How to Design a Skin Microbiome Study, Part II: Amplicon Sequencing

In this post, the second of a 2-part series on skin microbiome research, we will discuss technical issues surrounding sequencing of human skin microbes. Read the first blog post here.

At this point, the microbial ecologist conducting a skin microbiome study has now collected all the skin samples she needs, and the DNA has been extracted. We turn to the question of how to decide on the sequencing strategy. Metagenome shotgun sequencing, in which the entire community of microbes is sequenced in an untargeted manner, can provide invaluable information about the functional potential of the microbiome, but – despite continually dropping sequencing costs – it is still expensive. The researcher in this case settles for 16S marker gene sequencing, which targets a specific region of the gene. Now, which primer pair should she choose?

The current dogma in the field is that primers targeting regions V1-V3 are better at describing skin bacterial communities than the V4 region primer pair. (The V4 region is commonly used for studying gut communities and other environments.) This is because V1-V3-sequenced communities better recapitulate the taxonomic composition and relative abundance of “mock community” controls (Meisel et al., 2016). And V4 primers poorly amplify typical skin microbes, notably Propionibacterium and some Staphylococcus species (Meisel et al., 2016). But should V4 be discarded in favor of V1-V3?

The reason behind the V4 region’s underestimation of Propionibacterium is a single mismatch at the end of the primer that prevents efficient binding to a specific group of bacteria. To evaluate if V4 region may be a suitable target for characterizing skin bacteria, our team re-designed the V4 primer pair and tested in silico its ability to improve the coverage of underrepresented propionibacteria. With these new candidate primers, we are able (theoretically) to increase the coverage of Propionibacterium to over 67%–from less than 3%–without losing coverage of the other bacterial groups. Our next step is to evaluate the accuracy of this approach using a mock community as the standard.

There are advantages to using existing V4 primers. They can detect the genera Finegoldia and Peptoniphilus, which are increased in persons with primary immunodeficiencies (Oh et al ., 2013). Zeeuwen et al., citing previous work, also pointed out that the 27F primer used for the V1-V3 region inefficiently amplifies Gardnerella and Lactobacillus, which have been found to be associated with females (Zeeuwen et al., 2012). In general, V1-V3 classifies fewer populations down to the genus level (Meisel et al., 2016). Because the V1-V3 region is longer than the V4 region, paired-end reads generated with the Illumina MiSeq will not fully overlap. And without full overlap, denoising of reads is not as effective. Using the V3 chemistry (a 600-cycle kit, longer than the 500-cycle kit of the V2 version) will not solve the problem and may even make it worse, because the sequence quality drops after 500 cycles.

In this two-part blog series, we have discussed how to collect enough microbial biomass to run a skin microbiome study, and how to deal with environmental contamination. We have seen that even relatively minor changes in primer sequences may improve the detection of bacteria relevant to skin microbiomes. Feel free to reach out to our team for more information on designing your own skin microbiome study!

About the company

Microbiome Insights provides state-of-the art microbiome analysis and bioinformatics.

Our end-to-end service starts with experimental design and sample collection and extends to data analysis and bioinformatics interpretation.

Microbiome Insights is focused on providing our clients with a deeper understanding of functions and interactions of microbial communities across a range of human, animal, agricultural, and environmental research applications. Our team of experts and testing methods combine to provide fast, dependable, cost-effective results with highly comprehensive, publication quality bioinformatics. To learn more, see here.

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