Search Results (445)

Bacterial wilt, caused by Ralstonia solanacearum, is a major constraint to tomato production globally. To uncover resistance loci and develop efficient molecular tools for breeding, we conducted disease phenotyping over two growing seasons, which revealed consistent variation in resistance and moderate broad-sense heritability (H² = 0.22–0.28), suggesting a genetic basis. A genome-wide association study (GWAS) was performed on a diverse panel of 267 tomato accessions, evaluated against two R. solanacearum strains. A major resistance locus was identified on chromosome 12, with the strongest association observed at SNP S12_2992992, located within a gene encoding a leucine-rich repeat (LRR) receptor-like protein. Haplotype analysis indicated that the resistance-associated allele is relatively rare (~13.5%) in the population, underscoring its potential value in breeding programs. Functional validation in an F₂ population derived from a cross between the susceptible ‘Seedathip6’ and the resistant ‘Hawaii 7996’ confirmed that the TT genotype at S12_2992992 was significantly associated with enhanced resistance. A Kompetitive Allele Specific PCR (KASP) marker was developed for this SNP, facilitating cost-effective and high-throughput selection. Collectively, these findings establish S12_2992992 as a robust and functionally informative marker, offering a valuable tool for accelerating bacterial wilt resistance breeding in tomato through marker-assisted selection. Full article

Abdominal fat is a key indicator of chicken meat quality. Excessive deposition not only reduces meat quality but also decreases feed conversion efficiency, making the breeding of low-abdominal-fat strains economically important. Genomic selection (GS) uses information from genome-wide association studies (GWASs) and high-throughput sequencing data. It estimates genomic breeding values (GEBVs) from genotypes, which enables early and precise selection. Given that abdominal fat is a polygenic trait controlled by numerous small-effect loci, this study combined population genetic analyses with machine learning (ML)-based feature selection. Relevant single-nucleotide polymorphisms (SNPs) were first identified using a combined GWAS and linkage disequilibrium (LD) approach, followed by a two-stage feature selection process—Lasso for dimensionality reduction and recursive feature elimination (RFE) for refinement—to generate the model input set. We evaluated multiple machine learning models for predicting genomic estimated breeding values (GEBVs). The results showed that linear models and certain nonlinear models achieved higher accuracy and were well suited as base learners for ensemble methods. Building on these findings, we developed a Dynamic Adaptive Weighted Stacking Ensemble Learning Framework (DAWSELF), which applies dynamic weighting and voting to heterogeneous base learners and integrates them layer by layer, with Ridge serving as the meta-learner. In three independent validation populations, DAWSELF consistently outperformed individual models and conventional stacking frameworks in prediction accuracy. This work establishes an efficient GEBV prediction framework for complex traits such as chicken abdominal fat and provides a reusable SNP feature selection strategy, offering practical value for enhancing the precision of poultry breeding and improving product quality. Full article

Background: As a key rice breeding resource, aromatic rice is widely cultivated in agriculture due to its unique aroma. Badh2 mutations cause function loss, enabling rice’s characteristic aroma. Methods: In this study, we analyzed several badh2 mutation types across 8 japonica and 16 indica aromatic rice lines. Based on the 7 bp deletion in badh2-E2 identified in japonica aromatic lines, we developed a multiplex-ready PCR assay for badh2 genotyping. Additionally, leveraging the deletion mutation in badh2-E7 from the indica aromatic line Yexiang, we designed a KASP marker. Results: All 8 japonica aromatic lines carried a 7 bp deletion in badh2-E2, while 12 indica aromatic lines harbored an 8 bp deletion in badh2-E7, and 4 additional indica aromatic lines exhibited an 8 bp deletion in badh2-E2. The multiplex-ready PCR assay was used to screen 200 individual plants from the aromatic rice line Jia 58: 199 plants showed the expected results, while the remaining 1 exhibited two fluorescent signal peaks—suggesting that it may be a heterozygous individual. Using the KASP marker, we performed genotyping analysis on F₇ progeny individuals derived from the cross between Yexiang (aromatic line) and Yuenongsimiao (non-aromatic line). Combined with phenotypic observations, we successfully screened out an elite aromatic line named Zhexiangzhenhe, which not only possesses aroma but also maintains superior agronomic traits. Conclusions: The multiplex-ready PCR assay and KASP markers facilitate high-throughput genotyping in large-scale breeding populations, providing breeders with a rapid and efficient selection tool to accelerate aromatic trait improvement in rice. Full article

(1) Background: Hepatitis E (HE) is a novel zoonotic disease caused by hepatitis E virus (HEV). In particular, swine hepatitis E virus (SHEV) genotype IV is one of the main genotypes that infect humans. Open reading frame 3 (ORF3) is an important virulence protein of SHEV, which is involved in virus assembly, release, and regulation of host cell signaling pathways. Circular RNAs (circRNAs), as a type of competitive endogenous RNA (ceRNA), have a closed-loop structure and are special non-coding RNA molecules. They participates in the regulation of multiple biological processes by adsorbing microRNAs (miRNAs). Riboflavin, also known as vitamin B2, is a component of the coenzyme of flavoenzymes in the body. When there is a deficiency of riboflavin, it will affect the biological oxidation process of the host, leading to metabolic disorders. In addition, riboflavin can also affect the synthesis, transportation and decomposition of lipids in the body. It mainly maintains the normal transportation process of fat in the liver. Therefore, the deficiency of riboflavin will lead to the disorder of lipid metabolism in the body. Thus, viral hepatitis is closely related to riboflavin metabolism. However, there are very few reports on SHEV ORF3 affecting the riboflavin metabolism of target cells and thereby influencing viral infection. Therefore, this study investigates this highly significant scientific issue. (2) Methods: In the previous research of our group, adenovirus was used to mediate the overexpression of SHEV ORF3 genotype IV in HepG2 cells. Total RNA was extracted for high-throughput sequencing of circRNAs and transcriptome. KEGG functional enrichment analysis was performed on the data to identify the differentially expressed circRNAs and miRNAs after SHEV infection, and the relevant circRNA-miRNA network in the riboflavin metabolism pathway in HepG2 cells was found. (3) Results: We identified 4 circRNAs in the riboflavin metabolism pathway of HepG2 cells expressing the ORF3 protein of SHEV genotype IV and successfully found 26 relevant circRNA-miRNA networks. (4) Conclusion: We successfully screened and identified circRNAs related to riboflavin metabolism, further identifying the circRNA-miRNA network and its functional targets. For the first time, we investigated the key mechanism by which ORF3 protein influences riboflavin metabolic pathways in target cells through circRNAs, preliminarily revealing that ariboflavinosis can lead to lipid metabolic disorder in the organism. This indicates a close association between viral HE and riboflavin metabolism. Full article

Watermelon (Citrullus lanatus) and melon (Cucumis melo) are globally important cucurbit crops, with China being the largest producer and consumer. Traditional breeding methods face difficulties in significantly improving yield and quality. Smart breeding, which combines genomics, gene editing, and artificial intelligence (AI), holds great promise but fundamentally depends on understanding the molecular mechanisms controlling important agronomic traits. This review summarizes the progress made over recent decades in discovering and understanding the functions of genes that control essential traits in watermelon and melon, focusing on plant architecture, fruit quality, and disease resistance. However, major challenges remain: relatively few genes have been fully validated, the complex gene networks are not fully unraveled, and technical hurdles like low genetic transformation efficiency and difficulties in large-scale trait phenotyping limit progress. To overcome these and enable the development of superior new varieties, future research priorities should focus on the following: (1) systematic discovery of genes using comprehensive genome collections (pan-genomes) and multi-level data analysis (multi-omics); (2) deepening the study of gene functions and interactions using advanced gene editing and epigenetics; (3) faster integration of molecular knowledge into smart breeding systems; (4) solving the problems of genetic transformation and enabling efficient large-scale trait and genetic data collection (high-throughput phenotyping and genotyping). Full article

Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate–resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought–heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches—genomics, transcriptomics, proteomics, and metabolomics—to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA–SnRK2 signaling cascade, HSF–HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This “pixels-to-proteins” paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties. Full article

Cold stress severely limits legume productivity, threatening global food security, particularly in climate-vulnerable regions. This review synthesizes advances in understanding and enhancing cold tolerance in key legumes (chickpea, soybean, lentil, and cowpea), addressing three core questions: (1) molecular/physiological foundations of cold tolerance; (2) how emerging technologies accelerate stress dissection and breeding; and (3) integration strategies and deployment challenges. Legume cold tolerance involves conserved pathways (e.g., ICE-CBF-COR, Inducer of CBF Expression, C-repeat Binding Factor, Cold-Responsive genes) and species-specific mechanisms like soybean’s GmTCF1a-mediated pathway. Multi-omics have identified critical genes (e.g., CaDREB1E in chickpea, NFR5 in pea) underlying adaptive traits (membrane stabilization, osmolyte accumulation) that reduce yield losses by 30–50% in tolerant genotypes. Technologically, AI and high-throughput phenotyping achieve >95% accuracy in early cold detection (3–7 days pre-symptoms) via hyperspectral/thermal imaging; deep learning (e.g., CNN-LSTM hybrids) improves trait prediction by 23% over linear models. Genomic selection cuts breeding cycles by 30–50% (to 3–5 years) using GEBVs (Genomic estimated breeding values) from hundreds of thousands of SNPs (Single-nucleotide polymorphisms). Advanced sensors (LIG-based, LoRaWAN) enable real-time monitoring (±0.1 °C precision, <30 s response), supporting precision irrigation that saves 15–40% water while maintaining yields. Key barriers include multi-omics data standardization and cost constraints in resource-limited regions. Integrating molecular insights with AI-driven phenomics and multi-omics is revolutionizing cold-tolerance breeding, accelerating climate-resilient variety development, and offering a blueprint for sustainable agricultural adaptation. Full article

Porcine Teschovirus (PTV) is a highly prevalent pathogen within swine populations, primarily associated with encephalitis, diarrhea, pneumonia, and reproductive disorders in pigs, thereby posing a significant threat to the sustainable development of the pig farming industry. In this study, a novel strain of PTV was isolated from the feces of a pig exhibiting symptoms of diarrhea, utilizing PK-15 cell lines. The structural integrity of the viral particles was confirmed via transmission electron microscopy, and the viral growth kinetics and characteristics were evaluated in PK-15 cells. High-throughput sequencing facilitated the acquisition of the complete viral genome, and subsequent phylogenetic analysis and full-genome alignment identified the strain as belonging to the PTV 2 genotype. Further investigation revealed that infection with the PTV-GXLZ2024 strain induces phosphorylation of the eukaryotic translation initiation factor 2α (eIF2α) in PK-15 cells, indicating activation of the unfolded protein response (UPR) through the PERK pathway, with minimal involvement of the IRE1 or ATF6 pathways. Notably, ATF4 protein expression was progressively downregulated throughout the infection, while downstream CHOP protein levels remained unchanged, indicating an incomplete UPR induced by PTV-GXLZ2024. Furthermore, PERK knockdown was found to enhance the replication of PTV-GXLZ2024. This study provides critical insights into the molecular mechanisms underlying PTV pathogenesis and establishes a foundation for future research into its evolutionary dynamics and interactions with host organisms. Full article

This review synthesizes advances in livestock genomics by examining the interplay between candidate genes, molecular markers (MMs), signatures of selection (SSs), and quantitative trait loci (QTLs) in shaping economically vital traits across livestock species. By integrating advances in genomics, bioinformatics, and precision breeding, the study elucidates genetic mechanisms underlying productivity, reproduction, meat quality, milk yield, fibre characteristics, disease resistance, and climate resilience traits pivotal to meeting the projected 70% surge in global animal product demand by 2050. A critical synthesis of 1455 peer-reviewed studies reveals that targeted genetic markers (e.g., SNPs, Indels) and QTL regions (e.g., IGF2 for muscle development, DGAT1 for milk composition) enable precise selection for superior phenotypes. SSs, identified through genome-wide scans and haplotype-based analyses, provide insights into domestication history, adaptive evolution, and breed-specific traits, such as heat tolerance in tropical cattle or parasite resistance in sheep. Functional candidate genes, including leptin (LEP) for feed efficiency and myostatin (MSTN) for double-muscling, are highlighted as drivers of genetic gain in breeding programs. The review underscores the transformative role of high-throughput sequencing, genome-wide association studies (GWASs), and CRISPR-based editing in accelerating trait discovery and validation. However, challenges persist, such as gene interactions, genotype–environment interactions, and ethical concerns over genetic diversity loss. By advocating for a multidisciplinary framework that merges genomic data with phenomics, metabolomics, and advanced biostatistics, this work serves as a guide for researchers, breeders, and policymakers. For example, incorporating DGAT1 markers into dairy cattle programs could elevate milk fat content by 15-20%, directly improving farm profitability. The current analysis underscores the need to harmonize high-yield breeding with ethical practices, such as conserving heat-tolerant cattle breeds, like Sahiwal. Full article

Group A rotavirus (RVA) is a leading causative agent of diarrhea in both young animals and humans. In China, multiple genotypes are commonly found within the bovine population. In this study, we investigated 1917 fecal samples from calves with diarrhea between 2022 and 2025, with 695 testing positive for RVA, yielding an overall detection rate of 36.25%. The highest positivity rate was observed in Hohhot (38.98%), and annual detection rates ranged from 26.75% in 2022 to 42.22% in 2025. A bovine rotavirus (BRV) strain, designated 0205HG, was successfully isolated from a fecal sample of a newborn calf. Its presence was confirmed through cytopathic effects (CPEs), the indirect immunofluorescence assay (IFA), electron microscopy (EM), and high-throughput sequencing. Genomic characterization identified the strain as having the G6-P[1]-I2-R2-C2-M2-A3-N2-T6-E2-H3 genotype constellation. The structural proteins VP2 and VP7, along with nonstructural genes NSP1–NSP4, shared high sequence identity with Chinese bovine strains, whereas VP1, VP4, and NSP5 clustered more closely with human rotaviruses, and VP3 was related to feline strains. These findings highlight the genetic diversity and interspecies reassortment of BRVs in China, underlining the importance of continued surveillance and evolutionary analysis. Full article

Ujimqin sheep, known for its distinctive multi-vertebrae phenotypes (T13L7, T14L6, and T14L7) and economic value, has garnered significant attention. However, conventional phenotypic detection methods suffer from low efficiency and high costs. In this study, based on a key SNP locus (ABCD4 gene, Chr7:89393414, C > T) identified through a genome-wide association study (GWAS), a TaqMan-MGB (minor groove binder) genotyping system was developed. the objective was to establish a high-throughput and efficient molecular marker-assisted selection (MAS) tool. Specific primers and dual fluorescent probes were designed to optimize the reaction system. Standard plasmids were adopted to validate genotyping accuracy. A total of 152 Ujimqin sheep were subjected to TaqMan-MGB genotyping, digital radiography (DR) imaging, and Sanger sequencing. the results showed complete concordance between TaqMan-MGB and Sanger sequencing, with an overall agreement rate of 83.6% with DR imaging. For individuals with T/T genotypes (127/139), the detection accuracy reached 91.4%. This method demonstrated high specificity, simplicity, and cost-efficiency, significantly reducing the time and financial burden associated with traditional imaging-based approaches. the findings indicate that the TaqMan-MGB technique can accurately identify the T/T genotype at the SNP site and its strong association with the multi-vertebrae phenotypes, offering an effective and reliable tool for molecular breeding of Ujimqin sheep. Full article

Efficient and consistent DNA extraction is crucial for genotyping but often hindered by the limitations of traditional manual processes, which are labour-intensive, error-prone, and costly. We introduce a semi-automated, robotic-assisted DNA extraction (RoboCTAB) tailored for large-scale plant genotyping, leveraging advanced yet affordable liquid-handling robotic systems. The protocol/workflow integrates a CTAB extraction protocol specifically adapted for a robotic liquid-handling system, making it compatible with high-throughput genotyping techniques such as SNP genotyping and sequencing. Various plant parts (leaves, roots, manual seed chip) were explored as the source material for DNA extractions, with the aim of identifying the tissue best suited for collection on a large scale. Young roots (radicle) proved the easiest to harvest at scale, while the harvest of leaves and seed chips were more laborious and error-prone. DNA yield and quality from both leaves and roots (but not seed chips) were similar and sufficient for downstream analysis. Interestingly, root tissue could still be extracted from imbibed seeds, even if the seeds failed to germinate, thus proving useful for DNA extraction. Cost analysis indicates significant savings in labour costs, highlighting the approach’s suitability for large-scale projects. Quality assessments demonstrate that the robotic process yields high-quality DNA, maintaining integrity for downstream applications. This semi-automated DNA extraction system represents a scalable, reliable solution for large-scale genotyping that is accessible to many users who cannot implement highly sophisticated and costly systems as are known to exist in large multinational seed companies. RoboCTAB, a low-cost, optimized method for high-throughput DNA extraction, minimizes the risk of cross-contamination. RoboCTAB is capable of processing up to four 96-well plates (384 samples) simultaneously in a single run, improving cost-efficiency and providing seamless integration with laboratory workflows, potentially setting new standards for efficiency and quality in DNA processing and sequencing at scale. Full article

Introduction: Scattering of kernels due to angular dehiscence is a key bottleneck in mechanized harvesting of oilseed rape. Materials and Methods: In this study, a dual-track “genotype–phenotype” screening strategy was established by innovatively integrating high-throughput KASP molecular marker technology and a standardized random collision phenotyping system for the complex quantitative trait of angular resistance. Results: Through the systematic evaluation of 634 oilseed rape hybrid progenies, it was found that the KASP marker S12.68, targeting the cleavage resistance locus (BnSHP1) on chromosome C9, achieved a 73.34% introgression rate (465/634), which was significantly higher than the traditional breeding efficiency (<40%). Phenotypic characterization screened seven excellent resources with cracking resistance index (SRI) > 0.6, of which four reached the high resistance standard (SRI > 0.8), including the core materials NR21/KL01 (SRI = 1.0) and YuYou342/KL01 (SRI = 0.97). Six breeding intermediate materials (44.7–48.7% oil content, mycosphaerella resistance MR grade or above) were created, combining high resistance to chipping and excellent agronomic traits. For the first time, it was found that local germplasm YuYou342 (non-KL01-derived line) was purely susceptible at the S12.68 locus (SRI = 0.86), but its angiosperm vascular bundles density was significantly increased by 37% compared with that of the susceptible material 0911 (p < 0.01); and the material 187308 (SRI = 0.78), although purely susceptible at S12.68, had a 2.8-fold downregulation in expression of the angiosperm-related gene, BnIND1, and a 2.8-fold downregulation of expression of the angiosperm-related gene, BnIND1. expression was significantly downregulated 2.8-fold (q < 0.05), indicating the existence of a novel resistance mechanism independent of the primary effector locus. Conclusions: The results of this research provide an efficient technical platform and breakthrough germplasm resources for oilseed rape crack angle resistance breeding, which is of great practical significance for promoting the whole mechanized production. Full article

We report the development of a procedure for ultrasound-assisted microscale extraction of metabolites from the flowers of Saint John’s wort (Hypericum perforatum L.), designed for comparative metabolite analysis of plants from genetic resource collections and natural and segregating populations. The procedure involves high-throughput methanol extraction of metabolites from ground-frozen flowers at a selected stage of flower development, which is carried out in a standard 2 mL Eppendorf tube. A total of 18 compounds, including chlorogenic acid, catechins, glycosylated flavonoids, hypericins, and hyperforin, were identified based on LC/DAD/QTOF analysis, of which 16 could be detected in the UV-Vis spectrum. Two alternative versions of the procedure were evaluated: the “single-flower” procedure, including repeated collection and analysis of single flowers from the tested plant, and the “bulk-flower” procedure, employing the collection of a bulk flower sample from the tested plant and analysis of a portion of the ground sample. The results showed excellent technical reproducibility of the “single-flower” procedure when used with the suggested combination of the peak areas for the proto- and stable forms of pseudohypericin and hypericin. Application of the developed “single-flower” procedure for comparison of the plants derived from seed progeny of the apomictic line Hp93 revealed significantly lower metabolite variation among the apomictic progeny plants compared to the variation observed among plants belonging to different genotypes. Full article

Perennial ryegrass (Lolium perenne L.) is a cornerstone forage species in temperate dairy systems worldwide, valued for its high yield potential, nutritive quality, and grazing recovery. However, current regional evaluation systems face challenges in accurately assessing complex traits like seasonal dry matter yield due to polygenic nature, environmental variability, and lengthy evaluation cycles. This review examines the evolution of perennial ryegrass evaluation systems, from regional frameworks—like Australia’s Forage Value Index (AU-FVI), New Zealand’s Forage Value Index (NZ-FVI), and Ireland’s Pasture Profit Index (PPI)—to advanced genomic prediction (GP) approaches. We discuss prominent breeding frameworks—F2 family, Half-sib family, and Synthetic Population—and their integration with high-throughput genotyping technologies. Statistical models for GP are compared, including marker-based, kernel-based, and non-parametric approaches, highlighting their strengths in capturing genetic complexity. Key research efforts include representative genotyping approaches for heterozygous populations, disentangling endophyte–host interactions, extending prediction to additional economically important traits, and modeling genotype-by-environment (G × E) interactions. The integration of multi-omics data, advanced phenotyping technologies, and environmental modeling offers promising avenues for enhancing prediction accuracy under changing environmental conditions. By discussing the combination of regional evaluation systems with GP, this review provides comprehensive insights for enhancing perennial ryegrass breeding and evaluation programs, ultimately supporting sustainable productivity of the dairy industry in the face of climate challenges. Full article