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From grasslands to genes: exploring the major microbial drivers of antibiotic-resistance in microhabitats under persistent overgrazing

Microbiome volume 12, Article number: 245 (2024) Cite this article

Abstract

Background

The extensive use of antibiotics in the global livestock industry in recent decades has accelerated the accumulation and dissemination of antibiotic-resistance genes (ARGs) within terrestrial ecosystems. This occurs due to the limited absorption of most antibiotics, leading to their release into the environment through feces and urine. This poses a significant threat to both the environment and human health. However, the response of antibiotic-resistant microorganisms and their ARGs in grasslands to prolonged grazing, as well as the primary microbial taxa driving the ARG distribution, remain poorly understood, especially within various microhabitats. In this study, we characterized ARGs in the phyllosphere, litter, and soil after decades of livestock grazing in a meadow steppe. We particularly focused on identifying the major members of the microbial community influencing ARGs and the distinction between microbial generalists and specialists.

Results

Our findings indicate that a core set of ARGs accounted for 90% of the abundance in this plant-soil ecosystem. While the soil exhibited the highest ARG abundance, the phyllosphere, and litter displayed higher ARG diversity and diverse distribution patterns after overgrazing. Grazing increased ARG abundance by elevating the proportion of core ARGs and suppressing stochastic ARGs in the phyllosphere and litter, while it had little effect on the ARGs in the soil. Additionally, microbial generalist abundance increased, but specialist abundance decreased in the phyllosphere and litter, with no effect in the soil, under grazed conditions. Ultimately, microbial microhabitats and grazing influenced ARG community characteristics through direct (i.e., feces and other exogenous ARG input) and indirect (i.e., trampling and selective feeding) effects on nutrient availability, microbial community composition, and mobile genetic elements. The generalist community, with its broad ecological niches and phylogenetic composition, made the most significant contribution to the ARG characteristics.

Conclusions

This study underscores the impact of environmental disturbances on the distributional patterns of ARGs in ecosystems, mediated by the regulation of microbial generalized species. These insights enhance our understanding of microbial control over ARGs and facilitate predictions regarding the dynamics and risk of ARGs in diverse ecological niches subjected to anthropogenic disturbances.

Video Abstract

Introduction

In recent decades, the widespread overuse and sustained release of antibiotics have significantly accelerated the occurrence and dissemination of antibiotic resistance genes (ARGs), which has led to the rapid proliferation of ARGs in the environment [1,2,3,4]. Beyond compromising the efficacy of antibiotic treatment, the spread of ARGs carries additional potential negative effects, including allergic reactions and the toxic effects of prolonged exposure for humans and livestock [5,6,7,8]. Consequently, the World Health Organization (WHO) has identified antimicrobial resistance as one of the greatest threats to health and food security, garnering increasing attention from the general public and researchers alike [1, 9]. Notably, increasing amounts of antibiotics in livestock production for disease prevention and treatment have become a significant concern [1, 10]. However, the incomplete absorption (10–70%) of these extensively used antibiotics, coupled with their excretion into the terrestrial ecosystem through livestock feces and urine, exacerbates the emergence of drug antibiotic resistance in environmental microbiomes [11, 12]. Consequently, livestock farming has been recognized as a prevalent source of environmental antibiotic-resistant microorganism pollution [13,14,15]. Many microcosm experiments have consistently demonstrated that the application of livestock manure significantly increases ARG abundance and strongly affects their distribution pattern in soil [6, 14, 16]. Despite these findings, the majority of these ARG studies have primarily involved culture experiments conducted in controlled environments like greenhouses and agroecosystems, with limited research focused on natural ecosystems. Although research on the effects of grazing on ARG has received greater emphasis in recent years [10, 13, 17], there is a paucity of reports suitable for evaluating the spread of ARGs in natural grassland ecosystems subject to anthropogenic disturbance.

The concentration of ARGs in the soil is gradually increasing due to livestock husbandry practices and manure application, although a level of antibiotic resistance is ancient in the natural environment [9, 14, 18]. Meanwhile, recent studies have revealed the plant phyllosphere as an equally vital repository of environmental ARGs, deriving from soil or airborne diffusion [19, 20]. The vast expanse of leaf surface, which is cumulatively estimated to exceed 109 km2, is one of the largest microbial pools on Earth [19,20,21]. Moreover, the plant phyllosphere has been identified as a facilitator of conjugative plasmids, amplifying the risk of spreading antibiotic resistance [22, 23]. Therefore, a more comprehensive exploration of phyllosphere ARGs is essential for gaining insights into the distribution and hazards associated with ARGs in the environment. Livestock grazing not only introduces antibiotic-resistant microorganisms directly into the soil through livestock excreta but also leads to the colonization of the phyllosphere by antibiotic-resistant microorganisms from manure, facilitated through bioaerosols and plant-soil interactions [9, 10, 13]. The response of ARG profiles to grazing in the soil and the phyllosphere may differ significantly due to the considerable variation in habitat types and microbial community compositions between the phyllosphere and the soil [24, 25]. Despite this, existing studies investigating the effects of grazing on the grassland microbiome and resistance groups have predominantly focused on soil, omitting plant considerations. Notably, comparative studies encompassing all these microbial pools and microhabitats remain absent from the current literature.

There is growing evidence that supports the categorization of microbial species into generalists and specialists, based on their life strategies and adaptability to environmental conditions [26,27,28]. These microbial archetypes, generalists and specialists, often exhibit distinct functional traits and responses to environmental disturbance [29,30,31]. Generalists, thriving across a wide range of habitats, demonstrate heightened resistance owing to their environmental tolerance. In contrast, specialists, restricted to specific habitats, prove vulnerable to environmental changes due to their narrower environmental tolerance [26, 31]. Traditionally, the prevailing perspective posits that the extensive distribution and robust population densities of generalists render them more influential in shaping ecosystem function and stability [28, 32]. Conversely, specialists, characterized by narrower ecological niches and lower population abundance, exhibit a more sensitive response to environmental disturbance [27, 30, 31]. Meanwhile, microbial community composition has been regarded as having a pivotal role in structuring resistomes, thus changes in the relative abundance of different microbes can strongly affect ARG abundance [10, 13, 33]. However, our knowledge of the specific contributions of different microbes, such as generalists and specialists, to the distribution of ARGs under environmental disturbances (e.g., grazing) remains limited.

The Songnen grassland, covering an area of approximately 17.0 × 106 ha, constitutes a prominent grassland in northeast China, facing severe degradation due to decades of relentless overgrazing by livestock [34, 35]. Meanwhile, local herders extensively employ veterinary antibiotics for the prevention and treatment of livestock diseases. To examine the impacts of prolonged grazing on resistome characteristics across diverse microhabitats, we performed an in situ experiment in both grazed and ungrazed (fenced) fields within this region. Our investigation, inclusive of microbial generalist and specialist communities, aimed to further explore the relative contributions of different microbes in regulating the distributional patterns of ARGs. Given the recognized potential of livestock as a significant source of ARGs and its role as a potent environmental filter, we hypothesized that grazing would increase the overall abundance of ARGs due to the introduction of antibiotic-resistance microbiomes and antibiotic residues from animal feces. Furthermore, we hypothesized that the response of ARGs to grazing would vary across microhabitats due to disparities in microenvironmental and microbial characteristics among them. Given the profound impact of grazing on microbial community composition and functions, we hypothesized that microbial generalists, due to their greater environmental tolerance and broader functional capabilities, would have a more substantial role than specialists in influencing the distribution of ARGs. Overall, we postulate that ecosystem-level stress induced by prolonged grazing establishes distinct microbial communities in various microhabitats, subsequently shaping the distributional patterns of ARGs. By examining how long-term grazing disturbance changes the microbial community and ARG distribution pattern in a degraded grassland, our work would contribute valuable insights to contamination risk assessment databases for ARGs and contribute to developing management strategies in grazing systems.

Materials and methods

Study site and sample collection

This study was performed in a meadow steppe at the Songnen Grassland Ecosystem National Research Station (123°44′–123°47′ E, 44°40′–44°44′ N), with an average altitude of 140–160 m. The region has a temperate continental sub-humid semi-arid monsoon climate, with a mean annual temperature of 6.1 °C. The mean annual precipitation is 350–450 mm, with most precipitation occurring between June and August [36]. The study area is a lowland area with mainly alkaline meadow soils and saline soils with a pH ranging from 7.5 to 10.5. Soil salts are mainly carbonate (Na2CO3) and sodium bicarbonate (NaHCO3) with sodium sulfate (Na2SO4) and sodium chloride (NaCl). The soils are poor in nutrients, and the nitrogen and phosphorus concentrations (0-10 cm) are approximately 0.75 and 0.35 g kg−1, respectively. The organic matter content is approximately 6.9–8.6 g kg−1 (Table S1) [34, 36]. The study area has been overgrazed since the 1980s and partly fenced to exclude grazing since 1993, for grassland protection and restoration. The dominant plants in both grazed and ungrazed areas are Leymus chinensis, accompanied by Phragmites australis, Kochia sieversiana, Chloris virigata, Puccinellia tenuiflora, and Artemisia anethifolia [34, 37].

Samples were collected during the plant growing season in late August 2021 from four pairs of randomly set 50 m × 50 m plots, including grazed and ungrazed (fenced) treatments. Within each grazed and ungrazed plot, three 2 m × 2 m quadrants were randomly selected with a distance of at least 10 m for samples. Composite samples from 3 microhabitats (phyllosphere, litter, and soil) were randomly collected from each quadrat. As a result, a total of 72 samples were collected (4 plots × 2 grazing treatments × 3 microhabitats × 3 replicate quadrats = 72 total samples). The soil at a depth of 10 cm (2.5 cm diameter) was taken from five randomly selected sites in each sample quadrat and mixed into one sample. Simultaneously, composite fresh leaf and litter samples were randomly collected within the same sample quadrat. Intact and mature plant leaves were randomly and carefully collected using sterilized scissors from many different plants in each sample quadrat and immediately placed in a sterilized plastic bag, according to previous studies [38, 39]. The collected leaf, litter, and soil samples were immediately transported to the laboratory in ice boxes. Upon arrival at the laboratory, part of the samples was stored at 4 °C for microbial DNA extraction within a few days. The remaining samples were weighed before and after freeze-drying, and used to determine elemental concentrations by inductively coupled plasma-optical emission spectrometry (ICP-OES) and elemental analyzer (NCS 2500, Carlo Erba Instruments, Milan, Italy). Nutrient concentration and water content were summarized in Table 1.

Table 1 Sample nutrient content in different microhabitats between the grazed and ungrazed plots
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DNA extraction, sequencing, and high-throughput quantitative PCR (HT-qPCR)

Microbial DNA was extracted from phyllosphere, litter, and soil samples using a DNA kit (MP Biomedical, Santa Ana, CA, USA) [39, 40]. Fifteen grams of fresh leaf or litter composite samples from each quadrat were placed in a flask containing 100 ml of 0.01 M phosphate-buffered saline. Following one hour of shaking at 180 rpm, the suspension was filtered through a 0.22-μm filter membrane (each membrane for one sample) with a diameter of 55 mm. Subsequently, the filter membrane was cut into small pieces using sterilized scissors. Microbial DNA was extracted from both the filter membranes and soil samples according to the instructions in the DNA kit. The quality and concentration of the extracted DNA were then assessed through agarose gel electrophoresis and a NanoDrop ND-2000 UV–vis spectrophotometer (NanoDrop ND-1000, Thermo Scientific, Waltham, MA, USA), respectively.

The V4–V5 region of 16S rRNA was amplified with primers F515 and R907. Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). After demultiplexing, the resulting sequences were merged with FLASH (v1.2.11) and quality filtered with fastp (0.1.9.6). Then the high-quality sequences were de-noised using the DADA2 plugin in the Qiime2 pipeline with recommended parameters, which obtains single nucleotide resolution based on error profiles within samples. DADA2-denoised sequences are usually called amplicon sequence variants (ASVs). To complete downstream microbial diversity and composition analyses, we removed the sequences of chloroplast and mitochondrial ASVs, and thereafter, amplicon sequence variants (ASVs) were generated, and sequence data were rarefied to the lowest number per sample to ensure sufficient sequencing coverage based on rarefaction curves, which still yielded an average Good’s coverage of 97.90%. The taxonomy of amplicon sequence variants (ASVs) was analyzed by QIIME2 against a 16S rRNA database (Silva v138).

The diversity and abundance of ARGs were determined by the high-throughput qPCR using a SmartChip Real-time PCR system (TakaraBio, Shiga, Japan) as described previously [20, 41,42,43], with primers targeting 45 mobile genetic elements (MGEs), 331 ARGs, and one 16S rRNA gene (Table S2 and S3). The PCR amplification program was initial enzyme activation at 95 °C for 10 min, 40 cycles of denaturation at 95 °C for 30 s, and annealing at 60 °C for 30 s, with the fluorophore SYBR® Green I (IN, USA). The spurious results were removed based on the pre-set default amplification efficiency. Each sample was evaluated with three technical replicates, and the detection threshold cycle (CT) for successful amplification was set at 31, based on previous studies [8, 16]. The relative abundance of ARGs was calculated according to published papers and expressed as copies per 16S rRNA gene [21, 44].

$$\text{Relative gene copy number}={10}^{(31-{C}_{T})/(10/3)}$$

Definition of core, common, and stochastic ARGs

ARGs were classified into core, common, and stochastic categories based on their frequency [20, 45]. Core ARGs were defined as those detected in more than 80% of the samples, while common and stochastic ARGs were identified in more or less than 50% of the samples, respectively.

Statistical analysis

Microsoft Excel was used for the preliminary statistical analysis and all data were appropriately standardized and transformed to meet the needs of subsequent analyses if necessary. Microbial generalists and specialists in the microbial community were identified based on the Levins’ niche breadth (B) index with 1000 permutation algorithms using EcolUtils v0.1 (https://github.com/GuillemSalazar/EcolUtils). Based on the ASV information, microbial alpha diversity indices, including community richness (Sobs) and diversity (Shannon diversity index), were calculated using Mothur after eliminating the sequences of chloroplast and mitochondrial ASVs. Differences in microbial community and ARGs properties were determined using a mixed-effect model of the form: lme (response variable ~ grazing *microhabitat, random = ~ 1|blocks/pairs, data = da) (models included interactions between grazing and microhabitat as fixed effects, and random intercept terms for plots and quadrats within plots, with Tukey’s post-hoc tests, using R package “nlme”. Principal coordinate analysis (PCoA) and analysis of similarities (ANOSIM) were used to test the differences in ARGs and microbial community composition between treatments, using R packages “vegan” and “ggplot2”. Microbial community dissimilarities and composition heterogeneity were assessed based on the dissimilarity of the Bray–Curtis distance metric to characterize community traits using the R packages “vegan” and “ggpubr”. Spearman correlation, Mantel test, and Procrustes test were used to determine the correlation between ARGs, microbial community, and nutrient concentration, performed on the Majorbio Cloud Platform (https://cloud.majorbio.com/page/tools/). Co-occurrence network analysis was analyzed using molecular ecological network analysis, with default settings (MENA; http://ieg4.rccc.ou.edu/mena/) to determine the internal community relationships between ARGs and microbial community [46, 47]. A combined approach of redundancy analysis (RDA) and variation partitioning analysis (VPA) was used to explore the factors affecting ARGs, with analyses performed on the Majorbio Cloud Platform (https://cloud.majorbio.com/page/tools/). Structural equation modeling (SEM), using AMOS graphic (IBM, USA), was further conducted to identify the direct and indirect effects of nutrient concentration, MEGs, and microbial generalist and specialist communities on ARGs. We first transformed and standardized the individual parameters using Z-score with SPSS (IBM Corp, Armonk, NY, USA). The standardized parameters were entered into the AMOS graphic application. All the figures were generated on the Majorbio Cloud Platform (https://cloud.majorbio.com/page/tools/) and R Version 4.2.3 [48].

Results

ARG abundance and profiles

A total of 165 ARGs and 26 MGEs were detected across all samples. Grazing significantly increased the ARG abundance in the phyllosphere and litter (p < 0.05), but had no effect on ARG abundance in the soil (p > 0.05), although soil had the highest ARG abundance (Fig. 1A). In contrast, the diversity of ARGs was diminished in the phyllosphere and litter under grazed treatment and decreased from the phyllosphere to the soil microhabitat (Fig. 1B, p < 0.05). Meanwhile, the count of detected ARGs was not affected by grazing across microhabitats, and exhibited a decreasing trend from the phyllosphere to the soil, with mean numbers of 92, 84, and 70, respectively (Fig. 1C). In addition, MGEs were notably influenced by microhabitat and grazing, displaying a similar increasing trend from the phyllosphere to the soil (Fig. 1D, p < 0.05).

Fig.1
figure 1

Characteristics of antibiotic resistance genes (ARGs). A Detected number, B abundance, and C diversity of ARGs, and D mobile genetic elements (MGEs) abundance among different microhabitats and grazing treatments. E, F, G, and H Principal coordinate analysis (PCoA) of ARGs. Different lower and upper case letters indicate significant differences at p < 0.05 level among different microhabitats in grazed and ungrazed treatments, respectively. * indicates significant differences at p < 0.05 level between grazed and ungrazed plots

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In the present study, ARGs were classified into ten categories according to their associated drug class, as described previously [16]. Among these, Multi-drug resistance genes were the predominant ARGs, accounting for 85% of the total ARGs. Aminoglycoside resistance genes accounted for 7%, Tetracycline resistance genes for 2%, and Phenicol resistance genes for 1% (Figure S1). Furthermore, efflux pump (85%) was the most important mechanism involved in antibiotic resistance across all determined ARGs, followed by antibiotic inactivation (10%) and cellular protection (5%) (Figure S1). Microhabitats demonstrated significant effects on the relative proportions of drug classes and mechanisms, and grazing similarly influenced their relative proportions in the phyllosphere. However, grazing exhibited no discernible impacts on these proportions in litter or soil (Figure S1).

The PCoA analysis revealed that the first and second principal components accounted for 87% of the total variation in ARGs among all samples (Fig. 1E). The Analysis of Similarity (ANOSIM) test demonstrated a significant difference in ARG profiles among microhabitats. The grazing treatment significantly altered the ARG profiles in the phyllosphere and litter but had no discernible effect on the ARG profiles in the soil (Fig. 1F–H).

Core, common, and stochastic ARGs

There were 7 to 15 unique ARGs detected in the phyllosphere, litter, and soil under grazed and ungrazed treatments, respectively (Fig. 2A). While 118 shared ARGs were found across microhabitats, accounting for 72% of the total ARGs detected, and 7, 5, and 1 unique ARGs were detected in the phyllosphere, litter, and soil, respectively (Fig. 2A).

Fig. 2
figure 2

The distribution of antibiotic resistance genes (ARGs). A Shared and unique ARGs in different microhabitats and grazing treatments. B Core, common, and stochastic ARGs in different microhabitats and grazing treatments. The area plots display the percentage changes

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The core ARGs included 57 ARGs that were detected in more than 80% of all samples, accounting for 89% to 99% of the total ARG abundance in each sample (Fig. 2B). A total of 72 ARGs were detected in more than 50% of the samples (common ARGs), accounting for more than 99% of the total ARGs detected abundance and at least 96% of the total ARG abundance in each sample (Fig. 2B). In contrast, 93 ARGs were detected in less than half of the samples (stochastic ARGs). These stochastic ARGs accounted for less than 1% of the total ARGs detected abundance and were suppressed under the grazed treatment in phyllosphere and litter (Fig. 2B).

Habitat generalist and specialist communities

A total of 9295 ASVs were presented throughout the entire dataset. Our results show that microbial community dissimilarity and composition heterogeneity were depressed by grazing in the phyllosphere and litter, but increased in the soil (Fig. 3A). In addition, the diversity and richness of the whole microbial community increased from the phyllosphere to the soil, and grazing treatment showed a positive effect on the microbial diversity and richness in the phyllosphere and litter but not in the soil (Table 2 and Figure S2). Similarly, the complexity and connectivity of the microbial co-occurrence network increased from the phyllosphere to the soil (Table S4). A total of 177 identified microbe species were classified as generalists, accounting for 18% of the average relative abundance in the whole community. The relative abundance of generalists significantly increased from the phyllosphere to the soil, and grazing treatment had a boosting effect on the relative abundance of generalists in the phyllosphere and litter but not in the soil (Fig. 3B). In contrast, 229 identified microbe species were classified as specialists, with the relative abundance of specialists significantly decreasing from the phyllosphere to the soil, and significantly reducing under the grazed treatment in the phyllosphere and litter, but not in the soil (Fig. 3B). Furthermore, both the microhabitats and grazing treatment had a significant effect on the diversity and richness of the generalist and specialist communities (Table 2), and generalists showed a higher habitat niche breadth than specialists (Figure S3). The PCoA analysis revealed that microhabitats and grazing also played a significant role in shaping different community compositions and profiles for generalists and specialists (Fig. 3C, D, and Figure S4). The relative abundance of Proteobacteria in generalists was highest in the phyllosphere and decreased undergrazing across microhabitats. In contrast, the proportion of Actinobacteriota increased after grazing (Fig. 3).

Fig. 3
figure 3

Characteristics of microbial generalist and specialist communities. A The whole microbial community dissimilarity and composition heterogeneity. B Relative abundance of generalist and specialist communities, C Principal coordinate analysis (PCoA) of generalist and specialist communities, D Composition of generalist and specialist communities in different microhabitats and grazing treatments. Different lower and upper case letters indicate significant differences at p < 0.05 level among different microhabitats in grazed and ungrazed treatments, respectively. * indicates significant differences at p < 0.05 level between grazed and ungrazed plots

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Table 2 Generalist and specialist community richness and diversity index in different microhabitats between the grazed and ungrazed plots
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Co-occurrence patterns between ARGs, microbial community, and nutrient concentration

Our results showed that ARG abundance was negatively correlated with ARG richness and diversity, and ARG richness decreased with the increasing abundance of generalists (Figure S5 and Figure S6). The correlation analysis further showed that ARGs and MGE abundances were positively and negatively related to the abundance of generalist and specialist communities, respectively (Fig. 4A, B). Both the diversity and richness of the whole microbial community were positively correlated with ARG and MGE abundances (Fig. 4C and Figure S7A). In addition, the Procrustes test and Mantel test found that ARG profiles were significantly correlated with generalist and specialist community composition (Fig. 5A, B). Co-occurrence network analysis between ARGs, MGEs, generalist, and specialist communities showed a close interaction between them, and generalists had more connected edges with ARGs and MGEs than specialists did (Fig. 5C and Table S5). The abundance of ARGs showed a significant positive correlation with the abundance of MGEs (Figure S7B, R2 = 0.81, p < 0.001). Furthermore, generalist community characteristics (including relative abundance, community diversity, and richness) were positively correlated with nutrient concentrations, but the ARGs and specialist community characteristics were negatively correlated with nutrient concentrations (Figure S8, Figure S9, and Figure S10).

Fig. 4
figure 4

Spearman correlation between antibiotic resistance genes (ARGs) and microbial community characteristics. The correlation between the abundance of A ARGs and B mobile genetic elements (MGEs) and the relative abundance of generalist and specialist communities. C The correlation between the abundance of ARGs and the whole microbial community diversity and richness

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Fig.5
figure 5

Relationship between antibiotic resistance genes (ARGs) and microbial generalist and specialist communities. A, and B Procrustes and Mantel test between ARGs and generalist and specialist communities. C Co-occurrence networks analysis between ARGs, generalist, and specialist communities

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To further investigate the drivers of the abundance and profiles of ARGs, we used RDA, VPA, and SEM to analyze the relationship between ARGs, nutrient concentration, and generalist and specialist communities (Fig. 6). RDA analysis revealed that 54% of the ARG profile could be explained by the sample nutrient concentration, MGEs, and generalist and specialist community composition (Fig. 6A). VPA results further showed that 84% of the variance in ARG profiles could be explained by these four group factors (Fig. 6B). SEM results suggested that microhabitat and grazing could, directly and indirectly, influence the abundance of ARGs through a complex interplay with MGEs, nutrient concentration, and generalist and specialist communities (Fig. 6C). Notably, generalists exerted a more substantial effect on ARG abundance than specialists.

Fig. 6
figure 6

Potential drivers of antibiotic resistance genes (ARGs). A Redundancy analysis (RDA) of the correlation between ARGs, generalist and specialist communities, nutrient content, and mobile genetic elements (MGEs). B Variation partitioning analysis (VPA) differentiates generalist and specialist communities, nutrient content, and MGEs effects on the ARGs profiles. C Structural equation model predictions of microhabitats, grazing, nutrient content, microbial generalist and specialist communities, and MGEs effect on ARGs. Dashed lines indicate a lack of effect, and solid lines indicate a significant effect. Line thickness and asterisk indicate: *p < 0.05, **p ≤ 0.01, and ***p ≤ 0.001

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Discussion

Grazing increased the ARG abundance depending on the microhabitats

We investigated the ARGs in the phyllosphere, litter, and soil subject to long-term livestock grazed and ungrazed areas in a grassland ecosystem. Our findings revealed the widespread colonization of these natural ecosystems by antibiotic-resistant bacteria, predominantly harboring ARGs associated with human pathogens. The coexistence of abundant MGEs suggests that ARGs can undergo horizontal gene transfer within ecosystems, posing potential threats to human health [2, 49, 50]. The results also show that more than 70% of the identified ARGs were concurrently presented in the various microhabitats, as well as between grazed and ungrazed treatments. ARG abundance in phyllosphere and litter was suppressed under the grazed treatment. The core ARGs accounted for more than 90% of total ARG abundance within each niche. These results imply the existence of a core set of antibiotic-resistant microorganisms through continuous intense grazing by human livestock, underscoring their crucial role in resistance dynamics [20, 45]. In addition, the identification of overlapping ARGs across the phyllosphere, litter, and soil suggests widespread dissemination of antibiotic-resistant microorganisms in plant-soil ecosystems. This dissemination was likely facilitated by constant microbial exchange within the ecosystem, including processes such as soil particle splash and leaf fall [7, 16, 49].

Beyond the ARGs shared among various microhabitats and treatments, our findings indicate that the phyllosphere and litter had more unique ARGs and higher ARG diversity compared to the soil. This reinforces the understanding that plant leaves inherently possess an antibiotic resistome, solidifying their role as significant environmental reservoirs of ARGs in ecosystems [7, 19]. This knowledge is vital for establishing a baseline of antibiotic resistance groups before assessing the spread of ARGs through plants. The divergent characteristics of ARGs between microhabitats may be due to the fact that the phyllosphere is an oligotrophic and fluctuating microbial habitat, leading to the formation of unique and diverse microbiomes, and harboring multiple antibiotic-resistant microbes within the stressful environment [51, 52]. Moreover, the soil is recognized as a reservoir of plant microbiomes, and antibiotic-resistant microorganisms can be transferred to the exposed phyllosphere habitat through a variety of potential pathways, including atmospheric deposition, rainfall, and aerosols [19, 25]. For instance, airborne particles carry a wide variety of ARGs, and the air is an essential source of phyllosphere ARGs through atmospheric deposition [53, 54]. Thus, the higher diversity of ARGs in the phyllosphere compared to other microhabitats may result from a combination of habitat variation and the complexity of the phyllosphere microbial sources. In contrast, the higher abundance of ARGs in the soil suggests that soil is still one of the most significant environmental reservoirs and origins of ARGs [12, 18], which may be because the soil is a more favorable habitat for abundant microbial growth compared to leaves [6, 55]. As anticipated, our findings reveal distinct profiles of ARGs across the phyllosphere, litter, and soil, signifying that antibiotic-resistant microbial communities differ in response to their microhabitats and substrate availability [9, 16, 20].

It was proposed that an increased prevalence of ARGs may increase health risks, thus giving the tendency of ARGs and pathogenic genomes to co-evolve [1, 56, 57]. Therefore, the increased abundance of ARGs in the phyllosphere and litter in the grazed treatment of our study indicates that grazing may increase the potential health risks from ARGs. Meanwhile, we found that grazing increased the abundance of core ARGs but depressed the stochastic ARGs, and the abundance of ARGs was negatively correlated to their richness and diversity. These results indicate that livestock grazing introduces and fosters the accumulation of more ARGs, concurrently acting as an environmental stressor that alters the composition of ARGs in local regions [9, 14, 56]. This is understandable because antibiotic residues present in livestock manure may retain antimicrobial activity, thereby promoting the proliferation of ARGs [6, 14]. Moreover, livestock grazing, serving as an environmental stressor, can significantly affect grassland ecosystem microbial biodiversity and composition through various pathways, including manure deposition, trampling, and selective foraging effect on the nutrient status and physicochemical properties of the pant-soil system [58, 59]. However, the increase in ARG abundance under the grazed treatment was not in the soil microhabitat. Similar results have been found in recent studies showing that the impact of manure on ARGs is more pronounced in the phyllosphere than in the soil, and grazing exerts no discernible effect on soil antibiotic resistance in different grassland types [3, 10, 13]. The different responses of ARGs to grazing in various microhabitats could be attributed to the inherent competition and cooperation of microbial communities [13, 60]. Considering that soil is one of the largest reservoirs of natural microorganisms, ARG communities within it are shaped by the inherent mechanisms governing interactions between microbiomes [14, 18, 61]. As a result, the endemic microbial community in the soil may hinder the proliferation of antibiotic-resistant microorganisms through limitations on the dissemination of ARGs, stemming from higher microbial abundance, diversity, and interactions between different microbial communities [9, 62]. Correspondingly, we found a relatively high microbial diversity and richness in the soil compared to the phyllosphere and litter. In addition, the results of microbial co-occurrence network analysis show that the connection was higher in the soil than in the phyllosphere and litter, further supporting the greater interactions within the microbial community in the soil than in the phyllosphere and litter. An alternative explanation of the grazing effect on the ARGs in the phyllosphere and litter is that the plant communities may change as a result of the selective feeding of livestock. Plants possess the capacity to attract and enlist diverse microbial populations based on the growth requirements and physiological properties of the microbes [63, 64]. Distinct plant species might attract varying microorganisms or exert selection pressure owing to variations in leaf structure, indirectly affecting the abundance of plant resistomes by altering plant-associated microbiomes [20, 40, 65]. Furthermore, the higher frequency of conjugative plasmid transfer caused by the formation of cell aggregates in plant leaves may also contribute to the greater sensitivity of ARGs in the phyllosphere and litter [22, 23].

Generalists play a critical role in determining the ARGs

In this study, we examined the effect of grazing on microbial generalist and specialist communities within the phyllosphere, litter, and soil. Recognizing that the variation of microhabitats and external disturbance could drive the community composition and ecological functions of different microbial taxa [27, 30, 31], our results show significant changes in the profiles of generalist and specialist communities due to different microhabitats and grazing treatments. Substrate nutrient availability is one of the key factors regulating the survival of different microbial communities [66, 67]. Our study revealed significant differences in nutrient concentrations across microhabitats and grazing treatments, closely correlated with the community properties of generalist and specialist communities. Notably, the abundance of generalists increased, while the abundance of specialists decreased in the order phyllosphere > litter > soil. This pattern may be attributed to the relative stability of the soil microhabitat, creating a more favorable environment for the growth and development of generalists possessing competitive advantages [55, 68]. Moreover, grazing induces an increase in the abundance of generalist species but a reduction in specialist species under the impacted phyllosphere and litter, as generalists demonstrate stronger anti-interference capabilities than specialists due to environmental stressors caused by livestock grazing [26, 28]. Additional grazing-associated effects, such as changes in plant community composition, biomass, and nutrient availability, further contribute to the changes in the distribution of diverse microbial taxa in the phyllosphere and litter [59, 69].

We further analyzed the role of generalists and specialists in influencing antibiotic resistomes. The results reveal a significant negative correlation between the abundance of ARGs and specialists, while a positive relationship was observed with the abundance of generalists. The co-occurrence network correlation, recognized as one of the effective ways of tracking potential ARG hosts, provides insights into the transfer potential of ARGs among microorganisms [56, 70]. Our research demonstrates that the connectivity between generalists and ARGs surpasses that between specialists and ARGs, aligning with our hypothesis that microbial generalists contribute more than specialists in driving ARG abundance. We speculate that the mechanisms may be related to the differences in ecological niches, reflecting distinct survival strategies concerning resource (including the available substrates and nutrients) competitiveness, and environmental adaptation across various habitats and disturbances [30, 71]. Generally, microbes with broader ecological niches exhibit greater metabolic plasticity and enhanced interspecific competitiveness [30, 72]. Given that antibiotics are the product of natural competition within different microbiomes [14, 61], generalists with expansive ecological niches emerge as key contributors to shaping the ARGs. A growing number of studies have found that microbial habitat generalists contribute more to performing ecosystem functions than microbial specialists [27, 29, 31]. An additional potential explanatory mechanism could be horizontal gene transfer facilitated by MGEs, which is a recognized mechanism for the transmission of ARG, playing a critical role in microbial antibiotic resistance [17, 73]. Our results also revealed a significant positive correlation between ARG abundance and MGE abundance, as well as a positive association between the abundance of generalists and MGEs. The close connection between generalists and MGEs, suggests that generalists with broader ecological niches may have increased opportunities to acquire ARGs through MGEs. Therefore, generalists with broader ecological niches might have increased opportunities to acquire ARGs via MGEs. However, it should be noted that the observed correlation between ARGs and MGEs also might be due to they have a common source, such as animal feces.

Microbial phylogeny has been regarded as a pivotal role in structuring microbial resistomes, thus changes in microbial diversity can strongly affect the ARGs-bearing microorganisms [10, 13, 33]. Consistent with this, our results reveal significant positive correlations between the abundance of ARGs and microbial diversity and richness. Previous studies have also suggested that microbial community composition is a primary determinant of environmental ARGs [13, 14, 33]. For instance, Actinobacteria, known for a higher genetic potential for antibiotic production, houses numerous multi-resistant ARGs [74, 75]. Notably, in this study, a relatively high proportion of Actinobacteria was identified among generalists compared to specialists. However, interpreting microbial community structure as a regulatory factor for ARGs requires caution, as this correlation can be intricate and influenced by various factors, including the physicochemical properties of the habitat [4, 40]. Microbial habitat characteristics may indirectly affect the abundance and distribution of ARGs by influencing the frequency of horizontal gene transfers in microbial communities [13, 19]. Nutrient availability can also strongly influence the distribution of environmental ARGs through indirect effects on microbial community structure and diversity [14, 76]. Accordingly, significant correlations between nutrient concentrations and microbial community properties were found in this study. Despite considering these potential regulators and employing structural equation modeling for further analysis, it remains noteworthy that the largest contribution to the ARGs community is attributed to the microbial generalists.

Conclusion

In this study, we characterized microbial generalists, specialists, ARGs, and their potential drivers across diverse microhabitats subjected to decades of overgrazing. The abundance of generalists increased, while that of specialists decreased, following the order: phyllosphere > litter > soil. Grazing further intensified this pattern by augmenting generalist abundances and diminishing specialist abundances in the phyllosphere and litter, while it left the abundances of both generalists and specialists in the soil unaffected. Moreover, grazing increased the overall abundance of ARGs by elevating ARG abundance through an increase in the proportion of core ARGs and a suppression of stochastic ARGs. This influence of grazing extended to the ARG profiles in the phyllosphere and litter, while it exhibited no effect on either the abundance or profiles in the soil. Furthermore, shifts in microhabitats and grazing treatment significantly influenced the profiles of generalist and specialist microbial communities. Ultimately, microbial habitat and grazing practices emerged as influential factors in shaping environmental ARGs through direct and indirect effects on nutrient availability, various microbes, and MGEs. Remarkably, the microbial generalist community stood out as the primary contributor to these factors. These novel insights provide valuable support for predicting the risk and dynamics of ARGs within microhabitats experiencing anthropogenic disturbance.

Data availability

No datasets were generated or analysed during the current study.

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Funding

This work was supported by the National Natural Science Foundation of China (42021005, 42477231), Ningbo S&T project (2021-DST-004), the Alliance of International Science Organizations (Grant No. ANSO-PA-2023–18), the National Natural Science Foundation of China (42207262, 41977201, 42277110, 32101438), the Fujian Provincial Natural Science Foundation of China (2024J01197), the Natural Science Foundation of Jilin Province (YDZJ202201ZYTS486), the Science and Technology Project of the Jilin Provincial Education Department (JJKH20211300KJ), and the Basic Scientific Research Funds for Natural Science (2412022ZD029). Josep Peñuelas and Jordi Sardans were funded by the Spanish Ministry of Science (grants PID2020115770RB-I, PID2022-140808NB-I00, and TED2021-132627B-I00 funded by MCIN, AEI/https://doiorg.publicaciones.saludcastillayleon.es/10.13039/501100011033 and European Union NextGenerationEU/PRTR), the Catalan government (grant SGR2021-1333) and the Fundación Ramón Areces (grant CIVP20A6621).

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Authors and Affiliations

  1. Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China

    Jian Li, Mingkang Jin, Lijie Huang, Xiaoru Yang & Yong-Guan Zhu

  2. Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, 315830, China

    Jian Li, Xiaoru Yang & Yong-Guan Zhu

  3. Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot, 010021, China

    Quanhui Ma & Yu Zhu

  4. Institute of Grassland Science, Key Laboratory of Vegetation Ecology, Ministry of Education/Jilin Songnen, Grassland Ecosystem National Observation and Research Station, Northeast Normal University, Changchun, 130024, China

    Quanhui Ma & Ling Wang

  5. School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, 071002, China

    Lijie Huang

  6. Department of Biological Sciences, Tennessee State University, Nashville, TN, 37209, USA

    Dafeng Hui

  7. Global Ecology Unit, CSIC, CREAF-CSIC-UAB, Bellaterra, Barcelona, Catalonia, 08193, Spain

    Jordi Sardans & Josep Peñuelas

  8. CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, 08193, Spain

    Jordi Sardans & Josep Peñuelas

  9. Centre for Global Food and Resources, University of Adelaide, Adelaide, 5005, Australia

    Patrick O’Connor

  10. State Key Laboratory of Black Soils Conservation and Utilization & Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China

    Yu Zhu

  11. State Key Lab of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Yong-Guan Zhu

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  1. Jian Li
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  2. Quanhui Ma
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Contributions

JL: Investigation, Data curation, Methodology, Writing - original draft, review & editing, Funding; Q-HM: Investigation, Data curation, Methodology, Writing - review & editing, Funding; M-KJ: Data curation, Methodology; L-JH: Data curation, Methodology, D-FH: Writing - review & editing; JS: Writing - review & editing; JP: Discussion of methods and results, Writing - review & editing; PC: Writing - review & editing; YZ: Investigation, Data curation, Methodology and Writing - review & editing; X-RY: Writing - review & editing, Funding; LW: Methodology, Writing - review & editing; Y-GZ: Methodology, Writing - review & editing, Funding, Supervision.

Corresponding authors

Correspondence to Jian Li or Yu Zhu.

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Supplementary Material 1: Figure S1. Bar chart of Kruskal-Wallis test results of antibiotic resistance genes (ARGs) classified based on their suggested drug class and mechanism in different microhabitats and grazing treatments. *: p < 0.05; **: p < 0.01; ***: p < 0.001. Figure S2. Diversity and richness of the whole microbial community among different microhabitats and grazing treatments. Different lower and upper case letters indicate significant differences at p< 0.05 level among different microhabitats in grazed and ungrazed treatments, respectively. * indicates significant differences at p < 0.05 level between grazed and ungrazed plots. Figure S3. Habitat niche breadth of generalist and specialist communities. Different letters indicate significant differences at p < 0.05 level. Figure S4. Principal coordinate analysis (PCoA) of generalist and specialist communities among different microhabitats and grazing treatments. Figure S5. Spearman correlation correlations between antibiotic resistance genes (ARGs) abundance and their diversity and richness. Figure S6. Spearman correlation correlations between diversity and richness of antibiotic resistance genes (ARGs) and relative abundance of generalist and specialist communities. Figure S7. (A) Spearman correlation correlations between mobile genetic elements (MGEs) and the whole microbial community diversity and richness. (B) Correlation between ARGs and mobile genetic elements (MGEs). Figure S8. Spearman correlations between nutrient content and generalist community relative abundance, community diversity and richness. Figure S9. Spearman correlation correlations between nutrient content and specialist community relative abundance, community diversity, and richness. Figure S10. Spearman Correlation correlations between nutrient content and relative abundance of antibiotic resistance genes (ARGs). Table S1. Basic soil properties in the sampling site. Table S2. List of primer pairs used in the present study. Bold font indicates the primer pairs with positive results. Table S3. List of primer pairs of mobile genetic elements (MGEs) in the present study. Bold font indicates the primer pairs with positive results. Table S4. Microbial co-occurrence network properties in phyllosphere, litter, and soil. Table S5. Microbial co-occurrence network properties between antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and generalist and specialist communities.

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Li, J., Ma, Q., Jin, M. et al. From grasslands to genes: exploring the major microbial drivers of antibiotic-resistance in microhabitats under persistent overgrazing. Microbiome 12, 245 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40168-024-01965-z

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Keywords

Microbiome

ISSN: 2049-2618

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