Search Results (1,762)

Background: The regulation of oil and protein contents in cottonseed is governed by a complex genetic network. Gaining insight into the mechanisms controlling these traits is necessary for dissecting the formation patterns of cottonseed quality. Method: In this study, Xinluzhong 37 (P₁) and Xinluzhong 51 (P₂) were selected as parental lines for two reciprocal crosses: P₁ × P₂ (F₁) and its reciprocal P₂ × P₁ (F₁′). Each F1 was selfed and backcrossed to both parents to generate the F₂ (F₂′), B₁ (B₁′), and B₂ (B₂′) generations. To assess nutritional traits in hairy (non-delinted) and lint-free (delinted) seeds, two indicators, oil content and protein content, were measured in both seed types. Joint segregation analysis was employed to analyze the inheritance of these traits, based on a major gene plus polygene model. Results: In the orthogonal crosses, the CVs for the four nutritional traits ranged at 2.710–7.879%, 4.086–11.070%, 2.724–6.727%, and 3.717–9.602%. In the reciprocal crosses, CVs ranged at 2.710–8.053%, 4.086–9.572%, 2.724–6.376%, and 3.717%–8.845%. All traits exhibited normal or skewed-normal distributions. For oil content in undelinted/delinted seeds, polygenic heritabilities in the orthogonal cross were 0.64/0.52, and 0.40/0.36 in the reciprocal cross. For protein content, major-gene heritabilities in the orthogonal cross were 0.79 (undelinted) and 0.78 (delinted), while those in the reciprocal cross were both 0.62. Conclusions: Oil and protein contents in cottonseeds are quantitative traits. In both orthogonal and reciprocal crosses, oil content is controlled by multiple genes and is shaped by additive, dominance, and epistatic effects. Protein content, in contrast, is largely controlled by two major genes along with minor genes. In the P₁ × P₂ combination, major genes act through additive, dominance, and epistatic effects, while in the P₂ × P₁ combination, their effects are additive only. In both combinations, minor genes contribute through additive and dominance effects. In summary, the oil content in cottonseed is mainly regulated by polygenes, whereas the protein content is primarily determined by major genes. These genetic features in both linted, and lint-free seeds may offer a theoretical foundation for molecular breeding aimed at improving cottonseed oil and protein quality. Full article

Background/Objectives: Gastric cancer (GC) remains a significant global health burden due to its high mortality rate and frequent diagnosis at advanced stages. This study aimed to identify reliable diagnostic biomarkers and elucidate molecular mechanisms underlying GC by integrating transcriptomic data from independent platforms and applying machine learning techniques. Methods: Two transcriptomic datasets from the Gene Expression Omnibus were analyzed: GSE26899 (microarray, n = 108) as the discovery dataset and GSE248612 (RNA-seq, n = 12) for validation. Differential expression analysis was conducted using limma and DESeq2, selecting genes with |log2FC| > 1 and adjusted p < 0.05. The top 200 differentially expressed genes (DEGs) were used to develop machine learning models (random forest, logistic regression, neural networks). Functional enrichment analyses (GO, KEGG, Hallmark) were applied to explore relevant biological pathways. Results: In GSE26899, 627 DEGs were identified (201 upregulated, 426 downregulated), with key genes including CST1, KIAA1199, TIMP1, MSLN, and ATP4A. The random forest model demonstrated excellent classification performance (AUC = 0.952). GSE248612 validation yielded 738 DEGs. Cross-platform comparison confirmed 55.6% concordance among core genes, highlighting CST1, TIMP1, KRT17, ATP4A, CHIA, KRT16, and CRABP2. Enrichment analyses revealed involvement in ECM–receptor interaction, PI3K-Akt signaling, EMT, and cell cycle. Conclusions: This integrative transcriptomic and machine learning framework effectively identified high-confidence biomarkers for GC. Notably, CST1, TIMP1, KRT16, and ATP4A emerged as consistent, biologically relevant candidates with strong diagnostic performance and potential clinical utility. These findings may aid early detection strategies and guide future therapeutic developments in gastric cancer. Full article

Arteriovenous malformations (AVMs) are congenital vascular anomalies defined by abnormal direct connections between arteries and veins due to their complex structure or endovascular approaches. Pharmacological strategies targeting the underlying molecular mechanisms are thus gaining increasing attention in an effort to determine the mechanism involved in AVM regulation. In this study, we examined 30 human tissue samples, comprising 10 vascular samples, 10 human fibroblasts derived from AVM tissue, and 10 vascular samples derived from healthy individuals. The pharmacological agents thalidomide, U0126, and rapamycin were applied to the isolated endothelial cells (ECs). The pharmacological treatments reduced the proliferation of AVM ECs and downregulated miR-135b-5p, a biomarker associated with AVMs. The expression levels of angiogenesis-related genes, including VEGF, ANG2, FSTL1, and MARCKS, decreased; in comparison, CSPG4, a gene related to capillary networks, was upregulated. Following analysis of these findings, skin samples from 10 AVM patients were reprogrammed into induced pluripotent stem cells (iPSCs) to generate AVM blood vessel organoids. Treatment of these AVM blood vessel organoids with thalidomide, U0126, and rapamycin resulted in a reduction in the expression of the EC markers CD31 and α-SMA. The establishment of AVM blood vessel organoids offers a physiologically relevant in vitro model for disease characterization and drug screening. The authors of future studies should aim to refine this model using advanced techniques, such as microfluidic systems, to more efficiently replicate AVMs’ pathology and support the development of personalized therapies. Full article

The yak (Bos grunniens) has exceptional hypoxia resilience, making it an ideal model for studying high-altitude adaptation. Here, we investigated the effects of oxygen concentration on yak cardiac fibroblast proliferation and the underlying molecular regulatory pathways using RNA sequencing (RNA-seq) and metabolic analyses. Decreased oxygen levels significantly inhibited cardiac fibroblast proliferation and activity. Intriguingly, while the mitochondrial DNA (mtDNA) content remained stable, we observed coordinated upregulation of mtDNA-encoded oxidative phosphorylation components. Live-cell metabolic assessment further demonstrated that hypoxia led to mitochondrial respiratory inhibition and enhanced glycolysis. RNA-seq analysis identified key hypoxia adaptation genes, including glycolysis regulators (e.g., HK2, TPI1), and hypoxia-inducible factor 1-alpha (HIF-1α), with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses highlighting their involvement in metabolic regulation. The protein–protein interaction network identified three consensus hub genes across five topological algorithms (CCNA2, PLK1, and TP53) that may be involved in hypoxia adaptation. These findings highlight the importance of metabolic reprogramming underlying yak adaptation to hypoxia, providing valuable molecular insights into the mechanisms underlying high-altitude survival. Full article

Translation initiation site (TIS) prediction in mRNA sequences constitutes an essential component of transcriptome annotation, playing a crucial role in deciphering gene expression and regulation mechanisms. Numerous computational methods have been proposed and achieved acceptable prediction accuracy. In our previous work, we developed NeuroTIS, a novel method for TIS prediction based on a hybrid dependency network combined with a deep learning framework that explicitly models label dependencies both within coding sequences (CDSs) and between CDSs and TISs. However, this method has limitations in fully exploiting the primary structural information within mRNA sequences. First, it only captures label dependency within three neighboring codon labels. Second, it neglects the heterogeneity of negative TISs originating from different reading frames, which exhibit distinct coding features in their vicinity. In this paper, under the framework of NeuroTIS, we propose its enhanced version, NeuroTIS+, which allows for more sophisticated codon label dependency modeling via temporal convolution and homogenous feature building through an adaptive grouping strategy. Tests on transcriptome-wide human and mouse datasets demonstrate that the proposed method yields excellent prediction performance, significantly surpassing the existing state-of-the-art methods. Full article

Cardiovascular disease and hypertension are major global health challenges, and increasing dietary salt intake is a known contributor. Emerging evidence suggests that excessive salt exposure during pregnancy may impact fetal development, yet its effects on early embryogenesis remain poorly understood. In this study, we used zebrafish (Danio rerio) embryos as a model to investigate the developmental and molecular consequences of high-salt exposure during early vertebrate development. Embryos subjected to elevated salt levels exhibited delayed hatching, reduced heart rates, and significant alterations in gene expression profiles. Transcriptomic analysis revealed over 4000 differentially expressed genes, with key disruptions identified in calcium signaling, MAPK signaling, cardiac muscle development, and vascular smooth muscle contraction pathways. These findings indicate that early salt exposure can perturb crucial developmental processes and signaling networks, offering insights into how prenatal environmental factors may contribute to long-term cardiovascular risk. Full article

Soybean (Glycine max) peptide lunasin exhibits significant cancer-preventive, antioxidant, and hypocholesterolemic effects. This study aimed to identify quantitative trait nucleotides (QTNs) associated with lunasin content and to annotate the candidate genes in the soybean genome. The mapping panel of 144 accessions was gathered from the USDA Soybean Germplasm Collection, encompassing diverse geographical origins and genetic backgrounds, and was genotyped using SoySNP50K iSelect Beadchips. The lunasin content in soybean seeds was measured using the enzyme-linked immunosorbent assay (ELISA) method, with lipid-adjusted soybean flour prepared from seeds obtained from the Germplasm Resource Information Network (GRIN) of USDA-ARS in 2003 and from North Carolina in 2021, respectively. QTNs significantly related to lunasin content in soybean seeds were detected on 15 chromosomes, with LOD scores greater than 3.0, explaining various phenotypic variations identified using the R package mrMLM (v4.0). Significant QTNs on chromosomes 3, 13, 16, 18, and 20 were consistently identified across multiple models as being significantly associated with soybean lunasin content, based on assessment data from two years. Twenty-nine candidate genes were found, with 12 identified in seeds from 2003 and 17 from 2021. Our study is an important effort to understand the genetic basis and functional genes for lunasin production in soybean seeds. Full article

To support the national goals of carbon peaking and carbon neutrality, this study proposes a household carbon emission prediction model based on Gene Expression Programming (GEP) for low-carbon retrofitting of aging residential areas in arid-cold regions. Focusing on 15 typical aging communities in Kundulun District, Baotou City, a 17-dimensional dataset encompassing building characteristics, demographic structure, and energy consumption patterns was collected through field surveys. Key influencing factors (e.g., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. Experimental results demonstrated that the model achieved an R² value of 0.81, reducing RMSE by 77.1% compared to conventional GEP models and by 60.4% compared to BP neural networks, while significantly improving stability. By combining data dimensionality reduction with adaptive evolutionary algorithms, this model overcomes the limitations of traditional methods in capturing complex nonlinear relationships. It provides a reliable tool for precision-based low-carbon retrofits in aging residential areas of arid-cold regions and offers a methodological advance for research on building carbon emission prediction driven by urban renewal. Full article

Bacterial structures formed from the outer membrane and the periplasm components carry biomolecules to expel cellular material and interact with other cells. These outer membrane vesicles (OMVs) can encapsulate bioactive content, which confers OMVs with high potential as alternative drug delivery vehicles or as a platform for novel vaccine development. Single-gene mutants derived from Escherichia coli JC8031 were engineered to further enhance OMV production based on metabolic network modelling and in silico gene knockout design (ΔpoxB, ΔsgbE, ΔgmhA, and ΔallD). Mutants were experimentally obtained by genome editing using CRISPR-Cas9 and tested for OMVs recovery observing an enhanced OMV production in all of them. Lipidomic analysis through LC-ESI-QTOF-MS was performed for OMVs obtained from each engineered strain and compared to the wild-type E. coli JC8031 strain. The lipid profile of OMVs from the wild-type E. coli JC8031 did not change significantly confirmed by multivariate statistical analysis when compared to the mutant strains. The obtained results suggest that the vesicle production can be further improved while the obtained vesicles are not altered in their composition, allowing further study for stability and integrity for use in therapeutic settings. Full article

The SCARECROW (SCR) transcription factor governs cell-type patterning in plant roots and Kranz anatomy of leaves, serving as a master regulator of root and shoot morphogenesis. Foxtail millet (Setaria italica), characterized by a compact genome, self-pollination, and a short growth cycle, has emerged as a C₄ model plant. Here, we revealed two SCR paralogs in foxtail millet—SiSCR1 and SiSCR2—which exhibit high sequence conservation with ZmSCR1/1h (Zea mays), OsSCR1/2 (Oryza sativa), and AtSCR (Arabidopsis thaliana), particularly within the C-terminal GRAS domain. Both SiSCR genes exhibited nearly identical secondary structures and physicochemical profiles, with promoter analyses revealing five conserved cis-regulatory elements. Robust phylogenetic reconstruction resolved SCR orthologs into monocot- and dicot-specific clades, with SiSCR genes forming a sister branch to SvSCR from its progenitor species Setaria viridis. Spatiotemporal expression profiling demonstrated ubiquitous SiSCR gene transcription across developmental stages, with notable enrichment in germinated seeds, plants at the one-tip-two-leaf stage, leaf 1 (two days after heading), and roots during the seedling stage. Co-expression network analysis revealed that there is a correlation between SiSCR genes and other functional genes. Abscisic acid (ABA) treatment led to a significant downregulation of the expression level of SiSCR genes in Yugu1 roots, and the expression of the SiSCR genes in the roots of An04 is more sensitive to PEG6000 treatment. Drought treatment significantly upregulated SiSCR2 expression in leaves, demonstrating its pivotal role in plant adaptation to abiotic stress. Analysis of heterologous expression under the control of the 35S promoter revealed that SiSCR genes were expressed in root cortical/endodermal initial cells, endodermal cells, cortical cells, and leaf stomatal complexes. Strikingly, ectopic expression of SiSCR genes in Arabidopsis led to hypersensitivity to ABA, and ABA treatment resulted in a significant reduction in the length of the meristematic zone. These data delineate the functional divergence and evolutionary conservation of SiSCR genes, providing critical insights into their roles in root/shoot development and abiotic stress signaling in foxtail millet. Full article

Chronic inflammation plays a pivotal role in human health, with certain inflammatory conditions significantly increasing the risk of cancer, while others do not. However, the molecular mechanisms underlying this divergent risk remain poorly understood. In this study, we propose FR-BINN, a biologically informed neural network framework for disease prediction and interpretability. Incorporating Fenton reaction (FR)-related biological priors and leveraging multiple interpretability methods, FR-BINN identifies key genes driving cancer-prone and non-cancer-prone chronic inflammatory diseases. The experimental results demonstrate that FR-BINN achieves superior classification performance while offering biologically interpretable insights. Moreover, attribution results derived from different explainable techniques show high consistency, and intra-method results exhibit distinct patterns across disease categories. We further combine large language models with feature attributions to identify candidate biomarkers, and independent datasets confirm the robustness of these findings. Notably, genes such as NCOA1 and SDHB are identified as being associated with cancer susceptibility. The framework further reveals distinct patterns in energy metabolism, oxidative stress, and pH regulation between cancer-prone and non-cancer-prone inflammatory diseases. These insights enhance our understanding of inflammation-associated tumorigenesis and contribute to the identification of potential biomarkers and therapeutic targets. Full article

Haploinsufficiency disorders are genetic diseases caused by reduced gene expression, leading to developmental, metabolic, and tumorigenic abnormalities. The dosage-sensitive Retinoic Acid Induced 1 (RAI1) gene, located within the 17p11.2 region, is central to the core features of Smith––Magenis syndrome (SMS) and Potocki––Lupski syndrome (PTLS), caused by the reciprocal microdeletions and microduplications of this region, respectively. SMS and PTLS present contrasting phenotypes. SMS is characterized by severe neurobehavioral manifestations, sleep disturbances, and metabolic abnormalities, and PTLS shows milder features. Here, we detail the molecular functions of RAI1 in its wild-type and haploinsufficiency conditions (RAI1+/−), as studied in animal and cellular models. RAI1 acts as a transcription factor critical for neurodevelopment and synaptic plasticity, a chromatin remodeler within the Histone 3 Lysine 4 (H3K4) writer complex, and a regulator of faulty 5′-capped pre-mRNA degradation. Alterations of RAI1 functions lead to synaptic scaling and transcriptional dysregulation in neural networks. This review highlights key molecular mechanisms of RAI1, elucidating its role in the interplay between genetics and phenotypic features and summarizes innovative therapeutic approaches for SMS. These data provide a foundation for potential therapeutic strategies targeting RAI1, its mRNA products, or downstream pathways. Full article

Air pollution is a known contributor to cancer risk, although its specific impact on endometrial cancer (EC) remains unclear. This study integrates network toxicology, transcriptomics, molecular docking, and machine learning to investigate pollutant–gene interactions in EC. We identify 83 air pollution-associated EC genes (APECGs), with TNF, ESR1, IL1B, NFKB1, and PTGS2 as the hub genes. A 13-gene RSF-SuperPC model, including CCNE1, SLC2A1, AHCY, and CDC25C, shows effective prognostic stratification. Molecular docking reveals strong binding between pollutants (e.g., benzene, toluene, and ethylbenzene) and key APECGs. The enrichment and SHAP analyses suggest that pollutant-driven EC progression involves DNA damage, metabolic reprogramming, epigenetic dysregulation, immune suppression, and inflammation. These findings reveal potential mechanisms linking air pollution to EC and support the development of biomarkers for high-exposure populations. Further experimental and epidemiological validation is needed to enable clinical translation. Full article

Heat stress (HS) in dairy cattle triggers systemic physiological disruptions, including milk yield decline, immune suppression, and reproductive dysfunction, jeopardizing sustainable livestock production. While conventional single-omics or phenotypic studies have provided fragmented insights, they fail to decipher the multi-layered regulatory networks and gene–environment interactions underlying HS. This review integrates current knowledge on HS-induced physiological responses and molecular adaptations in dairy cattle from a multi-omics perspective, highlighting integrative approaches that combine IoT-enabled monitoring and AI-driven genetic improvement strategies. However, key challenges persist, such as complexities in bioinformatic data integration, CRISPR off-target effects, and ethical controversies. Future directions will emphasize the development and application of AI-aided predictive models to enable precision breeding, thereby advancing climate-resilient genetic improvement in dairy cattle. Full article

The selection of appropriate biomarkers in clinical practice aids in the early detection, treatment, and prevention of disease while also assisting in the development of targeted therapeutics. Recently, multi-omics data generated from advanced technology platforms has become available for disease studies. Therefore, the integration of this data with associated clinical data provides a unique opportunity to gain a deeper understanding of disease. However, the effective integration of large-scale multi-omics data remains a major challenge. To address this, we propose a novel deep learning model—the Multi-Omics Graph Attention biomarker Discovery network (MOGAD). MOGAD aims to efficiently classify diseases and discover biomarkers by integrating various omics data such as DNA methylation, gene expression, and miRNA expression. The model consists of three main modules: Multi-head GAT network (MGAT), Multi-Graph Attention Fusion (MGAF), and Attention Fusion (AF), which work together to dynamically model the complex relationships among different omics layers. We incorporate clinical data (e.g., APOE genotype) which enables a systematic investigation of the influence of non-omics factors on disease classification. The experimental results demonstrate that MOGAD achieves a superior performance compared to existing single-omics and multi-omics integration methods in classification tasks for Alzheimer’s disease (AD). In the comparative experiment on the ROSMAP dataset, our model achieved the highest ACC (0.773), F1-score (0.787), and MCC (0.551). The biomarkers identified by MOGAD show strong associations with the underlying pathogenesis of AD. We also apply a Hi-C dataset to validate the biological rationality of the identified biomarkers. Furthermore, the incorporation of clinical data enhances the model’s robustness and uncovers synergistic interactions between omics and non-omics features. Thus, our deep learning model is able to successfully integrate multi-omics data to efficiently classify disease and discover novel biomarkers. Full article