Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• 16S rRNA gene profiling is crucial for microbiome studies, typically using combined hypervariable regions like V3-V4 for enhanced bacterial identification.
• Chronic respiratory diseases are often associated with altered lung microbiomes, necessitating accurate taxonomic profiling.

Purpose of the Study:

• To compare the resolving power of different 16S rRNA hypervariable regions (V1-V2, V3-V4, V5-V7, V7-V9) for microbiome analysis in sputum samples.
• To identify the optimal region combination for accurate taxonomic identification of respiratory bacteria.

Main Methods:

• DNA extraction from 33 human sputum samples and a mock community standard.
• Library preparation using a QIASeq screening panel for Illumina platforms.
• Analysis of amplicon sequence variants (ASVs) using the Deblur algorithm.

Main Results:

• Alpha diversity was significantly higher for V1-V2, V3-V4, and V5-V7 compared to V7-V9.
• Significant compositional differences were observed between region pairs, with V3-V4 and V5-V7 showing high similarity.
• The V1-V2 region combination demonstrated the highest resolving power, sensitivity, and specificity for identifying respiratory bacterial taxa.

Conclusions:

• Different 16S rRNA hypervariable regions yield significant variations in taxonomic identification from sputum samples.
• The V1-V2 region combination is recommended for accurate and sensitive taxonomic profiling of the sputum microbiome in respiratory diseases.
• This finding provides a valuable option for taxonomic identification as full-length 16S rRNA sequencing technologies advance.