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Article

Contrasting Key Bacteria and Fungi Related to Sugar Beet (Beta vulgaris L.) with Different Resistances to Beet Rot under Two Farming Modes

by
Ming Lin
1,2,
Yuanhang Zhou
2,
Runlai Xu
3,
Chenghang Du
3,
Ronghua Wang
4,
Weidan Lu
2,
Kuerban Abudukadier
1 and
Zhencai Sun
3,*
1
Economic Crop Research Institute, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
2
Manas Agricultural Experimental Station of Xinjiang Academy of Agricultural Sciences, Changji 832200, China
3
Department of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
4
Xinjiang Shihezi Academy of Agricultural Sciences, Shihezi 832061, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(3), 825; https://doi.org/10.3390/agronomy13030825
Submission received: 15 February 2023 / Revised: 3 March 2023 / Accepted: 10 March 2023 / Published: 11 March 2023
(This article belongs to the Special Issue Effects of Tillage, Cover Crop and Crop Rotation on Soil)
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Abstract

:
Sugar beet production is threatened by beetroot rot, which can be triggered by consecutive monoculture. Previous studies have shown the beneficial function of microbes affiliated with different plant compartments in inhibiting various plant pathogens. However, whether sugar beet root can recruit particular microbes at the risk of beet rot is still unclear. Therefore, this study explored the composition and community structure of bacteria and fungi of the different compartments (endosphere root, rhizosphere, bulk soil) under two farming modes (monoculture and rotation). Our result showed that the farming mode significantly affected the community structure of bacteria and fungi in bulk soil. In the rhizosphere, the community structures of bacteria between the two varieties were similar under rotation mode, and markedly different under monoculture mode. The bacterial and fungal diversity in the rhizosphere and endophytic root of the rot-suppressive variety was higher than in the rot-conducive variety. Under monoculture mode, the beneficial microbes as biomarkers were enriched in the rot-resistant variety, e.g., operational taxonomic units (OTUs) affiliated to the genus of Sordariomycetes, Cordycipitaceae, Lecanicillium, Plectosphaerellaceae, S085, Pedosphaeraceae in the rhizosphere and the genus of Actinobacteria, and Pseudonocardia, Exobasidiomycetes in the endophytic root, while for the rot-conducive variety, OTUs affiliated to the genus of Chitinophagaceae, Flavisolibacter in the rhizosphere and the Novosphingobium, Sphingobacterium, Tilletiopsis_washingtonensis, and Flavobacterium in the endophytic root. The network analysis showed that OTUs affiliated to the order of Saccharimonadales, Anaerolineae, the family of Saprospiraceae, the genus of Subgroup_10 (belonging to the family of Thermoanaerobaculaceae), Lysobacter, and AKYG587 were the keystone taxa in the rot-suppressive variety, while both beneficial and harmful microbes in the rot-conducive variety, such as Pedobacter, Ferruginibacter, and P3OB-42, were present. The variation in soil pH was shown to be the critical contributor to the microbial difference. In summary, the farming mode is critical in shaping bulk soil microbial structure by changing soil pH. Under monoculture mode, the rot-suppressive variety has more microbial diversity in both the rhizosphere and endophytic root, and enriched different beneficial microbes relative to the rot-conducive variety; the underlying mechanisms and associations of critical microbes are worth further investigation.

1. Introduction

Beets are a biennial herb affiliated with the amaranth family, a genus of beets, and one of the most important economic crops in the world. Xinjiang is China’s largest sugar beet-producing region, producing more than half of the national sugar beet production. Beetroot rot is a serious soil-borne disease that occurs in growing sugar beet and is caused by a variety of fungi and bacteria, either separately or in combination, that can cause up to 40% loss of sugar beet. Monoculture of sugar beet is one of the main contributors to the occurrence of sugar beet rot through gradually excreting allelopathic substances to form a feasible condition for pathogenic microbes. However, targeting methods for inhibiting sugar beet rot are still missing, since the underlying mechanisms of sugar beet rot development and occurrence are largely unclear.
Generally, healthy soil has a stable system with high microbiological diversity and activity levels. Relative to healthy soil, previous studies have shown that the soil microbial community under soil-borne diseases is different [1], especially for the microbes in the rhizosphere, which are the critical microdomains in plant–soil microbe interaction [2,3,4]. Disturbance by incorrect soil management could cause oscillations in the soil microbial community and trigger high pathogenic microbial abundance and infection [2], indicating that the potentially beneficial pathogenic microbe-inhibiting microbe may have been disturbed. For instance, Wen et al. [5] found that the microbiome in a diseased rhizosphere caused by fusarium wilt was significantly reassembled. Through amplicon sequencing and isolation culture trials on disease-suppressive and -conducive soils, Zheng et al. [4] found that beneficial bacteria resistant to tomato wilt also play the role of keystone taxa in the microbial community network. In addition, the endophytes also resist pathogenic microorganisms directly or indirectly. Recent evidence has shown that the endophytic root microbiome participated in the suppressive function of the soil-borne disease caused by Rhizoctonia solani [6], in which fungal infection occurred in plant roots enriched for Chitinophagaceae and Flavobacteriaceae in the root endosphere, and for chitinase genes and various unknown biosynthetic gene clusters encoding the production of nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs). Matsumoto et al. [7] reported that bacterial seed endophytes, e.g., Sphingomonas melonis, can shape disease resistance in rice by releasing small signaling molecules to synergize with the host response to control the intrusive Burkholderia plantarii. Therefore, these findings provide new sight into the role of plant endophytes in plant disease infection.
Because of their varied genetic background, identical plants of different varieties can exhibit markedly different abilities to inhibit soil-borne diseases. For instance, some varieties are susceptible to pathogenic diseases, while others are resistant. Furthermore, plants, as the host of rhizosphere microbes and endophytes, generally interact with them to defend against pathogenic diseases. Gu et al. [8] reported that root exudates determine the structure of rhizosphere microbes and the function of pathogen suppression in tomato wilt. Similar findings were addressed by Kwak et al. [3], who found that flavobacteria were enriched in the tomato rhizosphere of the Hawaii7996 (a resistant variety), which can inhibit wilt occurrence compared with Moneymaker (a susceptible variety). More and more evidence shows that plants can recruit some beneficial microbes from soil to resist pathogenic infection in several ways, such as promoting plant growth and triggering the jasmonic acid and salicylic acid disease-resistant signaling pathways [9,10,11,12]. However, to our knowledge, it is not known whether sugar beet with different resistance to beet rot can interact with some beneficial microbes. If so, these microbes could potentially provide new insight into further prohibiting beet rot.
In this study, based on a 35-year history of sugar beet monoculture and a sugar beet–wheat rotation experimental field, we explored the microbial composition and community structure in different compartments of sugar beet rot, i.e., the rhizosphere, root endophytes, and soil, using different resistant varieties. Our aim is to explore the beneficial microbes affiliated with different varieties under two farming modes. We hypothesized that the beet rot-suppressing sugar beet might recruit some beneficial microbes in the rhizosphere, endophytes, or both compartments, compared with the beet rot-conducive variety under monoculture conditions. The study aims to provide fundamental knowledge for a deeper understanding of the mechanisms of sugar beet rot from potentially correlated microbes, and eventually to inhibit sugar beet rot.

2. Materials and Methods

2.1. Field Experiment and Sampling

The field sites are located on North Meridian Road, Agricultural Science Center Community Sugar Beet Research Institute, City of Shihezi, Xinjiang autonomous region (86°03′ E, 44°19′ N). This area belongs to a typical temperate continental climate. In recent decades, the annual temperature and rainfall have been 15 ℃ and 125–200 mm, respectively. The latitude is 442.9 m. The soil has a loamy texture. There are two adjacent experimental sites. One field site of 667 m2 has a 35-year history of monoculture of sugar root, and another field site of 667 m2 has a rotation mode of winter wheat–sugar beet. Two sugar beet varieties with different resistance to beet rot were planted in each field site, of which HDTY191 is a rot-conducive variety and CN0417 is a rot-suppressive variety. For convenience, HDTY191 was marked as the number “1” and “6” in monoculture mode and rotation mode, respectively; CN0417 was marked as the number of “4” and “7” in monoculture mode and rotation mode, respectively. Both varieties received the same fertigation management in the two field sites. While sampling, there no disease symptoms in the two varieties under rotation conditions; the rot disease rate was 11.0% and 32.8% for CN0417 and HDTY191 under monoculture conditions, respectively.
Prior to the sugar beet harvest, field sampling was conducted. In brief, three sampling sites along the diagonal were selected for each variety from each field site. Four plants and the surrounding bulk soil from each selected plant were sampled in each sampling site. The collected bulk soil from each sampling site was sieved through a 2 mm mesh and then merged. Subsequently, part of the bulk soil sample was put in a box with dry ice and later stored at −80 °C; the remaining bulk soil was air-dried for later soil properties measurement. After hand-shaking the root, soil tight to the root was collected using the fine brush as rhizosphere soil for each sampled sugar beet. The rhizosphere soil from each sampling site was merged as one sample, which was stored at −80 °C. After sampling the rhizosphere soil, the root’ssurface was disinfected with alcohol, washed using sterile deionized water and put in the box with dry ice. After being brought inside, the root was cut and ground in a mortar with liquid nitrogen. Similarly to the rhizosphere soil, each sampling site’s ground root was merged as one sample and saved at −80 °C.

2.2. DNA Extraction and Amplicon Sequencing

Following the previous method [13], total genomic DNA was extracted from samples of 0.5 g bulk soil, rhizosphere soil, and root using a Fast DNA spin kit (MP Biomedicals, LLC, Solon, OH, USA) following the manufacturer’s standard operation protocol. After checking the concentration, DNA samples were sent to Guangdong Magigene Biotechnology Co., Ltd., City of Guangzhou, China, for sequencing via Illumina® MiSeq. The following primers were used for amplifying bacteria and fungi in different samples. The bacterial primers of bulk soil and rhizosphere soil are 515F/806R, which is 799F/1193R for endophytic root bacteria. The fungal primers of bulk soil and rhizosphere soil are ITS3F/ITS4R and ITS2-2043R/ITS5-1737F for endophytic root fungi. Primers were synthesized by Invitrogen (Invitrogen, Carlsbad, CA, USA). PCRs containing 25 μL of 2× Premix Taq (Takara Biotechnology, Dalian Co. Ltd., China), 1 μL of each primer (10 mM), and 3 μL of DNA (20 ng/μL) in a volume of 50 µL were amplified by thermocycling (5 min at 94 °C for initialization, 30 cycles of 30 s denaturation at 94 °C, 30 s annealing at 52 °C, and 30 s extension at 72 °C, followed by 10 min of final elongation at 72 °C). The PCR instrument was a BioRad S1000 (Bio-Rad Laboratory, CA, USA). Afterwards, the clean paired-end reads were aligned by FLASH (V1.2.11, https://ccb.jhu.edu/software/FLASH/, accessed on 15 November 2022) and filtered to remove the barcode and primers using Mothur (V1.35.1) to obtain effective clean tags. By using USEARCH software V10 (http://www.drive5.com/usearch/, accessed on 18 November 2022), sequences with ≥97% similarity were assigned to the same operational taxonomic units (OTUs). The SILVA database (https://www.arb-silva.de/, accessed on 1 December 2022) and the UNITE (http://unite.ut.ee/index.php, accessed on 12 December 2022) database were used to annotate the taxonomic information of the 16S data and ITS, respectively. The OTUs annotated as chloroplasts or mitochondria (16S amplicons), and tags that could not be annotated to the kingdom, were removed. Finally, the total number of sequences and OTUs were counted.

2.3. Soil Chemical Properties

Soil organic carbon (OC) was determined using an elemental analyzer (Vario EL Ⅲ, CHNOS Elemental Analyzer, Elementar, Germany). Before the OC measurement, carbonates in the soil were removed by soaking the soil in 1M HCl with a ratio of 1:10 (w/v), then air dried at 60 for 24 h. Soil total nitrogen content (TN) was measured following the Dumas combustion method using an elemental analyzer (Vario EL III, CHNOS Elementar, Germany). Soil nitrate content was extracted with 0.01 mol L−1 CaCl2, and then the filtered supernatant was measured using a continuous flow analytical system (AA3, Braun rupee, Germany). Soil Olsen-P was measured based on the sodium hydrogen carbonate solution-Mo-Sb Anti spectrophotometric method, following the description of Olsen et al. [14]. Soil pH was determined using a pH meter by soaking the soil in water with a ratio of 1:4 (w/v).

2.4. Statistical Analysis

All data were checked for normality by Shapiro–Wilk tests and equal variance. A Hellinger transformation was employed to transform data to a normal distribution. A two-way analysis of variance (ANOVA) was performed using R (R3.5.0, R Development Core Team). The indices of Richness, Chao1, Shannon, and Simpson were calculated based on the OTU data, following the equation described by Sogin et al. [15]. Briefly, indices of Richness and Chao1 represent the microbial richness independent of the abundance of each OTU, higher richness, and Chao1 represents higher microbial richness; indices of Shannon and Simpson reflect the microbial diversity, and higher Shannon or lower Simpson reflect higher microbial diversity. The vegan package in R software was employed to conduct the following analysis. Principal coordinates analysis (PCoA) was used to investigate the variation in the bacterial and fungal community structures among the treatments using the cmdscale function; analysis of similarities (ANOSIM or AMOVA) was used to examine the difference in the community compositions of the bacteria or fungi; the linear discriminant analysis (LDA) effect size (LEfSe) method was used to find significant differences in the taxa of bacteria and fungi among the treatments; redundancy analysis (RDA) was conducted to investigate the relationships between microbial compositions and soil properties.
Following the previous analysis [13], co-occurrence networks were used to visualize the associations among bacterial and fungal communities in Cytoscape 3.6.1 [16]. The OTUs were filtered by setting ten as the minimum occurrence across samples. Spearman’s correlation was used to calculate the correlation. The initial threshold permutations and 1000 bootstrap scores were generated for each treatment. Filter criteria to reduce the chance of obtaining false-positive results followed Brown’s method [17] and the Benjamini–Hochberg method [18]. Then, the network was subjected to permutation as the null distribution and to bootstrap the random distribution. Then, Gephi was used to visualize the final networks and to calculate the topological characteristics of the networks. Keystone species were considered OTUs with high degrees, eigenvector centrality, and closeness.

3. Results

For bulk soil, the Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes, Gemmatimonadetes, Chloroflexi, and Verrucomicrobia comprised 94.3–96.1% of the bacterial community across the treatments (Figure 1). Considering different farming modes, the Acidobacteria abundance was higher under monoculture than that under rotation; the converse trend was observed for the Gemmatimonadetes abundance. Richness, Chao1, Shannon, and Simpson indices were not significantly different across the treatments (Table S1). The Anosim analysis showed a significant difference in the bacterial community of bulk soil across the treatments. The PCoA analysis showed that PCoA1 and PCoA2 explain a 50.3% and 17.2% treatment difference, respectively (Figure S1). Furthermore, on the PCoA1 axis, the bulk soil bacterial community under monoculture conditions was isolated from that under rotation conditions.
The primary fungi in bulk soil are Ascomycota, Mortierellomycota, unassigned, Ciliophora, and Basidiomycota, occupying more than 93.6% of fungi (Figure 1). The Ascomycota abundance was higher under rotation conditions compared with that under monoculture conditions. The fungi alpha diversity indicated by the indices of richness, Chao1, Shannon, and Simpson, did not change in the two farming systems (Table S1). The Anosim analysis showed that there was a significant difference among the treatments. The PCoA analysis showed that PCoA1 and PCoA2 explain the differences between 37.5% and 20.3% treatments (Figure S1). On the PCoA1 axis, the fungi group under monoculture conditions was isolated from that under the rotation group.
For the root endophytes, the phylum of Proteobacteria, Actinobacteria, and Bacteroidetes consist of 88.8–99.9% of the bacterial community (Figure 1). Of these, under both rotation and monoculture conditions, the abundance of Bacteroidetes was higher in the rot-conducive variety than that in the rot-suppressive variety, and more pronounced under monoculture conditions. Furthermore, for the same variety, the abundance of Bacteroidetes was higher under monoculture conditions than that under rotation conditions. The abundance of Proteobacteria was higher in the rot-conducive variety than in the rot-suppressive variety, no matter the farming conditions. The abundance of Actinobacteria was higher in the rot-suppressive variety than that in the rot-conducive variety, especially under monoculture conditions, with a 125.1% increase. For the alpha diversity, the indices of Richness and Chao1 showed a similar trend across the different varieties, i.e., the rot-suppressive variety had significantly higher Richness and Chao1 indices than the rot-conducive variety under monoculture conditions (p < 0.05) (Table 1). A similar scenario was observed for the Shannon index under monoculture and rotation conditions (Table 1). The Simpson index was higher in the rot-conducive variety than the rot-suppressive variety under both farming conditions, while not reaching a significant level. The PCoA analysis showed different bacterial community structures among the varieties, independent of the farming conditions. In addition, between two different farming conditions, the difference in bacterial community structure appeared to be more pronounced for the rot-suppressive variety relative to the rot-conducive variety. Based on the LEfSe analysis, the biomarkers showed that the Actinobacteria and Pseudonocardia were enriched in the endophyte of the rot-suppressive variety under monoculture conditions (Figure 2). Under rotation conditions, the Thermoleophilia, Acidimicrobiia, Chloroflexi, Gemmatimonadetes, and so on, were enriched in the endophyte of the rot-suppressive variety (Figure 2).
For endophytic root fungi, the Ascomycota, unidentified, Basidiomycota, and unassigned consist of 98.9–99.9% of fungi (Figure 1). Under monoculture condition, the Ascomycota abundance was slightly higher in the rot-conducive variety than that in the rot-suppressive variety. However, under rotation conditions, the Ascomycota abundance was 75.6% higher in the rot-suppressive variety than in the rot-conducive variety. The Basidiomycota abundance was higher in the rot-suppressive variety than the rot-conducive variety under both farming conditions, and more pronounced under monoculture conditions.
The alpha diversity showed that the rot-suppressive variety had higher indices of Richness, Chao1, and Shannon under each farming condition, while a significant difference was shown for the Shannon index under rotation conditions (Table 2). In contrast, the Simpson index was significantly lower in the rot-suppressive variety under each farming condition than in the rot-conducive variety. The Amova analysis showed a significant difference among the community of endophytic root fungi of all the treatments (Figure 3). The rot-suppressive variety had significantly different fungi community structures between the two farming systems. The PCoA1 and PCoA2 explain 54.1% and 26.2% of fungi community differences, respectively. The LEfSe analysis showed that some fungi as biomarkers were enriched in the endophyte of the root of the rot-suppressive variety under monoculture conditions, e.g., Entylomatales, Exobasidiomycetes, Tilletiopsis_washingtonensis, Tilletiopsis, Entylomatales_fam_Incertae_sedis (Figure 2). Furthermore, the result showed that the rot-conducive variety could enrich different fungi under rotation conditions to those under monoculture condition, such as the Erysiphe, Leotiomycetes, Erysiphaceae, Eurotiomycetes, and so on (Figure 2).
For the bacterial community in the rhizosphere, the phylum of Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, Gemmatimonadetes, Chloroflexi, Patescibacteria, and Verrucomicrobia consist of 93.7–96.2% of rhizosphere bacteria (Figure 1). The abundances of Proteobacteria and Bacteroidetes were higher in the rot-conducive variety than those in the rot-suppressive variety under both monoculture and rotation conditions, and a contrasting trend was observed for the Acidobacteria, Gemmatimonadetes, and Chloroflexi. Under rotation conditions, the abundance of Patescibacteria was higher in the rot-conducive variety relative to the rot-conducive variety.
In the bacterial alpha diversity, the indices of Richness and Chao1 between the varieties were not significantly different, independent of the farming condition (Table 1). The Shannon index was higher in the rot-suppressive variety than the rot-conducive variety, reaching a significant level under the rotation condition (p < 0.05). In contrast, the Simpson index was lower in the rot-suppressive variety than that in the rot-conducive variety, reaching a significant level under the monoculture condition (p < 0.05). The ANOVA analysis showed that there is a significant difference across the treatments. The result of PCoA analysis showed that PCoA1 and PCoA2 explain the 34.4% and 26.2% group difference, respectively (Figure 3). On the PCoA1 axis, the bacterial community structure difference between the two varieties was clearer under monoculture conditions than that under rotation conditions. The LEfSe analysis showed that o_Acidimicrobiia, g_S085_Unclassfied_Unclassified, g_Actinomarinales_uncultured_Unclassified, p_Planctomycetes, g_Pedosphaeraceae_Unclassified, and so on, were enriched in R4 (Figure 2). In R1, o_Rhizobiales, f_Micrococcaceae, f_Blastocatellaceae, g_Flavisolibacter, g_Chitinophagaceae, were enriched in R1 (Figure 2).
The Ascomycota, Basidiomycota, ciliophoran, Nematoda, Mortierellomycota, Arthropoda, unassigned, and unidentified consist of 98.9–99.8% rhizosphere fungi among the treatments (Figure 1). The abundances of Ascomycota, Arthropoda, and Mortierellomycota were higher in the rot-suppressive variety than the rot-conducive variety under monoculture conditions. The Basidiomycota abundance was lower in the rot-suppressive variety than the rot-conducive variety under monoculture and rotation conditions, especially for the former, with a more than ten times decrease. The richness and Chao1 rhizosphere fungi indices showed no difference between the two varieties under both farming conditions (Table 2). Under monoculture conditions, the Shannon index was significantly higher in the rot-suppressive variety than the rot-conducive one. The Simpson index was lower in the rot-suppressive variety than the rot-conducive variety under both monoculture and rotation conditions, while not reaching a significant difference (p < 0.05). The Anosim analysis showed a significant difference in the fungi community structure across the treatments. The PCoA analysis showed that PCoA1 and PCoA2 demonstrate a 43.8% and 24.4% fungi community difference, respectively (Figure 3). Furthermore, on the PCoA1 axis, a marked difference was observed between the different varieties under monoculture conditions. The biomarkers analysis showed that p_Basidiomycota and c_Agaricomycetes were enriched in R1 (Figure 4), while g_Sordariomycetes_Unclassified, g_Cordycipitaceae, f_Ascomycota, g_Lecanicillium, g_Plectosphaerellaceae, and so on, were enriched in R4 (Figure 4).

4. Discussion

Sugar beet rot has threatened sugar beet production for numerous years, and targeting and efficiency solutions are lacking due to its complicated mechanisms. For soil-borne disease, more and more recent evidence has shown that plants can defend against the invasion of pathogenic microbes by recruiting some microbes [3,5,6]. However, whether sugar beet also can recruit certain essential microbes is unclear. In this study, we found that both the microbes in the rhizosphere and endophyte varied significantly, depending on the variety. Firstly, from the evidence of the isolated group based on the PCoA analysis of bacteria and fungi in the bulk soil, the monocultural farming mode has changed the soil microbial community structure relative to the rotational mode. The changed bulk soil microbial community due to monoculture was consistent with the previous findings that monoculture can shape the soil microbial community [19,20]. Furthermore, Zhou et al. [19] found that potential plant pathogens and antagonistic microorganisms were increased from rotation to monoculture of cucumber.
On the other hand, in the rhizosphere, compared with the rot-conducive variety, the rot-resistant variety demonstrated a significantly lower bacterial Simpson index and a diverse bacterial community structure under monoculture conditions. Nevertheless, the Shannon index of fungi was significantly higher in the rot-resistant variety than in the rot-conducive variety under monoculture conditions. In addition, the fungi community structure is also apparently isolated between the two different varieties, as evidenced by the PCoA, illustrating different community structures. Therefore, rot-resistant varieties can form more diverse communities of bacteria and fungi than rot-conducive varieties. Mendes et al. [21] also found that the common beans resistant to Fusarium oxysporum had a more complex microbial community structure in the rhizosphere, and enriched the known beneficial microbes from Pseudomonas and Baclostridae. Kwak et al. [3] found that wilt-resistant varieties can enrich unique microbes such as Flavobacterium; this was clarified to suppress R. solanacearum disease development. Using amplicon sequencing and non-targeted metabolomics, Wen et al. [5] found the cucumber variety conducive to Fusarium oxysporum f.sp. cucumerinum can enrich beneficial microbes such as Comamonadaceae and Xanthomonadaceae by releasing organic acid. In addition, a recent study showed that plants could alter the root secretion to enrich microbes that can induce the plant’s immune system resistance to defend itself from invading pathogens [22]. The g_Sordariomycetes_Unclassified, as the fungi biomarker, was enriched in the rot-resistant variety. A similar finding was reported by Liu et al. [23], who found that the Sordariomycetes could inhibit the Fusarium graminearum. However, in this study, the nature of the potential function of other enriched biomarkers such as g_Cordycipitaceae, g_Lecanicillium, and g_Plectosphaerellaceae in rot-resistant variety and the way in which they were recruited are largely unclear. In the bacteria community, the genus of S085 was enriched in the rot-resistant variety. It was then lost following the infection of root-knot nematodes with crop pathogens, but still existed in the rhizosphere of the crop without infection [24], indicating that S085 could be a potential beneficial rhizosphere bacterium. An unclassified bacterium affiliated with the genus of Pedosphaeraceae was enriched in a resistant variety. A recent study addressed that Pedosphaeraceae was a plant-promoting rhizobacterium in Cd-contaminated soil [25]. In addition, although H is a rot-conducive variety, we found that its root can significantly enrich the beneficial bacteria g_Chitinophagaceae and g_Flavisolibacter, in which the former has been identified to inhibit Rhizoctonia solani potentially through secreting cell wall-degrading enzymes [6]. The latter was reported to promote root growth [26]. Thus, the different varieties can recruit beneficial microbes, and the underlying reasons for this are certainly worth further study. For instance, soil pH was a critical factor in the variety (Figure 5).
Besides the change in rhizosphere microbes, we also found that the rot-resistant variety can form a more prosperous and diverse bacterial community in root endophytes relative to that of the rot-conducive variety under monoculture conditions, as evidenced by the significantly higher indices of richness, Chao1 and Shannon. Nevertheless, the fungal beta diversity of root endophyte also presented a significant difference between the variety with different resistant abilities. The rot-resistant variety significantly enriched Actinobacteria and Pseudonocardia. The genus of Pseudonocardia was previously found enriched in the Verticillium dahliae-resistant olive tree endophyte relative to the disease-conducive variety [27]. Lee et al. [28] reported that the rhizosphere of healthy tomatoes had higher Actinobacteria abundance than tomatoes with wilt disease; further analysis clarified that Actinobacteria could induce immune system resistance to tomatoes. In addition, the rot-conducive variety also enriches the different beneficial bacteria, such as Novosphingobium, Sphingobacterium, and Flavobacterium, which can inhibit V. dahlia as a soil-borne fungus [6,29].
Furthermore, we found that the fungi community in the root endophyte of the rot-resistant variety was more diverse than the rot-conducive variety, as evidenced by the lower Simpson index. The rot-resistant variety enriched the Entylomatales, Exobasidiomycetes, Tilletiopsis_washingtonensis, Tilletiopsis, Entylomatales_fam_Incertae_sedis, and Saccharomycetes. The microbes belonging to Exobasidiomycetes were reported to control the number of powdery mildew pustules [30,31], possibly by producing compounds [32]. Furthermore, a previous study reported that Tilletiopsis_washingtonensis could germinate into yeasts or mycelium in response to particular environmental conditions, such as the vicinity of other fungal colonies [33]. However, the potential linkage of Tilletiopsis_washingtonensis with rot development is still largely unknown. The Saccharomycetes were found to have good antifungal activity on other plant diseases via reproducing rapidly and producing several active substrates such as proteins, amino acids, and vitamins [34,35,36]. In addition, whether there were interactions within the enriched endophytes or enriched rhizosphere microbes and microbes between the compartments needs further research.
Besides the biomarkers, keystone taxa were thought as the drivers of microbial structure and functioning [37]. Little is known about the bacterial and fungal keystone taxa affiliated with different sugar beet compartments. In this study, beneficial bacterial keystone taxa were found in the rhizosphere of the disease-suppressive variety, e.g., OTUs affiliated to the order of Saccharimonadales, Anaerolineae, the family of Saprospiraceae, the genus of Subgroup_10 (belonging to the family of Thermoanaerobaculaceae), Lysobacter, and AKYG587 (Figure 6, Tables S2 and S3). Jia et al. [38] also found that the abundance of Saccharimonadales in the rhizosphere was inversely proportional to the disease incidence of wheat dwarf bunt, indicating the beneficial function of Saccharimonadales to plant pathogens. A similar phenomenon was reported on the Anaerolineae, which was negatively related to the disease incidence of Fusarium oxysporum f. sp. lactucae, Rhizoctonia solani, and Sclerotiorum (Bonanomi et al., 2022) [39]. The observed keystone taxa Lysobacter in the rhizosphere of the disease-suppressive variety is known for its ability to prey on other microorganisms by secreting antibiotics and lytic enzymes [40,41]. A previous study has reported that the abundance of AKYG587 was associated with disease suppression through its action as a helper, symbiont, or mutualist to improve the efficacy of Pseudomonas or Bacillus biocontrol agents [42].
On the other hand, compared with the disease-suppressive variety, the keystone taxa were markedly different in the rhizosphere of the disease-conducive variety. Firstly, Pedobacter as the plant pathogen was found as the observed keystone taxa, which was also enriched in consecutive monoculture of sweet potato [43] and in wheat with patch disease (Triticum aestivum L.) [44]. However, disease-conducive sugar beet can also recruit some beneficial bacteria, such as the observed Ferruginibacter [45] and P3OB-42 [46]. Different varieties recruited varied beneficial bacteria, which could be attributed to different resistant abilities and potential mechanisms that may be worth further detection. Nevertheless, the fungal keystone taxa in the rhizosphere also varied, depending on the variety type. The family of Aspergillaceae, Ceratobasidiaceae, and Anteholosticha_sp were the keystone taxa of the disease-suppressive variety, while the species of Hypocreales_sp, Rozellomycota_sp, Aspergillus_thesauricus, and Kernia_sp, the order of Sordariales were the keystone taxa of the disease-conducive variety. However, the related mechanisms underlying the variation in fungal keystone taxa were nearly blank.
Besides the rhizosphere, we investigated the keystone taxa in root endophytic bacteria and fungi in two different varieties. The OTUs affiliated with the genus of Aliidiomarina, Paracoccus, Novosphingobium, Methylobacterium, the order of Gaiellales, the family of Solirubrobacteraceae, Methylopilaceae, and the species of Sphingobacterium_gobiense, Nocardioides_sp._RCML-51, and Saccharothrix_sp._sj68, were the bacterial keystone taxa in disease-suppressive sugar beet. Meanwhile, the genus of Aeromicrobium and the species of Nocardioides_bigeumensis and uncultured_bacterium were the bacterial keystone taxa in the disease-conducive sugar beet (Figure 6, Tables S4 and S5). The order of Gaiellales in the disease-suppressive sugar beet root was recently found to suppress common scabs of potatoes [47]. In addition, the Nocardioides observed in the disease-conducive sugar beet were found to enhance disease-suppressive ability by activating the pathogen-associated molecular patterns and triggering the immune pathway [48]. Thus, disease-conducive roots can also recruit beneficial bacteria, although they are different from the recruited microbes in the disease-suppressive variety. For fungi, the species of Saccharomycetales_sp, Malassezia_globosa, Dactylonectria_anthuriicola, Rhizophlyctis_rosea, Malasseziales_sp, and the genus of Aspergillus were the keystone taxa in disease-suppressive sugar beet root. At the same time, these were the species of Sporobolomyces_roseus, Tricholoma_matsutake, Helotiales_sp, the family of Cordycipitaceae, Mycosphaerellaceae, and Ophiocordycipitaceae, and the order of Chaetothyriales in the disease-conducive sugar beet root. However, these microbes’ potential functions and their associations are largely unknown. In addition, interestingly, we found some microbes play the roles of both the biomarkers and keystone taxa, e.g., OTUs affiliated to the class of Anaerolineae and Eurotiomycetes in the rhizosphere of the disease-suppressive variety, OTUs affiliated to the order of Gaiellales and Saccharomycetales inside the root of the disease-suppressive variety, and OTUs affiliated to the genus of Pedobacter and Chitinophagaceae in the rhizosphere of the disease-conducive variety. To our knowledge, this rare study reported a linkage between biomarkers and keystone taxa. These microbes may provide new insight for further inhibiting sugar beet rot.

5. Conclusions

Based on two farming modes and two sugar beet varieties with different resistant abilities to beet rot, we found that long-term monoculture has significantly altered the soil microbial composition and community structure relative to the rotation mode. The microbial community differed between the two varieties in the rhizosphere and endophytic root, and this difference was more pronounced in the monoculture mode than in the rotation mode. We found that the rot-suppressive variety can enrich the beneficial and particular bacteria and fungi in the rhizosphere and endophytic root, as evidenced by the biomarker and keystone taxa analysis. In contrast, besides the disease-enriched microbe, the rot-conducive variety also enriched potential beneficial bacteria and fungi, although these were different from the rot-suppressive variety. To our knowledge, this is the first finding of this kind in the study of sugar rot. These different enriched biomarkers and keystone taxa could be attributed to the different resistant abilities of sugar beet root. The key microbes enriched or recruited by the rot-suppressive variety might be the candidates for key biocontrol agents in sugar beet rot, and further clarification of their underlying mechanisms is needed. In addition, the varied soil pH due to the different farming modes was shown to be the crucial exogenous factor in shaping microbial differences in different varieties; this may be referenced in further soil amendments to inhibit sugar beet root rot.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13030825/s1, Figure S1: PCoA of the bacterial and fungal communities of bulk soil across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode); Table S1: The α-diversity of bacteria and fungi in bulk soil under different farming modes; Table S2: The keystone taxa of bacteria in rhizosphere of different varieties; Table S3: The keystone taxa of fungi in rhizosphere of different varieties; Table S4: The keystone taxa of in endophytic bacteria in root of different varieties; Table S5. The keystone taxa of in endophytic fungi in root of different varieties.

Author Contributions

Conceptualization, M.L. and Z.S.; methodology, Y.Z.; software, R.X.; validation, Y.Z. and Z.S.; formal analysis, C.D.; investigation, K.A.; resources, M.L.; data curation, M.L.; writing—original draft preparation, M.L.; writing—review and editing, R.W.; visualization, Z.S.; project administration, W.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the project of Renovation Capacity Building for the Young Sci-Tech Talents, sponsored by Xinjiang Academy of Agricultural Sciences (xjnkq-2022009, xjnkq-2020014), and supported by China Agriculture Research System of MOF and MARA (CARS-170724); Xinjiang Science and Technology Achievements Transformation Projects (2020B007) and the National Wheat Industry Technology System (CARS301).

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank Xu Xingchun and Chen Fei for helping to arrange the field experiment.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The abundance of the predominant bacteria and fungi at the phylum level (≥1%) of different compartments (S, R, and B indicate root endosphere, rhizosphere, and bulk soil, respectively) across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
Figure 1. The abundance of the predominant bacteria and fungi at the phylum level (≥1%) of different compartments (S, R, and B indicate root endosphere, rhizosphere, and bulk soil, respectively) across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
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Figure 2. LEfSe analysis of the root endophytic bacterial and fungi community from the phylum to species rank across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
Figure 2. LEfSe analysis of the root endophytic bacterial and fungi community from the phylum to species rank across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
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Figure 3. PC and PcoA of the bacterial and fungal communities of different compartments (S, R, and B indicate root endosphere, rhizosphere, and bulk soil, respectively) across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
Figure 3. PC and PcoA of the bacterial and fungal communities of different compartments (S, R, and B indicate root endosphere, rhizosphere, and bulk soil, respectively) across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
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Figure 4. LEfSe analysis of the rhizosphere bacterial and fungi community from the phylum to species rank across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
Figure 4. LEfSe analysis of the rhizosphere bacterial and fungi community from the phylum to species rank across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
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Figure 5. RDA of the rhizosphere bacterial community (left part) and fungal community (right part) of different compartments across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
Figure 5. RDA of the rhizosphere bacterial community (left part) and fungal community (right part) of different compartments across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
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Figure 6. Co-occurrence network of the bacterial and fungal communities of different compartments (S and R indicate root endosphere and rhizosphere, respectively) across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
Figure 6. Co-occurrence network of the bacterial and fungal communities of different compartments (S and R indicate root endosphere and rhizosphere, respectively) across the different varieties (1,6 indicate the rot-conducive variety; 4,7 indicate the rot-suppressive variety) and farming modes (1,4 belong to monoculture mode; 6,7 belong to rotation mode).
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Table
Table 1. The α-diversity of bacteria in the root endosphere and rhizosphere in different varieties under different farming modes.
Table 1. The α-diversity of bacteria in the root endosphere and rhizosphere in different varieties under different farming modes.
FarmingVarietyα-Diversity of Bacteria
Root EndosphereRhizosphere
RichnessChao1ShannonSimpsonRichnessChao1ShannonSimpson
M ‡H ‡319.3 c321.8 c4.64 c0.074 a2783.7 ab2785.2 ab9.10 ab0.006 a
M ‡C ‡547.3 b548.4 b6.51 a0.035 a2874.0 a2875.3 a9.35 a0.004b
R ‡H ‡646.3 ab647.3 ab5.45 b0.125 a2593.3 b2595.2 b8.66 b0.008 a
R ‡C ‡896.0 a896.6 a7.77 a0.019 a2838.3 ab2839.8 ab9.29 a0.005 ab
Farming*******nsnsns*ns
Variety*********nsns***
Farming × Varietynsnsnsnsnsnsnsns
‡ The M, R, H, and C represent monoculture mode, rotation mode, rot-conducive variety, and rot-suppressive variety, respectively. Different letters indicate a significant difference at p < 0.05. “*”, ”**”, and ”***”, represent significant difference at p < 0.05, p < 0.01, and p < 0.001, respectively.
Table
Table 2. The α-diversity of fungi in the root endosphere and rhizosphere in different varieties under different farming modes.
Table 2. The α-diversity of fungi in the root endosphere and rhizosphere in different varieties under different farming modes.
FarmingVarietyα-Diversity of Fungi
Root EndosphereRhizosphere
RichnessChao1ShannonSimpsonRichnessChao1ShannonSimpson
M ‡H ‡62.7 a68.4 a2.20 ab0.34 a463.3 a465.4 a514.1 b0.315 a
M ‡C ‡68.7 a76.4 a3.56 a0.17 b386.7 a388.1 a403.0 a0.054 a
R ‡H ‡67.0 a70.3 a2.18 b0.45 a354.3 a356.3 a426.5 ab0.116 a
R ‡C ‡69.7 a82.0 a3.94 a0.15 b386.7 a388.4 a457.4 a0.057 a
Farmingnsnsnsnsnsnsnsns
Varietynsns****nsns***
Farming × Varietynsnsnsnsnsnsnsns
‡ The M, R, H, and C represent monoculture mode, rotation mode, rot-conducive variety, and rot-suppressive variety, respectively. Different letters indicate a significant difference at p < 0.05. “*” and ”**” represent significant difference at p < 0.05 and p < 0.01, respectively.
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MDPI and ACS Style

Lin, M.; Zhou, Y.; Xu, R.; Du, C.; Wang, R.; Lu, W.; Abudukadier, K.; Sun, Z. Contrasting Key Bacteria and Fungi Related to Sugar Beet (Beta vulgaris L.) with Different Resistances to Beet Rot under Two Farming Modes. Agronomy 2023, 13, 825. https://doi.org/10.3390/agronomy13030825

AMA Style

Lin M, Zhou Y, Xu R, Du C, Wang R, Lu W, Abudukadier K, Sun Z. Contrasting Key Bacteria and Fungi Related to Sugar Beet (Beta vulgaris L.) with Different Resistances to Beet Rot under Two Farming Modes. Agronomy. 2023; 13(3):825. https://doi.org/10.3390/agronomy13030825

Chicago/Turabian Style

Lin, Ming, Yuanhang Zhou, Runlai Xu, Chenghang Du, Ronghua Wang, Weidan Lu, Kuerban Abudukadier, and Zhencai Sun. 2023. "Contrasting Key Bacteria and Fungi Related to Sugar Beet (Beta vulgaris L.) with Different Resistances to Beet Rot under Two Farming Modes" Agronomy 13, no. 3: 825. https://doi.org/10.3390/agronomy13030825

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