Understanding respiratory tract infections through minimally-invasive, daily nasal sampling in children

Introduction

Pneumonia is the number one infectious cause of death in children worldwide. Many of the viruses and bacteria that cause pneumonia regularly infect, or colonize, the upper respiratory tract (URT) without causing disease. This drives community transmission but is also an important source of immunity. The processes and key host immune and microbiota factors that determine the infection kinetics, transmission, and development of immunity during such infections need elucidation.

Methodology

I have recently optimized minimally-invasive nasal sampling analysis methods using Synthetic Absorptive Matrix (SAM) strips that now allow me to address these knowledge gaps. Through the daily collection of such well-tolerated nasal samples in children, I will study:

1. Non-pathological, naturally-acquired URT infections.
2. Controlled infections in an ethical and safe manner using the live attenuated influenza vaccine.

In addition, I will perform high-frequency nasal sampling in groups of schoolchildren to precisely measure transmission events over time and even infer exposure.

Data Collection

Incoming bacteria, viruses, and the resident URT microbiome, as well as mucosal host innate and adaptive immune responses, will be quantified in parallel throughout infections using existing and new high-throughput assays, including:

• An antigen array
• Microfluidic qPCR for 32 pathogens

Analysis

Multi-omics integrative time-series analyses and mathematical modeling will be used to identify parameters that are central and predictive for:

1. Pathogen acquisition
2. Replication and clearance
3. Transmission and immune boosting

Key novel markers and concepts will be validated using state-of-the-art in vitro mucosal models.

Impact

The comprehensive and detailed understanding of URT infections obtained in this project can lead to better diagnostics, mucosal targeted therapies, and vaccines. It will also provide a basis for improved predictions of pathogen spread and public health effects of interventions at the population level.