Identification of Pathogenic Missense Mutations of NF1 Using Computational Approaches.

Neurofibromatosis type 1 (NF1) is a prevalent autosomal dominant disorder caused by mutations in the NF1 gene, leading to multisystem disorders. Given the critical role of cysteine residues in protein stability and function, we aimed to identify key NF1 mutations affecting cysteine residues that significantly contribute to neurofibromatosis pathology. To identify the most critical mutations in the NF1 gene that contribute to the pathology of neurofibromatosis, we employed a sophisticated computational pipeline specifically designed to detect significant mutations affecting the NF1 gene. Our approach involved an exhaustive search of databases such as the Human Gene Mutation Database (HGMD), UniProt, and ClinVar for information on missense mutations associated with NF1. Our search yielded a total of 204 unique cysteine missense mutations. We then employed in silico prediction tools, including PredictSNP, iStable, and Align GVGD, to assess the impact of these mutations. Among the mutations, C379R, R1000C, and C1016Y stood out due to their deleterious effects on the biophysical properties of the neurofibromin protein, significantly destabilizing its structure. These mutations were subjected to further phenotyping analysis using SNPeffect 4.0, which predicted disturbances in the protein's chaperone binding sites and overall structural stability. Furthermore, to directly visualize the impact of these mutations on protein structure, we utilized AlphaFold3 to simulate both the wild-type and mutant NF1 structures, revealing the significant effects of the R1000C mutation on the protein's conformation. In conclusion, the identification of these mutations can play a pivotal role in advancing the field of precision medicine and aid in the development of effective drugs for associated diseases.

Molecular features of NSCLC patients with liver metastasis.

Background: Metastasis is the primary cause of lung cancer-related death. Primary cancer cells invade through the lymphatic or blood vessels to distant sites. Recently, it was proposed that lymphatic metastasis was more a hallmark of tumor aggressiveness or metastatic potential than a gateway to metastases. Therefore, the underlying molecular mechanism of metastasis is not entirely clear. Objectives: This study aimed to explore the genetic mechanisms underlying liver metastases from lung cancer and to evaluate the efficacy of different therapies in these patients. Design: We retrospectively analyzed the mutation spectrum of different biopsy samples including primary lung tumors, liver, lymph node metastasis, and circulating tumor DNA (ctDNA) from 1090 non-small-cell lung cancer (NSCLC) patients with liver metastasis between the years 2017 and 2022. Methods: Demographic and disease characteristics were summarized using descriptive parameters. Time to treatment discontinuation was used to analyze the clinical outcome. Results: More liquid biopsies were performed than tissue biopsies, especially in the treated advanced NSCLC patients. Liver metastasis before treatment was associated with poor response to immune checkpoint inhibitors and targeted therapy. Liver and lymph node metastasis had higher levels of single nucleotide variants and copy number variants than primary lung tumors. In paired lung and liver, lymph nodes, and simultaneous ctDNA, we found actionable mutations were always shared, while metastasis samples had multiple private mutations. Serial ctDNA analysis identifies potential resistant mutations and describes the evolution of tumor cells. Conclusion: Liver and lymph node metastasis in NSCLC showed shared actionable mutations. Of note, the discrepancy of private mutations in liver and lymph node metastases indicated that liver metastases are mainly seeded by the primary tumor rather than the earlier colonized lymph node metastases.

Causal relationship between immune cells and hepatocellular carcinoma: a Mendelian randomisation study.

Background: Hepatocellular carcinoma (HCC), the predominant malignancy of the digestive tract, ranks as the third most common cause of cancer-related mortality globally, significantly impeding human health and lifespan. Emerging immunotherapeutic approaches have ignited fresh optimism for patient outcomes. This investigation probes the link between 731 immune cell phenotypes and HCC through Mendelian Randomization and single-cell sequencing, aiming to unearth viable drug targets and dissect HCC's etiology. Methods: We conducted an exhaustive two-sample Mendelian Randomization analysis to ascertain the causal links between immune cell features and HCC, utilizing publicly accessible genetic datasets to explore the causal connections of 731 immune cell traits with HCC susceptibility. The integrity, diversity, and potential horizontal pleiotropy of these findings were rigorously assessed through extensive sensitivity analyses. Furthermore, single-cell sequencing was employed to penetrate the pathogenic underpinnings of HCC. Results: Establishing a significance threshold of pval_Inverse.variance.weighted at 0.05, our study pinpointed five immune characteristics potentially elevating HCC risk: B cell % CD3- lymphocyte (TBNK panel), CD25 on IgD+ (B cell panel), HVEM on TD CD4+ (Maturation stages of T cell panel), CD14 on CD14+ CD16- monocyte (Monocyte panel), CD4 on CD39+ activated Treg ( Treg panel). Conversely, various cellular phenotypes tied to BAFF-R expression emerged as protective elements. Single-cell sequencing unveiled profound immune cell phenotype interactions, highlighting marked disparities in cell communication and metabolic activities. Conclusion: Leveraging MR and scRNA-seq techniques, our study elucidates potential associations between 731 immune cell phenotypes and HCC, offering a window into the molecular interplays among cellular phenotypes, and addressing the limitations of mono-antibody therapeutic targets.

Genotypic and Phenotypic Spectrum of maple syrup urine disease in Zhejiang of China.

BACKGROUND: Maple Syrup Urine Disease (MSUD) is an autosomal recessive metabolic disorder originating from defects in the branched-chain α-ketoacid dehydrogenase (BCKDH) complex encoded by BCKDHA, BCKDHB, and DBT. This condition presents a spectrum of symptoms and potentially fatal outcomes. Although numerous mutations in the BCKDH complex genes associated with MSUD have been identified, the relationship between specific genotypes remains to be fully elucidated. AIM: Our objective was to predict the pathogenicity of these genetic mutations and establish potential links between genotypic alterations and the clinical phenotypes of MSUD. DESIGN: Retrospective population-based cohort. METHODS: We analyzed 20 MSUD patients from the Children's Hospital at Zhejiang University School of Medicine (Hangzhou, China), recorded from January 2010 to May 2023. Patients' blood samples were collected by heel-stick through neonatal screening, and amino acid profiles were measured by tandem mass spectrometry. In silico methods were employed to assess the pathogenicity, stability, and biophysical properties. Various computation tools were utilized for assessment, namely PredictSNP, MAGPIE, iStable, Align GVGD, ConSurf and SNP effect. RESULTS: We detected 25 distinct mutations, including 12 novel mutations. The BCKDHB gene was the most commonly affected (53.3%) compared to the BCKDHA gene (20.0%) and DBT gene (26.7%). In silico webservers predicted all novel mutations were disease-causing. CONCLUSIONS: This study highlights the genetic complexity of MSUD and underscores the importance of early detection and intervention. Integrating neonatal screening with advanced sequencing methodologies is pivotal in ensuring precise diagnosis and effective management of MSUD, thereby significantly improving the prognosis for individuals afflicted with this condition.

Health-related quality of life measurements in children and adolescents with refractive errors: A scoping review.

Background: Refractive errors, particularly myopia, are the leading visual disorders worldwide, significantly affecting the quality of life (QOL) even after correction. This scoping review focuses on health-related quality of life (HRQOL) measurements for children and adolescents with refractive errors. Main text: We explored generic and disease-specific HRQOL tools, examining their content, psychometric properties, and the impact of various interventions on QOL. Two English databases-PubMed, Embase, and one Chinese database, CNKI, were searched for relevant studies published from January 2001 to October 2023. Inclusion criteria encompassed studies using standardized instruments to assess the QOL of children aged 0-18 with refractive errors. The review compares prevalent HRQOL measurements, analyzes children's refractive error assessments, and discusses intervention effects on patient QOL. Conclusions: The study underlines the necessity of developing disease-specific QOL instruments for very young children and serves as a practical guide for researchers in this field. The findings advocate for a targeted approach in HRQOL assessment among the pediatric population, identifying critical gaps in current methodologies.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics.

BACKGROUND: Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment. METHODS: Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses. RESULTS: Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions. CONCLUSION: Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

Double Cation-π Directed Two-Dimensional Metallacycle-Based Hierarchical Self-Assemblies for Dual-Mode Catalysis.

Hierarchical self-assembly of Pt(II) metallacycles for the construction of functional materials has received considerable research interest, owing to their potential to meet increasing complexity and functionality demands while being based on well-defined scaffolds. However, the fabrication of long-range-ordered Pt(II) metallacycle-based two-dimensional hierarchical self-assemblies (2D HSAs) remains a challenge, primarily because of the limitations of conventional orthogonal noncovalent interaction (NCI) motifs and the intrinsic characteristics of Pt(II) metallacycles, making the delicate self-assembly processes difficult to control. Herein, we prepare well-regulated Pt(II)-metallacycle-based 2D HSAs through a directed strategy involving double cation-π interactions derived from C3-symmetric hexagonal Pt(II) metallacycles and C2-symmetric sodium phenate monomers. Spatially confined arrays of planar Pt(II) metallacycles and the selective growth of self-assemblies at desired locations are achieved by employing strong cation-π driving forces with well-defined directionality as the second orthogonal NCI, realizing the bottom-up, three-stage construction of Pt(II)-metallacycle-based 2D HSAs. The resultant 2D HSAs are applied as dual-mode catalysis platforms, which are loaded with two different nanocatalysts, one promoting catalytic oxidation and the other promoting photocatalytic reduction.