Adenylate Cyclase-Mediated cAMP Elevation Enhances Salt Stress Tolerance and Disease Resistance via Transcriptional and ER Stress Regulatory Networks in Arabidopsis thaliana

1. Introduction

Cyclic adenosine monophosphate (cAMP) is a canonical second messenger extensively characterized in animals and microorganisms, where it regulates metabolic homeostasis, transcriptional networks, and stress adaptation [1]. In higher plants, however, cAMP has long been considered scarce, and its physiological roles remained ambiguous due to low detection levels and poorly defined targets [2]. Recent advances in sensitive detection methods, molecular genetics, and transcriptomics have begun to reveal that cAMP is an active regulatory signal in plants, modulating ion channel activity, hormone signaling, transcriptional control, and stress-responsive pathways [3,4,5].

Functional adenylate cyclases (ACs), though often cryptic or embedded within multifunctional proteins, provide a mechanism for dynamic cAMP production in response to developmental or environmental cues. A recent study established a glucocorticoid-inducible AC system in Arabidopsis thaliana based on the catalytic center of AtKUP7. Induction of this system elevates endogenous cAMP levels and triggers extensive transcriptomic adjustments, highlighting potential regulatory scope of cAMP [2].

Salt stress poses a major challenge for plant growth and survival, disrupting ion homeostasis, protein stability, redox balance, and hormone signaling. Understanding how signaling molecules integrate stress responses is crucial for engineering tolerance [2,5]. In higher plants, cAMP regulates key physiological responses to both biotic and abiotic stresses by mediating complex intracellular signaling cascades [6,7]. For instance, cAMP influences ion channel activity, gene expression, and hormone signaling networks, thereby fine-tuning adaptive responses [4]. Studies have demonstrated that enhanced cAMP levels can lead to alterations in transcriptomic profiles, shedding light on the downstream pathways and target genes modulated by this molecule [8]. The emerging concept of integrating cAMP dynamics into the broader framework of plant signaling is of particular relevance to transgenic research, where it can be leveraged to create more robust and adaptive phenotypes.

Building upon previous work, the present study aimed to: (i) elevate cAMP through inducible AC activity, (ii) characterize the temporal transcriptomic landscape and identify constitutively expressed cAMP-responsive genes (CRGs)/ anchor CRGs, (iii) map the regulatory networks modulated by cAMP, and (iv) determine whether elevated cAMP enhances salt stress tolerance. By integrating high-resolution transcriptomics, network analyses, functional annotation, and physiological assays, this study delineates a mechanistic framework in which cAMP orchestrates transcriptional regulation, ER-mediated protein quality control, and stress-response pathways, collectively improving salinity tolerance in Arabidopsis thaliana [9].

2. Materials and Methods

2.1. Plant Material

Arabidopsis thaliana (Col-0) seedlings expressing a glucocorticoid-inducible adenylate cyclase construct (pTA7001-AC), containing the N-terminal catalytic domain of AtKUP7, were used as described by Xu et al. (2021). Seeds were surface sterilized, stratified at 4°C for 2 days, and grown on ½ MS medium (1% sucrose, 0.2× MS salts, 1% agar, pH 5.7) under long-day photoperiods (16 h light/8 h dark at 22°C). AC activity was induced by applying 10 µM dexamethasone (DEX) to seedlings [2,10].

2.2. Transcriptomic Profiling and Bioinformatics Analysis

Seedlings were harvested at 1, 3, 12, 24, and 72 h post-induction. RNA was extracted from three biological replicates per time point, and cDNA libraries were sequenced. Reads were mapped to the TAIR Arabidopsis reference genome [11] and transcript abundance was quantified using StringTie. Differentially expressed genes (DEGs) were identified using edgeR (FDR < 0.05, log2FC > 1). DEGs shared across all time points were defined as anchor CRGs. Functional enrichment of CRGs was performed using Gene Ontology (GO) and KEGG pathways via DAVID, KEGG Mapper, and clusterProfiler. Gene Set Enrichment Analysis (GSEA; fgsea) was applied to ranked gene lists (FDR < 0.25) to identify significantly enriched biological processes and pathways.

2.3. K-Means Clustering

Temporal clustering of CRGs was performed using k-means clustering to resolve distinct transcriptional modules across the 1–72 h time course. Log₂-normalized FPKM values were scaled and clustered using Euclidean distance, with the optimal number of clusters determined by within-group sum of squares and silhouette analysis. Heatmaps and centroid profiles were generated using pheatmap and ggplot2 to visualize phase-specific transcriptional dynamics.

2.4. Salt Stress Assays and cAMP Quantification

Seedlings were transferred to hydroponic ½ MS medium supplemented with 100 mM NaCl. For AC induction experiments, 10 µM DEX was applied prior to salt exposure. Root length and overall growth were monitored under control, DEX, and DEX+NaCl conditions [2]. Moreover, endogenous cAMP levels were measured using HPLC with UV detection at 259 nm. Standard curves were generated using 0.1–100 μM cAMP. Seedlings were homogenized, extracted in ultrapure water, filtered, and analyzed as described previously [2].

2.5. qPCR Expression Profiling and Functional Validation

Gene expression validation was conducted using quantitative real-time PCR (qPCR). Total RNA was reverse transcribed using oligo(dT) primers. qPCR assays were performed using gene-specific primers. Expression was normalized to TUB4 and analyzed using the ΔΔCt method. Functional validation included differential expression analysis across Col-0 and AC genotypes under mock, salt-treated and Pseudomonas syringae pv. tomato DC3000 infection.

2.6. Disease Onset Assays

For disease resistance assays, 4-week-old plants were syringe-infiltrated with Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) at OD₆₀₀ = 0.5–1.5. Symptom progression was monitored at 24, 48, and 72 h post-inoculation.

2.7. Physiological Testing

DAB (3,3′-diaminobenzidine) staining was used to visualize H₂O₂ accumulation, while Trypan Blue staining was used to assess cell death. Image acquisition was performed with a stereo-microscope equipped with a digital imaging system. Disease severity and staining intensities were observed thoroughly.

3. Results

3.1. Transcriptomic Dynamics Under AC Induction

Transcriptome profiling was conducted on cDNA libraries from samples collected at 1h, 3h, 12h, 24h, and 72h time points, each in triplicates. The RNA-Seq data from these time points, labeled as T1, T3, T12, T24, and T72 respectively, was analyzed relative to the control. Data processing included mapping the reads to the Arabidopsis genome using TAIR [11], followed by transcript quantification using StringTie to obtain read counts and FPKM values for gene expression estimation. Differentially expressed genes (DEGs) for each time interval were identified using edgeR package [12], by preprocessing the data with standardization method and setting threshold of false discovery rate (FDR) adjusted p-value < 0.05 and an absolute fold change (log2FC > 1) as the cutoff with dplyr and tidyverse packages. RNA-Seq analysis revealed time-dependent transcriptional shifts, with 607, 2,915, 4,041, 3,922, and 2,924 DEGs detected at 1, 3, 12, 24, and 72 h post-DEX induction as shown in volcano plots (Figure 3.1a), respectively. The overlap of DEGs across these time points was determined using the JVenn tool [13] (Figure S1) and visualized through an Upset plot (Figure 3.1b).

Figure 3.1a. Volcano plots of RNASeq data from all timepoints as a result of DESeq2 analysis highlight the dynamic gene expression changes over time in response to the experimental conditions. Up and down-regulated genes are shown in red and blue colors, whereas non-DEGs are represented in grey.

Figure 3.1a. Volcano plots of RNASeq data from all timepoints as a result of DESeq2 analysis highlight the dynamic gene expression changes over time in response to the experimental conditions. Up and down-regulated genes are shown in red and blue colors, whereas non-DEGs are represented in grey.

This analysis identified 292 common DEGs, designated as CRGs, which were selected for further bioinformatics analyses. The up-regulated and down-regulated DEGs were identified for all time points, as follows: T1 (124 up-regulated, 483 down-regulated), T3 (1358 up-regulated, 1557 down-regulated), T12 (1856 up-regulated, 2185 down-regulated), T24 (1953 up-regulated, 1969 down-regulated), and T72 (1506 up-regulated, 1418 down-regulated).

These results highlight the dynamic gene expression changes over time in response to the experimental conditions. By utilizing ComplexUpset package in R, upset plot analysis identified 292 CRGs consistently regulated across all time points, indicating that elevated cAMP exerts a stable regulatory influence on core cellular pathways. Of these, 230 were down-regulated and 62 up-regulated.

Figure 3.1b. Comparative analysis of differentially expressed CRGs and timepoint specificity of gene expression. The upset plot illustrates the number of overlapping and unique DEGs across all timepoints. The connectors at the bottom indicate the groups with overlapping DEGs corresponding to the bars above. The different connector colors represent the degree of overlap: red for DEGs common to all five groups, green for four groups, brown for three groups, maroon for two groups, and yellow for unique DEGs in each group. The total number of DEGs for each individual group is displayed in a distinct bar plot segment positioned at the bottom left of the upset plot.

Figure 3.1b. Comparative analysis of differentially expressed CRGs and timepoint specificity of gene expression. The upset plot illustrates the number of overlapping and unique DEGs across all timepoints. The connectors at the bottom indicate the groups with overlapping DEGs corresponding to the bars above. The different connector colors represent the degree of overlap: red for DEGs common to all five groups, green for four groups, brown for three groups, maroon for two groups, and yellow for unique DEGs in each group. The total number of DEGs for each individual group is displayed in a distinct bar plot segment positioned at the bottom left of the upset plot.

These results illustrate that the majority of CRGs are down-regulated in response to cAMP elevation, indicating potential suppression of specific biological processes under these conditions. In contrast, the up-regulated genes may represent key pathways that are activated or enhanced. The balance between up- and down-regulation highlights the dynamic role of cAMP signaling in modulating gene expression, possibly in response to environmental stimuli or stress factors [14].

3.2. Principal Component Analysis

Plotly package was employed to design 3D PCA plot in R. The 3D PCA analysis of CRG expression revealed a strong separation between control and AC-induced samples along the first three principal components. PC1, PC2, and PC3 accounted for 74.77%, 7.46%, and 4.44% of the total variance, respectively, capturing over 86% of the transcriptional variability in the dataset (Figure 3.2). The pronounced divergence along PC1 indicates that cAMP elevation is the dominant driver of global transcriptional differences. Separation along PC2 and PC3 further highlights secondary layers of regulation, collectively demonstrating a robust and multidimensional transcriptional reprogramming in response to AC induction.

Figure 3.2. Principal component analysis (PCA) of CRGs. PCA analysis of transcriptome changes associated with induction of cAMP elevation using RNA-seq data of six-week-old AC transgenic plants at 1h, 3h, 12h, 24h and 72h after spraying dexamethasone (DEX), means of three biological replicates of each group were taken for analysis.

Figure 3.2. Principal component analysis (PCA) of CRGs. PCA analysis of transcriptome changes associated with induction of cAMP elevation using RNA-seq data of six-week-old AC transgenic plants at 1h, 3h, 12h, 24h and 72h after spraying dexamethasone (DEX), means of three biological replicates of each group were taken for analysis.

3.3. Functional Annotation of CRGs

GO enrichment performed by DAVID tool identified 64 terms: 38 biological processes, 22 molecular functions, and 4 cellular components. Significant enrichments included transcription factor activity, regulation of DNA-templated transcription, phytohormone signaling (e.g., ABA, cytokinin), abiotic stress responses (heat, cold, hypoxia, osmotic stress), defense regulation, and protein complex oligomerization. 35 transcription factors were among the CRGs, emphasizing a broad regulatory role.

Figure 3.3. Functional categorization of cAMP-responsive genes (CRGs). The heatmap illustrates the functional terms of Biological Processes (BPs), Molecular Functions (MFs), and Cellular Components (CCs) derived from Gene Ontology (GO) analysis of CRGs across all timepoints. The BP and MF terms indicate significant enrichment at a false discovery rate (FDR) < 0.05, while CC terms reflect enrichment at FDR < 0.5. Terms in bold are represented as the significant ones.

Figure 3.3. Functional categorization of cAMP-responsive genes (CRGs). The heatmap illustrates the functional terms of Biological Processes (BPs), Molecular Functions (MFs), and Cellular Components (CCs) derived from Gene Ontology (GO) analysis of CRGs across all timepoints. The BP and MF terms indicate significant enrichment at a false discovery rate (FDR) < 0.05, while CC terms reflect enrichment at FDR < 0.5. Terms in bold are represented as the significant ones.

Although no CC terms were significantly enriched, a substantial number of CRGs were associated with the plasma membrane. It is noteworthy that GO terms falling short of the strict significance threshold should not be disregarded, as they may still have potential relevance to the effects of cAMP elevation. These include biological processes such as gibberellin and cytokinin catabolic processes, phosphorylation, protein folding, and plant organ senescence, which refers to the wide-ranging regulatory functions of cAMP signaling in plants. The CRGs involved in these biological processes are listed in Figure 3.3. Furthermore, the additional molecular functions such as protein kinase activity, adenylyl sulfate reductase activity, ATP binding, calmodulin binding, and transmembrane receptor protein serine/threonine activity were also identified apart from the selective criteria (FDR <0.05). The cellular components linked to these CRGs include the plant cell wall, vacuole membrane, and NADPH oxidase complex, underscoring the broad spectrum of cAMP-mediated regulatory functions across various cellular and molecular contexts.

3.4. KEGG and GSEA Pathway Analysis

KEGG enrichment highlighted biosynthesis of secondary metabolites, protein processing in the endoplasmic reticulum and zeatin biosynthesis, reflecting cAMP-mediated modulation of protein quality control and hormone metabolism. Secondary metabolites play crucial roles in plant defense, signaling, and adaptation to environmental stress, and their biosynthesis may be influenced by cAMP signaling [15]. In contrary, GSEA confirmed enrichment of sequence-specific DNA-binding transcription factors and ER protein processing machinery, underscoring dual influence of cAMP on transcriptional and protein-folding networks.

Figure 3. 4. KEGG Pathway Enrichment and GSE Analysis of CRGs. (A) Heatmap illustrating the enriched KEGG pathways associated with CRGs across all the timepoints. Significantly enriched pathways include; biosynthesis of secondary metabolites, zeatin biosynthesis and protein processing in the endoplasmic reticulum (ER). The significance of pathway enrichment was determined by a false discovery rate (FDR) threshold of <0.05. These pathways highlight the roles of cAMP signaling in cytokinin-mediated growth regulation and protein homeostasis under stress conditions. (B) GSEA plots for CRGs gene sets based on top GO and KEGG terms. Sequence-specific DNA binding (GO:0043565) and Protein processing in the endoplasmic reticulum (ko04141). LES: Leading Edge Subset; NES: Normalized Enrichment Score; p-value: nominal p-value of the Enrichment Score (ES); Leading edge subset: the core genes contributing most to the enrichment result.

Figure 3. 4. KEGG Pathway Enrichment and GSE Analysis of CRGs. (A) Heatmap illustrating the enriched KEGG pathways associated with CRGs across all the timepoints. Significantly enriched pathways include; biosynthesis of secondary metabolites, zeatin biosynthesis and protein processing in the endoplasmic reticulum (ER). The significance of pathway enrichment was determined by a false discovery rate (FDR) threshold of <0.05. These pathways highlight the roles of cAMP signaling in cytokinin-mediated growth regulation and protein homeostasis under stress conditions. (B) GSEA plots for CRGs gene sets based on top GO and KEGG terms. Sequence-specific DNA binding (GO:0043565) and Protein processing in the endoplasmic reticulum (ko04141). LES: Leading Edge Subset; NES: Normalized Enrichment Score; p-value: nominal p-value of the Enrichment Score (ES); Leading edge subset: the core genes contributing most to the enrichment result.

3.5. Chromosomal Distribution and clustering

All CRGs were mapped to chromosomes using the Chromosome Map Tool from The Arabidopsis Information Resource (TAIR) [16]. The number of CRGs mapped across chromosomes 1 to 5 were 81 (15 up-regulated and 65 down-regulated, 1 mixed), 33 (6 up-regulated and 26 down-regulated, 1 mixed), 50 (12 up-regulated and 38 down-regulated), 44 (13 up-regulated and 31 down-regulated), and 81 (16 up-regulated and 65 down-regulated), respectively out of a total of 289 mapped genes (Figure 3.5). Three novel genes which could not mapped included an up-regulated and 2 down-regulated genes.

Figure 3.5. Chromosomal Distribution of CRGs. Black indicates the gene IDs and locations of down-regulated CRGs, while red denotes the gene IDs and locations of up-regulated CRGs that are dispersed across the chromosomes of Arabidopsis. Whereas, gene IDs in olive color refers to mixed/dynamic gene expression.

Figure 3.5. Chromosomal Distribution of CRGs. Black indicates the gene IDs and locations of down-regulated CRGs, while red denotes the gene IDs and locations of up-regulated CRGs that are dispersed across the chromosomes of Arabidopsis. Whereas, gene IDs in olive color refers to mixed/dynamic gene expression.

The throughout distribution of CRGs was found uneven, with chromosomes 1 and 5 each harboring the highest number of CRGs (81 genes, 27.7% each), while chromosome 2 had the fewest (33 genes, 11.3%) (Figure 3.5). Analysis of expression trends showed that down-regulated CRGs predominated on all chromosomes. For instance, on chromosome 1, 64 of 81 CRGs (79.0%) were down-regulated, whereas only 15 (18.5%) were up-regulated. Similarly, chromosome 5 had 66 down-regulated CRGs (81.5%) and 15 up-regulated (18.5%). The proportion of up-regulated genes was relatively higher on chromosome 4, where 13 of 44 CRGs (29.6%) showed increased expression.

Table 3.1. Chromosomal distribution statistics of anchor CRGs.

Chr	CRGs	CRGs%	Up	Up/CRGs%	Up/Total%	Dn	Dn/CRGs%	Dn/Total%
1	81	27.74	15	18. 51	5.13	64	79.01	21.92
2	33	11.30	6	18.18	2.05	25	75.76	8.56
3	50	17.12	12	24	4.11	38	76	13.01
4	44	15.06	13	29.55	4.45	31	70.45	10.62
5	81	27.74	15	18.51	5.13	66	81.48	22.60
Novel	3	1.02	–	–	–	–	–	–
Total	292

Table 3.1. Chromosomal distribution statistics of anchor CRGs.

Chr	CRGs	CRGs%	Up	Up/CRGs%	Up/Total%	Dn	Dn/CRGs%	Dn/Total%
1	81	27.74	15	18. 51	5.13	64	79.01	21.92
2	33	11.30	6	18.18	2.05	25	75.76	8.56
3	50	17.12	12	24	4.11	38	76	13.01
4	44	15.06	13	29.55	4.45	31	70.45	10.62
5	81	27.74	15	18.51	5.13	66	81.48	22.60
Novel	3	1.02	–	–	–	–	–	–
Total	292

When considering the total set of CRGs, up-regulated genes represented a smaller fraction overall (2–5.1% per chromosome), whereas down-regulated genes constituted the majority (8.5–22.6% per chromosome). Only three novel genes (1.0% of total CRGs) could not be assigned to any chromosome, indicating that the mapped CRGs cover nearly the entire genome. Overall, the chromosomal mapping indicates that CRGs are distributed across all chromosomes but show a strong bias toward down-regulation, suggesting widespread transcriptional repression associated with the studied condition.

3.6. K-Means Clustering Analysis

K-means clustering of 292 Anchor-CRGs across six time points revealed six distinct temporal expression patterns or K-clusters (KC), reflecting a highly coordinated and dynamic stress response (Figure 3.6). Early-response clusters (KC1 and KC6), featuring transcription factors of the WRKY family and upstream kinases of the MAPKKK/MAPK cascade, exhibited rapid repression or oscillatory expression, indicating immediate modulation of stress signaling and regulatory feedback. The WRKY family is widely acknowledged as a key regulator of plant responses to abiotic stresses, including drought, salt and temperature stress, through modulation of hormone signaling, ROS homeostasis, and downstream gene expression [17,18,19]. Similarly, MAPKKKs (MAP3Ks) have been shown to mediate signal transduction under abiotic stress, acting as multidimensional regulators of stress tolerance via MAPK cascade pathways [20]. Mid-phase genes in KC3 likely mediate intermediate signaling and metabolic adjustments, bridging early stress sensing and later recovery. Such transient transcriptional activation is consistent with the dynamic reprogramming observed in plant stress responses, where central metabolism is rapidly reorganized to maintain energy and redox balance under abiotic stress [21,22]. Late-phase clusters (KC2 and KC4) displayed delayed or biphasic induction, consistent with coordinated recovery, hormonal regulation, and protective metabolism, a behavior characteristic of stress-recovery and adaptation phases in long-term stress studies [23,24]. Meanwhile, KC5 comprised genes that were persistently repressed, suggesting strategic suppression of non-essential processes to conserve energy under stress conditions [21,25]. Altogether, these temporal clusters reveal phase-specific regulation, functional complementarity, and dynamic feedback, underscoring a sophisticated orchestration of transcriptional reprogramming during stress adaptation.

Figure 3.6. K-means clustering Analysis was applied to the Z-score normalized expression profiles of the 292 anchor CRGs across all timepoints, classifying them into six clusters (KC1– KC6) based on Euclidean distance. Each cluster represents distinct transcriptional behavior.

Figure 3.6. K-means clustering Analysis was applied to the Z-score normalized expression profiles of the 292 anchor CRGs across all timepoints, classifying them into six clusters (KC1– KC6) based on Euclidean distance. Each cluster represents distinct transcriptional behavior.

Overall, the KC clusters demonstrate phase-specific regulation, functional complementarity, and dynamic feedback control. Early-response clusters (KC1, KC6) initiate stress adaptation, mid-phase effectors (KC3) facilitate transitional adjustments, late-phase clusters (KC2, KC4) coordinate recovery, and sustained repression (KC5) preserves cellular resources. This framework provides mechanistic insights and identifies candidate biomarkers and regulatory targets for stress adaptation. Furthermore, this analysis demonstrates phase-specific activation, functional complementarity, and dynamic feedback mechanisms across KC clusters. KC1 and KC6 initiate early response, KC2 and KC4 mark recovery and regulation, KC3 supports transition, while KC5 downregulates non-essential genes.

3.7. Salt Stress Assay and Phenotypic Analysis of Col-0 vs AC Transgenic

Col-0 seedlings exhibited reduced root growth and chlorosis under 100 mM NaCl. AC transgenic seedlings, however, maintained significantly longer roots and improved overall vigor. DEX-induced cAMP elevation did not completely prevent salt stress damage but conferred enhanced tolerance as reported in previous study [2].

Figure 3.6 shows a graphical comparison of untreated and salt-treated seedlings of Col-0, as well as AC transgenic line. It is evident that Col-0 exhibits significant susceptibility to salt stress at a concentration of 100 mM, resulting in poor growth and underdeveloped roots following prolonged exposure. In contrast, the roots of transgenic line pTA7001-AC demonstrate improved development even under extended stress conditions. Specifically, the roots of transgenic line exhibit relatively faster and more resilient growth under prolonged stress, while those of wildtype display slower growth throughout the treatment period.

Figure 3.7a. Salt stress assay studies of AC transgenics at 2-weeks stage. Under salt stress alone, exposure to 100 mM NaCl resulted in significantly longer roots in AC transgenic lines (1.46 ± 0.23 cm) compared to Col-0 (1.08 ± 0.22 cm; p < 0.0001, n = 17), demonstrating the improved tolerance in transgenic line to severe salt stress. However, when subjected to combined DEX and salt treatments, AC transgenic lines consistently outperformed Col-0 plants, showing significantly greater root lengths under 30 µM DEX + 100 mM NaCl (AC: 2.53 ± 0.50 cm vs. Col-0: 2.01 ± 0.40 cm; p = 0.006, n = 12).

Figure 3.7a. Salt stress assay studies of AC transgenics at 2-weeks stage. Under salt stress alone, exposure to 100 mM NaCl resulted in significantly longer roots in AC transgenic lines (1.46 ± 0.23 cm) compared to Col-0 (1.08 ± 0.22 cm; p < 0.0001, n = 17), demonstrating the improved tolerance in transgenic line to severe salt stress. However, when subjected to combined DEX and salt treatments, AC transgenic lines consistently outperformed Col-0 plants, showing significantly greater root lengths under 30 µM DEX + 100 mM NaCl (AC: 2.53 ± 0.50 cm vs. Col-0: 2.01 ± 0.40 cm; p = 0.006, n = 12).

Figure 3.7b. Comparative analysis of root length under DEX and salt stress at 2-weeks stage. Statistical analysis using t-tests was conducted to compare root length between each treatment and the control group. No significant difference was observed between the Col-0 and AC under mock, whereas all other treatment groups exhibited significant differences in root length and AC grew longer as compared to Col-0.

Figure 3.7b. Comparative analysis of root length under DEX and salt stress at 2-weeks stage. Statistical analysis using t-tests was conducted to compare root length between each treatment and the control group. No significant difference was observed between the Col-0 and AC under mock, whereas all other treatment groups exhibited significant differences in root length and AC grew longer as compared to Col-0.

These findings collectively indicate that the transgene confers DEX-inducible root growth enhancement and provides significant protection against salt stress, particularly under more severe conditions. The results highlight the potential of this genetic modi- fication to improve plant resilience in challenging environments. Nevertheless, the results refer to a notable role for cAMP in stress tolerance. It is essential to highlight, however, that cAMP elevation did not mitigate the detrimental effects associated with prolonged salt stress, indicating a complex regulatory role for cAMP in plant development. Previous studies have shown that cyclic nucleotides (cAMP / cGMP) can reduce Na⁺ uptake via non-selective voltage-independent cation channels (VICs), thereby lowering tissue Na⁺ accumulation and improving survival under salinity stress [26]. Further, global transcriptomic analyses of cAMP-elevated transgenic Arabidopsis identified hundreds of CRGs, many of which are involved in hormone signaling, ion transport, stress responses, and transcription regulation indicating that cAMP triggers wide transcriptional reprogramming relevant for salt, hormonal and stress signalling [2].

3.8. Functional Validation of Salt-Tolerance in AC Transgenic with qPCR

The qPCR expression profiles of CNGC11, CRK10, CML11, FLS2, RBOHC, and SOS1 reveal pronounced transcriptional reprogramming between Col-0 and AC transgenic Arabidopsis plants across the four treatment conditions (CK, DEX-treated (DT), salt-treated (ST), and DST) (Figure 3.8). Under mock conditions, all genes displayed comparable and low expression levels, confirming no inherent baseline differences between genotypes. Following DEX treatment, AC transgenic plants exhibited clear induction of CNGC11 and CRK10, while FLS2 showed a moderate increase, indicating that activation of the adenylyl cyclase transgene elevates cAMP levels sufficiently to stimulate early components of calcium and receptor-mediated defense signaling. This is consistent with mounting evidence that cAMP functions as a signaling molecule in plants and can influence transcriptional networks [27,28].

Figure 3.8. Comparative analysis of salt tolerance in Col-0 and AC Lines. Pairwise comparison of relative gene expression was performed using SPSS across Mock, DT (DEX-treated), ST (salt- treated), and DST (DEX+salt-treated) groups.

Figure 3.8. Comparative analysis of salt tolerance in Col-0 and AC Lines. Pairwise comparison of relative gene expression was performed using SPSS across Mock, DT (DEX-treated), ST (salt- treated), and DST (DEX+salt-treated) groups.

Salt stress alone triggered a substantial transcriptional response, particularly in genes associated with calcium influx and stress perception. CRK10 and FLS2 were strongly induced in Col-0 but exhibited dramatic, multi-fold higher expression in AC lines, demonstrating heightened sensitivity of these pathways to cAMP-primed signaling. CNGC11, although only modestly induced in Col-0, displayed pronounced upregulation in AC plants, consistent with enhanced cAMP-dependent modulation of Ca²⁺ channel activity during salt stress; CNGCs are well established as cyclic-nucleotide-sensitive Ca²⁺-permeable channels implicated in both biotic and abiotic stress responses [29,30].

Strikingly, the combined DEX + salt treatment (DST) elicited the highest expression levels for nearly all genes, with AC plants showing synergistic activation far exceeding the response to either treatment alone. This pattern is especially evident in CRK10, RBOHC, CML11, and particularly SOS1, which exhibited extremely high transcript accumulation in AC transgenics under DST conditions. The massive induction of SOS1, a central Na⁺/H⁺ antiporter required for salt tolerance, along with elevated RBOHC (ROS production) and CML11 (Ca²⁺ sensing) suggests that elevated cAMP intensifies both signal perception and downstream adaptive responses; the SOS regulatory module and ROS/Ca²⁺ interplay are key determinants of ionic homeostasis and stress acclimation [31,32,33].

Together, these findings indicate that the AC transgene potentiates salt-induced transcriptional activation via amplified cAMP signaling, leading to stronger induction of ion transporters, calcium sensors, receptor-like kinases, and ROS machinery. The robust, synergistic transcriptional response under DST supports a model in which cAMP acts as an amplifier that coordinates Ca²⁺ influx (via CNGCs), ROS signaling (via RBOHs), and Na⁺ extrusion (via SOS1 and its regulatory network) to enhance salt-stress resilience in AC transgenic Arabidopsis [31,32,33].

3.9. HPLC-Based cAMP Quantification

HPLC analysis confirmed that AC transgenic seedlings accumulated nearly double the cAMP levels of Col-0 controls (20.50 ± 9.27 vs. 11.40 ± 2.01 µM), validating effective biochemical modulation.

Figure 3.9. cAMP content quantification via HPLC. Pairwise comparison of cAMP quantification data across all timepoints revealed a significant increase in cAMP levels in AC transgenic plants from 12 to 72 hours, as indicated by the grey dotted line trend. In contrast, Col-0 plants showed a modest elevation at 12 and 24 hours, followed by a decline at 72 hours.

Figure 3.9. cAMP content quantification via HPLC. Pairwise comparison of cAMP quantification data across all timepoints revealed a significant increase in cAMP levels in AC transgenic plants from 12 to 72 hours, as indicated by the grey dotted line trend. In contrast, Col-0 plants showed a modest elevation at 12 and 24 hours, followed by a decline at 72 hours.

Time-course analysis following DEX treatment (sampling at 0, 1, 3, 12, 24 and 72 h) revealed dynamic cAMP fluctuations: AC-transgenic plants showed a marked and sustained increase in cAMP levels from 12 to 72 h, while wild-type plants exhibited only a modest transient elevation at 12 and 24 h followed by a decline at 72 h (Figure 3.8). Similar timepoints were selected as in section 3.1 for RNASeq analysis for confirmation of enhanced cAMP content. These results demonstrate that the HPLC method reliably detects temporal changes in cAMP in plant tissues under inducible conditions. Given that cAMP has been increasingly recognized as a bona fide signaling molecule in plants implicated in stress responses, hormonal signaling, ion flux regulation, and transcriptional reprogramming, our findings provide both methodological and biological support for a functional role of cAMP in Arabidopsis physiology.

3.10. Physiological Testing by DAB and Trypan Blue Staining

Physiological staining assays further demonstrated the enhanced stress tolerance conferred by elevated cAMP in AC transgenic Arabidopsis plants. Trypan Blue staining, which marks dead or severely damaged cells, revealed extensive blue coloration in Col-0 leaves, indicating widespread membrane injury and cell death which has caused lesions about 3-times severe than that of AC (Figure 3.10a). In contrast, AC transgenic plants exhibited markedly reduced blue staining, reflecting significantly lower levels of cellular damage under identical stress conditions. This reduction in cell death is consistent with the notion that cAMP enhances cellular resilience and mitigates cytotoxicity.

Figure 3.10a. Trypan 0042lue staining of Col-0 and AC transgenic seedlings clearly shows the tissue damage is worse in case of Col-0 while that of AC is minimal.

Figure 3.10a. Trypan 0042lue staining of Col-0 and AC transgenic seedlings clearly shows the tissue damage is worse in case of Col-0 while that of AC is minimal.

Conversely, DAB staining, which detects hydrogen peroxide (H₂O₂), showed stronger and more widespread brown precipitates in AC plants compared with Col-0 (Figure 3.10b). Rather than indicating oxidative injury, this controlled accumulation of H₂O₂ likely reflects an active and regulated ROS-signaling response, which is crucial for stress acclimation, ion homeostasis, and activation of downstream defense pathways. Elevated ROS production through enzymes such as RBOHs is well known to function as an adaptive signaling mechanism during early stress perception, triggering transcriptional and physiological adjustments that promote tolerance.

Together, the contrasting staining patterns, lower cell death (Trypan Blue) and higher regulated ROS signaling (DAB) support the conclusion that cAMP elevation in AC lines enhances stress adaptation by strengthening protective signaling pathways while preventing cellular injury. These results reinforce the functional role of cAMP as a critical mediator of plant stress resilience.

Figure 3.10b. DAB staining for H₂O₂ accumulation in Col-0 and AC transgenic seedlings represents that Col-0 leaf skeleton has relatively less brown staining as compared to AC. Hence, AC confer more stress tolerance.

Figure 3.10b. DAB staining for H₂O₂ accumulation in Col-0 and AC transgenic seedlings represents that Col-0 leaf skeleton has relatively less brown staining as compared to AC. Hence, AC confer more stress tolerance.

3.11. Disease Severity Index (DSI)

Compared to Col-0, AC transgenic lines showed delayed onset of symptoms, reduced disease severity, and complete avoidance of tissue collapse. The absence of wilting under high pathogen pressure suggests that elevated adenylate-cyclase activity and the resultant increase in intracellular cAMP significantly enhances the plant defense capacity. This observation is consistent with growing evidence that cAMP serves as a bona fide second messenger in plant innate immunity: cAMP elevation activates cyclic-nucleotide-gated channels (CNGCs), leading to a Ca²⁺ influx, ROS generation via NADPH oxidases (e.g., RBOHs), and induction of downstream defense pathways including salicylic-acid–mediated and pathogenesis-related (PR) responses, culminating in increased resistance to pathogens and reduced cell death [9,34].

Figure 3.11. Differential disease progression in Col-0 and AC lines following DC3000 infection. Col-0 plants exhibited early and progressively severe disease symptoms following pathogen inoculation i.e., Pseudomonas syringae pv. tomato DC3000 (Pst DC3000): slight yellowing appeared by 24 h under the highest inoculum (1.5 OD₆₀₀), spreading to all inoculated leaves by 48 h, and culminating in complete wilting of infected leaves by 72 h. In contrast, AC-transgenic plants displayed a markedly enhanced resistance phenotype. No visible symptoms were apparent at 24 h across all inoculum concentrations; mild chlorosis emerged only at higher doses (1.0–1.5 OD₆₀₀) by 48 h, and though yellowing intensified by 72 h in these treatments, wilting was entirely absent. Mock-treated AC leaves remained healthy throughout the assay.

Figure 3.11. Differential disease progression in Col-0 and AC lines following DC3000 infection. Col-0 plants exhibited early and progressively severe disease symptoms following pathogen inoculation i.e., Pseudomonas syringae pv. tomato DC3000 (Pst DC3000): slight yellowing appeared by 24 h under the highest inoculum (1.5 OD₆₀₀), spreading to all inoculated leaves by 48 h, and culminating in complete wilting of infected leaves by 72 h. In contrast, AC-transgenic plants displayed a markedly enhanced resistance phenotype. No visible symptoms were apparent at 24 h across all inoculum concentrations; mild chlorosis emerged only at higher doses (1.0–1.5 OD₆₀₀) by 48 h, and though yellowing intensified by 72 h in these treatments, wilting was entirely absent. Mock-treated AC leaves remained healthy throughout the assay.

Thus, the enhanced resistance phenotype of AC-overexpressing plants underscores a functional role for cAMP-mediated signaling in orchestrating robust immune responses, likely through rapid activation of Ca²⁺/ROS/defense pathways and efficient pathogen containment. These results warrant further mechanistic investigation into how cAMP-dependent signaling cascades modulate plant immunity.

3.12. qPCR-Based Functional Validation of Disease-Resistance in AC Transgenic

Leaves from 24 h stage post-infection were selected for qPCR analysis for further in-depth study. Quantitative PCR profiling across Col-0, DT_Col-0, and DT_AC genotypes revealed contrasting immune strategies upon Pst DC3000 infection. In DT_AC, PR5 was constitutively upregulated even in the absence of pathogen challenge, indicating a primed basal defense state, while RD20 maintained moderate expression under both mock and infected conditions, consistent with a sustained readiness for abiotic and biotic stress. Upon infection, DT_AC displayed the strongest induction of CNGC11, suggesting enhanced cAMP-dependent Ca²⁺ influx through cyclic nucleotide-gated channels, a process recognized as a crucial early component of pattern-triggered immunity (PTI) . Concurrently, FLS2, the primary receptor for bacterial flagellin, was markedly elevated in DT_AC, indicating a sensitized PAMP perception system and strengthened PTI activation. CML11, a calmodulin-like Ca²⁺ sensor, was similarly upregulated, consistent with enhanced decoding of Ca²⁺ signatures into downstream immune signaling. By contrast, classical SA- and ROS-associated defense markers including PR1, WRKY29, and RBOHC were more strongly induced in Col-0 and DT_Col-0 following infection, reflecting a reactive immune strategy characterized by strong transcriptional activation and oxidative bursts. This divergence suggests that Col-0 and DT_Col-0 rely predominantly on SA-mediated and ROS-dependent defenses, whereas DT_AC employs a pre-primed, Ca²⁺-centered, low-damage immune strategy. CRK10 expression remained relatively stable across genotypes and treatments, showing only modest induction in DT_AC, consistent with its proposed modulatory rather than primary defensive role.

Figure 3.12. qPCR analysis of PR genes and AC-induced defense-related genes. Pairwise comparisons were performed between Col-0 and AC transgenic plants under DEX-treated (DT), infected, and uninfected conditions.

Figure 3.12. qPCR analysis of PR genes and AC-induced defense-related genes. Pairwise comparisons were performed between Col-0 and AC transgenic plants under DEX-treated (DT), infected, and uninfected conditions.

Collectively, these data indicate that AC-mediated cAMP elevation reprograms Arabidopsis immunity toward a proactive, Ca²⁺-driven and energy-efficient defense network strengthening early pathogen detection and signaling while reducing reliance on high-amplitude ROS or SA bursts. This mechanistic shift likely underlies the enhanced disease resistance and reduced tissue damage observed in AC transgenic plants.

4. Discussion

This study demonstrates that inducible elevation of cAMP through a glucocorticoid-activated adenylate cyclase (pTA7001-AC) profoundly reprograms transcriptional networks and enhances salt stress tolerance in Arabidopsis thaliana. Our integrated transcriptomic, network, and physiological analyses reveal that cAMP functions as a central signaling hub, orchestrating transcriptional regulation, protein quality control, hormone metabolism, and stress-responsive pathways.

4.1. cAMP as a Broad Transcriptional Regulator

The identification of 292 anchor CRGs underscores the persistent influence of elevated cAMP on core cellular pathways. GO and GSEA analyses showed strong enrichment for DNA-binding transcription factors, stress-responsive proteins, and protein complex oligomerization, highlighting capacity of cAMP to modulate gene expression at multiple regulatory levels. The predominance of transcriptional regulators among CRGs suggests that cAMP does not act solely as a metabolic effector but orchestrates broad transcriptional reprogramming, consistent with previous reports of cAMP-mediated transcriptional control in plants [1,2].

4.2. cAMP Links ER Protein Processing and Hormonal Regulation

KEGG and clustering analyses revealed significant enrichment of pathways associated with endoplasmic reticulum (ER) protein processing and zeatin biosynthesis. ER stress pathways, including molecular chaperones and folding enzymes, were prominently represented among CRG clusters, indicating that cAMP supports protein homeostasis under potentially stress-inducing conditions. Concurrent modulation of cytokinin biosynthesis suggests that cAMP may also fine-tune hormonal balance to optimize growth under stress, a mechanism previously hypothesized in plant cAMP signaling studies [3,10].

4.3. Integration with Salt Stress Responses

Fifteen CRGs were annotated as salt stress-responsive, including RAP2.6 (an ERF transcription factor), small heat shock proteins (HSP17.4, HSP17.6A, HSP17.6II), RD20, ERD7, and ERD9. These genes collectively contribute to membrane stability, ROS homeostasis, protein folding, and lipid metabolism, providing a mechanistic basis for the observed physiological resilience of AC transgenic seedlings under 100 mM NaCl. These findings reinforce the concept that cAMP serves as an upstream integrator of stress-responsive pathways, linking transcriptional regulation, protein quality control, and metabolic adaptation [2,4].

4.4. Temporal Dynamics and Network Coordination

K-means clustering highlighted that CRGs participate in temporally coordinated clusters, reflecting diverse expression patterns and functional specializations. The largest CRG module contained hub genes regulating olefinic compound metabolism and ER protein folding, suggesting that cAMP-driven networks coordinate membrane integrity and proteostasis, which are critical under salt stress conditions. Such modular organization parallels findings in other signaling systems, where second messengers act as master regulators of networked gene sets [1].

4.5. Physiological Validation of cAMP-Mediated Tolerance

Phenotypic assays confirmed that AC transgenic seedlings maintained longer roots and higher overall vigor under salt stress, while HPLC quantification validated elevated cAMP accumulation. This direct correlation between biochemical modulation and physiological outcome underscores the functional significance of cAMP signaling in mediating adaptive responses. Although cAMP elevation did not entirely eliminate stress damage, it provided substantial mitigation, highlighting its potential for engineering abiotic stress resilience.

4.6. Implications and Future Directions

Our findings position cAMP as a central integrator of transcriptional, protein-folding, and hormonal pathways in plants. Exploiting this signaling axis could enable rational design of crops with enhanced tolerance to salinity and other abiotic stresses. Future studies should investigate cAMP cross-talk with calcium signaling, ROS pathways, and phytohormone networks to fully elucidate its regulatory scope and potential for crop improvement.

5. Conclusions

Engineered elevation of cAMP via adenylate cyclase induces extensive transcriptional and proteostatic reprogramming in Arabidopsis, activating genes and pathways that contribute to salt tolerance. The findings position cAMP as a central regulatory molecule capable of coordinating multilevel stress adaptation. Harnessing cAMP signaling holds promise for engineering crops with improved resilience to salinity and other environmental stresses