Transcriptomic insights into drought response in wild Arachis relatives A. dardani and A. ipaënsis

Abstract

Drought is a major environmental constraint limiting global peanut productivity. Wild peanut species, characterized by greater genetic diversity, represent valuable resources for improving drought resilience in cultivated peanut. However, the molecular mechanisms underpinning drought tolerance in wild peanut species remain largely unexplored. This study evaluated the drought tolerance of three wild-type peanut accessions from two different species, Arachis dardani GK12946, Arachis dardani V7215, and Arachis ipaënsis K30076. Physiological measurements such as fresh weight and dry weight revealed statistically non-significant differences between drought-stressed and well-watered conditions, indicating strong inherent drought tolerance. Transcriptome analysis revealed that 3272, 3648, and 1181 genes in leaf samples of A. dardani GK12946, A. dardani V7215, and A. ipaënsis K30076 were differentially expressed, respectively. In root samples, 3014, 3472, and 2033 genes were differentially expressed in the same accessions. Notably, differentially expressed genes (DEGs) and set intersection (Venn) analysis suggests A. dardani V7215 exhibited the highest number of DEGs (1155) uniquely expressed in leaves, and 899 DEGs uniquely expressed in roots, suggesting accession-specific gene expression. Gene Ontology enrichment revealed that upregulated genes were associated with abiotic stress responses, temperature stimulus, heat stress, and DNA-binding transcription factor activity. Co-expression network analysis using WGCNA identified key drought-responsive modules, enriched for GO terms like stress regulation, protein folding, as well as GST family amino acid metabolic processes. Overall, this study provides comprehensive insights into the molecular basis of drought tolerance in wild peanut accessions. Our findings establish a valuable resource for functional genomics and crop improvement under water-limited conditions.

Supplementary Information: The online version contains supplementary material available at 10.1186/s12870-025-07799-7.

Keywords: Arachis dardani; Arachis ipaënsis; Crop improvement, WCGNA (Weighted gene co-expression network analysis); Differentially expressed genes (DEGs); Drought tolerance; Transcriptome analysis; Wild peanut species.

Fig. 1

Principal component analysis (PCA) and differential gene expression analysis. PCA biplot of (A) leaf samples and (B) root samples, illustrating the separation of samples based on treatment and cultivar. Each point represents a biological replicate (in = 4 per group), and samples are labeled by accession and treatment group. The analysis includes two cultivars of A. dardani GK12946 and A. dardani V7215 and one cultivar of A. ipaënsis K30076. Differentially expressed genes (DEGs) identified in (C) leaf samples and (D) root samples. Differential expressions were determined by separately comparing drought-treated versus watered samples within each cultivar and tissue type. Genes with a log2 fold change ≥ ±2 and FDR ≤ 0.05 were considered significantly up- or downregulated in drought vs well-watered conditions

Fig. 2

Gene Ontology (GO) enrichment analysis of differentially upregulated genes. GO enrichment in (A) Arachis dardani GK12946 leaf, (B) Arachis dardani V7215 leaf, (C) Arachis ipaënsis K30076 leaf, (D) Arachis dardani GK12946 root, (E) Arachis dardani V7215 root, and (F) Arachis ipaënsis K30076 root. The top ten significantly enriched GO terms from all categories of biological processes, molecular functions, and cellular components are presented. Enrichment analysis was performed using the Cluster Profiler R package to identify GO terms overrepresented among upregulated genes compared to the background gene set, with significance determined by a false discovery rate (FDR) cutoff of < 0.05. The point color corresponds to the significance level, and the size corresponds to the gene count of a particular enriched GO term

Fig. 3

Venn diagrams and Gene Ontology (GO) enrichment analysis of core differentially expressed genes (DEGs) under drought versus watered conditions. (A-C) leaf samples and (D-F) root samples. (A, D) All DEGs, (B, E) upregulated DEGs, and (C, F) downregulated DEGs, with comparisons performed between drought-treated and well-watered plants within each cultivar and tissue type. Overlapping regions in the Venn diagrams indicate genes commonly differentially expressed across all three cultivars. GO enrichment analysis for genes commonly upregulated in (G) leaf and (H) root samples. The top ten significantly enriched GO terms were identified across the categories of biological process, molecular function, and cellular component. Enrichment analysis was conducted using the clusterProfiler R package with an FDR threshold of < 0.05. Dot size reflects the number of genes associated with each GO term, and color indicates the adjusted p-value

Fig. 4

Gene co-expression and Gene Ontology (GO) enrichment analysis of drought-induced gene modules. Heatmap of drought-induced modules of A leaf and B root samples. The GO enrichment analysis of the modules C leaf and D root samples. The top ten significantly enriched GO terms from biological processes are presented. Enrichment analysis was performed using the Cluster Profiler R package to identify GO terms overrepresented among upregulated genes compared to the background gene set, with significance determined by a false discovery rate (FDR) cutoff of ≤ 0.05. The point color corresponds to the significance level, and the size corresponds to the gene count of a particular enriched GO term

Fig. 5

Gene regulatory network (GRN) inference and Sanky plot of selected genes under drought stress in peanut. GRNs were constructed from DEGs in (A) leaf and (B) root samples. Expression data were normalized by VST normalization, and regulatory relationships were inferred using the GENIE3 algorithm, which applies a random forest-based ensemble method to predict gene-gene interactions. From the full DEG set, a subset of transcription factors (TFs) overlapped with the expression matrix and were used as candidate regulators. The analysis yielded high-confidence regulatory interactions (edge weight >0.045), forming a directed network of genes. Network visualization was performed using the ggraph package with a Fruchterman-Reingold force-directed layout to highlight network structure and connectivity. Transcription factors are shown in red and are often central nodes, regulating multiple downstream targets. (C) The Sanky flow diagram revealed the key genes potentially involved in drought stress responses (from left to right gene family, genes, accessions, tissue)

Fig. 6

Coordinated ABA and ethylene signalling networks drive tissue-specific drought responses in peanut leaves and roots.The figure presents an integrated view of how drought-responsive gene expression in peanut is shaped by hormonal signalling and regulatory networks in leaf (left) and root (right) tissues. Differentially expressed genes (DEGs) responsive to abscisic acid (ABA) and ethylene were identified and visualized through clustered heatmaps, highlighting genotype-specific transcriptional patterns. Gene regulatory networks (GRNs) reveal hormone-driven regulatory hubs that orchestrate downstream responses. These include enhanced expression of genes involved in reactive oxygen species (ROS) detoxification and other key stress-adaptive functions. In leaves, the networks align with physiological processes such as stomatal regulation and senescence, while in roots they support structural and metabolic adaptations for drought resilience. Together, this model underscores the complexity and tissue-specific nature of hormonal crosstalk during drought stress. Abbreviations: DEG, differentially expressed gene; ABA, abscisic acid; GRN, gene regulatory network; ROS, reactive oxygen species