An interactive visualization tool to enable studying the gut microbiome of allergic infants.

Ehud Dahan ¹ Sivan Betzalel ¹ Victoria J. Martin ² Yamini V. Virkud ² Qian Yuan ² Wayne G. Shreffler ² Larson Hogstrom ³ Hera Vlamakis ³ Ramnik J. Xavier ^3,4 Moran Yassour ^1,5

¹Microbiology and Molecular Genetics, Faculty of Medicine, The Hebrew University of Jerusalem, Israel
²Department of Pediatrics, Massachusetts General Hospital, USA
³Microbiome and Infectious Diseases, The Broad Institute of Mit and Harvard, USA
⁴Center for Computational and Integrative Biology, Massachusetts General Hospital, USA
⁵School of Computer Science & Engineering, The Hebrew University of Jerusalem, Israel

Previous studies have shown that infant gut microbiome composition can increase the risk of developing immune system-related diseases, like asthma and inflammatory bowel disease. This is thought to be related to the immune-system education via the developing gut microbiome. Exploring the complex relationships between the microbiome and disease outcomes collaboratively is often hampered by the specialization of expertise within the group. We developed an interactive R-based visualization tool to summarize linear regression results, and enable our collaborators to search for microbial features associated with a form of non IgE-mediated infant milk allergy.

Although most children eventually grow out of this childhood allergy, the symptoms of abdominal discomfort, blood-streaked and mucousy stools, vomiting, and diarrhea are stressful for both children and their parents, may lead to dietary restrictions, and have been recently associated with an increased risk of other disease.

Using a Massachusetts-based healthy infant cohort that we have recently established, we selected 90 cases and 87 matched controls with 6-8 stool samples from each during their first year of life. We performed 16S ribosomal gene sequencing on a total of 1088 samples to characterize the gut microbiome dynamics in the first year of life.

In our preliminary results, we find that allergic children have higher abundance of lactobacillus species following the allergy resolution. We will present the tool and its application for enabling identification of additional such associations. Such an approach can help teams make discoveries that improve early diagnosis, intervention and perhaps even prevention of infant cow’s milk allergy.