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Yun Ma

Publications and source records attributed to Yun Ma.

2 recordsLinked to original sources
PubMed2026

Identification and expression validation of key genes of Xiaozhengtongluo formula in the treatment of diabetic nephropathy by Mendelian randomization.

Xiaozhengtongluo formula (XZTL) has a positive effect on the treatment of diabetic nephropathy (DN), but its mechanism is not fully understood. Therefore, it is important to explore the key genes of XZTL in the treatment of DN. Differentially expressed genes (DEGs) between DN and control obtained from GSE96804, drug target genes of XZTL, and disease target genes of DN obtained from public databases were intersected. Genes of intersection were defined as candidate genes. Next, Mendelian randomization (MR) analysis was used to ascertain the causal associations between candidate genes and DN. Afterwards, key genes were confirmed through receiver operating characteristic (ROC) curve analysis and expression validation. Subsequently, enrichment analysis, molecular regulatory network analysis, and molecular docking were conducted. Finally, experimental verification of the expression levels of key genes was performed through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Altogether, 29 candidate genes were screened via MR analysis, identifying APOD, IGFBP3, and LPL as significantly associated with DN. IGFBP3 and APOD were risk factors, whereas LPL was protective. Consistent expression trends across training and validation datasets defined them as key genes. All three were co-enriched in 26 pathways, including oxidative phosphorylation. Regulatory networks showed MIR497HG/hsa-miR-19a-3p regulated IGFBP3, and NEAT1/hsa-miR-29a-3p regulated LPL; IGFBP3 and LPL were co-targeted by SP3 and SP1. Molecular docking revealed APOD-baicalein, LPL-oleic acid, and IGFBP3-quercetin binding, suggesting therapeutic potential. RT-qPCR confirmed aberrant expression of these genes in DN, which was normalized by XZTL intervention. In this study, three key genes (APOD, IGFBP3, and LPL) of XZTL in the treatment of DN were finally obtained, providing mechanistic clues for understanding XZTL's multi-target mechanism and providing experimentally tractable candidate targets for DN molecular subtyping, targeted therapeutic development, and precision medicine approaches in TCM.

Diabetic Nephropathies↗
PubMed2026

Livestock Multi-Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation.

Livestock multi-omics integration is key to unraveling complex trait regulation, yet systematic, livestock-specific strategies remain scarce. This review traces the progression from single-omics accumulation to multi-dimensional integration, highlighting how large-scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi-omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls-overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome-host integration lacking environmental context, and systematic neglect of metabolic fluxomics-and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock-adapted three-tier analytical framework: (1) statistical association of cross-omics covariation patterns; (2) machine learning-driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior-knowledge-guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single-cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock-specific benchmarking, and translational pipelines, charting a path from correlation-centric reporting to mechanistic causality and precision breeding.

Animals↗