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. 2019 Dec 6;7:e7950. doi: 10.7717/peerj.7950

Genome-wide identification and expression analysis of the 14-3-3 gene family in soybean (Glycine max)

Yongbin Wang 1,2, Lei Ling 3, Zhenfeng Jiang 2, Weiwei Tan 1, Zhaojun Liu 1, Licheng Wu 1, Yuanling Zhao 4, Shanyong Xia 4, Jun Ma 4, Guangjin Wang 5,✉, Wenbin Li 2,✉
Editor: Shalu Jhanwar
PMCID: PMC6901008  PMID: 31824753

Abstract

In eukaryotes, proteins encoded by the 14-3-3 genes are ubiquitously involved in the plant growth and development. The 14-3-3 gene family has been identified in several plants. In the present study, we identified 22 GmGF14 genes in the soybean genomic data. On the basis of the evolutionary analysis, they were clustered into ε and non-ε groups. The GmGF14s of two groups were highly conserved in motifs and gene structures. RNA-seq analysis suggested that GmGF14 genes were the major regulator of soybean morphogenesis. Moreover, the expression level of most GmGF14s changed obviously in multiple stress responses (drought, salt and cold), suggesting that they have the abilities of responding to multiple stresses. Taken together, this study shows that soybean 14-3-3s participate in plant growth and can response to various environmental stresses. These results provide important information for further understanding of the functions of 14-3-3 genes in soybean.

Keywords: Soybean, 14-3-3 genes, Expression, Abiotic stress

Introduction

The 14-3-3 genes are first isolated from brain tissue, and they are ubiquitously found in eukaryotes (Li et al., 2015; Yang et al., 2017; Kumar et al., 2015; Takahashi, 2006). 14-3-3 proteins are highly conserved small acidic proteins in different organisms, encoded by a large gene family (27–32 kDa) (Ferl, Lu & Bowen, 1994; Cao & Tan, 2018). These proteins can form dimers (homo- or hetero- dimers) and have approximately nine antiparallel α-helices (Ferl, Manak & Reyes, 2002; Rodriguez & Guan, 2010). These structures work as binding sites and interact with 14-3-3 proteins and their target, and they also bring two proteins together as a protein complex based on their dimeric properties (Sijbesma et al., 2017; Valente et al., 2012; Li & Dhaubhadel, 2012). 14-3-3s are involved in several protein-protein interactions, such as responding to biotic/abiotic stress, participating in plant hormone signaling and regulating tissue development in various plants (Roberts, Salinas & Collinge, 2002; Camoni et al., 2018; Zhang et al., 2010).

To date, more 14-3-3s have been reported in several plants, such as Arabidopsis, rice, tobacco, populus and Medicago truncatula (Chen et al., 2006; Rosenquist et al., 2001; Xu & Shi, 2006; Tian et al., 2015; Cheng et al., 2016). In plants, the 14-3-3 proteins were named as GF14 or GRF due to they are a part of protein/G-box complex (De Vetten & Ferl, 1994; Rosenquist et al., 2001). 14-3-3s are distributed in different organelles, such as cytoplasm, cell membrane, nucleus, chloroplast and mitochondria (Bihn et al., 2010; Sehnke et al., 2000; Ferl, Manak & Reyes, 2002). In plants, they were regulated by several biological processes (Cheng et al., 2016; Tian et al., 2015); for example, multiple mutant analysis suggested that Arabidopsis 14-3-3 genes regulate root growth, chloroplast division, photosynthesis and leaf longevity (Liesbeth et al., 2015). GhGRFs were found involving in plant development and signaling transduction in cotton fiber (Zhang et al., 2010). In addition, an increasing number of works were carried out to investigate the roles of 14-3-3s in plants under multiple stresses (Roberts, Salinas & Collinge, 2002). Most of the OsGRF genes’ expression changed under heat, cold and salt stresses (Yashvardhini et al., 2017). The results of overexpression of AtGRF6 in transgenic cotton showed a stay-green phenotype, indicating that they can improve plant tolerance to drought stress (Juqiang et al., 2004).

Soybean is an important cash crop in the world, while its production is often influenced by various environmental stresses (Masuda & Goldsmith, 2009). However, not enough attention is focused on soybean GmGF14s. Previous studies identified 18 GmGF14 genes in soybean, which was different from our study (Li & Dhaubhadel, 2011). In this study, we identified a total of 22 GmGF14 genes in soybean genome. Phylogenic relationship, gene structures, protein motifs and expression patterns of all the GmGF14 genes were analyzed, together with their responses to various stresses in soybean. These results will provide important information for further study to understand the regulating mechanism of GmGF14 genes during their growth and their responding abilities to various environmental stresses.

Materials & Methods

Identification of 14-3-3 genes

The Hidden Markov Model (HMM) profiles of the 14-3-3 motif (PF00244) were downloaded from the Pfam database (Punta et al., 2004). HMM searched 14-3-3 motif (PF00244) from the Glycine max protein database with values (e-value) cut-off at 0.1 (Punta et al., 2004). The integrity of the 14-3-3 motif was determined using the online program SMART (http://smart.embl-heidelberg.de/) with an e-value < 0.1 (Ivica, Tobias & Peer, 2012). In addition, the three fields (length, molecular weight, and isoelectric point) of each 14-3-3 protein was predicted by the online ExPASy program (http://www.expasy.org/tools/) (Johana et al., 2015).

Phylogenetic analysis

To investigate the phylogenetic relationship of the 14-3-3 gene families in Arabidopsis thaliana, Oryza sativa, Medicago truncatula and Glycine max, 14-3-3 protein sequences were downloaded from Phytozome v12.1 (http://www.phytozome.org) (Goodstein et al., 2012). 14-3-3s were aligned using the BioEdit program. A neighbor-joining (NJ) phylogenetic tree was constructed using the MEGA 5.0 program (Tamura et al., 2011). Bootstrapping was performed with 1000 replications. Genes were classified according to the distance homology with Arabidopsis thaliana genes (Ferl, Lu & Bowen, 1994).

Sequence alignment, motif prediction and gene structure of 14-3-3 genes

The 3D structure of 14-3-3 proteins were predicted by using Phyre2 and ESPript 3.0 software (Gouet, Robert & Courcelle, 2003; Kelley et al., 2015). Multiple alignments of proteins were conducted using Jalview software with ClustalW method (Clamp et al., 2004). The online MEME analysis is used to identify the unknown conserved motifs (http://meme.ebi.edu.au/) by using the following parameters: site distribution: zero or one occurrence (of a contributing motif site) per sequence, maximum number of motifs: 20, and optimum motif width ≥6 and ≤200 (Bailey et al., 2015). A gene structure display server program (http://gsds.cbi.pku.edu.cn/) was used to display the G. max 14-3-3 gene structures.

Gene duplication and collinearity analysis

The physical locations of the GmGF14 genes on the soybean chromosomes were mapped by using MG2C website (http://mg2c.iask.in/mg2c_v2.0/). The analysis of synteny among the soybean genomes was conducted locally using a method similar to that developed for the PGDD (http://chibba.agtec.uga.edu/duplication/) (Krzywinski & Schein, 2009). First, BLASTP, OrthoMCL software (http://orthomcl.org/orthomcl/about.do#release) and MCScanX software (Wang et al., 2012) were used to search for potential homologous gene pairs (E <1 e−5, top 5 matches) across multiple genomes. Then, these homologous pairs were used as the input for the PGDD database (http://chibba.agtec.uga.edu/duplication/). Ideograms were created by using Circos (Krzywinski & Schein, 2009).

Calculating Ka and Ks

The Ka and Ks were used to assess selection history and divergence time (Li, Gojobori & Nei, 1981). The number of synonymous (Ks) and nonsynonymous (Ka) substitutions of duplicated 14-3-3 genes were computed by using the KaKs_Calculator 2.0 with the NG method (Wang et al., 2010). The divergence time (T) was calculated using the formula T = Ks/ (2 × 6. 1 × 10−9) ×10−6 million years ago (MYA) (Kim et al., 2013).

14-3-3 genes expression analysis of soybean

The expression data of 14-3-3 genes in different tissues, including root, root hair, flower, nodule, pod, stem, leaf, SAM and seed, were available in Phytozome v12.1 database (https://phytozome.jgi.doe.gov/pz/portal.html). We retrieved the fragments per kilobase per million reads (FPKM) value representing the expression level of GmGF14 genes, then generated the heatmap and k-means clustering by using R 3.2.2 software.

Plant material and treatments

Glycine max (Williams 82) was used in this study. Seeds were planted in a 3:1 (w/w) mixture of soil and sand, germinated, and irrigated with half-strength Hoagland solution once every 2 days. The seedlings were grown in a night temperature of 20 °C and day temperature of 22 °C, relative humidity of 60%, and a 16/8 h photoperiod (daytime: 05:00–21:00). After 4 weeks, the germinated seedlings were treated with 20% PEG6000 (drought), 250 mM NaCl solution (salt), and 4 °C (cold). Control and treated seedlings were harvested 1 h, 6 h, and 12 h after treatment. All samples were frozen in liquid nitrogen and stored at −80 °C until use.

RNA extraction and Quantitative real-time PCR (qRT-PCR)

Total RNA was extracted from the root of soybean using RNAiso Plus (TaKaRa, Toyoto, Japan) according to manufacturer’s instructions. 2 µg RNA was extracted using PrimeScript RT reagent Kit with gDNA Eraser (TaKaRa,Toyoto, Japan). The cDNA samples were diluted to 2.5 ng/L. Quantitative Real-time PCR (qRT-PCR) was performed using SYBR Premix Ex Taq II (TaKaRa, Toyoto, Japan) on an ABI Prism 7000 sequence detection system (Applied Biosystems, USA) with the primers listed in Table S1. PCR amplification was performed in accordance with SYBR Premix Ex Taq (TaKaRa, Toyoto, Japan) response system. For each sample, three biological replicates were conducted. Relative expression was calculated by the 2−ΔΔCt method (Livak & Schmittgen, 2001). The actin and GAPDH genes were used as internal control.

Gene ontology enrichment

Once the sequences were obtained ran a BLASTX search against the Uniref100 database at a 1e−30 significance level. The matches were extracted and compared to the GO annotation generated against Uniref100 hits located at EBI. The GO annotation of the GmGF14 genes by using WEGO 2.0 website (http://wego.genomics.org.cn/).

Results

Identification and multiple sequences alignment of GmGF14 genes

We identified 22 GmGF14 genes, which were named from GmGF14a to GmGF14v based on their physical locations on chromosomes. ExPASy predicted that 22 GmGF14 proteins have different physical and chemical properties as their amino acid lengths ranged from 71 aa (GmGF14v) to 754 aa (GmGF14j), with an average of 295 aa, and their molecular weights ranged from 7.92 kDa (GmGF14v) to 81.75 kDa (GmGF14j), the isoelectric points ranged from 4.67 (GmGF14c/e) to 5.7 (GmGF14v). Detail information of GmGF14 proteins is provided in Table 1. Besides, we found that most of the GmGF14s contain highly conserved domains, and ten α-helices were identified in their secondary structures (Fig. 1). In addition, the C-terminal end of 14-3-3 proteins are quite unique in sequence and length.

Table 1. List of all GmGF14 genes identified in the Glycine max genome.

Gene name Gene locus Chromosome location Length (aa) pI Molecular weight (Da) Group
GmGF14a Glyma.01G058000 Chr01:7642485-7646277 260 4.83 29,487.17 ε
GmGF14b Glyma.02G115900 Chr02:11280858-11285984 260 4.83 29,498.19 ε
GmGF14c Glyma.02G208700 Chr02:39388574-39391014 263 4.67 29,353.86 non-ε
GmGF14d Glyma.04G092600 Chr04:8158031-8160711 251 4.81 28,208.84 non-ε
GmGF14e Glyma.04G099900 Chr04:9132954-9135203 289 4.67 32,432.54 non-ε
GmGF14f Glyma.04G183400 Chr04:45129363-45133820 727 4.94 79,220.24 ε
GmGF14g Glyma.05G158100 Chr05:35025422-35029392 260 4.8 29,249.8 ε
GmGF14h Glyma.06G094400 Chr06:7432085-7434388 251 4.81 28,365.07 non-ε
GmGF14i Glyma.06G101500 Chr06:8052625-8054939 280 5.46 31,708.21 non-ε
GmGF14j Glyma.06G182800 Chr06:15705290-15709591 754 5.06 81,749.77 ε
GmGF14k Glyma.07G226000 Chr07:40298318-40302692 260 4.79 29,579.23 ε
GmGF14l Glyma.08G115800 Chr08:8877809-8881104 260 4.9 29,247.74 ε
GmGF14m Glyma.08G363800 Chr08:47528826-47532060 261 4.81 29,384.49 non-ε
GmGF14n Glyma.12G210400 Chr12:36943077-36946491 262 4.73 29,461.02 ε
GmGF14o Glyma.12G229200 Chr12:38919217-38923409 266 4.85 30,493.26 ε
GmGF14p Glyma.13G270600 Chr13:37265741-37269626 264 4.84 30,207.93 ε
GmGF14q Glyma.13G290900 Chr13:39120795-39124124 262 4.77 29,518.07 ε
GmGF14r Glyma.14G176900 Chr14:43637893-43642553 315 4.71 35,233.85 non-ε
GmGF14s Glyma.17G208100 Chr17:34108328-34108849 160 5.61 18,687.61 non-ε
GmGF14t Glyma.18G298300 Chr18:57587135-57590454 258 4.7 29,063.69 non-ε
GmGF14u Glyma.20G025900 Chr20:2845106-2852380 261 4.79 29,640.22 ε
GmGF14v Glyma.20G043700 Chr20:7939112-7939943 71 5.7 7920.14 ε
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Figure 1. Multiple sequence alignment of all 14-3-3 proteins from soybean.

Figure 1

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Phylogenetic analysis of the GmGF14s

We constructed a phylogenetic tree to show the phylogenetic and evolutionary relationships of GmGF14 genes among A. thaliana, O. sativa, M. truncatula and G. max (Fig. 2). The 22 GmGF14 proteins were composed of ε group or non-ε group, 13 GmGF14 proteins (GmGF14a/b/f/g/j/k/l/n/o/p/q/u/v) belonged to the former group, while the other 9 GmGF14 proteins (GmGF14c/d/e/h/i/m/r/s/t) belonged to the non-ε group.

Figure 2. Phylogenetic tree analysis of the 14-3-3 genes in Glycine max, Arabidopsis thaliana, Medicago truncatula and Oryza sativa.

Figure 2

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The phylogenetic tree was constructed using MEGA 5.0 by the neighbor-joining method. The Bootstrap value was 1,000 replicates. The two clusters were designated as non-ε group and ε group, and indicated them in a specific color.

Gene structure and motif analysis

Exon/intron pattern divergence plays a crucial role during evolution. We analyzed the exon/intron pattern of GmGF14s and found that genes of soybean contained 1–6 introns. Among them, non-ε group GmGF14 genes contained 1–4 introns, whereas ε group genes had 1–6 introns. The exon/intron pattern were obviously different in the two groups of GmGF14 genes, suggesting the diversity of GmGF14 genes during the evolution (Fig. 3A, Tables S2 and S3). A total of 15 conserved motifs in GmGF14 genes were identified by MEME software. As shown in Fig. 3B, 5 motifs (motifs 1–5) were annotated as 14-3-3 domains, and most of GmGF14 proteins contained these motifs. All non-ε group GmGF14 proteins shared motifs 3, 4 and 15, whereas most ε group soybean 14-3-3 proteins contained the motifs 1–7 and motif 15. In addition, the GmGF14f/j in ε group contained motifs 8-14, and GmGF14v only had motif 6.

Figure 3. Conserved motifs and gene structure in GmGF14s.

Figure 3

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(A) Exon/intron structures of GmGF14 genes. (B) Conserved motifs of the GmGF14s. Each motif is represented by a number in colored box.

Chromosomal location and duplication analysis

A chromosomal location map of GmGF14 genes on each chromosome was drawn. As shown in Fig. 4, 22 GmGF14 genes were mapped to thirteen of twenty chromosomes unevenly, and they were densely distributed on chromosome 4 and chromosome 6, containing 3 members, respectively (Fig. 4). Most of them were distributed on the two ends of the chromosomes. To better understand the evolution of soybean 14-3-3 genes, we checked genome duplication events in this gene family. The GmGF14 gene pairs had 19 segmental duplication events without tandem duplication (Table S4). Among the duplication events, genes on chromosome 8 had the largest number of that (Fig. 4).

Figure 4. Chromosome location and duplication events analysis in Glycine max.

Figure 4

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Evolution and divergence of the 14-3-3 gene family

We found 19 pairs of paralogous in soybean, 27 orthologous pairs between soybean and Arabidopsis, 15 orthologous pairs in soybean and M. truncatula (Table 2). Additionally, we found that two 14-3-3 genes (GmGF14i and GmGF14s) did not have any homology genes. Two or more GmGF14 genes matched to one AtGRF gene or Mt14-3-3 gene, implying that these genes might play key roles in the GmGF14 genes’ expansion during evolution. In addition, to examine the evolutionary selection process, we calculated Ka/Ks ratios of 19 GmGF14 paralogous pairs (Table 3). All the Ka/Ks value were under 0.3, indicating that they had evolved mainly in strong purifying selection. The gene differentiation of the 19 gene pairs were approximately occurred in the 5–20 MYA.

Table 2. List of paralogous and orthologous pairs between soybean and Arabidopsis thaliana and Medicago truncatula.

Paralogous (Gm-Gm) and orthologous (Gm-Mt and Gm-At) gene pairs.

Gm-Gm Gm-Mt Gm-At
GmGF14a/GmGF14b GmGF14c/Mt14-3-3i GmGF14f/AtGRF16
GmGF14a/GmGF14k GmGF14r/Mt14-3-3i GmGF14j/AtGRF16
GmGF14a/GmGF14u GmGF14a/Mt14-3-3h GmGF14d/AtGRF8
GmGF14b/GmGF14k GmGF14b/Mt14-3-3h GmGF14d/AtGRF6
GmGF14b/GmGF14u GmGF14d/Mt14-3-3f GmGF14h/AtGRF8
GmGF14c/GmGF14e GmGF14h/Mt14-3-3f GmGF14h/AtGRF6
GmGF14c/GmGF14r GmGF14l/Mt14-3-3j GmGF14c/AtGRF1
GmGF14d/GmGF14 h GmGF14g/Mt14-3-3j GmGF14c/AtGRF4
GmGF14f/GmGF14j GmGF14k/Mt14-3-3g GmGF14c/AtGRF2
GmGF14g/GmGF14l GmGF14u/Mt14-3-3g GmGF14e/AtGRF1
GmGF14g/GmGF14n GmGF14n/Mt14-3-3c GmGF14e/AtGRF4
GmGF14g/GmGF14q GmGF14q/Mt14-3-3c GmGF14e/AtGRF2
GmGF14k/GmGF14u GmGF14e/Mt14-3-3e GmGF14r/AtGRF1
GmGF14l/GmGF14n GmGF14o/Mt14-3-3b GmGF14r/AtGRF4
GmGF14l/GmGF14q GmGF14p/Mt14-3-3b GmGF14r/AtGRF2
GmGF14m/GmGF14t GmGF14g/AtGRF9
GmGF14n/GmGF14q GmGF14l/AtGRF9
GmGF14o/GmGF14p GmGF14n/AtGRF9
GmGF14r/GmGF14e GmGF14q/AtGRF9
GmGF14m/AtGRF3
GmGF14m/AtGRF7
GmGF14m/AtGRF5
GmGF14t/AtGRF3
GmGF14t/AtGRF7
GmGF14t/AtGRF5
GmGF14a/AtGRF12
GmGF14b/AtGRF12
GmGF14k/AtGRF12
GmGF14u/AtGRF12
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Table 3. List of Ka, Ks and Ka/Ks values calculated for paralogous GmGF14 gene pairs.

Gene 1 Gene 2 Ka Ks Ka/Ks ratio
GmGF14a GmGF14b 0.006657464 0.098400891 0.067656541
GmGF14a GmGF14k 0.050954843 0.601979458 0.084645484
GmGF14a GmGF14u 0.052668354 0.646064072 0.081521874
GmGF14b GmGF14k 0.053326335 0.624443785 0.085398135
GmGF14b GmGF14u 0.05474254 0.686620201 0.07972754
GmGF14c GmGF14e 0.041987099 0.46411507 0.090467003
GmGF14c GmGF14r 0.007777127 0.133330521 0.058329686
GmGF14d GmGF14 h 0.014039603 0.130901237 0.1072534
GmGF14f GmGF14j 0.038562958 0.163525641 0.235822089
GmGF14g GmGF14l 0.016897364 0.089125506 0.189590661
GmGF14g GmGF14n 0.095210598 1.445569001 0.065863752
GmGF14g GmGF14q 0.096117116 1.432594886 0.067093019
GmGF14k GmGF14u 0.006698342 0.14254911 0.046989715
GmGF14l GmGF14n 0.08659209 1.528599239 0.056648
GmGF14l GmGF14q 0.087490577 1.514174801 0.057781028
GmGF14m GmGF14t 0.052328317 0.201934457 0.259135156
GmGF14n GmGF14q 0.008325325 0.127514764 0.065289105
GmGF14o GmGF14p 0.015822932 0.069501128 0.227664399
GmGF14r GmGF14e 0.037835791 0.428494041 0.088299457
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Cis-elements in GmGF14s promoters

Cis-elements involved in transcriptional regulation and can response to variety stresses. We isolated the sequence which is 1.5 kb upstream of the GmGF14 genes to explore their potential function (Table 4). We found nine potential elements, such as ABRE, AuxRR-core, GARE-motif, CGTCA/TGACG-motif, P-box, TATC-box, TCA-element and TGA-element, were involved in ABA (abscisic acid), IAA (auxin), GA (gibberellin), MeJA (methyl jasmonate) and SA (salicylic acid) regulating mechanism. Additionally, there were four elements (TC-rich repeats, ARE, MBS and LTR) involved in defense/stress, anaerobic induction, drought and low-temperature responses, respectively. In the GmGF14 promoters, we found different types and numbers of cis-elements, indicating that they participated in different regulatory mechanisms during plant growth and development.

Table 4. The number and composition of cis-acting regulatory elements of each GmGF14 gene.

Gene ABRE (ABA) AuxRR-core (IAA) TGA-element (IAA) CGTCA-motif (MeJA) TGACG-motif (MeJA) GARE-motif (GA) P-box (GA) TATC-box (GA) TCA-element (SA) TC-rich repeats (Defense/stress) LTR (cold) ARE (anaerobic) MBS (drought)
GmGF14a 1 1 1
GmGF14b 1 1
GmGF14c 1 2 2
GmGF14d 3 1 2 2 1
GmGF14e 4 1 1 1 1
GmGF14f 2 1 1 1 2 1 1 1
GmGF14g 3 1 2 1
GmGF14 h 7 1 1
GmGF14i 3 1 1 1 1
GmGF14j 1 1 1 2 1
GmGF14k 1 1 1 1
GmGF14l 2 1 1 1
GmGF14m 3 1 1
GmGF14n 1 2
GmGF14o 1 1
GmGF14p 1
GmGF14q 1 1 1 1
GmGF14r 3 2 2 1
GmGF14s 1 1
GmGF14t 1 2 2
GmGF14u 2 2 2 1
GmGF14v
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Expression analysis of GmGF14 genes in different tissues

We analyzed the expression level of GmGF14 genes in different soybean tissues and organs (e.g., root, root hair, flower, nodule, pod, stem, leaf, SAM and seed) based on RNA-seq data (Fig. 5). Results showed that the expression level of most GmGF14 genes varies in different tissues, suggesting the diversity of their roles. Significantly, most GmGF14s’ expression level in vegetative organs (e.g., root, root hair, stem, leaf, and SAM) were higher than that of reproductive organs (e.g., flower, pod and seed). Ten GmGF14 genes (GmGF14e/i/h/c/r/m/n/q/g/t) were highly expressed in all tested tissues, suggesting that they regulated the growth and development of soybean. GmGF14k was specifically expressed in root, GmGF14p was highly expressed in pod and stem, GmGF14o was highly expressed in pod. In addition, GmGF14s and GmGF14v can’t be detected in these tissues.

Figure 5. Expression analysis of GmGF14 genes in different tissues.

Figure 5

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The gene expression values are square-root transformed fragments per kilo-bases per million mapped reads (FPKM). Different colors in map represent FPKM values as shown in bar at top of figure.

Expression patterns of GmGF14s under abiotic stress

Drought, salinity and cold are major factors affecting the production of soybean under natural conditions. We selected 19 genes from GmGF14 which were differentially expressed (except GmGF14s, GmGF14u and GmGF14v) to further explore their expression pattern by qRT-PCR under abiotic stresses (Figs. 6–8, Tables S5–S8). The expression levels of them were changed over time during the stresses, showing that there were dynamic processes in the GmGF14s when they responding to stresses. In addition, we found that different gene duplication pairs have different expression patterns under the abiotic stresses (Table S5). During drought treatment, the expression pattern of three genes (GmGF14a/b/g) were all up-regulated in all the time point (Fig. 6, Table S6). Five genes (GmGF14a/b/e/g/t) were highly induced at 1 h, while the expression levels of GmGF14c/d/q/r did not significantly changed at all the time after drought treatment. Conversely, under drought treatment, four GmGF14 genes (GmGF14c/d/q/r) were obviously down-regulated. Under salt stress, the expression levels of GmGF14a/b/c/e/g/i/q/r/t at 1 h time point up-regulated considerably, then the expression levels decreased (Fig. 7, Table S7). The expression levels of three GmGF14 genes (GmGF14f/h/p) were generally down-regulated at one time point. 5 of 19 GmGF14 genes (GmGF14g/h/l/m/t) were expressed essentially identical, with expression peaking at the first time point (1h) under cold stress (Fig. 8, Table S8). Eight genes (GmGF14a/b/c/d/e/n/q/r) were not significantly changed at first time points (1h), followed by a strongly decrease under cold stress.

Figure 6. qRT-PCR analysis reveals GmGF14 genes under PEG (drought) treatment compared to the controls.

Figure 6

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Stress treatments and time course are described in ‘Materials and Methods’. (A–S) represent different genes which were used in qRT-PCR analysis. Asterisks on top of the bars indicating statistically significant differences between the stress and counterpart controls (*p < 0.05, **p < 0.01). Error bars represent SD of biologic replicates.

Figure 8. qRT-PCR analysis reveals.

Figure 8

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GmGF14 genes under cold treatment compared to the controls. tress treatments and time course are described in ‘Materials and Methods’.(A–S) represent different genes which were used in qRT-PCR analysis. Asterisks on top of the bars indicating statistically significant differences between the stress and counterpart controls (*p < 0.05, **p < 0.01). Error bars represent SD of biologic replicates.

Figure 7. qRT-PCR analysis reveals.

Figure 7

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Stress treatments and time course are described in ‘Materials and Methods’. (A–S) represent different genes which were used in qRT-PCR analysis. Asterisks on top of the bars indicating statistically significant differences between the stress and counterpart controls (*p < 0.05, **p < 0.01). Error bars represent SD of biologic replicates.

Gene ontology enrichment

To further understand the functions of the GmGF14s, we performed GO annotation and GO enrichment analyses (Fig. S2 and Table S9). Usually, the GO terms included biological process, molecular function and cellular component. Within the biological process, most genes were assigned to the cellular process (16/22), single-organism process (16/22), biological regulation (14/22), regulation of biological process (14/22) and response to stimulus (14/22). In terms of cellular process, most GmGF14 proteins were predicted to be involved in cell (20/22), cell part (20/22), membrane (16/22), organelle (16/22) and organelle part (14/22). Few genes had predicted distributions in cell junction (2) and symplast (2). Within the molecular function category, only 20 GmGF14 proteins were predicted to be involved in binding. The number of GmGF14 proteins predicted to cell and binding were very high, suggesting that the GmGF14 gene family may play a crucial role in protein binding and cell development.

Discussion

In eukaryotes, the 14-3-3s were highly conserved and could form homo- or hetero- dimers, which then produced different proteins in a protein complex (Takahashi et al., 2003; Ferl, Manak & Reyes, 2002). They played important roles in various biological progresses and signal transduction process (Yoon et al., 2012; Wilson, Swatek & Thelen, 2016). Hence, we carried out genome-wide analysis of GmGF14 genes by bioinformatics analysis and qRT-PCR to investigate their regulation during development processes and/or stress responses. Li identified 18 GmGF14 genes in soybean (Li & Dhaubhadel, 2011), however, we found 22 GmGF14 genes. This may due to the fact that we used a newer version database compared with previous study. Recently, the 14-3-3s has been reported in several plants, such as Arabidopsis (13), tobacco (17), rice (18), Populus (12), cotton (6), banana (25) and grape (11) (Saalbach et al., 1997; Ferl, Lu & Bowen, 1994; Yashvardhini et al., 2017; Tian et al., 2015; Zhang et al., 2010; Li et al., 2012; Cheng et al., 2018).

In soybean, 14-3-3s were divided into two groups, ε group (13 members) and non-ε group (9 members) based on their phylogenetic analysis. Besides, there is a very close relationship between soybean and M. truncatula, suggesting that the 14-3-3 family members in legumes are relatively conserved. In addition, ε group GmGF14 genes have much more exons/intron than non- ε group genes, while the first intron of non- ε group was longer than that of ε group. Besides, the members in ε group contained eight motifs, while non-ε group members had less, usually 3-4 motifs. Furthermore, protein structure analysis show that compared with other species such as banana, grape and rice, the members of 14-3-3s had ten typical antiparallel α-helices (Yashvardhini et al., 2017; Cheng et al., 2018; Li et al., 2012). The result of 14-3-3 proteins was different from that in other species, that might due to the soybean genome has undergone two gene duplication events and has more gene diversity in the process of evolution (Wang et al., 2017).

Gene duplication events is important in gene family expansion and could gain functional diversity during evolution, including tandem, transposition and segment duplication events (Kaessmann, 2010). There were 19 gene pairs involved in segment duplication, while no tandem duplication event occurred in GmGF14s, indicating that the segment duplication maybe the major gene duplication for this gene family’s expansion (Cheng et al., 2018). Among them, ε group had more gene duplication events (14/19; 73.68%) than non-ε group (5/19; 26.32%) in soybean. In addition, we calculated the Ks value of each paralogous pairs, and found the most recent duplication event in soybean appeared between 5 to 20 MYA, which is consisting with the recent whole genome duplication (WGD) event in soybean (Wang et al., 2017). The Ka/Ks of all the GmGF14 gene pairs were less than 0.3, suggesting that they were evolved in mainly under strong purifying selection. This result was similar to other plants, meaning that 14-3-3 genes evolved more slowly at the protein level in plants, and they have a conserved evolutionary pattern in GmGF14 genes.

It has been reported that the 14-3-3 genes were expressed in different tissues in many plants. PvGRFr might involve in flower development based on the expression patterns in switchgrass (Wu et al., 2016). In banana, the expression quantity of most MaGRFs were accumulated during fruit ripening obviously (Li et al., 2012). The expression levels of most GmGF14 genes in vegetative organs were higher than that of reproductive organs in plants, suggesting that 14-3-3 genes may participate in morphogenesis directly or indirectly. In soybean, 14-3-3 genes involved in nodule mature, they can affect the formation of the early nodule development when the expression levels of SGF14c and SGF14l reduced (Radwan et al., 2012). In M. truncatula, Mt14-3-3 genes were involved in rhizobium infections, Mt14-3-3c was involved in the early stage of nodule formation (Chen et al., 2006). These results suggesting that the 14-3-3 gene family has various functions, they were similar to the results of the GO enrichment. For the GO enrichment, GmGF14 genes were mainly concentrated in cell development and protein binding. Except this, different GmGF14 genes had similar expression patterns in different tissues. For example, paralogous pairs GmGF14a/b, GmGF14k/u, and GmGF14o/p had similar expression patterns in most tested tissues, meanwhile, they also had gene duplication relationship, indicating that they might have similar functions in different tissues, in accordance with the results of GO enrichment.

More and more evidences had suggested that 14-3-3 genes response to environmental stimuli in many plants (Xu & Shi, 2006; Chen et al., 2006; Li et al., 2015). Plant 14-3-3 genes are signal moderators, playing an important role in response to abiotic stress (Li et al., 2015). Overexpression of AtGRF9 resulted in more carbon distribution from the shoot to the root, and enhanced the drought tolerance of plant by increasing proton secretion in the root growing zone (He et al., 2015). As homologous gene of AtGRF9, GmGF14g was up regulated during the treatment, and the 1 h drought stress treatment caused a threefold increase of its expression level (its expression increased 3-fold with 1 h drought stress treatment). In tomato, transcription level of four 14-3-3 genes were significantly up-regulated under salt stress (Xu & Shi, 2006). In this study, nine genes (GmGF14a/b/c/e/g/i/q/r/t) were first up-regulated and then decreased after salt treatment, indicating that soybean 14-3-3 genes have different regulatory mechanisms under stress. In addition, many 14-3-3 genes (e.g., GmGF14b/c/g/j) of soybean changed distinctly under cold treatment, most of the gene expression levels decreased at 6 h and 12 h treatment time points, suggested that they might play a potential role in responding to cold stress. The results of GO enrichment suggested that GmGF14 genes can respond to stimuli.

The ABA signaling pathway is a major pathway in response to the drought, salt and cold stresses (Zhang et al., 2006; Yu & Qi, 2017). 14-3-3s promoter region contain ABRE promoters, and they could response to stresses directly or indirectly by involving in the ABA signal pathway. In addition, four cis-elements of GmGF14 genes (TC-rich repeats, ARE, MBS and LTR) involved in responding to different abiotic stresses, while other eight cis-elements were involved in multiple plant hormone stress responses. Taken together, these results reported that GmGF14s may have various functions, including regulate plant growth and response to abiotic stresses.

Conclusions

All 22 GmGF14s were classified into the ε group and non-ε group based on their phylogenetic relationship among A. thaliana, O. sativa and M. truncatula. Gene structure and duplication event showed that 14-3-3 gene family was relatively conserved. RNA-seq and qRT-PCR were used to explore the function of GmGF14s. The expression levels of most GmGF14s showed that they could response to multiple stresses. In summary, these results suggest the potential roles of GmGF14 genes in plant development and multiple stress responses, therefore provide scientific references for the further study of GmGF14 genes’ function.

Supplemental Information

Figure S1. Sequence logo of motifs in GmGF14 genes.
Click here for additional data file. (3.4MB, png)
DOI: 10.7717/peerj.7950/supp-1
Figure S2. GO enrichment of the GmGF14 genes.
Click here for additional data file. (383.5KB, png)
DOI: 10.7717/peerj.7950/supp-2
Table S1. List of primers used in qRT-PCR.
Click here for additional data file. (13.4KB, docx)
DOI: 10.7717/peerj.7950/supp-3
Table S2. The sequence information of GmGF14 genes.
Click here for additional data file. (15.1KB, docx)
DOI: 10.7717/peerj.7950/supp-4
Table S3. The GFF file for GF14 genes.
Click here for additional data file. (165.7KB, docx)
DOI: 10.7717/peerj.7950/supp-5
Table S4. Duplication relationship of GmGF14 genes.
Click here for additional data file. (13.4KB, docx)
DOI: 10.7717/peerj.7950/supp-6
Table S5. Similarity of duplication gene pairs under abiotic stresses (drought, salt and cold).
Click here for additional data file. (14.1KB, docx)
DOI: 10.7717/peerj.7950/supp-7
Table S6. Raw data for the drought stress.
Click here for additional data file. (16.6KB, docx)
DOI: 10.7717/peerj.7950/supp-8
Table S7. Raw data for the salt stress.
Click here for additional data file. (17.2KB, docx)
DOI: 10.7717/peerj.7950/supp-9
Table S8. Raw data for the cold stress.
Click here for additional data file. (17KB, docx)
DOI: 10.7717/peerj.7950/supp-10
Table S9. GO terms of the GmGF14 genes.
Click here for additional data file. (39.5KB, xls)
DOI: 10.7717/peerj.7950/supp-11

Acknowledgments

The authors would like to thank the Key Laboratory of Crop and Livestock Molecular Breeding of Heilongjiang Province for providing plenty of helpful manpower and material support.

Funding Statement

This work was supported by the National Key R&D Program for Crop Breeding (2016YFD0102105), the Science and Technology Program for Innovation Talents of Harbin (2014RFQYJ016, 2014RFXYJ011) and the Project of the Heilongjiang Academy of Agricultural Sciences (HAAS) (2017BZ12). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Contributor Information

Guangjin Wang, Email: gjw1962@yeah.net, wenbinli@yahoo.com.

Wenbin Li, Email: wenbinli@neau.edu.cn.

Additional Information and Declarations

Competing Interests

The authors declare there are no competing interests.

Author Contributions

Yongbin Wang performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Lei Ling analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Zhenfeng Jiang performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, approved the final draft.

Weiwei Tan Zhaojun Liu performed the experiments, prepared figures and/or tables, approved the final draft.

Licheng Wu, Shanyong Xia and Jun Ma performed the experiments, contributed reagents/materials/analysis tools, prepared figures and/or tables, approved the final draft.

Yuanling Zhao performed the experiments, prepared figures and/or tables, approved the final draft.

Guangjin Wang conceived and designed the experiments, prepared figures and/or tables, approved the final draft.

Wenbin Li conceived and designed the experiments, authored or reviewed drafts of the paper, approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The raw measurements are available in the Supplementary Files.

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Associated Data

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

Supplementary Materials

Figure S1. Sequence logo of motifs in GmGF14 genes.
Click here for additional data file. (3.4MB, png)
DOI: 10.7717/peerj.7950/supp-1
Figure S2. GO enrichment of the GmGF14 genes.
Click here for additional data file. (383.5KB, png)
DOI: 10.7717/peerj.7950/supp-2
Table S1. List of primers used in qRT-PCR.
Click here for additional data file. (13.4KB, docx)
DOI: 10.7717/peerj.7950/supp-3
Table S2. The sequence information of GmGF14 genes.
Click here for additional data file. (15.1KB, docx)
DOI: 10.7717/peerj.7950/supp-4
Table S3. The GFF file for GF14 genes.
Click here for additional data file. (165.7KB, docx)
DOI: 10.7717/peerj.7950/supp-5
Table S4. Duplication relationship of GmGF14 genes.
Click here for additional data file. (13.4KB, docx)
DOI: 10.7717/peerj.7950/supp-6
Table S5. Similarity of duplication gene pairs under abiotic stresses (drought, salt and cold).
Click here for additional data file. (14.1KB, docx)
DOI: 10.7717/peerj.7950/supp-7
Table S6. Raw data for the drought stress.
Click here for additional data file. (16.6KB, docx)
DOI: 10.7717/peerj.7950/supp-8
Table S7. Raw data for the salt stress.
Click here for additional data file. (17.2KB, docx)
DOI: 10.7717/peerj.7950/supp-9
Table S8. Raw data for the cold stress.
Click here for additional data file. (17KB, docx)
DOI: 10.7717/peerj.7950/supp-10
Table S9. GO terms of the GmGF14 genes.
Click here for additional data file. (39.5KB, xls)
DOI: 10.7717/peerj.7950/supp-11

Data Availability Statement

The following information was supplied regarding data availability:

The raw measurements are available in the Supplementary Files.

Articles from PeerJ are provided here courtesy of PeerJ, Inc

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