For effective assessment of microbial communities and their possible effects on human health and disease, it’s not enough to simply identify what bacterial species are present in a sample. It’s also vital to assess their growth dynamics, which have been associated with multiple disease characteristics. Changes in growth rates can change the character of a microbial community, and the most common species are not necessarily the fastest growing.
Existing techniques for estimating growth rates from microbiome sequencing data have important limitations, reducing their utility for many research applications. To improve growth-rate estimates, Julia Oh, Ph.D.Our central goal is to develop microbiome therapeutics to treat human disease. We use diverse tools like genomics and synthetic biology to investigate our microbiome’s role in our health and engineer therapeutics.JAX Assistant Professor Julia Oh and Postdoctoral Associate Akintunde Emiola developed Growth Rate InDex (GRiD), as presented in a paper published in Nature Communications, “High throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage.”
GRiD provides a more accurate inference of growth rate bacteria in a microbiome, even those that are very low abundance or those which have no known genome reference. Importantly, GRiD can be applied to thousands of different bacteria simultaneously, allowing biological insights at the microbial community, and not single isolate, level. Applying it to different human skin and environmental datasets, the authors were able uncover new associations between growth rates and patient disease status in psoriasis. For example, they observed competitive exclusion interactions between Staphylococcus and Corynebacterium species and identified a likely advantage gained by S. pasteuri through antimicrobial production, previously unknown in that species.
In the end, GRiD and GRiD-MG provide researchers with a highly effective tool for estimating bacterial growth rate using metagenomic data. Their performance even at low sequencing coverage make them excellent tools for work with a variety of datasets. Together, they allow researchers to infer accurate growth data and greatly expand their ability to identify new associations between bacterial species and between bacteria and host.