Biomedical subjects

Bin Zeng

Publications and source records attributed to Bin Zeng.

3 recordsLinked to original sources

PubMed2026

A systematic review and network meta-analysis of single nucleotide polymorphisms associated with oral submucous fibrosis risk.

BACKGROUND: Oral submucous fibrosis (OSF) is a chronic and insidious oral disease characterized by hyalinization of the subepithelial connective tissue and progressive fibrosis of the oral submucosa. It is a precancerous condition of oral squamous cell carcinoma. Studies have demonstrated that single nucleotide polymorphisms (SNPs) are closely associated with susceptibility to OSF. This study aims to comprehensively evaluate the association between SNPs and OSF risk and to rank the strength of the association between different genetic models and OSF susceptibility. METHODS: Literature related to OSF was comprehensively searched from PubMed, Web of Science, Embase, Cochrane Library, CNKI, and Wangfang databases up to July 2025. Full-text case-control studies with patients diagnosed with OSF were included. Quality assessment was performed to evaluate the risk of bias. RevMan 5.4, GeMTC 0.14.3, and STATA 17.0 were used for the pairwise and Bayesian network meta-analysis. RESULTS: A total of 24 studies with 2545 cases and 3772 controls, covering 13 SNPs in 11 genes, were included in our meta-analysis. We found that CYP1A1 rs4646903:T>C, CYP1A1 rs1048943:A>G, GSTT1 null genotype, GSTM1 null genotype, and XRCC3 rs861539:C>T were associated with an increased risk of OSF, while MMP2 rs243865:C>T and MMP3 rs3025058: 5A>6A were associated with a decreased risk of OSF. Further Bayesian network meta-analysis indicated the top 5 genetic models with the highest association with OSF risk in network group 1 were the dominant model, homozygous model, allelic model, and recessive model of CYP1A1 rs1048943:A>G (ranked 1-4), and the heterozygous/dominant model of CYP1A1 rs4646903:T>C (both ranked 5). While the allelic models of XRCC3 rs861539:C>T and MMP3 rs3025058: 5A>6A ranked first for predicting OSF in group 2 and group 3, respectively. CONCLUSION: Some specific SNPs are significantly related to the risk of OSF. Among them, the dominant model of CYP1A1 rs1048943:A>G may be the most strongly associated genetic model with OSF risk. Future large-sample, well-designed studies with detailed genotype data are needed to validate the roles of these SNPs in OSF risk.

Humans↗

PubMed2026

Historical metabolic adaptation potentiates the rapid evolution of flonicamid resistance in Myzus persicae.

Rapid adaptation to novel environments is often shaped not only by newly acquired mutations but also by historical genetic backgrounds established through prior evolutionary events. However, the extent to which such historical contingency contributes to the rapid evolution of insecticide resistance remains poorly understood. Here, we investigated the emergence of resistance to flonicamid, a recently deployed insecticide, in the green peach aphid, Myzus persicae. We show that constitutive overexpression of the P450 enzymes CYP6CY3 and CYP6CY4, already widespread in populations of M. persicae before flonicamid deployment, confers a previously cryptic tolerance phenotype to flonicamid. However, biochemical and transgenic analyses demonstrated that these metabolic adaptations provide only weak protection against flonicamid. Following flonicamid deployment, however, a novel target-site mutation, NaamV251I, in the recently identified molecular target of 4-trifluoromethylnicotinamide (TFNA-AM), emerged in M. persicae on a genetic background of CYP6CY3 or CYP6CY4 overexpression. Structural modeling, enzymatic assays, and CRISPR-Cas9 genome editing demonstrated that this mutation reduces target sensitivity and independently confers moderate resistance. Strikingly, combining the nicotinamidase (Naam) mutation with pre-existing CYP6CY3 or CYP6CY4 overexpression produced substantially elevated resistance phenotypes that far exceeded the effects of either mechanism alone. Our results demonstrate that the pre-existing metabolic background did not itself evolve further following flonicamid deployment but fundamentally altered the phenotypic consequences of a subsequently acquired target-site mutation. These findings provide direct evidence that historical adaptive variation can potentiate rapid resistance evolution to newly introduced insecticides and reveal how interactions between past and contemporary adaptations shape evolutionary responses to novel environmental challenges.

Animals↗

PubMed2026

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics↗