Targeted next-generation sequencing reveals the genetic mechanism of Chinese Marfan syndrome cohort with ocular manifestation.

BACKGROUND: Marfan syndrome (MFS) is a hereditary connective tissue disorder involving multiple systems, including ophthalmologic abnormalities. Most cases are due to heterozygous mutations in the fibrillin-1 gene (FBN1). Other associated genes include LTBP2, MYH11, MYLK, and SLC2A10. There is significant clinical overlap between MFS and other Marfan-like disorders. PURPOSE: To expand the mutation spectrum of FBN1 gene and validate the pathogenicity of Marfan-related genes in patients with MFS and ocular manifestations. METHODS: We recruited 318 participants (195 cases, 123 controls), including 59 sporadic cases and 88 families. All patients had comprehensive ophthalmic examinations showing ocular features of MFS and met Ghent criteria. Additionally, 754 cases with other eye diseases were recruited. Panel-based next-generation sequencing (NGS) screened mutations in 792 genes related to inherited eye diseases. RESULTS: We detected 181 mutations with an 84.7% detection rate in sporadic cases and 87.5% in familial cases. The overall detection rate was 86.4%, with FBN1 accounting for 74.8%. In cases without FBN1 mutations, 23 mutations from seven Marfan-related genes were identified, including four pathogenic or likely pathogenic mutations in LTBP2. The 181 mutations included 165 missenses, 10 splicings, three frameshifts, and three nonsenses. FBN1 accounted for 53.0% of mutations. The most prevalent pathogenic mutation was FBN1 c.4096G>A. Additionally, 94 novel mutations were detected, with 13 de novo mutations in 14 families. CONCLUSION: We expanded the mutation spectrum of the FBN1 gene and provided evidence for the pathogenicity of other Marfan-related genes. Variants in LTBP2 may contribute to the ocular manifestations in MFS, underscoring its role in phenotypic diversity.

Single-cell transcriptomic sequencing identifies subcutaneous patient-derived xenograft recapitulated medulloblastoma.

BACKGROUND: Medulloblastoma (MB) is one of the most common malignant brain tumors that mainly affect children. Various approaches have been used to model MB to facilitate investigating tumorigenesis. This study aims to compare the recapitulation of MB between subcutaneous patient-derived xenograft (sPDX), intracranial patient-derived xenograft (iPDX), and genetically engineered mouse models (GEMM) at the single-cell level. METHODS: We obtained primary human sonic hedgehog (SHH) and group 3 (G3) MB samples from six patients. For each patient specimen, we developed two sPDX and iPDX models, respectively. Three Patch+/- GEMM models were also included for sequencing. Single-cell RNA sequencing was performed to compare gene expression profiles, cellular composition, and functional pathway enrichment. Bulk RNA-seq deconvolution was performed to compare cellular composition across models and human samples. RESULTS: Our results showed that the sPDX tumor model demonstrated the highest correlation to the overall transcriptomic profiles of primary human tumors at the single-cell level within the SHH and G3 subgroups, followed by the GEMM model and iPDX. The GEMM tumor model was able to recapitulate all subpopulations of tumor microenvironment (TME) cells that can be clustered in human SHH tumors, including a higher proportion of tumor-associated astrocytes and immune cells, and an additional cluster of vascular endothelia when compared to human SHH tumors. CONCLUSIONS: This study was the first to compare experimental models for MB at the single-cell level, providing value insights into model selection for different research purposes. sPDX and iPDX are suitable for drug testing and personalized therapy screenings, whereas GEMM models are valuable for investigating the interaction between tumor and TME cells.

Comparative Dynamics and Functional Mechanisms of the CYP17A1 Tunnels Regulated by Ligand Binding.

As an important member of cytochrome P450 (CYP) enzymes, CYP17A1 is a dual-function monooxygenase with a critical role in the synthesis of many human steroid hormones, making it an attractive therapeutic target. The emerging structural information about CYP17A1 and the growing number of inhibitors for these enzymes call for a systematic strategy to delineate and classify mechanisms of ligand transport through tunnels that control catalytic activity. In this work, we applied an integrated computational strategy to different CYP17A1 systems with a panel of ligands to systematically study at the atomic level the mechanism of ligand-binding and tunneling dynamics. Atomistic simulations and binding free energy computations identify the dynamics of dominant tunnels and characterize energetic properties of critical residues responsible for ligand binding. The common transporting pathways including S, 3, and 2c tunnels were identified in CYP17A1 binding systems, while the 2c tunnel is a newly formed pathway upon ligand binding. We employed and integrated several computational approaches including the analysis of functional motions and sequence conservation, atomistic modeling of dynamic residue interaction networks, and perturbation response scanning analysis to dissect ligand tunneling mechanisms. The results revealed the hinge-binding and sliding motions as main functional modes of the tunnel dynamic, and a group of mediating residues as key regulators of tunnel conformational dynamics and allosteric communications. We have also examined and quantified the mutational effects on the tunnel composition, conformational dynamics, and long-range allosteric behavior. The results of this investigation are fully consistent with the experimental data, providing novel rationale to the experiments and offering valuable insights into the relationships between the structure and function of the channel networks and a robust atomistic model of activation mechanisms and allosteric interactions in CYP enzymes.