Plant Genome最新文献

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.

Root architectural traits play pivotal roles in plant adaptation to drought stress, and hence they are considered promising targets in breeding programs. Here, we phenotyped eight root architecture traits in response to well-watered and drought stress conditions in 200 spring barley (Hordeum vulgare L.) inbred lines over two consecutive field seasons. Root architecture traits were less developed under drought in both seasons when compared with control treatments. Genetic variation in root architectural traits was dissected employing a genome-wide association study (GWAS) coupled with linkage disequilibrium mapping. GWAS uncovered a total of 186 significant single nucleotide polymorphism-trait associations for eight root traits under control, drought, and drought-related indices. Of these, a few loci for root traits were detected on chromosomes 3 and 5, which co-located with QTL identified in previous studies. Interestingly, 13 loci showed simultaneou associations with multiple root traits under drought and drought-related indices. These loci harbored candidate genes, which included a wide range of drought-responsive components such as transcription factors, binding proteins, protein kinases, nutrient and ion transporters, and stress signaling factors. For instance, two candidate genes, HORVU7Hr3G0713160 and HORVU6H r3G0626550, are orthologous to AtACX3 and AtVAMPs, which have reported functions in root length-mediated drought tolerance and as a key protein in abiotic stress tolerance, respectively. Interestingly, one of these loci underlying a high-confidence candidate gene NEW ENHANCER OF ROOT DWARFISM1 (NERD1) showed involvement with root development. An allelic variation of this locus in non-coding region was significantly associated with increased root length under drought. Collectively, these results offer promising multi-trait affecting loci and candidate genes underlying root phenotypic responses to drought stress, which may provide valuable resources for genetic improvement of drought tolerance in barley.

Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food-grade oats. Conventional selection for milling quality is costly and burdensome. Multi-trait genomic selection (MTGS) combines information from genome-wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across 2 years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single-trait models by 52% and 59%, respectively. In contrast, MTGS models for groat percentage were not significantly better than the single-trait model. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thins percentage.

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.

Microsatellite markers are widely used in population genetics and breeding. Despite the economic significance of yams in developing countries, there is a paucity of microsatellite markers, and as of now, no comprehensive microsatellite marker database exists. In this study, we conducted genome-wide microsatellite marker development across four yam species, identified cross-species transferable markers, and designed an easy-to-use web portal for the yam researchers. The screening of Dioscorea alata, Dioscorea rotundata, Dioscorea dumetorum, and Dioscorea zingiberensis genomes resulted in 318,713, 322,501, 307,040, and 253,856 microsatellites, respectively. Mono-, di-, and tri-nucleotides were the most important types of repeats in the different species, and a total of 864,128 primer pairs were designed. Furthermore, we identified 1170 cross-species transferable microsatellite markers. Among them, 17 out of 18 randomly selected were experimentally validated with good discriminatory power, regardless of the species and ploidy levels. Ultimately, we created and deployed a dynamic Yam Microsatellite Markers Database (Y2MD) available at https://y2md.ucad.sn/. Y2MD is embedded with various useful tools such as JBrowse, Blast, insilicoPCR, and SSR Finder to facilitate the exploitation of microsatellite markers in yams. This study represents the first comprehensive microsatellite marker mining across several yam species and will contribute to advancing yam genetic research and marker-assisted breeding. The released user-friendly database constitutes a valuable platform for yam researchers.

Selecting and mating parents in conventional phenotypic and genomic selection are crucial. Plant breeding programs aim to improve the economic value of crops, considering multiple traits simultaneously. When traits are negatively correlated and/or when there are missing records in some traits, selection becomes more complex. To address this problem, we propose a multitrait selection approach using the Multitrait Parental Selection (MPS) R package-an efficient tool for genetic improvement, precision breeding, and conservation genetics. The package employs Bayesian optimization algorithms and three loss functions (Kullback-Leibler, Energy Score, and Multivariate Asymmetric Loss) to identify parental candidates with desirable traits. The software's functionality includes three main functions-EvalMPS, FastMPS, and ApproxMPS-catering to different data availability scenarios. Through the presented application examples, the MPS R package proves effective in multitrait genomic selection, enabling breeders to make informed decisions and achieve strong performance across multiple traits.

The identification of genomic regions underlying the root system architecture (RSA) is vital for improving crop abiotic stress tolerance. To improve sorghum (Sorghum bicolor L. Moench) for environmental stress tolerance, information on genetic variability and genomic regions linked to RSA traits is paramount. The aim of this study was, therefore, to investigate common quantitative trait nucleotides (QTNs) via multiple methodologies and identify genomic regions linked to RSA traits in a panel of 274 Ethiopian sorghum accessions. Multi-locus genome-wide association study was conducted using 265,944 high-quality single nucleotide polymorphism markers. Considering the QTN detected by at least three different methods, a total of 17 reliable QTNs were found to be significantly associated with root angle, number, length, and dry weight. Four QTNs were detected on chromosome SBI-05, followed by SBI-01 and SBI-02 with three QTNs each. Among the 17 QTNs, 11 are colocated with previously identified root traits quantitative trait loci and the remaining six are genome regions with novel genes. A total of 118 genes are colocated with these up- and down-streams of the QTNs. Moreover, five QTNs were found intragenic. These QTNs are S5_8994835 (number of nodal roots), S10_55702393 (number of nodal roots), S1_56872999 (nodal root angle), S9_1212069 (nodal root angle), and S5_5667192 (root dry weight) intragenic regions of Sobic.005G073101, Sobic.010G198000, Sobic.001G273000, Sobic.009G013600, and Sobic.005G054700, respectively. Particularly, Sobic.005G073101, Sobic.010G198000, and Sobic.009G013600 were found responsible for the plant growth hormone-induced RSA. These genes may regulate root development in the seedling stage. Further analysis on these genes might be important to explore the genetic structure of RSA of sorghum.

The clustered regularly interspaced short palindromic repeats (CRISPR) systems have been demonstrated to be the foremost compelling genetic tools for manipulating prokaryotic and eukaryotic genomes. Despite the robustness and versatility of Cas9 and Cas12a/b nucleases in mammalian cells and plants, their large protein sizes may hinder downstream applications. Therefore, investigating compact CRISPR nucleases will unlock numerous genome editing and delivery challenges that constrain genetic engineering and crop development. In this study, we assessed the archaeal miniature Un1Cas12f1 type-V CRISPR nuclease for genome editing in rice and tomato protoplasts. By adopting the reengineered guide RNA modifications ge4.1 and comparing polymerase II (Pol II) and polymerase III (Pol III) promoters, we demonstrated uncultured archaeon Cas12f1 (Un1Cas12f1) genome editing efficacy in rice and tomato protoplasts. We characterized the protospacer adjacent motif (PAM) requirements and mutation profiles of Un1Cas12f1 in both plant species. Interestingly, we found that Pol III promoters, not Pol II promoters, led to higher genome editing efficiency when they were used to drive guide RNA expression. Unlike in mammalian cells, the engineered Un1Cas12f1-RRA variant did not perform better than the wild-type Un1Cas12f1 nuclease, suggesting continued protein engineering and other innovative approaches are needed to further improve Un1Cas12f1 genome editing in plants.

Aggressive strains of Neopestalotiopsis sp. have recently emerged as devastating pathogens of strawberry (Fragaria × ananassa Duchesne ex Rozier), infecting nearly all plant parts and causing severe outbreaks of leaf spot and fruit rot in Florida and globally. The development of host resistance is imperative due to the absence of fungicides that effectively inhibit Neopestalotiopsis sp. growth on an infected strawberry crop. Here, we analyzed 1578 individuals from the University of Florida's (UF) strawberry breeding program to identify and dissect genetic variation for resistance to Neopestalotiopsis sp. and to explore the feasibility of genomic selection. We found that less than 12% of elite UF germplasm exhibited resistance, with narrow-sense heritability estimates ranging from 0.28 to 0.69. Through genome-wide association studies (GWAS), we identified two loci accounting for 7%-16% of phenotypic variance across four trials and 3 years. Several candidate genes encoding pattern recognition receptors, intra-cellular nucleotide-binding leucine-rich repeats, and downstream components of plant defense pathways co-localized with the Neopestalotiopsis sp. resistance loci. Interestingly, favorable alleles at the largest-effect locus were rare in elite UF material and had previously been unintentionally introduced from an exotic cultivar. The array-based markers and candidate genes described herein provide the foundation for targeting this locus through marker-assisted selection. The predictive abilities of genomic selection models, with and without explicitly modeling peak GWAS markers as fixed effects, ranged between 0.25 and 0.59, suggesting that genomic selection holds promise for enhancing resistance to Neopestalotiopsis sp. in strawberry.