Search Results (73)

Gut microbiota, through both its species composition and its metabolites, impacts expression and activity of intestinal drug transporters. This phenomenon directly affects absorption process of orally administered drugs and contributes to the observed inter-individual variability in pharmacotherapeutic responses. This review summarizes mechanistic evidence from in vitro and animal studies and integrates clinical observations in which alterations in gut microbiota are associated with changes in oral drug exposure, consistent with potential regulation of key intestinal drug transporters—such as P-glycoprotein (P-gp, ABCB1), Breast Cancer Resistance Protein (BCRP, ABCG2), MRP2/3 proteins (ABCC2/3), and selected Organic Anion-Transporting Polypeptides (OATPs, e.g., SLCO1A2, SLCO2B1)—by major bacterial metabolites including short-chain fatty acids (SCFAs), secondary bile acids, and tryptophan-derived indoles. The molecular mechanisms involved include activation of nuclear and membrane receptors (PXR, FXR, AhR, TGR5), modulation of transcriptional and stress-response pathways (Nrf2, AP-1) with simultaneous suppression of pro-inflammatory pathways (NF-κB), and post-translational modifications (e.g., direct inhibition of P-gp ATPase activity by Eggerthella lenta metabolites). The review also highlights the pharmacokinetic implications of, e.g., tacrolimus, digoxin, and metformin. In conclusion, the significance of “drug–transporter–microbiome” interactions for personalized medicine is discussed. Potential therapeutic interventions are also covered (diet, pre-/probiotics, fecal microbiota transplantation, modulation of PXR/FXR/AhR pathways). Considering the microbiota as a “second genome” enables more accurate prediction of drug exposure, reduction in toxicity, and optimization of dosing for orally administered preparations. Full article

Alfalfa (Medicago sativa L.) is a high-nutritive-value forage crop that provides livestock with abundant protein and essential nutrients. Breeding elite cultivars with superior quality has become a major goal in modern alfalfa improvement. This study systematically evaluated 12 quality-related traits under field conditions using a diverse panel of 176 alfalfa accessions and investigated the genetic basis underlying these traits. Phenotypic analysis revealed variability across all traits, with coefficients of variation ranging from 2.56% to 15.72%. Based on multi-trait clustering analysis, 16 accessions with overall superior quality were identified. Genome-wide association studies (GWAS) detected 45 significant single nucleotide polymorphisms (SNPs) and 12 structural variants (SVs). Within the associated genomic regions, eight candidate genes were prioritized. RT-qPCR validation indicated that three of these genes (Msa.H.0301430, Msa.H.0290550, and Msa.H.0313490) negatively regulate quality traits, while one gene (Msa.H.0479570) acts as a positive regulator. Haplotype analysis further revealed a positive correlation between the number of favorable haplotypes and phenotypic performance. Genomic prediction (GP) achieved accuracies ranging from 0.71 to 0.86 for the traits when incorporating the top 5000 SNPs identified from GWAS. This study provides valuable insights into the genetic architecture of quality-related traits in alfalfa and lays a solid foundation for future molecular design breeding. Full article

Anthracnose, predominantly caused by Colletotrichum fructicola, severely reduces yield in Fragaria × ananassa production. We assessed ensemble machine learning (ML) frameworks to improve genomic prediction (GP) of resistance using a training population of 300 individuals from six full-sib families. Genotyping with the Axiom^® 50K FanaSNP array and phenotyping by AUDPC after artificial inoculation enabled evaluation of five algorithms—G-BLUP, LASSO, LightGBM, Random Forest, and XGBoost—combined with informed feature selection and resampling-based data augmentation (3×, 5×). Ensemble ML models consistently outperformed linear approaches, with Random Forest, LightGBM, and XGBoost achieving the highest accuracies. Marker prioritization revealed that a reduced SNP panel of ~200 markers provided near-maximal predictive performance (R² up to 0.991), demonstrating that compact subsets can support cost-efficient GP. Data augmentation, implemented through the resampling of existing observations rather than the creation of new alleles, improved statistical power and model stability under limited sample sizes. Collectively, this study demonstrates that (i) ensemble ML models deliver superior accuracy for predicting polygenic resistance, (ii) small SNP panels can achieve high efficiency, and (iii) augmentation enhances robustness in resource-constrained breeding populations. These findings establish a scalable and breeder-oriented GP pipeline to accelerate the development of anthracnose-resistant strawberry cultivars. Full article

In recent years, phenotyping approaches in plant breeding have expanded in both methodology and data collection capacity. One such tool, Near-Infrared Spectroscopy (NIRS) generates a wealth of reflectance values for biological samples. To test the potential of NIRS-based predictions, a hundred grain sorghum hybrids generated from a 10 × 10 factorial mating design were evaluated across eight environments. Hybrids were phenotyped for grain yield, days to anthesis, plant height, kernel hardness index, kernel diameter, and kernel weight. Hybrid grain samples were scanned with NIRS to generate phenomic data while parental lines were genotyped using genotyping by sequencing. Three different predictive models: genomic prediction (GP), phenomic prediction (PP), and GP + PP were fitted. Three different cross-validation schemes of untested hybrids in characterized environments (CV1), tested hybrids in uncharacterized environments (CV2), and untested hybrids in uncharacterized environments (CV3) were completed. GP + PP significantly improved over GP for days to anthesis, kernel hardness index, kernel diameter, and kernel weight for CV1. Prediction accuracy of GP + PP was also significantly improved for the kernel hardness index and kernel weight for CV2 and CV3. Depending on logistics, phenomic prediction has the potential to complement or supplement genomic data for predictive strategies in sorghum. Full article

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain—including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery. Full article

Background/Objectives: Next-generation-sequencing-based genomic profiling (GP) of advanced breast cancer (BC) has been increasingly integrated into clinical practice. The growing number of biomarker-based therapies in BC increasingly complicates treatment decisions. As a result, molecular tumor boards (MTBs) have become pivotal. However, real-world data on the utility of MTBs in advanced BC remain limited. This study evaluates the translation of molecular findings in BC patients into MTB recommendations and examines their implementation and outcomes in real-world clinical practice. Methods: This retrospective, single-center study included 103 BC patients who received GP between January 2018 and December 2023. Patients were discussed at the weekly multidisciplinary MTB of our institution. Data retrieved included patient characteristics, GP results, and MTB recommendations, which were consecutively matched with treatment outcomes, namely the proportion of patients receiving an MTB treatment recommendation, proportion of patients receiving molecularly matched targeted therapy (MTT), and best treatment response. Results: The MTB reviewed 94 patients and provided 155 recommendations to 68 patients (72.3%), including systemic anti-cancer treatment (n = 123), clinical study participation (n = 4), genetic counseling (n = 12), and additional molecular testing (n = 16) recommendations. Treatment recommendations were provided to 63 patients (67%), of whom 38 (60.3%) received MTT. Of the 35 patients eligible for response assessment, 16 (45.7%) demonstrated clinical benefit: three achieved a complete response, six a partial response, and ten a stable disease > 6 months. Conclusions: GP and MTBs expand biomarker-matched treatment options to BC patients beyond the standard of care. Around half of the patients who receive MTT experience a clinical benefit. The standardization of procedures, the development of multi-biomarker-based prediction, and the enhancement in MTT delivery to patients are key challenges, which should be addressed in future initiatives. Full article

Background/Objectives: Improving sparse testing is essential for enhancing the efficiency of genomic prediction (GP). Accordingly, new strategies are being explored to refine genomic selection (GS) methods under sparse testing conditions. Methods: In this study, a sparse testing approach was evaluated, specifically in the context of predicting performance for tested lines in untested environments. Sparse testing is particularly practical in large-scale breeding programs because it reduces the cost and logistical burden of evaluating every genotype in every environment, while still enabling accurate prediction through strategic data use. To achieve this, we used training data from CIMMYT (Obregon, Mexico), along with partial data from India, to predict line performance in India using observations from Mexico. Results: Our results show that incorporating data from Obregon into the training set improved prediction accuracy, with greater effectiveness when the data were temporally closer. Across environments, Pearson’s correlation improved by at least 219% (in a testing proportion of 50%), while gains in the percentage of matching in top 10% and 20% of top lines were 18.42% and 20.79%, respectively (also in a testing proportion of 50%). Conclusions: These findings emphasize that enriching training data with relevant, temporally proximate information is key to enhancing genomic prediction performance; conversely, incorporating unrelated data can reduce prediction accuracy. Full article

Soybean frogeye leaf spot (FLS) disease has been reported globally and is caused by the fungus Cercospora sojina, which affects the growth, seed yield, and quality of soybean. Among the 15 physiological microspecies of C. sojina soybean in China, Race 7 is one of the main pathogenic microspecies. A few genes are involved in resistance to FLS, and they cannot meet the need to design molecular breeding methods for disease resistance. In this study, a soybean recombinant inbred line (RIL3613) population and a germplasm resource (GP) population were planted at two sites, Acheng (AC) and Xiangyang (XY). Phenotypic data on the percentage of leaf area diseased (PLAD) in soybean leaves were obtained via image recognition technology after the inoculation of seven physiological species and full onset at the R3 stage. Quantitative trait loci (QTLs) and quantitative trait nucleotides (QTNs) were mapped via linkage analysis and genome-wide association studies (GWASs), respectively. The resistance genes of FLS were subsequently predicted in the linkage disequilibrium region of the collocated QTN. We identified 114 QTLs and 18 QTNs in the RIL3613 and GP populations, respectively. A total of 14 QTN loci were colocalized in the two populations, six of which presented high phenotypic contributions. Through haplotype–phenotype association analysis and expression quantification, three genes (Glyma.06G300100, Glyma.06G300600, and Glyma.13G172300) located near molecular markers AX-90524088 and AX-90437152 (QTNs) are associated with FLS Chinese Race 7, identifying them as potential candidate resistance genes. These results provide a theoretical basis for the genetic mining of soybean antigray spot No. 7 physiological species. These findings also provide a theoretical basis for understanding the genetic mechanism underlying FLS resistance in soybeans. Full article

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

Soybean (Glycine max) seeds are rich in amino acids, offering key nutritional and physiological benefits. In this study, 290 soybean accessions from the USDA Germplasm Collection based in Urbana, IL Information Network (GRIN) were analyzed. Four Genome-Wide Association Study (GWAS) models—Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), Mixed Linear Model (MLM), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Multi-Locus Mixed Model (MLMM)—identified two significant Single Nucleotide Polymorphisms (SNPs) associated with arginine content: Gm06_19014194_ss715593808 (LOD = 9.91, 3.91% variation) at 19,014,194 bp on chromosome 6 and Gm11_2054710_ss715609614 (LOD = 9.05, 19% variation) at 2,054,710 bp on chromosome 11. Two candidate genes, Glyma.06g203200 and Glyma.11G028600, were found in the two SNP marker regions, respectively. Genomic Prediction (GP) was performed for arginine content using several models: Bayes A (BA), Bayes B (BB), Bayesian LASSO (BL), Bayesian Ridge Regression (BRR), Ridge Regression Best Linear Unbiased Prediction (rrBLUP), Random Forest (RF), and Support Vector Machine (SVM). A high GP accuracy was observed in both across- and cross-populations, supporting Genomic Selection (GS) for breeding high-arginine soybean cultivars. This study holds significant commercial potential by providing valuable genetic resources and molecular tools for improving the nutritional quality and market value of soybean cultivars. Through the identification of SNP markers associated with high arginine content and the demonstration of high prediction accuracy using genomic selection, this research supports the development of soybean accessions with enhanced protein profiles. These advancements can better meet the demands of health-conscious consumers and serve high-value food and feed markets. Full article

The yak (Bos grunniens), the only large domesticated species endemic to the Qinghai–Tibet Plateau, is a vital resource for local livelihoods and regional economic sustainability. However, yak breeding faces significant challenges, including limited understanding of the genetic architecture underlying quantitative traits, inadequate advanced breeding strategies, and the sterility of hybrid offspring from yak–cattle crosses. These constraints have hindered genetic progress in key production traits. To address these issues, integrating modern genomic tools into yak breeding programs is imperative. This review explores the application and potential of molecular marker-assisted selection (MAS) and genomic prediction (GP) in yak genetic improvement. We systematically evaluate critical components of genomic breeding pipelines, including: (1) phenotypic trait assessment, (2) sample collection strategies, (3) reference population design, (4) high-throughput genotyping (via genome sequencing and SNP arrays), (5) predictive model development, and (6) heritability estimation. By synthesizing current advances and methodologies, this work aims to provide a framework for leveraging genomic technologies to enhance breeding efficiency, preserve genetic diversity, and accelerate genetic gains in yak populations. Full article

Machine learning has attracted much attention in the field of genomic prediction due to its powerful predictive capabilities, yet the lack of an explanatory nature in modeling decisions remains a major challenge. In this study, we propose a novel machine learning method, ExAutoGP, which aims to improve the accuracy of genomic prediction and enhance the transparency of the model by combining automated machine learning (AutoML) with SHapley Additive exPlanations (SHAP). To evaluate ExAutoGP’s effectiveness, we designed a comparative experiment consisting of a simulated dataset and two real animal datasets. For each dataset, we applied ExAutoGP and five baseline models—Genomic Best Linear Unbiased Prediction (GBLUP), BayesB, Support Vector Regression (SVR), Kernel Ridge Regression (KRR), and Random Forest (RF). All models were trained and evaluated using five repeated five-fold cross-validation, and their performance was assessed based on both predictive accuracy and computational efficiency. The results show that ExAutoGP exhibits robust and excellent prediction performance on all datasets. In addition, the SHAP method not only effectively reveals the decision-making process of ExAutoGP and enhances its interpretability, but also identifies genetic markers closely related to the traits. This study demonstrates the strong potential of AutoML in genomic prediction, while the introduction of SHAP provides actionable biological insights. The synergy of high prediction accuracy and interpretability offers new perspectives for optimizing genomic selection strategies in livestock and poultry breeding. Full article

The compound CNI1, identified as a novel antitumor agent based on the cytisine N-isoflavones scaffold, and its series of cytisine N-isoflavones derivatives (CNI2, CNI3, and CNI4), were first isolated from bitter bean seeds, a traditional Chinese medicinal source, by our research team. Cellular activity assays combined with virtual screening targeting P-gp revealed that CNI1, along with the three cytisine N-isoflavones derivatives, CNI2, CNI3, and CNI4, exhibited significant multidrug resistance (MDR) reversal activity in breast cancer. Despite this promising outcome, the precise molecular mechanisms and key targets involved in the MDR reversal of these compounds remain to be elucidated. To explore potential mechanisms, targets for CNI1, CNII2, CNI3, and CNI4 (CNI1-4) were predicted using SwissTargetPrediction and Pharmmapper databases, while MDR-related targets in breast cancer were retrieved from OMIM and GeneCards. The overlapping targets were utilized to construct a protein–protein interaction (PPI) network to identify core targets. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the DAVID database to identify relevant signaling pathways. Molecular docking simulations were employed to evaluate the binding sites and energies of CNI1-4 with the identified key targets, with the highest binding energy complexes selected for subsequent molecular dynamics simulations. This study identified 81 intersecting multidrug resistance (MDR) targets and 19 core targets in breast cancer. GO and KEGG pathway enrichment analyses revealed that MDR was primarily mediated by genes involved in cellular processes, apoptosis, protein phosphorylation, as well as the MAPK and PI3K-Akt signaling pathways. Molecular docking studies demonstrated that the binding energies of P-gp, AKT1, and SRC to CNI1-4 were all lower than −10 kcal/mol, indicating strong binding affinities. Molecular dynamics simulations further confirmed the stable and favorable binding interactions of CNI1-4 with AKT1 and P-gp. This study provides preliminary insights into the potential targets and molecular mechanisms of cytisine N-isoflavones compounds in reversing MDR in breast cancer, offering crucial data for the pharmacological investigation of CNI1-4 and supporting the development of P-gp inhibitors. Full article

Genomic prediction (GP), which uses genome-wide markers to estimate breeding values, is a crucial tool for accelerating genetic progress in livestock and plant breeding. The accuracy of GP depends on several factors, including the statistical model, marker density, and cross-validation strategy. This study evaluated these factors to optimize GP accuracy for eight economically important carcass and body traits in a Duroc × (Landrace × Yorkshire) (DLY) pig population. This study used 50 K SNP chip data from 1494 DLY pigs, which were imputed to the whole genome sequence (WGS) level. Seven different models were compared, including GBLUP, ssGBLUP, and five Bayesian models. The ssGBLUP model consistently outperformed other models across all traits, with prediction accuracies ranging from 0.371 to 0.502. Further analyses showed that prediction accuracy improved with increasing cross-validation folds and marker density, particularly in the low-density panel. However, the improvement plateaued in medium-to-high-density scenarios. These findings underscore the importance of carefully selecting the model, marker density, and cross-validation strategy to optimize GP accuracy for carcass and body traits in commercial pigs. The insights from this study can guide breeders and researchers in maximizing genetic progress in pig breeding programs. Full article

Soybean mosaic virus (SMV), a pathogen responsible for inducing leaf mosaic or necrosis symptoms, significantly compromises soybean seed yield and quality. According to the classification system in the United States, SMV is categorized into seven distinct strains (G1 to G7). In this study, we performed a genome-wide association study (GWAS) in GAPIT3 using four analytical models (MLM, MLMM, FarmCPU, and BLINK) on 218 soybean accessions. We identified 22 SNPs significantly associated with G1 resistance across chromosomes 1, 2, 3, 12, 13, 17, and 18. Notably, a major quantitative trait locus (QTL) spanning 873 kb (29.85–30.73 Mb) on chromosome 13 exhibited strong association with SMV G1 resistance, including the four key SNP markers: Gm13_29459954_ss715614803, Gm13_29751552_ss715614847, Gm13_30293949_ss715614951, and Gm13_30724301_ss715615024. Within this QTL, four candidate genes were identified: Glyma.13G194100, Glyma.13G184800, Glyma.13G184900, and Glyma.13G190800 (3Gg2). The genomic prediction (GP) accuracies ranged from 0.60 to 0.83 across three GWAS-derived SNP sets using five models, demonstrating the feasibility of GP for SMV-G1 resistance. These findings could provide a useful reference in soybean breeding targeting SMV-G1 resistance. Full article