Frontiers in Genetics最新文献

Introduction: Obesity is a disease characterized by the excessive accumulation of fat. Concurrently, thyroid carcinoma (THCA) stands as the foremost endocrine malignancy. Despite the observed escalation in concurrent prevalence of both conditions, the underlying interconnections remain elusive. This indicates the need to identify potential biomarkers to predict the pathways through which obesity and THCA coexist.

Methods: The study employed a variety of methods, including differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and gene enrichment analysis. It was also supplemented with immunohistochemical data from the Human Protein Atlas (HPA), advanced machine learning techniques, and related experiments such as qPCR, to identify important pathways and key genes shared between obesity and THCA.

Results: Through differential gene expression analysis, WGCNA, and machine learning methods, we identified three biomarkers (IL6R, GZMB, and MSR1) associated with obesity. After validation analysis using THCA-related datasets and biological experiments, we selected Macrophage Scavenger Receptor 1 (MSR1) as a key gene for THCA analysis. The final analysis revealed that MSR1 is closely related to the degree of immune cell infiltration in patients with obesity and THCA, suggesting that this gene may be a potential intervention target for both obesity and THCA.

Discussion: Our research indicates that MSR1 may influence the occurrence and development of obesity and THCA by regulating the infiltration level of immune cells. This lays the foundation for future research on targeted therapies based on their shared mechanisms.

Introduction: Scoliosis is a pathological spine structure deformation, predominantly classified as "idiopathic" due to its unknown etiology. However, it has been suggested that scoliosis may be linked to polygenic backgrounds. It is crucial to identify potential Adolescent Idiopathic Scoliosis (AIS)-related genetic backgrounds before scoliosis onset.

Methods: The present study was designed to intelligently parse, decompose and predict AIS-related variants in ClinVar database. Possible AIS-related variant records downloaded from ClinVar were parsed for various labels, decomposed for Dinucleotide Compositional Representation (DCR) and other traits, screened for high-risk genes with statistical analysis, and then learned intelligently with deep learning to predict high-risk AIS genotypes.

Results: Results demonstrated that the present framework is composed of all technical sections of data parsing, scoliosis genotyping, genome encoding, machine learning (ML)/deep learning (DL) and scoliosis genotype predicting. 58,000 scoliosis-related records were automatically parsed and statistically analyzed for high-risk genes and genotypes, such as FBN1, LAMA2 and SPG11. All variant genes were decomposed for DCR and other traits. Unsupervised ML indicated marked inter-group separation and intra-group clustering of the DCR of FBN1, LAMA2 or SPG11 for the five types of variants (Pathogenic, Pathogeniclikely, Benign, Benignlikely and Uncertain). A FBN1 DCR-based Convolutional Neural Network (CNN) was trained for Pathogenic and Benign/ Benignlikely variants performed accurately on validation data and predicted 179 high-risk scoliosis variants. The trained predictor was interpretable for the similar distribution of variant types and variant locations within 2D structure units in the predicted 3D structure of FBN1.

Discussion: In summary, scoliosis risk is predictable by deep learning based on genomic decomposed features of DCR. DCR-based classifier has predicted more scoliosis risk FBN1 variants in ClinVar database. DCR-based models would be promising for genotype-to-phenotype prediction for more disease types.

Low temperature chilling is one of the major abiotic stresses affecting growth and yield of Triticum aestivum L. With global climate change, the risk of cold damage in wheat production has increased. In recent years, with the extensive research on wheat chilling resistance, especially the development of genetic engineering technology, the research on wheat chilling resistance has made great progress. This paper describes the mechanism of wheat cold damage, including cell membrane injury, cytoplasmic concentration increased as well as the imbalance of the ROS system. Mechanisms of cold resistance in wheat are summarised, including hormone signalling, transcription factor regulation, and the role of protective enzymes of the ROS system in cold resistanc. Functions of cloned wheat cold resistance genes are summarised, which will provide a reference for researchers to further understand and make use of cold resistance related genes in wheat. The current cold resistant breeding of wheat relies on the agronomic traits and observable indicators, molecular methods are lacked. A strategy for wheat cold-resistant breeding based on QTLs and gene technologies is proposed, with a view to breeding more cold-resistant varieties of wheat with the deepening of the research.

Introduction: Hearing loss is a frequent sensory impairment type in humans, with about 50% of prelingual cases being attributed to genetic factors. Autosomal recessive hearing loss (ARHL) exhibits great locus heterogeneity and is responsible for 70%-80% of hereditary nonsyndromic cases.

Methods: A total of 90 unrelated Brazilian individuals were selected for having hearing loss of presumably autosomal recessive inheritance, either born from consanguineous marriages or belonging to families with two or more affected individuals in the sibship and most cases were of normal hearing parents. In all cases, common pathogenic variants in GJB2 (c.35delG), GJB6 [del(GJB6-D13S1830) and del(GJB6-D13S1854)] and MT-RNR1 (m.1555A>G) were discarded and most were previously assessed by complete Sanger sequencing of GJB2. Their genetic material was analyzed through next-generation sequencing, targeting 99 hearing loss-related genes and/or whole exome sequencing.

Results: In 32 of the 90 probands (36,7%) causative variants were identified, with autosomal recessive inheritance confirmed in all, except for two cases due to dominant variants (SIX1 and P2RX2). Thirty-nine different causative variants were found in 24 different known hearing loss-associated genes, among which 10 variants are novel, indicating wide genetic heterogeneity in the sample, after exclusion of common pathogenic variants. Despite the genetic heterogeneity, some genes showed greater contribution: GJB2, CDH23, MYO15A, OTOF, and USH2A.

Conclusion: The present results confirmed that next-generation sequencing is an effective tool for identifying causative variants in autosomal recessive hearing loss. To our knowledge, this is the first report of next-generation sequencing being applied to a large cohort of pedigrees with presumable autosomal recessive hearing loss in Brazil and South America.

[This corrects the article DOI: 10.3389/fgene.2023.1282711.].

The complex nature of microbiome data has made the differential abundance analysis challenging. Microbiome abundance counts are often skewed to the right and heteroscedastic (also known as overdispersion), potentially leading to incorrect inferences if not properly addressed. In this paper, we propose a simple yet effective framework to tackle the challenges by integrating Poisson (log-linear) regression with standard error estimation through the Bootstrap method and Sandwich robust estimation. Such standard error estimates are accurate and yield satisfactory inference even if the distributional assumption or the variance structure is incorrect. Our approach is validated through extensive simulation studies, demonstrating its effectiveness in addressing overdispersion and improving inference accuracy. Additionally, we apply our approach to two real datasets collected from the human gut and vagina, respectively, demonstrating the wide applicability of our methods. The results highlight the efficacy of our covariance estimators in addressing the challenges of microbiome data analysis. The corresponding software implementation is publicly available at https://github.com/yimshi/robustestimates.

Introduction: 5p deletion syndrome, also called Cri-du-chat syndrome 5p is a rare genetic syndrome with reports up to 36% of patients are associated with congenital heart defects. We investigated the association between left outflow tract obstruction and Cri-du-chat syndrome.

Methods: A retrospective review of the abnormal microarray cases with congenital heart defects in Children's Hospital of Pittsburgh and the Cytogenomics of Cardiovascular Malformations Consortium.

Results: A retrospective review at nine pediatric centers identified 4 patients with 5p deletions and left outflow tract obstruction (LVOTO). Three of these patients had additional copy number variants. We present data suggesting an association of LVOTO with 5p deletion with high mortality in the presence of additional copy number variants.

Conclusion: A rare combination of 5p deletion and left ventricular outflow obstruction was observed in the registry of copy number variants and congenital heart defects.