Abundance and diversity of snow bacteria in two glaciers at the Tibetan Plateau

Abstract

Snow bacterial abundance and diversity at the Guoqu Glacier and the East Rongbuk Glacier located in the central and southern Tibetan Pateau were investigated using a 16S rRNA gene clone library and flow cytometry approach. Bacterial abundance was observed to show seasonal variation, with different patterns, at the two glaciers. High bacterial abundance occurs during the monsoon season at the East Rongbuk Glacier and during the non-monsoon season at the Guoqu Glacier. Seasonal variation in abundance is caused by the snow bacterial growth at the East Rongbuk Glacier, but by bacterial input from the dust at the Guoqu Glacier. Under the influence of various atmospheric circulations and temperature, bacterial diversity varies seasonally at different degrees. Seasonal variation in bacterial diversity is more distinct at the Guoqu Glacier than at the East Rongbuk Glacier. Bacterial diversity at the two glaciers exhibits different responses to various environmental conditions. More bacteria at the Guoqu Glacier are connected with those from a soil environment, while more bacteria affiliated with a marine environment occur at the East Rongbuk Glacier.

Keywords

bacteria / abundance / diversity / snow / glacier / Tibetan Peau

Introduction

The Tibetan Pateau, the world’s highest Plateau, contains the most glaciers within the high latitudes, with an area of 4.9×10⁵ km² (Shi, 2000). Glaciers and snow that cover the Plateau not only influence the global climate but also sensitively indicate global changes (Barnett et al., 1989; Thompson et al., 2000; Duan et al., 2006; Tian et al., 2006). Temporal and spatial characteristics of major ions, organic compounds, and dust particles in the snow have been studied in detail. Snow chemical compositions all exhibit strong seasonal variation and signify climatic change (Xie et al., 2000; Kang et al., 2002, 2004, 2007a; Xiao et al., 2002a; Xu et al., 2006; Zhang et al., 2008). The snow chemistry spatial pattern decreasing from north to south implies differing atmospheric circulation (Xiao et al., 2002a, b; Xu et al., 2006). However, less is reported on bacterial features in snow on the plateau. Only bacteria in the snow at Xixiabangma Peak and Mt. Everest in the Himalayas have been reported (Xiong et al., 1999; Liu et al., 2006, 2007). Up to now, the snow bacterial response to climatic and environmental changes in different regions on the plateau has not been studied.

In recent years, bacteria in glacial ice cores over the Tibetan Plateau have received more and more attention (Zhang et al., 2001; Christner et al., 2003; Xiang et al., 2005; Zhang et al., 2007). It was found that microbial records from ice cores could potentially reflect past climatic and environmental changes (Yao et al., 2006). A glacier is formed from snow as a result of gradual compression and transformation into ice; an understanding of bacteria in snow is important for the study of ice core records.

In this study, we investigated the bacterial abundance and diversity and the related chemical parameters of snow at the East Rongbuk Glacier and Guoqu Glacier. The two glaciers are located at the central and the southern Tibetan Plateau, respectively (Fig. 1). Our aim was to explore the seasonal and spatial variations of snow bacterial communities and their relationship with climate and the environment at the different glaciers in the Tibetan Plateau.

Sample and methods

Study area

Samples analyzed herein were collected from two snowpits (G and RBL) at the southern and the central Tibetan Plateau. Snowpit G (33°34′37.8″N, 91°10′35.3″E, 5720 m) was located at the Guoqu Glacier to the north of Mt. Geladaindong, the summit of the Tanggula Mountain, with the samples being taken on November, 2005. Snowpit RBL (27°59′N, 86°55′E, 6500 m) was situated at the East Rongbuk Glacier on the northern slope of Mt. Everest, the highest mountain in the world, with the samples being collected on April 2005.

The East Rongbuk Glacier at Mt. Everest in the Himalayas is located at the boundary region between the Indian monsoon-dominated wet and warm climate in the south and the westerly jet stream-dominated continental climate in the north. Snowfall at the glacier is caused by the moisture transported by the Indian monsoon and by local moisture from a short distance convective air mass in the summer, and by the westerly jet stream during other seasons.

The Guoqu Glacier on Mt. Geladaindong represents the northernmost extent of the summer Indian monsoon. Its summertime precipitation is derived from the moisture transported by the Indian monsoon and regional convection, with limited precipitation during the winter due to the occasional influx of westerly disturbances.

Sampling

Snowpit RBL was 1.7 m deep. Snow samples for bacterial analysis were collected continuously at 15 cm depth intervals, and at 5 cm intervals for the determination of δ¹⁸O and the concentrations of major dissolved ions.

Snowpit G was 0.86 m deep. Snow samples for bacterial abundance, δ¹⁸O and the concentrations of major dissolved ions were collected continuously at 5 cm depth intervals along the pit wall. Snow samples for bacterial diversity analysis were collected at 15 cm intervals.

Extreme care was taken at all times to ensure minimal contamination. At the beginning of sampling, the surface in contact with air was systematically scratched using a sterile spoon which was discarded. Non-particulating sterile suits, sterile gloves and masks were worn during the entire sampling process. Scoops for collecting snow were sterilized beforehand and used only once, for one sample. Snow samples forδ¹⁸O and major ions were put into Whirl Pack bags. Samples for bacterial analysis were placed in sterile Nalgene bottles. Snow samples were kept frozen until analysis in the laboratory.

Enumeration of bacterial cells

Cell abundance was analyzed using flow cytometry (Beckman Coulter, Epics Altra II). Snow meltwater (1 mL) was fixed with glutaraldehyde (final concentration: 1%) for flow cytometry analysis. SYBR Green I was applied as the nucleic acid stain (Marie et al., 1997). Samples were run on an EPICS ALTRA II flow cytometer (Beckman Coulter), equipped with a 100-mW 488-nm water-cooled argon-ion laser (Cohenrent Inc., USA) and a standard filter set-up. An external sample injector (Harvard Apparatus PHD 2000) was employed for accurate quantification (Jiao et al., 2005). 1 μm (diameter) fluorescent beads (Polyscience Inc.) were added to the sample for internal reference.

Measuring ofδ¹⁸O, concentration of major dissolved ions

Theδ¹⁸O values were determined using MAT-253. The concentrations of major dissolved ions (Ca²⁺, Mg²⁺, Na⁺, Cl¹, SO₄^2-, NO₃^-, and NH₄⁺) were measured using a Dionex Ion Chromatograph model 2010 (Buck et al., 1992).

Community DNA extraction, PCR amplification and 16S rRNA gene clone library construction

Two samples from the Guoqu Glacier and six samples from the East Rongbuk Glacier were taken for molecular analysis. Snow samples were melted overnight at 4°C, and approximately 1 L meltwater was filtered through a 0.22 μm filter (Millipore). The membrane was immediately treated with 1 mL GTE buffer (25 mmol/L Tris, 10 mmol/L EDTA, 50 mmol/L Sucrose, 20 mg/mL, pH 8.0) at 37°C for 2 h. Then proteinase K, NaCl, and sodium dodecyl sulfate were added at final concentrations of 0.2 mg/mL, 0.7 mol/L, and 1%, respectively. The mixture was incubated at 65°C for 1.5 h. After extraction with phenol-chloroform-isoamyl alcohol (25∶24∶1) and chloroform-isoamyl alcohol (24∶1), DNA in the aqueous phase was precipitated with an equal volume of isopropanol overnight at –20°C. After being centrifuged and washed with 75% ethanol, the DNA was dissolved in TE buffer. To assess any contamination in the DNA extraction, a negative parallel control was established by filtering 1 L of autoclaved deionized water using the same method as described above. DNA preparations from both the samples and negative control were used as templates to amplify the bacterial 16S rRNA genes with the universal primer pair 27F (forward, 5′-AGA GTT TGA TCM TGG CTC AG-3′) and 1392R (reverse, 5′-ACG GGC GGT GTG TRC-3′) (Brosius et al., 1978; Christner, 2002) and La Taq polymerase (TaKaRa Co., Dalian, China). The reaction mixture (30 μL) consisted of 1 U of La Taq (TaKaRa Co., Dalian, China), 0.2 mM each dNTP, 3 μL of 10×Buffer, 0.15 mM of each primer and 1 μL (ca. 10 ng) DNA of template. The PCR program was as follows: initial incubation at 94°C for 5 min, followed by 30 cycles (94°C for 1 min, 56°C for 1 min and 72°C for 1.5 min), and then by a final extension at 72°C for 8 min.

The PCR products were purified using an agarose gel DNA purification kit (TaKaRa Co., Dalian, China), ligated into pGEM-T vector (TaKaRa Co., Dalian, China), and then transformed into E. coli DH5α. The presence of the inserts was checked by colony PCR. Clones from each library were picked randomly for re-amplification and restriction digestion by enzymes Hha I and Afa I. The digested fragments were visualized on a 3% agarose gel, and different clones were discriminated according to their RFLP patterns. One clone of each RFLP type was sequenced with 27F as the sequencing primer.

Phylogenetic analysis

All sequences obtained were checked for chimeric artifacts using the CHIMERA_CHECK program (Maidak et al., 2001). Clones identified as potential chimeras were discarded. Sequences were calculated with DOTOUR (Schloss and Handelsman 2005) and similarities greater than 97% were grouped into one operational taxonomic unit (OTU). Sequences were assigned to genus level grouping with 80% confidence using the “Classifier” program of RDP (Cole et al., 2005). The closest neighbors were retrieved from the NCBI (http://www.ncbi.nih.gov/ BLAST) through blasting. A phylogenetic tree including obtained OTUs and their closest relatives were constructed using MEGA software 3.1 (Kumar et al., 2004). Neighbor-joining phylogenies were constructed from dissimilatory distances and pair-wise comparisons with the Jukes-Cantor distance model. Bootstrap analysis of 1000 replicates was performed.

Statistical analyses

Coverage of clone libraries was calculated using the equation

Coverage=\mathrm{1}-(N/Individuals)

,

where N is the number of clones that occurred only once (Kemp and Aller, 2004). Diversity index (Shannon) was calculated using the statistical program PAST (http://folk.uio.no/ohammer/past).

Nucleotide sequence accession numbers

The nucleotide sequences of partial 16S rRNA genes obtained in this study have been deposited in the GenBank database under accession numbers: DQ323081-DQ323115, EU152996-EU153042 .

Results

δ¹⁸O value and identification of seasonal snow

The δ¹⁸O values of snow ranged from-19.95‰ to-10.68‰ in snowpit RBL, and from-23.68‰ to-9.65‰ in snowpit G. The δ¹⁸O values of rainfall in the Tibetan Plateau are seasonally different due mainly to a precipitation “amount effect” (Tian et al., 2001a, b,2003). In general, δ¹⁸O is enriched during the dry non-monsoon season and is depleted during the wet monsoon season. Based on the δ¹⁸O value, we identified the seasonal precipitation at the two snowpits. The 1.7 m-depth snowpit RBL spanned two years (2003 and 2004). Two intervals with high δ¹⁸O values corresponded to snow deposition in the non-monsoon season, and other intervals with low δ¹⁸O values were monsoon snow (Fig. 2(a)). The 0.86 m-depth snowpit G recorded one year (2005) of snow deposition. Snow from 0.25 to 0.55 m with the depleted δ¹⁸O value deposit during the monsoon season (Fig. 2(b)).

Major ion concentrations

The vertical profiles of major ions Ca²⁺, Mg²⁺, Na⁺, Cl^-, and SO₄^2- in snowpit G are characterised by their increased concentrations in non-monsoon snow, consistent with previous results (Xiao et al., 2002a). Major ion concentrations are very high from the surface downward to a 0.15 m depth in the snow profile, corresponding to the non-monsoon period, suggesting the occurrence of a sandstorm event. However major ions do not show any obvious seasonal variation in snowpit RBL. The major ion concentrations are much higher in snowpit G than in RBL. The average concentration values of Ca²⁺, Mg²⁺, Na⁺, Cl^-, and SO₄^2- in snowpit G are about 49, 20, 65, 13, and 22 times of those in RBL. The average content values of NO₃^- and NH₄⁺ in snowpit G are about 4 and 3 times those in RBL.

Bacterial abundance

Bacterial abundance in snowpit RBL was from 5.7×10³ to 2.3×10⁴ cell/mL, with an obvious seasonal difference. Bacteria in the monsoon snow were more abundant than those in non-monsoon snow, with the average bacterial abundance of the monsoon and the non-monsoon seasons being 1.5×10⁴ and 9.2×10³ cell/mL, respectively.

Bacterial abundance in snowpit G ranged from 877×10⁵ to 4.5×10⁵ cell/mL. The layers with high Ca²⁺ concentrations from the post-monsoon season contained the most abundant bacteria among three seasons, with an average of 2.5×10⁵ cell/mL. In the layers with similar Ca²⁺ concentrations, bacterial abundance was slightly higher during the monsoon season than during the pre-monsoon period, with the average abundance being 1.7×10³ and 1.3×10³ cell/mL, respectively.

16S rRNA gene clone library analysis

Four 16S rRNA gene clone libraries of the snow bacteria were contracted. RBL-m and RBL-n are the libraries representing the monsoon and the non-monsoon snow in the snowpit RBL, respectively. The RBL-m library contains clones from samples RBL-3, RBL-4, RBL-9, and RBL-10, and RBL-n contains clones from RBL-5 and RBL-7. G2 and G6 are the libraries corresponding to the monsoon and the non-monsoon seasons, respectively, in the snowpit G. In total, 349 clones were subjected to RFLP screening. The negative control did not yield any amplification products, supporting the credibility of the DNA extraction procedures. After RFLP screening, 81 unique patterns were obtained. One clone was chosen from each pattern and subjected to sequence analysis. The average sequence length was ca. 900 bp, covering V1-5 regions of 16S rRNA genes. Seventy-eight sequences were identified as normal bacterial 16S rRNA gene sequences after being checked using the CHIMERA_CHECK program. Coverage analyses showed that the sequenced clones represent 82%, 84%, 73% and 96% of the RBL-m, RBL-n, G2 and G6 libraries, indicating that the number of clones sequenced in this study provides a good inventory of the bacterial 16S rRNA gene sequences in snow from various seasons. All the sequences are associated with eight phylogenetic groups; they include the Alpha-, Beta-, and Gamma-proteobacteria, Actinobacteria, Cytophaga-Flavobacterium-Bacterioides (CFB), Firmicutes, Cyanobacteria, andTM7 candidate phylum. Of these, members of Gamma-proteobacteria dominate, and account for 46% of the total clones.

RBL-m and RBL-n libraries

Sequences in RBL-m and RBL-n are both affiliated with Alpha-, Beta-, and Gamma-proteobacteria, Actinobacteria, Firmicutes, CFB group, and Cyanobacteria. The Gamma-proteobacteria dominate in the two libraries (Fig. 2). Eight sequences, representing 77% of the total clones of the two libraries, are common in RBL-n and RBL-m. They are affiliated with genera Acidovorax, Acinetobacter, Enterobacter, Kocuria, Anoxybacillus, Chamaesiphon, and unclassified (Table 1). Among them, 35% of clones are closely related to soil bacteria, and 41% of clones are related to bacteria from the Arctic or Antarctic.

There are 14 sequences present only in RBL-m. They belong to ten genera, and four were unclassified (Table 1). Eight sequences are similar to bacteria from aquatic environments, e.g. marines, lakes, springs, and sediments in bays.

Ten sequences in different genera only occur in RBL-n. Their close relative is from Antarctica, marines, lakes, and sludges.

G2 and G6 libraries

Bacteria in the G2 library are different from those in the G6 library. Sequences in G2 are distributed in the Alpha-, Beta-, and Gamma-proteobacteria, Actinobacteria, Firmicutes, and the CFB groups (Fig. 2). Of these, members of Alpha-proteobacteria and Actinobacteria dominate, accounting for 35% and 31% of the total clones, respectively. Furthermore, they relate closely to sequences isolated from glaciers, permafrost, soil, high altitude plants, Antarctica sea water, ice and soil, sediment from the deep sea, lakes and estuaries, water from oceans, lakes and rivers, and animals (including humans) (Table 1).

The G6 library is composed of three (sub-)phyla, the CFB group, Beta-proteobacteria and Actinobacteria (Table 1). Sequences of the CFB group and Beta-proteobacteria are dominant, and account for 58% and 40% of the total clones, respectively. Sequences in G6 are associated with those from glacial and soil environments (Table 1). Seventy-six percent of the total clones’ nearest neighbors have been isolated from cold environments, i.e., high mountains, the Arctic, and Antarctica.

Discussion

Seasonal variation of bacterial abundance

Bacterial abundance at the two glaciers varies during the monsoon and the non-monsoon seasons. However, they exhibit different change trends at the East Rongbuk and Guoqu Glaciers. Bacterial abundance is correlated positively with Ca²⁺, Mg²⁺, Na⁺, Cl^-, and SO₄^2- concentrations (r=0.75, 0.73, 0.62, 0.72, 0.65 and 0.72, P<0.01) at the Guoqu Glacier. The bacterial abundance is higher in the non-monsoon season than in the monsoon season. In contrast, bacterial abundance does not show any obvious relationship with ion concentrations at the East Rongbuk Glacier. Meanwhile, the snow contains more bacteria in the monsoon season than in the non-monsoon season.

It has been reported that bacteria in snow are supplied by the atmosphere by mineral particles, and some psychrophilic or psychrotrophic bacteria grow rapidly after deposition (Segawa et al., 2005). Major ions in the snow at the Guoqu Glacier originate mainly from the desert to the north. The ion concentrations indicate the amount of input from the atmosphere (Xiao et al., 2002a). Ca²⁺ concentrations in the snow provide an index of dust concentrations in the Tibetan Plateau (Yao et al., 2004). The significant correlation between bacterial abundance and ion concentration indicates that snow bacteria in the Guoqu Glacier are strongly influenced by the dust input. Sandstorms often happen in the region during the non-monsoon season. A large number of dust resulting from sandstorm events transports a relatively great amount of bacteria into the glacier in the non-monsoon season. However, bacterial abundance is slightly lower in snow deposited in the non-monsoon season than in the monsoon season. It is suggested that bacteria grow weakly during the monsoon season. Therefore, both the bacterial amount supplied from the atmosphere and the growth conditions impact on the bacterial abundance in the Guoqu Glacier, with the supplied amount being the key role.

At the East Rongbuk Glacier, snow bacterial abundance in the monsoon season (average 15.2 cell/mL) is higher than in the non-monsoon season (average 9.2 cell/mL). The ion concentrations in the snow do not show any seasonal variation. It is suggested that the abundant bacteria in the monsoon season are not transported by dust. Bacterial growth after deposition is the possible reason causing such high bacterial abundance during the monsoon season. The automatic weather station at the sample site in our study recorded that the daily maximum temperature of most days in June and July 2005 was above 0°C (Xie et al., 2007). The temperature makes bacterial growth possible. However, the bacterial growth conditions are currently not quite clear and await further investigation. Based on our data, it could be deduced that bacterial growth after deposition plays a more important role on the seasonal variation of bacterial abundance.

Seasonal variation of bacterial diversity

Snow bacteria at the Guoqu Glacier show significant seasonal differences. Firstly, bacterial diversity is greater during the monsoon season than during the non-monsoon season. Bacteria found in the samples from the monsoon season belong to 7 phyla and 15 genera, and 2 were unclassified. However, bacteria from the non-monsoon season belong to only 3 phyla and 3 genera, and 2 were unclassified. Secondly, the bacterial components of the two seasons are distinctly different; the CFB group dominates during the non-monsoon season, but contributes only a minor portion during the monsoon season. Beta-proteobacteria account for nearly half of the total in the non-monsoon season samples, but account for only 4% in the monsoon season samples. Alpha-proteobacter and Actinobacteria are the dominant components in the monsoon season. Thirdly, bacteria from different seasons were identified to originate from different environments. Bacteria in the non-monsoon season are characterized by having originated from two kinds of environments, cold environments and soil. Bacteria in the monsoon season are multifarious in origin, including marine rivers, lakes, plants, animals and humans, as well as cold environments and soil.

Snow bacteria at the East Rongbuk Glacier also exhibit seasonal variations. But the bacterial difference between the two seasons are not so distinct as that in the Guoqu Glacier. The bacterial components in the two seasons are similar (Fig. 2). The bacterial diversity of the Gamma-proteobacter subgroup decreases in the non-monsoon season, while the bacterial diversities of the CFB and Cyanobacteria groups increase. Both the monsoon and the non-monsoon snow contain their own genera, but the major clones are common in both seasons. The two seasons’ bacteria originate from similar environments, i.e. soil, marine lakes, the Arctic and Antarctic.

The seasonal difference in bacterial diversity is likely influenced by the atmospheric circulation and the temperature. Snow deposited in the monsoon and the non-monsoon seasons comes from different sources at the two glaciers. The summer Indian monsoon and the local convective water vapor contribute to the precipitation during the monsoon season, but the westerlies carry water vapor during the non-monsoon season (Kang et al., 2007a, b). Differing atmospheric circulation carries various kinds of bacteria which are later loaded in the glacier. Otherwise, relatively high temperatures and wet climatic conditions during the monsoon season could result in the survival of more bacteria. Under the influence of atmospheric circulation and temperature, the seasonal variation in bacterial diversity at the East Rongbuk Glacier is different from that at the Guoqu Glacier. It has been reported that marine moisture is westerly transported to the middle Himalayas from the North Atlantic during winter (Tian et al., 2005). Therefore, marine vapors could reach the East Rongbuk Glacier during both the monsoon and the non-monsoon seasons, but come to the Guoqu Glacier only during the monsoon season. The seasonal variations in temperature are also different at the two glaciers. The daily average temperature from the nearest meteorological station, Dingri Station, during the monsoon season is similar to that during the non-monsoon season, being at 18.1 and 10.7°C, respectively, while the temperatures measured from Anquo Station, which is the nearest station to the Guoqu Glacier, are vastly different during the two seasons, at 7.1 and-5.3°C. The distinct differences in atmospheric cycling and the temperatures between the monsoon and the non-monsoon seasons cause the significant difference in bacterial diversity at the Guoqu Glacier.

Spatial variation of snow bacteria

Three genera identified occur in both the East Rongbuk Glacier and the Guoqu Glacier; they include Brevibacterium in Actinobacteria, Sphingomonas in Alpha-proteobacteria, and Polaromonas in Gamma-proteobacteria. These genera are widely distributed in various glaciers on the Tibetan Plateau, such as the Manlan Glacier (Xiang et al., 2004), the Guliya Glacier (Christner et al., 2003), and the Puruogangri Glacier (Zhang et al., 2006). They were also detected in Arctic and Antarctic glaciers (Christner et al., 2000; Miteva et al., 2004; Miteva, 2007).

Except for the three common genera, the other bacteria in the two glaciers are affiliated with different genera and show different features. Sixteen of 46 sequences in the Guoqu Glacier are similar to soil bacteria. However, only 5 of 32 sequences in the East Rongbuk Glacier are relative to the bacteria in soil environments. Several genera, such as the Stappia, Roseobacter, Ruegeria, Rhodoferax at the East Rongbuk Glacier, are connected with those from marine environments (Finneran et al., 2003; Moran et al., 2003; Kim et al., 2006). Bacterial diversity variation at the two glaciers indicates that snow bacteria are responsive to environmental conditions. The Guoqu Glacier is located at the central plateau and near the deserts, and thus receives relatively much continental dust. The East Rongbuk Glacier is located at the southern part and is strongly influenced by moisture from the Indian Ocean. The spatial variation of snow bacteria observed herein is in line with the snow chemistry. The spatial patterns of snow chemistry demonstrate that the continental dust originates mainly from the deserts to the north, while monsoon sources of the moisture exert greater influence on snow chemistry on the southern plateau (Xiao et al., 2002a). Our data suggest that snow bacteria could be an index of climatic and environmental conditions, as good as other chemical parameters in the snow.

Conclusions

Abundant snow bacteria are present at the East Rongbuk Glacier and the Guoqu Glacier, ranging in abundance from 877×10⁵ to 4.5×10⁵ cell/mL. Both the bacterial abundance and the diversity show various patterns during the monsoon and the non-monsoon seasons at the two glaciers. At the Guoqu Glacier, more bacteria are transported in the non-monsoon season than in the monsoon season, with significant seasonal variation being observed in bacterial diversity. However, at the East Rongbuk Glacier, bacterial abundance is higher in the monsoon season than in the non-monsoon season due to preferential bacterial growth during the monsoon season. Bacterial differences between the two seasons are not so distinct as that seen in the Guoqu Glacier. The seasonal difference is likely influenced by various atmospheric circulations and temperature. Bacterial diversity at the two glaciers exhibits different features in response to various environments.

References

[1] Barnett T P, Dumenil L, Schlese U, Roeckner E, Latif M (1989). The effect of Eurasian snow cover on regional and global climate variations. J Atmos Sci, 46: 661-685

[2] Brosius J, Palmer M L, Kennedy J P, Noller F H (1978). Complete nucleotide sequence of a 16S ribosomal RNA gene from Escherichia coli. Proc Natl Acad Sci U S A, 75: 4801-4805

[3] Buck C F, Mayewski P A, Spencer M J, Whitlow S, Twickler M S, Barrett D (1992). Determination of major ions in snow and ice cores by ion chromatography. J Chromatogr, 594: 225-228

[4] Christner B C (2002). Detection, recovery, isolation and characterization of bacteria in glacial ice and lake Vostok accretion ice [Dissertation]. Columbus: The Ohio State University

[5] Christner B C, Mosley-Thompson E, Thompson L G, Reeve J N (2003). Bacterial recovery from ancient glacial ice. Environmental Microbiology, 5: 433-436

[6] Christner B C, Mosley-Thompson E, Thompson L G, Zagorodnov V, Sandman K, Reeve J N (2000). Recovery and identification of viable bacteria immured in glacial ice. Icarus, 144: 479-485

[7] Cole J R, Chai B, Farris R J, Wang Q, Kulam S A, McGarrell D M, Garrity G M, Tiedje J M (2005). The ribosomal database project (RDP-II): Sequences and tools for high-throughput rRNA analysis. Nucl Acids Res, 33: D294-296

[8] Duan K Q, Yao T D, Thompson L G (2006). Response of monsoon precipitation in the Himalayas to global warming. Journal of Geophysical Research-Atmospheres, 111

[9] Finneran K T, Johnsen C V, Lovley D R (2003). Rhodoferax ferrireducens sp. nov., a psychrotolerant, facultatively anaerobic bacterium that oxidizes acetate with the reduction of Fe(III). Int J Syst Evol Microbiol, 53: 669-673

[10] Jiao N Z, Yang Y H, Hong N, Ma Y, Harada S, Koshikawa H, Watanabe M (2005). Dynamics of autotrophic picoplankton and heterotrophic bacteria in the East China Sea. Continental Shelf Research, 25: 1265-1279

[11] Kang S C, Qin D H, Mayewski P A, Sneed S B (2002). Chemical composition of fresh snow on Xixiabangma peak, central Himalaya, during the summer monsoon season. Journal of Glaciology, 48: 337-339

[12] Kang S C, Mayewski P A, Qin D H, Sneed S A, Ren J W, Zhang D Q (2004). Seasonal differences in snow chemistry from the vicinity of Mt. Everest, central Himalayas. Atmos Environ, 38: 2819-2829

[13] Kang S C, Zhang Q G, Kaspari S, Qin D H, Cong Z Y, Ren J W, Mayewski P A (2007a). Spatial and seasonal variations of elemental composition in Mt. Everest (Qomolangma) snow/firn. Atmos Environ, 41: 7208-7218

[14] Kang S C, Zhang Y J, Qin D H, Ren J W, Zhang Q G, Grigholm B, Mayewski P A (2007b). Recent temperature increase recorded in an ice core in the source region of Yangtze River. Chin Sci Bull, 52: 825-831

[15] Kemp P F, Aller J Y (2004). Bacterial diversity in aquatic and other environments: What 16S rDNA libraries can tell us. Fems Microb Ecol, 47: 161-177

[16] Kim B C, Park J R, Bae J W, Rhee S K, Kim K H, Oh J W, Park Y H (2006). Stappia marina sp. nov., a marine bacterium isolated from the Yellow Sea. Int J Syst Evol Microbiol, 56: 75-79

[17] Kumar S, Tamura K, Nei M (2004). <patent>MEGA3</patent>: Integrated software for molecular evolutionary genetics analysis and sequence alignment. Briefings in Bioinformatics, 5: 150-163

[18] Liu Y Q, Yao T D, Kang S C, Jiao N Z, Zeng Y H, Shi Y, Luo T W, Jing Z F, Huang S J (2006). Seasonal variation of snow microbial community structure in the East Rongbuk glacier, Mt. Everest. Chin Sci Bull, 51: 1476-1486

[19] Liu Y Q, Yao T D, Kang S C, Jiao N Z, Zeng Y H, Shi Y, Luo T W, Jing Z F, Huang S J (2007). Microbial community structure in major habits above 6000 m on Mount Everest. Chin Sci Bull, 52: 2350-2357

[20] Maidak B L, Cole J R, Lilburn T G, Parker C T, Jr., Saxman P R, Farris R J, Garrity G M, Olsen G J, Schmidt T M, Tiedje J M (2001). The RDP-II (ribosomal database project). Nucl. Acids Res, 29: 173-174

[21] Marie D, Partensky F, Jacquet S, Vaulot D (1997). Enumeration and cell cycle analysis of natural populations of marine picoplankton by flow cytometry using the nucleic acid stain SYBR Green I. Appl Environ Microbiol, 63: 186-193

[22] Miteva V (2007). Bacteria in snow and glacier ice. In: Margesin R, Schinner F M J C, Gerday C, eds. Psychrophiles: From Biodiversity to Biotechnology. Berlin Heidelberg: Springer, 31-50

[23] Miteva V I, Sheridan P P, Brenchley J E (2004). Phylogenetic and physiological diversity of microorganisms isolated from a deep Greenland glacier ice core. Appl Environ Microbiol, 70: 202-213

[24] Moran M A, Gonzalez J M, Kiene R P (2003). Linking a bacterial taxon to sulfur cycling in the sea: Studies of the marine Roseobacter Group. Geomicrobiology Journal, 20: 375-388

[25] Schloss P D, Handelsman J (2005). Introducing DOTUR, a computer program for defining operational taxonomic units and Estimating Species Richness. Appl Environ Microbiol, 71: 1501-1506

[26] Segawa T, Miyamoto K, Ushida K, Agata K, Okada N, Kohshima S (2005). Seasonal change in bacterial flora and biomass in mountain snow from the Tateyama Mountains, Japan, analyzed by 16S rRNA gene sequencing and real-time PCR. Appl Environ Microbiol, 71: 123-130

[27] Shi Y (2000). Glacier and Their Environment in China. Beijing: Science Press (in Chinese)

[28] Thompson L G, Yao T D, Mosley-Thompson E, Davis M E, Henderson K A, Lin P N (2000). A high-resolution millennial record of the South Asian monsoon from Himalayan ice cores. Science, 289: 1916-1919

[29] Tian L, Masson-Delmotte V, Stievenard M, Yao T D, Jouzel J (2001a). Tibetan Plateau summer monsoon northward extent revealed by measurements of water stable isotopes. Journal of Geophysical Research-Atmospheres, 106: 28081-28088

[30] Tian L, Yao T D, Schuster P F, White J W C, Ichiyanagi K, Pendall E, Pu J, Wu Y (2003). Oxygen-18 concentrations in recent precipitation and ice cores on the Tibetan Plateau. Journal of Geophysical Research-Atmospheres, 108

[31] Tian L, Yao T D, Sun W Z, Stievenard M, Jouzel J (2001b). Relationship between delta D and delta O-18 in precipitation on north and south of the Tibetan Plateau and moisture recycling. Science in China (Series D), 44: 789-796

[32] Tian L D, Yao T D, White J W C, Yu W S, Wang N L (2005). Westerly moisture transport to the middle of Himalayas revealed from the high deuterium excess. Chin Sci Bull, 50: 1026-1030

[33] Tian L D, Yao T D, Li Z, MacClune K, Wu G J, Xu B Q, Li Y F, Lu A X, Shen Y P (2006). Recent rapid warming trend revealed from the isotopic record in Muztagata ice core, eastern Pamirs. Journal of Geophysical Research-Atmospheres, 111

[34] Xiang S R, Yao T D, An L Z, Li Z, Wu G J, Wang Y Q, Xu B Q, Wang J X (2004). Change of bacterial community in the Malan ice core and its relation to climate and environment. Chin Sci Bull, 49: 1869-1875

[35] Xiang S R, Yao T D, An L Z, Wu G J, Xu B Q, Ma X J, Li Z, Wang J X, Yu W S (2005). Vertical quantitative and dominant population distribution of the bacteria isolated from the Muztagata ice core. Science in China (Series D), 48: 1728-1739

[36] Xiao C, Kang S C, Qin D, Yao T D, Ren J W (2002a). Transport of atmospheric impurities over the Qinghai-Xizang (Tibetan) plateau as shown by snow chemistry. Journal of Asian Earth Sciences, 20: 231-239

[37] Xiao C D, Qin D H, Ren J W, Li Z Q, Wang X X. (2002b). Glaciochemistry distribution in the surface snow/ice in some key regions of the cryosphere: The environmental significance. Journal of Glaciology and Geocryology, 24: 492-499

[38] Xie A H, Dahe Q, Ren J W, Xiang Q, Xiao C D, Hou S G, Kang S C, Yang X G, Jiang Y Y (2007). Meteorological observations on Mount Everest in 2005. Progress in Natural Science, 17: 828-837

[39] Xie S C, Yao T D, Kang S C, Xu B Q, Duan K Q, Thompson L G (2000). Geochemical analyses of a Himalayan snowpit profile: Implications for atmospheric pollution and climate. Org Geochem, 31: 15-23

[40] Xiong K J, Tang X, Xie S C (1999). Yeast isolated from snow at Tibet. J Microbio, 19: 58-62 (in Chinese with English abstract)

[41] Xu B Q, Yao T D, Liu X Q, Wang N L (2006). Elemental and organic carbon measurements with a two-step heating-gas chromatography system in snow samples from the Tibetan Plateau. Annals of Glaciology, 43: 257-262

[42] Yao T D, Wu G J, Pu J C, Jiao K Q, Huang C L (2004). Relationship between calcium and atmospheric dust recorded in Guliya ice core. Chin Sci Bull, 49: 706-710

[43] Yao T D, Xiang S R, Zhang X J, Wang N L, Wang Y Q (2006). Microorganisms in the Malan ice core and their relation to climatic and environmental changes. Global Biogeochemical Cycles, <patent>20: GB1004, doi:1010.1029/2004GB002424</patent>

[44] Zhang G S, Niu F J, Ma X J, Liu W, Dong M X, Feng H Y, An L Z, Cheng G D (2007). Phylogenetic diversity of bacteria isolates from the Qinghai-Tibet plateau permafrost region. Can J Microbiol, 53: 1000-1010

[45] Zhang Q, Kang S, Cong Z, Hou S, Liu Y (2008). Elemental composition in surface snow from the ultra-high elevation area of Mt. Qomolangma (Everest). Chin Sci Bull, 53: 289-294

[46] Zhang X F, Yao T D, An L Z, Tian L D XuS J(2006). A study on the vertical profile of bacterial DNA structure in the Puruogangri (Tibetan Plateau) ice core using denaturing gradient gel electrophoresis. Annals of Glaciology, 43: 160-166

[47] Zhang X J, Yao T D, Ma X J, Wang N L (2001). Analysis of the characteristics of microorganisms packed in the ice core of Malan glacier, Tibet, China. Science in China (Series D), 44: 369-374

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg