[HTML][HTML] Gut microbiota analysis results are highly dependent on the 16S rRNA gene target region, whereas the impact of DNA extraction is minor

A Rintala, S Pietilä, E Munukka, E Eerola… - Journal of …, 2017 - ncbi.nlm.nih.gov
A Rintala, S Pietilä, E Munukka, E Eerola, JP Pursiheimo, A Laiho, S Pekkala, P Huovinen
Journal of biomolecular techniques: JBT, 2017ncbi.nlm.nih.gov
Next-generation sequencing (NGS) is currently the method of choice for analyzing gut
microbiota composition. As gut microbiota composition is a potential future target for clinical
diagnostics, it is of utmost importance to enhance and optimize the NGS analysis
procedures. Here, we have analyzed the impact of DNA extraction and selected 16S rDNA
primers on the gut microbiota NGS results. Bacterial DNA from frozen stool specimens was
extracted with 5 commercially available DNA extraction kits. Special attention was paid to …
Abstract
Next-generation sequencing (NGS) is currently the method of choice for analyzing gut microbiota composition. As gut microbiota composition is a potential future target for clinical diagnostics, it is of utmost importance to enhance and optimize the NGS analysis procedures. Here, we have analyzed the impact of DNA extraction and selected 16S rDNA primers on the gut microbiota NGS results. Bacterial DNA from frozen stool specimens was extracted with 5 commercially available DNA extraction kits. Special attention was paid to the semiautomated DNA extraction methods that could expedite the analysis procedure, thus being especially suitable for clinical settings. The microbial composition was analyzed with 2 distinct protocols: 1 targeting the V3–V4 and the other targeting the V4–V5 area of the bacterial 16S rRNA gene. The overall effect of DNA extraction on the gut microbiota 16S rDNA profile was relatively small, whereas the 16S rRNA gene target region had an immense impact on the results. Furthermore, semiautomated DNA extraction methods clearly appeared suitable for NGS procedures, proposing that application of these methods could importantly reduce hands-on time and human errors without compromising the validity of results.
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