We have recently discovered that commensal bacteria in the human body are continually evolving within individual people under the pressure of adaptive evolution—with mutations emerging and being fixed in the population within months (Zhao & Lieberman et al, bioRxiv). Inspired by this finding, our lab is addressing a number of questions about human-associated microbiomes, using a variety of model systems, with a heavy emphasis on the skin microbiome. Our work is focused in four areas:

  1. Consequences of rapid within-person evolution for host health and community structure
  2. Within-person evolution as a tool to understand colonization, transmission, and survival strategies of microbiome members
  3. General principles of rapid adaptation across environments (theory and modeling), using and inspired by microbiome data
  4. Tool development for strain-level analysis of microbiome data

 

More about some specific questions we are particularly working on at the moment:

Within-person evolution of the microbiome, its specificity, and connection to disease


We have recently discovered that commensal bacteria in the human gut are continually evolving within individual people under the pressure of adaptive evolution—with mutations emerging and being fixed in the population within months (Zhao & Lieberman et al, bioRxiv). This evolution reveals genes and pathways critical to bacterial colonization in vivo, which may aid in the design of rational probiotic therapy (Lieberman & Michel et al, Nature Genetics, 2011).

The Lieberman Lab seeks to address the many questions brought on by this discovery. Can we use the mutations occurring in vivo to better design personalized probiotics? How specific is within-person evolution to an individual’s genetics, diet, and microbial community? What are the impacts of within-person evolution on community structure and host function? Crucially, studies of within-person evolution can be performed without longitudinal studies, because bacterial strains diversify within hosts to form co-existing lineages that preserve a record of their natural history within the host (Lieberman et al, Nature Genetics, 2014).

Transmission of commensals within and between people

We are interested in how and when bacteria colonize our microbiomes, how they move around between body sites, and how within-host transmission impacts community structure.

Previously, we have used whole-genome approaches to understand how Mycobacterium tuberculosis spreads across the body in people with HIV (Lieberman et al, Nature Medicine, 2016).

Strain-level identification for disease association and epidemiology
We are developing novel methods for strain-level identification in metagenomic data, leveraging the growing number of microbial genomes and human metagenomes publicly available.

Niche characterization in the microbiome
We employ novel sampling strategies, experimental approaches, and whole-genome evolutionary inference to address questions related to niche range in the human microbiome. Can the same bacterial strain the lives on your cheek also live on your back? How many niches exist in your microbiome? What determines if a particular bacterial strain can colonize a particular microbiome?

Methods and approaches
We leverage the mutations that bacteria accumulate during colonization of individual people and evolutionary inference methods to infer past migrations within and across body sites, selective pressures faced by bacteria in vivo, and the molecular strategies used to adapt to these pressures. The main method we use for identifying these mutations is whole-genome sequencing of collections of cultured isolates. Crucially, these inferences can be performed without longitudinal studies, because bacterial strains diversify within hosts to form co-existing lineages that preserve a record of their natural history within the host.

Other favored approaches include high-throughput culturing and experiments, computational tool development, and interrogation of spatial structure. When possible, we focus on the human environment, in order to rapidly translate discoveries from these complex ecosystems.