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Article

Effects of Drought Stress during the Flowering Period on the Rhizosphere Fungal Diversity of Broomcorn Millet (Panicum miliaceum L.)

by
Yuhan Liu
1,2,3,
Jiangling Ren
1,2,3,
Yulu Hu
1,2,3,
Shu Wang
1,2,3,
Jiao Mao
1,2,3,
Yuanmeng Xu
1,2,3,
Mengyao Wang
1,2,3,
Sichen Liu
1,2,3,4,
Zhijun Qiao
1,2,3,4 and
Xiaoning Cao
1,2,3,4,*
1
Center for Agricultural Genetic Resources Research, Shanxi Agricultural University, Taiyuan 030031, China
2
College of Agriculture, Shanxi Agricultural University, Jinzhong 030801, China
3
Key Laboratory of Gene Resources and Germplasm Enhancement, Ministry of Agriculture, Taiyuan 030031, China
4
Key Laboratory of Cereal Germplasm Resources Exploration and Genetic Improvement, Taiyuan 030031, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(12), 2896; https://doi.org/10.3390/agronomy13122896
Submission received: 7 November 2023 / Revised: 19 November 2023 / Accepted: 20 November 2023 / Published: 25 November 2023
Browse Figures
Versions Notes

Abstract

:
Drought stress restricts plant growth and development. The flowering stage is a period of abundant microbial diversity in the rhizosphere of broomcorn millet. However, the diversity and community structure of rhizosphere fungi during the flowering stage of broomcorn millet and the response mechanism to drought stress are still unclear. In this study, two broomcorn millet varieties, ‘Hequ red millet’ (A1) and ‘Yanshu No.10′ (A2), with different drought resistances, were used as experimental materials. Using the pot water control method, drought treatment at the flowering stage was carried out, and normal watering (A1CK, A2CK) was used as the control. High-throughput sequencing technology was used to study the diversity and structural changes in rhizosphere fungi in broomcorn millet. The results show that the number of fungi OTUs in the A1, A2, A1CK and A2CK samples were 445, 481, 467 and 434, respectively, of which 331 OTUs were shared by all groups. The fungal community in the rhizosphere of broomcorn millet was mainly composed of Ascomycota and Basidiomycota. Drought treatment significantly reduced the abundance of Mortierella and significantly increased the abundance of Phoma. The abundance of Nectriaceae in the rhizosphere soil of ‘Hequ Red millet’ was significantly increased. The abundance of Pseudocercospora in the rhizosphere soil of ‘Yanshu No.10′ was higher, and the lower was Hypocreales and Nectriaceae. However, there was no significant difference in the alpha diversity of fungal communities in the four treatments, and the fungal community structure between A2 and A1CK was more similar, whereas the difference between A1 and A2CK was larger. Correlation analysis showed that drought stress had little effect on the interaction of rhizosphere fungi, and metabolic functions such as nucleotide metabolism and electron transport in rhizosphere fungi accounted for a relatively high proportion. The results show that the diversity and community structure of rhizosphere fungi were less affected by drought, which may have been due to the close interaction between species, which made the fungal community more stable under drought stress, and the difference in planting varieties may have affected the enriched rhizosphere fungal species.

1. Introduction

Drought is one of the most serious and frequently occurring non-biological factors worldwide that restrict plant growth and yield, exerting adverse effects on the growth and development of plants [1]. Broomcorn millet is a primary crop of coarse cereals in the Loess Plateau and exhibits higher drought tolerance compared to other crops [2]. When confronted with drought, broomcorn millet undergoes a series of physiological and biochemical changes, such as heightened oxidative enzyme activity, reduced accumulation of reactive oxygen species (ROS) and an increase in relative water content to combat drought [3]. Drought stress impacts crop metabolism, reducing transpiration rates from photosynthesis and suppressing aboveground biomass, ultimately resulting in a significant decrease in yield [4]. The rhizosphere plays a crucial role in water and nutrient uptake [5], and rhizospheric microorganisms, as an integral component of the rhizosphere, profoundly influence the interaction between plants and the soil environment [6,7].
When microorganisms encounter drought stress, they may experience death and cell lysis due to osmotic stress. During this period, the secretion of plant root exudates undergoes changes in response to photosynthesis, subsequently altering the rhizospheric environment, thus indirectly influencing microbial growth [8]. Microbial diversity can enhance soil health, which is essential for the growth and development of plants, and it plays a crucial role in nutrient cycling, the decomposition of organic matter, fertility improvement, the maintenance of soil balance and the promotion of plant health [9]. Microbes have the ability to synthesize growth-regulating substances to mitigate the adverse effects of stress on host plants. For instance, arbuscular mycorrhizal fungi (AMF) may bolster plant drought resistance by influencing the secretion of hormones in the host plant [10]. Certain signaling pathways mediated by plant hormones induce immune responses in plants against pathogens by regulating rhizospheric bacteria that promote plant growth [11]. Microorganisms can also alter soil pH, soil structure [12] and soil fertility [10]. Furthermore, microorganisms like pseudomonads [13] and rhizobia [14] can facilitate nutrient absorption in plants.
Broomcorn millet, recognized as an ideal crop for investigating stress resistance mechanisms [15], has been the subject of research concerning the composition of its rhizospheric microbial communities [16], as well as the influencing factors. The annual average temperature and soil pH emerge as pivotal driving factors that regulate the composition of broomcorn millet’s rhizospheric microbial communities, key species and modularity [17]. Furthermore, soil nutrients [18], cultivation methods [19] and growth stages [20] exert influences on rhizospheric microbial diversity and community structure. In challenging environments, the interconnections between rhizospheric communities in broomcorn millet are notably more intimate when compared to other crops [21]. Na et al. found that drought does not have a significant impact on the diversity and structure of the rhizospheric bacterial community in broomcorn millet. Some bacteria exhibit distinct response mechanisms to drought [22]. However, there are few studies on the response of rhizosphere fungal diversity and structure to drought stress in broomcorn millet. The flowering stage is known to be the period of the highest rhizospheric microbial diversity in broomcorn millet, and it is notably sensitive to moisture variations [22,23]. In this study, varieties of broomcorn millet with varying levels of drought resistance were individually subjected to drought stress during the flowering stage, with a normal treatment serving as a control. The diversity, community structure and metabolic function of rhizosphere fungi in broomcorn millet under two conditions were analyzed. The structure and diversity of rhizosphere fungal communities in different broomcorn millet varieties and the effects of drought on rhizosphere fungal communities were revealed. It is helpful to understand the differences in the drought tolerance of different broomcorn millet varieties. It is of great theoretical significance to improve the study of the drought tolerance mechanism of broomcorn millet and to improve the theory of organic dry farming.

2. Materials and Methods

2.1. Test Materials, Drought Stress Treatment and Sample Collection

The experiment was carried out at the Hequ Experimental Base of Agricultural Gene Resources Research Center of Shanxi Agricultural University. The pot experiment was carried out. The soil samples were collected from local farmland. Two broomcorn millet varieties, ‘Hequ Red millet’A1 (drought-tolerant) and ‘Yanshu No.10′ A2 (drought-sensitive) [23], with different drought resistances, were used as experimental materials and sowed in flowerpots. Each pot contained 10 kg of air-dried soil. Soil samples (sandy loam) were collected from local farmland (soil auger, 5 cm in diameter, 20 cm in length), which was not previously used for planting broomcorn millet. The field soils were air dried and sieved to 2 mm to remove rocks and plant debris.
Before sowing, the broomcorn millet seeds were sterilized, bleached for 5 min and washed with sterile water at least 3 times. All experiments were carried out in a dry shed. The experiment used a completely randomized block design. When the broomcorn millet grew normally to the heading stage, half of the pots decreased to 15% soil water content without watering (about 4 days), and 15% soil water content was maintained under drought stress (weighing every day) for 15 days. The remaining pots maintained 55% soil water content A1CK, A2CK (control) and were irrigated with sterile water throughout the process.
After 15 days of drought stress, the rhizosphere soil was sampled and shaken to remove loose soil, and the roots were rinsed with 5 mL of a 0.9% sterile NaCl solution. The obtained solution was centrifuged at 4 °C and 12,000 rpm for 10 min, and the deposition was defined as rhizosphere soil samples. Then, these rhizosphere soil samples were transferred into 5 mL sterilization tubes and stored at −20 °C for further analysis. Each 3 pots was repeated once, and each treatment was repeated three times.

2.2. Investigation of Agronomic Traits

The plant height, number of internodes, panicle length, dry weight of panicles and dry grain weight per plant of broomcorn millet were investigated. The plant height and panicle length were measured with a ruler. The culm diameter of the second section of broomcorn millet was measured with a vernier caliper. The dry weight of panicles and dry grain weight per plant of broomcorn millet were measured with an electronic balance.

2.3. DNA Extraction, PCR Amplification and Illumina MiSeq Sequencing

The extraction of microbial DNA, PCR amplification and Illumina sequencing were completed by Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China). Q5 high-fidelity DNA polymerase from the company NEB was used for PCR amplification, and the number of amplification cycles was strictly controlled to make the number of cycles as low as possible while ensuring that the amplification conditions of the same batch of samples were consistent. Paired-end sequencing of community DNA fragments was performed using the Illumina MiSeq platform.

2.4. Original Data Processing, Operation Classification Unit Division and Diversity Analysis

QIIME 1.8.0 software (Quantitative Insights Into Microbial Ecology) was used to identify and splice the obtained sequencing data, and low-quality, non-specific amplification sequences and chimeric sequences were removed [24]. By calling the sequence alignment tool UCLUST [25], the obtained sequences were merged, and OTUs were divided according to 97% sequence similarity. In addition, the most abundant sequence in each OTU was selected as the representative sequence of the OTU. Subsequently, according to the number of sequences contained in each OTU in each sample, a matrix file (OTU table) of the abundance of OTUs in each sample was constructed. Based on the UNITE database [25], a species annotation analysis of the OTUs was performed to obtain the taxonomic information of each OTU and construct the dilution curve, species accumulation curve and hierarchical abundance curve. QIIME software v.1.8.0 was used to calculate the alpha diversity index, including the Chao1 richness index [26], Shannon diversity index, Simpson index and ACE indexes [26]. The species accumulation curve and the grade abundance curve were drawn with the R language.
The beta diversity analysis was used to test the similarity of the community structure between different samples, mainly through principal component analysis (PCA) and multidimensional scaling (MDS).

2.5. Association Network Analysis and Metabolic Function Prediction of the Microbial Community

The species correlation network constructs a species correlation network by calculating the correlation between species. In the correlation network diagram of microbial ecology, each point can be called a node (or vertex), which can represent an ASV/OTU in the community or a taxonomic unit. The connection line between the two points can be called an edge, which represents the distribution trend with a positive or negative correlation between the two connected points. Through the method of correlation analysis, the inherent patterns of co-occurrence or co-exclusion of specific microbial communities driven by spatial and temporal changes and environmental processes are found, and then the correlation network of dominant microbial groups is constructed to explore the ecological significance between them. The top 50 species with total abundance at the classification level of soil fungal species were selected. By simulating the distribution of nodes in the network, we can identify complex species correlation information between soil microbial communities [27].
Through the PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) method, the existing ITS1 gene prediction data were compared with the microbial reference gene database with known metabolic functions to predict the metabolic function of microorganisms. Based on the full-length gene sequence of microorganisms, the gene function spectrum of common ancestors with significantly differentially expressed OTUs was predicted. The functional spectrum of other untested species in the full-length sequence database Greengenes was inferred, and the functional spectrum of archaea and bacteria was constructed. Then, the gene sequence data obtained from sequencing were compared with the Greengenes database to find the ‘nearest neighbor of the reference sequence’ of each sequence and classify it as a reference OTU. The OTU abundance matrix was corrected according to the rRNA gene copy number of the ‘nearest neighbor of the reference sequence’, and the bacterial composition data were ‘mapped’ to a database of known gene function profiles to predict the metabolic function of microorganisms.

2.6. Statistical Analysis

The data of agronomic traits were analyzed with IBM SPSS Statistics (version 25.0). The data are presented as the mean ± standard deviation (SD). A one-way analysis of variance was used for comparisons between groups. A p value < 0.05 was considered significant.

3. Results

3.1. The Impact of Drought on Agronomic Traits of Broomcorn Millet

From Figure 1, it is evident that drought stress during the flowering stage had a significant impact on the growth and development of broomcorn millet, and these effects varied notably among different varieties. In contrast to the drought-resistant A1, the growth of A2 was more severely affected by drought. In terms of physiological indicators, A2 showed a 6.6% increase in plant height compared to A1 but a 9.1% reduction in culm diameter. Regarding yield, A2 exhibited a 20.7% decrease in panicle length, a 9.7% decrease in panicle weight and a 4.7% decrease in dry grain weight per plant compared to A1 (Figure 1a,c,d–f).

3.2. Analysis of High-Throughput Sequencing Results

We employed Illumina high-throughput sequencing technology to sequence the fungal ITS1, yielding a total of 757,763 clean tags, which were subsequently categorized into operational taxonomic units (OTUs). Using a similarity threshold of ≥97%, multiple OTUs suitable for species classification were obtained. Specifically, the total number of OTUs for A1, A2, A1CK and A2CK were 445, 481, 467 and 434, respectively, with 331 OTUs shared among all groups (Figure 2a). Following OTU identification, the OTUs for each sample were classified at six taxonomic levels, including phylum, class, order, family, genus and species. The findings reveal that drought stress during the flowering period led to a reduction in broomcorn millet’s OTUs of rhizospheric fungi, with a pronounced decrease in fungal diversity in the A1 group. Under well-hydrated conditions, the rhizospheric microorganism A1CK was more enriched compared to that of A2CK (Figure 2b).

3.3. Alpha Diversity Analysis

Alpha diversity analysis was conducted to assess species richness and evenness within individual microbial ecosystems. The rarefaction curves for fungal communities indicate that, when the sequence count reached 20,000, the rarefaction curves for all samples essentially leveled off. This suggests that the sampling was reasonably adequate, and there is a higher degree of confidence in the samples accurately reflecting the fungal community structure of soil samples in their natural environment (Figure 3a). Additionally, under the condition of extracting an equal number of sequences, A2 and A1CK showed a higher number of OTUs compared to the A2CK and A1 samples. This indicates a greater fungal richness in the A2 and A1CK samples. Furthermore, the species accumulation curves demonstrate that the sample size was sufficient to accurately represent the richness of the fungal population (Figure 3b). The rank abundance curves also reveal that A1CK and A2 exhibited flatter patterns compared to the other two samples, indicating a smaller difference in abundance among OTUs and higher evenness (Figure 3c).
Several indices are available to assess the alpha diversity of microbial communities. The Shannon index reflects the degree of species diversity within a community, where a higher Shannon index signifies greater species richness and a more even distribution. Other commonly used metrics include the Chao 1 and ACE indices, which gauge community richness, and the Simpson index, which assesses community evenness. The alpha diversity indices for each sample were individually calculated and are detailed in Table 1. The results demonstrate that both A1CK and A2 exhibit significantly higher alpha diversity indices compared to the other two groups. This indicates that A1CK and A2 possess greater species richness and evenness.

3.4. Taxonomic Analysis

Based on the results of the OTU classification, the distinctive composition of microbial taxa at the phylum, class, order, family, genus and species levels was determined for each sample. Microbial communities in different samples exhibited variations at each taxonomic level, with A1CK consistently demonstrating a greater number of microbial taxa at the class, order, genus and species levels in comparison to the other samples (in Table 2).
Based on the results of OTU classification and taxonomic identification, we can obtain the specific composition of each sample at various taxonomic levels. These levels include phylum, class, order, family, genus and species, and they provide different resolutions for examining the structure of community compositions. In the analysis of broomcorn millet’s rhizospheric microbial diversity at the phylum level, the majority of fungi belong to phyla such as Ascomycota, Basidiomycota, Zygomycota, Ciliophora, Glomeromycota and Chytridiomycota. Among these, Ascomycota is the dominant phylum. When comparing the rhizosphere soil of A1 with normal watering conditions to broomcorn millet, the abundance of Ascomycota in A1’s rhizosphere soil is significantly reduced, whereas Basidiomycota is significantly increased (Figure 4a).
At the taxonomic level of class, the majority of soil fungi are classified into classes, including Sordariomycetes, Dothideomycetes, Agaricomycetes, Eurotiomycetes, Incertae sedis and Saccharomycetes. When compared to the other two sample groups, the abundance of Sordariomycetes in the rhizosphere soil of A1 and A1CK is significantly reduced. Conversely, the abundance of Dothideomycetes in the rhizosphere soil of A2 and A1CK is significantly increased. In the rhizosphere soil of A1 and A2CK, the abundance of Dothideomycetes is significantly decreased (Figure 4b).
At the taxonomic level of order, the majority of microorganisms fall into orders such as Hypocreales, Pleosporales, Sordariales, Capnodiales, Microascales and Agaricales. When compared to drought-resistant varieties of broomcorn millet, there is a significant increase in the abundance of Hypocreales in the rhizosphere soil of A2 and A2CK, along with a significant decrease in the abundance of Sordariales. In contrast to non-drought-resistant varieties of broomcorn millet, the rhizosphere soil of A1 and A1CK shows a significant decrease in the abundance of Hypocreales (Figure 4c).
At the taxonomic level of family, most of the microbes in the rhizosphere soil of broomcorn millet belong to families such as Nectriaceae, Hypocreales (Incertae sedis), Pleosporales (Incertae sedis), Mycosphaerellaceae, Chaetomiaceae and Sporormiaceae. Specifically, when compared to drought-resistant varieties of broomcorn millet, there is a significant increase in the abundance of Nectriaceae in the rhizosphere soil of A2 and A2CK. Conversely, the rhizosphere soil of A1 and A1CK exhibits a significant decrease in the abundance of Nectriaceae. Moreover, under the influence of drought, there is a significant decrease in the abundance of Hypocreales (Incertae sedis) in the rhizosphere fungi of A1 and A2 (Figure 4d).
At the taxonomic level of genus, the majority of microbes in the rhizosphere soil of broomcorn millet belong to genera such as Fusarium, Acremonium, Phoma, Gibberella, Pseudocercospora and Mortierella. When compared to the rhizosphere soil of broomcorn millet under normal watering conditions, there is a significant increase in the abundance of the genus Phoma in the rhizosphere soil of A1 and A2. Conversely, there is a significant decrease in the abundance of the genus Mortierella in the rhizosphere soil of A1 and A2 (Figure 4e).
The phylogenetic tree analysis indicates that the phyla Ascomycota, Basidiomycota and Zygomycota were the most abundant in the rhizosphere soil of broomcorn millet. Additionally, the most abundant class was Sordariomycetes (Figure 5a). Based on the abundance distribution of taxonomic groups or the similarity between samples, a clustering analysis was conducted for the top 50 genera in different samples. In comparison to the rhizosphere soil for the control, the genera Preussia and Pseudocercospora were relatively more abundant in the rhizosphere soil of broomcorn millet under drought conditions (Figure 5b).

3.5. Beta Diversity Analysis

To observe the variation in fungal communities among different samples, we conducted a beta diversity analysis using Principal Component Analysis (PCA) and Multidimensional Scaling (MDS). The analysis revealed that the first principal component (PCA1) explains 53.69% of all the variables, whereas the second principal component (PCA2) explains 19.77% of the used variables. The cumulative contribution of variance for these two principal components reached 73.46%, indicating that the results can effectively represent the compositional characteristics of microbial communities. Further analysis demonstrated that the structures of the microbial community in the rhizospheric samples (A2 and A1CK) of broomcorn millet were relatively similar (Figure 6a). The clustering analysis results, displayed in the form of a hierarchical tree, showed that the rhizosphere fungal community structure of A1CK and A2 was similar, and the rhizosphere fungal community structure of A1 and A2CK was similar to those of A1 and A2CK, respectively (Figure 6b). To provide a more comprehensive description of the structural differences in fungal communities within and between groups, weighted UniFrac distances were calculated. The results indicate a significant UniFrac distance between A1 and the other three samples, with a smaller UniFrac distance observed between A1CK and A2 (Figure 6c).
Through the application of Partial Least Squares Discriminant Analysis (PLS-DA) to the microbial community data generated from high-throughput sequencing, which is based on species abundance matrices and data from sample grouping, the results indicate that A1CK and A2CK are more similar to each other (Figure 6d).

3.6. Microbial Network Analysis

Using the SparCC method, correlations among microbial members were estimated based on the composition data of microbial communities to identify the relationships between different microbial members. Visualization was conducted using the ggraph package. Among all 12 samples, the correlation networks of species displayed distinct patterns under different treatments. The phylum Ascomycota represented the largest proportion of all nodes. The interaction between species in group A1 was weaker than that in the other three groups, indicating that drought stress has a relatively minor impact on the correlations among rhizospheric fungi. In A2CK, species interactions were the most tightly connected, indicating stronger correlations among species within the group. Conversely, A1CK showed lower interactions among species, suggesting that varieties of broomcorn millet also influenced correlations among rhizospheric fungi (Figure 7).

3.7. Functional Prediction of Fungal Community

High-throughput sequencing data were aligned with the KEGG (KEGG Pathway Database, http://www.genome.jp/kegg/pathway.html (accessed on 6 November 2023)) database, and functional predictions of the fungal communities in the four soil types were carried out using PICRUSt. The results were annotated into six primary metabolic pathways, namely metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems and human diseases. There were 45 secondary metabolic pathways. The third level corresponds to metabolic pathway diagrams, and the fourth level corresponds to specific annotation information for each KO (KEGG orthologous groups). The secondary metabolic functions of the broomcorn millet’s rhizospheric fungi, ranked from the most to the least abundant, include nucleoside and nucleotide biosynthesis, respiration, electron transfer, cofactor, prosthetic group and electron carrier, as well as vitamin biosynthesis, amino acid biosynthesis, fatty acid and lipid biosynthesis and carbohydrate biosynthesis (Figure 8).

4. Discussion

Drought stress can adversely affect crop growth and development, and in severe cases, it can lead to plant mortality [28]. In such circumstances, crops have their own defense mechanisms [29], and the rhizospheric microorganism also plays a role in mitigating and supporting the plants [30]. There is evidence suggesting that interactions between certain plants and microbes can enhance plants’ resistance to stress [31]. Therefore, investigating the role of microbes in response to environmental changes and their interactions with plants becomes highly significant. Microbial communities are regulated by environmental factors, interactions among microorganisms and communication between plants and microorganisms [32]. Plants influence microbial communities by releasing substances from their roots into the soil [33]. Environmental factors, including drought, salinity, soil structure and fertility, also impact microbial communities [8,34].
In this study, high-throughput sequencing of microbial genes was employed to detect differences in the rhizospheric microorganisms of broomcorn millet under drought conditions. The research results indicate that there is relatively little difference in the diversity of fungal community under the two treatment conditions (Table 1 and Table 2). We believe there are three reasons for this outcome. Firstly, the 15-day drought treatment had a limited impact on the structure of broomcorn millet’s fungal community. Despite significant inhibition of broomcorn millet’s growth and development under drought stress (Figure 1), it did not affect the structure of the rhizospheric fungal community [35]. Interactions among species remained relatively close (Figure 7). Previous studies have suggested that, although drought reduces the allocation of assimilated carbon by broomcorn millet belowground, it does not reduce the transfer of assimilated carbon to fungi [36]. Second, broomcorn millet requires less water for growth and development [37,38], so short-term drought stress from heading to flowering may not significantly affect the interaction between roots and soil microorganisms. Finally, the diversity of fungal community detected under well-watered and drought stress conditions may represent the normal state of broomcorn millet’s growth in arid and semi-arid regions [22,39,40]. Therefore, under the current drought conditions, the overall composition of the rhizospheric fungal community in broomcorn millet remains relatively stable. To understand the reasons for changes in the rhizospheric fungi of broomcorn millet, it may be necessary to improve the experimental methods by extending the duration of drought stress and considering other measures.
Research on correlation networks suggests that the most abundant interactions among species occur within the phyla Ascomycota and Basidiomycota. These species are widely distributed in various soil types, indicating that their physiological adaptations enable them to thrive in diverse environments and play crucial roles in ecological processes [28,41]. A1 is affected by drought stress, and it is relatively difficult to form material exchange between species. Therefore, the distribution pattern is more dispersed (Figure 7). As crops are in a growth state, they require inter-species interactions to overcome environmental changes and achieve stable material cycling and energy flow [42]. Furthermore, in the rhizosphere soil of A2CK, interactions among species are more intimate. Previous studies have indicated that healthy soil environments harbor more complex and highly interconnected ecological networks [43,44]. The complexity of ecological networks is associated with stronger resistance to pathogens and adversity and better maintenance of the potential stability of ecosystem functions [45,46]. Therefore, we hypothesize that the intricate interactions among dominant phyla like Ascomycota and Basidiomycota contribute to the stability of the rhizospheric fungal community, which may help plants resist drought or other biological stressors.
Different varieties of broomcorn millet enrich different rhizospheric fungal species and their relative abundances. Taxonomic and clustering analysis indicated that, under drought conditions, ‘Yanshu No.10′ had a greater number of rhizospheric fungal OTUs compared to ‘Hequ Red Millet’, with higher evenness and richness (Figure 3). The fungal species enriched in the rhizosphere of broomcorn millet varied significantly between the two treatments (Figure 4 and Figure 5). In the rhizosphere soil of ‘Hequ Red Millet’, there were higher abundances of genera such as Pseudogymnoascus, Chaetomium, Nothophoma and Trichocladium. Among these, Chaetomium is known to promote plant growth [47] and have a role in biological control [48,49]. They can produce secondary metabolites with antitoxic [50] and antibacterial properties [51], degrade cellulose and organic matter [52,53,54], promote plant absorption of nutrients and induce systemic resistance in plants themselves [55]. In the rhizosphere soil of ‘Yanshu No.10′, there were higher abundances of genera like Pseudocercospora, Botryotrichum, Cladosporium and Preussia. Preussia can produce plant hormones (indole-3-acetic acid) and extracellular enzymes (phosphatases and glucosidases) and promote plant growth under drought conditions [56]. However, the two varieties of broomcorn millet exhibited distinct rhizospheric fungal species, likely due to the distinct interaction mechanisms between different varieties and rhizospheric fungi. As a consequence of these interactions, the enriched microorganisms undergo alterations. Different varieties release rhizospheric secretions containing varying types of carbon substrates, leading to differences in rhizospheric fungal species [57,58]. Additionally, arbuscular mycorrhizal fungi recognize the host plant through the secretion of polyamines (PAs), enhancing interactions with the host [59]. There is also a symbiotic structure between microorganisms and plants that can directly participate in the nutrition between plants and microorganisms [60,61], thereby regulating host physiology and enhancing plant stress resistance. Therefore, these enriched fungi may establish connections with different host plants through various pathways, participating in the regulation of physiological and biochemical responses under drought conditions, ultimately contributing to improved drought resistance. However, further research is needed to elucidate the specific mechanisms by which they enhance drought resistance.

5. Conclusions

The diversity and community structure of the rhizosphere fungi of broomcorn millet with different drought tolerances under drought stress at the flowering stage were studied with high-throughput sequencing technology. The varieties of broomcorn millet had different effects on the species and abundance of rhizosphere fungi under drought stress. The abundance of Pseudogymnoascus and Chaetomium in the rhizosphere soil of ‘Hequ red millet’ was higher, and the fungi enriched by ‘Yanshu No.10′ were Pseudocercospora and Botryotrichum. The rhizosphere fungal communities of the two varieties showed strong drought resistance to drought stress. These results may help us understand the drought tolerance mechanism of the interaction between broomcorn millet and its related microbial communities.

Author Contributions

X.C., S.L. and Z.Q. conceived and designed the experiment. X.C. performed sample collection. Y.L., J.R., Y.H. and S.W. carried out the experiments. Y.L., J.R., J.M., Y.X. and M.W. performed DNA extraction and detection. Y.L. analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Central Guiding Local Science and Technology Development Funds (YDZJSX2022A044), the earmarked fund for CARS (Program No. CARS-06-14.5), the construction of a modern agricultural industrial technology system in Shanxi Province (2023CYJSTX03-23), College of Agriculture, Shanxi Agricultural university graduate innovation project (2023YCX33).

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available, as the project is not finished.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Effects of drought stress on agronomic traits of broomcorn millet. (a) Plant height; (b) Number of internode; (c) Culm diameter; (d) Panicle length; (e) Dry weight of panicle; (f) Dry grain weight per plant.
Figure 1. Effects of drought stress on agronomic traits of broomcorn millet. (a) Plant height; (b) Number of internode; (c) Culm diameter; (d) Panicle length; (e) Dry weight of panicle; (f) Dry grain weight per plant.
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Figure 2. OTUs of each sample. (a) Venn diagram of the number of sample OTUs; (b) The number of sample OTUs at different classification levels.
Figure 2. OTUs of each sample. (a) Venn diagram of the number of sample OTUs; (b) The number of sample OTUs at different classification levels.
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Figure 3. Diversity analysis of rhizosphere fungi in drought-tolerant and weak drought-tolerant broomcorn millet. (a) Rarefaction curves; (b): Species accumulation curves; (c): Rank abundance curves.
Figure 3. Diversity analysis of rhizosphere fungi in drought-tolerant and weak drought-tolerant broomcorn millet. (a) Rarefaction curves; (b): Species accumulation curves; (c): Rank abundance curves.
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Figure 4. Distribution and abundance of taxa. (a–e) The percentage of taxa at the phylum, order, class, genus and species levels.
Figure 4. Distribution and abundance of taxa. (a–e) The percentage of taxa at the phylum, order, class, genus and species levels.
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Figure 5. Taxonomic analysis through a phylogenetic tree and heat map. (a) Classification hierarchy tree showing the hierarchical relationships of all taxa from the phylum to the genus level in the sample population. (b) Combined heat level map of the community composition with cluster analysis.
Figure 5. Taxonomic analysis through a phylogenetic tree and heat map. (a) Classification hierarchy tree showing the hierarchical relationships of all taxa from the phylum to the genus level in the sample population. (b) Combined heat level map of the community composition with cluster analysis.
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Figure 6. Beta diversity analysis. (a) Two-dimensional ranking of the PCA analysis. (b) Weighted UniFrac distance matrix. Samples are clustered according to their similarity. The shorter the branch length between samples, the more similar the two samples are. (c) Multiple sets of box plots for the weighted UniFrac distance. (d) PLS-discriminant analysis. Each point represents a sample, the same color points belong to the same group, and the same group points are marked by ellipses. The samples belonging to the same group are closer to each other, and the distances between the points of the different groups are farther apart.
Figure 6. Beta diversity analysis. (a) Two-dimensional ranking of the PCA analysis. (b) Weighted UniFrac distance matrix. Samples are clustered according to their similarity. The shorter the branch length between samples, the more similar the two samples are. (c) Multiple sets of box plots for the weighted UniFrac distance. (d) PLS-discriminant analysis. Each point represents a sample, the same color points belong to the same group, and the same group points are marked by ellipses. The samples belonging to the same group are closer to each other, and the distances between the points of the different groups are farther apart.
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Figure 7. Species-related network diagram of the top 50 species at each treatment level. In the figure, the default shows the species with p < 0.01. The size of the nodes in the graph represents the abundance of the species, and different colors represent different species. Line color represents positive correlations and negative correlations: yellow represents a positive correlation, and gray represents a negative correlation. The thickness of the line indicates the correlation size. With the increase of line thickness, the correlation between species increased. More spectral lines indicate a closer relationship between species and other species. (a): A1; (b): A2; (c): A1CK; (d): A2CK.
Figure 7. Species-related network diagram of the top 50 species at each treatment level. In the figure, the default shows the species with p < 0.01. The size of the nodes in the graph represents the abundance of the species, and different colors represent different species. Line color represents positive correlations and negative correlations: yellow represents a positive correlation, and gray represents a negative correlation. The thickness of the line indicates the correlation size. With the increase of line thickness, the correlation between species increased. More spectral lines indicate a closer relationship between species and other species. (a): A1; (b): A2; (c): A1CK; (d): A2CK.
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Figure 8. Difference analysis of rhizosphere microbial metabolic pathways.
Figure 8. Difference analysis of rhizosphere microbial metabolic pathways.
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Table 1. Microbial alpha diversity index.
Table 1. Microbial alpha diversity index.
SampleChao1 IndexACE IndexSimpson IndexShannon Index
A1253.34 ± 24.03288.24 ± 48.390.72 ± 0.053.02 ± 0.28
A1CK270.00 ± 46.87302.43 ± 43.680.81 ± 0.043.50 ± 0.51
A2277.00 ± 60.67306.05 ± 61.230.75 ± 0.013.22 ± 0.30
A2CK229.00 ± 50.48253.41 ± 43.380.77 ± 0.023.13 ± 0.47
Table 2. Statistics of the number of microbial groups at each classification level.
Table 2. Statistics of the number of microbial groups at each classification level.
SamplePhylumClassOrderFamilyGenusSpecies
A1286.0 ± 12.00250.0 ± 10.69230.0 ± 8.72199.3 ± 8.62143.3 ± 6.42303.3 ± 13.01
A1CK308.0 ± 35.59270.7 ± 33.50248.3 ± 32.01215.7 ± 28.01159.3 ± 23.09323.3 ± 35.35
A2302.0 ± 40.04264.3 ± 36.12243.3 ± 34.67210.0 ± 32.91157.0 ± 28.35319.7 ± 42.00
A2CK264.0 ± 31.23232.7 ± 29.30213.0 ± 28.79185.3 ± 27.47135.7 ± 20.43275.3 ± 33.71
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Liu, Y.; Ren, J.; Hu, Y.; Wang, S.; Mao, J.; Xu, Y.; Wang, M.; Liu, S.; Qiao, Z.; Cao, X. Effects of Drought Stress during the Flowering Period on the Rhizosphere Fungal Diversity of Broomcorn Millet (Panicum miliaceum L.). Agronomy 2023, 13, 2896. https://doi.org/10.3390/agronomy13122896

AMA Style

Liu Y, Ren J, Hu Y, Wang S, Mao J, Xu Y, Wang M, Liu S, Qiao Z, Cao X. Effects of Drought Stress during the Flowering Period on the Rhizosphere Fungal Diversity of Broomcorn Millet (Panicum miliaceum L.). Agronomy. 2023; 13(12):2896. https://doi.org/10.3390/agronomy13122896

Chicago/Turabian Style

Liu, Yuhan, Jiangling Ren, Yulu Hu, Shu Wang, Jiao Mao, Yuanmeng Xu, Mengyao Wang, Sichen Liu, Zhijun Qiao, and Xiaoning Cao. 2023. "Effects of Drought Stress during the Flowering Period on the Rhizosphere Fungal Diversity of Broomcorn Millet (Panicum miliaceum L.)" Agronomy 13, no. 12: 2896. https://doi.org/10.3390/agronomy13122896

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