Search Results (2,685)

Background/Objectives: The Chinese dwarf cherry (Cerasus humilis) is an endemic shrub fruit tree species in China. Its fruit is flavorful, nutrient-rich, and has considerable research and utilization potential. However, most currently cultivated varieties of C. humilis are highly acidic and primarily used for processing. Consumer-preferred, low-acid, fresh-eating varieties are scarce, limiting industrial development. We used 208 F₁ individuals derived from a cross between high-acid “Nongda 4” and the low-acid “DS-1”. Methods: Restriction site-associated DNA sequencing (RAD-seq) was used to develop single-nucleotide polymorphism (SNP) markers and construct a high-density genetic linkage map. Using two years of fruit titratable acidity phenotypic data, quantitative trait locus (QTL) mapping and candidate gene screening were performed. Results: The genetic map contained 2491 SNP markers, assigned to eight linkage groups. The total genetic distance was 672.71 cm, with an average distance of 0.27 cm between markers, indicating high map quality. QTL mapping identified 18 loci associated with fruit titratable acidity, including 11 major-effect QTLs (logarithm of odds, LOD ≥ 3.5). These major-effect QTLs were concentrated on linkage groups LG2 and LG5, with an explained phenotypic variation of 8.6–31.13%. Two candidate genes were identified within QTL intervals: phosphoester phosphatase and MATE transmembrane transporter. The phosphatase gene’s expression showed a strong correlation with titratable acid content (p < 0.01, correlation coefficient 0.93), suggesting that it plays an important role regulating fruit acidity in C. humilis. Conclusions: This study supports marker-assisted breeding of low-acid, fresh-eating varieties, aiding commercial promotion of C. humilis. Full article

Skin color is a crucial phenotypic trait in poultry that influences consumer preference, market value, and breed identification. However, the mechanisms underlying skin color variation in Lingshan chickens remain poorly understood. This study aimed to elucidate the physiological, metabolic, and microbial characteristics associated with skin color differences in male Lingshan chickens. A total of 210 castrated male Lingshan chickens were categorized into white-shanked (WS), yellow-shanked (YS), and red-shanked (RS) groups based on the Roche color fan scores. The results showed that chickens in the YS and RS groups exhibited significantly higher body weights and pigmentation levels in the shank, breast, and abdominal skin compared to those in the WS group (p < 0.05). Serum concentrations of triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and very-low-density lipoprotein (VLDL) were markedly elevated in RS chickens. Additionally, carotenoid profiles revealed higher deposition of lutein and β-carotene in the skin and adipose tissues of YS and RS birds. Gene expression analysis indicated differential regulation of carotenoid transport and metabolism-related genes among groups. Furthermore, 16S rRNA sequencing of cecal microbiota revealed significant compositional shifts in microbial communities associated with shank pigmentation. Collectively, these findings suggest that differences in shank color in Lingshan chickens are closely linked to lipid metabolism, carotenoid transport, and gut microbiota composition. This study provides novel insights into the biological mechanisms driving skin pigmentation, offering valuable implications for breeding and functional trait selection in indigenous chicken populations. Full article

Potato (Solanum tuberosum L.) is a globally important food crop, but its tetrasomic inheritance and diploid self-incompatibility have limited the discovery of potato genes and progress in breeding. Here, we developed an F₂ segregating population consisting of 174 lines by crossing a self-compatible genome-homozygous diploid line (Y8, female parent) with a heterozygous diploid line (IVP101, male parent), followed by selfing. Using whole-genome resequencing, we constructed a high-density genetic map containing 4464 recombinant bin markers with an average physical distance of 165.51 Kb. Phenotypic evaluation of 8 traits related to yield, tuber shape, and tuber eye number across three environments revealed significant parental differences and wide phenotypic variation within the F₂ population. QTL (Quantitative trait loci) mapping using this genetic map and multi-environment phenotypic data identified 89 QTLs, including 7 previously reported QTLs/genes. In addition, 10 QTLs were stably detected across multiple seasons (stable QTLs). Further genetic effect analysis showed that favorable alleles of these stable QTLs significantly enhanced phenotypic values. Notably, two pleiotropic QTLs were identified on chromosomes 5 and 12; the major-effect QTL on chromosome 12 (qTY-12-6, qTS-12-3, and qTE-12-4) exhibited high phenotypic variance explained (PVE). Its favorable allele from Y8 significantly increased mean tuber weight, tuber number per plant, and promoted rounder tuber shape while reducing eye number, simultaneously improving yield and quality. Collectively, this study provides a reference for genetic mapping using homozygous and heterozygous diploid parents, and the identified QTLs offer valuable genetic resources for potato breeding and molecular mechanism research, enhancing our understanding of the genetic regulation of yield, tuber shape, and eye number in potato. Full article

Chronic wasting disease (CWD) is a prion disease of cervids marked by growing strain diversity and variation in host susceptibility. Central to this complexity are prion protein gene (Prnp) polymorphisms, which can modulate pathogenesis by altering the ability of cellular prion protein (PrP^C) to misfold into infectious prions (PrP^Sc), or by promoting the emergence of novel strains. Studies in cervids and transgenic rodent models demonstrate that individual polymorphisms influence PrP stability, conversion efficiency, and the selection of PrP^Sc conformers, with most variants conferring partial resistance but none offering complete protection. These host–strain interactions define transmission barriers and disease phenotype. Understanding how Prnp genotypes shape CWD strain diversity is essential for predicting transmission dynamics, refining surveillance, and assessing zoonotic potential as the disease continues to expand geographically and genetically. Full article

Stomata play a crucial role in plant immune responses, with their morphological characteristics closely linked to disease resistance. Accurate detection and analysis of stomatal phenotypic parameters are essential for soybean disease resistance research and variety breeding. However, traditional stoma detection methods are challenged by complex backgrounds and leaf vein structures in soybean images. To address these issues, we proposed a Soybean Stoma-YOLO (You Only Look Once) model (SS-YOLO) by incorporating large separable kernel attention (LSKA) in the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8 and Deformable Large Kernel Attention (DLKA) in the Neck part. These architectural modifications enhanced YOLOV8′s ability to extract multi-scale and irregular stomatal features, thus improving detection accuracy. Experimental results showed that SS-YOLO achieved a detection accuracy of 98.7%. SS-YOLO can effectively extract the stomatal features (e.g., length, width, area, and orientation) and calculate related indices (e.g., density, area ratio, variance, and distribution). Across different soybean rust disease stages, the variety Dandou21 (DD21) exhibited less variation in length, width, area, and orientation compared with Fudou9 (FD9) and Huaixian5 (HX5). Furthermore, DD21 demonstrated greater uniformity in stomatal distribution (SEve: 1.02–1.08) and a stable stomatal area ratio (0.06–0.09). The analysis results indicate that DD21 maintained stable stomatal morphology with rust disease resistance. This study demonstrates that SS-YOLO significantly improved stoma detection and provided valuable insights into the relationship between stomatal characteristics and soybean disease resistance, offering a novel approach for breeding and plant disease resistance research. Full article

Eyespots are functionally complex and highly variable elements of butterfly wing patterns. The Meadow Brown, Maniola jurtina, is a classic model species studied for variation in eyespots as an index of evolutionary divergence and adaptation. However, the role of fine-scale ecogeographic conditions on eyespot variation remains poorly understood. In this study, we examined hindwing eyespot number, distribution, and combination patterns in male M. jurtina across climatically and topographically diverse north-western Balkans. Compared to the species average, males in this region displayed greater spottiness and phenotypic diversity. While the typical two-spot phenotype was dominant and stable, in some populations, three-spotted and even four-spotted males occurred at similar frequencies. Rare six-spotted individuals were recorded only at mountain localities above 1200 m. Geographic and climatic factors together influenced this variation: higher altitudes and cooler, thermally stable environments promoted increased eyespot number and greater phenotypic plasticity than warmer, more variable environments. This pattern contrasts with large-scale latitudinal trends previously described for the species, emphasizing the importance of local climatic heterogeneity. Our findings suggest the north-western Balkans as a possible transitional zone where environmental complexity promotes elevated eyespot variability, contributing to the understanding of adaptive morphological plasticity in M. jurtina. Full article

Snake melons have been present for a millennia, despite their present limited use, and host a large degree of genetic and phytochemical diversity. The current study evaluated the genomic and biochemical diversity of Snake melon landraces of Cypriot and Greek origin, revealing significant degrees of genetic and mineral/phytochemical variation. Landraces showed a high potential for improving nutritional quality and a possible contribution to stress resilience. Whole-genome analysis highlighted a high degree of SNPs, InDels, SVs, and CNVs, especially in genotypes like Atzouri and ARI001024, indicating that functional variants influence phenotypic/chemical diversity. Biochemical profiling demonstrated great differences in the concentration of pigments, antioxidants, and minerals, with ARI001024 and ARI00894 exhibiting elevated levels of nutrients/phytochemicals. Hierarchical clustering and PCA analyses established relationships among traits, and reinforced the concept that these genotypes may offer valuable genetic resources for breeding programs under climate-resilient production schemes, emphasizing the need for conservation and further genomic characterization. Full article

The field of bacterial systems biology is rapidly advancing beyond static genomic analyses, and moving toward dynamic, integrative approaches that connect genetic variation with cellular function. This review traces the progression from genome-wide association studies (GWAS) to multi-omics frameworks that incorporate transcriptomics, proteomics, and interactome mapping. We emphasize recent breakthroughs in high-resolution transcriptomics, including single-cell, spatial, and epitranscriptomic technologies, which uncover functional heterogeneity and regulatory complexity in bacterial populations. At the same time, innovations in proteomics, such as data-independent acquisition (DIA) and single-bacterium proteomics, provide quantitative insights into protein-level mechanisms. Experimental and AI-assisted strategies for mapping protein–protein interactions help to clarify the architecture of bacterial molecular networks. The integration of these omics layers through quantitative trait locus (QTL) analysis establishes mechanistic links between single-nucleotide polymorphisms and systems-level phenotypes. Despite persistent challenges such as bacterial clonality and genomic plasticity, emerging tools, including deep mutational scanning, microfluidics, high-throughput genome editing, and machine-learning approaches, are enhancing the resolution and scope of bacterial genetics. By synthesizing these advances, we describe a transformative trajectory toward predictive, systems-level models of bacterial life. This perspective opens new opportunities in antimicrobial discovery, microbial engineering, and ecological research. Full article

As nutrient allocation trade-offs occur between reproductive and vegetative development in crops, optimizing their partitioning holds promise for improving agricultural productivity and quality. Herein, we characterize the phenotypic diversity of the fruitfulness trait and identify associated genes in tea plants (Camellia sinensis). Over three consecutive years, we monitored the fruitfulness of an F₁ hybrid population (n = 206) derived from crosses of ‘Emei Wenchun’ and ‘Chuanmu 217’. A marked variation was observed in the yield of individual plants, ranging from complete sterility (zero fruits) to exceptionally high fertility (1612 fruits). Using the high-resolution genetic linkage map and the fruitfulness data, we identified a stable major QTL designated as qFN5. To fine-map the underlying gene(s), artificial pollination experiments were conducted with extreme phenotype individuals (with the highest vs. lowest fruit numbers). Bulked segregant RNA sequencing (BSR-Seq) with ovules collected at two and seven days post-pollination (DPP) identified the genomic intervals that exhibit a high degree of overlap with qFN5. Analysis of expression dynamics combined with functional genomics data revealed a prominent candidate gene, CsETR2 (TGY048509), which encodes an ethylene receptor protein. When CsETR2 was overexpressed in Arabidopsis thaliana, the transgenic lines exhibited significantly decreased reproductive performance relative to the wild-type plants. Relative to the wild type, the transgenic lines exhibited a significant decline in several key traits: the number of effective panicles decreased by 72.5%, the seed setting rate dropped by 67.7%, and the silique length shortened by 38%. These findings demonstrate its role in regulating plant fruitfulness. Furthermore, yeast one-hybrid and dual-luciferase assays verified that CsMYB15 (TGY110225) directly binds to the CsETR2 promoter, thus repressing its transcription. In summary, our findings expand the understanding of genetic regulation underlying fruitfulness in tea plants and provide candidate target loci for breeding. Full article

Nitrogen is an essential macronutrient for crop growth. Although sorghum can tolerate poor soils, its low-nitrogen (LN) tolerance mechanisms remain underexplored. We conducted a genome-wide association study (GWAS) and RNA sequencing (RNA-seq) to dissect LN tolerance mechanisms in a diverse panel of 232 sorghum accessions. Phenotypic analyses revealed extensive variation in nitrogen-use efficiency traits, with shoot dry weight and shoot nitrogen accumulation in (SNAcc) showing the highest diversity. GWAS identified 10 quantitative trait loci harboring pleiotropic single-nucleotide polymorphisms (SNPs), including q1 (Chr3: 8.59–8.68 Mb), which is associated with biomass and nitrogen accumulation. Transcriptome profiling under LN stress revealed 6208 differentially expressed genes, with nitrate transporters showing genotype-specific regulation. Integration prioritized SORBI_3004G286700, where Hap2 accessions (14.66%) showed superior agronomic performance under LN conditions. We also identified pivotal transcription factors (TFs) that govern LN tolerance in sorghum, notably bHLH35 (SORBI_3007G051800) and three WRKY TFs, demonstrating constitutive upregulation in tolerant genotypes, whereas three previously uncharacterized TFs (MYB, bZIP, and B3) exhibited > 5-fold genotype-specific induction under LN. The integration of GWAS and transcriptome analyses offers an effective strategy for exploring candidate genes and elucidating nitrogen adaptation mechanisms in sorghum, while providing actionable molecular targets for precise breeding of nitrogen-efficient cultivars. Full article

Advancements in unmanned aerial vehicle (UAV) multispectral systems offer robust technical support for the precise and efficient estimation of japonica rice yield in cold regions within the framework of precision agriculture. These innovations also present a viable alternative to conventional yield estimation methods. However, recent research suggests that reliance solely on vegetation indices (VIs) may result in inaccurate yield estimations due to variations in crop cultivars, growth stages, and environmental conditions. This study investigated six fertilization gradient experiments involving two conventional japonica rice varieties (KY131, SJ22) and two hybrid japonica rice varieties (CY31, TLY619) at Yanjiagang Farm in Heilongjiang Province during 2023. By integrating UAV multispectral data with machine learning techniques, this research aimed to derive critical phenotypic parameters of rice and estimate yield. This study was conducted in two phases: In the first phase, models for assessing phenotypic traits such as leaf area index (LAI), canopy cover (CC), plant height (PH), and above-ground biomass (AGB) were developed using remote sensing spectral indices and machine learning algorithms, including Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). In the second phase, plot yields for hybrid rice and conventional rice were predicted using key phenotypic parameters at critical growth stages through linear (Multiple Linear Regression, MLR) and nonlinear regression models (RF). The findings revealed that (1) Phenotypic traits at critical growth stages exhibited a strong correlation with rice yield, with correlation coefficients for LAI and CC exceeding 0.85 and (2) the accuracy of phenotypic trait evaluation using multispectral data was high, demonstrating practical applicability in production settings. Remarkably, the R² for CC based on the RF algorithm exceeded 0.9, while R² values for PH and AGB using the RF algorithm and for LAI using the XGBoost algorithm all surpassed 0.8. (3) Yield estimation performance was optimal at the heading (HD) stage, with the RF model achieving superior accuracy (R² = 0.86, RMSE = 0.59 t/ha) compared to other growth stages. These results underscore the immense potential of combining UAV multispectral data with machine learning techniques to enhance the accuracy of yield estimation for cold-region japonica rice. This innovative approach significantly supports optimized decision-making for farmers in precision agriculture and holds substantial practical value for rice yield estimation and the sustainable advancement of rice production. Full article

Chickpea (Cicer arietinum L.) is a key legume crop of global economic and nutritional importance, yet its cultivation in Kazakhstan is constrained by a narrow genetic base and exposure to stress-prone environments. To characterize the diversity available for breeding and conservation, 27 accessions (22 kabuli and 5 desi) were evaluated for phenotypic and molecular diversity to assess its potential for use in breeding programs. Seven agronomic traits were assessed, including plant height, the first pod’s height, the number of main stems per plant, and seed yield components. The collection showed considerable variability across traits, with the plant height ranging from 37 to 75 cm and hundred-seed weight ranging from 21 to 42 g. Strong positive correlations between the number of fertile nodes, number of seeds per plant, and yield per plant (r > 0.83) highlighted their utility as indirect selection criteria. Genotyping with 28 SSR markers revealed 110 alleles (mean 3.9 ± 0.4 per locus) with moderate polymorphism (PIC = 0.493 ± 0.089). Loci CaM00495 and TAI71 were highly informative (PIC > 0.804), while two accessions showed low polymorphism, indicating genetic uniformity. Population structure analysis grouped accessions into four highly admixed clusters. Overall, Kazakh chickpea germplasm exhibits substantial phenotypic and genetic diversity under optimal conditions, providing valuable preliminary data for selecting parental lines for future breeding programs, which should include targeted stress screening to evaluate resilience. Full article

Wheat landraces are considered a valuable resource of potential phenotypic variation that could be used in germplasm improvement. Here, we examined 588 local wheat genotypes collected from farmers’ fields at 127 locations around Van Lake Basin and evaluated the morphological diversity and trait associations using Multidimensional Scaling Analysis. Spike and yield traits were measured and scored according to the UPOV and ICARDA phenotypic characterization criteria. Multidimensional Scaling Analysis divided the wheat samples into four main groups based on the number of spikelets (NOS), number of fertile spikelets (NFS), thousand-grain weight (TGW), and number of seeds per spike (NSS) and indicated a strong correlation between NOS and NFS. Furthermore, the analysis revealed that the glume and awn color of most of the genotypes was black, and they were within the locally known Karakılçık group. Only two genotypes were excluded from the Karakılçık group; No. 231 was within the Geverik local wheat group, and genotype No. 579 was found to be Tir. The Hevidik and Kirik groups had the same spike color, but the Hevidik group had spikes similar to compactum wheat, whereas the Kirik group had larger spikes. Finally, genotype No. 57 varied from all other genotypes when all the measured traits were taken into consideration. Overall, the Van Lake Basin landraces combine broad similarity with meaningful phenotypic heterogeneity shaped by local environments and traditional on-farm selection. These findings provide practical cues for conservation efforts and for the use of landraces as valuable resources in future wheat breeding programs. Full article

Background/Objectives: Leaf sheath hairiness (LSH) is an adaptive trait in wheat that improves tolerance to biotic and abiotic stresses. Although trichome development has been extensively studied in model plants, the genetic basis of LSH in Triticeae crops remains poorly defined. Methods: In this study, the inheritance and genetic architecture of LSH were investigated. Two F₂ populations were used, derived from crosses between the glabrous lines ‘Shumai 830’ and ‘Shumai 2262’ and the hairy line ‘Zhongkelanmai 1’. BSA-seq was combined with KASP marker genotyping to map and refine the trait locus. Candidate genes were evaluated through comparative genomics; sequence variation; and subcellular localization prediction. Results: Phenotypic evaluation revealed that LSH is a dominant trait, segregating at a 3:1 ratio in F₂ populations. BSA-seq identified a major locus, QLsh.cwnu-4D, on chromosome 4DL. Fine mapping with KASP markers refined this region to a 1.67 Mb interval overlapping a 530 kb trichome-associated linkage disequilibrium block in Aegilops tauschii. Within this interval, TaSAIN1-4D, a salt-inducible protein unique to Triticeae, was identified as the strongest candidate gene. Extensive sequence variation among alleles (TaSAIN1-4Da; TaSAIN1-4Db; TaSAIN1-4Dc), including large insertions and multiple SNPs, indicated potential functional diversification. Predicted nuclear localization of TaSAIN1-4D supports a role in trichome regulation and stress adaptation. The co-dominant KASP marker K-cwnu-4D-502238348 was tightly linked to LSH and cosegregated perfectly, making it a reliable tool for marker-assisted selection. Conclusions: This study clarifies the genetic architecture of leaf sheath hairiness in wheat and identifies TaSAIN1-4D as a likely regulator. These findings provide a practical marker-assisted selection tool that can accelerate the development of improved wheat varieties with desirable leaf surface traits. Full article

Cauliflower landraces (Brassica oleracea var. botrytis) safeguard allelic diversity for adaptation, yet their phenotypic breadth under winter field conditions remains under-documented. We evaluated 69 Spanish landraces and two commercial checks from the COMAV-UPV genebank using 15 quantitative and 21 qualitative descriptors. Seed viability ranged from 0 to 92%, and mature plants showed wide ranges in stem length (coefficient of variation ≈ 72%), leaf size, and head weight (100–723 g). Six curd-colour classes—including uncommon purple and Romanesco green—were recorded. Most accessions (>88%) required more than 120 days from sowing to harvest, but a distinct subset (12%) matured within 60–120 days. Plant stature tended to be positively associated with head mass, whereas highly branched inflorescences matured earlier. Variation was dominated by curd size and plant architecture. Multivariate analyses—principal component analysis for quantitative traits, multiple correspondence analysis for qualitative traits, factor analysis of mixed data, and clustering of FAMD scores by k-means—resolved three phenotypic clusters spanning a gradient of curd size/architecture and plant stature. The collection includes accessions with compact curds, earliness, or distinctive pigmentation that are immediately useful for breeding and for prioritizing regeneration. These results provide a phenotypic baseline for future genomic association studies and the development of cultivars adapted to winter production. Full article