DNA barcoding for pollen forecasting

PollerGEN is a group of interdisciplinary researchers funded by NERC to understand grass pollen deposition. We aim to revolutionise the way that pollen is measured, model spatial and temporal deposition from different species of grass pollen and identify linkages to human health.

DNA barcoding of pollen is not a new invention. It is not easy either but has been shown to provide extremely valuable information, e.g. for understanding plant-pollinator interactions, honey bee foraging, or the characterization of honeybee pollen pellets. It should come to no surprise that researchers are also working on an application that intends to improve a forecasting system that has become more and more important for a large portion of the human population - pollen forecasting for hay fever and other allergic reactions.

At this point most forecasts are build using data from a network of pollen traps which operate throughout the main pollen seasons. These traps measure how many pollen grains are present on a daily basis and identifications of species are done using morphology-based methods. The latter is extremely challenging when it comes to species with very uniform appearance, e.g. grasses. However, the species identity often makes a big difference. It is fairly rare that somebody is allergic to all grass pollen but we are having difficulties to tell which pollen in the mix is the culprit.

PollerGen, a project run out of Bangor University wants to change this by using a DNA-based approach. 

The colleagues are now working on a way to detect airborne pollen from different species of allergenic grass. We’re also developing new pollen source maps, and modelling how pollen grains likely move across landscapes, as well as identifying which species are linked with the exacerbation of asthma and hay fever.

We’re going to be using a new UK plant DNA barcode library, as well as environmental genomic technologies to identify complex mixtures of tree and grass pollens from a molecular genetic perspective. By combining this information with detailed source maps and aerobiological modelling, we hope to redefine how pollen forecasts are measured and reported in the future.

We have just started the third year of pollen collection and hope to road test the combined forecasting methods over the next year. In the long run, our vision is to be able to provide specific pollen forecasts for grass, and unravel which species of grass pollen are most likely causing allergic responses. More broadly, we also want to provide information to healthcare professionals and charities, who can translate this information to help pollen allergy sufferers live healthier and more productive lives.

Pretty cool.