Introduction
The snowpack has been shown to act as an important matrix for (photo)
chemical and biological reactions of organic compounds
(Ariya et al., 2011). Snow and ice provide large
surface areas which consist of interstitial air, water and ice that may
exchange chemical and biological matter with the atmospheric boundary layer.
Trace gas exchange, scavenging, photolysis, adsorption (Kos et al., 2014),
and more recently, biological transformations in snowpack have been
considered (Amoroso et al., 2010,
2009; Fujii et al., 2010; Segawa et al., 2005). Yet, the role of biomolecules, including
microorganisms, in oxidation, ice nucleation, gas-particle transfer and
aerosol formation remains poorly understood.
Climate change has been linked to changes in snow and ice patterns in the
Arctic, potentially impacting the Earth's albedo and atmospheric energy
balance (Grenfell and Maykut, 1977; Grenfell and Perovich, 1984, 2004;
Hanesiak, 2001). Atmospheric transport events such as dust storms initiated
long distances away have been considered to influence the Arctic climate.
Saharan dust, for instance, has been reported as a source of certain
biological particles to reach the Arctic region (Barkan and Alpert, 2010). In
1976, an Asian dust storm was responsible for bringing as much as 4000 t
of dust per hour to the Arctic (Rahn et al., 1977). Dust has also been shown
to transport microorganisms (S. Zhang et al., 2007, 2008; X. F. Zhang, 2008).
Bacteria and fungi have been detected in Asian dust (Choi et al., 1977; Yeo
and Kim, 2002; Wu et al., 2004; Ho et al., 2005) and in African desert winds
(Griffin et al., 2001, 2003, 2007, 2006; Kellogg et al., 2004; Prospero et
al., 2005), whereby some have been found to be viable (Griffin et al., 2001;
Prospero et al., 2005). Recently, the increase in the number of storms has
been associated with the efficient long-range transport of dust, microbial
and other chemicals to the Arctic regions (Clarke et al., 2001; Grousset et
al., 2003; Uno et al., 2009). During long distance transportation, air masses
may undergo chemical and physical transformation under extreme environmental
conditions such as high levels of solar radiation, multiple freeze–thaw
cycles, relatively acidic conditions, and predominantly inorganic salts
(Jickells, 1999; Ariya et al., 2002, 2009; Cote et al., 2008). Little is
known on the effects of the photochemical and aging processes of the chemical
and biological composition of dust particles, or whether chemical properties
and the genomic structure of microbial entities transported with dust are
altered, or mutated during long distance transport (Smith et al., 2010).
Pure water droplets homogeneously freeze in the atmosphere at approximately
-38 ∘C. Entities such as particles including mineral dust,
soot, and biological materials (DeMott et al., 2003; Möhler et al.,
2007) may serve as ice nuclei (IN) which enhance freezing at much higher
temperatures in a process known as heterogeneous nucleation
(Pruppacher and Klett, 1997). Depending on the nature of the
impurities, heterogeneous nucleation can occur over a wide range of temperatures. Although dust particles are generally assumed to be the most
important global effect on ice nucleation, several strains of Erwinia herbicola
(Lindow, 1978), Pseudomonas fluorescens (Maki and Willoughby, 1978),
Pseudomona.s viridflava (Paulin, 1978), and Xanthomonas campestris pathovar translucens (Kim et al., 1987) are
recognized amongst the most efficient IN in biological particles. Yet the
global effect of the importance of biological ice nuclei is still a subject
of debate. Some strains of Pseudomonas syringae can initiate water freezing at temperatures as
high as -2 ∘C (Orser et al., 1985) which have also been
detected in clouds and snow (Amato et al., 2005, 2007;
Vaïtilingom et al., 2012; Lohmann and Feichter, 2005; Joly et al.,
2013). Atmospheric microbial, besides being considered efficient ice nuclei
(Constantinidou et al., 1990; Kieft and Ahmadjian, 1989; Möhler et al.,
2007; Möhler et al., 2008; Pouleur, 1992; Ariya et al., 2014; Mortazavi et
al., 2008) have also been suggested to act as cloud condensation nuclei
(Bauer et al., 2003; Möhler et al., 2007).
Both natural (e.g. mineral dust, biogenic nucleators) and anthropogenic
(e.g. soot) sources can contribute to precipitation in Arctic regions
(Hansson et al., 1993; Hinkley, 1994). There is some
evidence for the observed increase in the number of storms in certain areas
of the globe which can alter the transport and distribution of chemicals or
biological entities (Wang et al., 2011; Zhang et al., 2007; Erel et al.,
2006) with potential impacts on precipitation patterns (Sempere and
Kawamura, 1994; Satsumabayashi et al., 2001). Although the pivotal role of
dust in the atmospheric global circulation (Dunion and Velden, 2004; Wu,
2007), radiative budget (Sokolik and Toon, 1996; Kaufman et al., 2001),
air pollution (Prospero, 1999; VanCuren, 2003) and cloud formation
(Toon, 2003) has been documented, there is little known about how
the newly introduced pool of transported microbial entities by dust to the
Arctic impacts the change of the total Arctic microbial pool or affects the
freezing and melting processes of snow and ice matrices in this region.
Several studies using standard microbiology techniques have shown that there
is a diverse population of bacteria in the snow (Carpenter et al., 2000;
Amato et al., 2007; Mortazavi et al., 2008; Amoroso et al., 2010; Moller et
al., 2011; Liu et al., 2011; Harding et al., 2011). Recent developments in
high-throughput sequencing (HTS) techniques (Loman et al., 2012a, b), such as next-generation sequencing (NGS), also allow for
metagenomic investigations of microbial populations in environmental
samples. The present study was performed as part of the international
Ocean–Atmosphere–Sea Ice–Snowpack (OASIS) campaign (2009) in Barrow, Alaska.
Five different types of Arctic snow: (i) accumulated snow, (ii) windpack,
(iii) blowing snow, (iv) surface hoar snow, (v) fresh snow and frost flowers
were used for this study (Fierz et al., 2009; Glossary
of Meteorology, 2009). Frost flowers are dendritic shape clusters of ice
crystals that form at the interface between warm ice surface and
sufficiently cold atmospheric temperature and humidity (Obbard
et al., 2009). The chemistry of frost flowers has garnered increased
interest because these salty ice crystals have been shown to act as a
source for the following: (i) sea-salt aerosol (Perovich and Richter-Menge,
1994), and (ii) BrO, which contributes to ozone depletion events
(Kaleschke et al., 2004). Increased bacterial
abundance have also been found in frost flowers (Bowman and
Deming, 2010). Yet, further research is still required to better understand
the mechanisms of physical, chemical and biological processes involving
frost flowers.
The aim of the study, in five different snow types and frost flowers in the
Arctic, was to evaluate the: (i) identification and quantification of the
number of viable bacterial and fungal colonies, (ii) determination of the ice
nucleation (IN) property of: (a) selected isolated bacteria and (b) melted
samples, and (iii) identification of the total bacterial pool using
next-generation sequencing. We herein provide further information on the
biological composition of Arctic snow and frost flowers at genomic level,
shed light on the potential influence of atmospheric transport on the change
of microbial diversity, and discuss their potential roles in the
freezing-melting processes of ice-snow in the Arctic.
Experimental methods
Study sites
Five different types of Arctic snow were studied: (i) accumulated snow, (ii)
windpack, (iii) blowing snow, (iv) surface hoar snow, (v) fresh snow and
frost flowers which were collected from 4–20 March 2009 during the
OASIS campaign in Barrow, AK, USA. Detailed snow sampling procedures have
been described elsewhere (Kos et al., 2014). Snow samples
were collected from a field dedicated to snow research in the clean air
sector at 71.31∘ N, 156.6∘ W, 400 m to the southeast of
the Barrow Arctic Research Center (BARC), and frost flower samples were
collected from sea ice at 71.36∘ N, 156.70∘ W. Vehicle
access was restricted to the snow sampling area, and equipment was
transported on foot with a hand-pulled sled to limit local pollution. Snow
sampling devices were sterile and single-use. A sterile high-density
polyethylene (HDPE) spoon (Fisher Scientific, Montreal, Canada) was used to
collect the first 3 cm of the surface snow to fill the HDPE (Fisher
Scientific, Montreal, Canada) sample containers (220 m L-1). Similarly, a total
of 900 m L-1 of frost flower samples were collected by carefully lifting the
frost flower off the surface with a shovel to minimize (but not completely
eliminate) brine content. Frost flower sampling was collected from a frost
flower “field” on a flat area of thick sea ice, in a single location about
5 km northwest of Barrow on 20 March 2009 (also described by: Beine et
al., 2012; Douglas et al., 2012). Snow temperature was measured using a
long-stemmed thermometer (Fisher Scientific, Montreal, Canada), and the
meteorological conditions were recorded (air temperature, wind direction,
and cloud cover). Average snow temperature was at -19 ∘C and air
temperature was at -21 ∘C. The frost flowers were characterized
as “old frost flowers” having coatings from increased vapor phase
deposition (Douglas et al., 2012). Samples were kept frozen (-20 ∘C) until shipped out by airfreight (transit time 41 hours) in commercial
coolers (Coleman). The maximum temperature upon arrival in the laboratory at
McGill University in Montreal was -5 ∘C. Arctic samples were
stored in a freezer at -20 ∘C (Viking) until analysis. Surface
snow samples from heavy snowfall regions in the province of Quebec, Canada;
Mont-Tremblant, the city of Montreal (urban snow) and its suburb,
Pierrefonds, were also collected using similar techniques. A minimum of three
samples for each snow type and frost flower were used for analysis.
Isolation of viable microorganisms and Drop-freezing assays
Snow and frost flower samples were melted directly by transferring from
freezer to refrigerator at 4 ∘C, under sterile conditions, and
these conditions were maintained at all times using sterile instruments and
materials or certified sterile single-use supplies. Once melted, they were
kept on ice for the IN experiment, or transferred to the laminar flow hood
to culture the microorganisms. To grow the microorganisms, 1 milliliter of
Arctic samples (snow and frost flower) were placed in standard 100 × 15 mm sterile plastic Petri dishes (Fisher Scientific, Montreal,
Canada). The media used for bacteria were tryptic soy agar (TSA), and R2A
agar, a low nutrient medium used to improve the recovery of stressed
bacteria. For fungi, mycological agar (Rybnikar, 1986) at neutral pH,
and sabouraud dextrose agar (SDA) at a low pH of approximately 5.6 (all
media by Becton, Dickson and Co, Mississauga, Canada) were used. In a flask,
agar was dissolved and heated in ultrapure Milli-Q water (18 ohms
resistance) according to the manufacturer's recommendation. After boiling
for 1 min or until the medium was completely dissolved, the flask was
autoclaved at 121 ∘C for 15 min. Plates were incubated at 4 ∘C and were regularly checked for growth, and the colonies were
counted.
Drop-freezing assays were done on: (i) viable isolated bacteria obtained from
Arctic samples, and (ii) melted Arctic samples. Viable bacteria isolated from
Arctic samples grown on Petri dishes were mixed with sterile ultrapure water
(Millipore, Mississauga, Canada). The optical density of 1 at 600 nm was
used to adjust the concentrations of bacteria to 108 cells m L-1.
IN experiments were performed using a homemade copper cooling plate (cooling
rate of 1 ∘C min-1), a technique first described by Vali (1971). The copper plate was coated evenly with commercial
Vaseline™
petroleum jelly. The samples were kept on ice and were loaded as 10 µL
droplets. A minimum of 150 drops was used for each experiment. Tap water was
also used as a control that showed IN activity between positive
(Pseudomonas syringae) and negative controls (ultrapure water). The temperature of each
frozen droplet was recorded. IN temperatures are a simple average of the
temperatures at which a sample group of drops freezes; i.e., the sum of the
freezing temperature of each drop in the ensemble divided by the total
number of drops.
Bacterial DNA isolation, amplification of 16S rDNA, sequencing and
identification
Part of each bacterial colony was picked by a sterile disposable inoculating
loop (VWR, Mississauga, Canada) and mixed with
Ready-Lyse™ Lysozyme (Epicentre Technologies,
Madison, USA) and proteinase K in 1.5 m L-1 Eppendorf tube. DNA was
extracted and purified equally well with either a DNAeasy kit (Qiagen ,
Toronto, Canada) or a Master Pure DNA purification kit (Epicentre
Technologies, Madison, USA) according to the manufacturer's instructions. The
conserved sequence of extracted DNA was amplified by polymerase chain
reaction (PCR) (Techne Flexigene Thermal
Cycler FFG02HSD) in a final volume of 25 µL using 16S universal
primers 27F and 149R (Forward primer 5′-AGAGTTTGATCCTGGCTCAG-3′ and
reverse primer 5′-ACGGCTACCTTGTTACGACTT-3′, Integrated DNA Technologies,
Coralville, USA) yielding a product of about 1465 bp. An
Eppendorf™ tube containing all the
ingredients, but DNA was used as control (blank) for every set of PCR. A
typical PCR reaction for one tube contained 2.5 µL of 10X buffer,
1 µL of each primer of 2.5 µM, 0.6 µL of 10 mM
dNTP, 1 µL of 0.1 µg µL-1 DNA,
0.1 µL of 5U / µL of Taq polymerase, 2.5 µL
of 25 mM of MgCl2, 0.8 µL of 1 M NaCl, and to
25 µL of nuclease free water (Promega, Madison, USA). PCR included
35 cycles of denaturing at 94 ∘C for 1 min, annealing at
55 ∘C for 1 min, and extending at 72 ∘C for 2 min,
followed by a 7 min final extension at 72 and 4 ∘C forever. The PCR
product was separated and analyzed in 1.2 % agarose gel electrophoresis
stained with ethidium bromide.
The PCR product of 16S rDNA genes obtained from the cultured bacterial
colonies were purified using QIAquick PCR Purification Kit (Qiagen, Toronto,
Canada), sequenced at McGill University and Génome
Québec Innovation Centre, Montreal, Canada. The 16S rDNA sequences were aligned and
compared with those available in the GenBank databases using the BLASTN
(Basic Local Alignment Search Tool for DNA/nucleic acid) through the NCBI
(National Center for Biotechnology Information server) to identify sequences
that share regions of homology with isolated sequences.
454 Pyrosequencing
We opted to use a conventional technique in concentrating the bacteria in
Arctic samples using filtration, sonication and precipitation using
high-speed centrifuge. For DNA analysis, bath sonication is a method that has
been used to dislodge adherent bacteria in environmental samples (Buesing and
Gessner, 2002; Bopp et al.,
2011; Joly et al., 2006; Kesberg and Schleheck,
2013) as well as in medically devised explanted prosthetic instruments
studied in hundreds of patients (Piper et al., 2009; Sampedro et al.,
2010; Tunney et al., 1999). The dislodge bacteria is viable and can be
cultured (Trampuz et al., 2007; Vergidis et al., 2011; Piper et
al., 2009; Sampedro et al., 2010; Tunney et al., 1999; Joly et al., 2006;
Kesberg and Schleheck, 2013; Solon et al., 2011). Melted snow was passed
through a 0.22 micron filter (Millipore, Mississauga, Canada). Filter was
sonicated in 17 m L-1 of 1X Tris/Borate/EDTA (TBE)
buffer in an ultrasound bath for 10 min. For removing the viable bacteria
from surface by sonication, no major differences were reported for duration
of exposure time at 5 or 10 min, and temperatures at 22 ∘C (room
temperature) or 6 ∘C; though the latter only slightly improved
bacterial viability (Monsen et al., 2009). In our experiment, all the
above factors were considered for removing the bacteria from
filters. All sequences obtained in this study have been submitted to NCBI/SRA
under bioproject ID PRJNA285322.
The liquid was collected in sterile 50 m L-1 polycarbonate centrifuge tubes
(Nalgen. Rochester, NY, USA). The liquid was centrifuged for 15 min at
18 000 g, and the pellet was re-suspended in 200 ul of 1X TBE buffer
(Moran et al., 2008; Gharaibeh et al., 2009; Gantner et al., 2011;
Medinger et al., 2010). Ready-Lyse™ Lysozyme (Epicentre Technologies,
Madison, USA) was used to lyse the cell, and DNA was extracted and purified
using either a DNAeasy kit (Qiagen, Toronto, Canada) or a Master Pure DNA
purification kit (Epicentre Technologies, Madison, USA) according to the
manufacturer's instructions. A barcoded 16S rDNA tag was used to amplify
three distinct regions (V1-V3) of the bacterial 16S rDNA gene (∼ 500 bp). The forward primer consisted of 454 Life Science adaptor A, a unique
10-base barcode rapid library Multiplex identifier (MID), and the specific forward primer sequence:
5′-CCATCTCATCCCTGCGTGTCTCCGACTCAGACGAGT GCGTATTACCGCGGCTGCTGG-3′, and
the reverse primer consisted of 454 Life Science adaptor B fused to the
specific reverse primer sequence: 5′-CCTATCCCCTGTGTGCCTTGGCAGTCTCAGAGAGTT TGATCCTGGCTCAG-3′.
Amplification was done in triplicate and performed in a 20 µL reaction
volume containing 13.85 µL of RNase and DNase free water, 2 µL of
5 ng µL-1 of DNA template, 2 µL of 10X AccuPrime PCR buffer
(Invitrogen, Burlington, Canada), 1 µL of 2 µM of each primer, and
0.15 µL of AccuPrime Taq DNA polymerase Hifi (Invitrogen, Burlington,
Canada). Cycling conditions were performed at 95 ∘C for 2 min, followed by 30 cycles at 95 ∘C
for 20 s, 56 ∘C (V1-V3 primer set), 72 ∘C for 5 min and 4 ∘C forever. PCR products were purified with AMPure XP beads
(Agencourt, Beckman Coulter, Canada), and eluted in 20 µL of ultrapure
water. The quality and size of the amplicons were assessed on a 2100
Bioanalyzer using a DNA 1000 kit (Agilent Technologies, Mississauga, Canada)
and quantified with the PicoGreen Assay (Invitrogen, Burlington, Canada).
The amplicons library was pooled in equimolar amounts. NGS sequencing was
performed using 1/8 of sequencing plate of GS FLX Titanium (454/Roche,
Mississauga, Canada) for reading. The pool was sequenced uni-directionally
from adaptor A with the Genome Sequencer FLX Titanium (454/Roche,
Mississauga, Canada) at McGill University and Génome Québec
Innovation Centre, Montreal, Canada. The generated sequences from
pyrosequencing was analyzed with software MOTHUR formatted version of the
RDP classifier using a Bayesian method (Wang et al., 2007) with
1000 bootstrap replicates for pre-processing (quality-adjustment, barcode
split), identification of operational taxonomic units (OTUs) defined at ≥ 97 % 16S rRNA sequence identity level, taxonomic assignment, community
comparison, and statistical analysis
(Schloss et al., 2009). Trimming was done
by quality from the 3′ end. Sequences containing ambiguous bases,
homopolymers longer than eight bases, or an average quality score below 20
over a 50 bp long window were excluded (Schloss et al., 2011).
The diversity of the bacterial communities for four different snow types and
frost flowers was estimated using the Simpson diversity index, species
richness using a rarefaction metric, and the nonparametric Chao index
(Chao, 1984). Chao1 index is a good estimator for obtaining true
species richness based on the observed species accumulation pattern wherein
the number of singletons and doubletons were used.
Electron microscopy analysis
Analysis transmission electron microscopy (TEM) in conjunction with
energy-dispersive X-ray spectroscopy (EDS) analyses were used on Arctic snow
samples and frost flowers to detect microbial and chemical compounds.
Samples were freeze-dried. A sample solution of 7 µL was put onto a
200 mesh carbon-coated copper grid for 1 min. It was negative stained with 2 % uranyle acetate for 30 s. Imaging was done under TEM (Hitachi H7500
operated at 100 keV and spot size 5), a Philips CM200 200 kV TEM equipped
with Gatan Ultrascan 1000 2 k × 2 k CCD camera system (Model 895) and EDAX
Genesis EDS Analysis System.
Results and discussion
Recent observations have indicated that snowpack is indeed a complex
microhabitat that permits the growth of diverse microorganisms allowing for
photo-chemical and biological reactions to occur
(Amoroso et al., 2010). Nitrification
(Amoroso et al., 2010), transformation of mercury
(Moller et al., 2011) and other pollutants within the
snowpack have been detected. Ammonia-oxidizing Betaproteobacteria are active
nitrifiers in glacial ice microcosms (Miteva et al., 2007), and the
presence of nifH genes has been previously suggested the potential for
nitrogen fixation in supraglacial snow (Boyd et al., 2011). A clear
understanding of the bacterial population and their interactions will be
required to further reveal the role these play in altering the Arctic
environment and climate.
In this study, the next-generation sequencing (NGS) technique in conjunction
with a classical cultural method was used to identify and compare the
bacterial community in different types of Arctic snow and frost flowers.
Moreover, the Arctic microbial population was compared to urban snow from
the cold North American city of Montreal. Using GS FLX Titanium (450/Roche),
a total of 88 937 reads was made for all the samples with the average number
of total reads being 17 787. The average read length for all the reading was
a 373 base with an average read quality of 34. After trimming and passing through
quality control, the final read length was recovered as follows: 319 ± 18
bases (urban snow, US), 299 ± 17 bases (blowing snow, BS), 401 ± 20 bases (surface hoar snow, SH), 385 ± 20 bases (windpack, WP), and
419 ± 20 bases (frost flowers, FFs).
The diversity of the bacterial communities for four different snow types and
frost flowers was estimated using the Simpson diversity index, species
richness using rarefaction metric, and the nonparametric Chao index
(Chao, 1984). The Simpson diversity index which takes into account
both species' richness, and an evenness of abundance among the species
present reached a plateau after the sequencing of sampling of about 5000 for
BS, 6000 for WP, 7000 for US, 8000 for SH, and 10 000 for FFs (Fig. A1a). The Chao index gave values between 1500 and 7500 with BS exhibiting the
lowest richness (Fig. A1b). The richness in total bacterial
communities of Arctic samples was estimated by rarefaction analysis. The
shapes of the rarefaction curves did not reach asymptote, indicating that
bacterial richness for most samples especially for urban snow, and windpack
is not yet complete (Fig. A1c). Using the 3 % cut-off value in
sequence differences for OTU, the estimates of the richness of total
bacterial communities ranged from 1033 in BS, 1971 in WP, 1956 in SH, 1933
in US and 1605 in FFs (Fig. A1c). Based on these analyses, the order
of the highest diversity of bacteria to the lowest was observed in windpack,
surface hoar snow, urban snow, frost flowers, and blowing snow, under
experimental conditions herein used.
In the next-generation sequencing part of this study, pyro-sequencing was
done only for bacteria and not fungi which was feasible under our existing
facilities. However, high resolution electron microscopy (Fig. 1) further
revealed the appearance of the existence of several biological materials,
remnants of biological activities, and not only biological entities in their
entirety. The individual sequences represented known phyla or candidate
divisions as follows: 11 (urban snow), Arctic samples: 18 (WP), 16 (SH), 15 (BS),
and 18 (FFs) (see also the Appendix Table A1a). The majority of sequences
(12.3–83.1 %) belonged to one of the five major phyla: Proteobacteria,
Actinobacteria, Bacteroidetes, Firmicutes, and Cyanobacteria. The major
phyla for urban snow and Arctic samples were as follows: (i) urban snow –
Proteobacteria (49.04 %), Bacteroidetes (47.5 %); (ii) windpack –
Proteobacteria (66.1 %), Cyanobacteria (12.3 %); (iii) surface hoar
snow –
Proteobacteria (67 %), Firmicutes (13.6 %); (iv) blowing snow –
Proteobacteria (83.1 %), Firmicutes (6 %), Actinobacteria (5.09 %) and
(v) frost flowers – Proteobacteria (50.2 %), and Actinobacteria (32.8 %)
(Appendix Table A1a). Proteobacteria was the most widely expressed phylum
among all the Arctic samples tested with the greatest abundance observed in
blowing snow.
Analysis of snow-associated microorganisms by transmission
electron microscopy (TEM) in conjunction with energy-dispersive X-ray
spectroscopy (EDS). Microorganisms (a) and chemicals (b) were
detected in selected Arctic samples: blowing snow, BS; surface hoar snow,
SH; windpack, WP, and frost flowers, FFs.
![]()
Bacterial community composition in Arctic samples and urban
snow at genera level as detected by Roche 454 GS-FLX Titanium. (a):
distribution of bacterial genus observed at greater than 1 %. (b):
number of occurrence for each percentage observed for any genus in total
bacteria. (The name of corresponding genera for each percentage
(>1 %) is listed in the Appendix Table A2).
![]()
At the genus level, sequences represented 134 different genera for urban
snow; Arctic samples: 245 for windpack, 139 for surface hoar snow, 101 for
blowing snow, and 158 for frost flowers. The distribution of bacterial
genera observed at greater than 1 % and the number of occurrence for each
percentage observed for any genus in total bacteria is shown in Fig. 2.
The name of corresponding genera for each percentage (> 1 %) is
listed in the Appendix Table A2. The top four genera with the highest
percentage detected for each sample were as follows: urban snow (US) –
Flavobacterium (40 %), Polaromonas (11.2 %), Variovorax (6.6 %) and Sandarakinorhabdus (6 %); windpack (WP) – Methylobacterium (9 %), Sphingomonas (4.9 %), Lamprocystis (4.5 %), and Roseateles (4.4 %); surface hoar snow
(SH) – Roseateles (18.6 %), Methylobacterium (14.1 %), Bacillus (5.7 %), and Streptococcus (5.7 %); blowing snow (BS) – Methylobacterium (15.3 %), Bradyrhizobium (1.6 %), Bacillus (1 %), Sphingomonas (1 %); and for frost flowers
(FFs) – Propionibacterineae (32.9 %), Roseateles (8 %), Staphylococcus (6.7 %), and Candidatus Pelagibacter (6.1 %) (Appendix Table A1b).
Bhatia et al. (2006) compared bacterial communities from solid snow and snow
melt water from the high Arctic John Evans glacier with basal ice and
sub-glacial communities of the same glacier. Distinct bacterial communities
were found in each one of these different environments with very few common
profiles. Similar to this study, our NGS analysis clearly showed variation
of distinct sets of microorganisms among different Arctic samples and urban
snow. Our observation also suggests the importance of the selective pressure
of specific physical and chemical characteristics of each snow type that may
serve as a predictor of microbial abundance and composition (Miteva,
2008). It may specifically favor the growth conditions for microbial
communities that originated from diverse sources. Interestingly, few
Geobacter bacteria (at 0.09 %) were only detected in the windpack. Some of
which have been suggested in previous studies, to catalyze anaerobic U (IV)
oxidation with nitrate serving as a potential electron acceptor leading to
the subsequent mobilization of uranium (Finneran et al.,
2002). Geobacter species have also shown to reduce soluble U(VI) to the less
soluble U(IV) (Lovley, 1991). The Arctic region is exposed to
further uranium originating from radioactive waste due to military activity,
oil and gas, and uranium mining exploitation (Thomas et al., 1992;
Dowdall et al., 2004; Convey, 2010; Emmerson and Lahn, 2012). However,
additional research is required to evaluate the role of microorganisms in
chemical transformation of molecules in the Arctic region.
Relative abundance of origin and physical properties of
analyzed NGS bacteria. Bacteria in Arctic samples: blowing snow (BS), surface hoar snow (SH),
windpack snow (WP), frost flowers (FFs); and urban snow (US) were analyzed
for their origin, and ice nucleation/melting properties as detected by Roche 454
GS-FLX Titanium; (a) total bacteria in Arctic samples: (i) previously
observed in Asian/African (dust); (ii) cold oceanic water, (iii) with
antifreeze (AF), (iv) ice nucleation (IN), and (v) IN and AF property; (b) in subtotal of total Arctic bacterial pool originated from Asian/African
dust: (i) with AF, or (ii) IN property.
(a) Total bacteria in Arctic
Snow
Dust
Cold Oceanic
AF
IN
IN & AF
Categories
Water
US
47 %
0 %
7 %
15 %
0.1 %
BS
36 %
0.01 %
0.10 %
15 %
0.03 %
SH
44 %
0.04 %
0.20 %
16 %
0.01 %
WP
39 %
0.01 %
1 %
16 %
0.01 %
FFs
44 %
0.01 %
0.10 %
14 %
0.01 %
(b) Subtotal of Arctic bacteria in Asian/African dust origin
Snow
AF
IN
Categories
US
13 %
2 %
BS
1 %
3 %
SH
1 %
3 %
WP
6 %
2 %
Ffs
0.4 %
0.2 %
Table 1 shows the analysis of NGS results encompassing bacteria at genus
level that have been previously detected: (i) in Asian or African dust
storms, (ii) with antifreeze and/or ice nucleation properties, and (iii) in
cold oceanic water. Note that the existence of these bacteria does not
ensure the expression of their property, and thus the existence of ice
nucleating and freezing bacteria does not reflect their expression in
environmental matrices. The percentage of bacteria (at genus level) that
were previously observed in Asian or African dust samples were in the range
of 36–47 % for all the snow categories and frost flower samples. Only a
very small percentage of identified bacteria with previously demonstrated
antifreeze property (Yamashita et al., 2002), were
detected in: windpack – 1 %; surface hoar snow – 0.2 %; and frost
flowers – 1 %. Urban Montreal snow samples had the highest number (7 %). 14–16 %
of samples contained bacteria with ice nucleation properties, as shown in
Table 1a. A very small percentage of identified bacteria showed both ice
nucleation and antifreeze properties. Some bacteria such as Pseudomonas fluorescens KUAF-68 and
Pseudomonas borealis DL7 have been reported to have both antifreeze and ice nucleation activity
(Kawahara et al., 2004; Wilson et al., 2006). Having these two properties has been suggested to enhance the freeze-tolerance survival of bacteria by
maintaining small ice crystals with ice recrystallization inhibition
protecting against freeze–thaw stress with antifreeze proteins (Xu
et al., 1998), minimizing damage from explosive ice crystal growth and
stabilizing the outer membrane with the low thermal hysteresis value
(Xu et al., 1998), and minimizing the supercooling point with ice
nucleation proteins (Kawahara et al., 2004). In our
study, under our experimental conditions, the highest percentage was
observed in urban snow samples (0.1 %), and the lowest was observed in
blowing snow (0.03 %). Only 0.01 % of bacteria in windpack snow, surface
hoar snow and frost flowers had both ice nucleation and antifreeze
properties. Similarly, Arctic samples showed a minute number of bacteria
that have been previously detected in cold oceanic water (0.01–0.04 %);
none were detected in urban snow samples, under the experimental conditions
in this work.
Some of the bacteria in Arctic samples have previously been identified in
Asian or African storms with ice nucleation (Kellogg et al., 2004;
Griffin, 2007) or antifreeze properties (Smith et al., 2013). Within
these bacterial genera pool, 2–3 % of Arctic snow samples and urban snow
showed ice nucleation properties with only 0.2 % in frost flowers (Table 1b). Bacteria with antifreeze properties were observed for only 0.4 % in
frost flowers and 1 % in both blowing snow and surface hoar snow. Higher
numbers of such bacteria were observed for windpack (6 %). Interestingly,
13 % of bacteria originating from dust storms in urban snow had antifreeze
properties. The possible introduction of antifreeze bacteria from the ocean
into the air by different mechanisms such as the bursting of frost flowers
by wind and fresh snowfall may further provide and facilitate infiltration
into the snowpack (Rankin et al., 2002). The detection of a high
number of bacteria with a vast genetic diversity pool, using NGS analysis,
further illustrates that the snowpack is a heterogeneous soup of microbial
entities. The chemical environment of the snowpack is constantly evolving by
novel streams of chemicals through fresh precipitations, wind transportation
and metabolic activity of microbial. On a speculative basis, the increased
incidence of dust storms, possibly due to climate change, the detection of
specific bacteria with possible mid-latitude desert origins into the Arctic
environment may suggest a shift in the balance of “native bacterial
populations” in the Arctic, yet, there is no current evidence to firmly
support this hypothesis and further research is required. One may also
speculate that it might be conceivable to consider interactions among the
heterogeneous population of microbial in Arctic samples, including
non-native taxa adaptation in the Arctic snow-ice genome. Though the Arctic
does not provide a native habitat for non-native bacteria or biological
species originating from elsewhere in the world, their entrance into the
Arctic may affect certain bio-chemical reactions, or alter the nutrient pool
for the other native microbial entities. In turn, it might affect the ratio
and the survival rate of certain populations of microbial with freezing or
anti-freezing properties, impacting the melting of ice or snowpack in the
Arctic region. Yet, further studies are required to evaluate such
speculations.
With Arctic regions currently warming at rapid rates (Hansen et al.,
2006; Convey et al., 2009), the interrelationship of ice/snow microbial, and
increased water availability is yet to be determined. Though fungi species
and their spore are widespread in the atmosphere, little is known about
their role and presence in the Arctic. Interestingly, a few studies have
shown that fungi like bacteria can be effective ice nucleators, capable of
initiating ice nucleation at temperatures as high as -2 ∘C
(Kieft and Ahmadjian, 1989; Pouleur et al., 1992). Some fungi have shown
to exhibit an effect to prevent ice crystal expansion by synthesizing
antifreeze proteins permitting their growth at subzero temperatures (Tojo
and Newsham, 2012; Hoshino et al., 1998). Present study contributes also to
fungal population in snow and frost flowers at an Arctic site of Barrow,
Alaska, USA.
Only a small fraction of a microbial community, especially from extreme
environments such as the Arctic can be grown under laboratory conditions
since many factors such as the composition of the medium that fully supports
the basic needs of microorganisms for growth is not known. This notion was
further confirmed as cultivable bacteria encompassing 0.1 to 3 % of the
total bacteria, which was detected by NGS technique. Thus, the identified
number of cultivated bacteria and fungi independent from different snow
categories and frost flowers does not reflect the actual number of microbial
and should be considered as the lower limit, and therefore more metagenomic
analysis (such as NGS which was deployed in this study) is essential to
decipher the complex pool of microorganisms in the Arctic. The cultivable
bacteria might be representative of the active fraction of cultivable
bacterial snow communities (Ellis et al., 2003; Frette et
al., 2004), as was detected for bacteria living in different environmental
samples such as soil, and marine samples (Pinhassi et al., 1997; Rehnstam
et al., 1993). Table 2 contains the identified cultivable bacteria found in
each category of Arctic samples (snow and frost flowers). Figure 3a
(bacteria) and 3b (fungi) show the variability in numbers of colony-forming
units (CFUs) within and between the different sample types using two
different media (R2A and TSA) for bacteria and (SDA and mycological) for
fungi. The average number of viable bacteria was higher than the number of
fungi in Arctic samples. Overall, a higher number of CFUs was observed in the
R2A medium with a more limited nutrient content than TSA, wherein most
aerobic bacteria are able to grow. In the R2A plates, the highest number of
bacteria was observed in frost flowers (FFs) and accumulated snow (AS) with
325 and 314 CFU m L-1, respectively (Fig. 3a). However, the highest
number of fungi grown in the mycological plate was observed in windpack (WP)
and blowing snow (BS) with 5 CFU m L-1 (Fig. 3b).
Identification of viable cultivable bacteria in Arctic
samples. Some of the bacterial colonies were identified in each snow
categories: accumulated snow (AS), blowing snow (BS), fresh snow (FS),
windpack (WP), and frost flowers (FFs); accession number, the nearest
neighbor found in the database, a unique identifier given to a DNA
sequence; identified species; and % similarity, the ratio of identical
query bases to known bases in the database.
Snow category
Bacterial colony #
Accession #
Species
% Similarity
FFs
1
GU975796.1
Curtobacterium sp.D2.2
97
JF798380.1
Curtobacterium luteum
97
FN178369.1
Curtobacterium citreum
97
HM045842.1
Bacillus sp. WJ18
97
2
EU196527.1
Paracoccus sp. B10
97
DQ195864.1
Rhodobacteraceae bacterium
97
3
HQ425309.1
Kocuria sp. M1-36
98
FR682683.1
Kocuria rhizophila
98
4
JN084144.1
Curtobacterium oceanosedimentum
93
EF592577.1
Flavobacterium oceanosedimentum
93
EU373393.1
Bacillus subtilis
93
FS
1
JN208198.1
Bacillus sp. DG7
96
AB648987.1
Bacillus megaterium
96
JN092792.1
Bacillus flexus
96
HQ857752.1
Bacillus aryabhattai
96
2
JN085952.1
Microbacterium sp. ZL2
98
JF700471.1
Microbacterium hydrocarbonoxydans
98
HQ113206.1
Microbacterium oxydans
98
AS
1
EU379295.1
Micrococcus luteus
92
2
HE578790.1
Micrococcus luteus
95
HM209728.1
Micrococcus yunnanensis
95
3
EU584512.1
Frigoribacterium sp. Everest-gws-26
97
AY599739.1
Actinobacterium TB3-4-I
97
4
JF969180.1
Bacterium REGD8
95
JF778689.1
Sporosarcina sp. DRB20
94
WP
1
JF728909.1
Leifsonia sp. DAB_MOR27
96
DQ172984.2
Bacterium TSBY-9
96
NR_042669.1
Leifsonia kafniensis
96
NR_041548.1
Microterricola viridarii
96
BS
1
EF540454.1
Brevundimonas sp. d1M
96
JN020187.1
Uncultured alpha proteobacterium clone cher4_1B_11
96
NR_037106.1
Brevundimonas variabilis
96
2
FR691407.1
Brevundimonas sp. R-36741
96
AB452982.1
Alpha proteobacterium HIBAF003
96
3
JF778709.1
Paenisporosarcina macmurdoensis
99
JF778708.1
Sporosarcina sp. GRT2
99
4
JF969180.1
Bacterium REGD8
98
HM224487.1
Sporosarcina sp. TPD39
98
JF778709.1
Paenisporosarcina macmurdoensis
98
5
JN082256.1
Bacillus sp. cf30
97
JN092792.1
Bacillus flexus
97
HQ143640.1
Geobacillus stearothermophilus
97
6
FJ487574.1
Paenibacillus amylolyticus
95
NR_044524.1
Paenibacillus xylanexedens
95
HQ202814.1
Paenibacillus tylopili
95
Concentration of viable cultivable bacteria and
fungi in Arctic samples. Mean number of colony-forming units (CFUs) m L-1
of snow on two media for: (a) bacteria (TSA and R2A) and (b)
fungi (SDA and mycological agar) in each Arctic snow categories: accumulated
snow (AS), blowing snow (BS), fresh snow (FS), surface hoar snow (SH),
windpack (WP), and frost flowers (FFs). Samples from several urban sites
collected in Quebec, Canada: Pierrefonds (suburb of Montreal; “PFDS”), and
Mont-Tremblant (Laurentians; “Tremblant”) are included for comparison.
Error bars indicate standard deviation (SD) of the mean for three
experiments.
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Both NGS and culture method analysis revealed a very high number of bacteria
in frost flowers as compared to the other snow types that we tested. In
recent years, special attention has been focused on the role of frost
flowers as a contributing factor to changing the chemistry of the atmosphere
in the Arctic. Frost flowers are (i) an important source of sea-salt aerosol
(Rankin et al., 2002; Perovich and Richter-Menge, 1994; Martin et al.,
1995), (ii) a contributing factor in releasing the ozone-depleting molecule,
bromine monoxide (BrO), as was detected by satellite
(Kaleschke et al., 2004), and (iii) a source of sea
ice bacteria (Collins et al., 2010). Moreover, with their physical
structure and chemical composition, frost flowers might provide a habitat
for microbiological bodies such as bacteria, as well as protective and
favorable conditions for metabolic and photochemical reactions (Bowman and
Deming, 2010). The observed simple organic compounds and increased
concentrations of both formaldehyde (Barret et
al., 2009), hydrogen peroxide (Beine and Anastasio, 2009)
within frost flower, may suggest that selected bacterial strains can act as
a substrate for the photolytic production of oxidants (Bowman and
Deming, 2010), and simple organic compounds (Ariya et al., 2002).
The regular release mechanism of bacteria through frost flower, such as
those with high ice nucleation activity, into the atmosphere, with potential
transportation, may provide an additional impact on bioaerosol lower
tropospheric mixing ratios (Jayaweera and Flanagan, 1982).
Ice nucleation activity of viable cultivable
bacteria in Arctic samples. The average ice nucleation temperature of: (a) individual bacterial isolates and (b) total bacterial isolates
and melted Arctic samples. Arctic snow categories: accumulated snow (AS),
blowing snow (BS), fresh snow (FS), surface hoar snow (SH), windpack (WP),
and frost flowers (FFs). Controls: ultrapure water (Milli-Q-water), tap
water, and a suspension of laboratory-grown Pseudomonas syringae (P. syringae). Error bars indicate
standard deviation (SD) of the mean for three experiments.
![]()
As opposed to frost flowers, accumulated snow is characterized by several
layers of snowfall, which may have experienced repeated freeze–thaw cycles,
and solar irradiation exposure. Analysis by cultural method showed that the
highest number of bacteria is present in accumulated snow samples. Each fresh
snowfall adds new nutrients and microorganisms to the old pool of accumulated
snow. With the detection of more than 100 organic species in the aerosols at Alert in the
Canadian High arctic (February–June) (Fu et al., 2008), the snow layers
could be further enriched with nutrients by the air/snow exchange (Xie et
al., 2007; Cincinelli et al., 2005). Over time, bacterial populations in
accumulated snow may increase by their sustainability and slow growth at very
low temperature (-2 to -35 ∘C) (Junge et al., 2004; Gilichinsky
et al., 1995; Panikov and Sizova, 2007; Bakermans et al., 2003).
Different types of Arctic snow and frost flower samples were tested for IN
activities using obtained cultured bacterial colonies as well as whole
melted Arctic samples. Ultrapure Milli-Q water (18 ohms resistance), tap
water, and P. syringae mixed in ultrapure water were used as controls. Tap water
contains organic impurities that allow ice to nucleate at warmer
temperatures than ultrapure water. The individual average freezing
temperature of bacterial isolates from different types of Arctic samples is
shown in Fig. 4a. To explore the impact of undetected biological and
chemical contents of snow samples on IN activity, the IN activity of each
melted Arctic sample was directly measured and its IN activity was compared
with the corresponding freezing temperature of the average sum of the total
individual isolated bacterial colonies (Fig. 4b). The freezing temperatures
of the average sum of the total individual (ASTI) isolated bacterial
colonies fall at intermediate values between sterile ultrapure water (-24.3 ± 1.2 ∘C) and tap water (-15.3 ± 0.9 ∘C).
The highest and lowest ice nucleation activity of bacteria was observed in
fresh snow (ASTI: -15.7 ± 5.6 ∘C) and frost flowers (ASTI:
-20.3 ± 1.5 ∘C), respectively. The ice nucleation activity
of fresh snow was comparable to tap water (-15.3 ± 0.9 ∘C)
(Fig. 4b).
Many of the bacterial isolates in different categories of Arctic samples
showed a moderate IN activity at -15.9 ± 0.4 and -17.2 ± 0.8 ∘C in windpack (WP), -15.2 ± 1
and -16.1 ± 1.4 ∘C in blowing snow (BS), -15.2 ± 0.6, -12.9 ± 0.2, and -17.3 ± 0.3 ∘C
in accumulated snow (AS), and -14.0 ± 0.4, -13.7 ± 0.2, and -6.8 ± 0.2 ∘C in
fresh snow (FS) (Fig. 4a). Interestingly, bacteria with a type 2 ice
nucleation ability at -6.8 ± 0.2 ∘C was isolated in fresh
snow. This bacterium was identified with 96 % similarity to the Bacillus species
(Table 3).
Among tested bacterial colonies, fresh snow showed the highest variation in
ice nucleation activities; higher variation was observed for accumulated
snow as compared to frost flowers. Different factors such as nutrient
limitation and low temperature observed in the Arctic might have further
shifted the ice nucleation activity of bacteria to the higher temperature
(Nemecek-Marshall et al., 1993). Frost flower, a bridge between sea ice
and the atmosphere and linking biogenic to non-biogenic materials, had the
lowest average ice nucleation activity. This may be related to its salinity
and ability to accumulate different chemicals, but further studies are
required to provide insight on the physical and chemical processes in frost
flowers.
The observed range of IN activity in melted snow and frost flower samples
was between -9.5 ± 1.0 ∘C (FS) and -18.4 ± 0.1 ∘C (FFs) (Fig. 4b). Interestingly, all the melted Arctic snow
samples and frost flowers showed ice nucleation activity at the range very
close to the lowest recorded ice nucleation activity of individual
cultivable bacterial colonies. This observation may indicate an additional
role of other non-cultivable microbial and components in Arctic snow samples
and frost flowers which are important in increasing the ice nucleation
temperature. Culture-dependent methods selectively isolate a
plate-growth-adapted subpopulation from the microbial communities which may
represent the majority of the total bacterial numbers in samples
(Pinhassi et al., 1997; Rehnstam et al., 1993), but not necessarily the
total richness (number of different species) of the bacterial population
(Amann et al., 1995; Onstott et al., 1998).
Ice nucleation of identified viable cultivable bacteria in
Arctic sample. Ice nucleation temperature of identified bacterial isolates
in each snow categories: blowing snow (BS), fresh snow (FS), windpack (WP),
and frost flowers (FFs); accession number, a unique identifier given to a DNA
sequence; identified species; and % similarity, the ratio of identical
query bases to known bases in the database.
Snow
Bacterial
Ice nucleation
Accession #
Species
%
category
colony
temp. (∘C)
similarity
WP
A
-18.9 ± 2.5
U87778.1
Afipia genosp
86
JF799916.1
Bradyrhizobium
87
FR691406.1
Bosea
87
BS
A
-18.9 ± 1.6
JN082256.1
Bacillus sp.
97
JN092792.1
Bacillus flexus
97
HQ143640.1
Geobacillus stearothermophilus
97
B
-15.2 ± 1
JF343205.1
Paenibacillus amylolyticus
95
NR_044524.1
Paenibacillus xylanexedens
95
HQ202814.1
Paenibacillus tylopili
95
FS
A
-6.8 ± 0.2
JN208198.1
Bacillus sp.
96
AB648987.1
Bacillus megaterium
96
JN092792.1
Bacillus flexus
96
HQ857752.1
Bacillus aryabhattai
96
B
-21.6 ± 1
JN085952.1
Microbacterium sp.
98
JF700471.1
Microbacterium hydrocarbonoxydans
98
HQ113206.1
Microbacterium oxydans
98
FFs
A
-20.0 ± 1.5
HQ425309.1
Kocuria sp.
98
FR682683.1
Kocuria rhizophila
98
Table 3 enlists the identified cultivable bacteria with their ice nucleation
activity in each category of Arctic samples (snow and frost flowers).
Isolated bacteria were identified belonging to different genus, such as:
Afipia genosp, Bacillus, Paenibacillus, Microbacterium, and Kocuria. The highest IN activity corresponded to genus: Bacillus and
Paenibacillus with -6.8 ± 0.2 and
-15.2 ± 1 ∘C, respectively (Table 3). At the 16S rDNA gene level, they exhibited about
96 and 95 % similarity to reported species of Bacillus sp., Bacillus megaterium, Bacillus flexus, and Bacillus
aryabhattai; and Paenibacillus amylolyticus, Paenibacillus xylanexedens, and Paenibacillus tylopili, respectively.
The elemental composition of Arctic samples was determined using HR-TEM with
EDS. As shown in Fig. 1b, the presence of elements such as Mg, Al, Cl, Ca,
and U was detected. The presence of Si and Al might be an indication of soil
or through deposit of the dust transported from soil (Sposito, 2008;
Shridhar et al., 2010). The source of calcium might be related to either
marine or soil origins. Since phosphorous is the limiting macronutrient in
marine ecosystems (Toggweiler, 1999; Tyrrell, 1999), the positive
observation of this element in frost flower encourages further research in
the role of frost flowers in Arctic ecosystem.
It is noteworthy that although we focused on biomolecules materials, as we
can see from HR-TEM/EDS analysis, the detection of inorganic matter in
Arctic snow and frost flowers can contribute to ice nucleation. Hence, to
evaluate the snow freezing properties, the complex chemical and bio-chemical
pool of molecules and particles should be considered.