TY - ABST

T1 - Metabarcoding-based assessment of airborne pollen assemblages

AU - Potter, Caitlin

AU - Brennan, Georgina

AU - Creer, Simon

AU - de Vere, Natasha

AU - Skjoth, Carsten Ambelas

AU - Osborne, Nicholas

AU - Wheeler, Benedict

AU - McInnes, Rachel

AU - Clewlow, Yolanda

AU - Barber, Adam

AU - Hanlon, Helen

AU - Hegarty, Matthew

AU - Jones, Laura

AU - Kurganskiy, Alexander

AU - Rowney, Francis

AU - Armitage, Charlotte

AU - Adams-Groom, Beverley

AU - Ford, Col

AU - Petch, Geoff

AU - Griffiths, Gareth

PY - 2019

Y1 - 2019

N2 - Background: Airborne pollen is a common trigger for both allergic rhinitis (hay fever) and asthma, which globally affect 400 million and 300 million people, respectively. Accurate pollen forecasts are important in managing these conditions, enabling sufferers to minimise their exposure. However, current UK pollen forecasts are based on counting individual grains under a light microscope, which is time consuming and requires highly trained personnel. Moreover, it is often not possible to morphologically identify pollen grains to the species or genus level, and counts may not be consistent between different collectors. Here, we assess the use of DNA metabarcodingas an alternative to light microscopy that avoids these drawbacks.Results: In this study, pollen was collected at up to 12 sites across the UK over multiple years and analysed using both metabarcoding and light microscopy. Airborne pollen was dominated by a few groups of wind-pollinated species, but in some instances high levels of pollen from insect-pollinated plants were also present. Pollen assemblages varied considerably across the season, but also differed between sampling sites. We demonstrate that metabarcoding and light microscopy were broadly in agreement about the time window over which pollen of a given family was present in the air. Significance: These results suggest the potential for high-throughput sequencing to be incorporated into current workflows for generating pollen forecasts, reducing costs and avoiding the limitations of microscopy-based pollen counts. By contributing to improved pollen forecasts and a better understanding of seasonal pollen dynamics, a better understanding can be developed of the impact of airborne pollen on human health.

AB - Background: Airborne pollen is a common trigger for both allergic rhinitis (hay fever) and asthma, which globally affect 400 million and 300 million people, respectively. Accurate pollen forecasts are important in managing these conditions, enabling sufferers to minimise their exposure. However, current UK pollen forecasts are based on counting individual grains under a light microscope, which is time consuming and requires highly trained personnel. Moreover, it is often not possible to morphologically identify pollen grains to the species or genus level, and counts may not be consistent between different collectors. Here, we assess the use of DNA metabarcodingas an alternative to light microscopy that avoids these drawbacks.Results: In this study, pollen was collected at up to 12 sites across the UK over multiple years and analysed using both metabarcoding and light microscopy. Airborne pollen was dominated by a few groups of wind-pollinated species, but in some instances high levels of pollen from insect-pollinated plants were also present. Pollen assemblages varied considerably across the season, but also differed between sampling sites. We demonstrate that metabarcoding and light microscopy were broadly in agreement about the time window over which pollen of a given family was present in the air. Significance: These results suggest the potential for high-throughput sequencing to be incorporated into current workflows for generating pollen forecasts, reducing costs and avoiding the limitations of microscopy-based pollen counts. By contributing to improved pollen forecasts and a better understanding of seasonal pollen dynamics, a better understanding can be developed of the impact of airborne pollen on human health.

U2 - 10.1139/gen-2019-0083

DO - 10.1139/gen-2019-0083

M3 - Conference abstract in journal

VL - 62

SP - 420

JO - Genome

JF - Genome

SN - 0831-2796

IS - 6

ER -