MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data
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 inc...
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MDPI AG
2020-05-01
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| Series: | Microorganisms |
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| Online Access: | https://www.mdpi.com/2076-2607/8/5/684 |
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| author | Nathanael J. Bangayan Baochen Shi Jerry Trinh Emma Barnard Gabriela Kasimatis Emily Curd Huiying Li |
| author_facet | Nathanael J. Bangayan Baochen Shi Jerry Trinh Emma Barnard Gabriela Kasimatis Emily Curd Huiying Li |
| author_sort | Nathanael J. Bangayan |
| collection | DOAJ |
| description | 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, <i>Propionibacterium acnes</i>, 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. |
| first_indexed | 2024-03-10T19:58:50Z |
| format | Article |
| id | doaj.art-dfb4bb86c2584ce9800e15e5f76e76d5 |
| institution | Directory Open Access Journal |
| issn | 2076-2607 |
| language | English |
| last_indexed | 2024-03-10T19:58:50Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Microorganisms |
| spelling | doaj.art-dfb4bb86c2584ce9800e15e5f76e76d52023-11-19T23:45:14ZengMDPI AGMicroorganisms2076-26072020-05-018568410.3390/microorganisms8050684MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic DataNathanael J. Bangayan0Baochen Shi1Jerry Trinh2Emma Barnard3Gabriela Kasimatis4Emily Curd5Huiying Li6Department of Molecular and Medical Pharmacology, Crump Institute for Molecular Imaging, University of California, Los Angeles, CA 90095, USADepartment of Molecular and Medical Pharmacology, Crump Institute for Molecular Imaging, University of California, Los Angeles, CA 90095, USADepartment of Molecular and Medical Pharmacology, Crump Institute for Molecular Imaging, University of California, Los Angeles, CA 90095, USADepartment of Molecular and Medical Pharmacology, Crump Institute for Molecular Imaging, University of California, Los Angeles, CA 90095, USADepartment of Molecular and Medical Pharmacology, Crump Institute for Molecular Imaging, University of California, Los Angeles, CA 90095, USADepartment of Molecular and Medical Pharmacology, Crump Institute for Molecular Imaging, University of California, Los Angeles, CA 90095, USADepartment of Molecular and Medical Pharmacology, Crump Institute for Molecular Imaging, University of California, Los Angeles, CA 90095, USAThe 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, <i>Propionibacterium acnes</i>, 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.https://www.mdpi.com/2076-2607/8/5/684microbiomestrainmetagenomicsMLST<i>Propionibacterium acnes</i>method |
| spellingShingle | Nathanael J. Bangayan Baochen Shi Jerry Trinh Emma Barnard Gabriela Kasimatis Emily Curd Huiying Li MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data Microorganisms microbiome strain metagenomics MLST <i>Propionibacterium acnes</i> method |
| title | MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data |
| title_full | MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data |
| title_fullStr | MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data |
| title_full_unstemmed | MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data |
| title_short | MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data |
| title_sort | mg mlst characterizing the microbiome at the strain level in metagenomic data |
| topic | microbiome strain metagenomics MLST <i>Propionibacterium acnes</i> method |
| url | https://www.mdpi.com/2076-2607/8/5/684 |
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