Dataset Information

Identification of chronological and photoageing-associated microRNAs in human skin.

ABSTRACT: MicroRNAs are short non-coding RNAs that play key roles in regulating biological processes. In this study, we explored effects of chronological and photoageing on the miRNome of human skin. To this end, biopsies were collected from sun-exposed (outer arm, n?=?45) and sun-protected (inner arm, n?=?45) skin from fair-skinned (phototype II/III) healthy female volunteers of three age groups: young, 18-25?years, middle age, 40-50?years and aged,?>?70?years. Strict inclusion criteria were used for photoageing scoring and for chronological ageing. Microarray analysis revealed that chronological ageing had minor effect on the human skin miRNome. In contrast, photoageing had a robust impact on miRNAs, and a set of miRNAs differentially expressed between sun-protected and sun-exposed skin of the young and aged groups was identified. Upregulation of miR-383, miR-145 and miR-34a and downregulation of miR-6879, miR-3648 and miR-663b were confirmed using qRT-PCR in sun-exposed skin compared with sun-protected skin. qRT-PCR analysis revealed that miR-383, miR-34a and miR-134 were differentially expressed in all three age groups both in chronological and photoageing, suggesting a synergetic effect of intrinsic and extrinsic ageing on their expression. In conclusion, our study identifies a unique miRNA signature which may contribute to skin ageing.

SUBMITTER: Srivastava A

PROVIDER: S-EPMC6113407 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Publications

Identification of chronological and photoageing-associated microRNAs in human skin.

Srivastava Ankit A Karlsson Magnus M Marionnet Claire C Bernerd Françoise F Gueniche Audrey A Rawadi Charles E L CEL Ståhle Mona M Sonkoly Enikö E Breton Lionel L Pivarcsi Andor A

Scientific reports 20180828 1

MicroRNAs are short non-coding RNAs that play key roles in regulating biological processes. In this study, we explored effects of chronological and photoageing on the miRNome of human skin. To this end, biopsies were collected from sun-exposed (outer arm, n = 45) and sun-protected (inner arm, n = 45) skin from fair-skinned (phototype II/III) healthy female volunteers of three age groups: young, 18-25 years, middle age, 40-50 years and aged, > 70 years. Strict inclusion criteria were used for pho ...[more]

PMID: 30154427

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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

Identification of chronological and photoageing-associated microRNAs in human skin.

Publications

Identification of chronological and photoageing-associated microRNAs in human skin.

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