Plant Genome最新文献

Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

Mid-density targeted genotyping-by-sequencing (GBS) combines trait-specific markers with thousands of genomic markers at an attractive price for linkage mapping and genomic selection. A 2.5K targeted GBS assay for potato (Solanum tuberosum L.) was developed using the DArTag technology and later expanded to 4K targets. Genomic markers were selected from the potato Infinium single nucleotide polymorphism (SNP) array to maximize genome coverage and polymorphism rates. The DArTag and SNP array platforms produced equivalent dendrograms in a test set of 298 tetraploid samples, and 83% of the common markers showed good quantitative agreement, with RMSE (root mean squared error) <0.5. DArTag is suited for genomic selection candidates in the clonal evaluation trial, coupled with imputation to a higher density platform for the training population. Using the software polyBreedR, an R package for the manipulation and analysis of polyploid marker data, the RMSE for imputation by linkage analysis was 0.15 in a small half-diallel population (N = 85), which was significantly lower than the RMSE of 0.42 with the random forest method. Regarding high-value traits, the DArTag markers for resistance to potato virus Y, golden cyst nematode, and potato wart appeared to track their targets successfully, as did multi-allelic markers for maturity and tuber shape. In summary, the potato DArTag assay is a transformative and publicly available technology for potato breeding and genetics.

The small heat shock proteins (sHSPs) are important components in plant growth and development, and stress response. However, a systematical understanding of the sHSP family is yet to be reported in five diploid Gossypium species. In this study, 34 GlsHSPs, 36 GrsHSPs, 37 GtsHSPs, 37 GasHSPs, and 38 GhesHSPs were identified in Gossypium longicalyx, Gossypium raimondii, Gossypium turneri, Gossypium arboreum, and Gossypium herbaceum, respectively. These sHSP members can be clustered into 10 subfamilies. Different subfamilies had different member numbers, motif distributions, gene structures, gene duplication events, gene loss numbers, and cis-regulatory elements. Besides, the paleohexaploidization event in cotton ancestor led to expanding the sHSP members and it was also inherited by five diploid Gossypium species. After the cotton ancestor divergence, the sHSP members had the relatively conserved evolution in five diploid Gossypium species. The comprehensive evolutionary history of the sHSP family was revealed in five diploid Gossypium species. Furthermore, several GasHSPs and GhesHSPs were important candidates in plant growth and development, and stress response. These current findings can provide valuable information for the molecular evolution and further functional research of the sHSP family in cotton.

Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI-based FDK (FDK_QVIS/FDK_QNIR) showed a two-fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI-based FDK was evaluated along with other traits in multi-trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single-trait GS model. We next used hyperspectral imaging of FHB-infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single-trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB-infected wheat kernels and observed an accuracy (R² = 0.45) comparable to best-performing MT GS models. This study demonstrates the potential application of AI and vision-based platforms to improve PA for FHB-related traits using genomic and phenomic selection.

In plantae, basic leucine zipper (bZIP) transcription factors (TFs) are widespread and regulate a variety of biological processes under abiotic stress. However, it has not been extensively studied in Rosaceae, and the functional effects of bZIP on Eriobotrya japonica under salt stress are still unknown. Therefore, in this study, the bZIP TF family of 12 species of Rosaceae was analyzed by bioinformatics method, and the expression profile and quantitative real-time polymerase chain reaction of E. japonica under salt stress were analyzed. The results showed that a total of 869 bZIP TFs were identified in 12 species of Rosaceae and divided into nine subfamilies. Differences in promoter cis-elements between subfamilies vary depending on their role. Species belonging to the same subfamily have a similar number of chromosomes and the number of genes contained on each chromosome. Gene duplication analysis has found segmental duplication to be a prime force in the evolution of Rosaceae species. In addition, nine EjbZIPs were significantly different, including seven up-regulated and two down-regulated in E. japonica under salt stress. Especially, EjbZIP13 was involved in the expression of SA-responsive proteins by binding to the NPR1 gene. EjbZIP27, EjbZIP30, and EjbZIP38 were highly expressed in E. japonica under salt stress, thus improving the salt tolerance capacity of the plants. These results can provide a theoretical basis for exploring the characteristics and functions of the bZIP TF family in more species and breeding salt-tolerant E. japonica varieties. It also provides a reference for resolving the response mechanism of bZIP TF in 12 Rosaceae species under salt stress.

Seven in absentia proteins, which contain a conserved SINA domain, are involved in regulating various aspects of wheat (Triticum aestivum L.) growth and development, especially in response to environmental stresses. However, it is unclear whether TaSINA family members are involved in regulating grain development until now. In this study, the expression pattern, genomic polymorphism, and relationship with grain-related traits were analyzed for all TaSINA members. Most of the TaSINA genes identified showed higher expression levels in young wheat spikes or grains than other organs. The genomic polymorphism analysis revealed that at least 62 TaSINA genes had different haplotypes, where the haplotypes of five genes were significantly correlated with grain-related traits. Kompetitive allele-specific PCR markers were developed to confirm the single nucleotide polymorphisms in TaSINA101 and TaSINA109 among the five selected genes in a set of 292 wheat accessions. The TaSINA101-Hap II and TaSINA109-Hap II haplotypes had higher grain weight and width compared to TaSINA101-Hap I and TaSINA109-Hap I in at least three environments, respectively. The qRT-PCR assays revealed that TaSINA101 was highly expressed in the palea shell, seed coat, and embryo in young wheat grains. The TaSINA101 protein was unevenly distributed in the nucleus when transiently expressed in the protoplast of wheat. Three homozygous TaSINA101 transgenic lines in rice (Oryza sativa L.) showed higher grain weight and size compared to the wild type. These findings provide valuable insight into the biological function and elite haplotype of TaSINA family genes in wheat grain development at a genomic-wide level.

This study focuses on meeting end-users' demand for cassava (Manihot esculenta Crantz) varieties with low cyanogenic potential (hydrogen cyanide potential [HCN]) by using near-infrared spectrometry (NIRS). This technology provides a fast, accurate, and reliable way to determine sample constituents with minimal sample preparation. The study aims to evaluate the effectiveness of machine learning (ML) algorithms such as logistic regression (LR), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) in distinguishing between low and high HCN accessions. Low HCN accessions averagely scored 1-5.9, while high HCN accessions scored 6-9 on a 1-9 categorical scale. The researchers used 1164 root samples to test different NIRS prediction models and six spectral pretreatments. The wavelengths 961, 1165, 1403-1505, 1913-1981, and 2491 nm were influential in discrimination of low and high HCN accessions. Using selected wavelengths, LR achieved 100% classification accuracy and PLS-DA achieved 99% classification accuracy. Using the full spectrum, the best model for discriminating low and high HCN accessions was the PLS-DA combined with standard normal variate with second derivative, which produced an accuracy of 99.6%. The SVM and LR had moderate classification accuracies of 75% and 74%, respectively. This study demonstrates that NIRS coupled with ML algorithms can be used to identify low and high HCN accessions, which can help cassava breeding programs to select for low HCN accessions.

The rhomboid-like (RBL) gene encodes serine protease, which plays an important role in the response to cell development and diverse stresses. However, genome-wide identification, expression profiles, and haplotype analysis of the RBL family genes have not been performed in wheat (Triticum aestivum L.). This study investigated the phylogeny and diversity of the RBL family genes in the wheat genome through various approaches, including gene structure analysis, evolutionary relationship analysis, promoter cis-acting element analysis, expression pattern analysis, and haplotype analysis. The 41 TaRBL genes were identified and divided into five subfamilies in the wheat genome. RBL family genes were expanded through segmented duplication and purification selection. The cis-element analysis revealed their involvement in various stress responses and plant development. The results of RNA-seq and quantitative real-time-PCR showed that TaRBL genes displayed higher expression levels in developing spike/grain and were differentially regulated under polyethylene glycol, NaCl, and abscisic acid treatments, indicating their roles in grain development and abiotic stress response. A kompetitive allele-specific PCR molecular marker was developed to confirm the single nucleotide polymorphism of TaRBL14a gene in 263 wheat accessions. We found that the elite haplotype TaRBL14a-Hap2 showed a significantly higher 1000-grain weight than TaRBL14a-Hap11 in at least three environments, and the TaRBL14a-Hap2 was positively selected in wheat breeding. The findings will provide a good insight into the evolutionary and functional characteristics of the TaRBL genes family in wheat and lay the foundation for future exploration of the regulatory mechanisms of TaRBL genes in plant growth and development, as well as their response to abiotic stresses.

Ambrosia artemisiifolia and Ambrosia trifida (Asteraceae) are important pest species and the two greatest sources of aeroallergens globally. Here, we took advantage of a hybrid to simplify genome assembly and present chromosome-level assemblies for both species. These assemblies show high levels of completeness with Benchmarking Universal Single-Copy Ortholog (BUSCO) scores of 94.5% for A. artemisiifolia and 96.1% for A. trifida and long terminal repeat (LTR) Assembly Index values of 26.6 and 23.6, respectively. The genomes were annotated using RNA data identifying 41,642 genes in A. artemisiifolia and 50,203 in A. trifida. More than half of the genome is composed of repetitive elements, with 62% in A. artemisiifolia and 69% in A. trifida. Single copies of herbicide resistance-associated genes PPX2L, HPPD, and ALS were found, while two copies of the EPSPS gene were identified; this latter observation may reveal a possible mechanism of resistance to the herbicide glyphosate. Ten of the 12 main allergenicity genes were also localized, some forming clusters with several copies, especially in A. artemisiifolia. The evolution of genome structure has differed among these two species. The genome of A. trifida has undergone greater rearrangement, possibly the result of chromoplexy. In contrast, the genome of A. artemisiifolia retains a structure that makes the allotetraploidization of the most recent common ancestor of the Heliantheae Alliance the clearest feature of its genome. When compared to other Heliantheae Alliance species, this allowed us to reconstruct the common ancestor's karyotype-a key step for furthering of our understanding of the evolution and diversification of this economically and allergenically important group.