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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Oral microbiome profiles: 16S rRNA pyrosequencing and microarray assay comparison.

Jiyoung Ahn¹, Liying Yang, Bruce J Paster

¹Division of Epidemiology, Department of Environmental Medicine, New York University School of Medicine, New York, New York, United States of America. jiyoung.ahn@nyumc.org

|August 11, 2011

Summary

Comparing 16S rRNA gene sequencing and DNA microarray for oral microbiome analysis, both methods showed high correlation at the phylum and genus levels. 16S rRNA sequencing offers broader taxa identification and greater sensitivity for disease risk studies.

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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

Area of Science:

Microbiome research
Molecular biology techniques
Human health and disease

Background:

The human oral microbiome plays a role in various health conditions.
High-throughput technologies enable detailed analysis of microbial communities.
Comparing different molecular methods is crucial for accurate microbiome profiling.

Purpose of the Study:

To compare the effectiveness of 16S rRNA gene sequencing and DNA microarray for oral microbiome analysis.
To assess the correlation between these two methods at phylum and genus levels.
To determine the suitability of each method for large-scale epidemiological studies.

Main Methods:

Oral wash samples from 20 individuals were analyzed using 454 pyrosequencing of the 16S rRNA gene (V3-V5 region).
Targeted microbial identification was performed using the Human Oral Microbe Identification Microarray (HOMIM).
Correlation and relative abundance were compared between sequencing read ratios and microarray hybridization intensities.

Main Results:

Both 16S rRNA sequencing and HOMIM identified major oral phyla (Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, Fusobacteria) with high correlation (r=0.70–0.86).
16S rRNA sequencing identified more genera (77) than HOMIM (49), with 37 common genera accounting for over 98% of classified bacteria.
High concordance and correlation were observed for common genera (e.g., Streptococcus, Prevotella) between the two methods (r=0.70-0.84).

Conclusions:

Microbiome profiles from 16S rRNA pyrosequencing and HOMIM are highly correlated at phylum and genus levels.
Both methods are suitable for high-throughput epidemiological studies linking oral microbes to disease.
16S rRNA pyrosequencing may offer advantages in broader taxa identification, sequence data, and detection sensitivity.