Model Can Predict Which Bacteria will Engraft after FMT

Eric J. Alm, PhD

Researchers have created a statistical model for predicting which bacteria will engraft in the guts of Clostridium difficile (C. difficile) patients after undergoing fecal microbial transplants (FMT), according to a recent study.

The team from Harvard and MIT studied the guts of 18 C. difficile patients after undergoing FMT to develop their model. They wrote in their study that the governing forces behind successful engraftment are unknown in humans and they wanted to dig deeper into the process.

Specifically, by studying the current treatments for C. difficile, they thought they would be able to “uncover the rules of engraftment in humans,” they wrote.

First, the team utilized high-resolution metagenomic genomic sequencing to examine the guts of donors and recipients before and after the transplant for up to four months. Using that data, which included strain types and abundance, they built a predictive statistical model which calculated what strains would engraft in a given host. The study authors named the final model “Strain Finder,” which inferred strain genotypes and tracked them over time.

“We describe a model focused on three elements, including bacterial engraftment, growth, and mechanism of action, that need to be considered when developing these live therapies targeting the gut microorganisms, or microbiome,” co-senior study author Eric J. Alm, PhD, said in a statement.

The researchers learned the more abundant the strains, the more likely they were to engraft.

“That’s important to know when designing a microbiome-based therapeutic like this,” second co-senior author Ramnik J. Xavier, MD, PhD, said.

They also noted that after the FMT, about a third of the donor bacteria engrafted; they extrapolated that to mean that the maximum efficacy of a drug designed to expand on that theory is only about 30%.

Instead, they used their model to show that the recipient engraftment could be predicted. They showed that if a donor had five various strains of a bacterial species, all five strains were transferred to the patient. If the recipient already had some of those strains, the researchers demonstrated that the probability of those strains engrafting was higher.

The researchers were also able to show that they could predict the amount of each engrafted strain grown in the recipient.

“Again, that is an essential piece of information because you want to know whether a bacterial strain will be found in trace levels or at high levels so that it can actually produce the metabolite that you want,” Xavier said.

Eventually, the team believes their research can expand to diseases beyond C. difficile, such as metabolic syndrome.

“We are in the midst of one of the largest disease therapeutics that are being developed based on a human source—bugs within us,” Xavier said. “These bugs within us, or the microbiome, are going to have a potential impact for many diseases.”

The study, “Strain Tracking Reveals the Determinants of Bacterial Engraftment in the Human Gut Following Fecal Microbiota Transplantation,” was published online in Cell Host & Microbe last month.

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