The methodology is revealing interesting relationships between bacterial growth rates and conditions such as type 2 diabetes and inflammatory bowel disease. The new computational approach can shed light on a dynamic process such as growth from a static “snapshot” of a single sample, and it may could have implications for both diagnostics and new avenues of research.
Tal Korem and David Zeevi, research students in the lab of Prof. Eran Segal of the Department of Computer Science and Applied Mathematics at the Weizmann Institute, directed the research, with collaborators Jotham Suez, a research student in Dr. Eran Elinav’s lab in the Department of Immunology, and Dr. Adina Weinberger, a research associate in Prof. Segal’s lab.
The researchers started with the advanced genomic sequencing techniques used in many microbiome studies, which sequence all of the bacterial DNA in a sample. From the short sequences, researchers develop a picture of the types of bacteria and their relative abundance, but the Weizmann Institute group realized that this sequencing technique offered another type of information.
According to Prof. Segal, “The sample’s bacteria are doing what bacteria do best: making copies of their genomes so they can divide. Most of the bacterial cells contain more than one genome – a genome and a half, for example, or a genome and three quarters.”
Because most bacterial strains have pre-programmed “start” and “finish” codes, the team could identify the “start” point as the short sequence that was most prevalent in the sample. They found that the least prevalent, at the other end of the genome, was the DNA that gets copied last and that analyzing the relative amounts of starting DNA and ending DNA could be translated into the growth rate for each strain of bacteria. The researchers tested this concept experimentally, first in single-strain cultures for which the growth rate could be controlled and observed, then in multiple animal model systems, and finally in the DNA sequences of human microbiomes, in their full complexity.
They said that their method worked even better than they expected: Estimated bacterial growth rates were nearly identical to observed growth rates. According to Dr. Elinav, “Now we can finally say something about how the dynamics of our microbiome are associated with a propensity to disease. Microbial growth rate reveals things about our health that cannot be seen with any other analysis method.”
For example, in the examination of human microbiome data, the researchers determined that particular changes in bacterial growth rates are uniquely associated with type 2 diabetes, while other ones are tied to inflammatory bowel disease. Because these associations were not observed in the static microbiome “population” studies, the method could be used in the future as a diagnostic tool for early detection of disease or pathogen infection, or to determine the effects of probiotic or antibiotic treatment. In addition, the scientists hope this new understanding of the microbiome will spur further research into the connections between the complex, dynamic ecosystem inside of humans and human health.