*Article* **In Silico and Transcription Analysis of Trehalose-6-phosphate Phosphatase Gene Family of Wheat: Trehalose Synthesis Genes Contribute to Salinity, Drought Stress and Leaf Senescence**

**Md Ashraful Islam 1,† , Md Mustafizur Rahman 2,† , Md Mizanor Rahman <sup>2</sup> , Xiujuan Jin <sup>1</sup> , Lili Sun <sup>1</sup> , Kai Zhao <sup>1</sup> , Shuguang Wang <sup>1</sup> , Ashim Sikdar <sup>3</sup> , Hafeez Noor <sup>1</sup> , Jong-Seong Jeon <sup>2</sup> , Wenjun Zhang <sup>1</sup> and Daizhen Sun 1,\***


**Abstract:** Trehalose-6-phosphate phosphatase (*TPP*) genes take part in trehalose metabolism and also in stress tolerance, which has been well documented in many species but poorly understood in wheat. The present research has identified a family of 31 *TPP* genes in *Triticum aestivum* L. through homology searches and classified them into five clades by phylogenetic tree analysis, providing evidence of an evolutionary status with *Hordeum vulgare, Brachypodium distachyon* and *Oryza sativa.* The exon-intron distribution revealed a discrete evolutionary history and projected possible gene duplication occurrences. Furthermore, different computational approaches were used to analyze the physical and chemical properties, conserved domains and motifs, subcellular and chromosomal localization, and three-dimensional (3-D) protein structures. *Cis*-regulatory elements (CREs) analysis predicted that *TaTPP* promoters consist of CREs related to plant growth and development, hormones, and stress. Transcriptional analysis revealed that the transcription levels of *TaTPPs* were variable in different developmental stages and organs. In addition, qRT-PCR analysis showed that different *TaTPPs* were induced under salt and drought stresses and during leaf senescence. Therefore, the findings of the present study give fundamental genomic information and possible biological functions of the *TaTPP* gene family in wheat and will provide the path for a better understanding of *TaTPPs* involvement in wheat developmental processes, stress tolerance, and leaf senescence.

**Keywords:** in silico;*Cis*-regulatory elements; gene transcription; trehalose-6-phosphate phosphatase; wheat

#### **1. Introduction**

Cereals are indeed the single most significant part of the diet for the majority of the global population, with about 60% to 80% of carbohydrates coming straightly from them in developing and under-developing nations, respectively [1]. According to the FAO's most current predictions, global grain production in 2021 will increase by 1.7% over 2020, achieving 2817 million tons [2]. Wheat (*Triticum aestivum* L.) is the world's largest extensively grown cereal crop and is among the most often eaten cereals by the world population [3]. The major abiotic stresses that decrease wheat productivity throughout the growing period include water shortages, high temperatures, and salinity [4]. Among them, salinity is a major barrier to crop production, especially in wheat, resulting in a yield loss of 65% in moderately saline soils, by influencing nearly every stage of plant growth and

**Citation:** Islam, M.A.; Rahman, M.M.; Rahman, M.M.; Jin, X.; Sun, L.; Zhao, K.; Wang, S.; Sikdar, A.; Noor, H.; Jeon, J.-S.; et al. In Silico and Transcription Analysis of Trehalose-6-phosphate Phosphatase Gene Family of Wheat: Trehalose Synthesis Genes Contribute to Salinity, Drought Stress and Leaf Senescence. *Genes* **2021**, *12*, 1652. https://doi.org/10.3390/ genes12111652

Academic Editor: Patrizia Galeffi

Received: 22 September 2021 Accepted: 19 October 2021 Published: 20 October 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

development, including germination, vegetative growth, and reproductive growth [5,6]. This abiotic stress condition results in a decrease in yield related traits that directly affect the yield of cereal crops. Thus, one of the most significant tasks for plant breeders right now is to uncover the genes associated with abiotic stress responses and to cultivate genetically engineered varieties with improved stress tolerance [7,8].

Plants generate various organic molecules, such as soluble sugar and free amino acids, in response to stress exposure. Trehalose is one of these non-reducing disaccharides composed of two molecules of α-glucose that may accumulate in the cell up to 12% of its dry mass to maintain its integrity and is associated with plant abiotic stress tolerance, including high and low temperature, drought, and osmotic stress tolerance [9–12]. Many species, including yeast, fungus, invertebrates, plants, bacteria, insects, green weed, and cyanobacteria synthesize this sugar substance [12–15]. Except for vertebrates, the synthesis of trehalose in plants and other organisms involves two phases with two catalytic enzymes, trehalose-6-phosphate synthase (TPS) and trehalose-6-phosphate phosphatase (TPP). TPS produces trehalose-6-phosphate (T6P), a phosphorylated intermediate, from Uridine diphosphate-glucose (UDPG) and Glucose-6-phosphate (G6P) in the first phase, and the TPP dephosphorylates T6P to produce trehalose in the second phase (Figure 1). Trehalose is then hydrolyzed by an enzyme called trehalase (TRE) to synthesize two molecules of glucose, which suggests that TPS, TPP, and TRE are the three enzymes involved in the trehalose biosynthesis pathway [16]. The *TPS* and *TPP* families encode multiple genes, but *TRE* is denoted by a single copy of the gene [17–19].

**Figure 1.** Trehalose biosynthesis pathway in plants. Uridine diphosphate glucose (UDP), Trehalose-6 phosphate synthase (TPS) and Trehalose-6-phosphate phosphatase (TPP).

In addition to providing a route for the production of trehalose, *TPS* and *TPP* have been shown to serve as signaling molecules in higher plants by modulating a variety of plant metabolic and developmental processes. T6P is a signaling metabolite in plants that links growth and development to carbon metabolism and serves as a signal of sucrose status at various phases of the plant's development [20–22]. *TPS* genes were discovered to be involved in the germination of seeds, stress signaling, vegetative phase separation, shoot branching, and flowering time regulation in *Arabidopsis* and rice, with *TPS1* being the most studied [23–27]. Instead, TPP was found to inhibit SnRK1 (Sn1-related protein kinase) activity, a well-known transcriptional regulatory pathway under stress and energy metabolism [28]. The Ramosa1 (RA1) transcription factor activates the transcription of *TPP* to regulate flower branching, which suggests that trehalose may have a role in specific developmental processes [29]. Tobacco plant overexpressing *Escherichia coli TPS* gene *ostA* improved photosynthesis efficiency by enhancing RUBISCO concentration, although *ostB*, a *TPP* gene, exhibited the opposite impact, further suggesting the significance of trehalose in plant photosynthesis [30].

Various studies have reported trehalose enzymes to enhance abiotic and biotic stress tolerance, such as in *Arabidopsis* [31,32]. For example, *ZxTPP* (*Zygophyllum xanthoxylum*) or *ostA* and *ostB* containing tobacco transgenic plants were significantly tolerant to drought [33,34]. Likewise, *ostA* and *ostB* transformed rice plants showed increased trehalose levels and enhanced performance against cold, salt, and drought stresses [35]. Exogenous trehalose triggered a signal transduction pathway including calcium and reactive oxygen species (ROS) and *OsTPP1* or *OsTPP3* transgenic rice and maize plants induced stress-related genes that conferred drought tolerance [36–38]. After drought stress, vulnerable maize seedlings had lower *ZmTPP1* expression, whereas resistant seedlings

had higher expression [39]. *TPP* promoters' *Cis*-regulatory elements (CREs) stimulate trehalose metabolism and improve stress response. In *Arabidopsis*, *ABF1*, *ABF2*, and *ABF4* are ABA-responsive elements that directly influence *AtTPPI* expression to increase drought tolerance by changing stomatal apertures [40]. The transcription factor that responds to ABA in the presence of ABA, ABF2 binds directly to the *AtTPPE* promoter, triggering its expression for root elongation and stomatal movement via producing ROS [41]. DREB1A, which binds to the DRE/CR motif in the *AtTPPF* promoter, is thought to upregulate *AtTPPF* transcription in drought-stressed plants [32]. T6P role as a signal for increased carbon availability might have implications for leaf senescence control, as the accumulation of sugars has been demonstrated during leaf senescence in *Arabidopsis*, wheat, tobacco, and maize. The phenotype of mature *otsB*-overexpressing *Arabidopsis* plants included delayed senescence and decreased anthocyanin accumulation, suggesting that the role of *TPP* may perform a crucial role during leaf senescence in plants [42–45]. To date, *TaTPP-6AL1* and its functional marker have been shown to improve crop yield in wheat [46]. However, the gene structure and regulatory mechanism of wheat *TPPs* are not well studied.

The present study intends to investigate wheat *TPPs* in silico by identification of *TaTPPs,* gene duplication analysis, phylogenetic relationship with other species, subcellular localization prediction, motif and domain analyses, proteins 3-D structure modeling, investigation of CREs, and gene transcription analysis that have all been performed to better understand *TaTPPs* functions in wheat.

#### **2. Materials and Methods**

#### *2.1. Identification of Putative TPPs in the Wheat Genome*

To find putative *TPPs* in wheat, we utilized *TPPs* from *Arabidopsis* and rice. Ensembl Plants database was used to collect TPP protein sequences from *Arabidopsis* and rice and a BLASTp search was conducted against the most recent wheat assembly from the IWGSC (RefSeq v1.0) (http://plants.ensembl.org/index.html, 10−<sup>5</sup> cut-off e-value and bit-score > 100, accessed on 12 March 2021). After eliminating duplicated sequences, SMART (http://smart.embl-heidelberg.de/, accessed on 12 March 2021) or InterPro (https: //www.ebi.ac.uk/interpro, accessed on 12 March 2021) and NCBI CDD (https://www.ncbi. nlm.nih.gov/Structure/cdd/wrpsb.cgi, accessed on 12 March 2021) were used to examine the remaining sequences for the presence of transmembrane domains. TPP-related domaincontaining protein sequences were collected and designated consecutively according to their chromosomal locations after the sequences without transmembrane domains were deleted. The ProtParam software (https://web.expasy.org/protparam/, accessed on 13 March 2021) was used to calculate the length, molecular weight, isoelectric point (pI), and grand average of hydropathicity (GRAVY) of TPP proteins.

#### *2.2. Chromosome Localization, Gene Duplication and Synteny Analysis*

*TPPs* genomic locations were acquired from the Ensembl Plants BioMart (http://plants. ensembl.org/biomart/martview, accessed on 14 March 2021) for chromosomal distribution. The *TPPs* were given a 'Ta' prefix and were numbered in ascending order according to their ascending chromosomal location. The *TaTPPs* on the wheat chromosomes were represented using TBtools. A NCBI BlastP search (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp& PAGE\_TYPE=BlastSearch&BLAST\_SPEC=&LINK\_LOC=blasttab&LAST\_PAGE=blastn, query conditions: percent identity between 75 and 100 and query coverage between 80 and 100, accessed on 14 March 2021) based on the proportion of query cover to the identity of the *TaTPPs* against each other was performed to check for gene duplication [47]. Based on a BLAST search and a phylogenetic tree, duplicate gene pairs were identified. TBtools was used to determine the non-synonymous substitution rate (Ka), synonymous substitution rate (Ks), and Ka/Ks ratio [48]. The synteny relationships of wheat *TPP* genes with different plant species were analyzed using TBtools.

#### *2.3. Phylogenetic Analysis, Exon-Intron Distribution and 3-D Structure Modeling*

ClustalW in MEGA X was used to align full-length protein sequences from various species [49]. Following the alignment, MEGA X was used to create a phylogenetic tree with the Maximum Likelihood method [50] and 1000 bootstrap values [51]. To examine the exon-intron distribution of *TaTPPs*, the TBtool was used to align the CDSs and genomic sequences. SWISS-MODEL Workspace web tools (https://swissmodel.expasy. org/interactive#sequence, accessed on 16 March 2021), GASS and SOPMA secondary structural method (https://npsa-prabi.ibcp.fr/cgi-bin/npsa\_automat.pl?page=npsa%20 \_sopma.html, accessed on 16 March 2021) and MolProbity server (http://molprobity. biochem.duke.edu/, accessed on 16 March 2021) were used to conduct 3-D structure analyses of TaTPP proteins [52–57].

#### *2.4. Subcellular Localization Prediction and Protein Domain Analysis*

PredSL (http://aias.biol.uoa.gr/PredSL/index.html, accessed on 17 March 2021) was used to predict subcellular localizations. The TPP domain (trehalose-phosphatase (Trehalose PPase); PF02358) was retrieved from the Pfam database and the structures were created with TBtools [58,59]. We utilized MEME suite 5.1.1 to examine TaTPP motifs and The site distribution was set to any number of repetitions, the maximum number of motifs to locate was set to 9, the minimum width was set to 6, the maximum width was set to 50, and the maximum number of motifs to locate was set to 9 [60].

#### *2.5. Analysis of Publicly Accessible Expression Data and Cis-Regulatory Elements (CREs)*

We used the NCBI database (https://www.ncbi.nlm.nih.gov/, accessed on 19 March 2021) to obtain 2 kb upstream from start codon promoter sequences of 11 *TaTPPs*, which we subsequently submitted to PlantCARE to find the CREs [61]. Netbeans IDE 8.0 (https: //netbeans.org., accessed on 25 March 2021) was used to organize data [62] and subsequently TBtools Heatmap was used for data visualization. The Genevestigator RNAseq public anatomy was used to examine gene expression [63] and the MeV tool was then used to visualize expression [64].

#### *2.6. Plant Materials and Treatments*

*T. aestivum* L. cultivar Jinmai39 was used to investigate the transcription of *TaTPPs* in the presence of salt, drought, and ABA treatments. The seedlings were grown in a growth chamber at 22 ◦C with 16 h/8 h of light/ darkness and a light intensity of 9000 lux. Wheat plants were treated with either double-distilled water (control) or a 20% PEG-6000 or a 250 mM NaCl solution at the 2–3 leaf stage for drought and salt stress, respectively. For abscisic acid (ABA) treatment, plants at the same stage are sprayed with 100 mM abscisic acid (ABA) or 0.1% (*v*/*v*) ethanol (control). To analyze the expression of *TaTPPs* during leaf senescence, the delayed senescence wheat cultivar Yannong19 was grown in field conditions and collected samples from flag leaf at 0, 7, 10, 16, 19, 22, 24, and 25 days after anthesis. All the leaves after collection are immediately frozen into liquid nitrogen and stored at −80 ◦C for further RNA extraction.

#### *2.7. RNA Extraction, Quantitative Real-Time Reverse Transcription PCR Analysis and Protein Interaction Network*

The Quick RNA isolation Kit (Huayueyang Biotechnology, Beijing, China) was used to extract RNA according to the manufacturer's instructions and DNase I treatment was used to remove DNA contamination. The RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific, Waltham, MA, USA) was used to synthesize cDNA from a 3-µg aliquot of total RNA from each sample. To measure the expression of *TaTPPs* qRT-PCR analysis was performed with specific primers (Table S1), as described previously [65]. The ABI PRISM 7500 system (Applied Biosystems, Foster City, CA, USA) was used to generate threshold values (CT) and the transcription level of *TaTPPs* was measured using the comparative 2 <sup>−</sup>∆∆CT technique that was standardized with the *Elongation factor 1α* (*TaEF-1α*) (GenBank

accession no. Q03033) [66,67] (Table S1). All of the studies were carried out three times. The TaTPP protein interaction network was examined using the STRING online server (https://string-db.org/, accessed on 27 April 2021).

#### **3. Results**

#### *3.1. Identification and Annotation of Wheat TPPs*

We identified a total of 31 TPP protein sequences in the wheat genome (Tables 1 and S2). This number is relatively large when compared to TPPs previously identified in *Arabidopsis*, rice, and maize (Table S3). Wheat has a greater ploidy level and a larger genome size as it originated from the natural hybridization of three closely related genomes (A, B, and D), which may justify this result [68]. These protein sequences were encoded by 31 genes, three of which were chosen as representatives because they showed splice variants with full domains. A detailed description of *TaTPPs* is summarized in Table 1. The ORF of *TaTPPs* ranged from 750 to 1755 bp, with protein lengths ranging from 249 to 584 amino acids (Table 1). The molecular weight of the genes ranged from 28.67 KDa to 65.02 KDa. (Table 1). Fifteen genes were found to be basic (>7) and 16 genes were found to be acidic (<7) based on the predicted pI value (Table 1).

In addition, the Aliphatic Index and Instability Index were computed. The Aliphatic Index measures how much space is taken up by aliphatic side chains in Alanine, Isoleucine, Leucine, and Valine amino acids [69]. The Aliphatic Index ranges observed were 72.28 to 86.42, and the Instability Index ranges were 32.59 to 55.74 (Table 1). The high Aliphatic Index of a protein sequence suggests that it can function at a broad range of temperatures, whereas the Instability Index shows whether the protein is stable or unstable [70]. All the TaTPPs had negative GRAVY values ranging from −0.700 to −0.142 (Table 1). A protein with a negative GRAVY value is non-polar and hydrophilic in nature [69].

#### *3.2. Subcellular Localization Prediction and Chromosomal Distribution of TaTPPs*

PredSL (http://aias.biol.uoa.gr/PredSL/index.html accessed on 22 September 2021) was used to predict subcellular localization. Subcellular localization of the TaTPPs was predicted mostly in the chloroplast, whereas, TaTPP1-A, TaTPP7-D, TaTPP10-B appeared to be localized in the mitochondrion (Table 1). Moreover, TaTPP5-B, TaTPP7-A were predicted as secreted proteins (Table 1). However, TaTPP5-A, TaTPP10-A, TaTPP10-D were predicted with unknown localization (Table 1). A schematic diagram was created to explain the chromosomal location of *TaTPPs*. The *TaTPPs* are present on 17 wheat chromosomes (Figure 2 and Table 1). On the chromosomes of the A subgenome, the highest number of *TaTPP* genes (11 genes) were mapped. B and D subgenomes had 10 *TaTPP* genes in each subgenome. The maximum 14 genes of *TaTPPs* were located on chromosome 2 (Figure 2). Chromosome 6A, 6B and 6D, had 2 genes on each chromosome and 1A, 1B, 1D, 3A, 3D, 5A, 5B, and 5D had only a single gene. On the other hand, no *TaTPPs* were found on chromosomes 3B, 4A, 4B, or 4D (Figure 2 and Table 1), suggesting that *TPP* family genes were unevenly distributed throughout the three subgenomes of wheat.

*Genes* **2021**, *12*, 1652

