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Environmental Microbiology (2015)

doi:10.1111/1462-2920.12911

Antagonistic interactions between endophytic cultivable bacterial communities isolated from the medicinal plant Echinacea purpurea

Isabel Maida,1† Carolina Chiellini,1,2† Alessio Mengoni,1 Emanuele Bosi,1 Fabio Firenzuoli,3 Marco Fondi1 and Renato Fani1* 1 Department of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, Florence I-50019, Italy. 2 CRA-ABP Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Centro di ricerca per l’Agrobiologia e la Pedologia, Piazza M. D’Azeglio 30, Firenze, FI 50121, Italy. 3 Center for Integrative Medicine, Careggi University Hospital, University of Florence, Florence, Italy. Summary In this work we have studied the antagonistic interactions existing among cultivable bacteria isolated from three ecological niches (rhizospheric soil, roots and stem/leaves) of the traditional natural medicinal plant Echinacea purpurea. The three compartments harboured different taxonomic assemblages of strains, which were previously reported to display different antibiotic resistance patterns, suggesting the presence of differential selective pressure due to antagonistic molecules in the three compartments. Antagonistic interactions were assayed by the crossstreak method and interpreted using a network-based analysis. In particular ‘within-niche inhibition’ and ‘cross-niche inhibition’ were evaluated among isolates associated with each compartment as well as between isolates retrieved from the three different compartments respectively. Data obtained indicated that bacteria isolated from the stem/leaves compartment were much more sensitive to the antagonistic activity than bacteria from roots and rhizospheric soil. Moreover, both the taxonomical position and the ecological niche might influence the antagonistic ability/sensitivity of different strains. Antagonism

Received 4 July, 2014; revised 15 May, 2015; accepted 16 May, 2015. *For correspondence. E-mail [email protected], renato.fani@ virgilio.it; Tel. +390554574742; Fax (+39) 055 4574906. †These authors equally contributed to the work.

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could play a significant role in contributing to the differentiation and structuring of plant-associated bacterial communities. Introduction The last 15 years have witnessed an increasingly stronger interest towards plant-associated bacteria, in particular the commensal bacterial endophytes, which inhabit the internal tissues of plant, thriving as nonpathogenic commensal or mutualistic symbionts (Holliday, 1989; Shultz and Boyle, 2006; Reinhold-Hurek and Hurek, 2011). In several cases, bacterial endophytes have been claimed as promoters of plant growth through the production or modulation of phytohormones, or as through the fixation of atmospheric nitrogen (Ryan et al., 2008). Rhizobia form specialized structures in roots or stems of host plant in which atmospheric nitrogen is fixed to ammonium (Masson-Boivin et al., 2009; Mengoni et al., 2013). Moreover, several commensal endophytic strains from different taxa have been found to harbour genes encoding 1-aminocyclopropane-1-carboxylic acid deaminases. This enzyme is supposed to decrease the level of the phytohormone ethylene by cleaving its precursor (Hardoim et al., 2008; Rajkumar et al., 2009), thereby decreasing plant senescence. Also, other genes (most of which with an unknown function) have been found significantly enriched in the genomes of plant-associated bacteria (Pini et al., 2011). Finally, several commensal bacterial endophytes may help the plant to prevent infections by plant pathogenic microorganism, through antagonistic effect (Rosenblueth and Martinez-Romero, 2006; Ryan et al., 2008). A strong structuring of endophytic bacterial communities in relation to plant organs, season of sampling and individual plant variation has been found. Indeed, different plant organs harbour different bacterial assemblages (Mengoni et al., 2003; 2009; Mocali et al., 2003; Barzanti et al., 2007; Mano and Morisaki, 2008; Ulrich et al., 2008; Pini et al., 2012). Finally, endophytic bacteria have been claimed also as biocontrol agents, thanks to the antagonistic effects versus pathogenic bacteria and fungi displayed by some member of the endophytic community. The antagonistic mechanisms include the production of

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secondary metabolites, ecological niche competition and the induction of systemic acquired resistance of the host (Chanway, 1998; van Loon et al., 1998; Ryan et al., 2008). Several studies have been performed testing isolated endophytes towards a panel of known pathogens, to search for biocontrol agents (Rosenblueth and Martinez-Romero, 2006; Ryan et al., 2008). To the best of our knowledge, no studies have been performed testing a cross interaction among isolates from the same healthy plant with respect to the ecological niches, which can be identified in the different plant organs. In spite of the huge amount of data concerning the endophytic bacterial communities isolated from different plants, very little is known about the endophytic bacteria inhabiting the tissues of medicinal plants, a potential reservoir of important bioactive molecules. The only examples of endophytic bacterial communities characterized so far from medicinal plants concern Lavandula officinalis (Emiliani et al., 2014), Echinacea purpurea and Echinacea angustifolia (Chiellini et al., 2014). The whole body of data obtained on the endosphere of those plants revealed that they harbour different and complex bacterial communities. Besides, the molecular fingerprinting analysis carried out through random amplified polymorphic DNA (RAPD) analysis on the E. purpurea bacterial isolates revealed (i) a nonclonal structure of bacterial populations and (ii) an extremely low degree of strain sharing between different compartments (stem/leaves, roots) of the same plant as well as with rhizospheric soils. This finding points to the existence of selective pressures that may differentiate the communities present in the different compartments. Indeed, we recently showed (Mengoni et al., 2014) that the different plant compartments harbour strains with differential resistance patterns towards antibiotics as ciprofloxacin, tetracycline, rifampicin, and so on, suggesting that one of the above-mentioned selective pressures could be the production of different antibiotic molecules within the bacterial communities colonizing the different compartments. We can then speculate that antagonistic interactions between bacteria inhabiting different compartments of plants could take place. In principle, antagonism could be either direct (through the synthesis of antimicrobial compounds produced by bacteria (Ratcliff and Denison, 2009)) and/or indirect (by exclusive niche colonization which may trigger a plant response against other bacteria (Denison, 2000; Kiers and Denison, 2008)), accumulation of toxic catabolites or signal molecules. In other words, bacterial endophytes might be able to perform an antagonistic activity not only towards plant pathogens, but also other microorganisms inhabiting the same or a different ecological and/or spatial niche within the same plant. Antagonism between microbial communities inhabiting the same or different ecological niches has been poorly analysed in detail. Most of the

work has been done towards protection against plant pathogens (Sessitsch et al., 2004; Latz et al., 2012). However, in recent years, antagonism among bacteria has also been considered in frameworks related to sociobiology and general ecology (Schoenian et al., 2011; Becker et al., 2012; Perez-Gutierrez et al., 2013). Concerning bacterial communities of multicellular eukaryotes, we studied the antagonistic interactions existing among cultivable bacteria associated with the Antarctic sponges Anoxycalyx joubini and Lissodendoryx nobilis (Mangano et al., 2009). The two sponges harboured microorganisms belonging to different species/genera, previously retrieved from polar marine environments. Antagonistic interactions, assayed by the cross-streak method and analysed using a ‘network modeling’, were checked among isolates associated with the same sponge as well as between isolates retrieved from the two sponge species (‘cross-niche inhibition’). Data obtained in that work revealed the existence of a sort of ‘hierarchical’ structure of the bacterial community with some strains able to strongly inhibit the growth of bacteria inhabiting the same (or different) sponge(s), and playing a possible role in shaping sponges biodiversity. The aim of this work was to check the possible antagonism existing between endophytic bacteria inhabiting the same or different plant compartments and to correlate it with the observed endophytic biodiversity. To this purpose we used as model systems the bacterial communities previously isolated from a traditional natural medicinal plant, that is, E. purpurea (L.) Moench (Compositae), whose different compartments (roots and stem/leaves) exhibited both a different degree of biodiversity and different endophytic bacterial communities at strains, species and genus level (Chiellini et al., 2014). The choice of E. purpurea as a model system to study the antagonism between bacteria inhabiting different compartments of this plant relies on the large popularity of Echinacea derived extracts to treat common cold (Karsch-Völk et al., 2014). Results The panel of bacterial strains used in this work represents a subset of a larger panel of bacteria isolated from different compartments of E. purpurea plants, namely rhizospheric soil, roots, stem/leaves (hereinafter RS, R and S/L respectively). The composition of cultivable bacterial community from each compartment was determined through 16S rRNA gene amplification, sequencing and analysis (Chiellini et al., 2014) performed on 200 isolates per compartment, which showed a (very) different composition of the three communities at the genus/species level. Moreover, a RAPD analysis revealed a very high degree of biodiversity at the strain level in all the three

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology

Antagonism among E. purpurea endophytic bacteria Total

Stem/Leaves

Achromobacter sp. Agrococcus sp. Agrobacterium sp Arthrobacter sp. Flavobacterium sp.

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Fig. 1. Schematic representation of the taxonomic composition of the bacterial communities used in this work. The coloured circles indicate the different taxonomy of the strains as follows: green for Alphaproteobacteria, blue for Betaproteobacteria, violet for Gammaproteobacteria, yellow for Actinobacteria, black for Firmicutes, orange for Flavobacteria.

Frigoribacterium sp. Kineococcus sp. Methylobacterium sp. Microbacterium sp. Pseudomonas sp.

Rhodobacter sp. Sphingomonas sp. Staphylococcus sp.

Roots

Rhizospheric Soil

compartments, since more than 400 different RAPD profiles were obtained from the 600 bacterial isolates; besides, a very low degree of strain sharing between two or three compartments was also found (Chiellini et al., 2014). The subset of bacteria used in this work (Supporting Table S1) was randomly chosen among the panel of strains exhibiting different RAPD profiles, e.g. the 140 bacteria correspond to 140 different bacterial strains (43, 49 and 48, from RS, R and S/L respectively). Since isolation medium used Tryptone Soy Agar (TSA) was intended to enrich for copiotrophic bacteria (Chiellini et al., 2014), in agreement with taxa proportion of the whole collection reported in Chiellini and colleagues (2014), datasets RS and R contained mainly strains belonging to the genus Pseudomonas, whereas the community from S/L compartment is much more heterogeneous (Fig. 1). High variability of inhibitory patterns: the stem/leaves compartment harbours the most sensitive strains The pairwise inhibitory interaction between bacteria isolated either from the same or from a different E. purpurea compartment was tested for each of the 140 different strains of the entire panel. Therefore each strain was used either as a tester or target through the cross-streaking method as described in ‘Materials and methods’ for a total of 19 600 tests. The results of the tests were scored as follows: complete (3), strong (2), weak (1) and absence (0) of inhibition. Data obtained are portrayed in Fig. 2 and schematically represented in Supporting Information Tables S2 and S3. Moreover, an overall representation of the inhibitory

pattern among strains from the same compartment and from different compartments is reported in Fig. 3. The analysis of obtained data revealed that (i) All the 140 tested strains were able to inhibit at least another strain (i.e. the 140 strains were ‘active’). Accordingly, no overall ‘sensitive’ or ‘resistant’ strain was detected. Moreover, 11 active strains were also resistant to the activity of all the other 129 strains. They belong to the genera Arthrobacter (4), Flavobacterium (1) and Pseudomonas (6). All of them came from the RS compartment. (ii) As shown in Supporting Information Table S3, selfinhibition (SI) was observed for 6, 7 and 31 bacterial isolates associated with RS, R, and S/L respectively. (iii) Bacteria isolated from S/L compartment were more sensitive to the antagonistic effect of other endophytes and rhizospheric strains than bacteria isolated from RS and R. Strains isolated from S/L exhibited a high sensitivity also to the inhibiting effect of strains isolated from the same compartment. In principle, this higher sensitivity of S/L strains to the antagonistic effect of other endophytic or RS bacteria might be attributed (at least) to (i) the taxonomical position of strains (i.e. phylogenetically related bacteria strains may show similar behaviour), (ii) the ecological niche they inhabit or (iii) both of them. To discriminate between these three different scenarios, the absolute and the mean In-Inhibition (II) (which is the inhibition of the growth of a given strain due to the activity of other ones), Out-Inhibition (OI) (referring to the ability of a given strain to inhibit the growth of other ones) and the SI (which refers to the ability of a

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Fig. 2. Heat map showing the antagonistic interactions existing between endophytic and rhizospheric soil bacteria isolated from the medicinal plant E. Purpurea. Each strain (in row) was tested either as target or tester strain versus all the other ones (columns). The inhibition values reflect three different inhibition levels observed during the cross-streak experiments, that is, complete (3, red), strong (2, orange), weak (1, salmon), and absence (0, white) of inhibition. Nd (not detected) refers to results that were not obtained.

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Antagonism among E. purpurea endophytic bacteria 3147

Fig. 3. Schematic representation of inhibiting activity of bacterial strains isolated from the medicinal plant E. purpurea rhizospheric soil (RS), roots (R), and stem/leaves (S/L). Each node represents a plant compartment whereas numbers represent the inhibiting scores (the sum of the entries of each row, as displayed in Fig. 2) of the bacteria isolated from those compartments. Directed links represent inhibition patterns. Dashed links indicate the occurrence and the extent of self-inhibition.

61

S/L 77

261 1733

9

RS 342

5

3447 10

341

R

382

419

nomical affiliation of strains. Indeed, S/L bacteria exhibited a wide range of inhibition score; as an example, bacteria affiliated to Frigoribacterium, Methylobacterium and Sphingomonas exhibited an II score (1.57, 2.27 and 2.6, respectively) much higher than that of bacteria affiliated to Arthrobacter, Bacillus or Staphylococcus (0.28, 0.27, and 0.55 respectively). Hence, it appeared that also the taxonomical affiliation of bacterial strains might play an important role in this ‘antagonistic scenario’. However, it is quite possible that the two parameters (ecological niche and taxonomy) might be, in turn, (strictly) interconnected. It is remarkable that bacteria exhibiting the highest II degree belong to genera that have been disclosed only in the S/L compartment of E. purpurea (Chiellini et al., 2014). Thus, the existence of a ‘driving force’ responsible for the shaping of the

given strain to inhibit its own growth) score for each strain and for each compartment was calculated as the sum of the score of each strain belonging to the same compartment. Data obtained (Supporting Information Table S4) are resumed in Fig. 3. The analysis of this latter result suggested that the bacterial community from E. purpurea S/L compartment was much more sensitive to the antagonistic effect exerted by endophytic and rhizospheric bacteria than those isolated from the other two compartments. Thus, apparently, the ecological niche inhabited by bacterial strains might influence the different antagonistic activity observed. However, a deeper analysis (Table 1) performed on the different taxonomic units belonging to the S/L compartment revealed that the degree of bacterial sensitivity to the antagonistic activity of other strains was also related to the taxo-

Table 1. Self-inhibition, in-inhibition, out-inhibition (SI) scores for the different genera isolated from E. purpurea S/L compartment. The score is defined as the sum of the entries of each row (numeric integer values reflecting the inhibitory efficiency) from the inhibition matrix. Score Self-inhibition

In-inhibition

Out-inhibition

Genus

Taxonomy

Number of strains

Absolute number

Mean value

Absolute number

Mean value

Absolute number

Mean value

Agrococcus Arthrobacter Curtobacterium Frigoribacterium Kineococcus sp. Microbacterium Bacillus Staphylococcus Methylobacterium Rhodobacter Sphingomonas Pseudomonas

Actinobacteria Actinobacteria Actinobacteria Actinobacteria Actinobacteria Actinobacteria Firmicutes Firmicutes Alphaproteobacteria Alphaproteobacteria Alphaproteobacteria Gammaproteobacteria

1 3 2 7 1 2 1 10 4 5 7 5

1 0 1 10 2 6 1 9 7 2 18 4

1.00 0.00 0.50 1.43 0.5 3.00 1.00 0.9 2.30 0.40 2.57 1

215 115 257 1499 373 284 38 1048 1079 794 2127 498

1.57 0.27 0.95 1.57 1.36 1.07 0.27 1.07 2.27 1.27 2.55 0.74

15 455 117 366 42 191 166 683 285 308 443 414

0.11 1.09 0.42 0.38 0.30 0.68 1.21 0.49 0.52 0.44 0.45 0.62

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composition of the different bacterial communities inhabiting different compartments of the same plant cannot be a priori excluded. Finally, the 11 most active strains (those exhibiting an out-degree >1 and highlighted in Table S4) were isolated from S/L (5 strains), R (4 strains), and RS (2 strains) and were affiliated to the genera Achromobacter (1 strain), Arthrobacter (3 strains), Bacillus (1 strain), Methylobacterium (1 strain), Pseudomonas (4 strains) and Rheinheimera (1 strain). (iv) A large heterogeneity in the antagonistic/resistance/ sensitivity behaviour of different strains was detected. Some of them (those mentioned in the previous paragraph) are highly sensitive and showed a low capacity to antagonize others (Rhodobacter, Sphingomonas, Methylobacterium and Frigoribacterium from S/L compartment). Other strains, such as strain Ep RS 3 (affiliated to Rheinheimera) are sensitive, but are also extremely active versus a large percentage of S/L, R and RS strains. Moreover, other strains are resistant (see e.g. Ep RS 2, Ep RS 7, Ep RS 4, Ep RS 46 affiliated to the Pseudomonas genus). (v) Overall, inhibition patterns varied in different bacterial isolates. Just in few cases did the inhibition pattern vary between strains probably belonging to the same species (Chiellini et al., 2014). This finding suggests that antagonism might be due to different inhibitory mechanisms within the same species. To get a systemic view of the identified bacterial interactions we used a graph-based approach, representing antagonistic relationships in the form of directed networks. Results obtained are reported in Supporting Information Fig. S1. This analysis confirmed the inhibition pattern among endophytic and rhizospheric bacteria isolated from E. purpurea. Importantly, we observed the same overall trend when such antagonistic interactions were analysed in a reduced taxonomic space, that is, focusing on all the isolates belonging from the same taxonomic unit (the Pseudomonas genus). Results of this analysis are shown and commented in Fig. S2. Discussion To the best of our knowledge, this is the first report on a study regarding the antagonistic interaction between bacteria isolated from medicinal plants and involving a high number of bacterial strains. Data obtained revealed a complex scenario in which both taxonomy and the ecological niche appeared to play a role in structuring the endophytic bacterial communities. Overall, different strains belonging to different species/ genera exhibited a different antagonistic activity. The whole body of data obtained revealed that:

(i) Strains isolated from RS and R compartments exhibit a (very) low degree of SI, intra- and inter-compartment sensitivity to the bacterial antagonistic ability (also captured by in- and out- degree analysis, Supplementary Table S5). This suggests that the ‘antagonistic force/s’, if any, do not (strongly) interfere with the growth of RS and R bacteria in spite of the fact that these strains are not shared by the two compartments (Chiellini et al., 2014). (ii) On the other side, strains isolated from S/L exhibited a high degree of SI, as well as an intra- and intercompartment sensitivity to the antagonistic activity (Supplementary Table S5). Most of this sensitivity was mainly related to the taxonomical position of S/L bacteria; indeed, the most sensitive bacteria were affiliated to three Alphaproteobacteria taxa (i.e. Sphingomonas, Methylobacterium and Rhodobacter) and one Actinobacteria taxon (Frigoribacterium), which were absent (or rarely detected) among the cultivable RS and R communities. In particular, concerning Alphaproteobacteria, the root compartment was dominated by members of genera Achromobacter, Agrobacterium and Rhizobium not represented in the S/L community, indicating that differential antagonistic activities are present in genera from the same class (Alphaproteobacteria) associated with the different plant compartments. Concerning SI as previously reported (Nair and Simidu, 1987; Hentschel et al., 2001; Mangano et al., 2009) this phenomenon, acting as a controlling factor in the maintenance of species diversity, is frequent in environments inhabited by taxonomically different bacteria. It is generally related to the synthesis of bacteriocins, with polypeptide killing belonging to strongly related species, providing the producer bacteria a selective advantage, that is, partially limiting itself and coexisting with competitors (see for instance Perez-Gutierrez et al., 2013). However, it is clear that a disproportion in the SI score exists in the three bacterial communities, with bacteria belonging to S/L compartment exhibiting the highest degree of SI. Additionally, the analysis of the sub-network constructed using data obtained from the only genus shared by the three compartments (the Pseudomonas sp. strains, see Fig. S2) may suggest the absence of a taxonomic bias in the analysis of the antagonistic relationships among the isolated samples, and that other biological explanations may exist to account for the higher sensitivity of S/L strains to the antagonistic effect of the other bacteria tested in this work. Indeed, previous observation of differential antibiotic resistance patterns of strains with respect to plant organs (Mengoni et al., 2014) seem to mirror those on antagonistic patterns reported in this

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology

Antagonism among E. purpurea endophytic bacteria work. These concomitant evidences may suggest that biotic interactions, mediated through molecules controlling cell growth and viability (e.g. quorum sensing/ quenching molecules, bacteriocins, other antibacterial compounds) within the endophytic communities could contribute to the fixation of differential antagonistic patterns along the plant compartments. Overall, the depicted scenario allow us to hypothesize that antagonistic interactions between bacterial strains belonging to the same or to different taxa might play a (key) role in driving the structuring of microbial communities interacting with eukaryotic macro-organisms. These data suggest that the endophytic bacterial communities inhabiting different plant compartments (R and S/L) cannot intermix with each other, at least at the strain level. This hypothesis could be tested in the future by performing synthetic community studies (De Roy et al., 2014), also with the use of fluorescently tagged strains which allow the localization of bacteria in plant tissues, helping to understand the dynamics of bacterial community differentiation in plant organs. Indeed, the antagonistic patterns reported may be an oversimplification of the in vivo behaviour of strains, since plate growth conditions on a rich medium are very distant from the growth, or living, conditions inside plant tissues. Inhibitory patterns may be the consequence of the medium used and could not be present, or limited, in vivo, as well as strains which do not show competition in plate, may be strong competitors (and antagonists) in different environmental conditions, as those experiences in the plant apoplast. However, we are aware that the scenario described in this work might be an (over)simplification of the actual situation and we cannot a priori exclude that the diversity of microbial communities inhabiting different compartments might be related to the synthesis of a mix of antimicrobial compounds produced by the plant itself and/or some of the bacterial strains living in the plant. Moreover, the biological rationale of such differences in antagonistic activities from strains of different plant compartments remains elusive. Nonetheless, the large-scale analysis of the antagonistic interactions reported herein has served as a solid platform for the generation of testable hypothesis on the actual in vivo relationships among endophytic bacterial communities.

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All bacterial strains were grown on Tryptic Soy Agar (TSA, Oxoid SpA. Strada Rivoltana, 20090 Rodano, MI, Italy) plates at 30°C.

Screening for antagonistic interactions among bacterial isolates Bacteria isolated from each compartment were screened for antagonistic interactions by the cross-streak method (Arasu et al., 2013; Thirumurugan and Vijayakumar, 2015) using a 140 × 140 (19 600) array of tests, to check both the ‘intraniche’ and ‘cross-niche inhibition’. This means that each strain was tested against one another. Hereafter, bacterial isolates tested for inhibitory activity will be termed ‘tester’ strains, whereas those used as targets will be called ‘target’ strains. Briefly, tester strains were streaked across one-half of a TSA plate and incubated at 30°C until satisfactory growth was obtained (usually 48 h). Target strains were then streaked perpendicular to the initial streak and plates were further incubated at 30°C. The antagonistic effect was indicated by failure of the target strain to grow in the confluence area. Each interaction was tested twice. The presence of interaction was rated subjectively on each observation as follows: complete (3), strong (2), weak (1) and absence (0) of inhibition. Nd (not detected) refers to results that were not obtained. All the observations were performed by at least two observers using a blind experimental setup, in the sense that each evaluator recorded the degree of growth inhibition without having any previous knowledge of the target and tester strains. Therefore, results might be considered semi-quantitative. In Fig. S3, a schematic representation of the cross-streak experiment (Fig. S3A) and some images of the results obtained (Fig. S3B) are reported. Bacterial isolates were then operationally distinguished into three different groups of competitiveness, termed (1) active, if they were active competitors (able to inhibit growth of at least one bacterial target); (2) sensitive, if they showed low competitiveness (their growth was inhibited by at least one isolate used as a tester); and (3) resistant to competition (their growth was never inhibited by tester strains). It must be noted that an individual strain could be included in one or two interactivity clusters. Thus, in ‘Results and discussion’ section the ‘active cluster’ includes strains which proved to be active plus sensitive as well as active plus resistant; (2) the ‘sensitive cluster’ includes strains shown to be exclusively sensitive, as well as sensitive plus active; and (3) the ‘resistant cluster’ includes strains shown to be resistant plus active, as well as exclusively resistant.

Heatmap Inhibition matrix

Experimental procedures Bacterial strains and growth conditions The 140 endophytic bacterial strains (48 from S/L, 49 from R and 43 from RS) used in this work are listed in Table S1, and they have been previously phylogenetically assigned through 16SrRNA gene sequence and analysis (Chiellini et al., 2014).

The results from the cross-streak inhibition assay were organized in the form of an inhibition matrix. In this matrix each row stands for a target strain, while each column stands or a tester strain. The inhibition values reflect three different inhibition levels observed during the cross-streak experiments, that is, complete (3), strong (2), weak (1) and absence (0) of inhibition.

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Heatmap The inhibition matrix was graphically represented as a heatmap with a colour key code indicating the different inhibition levels.

Inhibition score To measure the inhibitory power of a tester strain an inhibition score was computed for each strain as the sum of the entries of each row (numeric integer values reflecting the inhibitory efficiency) from the inhibition matrix. This score can also be computed for the target strain, to measure its sensitivity, by summing up those entries of the correspondent column.

Inhibitory networks analysis and visualization The inhibition matrix obtained with the cross-streak matrix was converted into a directed graph in which the nodes represent isolates and the (directed) links connecting them the occurrence of growth inhibition of one strain with respect to the others. This graph was then analysed by computing for each node, its degree, in-degree and out-degree. The degree of a node accounts for the number of links possessed by that node whereas out-degree and in-degree represent the number of tail endpoints and the number of tail endpoints departing from that node respectively. Graph topology and statistical tests (Wilcoxon rank sum test) were performed with the igraph library of the R statistical package (http://www.r-project.org/) and in-house developed Perl scripts. Network visualization and post-processing was done using Gephi software (Bastian et al., 2009; Kohl et al., 2011).

Acknowledgements This work was supported by Ente Cassa di Risparmio di Firenze (project 2013.0657). We are very grateful to two anonymous referees for their helpful suggestions in improving the paper.

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Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Fig. S1. Inhibition network of S/L, R and RS strains and taxonomy of isolated strains. In this graph each node represents an isolate and the directed links the presence of an antagonistic relationship. Nodes are coloured according to the genus of the corresponding isolate. In (A) nodes size is proportional to their degree (the larger the node, the higher the degree) whereas in (B) and (C) to their in- and outdegree values respectively. The degree of a node accounts for the number of links possessed by that node. Out-degree and in-degree of a node represent the number of tail endpoints and the number of tail endpoints departing from that node respectively. Each node is coloured according to the genus of the corresponding strain and the percentage of each genus over the total number of strains is reported in the legend. Fig. S2. Inhibition network of Pseudomonas strains. In this graph each node represents a Pseudomonas isolate and the directed links the presence of an antagonistic relationship among all the tested strains. In this graph nodes size is proportional to their degree values. Fig. S3. Schematic representation of the cross-streak experiment (Fig. S3A) and some images of the results obtained for the strains Ep RS 3 and Ep RS 1 and in one control plate (Fig. S3B). Table S1. List of bacterial strains used in this work. Table S2. Number of bacterial strains isolated from different compartments of the medicinal plant Echinacea purpurea and belonging to the ‘sensitive’, ‘resistant’ or ‘active’ group. Table S3. Number of self-inhibiting bacterial strains isolated from different compartments of the medicinal plant Echinacea purpurea. Table S4. Intra-compartment inhibition, between compartment inhibition, self-inhibition score calculated for the 140 bacterial strains analysed in this work. Table S5. Average degree, in- and out-degree values for stem/leaves (S/L), root (R) and rhizospheric soil (RS) samples. The degree of a node accounts for the number of links possessed by that node. Out-degree and in-degree of a node represent the number of tail endpoints and the number of tail endpoints departing from that node respectively. Standard deviation is reported in parentheses.

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