Frontiers in Genetics最新文献

Investigating the involvement of long non-coding RNAs (lncRNAs) and epigenetic processes in bovine adipocytes can provide valuable new insights into controlling adipogenesis in livestock. Long non-coding RNAs have been associated with forming chromatin loops that facilitate enhancer-promoter interactions during adipogenesis, as well as regulating important adipogenic transcription factors like C/EBPα and PPARγ. They significantly influence gene expression regulation at the post-transcriptional level and are extensively researched for their diverse roles in cellular functions. Epigenetic modifications such as chromatin reorganization, histone alterations, and DNA methylation subsequently affect the activation of genes related to adipogenesis and the progression of adipocyte differentiation. By investigating how fat deposition is epigenetically regulated in beef cattle, scientists aim to unravel molecular mechanisms, identify key regulatory genes and pathways, and develop targeted strategies for modifying fat deposition to enhance desirable traits such as marbling and meat tenderness. This review paper delves into lncRNAs and epigenetic factors and their role in regulating bovine adipocytes while focusing on their potential as targets for genetic improvement to increase production efficiency. Recent genomics advancements, including molecular markers and genetic variations, can boost animal productivity, meeting global demands for high-quality meat products. This review establishes a foundation for future research on understanding regulatory networks linked to lncRNAs and epigenetic changes, contributing to both scholarly knowledge advancement and practical applications within animal agriculture.

Common variable immunodeficiency (CVID) is a group of genetic disorders involving more than a dozen genetic loci and characterized by a deficiency in specific antibody isotypes leading to poor immune responses and recurrent infection. CVID affects approximately 1 in 10,000 to 1 in 50,000 people worldwide with substantial heterogeneity in disease severity, including asymptomatic individuals designated as hypogammaglobulinemia of undetermined significance (HGUS). As expected of humoral immunodeficiency, the molecular causes of CVID primarily affect the maturation, activation, or survival of B cells and plasma cells. In this retrospective analysis, we defined a cohort of 21 patients with a primary CVID or HGUS diagnosis in the v7 release of the All of Us Research Program database and performed gene annotation and variant effect prediction. Our analysis identified both known disease-causing variants and rare genetic variants overlapping with other immunodeficiency syndromes.

Optimizing immunologic compatibility in organ transplantation extends beyond the conventional approach of Human Leukocyte Antigen (HLA) antigen matching, which exhibits significant limitations. A broader comprehension of the roles of classical and non-classical HLA genes in transplantation is imperative for enhancing long-term graft survival. High-resolution molecular HLA genotyping, despite its inherent challenges, has emerged as the cornerstone for precise patient-donor compatibility assessment. Leveraging understanding of eplet biology and indirect immune activation, eplet mismatch calculators and the PIRCHE-II algorithm surpass traditional methods in predicting allograft rejection. Understanding minor histocompatibility antigens may also present an opportunity to personalize the compatibility process. While the application of molecular matching in deceased donor organ allocation presents multiple technical, logistical, and conceptual barriers, rendering it premature for mainstream use, several other areas of donor-recipient matching and post-transplant management are ready to incorporate molecular matching. Provision of molecular mismatch scores to physicians during potential organ offer evaluations could potentially amplify long-term outcomes. The implementation of molecular matching in living organ donation and kidney paired exchange programs is similarly viable. This article will explore the current understanding of immunologic matching in transplantation and the potential applications of epitope and non-epitope molecular biology and genetics in clinical transplantation.

The innovation in ultrasound has greatly promoted the prenatal diagnosis of ventricular septal defect. As a minor lesion of congenital heart disease, the prenatal genetic counseling of isolated ventricular septal defect faces some challenges, including the true genetic correlationship, selection of appropriated testing methods to identify deleterious mutations, and avoidance of overdiagnosis and overintervention. Researchers have explored the prenatal diagnosis efficiency of commonly used cytogenetic and molecular genetic technologies. Small insertions/deletions and monogenic variants with phenotypic heterogeneity play important role and contribute to the comprehend of pathogenesis. Isolated ventricular septal defect fetuses without genetic finding and extracardiac structural abnormality generally have good pregnancy outcome. Long-term follow-up data is needed to describe the comprehensive map, such as the potential missed diagnosis especially late-onset syndromes, the impact on the quality of life and life expectancy. When conducting prenatal genetic counseling, strict adherence to ethical principles is needed to ensure that the rights of all parties involved are fully protected. Clinicians should carefully evaluate the risks and benefits and provide parents with sufficient information and advice to enable them to make informed decisions.

Background: Short stature is a complex disorder with phenotypic and genetic heterogeneity. This study aimed to investigate clinical phenotypes and molecular basis of a cohort of patients with short stature.

Methods: Trio whole-exome sequencing (Trio-WES) was performed to explore the genetic aetiology and obtain a molecular diagnosis in twenty Chinese probands with syndromic and isolated short stature.

Results: Of the twenty probands, six (6/20, 30%) patients with syndromic short stature obtained a molecular diagnosis. One novel COMP pathogenic variant c.1359delC, p.N453fs*62 and one LZTR1 likely pathogenic variant c.509G>A, p.R170Q were identified in a patient with short stature and skeletal dysplasia. One novel de novo NAA15 pathogenic variant c.63T>G, p.Y21X and one novel de novo KMT2A pathogenic variant c.3516T>A, p.N1172K was identified in two probands with short stature, intellectual disability and abnormal behaviours, respectively. One patient with short stature, cataract, and muscle weakness had a de novo POLG pathogenic variant c.2863 T>C, p.Y955H. One PHEX pathogenic variant c.1104G>A, p.W368X was identified in a patient with short stature and rickets. Maternal uniparental disomy 7 (mUPD7) was pathogenic in a patient with pre and postnatal growth retardation, wide forehead, triangular face, micrognathia and clinodactyly. Thirteen patients with isolated short stature had negative results.

Conclusion: Trio-WES is an important strategy for identifying genetic variants and UPD in patients with syndromic short stature, in which dual genetic variants are existent in some individuals. It is important to differentiate between syndromic and isolated short stature. Genetic testing has a high yield for syndromic patients but low for isolated patients.

Introduction: Predicting TCR-peptide binding is a complex and significant computational problem in systems immunology. During the past decade, a series of computational methods have been developed for better predicting TCR-peptide binding from amino acid sequences. However, the performance of sequence-based methods appears to have hit a bottleneck. Considering the 3D structures of TCR-peptide complexes, which provide much more information, could potentially lead to better prediction outcomes.

Methods: In this study, we developed TCRcost, a deep learning method, to predict TCR-peptide binding by incorporating 3D structures. TCRcost overcomes two significant challenges: acquiring a sufficient number of high-quality TCR-peptide structures and effectively extracting information from these structures for binding prediction. TCRcost corrects TCR 3D structures generated by protein structure tools, significantly extending the available datasets. The main and side chains of a TCR structure are separately corrected using a long short-term memory (LSTM) model. This approach prevents interference between the chains and accurately extracts interactions among both adjacent and global atoms. A 3D convolutional neural network (CNN) is designed to extract the atomic features relevant to TCR-peptide binding. The spatial features extracted by the 3DCNN are then processed through a fully connected layer to estimate the probability of TCR-peptide binding.

Results: Test results demonstrated that predicting TCR-peptide binding from 3D TCR structures is both efficient and highly accurate with an average accuracy of 0.974 on precise structures. Furthermore, the average accuracy on corrected structures was 0.762, significantly higher than the average accuracy of 0.375 on uncorrected original structures. Additionally, the average root mean square distance (RMSD) to precise structures was significantly reduced from 12.753 Å for predicted structures to 8.785 Å for corrected structures.

Discussion: Thus, utilizing structural information of TCR-peptide complexes is a promising approach to improve the accuracy of binding predictions.

Dilated cardiomyopathy (DCM) is a heart condition that causes enlarged and weakened left ventricles and affects the heart's ability to pump blood effectively. Most genetic etiology still needs to be understood. Previously, we have used the known germline hereditary fusion genes (HFGs) to identify HFGs associated with multiple myeloma and leukemia. In this study, we have developed a statistical model to study fusion transcripts discovered from the left ventricles of 122 DCM patients and 252 GTEx (Genotype Tissue Expression) healthy controls to discover novel HFGs, ranging from 4% to 87.7%, and EFGs, ranging from 4% to 99.2%, associated with DCM. This discovery of numerous novel HFGs and EFGs associated with DCM provides first-hand evidence that DCM results from interactive developmental consequences between germline genetic and environmental abnormalities and paves the way for future research and diagnostic and therapeutic applications, instilling hope for the future of DCM treatment.

Introduction: The identification of significant DNA markers of primary osteoporosis may gain new insights by studying genome regions involved in mechanisms of epigenetic regulation through interactions with microRNAs.

Methods: The authors searched for associations of polymorphic variants of microRNA binding sites of mRNA target genes and polymorphic loci of microRNA genes with primary osteoporosis in a cohort of women and men from the Volga-Ural region of Russia (N = 1.177).

Results: Using case-control association analysis, the authors found that rs1061947 (COL1A1), rs10793442 (ZNF239), rs6854081 (FGF2), and rs11614913 (miR-196a) were associated with osteoporotic fractures; rs5854 (MMP1) and rs2910164 (miR-146a) were associated with low bone mineral density; and rs10098470 (TPD52), rs11540149 (VDR), rs1042673 (SOX9), rs1054204 (SPARC), and rs1712 (FBXO5) were markers of both fractures and low bone mineral density. Among the identified associations, ethno specific trends were found, as well as sex-specific associations. Prognostic models were developed, among which the model for predicting osteoporosis in general in women (Area Under Curve = 0.909) achieved the highest level of predictive value. Thus, the potential role of polymorphic variants of microRNA binding sites in the development of primary osteoporosis in men and women from the Volga-Ural region of Russia was demonstrated.

Introduction: Nitraria tangutorum Bobr., a prominent xerophytic shrub, exhibits remarkable adaptability to harsh environment and plays a significant part in preventing desertification in northwest China owing to its exceptional drought and salinity tolerance.

Methods: To investigate the drought-resistant mechanism underlying N. tangutorum, we treated 8-week-old seedlings with polyethylene glycol (PEG)-6000 (20%, m/m) to induce drought stress. 27 samples from different tissues (leaves, roots and stems) of N. tangutorum at 0, 6 and 24 h after drought stress treatment were sequenced using PacBio single-molecule real-time (SMRT) sequencing and Illumina RNA sequencing to obtain a comprehensive transcriptome.

Results: The PacBio SMRT sequencing generated 44,829 non-redundant transcripts and provided valuable reference gene information. In leaves, roots and stems, we identified 1162, 2024 and 232 differentially expressed genes (DEGs), respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that plant hormone signaling and mitogen-activated protein kinase (MAPK) cascade played a pivotal role in transmitting stress signals throughout the whole N. tangutorum plant following drought stress. The interconversion of starch and sucrose, as well as the biosynthesis of amino acid and lignin, may represent adaptive strategies employed by N. tangutorum to effectively cope with drought. Transcription factor analysis showed that AP2/ERF-ERF, WRKY, bHLH, NAC and MYB families were mainly involved in the regulation of drought response genes. Furthermore, eight physiological indexes, including content of proline, hydrogen peroxide (H2O2), malondialdehyde (MDA), total amino acid and soluble sugar, and activities of three antioxidant enzymes were all investigate after PEG treatment, elucidating the drought tolerance mechanism from physiological perspective. The weighted gene co-expression network analysis (WGCNA) identified several hub genes serve as key regulator in response to drought through hormone participation, ROS cleavage, glycolysis, TF regulation in N. tangutorum.

Discussion: These findings enlarge genomic resources and facilitate research in the discovery of novel genes research in N. tangutorum, thereby establishing a foundation for investigating the drought resistance mechanism of xerophyte.

While it is important to find the key biomarkers and improve the accuracy of disease models, it is equally important to understand their interaction relationships. In this study, a transparent sparse graph pathway network (TSGPN) is proposed based on the structure of graph neural networks. This network simulates the action of genes in vivo, adds to prior knowledge, and improves the model's accuracy. First, the graph connection was constructed according to protein-protein interaction networks and competing endogenous RNA (ceRNA) networks, from which some noise or unimportant connections were spontaneously removed based on the graph attention mechanism and hard concrete estimation. This realized the reconstruction of the ceRNA network representing the influence of other genes in the disease on mRNA. Next, the gene-based interpretation was transformed into a pathway-based interpretation based on the pathway database, and the hidden layer was added to realize the high-dimensional analysis of the pathway. Finally, the experimental results showed that the proposed TSGPN method is superior to other comparison methods in F1 score and AUC, and more importantly, it can effectively display the role of genes. Through data analysis applied to lung cancer prognosis, ten pathways related to LUSC prognosis were found, as well as the key biomarkers closely related to these pathways, such as HOXA10, hsa-mir-182, and LINC02544. The relationship between them was also reconstructed to better explain the internal mechanism of the disease.