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Trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture cultivation system
Applied Soil Ecology 148 (2020) 103508
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Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil
Trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture cultivation system
T
Pengfa Lia,b, Jia Liua,c, Chunyu Jianga, Meng Wua, Ming Liua, Shiping Weia,b, Cunpu Qiua,b, ⁎ Guilong Lia,b, Changxu Xuc, Zhongpei Lia,b, a
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China University of Chinese Academy of Sciences, Beijing 100049, China c Soil and Fertilizer & Resources and Environment Institute, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Monoculture Non-phytopathogenic fungi Peanut Potential phytopathogenic fungi Trade-off
Phytopathogenic fungi are a major cause of plant disease. However, the interrelationships between phytopathogenic and non-phytopathogenic fungi in the peanut (Arachis hypogaea) monoculture system remain unclear. In this study, rhizosphere soils from four fields that had been monocropped with peanut for different durations were investigated, including monoculture for 1 year (CK), 4 years (P4), 10 years (P10), and 20 years (P20). Illumina sequencing of fungal ITS regions and FUNGuild were used to investigate the interrelationships between potential phytopathogenic and non-phytopathogenic fungi. Our results showed that the relative abundance of potential phytopathogenic fungi was significantly negatively correlated with that of non-phytopathogenic fungi (r = −0.650, P = 0.022); and the relative abundance of the key potential phytopathogenic species, Fusarium, was also significantly negatively correlated with that of the non-phytopathogenic fungi, Penicillium (r = −0.815, P = 0.001). The greater negativity between potential phytopathogenic and nonphytopathogenic fungi in the co-occurrence network implied an antagonism between them; and the greater negativity between Penicillium species and Fusarium species in the network combining with the results of confrontation experiment suggested the suppression of Penicillium species on Fusarium species. In conclusion, there is a trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture system. Our results provide better insight towards understanding the obstacles of continuous peanut cropping.
1. Introduction Continuous monoculture can result in sharp declines in yield and quality. This has been demonstrated across many studies on a variety of crops, such as watermelon (Ling et al., 2013), soybean (Jie et al., 2019), cucumber (Elliott et al., 1986), and banana (Wang et al., 2015). Peanut (Arachis hypogaea L.) is one of the most important oilseed and economic crops in China. Peanuts are continuously cropped due to the relatively high economic benefits and the limited available cultivation areas. Intensive peanut monoculture in the hilly red soil regions of subtropical China has resulted in a significant decline in peanut yield and quality. The issues associated with continuous cropping are complex and pertain largely to soil environmental issues, particularly alterations in the soil microbial communities (Peruzzi et al., 2017). An imbalance in the microbial flora is considered to be an important mechanism underlying the issues associated with continuous cropping (Sudini et al., 2011; Chen et al., 2018). With regards to fungi, a previous study
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demonstrated that pathogenic fungi, such as Fusarium oxysporum, Bionectria ochroleuca, Leptosphaerulina australis, and Phoma sp., continuously accumulated in a peanut monoculture system, and the accumulation of pathogenic fungi resulted in yield declines (Li et al., 2014). In addition to peanut, a previous study on cotton also demonstrated that a key fungal pathogen, Fusarium oxysporum, significantly increased with an increase in the duration of continuous monoculture. Simultaneously, some plant-beneficial and disease-suppressive bacterial taxa declined (Li et al., 2015). The increase in phytopathogenic fungi and decrease in plant-beneficial microbes under monoculture systems appears to be prevalent. However, some current studies demonstrated that the plants can protect themselves from infection through suppressing phytopathogenic microbes by recruiting protective microorganisms, and enhancing microbial pathogen-suppression activity (Berendsen et al., 2012). For instance, Arabidopsis can recruit pathogen-suppression microbe thorough secreting malic acid after being infected by pathogens (Rudrappa et al., 2008). In addition to the suppression of
Corresponding author at: Institute of Soil Science, Chinese Academy of Sciences, East Beijing Road 71, Nanjing 210008, China. E-mail address: [email protected] (Z. Li).
https://doi.org/10.1016/j.apsoil.2020.103508 Received 20 February 2019; Received in revised form 3 January 2020; Accepted 7 January 2020 0929-1393/ © 2020 Elsevier B.V. All rights reserved.
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this study. The peanut cultivar (Ganhua 1, a cultivar that is widely cultured in south China), the fertilization scheme, and all other field management methods were the same in the four fields. Peanut was cultivated at the beginning of April. About 300 kg ha−1 of urea, 750 kg ha−1 of calcium magnesium phosphate, and 225 kg ha−1 of potassium chlorate were applied along the surface of the opposite slope of furrows that were 10 cm wide × 10 cm deep, into which peanut seeds were planted. Weeds were controlled with herbicides (Bentazon, Shanghai, China). Plant and soil samples were collected on July 26 when the peanuts were in the late reproductive stage. Ten random plants were carefully removed from each of the subplots using a spade, after which the soil attached to the roots was collected and pooled to represent a composite rhizosphere soil sample. Fresh soil samples were sieved through 2 mm mesh and then subdivided into two subsamples. One subsample was stored at 4 °C for determination of physical and chemical properties, while the other was stored at −20 °C until DNA extraction and analyses. Unlike the soils, the plants were not immediately pooled because plant yield needed to be determined. Peanut pods were separated from plants, and pods of each plant were then weighted separately to indicate peanut yield. The peanut yield significantly decreased from the CK to P20 treatment, and the data are shown in Fig. S1.
phytopathogenic fungi by plants, the natural soil also has the ability to suppress pathogens to a certain extent (Hoitink and Boehm, 1999). Generally, the suppression ability of the natural soil can be classified into two types, namely ‘general suppression’ and ‘specific suppression’. ‘General suppression’ occurs when total microbial community cause soils to be suppressive to a board range of pathogens; and the ‘specific suppression’ occurs when specific microorganisms cause soils to be suppressive to a disease; for instance, the suppression of Fusarium after several years of crop monoculture (Bennett et al., 2012). Such a phenomenon has been observed in many monoculture systems (Mendes et al., 2011; Raaijmakers et al., 2009; Weller et al., 2002). If peanut plants and peanut-cultivated soils also have the ability to suppress pathogens, we hypothesize that the phytopathogenic fungi in peanut rhizosphere would not increase without limits in the peanut monoculture system. However, this has been merely tested in the peanut cultivation system. Furthermore, previous studies on the changes in phytopathogenic fungi in a peanut monoculture system have only focused on the changes in relative abundance of specialized fungi species or the effects of specialized chemical substances on microbial communities. However, the interrelationships between phytopathogenic and non-phytopathogenic fungi, and the key factors shaping phytopathogenic fungal communities, remain unclear. Next-generation sequencing (NGS) has made it easier to conduct systematic and comprehensive studies on fungal communities (Buee et al., 2009). However, the lack of effective analytical methods has limited studies on phytopathogenic fungi to a great extent in the past. For example, one soil sample may contain hundreds or thousands of phytopathogenic fungi, making it almost impossible to artificially select the operational taxonomic units (OTUs) related to plant pathogens in order to investigate plant pathogen communities. Thus, the majority of research on phytopathogenic fungal communities has simply focused on specialized pathogens (Caputo et al., 2015; Romano et al., 2016). Presently, FUNGuild (Nguyen et al., 2016), an annotation tool for parsing fungal community datasets, has made it possible to identify almost all phytopathogenic fungi in a soil sample, thus facilitating the investigation of phytopathogenic fungal communities in a more effective and credible manner. In this study, using next-generation sequencing and FUNGuild, the interrelationships between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture system were investigated. Specifically, the following questions were addressed: 1) the interrelationships between potential phytopathogenic and non-phytopathogenic fungi; and 2) how the above interrelationships were shaped.
2.2. Soil properties analysis Soil samples were further sieved through 0.149 mm mesh after air drying. Soil pH was measured using a pH meter (Mettler Toledo FE20, Shanghai, China) after creating a 1:5 wt/vol soil water suspension and shaking for 30 min (Pansu and Gautheyrou, 2006). Soil moisture was measured gravimetrically. SOC (soil organic carbon) was determined using the sulfuric acid-potassium dichromate oxidation method (Pansu and Gautheyrou, 2006), while total and available N were measured as Kjeldahl-N (Keeney, 1982). Total P and available P were determined using a HF–HClO4 digestion and sodium bicarbonate extraction using the molybdenum blue method, respectively (Pansu and Gautheyrou, 2006). Lastly, total K and available K were determined using a HFHClO4 digestion and ammonium acetate extraction using a flame photometer, respectively (Pansu and Gautheyrou, 2006). The results of the soil properties are shown in Table S1. 2.3. Soil DNA extraction Soil DNA was extracted from 0.5 g of soil (fresh weight) using a Fast®DNA SPIN Kit (MP Biomedicals, CA, USA) and then subsequently purified using a PowerClean® DNA Clean-up Kit (MoBio, CA, USA) according to the manufacturer's instructions. The concentration and quality of the extracted DNA were measured using a NanoDrop ND1000 spectrophotometer (NanoDrop Technologies, DE, USA).
2. Materials and methods 2.1. Experimental site and sampling The study was conducted in the fields of the Ecological Experimental Station of Red Soil at the Chinese Academy of Sciences in Yujiang, Jiangxi province (28°13′ N, 116°55′ E) during the 2017 cropping season. The experimental soil was classified as Udic Ferrosol, which is commonly known as red soil in China. The 50-year mean annual precipitation in the region is 1750 mm, with most rainfall occurring between April and June. The monthly average temperature varies from a minimum of 5.9 °C in January to a maximum of 30 °C in July. Four peanut fields were selected for this research, each of which was subdivided into three equal-area subplots (4 m × 6 m) to perform experiments in triplicate. The control field (CK) had previously been planted with peanut for two years, but was fallow for five years before peanut was again planted for this study. Field P4 and field P10 were consecutively monocultured with peanut for 4 years and 10 years before sampling, respectively. Field P20 was consecutively monocultured with peanut for > 20 years prior to sampling. Fields P4, P10, and P20 were also fallow for more than five years before cultivating peanut for
2.4. Amplification, Illumina sequencing, and sequence processing Each of the 12 DNA samples was amplified separately using the fungal PCR primers ITS1F (5′- CTTGGTCATTTAGAGGAAGTAA −3′) (Gardes and Bruns, 1993) and ITS2 (5′- GCTGCGTTCTTCATCGATGC3′) (White et al., 1994) that target the internal transcribed spacer 1 (ITS1) region. The PCR products were then sequenced on the Illumina MiSeq PE250 platform. Raw sequence data were analyzed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline (http:// qiime.sourceforge.net/) (Caporaso et al., 2010). Reads with length < 200 bp or with average quality scores < 25 were removed, resulting in 796,863 high-quality sequences. High-quality sequences were then combined using the Mothur software package (Schloss et al., 2009). Clustering of sequences into OTUs at a 97% nucleotide similarity level was performed using UCLUST (Edgar, 2010). The taxonomic identity of each OTU was then determined based on comparisons against the UNITE database (https://unite.ut.ee/).The raw sequences were 2
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submitted to NCBI Sequence Read Archive (SRA) under the accession number SRP 167123.
except for the unidentified ones were defined as non-phytopathogenic fungi (Supplementary Data1).
2.5. Confrontation experiment
3. Results
A confrontation experiment was conducted to investigate the interrelationships between Penicillium and Fusarium. The most enriched Penicillium species across all soil samples, P. citrinum, and three Fusarium strains, F. solina, F. moniliforme, and F. oxysporum, were used to investigate whether P. citrinum exercised obvious antagonism against the Fusarium species. Penicillium citrinum, F. solina, and F. moniliforme were obtained from BeNa Culture Collection (BNCC, Beijing, China). F. oxysporum was obtained from the Agricultural Culture Collection of China, Chinese Academy of Agricultural Science (ACCC, Beijing, China). The culture medium used in this study was the widely-used potato dextrose agar (PDA) medium. The culture medium contained fresh potato filtrate, agar powder, and glucose at a ratio of 10:1:1 in weight. The flat plates were amicrobic commercial products. In general, three groups were set: 1) P. citrinum × F. solina; 2) P. citrinum × F. moniliforme, and 3) P. citrinum × F. oxysporum. The two strains were 3 cm apart on each flat plate. In addition, each group was paired with a control group in which the Fusarium strain was inoculated alone on the center of the same flat plate simultaneously. The inoculated flat plates were cultured in a 25 °C constant-temperature incubator to observe any bacteriostasis after 5 d.
3.1. General information of the potential phytopathogenic and nonphytopathogenic fungal communities In total, 796,863 full-length ITS1 high quality sequences were obtained, and each sample contained an average of 55,779 sequences. After normalizing datasets for sequence numbers using multiple rarefactions, there were 2027 OTUs at the 97% similarity cut-off level. Of the representative samples, 71% were taxonomically classified using the UNITE database. The dominant phyla across all soil samples were Ascomycota and Basidiomycota, totally accounting for > 60% of the sequences on average (Fig. S2). At the genus level, Fusarium was dominant in all samples and accounted for > 31% of the sequences on average. Penicillium accounted for 17% on average, second in importance to Fusarium (Table S2). Seven hundred and five OTUs, accounting for > 69% of the sequences on average, could be assigned to fungal functional guilds. A total of 245 OTUs and 460 OTUs were identified as potential phytopathogenic and non-phytopathogenic fungi, respectively (Supplementary Data1). The potential phytopathogenic community was comprised of 36 genera. Based on average relative abundance, Fusarium, Aspergillus, and Haematonectria dominated the potential phytopathogenic fungal communities across all samples. The non-phytopathogenic fungal community was comprised of 61 genera. Penicillium, Bionectria, and Pseudallescheria dominated the non-phytopathogenic communities across all samples. Fusarium accounted for 75% of the sequences in the potential phytopathogenic fungal communities, and Penicillium accounted for 63% of the sequences in the non-phytopathogenic fungal communities. The relative abundance of potential phytopathogenic fungi especially Fusarium and Haematonectria peaked in P4 (Fig. 1a), and the relative abundance of non- phytopathogenic fungi especially Penicillium bottomed in P4 (Fig. 1b). A Pearson correlation test showed that the relative abundance of potential phytopathogenic fungi was significantly negatively correlated with that of non- phytopathogenic fungi (Fig. 1c); and the relative abundance of the most abundant potential phytopathogenic fungi, Fusarium, was also significantly negatively correlated with that of the most abundant non- phytopathogenic fungi, Penicillium (Fig. 1d).
2.6. Statistical analysis Statistically significant differences in peanut biomass, soil properties, OTU numbers, and relative abundances among the treatments were determined with one-way analysis of variance (ANOVA) tests, along with the use of Duncan's test for multiple comparisons. Dissimilarities in community composition and functions among samples were calculated using Bray-Curtis distance matrices, followed by ordinated using principal coordinate analysis (PCoA). Significant differences among the groups were determined using analysis of similarity (ANOSIM) tests in the vegan package for R (Version 3.4.3) (R Development Core Team, 2013). Mantel tests were used to identify biotic and abiotic factors that were significantly correlated with plant pathogen community composition. Additionally, network analysis based on the OTUs was conducted using software CoNet (Faust and Raes, 2016) in Cytoscape 2.8 (Shannon et al., 2003), while Gephi (Bastian et al., 2009) was used to visualize the networks. Each network was constructed with all the samples pooled together. We set a minimum of occurrences among replicates to 20–25% and normalized the values. The co-occurrences were tested statistically with Pearson, Spearman, and Kendall tests as well as with the dissimilarity index of Bray–Curtis. For all tests, only correlations > 0.5 (and Bray–Curtis distances < 0.5) and with P < 0.05 were considered as significant. Edges were established when the co-occurrences/exclusions were supported by at least three out of the four (correlations/dissimilarity) indices. The values of the edges corresponded to the average value among indexes. We also applied a multi-test correction with both a Fisher's Z and the Benjamini–Hochberg procedure (Benjamini and Hochberg, 1995), with q-values set to 0.05. The fungal guild was analyzed with FUNGuild v1.0 (https://github. com/UMNFuN/FUNGuild) (Nguyen et al., 2016). FUNGuild v1.0 is a flat database that contains a total of 9476 entries, with 66% at the genus level and 34% at the species level. Fungal OTU tables with OTUs in rows, samples in columns, and a ‘taxonomy’ column were inputs (at http://www.stbates.org/guilds/app.php). Outputs included the original OTU table, sorted by sequence abundance, with trophic mode, guild, and confidence data. OTUs that were identified as plant pathogens were defined as potential phytopathogenic fungi; and OTUs that were simultaneously identified as plant pathogens and other guilds were also defined as potential phytopathogenic fungi; while the rest of the OTUs
3.2. Interrelationships between potential phytopathogenic and nonphytopathogenic fungi A co-occurrence network analysis was used to investigate the interrelationships between the potential phytopathogenic and non-phytopathogenic fungal communities (Fig. 2). Fusarium species dominated the inter-potential-phytopathogenic network, while Penicillium species dominated the inter-non-phytopathogenic network. In addition, Aspergillus species were largely present in both the inter-potential-phytopathogenic and inter-non-phytopathogenic network. This was because some Aspergillus species were assigned to potential phytopathogenic fungi, while others were assigned to non-phytopathogenic fungi. Positive co-occurrence dominated both the inter-potential-phytopathogenic fungal communities and inter-non-phytopathogenic fungal communities. However, co-occurrence between the potential phytopathogenic and non-phytopathogenic fungal communities was mainly negative (66%). This implied antagonism between the non-phytopathogenic fungi and potential phytopathogenic fungi. Mantel tests was conducted to investigate which non-phytopathogenic fungal genera played key roles in influencing the potential phytopathogenic fungal communities. Mantel tests indicated a significant correlation between Penicillium and potential phytopathogenic fungal 3
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Fig. 1. The relative abundance of potential phytopathogenic (a) and non-phytopathogenic fungi (b) and the correlation between them (c); and the correlation between the relative abundance of Fusarium species and Penicillium species (d).
3.3. Interrelationships between Fusarium species and Penicillium species
communities (P = 0.001, Table 1). However, the remainder of the genera showed no significant correlation with potential phytopathogenic fungal communities. This indicated that Penicillium played a vital role in shaping the potential phytopathogenic fungal communities.
The Penicillium species and Fusarium species dominated the nonphytopathogenic and potential phytopathogenic fungal communities, respectively. A network was thus constructed to further assess the relationships between Fusarium and Penicillium (Fig. 3). Ninety-two OTUs
Fig. 2. Network of the interrelationships between potential phytopathogenic and non-phytopathogenic fungi. The red lines represent negative interrelationships, and the blue lines represent positive interrelationships. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 4
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Table 1 Effects of non-phytopathogenic fungal genera on potential phytopathogenic fungal communities. The coefficients were determined by Mantel tests. Variable
r
P
Variable
r
P
Variable
r
P
Penicillium Zopfiella Pseudallescheria Corynespora Arthrinium Aspergillus Schizophyllum Emericella Chaetosphaeria Candida Monacrosporium Paecilomyces Hypocrea Myrothecium Stromatonectria Podospora Eupenicillium Eucasphaeria Cordyceps Arthrobotrys Nomuraea
0.599 0.584 0.395 0.248 0.190 0.174 0.150 0.149 0.149 0.149 0.140 0.123 0.117 0.092 0.082 0.062 0.054 0.050 0.049 0.048 0.043
0.001 0.083 0.088 0.125 0.122 0.127 0.081 0.128 0.162 0.166 0.153 0.194 0.181 0.218 0.276 0.248 0.248 0.26 0.235 0.219 0.307
Ceratobasidium Myrmecridium Phialophora Metarhizium Eurotium Chaetomium Lophiostoma Trichoderma Vaginatispora Xylaria Gymnopilus Talaromyces Sagenomella Arrhenia Pyrenochaeta Hannaella Paraconiothyrium Whalleya Bionectria Hypochnicium Williopsis
0.032 0.032 0.018 0.011 0 −0.013 −0.017 −0.026 −0.029 −0.031 −0.042 −0.046 −0.060 −0.079 −0.080 −0.093 −0.098 −0.101 −0.110 −0.110 −0.111
0.302 0.323 0.338 0.349 0.403 0.471 0.501 0.494 0.594 0.683 0.539 0.553 0.595 0.678 0.617 0.679 0.699 0.68 0.711 0.709 0.682
Rhodotorula Postia Cyphellophora Lyophyllum Scolecobasidium Neosartorya Marasmius Thermoascus Conocybe Glomus Lecythophora Cladorrhinum Hyphoderma Tetraplosphaeria Phialosimplex Allomyces Doratomyces Stachybotrys Rhizophagus
−0.120 −0.121 −0.122 −0.128 −0.128 −0.142 −0.155 −0.161 −0.161 −0.161 −0.162 −0.167 −0.206 −0.209 −0.209 −0.209 −0.211 −0.229 −0.257
0.755 0.728 0.757 0.757 0.781 0.824 0.777 0.814 0.818 0.847 0.819 0.829 0.879 0.92 0.912 0.918 0.898 1.000 1.000
showed that all Fusarium strains had been suppressed when compared to the control, and clear bacteriostasis was observed when P. citrinum was co-cultured with F. oxysporum and F. moniliforme (Fig. 4).
related to Fusarium and 113 OTUs related to Penicillium were selected from the entire OTU table to construct the co-occurrence network, and 49 nodes entered the network. Twenty-six nodes represented Fusarium, while 23 nodes represented Penicillium. Positive interrelationships dominated the network (75%). The proportion of positive interrelationships was > 97% in both the inter-genus co-occurrence networks. However, the negative interrelationships (84%) between Fusarium and Penicillium were much greater than the positive interrelationships (16%), implying an antagonism between Fusarium and Penicillium. A confrontation experiment was then conducted to validate the antagonism between Fusarium and Penicillium. P. citrinum and three Fusarium strains, F. oxysporum, F. moniliforme, and F. solina, were selected as the experimental strains. Penicillium.citrinum accounted for 85% of the Penicillium sequences across all samples, while F. oxysporum, F. moniliforme, and F. solina were the top three Fusarium species. The four strains were also network-derived. The confrontation experiment
4. Discussion Previous studies demonstrated that continuous monoculture results in significant decrease in crop yield (Nevens and Reheul, 2001). Our results also showed that peanut yield decreased significantly with an increase in monoculture duration (Fig. S1). This demonstrated that continuous cropping of peanuts would also result in reduced yields. Many studies have attempted to explain the microbiological mechanisms that cause decreases in yield in monoculture systems. The majority of studies suggest that the accumulation of pathogenic fungi would cause serious disease in peanut monoculture systems (Li et al., 2014). For instance, the relative abundance of F. oxysporum, a key pathogen that causes peanut root rot, increased from 5.5% to 16.6% with Fig. 3. Network of the interrelationships between Penicillium species and Fusarium species. The red lines represent negative interrelationships, and the blue lines represent positive interrelationships. The size of the node represents the connecting degree. The width of the lines represents the strength of the correlation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Fig. 4. Confrontation experiment between three Fusarium strains and Penicillium citrinum. Colonies of three Fusarium strains were cultured alone (first row) or cocultured with P. citrinum (second row).
Fusarium wilt of watermelon (De Cal et al., 2009), and Sclerotinia blight of peanuts (Jackson and Melouk, 1992). Combining the network analysis (Fig. 3) and the confrontation experiment, we postulated that Penicillium might suppress the development of Fusarium. A previous study also demonstrated that Penicillium can suppress the growth of Fusarium. For instance, Penicillium sp. was demonstrated to have a significant suppressive effect on Fusarium wilt diseases, caused by F. oxysporum, through dual culture experiments (Alam et al., 2011). Another confrontation experiment showed that P. oxalicum could inhibit the hyphal growth of F. oxysporum, F. moniliforme, F. proliferatum, and F. solani to variable extents, and increased the inhibition rates by 42%, 74%, 47%, and 55% on PDA medium (Zhang et al., 2016). In addition to the microbial interactions, plants are able to recruit protective microorganisms, mainly plant-beneficial microbes such as Penicillium species, and enhance microbial activity to suppress pathogens in the rhizosphere (Berendsen et al., 2012) after surviving a severe outbreak of a disease (Cook et al., 1995). Plant-beneficial microbes can secrete some secondary metabolites to suppress plant pathogens (Rudrappa et al., 2008). Furthermore, these microbes stimulate the plant immune system to compete with phytopathogenic fungi for nutrients and space (Nihorimbere et al., 2011). As these plant-beneficial microbes only have a significant pathogen-suppressing effect if they are present in sufficiently high numbers (Raaijmakers et al., 1995; Bull et al., 1991), the relative number and relative abundance of the potential phytopathogenic fungal communities decreased sharply in the P10 and P20 treatments rather than the P4 treatment in our study. Furthermore, soil itself may also play an important role in suppressing phytopathogenic fungi. Every natural soil has the ability to suppress a pathogen to a certain extent, known as general disease suppression (Berendsen et al., 2012). Once phytopathogenic microbes cause soils to suppress a disease, ‘specific suppression’ on these phytopathogenic microbes would occur (Berendsen et al., 2012). Specially, many soils develop ‘suppressiveness’, including the suppression of
increased monoculture duration from 1 year to > 20 years (Li et al., 2014). In our study, ITS sequencing and FUNGuild indicated a trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture system (Fig. 1). While the relative abundance of some potential phytopathogenic genera also showed a distinct increase (Fig. 1), our results differ from those of a previous study whereby the pathogenic fungi continuously increased in a peanut monoculture system (Li et al., 2014). These contrasting results are possibly because the previous study was only based on the changes in relative abundance of four pathogenic fungi. In contrast, in our study, 245 OTUs were assigned to potential phytopathogenic fungi based on the FUNGuild output. The trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture system may be a result of their interrelationships. A Pearson test indicated a significant negative correlation between the relative abundance of potential phytopathogenic fungi and non-phytopathogenic fungi (Fig. 1c); and a network analysis showed that co-occurrence between potential phytopathogenic and non-phytopathogenic fungal communities was mainly negative (66%; Fig. 2). These implied that competitive relationships between non-phytopathogenic fungi and potential phytopathogenic fungi may be much stronger than cooperative relationships (Feng et al., 2017), reflecting an antagonism between the non-phytopathogenic fungi and potential phytopathogenic fungi. A combination of network analysis (Fig. 2) and Mantel tests (Table 1) indicated that Penicillium species played significant roles in shaping the potential phytopathogenic fungal communities. The antifungal activities of Penicillium have been well explored and widely documented (Nicoletti et al., 2007; Petit et al., 2009). Penicillium species are able to produce a diverse range of bioactive secondary metabolites, including antifungal metabolites (Nicoletti et al., 2007; Zhelifonova et al., 2010). Actually, Penicillium species have been widely used to control plant diseases that caused by various phytopathogenic fungi such as Fusarium wilt of tomato (Sabuquillo et al., 2005), 6
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Fusarium species after several years of monoculture (Bennett et al., 2012). This self-organized suppressiveness in the soil contributes largely to limiting the continuous accumulation of phytopathogenic fungi. Furthermore, soil type was considered to be an important driver of the microbial community composition in the rhizosphere (Berg and Smalla, 2009). Among all soil properties, soil pH is frequently considered as the most important determinant of fungal community structure (Siciliano et al., 2014). In our study, Mantel tests indicated that no soil property except for AK was significantly correlated with potential phytopathogenic fungal communities (Table S3). Thus, pH did not decisively influence community structure in the current study, and it was not determined to be more important than the soil nutrients. Similar results have also been observed in other experiments (Zhong et al., 2015; Huang et al., 2016). Thus, the response mechanism of the potential phytopathogenic fungal community composition to soil properties may differ from those of other fungal communities (Alkan et al., 2013; Liu et al., 2018). This may be attributed to the differences in local environmental conditions (Nunan, 2017) and the microbial population (Daquiado et al., 2016). Although the relative abundance of potential phytopathogenic fungi did not continuously increase, the peanut yields of peanut continuously decreased from the CK to P20 treatments in our research. We postulated that regardless of the relative abundance, the absolute quantity of phytopathogenic fungi may increase significantly along with the monoculture duration. This would aggravate the disease occurrence and lead a yields loss. Furthermore, some peanut-specialized phytopathogenic fungi, such as Aspergillus flavus that can induce preemergence rotting of peanuts at any stage of crop development (Subrahmanyam et al., 1987), may accumulate because of long-term selection (Fig. 1a), resulting a decline in yields.
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5. Conclusions Our research reviled that the potential phytopathogenic fungi, mainly Fusarium species, would not increase without limits in the peanut monoculture system, as a result of the possible suppression of non-phytopathogenic fungi such as Penicillium species on these potential phytopathogenic fungi. That is, there is a trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture system. Our results provides better insight towards understanding the obstacles of continuous peanut cropping. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apsoil.2020.103508. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment The work was supported by the National Natural Science Foundation of China (grant number 41771298) and the National Basic Research Program of China (973 Program) (grant number 2015CB150501). References Alam, S.S., Sakamoto, K., Inubushi, K., 2011. Biocontrol efficiency of Fusarium wilt diseases by a root-colonizing fungus Penicillium sp. Soil Sci. Plant Nutr. 57, 204–212. Alkan, N., Espeso, E.A., Prusky, D., 2013. Virulence regulation of phytopathogenic fungi by pH. Antioxid Redox Sign 19, 1012–1025. Bastian, M., Heymann, S., Jacomy, M., 2009. Gephi: an open source software for exploring and manipulating networks. Icwsm 8, 361–362. Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 57, 289–300.
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and fungitoxic activity of Sch 642305, a secondary metabolite of Penicillium canescens. Mycopathologia 163, 295–301. Nihorimbere, V., Ongena, M., Smargiassi, M., Thonart, P., 2011. Beneficial effect of the rhizosphere microbial community for plant growth and health. Biotechnol Agron Soc 15, 327–337. Nunan, N., 2017. The microbial habitat in soil: scale, heterogeneity and functional consequences. J Plant Nutr Soil Sc 180, 425–429. Pansu, M., Gautheyrou, J., 2006. Handbook of Soil Analysis: Mineralogical, Organic and Inorganic Methods. Springer, Berlin Heidelberg, Berlin. Peruzzi, E., Franke-Whittle, I.H., Kelderer, M., Ciavatta, C., Insam, H., 2017. Microbial indication of soil health in apple orchards affected by replant disease. Appl. Soil Ecol. 119, 115–127. Petit, P., Lucas, E.M.F., Abreu, L.M., Pfenning, L.H., Takahashi, J.A., 2009. Novel antimicrobial secondary metabolites from a Penicillium sp isolated from Brazilian cerrado soil. Electron. J. Biotechnol. 12. R Development Core Team, 2013. R: A Language and Environment for Statistical Computing. Raaijmakers, J.M., Leeman, M., Vanoorschot, M.M.P., Vandersluis, I., Schippers, B., Bakker, P.A.H.M., 1995. Dose-response relationships in biological-control of fusarium-wilt of radish by Pseudomonas spp. Phytopathology 85, 1075–1081. Raaijmakers, J.M., Paulitz, T.C., Steinberg, C., Alabouvette, C., Moenne-Loccoz, Y., 2009. The rhizosphere: a playground and battlefield for soilborne pathogens and beneficial microorganisms. Plant Soil 321, 341–361. Romano, N., Lignola, G.P., Brigante, M., Bosso, L., Chirico, G.B., 2016. Residual life and degradation assessment of wood elements used in soil bioengineering structures for slope protection. Ecol. Eng. 90, 498–509. Rudrappa, T., Czymmek, K.J., Pare, P.W., Bais, H.P., 2008. Root-secreted malic acid recruits beneficial soil bacteria. Plant Physiol. 148, 1547–1556. Sabuquillo, P., De Cal, A., Melgarejo, P., 2005. Dispersal improvement of a powder formulation of Penicillium oxalicum, a biocontrol agent of tomato wilt. Plant Dis. 89, 1317–1323. Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., Lesniewski, R.A., Oakley, B.B., Parks, D.H., Robinson, C.J., Sahl, J.W., Stres, B.,
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Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil
Trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture cultivation system
T
Pengfa Lia,b, Jia Liua,c, Chunyu Jianga, Meng Wua, Ming Liua, Shiping Weia,b, Cunpu Qiua,b, ⁎ Guilong Lia,b, Changxu Xuc, Zhongpei Lia,b, a
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China University of Chinese Academy of Sciences, Beijing 100049, China c Soil and Fertilizer & Resources and Environment Institute, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Monoculture Non-phytopathogenic fungi Peanut Potential phytopathogenic fungi Trade-off
Phytopathogenic fungi are a major cause of plant disease. However, the interrelationships between phytopathogenic and non-phytopathogenic fungi in the peanut (Arachis hypogaea) monoculture system remain unclear. In this study, rhizosphere soils from four fields that had been monocropped with peanut for different durations were investigated, including monoculture for 1 year (CK), 4 years (P4), 10 years (P10), and 20 years (P20). Illumina sequencing of fungal ITS regions and FUNGuild were used to investigate the interrelationships between potential phytopathogenic and non-phytopathogenic fungi. Our results showed that the relative abundance of potential phytopathogenic fungi was significantly negatively correlated with that of non-phytopathogenic fungi (r = −0.650, P = 0.022); and the relative abundance of the key potential phytopathogenic species, Fusarium, was also significantly negatively correlated with that of the non-phytopathogenic fungi, Penicillium (r = −0.815, P = 0.001). The greater negativity between potential phytopathogenic and nonphytopathogenic fungi in the co-occurrence network implied an antagonism between them; and the greater negativity between Penicillium species and Fusarium species in the network combining with the results of confrontation experiment suggested the suppression of Penicillium species on Fusarium species. In conclusion, there is a trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture system. Our results provide better insight towards understanding the obstacles of continuous peanut cropping.
1. Introduction Continuous monoculture can result in sharp declines in yield and quality. This has been demonstrated across many studies on a variety of crops, such as watermelon (Ling et al., 2013), soybean (Jie et al., 2019), cucumber (Elliott et al., 1986), and banana (Wang et al., 2015). Peanut (Arachis hypogaea L.) is one of the most important oilseed and economic crops in China. Peanuts are continuously cropped due to the relatively high economic benefits and the limited available cultivation areas. Intensive peanut monoculture in the hilly red soil regions of subtropical China has resulted in a significant decline in peanut yield and quality. The issues associated with continuous cropping are complex and pertain largely to soil environmental issues, particularly alterations in the soil microbial communities (Peruzzi et al., 2017). An imbalance in the microbial flora is considered to be an important mechanism underlying the issues associated with continuous cropping (Sudini et al., 2011; Chen et al., 2018). With regards to fungi, a previous study
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demonstrated that pathogenic fungi, such as Fusarium oxysporum, Bionectria ochroleuca, Leptosphaerulina australis, and Phoma sp., continuously accumulated in a peanut monoculture system, and the accumulation of pathogenic fungi resulted in yield declines (Li et al., 2014). In addition to peanut, a previous study on cotton also demonstrated that a key fungal pathogen, Fusarium oxysporum, significantly increased with an increase in the duration of continuous monoculture. Simultaneously, some plant-beneficial and disease-suppressive bacterial taxa declined (Li et al., 2015). The increase in phytopathogenic fungi and decrease in plant-beneficial microbes under monoculture systems appears to be prevalent. However, some current studies demonstrated that the plants can protect themselves from infection through suppressing phytopathogenic microbes by recruiting protective microorganisms, and enhancing microbial pathogen-suppression activity (Berendsen et al., 2012). For instance, Arabidopsis can recruit pathogen-suppression microbe thorough secreting malic acid after being infected by pathogens (Rudrappa et al., 2008). In addition to the suppression of
Corresponding author at: Institute of Soil Science, Chinese Academy of Sciences, East Beijing Road 71, Nanjing 210008, China. E-mail address: [email protected] (Z. Li).
https://doi.org/10.1016/j.apsoil.2020.103508 Received 20 February 2019; Received in revised form 3 January 2020; Accepted 7 January 2020 0929-1393/ © 2020 Elsevier B.V. All rights reserved.
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this study. The peanut cultivar (Ganhua 1, a cultivar that is widely cultured in south China), the fertilization scheme, and all other field management methods were the same in the four fields. Peanut was cultivated at the beginning of April. About 300 kg ha−1 of urea, 750 kg ha−1 of calcium magnesium phosphate, and 225 kg ha−1 of potassium chlorate were applied along the surface of the opposite slope of furrows that were 10 cm wide × 10 cm deep, into which peanut seeds were planted. Weeds were controlled with herbicides (Bentazon, Shanghai, China). Plant and soil samples were collected on July 26 when the peanuts were in the late reproductive stage. Ten random plants were carefully removed from each of the subplots using a spade, after which the soil attached to the roots was collected and pooled to represent a composite rhizosphere soil sample. Fresh soil samples were sieved through 2 mm mesh and then subdivided into two subsamples. One subsample was stored at 4 °C for determination of physical and chemical properties, while the other was stored at −20 °C until DNA extraction and analyses. Unlike the soils, the plants were not immediately pooled because plant yield needed to be determined. Peanut pods were separated from plants, and pods of each plant were then weighted separately to indicate peanut yield. The peanut yield significantly decreased from the CK to P20 treatment, and the data are shown in Fig. S1.
phytopathogenic fungi by plants, the natural soil also has the ability to suppress pathogens to a certain extent (Hoitink and Boehm, 1999). Generally, the suppression ability of the natural soil can be classified into two types, namely ‘general suppression’ and ‘specific suppression’. ‘General suppression’ occurs when total microbial community cause soils to be suppressive to a board range of pathogens; and the ‘specific suppression’ occurs when specific microorganisms cause soils to be suppressive to a disease; for instance, the suppression of Fusarium after several years of crop monoculture (Bennett et al., 2012). Such a phenomenon has been observed in many monoculture systems (Mendes et al., 2011; Raaijmakers et al., 2009; Weller et al., 2002). If peanut plants and peanut-cultivated soils also have the ability to suppress pathogens, we hypothesize that the phytopathogenic fungi in peanut rhizosphere would not increase without limits in the peanut monoculture system. However, this has been merely tested in the peanut cultivation system. Furthermore, previous studies on the changes in phytopathogenic fungi in a peanut monoculture system have only focused on the changes in relative abundance of specialized fungi species or the effects of specialized chemical substances on microbial communities. However, the interrelationships between phytopathogenic and non-phytopathogenic fungi, and the key factors shaping phytopathogenic fungal communities, remain unclear. Next-generation sequencing (NGS) has made it easier to conduct systematic and comprehensive studies on fungal communities (Buee et al., 2009). However, the lack of effective analytical methods has limited studies on phytopathogenic fungi to a great extent in the past. For example, one soil sample may contain hundreds or thousands of phytopathogenic fungi, making it almost impossible to artificially select the operational taxonomic units (OTUs) related to plant pathogens in order to investigate plant pathogen communities. Thus, the majority of research on phytopathogenic fungal communities has simply focused on specialized pathogens (Caputo et al., 2015; Romano et al., 2016). Presently, FUNGuild (Nguyen et al., 2016), an annotation tool for parsing fungal community datasets, has made it possible to identify almost all phytopathogenic fungi in a soil sample, thus facilitating the investigation of phytopathogenic fungal communities in a more effective and credible manner. In this study, using next-generation sequencing and FUNGuild, the interrelationships between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture system were investigated. Specifically, the following questions were addressed: 1) the interrelationships between potential phytopathogenic and non-phytopathogenic fungi; and 2) how the above interrelationships were shaped.
2.2. Soil properties analysis Soil samples were further sieved through 0.149 mm mesh after air drying. Soil pH was measured using a pH meter (Mettler Toledo FE20, Shanghai, China) after creating a 1:5 wt/vol soil water suspension and shaking for 30 min (Pansu and Gautheyrou, 2006). Soil moisture was measured gravimetrically. SOC (soil organic carbon) was determined using the sulfuric acid-potassium dichromate oxidation method (Pansu and Gautheyrou, 2006), while total and available N were measured as Kjeldahl-N (Keeney, 1982). Total P and available P were determined using a HF–HClO4 digestion and sodium bicarbonate extraction using the molybdenum blue method, respectively (Pansu and Gautheyrou, 2006). Lastly, total K and available K were determined using a HFHClO4 digestion and ammonium acetate extraction using a flame photometer, respectively (Pansu and Gautheyrou, 2006). The results of the soil properties are shown in Table S1. 2.3. Soil DNA extraction Soil DNA was extracted from 0.5 g of soil (fresh weight) using a Fast®DNA SPIN Kit (MP Biomedicals, CA, USA) and then subsequently purified using a PowerClean® DNA Clean-up Kit (MoBio, CA, USA) according to the manufacturer's instructions. The concentration and quality of the extracted DNA were measured using a NanoDrop ND1000 spectrophotometer (NanoDrop Technologies, DE, USA).
2. Materials and methods 2.1. Experimental site and sampling The study was conducted in the fields of the Ecological Experimental Station of Red Soil at the Chinese Academy of Sciences in Yujiang, Jiangxi province (28°13′ N, 116°55′ E) during the 2017 cropping season. The experimental soil was classified as Udic Ferrosol, which is commonly known as red soil in China. The 50-year mean annual precipitation in the region is 1750 mm, with most rainfall occurring between April and June. The monthly average temperature varies from a minimum of 5.9 °C in January to a maximum of 30 °C in July. Four peanut fields were selected for this research, each of which was subdivided into three equal-area subplots (4 m × 6 m) to perform experiments in triplicate. The control field (CK) had previously been planted with peanut for two years, but was fallow for five years before peanut was again planted for this study. Field P4 and field P10 were consecutively monocultured with peanut for 4 years and 10 years before sampling, respectively. Field P20 was consecutively monocultured with peanut for > 20 years prior to sampling. Fields P4, P10, and P20 were also fallow for more than five years before cultivating peanut for
2.4. Amplification, Illumina sequencing, and sequence processing Each of the 12 DNA samples was amplified separately using the fungal PCR primers ITS1F (5′- CTTGGTCATTTAGAGGAAGTAA −3′) (Gardes and Bruns, 1993) and ITS2 (5′- GCTGCGTTCTTCATCGATGC3′) (White et al., 1994) that target the internal transcribed spacer 1 (ITS1) region. The PCR products were then sequenced on the Illumina MiSeq PE250 platform. Raw sequence data were analyzed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline (http:// qiime.sourceforge.net/) (Caporaso et al., 2010). Reads with length < 200 bp or with average quality scores < 25 were removed, resulting in 796,863 high-quality sequences. High-quality sequences were then combined using the Mothur software package (Schloss et al., 2009). Clustering of sequences into OTUs at a 97% nucleotide similarity level was performed using UCLUST (Edgar, 2010). The taxonomic identity of each OTU was then determined based on comparisons against the UNITE database (https://unite.ut.ee/).The raw sequences were 2
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submitted to NCBI Sequence Read Archive (SRA) under the accession number SRP 167123.
except for the unidentified ones were defined as non-phytopathogenic fungi (Supplementary Data1).
2.5. Confrontation experiment
3. Results
A confrontation experiment was conducted to investigate the interrelationships between Penicillium and Fusarium. The most enriched Penicillium species across all soil samples, P. citrinum, and three Fusarium strains, F. solina, F. moniliforme, and F. oxysporum, were used to investigate whether P. citrinum exercised obvious antagonism against the Fusarium species. Penicillium citrinum, F. solina, and F. moniliforme were obtained from BeNa Culture Collection (BNCC, Beijing, China). F. oxysporum was obtained from the Agricultural Culture Collection of China, Chinese Academy of Agricultural Science (ACCC, Beijing, China). The culture medium used in this study was the widely-used potato dextrose agar (PDA) medium. The culture medium contained fresh potato filtrate, agar powder, and glucose at a ratio of 10:1:1 in weight. The flat plates were amicrobic commercial products. In general, three groups were set: 1) P. citrinum × F. solina; 2) P. citrinum × F. moniliforme, and 3) P. citrinum × F. oxysporum. The two strains were 3 cm apart on each flat plate. In addition, each group was paired with a control group in which the Fusarium strain was inoculated alone on the center of the same flat plate simultaneously. The inoculated flat plates were cultured in a 25 °C constant-temperature incubator to observe any bacteriostasis after 5 d.
3.1. General information of the potential phytopathogenic and nonphytopathogenic fungal communities In total, 796,863 full-length ITS1 high quality sequences were obtained, and each sample contained an average of 55,779 sequences. After normalizing datasets for sequence numbers using multiple rarefactions, there were 2027 OTUs at the 97% similarity cut-off level. Of the representative samples, 71% were taxonomically classified using the UNITE database. The dominant phyla across all soil samples were Ascomycota and Basidiomycota, totally accounting for > 60% of the sequences on average (Fig. S2). At the genus level, Fusarium was dominant in all samples and accounted for > 31% of the sequences on average. Penicillium accounted for 17% on average, second in importance to Fusarium (Table S2). Seven hundred and five OTUs, accounting for > 69% of the sequences on average, could be assigned to fungal functional guilds. A total of 245 OTUs and 460 OTUs were identified as potential phytopathogenic and non-phytopathogenic fungi, respectively (Supplementary Data1). The potential phytopathogenic community was comprised of 36 genera. Based on average relative abundance, Fusarium, Aspergillus, and Haematonectria dominated the potential phytopathogenic fungal communities across all samples. The non-phytopathogenic fungal community was comprised of 61 genera. Penicillium, Bionectria, and Pseudallescheria dominated the non-phytopathogenic communities across all samples. Fusarium accounted for 75% of the sequences in the potential phytopathogenic fungal communities, and Penicillium accounted for 63% of the sequences in the non-phytopathogenic fungal communities. The relative abundance of potential phytopathogenic fungi especially Fusarium and Haematonectria peaked in P4 (Fig. 1a), and the relative abundance of non- phytopathogenic fungi especially Penicillium bottomed in P4 (Fig. 1b). A Pearson correlation test showed that the relative abundance of potential phytopathogenic fungi was significantly negatively correlated with that of non- phytopathogenic fungi (Fig. 1c); and the relative abundance of the most abundant potential phytopathogenic fungi, Fusarium, was also significantly negatively correlated with that of the most abundant non- phytopathogenic fungi, Penicillium (Fig. 1d).
2.6. Statistical analysis Statistically significant differences in peanut biomass, soil properties, OTU numbers, and relative abundances among the treatments were determined with one-way analysis of variance (ANOVA) tests, along with the use of Duncan's test for multiple comparisons. Dissimilarities in community composition and functions among samples were calculated using Bray-Curtis distance matrices, followed by ordinated using principal coordinate analysis (PCoA). Significant differences among the groups were determined using analysis of similarity (ANOSIM) tests in the vegan package for R (Version 3.4.3) (R Development Core Team, 2013). Mantel tests were used to identify biotic and abiotic factors that were significantly correlated with plant pathogen community composition. Additionally, network analysis based on the OTUs was conducted using software CoNet (Faust and Raes, 2016) in Cytoscape 2.8 (Shannon et al., 2003), while Gephi (Bastian et al., 2009) was used to visualize the networks. Each network was constructed with all the samples pooled together. We set a minimum of occurrences among replicates to 20–25% and normalized the values. The co-occurrences were tested statistically with Pearson, Spearman, and Kendall tests as well as with the dissimilarity index of Bray–Curtis. For all tests, only correlations > 0.5 (and Bray–Curtis distances < 0.5) and with P < 0.05 were considered as significant. Edges were established when the co-occurrences/exclusions were supported by at least three out of the four (correlations/dissimilarity) indices. The values of the edges corresponded to the average value among indexes. We also applied a multi-test correction with both a Fisher's Z and the Benjamini–Hochberg procedure (Benjamini and Hochberg, 1995), with q-values set to 0.05. The fungal guild was analyzed with FUNGuild v1.0 (https://github. com/UMNFuN/FUNGuild) (Nguyen et al., 2016). FUNGuild v1.0 is a flat database that contains a total of 9476 entries, with 66% at the genus level and 34% at the species level. Fungal OTU tables with OTUs in rows, samples in columns, and a ‘taxonomy’ column were inputs (at http://www.stbates.org/guilds/app.php). Outputs included the original OTU table, sorted by sequence abundance, with trophic mode, guild, and confidence data. OTUs that were identified as plant pathogens were defined as potential phytopathogenic fungi; and OTUs that were simultaneously identified as plant pathogens and other guilds were also defined as potential phytopathogenic fungi; while the rest of the OTUs
3.2. Interrelationships between potential phytopathogenic and nonphytopathogenic fungi A co-occurrence network analysis was used to investigate the interrelationships between the potential phytopathogenic and non-phytopathogenic fungal communities (Fig. 2). Fusarium species dominated the inter-potential-phytopathogenic network, while Penicillium species dominated the inter-non-phytopathogenic network. In addition, Aspergillus species were largely present in both the inter-potential-phytopathogenic and inter-non-phytopathogenic network. This was because some Aspergillus species were assigned to potential phytopathogenic fungi, while others were assigned to non-phytopathogenic fungi. Positive co-occurrence dominated both the inter-potential-phytopathogenic fungal communities and inter-non-phytopathogenic fungal communities. However, co-occurrence between the potential phytopathogenic and non-phytopathogenic fungal communities was mainly negative (66%). This implied antagonism between the non-phytopathogenic fungi and potential phytopathogenic fungi. Mantel tests was conducted to investigate which non-phytopathogenic fungal genera played key roles in influencing the potential phytopathogenic fungal communities. Mantel tests indicated a significant correlation between Penicillium and potential phytopathogenic fungal 3
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Fig. 1. The relative abundance of potential phytopathogenic (a) and non-phytopathogenic fungi (b) and the correlation between them (c); and the correlation between the relative abundance of Fusarium species and Penicillium species (d).
3.3. Interrelationships between Fusarium species and Penicillium species
communities (P = 0.001, Table 1). However, the remainder of the genera showed no significant correlation with potential phytopathogenic fungal communities. This indicated that Penicillium played a vital role in shaping the potential phytopathogenic fungal communities.
The Penicillium species and Fusarium species dominated the nonphytopathogenic and potential phytopathogenic fungal communities, respectively. A network was thus constructed to further assess the relationships between Fusarium and Penicillium (Fig. 3). Ninety-two OTUs
Fig. 2. Network of the interrelationships between potential phytopathogenic and non-phytopathogenic fungi. The red lines represent negative interrelationships, and the blue lines represent positive interrelationships. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 4
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Table 1 Effects of non-phytopathogenic fungal genera on potential phytopathogenic fungal communities. The coefficients were determined by Mantel tests. Variable
r
P
Variable
r
P
Variable
r
P
Penicillium Zopfiella Pseudallescheria Corynespora Arthrinium Aspergillus Schizophyllum Emericella Chaetosphaeria Candida Monacrosporium Paecilomyces Hypocrea Myrothecium Stromatonectria Podospora Eupenicillium Eucasphaeria Cordyceps Arthrobotrys Nomuraea
0.599 0.584 0.395 0.248 0.190 0.174 0.150 0.149 0.149 0.149 0.140 0.123 0.117 0.092 0.082 0.062 0.054 0.050 0.049 0.048 0.043
0.001 0.083 0.088 0.125 0.122 0.127 0.081 0.128 0.162 0.166 0.153 0.194 0.181 0.218 0.276 0.248 0.248 0.26 0.235 0.219 0.307
Ceratobasidium Myrmecridium Phialophora Metarhizium Eurotium Chaetomium Lophiostoma Trichoderma Vaginatispora Xylaria Gymnopilus Talaromyces Sagenomella Arrhenia Pyrenochaeta Hannaella Paraconiothyrium Whalleya Bionectria Hypochnicium Williopsis
0.032 0.032 0.018 0.011 0 −0.013 −0.017 −0.026 −0.029 −0.031 −0.042 −0.046 −0.060 −0.079 −0.080 −0.093 −0.098 −0.101 −0.110 −0.110 −0.111
0.302 0.323 0.338 0.349 0.403 0.471 0.501 0.494 0.594 0.683 0.539 0.553 0.595 0.678 0.617 0.679 0.699 0.68 0.711 0.709 0.682
Rhodotorula Postia Cyphellophora Lyophyllum Scolecobasidium Neosartorya Marasmius Thermoascus Conocybe Glomus Lecythophora Cladorrhinum Hyphoderma Tetraplosphaeria Phialosimplex Allomyces Doratomyces Stachybotrys Rhizophagus
−0.120 −0.121 −0.122 −0.128 −0.128 −0.142 −0.155 −0.161 −0.161 −0.161 −0.162 −0.167 −0.206 −0.209 −0.209 −0.209 −0.211 −0.229 −0.257
0.755 0.728 0.757 0.757 0.781 0.824 0.777 0.814 0.818 0.847 0.819 0.829 0.879 0.92 0.912 0.918 0.898 1.000 1.000
showed that all Fusarium strains had been suppressed when compared to the control, and clear bacteriostasis was observed when P. citrinum was co-cultured with F. oxysporum and F. moniliforme (Fig. 4).
related to Fusarium and 113 OTUs related to Penicillium were selected from the entire OTU table to construct the co-occurrence network, and 49 nodes entered the network. Twenty-six nodes represented Fusarium, while 23 nodes represented Penicillium. Positive interrelationships dominated the network (75%). The proportion of positive interrelationships was > 97% in both the inter-genus co-occurrence networks. However, the negative interrelationships (84%) between Fusarium and Penicillium were much greater than the positive interrelationships (16%), implying an antagonism between Fusarium and Penicillium. A confrontation experiment was then conducted to validate the antagonism between Fusarium and Penicillium. P. citrinum and three Fusarium strains, F. oxysporum, F. moniliforme, and F. solina, were selected as the experimental strains. Penicillium.citrinum accounted for 85% of the Penicillium sequences across all samples, while F. oxysporum, F. moniliforme, and F. solina were the top three Fusarium species. The four strains were also network-derived. The confrontation experiment
4. Discussion Previous studies demonstrated that continuous monoculture results in significant decrease in crop yield (Nevens and Reheul, 2001). Our results also showed that peanut yield decreased significantly with an increase in monoculture duration (Fig. S1). This demonstrated that continuous cropping of peanuts would also result in reduced yields. Many studies have attempted to explain the microbiological mechanisms that cause decreases in yield in monoculture systems. The majority of studies suggest that the accumulation of pathogenic fungi would cause serious disease in peanut monoculture systems (Li et al., 2014). For instance, the relative abundance of F. oxysporum, a key pathogen that causes peanut root rot, increased from 5.5% to 16.6% with Fig. 3. Network of the interrelationships between Penicillium species and Fusarium species. The red lines represent negative interrelationships, and the blue lines represent positive interrelationships. The size of the node represents the connecting degree. The width of the lines represents the strength of the correlation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Fig. 4. Confrontation experiment between three Fusarium strains and Penicillium citrinum. Colonies of three Fusarium strains were cultured alone (first row) or cocultured with P. citrinum (second row).
Fusarium wilt of watermelon (De Cal et al., 2009), and Sclerotinia blight of peanuts (Jackson and Melouk, 1992). Combining the network analysis (Fig. 3) and the confrontation experiment, we postulated that Penicillium might suppress the development of Fusarium. A previous study also demonstrated that Penicillium can suppress the growth of Fusarium. For instance, Penicillium sp. was demonstrated to have a significant suppressive effect on Fusarium wilt diseases, caused by F. oxysporum, through dual culture experiments (Alam et al., 2011). Another confrontation experiment showed that P. oxalicum could inhibit the hyphal growth of F. oxysporum, F. moniliforme, F. proliferatum, and F. solani to variable extents, and increased the inhibition rates by 42%, 74%, 47%, and 55% on PDA medium (Zhang et al., 2016). In addition to the microbial interactions, plants are able to recruit protective microorganisms, mainly plant-beneficial microbes such as Penicillium species, and enhance microbial activity to suppress pathogens in the rhizosphere (Berendsen et al., 2012) after surviving a severe outbreak of a disease (Cook et al., 1995). Plant-beneficial microbes can secrete some secondary metabolites to suppress plant pathogens (Rudrappa et al., 2008). Furthermore, these microbes stimulate the plant immune system to compete with phytopathogenic fungi for nutrients and space (Nihorimbere et al., 2011). As these plant-beneficial microbes only have a significant pathogen-suppressing effect if they are present in sufficiently high numbers (Raaijmakers et al., 1995; Bull et al., 1991), the relative number and relative abundance of the potential phytopathogenic fungal communities decreased sharply in the P10 and P20 treatments rather than the P4 treatment in our study. Furthermore, soil itself may also play an important role in suppressing phytopathogenic fungi. Every natural soil has the ability to suppress a pathogen to a certain extent, known as general disease suppression (Berendsen et al., 2012). Once phytopathogenic microbes cause soils to suppress a disease, ‘specific suppression’ on these phytopathogenic microbes would occur (Berendsen et al., 2012). Specially, many soils develop ‘suppressiveness’, including the suppression of
increased monoculture duration from 1 year to > 20 years (Li et al., 2014). In our study, ITS sequencing and FUNGuild indicated a trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture system (Fig. 1). While the relative abundance of some potential phytopathogenic genera also showed a distinct increase (Fig. 1), our results differ from those of a previous study whereby the pathogenic fungi continuously increased in a peanut monoculture system (Li et al., 2014). These contrasting results are possibly because the previous study was only based on the changes in relative abundance of four pathogenic fungi. In contrast, in our study, 245 OTUs were assigned to potential phytopathogenic fungi based on the FUNGuild output. The trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture system may be a result of their interrelationships. A Pearson test indicated a significant negative correlation between the relative abundance of potential phytopathogenic fungi and non-phytopathogenic fungi (Fig. 1c); and a network analysis showed that co-occurrence between potential phytopathogenic and non-phytopathogenic fungal communities was mainly negative (66%; Fig. 2). These implied that competitive relationships between non-phytopathogenic fungi and potential phytopathogenic fungi may be much stronger than cooperative relationships (Feng et al., 2017), reflecting an antagonism between the non-phytopathogenic fungi and potential phytopathogenic fungi. A combination of network analysis (Fig. 2) and Mantel tests (Table 1) indicated that Penicillium species played significant roles in shaping the potential phytopathogenic fungal communities. The antifungal activities of Penicillium have been well explored and widely documented (Nicoletti et al., 2007; Petit et al., 2009). Penicillium species are able to produce a diverse range of bioactive secondary metabolites, including antifungal metabolites (Nicoletti et al., 2007; Zhelifonova et al., 2010). Actually, Penicillium species have been widely used to control plant diseases that caused by various phytopathogenic fungi such as Fusarium wilt of tomato (Sabuquillo et al., 2005), 6
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Fusarium species after several years of monoculture (Bennett et al., 2012). This self-organized suppressiveness in the soil contributes largely to limiting the continuous accumulation of phytopathogenic fungi. Furthermore, soil type was considered to be an important driver of the microbial community composition in the rhizosphere (Berg and Smalla, 2009). Among all soil properties, soil pH is frequently considered as the most important determinant of fungal community structure (Siciliano et al., 2014). In our study, Mantel tests indicated that no soil property except for AK was significantly correlated with potential phytopathogenic fungal communities (Table S3). Thus, pH did not decisively influence community structure in the current study, and it was not determined to be more important than the soil nutrients. Similar results have also been observed in other experiments (Zhong et al., 2015; Huang et al., 2016). Thus, the response mechanism of the potential phytopathogenic fungal community composition to soil properties may differ from those of other fungal communities (Alkan et al., 2013; Liu et al., 2018). This may be attributed to the differences in local environmental conditions (Nunan, 2017) and the microbial population (Daquiado et al., 2016). Although the relative abundance of potential phytopathogenic fungi did not continuously increase, the peanut yields of peanut continuously decreased from the CK to P20 treatments in our research. We postulated that regardless of the relative abundance, the absolute quantity of phytopathogenic fungi may increase significantly along with the monoculture duration. This would aggravate the disease occurrence and lead a yields loss. Furthermore, some peanut-specialized phytopathogenic fungi, such as Aspergillus flavus that can induce preemergence rotting of peanuts at any stage of crop development (Subrahmanyam et al., 1987), may accumulate because of long-term selection (Fig. 1a), resulting a decline in yields.
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5. Conclusions Our research reviled that the potential phytopathogenic fungi, mainly Fusarium species, would not increase without limits in the peanut monoculture system, as a result of the possible suppression of non-phytopathogenic fungi such as Penicillium species on these potential phytopathogenic fungi. That is, there is a trade-off between potential phytopathogenic and non-phytopathogenic fungi in the peanut monoculture system. Our results provides better insight towards understanding the obstacles of continuous peanut cropping. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apsoil.2020.103508. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment The work was supported by the National Natural Science Foundation of China (grant number 41771298) and the National Basic Research Program of China (973 Program) (grant number 2015CB150501). References Alam, S.S., Sakamoto, K., Inubushi, K., 2011. Biocontrol efficiency of Fusarium wilt diseases by a root-colonizing fungus Penicillium sp. Soil Sci. Plant Nutr. 57, 204–212. Alkan, N., Espeso, E.A., Prusky, D., 2013. Virulence regulation of phytopathogenic fungi by pH. Antioxid Redox Sign 19, 1012–1025. Bastian, M., Heymann, S., Jacomy, M., 2009. Gephi: an open source software for exploring and manipulating networks. Icwsm 8, 361–362. Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 57, 289–300.
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