Metagenomic Analysis of the Rhizosphere Microbiome of Poa alpigena in the Qinghai Lake Basin Grasslands

Abstract

Poa alpigena Lindm is a dominant forage grass in the temperate grasslands of the Qinghai Lake Basin, commonly used for grassland restoration. Soil microorganisms are crucial in material cycling within terrestrial ecosystems. This study aimed to investigate the effects of P. alpigena on the microbial community composition and structure in rhizosphere and non-rhizosphere soils in the Qingbaya grassland area. Using high-throughput sequencing, we identified microbial gene pools and compared microbial diversity. Metagenomic analysis showed that non-rhizosphere soil contained 35.42–36.64% known microbial sequences, with bacteria making up 79.25% of the microbiota. Alpha diversity analysis indicated significantly higher microbial richness and diversity in non-rhizosphere soil, influenced by electrical conductivity, total carbon, and total nitrogen content. LEfSe analysis revealed that Alphaproteobacteria and Betaproteobacteria were major differential taxa in rhizosphere and non-rhizosphere soils, respectively. Key metabolic pathways in rhizosphere microorganisms were related to AMPK signaling, secondary metabolite biosynthesis, and starch metabolism, while non-rhizosphere microorganisms were involved in aromatic compound degradation, purine metabolism, and microbial metabolism in diverse environments. The enrichment of microbial taxa and functional pathways related to methane oxidation in rhizosphere soil suggests a potential role of P. alpigena in shaping microbial processes linked to greenhouse gas regulation, although direct evidence of methane flux changes was not assessed. Similarly, the presence of aromatic compound degradation pathways in non-rhizosphere soil indicates microbial potential for processing such compounds, but no direct measurements of specific contaminants were performed.

1. Introduction

The Qinghai Lake Basin, located in the northeastern part of the Qinghai–Tibet Plateau, is renowned for its striking landscapes and rich biodiversity, playing a crucial role in soil and water conservation [1]. The region’s unique geographical and ecological characteristics support a wide variety of life forms, yet it remains ecologically fragile [2,3]. The environmental conditions of the plateau, including fluctuations in temperature, humidity, and light [4], make soil microorganisms particularly sensitive, and changes in microbial populations can significantly impact overall biodiversity [5]. The area is home to diverse vegetation types, such as grasslands, shrubs, meadows, alpine rock vegetation, and desert landscapes, with purple needle grass prevalent in the alpine grasslands and plateau bluegrass dominating the alpine meadows [6]. However, climate change and human activities—such as overgrazing, land reclamation, infrastructure development, and tourism—have driven significant ecological changes in the Qinghai Lake Basin. These activities have altered land use patterns, shifted vegetation types, degraded soil structures, and impacted microbial communities [7,8,9]. Consequently, these changes have led to declines in pasture biomass, vegetation cover, and grassland quality, as well as the loss of plant diversity and soil degradation. Consequently, understanding the interaction between soil quality and microbial communities is crucial to addressing the vitality and sustainability of the grasslands in the region [10,11].

Soil degradation poses a growing threat to the ability of soils to support plant growth [12]. Thus, understanding the causes of these soil changes is essential for conserving the meadow ecosystem. Soil microorganisms play a key role in the functioning of ecosystems, and their biological indicators are vital for assessing soil quality and fertility [13,14]. Recent advancements in metagenomic sequencing technologies have enabled detailed studies of the diversity, abundance, and interactions of soil microorganisms with their environment [15,16]. Despite these advances, research on soil microorganisms in the meadow regions of the Qinghai Lake Basin remains limited by sampling challenges and gaps in methodology, especially regarding microbial community structure and interspecies interactions.

This study focuses on the rhizosphere and non-rhizosphere microbial communities associated with P. alpigena, a dominant and high-quality forage species in the Qingbaya area of Qinghai Lake. By utilizing metagenomic sequencing technology, this research aims to investigate the composition, structural distribution, and diversity of these soil microorganisms, as well as to examine the interactions between rhizosphere and non-rhizosphere communities. The ultimate goal is to better understand the microbial communities in the Qingbaya meadow ecosystem and their responses to environmental and biological fluctuations, contributing to the sustainable development of the Qinghai Lake region.

2. Materials and Methods

2.1. Study Area

The sampling site is located in the northwest sector of Qinghai Lake, specifically in the Qingbaya grassland area (37.05° N, 99.72° E; Figure 1). The map illustrating the location of the study area was prepared using ArcMap 10.8 (Esri, Redlands, CA, USA). This temperate grassland is situated between two mountains at an elevation of 3255 m. The region has an average annual temperature of −0.7 °C, with maximum and minimum temperatures reaching 28 °C and −31 °C, respectively. The average annual precipitation is 322.7 mm. The study area lies within the semiarid and alpine climatic zone of the plateau. The surface vegetation is primarily composed of Poa alpigena L., Stipa purpurea Griseb., Carex rigescens F.J. Herm., Leymus secalinus (L.) Hochst., Polygonum sibiricum Laxm., Allium przewalskianum Regel, and Astragalus adsurgens Pall.

2.2. Sampling Method and Treatment

Soil samples were collected from four quadrats (2 m × 2 m each) exhibiting consistent growth of P. alpigena, using the S-type five-point sampling technique. The rhizosphere soil of P. alpigena (QBG) was obtained by vigorously shaking the roots, while bulk soil (QBC) was collected from the surrounding area at a depth of 0–10 cm. Five soil samples, each weighing approximately 10 g, were collected from each rhizosphere quadrant and combined to form a pooled sample. This mixed sample was labeled QBG1 for quadrant 1, with similar samples QBG2, QBG3, and QBG4 created for the other quadrants. Bulk soil samples were collected using the same procedure.

Both rhizosphere and bulk soil samples were divided into two aliquots. One aliquot was preserved in liquid nitrogen for DNA extraction and subsequent metagenomic sequencing. DNA degradation and potential contamination were assessed via 1% agarose gel electrophoresis. DNA concentration was measured using a NanoDrop2000 (Thermo Fisher Scientific, Wilmington, DE, USA). After DNA extraction, precipitation, and purification, the samples were sent to BGI Technology Co., Ltd. (Shenzhen, China) for metagenomic sequencing on the BGI-SEQ-500 platform (BGI, Shenzhen, Guangdong, China).

The second aliquot was used for analysis of various soil properties, including water content, total nitrogen, total carbon, pH, and conductivity. Soil water content was determined using a JK-100 F moisture content analyzer (Jingke Scientific Instrument Co., Ltd., Shanghai, China), total nitrogen was measured via the Kjeldahl method, total carbon was analyzed using a CE-440 elemental analyzer (Exeter Analytical Inc., North Chelmsford, MA, USA), pH was measured with a pHS-25 m, and conductivity was assessed using a DDS-307 conductivity meter (INESA Scientific Instrument Co., Ltd., Shanghai, China).

2.3. Data Analysis

2.3.1. Quality Control of Sequencing Reads

The quality of the 150 bp reads was assessed using the FASTQC software version 0.12.1 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/, accessed on 3 January 2025). To ensure high-quality DNA sequences from soil microbial organisms, the Trimmomatic software (v3.3) was employed to remove linker sequences and low-quality base calls. The following parameters were applied: LEADING:3, TRAILING:3, SLIDINGWINDOW:4:15, HEADCROP:12, and MINLEN:36. The cleaned linker sequences used for trimming are as follows:

PrefixPE/1: AAGTCGGAGGCCAAGCGGTCTTAGGAAGACAA;

PrefixPE/2: AAGTCGGATCGTAGCCATGTCGTTCTGTGAGCCAAGGAGTTG.

2.3.2. Microbial Taxonomic Classification

The clean reads were used to classify microbial species composition using the Kraken2 software version 2.1.3 (Derrick Wood, Johns Hopkins University, Baltimore, MD, USA) [17]. Genomes of bacteria, fungi, archaea, protozoa, and viruses and the NCBI taxonomy database were downloaded from NCBI and used to build the reference database via kraken2-build. Taxonomic information at various levels (phylum, class, order, family, genus, species) was obtained through the combined use of Kraken v2 and Bracken, following the established protocol.

2.3.3. Diversity Analysis

To assess microbial diversity, the α-diversity of microorganisms was analyzed using the Vegan software version 2.6-4 (Jari Oksanen, University of Oulu, Oulu, Finland). Principal component analysis (PCA) was conducted to evaluate the β-diversity index of microorganisms.

2.3.4. Metagenomic Assembly

All clean reads were assembled into a metagenome using the Megahit software version 1.2.9 (Dinghua Li, University of Hong Kong, Hong Kong, China), with the parameters “--min-contig-len 500 --preset meta-large”. The assembly quality was evaluated using the Metaquast software version 5.2.0 (Alla Mikheenko, Saint Petersburg State University, Saint Petersburg, Russia), which compares assembly results with reference sequences and provides metrics such as contig quality and N50 values.

2.3.5. Dominant Microorganisms and Metabolic Pathway Analysis

LEfSe analysis was performed on the raw data to examine the microbial differences between groups and to identify the dominant microorganisms. KEGG pathway enrichment analysis was conducted to explore the metabolic pathways of these microorganisms.

2.3.6. Data Upload and Sequencing Accession

The metagenomic sequencing data were uploaded to NCBI (https://www.ncbi.nlm.nih.gov/sra/PRJNA867494, accessed on 3 January 2025), with the following Accession numbers:

QBG group: SRX17159225, SRX17159226, SRX17159227, and SRX17159228;

QBC group: SRX17159229, SRX17159230, SRX17159231, and SRX17159232.

2.3.7. Statistical Analysis

Pearson’s correlation coefficients were computed using the psych (v2.3.6) package in R (v4.2), with p-values adjusted using the False Discovery Rate (FDR) method during the correlation analysis. Corrections for multiple testing were applied via the Benjamini–Hochberg (BH) method. Correlation data were filtered to include only those with absolute values of |r| > 0.8 and p < 0.05 from the corrected results. The data were visualized using the Cytoscape 3.10 software, where the red lines indicate positive correlations, the blue lines indicate negative correlations, and the thickness of the lines represents the strength of the correlation. Statistical analyses and data processing were performed using SPSS 26.0.

3. Results

3.1. Heterogeneity Analysis of Physicochemical Indices in the Rhizosphere and Bulk Soil of Temperate Grassland

To gain insights into the heterogeneity of physicochemical indices in the rhizosphere soil (QBG) and bulk soil (QBC) of temperate grassland, we assessed parameters such as moisture content, total nitrogen, total carbon, pH, and electrical conductivity. As shown in Figure 2, there were distinct differences between QBG and QBC in terms of these soil properties.

The moisture content in QBG (1.54%) was lower than that in QBC (2.01%), but the difference was not statistically significant (p > 0.05) (Figure 2A). Similarly, the pH in QBG (8.13) was slightly lower than in QBC (8.48), but this difference was also not significant (p > 0.05) (Figure 2B). In contrast, electrical conductivity in QBG (0.08 mS/cm) was significantly higher than in QBC (0.04 mS/cm) (p < 0.05) (Figure 2C), indicating a higher concentration of dissolved ions in the rhizosphere soil. The total carbon content in QBG (5.03%) was significantly higher than in QBC (3.90%) (p < 0.05) (Figure 2D), suggesting that rhizosphere processes, such as root exudation and microbial activity, contribute to carbon accumulation. Similarly, the total nitrogen content in QBG (0.20%) was significantly higher than in QBC (0.11%) (p < 0.05) (Figure 2E), further supporting the role of rhizosphere processes in nitrogen enrichment.

3.2. The Microbial Composition of Soil in Temperate Grassland Alpine Poa Pratensis

The metagenomic sequencing results show that each sample’s sequencing depth exceeds 10 GB, with an average of approximately 37 million paired-end reads, indicating a high-quality sequencing effort with minimal experimental variation (

Table 1. Microbial sequencing results. Comparison of clean reads and taxonomic composition in rhizosphere and non-rhizosphere soils of P. alpigena.

Project	QBC	QBG
Number of clean reads	36,920,777.00 ± 8354.47 a	37,091,595.50 ± 836,079.71 a
Unclassified reads (%)	57.86 ± 0.57 a	55.37 ± 0.57 b
Classified reads (%)	42.15 ± 0.57 b	44.63 ± 0.57 a
Chordate reads (%)	5.97 ± 0.03 b	6.42 ± 0.62 a
Microbial reads (%)	36.03 ± 0.61 b	38.07 ± 1.15 a
Bacterial reads (%)	28.56 ± 0.59 b	30.14 ± 1.62 a
Fungal reads (%)	0.15 ± 0.001 a	0.16 ± 0.004 a
Viral reads (%)	1.33 ± 0.002 a	1.38 ± 0.045 a
Protozoan reads (%)	0.17 ± 0.002 a	0.19 ± 0.024 a

Note: the values are mean ± SD, and different letters are used to indicate the significance of various soils (p < 0.05).

). The total number of clean reads does not significantly differ between non-rhizosphere soil (QBC: 36,920,777) and rhizosphere soil (QBG: 37,091,595). However, the proportion of unclassified reads is significantly higher in QBC (57.86%) than in QBG (55.37%) (p < 0.05), whereas classified reads are significantly more abundant in QBG (44.63%) than in QBC (42.15%) (p < 0.05).

The microbial community primarily consists of bacteria, fungi, protozoa, and viruses. In QBC, microbial reads account for 36.03% of the total sequences, which is significantly lower than in QBG (38.07%) (p < 0.05). Similarly, bacterial sequences are more prevalent in QBG (30.14%) compared to QBC (28.56%) (p < 0.05). While the fungal and viral proportions do not show significant differences between the two soils, protozoan reads tend to be slightly higher in QBG (0.19%) than in QBC (0.17%), though this difference is not statistically significant (p > 0.05).

3.3. Analysis of α-Diversity of Microorganisms in Rhizosphere and Non-Rhizosphere Soils of Temperate Grassland

This study employed multiple α-diversity indices, including Chao1, Richness, Simpson, and Shannon indices, to evaluate and compare the microbial communities in rhizosphere (QBC) and non-rhizosphere (QBG) soils of the Qingbaya grassland (Figure 3). The Chao1 index, which estimates species richness, revealed significant differences between QBC and QBG (p < 0.05). The average Chao1 index for QBC was 5735, while QBG averaged 5700 (Figure 3A). This indicates slightly higher richness in QBC soils. The Richness index results aligned with the Chao1 index, showing minimal differences between QBC (average 5731) and QBG (average 5705), with no statistically significant variation observed (Figure 3B). Together, these results suggest comparable species abundance in both soil types, with a marginally higher richness in rhizosphere soils. The Simpson index, which measures community evenness, showed that QBG (0.00110–0.00115) had significantly higher evenness compared to QBC (0.00100–0.00105, p < 0.05; Figure 3C). This result indicates that microbial species in non-rhizosphere soils were more evenly distributed than those in rhizosphere soils. The Shannon index, which evaluates both richness and evenness, was computed using bases of 2, e, and 10. For all bases, the Shannon index values were slightly higher in QBC compared to QBG, with averages of 11.25, 7.800, and 3.390 for QBC, and 11.15, 7.775, and 3.375 for QBG, respectively (Figure 3D–F). The differences were statistically significant (p < 0.05), indicating higher microbial diversity in rhizosphere soils.

3.4. Analysis of β-Diversity of Microorganisms in Rhizosphere and Non-Rhizosphere Soils

The results of the principal component analysis (PCA) show a significant separation between rhizosphere soil (QBC) and non-rhizosphere soil (QBG) microbial communities along the primary dimension (PC1), which explains 97.0% of the variation in the data (Figure 2F). This indicates significant differences in the composition or functional characteristics of the microbial communities in the two soil types. In contrast, along the secondary dimension (PC2), which explains 2.7% of the variation, there is considerable overlap between the two groups, with no significant differences observed. Furthermore, based on the confidence ellipses of sample distributions, QBC samples exhibit a wider distribution range, suggesting that rhizosphere soil is influenced by multiple factors, such as plant exudates and microbial activity, resulting in higher within-group variability. In contrast, QBG samples are more narrowly distributed, indicating greater stability in non-rhizosphere soil. These findings suggest that the differences in microbial communities between rhizosphere and non-rhizosphere soils are primarily driven by variables associated with PC1.

3.5. Dominant Microorganisms in Rhizosphere and Bulk Soils of Temperate Grassland

Through rigorous statistical analysis, we identified the dominant microbial taxa in the sampled soils. At the phylum level (Figure 4A), the 15 most abundant phyla in both rhizosphere and non-rhizosphere soils were analyzed. Proteobacteria was the most dominant, with a relative abundance of 49.98% in non-rhizosphere soil and 45.59% in rhizosphere soil. Actinobacteria followed as the second most abundant phylum, comprising 37.18% and 41.55% in non-rhizosphere and rhizosphere soils, respectively. Other relatively abundant phyla, ranked in descending order, included Firmicutes, Acidobacteria, Cyanobacteria, Gemmatimonadetes, Euryarchaeota, Verrucomicrobia, Deinococcus-Thermus, Chloroflexi, Thaumarchaeota, Ascomycota, and Nitrospirae.

At the genus level (Figure 4B), the 15 most prevalent genera across both soil types, listed in order of decreasing abundance, were Streptomyces, Nocardioides, Sphingomonas, Bradyrhizobium, Pseudomonas, Conexibacter, Mycobacterium, Mesorhizobium, Mycolicibacterium, Burkholderia, Microbacterium, Amycolatopsis, Variovorax, Sorangium, and Rubrobacter. Streptomyces was the most abundant genus, accounting for 7.23% in non-rhizosphere soil and 7.19% in rhizosphere soil, followed by Nocardioides, with relative abundances of 2.46% and 1.92%, respectively.

3.6. Dominant Microbial Taxa in Rhizosphere and Non-Rhizosphere Soils

To better understand the microbial community composition in rhizosphere and non-rhizosphere soils, LEfSe analysis was performed, identifying taxa with significant differences based on a linear discriminant analysis (LDA) score threshold of >3.0 (Figure 5). The results revealed that non-rhizosphere soil exhibited significantly higher abundances of the following microbial taxa: Pseudomonadaceae, Burkholderia, Pseudomonas, Burkholderiaceae, Gammaproteobacteria, Burkholderiales, Betaproteobacteria, and Proteobacteria. In contrast, rhizosphere soil showed significantly greater abundances of the following taxa: Chitinophagaceae, Chitinophagia, Rubrobacterales, Rubrobacteria, Nocardioides, Bacteroidetes, Sphingomonas, Propionibacteriales, Nocardioidaceae, Alphaproteobacteria, Sphingomonadaceae, and Sphingomonadales.

3.7. Differential Metabolic Pathways Between Rhizosphere and Bulk Soil Microorganisms

To analyze the key metabolic pathways differentiating rhizosphere (QBC) and bulk soil (QBG) microorganisms, the LEfSe software version 1.1.2 (Nicola Segata, University of Trento, Trento, Italy) was used. The original gene abundance data were processed with the Salmon software version 1.10.2 (Rob Patro, Stony Brook University, Stony Brook, NY, USA), quantifying the number of metabolic pathways. Only pathways with a linear discriminant analysis (LDA) score > 2.0 are shown in Figure 6. A total of 43 differential metabolic pathways were identified, with 21 enriched in QBG and 19 in QBC.

In the QBG group, significant pathways included the following: glucagon signaling, AMPK signaling, secondary metabolite biosynthesis, starch and sucrose metabolism, pentose and glucuronate interconversions, porphyrin and chlorophyll metabolism, chemical carcinogenesis, antigen processing and presentation, carotenoid biosynthesis, nonribosomal peptide structures, relaxin signaling, flavone and flavonol biosynthesis, JAK-STAT signaling, siderophore group nonribosomal peptide biosynthesis, mRNA surveillance, antifolate resistance, and MAPK signaling.

In the QBC group, key pathways enriched included the following: dioxin degradation, inositol phosphate metabolism, ethylbenzene degradation, phosphotransferase system (PTS), furfural degradation, carbon fixation in photosynthetic organisms, fluorobenzoate degradation, toluene degradation, butanoate metabolism, chlorocyclohexane and chlorobenzene degradation, sulfur relay system, aminobenzoate degradation, sulfur metabolism, benzoate degradation, arginine and proline metabolism, biofilm formation in Escherichia coli, phenylalanine metabolism, glycine, serine, and threonine metabolism, methane metabolism, meiosis in yeast, degradation of aromatic compounds, biofilm formation in Vibrio cholerae and Pseudomonas aeruginosa, purine metabolism, two-component systems, and microbial metabolism in diverse environments.

The metabolic pathways of rhizosphere and bulk soil microorganisms exhibit significant differences, highlighting metabolic adaptations in the rhizosphere microbial communities. In the rhizosphere, two enriched pathways—“Mismatch repair” and “Homologous recombination”—belong to the “Genetic Information Processing” category, crucial for DNA replication and repair during bacterial cell division. In “Carbohydrate Metabolism”, rhizosphere microorganisms were enriched in “Starch and sucrose metabolism” and “Carotenoid biosynthesis”, while bulk soil microorganisms were enriched in “Photosynthesis” and “Glycolysis/Gluconeogenesis”. This suggests differences in energy cycling and substrate utilization between the two environments. Additionally, the rhizosphere exhibited enrichment in the “Peptidoglycan biosynthesis” pathway, vital for bacterial cell wall synthesis, with a related pathway, “Vancomycin resistance”, also more prevalent. This indicates high metabolic activity near plant roots. In contrast, bulk soil microorganisms were enriched in pathways associated with environmental adaptation, including “Chloroalkane and chloroalkene degradation”, “Benzoate degradation”, “Aminobenzoate degradation”, “ABC transporters”, “Degradation of aromatic compounds”, and “Microbial metabolism in diverse environments”. These pathways are linked to the degradation of organic pollutants, underscoring the influence of environmental factors on microbial community functions.

3.8. Correlation Analysis of Rhizosphere and Bulk Soil Microbial Communities

To investigate the interrelationships among microorganisms, this study examined the 26 most prominent soil microorganisms associated with both the rhizosphere and non-rhizosphere in the Qingbayahuo region, as illustrated in Figure 7. At the genus level, Azospirillum—a group of nitrogen-fixing spiral bacteria—was identified as having the strongest correlation, followed by Actinoplanes, motile actinobacteria, and Cupriavidus, known for its copper affinity. The genera were ranked in order of correlation from highest to lowest as follows: Burkholderia, Paraburkholderia, Pseudomonas, Variovorax, Nocardioides, Sphingomonas, Corynebacterium, Rhizobium, Mesorhizobium, Paenibacillus, Baekduia, Bradyrhizobium, Massilia, Sphingopyxis, Sphingobium, Nocardia, Microbacterium, Methylobacterium, Rhodococcus, Streptomyces, Amycolatopsis, Conexibacter, and Micromonospora. The figure’s deep red and light blue connecting lines represent positive and negative correlations, respectively, among the soil microorganisms. For example, Sphingobium and Sphingomonas show a mutually beneficial relationship and Rhizobium and Paenibacillus exhibit a positive correlation, while Azospirillum and Rhodococcus are negatively correlated.

4. Discussion

4.1. Differential Analysis of Rhizosphere Microorganisms of P. alpigena in the Temperate Grassland of Qinghai Lake

Different plants exhibit significant variations in their rhizosphere microbial communities, highlighting the co-evolutionary relationship between plants and microorganisms [18]. Quattrone et al. highlight that these microbes contribute to plant resilience by facilitating nutrient uptake and supplying essential nutrients, thereby reinforcing a mutually beneficial symbiotic relationship [19]. Root exudates play a critical role in shaping the structure and diversity of microbial communities [20], favoring specific populations and leading to lower diversity in the rhizosphere compared to non-rhizosphere soils. For instance, Ran et al. found that in Eucommia ulmoides, microbial abundance was higher in the rhizosphere, which also exhibited faster carbon source utilization. Rhizosphere soil tends to be richer in nutrients and shows greater evenness. Additionally, roots support the metabolic activities of microorganisms in deeper soil layers, enhancing organic matter decomposition and increasing microbial metabolic rates [21]. LEfSe analysis reveals significant species variation among soil microorganisms associated with P. alpigena in the temperate grasslands of the Qinghai Lake Basin, with Proteobacteria enriched in non-rhizosphere soil and Sphingomonadales more abundant in the rhizosphere.

Investigations by Li et al. suggest that Firmicutes and Proteobacteria play a key role in denitrification, facilitating biological nitrogen removal, while certain genera of Nitrospirales exhibit nitrification capabilities. These intricate interactions enable plants to utilize ammonia nitrogen produced by soil microbes, resulting in lower nitrogen content in rhizosphere soil compared to non-rhizosphere soil, reflecting the selective interactions between plants and beneficial microorganisms [22]. The phylum Proteobacteria encompasses a diverse array of vital strains involved in several key ecological processes, such as carbon and nitrogen cycling, suppression of plant pathogenic fungi, and the establishment of plant symbiosis [23]. For example, species within the genus Pseudomonas significantly enhance nitrogen utilization efficiency in plants, leading to increased biomass in both aboveground and root structures. Proteobacteria are characterized by their high abundance in both rhizosphere and non-rhizosphere soils, correlating with elevated carbon content. This suggests that the distribution of Proteobacteria is closely influenced by the availability of organic matter. Interestingly, despite the substantial release of root exudates in the rhizosphere, non-rhizosphere soils often exhibit a relatively high abundance of these bacteria. Rhizosphere exudates are significantly involved in regulating the nitrogen (N) cycle and facilitating belowground chemical communication between plants and soil microbes [24]. However, increased competition in the rhizosphere, where specialized microbial communities outcompete Proteobacteria for available resources, may explain their relatively lower abundance in this zone.

Furthermore, Actinobacteria are renowned for their multifaceted roles, including disease resistance and growth promotion [25,26]. A higher presence of Actinobacteria is often associated with a reduced risk of root infections by fungal pathogens, thereby enhancing soil health [27,28]. Noteworthy genera, such as Streptomyces and Nocardia, are known for producing bioactive compounds, including antibiotics and plant growth hormones, which support healthy plant development and increase resilience against environmental stresses [29].

4.2. The Influence of Rhizosphere and Non-Rhizosphere Soils on Microbial Metabolism

The KEGG enrichment analysis results reveal significant enrichment of specific pathways in rhizosphere soil microorganisms, particularly the glucose signaling pathway and the AMPK signaling pathway. These pathways are closely linked to nutrient metabolism and energy conversion processes [30]. Moreover, the AMPK signaling pathway serves as a critical regulatory mechanism for glucose and various other metabolic substrates. Rodriguez-Concepcion et al. discovered that modulating the AMPK signaling pathway can effectively enhance glucose consumption [31]. This pathway plays a vital role in regulating the metabolism of energy substrates, including glucose, and its enrichment suggests that plants require substantial energy, likely in response to environmental stressors.

In addition, the enrichment of pathways associated with the biosynthesis of secondary metabolites, starch and sucrose metabolism, and the conversion of pentoses and glucuronic acid indicates that soil microorganisms are actively involved in dry matter accumulation and energy metabolism. A number of genes involved in chlorophyll synthesis are encompassed within the porphyrin and chlorophyll metabolism pathways, playing a crucial role in regulating chlorophyll biosynthesis. Furthermore, the promotion of carotenoid biosynthesis can significantly enhance plant survival and resilience to environmental stresses [32,33]. The JAK-STAT signaling pathway and the MAPK signaling pathway are key signal transduction systems in living organisms, orchestrating various aspects of cellular growth and developmental differentiation [34,35].

4.3. Soil Microbial Correlation Analysis

Soil microorganisms engage in complex interactions that can either promote or inhibit each other, enhancing ecological adaptability through mechanisms of competition, symbiosis, or mutualistic relationships [36]. The results of this study highlight key microbial associations within the soil microbiome of P. alpigena, revealing both positive and negative correlations among various genera. For instance, the genus Sphingomonas forms a synergistic relationship with Sphingobium, with a positive correlation observed between Rhizobium and Bacillus. Conversely, a negative correlation is noted between Nostoc and Rubrivivax. Notably, Sphingomonas enhances the abundance of Sphingobium, likely due to its unique metabolic regulatory mechanisms that enable it to thrive in nutrient-deficient environments. This adaptability allows Sphingomonas to modulate its growth in response to various adverse environmental conditions, thus benefiting the overall microbial community [37]. Sphingobium, in turn, is recognized for its remarkable capacity to degrade environmental pollutants, utilizing a wide range of substrates while maintaining resilience in nutrient-poor soils. These two genera contribute to ecological balance by promoting plant health and supporting microbial community dynamics, both through direct interactions and the enhancement of population abundance.

The positive correlation between Rhizobium and Bacillus underscores a mutualistic relationship that benefits both genera. Extensive studies have shown that, in addition to its nitrogen-fixing capabilities, Rhizobium plays a pivotal role in enhancing soil stability and promoting soil remediation [38,39,40]. For example, Huang et al. demonstrated that Rhizobium interacts with various soil microorganisms, influencing the composition of microbial communities in the rhizosphere and promoting the growth of bacteria essential to the nitrogen cycle [41]. This interaction further emphasizes the significance of Rhizobium in altering soil microbial diversity and contributing to improved plant health. Meanwhile, Bacillus functions as both a microbial fertilizer and a biopesticide, sustaining plant growth and protecting crops from a range of diseases [42,43]. The co-inoculation of Bacillus and Rhizobium has been shown to improve soil fertility, increase soil biomass, and enhance microbial community structure. These findings are consistent with the results of a metagenomics-based study of the rhizospheric microorganisms of P. alpigena in Qinghai Lake, Ganzi River Plateau, which revealed complex microbial relationships characterized by mutual facilitation or inhibition [44]. The interactions between these genera highlight the importance of soil microbial diversity and the symbiotic relationships that contribute to the overall health and resilience of the ecosystem.

5. Conclusions

This study examined the microbial composition and functional characteristics of rhizosphere and non-rhizosphere soils of P. alpigena in the Qinghai Lake Basin using metagenomic sequencing. The results revealed significant differences in soil properties, microbial diversity, and metabolic functions between the two soil types. Rhizosphere soil exhibited higher total carbon and nitrogen contents, likely due to root exudates and microbial activity, while non-rhizosphere soil had greater microbial richness and diversity. PCA confirmed distinct microbial community compositions, primarily shaped by root–microbe interactions. Proteobacteria and Actinobacteria were dominant in both soils, with Alphaproteobacteria enriched in rhizosphere soil and Betaproteobacteria in non-rhizosphere soil. Functional analysis showed that rhizosphere soil favored pathways related to secondary metabolite biosynthesis and carbohydrate metabolism, whereas non-rhizosphere soil was enriched in pathways for aromatic compound degradation and microbial metabolism in diverse environments. The presence of microbial taxa and functional pathways related to methane oxidation in rhizosphere soil suggests a potential role of P. alpigena in shaping microbial processes linked to greenhouse gas regulation. However, direct evidence of methane flux changes was not assessed in this study, and further research is required to confirm this ecological function. Similarly, the enrichment of aromatic compound degradation pathways in non-rhizosphere soil indicates the potential for microbial processing of such compounds, but no direct measurements of specific contaminants were performed in this study. Future studies incorporating soil contaminant analysis are needed to verify the presence and ecological implications of these pathways. This study enhances our understanding of plant–soil–microbe interactions in temperate grasslands, providing insights for ecosystem conservation and management.