Introduction to Omics
Metabolomics has its history as part of the wider omics field. Science has come a very long way since the mid-1970s when it was commonly believed that skin was colonised by only a few microbial species (Kligman, 1976). Today, the new omics techniques are revealing the true diversity and complexity of the microbiome and its interactions with skin. For example, genomic techniques show that, far from a few microbial species , the human skin microbiome actually contains around 1,000 species of bacteria plus numerous archaea, viruses, fungi and even small arthropods (Eisenstein, 2020). The story for proteins is similar. Back in the 1970s when researchers relied on very basic techniques such as counting stained-bands on electrophoretic gels, antibody-antigen binding tests and substrate specific enzyme assays, it was believed that there were relatively few proteins on skin. Proteomics and metaproteomics, which use advanced mass spectrometry and chromatographic techniques, have changed this view. Recently over 20,000 peptides and a further 4,785 proteins were identified in samples taken from the surface of skin (Gonindard, 2022). The omic revolution is really amazing and is revealing for the first time, the true complexity of skin and its microbiome.
Metabolomics: measuring nature and nurture
Metabolomics is the stand-out technology among all the other omics (even more than genomics, proteomics, epigenomics and transcriptomics) because, by systematically identifying and quantifying all the metabolites in a living sample, it gives researchers a broad vision of the biological processes happening at a particular moment (Nalbantoglu, 2019) . This ‘metabolone‘ is a true measure of both nature and nurture. The metabolites found on skin are there as a result of both the actions of proteins transcribed from genes and as a result of lifestyle choices such as the foods we choose to eat, medicines taken, the cosmetics we use as well as other environmental influences (Gaynor, 2017). Metabolomics reveals, in a moment of time, the presence and concentrations of small metabolites of around 50-1500 Da within a biological sample (Newsom SN, 2018). Metabolomics is unveiling the vast number of small molecules, which are constantly being produced and used by skin and its microbiota. One source estimates that the human metabolome consists of at least 500,000 different types of metabolite and if combinations such as those found between lipids and oligosaccharides are also included, then the number of measurable metabolites in the metabolome appear to be near infinite. This same source points out that the ”human microbiome and metabolome far exceeds the complexity of the human genome” (Pieter C. Dorrestein, 2014). Historically, biochemical analysis relied on one or a very few molecular markers as indicators of a condition. The sampling methods and analytical techniques involved often have inherent practical problems and the resulting level of error is managed by analysing a valid number of replicates with the appropriate statistical methods. Because omics is revealing huge amounts of information from a single or very few samples, it raises questions about the adequacy of the methods being used and how the data is being analysed (Jackson O. Lay, 2006). To be dependable, the algorithms, filters and mathematics that lead to the interpretation of omic metadata should therefore always follow the FAIR principle, i.e., be Findable, Accessible, Interoperable and Reusable (Wilkinson, 2016).
Metabolomics and machine learning
With thanks to the recent publicity surrounding artificial intelligence-based chatbots such as ChatGPT, the pros and cons of machine learning are becoming well known. Although far from perfect, the use of deep learning architecture to process entire sequences of words at once has already led to some impressive results as well as to amusing failures. Unsurprisingly then, Machine Learning is being seen as the way to resolve the omics-data dilemma of how to automate extracting information from large datasets derived from few samples with many features (Feldner-Busztin, 2023). This is early days for omics and while method developers, statisticians and bioinformatics experts grapple with these problems, and chatbots amuse us with their errors, it should not be surprising that many omic findings will not be supported by subsequent research.
Homeostasis, the molecular balancing act
Maintaining a healthy steady state between skin and its microbiome depends on many factors but primarily on skin–microbe interactions that activate skin’s immune systems and microbe–microbe interactions that resist dysbiosis. Unsurprisingly, sebum, cellular debris and metabolic by-products derived from skin are the microbiome’s main sources of energy and nutrients. However, the skin surface is also home to a large number of specialised functional molecules including lipids, which are not present in other parts of the body. For example, the dominant unsaturated fatty acid in sebum, sapienic acid, is unique to humans and at high concentrations it can be antimicrobial, prevent staphylococcal virulence factors, (including reducing the production of toxins) and work against psoriasis. The authors of one transcriptomic study on Staphylococcus aureusargue that by reducing the virulence of S. aureus, sapienic acid allows S. aureus, to colonise as part of the commensal flora. If the S. aureus were in a more virulent state they would trigger an inflammatory response, which would result in their comfortable niche being destroyed (Neumann, 2015). Members of the microbiome can transform skin derived triglycerides into short chained free fatty acids and glycerides that can act against other microbes or stimulate skin cells. For example, when oxygen levels are low Cutibacterium acnes produces propionate and valerate from skin lipids, which indirectly constrain the production of key proinflammatory mediators produced by keratinocytes, yet these short chained fatty acids can also cause an increase in sebocytes inflammatory gene expression (Sanford JA, 2019). Through a complicated balancing act of metabolic pathways, starting with the inhibition of histone deacetylases and activating fatty acid receptors, short chain fatty acids produced by various members of the skin microbiome, lower the levels of inflammatory cytokines released by keratinocytes in response to stimuli and so suppress monocyte/macrophage and neutrophil recruitment. Whereas at higher concentrations through other pathways in the balancing act, they can be proinflammatory by promoting cytokine expression and encouraging neutrophil accumulation. These results although confusing, confirm that the products of the microbiome can, in different ways, influence skins inflammatory processes (Chen, 2018) (Vinolo MA, 2011).
Metabolomics and revealing the Microbial mini factories
Thinking of microbes within the microbiome as mini factories is useful for understanding microbial metabolism and interpreting the relationship between microbes (Sharma, 2018). Skin’s microbiota can be thought of as a microbial scale industrial park with around 1,000 types of different mini factories. Each type of mini factory is processing metabolites in order to survive and flourish. Some of the secreted metabolites fuel the food web between the members of the microbiota. These Business-to-business-style interdependencies mean the success and survival of microbes can be dependent on the success of others. Metabolites that alter the local pH, or are defensive, or help with quorum sensing, or form protective biofilms, and those, as just discussed, that enable microbes to influence the host-microbe interface, together create the dynamic steady-state that keeps community of microbes in balance (Pieter C. Dorrestein, 2014).
Interestingly, general effects such as altering skin pH, can transform at least one, otherwise friendly microbe. Staphylococcus epidermidis, an important member of skin microbiota, is considered to be commensal and actively primes the immune response while its presents prevents opportunistic pathogens. However recent research into medical device infections shows that S. epidermidis can become an opportunistic pathogen when the pH is increased. Using next-generation proteomics and 1H NMR-based metabolomics, researchers demonstrated how returning a commensal strain of S. epidermidis from pH 7.4 (the pH of blood) to skin pH 5.5 could make them metabolically more active. Lowering the pH back to pH 5.5 induced oxidative phosphorylation and the synthesis of peptidoglycan, lipoteichoic acids and betaine. Whereas, at pH 7.4 the same strain of S. epidermidis underwent more glycolysis and fermentation. More importantly, at blood pH these microbes started synthesising some extracellular proteases associated with virulence and membrane ion transporters involved in immune evasion. (Gonçalves Luis Gafeira, 2022). Admittedly, the pH of skin is normally acidic due to skin’s acid mantle. Only very rarely do microbes produce metabolites that increase pH. More commonly, microbes such as lactic acid bacteria reenforce skin’s acidity, however the pH of skin can rise when its barrier function is disrupted because of inflammatory skin diseases such as atopic dermatitis or because of a topical product. Knowing how pH can transform strains of S. epidermidis, could a high pH cosmetic trigger a Jekyll and Hyde moment turning a commensal species into a pathogen and so disturb the balance of a healthy microbiome?
Metabolomics, the chief of Omic technologies
Omic technologies provide a revolutionary approach to understanding biological systems. They are generating huge amounts of information that can overwrite long accepted ideas. However, with this data bonanza comes issues of reliability, the need to follow the FAIR principles and to create innovative ways to process the large amounts of omic information. Data derived from omics technologies should therefore be seen as the starting point and not the end point for new discoveries.
Chief among the omic technologies is metabolomics which gives a snapshot in time of all the processes and products that are being used, made, stored and exported from skin and its microbiota. This powerful approach captures both nature and nurture and is leading to a more thorough understanding of the relationship between skin and its invisible microbial shield. Scientists characterising the metabolone on the surface of skin can help formulators demonstrate efficacy and foresee problems such as the potential damaging effects, which might be brought about if their skin care formula causes an increase in pH. Undoubtedly, omic studies will lead to a more profound understanding of the factors needed for healthy skin.
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References
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