A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function

Climate change, airborne allergens, and three translational mitigation approaches

One of the important adverse impacts of climate change on human health is increases in allergic respiratory diseases such as allergic rhinitis and asthma. This impact is via the effects of increases in atmospheric carbon dioxide concentration and air temperature on sources of airborne allergens such as pollen and fungal spores. This review describes these effects and then explores three translational mitigation approaches that may lead to improved health outcomes, with recent examples and developments highlighted. Impacts have already been observed on the seasonality, production and atmospheric concentration, allergenicity, and geographic distribution of airborne allergens, and these are projected to continue into the future. A technological revolution is underway that has the potential to advance patient management by better avoiding associated increased exposures, including automated real-time airborne allergen monitoring, airborne allergen forecasting and modelling, and smartphone apps for mitigating the health impacts of airborne allergens.

Pollen Allergy in a Changing Planetary Environment

Airborne pollens are one of the common causative and triggering agents of respiratory allergy in a changing planetary environment. A growing number of people worldwide are contracting allergic diseases caused by pollens. The seasonal variations in pollens have occurred everywhere and the sensitization rate to pollens has increased in children as well as in adults. Moreover, allergenic plants, such as ragweed and Japanese hop, grow in soil damaged by human’s activities and deforestation with air pollution. It is impossible to avoid plants that cause allergies, because pollens can travel many kilometers in the breeze or wind. Hence, it is essential to survey and forecast pollens for the management of pollen allergy. Weather conditions may alter pollen concentrations. A number of studies have shown that increases in CO₂ concentration and atmospheric temperature raise pollen concentration. Hence most of the studies on the impact of climate change on aeroallergens must include the amount and allergenicity of pollens. It is yet unknown whether complex interactions with pollens, meteorological variables, and air pollutants in the changing environment. Considering the effect of climate change on the long-term trends in pollen levels and emerging viral infection, it is crucial to forecast and eliminate the associated risk for human health in future and take appropriate measures to reduce it.

Allergen Homologues, Pathogenesis-Related 1, Polygalacturonase, and Pectin Methyl Esterase from a Japanese Hop

BACKGROUND

Japanese hop is an important cause of weed pollinosis in East Asia. Its pollen is abundant in autumn. This pollen is known to be the cause of many allergic diseases. However, molecular characteristics of its allergens have not been elucidated.

OBJECTIVE

In this study, we produced recombinant proteins of allergen homologues from Japanese hop by the analysis of expressed sequence tags (EST), and evaluated its allergenicity.

METHODS

cDNA library was constructed using as little as 50 ng of total RNA from Japanese hop pollen. Allergen homologues were identified by the initial screening of 963 EST clones. Recombinant proteins were overexpressed in the E. coli expression system and purified using Ni-nitrilotriacetic acid-agarose. Purified proteins were analyzed by ELISA.

RESULTS AND DISCUSSION

Japanese hop pathogenesis-related 1 protein (PR-1) shares 37.0 to 44.4% of amino acid sequence identity with Art v 2, Cuc m 3, and Cyn d 24. Pectin methyl esterase (PME) shows 23.2 to 50.2% of identities to Act d 7, Ole e 11, and Sal k 1. Polygalacturonase (PGs) shows 16.7 to 19.3% of identities to Phl p 13, Cry j 2, Cha o 2, Jun a 2, Pla a 2, and Pla or 2. IgE antibodies from Japanese hop allergy patients' sera recognized PR-1 (3.4%), PME (13.8%), PGs (3.7%), and profilin (13.8%), respectively.

CONCLUSION

Novel allergenic components were identified, even though low IgE reactivity was displayed reflecting the low degree of cross-reactivity with other pollen allergens. We believe that these molecules have worth further studies.

Forecast for Pollen Allergy: A Review from Field Observation to Modeling and Services in Korea

Pollen, a major causal agent of respiratory allergy, is mainly affected by weather conditions. In Korea, pollen and weather data are collected by the national observation network. Forecast models and operational services are developed and provided based on the national pollen data base. Using the pollen risk forecast information will help patients with respiratory allergy to improve their lives. Changes in temperature and CO₂ concentration by climate change affect the growth of plants and their capacity of producing more allergenic pollens, which should be considered in making the future strategy on treating allergy patients.

Allergenic Pollen Calendar in Korea Based on Probability Distribution Models and Up-to-Date Observations

PURPOSE

The pollen calendar is the simplest forecasting method for pollen concentrations. As pollen concentrations are liable to seasonal variations due to alterations in climate and land-use, it is necessary to update the pollen calendar using recent data. To attenuate the impact of considerable temporal and spatial variability in pollen concentrations on the pollen calendar, it is essential to employ a new methodology for its creation.

METHODS

A pollen calendar was produced in Korea using data from recent observations, and a new method for creating the calendar was proposed, considering both risk levels and temporal resolution of pollen concentrations. A probability distribution was used for smoothing concentrations and determining risk levels. Airborne pollen grains were collected between 2007 and 2017 at 8 stations; 13 allergenic pollens, including those of alder, Japanese cedar, birch, hazelnut, oak, elm, pine, ginkgo, chestnut, grasses, ragweed, mugwort and Japanese hop, were identified from the collected grains.

RESULTS

The concentrations of each pollen depend on locations and seasons due to large variability in species distribution and their environmental condition. In the descending order of concentration, pine, oak and Japanese hop pollens were found to be the most common in Korea. The pollen concentrations were high in spring and autumn, and those of oak and Japanese hop were probably the most common cause of allergy symptoms in spring and autumn, respectively. High Japanese cedar pollen counts were observed in Jeju, while moderate concentrations were in Jeonju, Gwangju and Busan.

CONCLUSIONS

A new methodology for the creation of a pollen calendar was developed to attenuate the impact of large temporal and spatial variability in pollen concentrations. This revised calendar should be available to the public and allergic patients to prevent aggravation of pollen allergy.

Deep Neural Network-Based Concentration Model for Oak Pollen Allergy Warning in South Korea

Purpose

Oak is the dominant tree species in Korea. Oak pollen has the highest sensitivity rate among all allergenic tree species in Korea. A deep neural network (DNN)-based estimation model was developed to determine the concentration of oak pollen and overcome the shortcomings of conventional regression models.

Methods

The DNN model proposed in this study utilized weather factors as the input and provided pollen concentrations as the output. Weather and pollen concentration data were used from 2007 to 2016 obtained from the Korea Meteorological Administration pollen observation network. Because it is difficult to prevent over-fitting and underestimation by using a DNN model alone, we developed a bootstrap aggregating-type ensemble model. Each of the 30 ensemble members was trained with random sampling at a fixed rate according to the pollen risk grade. To verify the effectiveness of the proposed model, we compared its performance with those of models of regression and support vector regression (SVR) under the same conditions, with respect to the prediction of pollen concentrations, risk levels, and season length.

Results

The mean absolute percentage error in the estimated pollen concentrations was 11.18%, 10.37%, and 5.04% for the regression, SVR and DNN models, respectively. The start of the pollen season was estimated to be 20, 22, and 6 days earlier than that predicted by the regression, SVR and DNN models, respectively. Similarly, the end of the pollen season was estimated to be 33, 20, and 9 days later that predicted by the regression, SVR and DNN models, respectively.

Conclusions

Overall, the DNN model performed better than the other models. However, the prediction of peak pollen concentrations needs improvement. Improved observation quality with optimization of the DNN model will resolve this issue.