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Summary: Hybridization, the process of crossing individuals from diverse genetic backgrounds, plays a pivotal role in evolution, biological invasiveness, and crop breeding.At the transcriptional level, hybridization often leads to complex nonadditive effects, presenting challenges for understanding its consequences. Although standard transcriptomic analyses exist to compare hybrids to their progenitors, such analyses have not been implemented in a software package, hindering reproducibility.We introduce hybridexpress, an R/Bioconductor package designed to facilitate the analysis, visualization, and comparison of gene expression patterns in hybrid triplets (hybrids and their progenitors). hybridexpress provides users with a user‐friendly and comprehensive workflow that includes all standard comparative analyses steps, including data normalization, calculation of midparent expression values, sample clustering, expression‐based gene classification into categories and classes, and overrepresentation analysis for functional terms.We illustrate the utility of hybridexpress through comparative transcriptomic analyses of cotton allopolyploidization and rice root trait heterosis. hybridexpress is designed to streamline comparative transcriptomic studies of hybrid triplets, advancing our understanding of evolutionary dynamics in allopolyploids, and enhancing plant breeding strategies. hybridexpress is freely accessible from Bioconductor (https://bioconductor.org/packages/HybridExpress) and its source code is available on GitHub (https://github.com/almeidasilvaf/HybridExpress). [ABSTRACT FROM AUTHOR]

Miscanthus ×giganteus (giant miscanthus), first found as a naturally occurring hybrid, has shown promise as a bioenergy/biomass crop throughout much of the temperate world. This allotriploid (2n = 3x = 57) hybrid resulted from a cross between tetraploid Miscanthus sacchariflorus (2n = 4x = 76) and diploid Miscanthus sinensis (2n = 2x = 38) and is particularly desirable due to its low fertility that minimizes reseeding and potential invasiveness. However, there is limited genetic diversity in commonly grown cultivars of triploid M. ×giganteus and breeding and development efforts to improve and domesticate this crop have been minimal. Here, we report on newly developed M. ×giganteus hybrids compared with the industry standard M. ×giganteus '1993‐1780'. Dry biomass yields of new hybrids ranged from 19.5 to 32.4 Mg/ha/year for the fourth growing season, compared with 21.0 Mg/ha/year for M. ×giganteus '1993‐1780'. Plant reproductive fertility remained low for all accessions with overall fertility [(seed set × seed germination)/100] ranging from 0.3% to 4.5% for new hybrids compared to 0.4% for M. ×giganteus '1993‐1780'. Culm density and height varied among accessions and were positively correlated with increased biomass. Based on compositional analyses, theoretical ethanol yields ranged from 9, 740 to 16,278 L/ha/year for new hybrids compared to 10,406 L/ha/year for M. ×giganteus '1993‐1780'. Relative feed value indices were low overall and ranged between 66.0 and 72.8 for new hybrids compared to M. ×giganteus '1993‐1780' with 71.3. The genetic diversity of new hybrids, compared with existing cultivars, was characterized using whole genome sequences. Based on pair‐wise distances, cluster analysis clearly showed increased diversity of new hybrids compared with earlier selections. These results document new triploid hybrids of M. ×giganteus with enhanced biomass and theoretical ethanol yields in combination with broader genetic diversity and lowreproductive fertility. [ABSTRACT FROM AUTHOR]

Summary: Plants grow rapidly for maximal production under optimal conditions; however, they adopt a slower growth strategy to maintain survival when facing environmental stresses. As salt stress restricts crop architecture and grain yield, identifying genetic variations associated with growth and yield responses to salinity is critical for breeding optimal crop varieties. OsDSK2a is a pivotal modulator of plant growth and salt tolerance via the modulation of gibberellic acid (GA) metabolism; however, its regulation remains unclear. Here, we showed that OsDSK2a can be phosphorylated at the second amino acid (S2) to maintain its stability. The gene‐edited mutant osdsk2aS2G showed decreased plant height and enhanced salt tolerance. SnRK1A modulated OsDSK2a‐S2 phosphorylation and played a substantial role in GA metabolism. Genetic analysis indicated that SnRK1A functions upstream of OsDSK2a and affects plant growth and salt tolerance. Moreover, SnRK1A activity was suppressed under salt stress, resulting in decreased phosphorylation and abundance of OsDSK2a. Thus, SnRK1A preserves the stability of OsDSK2a to maintain plant growth under normal conditions, and reduces the abundance of OsDSK2a to limit growth under salt stress. Haplotype analysis using 3 K‐RG data identified a natural variation in OsDSK2a‐S2. The allele of OsDSK2a‐G downregulates plant height and improves salt‐inhibited grain yield. Thus, our findings revealed a new mechanism for OsDSK2a stability and provided a valuable target for crop breeding to overcome yield limitations under salinity stress. [ABSTRACT FROM AUTHOR]

Background: Seed endophytes have a significant impact on plant health and fitness. They can be inherited and passed on to the next plant generation. However, the impact of breeding on their composition in seeds is less understood. Here, we studied the indigenous seed microbiome of a recently domesticated perennial grain crop (Intermediate wheatgrass, Thinopyrum intermedium L.) that promises great potential for harnessing microorganisms to enhance crop performance by a multiphasic approach, including amplicon and strain libraries, as well as molecular and physiological assays. Results: Intermediate wheatgrass seeds harvested from four field sites in Europe over three consecutive years were dominated by Proteobacteria (88%), followed by Firmicutes (10%). Pantoea was the most abundant genus and Pantoea agglomerans was identified as the only core taxon present in all samples. While bacterial diversity and species richness were similar across all accessions, the relative abundance varied especially in terms of low abundant and rare taxa. Seeds from four different breeding cycles (TLI C3, C5, C704, C801) showed significant differences in bacterial community composition and abundance. We found a decrease in the relative abundance of the functional genes nirK and nifH as well as a drop in bacterial diversity and richness. This was associated with a loss of amplicon sequence variants (ASVs) in Actinobacteria, Alphaproteobacteria, and Bacilli, which could be partially compensated in offspring seeds, which have been cultivated at a new site. Interestingly, only a subset assigned to potentially beneficial bacteria, e.g. Pantoea, Kosakonia, and Pseudomonas, was transmitted to the next plant generation or shared with offspring seeds. Conclusion: Overall, this study advances our understanding of the assembly and transmission of endophytic seed microorganisms in perennial intermediate wheatgrass and highlights the importance of considering the plant microbiome in future breeding programs. [ABSTRACT FROM AUTHOR]

Summary: The rhizosphere microbiome plays critical roles in plant growth and provides promising solutions for sustainable agriculture. While the rhizosphere microbiome frequently fluctuates with the soil environment, recent studies have demonstrated that a small proportion of the microbiome is consistently assembled in the rhizosphere of a specific plant genotype regardless of the soil condition, which is determined by host genetics. Based on these breakthroughs, which involved exploiting the plant‐beneficial function of the rhizosphere microbiome, we propose to divide the rhizosphere microbiome into environment‐dominated and plant genetic‐dominated components based on their different assembly mechanisms. Subsequently, two strategies to explore the different rhizosphere microbiome components for agricultural production are suggested, that is, the precise management of the environment‐dominated rhizosphere microbiome by agronomic practices, and the elucidation of the plant genetic basis of the plant genetic‐dominated rhizosphere microbiome for breeding microbiome‐assisted crop varieties. We finally present the major challenges that need to be overcome to implement strategies for modulating these two components of the rhizosphere microbiome. [ABSTRACT FROM AUTHOR]

The selection of highly productive genotypes with stable performance across environments is a major challenge of plant breeding programs due to genotype-by-environment (GE) interactions. Over the years, different metrics have been proposed that aim at characterizing the superiority and/or stability of genotype performance across environments. However, these metrics are traditionally estimated using phenotypic values only and are not well suited to an unbalanced design in which genotypes are not observed in all environments. The objective of this research was to propose and evaluate new estimators of the following GE metrics: Ecovalence, Environmental Variance, Finlay–Wilkinson regression coefficient, and Lin–Binns superiority measure. Drawing from a multi-environment genomic prediction model, we derived the best linear unbiased prediction for each GE metric. These derivations included both a squared expectation and a variance term. To assess the effectiveness of our new estimators, we conducted simulations that varied in traits and environment parameters. In our results, new estimators consistently outperformed traditional phenotype-based estimators in terms of accuracy. By incorporating a variance term into our new estimators, in addition to the squared expectation term, we were able to improve the precision of our estimates, particularly for Ecovalence in situations where heritability was low and/or sparseness was high. All methods are implemented in a new R-package: GEmetrics. These genomic-based estimators enable estimating GE metrics in unbalanced designs and predicting GE metrics for new genotypes, which should help improve the selection efficiency of high-performance and stable genotypes across environments. [ABSTRACT FROM AUTHOR]

Cowpeas (Vigna unguiculata L. Walp) have been credible constituents of nutritious food and forage in human and animal diets since the Neolithic era. The modern technique of Diversity Array Technology (DArTseq) is both cost-effective and rapid in producing thousands of high-throughputs, genotyped, single nucleotide polymorphisms (SNPs) in wide-genomic analyses of genetic diversity. The aim of this study was to assess the heterogeneity in cowpea genotypes using DArTseq-derived SNPs. A total of 92 cowpea genotypes were selected, and their fourteen-day-old leaves were freeze-dried for five days. DNA was extracted using the CTAB protocol, genotyped using DArTseq, and analysed using DArTsoft14. A total of 33,920 DArTseq-derived SNPs were recalled for filtering analysis, with a final total of 16,960 SNPs. The analyses were computed using vcfR, poppr, and ape in R Studio v1.2.5001-3 software. The heatmap revealed that the TVU 9596 (SB26), Orelu (SB72), 90K-284-2 (SB55), RV 403 (SB17), and RV 498 (SB16) genotypes were heterogenous. The mean values for polymorphic information content, observed heterozygosity, expected heterozygosity, major allele frequency, and the inbreeding coefficient were 0.345, 0.386, 0.345, 0.729, and 0.113, respectively. Moreover, they validated the diversity of the evaluated cowpea genotypes, which could be used for potential breeding programmes and management of cowpea germplasm. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) is a groundbreaking statistical machine learning method for advancing plant and animal breeding. Nonetheless, its practical implementation remains challenging due to numerous factors affecting its predictive performance. This research explores the potential of data augmentation to enhance prediction accuracy across entire datasets and specifically within the top 20% of the testing set. Our findings indicate that, overall, the data augmentation method (method A), when compared to the conventional model (method C) and assessed using Mean Arctangent Absolute Prediction Error (MAAPE) and normalized root mean square error (NRMSE), did not improve the prediction accuracy for the unobserved cultivars. However, significant improvements in prediction accuracy (evidenced by reduced prediction error) were observed when data augmentation was applied exclusively to the top 20% of the testing set. Specifically, reductions in MAAPE_20 and NRMSE_20 by 52.86% and 41.05%, respectively, were noted across various datasets. Further investigation is needed to refine data augmentation techniques for effective use in genomic prediction. [ABSTRACT FROM AUTHOR]

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. [ABSTRACT FROM AUTHOR]

Breeding salt-tolerant crops is necessary to reduce food insecurity. Prebreeding populations are fundamental for uncovering tolerance alleles from wild germplasm. To obtain a physiological interpretation of the agronomic salt tolerance and better criteria to identify candidate genes, quantitative trait loci (QTLs) governing productivity-related traits in a population of recombinant inbred lines (RIL) derived from S. pimpinellifolium were reanalyzed using an SNP-saturated linkage map and clustered using QTL meta-analysis to synthesize QTL information. A total of 60 out of 85 QTLs were grouped into 12 productivity MQTLs. Ten of them were found to overlap with other tomato yield QTLs that were found using various mapping populations and cultivation conditions. The MQTL compositions showed that fruit yield was genetically associated with leaf water content. Additionally, leaf Cl− and K+ contents were related to tomato productivity under control and salinity conditions, respectively. More than one functional candidate was frequently found, explaining most productivity MQTLs, indicating that the co-regulation of more than one gene within those MQTLs might explain the clustering of agronomic and physiological QTLs. Moreover, MQTL1.2, MQTL3 and MQTL6 point to the root as the main organ involved in increasing productivity under salinity through the wild allele, suggesting that adequate rootstock/scion combinations could have a clear agronomic advantage under salinity. [ABSTRACT FROM AUTHOR]

The increase in the global human population and the accompanying challenges in meeting nutritional needs amidst climate change are a worldwide concern. Widespread protein and micronutrient deficiencies contribute to a significant number of individuals experiencing malnutrition, leading to severe health repercussions. This issue can be addressed through genomics-assisted breeding, particularly in enhancing the nutritional profile of vital staple crops like chickpea (Cicer arietinum L.). Chickpea, beyond being a rich source of protein, provides a diverse nutritional spectrum encompassing carbohydrates, fats, and minerals. To explore and improve the genetic basis of nutritional traits in chickpea, a study was conducted using 93 kabuli-type single plant derived lines and five cultivars in 2018, 2022, and 2023. Genotyping by sequencing revealed a total of 165K single nucleotide polymorphisms (SNPs) within this kabuli chickpea mini-core collection. After filtering for a minor allele frequency greater than 5%, 113,512 SNPs were utilized, distributed across eight chromosomes of the chickpea genome. Marker-trait associations were analyzed using genome wide association study, leading to the identification of 27 significantly associated SNPs from across all eight chromosomes linked to three seed nutritional concentrations and 100-seed weight. To unravel the molecular mechanisms governing seed protein, fiber, fat concentrations, and 100-seed weight, 31 candidate genes were determined within a 30 kb window size. This comprehensive approach holds promise for advancing crop breeding strategies to combat malnutrition and improve global food security. [ABSTRACT FROM AUTHOR]

Summary: Professor Rajeev K. Varshney's transformative impact on crop genomics, genetics, and agriculture is the result of his passion, dedication, and unyielding commitment to harnessing the potential of genomics to address the most pressing challenges faced by the global agricultural community. Starting from a small town in India and reaching the global stage, Professor Varshney's academic and professional trajectory has inspired many scientists active in research today. His ground‐breaking work, especially his effort to list orphan tropical crops to genomic resource‐rich entities, has been transformative. Beyond his scientific achievements, Professor Varshney is recognized by his colleagues as an exemplary mentor, fostering the growth of future researchers, building institutional capacity, and strengthening scientific capability. His focus on translational genomics and strengthening seed system in developing countries for the improvement of agriculture has made a tangible impact on farmers' lives. His skills have been best utilized in roles at leading research centres where he has applied his expertise to deliver a new vision for crop improvement. These efforts have now been recognized by the Royal Society with the award of the Fellowship (FRS). As we mark this significant milestone in his career, we not only celebrate Professor Varshney's accomplishments but also his wider contributions that continue to transform the agricultural landscape. [ABSTRACT FROM AUTHOR]

Summary: Rice blast, caused by Magnaporthe oryzae, significantly impacts grain yield, necessitating the identification of broad‐spectrum resistance genes and their functional mechanisms for disease‐resistant crop breeding. Here, we report that rice with knockdown OsHDAC1 gene expression displays enhanced broad‐spectrum blast resistance without effects on plant height and tiller numbers compared to wild‐type rice, while rice overexpressing OsHDAC1 is more susceptible to M. oryzae. We identify a novel blast resistance transcription factor, OsGRAS30, which genetically acts upstream of OsHDAC1 and interacts with OsHDAC1 to suppress its enzymatic activity. This inhibition increases the histone H3K27ac level, thereby boosting broad‐spectrum blast resistance. Integrating genome‐wide mapping of OsHDAC1 and H3K27ac targets with RNA sequencing analysis unveils how OsHDAC1 mediates the expression of OsSSI2, OsF3H, OsRLR1 and OsRGA5 to regulate blast resistance. Our findings reveal that the OsGRAS30–OsHDAC1 module is critical to rice blast control. Therefore, targeting either OsHDAC1 or OsGRAS30 offers a promising approach for enhancing crop blast resistance. [ABSTRACT FROM AUTHOR]

Summary: Rhizomes are modified stems that grow underground and produce new individuals genetically identical to the mother plant. Recently, a breakthrough has been made in efforts to convert annual grains into perennial ones by utilizing wild rhizomatous species as donors, yet the developmental biology of this organ is rarely studied. Oryza longistaminata, a wild rice species featuring strong rhizomes, provides a valuable model for exploration of rhizome development. Here, we first assembled a double‐haplotype genome of O. longistaminata, which displays a 48‐fold improvement in contiguity compared to the previously published assembly. Furthermore, spatiotemporal transcriptomics was performed to obtain the expression profiles of different tissues in O. longistaminata rhizomes and tillers. Two spatially reciprocal cell clusters, the vascular bundle 2 cluster and the parenchyma 2 cluster, were determined to be the primary distinctions between the rhizomes and tillers. We also captured meristem initiation cells in the sunken area of parenchyma located at the base of internodes, which is the starting point for rhizome initiation. Trajectory analysis further indicated that the rhizome is regenerated through de novo generation. Collectively, these analyses revealed a spatiotemporal transcriptional transition underlying the rhizome initiation, providing a valuable resource for future perennial crop breeding. [ABSTRACT FROM AUTHOR]

Background: Extreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa. Results: This study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested. Conclusions: In the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change. [ABSTRACT FROM AUTHOR]

Background: There is a lack of knowledge on the combined effects of different stresses on plants, in particular different stresses that occur during winter in temperate climates. Perennial herbaceous plants in temperate regions are exposed to many different stresses during winter, but except for the fact that cold temperatures induce resistance to a number of them, very little is known about their interaction effects. Knowledge about stress interactions is needed in order to predict effects of climate change on both agricultural production and natural ecosystems, and to develop adaptation strategies, e.g., through plant breeding. Here, we conducted a series of experiments under controlled conditions to study the interactions between cold (low positive temperature), clover rot infection (caused by Sclerotinia trifoliorum) and freezing, in red clover (Trifolium pratense) accessions. We also compared our results with winter survival in field experiments and studied associations between stress and shoot growth. Results: Exposure to low positive temperatures (cold acclimation) induced resistance to clover rot. There was a clear negative interaction effect between freezing stress and clover rot infection, resulting in up to 37% lower survival rate compared to what would have been expected from the additive effect of freezing and infection alone. Freezing tolerance could continue to improve during incubation under artificial snow cover at 3 °C in spite of darkness, and we observed compensatory shoot growth following freezing after prolonged incubation. At the accession level, resistance to clover rot was negatively correlated with growth in the field during the previous year at a Norwegian location. It was also negatively correlated with the shoot regrowth of control plants after incubation. Clover rot resistance tests under controlled conditions showed limited correlation with clover rot resistance observed in the field, suggesting that they may reveal variation in more specific resistance mechanisms. Conclusions: We here demonstrate, for the first time, a strong negative interaction between freezing and infection with a winter pathogen. We also characterize the effects of cold acclimation and incubation in darkness at different temperatures on winter stress tolerance, and present data that support the notion that annual cycles of growth and stress resistance are associated at the genetic level. [ABSTRACT FROM AUTHOR]

Background: Isolated microspore culture is a useful biotechnological technique applied in modern plant breeding programs as it can produce doubled haploid (DH) plants and accelerate the development of new varieties. Furthermore, as a single-cell culture technique, the isolated microspore culture provides an excellent platform for studying microspore embryogenesis. However, the reports on isolated microspore culture are rather limited in rice due to the low callus induction rate, poor regeneration capability, and high genotypic dependency. The present study developed an effective isolated microspore culture protocol for high-frequency androgenesis in four japonica rice genotypes. Several factors affecting the isolated microspore culture were studied to evaluate their effects on callus induction and plantlet regeneration. Results: Low-temperature pre-treatment at 4 ℃ for 10–15 days could effectively promote microspore embryogenesis in japonica rice. A simple and efficient method was proposed for identifying the microspore developmental stage. The anthers in yellow-green florets located on the second type of primary branch on the rice panicle were found to be the optimal stage for isolated microspore culture. The most effective induction media for callus induction were IM2 and IM3, depending on the genotype. The optimal concentration of 2, 4-D in the medium for callus induction was 1 mg/L. Callus induction was negatively affected by a high concentration of KT over 1.5 mg/L. The differentiation medium suitable for japonica rice microspore callus comprised 1/2 MS, 2 mg/L 6-BA, 0.5 mg/L NAA, 30 g/L sucrose, and 6 g/L agar. The regeneration frequency of the four genotypes ranged from 61–211 green plantlets per 100 mg calli, with Chongxiangjing showing the highest regeneration frequency. Conclusions: This study presented an efficient protocol for improved callus induction and green plantlet regeneration in japonica rice via isolated microspore culture, which could provide valuable support for rice breeding and genetic research. [ABSTRACT FROM AUTHOR]

Background: Yield and quality are the two most important traits in crop breeding. Exploring the regulatory mechanisms that affect both yield and quality traits is of great significance for understanding the molecular genetic networks controlling these key crop attributes. Expansins are cell wall loosening proteins that play important roles in regulating rice grain size. Results: We investigated the effect of OsEXPA7, encoding an expansin, on rice grain size and quality. OsEXPA7 overexpression resulted in increased plant height, panicle length, grain length, and thousand-grain weight in rice. OsEXPA7 overexpression also affected gel consistency and amylose content in rice grains, thus affecting rice quality. Subcellular localization and tissue expression analyses showed that OsEXPA7 is localized on the cell wall and is highly expressed in the panicle. Hormone treatment experiments revealed that OsEXPA7 expression mainly responds to methyl jasmonate, brassinolide, and gibberellin. Transcriptome analysis and RT-qPCR experiments showed that overexpression of OsEXPA7 affects the expression of OsJAZs in the jasmonic acid pathway and BZR1 and GE in the brassinosteroid pathway. In addition, OsEXPA7 regulates the expression of key quantitative trait loci related to yield traits, as well as regulates the expression levels of BIP1 and bZIP50 involved in the seed storage protein biosynthesis pathway. Conclusions: These results reveal that OsEXPA7 positively regulates rice yield traits and negatively regulates grain quality traits by involving plant hormone pathways and other trait-related pathway genes. These findings increase our understanding of the potential mechanism of expansins in regulating rice yield and quality traits and will be useful for breeding high-yielding and high-quality rice cultivars. [ABSTRACT FROM AUTHOR]

Background: X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice (Oryza sativa) root segments in CT volumes. Results: VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data. Conclusions: A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating. [ABSTRACT FROM AUTHOR]

Coleus scutellarioides (L.) Benth. is a globally spread species, known for its characteristic spectacularly colorful leaves of decorative value. Thanks to its rich chemical composition, the plant is used in ethnopharmacology, and it is also regarded as having high medicinal potential. The application of in vitro cultures enables the acquisition of homogeneous certified material of high quality. Additionally, excluding the effect of biotic and abiotic factors on the plants is a way to fully recognize the influence of phytohormones on the plant morphology and the biosynthetic pathways of compound production. The best way to grow C. scutellarioides "Electric Lime" under in vitro conditions is to use the basic MS medium (Murashige and Skoog medium), enriched with naphthyl-1-acetic acid at a concentration of 0.5 mg dm−3. The analysis of volatile compounds demonstrated that the content of volatile compounds in the plants cultivated under in vivo conditions was expressed at a level of 2848.59 µg g−1, whereas in the plants bred in vitro without supplementation with phytohormones, the level was 8191.47 µg g−1. The highest content was noted for copaene, α-pinene, 1-octene-3-ol, α-selinene, sabinen, γ- and δ-cadinene, 3-octanol, and β-pinene. Aroma profiling revealed a lack of boranyl acetate, 2-hexenal, and 2-hexen-1-ol in the plants cultivated under in vivo conditions. Differences were found in the volatile composition between plants bred in vivo and in vitro, with the most significant recorded for the contents of 1-octen-3-ol and 3-octanol. The addition of plant growth regulators into the basic medium under in vitro conditions affected the percentage ratio and contents of specific compounds in plant tissues. The most intense biosynthesis of volatile compounds took place in the plants cultivated on the medium enriched with NAA at 10,579.11 µg g−1, whereas the least intense was noted for plants cultivated on the medium supplemented with BA, where it was recorded at the level of 5610.02 µg g−1. So far, there has been no research published which would pertain to the profiling of volatile compounds performed using the SPME (solid-phase microextraction) technique. Moreover, the very few studies conducted on the chemical composition of these compounds do not mention the specific variety of C. scutellarioides under analysis. [ABSTRACT FROM AUTHOR]

As a physical mutagen, carbon ion beam (CIB) irradiation can induce high-frequency mutation, which is user-friendly and environment-friendly in plant breeding. In this study, we resequenced eight mutant lines which were screened out from the progeny of the CIB-irradiated dehulled rice seeds. Among these mutants, CIB induced 135,535 variations, which include single base substitutions (SBSs), and small insertion and deletion (InDels). SBSs are the most abundant mutation, and account for 88% of all variations. Single base conversion is the main type of SBS, and the average ratio of transition and transversion is 1.29, and more than half of the InDels are short-segmented mutation (1–2 bp). A total of 69.2% of the SBSs and InDels induced by CIBs occurred in intergenic regions on the genome. Surprisingly, the average mutation frequency in our study is 9.8 × 10−5/bp and much higher than that of the previous studies, which may result from the relatively high irradiation dosage and the dehulling of seeds for irradiation. By analyzing the mutation of every 1 Mb in the genome of each mutant strain, we found some unusual high-frequency (HF) mutation regions, where SBSs and InDels colocalized. This study revealed the mutation mechanism of dehulled rice seeds by CIB irradiation on the genome level, which will enrich our understanding of the mutation mechanism of CIB radiation and improve mutagenesis efficiency. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability. [ABSTRACT FROM AUTHOR]

Background: The formation of shoots plays a pivotal role in plant organogenesis and productivity. Despite its significance, the underlying molecular mechanism of de novo regeneration has not been extensively elucidated in Capsicum annuum 'Dempsey', a bell pepper cultivar. To address this, we performed a comparative transcriptome analysis focusing on the differential expression in C. annuum 'Dempsey' shoot, callus, and leaf tissue. We further investigated phytohormone-related biological processes and their interacting genes in the C. annuum 'Dempsey' transcriptome based on comparative transcriptomic analysis across five species. Results: We provided a comprehensive view of the gene networks regulating shoot formation on the callus, revealing a strong involvement of hypoxia responses and oxidative stress. Our comparative transcriptome analysis revealed a significant conservation in the increase of gene expression patterns related to auxin and defense mechanisms in both callus and shoot tissues. Consequently, hypoxia response and defense mechanism emerged as critical regulators in callus and shoot formation in C. annuum 'Dempsey'. Current transcriptome data also indicated a substantial decline in gene expression linked to photosynthesis within regenerative tissues, implying a deactivation of the regulatory system governing photosynthesis in C. annuum 'Dempsey'. Conclusion: Coupled with defense mechanisms, we thus considered spatial redistribution of auxin to play a critical role in the shoot morphogenesis via primordia outgrowth. Our findings shed light on shoot formation mechanisms in C. annuum 'Dempsey' explants, important information for regeneration programs, and have broader implications for precise molecular breeding in recalcitrant crops. [ABSTRACT FROM AUTHOR]

The limited engagement of farmers and their community agents in the process of variety development and evaluation is one of the main factors for lower rates of variety adoption, which resulted in lower crop productivity. Participatory bread wheat variety evaluation was jointly conducted by breeders, members of seed producer cooperative (SPC) and local experts with the objectives to identify farmers' selection criteria, to assess farmers' preferred varieties and to increase the SPC's variety portfolios. Twelve released bread wheat varieties were evaluated during 2019 cropping season at Sekela district of Amhara region, Ethiopia. Pair-wise ranking was used to identify farmers' selection criteria and direct matrix ranking to prioritize those selected criteria. Varieties were planted in two sets with and without lime application. Grain yield, followed by earliness, disease tolerance, and tillering capacity, was identified the first preferred criterion by farmers. The mean values showed that higher yield was recorded with the lime application compared to no lime application. Lemu (6.4 t/ha), Alidoro (6.3 t/ha), Dambal (6.3 t/ha), Obora (6.1 t/ha) and Liben (6.1 t/ha) had greater yield with lime application. The t-test results indicated that significant difference between with and without liming for the number of tillers per plant, spike length per plant, plant height, 1000 seed weight, and hectoliter weight, but non-significant difference for grain yield. The selected varieties should be included in the production plan of the cooperatives for large scale production to increase the variety portfolios and to address the demand of the farming community. [ABSTRACT FROM AUTHOR]

Genetic diversity in plant breeding is key to dissecting the genetic architecture of complex economical traits. Genome-wide association studies (GWAS) and genomic selection (GS) have emerged as promising tools to accelerate agronomic trait improvement efficiency, defining current breeding strategies. The key objective of this study was to investigate the genetic architecture and perform genomic prediction of yield, specific gravity (SG), and dry matter (DM) traits, using a diversity panel of 568 Solanum tuberosum Andigenum accessions. Analyses were carried out using a total of 4271 Single Nucleotide Polymorphisms (SNPs) obtained using the SolCAP SNP array 8 K version. Yield data were collected from field evaluations during three consecutive years, while tuber quality traits (SG and DM) were evaluated in 1 year. To this end, population structure was first examined and revealed two main subpopulations. GWAS analysis identified candidate loci for yield in chromosomes 1, 3, 4 and 11, with minor effects on phenotypic variation (< 1–10%). Important genomic regions were detected for SG in chromosomes 5, 6, and 8, and in DM in chromosome 7, though they explained only a small fraction of the phenotypic variation (< 1–2% for SG and 3% for DM). Functional annotation of candidate genes showed promising genomic regions related to tuber growth, quality and development that could further be verified by other tools (i.e., gene editing). Furthermore, the predicted capacity of the cross-validation process between subpopulations was low for yield and specific gravity. However, when the prediction process was conducted across the entire population, the predictive power increase, as a greater diversity in the data enables better prediction. Instead, dry matter genomic prediction accuracies were higher in accordance with its higher narrow sense heritability value. These findings enriched GWAS analyses in an Andigenum potato collection and reinforce the potential of applying GS in Colombian breeding programs using diverse populations. [ABSTRACT FROM AUTHOR]

Ogura cytoplasmic male sterility (Ogura CMS) is extensively applied in hybrid seed production in cruciferous crops. However, the posttranscriptional molecular basis of Ogura CMS in cruciferous crops remains elusive. Here, a data-independent acquisition-based proteomic approach coupled with a parallel reaction monitoring-based targeted proteomic assay was used to analyze the proteome dynamics of Ogura CMS cabbage line RM and its maintainer line RF during floral bud development to obtain insights into the mechanism underlying Ogura CMS in cruciferous crops. A total of 9 162 proteins corresponding to 61 464 peptides were identified in RM and RF floral buds. The proteomic fluctuation of RM was weaker than that of RF. Differences in protein expression between RM and RF gradually enlarged with floral bud development. Fifteen continually up-regulated and eight continually down-regulated proteins were found in RM relative to RF throughout floral bud development. Differentially expressed proteins between RM and RF during floral bud development were implicated in the endoplasmic reticulum (ER)-associated protein processing pathway, in which most of them exhibited down-regulated expression in RM. These data suggest that ER-associated protein processing may be involved in pollen abortion in Ogura CMS cabbage by inhibiting the expression of critical factors. Our findings not only deepen the understanding of the molecular mechanisms of Ogura CMS in cruciferous crops but also provide better guidance for applying Ogura CMS in the hybrid breeding of cruciferous crops. [ABSTRACT FROM AUTHOR]

Selecting wheat hybrids is vital for enhancing crop yield, adapting to changing climates, and ensuring food security. These hybrids align with market demands and sustainable farming practices, contributing to efficient crop management. Traditional methods for wheat hybrid selection, such as molecular techniques, are costly and time-consuming, and are prone to human error. However, advancements in artificial intelligence and machine learning offer non-destructive, objective, and more efficient solutions. This study is explored the classification of wheat varieties and hybrids using two deep learning models, MobileNetv2 and GoogleNet. These models are achieved impressive classification accuracy, with MobileNetv2 reaching 99.26% and GoogleNet achieving 97.41%. In the second scenario, the deep features obtained from these models are classified with Support Vector Machine (SVM). In the classification made with the MobileNetv2-SVM hybrid model, an accuracy of 99.91% is achieved. This study is provided rapid and accurate wheat variety and hybrid identification method, as well as contributing to breeding programs and crop management. [ABSTRACT FROM AUTHOR]

This study utilized the machine learning algorithm called "Bagging" to gain a better understanding of the relationships between climate variability, crop yields, and the agricultural contributions to the gross domestic product (agrigdp) in the Nile basin. The multiple stepwise regression model (MSRM) was also used as a comparison. The time series data collected from 1961 to 2016 for four main crops (groundnuts, sorghum, wheat, and sugarcane), rainfall, air temperature (minimum, mean, and maximum), and agrigdp were divided into 70% for training and 30% for testing using R packages. The results indicated that the Bagging algorithm improved crop yield predictions by 18%—63% compared to the MSRM. All crops demonstrated a greater sensitivity to climate variability, resulting in a low probability of achieving the highest attainable crop yield: 18% – 33% for groundnut, 18%—38% for sorghum, 21%—41% for wheat, and 22%—56% for sugarcane. The climate variability also led to a basin-wide loss of 35 million US$ in the agrigdp, with a basin-wide probability of 29%—61% for attaining the highest agrigdp. The algorithm clearly identified the ideal climatic conditions for reaching the highest attainable crop yields and agrigdp in the Nile Basin. The central factor was found to be air temperature, particularly the mean temperature, with anomalies of ± 0.5 °C responsible for 17%—69% of crop yield losses. Strategies for adapting to and managing risks associated with climate variability should focus on on-farm air temperature management practices, technologies such as greenhouses, and plant breeding. [ABSTRACT FROM AUTHOR]

Cotton is an important economic crop; therefore, enhancing cotton yield and cultivating superior varieties are key research priorities. The seedling stage, a critical phase in cotton production, significantly influences the subsequent growth and yield of the crop. Therefore, breeding experts often choose to measure phenotypic parameters during this period to make breeding decisions. Traditional methods of phenotypic parameter measurement require manual processes, which are not only tedious and inefficient but can also damage the plants. To effectively, rapidly, and accurately extract three-dimensional phenotypic parameters of cotton seedlings, precise segmentation of phenotypic organs must first be achieved. This paper proposes a neural network-based segmentation algorithm for cotton seedling organs, which, compared to the average precision of 75.4% in traditional unsupervised learning, achieves an average precision of 96.67%, demonstrating excellent segmentation performance. The segmented leaf and stem point clouds are used for the calculation of phenotypic parameters such as stem length, leaf length, leaf width, and leaf area. Comparisons with actual measurements yield coefficients of determination R 2 of 91.97%, 90.97%, 92.72%, and 95.44%, respectively. The results indicate that the algorithm proposed in this paper can achieve precise segmentation of stem and leaf organs, and can efficiently and accurately extract three-dimensional phenotypic structural information of cotton seedlings. In summary, this study not only made significant progress in the precise segmentation of cotton seedling organs and the extraction of three-dimensional phenotypic structural information, but the algorithm also demonstrates strong applicability to different varieties of cotton seedlings. This provides new perspectives and methods for plant researchers and breeding experts, contributing to the advancement of the plant phenotypic computation field and bringing new breakthroughs and opportunities to the field of plant science research. [ABSTRACT FROM AUTHOR]

It is of great significance to explore the molecular mechanism of gametophytic sexual reproduction in the genetic improvement and breeding of tea plants [Camellia sinensis (L.) O. Kuntze]. Imported beta family members (IMBs) are a class of widely distributed nucleoplasmic transport receptor proteins in eukaryotes, affecting plant development and reproduction, and participating in flowering time and sexual reproduction. Still missing, though, is a thorough examination of IMB members in tea plants. Here, seven members of the IMB gene family were screened by a genome-wide investigation in tea plants. These members were scattered unevenly throughout five chromosomes. All of them contained the conserved KAP95 and HEAT Repeat domains. Additionally, the promoter regions of CsIMBs harbored cis-acting elements associated with plant hormones, light, and abiotic stress responses. In order to further confirm the function of CsIMBs in the sexual reproduction of tea plants, the expression patterns of CsIMBs in different flower development stages and the ovary (before and after pollination) were analyzed. The expression results highlighted that CsIMBs were related to the fertility and fruiting of tea plants. Furthermore, five of the seven CsIMBs (CsIMB1a, CsIMB1b, CsIMB2, CsIMB3a, and CsIMB3b) were found to be localized in the nucleus revealed by subcellular localization analysis. These results offer a comprehensive characterization of IMB genes as well as insights into the potential roles of CsIMBs participating in the gametophytic sexual reproduction of C. sinensis. [ABSTRACT FROM AUTHOR]

Rice is a staple food for more than half of the world's population. However, the pervasive problem of salinity is severely undermining rice production, especially in coastal and low-lying areas where soil salinization is widespread. This stress, exacerbated by climate change, necessitates the development of salt-tolerant rice varieties to ensure food security. In this study, an F2:3 population (n = 454) from a cross of KDML105 and its chromosome segment substitution line (CSSL) was used to identify genomic regions associated with salt tolerance at the seedling stage. Using the QTL-seq approach, a QTL significantly associated with salt tolerance was identified on chromosome 1. Annotation of candidate genes in this region revealed the potential regulators of salt tolerance, including MIKC-type MADS domain proteins, calmodulin-binding transcription factors, and NB-ARC domain-containing proteins. These and other identified genes provide insights into the genetic basis of salt tolerance. This study underscores the importance of using advanced genomics tools and CSSL populations in the study of complex traits such as salt tolerance in rice. Several candidate genes identified in this study could be used in further studies on molecular or physiological mechanisms related to the salt response and tolerance mechanism in rice. Additionally, these genes could also be utilized in plant breeding programs for salt tolerance. [ABSTRACT FROM AUTHOR]

Rice blast, caused by the fungus Magnaporthe oryzae, and brown planthopper (BPH) infestation are two of the most destructive problems of rice production in China. The development of multi-resistant varieties is widely recognized as the most efficient and environmentally friendly approach to controlling crop diseases and pests. Functional molecular markers (FMMs) have been developed from functional variants in the genic region associated with trait variation, greatly enhancing the efficiency of identifying and pyramiding valuable genes in crop breeding. In this study, two FMMs and a multiplex PCR amplification system were developed for two major broad-spectrum BPH resistance genes, Bph6 and Bph9. With the assistance of FMMs in the multi-resistant rice restorer-line development pipeline, two lines (Huahui7713 and Huahui3006) with blast and BPH resistance were developed by pyramiding three resistance genes Pigm, Bph6 and Bph9. Two new hybrid rice varieties, Weiliangyou7713 and Xuanliangyou3006, derived from Huahui7713 and Huahui3006, have been developed and commercialized in China. Weiliangyou7713 and Xuanliangyou3006 exhibit enhanced resistance to both blast and BPH, while maintaining optimal yield and grain quality. The adoption of Weiliangyou7713 continues to expand, now being cultivated on a large scale, which is promising for its future role in reducing the dependence on chemical fungicides and pesticides in rice production. This suggests that the implementation of Huahui7713 and Huahui3006 in targeted breeding programs could be highly beneficial for developing rice varieties with strong resistance to blast and BPH. [ABSTRACT FROM AUTHOR]

Summary: Plants have evolved a multi‐layered immune system to fight off pathogens. However, immune activation is costly and is often associated with growth and development penalty. In crops, yield is the main breeding target and is usually affected by high disease resistance. Therefore, proper balance between growth and defence is critical for achieving efficient crop improvement. This review highlights recent advances in attempts designed to alleviate the trade‐offs between growth and disease resistance in crops mediated by resistance (R) genes, susceptibility (S) genes and pleiotropic genes. We also provide an update on strategies for optimizing the growth–defence trade‐offs to breed future crops with desirable disease resistance and high yield. [ABSTRACT FROM AUTHOR]

Summary: Base editors enable precise nucleotide changes at targeted genomic loci without requiring double‐stranded DNA breaks or repair templates. TALE‐adenine base editors (TALE‐ABEs) are genome editing tools, composed of a DNA‐binding domain from transcription activator‐like effectors (TALEs), an engineered adenosine deaminase (TadA8e), and a cytosine deaminase domain (DddA), that allow A•T‐to‐G•C editing in human mitochondrial DNA. However, the editing ability of TALE‐ABEs in plants apart from chloroplast DNA has not been described, so far, and the functional role how DddA enhances TadA8e is still unclear. We tested a series of TALE‐ABEs with different deaminase fusion architectures in Nicotiana benthamiana and rice. The results indicate that the double‐stranded DNA‐specific cytosine deaminase DddA can boost the activities of single‐stranded DNA‐specific deaminases (TadA8e or APOBEC3A) on double‐stranded DNA. We analysed A•T‐to‐G•C editing efficiencies in a β‐glucuronidase reporter system and showed precise adenine editing in genomic regions with high product purity in rice protoplasts. Furthermore, we have successfully regenerated rice plants with A•T‐to‐G•C mutations in the chloroplast genome using TALE‐ABE. Consequently, TALE‐adenine base editors provide alternatives for crop improvement and gene therapy by editing nuclear or organellar genomes. [ABSTRACT FROM AUTHOR]

Most research on trinucleotide repeats (TRs) focuses on human diseases, with few on the impact of TR expansions on plant gene expression. This work investigates TRs' effect on global gene expression in Psidium guajava L., a plant species with widespread distribution and significant relevance in the food, pharmacology, and economics sectors. We analyzed TR-containing coding sequences in 1,107 transcripts from 2,256 genes across root, shoot, young leaf, old leaf, and flower bud tissues of the Brazilian guava cultivars Cortibel RM and Paluma. Structural analysis revealed TR sequences with small repeat numbers (5–9) starting with cytosine or guanine or containing these bases. Functional annotation indicated TR-containing genes' involvement in cellular structures and processes (especially cell membranes and signal recognition), stress response, and resistance. Gene expression analysis showed significant variation, with a subset of highly expressed genes in both cultivars. Differential expression highlighted numerous down-regulated genes in Cortibel RM tissues, but not in Paluma, suggesting interplay between tissues and cultivars. Among 72 differentially expressed genes with TRs, 24 form miRNAs, 13 encode transcription factors, and 11 are associated with transposable elements. In addition, a set of 20 SSR-annotated, transcribed, and differentially expressed genes with TRs was selected as phenotypic markers for Psidium guajava and, potentially for closely related species as well. [ABSTRACT FROM AUTHOR]

Background: Floral scents play a crucial role in attracting insect pollinators. Among the compounds attractive to pollinators is 1,4-dimethoxybenzene (1,4-DMB). It is a significant contributor to the scent profile of plants from various genera, including economically important Cucurbita species. Despite its importance, the biosynthetic pathway for the formation of 1,4-DMB was not elucidated so far. Results: In this study we showed the catalysis of 1,4-DMB in the presence of 4-methoxyphenol (4-MP) by protein extract from Styrian oil pumpkin (Cucurbita pepo) flowers. Based on this finding, we identified a novel O-methyltransferase gene, Cp4MP-OMT, whose expression is highly upregulated in the volatile-producing tissue of pumpkin flowers when compared to vegetative tissues. OMT activity was verified by purified recombinant Cp4MP-OMT, illustrating its ability to catalyse the methylation of 4-MP to 1,4-DMB in the presence of cofactor SAM (S-(5′-adenosyl)-L-methionine). Conclusions: Cp4MP-OMT is a novel O-methyltransferase from C. pepo, responsible for the final step in the biosynthesis of the floral scent compound 1,4-DMB. Considering the significance of 1,4-DMB in attracting insects for pollination and in the further course fruit formation, enhanced understanding of its biosynthetic pathways holds great promise for both ecological insights and advancements in plant breeding initiatives. [ABSTRACT FROM AUTHOR]

Simple Summary: This experiment is based on the treatment of two forms of R. chrysanthum with UV-B radiation and transcriptomic and metabolomic analyses. It aims to explore how plants adapt to UV-B stress from metabolomic and transcriptomic perspectives. Through this comparative study, potential UV-B stress biomarker and UV-B-responsive metabolites were identified. The main concentration sites of some differentially expressed genes under UV-B stress were summarized. The metabolic synthesis pathway of R. chrysanthum under UV-B stress and the relationship network of key metabolite-related and synthetic enzymes were constructed. The experimental results show that domesticated R. chrysanthum has a stronger UV-B tolerance. By comparing the differences in response to UV-B stress between the two forms of R. chrysanthum, we provide a reference for breeding domesticated plants with UV-B tolerance characteristics. These results will help to study the complex mechanisms of plant adaptation to UV-B radiation. Rhododendron chrysanthum Pall. (R. chrysanthum), a plant with UV-B resistance mechanisms that can adapt to alpine environments, has gained attention as an important plant resource with the ability to cope with UV-B stress. In this experiment, R. chrysanthums derived from the same origin were migrated to different culture environments (artificial climate chamber and intelligent artificial incubator) to obtain two forms of R. chrysanthum. After UV-B irradiation, 404 metabolites and 93,034 unigenes were detected. Twenty-six of these different metabolites were classified as UV-B-responsive metabolites. Glyceric acid is used as a potential UV-B stress biomarker. The domesticated Rhododendron chrysanthum Pall. had high amino acid and SOD contents. The study shows that the domesticated Rhododendron chrysanthum Pall. has significant UV-B resistance. The transcriptomics results show that the trends of DEGs after UV-B radiation were similar for both forms of R. chrysanthum: cellular process and metabolic process accounted for a higher proportion in biological processes, cellular anatomical entity accounted for the highest proportion in the cellular component, and catalytic activity and binding accounted for the highest proportion in the molecular function category. Through comparative study, the forms of metabolites resistant to UV-B stress in plants can be reflected, and UV-B radiation absorption complexes can be screened for application in future specific practices. Moreover, by comparing the differences in response to UV-B stress between the two forms of R. chrysanthum, references can be provided for cultivating domesticated plants with UV-B stress resistance characteristics. Research on the complex mechanism of plant adaptation to UV-B will be aided by these results. [ABSTRACT FROM AUTHOR]

Grapevine (Vitis vinifera) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20–25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype, and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on 2 existing grapevine breeding programs: Rosé wine and Cognac production. In these programs, several families were created through crosses of emblematic and interspecific resistant varieties to powdery and downy mildews. Thirty traits were evaluated for each program, using 2 genomic prediction methods: Genomic Best Linear Unbiased Predictor and Least Absolute Shrinkage Selection Operator. The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector. [ABSTRACT FROM AUTHOR]

In clonal progenies tests, using experiments with one single tree plot, the number of repetitions (cloned plants of each individual of the progenies under evaluation) must be smaller than the traditionally employed. This is because obtaining clones of each individual from the progenies must be carried out in the shortest possible time and, thus, contribute to the greatest advantage of clonal progenies test, which is to identify new clones earlier than other selection alternatives. With the objective of obtaining the minimum number of repetitions necessary in one single tree plot for use in clonal progenies tests, without significantly affecting the selection accuracy, this work was carried out. To estimate the minimum number of repetitions (number of plants of each clone), data obtained from the evaluation of 60 eucalyptus clones, with 30 repetitions in one single tree plot, conducted in six sites, were used. In these experiments, the mean annual increment (cubic meter per hectare per year) was evaluated. Through 1000 resamplings, without replacement, experimental data were obtained with the number of repetitions ranging from 2 to 29. The estimates of the general mean, genetic variance between clones and the selection accuracy obtained showed that, the estimates of general mean, genetic variance between clones and selection accuracy in means were very similar. It was inferred that for clonal progenies tests, in which the experiments will certainly be carried out in more sites, it is feasible to use five repetitions per experiment. [ABSTRACT FROM AUTHOR]

At present, the development of plants with improved traits like superior quality, high yield, or stress resistance, are highly desirable in agriculture. Accelerated crop improvement, however, must capitalize on revolutionary new plant breeding technologies, like genetically modified and gene-edited crops, to heighten food crop traits. Genome editing still faces ineffective methods for the transformation and regeneration of different plant species and must surpass the genotype dependency of the transformation process. Tomato is considered an alternative plant model system to rice and Arabidopsis, and a model organism for fleshy-fruited plants. Furthermore, tomato cultivars like Micro-Tom are excellent models for tomato research due to its short life cycle, small size, and capacity to grow at high density. Therefore, we developed an indirect somatic embryo protocol from cotyledonary tomato explants and used this to generate epigenetically edited tomato plants for the SlWRKY29 gene via CRISPR-activation (CRISPRa). We found that epigenetic reprogramming for SlWRKY29 establishes a transcriptionally permissive chromatin state, as determined by an enrichment of the H3K4me3 mark. A whole transcriptome analysis of CRISPRa-edited pro-embryogenic masses and mature somatic embryos allowed us to characterize the mechanism driving somatic embryo induction in the edited tomato cv. Micro-Tom. Furthermore, we show that enhanced embryo induction and maturation are influenced by the transcriptional effector employed during CRISPRa, as well as by the medium composition and in vitro environmental conditions such as osmotic components, plant growth regulators, and light intensity. [ABSTRACT FROM AUTHOR]

Summary: Asteraceae represent one of the largest and most diverse families of plants. The evolutionary success of this family has largely been contributed to their unique inflorescences, capitula that mimic solitary flowers but are typically aggregates of multiple florets. Here, we summarize the recent molecular and genetic level studies that have promoted our understanding of the development and evolution of capitula. We focus on new results on patterning of the enlarged meristem resulting in the iconic phyllotactic arrangement of florets in Fibonacci numbers of spirals. We also summarize the current understanding of the genetic networks regulating the characteristic reproductive traits in the family such as floral dimorphism and differentiation of highly specialized floral organs. So far, developmental studies in Asteraceae are still limited to a very narrow selection of model species. Along with the recent advancements in genomics and phylogenomics, Asteraceae and its relatives provide an outstanding model clade for extended evo‐devo studies to exploit the morphological diversity and the underlying molecular networks and to translate this knowledge to the breeding of the key crops in the family. [ABSTRACT FROM AUTHOR]

Tobamoviruses are a group of plant viruses that pose a significant threat to agricultural crops worldwide. In this review, we focus on plant immunity against tobamoviruses, including pattern-triggered immunity (PTI), effector-triggered immunity (ETI), the RNA-targeting pathway, phytohormones, reactive oxygen species (ROS), and autophagy. Further, we highlight the genetic resources for resistance against tobamoviruses in plant breeding and discuss future directions on plant protection against tobamoviruses. [ABSTRACT FROM AUTHOR]

The article focuses on the development of a new olive cultivar, 'Sultana', specifically designed for hedgerow orchards in response to the growing demand for low-vigor cultivars suitable for superhigh density olive systems. Topics include the breeding program's aim to improve agronomical characteristics like oil content, the evaluation of 'Sultana' against traditional cultivars, and the detailed morphological description and agronomical traits of 'Sultana'.

Agrobacterium tumefaciens mediated plant transformation is a versatile tool for plant genetic engineering following its discovery nearly half a century ago. Numerous modifications were made in its application to increase efficiency, especially in the recalcitrant major cereals plants. Recent breakthroughs in transformation efficiency continue its role as a mainstream technique in CRISPR/Cas-based genome editing and gene stacking. These modifications led to higher transformation frequency and lower but more stable transgene copies with the capability to revolutionize modern agriculture. In this review, we provide a brief overview of the history of Agrobacterium-mediated plant transformation and focus on the most recent progress to improve the system in both the Agrobacterium and the host recipient. A promising future for transformation in biotechnology and agriculture is predicted. [ABSTRACT FROM AUTHOR]

The common bean collection (Phaseolus vulgaris L.) from the Genebank of Institute for Fundamental Research in Tropical Agriculture "Alejandro de Humboldt" (INIFAT), has the potential to be used as genetic material in breeding programs for the crop, in the development of strategies for the conservation of local varieties, for confronting climate change, among other functions. The optimal exploitation of the genetic potential of this collection is only achieved through characterization. The following work aims to characterize the genetic variability of a sample of 35 accessions of common bean (Phaseolus vulgaris L.) through the use of 31 morphological descriptors. The selected sample presented less variability in terms of maturity and harvest times, also presented a significant correlation between the traits related to the architecture of the plant. Principal component analysis indicated that first two components explained 63.8 % of total variance. The type and size of grains have an important role in the grouping of the accessions. Several genotypes evaluated have potential as basic breeding lines. [ABSTRACT FROM AUTHOR]

Research conducted at the Plant Breeding and Acclimatization Institute—NIR in Poland in 2020–2022 assessed the commercial quality of potato tubers depending on the production system (organic, conventional), variety, and weather conditions prevailing in the years of research. The tuber size distribution and the share of tuber defects was assessed. It was found that all tested factors influenced the commercial quality of the yield but to different extents. The production system had the greatest influence on most of the examined features, followed by weather conditions and the least influential factor being the variety. The production system had the greatest impact on such features as: tuber size distribution (all fractions), pest damage, scab infection, tuber deformations, and share of green tubers. Weather conditions had the greatest impact on scab infection, pest damage, deformation, cracks and share of large tubers. The variety factor had the greatest influence on such features as: deformations, cracks, green tubers, black scarf, and share of the smallest (non-commercial) tubers. In the conventional system, a significantly better tuber yield distribution was found. The share of tuber defects varied depending on the production system. In the organic system, a greater share of defects such as scab (68.3 and 41.3) and pest damage was found (6.8 and 0.2), while in the conventional system, a greater share of deformations (9.2 and 4.9), cracks (5.4 and 2.4), and rust spots (0.61 and 0.06) occurred. [ABSTRACT FROM AUTHOR]

The climatic and edaphic conditions of the semi-arid Brazilian Northeast region limit the production of several annual crops, including cotton. The aim of this study was to estimate the genetic parameters and select water stress tolerant cotton lines based on yield and fiber quality traits. Twenty cotton lines were evaluated in Alagoinha-PB over two years under rainfed conditions. Individual and joint analysis of variance was performed on the data. Genetic parameters were determined, and lines were selected using the selection index. The lines CNPA SA 2019-115, CNPA SA 2019-185, CNPA SA 2019-109, and CNPA SA 2019-165 were selected for further tests in the region for recommendation of new cultivars. These lines can form new blocks of crosses, together with the BRS 286 check cultivar, as they have the best mean yield and fiber quality values, with the expectation of significant genetic gains. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement. [ABSTRACT FROM AUTHOR]

Application of machine learning in plant breeding is a recent concept, that has to be optimized for precise utilization in the breeding program of high yielding crop plants. Identification and efficient utilization of heterotic grouping pattern aided with machine learning approaches is of utmost importance in hybrid cultivar breeding as it can save time and resources required to breed a new plant hybrid/variety. In the present study, 109 genotypes of sunflower were investigated at morphological, biochemical (SDS-PAGE) and molecular levels (through micro-satellites (SSR) markers) for heterotic grouping. All the three datasets were combined, scaled, and subjected to unsupervised machine learning algorithms, i.e., Hierarchical clustering, K-means clustering and hybrid clustering algorithm (hierarchical + K-means) for assessment of efficiency and resolution power of these algorithms in practical plant breeding for heterotic grouping identification. Following the application of machine learning unsupervised clustering approach, two major groups were identified in the studied sunflower germplasm, and further classification revealed six smaller classes in each major group through hierarchical and hybrid clustering approach. Due to high resolution, obtained in hierarchical clustering, classification achieved through this algorithm was further used for selection of potential parents. One genotype from each smaller group was selected based on the maximum seed yield potential and hybridized in a line × tester mating design producing 36 F1 cross combinations. These F1s along with their parents were studied in open field conditions for validating the efficacy of identified heterotic groups in sunflowers genetic material under study. Data for 11 agronomic and qualitative traits were recorded. These 36 F1 combinations were tested for their combining ability (General/Specific), heterosis, genotypic and phenotypic correlation and path analysis. Results suggested that F1 hybrids performed better for all the traits under investigation than their respective parents. Findings of the study validated the use of machine learning approaches in practical plant breeding; however, more accurate and robust clustering algorithms need to be developed to handle the data noisiness of open field experiments. [ABSTRACT FROM AUTHOR]

Allelopathy has been considered as a natural method of weed control. Despite the nature of allelochemical compounds has been studied, little is known about the genetic basis underlying allelopathy. However, it is known that rice exhibits diverse allelopathic potentials across varieties, and breeding for rice plants exhibiting allelopathic potential conferring an advantage against weeds in paddy fields would be highly desirable. Knowledge of the gene factors and the identification of the genomic regions responsible for allelopathy would facilitate breeding programs. Taking advantage of the existing genetic diversity in rice, particularly in temperate japonica rice, we conducted a comprehensive investigation into the genetic determinants that contribute to rice allelopathy. Employing Genome-Wide Association Study, we identified four Quantitative Trait Loci, with the most promising loci situated on chromosome 2 and 5. Subsequent inspection of the genes located within these QTLs revealed genes associated with the biosynthesis of secondary metabolites such as Phenylalanine Ammonia Lyase (PAL), a key enzyme in the synthesis of phenolic compounds, and two genes coding for R2R3-type MYB transcription factors. The identification of these two QTLs associated to allelopathy in rice provides a useful tool for further exploration and targeted breeding strategies. [ABSTRACT FROM AUTHOR]