Plant Genome最新文献

Climate change represents a significant challenge to global food security by altering environmental conditions critical to crop growth. Plant breeders can play a key role in mitigating these challenges by developing more resilient crop varieties; however, these efforts require significant investments in resources and time. In response, it is imperative to use current technologies that assimilate large biological and environmental datasets into predictive models to accelerate the research, development, and release of new improved varieties that can be more resilient to the increasingly variable climatic conditions. Leveraging large and diverse datasets can improve the characterization of phenotypic responses due to environmental stimuli and genomic pulses. A better characterization of these signals holds the potential to enhance our ability to predict trait performance under changes in weather and/or soil conditions with high precision. This paper introduces characterization and integration of driven omics (CHiDO), an easy-to-use, no-code platform designed to integrate diverse omics datasets and effectively model their interactions. With its flexibility to integrate and process datasets, CHiDO's intuitive interface allows users to explore historical data, formulate hypotheses, and optimize data collection strategies for future scenarios. The platform's mission emphasizes global accessibility, democratizing statistical solutions for situations where professional ability in data processing and data analysis is not available.

End-use and processing traits in wheat (Triticum aestivum L.) are crucial for varietal development but are often evaluated only in the advanced stages of the breeding program due to the amount of grain needed and the labor-intensive phenotyping assays. Advances in genomic resources have provided new tools to address the selection for these complex traits earlier in the breeding process. We used association mapping to identify key variants underlying various end-use quality traits and evaluate the usefulness of genomic prediction for these traits in hard red spring wheat from the Northern United States. A panel of 383 advanced breeding lines and cultivars representing the diversity of the University of Minnesota wheat breeding program was genotyped using the Illumina 90K single nucleotide polymorphism array and evaluated in multilocation trials using standard assessments of end-use quality. Sixty-three associations for grain or flour characteristics, mixograph, farinograph, and baking traits were identified. The majority of these associations were mapped in the vicinity of glutenin/gliadin or other known loci. In addition, a putative novel multi-trait association was identified on chromosome 6AL, and candidate gene analysis revealed eight genes of interest. Further, genomic prediction had a high predictive ability (PA) for mixograph and farinograph traits, with PA up to 0.62 and 0.50 in cross-validation and forward prediction, respectively. The deployment of 46 markers from GWAS to predict dough-rheology traits yielded low to moderate PA for various traits. The results of this study suggest that genomic prediction for end-use traits in early generations can be effective for mixograph and farinograph assays but not baking assays.

Alfalfa (Medicago sativa L.) is a perennial forage legume esteemed for its exceptional quality and dry matter yield (DMY); however, alfalfa has historically exhibited low genetic gain for DMY. Advances in genotyping platforms paved the way for a cost-effective application of genomic prediction in alfalfa family bulks. In this context, the optimization of marker density holds potential to reallocate resources within genomic prediction pipelines. This study aimed to (i) test two genotyping platforms for population structure discrimination and predictive ability (PA) of genomic prediction models (G-BLUP) for DMY, and (ii) explore optimal levels of marker density to predict DMY in family bulks. For this, 160 nondormant alfalfa families were phenotyped for DMY across 11 harvests and genotyped via targeted sequencing using Capture-seq with 17K probes and the DArTag 3K panel. Both platforms discriminated similarly against the population structure and resulted in comparable PA for DMY. For genotyping optimization, different levels of marker density were randomly extracted from each platform. In both cases, a plateau was achieved around 500 markers, yielding similar PA as the full set of markers. For phenotyping optimization, models with 500 markers built with data from five harvests resulted in similar PA compared to the full set of 11 harvests and full set of markers. Altogether, genotyping and phenotyping efforts were optimized in terms of number of markers and harvests. Capture-seq and DArTag yielded similar results and have the flexibility to adjust their panels to meet breeders' needs in terms of marker density.

Phytocytokines belong to a category of small secreted peptides with signaling functions that play pivotal roles in diverse plant physiological processes. However, due to low levels of sequence conservation across plant species and poorly understood biological functions, the accurate detection and annotation of corresponding genes is challenging. The availability of a high-quality apple (Malus domestica) genome has enabled the exploration of five phytocytokine gene families, selected on the basis of their altered expression profiles in response to biotic stresses. These include phytosulfokine, inflorescence deficient in abscission/-like, pathogen-associated molecular pattern induced secreted peptide, plant peptide containing sulfated tyrosine, and C-terminally encoded peptide. The genes encoding the precursors of these five families of signaling peptides were identified using a customized bioinformatics protocol combining genome mining, homology searches, and peptide motif detection. Transcriptomic analyses showed that these peptides were deregulated in response to Erwinia amylovora, the causal agent of fire blight in pome fruit trees, and in response to a chemical elicitor (acibenzolar-S-methyl). Finally, gene family evolution and the orthology relationships with Arabidopsis thaliana homologs were investigated.

Durum wheat (Triticum turgidum ssp. durum L.) is an important world food crop used to make pasta products. Compared to bread wheat (Triticum aestivum L.), fewer studies have been conducted to identify genetic loci governing yield-component traits in durum wheat. A potential source of diversity for durum is its immediate progenitor, cultivated emmer (T. turgidum ssp. dicoccum). We evaluated two biparental populations of recombinant inbred lines (RILs) derived from crosses between the durum lines Ben and Rusty and the cultivated emmer wheat accessions PI 41025 and PI 193883, referred to as the Ben × PI 41025 (BP025) and Rusty × PI 193883 (RP883) RIL populations, respectively. Both populations were evaluated under field conditions in three seasons with an aim to identify quantitative trait loci (QTLs) associated with yield components and seed morphology that were expressed in multiple environments. A total of 44 and 34 multi-environment QTLs were identified in the BP025 and RP883 populations, respectively. As expected, genetic loci known to govern domestication and development were associated with some of the QTLs, but novel QTLs derived from the cultivated emmer parents and associated with yield components including spikelet number, grain weight, and grain size were identified. These QTLs offer new target loci for durum wheat improvement, and toward that goal, we identified five RILs with increased grain weight and size compared to the durum parents. These materials along with the knowledge of stable QTLs and associated markers can help to expedite the development of superior durum varieties.

Iodes seguinii is a woody vine known for its potential therapeutic applications in treating rheumatoid arthritis (RA) due to its rich bioactive components. Here, we achieved the first chromosome-level assembly of the nuclear genome of I. seguinii using PacBio HiFi and chromatin conformation capture (Hi-C) sequencing data. The initial assembly with PacBio data produced contigs with an N50 length of 9.71 Mb, and Hi-C data anchored these contigs into 13 chromosomes, achieving a total length of 273.58 Mb, closely matching the estimated genome size. Quality assessments, including BUSCO, long terminal repeat assembly index, transcriptome mapping rates, and sequencing coverage, confirmed the high quality, completeness, and continuity of the assembly, identifying 115.28 Mb of repetitive sequences, 1062 RNA genes, and 25,270 protein-coding genes. Additionally, we assembled and annotated the 150,599 bp chloroplast genome using Illumina sequencing data, containing 121 genes including key DNA barcodes, with maturase K (matK) proving effective for species identification. Phylogenetic analysis positioned I. seguinii at the base of the Lamiales clade, identifying significant gene family expansions and contractions, particularly related to secondary metabolite synthesis and DNA damage repair. Metabolite analysis identified 84 active components in I. seguinii, including the discovery of luteolin, with 119 targets predicted for RA treatment, including core targets like AKT1, toll-like receptor 4 (TLR4), epidermal growth factor receptor (EGFR), tumor necrosis factor (TNF), TP53, NFKB1, janus kinase 2 (JAK2), BCL2, mitogen-activated protein kinase 1 (MAPK1), and spleen-associated tyrosine kinase (SYK). Key active components such as flavonoids and polyphenols with anti-inflammatory activities were highlighted. The discovery of luteolin, in particular, underscores its potential therapeutic role. These findings provide a valuable genomic resource and a scientific basis for the development and application of I. seguinii, addressing the genomic gap in the genus Iodes and the order Icacinales and underscoring the need for further research in genomics, transcriptomics, and metabolomics to fully explore its potential.

Durum wheat (Triticum turgidum ssp. durum) is globally cultivated for pasta, couscous, and bulgur production. With the changing climate and growing world population, the need to significantly increase durum production to meet the anticipated demand is paramount. This review summarizes recent advancements in durum research, encompassing the exploitation of existing and novel genetic diversity, exploration of potential new diversity sources, breeding for climate-resilient varieties, enhancements in production and management practices, and the utilization of modern technologies in breeding and cultivar development. In comparison to bread wheat (T. aestivum), the durum wheat community and production area are considerably smaller, often comprising many small-family farmers, notably in African and Asian countries. Public breeding programs such as the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) play a pivotal role in providing new and adapted cultivars for these small-scale growers. We spotlight the contributions of these and others in this review. Additionally, we offer our recommendations on key areas for the durum research community to explore in addressing the challenges posed by climate change while striving to enhance durum production and sustainability. As part of the Wheat Initiative, the Expert Working Group on Durum Wheat Genomics and Breeding recognizes the significance of collaborative efforts in advancing toward a shared objective. We hope the insights presented in this review stimulate future research and deliberations on the trajectory for durum wheat genomics and breeding.

Circular RNAs (circRNAs) are closed-loop RNAs forming a covalent bond between their 3' and 5' ends, the back splice junction (BSJ), rendering them resistant to exonucleases and thus more stable compared to linear RNAs. Identification of circRNAs and distinction from their cognate linear RNA is only possible by sequencing the BSJ that is unique to the circRNA. CircRNAs are involved in the regulation of their cognate RNAs by increasing transcription rates, RNA stability, and alternative splicing. We have identified circRNAs from C. sativa that are associated with the regulation of germination, light response, and lipid metabolism. We sequenced light-grown and etiolated seedlings after 5 or 7 days post-germination and identified a total of 3447 circRNAs from 2763 genes. Most circRNAs originate from a single homeolog of the three subgenomes from allohexaploid camelina and correlate with higher ratios of alternative splicing of their cognate genes. A network analysis shows the interactions of select miRNA:circRNA:mRNAs for regulation of transcript stabilities where circRNA can act as a competing endogenous RNA. Several key lipid metabolism genes can generate circRNA, and we confirmed the presence of KASII circRNA as a true circRNA. CircRNA in camelina can be a novel target for breeding and engineering efforts.

Enhancing predictive modeling accuracy in wheat (Triticum aestivum) breeding through the integration of high-throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. In this study, spanning four locations in the southeastern United States over 3 years, models to predict grain yield (GY) were investigated through different cross-validation approaches. The results demonstrate the superiority of multivariate comprehensive models that incorporate both genomic and HTP data, particularly in accurately predicting GY across diverse locations and years. These HTP-incorporating models achieve prediction accuracies ranging from 0.59 to 0.68, compared to 0.40-0.54 for genomic-only models when tested under different prediction scenarios both across years and locations. The comprehensive models exhibit superior generalization to new environments and achieve the highest accuracy when trained on diverse datasets. Predictive accuracy improves as models incorporate data from multiple years, highlighting the importance of considering temporal dynamics in modeling approaches. The study reveals that multivariate prediction outperformed genomic prediction methods in predicting lines across years and locations. The percentage of top 25% lines selected based on multivariate prediction was higher compared to genomic-only models, indicated by higher specificity, which is the proportion of correctly identified top-yielding lines that matched the observed top 25% performance across different sites and years. Additionally, the study addresses the prediction of untested locations based on other locations within the same year and in new years at previously tested locations. Findings show the comprehensive models effectively extrapolate to new environments, highlighting their potential for guiding breeding strategies.

Brassicas are an economically important crop species that provide a source of healthy oil and vegetables. With the rising population and the impact of climate change on agriculture, there is an increasing need to improve agronomically important traits of crops such as Brassica. The genomes of plant species have significant sequence presence absence variation (PAV), which is a source of genetic variation that can be used for crop improvement, and this species variation can be captured through the construction of pangenomes. Graph pangenomes are a recent reference format that represent the genomic variation with a species or population as alternate paths in a sequence graph. Graph pangenomes contain information on alignment, PAV, and annotation. Here we present the first multi-species graph pangenome for Brassica visualized with pangenome analyzer with chromosomal exploration (Panache).