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

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Accurate microbial genetic variation (MGV) detection is crucial for disease diagnosis, pathogen surveillance, and research reproducibility.
• Existing methods for MGV detection are limited by sensitivity, specificity, and reliance on culturing.
• There is a need for advanced, culture-independent techniques for high-resolution strain differentiation.

Purpose of the Study:

• To develop and validate MGV-Seq, a novel culture-independent method for high-resolution microbial strain identification.
• To analyze microbial genetic variation using multiple dispersed nucleotide polymorphism (MNP) markers.
• To overcome the limitations of current culture-dependent and metagenomics-based approaches.

Main Methods:

• Developed MGV-Seq by integrating multiplex PCR, high-throughput sequencing, and bioinformatics to analyze MNP markers.
• Designed 213 MNP markers from 458 genome assemblies of *Xanthomonas oryzae*.
• Validated method performance including reproducibility, accuracy, sensitivity (down to 0.1%), and specificity using various samples and benchmarking against whole-genome sequencing (WGS) and LoFreq.

Main Results:

• MGV-Seq demonstrated 100% reproducibility and accuracy in major allele detection.
• Achieved high sensitivity (0.1%) for low-abundance variants and superior specificity compared to LoFreq.
• Revealed widespread genetic heterogeneity (90% of strains) and misidentification in *X. oryzae*, with successful identification of dominant and low-frequency strains in complex samples.

Conclusions:

• MGV-Seq provides a robust, high-throughput solution for microbial strain identification, microevolution monitoring, and authentication.
• The method overcomes limitations of traditional culture-dependent and metagenomics approaches.
• MGV-Seq has broad applicability in clinical, agricultural, and forensic diagnostics for various microorganisms.