Plant Genome最新文献

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.

Bananas (Musa spp.) are one of the most highly consumed fruits globally, grown in the tropical and sub-tropical regions. We evaluated 856 Musa accessions from the breeding programs of the International Institute of Tropical Agriculture of Nigeria, Tanzania, and Uganda; the National Agricultural Research Organization of Uganda; the Brazilian Agricultural Research Corporation (Embrapa); and the National Research Centre for Banana of India. Accessions from the in vitro gene bank at the International Transit Centre in Belgium were included to provide a baseline of available global diversity. A total of 16,903 informative single nucleotide polymorphism markers were used to estimate and characterize the genetic diversity and population structure and identify overlaps and unique material among the breeding programs. Analysis of molecular variance displayed low genetic variation among accessions and diploids and a higher variation among tetraploids (p < 0.001). Structure analysis revealed two major clusters corresponding to genomic composition. The results indicate that there is potential for the banana breeding programs to increase the diversity in their breeding materials and should exploit this potential for parental improvement and to enhance genetic gains in future breeding efforts.

Spike length (SL) is one of the major contributors to wheat yield. Uncovering major genetic regions affecting SL is an integral part of elucidating the genetic basis of wheat yield traits and goes further pivotal for marker-assisted selection breeding. A genome-wide meta-quantitative trait locus (MQTL) analysis of wheat SL resulted in the refinement of 48 MQTLs using 227 initial QTLs retrieved from previous studies published over the past decades. The average confidence interval (CI) of these MQTLs amounted to a 5.16-fold reduction compared to the mean CI of the initial QTLs. As many as 2240 putative candidate genes (CGs) were identified from the MQTL intervals using transcriptomics data in silico of wheat, of which 58 CGs were identified based on wheat-rice homology analysis. For the key CG affecting SL, a functional kompetitive allele-specific PCR (KASP) marker, TaPP2C-3B-KASP, was developed to distinguish TaPP2C-3B-Hap I and TaPP2C-3B-Hap II based on the single nucleotide polymorphism at the 272 bp (A/G). The frequency of the elite allelic variation TaPP2C-3B-Hap II with high SL remained relatively stable at about 49.62% from the 1960s to 1990s. Integration of MQTL analysis and in silico transcriptome data led to a significant increase in the reliability of CGs for the genetic regulation of wheat SL, and the haplotype analysis for key CGs TaPP2C-3B of SL provided insights into the biological function of the TaPP2C-3B gene.

Phenotypic selection of complex traits such as seed yield and protein in the preliminary yield trial (PYT) is often constrained by limited seed availability, resulting in trials with few environments and minimal to no replications. Multi-trait multi-environment enabled genomic prediction (MTME-GP) offers a valuable alternative to predict missing phenotypes of selection candidates for multiple traits and diverse environments. In this study, we assessed the efficiency of MTME-GP for improving seed protein and seed yield in field pea, the top two breeding targets but highly antagonistic traits in pulse crop. We utilized a set of 300 selection candidates in the PYT that virtually represented all possible families of the North Dakota State University field pea breeding program. Selection candidates were evaluated in three diverse, contrasting environments, as indicated by a range of heritability. Using whole- and split-environment cross validation schemes, MTME-GP had higher predictive ability than a standard additive G-BLUP model. Integrating a range of overlapping genotypes in between environments showed improvement on the predictive ability of the MTME-GP model but tends to plateau at 50%-80% training set size. Regardless of the cross-validation scheme, accuracy was among the lowest in stressed environments, presumably due to low heritability for seed protein and yield. This study provided insights into the potential of MTME-GP in a public pulse crop breeding program. The MTME-GP framework can be further improved with more testing environments and integration of additional orthogonal information in the early stages of the breeding pipeline.

Helicoverpa armigera (also known as gram pod borer) is a serious threat to chickpea production in the world. A set of 173 chickpea genotypes were evaluated for H. armigera resistance, including mean larval population (MLP), percentage pod damage (PPD), and pest resistance (PR) for 2 consecutive years (year 2020 and 2021). The same core set was also genotyped with 50K Axiom CicerSNP Array. The trait data and 50,000 single nucleotide polymorphism genotypic data were used together to work out marker-trait associations (MTAs) using different genome-wide association studies models. For MLP, a total of 53 MTAs were identified, including 25 MTAs in year 2020 and 28 MTAs in year 2021. A set of three MTAs was found common in both environments. For PPD, two MTAs in year 2020 and five MTAs in year 2021 were identified. A set of two MTAs were common in both environments. Similarly, for PR, only two MTAs common in both environments were identified. Interestingly, a common MTA (Affx_123255526) on chromosome 2 (Ca2) was found to be associated with all the three component traits (MLP, PPD, and PR) of pod borer resistance in chickpea. Further, we report key genes that encode SCAMPs (that facilitates the secretion of defense-related molecules), quinone oxidoreductase (enables the production of reactive oxygen species that promotes diapause of gram pod borer), and NB-LRR proteins that have been implicated in plant defense against H. armigera. The resistant chickpea genotypes, MTAs, and key genes reported in the present study may prove useful in the future for developing pod borer-resistant chickpea varieties.