Search Results (1,756)

1,3-butadiene (BD) is a volatile organic pollutant. Upon inhalation, it is metabolically activated to reactive epoxides which alkylate genomic DNA and form potentially mutagenic monoadducts and DNA–DNA crosslinks including N7-(1-hydroxyl-3-buten-1-yl)guanine (EB-GII) and 1,4-bis-(guan-7-yl)-2,3-butanediol (bis-N7G-BD). While metabolic activation resulting in mutagenicity is a well-established mode of action for 1,3-butadiene, characterization of the extent of inter-individual variability in response to BD exposure is a gap in our knowledge. Previous studies showed that population-wide mouse models can be used to evaluate variability in 1,3-butadiene DNA adducts; therefore, we hypothesized that this approach can be used to also study variability in the formation and loss of BD DNA adducts across tissues and between sexes. To test this hypothesis, female and male mice from five genetically diverse Collaborative Cross (CC) strains were exposed to filtered air or 1,3-butadiene (600 ppm, 6 h/day, 5 days/week for 2 weeks) by inhalation. Some animals were kept for two additional weeks after exposure to study DNA adduct persistence. EB-GII and bis-N7G-BD adducts were quantified in liver, lungs and kidney using established isotope dilution ESI-MS/MS methods. We observed strain- and sex-specific effects on both the accumulation and loss of both DNA adducts, indicating that both factors play important roles in the mutagenicity of 1,3-butadiene. In addition, we quantified the intra-species variability for each adduct and found that for most tissues/adducts, variability values across strains were modest compared to default uncertainty factors. Full article

Precision medicine approaches rely on accurate somatic variant detection, where the DNA input into genomic workflows is a key variable. However, there are no gold standard methods for total DNA quantification. In this study, a pentaplex reference gene panel using digital PCR (dPCR) was developed as a candidate reference method. The multiplex approach was compared between two assay chemistries, applied to healthy donor genomic DNA and plasma cell-free DNA (cfDNA) to measure the ERBB2 (HER2) copy number variation in cancer cell line DNA. The multiplex approach demonstrated robust performance with the two assay chemistries, demonstrating comparable results and a wide dynamic range. Ratios of reference genes were close to the expected 1:1 in healthy samples; however, some small but significant differences (<1.2-fold) were observed in one of the five targets. Expanded relative measurement uncertainty was 12.1–19.8% for healthy gDNA and 9.2–25.2% for cfDNA. The multiplex approach afforded lower measurement uncertainty compared to the use of a single reference for total DNA quantification, which is an advantage for its potential use as a calibration method. It avoided potential biases in the application to CNV quantification of cancer samples, where cancer genome instability may be prominent. Full article

Cannabis sativa L. (2n = 2x = 20) is a widely recognized species within the Cannabaceae family. Despite its utilization for medicinal, recreational, and industrial purposes, alongside its extensive historical background, the number of genetic and biotechnological studies of this plant species has decreased due to legal ramifications and prohibition campaigns associated with its use and cultivation. For many years, the development of novel varieties has been pursued solely by cultivators, as domestic growers have transitioned their work from cultivation to breeding Cannabis lineages. Recently, the application of genomics has facilitated a surge in methodologies aimed at marker-assisted selection, germplasm management, genetic differentiation, authentication of cultivated varieties or cultivars, and forensic applications such as safeguarding intellectual property rights. Nevertheless, the utilization of molecular markers for the advancement of commercial varieties through marker-assisted breeding (MAB) frameworks remains rare. This investigation was designed to evaluate a previously established informative microsatellite (SSR) array for the genotyping of drug-type Cannabis sativa cultivars derived from seeds of European origin. A total of 171 samples from 20 varieties were collected from European distributors and analyzed for genetic uniformity and population structure. The results were then compared with previously analyzed hemp samples and drug-type samples of Canadian origin, revealing the identification capabilities of our SSR genotyping method. Full article

Background/Objectives: Prenatal exposure to famine can lead to lasting health effects through changes in DNA methylation. This study aims to evaluate the impact of prenatal exposure to the Chinses Great Famine (1959–1961) on human epigenome and the subsequent influence on blood lipids. Methods: We conducted an epigenome-wide association study (EWAS) of peripheral blood-based DNA methylation and prenatal exposure to the Chinese Great Famine as well as blood lipids among eight participants exposed to famine and eight sex-matched participants (born ≤ 3 years after the famine). Genome-wide DNA methylation sites were profiled using the Illumina EPIC BeadChip, which covers 850K methylation positions. Results: After EWAS analyses, seven probes in genes C8orf31, ELAVL1, U6, GBA2, SHOX2, SLC1A4, and NPHP4 reached p < 1 × 10⁻⁵. Of these, famine exposure was associated with decreased methylation levels of a GBA2 exonic probe cg08258661 (p = 4.9 × 10⁻⁶). After false discovery rate (FDR) correction, pathway enrichment analyses for genes harboring nominally significant (p < 0.05) probes identified 44 significant pathways (q < 0.05), and 5 pathways were related to lipid metabolism. After FDR correction in each pathway, probes cg02622866 (5’UTR of ATF2, p = 1.09 × 10⁻³), cg07316730 (body of GRB2, p = 1.32 × 10⁻³), and cg01105385 (body of PIK3R1, p = 1.94 × 10⁻³) in the PI2K-Akt signaling pathway were associated with blood LDL-C (q ≤ 0.04); probes cg09180702 (3’UTR of PIGQ, p = 9.21 × 10⁻⁵, and q = 0.04) and cg01421548 (body of HS3ST4, p = 5.23 × 10⁻⁵, and q = 0.01) in the metabolism pathway were associated with blood LDL-C and HDL-C, respectively; In addition, probe cg08460387 (5’UTR of MAN1C1, p = 1.09 × 10⁻⁴, and q = 0.02) in the vesicle-mediated transport pathway was associated with log-transformed blood triglycerides. Conclusions: Through an epigenetic study of the Chinese Great Famine, we identified six novel genes involved in lipid metabolism. Full article

Soybean (Glycine max (L.) Merr.) is one of the most important global economic crops, extensively utilized in the production of food, animal feed, and industrial raw materials. As the demand for soybeans continues to rise, improving both the yield and quality of soybeans has become a central focus of agricultural research. To accelerate the genetic improvement of soybean, genome selection (GS) and genome-wide association studies (GWAS) have emerged as effective tools and have been widely applied in various crops. In this study, we conducted GWAS and GS model evaluations across five soybean phenotypes (Glycitin content, Oil, Pod, Total isoflavone content, and Total tocopherol content) to explore the effectiveness of different GWAS methods and GS models in soybean genetic improvement. We applied several GWAS methods, including fastGWA, BOLT-LMM, FarmCPU, GLM, and MLM, and compared the predictive performance of various GS models, such as BayesA, BayesB, BayesC, BL, BRR, SVR_poly, SVR_linear, Ridge, PLS_Regression, and Linear_Regression. Our results indicate that markers selected through GWAS, when used in GS, achieved a prediction accuracy of 0.94 at a 5 K density. Furthermore, Bayesian models proved to be more stable than machine learning models. Overall, this study offers new insights into soybean genome selection and provides a scientific foundation for future soybean breeding strategies. Full article

Background: Wide application of genome sequencing technologies has highlighted extensive genetic diversity in Acute Myeloid Leukemia (AML), yet the specific roles of individual genes remain unclear. This systematic review and meta-analysis aims to provide robust evidence for the prognostic impact of somatic gene mutations in de novo AML patients, while also exploring the prevalence of these mutations. Methods: Eligible studies were identified from PubMed and Scopus, with a focus on those reporting the prognostic influence of somatic gene mutations on overall survival (OS) or relapse-free survival (RFS) when compared to wild-type carriers. We calculated the pooled prevalence with 95% confidence intervals to assess the frequency of these mutations, and the pooled Hazard Ratio (HR) to compare OS and RFS associated with specific gene mutations. Results: We evaluated 53 somatic gene mutations using 80 studies, involving 20,048 de novo AML patients. The analysis revealed that the most prevalent affected genes were NPM1 (27%), DNMT3A (26%), and FLT3-ITD (24%). Mutations in CSF3R, TET2, and TP53 were significantly associated with poorer OS or RFS (p < 0.05). Sensitivity analysis confirmed that ASXL1, DNMT3A, and RUNX1 mutations were consistently linked to inferior OS or RFS. In contrast, CEBPAdm mutations were associated with favorable OS [HR = 0.39 (0.30–0.50)] and RFS [HR = 0.44 (0.37–0.54)]. Subgroup analysis showed that FLT3-ITD mutations were consistently associated with worse OS or RFS across all subgroups, though no significant subgroup differences were noted. No significant impact on OS or RFS was observed for mutations in GATA2, FLT3-TKD, KRAS, NRAS, IDH1, and IDH2. Conclusions: These findings provide critical insights into AML prognosis, aiding clinical decision-making and improving risk stratification strategies. Full article

Stomata on the leaves of wheat serve as important gateways for gas exchange with the external environment. Their morphological characteristics, such as size and density, are closely related to physiological processes like photosynthesis and transpiration. However, due to the limitations of existing analysis methods, the efficiency of analyzing and mining stomatal phenotypes and their associated genes still requires improvement. To enhance the accuracy and efficiency of stomatal phenotype traits analysis and to uncover the related key genes, this study selected 210 wheat varieties. A novel semantic segmentation model based on transformer for wheat stomata, called Wheat Stoma Former (WSF), was proposed. This model enables fully automated and highly efficient stomatal mask extraction and accurately analyzes phenotypic traits such as the length, width, area, and number of stomata on both the adaxial (Ad) and abaxial (Ab) surfaces of wheat leaves based on the mask images. The model evaluation results indicate that coefficients of determination (R²) between the predicted values and the actual measurements for stomatal length, width, area, and number were 0.88, 0.86, 0.81, and 0.93, respectively, demonstrating the model’s high precision and effectiveness in stomatal phenotypic trait analysis. The phenotypic data were combined with sequencing data from the wheat 660 K SNP chip and subjected to a genome-wide association study (GWAS) to analyze the genetic basis of stomatal traits, including length, width, and number, on both adaxial and abaxial surfaces. A total of 36 SNP peak loci significantly associated with stomatal traits were identified. Through candidate gene identification and functional analysis, two genes—TraesCS2B02G178000 (on chromosome 2B, related to stomatal number on the abaxial surface) and TraesCS6A02G290600 (on chromosome 6A, related to stomatal length on the adaxial surface)—were found to be associated with stomatal traits involved in regulating stomatal movement and closure, respectively. In conclusion, our WSF model demonstrates valuable advances in accurate and efficient stomatal phenotyping for locating genes related to stomatal traits in wheat and provides breeders with accurate phenotypic data for the selection and breeding of water-efficient wheat varieties. Full article

Understanding trait relationships is fundamental in soybean breeding because the goal is to maximize simultaneous gains. Standard multi-trait genome-wide association studies (MT-GWAS) identify variants linked to multiple traits but fail to capture phenotypic structures or interrelations. Structural Equation Models (SEM) account for covariances and recursion, enabling the decomposition of single nucleotide polymorphism (SNP) effects into direct or indirect components and identifying pleiotropic regions. We applied SEM to analyze morphology (pod thickness, PT) and yield traits (number of pods, NP; number of grains, NG; hundred-grain weight, HGW). The dataset comprised 96 soybean individuals genotyped with 4070 SNP markers. The phenotypic network was constructed using the hill-climbing algorithm, a class of score-based methods commonly applied to learn the structure of Bayesian networks, and structural coefficients were estimated with SEM. According to coefficient signs, we identified negative interrelationships between NG and HGW, and positive ones between NP and NG, and HGW and PT. NG, HGW, and PT showed indirect SNP effects. We also found loci jointly controlling traits. In total, 46 candidate genes were identified: 7 associated exclusively with NP and 4 associated with NG. An additional 15 genes were common to NP and NG, 3 were common to NP and HGW, 6 were common to NG and HGW, and 11 were common to NP, NG, and HGW. In summary, SEM-GWAS revealed novel relationships among soybean traits, including PT, supporting breeding programs. 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/Objectives: The basic helix–loop–helix (bHLH) transcription factor family regulates plant development, metabolism, and stress responses. Yet, its genome-wide composition remains unexplored in Lilium bakerianum var. rubrum (LBVR), an ornamental lily valued for its floral traits. This study aimed to identify, classify, and profile the bHLH family in LBVR using full-length transcriptomic resources. Methods: PacBio HiFi full-length transcriptome sequencing was combined with Illumina RNA-seq for accurate structural annotation and expression quantification. Candidate bHLHs were identified by iTAK and HMMER-Pfam, and their physicochemical properties, secondary structures, motifs, and phylogenetic positions were examined. Expression patterns were analyzed across four floral stages (bud, initial bloom, full bloom, and late bloom). Results: A total of 113 high-confidence bHLH genes were identified, with ~90% successfully annotated. The proteins displayed variation in molecular weight, isoelectric point, structural features, and motif composition. Phylogenetic analysis placed them into 13 clades consistent with Arabidopsis subfamilies, revealing lineage-specific expansions and contractions. Expression profiling showed that 95 genes were active in at least one stage, with two transcriptional waves: a strong bud-to-initial-bloom activation and a secondary wave spanning anthesis. Seventeen genes were expressed exclusively at the bud stage, suggesting roles in early floral-organ initiation and pigmentation. Conclusions: This work provides the first genome-wide characterization of bHLHs in LBVR. The integrated sequencing approach generated a robust catalogue and developmental expression map, offering candidates for functional studies and resources for breeding in lilies. Full article

Background: High-temperature stress is one of the major abiotic stresses limiting cotton production. Identifying genetic loci and genes for heat tolerance is crucial for breeding heat-tolerant varieties. Methods: Given the complexity of heat tolerance phenotypes in cotton, this study, which focused on resource materials, identified an A/C SNP mutation at position 5486185 on chromosome D06 within the heat tolerance interval through genome-wide association studies (GWAS) of natural Gossypium hirsutum populations. Results: A total of 308 resource materials were identified and evaluated for their heat tolerance phenotypes over two years of field research. Kompetitive allele-specific PCR (KASP) molecular markers were developed on the basis of the D06-5486185 SNP to characterize the heat tolerance phenotypes of these 308 resource materials. Genotyping for heat tolerance-related traits and agronomic traits was also performed. Materials with the C/C haplotype at position D06-5486185 presented increased heat tolerance (higher pollen viability (PV), leaf area (LA), chlorophyll (Chl) and number of bolls on the third fruit branch (FB3) and a lower number of dry buds (DBs) and drop rate (DR)) without negatively impacting key yield traits. This locus is located in the intergenic region of two adjacent bZIP transcription factor genes (GH_D06G0408 and GH_D06G0409). Expression analysis revealed that the expression levels of these two genes were significantly greater in heat-tolerant accessions (C/C type) than in sensitive accessions and that their expression levels were significantly correlated with multiple heat-tolerant phenotypes. Conclusions: In summary, this study developed a Kompetitive Allele Specific PCR (KASP) marker associated with heat tolerance in G. hirsutum and identified two key heat tolerance candidate genes. These results provide an efficient marker selection tool and important genetic resources for the molecular breeding of heat-tolerant G. hirsutum, laying an important foundation for further establishing a molecular marker-assisted breeding system for heat tolerance in G. hirsutum. Full article

The concept of a genome signature broadly refers to characteristic patterns in DNA sequences that enable the identification and comparison of species or individuals, often without requiring sequence alignment. Such signatures have applications ranging from forensic identification of individuals to cancer genomics. In comparative genomics and evolutionary biology, genome signatures typically rely on statistical properties of DNA that are species-specific and carry phylogenetic information reflecting evolutionary relationships. We propose a novel genome signature based on the compositional structure of DNA, defined by the distributions of strong/weak, purine/pyrimidine, and keto/amino ratios across DNA segments identified through entropic segmentation. We observe that these ratio distributions are similar among closely related species but differ markedly between distant ones. To quantify these differences, we employ the Jensen–Shannon distance—a symmetric and robust measure of distributional dissimilarity—to define a genome-to-genome distance metric, termed Segment Compositional Distance ($\mathcal{D}$). Our results demonstrate a clear correlation between $\mathcal{D}$ and species divergence times, and also that this metric captures a strong phylogenetic signal. Our method employs a genome-wide approach rather than tracking specific mutations; thus, $\mathcal{D}$ offers a coarse-grained perspective on genome compositional evolution, contributing to the ongoing discussion surrounding the molecular clock hypothesis. Full article

Background/Objectives: Gastrointestinal nematode infections represent a major constraint to sheep production globally, with widespread drug resistance requiring alternative control strategies. Methods: This systematic review combined genome-wide association study findings to understand the genetic basis underlying parasite resistance traits in sheep. Following PRISMA guidelines, we identified 22 studies including 28,033 samples from 32 breeds across 11 countries, extracting 1580 candidate genes associated with resistance traits, including fecal egg count, packed cell volume, and immunoglobulin levels. Gene prioritization analysis using ToppGene identified 75 high-confidence candidate genes. Results: Functional enrichment analysis revealed significant involvement of the JAK-STAT signaling pathway, inflammatory response processes, and immune-related biological functions. Protein–protein interaction network analysis identified nine key hub genes: TNF, STAT3, STAT5A, PDGFB, ADRB2, MAPT, ITGB3, SMO, and GH1. The JAK-STAT pathway emerged as particularly important, with multiple core genes involved in cytokine signaling and immune cell development. These findings demonstrate that parasite resistance involves complex interactions between inflammatory responses, immune signaling networks, and metabolic processes. Conclusions: This comprehensive genetic framework provides essential insights for developing genomic selection strategies and marker-assisted breeding programs to enhance natural parasite resistance in sheep, offering a sustainable approach to reducing drug dependence and improving animal welfare in global sheep production systems. Full article

Background: The ubiquitin–proteasome system plays a critical role in plant antiviral defense, with HECT-type E3 ubiquitin ligases serving as key regulators of protein turnover. To explore the potential involvement of the HECT gene family in host resistance against tomato yellow leaf curl virus (TYLCV), a comprehensive analysis was conducted in Nicotiana benthamiana. Methods: In this study, the HECT gene family in N. benthamiana was systematically investigated using a genome-wide bioinformatic analysis. The potential roles of these genes in the response to TYLCV infection were further examined using a virus-induced gene silencing (VIGS) technique. Results: Using a Hidden Markov Model approach, 18 NbHECT genes were identified that phylogenetically clustered into four subfamilies with distinct structural features. Chromosomal location and synteny analyses indicated that these genes were unevenly distributed across 11 chromosomes, with 10 instances of segmental duplication identified. Tissue-specific expression profiling demonstrated that 17 NbHECTs were constitutively expressed, with Group III members showing the highest expression in reproductive tissues. Following TYLCV infection, NbHECT6 was significantly downregulated while NbHECT13 was upregulated in both inoculated and systemic leaves. Functional validation through the VIGS approach revealed that suppression of NbHECT6 and NbHECT13 increased host susceptibility, as evidenced by exacerbated symptom severity and enhanced viral DNA accumulation compared to controls. Conclusions: These findings establish NbHECT6 and NbHECT13 as critical components of the plant antiviral response, providing new insights into ubiquitin-mediated defense mechanisms against geminiviruses. Full article

Background/Objectives: This study aimed to identify genes linked to phenotypic traits in Apis cerana cerana through a genome-wide association study. Methods: Genomic data was collected from 116 workers across 12 regions in Guizhou Province, China, and 15 morphological traits were measured, including proboscis length, femur length, tibia length, tarsus length, tarsus width, tergite III and IV length, sternite III length, wax mirror length on sternite III, wax mirror slanted length on sternite III, wax mirror interval on sternite III, sternite VI length, sternite VI width, forewing length, forewing width, and cubital index. Then, a genome-wide association study was performed on these traits. Results: The analysis identified 12 SNPs significantly associated with tergite III and IV length, along with 2 SNPs linked to wax mirror length on sternite III, and 7 SNPs related to the wax mirror interval on sternite III. Eleven candidate genes for tergite III and IV length, two genes for wax mirror length on sternite III, and seven genes for wax mirror interval on sternite III were identified. These genes encode proteins involved in Longitudinals, Zinc, Lamin, BTB/POZ, Dyneins, and Phospholipases. Conclusions: The discovered SNPs and their corresponding genes may regulate the lateral and longitudinal development of the tergum and sternum in the A. c. cerana. Continued in-depth research on these aspects will help clarify how these SNPs regulate the tergum and sternum, thereby enhancing economic returns for beekeepers and promoting the conservation of germplasm resources in the native Apis cerana cerana. Full article