Abstract
During aging of human skin, a number of intrinsic and extrinsic factors cause the alteration of the skin's structure, function and cutaneous physiology. Many studies have investigated the influence of the skin microbiome on these alterations, but the molecular mechanisms that dictate the interplay between these factors and the skin microbiome are still not fully understood. To obtain more insight into the connection between the skin microbiome and the human physiological processes involved in skin aging, we performed a systematic study on interconnected pathways of human and bacterial metabolic processes that are known to play a role in skin aging. The bacterial genes in these pathways were subsequently used to create Hidden Markov Models (HMMs), which were applied to screen for presence of defined functionalities in both genomic and metagenomic datasets of skin-associated bacteria. These models were further applied on 16S rRNA gene sequencing data from skin microbiota samples derived from female volunteers of two different age groups (25-28 years ('young') and 59-68 years ('old')). The results show that the main bacterial pathways associated with aging skin are those involved in the production of pigmentation intermediates, fatty acids and ceramides. This study furthermore provides evidence for a relation between skin aging and bacterial enzymes involved in protein glycation. Taken together, the results and insights described in this paper provide new leads for intervening with bacterial processes that are associated with aging of human skin.
Highlights
In the past decade, there has been increased appreciation of the influence of the host microbiome composition and functionalities on human cellular processes, such as gut and skin health and immunity, and visible aging of the skin
To increase our understanding of the influence of microbial functionalities on molecular processes involved in aging of human skin, there is a need for improved computational data analysis workflows that allow for integration of knowledge and data on both aspects
To define the influence of microbial functionalities represented by the skin microbiome on molecular processes involved in aging of human skin, a multistep bioinformatics and data analysis approach was applied (Fig 1)
Summary
There has been increased appreciation of the influence of the host microbiome composition and functionalities on human cellular processes, such as gut and skin health and immunity, and visible aging of the skin. In order to do this correctly, profound knowledge of the physiology of bacterial skin residents needs to be combined with knowledge on the physiology of the host that is related to the skin condition of interest Analysis of such multidimensional data is performed by mapping to canonical knowledge that is stored in databases, for instance to determine enriched biological processes that are modified upon intervention or during the progression of a disease [15]. To increase our understanding of the influence of microbial functionalities (represented by the skin microbiome) on molecular processes involved in aging of human skin, there is a need for improved computational data analysis workflows that allow for integration of knowledge and data on both aspects. The described methodology was validated on a publicly available skin-related shotgun metagenomics dataset
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