No consistency in the mechanism of assembly and potential effects on the host of various dimensions of Eucommia ulmoides rhizosphere microbiota

doi:10.21203/rs.3.rs-2994767/v1

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No consistency in the mechanism of assembly and potential effects on the host of various dimensions of Eucommia ulmoides rhizosphere microbiota

https://doi.org/10.21203/rs.3.rs-2994767/v1

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Background and Aims

Rhizosphere microbiota is regarded as the second genome of plants and can promote plant growth, development and metabolism. However, little is known about the dominant microbial genera, diversity, and mechanisms of assembly of co-occurrence networks of the plant rhizosphere microbiota and their effects on host secondary metabolites.

Methods: Using 33 Eucommia ulmoides rhizosphere soil samples from 11 regions of China, the dominant microbial genera, α-diversity, and co-occurrence networks of the rhizosphere microbiota were determined. Their responses to environmental factors (soil and climatic variables) and effects on the host secondary metabolites (assessed by LC-QTOF MS/MS) were then analyzed.

Results: The dominant fungal genus and bacterial genus in the E. ulmoides rhizosphere microbiota were Eremothecium (relative abundance, 50.54%) and Sphingomonas (8.33%), respectively. The dominant microbial genera, α-diversity, and co-occurrence network properties significantly differed by region. The three dimensions of the rhizosphere microbiota (dominant microbial genera, α-diversity, and co-occurrence networks) clearly responded inconsistently to environmental factors, with total and available nitrogen having key effects on dominant microbial genera, total nitrogen and rainfall having key effects on α-diversity, and soil organic matter and temperature having key effects on co-occurrence network properties. There were also inconsistent effects on secondary metabolites, with the co-occurrence network properties being significantly correlated with pinoresinol diglucoside, and dominant bacteria genera (Bradyrhizobium, Streptomyces, Burkholderia, and Mortierella) being significantly correlated with aucubin.

Conclusion: This study provides a new theoretical basis regarding the mechanisms underlying rhizosphere microbiota assembly, and the microbiota effects on host secondary metabolites.

Eucommia ulmoides

rhizosphere microbiota

plant-microbiota interactions

co-occurrence pattern

secondary metabolities

The rhizosphere is an active microdomain of plant–microbe interactions. The rhizosphere microbiota is considered to be the second genome of the plant and has an important role in healthy plant growth, organic matter decomposition and resistance to biotic/abiotic stresses (Dong et al. 2022a; Tian et al. 2022). It is generally accepted that the assembly of the rhizosphere microbiota is influenced by host genotype, environment, and microbial interactions (Trivedi et al. 2020). Compared to the plant endophytic microbiota, rhizosphere microbiota is influenced to a greater degree by abiotic factors, with soil pH, organic matter, moisture, and carbon-to-nitrogen ratio being the main drivers of microbial community assembly associated with the below-ground part of the plant (Abdullaeva et al. 2021; Chialva et al. 2021). In Populus cathayana, pH significantly influenced the relative abundance of the dominant phyllosphere taxon Proteobacteria and the dominant rhizosphere taxa Chloroflexi, Actinobacteria, and Proteobacteria, along with Shannon's index of the rhizosphere (Guo et al. 2022). With the rapid development of sequencing technologies, the composition, assembly, and function of the endophytic microbiota of plants have become better understood. Numerous studies indicate that endophytes form complex microbial co-occurrence networks through positive, negative, and neutral interactions (Li et al. 2021). However, the mechanisms underlying the assembly of co-occurrence networks of the plant rhizosphere microbiota are rarely studied and, in particular, little is known about whether rhizosphere microbial co-occurrence patterns are consistent with the responses of microbial taxa and diversity to environmental factors.

The plant rhizosphere microbiota has profound impacts on plant health and production, either directly or indirectly by regulating and participating in secondary metabolite synthesis in plants (Pang et al. 2021; Korenblum et al. 2020). The abundance of the soybean rhizosphere microbes Xanthomonadaceae and Comamonadaceae significantly affected the isoflavone content (White et al. 2017). In addition, cyanide production by Manihot esculenta triggered ethylene production in the adjacent Arachis hypogaea rhizosphere, thereby altering the microbial composition and co-occurrence network of the A. hypogaea rhizosphere (Chen et al. 2020).Recent studies have highlighted that the microbiota may be a key determinant of the quality of medicinal herbs (Dong et al. 2022b; Huang et al. 2018). In Salvia miltiorrhiza, terpenoids, limonene, pinene, and geraniol were significantly correlated with Gammaproteobacteria, Alphaproteobacteria, Betaproteobacteria, Sphingobacteria, Bacilli, and Actinobacteria (Chen et al. 2018). Microorganisms may be important factors affecting the quality of Eucommia ulmoides herbs; for example, the dominant taxa Lactobacillus, Escherichia, Bacteroides, and Faecalibacterium in E. ulmoides bark were significantly associated with several pharmacologically active ingredients of E. ulmoides (Dong et al. 2022a). Notably, a growing body of research suggests that a single microbial species may not be able to perform ecological functions, and that, instead, synergies between multiple compatible microbial strains can better adapt to new environments while providing a broader range of plant-promoting functions to improve crop phenotypes and, ultimately, crop productivity (Trivedi et al. 2021; Schmitz et al. 2022). Therefore, exploring the effects of dominant plant rhizosphere microbial genera, α-diversity, and co-occurrence network topological properties on host secondary metabolite levels is key to developing scientific approaches for plant management, such as the use of plant microbes to precisely regulate sustainable agricultural production.

Eucommia ulmoides Oliver is a species of the family Eucommiaceae. It is a valuable medicinal plant that contains medicinal components such as pinoresinol diglucoside, kynurenine glycosides, and chlorogenic acid, which are often used as indicators of the quality of the E. ulmoides medicinal herbs and have anti-tumor, liver and kidney tonic effects, and hypotensive properties (He et al. 2014). E. ulmoides gum is a unique polymeric resource of great strategic importance for the sustainable green development of the rubber industry (Wei et al. 2021). E. ulmoides bark microorganisms mainly comprise taxa such as Sordariomycetes, Bacillus, and Sphingomonas, and their assembly is influenced by environmental factors such as air humidity, soil pH, and soil organic matter (Dong et al. 2022a, b). In addition, Lactobacillus, Escherichia, Bacteroides, and Faecalibacterium are significantly associated with indicators of the quality of E. ulmoides (Dong et al. 2022b) However, the assembly mechanism of the E. ulmoides rhizosphere microbiota composition and structure are not clear, and nor is the influence of the microbial co-occurrence network on plant host physiology and ecology.

Therefore, E. ulmoides from 11 regions was selected as the research object in this study. Based on amplicon sequencing, we explored whether the dominant microbial genera, α-diversity, and co-occurrence network topological properties of the rhizosphere microbiota are consistent in their response to environmental factors (soil physicochemical and climatic variables). The effects of dominant microbial genera, α-diversity, and co-occurrence network topological properties on E. ulmoides secondary metabolite levels were also explored. The results provide an important scientific basis for breeding, cultivation, and scientific management of E. ulmoides.

Sample collection

33 rhizosphere soil and bark samples and 33 bark samples were collected from the following 11 E. ulmoides production regions in China: CL (Cili), HS (Heishui), LB (Lingbao), LS (Longshi), LY (Liuyang), PZ (Panzhou), QM (Qimen), SL (Shuanglong), SY (Shiyan), WC (Wangcang), and ZY (Zunyi).

Regarding the rhizosphere soil samples, three healthy E. ulmoides trees (approximately 10 m from one another) were selected in each region, and a rhizosphere soil sample was collected from each tree at a depth of 20 cm (approximately 1 m from the trunk). They were uniformly mixed into a sample and then subjected to total genomic DNA extraction.

Regarding the bark samples, three healthy E. ulmoides trees (approximately 10 m from one another) were selected in each region, and a bark sample was collected from each tree at 1.5 m above the ground. The samples were subjected to tandem liquid chromatography quadrupole time of flight mass spectrometry (LC-QTOF MS/MS) to assess the levels of secondary metabolites (pinoresinol diglucoside, aucubin, and geniposidic acid).

The data on the soil physicochemical properties and secondary metabolites are shown in Tables S1–2.

DNA extraction, PCR amplification, and Illumina MiSeq sequencing of rhizosphere soil

Total genomic DNA was extracted from the E. ulmoides rhizosphere soil samples using a FastDNA Spin Kit for Soil (MP, USA). A DNA concentration of 40–60 ng/µL was considered acceptable.

The 16S rRNA gene was amplified using the universal primer pair 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), as described by Dong et al. (2022a). The PCR products were sequenced by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China) using an Illumina MiSeq PE300 platform (Illumina, San Diego, USA). The analyses were performed on BMK Cloud (https://console.biocloud.net/).

The ITS gene was amplified using the primers ITS1F (5’-CTTGGTCATTTAGAGGAAGTAA-3’) and ITS2R (5’-GCTGCGTTCTTCATCGATGC-3’) on a GeneAmp 9700 PCR system (ABI, USA) as follows: initial denaturation at 95℃ for 3 min, followed by 27 cycles of denaturing at 95℃ for 30 s, annealing at 55℃ for 30 s, and extension at 72℃ for 30 s, a single extension at 72℃ for 10 min, and storage at 4℃. The 20-µL PCR system consisted of 4 µL 5×Buffer, 2 µL (2.5 mmol/L) dNTPs, 0.8 µL forward primer ITS1F, 0.8 µL reverse primer ITS2R, 0.4 µL rTaq DNA polymerase, 0.2 µL bovine serum albumin, 10 ng template DNA, and ddH₂O (up to 20 µL). Purified amplicons were combined in equimolar amounts and paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, USA) according to the standard protocols of Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The raw data was uploaded to the National Center for Biotechnology Information (NCBI) database (BioProject ID: PRJNA771480; BioSample: SAMN22311478).

Co-occurrence network construction

Co-occurrence networks were constructed as described previously (Dong et al. 2021a). For the three overall networks (fungal, bacterial, and fungal–bacterial networks), to eliminate the influence of a single operational taxonomic unit (OTU) in a specific sample on the correlation matrix, only OTUs that were present in 80% of samples were used. Next, each co-occurrence network was constructed based on a Spearman's correlation coefficient R > 0.8 and P < 0.01. The network topological properties (number of nodes, number of edges, average degree, modularity, diameter, and density) were calculated using the R package igraph. Network visualization was conducted using Gephi version 0.9.2.

For the regional fungal, bacterial, and fungal–bacterial networks, OTUs that were present in 80% of the samples were used. Next, OTUs were selected according to the lowest number of OTUs (bacteria: 288 OTUs, fungi: 91 OTUs) present in any of the 11 regions according to the relative abundance. The networks were then constructed according to the above method, and the network topological properties were calculated.

Statistical analyses

To evaluate the responses of the dominant microbial genera, α-diversity, and network topological properties of the rhizosphere microbiota to the environment, the associations of environmental factors (soil and climatic variables) with the top 10 most abundant fungal and bacterial genera, α-diversity indexes (Chao1, Shannon, and Simpson), and network topological properties (number of nodes, number of edges, average degree, modularity, diameter, and density) were assessed using Spearman's rank correlation analysis. Similarly, Spearman's rank correlation analysis was used to explore the relationships of secondary metabolites (pinoresinol diglucoside, geniposidic acid, and aucubin) with the dominant microbial genera, α-diversity, and network topological properties.

Structure and composition of E. ulmoides rhizosphere microbiota

Illumina MiSeq sequencing was performed on 33 E. ulmoides rhizosphere soil samples from 11 regions. The fungal analysis involved 2,611,379 valid sequences, with an average of 79,133 per sample (Fig. S1a). The bacterial analysis involved 2,621,099 valid sequences, with an average of 79,427 per sample (Fig. S1b). The dilution curves indicate that the sequencing data provide a good representation of the rhizosphere microbiota of E. ulmoides (Fig. S1 c,d).

Ascomycota (80.77%), Basidiomycota (12.88%), and Mortierellomycota (2.33%) were the dominant fungal phyla. Eremothecium (50.54%), Marasmius (9.10%), Fusarium (4.35%), Alternaria (2.91%), and Cladosporium (2.36%) were the dominant fungal genera (Fig. 1a). Actinobacteria (35.38%), Proteobacteria (26.85%), and Acidobacteria (8.20%) were the dominant bacterial phyla. Sphingomonas (8.33%) Bryobacter (5.63%), Bradyrhizobium (5.50%), Nitrospira (4.55%), and Streptomyces (3.42%) were the dominant bacterial genera (Fig. 1b).

The Shannon index showed that the fungal α-diversity was significantly higher in sample QM than samples HS, LB, SL, and SY (Fig. 1c); however, the bacterial α-diversity was significantly lower in sample QM than sample LB, while the microbial α-diversity of the remaining samples did not differ significantly (Fig. 1d). In addition, the principal co-ordinates analysis revealed that there were no significant differences in the fungal communities among samples except for sample QM (Fig. 1e), while the bacterial communities significantly differed among samples (Fig. 1f). These results suggest that the assembly mechanisms of fungal and bacterial communities may be inconsistent.

Microbe interaction and network topology characteristics

To clarify the co-occurrence patterns of the rhizosphere microbiota in E. ulmoides, three networks (fungal, bacterial, and fungal–bacterial networks) were constructed (Fig. 2). The fungal network consisted of 138 nodes and 532 edges, the bacterial network consisted of 463 nodes and 1387 edges, and the fungal–bacterial network consisted of 608 nodes and 2655 edges. The fungal–bacterial network was the most complex, followed by the bacterial network and then the fungal network (average degree represents network complexity). The network modularity value was > 0.4, indicating a clear modular structure of the rhizosphere microbial network of E. ulmoides.

In addition, 11 regional networks representing the 11 studied regions were constructed (Fig. S2–4). The number of nodes, number of edges, mean degree, and diameter were significantly larger for the fungal–bacterial networks than the bacterial and fungal networks (Table 1), suggesting that there are considerable fungal–bacterial interactions. The bacterial network (non-regional) was more complex and had stronger interactions than the fungal network. In addition, the three non-regional networks all had large modularity values, ranging from 0.702 to 0.96.

Table 1

Key topological features of regional networks.
Community	Topological features	CL	HS	LB	LS	LY	PZ	QM	SL	SY	WC	ZY
Fungi	Node	59	58	52	58	67	64	56	52	53	61	59
	Edge	43	45	52	40	137	120	46	40	36	48	62
	Average degree	1.46	1.55	2.00	1.38	4.09	3.75	1.64	1.54	1.36	1.57	2.10
	Modularity	0.94	0.91	0.91	0.93	0.71	0.72	0.9	0.92	0.93	0.92	0.85
	Diameter	3	4	3	3	7	12	5	4	2	4	9
	Density	0.025	0.027	0.039	0.024	0.062	0.06	0.03	0.03	0.026	0.026	0.036
Bacteria	Node	264	270	271	262	379	273	264	270	270	271	263
	Edge	661	474	836	417	935	448	567	491	446	472	754
	Average degree	5.01	3.51	6.17	3.18	4.93	3.28	4.30	3.64	3.30	3.48	5.73
	Modularity	0.89	7.02	0.76	0.95	0.95	0.95	0.91	0.91	0.96	0.94	0.85
	Diameter	13	12	26	21	16	9	8	19	6	10	25
	Density	0.019	0.013	0.023	0.012	0.013	0.012	0.016	0.014	0.012	0.013	0.022
Fungi & Bacteria	Node	398	402	396	401	400	398	399	404	405	401	400
	Edge	1053	882	1139	851	1059	1010	1109	920	816	885	1352
	Average degree	5.29	4.39	5.75	4.24	5.30	5.08	5.56	4.56	4.03	4.41	6.76
	Modularity	0.919	0.96	0.86	0.96	0.91	0.93	0.93	093	0.97	0.95	0.84
	Diameter	27	12	24	13	25	20	18	17	15	24	23
	Density	0.013	0.011	0.015	0.011	0.013	0.013	0.014	0.011	0.01	0.011	0.017

It is worth noting that the network topology of the same community (fungal, bacterial, or fungal–bacterial) clearly differed by region. For example, for fungi, the average degree and density were higher in LY than other networks. For bacteria, the average degree was higher in ZY than other networks, but the numbers of nodes and edges were higher in LY than other networks (Table 1). These results suggest that the interactions of the E. ulmoides rhizosphere microbiota may not respond uniformly to the environment.

Factors influencing microbial community α-diversity and co-occurrence networks

To investigate the factors influencing the α-diversity of the rhizosphere microbiota and the co-occurrence networks, changes in soil pH, available nitrogen (AN), soil organic matter (SOM), available phosphorous (AP), total nitrogen (TN), total phosphorus (TP), relative humidity, mean annual temperature, rainfall, and altitude were measured (Table S1). The ranges of soil pH, AN, SOM, AP, TN, and TP for the 11 regions were 5.36–7.71, 95.60–337.65 mg/kg, 0.94–20.93 mg/kg, 7.75–67.65 mg/kg, 1.85–4.95 g/kg, and 0.12–0.91 g/kg, respectively. The ranges of relative humidity, average annual temperature, rainfall, and altitude were 73–96, 8.34–18.05℃, 652.93–2239.47 mm, and 67.12–2203.63 m, respectively.

Based on Spearman's correlation, the top 10 most abundant fungal and bacterial genera, α-diversity indexes, and network topological properties responded differently to environmental factors (Fig. 3). Regarding the top 10 most abundant fungal and bacterial genera, only the bacterial taxon Hailiangium was significantly associated with TN. The fungal taxa Eremothecium, Marasmius, Alternaria, Cladosporium, and Vishniacozyma were significantly associated with AN, Mortierella was significantly associated with TN, Hygrocybe was significantly associated with temperature, and Filobasidium and Aspergillus were significantly associated with rainfall and altitude (Fig. 3a). Regarding the α-diversity indexes, the bacterial α-diversity indexes were not significantly correlated with environmental factors, but TN and rainfall were significantly correlated with the fungal Chao1 and Shannon indexes (Fig. 3b). In contrast, network topological parameters of fungal communities were significantly correlated with SOM, temperature, and altitude, while the bacterial network topological parameters were not significantly correlated with the environmental factors. In addition, the fungal–bacterial network topological parameters were significantly correlated with SOM, AP, and temperature.

Effects of rhizosphere microbiota on host secondary metabolites

To further investigate the potential effects of the rhizosphere microbiota on indicators of the quality of E. ulmoides, we determined the levels of pinoresinol diglucoside, geniposidic acid, and aucubin in E. ulmoides bark from the 11 regions (Table S2). The bacteria Bradyrhizobium, Streptomyces, Burkholderia, and Mortierella were significantly correlated with the level of aucubin, and the fungi Mortierella and Vishniacozyma were significantly correlated with the level of pinoresinol diglucoside (Fig. 4a). The Chao1 and Shannon indexes of the bacterial community were significantly correlated with the level of aucubin (Fig. 4b). In addition, the average degree, number of edges, and modularity of the fungal network were significantly correlated with the level of pinoresinol diglucoside (Fig. 4c). This suggests that fungi–fungi interactions may have a greater impact on plant host physiology and adaptations.

In this study, the dominant microbial genera, α-diversity, and network topological properties of the rhizosphere microbiota of E. ulmoides were explored in terms of their responses to environmental factors and their effects on the secondary metabolites of the host. The results show that the three dimensions (dominant microbial genera, α-diversity, and co-occurrence network topological properties) of the rhizosphere microbiota not only responded inconsistently to environmental factors, but also had inconsistent effects on the levels of secondary metabolites in the host (pinoresinol diglucoside, aucubin, and geniposidic acid). This study provides new insights into the mechanisms of plant microbiota assembly and physiological and ecological impacts on the host.

During the assembly of a plant rhizosphere microbiota, different microbial taxa respond differently to different environmental factors (Dastogeer et al. 2020; Hamonts et al. 2018). The assembly of plant rhizosphere fungi is more affected by random processes, while that of bacteria is more affected by environmental factors (Trivedi et al. 2020). In this study, β-diversity analysis showed that there were no significant differences in the E. ulmoides rhizosphere fungal communities among the 11 regions, but there were significant differences in the bacterial communities (Fig. 1). This suggests that the environment is a key factor influencing the assembly of the E. ulmoides rhizosphere bacterial communities, as reported in many previous studies of rhizosphere microbial communities. In general, the physicochemical properties of the soil directly influence the assembly of the rhizosphere microbiota. For example, soil pH was reported to be significantly positively correlated with bacterial diversity in neutral soils and negatively correlated in acidic soils (Tecon and Or, 2017). In addition, the physicochemical properties of soil indirectly regulate the composition of the rhizosphere microbiota by influencing the root exudates (Bai et al. 2022). For example, the phosphorus starvation response can affect the composition of the rhizosphere microbiota, creating an atypical microbial community (Castrillo et al. 2017).

Co-occurrence network analysis can provide insights into microbiota structures and microbial interactions (Faust & Raes 2012). Our co-occurrence network analysis showed that the fungal–bacterial network was the most complex, followed by the bacterial network and then the fungal network (Fig. 2). The bacteria–fungi interactions were more common and diverse than previously known (Bastías et al. 2020). Bacteria and fungi influence a series of physiological processes including host morphology, growth, reproduction, transport/movement, nutrition, stress resistance, and pathogenicity through complex interactions (Tan et al. 2021). In addition, there were significant differences in the complexity, modularity, and average degree among the 11 regional networks of fungi, the 11 regional networks of bacteria, and the 11 regional networks of fungal–bacteria, indicating that the rhizosphere microbiota of E. ulmoides may be related to environmental factors. Ma et al. (2012) reported that SOM, iron, nitrogen, and pH are important environmental factors influencing the co-occurrence network of soil microbiota. Similarly, by studying the E. ulmoides rhizosphere microbiota, we revealed that SOM, temperature, and altitude significantly influenced the modularity of the bacterial network, and SOM, AP, and mean annual temperature significantly influenced the diameter of the fungal–bacterial network (Fig. 3c), similar to previous microbial biogeography studies (Tian et al. 2022; Zhang et al. 2018). For example, Lin et al. (2019) showed that SOM is a key factor influencing the composition and co-occurrence network of soil microbial communities.

The three dimensions (dominant microbial genera, α-diversity, and co-occurrence network topological properties) of the rhizosphere microbiota had different responses to environmental factors. Regarding the dominant microbial genera and α-diversity, the fungal communities were more influenced by environmental factors than the bacterial communities. For example, only Haliangium was significantly correlated with TN, while many fungal taxa were significantly correlated with AN, SOM, and relative humidity. The Chao1 and Shannon indexes of fungi were significantly correlated with AN and relative humidity, respectively. However, the Chao1 and Shannon indexes of bacteria were not significantly correlated with environmental factors (Fig. 3), which is similar to the results of previous studies (Faticov et al. 2021; Wang et al. 2021). The fungal community was more sensitive to nitrogen deposition than the bacterial community, which highlights the importance of simultaneously assessing the response of soil bacterial and fungal communities to global environmental changes. Wang et al. (2021) revealed that the environment had a greater impact on the forest soil fungal community than the bacterial community, and they reported that the fungal community was more sensitive to nitrogen deposition than the bacterial community. In contrast, the environmental factors had no significant effects on our fungal co-occurrence network, but the modularity of the bacterial network was significantly correlated with SOM, temperature, and altitude, and the diameter of the fungal–bacterial network was significantly correlated with SOM, AP, and temperature (Fig. 3). In short, different types of rhizosphere microbiota co-occurrence networks respond differently to environmental factors, and the assembly mechanism related to microbial community composition and α-diversity is not consistent with the assembly mechanism related to the co-occurrence network.

Furthermore, the dominant microbial genera, α-diversity, and network topological properties of the rhizosphere microbiota had inconsistent effects on active components in the host (pinoresinol diglucoside, aucubin, and geniposidic acid). Liu et al. (2021) emphasized the importance of dominant rhizosphere microbial taxa for plant growth and development. In our study, several dominant microbial taxa (e.g., Bradyrhizobium, Burkholderia, Streptomyces, and Mortierella) were significantly associated with the level of aucubin. Studies have reported that Bradyrhizobium and Burkholderia spp. feedback regulate plant growth and metabolism by assisting plants to regulate soil nitrogen cycling and to secrete phytohormones such as indoleacetic acid (Lu et al. 2018). However, many recent studies have emphasized that microbial communities are not merely collections of discrete individuals, but rather complex ecological communities that co-occur, communicate, and collaborate with each other (Eichmann et al. 2021; Trivedi et al. 2022). Our study showed that the average degree, number of edges, and modularity of the fungal co-occurrence network were significantly correlated with the level of pinoresinol diglucoside. Indeed, the higher the microbial abundance, α-diversity, and complexity of co-occurrence networks, the greater the role they play in ecosystem function (Wagg et al. 2019). These results suggest that the interactions of rhizosphere fungi of E. ulmoides may have an important role in host metabolism.

Acknowledgements

The work was supported by the Gui Da Tegang He Zi (2022) 57, Natural Science Foundation of China (32260003, 32060011), “Hundred” Talent Projects of Guizhou Province (Qian Ke He [2020] 6005), Construction Program of Biology First-class Discipline in Guizhou (GNYL [2017] 009).

Abdullaeva Y, Manirajan BA, Honermeier B, Schnell S, Cardinale M (2021) Domestication affects the composition, diversity, and co-occurrence of the cereal seed microbiota. J Adv Res 31:75-86.
Bai B, Liu W, Qiu X, Zhang J, Zhang J, Bai Y (2022) The root microbiome: community assembly and its contributions to plant fitness. J Integr Plant Biol 64(2):230-243.
Bastías DA, Johnson LJ, Card SD (2020) Symbiotic bacteria of plant-associated fungi: friends or foes? Curr Opin Plant Biol 56:1-8.
Castrillo G, Teixeira PJPL, Paredes SH, Law TF, De Lorenzo L, Feltcher ME, Finkel OM, Breakfield NW, Mieczkowski P, Jones CD, Paz-Ares J, Dangl JL (2017) Root microbiota drive direct integration of phosphate stress and immunity. Nature 543(7646):513-518.
Chen H, Wu H, Yan B, Zhao H, Liu F, Zhang H, Sheng Q, Miao F, Liang Z (2018) Core microbiome of medicinal plant Salvia miltiorrhiza seed: a rich reservoir of beneficial microbes for secondary metabolism? Int J Mol Sci 19(3):672.
Chen Y, Bonkowski M, Shen Y, Griffiths BS, Jiang Y, Wang X, Sun B (2020) Root ethylene mediates rhizosphere microbial community reconstruction when chemically detecting cyanide produced by neighbouring plants. Microbiome 8(1):1-17.
Chialva M, De Rose S, Novero M, Lanfranco L, Bonfante P (2021) Plant genotype and seasonality drive fine changes in olive root microbiota. Curr Plant Biol 28:100219.
Dong C, Shao Q, Zhang Q, Yao T, Huang J, Liang Z, Han Y (2021a) Preferences for core microbiome composition and function by different definition methods: evidence for the core microbiome of Eucommia ulmoides bark. Sci Total Environ 790:148091.
Dong C, Zhang X, Shao Q, Yao T, Liang Z, Han Y (2021b) Mycobiota of Eucommia ulmoides bark: diversity, rare biosphere and core taxa. Fungal Ecol 53:101090.
Dastogeer KM, Tumpa FH, Sultana A, Akter MA, Chakraborty A (2020) Plant microbiome–an account of the factors that shape community composition and diversity. Curr Plant Biol 23:100161.
de Vries FT, Griffiths RI, Bailey M, Craig H, Girlanda M, Gweon HS, Hallin S, Kaisermann A, Keith AM, Kretzschmar M, Lemanceau P, Lumini E, Mason KE, Oliver A, Ostle N, Prosser JI, Thion C, Thomson B, Bardgett RD (2018) Soil bacterial networks are less stable under drought than fungal networks. Nat Commun 9(1): 3033.
Dong CB, Shao QY, Ren YL, Ge W, Yao T, Hu HY, Huang JZ, Liang ZQ, Han YF (2022a) Assembly, core microbiota, and function of the rhizosphere soil and bark microbiota in Eucommia ulmoides. Front Microbiol 13:855317.
Dong CB, Zhang ZY, Shao QY, Yao T, Hu HY, Huang JZ, Liang ZQ, Han YF (2022b) Deciphering the effects of genetic characteristics and environmental factors on pharmacological active ingredients of Eucommia ulmoides. Ind Crop Prod 175:114293.
Eichmann R, Richards L, Schäfer P (2021) Hormones as go‐betweens in plant microbiome assembly. Plant J 105(2):518-541.
Faticov M, Abdelfattah A, Roslin T, Vacher C, Hambäck P, Blanchet FG, Tack AJ (2021) Climate warming dominates over plant genotype in shaping the seasonal trajectory of foliar fungal communities on oak. New Phytol 231(5):1770-1783.
Faust K, Raes J (2012) Microbial interactions: from networks to models. Nat Rev Microbiol 10(8):538-550.
Guo Q, Liu L, Liu J, Korpelainen H, Li C (2022) Plant sex affects plant-microbiome assemblies of dioecious Populus cathayana trees under different soil nitrogen conditions. Microbiome 10(1):1-15.
Hamonts K, Trivedi P, Garg A, Janitz C, Grinyer J, Holford P, Botha FC, Anderson IC, Singh BK (2018) Field study reveals core plant microbiota and relative importance of their drivers. Environ Microbiol 20:124-140.
He X, Wang J, Li M, Hao D, Yang Y, Zhang C, He R, Tao R (2014) Eucommia ulmoides Oliv.: ethnopharmacology, phytochemistry and pharmacology of an important traditional Chinese medicine. J Ethnopharmacol 151:78-92.
Huang W, Long C, Lam E (2018) Roles of plant-associated microbiota in traditional herbal medicine. Trends in Plant Science 23(7):559-562.
Korenblum E, Dong Y, Szymanski J, Panda S, Jozwiak A, Massalha H, Meir S, Rogachev I, Aharoni A (2020) Rhizosphere microbiome mediates systemic root metabolite exudation by root-to-root signaling. Proc Natl Acad Sci U S A 117(7):3874-3883.
Li Y, Li T, Zhao D, Wang Z, Liao Y (2021) Different tillage practices change assembly, composition, and co-occurrence patterns of wheat rhizosphere diazotrophs. Sci Total Environ 767:144252.
Lin Y, Ye G, Kuzyakov Y, Liu D, Fan J, Ding W (2019) Long-term manure application increases soil organic matter and aggregation, and alters microbial community structure and keystone taxa. Soil Biol Biochem 134:187-196.
Liu, J. W., Li, X. Z., & Yao, M. J. (2021). Research progress on assembly of plant rhizosphere microbial community. Acta Microbiol Sin 61(2), 231-248.
Lu T, Ke M, Lavoie M, Jin Y, Fan X, Zhang Z, Fu Z, Sun L, Gillings M, Peñuelas J, Qian H, Zhu YG (2018) Rhizosphere microorganisms can influence the timing of plant flowering. Microbiome 6(1):1-12.
Ma B, Wang H, Dsouza M, Lou J, He Y, Dai Z, Brookes PC, Xu J, Gilbert JA (2016) Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J 10(8):1891-1901.
Pang Z, Chen J, Wang T, Gao C, Li Z, Guo L, Xu J, Cheng Y (2021) Linking plant secondary metabolites and plant microbiomes: a review. Front Plant Sci 12:621276.
Rodriguez PA, Rothballer M, Chowdhury SP, Nussbaumer T, Gutjahr C, Falter-Braun P (2019) Systems biology of plant-microbiome interactions. Mol Plant 12(6):804-821.
Schmitz L, Yan Z, Schneijderberg M, de Roij M, Pijnenburg R, Zheng Q, Franken C, Dechesne A, Trindade LM, van Velzen R, Bisseling T, Geurts R, Cheng X (2022) Synthetic bacterial community derived from a desert rhizosphere confers salt stress resilience to tomato in the presence of a soil microbiome. ISME J 16(8):1907-1920.
Tan L, Zeng WA, Xiao Y, Li P, Gu S, Wu S, Zhai Z, Feng K, Deng Y, Hu Q (2021) Fungi-bacteria associations in wilt diseased rhizosphere and endosphere by interdomain ecological network analysis. Front Microbiol 12:722626.
Tecon R, Or D (2017) Biophysical processes supporting the diversity of microbial life in soil. FEMS Microbiol Rev 41(5):599-623.
Tian L, Chen P, Gao Z, Gao X, Feng B (2022) Deciphering the distinct mechanisms shaping the broomcorn millet rhizosphere bacterial and fungal communities in a typical agricultural ecosystem of Northern China. Plant Soil 474(1-2):469-484.
Trivedi P, Batista BD, Bazany KE, Singh BK (2022) Plant–microbiome interactions under a changing world: Responses, consequences and perspectives. New Phytol 234(6):1951-1959.
Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK (2020) Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol 18(11):607-621.
Trivedi P, Mattupalli C, Eversole K, Leach JE (2021) Enabling sustainable agriculture through understanding and enhancement of microbiomes. New Phytol 230(6):2129-2147.
Wagg C, Schlaeppi K, Banerjee S, Kuramae EE, van der Heijden MG (2019) Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat Commun 10(1):4841.
Wang J, Shi X, Zheng C, Suter H, Huang Z (2021) Different responses of soil bacterial and fungal communities to nitrogen deposition in a subtropical forest. Sci Total Environ 755:142449.
Wei X, Peng P, Peng F, Dong J (2021) Natural polymer Eucommia ulmoides rubber: a novel material. J Agric Food Chem 69:3797-3821.
White LJ, Ge X, Brözel VS, Subramanian S (2017) Root isoflavonoids and hairy root transformation influence key bacterial taxa in the soybean rhizosphere. Environ Microbiol 19(4):1391-1406.
Zhang B, Zhang J, Liu Y, Shi P, Wei G (2018) Co-occurrence patterns of soybean rhizosphere microbiome at a continental scale. Soil Biol and Biochem 118:178-186.

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No consistency in the mechanism of assembly and potential effects on the host of various dimensions of Eucommia ulmoides rhizosphere microbiota

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Abstract

Figures

Introduction

Materials and methods

Sample collection

DNA extraction, PCR amplification, and Illumina MiSeq sequencing of rhizosphere soil

Co-occurrence network construction

Statistical analyses

Results

Microbe interaction and network topology characteristics

Factors influencing microbial community α-diversity and co-occurrence networks

Effects of rhizosphere microbiota on host secondary metabolites

Discussion

Declarations

References

Supplementary Files

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