By Blake Jackson
A study led by Penn State researchers has introduced landscape transcriptomics, a novel method for analyzing gene expression patterns, to understand the stressors impacting bumble bees. This approach could provide crucial insights into the ongoing decline of bee populations worldwide.
The study, a first test of landscape transcriptomics, focused on identifying stress signatures in wild organisms.
Researchers utilized machine learning, an artificial intelligence technique, to evaluate gene expression profiles in individual bumble bees, successfully pinpointing genetic markers of stressors like extreme heat and cold in both lab and wild environments.
Gabriela Quinlan, who spearheaded the study during her postdoctoral research, emphasized the potential of landscape transcriptomics to accelerate conservation efforts.
"This is a really big step in demonstrating this new strategy for identifying at-risk populations and showing how these machine learning models can be used both in the lab and out in the field," she said. "We also found directly applicable insights such as sets of genes that are associated with different stressors in bumble bees, which we didn’t have before."
Christina Grozinger, Publius Vergilius Maro Professor of Entomology, likened the method to "forensic biology," explaining, "It’s like forensic biology, where you can look at an organism’s gene expression patterns and identify a signature or fingerprint that relates to the stress it’s experiencing. Landscape transcriptomics should allow us to identify stressed populations of target species much more rapidly than traditional approaches."
The research team conducted both lab and field experiments. In the lab, they exposed bees to various stressors and used the resulting RNA profiles to train a machine learning model. This model achieved 92% accuracy in identifying stress-related gene expression patterns.
In the field, wild bees were collected from two distinct sites to observe how environmental differences influenced their stress levels.
The model accurately predicted the stressors experienced by these bees. However, the study revealed that stress signatures in RNA are transient, disappearing quickly after the stressor subsides.
This transient nature allowed researchers to observe daily stress patterns, such as starvation stress in morning-collected bees.
Future research will focus on developing models that can detect longer-term stress signatures, providing deeper insights into the factors contributing to bee population decline.
Photo Credit: istock-kerem-hanci
Categories: Pennsylvania, Education