Culture independent bacterial diversity of Changme Khang and Changme Khangpu glaciers of North Sikkim, India

  • Mingma Thundu Sherpa
  • Ishfaq Nabi Najar
  • Sayak Das
  • Nagendra ThakurEmail author
Original Article


Microbial communities at cryosphere are the cosmopolitan buffers of important biogeochemical processes stationed at extreme archaic and frigid conditions. In the present study microbial diversity analysis from accumulation zone of two glaciers of North Sikkim, India has been carried by two culture independent methods. The phospholipid fatty acids analysis of Changme Khang and Changme Khangpu glacier showed that both of these were dominated by Gram-positive bacteria followed by Gram-negative bacteria. Among the two glaciers, Changme Khang (54.04%) had higher percentage of Gram-positive bacteria than Changme Khangpu (24.84%), while Gram-negative bacteria were higher in Changme Khangpu (22.65%) than Changme Khang (4.41%). The metagenomic analysis shows the dominance of Proteobacteria followed by Firmicutes and Actinobacteria. Betaproteobacteria were the dominant class among Proteobacteria. Similar kind of bacterial diversity was also observed from other polar and non-polar glaciers.


Psychrophiles Changme Khang Changme Khangpu Glacier Phospho lipid fatty acid analysis (PLFA) Metagenomics 


Himalayas are vast hot spots of the world, starting from Hindu Kush range, stretching to Tibetan plateau covering surface area of over 4.3 million km2. Himalayan cryosphere contains the largest deposits of snowfall and houses huge number of glaciers apart from the Polar Regions (Bajracharya and Shrestha 2011) and hence this region is regarded as “Third pole of the world”. These regions are the vital pool of global freshwater resources and provide electricity to around 150 million populations and also used for their agriculture, industry, and drinking purposes (Singh and Bengtsson 2004). In 2012, Committee on Himalayan Glaciers and Hydrology (CHGH 2012), emphasized the possible impacts of climate change on Himalayan glaciers leading to water scarcity and which in turn may possibly play an escalating role in geopolitical tautness. Therefore, melting glaciers of Himalayan region are going to be in very critical stage in the next few decades (Jianchu et al. 2007). The component that controls and regulates the Himalayan glacier dynamics is unknown (Byers 2012). The retreat of glaciers in the Himalayas directly affects the various atmospheric, climatic, and ecological phenomena (Bhutiyani et al. 2008). When the glaciers retreat, the volume of ice or snow decreases and the inner depth ice core gets exposed, the surface area of fore field increases and new top layer of soil develops ecological succession (Garcia-Lopez and Cid 2017; Hagen et al. 2003). Due to global climate change the cryospheric conditions are getting altered and this might influence the growth of extreme tolerant microbes of mesophilic nature which may cause paradigm shift in transient flora over habitat flora resulting in change of biodiversity (Nowak and Hodson 2014; Hell et al. 2013).

Two distinct zones are found in the glaciers, ablation and accumulation zones. Upper portion of the glacier is called as accumulation zone where snow and ice accumulate by snowfall, hail, drifting snow, avalanche and frozen rainfall. Accumulation zone can be easily identified on a glacier as a snow or ice surface without any supra-glacier debris cover. Whereas, the lower portion of the glacier is called as ablation zone, where loss of ice takes place through the process of melting, evaporation, calving, and deflation. It is generally debris-covered and often the surface in this zone is marked by supraglacial lakes. The zone that separates accumulation and ablation zones on glacier surfaces is regarded as equilibrium line altitude (ELA).

The microorganisms present in such glaciers can flourish under extremely cold temperature. The microbes which show minimum, optimum and maximum growth temperatures at or below 0 °C, 15 °C, and 20 °C are called psychrophiles (Morita 1975). Psychrophilic bacteria isolated from different habitats belong to phyla Proteobacteria, (Auman et al. 2006; Huston et al. 2000), Bacteroidetes (Mc Cammon and Bowman 2000) and Firimcutes (Junge et al. 2011; Yoon et al. 2001). Bacteria found in the upper layers of glaciers can be considered as a recent deposition events and show community diversity profile and relation to local environments. Microbes from polar and non-polar environments are dominated by phylum Proteobacteria, Cyanobacteria, Actinobacteria, Bacteroidetes and Firmicutes, whereas Betaproteobacteria are the dominant class detected by many researchers in the glaciers. Many bacteria were isolated from various polar and non-polar glaciers. Among them the dominant genus are Arthrobacter, Clavibacter, and Mycobacterium from phylum Actinobacteria; Methylobacterium, Sphingomonas, Acinetobacter and Pseudomonas, from phylum Proteobacteria; Chryseobacterium, and Flavobacterium from phylum Bacteroidetes and Bacillus, Exiguobacterium, Paenibacillus, and Planococcus from Firmicutes (Sherpa et al. 2018a).

Psychrophilic and psychrotolerant microorganisms are crucial economically because of cryozymes production (Margesin et al. 2007). These have several industrial applications, such as cryo-enzyme based detergents, hydrolysis of lactose from milk, industrial dehairing of skins, stone washing, bio-polishing of textile products, food and meat processing, softening of wool or cleaning of contact lenses, bioremediation of waste water and solids, and production of biofuels in frigid conditions (Ueda et al. 2010).

Glacial ecosystems harboring extreme psychrophilic microbes might act as potential bio-indicators of climate change (Cavicchioli et al. 2002; Ramana et al. 2002). Melting of these glaciers can cause an increase in the production of greenhouse gases and might help in growth of extreme tolerating mesophiles. Therefore, it is important to study the role of these microorganisms in climate change and how these microorganisms can be utilized to curb emissions (Davidson and Janssens 2006).

Sikkim Himalayas come under the seismic zone and hence the probabilistic of extreme changes in local weather and climate might cause further glacier retreats in future (Bajracharya et al. 2007). In Sikkim, there are around 84 glaciers both small and large in size covering over an area of 440 km2 (Bahuguna et al. 2001). All these glaciers are located at Mt. Kanchenjunga range of North and West Sikkim, which is considered as the biological hotspot in Eastern Himalaya and also identified as World Heritage Site by UNESCO in 2016.

Eastern Himalayas, despite playing important roles in livelihood of billions of people and different organisms, still almost negligible studies have been done on microbial diversity of these glaciers (Kumar et al. 2015a, b, 2016; Himanshu et al. 2016), therefore, it is important to check them.

In our previous studies we have reported the cultural bacterial diversity from debris-free Changme Khang (CKG) and debris-cover Changme Khangpu (CK) glacier, North Sikkim, India. The dominant phyla present in these glaciers were Firmicutes followed by Actinobacteria, Alphaproteobacteria and Betaproteobacteria (Sherpa et al. 2018a). Analysis of all the available 16S rRNA from public databases, have shown that the cultured species and genera of bacteria and Archaea are less than 5% and thus majority of them are uncultured bacteria or archaea. Therefore, to understand the complete bacterial diversity of these two glaciers, the metagenomic analysis as well as phospholipid fatty acid analysis (PLFA) was carried out in the present study.

Materials and methods

Study site and sampling

GPS MAP 78S, an automated global positioning device was used for GPS mapping. The ice core samples were obtained by drilling the surface ice about two meters from the two glaciers accumulation zone and processed as per our earlier studies (Sherpa et al. 2018a).

Methods for the determination of the chemical characteristics of the glacial samples

A total of 25 different chemicals were analyzed from Changme Khang and Changme Khangpu glaciers ice samples with the help of Inductive Couple Mass Spectroscopy (ICP-MS), Perkin Elmer, Nex-ION 300X ICPMS, USA. Along with the elemental analysis, various other parameters such as pH, total hardness, turbidity, total alkalinity, phenolic compounds, color, total dissolved solids (TDS), and electrical conductivity were measured by the help of ICPMS and dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD) were checked as per American Public Health Association (APHA) standards (Ewa and Bosnak 2012). Piper plotting was done to verify the chemical composition of glaciers' ice samples on the basis of relative concentration of its constituents (Piper 1944). The piper diagram was plotted by using AqQA software (Version v1.x. Rockware).

Culture independent techniques

Phospholipid fatty acid analysis (PLFA)

The phospholipids were extracted as per the standard protocol of Fan et al. (2017), Quideau et al. (2016), and these extracted phospholipids were analyzed using Sherlock-MIDI identification system (Najar et al. 2018).

Metagenomic DNA extraction

Glacier ice community genomic DNA was extracted by using DNeasy Power water kits (MoBio Laboratories, Carlsbad, CA, USA) as per the manufacturer instructions (Edwards et al. 2013). By DNA gel electrophoresis quality of the DNA was checked and quantified using Qubit fluorimeter (Thermofisher, USA).

Metagenomic library preparation and sequencing

Shotgun sequencing libraries were prepared using NEB Next UltraII DNA library preparation kit (Illumina) followed by the standard manufacturer’s protocol (Kayani et al. 2018). The fragment size distribution of each library was analyzed by Bioanalyzer and High Sensitivity DNA Kit (Agilent). Each library was sequenced using 100 bp paired-end, HiSeq 2000, Illumina platform after its quantification by qPCR. Raw metagenomics reads were submitted to Sequence Read Archive (SRA), NCBI to obtain the Bio-Sample and Sequence Read Archive (SRA) accession numbers The accession numbers obtained are SAMN08732116, SRP136080 and SRS3073134 for Changme Khang glacier with sample name as Changmekhang Glacier and SAMN08741619, SRP136079, and SRS3073133 for Changme Khangpu glaciers with sample name as CKVV4.

Bioinformatics analysis

The 18.72 million reads were optimally assembled using the metaSPAdes (Nurk et al. 2017), and the optimal assemblies were performed with K-mer length of 20 in increments of 20 and also scaffolded. Out of the 5 assemblies (i.e. 41,61,81,101 kmers and scaffold) it was concluded based on N50, the number of contigs assembled and contig length distribution that scaffolded assembly performed the best and was taken for further analysis. The assembly statistics were calculated using QUAST software and metaSPAdes after scaffolding assembled into contig length greater than 500 bp (Gurevich et al. 2013). The reads from the sample were mapped onto the contigs using BOWTIE2 in order to check for the assembly quality. More than 60% of sequence reads mapped back to the contigs successfully (Langmead and Salzberg 2012). The microbial abundance was estimated by using METAPHLAN-2 (Truong et al. 2015), a tool that profiles and classifies the sequencing data from shotgun metagenome samples, with species-level resolution and a set of one million clade-specific marker genes from more than 17,000 different microbes (~ 13,500 bacteria and archaea, ~ 3500 virus, and ~ 110 eukaryotes). The assembled contigs were annotated using BLASTn against the NCBI-nucleotide custom metagenomics database that included all nucleotide entries for organisms that belong to the archaea, bacteria, fungi and viruses; with minimum sequence similarity of 60% and e-value lesser than 1e-07 (Altschul et al. 1990). The contigs were annotated with ~ 80% contigs with similarity > 90%.


Physicochemical analysis of samples

The parameters which were used to describe the physical properties of water were temperature, turbidity, color, pH, and DO. The glacier accumulation zone temperature was monitored at approximately six-month interval for 3 years in the glacier ice pit at depths of 2 m using IR Gun (China). The mean temperature showed by Changme Khangpu glacier was − 41 °C followed by Changme Khang glacier sample (− 33 °C) (Table 1). The nephelometric turbidity (8 NTU) and TDS (1.03 gL−1) showed by Changme Khangpu glacier sample was higher as compared to Changme Khang glacier. pH of these two glaciers were similar (~ 7.4). ICP-MS results suggested that the Changme Khang glacier was found to be rich in calcium, magnesium, and total alkalinity as compare to Changme Khangpu glacier. Similarly from Changme Khangpu glacier chemicals such as nitrate, sulfate, colloidal sulfur as well as COD and BOD were showed higher than Changme Khang glacier (Supplementary Table 1). Ionic concentrations of elements in the glacier were plotted as piper diagram (Fig. 1) for classification on the basis of chemical composition (Piper 1944). Piper diagram is a combination of triangle plots representing anionic and cationic elements on a common baseline. The apexes of the cations plot were magnesium, calcium, sodium, and potassium cations, while the apexes of the anion plot were chloride, sulfate, carbonate, and hydrogen carbonate anions. The two ternary plots are then anticipated onto a diamond which can be used to describe different water types. The piper diagram showed that Changme Khang and Changme Khangpu glacier water fell under Ca2+ and \({\text{HCO}}_{3}^{ - }\) type which indicates the dominance of calcium and bicarbonate weathering as the major source of dissolved ions in the glacier ice.
Table 1

Physical analysis of glacier samples




Electrical conductivity (in µS cm)

Turbidity (in NTU)

D.O. (in mg L−1)

TDS (in g L−1)

Color (in Hazen)

Temperature (in °C)

Changme Khang

27°56′38.80″N longitude

88°39′56.91″ latitude






< 1

− 33

Changme Khangpu

27°58′04.16″N longitude 88°40′56.68″ latitude






< 1

− 41

Fig. 1

Piper diagram of Changme Khang (CKG) and Changme Khangpu (CK) glaciers' physiochemical analysis

Microbial diversity

Our previous studies with culture dependent method showed the dominance of phylum Firmicutes in Changme Khang and Changme Khangpu glaciers (Sherpa et al. 2018b). To understand the complete microbial diversity of these two glaciers, two culture independent analysis, i.e., the 16S rRNA metagenomic analysis as well as PLFA was carried out.

Phospholipid fatty acid analysis (PLFA)

Phospholipids are the fundamental components of microbial membranes and it has been described that they diverge between different species among prokaryotes which makes it a significant chemotaxonomic marker (Powl et al. 2007). PFLA of two glaciers suggests that the major fatty acids significantly varied amongst the Changme Khang and Changme Khangpu glaciers. It was found that the straight and branch chain fatty acids were abundant in case of Changme Khangpu glacier (51.34%) as compare to ChangmeKhang glacier (15.15%), whereas branch-chain fatty acids were higher in Changme Khang glacier (45.92%) than Changme Khangpu glacier (12.26%) (Table 2). The PLFA results showed that the two glaciers, i.e., Changme Khangpu (801.22 nmoles g−1) and Changme Khang (813.54 nmoles g−1) had similar biomass content. Fatty acid marker analysis with Sherlock PLFA tool showed the dominance of aerobic Gram-positive bacteria, followed by aerobic Gram-negative bacteria and subsequently anaerobic bacteria, actinomycetes, fungi, and eukaryotes in these glaciers. However, more Gram-positive bacteria were present in Changme Khang glacier (54.04%) than Changme Khangpu glacier (24.84%), whereas Gram-negative bacteria were more in Changme Khangpu glacier (22.65%) than Changme Khang glacier (4.41%) (Fig. 2). The proportion of signature fatty acids related to fungi, anaerobe and actinomycetes were higher in Changme Khangpu glacier compare to Changme Khang glacier (Fig. 2).
Table 2

The abundance of various fatty acids in two glaciers

Fatty acids

Changme Khang

Changme Khangpu







PUFA (Poly unsaturated fatty acids)



MUFA (Mono unsaturated fatty acids)



Cyclo fatty acids


DMA (Di-methyl acetal)









10-methyl fatty acids



Fig. 2

Community structure of CK and CKG glacier based on PLFA studies

The correlation among fatty acids with respect to two different studied glaciers were carried out with the help of Principal component analysis (PCA). The results have shown that the F1-represents the maximum variability of 97.94% as shown in (Table 3). There was significant Pearson (n) correlation in both the two glaciers such as Changme Khang and Changme Khangpu glacier with the significant p value of < 0.05. It has been shown that the Changme Khang and Changme Khangpu glacier are positively correlated with each other and with respect to parameters such as branch chain fatty acids. However, other fatty acids such as poly unsaturated fatty acids (PUFA), straight chain fatty acids, di methyl acetal (DMA), 18:1W6c, 9c and 18:1w9c were negatively correlated (Fig. 3).
Table 3

Principal component analysis (Eigenvalues)







Variability (%)



Cumulative (%)



Fig. 3

Principal Component analysis of various fatty acids of Changme Khang and Changme Khangpu glaciers

Metagenomic Analysis

Shotgun metagenomic sequencing of Changme Khang glacier revealed a total of 1,872,786 reads and 18,324 contigs with an average sequence length of 500 bp (Table 4). The G + C content was around 52%; while, 2,112,210 reads were obtained from Changme Khangpu glacier and 10,211 contigs with an average sequence length of 500 bp. The G + C content was estimated to be around 51%. The reads from both the samples were mapped onto the respective contigs using the BOWTIE2 software in order to check for the assembly quality. More than 60% of the sequence reads mapped back to the contigs successfully. The microbial abundance was estimated using METAPHLAN2 software, a tool that profiles and classifies the sequencing data from shotgun metagenome samples, with species-level resolution using a set of 1 million clade-specific marker genes from more than 17,000 different microbes.
Table 4

Diversity indices of Changme Khang and Changme Khangpu glaciers' microbial communities


Total number of reads

G + C content

Shannon H-indices

Fisher alpha

Chao 1













Diversity index and rarefaction curve

By using two software packages such as PAST and Estimate S-software, the diversity indices such as Shannon H, Fisher Alpha, and Chao1 were estimated. The results have shown that the Changme Khang glacier is more diverse than Changme Khangpu glacier. The Shannon index was 1.29 and 1.15 for Changme Khang and Changme Khangpu respectively (Table 4). The Fisher alpha and Chao1 was also higher in the case of Changme Khang glacier (Table 4). Rarefaction curve which allows the calculation of species richness in a sample, suggests that Changme Khang glacier has higher richness of species compared to Changme Khangpu glacier (Fig. 4).
Fig. 4

Rarefaction curve, red curve shows species richness of Changme Khangpu glacier whereas blue line represents Changme Khang glacier. The x-axis represents the number of sequence reads while the y-axis represents the species counts

The bacterial community composition of these two glaciers showed little variation. The phylum wise diversity showed that the dominance of Proteobacteria (75.5%), Firmicutes (5.1%), unidentified Virus (0.7%), Actinobacteria (7.2%), and Ascomycota (0.9%) in Changme Khangpu glacier whereas Changme Khang glacier was dominated by phylum Proteobacteria (81.2%), followed by Firmicutes (7.1%) and Actinobacteria (3.1%) (Fig. 5a, b). In phylum Proteobacteria, the dominant class was Betaproteobacteria (Changme Khang 66.22% and Changme Khangpu 52.32%), followed by Gammaproteobacteria (Changme Khangpu 35.97% and Changme Khang 18.42%) and Alphaproteobacteria (Changme Khang 11.27% and Changme Khangpu 14.61%). The other class such as Deltaproteobacteria, Epsilonproteobacteria, and Zetaproteobacteria were not detected in both the glaciers. At genus level classification there was not much variation in these two glaciers. The major dominated genus demonstrated in Changme Khang glacier were Delftia (49.35%), Serratia (31.19%), Brevundimonas (11.23%), Stenotrophomonas (3.27%), Massilia (2.26%), Commomonas (0.66%), and Pseudomonas (0.51%). Similarly, major genera demonstrated in Changme Khangpu glacier were Delftia (62.39%), Serratia (16.58%), Brevundimonas (14.52%), Massilia (2.95%), Stenotrophomonas (1.02%), and Commomonas (0.84%) as shown in (Fig. 6a, b). The metagenomic data obtained by Hiseq platforms provide shorts reads and species level differentiation is difficult, however, it possibly suggests that the diversity at species level varies significantly among two glaciers. In both the glaciers the major bacterial flora was uncultured with 40.68% in Changme Khangpu glacier and 35.01% in Changme Khang glacier. In case of Changme Khang glacier, major species detected were Serratia marcescens (31.19%) followed by Delftia acidovorans (19.79%), Stenotrophomonas maltophilia (2.26%), Pseudomonas putida (0.51%) and Escherichia coli (0.44%). In case of Changme Khangpu glacier major leading bacterial species were Delftia acidovorans (37.59%), subsequently by Serratia marcescens (24.80%) Stenotrophomonas maltophilia (0.86%), Bacillus cereus and Bacillus thuringinensis together account for about 0.41% of the total sequences, and Pseudomonas putida accounted for 0.11% of the total sequences, respectively (Fig. 7a, b).
Fig. 5

a Phylum level classification of Changme Khang glacier. b Phylum level classification of Changme Khangpu glacier

Fig. 6

a Genus level classification of Changme Khang glacier. b Genus level classification of Changme Khangpu glacier

Fig. 7

a Species-level classification of Changme Khang glacier. b Species-level classification of Changme Khangpu glacier


Glaciers are considered as large repositories of microbial life. Around 25% of the land surface on the earth are classified as cold environments (Choudhari et al. 2014), and biological activity in these low-temperature habitats is generally thought to be restricted. Glaciers are simple and fairly closed-ecosystems which are inhabited by primary producers such as photosynthetic algae and bacteria (Choudhari 2015). However, due to the uncultivated status of the major taxa in the glaciers, culture-dependent technique led to recognition of only a few distinct genera from the glaciers. The microbial diversity of Himalayan glaciers has been less investigated compared to other cold habitats around the world. The Himalayan glaciers might harbor microbial communities markedly different from those colonizing glaciers of Polar Regions, and thus studying their microbial diversity is of extreme importance for several reasons; first, glacial ecosystems are considered as massive repositories of a virtually unexplored genomic diversity (Edwards 2015), second, this largely unexplored biological diversity faces a real risk of extinction owing to the loss of its harboring ecosystems (Griffiths 2012), third, rapid meltdown of these glaciers might contribute to the reactivation and release of human, animal, and plant pathogens that have remained contained in glacial ice for centuries and even thousands of years (Rogers et al. 2004). Thus the aim of the present study was to explore the microbial diversity, mainly bacterial, present in the two glaciers of North Sikkim (Changme Khang and Changme Khangpu) to correlate these with the physiochemical parameters.

The abiotic and biotic matters determine the suitability of glacier water for human use. Therefore, several physical and chemical parameters of water from the glaciers were checked. The physicochemical analysis of two glaciers suggested that besides being two different types of glaciers, i.e., debris covered and debris free and closely located, these glaciers possess similar elemental concentrations. As per the APHA (2017), the elements present in these glaciers were under permissible limits. The piper analysis shows the nature of glaciers and it has been predicted that both the glaciers are calcium bicarbonate, thus it may be concluded that both are from the calcium carbonate weathering regions. The TDS were higher in Changme Khangpu glacier which is not surprising as in debris-covered glacier fine sediments are mostly present in accumulation zone where the snow gets accumulated.

Our previous culture dependent bacterial diversity studies suggested that the closely situated glaciers (Changme Khang, Changme Khangpu and Chumbu glacier) (Sherpa et al. 2018a, b) have similar bacterial diversity compared to that of the distantly located glacier (Kanchengayao) (Sherpa et al. 2019). These three glaciers are located in the adjoining area (Changme Khang, Changme Khangpu and Chumbu glacier), only a few miles apart and almost similar altitude with comparable chemical constituents. Consequently having related geological and geographical attributes; this might be the reason for having similar bacterial diversity in the three glaciers. Another reason might be due to divergence in Indian monsoon air masses as well as due to regional or local mineral or aerosol deposition patterns in glaciers (Zhang et al. 2007). Also globally it has been seen that among the cultural bacterial diversity, phylum Proteobacteria, Firmicutes and Actinobacteria were most versatile in the glacier ecosystems (Shen et al. 2012; Zhang et al. 2010; Rondon et al. 2016; Shivaji et al. 2011; Segawa et al. 2010; Schutte et al. 2010; Wu et al. 2012; Grzesiak et al. 2015; Zhang et al. 2015; Franzetti et al. 2017; Cameron et al. 2012; Zdanowski et al. 2017; Edwards et al. 2013; Yao et al. 2006; Liu et al. 2011). Other reasons for their dominance in glaciers might be due to the production of antifreeze proteins and spores in Gram-positive bacteria (Munoz et al. 2017; Singh et al. 2014; Zhang et al. 2016).

The PLFAs of Changme Khang and Changme Khangpu glacier samples showed the abundance of Gram-positive bacterial signature fatty acids. Our PLFAs results are correlated with culture-dependent methods as these two glaciers showed the dominance by Gram-positive bacteria (Sherpa et al. 2018a). The metagenomic analysis of Changme Khang and Changme Khangpu glacier suggested the dominance of Gram-negative bacteria followed by high-G + C Gram-positive bacteria and low-G + C Gram-positive bacteria. These glaciers were closely located with each other and thus the bacterial community shows little variation between them. The phylum wise diversity showed the dominance of Proteobacteria, Actinobacteria and Firmicutes. However, the result showed that at phylum Ascomycota, Tenericutes, and few unidentified viruses were detected in very small percentage (< 1%) of all classified sequences in both the glacial samples. Most proteobacterial sequences were assigned to the class Betaproteobacteria. This corresponded to other studies of glacier ice (Foght et al. 2004), subglacial habitats (Cheng and Foght 2007), and mountain snow (Segawa et al. 2005). Phylum Actinobacteria appear to be second abundant phyla but their representation was also less than 10% of all classified sequences. It has been shown that Actinobacteria have a higher ability to survive in a cold environment than low-G + C Gram-positive bacteria and Gram-negative bacteria (Willerslev et al. 2004). This is probably caused by the ability of Actinobacteria to develop resting forms with low metabolic activity. Thus Proteobacteria, Actinobacteria, and Firmicutes represented the predominant phyla in accumulation zone of glacier ice from Changme Khang and Changme Khangpu. These groups were also predominant in several other glaciers (Miteva et al. 2004, Willerslev et al. 2004; Turchetti et al. 2008) and other permanently cold habitats, such as sub-glacial melt-water (Cheng and Foght 2007; Foght et al. 2004), snow (Segawa et al. 2005), Antarctic lakes (Mosier et al. 2007) and Cryoconite hole (Edwards et al. 2013).

Our previous culture dependent studies (Sherpa et al. 2019) and PLFA results are correlated, i.e., both of these methods showed the dominance of Gram-positive bacteria (mainly Firmicutes) in these glaciers. Isolation of psychrophilic bacteria using different media along with different pH and temperature did not affect these results. The dominance of phylum Firmicutes might me due their ability to adopt a wide range of temperature and pH. Surprisingly, next generation sequencing (NGS) data showed the dominance of Gram-negative (mainly Proteobacteria) over Gram-positive bacteria in these glaciers. Possibly, these Gram-negative bacteria are unculturable. The dominance of Gram-negtive bacteria in NGS data and dominance of Gram-positive in culture dependent and PLFA studies were also found in our previous studies with thermophilic environments (Najar et al. 2018). PLFA and metagenomic studies are culture independent studies, however both of these are based on two different molecules, i.e., phospholipid fatty acids and DNA. PLFA analysis are based on live microbiota present in the sample of any ecosystem, as the phospholipids are unstable and decompose rapidly after cell death (Lanekoff and Karlsson 2010). Therefore it is not surprising that our culture-dependent and PLFAs results are correlated with each other. On the other hand in case of metagenomic studies, DNA which is more stable than phospholipids is estimated from both live and dead bacteria. Thus, the study of microbial diversity of such habitats requires different culture dependent and culture independent studies. Our results support the insight that interrelated phenotypes persist in geographically diverse cold habitats because of analogous strategies for survival and enduring activity at low temperature (Priscu and Christner 2004).


The Himalayas which is also called as “third pole of the world” houses large deposits of snowfall and many glaciers, however, in recent times it is experiencing rapid decline in snow cover. Despite playing important roles on livelihood of billions of people limited microbiological studies have been carried out on Himalayan glaciers. There are almost 80 glaciers in Sikkim and most of them are debris-covered valley type. Majority of these glaciers are located under Kanchenjunga National Park which is recognized as UNESCO heritage site in 2016, one of the biodiversity hotspot states of India. It prompted us to inspect the culture independent bacterial diversity of two glaciers, i.e., Changme Khangpu and Changme Khang glacier of North Sikkim, India by PLFA and 16S rRNA gene metagenomics. The PLFA analysis and culture-dependent analysis of Changme Khang and Changme Khangpu glacier were correlated with each other as both the methods revealed the abundance of Gram-positive psychrophilic bacteria as compared to Gram-negative bacteria. On the basis of the metagenomic analysis, a wide bacterial diversity was detected in both the glaciers. These glaciers harbour many novel and unknown microbes which are indicated by the abundance of 40% and 35% unclassified bacterial sequences as showed by metagenomic analysis of Changme Khangpu and Changme Khang glacier respectively. However, the most abundant phyla from both the glaciers were Proteobacteria, Firmicutes, and Actinobacteria. Our report of metagenomic analysis from glaciers of Sikkim is of very significance in relation to maintain and understand the dynamics of environment sustainability. The bacteriomes which are known to be responsible for dissimilatory nitrate reduction namely nitrate to ammonia, alanine, aspartate, and glutamate metabolism were detected. These bacteria can possess many genes involved in the synthesis of osmoprotectants and cryo-protectants such as glycine, betaine, choline, and glutamate. The adaption strategies of microbes in sub-zero conditions can also provide information to understand the extraterrestrial environments. Such studies also promote the knowledge which is assumed to be useful in the future for controlling pathogenic microbes, which endure and flourish in cold-stored food and feed materials.

To the best of our knowledge, this is the first study which revealed community composition and diversity from the accumulation zone of the Alpine glacier.



This study has been funded by the Department of Science and Technology, Govt. of India (IUCCC) and Department of Biotechnology (BT/20/NE/2011). We are grateful to Forest Department, Govt. of Sikkim for providing research permit and access to the sampling sites. Authors are thankful to Dr. Uttam Lal, Dr. Rakesh Ranjan and Dr. Smriti Basnet for their support during the field study. The authors would like to thank Department of Microbiology for all the laboratory facilities.

Author contributions

MTS performed sample collection, did the field study, experimental works, analysis and prepared the manuscript, NT designed the study, reviewed and edited manuscript, INN and SD helped in some experimental work.

Compliance with ethical standards

Conflict of interest

Authors have no conflict of interests.

Supplementary material

42398_2019_67_MOESM1_ESM.docx (17 kb)
Supplementary material 1 (DOCX 16 kb)


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Copyright information

© Society for Environmental Sustainability 2019

Authors and Affiliations

  • Mingma Thundu Sherpa
    • 1
  • Ishfaq Nabi Najar
    • 1
  • Sayak Das
    • 1
  • Nagendra Thakur
    • 1
    Email author
  1. 1.Department of Microbiology, School of Life SciencesSikkim UniversityGangtokIndia

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