ORIGINAL RESEARCH
published: 14 August 2018
doi: 10.3389/fmicb.2018.01752
Airborne Bacteria in Earth’s Lower
Stratosphere Resemble Taxa
Detected in the Troposphere: Results
From a New NASA Aircraft
Bioaerosol Collector (ABC)
David J. Smith 1*, Jayamary Divya Ravichandar 2 , Sunit Jain 2 , Dale W. Griffin 3 ,
Hongbin Yu 4 , Qian Tan 5 , James Thissen 6 , Terry Lusby 1 , Patrick Nicoll 7 , Sarah Shedler 8 ,
Paul Martinez 9 , Alejandro Osorio 10 , Jason Lechniak 9 , Samuel Choi 10 , Kayleen Sabino 2 ,
Kathryn Iverson 2 , Luisa Chan 2 , Crystal Jaing 6 and John McGrath 9
1
Edited by:
Pierre Amato,
UMR6296 Institut de Chimie de
Clermont-Ferrand (ICCF), France
Reviewed by:
Noelle C. Bryan,
Massachusetts Institute of
Technology, United States
Christopher E. Carr,
Massachusetts Institute of
Technology, United States
*Correspondence:
David J. Smith
david.j.smith-3@nasa.gov
Specialty section:
This article was submitted to
Extreme Microbiology,
a section of the journal
Frontiers in Microbiology
Received: 27 April 2018
Accepted: 13 July 2018
Published: 14 August 2018
Citation:
Smith DJ, Ravichandar JD, Jain S,
Griffin DW, Yu H, Tan Q, Thissen J,
Lusby T, Nicoll P, Shedler S,
Martinez P, Osorio A, Lechniak J,
Choi S, Sabino K, Iverson K, Chan L,
Jaing C and McGrath J (2018)
Airborne Bacteria in Earth’s Lower
Stratosphere Resemble Taxa
Detected in the Troposphere: Results
From a New NASA Aircraft Bioaerosol
Collector (ABC).
Front. Microbiol. 9:1752.
doi: 10.3389/fmicb.2018.01752
NASA Ames Research Center, Space Biosciences Division, Moffett Field, CA, United States, 2 Second Genome Inc., South
San Francisco, CA, United States, 3 United States Geological Survey, Environmental Health, St. Petersburg, FL,
United States, 4 Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States,
5
Earth Science Division, Bay Area Environmental Research Institute, Moffett Field, CA, United States, 6 Lawrence Livermore
National Laboratory, Livermore, CA, United States, 7 Space Biosciences Division, Blue Marble Space Institute of Science,
Moffett Field, CA, United States, 8 Biological Oceanography Department, University of South Florida, College of Marine
Sciences, St. Petersburg, FL, United States, 9 NASA Armstrong Flight Research Center, Palmdale, CA, United States,
10
Jacobs Technology Inc., NASA Armstrong Flight Research Center, Palmdale, CA, United States
Airborne microorganisms in the upper troposphere and lower stratosphere remain elusive
due to a lack of reliable sample collection systems. To address this problem, we
designed, installed, and flight-validated a novel Aircraft Bioaerosol Collector (ABC) for
NASA’s C-20A that can make collections for microbiological research investigations
up to altitudes of 13.7 km. Herein we report results from the first set of science
flights—four consecutive missions flown over the United States (US) from 30 October
to 2 November, 2017. To ascertain how the concentration of airborne bacteria
changed across the tropopause, we collected air during aircraft Ascent/Descent (0.3
to 11 km), as well as sustained Cruise altitudes in the lower stratosphere (∼12 km).
Bioaerosols were captured on DNA-treated gelatinous filters inside a cascade air
sampler, then analyzed with molecular and culture-based characterization. Several
viable bacterial isolates were recovered from flight altitudes, including Bacillus sp.,
Micrococcus sp., Arthrobacter sp., and Staphylococcus sp. from Cruise samples
and Brachybacterium sp. from Ascent/Descent samples. Using 16S V4 sequencing
methods for a culture-independent analysis of bacteria, the average number of total
OTUs was 305 for Cruise samples and 276 for Ascent/Descent samples. Some
taxa were more abundant in the flight samples than the ground samples, including
OTUs from families Lachnospiraceae, Ruminococcaceae and Erysipelotrichaceae as well
as the following genera: Clostridium, Mogibacterium, Corynebacterium, Bacteroides,
Prevotella, Pseudomonas, and Parabacteroides. Surprisingly, our results revealed a
homogeneous distribution of bacteria in the atmosphere up to 12 km. The observation
could be due to atmospheric conditions producing similar background aerosols across
the western US, as suggested by modeled back trajectories and satellite measurements.
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August 2018 | Volume 9 | Article 1752
Smith et al.
NASA Aircraft Bioaerosol Collector (ABC)
However, the influence of aircraft-associated bacterial contaminants could not be fully
eliminated and that background signal was reported throughout our dataset. Considering
the tremendous engineering challenge of collecting biomass at extreme altitudes where
contamination from flight hardware remains an ever-present issue, we note the utility of
using the stratosphere as a proving ground for planned life detection missions across the
solar system.
Keywords: bioaerosols, bacteria, C-20A, troposphere, stratosphere, Aircraft Bioaerosol Collector (ABC)
INTRODUCTION
established. High-altitude aircraft are a compelling choice for
capturing airborne microorganisms considering the multitude
of government and commercially operated platforms flying
into the upper atmosphere every day. Ride-along sample
collections on aircraft would enable transformative opportunities
for the aerobiology research community by providing substantial
spatiotemporal coverage. Despite an abundance of aircraft
worldwide, surprisingly few studies have attempted in-flight
collections of bioaerosols, perhaps because no standard hardware
package exists for sample acquisition. Detailed engineering
schematics and discussions of contamination control techniques
onboard aircraft are notably absent in the small group of
aircraft-based aerobiology literature (Meier and Lindbergh, 1935;
Polunin and Kelly, 1952; Timmons et al., 1966; Trägårdh,
1977; Borodulin et al., 2005; Hill et al., 2007; DeLeonRodriguez et al., 2013; Maki et al., 2013). As the sensitivity and
affordability of molecular methods in microbiology improves
each year, so should a general awareness that hardware used for
aerobiology surveys can be highly susceptible to contaminants
(Smith and Griffin, 2013; Griffin et al., 2017). In the era
of molecular assays, the aerobiology research community
must adopt stricter quality control procedures—reporting
detailed hardware designs, contamination control approaches
and monitoring methods—similar to standard practices in
space exploration (e.g., life detection and planetary protection)
(Vaishampayan et al., 2013; Benardini et al., 2014; Summons
et al., 2014) and industry (e.g., pharamceutical manufacuturing
facilities). Improvements evaluating contamination have been
recently made in studies using high altitude balloons (Bryan et al.,
2014) but such stringency has not yet been implemented with
aircraft experiments.
Accordingly, the primary aim of our study was to develop
a low cost, reproducible window-mounted aircraft hardware
system (Figure 1A) for microbiology collections in the upper
troposphere and lower stratosphere. Our new system, the
Aircraft Bioaerosol Collector (ABC), was designed to (1) function
at extreme altitude and high aircraft velocities; (2) capture
free stream atmospheric air samples at subzero temperatures;
(3) regulate air flow (on/off) for collections at specific altitudes
of interest; (4) use DNA-treated collection filters, replaceable in
flight; (5) size separate bioaerosols; and (6) have components
that could be easily installed, removed and periodically cleaned.
After building and installing the ABC, we flew 4 test flights over
the continental USA at altitudes up to 12.2 km demonstrating its
operability. We sampled above and below the lower stratosphere
to test the hypothesis that the tropopause serves as Earth’s
Microbial “highways” flow naturally overhead in Earth’s
atmosphere (Schmale and Ross, 2015) but “traffic patterns”
elude the international aerobiology research community due
to a widespread shortage of sampling opportunities. Although
airborne biomass eventually returns to the surface, low
sedimentation rates of microorganisms allows potentially long
periods aloft in the upper atmosphere and, consequently, long
distances traveled downwind (Bovallius et al., 2006; Reche et al.,
2018). A variety of ground and airborne studies have recently
reported that viable microorganisms can be delivered across
continents and oceans (Prospero et al., 2005; Bowers et al., 2011;
Toepfer et al., 2011; Favet et al., 2012; Smith et al., 2012, 2013;
Yamaguchi et al., 2012; Barberán et al., 2015; Tang et al., 2016;
Gat et al., 2017; Maki et al., 2017; Weil et al., 2017), microbes
at lower altitudes in clouds might be temporarily active (Klein
et al., 2016; Amato et al., 2017c), biomass (intact cells, spores, or
debris) can influence cloud chemistry and precipitation patterns
(Deguillaume et al., 2008; Bowers et al., 2009; Vaïtilingom et al.,
2010, 2013; Amato et al., 2015), and biosignatures can still be
detected up to 38 km (Smith, 2013). Since all marine and surface
environments are impacted by winds, the emanation/deposition
of airborne microorganisms (hereafter referred to as bioaerosols)
contributes to ecosystem dynamics. For instance, airborne
microorganisms surviving harsh conditions while aloft,
including strong levels of mutagenic ultraviolet radiation, might
be altered at the genomic, transcriptomic, and proteomic level
upon germination in a new environment (Smith et al., 2011;
Chudobova et al., 2015; Waters et al., 2015; Khodadad et al.,
2017). With hundreds of teragrams of microbe-laden dusts from
deserts and agricultural soils moving through Earth’s atmosphere
each year (Acosta-Martinez et al., 2015), additional surveys are
needed to better understand the ecological consequences of
airborne biomass exchange, including disease dispersal (Brown
and Hovmoller, 2002; Fröhlich-Nowoisky et al., 2016; Mahaffee
and Stoll, 2016; Van Leuken et al., 2016). Ultimately, more
sampling opportunities above the boundary layer (i.e., >2 km
above the Earth’s surface) will improve long range modeling
efforts aimed at providing predictive tools for aerobiology studies
at regional and global scales (Burrows et al., 2009a,b; Griffin
et al., 2017).
Before future investigations addressing the origin, destination,
survival, and mutation of airborne microorganisms can be
realistically implemented, routine and reliable access to the
upper atmosphere for collecting bioaerosols must first be
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Smith et al.
NASA Aircraft Bioaerosol Collector (ABC)
naturally occurring altitudinal biosphere boundary; specifically,
we expected a significant drop in bioaerosol abundance and
richness in the lower stratosphere (∼12 km) compared to the
troposphere (0.3 to 12 km). To evaluate our hypothesis we
characterized airborne bacteria using molecular and culturebased methods, and we determined the atmospheric transport
history of sampled air using meteorological models and satellite
data.
Basin (Science Flight #958). Each flight path remained above the
state of California (CA) except for the Slumgullion flight that also
crossed over Arizona, Nevada, Utah, and Colorado.
Aircraft Bioaerosol Collector (ABC)
Sampling free stream air (King, 1984) was an essential system
design feature. The atmospheric probe (Figure 1B) was a
miniaturized version of larger pitot-style inlets used on the NASA
DC-8 aircraft for collecting atmospheric aerosols < 4 µm (Talbot
et al., 1998; Dibb et al., 1999; Scheuer et al., 2003). STAR-CCM+
modeling (Siemens PLM Software, Plano, TX) and aerodynamic
calculations were performed to determine a position for the
probe that would pass through free stream air uninfluenced by C20A engines or surfaces. We built a custom 29 cm (l) x 16 cm (w)
x 3 cm (h) probe from aluminum (AL7050-T7452 per AMS 4050)
with an inner diameter of 0.3 cm and clam shell construction
that was secured with 6 screws (NAS11530-9), sealed using
AMS-S-8802 sealant, and anodized (MIL-A-8625 type II). When
mounted, the probe mass was ∼1 kg and it was positioned 16 cm
from the window plate, angled at 1.5 degrees below horizontal.
Depicted in Figure 1C, the probe-captured air traveled
through hose lines (Part # 101001-3CR-0240; 101001-3CR-0065;
101001-3CR-0064; 101003-3CR-0360, Aviall Hose Shop, Van
Nuys, CA) and into a sterilized 2-stage cascade sampler (Product
MATERIALS AND METHODS
Aircraft Description
NASA’s C-20A Gulfstream III aircraft is available through the
Airborne Science Program at Armstrong Flight Research Center
(AFRC). The aircraft can reach altitudes of 13.7 km and cover
a range of 6.3 km with an air speed up to 237 m·s−1 . Flights
can last 5–7 h and carry about 1,000 kg of useful scientific
payload. During the course of our study, C-20A ride-along flight
opportunities were provided by AFRC and the Uninhabited
Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team from
NASA Jet Propulsion Laboratory (Koo et al., 2012). We flew
4 research flights across regions of the western USA: Eel River
(Science Flight #955); California San Andreas Fault (CSAF)
(Science Flight #956); Slumgullion (Science Flight #957); and LA
FIGURE 1 | The Aircraft Bioaerosol Collector (ABC) system on the NASA C-20A aircraft. (A) Engineering diagram for components inside and outside of the aircraft;
(B) Photograph of the window plate mounted probe for capturing free stream air; and (C) View from inside of the C-20A during in-flight operations with system
elements labeled.
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NASA Aircraft Bioaerosol Collector (ABC)
flow meter (Model RMC, Dwyer, Michigan City, IN). Flow rates
were monitored during each flight and averaged 8.5 l·min−1 at
the aircraft’s cruise altitude of ∼12 km. After passing the flow
meter, air traveled into an exhaust line and back out the window
port. To prevent air flow during take-off or landing, a ball valve
(Part # 2F-B2LJ2-SSP-LD, Valin, San Jose, CA) was installed
in-line and upstream of the cascade sampler.
# TE-10-860; Tisch Environmental, Cleves, OH) with successive
aluminum stages fastened together by a butterfly cap and clamp
system with silicone o-rings for air-tight sealing (039S70 and
044S70). The cascade sampler sat on a custom-built workbench
containing all system components and supplies needed for flight
operations. We modified the top portion of the sampler with
quick release fasteners to allow for filter replacement in flight.
Each sampler stage had 400 small round drilled orifices (1.18 mm
on first stage; 0.25 mm on second stage). Gamma-irradiated (i.e.,
DNA-treated) gelatinous filter membranes (Part #16799-100102500, Sartorius, Bohemia, NY) sat underneath each aluminum
stage for bioaerosol capture. Each filter was supported by a
manufacturer-provided grid of polyethylene that was cut down to
size and sterilized before use with a 90% isopropyl alcohol rinse
(Sigma-Aldrich, St. Louis, MO). Plating the sterilized support
grids on R2A (Difco, Sparks, MD) media did not yield any
bacterial growth after 1 week of incubation at 25◦ C. Once
atmospheric air traveled across the cascade sampler stages and
gelatinous filter membranes, it then passed through a volumetric
Experimental Design and Sample
Description
Table 1 summarizes the collections in our study and Figure 2
shows photographs of the sample locations. Prior to flying,
accessible ABC components and tools were sterilized by
autoclaving or with isopropyl alcohol rinses. The window plate
and probe inlet were assayed for contaminants using a pre-wetted
sterile applicator (part # 25-8062WC, Puritan, Guilford, ME)
before the C-20A departed the aircraft hangar; these samples were
Hardware controls. Swabs were stored in 5 ml of sterile deionized
water within a 15 ml Falcon tube and kept at 4◦ C until laboratory
TABLE 1 | Summary of samples.
Date
(2017)
Flight
Type
1
30 Oct
Eel River
Ground
2
30 Oct
Eel River
Ground
3
30 Oct
Eel River
4
30 Oct
5
CFUs
PCR Yield (ng·µl−1 )
–
4
20.99
Ground, pre-flight, side of aircraft
–
1
20.99
Ground, post-flight, side of aircraft
Atmosphere
42
1
12.82
Ascent/Descent, in-flight, top stage of sampler
Eel River
Atmosphere
42
0
20.11
Ascent/Descent, in-flight, bottom stage of sampler
30 Oct
Eel River
Atmosphere
141
0
20.66
Cruise, in-flight, top stage of sampler
6
30 Oct
Eel River
Atmosphere
141
0
20.99
Cruise, in-flight, bottom stage of sampler
7
31 Oct
CSAF
Ground
–
7
20.99
Ground, pre-flight, side of aircraft
8
31 Oct
CSAF
Ground
–
7
14.22
Ground, post-flight, side of aircraft
9
31 Oct
CSAF
Atmosphere
41
0
3.43
Ascent/Descent, in-flight, top stage of sampler
10
31 Oct
CSAF
Atmosphere
41
1
0.01
Ascent/Descent, in-flight, bottom stage of sampler
11
31 Oct
CSAF
Atmosphere
250
0
0.28
Cruise, in-flight, top stage of sampler
12
31 Oct
CSAF
Atmosphere
250
0
0.86
Cruise, in-flight, bottom stage of sampler
13
1 Nov
Slumgullion
Ground
–
0
3.31
Ground, pre-flight, side of aircraft
14
1 Nov
Slumgullion
Ground
–
0
2.82
Ground, post-flight, side of aircraft
15
1 Nov
Slumgullion
Atmosphere
43
0
9.88
Ascent/Descent, in-flight, top stage of sampler
16
1 Nov
Slumgullion
Atmosphere
43
0
8.59
Ascent/Descent, in-flight, bottom stage of sampler
17
1 Nov
Slumgullion
Atmosphere
240
6
8.05
Cruise, in-flight, top stage of sampler
18
1 Nov
Slumgullion
Atmosphere
240
0
8.1
19
2 Nov
LA Basin
Ground
–
2
18.13
Ground, pre-flight, side of aircraft
20
2 Nov
LA Basin
Ground
–
0
19.62
Ground, post-flight, side of aircraft
21
2 Nov
LA Basin
Atmosphere
52
0
11.63
Ascent/Descent, in-flight, top stage of sampler
22
2 Nov
LA Basin
Atmosphere
52
0
20.4
23
2 Nov
LA Basin
Atmosphere
181
0
1.11
24
2 Nov
LA Basin
Atmosphere
181
0
11.75
Cruise, in-flight, bottom stage of sampler
25
2 Nov
LA Basin
Ground
–
0
10.13
Negative control, blank filter loaded into upper stage of sampler
26
2 Nov
LA Basin
Ground
–
0
8.24
27
30 Oct
Eel River
Ground
–
1
14.84
Hardware, pre-flight, probe sample
28
30 Oct
Eel River
Ground
–
0
20.99
Hardware, pre-flight, window plate sample
29
30 Oct
Eel River
Ground
–
0
19.24
Hardware, post-flight, probe sample
30
30 Oct
Eel River
Ground
–
2
11.8
Hardware, post-flight, window plate sample
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Time
(min)
4
Notes
Cruise, in-flight, bottom stage of sampler
Ascent/Descent, in-flight, bottom stage of sampler
Cruise, in-flight, top stage of sampler
Negative control, blank filter loaded into lower stage of sampler
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NASA Aircraft Bioaerosol Collector (ABC)
FIGURE 2 | Experimental design overview. (A) Cruise samples at ∼12 km; (B) Ascent/Descent samples ranging from 0.3 to 12 km; (C) Ground hardware samples
(probe and window plate, red X indicating area swabbed); (D) Ground samples (side of aircraft, red X indicating area swabbed); and (E) Negative control sample
where blank filter was loaded into cascade sampler.
After the Cruise sample collection and prior to aircraft Descent
below 10 km, the Ascent sample filters were returned into the
cascade sampler, combining the Ascent & Descent samples for
this study. The decision to pool Ascent/Descent samples (300–
10 km) was made in order to focus our analysis on the potential
difference between bacterial concentration and diversity in the
troposphere vs. lower stratosphere. Air flow into the ABC was
stopped again at ∼300 m above ground level on runway approach
for the C-20A at AFRC. Upon completion of each flight, cascade
sampler components were cleaned with isopropyl alcohol; swabs
and filters were kept at 4◦ C until transport back to NASA Ames
(Moffett Field, CA).
processing. Following the swab assay, the probe inlet was rinsed
with isopropyl alcohol and sprayed dry with sterile air canisters.
Before take-off and after landing, an exterior portion of the C20A directly upstream of the probe was assayed using the swab
method; these samples were Ground controls. The same area was
sampled on consecutive days of flight operations to determine the
bioburden of airborne microorganisms settling onto the C-20A
during fueling, take-off and landing operations.
Each flight day, the C-20A departed from and returned to
AFRC (Palmdale, CA). Free stream atmospheric air collection
began ∼300 m above ground level after take-off by opening the
ABC ball valve and flow meter. Upon reaching 10 km (∼10 min
into each flight), air flow for Ascent samples was stopped and new
filters for Cruise samples were aseptically loaded into the cascade
sampler for collections at the sustained altitude of ∼12 km
which lasted 141–250 min during the flight campaign. Two
types of agarose witness plates (TSA and R2A, Remel, Fremont,
CA) were installed on the workbench and exposed to ambient
aircraft cabin air during flight operations to assess the amount
of contamination inside the cabin air influencing filters during
installation and removal procedures. A particle counter (Model
GT-521, Met One Instruments, Inc., Grants Pass, OR) with
0.3 and 0.5 µm counting channels measured on average 13,400
particles and 300 particles, respectively, in the aircraft cabin air.
Used filters were stored inside sterile Whirl-Paks (Part #B01297,
Nasco, Modesto, CA) and kept inside an insulated cooler at 4◦ C.
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Microbiological Methods
Sample Concentration
Using sterile scissors and forceps, gelatinous filters were halved
inside a Class II Type A Biosafety Cabinet (NU-540-600, Nuaire,
Plymouth, MN). For each sample, one filter half was archived
in a −80◦ C freezer (TSU-600A, Thermo Scientific, Asheville,
NC) and the other half was dissolved in 40 ml of warm (37◦ C)
molecular grade water (H2OMB0124, Millipore, Billerica, MA).
Next, we used a concentrating pipette (CP Select, InnovaPrep,
Drexel, MO) which passed the entire dissolved sample volume
(40 ml) through a 0.1 µm flat membrane polyethersulfone
membrane (part number CC08001), followed by elution with
Tris buffer into a final output volume of 1 ml. Similarly, swabs
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Smith et al.
NASA Aircraft Bioaerosol Collector (ABC)
contained in 15 ml tubes (wetted with 5 ml of sterile water)
from Ground and Hardware control samples obtained from the
window plate, probe, and side of the aircraft were concentrated in
1 ml of Tris. For each concentrated volume, 800 µl of was used for
DNA extraction and subsequent 16S V4 sequencing; 100 µl was
used for culture-based recovery assays; and 100 µl was archived
at −80◦ C in the freezer.
Hochberg, 1995; Anderson, 2001; Oksanen et al., 2010;
McDonald et al., 2012; Edgar, 2013; McMurdie and Holmes,
2013, 2014; Love et al., 2014). Briefly, a custom software package
pre-processed, summarized, and normalized data followed
by calculations of alpha diversity metrics (within sample
diversity), beta diversity metrics (sample-to-sample similarity),
ordination/clustering, sample classification, and significance
testing. Representative Operation Taxonomic Unit (OTU)
sequences were assigned using mothur’s bayesian classifier,
with clusters referenced at 99% alignment to the Greengenes2
database of 16S gene sequences. For additional information on
reproducing the data pipeline we refer readers to Mohan et al.
(2016), Alhasson et al. (2017), and Reveles et al. (2017). Access to
raw sequencing data can be downloaded in the supporting files
associated with this project archived at GeneLab3 using accession
GLDS-170. To account for possible contaminants associated with
the experimental design, filtered taxonomic tables were generated
after removing a subset of OTUs identified from extractionnegative and no-template PCR control samples. Both raw and
filtered OTU tables are available in the GLDS-170 project folder.
Recovery of Viable Isolates and Identification
Concentrated 100 µl aliquots were evenly spread onto R2A
to encourage the recovery of viable heterotrophic bacteria.
All samples were wrapped with Parafilm (American National
Can, Chicago, IL) and placed in a dark incubator (SHKE6000,
MaxQ 6000, Thermo Scientific, Manetta, OH) at 25◦ C for 2
weeks while monitoring for signs of growth. Table 1 summarizes
the number of colony forming units (CFUs) for each sample.
Individual colonies were sub-cultured on R2A until isolated
and cryopreserved with 10% sterile glycerol (Amresco, Solon,
OH) and nutrient broth (Difco, Sparks, MD) at −80◦ C.
Deoxyribonucleic acid (DNA) extraction was performed on
each isolate, followed by the polymerase chain reaction (PCR)
amplification of 16S ribosomal ribonucleic acid (rRNA). A 466
bp 16S universal primer set from Nadkarni et al. (2002) [forward
primer: 5′ -TCCTACGGGAGGCAGCAGT-3′ (Tm , 59.4◦ C);
reverse primer: 5′ -GGACTACCAGGGTATCTAATCCTGTT-3′
(Tm , 58.1◦ C)] was used to generate bacterial amplicons. Sequence
data from GENEWIZ (South Planfield, NJ) were then mapped to
the most probable taxonomic affiliation of the bacteria using the
Basic Local Alignment Search Tool (BLAST)1 .
Environmental Data and Atmospheric
Modeling
We collected GPS position, altitude, wind speed, aircraft
speed, and air temperature data from the C-20A during flight
operations. To understand the transport history of the air masses
sampled, we calculated 2-day kinematic back-trajectories over
the flight paths using the Hybrid Single-Particle Lagrangian
Integrated Trajectory (HYSPLIT) model (Stein et al., 2015)
which uses global meteorological data from the Global Data
Assimilation System archive. Trajectories were run at 3 heights
(300, 5,000, and 12,000 m above ground level). In order to gain
1 km-scale resolution of the vertical distribution of aerosols
and components (e.g., SO4 , organic carbon, black carbon, dust,
sea salt), we retrieved satellite data from MODIS and produced
aerosol optical depth (AOD) maps at 470 nm based on the
MAIAC retrieval algorithm (Lyapustin et al., 2011). These
resulted in a 3-D model of aerosol distribution along flight
lines (matching latitude, longitude, and altitude) using MERRA-2
methods (Gelaro et al., 2017).
16S V4 Sequencing
DNA from each 800 µl concentrated sample aliquot was
extracted using an AllPrep PowerViral DNA/RNA kit (product
# 28000-50, Qiagen) then quantified by the Quant-iT PicoGreen
dsDNA Assay Kit (Invitrogen, Life Technologies, Grand Island,
NY). PCR amplification of the V4 hypervariable region of the
bacterial 16S rDNA gene enriched samples prior to MiSeq
(Illumina, San Diego, CA) sequencing. Post-amplification yields
averaged 12.2 ng·µl−1 across samples with the upper limit of
the reaction at 20.99 ng·µl−1 based on the ladders utilized. For
library construction and normalization, we used two differently
bar coded V4 fusion primers designed against the surrounding
conserved regions tailed with sequences to incorporate Illumina
adapters and indexing barcodes. Post amplification, each sample
was quantified by fluorometric methods (Qubit or PicoGreen
from Invitrogen, Life Technologies, Grand Island, NY) before
sequencing. Every amplicon (containing 16S V4 enriched,
amplified, barcoded samples) was loaded into a single MiSeq
cartridge and flow cell for paired-end sequencing runs.
After sequencing, a bioinformatics analysis by Second
Genome Inc. (South San Francisco, CA) filtered and trimmed
all reads, followed by mapping to taxonomic databases for
identifying bacterial composition. Detailed explanations for
statistical methods, including quality control and assurance
techniques, have been described elsewhere (Benjamini and
RESULTS
Flight Data and Atmospheric Modeling
Pertinent flight data are summarized in Table 2. The modeled
air mass back trajectories and aerosol distribution suggest
our flights passed through air with similar transport histories
and bulk aerosol composition. Figure 3 depicts the HYSPLIT
model based on GDAS meteorological data at heights and
positions within the range of C-20A flight lines. Generally,
the 48-h back trajectories resemble each other across the
modeled heights, showing air masses traveling eastward off
the Pacific Ocean into the US. Mostly uniform aerosol
mass loading was observed across the flight lines (Figure 4)
2 http://greengenes.secondgenome.com/downloads
3 https://genelab.nasa.gov/
1 https://blast.ncbi.nlm.nih.gov/Blast.cgi.
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TABLE 2 | Environmental and positional data averaged across flights.
Flight
Time (UTC)
Air Temp (◦ C)
Altitude (km)
Air Speed (m·s−1 )
Wind Speed (m·s−1 )
Wind Direction (◦ )
Eel River
17:16 to 21:07
−26.7
12.15
231.3
17.3
308.9
CSAF
16:48 to 22:10
−28.9
12.17
225.4
13.2
127.2
Slumgullion
16:14 to 21:18
−32.4
12.17
226.0
16.6
287.1
LA Basin
17:34 to 23:09
−27.6
12.06
226.3
22.8
236.9
FIGURE 3 | HYSPLIT kinematic back trajectories model air transport history for flights 30 Oct to 2 Nov 2017.
with Aerosol Optical Depth (AOD) measurements at 470 nm
using the MAIAC retrieval algorithm derived from combined
satellite datasets (MODIS/Terra and MODIS/Aqua). Satellite
observations of modeled aerosol concentrations at ∼12 km
were higher during the Eel River (30 Oct 2017) and CSAF
(31 Oct 2017) flights and lower on Slumgullion (1 Nov
2017) and LA Basin (2 Nov 2017) flights, consisting primarily
of SO4 , organic carbon, dust, black carbon, and sea salt
(Figure 5). Satellite data also showed smoke influence near
ground levels along the CSAF flight track. MERRA-2 aerosol
simulations (Figures 6, 7) were produced using C-20A flight
positions (latitude, longitude, and altitude) and were consistent
with the MAIAC derived AOD distribution. MERRA-2 results
show aerosol concentrations decreasing sharply with altitude
(i.e., highest concentrations were in the lower atmosphere at
Ascent/Descent). The concentration of aerosols was low (<0.5
µg·m−3 ) most of the flight time at Cruise altitudes of ∼12 km.
However, one exception at 12 km was the CSAF flight (31
Oct 2017) when the aerosol concentration briefly reached up
to 10 µg·m−3 .
in-flight collections, including Bacillus sp., Micrococcus sp.,
Arthrobacter sp., and Staphylococcus sp. from Cruise samples
and Brachybacterium sp. from Ascent/Descent samples.
However, none of the flight isolates were identifiable to the
species level with Sanger sequencing of the 16S rRNA gene
because of the relatively short region (∼466 bp) covered by
the assay.
Witness plates were used during each flight to monitor
bacteria from C-20A cabin air settling onto the surface of the
workbench. Although the ABC cascade sampler housed DNAtreated filters were sealed off from the cabin air during collection
periods, filter change-out procedures exposed samples to cabin
air for several minutes. Blank filters loaded did not yield any
CFUs, however witness plates exposed to cabin air during the
entire duration of flights (∼250 min on average) resulted in a
substantial amount of culture-based growth. The mean number
of bacterial and fungal CFUs collected on witness plates was 0.663
CFU·cm−2 .
Culture-Based Results
Altogether, 2,557 OTUs were observed from 3,911,495 sequences.
Of the OTUs, 98.86% of sequences were classified at the
family level; 95.11% at genus; 2.837% at species; and 2.38% at
strain. Prevalence filtering was applied to remove any spurious
OTUs that were observed in less than 5% of the samples.
After removal of the spurious OTUs, the number of total
OTUs was reduced from 2,557 to 1,181 and the number
of total sequences dropped from 3,911,495 to 3,886,523. The
number of reads in the filtered library for Ascent/Descent
group ranged from 2,470 to 160,341 (N = 97,689); Cruise
Culture-Independent Results: 16S V4
Sequencing
Most of the culturable bacteria collected in the study came
from (Ground) swabs of the exterior portion of the C20A before and after flights. Bacterial isolates included
numerous Bacillus carboniphilus, B. crescens, B. fumarioli,
B. megaterium, B. aryabhattai, B. humi, and B. timonesis, as
well as Gordonia paraffinivorans, Bhargavaea ginseng, and
Streptomyces sp. Bacillus timonesis was also obtained from a
post-flight swab of the window plate where the ABC probe
was mounted. Several bacterial isolates were recovered from
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FIGURE 4 | Aerosol Optical Depth (AOD) measurements at 470 nm based on the MAIAC retrieval algorithm derived from combined satellite datasets (MODIS/Terra
and MODIS/Aqua) shows vertically integrated aerosols over relevant flight lines.
Betaproteobacteria, Clostridia, Actinobacteria, Erysipelotrichi,
and Alphaproteobacteria. The eight most abundant families
detected from all samples were Staphylococcaceae, Moraxellaceae,
Oxalobacteraceae,
Lachnospiraceae,
Ruminococcaceae,
Comamonadaceae, Geodermatophilaceae, and Burkholderiaceae.
Figure 8 depicts the richness of samples by showing the
number of unique OTUs and Shannon diversity indices across
each category based on abundance data. Means for OTU richness
were higher for Ground (449; sd = 94.5) and Hardware (407;
sd = 56) samples than flight samples from Ascent/Descent
(276; sd = 96.3) and Cruise (305; sd = 107) altitudes. Table 4
summarizes the significant alpha diversity differences between
groups using an unpaired Kruskal-Wallis test. There were
significant differences between flight groups (Ascent/Descent
and Cruise) compared to Ground and Hardware groups. Similar
to the OTU richness results, Shannon diversity indices were
group ranged from 12,693 to 707,558 (N = 188,541); Ground
group ranged from 98,459 to 234,313 (N = 136,160); and
Hardware group ranged from 108,177 to 147,995 (N = 133,094).
After filtering, the number of unique categories at each
taxonomic rank was 879 species, 609 genera, and 268
families.
Table 3 summarizes the eight most abundant phyla detected
in the study (Firmicutes, Proteobacteria, Actinobacteria,
Bacteroidetes, Cyanobacteria, an Unclassified phylum,
Euryarchaeota,
and
Fusobacteria).
Firmicutes
and
Proteobacteria, and to a lesser extent Actinobacteria,
represent the majority of samples. Hardware samples had
a slightly higher proportion of Proteobacteria and a lower
abundance of Firmicutes compared to Ascent/Decent, Cruise,
and Ground samples. The seven most abundant classes
across all categories were Bacilli, Gammaproteobacteria,
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also higher for Ground (1.53; sd = 0.523) and Hardware
(1.93; sd = 0.421) samples compared to flight samples from
Ascent/Descent (1.38; sd = 0.296) and Cruise (1.26; sd = 0.446).
To visualize the relationship between sample groups
(Cruise, Ascent/Descent, Ground, Hardware) and ascertain
sample-to-sample dissimilarity based on whole microbiome
abundance profiles, we used a Principal Coordinate Analysis
(PCoA). While the weighted ordination in Figure 9 (based
on relative abundance) did not show clustering or any
significant differences by sample group with a PERMANOVA
analysis using Bray-Curtis dissimilarity (p-value = 0.081), an
unweighted ordination in Figure 10 (based on presence or
absence) depicted non-significant but noticeable differences
across groups using Jaccard distance (p-value = 0.097).
Specifically, the Ground and Hardware samples clustered
separately from the Ascent/Descent and Cruise samples. One
possible explanation for the perceived separation could be the
relative enrichment in OTUs from phylum Actinobacteria in
Ground samples, compared to the enrichment in OTUs from
phylum Firmicutes in Ascent/Descent and Cruise samples.
Ten strains identified as significantly enriched (adjusted pvalue < 0.05) in Ground samples compared to flight samples
potentially contributed to the separation between groups:
Blastococcus sp. BC412; Georgenia sp. JC82; Modestobacter
multiseptatus;
Modestobacter
versicolor;
Modestobacter
marinus; Clostridium sordellii; Ornithinimicrobium kibberense;
FIGURE 5 | MERRA-2 data summarizing SO4 , organic carbon, dust, black
carbon, and sea salt aerosol concentrations averaged across the cruise
altitude of ∼12 km for each flight.
FIGURE 6 | MERRA-2 results depicting bulk aerosol concentration (µg·m−3 ) for Eel River (Left) and CSAF (Right) flights; top panel shows horizontal cross section
and bottom panel shows vertical cross section.
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FIGURE 7 | MERRA-2 results depicting bulk aerosol concentration (µg·m−3 ) for Slumgullion (Left) and LA Basin (Right) flights; top panel shows horizontal cross
section and bottom panel shows vertical cross section.
In order to determine if the differing daily flight paths
contributed to microbiome diversity, we performed a
PERMANOVA analysis for testing significant differences
between each set of flight samples. Despite significant differences
based on PERMANOVA (p-value = 0.013), a weighted
ordination plot in Figure 12 using abundance data showed
that flight samples did not cluster according to flight location.
Airborne bacteria collected over Eel River, CSAF, Slumgullion,
and LA Basin were more similar than different. An unweighted
ordination shown in Figure 13 using presence/absence data also
showed no clear clustering by flight.
Finally, we analyzed samples on each of the two cascade
sampler stages to ascertain if bacteria collected in the ABC
were size sorted. The first stage of the sampler had 1.18 mm
drilled orifices, the second stage had 0.25 mm drilled orifices.
According to manufacturer specifications4 , at ground-normal
STP conditions using a flow rate of 28.3 L·min−1 , the first stage
would select for particles 5.8 to 9 µm and the second stage 0.7 to
1.1 µm. Figure 14 summarizes the OTU richness and Shannon
Yaniella sp. G5; Nocardioides sp. MSL 22; and Blastococcus
jejuensis.
Because the ordination data showed Ascent/Descent
samples and Cruise samples were more similar to each
other than different, Figure 11 was generated to highlight
which OTUs stood out from Ground samples. A total of
28 OTUs were found to be differentially abundant between
Ground samples and Ascent/Descent samples. Notably, OTUs
from the families Lachnospiraceae and Erysipelotrichaceae
and the genera Clostridium, Mogibacterium, Bacteroides,
Prevotella, and Parabacteroides were more abundant in the
Ascent/Descent samples than the Ground samples. When
comparing differentially abundant OTUs in Ground samples
against Cruise samples, a total of 25 OTUs were identified.
The taxa more abundant in Cruise samples included OTUs
from the families Erysipelotrichaceae and Ruminococcaceae
and the following genera: Clostridium, Mogibacterium,
Corynebacterium, and Prevotella. Pseudomonas stutzeri
was the only OTU identified at the species level that was
significantly more abundant in Cruise samples than Ground
samples.
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TABLE 3 | Mean and standard deviation (sd) values for the percent relative abundances of selected taxa at the Phylum level.
Phylum
Ascent/Descent
Cruise
Ground
Hardware
Firmicutes
80.6 (5.99)
78.1 (13)
76.1 (10.9)
65.1 (12.6)
Proteobacteria
16.7 (6.77)
20.1 (12.5)
19 (7.45)
29.7 (10.2)
Actinobacteria
0.887 (0.292)
0.798 (0.391)
3.67 (3.05)
3.92 (2.49)
Bacteroidetes
1.27 (1.5)
0.452 (0.133)
0.542 (0.169)
0.574 (0.0795)
Cyanobacteria
0.176 (0.164)
0.189 (0.202)
0.231 (0.258)
0.18 (0.165)
Unclassified
0.0751 (0.0931)
0.0691 (0.054)
0.0986 (0.0955)
0.0639 (0.0288)
Euryarchaeota
0.0757 (0.0837)
0.0865 (0.104)
0.0671 (0.0597)
0.0358 (0.0201)
Fusobacteria
0.111 (0.237)
0.0493 (0.0875)
0.0322 (0.0202)
0.0431 (0.0185)
Others
0.156 (0.139)
0.106 (0.0969)
0.308 (0.249)
0.394 (0.264)
FIGURE 8 | Alpha diversity estimates. (Left) OTU richness across sample groups. (Right) Shannon Diversity Index based on richness and evenness of OTUs within a
sample.
second stage across all flights, the mean OTU richness was
276 (sd = 140) and 288 (sd = 57.1) for Ascent/Descent
and Cruise groups, respectively. Without additional species-level
identifications and microscopy data, we could not determine the
size of bacterial cells, spores, and fragments contributing to OTUs
measured.
TABLE 4 | Alpha diversity differences across groups using an unpaired
Kruskal-Wallis test for significance.
Comparison
Z-value
p-value
Ascent/Descent vs. Cruise
−0.043
0.6704
Ascent/Descent vs. Ground
−3.05
0.0023
Cruise vs. Ground
−2.63
0.0086
Ascent/Descent vs. Hardware
−2.22
0.0263
Cruise vs. Hardware
−1.87
0.061
Ground vs. Hardware
−0.027
0.7849
DISCUSSION
Our results showed no clear differences in the richness or
diversity of airborne bacteria collected at lower altitudes 0.3 to
12 km (Ascent/Descent samples) compared to higher altitudes
∼12.1 km (Cruise samples). Since the bacteria from these
regions were more similar than different, our initial hypothesis
of the tropopause serving as a naturally occurring altitude
boundary for bioaerosols was not supported. Instead, our data
Diversity between the two sampler stages. For samples collected
on the first stage across all flights, the mean OTU richness
was 275 (sd = 44.9) and 322 (sd = 150) for Ascent/Descent
and Cruise groups, respectively. For samples collected on the
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FIGURE 9 | Weighted ordination based on relative abundance showed no clustering by sample group. The first two ordination axes accounted for 77.5% of sample
variation.
FIGURE 10 | Unweighted ordination based on presence/absence showed non-significant clustering in sample groups. Flight samples (Cruise and Ascent/Descent)
separated from the Ground and Hardware samples. The first two ordination axes accounted for 23.2% of sample variation.
OTUs measured across the troposphere and lower stratosphere.
While our new ABC system showed its ability to collect
bioaerosols at extreme altitudes, the influence of contaminants
from hardware remained apparent—thus, inherent limitations
of our dataset and areas for improvements will be discussed
later.
suggested that bacteria in the atmosphere up to 12 km were
homogenously distributed. However, we had no way of testing
whether a subset of bioaerosols from lower altitudes (i.e.,
Ascent/Descent samples) remained in the ABC inlet lines prior
to the initiation of higher altitude collections. Such “carry-over”
would be an alternative explanation for the even distribution of
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FIGURE 11 | Points on the left depict OTUs in Ascent/Descent and Cruise samples that were more abundant compared to Ground samples; points on the right
depict OTUs enriched in Ground samples. Features were considered significant if FDR-corrected p-values were < 0.05 and the absolute value of the log-2 fold
change was ≥ 1.
FIGURE 12 | Weighted ordination plot based on OTU abundance showing that samples did not cluster by flight. The first two ordination axes accounted for 77.5% of
sample variation.
most abundant in our study—Firmicutes, Proteobacteria,
Actinobacteria, Bacteroidetes, Cyanobacteria, Euryarchaeota
and Fusobacteria—are also reported widely across aerobiology
Although in relatively low abundance, the diversity of
bacteria in the atmosphere seems to mirror taxa found in
numerous terrestrial and aquatic ecosystems. The phyla
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FIGURE 13 | Unweighted ordination plot based on OTU presence/absence showing that samples did not cluster by flight. The first two ordination axes accounted for
23.2% of sample variation.
FIGURE 14 | Relative abundance and diversity of flight samples (Ascent/Descent and Cruise) collected on the two internal stages of the ABC cascade sampler.
soils and desert dust transport embedded microorganisms
into the atmosphere (Griffin et al., 2017). In addition to
Staphylococcus sp., many of the atmosphere sampled OTUs from
the Lachnospiraceae and Ruminococcaceae families are associated
with fecal matter from agriculture fields, livestock feedlots,
and human wastewater. Recent work by Bowers et al. (2013)
also measured these taxa in high abundance throughout the
atmospheric boundary layer. Many of the phyla from our flight
samples, including Alpha- Beta- and Gammaproteobacteria,
Firmicutes, and Bacteroidetes, are commonly found in air
samples adjacent to coastal regions (Urbano et al., 2011). With
literature, as reviewed by Amato et al. (2017a). Firmicutes (gram
positive), Proteobacteria (gram negative), and to a lesser extent
Actinobacteria (gram positive), represented the majority of
OTUs and culture-based isolates detected in our survey. By
far, the most common phyla was Firmicutes, with Bacilli and
Clostridia classes in significant abundance, followed distantly
by Erysipelotrichi. Firmicutes were also the predominant phyla
in another upper atmosphere microbiology study conducted at
an alpine observatory in central Oregon collecting transpacific
bioaerosols (Smith et al., 2013). The relative abundance of
Bacillus sp. and Staphylococcus sp. was expected since desiccated
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culture-based recoveries and subsequent Sanger sequencing
with 16S. Encouragingly, our ABC system demonstrated that
it can collect enough biomass in the stratosphere for DNA
extraction and library preparation; and for the first time, cultureindependent methods can be used to provide species-level
resolution for bioaerosols above the Earth’s troposphere.
We undertook extensive sampling of the C-20A aircraft
itself to ensure a greater degree of confidence about in situ
measurements. Based on 16S V4 sequencing data, our ground
samples were clearly enriched in Blastococcus sp. BC412;
Georgenia sp. JC82; Modestobacter multiseptatus; Modestobacter
versicolor; Modestobacter marinus; Clostridium sordellii;
Ornithinimicrobium kibberense; Yaniella sp. G5; Nocardioides
sp. MSL 22; and Blastococcus jejuensis. These results identified
the bacterial species abundant on the outside of the aircraft
and, thus, potential influences on flight samples if free stream
air models for our ABC probe’s position were inaccurate. Many
of the cultured isolates from ground samples were soil- and
plant-associated Bacillus sp., including B. carboniphilus, B.
crescens, B. fumarioli, B. megaterium, B. aryabhattai, B. humi,
and B. timonesis. Other common soil bacteria Bhargavaea
ginseng and Streptomyces sp. were detected on exterior portions
of the aircraft, as well as Gordonia paraffinivorans, elsewhere
associated with oil field environments (Xue et al., 2003). We
did not determine species or strain level identities of Sanger
sequenced isolates because of the short 466 bp region measured.
Wider sequencing depth may have helped determine whether
bacteria from ground samples were deposited during C-20A taxi
time, take-off, landing, or maintenance and fueling operations.
Significant changes between pre- and post-flight ground samples
assayed over the same surfaces suggest that all aircraft probably
shed microorganisms into the atmosphere and inherit new
biomass as well (Pfaender and Swatek, 1970). But airplane traffic
is probably a minor contributor to bioaerosols injected into the
global atmosphere compared to more prevalent natural and
human emission sources (Wéry et al., 2017).
We expected significant differences in the relative abundance
and richness of bioaerosols owed to our study’s wide spatial
coverage across the US. Yet, OTUs acquired from spatially
diverse flights seemed strikingly similar. The lack of regional
patterns contradicts another continental scale study by Barberán
et al. (2015) that acquired bioaerosols from dust samples at
ground locations across the US. Because our study collected
samples at higher altitudes, more mixing would be expected for
longer-lived aerosol species. Other explanations for differences
reported across the two studies might include seasonal influences,
nearby emission sources and meteorological conditions. Longerlasting studies using the C-20A would help reveal if bioaerosol
concentrations shift during spring/summer months due to drier
surface biomass getting aerosolized and stronger atmospheric
convection patterns (Amato et al., 2017a; Wéry et al., 2017).
The incorporation of satellite data in our study offered
supporting evidence for the homogenous distribution of bacterial
taxa across separate flights. Both the MAIAC and MERRA2 aerosol models showed similarities in the bulk type and
abundance of atmospheric aerosols. One noteworthy exception
was the CSAF flight that may have captured fire-related smoke
most of our flights occurring over the state of California, the
prevailing wind direction (traveling eastward) might have
carried marine bioaerosols from the Pacific Ocean. Indeed, sea
salt was present in the aerosol composition modeled at ∼12 km
for all flight days. Moraxellaceae was the most abundant family
from Gammaproteobacteria measured in our study samples;
Oxalobacteraceae, Comamonadaceae, and Burkholderiaceae
were abundant families in the Betaproteobacteria class. While
their overall abundance was very low in our study (< 1%),
the detection of Cyanobacteria (phototrophic), Euryarchaeota
(methanogenic) and Fusobacteria (potentially pathogenic),
remains noteworthy considering the wide range of phenotypes
and environmental impacts of these phyla.
Clostridium,
Mogibacterium,
Bacteroides,
Prevotella,
Parabacteroides, and Corynebacterium were significantly
more abundant in flight samples (Ascent/Descent and Cruise)
than in ground samples. The genus Clostridium is known for
endospore forming bacteria, an adaptation known to enhance
the resistance to biocidal factors in the atmosphere such as
ultraviolet light, desiccation, and freeze/thaw cycles. Another
endospore-forming genus measured in our culture-based
isolates was Bacillus. However, interestingly, it was not among
the most abundant genera measured with 16S V4 sequencing
perhaps due to the inefficiency of lysing bacterial endospores
with commercial DNA extraction kits. Besides Clostridium,
genera identified as more abundant in flight samples (based on
culture-independent sequencing) were non-spore forming OTUs
from Mogibacterium, Bacteroides, Prevotella, Parabacteroides,
and Corynebacterium. Mogibacterium and Corynebacterium
are Gram-positive bacteria while Bacteroides, Prevotella, and
Parabacteroides are Gram-negative bacteria; these genera are
widely distributed in nature and commonly found in animal
gut and oral microbiomes. Based on the prevalence of these
genera, wastewater treatment facilities and agriculture producing
aerosolized fecal matter could be possible sources of bacteria
captured at high altitudes in this study; but establishing
connections to specific emission sources was not achievable
within our experimental framework.
Altogether, our study’s detection of airborne bacteria in
the lower stratosphere supports other reported evidence of
microorganisms present at extreme altitudes (Griffin, 2004;
Smith et al., 2009). In addition to Bacillus sp., other viable
isolates recovered from lower stratosphere samples included
Micrococcus sp., Arthrobacter sp., and Staphylococcus sp. The
only other upper atmosphere microbiology studies previously
reporting Micrococcus sp. were from Imshenetsky et al. (1978)
and Wainwright et al. (2003) who used a sounding rocket
and balloon for collections, respectively. Arthrobacter sp. and
Staphylococcus sp. have not appeared in upper atmosphere
aerobiology literature, to our knowledge. Independent of
culturing, we were able to identify Pseudomonas stutzeri as
a significantly abundant OTU in Cruise samples. This was
significant for two reasons: first, P. stutzeri has not appeared in
other stratosphere microbiology studies; and second, no specieslevel identification of bacteria acquired from the stratosphere has
been reported using culture-independent methods. To date, all
stratosphere species identifications have been made using limited
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also consider standardizing primer sets and pursuing shotgun
metagenomics assays in order to reduce bias associated with 16S
reference databases which still contain many unclassified taxa.
Our 16S V4 primers were based on the Earth Microbiome Project
(Apprill et al., 2015; Parada et al., 2015; Walters et al., 2016) aimed
at maximally inclusive coverage, but marine bacteria would still
be under-detected due to repository biases.
By introducing the ABC, our hope is that other investigators
can utilize the system for future aircraft studies or reproduce its
basic components. All parts were easily manufactured or took
advantage of relatively low cost commercially available products
that can be obtained and installed on aircraft by follow-on
investigator teams. For instance, the gelatinous filter we used
was DNA-treated with gamma irradiation; often, field teams use
sterile filters but such products still have a substantial DNA signal.
Studies using other types of filters typically include aggressive
methods to dislodge biomass (Smith et al., 2013), like vortexing
or bead beating, introducing bias or damaging biomolecules
(DeSantis et al., 2005). Another advantage of the gelatinous filter
was that it could be dissolved in buffer, making the recovery
of embedded bacteria 100% efficient for both culture-based and
molecular methods. Because our flights were in arid regions
and most of the sampling time was in the stratosphere with
very low relative humidity, premature filter dissolving was not
an issue for our system. In future years, we will pursue several
modifications to the ABC system for improving bioaerosol
capture and reducing system contaminants: (1) manufacturing
a probe with a larger diameter to increase air flow rates above
8.5 l·min−1 ; (2) installing a valve at the probe opening to stop
air from entering the inlet lines during take-off and landing; (3)
building a glovebox on the C-20A workbench to accommodate
the cascade sampler and prevent the cabin air from settling onto
system surfaces; (4) creating a new filter membrane support grid
from aluminum (not plastic) for easier pre-flight sterilization;
and (5) recording system air volumes processed with a digitallyrecorded flow meter. If combined with post-flight quantitative
assays (e.g., cell counts though fluorescent microscopy or DNA
abundance estimates with qPCR reactions), more precise air flow
measurements would help establish global models of bioaerosol
concentration, vertical distribution and atmospheric residence
time.
It will always be difficult to determine bioaerosol origins
given the tapestry of known emission sources and potential
for long range atmospheric dispersal. A useful regional scale
study might have sampling stations proximally-positioned to
major aerosolization sources (e.g., wastewater treatment facilities,
livestock feedlots). Downwind areas could then be sampled
extensively with aircraft sweeps at multiple heights to make
better correlations across time and space. Huffman and Santarpia
(2017) discuss the importance of including “online” instruments
on aircraft used for future aerobiology missions. Particle counters
or LIDARs would be valuable for making real-time decisions in
the air about where to sample and also for establishing stronger
correlations between spectroscopic/fluorescent measurements
and microorganisms identified later in the laboratory. Ultimately,
studies should strive toward more routine and widespread
flux measurements, analogous to other globally-tracked gaseous
plumes. The concentration of solvable SO4 was also higher on
the CSAF flight compared to other days, suggesting the air
mass sampled was drier. Precipitation is known to be associated
with bioaerosol deposition (Deguillaume et al., 2008), so the
relationship between regional smoke plumes, SO4 , water vapor
and the presence of bioaerosols warrants future investigation.
Even with statistical testing for significance and the removal
of suspected contaminants, deciphering between taxa collected
in situ and taxa associated with the aircraft/hardware or
sample processing was challenging. Generally, flight samples
yielded more reads and unique OTUs than measured in
experimental controls, but filter blanks seated into the
sampler inside the aircraft still resulted in numerous reads,
including some totals that were higher than flight samples. For
instance, Staphylococcaceae were commonly detected (especially
dnOTU1), a bacterial family associated with human skin.
Considering all sample filters were loaded into the cascade
sampler inside the C-20A cabin, exposure to circulating air
would be a likely source of background OTUs detected in our
dataset. DNA-treated filters with small fragments of irradiated
DNA and 16S sequencing reagents might also have contributed
to a background signal. However, the primer set used for library
preparation should not have amplified highly degraded DNA
fragments. Tests of pristine filters and sample reagents resulted
in no amplification of DNA using 16S qPCR reactions based on a
standard curve derived from Acinetobacter baumannii. Thus, the
source of baseline reads from negative controls were probably
due to the influence of cabin air inside the C-20A. To address
this baseline signal, we discarded OTUs from negative controls
that had a higher mean in negative controls than in samples and
yielded a mean relative abundance in negative controls > 1%.
Our pitot-style probe method of bioaerosol collection surely
affected the type, size and number of bacteria captured in
flight. For instance, biomass > 4 µm likely could not enter
the probe. Some bioaerosols could have been lost inside the
probe and inlet lines, too, though by mounting the hardware to
the C-20A window plate we reduced the distance air traveled
through the system. Overall, there remains a general need for
standardized sampling methods in aerobiology because disparate
collection techniques introduce variation and make horizontal
comparisons difficult (Griffin et al., 2010; Amato et al., 2017b).
Even an imperfect sampler used repeatedly by investigators
sharing downstream methods could offer important insight
into currently unknown aerobiology patterns. Historically,
aerobiology studies using aircraft have relied upon culturebased recovery methods (with a bias toward heterotrophic plate
counts) that underestimate the true quantity and diversity of
bacteria in the upper atmosphere–this approach cannot identify
slow-growing, unculturable, or inactivated bacteria, including
fragmented cell components. Molecular methods address the
limitation but do not readily distinguish between living and dead
bacteria. Thus, a substantial portion of microbes detected by
DNA sequencing might exist only as debris. In the spaceflight
research community, a propidium monoazide (PMA) sequencing
pre-treatment has been used as a viability marker to differentiate
microbial sequences from dead cells vs. intact cells (Checinska
et al., 2015). Future studies utilizing molecular methods might
Frontiers in Microbiology | www.frontiersin.org
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August 2018 | Volume 9 | Article 1752
Smith et al.
NASA Aircraft Bioaerosol Collector (ABC)
engineering for the ABC design, instrument manufacturing and
C-20A support. KI, LC, JT, and CJ reviewed the analysis, helped
with data interpretation, and provided technical support. All
authors contributed to reviewing, editing, and finalizing the
manuscript.
aerosol species (e.g., carbon dioxide, water vapor, ozone, nitrogen
compounds) (Wéry et al., 2017).
Worldwide, bioaerosols are thought to represent 5–50% of
atmospheric particles > 0.2 µm in diameter (Jaenicke, 2005;
Després et al., 2012) and yet our current understanding of
emission sources, transport history, and vertical distribution
is quite rudimentary. Considering airborne biomass can travel
thousands of kilometers over days if not weeks (Smith et al.,
2013), atmospheric disease corridors likely exist (Prospero,
1999; Prospero et al., 2005), biodiversity impacts occur with
microbes landing in new environments (Morris and Sands,
2017), and cloud chemistry and precipitation rates can be
altered by cells (Delort et al., 2017; Hill et al., 2017), more
coordinated, international research campaigns using aircraft in
the troposphere and stratosphere should fly in future years.
While addressing basic research questions about the dynamics of
airborne microbes in the Earth’s upper atmosphere, we can also
gain knowledge about how to reliably collect and characterize
trace levels of biomass present in extreme environments—such
techniques will be relevant to astrobiology and the search for life
on other worlds.
ACKNOWLEDGMENTS
Our study was funded by the NASA Office of the Chief
Scientist (FY17 Science Innovation Fund award), a FY17/18
Biodiversity grant from the NASA Earth Science Division,
internal awards from NASA Ames Research Center and
Lawrence Livermore National Laboratory (LLNL) in FY18,
and the U.S. Geological Survey’s Environmental Health Toxic
Substances Hydrology and Contaminate Biology Programs. C20A ride along flights were generously provided by the Airborne
Science Program at NASA Armstrong Flight Research Center
and members of the UAVSAR science team from the Jet
Propulsion Laboratory. We thank J. Green and H. Wilson at
the University of Oregon, W. K. Kan at NASA Ames, and
J. Payne from InnovaPrep LLC for loaning equipment and
instruments to our team. Logistical support was invaluably
provided by S. Bhattacharya (NASA Ames) and S. Parker (Wyle
Labs). S. Palacios from the Bay Area Environmental Research
Institute, L. Rothschild at NASA Ames, A. Schuerger from the
University of Florida, and M. Dillon at LLNL all provided
feedback during the project planning phase and we are grateful
for their time and insight. Finally, we acknowledge several
open source products and data used in our study, including
HYSPLIT from NOAA, BLAST from NIH, and MODIS/MAIAC
from NASA.
AUTHOR CONTRIBUTIONS
DS led the study, designed the experiment and wrote the first
draft of the manuscript. JDR and SJ performed the statistical
analyses and compiled results from sequencing data. DG, PN,
and SS did culture-based isolations, nucleic acid extractions
and Sanger sequencing. KS extracted DNA for 16S V4 samples
and conducted sequencing runs. HY and QT generated aerosol
modeling data and analysis. TL, PM, AO, JL, SC, and JM provided
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The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
Copyright © 2018 Smith, Ravichandar, Jain, Griffin, Yu, Tan, Thissen, Lusby,
Nicoll, Shedler, Martinez, Osorio, Lechniak, Choi, Sabino, Iverson, Chan, Jaing and
McGrath. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (CC BY). The use, distribution or reproduction in
other forums is permitted, provided the original author(s) and the copyright owner(s)
are credited and that the original publication in this journal is cited, in accordance
with accepted academic practice. No use, distribution or reproduction is permitted
which does not comply with these terms.
Conflict of Interest Statement: JDR, SJ, KS, KI, and LC are employees of Second
Genome, Inc., a sequencing services company that produced the MiSeq data
for this study. In exchange for paid services, they assisted with experiment
planning and data generation. Reference herein to any specific commercial
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