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Niche differentiation and biogeography of Bathyarchaeia in paddy soil ecosystems: a case study in eastern China

Environmental Microbiome volume 19, Article number: 13 (2024) Cite this article

Abstract

Bathyarchaeia (formerly Bathyarchaeota) is a group of highly abundant archaeal communities that play important roles in global biogeochemical cycling. Bathyarchaeia is predominantly found in sediments and hot springs. However, their presence in arable soils is relatively limited. In this study, we aimed to investigate the spatial distributions and diversity of Bathyarchaeia in paddy soils across eastern China, which is a major rice production region. The relative abundance of Bathyarchaeia among total archaea ranged from 3 to 68% in paddy soils, and Bathy-6 was the dominant subgroup among the Bathyarchaeia (70–80% of all sequences). Bathyarchaeia showed higher migration ability and wider niche width based on the neutral and null model simulations. Bathy-6 was primarily assembled by deterministic processes. Soil pH and C/N ratio were identified as key factors influencing the Bathyarchaeia composition, whereas C/N ratio and mean annual temperature influenced the relative abundance of Bathyarchaeia. Network analysis showed that specific Bathyarchaeia taxa occupied keystone positions in the archaeal community and co-occurred with some methanogenic archaea, including Methanosarcina and Methanobacteria, and ammonia-oxidizing archaea belonging to Nitrososphaeria. This study provides important insights into the biogeography and niche differentiation of Bathyarchaeia particularly in paddy soil ecosystems.

Introduction

Archaea drives a series of global biogeochemical cycling of carbon and nitrogen [1,2,3]. Bathyarchaeia belongs to the kingdom archaea, which was initially discovered in hot springs. They were previously placed in the Miscellaneous Crenarchaeotal Group (MCG) [4, 5]. Bathyarchaeia has been found in various other environments, including sediments, volcanic mud, termite guts, bioreactors, and soils [6,7,8,9,10,11]. Bathyarchaeia is highly abundant in marine sediments, making them one of the most abundant groups of microorganisms on the earth [12, 13]. However, previous studies on Bathyarchaeia have mainly focused on sediments, whereas the distribution of Bathyarchaeia in arable soils is not much studied.

To date, pure cultures of Bathyarchaeia have not been successfully isolated. However, cultivation-independent studies suggest that this group of organisms possesses high physiological and metabolic diversity [14]. Members of Bathyarchaeia can grow on different substrates, such as detrital proteins, polymeric carbohydrates, fatty acids, methane, and other organic matter based on genome sequences [15,16,17]. Four genomes of Bathyarchaeia were reconstructed from White Oak River sediments. They contained genes encoding enzymes involved in acetogenesis using the reductive acetyl-CoA pathway, indicating an anaerobic lifestyle [16]. Furthermore, some Bathyarchaeia members are likely to perform dissimilatory nitrite reduction to ammonium [16], and a possible role in methane metabolism has also been suggested [15]. A previous study reported that supplementing rice paddy soil with fulvic acid significantly increased the relative abundance of Bathyarchaeia [18]. Therefore, Bathyarchaeia may play a role in the biodegradation of humus, which is abundantly present in paddy soils due to the slow microbial decomposition of plant and animal residues under flooding conditions. Paddy soil is an active zone of global carbon and nitrogen cycling. Therefore, studying the distribution and activity of Bathyarchaeia can be important for food production and climate change regulation.

Previous phylogenetic studies have classified Bathyarchaeia into 25 subgroups [13, 19], and different subgroups exhibit different ecological functions and distribution. Therefore, elucidating the mechanisms underlying Bathyarchaeia biogeography and community assembly in paddy soils can help predict corresponding ecological processes. Generally, the microbial community assembly can be described using the Niche-based theory or Neutral-based theory [20, 21]. Niche-based approaches consider that the community structure is influenced primarily by deterministic processes such as environmental filtering and species interactions [22, 23]. For instance, previous studies have revealed that the specific Bathyarchaeia subgroups show niche differentiation and exhibit different habitat preferences. Members of Bathy-6 grow in suboxic zones and sulfide-depleted shallow layers of sediments, whereas members of Bathy-8 prefer deeper and anoxic layers [24]. Furthermore, Bathy-8 is considered an indicator of saline environments [24], whereas Bathy-11 and Bathy-5 are indicators of freshwater environments [25]. Moreover, salinity and total organic matter (TOC) are crucial factors affecting the abundance and composition of the Bathyarchaeia community [8, 10, 11]. The neutral theory hypothesizes that all individuals are ecologically identical, and the community structure is primarily influenced by stochastic processes such as random death and dispersal [26, 27]. Stochastic processes play crucial roles in influencing microbial community structures in various environments [21, 28]. However, the assembly processes of the Bathyarchaeia community have garnered less attention in arable soils.

A recent global meta-analysis reported that Bathyarchaeia is globally distributed in paddy soils with high abundance, and the predominant subgroup is Bathy-6 [29]. The meta-analysis showed that the mean annual precipitation and the mean annual temperature could be associated with the relative abundance of Bathyarchaeia and Bathyarchaeial community structure, respectively [29]. However, this meta-analysis had some limitations, such as the limited availability of soil physicochemical parameters data and distribution. Therefore, studying niche differentiation governed by soil type-related factors was difficult. Furthermore, it is important to note that this meta-analysis could potentially be affected by various additional factors associated with soil management practices, including irrigation, anthropogenic interventions like flooding, the specific growth stages of rice, and the absence of uniform approaches for soil sampling procedures (e.g., sampling depth), DNA extraction techniques, and primer selection for sequencing. Such disparities can introduce potential biases into the results. Consequently, our approach involved the sampling of paddy soils from contrasting pedoclimatic regions across eastern China, all at the same stage of rice growth. We then conducted an examination of their taxonomic composition through Illumina sequencing of the 16 S rRNA genes. The primary objectives of this study encompassed characterizing the composition and diversity of Bathyarchaeia in paddy soils across eastern China, exploring the mechanisms governing the assembly of Bathyarchaeia communities in paddy soil, and delving into the differentiation of ecological niches and potential ecological functions of Bathyarchaeia within paddy soils.

Materials and methods

Soil sampling and property measuring

Paddy soil samples (n = 57) with different chemical characteristics were collected from 19 sites (3 samples/site) in June 2020 across eastern China (Fig. 1a), which are the main rice-producing areas in China. While sampling, the paddy soils were flooded for over a month, with rice plants at the tillering stage. Three composite samples were collected from each site, indicating three replicates. These composites were created by combining five surface (< 10 cm) paddy soil cores. The samples were kept under ice and transported to the lab. Each soil sample was divided into the following two aliquots: one was freeze-dried for DNA extraction, and the other was stored at 4 °C for further soil chemical analysis.

Fig. 1
figure 1

Paddy soils selected for the study. Map showing the geographical location of all sampling sites across eastern China (a). The color of the points represents the pH of the soil samples. pH of all the paddy soil samples (b)

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Soil chemical parameters were evaluated, as reported previously [30]. Soil pH was detected with air-dried soil at a water ratio of 1:2.5 using a pH meter. Total carbon (TC) and total nitrogen (TN) were analyzed using an Elemental Analyzer (Vario EL, Elementar, Germany), and the resulting data was used for the calculation of the C/N ratio. Soil organic matter (SOM) was measured using a Muffle furnace (VULCAN 3–1750 A, NEYTECH, America). Nitrate (NO3–) and ammonium (NH4+) were analyzed using a Continuous Flow Analytical System (AA3, SEAL, Germany). Total iron (Fe) and manganese (Mn) analyses were performed using an Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES). The physicochemical properties of the soils are shown in Table S1. The soil properties differed significantly among different sites; for instance, the soil pH ranged from 4.8 to 8.1 (Fig. 1b), the C/N ratio ranged from 8.6 to 19.8, and the SOM content ranged from 2.81 to 39.37% (Table S1). Climatic indexes of different sites were obtained from the WorldClim database (www.worldclim.org), and the mean annual temperature (MAT) differed from 6.2 to 21.4 °C.

DNA extraction and Illumina sequencing of the 16 S rRNA gene

DNA was extracted following the manufacturer’s protocol using the FastDNA SPIN kit (MP bio, CA, USA). The DNA was dissolved in 100 µL of sterilized deionized water and stored at − 18°C until further analysis. NanoDrop (ND-1000, USA) was used to detect DNA concentration and OD260/OD280. For the Illumina sequencing, the archaeal 16S rRNA genes were amplified using the primer pair 524F10extF/Arch958RmodR (5’-TGYCAGCCGCCGCGGTAA-3’/5’-YCCGGCGTTGAVTCCAATT-3’) [31], with 2 µL of DNA samples used as the template. Purified amplicons were sequenced using Illumina MiSeq PE300 (Illumina, San Diego, USA) by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).

Statistical analyses

Raw sequence data were analyzed using the R 4.0.1 platform using “dada2” packages for the analysis of the 16 S rRNA gene sequence (https://benjjneb.github.io/dada2/tutorial.html) [32]. Briefly, the adapters and primer sequences were first removed from raw sequence data using “cutadapat.” Moreover, clean sequences underwent trimming and merging. Amplicon sequence variants (ASVs) were derived following the removal of chimeric sequences, and their categorization was achieved using the Silva database release 138 to attain taxonomic insights [33, 34]. The ASV table was subsampled to the minimum requisite sequence count for subsequent statistical assessments. Calculation of α-diversity (Shannon and Chao1 indices) was executed with the “microeco” and “vegan” packages [35, 36]. The α-diversity and community composition visualizations were produced using Origin 2020 and the “ggplot2” packages in R [37]. Non-metric multidimensional scaling (NMDS) based on Bray-Curtis distances was performed using “micreco” packages to visualize the similarity between samples.

To analyze the community composition of Bathyarchaeia, a phylogenetic tree was constructed employing reference sequences from a prior study to classify the Bathyarchaeia subgroup [13]. The outgroup sequences belonged to Crenarchaeum (Cenarchaeum symbiosum) and Nitrosoarchaeum (Nitrosoarchaeum koreensis). These reference sequences encompassed 15 Bathyarchaeial subgroups [13]. ASVs affiliated with Bathyarchaeia, as per the Silva 138 database, were also selected. The construction of the phylogenetic tree was executed within the MEGA11 platform [38]. The alignment of all sequences was performed using ClustalW, and the Maximum Likelihood tree was employed for the construction, with a Bootstrap analysis (1000) being carried out to evaluate tree topology [13]. Based on the tree, the subgroup information of Bathyarchaeial ASVs was obtained and used for downstream statistical analyses. ArcMap software was used to predict and visualize the large-scale distribution pattern of Bathy-6 across eastern China paddy soils for the analysis of predictive atlas maps. The Kriging interpolation method was used to estimate the relative abundance of Bathy-6 across the whole map after the input of site information, including geographical coordinates and the relative abundance of Bathy-6. Further, the predictive maps were obtained using a province mask. For the heatmap of Bathyarchaeial ASVs, the figure was constructed using Evolview [39].

To investigate the determinism and stochasticity in influencing archaeal and Bathyarchaeial community structure, the Sloan neutral community model (NCM) was used to determine the effect of stochasticity on the archaeal and Bathyarchaeial community assembly using the “Hmisc” package [40, 41]. The “spaa” package was used to evaluate the width and overlap of the niche [42]. A cognitive assessment was employed to ascertain the connection between environmental factors and microbial communities with the utilization of the “linkET” package [43]. Structural equation modeling (SEM) was employed to quantify the direct and indirect influences of environmental factors on the shaping of both the archaeal and Bathyarchaeial communities, utilizing SPSS and AMOS software. To elucidate the correlational association between environmental factors and the relative abundance of Bathyarchaeial subgroups, Pearson’s correlation analysis was conducted through the “microeco” packages [35]. The graphical representations were generated using Origin 2020. For the co-occurrence network analysis, Spearman’s correlation coefficients between ASVs were initially calculated through the “microeco” packages on the R platform. The Spearman’s correlation threshold was set at a coefficient > 0.7 or < -0.7 with a significance level of p < 0.01. Subsequently, the networks were visualized using Gephi software.

Availability of data and materials

The 16 S rRNA gene sequence data is deposited at the GenBank with BioProject accession number PRJNA1023015.

Results

Archaeal community composition and diversity

Archaeal α-diversity (Shannon and Chao1 indices) varied among different soil types (Fig. S1, p < 0.05). Generally, soils with medium pH (6.0 < pH < 7.0) showed higher archaeal α-diversity than soils with pH over 7.5. Archaeal community composition also differed between the different sites (Fig. S1). Crenarchaeota was the most abundant in most samples at the phylum level, accounting for 20–70% of total archaea, followed by Halobacterota (1–70%) as the second most abundant phylum (Fig. S2). The relative abundance of Euryarchaeota and Thermoplasmatota was 2–33% and 5–20%, respectively. Asgardarchaeota and Micrarchaeota were also detected in some samples with lower relative abundance. Bathyarchaiea and Nitrososphaeria were the two most dominant classes in most samples at the class level (Fig. 2a). However, the relative abundance of Bathyarchaeia differed drastically among different sites, ranging from 3 to 68% of total archaea. Methanosarcina and Methanobacteria were also detected at relatively high abundance in most samples, which belong to Halobacterota and Euryarchaeota, respectively.

Fig. 2
figure 2

The composition and diversity of archaea and Bathyarchaeia community in paddy soils. The composition of the archaea community at class level (a). Phylogenetic tree of Bathyachaeial amplicon sequence variants (ASVs) (b). The star represents Bathyarchaeial ASVs from paddy soils in this study. The composition of the Bathyarchaeial subgroup in paddy soils (c). The heatmap of Bathyarchaeial ASVs (d). The 40 top abundant Bathyarchaeial ASVs were selected. The size of bubbles represents the relative abundance of ASVs. The color of the bubbles represents the pH of the soil samples. The color of ASV ID represents the subgroup of Bathyarchaeia

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Distribution and diversity of Bathyarchaeia in paddy soils

The α-diversity of Bathyarchaeia in various paddy soils exhibited significant differences among distinct paddy soils, with the Shannon index ranging from 2.16 to 5.63 (refer to Fig. S3, p < 0.05). Notably, soils characterized by a pH > 7.5 displayed a reduced Bathyarchaeial α-diversity when compared to soils with a pH < 7.5. Furthermore, the NMDS analysis indicated that Bathyarchaeia within paddy soils clustered distinctly based on the sampling sites and pH (see Fig. S3).

Five subgroups (Bathy-6, Bathy-11, Bathy-15, Bathy-17, and Bathy-5bb) were identified within all the paddy soils (Fig. 2b). Bathy-6 constituted the predominant subgroup across most samples, accounting for 70–80% of the total Bathyarchaeia (as depicted in Fig. 2c). Conversely, Bathy-11, Bathy-17, and Bathy-5bb were detected as less prevalent groups in numerous samples. It is noteworthy that paddy soils in Southeast China exhibited a higher relative abundance of Bathy-6 in comparison to their counterparts in Northwest China (Fig. S4).

The heatmap, based on the 40 most abundant Bathyarchaeial ASVs, was generated with a focus on soil pH (as demonstrated in Fig. 2d). Out of the 40 most abundant Bathyarchaeial ASVs, 36 ASVs were affiliated with Bathy-6, while three ASVs were associated with Bathy-15, and one ASV was linked to Bathy-17. Furthermore, soil pH had a substantial impact on the distribution of these dominant ASVs (see Fig. 2d). Notably, two ASVs (ASV1970 and ASV1998) affiliated with Bathy-6 were present at elevated levels in all the samples. Nevertheless, most other ASVs were solely detected in samples with acidic soil conditions (pH < 7.0).

Bathyarchaeial community assembly in paddy soils

The NCM results at ASV levels indicated that stochastic processes were dominant in archaea community assembly, with an R2 value of 0.613 (Fig. 3a). We found that 86.2% of archaeal ASVs and 87.0% of Bathyarchaeial ASVs fitted to the neutral model, indicating that a majority of archaeal ASVs assembled in paddy soils following stochastic processes. However, the neutral model fitted ASVs accounted for only 40.9% and 47.0% relative abundance of total archaeal and Bathyarchaeial community, respectively. Approximately 12.6% of Bathyarchaeial ASVs deviated from the neutral expectation, accounting for more than 45.0% relative abundance. These results indicate that most low-abundance Bathyarchaeia assembled following stochastic processes, whereas high-abundance Bathyarchaeia mainly assembled following deterministic processes. This phenomenon can be further supported by the assembly process of the highest abundant Bathy-6 (Fig. 2), exhibiting a higher percentage of the deterministic process (about 38.5%) than total archaea (13.8%) and total Bathyarchaeia (13%, Fig. 3b).

Fig. 3
figure 3

Community assembly progress of archaea and Bathyarchaeia in paddy soils. Neutral community model (NCM) of archaea (a). The solid blue lines mean the best fit to the NCM, and the dashed blue lines represent fit to the model under 95% confidence intervals. Nm means the metacommunity size times immigration, and R2 indicates the fit to this model. The proportions of richness (Amplicon sequence variants [ASV] number) and abundance (sequence number) of archaea and Bathyarchaeia based on the fitting to the model (b). Null model of Niche width of archaea at class level (c)

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Among the 13% of Bathyarchaeial ASVs that diverged from neutral expectations, 12.6% were more frequently observed than anticipated by the NCM prediction, indicating that they exceeded the neutral prediction. ASVs exceeding the prediction are believed to possess greater migratory capabilities and a heightened ability to relocate to new habitats. Notably, Bathy-6, as previously mentioned, was the most prevalent Bathyarchaeial subgroup and exhibited a higher proportion of ASVs surpassing the NCM prediction compared to other archaea and Bathyarchaeia. This suggests that Bathy-6 may have more robust migratory abilities and experience less constraint on dispersal than other subgroups. The breadth of ecological niches at the class level, as determined by the null model, was computed to assess the diversity of resource utilization among Bathyarchaeia. The outcomes demonstrated that the niche width of Bathyarchaeia in paddy soils surpassed that of other archaea, indicating a greater diversity in resource utilization among Bathyarchaeia in paddy soils (as visualized in Fig. 3c).

Factors affecting the distribution of the Bathyarchaeial community

Our findings revealed that deterministic processes played a substantial role in the assembly of highly prevalent Bathyarchaeia, particularly the Bathy-6 subgroup. This suggests that environmental filtering is a pivotal factor influencing their community structure. Consequently, we conducted an in-depth exploration of the relationship between environmental parameters and the distribution of Bathyarchaeia and Bathy-6.

The statistical analysis demonstrated that the relative abundance of Bathyarchaeia exhibited a significant negative correlation with soil C/N and a positive correlation with the mean annual temperature (MAT) (as portrayed in Fig. 4a, p < 0.01). In the case of the Bathyarchaeial community, soil pH and C/N emerged as the two principal factors shaping the communities of Bathyarchaeia and Bathy-6. This observation was reinforced by the results of structural equation modeling (SEM), which assessed the direct and indirect impacts of environmental parameters on the Bathyarchaeial community and Bathy-6 (Fig. 4b). SEM outcomes indicated that the Bathy-6 community could be directly influenced by soil pH and the Bathyarchaeial community. Moreover, the relative abundance of Bathy-6 was positively affected by MAT and indirectly influenced by soil C/N and pH.

Fig. 4
figure 4

The effects of environmental parameters on the distribution of Bathyarchaeia and Bathy-6. The relationship between Bathyarchaeia and Bathy-6 communities and environment parameters (a). SEM analyses reveal the direct and indirect effects of climate parameters, pH, and C/N on the relative abundance of Bathy-6 (b). Red lines represent a significant correlation, and grey lines represent no significant correlation. R2 represents the proportion of variance explained. Pearson’s correlations between Bathyarchaeia and environment parameters at Subgroup level (c). * and ** represent the significance at 0.05 and 0.01 level

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Furthermore, we performed Pearson’s analysis to assess the influence of environmental parameters on the abundance of Bathyarchaeial subgroups detected in this study. The results indicated that soil EC, SOM, NH4+, NO3–, Fe, TN, and TC exhibited predominantly positive correlations with Bath-5bb, Bathy-15, Bthy-11, and negative correlations with Bathy-17. The relative abundance of Bathy-6 displayed significant negative correlations with pH and C/N and positive correlations with MAT (Fig. 4c).

Moreover, Random Forest analysis provided further insights, indicating that MAT, C/N, and pH are the primary factors driving the distribution of Bathy-6 (Fig. 5). These results underscore that Bathy-6 tends to have a higher abundance in environments characterized by higher temperatures, lower C/N ratios, and slightly acidic conditions (Fig. 5).

Fig. 5
figure 5

Drivers of distribution of Bathy-6 were estimated. Environmental parameters predicting the relative abundance of Bathy-6 according to Random Forest analysis (a). * and ** represent the significance at 0.05 and 0.01 level. The high value of the increase in MSE indicates more importance in the Random Forest Model. Linear least-squares regression analysis of the relative abundance of Bathy-6 and MAT (b), C/N (c), and pH (d) was performed. The green and red bar charts overlaid on the axes represent the marginal distributions

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Co-occurrence of Bathyarchaeia with soil microorganisms

Co-occurrence network analysis was performed to determine the interactions of Bathyarchaeia with other archaea to examine the potential ecological functions of Bathyarchaeia in paddy soils. Bathyarchaeia mainly co-occurred with the members of Nitrososphaeria, Methanosarcina, and Methanobacteria (Fig. 6a). The co-occurred ASVs were selected to establish a phylogenetic tree (Fig. S5). The phylogenetic tree indicated that the co-occurrence archaea have various potential metabolisms (Fig. S5). Some co-occurrence archaea belonging to methanogens can use acetate and H2/CO2 to produce methane. The Zi-Pi plot indicated that Bathyarchaeia and Nitrososphaeria included keystone “species” (Fig. 6b). The keystone ASVs were ASV 1790 (Bathyarchaeia), ASV 2007 (Bathy-6), ASV 1248 (Nitrososphaeria), indicating their crucial role in maintaining the stability of microbial community structure. ASVs belonging to Bathyarchaeia were selected to establish the network (Fig. 6c). Bathyarchaeia and Bathy-6 were divided into different modules. Different modules showed significant correlations with other environmental parameters, indicating a high diversity within Bathyarchaeia and Bathy-6 in paddy soils (Fig. 6d). Niche overlap was evaluated to explore the interaction between Bathyarchaeia and other archaea. Bathyarchaeia showed a higher niche overlap with archaea, whereas Nitrososphaeria showed a lower niche overlap with archaea (Table S2).

Fig. 6
figure 6

Co-occurrence network analyses of archaea and Bathyarchaeia in paddy soils. Co-occurrence network of archaea community based on the archaeal amplicon sequence variants (ASVs) with relative abundance higher than 1% (a). The numbers outside the circular plot represent the number of edges related with Class. Zi-Pi plot showing the distribution of archaeal ASVs based on their topological roles (b). Co-occurrence network of Bathyarchaeial community based on the Bathyarchaeial ASVs (c). Heatmap of the relationship between Bathyarchaeia modules and environmental parameters (f). * and ** represent the significance at 0.05 and 0.01 level

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Discussions

Bathyarchaeia inhabiting paddy soil is highly abundant but not diverse

Bathyarchaeia exhibits a widespread distribution in diverse environments. Past investigations have predominantly concentrated on marine, mangrove, and freshwater sediments, where Bathyarchaeia have been notably abundant [1, 8, 10, 24]. Nevertheless, the presence of Bathyarchaeia in arable soils has received limited scrutiny. Within this study, we have determined that the proportion of Bathyarchaeia relative to all archaea varies significantly among distinct paddy soils, constituting an average of approximately 31.2%. Concerning the community structure of Bathyarchaeia in paddy soils, the Bathy-6 subgroup exclusively predominates across all surveyed sites, accompanied by a smaller representation of ASVs associated with Bathy-11, Bathy-15, Bathy-5bb, and Bathy-17. In comparison with our recent meta-analysis [29], the subgroup diversity of Bathyarchaeia in paddy soils within eastern China (comprising 5 subgroups) appeared lower than the global scale (comprising 7 subgroups), as Bathy-18 and Bathy-5b were not detected in our study. For these rare Bathycechaeial subgroups in paddy soil in this study, they have been detected in various freshwater environment. Bathy-18 and Bathy-5bb exhibited higher abundance in peatland with a pH ranging from 4.5 to 5 [44]. Bathy-11 and Bathy-15 showed higher abundance in in deeper sediments [44, 45]. They showed different niche preference. Furthermore, previous studies has also showed that Bathyarchaeia exhibit variable relative abundance in different paddy soils types [23, 46], highlighting the influence of pedoclimatic conditions on Bathyarchaeia. Therefore, it would be beneficial to explore the specific soil conditions that favor either low or high relative abundance of Bathyarchaeia in future research.

Different assembly progress of abundant and rare bathyarchaeial taxa in paddy soils

NCM results indicated that stochastic processes dominated the archaea community assembly in paddy soils. The results from the NCM revealed that abundant Bathyarchaeia ASVs exhibit higher migratory capabilities within paddy soils and experience less dispersal limitation, implying a broader distribution range and a greater propensity for colonization within these soils [47]. The null model also indicated that Bathyarchaeia displays a wider niche breadth at the class level compared to other archaea in paddy soil. A broader niche breadth signifies that Bathyarchaeia possesses a heightened ability to adapt to various environments, accompanied by metabolic versatility, which in turn results in a widespread and abundant distribution pattern [28, 48, 49]. This characteristic is also supported by previous studies indicating that Bathyarchaeia were detected in various environments and were suggested to show multiple metabolisms. NCM results also showed that most low abundant Bathyarchaeia (87.0% of ASVs) fitted the neutral model. Soil nutrients in arable soils can decrease the effect of environmental filtering for microbial distribution [50]. Due to fertilization practice, paddy soils contain much higher nutrients than other environmental habitats, which increases the importance of stochastic processes in Bathyarchaeia community assembly and affects rare Bathyarchaeia taxa.

Niche differentiation of Bathyarchaeia groups in paddy soils

To the best of our knowledge, this represents the inaugural study investigating the niche differentiation of Bathyarchaeia in relation to the physicochemical characteristics of arable soils, including pH and the C/N ratio. Our Random Forest analysis unveiled MAT as the preeminent factor influencing the abundance of Bathyarchaeia in paddy soil (as depicted in Fig. 5a). The relative abundance of Bathyarchaeia and Bathy-6 exhibits a notably positive correlation with MAT (as seen in Fig. 4). This observation harmonizes with our recent global meta-analysis [29], underscoring the influence of temperature on Bathyarchaeia subgroups in soils, as similarly documented by previous study [9], who explored the impact of temperature on Bathyarchaeia subgroups in soils via multivariate regression tree analysis [9]. The discovery of Bathyarchaeia in hot springs further accentuates its remarkable adaptability to high-temperature environments [4].

We found the significance of the C/N ratio as an important factor in regulating the relative abundance of Bathyarchaeia in paddy soils (Figs. 4 and 5). However, in oligotrophic environments, such as sea and mangrove sediments, TOC was reported to be the major limiting factors for the abundance of Bathyarchaeia, regulating the quantity of Bathyarchaeia [8, 10]. However, in paddy soils, organic matter and ammonia are abundant due to fertilization; therefore, the C/N ratio becomes the major factor associated with the abundance of Bathyarchaeia. The dominant subgroup differs in sediments and paddy soils, causing a niche differentiation. These results can elaborate our understanding of the niche preference of Bathyarchaeia in different environments and give suggestions for the enrichment of Bathyarchaeia.

The results of Mantel analysis suggested that soil pH is also a key factor regulating the Bathyarchaeia and Bathy-6 subgroups. SEM results further supported the important role of soil pH in influencing the community structure. The heatmap results also indicated that the abundance and number of ASVs also significantly correlate with soil pH. pH is a crucial factor in influencing bacterial and archaeal community structures in soils [23, 51], also influencing the community structure of Bathyarchaeia in mangrove sediments [8].

Bathyarchaeia co-occurred with methanogens and ammonia oxidizers

Genomic analysis suggested that Bathyarchaeia may play a crucial role in global carbon and nitrogen cycling [13]. In this study, we found the interactions between Bathyarchaeia and other archaea were very complex, and Bathyarchaeia play an important role in the construction of the archaeal network (Fig. 5a). The co-occurrences of Bathyarchaeia and acetoclastic methanogens (i.e. Methanosarcina) have also been found in other environments [9, 11]. These results suggest that Bathyarchaeia can be involved in carbon cycling by producing acetate for acetoclastic methanogens. Co-occurrences of Bathyarchaeia and hydrogenotrophic methanogens such as Methanobacteria were also found in paddy soils in this study suggesting transfer of hydrogen from Bathyarchaeia to the methanogens.

Bathyarchaeia also frequently co-occurred with members of Nitrososphaeria (Fig. 5d), which is consistent with the results of the global meta-analysis [29]. Genes involved in ammonia and urea production were found in Bathyarchaeial MAGs, suggesting Bathyarchaeia serve as a transfer station for nitrogen compounds in the global nitrogen cycle [17]. Moreover, the metagenomic analysis suggested that Nitrososphaeria could use ammonia and urea [52]. Ammonia and urea might the bridges link specific Bathyarchaeia and Nitrososphaeria taxa [29]. However, as two dominant classes in the soil, their relative abundances exhibit a negative correlation in Fig. 4a. Bathyarchaeia and Nitrososphaera both play roles in nitrogen cycling, their specific ecological niches and metabolic activities likely determine their interactions in complex microbial communities. Agricultural systems depend on significant nitrogen fertilizer inputs for farm yield; therefore, the role of Bathyarchaeia in agricultural soils on the nitrogen cycle and factors influence the relationship between Bathyarchaeia and Nitrososphaeria warrants further research.

We also found that Bathyarchaeia indicated higher niche overlap with other archaea. Higher overlap means higher association with other microorganisms, whereas no higher competition was found in paddy soils. This view is supported by network analysis that Bathyarchaeia plays a crucial role in the structure of a network [53, 54]. In the future, more research is needed to investigate the ecological function of Bathyarchaeia in paddy soils.

Bathy-6 is the dominant subgroup in paddy soils with broad environmental adaptation

Bathy-6 exhibits a widespread presence in terrestrial environments, including soil, freshwater sediments, and mangrove sediments [8, 9, 11], although it has also been detected in certain marine sediments [55]. In numerous prior studies, Bathyarchaeia were primarily observed in anaerobic sediments, consistent with their assumed anaerobic lifestyle [13]. Notably, Lazar et al. identified genes encoding enzymes responsible for responding to oxidative stress in Bathy-6, suggesting that Bathy-6 members possess an ability to adapt to fluctuations in oxygen levels [16]. Additionally, Pan et al. reported the presence of oxygen-dependent metabolic pathways within certain Bathy-6 genomes, hinting at a microaerophilic lifestyle for Bathy-6 [17]. In our study, the results underscored that Bathy-6 stands as the predominant subgroup within the Bathyarchaeia in all the paddy soils (see Fig. 3a). Flooded paddy soils are characterized by microaerophilic conditions due to the presence of dissolved oxygen in soil porewater and oxygen released by rice roots [56, 57]. This microoxic nature of paddy soil could elucidate why the anaerobic Bathyarchaeia subgroups are less prevalent, while Bathy-6 becomes dominant in these environments. Collectively, these findings indicate that Bathy-6 possesses wide-ranging environmental adaptability, accommodating both microoxic and anaerobic conditions. This suggests that Bathy-6 may potentially play a role in the evolutionary transition of life from anaerobic to aerobic environments. Further investigations are necessary to unravel the mechanisms underpinning the high prevalence of Bathy-6 in paddy soils.

Furthermore, beyond adapting to diverse oxygen conditions, certain Bathy-6 ASVs exhibit the capability to thrive across a broad pH range from 5 to 8. Bathy-6 demonstrates resilience not only in oligotrophic marine sediments but also in eutrophic paddy soils with elevated TC. Our network analysis indicated that Bathy-6 members are subdivided into several groups. These results collectively underscore that Bathy-6 possesses versatile metabolic capabilities, thrives in diverse habitats, and exhibits varied lifestyles, consistent with genomic predictions [17].

Conclusions

Our results extend our knowledge on Bathyarchaeia niche differentiation in paddy soils. Bathyarchaeia was shown to be highly abundant in most paddy soils across eastern China, which were predominated by Bathy-6. The abundance and diversity of Bathyarchaeia varied considerably among different soils. The relative abundance of Bathyarchaeia in paddy soils was linked to soil C/N ratio and MAT, whereas soil pH was the key factor influencing Bathyarchaeia community structure. Bathy-6 is assembled mainly by deterministic progress with higher relative abundance in high temperature, low C/N, and slightly acidic environments. The network analysis revealed that Bathyarchaeia helps in maintaining the stability of archaeal community structure, with high co-occurrence with acetoclastic methanogens, some hydrogenotrophic methanogens, and archaeal ammonia oxidizers. Further research is required to reveal the specific functions of these organisms in paddy soils.

Data availability

The datasets analysed during the current study are available in the GenBank repository, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1023015/.

Abbreviations

ASV:

Amplicon sequence variant

TC:

Total carbon

TN:

Total nitrogen

MAT:

Mean annual temperature

NMDS:

Non-metric multidimensional scaling

NCM:

Neutral community model

SEM:

Structural equation modeling

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Acknowledgements

We are grateful to Yongguan Zhu for his help with sample collection and data discussion.

Funding

This work was supported by the National Natural Science Foundation of China (grant no. 43991332 and 41977323) and Chinese Scholarship Council of the Ministry of Education.

Author information

Authors and Affiliations

  1. State Key Lab of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, 100085, Beijing, China

    Xingyun Yi, Shudan Xue, Yifei Wang, Ye Deng & Guilan Duan

  2. Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg, Denmark

    Xingyun Yi & Kristian Koefoed Brandt

  3. Sino-Danish Center (SDC), 101408, Beijing, China

    Kristian Koefoed Brandt

  4. University of Chinese Academy of Sciences, 100049, Beijing, China

    Shudan Xue, Ye Deng & Guilan Duan

  5. College of Resources and Environmental Sciences, China Agricultural University, 10093, Beijing, China

    Jingjing Peng

  6. Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, 518060, Shenzhen, Guangdong, China

    Meng Li

  7. Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, 518060, Shenzhen, Guangdong, China

    Meng Li

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  1. Xingyun Yi
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Contributions

X.Y. and G.D designed and performed the experiments. X.Y., S.X., Y.W. and J.P. analyzed the data. X.Y and G.D. drafted the paper. K.K.B., Y.D. and M.L. revised the paper.

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Correspondence to Guilan Duan.

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Additional file 1:

It’s.xlsx file and include two tables. The title of the tables are Physicochemical parameter details of the samples (Table S1) and Nice overlap of Bathyarchaeia and other archaea (Table S2), respectively.

Additional file 2:

It’s.docx file and include five figures. The title of the figures are The archaeal diversity of paddy soil (Fig. S1), The archaeal community in paddy soils at the phylum level (Fig. S2), The Bathyarchaeial diversity of paddy soil (Fig. S3), The atlas maps predicted the distribution of Bathy-6 across paddy soils (Fig. S4) and Phylogenetic tree of ASVs co-occurrence with Bathyarchaeia (Fig. S5).

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Yi, X., Brandt, K.K., Xue, S. et al. Niche differentiation and biogeography of Bathyarchaeia in paddy soil ecosystems: a case study in eastern China. Environmental Microbiome 19, 13 (2024). https://doi.org/10.1186/s40793-024-00555-8

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Environmental Microbiome

ISSN: 2524-6372