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NIH grant 1R01AI204107-01: Quantifying Pollen Allergens with DNA Metabarcoding
Institute: {'code': 'AI', 'abbreviation': 'NIAID', 'name': 'National Institute of Allergy and Infectious Diseases', 'admin_org_id': '12500003', 'admin_funding_url': ''}. PI: Berry Justice Brosi. Award: $378758.
Start: 2026-09-30T00:00:00.
PROJECT SUMMARY
Pollen allergy incurs a substantial burden of disease in the US, resulting in millions of lost work
and school days and a negative economic impact of billions of dollars. Despite this burden,
pollen monitoring has scarcely changed since the 1950s, consisting of volunteer-run monitoring
stations in which pollen is counted under a microscope. This monitoring scheme has very low
spatial resolution: there are only ~80 such stations in the US, with most states having only a
single monitoring station even across highly disparate geography and vegetation. This system is
also very low taxonomic resolution, as pollen grains are visually identified to coarse taxonomic
categories (e.g., “tree”, “grass”). This is of limited utility for someone with an allergy to one (out
of >11,000) grass species because grass pollen counts could be high but with zero grains of the
offending allergenic species. Alternatively, counts could be relatively low but with a high
proportion of the allergenic species.
To address these issues, we propose to quantify species-specific pollen loads using DNA
metabarcoding. This represents a key research advance that would make DNA metabarcoding
(the genetic identification of mixed-species samples using high-throughput sequencing) suitable
for use in pollen aeroallergy monitoring, in turn creating the potential to positively transform
pollen monitoring in terms of taxonomic specificity and geographic coverage. At present, DNA
metabarcoding is not quantitative because pollen grains from different plant species vary in
marker gene copy number and the efficiency of DNA isolation, among other attributes. To
address this, we will innovate on traditional pollen DNA metabarcoding by applying quantitative
corrections to metabarcoding outputs. Then, to transform these outputs into absolute counts, we
will incorporate known, quantified levels of pollen into each sample. In conjunction with AI-based
and phylogenetic imputation methods to correct for the differential detectability of species, our
proposed laboratory and computational innovations will synergistically develop and advance a
modern aeroallergen monitoring toolkit. DNA metabarcoding is inexpensive on a per-sample
basis (allowing broad geographic coverage) and also has high taxonomic resolution
(distinguishing >90% of all plant species). This work has transformative potential for improving
pollen aeroallergen monitoring and ultimately reducing the massive burden of disease of pollen
allergy in the US.document fetched 2026-10-15 02:48