Inferring the underlying ecological networks of microbial
communities is important to understanding their structure and responses to external
stimuli. But it can be very challenging to make accurate network inferences.
Scientists now detail a method to make the network inference easier by
utilizing steady-state data without altering microbial communities.
"Existing methods require assuming a particular population
dynamics model, which is not known beforehand," said Yang-Yu Liu, PhD, of
the Channing Division of Network Medicine. "Moreover, those methods
require fitting temporal abundance data, which are often not informative enough
for reliable inference."
To obtain more informative temporal data, researchers have to
introduce large perturbations to alter the microbial communities, which are not
only difficult in practice but also potentially ethically questionable,
especially for human-associated microbial communities. The new method developed
by BWH investigators avoids this dilemma.
"The basic idea is very simple. If one steady-state sample
differs from another only by addition of one species X, and adding X brings
down the absolute abundance of Y, then we can conclude X inhibits the growth of
Y," said Liu. The team showed that this simple idea can be extended to
more complicated cases where steady-state samples differ from each other by
more than one species. They verified that, if enough independent steady state
data were collected from the microbial communities, then the microbial
interaction types (positive, negative and neutral interactions) and the
structure of the network could be inferred without requiring any population dynamics
modeling.
The method proposed by the team resembles other network
reconstruction methods based on steady-state data, but unlike the previous
methods, no perturbations are required to be applied to the system.
Furthermore, a rigorous criterion was established by the team to check if any
given steady-state data was consistent with the Generalized Lotka-Volterra
(GLV) model -- a classical population dynamics model in ecology that
mathematically describes the relationships between species. The team found that
if the microbial community followed the GLV model, then the steady-state data
could also be used to deduce the model parameters -- interspecies interaction
strengths and growth rates.
The method was systematically validated using simulated data generated
from different classic population dynamics models with various levels of
complexity. Then it was applied to real data collected from four different
synthetic microbial communities, finding that the inferred ecological networks
either agree well with the ground truth or can predict the response of systems
to perturbations.
Additional insights into microbial ecosystems will emerge from a
better understanding of their underlying ecological networks. Inferring
ecological networks of human-associated microbial communities using the method
developed here will facilitate the design of personalized microbe-based
"cocktails," as the authors write, to treat diseases related to
microbial dysbiosis.
"I am quite excited about this method, because it may pave
the way to mapping more complex microbial communities such as the human gut
microbiota, which in turn will help us design better microbiome-based
therapies," said Liu.
See:
Posted by Dr. Tim Sandle