Yams (Dioscorea spp.) remain an important staple food crop for millions of people in Sub-Saharan Africa. Pre-breeding is a key strategy for broadening the genetic base of cultivated yams, particularly for traits related to biotic stress, yield and environmental adaptation. However, the efficiency of transferring useful alleles from wild species into cultivated backgrounds and their relationship with early phenotypic expression remains poorly understood. This study investigated introgression patterns and early phenotypic expression in interspecific hybrids derived from wild yams (Dioscorea abyssinica × D. praehensilis) and cultivated D. rotundata using SNP marker analysis and seedling-stage phenotypic characterisation. A total of 163 progenies and two parental genotypes were evaluated using 1358 SNP markers across 20 chromosomes alongside six vegetative traits. Early phenotypic assessment revealed substantial segregation across all traits, with leaf number, an indicator of seedling vigour, showing the highest variability (CV = 78.3%). SNP analysis confirmed genetic diversity and hybrid origin, with expected heterozygosity of 0.17 and 40.6% polymorphic loci, while population structure revealed two major genetic clusters corresponding to wild and cultivated ancestry. Introgression analysis showed an average wild (35%) and cultivated (65%) ancestries, with no SNP locus reaching complete fixation for either wild or cultivated alleles across the progeny population. Chromosomal analysis indicated heterogeneous introgression, with wild allele proportions ranging from 22% (chromosome 20) to 43% (Chromosome 12). However, Mantel analysis revealed no significant correlation between genetic and phenotypic distances (r = 0.004, p = 0.931), suggesting possible influence of specific genomic regions rather than overall ancestry. These findings demonstrate that early-generation introgression populations retain substantial genetic diversity and wild genomic contributions, providing a valuable foundation for targeted selection and future marker-assisted yam breeding programmes.
Paclitaxel (PTX) is a valuable anticancer diterpenoid traditionally sourced from Taxus species, but its limited natural availability necessitates alternative production strategies. Comprehensive identification, characterization, and functional analysis were conducted on DBAT (10-deacetyl baccatin III-10-O-acetyltransferase) genes in Corylus avellana and evaluated their role in paclitaxel biosynthesis under nanoparticle elicitation. Totally, 14 CaDBAT genes were characterized and mapped to seven chromosomes, exhibiting conserved BAHD acyltransferase motifs and catalytic domains alongside diverse exon–intron structures, subcellular localizations, and duplication events. Structural modeling revealed conserved α-helices and β-sheets with >90% residues in favorable Ramachandran plot regions, validating model stability and catalytic potential. Expression profiling under Al2O3NPs and WO3NPs treatments demonstrated significant transcriptional activation of most CaDBAT genes, with CaDBAT1, CaDBAT4, CaDBAT8, CaDBAT9, and CaDBAT13 showing the strongest induction, particularly under WO3. Interestingly, gene-specific stress responses were evident, with CaDBAT3 responding exclusively to WO3, while CaDBAT6 was selectively induced by Al2O3. HPLC based metabolite quantification confirmed that nanoparticle elicitation significantly enhanced taxane accumulation compared to control, with Al2O3 yielding the highest paclitaxel content (139.15 μg g−1 FW), while WO3 preferentially increased intermediates such as 10-deacetyl baccatin III and 7-epi-10-deacetyl taxol. Together, these findings highlight a direct link between nanoparticle-induced stress, CaDBAT gene regulation, and increased paclitaxel production. This work not only establishes C. avellana as a promising alternative source of paclitaxel but also provides a molecular and biochemical framework for developing nanoparticle-based elicitation and metabolic engineering strategies to enhance high-value secondary metabolite production in plants.
Cold stress is one of the most significant abiotic factors affecting rice yields worldwide. Therefore, it is crucial to identify genes that enhance rice cold tolerance without adversely affecting plant growth. In this study, we analyzed the expression patterns of CISP1, and CISP2 cold-tolerance genes isolated from barley at the tissue level and examined their potential contributions to rice cold tolerance. First, using in situ hybridization, we confirmed that CISP2 transcripts accumulate primarily in meristematic tissues under prolonged cold stress. This suggests that the CISP2 protein is involved in maintaining intracellular homeostasis, including metabolism. Furthermore, in experiments using rice callus, while wild-type rice callus cultured at low temperatures turned brown and died, CISP-expressing transgenic rice callus continued to proliferate. Furthermore, seeds obtained from transgenic rice plants overexpressing CISP1 exhibited a germination rate of approximately 92% even at low temperatures (10 °C), confirming that a certain level of metabolism is maintained even under cold conditions. We confirmed that the transgene was stably expressed in the transgenic rice plants used in experiments from the T0 to T3 generations. Next, phenotypic analysis of the transgenic rice revealed that while overexpression of CISP2 enhanced cold tolerance, it also caused severe growth retardation, including a reduction in the number of tillers, shorter panicles, poor grain filling, and lower 1000-grain weight. In contrast, CISP1 promoted root elongation, tillering, and plant height but did not affect yield-related traits. Furthermore, CISP1 transgenic seedlings exhibited better growth even under low-temperature stress, with longer roots and stems and a higher number of roots, whereas the CISP2-GFP line showed increased taproot elongation. Based on these results, it was confirmed that CISP1 can confer cold tolerance to rice without significantly limiting growth. Therefore, it has become clear that, of the two CISP genes, CISP1 is the one that helps improve cold tolerance in rice without adversely affecting other traits. Going forward, it is expected that CISP1 will be useful in molecular breeding aimed at improving cold tolerance in crops, including rice.
Camptostemon philippinensis, an endangered mangrove species distributed in Southeast Asia, is increasingly threatened by coastal development and habitat degradation, including the construction of a new capital city, highlighting the need for genetic data to support conservation. Despite its ecological importance and the conservation urgency, C. philippinensis remains underrepresented in major genomic databases, limiting comparative and evolutionary studies within Malvaceae. This study characterized the chloroplast genome of C. philippinensis and compared it with related Malvaceae species, with particular emphasis on its relationship to C. schultzii. Genomic DNA was sequenced to characterize the chloroplast genome, including analyses of amino acid frequency, codon usage, and repeat identification. Phylogenetic trees were generated for the complete and partial chloroplast genomes using four markers (trnK-matK, rbcL, trnL-trnF, and ndhF). Comparative analyses of wholegenome alignment, amino acid and repeat patterns, inverted repeat junction patternsand nucleotide polymorphisms were performed. The assembled chloroplast genome, measuring 160,586 bp, exhibits a quadripartite structure. A total of 130 genes were annotated, including 86 protein-coding genes, 8 rRNA genes, and 36 tRNA genes. Both complete and partial chloroplast genome analyses consistently placed C. philippinensis within the subfamily Malvoideae. The complete chloroplast genome analysis provided improved phylogenetic resolution compared with analyses based on partial chloroplast markers. Comparative analysis revealed that chloroplast genomes within Malvaceae are highly conserved, although several polymorphic regions were identified between the two Camptostemon species. The chloroplast genome generated in this study provides a useful reference for future phylogenetic, evolutionary, and conservation-related studies of this endangered mangrove species.
Improvement of agronomic traits in sesame (Sesamum indicum L.) depends on identifying functionally important genes and applying precise genome editing approaches. In the present study, a trait-guided computational strategy was followed in which multivariate analysis of morphological traits was used to identify flowering related processes for downstream CRISPR/Cas9 target prioritization. Thirteen quantitative traits were analysed using a Python-based workflow, which revealed that flowering-related traits were strongly represented in the principal components and exhibited high inter-correlation. Based on these multivariate associations, a flowering-associated candidate gene (SIN_1017333) was selected for subsequent CRISPR/Cas9 target analysis. A total of 44 sgRNA candidates were generated using CRISPOR and assessed for their efficiency, specificity, and potential off-target effects. Among them, three sgRNAs showed high specificity with negligible off-target risk and were selected for further consideration. All selected guides were located within coding regions, suggesting their suitability as candidate targets for future functional genome editing studies. Overall, this study presents a reproducible computational framework that integrates multivariate phenotypic analysis with biologically guided CRISPR/Cas9 target prioritization for sesame improvement.
γ-Glutamyl cyclotransferases (GGCTs) play an important role in glutathione catabolism and redox regulation in plants under diverse stress conditions. In this study, two GGCT isoforms were identified in rice: OsGGCT1 (chromosome 4) and OsGGCT2;1 (chromosome 2), sharing 64.7% nucleotide identity. Phylogenetic analysis showed that both isoforms are conserved and cluster with monocot homologs, suggesting divergence from a common ancestor. Physicochemical characterization indicated differences in stability, with OsGGCT1 exhibiting a lower instability index compared to OsGGCT2;1. Secondary structure predictions showed random coils as the dominant feature in both proteins, while domain analysis confirmed their belonging to the ChaC family. Promoter cis-regulatory element analysis indicated robust basal transcriptional potential and efficient recruitment of transcriptional machinery. Structural modeling with AlphaFold generated high-confidence 3D structures, which were further validated through stereochemical quality assessments. Molecular docking and molecular dynamics simulations revealed distinct interaction patterns with glutathione: GGCT2;1 displayed stronger binding affinity but greater conformational flexibility, whereas GGCT1 maintained lower structural fluctuations and compactness. Principal component analysis supported these findings, highlighting restricted motions in GGCT1 compared to the dynamic rearrangements in GGCT2;1. Gene expression analysis demonstrated functional divergence under stress conditions: OsGGCT2;1 was strongly induced by arsenate and salt stress, while OsGGCT1 was predominantly upregulated under cold and Fusarium infection. Collectively, these results provide new insights into the structural-functional divergence of rice GGCT isoforms, highlighting their distinct roles in abiotic and biotic stress adaptation.
Green leaf volatiles rapidly trigger stress-related transcription, but the gene-level features that shape volatile responsiveness remain unclear. DNA methylation profiles provide stable genomic annotations linked to expression breadth, evolutionary conservation and regulatory variation. Here, we reanalyzed publicly deposited RNA-seq datasets generated by our group from 7-day-old Arabidopsis seedlings exposed to a 30-min trans-2-hexenal (T2H) treatment and integrated them with public gene-level methylome annotations from Shahzad et al. (2025). The RNA-seq dataset included Columbia-0 (Col-0), the HEAT SHOCK TRANSCRIPTION FACTOR A2 loss-of-function mutant (hsfa2), the HISTONE DEACETYLASE 6 mutant (hda6) and the natural accession Cvi-0. All four backgrounds contained matched non-stress and T2H libraries, and Col-0 and hsfa2 additionally contained heat-stress libraries. The methylation data were not generated from T2H-treated plants and were used as pre-existing gene-level annotations. T2H-responsive genes were non-randomly distributed across unmethylated, gene-body methylated and transposable-element-like methylated categories. Among gene-body methylated genes, low or intermediate conservation was associated with stronger transcriptional responsiveness, whereas highly conserved gene-body methylation was associated with comparatively stable expression behavior. Methylation-associated expression quantitative trait locus (methylation-eQTL) and expression-variance annotations further linked volatile responsiveness with natural methylation-associated expression architecture and prioritized candidates including ATHSFA2, ATHSP101, AtFBS1, SARD1, DOGT1, AtC3H29, AtC3H47 and AtBCS1. These findings indicate that pre-existing methylation architecture is associated with the magnitude and variability of volatile-induced transcriptional responses, without implying rapid T2H-induced DNA methylation remodeling.
Gene duplication is a core driver of plant genome diversification and adaptive evolution. Calcineurin B-like protein 2 (CBL2), a plant-specific calcium sensor, forms signaling complexes with CBL-interacting protein kinases (CIPKs) to regulate ion homeostasis and stress responses. Turnip (Brassica rapa var. rapa) is a traditional crop cultivated on the Qinghai-Tibet Plateau, yet the functional divergence and adaptive mechanisms of its CBL2 genes remain unclear. Using the turnip genome, we identified two segmentally duplicated CBL2 copies, designated BrrCBL2.1 and BrrCBL2.2. Phylogenetic analyses placed the two copies in distinct clades and revealed sequence differences in the C-terminal PFPF motif, critical for CIPK interaction, suggesting functional divergence. Quantitative real-time PCR showed that BrrCBL2.1 is strongly up-regulated under ion stresses such as low potassium and high magnesium, whereas BrrCBL2.2 responds specifically to 4 °C cold stress. In vivo luciferase complementation (LUC) assays in tobacco demonstrated pronounced specificity in their interaction patterns with members of the BrrCIPK family, with BrrCBL2.2 forming complexes with a broader set of CIPKs. Transgenic validation in Arabidopsis thaliana further showed that BrrCBL2.1 effectively rescues the growth phenotype of the cbl2 mutant under low-potassium stress, retaining the ancestral CBL2 function in ion-homeostasis control; by contrast, BrrCBL2.2 markedly enhances cold tolerance in transgenic plants. These findings reveal that duplicated BrrCBL2 genes achieved functional division through divergence in the PFPF motif, specialized expression, and expanded interaction networks. This study offers insights into plant adaptation to high-altitude environments and provides genetic resources for stress-resilience breeding in Brassica crops.
In recent years, researchers have recognized the importance of biofertilizer development, especially in the context of climate change and the growing global population and its increasing food demand. Therefore, the development of effective biofertilizers requires prior identification and understanding of the microbes involved in the formulation. Information on PGPR-related microbial genes is important not only for biofertilizer development but also for biocontrol, stress tolerance studies, rhizosphere research, and screening of efficient microbial strains. Although certain dedicated tools for studying microbes exist, they are not accessible to all types of users, and gene-level classification of PGPR-associated sequences remains limited. Therefore, we developed a webbased application known as the PGPR Gene Analyzer, which classifies protein sequences as PGPR-associated or non-PGPR on the basis of normalized pairwise similarity scoring against a curated reference dataset. The reference dataset consists of 265 high-quality non-redundant protein sequences representing 15 functional genes across six PGPR trait categories, retrieved and filtered from 1000 NCBI GenBank entries. This platform classifies query sequences using the Biopython PairwiseAligner API in local alignment mode with a >= 70% normalized similarity threshold. Overall, the PGPR Gene Analyzer provides an accessible, reproducible, and continuously expandable resource for gene-centric PGPR annotation, supporting research in sustainable agriculture.
In the context of fully artificial raft cultivation of kelp, these organisms frequently experience elevated light exposure at the water surface due to drifting, which can detrimentally influence their normal growth and development, particularly affecting vulnerable seedlings. To elucidate the kelp's response mechanisms to high light stress and to identify functional genes that may facilitate improved seedling cultivation, this study performed a transcriptomic analysis on "Benniu" kelp seedlings subjected to medium and high light stress conditions. A cDNA library was constructed and sequenced using Illumina technology. A total of 1263 DEGs were identified, with 874 between high and low light treatments, 192 between medium and low light, and 182 between high and medium light conditions. GO functional annotation indicated that biological processes such as xylan catabolism, lipid metabolism, ubiquitin-independent endoplasmic reticulum-associated degradation, extracellular region activity, and ferulic esterase activity were significantly influenced and upregulated under light stress. Furthermore, KEGG pathway analysis revealed that medium and high light stress affected the linoleic acid metabolic pathway, with high light stress additionally impacting arachidonic acid metabolism and glutathione metabolism. WGCNA identified that genes within the "dark green" module exhibited a positive correlation with light intensity (r = 0.80, P = 0.009), whereas genes in the "coral 1" module showed a negative correlation (r = -0.76, P = 0.019). Based on these results, it is recommended that light intensity be maintained below 10,000 lx for kelp seedlings cultivated at approximately 15 cm depth prior to seedling separation to optimize growth conditions.
The mitigative impacts of exogenous melatonin have been reported in salt-stressed glycophytic plants. However, its role in the adaptation of halophytes to salinity remains elusive. There are four key enzymes in melatonin biosynthesis: tryptophan decarboxylase (TDC), tryptamine 5-hydroxylase (T5H), N-acetylserotonin O-methyltransferase (ASMT), and serotonin N-acetyltransferase (SNAT). This work aims to identify and analyze the melatonin biosynthetic genes in salt cress (Eutrema salsugineum) using bioinformatics and transcriptome data under salt stress. Totally, 18 genes were identified: 2 TDCs, 10 T5Hs, 4 SNATs, and 2 ASMTs. Based on phylogenetic analysis, EsTDC1, and EsTDC2 showed a close relationship with OsTDC2, and AtTDC. The T5H1, T5H2, and T5H8 were related to M. notabilis T5H6, G. soja T5H, G. arboreum T5H and C. cajan T5H proteins. The EsSNATs grouped into two clusters. Cis-Elements were functionally divided into 5 classes. The highest number was found in stress-responsive cis-elements, suggesting a possible role for these genes in response to stress factors. RNA-seq data showed that the TDC, T5H ASMT, and SNAT genes respond to salt in both leaves and roots. The T5H6 and T5H9 genes significantly increased in leaves, only. This work provides new insights into the potential roles of melatonin biosynthesis in E. salsugineum.
Chloroplasts contribute not only to primary metabolism but also to the regulation of plant growth and stress adaptation through metabolic signaling. AT4G33780 is annotated as a putative ATP phosphoribosyltransferase (ATP-PRT) regulatory subunit and is predicted to localize to chloroplasts. However, its physiological function in planta remains unclear. Here, we characterized AT4G33780 in Arabidopsis thaliana using CRISPR/Cas9 knockout and overexpression lines. AT4G33780 localized to chloroplasts and displayed tissue-specific expression. Genetic perturbation of AT4G33780 resulted in dosage-dependent phenotypes affecting seed germination, early seedling establishment, vegetative growth, and root responses to nickel stress. Transcriptomic analysis revealed extensive transcriptional reprogramming, including coordinated changes in cell wall-associated gene families, while untargeted metabolomics identified broad alterations in central carbon metabolism. Integrated analyses suggest that AT4G33780 functions as a chloroplast-associated modulatory factor linking metabolic status with developmental outputs under variable conditions. These findings identify AT4G33780 as a regulator of development and stress-associated responses and provide a basis for future mechanistic investigation.
The study of spontaneous and induced chlorophyll mutants has long served as a crucial avenue to understand the genetic and physiological underpinnings of these phenotypes. Recent advancements in molecular biology and bioinformatics have introduced powerful tools, significantly augmenting the strategies used to investigate the chlorophyll mutants. By employing these modern techniques, researchers can now do more than just determine the direct cause of a mutant phenotype; they can also explore the mutation's broader effect on global gene expression within plants. The present article reviews the reports on the studies on chlorophyll mutants published in the relatively recent past. It should be emphasised that availability of modern tools has provided a new dimension to the evaluation of chlorophyll mutants. These mutants are now being investigated at transcriptomic, proteomic and metabolomic level in addition to map-based cloning of candidate gene. This has certainly opened-up a new arena to investigate and also has created immense possibilities to understand and utilise the process of pigment biosynthesis, chloroplast biogenesis and photosynthesis from a larger perspective. Leveraging -omic data is essential to gain deeper insights into the regulatory mechanisms involved in these processes. This enhanced understanding of photosynthesis will directly aid the development of efficient strategies to breed high-yielding crop varieties. Concurrently, prioritising the isolation and exploration of chlorophyll mutants in other plant species is necessary to fully grasp physiological and molecular diversity among plants. Such foundational knowledge will ultimately facilitate the breeding of climate-resilient crop varieties.
Oil palm (Elaeis guineensis Jacq.) is an economically important perennial crop in Malaysia that requires a substantial amount of phosphate fertilizer on a regular basis to maintain optimum oil output. Phospate STARVATION RESPONSE 1 (PHR1) and its homologues, PHR1-like 1 (PHL1) and PHR1-like 2 (PHL2), regulate systemic responses in Arabidopsis under Pi deprivation condition, but their function in oil palm remains largely unknown. In this study, a protein designated as EgPHL7 which contains the MYB and coiled-coil (CC) domains, was identified in oil palm. The GFP protein that served as control was detected throughout the entire transformed onion cell, where else the EgPHL7-GFP fusion protein accumulated predominantly in the onion nucleus, confirming nuclear-localization of its encoded product. Our results demonstrated that EgPHL7 and its homologues, EgPHR1 and EgPHR2 can directly bind to P1BS in vitro and in vivo. Notably, a novel cis-acting element, AGATACT motif was identified in the promoter of EgSPX1. This novel motif combined with P1BS to form a P1BS::P1BS-like structure which showed the highest DNA-binding ability with the oil palm MYB-CC transcription factors. Further in silico promoter analysis revealed the present of this novel motif with variant bases in the promoter of several phosphate-starvation induced genes in oil palm. Together, the findings suggest that these MYB-CC TFs may form an integrative regulatory network to manipulate Pi-starvation molecular responses in oil palm. These findings provide new valuable knowledge of the common DNA interaction property between PHR1 family of transcription factors that govern the transcriptional responses under Pi-deficiency stress.
Nutrition is fundamental to human health and environmental sustainability, with cereal crops serving as a vital component in feeding the universal population. However, variability in climate poses significant environmental stresses to crop productivity, particularly in developing countries. This study focuses on enhancing rice productivity in eastern India, a region highly vulnerable to droughts, by employing statistical and machine learning approaches. A population of Recombinant Inbred Lines (RILs), generated from drought-tolerant Banglami and drought-susceptible Ranjit rice varieties, was examined under conditions of reproductive stage drought stress (RS) and non-stress (NS). A range of phenotypic traits was analyzed, and machine learning models, such as Random Forest (RF) and Support Vector Machines (SVM), were employed to distinguish genotypes based on drought resistance. The results indicate that specific traits significantly correlate with yield under stress conditions, and SVM outperforms other models in accurately classifying stress-resistant genotypes. This research provides valuable insights for breeders to develop cultivars with enhanced stress tolerance, contributing to sustainable food security.
Environmental accumulation of toxic metalloids such as arsenic (As), cadmium (Cd), and antimony (Sb) has emerged as a major challenge for plant productivity, food safety, and environmental sustainability. Rice (Oryza sativa), a staple crop globally, is particularly vulnerable due to its cultivation in flooded conditions that facilitate metalloid uptake. Recent research highlights the central regulatory roles of non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and small interfering RNAs (siRNAs) in orchestrating plant stress responses at transcriptional, post-transcriptional, and epigenetic levels. Advancements in high-throughput omics technologies, encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics, have enabled systems-level insights into these regulatory networks. Integrative multi-omics approaches, complemented by artificial intelligence (AI) and machine learning (ML) tools, have greatly improved our ability to mine large datasets, identify stress-responsive ncRNAs, and decode complex gene-environment interactions. Although comprehensive multi-omics datasets for stress biology in plants remain limited, emerging AI-guided frameworks show promise in accelerating the discovery of ncRNA biomarkers and their functional roles. This review underscores the importance of omics-driven exploration of non-coding regulatory layers in rice plants and advocates for their strategic application in developing stress-resilient crop varieties through molecular breeding, genome editing, and ncRNA-based biotechnological interventions.
