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Article

Watering Shapes a Robust and Stable Microbial Community under Fusarium Crown Rot Infection

by
Runlai Xu
,
Chenghang Du
,
Yutian Gao
,
Xiaohan Zhou
,
Irsa Ejaz
,
Jieru Guo
,
Kunhu Chen
,
Jun Ma
,
Yinghua Zhang
,
Zhimin Wang
and
Zhencai Sun
*
College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(5), 1356; https://doi.org/10.3390/agronomy13051356
Submission received: 12 April 2023 / Revised: 3 May 2023 / Accepted: 8 May 2023 / Published: 12 May 2023
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
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Abstract

:
Wheat crown rot, caused by Fusarium pseudograminearum, is an emerging soil-borne fungal disease and causes serious damage in areas where water is scarce. However, the interactions between plant, microbiome, and pathogen under different watering regimes are rarely known. In our study, we designed three watering patterns, including the early-drought (DR1), late-drought (DR2), and well-watered (WAT) patterns, and sampled at heading and grain filling, to investigate the effect of different watering regimes on the microbial community and disease severity. These findings showed that well-watered pattern at grain filling decreased the disease index compared to other patterns, and the bacterial community in the WAT and DR2 at grain filling showed higher alpha diversity (rhizosphere and root) and more stable structures (root). For the microbial network, irrespective of compartments, bacterial networks in the WAT and DR2 were more complex and connected with a higher average degree and clustering coefficient than DR1 at both periods. Furthermore, several potential beneficial microbes as biomarkers were enriched under good water conditions, specifically during the heading of DR2 and grain filling of WAT, including operational taxonomic units (OTUs) affiliated with the taxa of Arenimonas, Sphingomonas, Pseudoxanthomonas, Devosia, Lysobacter, Chitinophagaceae, and Gaiellales in the rhizosphere and root. Overall, the microbiome reshaped by good moisture or avoiding early drought should be emphasized and further used in controlling Fp-caused wheat crown rot.

1. Introduction

Crops, from sowing to harvesting, are often restricted by unfavorable factors, such as pests, diseases, and poor environmental conditions [1,2]. Drought stress is widely recognized as one of the most decisive factors that limit plant growth and development, with a substantial impact on global crop productivity and food security [3,4,5]. Furthermore, drought is related to the occurrence and prevalence of diseases and takes part in the plant’s response to the pathogen. Previous studies provided evidence that drought may enhance or suppress the incidence of plant disease, due to its effects on both the invasiveness of pathogen and the performance of host plant. As a result of the complex interaction between drought and pathogen, plants will exhibit varying degrees of disease [6]. Chilakala et al. [7] and Rai et al. [8] reported the effect of drought on the pathogen of dry root rot (DRR) of chickpeas (Cicer arietinum L.) and the negative correlation between disease severity and plant water content. Drought increased the DRR incidence and plant mortality resulting in lower yields and economic losses. In contrast, some moisture-dependent diseases, such as Ramularia leaf spot, were suppressed in an unfavorable drought environment [9].
Studies have found that when plants face external stress, they can establish relationships with their associated microbiome to form a symbiont, which can help alleviate the damage either by directly modifying the adverse factors, or by improving the host’s performance [10,11]. The rhizosphere is a communication hotspot among plants, microbes, and soil. The microorganisms can be enriched or depleted in rhizosphere soil or the root endosphere when plants face pathogen threats or poor living space [12]. The microbial community is drastically changed after the invasion of disease. Wu et al. [13] found that wheat (Triticum aestivum L.) seriously infected with the yellow mosaic virus would recruit different bacterial and fungal biomarkers in the rhizosphere and root to construct a larger and more complex co-occurrence network. Microorganisms can directly help or cooperate with their hosts, by improving plant performance or living environment. For instance, bacteria or arbuscular mycorrhiza fungi (AMF) have been reported to increase water content, stimulate root growth, and enhance plants’ antioxidant and osmotic protection to adapt to drought [14,15,16]. Inoculation of the bush mycorrhizal fungus Claroideoglomus etunicatum W.N. Becker & Gerd enhanced the phosphorus uptake, leaf stomatal sensitivity, and transpiration rate of perennial ryegrass (Lolium perenne L.), to mitigate the damage of the combined pressure of leaf spot disease and drought [17]. Rhizosphere microbial members can directly inhibit growth or activate the host’s induced systemic resistance (ISR) to resist the disease [10,18,19,20]. The beneficial bacteria such as Pseudomonas sp. showed an important role in controlling the pathogen of rice blast disease via stimulating the jasmonic acid (JA)- and ethylene (ET)-dependent immune pathway of hosts [21]. Furthermore, recent studies have shown that specific genotypes could build a special microbiome and improve plant disease resistance by calling microbial taxa for help [22,23].
The Fusarium crown rot (FCR) is a worldwide soil-borne fungal disease to the major food crop wheat [24,25] and has become one of the crucial factors restraining wheat production in the North China Plain since it was first reported in China in 2012 [26]. Previous studies have shown that F. pseudograminearum O’Donnell & Aoki (Fp) can survive for a long time in the residues of plants or soil due to straw return or other field managements [25,27]. Liu and Liu [28] showed that drought stress prolonged the initial infection of Fusarium and promoted its colonization and spread after the infestation. Nowadays, many researchers have reported plant disease induced by Fusarium sp. [29,30], and there is still a lack of effective resistant varieties and environmentally friendly control agents, such as microbes, for the suppression of the FCR under water-deficit conditions. Therefore, digging out the beneficial microbes enriched in the root zone of winter wheat may provide a new horizon for further controlling FCR.
To investigate the interactions among the wheat, watering regime, Fp, and potential beneficial microbes, we designed a pot experiment with different watering conditions, briefly the DR1 (early-drought), DR2 (late-drought), and WAT (well-watered), and collected soil and plant samples at heading and grain filling. We aimed to explore the variation in disease severity and plant-associated microbiome response to different watering treatments in the presence of Fp. We hypothesized that a microbial community with high diversity and stable structure was shaped by great soil moisture in the rhizosphere, root, or stem, and potentially beneficial microbes were enriched to assist in controlling FCR.

2. Materials and Methods

2.1. Plant Material and Inoculum Preparation

The winter wheat cultivar “Jimai 22” was used in this study, planted in the greenhouse after 30 d of vernalization. For vernalization, the seeds were soaked in 1% hydrogen peroxide for 24 h, rinsed twice with sterile water, then kept in Petri dishes containing humid filter paper and germinated at 4 °C.
The pathogen isolate was sequestered from the disease-infected wheat in the field and identified as Fp. The pathogen inoculum was prepared by mixing sterilized wheat seeds with Fp plugs. In brief, 500 g seeds and 500 mL sterile water were autoclaved twice in a sterilization bag. Fp was activated first on PDA (potato dextrose agar) media plates for 1 week under room temperature, and then the fungal hyphae and agar were cut from a plate and transferred into the bag with wheat grain. Afterward, the mixture was placed at room temperature until the Fp colonization of the seed substrate (about 10 to 15 d).

2.2. Greenhouse Experiments Design

The soil used in greenhouse experiments was collected from fields without Fp. After soil sampling, the plant debris in the soil was removed, air-dried, and passed through a 4 mm sieve, and then sieved soil was ready for the later experiment. A pot (diameter: 20 cm, height: 13.5 cm) contained 4.1 kg mixed soil, 0.976 g nitrogen, 2.15 g P2O5, and 0.542 g K2O. Twelve germinated seedings were sown in each pot, covered with a layer of Fp-infected millet inoculum (3.14 g) and then covered with another layer of soil (225 g) on the top side. The watering holding capacity (WHC) was determined before sowing as described in the previous method [31], and the pots were watered by weighting. Deionized water was added to the soil daily to keep the WHC at 75%, allowing seedlings to grow normally until jointing. The plants were thinned to 6 seedings at the 3-leaves period.
The pot experiment was designed with three watering patterns as shown in Figure 1a: DR1 (early-drought), initiating drought after jointing; DR2 (late-drought), initiating drought after anthesis; WAT (well-watered), watering throughout the reproductive period. During the watering stage, the pots were watered with deionized water every 24 h to keep the WHC at 75%. Conversely, the soil was watered daily during drought to keep the WHC at 35%.
Each treatment had three biological replicates, and the pots were placed by a complete random blocks design under the same controlled conditions including temperature (25 ± 5 °C) and light (16/8 h light/darkness). The position of the pots was randomly changed every 7 d.

2.3. Sample Collection and Processing

The soil and plant samples of different watering regimes were collected at heading (HD) and grain filling (GF) (Figure 1a). The plants inoculated with Fp showed FCR symptoms but with different infection levels. The diseased plants were uprooted carefully by a shovel and the disease index was quickly investigated. The bulk soil was collected by shaking the roots vigorously and used to determine the soil moisture and other physicochemical properties. The base stems, roots, and rhizosphere soils were separated and stored at −80 °C for further amplicon sequencing. Five plants per pot were selected and mixed as one replicate at each sampling period, with each treatment containing three replicates.
The base stem, defined as the first and second stem and internode covered with an area of an Fp-infected spot, was scored for the disease index visually and then ground to powder with liquid nitrogen for later DNA extraction.
The adhering rhizosphere soil was separated from roots by oscillation in a 50 mL plastic centrifuge tube containing 25 mL sterile 1× PBS (phosphate buffer solution, 100 mL 10× PBS (Solarbio Science & Technology Co., Ltd., Beijing, China) added into 900 mL deionized water) at 200 rpm for 10 min. The roots were transferred into a new centrifuge tube, and the rhizosphere soil pellet was collected by centrifugation at 6000 rpm for 15 min. The pellet was stored in an ultra-low temperature freezer (−80 °C) before extracting the DNA.
Roots without soil were fully surface sterilized in 75% ethanol at 200 rpm for 5 min, and then washed three times with sterile water. The root was grounded in the sterile mortar with liquid nitrogen and stored at −80 °C until DNA extraction.

2.4. Soil Physicochemical Properties

A thermometer was used to measure soil temperature (ST). Fresh soil loosely bound to the root was considered as bulk soil samples to determine physicochemical properties. Five grams of soil was used to determine the content of nitrate nitrogen (NO3−-N) by adding 0.01 mol L−1 KCL, based on the flow injection analysis method [32]. The soil moisture was measured after drying the soil for 24 h at 105 °C. The soil pH was determined using a pH meter in a mixture of soil and water at a ratio of 1:4 (w/v). The soil electrical conductivity (EC) was determined in a 1:5 (w/v) soil suspension by using an EC meter, as in the previous research [33].

2.5. Assessment of FCR Disease Severity

The FCR disease severity was assessed mainly based on the lesion area regarding former reports [34], and the scale was from level 0 to level 5 as follows. Level 0: the plant was generally healthy and had no obvious brown lesion; Level 1: the sheath was browned, and the first stem was discolored by 50 to 100%; Level 2: the first stem was browned completely; Level 3: the first and second stem were completely browned; Level 4: the first and second stem were 100% discolored, and the plant was close to death; Level 5: the plant died.
The disease index of FCR was calculated with the following formula: Disease index (DI) = ∑(n × S)/5 × N × 100%. S is the scale level, n is the number of plants in each level, and N is the number of plants used to investigate the disease severity.

2.6. DNA Extraction and Amplicon Sequencing

Genomic DNA extraction was conducted with a PowerSoil™ DNA Isolation kit (Mo Bio Laboratories, Carlsbad, CA, USA) following the operating instructions, and the concentration and purity were checked with a Thermo NanoDrop One (Nanodrop Technologies, Wilmington, DE, USA). An amount of 1 g of soil was used for the rhizosphere (RZ), and 1 g of plant powder was used for base stem (SE) and root (RE), respectively.
Universal primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) targeting the V3-V4 region were used for amplifying the bacterial 16S rRNA gene. The primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) targeting the ITS1 region were used for the amplification of fungal amplicon sequencing. PCR was performed in a system containing 25 μL 2× Premix Taq, 1 μL primer-F, 1 μL primer-R, 50 ng DNA template, and added nuclease-free water to a total of 50 μL. The PCR reaction was conducted as follows: 94 °C for 5 min for initial denaturation; 30 cycles of 94 °C for 30 s for denaturation; 52 °C for 30 s for annealing; 72 °C for 30 s for elongation; followed by 72 °C for 10 min for final extension and holding under 4 °C. PCR was conducted three times for each sample and mixed finally. The qualification of PCR products was detected by 1% AGE (agarose gel electrophoresis). The amplicon sequencing was performed in the Illumina Nova 6000 platform (Guangdong Magigene Biotechnology Co., Ltd., Guangzhou, China).
Fastp (v0.14.1) was used to filter, quality control, and remove the primer sequences for the raw reads to obtain clean tags. The reads were further denoised and clustered at 97% similarity using UPARSE in qiime2 (v2020.11.0). The OTUs (operational taxonomic units) of bacteria and fungi were annotated using the SILVA (v132) and UNITE (v8.0) databases, respectively. The OTUs belonging to Mitochondria, Chloroplast, and Archaea were removed to avoid an error.

2.7. Statistical Analyses

Statistical analyses were conducted in R (v4.1.0), and the data were transformed to ensure normality. The alpha diversity Chao1 and Shannon indices of microbial communities were calculated using the “vegan” package; the significances were compared by Duncan’s multiple means comparison method. The microbial community structures were assessed by principal coordinate analysis (PCoA) based on Bray–Curtis distances, and the Adonis function in the “vegan” package was used to perform permutational multivariate analysis of variance (PERMANOVA) with 999 permutations to evaluate the difference significance among watering and stage treatments. The ggplot2 package was used to create the graphs.
The correlation matrixes of the co-occurrence network were constructed by the rcorr function of the “Hmisc” package based on the Spearman method, and the OTUs with relative abundance < 0.1% were removed to reduce the bias of calculation and to ensure accuracy [35]. The robust correlations (|Spearman correlation coefficient| > 0.60 and p < 0.05) were used for co-occurrence network construction, and the Benjamini–Hochberg method was used to verify and adjust the p values [36]. Gephi was used to distinguish modules and to visualize co-occurrence networks. In addition, random networks based on the random graph model (well known as the Erdös–Réyni model [37]) were constructed with the same nodes and edges as empirical networks to ensure the correctness of the network. Additionally, network topological characteristics such as clustering coefficient, betweenness centralization, and modularity of empirical networks and random network (average of 1000 networks for each watering pattern) were calculated using the “igraph” package [38].

3. Results

3.1. The Relationship between Soil Water Content and Disease Severity

The soil water content of the DR2 regime was significantly higher than the DR1 regime at heading (DR2HD vs. DR1HD). At grain filling, the well-watered treatment (WATGF) had significantly higher water content than the early- and late-drought conditions (DR1GF and DR2GF), but there was no difference between the two drought treatments (Figure 1b and Table S1).
The severity of FCR was assessed by visual observation to determine the disease index, which was graded into five levels, with higher levels indicating a more severe disease symptom. The results were referenced from unpublished data from Gao et al., and showed the effect of watering regimes on the disease index (Figure 1c,d and Table S1). Briefly, the DR1 regime exhibited a higher disease index than the DR2 regime at heading. At grain filling, the disease index was significantly lower in the WAT mode than that in the DR1 and DR2, while there was no significant difference between the two drought patterns. Interestingly, the disease index significantly increased under the same drought conditions from heading to grain filling. Based on samples from all treatments, the disease index showed a negative correlation with water content (Figure 1d).

3.2. The Effect of Watering Regimes on Soil Physiochemical Properties

Soil physiochemical properties were significantly changed by watering regimes (Table 1). In the DR1 pattern, the ST was decreased from heading to grain filling, while at grain filling, the ST in the DR1 was significantly higher than in the DR2. At grain filling, the difference between the DR1 and DR2 disappeared, and the ST of these two treatments was higher than the WAT (Duncan test: p < 0.05). The pH showed a significant difference only at grain filling and was higher in the WAT than in the DR1 (Duncan test: p < 0.05). NO3−-N and EC were measured only at the grain-filling stage, and both of them showed similar variation trends among the three watering patterns, where the WAT regimes showed a 39.83 and 37.59% decrease in NO3−-N and a 20.46 and 17.67% decrease in EC compared to the DR1 and DR2, respectively (Duncan test: p < 0.05).

3.3. The Effect of Watering Regimes on Microbial Community Diversity and Composition in Different Compartment Niches

After amplicon sequencing of the 16S region for bacteria and ITS region for fungi, we received 1,019,534 bacterial sequences (rhizosphere soil: 681,174; root endosphere: 75,038; stem endosphere: 263,322) and 3,063,089 fungal sequences (rhizosphere soil: 1,101,904; root endosphere: 736,284; stem endosphere: 1,224,899) from all samples. Irrespective of watering regimes and periods, these sequences were assigned to 17,920 bacterial OTUs and 1700 fungal OTUs and annotated, respectively. The number of bacterial OTUs obtained was much greater than the number of fungal OTUs, and the highest number of bacterial and fungal OTUs was found in the rhizosphere compared to the other two niches (Figure 2a,b). The number of bacterial OTUs was greater in the stem than in the root, while fungal OTUs showed the opposite trend. In all three compartments, the bacterial phyla were dominated by Actinobacteria and Proteobacteria (average relative abundance of 46.55% in the rhizosphere, 64.47% in the root, and 65.75% in the stem; Figure 2c). Strikingly, Acidobacteria, Chloroflexi, Gemmatimonadetes, and Verrucomicrobia were more abundant in the rhizosphere than in the root and stem, and Firmicutes showed a significantly higher abundance in the root compared with the other two compartments, while Bacteroidetes showed the opposite trend (Figure S1a). For fungal communities in different compartment niches, the Phylum Ascomycota was more abundant in the stem than in the root, and the abundance of Mortierellomycota was significantly higher in the root than in the rhizosphere soil and base stem, irrespective of watering treatments and periods (Figure S1b).
Bacterial and fungal community composition varied widely among watering patterns and periods (Figure 2c,d). At heading, the abundance of Acidobacteria and Gemmatimonadetes in the rhizosphere and Actinobacteria in the root were significantly higher in the DR1 than in the DR2, while Chloroflexi in the rhizosphere and Firmicutes and Proteobacteria in the root showed the opposite trend. At grain filling, Actinobacteria, Chloroflexi and Firmicutes, Gemmatimonadetes, Patescibacteria, and Proteobacteria were enriched in the rhizosphere, root, or stem of the DR1 and the WAT compared with the DR2. The Verrucomicrobia were only enriched in the DR2 rhizosphere compared with the DR1 and WAT at grain filling. In the root of the DR1 pattern, the abundance of Actinobacteria and Firmicutes increased from heading to grain filling, while the opposite trend was found for Patescibacteria in the rhizosphere. The composition of the fungal community differed less among watering regimes, and comparative analysis revealed that Ascomycota in the root and Zoopagomycota in the rhizosphere were significantly enriched in the DR2 more than in the DR1 at heading, while Glomeromycota in the root showed the opposite trend. In addition, the abundance of Chytridiomycota was significantly lower in the WAT than in the DR2 at grain filling.
The effect of the watering regime on microbial alpha diversity was observed in the rhizosphere and root (Figure 3). The Chao1 index of bacteria was significantly higher in the rhizosphere of the WAT than the DR2 regime at grain filling, while the Shannon index in the DR1 root was 27.83 and 28.22% lower than the DR2 and WAT regime, respectively (Duncan test: p < 0.05). Additionally, there were no significant differences in the stem of different watering patterns. Fungal alpha diversity did not differ significantly among watering regimes at both periods.
Without considering watering treatments and periods, bacterial (adonis: R2 = 0.656 and p = 0.001) and fungal (adonis: R2 = 0.552 and p = 0.001) communities significantly differed among different compartments based on permutational multivariate analysis of variance (PERMANOVA) tests (Figure S2a,c). Based on the Bray–Curtis distance of bacterial and fungal community structures, the dissimilarity was significantly higher in the rhizosphere than in the root and stem (Figure S2b,d). Furthermore, PERMANOVA tests showed that the communities of bacteria in the rhizosphere (adonis: R2 = 0.469 and p = 0.001) and root (adonis: R2 = 0.363 and p = 0.041) and fungi in the stem (adonis: R2 = 0.507 and p = 0.017) were significantly different among different watering regimes and periods (Figure 4a,b). PCoA plots showed that the treatments in the rhizosphere bacterial community were dispersed in the first coordinate axis, except for the samples from two periods of the DR1 pattern being clustered together. In the root, the WAT pattern was distinguished from the DR2 pattern at grain filling but not separated from the DR1 pattern. Additionally, the fungal community of the stem in the DR2 and DR1 patterns clustered together at grain filling, separated from the WAT pattern. Interestingly, at heading, both bacterial and fungal communities were significantly affected by the watering regime; the DR1 pattern showed a separation from the DR2 pattern (Figure 4a,b).
The structure dissimilarity only showed a statistical difference in the root bacterial community, while the dissimilarity of the DR1 was significantly higher than the DR2 and WAT regime at grain filling (Figure 5a,b), indicating that a more unstable bacterial community was built at a later stage under an early-drought condition in the root.

3.4. Microbial Biomarker Enrichment under Different Watering Regimes

The samples greatly influenced by watering regimes, i.e., the bacterial community in rhizosphere soil and root and fungal community in the stem were chosen to conduct the linear discriminant analysis (LDA) effect size (LEfSe) (Figure 6, Figures S3 and S4). The LEfSe analysis was used to identify the biomarkers at lower levels of the taxonomy based on the differences in the relative abundance of microbial taxonomy. In the rhizosphere, 12 and 9 bacterial taxa were found in the DR1 and DR2 regimes at heading, respectively, while 6 and 37 bacterial biomarkers were found in the DR1 and WAT regimes at grain filling, and interestingly, the DR2 regime did not have a specific biomarker (Figure 6a,b and Figure S3a,b). Micrococcaceae was identified as a shared biomarker between the two sampling times of the DR1 regime, while Sphingomonas was present at both the heading of the DR2 regime and the grain filling of the WAT regime. In the root, 25 and 4 bacterial biomarkers were enriched in DR1 at two periods, while 6 and 12 were enriched in DR2 at heading and grain filling, respectively. Furthermore, we detected many bacterial biomarkers enriched in the WAT regime, including Anaerolineae, Gaiellales, BIrii41, 0319-6G20, Arenimonas, Lysobacter, Iamia, Altererythrobacter, Devosia, Fluviicola, Lacibacter, Saccharimonadales, Bdellovibrio, and Pseudoxanthomonas (Figure 6c,d and Figure S3c,d).
Only one fungal taxon was found in the stem of the DR2 at the heading stage. In contrast, 11 fungal biomarkers were enriched in the DR1 at the same stage, with no known beneficial microorganisms (Figure 7a and Figure S4a). Ten fungal biomarkers containing known phytopathogenic bacteria, Leotiomycetes and Erysiphales, were significantly more abundant in the WAT regime at the grain-filling stage. Similarly, no fungal biomarkers were detected in the stem of the DR1 (Figure 7b and Figure S4b).

3.5. Microbial Co-Occurrence Networks under the Different Watering Regimes

The co-occurrence networks of different watering regimes were further detected in bacterial and fungal communities. The samples from different compartments of the same watering treatment were mixed for the network construction, thus highlighting the effect of the watering regime. The watering regimes differed in the topological structure of bacterial and fungal networks, in which the average degree, clustering coefficient, betweenness centralization, density, and modularity of the empirical networks were greater than the random networks (Tables S2 and S3). At heading, bacterial networks of DR2 had more nodes (102 vs. 95) and edges (1271 vs. 951) than DR1, while the WAT network consisted of the most nodes (124) and edges (1374) at grain filling. The DR2 regime showed a greater average degree (24.922 and 0.281), clustering coefficient (23.623 and 0.703), and density (0.247 and 0.225) at heading and grain filling, respectively, than the other treatments at a corresponding period, indicating more complex bacterial network structures (Figure 8a and Table S2).
In the fungal networks, DR1 had 88 nodes and 571 edges at the heading stage (Figure 8b and Table S3), showing a larger network, but the topological characters associated with network complexity were similar to DR2. Moreover, the WAT’s average degree and clustering coefficient were greater than the other two treatments at grain filling. The OTUs with the TOP10 degree were selected as hub nodes (keystone taxa) of each network (Figure 8 and Tables S4 and S5). OTU 10, OTU 18, OTU 52, and OTU 2007 were common keystone taxa in the bacterial co-occurrence network of the DR1 regime at two periods, and OTU 57 and OTU 490 shared in the networks of DR2 at heading and WAT at grain filling. Interestingly, DR2 at grain filling possessed ten unique hub nodes. OTU 17 was a conserved keystone taxon in all treatments and represented the core OTU in the fungal network.
The bacterial co-occurrence networks were dominated by Actinobacteria, Bacteroidetes, and Proteobacteria, and most nodes of the fungal networks belonged to Ascomycota and Basidiomycota (Figure S5), while the percentage of taxonomy for the keystone taxa of these networks was similar.

4. Discussion

This study concentrated on the effect of different watering patterns on the wheat crown rot induced by F. pseudograminearum (Fp) following wheat development by studying the symptoms of diseased plants and the host-associated microbiome. We set up a pot experiment with three watering regimes, including the DR1, DR2, and WAT regimes, representing early-drought, late-drought, and well-watered, respectively. After stopping watering from the anthesis, we found more serious diseases in drought treatments, including the DR1 and DR2 regimes. The disease index and measured water content were negatively correlated across all samples, revealing that water stress may contribute to a severe disease state and poor phenotype of winter wheat. A similar conclusion has been reported in previous works.
For instance, the Fusarium crown rot happened more severely in bread wheat under the extra drought stress, and the double stresses of drought and pathogen reduced the plant height and yield components such as seeds per spike and seed weight [39]. The drought stress caused by rainfall fluctuations also increased the prevalence of soil-borne disease [40,41]. However, most previous studies focused on the disease induced by multiple Fusarium spp. rather than a single Fp. Our study’s drought treatments under inoculation conditions increased the ST, NO3−-N, and EC while decreasing the soil pH only at grain filling. The high soil temperature accompanied by prolonged droughts improved the early prevalence of wheat crown rot [42], and a positive correlation between EC and FCR disease index was also found in previous field research [43]. The NO3−-N content was increased in the drought treatments, indicating the imbalance of the form of nitrogen for shaping disease-resistant soil [44]. Plants with excess nitrogen can also consume soil water and then induce severe disease once they are without a sufficient water supplement [45].
The health state of plants depends on environmental factors and might be associated with the plant-related microbiome. The plant-associated microbial community was closely in touch with plant growth, development, and disease resistance, and its structure and function generally differed by various factors. Host genotypes and ecological niches shaped special microbiome construction and complex networks [46,47]. Some key microorganisms enriched in a particular compartment under specific conditions might promote plant growth [48] or resist biological [49] and abiotic stress [50]. The microbial community in the root zone of plants, including rhizosphere soil and the root inner side, were closely linked with soil-borne disease [12,51], and the base stem where the FCR symptoms occurred may also accumulate or diminish special microorganisms. As we hypothesized, the plant-related microbiome differed when exposed to Fp under different watering modes. The higher Chao1 and Shannon index was found in the rhizosphere and root bacterial community under the well-watered treatment at grain filling, revealing that adequate water content could shape a bacterial community with great richness and diversity, similar to the former research [52]. Additionally, the low alpha diversity might be the toxicity of high-level NO3−-N under the drought treatments [53]. The high bacterial diversity might relieve the outbreaks of disease, and this view was supported by much existing research; for instance, resistant cultivars held a higher microbial diversity to protect from Chinese wheat yellow mosaic virus (CWMV) [54], and the intercropping of wheat controlled the cucumber Fusarium wilt disease by enhancing the alpha diversity [55]. The differences among treatments were not shown in the alpha diversity of fungi, indicating a more stable fungal community to perturbation of the environment [56]. The alpha diversity of bacteria and fungi was decreased from the rhizosphere to the root and stem, and this result was consistent with the previous theory of host selection, which might be driven by root exudates [57].
Additionally, significant differences were detected in the rhizosphere, root bacterial, and stem fungal communities among the different watering regimes, indicating a water-dependent microbiome selection on various compartments. Soil moisture could affect disease severity by directly controlling pathogen density [58] or acting synergistically with the disease over time to inhibit plant growth [59]. In addition, soil moisture can interact with the microbiome to suppress plant disease. Recent studies have verified that proper soil water content plays an important role in disease suppression by building a disease-control microbiome [60]. The soil with a beneficial bacterial and fungal microbiome was enriched with numerous antibiotic biosynthetic genes upregulated to respond to pathogens, such as potato common scab [61]. When the soil faced water stress, the beneficial taxa, such as Bacillus and Pseudomonas, were inhibited by this poor environment [60]. In contrast, pathogens could reproduce and harm plants indiscriminately without natural enemies.
The variation in microbial composition over watering regimes might be an important factor in controlling disease severity. The key taxonomy enriched in the special compartment niches under proper soil moisture was identified and cultivated to antagonize pathogens or improve the host’s induced systemic resistance (ISR) [62]. The drought regimes tended to accumulate desiccation-preferred bacteria Actinobacteria, consistent with previous studies [63]. Chloroflexi and Firmicutes were enriched in the watering treatments and were sources of beneficial microorganisms that promote plant growth and disease suppression [64]. The LEfSe analysis was used to determine the biomarkers associated with disease resistance under a proper water content. Chitinophagaceae, Gaiellales, Arenimonas, Sphingomonas, Pseudoxanthomonas, Devosia, and Lysobacter were significantly enriched in the well-watered treatments, including DR2HD and WATGF in the rhizosphere and root, which were previously reported as plant growth-promoting bacteria (PGPRs) and plant disease inhibitors. Chitinophagaceae was identified in sugar beet (Beta vulgaris L.) root faced with a fungal infection, showing a robust relationship with enzymatic activities of the degradation of fungal cell walls and genes of secondary metabolite biosynthesis associated with disease suppression [65]. Gaiellales was found as a healthy indicator enriched in the rhizome of Polygonatum plants [66]. Arenimonas was a kind of denitrifier involved in nitrogen cycling and might prevent upcoming diseases by alleviating the toxic effects of nitrate on microbial communities [67]. Sphingomonas spp. was chosen to create a core phyllosphere microbiome and suppress foliar pathogens by weakening essential virulence genes’ expression to keep a harmonious phyllosphere [68]. Lysobacter and Pseudoxanthomonas were enriched in the tomato (Solanum lycopersicum L.) rhizosphere with bio-organic fertilizer and were identified to be responsible for the resistance to Ralstonia solanacearum by producing secondary metabolites [69]. Devosia was enriched in the rhizosphere and root of well-watered plants and was identified as a PGPR and positively correlated with organic acids, such as succinic acid [70], which plays a vital role in the recruitment of beneficial microbes [71]. Some bacterial biomarkers appeared in multiple periods and compartments with high soil moisture (DR2HD or WATGF), revealing the conservation of these water-dependent microbes.
Interestingly, after stopping watering in the DR2 regime, no beneficial taxa were inherited relative to the previous watering stage, and the disease index increased, suggesting that the later-drought regime did not alleviate the disease by improving the microbiome compared to the earlier-drought regime. Sphingobium and Niastella were enriched in the rhizosphere and root of the drought treatments, which were reported as indicators of diseased soil and plants [72,73]. Other biomarkers, including Micrococcales, Herbidospora, BIyi10, Polaromonas, Caulobacter, Micavibrionaceae, Gitt-GS-136, Crocinitomix, Peredibacter, Fibrobacteraceae_possible_genus_04, and Oscillatoria_PCC-6304 in the rhizosphere; IMCC26256, Iamia, Lacibacter, Saccharimonadales, Bdellovibrio, BIrii41, and 0319-6G20 in the root; and Altererythrobacter and Fluviicola in both compartments, had not been reported to be associated with pathogen suppression or plant growth promotion. The number of fungal biomarkers was less than bacteria; most were potential pathogens. Leotiomycetes and Erysiphales were two fungal pathogens enriched in the stem of the well-watered treatment, and it might be explained by the mutual antagonism between different pathogens mitigating the occurrence of the mainly studied one [74,75].
The microbial co-occurrence networks analysis was a great method to assess the closeness and complexity between bacterial and fungal microbiome taxa under different watering regimes. Network analysis could also explore the keystone taxa enriched in the well-watered treatments, potentially taking part in disease suppression. Many previous studies had reported that unsuitable moisture conditions might shape a specific microbial network to aggravate the disease or damage the plant traits [60,76]. At the heading, we found that the watering treatment (DR2HD) constructs a more complicated and connected bacterial network than the drought treatment (DR1HD), revealing that a well-connected and compact microbial network could help resist disease infestation. At grain filing, the network parameters representing complexity and connectivity in the well-watered treatment (WATGF) and late-drought treatment (DR2GF) were higher than in the early-drought treatment (DR1GF), indicating that watering at an early stage helped build a strong microbial network, even when drought occurs at a late stage. The difference in water content not only influenced the network topology but also enriched some nodes to a high degree, and these hub taxa might play a role in promoting plant growth and disease resistance. OTU 478 (Sphingomonas), OTU40 (Sphingomonas), OTU 93 (Sphingomonas), OTU 57 (Pseudoxanthomonas), OTU 74 (Lysobacter), and OTU 47 (Lysobacter) were the keystone taxa enriched in the high soil moisture treatments (DR2HD and WATGF), as described above with the potential function of disease control. OTU 555 (Paenibacillus) was the hub node with a degree of 41 and was enriched in the DR2 regime at heading. Paenibacillus was commercially used for inhibiting plant disease and promoting plant growth [77].

5. Conclusions

High soil water content at both heading and grain filling helps mitigate FCR and improves the rhizosphere and root of bacterial, not fungal, community diversity and structure. In contrast, drought disturbs the native microbial community, inducing the imbalance between beneficial microorganisms and Fp and resulting in more severe disease. We also find that the DR2 regime at heading and grain filling and the WAT regime at filling establish a stronger bacterial network than DR1, coupled with lower disease severities. Thus, in areas where irrigation water is sufficient, disease incidence can be reduced by increasing irrigation. Some biomarkers found in our study, including Sphingomona and Devosia in the rhizosphere, Chitinophagaceae and Gaiellales in the root, and Arenimonas, Lysobacter, and Pseudoxanthomonas in the both compartments under high soil moisture, have been identified previously as beneficial members in other diseases and can be used in the next research study of isolation and culture, which may relieve the FCR symptoms in the field even under reduced irrigation regimes. Other biomarkers, such as Herbidospora and Polaromonas in the rhizosphere and Iamia and Lacibacter in the root, which have not been reported to be relevant to plant disease control, could be further explored as potential biocontrol agents in the future. Furthermore, various anti-fungal strains with different functions can be mixed to build a synthetic microbiota to prevent Fp and promote plant growth and development, and validation experiments and multi-omics analysis should further demonstrate this.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy13051356/s1, Figure S1: The relative abundance difference of microbial taxonomy under different watering treatment in rhizosphere, root, and stem; Figure S2: Distribution of the microbial community among different compartments; Figure S3: Cladogram of bacterial biomarkers under different watering regimes; Figure S4: Cladogram of fungal biomarkers under different watering regimes; Figure S5: The taxonomy of bacterial and fungal co-occurrence network; Table S1: Water capacity and disease index in the different watering treatment; Table S2: Topological characters of empirical and random bacterial co-occurrence networks under the different watering patterns; Table S3: Topological characters of empirical and random fungal co-occurrence networks under the different watering patterns; Table S4: Hub nodes of bacterial co-occurrence networks under the different watering patterns; Table S5: Hub nodes of fungal co-occurrence networks under the different watering patterns.

Author Contributions

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

Funding

This work was supported by the Key Research Projects of Hebei Province (Grant number: 21326411D), the National Key Research Projects (Grant number: 2022YFD2300801), and the National Wheat Industry Technology System (CARS301).

Data Availability Statement

Raw data of amplicon sequencing have been deposited in the Sequence Read Archive (SRA) database of National Center for Biotechnology Information (NCBI) under the accession number PRJNA962815 (bacteria) and PRJNA962796 (fungi).

Acknowledgments

All authors greatly appreciate the technical staff at China Agricultural University’s help during the sampling campaign.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Effect of different watering regimes on the severity of wheat crown rot. (a) Schematic representation of three watering patterns. The dotted line indicates the stage of drought onset and the red arrow indicates the sampling time. (b) Differences in soil water capacity among treatments. (c) Differences in disease index among treatments. (d) Correlation between water capacity and disease index across all samples. HD: heading, GF: grain filling. DR1: early-drought pattern, DR2: late-drought pattern, WAT: well-watered pattern. The disease index in (c,d) was cited from unpublished data from Gao et al.
Figure 1. Effect of different watering regimes on the severity of wheat crown rot. (a) Schematic representation of three watering patterns. The dotted line indicates the stage of drought onset and the red arrow indicates the sampling time. (b) Differences in soil water capacity among treatments. (c) Differences in disease index among treatments. (d) Correlation between water capacity and disease index across all samples. HD: heading, GF: grain filling. DR1: early-drought pattern, DR2: late-drought pattern, WAT: well-watered pattern. The disease index in (c,d) was cited from unpublished data from Gao et al.
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Figure 2. Differences in microbial community composition between compartments, watering patterns, and development periods. OTU distribution of bacteria (a) and fungi (b) among rhizosphere, root, and stem. Relative abundance of the microbial community under different watering regimes at phylum level of bacteria (c) and fungi (d). RZ: rhizosphere soil, RE: root, SE: stem. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
Figure 2. Differences in microbial community composition between compartments, watering patterns, and development periods. OTU distribution of bacteria (a) and fungi (b) among rhizosphere, root, and stem. Relative abundance of the microbial community under different watering regimes at phylum level of bacteria (c) and fungi (d). RZ: rhizosphere soil, RE: root, SE: stem. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
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Figure 3. Effect of watering regimes on microbial alpha diversity. (a,b) Chao1 and Shannon index of bacterial community. (c,d) Chao1 and Shannon index of the fungal community. According to Duncan’s multiple means comparison tests, different lowercase letters indicate significant differences at p < 0.05. RZ: rhizosphere soil, RE: root, SE: stem. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
Figure 3. Effect of watering regimes on microbial alpha diversity. (a,b) Chao1 and Shannon index of bacterial community. (c,d) Chao1 and Shannon index of the fungal community. According to Duncan’s multiple means comparison tests, different lowercase letters indicate significant differences at p < 0.05. RZ: rhizosphere soil, RE: root, SE: stem. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
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Figure 4. Distribution of the bacterial community among watering regimes in the rhizosphere, root, and stem. (a) PCoA was used to analyze the beta diversity of bacterial communities. (b) The dissimilarity of bacterial community structure based on Bray–Curtis distance. According to Duncan’s multiple means comparison tests, different lowercase letters indicate significant differences at p < 0.05. RZ: rhizosphere soil, RE: root, SE: stem. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
Figure 4. Distribution of the bacterial community among watering regimes in the rhizosphere, root, and stem. (a) PCoA was used to analyze the beta diversity of bacterial communities. (b) The dissimilarity of bacterial community structure based on Bray–Curtis distance. According to Duncan’s multiple means comparison tests, different lowercase letters indicate significant differences at p < 0.05. RZ: rhizosphere soil, RE: root, SE: stem. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
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Figure 5. Distribution of the fungal community among watering regimes in the rhizosphere, root, and stem. (a) PCoA was used to analyze the beta diversity of fungal communities. (b) The dissimilarity of fungal community structure based on Bray–Curtis distance. According to Duncan’s multiple means comparison tests, different lowercase letters indicate significant differences at p < 0.05. RZ: rhizosphere soil, RE: root, SE: stem. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of well-watered pattern.
Figure 5. Distribution of the fungal community among watering regimes in the rhizosphere, root, and stem. (a) PCoA was used to analyze the beta diversity of fungal communities. (b) The dissimilarity of fungal community structure based on Bray–Curtis distance. According to Duncan’s multiple means comparison tests, different lowercase letters indicate significant differences at p < 0.05. RZ: rhizosphere soil, RE: root, SE: stem. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of well-watered pattern.
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Figure 6. Bacterial biomarkers in rhizosphere and root under the different watering regimes. The linear discriminant analysis effect size (LEfSe) analysis showed the special biomarkers of the bacterial community in the rhizosphere (a,b) and root (c,d). The difference significance was confirmed using the Kruskal–Wallis test with p < 0.05. RZ: rhizosphere soil, RE: root. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
Figure 6. Bacterial biomarkers in rhizosphere and root under the different watering regimes. The linear discriminant analysis effect size (LEfSe) analysis showed the special biomarkers of the bacterial community in the rhizosphere (a,b) and root (c,d). The difference significance was confirmed using the Kruskal–Wallis test with p < 0.05. RZ: rhizosphere soil, RE: root. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
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Figure 7. Fungal biomarkers in stem under the different watering regimes. The linear discriminant analysis effect size (LEfSe) analysis shows the special biomarkers of the fungal community in the stem (a,b). The difference significance was confirmed using the Kruskal–Wallis test with p < 0.05. SE: stem. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
Figure 7. Fungal biomarkers in stem under the different watering regimes. The linear discriminant analysis effect size (LEfSe) analysis shows the special biomarkers of the fungal community in the stem (a,b). The difference significance was confirmed using the Kruskal–Wallis test with p < 0.05. SE: stem. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
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Figure 8. Microbial co-occurrence networks under different watering regimes. (a) Bacterial co-occurrence networks of different watering regimes at heading and grain filling. (b) Fungal co-occurrence networks of different watering regimes at heading and grain filling. The colors of nodes indicate the taxa of Phylum. The colors of the edges indicate positive (red) and negative (green) correlations. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
Figure 8. Microbial co-occurrence networks under different watering regimes. (a) Bacterial co-occurrence networks of different watering regimes at heading and grain filling. (b) Fungal co-occurrence networks of different watering regimes at heading and grain filling. The colors of nodes indicate the taxa of Phylum. The colors of the edges indicate positive (red) and negative (green) correlations. DR1HD: heading of early-drought pattern, DR2HD: heading of late-drought pattern, DR1GF: grain filling of early-drought pattern, DR2GF: grain filling of late-drought pattern, WATGF: grain filling of the well-watered pattern.
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Table
Table 1. Soil physiochemical properties of different watering treatments.
Table 1. Soil physiochemical properties of different watering treatments.
TreatmentSTpHNO3−-NEC
DR1HD26.33 ± 0.33 a7.46 ± 0.02 ab--
DR2HD24.00 ± 0.58 b7.57 ± 0.05 ab--
DR1GF23.67 ± 0.44 bc7.43 ± 0.03 b27.88 ± 0.95 a365.00 ± 0.58 a
DR2GF22.50 ± 0.58 c7.47 ± 0.15 ab26.88 ± 1.03 a352.67 ± 8.09 a
WATGF20.13 ± 0.19 d7.73 ± 0.08 a16.77 ± 1.07 b290.33 ± 16.48 b
ST: soil temperature (°C); NO3−-N: soil nitrate nitrogen (mg kg−1); EC: electrical conductivity (μS cm−1). The data are mean ± standard error. According to Duncan’s multiple means comparison tests, letters indicate significant differences at p < 0.05.
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MDPI and ACS Style

Xu, R.; Du, C.; Gao, Y.; Zhou, X.; Ejaz, I.; Guo, J.; Chen, K.; Ma, J.; Zhang, Y.; Wang, Z.; et al. Watering Shapes a Robust and Stable Microbial Community under Fusarium Crown Rot Infection. Agronomy 2023, 13, 1356. https://doi.org/10.3390/agronomy13051356

AMA Style

Xu R, Du C, Gao Y, Zhou X, Ejaz I, Guo J, Chen K, Ma J, Zhang Y, Wang Z, et al. Watering Shapes a Robust and Stable Microbial Community under Fusarium Crown Rot Infection. Agronomy. 2023; 13(5):1356. https://doi.org/10.3390/agronomy13051356

Chicago/Turabian Style

Xu, Runlai, Chenghang Du, Yutian Gao, Xiaohan Zhou, Irsa Ejaz, Jieru Guo, Kunhu Chen, Jun Ma, Yinghua Zhang, Zhimin Wang, and et al. 2023. "Watering Shapes a Robust and Stable Microbial Community under Fusarium Crown Rot Infection" Agronomy 13, no. 5: 1356. https://doi.org/10.3390/agronomy13051356

APA Style

Xu, R., Du, C., Gao, Y., Zhou, X., Ejaz, I., Guo, J., Chen, K., Ma, J., Zhang, Y., Wang, Z., & Sun, Z. (2023). Watering Shapes a Robust and Stable Microbial Community under Fusarium Crown Rot Infection. Agronomy, 13(5), 1356. https://doi.org/10.3390/agronomy13051356

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