It’s become commonplace for researchers to genetically modify trees. They do so for a variety of different applications including biofuels and paper production. Of course, they want to steer clear of modifications to one gene that results in changes of other genes in the same organism. Why? In order to know if the modification they make is effective, they need to limit the variables of the modification.
Much of the recent research focuses on lignin. What is lignin? It’s a complex material found in trees that helps to give a tree its structure. Essentially, it’s what makes wood feel like wood.
Regardless of whether a tree is to be used as a biofuel or for creating paper and pulp-like products, there is a strong desire to genetically modify the chemical structure of its lignin. The way scientists do this is by manipulating lignin-specific genes. This way lignin becomes easier to ‘break down.’ Cranos Williams, an associate professor of electrical and computer engineering at North Carolina State University says that “you don’t want to make changes to a tree’s genome that compromise its ability to grow or thrive.”
Previous research into lignin generated models that predict how independent changes to the expression of lignin genetics impacted lingin’s characteristics. The models, created by the researchers, did not account for cross-regulatory influences that exist between genes. So, when they targeted a specific gene, the existing models did not accurately predict the changes scientists saw in how the non-targeted genes are expressed. The inability to capture such data hinders scientists’ ability to develop accurate gene-modification strategies. As the research currently stands, the possibility of unintended outcomes in lignin modification and wood traits is relatively high.
To address this challenge, a model was created to predict the direct and indirect changes across all of the lignin genes thus capturing the effects of multiple types of regulation. This allows scientists to predict how the expression of non-targeted genes is impacted as well as the expression of targeted genes. Seems like an elegant solution, right? Well, it is.
Another of the key merits of this work is that previous models only looked at how the RNA is impacted when genes are modified. Those models ‘assume’ that proteins will be impacted in the same way, which is not always the case. The new model does capture some of those changes. As a result, it’s reasonable to say that this model can be incorporated into larger, multi-scale models, thus providing a computational tool for exploring new approaches to genetically-modifying tree species in an effort to improve lignin traits for use in a variety of different industries.
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