Molecular modelling and gene expression analysis to probe the GT-γ trihelix transcription factors in Solanum tuberosum under drought stress

Abstract

GT-γ transcription factors, a subfamily known for their involvement in stress responses, remain uncharacterized in Solanum tuberosum under drought stress. This study employed in-silico approaches and in-vitro expression profiling in differential tissues to investigate StGTγ-1, StGTγ-2, StGTγ-3, and StGTγ-4 potential role in the potato’s drought tolerance mechanisms. Analysis of cis-regulatory elements showed complex networks controlling stress response. Alpha helices were prevalent in their structures, possibly aiding protein stability and interaction. Additionally, intrinsically disordered regions were observed in some StGT-γ proteins, suggesting their role in stress adaptation through flexibility. Protein structure modeling and validation revealed structural diversity within the GT-γ family, potentially reflecting variations in functionalities. Physicochemical analysis highlighted differences in protein properties that could influence their nuclear function. Post-translational modifications further diversified their functionalities. Subcellular localization prediction and topology analysis confirmed their nuclear localization, aligning with the anticipated role in transcriptional regulation. GT-γ proteins likely regulate genes due to structural variations. This is based on the presence of DNA-binding domains and functional annotation suggesting roles in metabolism, gene expression, and stress response. Molecular docking predicted partners involved in drought response, indicating GT-γ proteins’ role in drought tolerance networks. Identified StGT-γ genes were highly expressed in leaves after 14 days of drought stress, indicating their key role in protecting this vulnerable tissue during drought. This study enhances understanding of GT-γ factors and provides a foundation for the functional characterization and in-depth exploration of the role and regulatory mechanisms of GT-γ genes in potato’s response to drought stress.

Introduction

Solanum tuberosum, commonly known as potato, is a tetraploid and highly heterozygous crop that is widely cultivated throughout the world. As a staple food, feed, and industrial crop, it holds significant importance globally, ranking fourth among non-cereal crops¹. Despite its importance, the shallow root system makes it extremely vulnerable to drought stress during cultivation. Under drought conditions, potato plants experience significant challenges in their growth and yield². Lack of water severely disrupts many essential physiological and biochemical processes within the potato plants. This results in a notable impact on their overall performance and productivity. It is expected that by 2050, the projected decrease in total potato yield due to the impact of abiotic stress factors could potentially reach up to 32% (Demirel 2023). Potato plants adapt to many mechanisms at the molecular, biochemical, and physiological levels to lessen the effects of drought. At the molecular and genomic levels, drought tolerance has been achieved through the activation of diverse stress-responsive genes, which produce proteins such as transcription factor DREB, bZIP, NAC, MYB, Trihelix, and WRKY, and enzymes crucial for coping with drought stress. and enzymes crucial for coping with drought stress. Understanding the genetic basis of drought adaptation is important for the development of potato varieties capable of effectively adapting to drought conditions^3,4.

Trihelix transcription factors, found in plants, constitute a family of plant transcription factors distinguished by the presence of three consecutive helix structures (helix-loop-helix-loop-helix) within their DNA-binding structural domain ⁵. This domain has a trihelix structure, with conserved bas acidic amino acids, proline, and glutamine residues, which allows the transcription factors to bind to GT elements⁶. GT elements, identified as degenerate cis-elements with core sequences rich in A/T, have been linked to the intricate transcriptional control of numerous plant genes through their interaction with GT factors or Trihelix transcription factors. The expression of GT factors has also been detected in numerous plant tissues and organs; thus, it is assumed that these factors participate exclusively in the regulation of the development of plant species and the abiotic and biotic stress response⁷. The research on GT factors began after the discovery of the GT element (Box II) in the promoter of the pea rbcS-3A gene. Subsequently, the first GT factor, designated as GT-2, was isolated in rice, and a Nicotiana factor, termed GT-1a, or B2F, was identified using affinity screens with radiolabeled G elements. Following these discoveries, research has expanded to include genome-wide analyses of GT factor subfamily (potentially including GT-1, GT-2, GT-γ, SIP1, and SH4) in various plant species⁸.

In the GT-γ subfamily, the distribution of the genes in different plant species is as follows; 10 in Oryza sativa, 7 in Ananas comosus, 4 in Sorghum bicolor, 7 in Solanum tuberosum, 3 in Arabidopsis thaliana, and 9 in Solanum lycopersicum, which indicates the profound and broad function of GT-γ subfamily in plant stress response pathways and plant development^{9,10,11,12,13}. GT-γ factors attempt to impact plant molecular processes through either binding their specific GT element or interaction with other transcription factors, resulting in the regulation of genes¹⁴. Additionally, another investigation in tomato indicated that GT-γ genes, suppressed during hypertrophy induced by SlbHLH22 overexpression, potentially enhance salt resistance, and respond to hormonal stimuli, with a predicted phosphorylation site at S⁹⁶ suggesting post-translational modification’s role in regulating hypertrophy development¹⁵. Similarly, a study in rice highlights the role of GT-γ members in increasing tolerance to drought, salt, and low-temperature stresses¹⁶. The OsGT-γ genes in rice, OsGTγ-1, OsGTγ-2, and OsGTγ-3, are involved in salt stress response and response to other abiotic stresses⁹. Further molecular mechanism analysis suggested that OsGTγ-2 plays a role in the salt stress response pathway by regulating the expression of stress-responsive genes, ion transporters, and other transcription factors. Interestingly, OsGTγ-2 binds to the GAAAAA element in the OsRAV2 promoter, suggesting its role in the ABA signaling pathway. Particularly, OsGTγ-2 showed specificity in regulating Na⁺ tolerance and interacts with the promoters of major ion transporter genes OsHKT2; 1, OsNHX1, and OsHKT1; 3 (Hamamoto et al. 2015; Rosas-Santiago et al. 2015). These interactions contribute to the maintenance of ionic balance and increased plant adaptation to salt stress. Moreover, OsGTγ-2 is active in response to abscisic acid (ABA) and is concerned with seed germination and development in ABA stress conditions¹⁵,Jiang et al. 2019). OsGTγ-2 regulates early-responsive ABA biosynthesis genes such as OsNCED3 and the late-responsive genes OsLEA3 and OsRab16. Therefore, OsGTγ-2 is associated with salt resistance in rice¹⁷.

This work presents an extensive investigation to probe the role of GT-γ trihelix factors under drought stress in potato plants. We employed a multi-faceted approach to gain comprehensive insights into (StGTγ-1, StGTγ-2, StGTγ-3 and StGTγ-4) . In silico analyses included structural elucidation through protein structure prediction and post-translational modification site identification. Additionally, physiochemical properties were determined. Functional characterization involved motif and domain identification, gene structure analysis, subcellular localization prediction, and in silico transcriptome analysis. Protein–protein interaction (PPI) networks and protein–protein docking simulations were also performed. To complement these in silico findings, we employed qRT-PCR to study the expression patterns of StGTγ-1, StGTγ-2, StGTγ-3, and StGTγ-4 trihelix factors in potatoes under water-deficient conditions. The findings from this study not only deepen our understanding of GT-γ genes in potatoes but also offer valuable insights for future biotechnological endeavors aimed at developing multi-stress tolerant crops. Moreover, the insights gained here lay the groundwork for further exploration of GT-γ factors in other economically important plant species, opening avenues for broader applications in agricultural research and crop improvement.

Methodology

Identification of GT-γ trihelix genes in the potato genome

A homology-based approach was employed to identify the GT-γ trihelix genes in the potato genome. Protein sequences of known Trihelix members from Arabidopsis thaliana were retrieved from The Arabidopsis Information Resource (TAIR) (https://www.arabidopsis.org/ ). These sequences served as query sequences for a BLASTp search against the Solanum tuberosum genome version v6.1 available on Phytozome (https://phytozome-next.jgi.doe.gov/) (Supplementary Table S1).

Protein secondary structure and disordered region prediction

The secondary structure and disorder of the potato GT-γ trihelix proteins (StGTγ-1, StGTγ-2, StGTγ-3 and StGTγ-4) were predicted using SOPMA (https://npsa-prabi.ibcp.fr/NPSA/npsa_sopma.html)¹⁸, PSIPRED (http://bioinf.cs.ucl.ac.uk/psipred/)¹⁹, and PrDOS (https://bio.tools/disopred3) respectively²⁰. Using SOPMA with default settings (a window of width 17 amino acids, 4 states, and similarity threshold of 8), the secondary structural elements (α-helices, β-strands, and coils) of the GT-γ protein were predicted. Specially designed, PSIPRED predicted the secondary structure of GT-γ protein, while PrDOS identified the disordered regions within its sequence.

Protein structure modeling and refinement

The three-dimensional model of the Solanum tuberosum GT-γ transcription factor was generated using the SWISS-MODEL homology modeling pipeline (https://swissmodel.expasy.org/)²¹. The server found relevant structural templates matching the sequence similarity, and the top-scoring template chosen for the model building. YASARA energy minimization server further refined the model through geometrical optimization and eliminating unwanted atom clashes. This was done using an AMBER force field-based minimization algorithm in “minimize energy” mode (https://www.yasara.org/)²². The quality of the refined model was then evaluated using Root-Mean-Square Deviation (RMSD) analysis with PyMOL software (https://pymol.org/).²³. RMSD calculates the average displacement in atomic positions between the model and a reference structure to show the model’s accuracy.

To further refine the model, the Galaxy refine tool (https://galaxy.seoklab.org/) portal was utilized ²⁴. This server enables the usage of several techniques to provide a more satisfying geometric and chemical plausibility of the structure. For example, a Ramachandran plot analysis is applied as well as notarization. Lastly, the refined 3D model of the GT-γ transcription factor was visualized and examined using PyMOL and Discovery Studio software.

Model validation and quality assessment

The validation of the 3D models for GT-γ transcription factors was done through different servers. Procheck (https://servicesn.mbi.ucla.edu/PROCHECK/) (Laskowski et al., 2006) assessed stereochemical quality, Errat (https://servicesn.mbi.ucla.edu/ERRAT/) evaluated non-bonded interactions, and QMEAN (https://swissmodel.expasy.org/qmean/) calculated global and local quality scores. Additionally, WHATIF (https://swift.cmbi.umcn.nl/servers/html/index.html) identified potential errors, while the SWISS-MODEL Structural Evaluation tool (https://swissmodel.expasy.org/assess) provided comprehensive validation²¹. These methods collectively ensured the accuracy and reliability of the models for subsequent analyses.

Physicochemical property analysis

ExPASy’s ProtParam tool (http://web.expasy.org/protparam/) was employed to determine various physical and chemical parameters of the Trihelix GT-γ proteins²⁵. These parameters included: Molecular weight, Amino acid composition, Theoretical isoelectric point (pI), Instability index, Estimated half-life, Aliphatic index, and Grand average of hydropathicity (GRAVY).

Post-translational modifications

Ubiquitination, N-Glycosylation, O-Glycosylation, and phosphorylation analyses of trihelix GT-γ proteins were conducted using bioinformatics tools: NetNGlyc (http://www.cbs.dtu.dk/services/NetNGlyc/), NetOGlyc 4.0.0.13 (http://www.cbs.dtu.dk/services/NetOGlyc/)²⁶, and NetPhos 3.1 (http://www.cbs.dtu.dk/services/NetPhos/ ) ²⁷. These tools utilize neural networks to predict modification sites inside the protein chains, with stringent thresholds applied for high-confidence predictions.

Functional annotation analysis

The protein domains of the four GT-γ trihelix proteins were predicted using InterProScan (http://www.ebi.ac.uk/interpro/) to integrate predictions from multiple domain prediction methods, including Pfam (http://pfam.xfam.org/)²⁸, SMART (http://smart.embl-heidelberg.de/)²⁹, and PROSITE (https://prosite.expasy.org/) ³⁰, thereby providing comprehensive domain annotations. Additionally, individual domain predictions were obtained using specific databases, including Pfam, SMART, and the Conserved Domain Database (CDD) (https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml), which uses position-specific scoring matrices derived from the alignment of domain families³¹.

The identification and analysis of conserved motifs within the protein sequences were conducted using the MEME Suite (http://meme-suite.org/)³², specifically the MEME server (http://meme-suite.org/tools/meme). MEME employs an expectation–maximization algorithm to discover motifs in a set of protein sequences. The protein functions were predicted using FFPred, which employed Support Vector Machines (SVMs) and assigned Gene Ontology (GO) terms across all evidence code types³³. FFPred categorizes the reliability of these predictions as "High (H)" when specific criteria like Matthews Correlation Coefficient (MCC), sensitivity, specificity, and precision all exceed a set threshold. Otherwise, the predictions are classified as "Low (L)".

Gene structure and abiotic stress related cis-regulatory elements analysis

The gene structure of these GT-γ factors was elucidated using the Gene Structure Display Server 2.0 (GSDS 2.0) (http://gsds.gao-lab.org/), a tool that facilitates the visualization and analysis of gene structures, thus providing insights into the organization and potential regulatory elements of the genes encoding the GT-γ trihelix proteins (Hu et al. 2015).

To detect cis-acting regulatory elements (CAREs), the 2000 bp upstream sequences of GT-γ factors were obtained from Phytozome and subjected to analysis using the PlantCARE online server (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/). The frequency of abiotic stress-related CARE motif occurrence was tallied for GT-γ genes, and the most prevalent CAREs involved in abiotic stress were utilized to generate figures in TBtools-II v2.052.

Subcellular localization prediction

To predict the subcellular localization and topology of specific Trihelix GT-γ proteins within Solanum tuberosum CELLO v.2.5 (http://cello.life.nctu.edu.tw/)³⁴ was employed. Comparative assessments using additional tools were done like BUSCA (https://bioinformaticshome.com/db/tool/BUSCA)³⁵ for comprehensive localization insights, DeepLoc 2.0 (https://services.healthtech.dtu.dk/services/DeepLoc-2.0/)³⁶ for machine learning-based predictions, and WoLF PSORT (https://wolfpsort.hgc.jp/) for its algorithmic predictions based on sorting signals, localization features, and amino acid compositions.

TMHMM (http://www.cbs.dtu.dk/services/TMHMM/)³⁷ and HMMTOP (http://www.enzim.hu/hmmtop)³⁸ were used for the topology predictions to predict transmembrane helices. These predictions have great significance in relating links to the proteins’ potential interactions within the cellular environment regarding their spatial organization and possible membrane-spanning portion.

Analysis of binding sites and ligand affinity in trihelix GT-γ proteins

Ligands that possess an affinity for GT-γ can further highlight their functional mechanisms. The COACH online prediction tool (https://zhanggroup.org/COACH/), was employed to predict protein–ligand binding sites and potential ligands³⁹. This tool utilizes binding-specific substructure and sequence profile comparisons to identify ligand-binding templates from the GT-γ proteins function database. The predicted binding affinity of ligands is quantitatively expressed by the binding constant (K_d​), using the equation [PL] = [P][L]/K_d. Here, [PL]represents the concentration of the protein–ligand complex at equilibrium, while [P] and [L] represent the concentrations of the free protein and ligand, respectively. CASTp server (http://sts.bioe.uic.edu/) was used to identify potential binding sites within the trihelix GT-γ proteins. It offers services for identifying, outlining, and quantifying the geometric and topological characteristics of protein structures⁴⁰.

Protein–Protein Interaction Prediction and Docking

The GT-γ proteins’ putative protein partners were found using the STRING database: (https://version11.string-db.org/)⁴¹. The interaction network was then filtered to identify interactors potentially involved in drought stress response. This filtration was made possible by the Gene Ontology (GO) terms focusing on drought stress response within the STRING interface. The filtered PPI network was then taken into account to further analysis with Cytoscape version 3.10.2 (https://cytoscape.org/), a versatile tool for the visualization and analysis of complex biological networks. Cytoscape facilitated the exploration of network properties and the identification of key nodes and pathways potentially implicated in drought stress response⁴². After the PPI network was analyzed and the potential connections to drought stress were identified, protein–protein dockings were performed using the ClusPro server (https: cluspro.org/). Also, docking simulations were carried out using HDOCK to give further credibility to the possible binding affinities and interaction patterns of the GT-γ proteins with their interacting partners under drought stress. Lastly, the interactions between proteins after docking studies were analyzed using multiple tools, including PDBsum (https://www.ebi.ac.uk/thornton-srv/databases/pdbsum/)⁴³, PBDePISA (https://www.ebi.ac.uk/pdbe/pisa/) (Krissinel, 2009), and HDOCK (http://hdock.phys.hust.edu.cn/)⁴⁴.

In silico transcriptome of trihelix genes in response to drought

Gene Expression Omnibus (GEO) dataset GSE77826.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE77826) was used, specifically focusing on the greenhouse experiment involving four cultivars: Milva (sensitive), Alegria (sensitive), Desiree (tolerant), and Saturna (tolerant). Leaf tissue gene expression data (RPKM values) of trihelix GT-γ genes was extracted and transformed to log₂ base for analysis⁴⁵. A heatmap was constructed by TBtools-II v2.052 to illustrate the expression profiles of these genes across the cultivars under drought stress.

Experimental setup and drought treatments

Potato tubers var. Kuroda was utilized for experimental setup in glasshouse chamber at Atta-ur-Rahman School of Applied Biosciences (ASAB) at National University of Sciences and Technology (NUST), Islamabad, Pakistan. The tubers were planted in pots (9.5 cm in bottom diameter, 14.5 cm in upper diameter, and 15 cm in depth) filled with soil and placed in a growth chamber set to controlled environmental conditions: a day/night temperature of 25/17 °C, a 14-h light/10-h dark cycle, and 51% humidity. These conditions were maintained for a period of 14 days, with a constant temperature of 25 °C and a light/dark cycle of 13 h of light followed by 11 h of darkness.

The experimental setup consisted of two distinct groups: a control group, where plants were regularly watered every other day to sustain optimal hydration levels, and a drought-treated group. In the drought-treated group, progressive drought stress was achieved by withholding water for approximately two weeks, followed by a re-watering phase lasting three days. Root, shoot, and leaf samples of drought-stressed and well-watered triplicate plants were collected for analysis at intervals of 0, 14, and 17 days (rewatering) after the onset of drought stress. Immediately upon harvesting, the roots stems, and leaves were flash-frozen in liquid nitrogen and stored at -80 °C until RNA extraction was performed⁴⁶.

RNA extraction and qRT-PCR validation

Total RNA was extracted from potato tissues (roots, shoots, leaves) using TRIzol Reagent (Invitrogen, USA) according to the manufacturer’s protocol and quantified using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA). The 260/280 nm ratio of the isolated RNA was greater than 1.6, indicating good quality. cDNA synthesis was then achieved from 1 μg of RNA using a custom reaction mixture with essential reverse transcription components (e.g., primers, dNTPs, enzyme) and thermocycler incubation (Bio-Rad, USA). Following synthesis, the cDNA was diluted 1:10 and was stored at − 20 °C.

To investigate the expression of GT-γ and validate the RNA-Seq findings, we selected StTH-1, StTH-16, StTH-21, and StTH-26 for analysis using RT-qPCR. Primers (Supplementary Table S2) were designed using Primer 3 plus software (http://biotools.Umassmed.Edu/bioapps/primer3-www.cgi) specifically for RT-qPCR and validated through In silico PCR by AmpliFX. The expression levels of the selected GT-γ genes were normalized against actin widely recognized as a housekeeping gene with relatively stable expression in various plant tissues and under different experimental conditions, making it a suitable reference gene for qRT-PCR normalization^47,48. Reactions were conducted in a Biorad Real-Time PCR system using a 20µL reaction mixture composed of 1 µL of template cDNA, 10 µL of 2 × SybrGreen qPCR Master Mix (Thermo Fisher Scientific, USA), 0.8 µL (10 μM) of each primer, and 7.4 µL of water. Thermal cycling conditions included a 10-min denaturation at 95 °C, followed by 40 cycles (15 s at 94 °C, 30 s at 60 °C, 30 s at 72 °C). We performed three biological replicates for differential tissue (root, shoot, leaf) at each time point (0, 14, and rewatering) for each selected gene, and the relative gene expression levels were determined using the 2^−ΔΔCT method⁴⁹.

The presented data are expressed as the mean ± standard error (SE) of three biological replicates. Statistical analysis was performed using two-way ANOVA followed by Bonferroni’s multiple comparisons test to assess the significance of differences among treatments, with significance levels set at NEJM: 0.12 (ns), 0.033 (*), 0.002 (**), < 0.001 (***). All analyses were conducted using GraphPad Prism version 10.2.0.

Results:

Protein secondary structure and disordered region prediction

The secondary structure analysis of GT-γ transcription factors (StTH-1, StTH-16, StTH-21, and StTH-26) highlights a predominant composition of alpha helices, with percentages ranging from 36.90% to 46.43%. Notably, StTH-1 exhibits the highest alpha helix content at 46.43%, suggesting a structured framework that could impact its functional interactions. None of the proteins exhibit beta turns. The absence of beta turns suggests that these proteins may adopt more linear or elongated structures, as opposed to tightly packed globular forms. Random coils, representing the flexible regions, vary between 52.44% and 61.90%, underscoring the adaptability and potential for diverse interactions within the GT-γ family (Supplementary Table S3). The secondary structures predicted by PSIPRED are shown in Fig. 1B, Supplementary Figs. 1, 2, 3, and 4. This analysis reveals the structural diversity and complexity of StGTγ-1, StGTγ-2, StGTγ-3, and StGTγ-4 transcription factors, suggesting their varied roles and mechanisms in cellular processes.

Fig. 1

(A) Protein disorder in GT-γ proteins predicted by PrDOS (cutoff of 0.5). (B) PSIPRED secondary structure prediction of GT-γ proteins (α-helix: pink, β-strand: yellow, coil: gray). (C) Cartoon representation of a three-dimensional structural model for a GT-γ protein by Swiss model.

PrDOS analysis reveals GT-γ transcription factors’ disordered regions, suggesting their role in drought stress adaptation. StTH-1 and StTH-16 exhibit extensive disordered areas, indicative of structural flexibility crucial for drought response. StTH-21 and StTH-26 show localized disordered sequences, highlighting their potential for stress adaptation through flexible interactions (Fig. 1A). Figure 1 illustrates these findings, linking structural dynamics to drought resilience. This integration of disorder and structure analyses provides insights into GT-γ proteins’ balance between stability and adaptability, emphasizing their importance in drought stress response mechanisms.

Energy minimization and RMSD analysis

Supplementary Table S4 presents the outcomes of energy minimization using Yasara software for four protein structures (StTH-1, StTH-16, StTH-21, and StTH-26). It displays initial energy values (Start Energy) and energies after minimization (Minimized Energy), showcasing the achieved improvement (Energy Improvement). Additionally, the score change indicates alterations due to energy minimization. Significant energy enhancements were observed across all structures, with StTh-16 showing the largest improvement (63,736.8 kJ/mol), followed by StTH-21 (59,251.3 kJ/mol), StTH-26 (38,560.5 kJ/mol), and StTH-1 (15459.1 kJ/mol), indicating improved stability and conformation.

Supplementary Table S5 presents the results of RMSD (Root Mean Square Deviation) analysis for four structure pairs (StTH-1 vs. Reference, StTH-16 vs. Reference, StTH-21 vs. Reference, and StTH-26 vs. Reference). RMSD values, indicating structural similarity by measuring average atom distances between superimposed protein structures, exhibit varying degrees of similarity. StTH-1 and StTH-21 display relatively high structural similarity (RMSD values: 0.48 and 0.43), while StTH-16 and StTH-26 show moderate similarity (RMSD values: 0.72 and 0.91), suggesting differences in overall structural conformations.

Model validation and quality assessment

Protein structure evaluations revealed good quality overall. ProCheck Analysis using Ramachandran plots showed a high percentage of residues residing in favorable and allowed regions for all models, ranging from 91.0% to 96.8% within the core area (Fig. 2 and Supplementary Figs. 5–12). The global QMEAN scores fell within the moderate quality range (0.49—0.56) as shown in Supplementary Table S6 and Supplementary Figs. 13–16. Further reinforcing these findings, Probity analysis confirmed minimal occurrences of poor rotamers, low Cβ deviations, and a scarce presence of bad bonds and bad angles across all models. Additionally, the Ramachandran distribution Z-scores, which assess the quality of backbone torsion angles, fell within the acceptable range for all models, including model StTH-1 despite its slightly higher outlier percentage. This signifies an overall satisfactory level of quality for the protein backbone conformations. Although specific areas warrant further investigation, such as the Ramachandran outliers in model StTH-1, these evaluations collectively suggest well-defined protein structures with promising potential (Supplementary Table S7).

Fig. 2

Ramachandran plot for homology models. Ramachandran plot of homology models of (A) StTH-1 (B) StTH-16 (C) StTH-21 (D) StTH-26. Color Key: Red—Most favored region, Yellow—Allowed region, Pale yellow—Generously allowed region, White—Additionally allowed region.

Physicochemical property analysis

The physicochemical properties of the Trihelix GT-γ proteins in Solanum tuberosum, were examined using the ProtParam tool provided by the ExPASSy service as shown in Supplementary Table S8. This comprehensive analysis provided profound insights into the structural and functional characteristics of the proteins. Across different proteins (StTH-1, StTH-16, StTH-21, and StTH -26), the protein displayed varying lengths, with StTH-21 exhibiting the highest number of amino acids (496) and StTH-1 the lowest (392). Molecular weights ranged from approximately 45 kDa to 55 kDa, with StTH-21 demonstrating the highest molecular weight. Theoretical isoelectric point (pI) values fell within the range of approximately 5.6 to 6.9, collectively indicating an alkaline nature for all proteins, with StTH-1 showing the highest pI. Variations in the distribution of charged residues across variants underscored differences in charge distribution within the protein structure. Total atom counts ranged from approximately 6307 to 7667, with StTH-21 possessing the highest number of atoms. Notably, the estimated half-life of the GT-γ protein remained consistent across all variants, suggesting stability over a period of 30 h. Instability index values ranged from approximately 40.96 to 67.50, indicating varying degrees of stability across proteins, with StTH-26 exhibiting the lowest instability index. Aliphatic index values ranged from approximately 54.40 to 78.29, revealing differences in temperature stability across variants, with StTH-1 displaying the highest aliphatic index. The grand average of hydropathicity (GRAVY) scores ranged from approximately -0.686 to -1.173, affirming hydrophilic properties for all proteins, with StTH-1 demonstrating the least hydrophilic. These comprehensive findings offer valuable insights into the physicochemical properties of the GT-γ proteins, providing a solid foundation for further studies investigating its role in drought stress.

Post translational modifications

The post-translational modifications (PTMs) analysis in four trihelix transcription factors revealed significant phosphorylation occurring across all proteins (Supplementary Fig. 17). Among the proteins, StTH-21 showed the highest number of residues at the level of phosphorylation (36), signifying its possible function in cellular signaling ways. The ubiquitylation pattern for StTH-21 and StTH-16 was the highest with four lysine residues modified, indicating an involvement in proteolytic processes for protein degradation. N-glycosylation, which is the determining factor for protein stability, the occurrence of these sites varied, with the highest count reported for StTH-16 (4 sites) and StTH-26. O-glycosylation patterns particularly in StTH-16, which showed the highest count (30 sites), suggesting a role in cellular processes (Supplementary Table S9).

Subcellular localization prediction

StTH-1, StTH-16, StTH-21, and StTH-26 were consistently predicted to localize within the nucleus across all tools employed (BUSCA, DeepLoc 2.0, CELLO v.2.5, and WoLF PSORT). Similar results from different computational models serve as validation to the predictions. Furthermore, topology analysis via TMHMM and HMMTOP confirmed the absence of transmembrane helices in all studied proteins (Supplementary Table S10). This finding is in alignment with their nuclear localization, as transmembrane regions are generally not associated with proteins localized in the nucleus. Such localization implies their functional roles within the nucleus, related to modulating gene expression or nucleic components ‘interactions that are of great importance to plants´ development and response to various stresses.

Functional annotation

In our study, the functional annotation of the four GT-γ trihelix proteins revealed the presence of a Myb/SANT-like domain across all sequences, indicative of their potential regulatory roles in gene expression. Specifically, the Myb/SANT-like domain was identified in StTH-1 from amino acids 80 to 160, in StTH-16 from 373 to 429, in StTH-21 from 172 to 224, and in StTH-26 from 124 to 214 as shown in Fig. 3C. This domain is known for its involvement in DNA binding and is crucial for the regulation of transcription. The identification of the Myb/SANT-like domain aligns with the known functions of GT-γ trihelix proteins in transcriptional regulation. The presence of this domain suggests a conserved mechanism of action among these proteins. Additionally, four discovered motifs are shown in Fig. 3A.

Fig. 3

Functional annotation and gene structure analysis of StGTγ-1, StGTγ-2, StGTγ-3, and StGTγ-4 transcription factors: (A) Conserved motifs in GT-γ transcription factors (colored boxes). (B) Gene structure, including exons/CDS (red boxes), introns (black lines), and upstream/downstream regions (blue boxes). (C) Gene ontology of StGT-γ factors by FFpred. (D) Domain structure of GT-γ proteins, highlighting Myb/Sant-like domains (blue boxes).

In addition to this, FFPred highlighted the multifaceted roles of GT-γ trihelix proteins, particularly underlining their significant involvement in regulating metabolic processes, gene expression, and cellular responses to environmental stimuli such as drought. These proteins are implicated in the regulation of metabolic activities (GO:0019222; score: 0.964), regulation of nucleic acid-templated transcription (GO:1903506; score: 0.866), and the modulation of gene expression (GO:0010468; score: 0.924), underscoring their essential contributions to cellular homeostasis and the orchestration of genetic information in response to abiotic stress. Molecular function predictions emphasize their capacity for nucleic acid binding (GO:0,003676; score: 0.937) and metal ion binding (GO:0046872; score: 0.904), suggesting a pivotal role in gene regulatory mechanisms and enzymatic activities crucial for plant adaptation to drought conditions. Furthermore, cellular component predictions indicate a substantial nuclear presence (nucleolus GO:0,005730; score: 0.724; nuclear lumen GO:0031981; score: 0.822), aligning with their proposed functions in transcriptional regulation and nuclear processes essential for stress response. The involvement of these proteins in responses to a stimulus (GO:0,050896; score: 0.795) and cellular response to a stimulus (GO: 0050896; score: 0.806) particularly highlights their importance as shown in Fig. 3D in the plant’s adaptive mechanisms to drought stress, offering new insights into potential targets for improving drought tolerance through genetic and molecular approaches.

Gene structure and abiotic stress-related cis-regulatory element analysis

Moreover, our analysis of the gene structure for these proteins demonstrated a consistent coding sequence (CDS) region across all sequences, with an exception in StTH-16, where the CDS was interrupted by a single intron as shown in Fig. 3B. Cis-regulatory element analysis shows a significant number of elements associated with specific stresses: ABA-related drought and osmotic stress (10 ARE motifs), drought stress (1 DRE core element), and general stress response (13 STRE elements). Additionally, 38 occurrences of MYB-related motifs suggest regulation by these stress-responsive transcription factors (Supplementary Fig. 18, 19). Interestingly, there were also 3 instances of TC-rich repeats, which have been linked to stress response in some plants, although their exact function remains unclear⁵⁰.

Binding sites prediction of trihelix GT-γ proteins

The COACH server identified a range of biomolecules as potential ligands interacting with four different proteins of the GT-γ clade. For StTH-1, peptides, CVM, ZN, and N5P were identified. Similarly, for StTH-16, ZN was identified as a potential ligand. For StTH-21, biomolecules such as MG, nucleic acid, and RTL were identified as potential ligands. Additionally, for StTH-26, biomolecules including TBR, GLC, DIO, and MG were identified as potential ligands (Supplementary Table S11). Notably, ligands scoring 0.04 or higher on the C-Score were considered more reliable, all depicted in Fig. 4B-E and visualized in PyMOL⁵¹.

Fig. 4

(A) Binding site of GT-γ proteins predicted by Castp (B) StTH-1 interactions with the ligand interactions at the predicted active sites with a focus on Zn (magenta ball), peptide (red), N5P (yellow), CVM (blue). (C) StTH-16 interactions with the ligand interactions at the predicted active sites with a focus on RTL (blue), nucleic acid (orange), and MG (grey). (D) StTH-21 interactions with the ligand interactions at the predicted active sites with a focus on Zn (blue). (E) StTH-26 interactions with the ligand interactions at the predicted active sites with a focus on MG (yellow), ETI (pink), and TBR (red).

Additionally, the binding site analysis for GT-γ proteins using the Castp revealed varied binding site characteristics, indicative of differing interaction potentials. For StTH-1, the surface area was measured at 17,132.917 Ų, with a corresponding volume of 52,436.374 ų. In comparison, StTH-16 exhibited a larger surface area of 20,432.779 Ų and a substantially increased volume of 131,707.006 ų, suggesting a more expansive structure. StTH-21 displayed further increases, with an area of 22,091.489 Ų and a volume of 140,854.614 ų, highlighting progressive dimensional growth among these factors. The most substantial measurements were observed in StTH-26, which had a surface area of 24,721.058 Ų and a markedly higher volume of 291,768.492 ų. This progressive increase in both surface area and volume from stTH-1 to stTH-26 suggests potential variations in functional capacity or interaction dynamics among these transcription factors (Fig. 4A).

Protein–protein interaction prediction and docking

Initially, the PPI network was constructed, identifying a network comprising 66 nodes and 163 edges (Supplementary Fig. 20). This network was characterized by an average node degree of 4.94 and an average local clustering coefficient of 0.554, indicating a tightly knit network with a significant degree of interaction among proteins. The interacting partners with GT-γ proteins are listed in Supplementary Table S12. Topological analysis was performed to assess the network’s connectivity and biological relevance, focusing on metrics such as betweenness centrality (BC), closeness centrality (CC), and degree, which helped identify key proteins within the network.

The network was crucial for understanding the central proteins that play significant roles in the network’s organization and stability, particularly in the context of drought stress responses (Fig. 5).

Fig. 5

Available at: https://cytoscape.org.. The network visualizes potential target proteins identified from the STRING database. These target proteins are represented by pink circles within the network. The size of each circle corresponds to the degree, or the number of interaction partners, a target protein has within the network. The connecting lines (edges) illustrate the established relationships between these different target proteins.

Protein–protein interaction network generated using Cytoscape software, version 3.10.2.

Molecular docking was then conducted between the GT-y trihelix transcription factors (StTH-1, 16, 21, 26) and proteins associated with drought stress. StTH-1 was subjected to docking studies with drought stress-related proteins, including M1AD25_SOLTU, M1AN54_SOLTU, and M1CWQ8_SOLTU. Additionally, StTH-16 was docked with M0ZXP8_SOLTU, DREB2, M1C3C5_SOLTU, M1CII0_SOLTU, and M1B7W3_SOLTU. StTH-21 underwent docking with M1AYC3_SOLTU, M1B5R6_SOLTU, and M1A1E2_SOLTU. Lastly, StTH-26 was docked with M1BSD7_SOLTU as shown in Fig. 6. These simulations predicted the binding affinities and interaction patterns, providing insights into the molecular mechanisms through which these transcription factors may influence plant responses to drought stress.

Fig. 6

(A) StTH1-MIAN54 docking where both interacting partners are presented in cartoon model and surface view (B) Surface structure and cartoon structure of StTH1 protein and MICWQ8 protein, (C) Surface view and cartoon view of StTH16-MOXP8 protein complex, (D) StTH16-M1ADM5 docking where both interacting partners are presented as cartoon and surface, (E) Surface structure and cartoon structure of StTH16 protein and M1CIIO protein, (F) Cartoon view and Surface view of StTH16-MIAYC3 protein complex (G) StTH21-Ethylene docking where both interacting partners are presented as cartoon and surface, (H) Surface view and cartoonic view of StTH21-M1BSD7 protein complex.

The lowest scoring complex with M1AN54 (-280.66) involved StTH-1 offering 32 binding pocket residues (amino acids like LEU, ASN, PRO, ARG, and GLU). Interestingly, neighboring protein chains contributed 38 residues that interacted with the small ligand. Potential hydrogen bonds were identified between LEU 48A on StTH-1 and ARG 21A from a neighboring chain. Close interactions (within ~ 4 Å) between receptor residues like ARG 145A and GLU 331A, and ligand residues such as PHE 20A and LYS 52A, support strong, diverse binding interactions, underscoring the complex’s stability. Similarly, the highest scoring complex with M1CWQ8 (-291.34) showed StTH-1 offering 42 binding pocket residues and neighboring chains contributing 27 interacting residues. Here, potential hydrogen bonds were observed between GLU 52A on StTH-1 and ARG 362A from a neighboring chain, as well as between ASN 104A on StTH-1 and GLN 180A from another neighboring chain. The complex with the highest docking score (MOXP8, 354.71) involved StTH-16 offering 50 binding pocket residues (including ARG 35A, LYS 41A, TRP 39A). Similar to StTH-1, 38 residues from neighboring chains interacted with the small ligand MOXP8. Potential hydrogen bonds were observed between GLU 93A on StTH-16 and LYS 41A from a neighboring chain. Interestingly, the complex with the lowest score (DREB, -265.07) likely represents a nucleic acid fragment interaction. While StTH-16 offered 45 binding residues, 6 residues from DREB interacted with the protein. Notably, the receptor interface residues THR 60A, GLN 61A, and PHE 64A exhibited close distances to ligand interface residues ARG 102A and GLN 103A, suggesting significant interactions. These interacting residues from DREB likely belong to the sugar-phosphate backbone (e.g., PRO and VAL) rather than the nucleobases themselves, enhancing the overall binding affinity and stability of the complex. The lowest scoring complex with ethylene (-277.33) involved StTH-21, which provided 23 binding pocket residues, including MET, GLU, ARG, and PHE. Notably, ILE 59A, THR 60A, and PHE 63A, among others, were key receptor interface residues, facilitating interactions with the ligand. Additionally, hydrogen bonds were identified between MET 1A on StTH-21 and GLN 102A from a neighboring chain, while the receptor-ligand interactions highlighted several significant pairs, including ILE 59A—VAL 496A. The complex with the highest docking score (M1AYC3, -298.10) exhibited strong molecular association with 34 hydrogen bonds and 10 salt bridges. Notably, the hydrogen bonds involved amino acids like ARG, LYS, SER, GLU, ASN, HIS, and GLN, indicating significant interactions between the molecules. StTH-26—M1BSD7 Interaction had a docking score of -245.50 and revealed a comprehensive network of interactions, including 34 hydrogen bonds (involving residues like ARG, GLU, SER, and ASN), 10 salt bridges formed between oppositely charged amino acids (like LYS and GLU), and 407 non-bonded contacts likely involving hydrophobic and van der Waals forces (Supplementary Table S13). These findings offer valuable insights into potential binding modes and interactions between drought stress proteins and GT-y trihelix transcription factors, highlighting specific amino acid interactions offer valuable insights into potential binding modes and interactions between the molecules, highlighting the complex molecular mechanisms underlying their association and function in response to environmental stressors like drought.

In-silico transcriptome analysis of trihelix GT-γ transcription factors.

In-silico transcription profiling in Fig. 7 enables the comparative analysis of gene expression under various conditions, such as drought versus control, and across different potato varieties. The data provided focuses on four potato varieties—Algeria, Desiree, Milva, and Saturna—under both control and drought conditions for four specific GT-γ transcription factors.

Fig. 7

In-silico Transcriptome of trihelix genes in response to drought in four potato varieties Algeria, Desiree, Milva, and Saturna, under both control and drought conditions.

For the gene StTH-1, expression significantly decreases under drought conditions in Algeria, dropping from 1.55 to 0.68. In contrast, expression remains relatively stable or slightly increases in Desiree, Milva, and Saturna under drought stress. This variation suggests that StTH-1 may be more sensitive to drought stress in Algeria compared to the other varieties. The gene StTH-16 exhibits relatively stable expression across all varieties under both control and drought conditions, with only minor variations. This stability implies that StTH-16 could play a role in maintaining homeostasis during drought stress.

StTH-21 shows high expression levels across all varieties and conditions, with only minor decreases under drought. The consistent expression of this gene suggests it may be involved in critical functions during both normal and stress conditions. Lastly, StTH-26 displays the highest expression among the four genes, maintaining consistent levels across all varieties and conditions, and even slightly increasing under drought. This pattern indicates that StTH-26 may be a key regulator in the drought response, potentially contributing to the resilience of the potato varieties during stress.

Expression profiling analysis of trihelix GT-γ transcription factors by qRT-PCR in potato

In the conducted study, quantitative real-time PCR (qRT-PCR) was employed to analyze the expression patterns of GT-γ clade genes (StTH-1, StTH-16, StTH-21, and StTH-26) in different tissues of Solanum tuberosum var. Kuroda is under drought stress. The data revealed significant differences in gene expression levels across roots, stems, and leaves, showing a distinct tissue-specific response to drought conditions.

The highest expression levels were observed in the leaves, where the fold changes were markedly greater compared to other tissues. Specifically, after 14 days of drought stress, the fold change in leaves for StTH-1 was 4.1, significantly higher than its expression in stems (2.0) and roots (1.5). Similarly, StTH-16 exhibited a fold change of 3.3 in leaves, compared to 2.3 in stems and 1.4 in roots. StTH-21 and StTH-26 both followed this trend, with leaves showing the highest fold change (3.9 and 4.1, respectively) relative to stems (2.5 and 2.3) and roots (1.6 and 1.5). Upon rewatering after 17 days, all genes showed a reduction in expression across all tissues, yet leaves continued to display relatively higher expression levels. For example, StTH-1 decreased to 3.2 in leaves, while it reduced to 1.4 in shoots and 1.2 in roots. This pattern was consistent for the other genes, with leaves consistently showing higher residual expression levels post-rewatering than other tissues (Fig. 8).

Fig. 8 

Differential expression pattern analysis of potato StGTγ-1, StGTγ-16, StGTγ-21, StGTγ-26. (A) The relative expression level of StGTγ-1 in roots stems, and leaves of potatoes under 14-day drought stress followed by rewatering (3 days). (B) The relative expression level of StGTγ-16 in roots stems, and leaves of potatoes under 14-day drought stress followed by rewatering (17 days). (C) Differential expression pattern analysis of StGTγ-21 under 14-day drought stress followed by rewatering (17 days) (D) Expression changes in the potato StGTγ-26 gene were assessed under 14-day drought stress followed by rewatering. Using qRT-PCR, expression was measured relative to actin, presented as mean values ± SE for three replicates, with significance indicated by ns (0.12), * (0.033), ** (0.002), and *** (< 0.001).

This differential expression across tissues suggests that the GT-γ clade genes may play a critical role in modulating drought response, particularly in leaf tissues where the demand for water conservation and stress mitigation is most acute. These findings underscore the importance of these genes in enhancing drought resilience, offering potential targets for genetic enhancement of drought tolerance in crops.

Discussion

Previous studies of the GT-γ subfamily have been reported on salinity stress in rice and tomato^9,15. However, this subfamily lacks systematic characterization in potatoes under drought stress. Therefore, in the present study, we performed in-depth bioinformatics characterization of the identified GT-γ factors and used qRT-PCR to investigate their drought stress-specific transcriptional regulation. A research investigation detected multiple cis-regulatory elements within the promoters of trihelix transcription factors, such as GGTTAA, GGTAATT, TACAGT, GGTAAA, and GAAAAA. Notably, GAAAAA had been previously recognized as a fundamental cis-element responsible for salt induction in OsRAV2 ⁵². In this study, the analysis of cis-regulatory elements is associated with various stresses, including ABA-related drought and osmotic stress (ARE motifs), drought stress (DRE core element), and general stress response (STRE elements). This suggests a regulatory network controlling gene expression in response to environmental stresses. The occurrence of MYB-relevant motifs and TC-rich repeat sequences indicates the involvement of additional regulatory mechanisms in this plant species’ stress response system⁵⁰. The identification of the regulatory elements associated with these genes with gene expression patterns will facilitate the comprehension of their mechanism of action in response to environmental stress^53,54.

Plants show different gene expression patterns across different tissues and developmental stages. These patterns can provide clues about a gene’s function ⁵⁵. An investigation in tomato plants revealed that both SlGT-7 and SlGT-36 were highly expressed in leaves and could be induced by abscisic acid (ABA), with SlGT-7 displaying sensitivity to various hormonal stimuli¹⁵. Similarly, the expression of OsGT-γ genes (OsGT-γ1, OsGT-γ2, OsGT-γ3, OsGT-γ4) varies across different tissues. OsGT-γ genes show high expression levels in leaves, whereas expression is lower in roots and stems. Notably, the expression patterns of OsGT-γ1, OsGT-γ2, OsGT-γ3, and OsGT-γ4 are quite similar across different tissues. These findings suggest that members of the OsGT-γ gene family play roles in response to salt stress, with a possibility of overlapping functions between OsGT-γ1 and OsGT-γ2. Furthermore, it has been observed that OsGTγ-2, OsGTγ-1, and OsGTγ-3 exhibit similar expression profiles, showing high expression in leaf blades, sheaths, and young panicles⁹. Similar to studies on the OsGT-γ genes in rice, a shared expression pattern suggested potentially related functions, our finding that all StGT-γ genes have the highest expression in leaves, then shoots and roots, also suggests a functional link.

The highest expression in leaves aligns with the observed vulnerability of leaves during drought⁵⁶. Since all StGT-γ genes share this pattern, they are likely involved in processes crucial for leaves to cope with drought stress within the potato trihelix family. Our in-silico transcriptome analyses revealed the roles of StGT-γ genes in stress response across different potato varieties. Given the economic importance of the Kuroda variety in Pakistan and the current literature gap, including Kuroda in the study is essential to understand its molecular response to drought stress and to provide valuable insights for enhancing its resilience and productivity. From expression analysis and transcriptomic analysis, we confirm that StGT-γ genes play a crucial role in mitigating abiotic stress responses.

Primary structure helps to predict the protein’s secondary structure, which in turn provides valuable clues about its ultimate tertiary structure⁵⁷. The secondary structure analysis revealed a dominance of alpha helices across all GT-γ transcription factors, suggesting a well-defined structural framework. This finding aligns with observations in other plant stress-responsive proteins, where alpha-helices often contribute to stability and protein–protein interactions. StTH-1 has the highest alpha helix content (46.43%) which could impact how it interacts with other molecules. PrDOS analysis indicated disordered regions in StTH-1 (2.8%) and StTH-16 (6.0%), which suggest their role in drought stress adaptation through flexibility⁵⁸. The occurrence of structured regions and localized disorder in StTH-21 and StTH-26 suggests a potential mechanism for stress adaptation through controlled interactions. The observation of different structural features of these proteins including both alpha-helical structures and regions of disorder, within the GT-γ family, suggests functional diversities that might confer adaptation and regulatory systems in the drought response⁵⁹.

The three-dimensional structure of proteins was built by SWISS-MODEL. PROCHECK, Verify 3D, QMEAN, and ERRAT were used to confirm their quality. Furthermore, the energy minimization of YASARA improved the stability of these structures, showing their validity. The modeled protein structures were then compared to the template protein through superposition analysis to calculate Root Mean Square Deviations (RMSDs). RMSD values provide a quantitative measure of structural similarity between two proteins. Analyzing these differences is crucial for understanding how variations in protein structure can influence function⁶⁰. These results highlight the structural characteristics of trihelix transcription factors and pave the path for further exploration into their biological functions and regulatory mechanisms. To gain more understanding, the proteins’ physicochemical properties and post-translational modifications (PTMs) were examined.

Physicochemical analysis showed that the StGT-γ proteins differed in terms of the protein length, molecular weight, isoelectric point (pI), and surface properties. These variations in physicochemical properties can influence protein function in several ways. Protein size and molecular weight can affect their diffusion rates within the nucleus⁶¹. Despite these differences, all proteins exhibited a slightly alkaline nature and hydrophilic properties, with stable estimated half-lives. Slightly alkaline pI values might suggest these proteins function best in the slightly basic environment of the nucleus (pH around 7.2–7.4)⁶². Hydrophilic properties are often observed in proteins that interact with water and DNA within the nucleus⁶³. The stable estimated half-lives suggest these proteins might persist for a certain duration within the nucleus, potentially allowing for sustained regulation of gene expression during drought stress response. Post-translational modifications (PTMs) enhance the stability and broaden the functional capabilities of a polypeptide sequence by changing its physicochemical characteristics, including charge and conformation. Additionally, PTMs introduce new interaction patterns and interfaces that were not originally present in the protein molecule. In tomatoes, computational modeling predicted a putative phosphorylation site at S⁹⁶ on the GT-γ protein¹⁵. In this study, StTH-16 showed the highest phosphorylation level suggesting its potential role in signaling pathways. In contrast, StTH-21 exhibited the highest ubiquitylation pattern, showing a potential role in protein degradation. Variations in N-glycosylation and O-glycosylation further highlight the diverse functions of these factors. These findings deepen our understanding of trihelix transcription factor regulation and provide insights for future functional studies.

The subcellular localization prediction analysis indicates GT-γ members are localized within the nucleus. This finding aligns with their proposed functional roles in transcriptional regulation for plant development and stress response^5,64. Furthermore, topology analysis confirms the absence of transmembrane helices in all studied proteins, further supporting their nuclear localization⁶⁵. The domain analysis showed the presence of Myb/SANT-like domain in the amino acid sequences of GT-γ trihelix proteins thereby, this hint to its involvement in transcriptional regulation, a function that is very well characterized in this protein family. This domain is known for its role in DNA binding and is important in regulating gene expression⁶⁶. Additionally, gene structure analysis showed a consistent coding sequence region across all proteins, except for StTH-16, where a single intron interrupted the CDS. The structural variation of the gene sequence could represent the functional divergence among the GT-γ trihelix group members, influencing their roles in plant development and stress responses⁶⁷. Furthermore, our functional annotation using FFPred offer valuable insights into the functional characteristics and potential targets for improving drought tolerance through genetic and molecular approaches, contributing to the broader understanding of GT-γ trihelix proteins in plant biology and stress response pathways³³.

Plant transcription factors bind ligands to act as molecular switches, regulating gene expression based on specific signals⁵¹. The identification of unique potential ligands for each Trihelix GT-γ transcription factor (StTH-1, StTH-16, StTH-21, StTH-26) offers intriguing insights into their potential regulatory roles. StTH-1 binding to peptides (C14H18N4) suggests its involvement in protein–protein interactions, potentially influencing protein activity⁶⁸. CVM (4-cyclohexylbutyl 4-O-alpha-D-glucopyranosyl-beta-D-glucopyranoside) implies participation in carbohydrate metabolism or cell wall synthesis as it demonstrates the structure of a molecule containing glucose moieties, which are building blocks of carbohydrates found in plant cell walls. The exclusive binding of ZN (Zinc) to StTH-16 highlights a potential role in zinc-finger mediated DNA binding or protein stabilization, which could differ from the other proteins⁶⁹. Moreover, the identification of MG (Magnesium), nucleic acid, and RTL (Retinol) as potential ligands for StTH-21 implies involvement in metabolic regulation or nucleic acid binding activities, while potential interactions of StTH-26 with TBR, GLC (Glucose), ETI (Iodoethane), and MG (Magnesium) suggest diverse roles possibly in carbohydrate metabolism, xenobiotic detoxification, or cellular signaling^70,71. These findings provide valuable insights into the putative functions and regulatory mechanisms of StTH proteins, crucial in plant physiology and stress responses.

This study also investigated protein–protein interactions of GT-γ proteins, focusing on potential roles in abiotic stress response, particularly drought. Molecular Docking was performed between GT-γ proteins and predicted partners from STRING analysis, involved in drought stress. Here, we discuss the interacting partners in the context of their established functions and potential connections to abiotic stress response. The association of StTH-1 with proteins like M1AD25 and M1CWQ8 highlights their involvement in ethylene signaling pathways and cell death regulation, respectively, pointing to a nuanced control over stress-induced ethylene responses and programmed cell death—a common plant response to adverse conditions⁷². StTH-16 interacts with DREB2 (M1ADM5), a subfamily protein known for its function in resistance to diverse environmental stresses, including drought⁷³. Similarly, the AP2/ERF domain-containing protein (MOZXP8_Soltu) is another transcription factor associated with abiotic stress responses⁷⁴. Interestingly, StTH-16 also interacts with a UDP-glucosyltransferase (M1C3C5_Soltu) whose overexpression enhances drought tolerance in rice⁷⁵. Furthermore, StTH-16 interacts with an ATP synthase beta subunit (M1CIIO_Soltu) whose expression is upregulated during drought stress⁷⁶. Finally, the interaction with indole-3-glycerol phosphate synthase (M1B7W3_Soltu) suggests a potential role for tryptophan metabolism in drought stress response, as tryptophan is known to contribute to plant stress tolerance⁷⁷. StTH-21 interacting partners, LOB domain-containing proteins (M1B5R6, M1AYC3), are differentially expressed under various abiotic stresses, including drought⁷⁸. Additionally, StTH-21 interacts with an ethylene-responsive factor (ERF1) known to be involved in stress tolerance through the regulation of stress-specific genes⁷⁹. StTH-26 interacts with a protein containing an LSM domain (M1BSD7). Recent research highlights the role of LSM proteins in regulating plant tolerance to abiotic stress, including drought. LSM complexes function by selecting specific RNA targets based on environmental conditions, potentially influencing stress response gene expression.⁸⁰. The identified interactions and docking results provide strong evidence for the involvement of GT-γ proteins in a complex regulatory network governing plant responses to drought stress.

Conclusion

In conclusion, this research has provided valuable insights into the potential role of GT-γ transcription factors in potato’s response to drought stress. The presence of specific regulatory elements (DRE, AREs, STREs, MYB related motif) in their promoters and their high expression (3.3 to 4.1-fold) in leaves suggest a complex regulatory network focused on protecting this vulnerable tissue during drought. We also observed structural diversity within the GT-γ family, with alpha helices likely contributing to stability and interactions, while disordered regions might play a role in stress adaptation. Furthermore, predicted interactions with known stress-responsive proteins strengthen the evidence for GT-γ involvement in drought tolerance through a complex regulatory network. StTH-1 formed complexes with M1AN54_SOLTU (-280.66) and M1CWQ8_SOLTU (-291.34), featuring notable hydrogen bonds; StTH-16 showed a high docking score with MOXP8_SOLTU (354.71) and a weaker interaction with DREB2 (-265.07); StTH-21’s interaction with ethylene scored -277.33; StTH-26 engaged in complex interactions, scoring -298.12 with M1AYC3_SOLTU and -245.50 with M1BSD7_SOLTU, featuring multiple hydrogen bonds and salt bridges. This knowledge paves the way for future research. By manipulating the interactions between GT-γ proteins (StGTγ-1, StGTγ-2, StGTγ-3 and StGTγ-4) and their partners, we can potentially engineer drought-resistant potato cultivars. Additionally, in vitro and in vivo experiments are crucial to validate these predictions and gain a deeper understanding of individual GT-γ protein functions in potato’s drought response.