Modeling Microbes: New Advances Could Help Keep Us Healthier

We might like to think of ourselves as humans through and through, but there’s no denying that each of us carries around more microbial cells than human cells. (If that’s news to you, don’t worry. I still remember how stunned I was to learn that.) Many of our microbes actually help keep us healthy or give us abilities we wouldn’t normally have — hello, lactose digesters! — but it’s the occasionally invading microbes that can make us really sick.

That’s why two recent pieces of microbe-related news caught my eye. Both have to do with using large amounts of data to predict how microbes will evolve.

I’ll start with the one about Clostridioides difficile because it has obvious human health implications. C. diff, as it’s more commonly known, is a humdinger of a pathogen for some people — this gastrointestinal invader can even be fatal — and somehow completely harmless for others. As infection control teams have succeeded in reducing the incidence of hospital-acquired C. diff cases, a curious thing has happened: the community spread of C. diff has increased substantially, but with very little negative health impact. It’s estimated that as many as 40% of adults are walking around with C. diff but have no symptoms from it.

There is, somehow, a tipping point between harmless colonization of C. diff and an active C. diff infection. Scientists at the Institute for Systems Biology in Seattle aimed to answer an important question: is it possible to predict colonization susceptibility and, from there, determine which people will go on to suffer an actual infection? They started by gathering information about the microbial communities living in the guts of more than 15,000 people. After analyzing that data, they built modeling tools to predict the risk of colonization and response to probiotic treatments.

The team found that C. diff colonization is not a simple state; it looks different from one person to the next. C. diff employs a variety of growth strategies, such that no single medical intervention is likely to work for all people. Personalized probiotics, for example, might be needed to try to clear C. diff from an asymptomatic carrier. The ability to use modeling tools is a big step toward being able to develop that kind of targeted approach. The scientists also showed they could predict the ability to suppress C. diff growth for infection treatment.

Research like this has benefited significantly from foundational studies of microbes that continue to pay dividends for the scientific community (and all of us microbe-toting humans). I’m thinking in particular of the E. coli Long-Term Evolution Experiment that began back in 1988 and is still going today. Evolutionary biologist Richard Lenski began growing non-pathogenic strains of E. coli in his lab — something that could be done in any lab, really — and put his own spin on it by just not stopping. I first wrote about this work probably 20 years ago, when the study had reached 20,000 generations. Today, the project has gone beyond 80,000 generations (and along the way achieved something like celebrity status with its own website and Wikipedia page).

Growing the same 12 populations of E. coli for nearly 40 years is really something. Just think about the common events that could have utterly derailed it: power outages, staff turnover, a bad batch of lab consumables. But this experiment just keeps on going. And having access to the closely tracked data from more than 80,000 generations of E. coli has been a huge boon to the research community; it’s the kind of data trove that helps other scientists create better models for their own microbial studies.

Recently, Lenski’s team released a new software tool to do even more for other scientists. Based on data gleaned from the E. coli experiment, this simulator can predict the evolutionary dynamics of a broad range of microbial populations, making it useful far beyond the realm of E. coli science. While this is a research tool, it’s easy to imagine how it could inform more clinically oriented studies of microbial pathogens as well.

After all, the better we understand our microbes, the better we’ll understand ourselves.