Composts from green sources show an increased suppressiveness to soilborne plant pathogenic fungi: Relationships between physicochemical properties, disease suppression, and the microbiome

Crop Protection 124 (2019) 104870

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Composts from green sources show an increased suppressiveness to soilborne plant pathogenic fungi: Relationships between physicochemical properties, disease suppression, and the microbiome Ugo De Corato a, *, Luigi Patruno b, Nicola Avella b, Giovanni Lacolla c, Giovanna Cucci c a

Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Department of Bioenergy, Biorefinery and Green Chemistry, Territorial Office of Bari, Via Giulio Petroni 15/F, Bari, 70124, Italy BIO-PLANTA Consortium, S.S. 96, Bari, 70100, Italy c University of Bari ‘Aldo Moro’, Department of Agricultural and Environmental Science (DiSAAT), Via Amendola 165/A, Bari, 70126, Italy b

A R T I C L E I N F O

A B S T R A C T

Keywords: Amplicon sequencing Bacteria Disease suppression Fungi Organic amendment

The need for studying tailored composts, as new feedstocks become available with time, increases the in-depth studies of suppressive composts derived from green sources and agro-wastes recycling. The composition, di­ versity and variability of microbiomes within a collection of 10 composts were investigated by amplicon sequencing and bioinformatics analyses. The observed differences in microbiome structure were related to the different compost origin. The multi-suppressive properties of composts from agricultural residues, agro-industrial co/by-products, and plant green-waste showed the most complex microbiome structure, which included either biocontrol agents associated with the control of Rhizoctonia damping-off in bean and Verticillium wilt in eggplant or microbial consortia for controlling Pythium damping-off in cucumber and zucchini and Phytophthora root rot in tomato and azalea. In contrast, the pathogen-specific property of composts from municipal solid waste and co-composted cow manure household waste showed a microbiome that overall included biocontrol agents against Fusarium wilt in tomato, melon and basil. The highest correlations between physicochemical properties, disease suppression, and the microbiome have allowed to make a helpful matrix to know how relationships among these variables of composts could be established and quantified for predicting their suppressive properties basing on the physicochemical properties and the microbiome.

1. Introduction

et al., 2018b, 2018c), the suppressiveness of compost is often inconsis­ tent or unpredictable (Bonanomi et al., 2017). The mechanisms under­ lying the suppressive effect are not fully understood, although these mechanisms are primarily associated with the biological activity of the microbiomes, which interact with the soil and the host plant (Avil�es et al., 2011). A general suppression effect is attributed to microbial consortia affecting more than one pathogen simultaneously, where diversified mechanisms offer a basal protection against a broad range of pathogens (Cha et al., 2016). In contrast, a specific suppression effect is related to restricted groups of microbes affecting in a more effective biocontrol activity, where specific mechanisms induce a greater pro­ tection to one or few pathogens (Termorshuizen et al., 2006; Termor­ shuizen and Jeger, 2008). As compost microbiota depends on the origin and quality of the organic matter chosen before the composting process (De Corato et al., 2018c), the selection of suitable residual feedstocks

Composting is a powerful technology that converts organic waste to ecofriendly soil amendment by attenuating the pressure of landfill and incineration of industrial waste and biowaste (Kumar, 2011; Amore et al., 2013). Consequently, on the one hand, the development of new composting techniques has rapidly proceeded within a circular economy approach (De Corato et al., 2018a). On the other hand, the use of banned substances in organic cropping systems for soil disinfection by fumiga­ tion with methyl bromide, 1,3-dichloropropene and chloropicrin against soil-borne plant diseases threatens to the global food security for a sustainable growing population. Although addition of suppressive compost into plant growth media is considered a consolidated strategy for effective biological control of soil-borne pathogens to reduce the use of synthetic chemicals overall under greenhouse condition (De Corato

* Corresponding author. E-mail address: [email protected] (U. De Corato). https://doi.org/10.1016/j.cropro.2019.104870 Received 2 October 2018; Received in revised form 3 April 2019; Accepted 28 June 2019 Available online 1 July 2019 0261-2194/© 2019 Elsevier Ltd. All rights reserved.

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Crop Protection 124 (2019) 104870

result is crucial to improve the efficacy of suppression, regulation and maintenance of the compost microbial consortia into soil during plant growth (Tejada et al., 2009; Hadar and Papadopoulou, 2012; Borrero et al., 2013). The control of soil-borne diseases by compost has been improved using selected fungal and/or bacterial strains (Hoitink et al., � o et al., 2013) or 2001; Trillas et al., 2006; Pugliese et al., 2008; Castan by adding microbiome inputs from composted green wastes and bio-energy co/by-products for enhancing the suppressive capacity of weakly suppressive or conductive media (De Corato et al., 2018c). On the other hand, to limit possible drawbacks related to use of unselected or poorly selected microbial consortia sourced from composted wastes of different origin and provenience (McGee et al., 2016), a detailed un­ derstanding of the microbiomes is needed whenever their ecological roles should be elucidated in relation to the microbial communities inhabiting compost-amended organic media. Amplicon sequencing and bioinformatics analyses are recent and powerful tools for studying soil microbiomes because of high reliability and sensitivity as alternatives to plate counting methods for studying culturable fungi and bacteria from organic media (De Corato et al., 2016). Amplicon sequencing has been developed to evaluate (i) the ecological roles of different soil organic amendments and the effects of their use with different application fre­ quencies/doses/rates on crop cultivation, biomass yield, soil properties, fertility and microbiome composition (Pane et al., 2015; Cesarano et al., 2017); (ii) the ecological impact of preconditioning soil with activated biochar through the manipulation of bacterial and fungal microbiomes on soil productivity and crop yield (Jaiswal et al., 2018); (iii) the reliable efficacy of different organic media in controlling Phytophthora root rot (Blaya et al., 2016); (iv) the influence of organic and conventional farming systems on bacterial communities (Li et al., 2012); and (v) how belowground microbiota interactions can influence growth and pro­ ductivity of tree crops (Mercado-Blanco et al., 2018). However, the taxonomic structure, richness and diversity of microbiomes in suppres­ sive composts for managing soil-borne pathogenic fungi remain un-investigated in depth. In this study, first, amplicon sequencing and bioinformatics analyses were performed to quantify and characterise at the genus/species levels the widest spectrum of biological control agents (BCAs) related to the suppressive properties of a compost collection derived from diversified feedstocks. Second, physicochemical features, suppressiveness, and the microbiome variables of the composts were correlated for creating a correlation matrix based on the awareness of microbiome structure in terms of composition, richness and diversity for fungi and bacteria in relation to the compost primary physicochemical property.

with household waste into a forced-aeration pile at a local cattle farm. Table 1 classifies the composts under consideration into three groups (AC, MC and LC) on the basis of (i) composition, origin and provenience of feedstock (Table 1a); and (ii) composting cycle and plant type (Table 1b). Briefly, the group-AC composts were obtained from green source by an accelerated composting cycle into a semi-industrial plant, the group-MC composts from green source by an on-farm middle cycle into an organic farming system and the group-LC composts from municipal solid bio-waste by a long cycle into an accredited industrial plant. Once composting process was completed after curing, the compost piles were air-dried until a constant weight was reached and then milled and passed through a sieve (mesh 2 mm). Each compost sample was made of 15 sub-samples (approximately 5 kg dry weight for each subsample) taken from randomised points in the piles, pooled, air-dried again and stored at 4 � C to avoid undesidered microbial contamination. 2.1.2. Physicochemical property Physicochemical parameters of each sample were determined within one week of storage using sub-samples of 500 g dry weight (DW) each. The sub-samples were randomly collected from five different points of the sample with five independent replications for each sample. The technical description used for the analysis to determine the following physicochemical properties of composts has been accurately detailed in a previous work (De Corato et al., 2016). Briefly, water content was quantified after drying samples at 105 � C for 72 h in a forced-air oven. Electrical conductivity (EC) and pH were measured in water extracts (1:10 w/v) according to the official methods of the International Society of Soil Science. The parameters total organic carbon (TOC), total extractable carbon (TEC), humic carbon (CH), humification index (HI), humification degree (HD) and humification ratio (HR) were determined according to the Italian directive (G.U. n. 21 of 26th January 2001). The parameters total organic nitrogen (TON), ammonium nitrogen (N–NHþ 4 ), nitric nitrogen (N–NO3) and total phosphorus (P) were determined according to the Regalement UE n. 2003 of 13 October 2003. The contents of various micronutrients including potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), copper (Cu), iron (Fe), manganese (Mn) and zinc (Zn) were determined after acid digestion using a microwave oven with an ICP-OES system (ICAP 6000 DUO Se­ ries, Thermo Fisher Scientific; Waltham, MA, USA). 2.1.3. Suppressive capacity The following pathogen/host systems (pathosystems) Pythium ulti­ mum/Cucumber, Pythium irregulare/Zucchini, Rhizoctonia solani/Bean, Phytophthora nicotianae/Tomato, Phytophthora cinnamomi/Azalea, Ver­ ticillium dahliae/Eggplant, Fusarium oxysporum f. sp. melonis/Melon, Fusarium oxysporum f. sp. lycopersici/Tomato and Fusarium oxysporum f. sp. basilici/Basil were tested for compost capability to induce suppres­ sion in a nursery soilless system using compost-amended container media according to the methodology described by De Corato et al. (2016; 2018b). Pathogens were obtained from a collection located at the University of Bari (Apulia). Isolates were derived from naturally infected plants belonging to South Italy (Apulia, Basilicata and Calabria), cultured in Petri plates filled with potato dextrose agar (PDA; Sigma-Aldrich, Milan, Italy), monthly sub-cultured in tubes and stored at 4 � C in the dark until use. Each substrate was prepared by mixing each compost at 30% (v/v) with sterilised Sphagnum peat (50% light peat þ 50% dark peat: fraction 0–15 mm, air content 15–25%, volume porosity more than 85% and pH adjusted to 5.75 with 4 g L 1 CaCO3) autoclaved twice at 121 � C for 1 h. Sterilised peat without compost amendment was used as control. All plant growing media were fertilised with 4 g L 1 Osmocote 10-11-18 NPK (Scotts Italy), infested with each pathogen and maintained in 7-L bag in the dark with 40% water by volume at 25 � C for at least 20 days to allow pathogen growth before host plant transplantation. Table 1S summarises the preparation of pathogen inoculum and its addition into plant growth media. Plant growth media were dispensed

2. Materials and methods 2.1. Composts 2.1.1. Origin and classification A total of 10 composts (Com-A–Com-L) were obtained in seven geographical areas in South Italy. A feedstock of agricultural and agroindustrial co-products (feedstock #1) was purchased from local fac­ tories, while plant green wastes (feedstock #2) were obtained from agricultural fields. Prior to composting, the feedstocks were air-dried for 20 days until a constant weight was reached; then they were finely powdered using a ball mill and stored at room temperature. Municipal solid waste (MSW), labelled with the European waste code (EWC) 200201 as ‘green MSW’ (vegetable processing leftovers, large pruning, cuttings and garden waste) and ‘wet MSW’ by the EWC 200108 (un­ marketable products as vegetable and fruit by-products; food waste such as meat, fish, bread, eggs, cheese, pastries, coffee ground and tea bags; cropping residues such as grass, leaves, flowers, twigs and extinguished wood ashes, etc.), was purchased (feedstock #3) from an accredited industrial plant to produce high-quality compost and in situ composted according to ISO/IEC 17025:2005. Compost from cow manure and household waste (feedstock #4) was obtained by co-composting faeces 2

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Table 1 Three compost groups classified on the basis of (A) composition, origin and provenience of feedstock; (B) composting cycle and plant type. (A) Compost group

Compost

Group-AC

Group-MC

Group-LC

Agricultural and agro-industrial residues (feedstock #1) a

Plant green-waste (feedstock #2)

Com-A

Defatted olive marc (50%)

Fennel (48%)

Com-B Com-C Com-D

Un-defatted olive marc (50%) Coffee ground (50%) Tea bag (50%)

Com-E

a

Composting starter (2%)

Geographical provenience

-

Yes

Artichoke (48%) Celery (28%) þ Carrot (20%) Tomato (28%) þ Lettuce (20%)

-

Yes Yes Yes

Bitonto (Bari, Italy) Bitonto (Bari) Foggia (Italy) Monopoli (Bari)

Wood chip (50%)

Tomato (28%) þ Escarole (20%)

-

Yes

Com-F Com-G

Aspen chip (50%) Vineyard pruning wastes (21%) þ Vinery residues (21%) þ Straw (8%)

Artichoke (28%) þ Fennel (20%) Potato (24%) þ Pepper (24%)

-

Yes Yes

Policoro (Matera, Italy) Policoro (Matera) Turi (Bari)

Com-H

-

-

No

Modugno (Bari)

-

-

Green MSW (EWC d 200201) (50%) þ wet MSW (EWC, 200108) (50%) Wet MSW (100%) Cow manure (70%) þ Household waste (30%)

No No

Modugno (Bari) Lavello (Potenza, Italy)

Com-I Com-L

c

Organic fraction of municipal solid waste (feedstock #3), cattle manure and others biowaste (feedstock #4) a

a,b

(B) Compost group

Composting cycle and plant type

Biomass composted as dry weight (kg)

Pile size (m3)

Maximum temperature (� C) reached

Thermophilic stage

Curing Stage

Reference for describing composting technology

Group-AC

Accelerated cycle into a semi-industrial plant

~500

~5

68

65 days

De Corato et al. (2016)

Middle cycle into an organic farming system

~500

~5

70

90 days

Long cycle into an accredited industrial plant

~500

~5

75

3 months

2 months 6 months 2 years

Group-MC Group-LC a b c d

c

Pane et al. (2015) ISO/IEC 17025:2005

Percentage value is considered as dry weight. Household waste used as composting starter is 3-years-old. Com-L is obtained by a long co-composting cycle into a cattle farm. EWC ¼ European Waste Code.

into rows (20 � 1.5 � 0.35 m, length � width � depth), covered with a hydrolysed protein-based mulching coating to avoid solarization effects (Sartore et al., 2018), maintained at 25 � C for 7 days before plant transplantation in a conditioned greenhouse and daily watered. Healthy plants used as susceptible hosts [cucumber (cv. Marketmore 76), zucchini (cv. Vitulia), bean (cv. Sarconi), tomato (cv. Supermarmande), azalea (cv. Gloria), eggplant (cv. Florida Market), melon (cv. Rugoso di Cosenza) and basil (cv. Fine Verde)] were sowed into paper pots (90 holes, 5 cm diameter/hole) containing sterilised vermiculite, trans­ planted into the rows at the one to two true-leaf stage and maintained in greenhouse at 25 � 2 � C and 90 � 5% RH under a 12 h/12 h light/dark cycle for 10–40 days. Symptoms of damping-off on cucumber, zucchini and bean; root rot on tomato and azalea; and vascular wilt on eggplant, tomato, melon and basil were recorded by counting the number of diseased plants. Two experimental trials were carried out per each pathosystem to evaluate statistical differences in suppressiveness among the composts: the first one evaluated suppressiveness of the non-sterilised compostamended container media, while the second one tested the same compost-amended media after sterilisation. A completely randomised block of 10 composts � 6 replicates � 20 seedlings was designed for each trial. Four replications were inoculated with the pathogen, while the remaining two were not inoculated and used as negative control. Each trial was repeated twice in two consecutive years. To assess the sup­ pressiveness of the composts in each pathosystem, the suppression level of the compost-amended plots was compared to that of the peatamended plots (control) by calculating disease suppression index (DSI %):

(where, NCA is the number of diseased plants in compost-amended media and NPA in peat-media). 2.1.4. Microbiomes characterisation Microbiomes of compost microbial communities were characterised using amplicon sequencing technology and bioinformatics analyses on three independent replicates of 0.25 g DW, each collected from the sample within three days of the end of curing. Total DNA was extracted using the FastDNA1 Spin Kit for soil (Q-Biogene, Carlsbad, CA, USA) following the manufacturer’s instructions, and the DNA samples were frozen at 80 � C until required. As one of the main objectives of this study was to evaluate the differences in microbiome among the com­ posts, DNA was extracted in triplicate and the successive amplification was done by pooling DNA. 2.1.4.1. Amplicon sequencing of the ITS rDNA gene region and 16S rDNA gene. The DNA samples were amplified by PCR from the ITS1 and ITS2 regions of the rDNA gene using the ITS5/ITS2 and ITS3/ITS4 primer pair sets for characterising compost fungal community (White et al., 1990; Schoch et al., 2012; Antoniou et al., 2017), while the 16S rDNA gene was amplified using the 8F/120R, F388/R534, F968/R1073 and 8F/R361 primer pair sets for identifying bacterial community (Sundquist et al., 2007; Wang and Qian, 2009; Antoniou et al., 2017). The genus Tricho­ derma was identified by sequencing the ITS1-5.8S-ITS2 gene region of the rDNA, amplified with the universal primers ITS1 and ITS4 (White et al., 1990). The species Trichoderma harzianum, T. asperellum and T. atroviride were identified by amplification of the chitinase gene region with the primers Chit42-1a and Chit42-2a (Kullnig-Gradinger et al., 2002; Aleandri et al., 2015). PCR amplification was carried out in a C100R Thermal Cycler (Bio-Rad; Hercules, CA, USA) in 25-μL PCR

DSI % ¼ [(NPA – NCA) / NPA] � 100

3

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reactions containing 1 � KAPA2G Fast HotStart ReadyMix2 (2 � ) (Kapa Biosystems, Boston, MA, USA), 1.5 mM MgCl2, 0.5 μM for each primer and 5 μL of DNA template. PCR amplifications with the primer sets 8F/120R, F968/R1073 and 8F/R361 were performed as 15 cycles of denaturation at 90 � C for 30 s, amplification at a temperature gradient from 70 � C to 50 � C for 30 s and a final extension at 72 � C for 30 s. PCRs using the primer pair F388/R534 and those amplifying the ITS region had an initial denaturation step at 95 � C for 3 min, followed by 25–40 cycles of denaturation at 95 � C for 15 s, amplification at 60 � C for 15 s, extension at 72 � C for 15 s and a final extension at 72 � C for 1 min. Approximately 200 ng of each PCR product was used to generate a li­ brary, and amplicons were sequenced according to the amplicon sequencing protocol of the DNA service facility (http://www.rrc.uic. edu/dnas). Amplicons were purified using a QIAquick PCR Purifica­ tion Kit (Qiagen, Hilden, Germany) and quantified using a Quanti­ Fluor™-ST (Promega, Wisconsin, USA). Amplicons were pooled at an equimolar concentration using a GS Junior platform (Life Sciences, Roche Diagnostics, IT) prior to further processing. The sequencing products were purified to eliminate excess fluorescent dyes by NaOA­ c/EtOH precipitation (Antoniou et al., 2017) and analysed with an ABI 3130 Genetic Analyser (Applied Biosystems). A library was created using the Ion Plus Fragment Library Kit (Thermo Fisher Scientific, Catalogue No. 4471252), and barcodes were added using the Ion Xpress™ Barcode Adapters 1–96 Kit. Templates were prepared using an Ion OneTouch™ 2 System and an Ion PGM™ Template Kit OT2 400. Finally, the samples were sequenced using an Ion Torrent PGM platform (Life Technologies, Carlsbad, CA, USA) with the Sequencing Kit Ion PGM 400 and the Ion 318 and Ion 314 Chip kits.

and species in each sample with a relative abundances higher than 0.1%, was analysed by LefSe Program (http://huttenhower.sph.harvard. edu/galaxy/root/index). OTUs generated from the data processing were used to determine the alpha-diversity indices (number of observed OTUs, Chao1 richness and Shannon diversity) of fungal and bacterial consortia (Schloss et al., 2009). To avoid biases due to the different sequencing depths for alpha-diversity analyses, sequences from each sample were evenly sub-sampled and rarefied to the lowest number of sequences per sample (3048 and 3696 for bacteria and fungi, respec­ tively). Alpha-diversity analyses were performed using the QIIME pipe­ line with rarefied OTU tables imported into the R environment for analyses and plotting (http://www.r-project.org). The diversity of the bacterial and fungal communities was assessed using Shannon’s algo­ P rithm H’ ¼ – (pi ln pi) (where p represents the proportion of each taxa i into the microbial population). 2.2. Statistical analyses Compost physicochemical properties, suppression index percentage and alpha-diversity indices, as well as relative abundance of fungi and bacteria identified at the genus/species levels were averaged and sub­ jected to one-way analyses of variance (one-way ANOVA) with the factor ‘compost’ to evaluate significant differences among the composts under consideration. The averaged data of DSI % obtained from the two experiments were pooled and treated as a mean alone whenever a pre­ liminary statistical analysis performed by the Bartlett’s test determined homogeneity of variance. Whenever F-value was significant (P � 0.05), the Duncan’s multiple range test (DMRT) was used to separate the mean values at the P � 0.05 level. To assess the role assumed by microbiota in suppression, the effect of autoclaving treatment was empirically quan­ tified by the DSI ratio between the index occurred in sterilised media and those measured in non-sterilised media; values ranging from 0.7 to 1 indicate no effect on suppression loss after sterilisation, while values less than 0.7 show a significant suppressive effect loss. To correlate the microbiome variables with the physicochemical features of composts primarily related to suppression, the significant interactions between the OTU relative abundance, Chao1 richness and Shannon diversity for fungi and bacteria with the EC, pH, TOC/TON, N–NHþ 4 , N–NO3, CH, HI, HD and HR for composts were established by the Pearson correlation coefficients. The assumption is that relationships among the microbiome variables with the primary physicochemical properties of composts were linearly correlated according to previous tests (un-shown data). A correlation matrix was generated, and the significant positive or negative interactions were assessed at the P � 0.05 and P � 0.01 levels. All data analyses were performed using STATISTICA 7 SPSS v.19.0 (SPSS Inc., Chicago, IL, USA).

2.1.4.2. Bioinformatics analyses. Paired-end FASTQ files were merged using PEAR (Jaiswal et al., 2018), and the resulting sequences were filtered for quality and trimmed for length using CLC software (CLC Bio, Aarhus, Denmark). Raw reads were filtered and analysed using the QIIME v.1.9.1. pipeline (Caporaso et al., 2010) and USEARCH v.7.0.1090 soft­ ware. Reads shorter than 300 bp, with more than 1 primer mismatch and with average minimum quality score lower than 25 were discarded. Primers and barcodes were removed, and chimaeras were filtered using the UCHIME v.4.2.40 database (Edgar, 2013). The remaining high-quality sequences were clustered into operational taxonomic units (OTUs) at 97% sequence similarity using the UCLUST algorithm. The open reference method was used whenever sequences were clustered against the GreenGenes v.13_8 and UNITE/QIIME 12_11 ITS databases for bacterial and fungal communities, respectively. Sequences that did not match the database were subsequently clustered de novo. Sequences were compared and identified by a database similarity search in the GenBank database using the BLAST algorithm whenever a NCBI Nucleotide Sequence Database (http://www.blast.ncbi.nlm.nih. gov/Blast.cgi) was available. A representative sequence set from each OTU was generated, and taxonomic assignments were made. For bac­ terial taxonomic assignments, OTUs were selected and aligned using PYNAST to the Silva bacterial database SSU/LSU rRNA gene database v.119 (Quast et al., 2013) available at https://www.arb-silva.de/. For fungal taxonomic assignments, OTUs were selected and aligned using MAFFT (Yamada et al., 2016). Each representative sequence was assigned to taxonomic groups using the UCLUST algorithm and the Silva database for bacterial sequences, while the UCLUST and UNITE database v.7.1 (https://unite.ut.ee/) was used for fungi. A OTUs matrix was created using the taxonomic assignments and the alignment of the representa­ tive sequences. Sequences identified as chimaeras, singletons, chloro­ plasts and mitochondria by UCHIME mapping onto the gold and UNITE databases for bacteria and fungi, respectively, were removed from the analysis and the relative abundances of other taxa recalculated. On the basis of the results of taxonomic classification of all samples, the top phylum with a relative abundances higher than 1% in at least one sample, as well as the most abundant classes, orders, families, genera

3. Results 3.1. Physicochemical property The physicochemical properties for which P-value�0.05 showed significant difference among the composts under study as shown in Table 2. In particular, should be to noted that an opposite trend for EC and pH was observed from group-AC to group-LC composts, where the most salty composts were also the most acidic (from Com-H to Com-L) and vice versa (from Com-A to Com-G). In addition, an opposite trend was observed for TOC and TON content, where the composts with the highest TOC content showed the lowest TON content (from Com-A to Com-G) and vice versa (from Com-H to Com-L). A diverse pattern was observed for the N–NHþ 4 and N–NO3 contents, where these two nitrogen forms were observed to decrease from group-AC to group-LC composts. In contrast, a specific trend for the different humified carbon forms (TEC, CH, HI, HD and HR) was not observed among the composts. Finally, the phosphorus content increased from group-AC to group-LC composts, while the highest copper content was observed in Com-G. 4

U. De Corato et al.

Table 2 Physicochemical property of 10 suppressive composts (Com-A–Com-L) measured using the official methods of the International Society of Soil Science.

5

Physicochemical property

Com-A

Com-B

Com-C

Com-D

Com-E

Com-F

Com-G

Com-H

Com-I

Com-L

Fvalue

Pvalue

Water content (%) EC (mS cm 1) a pH TOC (%) b TON (%) c TOC/TON ratio d N–NHþ 4 (%) N–NO–3 (%) e TEC (%) f CH (%) g HI (%) h HD (%) i HR (%) l P (%) K (%) Ca (%) Mg (%) Na (%) Cu (ppm) Fe (ppm)

45 � 0.8 a 1.9 � 0.05 d 8.18 � 0.03 a 43.77 � 0.4 ab 1.97 � 0.02 c 22.22 c 0.11 � 0.01 bc 0.63 � 0.03 bc 15.44 � 0.6 b 4.67 � 0.02 c 10.77 d 30.25 b 10.67 c 0.3 � 0.01 c 2.36 � 0.12 a 4.15 � 0.08 a 1.29 � 0.01 a 0.78 � 0.08 a 50.68 � 0.2 c 165.97 � 1.42 a 248.65 � 1.87 a 54.87 � 0.6 a

49 � 0.8 a 1.6 � 0.05 d 8.01 � 0.03 a 48.97 � 0.4 a 1.87 � 0.02 c 26.19 bc 0.15 � 0.01 b 0.52 � 0.03 c 19.60 � 0.7 ab 4.27 � 0.02 c 15.33 c 21.78 d 8.72 d 0.2 � 0.01 c 2.78 � 0.32 a 4.26 � 0.08 a 1.39 � 0.01 a 0.42 � 0.06 a 47.95 � 0.2 c 189.53 � 1.72 a 226.75 � 1.87 a 46.48 � 0.6 a

41 � 0.8 a 1.8 � 0.05 d 8.25 � 0.03 a 45.16 � 0.4 ab 1.52 � 0.01 cd 29.71 b 0.12 � 0.01 bc 0.48 � 0.02 c 27.65 � 0.8 a 4.48 � 0.02 c 23.17 ab 16.20 e 9.92 cd 0.4 � 0.02 c 2.02 � 0.12 a 5.12 � 0.08 a 1.73 � 0.02 a 0.47 � 0.06 a 45.02 � 0.2 c 157.13 � 1.42 a 260.96 � 1.88 a 47.06 � 0.6 a

52 � 0.8 a 1.7 � 0.05 d 8.35 � 0.03 a 42.16 � 0.4 c 1.62 � 0.01 cd 26.02 bc 0.17 � 0.01 a 0.58 � 0.04 c 29.15 � 0.8 a 5.23 � 0.04 b 23.92 ab 17.94 e 12.41 c 0.5 � 0.02 c 2.12 � 0.12 a 4.86 � 0.08 a 1.13 � 0.01 a 0.56 � 0.07 a 148.02 � 0.7 b 123.13 � 1.27 a 250.46 � 1.77 a 87.06 � 0.6 a

48 � 0.8 a 2.8 � 0.06 c 7.40 � 0.03 b 49.34 � 0.4 a 1.25 � 0.009 d 39.47 a 0.16 � 0.01 a 0.65 � 0.04 bc 32.43 � 0.9 a 6.12 � 0.05 a 26.31 a 18.87 e 12.40 c 0.2 � 0.01 c 2.46 � 0.22 a 3.87 � 0.08 a 1.02 � 0.01 a 0.25 � 0.03 a 140.16 � 0.7 b 142.87 � 1.34 a 228.79 � 1.87 a 84.72 � 0.6 a

40 � 0.8 a 2.6 � 0.06 c 7.68 � 0.03 b 45.87 � 0.3 ab 1.58 � 0.01 cd 29.03 b 0.14 � 0.01 b 0.70 � 0.03 b 13.34 � 0.6 c 6.78 � 0.06 a 6.56 e 50.82 a 14.78 bc 0.5 � 0.02 c 2.11 � 0.03 a 4.42 � 0.05 a 1.52 � 0.02 a 0.43 � 0.06 a 23.6 � 0.1 d 134.5 � 1.18 a

46 � 0.8 a 2.2 � 0.06 c 6.16 � 0.02 c 43.60 � 0.3 ab 1.45 � 0.01 cd 30.01 b 0.18 � 0.01 a 0.81 � 0.04 a 15.30 � 0.6 b 5.65 � 0.05 b 9.65 e 36.93 b 12.96 c 0.3 � 0.01 c 2.42 � 0.22 a 4.17 � 0.08 a 1.38 � 0.01 a 0.48 � 0.08 a 363.68 � 0.9 a 147.13 � 1.38 a 228.75 � 1.87 a 57.06 � 0.6 a

51 � 0.9 a 4.7 � 0.12 b 5.66 � 0.01 d 35.30 � 0.2 d 2.14 � 0.02 b 18.18 d 0.04 � 0.003 d 0.23 � 0.003 d 22.40 � 0.7 ab 6.27 � 0.06 a 16.13 c 28.00 c 16.11 b 0.98 � 0.08 b 2.02 � 0.12 a 4.86 � 0.08 a 1.39 � 0.01 a 0.42 � 0.06 a 49.68 � 0.2 c 179.53 � 1.83 a 257.96 � 1.57 a 77.06 � 0.6 a

52 � 0.9 a 3.9 � 0.09 b 5.25 � 0.01 d 33.80 � 0.2 d 2.76 � 0.02 b 12.28 e 0.02 � 0.003 d 0.34 � 0.004 d 19.60 � 0.7 ab 3.48 � 0.01d 16.12 c 17.76 e 10.27 c 0.93 � 0.08 b 2.06 � 0.12 a 3.87 � 0.08 a 1.83 � 0.03 a 0.47 � 0.06 a 41.95 � 0.2 c 167.13 � 1.42 a 248.46 � 1.72 a 74.72 � 0.6 a

58 � 0.9 a 5.1 � 0.15 a 4.31 � 0.01 e 25.60 � 0.1 e 3.43 � 0.02 a 7.47 f 0.08 � 0.003 d 0.22 � 0.003 d 18.80 � 0.7 ab 5.14 � 0.04 b 13.66 c 27.34 c 19.85 a 1.23 � 0.1 a 2.77 � 0.42 a 4.42 � 0.05 a 1.55 � 0.02 a 0.56 � 0.07 a 29.02 � 0.2 d 133.13 � 1.19 a 228.79 � 1.62 a 61.8 � 0.6 a

41.5 28.2 48.6 32.7 35.6 43.7 29.2 21.9 32.2 31.8 38.3 43.7 25.2 41.9 42.2 35.8 32.8 25.9 34.7 45.6

0.321 0.044* 0.024* 0.013* 0.026* 0.033* 0.028* 0.012* 0.005* 0.009* 0.004* 0.002* 0.005* 0.046* 0.185 0.252 0.379 0.471 0.044* 0.198

29.5

0.445

46.7

0.357

Mn (ppm) Zn (ppm) a

b

c

d

e

193.4 � 1.28 a 71.8 � 0.6 a f

g

h

Electrical conductivity. Total organic carbon. Total organic nitrogen. Ammonium nitrogen. Nitric nitrogen. Total extractable carbon. Humic carbon. Humification index ¼ TEC–CH (simplified formula). i Humification degree ¼ CH/TEC � 100. l Humification ratio ¼ CH/TOC � 100. Values are means, F-values and P-values as resulting from one-way analyses of variance (one-way ANOVA) with the factor ‘compost’. Each value is the pooled mean of five replicates of 500 g dry weight each � standard deviation. Value with asterisk is significant (P � 0.05). Values within each line followed by different lowercase letters indicate significant differences according to Duncan Multiple Range Test (P � 0.05). Crop Protection 124 (2019) 104870

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3.2. Suppressive capacity

sp. melonis/Melon, F. oxysporum f. sp. lycopersici/Tomato and F. oxysporum f. sp. basilici/Basil. In contrast, an opposite trend was observed for the remaining three composts (Com-H–Com-L) whenever Fusarium wilts were more suppressed than Pythium and Rhizoctonia damping-off and Phytophthora root rot, whereas Verticillium wilt on eggplant was suppressed in a similar manner by all the composts (with exception of Com-L). Suppression ratio for DSI sterilised and nonsterilised media is shown in Fig. 1b. The seven green composts lost their ability to suppress P. ultimum/Cucumber, P. irregulare/Zucchini, R. solani/Bean, P. nicotianae/Tomato and P. cinnamomi/Azalea after

Table 2S shows the significant differences between the DSI % mean values amongst the non-sterilised compost-amended container media in all pathosystems according to one-way ANOVA (P-value�0.05). Fig. 1a shows DSI % mean values of the composts in the same pathosystem divided among them according to DMRT (P � 0.05). The figure also shows that the seven green composts (Com-A–Com-G) were more sup­ pressive to P. ultimum/Cucumber, P. irregulare/Zucchini, R. solani/Bean, P. nicotianae/Tomato and P. cinnamomi/Azalea than to F. oxysporum f.

Fig. 1. (a) Disease suppression index percentage (DSI %) measured in 9 pathogen/host systems in container media amended with 10 non-sterilised compostsa. DSI is the pooled mean of two experiments each set up with 10 composts � 4 replicates � 20 seedlings per pathosystem. Bar indicates � standard error (SE) of the mean. Columns for each pathosystem labelled with different lowercase letters indicate diverse values among the composts according to Duncan’s Multiple Range Test (DMRT) at P � 0.05 level. (b) DSI sterile/non-sterile ratio without asterisk indicates no significant effect on suppression loss after sterilisation, value with asterisk suggests a significant suppression loss. aComposts A, B, C, D (group AC) derive from green source by an accelerated composting cycle; composts E, F, G (group MC) from green source by an on-farm middle cycle; composts H, I, L (group LC) from Municipal Solid Waste (MSW) and bio-wastes by a long cycle. 6

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solani, followed by a decreased abundance of amplicons of Alternaria and Hypocrea according to DMRT (P � 0.05). The green composts of groupMC showed lower abundances of amplicons of Trichoderma species than composts of group-AC; particularly, Com-G exhibited the lowest abundance of the Trichoderma species. Similar abundances of fusaria amplicons were observed in group-MC composts. The two MSW-based composts were instead predominantly colonised by Aspergillus terreus, Aspergillus fumigatus, Aspergillus niger, Aspergillus ochraceus and Asper­ gillus versicolor, followed by the yeast Candida oleophila and Pichia guil­ liermondii, and by the genus Mortierella. Compost-L was predominantly colonised by Penicillium ilerdanum, Penicillium piceum, Penicillium chrys­ ogeum, C. oleophila, P. guilliermondii and by the genus Mortierella. It is important to notice that the fungal orders Agaricales, Helotiales, Hypo­ creales and Microascales were not detected in Com-L (Fig. 3a).

sterilisation, while the three remaining composts lost their ability to suppress Fusarium wilts on tomato, melon and basil; finally, all the composts lost their ability to suppress Verticillium wilt on eggplant. 3.3. Microbiomes characterisation 3.3.1. Richness and diversity The alpha-diversity indices trends showed how the fungal commu­ nity was less abundant in OTUs, rich and diverse than the bacterial community, where in the same compost the highest numbers of observed OTUs, richness and diversity in the bacterial community were accompanied by the lowest OTUs, richness and diversity in the fungal community. Table 3S shows significant difference in the number of observed OTUs, Chao1 richness and Shannon diversity of the fungal and bacterial communities among the composts according to one-way ANOVA (P � 0.05). Fig. 2 shows significant difference in the fungal and bacterial communities of composts from the group-AC to group-LC composts according to DMRT (P � 0.05) in terms of number of observed OTUs (Fig. 2a), Chao1 richness (Fig. 2b) and Shannon diversity (Fig. 2c).

3.3.3. Bacterial community composition For the bacterial community, 2524 high-quality sequences were obtained (data not shown). Fig. 3b shows that these sequences were affiliated with the members of Acidobacteria, Actinobacteria, Firmicutes, Gemmatimonadetes, Planctobacteria, Alpha-Proteobacteria, Beta-Proteo­ bacteria, Gamma-Proteobacteria and Sphingobacteria, while the remaining sequences were assigned to other phyla, Proteobacteria and Prokarya. The most abundant sequence reads were identified as belonging to Fir­ micutes, accounting for 22.42% of total sequence reads, followed by Gamma-Proteobacteria (17.51%), Actinobacteria (10.38%), Sphingobac­ teria (5.9%), Alpha-Proteobacteria (5.43%), Beta-Proteobacteria (3.92%) and Acidobacteria (3.1%). Bacterial relative abundances showed signif­ icant difference at the genus/species levels according to one-way ANOVA (P � 0.05) (Table 3S). Fig. 4c shows the green composts of group-AC were characterised by higher abundances of amplicons iden­ tified as Bacillus subtilis, Bacillus licheniformis and Bacillus pumilus, fol­ lowed by Pseudomonas fluorescens, Agrobacterium and Flavobacterium according to DMRT (P � 0.05). When compared to group-AC green composts, group-MC composts showed a similar abundance of B. subtilis and B. pumilis, a higher abundance of B. licheniformis and a lower

3.3.2. Fungal community composition For the fungal community, 1962 high-quality sequences were ob­ tained (data not shown). Fig. 3a shows that they were affiliated with the members of Ascomycota, Basidiomycota and Zygomycota, with Ascomy­ cota being the most abundant accounting for 79% of total sequence reads, followed by Basidiomycota (12.74%), Zygomycota (4.18%) and Saccharomycetales (4.08%). More specifically, the most abundant sequence reads were identified as belonging to the orders HypocrealesTrichoderma (23.2%), Hypocreales-Fusarium (21.04%), Eurotiales (18.06%), Agaricales (6.62%), Microascales (5.45%) and Mortierellales (4.18%). Fungal relative abundances showed significant difference at the genus/species levels according to one-way ANOVA (P � 0.05) (Table 3S). Fig. 4a and 4b shows that the green composts of group-AC were characterised by higher abundances of amplicons identified as T. atroviride, T. harzianum, T. asperellum, F. oxysporum and Fusarium

Table 3 Pearson correlation coefficients measured among the compost primary physicochemical properties with the microbiomes of fungi (1a, 2a, 3a) and bacteria (1b, 2b, 3b) identified at the phylum and order levels (for fungi) and at the phylum and class levels (for bacteria) by using amplicon sequencing of the ITS rDNA gene region and 16S rDNA gene, respectively, in 10 suppressive composts (Com-A–Com-L). Microbiome variable

EC

pH

TOC/TON

N–NHþ 4

N–NO–3

CH

HI

HD

HR

(1a) OTUs relative abundance for fungi: Agaricales Eurotiales Helotiales Hypocreales (Trichoderma) Hypocreales (Fusarium) Microascales Saccharomycetales Mortierellales Ascomycota Basidiomycota (2a) Chao1 richness for fungi (3a) Shannon diversity for fungi

þ0.15 þ0.84** þ0.15 0.83** 0.79** þ0.12 þ0.58 þ0.74* þ0.15 þ0.12 0.71* 0.85**

0.09 0.81** 0.12 þ0.79** þ0.88** 0.12 0.57 0.78** 0.12 0.12 þ0.65* þ0.75**

0.18 0.42 0.37 0.44 0.47 0.13 0.46 0.42 0.11 0.13 þ0.58 þ0.52

þ0.13 0.77** þ0.39 þ0.86** þ0.79** þ0.10 þ0.56 0.80** þ0.12 þ0.10 þ0.71* þ0.82**

þ0.17 0.86** þ0.33 þ0.71* þ0.83** þ0.15 þ0.58 0.81** þ0.18 þ0.15 þ0.74* þ0.84**

þ0.39 þ0.72* þ0.42 þ0.68* þ0.69* þ0.12 þ0.09 þ0.62* þ0.12 þ0.32 þ0.71* þ0.79**

þ0.39 þ0.65* þ0.27 þ0.67* þ0.71* þ0.12 þ0.11 þ0.68* þ0.13 þ0.33 þ0.80** þ0.78**

þ0.11 þ0.82** þ0.47 þ0.69* þ0.80** 0.13 þ0.04 þ0.72* þ0.10 þ0.10 þ0.68* þ0.70*

þ0.37 þ0.84** þ0.42 þ0.77** þ0.78** þ0.10 þ0.05 þ0.84** þ0.15 þ0.35 þ0.67* þ0.83**

(1b) OTUs relative abundance for bacteria: Acidobacteria þ0.61* Actinobacteria þ0.82** Firmicutes þ0.12 Gemmatimonadetes 0.57 Planctobacteria 0.39 Alpha-Proteobacteria þ0.58 Beta-Proteobacteria þ0.59 Gamma-Proteobacteria þ0.58 Proteobacteria þ0.61* Sphingobacteria þ0.39 Prokarya þ0.39 (2b) Chao1 richness for bacteria 0.68* (3b) Shannon diversity for bacteria 0.82**

þ0.58 0.85** 0.12 þ0.55 þ0.39 0.57 0.58 0.62* 0.59 0.33 0.11 þ0.68* þ0.79**

þ0.53 0.43 0.13 þ0.46 þ0.11 0.07 0.09 0.07 0.05 0.21 0.37 þ0.59 þ0.52

0.58 0.82** þ0.10 0.56 0.37 þ0.56 þ0.59 þ0.57 þ0.53 þ0.36 þ0.39 þ0.67* þ0.80**

0.64* 0.70* þ0.15 0.58 0.44 þ0.50 þ0.52 þ0.42 þ0.56 þ0.51 þ0.33 þ0.71* þ0.87**

þ0.73* þ0.69* þ0.68* þ0.12 þ0.12 þ0.12 þ0.12 þ0.80** þ0.12 þ0.12 þ0.12 þ0.79** þ0.71*

þ0.67* þ0.65* þ0.80** þ0.13 þ0.12 þ0.13 þ0.13 þ0.74* þ0.13 þ0.13 þ0.12 þ0.78** þ0.80**

þ0.63* þ0.66* þ0.74* þ0.10 þ0.13 þ0.10 þ0.10 þ0.64* þ0.10 þ0.10 þ0.13 þ0.73* þ0.68*

þ0.81** þ0.78** þ0.65* þ0.15 þ0.10 þ0.15 þ0.15 þ0.78** þ0.15 þ0.15 þ0.10 þ0.83** þ0.67*

Asterisks indicate significant and highly significant correlations at the P � 0.05 (*) and P � 0.01 (**) levels, respectively. 7

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Fig. 2. Alpha-diversity indices of the fungal and bacterial communities in 10 suppressive compostsa showing (a) the number of operational taxonomic units (OTUs), (b) Chao1 richness and (c) Shannon diversity using amplicon sequencing of the ITS rDNA gene region and 16S rDNA gene, respectively. Value is the pooled mean of three replicates of 0.25 g dry weight (DW) each. Bar indicates � SE. Columns for each microbial group (fungi/bacteria) labelled with different lowercase letters indicate diverse values among the composts according to DMRT (P � 0.05). a To see Fig. 1.

abundance of P. fluorescens. In contrast, group-LC composts were pri­ marily colonised by Streptomyces griseus, P. fluorescens and Pseudomonas putida, followed by several members of Bacillus, Acidobacterium, Agro­ bacterium, Acidovorax and Flavobacterium. Importantly, group-LC com­ posts were inconsistently colonised by other Proteobacteria, Sphingobacteria and Prokarya, and all green composts were not colonised by Acidobacteria, or inconsistently colonised by Actinobacteria (Fig. 3b).

3.4. Pearson correlation Table 3 shows the Pearson coefficients correlation matrix for the microbiomes alpha-diversity indices with the primary physicochemical features of composts. It should be to noted that EC was positively correlated with Mortierellales, Acidobacteria and other Proteobacteria (P � 0.05) and also with Eurotiales and Actinobacteria (P � 0.01), while it was negatively correlated with Hypocreales (Trichoderma and Fusarium) (P � 0.01). pH was negatively correlated with Gamma-Proteobacteria (P � 0.05) and Eurotiales, Mortierellales, Actinobacteria (P � 0.01), and it 8

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Fig. 3. Relative abundance of (a) fungi and (b) bacteria (expressed as OTUs percentage) identified in 10 suppressive compostsa at the phylum and order levels (for fungal amplicons) and at the phylum and class levels (for bacterial amplicons) using sequencing of the ITS rDNA gene region and 16S rDNA gene, respectively (only abundances >1% are displayed). Value is the pooled mean of three replicates of 0.25 g DW each. aTo see Fig. 1.

was positively correlated with Hypocreales (P � 0.01). The ammonium nitrogen content was negatively correlated with Eurotiales, Mortierellales and Actinobacteria, while it was positively correlated with Hypocreales (P � 0.01). In addition, the nitric nitrogen content showed the same correlation pattern with regard to the ammonium nitrogen content. In contrast, the parameters related to the humified carbon forms (CH, HI, HD and HR) were positively correlated with Eurotiales, Hypocreales, Mortierellales, Acidobacteria, Actinobacteria, Firmicutes and Gamma-Pro­ teobacteria. Finally, Chao1 richness and Shannon diversity indices were positively correlated to the most physicochemical properties, with electrical conductivity being the only exception observed. Moreover, the TOC/TON ratios did not show significant correlation with any param­ eter considered.

pathogens, simultaneously. Specifically, populations of Bacillus, Tricho­ derma, Fusarium and other Eukarya belonging to Ascomycota and Basi­ diomycota, which were consistently found in green composts AC and MC, could account for the multisuppressive properties toward Rhizoctonia and Pythium damping-off, Phytophthora root rot and Verticillium wilt. The green composts of the groups AC and MC could have provided the needed substrates able to support microbial biomass growth for disease suppression, a finding in agreement with the findings of Hoitink et al. (1997) that demonstrated as composts from lignocellulosic sources suppressed more than one pathogen, simultaneously. In addition, the green composts of group AC exhibited higher levels of Alternaria and fusaria amplicons, as well as others such as Ascomycota, Basidiomycota and Prokarya, which were potentially capable of conferring suppressive properties to Pythium damping-off and Phytophthora root rot, a finding in agreement with the findings of Chen et al. (1988); Hardy and Siva­ sithamparam (1991); Manici et al. (2004); Scheuerell et al. (2005); and De Corato et al. (2016; 2018b, 2018c). Moreover, the green composts of group MC overall exhibited higher levels of microbial biomass than specific taxa of their microbiota, which are able to induce a general suppression to the aforementioned diseases. In contrast, populations of Aspergillus, Penicillium, Mortierella, Streptomyces and fluorescent Pseu­ domonas in group-LC composts could account for the suppression of �rez-Estrella et al., 2007) and Verticillium wilt Fusarium diseases (Sua � o and Avil�es, 2013). Specifically, certain species of Trichoderma, (Castan Aspergillus, Penicillium, Streptomyces, Bacillus and fluorescent Pseudo­ monas are known in literature as the BCAs able to control Rhizoctonia damping-off, Fusarium and Verticillium wilts, a finding in agreement with the findings of Mercado-Blanco et al. (2004); Pane et al. (2013);

4. Discussion All green composts were capable of effectively suppressing the pathosystems under consideration. Furthermore, the observed pathogen suppressive effects of these composts were more presumably related to biological activity of their microbiomes, as significant suppressiveness losses seemed to be consistent after sterilisation. Our findings on compost microbiomes are in agreement with those reported in previous research, showing that they are primarily influ­ � and enced by the feedstock from which they are derived (Vo�rí�skova �pez-Gonza �lez et al., 2015). The composts from green Baldrian, 2013; Lo biomass are colonised by a wide spectrum of fungi and bacteria belonging to highly diversified taxonomic groups, most of them being potentially capable of effectively controlling either one or a range of 9

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Fig. 4. Relative abundance of (a, b) fungi and (c) bacteria (expressed as high-quality sequences percentage) identified in 10 suppressive compostsa at the genus/ species levels using amplicon sequencing of the ITS rDNA gene region and 16S rDNA gene, respectively (only abundances >0.1% are displayed). Value is the pooled mean of three replicates of 0.25 g DW each. Bar represents � SE. Columns for each genus/species labelled by different lowercase letters indicate diverse values among the composts according to DMRT (P � 0.05). aTo see Fig. 1.

10

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Avil�es and Borrero (2017); and De Corato et al. (2016; 2018b, 2018c). The genus Mortierella, which is less described in the literature than other genera, may instead serve as a new indicator and/or enhancer of Fusa­ rium wilt suppression in vanilla (Xiong et al., 2017). With regard to fungal species acting as potential BCAs for controlling R. solani, V. dahliae and F. oxysporum, it is important to note that amplicons of Trichoderma and Fusarium from the green composts, amplicons of Aspergillus and Mortierella from the MSW-based composts, and amplicons of Penicillium and Mortierella from the co-composted cow manure with household wastes, were more abundant than amplicons of other fungal genera. Specifically, R. solani is a highly problematic pathogen in many cropping systems (Chet and Baker, 1980). The sup­ pressive properties of composts to Rhizoctonia diseases were related to their colonisation by Trichoderma species including T. asperellum strain T34 (Segarra et al., 2013). Many studies showed that composts derived from hardwood bark and plant green-wastes suppressed Rhizoctonia damping-off in cucumber (Trillas et al., 2006), bean (Kwok et al., 1987) and cress (Pane et al., 2013). Other studies have instead established that composted pine bark failed to suppress Rhizoctonia damping-off in radish (Krause et al., 2001), while on-farm compost from woody plant pruning and grass clippings was even conducive to Rhizoctonia disease on lavender (Chilosi et al., 2017). The primary mechanism of action ascribed to Trichoderma populations for suppressing Rhizoctonia dis­ eases is related to biological activity of many enzyme groups belonging to β-1,3-glucanase, chitobiase and chitinase (de la Cruz et al., 1993) that are capable of degrading fungal cell walls of pathogens and overwinter resistant propagules of Rhizoctonia species into soil (Nguyen et al., 2008). On the other hand, authors demonstrated that a significant suppressive potential of the purified compounds from filtrates of B. subtilis subsp. subtilis strain C9 for use as a BCA against R. solani on Zoysia grass, as well as a plant growth promoter with the ability to trigger induced systemic resistance of grass plants, was found in a pre­ vious study (Islam et al., 2012). V. dahliae is another problematic soil-borne pathogen because it produces microsclerotia highly persistent into soil. Kanaan et al. (2017) have demonstrated that the increased suppressive effect of compost to Verticillium wilt in eggplant can be attributed to direct activity of Bacillus and fluorescent Pseudomonas populations or systemic resistance induced by Trichoderma populations isolated from host plants (Hoitink et al., 2006). Other studies have noted that a high abundance of Actinobacteria populations associated with the compost enzymatic diversity, β-glucosidase activity, pH and EC may be considered predictive in suppressing Verticillium wilt in cotton using composted olive mill (Avil� es and Borrero, 2017). Finally, F. oxysporum is also considered to be a pathogen difficult to eradicate, as it produces chlamydospores highly persistent into soil (Ocamb and Kommedahl, 1994). The suppressiveness of the composts used in this study for con­ trolling F. oxysporum in melon, tomato and basil could be potentially related to the relative abundance of Aspergillus, Penicillium, Streptomyces �rez-Estrella et al. and fluorescent Pseudomonas. With regard to this, Sua (2007) indicated that Aspergillus species are considered the primary biocontrol agents of F. oxysporum f. sp. melonis of melon. While Hussain et al. (2016) concluded that the genus Penicillium acts as BCA against F. oxysporum f. sp. lycopersici of tomato. However, a combination of T. asperellum strain T34 with the endophytic non-pathogenic F. oxysporum strain Fo47 and sewage sludge compost effectively sup­ pressed F. oxysporum f. sp. melonis in tomato (Cotxarrera et al., 2002). Finally, the dominance of Mortierella in suppressive soils against Fusa­ rium wilt in vanilla is considered to be the primary enhancer of Fusa­ rium wilt suppression (Xiong et al., 2017). Therefore, the members of Zygomycota present in group-LC composts, which are often undervalued in the literature, may be an interesting and new BCA group that potentially acts as a key factor against Fusarium wilt disease overall if affiliated to the genus Mortierella. On the other hand, some isolates of Mortierella produce antagonistic metabolites against potato scab path­ ogens (Tagawa et al., 2010). With regard to bacterial species acting as potential BCAs, it is

important to underline that the higher abundances of Firmicutes, Gamma-Proteobacteria and Actinobacteria in groups AC and LC composts are associated with the suppression of wilt diseases (Shi et al., 2017). The genera Bacillus (P� erez-García et al., 2011; Pane et al., 2012) and Pseudomonas (Jan et al., 2011) are both verified as two BCA represen­ tatives for effectively controlling F. oxysporum f. sp. lycopersici in tomato (Aydi Ben Abdallah et al., 2016). The primary mechanism of action ascribed to Firmicutes, Gamma-Proteobacteria and Actinobacteria pop­ ulations for suppressing diseases is related to biological activity of an­ tibiotics. Many bacterial species belonging to the genera Bacillus, Pseudomonas and Streptomyces produce high concentrations of secondary metabolites that act either as antibiotics or as potential BCAs (B�erdy, 2005; Palaniyandi et al., 2013). Strains of fluorescent Pseudomonas producing antibiotics are currently used in biological treatments for enhancing soil suppressiveness (Haas and D� efago, 2005). It is reported in the literature that over two-thirds of all natural antibiotics are pro­ duced by several strains of Streptomyces species (Reuben et al., 2008), such as Streptomyces violaceus-niger that produce tuberdicidin, phos­ phalactomycin and candicidin (Hwang et al., 1994; Shekhar et al., 2006), while Streptomyces albospinus strain CT205 (Wang et al., 2016) and Bacillus amyloliquefaciens strain SN16-1 (Wan et al., 2017) have been identified as BCAs to inhibit Fusarium wilt in cucumber and to­ mato, respectively. Significant abundances of specific microbial phyla could be associ­ ated with BCAs for effectively suppressing the pathosystems under consideration by conferring them potential suppressive properties. It is to be underlined that fungal amplicons belonging to the genera Alter­ naria, Aspergillus, Fusarium and Penicillium, as well as bacterial amplicons of the genera Bacillus, Pseudomonas and Streptomyces, also include important plant pathogens. The certainty of the species levels has been established by the compost microbiomes analyses, but the phytopatho­ genic attitudes of a representative number of colonies of each potential pathogen should be tested in planta in further researches, although one the main assumption of this work is that the composts under consider­ ation are considered as free of plant pathogens. All the primary physicochemical properties of composts, with the only exception of TOC/TON ratios, can be considered significantly correlated with the richness and diversity of the microbiomes in sup­ pressive composts, as being significantly correlated with the microbiota composition. The relative abundances of Eurotiales (Aspergillus and Penicillium), Hypocreales (Trichoderma and Fusarium), Mortierellales (Mortierella), Acidobacteria (Acidobacterium), Actinobacteria (Strepto­ myces), Firmicutes (Bacillus) and Gamma-Proteobacteria (Pseudomonas) may be at least qualitatively expected on the basis of some physico­ chemical properties of composts. Unfortunately, other information of this topic is not available in the literature, which hampers comparisons with existing studies. The highest significant Pearson correlations have allowed us to make a helpful correlation matrix to know how relation­ ships among the microbiomes variables with the primary physico­ chemical properties of the composts could be established and quantified. Nevertheless, our future objective is to elaborate a clear, simple and coherent graphical model to simultaneously correlate compost physi­ cochemical properties, suppressive capacities and the microbiota vari­ ables of a larger number of tailored green composts for predicting their suppressive potential. 5. Conclusions This work has pointed out a reliable and faster alternative method to traditional plate count of culturable fungi and bacteria for identifying a wider range of potential BCAs in a suppressive compost collection in relation to the current state of art contributing to the study of the compost complexed systems. This approach has also allowed us to identify and quantify other Eukarya and Prokarya that are probably involved in suppression, but less studied. The green composts provided most complex microbiomes than the composted municipal solid waste 11

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for suppressing Rhizoctonia and Pythium damping-off, Phytophthora root rot and Verticillium wilt, simultaneously. At the same time, re­ lationships among the microbiomes variables with the primary physi­ cochemical properties of composts were quantitatively established. Requirements by the organic amendment market to use new suppressive composts in organic cropping systems will be increased in the next years using the profitable way of circular economy whenever an increasing number of tailored suppressive composts sourced from agro-wastes, agro-industrial residues and bio-energy co/by-products will be avail­ able over time in organic food processing (Kahl et al., 2014).

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