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Biomedical subjects

Ye Sun

Publications and source records attributed to Ye Sun.

2 recordsLinked to original sources

Reframing the asthma microbiome: Multikingdom, multisite, and multiomic perspectives.

The field of asthma microbiome research has shifted rapidly in recent years. Advances in sequencing technology have led to an increased ability to characterize multikingdom microbial species and integration with host -omics profiling to enhance future translational applications. Traditional bacteria-centric, cross-sectional studies are giving way to mechanistic frameworks that incorporate fungi, viruses, and host-immune interactions. In this state-of-the-art review of emerging concepts in microbiome asthma research, we first propose a structured framework to consider microbiome studies across 5 major domains-microbial kingdom, site of sampling, integration with host -omics, clinical outcome domain, and translational relevance-in order to synthesize recent high-impact human microbiome studies in asthma. We highlight emerging evidence that fungal and viral communities contribute independently to asthma risk and that human microbial communities are linked to distinct inflammatory and immune pathways shaped by host genetic susceptibility.

Asthma

Integrative multi-omics analysis unravels the metabolic landscape and reveals serum biomarkers for early diagnosis of hyperuricemia.

BACKGROUND: Hyperuricemia (HUA) is a major risk factor for gout and multiple metabolic disorders. Although serum uric acid (UA) is the gold standard for HUA diagnosis, it fails to reflect early metabolic disturbances and shows limited predictive value for asymptomatic HUA. This study sought to elucidate the pathological mechanisms underlying HUA and identify novel diagnostic biomarkers beyond UA. METHODS: This study enrolled 195 patients with HUA and 98 healthy controls. Global metabolomics and proteomics profiling were performed to characterize molecular alterations underlying HUA. Based on the biological relevance of the shared dysregulated pathways, a pathway correlation network was constructed to elucidate the pathological mechanisms driving HUA initiation and progression. Furthermore, diagnostic biomarkers for HUA were identified using machine learning algorithms, and were validated with an external cohort. RESULTS: HUA patients exhibited distinct metabolic and proteomic profiles compared with healthy controls. Integrated multi-omics pathway analysis revealed that peroxisome proliferators-activated receptor signaling pathway, arachidonic acid metabolism, purine metabolism, pyrimidine metabolism and sphingolipid signaling pathway were significantly dysregulated in HUA. Among them, arachidonic acid metabolism was identified as a hub pathway involved in HUA progression. Furthermore, a metabolite panel consisting of cysteine-S-sulfate, glycerophosphocholine and 4-hydroxyphenylpyruvic acid was screened by machine learning and validated in an independent cohort, which showed slightly higher diagnostic performance for HUA than UA. CONCLUSIONS: This study reveals the core metabolic and protein regulatory networks of HUA, and identifies a novel serum metabolite panel for the diagnosis of HUA. These findings provide new insights for improved clinical diagnosis and management.

Humans