Sulphotransferase (SOT) genes in wheat providing tolerance against drought and heat

doi:10.21203/rs.3.rs-3770369/v1

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Sulphotransferase (SOT) genes in wheat providing tolerance against drought and heat

https://doi.org/10.21203/rs.3.rs-3770369/v1

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Sulphotransferase (SOT) genes that are involved in sulphonation during sulphate metabolism, represent a conserved family of genes, which provide tolerance against abiotic stresses like heat and drought in plants. In plants, sulphate metabolism is a component of primary metabolism, where SOTs catalyze sulphonation. In the present study in wheat, 107 TaSOT genes were identified; 78 of these genes were identified through genome-wide search and the remaining 29 identified through SOT domain (PF00685) (sulfotransferase family protein). All 107 genes were mapped onto individual chromosomes. The length of these ranged from 969bp to 7636bp; 73 (68%) of these 107 genes are intronless. The gene sequences were associated with 48 different cis regulatory elements. The genes also carry some tandem/segmental duplications, SSRs (simple sequence repeats), target sites for miRNAs and sequences for some lncRNAs. The promoters of some genes also carried transcription factor binding sites (TFBS). The SOT proteins carried two highly conserved 5’-phosphosulphate-binding (5’ PSB) regions and a 3’-phosphate-binding (3’ PB) motif that are essential for SOT activities. Homology modeling of TaSOT proteins and docking analysis of their ligand-binding sites revealed the presence of important amino acids in their active sites. An expression assay of TaSOT genes via wheat RNA-seq data suggested engagement of these genes in growth, development and responses to various hormones and biotic/abiotic stresses. The results of the present study should help in further functional characterization of SOT genes in wheat and other related crops.

Wheat

Abiotic stress

SOT

5” PSB

3” PB

Drought and heat occur concurrently and cause major yield losses in all major crops including wheat (Zampieri et al. 2017). Grain quality is also adversely affected by drought and heat stresses individually and together. Globally, > 40% of wheat-growing areas experience heat stress, causing a yield loss of ~ 10% with increase of every 1°C (Narayanan et al. 2018). Also, the climate models predict that in future, crops will be more frequently affected by combined drought and heat waves (IPCC,2022). Due to the elevated global temperature, the estimated future worldwide demand cannot be met with the current rate of annual increase in production (Gaupp et al. 2019 and Tomás et al. 2022, Tanin et al.2022). Therefore, studies on the ability of plants to cope with water limitation and heat stress is important and continue to receive attention (Sobhanian et al.2020). These studies include use of various -omic technologies, and should help developing in-depth insights on effects of drought and heat stress, thus facilitating development of strategies for mitigating the effects of drought and heat stresses and for the development of drought and heat-tolerant wheat varieties, using both conventional and molecular breeding approaches (Lal et al.2021).

A number of metabolic changes involving regulation of expression of a large number of genes occur to enable plants sense and survive under both biotic and abiotic stresses (Mostofa at al. 2018). For instance, it has been shown that as soon as the roots sense water deficiency, they begin to synthesize ABA, which is then transported through xylem vessels to the leaves leading to the closure of stomata, thus reducing loss of water due to transpiration (Schroeder et al.2001).

Suplphur metabolism is one of the important components of metaolic processes affected by drought and heat, because sulfur (S) occurs in amino acids [cysteine (Cys) and methionine (Met)], vitamins (e.g., thiamin and biotin), co-enzymes (e.g., S-adenosyl methionine), iron–sulfur complexes, prosthetic substances, glutathione (GSH) antioxidants, and others natural secondary metabolites (Takahashi et al. 2012). Therefore, adequate S in the soil and its uptake and utilization for sulphur metabolism is necessary for proper plant growth and development, such that deficiency of sulphur may not cause any major losses in grain yield and quality (d’Hooghe et al. 2014).

Sulphotransferases (SOTs) (EC 2.8.2.) are important enzymes involved in sulphur transfer during primary and secondary sulpur metabolisms (Fig. 1). In plants, reactions involving sulphate play a vital role in plant growth and development that are affected as a response to various stresses (Varin et al. 1997). Sulphate ions are utilized in two subsequent steps for the formation of adenosine-5’phosphosulfate (APS) and 3’phosphoadenosine- 5’phosphosulfate (PAPS) before being involved in further biochemical reactions (Schmidt et al. 1997). SOTs catalyze the transfer of a sulfate group from PAPS to a hydroxyl group in a variety of compounds (Glendening et al. 1990). Sulfated substances generated thus function as secondary metabolites, which include not only the hormones that cope with stimulus, but also also some important S storage substances (Klein et al. 2004). Plant SOTs are also directly involved in the sulphation process of desulpho-glucosinolate compounds (ds-Gl), which are also important secondary metabolites that provide resistance against multiple biotic/abiotic stimuli, as demonstrated in case of crops of mustard family, brassicales (Rausch et al. 2005).

PAP, a by-product of the above sulfation reaction also has gene regulatory attributes, and is itself regulated by the the enzyme adenosine-bisphosphate phosphatase (SAL1) that dephosphorylates PAP to adenosine monophosphate (AMP). The concentration of this PAP increases due to drought stress and intense light conditions (Klaassen et al.1997). Mutation studies also suggested that PAP inhibits 5’–3’_exoribonucleases in the cytosol and nucleus, thus causing changes in expression of stress-responsive genes. It has also been suggested that a PAP/SAL1 retrograde pathway alters expression of many genes as part of stress response (Estavillo et al. 2011).

SOTs are also involved in the biosynthesis of flavonoids, which are secondary metabolites and have antioxidant properties to protect plants from oxidative damage under stress. SOTs also respond to drought through accumulation of metabolites, which function as osmoprotectants, osmolytes, antioxidants, and/or stress signals (Chan et al.2013). Several studies also suggest that SOT enzymes play an important role in heat and drought stress tolerance in plants. For instance, in chinese cabbage (Brassica. rapa), differential expression of BraSOT genes was observed after exposure to drought and salinity and also after treatment with ABA (Jin et al. 2019).

Studies on SOTs have also shown that sulfated compounds are involved in response to biotic and abiotic stresses. This is supported by several expression studies on SOTs, which include the following. First, in Arabidopsis, the expression of AtSOTs increased significantly due to treatment with jasmonate (Lacomme and Roby,1996; Gidda et al. 2003; Piotrowski et al. 2004). Second, in potato, StSOT genes were shown to play a significant role in response to abiotic stresses (Faraji et al. 2021). Third, in cotton, under abiotic stresses, GhSOT genes were shown to exhibit high level of expression in the stem, leaf, and the initial stage of fiber development (Wang et al. 2019). Fourth, in algal genus Caulerpa, SOTs were shown to catalyse the nodal step for the biosynthesis of sulfated polysaccharides (SPs), which are used as pharmacuticals (Landi et al.2020). Fifth, in rice, using biochemical analyses, it was shown that OsSOT9 sulfonates a range of substrates (flavonoids, alkaloids and phenolic acids), which are known to play a role in stress tolerance (Cao et al. 2016). The resistant allele of rice STV11 (STV11-R) also encodes a sulfotransferase (OsSOT1) catalysing the conversion of salicylic acid (SA) into sulphonated SA (SSA) that confers durable resistance against rice stripe virus (Wang et al. 2014).

Overall, SOTs are widely known to have functions in plant defense, stress response, signaling and developmental regulation. Sulfation through SOTs can either lead to activation or deactivation of a number of substrates. SOT expression takes place basically in all organs and at all stages in plant development. Interestingly, all SOTs studied so far were induced by several conditions or stress signalling compounds, indicating a general stress response (Hirschmann et al. 2014).

In view of the above, the study of SOT enzyme in wheat is important for understanding the metabolic processes involved in stress responses of this important crop, and for developing new strategies for enhancing resilience. Increasing the expression or activity of SOT enzymes in wheat may improve the plants’ ability to tolerate heat and drought stress. Keeping this in mind, the present study was undertaken to identify SOT genes in wheat for the first time with the hope that the results of this study may prove useful to fully understand the role of SOTs in stress tolerance, and threfore in the development wheat cultivars that are tolerant to drought and heat.

Gene sequence analysis

Identification of SOT genes and proteins

Full-length cDNA sequences of 18 Arabidopsis genes (AtSOTs) and 23 rice genes (OsSOTs) were identified, and retrieved from from Ensembl Plants and tBLASTEDx following the criteria described in our earlier study (Chaudhary et al.2023). Naming of TaSOT genes was done according to phylogenetic relationships and protein similarity with corresponding rice genes using MEGA 11 and clustalW (Klein et al. 2004). Information on wheat SOT genes including genomic, cDNA and CDS sequences and also the information on splice variants, chromosomal positions of SOT genes were retrieved from EnsemblPlants. Same approach was followed for identification of SOT genes and related information for the following related species: T. urartu, A. tauschi, T. turgidum, Z. mays, B. distachyon and Hordeum vulgare. The genes thus retrieved were examined for the presence of complete SOT domain using conserved domain database (CDD) batch search tool at NCBI (https://www.ncbi.nlm.nih.gov/Structure/bwrpsb/bwrpsb.cgi).

SOT domain (PF00685) was also used for identification of SOT proteins in wheat. For this purpose, HMM profile related to the SOT domain was done according to the criteria described in an earlier study (Batra et al. 2020). The proteins without SOT domain and sequences without complete reading frames (domains) were manually deleted.

Chromosome mapping and structural analysis of genes

SOT genes were assigned to chromosome and chromosome maps were prepared using routine methods described in our earlier study (Chaudhary et al. 2023). Gene duplications were identified following Ning et al. (2017). Synonymous substitutions (Ks) and non-synonymous substitutions (Ka) were calculated using DnaSP 5.0 software. The time of duplication and divergence for each duplicate gene pair was calculated following Patil et al. (2015). The approximate divergence time between duplicate gene pairs was calculated by using criteria used by by El Baidouri et al. (2017).

Genomic and CDS sequences of wheat SOT genes were used for the study of gene structure and promoter analysis; synteny and collinearity analyses between SOT genes of wheat and those of four other species (O. sativa, Z. mays, B. distachyon, and H. vulgare) were carried using the criteria described earlier; the methods for preparation of circular plot using cicos tool was also described in this earlier study (Chaudhary et al. 2023).

SSRs, lncRNAs microRNAs (miRNA) and in silico expression analysis

Simple sequence repeats (SSRs), lncRNAs and miRNAs within SOT genes were identified following routine methods. The regulatory network between the miRNA and TaSOT genes was visualized using cytoscape tool. The miRNAs that could interact with lncRNAs (as the targets) were predicted using psRNATarget tool, with maximum expectation value of 3.0 or less (more stringent) (https://plantgrn.noble.org/psRNATarget/).

Expression analysis was conducted using small RNA datasets for abiotic stress analysis (heat and drought) available from previous studies (Pandey et al. 2014, and Ma et al. 2015). Differential expression values of miRNAs and the normalised value in TPM (transcripts per million) were taken from the database available at http://pmirexat.nabi.res.in/ index.html). Fold change values for gene expression were taken from wheatomics (leaf, root and seedling tissue) and Plant miRNA Expression Atlas (PmiRExAt) available at http://pmirexat.nabi.res.in/). PmiRExAt is a web-based resource comprising miRNA expression profiles and searching tool for a large number of miRNAs including 1,859 wheat, 2,330 rice, and 283 maize miRNAs (Gurjar et al.2016). Gene ontology (GO) was conducted using protpram of Expasy. The TaSOT genes belonging to different GO classes (molecular functions, biological process, and cellular component) were documented.

Protein Analysis

Physico-chemical properties

Major domains in TaSOT protein sequences were identified through conserved domain (CD)-search program of conserved domain database (CDD) at NCBI. The ProtParam tool (http://web.expasy.org/protparam/) of ExPASy was used to compute physico-chemical properties, following the criteria used by us earlier (Chaudhary et al. 2023).

Putative transmembrane domains were predicted using the SCAMPI (Bernsel et al.2008) and topologies of the TMDs were analyzed using TMHMM (http://www.cbs.dtu.dk/services/TMHMM/); post-translational phosphorylation changes were predicted using NetPhos 3.1 server (https://services.healthtech.dtu.dk/ service.php?NetPhos-3.1). The location of the TaSOT proteins in the cell was determined with the Omicsbox program (https://www.biobam.com/omicsbox).

Domains in protein were visualised through Pfam database and the motifs in the sequences of TaSOTs proteins were identified; annotation of identified motifs and secondary structure prediction was done using the criteria used by us earlier (Chaudhary et al.2023). Multiple sequence alignment (MSA) involving wheat was done through MEGAX.

Protein–protein interactions

Protein-protein interactions were examined using only 10 representative TaSOT proteins belonging to same 10 genes that were used for qRT-PCR; each of these 10 TaSOT proteins had a SOT domain, and each had high expression in seedling and at other developmental stages. The protein–protein interactions (PPIs) of these TaSOTs were examined using criteria described earlier (Chaudhary et al. 2023). The protein association network involving the above 10 TaSOTs and other interacting proteins was developed using the following criteria: confidence parameter set to a threshold of 0.40, the maximum number of interactors in the first shell limited to 10, and none in the second shell. The amino acid sequences of the 10 TaSOT proteins were used as query subjects to analyse the network model of putative interacting partners. The protein with the highest bit score was considered for this purpose. The interactome was used to understand the physical and functional role of the proteins involved.

3D structure and its analysis

The 3D monomeric structures of TaSOT proteins were predicted using homology modelling. Suitable homologous templates (crystal and NMR 3D structures), 5mek (as a cytosolic sulfotransferase 18) and 1q44 and 1fmj (as the P-loop containing PAPS sulfotransferases in Arabidopsis) were obtained using criteria described earlier; verification of predicted 3D structures of proteins was also done using criteria described earlier (Chaudhary et al. 2023). The molecular voids and pocket/channel numbers were estimated via the BetaCavity Web server (Kim et al.2015). Protein secondary structures related to TaSOTs were also identified by utilizing GOR4 program. Validation of the structures were done by the ProSA server for evaluation of probable errors within the protein models, by calculating Z values of the 3D structures.

Interaction with a ligand

Interactions between TaSOT proteins and the ligand Adenosine-3'-5'-diphosphate (A3P) CID_94310 were examined using molecular docking. For this purpose, the chemical structure of A3P was extracted from Pubchem databse in MOL2 format, and protein structures were generated by swiss model in pdb format. Hydrogen atoms were added to the predicted 3D protein structures using AMBER7 FF99 charges. The 3D structures, thus obtained, were optimized by applying 100 steps of Powell method and conjugate gradient algorithm with Tripos force field. Grid generation was done by using UCSF CHIMERA 1.10(https://www.cgl.ucsf.edu/chimera/). The molecular docking reactions were performed using the Auto Dock vina program (Trott et al.2010), which achieves an approximately two orders of magnitude speed-up and also significantely improves the accuracy of the binding mode predictions. Best structures of two docking complexes were selected and analyzed using Pymol software (https://pymol.org/2).

Structure simulation

TaSOT proteins were subjected to system preprations (topology preprations) with GROMACS v2021(Case et al. 2018) using criteria used earlier in (Prasad et al.,2023). To equilibrate the system, Solutes were subjected to restrain minimization by applying 50,000 steps for two Ensemble class NPT and NVT at 300 K for 100psec. Finally, fully minimized systems were subjected to MD simulation for 80 nsec. The RMSD was calculated to predict the protein and protein-protein complex stability. The RMSF was also measured for predicting the flexibility of each amino acid residue after ligand binding. The plots were generated by xmgrace plotting program.

Superimposition of 3D structures

The 3D structures of wheat SOT proteins were superimposed over the corresponding 3D structure of rice proteins; 3D sructures with minimized energy were used for this purpose. The accuracy and topology of predicted proteins and RMSD value of the Cα atoms were assessed by using criteria described earlier (Chaudhary et al. 2023).

In Silico expression profiling of TaSOT genes

In silico expression analysis of TaSOT genes in five different tissues (grain, spike, leaf, stem and root) under abiotic stress (heat and drought) and the preparation of heat maps was carried out using criteria described earlier (Chaudhary et al. 2023).

qRT-PCR for validation of expression data

On the basis of in silico expression analysis, following ten TaSOT genes were selected for quantitative real-time PCR (qRT-PCR) to examine in planta changes of expression: TaSOT30-1A1, TaSOT5b-2D, TaSOT35c-2D, TaSOT20c-3D, TaSOT18b-7B, TaSOT6-7D, TaSOT37-2D, TaSOT9-2D, TaSOT43-5D1, TaSOT20c-Un2). Following three stress conditions were used: heat, drought and heat + drought (each for 1 hour and 6 hours): The contrasting wheat genotypes used for qRT-PCR included the following: (i) sensitive cv. PBW343 and (ii) tolerant Excalibur.

For RNA isolation, seedlings of two cultivars, namely Excalibur (heat tolerant) and PBW343 (heat sensitive) were raised in growth chambers. Total RNA from seedlings was isolated using the RNeasy Plant Mini Kit (Qiagen) with DNase I treatment dollowing criteria described in our earlier study (Chaudhary et al. 2023).

Genes and their analysis

Identification and structure analysis

Using whole genome search and SOT domains, 107 full-length TaSOT genes were identified from the wheat genome. Of these 107 genes, 78 genes were identified using 17 of the 35 OsSOT genes (Table 1); for the remaining 18 rice genes, no wheat orthologues were available. These 78 genes were given names according to their orthology with corresponding 17 rice genes and placed in 35 groups (TaSOT1 to TaSOT35). The remaining 29 wheat genes were identified on the basis of SOT domain, and were placed in 9 groups that were given names serially from TaSOT36 to TaSOT44, since these genes did not have their orthologues in rice genome (Table 2). In addition to these 107 genes, there were 28 additional incomplete wheat TaSOT genes, which carried one or more incomplete domains (Supplementary Table S4) and were therefore not included in the number of TaSOT genes.

Table 2

A list of 29 wheat SOT genes (identified only using SOT domain), without any homology with known
Gene name	Gene length(bp)/cDNA/CDS	Exons	Gene name	Gene length(bp)/cDNA/CDS	Exons
1.TaSOT36-7A	2745/1044/1044	2	16.TaSOT40-2D	1218/1218/1017	1
2.TaSOT36-1B	1342/1342/1068	1	17.TaSOT40-2B	1023/1023,582	1
3.TaSOT36-1D	1347/1347/1047	1	18.TaSOT41-2A	1822/1822/1128	1
4.TaSOT37-2A	1247/1247/1026	1	19.TaSOT41-2B*	1918/888/888	2
5.TaSOT37-2D	1760/1460/1026	1	20.TaSOT41-2D3*	1495/1405/852	2
6.TaSOT38-2A*	1179/1179/1029	1	21.TaSOT41-2D2	1320/1137,1495	1
7.TaSOT38-2B2	1233/1233/921	1	22.TaSOT41-2D1	1071/1071, 1014	1
8.TaSOT38-2B1	1251/1251/1038	1	23.TaSOT42-3D	1517/1059, 936	3
9.TaSOT38-2D*	1480/1480/1035	1	24.TaSOT43-4B	1373/987, 1373	1
10.TaSOT39-7B2*	1338/1338/1089	1	25.TaSOT43-5B*	1364/1146, 1364	1
11.TaSOT39-7B1*	1426/1426/984	1	26.TaSOT43-5D2	1062/1038,1062	2
12.TaSOT39-2D1	1002/1002/1002	1	27.TaSOT43-5D1*	1023/1023, 1023	1
13.TaSOT39-2D2	1391/1391/1002	1	28.TaSOT44-6A	1004/1002,1004	2
14.TaSOT39-2B	1273/1273/1089	1	29.TaSOT44-6B	1068/1068, 1068	1
15.TaSOT 40-2A	1231/1231/1017	1
rice genes, (genes marked with * carry duplication events)

The length of individual TaSOT genes ranged from 969 bp (TaSOT20a-2A) to 7636 bp (TaSOT2-2B and TaSOT5a-2B) (Table 2). Similarly, the length of cDNA sequences of TaSOT genes ranged from 825 (TaSOT35e-7D) to 2322 bp (TaSOT11-2A) (Tables 1, 2), while lengths of CDS ranged from 582 (TaSOT40-2B) to 1128 nt (TaSOT41-2A).

Table 1

A list of 78 wheat SOT genes having homology with only 17 corresponding rice genes, (genes marked with * carry duplication events)
Gene name	Gene length(bp)/cDNA/CDS	Exons	Gene name	Gene length(bp)/cDNA/CDS	Exons
1.TaSOT1-6D	1660/1398/1038	2	1. OsSOT1	1407/1407/1041	1
2. TaSOT2-2D	1260/1260/1065	1	2. OsSOT2	1300/1300/1158	1
3. TaSOT2-2A	1056/1056/1056	1
4. TaSOT2-2B	7636/1278/1044	1
5.TaSOT3-1B	5689/1480/1023	6	3. OsSOT3	3729/1744/1035	7
6.TaSOT3-1D	2557/1359/1023	6
7.TaSOT3-1A	5520/2236/1017	6
8.TaSOT5-3D	1250/1250/1014	1	4. OsSOT5	1041/1041/1041	1
9.TaSOT5a-2B*	7636/1244/1074	1
10.TaSOT5b-2B*	1244/2162/1026	1
11.TaSOT5a-2D*	1260/1334/1023	1
12.TaSOT5b-2D	1334/1026/1026	1
13.TaSOT5c-2A1*	1347/1327/1083	1
14.TaSOT5c-2A2*	1319/1319/1083	1
15.TaSOT5a-2A*	1056/1167/1023	1
16.TaSOT5b-2A	1167/1199/1026	1
17.TaSOT6-7B	1080/1080/1080	1	5. OsSOT6	1258/1258/615	1
18.TaSOT6-7D	1963/1585/1032	1	5. OsSOT6	1258/1258/615	1
19.TaSOT9-2D	3744/1623/1020	1	6. OsSOT9	1362/1362/1032	1
20.TaSOT9-2B	3242/1305/1110	1	6. OsSOT9	1362/1362/1032
21.TaSOT11-2B	2585/1758/1017	5	7. OsSOT11	3018/1350/1014	7
22.TaSOT11-2D	3744/2310/1017	5
23.TaSOT11-2A	3620/2322/1071	6
24.TaSOT12-1A	1151/1151/1026	2	8. OsSOT12	1128/1083,4643	2
25.TaSOT13-1D	1800/1240/1020	2	9. OsSOT13	3069/1234/1047	2
26.TaSOT13-7D1	1601/1271/1014	2
27.TaSOT13-7D2	1980/1141/1014	2
28.TaSOT18d-6D	1394/1281/978	1	10. OsSOT18	1050/1050,3376	2
29.TaSOT18a-3D1	1179/1179/963	1
30.TaSOT18b-7B	1284/1284/984	1
31.TaSOT18d-7A	1187/1187/969	2
32.TaSOT20a-2A*	969/969/969	1	11. OsSOT20	1084/855,1084	1
33.TaSOT20a-2B*	1185/1185/981	1
34.TaSOT20a-2D*	1221/1221/966	1
35.TaSOT20c-3D*	1147/1147/1002	1
36.TaSOT20b-7A	1134/1134/978	1
37.TaSOT20b-7B	1151/1151/969	1
38.TaSOT20c-Un1*	1256/1256/1002	1
39.TaSOT20c-Un2*	1228/1228/1002	1
40.TaSOT21-Un1*	1071/1071/1071	1	12. OsSOT21	1466/1466/1158	1
41.TaSOT21-7D*	1071/1071/1071	1
42.TaSOT21e-7D2*	1312/1312/1041	1
43.TaSOT21e-7D1*	1408/1408/1041	1
44.TaSOT21-Un2*	1452/1452/1071	1
45.TaSOT21-6D	1545/1545/1053	1
46.TaSOT21c-5A	1492/1492/1107	1
47.TaSOT21a-1A*	1425/1425/1053	1
48.TaSOT21a-1D*	1053/1053/1053	1
49.TaSOT21b-1D	1214/1214/1086	1
50.TaSOT27-2B1	1038/1038/1038	1	13. OsSOT27	1103/1103/984	1
51.TaSOT28c-6B2	1250/1095/1095	3	14. OsSOT28	1817/1225/987	2
52.TaSOT28a-1B*	1239/1239/1014	1
53.TaSOT28a-1D*	1039/1002/1002	2
54.TaSOT28b-7B1*	1290/1290/1014	1
55.TaSOT28b-7B2*	1300/1300/1029	1
56.TaSOT30-1A1	1012/987/987	2	15. OsSOT30	1222/1222/1131	1
57.TaSOT30-1A2	1267/1267/1062	1	15. OsSOT30	1222/1222/1131	1
58.TaSOT32a-3A2*	1369/1369/1032	1	16. OsSOT32	1256/1256/1014	1
59.TaSOT32b-3B2*	1115/1115/1035	1
60.TaSOT32b-3B1*	1032/1032/1032	1
61.TaSOT32a-3D*	1212/1212/1035	1
62.TaSOT32-3D*	1407/1407/1032	1
63.TaSOT32-5A	1095/1095/1038	1
64.TaSOT32-5D	6372/1047/1047	3
65.TaSOT35c-1A	1317/1317/1017	1	17. OsSOT35	1650/1207/1017	2
66.TaSOT35b-2A	1813/1216/1062	2
67.TaSOT35c-2A	1638/1298/1017	2
68.TaSOT35b-2B	1630/1038/1038	2
69.TaSOT35a-2D	1350/1070/1041	2
70.TaSOT35c-2D	1664/1352/1020	2
71.TaSOT35d-4A*	1114/1026/1026	2
72.TaSOT35e-4A	1389/1328/1005	2
73.TaSOT35d-4B*	1573/1503/1026	2
74.TaSOT35e-Un	1183/1014/1014	2
75.TaSOT35e-7A	1220/1005/1005	2
76.TaSOT35e-7D	1173/825/825	2
77.TaSOT35d-1D	2083/1047/1047	2
78.TaSOT35a-2B	1226/888/888	2

Table 2

A list of 29 wheat SOT genes (identified only using SOT domain), without any homology with known rice genes, (genes marked with * carry duplication events)
Gene name	Gene length(bp)/cDNA/CDS	Exons	Gene name	Gene length(bp)/cDNA/CDS	Exons
1.TaSOT36-7A	2745/1044/1044	2	16.TaSOT40-2D	1218/1218/1017	1
2.TaSOT36-1B	1342/1342/1068	1	17.TaSOT40-2B	1023/1023,582	1
3.TaSOT36-1D	1347/1347/1047	1	18.TaSOT41-2A	1822/1822/1128	1
4.TaSOT37-2A	1247/1247/1026	1	19.TaSOT41-2B*	1918/888/888	2
5.TaSOT37-2D	1760/1460/1026	1	20.TaSOT41-2D3*	1495/1405/852	2
6.TaSOT38-2A*	1179/1179/1029	1	21.TaSOT41-2D2	1320/1137,1495	1
7.TaSOT38-2B2	1233/1233/921	1	22.TaSOT41-2D1	1071/1071, 1014	1
8.TaSOT38-2B1	1251/1251/1038	1	23.TaSOT42-3D	1517/1059, 936	3
9.TaSOT38-2D*	1480/1480/1035	1	24.TaSOT43-4B	1373/987, 1373	1
10.TaSOT39-7B2*	1338/1338/1089	1	25.TaSOT43-5B*	1364/1146, 1364	1
11.TaSOT39-7B1*	1426/1426/984	1	26.TaSOT43-5D2	1062/1038,1062	2
12.TaSOT39-2D1	1002/1002/1002	1	27.TaSOT43-5D1*	1023/1023, 1023	1
13.TaSOT39-2D2	1391/1391/1002	1	28.TaSOT44-6A	1004/1002,1004	2
14.TaSOT39-2B	1273/1273/1089	1	29.TaSOT44-6B	1068/1068, 1068	1
15.TaSOT 40-2A	1231/1231/1017	1

Location of genes and duplications

The TaSOT genes are distributed equally across the three sub-genomes of wheat (sub-genome A has 28 TaSOT genes; sub-genome B has 32 genes, sub-genome D has 42 genes); 5 genes could not be assigned to any chromosomes and were, therefore, labeled as Un. The number of the genes on each chromosome vary from 1 (6A and 5B) to 17 (2D); no gene was found on chromosome 4D (Fig. 2a,2b). Majority of gene were available in telomeric and sub-telomeric regions of chromosomes (Fig. 2b)

No correlation was observed between the number of TaSOT genes and the size of corresponding chromosomes. For instance, the smallest chromosome (6D, 473.6 Mb) carries three TaSOT genes, while the longest chromosome (3B, 830.8 Mb) contains only two TaSOT genes. There were 7 tandem and 26 segmental duplications, which together involved 40 genes (Fig. 2b and 2c) (Supplimentary Table S2).

Synonymy among genes (Ks, Ka and Ka/Ks ratio)

The Ka/Ks values of wheat SOT genes ranged from 0.2 (TaSOT35d-4A-TaSOT35d-4B) to 0.49 (TaSOT5b-2D and TaSOT5b-2A) (Fig. 3, Supplementary Table S3). For two pairs of genes, the Ks values were zero (0), and therefore, no Ka/Ks ratio could be calculated. The results suggested a time-line for duplication events from recent to as old as 15 MYA.

Gene structure

Of the 107 TaSOT genes detected in the present study, 34 genes were split genes (each with one or more introns, and with 2 to 6 exons); the remaining 73 genes had no introns (each with only one exon). The number of exons in individual genes ranged from 1–6; 73 genes with 1 exon (no intron), 25 genes with 2 exons; 3 genes with 3 exons; no gene with 4 exons; 2 genes with 5 exons and 4 genes each with 6 exons (Table 1).

Identification of orthologs between wheat and its relatives

As mentioned above, the 107 TaSOT genes belonged to 26 orthologs/paralogs (17 based on rice genes + 9 based on SOT domain). In the following other six related species, paralogs for all these 26 were not available; T. urartu, Ae. tauschii, T. turgidum, H. vulgare, B. distachyon and Z. mays (Supplimentary Table S4). Among these related species, T. urartu (progenitor of subgenome A) and Ae. tauschii (donor of D subgenome) each had 45 genes (TuSOT and AetSOT), the former with 14 duplicates and the latter with 7 duplicates. T. turgidum (AABB) had 83 genes with 20 duplicates. Minimum number of SOT genes are present in Z. mays (15) and maximum in T. aestivum (107). For one or more of four wheat SOT genes (TaSOT39, TaSOT32, TaSOT27, TaSOT12 and TaSOT13), orthologues were not available in the following related species B. distachyon, H. vulgare and Z. mays:

Synteny/collinearity between SOT genes of wheat and related species

Synteny was observed between TaSOT genes and SOT genes of the following related species: B. distachyon, H. vulgare, O. sativa Z. mays, Ae. tauschii and T. turgidum and Sorghum bicolar. The results of syntney are represented by Circos circular ideogram included in Figs. 5a. 5b and 5c. Maximum numbers of 34 syntenic genes were observed in H. vulgure (with 12–80% synteny) followed by 12 genes each in S. bicolor (40–96%) and O. sativa (30–70%) and 10 genes in B. distachyon (19–67%). The minimum number of five syntetic genes were present in Z. mays (40–54%). TaSOT11-2B gene was unique in showing synteny with SOT genes of all the seven species (Fig. 5a, 5b and5c). Synteny between TaSOT genes with those of Ae. tauschii (20–83%) and T. turgidum (20–90%) both had a wide range (Figs. 5b, 5c).

Promoter analysis and identification of cis-regulatory elements (CREs)

Promoter analysis and search for CREs gave 48 CREs in all 107 TaSOT genes. These CREs were grouped into the following four broad categories on the basis of their function and response to stimuli (Fig. 6; Supplementary Table S5): (i) 16 CREs for light (ii) 12 CREs for biotic/abiotic stresses; (iii) 8 CREs for hormones;, (iv) 12 CREs for growth and biological processes. Statistical analysis suggested that light response elements comprised 33.3% of all CREs, followed by abiotic stress response elements (25%), elements for growth and biological processes (25%), and hormone response elements (16.6%).

The number of CREs for individual TaSOT gene ranged from a minimum of six CREs for growth/ development to a mximum of 14 light response CREs. The number of CREs also differed for different TaSOT genes, ranging from a minimum of 10 CREs for TaSOT21-Un2 to a maximum of 22 CREs each for three genes (TaSOT12-1A, TaSOT35c-2D, TaSOT40-2A) (Supplementary Table S5).

There CREs in different TaSOT genes were classified in 28 different types, depending upon the stimuli and responses involved. The most important stimuli involved in a large number of CREs included light, wound, cold, heat, anaerobic induction, and drought. The CRE for the transcription factor MYB was found in almost all identified genes; this was followed by MYC, STRE, ARE, as-1 and MBS, WRE3, thus indicating that many TaSOTs are involved in drought stress response (Fig. 6).

The CREs in TaSOT genes were grouped into four major categories (Light responsive, stress responsive, hormone responsive and growth and biological processes responsive CREs);based upon function and their response to stimuli. These CREs did not include the common eukaryotic regulatory elements, such as TATA and CAAT boxes. Hormone-responsive CREs including ABRE and MeJA-REs, were found in fairly high proportions. The distribution of these CREs is shown in Fig. 7.

SSRs/miRNAs/lncRNAs

There were 42 SSRs in 34 of the 107 genes, which included a maximum of 4 in TaSOT11-2A, 2 each in TaSOT41-2A, TaSOT40-2B ,TaSOT5a-2D and TaSOT5b-2D and the remaining genes each with a solitary SSR (Supplimentary Table S6). Tri-nucleotide repeats were most frequent (27 SSRs) followed by di- and tetra –nucleotide repeats (5 SSRs each) and the hexa-nucleotide repeats (4 SSRs). The miRNAs (13 in number) were targeted on TaSOT genes (12 in number) in the CDS region but ahead of the SOT domain. 11 TaSOT genes were inhibited by miRNA via cleavage (87.5%), while the two genes were inhibited via translation [TaSOT35e-Un, TaSOT3-1B inhibited by tae-miR1136, tae-miR1127b-3p respectively through translation (12.5%)]; (Supplimentary Table S7) .LncRNA analysis showed six (6) TaSOT genes carried partial or complete sequences encoding nine lncRNAs (five of these TaSOT genes carrying sequences for 2 lncRNAs and one TaSOT gene carrying coding sequence for only one lncRNA (different TaSOT genes could also have the coding sequence for same lncRNA). The length of lncRNAs ranged from 208bp to 52000 bp; the maximum size of lncRNA genes sometimes exceeding the maximum length of TaSOT gene, so that the TaSOT carried only part of a gene for lncRNA (supplementary Table S8).

\Network of TaSOT genes with miRNAs and lncRNAs

The miRNA-targeted TaSOT genes were analysed to assess the network of miRNA and TaSOT genes; 13 miRNAs were predicted to target 12 TaSOT genes. Based on their target relationships, miRNA-TaSOT interactions were inferred (Fig. 8a); where most of the 12 genes (9 in number ) were tragetted by single miRNA but few were targeted by more than one miRNA,such as TaSOT35e-Un was targeted by three miRNAs wheraes TaSOT35b-2A and TaSOT35b-2B were targeted by two miRNAs. (Fig. 8a). We have found that TaSOT5b-2A is the only gene having lncRNA (STRG_Seedling.33016.1 and STRG_Seed.29606.1), on which miRNA (tae-miR1138) is targetted.

Majority of genes (including TaSOT3-1A, TaSOT3-1B, TaSOT35e-Un, TaSOT21-Un2, TaSOT32-5D, TaSOT35b-2B, TaSOT35b-2B, TaSOT35e-7D and TaSOT41-2B) showed basal level of expression with no association with any miRNA (Fig. 8b). Under heat stress, TaSOT5b-2A exhibited slightly increased expression associated with downregulation of associated miRNA; same is true with the following TaSOT genes, each with high expression and downregulation of corresponding miRNAs: TaSOT18a-3D1, TaSOT18b-7B, TaSOT35e-Un, TaSOT35b-2A, TaSOT35e-7D, TaSOT41-2B. TaSOT3-1A, TaSOT3-1B, TaSOT35e-Un are upregulating along with its target miRNA.

Protein analysis

Physio-chemical properties and annotation of proteins

For 107 TaSOT genes, the corresponding 107 SOT protein were available, which ranged in length from 274 to 381 amino acids, and in molecular weight from 31.4 KDa (TaSOT41-2D3) to72.7 KDa (TaSOT35b-2A). (Supplimentary Table S12). Based on the predicted value of isoelectric point, 22 genes were basic (pH > 7) and 85 genes were acidic (pH < 7) (pI; Supplimentary Table S9). These proteins could be either stable or unstable. Similarly, the Aliphatic Index (AI) ranged from 70.8 (TaSOT5a-2D) to 94.3 (TaSOT35e-7D), suggesting that proteins can be unstable (low AI) or stable (high AI). (Supplimentary Table S9). All TaSOT proteins had negative GRAVY values ranging from − 0.02 (TaSOT35e-7A) to -0.5 (TaSOT21b-1D) (Supplimentary Table S9).

Subcellular localisation of proteins

Within the cell, the TaSOT proteins could occupy different locations including the cytoplasm, chloroplast, mitochondria, peroxisome, nucleus, and plasma membrane (Supplimentary Table S10). Of these, approximately 73% were either solely cytoplasmic or were also located in chloroplast and mitochondria along with nucleus and plasma membrane.

Post- translational phosphorylation

Post translational phosphorylation of proteins mainly involved serine (S), threonine (T) and tyrosine (Y) s (Supplementary Table S17). Some of the drought/heat responsive TaSOTs were shown to be highly phosphorylated SOTs. Two SOT proteins, namely TaSOT30-1A1 and TaSOT18b-7B were each phosp horylated at only one or two residues (Supplimentary Table S11).

Functional domains and motifs

TaSOT proteins belong to the superfamily P-loop containing nucleoside triphosphate hydrolases. SOT-related Pfam domain (PF00685) was of the following three types: (i) SOT_1, present in 110 positions in all genes within the range of 38–344; (ii) SOT 3, present at 80 positions in genes, ranging in length from 104 aa to 342 aa and (iii) SOT transmembrane domain (TMD) present in two protein TaSOT3 and TaSOT11 in subgenome A, B and D. Their spanning in the transmembrane, range from 20aa to 47aa (TaSOT3) and 20aa to 37aa (TaSOT11). (Supplementary Figure S2, Supplimentary Table S14 and Fig. 9c).

The TaSOT proteins also carried 20 distinct motifs (motif 1 to motif 20) (Fig. 9a). The logo of 13 motifs and associated amino acids are summarized in Fig. 9 (b). The details regarding E-value, sequences, and functions of motifs are shown in Supplementary Tables S12 and 13). In brief, five of the 20 motifs that were annotated carried SOT activity, the remaining motifs having no known function. The number of conserved motifs in individual SOT genes ranged from 8 to 14 with motif 8 occurring in almost all TaSOT genes, followed by motifs 13, 9, 1, 2 in this order (appearing in more than 100 genes). Genes on chromosomes of homoeologous group 7 (7A,7B and 7D) in the phylogenetic tree did not show any conserved motif and were different from other genes. SOT family members are specified by four conserved regions (I to IV) in their protein, which are involved in PAPS binding; only two highly conserved regions are found in TaSOTs, namely group-I present in motif 1 and group-IV in motif 9, each having PAPS-binding regions.

Multiple sequence alignment (MSA)

Multiple sequance alignement revealed the following two characteristic regions: (1) 5′ N terminal phospho-sulphate binding (PSB) loop in motif 1 involving five successive residues, and (2) 3′ phosphate binding (PB) regions in motif 2 (Supplimentary Fig S1 and Fig. 9b). Motif 1 in PSB loop is engaged in an interaction with 5′-phosphate region of PAPS, which is engaged in sulfotransferase catalytic activity; the residues involved include Lys-154, Thr-157 and Thr-158 (Supplementary Figure S1). Similarly, the conserved 3′ PB motif in the C-terminal part of the TaSOTs encompassed β-sheet 8 and α-helix 6, which contains Arg-287 and Ser-295 (Supplementary Figure S1). Histidine repeats are present at 3′ end of the sequences as positioned on 300aa.

Protein- protein interactions

As mentioned earlier, only 10 TaSOT proteins (encoded by 10 genes used for qRT-PCR) were used for a study of protein-protein interactions. The analysis revealed interactions of these 10 TaSOT proteins with each other and with the six other SOT proteins, which are co-regulated with these 10 selected TaSOT proteinas (Fig. 10). In addition to these 16 proteins, 13 other proteins were involved in the analysis of protein-protein interactions; these 13 proteins included five UDP-glycotransferase proteins, four cytochrome P450 proteins and four aminotransferase proteins (Table.3). The key regulatory proteins, which are crucial in synthesis of secondary metabolites, like phytohormones and plant defence are showing interaction with each other.

Table 3

Proteins from different families involved in interactions with 10 TaSOTs.
Protein name/family	Functions
Aminotransferase	Transaminase activity, pyridoxal phosphate binding
Glycotransferase	Transfer of a glycosyl moiety from UDP sugars to flavanoid acceptors, biosynthesis of secondary metabolites and sugar conjugation
Cytochtome P450	Synthesis of jasmonic acid (JA); craboxyl acid metabolic process
Sulphotransferase	SOT activity (GO:0008146)

Secondary and tertiary (3D ) structures

In secondary structure, random coils are more frequent relative to α helix and β sheet (Supplementary Table S15). Also, a high proportion of aa residues were present in the favoured region relative to those falling in the ‘allowed’ and ‘disallowed’ regions.

Similarity of 3D structures of SOT proteins of wheat and rice ranged from 40.6–100%. All TaSOT proteins had monomeric structures (Supplementary Table S15), although together, they make a multifamily. Global model quality estimation (GMQE) values of the 3D structures ranged from 0.7 to 0.9, suggesting high-quality protein models. The Qualitative Model Energy ANalysis (QMEAN) values ranged from 0.2 to 0.7 (Supplementary Table S16). The quality factors (determined using ERRAT) ranged from 79.8–100% and the 3D-1D score (determined using VERIFY3D) ranged from 68.6–95.5% (Supplementary Table S16). Channel number in proteins ranged from 1 to 11.

Alignment and functional annotation of 3D structures

The similarity of 3D structures of three TaSOT proteins (TaSOT5b-2D, TaSOT9-2D and TaSOT18) with corresponding 3D structure of rice SOT proteins (OsSOT5, OsSOT9 and OsSOT18) ranged from 71.2–81.8%, with values for RMSD ranging from 0.49 Å to 0.72 Å (Suplimentary Table S18).

Molecular dynamic simulation and docking

Docking analysis revealed that the ligand adenosine 3-phosphate (A3P) binds effectively within active sites in amino acids in three TaSOT proteins, namely, TaSOT5b-2D, TaSOT9-2D andTaSOT18b-7B. This binding involves strong hydrogen bonds, which range from 2 to 10 in number (Fig. 12a). TaSOT5b-2D also form two parallel pi bonds along with hydrogen bond (Fig. 12a). Further analysis of binding revealed differences in the interactions of A3P with active sites of TaSOTs. These differences involved binding with one or more of the following amino acid residues His246, Asp238, Arg235, Pro228, and Leu 336 in two proteins (TaSOT9-2D and TaSOT18b-7B) whereas Cys277, Trp89, Phe279, Arg307/171, The305 and Thr87/88 are showing interaction with protein TaSOT5b-2D (Fig. 12a).

The 3D structures of three monomeric TaSOT proteins, namely TaSOT5b-2D, TaSOT9-2D and TaSOT18b-7B, were assessed through homology modelling; these had Ramachandran score ranging from 93.8 to 96.1, the favoured regions ranging from 91.7 to 93.3 and the quality factor ranging from 87.5 to 92.6 (Suplimentary Table S17). These models were subjected to MD-simulation to improve their structure and stability. Total energy (TE) of the final simulated structure of the three TaSOT proteins were as follows: (i) TaSOT5b-2D, -5.2e + 05kJ/mol; (ii) TaSOT9-2D, − 5.52e + 05kJ/mol and (iii) TaSOT18b-7B, -5.08e + 05kJ/mol. Potential energy (PE) was − 6.46e + 05kJ/mol, -6.84e + 05kJ/mol and − 6.32e + 05kJ/mol. RMSD values of TaSOT5b-2D, ranged from 2 to 9nm for 10ns and then constantaly lies in the range of 7nm for upto 80 ns (Fig. 13a). Similar changes are seen in TaSOT9-2D, where RMSD values range from 2 to 11 nm continuing upto 80ns. TaSOT18b-7B differed with very low RMSD ranging from 1.0 to 1.5nm continuously from starting without any variation. had the maximum RMSF (0.1–0.5 nm) was maximum for TaSOT5b-2D and minimum (0.1-0.3nm) for TaSOT18b-7B [Fig. 13b (green colour in graph)]. The radius of gyration of the simulated complexes was analysed to understand the mobile nature and flexibility of the complexes (Fig. 13c). The complex of TaSOT18b-7B maintained a steady-state in Rg graph (Fig. 13c) throughout the whole simulation time. The TaSOT5b-2D and ligand complex had flexibility after 20 ns time, and higher Rg than the other two complex.

In-silico expression of genes

In silico expression of TaSOT genes was examined in five different tissues (root, stem, leaf, spike, and grain) of tolerant cultivar ‘Gemmiza 10’, each sampled at three different developmental stages under normal conditions (Fig. 14a) and also under drought and heat (14b). The expression patterns at the seedling stage (7 days) were examined under following three stress conditions: (i) heat stress (1h and 6h); (ii) drought stress (1h and 6h) and (iii) heat + drought combined (1h and 6h);

In grain/stem/leaf/root/spike, high expression (TPM > 2) was observed in 52 genes, of which TaSOT20c-Un2 exhibited highest expression in most of the tissues and at each developmental stage, except grain, root, leaf-2 dpa, and stem/ anthesis (Fig. 14a).

In seedlings, high expression (TPM > 2) was observed in 23 genes, of which TaSOT6-7D (8.2-fold change) had the highest upregulation in H + D (1h) and TaSOT39-7B2 (-5.1-fold change) had maximum downregulation (2 h) (Fig. 14b).

qRT-PCR for validation

As mentioned in Material and Methods, the expression the following ten genes, each in sensitive cultivar PBW343 and tolerant cultivar Excalibur, was examined using qRT-PCR: TaSOT30-1A1, TaSOT5b-2D, TaSOT35c-2D, TaSOT20c-3D, TaSOT18b-7B, TaSOT6-7D, TaSOT37-2D, TaSOT9-2D, TaSOT43-5D1, TaSOT5b-2A). Most of these genes showed differential expression (≥ 2 fold) under different stress conditions (Fig. 15); following are some details: (i) Under drought, the expression levels for eight of the ten genes (except TaSOT43-5D1 and TaSOT9-2D) was high in tolerant cv. Excalibur relative to that in susceptible PBW343; maximum of 20-fold expression was shown by TaSOT35c-2D (Fig. 15) (ii) Under heat, high expression; was observed in TaSOT5b-2A, TaSOT9-2D and TaSOT6-7D exhibited. (iii) Under heat + drought, differential expression was observed only in TaSOT6-7D and TaSOT43-5D1. The above results suggest that several wheat SOT genes give high expression under drought stress and few genes also show relatively high expression under heat and drought + heat conditions.

Genetics of drought/heat tolerance and breeding for drought tolerantce in crop plants have been areas of current active research in crop plants. Several reviews on drought tolerance in crops have also been written, providing updated information on the subject (Hirschmann et el. 2014, Abuelsoud et al. 2016; Bapela et al. 2022, Adel et al. 2023,). As described in these reviews, genes associated with a variety of metabolic pathways, including photosynthesis, respiration, nitrogen metabolism and sulphur metabolism are involved in response to drought in crop plants. A number of traits including depression of canopy temperature, shrinkage of root traits, closure of stomatal have also been utilized as marker traits for selection of drought tolerant genotypes in a crop (Ashfaq et al. 2023). Thus, a large number of genes (numbering into hundreds), many of these genes occurring as multigene families, seem to be involved in drought tolerance, rendering development of drought tolerant cultivars a challenge indeed. Some major genes/QTLs, QTL hot spots and metaQTLs have also been identified in some crops (Gupta et al. 2012, 2017; Barmukh et al. 2022); in some cases, these QTLs/genes have also been utilised in developing drought tolerant cultivars, as done in chickpea (Bharadwaj et al. 2021). Transcription factors and meta-QTLs for drought tolerance have also been identified (Gahlaut et al. 2016; Kumar et al. 2021; Tanin et al. 2022). Recently in Argentina in South America, a sunflower gene Hahb4 encoding a transcription factor has also been utilized for developing drought tolerant transgenic HB4 wheats that have been approved for consumption as feed and food; in Agentina and Brazil, HB4 wheat has also been approved for commercial cultivation (Gupta, 2023a).

Despite the above developments, a search for genes responding to drought continues unabated. The present study is yet another such effort involving identification of drought responsive SOT genes in wheat and the related species (Hirschmann et el. 2014; Abuelsoud et al. 2016). In wheat, the present study is the first systematic analysis of the family of SOT genes and SOT proteins. Our results involving gene and protein structure, gene distribution and protein classification will facilitate further investigations on the functions of TaSOT genes in T. aestivum. Hopefully, these results will also provide a useful reference for the study of SOT genes in other crops. Our data should also promote further studies on the evolution of SOT genes/proteins in polyploid genomes and the genetic improvement of bread wheat for drought tolerance.

In wheat, individual genes range from one gene per genome to dozens of genes per genome, such that many genes are present as multigene families. In several cases, examined so far in our own laboratory also, more than one gene occurs in the same genome (for a review, see Gupta et al. 2023). Following are some examples (number of genes in parentheses): (iii) RuvBL genes (9) (iv) RWP-PK (37) (iv) SET domain genes = SDGs (166); (iv) 20S proteasome genes (67); (v) NAAT genes (24); (vi) VMT genes (6); (vii) SWEET genes (108).

Like some of the above examples, the number of SOT genes varies from two SOT genes in the genus Flaveria (an ornamental genus of Asteraceae) to 107 SOT genes in bread wheat (the present study). Among other species examined, 18 genes were reported in Arabidopsis thaliana (Klein et al. 2006), 35 in rice (Oryza sativa; Chen et al. 2012), 77 in cotton, (G. hirsutum; Wang et al. 2019), 56 in Chinese cabbage (Brassica rapa L.; Jin et al.2019), an average of 45 in 11 species of the algal genus Caulerpa (Landi et al. 2020) and 29 in potato (Solanum tuberosum; Faraji et al. 2021). Based on the hexaploid nature of wheat genome and availability of 35 SOT genes in diploid rice genome, it is not surprising that 107 genes were identified in wheat in the present study, assuming that for each rice gene, there would be three genes in hexaploid wheat. However, not all the seven homoeologopus groups carried TaSOT genes and also not all genes had three copies, one on each of the three chromosomes of the same homoeologous group. For instance, 4 D chromosome did not have any gene. In earlier studies, this type of uneven distribution was also noticed SOT family in rice, where 35 SOT genes are unevenly distributed and genes were absent on chromosome 3 and 5 (Chen et al. 2012). Similar results were obtained in cotton, mustard and potato (Wang et al. 2019, Jin et al. 2019 and Faraji et al. 2021). In earlier studies also, D genome was found to be different (Marcussen et al. 2014 and IWGSC 2018).

The results of the structure of TaSOT genes obtained in the present study can also be related with funtion and evolution of these genes. It may be recalled that 34 of the 107 TaSOT genes identified during the present study were split genes, where the number of introns in an individual gene varied from 1 to 5. It means that majority of TaSOT genes [73 (68%) out of 107] were intronless. Similar results are available for SOT genes in other plant species studied so far. For instance, in Arabidopsis 7 of the 21 AtSOT genes (Klein et al. 2004), in potato, only four genes out of 29 StSOT genes (Faraji et al.2021), in Chinese cabbage (B. rapa) 18 out of 56 BraSOT genes (Jin et al. 2019) and in rice, 19 out of 35 OsSOT genes were split genes, suggesting that majority of genes are intronless (Klein et al. 2004; Chen et al.2012); also among rice genes, the gene OsSOT9, which provides tolerance for heat and drought stress is a intronless gene (Cao et al. 2016). In the present study, only 23 genes had high expression under drought, of these 23 genes, 19 genes are intronless and only 4 were split genes, suggesting that in general many more introless genes are associated with high expression under drought. The occurrence of miRNAs and lncRNA among split genes and intronless genes was also examined; it was found that of 16 miRNAs, 4 miRNAs occurred in 4 intronless genes (the remaining 12 miRNAs occur in 8 split genes). Based on this distribution, we may conclude as follows: (i) many more genes are intronless; (ii) many more intronless genes (26%) and relatively fewer split genes (11%) had high expression under drought.

The occurrence of relatively fewer split genes among plant SOT genes may have some significance. This may be examined keeping in view the function of introns in split genes, which are known to carry a variety of regulatory sequences including non-coding RNAs like miRNAs and lncRNAs. In the present study, the expression of only four of the 34 split genes were associated with high expression under drought. Asssociation of miRNA and lncRNA with split genes was also examined; it was found that among 12 genes carrying miRNAs, eight genes were split genes and the remaining four were intronless. In contrast, among nine lncRNAs six TaSOT genes, of which only two are split genes (TaSOT2-2B, TaSOT36-7A). This suggests that miRNA are more frequent in split genes and lncRNA are more frequent in intronless genes. These results also suggest that majority of TaSOT genes involved in response to drought/heat were intronless and also that miRNA and lncRNA are negatively correlated with drought and heat tolerance.

Among the intronless genes, TaSOT5b-2A is the only gene having target miRNA and the sequence for a lncRNA, associated with high expression under heat and drought as well. The relative distribution of miRNAs and lncRNA in split genes can also be also examined, showing that majority of genes involved in drought stress areintronless. This may indirectly support the earlier reports, where small nucleolar RNAs, small nuclear RNAs, and circular RNAs were associated with gene regulation (Kumari et al. 2022; Vakirlis et al. 2022). In the present study, miRNAs in (12 genes) in which 8 genes are split genes with interons and lncRNAs are found in 6 TasSOT genes, of which only 2 genes are split genes.

The occurrence of 7 tandem and 26 segmental duplications in TaSOT genes observed in the present study is nothing unique, since similar duplications have been reported in several other crops (potato, rice, cotton, cabbage) and the model plant species ,arabidopsis (Faraji et al. 2021; Chen et al. 2012; Appels et al. 2018). It has been advocated that gene duplications provide new genetic material for mutation, drift, and selection to act upon, the result of which is specialized for new gene functions. Without gene duplication, the plasticity of a genome or species in adapting to changing environments would be severely limited. The distribution of dupolications in genes can also be utilized to find out the evolutionary stage, when these duplications originated.

Orthologous relationships of TaSOT genes with SOT genes of other related species like rice and six other species (T. urartu, Ae. tauschii, T. turgidum, H. vulgare, B. distachyon and Z. mays), were analysed, where clear orhology of TaSOT was observed with these species. No orthology of TaSOT genes has seen with Arabidosis which is in accordance with the result of OsSOT genes, indicating their independent evoloution (Chen et al. 2012). In our analysis a clear gene duplication can be observed in monocots (15 genes in maize, 35 in rice and 107 in wheat) which shows that some homologous genes in wheat ancestor species were common; suggesting that these genes are highly conserved during evolution and may have similar functions. The divergences between homologous genes are due to deletion and duplication, revealing that some of them may lose function or acquire new function in evolutionary process (Li et al. 2020).

Synteny analysis shows that a majority of TaSOTgenes showed syntenic relationship with their orthologs in T. turgidum (20–90%) and a minimum of five TaSOT genes with Z. mays (40–54%), which shows that in course of evolution and with polyploidization, the number of syntenic SOTs increased by gene complementation to confer adaptive plasticity (Otto et al. 2000). In previous study also it is being analysed that approximately 35% of StSOT showed synteny relationships with the tomato and approximately 32% with arabidopsis (Faraji et al. 2021).

Promoter analysis of TaSOT genes showed that these genes contained cis-regulatory elements (CREs) with responding to abiotic stresses. In TaSOT out of 48, 11 were responding to abiotic stress which was in favour with the previous study of OsSOTs, where 21 types of CREs were involved in abiotic stress (Chen et al.2012). The largest portion of the ABRE regulative elements was also seen in the promoter regions of TaSOT genes, which is the key regulative element in heat and drought stress response (Batra et al. 2019, Chaudhary et al. 2023). These findings suggest potential cross talk between hormones in the expression of ABA receptors.

In our analysis most of the SSR found in TaSOT gene were trinucleotide(maximum) to hexanucleotide repeats(minimum). The abundance of tri-nucleotide repeat SSRs in TaSOT is in agreement with earlier reports in wheat (Varshney et al. 2005). no SSR analysis have been reported in SOT genes in any other plant species.

In protein analysis of TaSOT we found high aliphatic index of 71–83%; with negative value of GRAVY for all TaSOT protein. The high Aliphatic Index of a protein sequence suggests their significance in high thermostability (Ikai 1980, Chaudhary et al. 2023). A protein with a negative GRAVY value is non-polar and hydrophilic in nature (Bhattacharya et al. 2018). Most of the TaSOT protein are cytoplasmic, but protein of three genes is transmembranic and also found in mitochondria, chloroplast. these results are in favour with that of AtSOT, which were also found to localised in cytoplasm as well as in plastids (Kleain et al. 2004). But in

contrast with StSOTs where all protein were localised in cytoplasm only with no TMP (Faraji et al. 2021). This indicates that TaSOT proteins likely to have distinct functions related to membrane-associated processes and organelle-specific functions.

Post-translational phosphorylation of TaSOT proteins revealed a wide variety of phosphorylated residues, in potato StSOTs also, highly phosphorylated SOTs were noticed (Faraji et al. 2021). Since protein phosphorylation can mediate multiple biological processes, like plant development and stimuli responses (Heidari et al. 2019, Rezaee et al. 2020), it may be inferred that highly phosphorylated TaSOTs should play an important role during the wheat life cycle.

SOT domain (PF00685) is found in all TaSOT proteins; this is true of other plant species also, where SOT genes have been characterized (Faraji et al. 2021, Chen et al. 2012, Wang et al. 2019). SOT family members are specified by four conserved regions (I to IV) in their protein sequences (Varin et al. 1997), in which the I and IV regions are highly conserved sections, and are involved in PAPS binding (Klein et al. 2004, Zang et al. 2009). This is also obsereved in all the previous studies of SOTs (Faraji et al. 2021, Chen et al. 2012, Wang et al. 2019).

The specific amino acid residues responsible for SOT catalytic activity in these motifs are in accordance with Faraji et al. 2021. The conserved 3' PB motif located in the C-terminal region of the TaSOTs acts as the interacting sites with the 3'-phosphate group of PAPS, and it selectively modulates the binding of PAPS, as reported by Klaassen et al. in 1997 and Faraji et al. in 2021. The structural similarities observed in this motif may indicate a significant resemblance in expression patterns and regulatory functions within the cell, as discussed by Faraji et al. in 2021.

The results of protein-protein interactions involving SOT proteins suggested that the different genes families along with the SOT domain family are co-expressed and co-regulated and all the families are connected in a pathway with TaSOT gene family have transferase and catalytic activity, sulfur is a crucial element that contributes significantly to the structure, regulation, and catalytic functions of proteins. Previous research by Faraji et al. in 2021 has demonstrated the involvement of the SOT gene family in the biosynthesis of sulfur compounds, as well as the regulation of flavonoid and brassinosteroid metabolic processes. It is likely that TaSOT proteins work in collaboration with proteins from iron-sulfur complexes and amino acid metabolism, thereby playing a role in regulating plant responses to external stimuli (Faraji et al. 2021, Heidari ert al. 2020).

The 3D structures of three representative TaSOT proteins showed high proportion of amino acid residues (> 90%), falling in the most favoured regions, suggesting that the predicted models are reliable (Batra et al. 2017; Kumar et al. 2018, Chaudhary et al. 2023). Generally, our predicted 3D models were in good agreement with the parameters related to typical SOT proteins and can be utilized for peptide ligands and as a docking assay. We found either 10 or 11 channel proteins; previously also11-13 channels were identified in StSOT proteins (Faraji et al.2021). In protein structures, the channels and cavities modulate protein function and can determine their binding specificity (Fukao et al. 2012, Prasad et al. 2023).

Our docking assay revealed that the predicted residues in docked complex provide stability and maintain interaction between three TaSOTs and the A3P molecules. Most of these functional regions were included in the sulphotransfer_1 domain. Same result was obsereved in StSOT, where ligand binding comprises of proline, glycine, serine, and lysine in their active sites (Faraji et al.2021). The time-dependent fluctuation of Rg (radius of gyration) was estimated to establish the influence of ligand binding on the compactness and structural integrity of ligand- A3P and TaSOT proteins. TaSOT5b-2D shows higher Rg, than other two proteins, that indicates that the higher the Rg higher will be the level of labile nature of the complexes, whereas the lower Rg profile indicates the contracted nature of the complexes (Tiwari et al. 2022).

The differential expression of TaSOT genes, observed during in-silico expression analysis, was also confirmed in ten choosen TaSOT genes using qRT-PCR. In another earlier study in Brassica also, expression of SOTs were increases under abiotic stresses. (Marsolais et al. 2003; Michele et al. 1999) which was later confirmed using transgenic approach in OsSOT9 (Cao et al.2016). Similar results were also reported in rice (Chen et.al.2012) and cotton (Wang et al. 2019), Chinese cabbage (Jin et al.2019), Caulerpa (Landi etal. 2020) and Potato (Faraji et al. 2021). The expression and regulation of TaSOT genes in response to abiotic stresses indicate their participation in various cellular pathways. To gain a comprehensive understanding of the functions of TaSOT genes, further investigations in wheat are warranted. Moreover, exploring the allelic variations within these genes could be valuable for the development of thermotolerant wheat cultivars.

In the present study in wheat, 107 sulphotransfeaase (SOT) genes and the corresponding proteins were characterized for the first time, with an objective to find out their relevance to the development of drought/heat wheat cultivars. Large number of these genes [73 (68%) of 107 genes] were intronless, suggesting that the introns were not really needed for the function of these genes. The genes carried duplications, which must have originated during evolution of bread wheat. The TaSOT genes were also subjected to a study of in silico expression. Expression of SOT genes were also compared with tolerance against drought and heat; the same was validated using qRT-PCR, through a comparison between drought sensitive cultivar PBW343 and the the drought tolerant Australian cultivar Excalibur. The SOT genes were found to be associated with a variety of regulatory mechanisms involving presence of cis regulatory elements in the upstrean promoter regions. Since a variety of genes are now known, which respond to drought/heat, the SOT genes may be used along with other genes for designing a recipe for marker-assisted recurrent selection and/or geneomic selection (GS). In particular, the gene TaSOT5b-2A (with target for miRNA and carrying the sequence encoding lncRNA), which showed high expression in heat and drought, may be of special interest, and therefore should be examined for its utility in plant breeding.

Conflicts of interest

The authors declare no conflicts of interest.

Funding

No funding was received for conducting this study.

Author contributions

JC conceived and planned this study under mentorship of PKG. JC, did the analysis and prepared the first draft of the manuscript, which was improved, edited, and finalised by PKG. HG and VJ performed the Qrt-PCR analysis.

Abuelsoud W, Hirschmann F, Papenbrock J (2016) Sulfur metabolism and drought stress tolerance in plants. Drought Stress Tolerance in Plants. Physiol Biochem 227–249.
Adel S, Carels N (2023) Plant Tolerance to Drought Stress with Emphasis on Wheat. Plants 12: p.2170.
Akpinar, Bala Ani, Tugdem Muslu, Gadi VP Reddy, Munevver Dogramaci, Hikmet Budak (2023) Wheat long noncoding RNAs from organelle and nuclear genomes carry conserved microRNA precursors which may together comprise intricate networks in insect responses Int J Mol Science 3: 2226.
Appels R, Eversole K, Appels R, Eversole K, Feuillet C, Keller B (2018) Shifting the Limits in Wheat Research and Breeding Using a Fully Annotated Reference Genome Science 361.
Ashfaq W, Brodie G, Fuentes S, Pang A and Gupta D (2023) Silicon improves root system and canopy physiology in wheat under drought stress. Plant Soil 1–18
Bapela T, Shimelis H, Tsilo T.J, Mathew I (2022). Genetic improvement of wheat for drought tolerance: Progress, challenges and opportunities. Plants 11: p.1331.
Barmukh, R. (2022) Characterization of ‘QTL-hotspot’ introgression lines reveals physiological mechanisms and candidate genes associated with drought adaptation in chickpea. J Exp Bot 73:7255–7272.
Batra R, Gautam T, Pal S, Chaturvedi D, Rakhi Jan I, Balyan H.S, Gupta P.K (2020). Identification and characterization of SET domain family genes in bread wheat (Triticum aestivum L.). Sci. Reports. 10:14624.
Bernsel A, Viklund H, Falk J, Lindahl E, Von Heijne G, Elofsson A (2008) Prediction of membrane-protein topology from first principles. Proc Natl Acad Sci U.S.A 105: 7177–7181.
Bharadwaj C. et al. (2021) Introgression of “QTL-hotspot” region enhances drought tolerance and grain yield in three elite chickpea cultivars. Plant J 14: e20076.
Bhattacharya M, Hota A, Kar A, Chini D.S, Malick R.C, Patra B.C, Das B.K (2018) In silico structural and functional modelling of Antifreeze protein (AFP) sequences of Ocean pout (Zoarces americanus, Bloch & Schneider 1801). J Genet Eng Biotechnol 16: 721–730.
Cao X, Liao Y, Rong S, Hu C, Zhang X, Chen R, Xu Z, Gao X, Li L, Zhu J (2016) Identification and characterization of a novel abiotic stress responsive sulphotransferase gene (OsSOT9) from rice. Biotechnol Equip 30: 227–235.
Chan K.X, Wirtz M, Phua S.Y, Estavillo G.M, Pogson B.J (2013) Balancing metabolites in drought: the sulfur assimilation conundrum. Trends Plant Sci 18: 18–29.
Chaudhary J, Gautam T, Gahlaut V, Singh K, Kumar S, Batra R, Gupta P.K (2023) Identification and characterization of RuvBL DNA helicase genes for tolerance against abiotic stresses in bread wheat (Triticum aestivum L.) and related species. Funct Integr Genomics 23: p.255.
Chen R, Jiang Y, Dong J, Zhang X, Xiao H, Xu Z, Gao X (2012). Genome-wide analysis and environmental response profiling of SOT family genes in rice (Oryza sativa). Genes & Genomics 34: pp.549–560.
d’Hooghe P, Dubousset L, Gallardo K, Kopriva S, Avice J.-C, Trouverie J (2014) Evidence for proteomic and metabolic adaptations associated with alterations of seed yield and quality in sulfur-limited Brassica napus L. Mol Cell Proteom 13: 1165–1183.
Estavillo G.M, Crisp P.A, Pomsiriwong W, Wirtz M, Collinge D, Carrie C (2011) Evidencefora SAL1-PAP chloroplast retrograde pathway that functions in drought and high light signalingin Arabidopsis. Plant Cell 23: 3992–4012.
Faraji S, Heidari P, Amouei H, Filiz E, Poczai P (2021) Investigation and computational analysis of the sulfotransferase (SOT) gene family in potato (Solanum tuberosum): insights into sulfur adjustment for proper development and stimuli responses. Plants 10: p.2597.
Faraji S, Ahmadizadeh M, Heidari P (2021) Genome-wide comparative analysis of Mg transporter gene family between Triticum turgidum and Camelina sativa. Bio Metals 34: 639–660.
Faraji S, Filiz E, Kazemitabar S.K, Vannozzi A, Palumbo F, Barcaccia G, Heidari P (2020) The AP2/ERF Gene Family in Triticum durum: Genome-Wide Identification and Expression Analysis under Drought and Salinity Stresses. Genes 11:1464.
Faraji S, Hasanzadeh S, Heidari P (2021) Comparative in silico analysis of Phosphate transporter gene family, PHT, in Camelina sativa gemome. Gene Rep 101351
Fukao Y (2012) Protein–protein interactions in plants. Plant Cell Physiol 53: pp.617–625.
Gahlaut V, Jaiswal V, Kumar A and Gupta, PK. (2016) Transcription factors involved in drought tolerance and their possible role in developing drought tolerant cultivars with emphasis on wheat (Triticum aestivum L.). Theor Appl Genet. 129:2019–2042.
Gaupp F, Hall J, Mitchell D, Dadson S (2019) Increasing risks of multiple breadbasket failure under 1.5 and 2°C global warming. Agric Syst 175: 34–45
Gidda S.K, Miersch O., Levitin A, Schmidt J, Wasternack C, Varin L (2003). Biochemical and molecular characterization of a hydroxyjasmonate sulfotransferase from Arabidopsis thaliana. J. Biol. Chem 278: pp.17895–17900.
Glendening T.M, Poulton J.E (1990) Partial Purification and Characterization of a 30-Phosphoadenosine 50-Phosphosulfate: Desulfoglucosinolate Sulfotransferase from Cress (Lepidium sativum). Plant Physiol 94: 811–818.
Gupta P.K, H.S. Balyan, P.L. Kulwal, Gahlaut V (2012). Phenotyping, genetic dissection, and breeding for drought and heat tolerance in common wheat: status and prospects. Plant Breed Rev 37: 85–168.
Gupta P.K, Balyan H.S, Gahlaut V (2017). QTL Analysis for drought tolerance in wheat: present status and future possibilities. Agronomy 7: 1–21.
Gupta, P.K. (2023). Drought tolerant transgenic wheat HB4: A hope for the future. Trends in Biotech (in press).
Gupta PK, Chaudhary J, Gautam T (2023). Bioinformatics for gene identification and crop improvement in wheat. In Chen, Jen-Tsung (Ed). Bioinformatics for Plant Research and Crop Breeding. Wiley-Blackwell (in Press).
Gurjar A.K.S, Panwar A.S, Gupta R, Mantri S.S (2016). PmiRExAt: plant miRNA expression atlas database and web applications. Database p: baw060.
Heidari P, Ahmadizadeh M, Izanlo F, Nussbaumer T (2019) In silico study of the CESA and CSL gene family in Arabidopsis thaliana and Oryza sativa: Focus on post-translation modifications. Plant Gene 19: 100189.
Heidari P, Mazloomi F, Nussbaumer T, Barcaccia G (2020) Insights into the SAM synthetase gene family and its roles in tomato seedlings under abiotic stresses and hormone treatments. Plants 9: 586.
Hirschmann F, Krause F, Papenbrock J (2014) The multi-protein family of sulfotransferases in plants: composition, occurrence, substrate specificity, and functions. Frontiers in plant science 5: p.556.
IWGSC. A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome (2014) Science.1251788:345.
International Wheat Genome Sequencing Consortium (IWGSC), Appels R, Eversole K, Stein N, Feuillet C, Keller B, Rogers J, Pozniak CJ, Choulet F, Distelfeld A, Poland J (2018) Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361:7191.
Jin L, Ouyang N, Huang Y, Liu C, Ruan Y (2019) Genome-wide analysis of sulfotransferase genes and their responses to abiotic stresses in Chinese cabbage (Brassica rapa L.). Plos one 14: p.e0221422.
Kim J.K, Cho Y, Lee M, Laskowski R.A, Ryu S.E, Sugihara K, Kim D.S (2015) BetaCavityWeb: a webserver for molecular voids and channels. Nucleic Acids Res 43: pp. W413-W418.
Klaassen C.D, Boles J.W (1997) The importance of 3’ phosphoadenosine 5’ phosphosulfate (PAPS) in the regulation of sulfation. The FASEB J 11: pp.404–418.
Klein M, Papenbrock J (2004) The multi-protein family of Arabidopsis sulphotransferases and their relatives in other plant species. J Exp Bot 55: 1809–1820
Klein Marion, Michael Reichelt, Jonathan Gershenzon, Jutta Papenbrock (2006) The three desulfoglucosinolate sulfotransferase proteins in Arabidopsis have different substrate specificities and are differentially expressed." The FEBS J 273: 122–136.
Kumar A, Batra R, Gahlaut V, Gautam T, Kumar S, Sharma M, Tyagi S, Singh K.P, Balyan H.S, Pandey R, Gupta P.K (2018). Genome-wide identification and characterization of gene family for RWP-RK transcription factors in wheat (Triticum aestivum L.). PloS one 13: p.e0208409.
Kumar S, Singh V.P, Saini D.K, Sharma H, Saripalli G, Kumar S, Balyan H.S, Gupta P.K (2021) Meta-QTLs, ortho-MQTLs, and candidate genes for thermotolerance in wheat (Triticum aestivum L.). Mol Breeding 41:1–22.
Kumari A, Sedehizadeh S, Brook J.D, Kozlowski P, Wojciechowska M (2022). Differential fates of introns in gene expression due to global alternative splicing. Hum. Genet.1–17.
Lal J.P (2021) Genetic improvement for drought tolerance through conventional and molecular approaches in Oryza sativa L.
Lacomme C, Roby D (1996) Molecular cloning of a sulfotransferase in Arabidopsis thaliana and regulation during development and in response to infection with pathogenic bacteria. Plant Mol Biol 30: 995–1008.
Landi S, Esposito S (2020) Bioinformatic characterization of sulfotransferase provides new insights for the exploitation of sulfated polysaccharides in Caulerpa. Int J Mol Sci 21: p.6681.
Ma X, Xin Z., Wang, Z., Yang, Q., Guo, S., Guo, X., Cao, L. and Lin, T., 2015. Identification and comparative analysis of differentially expressed miRNAs in leaves of two wheat (Triticum aestivum L.) genotypes during dehydration stress. BMC Plant Biol.15:1–15.
Marcussen T, Sandve S.R, Heier L, Spannagl M, Pfeifer M, Jakobsen K.S, Wulff B.B, Steuernagel B, Mayer K.F.X, Olsen O.A(2014) Ancient hybridizations among the ancestral genomes of bread wheat. Sci 345:1250092.
Marsolais F, Sebastià C.H, Rousseau A, Varin L (2003) Molecular and biochemical characterization of BNST4, an ethanol-inducible steroid sulfotransferase from Brassica napus, and regulation of BNST genes by chemical stress and during development. Plant Sci 166: 1359–1370.
Michele R, Frederic M, Martine R (1999) Inactivation of brassinosteroid biological activity. J Biol Chem 274: 20925–20930.
Mostofa M. G, Ghosh A, Li Z. G, Siddiqui M. N, Fujita M, Tran L. S. P (2018) Methylglyoxal – a signaling molecule in plant abiotic stress responses. Free Radic Biol Med 122: 96–109
Narayanan S, Prasad PVV, Welti R (2018) Alterations in wheat pollen lipidome during high day and night temperature stress. Plant Cell Environ 41:1749–61.
Ning P, Liu C, Kang J, Lv J (2017) Genome-wide analysis of WRKY transcription factors in wheat (Triticum aestivum L.) and differential expression under water deficit condition. Peer J 5: 1–17
Otto S. P, Whitton J (2000) Polyploid incidence and evolution. Annu Rev Genet 34:401–437.
Pandey R, Joshi G, Bhardwaj A.R, Agarwal M, Katiyar-Agarwal S (2014) a comprehensive genome-wide study on tissue-specific and abiotic stress-specific miRNAs in Triticum aestivum. PLoS One, 9: p.e95800.
Patil G, Valliyodan B, Deshmukh R, Prince S, Nicander B, Zhao M, Sonah H, Song L, Lin L, Chaudhary J, Liu Y (2015) Soybean (Glycine max) SWEET gene family: insights through comparative genomics, transcriptome profiling and whole genome re-sequence analysis. Bmc Genomics 16: pp.1–16.
Piotrowski M, Schemenewitz A, Lopukhina A, Muller A, Janowitz T, Weiler E.W, Oecking C (2004). Desulfoglucosinolate sulfotransferases from Arabidopsis thaliana catalyze the final step in the biosynthesis of the glucosinolate core structure. J. Biol. Chem. 279: pp.50717–50725.
Prasad P, Jain N, Chaudhary J, Thakur R.K, Savadi S, Bhardwaj S.C, Gangwar O.P, Lata C, Adhikari S, Kumar, S, Balyan H.S (2023) Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis. Front Microbiol 14.
Rausch T, Wachter A (2005) Sulfur metabolism: A versatile platform for launching defence operations. Trends Plant Sci. 10:503–509.
Rezaee S, Ahmadizadeh M, Heidari P (2020) Genome-wide characterization, expression profiling, and post-transcriptional study of GASA gene family. Gene Rep. 20: 100795.
Schmidt A (1997) Distribution of APS-sulfotransferase activity among higher plants. Plant Sci Lett 5: 407–415.
Schroeder J.I, Allen G.J, Hugouvieux V, Kwak J.M, Waner D (2001) Guard cell signal transduction. Annu Rev Plant Biol 52: pp.627–658.
Sobhanian H, Pahlavan S, Meyfour A (2020) How does proteomics target plant environmental stresses in a semi-arid area?. Mol. Biol. Rep 47:3181–3194.
Takahashi H, Buchner P, Yoshimoto N, Hawkesford M.J, Shiu S.-H (2012) Evolutionary relationships and functional diversity of plant sulfate transporters. Front Plant Sci 2: 119.
Tanin M.J, Saini D.K., Sandhu K.S, Pal N, Gudi S, Chaudhary J, Sharma A (2022) Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding. Sci Rep 12:13680.
(IWGSC) Appels R, Eversole K, Stein N, Feuillet C, Keller B, Rogers J, Pozniak C.J, Choulet F, Distelfeld A (2018) Shifting the limits in wheat research and breeding using a fully annotated reference genome. Sci 361: eaar7191.
Tiwari A, Singh G, Choudhir G, Motiwale M, Joshi N, Sharma V, Srivastava R.K, Sharma S, Tutone M Singour, P.K (2022) Deciphering the Potential of Pre- and Pro-Vitamin D of Mushrooms against Mpro and PLpro Proteases of COVID-19: An In Silico Approach Mol 27: p.5620.
Tomás D, Viegas W, Silva M (2022) Grain Transcriptome Dynamics Induced by Heat in Commercial and Traditional Bread Wheat Genotypes. Front Plant Sci 13:842599
Varin L, Marsolais F, Richard M, Rouleau M (1997) Biochemistry and molecular biology of plant sulfotransferases. FASEB J 11: 517–525.
Varshney RK, Graner A, Sorrells ME (2005) Genic microsatellite markers in plants: features and applications. Trends Biotechnol 23:48–55.
Wang L, Liu X, Wang X, Pan Z, Geng X, Chen B, Liu B, Du X, Song X (2019) Identification and characterization analysis of sulfotransferases (SOTs) gene family in cotton (Gossypium) and its involvement in fiber development. BMC plant Biol 19: pp.1–13.
Wang Q, Liu Y, He J (2014) STV11 encodes a sulphotransferase and confers durable resistance to rice stripe virus. Nat Commun 5: 4768.
Zampieri M, Ceglar A, Dentener F, Toreti A (2017) Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environ Res Lett 12: p.064008.
Zang Y, Kim H.U, Kim J.A, Lim M, Jin M, Lee S.C, Kwon S, Lee S, Hong J.K, Park T (2009) Genome-wide identification of glucosinolate synthesis genes in Brassica rapa. FEBS J 276: 3559–3574.

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Sulphotransferase (SOT) genes in wheat providing tolerance against drought and heat

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Abstract

Figures

Introduction

Material and methods

Gene sequence analysis

Identification of SOT genes and proteins

Chromosome mapping and structural analysis of genes

Protein Analysis

Physico-chemical properties

Protein–protein interactions

3D structure and its analysis

Interaction with a ligand

Structure simulation

Superimposition of 3D structures

qRT-PCR for validation of expression data

Results

Genes and their analysis

Identification and structure analysis

Location of genes and duplications

Synonymy among genes (Ks, Ka and Ka/Ks ratio)

Gene structure

Identification of orthologs between wheat and its relatives

Synteny/collinearity between SOT genes of wheat and related species

Promoter analysis and identification of cis-regulatory elements (CREs)

SSRs/miRNAs/lncRNAs

Protein analysis

Physio-chemical properties and annotation of proteins

Subcellular localisation of proteins

Post- translational phosphorylation

Functional domains and motifs

Multiple sequence alignment (MSA)

Protein- protein interactions

Alignment and functional annotation of 3D structures

Molecular dynamic simulation and docking

qRT-PCR for validation

Discussion

Summary and conclusions

Declarations

References

Additional Declarations

Supplementary Files

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