Corrigendum: Species Richness, rRNA Gene Abundance, and Seasonal Dynamics of Airborne Plant-Pathogenic Oomycetes.

[This corrects the article DOI: 10.3389/fmicb.2018.02673.].

Species Richness, rRNA Gene Abundance, and Seasonal Dynamics of Airborne Plant-Pathogenic Oomycetes.

Oomycetes, also named Peronosporomycetes, are one of the most important and widespread groups of plant pathogens, leading to significant losses in the global agricultural productivity. They have been studied extensively in ground water, soil, and host plants, but their atmospheric transport vector is not well characterized. In this study, the occurrence of airborne Oomycetes was investigated by Sanger sequencing and quantitative PCR of coarse and fine aerosol particle samples (57 filter pairs) collected over a 1-year period (2006-2007) and full seasonal cycle in Mainz, Germany. In coarse particulate matter, we found 55 different hypothetical species (OTUs), of which 54 were plant pathogens and 29 belonged to the genus Peronospora (downy mildews). In fine particulate matter (<3 µm), only one species of Hyaloperonospora was found in one sample. Principal coordinate analysis of the species composition revealed three community clusters with a dependence on ambient temperature. The abundance of Oomycetes rRNA genes was low in winter and enhanced during spring, summer, and fall, with a dominance of Phytophthora, reaching a maximum concentration of ∼1.6 × 106 rRNA genes per cubic meter of sampled air in summer. The presence and high concentration of rRNA genes in air suggests that atmospheric transport, which can lead to secondary infection, may be more important than currently estimated. This illustrates the need for more current and detailed datasets, as potential seasonal shifts due to changing meteorological conditions may influence the composition of airborne Oomycetes. An insight into the dynamics of airborne plant pathogens and their major drivers should be useful for improved forecasting and management of related plant diseases.

Metaproteomic analysis of atmospheric aerosol samples.

Metaproteomic analysis of air particulate matter provides information about the abundance and properties of bioaerosols in the atmosphere and their influence on climate and public health. We developed and applied efficient methods for the extraction and analysis of proteins from glass fiber filter samples of total, coarse, and fine particulate matter. Size exclusion chromatography was applied to remove matrix components, and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was applied for protein fractionation according to molecular size, followed by in-gel digestion and LC-MS/MS analysis of peptides using a hybrid Quadrupole-Orbitrap MS. Maxquant software and the Swiss-Prot database were used for protein identification. In samples collected at a suburban location in central Europe, we found proteins that originated mainly from plants, fungi, and bacteria, which constitute a major fraction of primary biological aerosol particles (PBAP) in the atmosphere. Allergenic proteins were found in coarse and fine particle samples, and indications for atmospheric degradation of proteins were observed. Graphical abstract Workflow for the metaproteomic analysis of atmospheric aerosol samples.

Quantitative DNA Analyses for Airborne Birch Pollen.

Birch trees produce large amounts of highly allergenic pollen grains that are distributed by wind and impact human health by causing seasonal hay fever, pollen-related asthma, and other allergic diseases. Traditionally, pollen forecasts are based on conventional microscopic counting techniques that are labor-intensive and limited in the reliable identification of species. Molecular biological techniques provide an alternative approach that is less labor-intensive and enables identification of any species by its genetic fingerprint. A particularly promising method is quantitative Real-Time polymerase chain reaction (qPCR), which can be used to determine the number of DNA copies and thus pollen grains in air filter samples. During the birch pollination season in 2010 in Mainz, Germany, we collected air filter samples of fine (<3 µm) and coarse air particulate matter. These were analyzed by qPCR using two different primer pairs: one for a single-copy gene (BP8) and the other for a multi-copy gene (ITS). The BP8 gene was better suitable for reliable qPCR results, and the qPCR results obtained for coarse particulate matter were well correlated with the birch pollen forecasting results of the regional air quality model COSMO-ART. As expected due to the size of birch pollen grains (~23 µm), the concentration of DNA in fine particulate matter was lower than in the coarse particle fraction. For the ITS region the factor was 64, while for the single-copy gene BP8 only 51. The possible presence of so-called sub-pollen particles in the fine particle fraction is, however, interesting even in low concentrations. These particles are known to be highly allergenic, reach deep into airways and cause often severe health problems. In conclusion, the results of this exploratory study open up the possibility of predicting and quantifying the pollen concentration in the atmosphere more precisely in the future.

High diversity of fungi in air particulate matter.

Fungal spores can account for large proportions of air particulate matter, and they may potentially influence the hydrological cycle and climate as nuclei for water droplets and ice crystals in clouds, fog, and precipitation. Moreover, some fungi are major pathogens and allergens. The diversity of airborne fungi is, however, not well-known. By DNA analysis we found pronounced differences in the relative abundance and seasonal cycles of various groups of fungi in coarse and fine particulate matter, with more plant pathogens in the coarse fraction and more human pathogens and allergens in the respirable fine particle fraction (<3 microm). Moreover, the ratio of Basidiomycota to Ascomycota was found to be much higher than previously assumed, which might also apply to the biosphere.