The Identification of MATE Antisense Transcripts in Soybean Using Strand-Specific RNA-Seq Datasets

Abstract

Natural antisense transcripts (NATs) have been generally reported as negative regulators of their sense counterparts. Multidrug and toxic compound extrusion (MATE) proteins mediate the transport of various substrates. Although MATEs have been identified genome-wide in various plant species, their transcript regulators remain unclear. Here, using the publicly available strand-specific RNA-seq datasets of Glycine soja (wild soybean) which have the data from various tissues including developing pods, developing seeds, embryos, cotyledons and hypocotyls, roots, apical buds, stems, and flowers, we identified 35 antisense transcripts of MATEs from 28 gene loci after transcriptome assembly. Spearman correlation coefficients suggested the positive expression correlations of eight MATE antisense and sense transcript pairs. By aligning the identified transcripts with the reference genome of Glycine max (cultivated soybean), the MATE antisense and sense transcript pairs were identified. Using soybean C08 (Glycine max), in developing pods and seeds, the positive correlations between MATE antisense and sense transcript pairs were shown by RT-qPCR. These findings suggest that soybean antisense transcripts are not necessarily negative transcription regulators of their sense counterparts. This study enhances the existing knowledge on the transcription regulation of MATE transporters by uncovering the previously unknown MATE antisense transcripts and their potential synergetic effects on sense transcripts.

1. Introduction

Multidrug and toxic compound extrusion (MATE) transporters typically consist of 12 transmembrane domains (TMDs) [1] and play important roles in cellular transport, metabolism, and physiology [2,3,4]. MATEs are ancient proteins that can be found in all three domains of life [3,5]. Previous research suggested that prokaryotic MATE transporters use H⁺ or Na⁺ in exchange for their substrates such as ions and secondary metabolites while most eukaryotic MATE transporters use H⁺ in exchange for their target substrates [3,5]. In plants, MATE transporters have been reported to be involved in the transportation of various substrates, including ion chelators, phytohormones, alkaloids and flavonoids [4,6,7]. In terms of biological processes, MATE proteins have been reported to regulate various processes such as detoxification, stress tolerance, growth and development, and senescence of plants [8,9]. The diverse substrate specificities of MATE proteins to transport various substrates and regulate diverse biological processes could be the possible reason rendering the big families of MATEs in plant genomes. In a genome-wide survey based on the presence of the conserved MATE domain, 117 MATE genes were identified in the soybean genome [10]. Similarly, 49 MATE genes were identified in Zea mays [11], 67 in Solanum lycopersicum [12], 71 in Populus trichocarpa [13], 53 in Oryza sativa [14], 56 in Arabidopsis thaliana [15], 40 in Medicago truncatula [16], and 65 in Vitis vinifera [17]. Although MATE proteins are structurally similar, as characterized by the typical 12 TMDs [1], they have different substrate specificities [4,6,7], probably due to different substrate recognitions by different amino acid residues [6]. Some substrates transported by MATE proteins are specific to particular groups of plants. For example, anthocyanins are especially rich in the skin of berries and colored seed coats while isoflavones are especially rich the seeds of legumes [18]. VvMATE1 and VvMATE2 were reported to be putative proanthocyanidin transporters in seed berries [19] while GmMATE1 and GmMATE2 were reported to mediate isoflavone transport and storage in soybean seeds [7]. The rich diversity of metabolites in particular plant species hints at the need for a large number of transporters and thus the big MATE families in plants compared to other eukaryotes. Since MATE genes are differentially expressed in different tissue types and different developmental stages [7,20], it is plausible that there are additional regulatory roles that these genes play besides transcriptional regulation.

Natural antisense transcripts (NATs) were believed to negatively regulate their corresponding sense transcripts [21]. For example, in the model plant Arabidopsis, the antisense transcripts covering the entire FLOWERING LOCUS C (FLC locus) were suggested to silence FLC transcripts [22]; the antisense transcript of DOG1 (Delay of Germination 1) was reported to suppress the expression of the sense DOG1 transcript [23]; CDF5 (CYCLING DOF FACTOR 5) transcript and its antisense pair transcript FLORE (CDF5 LONG NONCODING RNA) were reported to inhibit each other’s expression [24]. However, accumulated evidence suggested that NATs are not necessarily the negative regulators of their sense counterparts [25]. For example, also in the model plant Arabidopsis, in a global analysis of antisense and sense transcript pairs that result in elevated small RNA levels, in terms of expression, the transcript pairs could have negative correlations, no correlations, or positive correlations [26]. In another study, MAS, the antisense transcript of MAF4 (MADS AFFECTING FLOWRING4), was reported to be the positive regulator of the transcription of its sense counterpart [27].

The previous soybean NAT database was built using many fewer tissue types [28] or predicted only by genome annotation [29]. Based on the annotation information, transcript orientation, and the degree of overlapping, in the Plant Natural Antisense Transcripts DataBase (PlantNATsDB), it was predicted that there are 436 cis-NATs and 77,903 trans-NATs in soybean [29]. In another genome-wide identification of NATs in soybean, in which sequences of small RNAs were aligned to the predicted cDNA sequences from the soybean gene sequence database, 994 cis-NATs and 25,222 trans-NATs were predicted [28]. However, such predictions relied heavily on the accuracy of transcript annotation [29] and had possibly left out NATs that do not lead to small RNA generation [28].

In this study, we performed a more robust genome-wide analysis to identify NATs that may regulate MATE genes in soybean.

2. Materials and Methods

2.1. Transcriptome Assembly and MATE Antisense Transcripts Identification

The antisense transcript identification was performed with the transcriptome datasets of the wild soybean accessions (Glycine soja) since Glycine soja has more comprehensive strand-specific RNA-seq data from different tissues compared to cultivated soybean (Glycine max) [30,31]. The strand-specific RNA-seq datasets used for transcriptome assembly and MATE antisense transcript identification are summarized in Table S1. Publicly available RNA-seq datasets of Glycine soja (Table S1) were collected. After that, adapter trimming and quality trimming were performed with the raw transcriptome reads using Trimmomatic (0.36) [32]. The clean read data were then aligned to the reference genome of Glycine soja (W05) [31] using HISAT2 (2.1.0) [33], followed by de novo transcriptome assembly using StringTie (1.3.4d) [34]. De novo transcriptome assembly was performed for each sample. After that, the data were merged using the merge function of StringTie [34]. The merged transcriptome data were integrated with the reference annotation of the reference genome of Glycine soja (W05) [31] using the transcript annotation script (NIAP_annotate_v1.1.pl) in the NIAP package (https://github.com/alanlamsiu/NIAP, last accessed on 9 April 2021). This merged transcriptome annotation was then used for MATE antisense transcript identification and expression analysis. Antisense transcripts of MATE loci were identified by intersecting the merged transcriptome annotation and predicted MATE loci for W05 [31] at opposite strands using BEDTools (v2.27.1) [35]. The sequences for these sense and antisense transcripts of MATEs from soybean accession W05 were then extracted. Considering that most research on soybean is done with cultivated soybean (Glycine max), the corresponding MATE antisense and sense transcripts in cultivated soybean were identified with the use of the reference genome of cultivated soybean (Gmax_275_Wm82.a2.v1) [36]. Reciprocal BLAST was performed with all protein sequences of the accession Williams 82 (Wm82) (Gmax_275_Wm82.a2.v1 [36]) and W05 [31] to identify corresponding genes between Wm82 and W05. The transcript sequences of W05 was aligned with the genome sequence of Wm82 (Gmax_275_Wm82.a2.v1 [36]) to identify the corresponding MATE antisense and sense transcripts, which were used later in designing the primers for the RT-qPCR validation experiment. Sequences and coordinates of the W05 and Wm82 antisense transcripts can be found in Supplementary Materials S1 and S2. Genomic loci in W05 corresponding to genes in Wm82 which were annotated as MATE [10] were selected for MATE antisense transcript identification.

2.2. Plant Sample Preparation

Soybean plants (cultivated soybean accession C08 (Glycine max) [37]), were grown on an experimental field in the Chinese University of Hong Kong, watered twice a day, under normal conditions. The developing pods were harvested at 40 days after flowering (DAF). For each biological replicate, the pods were harvested from three individual plants. Two biological replicates were harvested and used for RNA extraction, followed by expression analysis. The pod shells and the seeds with seed coat removed were frozen in liquid nitrogen, then stored at −80 °C before RNA extraction.

2.3. RNA Extraction from Plant Samples

Total RNA was extracted from the frozen pods and seeds without seed coat using Fruit-mate (Cat# 9192, TaKaRa, Shiga, Japan) supplemented with RNasin (recombinant, Cat# N2515, Promega, Madison, WI, USA), and TRIzol Reagent (Cat# 15596018, ThermoFisher Scientific, Waltham, MA, USA) according to the manufacturers’ instructions. The total RNA was treated with DNase I (Cat# 18068015, ThermoFisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocol and then used for expression analysis.

2.4. Expression Analysis

The expression levels of the antisense and sense transcripts were validated using One Step PrimeScript RT-PCR Kit (Perfect Real Time) (Cat# RR064B, TaKaRa, Shiga, Japan) according to the manufacturer’s protocol with the following modifications. The reverse primer was added to the DNaseI-treated RNA for reverse transcription. After that, the forward primer was added to the reaction mix for qPCR. The relative expressions of the antisense and sense transcripts were calculated using the 2^−ΔΔCT method with VPS as the normalizing gene [38]. Primers used in the experiments are listed in Table S2. Two biological replicates, with each replicate having the total RNA extracted from seeds (seed coats removed) or pods from three individual plants, were used for the expression analysis. Three RT-qPCR reactions were done for each template and primer pair combination. One RT-qPCR was regarded as one technical replicate.

3. Results

3.1. Genome-Wide Identification of MATE Antisense Transcripts

Using the publicly available, strand-specific RNA-seq datasets of a wide variety of tissue types including developing pods, developing seeds, embryos, cotyledons and hypocotyls, roots, apical buds, stems, and flowers, we identified 35 antisense transcripts of MATEs from 28 gene loci in the wild soybean accession W05 (Supplementary Material S1). The predicted relative expression levels of these antisense transcripts are illustrated in Figure 1. Subsequently, we identified the antisense MATE transcripts in the Wm82 by aligning the antisense transcripts identified from strand-specific RNA-seq datasets of W05 to the reference genome of Wm82 (Gmax_275_Wm82.a2.v1 [36]) (Supplementary Material S2).

3.2. Predicted Expression Correlations between MATE Antisense and Their Respective Sense Transcripts

Being NATs, the antisense MATE transcripts could potentially regulate the levels of the respective sense transcripts due to the sequence complementarity. As reflected by the Spearman correlation coefficients, there are several antisense MATE transcripts that exhibited positive expression correlations with their respective sense transcripts in the dataset (Figure 2).

3.3. Experimental Validations and Expression Analyses of the Positively Correlated Antisense-Sense MATE Transcript Pairs

MATE-type proteins are known to be transporters of secondary metabolites [18]. During pod development, nutrients are actively accumulated inside the developing seeds. In this study, developing pods of the soybean accession C08 at 40 DAF, the stage of active seed-filling [39], were used for experimental validation of the transcripts and comparisons of the expression levels between seed and pod (Figure 3). In line with the expression correlations between the antisense and the respective sense transcripts of soybean accession W05 (Figure 2), the antisense and sense MATE transcripts from soybean accession C08 showed similar expression trends between the developing seed and pod (Figure 3).

4. Discussion

MATE transporters have diverse physiological roles in growth, development, and stress responses [4,6]. The expression of MATE genes also exhibits differential patterns to cope with their functions [7,13,20]. MATE genes display tissue specific expression patterns. For example, the soybean genes GmMATE1, GmMATE2, and GmMATE4 were shown to have differential expression levels in different tissues such as seeds, seed coats, and pods [7,20]. In Hordeum vulgare, HvAACT1 is expressed in the whole root but has a higher expression level in the root tip [40]. In Nicotiana tabacum, Nt-JAT1 is expressed in leaf, stem, and root [41] while NtJAT2 is expressed in leaf but not stem or root [42]. In terms of the response to stimuli, both Nt-JAT1 and Nt-JAT2 were shown to be induced by methyl jasmonic acid treatment [41,42]. In Populus poplar, PtrMATE1 and PtrMATE2 were shown to be induced by aluminum stress [13]. In Solanum lycopersicu, Sl-ALMT9 was also demonstrated to be induced by aluminum stress [43]. The diverse expression patterns and responses to stimuli of MATE genes imply the possible existence of transcript regulators.

In this study, we identified novel antisense transcripts of MATEs and investigated the expression correlation between NATs and their sense transcripts to reveal the possible negative regulatory roles or the synergetic effects of the NATs on their sense transcripts.

We successfully employed a whole-genome approach to identify high confident NATs of the MATE transcript (Figure 1). These antisense transcripts were not listed in existing databases of soybean antisense transcripts [28,29]. We were able to validate the expressions of the predicted antisense transcripts using RT-qPCR (Figure 3). Among the experimentally validated transcripts (Figure 3), GmMATE4 (Glyma.19G120900, Figure 3E) was previously reported to be localized in the overlapping QTLs regulating the content of antioxidants, phenolics, and flavonoids in soybean seeds [20]. The antisense transcript of GmMATE4 was not reported in the previous study on the expression patterns in developing soybean seeds and pods [20]. The identification of novel MATE antisense transcripts enriches the knowledge of the expression of MATE genes in soybean.

Surprisingly, we found that the relative expression levels of many of the MATE NATs are positively correlated to the corresponding sense transcripts (Figure 2). Whether NATs promote or reduce the target transcript stability has been a subject of debate [25,44]. Nevertheless, there has been accumulating evidence to support the synergy brought forth by cis-NATs on the sense mRNA [25]. In the global identification of Arabidopsis long non-coding RNAs (lncRNAs), based on the Pearson correlation coefficients, the strong tendency of NATs to positively regulate the expression of their sense overlapping genes was suggested [27]. MAS, the NAT overlapping the sense transcript of MADS AFFECTING FLOWERING4 (MAF4), was found to be promoting MAF4 expression at the transcriptional level [27]. It was reported that MAS recruits WDR5a, a core component of COMPASS-like complexes, to enhance histone 3 lysine 4 trimethylation (H3K4me3), which brings forth transcriptional activation of MAF4 [27].

In this study, we predicted positive correlation between antisense transcripts of MATEs and their corresponding sense transcripts based on the Spearman correlation coefficients (Figure 2), and validated the positive correlation of the antisense and sense pairs in developing seeds and pods by RT-qPCR (Figure 3). These results are in line with the possible synergetic effects of the antisense transcripts on the expression of their sense counterparts.

5. Conclusions

In this study, using strand-specific RNA-seq datasets from the tissues of soybean accession W05 and the reference grade genome of W05 [31], we identified a set of MATE antisense transcripts. Based on Spearman correlation coefficients, several antisense transcripts were predicted to have positive correlations with their sense counterparts. By aligning the antisense transcripts identified in the dataset to the accession Williams 82 (Wm82) (Gmax_275_Wm82.a2.v1 [36]), MATE antisense transcripts in Wm82 were identified. The expressions of the antisense transcripts and their sense counterparts were validated in developing soybean seeds and pods. Results showed positively correlated differential expression patterns of the antisense transcripts and their corresponding sense transcripts in both seed and pod. This is consistent with a possible synergistic effect on sense transcript expressions by the antisense counterparts. This study provides new information on the genome-wide identification of MATE genes, demonstrates the enhanced capacity to detect novel antisense transcripts using strand-specific RNA-seq datasets, and enriches our understanding on the possible role of antisense transcripts in transcription regulation.