Dataset Information

Improved single-swab sample preparation for recovering bacterial and phage DNA from human skin and wound microbiomes.

ABSTRACT:

Background

Characterization of the skin and wound microbiome is of high biomedical interest, but is hampered by the low biomass of typical samples. While sample preparation from other microbiomes (e.g., gut) has been the subject of extensive optimization, procedures for skin and wound microbiomes have received relatively little attention. Here we describe an improved method for obtaining both phage and microbial DNA from a single skin or wound swab, characterize the yield of DNA in model samples, and demonstrate the utility of this approach with samples collected from a wound clinic.

Results

We find a substantial improvement when processing wound samples in particular; while only one-quarter of wound samples processed by a traditional method yielded sufficient DNA for downstream analysis, all samples processed using the improved method yielded sufficient DNA. Moreover, for both skin and wound samples, community analysis and viral reads obtained through deep sequencing of clinical swab samples showed significant improvement with the use of the improved method.

Conclusion

Use of this method may increase the efficiency and data quality of microbiome studies from low-biomass samples.

SUBMITTER: Verbanic S

PROVIDER: S-EPMC6729076 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

Improved single-swab sample preparation for recovering bacterial and phage DNA from human skin and wound microbiomes.

Verbanic Samuel S Kim Colin Y CY Deacon John M JM Chen Irene A IA

BMC microbiology 20190905 1

<h4>Background</h4>Characterization of the skin and wound microbiome is of high biomedical interest, but is hampered by the low biomass of typical samples. While sample preparation from other microbiomes (e.g., gut) has been the subject of extensive optimization, procedures for skin and wound microbiomes have received relatively little attention. Here we describe an improved method for obtaining both phage and microbial DNA from a single skin or wound swab, characterize the yield of DNA in model ...[more]

PMID: 31488062

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Project description:The human microbiome contributes significantly to the genetic content of the human body. Genetic and environmental factors help shape the microbiome, and as such, the microbiome can be unique to an individual. Previous studies have demonstrated the potential to use microbiome profiling for forensic applications; however, a method has yet to identify stable features of skin microbiomes that produce high classification accuracies for samples collected over reasonably long time intervals. A novel approach is described here to classify skin microbiomes to their donors by comparing two feature types: Propionibacterium acnes pangenome presence/absence features and nucleotide diversities of stable clade-specific markers. Supervised learning was used to attribute skin microbiomes from 14 skin body sites from 12 healthy individuals sampled at three time points over a >2.5-year period with accuracies of up to 100% for three body sites. Feature selection identified a reduced subset of markers from each body site that are highly individualizing, identifying 187 markers from 12 clades. Classification accuracies were compared in a formal model testing framework, and the results of this analysis indicate that learners trained on nucleotide diversity perform significantly better than those trained on presence/absence encodings. This study used supervised learning to identify individuals with high accuracy and associated stable features from skin microbiomes over a period of up to almost 3 years. These selected features provide a preliminary marker panel for future development of a robust and reproducible method for skin microbiome profiling for forensic human identification.IMPORTANCE A novel approach is described to attribute skin microbiomes, collected over a period of >2.5 years, to their individual hosts with a high degree of accuracy. Nucleotide diversities of stable clade-specific markers with supervised learning were used to classify skin microbiomes from a particular individual with up to 100% classification accuracy for three body sites. Attribute selection was used to identify 187 genetic markers from 12 clades which provide the greatest differentiation of individual skin microbiomes from 14 skin sites. This study performs skin microbiome profiling from a supervised learning approach and obtains high classification accuracy for samples collected from individuals over a relatively long time period for potential application to forensic human identification.

Dataset Information

Improved single-swab sample preparation for recovering bacterial and phage DNA from human skin and wound microbiomes.

Background

Results

Conclusion

Publications

Improved single-swab sample preparation for recovering bacterial and phage DNA from human skin and wound microbiomes.

Similar Datasets

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