Plant Communications最新文献

Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield, particularly in maize and rice. However, a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses. Genomic selection (GS) has emerged as a powerful tool to tackle this challenge, but its success in practical breeding depends on prediction accuracy. Several strategies have been explored to enhance prediction accuracy for complex traits, such as the incorporation of functional markers and multi-omics data. Metabolome-wide association studies (MWAS) help to identify metabolites that are closely linked to phenotypes, known as metabolic markers. However, the use of preselected metabolic markers from parental lines to predict hybrid performance has not yet been explored. In this study, we developed a novel approach called metabolic marker-assisted genomic prediction (MM_GP), which incorporates significant metabolites identified from MWAS into GS models to improve the accuracy of genomic hybrid prediction. In maize and rice hybrid populations, MM_GP outperformed genomic prediction (GP) for all traits, regardless of the method used (genomic best linear unbiased prediction or eXtreme gradient boosting). On average, MM_GP demonstrated 4.6% and 13.6% higher predictive abilities than GP for maize and rice, respectively. MM_GP could also match or even surpass the predictive ability of M_GP (integrated genomic-metabolomic prediction) for most traits. In maize, the integration of only six metabolic markers significantly associated with multiple traits resulted in 5.0% and 3.1% higher average predictive ability compared with GP and M_GP, respectively. With advances in high-throughput metabolomics technologies and prediction models, this approach holds great promise for revolutionizing genomic hybrid breeding by enhancing its accuracy and efficiency.

The unfolded protein response (UPR) is a vital cellular pathway that maintains endoplasmic reticulum (ER) homeostasis under conditions of ER stress and is associated with the degradation of misfolded proteins. However, the role of ER-associated degradation in plant-microbe interactions has yet to be explored. In this study, we identified a novel viral protein, βV1, encoded by the tomato yellow leaf curl betasatellite (TYLCCNB), which is localized to the ER and triggers ER aggregation. Transient expression of βV1 in Nicotiana benthamiana induces robust ER stress and activates the bZIP17/28 branch of the UPR signaling pathway. The induction of bZIP17/28 by βV1 is crucial for successful virus infection. Furthermore, we demonstrated that βV1 is unstable in N. benthamiana mesophyll cells, as it is targeted for autophagic degradation. The autophagy-related protein ATG18a, a key component of autophagosomes, participates in the degradation of βV1, thereby exerting an anti-viral role. Taken together, our results reveal a novel function of the βV1 protein and provide the first evidence for involvement of bZIP17/28 and ATG18a in ER-associated autophagic degradation during geminivirus infection. These findings significantly expand our understanding of the arms-race dynamics between plants and viruses.

Current methods used in genome-wide association studies frequently lack power owing to their inability to detect heterogeneous associations and rare and multiallelic variants. To address these issues, quantile regression is integrated with a three (compressed) variance component multi-locus random-SNP-effect mixed linear model (3VmrMLM) to propose q3VmrMLM for detecting heterogeneous quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs), and then design haplotype-based q3VmrMLM (q3VmrMLM-Hap) for identifying multiallelic haplotypes and rare variants. In Monte Carlo simulation studies, q3VmrMLM had higher power than 3VmrMLM, sequence kernel association test (SKAT), and integrated quantile rank test (iQRAT). In a re-analysis of 10 traits in 1439 rice hybrids, 261 known genes were identified only by q3VmrMLM and q3VmrMLM-Hap, whereas 175 known genes were detected by both the new and existing methods. Of all the significant QTNs with known genes, q3VmrMLM (179: 140 variance heterogeneity and 157 quantile effect heterogeneity) found more heterogeneous QTNs than 3VmrMLM (123), SKAT (27), and iQRAT (29); q3VmrMLM-Hap (121) mapped more low-frequency (<0.05) QTNs than q3VmrMLM (51), 3VmrMLM (43), SKAT (11), and iQRAT (12); and q3VmrMLM-Hap (12), q3VmrMLM (16), and 3VmrMLM (12) had similar power in identifying gene-by-environment interactions. All significant and suggested QTNs achieved the highest predictive accuracy (r = 0.9045). In conclusion, this study describes a new and complementary approach to mining genes and unraveling the genetic architecture of complex traits in crops.

The introduction of Reduced height (Rht) genes into wheat varieties has been pivotal in developing semi-dwarf plant architectures, significantly improving lodging resistance and harvest indices. Therefore, identifying new Rht gene resources for breeding semi-dwarf wheat cultivars has been a key strategy for ensuring high and stable grain yields since the 1960s. In this study, we report the map-based cloning of TaERF-A1, which encodes an AP2/ERF (APETALA2/ethylene responsive factor) transcription factor that acts as a positive regulator of wheat stem elongation, as a novel gene that regulates plant height and spike length. The natural variant, TaERF-A1^JD6, features a Phe (derived from 'Nongda3338') to Ser (derived from 'Jingdong6') substitution at position 178, which significantly reduces the stability of the TaERF-A1 protein. This substitution leads to partially attenuated transcriptional activation of downstream target genes, including TaPIF4 (Triticum aestivum Phytochrome Interacting Factor 4), thereby restricting stem and spike elongation. Importantly, the introgression of the semi-dwarfing allele TaERF-A1^JD6 into wheat can significantly enhance lodging resistance, particularly in dense cropping systems. Therefore, our study identifies TaERF-A1^JD6 as a new Rht gene resource for breeding semi-dwarf wheat varieties with increased yield stability.

Two principal growth regulators, cytokinins and ethylene, are known to interact in the regulation of plant growth. However, information about the underlying molecular mechanism and positional specificity of cytokinin/ethylene crosstalk in the control of root growth is scarce. We have identified the spatial specificity of cytokinin-regulated root elongation and root apical meristem (RAM) size, both of which we demonstrate to be dependent on ethylene biosynthesis. Upregulation of the cytokinin biosynthetic gene ISOPENTENYLTRANSFERASE (IPT) in proximal and peripheral tissues leads to both root and RAM shortening. By contrast, IPT activation in distal and inner tissues reduces RAM size while leaving the root length comparable to that of mock-treated controls. We show that cytokinins regulate two steps specific to ethylene biosynthesis: production of the ethylene precursor 1-aminocyclopropane-1-carboxylate (ACC) by ACC SYNTHASEs (ACSs) and its conversion to ethylene by ACC OXIDASEs (ACOs). We describe cytokinin- and ethylene-specific regulation controlling the activity of ACSs and ACOs that are spatially discrete along both proximo/distal and radial root axes. Using direct ethylene measurements, we identify ACO2, ACO3, and ACO4 as being responsible for ethylene biosynthesis and ethylene-regulated root and RAM shortening in cytokinin-treated Arabidopsis. Direct interaction between ARABIDOPSIS RESPONSE REGULATOR 2 (ARR2), a member of the multistep phosphorelay cascade, and the C-terminal portion of ETHYLENE INSENSITIVE 2 (EIN2-C), a key regulator of canonical ethylene signaling, is involved in the cytokinin-induced, ethylene-mediated control of ACO4. We propose tight cooperation between cytokinin and ethylene signaling in the spatially specific regulation of ethylene biosynthesis as a key aspect of the hormonal control of root growth.

Diatoms, a group of prevalent marine algae, contribute significantly to global primary productivity. Their substantial biomass is linked to enhanced absorption of blue-green light underwater, facilitated by fucoxanthin chlorophyll (Chl) a/c-binding proteins (FCPs), which exhibit oligomeric diversity across diatom species. Using mild clear native PAGE analysis of solubilized thylakoid membranes, we displayed monomeric, dimeric, trimeric, tetrameric, and pentameric FCPs in diatoms. Mass spectrometry analysis revealed that each oligomeric FCP has a specific protein composition, and together they constitute a large Lhcf family of FCP antennas. In addition, we resolved the structures of the Thalassiosira pseudonana FCP (Tp-FCP) homotrimer and the Chaetoceros gracilis FCP (Cg-FCP) pentamer by cryoelectron microscopy at 2.73-Å and 2.65-Å resolution, respectively. The distinct pigment compositions and organizations of various oligomeric FCPs affect their blue-green light-harvesting, excitation energy transfer pathways. Compared with dimeric and trimeric FCPs, the Cg-FCP tetramer and Cg-FCP pentamer exhibit stronger absorption by Chl c, redshifted and broader Chl a fluorescence emission, and more robust circular dichroism signals originating from Chl a-carotenoid dimers. These spectroscopic characteristics indicate that Chl a molecules in the Cg-FCP tetramer and Cg-FCP pentamer are more heterogeneous than in both dimers and the Tp-FCP trimer. The structural and spectroscopic insights provided by this study contribute to a better understanding of the mechanisms that empower diatoms to adapt to fluctuating light environments.

Sustainable alternative farming systems are gaining popularity worldwide because of the negative effects of conventional agriculture on global climate change and the environmental degradation caused by intensive use of synthetic inputs. The green farming system in China is an integrated production strategy that focuses on reducing chemical fertilizer use while increasing organic manure inputs. Despite their rapid growth as more sustainable systems over the past decades, green farming systems have not been systematically evaluated to date. We used apple production as a representative case to assess the sustainability of green farming systems. Across major apple-producing regions in China, green farming reduced the application of chemical fertilizer nitrogen (N) by 46.8% (from 412 to 219 kg ha^-1) and increased that of manure N by 33.1% (from 171 to 227 kg ha^-1) on average compared with conventional systems enhancing N use efficiency by 7.27-20.27% and reducing N losses by 8.92%-11.56%. It also slightly lowered yield by 4.34%-13.8% in four provinces. Soil fertility was improved in green orchards through increases in soil organic matter, total N, and available major nutrients. Our cradle-to-farm-gate life-cycle assessment revealed that green farming helped to mitigate greenhouse gas emissions by an average of 12.6%, potentially contributing to a reduction of 165 239 t CO₂ eq annually in major apple-producing areas. In addition, green farming achieved 39.3% higher profitability ($7180 ha^-1 year^-1) at the farmer level. Our study demonstrates the potential of green production of apples for the development of sustainable agriculture in China. These findings advance our understanding of sustainable alternative farming systems and offer perspectives for the sustainable development of global agriculture.

The transcriptome serves as a bridge that links genomic variation to phenotypic diversity. A vast number of studies using next-generation RNA sequencing (RNA-seq) over the last 2 decades have emphasized the essential roles of the plant transcriptome in response to developmental and environmental conditions, providing numerous insights into the dynamic changes, evolutionary traces, and elaborate regulation of the plant transcriptome. With substantial improvement in accuracy and throughput, direct RNA sequencing (DRS) has emerged as a new and powerful sequencing platform for precise detection of native and full-length transcripts, overcoming many limitations such as read length and PCR bias that are inherent to short-read RNA-seq. Here, we review recent advances in dissecting the complexity and diversity of plant transcriptomes using DRS as the main technological approach, covering many aspects of RNA metabolism, including novel isoforms, poly(A) tails, and RNA modification, and we propose a comprehensive workflow for processing of plant DRS data. Many challenges to the application of DRS in plants, such as the need for machine learning tools tailored to plant transcriptomes, remain to be overcome, and together we outline future biological questions that can be addressed by DRS, such as allele-specific RNA modification. This technology provides convenient support on which the connection of distinct RNA features is tightly built, sustainably refining our understanding of the biological functions of the plant transcriptome.