MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data.

Nathanael J Bangayan,Baochen Shi,Huiying Li,Emma Barnard,Emily Curd,Jerry Trinh,Gabriela Kasimatis

doi:10.3390/microorganisms8050684

Nathanael J Bangayan, Baochen Shi + Show 5 more

Open Access

https://doi.org/10.3390/microorganisms8050684

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Abstract

The microbiome plays an important role in human physiology. The composition of the human microbiome has been described at the phylum, class, genus, and species levels, however, it is largely unknown at the strain level. The importance of strain-level differences in microbial communities has been increasingly recognized in understanding disease associations. Current methods for identifying strain populations often require deep metagenomic sequencing and a comprehensive set of reference genomes. In this study, we developed a method, metagenomic multi-locus sequence typing (MG-MLST), to determine strain-level composition in a microbial community by combining high-throughput sequencing with multi-locus sequence typing (MLST). We used a commensal bacterium, Propionibacterium acnes, as an example to test the ability of MG-MLST in identifying the strain composition. Using simulated communities, MG-MLST accurately predicted the strain populations in all samples. We further validated the method using MLST gene amplicon libraries and metagenomic shotgun sequencing data of clinical skin samples. MG-MLST yielded consistent results of the strain composition to those obtained from nearly full-length 16S rRNA clone libraries and metagenomic shotgun sequencing analysis. When comparing strain-level differences between acne and healthy skin microbiomes, we demonstrated that strains of RT2/6 were highly associated with healthy skin, consistent with previous findings. In summary, MG-MLST provides a quantitative analysis of the strain populations in the microbiome with diversity and richness. It can be applied to microbiome studies to reveal strain-level differences between groups, which are critical in many microorganism-related diseases.

Highlights

Our knowledge of the human microbiome and its relationship to health and disease has been rapidly increasing in recent years
In this study we investigated whether we can utilize the program STRUCTURE to identify strain populations and quantify their relative abundances from microbiome data, which we named as the metagenomic multi-locus sequence typing (MG-multi-locus sequence typing (MLST)) method
With our clusters acting as representatives of the various strain populations, we can infer strain-level composition, thereby applying MLST on metagenomic samples

Summary

Introduction

Our knowledge of the human microbiome and its relationship to health and disease has been rapidly increasing in recent years. Within the species of Escherichia coli, strain Nissle 1917 has been used as a probiotic to treat ulcerative colitis [1], while strain O157:H7 is the most common cause of hemolytic uremic syndrome [2]. Another example is Propionibacterium acnes, a common commensal found on the human skin. By studying the strain composition of the microbiome, new correlations or causal relationships between microbial organisms and health or disease may be discovered

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