Science of the Total Environment 688 (2019) 83–86

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Science of the Total Environment

journal homepage: www.elsevier.com/locate/scitotenv

Short Communication

Uncovering prokaryotic biodiversity within aerosols of the pristine

Amazon forest
Felipe F.C. Souza a, Daniel V. Rissi b, Fabio O. Pedrosa c,d, Emanuel M. Souza c,d, Valter A. Baura d,
Rose A. Monteiro d, Eduardo Balsanelli d, Leonardo M. Cruz c,d, Rodrigo A.F. Souza e, Meinrat O. Andreae g,
Rodrigo A. Reis a, Ricardo H.M. Godoi f, Luciano F. Huergo a,c,d,⁎
a
Setor Litoral, UFPR, Matinhos, PR, Brazil
b
Department of Livestock Microbial Ecology, Institute of Animal Science, University of Hohenheim, Stuttgart, Germany
c
Programa de pós-graduação em Bioinformática, SEPTI, UFPR, Curitiba, PR, Brazil
d
Departamento de Bioquímica e Biologia Molecular, UFPR, Curitiba, PR, Brazil
e
Meteorology Department, State University of Amazonas – UEA, Manaus, AM, Brazil
f
Departamento de Engenharia Ambiental, UFPR, Curitiba, PR, Brazil
g
Max Planck Institute for Chemistry, Mainz, Germany

H I G H L I G H T S G R A P H I C A L A B S T R A C T

• An efficient method to collect the

microbiome of aerosol within pristin
amazon forest was developed.
• This is the first study describing the pro-
karyotic community of bioaerosols in
the Amazon.
• Metagenomic DNA was extracted from
air samples and the prokaryotic diver-
sity determined by 16S rRNA gene se-
quencing.
• The organisms identified in Amazon
bioaerosols are a potential source for
biotechnological applications.

a r t i c l e i n f o a b s t r a c t

Article history: Biological aerosols (bioaerosol) are atmospheric particles that act as a dispersion unit of living organisms across
Received 8 April 2019 the globe thereby affecting the biogeographic distribution of organisms. Despite their importance, there is virtu-
Received in revised form 11 June 2019 ally no knowledge about bioaerosols emitted by pristine forests. Here we provide the very first survey of the pro-
Accepted 14 June 2019
karyotic community of a bioaerosol collected inside pristine Amazon forest at 2 m above ground. Total
Available online 15 June 2019
atmospheric particles were collected at the Amazon Tall Tower Observatory, subjected to metagenomic DNA ex-
Editor: Pavlos Kassomenos traction and the prokaryotic diversity was determined by 16S rRNA gene amplicon sequencing. A total of 271,577
reads of 250 bp of the 16S rRNA gene amplicon were obtained. Only 27% of the reads could be classified using the
Keywords: 16S SILVA database. Most belonged to Proteobacteria, Actinobacteria and Firmicutes which is in good agreement
Bioaerosol with other bioaerosol studies. Further inspection of the reads using Blast searches and the 18S SILVA database re-
Amazon vealed that most of the dataset was composed of Fungi sequences. The identified microbes suggest that the atmo-
Biodiversity sphere may act as an important gateway to interchange bacteria between plants, soil and water ecosystems.
16S sequencing © 2019 Elsevier B.V. All rights reserved.
Metagenome

⁎ Corresponding author at: Setor Litoral, UFPR, Matinhos, PR, Brazil.

E-mail address: huergo@ufpr.br (L.F. Huergo).

https://doi.org/10.1016/j.scitotenv.2019.06.218
0048-9697/© 2019 Elsevier B.V. All rights reserved.
84 F.F.C. Souza et al. / Science of the Total Environment 688 (2019) 83–86

1. Introduction microbiology (Robertson et al., 2013). Very few studies reported the di-
versity of bacteria in aerosols sampled in open environments (Mayol
The Amazon Rainforest is the largest tropical forest in the world, and et al., 2017; Yoo et al., 2017). To our knowledge, there is no data on
this biome hosts a significant portion of the earth's biodiversity. Despite the Bacterial biodiversity of pristine forest aerosol. Here we describe
the well-studied diversity of plants and animals, there are just a few the first survey of the prokaryotic diversity within bioaerosols inside
studies analysing prokaryotic biodiversity in the Amazon, and these pristine Amazon forest.
studies focused mainly on soil (Fonseca et al., 2018) and freshwater eco-
systems in rivers and lakes (Ghai et al., 2011; Santos-Júnior et al., 2016). 2. Material and methods
Molecular approaches combining environmental metagenomic DNA
extraction, 16S rRNA gene sequencing and bioinformatics are helping to Air samples were collected at the Amazon Tall Tower Observatory
uncover environmental prokaryotic communities. The Earth (ATTO) (Andreae et al., 2015), set up in a pristine rain forest region in
Microbiome project has catalogued microbial communities on a global the central Amazon Basin, about 150 km northeast of the city of Manaus.
scale with efforts concentrated on soil, sediments, water and animals Adapted Harvard inertial impactor samplers were used to collect total
or plant material (Thompson et al., 2017). particulate matter were set 2 m above ground Polycarbonate filters
Cataloguing and comprehending the diversity and ecology of pro- (0.8 μm cut off) were assembled in their holders and sterilized at 120
karyotic life in biogenic aerosols (bioaerosol) is key to understanding °C for 20 min. The filters were placed in sterile Petri dishes which
the biogeographic distribution of these organisms. Increasing evidence were sealed under vacuum and were opened only at the time of sam-
supports the hypothesis that bioaerosols may affect the climate system pling. Other parts of the Harvard sampler were, extensively washed,
by acting as ice nuclei and/or cloud condensation nuclei (Pöschl et al., disinfected by rising with ethanol 70% (v/v) and exposing to a UV
2010). Furthermore, bioaerosols may act as a vehicle to spread disease lamp for 5 min inside a microbiological chamber. The Harvard apparatus
(Womack et al., 2010). Much of the literature on bioaerosols was fo- and filters were mounted, and air flow was achieved using a pump op-
cused on urban and or closed environments with emphasis on medical erating at 35 l·min−1. After 72 h of sampling, the filters were collected

A B

C D

Fig. 1. Taxonomic classification at Phylum level (A), Order (B), Class (C) and Family (D). The reads were classified using the RDP classifier with bootstrap cut off 80%. The most prevalent
taxa are show as % of total classified reads at each taxonomic level. Reads assigned to chloroplast were removed from the analysis to generate figures B, C and D.
 F.F.C. Souza et al. / Science of the Total Environment 688 (2019) 83–86 85

with sterilized tweezers, placed inside sterile disposable Petri dishes, The sequences were quality analysed using the Fastqc tool. The for-
sealed under vacuum and kept at approximately 4 °C until extraction ward sequences were processed using QUIIME v1.9 (Kuczynski et al.,
of DNA (between 3 and 10 days). Two filters that were used as negative 2012), Uchime 6.1 was used for detection of chimeras but no chimeras
controls were installed inside the Harvard samplers and left for 30 min were detected. The OTUs were picked and clustered with Uclust
without airflow. 1.2.21 at 97% sequence identity. Sequences were filtered for the pres-
Two filters were sampled for 72 h (Between 20 and 26 August 2018, ence of mitochondria and the taxonomy classification was performed
during the dry season, During all sampling time, the average humidity using the SILVA database 132 using 97% identity cut off (Quast et al.,
was 75% and the wind speed smaller than 1 m·s−1). The filters were 2013) and the RDP classifier tool using 80 bootstrap confidence cut off
combined, cut in small pieces using sterile scissors and processed (Wang et al., 2007). Sequences were deposited at Genbank Bioproject
using the DNA extraction kit DNeasy Power Water (QIAGEN). The PRJNA528294/SRR8799441).
DNA was extracted following the manufacture's protocol with the addi-
tion of a 30 min sonication step in a water bath at 65 °C before cell rup- 3. Results and discussion
ture. According, to previous studies, this step increases the yield of
metagenomic DNA recovered from bioaerosol filters (Luhung et al., In order to establish the prokaryotic community present in
2015). The two negative control filters were subjected to exactly the bioaerosols of pristine Amazon forest we used adapted Harvard inertial
same extraction protocol. impactor samplers to collect total particulate matter at the Amazon Tall
All the recovered metagenomic DNA was used as template for PCR Tower Observatory. Total metagenomic DNA was extracted and used as
amplification of the 16S rRNA gene V4 region. PCR, barcoding and template for 16S rRNA gene amplicon sequencing. Parallel controls with
Illumina sequencing was performed as described previously (Caporaso blank filters were processed exactly as the sampled filters but yield no
et al., 2012; Huergo et al., 2018). Amplicons were verified by electro- 16S amplicons. Hence, supporting that sequences obtained are not re-
phoresis and quantified by Qubit HS dsDNA kit (Invitrogen). Negative sult of spurious contamination.
control samples underwent exactly the same PCR conditions and did A total of 271,577 reads of 250 bp of the 16S rRNA gene amplicon
not produce amplicons as verified by electrophoresis. Sequencing reac- were obtained. The reads were clustered at 97% sequence identity into
tions were performed for each sample using a MiSeq platform with Operational Taxonomic Units (OTUs) resulting in 4296 OTUs. Rarefac-
MiSeq 300v2 Reagent Kit (Illumina). tion plots are shown in Fig. S1. OTUs were filtered for mitochondria
and subjected to taxonomy classification using the SILVA database 132
(Quast et al., 2013). Only 27% of the reads could be classified using the
16S database. Most of the OTUs classified as bacteria belonged to the
Luteimonas phyla Proteobacteria, Actinobacteria and Firmicutes (Fig. S2) which is
Streptomyces in good agreement with other bioaerosol studies (Archer et al., 2019;
Leucobacter Tanaka et al., 2019).
Sphingobacterium The taxonomic identity of each of the sequence reads was explored
Methylobacterium using the RDP classifier tool (Wang et al., 2007) and the RDP 16S data-
Brevibacterium base (accessed in February 2019). This approach classified 38,281
Paracoccus reads (14%) at the Phylum level. Only 6 reads were representative of Ar-
Bacillus chaea, the remaining were mostly Proteobacteria 36%, Actinobacteria
Brachybacterium 24% and Firmicutes 19% (Fig. 1A). The most prevailing classified Bacte-
Olivibacter rial Order, Classes and Families are depicted in Fig. 1. The most represen-
Gp3 tative Families were Bacillaceae 13%, Xanthomonadaceae 9%,
Tumebacillus Streptomycetaceae 7%, Sphingobacteriaceae 6%, Microbacteriaceae 5%
Stenotrophomonas and Enterobacteriaceae 5%. The most prevailing reads classified at the
Conexibacter genus level are depicted in Fig. 2 and Table S1.
Bacillariophyta We were intrigued by the fact that only a small fraction of the reads
Granulicella could be classified using the 16S rRNA sequence databases SILVA or RDP.
Chondromyces Manual inspection of the mitochondria unfiltered dataset using Blast
Sphingomonas searches revealed that most of the sequences belong to Fungal mito-
Gordonia chondrial ribosomal genes. In addition, nearly all the mitochondrial fil-
Acnomycetospora tered OTUs that could not be classified using the prokaryotic 16S
Corynebacterium SILVA database were classified as Fungi sequences using the SILVA 18S
database (Fig. S3). The prevailing fungi were Basidiomycota and Asco-
Escherichia/Shigella
mycota (Fig. S3) which is in good agreement with data in other
Pseudoxanthomonas
bioaerosol studies (Tanaka et al., 2019)
Lysinibacillus
It is well known that 16S rDNA primer pairs also exhibit affinity for
Phenylobacterium
mitochondrial and eukaryotic 18S rDNA and it is common to detect a
Staphylococcus
small fraction of eukaryotic DNA in bacterial 16S rDNA amplicon studies
Terriglobus
(Beckers et al., 2016). In our study, most of the reads obtained were
Chlorophyta
from Fungal related sequences suggesting that Fungal abundance over-
Pseudonocardia
comes Prokaryotic abundance in the biogenic aerosol inside Amazon.
Paenibacillus
Mycobacterium 4. Conclusions
0 500 1000 1500
Number of reads One the major challenges to study bioaerosol composition is the lack
of standard methods and the low yield of biomass per volume of air
Fig. 2. Taxonomic classification of the reads at Genus level. The most prevalent Genus is
(Luhung et al., 2015). The data reported exemplifies a successful analy-
shown as number of total reads. The plant/chloroplast genus Streptophyta was removed sis of forest bioaerosol using a metagenomics approach which can be
from this analysis. used as a framework for more complex ecological studies in the future.
86 F.F.C. Souza et al. / Science of the Total Environment 688 (2019) 83–86

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Tecnologia e Inovação (MCTI/FINEP contract 01.11.01248.00), suburban sites in Toyama, Japan suggest a new perspective for bioprospecting. Front.
Bioeng. Biotechnol. https://doi.org/10.3389/fbioe.2019.00012.
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Dixon – John Innes Centre – UK, for revising the manuscript. Earth's multiscale microbial diversity. Nature https://doi.org/10.1038/nature24621.
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