Bacteria are widely distributed in atmospheric aerosols and are
indispensable components of clouds, playing an important role in the
atmospheric hydrological cycle. However, limited information is available
about the bacterial community structure and function, especially for the
increasing air pollution in the North China Plain. Here, we present a
comprehensive characterization of bacterial community composition, function,
variation, and environmental influence for cloud water collected at Mt Tai
from 24 July to 23 August 2014. Using Miseq 16S rRNA gene sequencing, the highly
diverse bacterial community in cloud water and the predominant phyla of
Proteobacteria, Bacteroidetes, Cyanobacteria, and Firmicutes were
investigated. Bacteria that survive at low temperature, radiation, and poor
nutrient conditions were found in cloud water, suggesting adaption to an
extreme environment. The bacterial gene functions predicted from the 16S
rRNA gene using the Phylogenetic Investigation of Communities by
Reconstruction of Unobserved States (PICRUSt) suggested that the pathways
related to metabolism and disease infections were significantly correlated
with the predominant genera. The abundant genera
Clouds are an aerosol system composed of tiny droplets suspended in the atmosphere. In the atmosphere, pollutants attached to particles can be dissolved or incorporated into cloud droplets, which may have complex effects on environment security and human health. Over the past decades, studies on cloud water have mainly focused on the physical and chemical properties (Aikawa et al., 2001; Boris et al., 2016; Fernández-González et al., 2014). Recently, with the in-depth understanding of cloud characteristics, studies on bioaerosols have been on the rise.
Living microorganisms, including bacteria, fungi, and yeasts, have been shown to be present in clouds (Burrows et al., 2009). In the first study on biological particles in fog/cloud water, Fuzzi et al. (1997) suggested bacterial replication on foggy days. Later, with the development of detection techniques, microorganisms in fog/cloud water have been systematically studied (Amato et al., 2007c; Delort et al., 2010; Vaïtilingom et al., 2012). Combined with field investigations and laboratory experiments, diverse bacterial communities have been retrieved, and the bacterial metabolism active in cloud water has been further demonstrated. In the atmospheric aqueous phase, microorganisms can act as cloud condensation nuclei (CCN) and ice nuclei (IN), which have a potential impact on cloud formation and precipitation processes (Amato et al., 2015; Bauer et al., 2003; Mortazavi et al., 2015). Moreover, microorganisms in cloud water are available to metabolize organic carbon compounds (degrading organic acids, formate, acetate, lactate, and succinate) and are associated with carbon and nitrogen recycling (Amato et al., 2007a; Hill et al., 2007; Vaïtilingom et al., 2010). They can also influence photochemical reactions (Vaïtilingom et al., 2013) and participate in a series of complex biochemical metabolic activities.
Cloud occurrence is a complex process. In contaminated areas, clouds
typically contain numerous pollutants such as sulfate and nitrate ions,
organic carbon compounds, and bacteria (Badarinath et al., 2007; Després
et al., 2012; Fernández-González et al., 2014; Mohan and Payra,
2009). As an intensive agricultural and economic region in China, the North
China Plain has been affected by severe air pollution in recent years, for
instance, the severe fog and haze pollution in Beijing and Jinan in January
2013 (Huang et al., 2014; Wang et al., 2014). Mt Tai (36
Notably, atmospheric microorganisms are subject to a wide range of
environmental conditions including meteorological factors and the
physiochemical composition of aerosols (Womack et al., 2010). Community
structure and function are closely related to the environmental
characteristics in the atmosphere and the geomorphic characteristics (Dong et
al., 2016; Gao et al., 2016). For instance, studies about inhalable
bioaerosols suggest that environmental parameters including temperature,
relative humidity, PM
In the present study, samples from typical cloud episodes under polluted and non-polluted weather conditions were collected on the summit of Mt Tai in the North China Plain. To understand the bacterial community structure and function, Miseq 16S rRNA gene sequencing was performed, and the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) predictive function was applied to examine the metabolic and ecological function. Analysis of similarities (ANOSIM) and linear discriminant analysis effect size (LEfSe) were executed to clarify the discrepant bacterial taxa. Moreover, redundancy analysis (RDA) was applied to identify the pivotal environmental factors influencing the bacterial community. Air mass back trajectory was conducted to define the most likely source and transmission paths of the pollutants and bacteria.
Cloud water samples were collected using the Caltech Active Strand Cloud
Water Collector (CASCC) on the summit of Mt Tai (36
To avoid artificial and instrumental contamination, the Teflon tube and the
polyethylene bottles were pretreated with anhydrous ethanol and washed three times using the sterilized ultrapure water. Before sampling, the collector
was washed with the sterilized deionized distilled water filtered through
0.22
To distinguish the polluted and non-polluted cloud episodes, we firstly
checked the air pollution condition according to the 24 h WHO air quality
guideline (PM
Description cloud episodes at Mt Tai, China.
After adjustment, seven cloud episodes and 13 samples were obtained
during the whole sampling period (from 24 July to 23 August 2014), including
11 polluted and 2 non-polluted cloud water samples (Fig. S1 in the Supplement). The samples
for microbial community investigation were stored with dry ice in transit
and then frozen at
In cloud water, the pH and conductivity were detected with a multi
pH/conductivity/temperature handheld meter (pH/COND/TEMP, 6350) immediately after sampling. The liquid
water content (LWC) of cloud droplets was measured with a fog monitor FM-120
(Droplet Measurement Technologies Inc., USA). The organic carbon (OC) in
cloud water was detected using an an organic carbon–elemental carbon analyzer (Sunset Laboratory, Tigard,
OR, USA). The major inorganic ions (Cl
For each cloud episode, a 24 h back trajectory analysis was performed to
determine the air mass from the most likely source region using the Hybrid
Single-Particle Lagrangian Integrate Trajectories (HYSPLIT) model
(
Genomic DNA was extracted in triplicate with the FastDNA spin kit for soil (MP Biomedicals, Solon, OH, USA) according to the manufacturer's directions. The concentration of DNA was determined spectrophotometrically (Nano-Drop 2000, Thermo, Wilmington, Delaware, USA). To check sample contamination, DNA was extracted through an identical extraction procedure for the blank samples. These blanks were PCR amplified together with the DNA samples extracted from cloud water samples. For the blank, no obvious bands and target fragments were detected by the examination of electrophoretic gel images.
The designed primer sets with the V3-V4 region of 16S rRNA gene (338F-806R; Masoud et al., 2011), adapter and bar codes were selected in the Illumina
Miseq sequencing. For each sample, a 25
The PCR products were separated by 2 % agarose gel electrophoresis and
purified with the nucleic acid purification kit (AxyPrepDNA, Axygen, USA).
Purified PCR products were quantified using a Qubit 3.0 fluorometer
(Invitrogen, Carlsbad, CA) and then mixed to equal concentration. For each
sample, 4
Raw sequences were processed using the QIIME packages (Kuczynski et al.,
2011). The pair-end reads were firstly merged with an overlap length greater
than 10 bp. Then, the adapter, bar codes, and primers were removed from the
merged sequences. Subsequently, the trimmed sequences with a length shorter
than 200 bp, a quality score lower than 25, and homologous bases longer than 8 bp,
containing ambiguous characters, were screened. Finally, chimeric sequences
were distinguished using the Usearch61 algorithm and removed from the
dataset. Optimized sequences were clustered into operational taxonomic units (OTUs) at the threshold of
97 % similarity with the usearch61 algorithm. Single OTUs were removed and
taxonomy was assigned to each representative OTU using the Ribosomal
Database Project (RDP) classifier in QIIME, with a minimum confidence cutoff
of 80 % against the silva reference database (silva 119,
To acquire bacterial community function, Phylogenetic Investigation of
Communities by Reconstruction of Unobserved States (PICRUSt) was performed.
The PICRUSt can be used to predict the metabolic function pathway from
corresponding bacteria and archaea and provide a community's functional
capabilities based on the 16S rRNA gene sequence (Langille et al., 2013;
Corrigan et al., 2015; Wu et al., 2016). In the present study, the
phylogenetic and functional capacities for the bacteria in cloud water are
of great interest to help understand their roles in the atmosphere,
ecosystem, and health. Bacterial community functional profiles were
predicted from 16S rRNA gene using the PICRUSt program and annotated against
with the Kyoto Encyclopedia of Gene and Genomes (KEGG) database. Spearman's
correlation coefficients were calculated to link the pairwise comparison of
KEGG pathway and genus. Selected KEGG pathways related to metabolism and
disease infection, and predominant genera are included in the heat map.
Correlation is significant at a
Alpha diversity was assessed by examining the rarefaction curves,
Shannon–Wiener curves, and rank-abundance curves calculated with Mothur
(v.1.34.0;
Hierarchical cluster (Hcluster) and principal coordinate analysis (PCoA)
were performed to visualize the changes in bacterial community for the
collected samples. Hcluster and PCoA plots were constructed depending on the Bray–Curtis similarity index calculated with the abundance of OTUs using the
biodiversity package in R (Kindt and Coe, 2005). The difference in OTU
composition for samples collected in polluted and non-polluted cloud
episodes was tested by the analysis of similarity (ANOSIM; Clarke, 1993).
ANOSIM was implemented with the VEGAN package in R. Linear discriminant
analysis effect size (LEfSe;
Summary of bacterial diversity and richness of cloud water.
The diversity indexes including OTUs, ACE, Chao1, coverage, Shannon, and Simpson were defined at 97 % sequence similarity. Abbreviations: CE, cloud episodes; TSP, total suspended particulate matter.
Correlation between bacterial community and environmental variables was
first performed using a detrended correspondence analysis (DCA) to estimate
the gradient length. The resulting length (0.99) indicates that a linear model
was appropriate; hence, RDA was subsequently performed. RDA was elaborated
with the predominant bacterial matrix and environmental data matrix
including PM
In the present study, we first defined cloud water samples according to the
air pollution conditions. The collected cloud water was considered to be a polluted sample under air pollution. However, in Hcluster and PCoA analysis
(Fig. S3), sample CE1-2 (the non-polluted sample) was separated from other
non-polluted samples but closed to the polluted samples. The reclassification of
cloud water samples was combined with the major ions in water droplets. By
checking the major ions, we observed that although the PM
Bacterial community variation for cloud episodes at the
phylum
Although the predominant bacteria are similar between polluted and
non-polluted cloud episodes, significant disparity is also identified.
ANOSIM analysis suggest that OTUs from polluted samples were grouped into
one large cluster and separated from the non-polluted clusters (ANOSIM
comparison,
Information on the bacterial community in fog/cloud droplets is scarce; our
study provided a comprehensive investigation of the bacterial community. From the
13 samples collected during seven cloud episodes, a total of 232 148 high-quality
sequences were obtained after quality filtering, and OTUs ranged from 975 to
1258 (Table 2). This was similar to other sequence-based surveys such as
the atmospheric bacteria in a dust storm (1214l; Katra et al., 2014) and
bacteria in rainwater in July (1542; Cho and Jang, 2014). The identification
of OTUs at different taxonomic levels yielded 359 species, 411 genera, 152
families, 70 orders, 38 classes, and 26 phyla. Across all samples,
Proteobacteria were the dominant phylum, followed by Bacteroidetes,
Cyanobacteria, Firmicutes, Deinococcus-Thermus, Actinobacteria, and
Nitrospirae (Fig. 1). These taxa are predominant bacteria in clouds
determined by Sanger sequencing and tagged pyrosequencing (Bowers et al.,
2009), and they are also the typical culturable heterotrophic bacteria from
clouds broadly distributed in aquatic and terrestrial habitats (Amato et
al., 2005; Kourtev et al., 2011). In the present study, Fig. S5 shows the
dominant genera collected during cloud process. The predominant genera from
Proteobacteria (including
Bacterial taxa are related to KEGG pathways. Bacterial
gene functions were predicted based on 16S rRNA gene sequences using the
PICRUSt algorithm and annotated from KEGG databases. Spearman's correlation
coefficients were calculated for each pairwise comparison of genus and KEGG
pathway. Selected KEGG pathways related to metabolism and disease infection
and predominant genera are included in the heat map. Red color refers to the
positive correlation, and green indicates a negative correlation.
Correlation is significant at
In the bacterial community, the aforementioned taxa contained a series of
species participating in the atmospheric hydrological and biochemical cycle
(Amato et al., 2007b; Delort et al., 2010). Community function analysis
estimated with the PICRUSt algorithm confirmed this viewpoint. After PICRUSt
analysis, pathways with participants of less than 10 % were removed, leaving
225 non-human-gene KEGG pathways. These predominant pathways were mainly
related to the amino acid metabolism, the carbohydrate metabolism, cell motility,
cellular processes and signaling, energy metabolism, enzyme families,
folding, sorting and degradation, membrane transport, the nucleotide metabolism,
the nucleotide metabolism, replication and repair, signal transduction,
transcription, and translation (Fig. S6). Besides the pathways associated
with the microbial physiological metabolism, we focused on the pathways of the microbial
metabolism in a variety of natural environments. Fog/cloud droplets contain carbon and nitrogen compounds, which could be available substrate for
microbial growth in the atmosphere. The predicted function of the metabolism was
likely attributed to the bacterial gene from the identified taxa (Fig. 2).
Previous studies have demonstrated that the atmospheric bacterial community
contained a metabolically diverse group from a wide range of water/soil
habitats. For example,
The identified bacterial species in cloud water samples correlated with the potential ecological function.
CNN and IN refers to the bacteria participating in the formation of clouds or rain by acting as cloud condensation nuclei (CNN) and ice nuclei (IN). Biodegradation refers to the bacteria associated with the biodegradation of organic compounds, even toxic pollutants, e.g., aromatic compounds. Abbreviations: AP, Alphaproteobacteria; BP, Betaproteobacteria; GP, Gammaproteobacteria; AC, Actinobacteria; BA, Bacteroidetes; CY, Cyanobacteria; DT, Deinococcus-Thermus; FR, Firmicutes.
In cloud water, a series of genera adapted to harsh environments were also
identified. The ability to survive in low concentrations of nutrients has
been reported for
Although most bacterial ecophysiological roles in biogeochemical cycles are generally established based on soils and water habitats, information about bacterial activity in cloud water is available. The identification of microorganisms in the barren-nutrition, low-temperature, and radiation environment encountered in clouds is expected since similar bacterial species have been retrieved and proved to be active in harsh environments. Their adaption to the specific environments in fog/cloud water with a potential role in the nucleation and metabolism of organic pollutants demonstrated their potential importance in the atmospheric biochemistry cycle.
Schematic representation of bioaerosols' life cycle and potential influence on atmosphere, ecosystem, and human health, modified from Pöschl (2006). In clouds, the bacterial potential functions are indicated in the figure. Bioaerosols are emitted from various terrestrial environments, e.g., soil, water, plants, animals, or human beings, which may include pathogenic or functional species. These bacteria can be attached to particles or incorporated into water droplets of clouds/fog. Certain species can serve as biogenic nuclei for cloud condensation nuclei (CCN) and ice nuclei (IN), which induce rain formation, precipitation, and wet deposition of gases and particles. For the potential pathogens and functional bacteria, during cloud processes, they can be deposited back to land via deposition and possibly induce human infections and affect the diversity and function of aquatic and terrestrial ecosystems.
Bacteria in fog/cloud water have been known for decades, but detailed information on community composition and potential ecophysiological role is severely limited. Bioaerosols in fog/cloud are complex assemblages of airborne and exogenic microorganisms, likely due to emission and resuspension from various terrestrial environments, e.g., soil, water, plants, animals, or human beings. In the atmosphere, fog/clouds may be a favorable niche for bacteria and these bacteria could thrive and influence cloud processes by acting as cloud condensation nuclei and ice nuclei. Bacteria including pathogenic or beneficial species can also be attached to particles or incorporated into water droplets of fog/clouds. During fog/cloud or rain processes, they can be deposited back on land via deposition and possibly cause human infections and affect the diversity and function of aquatic/terrestrial ecosystems (Kaushik and Balasubramanian, 2012; Simmons et al., 2001; Vaïtilingom et al., 2012; Fig. 3 and Table 3).
Atmospheric bacteria are efficient cloud condensation nuclei, and water
vapor can be condensed on bacterial cell surface (Andreae and Rosenfeld, 2008). The
hygroscopic growth of bacteria below water saturation and supersaturations
has been observed for some species; e.g., Bauer et al. (2003) found that
Distinct bacterial taxa between polluted and non-polluted cloud episodes identified by linear discriminant analysis (LDA) coupled with effect size (LEfSe). The LDA effect sizes (left) were calculated using the default parameters. The taxonomic cladogram (right) was visualized with LDA values higher than 3.5 comparing all bacterial taxa. The significantly distinct taxon nodes are colored in red (polluted samples) and green circles (non-polluted samples). The nonsignificant bacterial taxa are indicated with yellow circles. The abbreviation in the cladogram tree: a: g_Rhodococcus, b: f_Nocardiaceae, c: f_Flavobacteriaceae, d: o_Flavobacteriales, e: c_Flavobacteriia, f: p_Bacteroidetes, g: f_Deinococcaceae, h: o_Deinococcales, i: c_Deinococci, j: p_Deinococcus-Thermus, k: f_Bacillaceae, l: o_Clostridiales, m: c_Clostridia, n: p_Firmicutes, o: f_Oxalobacteraceae, p: o_Burkholderiales, q: c_ Betaproteobacteria, r: f_Moraxellaceae, s: o_Pseudomonadales, t: f_Xanthomonadaceae, u: o_Xanthomonadales, v: c_Gammaproteobacteria, w: p_Proteobacteria. Abbreviations: p – phylum; c – class; o – order; f – family; g – genus; s – species.
In addition, microorganisms living in fog/cloud may play a vital role in
atmospheric biochemistry. The detection of bacteria in cloud water
associated with the biotransformation of organic compounds raises questions regarding a general
understanding of their potential role in atmospheric chemistry. The identified
species from
Biplot of the environmental variables and genus-level
community structure using a redundancy analysis model (RDA), describing the
variation in bacterial community explained by environmental variable. CE
refers to cloud episodes. Polluted episodes are indicated by red circles,
and non-polluted episodes are green squares. Species data are listed in
Table S2. The selected environmental variables are significant (
In addition, bacterial genera containing potential pathogens were
of particular interest after sequencing. By alignment with the reference pathogen
database, sequences highly similar to potential pathogens were identified.
In the present study, the presence of potential pathogen sequences indicated
occasional distribution and dispersion of pathogens in cloud water (Table 3). The identified opportunistic pathogens
from
Previous studies on potential pathogens are mostly focused on atmospheric particulate matter (PM
Air mass transport pathways for the cloud episodes using
the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model.
24 h backward trajectories were calculated for air parcels arriving at
the summit of Mt Tai (36
To distinguish indicator species within the polluted and non-polluted cloud episodes, LEfSe was performed, which showed statistically significant differences. A total of 70 bacterial groups were distinct using the default logarithmic (LDA – linear discriminant analysis) value of 2. Cladograms show taxa with LDA values higher than 3.5 for clarity (Fig. 4). Consequently, 8 and 19 representative bacterial taxa are detected in polluted and non-polluted cloud episodes.
In polluted cloud episodes, most enriched bacteria were ranked as
opportunistic pathogens, such as Proteobacteria, Gammaproteobacteria,
Xanthomonadales, Xanthomonadaceae, Stenotrophomonas, Moraxellaceae, and
Wind rose diagram to quantitative analyze wind speed
and wind direction during sampling time between polluted
In comparison, the majority of indicator species in the non-polluted samples
are from Bacteroidetes, Firmicutes, Betaproteobacteria, and
Deinococcus-Thermus. An important biomarker from Bacteroidetes was
Flavobacteriia. Comparative study has illustrated the marine sources for
Flavobacteria. Most of Flavobacteria sequences searched for against the NCBI database using BLAST (Basic Local Alignment Search Tool) are
mainly from marine sources, i.e., algae, oysters, and sea cucumbers (Cho and
Jang, 2014). The genus
By comparison, potential pathogens were significant groups in the polluted samples, whereas diverse ecological function groups were identified in the non-polluted samples originating from a wide range of habitats. An ecologically meaningful distinction of bacterial groups under polluted and non-polluted conditions is essential for an understanding of the variation of bacterial community structure and function, which reveals the community dynamics under pollution stress.
To clarity the vital environmental factors shaping the bacterial community
structure, RDA was performed to discern the genus-level structure with the
selected environmental factors (Fig. 5). The first two axes explained
65.9 % of the accumulated variance in the species-environment relation.
Interset
Cumulative fit indicated that the predominant genera affiliated with
Of the environmental characteristics measured, major ions in cloud water and
PM
During cloud processes, most atmospheric particles (including PM
The identified taxa from either polluted or non-polluted samples were typically found in soil, water, plants, or human beings. These bacterial groups aerosolized and dispersed into atmosphere either from local regional emissions or long-range transport. Source tracking analysis by backward trajectory indicated that the air mass of polluted cloud episodes came largely from northern and western China, moving east through Shanxi, Henan, and Hebei province to the study area, or from outer Mongolia, crossing the Jingjinji area to Mt Tai (Fig. 6). The areas passed were notable heavy industry regions with frequent coal mining activities and serious pollution. Moreover, the large population and agricultural activities resulted in numerous pathogenic microorganisms from human or animal fecal matter being dispersed in air. In contrast, air mass of non-polluted cloud episodes originated mostly from southern China, and the regions passed were rich in water resources, e.g., Dongting Hu, Huai He, Yangtze river. The marine-source bacteria (Flavobacteria, significant biomarker in non-polluted cloud water samples by LefSE, Fig. 4) dispersed in the atmosphere typically derived from the evaporation of lake and river water. These bacteria mainly originated from sea–air interactions, and the marine bacteria can be transported inland through long-range transport.
At the sampling site (the summit of Mt Tai, 1534 m a.s.l.), local
anthropogenic pollution might be minimized and air pollution is mainly
influenced by long-range transport. The wind rose diagram suggests a prevailing
west wind during polluted cloud episodes, and wind speeds ranged from 1.2 to 1.4 m s
The composition and potential function of microbial communities in the
atmospheric water phase (fog and clouds) remains rarely studied. Using 16S
rRNA gene sequencing, this study has presented a comprehensive investigation of
bacterial ecological diversity during polluted and non-polluted cloud
episodes and revealed a highly diverse bacterial community harbored in cloud
water. Correlation analysis for the predominant genera and PICRUSt function
predication enhanced our understanding of the distribution of bacteria and
their potential involvement in the atmosphere, ecosystem, and human health.
The identification of bacteria surviving in the poor-nutrition, low-temperature, and
radiation environments encountered in fog/cloud water demonstrated bacterial
activity in harsh atmospheric environments. They may act as efficient cloud
condensation nuclei or ice nuclei, associated with biogeochemical cycling
(nitrogen/carbon cycling), microbial degradation of organic compounds in
fog/clouds, and the spread of specific human, animal, and plant diseases by
potential pathogens. Moreover, community disparity between polluted and
non-polluted cloud episodes suggested that major ions in cloud water seem to be
pivotal in shaping bacterial communities. PM
The meteorological data are accessible at the China Meteorological
Administration (
The authors declare that they have no conflict of interest.
This work was supported by the National Natural Science Foundation of China (41605113, 41375126), the Taishan Scholar Grant (ts20120552), and the China Postdoctoral Science Foundation (no. 2015M582095). Edited by: T. Zhu Reviewed by: three anonymous referees