Biomedical subjects

Ziyan Zhang

Publications and source records attributed to Ziyan Zhang.

3 recordsLinked to original sources

PubMed2026

Conventional and Shared Genetic Association Analysis Between Diabetes Mellitus and Sensorineural Hearing Loss.

PURPOSE: This study aims to investigate the epidemiological and genetic associations between diabetes mellitus (DM) and sensorineural hearing loss (SNHL) across different subtypes. METHODS: We analyzed 502,490 participants from the UK Biobank using multivariate logistic regression to examine the association between DM and SNHL, considering gender, age, and HbA1c levels. Genetic correlations and causality were examined by linkage disequilibrium score regression and bidirectional Mendelian randomization. Cross-trait meta-analyses identified shared loci between DM and SNHL, followed by gene annotation, functional analysis, and drug candidate exploration for the shared traits. RESULTS: Observational analysis revealed significant associations between DM and SNHL, consistent in subgroups based on age, sex, and certain HbA1c levels. A positive genetic correlation was found between type 2 diabetes mellitus (T2D) and SNHL (Rg = 0.0982, p = 0.0095) between T2D and SNHL, and four loci were identified, with ARHGEF28 and TCF7L2 prioritized as credible pleiotropic genes. Enrichment was indicated in glucose metabolism and organogenesis, with shared heritability in metabolic tissues and outer hair cells. Metformin was identified as potential drug candidates for the T2D-SNHL comorbidity. CONCLUSION: These findings progress our understanding of the epidemiological association, shared genetic basis, and potential therapeutic targets between T2D and SNHL, which might contribute to the management of their comorbidity.

Humans↗

PubMed2026

Mitochondrial Function-Related Genes in Sleep Disorders: A Multi-Omics Mendelian Randomization Study.

Mitochondrial dysfunction is linked to sleep disorders in previous report, but the potential roles of specific genes remain unclear. This study aimed to dissect different subtype-specific genetic associations and their underlying mechanisms. A multi-omics Summary-data-based Mendelian Randomization (SMR) approach was performed to identify potential causal links between mitochondrial function-related genes and sleep disorders. We integrated GWAS data from FinnGen database (the discovery set), independent GWAS datasets (covering different sleep-disorder subtypes and used for validation), and cis-QTLs (including mQTLs, eQTLs, and pQTLs) to perform systematic exploration. Specially, we performed targeted validation of tissue-specific effects, leveraging gene expression data from disease-relevant brain regions within the GTEx database. Our SMR analysis identified mitochondrial function-related genes potentially modulating sleep disorders across biological layers, initially identifying 102 genes at the methylation level, 48 at the gene expression level, and 6 at the protein abundance level. Integrative analysis subsequently prioritized DCXR and ACADVL and revealed their distinct, subtype-specific associations. DCXR exhibited a protective role in sleep apnea while ACADVL showed a paradoxical risk conferring role in daytime sleepiness. In addition, the analysis identified an epigenetic regulatory mechanism for DCXR in which its expression and protein levels are modulated by DNA methylation. Finally, validation in brain-hypothalamus tissue confirmed DCXR as a significant potential protective factor (OR = 0.929, 95% CI: 0.887-0.973, P_HEIDI = 0.999, FDR = 0.2449). Our findings implicate key mitochondrial genes, particularly DCXR and ACADVL, in the pathophysiology of specific sleep disorder subtypes, highlighting potential avenues for precision medicine. Clinical trial number: Not applicable.

Humans↗

PubMed2026

NanoSSL: attention mechanism-based self-supervised learning method for protein identification using nanopores.

MOTIVATION: Nanopores are cutting-edge interdisciplinary tools that can analyze biomolecules at the single-molecule level for many applications, e.g. DNA sequencing. Efforts are underway to extend nanopores to proteomics, including the development of machine learning algorithms for protein sequencing and identification. However, single-molecule data are intrinsically noisy and hard to process. Moreover, the development and performance of machine learning for nanopore is jeopardized by data scarcity. Self-supervised learning is an emerging method that may yield advantages in nanopore scenarios. RESULTS: We propose and experimentally validate Nanopore analysis using Self-Supervised Learning (NanoSSL), a generative self-supervised learning framework based on attention mechanisms for the identification of protein signals from nanopores. Leveraging a two-step approach consisting of self-supervised pre-training and supervised fine-tuning, NanoSSL learns useful feature representations from empirical data to facilitate downstream classification tasks. Inspired by the concept of fragmentation in conventional protein sequencing technologies, during pretraining each translocation event is split into multiple non-overlapping fragments of equal size, some of which are randomly masked and reconstructed using a masked autoencoder. Learning the feature representations of the reconstructed nanopore events facilitates molecular identification in fine-tuning. In this study, we retested a publicly available nanopore multiplexed protein sensing dataset for model iteration, and subsequently measured Alzheimer's disease biomarker Aβ1-42 using homemade solid-state nanopores. Empirical results indicated NanoSSL achieved an unprecedented performance across four metrics: accuracy, precision, recall, and F1 score, when classifying two mutated Aβ1-42, E22G and G37R. The self-supervised learning and attention mechanism were verified as the source of performance gains. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://doi.org/10.5281/zenodo.17172822.

Nanopores↗