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Honeybees are vital pollinators with functional differentiation as a key survival strategy. The Chinese honeybee (Apis cerana cerana) exhibits exceptional nectar foraging in complex terrains, yet how alternative splicing (AS) and polyadenylation (APA) regulate its labor division remains unclear. Here, we applied PacBio full-length transcriptome sequencing to annotate worker bee transcriptomes across three developmental stages (Ac3d, Ac10d, Ac21d), calibrating the third-generation sequencing data with second-generation sequencing to enhance transcriptome annotation accuracy. We identified 17,961 isoforms and 1922 APA genes, finding that alternative first exon was the major type of AS, while APA enhances transcriptomic diversity via dual polyadenylation sites in most genes. Functional analyses revealed AS enrichment in growth signaling (Vg6, CYP15A1) and immune pathways (PTPRR), whereas APA regulated growth signaling (Vg6), energy metabolism (Acsl1, AcceFE), and oxidative stress (PTPRR, PPO2). Validation by PCR and 3′RACE confirmed stage-specific AS/APA events in key genes. These findings significantly enhance the A. cerana cerana reference genome annotation and provide valuable insights into the mechanisms of AS and APA regulation underlying honeybee development and functional transitions. Full article

Background: Genomic information is becoming integral to nursing practice, yet undergraduate curricula often provide limited opportunities to apply this knowledge in realistic settings. Objective: To evaluate the impact of a clinical simulation-based intervention on nursing students’ learning of genetic counseling, with a focus on knowledge acquisition, communication skills, and student satisfaction. Methods: A sequential mixed-methods study was conducted with 30 third-year nursing students enrolled in the elective Genetics Applied to Health Sciences. Quantitative data comprised (i) pre-/post-simulation knowledge tests, (ii) a satisfaction questionnaire, and (iii) final course grades, which were compared with grades of a cohort from the previous academic year that had no simulation component (n = 28). Qualitative insights were gathered through field notes and semi-structured interviews with six purposively selected participants. During the intervention each student rotated through the roles of genetic-counseling nurse, patient, and observer, followed by a facilitated debriefing. Results: Post-simulation knowledge scores and final course grades were significantly higher than both baseline values and the historical comparison cohort. Students reported very high satisfaction, highlighting the authenticity of the scenarios and the usefulness of immediate feedback. Qualitative analysis showed that role rotation fostered deeper understanding of counseling complexities, improved empathic communication, and bolstered self-confidence when discussing hereditary risk. Conclusions: Embedding standardized-patient simulation into undergraduate genetics courses measurably improves students’ knowledge, communication proficiency, and satisfaction. These findings support incorporating similar simulation-based learning activities to bridge the gap between theoretical genetics content and real-world nursing practice. Full article

Retroviruses are single-stranded RNA viruses that package two copies of their positively stranded RNA genomes as a non-covalent dimer into newly formed virions. This process is evolutionarily conserved, and disruption of genome dimerization results in production of non-infectious virus particles. Genome dimers can be packaged as homodimers, containing two identical RNAs, or heterodimers, consisting of two genetically distinct copies. Genome dimerization generates genetic diversity, and different retroviruses have preferences for the type of genome dimers packaged into virions. We developed a novel imaging approach to specifically label and detect retroviral genome heterodimers in cells using a modified bimolecular fluorescence complementation (BiFC) technique. This method utilizes viral genomes encoding two different RNA stem-loop cassettes that each specifically binds to an RNA-binding protein conjugated to a split fluorophore. When two genetically different genomes are within close proximity, the fluorophore halves come together to reconstitute fluorescence. These BiFC-labeled RNA dimers can be visualized and tracked in living cells and interact with retroviral Gag proteins. This method has the advantage of low background fluorescence and can be applied to the study of dimeric or double-stranded RNAs of viruses and other organisms. Full article

Background: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, with a multifactorial etiology involving genetic and environmental factors. Advanced proteomics offers valuable insights into the molecular mechanisms of cancer, identifying proteins that function as mediators in tumor biology. Methods: In this study, we used mass spectrometry-based data-independent acquisition (DIA) to analyze the proteomic landscape of CRC. We compared protein abundance in normal and tumor tissues from 16 patients with CRC to identify cancer-associated proteins and examine their roles in disease progression. Results: The analysis identified 10,329 proteins, including 531 cancer-associated proteins from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, and 48 proteins specifically linked to CRC. Notably, clusters of proteins showed consistent increases or decreases in abundance across disease stages, suggesting their roles in tumorigenesis and progression. Conclusions: Our findings suggest that proteome abundance trends may contribute to the identification of biomarker candidates and therapeutic targets in colorectal cancer. However, given the limited sample size and lack of subtype stratification, further studies using larger, statistically powered cohorts are warranted to establish clinical relevance. These proteins may provide insights into drug resistance and tumor heterogeneity. Limitations of the study include the inability to detect low-abundance proteins and reliance on protein abundance rather than functional activity. Future complementary approaches, such as affinity proteomics, are suggested to address these limitations. Full article

Consumers are increasingly concerned about the flavor quality of poultry meat, yet the relationship between inosine monophosphate (IMP), intramuscular fat (IMF), and the gut microbiota remains largely unclear. This study aimed to characterize the cecal microbiota associated with IMP/IMF deposition in Daheng broilers selectively bred for high-IMP/IMF levels (High group) and low levels (Control group). A two-stage microbiome analysis strategy was applied. Initially, 16S rRNA gene sequencing was conducted to assess microbial diversity and composition. Significant differences were observed between groups in alpha diversity indices (Chao1 and Faith_PD) and beta diversity (p < 0.05). LEfSe analysis identified 55 differentially abundant taxa (LDA > 3, p < 0.05), primarily within the Phylum bacteroidota. To achieve species-level and functional insights, whole-metagenome shotgun sequencing was performed. Taxonomic profiling of 62,443 microbial species revealed significant beta diversity differences (p < 0.05), with 120 dominant species differentially enriched (LDA > 3, p < 0.05), including 77 species in the High group such as Merdivivens faecigallinarum. Enriched functional genes were mainly involved in methane metabolism, starch and sucrose metabolism, and the nucleoside phosphate metabolic process. A total of 882 metagenome-assembled genomes (MAGs) were reconstructed and integrated with 19,628 publicly available chicken MAGs, resulting in 2609 non-redundant genomes, including 52 novel ones. These findings suggest that cecal microbial composition and function are associated with IMP/IMF levels in broilers, providing candidate bacterial species and functional pathways for further validation through gavage-based intervention and multi-omics analysis. Full article

Articular cartilage (AC) has a very limited capacity for self-healing once damaged. Chondrocytes maintain AC homeostasis and are key cells in AC tissue engineering (ACTE). However, chondrocytes lose their function due to oxidative stress. Umbilical cord mesenchymal stem cells (UCMSCs) are investigated as an alternative cell source for ACTE. MSCs are known to regulate tissue regeneration through host cell modulation, largely via extracellular vesicle (EV)-mediated cell-to-cell communication. The purpose of this study was to verify whether UCMSC-derived EVs (UCMSC-EVs) enhance chondrocyte function. The mean particle sizes of the UCMSC-EVs were 79.8 ± 19.05 nm. Transmission electron microscopy (TEM) revealed that UCMSC-EVs exhibited a spherical morphology. The presence of CD9, CD63, and CD81 confirmed the identity of UCMSC-EVs, with α-tubulin undetected. UCMSC-EVs maintained chondrocyte survival, and increased chondrocyte proliferation after intake by chondrocytes. UCMSC-EVs upregulated mRNA levels of SOX-9, collagen type II (Col-II), and Aggrecan, while decreasing collagen type I (Col-I) levels. UCMSC-EVs reduced the oxidative stress of chondrocytes by reducing mitochondrial superoxide production and increasing protein levels of SOD-2 and Sirt-3 in chondrocytes. The 50 most abundant known microRNAs (miRNAs) derived from UCMSC-EVs were selected for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. GO analysis revealed enrichment in pathways associated with small GTPase-mediated signal transduction, GTPase regulatory activity, and mitochondrial matrix. The KEGG analysis indicated that these miRNAs may regulate chondrocyte function through the PI3K-Akt, MAPK, and cAMP signaling pathways. In summary, this study shows that UCMSC-EVs enhance chondrocyte function and may be applied to ACTE. Full article

Cassava (Manihot esculenta Crantz) is essential for global food security, especially in tropical regions. As an important genetic resource, its genetics plays a key role in crop breeding, enabling the development of more productive and pest- and disease-resistant varieties. Scientometrics, which quantitatively analyzes the production and impact of scientific research, is crucial for understanding trends in cassava genetics. This study aimed to apply bibliometric methods to conduct a scientific mapping analysis based on yearly publication trends, paper classification, author productivity, journal impact factor, keywords occurrences, and omic approaches to investigate the application of genetics to the species from 1960 to 2022. From the quantitative data analyzed, 3246 articles were retrieved from the Web of Science platform, of which 654 met the inclusion criteria. A significant increase in scientific production was observed from 1993, peaking in 2018. The first article focused on genetics was published in 1969. Among the most relevant journals, Euphytica stood out with 36 articles, followed by Genetics and Molecular Research (n = 30) and Frontiers in Plant Science (n = 25). Brazil leads in the number of papers on cassava genetics (n = 143), followed by China (n = 110) and the United States (n = 75). The analysis of major methodologies (n = 185) reveals a diversified panorama during the study period. Morpho-agronomic descriptors persisted from 1978 to 2022; however, microsatellite markers were the most widely used, with 102 records. Genomics was addressed in 87 articles, and transcriptomics in 65. By clarifying the current landscape, this study supports cassava conservation and breeding, assists in public policy formulation, and guides future research in the field. Full article

The Evidential K-Nearest Neighbor (EK-NN) classifier has demonstrated robustness in handling incomplete and uncertain data; however, its application in high-dimensional big data for feature selection, such as genomic datasets with tens of thousands of gene features, remains underexplored. Our proposed Granular–Elastic Evidential K-Nearest Neighbor (GEK-NN) approach addresses this gap. In the context of big data, GEK-NN integrates an Elastic Net within the Genetic Algorithm’s fitness function to efficiently sift through vast amounts of data, identifying relevant feature subsets. This process mimics human cognitive behavior of filtering and refining information, similar to concepts in cognitive computing. A granularity metric is further employed to optimize subset size, maximizing its impact. GEK-NN consists of two crucial phases. Initially, an Elastic Net-based feature evaluation is conducted to pinpoint relevant features from the high-dimensional data. Subsequently, granularity-based optimization refines the subset size, adapting to the complexity of big data. Before applying to genomic big data, experiments on UCI datasets demonstrated the feasibility and effectiveness of GEK-NN. By using an Evidence Theory framework, GEK-NN overcomes feature-selection challenges in both low-dimensional UCI datasets and high-dimensional genomic big data, significantly enhancing pattern recognition and classification accuracy. Comparative analyses with existing EK-NN feature-selection methods, using both UCI and high-dimensional gene datasets, underscore GEK-NN’s superiority in handling big data for feature selection and classification. These results indicate that GEK-NN not only enriches EK-NN applications but also offers a cognitive-inspired solution for complex gene data analysis, effectively tackling high-dimensional feature-selection challenges in the realm of big data. Full article

Aflatoxin contamination, primarily caused by Aspergillus flavus, poses a significant threat to peanut (Arachis hypogaea L.) production, food safety, and global trade. Despite extensive efforts, breeding for durable resistance remains difficult due to the polygenic and environmentally sensitive nature of resistance. Although germplasm such as J11 have shown partial resistance, none of the identified lines demonstrated stable or comprehensive protection across diverse environments. Resistance involves physical barriers, biochemical defenses, and suppression of toxin biosynthesis. However, these traits typically exhibit modest effects and are strongly influenced by genotype–environment interactions. A paradigm shift is underway with increasing focus on host susceptibility (S) genes, native peanut genes exploited by A. flavus to facilitate colonization or toxin production. Recent studies have identified promising S gene candidates such as AhS5H1/2, which suppress salicylic acid-mediated defense, and ABR1, a negative regulator of ABA signaling. Disrupting such genes through gene editing holds potential for broad-spectrum resistance. To advance resistance breeding, an integrated pipeline is essential. This includes phenotyping diverse germplasm under stress conditions, mapping resistance loci using QTL and GWAS, and applying multi-omics platforms to identify candidate genes. Functional validation using CRISPR/Cas9, Cas12a, base editors, and prime editing allows precise gene targeting. Validated genes can be introgressed into elite lines through breeding by marker-assisted and genomic selection, accelerating the breeding of aflatoxin-resistant peanut varieties. This review highlights recent advances in peanut aflatoxin resistance research, emphasizing susceptibility gene targeting and genome editing. Integrating conventional breeding with multi-omics and precision biotechnology offers a promising path toward developing aflatoxin-free peanut cultivars. Full article

Al-Mg-Si (6XXX) series aluminum alloys are widely applied in aerospace and transportation industries. However, exploring how varying compositions affect alloy properties and deformation mechanisms is often time-consuming and labor-intensive due to the complexity of the multicomponent composition space and the diversity of processing and heat treatments. This study, inspired by the Materials Genome Initiative, employs high-throughput experimentation—specifically the kinetic diffusion multiple (KDM) method—to systematically investigate how the pop-in effect, indentation size effect (ISE), and creep behavior vary with the composition of Al-Mg-Si alloys at room temperature. To this end, a 6016/Al-3Si/Al-1.2Mg/Al KDM material was designed and fabricated. After diffusion annealing at 530 °C for 72 h, two junction areas were formed with compositional and microstructural gradients extending over more than one thousand micrometers. Subsequent solution treatment (530 °C for 30 min) and artificial aging (185 °C for 20 min) were applied to simulate industrial processing conditions. Comprehensive characterization using electron probe microanalysis (EPMA), nanoindentation with continuous stiffness measurement (CSM), and nanoindentation creep tests across these gradient regions revealed key insights. The results show that increasing Mg and Si content progressively suppresses the pop-in effect. When the alloy composition exceeds 1.0 wt.%, the pop-in events are nearly eliminated due to strong interactions between solute atoms and mobile dislocations. In addition, adjustments in the ISE enabled rapid evaluation of the strengthening contributions from Mg and Si in the microscale compositional array, demonstrating that the optimum strengthening occurs when the Mg-to-Si atomic ratio is approximately 1 under a fixed total alloy content. Furthermore, analysis of the creep stress exponent and activation volume indicated that dislocation motion is the dominant creep mechanism. Overall, this enhanced KDM method proves to be an effective conceptual tool for accelerating the study of composition–deformation relationships in Al-Mg-Si alloys. Full article

Background: The Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) was a 3-year, multicenter, randomized controlled trial to test the effects of the MIND diet on cognitive decline in 604 individuals at risk for Alzheimer’s dementia. Here, we describe the genotyping, imputation, and quality control (QC) procedures for the genetic data of trial participants. Methods: DNA was extracted from either whole blood or serum, and genotyping was performed using the Infinium Global Diversity Array. Established sample and SNP QC procedures were applied to the genotyping data, followed by imputation using the 1000 Genomes Phase 3 v5 reference panel. Results: Significant study-site, specimen type, and batch effects were observed. A total of 494 individuals of inferred European ancestry and 58 individuals of inferred African ancestry were included in the final imputed dataset. Evaluation of the imputed APOE genotype against gold-standard sequencing data showed high concordance (98.2%). We replicated several known genetic associations identified from previous genome-wide association studies, including SNPs previously linked to adiponectin (rs16861209, p = 1.5 × 10⁻⁵), alpha-linolenic acid (rs174547, p = 1.3 × 10⁻⁷), and alpha-tocopherol (rs964184, p = 0.003). Conclusions: This dataset represents the first genetic resource derived from a dietary intervention trial focused on cognitive outcomes. It enables investigation of genetic contributions to variability in cognitive response to the MIND diet and supports integrative analyses with other omics data types to elucidate the biological mechanisms underlying cognitive decline. These efforts may ultimately inform precision nutrition strategies to promote cognitive health. Full article

Potato (Solanum tuberosum L.) crop yields may be reduced by nitrogen deficiency stress tolerance. An evaluation of 144 tetraploid potato genotypes was carried out during two consecutive seasons (2019 and 2020), with the objective of characterizing their variability in key physiological and agronomic parameters. Physiological parameters included chlorophyll content and fluorescence, stomatal conductance, NDVI, leaf area, and perimeter, while agronomic characteristics such as yield, tuber fresh weight, tuber number, starch content, dry matter, and reducing sugars were evaluated. To genotype the population, the GGP V3 Potato array was used, generating 18,259 high-quality SNP markers. Marker–trait association analysis was conducted using the GWASpoly package in R, applying Q + K linear mixed models to enhance precision. This methodology enabled the identification of 18 SNP markers that exhibited statistically significant associations with the traits analyzed in both trials and periods, relating them to genes whose functional implication has already been described. Genetic loci associated with chlorophyll content and tuber number were detected across non-stress and stress treatments, while markers linked to leaf area and leaf perimeter were identified specifically under nitrogen deficiency stress. The genomic distribution of these markers revealed that genetic markers or single-nucleotide polymorphisms (SNPs) correlated with phenotypic traits under non-stress conditions were predominantly located on chromosome 11, whereas SNPs linked to stress responses were mainly identified on chromosomes 2 and 3. These findings contribute to understanding the genetic mechanisms underlying potato tolerance to nitrogen deficiency stress, offering valuable insights for the development of future marker-assisted selection programs aimed at improving nitrogen use efficiency and stress resilience in potato breeding. Full article

Chromosomal microarray analysis (CMA) was performed for 40 patients with B-ALL undergoing treatment according to the ALL-2016 protocol to investigate the copy number alterations (CNAs) and copy neutral loss of heterozygosity (cnLOH) associated with minimal residual disease (MRD)-positive remission. Aberrations involving over 20,000 genes were identified, and a random forest approach was applied to isolate a subset of genes whose CNAs and cnLOH are significantly associated with poor therapeutic response. We have assembled the triple matched healthy population data and used that data as a reference, but not as a matched control. We identified a recurrent cluster of cnLOH in the 19q13.2–19q13.31 region, significantly enriched in MRD-positive patients (70% vs. 47% in the reference group vs. 16% in MRD-negative patients). This region includes the pregnancy-specific glycoprotein (PSG) gene family and the oncogene ERF, suggesting a potential role in leukemic persistence and treatment resistance. Additionally, we observed significant deletions involving 7p22.3 and 16q13, often as part of large-scale losses affecting almost the entire chromosomes 7 and 16, indicative of global chromosomal instability. These findings highlight specific genomic regions potentially involved in therapy resistance and may contribute to improved risk stratification in B-ALL. Our findings emphasize the value of high-resolution CMA in diagnostics and risk stratification and suggest that PSG genes and other candidate genes could serve as biomarkers for predicting treatment outcomes. Full article

This study measured eight key phenotypic traits across 259 fresh maize inbred lines, including plant height and spike length. A total of 82 single nucleotide polymorphisms (SNPs) significantly associated with these phenotypes were identified by applying a mixed linear model to calculate the best linear unbiased prediction (BLUP) values and integrating genome-wide genotypic data through genome-wide association analysis (GWAS). A further analysis of significant SNPs contributed to the identification of 63 candidate genes with functional annotations. Notably, 11 major candidate genes were identified from multi-trait association loci, all of which exhibited highly significant P-values (<0.0001) and explained between 7.21% and 12.78% of phenotypic variation. These 11 genes, located on chromosomes 1, 3, 4, 5, 6, and 9, were functionally involved in signaling, metabolic regulation, structural maintenance, and stress response, and are likely to play crucial roles in the growth and physiological processes of fresh maize inbred lines. The functional genes identified in this study have significant implications for the development of molecular markers, the optimization of breeding strategies, and the enhancement of quality in fresh maize. Full article

Rice seed storage proteins (SSPs) play a critical role in determining the nutritional quality, cooking properties, and digestibility of rice. To enhance seed quality, CRISPR/Cas9 genome editing was applied to modify SSP composition by targeting genes encoding 13 kDa prolamins and type A glutelins. Three CRISPR/Cas9 constructs were designed: one specific to the 13 kDa prolamin subfamily and two targeting conserved GluA glutelin regions. Edited T₀ and T₁ lines were generated and analyzed using InDel analysis, SDS-PAGE, Bradford assay, and RP-HPLC. Insertions were more frequent than deletions, accounting for 56% and 74% of mutations in prolamin and glutelin genes, respectively. Editing efficiency varied between sgRNAs. All lines with altered protein profiles contained InDels in target genes. SDS-PAGE confirmed the absence or reduction in bands corresponding to 13 kDa prolamins or GluA subunits, showing consistent profiles among lines carrying the same construct. Quantification revealed significant shifts in SSP composition, including increased albumin and globulin content. Prolamin-deficient lines showed reduced prolamins, while GluA-deficient lines exhibited increased prolamins. Total protein content was significantly elevated in all edited lines, suggesting enrichment in lysine-rich fractions. These findings demonstrate that CRISPR/Cas9-mediated editing of SSP genes can effectively reconfigure the rice protein profile and enhance its nutritional value. Full article