TY - JOUR
T1 - The Bee Hemolymph Metabolome: A Window into the Impact of Viruses on Bumble Bees
AU - Wang, Luoluo
AU - Meulebroek, Lieven Van
AU - Vanhaecke, Lynn
AU - Smagghe, Guy
AU - Meeus, Ivan
PY - 2021/4/1
Y1 - 2021/4/1
N2 - State-of-the-art virus detection technology has advanced a lot, yet technology to evaluate
the impacts of viruses on bee physiology and health is basically lacking. However, such technology
is sorely needed to understand how multi-host viruses can impact the composition of the bee community. Here, we evaluated the potential of hemolymph metabolites as biomarkers to identify the
viral infection status in bees. A metabolomics strategy based on ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry was implemented. First, we constructed
a predictive model for standardized bumble bees, in which non-infected bees were metabolically
differentiated from an overt Israeli acute paralysis virus (IAPV) infection (R2Y = 0.993; Q2 = 0.906), as
well as a covert slow bee paralysis virus (SBPV) infection (R2Y = 0.999; Q2 = 0.875). Second, two sets
of potential biomarkers were identified, being descriptors for the metabolomic changes in the bee’s
hemolymph following viral infection. Third, the biomarker sets were evaluated in a new dataset
only containing wild bees and successfully discriminated virus infection versus non-virus infection
with an AUC of 0.985. We concluded that screening hemolymph metabolite markers can underpin
physiological changes linked to virus infection dynamics, opening promising avenues to identify,
monitor, and predict the effects of virus infection in a bee community within a specific environment.
AB - State-of-the-art virus detection technology has advanced a lot, yet technology to evaluate
the impacts of viruses on bee physiology and health is basically lacking. However, such technology
is sorely needed to understand how multi-host viruses can impact the composition of the bee community. Here, we evaluated the potential of hemolymph metabolites as biomarkers to identify the
viral infection status in bees. A metabolomics strategy based on ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry was implemented. First, we constructed
a predictive model for standardized bumble bees, in which non-infected bees were metabolically
differentiated from an overt Israeli acute paralysis virus (IAPV) infection (R2Y = 0.993; Q2 = 0.906), as
well as a covert slow bee paralysis virus (SBPV) infection (R2Y = 0.999; Q2 = 0.875). Second, two sets
of potential biomarkers were identified, being descriptors for the metabolomic changes in the bee’s
hemolymph following viral infection. Third, the biomarker sets were evaluated in a new dataset
only containing wild bees and successfully discriminated virus infection versus non-virus infection
with an AUC of 0.985. We concluded that screening hemolymph metabolite markers can underpin
physiological changes linked to virus infection dynamics, opening promising avenues to identify,
monitor, and predict the effects of virus infection in a bee community within a specific environment.
U2 - 10.3390/v13040600
DO - 10.3390/v13040600
M3 - Article
SN - 1999-4915
VL - 13
JO - Viruses
JF - Viruses
IS - 4
M1 - 600
ER -