Assessment of protocols for characterization of the human skin microbiome using shotgun metagenomics and comparative analysis with 16S metabarcoding
Bibliographic record
Abstract
ABSTRACT The skin microbiome includes bacteria, fungi, and viruses, with composition varying significantly across body sites. Although 16S rRNA gene sequencing is common, it excludes non-prokaryotic taxa and offers limited functional data. Shotgun metagenomics provides broader taxonomic and functional insights but is challenging for low-biomass skin samples due to limited microbial DNA and high host contamination. In this study, we characterized the microbiome of the forehead and armpits in healthy individuals using shotgun metagenomics and assessed the strategies to improve sequencing success. We compared collection kits, DNA extraction protocols, and tested multiple displacement amplification (MDA). We found that sampling with D-Squame discs followed by an in-house DNA extraction protocol was the most effective combination to maximize DNA yields. MDA introduced significant compositional biases and is not recommended. Shotgun sequencing, without MDA, produced microbial compositions and diversity indices broadly consistent with 16S rRNA metabarcoding, although it showed discrepancies in the relative abundance of some genera. Consistent with prior studies, the armpit microbiome was dominated by Staphylococcus spp ., whereas the forehead microbiome was dominated by Cutibacterium spp . Critically, shotgun sequencing provided additional insights into viral and eukaryotic microorganisms and revealed the functional potential of microbial communities, demonstrating its clear advantages over 16S rRNA metabarcoding for comprehensive skin microbiome research. IMPORTANCE With growing evidence of the role of microorganisms in maintaining healthy skin, accurately characterizing the skin microbiome remains a significant challenge. In this study, we demonstrate that shotgun sequencing, carried out with adapted wet lab protocols, provides deep insights into the microbiome composition of specific areas, such as the forehead or the armpits. Notably, it enables the characterization of fungi and viruses while offering direct functional insights into microbial communities, providing a clear advantage over 16S ribosomal RNA gene sequencing. Our findings highlight the potential of shotgun metagenomics as a powerful tool for comprehensive skin microbiome analysis. They emphasize the importance of tailored protocols for low-biomass samples, improving the reliability of shotgun sequencing and paving the way for more robust clinical studies focused on the skin microbiome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".