Li Lun, Kong Weiyu, Sun Jing, Jiang Yongzhong, Li Tiantian, Xia Zhihui, Zhou Junfei, Fang Zhiwei, Chen Lihong, Feng Shun, Song Huiyin, Xiao Huafeng, Zhang Baolong, Fang Bin, Peng Hai, Gao Lifen
Institute for Systems Biology, Jianghan University, Wuhan, Hubei, China.
Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China.
Front Microbiol. 2025 Jun 25;16:1603255. doi: 10.3389/fmicb.2025.1603255. eCollection 2025.
Precise detection of microbial genetic variation (MGV) at the strain level is essential for reliable disease diagnosis, pathogen surveillance, and reproducible research. Current methods, however, are constrained by limited sensitivity, specificity, and dependence on culturing. To address these challenges, we developed MGV-Seq, an innovative culture-independent approach that integrates multiplex PCR, high-throughput sequencing, and bioinformatics to analyze multiple dispersed nucleotide polymorphism (MNP) markers, enabling high-resolution strain differentiation.
Using as a model organism, we designed 213 MNP markers derived from 458 genome assemblies. Method validation encompassed reproducibility, accuracy, sensitivity (detection limit), and specificity using laboratory-adapted strains, artificial DNA mixtures, and uncultured rice leaf samples. Performance was benchmarked against whole-genome sequencing (WGS) and LoFreq variant calling.
MGV-Seq achieved 100% reproducibility and accuracy in major allele detection, with sensitivity down to 0.1% ( = 12 strains) for low-abundance variants and significantly higher specificity than LoFreq. Analysis to 40 strains revealed widespread heterogeneity (90% of strains) and misidentification (e.g., HN-P5 as ). Homonymous strains exhibited significant genetic and phenotypic divergence, attributed to contamination rather than mutation. MGV-Seq successfully identified dominant strains and low-frequency variants in rice leaf samples and authenticated single-colony strains with 100% major allele similarity.
MGV-Seq establishes a robust, high-throughput solution for strain identification, microevolution monitoring, and authentication, overcoming limitations of culture-dependent and metagenomics-based methods. Its applicability extends to other microorganisms, offering potential for clinical, agricultural, and forensic diagnostics.
在菌株水平上精确检测微生物遗传变异（MGV）对于可靠的疾病诊断、病原体监测和可重复的研究至关重要。然而，目前的方法受到灵敏度有限、特异性不足以及对培养的依赖等限制。为应对这些挑战，我们开发了MGV-Seq，这是一种创新的非培养方法，它整合了多重PCR、高通量测序和生物信息学来分析多个分散的单核苷酸多态性（MNP）标记，从而实现高分辨率的菌株区分。
以[具体生物名称]作为模式生物，我们从458个基因组组装中设计了213个MNP标记。方法验证涵盖了使用实验室适应菌株、人工DNA混合物和未培养的水稻叶片样本的可重复性、准确性、灵敏度（检测限）和特异性。性能以全基因组测序（WGS）和LoFreq变异检测作为基准进行评估。
MGV-Seq在主要等位基因检测中实现了100%的可重复性和准确性，对于低丰度变异的灵敏度低至0.1%（n = 12个菌株），并且特异性显著高于LoFreq。对40个[具体生物名称]菌株的分析揭示了广泛的异质性（90%的菌株）和错误鉴定（例如，将HN-P5鉴定为[错误名称]）。同名菌株表现出显著的遗传和表型差异，这归因于污染而非突变。MGV-Seq成功鉴定了水稻叶片样本中的优势菌株和低频变异，并以100%的主要等位基因相似性验证了单菌落菌株。
MGV-Seq为菌株鉴定、微进化监测和验证建立了一种强大的高通量解决方案，克服了基于培养和宏基因组学方法的局限性。其适用性扩展到其他微生物，为临床、农业和法医诊断提供了潜力。
