A new approach to detecting unintended changes in GM foods
The approach of Hoekenga’s team, in contrast, doesn’t decide ahead of time which metabolites are important to measure, suggesting it could be more likely to snare a truly unexpected impact. “We throw a net in the water and try to get as many fish as we can,” Hoekenga says.
Moreover, comparing a GM variety to diverse cultivars can help both scientists and consumers put into context any biochemical changes that are observed. “We accept that there isn’t just one kind of tomato at the farmer’s market. We look for diverse food experiences,” Hoekenga says. “So we think that establishing the range of acceptable metabolic variability [in food] can be useful for examining GM varieties.”
At the same time, this brand of “non-targeted” metabolomics is expensive, and the chemistry methods it employs aren’t robust enough yet to be used in official safety assessments, Hoekenga acknowledges.
Most importantly, making statistical comparisons of metabolic “fingerprints” is no easy task. In their study, Hoekenga’s group adapted a style of statistics, called network analysis, which was developed to compare overall patterns of gene expression, or transcription, in mice. The reason for this choice, he explains, is that just like gene transcripts, metabolites that participate in the same biochemical pathways or fall under the same regulatory control are expected to cluster together. And as the researchers hypothesized, network analysis allowed them to detect metabolic clusters in tomato and compare those patterns across different varieties.
But the techniques don’t apply only to tomato. “The method can be applied to any plant or crop,” Hoekenga says. “We’ve made something fundamentally useful that anyone can use and improve on.” His group has already characterized the corn metabolome, and he hopes plant breeders will begin to see the utility of metabolomics, as well.
When crossing parent plants, for example, breeders often like to track the genes underlying their trait of interest, such as resistance to a pathogen. That’s because pinpointing offspring that carry the right genes is often faster and easier than examining plants for the trait itself.
But sometimes, so many genes contribute to a single trait that figuring out which genes are involved in the first place becomes onerous. This is where Hoekenga thinks metabolomics and network analysis might one day help.
“The question is: Can we relate [everything] we measure to real-world traits that people care about?” he says. “We’re trying to describe at the biochemical level what might be responsible for a trait. And from that, you could extract genetic information to use in breeding.”
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