Defining the gastrointestinal immune and microbiome response to shigella infection in children

About This Grant

ABSTRACT Shigella causes severe diarrhea and dysentery in children under five years old living in low- and middle-income countries. Repeated bouts of this easily transmissible infection in children can lead to clinically severe malnutrition, impaired growth, lifelong morbidity, and death. The underlying mechanisms that are involved in the progression to severe disease in some children remain poorly understood. While host immunity can impact the course and severity of disease, most studies have focused on systemic immunity. However, mucosal immune markers—likely directly involved in pathogen clearance in the gut—are not well characterized. Additionally, the microbial communities in the infant gastrointestinal tract are crucial for the development and regulation of the mucosal immune system. The overall objective of this application is to identify mucosal immune and microbiome markers associated with susceptibility to Shigella infection in children. Our preliminary data indicate that Shigella infection and its abundance influence both microbiome and immune components in children. We will investigate the relationship between Shigella burden, microbiome composition, stool immune markers, and severity of diarrheal disease in a well-characterized cohorts of children less than 5 years of age who participated in the Vaccine Impact on Diarrhea in Africa (VIDA) study, a multi-site study designed to investigate the etiology of diarrheal diseases in three African countries. We will test samples from children stratified in four groups: those with Shigella infection (confirmed by qPCR) with or without diarrhea, and controls with or without diarrhea (caused by pathogens other than Shigella). We will examine age-related differences in mucosal immune and microbiome profiles across three age groups (0-11, 12-23, and 24-59 months) to assess age-associated features. Aim 1 will characterize the gastrointestinal microbiome profile associated with Shigella infection using metagenomic sequencing. Aim 2 will use proteomics to identify known and novel mucosal immune components elicited during Shigella infection. Aim 3 will apply statistical and machine learning tools to identify interaction patterns between microbiome and immune features that are associated with Shigella infection. This five-year career development program will expand my expertise in microbiome sequencing, mucosal immunology, and computational biology. The findings will add to our understanding of the gut immune-microbial interactions, shedding light on their mechanisms of action and informing the development of effective prevention and therapeutic tools for children in vulnerable populations.