Biomedical subjects

Quan Sun

Publications and source records attributed to Quan Sun.

4 recordsLinked to original sources

PubMed2026

Gravity-driven millifluidic platform for magnetic solid-phase extraction of Enterocytozoon hepatopenaei DNA from complex shrimp hepatopancreas.

Effective detection of Enterocytozoon hepatopenaei (EHP) in aquaculture is currently hindered by the lack of field-deployable extraction methods capable of processing complex, inhibitor-rich hepatopancreatic tissue. This study presents a gravity-driven millifluidic platform for the rapid extraction of EHP genomic DNA using an optimized, surfactant-compatible magnetic solid-phase extraction (MSPE) chemistry. Utilizing 5% PEG 8000 and 2.0 M NaCl, the platform facilitates the selectively capture of DNA from inhibitor-rich crustacean lysates. The 3D-printed device employs a tilting rocking plate to generate passive, gravity-driven flow, maintaining homogeneous magnetic bead suspension and maximizing solid-phase capture efficiency without external pumps. The integrated platform achieved a DNA yield of 2804.33 ± 15.31 ng/μL, a 5.9-fold increase over manual magnetic bead extraction. TaqMan quantitative PCR (qPCR) validation targeting the EHP SSU rRNA gene was developed. Using a standard curve spanning 101 to 107 plasmid copies (Ct = -3.611 log10 [copy] + 42.309, R2 = 0.998, amplification efficiency 89.2%), the on-chip MSPE achieved a validated analytical limit of detection (LOD) of 1 spore per reaction (100% detection rate, n = 21), whereas a commercial CTAB-based DNA extraction kit failed to achieve a validated LOD even at 10 spores (85.7%, 18/21). Nested PCR targeting the SWP gene was employed for field evaluation. A pilot study across two cohorts (N = 40) demonstrated consistent detection of confirmed EPH-positive cases; however, the small sample size precludes definitive diagnostic accuracy claims. With a total processing time under 30 min, this platform provides a high-efficiency extraction module. Future work will couple the device with isothermal amplification (e.g., LAMP or RPA) to realize a sample-to-answer system for resource-limited aquaculture.

Aquaculture diagnostics↗

PubMed2026

PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.

MOTIVATION: Integrating multi-omics data provides valuable insights into biological processes by capturing information across multiple molecular layers, enabling a comprehensive understanding of complex diseases and driving advancements in precision medicine. However, existing computational methods for multi-omics integration face significant challenges, such as low reliability and poor generalizability, due to the high dimensionality and low sample size nature of omics data. RESULTS: To address these challenges, we present PEARL (Pearson-Enhanced spectrAl gRaph convoLutional networks), a novel deep graph learning method for biomedical classification and functional important omics features identification. PEARL leverages a simple yet effective learning architecture to achieve superior and robust performance in high-dimensional, low-sample-size multi-omics settings. Our results demonstrate that PEARL significantly outperforms existing state-of-the-art methods on both synthetic and real biomedical datasets. Furthermore, applied to Alzheimer's disease (AD) brain multi-omics data, features prioritized by PEARL lead to functionally important genes that demonstrate significant enrichment in AD-related pathways. These findings highlight PEARL's practical utility in biomedical research and its potential to enhance biological interpretability in multi-omics studies. AVAILABILITY AND IMPLEMENTATION: The source code of our computational framework is available at https://github.com/zqq121017/PEARL.

Multiomics↗

PubMed2025

Genome-wide gene-sleep interaction study identifies novel lipid loci in 732,564 participants.

BACKGROUND AND AIMS: Deviations from the population mean in sleep duration have been associated with increased risk for developing dyslipidemia and atherosclerotic cardiovascular disease, but the mechanism of effect is poorly characterized. We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. METHODS: We collected data from 55 cohorts with a combined sample size of 732,564 participants (87 % European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20 % of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for one-degree of freedom tests of interaction and two-degree of freedom joint tests of the SNP-main and -interaction effect on lipid levels. RESULTS: The one-degree of freedom variant-sleep interaction test identified 10 novel loci (Pint<5.0e-9), and we additionally identify 7 loci within the two-degree of freedom analyses (Pjoint<5.0e-9 in combination with Pint<6.6e-6). Multiple loci, including those mapped to APSH (target for aspartic and succinic acid) and SLC8A1 showed biological plausibility and druggability potential based on literature. CONCLUSIONS: Collectively, the 17 (9 with short and 8 with long sleep) loci provided evidence into the biomolecular mechanisms underlying sleep-associated lipid changes, including potential involvement of the vitamin D receptor pathway. Collectively, these findings may contribute developing novel interventions for treating dyslipidemia in people with sleep disturbances.

Humans↗

PubMed2025

Genomic loci and molecular genetic mechanisms for hidradenitis suppurativa.

BACKGROUND: Hidradenitis suppurativa (HS) is a common, chronic and debilitating inflammatory disease that most commonly affects intertriginous skin. Despite its high heritability, the genetic underpinnings of HS remain poorly understood. OBJECTIVES: To identify genetic signals associated with HS, determine genetic relationships with other diseases and investigate potential molecular genetic mechanisms. METHODS: We performed a genome-wide association meta-analysis of six studies, totalling 4540 patients with HS and > 1 million control participants, and identified genetic correlations with other common diseases. We integrated the HS data with expression quantitative trait loci from 10 trait-relevant tissues, epigenomic and transcriptomic data from human scalp, differential expression data from HS lesions vs. adjacent skin and mesenchymal Hi-C chromatin looping data. To identify functional noncoding variants, we performed transcriptional reporter assays for signals near KLF5 and SOX9. RESULTS: We identified 11 significant HS signals across 7 loci: 4 corresponded to previously reported associations, 4 represented novel signals within known loci and 3 were signals in newly implicated loci. We identified significant genetic correlations between HS and other inflammatory conditions, particularly inflammatory bowel disease, rheumatoid arthritis, type 2 diabetes mellitus and asthma. We prioritized candidate genes for the 11 signals. The risk allele at KLF5 exhibited 10-fold greater transcriptional activity than the nonrisk allele, while risk alleles at SOX9 showed significantly reduced transcriptional activity. CONCLUSIONS: Our results provide insights into potential genetic mechanisms underlying HS and suggest potential therapeutic targets for this challenging condition.

Humans↗