Plant Genome最新文献

Improvements in long-read sequencing techniques have greatly accelerated plant genome sequencing. Current de novo assemblies are routinely achieved by assembling long-read sequence data into contigs that are assembled to chromosome level by chromatin conformation capture. We report here a chromosome-level mango genome using only PacBio high-fidelity (HiFi) long reads. HiFi reads at high coverage (204x) resulted in the assembly of 17 chromosomes, each as a single contig with telomeres at both ends. The remaining three chromosomes were represented each by two contigs, with telomeres at one end and ribosomal repeats at the other end. Analyzing contig ends allowed them to be paired and linked to generate the remaining three complete chromosomes, telomere-to-telomere but with ribosomal repeats of uncertain length. The assembled genome was 365 Mb with 100% completeness as assessed by Benchmarking Universal Single-Copy Orthologs analysis. The haplotypes assembled demonstrated extensive structural differences. This approach using very high genome coverage may be useful for assembling high-quality genomes for many other plants.

Banana is an important food security crop for millions of people in the tropics but it faces challenges from diseases and pests. Traditional breeding methods have limitations, prompting the exploration of precision genetic tools like genetic modification and genome editing. Extensive efforts using transgenic approaches have been made to develop improved banana varieties with resistance to banana Xanthomonas wilt, Fusarium wilt, and nematodes. However, these efforts should be extended for other pests, diseases, and abiotic stresses. The commercialization of transgenic crops still faces continuous challenges with regulatory and public acceptance. Genome editing, particularly CRISPR/Cas, offers precise modifications to the banana genome and has been successfully applied in the improvement of banana. Targeting specific genes can contribute to the development of improved banana varieties with enhanced resistance to various biotic and abiotic constraints. This review discusses recent advances in banana improvement achieved through genetic modification and genome editing.

Loss of genetic diversity in elite crop breeding pools can severely limit long-term genetic gains and limit ability to make gains in new traits, like heat tolerance, that are becoming important as the climate changes. Here, we investigate and propose potential breeding program applications of optimal haplotype stacking (OHS), a selection method that retains useful diversity in the population. OHS selects sets of candidates containing, between them, haplotype segments with very high segment breeding values for the target trait. We compared the performance of OHS, a similar method called optimal population value (OPV), truncation selection on genomic estimated breeding values (GEBVs), and optimal contribution selection (OCS) in stochastic simulations of recurrent selection on founder wheat genotypes. After 100 generations of intercrossing and selection, OCS and truncation selection had exhausted the genetic diversity, while considerable diversity remained in the OHS population. Gain under OHS in these simulations ultimately exceeded that from truncation selection or OCS. OHS achieved faster gains when the population size was small, with many progeny per cross. A promising hybrid strategy, involving a single cycle of OHS in the first generation followed by recurrent truncation selection, substantially improved long-term gain compared with truncation selection and performed similarly to OCS. The results of this study provide initial insights into where OHS could be incorporated into breeding programs.

For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across-environment sparse genomic prediction models. One phenomic data modality is whole grain near-infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500-kernel weight (KW) across 2 years (2011-2012) and two management conditions (water-stressed and well-watered) were conducted using combinations of reflectance data obtained from high-throughput, F₂ whole-kernel scans and genomic data obtained from genotyping-by-sequencing within four different cross-validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689 ± 0.024-genomic vs. 0.612 ± 0.045-phenomic), but phenomic data were better than genomic data for KW (0.535 ± 0.034-genomic vs. 0.617 ± 0.145-phenomic). Multi-kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single-kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single-kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00.

4-Coumarate-CoA ligase (4CL) gene plays vital roles in plant growth and development, especially the regulation of lignin metabolism and flavonoid synthesis. To investigate the potential function of 4CL in the lignin biosynthesis of Ginkgo biloba, this study identified two 4CL genes, Gb4CL1 and Gb4CL2, from G. biloba genome. Based on the phylogenetic tree analysis, Gb4CL1 and Gb4CL2 protein were classified into Class I, which has been confirmed to be involved in lignin biosynthesis. Therefore, it can be inferred that these two genes may also participate in lignin metabolism. The tissue-specific expression patterns of these two genes revealed that Gb4CL1 was highly expressed in microstrobilus, whereas Gb4CL2 was abundant in immature leaves. The onion transient expression assay indicated that Gb4CL1 was predominantly localized in the nucleus, indicating its potential involvement in nuclear functions, while Gb4CL2 was observed in the cell wall, suggesting its role in cell wall-related processes. Phytohormone response analysis revealed that the expression of both genes was upregulated in response to indole acetic acid, while methyl jasmonate suppressed it, gibberellin exhibited opposite effects on these genes. Furthermore, Gb4CL1 and Gb4CL2 expressed in all tissues containing lignin that showed a positive correlation with lignin content. Thus, these findings suggest that Gb4CL1 and Gb4CL2 are likely involved in lignin biosynthesis. Gb4CL1 and Gb4CL2 target proteins were successfully induced in Escherichia coli BL21 with molecular weights of 85.5 and 89.2 kDa, proving the integrity of target proteins. Our findings provided a basis for revealing that Gb4CL participated in lignin synthesis in G. biloba.

Drought represents a significant production challenge to maize farmers in West and Central Africa, causing substantial economic losses. Breeders at the International Institute of Tropical Agriculture have therefore been developing drought-tolerant maize varieties to attain high grain yields in rainfed maize production zones. The present review provides a historical overview of the approaches used and progress made in developing drought-tolerant hybrids over the years. Breeders made a shift from a wide area testing approach, to the use of managed screening sites, to precisely control the intensity, and timing of drought stress for developing drought-tolerant maize varieties. These sites coupled with the use of molecular markers allowed choosing suitable donors with drought-adaptive alleles for integration into existing elite maize lines to generate new drought-tolerant inbred lines. These elite maize inbred lines have then been used to develop hybrids with enhanced tolerance to drought. Genetic gains estimates were made using performance data of drought-tolerant maize hybrids evaluated in regional trials for 11 years under managed drought stress, well-watered conditions, and across diverse rainfed environments. The results found significant linear annual yield gains of 32.72 kg ha^-1 under managed drought stress, 38.29 kg ha^-1 under well-watered conditions, and 66.57 kg ha^-1 across multiple rainfed field environments. Promising hybrids that deliver high grain yields were also identified for areas affected by drought and variable rainfed growing conditions. The significant genetic correlations found among the three growing conditions highlight the potential to exploit the available genetic resources and modern tools to further enhance tolerance to drought in hybrids.

Sorghum [Sorghum bicolor (L.) Moench] is a cereal crop of critical importance in the semi-arid tropics, particularly in Africa where it is second only to maize (Zea mays L.) by area of cultivation. The International Crops Research Institute for the Semi-Arid Tropics sorghum breeding program for Eastern and Southern Africa is the largest in the region and develops improved varieties for target agro-ecologies. Varietal purity and correct confirmation of new crosses are essential for the integrity and efficiency of a breeding program. We used 49 quality control (QC) kompetitive allele-specific PCR single nucleotide polymorphism (SNP) markers to genotype 716 breeding lines. Note that 46 SNPs were polymorphic with the top 10 most informative revealing polymorphism information content (PIC), minor allele frequency (MAF), and observed heterozygosity (H_o) of 0.37, 0.43, and 0.02, respectively, and explaining 45% of genetic variance within the first two principal components (PC). Thirty-nine markers were highly informative across 16 Burkina Faso breeding lines, out of which the top 10 revealed average PIC, MAF, and H_o of 0.36, 0.39, and 0.05, respectively. Discriminant analysis of principal components done using top 30 markers separated the breeding lines into five major clusters, three of which were distinct. Six of the top 10 most informative markers successfully confirmed hybridization of crosses between genotypes IESV240, KARIMTAMA1, F6YQ212, and FRAMIDA. A set of 10, 20, and 30 most informative markers are recommended for routine QC applications. Future effort should focus on the deployment of these markers in breeding programs for enhanced genetic gain.

Fusarium wilt (FW) is the most severe soil-borne disease of chickpea that causes yield losses up to 100%. To improve FW resistance in JG 11, a high-yielding variety that became susceptible to FW, we used WR 315 as the donor parent and followed the pedigree breeding method. Based on disease resistance and yield performance, four lines were evaluated in station trials during 2017-2018 and 2018-2019 at Kalaburagi, India. Further, two lines, namely, Kalaburagi chickpea desi 5 (KCD 5) and KCD 11, which possesses the resistance allele for a specific single-nucleotide polymorphism marker linked with FW resistance, were evaluated across six different locations (Bidar, Kalaburagi, Raichur, Siruguppa, Bhimarayanagudi and Hagari) over a span of 3 years (2020-2021, 2021-2022 and 2022-2023). KCD 11 exhibited notable performance, showcasing yield advantages of 8.67%, 11.26% and 23.88% over JG 11, and the regional checks Super Annigeri 1 (SA 1) and Annigeri 1, respectively, with enhanced FW resistance in wilt sick plot. Further, KCD 11 outperformed JG 11, SA 1 and Annigeri 1 in multi-location trials conducted across three seasons in the North Eastern Transition Zone, North Eastern Dry Zone, and North Dry Zones of Karnataka. KCD 11 was also tested in trials conducted by All India Coordinated Research Project on chickpea and was also nominated for state varietal trials for its release as a FW-resistant and high-yielding variety. The selected line is anticipated to cater the needs of chickpea growers with the dual advantage of yield increment and disease resistance.

Torenia fournieri Lind. is an ornamental plant that is popular for its numerous flowers and variety of colors. However, its genomic evolutionary history and the genetic and metabolic bases of flower color formation remain poorly understood. Here, we report the first T. fournieri reference genome, which was resolved to the chromosome scale and was 164.4 Mb in size. Phylogenetic analyses clarified relationships with other plant species, and a comparative genomic analysis indicated that the shared ancestor of T. fournieri and Antirrhinum majus underwent a whole genome duplication event. Joint transcriptomic and metabolomic analyses identified many metabolites related to pelargonidin, peonidin, and naringenin production in rose (TfR)-colored flowers. Samples with blue (TfB) and deep blue (TfD) colors contained numerous derivatives of petunidin, cyanidin, quercetin, and malvidin; differences in the abundances of these metabolites and expression levels of the associated genes were hypothesized to be responsible for variety-specific differences in flower color. Furthermore, the genes encoding flavonoid 3-hydroxylase, anthocyanin synthase, and anthocyanin reductase were differentially expressed between flowers of different colors. Overall, we successfully identified key genes and metabolites involved in T. fournieri flower color formation. The data provided by the chromosome-scale genome assembly establish a basis for understanding the differentiation of this species and will facilitate future genetic studies and genomic-assisted breeding.

Bread wheat (Triticum aestivum L.) is a globally important food crop, which was domesticated about 8-10,000 years ago. Bread wheat is an allopolyploid, and it evolved from two hybridization events of three species. To widen the genetic base in breeding, bread wheat has been re-synthesized by crossing durum wheat (Triticum turgidum ssp. durum) and goat grass (Aegilops tauschii Coss), leading to so-called synthetic hexaploid wheat (SHW). We applied the quantitative genetics tools of "hybrid prediction"-originally developed for the prediction of wheat hybrids generated from different heterotic groups - to a situation of allopolyploidization. Our use-case predicts the phenotypes of SHW for three quantitatively inherited global wheat diseases, namely tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB). Our results revealed prediction abilities comparable to studies in 'traditional' elite or hybrid wheat. Prediction abilities were highest using a marker model and performing random cross-validation, predicting the performance of untested SHW (0.483 for SB to 0.730 for TS). When testing parents not necessarily used in SHW, combination prediction abilities were slightly lower (0.378 for SB to 0.718 for TS), yet still promising. Despite the limited phenotypic data, our results provide a general example for predictive models targeting an allopolyploidization event and a method that can guide the use of genetic resources available in gene banks.