A Microbiome-Based Index for Assessing Skin Health and Treatment Effects for Atopic Dermatitis in Children.
Zheng Sun,Shi Huang,Pengfei Zhu,Feng Yue,Helen Zhao,Ming Yang,Yueqing Niu,Gongchao Jing,Xiaoquan Su,Huiying Li,Chris Callewaert,Chris Callewaert,Rob Knight,Jiquan Liu,Edward Dewey Smith,Karl Shiqing Wei,Jian Xu +16 more
- Vol. 4, Iss: 4
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TLDR
MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations, suggesting applications in diagnosis and patient stratification.Abstract:
A quantitative and objective indicator for skin health via the microbiome is of great interest for personalized skin care, but differences among skin sites and across human populations can make this goal challenging. A three-city (two Chinese and one American) comparison of skin microbiota from atopic dermatitis (AD) and healthy pediatric cohorts revealed that, although city has the greatest effect size (the skin microbiome can predict the originated city with near 100% accuracy), a microbial index of skin health (MiSH) based on 25 bacterial genera can diagnose AD with 83 to ∼95% accuracy within each city and 86.4% accuracy across cities (area under the concentration-time curve [AUC], 0.90). Moreover, nonlesional skin sites across the bodies of AD-active children (which include shank, arm, popliteal fossa, elbow, antecubital fossa, knee, neck, and axilla) harbor a distinct but lesional state-like microbiome that features relative enrichment of Staphylococcus aureus over healthy individuals, confirming the extension of microbiome dysbiosis across body surface in AD patients. Intriguingly, pretreatment MiSH classifies children with identical AD clinical symptoms into two host types with distinct microbial diversity and treatment effects of corticosteroid therapy. These findings suggest that MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations.IMPORTANCE MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification.read more
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Method development for cross-study microbiome data mining: Challenges and opportunities.
TL;DR: This mini-review focuses on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining, and proposes the promising solutions to multi-omics data analysis.
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Computational Modeling of the Human Microbiome
TL;DR: By providing an overview of different human microbiome sites, this work hopes to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.
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Applications of Human Skin Microbiota in the Cutaneous Disorders for Ecology-Based Therapy.
TL;DR: The interactions between Dysbiosis and the cutaneous disorders, including homeostasis and dysbiosis of skin microbiota, microbial composition in skin diseases, and the mechanisms and applications of reversing or ameliorating the dysbiotic by the targeted manipulation of the skin microbiota are summarized to aid development of therapeutic modality for ecology-based therapy.
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Protecting the outside: biological tools to manipulate the skin microbiota
TL;DR: The aim of this review is to discuss the currently available literature on biological tools that have the potential to manipulate the skin microbiota, with particular focus on bacteriocins, phage therapy, antibiotics, probiotics, and targets of the gut-skin axis.
Journal ArticleDOI
The role of the skin microbiome in atopic dermatitis – correlations and consequences
TL;DR: Results of initial clinical studies on various approaches demonstrate promising results and knowledge of dermal dysbiosis yields new treatment options for the therapy of the disease.
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TL;DR: The Human Microbiome Project Consortium reported the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome as discussed by the authors.
Journal Article
Structure, function and diversity of the healthy human microbiome
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TL;DR: The Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far, finding the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals.
Journal ArticleDOI
Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes
Gary D. Wu,Jun Chen,Christian Hoffmann,Christian Hoffmann,Kyle Bittinger,Ying-Yu Chen,Sue A. Keilbaugh,Meenakshi Bewtra,Dan Knights,William A. Walters,Rob Knight,Rohini Sinha,Erin Gilroy,Kernika Gupta,Robert N. Baldassano,Lisa Nessel,Hongzhe Li,Frederic D. Bushman,James D. Lewis +18 more
TL;DR: Alternative enterotype states are associated with long-term diet, particularly protein and animal fat (Bacteroides) versus carbohydrates (Prevotella) and other enterotypes distinguished primarily by levels of Bacteroide and Prevotella.
Journal ArticleDOI
An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea
Daniel McDonald,Morgan N. Price,Julia K. Goodrich,Julia K. Goodrich,Eric P. Nawrocki,Todd Z. DeSantis,Alexander J. Probst,Alexander J. Probst,Gary L. Andersen,Rob Knight,Rob Knight,Philip Hugenholtz +11 more
TL;DR: A ‘taxonomy to tree’ approach for transferring group names from an existing taxonomy to a tree topology is developed and used to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences.