Investigating the role of skin and gut microbiome in the onset of atopic dermatitis in infants

Project Summary/Abstract Atopic dermatitis (AD) is a common chronic skin condition that affects over 10% of children in the US. AD can significantly impact the quality of life by causing unbearable itching, leading to sleep loss and infections. Unfortunately, there is currently no cure. The microbiome in both the skin and gut is significantly altered in patients with AD. However, very few studies have investigated the role of microbiome in the onset of AD in infants when AD symptoms usually appear. Moreover, little is known about how the skin and gut microbiome interact with each other during the onset of AD, known as the gut-skin axis. To fill this critical gap in our knowledge, Dr. Shen will utilize genomic, bioinformatic, and experimental approaches to an unprecedentedly large pool of skin swabs, nasal swabs, and stool samples from 608 infants, including 258 paired skin and stool samples, to facilitate the investigation of the gut-skin axis. Dr. Shen will use ultra-deep shotgun metagenomic sequencing to capture bacteria, fungi, and viruses in infants with AD at the species and strain level resolution. The preliminary data have achieved high classification rates of metagenomic reads, and have indicated a multi-kingdom decrease in skin microbial diversity and numerous differential gut microbial species associated with AD. These data lead to a central hypothesis that the microbiome of both the skin and gut plays an important role in the onset of AD in infants. This study aims to reveal full microbiome signatures and the gut-skin microbiome relationships associated with the onset of AD. In Aim 1, Dr. Shen will perform large-scale bioinformatic analyses to identify skin microbial taxa and genes associated with the early onset of AD in infants, in the context of host genetics. Additionally, Dr. Shen will experimentally test the effects of AD-associated microbes with neonatal wild-type mice. In Aim 2, Dr. Shen will extend the computational approaches from Aim 1 to the gut metagenomic data and integrate the skin and gut microbiome to reveal the gut-skin microbiome relationships in AD. Furthermore, Dr. Shen will apply deep learning techniques to build the first microbiome-based predictive model for AD outcomes, facilitating early prevention and intervention of the disease. The proposed research and training plan will build on Dr. Shen’s strengths in the fields of bioinformatics and microbiome, help him to develop necessary skills in experiments, and build his own professional networks through conferences. Dr. Shen’s long-term goal is to extend the computational and experimental techniques of this proposal to study the host-microbe interactions of the gut-skin axis in AD. The unique environment of the NIH/NHGRI and the strong mentoring team of Dr. Shen will enhance his research and career development, facilitating his transition to independence.