soil type-dependent effects of a potential biocontrol inoculant on indigenous bacterial communities...

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This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differ-ences between this version and the Version of Record. Please cite this article as doi: 10.1111/1574-6941.12430

This article is protected by copyright. All rights reserved.

Received Date : 09-Jul-2014

Revised Date : 09-Sep-2014

Accepted Date : 14-Sep-2014

Article type : Research Paper

Editor : Wietse de Boer

Corresponding author mail id: [email protected]

Soil type dependent effects of a potential biocontrol inoculant on indigenous

bacterial communities in the rhizosphere of field-grown lettuce

Susanne Schreiter1, 2, Guo-Chun Ding1,4, Rita Grosch2 , Siegfried Kropf3, Kai Antweiler3 and Kornelia Smalla1

1 Julius Kühn-Institut, Federal Research Centre for Cultivated Plants, Institute for Epidemiology and Pathogen Diagnostics, Messeweg 11-12, 38104 Braunschweig, Germany

2Leibniz Institute of Vegetable and Ornamental Crops Großbeeren/Erfurt e.V., Department Plant Health, Theodor-Echtermeyer-Weg 1, 14979 Großbeeren, Germany

3Department for Biometrics und Medical Informatics, Otto von Guericke University, Leipziger Straße 44, 39120 Magdeburg, Germany

4College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China

Email of corresponding author: [email protected]

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Key words: Pseudomonas jessenii RU47, biocontrol, total community DNA, 16S rRNA gene, DGGE, pyrosequencing

Running title: Effect of inoculant RU47 on rhizosphere bacterial community

Abstract

Bacterial biocontrol strains used as an alternative to chemical fungicides may influence bacterial

communities in the rhizosphere and effects might differ depending on the soil type. Here we present

baseline data on the effects of Pseudomonas jessenii RU47 on the bacterial community composition

in the rhizosphere of lettuce grown in diluvial sand, alluvial loam and loess loam at the same field

site. 16S rRNA gene fragments amplified from total community DNA were analyzed by denaturing

gradient gel electrophoresis (DGGE) and pyrosequencing. DGGE fingerprints revealed that in three

consecutive years (2010-2012) effects of RU47 had a slight but statistically significant effect on the

bacterial community composition in one (2010), two (2011), or all the three soils (2012). However,

these effects were much less pronounced compared to the influence of soil types. Additional

pyrosequence analysis of 2011 samples showed that significant changes in bacterial community

compositions in response to RU47 inoculation occurred only in alluvial loam. Different taxonomic

groups responded to the RU47 application depending on the soil type. Most remarkable was the

increased relative abundance of OTUs belonging to the genera Bacillus and Paenibacillus in alluvial

loam. Pyrosequencing allowed identifying side-effects of the application of bacterial inoculants into

the rhizosphere.

Introduction

The soilborne pathogen Rhizoctonia solani AG1-IB can be responsible for losses of up to 70% in field

production of lettuce (Davis et al., 1997). The pathogen is difficult to control because of the long

persistence of its sclerotia in soil (Ogoshi, 1996). Efficient control strategies against R. solani such as

the soil fumigant methyl bromide were phased out because of their stratospheric ozone depletion

potential and ability to contaminate groundwater (Guns, 1989). Additionally, it has a high acute tox-

icity risk for humans due to its carcinogenicity and neurotoxicity (Barry et al., 2012; Bulathsinghala &

Shaw, 2014). The use of bacterial strains represents an environmentally friendly control strategy to

chemical substances (Martin, 2003). In combination with other management activities such as crop

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rotation, biofumigation and removal of infected plant tissue, use of biocontrol inoculants could be an

important component of integrated pest management (Barriere et al., 2014).

In the last years a number of potential strains for biocontrol of R. solani AG1-IB were isolated

and characterized under greenhouse and field conditions (Adesina et al., 2009; Chowdhury et al.,

2013). Furthermore, the ability of inoculants to colonize the rhizosphere which is termed

rhizocompetence was in the focus of several studies (Götz et al., 2006; Adesina et al., 2009;

Lugtenberg & Kamilova, 2009; Chowdhury et al., 2013; Xue et al., 2013). In view of the legislation of

biocontrol strains information on potential effects on the soil microbiota should be considered as soil

microbes are increasingly recognized as important drivers for soil functions related to plant health

and growth (Berendsen et al., 2012; Pieterse et al., 2014). With the advent of 16S rRNA gene-based

molecular fingerprints detailed monitoring of rhizosphere microbial community changes in response

to inoculation became feasible (Adesina et al., 2009; Grosch et al., 2012). In order to assess the ex-

tent of shifts caused in response to inoculants in comparison to other factors shaping soil and

rhizosphere bacterial diversity such as the soil type, plant species, cultivar or plant growth stage,

appropriate experimental design is needed (Berg & Smalla, 2009). Furthermore, effects of inoculants

on the microbial community in the rhizosphere might differ depending on the soil type. However,

these effects are difficult to assess under field conditions as many parameters such as cropping histo-

ry, agricultural management practice and weather conditions influence the soil bacterial community

composition (Costa et al., 2006; Berg et al., 2014). For the first time, a unique experimental plot sys-

tem with three soil types under identical agricultural management practice and weather conditions

for more than 10 years (Rühlmann & Ruppel, 2005) opened the opportunity to study the effect of soil

type on the bacterial community composition in the bulk soil and in the rhizosphere under field con-

ditions.

Using different analyses of 16S rRNA genes it could be shown that soil types do not only

strongly shape the bacterial community composition in the rhizosphere, but do also influence the

extent of the so-called rhizosphere effect. In response to the growing lettuce plant several genera of

bacteria significantly increased in the rhizosphere in comparison to the corresponding bulk soil

(Schreiter et al., 2014a). Remarkably, the effects of the soil types on the rhizocompetence and

biocontrol activity of two potential biocontrol inoculants of R. solani AG1-IB, namely Pseudomonas

jessenii RU47 and Serratia plymuthica 3Re4-18, were negligible (Schreiter et al., 2014b). The root

exudate composition of lettuce, which was recently shown by Neumann et al. (2014) to only quanti-

tatively differ between the soil types, seemed to support the successful establishment of the inocu-

lant strains. DGGE analysis of 16S rRNA gene fragments amplified from total community DNA (TC-

DNA) of lettuce rhizosphere samples taken two weeks after planting (2WAP) in 2011 indicated a

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weak effect of the inoculant strains on the bacterial community composition. The effect of inoculants

on the bacterial community composition was clearly stronger for 3Re4-18 than for RU47 (Schreiter et

al., 2014b). Based on the less pronounced effect of RU47 and the reliable soil type-independent

rhizocompetence and biocontrol activity it was decided to focus the present study specifically on the

effects of RU47.

In the present study, the effect of the inoculant RU47 on the rhizosphere bacterial community of

field-grown lettuce was monitored for three consecutive years. We hypothesized that the effects of

the inoculant on the bacterial community composition in the rhizosphere of lettuce will be influ-

enced by the soil type. Lettuce rhizosphere pellets were obtained after destructive sampling and

recovery from a subset of the complete root system of three plants. DGGE analysis of 16S rRNA gene

fragments was performed for all samples and subjected to statistical analysis. Pyrosequence analysis

was done for the rhizosphere samples collected in 2011, the year when the best biocontrol effects by

RU47 were observed.

Material and methods

Design of field experiments

Lettuce (Lactuca sativa L.) was selected as a model plant to evaluate the effect of repeated applica-

tions of the inoculant P. jessenii RU47 on the bacterial community compositions in the rhizosphere in

three soil types - diluvial sand (DS), alluvial loam (AL), and loess loam (LL). The three soils were stored

at the same field site in a unique experimental plot system each arranged in separate blocks at the

Leibniz Institute of Vegetable and Ornamental Crops (IGZ, Großbeeren, Germany, 52° 33’ N, 13° 22’

E) in three consecutive years. Each block consists of 24 plots sized 2 m x 2 m with a depth of 75 cm

(Rühlmann & Ruppel, 2005). All three soil types were characterized by the same crop history. Follow-

ing crops were cultivated on each block in the experimental plot system in the seasons before the

start of the experiment: pumpkin, nasturtium, pumpkin, amaranth, wheat, wheat, pumpkin, nastur-

tium, wheat, wheat, and lettuce. Four plots with lettuce with and without RU47 inoculation were

established in each soil type from 2010 to 2012.

Lettuce seeds (cv. Tizian, Syngenta, Bad Salzuflen, Germany) were sown in seedling trays

filled with the respective soil type and incubated at 12°C for 48 h and transferred to the greenhouse

to grow at about 20/15°C (day/night). All seedling trays were watered daily and fertilized weekly

(0.2% Wuxal TOP N, Wilhelm Haug GmbH & Co. KG, Düsseldorf, Germany) to maintain the substrate

moisture. Lettuce seedlings were planted at the 3-4 leaf stage (four weeks after sowing) in six rows

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per plot (36 plants per plot) with a within-row and intra-row distance of 30 cm each. Fertilizer

(Kalkamon, 27% N, TDGmbh Lommatzsch, Germany) was added to each plot, based on a chemical

analysis of the soil type before planting. The same nitrogen amount of 157 kg ha-1 was adjusted to

each soil type. Lettuce was overhead irrigated based on the computer program ‘BEREST’ (Gutezeit et

al., 1993) as described in detail by Schreiter et al. (2014a). Each treatment included four replicates or

plots.

Bacterization of lettuce

The rifampicin-resistant inoculant P. jessenii RU47 was retrieved from the Julius Kühn-Institut strain

collection by restreaking the stock culture stored in Luria-Bertani broth (ROTH, Karlsruhe, Germany)

with 20% glycerol at -80°C on King agar B plates (Merck KGaA, Darmstadt, Germany) supplemented

with rifampicin (75 µg mL-1). For lettuce seed treatment a single colony of RU47 was resuspended

and spread on King agar B plates (Merck KGaA, Darmstadt, Germany) supplemented with rifampicin

(75 µg mL-1). Cells were scraped off from the bacterial lawn on the King B agar and were suspended

in 15 mL sterile 0.3 % NaCl solution with a density corresponding to 108 CFU mL-1 adjusted in a spec-

trometer. A total of approximately 100 lettuce seeds were coated with 250 µL of the bacterial sus-

pension or 0.3 % NaCl (control seeds).

For young lettuce plant inoculation, RU47 was grown in nutrient broth (NB II, SIFIN GmbH,

Berlin, Germany) supplemented with rifampicin (75 µg mL-1) on a rotary shaker (90 rpm) for 16 h at

29°C. The cells were harvested by centrifugation at 13,000 g for 5 min and resuspended in sterile 0.3

% NaCl solution and adjusted to a density corresponding to 107 CFU mL-1 or 108 CFU mL-1. Each let-

tuce seedling was treated with 20 mL bacterial solutions of RU47 (107 CFU mL-1) one week before

transplanting into the field and with 30 mL (108 CFU mL-1) at the 4-leaf stage two days after planting.

The seedlings of the control were drenched with 0.3% NaCl, respectively.

Sampling and DNA extraction

Lettuce rhizosphere samples were collected three weeks after planting (3WAP) in 2010 and two

weeks after planting (2WAP) in 2011 and 2012. The complete root system of three lettuce plants

with adhering soil was combined per replicate (four replicates per treatment and soil type). While

loosely adhering soil was removed from the root by vigorous shaking, in the following years an addi-

tional root wash was applied by dipping the lettuce roots of each replicate briefly into 300 mL sterile

tap water, followed by a three times repeated Stomacher blending step. To obtain the microbial

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rhizosphere/rhizoplane pellets, roots were treated as described by Schreiter et al. (2014b). TC-DNA

was extracted from the pellets obtained with the FastDNA® SPIN Kit for Soil (MP Biomedicals, Heidel-

berg, Germany) according to the manufacturer’s protocol after a harsh lysis step with the FastPrep®-

24 Instrument (MP Biomedicals, Heidelberg, Germany). The TC-DNA was purified with GENECLEAN

SPIN® Kit (MP Biomedicals, Heidelberg, Germany) and afterwards diluted 1:10 with 10 mM Tris HCl.

Analysis of 16S rRNA gene fragments by DGGE

PCR reactions with TC-DNA of rhizosphere samples were performed for amplification of 16S rRNA

gene fragments using the bacterial primers F984-GC and R1378 as described by Heuer et al. (1997)

but using GoTaq® Flexi (Promega, Mannheim, Germany) in 2012, instead of the Taq DNA polymerase

(Stoffel fragment, ABI, Darmstadt, Germany) used in 2010 and 2011. To analyze the Actinobacteria,

Alpha- and Betaproteobacteria in the 2010 and 2012 samples, a nested primer approach was used.

The PCR product of the first PCR was used as a template for the bacterial primers F984 and R1378. All

primers from this study are summarized in Table S1.The PCR products were analyzed by DGGE as

described by Weinert et al. (2009), and silver staining was performed according to Heuer et al.

(2001).

Bacterial DGGE fingerprints were evaluated with GELCOMPAR II version 6.5 (Applied Maths,

Sint-Martens-Latem, Belgium). The normalization and background subtraction was performed on the

basis of each DGGE gel image (Schreiter et al., 2014a). The Pearson correlation coefficient as a curve-

based method was chosen to obtain the similarity matrices. These were used for construction of a

dendrogram by an unweighted pair group method with arithmetic mean (UPGMA) as well as for sta-

tistical analysis by Permutation test, where the d-value was calculated as average overall correlation

coefficients within the groups minus the average overall correlation coefficients between samples

from different groups (Kropf et al., 2004) and displayed in %.

Analysis of 16S rRNA gene fragments by pyrosequencing

TC-DNA from rhizosphere samples (RU47 treatments and controls) obtained 2WAP from the

field experiment 2011 were sent to Biotechnology Innovation Center (BIOCANT,

Cantanhede, Portugal) for barcoded pyrosequencing. PCR reactions were performed in 40 µL

volumes with Advantage Taq (Clontech) using 0.2 M of both primers 338F and 802R (Table

S1), 0.2 mM dNTPs, 1X polymerase mix and 6% DMSO. The PCR conditions were 94ºC for

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4 min, followed by 25 cycles of 94ºC for 30 s, 44ºC for 45 s and 68ºC for 60 s and a final

elongation step at 68ºC for 10 min. The amplicons were quantified by fluorimetry with

PicoGreen (Invitrogen, Carlsbad, CA, USA), pooled at equimolar concentrations and se-

quenced in the A direction with GS 454 FLX Titanium chemistry, according to the manufac-

turer’s instructions (Roche, 454 Life Sciences, Branford, CT, USA). The raw pyrosequence

reads (fasta files) were processed using an automatic pipeline implemented at BIOCANT. In a

first step, sequencing reads were assigned to the appropriate samples based on the respective

barcode. Then, reads were quality filtered to minimize the effects of random sequencing er-

rors, by elimination of sequence reads with <100 bp and sequences that contained more than

two undetermined nucleotides. Sequences were additionally cut for the reverse primer if pre-

sent.

The prefiltered pyrosequence data provided by BIOCANT was analyzed according to

Ding et al. (2012a). Briefly, only those sequences matching the barcode and forward primer

were selected for BLASTN analysis against a SILVA 16S rRNA gene database to truncate the

unpaired regions for each sequence, only sequences with a length of more than 200 bp were

included. Operational taxonomic unit (OTU) was generated with the following steps: Se-

quences were assigned to OTUs (defined at > 97% sequences identity) with the program

Mothur 1.21 Software (Schloss et al., 2009). Naïve Bayesian Classifier (Wang et al., 2007)

was used to classify the sequences. The OTU assignment and the classification of each se-

quence were loaded into a MySQL-data base for producing the taxonomic OTU report.

Based on the OTU report a modified principal component test, termed PCUniRot was

performed (Ding et al., 2012b). This test supports multifactorial comparisons with high-

dimensional data including the investigation of interactions of the factors after an upstream

log transformation of the relative abundance in ‰ of the OTUs after adding “1”. It is exact

under the assumption of multivariate normal data. In a further development of the basic prin-

cipal component test (Läuter et al., 1996, 1998), the modification was directed towards a

more effective use of the principal components in case of small samples and a very large

number of variables included.

For the detection of OTUs determining the overall shift corresponding to the applica-

tion of RU47, a multiple permutation test as suggested by Westfall and Young (1989) with the

described pre-transformation of the data was used. With this test an overall family-wise error

rate based on all detected OTUs of 5% was not possible. For this reason OTUs occurring in a

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minimum of 10 samples were selected for further analysis. With this pre-filtering a total of the

most abundant 901 OTUs were used for significance test.

Furthermore, the ‘R (version 2.14) add-on package vegan’ was used to analyze the

community composition and calculate the rarefaction curves as well as the Pielous’s evenness

indices. A Tukey’s honestly significant difference test, abbreviated Tukey test, under a gener-

alized linear model via a logistic function for binomial data with the package multcomp

(Hothorn et al., 2008) was performed to identify the discriminative taxa between inoculation

with RU47 and control in the different soil types without a logarithmic pre-transformation of

the data. Bonferroni adjustment was used for the p value < 0.05. To visualize the microbial

community composition between the treatments a non-metric multidimensional scaling

(NMDS) was used based on the Bray-Curtis similarity. Pyrosequence data were deposited at

the NCBI Sequence Read Archive under the study accession number SRP029944.

Results

DGGE revealed minor effects of RU47 on the bacterial community in the rhizosphere of lettuce

To assess the effects of the inoculant RU47 on the bacterial community composition in the

rhizosphere of lettuce grown in three soil types in three consecutive years, comparative DGGE analy-

sis was done. Fingerprints generated from 16S rRNA gene fragments amplified from TC-DNA of the

four replicate samples per treatment showed little within- and between- treatment variability in each

of the three soil types. No separate clusters between inoculated and control treatments were found

(Fig. S1-S17) while a separate clustering depending on the soil types was observed for all three years.

The calculated d-values (Table 1) indicated low differences between the bacterial community com-

positions in the rhizosphere of RU47 inoculated lettuce plants and the corresponding control plants.

However, for DS soils these differences were significant in all three years. In 2011 the differences

were also significant for lettuce grown in LL soil while in 2012 significant differences between inocu-

lated and control treatments were observed for all three soil types. The differences of the bacterial

communities in the rhizosphere of inoculated and control treatments increased in all three soils with

the highest d-values (except for LL) observed in 2012.

Compared to the differences caused by the application of RU47, the differences in bacterial commu-

nity compositions caused by soil types were much higher (Table 1). In 2010 the differences between

the soil types were rather similar with d-values ranging from 18.8% to 19.9%, the differences of DS

versus LL and AL versus LL soil strongly increased over time. The highest differences were recorded

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between DS and LL soil with 46.6% in 2012 (Table 1). In comparison, the bacterial community com-

positions in the rhizosphere of lettuce grown in DS and AL soil became more similar with time as the

lowest d-value was observed in 2012.

In order to reveal soil type-dependent effects of the RU47 treatment on less abundant popu-

lations in the rhizosphere of lettuce, DGGE fingerprints were generated for Actinobacteria, Alpha-

and Betaproteobacteria from samples taken in 2010 and 2012 (Table 1). UPGMA analysis revealed

soil type dependent clusters for the taxonomic groups analyzed in both years (Fig. S8, S10, S16, S18).

Interestingly, Betaproteobacteria rhizosphere fingerprints of lettuce grown in DS and AL soils in 2012

showed higher similarities with each other compared to the 2010 LL rhizosphere fingerprints (Fig.

S14). In contrast to this, the Actinobacteria fingerprints of AL and LL rhizospheres were more similar

to each other compared to those of DS rhizospheres in 2010 and 2012 (Fig. S16, S18). The effect of

RU47 on the Betaproteobacteria community composition was only significant for lettuce grown in AL

in 2010, and in all soils the d-values decreased in 2012 compared to 2010 (Table 1), whereas the

RU47 inoculation significantly influenced the Actinobacteria community composition in the

rhizosphere of lettuce grown in DS soil in both years. In the two loamy soils rather strong effects of

RU47 on the Actinobacteria community composition were only detected in 2012. Overall, increased

d-values indicating higher differences between the Actinobacteria communities in the rhizosphere of

inoculated and control treatments were recorded for all three soils in 2012.

The analysis of the Actinobacteria, Alpha- and Betaproteobacteria confirmed that the effect of RU47

was minor compared to the effects of different soil types. However, it became also clear that both

the RU47 and the soil type effects differed for the three taxonomic groups analyzed.

Pyrosequence data

To determine the taxonomic composition of bacterial communities in the rhizosphere of lettuce and

to identify major responders to RU47 inoculation, pyrosequencing of 16S rRNA gene fragments am-

plified from a total of 24 rhizosphere samples of control and RU47 treatments from the three soil

types in 2011 was done (four replicates per soil type and treatment). Altogether 103,178 sequences

with a sequence length of more than 200 bp were used for the analysis. The sequences were classi-

fied into 18 phyla, 52 classes, 83 orders, 175 families and 394 genera, and clustered to 7,076 OTUs

based on 97% sequence identity. In both treatments and all three soil types the dominant phyla (with

more than 1% relative abundance) were the Proteobacteria, followed by Actinobacteria, Bacteroide-

tes, Firmicutes and Acidobacteria (Table 2). The classes within the Proteobacteria with the highest

relative abundance were the Betaproteobacteria followed by Alphaproteobacteria. Compared with

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these classes the Gamma- and Deltaproteobacteria were less abundant (Table 2). Based on the

Tukey test, statistically significant differences between RU47-treated and control samples at the phy-

lum level were observed only for the Firmicutes in AL rhizosphere. A 1.5-fold increase in the relative

abundance of the phylum Firmicutes was observed in response to the RU47 inoculation. All other

phyla and classes analyzed did not change significantly in their relative abundance in response to the

RU47 inoculation.

Rarefaction analysis was performed to compare bacterial richness between soil types and

treatments. The result revealed that bacterial richness varied between soil type, with the lowest for

DS soil and the highest for LL soil. However, the influence of RU47 on richness was not pronounced

for the three soil types (Fig. 1A). Interestingly, the evenness indicated by Pielous’s indices was lower

in the control compared to the RU47 treatment for the two loamy soils (Fig. 1B). But in the DS soil

the evenness was comparable between control samples and RU47 treated samples.

The comparison of the bacterial community compositions performed by NMDS using Bray-

Curtis similarities confirmed that the RU47 and control treatments did not cluster separately while

soil type-dependent clusters were observed (Fig. 2). Using the PCUniRot for each soil type separately,

a statistically significant effect of the RU47 inoculation on the bacterial community composition was

observed in AL rhizosphere only. Two OTUs responsible for this effect could be identified as OTU 402

and OTU 5304. These OTUs shared 98% sequence identity to P. brassicacearum (EU391388) and P.

reinekei (AM293556), respectively. Both of these OTUs also had a high sequence identity of 98-99%

to the applied biocontrol strain RU47.

Several genera with significant changes in relative abundance in response to the RU47 inocu-

lation were identified by means of the Tukey test. Most importantly, the genera with significantly

changed relative abundance in the lettuce rhizosphere differed among the soil types. The most

abundant responders to RU47 inoculation in AL rhizosphere were the genera Paenibacillus and Bacil-

lus (Table 3). In addition, the less abundant genus Bdellovibrio showed a twofold increase in relative

abundance in response to RU47 (Table 3). In DS rhizosphere, the genera Methylophilus, Fluviicola

and Cytophaga significantly increased in relative abundance (two- and threefold; Table 3). In con-

trast, the genera Nocardioides and Marmoricola, belonging to the Actinobacteria, decreased in rela-

tive abundance in DS rhizosphere of RU47 treatments compared to the control (Table 3). In the LL

soil only the genus Bdellovibrio displayed a significantly changed relative abundance in response to

the RU47 treatment. Similar to AL rhizosphere the relative abundance of Bdellovibrio doubled in LL

rhizosphere in response to RU47 inoculation (Table 3).

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The 50 most abundant OTUs were selected and their relative abundance was visualized in a

heatmap (Fig. 3). Among the 50 dominant OTUs, 42 were affiliated to the Proteobacteria, with the

majority being Betaproteobacteria (22) followed by Alpha- (10) and Gammaproteobacteria (10). The

remaining dominant OTUs belonged to the Firmicutes (4), Actinobacteria (3) and Bacteroidetes (1).

With a few exceptions most dominant OTUs displayed rather high sequence identities (97-100%) to

16S rRNA gene sequences of isolates. Significant changes in the relative abundance of dominant

OTUs in response to the application of RU47 were only observed for six OTUs. Among them were

three OTUs with high sequence identity to P. reinekei and P. brassicacearum which were detected in

the lettuce rhizosphere in all three soils. The fourth OTU identified as P. graminis did not show this

treatment-dependent response. In contrast to the few OTUs responding to the RU47 inoculation,

approximately two-third of the dominant OTUs showed a clear soil type-dependent relative abun-

dance (Fig. 3).

Discussion

Various factors such as soil type, plant species, cultivars, plant growth stage, cropping history, or

agricultural management systems were recently reported to shape the bacterial community compo-

sition in the rhizosphere of crops as reviewed by (Berg & Smalla, 2009; Buee et al., 2009; Berg et al.,

2014). In the present study, we tested the hypothesis that the effects of potential biocontrol inocu-

lants such as P. jessenii RU47 on the bacterial community composition depend on the soil type as the

inoculant is not only exposed to different soil physico-chemical conditions but also interacts with

different indigenous bacterial communities. Effects of the inoculant RU47 on the bacterial communi-

ties in the rhizosphere were assessed for lettuce grown under field conditions in three different soils

in three consecutive years and sampled at the same plant growth stage. The experimental design and

the methods used did not only allow the evaluation of shifts in the bacterial community composition

in response to the inoculant application in each year, but also made it possible to reveal differences

due to the repeated growth of lettuce with and without RU47 application and to identify responders

to the inoculant. Bacterial DGGE fingerprints revealed that the differences in the bacterial communi-

ty composition between RU47 and control treatments were minor in all three soil types compared to

the differences observed between soil types (Table 1). This observation was made for all soil types in

the three consecutive years. DGGE fingerprints indicated that the differences in the bacterial and

actinobacterial communities between RU47 and control treatments increased in 2012 in all three

soils, indicating that the repeated application of RU47 in successive lettuce cropping at the same field

site likely increased the effect of the inoculants (Table 1). However, we cannot exclude that other

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factors such as the weather conditions, might have also contributed to these increased differences.

Interestingly, the d-values for Alpha- and Betaproteobacteria were lower in 2012 compared to 2010,

indicating a weaker effect of RU47 after repeated lettuce growth which might well be caused by the

strong enrichment of Beta- and Alphaproteobacteria in the rhizosphere in consecutive lettuce culti-

vations (Schreiter et al., 2014a). The enrichment of Beta- and Alphaproteobacteria in the rhizosphere

in previous lettuce crops may increase the relative abundance of these populations in bulk soil.

Pyrosequence analyses were done for the samples from 2011 when the best biocontrol ef-

fects by RU47 were observed and the data largely confirmed the findings obtained by DGGE. Both

methods showed distinct soil type-dependent bacterial community composition in the rhizosphere of

lettuce, whereas only minor differences between the bacterial community compositions of the RU47-

inoculated and control treatments were detected. However, statistical analysis of pyrosequence data

showed a significant effect of RU47 in the AL rhizosphere, whereas the d-value of the same sample

set was not significantly different for DGGE fingerprints albeit that the highest d-values were ob-

served for the AL rhizosphere.

The soil type-depending responders to RU47 inoculation identified through pyrosequence

analysis further confirmed our hypothesis that the effects of RU47 were soil type specific. Although

the effects of RU47 on the bacterial community composition detected by DGGE fingerprints and

pyrosequence data were much less pronounced compared to the effects caused by the soil types,

changes in relative abundance of a few genera in response to RU47 application were observed by

statistical analysis of pyrosequence data. The significant increase of the already abundant genera

Bacillus and Paenibacillus in AL rhizosphere was particularly interesting as isolates belonging to these

genera often confer traits that are beneficial for plant growth and health (Beneduzi et al., 2008a;

Beneduzi et al., 2008b). Strains belonging to these genera are already used as biocontrol agents, for

example Bacillus subtilis CA32 and B. amyloliquefaciens FZB42.

In comparison with the composition of dominant OTUs in the rhizosphere of lettuce grown in

the same soils in 2010 (Schreiter et al., 2014a), a higher proportion of the 50 dominant OTUs from

the present study (2011) belonged to the Betaproteobacteria (Acidovorax, Burkholderia, Massilia,

Methylophilus, Duganella, Naxibacter, Piscinibacter, Rubrivivax, Variovorax) and

Gammaproteobacteria (Acinetobacter, Pantoea, Alkanindiges Pseudomonas). This finding might be

caused by the modification in the protocol for the extraction of the microbial pellet from the roots.

The additional root wash step was introduced to avoid large clumps of soil adhering to the root,

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which was in particular observed for the soils with high clay content (AL, LL). Thus the rhizosphere

community composition of samples from 2011 and 2012 mainly represent the fraction of soil very

tightly adhering to the root and the rhizoplane. Our data indicate that in particular

Betaproteobacteria were more closely attached to the roots of lettuce. In comparison,

Alphaproteobacteria dominated in the rhizosphere of lettuce samples from 2010 which were ana-

lyzed without root wash suggesting that they were less firmly adhering to the roots (Schreiter et al.,

2014a). Although the relative abundance of six dominant OTUs changed significantly in response to

RU47 only three OTUs strongly increased under the RU47 treatment. They displayed 98% sequence

identity to P. brassicacearum and P. reinekei. We cannot exclude that the inoculant RU47 contributed

to the OTU 5304, OTU 1077 and OTU 402 that showed sequence identity of >98%, respectively, to

the 16S rRNA gene sequence of RU47. Thus the heatmap data should not be overinterpreted as par-

tial 16S rRNA gene sequence clearly does not allow a reliable taxonomic identification at the species

level. Screening the abundance of functional genes involved in antibiotic production (prnD, PCA,

DAPG) in 2010 did not indicate any differences between controls and RU47 treatments (data not

shown).

The present study is unique, being the first to report on the assessment of the effects of a potential

biocontrol inoculant on the bacterial community composition in the rhizosphere in three soil types at

the same field site in three consecutive years. Despite a good and soil type-independent

rhizocompetence of RU47 in the different experiments (Schreiter et al., 2014b) the effects on the

bacterial community composition in the rhizosphere were minor compared to the soil type, and likely

transient. Effects of the inoculant treatment might be comparable to the effects of genetically modi-

fied potato plants which were within the range of natural cultivar variability as reported by Weinert

et al. (2009). Previous studies with other inoculants (Scherwinski et al., 2008; Grosch et al., 2012;

Chowdhury et al., 2013), all of them based on fingerprinting of 16S rRNA gene fragments (single-

strand conformation polymorphism (SSCP), DGGE and terminal restriction fragment length polymor-

phism (T-RFLP)), also reported only minor effects of inoculants. In the study by Erlacher et al. (2014),

the gammaproteobacterial microbiome in the rhizosphere and phyllosphere of lettuce grown under

greenhouse conditions was analyzed by pyrosequencing. These authors found the highest impact on

the indigenous gammaproteobacterial communities due to the R. solani AG1-IB attack which was

reduced in the treatments with the biocontrol strain B. amyloliquefaciens. A direct metagenome

sequencing approach was recently used to assess the effects of the inoculant B. amyloliquefaciens

FZB42 on the bacterial community in the rhizosphere of lettuce at three different plant growth stages

(Kröber et al., 2014). FZB42 was found to have almost no effect on the bacterial community composi-

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tion in the rhizosphere of lettuce while the growth stage had a more pronounced impact. However,

the absence of replicates also prevented a more rigorous testing for significant differences. The same

lettuce cultivar was used by Kröber et al. (2014) and, with a few exceptions, the metagenome se-

quencing revealed a similar taxonomic affiliation of the abundant classes, orders and genera as found

by amplicon sequencing of 16S rRNA gene fragments in the present study. The most striking differ-

ence was the high abundance of Mycobacteria revealed by direct metagenome sequencing which

might be due to a different bacterial community composition of the AL soil used in that study. In the

present study, we demonstrated the potential of next generation sequencing technologies to reveal

populations changing in relative abundance in response to RU47 inoculation. With increasing se-

quencing depth and decreasing costs of next generation sequencing the sensitivity of the methods

and feasibility to analyze large numbers of samples will provide an important tool to assess the ef-

fects of inoculants on the soil microbiota. Despite the limitations of the 16S rRNA gene based analy-

sis, by providing limited taxonomic and functional information, this is a big step forward in our ability

to detect even more subtle changes in the bacterial community composition in response to inoculant

application. It is important to note that changes of the bacterial community composition are not per

se negative but could well be beneficial, e.g. through the increased relative abundance of plant bene-

ficial bacteria or the decreased abundance of plant pathogens. Considering functional resilience in

soils we assume that the inoculation of RU47 did not affect soil functions, or only transiently. Chang-

es in the bacterial community composition could well contribute to the improved plant health in

inoculant treatments previously observed by Schreiter et al. (2014b).

Acknowledgements

The authors acknowledge that the project SM59/11-1/GR568121 was funded by the German Re-

search Foundation (DFG). We would also like to thank Petra Zocher, Ute Zimmerling, Sabine Breitkopf

and Angelika Fandrey for their skilled technical assistance and Ilse-Marie Jungkurth for her helpful

comments on the manuscript.

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Table 1. Differences (d-values in %) between taxonomic groups obtained by DGGE analysis of rhizosphere samples of lettuce grown in three soil types at the experimental plot system in Großbeeren (Germany) with and without application of the inoculant RU47. The asterisks indicate significant differences acquired by permutation test as suggested by Kropf et al. (2004).

differences caused by

RU47 inoculation

differences caused by the soil type

taxonomic group sampling year

sampling time

figure

DS AL LL

DS-AL DS-LL AL-LL

Bacteria 2010 3WAP S1 2.9* 2.1 2.0 18.8* 18.9* 19.9*

Bacteria 2011 2WAP S3 3.9* 5.8 4.5* 21.9* 36.8* 22.9*

Bacteria 2012 2WAP S5 8.9* 9.1* 3.2* 15.7* 46.6* 27.0*

Alphaproteobacteria 2010 3WAP S7 1.1 2.5 2.7* 27.6* 25.1* 37.9*

Alphaproteobacteria 2012 2WAP S9 0.4 0.9 5.1* 13.6* 24.0* 8.0*

Betaproteobacteria 2010 3WAP S11 3.5 7.8* 11.4 38.5* 51.4* 57.2*

Betaproteobacteria 2012 2WAP S13 0 0 3.4 6.8 29.9* 16.0*

Actinobacteria 2010 3WAP S15 2.4* 1.1 0 29.0* 28.5* 20.0*

Actinobacteria 2012 2WAP S17 8.1* 15.7* 14.4* 40.0* 35.4* 42.8*

DS: diluvial sand, AL: alluvial loam, LL: loess loam

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Table 2. Relative abundance (in %) of dominant phyla and classes from the rhizosphere of lettuce grown in three soil types in 2011 in the control plot and the plot inoculated with P. jessenii RU47 obtained by pyrosequencing. The asterisk indicate significantly enriched taxa in the rhizosphere between control and RU47 treated samples in each soil type, identified by Tukey test under a generalized linear model via logistic function for binomial data.

DS AL LL

phylum class control RU47 control RU47 control RU47

Proteobacteria 63.8±4 67.3±3 63.2±7 58.9±4 60.7±6 59.0 ±4

Alphaproteobacteria 25.8±6 27.7±3 22.2±2 21.8±4 18.9±1 19.8±2

Betaproteobacteria 26.7±1 30.0±3 30.8±9 23.6±6 31.0±7 26.5±2

Gammaproteobacteria 8.2±5 6.8±2 7.2±2 10.1±6 6.6±1 8.0±1

Deltaproteobacteria 1.8±0 1.8±0 1.8±0 2.0±0 2.6±1 3.2±0

Actinobacteria 18.5±2 15.4±1 15.1±4 16.8±2 15.7±2 16.3±2

Bacteroidetes 4.8±1 5.9±1 6.1±1 5.6±1 6.6±0 6.9±1

Firmicutes 4.8±2 4.1±1 5.8±1 8.6±1* 6.6±2 7.9±2

Acidobacteria 3.0±1 2.7±0 2.91± 3.2±1 3.0±1 2.9±0


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Table 3. Relative abundance (in %) of taxonomic groups in the rhizosphere of lettuce, grown in the three soil types responding to the treatment with the inocu-lants RU47. The asterisks indicate significantly enriched taxa in the rhizosphere between control and RU47 treated samples in each soil type, identified by Tukey test under a generalized linear model via logistic function for binomial data.

DS AL LL

phylum class order family genus control RU47 control RU47 control RU47

Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Nocardioides 1.4±0* 0.9±0 1.9±0 1.5±0 2.3±0 2.6±0

Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Marmoricola 0.1±0* 0.0±0 0.1±0 0.1±0 0.1±0 0.0±0

Bacteroidetes Flavobacteria Flavobacteriales Cryomorphaceae 0.1±0 0.2±0* 0.3±0 0.4±0 0.3±0 0.2±0

Bacteroidetes Sphingobacteria Sphingobacteriales Cytophagaceae Cytophaga 0.1±0 0.3±0* 0.1±0 0.0±0 0.0±0 0.0±0

Bacteroidetes Flavobacteria Flavobacteriales Cryomorphaceae Fluviicola 0.1±0 0.2±0* 0.2±0 0.4±0 0.3±0 0.2±0

Firmicutes 4.8±2 4.1±1 5.8±1 8.6±1* 6.6±2 7.9±2

Firmicutes Bacilli 4.4±2 3.5±1 5.0±1 7.6±1* 5.9±1 6.8±2

Firmicutes Bacilli Bacillales 4.4±2 3.5±1 5.0±1 7.6±1* 5.9±1 6.8±2

Firmicutes Bacilli Bacillales Bacillaceae 2.3±1 1.6±0 2.1±0 3.4±1* 3.0±0 3.6±1

Firmicutes Bacilli Bacillales Bacillaceae Bacillus 1.3±0 1.2±0 1.6±0 2.4±1* 1.5±0 1.7±0

Firmicutes Bacilli Bacillales Paenibacillaceae 1.7±0 1.3±0 2.0±0 3.3±1* 2.4±1 2.7±1

Firmicutes Bacilli Bacillales Paenibacillaceae Paenibacillus 1.3±0 1.0±0 1.6±0 3.0±1* 2.0±1 2.3±1

Proteobacteria Betaproteobacteria Methylophilales 1.3±0 2.4±0* 1.3±0 1.1±0 2.8±1 2.8±0

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Proteobacteria Betaproteobacteria Methylophilales Methylophilaceae 1.3±0 2.4±0* 1.3±0 1.1±0 2.8±1 2.8±0

Proteobacteria Betaproteobacteria Methylophilales Methylophilaceae Methylophilus 1.1±0 2.2±0* 0.5±0 0.4±0 1.6±0 1.8±0

Proteobacteria Deltaproteobacteria Bdellovibrionales Bdellovibrionaceae 0.1±0 0.1±0 0.1±0 0.2±0* 0.1±0 0.2±0*

Proteobacteria Deltaproteobacteria Bdellovibrionales Bdellovibrionaceae Bdellovibrio 0.1±0 0.1±0 0.1±0 0.2±0* 0.1±0 0.2±0*


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soil type-dependent effects of a potential biocontrol inoculant on indigenous bacterial communities...

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