Ragweed as an Example of Worldwide Allergen Expansion

Oswalt, Matthew L.; Marshall, Gailen D.

doi:10.1186/1710-1492-4-3-130

Cited by 60 publications

(48 citation statements)

References 46 publications

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“…The prevalence of IgE sensitization is on the rise (1-3) due to the continuous spreading of the plant, originally native to North America, within several European countries (4,5). As of today, 10 short ragweed pollen allergens have been registered by the International Union of Immunological Societies (6).…”

mentioning

confidence: 99%

“…To better understand the biochemical and structural basis for Amb a 11 allergenicity, we produced in the present study a recombinant proform of Amb a 11 (pro-rAmb a 11) 4 in Escherichia coli and determined its crystal structure. Herein, we demonstrate that Amb a 11 is a bona fide cysteine protease and that it triggers a strong allergic inflammation in a murine sensitization model.…”

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confidence: 99%

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Structural and Functional Characterization of the Major Allergen Amb a 11 from Short Ragweed Pollen

Groeme¹,

Airouche²,

Kopečný³

et al. 2016

Journal of Biological Chemistry

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Allergy to the short ragweed (Ambrosia artemisiifolia) pollen is a major health problem. The ragweed allergen repertoire has been recently expanded with the identification of Amb a 11, a new major allergen belonging to the cysteine protease family. To better characterize Amb a 11, a recombinant proform of the molecule with a preserved active site was produced in Escherichia coli, refolded, and processed in vitro into a mature enzyme. The enzymatic activity is revealed by maturation following an autocatalytic processing resulting in the cleavage of both N-and C-terminal propeptides. The 2.05-Å resolution crystal structure of pro-Amb a 11 shows an overall typical C1A cysteine protease fold with a network of molecular interactions between the N-terminal propeptide and the catalytic triad of the enzyme. The allergenicity of Amb a 11 was confirmed in a murine sensitization model, resulting in airway inflammation, production of serum IgEs, and induction of Th2 immune responses. Of note, inflammatory responses were higher with the mature form, demonstrating that the cysteine protease activity critically contributes to the allergenicity of the molecule. Collectively, our results clearly demonstrate that Amb a 11 is a bona fide cysteine protease exhibiting a strong allergenicity. As such, it should be considered as an important molecule for diagnosis and immunotherapy of ragweed pollen allergy.

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confidence: 99%

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confidence: 99%

Structural and Functional Characterization of the Major Allergen Amb a 11 from Short Ragweed Pollen

Groeme¹,

Airouche²,

Kopečný³

et al. 2016

Journal of Biological Chemistry

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“…asthma, hay fever, and allergic rhinitis (Oswalt and Marshall, 2008;Howard and Levetin, 2014). It is interesting that a variety of non-respiratory issues such as strokes (Low et al, 2006), and surprisingly, even suicide and attempted suicide (Matheson et al, 2008) have an association with the daily concentration of atmospheric particulates.…”

Section: Pollen Estimationmentioning

confidence: 99%

Machine Learning Applications for Earth Observation

Lary

Zewdie

Liu

et al. 2018

Earth Observation Open Science and Innovation

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Machine learning has found many applications in remote sensing. These applications range from retrieval algorithms to bias correction, from code acceleration to detection of disease in crops, from classification of pelagic habitats to rock type classification. As a broad subfield of artificial intelligence, machine learning is concerned with algorithms and techniques that allow computers to "learn" by example. The major focus of machine learning is to extract information from data automatically by computational and statistical methods. Over the last decade there has been considerable progress in developing a machine learning methodology for a variety of Earth Science applications involving trace gases, retrievals, aerosol products, land surface products, vegetation indices, and most recently, ocean applications. In this chapter, we will review some examples of how machine learning has already been useful for remote sensing and some likely future applications.

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“…Furthermore, these thresholds vary largely between different regions and ethnic groups. The likely range of such daily thresholds is 5-20 grains m −3 per day estimated by Oswalt and Marshall (2008). Very sensitive people can be affected by as few as 1-2 pollen grains m −3 per day (Bullock et al, 2012).…”

Section: Ragweed Pollen Distribution Pattern and Risk Assessmentsmentioning

confidence: 99%

Ragweed pollen production and dispersion modelling within a regional climate system, calibration and application over Europe

et al. 2016

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Abstract. Common ragweed (Ambrosia artemisiifolia L.) is a highly allergenic and invasive plant in Europe. Its pollen can be transported over large distances and has been recognized as a significant cause of hay fever and asthma (D'Amato et al., 2007;Burbach et al., 2009). To simulate production and dispersion of common ragweed pollen, we implement a pollen emission and transport module in the Regional Climate Model (RegCM) version 4 using the framework of the Community Land Model (CLM) version 4.5. In this online approach pollen emissions are calculated based on the modelling of plant distribution, pollen production, species-specific phenology, flowering probability, and flux response to meteorological conditions. A pollen tracer model is used to describe pollen advective transport, turbulent mixing, dry and wet deposition.The model is then applied and evaluated on a European domain for the period 2000-2010. To reduce the large uncertainties notably due to the lack of information on ragweed density distribution, a calibration based on airborne pollen observations is used. Accordingly a cross validation is conducted and shows reasonable error and sensitivity of the calibration. Resulting simulations show that the model captures the gross features of the pollen concentrations found in Europe, and reproduce reasonably both the spatial and temporal patterns of flowering season and associated pollen concentrations measured over Europe. The model can explain 68.6, 39.2, and 34.3 % of the observed variance in starting, central, and ending dates of the pollen season with associated root mean square error (RMSE) equal to 4.7, 3.9, and 7.0 days, respectively. The correlation between simulated and observed daily concentrations time series reaches 0.69. Statistical scores show that the model performs better over the central Europe source region where pollen loads are larger and the model is better constrained.From these simulations health risks associated to common ragweed pollen spread are evaluated through calculation of exposure time above health-relevant threshold levels. The total risk area with concentration above 5 grains m −3 takes up 29.5 % of domain. The longest exposure time occurs on Pannonian Plain, where the number of days per year with the daily concentration above 20 grains m −3 exceeds 30.

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Ragweed as an Example of Worldwide Allergen Expansion

Cited by 60 publications

References 46 publications

Structural and Functional Characterization of the Major Allergen Amb a 11 from Short Ragweed Pollen

Structural and Functional Characterization of the Major Allergen Amb a 11 from Short Ragweed Pollen

Machine Learning Applications for Earth Observation

Ragweed pollen production and dispersion modelling within a regional climate system, calibration and application over Europe

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