Microbes have evolved over millions of years to live in and on all parts of the human body. Scientists have created new ways to reconstruct how this evolution unfolded, using mathematical tools originally developed for geologists. They identified microbes that diverged into new species as they colonized one area of the body after another. The research could prompt new theories and treatments to manage our bacterial ecology and improve our personal health.
Only
recently have scientists begun to appreciate just how much our health depends
on the trillions of bacteria that call our bodies home. We now know that these
bacteria help digest the food we eat, boost our brain function, and regulate
our immune systems. But figuring out how our bacteria -- which by some accounts
outnumber our own cells by ten to one -- evolved to live with each other and
with us has proven particularly challenging.
Scientists
typically glean information on the microbiome by sampling a few million
bacteria -- say, from the gut or tonsils -- and sequencing them to count which
bacteria belong to each species. Then they compare those counts, generating
values that tell them the relative abundance of each type of bug. But relative
abundance data requires statistical methods that take into account how shifts
in one species might affect another.
The research could prompt new theories and treatments for
managing these bacterial communities, collectively known
as the human microbiome, to improve our personal health.
"Over
the last decade, there has been significant interest in developing probiotics
and transplants of beneficial bacteria to treat a wide variety of health
issues," said Lawrence A. David, Ph.D., senior author of the study and
assistant professor of molecular genetics and microbiology at Duke University
School of Medicine. "Our analysis gives us a window into how different bacteria
adapt and evolve so that we can more effectively predict which implanted
species will survive to make an impact on disease."
Scientists
typically glean information on the microbiome by sampling a few million
bacteria -- say, from the gut or tonsils -- and sequencing them to count which
bacteria belong to each species. Then they compare those counts, generating
values that tell them the relative abundance of each type of bug. But relative
abundance data requires statistical methods that take into account how shifts
in one species might affect another.
Justin
Silverman, an MD-PhD student in the David laboratory, searched the literature
for possible workarounds, and found one in an unlikely place -- the field of
geology. To make sense of the relative amounts of different elements like
calcium and aluminum found in rocks, geologists had devised a mathematical tool
called the PhILR transform. Silverman adapted this tool to study the relative
amounts of bacteria found in the microbiome.
The
new technique combined the sequencing counts for each species with information
on their position on the bacterial family tree. The resulting statistical
framework looks like a mobile you might find hanging over a baby's crib, with a
common ancestor at the top and all the subsequent generations suspended
underneath, connected by a series of cross-bars. By looking at how these
cross-bars tilted and swayed with the weight of the various species dangling
from their tips, Silverman and his colleagues could assess how microbial
communities grew and evolved in different body sites.
"This
technique unlocks a tremendous toolbox of statistical methods that wouldn't
have worked before, but that can now be used to analyze microbiome data,"
Silverman said.
The
researchers believe their technique could be applied in practically any
situation where high-throughput technologies are used to measure the
composition of a sample, from the genetic makeup of a tumor to the strains of
an influenza virus.
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Posted by Dr. Tim Sandle
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