Diversity, distribution, and function of bacteria in the supraglacial region hit by glacial lake outburst flood in northern Pakistan
Environmental Sciences Europe volume 34, Article number: 73 (2022)
Glaciers of the Hindu Kush region are highly susceptible to climate change. Recently, a severe glacial lake outburst flood (GLOF) hit the supraglacial region at the frontier of Dook Pal Glacier. Information on the bacterial community in a freshly appeared supraglacial lake after GLOF is essential to probe the bacterial distribution pattern after immediate unlocking from the supraglacial region. After GLOF, geochemistry and bacterial diversity, distribution, community structure, and function were examined in the lake-debris and melt-water.
In general, concentrations of dissolved free amino acids were similar between lake-debris and melt-water, potentially toxic elements and cations were greater in lake-debris, and anions concentrations were greater in melt-water. In addition, there was comparable diversity in the glacial melt-water and lake-debris; Proteobacteria dominated in lake-debris (33.1–94.5%), while Proteobacteria (36.7–50.5%) and Firmicutes (44–62%) dominated in melt-water. It is more likely that Proteobacteria and Firmicutes shifted towards melt-water after GLOF, creating an uneven distribution of communities in the lake-debris; however, a relatively even distribution was maintained in melt-water. Genes responsible for metabolism and energy production were higher in lake-debris than in melt-water bacteria; whereas, genes for other cellular mechanisms were higher in melt-water than in lake-debris bacteria.
This study offers valuable datasets in understanding the bacterial diversity, distribution and function as a consequence of GLOF at the glacial frontier.
Approximately 13% of the earth’s surface is covered by the cryosphere , which consists mainly of glaciers and ice sheets , but also includes permafrost and icebergs in the polar and non-polar regions. Biologically active life was once thought not to exist in glacier habitats [2, 3]; however, microbial life has been identified. In fact, glacial ecology has been gaining more attention in the past few decades , with studies reporting on diverse microbial communities, including psychrophiles and psychrotrophs in a biologically active form in glacial habitats [4,5,6,7]. The life of all domains, including Eukarya, Archaea, and Bacteria, has been described in glacial environments , with bacteria dominating the glacial ecological niches, followed by fungi and algae . These microorganisms face hostile environmental conditions, such as sub-zero temperature, nutrient deficiency, freeze–thaw cycles, and high salinity; however, they have adopted numerous defensive mechanisms such as the production of cold-active enzymes, exopolysaccharides, ice-nucleating proteins, unsaturated fatty acids and synthesis of pigments to cope with these ecological stresses .
The study of glacial microbes is crucial as they strongly influence glacial melting by producing pigments that impact the albedo effect . Glaciers are the vital modules of the climate system. Their melting interferes with freshwater availability and causes the sea level to rise , resulting in regional water shortage and geopolitical tension. Furthermore, glacier melting could unlock the hidden microbial world being harbored in these harsh environments. These microbes play a crucial role in the food web, geochemical processes, mineralization, and in immobilizing several xenobiotic compounds [4, 10], and are potential bio-indicators for climate change [11, 12]. In addition, these microbes might include potential pathogens and antibiotic-resistant components that could genetically alter the contemporary microbial world and enhance greenhouse gas emissions .
The diversity of microbes, and their ecological and biogeochemical processes, responses to climate change, and biotechnological potential, have been studied intensively in polar regions, such as Greenland and Antarctica [14,15,16,17], but not so in the non-polar region. However, studies on the diversity of microbiota in the non-polar region and their biogeochemical processes and bioprospecting potential are gaining momentum [6, 7, 18, 19]. This region, known as the third pole of the world, has the largest glacial reservoir globally and includes northern areas of Pakistan, India, and southern China. In addition, the Karakoram, Hindu Kush, and Himalaya regions contain 54,252 large and small glaciers that cover an area of approximately 60,000 km2. Very few studies have been made on microorganisms in the glaciers of the Hindu Kush region [19, 20] and none have used culture-independent techniques. The isolation and culturing of microbes in the laboratory have remained a major challenge in microbiology , but for a better understanding of the bacterial diversity and community structure in this region, a culture-independent technique is crucial.
Glacial ecosystems are divided into three ecological regions, supraglacial, subglacial, and englacial, among which only supraglacial have been studied in detail. Several studies reported different microbial communities in different portions of the same glacier, with few common groups. Greater bacterial numbers were observed beneath the glacial mass than in the glacier and these bacteria are crucial in weathering and carbon cycling . Sajjad et al.  reported dissimilar bacterial community compositions in glacial melt-water runoff and soil from the frontier region of Baishui Glacier in China. Yang et al.  reported similar bacterial communities in the glacial snow and soil from the Chongce Ice Cap, while Wilhelm et al.  reported an alteration in the microbial diversity and community structure composition due to glacial retreat in Alpine glacier-fed streams. Furthermore, significant shifts in community composition of microbes have been observed within and among geographically distinct glaciers , with several climatic and environmental parameters acting as driving factors for these variations [26, 27]. Climate change could affect bacterial growth and the distribution pattern over the glacial habitat, causing biodiversity shifts [7, 28, 29]. Microbes harbor in the supraglacial region carried out carbon and nutrient cycling and bedrocks weathering [22, 30] and could influence the physical properties of glaciers by lowering the albedo effect and enhancing glacier melting [31, 32]. When the temperature rises in the glacial environment, the melted water interacts primarily with the adjacent glacial debris and then flows into moraine lakes and streams ; consequently, glacial soil and debris is a pivotal microbial ecosystem of supraglacial regions, which regulates biogeochemical processes. Supraglacial ecosystem contains the top layer of ice that is in direct contact with the atmosphere and gets dust, aerosols and microbial deposition. Here three inter-connected but distinct habitats including bare snow, melting snow and surface debris host metabolically active microbes. Mostly heterotrophic microbes have been correlated with dust concentration on the ice surface . During the melt season, the melt-water provides a transient spot in this junction of habitats by which microbes can be transported from the ice surface towards melt-water [34, 35]. These supraglacial lakes and streams are vital components of the glacial ecology that strongly controls the ecology and geomorphology of these systems by connecting glacial processes and downstream ecosystems . These supraglacial ecosystems could substantially influence the downstream terrestrial and marine environments through melting and releasing locked organic carbon, ancient microbes, antibiotic resistance genes, and other nutrients [13, 37, 38].
In addition, the microbial world in the supraglacial ecosystem faces an intensive threat of demolition due to the ongoing climate change . Therefore, the supraglacial ecosystems immensely hit and destroyed by severe climate change events might release the long trapped microbes and affect their distribution pattern in freshly appeared supraglacial lake habitat. Due to the ongoing accelerated glacial retreat, glacial lake outburst flood (GLOF) and associated debris flow from supraglacial lakes have become more frequent in recent years. Therefore, it is vital to understand the bacterial community dynamics in the supraglacial region hit by GLOF. One such supraglacial ecosystem was recently hit by a severe GLOF event at the frontier of Dook Pal Glacier in Chitral valley located in the Hindu Kush (Fig. 1). Hindu Kush is the sub-range of the TP region situated in northern Pakistan and spreads towards central Afghanistan . Being a highly glaciated zone, the ecology of Chitral is subtle and highly susceptible to climate change. Dook Pal glacier is one of the most vulnerable glaciers in Golen’s Hindu Kush region, which is severely affected by a huge GLOF in 2019 and has never been studied before for microbial diversity. In addition, no study has been conducted so far for bacterial diversity released from the supraglacial ecosystem as a result of GLOF in the entire Hindu Kush region. Therefore, this study comprehensively examined the bacterial diversity, their distribution pattern and function after release from the supraglacial ecosystem as a result of GLOF at the Dook Pal Glacier (Additional file 1: Fig. S1). Glacial lake-debris and melt-water samples were collected near supraglacial lake freshly appeared as a result of recent GLOF event at the frontier region of the glacier to study the bacterial diversity, community structure composition, distribution pattern, and function by adapting a high-throughput Illumina sequencing method to the 16S rRNA gene. This study was guided with the following objectives; (i) to describe the bacterial richness, including alpha and beta diversity in supraglacial lake region at the glacial frontier; (ii) to determine taxonomic assignments and novelty of bacterial phylotypes; (iii) to determine physicochemical parameters that act as driving factors for the community structure composition and distribution pattern of bacteria in this closely linked ecosystem; (iv) to probe potential biomarkers in the melt-water and lake-debris bacteria; (v) and, to generate the predicted functional analysis of the phylotypes.
Materials and methods
The study area
Golen valley, with lush green meadows and pastures, is situated in the northern part of Pakistan, 25 km east of Chitral town (~ 80,000 people). The valley spans 529 km2 and comprises seven small catchments spreading in the southeast direction. According to United Nations Development Program (UNDP, 2015), 53 glaciers, ranging in elevation from 3917 to 6143 m above sea. level (a. s. l.), are present in the valley and they provide water for the 108 MW electricity project of the Water and Power Development Authority (WAPDA). The name Golen Gol is derived from two Khowar (local language) words that mean lots of nalas (water streams) joined together. These streams provide water for electricity and drinking for Chitral town. Pakistan is among the most vulnerable countries to face the impacts of climate change, as the average air temperature is increasing, which enhances glacier melting. Dook Pal Glacier (35.85545°N and 71.96885°E; 4482 m a. s. l) is one of the best-known glaciers in Golen Gol. The rapid melting ice and snow during summer feeds two supraglacial lakes downstream that were declared vulnerable by the lake assessment report of UNDP in 2014.
At high altitudes, a GLOF erodes and entrains materials and ultimately converts into a debris flow while propagating down a valley. The topography of the valley and material of the dams indicate that the dams were formed by rock avalanches/slides and debris flow from tributary valleys. In 2019, a subglacial/englacial rupture caused the lake eruption that released a huge amount of debris and water along the GLOF flow path  which caused devastation downstream by damaging houses, bridges, water channels, agricultural land, forests, electric poles, and the hydropower project of WAPDA. This area is extremely vulnerable to geological events, such as GLOF and land-slides, and climate change and has attracted attention for geological, geographical, and climate research. However, no microbiological research has been done in this ecologically unique environment.
In September 2019, the supraglacial region at the ablation zone of Dook Pal Glacier freshly appeared as a result of GLOF was visited for sample collections. Six glacial lake-debris (GLD) samples at different spots around the supraglacial lake at the frontier of the glacier were collected in sterile high-density polyethylene (HDPE) self-sealing bags (Qingdao Xinjie Industry, Qingdao, China) and 6 glacial melt-water (GMW) samples were collected at different spots using sterile screw-capped polypropylene bottles (Nalgene, Thermo Scientific, Waltham, MA, USA). All 12 samples were collected in triplicate at each sampling point and the triplicates of each site were pooled so that there were 6 GLD (NS-1 to NS-6) and 6 GMW (NW-1 to NW-6) samples. In addition, 250 mL melted water was filtered through a Millipore membrane (0.22 mm pore diameter fitted on a Sartorius polycarbonate filter holder) into a sterilized polypropylene bottle for geochemical analysis. Standard sampling techniques were used to prevent potential contamination during sampling. Samples were stored at − 4 ± 0.5 °C in Chitral town. The genomic DNA obtained from these samples was transported to Northwest Institute of Eco-environment and Resources (NIEER), Chinese Academy of Sciences Lanzhou, China, and kept at −20 °C. Description of the collected samples is presented in Additional file 1: Table S1.
Physical and geochemical analysis
Temperature of the sampling sites was recorded during sample collection. The pH of GMW samples was measured immediately while sampling. For GLD samples, a 0.1 (1 g lake-debris in 10 mL water) saturated solution was prepared in deionized water and the pH was measured with a Pt electrode having a reference with Ag/AgCl electrode (3.0 M KCl) (BPP-922). To quantify metal concentrations, the filtered water was acidified by adding HNO3, while the digestion of GLD samples was carried out by adapting the procedure of Sajjad et al. . Metals in the filtrate were measured by inductively coupled plasma atomic emission spectroscopy (ICP-AES) following an external conventional standardization procedure. To verify the accuracy of the analysis, certified solid sample material NIST 4355 (Lot number: 221) was run as a reference. The total organic carbon (TOC) concentration was determined in triplicate by the Total Organic Carbon Analyzer (SHIMADZU-TOC-VCPH, Tokyo, Japan). Dissolved free amino acids (DFAA), including alanine, adenosine, valine, tricine, tyrosine and aspartic acid, were measured isocratically in the CarboPac PA20 column of the Dionex ICS-3000 (Thermo Fisher, Waltham, MA, USA) ion chromatography system. The concentrations of anions (acetate, PO4(2−), SO4(−2), Cl−, NO3, and NO2− and cations (Mg2+, Na+, Ca2+, and NH4+) were determined based on particular retention times in the eluent gradient (MSA, KOH, K2CO3, and LiOH) using Dionex ICS-5000 reagent-free, capillary ion chromatography system. Ammonia concentration was measured using flow injection analysis (FIA).
Genomic DNA extraction
Five grams of lake-debris were sieved through a 2-mm mesh and larger debris was removed. Then, 1 g of sample was added to 100 mL phosphate-buffered saline (pH 7.0) and shaken in an incubator at 100 rpm for 2 h at 5 °C to free bacterial cells connected to GLD particles. The supernatant from the incubated GLD samples and thoroughly mixed GMW sub-samples (1000 mL) was filtered through a 0.2-µm pore size (EMD Millipore Sterivex-GV Polyvinylidene Fluoride filter, Millipore, Billerica MA, USA). To extract bacterial DNA, a kit (MoBio Power Soil DNA Isolation Kit, MoBio Laboratories, Carlsbad, CA, USA) was used following the manufacturer's instructions. To burst bacterial cells, especially Gram-positive bacteria, 35 mg of 1 mm sterile glass beads were added to the samples and homogenized for 30 s at 3000 rpm in a Mini-BeadBeater-16 (Model 607, BioSpec Products, Bartlesville, OK, USA). Bacterial DNA was extracted in triplicate and mixed before PCR amplification to reduce spatial variation. Quantitative and qualitative evaluation of the extracted DNA was carried out on 0.8% (w/v) agarose gel and NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). Extracted DNA with ≥ 1.8 absorbance ratio (OD260/OD280) was subsequently used. Details on the quantity and quality of the DNA are presented in Additional file 1: Table S2. The DNA was kept at −20 °C for further analyses.
For amplifying the bacterial hypervariable V3–V4 region, 515F–806R primers with essential sequences of adapter, barcode, and linker were used. PCR amplification used the Thermocycler PCR system (GeneAmp 9700, ABI, ThermoFisher, Waltham, MA, USA) with TransStart Fastpfu DNA Polymerase AP221-02. Details on sequencing primers and conditions of PCR amplification are available on the Earth Microbiome Project website (https://earthmicrobiome.org/protocols-and-standards/16s/). PCR amplifications were done according to the formal experimental conditions, and samples were amplified in triplicate with each reaction volume at 25 µL (75 µL of the total volume of each sample). The PCR reaction mixture was composed of 13 µL of PCR-grade water (MioBio cat. no. 17000-11), 10 µL PCR master mix (2×) (ThermoFisher cat. no. 13000014), primers both reverse and forward of 0.5 µL each (10 µM), and template DNA of 1 µL. PCR conditions followed denaturation for 3 min at 94 °C, 30–35 cycles at 94 °C for 45 s, annealing of primers at 50 °C for 60 s, extension at 72 °C for 90 s, and final elongation at 72 °C for 10 min. Different PCR cycles (30–35) were run for different samples to obtain strong DNA bands in limited possible PCR cycles to determine the real abundance of bacteria. Furthermore, the PCR amplicons were extracted from 2% agarose gel and further purified with a kit (AxyPrep DNA Gel Extraction Kit, Axygen Biosciences, Union City, CA, USA) following the manufacturer’s instructions and further quantified with QuantiFluorTM-ST (Promega, Madison, WI, USA). The obtained amplicons were combined in the equimolar concentration and paired-end sequencing (2× 250/300 bp) was done using the Illumina MiSeq platform (Allwegene, Beijing, China).
Bioinformatics, statistical analyses, and diversity estimation
The raw sequences were further de-multiplexed and filtered for quality using Quantitative Insights into Microbial Ecology (QIIME; version 1.17) and FASTQ files were used for subsequent alignment. Reads (300-bp) were trimmed to obtain an average of < 20 quality scores on a 10-bp sliding window. Reads of < 50-bp, ambiguous characters, and two nucleotides discrepancy during primer matching were discarded. The overlapping sequences having > 10 bp were assembled based on their overlapped sequence. In addition, chimeric sequences were also discarded with the help of the UCHIME algorithm, and non-chimeric sequences were clustered to operational taxonomic units (OTUs) having 97% similarity cutoff through UPARSE (version 7.1). The sequence dataset was used for taxonomic grouping, and sequences were aligned against the SILVA 138.1 database with a 70% confidence threshold. Details on sequences are presented in Additional file 1: Table S3. Diversity and richness estimators, including OTUs, Shannon, Simpson, Chao1, and coverage indices and the rarefaction curve were determined in Mothur v.1.42.1. The analyses of hierarchical clustering used Primer 6 software (Primer-E Ltd., UK). In addition, the relative abundances of dominant genera and phyla were estimated by dividing the number of reads allocated to a particular taxon by the total obtained sequences (%). Paleontological Statistics (PAST) Version 4.05 was used for clustering samples based on the Bray–Curtis similarity index using the unweighted pair group method with arithmetic mean (UPGMA). The normalized abundance of genera (√x) and environmental factors (X–mean/standard deviation) were used to estimate the correlation of bacterial community pattern and environmental variables based on Bray–Curtis similarity index and plotted redundancy analyses (RDA) in the R version 3.6.2 using the function of geom_ord_ellipse in yyplot, gglayer vegan package (support sample size ≥ 3). In addition, the ggrepel and yyplot packages in “VEGAN” were used to insert confidence ellipses into the samples. For variations based on OTUs abundance that reflect the bacterial taxonomic composition, nonmetric multidimensional scaling (NMDS) was plotted in the vegan R package (ggplot2) using normalized data. The Venn diagram was drawn using the “VennDiagram” package. The linear discriminant analyses effect size (LEfSe) analyses evaluated potential metagenomic biomarkers in the glacial water and solid groups having linear discriminant analysis (LDA) = 2 in the Galaxy server (http://huttenhower.sph.harvard.edu/galaxy/). Furthermore, the Spearman correlation, using the function of corr.test in “psych” package in R, tested relationships between dominant phyla and ecological parameters, while analyses of the correlation network were carried out using Gephi v.0.9.1 software. A one-way ANOVA, t-test and Mann–Whitney U test were applied to test significant differences among physicochemical parameters using PAST (Version 4.03) and accepting p < 0.05 as significant.
For functional gene prediction, reclassification of the obtained OTUs was carried out using the Greengenes database. Subsequently, phylogenetic examination of communities by reconstruction of unobserved states (PICRUSt v1.1.4) used the sequences to determine the gene function from phylogenetic information. The gene functions were predicted and precalculated in databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG). First, the data were normalized by copy number (each OTU divided by the known abundance of the 16S copy numbers) and then functional pathways were predicted. The PICRUST output consists of the functional gene table displaying KEGG orthologs (KOs). The nearest sequenced taxon index (NSTI) value indicated the reliability and validity of the predicted functional metagenomes and functional pathways.
Physical and geochemical analysis
The lake-debris was alkaline with a pH higher (p < 0.05) than melt-water, which was neutral, while the temperature of lake-debris was lower (p < 0.05) than melt-water (Table 1). The TOC concentration was greater (p < 0.05) in lake-debris (2.7–3.7%) than in melt-water (0.4–0.8%), as were the concentrations of total iron (Fe: 1533 to 2215 ppm in lake-debris and 10.2 to 22 ppm in melt-water), zinc (Zn: 43 to 60 ppm in lake-debris and 8 to 13 ppm in melt-water), Mg, Ni, Cu, and Cr (Table 1). Similar concentrations of dissolved free amino acids (DFAAs) were measured in lake-debris and melt-water, except for aspartic acid and tricine, which were below detection in water (Table 2). Among DFAAs, tyrosine was present both in lake-debris (range 0.9–1.3 µM) and melt-water (range 0.7–1.6 µM). Concentrations of cations, including acetate, Ca2+, NH4+, Na2+, and K+ were greater in lake-debris than melt-water, except for Mg2+, which did not differ between them. All anions were detected in melt-water, but only SO4(−2) was detected in lake-debris (Table 2).
Sequencing and diversity indices results
The Illumina sequencing analyses generated a total of 2,331,702 high-quality sequences, with an average of 194,308 sequences per sample (Additional file 1: Table S3). Details of read numbers, richness, and diversity estimators are presented in Table 3. Adequate sequencing depth was achieved as illustrated by the rarefaction curves and most of the samples displayed saturated levels (Additional file 1: Fig. S2). Moreover, alpha diversity (community within-sample), beta diversity (communities between samples), bacterial structural dynamics, distribution pattern, potential taxonomic biomarkers, and predicted functional analysis were determined.
Alpha diversity analyses
Bacterial diversity encompasses mainly the richness and evenness of bacterial taxa. Alpha diversity indices, including the abundance of OTUs, evenness, dominance, Fisher-Alpha, equitability, Shannon, Chao1 and Simpson were calculated (Table 3). A total of 9472 OTUs, ranging from 394 to 1551, did not differ between lake-debris and melt-water. A higher number of unique OTUs emerged in lake-debris (n = 1473; 41%) than in melt-water (n = 748; 21%), while they shared 1394 (39%) OTUs (Additional file 1: Fig. S3). Both diversity and richness of the bacterial taxa were higher in lake-debris than melt-water (NS-6 and NS-5). The richness estimator Chao1 index, ranging between 634 and 1586, was higher (p < 0.05) in lake-debris than in melt-water and was higher (p < 0.05) than the obtained OTUs, which indicated that additional phylotypes (missing species) were in the samples. Higher values than the estimated OTUs and Chao1 were found in sample NS-2 (565) and a lesser value was found in sample NS-6 (35), which were consistent with the rarefaction curves. The rarefaction curve that did not reach a complete asymptote indicated unobserved phylotypes, which might be due to real rare species and/or reads that were artificially generated through miscalculations in sequencing and PCR amplification. However, all phylotypes were described and fullness was achieved as indicated by the coverage. Both Shannon and Simpson indices were correlated with bacterial diversities, which provided detailed information about the community composition and species richness. The Shannon index ranged between 1.24 and 5.31 (mean 3.29, n = 6) in lake-debris and between 2.70 and 3.44 (mean 3.0, n = 6) in melt-water, which were consistent with the rarefaction curves. The Shannon and Simpson indices did not differ (p > 0.05) between glacial melt-water and lake-debris; the most diverse sample was NS-6 and the least diverse sample was NS-2. In addition, Equitability_J, Evennees_e^H/S, and Dominanace_D were comparable with the diversity pattern, and the distribution of individuals inside communities was not similar. Sample NS-4 displayed higher evenness and lower dominance in the community structure than sample NS-2 (Table 3). In terms of richness, the study area was diverse; however, the bacterial community structure was not distributed evenly.
Beta diversity analysis
Based on the OTUs, samples were clustered through a paired group algorithm (UPGMA) and the Bray–Curtis similarity index was calculated. The clusters showed higher segregation resolution of bacterial communities and both lake-debris (I) and melt-water (II) formed relatively distant clades. However, melt-water (II) samples exhibited a slightly greater similarity than lake-debris (I). Samples NS-1 and NS-2 clustered and showed similarity, as did NS-3 and NS-4, while NS-6 clustered in a separate clade. Water samples clustered more closely, except for NW-6 that clustered with NS-5 (Fig. 2). In addition, the branches indicated a hypothetical phylogenetic tree and the nodes showed divergent events. Similarly, the branches present in assemblage I (the clade including NS-1, NS-2, NS-3, NS-4, and NS-6) displayed recent divergent events compared to other assemblages that showed comparatively older divergent events, particularly samples NW-1, NW-2, NW-3, and NW4 (Fig. 2).
The NMDS plotting ordination estimated the structural similarities of bacteriomes and showed similar results (Fig. 3a). GMW samples were clustered relatively closer and showed greater similarity in the taxonomic composition than GLD samples. The analysis of similarity (ANOSIM) showed significant (R = 0.341; p = 0.0053; permutations = 9999) dissimilarity between lake-debris and melt-water (Fig. 3a). Moreover, the impact of physicochemical parameters (temp, pH, Cu, Fe, Zn, Cr, Mn, Ni, and TOC) on shaping the bacterial communities was examined by plotting two coordinates RDA. Surprisingly, GMW samples clustered closely while GLD samples displayed clear segregation (Fig. 3b). Coordinate 1 and coordinate 2 explained 37.45% and 26.59% of the variances, respectively. The environmental factors were strong drivers of the bacterial community structure in melt-water except for temperature that acted mainly on the lake-debris.
Taxonomic distribution of bacterial community
A total of 41 bacterial phyla were identified (Additional file 1: Fig. S4), with the relative abundance and phylogenetic distribution of the 14 dominant ones presented in Fig. 4a. The maximum portions of the obtained sequences (~ 87%) were allocated to Proteobacteria (~ 52%) and Firmicutes (~ 35%), which were dominant in melt-water and lake-debris, except for NS-1 and NS-2, which were dominated by Proteobacteria and Cyanobacteria. The relative abundance of Proteobacteria was greater in lake-debris (33.1–94.5%) than in melt-water (36.7–50.5%), but of Firmicutes was greater in melt-water (44–62%) than in lake-debris (0.3–47.5%). Bacteroidetes were observed in melt-water (0.5–3.7%) and Cyanobacteria (3.5%) in sample NW-5, while Acidobacteria were observed in samples NS-4 (9.5%) and NS-6 (4.3%). In addition, Bacteroidetes (0.8–8.3%), Actinobacteria (0.7–5%), Chloroflexi (0.2–4%), and Cyanobacteria (0.1–51%) were present in lake-debris. Maximum relative abundance of Cyanobacteria (51%) occurred in NS-1. A diverse community was observed at the genus level. The melt-water was dominated mainly by Lactococcus (21.3–32.5%), Pseudomonas (23–32.4%), and Geobacillus (7.8–14%), and to a lesser extent by Streptococcus (6–7%), Caulobacter (1.8–7%), Ralstonia (3–9.2%), Acinetobacter (3.5–9.5%), and Bacillus (0.2–6.3%). The genera composition in lake-debris showed greater variation than and differed from melt-water. Sample NS-2 was dominated by Acidophilum (74.3%) and Acidithiobacillus (25.3%), while NS-1 was dominated by Acidophilum (33.6%), Acidithiobacillus (24.7%), Lactococcus (11.6%), Pseudomonas (9.6%), and Bacillus (3.3%). Samples NS-3 and NS-4 were similar in genera composition. Acinetobacter dominated both these samples, 35% and 33.5%, respectively, followed by Staphylococcus, 33.5% and 28.6%, respectively. Other genera in these two samples included Pseudomonas (12.2% and 13.6%), Luteibacter (9.6% and 6.4%), and Stenotrophomonas (6% in each). However, samples NS-5 and NS-6 showed a different and more diverse pattern of genera composition. Sample NS-5 was dominated by Pseudomonas (28%) and Lactococcus (28%), Ralstonia (11%) and Geobacillus (10%), while NS-6 had more genera and was dominated by Acinetobacter (18%), Pseudomonas (16.9%), and Staphylococcus (11.5%) (Fig. 4b).
Correlations of environmental parameters with bacteria phyla
Firmicutes were correlated positively with air , but negatively with pH, TOC, Cr, Fe, Zn, Ni, Mn, and Cu. In contrast, Cyanobacteria was correlated negatively with air temperature and positively with Ni. In addition, Nitrospirae was correlated positively with TOC, Fe, Zn, and Cu; Chloroflexi was correlated positively with TOC, Fe, and Zn; and Gemmatimonadetes and Acidobacteria were correlated positively with Cu (Fig. 5).
Following co-occurrence networking, significant variations in the bacteria community structure due to environmental factors were observed in lake-debris and melt-water (Additional file 1: Fig. S5). Maximum edges of 256 emerged in the melt-water group, with 192 nodes having node connectivity of an average degree of 2.67, while lake-debris had 224 edges with 289 nodes having an average degree of 2.37 (Table 4). In lake-debris, the positive (49.5%) and negative (50.4%) correlations were similar, having modularity of 0.704; however, a significant difference was found in the positive (40.6%) and negative (59.4%) interactions in the water group with the modularity of 0.662. Interestingly, genera of the lake-debris and melt-water in combined form showed that the water bacterial communities were 53.3% and lake-debris were 46.7% correlated with environmental factors; however, the percentage of the negative correlations was higher (55.2%), while positive correlations were 44.8% (Additional file 1: Fig. S6 and Table 4). Similarly, in separate correlation analyses, maximum bacterial genera were linked with Ni (64%) in melt-water (Additional file 1: Fig. S5). The percentage of positive correlations was higher in lake-debris (49.5%) than melt-water (40.6%), while negative correlations were higher in melt-water (59.4%) than lake-debris (50.5%), as shown in Table 4. Overall, the significant difference in the environmental factors in both groups responded differently to the network correlation and significantly affected bacterial communities.
Analysis of biomarkers with statistical differences
Taxonomic apportionment of specific bacterial communities in melt-water and lake-debris was studied using LEfSe v1.0 . Based on LDA value 2, the findings showed an outline having 78 groups in which lake-debris had 58 and melt-water had 20 maximum biomarkers (Additional file 1: Fig. S7). The data at a known level from phylum to genera were analyzed and all other unknown, unclassified, and unclear lineages were removed, leaving a taxonomic outline of 37 bacteria groups (Fig. 6). There were higher biomarkers in lake-debris (32) than in melt-water (5). Bacterial lineages such as the order Xanthomonadales in Proteobacteria, and the order Nitrospirales, family Nitrospiraceae, genus Nitrospira in Nitrospirae were enriched in melt-water. Lake-debris was enriched with bacterial lineages at different taxonomic levels, which included phylum Firmicutes, class Bacilli, orders Lactobacillales and Mycoplasmatales, families Streptococcaceae, Pseudomonadaceae, Bacillaceae, Leuconostocaceae, and Mycoplasmataceae, genera Lactococcus, Pseudomonas, Geobacillus, Streptococcus, Bacillus, Leuconostoc, Anaerovibrio, Epilithonimonas, Anoxybacillus, Atopostipes, Acidaminococcus, Mycoplasma, Trichococcus, and Acidaminococcaceae, genus Moraxella of Proteobacteria and genera Myroides and Pedobacter of Bacteroidetes, and genus Trueperella of Actinobacteria (Fig. 6).
Using KEGG pathway level 1, 6 functional and metabolic classes, including unclassified, emerged (Fig. 7a). Among these classes, metabolic pathways were dominant (51.7–56.8%) in all samples, followed by genetic and environmental information processing classes (11.5–16%). In addition, 24 functional categories were predicted from the COG orthologues that determined the metabolic and functional variations in the bacteria communities. Among these categories, the top 17 in melt-water and lake-debris are presented in Fig. 7b. The metabolism of amino acids, nucleotides, carbohydrates, coenzymes, lipids and energy production and conversion were greater (p < 0.05) in lake-debris than melt-water. Moreover, general function and genes for transcription, replication, recombination, and repair were greater (p < 0.05) in lake-debris than melt-water, while genes responsible for defense mechanisms, signal transduction mechanisms, biosynthesis of secondary metabolites, transport and catabolism, cell motility, inorganic ion transport and metabolism were greater (p < 0.05) in melt-water than lake-debris. Genes responsible for cell wall and other membrane biogenesis, translation, and ribosomal structure biogenesis were greater (p < 0.05) in melt-water than lake-debris (Fig. 7b). The variation trends in these functional categories in individual samples are shown in Fig. 7c and they are consistent with the findings in melt-water and lake-debris.
In the glacial environment, the freshly formed supraglacial lake site as a result of GLOF at the frontier zone, where melt-water interacts with lake-debris, provides a unique ecosystem. Studying microbial diversity in the glacial ecosystem is extremely important since a huge, relatively unknown genomic pool is trapped here . Furthermore, this biological world faces a real risk of destruction as climate change is causing damage to the glaciers and ecosystem . Therefore, the glacial sites hit by severe climate change events might provide a unique microbial community, especially in terms of their distribution pattern. For these reasons, this study comprehensively determined the bacterial communities in the lake-debris and melt-water in the freshly formed supraglacial lake site as a result of GLOF at the frontier of Dook Pal Glacier in the Hindu Kush. This region was damaged severely following several huge GLOF events.
In the present study, the pH of lake-debris was higher (slightly alkaline) than of melt-water (neutral), which is in agreement with the results of Zhu et al.  and Sajjad et al. , who studied the frontiers of Laohugou glacier No. 12 and Baishui glacier No. 1, respectively. In addition, the TOC concentration was higher in lake-debris than melt-water, as organic matter accumulates in large quantities in glacial soil  and, with intense glacier melting, organic matter in the soil is released into the active layers . The TOC concentration in the water could have resulted from the interaction of melt-water with lake-debris during the huge GLOF event and, also, directly from airborne organic matter. In addition, the concentrations of metals were higher in lake-debris than in melt-water, and anions, cations, and DFAAs were observed in the lake-debris and melt-water. Long-lasting metabolic activities of microbes effectively cycled minerals and nutrients which were dissolved in the melt-water. These geochemical results are similar to the findings of Rafiq et al. , who studied the Tirich Mir glacier located in the same region. Mountains in the Chitral valley have metals such as iron, zinc, lead, and copper , which are important driving factors in shaping the microbial diversity in the glacial ecosystem . Moreover, geochemistry of the bedrocks and glacial soil directly affects the microbial communities' chemosynthetic characteristics . These driving factors become more prominent in such a vulnerable area affected by climate change and extreme environmental conditions.
Due to the ecological gradients possibly generated more prominently by recent GLOF at the site, enriched bacterial diversity (> 600 genera) and interesting distribution patterns were observed. In terms of OTUs abundance, variations were noted in these closely linked habitats (Additional file 1: Fig. S3) and the Chao1 estimator was higher than the OTUs. The reason might be due to the association of singleton species in the Chao1 estimation that influenced the bacterial diversity or due to possible errors during the PCR (chimeras and mutation) and high-throughput sequencing, which became ‘so-called unusual species’ that overestimated the richness [41, 49]. The Shannon diversity index, which is less affected by the PCR and sequencing-based errors, and the Simpson index showed a similar pattern. The Shannon indices of lake-debris (mean 3.3) and melt-water (mean 3.1) did not differ significantly, which indicated comparable bacterial diversities in lake-debris and melt-water after GLOF. These results are in disagreement with the findings of Sajjad et al. , who reported that, at the glacial frontier, the bacterial diversity in soil is significantly higher than melt-water and soil bacteria have no impact on the communities in melt-water runoff due to the species-sorting mechanism. Surprisingly, the hierarchical clustering and NMDS ordination showed dissimilar plotting of the lake-debris that denoted variations in the bacterial community. This uneven bacterial distribution pattern might be due to the GLOF event that disturbed the previous species-sorting mechanism in the region and washed the lake-debris microbial communities towards the melt-water pools. Normally, lake-debris offers a nutrient-enriched solid surface that is easily colonized by microbes, while water has as opposite effect and the dissolution of the potentially toxic elements in the water further decreases microbial diversity . In this study, differences were reported in the physicochemical parameters that showed both positive and negative correlations with major bacterial phyla, which further supported the possibility that the ecological gradients generated by GLOF contributed to the bacterial community composition.
High variation in ecological factors impacts microbiocenosis diversity [42, 50, 51]. However, despite the high variation in ecological parameters in the study area, the overall diversity difference was not substantial. Unlike previous studies that reported higher variation in bacterial diversity [23, 51], this study displayed a relatively constant diversity for the bacterial community in lake-debris and melt-water at the glacier frontier. Among metals, Zn (77%) and Fe (74%) showed high correlations, while TOC (35%) showed a lesser correlation with bacterial genera. The strong negative correlation between environmental factors and bacterial genera could be attributed to the competitive behavior of microorganisms and harsh ecological conditions. For example, the GLOF event disturbed the distribution pattern of bacteria, which were trying to get adapted to the new conditions. Malešević et al.  stated that the biological, chemical and anthropogenic activities could shape the bacterial communities; consequently, the GLOF event in the study area instead of ecological factors could be the main driving factor that affects the bacterial distribution pattern.
In addition, phylogenetically diverse bacterial phylotypes were noted after programmed pipeline analysis of the retrieved sequences. The retrieved sequences were mostly assigned to Proteobacteria and Firmicutes, two phyla that commonly exist in glacial environments and possess versatile metabolic activities . Recently, Zhu et al.  and Sajjad et al.  identified Proteobacteria as the dominant phylum at the Laohugou and Baishui glaciers, respectively. In the present study, lake-debris was dominated by Proteobacteria, which could be attributed to the fast-growing members present in this group , and they easily degrade and metabolize organic matter . Proteobacteria is widely distributed in different ecosystems that include species having diverse patterns of physiological and metabolic responses . Interestingly, in this GLOF affected area, melt-water provides suitable conditions to be dominated by Proteobacteria and Firmicutes. Therefore, GLOF might be associated with the distribution pattern of bacterial community structure. As the glacier surface is a transient spot, therefore, the GLOF event enhances this transient phenomenon and abruptly transported Proteobacteria and Firmicutes from the ice surface towards melt-water. Proteobacteria and Firmicutes were also reported in permafrost environments  and other glacial soil environments . Firmicutes consist of several endospore-forming taxa that withstand harsh environmental conditions . Actinobacteria was reported as the major bacterial group in freshwater; however, this phylum was observed in the lake-debris in this study, consistent with the cryospheric environments’ reports [7, 23, 44]. The culture-dependent studies of bacterial diversity showed that phyla including Proteobacteria, Firmicutes, and Actinobacteria were common in glacial environments [58,59,60]. Biosynthesis of antifreeze proteins and spore production enabled the Gram-positive bacteria to dominate the glacial environment [60, 61]. Bacteroidetes are reported in the soil due to their copiotrophic lifestyle  and the nutrition accumulated in the lake-debris that supports bacterial propagation. Chloroflexi and Cyanobacteria in lake-debris demonstrated that photosynthetic autotrophs preferred lake-debris to colonize [23, 63], which could be linked to the late periods of ecological succession . These findings are in agreement with Zhu et al.  and Ali et al.  in glacial habitats. Liu et al.  correlated the organic carbon accumulated in the soil with the photosynthetic genes cbbL abundance of Cyanobacteria. In addition, the neutral or slightly alkaline pH supports Cyanobacteria to proliferate. Cyanobacteria prefer warm conditions to proliferate; however, this study supported the results of Sajjad et al. , who demonstrated that Cyanobacteria could also colonize glacial soil. Cyanobacteria is the leading source of organic carbon production prior to plant colonization at the glacier frontier . Moreover, Cyanobacteria carries out nitrogen fixation and forms ammonia or nitrate that are released into the atmosphere. The diverse bacterial community and their distribution pattern in the glacial lake-debris play a pivotal ecological role in biogeochemical cycling and mineral nutrients.
In addition, the clustering results displayed relatively greater variations in the bacterial communities in lake-debris than in melt-water. This supports the possible involvement of the GLOF event in the reshaping of the bacterial distribution and community structure. Generally, a solid surface provides a stable colonization bed for microbes than water in such a confined area; however, the GLOF event acted here as a driving factor for comparable bacterial diversity in melt-water and their even distribution. Similarly, at the genus level, bacterial phylotypes also showed diverse patterns of distribution. Lactococcus, Pseudomonas, and Geobacillus dominated melt-water; however, different distribution trends were present in lake-debris. Pseudomonas is commonly distributed in glacial habitats . This distribution pattern of phyla and genera is unique in melt-water and contradicts several studies in glacial environments [7, 23, 64] that could be attributed to GLOF. Moreover, the aeolian deposition, precipitation, aerosol and dust cannot be ignored that could bring allochthonous bacteria to glacial habitats and further influence the dynamics of bacterial communities . However, this is long-term phenomenon since the sessional variations, including air temperature, nutrient concentrations, and solar radiation, affect the microbial community structure by supporting only those species that could cope with the environmental stresses . Glacial retreat is now accelerated in the study region; therefore, in terms of ecosystem development, glacial lake-debris is highly dynamic due to primary succession .
Diverse indicator groups were present in freshly exposed glacial lake-debris and melt-water. The diverse and mixed distribution of taxa might be due to the extreme ecological events in such a closely associated environment. Although the physical disturbance caused by GLOF influenced the distribution of bacteria, the lake-debris displayed a higher number of taxon indicators, which were distributed into numerous subgroup lineages, than melt-water (Fig. 6). The taxonomic trend in glacial lake-debris agreed with previous studies. For example, the genera Epilithonimonas was reported in alpine permafrost , Myroides in the Chongce Ice Cap glacial soil , Pedobacter in the Siachen glacier, Pakistan , and Pseudomonas in Batura glacier, Pakistan . In melt-water, Xanthomonadales and Nitrospirae were present in the current study, while Peter and Sommaruga  observed Nitrospirae in glacial water runoff at the glacial retreat. Lake-debris acts as a productive medium that is dominated by primary producers and contributes to the nutrient supply. Therefore, glacial lake-debris harbors diverse types of primary producers and consumers. In addition, saprophytic bacteria choose eutrophic environments to colonize and TOC enriched lake-debris in the study area supports this colonization.
To distribute, colonize and survive in these ecologically harsh conditions, bacteria switch functional and metabolic pathways on and off according to the situation. In this study, significant variations were reported in the pathways both in melt-water and lake-debris. The organic carbon concentration was highest in lake-debris; consequently, metabolic genes for amino acids, nucleotides, carbohydrates, coenzymes, lipids, and energy production and conversion were higher in lake-debris than in melt-water bacteria. A higher concentration of amino acids was found in lake-debris than melt-water, which is why the higher amino acid metabolic genes in lake-debris. The higher metabolic activities in lake-debris supported the strong presence of general function and genes for transcription, replication, recombination, and repair in lake-debris. In addition, intensive metabolic activities in the lake-debris might influence geochemical cycling in the region that needs further exploration. The bacterial groups unlocked by GLOF from the supraglacial region faced various challenges in melt-water, including dissolved metals, low nutrients, and lack of a solid surface to proliferate. Therefore, genes responsible for defense mechanisms, signal transduction mechanisms, biosynthesis of secondary metabolites, transport and catabolism, cell motility, and inorganic ion transport and metabolism were higher in melt-water than lake-debris bacteria. In addition, genes responsible for cell wall and other membrane biogenesis, translation, and ribosomal structure biogenesis were higher in melt-water than in lake-debris (Fig. 7b). Overall, the variation in metabolic and functional pathways is a defensive mechanism adopted by bacteria to cope with the ecologically stressful and oligotrophic conditions.
The present study is the first report of geochemistry, bacterial diversity and their distribution pattern in a freshly exposed supraglacial lake at the frontier of the Dook Pal Glacier hit by GLOF. The GLOF not only enhanced glacial retreat, but also unlocked trapped bacteria to affect the bacterial diversity and distribution pattern. Highly diverse and unique distribution patterns of the bacterial communities were observed in the glacial lake-debris and melt-water. Despite substantial differences in environmental factors, the overall difference in lake-debris and melt-water bacterial diversity was not significant following the GLOF. Moreover, the GLOF event likely resulted in an uneven distribution of bacterial phyla in lake-debris; however, the distribution in melt-water remained relatively even. This pattern of the bacterial structural communities was best explained by correlations between bacteria and environmental parameters and the shifts in expressions of metabolic pathways in melt-water. Glacial lake-debris and melt-water runoff were closely connected ecosystems, providing distinct niches for bacterial diversities and ecological traits. This study demonstrated that the even bacterial distribution in glacial melt-water likely occurred after GLOF. Thus, the freshly formed supraglacial lake after GLOF at the glacial frontier provides a unique site for studies on bacterial diversity and distribution. Additional systematic studies are needed to understand better how bacterial diversity responds to abrupt events of climate change and how their functioning is affected, especially with global warming and glacial retreat.
Availability of data and materials
The data supporting this study’s findings are available on reasonable request from the corresponding author [WS].
Glacial lake outburst flood
Water and Power Development Authority
United Nation Development Program
Total organic carbon
Operational taxonomic unit
Polymerase chain reaction
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Nikhat Ilahi acknowledges the financial support provided by the Chinese Scholarship Council (CSC) for Ph.D. completion. We would like to thank the Central Laboratory of the School of Life Science, Lanzhou University, for providing instruments and equipment.
A PIFI Fellowship supported Wasim Sajjad from the Chinese Academy of Sciences (2020PC0052). This work was supported by the Natural Science Foundation of China (31961143012), the Fundamental Research Funds for the Central Universities (lzujbky-2021-ct10), and the ‘111’ Programme 2.0 (BP0719040).
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: Table S1. Descriptions of the sampling sites and collected samples near supraglacial lake Dook Pal, Golen Gol Valley, Hindu Rush Range, Chitral, Northern Pakistan. Table S2. Quantification details of extracted DNA from lake-debris and melt-water bacteria. Table S3. Summary of the metagenome dataset of lake-debris and melt-water samples collected around the supraglacial lake in Dook Pal, Golen Gol Valley, Hindu Rush Range, Chitral, Northern Pakistan. Fig S1. View of Dook Pal glacier and the destruction caused by GLOF during July 2019. Fig S2. Rarefaction curves of samples collected at Dook Pal, Golen Gol Valley, Hindu Rush Range, Chitral, Northern Pakistan. Fig S3. Venn diagram presenting the OTUs distribution in glacial lake-debris and melt-water. Fig S4. Heat map of total 14 phyla. The abundance of each phylum is shown by color intensity. The top and left graphs show the clusterings of the bacterial phyla estimated by the Bray–Curtis method. Fig S5. Spearman rank correlation network analyses based on environmental factors interaction with bacterial genera in lake-debris and melt-water separately. The node size is directly proportional to the degree (number of connections). The red dots represent genera. The lines linked nodes (edges) display negative (pink) and positive (green) correlation relationships with P < 0.05. Fig S6. Spearman rank correlation network analysis based on environmental factors that interact with bacterial genera in both melt-water and lake-debris. The node size is directly proportional to the degree (number of connections). The red dots represent different genera. The lines linked nodes (edges) display negative (pink) and positive (green) correlations with P < 0.05. Fig S7: Comparison of the potential biomarkers in melt-water and lake-debris. (A) Indicator bacterial groups in solid and water groups with LDA values > 2. (B) the cladogram shows the phylogenetic distribution of bacterial lineage.
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Ilahi, N., Bahadur, A., Wang, W. et al. Diversity, distribution, and function of bacteria in the supraglacial region hit by glacial lake outburst flood in northern Pakistan. Environ Sci Eur 34, 73 (2022). https://doi.org/10.1186/s12302-022-00654-8