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Article

Potential Effects of Methane Metabolic Microbial Communities on the Glacial Methane Budget in the Northwestern Tibetan Plateau

by
Yuchan Guo
1,* and
Shuhong Zhang
2
1
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
2
College of Biology and Food, Shangqiu Normal University, Shangqiu 476000, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(9), 7352; https://doi.org/10.3390/su15097352
Submission received: 1 April 2023 / Revised: 24 April 2023 / Accepted: 27 April 2023 / Published: 28 April 2023
(This article belongs to the Special Issue Global Climate Change: What Are We Doing to Mitigate Its Effects)
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Abstract

:
With global warming, the dramatic retreat of glaciers in the Tibetan Plateau (TP) might accelerate release of stored methane (CH4) into the atmosphere; thus, this region might become a new source of CH4. CH4-metabolic microbial communities can produce or consume CH4 in the environment, which is critical for evaluating the CH4 budget of glaciers. However, studies on the influence of CH4-metabolic microbial communities on the CH4 budget during glacier retreat in the TP remain scarce. In this work, ice samples were collected at the terminus of the Guliya Ice Cap in the northwestern TP. The community composition of CH4-metabolic microorganisms, including methanogens and methanotrophs, was determined using genomic analysis, and the metabolic rates of the two microorganisms were further estimated. The abundance of methanotrophs in Guliya was one order of magnitude higher than that of methanogens. The CH4 consumption flux by the combined action of the two microorganisms was ca. 1.42 × 103 pmol·mL−1·d−1, suggesting that CH4 metabolic microbial communities in the glacier might be an important CH4 sink, and can reduce subglacial CH4 emission during glacier retreat. This is important for predicting the CH4 budget in glaciers on the TP and corresponding climate impacts during glacier retreat.

1. Introduction

Methane (CH4) is an important greenhouse gas, with a warming potential 28× larger than that of carbon dioxide (CO2) on a 100-year horizon [1]. CH4 concentrations in the atmosphere have been increasing since the industrial revolution [2]. The concentration of CH4 is determined by the balance between CH4 sources and sinks [3]; thus, it is important to understand global sources and sinks of CH4, as well as their feedbacks within the climate system. Glacier ecosystems are critical in the global CH4 cycle [4]; subglacial environments in areas such as Antarctica [5], the Arctic [6], and the Tibetan Plateau (TP) [7] are potential sources of CH4. Subglacial environments are typically high-pressure, low-temperature, and anoxic. They contain carbon-rich sediments that facilitate formation and preservation of CH4 [8,9]. CH4 reserves under the Antarctic Ice Sheet can reach up to 400 Pg (1 Pg = 1015 g) [5]. With global warming, glaciers and permafrost regions exhibit dramatic retreat, and if stored CH4 is released in large quantities during glacial retreat, atmospheric CH4 concentrations will substantially increase [5]. In addition, higher temperatures increase the metabolic activity of microorganisms, accelerating degradation of organic matter and contributing to release of more greenhouse gases into the atmosphere, which provides positive feedback for climate warming [10,11].
Recent studies have revealed that the flux of CH4 released into the atmosphere from glaciers and permafrost regions is influenced by factors such as the distribution of emissions over time and space, the presence and abundance of different substrates, temperature, and the composition of microbial communities [7,9,12,13]. Approximately 69% of atmospheric CH4 originates from microbial processes [3], indicating that the impact of microbial activity in glaciers and permafrost regions on CH4 release cannot be ignored. Microorganisms associated with CH4 metabolism include methanogens and methanotrophs. Taxonomically, methanogens include Methanobacteriales, Methanococcales, Methanomicrobiales, Methanopyrales, Methanosarcinales, Methanocellales, and Methanomassiliicoccales. Depending on the differences in available substrates, methanogens can also be divided into acetoclastic, methylotrophic, and hydrogenotrophic types [14,15]. Methanotrophs include Proteobacteria, Verrucomicrobia, and NC10. Proteobacteria and Verrucomicrobia belong to aerobic microorganisms, and Proteobacteria can be divided into Type I and Type II classifications [16,17]. The anoxic environment in subglacial ecosystems might be an important site for CH4 production by methanogenic microbial communities. Studies from the GISP2 ice core in Greenland [18,19], the East Rongbuk ice core in the TP [20], and the Sajama ice core in South America [13] suggest that anomalously high CH4 records have resulted from in situ metabolic activity of methanogens. During the melting season, supersaturated CH4 produced by methanogens has been observed in runoff from Greenland Ice Sheet catchments [6,21]. However, oxygen in the atmosphere might enter the subglacial ecosystem through ice cracks [22,23,24,25], enabling aerobic methanotrophs to maintain in situ metabolic activity that could mitigate release of subglacial CH4 into the atmosphere during glacier shrinkage [3,26]. Dieser et al. [21] revealed that there are active methane-oxidizing microorganisms in the Greenland Ice Sheet subglacial ecosystems and that they act as a buffer against release of subglacial CH4 into the atmosphere. Research by Michaud et al. [26] on Subglacial Lake Whillans (SLW) beneath the West Antarctic Ice Sheet reveals that methanotrophs consume >99% of subglacial CH4, reducing its release into the atmosphere. Therefore, both methanogenic and methanotrophic microbial communities have a combined impact on the subglacial CH4 budget [27].
The TP contains the largest glacier distribution outside the Antarctic and Arctic regions [28], and is an important reservoir of microbial resources [29]. In the context of climate change over the last 50 y, the TP has been warming at circa twice the global average rate, with an average increase of nearly 0.5 °C per decade between 1981 and 2020 [30], and the continued increases have resulted in substantial retreat among the TP glaciers [28,31,32]. During glacial melting season, cryoconite holes, subglacial sediments, glacial meltwater at the terminus, and glacier runoff in the northern, southern, and southeastern regions of the TP are possible sources of CH4 [7,12]. The permafrost rivers in the eastern TP are weak sources of CH4 [33], whereas soil at glacier forelands in the northern regions is a potential source that might transform from a sink into a source of CH4 [34]. However, research on the influence of CH4-metabolic microbial communities within glaciers on the CH4 budget remains scarce, especially for the northwestern TP regions. In this study, ice samples were collected from the Guliya Ice Cap in the northwestern TP to analyze the community composition of methanogens and methanotrophs, and estimate their metabolic rate; this aims to evaluate potential impacts of CH4 metabolic microbial communities on the glacier CH4 budget. This study investigated the community composition of methanogens and methanotrophs, and their impacts on the CH4 budget, in the northwestern TP, filling a gap in microbial research on glacial environments in this region. The findings provide support for understanding the CH4 cycle and predicting the response of glacier ecosystems to future climate change.

2. Materials and Methods

2.1. Site Description and Sample Collection

The Guliya Ice Cap, located on the southern slope of the Western Kunlun Mountains on the TP, is currently the highest in altitude (6700 m), largest in area (376 km2), thickest in ice layers (308.6 m), and has the lowest temperature (−18.6 °C) in central Asia [35]. Field observation data indicate that the Guliya Ice Cap has a negative surface mass balance from ca. 6000 m equilibrium line altitude to lower elevations, with ablation features at the glacier terminus [36]. In this study, ice samples (Figure 1) were collected along the southern edge of the Guliya glacier terminus (35°12′ N, 81°31′ E) in May 2021, placed in sterile sealed bags (CLEANWRAP, Seoul, South Korea), transported frozen to the lab, and maintained at −20 °C until further processing.

2.2. Ice Decontamination

The first step in this study was to remove the contaminants from the outer part of the ice samples. Traditional ice core decontamination generally involves preparing an artificial ice core with ultrapure water and applying known concentrations of cells, viruses, and DNA to the ice surface to simulate external contamination sources. After decontamination, the concentration of cells, viruses, and DNA remaining in the ice core samples are measured to determine the effectiveness of the decontamination [37]. However, this method imparts difficulties to simulating real contamination of the ice core, hindering determination of the effectiveness of the decontamination [38]. Tung et al. [18] discovered a substantial gradient change of the concentration of microorganisms in the ice core from the outer to inner layers (the outer layer was substantially higher than the inner layer) when studying microorganisms in the Greenland GISP2 ice core. This phenomenon was caused by a contaminated outer layer of the ice core due to external contamination, and this gradient change could indicate the extent of external contamination of ice samples. Based on that research, a continuous melting method was used in this study to plot the gradient of microbial concentrations from the outer to inner layers of the ice samples to evaluate contamination of the ice samples.
First, the ice samples were divided into six equal parts (each part weighing 250 g); two were used to evaluate sample contamination status (GLY-1 and GLY-2), three for subsequent microbial community analysis (GLY-3, GLY-4 and GLY-5), and the remaining one was stored at −20 °C for backup purposes (GLY-6). To evaluate the contamination status of the ice samples, GLY-1 and GLY-2 were first thawed at room temperature separately. The meltwater was collected from the outer to inner layers in chronological order. Eight portions of the collected meltwater were then placed in sterile beakers. After each collection of meltwater, sterile ultrapure water was used to flush the remaining ice samples to prevent microbial contamination from the outer layers mixing with the inner layers. Cell concentrations in each layer of collected meltwater were measured by direct cell counting, and sterile ultrapure water was used as the control group. Furthermore, the HumM2 gene copy number was also determined from these collected meltwaters as an indicator of potential human-induced contamination during experimental processing [39]. The experimental clean bench was sterilized with 75 vol% ethanol and researchers wore sterile cleanroom work clothes to avoid environmental contamination. All decontamination experiments were conducted at the Key Laboratory of Coast and Island Development of the Ministry of Education.

2.3. Direct Cell Count and HumM2 Gene Quantification

Microbial cells in meltwater were fixed with 4 vol% paraformaldehyde (filter sterilized with a 0.22 μm membrane) and stained with 300 μL of 100× SYBR Green I (Invitrogen™, Waltham, MA, USA) for 30 min, protected from light; this was followed by filtration through 0.22 μm polycarbonate membranes (Millipore, Burlington, MA, USA). The fluorescently stained microbial cells in the field of view were counted with a two-photon laser confocal microscope (Leica TCS SP8-MaiTai MP, Weztlar, Germany) at room temperature. Fifteen randomly selected fields were photographed on each filter; the total number of microbial cells in the field of view was counted and converted to cells per milliliter of meltwater (field area: 553.57 μm × 553.57 μm). A gradient map depicting changes in cell concentration from the outer to inner layers was then plotted.
The copy number of the HumM2 gene in each layer of meltwater was measured with a real-time fluorescence quantitative polymerase chain reaction (PCR) instrument (LightCycler® 480II, Roche, Germany) with the primers selected as Hum2F/Hum2R (Table 1). The reaction system for the quantitative real-time PCR (qPCR) was 30 μL; including 15 μL qPCR premix, 2 μL Mg2+ (25 mM), 0.5 μL of each forward and reverse primer (10 μM), 2 μL of template DNA, 0.5 μL of nucleic acid dye, and 10 μL of ddH2O. The thermal cycling parameters were denaturation at 95 °C for 3 min, followed by amplification cycles consisting of denaturation at 94 °C for 30 s, annealing at 50 °C for 30 s, and extension at 72 °C for 30 s. In total, 35 cycles were amplified with a final extension of 10 min. Three parallel experiments were performed for each sample, and sterile water was used as a negative control instead of the DNA template.

2.4. DNA Extraction and Microbial Community Studies

Based on the contamination of the ice samples obtained from the aforementioned experiments, the contaminated outer layers of the GLY-3, GLY-4, and GLY-5 ice samples were removed by melting at room temperature. Subsequently, the clean internal ice sample was fully melted and filtered through a 0.22 μm membrane (Millipore, USA). Genomic DNA was extracted with the Fast DNA SPIN Kit for Soil (MP Biomedicals, Irvine, CA, USA), in accordance with the instructions. The quality and concentration of genomic DNA were evaluated with NanoDrop One (Thermo Fisher Scientific, Waltham, MA, USA). Methanogens are a group of microorganisms that contain the mcrA (methyl-coenzyme M reductase α-subunit) gene, belonging to the phylum Archaea [15]. Taxonomically, methanogens have been identified in 3 classes, 7 orders, 12 families, and 41 genera, with over 200 species. Methanotrophs are a group of microorganisms that contain the pmoA (β-subunit of the particulate CH4 monooxygenase) gene. This group can be classified into two broad groups, aerobic and anaerobic methanotrophs, based on whether they require use of oxygen during CH4 oxidation [15]. The mcrA gene [46,47] and pmoA [42] were amplified using PCR with MLfF/MLrR and A189F/mb661R primers (Table 1), respectively. The amplification products were recovered with 2 wt% agarose gels, purified with an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), and detected by 2% agarose gel electrophoresis. The recovered products were also detected and quantified with a Quantus Fluorometer (Promega, Fitchburg, WI, USA). Libraries were built with a NEXTFLEX Rapid DNA-Seq Kit (Bioo Scientific, Austin, TX, USA) and sequenced on Illumina’s Miseq PE300 platform.
The raw sequences were quality-controlled with Fastp software (V0.23.2), spliced with Flash software (V1.2.11), and clustered into operational taxonomic units (OTUs) with Uparse software (V11) based on a 97% similarity threshold [48]. Based on the functional gene database of GeneBank (Release 7.3 http://fungene.cme.msu.edu/ (accessed on 24 April 2023)) [49], the species taxonomic annotation results were obtained by using the RDP classifier to annotate the OTU representative sequences (more than 97% similarity) with a confidence threshold of 0.7. Because of the low abundance of methanogens and methanotrophs in the glacial environment, it is difficult to directly determine their abundance using qPCR [26]. In this study, 515F/806R and 524F10extF/Arch958RmodR primers (Table 1) were further used to determine the diversity of 16S rRNA of bacteria and archaea in the ice samples. The proportions of methanogens and methanotrophs in the total OTUs of the archaea and bacteria, respectively, were obtained. After determining the absolute abundance of bacteria and archaea using qPCR (Table 1 lists the primers), the abundance of methanogens and methanotrophs was calculated [26,33]. The sequences were deposited into the GSA database of the National Data Centre for Genome Sciences (Project No. PRJCA015327).

2.5. Estimation of Metabolic Rates of Methanogens and Methanotrophs at the Guliya Glacier Terminus

The metabolic rates of CH4 metabolic microbial communities in the Guliya ice samples were estimated by using the metabolic rates of methanogens from the Robertson Glacier, Canada [50] and methanotrophs from the Styggedalsbreen Glacier, Norway [51], with reference to Lamarche-Gagnon et al. [6]. The rates of methanogenesis (methanogens) and CH4 oxidation (methanotrophs) were calculated with Equations (1) and (2), respectively:
RP = RP-cell × Cmethanogens
RO = RO-cell × Cmethanotrophs
where RP and RO are the methanogenic and CH4 oxidation rates at the Guliya glacier terminus, respectively; RP-cell is the methanogenic rate per cell in the Robertson Glacier, Canada sample; RO-cell is the CH4 oxidation rate per cell in the Styggedalsbreen Glacier, Norway sample; and Cmethanogens and Cmethanotrophs are the cell concentrations of methanogens and methanotrophs, respectively, in the Guliya ice samples.

3. Results and Discussion

3.1. Evaluation of the Decontamination Effect of the Ice Samples

Figure 2a,b shows the concentrations of microbial cells and the copy number of the HumM2 gene, respectively, in meltwater from the ice surface to ice center. Figure 2a indicates that the concentration of microbial cells from ice surface to ice center gradually decreases and tends to plateau. The concentration of microbial cells in the 0–50 mL meltwater layer was higher than in other meltwater layers, representing that there was substantial external contamination. By the inner layer of the ice sample, the cell concentration decreased to a stable level, which represents the microbial concentration inside the ice. The background fluorescence of the control group was 0, indicating that the anthropogenic contamination introduced during the experiments was low. In Figure 2b, the HumM2 gene was only detected in the 25–50 mL (22 copies·mL−1), 75–125 mL (8 copies·mL−1), and 175–200 mL (65 copies·mL−1) meltwater layers. This result is much lower than that of environmental samples such as wastewater samples [39]. The copy numbers of the HumM2 gene in the other meltwater layers were all below the detection limit of qPCR, indicating that the anthropogenic contamination introduced during the experiments was low, and the influence on the overall microbial community composition of the samples could be ignored. In summary, the outer 50 mL of the ice sample (20% of the ice volume) was contaminated with external sources and 80% of the inner ice sample was available for subsequent microbial community studies.

3.2. Analysis of the Microbial Community Composition of CH4-Metabolic Microbial Communities at the Guliya Glacier Terminus

In this study, based on the OTU classification and taxonomic identification results of the Guliya ice samples, taxonomic information on the phylum, order, family, genus, and species was analyzed. Two phyla, three classes, four orders, four families, four genera, and four species of methanogens were found; two phyla, four classes, four orders, four families, four genera, and four species of methanotrophs were found.
The methanogens in the ice samples consisted mainly of Methanobacteriaceae (2.6%), Methanosarcinaceae (84.2%), and Methanoregulaceae (8.1%) (Figure 3), with the highest relative abundance of methanogens sequences (GLY mcrA OTU3, 84.2%; GLY mcrA OTU4, 8.1%) exhibiting high similarity to the Canadian Robertson Glacier sequence (Robertson Glacier mcrA clone RG1) [50] (Figure 4a). Methanosarcinaceae predominated, and most of these methanogen communities were of the acetoclastic methanogen; thus, acetic acid compounds might have been the main substrate for this group in the ice samples. Compared with polar regions (see Table 2), the dominant genus of methanogen communities in the Greenland Ice Sheet [52] is Methanococcoides, which belongs to the methylotrophic type; in SLW [26], the dominant genus of methanogens is Methanohalophilus, also belonging to the methylotrophic type. In contrast, when compared with wetlands, lakes, and other glaciers on the TP, the Zoige wetland is dominated by the family Methanosarcinaceae [53], whereas Gongzhuzuo is dominated by Methanosaetaceae [54]; both belong to the methylotrophic type. Overall, methylotrophic methanogens predominate polar glaciers by using methylated compounds as substrates. However, since the natural abundance of methylated compounds such as methanol and trimethylamine is much lower than that of acetic acid compounds on a global scale, the distribution of this type of methanogen is relatively rare outside of polar regions. However, the Guliya Ice Cap on the TP along with wetlands and lakes mostly contain acetoclastic methanogens, which use acetic acid compounds as their substrate for producing two-thirds of the total biogenetic CH4 output in natural environments such as wetlands, lakes, temporary water bodies, glaciers, and permafrost [55,56].
The methanotrophs in the Guliya ice samples mainly consisted of Methylocystaceae (67.4%) and unidentified Gammaproteobacteria (31.6%) (Figure 3). Both types of methanotroph communities are aerobic and belong to Type II and Type I methanotrophs, respectively, with Type II dominating. In accordance with sequence alignment analysis (Figure 4b), the most abundant sequences of methanotrophs (GLY pmoA OTU3, 31.6%; and GLY pmoA OTU4, 67.4%) exhibited a high degree of homology with those found in the Greenland Ice Sheet runoff (Greenland Ice Sheet outlet water pmoA clone OTU1) [21] and the Norwegian Styggedalsbreen glacier sequence (MOB_OTU7) [51]. Table 2 indicates that the dominant genus of methanotrophs in SLW is Methylobacterium [26], whereas in the Zoige wetland on the TP, the dominant genera are Methylobacter and Methylococcus [57], and in Maqu marsh soil, it is again Methylobacter [58]; all of which belong to Type I methanotrophs. Type I and Type II methanotrophs differ in phylogeny and physiological function, with Type I having a more efficient carbon assimilation pathway, and Type II typically having the ability of nitrogen fixation and growing more optimally in low concentrations of oxygen. Therefore, Type I methanotrophs are distributed globally in wetlands and the rhizosphere of wetland plants [59]; whereas, Type II methanotrophs tend to thrive in environments with low oxygen concentration, low CH4 content, and poor nutrient availability [60]. Glaciers are characterized by low temperatures and anoxia with low organic matter content, so the relative abundance of Type II methanotrophs is higher. Therefore, the results are rational and reliable.
However, the copy number of the archaeal 16S rRNA gene in the ice samples was (1.28 ± 0.53) × 103 copies·mL−1, and that of the bacterial 16S rRNA gene was (7.10 ± 1.52) × 104 copies·mL−1. Calculated based on three 16S rRNA gene copies per archaeal cell and six 16S rRNA gene copies per bacterial cell [61], the abundance of archaea and bacteria in the ice samples was estimated to be (4.28 ± 1.76) × 102 and (1.18 ± 0.25) × 104 cells·mL−1 respectively. In accordance with the sequencing results, the methanogens accounted for 16.58% of all archaeal OTUs, whereas methanotrophs accounted for 6.47% of all bacterial OTUs in the ice samples. Furthermore, the abundance of methanogens and methanotrophs was estimated at (0.71 ± 0.29) × 102 and (7.66 ± 1.64) × 102 cells·mL−1, respectively. In comparison with microbial abundances involved in CH4 metabolic microbial communities found in polar glaciers (Table 3), the abundance of methanogens from Guliya is comparable to that found in SLW with both slightly lower than those observed from the Greenland GISP2 ice core; whereas, the abundance of methanotrophs was slightly lower than that measured from SLW but differing by no more than one order of magnitude. There was a certain degree of difference in the abundance of CH4 metabolic microbial communities in the three study areas. The abundance of microorganisms in glaciers is affected by multiple factors including dissolved oxygen concentration, temperature, organic matter content, pH, and salt concentration [62]. In addition, different measurement methods can also lead to some differences in abundance measurements [61]; thus, this difference of less than one order of magnitude is acceptable. Moreover, the relative abundance of methanogens and methanotrophs plays a more important role in the CH4 budget within glaciers. The abundance of methanotrophs was ca. 10× higher than that of methanogens in Guliya, whereas it was approximately 50× higher than that of methanogens in SLW. Therefore, the abundance of methanotrophs was substantially higher than that of methanogens in Guliya and SLW.

3.3. Impact of CH4-Metabolic Microbial Communities on the Glacial CH4 Budget

The production rate and oxidation rate of CH4-metabolic microbial communities directly affect the glacier CH4 budget, which is important for maintaining carbon balance in glacial regions and even global ecosystems [4]. As mentioned in Section 3.2, the CH4-metabolic microbial sequences from the Guliya ice samples are highly homologous to those from the Robertson Glacier in Canada [50] and the Styggedalsbreen Glacier in Norway [51]. Therefore, this study used samples from the Robertson Glacier and the Styggedalsbreen Glacier to measure production rates and oxidation rates through laboratory cultivation, evaluating the impact on methanogens and methanotrophs on the glacier CH4 budget in Guliya. Table 4 shows related data and calculation results.
The production rate of methanogens in the Guliya ice samples was (1.7 ± 0.7) × 10−2 pmol·mL−1·d−1, and the oxidation rate of methanotrophs was (1.42 ± 0.30) × 103 pmol·mL−1·d−1. The oxidation activities of methanotrophs might have a greater impact than methanogens on the glacier CH4 budget. Overall, when glaciers retreat, the combined action of methanogens and methanotrophs in every milliliter of glacial meltwater can consume 1.42 × 103 pmol of CH4 per day in a manner than forms a microbial CH4 sink. Greenland Ice Sheet runoff [21] and SLW [26] are both sinks for CH4, with methanotrophic oxidation rates ranging from 3.2 × 10−1 to 4.1 × 10−3 μmol·L−1·d−1 respectively; when converted, the microbial CH4 oxidation rate in Guliya was ca. 1.42 × 103 μmol·L−1·d−1, which is different by one to three orders of magnitude compared with these areas. Stibal et al. [64] reported that there is a difference of circa four orders of magnitude between the highest CH4 metabolism rates found in Arctic and Antarctic subglacial sediments, which might be caused by differences in microorganism communities and regional environmental conditions among different study areas [65]. Compared with the Greenland Ice Sheet runoff and SLW, CH4-metabolizing microorganisms in the Guliya Ice Cap have a greater potential for CH4 oxidation. This can reduce the emissions of subglacial CH4 to the atmosphere during glacier retreat, lower greenhouse gas contributions from glacier ecosystems, and play an important role in the global atmospheric CH4 budget as well as the carbon cycle. Additionally, the evaluation of the contribution to global greenhouse of methane emissions closely associated with microbial methanotrophic communities is also provided by the study of CH4 seeps in the fossil record [66]. Overall, our study suggests that CH4-metabolizing microorganisms within glaciers are a potential sink for CH4, providing new evidence for understanding the impact of microorganisms on the glacier CH4 budget. However, due to the regional variability in glacier temperature, salinity, pH, hydrology, and the presence of other microbial communities, there are differences in CH4-metabolizing microorganisms among different regions, which might be considered in evaluating the present research results.

4. Conclusions

The dominant methanogens and methanotrophs in ice samples from the Guliya Ice Cap terminus on the TP were Methanosarcinaceae and Methylocystaceae, respectively. The abundance of methanotrophs was one order of magnitude higher than that of methanogens. The production rate of methanogens in the ice samples was (1.7 ± 0.7) × 10−2 pmol·mL−1·d−1, whereas the oxidation rate of methanotrophs was (1.42 ± 0.30) × 103 pmol·mL−1·d−1. The overall consumption flux for CH4 under the combined action of these two types of microbial communities was ca. 1.42 × 103 pmol·mL−1·d−1, which suggests that they have potential to be sinks for CH4 and can help reduce subglacial emissions into the atmosphere, providing evidence for understanding microbial impacts on the glacier CH4 budget. In further studies, it will be important to investigate microbial communities across various glacial environments, deepen understanding regarding ecological characteristics and interactions between microorganisms as well as environmental factors, and ultimately provide a better basis for comprehending regional roles played by glaciers located within the TP in the global CH4 cycle.

Author Contributions

Conceptualization: Y.G. and S.Z.; methodology: Y.G. and S.Z.; experiments: Y.G.; data analysis: Y.G. and S.Z.; writing—original draft preparation: Y.G.; writing—review and editing: Y.G. and S.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Chinese Arctic and Antarctic Administration (CXPT2020012), the “333 Project” of Jiangsu Province (BRA2020030), National Natural Science Foundation of China (41830644, 91837102, 41771031, 41622605).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are openly available in the GSA database of the National Data Centre for Genome Sciences (Project No. PRJCA015327).

Acknowledgments

Thanks to the Key Laboratory of Coast and Island Development the Ministry of Education, Nanjing University for its assistance in sample processing. We would like to extend our special thanks to Wangbin Zhang, Jinhai Yu and others in the School of Geography and Ocean Science, Nanjing University for their hard fieldwork of ice sampling.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of the Guliya Ice Cap and sampling site in this study in 2021. (a) Location of the Guliya Ice Cap. (b) Sampling position in this study.
Figure 1. Location of the Guliya Ice Cap and sampling site in this study in 2021. (a) Location of the Guliya Ice Cap. (b) Sampling position in this study.
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Figure 2. Results of decontamination. (a) Cell concentration of each ice melting layer from the ice surface to ice center. (b) HumM2 gene copy number in each ice melting layer from the outer to inner parts.
Figure 2. Results of decontamination. (a) Cell concentration of each ice melting layer from the ice surface to ice center. (b) HumM2 gene copy number in each ice melting layer from the outer to inner parts.
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Figure 3. Distribution of methanogens and methanotrophs in the ice samples.
Figure 3. Distribution of methanogens and methanotrophs in the ice samples.
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Figure 4. Phylogenetic tree of methanogens and methanotrophs in the ice samples. (a) Neighbor-joining phylogenetic tree of the mcrA gene (GLY mcrA OTU1−GLY mcrA OTU4 are the sequences of methanogens detected in the Guliya ice samples). (b) Neighbor-joining phylogenetic tree of the pmoA gene (GLY pmoA OTU1−GLY pmoA OTU6 are the sequences of methanotrophs detected in the Guliya ice samples).
Figure 4. Phylogenetic tree of methanogens and methanotrophs in the ice samples. (a) Neighbor-joining phylogenetic tree of the mcrA gene (GLY mcrA OTU1−GLY mcrA OTU4 are the sequences of methanogens detected in the Guliya ice samples). (b) Neighbor-joining phylogenetic tree of the pmoA gene (GLY pmoA OTU1−GLY pmoA OTU6 are the sequences of methanotrophs detected in the Guliya ice samples).
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Table
Table 1. Oligonucleotide primers in this study.
Table 1. Oligonucleotide primers in this study.
Target GroupsPrimer NameSequence (5′→3′)ApplicationReferences
Prokaryotic
16S rRNA V4		515FGTGCCAGCMGCCGCGGTAAPCR[40]
806RGGACTACHVGGGTWTCTAAT
Methanogens mcrA geneMLfFGGTGGTGTMGGATTCACACARTAYGCWACAGCPCR[41]
MLrRTTCATTGCRTAGTTWGGRTAGTT
Methanotrophs pmoA geneA189FGGNGACTGGGACTTCTGGPCR[42,43]
mb661RCCGGMGCAACGTCYTTACC
Archara
16S rRNA V4 + V5		524F10extFTGYCAGCCGCCGCGGTAAPCR, qPCR[44]
Arch958RmodRYCCGGCGTTGAVTCCAATT
Bacteria
16S rRNA V3 + V4		338FACTCCTACGGGAGGCAGCAGqPCR[45]
806RGGACTACHVGGGTWTCTAAT
HumM2 geneHum2FCGTCAGGTTTGTTTCGGTATTGqPCR[39]
Hum2RTCATCACGTAACTTATTTATATGCATTAGC
Table
Table 2. CH4 microbial community composition in the TP, Arctic, and Antarctic glacial permafrost regions.
Table 2. CH4 microbial community composition in the TP, Arctic, and Antarctic glacial permafrost regions.
Site
Location		Sampling
Environment		Dominant
Methanogens		Methanogens
Types		Dominant
Methanotrophs		Methanotrophs TypesReferences
Guliya Ice Cap, TPTerminal ice samples
(35°12′ N, 81°31′ E)		MethanosarcinaceaeAcetoclasticMethylocystisType IIThis study
Zoige Wetland, TPSoil samples
(33°56′ N, 102°52′ E)		MethanosarcinaceaeAcetoclasticMethylobacter
Methylococcus		Type I[53,57]
Gongzhu Co, TPSediment samples
(30°37′ N, 82°06′ E)		MethanosaetaceaeAcetoclastic−−[54]
Maqu Swamp, TPSoil samples
(33°39′ N, 101°52′ E)		−−MethylobacterType I[58]
Greenland Ice SheetGISP2 ice core
(72°36′ N, 38°30′ W)		MethanococcoidesMethylotrophic−−[52]
SLW, West Antarctic Ice SheetSediment samples
(84°14′ S, 153°41′ W)		MethanohalophilusMethylotrophicMethylobacterType I[26]
“−” represents the lacking of relevant data in the reference.
Table
Table 3. CH4 microbial community abundance in the TP, Arctic, and Antarctic glacial permafrost regions.
Table 3. CH4 microbial community abundance in the TP, Arctic, and Antarctic glacial permafrost regions.
Site LocationSampling LocationMethanogens Abundance
/(Cells·mL−1)		Methanotrophs Abundance
/(Cells·mL−1)		MethodReference
Guliya Ice Cap, TPTerminal ice samples
(35°12′ N, 81°31′ E)		(0.71 ± 0.29) × 102(7.66 ± 1.64) × 102qPCRThis study
Greenland Ice SheetGISP2 ice core
(72°36′ N, 38°30′ W)		(3.95 ± 3.87) × 102−Direct cell counts[18]
SLW, West Antarctic Ice SheetWater samples
(84°14′ S, 153°41′ W)		(1.30 ± 0.40) × 102(6.87 ± 2.12) × 103Direct cell counts[63]
“−” represents the lacking of relevant data in the reference.
Table
Table 4. Estimated CH4 production (CH4 oxidation) rates in the Guliya ice samples.
Table 4. Estimated CH4 production (CH4 oxidation) rates in the Guliya ice samples.
Types of CH4 Metabolic Microbial CommunitiesCell-Specific Metabolic Rates
/(fmol·cell−1·d−1)		Microbial Abundance
/(cells·mL−1)		Methanogenesis Rates (+)/
CH4 Oxidation Rates (−)
/(pmol·mL−1·d−1)
Methanogens2.4 × 10−1 ①(0.71 ± 0.29) × 102+(1.7 ± 0.7) × 10−2
Methanotrophs1.85 × 103 ②(7.66 ± 1.64) × 102−(1.42 ± 0.30) × 103
① Methanogenesis rates referenced to the Robertson Glacier, Canada [50]; ② CH4 oxidation rates referenced to the Styggedalsbreen Glacier, Norway [51].
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Guo, Y.; Zhang, S. Potential Effects of Methane Metabolic Microbial Communities on the Glacial Methane Budget in the Northwestern Tibetan Plateau. Sustainability 2023, 15, 7352. https://doi.org/10.3390/su15097352

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Guo Y, Zhang S. Potential Effects of Methane Metabolic Microbial Communities on the Glacial Methane Budget in the Northwestern Tibetan Plateau. Sustainability. 2023; 15(9):7352. https://doi.org/10.3390/su15097352

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Guo, Yuchan, and Shuhong Zhang. 2023. "Potential Effects of Methane Metabolic Microbial Communities on the Glacial Methane Budget in the Northwestern Tibetan Plateau" Sustainability 15, no. 9: 7352. https://doi.org/10.3390/su15097352

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