Anthracnose changes the diversity and composition of rhizosphere soil microbial community in common vetch

doi:10.21203/rs.3.rs-3759892/v1

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Anthracnose changes the diversity and composition of rhizosphere soil microbial community in common vetch

https://doi.org/10.21203/rs.3.rs-3759892/v1

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published 17 Jul, 2024

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Anthracnose caused by Colletotrichum is an increasingly severe disease of common vetch that reduces plant yield and quality. In this study, the effects of anthracnose on the rhizosphere soil microbial community of common vetch were studied via high-throughput sequencing. The relationship among the rhizosphere soil microbes and soil properties were analyzed. Lower fungal diversity was observed in rhizosphere soils of diseased plants. Variations in relative abundance were observed in the fungal phyla Mortierellomycota and Glomeromycota and in the bacterial phyla Acidobacteria, Oxyphotobacteria, and Verrucomicrobia. Gammaproteobacteria and Deltaproteobacteria were significantly more abundant in the rhizosphere soil of healthy plant. The bacterial community in the rhizosphere soil of diseased plant was involved in the synthesis of amino acids (glycine, serine, threonine, alanine, aspartate, and glutamate). Results of a redundancy analysis (RDA) showed that Colletotrichum was negatively correlated with soil available potassium (AK) and the content of soil organic matter (SOC), however it was positively correlated with soil pH. The relative abundances of Bacillus showed a negative correlation with soil pH and a positive correlation with AK and SOC content. The present study demonstrated that anthracnose could affect the rhizosphere soil microbial communities in common vetch, and soil properties have a close relationship with the rhizosphere soil microbes of common vetch.

Common vetch

anthracnose

high-throughput sequencing

rhizosphere microbes

Common vetch (Vicia sativa) is one of the most economically important annual legumes in the world (Huang et al. 2017). It is widely grown throughout the Mediterranean basin, western Asia, and in countries of the former Soviet Union (Dhima et al.2007; Yolcu et al. 2010). Common vetch is widely used for pasture or as grain legume as feed for livestock. The crop can also be used as green manure, providing rich organic fertilizer for farmland soil and promoting the recycling of nutrients in agricultural systems (Papastylianou 1999; Rinnofner et al. 2008).

Anthracnose is a very prevalent, severe disease of common vetch. Symptoms, including blackened, necrotic spots on stems, are present on the whole plants (Xu et al. 2017). The disease is caused by pathogenic microorganisms including Colletotrichum viciae (Dearness 1926), Colletotrichum villosum (Vlachostergios et al. 2011), Colletotrichum sativum (Horn 1952), Colletotrichum viciae-sativae (Sawada 1959), and Colletotrichum lentis (Xu and Li 2015). The pathogen overwinters on the soil and diseased plant residues, infesting and spreading via the conidia the following next spring (Weimer 1945). Anthracnose often favors warm, humid weather conditions, which results in a dramatic decrease in yield and quality of common vetch (Horn 1952).

Soil is a common source of plant interfollicular colonizing microbes (Bell-Dereske and Evans 2021), and microbes can undergo endophytic ectomy via vascular bundles to the aboveground, such as Streptomyces (Kim et al. 2019). The invasion of pathogens is a key biotic stresses that affect the microbial community, correspondingly, plants can recruit microbes that provide protection against pathogens (Vogel et al. 2022 ). Plant fungal pathogens are closely related to soil microflora and community structure (Kent and Triplett 2002). Soil properties and soil biological characteristics in the rhizosphere soil of plants are factors that suppress pathogens. The physical and chemical properties, and nutrient level of the soil directly affect the type, quantity and distribution of soil microbes (Hayat et al. 2010). Soil microbes (bacteria, fungi and actinomycetes), as one of the most important components of soil, participate in the energy flow and material circulation of soil ecosystems, which are directly related to the availability of soil nutrients and are also closely related to plant diseases (Raaijmakers and Mazzola 2016; Berendsen et al. 2018; Carrion et al. 2018).

The diversity of rhizosphere soil microbial communities of healthy and diseased plants in the same plot and the dynamic balance between them are often different, particularly soil borne pathogens. For example, bacterial diversity in the rhizosphere soil of potato (Solanum tuberosum) infected with bacterial wilt disease was significantly lower than that of the rhizosphere soil of healthy plants (Gorissen et al 2004). Bacterial and fungal community composition in the rhizosphere soil of cucumber (Cucumis sativus) infected with Fusarium wilt compared with the rhizosphere soil of healthy cucumber showed that the rhizosphere soil microbial community was richer and more complex in healthy plants (Qiu et al. 2012). Biocontrol using microbes derived from soil is an ecologically-sound strategy to manage plant disease. Few research focused on the effect of foliar pathogen on community composition of rhizosphere soil microbial. Further studies on this strategy are merited to improve food security for the growing human population (Li et al. 2014; Romero et al. 2016).

In 2019, a new anthracnose disease caused by Colletotrichum spinaciae was found in a common vetch field on the Qinghai-Tibetan Plateau, China; this pathogen can severely affect the yield of common vetch (Wang et al. 2019). However, little is known about the effects of this disease and soil properties on soil microbes and the feedback of soil microbes to anthracnose and soil properties. In addition, there was a severe outbreak of this disease; thus, it is important to clarify the effects of the disease and soil properties on soil microbes, as this will provide insight into the feedback of soil microbes related to anthracnose infection in common vetch and soil properties. Therefore, herein, we studied the composition and diversity of the fungal and bacterial community in the rhizosphere soil of diseased and healthy common vetch plant using high-throughput sequencing technology. In addition, we measured soil properties to analyze the relationship between soil properties and microbial community diversity and composition. We hypothesized that the occurrence of anthracnose will change the composition and diversity of the microbial community in rhizosphere soils, and microbial community may be related to soil properties.

Field sites and experimental design

In 2018, the experimental site was established in Xiahe County (35°11′N, 102°35′E; altitude 2733 m), the southern part of Gansu Province, China. Environmental conditions are summarized in Table 1 [20]. The soil in the field is silt loam, and soil organic carbon 2.17%, and soil TN, TP were 1.4696 and 0.9843 mg.g^-1, respectively. The soil AK was 217.73 mg·Kg^-1.

Table 1

Conditions at the experiment sites Xiahe Tibet Plateau, Gansu Province, China.
Items		Values
Altitude (m)	2733	Frost free days per year	80 ~ 130
Mean temperature (℃)	3.5	Growing season (days per year)	110 ~ 135
Temperature range (℃)	-15.5 ~ 10.8	Annual rainfall (mm)	451
Annual mean accumulated temperature (> 10℃)^a	684 ~ 1200	Soil pH	8.64
Soil TN (mg·g^-1)	1.4696	Soil TP (mg·g^-1)	0.9843
Soil AK (mg·Kg^-1)	217.73	SOC (%)	2.17
^a mean daily temperatures above 10℃ accumulated over a year.

The trial was set up with the common vetch cultivar V. sativa var. Lanjian No.2 on April 28, 2018. The common vetch was a new cultivar and was approved by the National Grass Variety Approval Committee in 2015, and the variety registration number is 482. The cultivar was widely planted in the Tibet Plateau area for livestock feeding and green manure crop. The precious crops was the highland barley (Hordeum vulgare). The blocks was ploughed to a depth of 30 cm with a rotary cultivator and then flood irrigated thoroughly before sowing in keeping with local field management practices. There were no irrigation and fertilizer during the experiment. There were five completely separated replicates plots (5.0 m×10.0 m) with a 1.0-m cell spcing in this study.

Soil samples collection

Before the maturity stage of common vetch (more than 70% of the middle pods reached the maximum length, the pods turned from green to yellow, and the grains began to harden), ten rhizospheric soil samples from diseased plants and ten rhizospheric soil samples from healthy plants were collected using a 20 cm soil core and then mixed to form one composite sample for each plot (Xiao et al. 2017). Totally 5 plots sample for each of the disease plant and healthy plant. There was a distance of more than 2 m between the healthy plants and diseased plants. The sample from rhizosphere soil of healthy plant was marked with an (Healthy RS) and the sample from rhizosphere soil of diseased plant was marked with a (Diseased RS). The soil samples for DNA extracti-on were kept at -80°C.

Soil chemical properties determination

All soil samples were analyzed for the chemical properties. Soil were air-dried at room temperature and passed through a 0.20-mm sieve. The PHS-3S acidity meter was used to measure the soil pH of 1: 2.5 soil-water suspension. The available K in soil was extracted with ammonium acetate (Du et al. 2017) and determined by flame photometry (AAS novAA 400, Analytik Jena AG, Jena, Germany). Soil organic matter (SOC) was determined by potassium dichromate oxidation-external heating method. Soil total nitrogen (TN) and total phosphorus (TP) were measured using a continuous flflow analyzer (FIAstar 5000 Analyzer, Foss, Denmark) (Yeomans and Bremner 1988; Zhao et al. 2014).

DNA extraction and PCR

The genomic DNA of soil was extracted by CTAB method (Borges et al. 2009). 15 µL PCR Master Mix, 10 ng template DNA, and 0.2 µM of primers {fungal primers: ITS5-1737F (5′-GGAAGTAAAAGTCGTAACAAGG-3′) and ITS2-2043R (5′-GCTGCGTTCTTCATCGATGC-3′); bacterial primers: 515F (5'-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′)} were combined a total volume of 30 µL (White et al. 1990; Parada et al. 2016). The PCR conditions included 95°C for 1 min, then 30 cycles of denaturation at 98°C for 10 s, followed by 50°C for 30 s, extension at 72°C for 30 s, and then 72°C for 5 min. Mix same volume of 1X loading buffer (contained SYB green) with PCR products and operate electrophoresis on 2% agarose gel for detection. The PCR products were purified with GeneJET™ Gel Extraction Kit (Thermo Scientific).

Sequencing data

Ion S5TM XL platform was used to sequence the libraries for five rhizospheric soil samples from diseased plants and five rhizospheric soil samples from healthy plants. Any low-quality partial reads were omitted using Cutadapt (V1.9.1), and then each sample data from the reads were splited according to barcode. The raw reads were obtained by cutting the barcode and primer sequences. The chimeric sequences were detected using the UCHIME Algorithm (Edgar et al. 2011). The Uparse software was used to obtain the clean reads (Langille et al. 2014). For valid sequences of all samples, the sequences are clustered into operational taxonomic units (OTUs) with 97% similarity.

Alpha and beta diversity analysis of rhizosphere soil microbial community

The alpha and beta diversity of the microbial community were estimated using observed-species, Chao 1, Shannon, Simpson, ACE and Good's coverage indexes, and these indexes were calculated and displayed by using the QIIME (Version 1.7.0) and R software (Version 2.15.3). A principal component analysis (PCA) and non-metric multi-dimensional scaling (NMDS) analysis based on the fungal and bacterial OTU levels were conducted. The CANOCO for Windows 4.5 was used to conduct a redundancy analysis (RDA) between genera and soil properties. Fungal and bacterial functional potential were investigated by using FunGuild and PICRUSt.

Statistical analysis

All of the data were subjected to an analysis of variance (ANOVA) using the SPSS version 20.0 (SPSS, Chicago, IL, USA). Using R statistical program (Version 4.2.0), the below index were analyzed, based on OTUs, alpha diversity indices were calculated to assess species diversity and richness of the fungi and bacteria community. The non-metric multi-dimensional scaling (NMDS) visualization based on Bray-Curtis dissimilarity was used to display beta diversity of the fungi and bacteria community, which indicated the distance between each group. Pearson correlation coefficient between chemical properties and fungi and bacteria alpha diversity in alfalfa were calculated within the psych package in R. Redundancy analysis (RDA) was also carried out using the capscale function of the vegan package in R to determine factors that could best explain variations in the microbial communities. In all tests, P-value < 0.05 was considered statistically significant.

Soil chemical analysis

The AK and SOC content in samples from rhizosphere soil of healthy plant (Healthy RS) was 24.3% and 20.8% higher than that of samples from rhizosphere soil of diseased plant (Diseased RS) (P < 0.05). The pH of Healthy RS was significantly lower than that of Diseased RS (P < 0.05) (Table 2).

Table 2

Soil properties of samples from rhizosphere soil of healthy plant (Healthy RS) and samples from rhizosphere soil of diseased plant (Diseased RS).
Samples	pH	AK (mg.Kg^− 1)	TN (mg.g^− 1)	TP (mg.g^− 1)	SOC (%)
Healthy RS^*	8.64 ± 0.03 b	287.74 ± 47.19 a	1.7723 ± 0.4190 a	0.9843 ± 0.1158 a	2.74 ± 0.26 A
Diseased RS^**	8.74 ± 0.08 a	217.73 ± 28.57 b	1.4696 ± 0.1728 a	1.0809 ± 0.4704 a	2.17 ± 0.17 B
^The sample from rhizosphere soil of healthy plant. ^* The sample from rhizosphere soil of diseased plant. Lowercase letters refer to the comparison within Healthy RS and Diseased RS at P < 0.05. Capital letters refer to the comparison within Healthy RS and Diseased RS at P < 0.01. .

Soil rhizoshpere rungal and bacterial richness and diversity

The analysis of diversity and richness using the Chao 1, ACE, Shannon, and Simpson estimators indicated that Healthy RS and Diseased RS did not differ significantly (P > 0.05). For the fungal community, and Diseased RS exhibited high Good’s coverage (> 99%), and Healthy RS had higher phylogenetic diversity compared to Diseased RS (P < 0.05) (Table 3).

Table 3

Richness estimators and diversity indices for samples from rhizosphere soil of healthy plant (Healthy RS) and samples from rhizosphere soil of diseased plant (Diseased RS) at a 97% identity threshold.
Samples	Fungal community		Bacterial community
Samples	Healthy RS	Diseased RS	Healthy RS	Diseased RS
Reads	80134	80201	84494	82320
OUT	1380	1329	2928	2810
Observed species	809	803	2204	2187
Shannon	6.75 ± 0.20 a	6.97 ± 0.07 a	9.42 ± 0.15 a	9.46 ± 0.07 a
Simpson	0.9752 ± 0.0046 a	0.9780 ± 0.0029 a	0.9958 ± 0.0018 a	0.9968 ± 0.0045 a
Chao1	838.62 ± 45.16 a	837.52 ± 43.43 a	2330.70 ± 39.21 a	2303.44 ± 55.63 a
ACE	843.45 ± 45.98 a	833.12 ± 47.16 a	2339.60 ± 44.79 a	2302.24 ± 58.64 a
Good’s coverage(%)	99.90	99.90	99.36	99.36
PD whole tree	230.77 ± 8.08 a	214.22 ± 13.78 b	164.26 ± 4.40 a	167.59 ± 7.80 a
Lowercase letters refer to the comparison within Healthy RS and Diseased RS at P < 0.05. .

Spearman correlations results revealed that the Shannon and Simpson index of common vetch rhizosphere soil fungal communities was negatively and significantly (P < 0.05) associated with rhizosphere soil SOC (Table 4). The Simpson index of common vetch rhizosphere soil bacterial community was negatively and significantly (P < 0.05) associated with rhizosphere soil available potassium (AK) (Table 5). The Observed species, Chao1 and ACE index of V. sativa rhizosphere soil fungal and bacterial community had no significant (P > 0.05) correlation with rhizosphere soil properties (Table 4, Table 5).

Table 4

Correlations between chemical properties in rhizosphere soil of *V. sativa* with fungal alpha diversity was analyzed by Pearson’s correlation coeffificient.
Soil properties	Observed species	Shannon	Simpson	Chao1	ACE
pH	-0.534	0.305	0.313	-0.424	-0.484
AK	0.371	-0.176	0.094	0.197	0.295
TN	0.161	-0.566	-0.541	0.230	0.218
TP	-0.505	-0.034	0.065	-0.472	-0.521
SOC	0.133	-0.804**	-0.696**	0.019	0.104
The asterisk indicates significant differences at the 0.01 and 0.05 level (*P < 0.01; P < 0.05). .

Table 5

Correlations between chemical properties in rhizosphere soil of *V. sativa* with bacterial alpha diversity was analyzed by Pearson’s correlation coeffificient.
Soil properties	Observed species	Shannon	Simpson	Chao1	ACE
pH	0.128	0.337	0.262	0.253	0.193
AK	0.315	-0.486	-0.678*	0.080	0.188
TN	-0.061	0.359	0.430	-0.081	0.108
TP	0.157	0.402	0.265	0.082	0.047
SOC	0.218	0.092	-0.068	0.150	0.166
The asterisk indicates significant differences at the 0.05 level (*P < 0.05).

Fungal and bacterial community composition

The sequencing analysis identified a total of six fungal phyla and 8 fungal class with an average relative abundance of > 1% in all soils (Table 6). The phyla were Ascomycota, Mortierellomycota, Basidiomycota, Glomeromycota, Chytridiomycota, and Olpidiomycota, which contributed approximately 67.49%, 27.09%, 13.23%, 4.45%, 1.42%, and 1.00% of relative abundances, respectively. The class were Sordariomycetes, Leotiomycetes, Mortierellomycetes, Dothideomycetes, Tremellomycetes, Glomeromycetes, Agaricomycetes and Eurotiomycetes, which contributed approximately 33.5%, 15.36%, 27.09%, 11.34%, 9.63%, 3.92%, 2.94% and 1.85% of relative abundances, respectively.

Table 6

The relative abundances of the fungal and the bacterial phyla and class for samples from rhizosphere soil of healthy plant (Healthy RS) and samples from rhizosphere soil of diseased plant (Diseased RS).
Taxonomy of fungal community	Relative abundance (%)		Taxonomy of bacterial community	Relative abundance (%)
Taxonomy of fungal community	Healthy RS	Diseased RS	Taxonomy of bacterial community	Healthy RS	Healthy RS
Phylum			Phylum
Ascomycota	35.28a	32.21a	Proteobacteria	33.79A	26.05B
Mortierellomycota	12.04A	15.05B	Bacteroidetes	14.99	16.39
Basidiomycota	7.55	5.69	Actinobacteria	12.45	15.35
Glomeromycota	1.31b	3.14a	Acidobacteria	13.78b	15.97a
Olpidiomycota	0.12	0.85	Thaumarchaeota	3.94	5.00
Chytridiomycota	0.69	0.73	Firmicutes	3.67	1.82
Blastocladiomycota	0.13	0.03	Gemmatimonadetes	5.80	5.02
Rozellomycota	0.04	0.04	Chloroflexi	3.94	3.95
Zoopagomycota	0.03	0.02	Verrucomicrobia	1.91B	3.41A
Mucoromycota	0.03	0.02	Oxyphotobacteria	0.46B	1.42A
Others	42.79	42.23	Others	5.28	5.64
Class			Class
Sordariomycetes	15.42	18.08	Bacteroidia	14.64	16.03
Leotiomycetes	12.11A	3.25B	Alphaproteobacteria	16.47	14.35
Mortierellomycetes	12.04b	15.05a	Gammaproteobacteria	11.92A	7.86B
Dothideomycetes	4.60	6.74	Nitrososphaeria	3.94	5.00
Tremellomycetes	6.10a	3.53b	Gemmatimonadetes	5.00	4.38
Olpidiomycetes	0.12	0.85	Actinobacteria	5.78	5.89
Glomeromycetes	1.14B	2.78A	Acidobacteria	6.30	7.51
Agaricomycetes	0.96b	1.98a	Clostridia	1.99	0.48
Eurotiomycetes	0.38B	1.47A	Deltaproteobacteria	5.37A	3.82B
Blastocladiomycetes	0.13	0.03	Acidimicrobiia	3.95	4.55
Others	47.00	46.24	Others	24.63	30.15

In the bacterial community, a total of 10 phyla and class with an average relative abundance > 1% were identified in the soil (Table 6). The bacterial community was composed of Proteobacteria, Bacteroidetes, Acidobacteria, Actinobacteria, Gemmatimonadetes, Thaumarchaeota, Chloroflexi, Firmicutes, Verrucomicrobia, and Oxyphotobacteria, which contributed approximately 59.83%, 31.38%, 29.75%, 27.80%, 10.82%, 8.93%, 7.89%, 5.48%, 5.31%, and 1.89% of relative abundances, respectively. The class were Alphaproteobacteria, Bacteroidia, Gammaproteobacteria, Acidobacteria, Actinobacteria, Gemmatimonadetes, Deltaproteobacteria, Nitrososphaeria, Acidimicrobiia and Clostridia, which contributed approximately 30.82%, 30.67%, 19.78%, 13.81%, 11.67%, 9.38%, 9.19%, 8.94%, 8.50%, and 2.47% of relative abundances, respectively.

Differences in fungal and bacterial communities

Mortierellomycota was significantly more abundant in Healthy RS than Diseased RS (P < 0.01), but the abundance of Glomeromycota was higher in Diseased RS than Healthy RS (P < 0.05). Verrucomicrobia and Oxyphotobacteria were significantly more abundant in Diseased RS than Healthy RS (P < 0.01), however the abundance of Proteobacteria was higher in Healthy RS than Diseased RS (P < 0.01). When compared to Healthy RS, the relative abundance of Acidobacteria was increased in Diseased RS (P < 0.05).

Mortierellomycetes, Glomeromycetes, Agaricomycetes and Eurotiomycetes were significantly more abundant in Healthy RS than Diseased RS (P < 0.05), but the abundance of Leotiomycetes and Tremellomycetes were higher in Diseased RS than Healthy RS (P < 0.05). Gammaproteobacteria and Deltaproteobacteria were significantly more abundant in Healthy RS than Diseased RS (P < 0.01).

The PCA based on the OTU composition suggested strong variations in fungal and bacterial community structure between Healthy RS and Diseased RS. The first two principal components could be used to explain a total of 32.32% and 39.44% of the variation in the composition of fungal and bacterial communities (Fig. 1A, B). The NMDS based on average relative OTU abundance illustrated dissimilarities of the fungal and bacterial communities (Fig. 1C, D).

The top 35 functions and their abundance in each sample were used to draw a heat map. Among different functional groups, clusters were selected according to the functional annotation and abundance information in the sample database. The results showed that plant pathogen, fungal parasite, undefined saprotroph, dung saprotroph, ectomycorrhizal-ericoid mycorrhizal, and soil saprotroph were components of the fungal community of Healthy RS, whereas endophytic and arbuscular mycorrhizal were abundant in Diseased RS (Fig. 2A). Bacteria in Healthy RS were involved in methane metabolism, chaperones and folding catalysts, and ribosomal biosynthesis and other physiological and biochemical activities. The relative abundance of gene families in Diseased RS were involved in the synthesis of amino acids, including glycine, serine, threonine, alanine, aspartate, and glutamate (Fig. 2B).

RDA of Fungal and Bacterial Communities

The first axis and second axis of the RDA explained 44.5% and 17.3% of the variance, respectively. The results from the RDA indicated that pH had a positive effect on the abundances of Dactylonectria, Gibberella, Colletotrichum, and Boeremia and a negative effect on Solicoccozyma, Mortierella, Thelebolus, Plectosphaerella, and Botrytis. TN, SOC, and AK had positive effects on Mortierella, Thelebolus, Plectosphaerella, and Botrytis abundance. TP also had a positive effect on Dactylonectria abundance and a negative effect on Gibberella, Boeremia, and Filobasidium (Fig. 3A). Based on these results, pH had a positive effect on Colletotrichum abundance, while SOC and AK had negative effects on Colletotrichum.

In the bacterial communities, the results of the RDA showed that pH had a positive effect on the abundance of Sphingomonas, Bryobacter, Flavisolibacter, and Microvirga, and a negative impact on Skermanella, Haliangium, Blastococcus, and Bacillus. TN, SOC, and AK had positive effects on Blastococcus, Bacillus, Skermanella, and Haliangium abundance. TP had a positive effect on the abundance of Bacillus, Adhaeribacter, Flavisolibacter, and Bryobacter (Fig. 3B).

Previous study shown that soil borne pathogen could change the composition of soil microbial. Using high-throughput sequencing, the present research revealed that anthracnose, a foliar disease caused by fungal pathogen in common vetch could affect the microbial diversity and composition in soils. This change was closely related with soil nutrients, pH and soil organic matter.

Soil pH, AK, and SOC can affect the occurrence of diseases, and accordingly, soil properties can impact the external environment through the regulation of diseases. Our study found that the occurrence of anthracnose was positively correlated with soil pH. Soil pH had a great influence on the expansion of pathogens (Watanabe et al. 2011) For C. spinaciae, the cause of anthracnose in this study, the optimal condition for hyphal growth was under alkaline conditions, and the high pH created more favorable conditions for the growth of the pathogen, which aggravated the incidence of infection (Yant 2017). Therefore, pH was positively correlated with Colletotrichum.

SOC is an important indicator of soil fertility and are easily absorbed by plants (Ilumäe et al. 2009). Potassium is an important factor in plant defense response, as it can increase the osmotic pressure of leaf cells, promote the conversion of low molecular compounds (such as glucose) into high molecular compounds (such as fiber), and reduce soluble nutrients content, thereby inhibiting the growth and development of pathogens (Amtmann et al. 2008). With the decrease of the content of AK and SOC, the occurrence of anthracnose was more severe. Potassium, which was derived from the roots and absorbed from the soil, no longer or reduced the supply to the diseased lesions and was preferentially utilized by younger tissues, thereby reduced the AK content in the soil (Engels and Marschner 1992). After the plant was stressed by anthracnose, the potassium content in the soil became active, and soil AK enhanced the immune response of common vetch, which effectively reduced the occurrence of diseases (Sun et al. 2014). Overall, the occurrence of anthracnose can be affected by adjusting soil AK and SOC (Fent et al. 2017) .

High-throughput sequencing technology revealed that anthracnose significantly affected the relative abundance of Mortierellomycota and Glomeromycota. Mortierellomycota was primarily found in healthy-plant rhizosphere soil samples while Glomeromycota was predominantly found in diseased-plant rhizosphere soil samples. Thus, the decrease in the abundance probably weakens the effective protection of Mortierellomycota against pathogens (Kent 2002). Our hypothesis that anthracnose will change microbial community and diversity was supported. Glomeromycota can reduce disease occurrence and improve plant disease resistance by interacting with pathogens. For example, previous studies have shown that Glomeromycota can defend plants against pathogens by increasing plant antioxidant capacity (Maya and Matsubara 2013), reducing pathogen infection sites, enhancing the activity of defense related enzymes in plants (such as L-phenylalanine ammonia-lyase, peroxidase, superoxide dismutase, and chitinase) (Gao et al. 2018a; Li et al. 2018), mediating signal transduction pathways in plant stress (Gao et al. 2018b; Li et al. 2019), promoting nutrient uptake (Vázquez et al. 2000), promoting accumulation of effector protein SP 7 (Silke et al. 2011)[45], inducing up-regulation of PR protein gene (PR-4) (Li et al. 2019), and increasing the leaf area (Banuelos et al. 2011). During the stages of pathogenic infection, Glomeromycota forms a symbiotic mycorrhizal network to protect or compensate for the infection caused by the pathogen (Helgason and Fitter 2009), The higher abundance of Glomeromycota in diseased-plant rhizosphere soil samples indicate the pathogen infection induce plant recruit beneficial microbes such as Glomeromycota to help its anti-pathogen infection.

Plant rhizosphere habitats often contain antagonistic bacteria that inhibit targeting by pathogens (Rodrigo et al. 2013). It is much easier to select effective antagonistic bacteria from rhizosphere soils than from other habitats(Nagarajkumar and Jayaraj); these antagonistic organisms are derived from roots or rhizosphere soils (Romero et al. 2016). Proteobacteria, Acidobacteria, Actinobacteria, and Chloroflexi were always the most common phyla from rhizosphere soils, which roughly corresponds to the findings presented in previous studies on agricultural soils (Poulsen et al. 2013). The pH of diseased-plant rhizosphere soil samples was significantly higher than that of healthy-plant rhizosphere soil samples. The Acidobacteria abundance of diseased-plant rhizosphere soil samples was increased by 2.20% comparing to healthy-plant rhizosphere soil samples, suggesting that the anthracnose affected the abundance of Acidobacteria is closely related with the changing of the soil pH (Lauber et al. 2009; Rousk et al. 2010).

The rhizosphere-promoting bacteria, such as Bacillus and Pseudomonas, produced gibberellic acid and cytokinins (Yuttavanichakul et al. 2012), and also promoted the transformation and absorption of rhizosphere nutrients (such as nitrogen, phosphorus, and trace elements such as iron, manganese, zinc, and copper) (Bhattacharyya et al. 2012). Proteobacteria (Gammaproteobacteria and Deltaproteobacteria) were significantly more abundant in healthy-plant rhizosphere soil samples than that of diseased-plant rhizosphere soil samples; The healthy-plant rhizosphere soil samples contained a large number of nitrogen-fixing bacteria that can coexist with plants. Proteobacteria have significant effects on nitrogen, phosphorus, sulfur, and organic matter cycling in farmland soils (Dedysh et al. 2004;Lv et al. 2014).

Soil properties is a important factor affecting the root-associated microbial community changes in healthy-plant rhizosphere soil samples and diseased-plant rhizosphere soil samples. RDA indicated that soil properties have a close relationship to the composition of soil microbial communities. Bacillus is often reported as biocontrol bacteria, and the abundance of Bacillus showed significantly negative correlations with soil pH and a positive correlation with AK and SOC.

Long-term addition of organic matter could increase the abundance of Acidobacteria and Firmicutes in soil (Bonanomi et al. 2016). The improvement of organic matter can inhibit soil-borne diseases and reduce the abundance of pathogenic bacteria in soil (Bonanomi et al. 2010). It is possible that organic matter increases the antagonism of other soil microorganisms to pathogenic fungi, and/or that competition and organic matter catabolism produce substances that are harmful to pathogenic bacteria (Bonanomi et al. 2015). The addition of organic matter to the soil can inhibit a variety of pathogenic microorganisms, including Rhizoctonia solani, Pythium, Fusarium, and Phytophthora. Soil pH was the decisive factor of rhizosphere soil bacterial diversity (Zhang et al. 2015). Among other ecological factors, such as the terrestrial ecosystem, precipitation, and soil nitrogen, the increase of soil pH (from acid to alkaline) led to an increase first followed by a decrease in bacterial diversity. Soil pH as well as the content of AK and SOC can be adjusted to inhibit the growth of pathogenic microorganisms, improve plant disease resistance, and maintain soil ecological balance.

The occur of anthracnose changed the diversity and composition of rhizosphere soil microbial communities in common vetch, lower fungal diversity was observed in rhizosphere soils of diseased plants. Healthy plant had higher fungal phyla Mortierellomycota and Glomeromycota and higher bacterial phyla Acidobacteria, Oxyphotobacteria, and Verrucomicrobia. The changes of rhizosphere soil microbial communities is closely related with soil properties changes by anthracnose pathogen Colletotrichum, it was negatively correlated with soil available potassium (AK) and the content of soil organic matter (SOC), however it was positively correlated with soil pH. The relative abundances of Bacillus showed a negative correlation with soil pH and a positive correlation with AK and SOC content. The results of this study provide a better understanding of the relationship among rhizosphere pathogens in an agro-ecosystem.

Acknowledgements: This work was financially supported by a grant from the China Agriculture Research System-Green Manure (CARS-22).

Author Contributions: Writing of original draft: Q. Wang and R. Zhu; investigation: Q. Wang, F. Li and Y. Li; graph and statistics: Q. Wang, R. Zhu and M. Bai; funding acquisition and revising of manuscript: T. Duan. All authors have read and agreed to the published version of the manuscript.

Conflict of Interests: The authors declare that there are no conflict of interests.

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Anthracnose changes the diversity and composition of rhizosphere soil microbial community in common vetch

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Abstract

Figures

Introduction

Materials and Methods

Field sites and experimental design

Soil samples collection

Soil chemical properties determination

DNA extraction and PCR

Sequencing data

Alpha and beta diversity analysis of rhizosphere soil microbial community

Statistical analysis

Results

Soil chemical analysis

Soil rhizoshpere rungal and bacterial richness and diversity

Fungal and bacterial community composition

Differences in fungal and bacterial communities

RDA of Fungal and Bacterial Communities

Discussion

Conclusions

Declarations

References

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