Urban aerosols harbor diverse and dynamic
Eoin L. Brodie, Todd Z. DeSantis, Jordan P. Moberg Parker, Ingrid X. Zubietta, Yvette M. Piceno, and Gary L. Andersen*
Ecology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
Edited by Steven E. Lindow, University of California, Berkeley, CA, and approved November 7, 2006 (received for review September 20, 2006)
Considering the importance of its potential implications for human
health, agricultural productivity, and ecosystem stability, surpris-
ingly little is known regarding the composition or dynamics of the
atmosphere’s microbial inhabitants. Using a custom high-density
DNA microarray, we detected and monitored bacterial populations
in two U.S. cities over 17 weeks. These urban aerosols contained at
least 1,800 diverse bacterial types, a richness approaching that of
some soil bacterial communities. We also reveal the consistent
presence of bacterial families with pathogenic members including
environmental relatives of select agents of bioterrorism signifi-
cance. Finally, using multivariate regression techniques, we dem-
onstrate that temporal and meteorological influences can be stron-
ger factors than location in shaping the biological composition of
the air we breathe.
16S rRNA ? biosurveillance ? aerobiology ? microarray ? climate change
environment for microbial life. Little is known regarding the
atmospheric microbial composition and how it varies by location
or meteorological conditions. Plant canopies for example, are
known to be significant sources of bacterial aerosols with upward
flux of bacteria positively impacted by temperature and wind
speed (1). Aerosols created at the surface of aquatic systems are
known to concentrate and carry bacteria through the liquid–air
interface (2, 3). The relationship between environmental con-
ditions and bacterial aerial dispersal indicates that climate
change could potentially alter the microbial composition of
downwind areas, resulting in increased health risk from patho-
gens or allergenic components of unclassified environmental
bacteria. For instance, the last decade has seen a dramatic
increase in the amount of desertification and a concomitant
increase in upper atmospheric particulates (4). In sub-Saharan
regions of Africa, dust storms have been associated with regional
outbreaks of meningococcal meningitis caused by the bacterium
Neisseria meningitidis (5). Since the 1970s, El Nino weather
events have coincided with increased flux of African Dust across
the Atlantic (4) that, in turn, has been linked to coral reef disease
(6) and increased exacerbations of pediatric asthma (7) in the
Caribbean. Therefore, as particles from dust storms shield
bacterial and fungal passengers from the inactivating effects of
UV exposure, global transport of dust will have more far-
reaching affects than impaired visibility.
The consequences of natural environmental variation such as
meteorological shifts, combined with anthropogenic influences
such as land use changes, may alter atmospheric microbial
composition. To monitor the effects of climate change on
aerosol microbial composition, it first is necessary to establish
baselines that acknowledge the current microbial components
and how they fluctuate naturally. However, the potential heter-
ogeneity, both spatial and temporal, in species composition
coupled with low microbial biomass ensures this is not a facile
Natural shifts in bacterial composition also have implications
for atmospheric pathogen monitoring systems, such as the
Department of Homeland Security effort to monitor major U.S.
ow levels of moisture and nutrients combined with high levels
of UV radiation make the earth’s atmosphere an extreme
cites for intentional release of biowarfare agents (www.ostp.gov/
such pathogens and other closely related bacteria with undefined
pathogenicity already are endemic to the locations that are being
monitored (8) and so may interfere with detection networks (9),
but little is known regarding the frequency or variability of their
occurrence. Most aerobiology studies to date (e.g., refs. 10–12),
have used culture-based methods for determining microbial
composition. Although some studies recently have applied
culture-independent techniques (e.g., refs. 13 and 14), little is
known of what constitutes the breadth of diversity of ‘‘typical’’
organisms in the atmosphere (as opposed to those capable of
growth in laboratory media) and what influences their compo-
sition. To address these methodological limitations and to
augment our view of aerosol microbial diversity and dynamics,
we have designed a microarray (PhyloChip) for the comprehen-
sive identification of both bacterial and archaeal organisms. We
target the variation in the 16S rRNA gene, possessed by all
prokaryotes, to capture the broad range of microbial diversity
that may be present in the atmosphere. This tool allows bacteria
and archaea to be identified and monitored in any type of sample
without the need for microbial cultivation.
The two greatest obstacles to designing a 16S rRNA gene-
based microarray to identify individual organisms in a complex
environmental mixture are natural sequence diversity and po-
tential cross-hybridization. Sequence diversity is an issue as we
sample new and distinctive environments such as the atmo-
sphere. There may be many undocumented organisms with 16S
rRNA gene sequences that are similar, but not identical, to the
sequences that were used for array design. Microarrays based on
single sequence-specific hybridizations (single probes) may be
ineffective in detecting such environmental sequences with one
or several polymorphisms. To overcome this obstacle, we have
designed a minimum of 11 different, short oligonucleotide
probes for each taxonomic grouping, allowing for the failure of
one or more probes. On the other hand, nonspecific cross-
hybridization is an issue when an abundant 16S rRNA gene
shares sufficient sequence similarity to nontargeted probes, such
that a weak but detectable signal is obtained. We have found that
the perfect match (PM)-mismatch (MM) probe pair approach
effectively minimizes the influence of cross-hybridization.
Widely used on expression arrays as a control for nonspecific
binding (15), the central nucleotide is replaced with any of the
Author contributions: E.L.B. and T.Z.D. contributed equally to this work; E.L.B., T.Z.D., and
G.L.A. designed research; E.L.B., T.Z.D., J.P.M.P., I.X.Z., and Y.M.P. performed research;
E.L.B., T.Z.D., Y.M.P., and G.L.A. analyzed data; and E.L.B., T.Z.D., and G.L.A. wrote the
The authors declare no conflict of interest.
This article is a PNAS direct submission.
Freely available online through the PNAS open access option.
database (accession nos. DQ129237–DQ129666, DQ236245–DQ236250, and DQ515230–
*To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
This article contains supporting information online at www.pnas.org/cgi/content/full/
January 2, 2007 ?
vol. 104 ?
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three nonmatching bases so that the increased hybridization
intensity signal of the PM over the paired MM indicates a
sequence-specific, positive hybridization. By requiring multiple
PM-MM probe pairs to have a positive interaction, we substan-
tially increase the chance that the hybridization signal is due to
a predicted target sequence.
We grouped known 16S rRNA gene sequences ?600 bp into
distinct taxa such that a set of at least 11 probes that were specific
to the taxon could be chosen. The resulting 8,935 taxa (8,741 of
which are represented on the PhyloChip), each containing ?3%
sequence divergence, represented all 121 demarcated bacterial
and archaeal orders [supporting information (SI) Table 2]. For
a majority of the taxa represented on the PhyloChip (5,737,
65%), probes were designed from regions of gene sequences that
have been identified only within a given taxon. For 1,198 taxa
(14%), no probe-level sequence could be identified that was not
shared with other groups of 16S rRNA gene sequences, although
the gene sequence as a whole was distinctive. For these taxo-
nomic groupings, a set of at least 11 probes was designed to a
combination of regions on the 16S rRNA gene that taken
together as a whole did not exist in any other taxa. For the
remaining 1,806 taxa (21%), a set of probes were selected to
minimize the number of putative cross-reactive taxa. Although
more than half of the probes in this group have a hybridization
potential to one outside sequence, this sequence was typically
from a phylogenetically similar taxon. For all three probe set
groupings, the advantage of the hybridization approach is that
multiple taxa can be identified simultaneously by targeting
unique regions or combinations of sequence.
To assess the bacterial composition of environmental aerosols
and how it changes over time and with location, we examined
outdoor air collected at multiple locations in two cities, Austin
and San Antonio, TX. These cities are part of the U.S. mul-
tiagency biosurveillance effort that use aerosol collectors to
concentrate airborne particulate matter in search of pathogens
that potentially could be indicative of a bioterrorism threat. For
either city, aerosol monitors were used to draw in air and pass
it through filters designed to collect submicrometer particulates
for a 24-h period. The samplers were placed immediately adja-
cent to six Environmental Protection Agency air quality moni-
toring stations located throughout the urban area of each city,
and the filter eluents were pooled for each day before amplifi-
cation of the 16S rRNA gene products from the extracted DNA.
Although PCR amplification may introduce some bias in terms
of quantitative assessment of an organism’s abundance due to
factors such as preferential amplification (16, 17), the extremely
low bacterial biomass in aerosol samples necessitate such an
approach. Amplified products from 4 days within a 7-day period
gene sequences detected in an urban aerosol by both microarray and cloning. Also annotated are phyla detected by microarray only that subsequently were
confirmed by targeted PCR and sequencing. The Archaea are used as an outgroup. (Scale bar: 0.1 changes per nucleotide.)
Representative phylogenetic tree showing all known bacterial phyla (and individual classes in the case of proteobacteria) annotated to show 16S rRNA
www.pnas.org?cgi?doi?10.1073?pnas.0608255104Brodie et al.
were pooled into a single sample representing 1 week, and 17
consecutive weekly samples beginning May 2003 were analyzed
from both cities for bacterial composition.
Results and Discussion
PhyloChip results for one sample, representing bacteria recov-
ered from outdoor air at San Antonio from the week of July
14–20 (calendar week 29), 2003, were compared with clone
library sequence results from the same pool of amplified 16S
rRNA gene products (Fig. 1). A conservative comparison of the
PhyloChip and cloning approaches was made at a taxonomic
level below family and above species classification (see SI
Materials and Methods), termed ‘‘subfamily’’ for clarity. This
demonstrated that the PhyloChip correctly detected 90% of
cloned subfamilies (SI Table 3) and additionally detected almost
2.5-fold more diversity at the phylum level (Fig. 1). We subse-
quently have confirmed many of these PhyloChip-only hits
(which include known pathogenic genera), by cluster-specific
PCR and sequencing (Fig. 1 and SI Table 4). The most common
sequences in the air clone library (35%) were Bacilli most similar
to the species Bacillus bataviensis (previously isolated from soil
in a disused hay field) (18) and another Bacillus sp. associated
with biodeterioration of mural paintings (19), suggesting dis-
persal through aerosolization. The diversity of the remaining
clone sequences was quite high, with a majority of the clones
representing distinctive 16S rRNA gene sequences (SI Fig. 4).
Because of the relative dearth of information regarding aero-
sol bacterial diversity, we compared the diversity detected in this
aerosol sample by cloning with that found in a farm soil from a
previous study (20). Soils are considered to be highly diverse
microbial habitats with an estimate of up to 1 million distinct
genomes per gram (21). Rarefaction analysis revealed a similar
level of diversity (at the 16S rRNA gene biomarker level) in the
aerosol and soil samples (Fig. 2). Predicted estimates of richness
(Chao1 and ACE) indicated between 1,500 and 1,800 16S rRNA
phylotypes (by using a 99% identity cutoff) in the aerosol sample
(SI Fig. 5). However, because both ACE and Chao1 richness
prediction curves were nonasymptotic, this is likely to be an
underestimate because of insufficient clone sampling, a common
problem when assessing environmental microbial diversity by
using cloning approaches.
Microbial communities are characteristically dynamic, and it
is expected that aerosol communities are no exception, consid-
ering the turbulent and well mixed nature of the atmosphere.
Using a Latin Square type study containing mixtures of ampli-
cons from diverse bacterial species applied to the PhyloChips in
rotating concentrations (SI Table 5), we tested the ability of the
PhyloChip to track 16S rRNA amplicon dynamics quantitatively.
This demonstrated a strong linear relationship, spanning five
orders of magnitude between PhyloChip intensities and quan-
tities of bacterial 16S rRNA gene signatures applied to Phylo-
Chips (SI Fig. 6). Having determined the potential of the
PhyloChip for detecting changes in biomarker quantities, we
analyzed intensity data for the two cities over the 17-week period
of the study. We also collated a range of meteorological param-
eters to investigate whether local weather conditions could be
correlated with the observed fluctuations in aerosol bacterial
populations. Using multivariate regression tree analysis (22, 23),
we examined such correlations, with tree topology and splitting
parameters suggesting that sample location (in this case two
geographically proximate cities) was less of a factor in explaining
the variability of aerosol bacterial composition than temporal or
meteorological influences (Fig. 3). The week of the year at which
a sample was taken proved to be a stronger predictor of
community composition with samples taken after the first three
weeks in May (weeks 19–21) clustering separately from those
taken before this, regardless of city sampled. Unsurprisingly,
sample week was observed to correlate with weather conditions
(SI Table 6). For both cities, week was positively correlated with
temperature, air pressure, and visibility, whereas negatively
correlated with wind speed and particulate matter. It is impor-
tant to note from the composition of the PhyloChip generated
tree clusters that the clone library ‘‘snapshot’’ taken during week
29 in San Antonio was representative of only approximately
one-third of the samples collected (11 of 34 weeks clustered at
this node). Therefore, caution should be used in interpreting
snapshot analyses in such dynamic systems. Underlying these
changes in bacterial community composition was a differential
abundance of biomarkers for many spore-forming bacteria such
as Actinomycetes and Firmicutes. Indeed, most of the taxa with
significant correlations to weather conditions were Actinomy-
cetes, which showed positive correlations with temperature (SI
Table 7). Warmer temperatures may result in increased desic-
cation of soil/plant-based bacteria, leading to spore dispersal or
aerosolization. Additionally, alpha-proteobacteria such as phyl-
losphere-inhabiting Sphingomonas spp (24). were correlated
with sea-level pressure, week, and temperature (Fig. 3 and SI
Table 7). These and other alpha-proteobacteria are typically
oligotrophic and also may originate from freshwater and marine
ecosystems (3) in addition to plant surfaces. The most significant
correlation between any PhyloChip intensity pattern and an
environmental/temporal variable was between the gamma-
proteobacterium Pseudomonas oleovorans and week (r ? 0.83,
P ? 2.1 ? 10?5). Real-time quantitative PCR of the same
genomic DNA pools used for PhyloChip PCRs demonstrated
that changes in PhyloChip intensity were representative of the
dynamics of this organism (SI Fig. 7).
Despite the variable nature of the aerosol bacterial popula-
tion, we detected some groups of organisms in every sample over
the 17-week period (summarized in Table 1, with complete
details in SI Table 8). Between the two cities, more types of
bacteria consistently were detected in San Antonio aerosol
samples (80 subfamilies) compared with Austin (43 subfamilies),
although there was significant overlap in the consistent 16S
rRNA signatures between the two cities. Many of these organ-
isms (e.g., Acidobacteria and Verrucomicrobia) are major com-
ponents of the soil microbiota and may be particle-associated.
Sphingomonas species also were detected consistently, psychro-
tolerant strains of which have been detected in dust and air
samples from the Antarctic (25). Notably, other bacteria con-
sistently detected were spore formers such as the endospore-
forming Bacilli and Clostridia and the exospore-forming Acti-
nomycetes. Cyanobacteria such as Plectonema were also
for 1,874 sequences from the Minnesota farm soil library.
Rarefaction curves comparing bacterial diversity in a Minnesota farm
Brodie et al.
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frequently detected members of the aerosol community, as were
plant chloroplasts (presumably from pollen). Significantly, ep-
silon proteobacteria were consistently detected by PhyloChip in
both cities, including organisms within the families Campy-
lobacteraceae and Helicobacteraceae, both of which contain hu-
man and animal pathogens. The exact Campylobacteraceae taxon
detected by the PhyloChip contains the genus Arcobacter, whose
presence we subsequently confirmed by taxon-specific PCR and
sequencing (SI Table 4). This genus is known to cause bactere-
mia and severe gastrointestinal illnesses in humans, and together
with Helicobacter (a causative agent of gastric ulcers), could be
considered indicators of fecal contamination (26, 27), which is
known to occur through aerosolization from wastewater treat-
ment plants (11, 28).
The consistent detection of signatures from potentially patho-
genic bacteria led us to examine taxonomic clusters containing
other pathogens (and their relatives) of public health and
bioterrorism significance over the 17-week period (SI Table 9).
Environmental relatives of monitored pathogens have already
been implicated in multiple detection events in U.S. Homeland
Security monitoring systems (www.houstontx.gov/health/
NewsReleases/bacteria%20detection.htm). In fact, in response
to such a detection event, a recent survey of soils in Houston was
carried out to determine potential reservoirs of environmental
relatives (10). This study revealed a surprising diversity of
Francisella-like organisms that may have been responsible for
triggering detectors in the aerosol monitoring systems. Similarly,
in the aerosol samples analyzed here, we detected taxonomic
clusters containing organisms closely related to Francisella in
one week in Austin and two weeks in San Antonio, although the
causative agent of tularemia, Francisella tularensis, was never
encountered. We also consistently detected phylogenetic near-
neighbors to Bacillus anthracis with the taxonomic cluster con-
taining B. anthracis itself (also containing common soil relatives
B. cereus, B. thuringiensis, and B. mycoides) being detected in one
week in San Antonio. Tick-borne Rickettsia and Clostridium
botulinum types C (causes illness in mammals, fish, and birds)
explains 89.1% of variance in SI Data Set 1. Bars plotted under each cluster represent mean of normalized array intensities of phylogenetically related bacteria
shown to be significantly correlated with environmental/temporal parameters.
Multivariate regression tree analysis of the interaction between aerosol bacterial dynamics (array intensity) and environmental parameters. The model
Table 1. Bacterial groups detected in all weeks during sampling
Phylum, classSan AntonioAustin
Actinobacteria, BD2–10 group
in each of 17 weeks sampled per city; N, not detected in every sample.
www.pnas.org?cgi?doi?10.1073?pnas.0608255104Brodie et al.
and G (rarely illness causing) also were detected regularly, as
were Burkholderia mallei and Bu. pseudomallei, which cause
Yersinia pestis and Brucella spp. (melitensis, suis, and abortus)
were never encountered. The frequent occurrence of environ-
mental relatives of bacteria targeted by biosurveillance efforts in
urban aerosols makes prediction of natural occurrences of
endemic pathogens or their uncharacterized environmental rel-
atives critical for the implementation of a robust biosurveillance
This study represents a comprehensive molecular analysis of
airborne bacterial composition and dynamics. We have demon-
strated that the atmosphere contains a diverse assemblage of
microorganisms probably representing the amalgamation of
numerous point sources. The composition of this habitat varies
widely and may be subject to climatic regulation. A global-scale
study of this uncharacterized ecosystem is necessary to deter-
mine baselines for bioaerosol transport patterns. Such data will
enable an understanding of future anthropogenic impacts in-
cluding pollution, bioterrorism, and climate change in altering
the biological composition of the air we breathe.
Materials and Methods
Sample Collection and Pooling.Air samples were collected by using
an air filtration collection system under vacuum located within
six Environmental Protection Agency air quality network sites in
both San Antonio and Austin. Approximately 10 liters of air per
minute were collected on a Celanex polyethylene terephthalate,
1.0-?m filter (Calanese, Dallas, TX). Samples were collected
daily over a 24-h period. Sample filters were washed in 10 ml
buffer (0.1 M sodium phosphate/10 mM EDTA, pH 7.4/0.01%
Tween-20), and the suspension was stored frozen until extracted.
Samples were collected from 4 May to 29 August 2003. Sample
dates were divided according to a 52-week calendar year starting
January 1, 2003, with each Monday-to-Sunday cycle constituting
sample week were extracted. Each date chosen for extraction
consisted of a 0.6-ml filter wash from each of the six sampling
sites for that city (San Antonio or Austin) combined into a ‘‘day
pool’’ before extraction. In total, for each week, 24 filters were
DNA Extraction and 16S rRNA Gene Amplification. The ‘‘day pools’’
were centrifuged at 16,000 ? g for 25 min, and the pellets were
resuspended in 400 ?l of 100 mM sodium phosphate buffer (pH
8). DNA extraction was performed as described in DeSantis et
al. (29), but only a single bead-beating velocity and duration was
used (6.5 m?s?1for 45 s). DNA was quantified by using a
PicoGreen fluorescence assay according to the manufacturer’s
recommended protocol (Invitrogen, Carlsbad, CA). 16S rRNA
gene amplification was performed according to standard pro-
cedures as outlined in SI Materials and Methods.
PhyloChip Processing, Scanning, Probe Set Scoring, and Normaliza-
tion. The pooled PCR product was spiked with known concen-
trations of synthetic 16S rRNA gene fragments and non-16S
rRNA gene fragments as internal standards for normalization
with quantities ranging from 5.02 ? 108and 7.29 ? 1010
molecules applied to the final hybridization mix (SI Table 10).
Target fragmentation, biotin labeling, PhyloChip hybridization,
scanning, and staining were as described by Brodie et al. (30),
and background subtraction, noise calculation, and detection
and quantification criteria were essentially as reported in Brodie
et al. (30), with some minor exceptions. These exceptions were
as follows: For a probe pair to be considered positive, the
difference in intensity between the PM and MM probes must be
at least 130 times the squared noise value (N). A taxon was
considered present in the sample when 92% or more of its
assigned probe pairs for its corresponding probe set were
positive (positive fraction ? ? 0.92). This was determined based
on empirical data from clone library analyses. Hybridization
intensity (referred to as intensity) was calculated in arbitrary
units for each probe set as the trimmed average (maximum and
minimum values removed before averaging) of the PM minus
MM intensity differences across the probe pairs in a given probe
set. All intensities ?1 were shifted to 1 to avoid errors in
subsequent logarithmic transformations. When summarizing
PhyloChip results to the subfamily, the probe set producing the
highest intensity was used.
with Clone Library. To compare the diversity of bacteria detected
for cloning and sequencing and replicate PhyloChip analysis. One
San Antonio week 29 was made. One milliliter of the pooled PCR
product was gel-purified, and 768 clones were sequenced at the
DOE Joint Genome Institute (Walnut Creek, CA) by standard
methods. An aliquot of this same pooled PCR product also was
hybridized to a PhyloChip (three replicate PhyloChips performed).
Subfamilies containing a taxon scored as present in all three
Phrap (31–33), and were required to pass quality tests of Phred 20
(base call error probability ?10?2.0) to be included in the compar-
ison. Chimeric sequences were removed after Bellerophon (34)
analysis, and similarity of clones to PhyloChip taxa was calculated
with DNADIST (35) measurement of homology (DNAML-F84)
over 1,287 conserved columns identified by using the Lane mask
(36). Sequences were assigned to a taxonomic node by using a
sliding scale of similarity threshold (37). These steps are described
clone and PhyloChip analysis is available in SI Table 3.
Validation of PhyloChip-Detected Subfamilies Not Supported by the
Clone Library. Primers targeting sequences within particular taxa/
subfamilies were generated by using ARB’s probe design feature
(38) and based on regions targeted by PhyloChip probes or were
obtained from published literature (SI Table 4). Primer quality
control was carried out by using Primer3 (39).
Quantitative Detection of Changes in 16S rRNA Gene Concentration in
Heterogeneous Solutions. To determine whether changes in 16S
rRNA gene concentration could be detected by using the
PhyloChip, various quantities of distinct rRNA gene types were
hybridized to the PhyloChip in rotating combinations. We chose
environmental organisms, organisms involved in bioremedia-
tion, and a pathogen of biodefense relevance. 16S rRNA genes
were amplified from each of the organisms shown in SI Table 5.
Then each of these nine distinct 16S rRNA gene standards was
tested once in each concentration category, spanning five orders
of magnitude (0 molecules, 6 ? 107, 1.44 ? 108, 3.46 ? 108,
8.30 ? 108, 1.99 ? 109, 4.78 ? 109, 2.75 ? 1010, 6.61 ? 1010, and
1.59 ? 1011) with concentrations of individual 16S rRNA gene
types rotating between PhyloChips such that each PhyloChip
contained the same total of 16S rRNA gene molecules. This is
similar to a Latin Square design, although with a 9 ? 11 format
Real-Time Quantitative PCR Confirmation of PhyloChip-Observed
Shifts in Taxon Abundance. A taxon (no. 9389) consisting only of
two sequences of Pseudomonas oleovorans that correlated well
with environmental variables was chosen for quantitative PCR
confirmation of PhyloChip-observed quantitative shifts. Primers
for this taxon were designed by using the ARB (38) probe match
function to determine unique priming sites based on regions
Brodie et al.
January 2, 2007 ?
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detected by PhyloChip probes. These regions then were imputed
into Primer3 (39) to choose optimal oligonucleotide primers for
PCR. Primer quality was assessed further by using Beacon
Designer v3.0 (Premier BioSoft, CA). Primers 9389F2 (CGAC-
TACCTGGACTGACACT) and 9389R2 (CACCGGCAG-
TCTCCTTAGAG) were chosen to amplify a 436-bp fragment.
Validation of primer specificity and reaction conditions are
available in SI Materials and Methods.
Statistical Analyses. All statistical operations were performed in
the R software environment (ref. 40; www.R-project.org). For
each day of aerosol sampling, 15 factors including humidity,
wind, temperature, precipitation, pressure, particulate matter,
and week of year were recorded from the U.S. National Climatic
Data Center (www.ncdc.noaa.gov) or the Texas Natural Re-
source Conservation Commission (www.tceq.state.tx.us). The
weekly mean, minimum, maximum, and range of values were
calculated for each factor from the collected data. The changes
in ln(intensity) for each PhyloChip taxon considered present in
the study was tested for correlation against the environmental
conditions. The resulting P values were adjusted by using the
step-up false discovery rate controlling procedure (41).
Multivariate regression tree analysis (22, 23) was carried out
by using the package ‘‘mvpart’’ within the ‘‘R’’ statistical pro-
gramming environment. A Bray-Curtis-based distance matrix
was created by using the function ‘‘gdist.’’ The Bray-Curtis
measure of dissimilarity is generally regarded as a good measure
in this case, array probe-set intensity, because it allows for
nonlinear responses to environmental gradients (22, 42). Large
trees were calculated with splitting based on information gain
and then pruned (from 13 to 10 nodes) based on 100 cross-
validations to a complexity parameter of 0.025286, where cross-
validation relative error had reached a plateau.
Before clone library rarefaction analysis, a distance matrix
(DNAML homology) of clone sequences, was created by using
an online tool at http://greengenes.lbl.gov/cgi-bin/nph-
distance?matrix.cgi (43) after alignment of the sequences by
using the NAST aligner (http://greengenes.lbl.gov/NAST) (44).
DOTUR (45) was used to generate rarefaction curves, Chao1,
est neighbor joining was used with 1,000 iterations for boot-
We thank Dr. Phil Hugenholtz and Dr. Paul Richardson of the Joint
Genome Institute for clone library sequencing; Susannah Green Tringe
for providing the soil 16S rRNA gene sequences; Sonya Murray for
expert technical assistance; John Coates, Lisa Alvarez-Cohen (both of
University of California, Berkeley, CA), Hoi-Ying Holman, Terry Hazen
(both of Lawrence Berkeley National Laboratory), and Arthur Fried-
lander (U.S. Army Medical Research Institute of Infectious Diseases,
Frederick, MD) for the generous gifts of bacterial cultures or DNA; and
Sue Lynch, Terry Hazen, Jill Banfield, Tamas Torok, and two anony-
This work was performed under the auspices of the U.S. Department of
Energy by the University of California, Lawrence Berkeley National
part by Department of Homeland Security Grant HSSCHQ04X00037
and the Climate Change Research Division, Biological and Environ-
mental Research, Office of Science, U.S. Department of Energy. Com-
putational support was provided through the Virtual Institute for
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