Plant Genome最新文献

Genomic prediction is a modern approach that uses genome-wide markers to predict the genetic merit of unphenotyped individuals. With the potential to reduce the breeding cycles and increase the selection accuracy, this tool has been designed to rank genotypes and maximize genetic gains. Despite this importance, its practical implementation in breeding programs requires critical allocation of resources for its application in a predictive framework. In this study, we integrated genetic and data-driven methods to allocate resources for phenotyping and genotyping tailored to genomic prediction. To this end, we used a historical blueberry (Vaccinium corymbosun L.) breeding dataset containing more than 3000 individuals, genotyped using probe-based target sequencing and phenotyped for three fruit quality traits over several years. Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data-driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long-term implication, we carried out a simulation study and emphasized that data-driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. Altogether, we provided a data-oriented decision-making approach for breeders by demonstrating that critical breeding decisions associated with resource allocation for genomic prediction can be tackled through a combination of statistics and genetic methods.

In this study, the contents of four carotenoids in 244 maize inbred lines were detected and about three million single nucleotide polymorphisms (SNPs) for genome-wide association study to preliminarily analyze the genetic mechanism of maize kernel carotenoids. We identified 826 quantitative trait loci (QTLs) were significantly associated with carotenoids contents, and two key candidate genes Zm00001d029526 (CYP18) and Zm00001d023336 (wrky91) were obtained. In addition, we found a germplasm IL78 with higher carotenoids. The results of this study can provide a theoretical basis for screening genes that guide kernel carotenoids selection breeding.

Salt stress is one of the primary environmental stresses limiting plant growth and production and adversely affecting the growth, development, yield, and fruit quality of Citrus sinensis. bHLH (basic helix-loop-helix) genes are involved in many bioregulatory processes in plants, including growth and development, phytohormone signaling, defense responses, and biosynthesis of specific metabolites. In this study, by bioinformatics methods, 120 CsbHLHgenes were identified, and phylogenetic analysis classified them into 18 subfamilies that were unevenly distributed on nine chromosomes. The cis-acting elements of the CsbHLH genes were mainly hormone-related cis-acting elements. Seventeen CsbHLH genes exhibited significant differences in expression under salt stress. Six CsbHLH genes with significant differences in expression were randomly selected for quantitative real-time polymerase chain reaction (qRT-PCR) validation. The qRT-PCR results showed a strong correlation with the transcriptome data. Phytohormones such as jasmonic acid (JA) are essential for biotic and abiotic stress responses in plants, and CsbHLH55 and CsbHLH87 are considered candidate target genes for sweet orange MYC2 transcription factors involved in the JA signaling pathway. These genes are the main downstream effectors in the JA signaling pathway and can be activated to participate in the JA signaling pathway. Activation of the JA signaling pathway inhibits the production of reactive oxygen species and improves the salt tolerance of sweet orange plants. The CsbHLH55 and CsbHLH87 genes could be candidate genes for breeding new transgenic salt-resistant varieties of sweet orange.

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.

Acca sellowiana [Berg] Burret, a cultivated fruit tree originating from South America, is gaining the attention of the nutraceutical and pharmaceutical industries due to their high content of flavonoids and other phenolic compounds in fruits, leaves, and flowers. Flavonoids are a diverse group of secondary metabolites with antioxidant, anti-inflammatory, and antimicrobial properties. They also play a crucial role in plant immune response. Despite their importance, the lack of research on A. sellowiana genomics and transcriptomics hinders a deeper understanding of the molecular mechanisms behind flavonoid biosynthesis and its regulation. Here, we de novo assembled and benchmarked 11 A. sellowiana transcriptomes from leaves and floral tissues at three developmental stages using high-throughput sequencing. We selected and annotated the best assembly according to commonly used metrics and databases. This reference transcriptome consisted of 221,649 nonredundant transcripts, of which 107,612 were functionally annotated. We then used this reference transcriptome to explore the expression profiling of key secondary metabolite genes. Transcripts from genes involved in the flavonoid and anthocyanin biosynthesis pathways were identified. We also identified 4068 putative transcription factors, with the most abundant families being bHLH, C2H2, NAC, MYB, and MYB-related. Transcript expression profiling revealed distinct patterns of gene expression during flower development. Particularly, we found 71 differentially expressed transcripts representing 14 enzymes of the flavonoid pathway, suggesting major changes in flavonoid accumulation across floral stages. Our findings will contribute to understanding the genetic basis of flavonoids and provide a foundation for further research and exploitation of the economic potential of this species.

Salvia hispanica L. (Chia), a member of the Lamiaceae, is an economically important crop in Mesoamerica, with health benefits associated with its seed fatty acid composition. Chia varieties are distinguished based on seed color including mixed white and black (Chia pinta) and black (Chia negra). To facilitate research on Chia and expand on comparative analyses within the Lamiaceae, we generated a chromosome-scale assembly of a Chia pinta accession and performed comparative genome analyses with a previously published Chia negra genome assembly. The Chia pinta and Chia negra genome sequences were highly similar as shown by a limited number of single nucleotide polymorphisms and extensive shared orthologous gene membership. However, there is an enrichment of terpene synthases in the Chia pinta genome relative to the Chia negra genome. We sequenced and analyzed the genomes of 20 Chia accessions with differing seed color and geographic origin revealing population structure within S. hispanica and interspecific introgressions of Salvia species. As the genus Salvia is polyphyletic, its evolutionary history remains unclear. Using large-scale synteny analysis within the Lamiaceae and orthologous group membership, we resolved the phylogeny of Salvia species. This study and its collective resources further our understanding of genomic diversity in this food crop and the extent of interspecies hybridizations in Salvia.

Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome prediction (GP) models of crops with highly heterozygous polyploid genomes. This study incorporates non-additive genetic effects and pedigree information using machine learning methods to track sugarcane breeding lines and enhance the prediction by assessing the degree of association between genotypes. This study measured the stalk biomass and sugar content of 297 clones from 87 families within a breeding population used in the Japanese sugarcane breeding program. Subsequently, we conducted analyses based on the marker genotypes of 33,149 single-nucleotide polymorphisms. To validate the accuracy of GP in the population, we first predicted the prediction accuracy of the best linear unbiased prediction (BLUP) based on a genomic relationship matrix. Prediction accuracy was assessed using two different cross-validation methods: repeated 10-fold cross-validation and leave-one-family-out cross-validation. The accuracy of GP of the first and second methods ranged from 0.36 to 0.74 and 0.15 to 0.63, respectively. Next, we compared the prediction accuracy of BLUP and two machine learning methods: random forests and simulation annealing ensemble (SAE), a newly developed machine learning method that explicitly models the interaction between variables. Both pedigree and genomic information were utilized as input in these methods. Through repeated 10-fold cross-validation, we found that the accuracy of the machine learning methods consistently surpassed that of BLUP in most cases. In leave-one-family-out cross-validation, SAE demonstrated the highest accuracy among the methods. These results underscore the effectiveness of GP in Japanese sugarcane breeding and highlight the significant potential of machine learning methods.

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.

Phytophthora root rot, caused by oomycete pathogens in the Phytophthora genus, poses a significant threat to soybean productivity. While resistance mechanisms against Phytophthora sojae have been extensively studied in soybean, the molecular basis underlying immune responses to Phytophthora sansomeana remains unclear. In this study, we investigated transcriptomic and epigenetic responses of two resistant (Colfax and NE2701) and two susceptible (Williams 82 and Senaki) soybean lines at four time points (2, 4, 8, and 16 h post inoculation [hpi]) after P. sansomeana inoculation. Comparative transcriptomic analyses revealed a greater number of differentially expressed genes (DEGs) upon pathogen inoculation in resistant lines, particularly at 8 and 16 hpi. These DEGs were predominantly associated with defense response, ethylene, and reactive oxygen species-mediated defense pathways. Moreover, DE transposons were predominantly upregulated after inoculation, and more of them were enriched near genes in Colfax than other soybean lines. Notably, we identified a long non-coding RNA (lncRNA) within the mapped region of the resistance gene that exhibited exclusive upregulation in the resistant lines after inoculation, potentially regulating two flanking LURP-one-related genes. Furthermore, DNA methylation analysis revealed increased CHH (where H = A, T, or C) methylation levels in lncRNAs after inoculation, with delayed responses in Colfax compared to Williams 82. Overall, our results provide comprehensive insights into soybean responses to P. sansomeana, highlighting potential roles of lncRNAs and epigenetic regulation in plant defense.