New
multi-locus metabarcoding approach for pollen analysis uncovers what plants bee
species rely on
Date:
November 13, 2015
Source:
Botanical Society of America
To
uncover what plants honey bees rely on, researchers from The Ohio State
University are using the latest DNA sequencing technology and a supercomputer.
They spent months collecting pollen from beehives and have developed a
multi-locus metabarcoding approach to identify which plants, and what
proportions of each, are present in pollen samples.
A
single beehive can collect pollen from dozens of different plant species, and
this pollen is useful evidence of the hive's foraging behavior and nutrition
preferences.
"Knowing
the degree to which certain plants are being foraged upon allows us to infer
things like the potential for pesticide exposure in a given landscape, the
preference of certain plant species over others, and the degree to which
certain plant species contribute to the honey bee diet," says graduate
student Rodney Richardson. "One of the major interests of our lab is
researching honey bee foraging preferences so we can enhance landscapes to
sustain robust honey bee populations."
For
Richardson and his colleagues, metabarcoding is key to this research. It is a
DNA analysis method that enables researchers to identify biological specimens.
Metabarcoding
works by comparing short genetic sequence "markers" from unidentified
biological specimens to libraries of known reference sequences. It can be used
to detect biological contaminants in food and water, characterize animal diets
from dung samples, and even test air samples for bacteria and fungal spores. In
the case of pollen, it could save researchers countless hours of identifying
and counting individual pollen grains under a microscope.
Richardson
and his colleagues devised the new metabarcoding method using three specific
locations in the genome, or loci, as markers. They found that using multiple
loci simultaneously produced the best metabarcoding results for pollen. The
entire procedure, including DNA extraction, sequencing, and marker analysis, is
described in the November issue of Applications in Plant Sciences.
To
develop the new method, the researchers needed a machine powerful enough to
process millions of DNA sequences. For this work, the team turned to the Ohio
Supercomputer Center.
"As
a researcher, you feel like a kid in a candy store," Richardson says.
"You can analyze huge datasets in an instant and experiment with the
fast-evolving world of open source bioinformatics software as well as the vast
amount of publicly available data from previous studies."
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