Identification and characterization of novel genetic variants in the first Chinese family of mucopolysaccharidosis IIIC (Sanfilippo C syndrome).

Mucopolysaccharidosis type IIIC (MPS IIIC) is one of inherited lysosomal storage disorders, caused by deficiencies in lysosomal hydrolases degrading acidic mucopolysaccharides. The gene responsible for MPS IIIC is HGSNAT, which encodes an enzyme that catalyses the acetylation of the terminal glucosamine residues of heparan sulfate. So far, few studies have focused on the genetic landscape of MPS IIIC in China, where IIIA and IIIB were the major subtypes. In this study, we utilized whole-exome sequencing (WES) to identify novel compound heterozygous variants in the HGSNAT gene from a Chinese patient with typical MPS IIIC symptoms: c.743G>A; p.Gly248Glu and c.1030C>T; p.Arg344Cys. We performed in silico analysis and experimental validation, which confirmed the deleterious pathogenic nature of both variants, as evidenced by the loss of HGSNAT activity and failure of lysosomal localization. To the best of our knowledge, the MPS IIIC is first confirmed by clinical, biochemical and molecular genetic findings in China. Our study thus expands the spectrum of MPS IIIC pathogenic variants, which is of importance to dissect the pathogenesis and to carry out clinical diagnosis of MPS IIIC. Moreover, this study helps to depict the natural history of Chinese MPS IIIC populations.

AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms.

Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.

The risk of Alzheimer's disease and cognitive impairment characteristics in eight mental disorders: A UK Biobank observational study and Mendelian randomization analysis.

INTRODUCTION: The cognitive impairment patterns and the association with Alzheimer's disease (AD) in mental disorders remain poorly understood. METHODS: We analyzed data from 486,297 UK Biobank participants, categorizing them by mental disorder history to identify the risk of AD and the cognitive impairment characteristics. Causation was further assessed using Mendelian randomization (MR). RESULTS: AD risk was higher in individuals with bipolar disorder (BD; hazard ratio [HR] = 2.37, P < 0.01) and major depressive disorder (MDD; HR = 1.63, P < 0.001). MR confirmed a causal link between BD and AD (ORIVW = 1.098), as well as obsessive-compulsive disorder (OCD) and AD (ORIVW = 1.050). Cognitive impairments varied, with BD and schizophrenia showing widespread deficits, and OCD affecting complex task performance. DISCUSSION: Observational study and MR provide consistent evidence that mental disorders are independent risk factors for AD. Mental disorders exhibit distinct cognitive impairment prior to dementia, indicating the potential different mechanisms in AD pathogenesis. Early detection of these impairments in mental disorders is crucial for AD prevention. HIGHLIGHTS: This is the most comprehensive study that investigates the risk and causal relationships between a history of mental disorders and the development of Alzheimer's disease (AD), alongside exploring the cognitive impairment characteristics associated with different mental disorders. Individuals with bipolar disorder (BD) exhibited the highest risk of developing AD (hazard ratio [HR] = 2.37, P < 0.01), followed by those with major depressive disorder (MDD; HR = 1.63, P < 0.001). Individuals with schizophrenia (SCZ) showed a borderline higher risk of AD (HR = 2.36, P = 0.056). Two-sample Mendelian randomization (MR) confirmed a causal association between BD and AD (ORIVW = 1.098, P < 0.05), as well as AD family history (proxy-AD, ORIVW = 1.098, P < 0.001), and kept significant after false discovery rate correction. MR also identified a nominal significant causal relationship between the obsessive-compulsive disorder (OCD) spectrum and AD (ORIVW = 1.050, P < 0.05). Individuals with SCZ, BD, and MDD exhibited impairments in multiple cognitive domains with distinct patterns, whereas those with OCD showed only slight declines in complex tasks.