Study on diversity and temporal-spatial characteristics of eukaryotic microorganisms on Glacier No.1 at the Urumqi River Head, Tianshan

Wei Zhang , GaoSen Zhang , GuangXiu Liu *, ZhongQin Li , LiZhe An

1. Key Laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China

2. School of Life Science, Lanzhou University, Lanzhou, Gansu 730000, China

Study on diversity and temporal-spatial characteristics of eukaryotic microorganisms on Glacier No.1 at the Urumqi River Head, Tianshan

Wei Zhang1, GaoSen Zhang1, GuangXiu Liu1*, ZhongQin Li1, LiZhe An2

1. Key Laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China

2. School of Life Science, Lanzhou University, Lanzhou, Gansu 730000, China

Surface snow samples of different altitudes and snow pit samples were collected from Glacier No.1 at the Urumqi River Head,Tianshan. Denaturing gradient gel electrophoresis (DGGE) was used to examine the diversity and temporal-spatial characteristics of eukaryotic microorganisms with different altitudes and depths. Results show that the eukaryotic microorganisms belong to four kingdoms—Viridiplantae, Fungi, Amoebozoa, and Alveolata. Among them, algae (especially Chlamydomonadales)were the dominant group. The diversity of eukaryotic microorganisms was negatively correlated with altitude and accumulation time, but positively correlated with δ18O values. These results indicate that temperature is the main factor for the temporal-spatial change of eukaryotic microorganisms, and the diversity of eukaryotic microorganisms could be an index for climate and environmental change.

Glacier No.1 at the Urumqi River Head; eukaryotic microorganisms; diversity; temporal-spatial characteristics; DGGE

1. Introduction

Glacier No.1 at the Urumqi River Head (43°06′N,86°49′E) is located in the East Tianshan Mountain of Middle Asian. East Tianshan is located in the central Eurasia continent and surrounded by desert and Gobi. To the east is the Mongolian Desert and Gobi of Mongolia; to the south is the Taklimakan Desert of the Tarim Basin; to the west are Central Asian deserts such as Sare-Isch Kotra; to the north is the Gurbantunggut Desert of the Junggar Basin (Leeet al., 2003;Liet al., 2006). At present, research related to Glacier No.1 is mostly focused on inorganic chemistry, with limited research on microorganisms (Takeuchi and Li, 2008; Takeuchiet al., 2008).

The present study of glacier microorganisms is mainly focused on glacier bacteria, with limited research on eukaryotic microorganisms. The glacier ecosystem is a unique freshwater system, which contains bacteria and eukaryotic microorganisms such as algae, fungi and protozoa. Recently,more glaciers are being evaluated for eukaryotic microorganisms. Willerslevet al. (1999), using a molecular method,found that microorganisms at the Hans Tausen ice cap in Northern Greenland represents at least 57 taxa, revealing a diversity of fungi, plants, algae, and protists. These organisms are derived from distant sources as well as from the local arctic environment. Buzziniet al. (2005) found metabolically active yeasts isolated by culture-based laboratory procedures in glaciers of the Italian Alps. Many researchers have found that cryoconite contains fungi, green algae, diatoms, rotifers, tardigrades and nematodes (Muelleret al.,2001; Christneret al., 2003; Mueller and Pollard, 2004), and the microbial diversity in glaciers can be used as a biological indicator to reflect climate and environmental change(Abyzovet al., 1998; Xianget al., 2005). However, for Glacier No.1, the number of studies related to eukaryotic microorganisms is far less than prokaryotic microorganisms.

The ratio of cultivative microorganisms to actual number of microorganisms is about 0.1%-1% in nature, so the traditional culture and identification methods could not represent the true situation of the micro-environment. The denaturing gradient gel electrophoresis (DGGE) technique can both reflect the diversity of microbial community structure and easily determine the dominant type of microorganisms or functional microorganisms. Therefore, it has been widely used to analyze the composition and dynamic variation of microorganisms. In the present study, we used DGGE technology to analyze the eukaryotic microbial diversity of snow sampled from different altitudes and a snow pit in the Tianshan Glacier No.1 at the Urumqi River Head. This study provides data for research on the mechanism between ice snow microorganisms and their living environment. This data also supports further explanation of the relationship among ice snow microorganisms, climate and the environment.

2. Materials and methods

2.1. Sampling

Samples were collected in the eastern section of Glacier No.1 (43°06′N, 86°48′E) in October, 2007. Among these,nine surface snow samples were from different altitudes at 20 cm depth. Twelve snow pit samples were from a 240 cm depth sampled every 20 cm. Samples from the surface to 100 cm depth represented the 2007 snow pack, 100 cm to 160 cm accumulated during 2006, and 180 cm to 240 cm were from 2005 (observed by Tianshan Glaciological Station, Chinese Academy of Sciences, China). The remaining ten samples were collected from sub-surface snow at different altitudes ranging from 3,732 m to 4,099 m. Sterile gloves and a mask were worn during the sampling process and the samples were placed in sterile material wrapped in two successive bags that had been sterilized. Sampling was carried out using a sterile polyethylene HDPE container (500 mL)pushed into the snow to fill it without any need for extra manipulation. All samplers were stored and transported frozen from the glacier to the laboratory.

2.2. Analysis of δ18O values

The δ18O values of the samples was determined using a mass spectrometer (MAT-252) at the State Key Laboratory of Cryospheric Science, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences.

2.3. DNA extraction and 18S rDNA PCR amplification

DNA was extracted as described previously for snow sample material (Liuet al., 2007), and the 18S rDNA genes were amplified with the primers GC-Euk1F and Euk516R(Wilmset al., 2002).

2.4. Denaturing Gradient Gel Electrophoresis (DGGE),clone and sequence

A Dcode universal mutation detection system (Bio-Rad,USA) was used to perform DGGE analysis. Equal amounts of PCR products were loaded onto 6% polyacrylamide gels with a denaturing gradient ranging from 20% to 45% (100%denaturant is defined as a mixture of 7 M urea and 40% deionized formamide). The electrophoresis was run at 150 V,60 °C for 5 h. The gel was silver stained (Mccaiget al.,2001), the image scanned by scanistor, and then the lighter bands were cut (Diezet al., 2001). DNA of the bands was retrieved, and amplified with primers Euk1F and Euk516(Wilmset al., 2002). The PCR products were purified, and cloned into pGEM-T Easy Vectors (Promega). The positive clones were selected for sequencing.

2.5. Phylogenetic analysis

The 18S rRNA gene sequences were aligned against representative reference sequences of the most closely related members, obtained from the National Center for Biotechnology Information (NCBI) database(http://www.nvbi.nlm.nih.gov), by use of the multiple-alignment Clustal X software package (Thompsonet al., 1997). The phylogenetic dendrograms were constructed by the neighbor-joining method (Saitou and Nei,1987), and tree topologies were evaluated by performing bootstrap analysis of 1,000 data sets using the MEGA4.1 package (Kumaret al., 2001). The 18S rDNA sequences in this study have been deposited in GenBank database under the following accession numbers:GU908449-GU908472 (Table 1).

2.6. Statistical analysis

The Shannon-Weaver index (H') was used for the statistical analysis of the DGGE gel map. The formula isH'=-ΣPilgPi.Piis the relative peak intensity of a DGGE band, calculated asPi=ni/N, whereniis the peak area of the band andNis the sum of all the peak areas in DGGE lane (Weertet al., 2010). TheniandNwere given by the Quantity One software. Meanwhile, the SPSS software was used for correlation analysis.

3. Results

3.1. Analysis of DGGE results

Some differences between the communities structures of eukaryotic microorganisms in the snow samples of Glacier No.1 were detected (Figure 1).

Table 1 Blast results of 18S rDNA from DGGE clones

Based on the Shannon-Weaver index, the eukaryotic microorganism diversity of surface snow negatively correlated with altitude (R2=0.740,P=0.006) when the 4,099-m altitude sample was eliminated (Figure 2). The possible reason was that the sample site at 4,099 m was the observation site of the Tianshan Glaciological Station, where human activity was more frequent than at the other sites.Furthermore, eukaryotic microorganism diversity decreased with the increasing of accumulation years in snow pits (Figure 2).

The Shannon-Weaver index of snow samples positively correlated with δ18O values (R2=0.343,P=0.007) (Figure 3).

Figure 1 Eukaryotic microorganism profiles of snow sample by DGGE

Figure 2 Temporal-spatial distribution of eukaryotic microorganism diversity

Figure 3 The relationship between eukaryotic microorganism diversity and δ18O value of snow samples

4. Discussion

4.1. The diversity of eukaryotic microorganisms

The eukaryotic microorganisms detected in our study fall into four kingdoms: Viridiplantae, Fungi, Amoebozoa and Alveolata. Willerslevet al. (1999) found Viridiplantae,Fungi and Alveolata from a 2,000-yr-old ice-core sample of the Northern Greenland. Christneret al. (2003) found Viridiplantae, Fungi, Alveolata and Metazoa in an Antarctic cryoconite hole by constructing a 18S rDNA clone library.In research of a snow pit of Eastern Tianshan Mountains,where is near our study area, Maet al. (2008) found five kingdoms of eukaryotic microorganisms: Viridiplantae, Fungi,Cercozoa, Alveolata and Metazoa. Lawleyet al. (2004) found the distribution of eukaryotic microorganisms were mainly from Viridiplantae, Fungi, Stramenopila, Metazoa, Alveolata and uncertain categories in Antarctic soils. Our results, along with the aforementioned cold environment studies, suggest that Viridiplantae, Fungi and Alveolata could be more suitable to such cold environments. Meanwhile, differences between these studies were detected, such as different origin resources of microorganisms, temperature, moisture, UV radiation and nutrition level at different sites.

Algae are the main eukaryotic microorganisms in the study area, and belong to order Chlamydomonadales of phylum Chlorophyta. Yoshimuraet al. (2000, 2006) found that algae in the glacier were mainly constructed by several Chlamydomonadales, and formed simple algae distributed structures. Broady and Weinstein (1998) found 11 types of Chlorophyta and 6 types of Cyanophyta in the Antarctic.Takeuchiet al. (2006) found 5 types of algae in Akkem Glacier, and those algae belong to Chlorophyta and Cyanophyta. Algae are the primary producers in the glacier ecosystem, and support the growth of cold-tolerant animals and heterotrophic bacteria. Meanwhile, fungi and heterotrophic bacteria could degrade dead algae and animals into simple organic matter, which could provide nutrition for algae growth. Thus, algae differences decide the changes of other organisms with environmental change in the glacier surface(Maet al., 2008). In addition, Amoebozoa and Alveolata detected in our research mainly fall into genusEchinamoebidaeandOxytrichidae, respectively, both of which can feed on algae (Liuet al., 2005). Therefore, their growth in the glacier may be mainly related to the distribution of a large number of algae.

The fungi detected in our study fall into phylum Basidiomycota, Chytridiomycota and Ascomycota. Among them, Basidiomycota was the main fungi. Jumpponen (2007)found Ascomycota, Basidiomycota, Chytridiomycota, and Zygomycota in Lyman Glacier. Maet al. (2000) detected two phylum fungi, Basidiomycota and Ascomycota, in Greenland Glacial ice. Lawleyet al. (2004) found three phylum fungi: Ascomycota, Basidiomycota and Zygomycota in Antarctic soils. The similarity among these studies indicates that Ascomycota and Basidiomycota could be suitable for such cold environments.

Based on the blast results, the number of sequences is closely correlated with human activity. We propose that human activity significantly affected the distribution of microorganisms in the glacier. Houxia Town is 50 km down along the river valley, where power and cement plants were built since 1958. State Road 216 climbs over Shenglidaban where is 2 km away from the study area. The road climbs from 3,600 m to 4,280 m in altitude, and the pavement is mainly sandstone or soil so that traffic can easily raise the dust (Zhouet al., 2009). Air pollutants of the town and road could carry over to Glacier No.1 by appropriate weather conditions and affect microbial distribution.

4.2. The temporal-spatial characteristics of eukaryotic microorganisms

Previous studies show that the microorganisms were imported by atmospheric dust. When microbes enter the glacier, their growth depends on the particle size that they are adhered to (Stibalet al., 2006). Atmospheric dust not only provides the habitat for microbial growth, but also the energy for the microorganisms (Takeuchi, 2001). However,microorganisms that are imported by atmospheric dust were not entirely adapted to such a cold environment. Low temperatures could reduce or stop microbial metabolism. When the temperature falls below zero, freezing of the water in microbial plasma could result in the death of the microorganism. This effect could also decrease eukaryotic microbial diversity over time.

Our results show that eukaryotic microbial diversity of surface snow decreased with altitude, which was similar to previous studies. Liet al. (2008) found that soil microbial diversity was negatively correlated with altitude along the Qinghai-Tibet Railway. They suggested that strong radiation,low temperature and other unfavorable conditions at high altitudes restricted microbial growth. The effects of temperature on microorganisms were significant. Appropriate temperatures could stimulate growth, and inappropriate temperatures could change microbial form, metabolism,toxicity, and even lead microorganisms to death. Meanwhile,temperature could also affect the geographical distribution of microorganisms (Shi, 1982). Increasing evidence shows that δ18O values in precipitation are controlled by temperature, and decreases with the increasing of altitude (Poage and Chamberlain, 2001). The δ18O value of fresh snow could well represent the temperature change (Zhanget al., 2009). In this study, we argued that the diversity of eukaryotic microorganisms in snow samples are positively correlated with δ18O values and temperature is the main factor for the spatial distribution of eukaryotic microbial diversity.

Glaciers are very sensitive to environmental factors(Qin and Ding, 2009), and its formation and development record the variation and fluctuation of climate and environment (Shi, 2000). In this study, the correlation between eukaryotic microbial diversity and δ18O values in snow samples indicate that just like δ18O values, eukaryotic microbial diversity could be used to reveal climatic and environmental changes.

The study of microorganisms is an important factor in extending microbial diversity research and developing biological resources of earth. Glacier No.1 at the Urumqi River Head contains a large amount of eukaryotic microbial resources. Therefore, the study of eukaryotic microbial diversity in this area could promote further research in diversity,psychrophilic mechanisms and system evolution of microorganisms. Meanwhile, the investigation of eukaryotic microorganisms as a biological indicator of climate change provides data support for further explanation of the relationship among ice snow microorganisms, climate and environment.

The authors are very thankful to Tianshan Glaciological Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences,for their help in sampling and data providing. This project is supported by National Natural Science Foundation of China(Grant No. 30770329, No. 40971034, No. 30800154) and China Postdoctoral Science Fund (Grant No. 20080430794).

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10.3724/SP.J.1226.2011.00306

*Correspondence to: Dr. GuangXiu Liu, Professor of Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences. No.320, West Donggang Road, Lanzhou, Gansu 730000, China. Tel: +86-931-4967525; Email:liugx@lzb.ac.cn

10 January 2011 Accepted: 16 March 2011