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Are the predicted known bacterial strains in a sample really present? A case study.

Minerva Ventolero¹, Saidi Wang², Haiyan Hu³

¹Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, Florida, United States of America.

|October 13, 2023

Summary

Bacterial genomes evolve rapidly. This study found previously identified strains of Staphylococcus aureus and Staphylococcus epidermis were unlikely present in atopic dermatitis samples, highlighting limitations of strain-based studies.

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Area of Science:

Microbiology
Genomics
Bioinformatics

Background:

Bacterial genomes accumulate mutations, raising questions about the persistence of previously identified strains in current samples.
Atopic dermatitis (AD) is associated with complex microbial communities, including Staphylococcus species.

Purpose of the Study:

To investigate the presence of known Staphylococcus aureus and Staphylococcus epidermis strains in atopic dermatitis shotgun metagenomic samples.
To evaluate the reliability of strain identification tools in detecting previously characterized bacterial strains.

Main Methods:

Analysis of 68 atopic dermatitis shotgun metagenomic samples.
Evaluation of sixteen known S. aureus and S. epidermis strains using original study predictions and two popular bioinformatics tools.
Assessment of the likelihood of strain presence in the analyzed samples.

Main Results:

Only two known strains were consistently predicted between the original study and the current analysis using the same tool.
None of the sixteen known strains were likely present in the 68 analyzed atopic dermatitis samples.
Significant discrepancies were observed in strain prediction even with identical tools.

Conclusions:

Known-strain-based studies have limitations, particularly for rapidly evolving bacteria like Staphylococcus species.
Previously identified bacterial strains may not be reliably present in current environmental or clinical samples.
De novo bacterial strain identification directly from shotgun metagenomic data is recommended for greater accuracy.