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Article

Differences of Nitrogen Transformation Pathways and Their Functional Microorganisms in Water and Sediment of a Seasonally Frozen Lake, China

by
Zhiqiang Tian
1,2,
Sheng Zhang
1,*,
Junping Lu
1,*,
Xiaohong Shi
1,
Shengnan Zhao
1,
Biao Sun
1,
Yanjun Wang
1,3,
Guohua Li
1,
Zhimou Cui
1,
Xueru Pan
1,
Guoguang Li
4 and
Zixuan Zhang
1
1
College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
2
Department of Water Conservancy and Civil Engineering, Hetao College, Bayannur 015000, China
3
Vocational and Technical College of Inner Mongolia Agricultural University, Baotou 014109, China
4
College of Life Sciences, Inner Mongolia Agricultural University, Hohhot 010011, China
*
Authors to whom correspondence should be addressed.
Water 2023, 15(13), 2332; https://doi.org/10.3390/w15132332
Submission received: 14 May 2023 / Revised: 15 June 2023 / Accepted: 21 June 2023 / Published: 23 June 2023
(This article belongs to the Special Issue Water Environment Pollution and Control)
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Abstract

:
Nitrogen is one of the most important elements involved in ecosystem biogeochemical cycling. However, little is known about the characteristics of nitrogen cycling during the ice-covered period in seasonally frozen lakes. In this study, shotgun metagenomic sequencing of subglacial water and sediment from Lake Ulansuhai was performed to identify and compare nitrogen metabolism pathways and microbes involved in these pathways. In total, ammonia assimilation was the most prominent nitrogen transformation pathway, and Bacteria and Proteobacteria (at the domain and phylum levels, respectively) were the most abundant portion of microorganisms involved in nitrogen metabolism. Gene sequences devoted to nitrogen fixation, nitrification, denitrification, dissimilatory nitrate reduction to ammonium, and ammonia assimilation were significantly higher in sediment than in surface and subsurface water. In addition, 15 biomarkers of nitrogen-converting microorganisms, such as Ciliophora and Synergistetes, showed significant variation between sampling levels. The findings of the present study improve our understanding of the nitrogen cycle in seasonally frozen lakes.

1. Introduction

Nitrogen is an essential element of life [1,2]. Its role as a limiting nutrient means that nitrogen runoff can exacerbate eutrophication in lakes [3,4]. The nitrogen cycle and its environmental effects are, therefore, a focus of research around the world. Most processes that can alter the chemical form of nitrogen depend on microorganisms [5,6]. For a long time, research on the nitrogen cycle and nitrogen-processing microorganisms focused on marine and terrestrial environments [7,8]. However, more recent studies focus on the nitrogen cycle of lake ecosystems, partially in response to the increasing need for eutrophication prevention and control methods.
Nitrogen can exist in nine known chemical forms with valence charges ranging from −3 to +5 and can be exchanged through 14 known redox reactions. The main nitrogen cycle includes two oxidation pathways (anaerobic ammonium oxidation and nitrification) and four reduction pathways (denitrification, nitrogen fixation, assimilatory nitrate reduction to ammonium [ANRA], and dissimilatory nitrate reduction to ammonium [DNRA]). Ammonification can also occur without a change in charge [9,10]. Ammonium is generally the most preferred nitrogen source for nitrogen assimilation; however, polyamines and monoamines have recently been found as alternative nitrogen sources for bacterial nitrogen assimilation [11]. New nitrogen cycle pathways have been discovered in recent years such as anaerobic ammonium oxidation (anammox) [12], complete ammonia oxidation (comammox) [13], ANRA [14], DNRA [15], and aerobic denitrification [16]. These nitrogen transformation pathways are mainly driven by Bacteria, but novel microorganisms were identified as part of these processes, including symbiotic heterotrophic nitrogen-fixing Cyanobacteria [17], ammonia-oxidizing Archaea [18], Streptomyces [19], and Eukaryota [20,21,22].
Although our understanding of microbial nitrogen cycling in lake ecosystems has increased, studies have primarily been performed in open water lakes, largely ignoring ice-covered lakes. In fact, half of the world’s lakes, especially those located at high altitudes and latitudes in temperate and boreal climates, are covered with ice for more than 40% of the year [23,24,25]. Consequently, little information is known about microbial life and nitrogen cycling in these lakes. It is traditionally believed that lake ecosystems under the ice subjected to low light and low water temperature are “on hold” in winter [26,27]. Increased interest in declines in ice cover dynamics caused by global warming led to the discovery of an unexpectedly dynamic subglacial microbiome. For example, large-scale cyanobacterial blooms broke out under the ice of Lake Stechlin in Germany during the winter of 2009–2010 and triggered the active growth of heterotrophic bacteria [28]. Similarly, large-scale algal blooms have also occurred under the ice of Lake Michigan and Lake Erie [29,30]. Ice sheets have a significant role in shaping subglacial hydrodynamics, nutrient concentration, salinity, photosynthetically active radiation (PAR), and water temperature. These changes alter the subglacial microbial community structure, and the way nitrogen is processed [20,31].
Lake Ulansuhai, as the eighth largest freshwater lake in China, is an ideal study location. The lake has a mid-temperate continental climate with a current approximate ice cover duration of four months each year. Although this lake’s ecological functions and eutrophication have been studied in detail [32,33,34], like other seasonal frozen lakes, the subglacial microecology of Ulansuhai in winter has not been given enough attention. Metagenomics has emerged as a major research tool in microbial ecology around the world as one of the most comprehensive approaches to characterizing microbial communities, revealing the functional diversity of microorganisms and the interactions between microorganisms. In the present study, we used a metagenomic approach to study the microbial processing of nitrogen throughout the water column. The objectives of this present study are to identify and compare the nitrogen transformation pathways and their functional microbes observed in subglacial water and sediments.

2. Materials and Methods

2.1. Case Study Lake

Lake Ulansuhai (40°36′–41°03′ N, 108°41′–108°57′ E) is located in Bayannur City in the Inner Mongolia Autonomous Region, China (Figure 1). As the largest freshwater lake in the Yellow River Basin and the eighth largest freshwater lake in China, its entire area is 325.31 km2, of which 123.11 km2 is open water and the remaining area is inhabited by littoral Phragmites sp. [35]. It has a north–south length of 35–40 km and an east–west width of 5–10 km. As an important part of the Hetao Irrigation Area, one of the three largest irrigation areas in China, Lake Ulansuhai provides a reservoir capacity of 250–300 million m3. The mean water depth is approximately 1.5 m. More than 90% of the farmland drainage from the Hetao Irrigation Area flows into the lake, with 81% inflow through the Main Drainage Channel and only 10% outflow into the Yellow River through the Retreating Channel. As a typical shallow lake in cold and arid areas, it has a mid-temperate continental climate with mean annual precipitation, annual average evaporation, and annual average air temperature of 224 mm, 1502 mm, and 7.2 °C, respectively. The multi-year average temperature during the ice-covered period from November to March is −10.24 °C, with an ice thickness of 0.3–0.6 m and snowfall of less than 10 cm [36].

2.2. Sample Collection and Treatment

Three sampling sites (numbered WL1–WL3) were selected at the inlet, middle, and outlet according to observed water flow (Figure 1). During the ice-covered period in January 2021, holes were drilled at each sampling site using an ice auger. Surface water samples, bottom water samples, and sediment samples (numbered W1, W2, and S1, respectively) were collected from each sampling site. Surface water samples were collected at a depth of 0.5 m and bottom water samples were collected at 0.5 m above the water bottom. Duplicates of each sample were filled into 1 L sterile polyethylene sampling bottles. Water temperature (WT), oxidation-reduction potential (ORP), dissolved oxygen (DO), and pH were measured with a multi-parameter YSI Professional Plus handheld water quality monitor (YSI Inc., Yellow Spring, OH, USA) in situ. After water collection, surface sediments (0–10 cm) were collected using a Petersen grab sampler, and then were divided into two parts with a 5–10 g into sterilized tubes for DNA extractions using sterile spoons and the remaining portion in sterile plastic bags for physical and chemical analyses. Physical and chemical samples of water and sediment were kept on ice in the field and during transport, and at 4 degrees Celsius in the lab. All sediment and water samples collected for DNA extractions were frozen on dry ice and brought back to the lab for long-term storage at 80 °C.

2.3. Physical and Chemical Analyses of Samples

In addition to measuring the WT, pH, ORP, and DO of water samples by YSI, ammonium (NH4+-N), nitrate (NO3−-N), nitrite (NO2−-N), total nitrogen (TN), and total organic carbon (TOC) in water samples were analyzed in the laboratory according to methods reported previously [37,38]. The indophenol blue colorimetric method was utilized specifically to quantify the NH4+-N concentration. The amount of NO2−-N was determined calorimetrically using N-(1-naphthyl)-1,2-diaminoethane dihydrochloride, whereas the amount of NO3−-N was measured by the difference in UV absorbance at 220 and 275 nm. Ultraviolet spectrophotometry was used to measure TN levels after alkaline potassium persulfate digestion. After taking the TC (total carbon) value and subtracting the TIC (total inorganic carbon) value, the TOC value was obtained. Meanwhile, the chemical characteristics of sediment, including TN, NH4+-N, NO3−-N, NO2−-N, dissolved organic nitrogen (DON), and TOC, were determined according to methods reported previously [39]. In detail, the concentrations of NH4+-N, NO3−-N, NO2−-N, and DON were measured by extracting 5 g of fresh sediment with 25 mL of KCl (2 mol/L) and then placing it in an oscillator at room temperature for 2 h (180 r/min). The aforementioned extract was filtered via a 0.45 m ANPEL membrane filter prior to analysis. The filtrate concentration was determined following standard protocol using a UV-VIS spectrophotometer (SHIMADZU UV-1700, Kyoto, Japan). To measure TN and TOC, we filtered some dried sediment over a 100-mesh screen using an elemental analyzer (ELEMEN-TAR, Frankfurt, Germany). Dissolved inorganic nitrogen (DIN) was calculated by the addition of NH4+-N, NO3−-N, and NO2−-N; the total organic nitrogen (TON) in water samples was obtained by the difference between the concentration of TN and DIN (particulate inorganic nitrogen did not represent a significant fraction of any samples). Here, C/N equaled TOC/TN.

2.4. DNA Extraction, Library Construction and Metagenomics Sequencing

All water and sediment samples were processed in accordance with the manufacturer’s instructions for the FastDNATM SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) to extract genomic DNA. The concentration, purity, and integrity of the extracted DNA were assessed with a QuantusTM Fluorometer (Promega, Madison, WI, USA), NanoDropTM 2000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA), and 1% agarose gel electrophoresis, respectively. After the genomic DNA was qualified, it was fragmented to an average size of 400 bp using Covaris M220 (Gene Company Limited, Hong Kong, China) for paired-end library construction using NEXTflexTM Rapid DNA-Seq Kit (Bioo Scientific, Austin, TX, USA). The paired-end sequencing was performed on Illumina NovaSeq platform (Illumina Inc., San Diego, CA, USA) by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) according to the manufacturer’s instructions.

2.5. Sequence Processing and Bioinformatics Analysis

To generate the clean reads from metagenome sequencing, we utilized the fastp software (version 0.20.0) [40] to remove adaptor sequences, trim, and eliminate low-quality reads. These included reads with unknown nucleotide “N” bases, a minimum length threshold of 50 bp, and a minimum quality threshold of 20. This resulted in 743,197,588 high-quality clean reads from the initial 765,555,050 raw reads with 13,228,319,062 total bases. We then assembled these clean reads into contigs using MEGAHIT (version 1.1.2) [41] which employs succinct de Bruijn graphs. The 8,604,784 contigs with a minimum length of 300 bp were selected as the final assembling result. Open reading frames (ORFs) in contigs were identified using MetaGene (http://metagene.cb.k.u-tokyo.ac.jp/ (accessed on 1 May 2022)) [42]. A total of 10,673,571 genes were predicted from ORFs with a minimum length of 100 bp, retrieved, and translated into amino acid sequences. A comprehensive gene catalog with 6,445,882 microbial genes (spanning 3,114,468,057 bp) was created using CD-HIT (http://www.bioinformatics.org/cd-hit/ (accessed on 1 May 2022), version 4.6.1) [43]. The catalog was built with 90% sequence identity and 90% coverage. To determine gene abundances, SOAPaligner (http://soap.genomics.org.cn/ (accessed on 4 May 2022), version 2.21) [44] was used to map the clean reads of each sample to the catalog with 95% identity. All metagenomic sequencing statistics are displayed in Table S1 (Online Resource).
To annotate nitrogen-related genes and abundances, the non-redundant gene catalog of nitrogen metabolism was constructed from the PATHWAY subdatabase of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The non-redundant gene catalog of nitrogen metabolism was translated into amino acid sequences. These translated amino acid sequences were annotated based on the integrated non-redundant (NR) database of the NCBI (https://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/ (accessed on 11 May 2022), version 20200604) using blastp as implemented in the Diamond software (http://www.diamondsearch.org/index.php (accessed on 11 May 2022), version 0.8.35) with an e-value cutoff of 1 × 10−5 for N taxonomic annotations [45]. The KEGG annotation was conducted using Diamond against the KEGG Orthology (KO) database (http://www.genome.jp/keeg/ (accessed on 11 May 2022), version 94.2) (e-value = 1 × 10−5) for N functional analyses.

2.6. Statistical Analysis

Samples were grouped according to spatial location: surface water (n = 3), bottom water (n = 3), and lakebed sediment (n = 3). Physicochemical data of samples were expressed as mean ± standard error (SE). Relative abundances of nitrogen metabolic genes were measured in terms of instances per million sequencing reads, simplified as “parts per million” (ppm). Relative abundances of the taxonomic and functional profiles of nitrogen-related genes were also defined in ppm. Differences in the physicochemical properties of water samples and nitrogen transformation pathways were performed by a non-parametric Kruskal–Wallis test of independent samples in IBM SPSS Statistics 19.0 (IBM Corporation, Armonk, NY, USA). p ≤ 0.05 was defined as a statistically significant difference. The Bray–Curtis similarity between functional and taxonomic profiles consisting of relative abundances was ordinated using principal coordinate analysis (PCoA) with the significance of groupings assessed using analysis of similarities (ANOSIM) with 999 random permutations. Significantly different taxonomic profiles in multiple groups were identified using a linear discriminant analysis (LDA) effect size (LEfSe) method. Functional contributions of observed microbial taxa (phylum level) to the functional pathways were explored using customized R scripts.

3. Results

3.1. Physical and Chemical Properties of Samples

The physicochemical properties of the water and sediment samples are shown in Tables S2 and S3 (Online Resource). The concentrations of TN, NH4+-N, NO3−-N, NO2−−N, DIN, and TON were higher in the bottom water than in the surface water, but the differences between them were not significant (p > 0.05). The concentration of DIN was higher than that of TON in the water column, and its proportions in surface water and bottom water were 77.09% and 78.17%, respectively. The concentrations of NH4+-N and NO3−-N were much higher than that of NO2−-N in the water column. Overall, the concentration of nitrogen in Lake Ulansuhai was relatively high during the ice-covered period, and DIN (dominated by NH4+-N and NO3−-N) was the main form of nitrogen. The bottom water also displayed a higher TOC concentration than the surface water. There were significant differences (p ≤ 0.05) between the physical properties of the surface water and bottom water in WT and DO; however, the values of pH and ORP were comparable (p > 0.05). The concentration of DON was higher than that of DIN in the sediment, and NH4+-N was the main form of DIN. C/N was also present in higher concentrations in the sediment than in either water depth sampled.

3.2. Detection Frequency of Nitrogen-Related Genes

In this study, we analyzed the frequency of detection of nitrogen-related genes in the samples. After aligning the high-quality clean reads to the non-redundant gene catalog of nitrogen metabolism from the KEGG Pathway database, we identified a total of 588,894 nitrogen-related gene sequences. The abundance of nitrogen-related genes in each sample ranged from 1278 to 6247 ppm. On average, the surface water samples had 1751 ppm, bottom water samples had 2198 ppm, and sediment samples had 4655 ppm of nitrogen-related genes. Composition and comparison of nitrogen transformation pathways
KEGG Orthology (KO) is a collection of genes with the same or similar function in the KEGG database that can directly characterize specific metabolic pathways and identify the number of functionally equivalent genes (KEGG orthologs, or KOs). All nitrogen-related genes identified in this survey were classified by aligning them to the KO database. According to the annotation results, 53, 54, and 54 N functional genes (KOs) were identified in the surface water, bottom water, and sediment, respectively, involving seven, seven, and eight nitrogen transformation pathways. In the surface water and bottom water, ammonia assimilation and ammonification were the major nitrogen transformation pathways, and their abundances ranged from 1097.16 to 1402.57 ppm and from 287.47 to 321.04 ppm, respectively. In the sediment, the predominant pathways were ammonia assimilation, DNRA, and denitrification, and their abundances were 2632.20 ppm, 831.16 ppm, and 431.21 ppm, respectively (Figure 2). The functional gene abundance of ammonia assimilation was higher than that of other nitrogen transformation pathways, indicating that ammonia assimilation was a major nitrogen transformation pathway in the water and sediment of Lake Ulansuhai during the ice-covered period. In comparison, the functional gene abundance of anammox was the lowest (0.39 ppm), and this pathway did not exist in detectable quantities in surface water or bottom water.
Ordination of functional profiles based on the relative abundance of KOs demonstrated that genetic content variation with respect to water depth was significantly smaller than the variation between water and sediment samples (Figure 3). The ANOSIM analysis strongly supported this clustering (R = 0.56, p = 0.04), suggesting notable distinctions in nitrogen transformation pathways between subglacial water and sediment. A non-parametric Kruskal–Wallis test showed that the KO abundances of nitrogen fixation, nitrification, denitrification, DNRA, and ammonia assimilation in sediment were significantly (p ≤ 0.05) higher than those in subglacial water. No such significant difference was observed (p > 0.05) between surface water and bottom water. Anammox, ANRA, and ammonification showed no significant genetic signature difference (p > 0.05) between surface water, bottom water, and sediment samples (Figure 2).

3.3. Composition and Comparison of Nitrogen-Processing Microbiome

To determine the makeup of the microbiome at each level, all discovered N functional genes were cross-referenced with the NR database. At the domain level, Bacteria (82.08–99.46%) were the most common among all samples, with an average relative abundance of 86.22% in surface water, 91.92% in bottom water, and 96.76% in sediment. Intriguingly, the relative abundances of Eukaryota and Archaea varied greatly between water samples and sediment samples. A high percentage of Eukaryota (13.62% and 7.96%) were observed in surface water and bottom water, respectively, while the predicted abundance of Eukaryota was only 0.07% in sediment. Contrarily, the relative percentage of Archaea in sediment samples (3.07%) was greater than the measured percentages in surface water and bottom water (0.10% and 0.06%) (Figure 4A).
At the phylum level, a total of 121 phyla of nitrogen-transforming microorganisms were identified in all samples including 67 phyla in surface water, 77 phyla in bottom water, and 89 phyla in sediment. Proteobacteria was the dominant phylum in all samples, with a relative abundance ranging from 34.21% to 69.04%. In surface water, the average relative abundance of Proteobacteria was 42.99%, followed by Actinobacteria (23.20%) and Bacteroidetes (13.22%). Similar abundances of Proteobacteria (48.59%), Actinobacteria (20.83%), and Bacteroidetes (8.69%) were observed in the bottom water. Notably, compared with Bacteroidetes, the abundances of Cyanobacteria (7.32%) and Bacillariophyta (6.80%) (Eukaryota domain) were relatively high in WL2_W1, and Cyanobacteria (6.59%), Bacillariophyta (6.71%), and Verrucomicrobiota (6.59%) were more abundant in WL2_W2. Proteobacteria made up the majority of the phyla in the sediment, accounting for 54.88% of it, followed by Chloroflexota (14.67%) and Actinobacteria (6.61%) (Figure 4B).
The sample grouping observed for functional profiles (Figure 3) was also strongly reflected in the taxonomic profiles (Figure 5a,b). Whether at the domain or phylum level, there were two distinct clusters: one consisting of water samples from the surface and bottom water, and a second cluster consisting of sediment samples, based on PCoA plots with Bray–Curtis dissimilarity distance. These clusters were strongly supported by ANOSIM analysis (R = 0.47, p = 0.05; R = 0.55, p = 0.04), indicating significant differences in the community composition of nitrogen-transforming microorganisms at the domain and phylum levels between subglacial water and detritus, with fewer differences observed between surface and bottom water (Figure 5a,b). To further identify the difference in taxa between sample clusters, we conducted LEfSe analysis to compare the average relative abundances of the microbial community at different taxonomic levels. Considering the LEfSe outcomes, 15 species with an LDA score of at least two were considered to be significantly different in composition between clusters, which are hereafter referred to as taxa biomarkers. Among these taxa biomarkers, 5 and 10 taxa were significantly enriched in the surface water and sediment, respectively. In the surface water, an unclassified microbe derived from the domain Archaea and Ciliophora derived from the domain Eukaryota earned a significantly higher LDA score. In the sediment, Chordata derived from domain Eukaryota and candidate_division_Zixibacteria, Synergistetes, Candidatus_Latescibacteria, and candidate_division_NC10 derived from domain Bacteria were substantially more prevalent than those in the water column (Figure 6a–f).

4. Discussion

The nitrogen transformation pathways and the community structures of functional microorganisms in the water and sediment of Lake Ulansuhai during the ice-covered period require greater study. To the best of our knowledge, this is the only comprehensive analysis to date of the characteristics of the nitrogen cycle in subglacial water and sediment during the ice-covered period of Lake Ulansuhai.

4.1. Characterization of Nitrogen Transformation Pathways

Marker gene abundance in a pathway is usually used to determine nitrogen cycling capacity. A nitrogen transformation pathway often involves multiple enzyme-catalyzed reactions, so multiple functional genes are required to work together as marker genes to ensure the integrity of metabolic pathways [46,47,48]. Therefore, in our study, all the functional genes annotated to each nitrogen transformation pathway are considered to characterize the corresponding pathway, so that the results are more reliable. For example, we used the abundance of nifDHK gene clusters to jointly characterize the incidence of nitrogen fixation pathways.

4.2. Nitrogen Transformation Pathways and Their Differences

Among the eight nitrogen transformation pathways we analyzed, ammonia assimilation was the most frequently detected nitrogen transformation pathway during the ice-covered period. In low-temperature conditions, microorganisms may need more organic nitrogen to support cell synthesis and growth, such as amino acids and proteins [5]. Ammonia assimilation is a main nitrogen conversion pathway that can convert ammonia nitrogen into organic nitrogen. At the same time, ammonia nitrogen is a nitrogen source that is more easily used by microorganisms and can be used by almost all microorganisms through ammonia assimilation. As a result, the detection rate of genes with ammonia assimilation function is the highest [49,50]. For example, the only detected glutamine synthetase (GS) type-1 (GSI) with abundances ranging from 247.45 to 895.29 ppm. The actual ammonia assimilation pathway has long been known [51], and as the dominant pathway of nitrogen transformation, similar results have been observed not only in frozen lakes but also in a number of environments [52,53].
Nitrogen fixation, nitrification, and anammox pathways were detected less frequently in the two media, especially in anammox pathways. The previous study showed that there is a negative correlation between nitrogenase activity and ammonia nitrogen concentration [54], and the physical and chemical characteristics of our samples included high ammonia nitrogen concentration and low biological nitrogen fixation in subglacial water and sediments. These characteristics were also found in the permanently ice-covered Bonney Lake in Antarctica [55]. A weak potential for nitrification was found in this study, similar to the findings in other frozen lakes [56]. According to our investigation and a prior study [57], the level of DO in Ulansuhai’s subglacial water was higher than anticipated, which ruled out the possibility of anammox. In contrast to water, denitrification and anaerobic ammonia oxidation occur naturally and are significant in sediments. However, related studies have revealed that anaerobic ammoxidation’s standard free energy is lower than denitrification, making denitrification more thermodynamically feasible [58,59]. In addition, compared with anammox bacteria, denitrifying bacteria have a higher growth yield, so denitrification is often dominant [60,61] in an environment where two pathways exist at the same time. The seasonal death of aquatic plants in the lake’s littoral zone releases large amounts of organic carbon into the water and sediment, which hinders the anaerobic ammoxidation process [62].
There are significant differences in this study’s observed functional gene abundance of nitrogen fixation, nitrification, denitrification, DNRA, and ammonia assimilation in subglacial water and sediment. Generally speaking, compared with water bodies, sediments contain more nitrogen-metabolizing microbes, as seen in our results.
Active nitrogen is crucial to aquatic environments, and biological nitrogen fixation is a major contributor. Traditionally, nitrogen-fixing cyanobacteria are generally considered to be the main nitrogen-fixing bacteria in lakes. However, heteromorphic bacteria, chemotrophic bacteria, and archaea can also perform nitrogen fixation [63] in dark sediments. Lakebed sediment is generally rich in microorganisms carrying nifDHK gene clusters that demonstrate higher nitrogen fixation activity than microbes in the water column, which has been confirmed by related studies [64]. Iron and molybdenum are necessary components for synthesizing nitrogenase, and the content of these heavy metal elements in sediments is generally high [65,66]. These factors likely contribute to the disparity we observe in the distribution of nitrogen fixation genes. A large number of studies have shown that denitrification and nitrogen fixation occur simultaneously in sediments because the nitrogen loss caused by denitrification can be compensated by nitrogen fixation [50,67]. Temperature is also an important environmental factor affecting the nitrogen conversion pathway. When the temperature is below 5 °C, denitrification and anammox are negligible [62,68]. Our study shows that the temperature of the subglacial water body is only 5.0 ± 1.84 °C in the bottom water, while the sediment temperature is relatively high. Although we did not measure the sediment temperature, the heat released from the overlying water body during the ice-sealing period can be used as evidence of higher sediment temperature than that of the water column [69]. Therefore, denitrification should occur at different rates in the lake’s subglacial water and sediment. Supplementary Tables S2 and S3 (Online Resource) show that sediment samples contained higher levels of C/N and NH4+-N, which may be the main reason for the difference between DNRA and ammonia assimilation in water and sediments. Although we have discussed a variety of factors that may have produced the differences we observed, a definitive model of nitrogen transformation pathways in Lake Ulansuhai would require more thorough surveying of the lake.

4.3. Nitrogen Transformation Functional Microorganisms and Their Differences

It is well known that there are a surprising number of microorganisms that can transform nitrogen. Bacteria, Eukaryota, and Archaea may all drive nitrogen transformation, but relatively speaking, Bacteria are the main participants in nitrogen transformation. This has also been well verified in our study (Figure 4A). Proteobacteria was the most abundant phylum in the subglacial water and sediment of Ulansuhai during the ice-covered period, which corresponded with the outcomes of experiments conducted in Hulun Lake, Chaohu Lake, and Erhai Lake [70,71,72], indicating that Proteobacteria is the dominant species driving nitrogen transformation in lakes during both ice-covered and ice-free periods. In addition, Actinobacteria, and Bacteroidetes were detected at higher concentrations in subglacial water, while a greater proportion of Chloroflexota and Actinobacteria were detected in sediment, which was similar to the microbial community structure in aquaculture ponds and sediments [73]. Previous studies have shown that, regardless of nutritional status, Proteobacteria, Actinobacteria, Bacteroidetes, and Chloroflexota generally occupy a dominant position in freshwater lakes, and they actively participate in the nitrogen cycle process [74,75,76]. Our study also confirmed that the distribution of microbial communities is not uniform but shows a small number of dominant species and a large number of rare species, and a small number of common dominant flora can be observed in different environments [77].
For lake water, although there were significant differences in the environmental factors between surface water and water near the lakebed (Table S2 (Online Resource)), their microbial communities were similar. Fifteen species with statistically significant differences were identified in the two environmental media, with 5 and 10 taxa significantly enriched in surface water and sediment, respectively. Ciliophora was found in freshwater in a planktonic state for most times of the year [78], which may account for the greater abundance of Ciliophora in surface water in our study. The significant enrichment of Synergistetes in the sediment may be related to its environmental characteristics for anaerobic existence [79]. It is worth noting that these differential species are not the few common dominant species, indicating that they can adapt to the environment in surprisingly specific ways. These observations may reflect a wide range of environmental heterogeneity. Species differences caused by habitat heterogeneity are not very common at higher classification levels but occur at lower classification levels such as the genus or species level [80,81]. Our study showed that the habitat heterogeneity of different species in the water and sediment of Lake Ulansuhai during the ice-covered period also existed at the domain–kingdom–phylum level.
Although we generalized the functionality of microbial genes through KEGG Orthography, some functional genes will play a role in multiple nitrogen transformation pathways at the same time. For these multi-purpose functional genes, we only classified them as one nitrogen transformation pathway and did not distinguish their contribution to other pathways. Additionally, we discussed the characteristics and differences of nitrogen transformation in subglacial heterogeneous habitats based on transformation pathways and community structure; however, quantifying the relationship between them is a problem that needs to be solved in the future.

5. Conclusions

During the ice-covered period of Lake Ulansuhai, the characteristics of the nitrogen cycle were analyzed through the metagenomic approach of subglacial water and sediment. We found that ammonia assimilation was the main nitrogen transformation pathway used by water column and sediment microbes, and domain Bacteria and phylum Proteobacteria were the dominant taxa driving nitrogen transformation. Habitat heterogeneity had a significant impact on nitrogen transformation pathways and species taxa, and surface sediment was crucial to the nitrogen cycle in the lake during the time when the lake was covered in ice. A small number of common dominant taxa were similar in different habitats, while taxonomical differences were concentrated in a large number of rarer species.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w15132332/s1, Table S1: Statistics of metagenomic sequencing data; Table S2: Physical and chemical properties of water samples; Table S3: Physical and chemical properties of sediment samples.

Author Contributions

Z.T.: conceptualization, investigation, formal analysis, methodology, visualization, and writing—original draft; S.Z. (Sheng Zhang): conceptualization, funding acquisition, supervision, and writing—review and editing; J.L., X.S. and S.Z. (Shengnan Zhao): data curation, funding acquisition, project administration, resources, and visualization; B.S. and Y.W.: formal analysis and supervision; G.L. (Guohua Li), Z.C., X.P., G.L. (Guoguang Li) and Z.Z.: data curation and investigation. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Research Program of Science and Technology at Universities of Inner Mongolia Autonomous Region (NJZY21180 and NJZY21503), the National Natural Science Foundation of China (52260029, 52060022, 52260028, and 52160021), the Inner Mongolia Autonomous Region Science and Technology Plan (2021GG0089), and the National Key Research and Development Program of China (2017YFE0114800 and 2019YFC0409204).

Institutional Review Board Statement

The research complies with ethical standards.

Informed Consent Statement

Not applicable.

Data Availability Statement

The National Center for Biotechnology Information (NCBI) Short Read Archive database has received the sequencing raw data related to this project (Accession Numbers: SRR18899872-SRR18899880). The other data generated or analyzed during this study are not publicly available due but are available from the corresponding author on reasonable request.

Acknowledgments

We acknowledge Shanghai Majorbio Bio-Pharm Technology Co., Ltd., for their technical assistance with sequencing. We would also like to thank LetPub (www.letpub.com, accessed on 20 June 2023) for its linguistic assistance during the preparation of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Locations of Lake Ulansuhai and sampling sites.
Figure 1. Locations of Lake Ulansuhai and sampling sites.
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Figure 2. The relative abundances and differences of nitrogen transformation pathways in water and sediment during the ice-covered period. The error bars represent the standard error of the mean (n = 3). * represents a statistically significant difference at the 0.05 level. Abbreviation: ANRA, assimilatory nitrate reduction to ammonium; DNRA, dissimilatory nitrate reduction to ammonium.
Figure 2. The relative abundances and differences of nitrogen transformation pathways in water and sediment during the ice-covered period. The error bars represent the standard error of the mean (n = 3). * represents a statistically significant difference at the 0.05 level. Abbreviation: ANRA, assimilatory nitrate reduction to ammonium; DNRA, dissimilatory nitrate reduction to ammonium.
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Figure 3. PCoA plots using Bray–Curtis distance of KOs in different samples from the ice-covered period.
Figure 3. PCoA plots using Bray–Curtis distance of KOs in different samples from the ice-covered period.
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Figure 4. The relative abundances of the microbial community at (A) domain and (B) phylum levels in different samples during the ice-covered period. The unclassified, unidentified, and sequences with a relative abundance <1% are in the “Others” group.
Figure 4. The relative abundances of the microbial community at (A) domain and (B) phylum levels in different samples during the ice-covered period. The unclassified, unidentified, and sequences with a relative abundance <1% are in the “Others” group.
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Figure 5. PCoA plots using Bray–Curtis distance of microbial community at (a) domain and (b) phylum levels in different samples during the ice-covered period.
Figure 5. PCoA plots using Bray–Curtis distance of microbial community at (a) domain and (b) phylum levels in different samples during the ice-covered period.
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Figure 6. Comparison and difference of microbial community in surface water, bottom water, and surface sediment. (a,b) Cladogram and LDA score (log10) of LEfSe analysis involved in Bacteria (domain–kingdom–phylum). (c,d) Cladogram and LDA score (log10) of LEfSe analysis involved in Eukaryota (domain-kingdom-phylum). (e,f) Cladogram and LDA score (log10) of LEfSe analysis involved in Archaea (domain–kingdom–phylum).
Figure 6. Comparison and difference of microbial community in surface water, bottom water, and surface sediment. (a,b) Cladogram and LDA score (log10) of LEfSe analysis involved in Bacteria (domain–kingdom–phylum). (c,d) Cladogram and LDA score (log10) of LEfSe analysis involved in Eukaryota (domain-kingdom-phylum). (e,f) Cladogram and LDA score (log10) of LEfSe analysis involved in Archaea (domain–kingdom–phylum).
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Tian, Z.; Zhang, S.; Lu, J.; Shi, X.; Zhao, S.; Sun, B.; Wang, Y.; Li, G.; Cui, Z.; Pan, X.; et al. Differences of Nitrogen Transformation Pathways and Their Functional Microorganisms in Water and Sediment of a Seasonally Frozen Lake, China. Water 2023, 15, 2332. https://doi.org/10.3390/w15132332

AMA Style

Tian Z, Zhang S, Lu J, Shi X, Zhao S, Sun B, Wang Y, Li G, Cui Z, Pan X, et al. Differences of Nitrogen Transformation Pathways and Their Functional Microorganisms in Water and Sediment of a Seasonally Frozen Lake, China. Water. 2023; 15(13):2332. https://doi.org/10.3390/w15132332

Chicago/Turabian Style

Tian, Zhiqiang, Sheng Zhang, Junping Lu, Xiaohong Shi, Shengnan Zhao, Biao Sun, Yanjun Wang, Guohua Li, Zhimou Cui, Xueru Pan, and et al. 2023. "Differences of Nitrogen Transformation Pathways and Their Functional Microorganisms in Water and Sediment of a Seasonally Frozen Lake, China" Water 15, no. 13: 2332. https://doi.org/10.3390/w15132332

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