Plant Genome最新文献

Wheat (Triticum aestivum L.) is crucial to global food security but is often threatened by diseases, pests, and environmental stresses. Wheat-stem sawfly (Cephus cinctus Norton) poses a major threat to food security in the United States, and solid-stem varieties, which carry the stem-solidness locus (Sst1), are the main source of genetic resistance against sawfly. Marker-assisted selection uses molecular markers to identify lines possessing beneficial haplotypes, like that of the Sst1 locus. In this study, an R package titled "HaploCatcher" was developed to predict specific haplotypes of interest in genome-wide genotyped lines. A training population of 1056 lines genotyped for the Sst1 locus, known to confer stem solidness, and genome-wide markers was curated to make predictions of the Sst1 haplotypes for 292 lines from the Colorado State University wheat breeding program. Predicted Sst1 haplotypes were compared to marker-derived haplotypes. Our results indicated that the training set was substantially predictive, with kappa scores of 0.83 for k-nearest neighbors and 0.88 for random forest models. Forward validation on newly developed breeding lines demonstrated that a random forest model, trained on the total available training data, had comparable accuracy between forward and cross-validation. Estimated group means of lines classified by haplotypes from PCR-derived markers and predictive modeling did not significantly differ. The HaploCatcher package is freely available and may be utilized by breeding programs, using their own training populations, to predict haplotypes for whole-genome sequenced early generation material.

Genome-wide introgression and substitution lines have been developed in many plant species, enhancing mapping precision, gene discovery, and the identification and exploitation of variation from wild relatives. Created over multiple generations of crossing and/or backcrossing accompanied by marker-assisted selection, the resulting introgression lines are a fixed genetic resource. In this study we report the development of spring wheat (Triticum aestivum L.) chromosome segment substitution lines (CSSLs) generated to systematically capture genetic variation from tetraploid (T. turgidum ssp. dicoccoides) and diploid (Aegilops tauschii) progenitor species. Generated in a common genetic background over four generations of backcrossing, this is a base resource for the mapping and characterization of wheat progenitor variation. To facilitate further exploitation the final population was genetically characterized using a high-density genotyping array and a range of agronomic and grain traits assessed to demonstrate the potential use of the populations for trait localization in wheat.

Salinity is a major abiotic stress factor that can significantly impact crop growth, and productivity. In response to salt stress, the plant Salt Overly Sensitive (SOS) signaling pathway regulates the homeostasis of intracellular sodium ion concentration. The SOS1, SOS2, and SOS3 genes play critical roles in the SOS pathway, which belongs to the members of Na⁺/H⁺ exchanger (NHX), CBL-interacting protein kinase (CIPK), and calcineurin B-like (CBL) gene families, respectively. In this study, we performed genome-wide identifications and phylogenetic analyses of NHX, CIPK, and CBL genes in six Rosaceae species: Prunus persica, Prunus dulcis, Prunus mume, Prunus armeniaca, Pyrus ussuriensis × Pyrus communis, and Rosa chinensis. NHX, CIPK, and CBL genes of Arabidopsis thaliana were used as controls for phylogenetic analyses. Our analysis revealed the lineage-specific and adaptive evolutions of Rosaceae genes. Our observations indicated the existence of two primary classes of CIPK genes: those that are intron-rich and those that are intron-less. Intron-rich CIPKs in Rosaceae and Arabidopsis can be traced back to algae CIPKs and CIPKs found in early plants, suggesting that intron-less CIPKs evolved from their intron-rich counterparts. This study identified one gene for each member of the SOS signaling pathway in P. persica: PpSOS1, PpSOS2, and PpSOS3. Gene expression analyses indicated that all three genes of P. persica were expressed in roots and leaves. Yeast two-hybrid-based protein-protein interaction analyses revealed a direct interaction between PpSOS3 and PpSOS2; and between PpSOS2 and PpSOS1C-terminus region. Our findings indicate that the SOS signaling pathway is highly conserved in P. persica.

Global mean temperature is increasing at a rapid pace due to the rapid emission of greenhouse gases majorly from anthropogenic practices and predicted to rise up to 1.5°C above the pre-industrial level by the year 2050. The warming climate is affecting global crop production by altering biochemical, physiological, and metabolic processes resulting in poor growth, development, and reduced yield. Maize is susceptible to heat stress, particularly at the reproductive and early grain filling stages. Interestingly, heat stress impact on crops is closely regulated by associated environmental covariables such as humidity, vapor pressure deficit, soil moisture content, and solar radiation. Therefore, heat stress tolerance is considered as a complex trait, which requires multiple levels of regulations in plants. Exploring genetic diversity from landraces and wild accessions of maize is a promising approach to identify novel donors, traits, quantitative trait loci (QTLs), and genes, which can be introgressed into the elite cultivars. Indeed, genome wide association studies (GWAS) for mining of potential QTL(s) and dominant gene(s) is a major route of crop improvement. Conversely, mutation breeding is being utilized for generating variation in existing populations with narrow genetic background. Besides breeding approaches, augmented production of heat shock factors (HSFs) and heat shock proteins (HSPs) have been reported in transgenic maize to provide heat stress tolerance. Recent advancements in molecular techniques including clustered regularly interspaced short palindromic repeats (CRISPR) would expedite the process for developing thermotolerant maize genotypes.

Temperatures below or above optimal growth conditions are among the major stressors affecting productivity, end-use quality, and distribution of key staple crops including rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays L.). Among temperature stresses, cold stress induces cellular changes that cause oxidative stress and slowdown metabolism, limit growth, and ultimately reduce crop productivity. Perception of cold stress by plant cells leads to the activation of cold-responsive transcription factors and downstream genes, which ultimately impart cold tolerance. The response triggered in crops to cold stress includes gene expression/suppression, the accumulation of sugars upon chilling, and signaling molecules, among others. Much of the information on the effects of cold stress on perception, signal transduction, gene expression, and plant metabolism are available in the model plant Arabidopsis but somewhat lacking in major crops. Hence, a complete understanding of the molecular mechanisms by which staple crops respond to cold stress remain largely unknown. Here, we make an effort to elaborate on the molecular mechanisms employed in response to low-temperature stress. We summarize the effects of cold stress on the growth and development of these crops, the mechanism of cold perception, and the role of various sensors and transducers in cold signaling. We discuss the progress in cold tolerance research at the genome, transcriptome, proteome, and metabolome levels and highlight how these findings provide opportunities for designing cold-tolerant crops for the future.

Chickpea (Cicer arietinum) is a pulse crop that provides an integral source of nutrition for human consumption. The close wild relatives Cicer reticulatum and Cicer echinospermum harbor untapped genetic diversity that can be exploited by chickpea breeders to improve domestic varieties. Knowledge of genomic loci that control important chickpea domestication traits will expedite the development of improved chickpea varieties derived from interspecific crosses. Therefore, we set out to identify genomic loci underlying key chickpea domestication traits by both association and quantitative trait locus (QTL) mapping using interspecific F₂ populations. Diverse phenotypes were recorded for various agronomic traits. A total of 11 high-confidence markers were detected on chromosomes 1, 3, and 7 by both association and QTL mapping; these were associated with growth habit, flowering time, and seed traits. Furthermore, we identified candidate genes linked to these markers, which advanced our understanding of the genetic basis of domestication traits and validated known genes such as the FLOWERING LOCUS gene cluster that regulates flowering time. Collectively, this study has elucidated the genetic basis of chickpea domestication traits, which can facilitate the development of superior chickpea varieties.

Climate change causes extreme conditions like prolonged drought, which results in yield reductions due to its effects on nutrient balances such as nitrogen uptake and utilization by plants. Nitrogen (N) is a crucial nutrient element for plant growth and productivity. Understanding the mechanistic basis of nitrogen use efficiency (NUE) under drought conditions is essential to improve wheat (Triticum aestivum L.) yield. Here, we evaluated the genetic variation of NUE-related traits and photosynthesis response in a diversity panel of 200 wheat genotypes under drought and nitrogen stress conditions to uncover the inherent genetic variation and identify quantitative trait loci (QTLs) underlying these traits. The results revealed significant genetic variations among the genotypes in response to drought stress and nitrogen deprivation. Drought impacted plant performance more than N deprivation due to its effect on water and nutrient uptake. GWAS identified a total of 27 QTLs with a significant main effect on the drought-related traits, while 10 QTLs were strongly associated with the NUE traits. Haplotype analysis revealed two different haplotype blocks within the associated region on chromosomes 1B and 5A. The two haplotypes showed contrasting effects on N uptake and use efficiency traits. The in silico and transcript analyses implicated candidate gene coding for cold shock protein. This gene was the most highly expressed gene under several stress conditions, including drought stress. Upon validation, these QTLs on 1B and 5A could be used as a diagnostic marker for NUE and drought tolerance screening in wheat.

Effects of individual single-nucleotide polymorphism (SNP) markers and the size of "training" and "test" populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of "training" to "test" population size was also evaluated. Fourteen alfalfa populations sampled from long-term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of "training" to "test" population size increased. However, the prediction accuracy of the six GS methods reduced to a range of -0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge-regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for "training" sets, but rrBLUP tended to outperform Bayesian methods in independent "test" sets when SNP subsets with high mean-squared-estimated-marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement.

The aim of this study was to evaluate the accuracy of the ridge regression best linear unbiased prediction model across different traits, parent population sizes, and breeding strategies when estimating breeding values in common bean (Phaseolus vulgaris). Genomic selection was implemented to make selections within a breeding cycle and compared across five different breeding strategies (single seed descent, mass selection, pedigree method, modified pedigree method, and bulk breeding) following 10 breeding cycles. The model was trained on a simulated population of recombinant inbreds genotyped for 1010 single nucleotide polymorphism markers including 38 known quantitative trait loci identified in the literature. These QTL included 11 for seed yield, eight for white mold disease incidence, and 19 for days to flowering. Simulation results revealed that realized accuracies fluctuate depending on the factors investigated: trait genetic architecture, breeding strategy, and the number of initial parents used to begin the first breeding cycle. Trait architecture and breeding strategy appeared to have a larger impact on accuracy than the initial number of parents. Generally, maximum accuracies (in terms of the correlation between true and estimated breeding value) were consistently achieved under a mass selection strategy, pedigree method, and single seed descent method depending on the simulation parameters being tested. This study also investigated model updating, which involves retraining the prediction model with a new set of genotypes and phenotypes that have a closer relation to the population being tested. While it has been repeatedly shown that model updating generally improves prediction accuracy, it benefited some breeding strategies more than others. For low heritability traits (e.g., yield), conventional phenotype-based selection methods showed consistent rates of genetic gain, but genetic gain under genomic selection reached a plateau after fewer cycles. This plateauing is likely a cause of faster fixation of alleles and a diminishing of genetic variance when selections are made based on estimated breeding value as opposed to phenotype.

Wheat (Triticum aestivum L.) is a major source of nutrients for populations across the globe, but the amino acid composition of wheat grain does not provide optimal nutrition. The nutritional value of wheat grain is limited by low concentrations of lysine (the most limiting essential amino acid) and high concentrations of free asparagine (precursor to the processing contaminant acrylamide). There are currently few available solutions for asparagine reduction and lysine biofortification through breeding. In this study, we investigated the genetic architecture controlling grain free amino acid composition and its relationship to other traits in a Robigus × Claire doubled haploid population. Multivariate analysis of amino acids and other traits showed that the two groups are largely independent of one another, with the largest effect on amino acids being from the environment. Linkage analysis of the population allowed identification of quantitative trait loci (QTL) controlling free amino acids and other traits, and this was compared against genomic prediction methods. Following identification of a QTL controlling free lysine content, wheat pangenome resources facilitated analysis of candidate genes in this region of the genome. These findings can be used to select appropriate strategies for lysine biofortification and free asparagine reduction in wheat breeding programs.