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The underarm (axillary) microbiome plays a crucial role in body odour production. Although deodorants and fragranced cosmetic products are designed to prevent perspiration and malodour, their impact on these microbial communities has not been extensively studied. What We Know: The human armpit harbours a dense and diverse bacterial community, with recent studies revealing significant variation in armpit bacteria among individuals, more so than in other body areas. This variation is partly due to the use of personal hygiene products, especially deodorants and antiperspirants (Urban et al., 2016). Armpit bacterial communities primarily consist of Corynebacterium, Staphylococcus, Betaproteobacteria, Clostridiales, Lactobacillus, Propionibacterium and Streptococcus species. However, there is significant variability in these communities across different individuals (Urban et al., 2016). Research shows that deodorants and antiperspirants affect the diversity and composition of armpit bacterial communities. Despite these findings, more research is needed to understand the full impact of these products on human health (Chang & Wang, 2023). The high variability of armpit bacterial communities contrasts with the more stable bacterial compositions found in other skin areas, likely due to less frequent use of hygiene products there. Although this variability might be influenced by random factors or the presence of dead bacteria, studies indicate that the common armpit bacteria are among the most metabolically active and contribute significantly to body odour (Urban et al., 2016). Industry Impact and Potential: The broader health implications of antiperspirant and deodorant use are not well studied. While some have suggested a potential link between these products and breast cancer incidence or age of diagnosis, evidence supporting this association is inconsistent and not conclusive (McGrath, 2003; Hardefeldt, Edirimanne & Eslick, 2013). Nonetheless, due to these growing concerns about potential health risks and additionally the harmful effects of deodorants on the environment, researchers are seeking more sustainable alternatives (Chang & Wang, 2023). Additionally, exploring how hygiene products influence the axillary microbiome offers valuable insights into how human behaviour affects microbial communities, especially since over 90% of adults in the US use antiperspirants or deodorants regularly (Benohanian, 2001). We have been fortunate enough to work with pioneers in this field, like Arcaea - who developed their novel odour-preventative technology, based on the armpit microbiome in 2023. Our Solution: Sequential is a leading expert in comprehensive, end-to-end Microbiome Product Testing and Formulation. Our specialised and customisable services empower businesses to innovate microbiome-friendly products confidently, ensuring their effectiveness and compatibility for a healthier microbiome. Let us support your development efforts, particularly in facial, oral, scalp, and vaginal microbiome research and production formulation. References: Benohanian, A. (2001) Antiperspirants and deodorants. Clinics in Dermatology. 19 (4), 398–405. doi:10.1016/S0738-081X(01)00192-4. Chang, Y. & Wang, X. (2023) Sweat and odor in sportswear – A review. iScience. 26 (7). doi:10.1016/j.isci.2023.107067. Hardefeldt, P.J., Edirimanne, S. & Eslick, G.D. (2013) Deodorant Use and Breast Cancer Risk. Epidemiology. 24 (1), 172. doi:10.1097/EDE.0b013e3182781684. McGrath, K.G. (2003) An earlier age of breast cancer diagnosis related to more frequent use of antiperspirants/deodorants and underarm shaving. European Journal of Cancer Prevention. 12 (6), 479. Urban, J., Fergus, D.J., Savage, A.M., Ehlers, M., Menninger, H.L., Dunn, R.R. & Horvath, J.E. (2016) The effect of habitual and experimental antiperspirant and deodorant product use on the armpit microbiome. PeerJ. 4, e1605. doi:10.7717/peerj.1605.

Malaria remains one of the deadliest diseases of the last century, posing a significant global health challenge. Researchers are continually exploring innovative methods to treat and prevent the disease, with recent studies suggesting that the skin microbiome may play a crucial role in influencing malaria transmission and severity. What We Know: Malaria is caused by the Plasmodium parasite, which is carried by Anopheles mosquitoes, and reproduces inside a human host after a bite. Over 90 countries are affected by malaria, and although the mortality rate has decreased significantly over the last century, the disease remains a major global health challenge (Garcia, 2010). In 2022, there were still 247 million malaria cases and 619,000 deaths worldwide (World Health Organization, 2023). When selecting its blood host, the Anopheles mosquito is largely influenced by human body odour. Therefore, the skin microbiome plays a significant role in this process, as it is responsible for the composition of volatile organic compounds (VOCs), which are a major component of body odour (Verhulst et al., 2010). Skin secretions contain over 500 VOCs, including acids, alcohols, aldehydes, esters and ketones and Anopheles mosquitoes exhibbit electrophysiological and behavioural responses to several of these VOCs. Specific VOCs (butanoic acid, carbon dioxide, lactic acid and propanoic acid) have demonstrated an attractive quality for Anopheles mosquitoes. Meanwhile, other VOCs, including aldehydes (decanal, octanal, nonanal) and ketones (geranylacetone and 6-methyl-5-hepten-2-one) repelled mosquitoes (Showering et al., 2022). Industry Impact and Potential: Research found that Anopheles was attracted to microbial VOCs produced by Staphylococcus and was repelled by those produced by Corynebacterium. Abundance of the latter has been linked to increased levels of hexanoic acid in body odour, which may act as a contextual repellent (Showering et al., 2022). However, further insights into the mechanisms of attractive and repellent microbial VOCs are needed, and could pave the way for developing mosquito repellents with diverse modes of action (Showering et al., 2022). Modifying the human skin microbiome to produce fewer mosquito attractants or to generate repellents has the potential to decrease mosquito bites and prevent the spread of deadly mosquito-borne diseases (Coutinho-Abreu et al., 2023). Our Solution: At Sequential, we specialise in comprehensive Microbiome Product Testing tailored to meet your specific goals in formulating products, such as mosquito repellent. Our expertise and customised services empower businesses to innovate confidently in developing topical solutions. We facilitate microbiome studies to ensure these products are maintain the microbiome, promoting efficacy and compatibility for healthier skin. References: Coutinho-Abreu, I., Jamshidi, O., Raban, R., Atabakhsh, A., Merriman, J., Fischbach, M. & Akbari, O. (2023) Identification of human skin microbiome odorants that manipulate mosquito landing behavior. bioRxiv : the preprint server for biology. doi:10.1101/2023.08.19.553996. Garcia, L.S. (2010) Malaria. Clinics in Laboratory Medicine. 30 (1), 93–129. doi:10.1016/j.cll.2009.10.001. Showering, Martinez, J., Benavente, E., Gezan, S., Jones, R., Oke, C., Tytheridge, S., Pretorius, E., Scott, D., Allen, R., D’Alessandro, U., Lindsay, S., Armour, J., Pickett, J. & Logan, J. (2022) Skin microbiome alters attractiveness to Anopheles mosquitoes. BMC microbiology. 22 (1). doi:10.1186/s12866-022-02502-4. Verhulst, N.O., Andriessen, R., Groenhagen, U., Kiss, G.B., Schulz, S., Takken, W., Loon, J.J.A. van, Schraa, G. & Smallegange, R.C. (2010) Differential Attraction of Malaria Mosquitoes to Volatile Blends Produced by Human Skin Bacteria. PLOS ONE. 5 (12), e15829. doi:10.1371/journal.pone.0015829. World Health Organization (2023) Google-Books-ID: u6UOEQAAQBAJ. World malaria report 2023. World Health Organization.

Sensitive skin (SS), also known as cutaneous sensory syndrome, is characterised by abnormal hypersensitivity to various stimuli, leading to symptoms such as itching, irritation, redness, dryness, and sensations of tightness, stinging, and pain. While many factors contribute to SS, research is increasingly focusing on the role of the skin microbiome in this condition. What We Know: SS can affect individuals with both normal and disrupted skin barriers. Symptoms suggest the involvement of cutaneous nerve endings, which research has confirmed. Additionally, epidermal cells, sensory proteins, skin barrier disruption, and immune mechanisms may play roles. Some studies indicate that an impaired skin barrier and drier skin, associated with increased mast cell degranulation, underlie SS (Seite & Misery, 2018). Commensal bacteria reside in the epidermis, where pain and itch receptors (nociceptors and proprioceptors) are located. The skin microbiota influences mast cell movement, location, and development in the skin, and can directly stimulate pain and itch receptors. Therefore, it is suggested that bacteria may contribute to SS development, but more research is needed to fully elucidate this link​ (Seite & Misery, 2018). Studies have shown that the facial microbiome of individuals with SS differs from those with normal skin. Increased levels of Actinomyces, Microbotryomycetes, Dermabacter, Chryseobacterium, Rhodotorula, Peptoniphilus, Cutibacterium, Corynebacterium , and Staphylococcus  were observed in SS. Notably,  Dermabacter hominis  was also more prevalent in the SS group, while Streptococcus  strains and Acidimicrobiia  levels were decreased when compared to the controls​ (Lu, Cheng & Shi, 2024). Industry Impact and Potential: Skincare product formulations designed to manage inflammation and support the skin barrier and microbiota diversity are emerging. These advanced formulas often include prebiotics, which can stimulate or inhibit bacterial growth (Seite & Misery, 2018). Specifically, new moisturiser formulations that include prebiotic ingredients that act directly on the skin microbiota are promising. This is because bacterial growth is sensitive to free water, and while traditional moisturisers improve surface hydration by binding water to skin cells, they do not increase free water levels. Non-pathogenic bacterial extracts may also be added to effectively support skin health by modulating the microbiota and reducing inflammation (Seite & Misery, 2018). Our Solution: Sequential offers comprehensive services to evaluate product impacts and formulations, leveraging a vast database of over 20,000 microbiome samples and 4,000 ingredients, and a global network of over 10,000 testing participants. Our team of experts will help your business develop innovative skincare solutions for sensitive skin that work with the microbiome to achieve optimal skin health. References: Lu, Y.-N., Cheng, L. & Shi, X.-M. (2024) Correlation between the facial skin microbiome and sensitive skin using the 2bRAD-M technique. International Journal of Cosmetic Science. doi:10.1111/ics.12941. Seite, S. & Misery, L. (2018) Skin sensitivity and skin microbiota: Is there a link? Experimental Dermatology. 27 (9), 1061–1064. doi:10.1111/exd.13686.

Rosemary oil has become increasingly popular in the hair care cosmetics industry, praised for its potential to improve scalp health and promote hair growth. Subsequent research has focused on examining the impact of rosemary oil products on the scalp microbiome and the treatment of various scalp conditions. What We Know: Rosmarinus officinalis, commonly known as rosemary, is a familiar aromatic household plant characterised by needle-like leaves. This medicinal plant is also renowned for its various beneficial properties, including cardiovascular health, nervous disorders treatment and notably, benefits for hair and scalp health through its ability to enhance microcapillary perfusion (Begum et al., 2023). Rosemary is also known to possess anti-inflammatory properties due to its rich content of phenolic phytoconstituents (Hashem et al., 2024). Moreover, the chemical composition of the plant encompasses essential oils containing primary constituents such as camphene, camphor, cineol and borneol. Additionally, it is known to harbour abundant flavonoids, bitter principles, tannins and terpenoids, alongside amino acids, steroids, glycosides, volatile oils and vitamins (Begum et al., 2023). Industry Impact and Potential: A study found that rosemary oil was as clinically effective as 2% minoxidil for treating androgenetic alopecia. Additionally, participants using rosemary oil experienced less scalp itching compared to those using minoxidil (Panahi et al., 2015). Hair lotion with 1% methanolic extract of R. officinalis administered in a study on mice demonstrated significant hair growth promoting activity, when compared to the control of 2% minoxidil hair lotion (Begum et al., 2023). An additional study combined rosemary with neem (Azadirachta indica) to create hair gel and leave-in products to treat dandruff. The products were successful, proving more effective than ketoconazole (a conventional antifungal agent) at managing Malassezia furfur, a dandruff-causing fungus, and Trichophyton rubrum, which is also associated with scalp disorders. Their products showed strong anti-inflammatory activity and also proved more effective than minoxidil in promoting hair growth (Hashem et al., 2024). Research like this suggests the potential of medicinal herbs like rosemary as natural and cost-effective ingredients to explore for targeting the scalp microbiome to treat diverse scalp conditions, thereby enhancing overall scalp and hair health (Hashem et al., 2024). Our Solution: With a database of 20,000 microbiome samples and 4,000 ingredients and a global network of 10,000 testing participants, Sequential provides tailored solutions for custom microbiome studies and product formulation. Considering rosemary oil-based scalp health products, our commitment to creating microbiome-safe and friendly formulations ensures the preservation of biome integrity. References: Begum, A., S, S., N, A.K. & Ali, S.S. (2023) Evaluation of Herbal Hair Lotion loaded with Rosemary for Possible Hair Growth in C57BL/6 Mice. Advanced Biomedical Research. 12, 60. doi:10.4103/abr.abr_306_21. Hashem, M.M., Attia, D., Hashem, Y.A., Hendy, M.S., AbdelBasset, S., Adel, F. & Salama, M.M. (2024) Rosemary and neem: an insight into their combined anti-dandruff and anti-hair loss efficacy. Scientific Reports. 14 (1), 7780. doi:10.1038/s41598-024-57838-w. Panahi, Y., Taghizadeh, M., Marzony, E.T. & Sahebkar, A. (2015) Rosemary oil vs minoxidil 2% for the treatment of androgenetic alopecia: a randomized comparative trial. Skinmed. 13 (1), 15–21.

Although the ageing process is complex and individualised, research highlights the significant role of the skin microbiome in skin ageing. Various topical ingredients show promise in supporting the microbiome. What We Know: The skin microbiome is known to play a significant role in barrier function. A characteristic feature of ageing skin is the decline in barrier function, causing decreased moisture retention, increased vulnerability and a decrease in overall skin integrity (Woo & Kim, 2024). Research has also identified significant changes in the skin microbiota of elderly people, marked by a decrease in Cutibacterium and an increase in Corynebacterium and Proteobacteria. Specifically, studies have linked an increased abundance of Corynebacterium species to a higher incidence of erythrasma in the elderly (Salemi et al., 2022). These findings highlight a potential connection between age-related changes in skin microbiota and the occurrence of specific skin conditions, emphasising the importance of microbiota composition in maintaining skin health and understanding disease manifestation in older populations (Woo & Kim, 2024). Industry Impact and Potential: The pursuit of healthy, resilient skin has resulted in innovative therapies. Notably, specific moisturisers, antioxidant-rich products, probiotics, prebiotics, postbiotics and effective UV protection show promise for strengthening the ageing skin barrier and addressing dysbiosis (Woo & Kim, 2024). Topical antioxidant ingredients (including α-tocopherol (free vitamin E), vitamin C, ferulic acid, resveratrol and niacinamide) have the ability to strengthen impaired skin barriers and protect the skin against oxidative stress. In addition, UV-protecting products also offer benefit to the skin microbiome, but their properties are enhanced with the addition of barrier-enforcing lipid formulations (i.e., ceramide-containing sunscreens) and antioxidants (i.e., sunscreens containing pre-tocopheryl) (Woo & Kim, 2024). Probiotics, prebiotics and postbiotics have shown efficacy in enhancing stratum corneum hydration, reducing wrinkle depth and offering photoprotective properties, ultimately supporting skin barrier health (Woo & Kim, 2024). Topical Epidermidibacterium Keratini EPI-7 ferment filtrate applied twice daily for three weeks caused significant improvement in skin hydration, elasticity and dermal density. Furthermore, an increase in beneficial commensal microorganisms like Cutibacterium, Corynebacterium, Staphylococcus, Streptococcus, Clostridium, Lawsonella, Rothia, Lactobacillus and Prevotella was also observed (Kim et al., 2023). Our Solution: Sequential specialises in comprehensive microbiome product testing, customised to align with your unique anti-aging product development and formulation goals. With our expert guidance and tailored services, we empower businesses to pioneer innovative strategies for creating anti-aging solutions. References: Kim, J., Lee, Y.I., Mun, S., Jeong, J., Lee, D.-G., Kim, M., Jo, H., Lee, S., Han, K. & Lee, J.H. (2023) Efficacy and Safety of Epidermidibacterium Keratini EPI-7 Derived Postbiotics in Skin Aging: A Prospective Clinical Study. International Journal of Molecular Sciences. 24 (5). doi:10.3390/ijms24054634. Salemi, S.Z., Memar, M.Y., Kafil, H.S., Sadeghi, J., Ghadim, H.H., Alamdari, H.A., Nezhadi, J. & Ghotaslou, R. (2022) The Prevalence and Antibiotics Susceptibility Patterns of Corynebacterium minutissimum Isolates from Skin Lesions of Patients with Suspected Erythrasma from Tabriz, Iran M. Adnan (ed.). Canadian Journal of Infectious Diseases and Medical Microbiology. 2022, 4016173. doi:10.1155/2022/4016173. Woo, Y.R. & Kim, H.S. (2024) Interaction between the microbiota and the skin barrier in aging skin: a comprehensive review. Frontiers in Physiology. 15, 1322205. doi:10.3389/fphys.2024.1322205.

The skin microbiome, a complex ecosystem of bacteria, fungi, viruses, and other microorganisms living on our skin, plays a crucial role in maintaining skin health (Berg et al., 2020). The microbiome acts as a protective barrier, helps in wound healing, and regulates the immune system. An imbalance in the microbiome can result in various skin conditions, such as acne, eczema, and psoriasis. Historically, traditional skincare formulations have often taken a one-size-fits-all approach, which may not be effective for everyone due to individual differences in skin microbiomes. As a result, new approaches are being adopted within the industry to facilitate the transition to better researched solutions. With growing demand for unique formulations, and diagnostic tools, the industry has opened its arms to new technologies that can facilitate research within the space. Artificial Intelligence (AI) has become a central player in transforming our understanding and treatment of the skin microbiome, leading to innovative solutions in product development and clinical research (Sun et al., 2023). AI and machine learning is now being adopted on a global scale in various industries as a way of redefining workflows and increasing efficiency. This article will outline how AI can be applied to microbiome research, evaluating its potential uses as well as constraints. As this technology continues to develop, AI powered insights will likely play an important role in microbiome research and intervention in the future. What is Artificial Intelligence? Artificial intelligence (AI) “refers to the ability of any machines which can stimulate the intelligence of higher organisms” (Bhardwaj et al., 2022). AI is a branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence. These tasks include learning from data, recognizing patterns, making decisions, understanding natural language, and even mimicking human interactions. AI encompasses a wide range of technologies and methodologies, such as: Machine learning: where algorithms improve through experience Deep learning: which involves neural networks with many layers Artificial neural networks (ANN): computing systems inspired by biological neural networks, designed to recognize patterns and solve problems through a layered architecture of interconnected nodes or "neurons" that process information and learn from data Natural language processing: which allows machines to understand and respond to human language Computer vision: enabling machines to interpret and make decisions based on visual inputs​ At its core, AI operates by processing large amounts of data, identifying correlations and patterns, and making predictions or decisions based on this analysis. This capability has led to AI's integration into various fields, including healthcare and wellness. For example, AI is used in medical diagnostics to analyse medical images, predict disease outcomes, drug discovery, and even radiography (Al-Antari, 2023). The development of AI continues to advance, pushing the boundaries of what machines can achieve and transforming numerous aspects of different industries. AI’s application in microbiome work AI and classical machine learning methodologies have been used in microbiome studies for more than a decade, with research articles highlighting its uses as far back as 2013. Microbiome research at its core is driven by large amounts of data centred around different types of sequencing technologies: Amplicon sequencing - 16S, 18S, ITS gene sequencing for taxonomic identification Metagenomics - deep sequencing to characterise the collective genomes of microorganisms, and infer function Metatranscriptomics - RNA sequencing on multiple organisms (human, bacteria, fungi, viruses etc) to understand gene activity across organisms Metaproteomics - Assess protein expression to characterise multiple organisms (human, bacteria, fungi, viruses etc) Metabolomics - Sequence small molecule production/consumption on multiple organisms (human, bacteria, fungi, viruses etc) The results from these bioinformatic tools can be used effectively as inputs for AI models. AI excels with large and complex datasets and can manage data gaps that usually pose problems for traditional statistics. By employing AI and machine learning, we can efficiently process the huge amounts of data generated by microbiome testing. AI models use embedded feature selection to identify the most relevant data features during training, eliminating the need to analyse the entire dataset each time. Potential applications for AI in skin microbiome testing Microbiome Profiling and Diversity Analysis Disease Biomarker Discovery Microbiome-Host Interaction Analysis Skin Microbiome-Based Therapeutics Development Personalised Skincare and Treatment Optimization Microbiome-Based Product Development and Formulation Optimization Longitudinal Monitoring and Predictive Modeling Study 1: Microbiome Profiling and Neural Networks Recent advances in high-throughput sequencing technologies have made microbiome profiles publicly accessible, revealing distinct profiles for healthy and diseased individuals and suggesting their potential as diagnostic tools. However, the complexity of metagenomic data poses challenges for current machine learning models. To address this, the study proposes MetaNN, a neural network framework that uses a new data augmentation technique to reduce overfitting. MetaNN significantly improves classification accuracy for both synthetic and real metagenomic data, outperforming existing models and paving the way for personalised treatments for microbiome-related diseases (Lo & Marculescu, 2019). Study 2: AI and Longitudinal Data The study investigated how the human microbiome changes dynamically over time due to factors like diet and medical interventions. It introduced 'phyLoSTM,' a deep learning framework that combined Convolutional Neural Networks and Long Short Term Memory Networks (LSTM) to extract features and analyse temporal dependencies in longitudinal microbiome data along with environmental factors for disease prediction. The framework also managed variable time points and balanced weights between imbalanced cases and controls. Testing on 100 simulated datasets and two real longitudinal studies demonstrated that phyLoSTM achieved higher predictive accuracy, with AUC improvements of 5% in simulated studies and significant gains in real studies compared to Random Forest, enhancing the prediction of disease outcomes from microbiome data (Sharma & Shu, 2021). Limitations While it is undeniable that AI presents a promising future when it comes to advancing the workflows in microbiome testing, several notable limitations need to be addressed before AI models can reach their full potential in the field. Interpretability: “but why?” Transitioning from input to output in AI systems is straightforward, but in healthcare understanding the "why" behind decisions is crucial. AI, deep learning especially, often struggles with transparency, making it difficult to interpret how conclusions are reached. This lack of interpretability can lead to legal issues and the possibility of unknown factors influencing decisions. Without insight into the model's logic, trust in its output is limited. There's also the risk of confusing correlation with causation, meaning that just because data points are correlated, it doesn't imply one causes the other. Proper study design and longitudinal data are essential to provide context and distinguish between cases and controls. Efforts are underway to improve AI interpretability by identifying the importance of different predictors within models. Incorporating prior knowledge into model creation can help guide the AI, adding constraints, and enhancing performance. This approach not only improves the AI's accuracy but also makes its decision-making process more transparent and trustworthy. Data quality: “garbage in, garbage out” While the method used in AI is important, it's equally crucial to examine how the data is structured to ensure it is relevant and useful. Blindly trusting data produced by AI, especially in personal care and healthcare, can lead to significant problems. The quality of AI outputs heavily depends on the quality and quantity of input data.In the context of the microbiome, gathering suitable datasets can be challenging: the field is still evolving, and much remains unexplained. The field is still evolving, and much remains unexplained. Additionally, microbiome data is highly complex and influenced by numerous contextual factors. We need comprehensive datasets to train AI models effectively, and without them, the application of AI in microbiome research will be limited. Thus, it's vital to continue uncovering the intricacies of the microbiome to enhance the effectiveness of AI models in this area. Conclusion Moving forward, AI is poised to become increasingly prevalent in microbiome research, significantly simplifying the processes for bioinformaticians. By automating the analysis of complex datasets and enhancing the precision of predictive models, AI will streamline workflows, reducing the time and effort required for data interpretation and hypothesis generation. This technological advancement not only accelerates the pace of discovery but also enables more personalised and effective interventions, heralding a new era in microbiome research where scientists can leverage AI to unlock deeper insights and drive innovation in health and wellness. References Al-Antari MA. Artificial Intelligence for Medical Diagnostics-Existing and Future AI Technology! Diagnostics (Basel). 2023 Feb 12;13(4):688. doi: 10.3390/diagnostics13040688. PMID: 36832175; PMCID: PMC9955430. Berg G, Rybakova D, Fischer D, Cernava T, Vergès MC, Charles T, Chen X, Cocolin L, Eversole K, Corral GH, Kazou M, Kinkel L, Lange L, Lima N, Loy A, Macklin JA, Maguin E, Mauchline T, McClure R, Mitter B, Ryan M, Sarand I, Smidt H, Schelkle B, Roume H, Kiran GS, Selvin J, Souza RSC, van Overbeek L, Singh BK, Wagner M, Walsh A, Sessitsch A, Schloter M. Microbiome definition re-visited: old concepts and new challenges. Microbiome. 2020 Jun 30;8(1):103. doi: 10.1186/s40168-020-00875-0. Erratum in: Microbiome. 2020 Aug 20;8(1):119. doi: 10.1186/s40168-020-00905-x. PMID: 32605663; PMCID: PMC7329523. Lo C, Marculescu R. MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks. BMC Bioinformatics. 2019 Jun 20;20(Suppl 12):314. doi: 10.1186/s12859-019-2833-2. PMID: 31216991; PMCID: PMC6584521. Sharma D, Xu W. phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data. Bioinformatics. 2021 Nov 5;37(21):3707-3714. doi: 10.1093/bioinformatics/btab482. PMID: 34213529. Sun T, Niu X, He Q, Chen F, Qi RQ. Artificial Intelligence in microbiomes analysis: A review of applications in dermatology. Front Microbiol. 2023 Feb 1;14:1112010. doi: 10.3389/fmicb.2023.1112010. PMID: 36819026; PMCID: PMC9929457.

Alzheimer's disease (AD) is a progressive neurodegenerative condition characterised by memory loss, changes in personality and behaviour and affecting 32% of individuals aged 80 and older. Recent research has uncovered a characteristic "oral microbiome stamp" in AD patients, prompting inquiries into oral microbiota diversity and the consequences thereof. What We Know: Research has delved into the connection between gut microbiome dysbiosis and neurological disorders and this reciprocal relationship is believed to extend to the oral microbiota and various systemic diseases like AD (Maitre et al., 2021). Specifically, a potential link between changes in the oral microbiome, periodontal disease and the onset of cognitive decline and AD has been proposed (Issilbayeva et al., 2024). Epidemiological correlations between AD and periodontitis are of interest, as several researchers have proposed that the link between the two may stem from heightened systemic inflammation associated with the proliferation of periodontal pathogens. These pathogens may potentially contribute to AD development by playing a role in vascular disease progression (Maitre et al., 2021). Additional research has also demonstrated the potential of the oral microbiome to invoke neuroinflammation (Issilbayeva et al., 2024). Industry Impact and Potential: The oral microbiome of AD shows higher microbial diversity, with  increased levels of Firmicutes and decreased levels of Bacteroidetes, Proteobacteria and Actinobacteria compared to control samples (Issilbayeva et al., 2024). An additional study looking at salivary samples representative of the oral microbiome in AD patients found that these contained increased levels of Moraxella, Leptotrichia and Sphaerochaeta, as well as decreased Rothia (Liu et al., 2019). Another study demonstrated a specific imbalance in the oral microbiome of AD patients, characterised by the presence of certain periodontal bacteria, such as A. actinomycetemcomitans, P. gingivalis, T. denticola and F. nucleatum which researchers propose constitute a characteristic “stamp” of AD (Maitre et al., 2021). However, delving into the full spectrum of the oral microbiome's composition in individuals with AD necessitates further exploration (Issilbayeva et al., 2024). The current findings underscore the significance of maintaining a healthy oral microbiome. Therefore, it is crucial to use oral hygiene products and adopt practices that safeguard and enhance oral microbial balance, fostering advancements in such product developments. Our Solution: Sequential specialises in analysing the oral microbiome, along with skin, scalp, and vulvar microbiomes, and leads the industry in developing microbiome-friendly products. Our team of experts is poised to help your company formulate innovative substances that promote a healthy oral microbiome and enhance microbiota diversity among consumers. References: Issilbayeva, A., Kaiyrlykyzy, A., Vinogradova, E., Jarmukhanov, Z., Kozhakhmetov, S., Kassenova, A., Nurgaziyev, M., Mukhanbetzhanov, N., Alzhanova, D., Zholdasbekova, G., Askarova, S. & Kushugulova, A.R. (2024) Oral Microbiome Stamp in Alzheimer’s Disease. Pathogens (Basel, Switzerland). 13 (3), 195. doi:10.3390/pathogens13030195. Liu, X.-X., Jiao, B., Liao, X.-X., Guo, L.-N., Yuan, Z.-H., Wang, X., Xiao, X.-W., Zhang, X.-Y., Tang, B.-S. & Shen, L. (2019) Analysis of Salivary Microbiome in Patients with Alzheimer’s Disease. Journal of Alzheimer’s Disease. 72 (2), 633–640. doi:10.3233/JAD-190587. Maitre, Y., Mahalli, R., Micheneau, P., Delpierre, A., Amador, G. & Denis, F. (2021) Evidence and Therapeutic Perspectives in the Relationship between the Oral Microbiome and Alzheimer’s Disease: A Systematic Review. International Journal of Environmental Research and Public Health. 18 (21), 11157. doi:10.3390/ijerph182111157.

Both the gut and skin are colonised with distinct microbial communities and operate as crucial organs in the body. Several conditions that primarily affect the gut also manifest in the skin, and the primary cause of several skin conditions has been identified as an underlying gastrointestinal disorder. This demonstration of a bidirectional connection between the gut and skin is known as the gut-skin axis. A summary of what we know: The connection between the gut and the skin is thought to be mediated by the host immune system, however, the underlying mechanisms of how the gut microbiome alters the skin’s immune system, and vice versa, are currently being investigated (De Pessemier et al., 2021) Specific diets as well as the consumption of prebiotics or probiotics that are beneficial for the gastrointestinal system have shown the potential to prevent and manage various skin conditions such as acne, atopic dermatitis and psoriasis (De Pessemier et al., 2021) The gut-skin axis is not only governed by diet as research has found that skin exposure to UVB, and therefore indirectly to serum vitamin D levels, increased the alpha and beta diversity of the gut microbiome (Bosman et al., 2019) Several studies have shown the use of both topical and oral pre and probiotics to be beneficial to the skin’s microbiome and overall health (Gao et al., 2023) Our progress: Through work with Dr Whitney Bowe based in NYC, we found synergistic effects on the skin microbiome diversity, when combining topical and oral probiotics. Certain key microbes were found to be altered more significantly when topical and oral probiotics were consumed over a period of 30 days. References Bosman ES, Albert AY, Lui H, Dutz JP, Vallance BA. Skin Exposure to Narrow Band Ultraviolet (UVB) Light Modulates the Human Intestinal Microbiome. Front Microbiol. 2019 Oct 24;10:2410. doi: 10.3389/fmicb.2019.02410. PMID: 31708890; PMCID: PMC6821880. De Pessemier B, Grine L, Debaere M, Maes A, Paetzold B, Callewaert C. Gut-Skin Axis: Current Knowledge of the Interrelationship between Microbial Dysbiosis and Skin Conditions. Microorganisms. 2021 Feb 11;9(2):353. doi: 10.3390/microorganisms9020353. PMID: 33670115; PMCID: PMC7916842. Gao T, Wang X, Li Y, Ren F. The Role of Probiotics in Skin Health and Related Gut-Skin Axis: A Review. Nutrients. 2023 Jul 13;15(14):3123. doi: 10.3390/nu15143123. PMID: 37513540; PMCID: PMC10385652.

Skin ageing is a natural and inevitable process caused by structural and functional changes in skin cells due to intrinsic and extrinsic factors e.g. biological age and environmental exposures, respectively. Several studies have evaluated the changes in the skin microbiome with age and more recently researchers are exploring whether the skin microbiome might directly influence skin ageing. A summary of what we know: Immediately after birth, newborn skin is colonised by surrounding microorganisms, which have been shown to differ depending on the mode of delivery (Dominguez-Bello et al., 2010; Luna, 2020) By 4–6 weeks after birth, infant skin microbiome structure and function significantly expand and diversify, with prominent body site specificities similar to those of the maternal skin microbiome (Chu et al., 2017; Luna, 2020) An infants skin microbiome continues to diversify and mature throughout childhood, then during puberty, shifts to the more lipophilic Actinobacteria (Corynebacterium and Cutibacterium) due to sebum overproduction (Oh et al., 2012) During adulthood, the skin microbial composition in healthy individuals has been found to remain largely stable until age-related physiologic changes start to occur in older individuals such as a decrease in sebum and sweat production (Oh et al., 2016; Luna, 2020) Alongside age, gender and race/ethnicity have been found to influence the microbial community composition of skin (Li et al., 2019) Recently, researchers have found an association between strains of C.acnes and S.epidermis and a decline in collagen in Caucasian women aged 54-60. However further studies are required to determine whether collagen levels influence or are influenced by the skin microbiome (Zhou et al., 2023) Our progress: When it comes to formulating with the microbiome in mind it is important to consider the microbial composition of different age groups. We are currently working with a client to support the formulation and in vivo testing of skin care products that are tailored to restoring and maintaining the microbiomes of different age groups. References Chu DM, Ma J, Prince AL, Antony KM, Seferovic MD, Aagaard KM. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat Med. 2017 Mar;23(3):314-326. doi: 10.1038/nm.4272. Epub 2017 Jan 23. PMID: 28112736; PMCID: PMC5345907. Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, Knight R. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci U S A. 2010 Jun 29;107(26):11971-5. doi: 10.1073/pnas.1002601107. Epub 2010 Jun 21. PMID: 20566857; PMCID: PMC2900693. Li M, Budding AE, van der Lugt-Degen M, Du-Thumm L, Vandeven M, Fan A. The influence of age, gender and race/ethnicity on the composition of the human axillary microbiome. Int J Cosmet Sci. 2019 Aug;41(4):371-377. doi: 10.1111/ics.12549. PMID: 31190339. Luna PC. Skin Microbiome as Years Go By. Am J Clin Dermatol. 2020 Sep;21(Suppl 1):12-17. doi: 10.1007/s40257-020-00549-5. PMID: 32910437; PMCID: PMC7584528. Zhou W., Fleming E., Legendre G., Roux L., Latreille J., Gendronneau G., et al. (2023). Skin microbiome attributes associate with biophysical skin ageing. Exp. Dermatol. 32 (9), 1546–1556. 10.1111/exd.14863

Atopic dermatitis (AD), also known as atopic eczema, is a common chronic inflammatory skin condition that is characterised by inflamed, dry and itchy skin. Multiple factors contribute to AD such as genetics and environment, although the exact mechanism is not well understood. Treatment is often very difficult, involving the use of topical steroids or oral antibiotics which can cause adverse side effects. Studies have shown that individuals with AD have a disturbed skin microbiome and are more often colonised with Staphylococcus aureus compared to healthy individuals. A summary of what we know: S. aureus contributes to skin barrier defects and inflammation in AD (Totté et al., 2016) Recent research has reported that commensal bacteria residing on normal skin (S. hominis) produce antimicrobial peptides that exhibit activity against S. aureus (Nakatsuji et al., 2017) Topical application of S. hominis has been shown to improve skin condition and may improve various skin conditions such as AD (Ohshima, Kurosumi and Kanto, 2021) Current solutions: Clinical data suggests a probiotic product containing Lactobacillus reuteri DSM 17938 could be a promising topical product for the management of AD (Butler, Christoffer and Axelsson, 2020). References Butler É, Lundqvist C, Axelsson J. Lactobacillus reuteri DSM 17938 as a Novel Topical Cosmetic Ingredient: A Proof of Concept Clinical Study in Adults with Atopic Dermatitis. Microorganisms. 2020 Jul 11;8(7):1026. doi: 10.3390/microorganisms8071026. PMID: 32664536; PMCID: PMC7409218. Ohshima H, Kurosumi M, Kanto H. New solution of beauty problem by Staphylococcus hominis: Relevance between skin microbiome and skin condition in healthy subject. Skin Res Technol. 2021 Sep;27(5):692-700. doi: 10.1111/srt.13001. Epub 2021 Jan 28. PMID: 33511688. Nakatsuji T, Chen TH, Narala S, Chun KA, Two AM, Yun T, Shafiq F, Kotol PF, Bouslimani A, Melnik AV, Latif H, Kim JN, Lockhart A, Artis K, David G, Taylor P, Streib J, Dorrestein PC, Grier A, Gill SR, Zengler K, Hata TR, Leung DY, Gallo RL. Antimicrobials from human skin commensal bacteria protect against Staphylococcus aureus and are deficient in atopic dermatitis. Sci Transl Med. 2017 Feb 22;9(378):eaah4680. doi: 10.1126/scitranslmed.aah4680. PMID: 28228596; PMCID: PMC5600545. Totté JE, van der Feltz WT, Hennekam M, van Belkum A, van Zuuren EJ, Pasmans SG. Prevalence and odds of Staphylococcus aureus carriage in atopic dermatitis: a systematic review and meta-analysis. Br J Dermatol. 2016 Oct;175(4):687-95. doi: 10.1111/bjd.14566. Epub 2016 Jul 5. PMID: 26994362.

Acne is a well-known chronic inflammatory condition that impacts individuals of all age groups worldwide. Several factors have been found to influence the development of acne and its severity, such as increased sebum production and hyperkeratinization. More recent studies have revealed that dysbiosis - an imbalance of the skin’s microbiome - is implicated in the manifestation of inflammatory skin diseases, including acne. Specifically, an imbalance of the bacteria Cutibacterium acnes has been regarded as one of the major factors involved in acne pathogenesis. However, now it is known that there are significant differences between the many strains of C. acnes, some of which are being shown to benefit the skin. A summary of what we know: Metagenomic analyses have revealed that the strain structure of C. acnes in acne patients differs from that of healthy individuals (Huang et al., 2023) Type IV and V strains are particularly prevalent in acne-affected skin, suggesting a correlation between specific C. acnes strains and acne pathology (Fitz-Gibbon et al., 2013) The species is divided into three main phylotypes: phylotype I as C. acnes subsp. acnes, phylotype II as C. acnes subsp. defendens, and phylotype III as C. acnes subsp. elongatum (Rhee et al., 2023) Recent research has suggested that strains more associated with health come from the C. acnes subsp. defendens phylotype (Rhee et al., 2023) Clinical data suggests the skin health benefits of topical application of a modified C. acnes subsp. defendens strain, XYCM42, and its ferment (Rhee et al., 2023) Industry impact & potential: Crown Laboratories, Inc. launched the BIOJUVE skin care collection earlier this year, incorporating Xycrobe technology, whose efficacy is backed by the clinical study of C. acnes subsp. defendens strain XYCM42. Understanding the differences between the many strains of microbiome species could be key to developing prebiotic, probiotic and postbiotic solutions for acne as well as multiple other skin conditions. Our solution: Sequential uses shotgun metagenomic sequencing in our R&D to understand which critical species and strains are influenced by cosmetic products and influence skin health. We have developed highly specific probes to measure the absolute quantity of these species and strains, down to the copy number. Therefore, we can help to perform in vivo studies and validate the sequencing results at strain level. References: Fitz-Gibbon S, Tomida S, Chiu BH, Nguyen L, Du C, Liu M, Elashoff D, Erfe MC, Loncaric A, Kim J, Modlin RL, Miller JF, Sodergren E, Craft N, Weinstock GM, Li H. Propionibacterium acnes strain populations in the human skin microbiome associated with acne. J Invest Dermatol. 2013 Sep;133(9):2152-60. doi: 10.1038/jid.2013.21. Epub 2013 Jan 21. PMID: 23337890; PMCID: PMC3745799. Huang C, Zhuo F, Han B, Li W, Jiang B, Zhang K, Jian X, Chen Z, Li H, Huang H, Dou X, Yu B. The updates and implications of cutaneous microbiota in acne. Cell Biosci. 2023 Jun 21;13(1):113. doi: 10.1186/s13578-023-01072-w. PMID: 37344849; PMCID: PMC10283232. Rhee MS, Alqam ML, Jones BC, Phadungpojna S, Day D, Hitchcock TM. Characterization of a live Cutibacterium acnes subspecies defendens strain XYCM42 and clinical assessment as a topical regimen for general skin health and cosmesis. J Cosmet Dermatol. 2023 Mar;22(3):1031-1045. doi: 10.1111/jocd.15510. Epub 2022 Nov 14. PMID: 36374551.

In the world of skincare, light-emitting diode (LED) technology has emerged as a powerful tool, emitting specific wavelengths of light, such as red and blue, which penetrate the skin at different depths and trigger specific cellular responses. As interest in LED devices grows, so does the scrutiny of their impact on the skin microbiome, prompting a surge in research efforts. What We Know: Red light (620-750 nm) effectively accelerates wound healing by promoting collagen production, stimulating fibroblast proliferation, enhancing local microvasculature and boosting cellular metabolism to facilitate tissue regeneration. Red light LED masks are thus popularly used to prevent signs of ageing and to treat conditions like psoriasis and rosacea (Zhang et al., 2024). Blue light (380-500 nm) exhibits antimicrobial properties. Narrowband LED therapy utilising blue light has shown efficacy and safety as an adjunctive treatment for mild to moderate acne. This occurs through the inactivation of Cutibacterium acnes when blue light combines with oxygen, producing reactive oxygen species, damaging the bacteria (Dai, 2017). Industry Impact and Potential: The sequential use of blue light followed by red light treatment to address skin disorders associated with microbial agents is a promising approach. Although blue light effectively targets C. acnes, its depth of skin penetration is limited. Red light, on the other hand, penetrates deeper and complements blue light therapy with its anti-inflammatory effects, leading to greater clinical improvement, especially in inflammatory acne lesions (Nestor et al., 2016). The versatility of LED therapy, influenced by light parameters and clinical application, allows for tailored treatment of various skin disorders, each with unique biological effects to address (Sorbellini, Rucco & Rinaldi, 2018). Prevalent commensal bacteria, such as Staphylococcus spp., contain pigments and proteins responsive to blue light, potentially influencing inflammation. These photosensitive targets, like flavins and porphyrins, can significantly impact microbial behaviour when excited. For instance, Acinetobacter baumannii and certain skin commensals alter biofilm formation and virulence in response to blue light. However, understanding the distribution of these light-sensitive elements across the skin microbiome remains unclear (Serrage et al., 2024). Our Solution: At Sequential, our flagship service empowers you to conduct personalised, comprehensive Skin Microbiome Studies. Backed by a vast database of 20,000 microbiome samples and a worldwide network of 10,000 testing participants, our team of experts is dedicated to assisting you in crafting a tailored microbiome study. This enables in-depth exploration into the impact of advanced skincare technologies, such as LED masks, on the skin microbiome. References: Dai, T. (2017) The antimicrobial effect of blue light: What are behind? Virulence. 8 (6), 649–652. doi:10.1080/21505594.2016.1276691. Nestor, M.S., Swenson, N., Macri, A., Manway, M. & Paparone, P. (2016) Efficacy and Tolerability of a Combined 445nm and 630nm Over-the-counter Light Therapy Mask with and without Topical Salicylic Acid versus Topical Benzoyl Peroxide for the Treatment of Mild-to-moderate Acne Vulgaris. The Journal of Clinical and Aesthetic Dermatology. 9 (3), 25–35. Serrage, H.J., Eling, C.J., Alves, P.U., Xie, E., McBain, A.J., Dawson, M.D., O’Neill, C. & Laurand, N. (2024) Spectral characterization of a blue light-emitting micro-LED platform on skin-associated microbial chromophores. Biomedical Optics Express. 15 (5), 3200–3215. doi:10.1364/BOE.522867. Sorbellini, E., Rucco, M. & Rinaldi, F. (2018) Photodynamic and photobiological effects of light-emitting diode (LED) therapy in dermatological disease: an update. Lasers in Medical Science. 33 (7), 1431–1439. doi:10.1007/s10103-018-2584-8. Zhang, L., Jiang, X., Li, S., Lan, Y., Liu, H., et al. (2024) Stretchable electronic facial masks for photodynamic therapy. Nano Energy. 123, 109407. doi:10.1016/j.nanoen.2024.109407.