Plant Genome最新文献

Flooding has become a major threat to soybean [Glycine max (L.) Merr.] production as the frequency and intensity of extreme precipitations have been increasing due to climate change. While advances have been made in identifying soybean genetic resources and genomic regions associated with mid-season flood tolerance, there is limited understanding of early season flood tolerance at the vegetative growth stages V2/V4. This study aimed to identify genomic regions associated with early season flood tolerance using a diverse panel of 254 soybean accessions, as well as investigate the viability of implementing genomic prediction models for flood tolerance. Field trials were conducted over 2 years, with flooding imposed at the V2/V4 vegetative growth stages. Genome-wide association studies were performed using the Bayesian-information and linkage-disequilibrium iteratively nested keyway and the multiple locus mixed linear model. Forward stepwise genomic prediction models using random forest (RF) were developed to identify the set of single nucleotide polymorphisms (SNPs) yielding the highest prediction accuracy while assessing the negative impacts of multicollinearity and overfitting on prediction accuracy. Genomic regions on chromosomes 4, 17, and 20 associated with early season flood tolerance were identified, all distinct from regions previously identified for mid-season tolerance. The RF model achieved a prediction accuracy of 0.64 with 29 selected SNPs, significantly improving over RF and ridge regression best linear unbiased prediction models with higher SNP counts. These findings provide genomic tools for improving the efficiency of breeding for early season flood tolerance, supporting the need to develop season-long flood-tolerant soybean genotypes.

Elucidating the impacts of demographic history and genomic selection on species evolution is a central topic in phylogeography and evolutionary biology. Black walnuts (Juglans section Rhysocaryon) are native nut trees of the NEW WORLD, with a broad distribution ranging from southern Canada to northern Argentina. The demographic history and genomic dynamics of Rhysocaryon species remain poorly understood. Here, we employed population genomics and chloroplast data to construct a high-density map of genomic variation across 108 Rhysocaryon accessions. Despite gene introgression, these accessions were clearly delimited into four groups. Evolutionary scenarios analysis showed that the diversification of black walnuts might have occurred approximately 28.74 million years ago during the late Oligocene, with the clade comprising Juglans hindsii and Juglans californica diverging earliest. The gene introgression and hybridization analysis indicated that Juglans microcarpa might be a hybrid descendant of Juglans nigra and J. hindsii. As the climate oscillated, these ancestral populations kept diverging, laying the basis for their colonization of South America. Quaternary climatic oscillations also exerted a profound influence on black walnut population size, which exhibited sensitive fluctuations in response to alternation of glacial and interglacial periods. The selection sweeps analysis unveiled highly divergent genomic regions in the economic species J. nigra, which were associated with development, reproduction, disease resistance, and stress tolerance. The genes WRKY41 and ERF012 were identified as potential drivers of J. nigra's adaptation. Our findings illuminated the demographic history and selective signatures of black walnuts, thereby providing a genetic foundation for future breeding, conservation, and genomic studies.

Cotton (Gossypium barbadense L. and Gossypium hirsutum L.), a major economic crop, suffers severe yield losses due to Verticillium wilt caused by Verticillium dahliae Kleb. Although the lectin receptor-like protein GbMBL1.1A has been identified as a key activator of hypersensitive cell death in cotton resistance to this pathogen, its downstream defense mechanisms remain unknown. Here, we performed comparative transcriptome analyses between the GbMBL1.1A-overexpressing (OE) cotton line and its wild-type (WT) recipient counterpart (WC) under both normal (non-infected) and V. dahliae infected conditions. A total of 760 differentially expressed genes (DEGs) were identified by comparing GbMBL1.1A-OE and WT cotton lines under normal condition. Upon pathogen infection across three timepoints, the counts of 1679 (1063 up, 616 down) and 1648 (633 up, 1015 down) DEGs were identified uniquely from GbMBL1.1A-OE or WT cotton line, respectively, relative to their mock controls. Further analysis of these DEGs revealed three aspects of changes due to GbMBL1.1A overexpression: (1) Pre-infection priming via selective pattern recognition receptor modulation, mitochondrial cell death activation, and reactive oxygen species antioxidant perturbation under normal condition; (2) Significant enhancement of plant defense against pathogen through the remodeling of immune receptor activity, alteration of signal transduction and regulation, and modulation of secondary metabolic processes, with the most significant alteration occurring in lignin and phenylpropanoid metabolism. (3) Dynamic redox homeostasis regulation through metabolite interconversion enzymes during infection process. Collectively, these findings strengthen the theoretical foundation and provide novel candidate targets for developing improved control strategies to enhance cotton resistance against V. dahliae.

Genomic selection (GS) has revolutionized breeding practices by integrating genotype and phenotype data to predict genomic estimated breeding values, offering the potential to accelerate breeding cycles and intensify and enhance early-stage selections. This approach utilizes the concept of linkage disequilibrium (LD) between genetic markers and quantitative trait loci within populations. LD, the nonrandom association between alleles at different loci, provides valuable insights into historical recombination patterns, although it can change over time under strong selection or genetic drift. This study aimed to investigate the influence of recombination on haplotype sizes and LD, assess the impact of additive (A) versus additive + epistasis (A+I) genetic models on GS predictive ability (PA), and demonstrate how haplotype resolution in the training set (TS) impacts the PA of GS. For this, we used biparental (MP2) and multiparent (MP6-8) populations, where the main difference between them was the recombination rate. As expected, a strong correlation between LD decay and the number of recombination opportunities within populations was observed, with smaller haplotype blocks in populations experiencing more recombination. The use of A+I models increased heritability but did not improve PA. Finally, populations with smaller haplotype sizes in the TS exhibited enhanced PA. This study demonstrates the effect of haplotype size on GS accuracy, and its uniqueness lies in its focus on populations where the primary differentiating factor is haplotype size. It offers an important tool for breeders in designing GS strategies, providing valuable guidance for future breeding efforts.

Heat stress, exacerbated by global warming, can cause significant challenges to agriculture, adversely impacting plant growth, reproduction, and yield. This review examines the crucial role of microRNAs (miRNAs) in mediating plant responses to heat stress across various key crops, including Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa), wheat (Triticum aestivum), and other significant species. Under high temperatures, miRNAs regulate gene expression by targeting transcription factors (e.g., SPL, NF-YA, and Apetala 2 [AP2]), heat shock proteins, and antioxidant enzymes (e.g., copper/zinc superoxide dismutase), thereby modulating pathways involved in hormone signaling, oxidative stress mitigation, and developmental transitions. Advanced high-throughput sequencing technologies have identified heat-responsive miRNAs (e.g., miR156, miR398, miR172) and their functional networks, including crosstalk with small interfering RNAs, long noncoding RNAs, and circular RNAs via competing endogenous RNA (ceRNA) mechanisms. These findings highlight miRNAs as promising targets for engineering heat-resilient crops. However, gaps remain in understanding tissue-specific miRNA dynamics and their integration with epigenetic and multi-omics networks. Future research should employ integrative approaches to optimize miRNA-based strategies for sustainable agriculture in the context of climate change.

The International Maize and Wheat Improvement Center (CIMMYT) spring bread wheat (Triticum aestivum L.) program represents the largest and most diverse set of elite wheat germplasm globally. From 2013 to 2023, genotyping was conducted on 130,247 bread wheat lines advanced through CIMMYT's bread wheat breeding program with the objective to perform genomic prediction and identify genomic regions associated with important traits such as yield and disease resistance. We constructed and sequenced 636 genotyping-by-sequencing (GBS) libraries, multiplexed at 96- to 384-plex, and generated 30.7 terabases of sequence. Using an optimized TASSEL pipeline, we identified 24,125 high-quality single nucleotide polymorphism on 21 chromosomes. Population genetic clustering of 444 selected lines within 10 pedigrees supported the accuracy of the GBS approach. Genome-wide analysis of nucleotide diversity (π) and minor allele frequency across the entire dataset revealed significantly reduced genetic variation in pericentromeric regions of all chromosomes, which was confirmed by comparison to a genetic outgroup of diverse winter wheat lines. This pattern of low genetic diversity indicates fixation of large centromeric haplotype blocks. The limited diversity in non-recombining regions has critical implications for future genetic gains in wheat breeding. Temporal pairwise F_ST analyses further demonstrated signatures of selection that aligned with previously published genome-wide association studies for agronomic traits such as grain yield and disease resistance. These datasets have been implemented for the selection of superior breeding lines and are distributed as a publicly available resource for global wheat breeding efforts and genetic studies.

Ronald Phillips, a maize geneticist, developed his career exploiting maize and the genetics of other species to help bring plant science into the era of molecular genetics. He was driven by belief in the value of service for the common good and in the value and importance of science for its own sake and for agriculture and food security, in particular. His career was a journey along the frontiers of plant science-from early DNA isolation to whole genome sequence revelations and into agricultural biotechnology. He represented the progress along the way in the maize genetics community, in national and international science and at the highest levels of influence. He was a caring, celebrated scientist who made a difference for people and institutions and left plant science so much further advanced than when he joined it in the mid-1960s.

An ensemble of multiple genomic prediction models has grown in popularity due to consistent prediction performance improvements in crop breeding. However, technical tools that analyze the predictive behavior at the genome level are lacking. Here, we develop a computational tool called Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP) that uses circos plots to visualize how different genomic prediction models quantify contributions of marker effects to trait phenotypes. As a demonstration of EasiGP, multiple genomic prediction models, spanning conventional statistical and machine learning algorithms, were used to infer the genetic architecture of days to anthesis (DTA) in a maize mapping population. The results indicate that genomic prediction models can capture different views of trait genetic architecture, even when their overall profiles of prediction accuracy are similar. Combinations of diverse views of the genetic architecture for the DTA trait in the teosinte nested association mapping study might explain the improved prediction performance achieved by ensembles, aligned with the implication of the Diversity Prediction Theorem. In addition to identifying well-known genomic regions contributing to the genetic architecture of DTA in maize, the ensemble of genomic prediction models highlighted several new genomic regions that have not been previously reported for DTA. Finally, different views of trait genetic architecture were observed across subpopulations, highlighting challenges for between-population genomic prediction. A deeper understanding of genomic prediction models with enhanced interpretability using EasiGP can reveal several critical findings at the genome level from the inferred genetic architecture, providing insights into the improvement of genomic prediction for crop breeding programs.