Plant Genome最新文献

Salt stress is one of the primary environmental stresses limiting plant growth and production and adversely affecting the growth, development, yield, and fruit quality of Citrus sinensis. bHLH (basic helix-loop-helix) genes are involved in many bioregulatory processes in plants, including growth and development, phytohormone signaling, defense responses, and biosynthesis of specific metabolites. In this study, by bioinformatics methods, 120 CsbHLHgenes were identified, and phylogenetic analysis classified them into 18 subfamilies that were unevenly distributed on nine chromosomes. The cis-acting elements of the CsbHLH genes were mainly hormone-related cis-acting elements. Seventeen CsbHLH genes exhibited significant differences in expression under salt stress. Six CsbHLH genes with significant differences in expression were randomly selected for quantitative real-time polymerase chain reaction (qRT-PCR) validation. The qRT-PCR results showed a strong correlation with the transcriptome data. Phytohormones such as jasmonic acid (JA) are essential for biotic and abiotic stress responses in plants, and CsbHLH55 and CsbHLH87 are considered candidate target genes for sweet orange MYC2 transcription factors involved in the JA signaling pathway. These genes are the main downstream effectors in the JA signaling pathway and can be activated to participate in the JA signaling pathway. Activation of the JA signaling pathway inhibits the production of reactive oxygen species and improves the salt tolerance of sweet orange plants. The CsbHLH55 and CsbHLH87 genes could be candidate genes for breeding new transgenic salt-resistant varieties of sweet orange.

We aimed to identify HD-Zip (homologous domain leucine zipper) family genes based on the complete Sophora alopecuroides genome sequence. Eighty-six Sophora alopecuroides HD-Zip family (SaHDZ) genes were identified and categorized into four subclasses using phylogenetic analysis. Chromosome localization analysis revealed that these genes were distributed across 18 chromosomes. Gene structure and conserved motif analysis showed high similarity among members of the SaHDZ genes. Prediction analysis revealed 71 cis-acting elements in SaHDZ genes. Transcriptome and quantitative real-time polymerase chain reaction analyses showed that under salt stress, SaHDZ responded positively in S. alopecuroides, and that SaHDZ22 was significantly upregulated afterward. Functional verification experiments revealed that SaHDZ22 overexpression increased the tolerance of Arabidopsis to salt and osmotic stress. Combined with cis-acting element prediction and expression level analysis, HD-Zip family transcription factors may be involved in regulating the balance between plant growth and stress resistance under salt stress by modulating the expression of auxin and abscisic acid signaling pathway genes. The Sophora alopecuroides adenylate kinase protein (SaAKI) and S. alopecuroides tetrapeptide-like repeat protein (SaTPR; pCAMBIA1300-SaTPR-cLUC) expression levels were consistent with those of SaHDZ22, indicating that SaHDZ22 may coordinate with SaAKI and SaTPR to regulate plant salt tolerance. These results lay a foundation in understanding the salt stress response mechanisms of S. alopecuroides and provide a reference for future studies oriented toward exploring plant stress resistance.

The perennial grass Thinopyrum intermedium (intermediate wheatgrass [IWG]) is being domesticated as a food crop. With a deep root system and high biomass, IWG can help reduce soil and water erosion and limit nutrient runoff. As a novel grain crop undergoing domestication, IWG lags in yield, seed size, and other agronomic traits compared to annual grains. Better characterization of trait variation and identification of genetic markers associated with loci controlling the traits could help in further improving this crop. The University of Minnesota's Cycle 5 IWG breeding population of 595 spaced plants was evaluated at two locations in 2021 and 2022 for agronomic traits plant height, grain yield, and spike weight, and domestication traits shatter resistance, free grain threshing, and seed size. Pairwise trait correlations were weak to moderate with the highest correlation observed between seed size and height (0.41). Broad-sense trait heritabilities were high (0.68-0.77) except for spike weight (0.49) and yield (0.44). Association mapping using 24,284 genome-wide single nucleotide polymorphism markers identified 30 main quantitative trait loci (QTLs) across all environments and 32 QTL-by-environment interactions (QTE) at each environment. The genomic prediction model significantly improved predictions when parents were used in the training set and significant QTLs and QTEs used as covariates. Seed size was the best predicted trait with model predictive ability (r) of 0.72; yield was predicted moderately well (r = 0.45). We expect this discovery of significant genomic loci and mostly high trait predictions from genomic prediction models to help improve future IWG breeding populations.

Water absorption capacity (WAC) influences various aspects of bread making, such as loaf volume, bread yield, and shelf life. Despite its importance in the baking process and end-product quality, its genetic determinants are less explored. To address this limitation, a genome-wide association study was conducted on 337 hard wheat (Triticum aestivum L.) genotypes evaluated over 5 years in multi-environmental trials. Phenotyping was done using the solvent retention capacity (SRC) test with water (SRC-water), sucrose (SRC-sucrose), lactic acid (SRC-lactic acid), and sodium carbonate (SRC-carbonate) as solvents. Individuals were genotyped using genotyping-by-sequencing to detect single nucleotide polymorphisms across the wheat genome. To detect the genomic regions that underline the SRCs and gluten performance index (GPI), a genome-wide association study was performed using six multi-locus models using the mrMLM package in R. Adjusted means for SRC-water ranged from 54.1% to 66.5%, while SRC-carbonate exhibited a narrow range from 84.9% to 93.9%. Moderate to high genomic heritability values were observed for SRCs and GPI, ranging from h² = 0.61 to 0.88. The genome-wide association study identified a total of 42 quantitative trait nucleotides (QTNs), of which five explained over 10% of the phenotypic variation (R² ≥ 10%). Most of the QTNs were detected on chromosomes 1A, 1B, 3B, and 5B. Few QTNs, such as S1A_5190318, S1B_3282665, S4D_472908721, and S7A_37433960, were located near gliadin, glutenin starch synthesis, and galactosyltransferase genes. Overall, these results show WAC to be under polygenic genetic control, with genes involved in the synthesis of key flour components influencing overall water absorption.

Salvia hispanica L. (Chia), a member of the Lamiaceae, is an economically important crop in Mesoamerica, with health benefits associated with its seed fatty acid composition. Chia varieties are distinguished based on seed color including mixed white and black (Chia pinta) and black (Chia negra). To facilitate research on Chia and expand on comparative analyses within the Lamiaceae, we generated a chromosome-scale assembly of a Chia pinta accession and performed comparative genome analyses with a previously published Chia negra genome assembly. The Chia pinta and Chia negra genome sequences were highly similar as shown by a limited number of single nucleotide polymorphisms and extensive shared orthologous gene membership. However, there is an enrichment of terpene synthases in the Chia pinta genome relative to the Chia negra genome. We sequenced and analyzed the genomes of 20 Chia accessions with differing seed color and geographic origin revealing population structure within S. hispanica and interspecific introgressions of Salvia species. As the genus Salvia is polyphyletic, its evolutionary history remains unclear. Using large-scale synteny analysis within the Lamiaceae and orthologous group membership, we resolved the phylogeny of Salvia species. This study and its collective resources further our understanding of genomic diversity in this food crop and the extent of interspecies hybridizations in Salvia.

Acca sellowiana [Berg] Burret, a cultivated fruit tree originating from South America, is gaining the attention of the nutraceutical and pharmaceutical industries due to their high content of flavonoids and other phenolic compounds in fruits, leaves, and flowers. Flavonoids are a diverse group of secondary metabolites with antioxidant, anti-inflammatory, and antimicrobial properties. They also play a crucial role in plant immune response. Despite their importance, the lack of research on A. sellowiana genomics and transcriptomics hinders a deeper understanding of the molecular mechanisms behind flavonoid biosynthesis and its regulation. Here, we de novo assembled and benchmarked 11 A. sellowiana transcriptomes from leaves and floral tissues at three developmental stages using high-throughput sequencing. We selected and annotated the best assembly according to commonly used metrics and databases. This reference transcriptome consisted of 221,649 nonredundant transcripts, of which 107,612 were functionally annotated. We then used this reference transcriptome to explore the expression profiling of key secondary metabolite genes. Transcripts from genes involved in the flavonoid and anthocyanin biosynthesis pathways were identified. We also identified 4068 putative transcription factors, with the most abundant families being bHLH, C2H2, NAC, MYB, and MYB-related. Transcript expression profiling revealed distinct patterns of gene expression during flower development. Particularly, we found 71 differentially expressed transcripts representing 14 enzymes of the flavonoid pathway, suggesting major changes in flavonoid accumulation across floral stages. Our findings will contribute to understanding the genetic basis of flavonoids and provide a foundation for further research and exploitation of the economic potential of this species.

In this study, the contents of four carotenoids in 244 maize inbred lines were detected and about three million single nucleotide polymorphisms (SNPs) for genome-wide association study to preliminarily analyze the genetic mechanism of maize kernel carotenoids. We identified 826 quantitative trait loci (QTLs) were significantly associated with carotenoids contents, and two key candidate genes Zm00001d029526 (CYP18) and Zm00001d023336 (wrky91) were obtained. In addition, we found a germplasm IL78 with higher carotenoids. The results of this study can provide a theoretical basis for screening genes that guide kernel carotenoids selection breeding.

Phenotypic selection of complex traits such as seed yield and protein in the preliminary yield trial (PYT) is often constrained by limited seed availability, resulting in trials with few environments and minimal to no replications. Multi-trait multi-environment enabled genomic prediction (MTME-GP) offers a valuable alternative to predict missing phenotypes of selection candidates for multiple traits and diverse environments. In this study, we assessed the efficiency of MTME-GP for improving seed protein and seed yield in field pea, the top two breeding targets but highly antagonistic traits in pulse crop. We utilized a set of 300 selection candidates in the PYT that virtually represented all possible families of the North Dakota State University field pea breeding program. Selection candidates were evaluated in three diverse, contrasting environments, as indicated by a range of heritability. Using whole- and split-environment cross validation schemes, MTME-GP had higher predictive ability than a standard additive G-BLUP model. Integrating a range of overlapping genotypes in between environments showed improvement on the predictive ability of the MTME-GP model but tends to plateau at 50%-80% training set size. Regardless of the cross-validation scheme, accuracy was among the lowest in stressed environments, presumably due to low heritability for seed protein and yield. This study provided insights into the potential of MTME-GP in a public pulse crop breeding program. The MTME-GP framework can be further improved with more testing environments and integration of additional orthogonal information in the early stages of the breeding pipeline.

Genomic prediction is a modern approach that uses genome-wide markers to predict the genetic merit of unphenotyped individuals. With the potential to reduce the breeding cycles and increase the selection accuracy, this tool has been designed to rank genotypes and maximize genetic gains. Despite this importance, its practical implementation in breeding programs requires critical allocation of resources for its application in a predictive framework. In this study, we integrated genetic and data-driven methods to allocate resources for phenotyping and genotyping tailored to genomic prediction. To this end, we used a historical blueberry (Vaccinium corymbosun L.) breeding dataset containing more than 3000 individuals, genotyped using probe-based target sequencing and phenotyped for three fruit quality traits over several years. Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data-driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long-term implication, we carried out a simulation study and emphasized that data-driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. Altogether, we provided a data-oriented decision-making approach for breeders by demonstrating that critical breeding decisions associated with resource allocation for genomic prediction can be tackled through a combination of statistics and genetic methods.

Pea (Pisum sativum L.) is a key rotational crop and is increasingly important in the food processing sector for its protein. This study focused on identifying diverse high seed protein concentration (SPC) lines in pea plant genetic resources. Objectives included identifying high-protein pea lines, exploring genetic architecture across environments, pinpointing genes and metabolic pathways associated with high protein, and documenting information for single nucleotide polymorphism (SNP)-based marker-assisted selection. From 2019 to 2021, a 487-accession pea diversity panel, More protein, More pea, More profit, was evaluated in a randomized complete block design. DNA was extracted for genomic analysis via genotype-by-sequencing. Phenotypic analysis included protein and fat measurements in seeds and flower color. Genome-wide association study (GWAS) used multiple models, and the Pathways Association Study Tool was used for metabolic pathway analysis. Significant associations were found between SNPs and pea seed protein and fat concentration. Gene Psat7g216440 on chromosome 7, which targets proteins to cellular destinations, including seed storage proteins, was identified as associated with SPC. Genes Psat4g009200, Psat1g199800, Psat1g199960, and Psat1g033960, all involved in lipid metabolism, were associated with fat concentration. GWAS also identified genes annotated for storage proteins associated with fat concentration, indicating a complex relationship between fat and protein. Metabolic pathway analysis identified 20 pathways related to fat and seven to protein concentration, involving fatty acids, amino acid and protein metabolism, and the tricarboxylic acid cycle. These findings will assist in breeding of high-protein, diverse pea cultivars, and SNPs that can be converted to breeder-friendly molecular marker assays are identified for genes associated with high protein.