e-HoMIniCS - elucidation of Host Microbiome Interactions in Cosmetic Skin
Lead Research Organisation:
King's College London
Department Name: Dental Institute
Abstract
The recent advances in high through-put data generation for DNA/RNA, proteins and metabolites has resulted in a paradigm shift in how we seek to answer some of the fundamental questions of biology. Over the past decade, significant amounts of these large data sets encompassing resident microbial communities (microbiome), specific host responses and environmental conditions have been generated. To date the integration and exploitation of these complex datasets in a structured way has been highly problematic. However recent advancements in in-silico methodologies can for the first time help to unlock the full potential of these data, facilitating improved understanding of and discovery of novel interventions for host-microbiome interactions. With the advent of these technologies it has become apparent that interactions between environmental, host and microbial factors give rise to the various changes in skin homeostasis that result in cosmetic conditions such as dry skin and dandruff. Dandruff and dry skin are widespread conditions impacting over 50% of the world's population affecting quality of life including self/body confidence and their treatment is the basis of a sector worth over 10bn Euros annually.
In this study, in collaboration with our industrial partners, Unilever, we will investigate the physiological changes of normal, dry skin and dandruff through unique integration of computational biology and modelling with microbiology. We will develop a computational and experimental platform for skin host-microbiome interactions to reveal the microbial mechanisms involved in different skin states. Using this approach, we will identify and evaluate new therapeutic targets as well as reveal the underlying physiological events in skin homeostasis.
Using a combination of skin samples collected by tape strips from normal, dry skin and dandruff, as well as data generated from reconstituted skin models and keratinocyte monolayers, we will generate data that accurately describes skin-microbe interactions. we will also identify the key species and strains of Malassezia, Staphylococcus and Cutibacterium associated with different skin states. In parallel by using the available multi-omics data from Unilever and the public domain, we will generate computational models for microbes and host skin tissue and cells. Having both in-silico and in-vitro set ups, we will investigate the impact of key metabolites and anti-metabolites on the relationship between the skin and key microbes and microbial communities. Finally, we will explore the impact of key host factors, such as cytokines (e.g. IL-36, IL-1, IL-17, IL-20 family) and antimicrobial peptides (e.g. beta-defensins, S100, LL-37) on the resident microbial communities. We will then categorize these therapies based on their mode of action on skin-microbiomes interactions. The new therapeutic targets generated and validated through this combination of both computational and experimental techniques can then be tested for host toxicity and efficacy. This cutting-edge integrative platform could be easily extended to identify new targets or drugs for different microbial constituents in human body, their association with a range of hosts and pathologies. As such it will delineate an entirely novel approach to investigating host-microbiome interactions that will have broad applicability across a wide range of sectors, including medical, veterinary, cosmetic and agricultural.
In this study, in collaboration with our industrial partners, Unilever, we will investigate the physiological changes of normal, dry skin and dandruff through unique integration of computational biology and modelling with microbiology. We will develop a computational and experimental platform for skin host-microbiome interactions to reveal the microbial mechanisms involved in different skin states. Using this approach, we will identify and evaluate new therapeutic targets as well as reveal the underlying physiological events in skin homeostasis.
Using a combination of skin samples collected by tape strips from normal, dry skin and dandruff, as well as data generated from reconstituted skin models and keratinocyte monolayers, we will generate data that accurately describes skin-microbe interactions. we will also identify the key species and strains of Malassezia, Staphylococcus and Cutibacterium associated with different skin states. In parallel by using the available multi-omics data from Unilever and the public domain, we will generate computational models for microbes and host skin tissue and cells. Having both in-silico and in-vitro set ups, we will investigate the impact of key metabolites and anti-metabolites on the relationship between the skin and key microbes and microbial communities. Finally, we will explore the impact of key host factors, such as cytokines (e.g. IL-36, IL-1, IL-17, IL-20 family) and antimicrobial peptides (e.g. beta-defensins, S100, LL-37) on the resident microbial communities. We will then categorize these therapies based on their mode of action on skin-microbiomes interactions. The new therapeutic targets generated and validated through this combination of both computational and experimental techniques can then be tested for host toxicity and efficacy. This cutting-edge integrative platform could be easily extended to identify new targets or drugs for different microbial constituents in human body, their association with a range of hosts and pathologies. As such it will delineate an entirely novel approach to investigating host-microbiome interactions that will have broad applicability across a wide range of sectors, including medical, veterinary, cosmetic and agricultural.
Technical Summary
Current approaches to investigating host-microbiome interactions are somewhat piecemeal. We will take a fully integrated approach, combining state-of-the-art computational modelling approaches to predict events and targets for modulation, with conventional microbiological and cell biology techniques to generate omics data and validate target efficacy in an iterative fashion. We will generate high-quality multi-cellular genome-scale metabolic models (GEMs) of Skin host-microbes interaction for dandruff, dry and healthy skin alongside in vitro models. These models will be derived from metagenomic, transcriptomic and metabolomic data generated from skin and in vitro models cocultured with unique multi-kingdom microbial. We will then integrate the computational and experimental outputs to predict and refine these communities. We will next investigate the impact of known metabolites and drugs on host-microbiome interactions, targeting metabolites through anti-metabolite analysis, and enzyme inhibition or gene silencing for gene/enzyme targeting. The in silico anti-metabolite and gene silencing analysis will be utilized to identify novel modulation targets for skin state and microbiome while toxicity of targets will be tested in host cells to check the side effects. A select panel of computational candidate targets will be validated experimentally before any further analysis. Additionally, this project develops a directed bipartite graph-based algorithm to model metabolic, regulatory and signaling networks simultaneously, predicting the effect of cytokines in maintaining host-microbiome homeostasis at the skin. Thus, we propose a pipeline for generating skin host-microbiome interactions to elucidate the physiology of different states, as well as identifying new therapeutic targets for their modulation. This approach can be extended to predict targets or drugs for host-microbiome interactions of other sites, including medical, veterinary and agricultural.
Planned Impact
Skin is the body's largest organ covering an area of over 2 square metres and contributing about 15% of total body weight. Dandruff and dry skin are widespread skin conditions impacting over 50% of the worlds' population, impacting significantly on quality of life and self/body confidence. Their treatment is the basis of a sector worth over 10bn euros annually, with the NHS alone spends more than £4.5 million each year on anti-dandruff medicated shampoos. Despite such high prevalence, their aetiology is not well understood. Treatment for these conditions often consists of antifungal shampoos. However, this is not always successful, and has the potential to increase antimicrobial drug resistance. Whilst there have been several recent studies generating significant amounts of data regarding the processes involved in microbiome-host interactions in healthy and dandruff skin, little effort has been made to effectively integrate the different data sets. By integrating different data sets, we will generate a novel approach that combines in silico modelling with in vitro and in vivo investigations to delineate the key mechanisms and events in skin homeostasis. By doing so, it will be possible to formulate new treatment modalities that will be less aggressive, focussing more on altering host-microbiome interactions through changing the environment, rather than killing of or removing microbes from otherwise healthy skin.
Academic researchers: Results from this work and the tools generated will provide an invaluable resource set that can be applied to understanding basic interactions between microbial and their host in both a healthy and unbalanced microbiota. These tools will help to answer key questions surrounding our current knowledge of microbiome, including understanding how imbalances of a microbial community can lead to changes in host tissue homeostasis and concomitant pathologies. Further, it will also have an impact on how we approach investigating host-microbiome interactions. As well as investigations of skin-microbiome, these tools will be equally applicable to investigating other tissue-microbiome interactions and will pave the way for a truly integrative approach to investigating microbial communities.
Industry: The approach and tools validated through this proposal will provide a rapid screening methodology that will enable industrial research to comprehensively screen a large panel of drugs, metabolites and anti-metabolite treatments to identify promising candidates for further investigation. This will reduce the amount of time and money spent following false leads and thereby streamline the drug/target discovery pipeline. This will have an impact on pharmaceutical, personal care and food producers.
Public: Through public dissemination plans, this work will provide the public with a greater understanding of our microbiomes, and how we can manipulate these communities to our advantage in many avenues. Moreover, completion of this work will allow the identification of novel compounds and treatment modalities for these common but potentially debilitating conditions, improving the quality of life for millions.
Governmental and NGOs: Results from this study will guide future studies investigating the microbiome and host-microbiome interactions, as well as methods to manipulate these communities. Further, by reducing the need for antimicrobial use, results of this study will have a significant impact on antimicrobial drug resistance development. This will reduce the healthcare burden for many modern-day conditions, both in the UK and in developing countries, where long-term treatments are not a viable option for many.
Academic researchers: Results from this work and the tools generated will provide an invaluable resource set that can be applied to understanding basic interactions between microbial and their host in both a healthy and unbalanced microbiota. These tools will help to answer key questions surrounding our current knowledge of microbiome, including understanding how imbalances of a microbial community can lead to changes in host tissue homeostasis and concomitant pathologies. Further, it will also have an impact on how we approach investigating host-microbiome interactions. As well as investigations of skin-microbiome, these tools will be equally applicable to investigating other tissue-microbiome interactions and will pave the way for a truly integrative approach to investigating microbial communities.
Industry: The approach and tools validated through this proposal will provide a rapid screening methodology that will enable industrial research to comprehensively screen a large panel of drugs, metabolites and anti-metabolite treatments to identify promising candidates for further investigation. This will reduce the amount of time and money spent following false leads and thereby streamline the drug/target discovery pipeline. This will have an impact on pharmaceutical, personal care and food producers.
Public: Through public dissemination plans, this work will provide the public with a greater understanding of our microbiomes, and how we can manipulate these communities to our advantage in many avenues. Moreover, completion of this work will allow the identification of novel compounds and treatment modalities for these common but potentially debilitating conditions, improving the quality of life for millions.
Governmental and NGOs: Results from this study will guide future studies investigating the microbiome and host-microbiome interactions, as well as methods to manipulate these communities. Further, by reducing the need for antimicrobial use, results of this study will have a significant impact on antimicrobial drug resistance development. This will reduce the healthcare burden for many modern-day conditions, both in the UK and in developing countries, where long-term treatments are not a viable option for many.
Publications
Begum N
(2022)
Host-mycobiome metabolic interactions in health and disease.
in Gut microbes
Begum N
(2022)
Integrative functional analysis uncovers metabolic differences between Candida species.
in Communications biology
Carr VR
(2020)
Abundance and diversity of resistomes differ between healthy human oral cavities and gut.
in Nature communications
Carr VR
(2023)
Palidis: fast discovery of novel insertion sequences.
in Microbial genomics
Carr VR
(2021)
Probing the Mobilome: Discoveries in the Dynamic Microbiome.
in Trends in microbiology
Carr, V.R.
(2023)
Palidis: fast discovery of novel insertion sequences
in Microbial Genomics
Ezzamouri B
(2021)
Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies.
in Frontiers in microbiology
Ghaffari P
(2022)
Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions.
in Journal of translational medicine
Harzandi A
(2021)
Acute kidney injury leading to CKD is associated with a persistence of metabolic dysfunction and hypertriglyceridemia.
in iScience
Karimova M
(2022)
The human microbiome in immunobullous disorders and lichen planus.
in Clinical and experimental dermatology
Kataria R
(2023)
Leveraging circulating microbial DNA for early cancer detection.
in Trends in cancer
Moyes DL
(2021)
Editorial: Immunity to Fungal Infections: Insights From the Innate Immune Recognition and Antifungal Effector Mechanisms.
in Frontiers in microbiology
Pellon A
(2022)
Role of Cellular Metabolism during Candida-Host Interactions.
in Pathogens (Basel, Switzerland)
Pellon A
(2020)
New Insights in Candida albicans Innate Immunity at the Mucosa: Toxins, Epithelium, Metabolism, and Beyond.
in Frontiers in cellular and infection microbiology
Ponde NO
(2022)
Receptor-kinase EGFR-MAPK adaptor proteins mediate the epithelial response to Candida albicans via the cytolytic peptide toxin, candidalysin.
in The Journal of biological chemistry
Proffitt C
(2020)
Disease, Drugs and Dysbiosis: Understanding Microbial Signatures in Metabolic Disease and Medical Interventions.
in Microorganisms
Rosario D
(2021)
Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in Parkinson's disease.
in Cell reports
Uzochukwu I
(2023)
The key players of dysbiosis in Noma disease; A systematic review of etiological studies.
in Frontiers in oral health
Description | We have developed computational models for how skin microbes interact with the skin based on large datasets from both healthy and dandruff skin. Using these models, we have predicted some key compounds that will alter the behaviour of skin microbes with how they interact with the skin. In particular, we have identified a series of compounds which will alter the outcomes of interactions between microbes and skin cells which will decrease the likelihood of the negative symptoms of dandruff. These compounds are the natural products of microbial metabolism and are the result of normal metabolic activity of the bacteria involved. Using the models, we were able to predict a series of compounds that could be used at alter the behaviour of these microbes to one more in balance with the host skin, and thereby reverse any disease pathologies. We then went on to validate these findings in the laboratory, demonstrating that the computational model predictions are valid and that use of the predicted compunds reduced inflammation and other responses normally associated with dandruff. In addition, we have identified the key role played by both polymicrobial communities and disease-specific strains of bacteria in driving disease pathologies in dandruff. incorporating this information and the data regarding these strains allowed us to further refine the computational models we developed to improve our ability to predict important features of microbes in dandruff. In addition, we have been able to apply these models to other skin conditions - notably the inflammatory skin diseases, psoriasis and atopic dermatitis, which has led to further research and funding applications. The work has also provided a template for how to conduct multidisciplinary research that combines wet-lab investigations, multi-omics data generation, bioinformatic analysis and computational modelling to significantly speed up research into host-microbial interactions in disease. In taking this inter-disciplinary approach, significant training and experience was provided to researchers in working with researchers from different disciplines - i.e. for wet-lab researchers to work with computational researchers and vice versa. Both researchers thus developed a keen knowledge of the features that are important in both aspects of research. Data generated as part of this study is still being analysed to identify further pathways and compounds that are of interest in skin disease. |
Exploitation Route | The computational models developed in this study as well as the omics data generated to build and refine these models can form the basis for whole new lines of investigation for a variety of inflammatory skin conditions. This has been further supported by the laboratory and clinical validation we have provided. As such, the outcomes are of significant interest and value to both academic and industrial researchers. The identified compounds in this study will be of significant interest and value to our industrial partner, potentially leading to the development of new dandruff shampoo treatments etc, as well as providing tools that can be used to analyse other personal hygiene conditions. The approach we have taken and validated will be of high value in the research community, and the findings themselves will be of great interest to the general public, providing key information about how they can benefit their own personal health through use and consumption of specific supplements and dietary intake. |
Sectors | Agriculture Food and Drink Digital/Communication/Information Technologies (including Software) Education Healthcare Pharmaceuticals and Medical Biotechnology |
Description | The findings are currently being used in collaboration with Unilever (the co-funder) to impact on their product development research. |
First Year Of Impact | 2021 |
Sector | Healthcare |
Impact Types | Economic |
Description | Research Project for MSc in Microbiome course |
Geographic Reach | Europe |
Policy Influence Type | Influenced training of practitioners or researchers |
Description | An analysis of the skin meta-transcriptome, metabolome and single-cell host transcriptome in S. aureus-colonised atopic dermatitis |
Amount | £75,000 (GBP) |
Organisation | Sanofi |
Sector | Private |
Country | Global |
Start | 01/2020 |
End | 09/2021 |
Description | Identifying skin microbiome, metabolome and immune profile signatures in different clinical phenotypes of atopic dermatitis |
Amount | £117,325 (GBP) |
Organisation | Pfizer Ltd |
Sector | Private |
Country | United Kingdom |
Start | 03/2022 |
End | 10/2024 |
Description | Integrative omics analysis of gingivitis - Measurement of gene expression, protein (and lipid) expression and local microbiome in samples of healthy and inflamed gingival tissue. |
Amount | £263,880 (GBP) |
Organisation | Unilever |
Sector | Private |
Country | United Kingdom |
Start | 06/2019 |
Description | Using Multi-omics Approach To Understand Pathogenesis In Orofacial Granulomatosis, A Rare Inflammatory Disorder Of The Face And Mouth. |
Amount | £219,964 (GBP) |
Organisation | Medical Research Council (MRC) |
Department | Medical Research Foundation |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 06/2024 |
End | 06/2026 |
Title | BioFung analysis database and tool |
Description | BioFung is a functional annotation analysis tool and database for analysing fungi |
Type Of Material | Technology assay or reagent |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | better, more accurate modeling of fungal metabolic outputs |
URL | https://github.com/sysbiomelab/BioFung |
Title | MIGRENE |
Description | A computational toolbox for MIcrobial and personalised GEM, REactobiome and community NEtwork modeling. used for community mmodelling and functional analysis of microbial communities. |
Type Of Material | Technology assay or reagent |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | Improved modelling in silico of microbial communities in the context of human health and disease. |
URL | https://github.com/sysbiomelab/MIGRENE |
Title | Dandruff lesional/nonlesional skin metagenomc |
Description | Metagenomic sequencing of skin swabs from lesional and nonlesional dandruff sites |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | No |
Impact | Data has just been genertaed and is currently being processed |
Description | Markers of Gingival Health |
Organisation | Unilever |
Department | Research and Development Colworth |
Country | United Kingdom |
Sector | Private |
PI Contribution | We will be analysing the transcriptome, microbiome and metabolome of healthy and inflammed tissue in patients with mild/early gingivitis |
Collaborator Contribution | Unilever are funding this research |
Impact | Multidisciplinary, involving clinical sample collection, molecular biology, metabolomics, transcriptomics, microbiome/NGS sequencing and systems biology |
Start Year | 2020 |
Description | Invited talk at the Annual Meeting of the Microbiology Society |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Invited talk for the Microbiology Society, addressing the analysis of antimicrobial resistance and mobile genetic elements within the micorbiome using metagenomics |
Year(s) Of Engagement Activity | 2022 |
Description | Keynote talk to the JAMS-UK meeting |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Postgraduate students |
Results and Impact | Presentation of our work with metagenomic analyses to a group of PhD students and early career researchers |
Year(s) Of Engagement Activity | 2023 |
Description | School Visit (Fleet, Hampshire) |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Schools |
Results and Impact | School presentation about the research being carried out on skin microbiome to Year 10 students at an all-girls school. |
Year(s) Of Engagement Activity | 2020 |
Description | Webinar (Phylobioscience) |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Webinar for the mycobiome and skin microbiome |
Year(s) Of Engagement Activity | 2020 |