MGV-Seq: a sensitive and culture-independent method for detecting microbial genetic variation

Lun Li¹Weiyu Kong¹Jing Sun¹Yongzhong Jiang²Tiantian Li¹Zhihui Xia³Junfei Zhou¹Zhiwei Fang¹Lihong Chen¹Shun Feng¹Huiyin Song¹Huafeng Xiao¹Baolong Zhang¹Bin Fang²Hai Peng^1*Lifen Gao^1*

• ¹Institute for Systems Biology, Jianghan University, Wuhan, Hebei Province, China
• ²Hubei Provincial Center for Diseases Control and Prevention, Wuhan, Hubei Province, China
• ³Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, Hainan University, Haikou, Hainan Province, China

Background: 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.Methods: Using Xanthomonas oryzae 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.Results: MGV-Seq achieved 100% reproducibility and accuracy in major allele detection, with sensitivity down to 0.1% (n=12 strains) for low-abundance variants and significantly higher specificity than LoFreq. Analysis to 40 X. oryzae strains revealed widespread heterogeneity (90% of strains) and misidentification (e.g., HN-P5 as Xoc). 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.