SkinBug: an artificial intelligence approach to predict human skin microbiome-mediated metabolism of biotics and xenobiotics

Jaiswal, Shubham K.; Agarwal, Shitij Manojkumar; Thodum, Parikshit; Sharma, Vineet K.

doi:10.1016/j.isci.2020.101925

Cited by 11 publications

(11 citation statements)

References 93 publications

(97 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our framework sets the stage for new analyses implementing AI approaches focused on understanding the complex relationships between microbial communities and phenotypes, to predict any condition from microbiome samples. Indeed, considering the skin microbiome topic, a few, very recent works included data integration strategies and AI applications (79)(80)(81), showing the potential held by these approaches in advancing skin microbiome research.…”

Section: Discussionmentioning

confidence: 99%

SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata

Agostinetto¹,

Bozzi²,

Porro

et al. 2021

Preprint

View full text Add to dashboard Cite

Large amounts of data from microbiome-related studies have been (and are currently being) deposited on international public databases. These datasets represent a valuable resource for the microbiome research community and could serve future researchers interested in integrating multiple datasets into powerful meta-analyses. However, this huge amount of data lacks harmonization and is far from being completely exploited in its full potential to build a foundation that places microbiome research at the nexus of many subdisciplines within and beyond biology. Thus, urges the need for data accessibility and reusability, according to FAIR (Findable, Accessible, Interoperable, and Reusable) principles, as supported by National Microbiome Data Collaborative and FAIR Microbiome. To tackle the challenge of accelerating discovery and advances in skin microbiome research, we collected, integrated and organized existing microbiome data resources from human skin 16S rRNA amplicon sequencing experiments. We generated a comprehensive collection of datasets, enriched in metadata, and organized this information into data frames ready to be integrated into microbiome research projects and advanced post-processing analysis, such as data science applications (e.g. machine learning). Furthermore, we have created a data retrieval and curation framework built on three different stages to maximize the retrieval of datasets and metadata associated with them. Lastly, we highlighted some caveats regarding metadata retrieval and suggested ways to improve future metadata submissions. Overall, our work resulted in a curated skin microbiome datasets collection accompanied by a state-of-the-art analysis of the last 10 years of the skin microbiome field.

show abstract

Section: Discussionmentioning

confidence: 99%

SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata

Agostinetto¹,

Bozzi²,

Porro

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Both species are biologically relevant and have been repeatedly isolated from healthy subjects at different skin sites ( 26 ). Moreover, they are easily distinguishable from each other on agar plates and have a well-established potential for xenobiotic metabolism ( 26 , 50 ).…”

Section: Methodsmentioning

confidence: 99%

Commensal-Related Changes in the Epidermal Barrier Function Lead to Alterations in the Benzo[ a ]Pyrene Metabolite Profile and Its Distribution in 3D Skin

Lemoine

Bayrambey

Roloff

et al. 2021

mBio

View full text Add to dashboard Cite

show abstract

“…Analysis of multi-omics data from the skin microbiome will provide a more complete picture of the behaviors of skin microbes. Furthermore, machine learning and artificial intelligence methods are increasingly being applied to skin microbiome research, including determining changes in abundance or diversity of species and strains, integrating multi-omic microbiome data, and phenotypic prediction ( 41 – 43 ).…”

Section: Toolbox For Surveying Microbial Skin Communitiesmentioning

confidence: 99%

Evolving approaches to profiling the microbiome in skin disease

2023

View full text Add to dashboard Cite

Despite its harsh and dry environment, human skin is home to diverse microbes, including bacteria, fungi, viruses, and microscopic mites. These microbes form communities that may exist at the skin surface, deeper skin layers, and within microhabitats such as the hair follicle and sweat glands, allowing complex interactions with the host immune system. Imbalances in the skin microbiome, known as dysbiosis, have been linked to various inflammatory skin disorders, including atopic dermatitis, acne, and psoriasis. The roles of abundant commensal bacteria belonging to Staphylococcus and Cutibacterium taxa and the fungi Malassezia, where particular species or strains can benefit the host or cause disease, are increasingly appreciated in skin disorders. Furthermore, recent research suggests that the interactions between microorganisms and the host’s immune system on the skin can have distant and systemic effects on the body, such as on the gut and brain, known as the “skin-gut” or “skin-brain” axes. Studies on the microbiome in skin disease have typically relied on 16S rRNA gene sequencing methods, which cannot provide accurate information about species or strains of microorganisms on the skin. However, advancing technologies, including metagenomics and other functional ‘omic’ approaches, have great potential to provide more comprehensive and detailed information about the skin microbiome in health and disease. Additionally, inter-species and multi-kingdom interactions can cause cascading shifts towards dysbiosis and are crucial but yet-to-be-explored aspects of many skin disorders. Better understanding these complex dynamics will require meta-omic studies complemented with experiments and clinical trials to confirm function. Evolving how we profile the skin microbiome alongside technological advances is essential to exploring such relationships. This review presents the current and emerging methods and their findings for profiling skin microbes to advance our understanding of the microbiome in skin disease.

show abstract

SkinBug: an artificial intelligence approach to predict human skin microbiome-mediated metabolism of biotics and xenobiotics

Cited by 11 publications

References 93 publications

SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata

SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata

Commensal-Related Changes in the Epidermal Barrier Function Lead to Alterations in the Benzo[ a ]Pyrene Metabolite Profile and Its Distribution in 3D Skin

Evolving approaches to profiling the microbiome in skin disease

Contact Info

Product

Resources

About