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Article

High-Throughput Sequencing Reveals the Effect of the South Root-Knot Nematode on Cucumber Rhizosphere Soil Microbial Community

by
Fan Yang
1,†,
Huayan Jiang
1,2,†,
Shen Liang
1,†,
Gaozheng Chang
1,
Kai Ma
1,
Lili Niu
1,
Guoquan Mi
1,
Yanling Tang
1,
Baoming Tian
1,2,* and
Xuanjie Shi
1,*
1
Institute of Horticulture, Henan Academy of Agricultural Sciences, Graduate T&R Base of Zhengzhou University, Zhengzhou 450002, China
2
Henan International Joint Laboratory of Crop Gene Resources and Improvement, School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(7), 1726; https://doi.org/10.3390/agronomy13071726
Submission received: 25 May 2023 / Revised: 23 June 2023 / Accepted: 26 June 2023 / Published: 27 June 2023
(This article belongs to the Special Issue Metagenomic Analysis for Unveiling Agricultural Microbiome)
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Abstract

:
Due to long-term cultivation in greenhouses, cucumbers are susceptible to root-knot nematode (RKN), resulting in reduced yield and quality. The objective of this study was to investigate the effect of RKN on the rhizosphere microbial community of cucumber. Understanding the composition of rhizosphere bacterial and fungal communities and the possible interaction between microorganisms and RKN is expected to provide a reference for the eco-friendly control of M. incognita in the future. Three different groups were selected for sampling based on the RKN incidence and root galling scale (NHR, 0%, no root galling; NR, 5–15%, root galling scale 1–2; NS, 60–75%, root galling scale 4–5). Soil properties were determined to evaluate the effect of M. incognita on rhizosphere soil. High-throughput sequencing was used to examine the bacterial and fungal communities in rhizosphere soil. The results showed that the contents of soil nutrients and enzyme activities were significantly lower in the NS than in the NHR. The alpha diversity showed that M. incognita had a greater effect on rhizosphere soil bacteria than on fungi. In beta diversity, there were significant differences among the three groups by PCoA (p = 0.001). Furthermore, bacteria and fungi with significant differences in relative abundance were screened at the genus level for a correlation analysis with soil factors, and a correlation analysis between the bacteria and fungi was performed to study their relationships. A redundancy analysis (RDA) of rhizosphere microorganisms and soil properties showed a negative correlation between nematode contamination levels and soil nutrient content. Finally, we predicted the interaction among RKN, soil factors, and the rhizosphere microbial community, which provided evidence for the prevention of RKN via microecological regulation in the future.

1. Introduction

The cucumber (Cucumis sativus L.) is widely cultivated and provides a high number of vitamins and minerals and a high amount of fiber and roughage [1]. China plays an essential role in the cultivation and consumption of cucumbers. In terms of planting methods, greenhouse planting accounts for a considerable proportion, and it is still increasing year by year [2,3]. However, greenhouses with a single crop and a high multiple cropping index provide a suitable place for a variety of cucumber diseases and pests, among which, root-knot nematode (RKN) disease is the major disease affecting the yield and quality of cucumber [4]. RKN (Meloidogyne arenaria, M. enterolobii, M. incognita, and M. javanica) has been reported to cause large economic losses [5]. Among them, the southern root-knot nematode (SRKN, M. incognita) is particularly harmful, and it is known to be parasitic in cucumbers, tomatoes, watermelons, and many other plants [6]. Nematodes invade plant root cells, absorb nutrients, and suppress plant immune systems [7]. They will generate a large number of galls, which affects the absorption of nutrients by the host, causing a significant decrease in quality and yield [8].
Until now, conventional chemical nematicides have been used to control nematode diseases, whereas the massive use of nematicides has caused problems in food safety and environmental pollution and has ultimately affected human health and the sustainable development of agriculture. Therefore, it is very important and urgent to develop an environmentally friendly nematode control strategy to ensure food safety and crop production. By isolating plant endophytes and soil microorganisms, the development of biocontrol methods utilizing beneficial antagonists as a promising option has received increasing attention. In recent years, researchers have isolated many microorganisms from plant tissue or soil that are able to suppress different plant pathogens. For example, Bacillus subtilis YB-15 was isolated from wheat roots and soil and has been shown to be effective in reducing the growth of Fusarium graminearum [9]; Flavobacterium TRM1 isolated from rhizosphere soil can effectively inhibit Ralstonia solanacearum [10]; and Pseudomonas aeruginosa NXHG29 isolated from soil can also reduce the occurrence of tobacco bacterial wilt caused by R. solanacearum [11]. An increasing number of rhizosphere bacterial, fungal, and endophytic bacteria have been demonstrated to successfully reduce plant disease incidence [12]. In addition, some studies have confirmed that the interaction among various microorganisms may also be an important condition for plants to enhance their resistance to diseases [13]. Recent studies have shown that plant microbiota can confer a broad range of immune functions to plant hosts and that this process is strongly dependent on the interaction between soil nutrient status and the plant immune system [ss,sss]. The root microbial-mediated innate immune stimulation of plants can develop resistance to various pathogens. In Arabidopsis, the transcription factor MYB72 plays a key role in the regulation of induced systemic resistance triggered by Trichoderma spp. fungi and the bacterium Pseudomonas simiae [14,15]. Interestingly, MYB72 is also involved in the Arabidopsis response to iron deficiency [16], suggesting a direct interaction between nutritional stress and immunity. The direct suppression of pathogens by members of the microbiota in the plant root system has been reported several times [17,18]. Modalities include the secretion of antimicrobial compounds [19], hyperparasitism [20], and competition for resources such as nutrients or space to suppress pathogenic microorganisms [21].
Consequently, to develop biological agents for the biological control of M. incognita, it is essential to comprehend the microbial composition of the cucumber rhizosphere infected with M. incognita. However, research in this field is relatively limited. Based on the evidence obtained thus far, we speculated that the rhizosphere microbial community structure of M. incognita-infected cucumbers would be significantly changed. Moreover, RKN infection may have various influences on cucumber rhizosphere bacteria and fungi, to different degrees. To validate our hypotheses, different plots of rhizosphere soil were collected to explore the effect of M. incognita on the rhizosphere microbial community structure. Our findings will contribute to a better understanding of how M. incognita affects microbial communities in cucumber rhizosphere soils and provide a reference for the biological control of M. incognita.

2. Materials and Methods

2.1. Study Area and Experimental Description

Cucumber cultivar BoJie616 was planted in greenhouse facilities of Beiwang village in Luoyang, Henan province, China (34°66′ N, 112°52′ E), and the rhizosphere soil was obtained from different greenhouses. To study the effects of M. incognita on the cucumber rhizosphere microbial community, we selected greenhouses with different degrees of RKN incidence for sampling. The specific groups are as follows: NHR (no disease, no root galling), NR (incidence rate 5%–15%, root galling scale 1–2), and NS (incidence rate 60%–75%, root galling scale 4–5). The root galling scale was determined at the time of collection of the rhizosphere soil. Briefly, 10–15 cucumber plants were taken randomly from each plot to determine the degree of root galling, which was graded according to the method of Taylor and Sasser (1978) [22]. Consistent field management was applied during the experiment.

2.2. Soil Sampling

All cucumber seedlings in this study were factory planted to the three-leaf stage and then uniformly transplanted to different greenhouses for standardized management. Sampling was carried out in late May of 2022, which was approximately the 80th day after transplanting the cucumber seedlings to different greenhouses. Each greenhouse was approximately 8 m in width and 65 m in length, and the distance between adjacent greenhouses was 2 m. Based on the RKN incidence and root galling scale, the three adjacent greenhouses were divided into NHR, NR, and NS, and the rhizosphere soil was collected. Rhizosphere soil was sampled as described in a previous study [23]. Briefly, the roots were first carefully dug out from the soil, and large pieces of soil around the root system were removed. After gently shaking off the soil particles attached to the root surface and removing impurities such as fallen leaves and roots with a 1-mm sieve, the soil was finally collected into 15 mL centrifuge tubes. Soil tightly adhered to the root surface was referred to as rhizospheric soil. For each greenhouse, the soil samples were taken in an S-type sampling trajectory, and 5 soil samples were collected and pooled to make a composite sample. All soil samples were rapidly frozen in liquid nitrogen for 1 h and then stored in a −80 °C refrigerator for DNA extraction and detection of soil properties.

2.3. DNA Extraction, PCR Amplification, and Sequencing

Rhizosphere soil pellets were removed from the −80 °C freezer, and 0.2 g was transferred into 96-well DNA extraction plates. Microbial genomic DNA was extracted from 18 samples using the E.Z.N.A.® soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) according to the manufacturer’s instructions. Finally, a NanoDrop 2000 UV-vis spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) was used to detect the concentration and purity of DNA using standard methods. Bacterial 16S rRNA gene amplicons were amplified with universal primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), which anneal to the hypervariable region V3-V4 of the bacterial 16S rRNA gene. For identification of fungi, the internal transcribed spacer (ITS) region of nuclear rDNA was amplified using the primer pair ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) using an ABI GeneAmp® 9700 PCR thermocycler (Applied Biosystems, Foster City, CA, USA). PCR amplification was performed with default parameters. PCR was performed in triplicate. The PCR product was extracted from a 2% agarose gel, purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions, and quantified using a Quantus™ Fluorometer (Promega, Madison, WI, USA). Then, the 16S rRNA gene and the ITS region of fungal ribosomal DNA were sequenced on the NovaSeq PE250 platform (Illumina, San Diego, CA, USA) with six repetitions in each soil plot according to standard protocols.

2.4. Bioinformatics Analysis

Paired-end reads were assigned to each sample based on their unique barcode and truncated by cutting off the barcode and primer sequence. Paired-end reads were merged using FLASH [24] for 16S rRNA gene sequencing and PEAR (v0.9.6) [25] for ITS sequencing. To produce high-quality clean tags, raw reads were filtered under specific conditions using fqtrim (v0.94). Vsearch software (v2.3.4) was used to filter chimeric sequences [26]. DADA2 [27] was used to dereplicate features and obtain the feature sequence and feature table. Alpha and beta diversity were calculated using QIIME2 [28].

2.5. Soil Chemical Properties and Enzymatic Activity Assay

Twelve soil properties were measured according to previously reported methods. To determine soil available nitrogen (AN), alkaline hydrolysis diffusion was used [29]. Soil available phosphorus (AP) was quantified using molybdenum-antimony anti-spectrophotometry [30]. Available copper (ACu), available iron (AFe) and exchangeable calcium (ECa) were measured using atomic absorption spectrophotometry [31]. Electrical conductivity (EC) was determined using a conductivity monitor [32]. Soil organic matter (OM) was determined through oxidization [33]. Microbial biomass carbon (MBC) was determined using the chloroform fumigation extraction method [34]. Soil dehydrogenase (S-DHA) was assessed using a colorimetric procedure [35]. Soil sucrase activity (S-SR) was assayed using the 3,5-dinitrosalicylic acid colorimetric method [36]. Soil urease activity (S-UR) was assayed using the indophenol blue colorimetric method [37]. Soil acid phosphatase (S-ACP) was assayed using the standard method [38]. Soil properties were measured with three repetitions in each soil plot. The above experimental technology service was provided by Norminkoda Biotechnology Co., Ltd. (Wuhan, China).

2.6. Statistical Analyses

The IBM SPSS software program (IBM Corporation, V.20.0, New York, NY, USA) and R software (version 3.5.2) were used for statistical analysis. One-way analysis of variance (ANOVA) with Tukey’s post hoc test was performed for comparisons among multiple groups. Relative abundance differences at the phylum and genus levels between two groups were analyzed using the Wilcoxon rank sum test. All statistical tests performed in this study were considered significant and extremely significant at p < 0.05 and p < 0.01.

3. Results

3.1. Changes in Rhizosphere Soil Properties of Cucumber Infected with M. incognita

To evaluate the effects of M. incognita on the chemical properties and enzymatic activities of rhizosphere soil, we measured 12 soil properties. Our results revealed significant differences among the properties. AN, AP, and S-ACP exhibited consistent trends, with significantly higher levels observed in NR than in NHR, while NS showed significantly lower levels than NHR. The remaining nine properties showed a significant decrease in both NR and NS compared with NHR (Figure 1). These results indicated a significant effect of RKNs on cucumber rhizosphere soil properties.

3.2. Diversities of Rhizosphere Bacterial and Fungal Communities

After data filtering and removing low-quality reads for the bacteria and fungi, the filtered reads ranged from 73,068 to 80,495 and 68,048 to 104,095, respectively, and the average reads were 76,632 and 86,007, respectively. The Q30 of 16S rRNA and ITS sequencing was above 91.2% and 90.66%, respectively (Table S1). The above data indicated that the quality of the sequencing data was reliable. High-quality paired-end reads were then connected to tags and clustered into 11,381 and 1922 amplicon sequence variants (ASVs) in bacteria and fungi, respectively. Among the identified ASVs of the bacteria and fungi, 5027 and 294 were common among the three groups, respectively (Figure 2A,D).
To comprehensively evaluate the alpha diversity of rhizosphere bacteria and fungi, we used Chao1 and Observed_features to represent the richness, the Shannon, and the Simpson indices to indicate the diversity, the Pielou evenness index, and the Goods coverage (Table S2). The Observed_features and Simpson of the bacteria were significantly different among the three groups with p values = 0.009 and 0.004, respectively. The Observed_features and Simpson of the fungi showed no significant difference among the three groups, with p values of 0.13 and 0.135, respectively. The Goods coverage showed no significant difference in either the bacteria or the fungi, with p values of 0.067 and 0.126, respectively (Figure 2B,E). The beta diversity revealed the distinct clustering of the three soil groups by PCoA (Figure 2C,F). In brief, the ASV abundance of the NS was separated from that of the NHR and NS. However, NHR and NS had a small overlap, indicating that the microbial composition of NHR and NR was closer, which was consistent in the fungi and the bacteria. The ANOSIM analysis showed the different compositions of the three soil groups (R = 0.673, p = 0.001 for bacteria; R = 0.451, p = 0.001 for fungi).

3.3. Characterization of Bacterial and Fungal Communities at the Phylum and Genus Levels

To investigate the influence of microbial communities by M. incognita, the relative abundance of soil bacteria and fungi at the phylum and genus levels was compared (Figure 3). In bacterial communities, the dominant phyla were Proteobacteria Actinobacteriota, Acidobacteriota, Chloroflexi, Gemmatimonadota, and Myxococcota (Figure 3A). The dominant genera were Vicinamibacteraceae, A4b, MND1, Rokubacteriales, and RB41 (Figure 3B). In fungal communities, the dominant phyla were Ascomycota, Mortierellomycota, Basidiomycota, and Rozellomycota (Figure 3C). The dominant genera were Chaetomium, Mortierella, Acremonium, Scedosporium, Alternaria, and Nigrospora (Figure 3D).
To further confirm the differences in the microorganism communities at the genus level, we used the Kruskal-Wallis rank sum test to select microbial communities with significant differences in rhizosphere soils. A fold change ≥ 2 and p < 0.05 were set as the thresholds for significantly differential abundance. At the bacterial genus level, the number of significantly different genera in “NR vs. NHR” and “NS vs. NHR” was 50 and 141, respectively, of which 23 were shared (Figure 4A). A clustering heatmap was drawn to demonstrate their relative abundance levels (Figure 4B). Similarly, at the level of fungal genera, a total of four genera were screened. The clustering heatmap is shown in Figure 4C,D.

3.4. Potential Relationships among Microbial Community Composition and Soil Properties

We performed a Spearman correlation analysis to investigate the relationship between microbial community changes and soil properties (Figure 5). At the bacterial genus level, the relative abundance of seven genera was positively correlated with the soil factor, and eleven genera were negatively correlated (Figure 5A). At the fungal genus level, the abundance of three genera was positively correlated with the soil factors, while Pichia showed a lower level of negative correlation with the soil factors (Figure 5B). Additionally, we performed a correlation analysis of the relative abundance between the bacteria and the fungi at the genus level, and the results showed that there was a very significant positive correlation between Stromatonectria in fungi and Synechococcus_IR11 and Vulcaniibacterium in bacteria and a very significant negative correlation with Desulfuromonadaceae. Another fungus, Mortierella, had a very significant positive correlation with the bacteria Marine_Group_II and a very significant negative correlation with Ruminiclostridium (Figure 5C). A redundancy analysis (RDA) was conducted to further study the relationship between the community composition of microorganisms and soil factors. The detrended correspondence analysis (DCA1) values of bacteria and fungi were 2.07474 and 0.71634, respectively, both less than 3.0, indicating that the data may be linearly distributed, so we chose to use RDA rather than CCA as the method of analysis. The first and second axes in the bacterial and fungal communities of the RDA model explained 50.95% and 14.97% and 55.87% and 34.55% of the variation, respectively (Figure 5D,E).

3.5. The Effect of Rhizosphere Microorganism Interactions on M. incognita

Based on the interaction between soil factors and microbial communities and the correlation between microorganisms, we speculated that soil properties, rhizosphere bacteria, and fungi may have direct effects on the occurrence and development of M. incognita in the cucumber rhizosphere. Then, the difference analysis of Stromatonectria and Mortierella screened in RDA and the correlation analysis, as well as Desulfuromonadaceae, Synechococcus_IR11, Vulcaniibacterium, Ruminiclostridium, and Marine_Group_II, were performed (Figure 6A). A schematic diagram of soil factors and the interactions of rhizosphere microorganisms was drawn to reveal the possible mechanisms of interroot microbial resistance to M. incognita (Figure 6B). The results showed that the relative abundance of Stromatonectria and Mortierella in fungal genera was significantly lower in NHR than in NR and NS. In bacteria, the abundance of Desulfuromonadaceae and Ruminiclostridium in the NHR was significantly higher than that in the NR and NS. The abundance of Synechococcus_IR11, Vulcaniibacterium, and Marine_Group_II in the NR and NS was significantly higher than that in the NHR.

4. Discussion

Cucumber is a typical cold-sensitive plant that is usually grown in greenhouses. However, cucumbers grown in greenhouses are generally planted continuously for many years, leading to the accumulation of pathogenic microorganisms and nematodes, which can easily cause soil-borne diseases. For decades, RKN infestations in crops have been controlled through the application of chemical nematicides. However, the excessive use of chemical nematicides has a huge negative impact on the environment. Biological control is usually considered a much more cost-effective alternative with less impact on the environment compared with both chemical and physical methods [39]. Previous studies have also demonstrated that microorganisms play an important role in soil properties and plant disease resistance. Among them, Streptomyces platensis could inhibit the growth of the spores of Plasmodiophora brassicae [40]. In addition, single or multiple rhizosphere bacteria in the soil are separated and made into biological control agents, which can control RKN. For example, a mix of Bacillus amyloliquefaciens and B. subtilis strains can reduce the amount of M. incognita in the soil [41]. Therefore, it is of great value to study the composition of microbial communities in rhizosphere soil and the changes in the composition of bacteria and fungi with different pathogenicity degrees for the future biological control of M. incognita. In recent years, with the promotion of high-throughput sequencing technology, researchers have carried out a large number of studies on the diversity of soil microbial communities, which can provide an in-depth understanding of the composition of soil and rhizosphere microbial communities [42].

4.1. Changes in Rhizosphere Soil Properties of Cucumber Infected with M. incognita

Soil properties have an important influence on plant growth. Generally, the richness of microorganisms is in direct proportion to the nutrients of the soil. [43]. This study showed that different incidences of M. incognita could significantly reduce organic matter, microbial biomass carbon, soil fertility (AFe, ACu, ECa, OM, EC), and enzyme activity (S-SR, S-UR, S-DHA) (Figure 1). Another study also showed that the available phosphorus content in the soil was negatively correlated with the incidence of Fusarium wilt [44]. It may be that a higher content of nutrients such as OM, nitrogen, and phosphorus in the soil can promote plant growth and improve plant disease resistance. Another important factor in the soil is the activity of various soil enzymes, which is also crucial for the growth and development of plants [45]. As the incidence of RKN increased, the activities of soil sucrase, urease, and dehydrogenase were significantly reduced compared with NHR, suggesting that soil enzyme activities may enhance the protection of plant cells; however, it was unfavorable for M. incognita to affect plant nitrogen utilization and carbon metabolism.

4.2. Rhizosphere Soil Microbial Diversity and Community Compositions

Maintaining healthy and stable soil microbial diversity has a certain inhibitory effect on pathogens. The perturbation of the rhizosphere microbial community composition is a significant contributor to soil-borne diseases. The multifunctionality of ecosystems in soil is determined by soil biodiversity and microbial composition, which have a suppressive impact on plant pathogens [46,47]. In general, a more diverse microbial community composition is associated with a greater capacity to impede pathogens [48]. A study has reported that there is a significant correlation between the abundance of rhizosphere microbial communities and the ability of plants to suppress pathogens. It was shown that there is a close correlation between higher bacterial and fungal diversity and a lower incidence of wilt disease in tomato rhizosphere soils [49]. Similar to previous research, in terms of bacterial community composition, Proteobacteria, Actinobacteria, and Acidobacteria were the dominant phyla [50]. In addition, several other dominant bacterial phyla, such as Acidobacteria and Bacteroidetes, have also been reported to play important roles in element utilization and the decomposition of organic matter [51]. At the genus level, the results showed that the major microbial groups were Vicinamibacteraceae, Rokubacteriales, Nitrososphaeraceae, Bacillus, and Pseudomonas. Rokubacteriales were previously reported to be nitrogen-efficient bacteria that affect the nitrogen reduction process in rhizosphere soil and accelerate the loss of nitrogen sources from the rhizosphere soil, thus reducing the nitrogen metabolic pathways of rhizosphere microorganisms and plants [52]. The results were consistent with the constant decrease in available nitrogen in the NHR that we observed (Figure 1). Pseudomonas are one of the most abundant bacterial genera in the soil and inter-rhizosphere and play an important role in promoting plant health. It has been demonstrated that Pseudomonas putida Sneb821 can stimulate resistance to SRKNs in tomato. Pseudomonas putida Sneb821 was able to suppress the expression of Sly-miR482d, which in turn up-regulated the expression of its target gene NBS-LRR, promoted H2O2 accumulation in tomato roots, and regulated SOD and POD enzyme activities, thereby regulating the immune response of tomato to SRKN infestation [53]. Potato scab and blight are two major diseases that can cause severe crop losses. They are caused by the bacterium Streptomyces scabies and an oomycete known as Phytophthora infestans, respectively. Studies have confirmed that Pseudomonas can produce hydrogen cyanide (HCN) and cyclic lipopeptide (CLP), key compounds that inhibit Streptomyces scab and the disease-causing blight of potato, thereby suppressing disease development [54]. Moreover, we also observed a significant decrease in the relative abundance of Bacillus among rhizobacteria as the incidence of RKN increased. Bacillus spp. can directly inhibit RKN disease and promote other bacteria to inhibit the occurrence of plant diseases [55]. Another previous study showed that Bacillus spp. can effectively inhibit various potential plant pathogens, including soil bacterial pathogens, soil fungal pathogens, soil viruses, and nematodes [56]. Fungal communities are simpler in composition than bacterial communities. The main fungal phyla are Ascomycota, Morphomycota, and Basidiomycota. Similar results were obtained by other investigators [57]. At the fungal genus level, Trichoderma is an important biocontrol fungus that not only fights and controls ecologically important plant pathogenic fungi, viruses, and bacteria that are widely present, but also effectively controls nematodes, especially RKNs. Fungal species of the genus Trichoderma are considered as biocontrol agents against plant-associated fungal pathogens and can suppress root-knot nematodes through competitive action as well as by producing specific metabolites [58,59].

4.3. Mechanisms of Interactions between Rhizosphere Microorganisms Resisting M. incognita

Furthermore, root-associated microbial communities may play an important role in modulating root-soil nutrient–nematode interactions [60]. There are several species of bacteria and fungi that are closely associated with nematodes, and some have been proven to have harmful effects on them [61]. Some fungi in the soil can secrete toxins or lytic enzymes to kill or inhibit nematodes, such as the toxin produced by Pleurotus ostreatus, which is fudecenedioic acid [62]. In addition, Paecilomyces lilacinus and Trichoderma strictum can control RKN through parasitism [63,64]. Earlier research has demonstrated that some Mortierella spp. may generate antibiotics, and numerous isolates have been widely used as prospective antagonistic agents against various pathogens [65]. It has also been discovered that Acremonium strictum could reduce M. incognita populations in tomato roots, indicating that some of the identified microbial taxa may be useful for biocontrol strategies [64]. Previous studies focused on bacteria or fungi in the soil, and there has been more research on bacteria than fungi [18]; hence, fungal communities have sometimes been neglected. In the correlation analysis of bacteria and fungi in this study, some key bacteria and fungi had significant positive or negative associations, indicating that they may have synergistic or antagonistic effects against RKN (Figure 5C). As reported in previous studies, Mortierella spp. can suppress Fusarium oxysporum in the soil to reduce vanilla Fusarium wilt disease [66]. In addition, the latest results also prove that symbiotic bacteria in fungi can help to protect them from nematode attack, suggesting that the interaction with bacteria can inhibit the development of nematodes [67]. In response to M. incognita, rhizosphere bacteria and fungi may use the following four pathways: (1) causing parasitic effects on nematodes; (2) using toxic and harmful substances to nematodes; (3) changes in root exudates to affect nematode development; and (4) competition with nematodes for nutrition and space position. There may also be other pathways, such as promoting plant growth and inducing host resistance (Figure 6B).

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13071726/s1. Supplementary Table S1: Overview of bacterial and fungal diversity sequencing data; Table S2: Bacteria and fungi alpha diversity index table.

Author Contributions

Conceptualization, B.T. and X.S.; Software, H.J.; Validation, S.L., G.C. and K.M.; Formal Analysis, L.N.; Investigation, F.Y. and Y.T.; Resources, F.Y.; Data Curation, G.M.; Writing—Original Draft Preparation, F.Y.; Writing—Review and Editing, B.T. and X.S.; Visualization, F.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Scientific and Technological Breakthrough Foundation of Henan Province Project (212102110426, 222102110004) and the Technology System of Watermelon Industry in Henan Province (HARS-22-10-G1).

Data Availability Statement

All raw sequence data have been made available in the NCBI Sequence Read Archive (SRA) database under the bioproject accession numbers PRJNA907508 and PRJNA907474.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Effect of M. incognita on rhizosphere soil chemical properties and enzymatic activities. (A) AN, available nitrogen; (B) AP, available phosphorus; (C) ACu, available copper; (D) AFe, available iron; (E) ECa, exchangeable calcium; (F) EC, electrical conductivity; (G) OM, organic matter; (H) MBC, microbial biomass carbon; (I) S-DHA, soil dehydrogenase; (J) S-SR, soil sucrase activity; (K) S-UR, soil urease activity; (L) S-ACP, soil acid phosphatase. The symbol “*” indicates significant differences (p < 0.05, n = 3) among three groups, based on one-way ANOVA followed by the LSD test. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 1. Effect of M. incognita on rhizosphere soil chemical properties and enzymatic activities. (A) AN, available nitrogen; (B) AP, available phosphorus; (C) ACu, available copper; (D) AFe, available iron; (E) ECa, exchangeable calcium; (F) EC, electrical conductivity; (G) OM, organic matter; (H) MBC, microbial biomass carbon; (I) S-DHA, soil dehydrogenase; (J) S-SR, soil sucrase activity; (K) S-UR, soil urease activity; (L) S-ACP, soil acid phosphatase. The symbol “*” indicates significant differences (p < 0.05, n = 3) among three groups, based on one-way ANOVA followed by the LSD test. * p < 0.05, ** p < 0.01, *** p < 0.001.
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Figure 2. The alpha and beta diversity of rhizospheric bacterial and fungal communities. Venn diagram of bacterial (A) and fungal (D) ASVs identified in the three groups. Statistical analysis of differences in alpha diversity indicators (Goods coverage, Observed features, and Simpson index) of bacteria (B) and fungi (E) based on the Kruskal–Wallis rank sum test with Dunn’s test (* p < 0.05; ** p < 0.01). Principal coordinates analysis (PCoA) shows the beta diversity of bacteria (C) and fungi (F) based on Bray–Curtis distances. The p value represents the analysis of ANOSIM.
Figure 2. The alpha and beta diversity of rhizospheric bacterial and fungal communities. Venn diagram of bacterial (A) and fungal (D) ASVs identified in the three groups. Statistical analysis of differences in alpha diversity indicators (Goods coverage, Observed features, and Simpson index) of bacteria (B) and fungi (E) based on the Kruskal–Wallis rank sum test with Dunn’s test (* p < 0.05; ** p < 0.01). Principal coordinates analysis (PCoA) shows the beta diversity of bacteria (C) and fungi (F) based on Bray–Curtis distances. The p value represents the analysis of ANOSIM.
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Figure 3. The composition of microorganism communities at the phylum and genus levels (relative abundance top 20). Phylum (A) and genus (B) levels of bacteria. Phylum (C) and genus (D) levels of fungi.
Figure 3. The composition of microorganism communities at the phylum and genus levels (relative abundance top 20). Phylum (A) and genus (B) levels of bacteria. Phylum (C) and genus (D) levels of fungi.
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Figure 4. Differential analysis of rhizosphere soil microorganisms at the genus level. Venn diagrams (A) and clustering heatmap (B) of bacteria with significant differences at the genus level in “NR vs. NHR” and “NS vs. NHR”. Venn diagrams (C) and clustering heatmap (D) of fungi with significant differences at the genus level in “NR vs. NHR” and “NS vs. NHR”.
Figure 4. Differential analysis of rhizosphere soil microorganisms at the genus level. Venn diagrams (A) and clustering heatmap (B) of bacteria with significant differences at the genus level in “NR vs. NHR” and “NS vs. NHR”. Venn diagrams (C) and clustering heatmap (D) of fungi with significant differences at the genus level in “NR vs. NHR” and “NS vs. NHR”.
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Figure 5. Relationships among microbial community composition and soil properties. Correlation heatmap between the key rhizosphere soil bacterial community (A) and fungal community (B) at the genus level and soil properties of rhizosphere soil. (C) Correlation analysis between the key rhizosphere soil bacterial community and fungal community. Redundancy analysis (RDA) of the relationship between soil bacterial (D) and fungal (E) community structures and soil factors in different soil groups. In the correlation analysis, the legend shows the correlation coefficient value, red represents the positive correlation and blue represents the negative correlation; color depth indicates the strength of the correlation; * p < 0.05, ** p < 0.01.
Figure 5. Relationships among microbial community composition and soil properties. Correlation heatmap between the key rhizosphere soil bacterial community (A) and fungal community (B) at the genus level and soil properties of rhizosphere soil. (C) Correlation analysis between the key rhizosphere soil bacterial community and fungal community. Redundancy analysis (RDA) of the relationship between soil bacterial (D) and fungal (E) community structures and soil factors in different soil groups. In the correlation analysis, the legend shows the correlation coefficient value, red represents the positive correlation and blue represents the negative correlation; color depth indicates the strength of the correlation; * p < 0.05, ** p < 0.01.
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Figure 6. A predictive model for the synergistic effect of rhizosphere microorganisms combined with soil factors against M. incognita. (A) Boxplot of the relative abundance of microorganisms. Differences between groups were analyzed using the Wilcoxon rank sum test. (n = 6. * p < 0.05; ** p < 0.01). (B) Schematic diagram of the model for synergistic suppression of RKNs by soil factors and rhizosphere microorganisms. Red arrows indicate elevated microbial abundance and blue arrows indicate decreased microbial abundance.
Figure 6. A predictive model for the synergistic effect of rhizosphere microorganisms combined with soil factors against M. incognita. (A) Boxplot of the relative abundance of microorganisms. Differences between groups were analyzed using the Wilcoxon rank sum test. (n = 6. * p < 0.05; ** p < 0.01). (B) Schematic diagram of the model for synergistic suppression of RKNs by soil factors and rhizosphere microorganisms. Red arrows indicate elevated microbial abundance and blue arrows indicate decreased microbial abundance.
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Yang, F.; Jiang, H.; Liang, S.; Chang, G.; Ma, K.; Niu, L.; Mi, G.; Tang, Y.; Tian, B.; Shi, X. High-Throughput Sequencing Reveals the Effect of the South Root-Knot Nematode on Cucumber Rhizosphere Soil Microbial Community. Agronomy 2023, 13, 1726. https://doi.org/10.3390/agronomy13071726

AMA Style

Yang F, Jiang H, Liang S, Chang G, Ma K, Niu L, Mi G, Tang Y, Tian B, Shi X. High-Throughput Sequencing Reveals the Effect of the South Root-Knot Nematode on Cucumber Rhizosphere Soil Microbial Community. Agronomy. 2023; 13(7):1726. https://doi.org/10.3390/agronomy13071726

Chicago/Turabian Style

Yang, Fan, Huayan Jiang, Shen Liang, Gaozheng Chang, Kai Ma, Lili Niu, Guoquan Mi, Yanling Tang, Baoming Tian, and Xuanjie Shi. 2023. "High-Throughput Sequencing Reveals the Effect of the South Root-Knot Nematode on Cucumber Rhizosphere Soil Microbial Community" Agronomy 13, no. 7: 1726. https://doi.org/10.3390/agronomy13071726

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