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change the skin microbiota or physiological condition. An alternative (when studies investigate the hand microbiota) is to use glove-based sampling. It seems that the glove method recovers more unique Operational Taxonomic Units (OTUs) than swab-based sampling while being more reproducible at the same time, probably because it recovers greater total biomass and bacterial communities. Immediate freezing and storage at -80 °C is the norm. Skin bacterial community composition varies by sequencing technique (DNA isolation, amplification and quantification by PCR). Bioinformatics is necessary for the analysis and interpretation of microbiome data. This analysis includes identification of all bacterial taxa present in the samples. The DNA sequences are first quality-controlled to remove low quality reads and chimeric sequences possibly artificially created during the PCR amplification process. Then sequences that have passed the quality control filtering are clustered into OTUs, i.e. nearly identical sequences which will be used as surrogates to define species. An OTUs table is generated that includes the number of OTUs and the taxonomic classification. From this table can be prepared phylogenetic trees, and summary charts for each specific taxonomic level (e.g. phylum, family, genus, species) presented in different formats called bar charts and heat maps. Finally, a variety of statistical analyses can be performed to assess the differences in OTUs composition and abundance between samples or groups. Microbiome diversity is typically described using different metrics. Alpha diversity is a measure of the distance or dissimilarity between each sample pair. Among the other commonly used metrics, one can mention the Shannon’s diversity index which measures the richness and evenness (respectively the number of species and their relative abundances) and the Bray- Curtis distances between all samples. Specific statistical tests exist to explore whether a community composition changes (PERMANOVA). Because of the complexity of the data resulting from microbiome studies, it is interesting to have a more visual presentation of the results. Ordination techniques, such as the Principal Coordinates Analysis (PCoA), allow summarising the results in two- or three-dimensional scatterplots revealing the clustering patterns. To date, most published skin microbiome studies have been ecological surveys rather than controlled laboratory studies aiming at assessing the effects of specific perturbation such as hand hygiene, of toiletries products. A few studies have investigated the effect of skin care treatments, thermal water spray on skin microbiota. More research has been done on prebiotics and probiotics. This is hardly a surprise since the existence of a “gut-skin axis” was suggested many years
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ago, with the idea that diet and the composition of resident microbial populations in the gut are dynamic factors able to influence the skin condition. It has been proved that probiotics have the ability to restrain the population of harmful intestinal bacteria and improve skin condition. Prebiotics are considered ‘food’ for beneficial bacteria and are defined as “non-digestible food” ingredients that beneficially affect the host by selectively stimulating the growth of one or a limited number of bacterial species in the colon which have the potential to improve host health. Probiotics are beneficial microorganisms known to exert numerous positive effects on human health. Fermented dairy products have been proposed as a natural source of probiotics to promote intestinal health. Indeed there is growing evidence showing that modulation of the gastrointestinal tract microbiota can modulate skin health as well. But topical application of dairy products was also found to provide skin benefits. Investigating the effect of various substances on the cutaneous microbiome is still is its infancy. Following prebiotics/probiotics and toiletries products which were reasonably assumed to have an effect on bacteria, it is very likely that skin care products will be formulated and evaluated to check if cutaneous health can be achieved through new concept products.
Conclusion and perspectives We have almost reached the point where we can characterise nearly exhaustively the bacteria inhabiting the skin and microbiome research has already improved our understanding the pathogenesis of some diseases. However many questions still remain. Firstly, we are not assured to really understand of what defines a healthy microbiota. Scientists have started to find associations of some skin diseases or particular skin conditions with particular microbial species but the causality relationship is often not demonstrated yet. Secondly, little is known about the persistence of bacterial populations, the causes and consequences of their time fluctuations. We know virtually nothing about most metabolites that are produced by skin bacteria in vivo, even though these are the key molecules responsible for the cross-talks between microbes and their human host. This point is of utmost importance since we have to decipher the mechanistic bases (particularly the chemical signals) for interactions between members of microbial communities and their hosts or the mechanisms that underlie associations between specific skin areas and their respective microbiota. This knowledge will enable creation of new tools to manipulate the microbiota. Next generation high- throughput sequencing and development of novel bioinformatics approaches will certainly
help to fill these gaps in our knowledge of microbiome physiological effects. Then, we will have a chance to be able to modulate particular bacteria within the skin microbiota community, to use probiotics to modify the gut microbiome to achieve ‘beauty from within’ objectives, to manipulate host- microbial homeostasis without risking to create unforeseen adverse outcomes. Rational microbiome-based interventions using local application of selected bacteria or topical treatments modulating bacterial activity could thus become an essential tool in the field of personalised cosmetic and medical treatments. For instance, the feasibility of microbiome-based skin diagnosis has already been proven for psoriasis patients.
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References 1 Kong HH, Segre JA. The molecular revolution in cutaneous biology: investigating the skin microbiome. J Invest Dermatol. 2017; 137(5): e119-e122.
2 Harkins CP, Pettigrew KA, Oravcova K, et al. The microevolution and epidemiology of Staphylococcus aureus colonization during atopic eczema disease flare. J Invest Dermatol. 2018; 138(2): 336-343.
3 Dagnelie MA, Corvec S, Saint-Jean M, et al. Decrease of diversity of Propionibacterium acnes phylotypes in patients with severe acne on the back. Acta Derm Venereol. 2018; 98: 262-267.
4 Kong HH, Andersson B, Clavel T, et al. Performing skin microbiome research: a method to the madness. J Invest Dermatol. 2017; 137(3): 561-568.
5 Kong HH. Details matter: designing skin microbiome studies. J Invest Dermatol. 2016; 136(5): 900-902.
6 Goodrich JK, Di Rienzi SC, Poole AC, et al. Conducting a microbiome study. Cell. 2014; 158(2): 250-262.
7 Castelino M, Eyre S, Moat J, et al. Optimisation of methods for bacterial skin microbiome investigation: primer selection and comparison of the 454 versus MiSeq platform. BMC Microbiology 2017; 17(1): 23.
8 Zapka C, Leff J, Henley J, et al. Comparison of standard culture-based method to culture- independent method for evaluation of hygiene effects on the hand microbiome. mBio 2017; 8(2): e00093-17.
9 Mori N, Kano M, Masuoka N, et al. Effect of probiotic and prebiotic fermented milk on skin and intestinal conditions in healthy young female students. Biosci Microbiota, Food Health 2016; 35(3): 105-112.
10 Bouslimani A, Porto C, Rath CM, et al. Molecular cartography of the human skin surface in 3D. Proc Natl Acad Sci U S A. 2015; 112(17): e2120-2127.
11 Kumar R, Eipers P, Little RB, et al. Getting started with microbiome analysis: sample acquisition to bioinformatics. Curr Protoc Hum Genet. 2014; 82: 18.8.1-29.
12 Sharpton TJ. An introduction to the analysis of shotgun metagenomic data. Front Plant Sci. 2014; 5: 209.
September 2018
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