Frontiers in Genetics最新文献

Introduction: Genomic Prediction (GP) faces significant challenges in balancing model complexity with computational efficiency, particularly for high-dimensional genomic data under limited sample sizes.

Methods: We propose GViT-GP, a Vision Transformer architecture that injects the Genomic Relationship Matrix (GRM) as a biological prior via a dual-pathway cross-attention fusion mechanism, coupled with a Selective Patch Embedding strategy to reduce redundancy and improve data efficiency.

Results: We evaluated GViT-GP on 20 traits across four datasets from three species (soybean, cattle, and chicken). GViT-GP outperformed established linear and non-linear baselines (including GBLUP, LightGBM, and DNNGP), achieving the best accuracy in 16/20 tasks. Ablation studies supported the effectiveness of Selective Patch Embedding and cross-attention fusion, and visualization analyses suggest adaptive attention to informative genomic regions.

Discussion: These results indicate that injecting GRM-informed inductive bias improves robustness and generalization in "p ≫ n" settings. GViT-GP provides a practical, high-performance framework for capturing complex genotype-phenotype relationships in modern digital breeding.

Introduction: Phosphofructokinase (PFK) is a crucial rate-limiting enzyme in glycolysis, essential for sugar metabolism and fruit quality. This study provides the first pangenome-scale analysis of the PFK family across Solanum species.

Methods: Using pan-genome data, 156 PFK genes were identified across 12 Solanum species. Comprehensive bioinformatic analyses, protein-protein interaction predictions, and promoter motif scans were performed. Expression patterns across four fruit developmental stages were characterized via RNA-seq and validated by qRT-PCR.

Results: The PFK family, categorized into PFK and PFP subfamilies, expanded primarily through segmental duplication under strong purifying selection. We identified distinct, stage-specific expression patterns, with SolyPFK07 and SolyPFPA2 emerging as key regulators of sugar accumulation. Promoters contained numerous elements responsive to hormones and abiotic stresses.

Conclusion: PFK genes are vital for fruit development, sugar metabolism, and stress adaptation. These findings offer a theoretical basis and genetic resources for the molecular breeding of high-quality tomatoes.

Plant extracellular vesicles (EVs) serve as critical mediators of intercellular communication during plant-pathogen interactions, particularly through their cargo of regulatory small RNAs, enabling the transport of miRNAs to distant tissues during biotic stress. Potato virus Y (PVY), one of the most economically damaging plant viruses globally, poses significant threats to solanaceous crop production. However, the landscape of EV-associated miRNAs and their regulatory roles in PVY infection remain largely unexplored. In this study, we isolated and characterized EV-associated particles from the apoplastic fluid of both PVY-infected and healthy tomato leaves using differential ultracentrifugation, followed by transmission electron microscopy, nanoparticle size analysis, and western blotting. High-throughput small RNA sequencing revealed 96 significantly differentially expressed miRNAs in EV-associated particles upon viral challenge. Bioinformatic prediction revealed that 80% of these dysregulated miRNAs potentially target multiple genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses demonstrated significant overrepresentation of predicted target genes in pathways associated with transcription, ta-siRNA biogenesis involved in RNA interference, protein binding, RNAi-mediated antiviral immune response, oxidative phosphorylation, mRNA surveillance pathway, and eukaryotic ribosome biogenesis. Our findings demonstrate that PVY infection selectively modulates the miRNA composition within tomato EV-associated particles. These EV-associated particles delivered miRNAs may contribute to a sophisticated antiviral defense mechanism by co-regulating host immunity. This study provides novel insights into the role of EV-associated particles mediated RNA communication in plant immunity and lays a theoretical foundation for developing innovative miRNA- and EV-based antiviral strategies for crop protection.

The main causes of articular cartilage injury (ACI) encompass inflammation, trauma, chronic strain, degeneration, and so forth. ACI is one of the main pathological features of degenerative joint diseases such as osteoarthritis (OA), which significantly affects patients' normal work and life. Due to the absence of nerves, blood vessels, and lymphatic tissue in the cartilage, it is challenging for it to repair itself after injury.​ Non-coding RNAs, as crucial regulators of gene expression, have been increasingly implicated in the pathophysiology of various diseases. Among them, circular RNAs(circRNAs), as a new type of endogenous special non-coding RNAs, have been extensively discovered in eukaryotic cells. Owing to their unique closed-loop structure and potentially stable expression characteristics, circRNAs have demonstrated significant regulatory roles in the occurrence and development of various diseases. circRNAs are differentially expressed in OA chondrocytes and normal chondrocytes, and are involved in the inflammatory response, proliferation, apoptosis, and other processes of chondrocytes and the extracellular matrix (ECM). This article aims to review the recent research progress of circRNAs in ACI and explore their potential role in the pathogenesis of OA.

Background: Sciatica is a debilitating condition characterized by pain radiating along the sciatic nerve, often manifesting due to underlying neuroinflammatory processes. Understanding the molecular mechanisms linking neuroinflammation to sciatica is essential for developing targeted therapeutic interventions. Recent studies have suggested that specific neuroinflammatory genes may play a pivotal role in the pathophysiology of sciatica, offering a potential avenue for understanding this condition.

Methods: This study aimed to elucidate the contributions of neuroinflammatory genes to the development of sciatica. We used publicly available datasets GSE124272 and GSE150408 from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information. By thoroughly analyzing the expression matrices, we identified differentially expressed genes (DEGs) linked to neuroinflammatory pathways. Functional annotation was performed using Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA). To enhance predictive modeling, we employed Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods to assess neuroinflammatory gene expression. Lastly, we employed quantita-tive real-time PCR (qRT-PCR) to validate our results.

Results: The analysis revealed that the identified DEGs are significantly enriched in multiple biological pathways relevant to neuroinflammatory responses in patients with sciatica. Notably, LASSO regression and SVM techniques identified four key neuroinflammatory genes: KLRK1, LRRK2, NLRP3, and PLG. A bar graph was generated to illustrate the predictive weights of these genes concerning sciatica risk, further complemented by immune cell composition analysis via CIBERSORTx, which underscored significant correlations between these genes and the abundance of various immune cell types in affected individuals.

Conclusion: Our findings substantiate the critical roles of KLRK1, LRRK2, NLRP3, and PLG in the neuroinflammation-associated pathogenesis of sciatica, providing pivotal insights into the biological underpinnings of this condition. These neuroinflammatory genes serve as promising targets for advancing therapeutic strategies for sciatica management.

Objective: Eclampsia severely endangers maternal and neonatal health, being a major contributor to emergency admissions, maternal mortality, and long-term complications. This study aimed to identify reliable biomarkers and explore potential therapeutic targets for improving the diagnosis, prevention, and management of eclampsia. Methods: Differential gene expression analysis was performed on the GSE60438 dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct gene modules and screen modules associated with pre-eclampsia. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were employed to annotate the biological functions and pathways of candidate genes. Immune cell infiltration was evaluated via the xCell algorithm. LASSO regression was utilized to identify hub genes, which was validated by RT-qPCR and Western blot in clinical samples (placental tissues and serum from pre-eclampsia patients). DPP4 knockdown experiments were conducted in HTR-8 cells to assess its effects on pro-inflammatory cytokines (IL-6, TNF-α) and trophoblast cell functions (migration, invasion, lumen formation). Additionally, the p65/NLRP3/ASC/Caspase-1 signaling pathway was examined to clarify the underlying molecular mechanism. Results: A total of 4,642 upregulated and 2,193 downregulated genes were identified in pre-eclampsia samples. WGCNA revealed nine gene modules, with the red module showing the strongest positive correlation and the magenta module exhibiting a negative correlation with pre-eclampsia. GO analysis indicated enrichment of candidate genes in chromosome organization, mitochondrial function, and DNA repair. GSEA identified key immune-related pathways, including cytokine production and chemokine signaling. LASSO regression pinpointed DPP4 as a hub gene, which was significantly upregulated in pre-eclampsia clinical samples. DPP4 knockdown in HTR-8 cells reduced IL-6 and TNF-α levels, impaired trophoblast migration, invasion, and lumen formation, and inhibited the phosphorylation of p65, NLRP3, ASC, and Caspase-1 in the p65/NLRP3/ASC/Caspase-1 signaling pathway. Conclusion: Targeting DPP4 may serve as an innovative strategy for regulating inflammatory signaling in eclampsia, with potential to alleviate maternal symptoms and improve pregnancy outcomes.

Introduction: Next-generation sequencing (NGS) has transformed the genetic diagnosis of human diseases, yet many patients remain unsolved due to the complexity of variant interpretation. Manual curation of candidate variants is effective but time-consuming and requires specialized expertise. Artificial intelligence (AI)-driven platforms have emerged as scalable tools for variant prioritization, yet their performance compared with manual curation remains insufficiently evaluated. The aim of this study was to evaluate the performance of AI-driven platforms for variant prioritization in a cohort of patients with inborn errors of immunity (IEI) and to compare their strengths and limitations with manual curation.

Methods: We analyzed 22 unsolved IEI cases that had previously undergone inconclusive NGS studies. Whole-genome sequencing was performed, and variant prioritization was carried out using two AI-driven platforms -AIMARRVEL and AION (Nostos Genomics)- and by manual curation. Selected variants were classified according to clinical relevance (very high, high, medium, or low), integrating both molecular and phenotypic evidence.

Results: Across the cohort, AI platforms efficiently prioritized variants with clear pathogenic features, often reaching the same conclusions as manual curation but in a fraction of the time. One patient (5%) received a conclusive diagnosis (FAM111B), and four patients (18%) carried variants of high clinical relevance, including strong disease-causing candidates in CD247 and SH2B3. Additional medium-relevance variants were identified in 36% of cases, although evidence was insufficient for functional validation. Notably, concordance between AIMARRVEL and AION was limited, particularly for variants of uncertain significance (VUS), reflecting differences in algorithmic weighting of variant features versus clinical phenotype. Both platforms also highlighted potentially novel associations in RUNX1 and TRAF7, underscoring their capacity to extend beyond classical IEI genes.

Discussion: Our results show that AI-driven tools are powerful for detecting clearly pathogenic variants and can markedly accelerate the diagnostic process. However, their strong reliance on curated databases, limited incorporation of phenotypic data, and challenges in handling VUS may reduce their effectiveness. Enhancing phenotype integration, expanding annotations (including non-coding regions), and incorporating up-to-date literature could improve their performance. Ultimately, AI tools should complement expert curation, with future models evolving toward integrative approaches that better capture the complexity of human disorders.

Introduction: Congenital heart disease (CHD) comprises structural abnormalities of the heart and major blood vessels arising during fetal development. Protein disulfide isomerase family member 4 (PDIA4) facilitates protein folding processes. However, its potential involvement in CHD has not been investigated. In this study, we identified PDIA4 as a candidate gene potentially involved in cardiac development.

Methods: Whole-exome sequencing and targeted sequencing were performed to identify PDIA4 as a candidate gene of CHD. To investigate the functional role of PDIA4, PDIA4-knockdown human umbilical vein endothelial cells were generated, followed by cellular and transcriptomic analyses.

Results: A de novo PDIA4 mutation (NM004911: c.1249G>A: p.V417I) was found in a patient with complex CHD. Burden analysis demonstrated a significant enrichment of rare deleterious PDIA4 variants in patients with CHD compared with controls (Person's chi-squared test: OR: 4.08, 95% CI: 2.23-4.76, p = 7.46e-7). Deficiency of PDIA4 in human umbilical vein endothelial cells suppressed functionality and inhibited the protein levels of both total and nuclear β-catenin as well as the downstream activity of the WNT/β-catenin signaling pathway.

Conclusion: Our study suggests that PDIA4 may act as a susceptibility gene for CHD, and its deficiency may contribute to abnormal cardiac development by modulating the WNT/β-catenin signaling pathway.