Using Gene Expression data to make decisions.
There are many uses for gene expression data in agronomic research and biological product development. However, it has its limitations like any research technique, and it is important to understand how it can be appropriately used. For a refresher on what “gene expression” is, see this blog article.
What Gene Expression data can tell you:
How a plant responds to a specific treatment at a specific time in a specific tissue compared to a control.
The "volume" of that response compared to a control.
How quickly a response may occur.
If the response is systemic or localized to the treatment area.
What Gene Expression data does not tell you:
The full physiological picture - you still need the physiological data for accurate and validated gene expression data.
How genes will always express in different conditions.
Types of decisions you can use gene expression data to help make:
How you describe your product’s mechanism: Biological or synthetic product mode of action - some products that stimulate responses in plants (like biostimulants, biofertilizers, or biopesticides) elicit specific gene expression profiles consistently and are produced only as a response to the product. This type of data can identify a candidate mode of action or portion of the mode of action.
How you do not describe your product’s mechanism: What is *not* a mode of action - equally important to knowing what a product is doing is seeing what it is not doing. Biologicals are often assumed to have certain mechanisms based on the physiological effects seen in the field. However, there are many redundancies and parallel physiological mechanisms in plants, and they may be mistaken as the targeted mechanism when they are really secondary or peripheral to the actual main mechanism. Gene expression data can help parse out which mechanisms are occurring and, based on what stage of the signal transduction mechanisms are occurring, may be able to identify causal mechanisms vs. peripheral ones.
Label information like rates and application frequency: Identifying maximum rates for safe use. Signs of stress or drags on plant metabolism and the type of stress mechanisms can often be identified in gene expression data. This can be helpful to see if certain rates induce stress responses in the plant and if they are, how severely they are being expressed. Some stress is not always bad. Phytotoxic stress probably is.
Should you explore other applications? Other mechanisms occurring that had not been previously considered in the target product application can sometimes be seen with gene expression. For example, a biostimulant may affect certain defense responses in addition to abiotic stress resistance mechanisms. Depending on the defense responses, it's possible the product could be used as a biopesticide in addition to its uses as a biostimulant.
There are, of course, nuances to all of these uses for gene expression, and the importance of reliable study design to get valid and reliable data can't be overstated. When tens of thousands of genes are being expressed at any given time, in response to diverse environments, and on a vast spectrum of expression levels and effects, it is critical to use gene expression data within well-designed studies that give clear results amidst the noise.
If you’re interested in incorporating gene expression data into your agronomic research trials or would like more information, contact me to book a free 20-minute consultation to get started.
