UC Berkeley

Humans spend approximately 90% of their time indoors, yet we know very little about the microbial ecosystem of the built environment and how it impacts occupants. Here we use infants hospitalized in a neonatal intensive care unit (NICU) as a model system to track the exchange of microbes between room and occupants. By leveraging high-throughput sequencing and other “omics” technologies, we conducted four major studies to broadly address the composition of microbes populating NICU surfaces, how these microbes migrate to the infant gut, and once in the gut, how these microbes compete for resources. Over the course of these campaigns we collected and processed over 5,000 samples from hospital room surfaces and over 300 infant fecal samples creating the largest collection of hospital samples to be interrogated with next-generation sequencing techniques. Using an approach that reassembles the entire 16S rRNA gene from room amplicons and gut metagenomics data, we discovered several organisms on room surfaces before their detection in the infant gut. Once in the gut, we used a metaproteomics technique to investigate the metabolisms of early infant gut colonizers. Unlike the anaerobic gut environment of older children and adults, we discovered a relatively high utilization of aerobic pathways in many of the facultative anaerobes colonizing the infant gut. We also observed niche partitioning amongst closely related Citrobacter strains in our strain- resolved proteomics data, providing insight into how early colonizers compete in the nascent infant gut. To better understand biomass trends inside and outside the gut, we developed an assay to quantify 16S rRNA gene copies using droplet digital PCR (ddPCR). We discovered a surprising amount of variation in bacterial densities across different NICU environments. These data also allowed us to adapt a novel in silico data cleaning method that leverages the quantification of negative controls to provide data less impacted by the inherent noise of low- biomass amplicon workflows. Cleaner data allowed us to apply a machine learning classifier that showed each infant’s room had a distinct microbial fingerprint. To validate this result, we conducted a metagenomics campaign on pooled room samples from six different infants. After assembly and binning, we were able to recover hundreds of high quality genomes. Utilizing genomes from this dataset and previously isolated genomes from our lab, we discovered the same strains in the room as in infants. Further, we found several taxa frequently isolated from infant gut samples in this NICU are the same strains in the NICU room metagenomes. Overall, the analysis from this work suggests that where a premature infant is born and the history of room occupancy can impact its gut microbiome development.