyltransferases: Structures, functions, and mechanisms. Annu. Rev. Biochem. 77, 521–555 (2008).

    CAS  Article  Google Scholar 

  18. Lamport, D. T. A. & Varnai, P. Periplasmic arabinogalactan glycoproteins act as a calcium capacitor that regulates plant growth and development. New Phytol. 197, 58–64 (2013).

    CAS  Article  Google Scholar 

  19. Showalter, A. M., Keppler, B., Lichtenberg, J., Gu, D. & Welch, L. R. A bioinformatics approach to the identification, classification, and analysis of hydroxyproline-rich glycoproteins. Plant Physiol. 153, 485–513 (2010).

    CAS  Article  Google Scholar 

  20. Gotting, et al. Analysis of the DXD motifs in human Xylosyltransferase I required for enzyme activity. J. Biol. Chem. 41, 42566–42573 (2004).

    Article  Google Scholar 

  21. Ye, C. Y., Li, T., Tuskan, G. A., Tschaplinski, T. J. & Yang, X. Comparative analysis of GT14/GT14-like gene family in Arabidopsis, Oryza, Populus, Sorghum and Vitis. Plant Sci. 181(6), 688–695 (2011).

    CAS  Article  Google Scholar 

  22. Lee, C., Teng, Q., Zhong, R. & Ye, Z. H. Arabidopsis GUX proteins are glucuronyltransferases responsible for the addition of glucuronic acid side chains onto xylan. Plant Cell Physiol. 7, 1204–1216 (2012).

    Article  Google Scholar 

  23. Zhang, Y., Held, M. A. & Showalter, A. M. Elucidating the roles of three β-glucuronosyltransferases (GLCATs) acting on arabinogalactan-proteins using a CRISPR-Cas9 multiplexing approach in Arabidopsis. BMC Plant Biols 20, 221 (2020).

    CAS  Article  Google Scholar 

  24. Geshi, N. et al. A galactosyltransferase acting on arabinogalactan protein glycans is essential for embryo development in Arabidopsis. Plant J. 76, 128–137. https://doi.org/10.1111/tpj.12281 (2013).

    CAS  Article  PubMed  Google Scholar 

  25. Wiggins, C. A. R. & Munro, S. Activity of the yeast MNN1 α-1,3-mannosyltransferase requires a motif conserved in many other families of glycosyltransferases. PNAS 95(14), 7845–7950 (1998).

    Article  Google Scholar 

  26. Vamathevan, et al. The role of positive selection in determining the molecular cause of species differences in disease. BMC Evol Biol. 8, 273 (2008).

    Article  Google Scholar 

  27. Carroll, S. B., Grenier, J. K. & Weatherbee, S. D. Evolution at two levels: On genes and form. PLoS Biol. 3, 1159–1166 (2005).

    CAS  Google Scholar 

  28. Minic, Z. Physiological role of plant glycoside hydrolases. Planta 227, 723. https://doi.org/10.1007/s00425-007-0668-y (2008).

    CAS  Article  PubMed  Google Scholar 

  29. McKinley, B., Rooney, W., Wilkerson, C. & Mullet, J. Dynamics of biomass partitioning, stem gene expression, cell wall biosynthesis, and sucrose accumulation during development of Sorghum bicolor. Plant. J. https://doi.org/10.1111/tpj.13269 (2016).

    Article  PubMed  Google Scholar 

  30. Gómez-Maqueo, X and Gamboa-deBuen. A. The dynamics of plant cell wall in muro modifications and its physiological implications on seed germination. InTech. https://doi.org/10.5772/64085 (2016).

  31. Goodstein, D.M., Shu, S., Howson, R. et al. Phytozome: A comparative platform for green plant genomics. Nucleic Acids Res. 40, D1178–D1186. https://doi.org/10.1093/nar/gkr944 (2012).

    CAS  Article  Google Scholar 

  32. Gasteiger, E. et al. Protein identification and analysis tools on the ExPASy Server. In The Proteomics Protocols Handbook (ed. Walker, J. M.) 571–607 (Humana Press, Totowa, 2005).

    Chapter  Google Scholar 

  33. Waterhouse, et al. Jalview Version 2—sa multiple sequence alignment editor and analysis workbench. Bioinformatics 25, 1189–1191 (2009).

    MathSciNet  CAS  Article  Google Scholar 

  34. Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis sampling trees. BMC Evol Biol. 7, 214 (2007).

    Article  Google Scholar 

  35. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL): An online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128. https://doi.org/10.1093/bioinformatics/btl529 (2016).

    CAS  Article  Google Scholar 

  36. Voorrips, R. E. MapChart: Software for the graphical presentation of linkage maps and QTLs. J. Hered. 93, 77–78. https://doi.org/10.1093/jhered/93.1.77 (2002).

    CAS  Article  PubMed  Google Scholar 

  37. Hu, B. et al. GSDS 2.0: An upgraded gene feature visualization server. Bioinformatics 31, 1296–1297. https://doi.org/10.1093/bioinformatics/btu817 (2015).

    Article  PubMed  Google Scholar 

  38. Darzentas, N. Circoletto: Visualizing sequence similarity with Circos. Bioinformatics 26, 2620–2621. https://doi.org/10.1093/bioinformatics/btq484 (2010).

    CAS  Article  PubMed  Google Scholar 

  39. Bailey, et al. MEME SUITE. Tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    CAS  Article  Google Scholar 

  40. Gao, et al. EasyCodeML: A visual tool for analysis of selection using CodeML. Ecol. Evolut. 9, 3891–3898 (2019).

    Article  Google Scholar 

  41. Guindon, S., Rodrigo, A. G., Dyer, K. A. & Huelsenbeck, J. P. Modeling the site-specific variation of selection patterns along lineages. Proc. Natl. Acad. Sci. USA 101, 12957–12962 (2004).

    ADS  CAS  Article  Google Scholar 

  42. Wong, W. S., Yang, Z., Goldman, N. & Nielsen, R. Accuracy and power of statistical methods for detecting adaptive evolution in protein coding sequences and for identifying positively selected sites. Genetics 168, 1041–1051 (2004).

    CAS  Article  Google Scholar 

  43. Yang, Z., Wong, W. S. & Nielsen, R. Bayes empirical Bayes inference of amino acid sites under positive selection. Mol. Biol. Evol. 22, 1107–1118 (2005).

    CAS  Article  Google Scholar 

  44. Nielsen, R. & Yang, Z. Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics 148, 929–936 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Whelan, S. G. N. Distributions of statistics used for the comparison of models of sequence evolution in phylogenetics. Mol Biol Evol. 19, 1292 (1999).

    Article  Google Scholar 

  46. Zhang, J., Nielsen, R. & Yang, Z. Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol. Biol. Evol. 22, 2472–2479 (2005).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

Authors acknowledge Yuan Zhang from the Department of Environmental and Plant Biology, Ohio University, for proof-reading and providing quality feedback on the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

O.O.A laid out the study, retrieved and analyzed the data. O.O.A and A.M.S prepared figures, supervised the study, wrote and reviewed the manuscript.

Corresponding author

Correspondence to Allan M. Showalter.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ajayi, O.O., Showalter, A.M. Systems identification and characterization of β-glucuronosyltransferase genes involved in arabinogalactan-protein biosynthesis in plant genomes. Sci Rep 10, 20562 (2020). https://doi.org/10.1038/s41598-020-72658-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-020-72658-4

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing