Clinical severity prediction in children with osteogenesis imperfecta caused by COL1A1/2 defects.

Osteogenesis imperfecta (OI) is a genetic disease with an estimated prevalence of 1 in 13,500 and 1 in 9700. The classification into subtypes of OI is important for prognosis and management. In this study, we established a clinical severity prediction model depending on multiple features of variants in COL1A1/2 genes. INTRODUCTION: Ninety percent of OI cases are caused by pathogenic variants in the COL1A1/COL1A2 gene. The Sillence classification describes four OI types with variable clinical features ranging from mild symptoms to lethal and progressively deforming symptoms. METHODS: We established a prediction model of the clinical severity of OI based on the random forest model with a training set obtained from the Human Gene Mutation Database, including 790 records of the COL1A1/COL1A2 genes. The features used in the prediction model were respectively based on variant-type features only, and the optimized features. RESULTS: With the training set, the prediction results showed that the area under the receiver operating characteristic curve (AUC) for predicting lethal to severe OI or mild/moderate OI was 0.767 and 0.902, respectively, when using variant-type features only and optimized features for COL1A1 defects, 0.545 and 0.731, respectively, for COL1A2 defects. For the 17 patients from our hospital, prediction accuracy for the patient with the COL1A1 and COL1A2 defects was 76.5% (95% CI: 50.1-93.2%) and 88.2% (95% CI: 63.6-98.5%), respectively. CONCLUSION: We established an OI severity prediction model depending on multiple features of the specific variants in COL1A1/2 genes, with a prediction accuracy of 76-88%. This prediction algorithm is a promising alternative that could prove to be valuable in clinical practice.

Epigenetic clocks and gliomas: unveiling the molecular interactions between aging and tumor development.

Gliomas, the most prevalent and aggressive primary brain tumors, represent a diverse group of malignancies originating from glial cells. These tumors account for significant brain tumor-related morbidity and mortality, with higher incidence rates in North America and Europe compared to Asia and Africa. Genetic predispositions and environmental factors, particularly ionizing radiation, critically impact glioma risk. Epigenetics, particularly DNA methylation, plays a pivotal role in glioma research, with IDH-mutant gliomas showing aberrant methylation patterns contributing to tumorigenesis. Epigenetic clocks, biomarkers based on DNA methylation patterns predicting biological age, have revealed significant insights into aging and tumor development. Recent studies demonstrate accelerated epigenetic aging in gliomas, correlating with increased cancer risk and poorer outcomes. This review explores the mechanisms of epigenetic clocks, their biological significance, and their application in glioma research. Furthermore, the clinical implications of epigenetic clocks in diagnosing, prognosticating, and treating gliomas are discussed. The integration of epigenetic clock data into personalized medicine approaches holds promise for enhancing therapeutic strategies and patient outcomes in glioma treatment.

The interplay between metal ions and immune cells in glioma: pathways to immune escape.

This review explores the intricate roles of metal ions-iron, copper, zinc, and selenium-in glioma pathogenesis and immune evasion. Dysregulated metal ion metabolism significantly contributes to glioma progression by inducing oxidative stress, promoting angiogenesis, and modulating immune cell functions. Iron accumulation enhances oxidative DNA damage, copper activates hypoxia-inducible factors to stimulate angiogenesis, zinc influences cell proliferation and apoptosis, and selenium modulates the tumor microenvironment through its antioxidant properties. These metal ions also facilitate immune escape by upregulating immune checkpoints and secreting immunosuppressive cytokines. Targeting metal ion pathways with therapeutic strategies such as chelating agents and metalloproteinase inhibitors, particularly in combination with conventional treatments like chemotherapy and immunotherapy, shows promise in improving treatment efficacy and overcoming resistance. Future research should leverage advanced bioinformatics and integrative methodologies to deepen the understanding of metal ion-immune interactions, ultimately identifying novel biomarkers and therapeutic targets to enhance glioma management and patient outcomes.

Discovery of a novel flavonol O-methyltransferase possessing sequential 4'- and 7-O-methyltransferase activity from Camptotheca acuminata Decne.

The biosynthetic route for flavonol in Camptotheca acuminata has been recently elucidated from a chemical point of view. However, the genes involved in flavonol methylation remain unclear. It is a critical step for fully uncovering the flavonol metabolism in this ancient plant. In this study, the multi-omics resource of this plant was utilized to perform flavonol O-methyltransferase-oriented mining and screening. Two genes, CaFOMT1 and CaFOMT2 are identified, and their recombinant CaFOMT proteins are purified to homogeneity. CaFOMT1 exhibits strict substrate and catalytic position specificity for quercetin, and selectively methylates only the 4'-OH group. CaFOMT2 possesses sequential O-methyltransferase activity for the 4'-OH and 7-OH of quercetin. These CaFOMT genes are enriched in the leaf and root tissues. The catalytic dyad and critical substrate-binding sites of the CaFOMTs are determined by molecular docking and further verified through site-mutation experiments. PHE181 and MET185 are designated as the critical sites for flavonol substrate selectivity. Genomic environment analysis indicates that CaFOMTs evolved independently and that their ancestral genes are different from that of the known Ca10OMT. This study provides molecular insights into the substrate-binding pockets of two new CaFOMTs responsible for flavonol metabolism in C. acuminata.

The characteristics of excitatory lineage differentiation and the developmental conservation in Reeler neocortex.

The majority of neocortical projection neurons are generated indirectly from radial glial cells (RGCs) mediated by intermediate progenitor cells (IPCs) in mice. IPCs are thought to be a great breakthrough in the evolutionary expansion of the mammalian neocortex. However, the precise ratio of neuron production from IPCs and characteristics of RGC differentiation process are still unclear. Our study revealed that direct neurogenesis was seldom observed and increased slightly at late embryonic stage. Besides, we conducted retrovirus sparse labelling combined carboxyfluorescein diacetate succinimide ester (CFSE) and Tbr2-CreER strain to reconstruct individual lineage tree in situ. The lineage trees simulated the output of RGCs at per round of division in sequence with high temporal, spatial and cellular resolution at P7. We then demonstrated that only 1.90% of neurons emanated from RGCs directly in mouse cerebral neocortex and 79.33% of RGCs contributed to the whole clones through IPCs. The contribution of indirect neurogenesis was underestimated previously because approximately a quarter of IPC-derived neurons underwent apoptosis. Here, we also showed that abundant IPCs from first-generation underwent self-renewing division and generated four neurons ultimately. We confirmed that the intermediate proliferative progenitors expressed higher Cux2 characteristically at early embryonic stage. Finally, we validated that the characteristics of neurogenetic process in lineages and developmental fate of neurons were conserved in Reeler mice. This study contributes to further understanding of neurogenesis in neocortical development.

mvPPT: A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants.

Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed Pathogenicity Prediction Tool for missense variants (mvPPT), a highly sensitive and accurate missense variant classifier based on gradient boosting. mvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, frequencies (allele frequencies, amino acid frequencies, and genotype frequencies), and genomic context. Compared with established predictors, mvPPT achieves superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights into variant pathogenicity. mvPPT is freely available at http://www.mvppt.club/.