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Heat stress is an important abiotic factor that limits wheat production globally, including south-east Asia. The importance of micro (mi) RNAs in gene expression under various biotic and abiotic stresses is well documented. Molecular markers, specifically simple sequence repeats (SSRs), play an important role in the wheat improvement breeding programs. Given the role of miRNAs in heat stress-induced transcriptional regulation and acclimatization, the development of miRNA-derived SSRs would prove useful in studying the allelic diversity at the heat-responsive miRNA-genes in wheat. In the present study, efforts have been made to identify SSRs from 96 wheat heat-responsive miRNA-genes and their characterization using a panel of wheat genotypes with contrasting reactions (tolerance/susceptible) to heat stress. A set of 13 miRNA-derived SSR markers were successfully developed as an outcome. These miRNA-SSRs are located on 11 different common wheat chromosomes (2A, 3A, 3B, 3D, 4D, 5A, 5B, 5D, 6A, 6D, and 7A). Among 13 miRNA-SSRs, seven were polymorphic on a set of 37 selected wheat genotypes. Within these polymorphic SSRs, three makers, namely HT-169j, HT-160a, and HT-160b, were found promising as they could discriminate heat-tolerant and heat-susceptible genotypes. This is the first report of miRNA-SSR development in wheat and their deployment in genetic diversity and population structure studies and characterization of trait-specific germplasm. The study suggests that this new class of molecular makers has great potential in the marker-assisted breeding (MAB) programs targeted at improving heat tolerance and other adaptability or developmental traits in wheat and other crops.

Understanding the changes in peanut ( Arachis hypogaea L.) anther lipidome under heat stress (HT) will aid in understanding the mechanisms of heat tolerance. We profiled the anther lipidome of seven genotypes exposed to ambient temperature (AT) or HT during flowering. Under AT and HT, the lipidome was dominated by phosphatidylcholine (PC), phosphatidylethanolamine (PE), and triacylglycerol (TAG) species (> 50% of total lipids). Of 89 lipid analytes specified by total acyl carbons:total carbon–carbon double bonds, 36:6, 36:5, and 34:3 PC and 34:3 PE (all contain 18:3 fatty acid and decreased under HT) were the most important lipids that differentiated HT from AT. Heat stress caused decreases in unsaturation indices of membrane lipids, primarily due to decreases in highly-unsaturated lipid species that contained 18:3 fatty acids. In parallel, the expression of Fatty Acid Desaturase 3-2 ( FAD3-2 ; converts 18:2 fatty acids to 18:3) decreased under HT for the heat-tolerant genotype SPT 06-07 but not for the susceptible genotype Bailey. Our results suggested that decreasing lipid unsaturation levels by lowering 18:3 fatty-acid amount through reducing FAD3 expression is likely an acclimation mechanism to heat stress in peanut. Thus, genotypes that are more efficient in doing so will be relatively more tolerant to HT.

Background Heavy metal toxicity has become a major threat to sustainable crop production worldwide. Thus, considerable interest has been placed on deciphering the mechanisms that allow plants to combat heavy metal stress. Strategies to deal with heavy metals are largely focused on detoxification, transport and/or sequestration. The P 1B subfamily of the H eavy M etal-transporting P-type A TPases (HMAs) was shown to play a crucial role in the uptake and translocation of heavy metals in plants. Here, we report the locus-specific expression changes in the rice HMA genes together with several low-copy cellular genes and transposable elements upon the heavy metal treatment and monitored the transgenerational inheritance of the altered expression states. We reveal that plants cope with heavy metal stress by making heritable changes in gene expression and further determined gene-specific responses to heavy metal stress. Results We found most HMA genes were upregulated in response to heavy metal stress, and furthermore found evidence of transgenerational memory via changes in gene regulation even after the removal of heavy metals. To explore whether DNA methylation was also altered in response to the heavy metal stress, we selected a Tos17 retrotransposon for bisulfite sequencing and studied its methylation state across three generations. We found the DNA methylation state of Tos17 was altered in response to the heavy metal stress and showed transgenerational inheritance. Conclusions Collectively, the present study elucidates heritable changes in gene expression and DNA methylation in rice upon exposure to heavy metal stress and discusses implications of this knowledge in breeding for heavy metal tolerant crops.

Peanut allergies affect millions of people worldwide, often causing life-threatening reactions and necessitating strict avoidance. Recent advancements in oral immunotherapy, such as Palforzia™, and IgE-mediated treatments (e.g., Xolair), have improved care options; however, their high costs limit accessibility and widespread utility. To address these challenges, researchers are employing conventional breeding and advanced molecular tools, such as CRISPR editing, to develop peanut lines with reduced levels of major allergenic proteins (Ara h1, Ara h2, Ara h3, and Ara h6). These reduced-immunogenicity genotypes retain their agronomic viability, flavor, and nutritional quality to some extent, offering the potential for cost-effective oral immunotherapy and safe food options for use in public spaces by non-allergic individuals. Rigorous evaluation, including immunological assays and human feeding trials, is essential to confirm their effectiveness in reducing allergic reactions. Adoption will depend on the establishment of clear regulatory guidelines, stakeholder education, and transparent communication of the benefits and risks. With sustained research, public trust, and supportive policies, reduced-immunogenicity peanuts could substantially lower the global burden of peanut allergies. This communication examined the impact of peanut allergies worldwide and explored strategies to develop peanut genotypes with reduced allergen content, including conventional breeding and advanced genetic engineering. It also addressed the challenges associated with these approaches, such as policy and regulatory hurdles, and outlined key requirements for their successful adoption by farmers and consumers.

Hexaploid bread wheat (Triticum aestivum L., genome BBAADD) is generally more salt tolerant than its tetraploid wheat progenitor (Triticum turgidum L.). However, little is known about the physiological basis of this trait or about the relative contributions of allohexaploidization and subsequent evolutionary genetic changes on the trait development. Here, we compared the salt tolerance of a synthetic allohexaploid wheat (neo-6 x) with its tetraploid (T. turgidum ; BBAA) and diploid (Aegilops tauschii; DD) parents, as well as a natural hexaploid bread wheat (nat-6 x). We studied 92 morphophysiological traits and analyzed homeologous gene expression of a major salt-tolerance gene High-Affinity K ⁺ Transporter 1;5 (HKT1;5). We observed that under salt stress, neo-6 x exhibited higher fitness than both of its parental genotypes due to inheritance of favorable traits like higher germination rate from the 4 x parent and the stronger root Na ⁺ retention capacity from the 2 x parent. Moreover, expression of the D-subgenome HKT1;5 homeolog, which is responsible for Na ⁺ removal from the xylem vessels, showed an immediate transcriptional reprogramming following allohexaploidization, i.e., from constitutive high basal expression in Ae. tauschii (2 x) to salt-induced expression in neo-6 x . This phenomenon was also witnessed in the nat-6 x . An integrated analysis of 92 traits showed that, under salt-stress conditions, neo-6 x resembled more closely the 2 x than the 4 x parent, suggesting that the salt stress induces enhanced expressivity of the D-subgenome homeologs in the synthetic hexaploid wheat. Collectively, the results suggest that condition-dependent functionalization of the subgenomes might have contributed to the wide-ranging adaptability of natural hexaploid wheat.

Cotton (Gossypium spp.) is a major source of natural fiber and an important cash crop. The cotton growth habit and architecture determine its productivity and influence management strategies for commercial production. The GATA transcription factors (TFs) control various developmental processes in plants, such as flower, bract and embryo development, and petal differentiation. As stable transformation is still a bottleneck in many plant species, TRV-VIGS was used to manipulate gene expression in different plants, including Gossypium hirsutum L. In this study, we undertook the TRV-based VIGS to functionally characterize two candidate genes, Gohir.D05G103700 and Gohir.D12G153600, identified through expression QTL analysis for five floral induction and meristem identity genes using the upland cotton mini-core collection. Virus-induced silencing of the Gohir.D05G103700 gene resulted in up to a 1.4-fold reduction in the transcript level in two inoculated plants, G3 and G4, and Gohir.D12G153600 gene resulted in up to a 2.3-fold reduction in transcript level in a single inoculated plant P05 relative to the mock-treated plant. The TRV2-Gohir.D05G103700-inoculated plants G3 and G4 also exhibited loss of the supernumerary (fourth) floral bract in the squares, whereas the TRV2-Gohir.D12G153600-inoculated plants did not show any observable phenotypic change relative to the mock-treated plants. Altogether, this study suggested that TRV-VIGS can be used to characterize genes in cotton relatively rapidly, and the cotton Gohir.D05G103700 gene is a positive regulator of the indeterminate growth habit in cotton, which could be manipulated to obtain a cotton plant with architecture best suited for the cultivation area.

Cytosine methylation at CG sites (ᵐCG) plays critical roles in development, epigenetic inheritance, and genome stability in mammals and plants. In the dicot model plant Arabidopsis thaliana , methyltransferase 1 (MET1), a principal CG methylase, functions to maintain ᵐCG during DNA replication, with its null mutation resulting in global hypomethylation and pleiotropic developmental defects. Null mutation of a critical CG methylase has not been characterized at a whole-genome level in other higher eukaryotes, leaving the generality of the Arabidopsis findings largely speculative. Rice is a model plant of monocots, to which many of our important crops belong. Here we have characterized a null mutant of OsMet1-2 , the major CG methylase in rice. We found that seeds homozygous for OsMet1-2 gene mutation (OsMET1-2 ⁻/⁻), which directly segregated from normal heterozygote plants (OsMET1-2 ⁺/⁻), were seriously maldeveloped, and all germinated seedlings underwent swift necrotic death. Compared with wild type, genome-wide loss of ᵐCG occurred in the mutant methylome, which was accompanied by a plethora of quantitative molecular phenotypes including dysregulated expression of diverse protein-coding genes, activation and repression of transposable elements, and altered small RNA profiles. Our results have revealed conservation but also distinct functional differences in CG methylases between rice and Arabidopsis .

Background Yellow or stripe rust, caused by the fungus Puccinia striiformis  f. sp. tritici  ( Pst ) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. Results Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker–trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5 , Yr7 , Yr16 , Yr26 , Yr30 , Yr43 , Yr44 , Yr64 , YrCH52 , and YrH52 . Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. Conclusion The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.

Despite various methods for detecting and treating SARS-CoV-2, affordable and easily applicable solutions are still needed. Aptamers can potentially fill this gap. Here, we establish a workflow to identify aptamers that bind to the spike proteins of SARS-CoV-2, a process applicable to other targets as well. The spike protein is crucial for the virus’s entry into host cells. The aptamer development process for the spike protein’s receptor binding domain (RBD) begins with splitting the SARS-CoV-2’s genome into 40 nucleotide-long sequences, predicting their two-dimensional structure, and sorting based on the free energy. Selected oligomers undergo three-dimensional structure prediction and docking onto the viral spike protein’s RBD. Six RNA oligomers were identified as top candidates based on the RNA docking with the SARS-CoV-2 wild-type (WT) (Wuhan-Hu-1 strain) and Omicron variant BA.1 RBD and molecular dynamics simulations. Three oligomers also demonstrated strong predicted binding affinity with other SARS-CoV-2 variants, including BA.2, XBB.1.5, and EG.5, based on the protein–aptamer docking followed by stability evaluation using the MD simulations. The aptamer with the best fit for the spike protein RBD was later validated using biolayer interferometry. The process has resulted in identifying a single aptamer from a library of 29,000 RNA oligomers, which exhibited affinity in the submicromolar range and the potential to develop into a viral screen or therapeutic.

One of the thrust areas of research in plant breeding is to develop crop cultivars with enhanced tolerance to abiotic stresses. Thus, identifying abiotic stress‐responsive genes (SRGs) and proteins is important for plant breeding research. However, identifying such genes via established genetic approaches is laborious and resource intensive. Although transcriptome profiling has remained a reliable method of SRG identification, it is species specific. Additionally, identifying multistress responsive genes using gene expression studies is cumbersome. Thus, endorsing the need to develop a computational method for identifying the genes associated with different abiotic stresses. In this work, we aimed to develop a computational model for identifying genes responsive to six abiotic stresses: cold, drought, heat, light, oxidative, and salt. The predictions were performed using support vector machine (SVM), random forest, adaptive boosting (ADB), and extreme gradient boosting (XGB), where the autocross covariance (ACC) and K‐mer compositional features were used as input. With ACC, K‐mer, and ACC + K‐mer compositional features, the overall accuracy of ∼60–77, ∼75–86, and ∼61–78% were respectively obtained using the SVM algorithm with fivefold cross‐validation. The SVM also achieved higher accuracy than the other three algorithms. The proposed model was also assessed with an independent dataset and obtained an accuracy consistent with cross‐validation. The proposed model is the first of its kind and is expected to serve the requirement of experimental biologists; however, the prediction accuracy was modest. Given its importance for the research community, the online prediction application, ASRpro, is made freely available (https://iasri‐sg.icar.gov.in/asrpro/) for predicting abiotic SRGs and proteins. Core Ideas Identification of multiple abiotic SRGs is vital to breeding for stress tolerance. We developed a computational model using ML algorithms to predict abiotic stress‐responsive proteins. Being the first of its kind tool, expected to supplement the wet experiments for identifying SRGs. The online prediction tool ASRpro (https://iasri‐sg.icar.gov.in/asrpro/) is made freely available to the community.