Search Results (1,065)

As an elite indigenous poultry breed under national protection in China, the Xupu goose is renowned for its large body size, superior fatty liver production, premium meat quality, and high tolerance to roughage. To elucidate its genomic architecture, genetic diversity, and evolutionary selection signatures, we conducted whole-genome resequencing on 15 purposively selected, unrelated male Xupu geese. An average of 6.79 Gb of high-quality sequence data was generated per individual, yielding approximately 4.27 million single-nucleotide polymorphisms (SNPs) with a transition/transversion (Ti/Tv) ratio of 2.49. Population genomic analyses revealed that while the population retains a moderate genetic reservoir (H_E = 0.298), it exhibits a distinct heterozygote deficit (Ho = 0.217) and a moderate genomic inbreeding coefficient (F_roh = 0.204). This structural pattern underscores the genetic impact of historical ex situ closed-flock conservation and the consequent formation of cryptic family lineages. Furthermore, genome-wide integrated haplotype score (iHS) scans detected distinct regions under recent positive selection. Functional annotation of these regions highlighted candidate genes tightly associated with the breed’s hallmark traits, specifically lipid metabolism and hepatic fat deposition (ACSS2, ACSS3, PECR), alongside muscle development (CMYA5, MTPN, LEPR). Conclusively, this study delineates a comprehensive genomic landscape of the Xupu goose, providing a robust foundational resource for future germplasm conservation, molecular marker development, and precision breeding programs. Full article

Soybean (Glycine max (L.) Merr.) is a globally important legume valued for the high protein, oil, and carbohydrate content of its seeds. However, it is difficult to simultaneously optimize the content of these three macronutrients due to their negatively correlated metabolic pathways and complex quantitative inheritance patterns. In this study, a genome-wide association study (GWAS) was conducted to elucidate the genetic architecture underlying the seed protein, oil, and carbohydrate content in 328 soybean accessions evaluated over two years using near-infrared spectroscopy. Significant negative correlations (r = −0.323 to −0.656, p < 0.001) were observed between the three traits, confirming the trade-off in carbon partitioning during seed development. The GWAS identified nine significant loci distributed across chromosomes 4, 6, 8, 9, 10, 16, and 18 as stable quantitative trait loci (QTLs) regulating the three traits. Of these, three pleiotropic loci (qProOil.4, qProOil.16, and qOilCarb.6) were found to be associated with multiple seed compositional traits. Haplotype analysis revealed seven haplotype blocks with distinct phenotypic variation, indicating that they have the potential for use as trait-specific markers in marker-assisted selection. Functional annotation of the stable QTL regions identified 22 putative genes, among which five candidate genes, Glyma.06g201700, Glyma.08g281900, Glyma.09g164900, Glyma.13g155600, and Glyma.16g209800 were likely to be involved in carbon allocation, protein biosynthesis, lipid metabolism, and carbohydrate modification pathways based on their relative expression levels. Overall, this study enhances the understanding of the genetic and molecular mechanism controlling the composition of soybean seed and identifies promising genomic targets for precision breeding programs aimed at improving nutritional quality. Full article

Genomic improvement and sustainable breeding of Mediterranean buffalo are hampered by the lack of breed-specific genomic resources compared with bovine. To address this gap, we aimed to identify a comprehensive set of high-confidence single-nucleotide polymorphisms (SNPs) in Mediterranean buffalo and evaluate their informativeness across other buffalo populations. A total of 58 whole-genome sequencing samples, from three different sets, were merged through the integration of short- and long-read sequencing technologies. Variants, both unique and shared among datasets, were identified using a combination of bioinformatic tools to increase the reliability of the dataset. From these, over 11 million high-confidence biallelic SNPs were identified in the Mediterranean breed. The same SNPs were also tested in additional populations (other rivers and swamps) to evaluate their polymorphism and missingness. These results provide a robust genomic resource for Mediterranean buffalo, overcoming the limitations of bovine-derived genotyping tools available today. The identified SNPs dataset lays the basis for the development of a breed-specific SNP array, providing a dense and informative set of markers that could support cost-effective SNP chip development compared with existing arrays and sequencing technologies. This resource will facilitate more accurate genomic selection, precision breeding, and the conservation of genetic diversity in the Mediterranean buffalo population. Full article

Drought stress is a significant environmental factor affecting plant growth, fruit quality and distribution. Wild jujube is an important species of eco-economic forest tree. In this study, two wild jujube families, ‘NO. 1’ (tolerant) and ‘NO. 5’ (sensitive), which show significant differences in morphological and physiological indicators in drought treatment, are considered. Compared with the ‘NO. 5’, the ‘NO. 1’ exhibited lower water loss, leaf yellowing and abscission rates, as well as reduced malondialdehyde (MDA) content, while showing higher superoxide dismutase (SOD) activity and elevated levels of soluble sugars (SS), soluble proteins (SP), and proline (Pro). In contrast, the ‘NO. 5’ suffered more severe damage to leaf epidermal cells compared with the ‘NO. 1’, accompanied by a significant decline in net photosynthetic rate (A) and instantaneous water use efficiency (WUEi). Transcriptomic profiles between two wild jujube families with markedly different drought responses (withholding water for 15 days) are shown. The two wild jujube families included 3238 up-regulated and 2675 down-regulated differentially expressed genes (DEGs). Many DEGs enriched in the GO and KEGG pathways are related to antioxidant activity, transmembrane transport, carbohydrate biosynthesis and metabolism, plant hormones, and photosynthesis. The biosynthesis of amino acids, the MAPK signaling pathway, plant hormone signal transduction, and flavonoid and alkaloid biosynthesis were the transcriptome modifications most significantly altered by drought stress. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the precision of the RNA-seq data. ZjJIP23-1, ZjbZIP53, ZjSPS8, ZjCAO, ZjADH1 and ZjERF39 may play important roles in the drought tolerance of the wild jujube. This study provides a solid foundation for further studies on the complex mechanisms and breeding of drought-resistant plants in wild jujube. Full article

Quantitative analysis of leaf cell microstructure is crucial for deciphering agronomic traits in Chinese cabbage, including photosynthetic efficiency, stress tolerance, and yield potential. Traditional manual observation methods are inefficient and highly subjective, failing to meet the demands of large-scale breeding for high-throughput, reproducible microscopic phenotyping. To transition breeding practices from experience-driven to data-driven, there is an urgent need to establish automated, standardized systems for acquiring cell-scale phenotypes. Therefore, this study proposes an automated instance segmentation and phenotyping analysis framework for multi-tissue cells in Chinese cabbage leaf cross-sections. This framework systematically optimizes Mask R-CNN by introducing an attention mechanism to enhance cellular feature responses in complex backgrounds. It employs weighted multi-scale feature fusion to process densely distributed small-scale cells and integrates a refined boundary optimization module to improve recognition accuracy in adherent and blurred regions. On a microscopic image dataset spanning multiple varieties, this method achieves high-precision predictions in instance segmentation tasks. Based on the predicted cell masks, an interactive phenotyping analysis tool was further developed to automatically extract standardized single-cell morphological parameters, including area, perimeter, and Feret’s diameter. The measured parameters exhibit high consistency with manual annotations (correlation coefficients (r) all exceed 0.97). This framework enables high-throughput, standardized phenotypic analysis at the cellular level of leaf cross-sections, providing a reliable method for the digital and automated interpretation of crop microscopic traits. This technical solution not only supports the systematic integration of microscopic phenotypes in Chinese cabbage breeding but also offers a scalable solution for cellular-scale phenotypic research in other crops. Full article

The reference genome serves as a fundamental resource for sheep genetic research and molecular breeding, and iterative improvements in assembly quality have directly driven advances in these fields. A systematic literature review was conducted by retrieving relevant studies from major scientific databases using predefined keywords related to sheep reference genomes and genome assembly technologies, followed by structured screening and comparative analysis of eligible publications. This review systematically summarizes the developmental trajectory of the sheep reference genome from early fragmented assemblies to telomere-to-telomere (T2T) genome assembly approaches. Different genome versions are comparatively analyzed from multiple perspectives, including assembly strategies, quality metrics, and functional annotation. Importantly, we propose a genome-resolution-driven analytical framework that explicitly links successive improvements in reference genome completeness with paradigm shifts in sheep genetic analysis, ranging from marker-based studies to structural variation- and multi-omics-enabled trait dissection. Particular emphasis is placed on the potential applications and biological research value of T2T genomes in assembly methodologies and multi-omics integrative analyses. Furthermore, the practical significance of different reference genome versions in genetic dissection, trait mapping, and breeding applications is reviewed, and future directions for sheep genome research are discussed. This review provides both a systematic synthesis and a forward-looking conceptual roadmap for advancing sheep molecular genetics and precision breeding in the T2T genomics era. Full article

Yield-related traits of rice (Oryza sativa L.) are pivotal to safeguarding global food security. As a powerful and efficient strategy, genome-wide association study (GWAS) has identified numerous genes for yield-related traits in rice over recent decades, providing critical resources for germplasm improvement. Most yield-related traits are complex quantitative traits controlled by multiple genes with diverse effect sizes, and traditional GWAS approaches have limited power to detect small-effect loci. In this study, we employed Fast3VmrMLM, a compressed mixed linear model integrating genome-wide scanning and machine learning, to perform GWAS for 10 key yield-related traits using a panel of 529 rice accessions and 4,945,006 single-nucleotide polymorphisms (SNPs). The traits included heading date, plant height, panicle number, effective panicle number, yield per plant, spikelet length, grain length, grain width, grain weight, and grain thickness. We detected 141 significant quantitative trait nucleotides (QTNs) associated with target traits and identified 92 previously validated genes located near these QTNs. As a key environmental regulator, photoperiod directly controls flowering and indirectly modulates yield-related traits, and we further identified 182 photoperiod-responsive candidate genes via differential expression and Gene Ontology (GO) enrichment analysis. Through tissue-specific expression analysis, homology analysis with Arabidopsis genes, and haplotype-phenotype differential analysis, six pleiotropic candidate genes were confirmed; notably, LOC_Os02g02210 appears to contribute substantially to grain width and yield-related traits. In conclusion, Fast3VmrMLM proved effective for dissecting the genetic basis of yield-related traits, especially in detecting small-effect loci. These results not only establish a potential genetic link between photoperiod regulation and rice yield formation but also provide high-confidence candidate genes and loci that will accelerate functional genomic studies and precision molecular breeding for high-yield rice. Full article

Low temperature and drought are among the most pervasive abiotic stresses limiting crop productivity worldwide, and their frequent co-occurrence or alternation imposes compounded constraints on agricultural sustainability. Increasing evidence supports cross-tolerance, whereby exposure to one stress enhances resistance to another, as an emergent property of shared signaling networks and integrative regulatory layers. In this review, we summarize recent advances in understanding cold–drought cross-talk, from early stress perception and secondary messengers to hormonal coordination via abscisic acid, transcriptional reprogramming centered on dehydration responsive element binding protein/C repeat binding factor (DREB/CBF) modules, and longer-term regulatory memory mediated by chromatin remodeling and biomolecular condensates. Importantly, we further discuss how these mechanistic insights can be translated into precision breeding strategies, including genome editing, allele mining, and backcross-assisted introgression, to accelerate the development of crop varieties with stable multi-stress tolerance. Finally, we highlight future directions for integrating multi-omics, high-throughput phenotyping, and data-driven approaches to enable efficient molecular design breeding for complex stress environments. Full article

The most recent National Research Council Nutrient Requirements for Sheep and Goats was published in 2007, one of the most consequential nutrient requirement recommendations for sheep and goats in the world. The enhancement of production efficiency, minimization of carbon footprint, and maximization of resource economy, among others, motivate the continuing discussion of nutrient utilization and refinement of nutrient requirements in sheep and goats that are increasingly important in various parts of the world. Progress has been made in the estimation of energy and protein requirements in sheep and goats, mainly utilizing empirical feeding experimentation, comparative slaughter techniques and minimum endogenous loss methods. In sheep, newer estimates of energy and protein requirements for maintenance and growth and partial efficiencies has been reported since 2007. There were suggestions that energy and protein requirements could have been affected by breed, wool growth, gender and size, with these reported values being similar or lower than the recommended values in international feeding systems such as NRC, ARC, INRA and AFRC. In goats, energy and protein requirements for growing goats were reported to be either higher or lower than the established recommendations, depending upon meat or dairy breeds. Effect of gender on energy requirement appeared to be related to the stage of growth or degree of maturity. Newer data also suggested that existing recommendations on nutrient requirements may not be adequate for non-pregnant and non-lactating pubertal females. In multiparous pregnant goats, energy and protein requirements for maintenance did not appear to be affected by days of pregnancy, but efficiencies of metabolizable energy and metabolizable protein utilization for pregnancy were. There were suggestions that metabolizable protein can be predicted from energy intake using equations that encompass both sheep and goats, but more data on goats were called for to account for specific differences in nutrition. In addition to sulfur, there has been progress made on the estimation of maintenance and growth requirements of calcium, phosphorus, potassium and magnesium in goats, with suggestions on the consideration of gender and breed differences. While conventional factors such as breeds and species, genotype, stage of maturity, gender, body composition, mobilization of tissue energy for production, and additional activity energy required due to resource limitation and acclimatization remain as important considerations for the estimation of nutrient requirements in sheep and goats, emerging factors such as climate change, heat stress, parasitism and secondary plant compounds that can affect nutrient utilization should also be considered in the estimation of nutrient requirements. Model equations and partial efficiencies used by NRC to predict energy and protein requirements for maintenance, growth, lactation, and fiber have been highlighted and discussed for the purpose of a more focus discussion and refinement for the future. Potential limitations of both traditional and emerging methodologies in determining the nutrient requirements in sheep and goats were discussed. The advancement in nutrigenomics can potentially move nutrient requirements beyond its population-based guidelines. To justify the research investment, emerging methodologies such as nutrigenomics will have to be linked more directly to the improvement of production efficiency via more precise prediction of nutrient requirement. With the assistance of artificial intelligence and more data obtained from sensor technology, precision nutrition has the potential to deliver nutrients precisely to individual animals and meet nutrient requirements in sheep and goats. Full article

Soybean seed quality is defined by an inverse relationship between oil and protein content. Understanding the spatiotemporal regulation of this trade-off is crucial for breeding. This study aims to dissect the transcriptomic networks governing carbon and nitrogen partitioning during seed development. Here, transcriptomic and co-expression network analyses were performed on cotyledon and seedcoat tissues of high-protein (HP) and low-protein (LP) soybean cultivars across three seed developmental stages. We identified 4910 HP/LP-specific differentially expressed genes (DEGs), with striking transcriptional alterations in the early developmental stage. Notably, some important DEGs were enriched in carbon/lipid metabolism, protein folding, and hormone/circadian signaling pathways, among which key gene families (e.g., OLEs, SWEETs, HSPs), core regulators (e.g., LACS, L1L, ABF1), and QTL-localized candidate genes (e.g., FA9) were characterized. Mechanistically, C/VIF1-mediated post-translational inhibition of CWINV1 may restrict carbon flux to oil synthesis in HP seeds; upstream circadian/hormone signaling and L1L-sHSPs jointly promote protein deposition, uncoupling the oil–protein trade-off and enabling HP trait formation. In contrast, LP cultivars upregulated SWEETs, OLEs, and LTPs to facilitate high carbon flux into lipid biosynthesis and storage. These findings provide valuable genetic targets for precision breeding programs aimed at optimizing resource allocation. Full article

The important economic traits of ruminants result from interactions between genetic background and environmental factors, but key traits such as reproductive performance, feed efficiency, disease resistance, and livestock product quality are often not fully explained by DNA sequence variations alone. Increasing evidence suggests that epigenetic regulation serves as a crucial molecular bridge connecting environmental stimuli with changes in gene expression, allowing organisms to exhibit stable and plastic phenotypic differences without altering the DNA sequence. This review provides a structured synthesis of recent research in the field of epigenetics in ruminants, elucidating how multiple layers of epigenetic mechanisms, including DNA methylation, histone modifications, non-coding RNAs, and higher-order chromatin structures, coordinate to regulate growth, development, reproductive performance, metabolic and immune homeostasis, and livestock product traits across different tissues and developmental stages. These epigenetic marks not only demonstrate high responsiveness to nutrition, management, and environmental stressors, but can exhibit context-dependent stability within the same tissue and physiological stage when environmental conditions are comparable, thereby contributing to the regulation of phenotypic plasticity and offering potential value as predictive biomarkers. Furthermore, epigenetic information can supplement our understanding of phenotypic variation in ways that traditional genomic selection methods are unable to capture, offering new data dimensions for the prediction and improvement of low heritability, environmentally sensitive traits. Overall, integrating epigenetic information with genomic selection strategies may improve the accuracy of ruminant trait prediction and enhance environmental adaptability. This integration also offers a conceptual basis and technical pathway for developing more precise and sustainable breeding systems. Full article

This study presents a novel pneumatic seed-metering device for precision soybean breeding, engineered to deliver two seeds per hill with high operational reliability. Its design features a compartmentalized structure and an integrated seed-clearing mechanism, explicitly addressing the key limitations of conventional seeders, such as low automation levels and intervarietal contamination during seed switching. The seed-metering and clearing processes were analyzed using coupled discrete element method–computational fluid dynamics (DEM–CFD) simulations. The exploratory DEM–CFD analysis identified distinct operational thresholds for seeding failures: miss-seeding occurred at disc rotational speeds exceeding 2.55 rad s⁻¹, while multiple-seeding issues were frequent at applied vacuum pressures above 5.6 kPa. Following this, a Central Composite Design (CCD) experiment was conducted in a controlled laboratory setting to examine the effects of operational speed and vacuum pressure on seeding quality indices. A multi-objective numerical optimization identified an optimal operational compromise with a seed-metering disc speed of 2.65 rad s⁻¹ (approximately 1.82 km h⁻¹) and an applied negative pressure of 5.80 kPa. This operating point effectively balances the competing failure modes of multiple seeding and miss-seeding, resulting in rates of 2.95% and 0.85% respectively. Field validation in saline–alkali soil conditions confirmed the device’s high precision, with actual multiple and miss-seeding rates maintained below 2% and 0.5%, respectively. Overall, this device significantly enhances seeding efficiency and operational reliability, providing a practical and effective solution for high-throughput soybean breeding programmes. Full article

This study aimed to establish a model for investigating X chromosome inactivation using transgenic mouse strains expressing green fluorescent protein (GFP). The D4/XGFP-Tg (XGFP) strain carries the GFP transgene on the X chromosome; therefore, due to random X chromosome inactivation, female offspring from crosses between XGFP males and CD-1 females exhibit mosaic GFP expression. In contrast, the B5/EGFP-Tg (EGFP) strain harbours autosomal integration of the same reporter construct, resulting in uniform GFP expression in progenies. Analysis of CD-1 × XGFP attached blastocysts revealed strong GFP expression in giant trophoblast cells and primordial germ cells (PGCs) at E6.5, demonstrating paternal X-chromosome reactivation. In 14.5-day-old CD-1 × XGFP female embryos and CD-1 × EGFP embryos, intense CAG promoter-driven GFP signals were detected in the brain, heart, gonads, somites, and limbs. In line with random X-chromosome inactivation, only 56% of embryonic fibroblast cells, derived from CD-1 × XGFP female embryos, exhibited GFP expression. These findings validate that CD-1 × XGFP mice represent a valuable in vivo model for studying X chromosome inactivation during early embryonic development and PGC specification. Furthermore, CD-1 × XGFP embryonic fibroblasts represent a valuable in vitro model for investigating the molecular mechanisms governing X-chromosome activation and inactivation. Full article

Abiotic stresses such as drought, salinity, extreme temperatures, and heavy metal contamination severely limit global crop productivity and threaten food security. Plants have evolved epigenetic strategies, particularly DNA methylation, to perceive, adapt to, and memorize environmental challenges. This review systematically elucidates the dynamic regulatory mechanisms of DNA methylation—including establishment via RNA-directed DNA methylation (RdDM), maintenance by methyltransferases (MET1, CMT), and active removal by demethylases (ROS1)—in plant responses to diverse abiotic stresses. We highlight how stress-induced methylation reprogramming modulates gene expression, chromatin states, and physiological adaptations, contributing to both somatic and transgenerational stress memory. Furthermore, we discuss advanced detection technologies for profiling methylation patterns and evaluate their applications in epigenetic breeding, such as exploiting heritable epialleles, RdDM-based gene silencing, and methylation markers for heterosis prediction. Despite significant progress, translating epigenetic insights into predictable breeding tools remains challenging. Future efforts should focus on establishing causal links between methylation changes and stress phenotypes, improving epigenome editing precision, and integrating multi-omics approaches for the development of climate-resilient crops. This work provides a comprehensive epigenetic perspective for enhancing crop adaptability and sustainable agriculture. Full article

Accurate and efficient screening of nitrogen-efficient rice varieties is crucial for implementing precision agriculture and achieving green and sustainable development. However, traditional screening methods rely on destructive sampling and chemical analysis, which are inefficient and costly, and thus cannot meet the requirements of large-scale breeding applications. Therefore, this study aims to develop a non-invasive, high-throughput screening method for nitrogen efficiency of rice based on unmanned aerial vehicle (UAV) hyperspectral data and machine learning algorithms. Sixty rice varieties were selected as the target, and principal component analysis (PCA) was used to reduce the dimension of seven key agronomic parameters (such as yield, nitrogen utilization rate, etc.). A comprehensive evaluation index for nitrogen utilization efficiency was constructed, and K-means clustering was used to classify the varieties into three categories: nitrogen-efficient, medium-efficient, and low-efficient varieties. On this basis, four machine learning algorithms (decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN)) were used to establish a variety nitrogen efficiency classification model based on spectral indices. The results showed that the indicators constructed based on PCA and clustering could effectively distinguish different nitrogen-efficient varieties; among the four models compared, the DT model achieved the highest overall performance, with an accuracy of 0.75, precision of 0.80, and F1-score of 0.74. This study confirmed the feasibility of combining UAV hyperspectral data with decision tree models, providing a reliable technical solution for the large-scale, rapid, and non-invasive screening of nitrogen-efficient rice varieties. Full article