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Environment

Microarrays Detect GMOs

Food Analysis: Technique could screen raw foods for nearly all commercial genetically modified organisms

by Puneet Kollipara
January 9, 2014

GMO Detection
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Around the globe, consumer groups have called for laws that compel food producers to label products containing genetically modified organisms. Some nations have implemented such regulations, leading to a need for analytical methods that can analyze DNA in food to detect GMOs.
Woman protests for labels on foods that contain genetically modified organisms.
Credit: Shutterstock
Around the globe, consumer groups have called for laws that compel food producers to label products containing genetically modified organisms. Some nations have implemented such regulations, leading to a need for analytical methods that can analyze DNA in food to detect GMOs.

As more governments across the globe start requiring food producers to label products containing genetically modified organisms (GMOs) or even restricting the products’ sale, analytical chemists have raced to develop better methods to detect the presence of GMOs. A new microarray-based technique can screen raw foods for nearly all current GMOs on the market (Anal. Chem. 2013, DOI: 10.1021/ac403630a). Despite the new method’s comprehensive range, some experts warn that it is a long way from widespread use.

Leading scientific and international bodies such as the World Health Organization say current GMOs pose no greater risk for human consumption than conventional foods do. But public skepticism has prompted dozens of nations, such as France and Japan, to enact labeling laws and other restrictions for GMOs. To help them enforce these new laws, regulators need tools to identify DNA from the engineered organisms.

Current methods mainly rely on polymerase chain reaction to selectively copy known engineered genes to increase the ease of detection on DNA microarrays. But these methods can test only a small number of different DNA samples at a time and can’t screen for all GMOs on the market.

Litao Yang and Sheng-Ce Tao of Shanghai Jiao Tong University and coworkers sought a better method that could screen for more engineered genes at once. They used two microarray chips: one that amplified known genes with PCR and another that identified the genes. The team had developed the chip design previously (Lab Chip 2011, DOI: 10.1039/C1LC20526A).

After extracting DNA from a raw food sample, researchers apply the DNA to microwells in the first chip. Inside the wells, the researchers had attached short sequences of DNA that correspond to individual engineered genes. These sequences help start the process of replicating the genes from the sample via PCR. After a round of PCR on this chip, the scientists run PCR again on the genes in a tube. These reactions also label the genes with fluorescent dyes. Finally, the researchers add the twice-amplified DNA to the second microarray chip labeled with DNA sequences of the known engineered genes. They can determine if a sample contains a certain gene if its corresponding spot on the microarray glows due to the fluorescent dyes.

The team tested the method on GMOs, including corn, soybeans, canola, and cotton, with known genetic compositions. The technique was nearly 100% accurate at identifying the engineered genes present in the plants. Given the range of engineered genes these test samples had, the researchers concluded that the method could identify 97% of GMOs currently on the market. Moreover, the PCR chip can amplify up to 91 different DNA samples at once, far more than previous methods. However, right now the technique can’t detect GMOs in processed foods off the shelf.

Still experts doubt the method—as it exists now—will see widespread use. For example, the technique can provide only qualitative and not quantitative information, says Yves Bertheau, who works on GMO detection at France’s National Institute for Agricultural Research. Regulators need quantitative data to know whether raw foods exceed allowable trace levels of GMO genes in non-GMO products, Bertheau says. Also, the technique could be expensive and would require a high level of expertise to run, thus limiting its use by monitoring labs, he says.

Yang acknowledges that the current technique is only qualitative and agrees that it’s appropriate for simple GMO monitoring. His team is working on streamlining the method to cut its cost and complexity.

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