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Article

Differentiated Microbial Strategies in Carbon Metabolic Processes Responding to Salt Stress in Cold–Arid Wetlands

1
School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
2
Inner Mongolia Key Laboratory of Environmental Pollution Prevention and Waste, Resource Recycle, Hohhot 010021, China
3
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Hohhot 010021, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(8), 1607; https://doi.org/10.3390/land14081607
Submission received: 17 June 2025 / Revised: 28 July 2025 / Accepted: 1 August 2025 / Published: 7 August 2025

Abstract

With the rising concerns about climate change and continuous increase in the salinity of soil, it is essential to understand the C-cycling functioning of saline soil to better predict the ecological functions and health of soil. Microbes play critical roles in C-cycling. However, limited research has been conducted to understand the impact of soil salinity on the microbial functional genes involved in C-cycling. In this study, effects of varying soil salinity levels in wetlands on the C-cycling functions and diversity of soil microbes were investigated by metagenomic sequencing. The results showed a higher relative abundance of genes related to decomposition of easily degradable organic C at low salinity. On the other hand, higher abundance of genes participating in the decomposition of recalcitrant organic C were observed at high salinity. These findings indicate distinct metabolic bias of soil microbes based on the salinity levels. Proteobacteria and Actinobacteria were dominant in soils with low to medium salinity levels, while Bacteroidetes phyla was prominent in highly saline soils. Furthermore, partial least squares path modeling (PLS-PM) identified electrical conductivity, total nitrogen, and total phosphorus as key regulators of C-cycling gene expression. Overall, the present study highlights the intricate connections between salinity, microbial attributes, and carbon metabolism in soil, suggesting that the soil microbes adapt to saline stress through divergent eco-adaptations. The findings of this study highlight the significance of exploring these microbial interactions for effective management and conservation of saline wetlands.

1. Introduction

The global carbon cycle constitutes a fundamental biogeochemical process governing terrestrial ecosystem functioning. A balanced carbon cycle significantly helps in mitigating climate change [1]. There is very limited understanding of the global carbon cycle and the ‘missing carbon sink’ problem has still not been resolved properly [2,3,4]. To improve the understanding of global carbon cycle, it is important to address the challenge of missing carbon sinks [5].
Although wetlands cover only 5–8% of global land they store approximately 20–30% of the estimated 1500 Pg global soil carbon among the ecosystems with rich carbon stocks. In wetlands soil decomposition slows down due to anoxic conditions, leading to organic matter accumulation [6]. Consequently, wetlands are thought to have high long-term carbon accumulation. Nahlik and Fennessy [7] showed that the carbon holding capacity of inland wetlands was nearly 10-fold higher than that of tidal saltwater sites. Nevertheless, limited research has been conducted to understand the dynamics of soil carbon cycling in inland wetlands. To contribute to the ongoing efforts to mitigate climate change, further research on the soil carbon cycling is required to refine the models predicting soil carbon dynamics.
Functioning as core biogeochemical agents, microorganisms mediate carbon exchange across Earth’s spheres (e.g., biosphere, atmosphere) and modulate climate feedback [8,9,10]. Many reports have underscored the significance of soil microbes in the formation and persistence of soil organic carbon (SOC) [11,12,13,14]. In addition, soil microbes can impact the soil carbon cycling by degrading the SOC through anabolism and secretion of extracellular enzymes [15,16]. The carbon-fixing and carbon-degrading functions of soil microbes are analyzed by determining the abundance of genes involved in carbon cycling [17,18]. The C-cycling-related functional genes (i.e., the genes involved in the conversion and degradation of starch, lignin, cellulose, chitin) are crucial for the conversion of soil carbon and stabilization of carbon pools, and they exhibit strong correlations with environmental factors [19,20]. Several recent studies have demonstrated the effects of soil pH and the concentration of heavy metals and nutrients on the abundance of C-cycling genes [21,22,23]. Salinity is a significant physicochemical property of soil. Previous studies have shown a decrease in SOC content with rise in soil salinity levels, which further caused a decline in the abundance of most carbon-degrading genes [24,25,26]. However, the impacts of salinity levels on the C-cycling functional genes in inland wetland soils have not been elucidated properly.
Metagenomic sequencing simultaneously quantifies the abundance of key functional genes and reveals soil microbial population structures [27,28]. Here, the effects of soil salinity on C-metabolism in the soil microbiome of Ulansuhai wetland were investigated by analyzing the abundance of C-cycling genes. Metagenome sequencing was used to obtain the abundance data of seven types of C-cycling genes, which were responsible for carbon fixation and the degradation of various organic compounds, such as lignin, starch, cellulose, pectin, etc. In addition, the responses of these functional genes to changes in soil environmental factors were studied under different salinity levels. The study was based on the following three hypotheses: (i) SOC content depends on salinity in wetland ecosystem; (ii) salinity affects the metabolism of microorganisms, including those that control the soil carbon cycle in wetlands; and (iii) soil salinity induces changes in the properties of soil and plays critical role in regulation of SOC decomposition by microbiota and carbon emission.

2. Materials and Methods

2.1. Study Area and Soil Sampling

In June 2022, soil samples were collected from Ulansuhai Wetland (latitude N 40°47′–41°03′, longitude E 108°43′–108°57′, elevation 1018.79 m) in Inner Mongolia, China, which is the largest inland wetland in the Yellow River Basin. The diverse landscapes of Ulansuhai Wetland are the breeding grounds for several migratory birds. It also features many terrestrial and aquatic ecosystems. The wetland not only provides a natural barrier against floods but also helps with water purification and serves as a carbon sink, thus contributing to the regulation of the global climate.
Saline soils are commonly found in lake wetlands of arid and semi-arid regions. Still, the plant communities in the lakeside belt of Ulansuhai Wetland are quite distinct. Based on specific topography and vegetation, soils were sampled systematically from various zones of Ulansuhai Wetland along the lakeside. The collected samples included the soils beneath three different vegetations: (i) Phragmites australis in the radiant belt towards the lake (the key interface for the transition from the shore zone to the profundal zone) (S1–S4); (ii) Salicornia europaea in the zone with fluctuating water level (S5–S7); and (iii) Nitraria sibirica in the radiant belt towards the land (the key interface of the transition from the shore zone to the terrestrial zone) (S8–S10) (Figure 1). The five-point sampling method was employed to collect the samples of topsoil (0–20 cm depth), and the soil samples taken from each plot were combined. After collection, soil samples were put in an ice box and taken to the laboratory instantly. In the lab, each soil sample was sieved (sieve size: 2 mm). The sieved sample was further partitioned into two portions. One portion was air-dried, ground, and kept at room temperature for the assessment of physiochemical properties, while the other portion was kept at −80 °C and used for DNA extraction.

2.2. Analysis of the Physiochemical Characteristics of Soil

The soil pH and electrical conductivity (EC, ds/m) were determined using deionized soil-to-water ratios of 1:2.5 and 1:5, respectively (Seven Direct SD30, Mettler Toledo, Switzerland). The gravimetric method was employed to estimate the soil water content (WHC) and salinity of soil extraction solution (soil/water ratio = 1:5). Total organic carbon (TOC) content in soil was measured by a spectrophotometer using K2Cr2O7 oxidation. NO3-N, NO2-N, and NH4+-N were extracted by using KCl solution (1:5 w/v) and their contents were measured through spectrophotometry (HJ634-2012). A CHN elemental analyzer (Vario Macro cube, Elementar, Germany) was utilized to estimate total nitrogen (TN) content in soil. The total phosphorus (TP) content was determined by a Mo-Sb anti-spectrophotometric process after extraction through sodium hydrogen carbonate. For each sample, three parallel measurements were performed.

2.3. Metagenomic Sequencing and Processing of Raw Sequence Data

For DNA extraction, 0.7 g of mixed fresh soil was taken and a FastDNA® SPIN kit (MP Biomedicals, Santa Ana, CA, USA) was utilized to isolate the total DNA. Concentration and purity of DNA were assessed by a Tanao 800 spectrophotometer (Tuohe Electromechanical Technology, Shanghai, China). After a quality-check, all DNA samples were sequenced by Majorbio Biomedical Technologies (Shanghai, China) using an Illumina HiSeq 6000. The raw paired-end reads (PE150, 150 bp) obtained after sequencing were trimmed to filter out the low-quality reads. In this process, reads were trimmed from 3′ to 5′ ends and the reads with <50 bp length were eliminated by fastp (https://github.com/OpenGene/fastp, version 0.20.0, accessed on 19 December 2022). When the mean base quality inside a window was below 20, the remaining low-quality reads were trimmed. After trimming, reads with <50 bp length or those containing ambiguous bases were removed. Subsequently, MEGAHIT (version 1.1.2) was used to assemble the optimized sequences [29], resulting in selection of contigs with ≥300 bp.
MetaGene was used for the ORF prediction of contigs [30] (https://metagene.nig.ac.jp/metagene/metagene.html, accessed on 31 July 2025), leading to the selection and translation of genes with ≥100 bp into amino acid sequences. Subsequently, CD-HIT (version 4.6.1, parameters: 90% coverage, 90% identity) was used for the prediction of gene sequences for all samples [31]. Considering the longest gene from each class as the characteristic sequence, a non-redundant gene (NRG) set was constructed. SOAPaligner (version 2.21) was employed for the comparison of qualified reads obtained from each sample with the NRG set (95% identity), followed by determining the abundance of genes in that sample [32]. Diamond 0.8.35 was employed for the alignment of amino acid sequences of the NRG set with the sequences available in Kyoto Encyclopedia of Genes and Genomes (KEGG), with an expectation e-value of 1e-5 for BLASTP alignment [33]. For taxonomic classification of the subset of chosen genes, Diamond software was used for alignment of sequences against the Non-Redundant Protein Database (NR) in the National Center for Biotechnology Information (NCBI). All raw sequences were submitted to the Sequence Read Archive of NCBI (Accession No. PRJNA1140737).

2.4. Statistical Analysis

The variance of parameters across different zones with varying salinity levels was analyzed by conducting a one-way analysis of variance (ANOVA) and Duncan’s test. Furthermore, Spearman correlation analysis was performed to determine the correlations between the environmental variables and the abundance of functional genes involved in the carbon cycle. The distribution of C-cycling-related genes was examined by conducting principal coordinate analysis (PCoA), based on hierarchical clusters and Bray–Curtis distance. Furthermore, Origin 2018 (OriginLab, Northampton, MA, USA) was used for linear regression analysis. Potential pathways, through which the influencing factors mediated the changes in TOC, were determined by partial least squares path modeling (PLS-PM) using the “PLS-PM” package of R (version 4.3.1). The fitness of model was evaluated based on the goodness-of-fit (GoF) index.

3. Results

3.1. Soil Characteristics

The salinity of soil samples ranged from 0.19 to 14.48 ms cm−1. Based on the salinity level, soil samples were categorized as demonstrated previously by Abrol et al. [34] and Volik et al. [35]. Soil of four plots were considered low-salinity soil (LSS), with a salinity range of 0–4 mS cm−1. Soil samples of three plots were categorized as light-to-medium salinity soil (MSS), with a salinity range of 4–8 mS cm−1. Furthermore, samples of three plots were classified as high-salinity soil (HSS), with a salinity > 8 mS cm−1 (Figure S1). Compared to MSS and HSS, LSS had significantly (p < 0.01) higher WHC (Figure 2a), In LSS, MSS, and HSS, WHC ranges were 50–55%, 13–18%, and 12–19%, respectively. Similarly, TOC content was much lower in MSS and HSS (2.36–6.23 and 1.93–7.62 mg g−1, respectively), as compared to the TOC in LSS (15.68–36.81 mg g−1) (Figure 2b). As the salinity level increased, TN content in soil decreased. Compared to HSS, LSS had significantly higher TN content (p < 0.01) (Figure 2c). The content of NH4+ varied significantly among LSS, MSS, and HSS (Figure 2d). Significant differences in NO3 content between LSSs and MSSs, as well as between MSSs and HSSs, were noticed (Figure 2e). On the contrary, NO2 did not vary significantly among the soils with different salinity levels (Figure 2f). Overall, variations in soil salinity levels exhibited different effects on the physicochemical properties of soils in the lakeshore zone of the Ulansuhai Wetland.

3.2. Impact of Salinity Level on the Abundance of C-Cycling Genes

To assess the soil health and microbial activity, especially under varying salinity conditions, it is crucial to identify C-cycling-related genes. The results of PCoA revealed that the first principal component (PC1) explained 67.21% and 65.42% of variance in the abundance of C-degrading and C-fixing genes, respectively. There were clear differences between LSSs, MSSs, and HSSs (Figure 3a,b). A total of seven genes involved in various types of C-decomposition and C-fixation functions were detected in all soil samples, with varying abundance across different soil salinity categories. Among these, responses of the top three functional genes (based on relative abundance) were investigated further (Figure 4). The abundance of starch-degrading genes in all three types of soil was relatively higher than that of other genes related to carbon cycling, followed by cellulose- and lignin-degrading genes. Furthermore, compared to LSSs and HSSs, MSSs had higher relative abundance of genes participating in the decomposition of starch, pectin, and chitin. As the salinity of soil increased, a decrease in the abundance of C-fixing, lignin-degrading, and hemicellulose-degrading genes was observed. On the contrary, increasing soil salinity exhibited a positive impact on the relative abundance of cellulose-degrading genes. Among the starch-degrading genes, the gene encoding glycogen phosphorylase (K00688, PYG) had the highest relative abundance, which significantly declined with rising soil salinity. Among the cellulose-degrading genes, beta-glucosidase encoding gene (K05349, bglX) showed the highest abundance, which significantly increased with rising soil salinity levels. Among the carbon-fixing genes, the gene encoding carbon-monoxide dehydrogenase (K00198, cooS) had the highest abundance, which declined noticeably with the increase in soil salinity level (Figure 5).

3.3. Correlations of Salinity on the Composition with C-Cycling Microbial Populations in Soil

Stark variations were observed among LSSs, MSSs, and HSSs, in terms of the composition of microbial populations at the phylum and genus levels. In the LSS, the active microbial population had members of various phyla, including Proteobacteria, Planctomycetes, and Chloroflexi, as well as Euryarchaeota, Candidatus_Aminicenantes, and Bacteroidota (Figure 6a). Around 57.61% of starch-degrading microbes, 80.33%, of lignin-degrading microbes, and 65.6% of carbon-fixing microbes in LSSs belonged to Proteobacteria phylum. In MSSs, 55.48% of chitin-degrading microbes, 72.57% of lignin-degrading microbes, and 81.68% of carbon-fixing microbes were members of Proteobacteria. Unlike MSSs and LSSs, Bacteroidota was the dominant phylum in HSSs, accounting for 48.8% of cellulose-degrading microbes, 62.24% of hemicellulose-degrading microbes, and 90.29% of pectin-degrading microbes. Among the carbon-fixing microorganisms, those belonging to Proteobacteria maintained a high level of abundance across all soil salinity categories (65.6%, 81.68%, and 86.1% in LSSs, MSSs, and HSSs, respectively), indicating the key role of Proteobacteria in carbon sequestration in wetlands. Salinity significantly influenced the abundance of microbes involved in the degradation of organic carbon.
Furthermore, the five most abundant genera in each group were explored, which showed huge differences in their abundance across the three soil salinity categories (Figure 6b). Acinetobacter was the most abundant genus in LSS, while Halorubrum was the dominant genus in MSS, with 29–57% relative abundance. Compared to MSS, HSS had increased abundance of Mariniflexile and Halorubrum and reduced abundance of Marinobacter and Streptomyces. Notably, the three soils showed significant difference in terms of the relative abundance of halophilic bacteria.

3.4. Correlations of Carbon-Cycling Related Genes with Key Environmental Variables

To identify the environmental variables influencing the abundance of C-cycling genes, the random forest model and PLS-PM were used (Figure 7). The results revealed positive correlations of salinity with the genes that participate in the decomposition of pectin, cellulose, starch, and chitin. On the other hand, hemicellulose- and lignin-degrading genes as well as carbon-fixing genes exhibited negative correlations with salinity. The cellulose- and starch-degrading genes showed strong negative correlations with SOC content. Genes related to starch and pectin degradation exhibited strong correlations with the TN content in soil. EC, TOC, and TN also showed significant influence on the abundance of C-cycling genes. Therefore, these environmental factors were chosen for PLS-PM analysis to explore their relationships with C-cycling genes (Figure 8). PLS-PM could explain 98% of variations in the abundance of C-degrading genes, 72% of variations in C-fixing genes, and 93% of variance in TOC in LSS. For MSS, PLS-PM could explain 82% of the variations in the abundance of C-degrading genes, 40% of variations in the abundance of C-fixing genes, and 83% of variations in the TOC content. For HSS, PLS-PM could explain 89% of variations in the abundance of C-degrading genes, 74% of variations in C-fixing genes, and 48% of variance in TOC content. These observations underscored the impact of soil properties on the C-cycling genes, which in turn, exerted direct or indirect impact on TOC content in soil. In MSS, C-degrading genes (r = −0.78) exhibited direct negative effect on TOC content (p < 0.05). In HSS, C-fixing genes (r = 0.69) showed direct positive impact on TOC content. The random forest model further confirmed that TOC was most significantly influenced by EC, TN, and TP contents in LSSs, MSSs, and HSSs, respectively. EC and TN directly affected the TOC in LSSs and MSSs, while TP negatively influenced the TOC content in HSS by exerting negative effect on C-fixing genes.

4. Discussion

4.1. Changes in Soil Characteristics and Distribution of Microbes in Response to Increasing Salinity

As key indicators of soil salinity, salt content, WHC, and soil pH usually show collinearity [36]. In the present study, WHC declined noticeably with rising salinity in soil. TOC is a crucial indicator of the quality of soil. In saline soils, TOC is impacted by two contrasting factors. Firstly, toxic ions and high osmotic potential hinder the growth of plants, leading to lower carbon input [14,37,38]. Secondly, high salinity restricts the activity of microbes, leading to lower degradation and mineralization of soil organic matter into CO2, which may cause a rise in soil TOC. Here, a negative correlation was noticed between TOC and salinity, which was the same as observed by Morrissey et al. [39]. This negative correlations may be mainly attributed to poor plant growth under the effect of salinity, especially in highly saline soils (soil with salt crystals and poor vegetation). The restricted growth of plants leads to low carbon content in the soil [40]. Overall, these findings highlighted the complex connections between soil salinity levels, microbial activity, plant growth, and carbon dynamics in saline soils.
Negative effects of salinity on the diversity and composition of soil microbial populations have been consistently demonstrated by many studies conducted in salt lakes [41], grassland of semi-arid regions [26], and denitrification bioreactors [42]. The high salinity may increase the osmotic potential outside the cells of microbes in saline soils, thereby inhibiting the activity of soil microbes [43]. Furthermore, high salt ion levels in soils may cause toxicity in microbes [44]. In a previous study, different bacterial communities had different extents of tolerance against specific ions [45]. For instance, Na+ accretion in soil led to an instable structure in soil, causing osmotic stress and ion effects [44,46]. Similarly, high pH and Na+ levels resulted in the lower abundance of bacteria in soil [43]. As the K+ concentration in soil increased, the diversity and activity of soil microbes declined significantly [47]. Simultaneously, growth of some bacteria (such as Pseudochlormonas) was joined by the release of mineral potassium in a recent study [48]. Singh et al. [49] reported that Al+ may cause restrictions in some respiratory pathways, thereby re-routing microbial metabolism to inefficient C pathways. In this study, an increase in soil salinity led to a significant decline in the abundance of Proteobacteria, Planctomycetes, and Chloroflexi, and an increase in the abundance of Actinobacteria, Euryarchaeota, and Bacteroidetes. Similar results were reported by Yang et al. [26]. Furthermore, Balneolaeota was highly abundant in the saline soils in this study, which was in agreement with a previous study [50]. The members of this phylum are vital for the cycling and metabolism of carbon, especially at extreme salinity levels. In the wetland soil, salinity exhibited a noticeable influence on microbial populations. These observations suggest that the impacts of soil salinity must be considered when investigating the C-cycling process in wetland.

4.2. Response of C-Cycling Genes to Increasing Soil Salinity

So far, variations in the abundance of C-cycling microbial genes in wetland soils have been rarely investigated [26,51]. In this study, microbial communities in wetland soils exhibited dramatic shifts in carbon metabolic functions, as evidenced by changes in KEGG pathways and C-cycling gene abundance. In a previous study, significant variations were observed in terms of the utilization of sole C source by microbes in wetland soil [52]. Here, a rise in soil salinity caused a noticeable rise in the abundance of lignin- and hemicellulose-degrading genes and a decline in the abundance of starch-degrading gene families. Similar trends were observed by Yang et al. [53]. These findings indicate higher starch degradation by soil microbes at low salinity. Furthermore, the trends suggest that soil salinity may change the microbial pattern of utilizing carbon substrates (such as glucose) in wetland soils. This may be because the production and secretion of C-degrading enzymes mostly depend on the adequate availability of energy sources [54]. Among the starch-degrading genes, the relative abundance of PYG in LSSs was noticeably greater than that in the other two soils, suggesting rapid degradation of starch in LSSs. On the other hand, the relative abundance of the cellulose-degrading bglX gene in HSSs was substantially greater than those in other soils. The bglX gene encodes beta-glucosidase, which degrades cellobiose and cello-polysaccharide into D-glucose. Thus, it is an important enzyme for cellulose degradation. Furthermore, the cooS gene involved in the carbon sequestration pathway of prokaryotes showed a negative correlation with soil salinity. These findings suggested that hemicellulose and starch degradation was more prevalent in LSS, while lignin and cellulose degradation was low. With the increase in soil salinity, the production and secretion of extracellular enzymes by microbes increases, leading to the higher degradation of organic macromolecules, such as cellulose and lignin in HSS [13,55]. Overall, the findings highlight the intricate effects of salinity on microbial C metabolism in wetland soils, demonstrating shifts in the abundance of C-cycling-related genes with the surge in soil salinity.

4.3. Correlations of Microbial Community and C-Cycling Genes with Soil Factors

Soil properties are crucial determinants of the structure of soil microbial communities [56]. Among these properties, salinity is a crucial factor affecting the structure of microbial (bacterial and fungal) populations in soil [43,51]. Furthermore, salinity also affects the chemical variables in soil, which further influence the microbial populations [26]. Here, soil EC was found to be a crucial factor influencing the abundance of C-cycling genes. The results further indicated differences between the soil properties (those influencing the abundance of C-cycling genes) across LSSs, MSSs, and HSSs. In LSS, EC showed strong positive and negative relationships with carbon-fixing genes and carbon-degrading genes, respectively. However, in MSS, EC influenced TOC content by changing the abundance of C-degrading genes, C-fixing genes, as well as TN and TP contents. In MSSs, TOC was noticeably influenced by C-degrading genes, while in HSSs, it was impacted by C-fixing genes. Moreover, higher fixation and degradation of organic carbon were observed in LSSs and MSSs, while the degradation function of organic carbon in HSSs was significantly higher than those of organic carbon fixation. This may be because of the wide range and higher abundance of soil nutrients in LSSs and MSSs for microbes. On the contrary, lower levels of nutrients in HSSs leads to low microbial activity, causing a decline in the fixation and degradation of organic carbon [26,57]. This study suggested that TP caused an increase in the abundance of carbon-fixing genes in HSS, resulting in higher SOC accumulation. This finding is in agreement with a recent study, which reported a decline in SOC content with an increase in soil salinity [25]. Previously, Yang et al. [58] noticed that soil salinization in wetlands inhibited the activity of carbon-acquiring enzymes and promoted the activity of CO2-acquiring enzymes. Qu et al. [59] also reported a decrease in organic carbon degradation due to soil salinization in wetlands. Furthermore, soil salinization may induce selective flocculation and the persistence of humic substances in wetland soils, resulting in the slower decomposition of organic carbon [60]. Low carbon degradation and higher carbon sequestration in the HSS of wetlands suggests that saline–alkali wetlands may turn into stable organic carbon sinks. Therefore, the SOC stabilization mechanism in saline–alkali wetlands needs to be investigated further.

5. Conclusions

In this study, fluctuations in soil properties, microbial communities, and C-cycling functional genes in response to varying soil salinity levels were investigated for wetland ecosystems. With the rise in soil salinity, TOC content declined substantially, which further influenced the structure and composition of microbial populations in soil. The results showed that soil salinization could influence the utilization of organic carbon substrates by soil microbes. At low salinity, the relative abundance of carbon-degrading genes for easily degradable organic carbon was higher. On the other hand, the higher abundance of carbon-degrading genes for recalcitrant organic carbon was observed at high soil salinity. Especially, soil salinization led to a stronger impact of C-cycling genes on SOC. The stronger impact of organic carbon catabolism on SOC was observed at medium soil salinity, while carbon anabolism exerted a greater impact on SOC at high salinity. These results indicate different catabolic pathways through which soil microbes adapt to salt stress. EC and TN were key factors influencing the SOC content at low and medium salinity, respectively. TP was the most important factor affecting the carbon-fixing genes at high salinity. However, the effects of TP on carbon-degrading genes were higher at medium and low salinity levels, which highlighted the importance of TP in carbon fixation at high salinity. These findings suggest that it is important to examine the C-cycling processes at microscopic scale, especially in the saline soils of wetlands. The study advocates for the optimization of carbon sequestration processes for unique environmental conditions and emphasizes the key factors affecting the efficiency of carbon sequestration in such environments.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14081607/s1, Figure S1: Variations in soil characteristics across soil salinity gradient. Figure S2: Linear regression analysis of soil factors and abundance of C-cycling genes. Table S1: Post-QC processed reads.

Author Contributions

Y.W.: Visualization, Formal analysis, and Writing—original draft. M.W.: Visualization, Formal analysis, and Investigation. T.W.: Writing—original draft and Investigation. J.Z.: Investigation. J.L.: Investigation. H.X.: Investigation. L.W. (Lixin Wang): Supervision and Project administration. L.W. (Linhui Wu): Conceptualization, Writing—review and editing, and Project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the the Inner Mongolia Natural Science Foundation [grant number 2024MS03027]; Hohhot ‘Leading the Charge with Open Competition’ Science and Technology Project (2024- Leading the Charge with Open Competition- Agriculture-1-1); National Natural Science Foundation of China (NSFC) [grant numbers 42067037, 32160279, 32161143025]; and the Science and Technology Major Project of Inner Mongolia [grant number 2022YFHH0017].

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Schematic diagram of position of sampling points along the catena.
Figure 1. Schematic diagram of position of sampling points along the catena.
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Figure 2. Variations in soil characteristics (WHC (a), TOC (b), TN (c), NH4+ (d), NO3 (e), and NO2 (f)) across soil salinity gradient. The values given here are the mean values of three replicates of each sample. The bars denote the standard errors. Here, LSS: low-salinity soil; MSS: light-to-medium salinity soil; and HSS: high-salinity soil. NS: not significant; ** represents significant variation with p < 0.05; *** represents highly significant variation with p < 0.01.
Figure 2. Variations in soil characteristics (WHC (a), TOC (b), TN (c), NH4+ (d), NO3 (e), and NO2 (f)) across soil salinity gradient. The values given here are the mean values of three replicates of each sample. The bars denote the standard errors. Here, LSS: low-salinity soil; MSS: light-to-medium salinity soil; and HSS: high-salinity soil. NS: not significant; ** represents significant variation with p < 0.05; *** represents highly significant variation with p < 0.01.
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Figure 3. Results of PCoA, comparing the variations in genes related to carbon degradation (a) and carbon fixation (b). LSS: soil with low salinity; MSS: soil with light-to-medium salinity; HSS: soil with high salinity.
Figure 3. Results of PCoA, comparing the variations in genes related to carbon degradation (a) and carbon fixation (b). LSS: soil with low salinity; MSS: soil with light-to-medium salinity; HSS: soil with high salinity.
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Figure 4. Relative abundance data of genes participating in decomposition of starch (a), pectin (b), chitin (c), cellulose (d), lignin (e), and hemicellulose (f), and the genes related to carbon-fixation (g) at different salinities. LSS: soil with low salinity; MSS: soil with light-to-medium salinity; HSS: soil with high salinity.
Figure 4. Relative abundance data of genes participating in decomposition of starch (a), pectin (b), chitin (c), cellulose (d), lignin (e), and hemicellulose (f), and the genes related to carbon-fixation (g) at different salinities. LSS: soil with low salinity; MSS: soil with light-to-medium salinity; HSS: soil with high salinity.
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Figure 5. Relative abundance of C-cycling genes (outer loop) and their correlations with various environmental factors (inner loop). LSS: soil with low salinity; MSS: soil with light-to-medium salinity; HSS: soil with high salinity.
Figure 5. Relative abundance of C-cycling genes (outer loop) and their correlations with various environmental factors (inner loop). LSS: soil with low salinity; MSS: soil with light-to-medium salinity; HSS: soil with high salinity.
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Figure 6. Compositions of the microbial communities in different salinity categories of soil at phylum level (a) and genus level (b). LSS: soil with low salinity; MSS: soil with light-to-medium salinity; HSS: soil with high salinity.
Figure 6. Compositions of the microbial communities in different salinity categories of soil at phylum level (a) and genus level (b). LSS: soil with low salinity; MSS: soil with light-to-medium salinity; HSS: soil with high salinity.
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Figure 7. Linear regression analysis of soil factors and abundance of C-cycling genes. Figures (al) show linear regression models of starch, pectin, and cellulose degradation genes versus TDS, TOC, TP, and TN.
Figure 7. Linear regression analysis of soil factors and abundance of C-cycling genes. Figures (al) show linear regression models of starch, pectin, and cellulose degradation genes versus TDS, TOC, TP, and TN.
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Figure 8. PLS-PM analysis identifying primary drivers of total organic carbon (TOC) in LSSs (a), MSSs (b), and HSSs (c). Storage as affected by soil properties and C-cycling functional genes. Width of an arrow is proportional to the corresponding standardized path coefficient. The red arrow indicates positive correlation, whereas the blue arrow indicates negative correlation. The solid arrow denotes significant (p < 0.05) correlation, whereas the dashed arrow indicates insignificant (p > 0.05) correlation. The number of asterisks represent the significance threshold, with ** indicating p < 0.01 and * indicating p < 0.05. TP: total phosphorus; TN: total nitrogen; EC: electrical conductivity; FIX: carbon-fixing genes; DEG; carbon-degrading genes. Importance of variables in predicting TOC content in LSSs (d), MSSs (e), and HSSs (f). LSS: soil with low salinity; MSS: soil with light-to-medium salinity; HSS: soil with high salinity.
Figure 8. PLS-PM analysis identifying primary drivers of total organic carbon (TOC) in LSSs (a), MSSs (b), and HSSs (c). Storage as affected by soil properties and C-cycling functional genes. Width of an arrow is proportional to the corresponding standardized path coefficient. The red arrow indicates positive correlation, whereas the blue arrow indicates negative correlation. The solid arrow denotes significant (p < 0.05) correlation, whereas the dashed arrow indicates insignificant (p > 0.05) correlation. The number of asterisks represent the significance threshold, with ** indicating p < 0.01 and * indicating p < 0.05. TP: total phosphorus; TN: total nitrogen; EC: electrical conductivity; FIX: carbon-fixing genes; DEG; carbon-degrading genes. Importance of variables in predicting TOC content in LSSs (d), MSSs (e), and HSSs (f). LSS: soil with low salinity; MSS: soil with light-to-medium salinity; HSS: soil with high salinity.
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Wang, Y.; Wang, M.; Wu, T.; Zhao, J.; Li, J.; Xie, H.; Wang, L.; Wu, L. Differentiated Microbial Strategies in Carbon Metabolic Processes Responding to Salt Stress in Cold–Arid Wetlands. Land 2025, 14, 1607. https://doi.org/10.3390/land14081607

AMA Style

Wang Y, Wang M, Wu T, Zhao J, Li J, Xie H, Wang L, Wu L. Differentiated Microbial Strategies in Carbon Metabolic Processes Responding to Salt Stress in Cold–Arid Wetlands. Land. 2025; 14(8):1607. https://doi.org/10.3390/land14081607

Chicago/Turabian Style

Wang, Yongman, Mingqi Wang, Tiezheng Wu, Jialin Zhao, Junyi Li, Hongliang Xie, Lixin Wang, and Linhui Wu. 2025. "Differentiated Microbial Strategies in Carbon Metabolic Processes Responding to Salt Stress in Cold–Arid Wetlands" Land 14, no. 8: 1607. https://doi.org/10.3390/land14081607

APA Style

Wang, Y., Wang, M., Wu, T., Zhao, J., Li, J., Xie, H., Wang, L., & Wu, L. (2025). Differentiated Microbial Strategies in Carbon Metabolic Processes Responding to Salt Stress in Cold–Arid Wetlands. Land, 14(8), 1607. https://doi.org/10.3390/land14081607

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