A wood growth model promises to advance genetic engineering of trees for timber, paper, pulp, biofuels, and environmentally friendly products.
The idea is to simulate the effects of gene-tweaking on the biology of wood without having to create and then wait for live specimens to grow and be analyzed.
The wood growth model allows scientists to manipulate up to 21 genes that control the properties of lignin, a major component of wood. Image: pixabay-3342013
Professor Vincent Chiang of the Forest Biotechnology Group at North Carolina State, and colleagues describe their “biosynthesis model” in a paper that was published recently in the journal Nature Communications.
Manipulates up to 21 genes
The current prototype, or “base model,” allows scientists to observe what happens to wood growth when they manipulate up to 21 genes that control the properties of lignin, a major component of wood.
Prof. Chiang, who is an expert on cell wall biochemistry and using gene sequencing data to understand wood growth, has spent more than 30 years working on the underlying research.
Since development of the model itself started back in 2008, dozens of engineers, geneticists, mathematicians, and chemists have contributed to it.
Much of the painstaking effort involved growing thousands of genetically altered specimens of the black cottonwood tree, Populus trichocarpa, the first tree to have its genome published.
The researchers grew the specimens so that they could note the biochemical and physical results of each genetic manipulation and feed them into the model.
Dealing with lignin
Lignin is a tough, natural polymer that forms the woody component of cell walls of nearly all land-based plants.
It strengthens and supports the plant and the internal vessels that carry fluids between the leaves and the roots.
Lignin and cellulose are the most abundant polymers in living things. Their role in plants has been likened to the partnership between glass fibre and epoxy resin in boat structures: fibres of cellulose bear the load and the lignin matrix provides rigidity and strength.
It is because they contain more than 20 percent lignin that trees can grow very tall before they bend under their own weight. In contrast, grasses, being only 20 percent lignin, do not get very tall before they start to bend.
However, it is because lignin is rigid and strong that it has to be removed when wood is made into paper, pulp, and biofuels.
At present, the process of lignin removal is lengthy, costly, and not good for the environment because it uses harsh chemicals and high heat.
Applications of the wood growth model
One way to deal with the lignin problem is to alter its genetic makeup so that the wood grows to suit the end product.
The new wood growth model is an important tool in this approach, as it tracks 25 gene-driven properties of wood that are of interest to industry users.
For instance, engineers interested in getting trees to grow wood for pulp or biofuels, would be keen to get their hands on genes that alter the cellulose to lignin ratio, as this is a “direct indicator of the potential maximum cellulosic yield for wood pulp and maximum sugar yield for biofuels and other bioproducts.”
Others might wish to focus on altering genes to make the lignin in the wood easier to break down.
The researchers are already looking at other ways to use the model, such as to engineer trees that work with bacteria to make it easier to convert the wood into biofuels and biochemicals.
Another avenue of research is the engineering of trees for making nanocellulose to replace plastic and other materials made from petroleum.
Advancements and fine tuning
When the wood growth model is complete, scientists should be able to predict the effects of gene alterations on large complexes of biological processes, either across the entire organism, or along an entire pathway.
The next step for the researchers will be to incorporate more advanced levels of genetic fine-tuning into the model.
For this they will have to engineer trees using the advanced features, then grow masses of live specimens to see if the predicted, specific wood properties develop or not. The results can then be used to confirm or correct the model.
“We now have a long-awaited base model,” explains Prof. Chiang, “where new higher level regulatory factors and others important to growth and adaptation, can be incorporated to continuously improve the predictability and extend the application.”