www.sciencemag.org/cgi/content/full/science.1203980/DC1

Supporting Online Material for

Deciphering the Rhizosphere Microbiome for Disease-Suppressive Bacteria

Rodrigo Mendes, Marco Kruijt, Irene de Bruijn, Ester Dekkers, Menno van der Voort,

Johannes H. M. Schneider, Yvette M. Piceno, Todd Z. DeSantis, Gary L. Andersen, Peter A. H. M. Bakker, Jos M. Raaijmakers*

*To whom correspondence should be addressed. E-mail: jos.raaijmakers@wur.nl

Published 5 May 2011 on Science Express

DOI: 10.1126/science.1203980

This PDF file includes:

Materials and Methods Figs. S1 to S8 Tables S1 to S5

References and Notes

2

Materials and Methods

Soil sample collection and storage

The suppressive soil was collected at the end of the 2004 sugar beet growing season

from an agricultural field close to the town of Hoeven, the Netherlands (51º35’10”N

4º34”44’E). Soil was collected at a depth of 0-30 cm from 25 random sites across the

field. The conducive soil was harvested from the margin of the sugar beet field; this

margin was not cultivated to sugar beet and was covered with grasses and weeds, which

were removed prior to soil sampling. Both soils were air dried, sieved (0.5-cm-mesh) to

remove plant debris, and stored in buckets at ambient temperatures. Physical-chemical

analyses on each soil were performed by BLGG-AgroXpertus (Oosterbeek, The

Netherlands).

Bioassay to assess disease suppressiveness of soils

Sugar beet seeds (cv. Alligator) were sown in square PVC pots (width 6 cm; height

8 cm) containing 250 g of soil with an initial moisture content of 10% (v/w). Plants were

grown in a growth chamber at 24°C, 70% relative humidity and 16 hour light, and

watered weekly with standard Hoagland solution (macronutrients only). Disease

suppressiveness was determined for various treatments: i) suppressive soil (S), ii)

conducive soil (C), conducive soil amended with 10% (w/w) of suppressive soil (CS),

suppressive soil heat-treated at 50°C (S50) or 80°C (S80) for 1 hour, or gamma-irradiated

(60 kGray, Isotron, The Netherlands). For heat treatment, the suppressive soil (moisture

content set at 10% v/w) was transferred to a plastic bag and placed in a water bath at

50ºC or 80ºC for 1 hour. The bags with soil were made flat (~ up to 4 cm height) to

increase the contact area with the surrounding water. For each soil treatment, four

replicates were used in a complete randomized experimental design. Four days after seed

germination, the number of seedlings was reduced to eight per pot. The fungal pathogen

Rhizoctonia solani (anastomosis group AG2-2IIIB) was introduced into the soil by

transferring two mycelial agar plugs (5-mm-diameter) of a 1 week-old potato dextrose

agar (PDA) culture to two opposite corners of the pots at 1-cm underneath the soil

surface. The number of infected sugar beet seedlings was scored every two-three days for

a period up to 20 days after pathogen inoculation. The area under the disease progress

curve (AUDPC) was determined according to the statistical methods described by Shaner

& Finney (15).

Rhizosphere DNA isolation and PhyloChip analysis

The rhizosphere microbiomes of sugar beet seedlings grown in soils with different

levels of disease suppression were subjected to metagenomic-based community analysis.

For each of the six soil treatments (identified in Fig. S3), four replicates were used.

Metagenomic DNA was isolated in triplicate from each replicate by using the PowerSoil®

DNA isolation kit (MO BIO Laboratories, Inc.) according to the manufacturer’s

instructions. Microbial profiles for each sample were generated with the PhyloChip assay

(Second Genome, CA, USA). All PCR conditions and universal primers used for

amplification of 16S rDNA genes from bacteria and archaea were as previously described

(7). Fragmentation of the 16S rDNA amplicons, labelling, hybridizations, staining, and

scanning of the PhyloChip were performed according to methods described by Hazen et

3

al. (6). OTU selection for data analysis differed slightly from Hazen et al. (6) as follows.

All OTUs passing PhyCA analysis Stage 1 criteria in this data set were considered for

further analyses, allowing the inclusion of Unclassified OTU, which would be excluded

by Stage 2 analysis. Additionally, the cut-off values for an OTU to pass Stage 1 were rQ1

≥ 0.646, rQ2 ≥ 0.884, and rQ3 ≥ 0.945. Data analyses were performed with Primer-E

software (version 6.1.13, Plymouth Marine Laboratory).

Bacterial isolation from suppressive soil

For the functional analyses, the γ-Proteobacteria were isolated from sugar beet

rhizosphere on semi-selective media. Sixteen sugar beet seeds were sown in pots filled

with 250 g of suppressive or conducive soils and grown for 20 days (N=3). Rhizosphere

(roots with tightly adhering soil) suspensions were prepared, serially diluted and plated

onto i) 1/10th

strength Tryptic Soy Agar (TSA) supplemented with 100 µg ml-1

Delvocid

to prevent fungal growth, and ii) Pseudomonas Agar (PSA) supplemented with 40 µg ml-1

ampicillin, 12.5 µg ml-1

chloramphenicol (16), and 100 µg ml-1

Delvocid. TSA plates

were incubated at 25°C for 5 days and PSA plates for 3 days. From each of the replicate

samples and growth media (TSA and PSA), 96 randomly selected bacterial colonies were

purified and screened for in vitro antagonism towards R. solani. In these in vitro

inhibition assays, the bacterial isolates were point-inoculated at the periphery of 1/5th

strength PDA (pH 7.0) plates and incubated for two days at 25ºC, after which a fresh

mycelial PDA agar plug of R. solani was transferred to the centre of the plate. After an

additional three days of incubation at 25ºC, inhibition of hyphal growth of R. solani was

scored. Out of 576 bacterial isolates randomly collected from the suppressive soil, 19.3%

showed antifungal activity, whereas only 3.3% of 421 isolates randomly collected from

the conducive soil inhibited growth of R. solani.

16S rDNA sequencing and phylogeny

16S rDNA was sequenced by Macrogen Inc. (Seoul, South Korea). Sequences were

trimmed and submitted to the Ribosomal Database Project for species identification (17).

MEGA 3.1 (18) was used to align 16S rDNA sequences and to construct a phylogenetic

tree (UPGMA with 10,000 bootstraps).

Coupling PhyloChip-based metagenomics with culture-based analysis

Two strategies were used to couple the metagenomics-based PhyloChip data with

the results of the culture-based analysis. For the first strategy, using Basic Local

Alignment Search Tool (http://blast.ncbi.nlm.nih.gov), we aligned the 16S rDNA

sequences of representative strains of haplotype clusters I-III (Fig. 4A) with the 16S

rDNA sequences of the five Pseudomonadaceae identified by the PhyloChip approach as

the top 10% dynamic taxa associated with disease suppression (see Table S3). For the

second strategy, 16S rDNA sequences from strains representing the three haplotype

clusters (Fig. 4A) were used for BLAST searches in the GreenGenes database

(http://greengenes.lbl.gov) used for the PhyloChip array design. Subsequently, the best

hits were traced back in our experimental data set revealing that these haplotypes were

indeed more abundant in suppressive soils than in conducive soils (Fig. 4B). Their

respective abundances were calculated according to the conditions described above in the

‘Rhizosphere DNA isolation and PhyloChip analysis’ section.

4

Random transposon mutagenesis

Plasposon mutagenesis of Pseudomonas sp. strain SH-C52 was performed using

pTnMod-OTc (19). From an initial screen of approximately 1,500 random mutants, two

mutants of strain SH-C52 were obtained that had lost in vitro activity against R. solani.

Single plasposon insertions were confirmed by Southern blot analysis with a probe

directed against the tetracycline resistance gene on the plasposon. Plasposon rescue using

BamHI or PstI was performed as previously described (19).

Cloning and sequencing of the thaABCD genes

A fosmid library (7 X genome coverage) with 30-40 kb fragments of genomic DNA

of Pseudomonas sp. strain SH-C52 was constructed according to the protocols of the

manufacturer (Fosmid Library Production kit, Epicentre). Library clones were blotted

onto Hybond N+ membranes (Amersham) and hybridized with dig-labeled probes

directed against specific sequences in the thaB gene. Hybridizations were performed

under stringent conditions (65°C with 0.1xSSC (75 mM NaCl, 7.5 mM sodium citrate,

0.1% sodium dodecyl sulfate). Hybridization-positive clones were subjected to restriction

analyses. Contigs were constructed by cluster analysis of these experimental data by the

unweighted-pair group method using average linkages. Clones 5.1 and 10.1 were sent for

shotgun sequencing (Macrogen, Seoul, Korea). Sequence gaps were closed by primer

walking.

Bioinformatic analyses

Operons and genes were predicted by the Softberry FGENESB program (Softberry,

Inc., Mount Kisco, NY), and the identified open reading frames (ORFs) were analyzed

using BlastX in the NCBI database and PseudoDB (http://xbase.bham.ac.uk). Putative

promoter sequences were identified by the Softberry BPROM program, and putative

terminator sequences were identified by the RNA secondary structure prediction program

of Genebee (http://www.genebee.msu.su/). Specific domains in the deduced protein

sequences of the NRPS genes thaA, thaB and thaC1 were analyzed with PFAM

(http://pfam.sanger.ac.uk/search?tabsearchSequenceBlock). Protein sequences of specific

domains were aligned in ClustalX (version 1.81). Trees were inferred by neighbor joining

using 1,000 bootstrap replicates. Identification of the genes flanking the NRPS genes was

performed by BlastX analysis in NCBI, Pseudomonas.com

(http://v2.pseudomonas.com/), or PseudoDB and by comparison with genes in the

biosynthesis cluster for syringomycin. The C1-domain of thaA as well as the TE domain

of the ninth module of thaB (GenBank HQ888764) were used in BlastP comparisons with

whole genome sequences of Pseudomonas species available in the databases

Pseudomonas.com and PseudoDB. Adenylation (A), thiolation (T), condensation (C) and

thioesterase (TE) domains of the NRPS genes were identified by PFAM

(http://www.sanger.ac.uk/Software/Pfam/). For phylogenetic analyses of the different

domains, alignments were made with ClustalX (version 1.81) and software available at

http://www.ebi.ac.uk/clustalw/. Trees were inferred by Neighbor Joining in ClustalX

using 1,000 bootstrap replicates.

5

Fig. S1. Disease suppressiveness of soils and its microbiological nature. (A) Progress of

Rhizoctonia damping-off disease of sugar beet seedlings in disease suppressive soil (S),

conducive soil (C), conducive soil amended with 10% (w/w) of suppressive soil (CS),

suppressive soil heat-treated at 50°C (S50) or 80°C (S80) for 1 hour. Disease incidence

represents the percentage of sugar beet seedlings with damping-off symptoms (mean

values ±SEM, N=4). For each replicate, eight sugar beet seedlings were used. (B) Area

under disease progress curve (AUDPC) for each of the five different treatments (mean

values ±SEM, N=4). Different letters above the bars indicate statistically significant

differences (P<0.05, Student-Newman-Keuls test).

0

10

20

30

40

50

60

70

80

0 2 4 6 8 10 12 14 16 18 20

Dis

ease

incid

ence (

%)

dpi

S C CS S50 S80

0

10

20

30

40

50

60

S C CS S50 S80

AU

DP

C

A

B

C

A

BC

BC

AB

6

Fig. S2. Effect of γ-irradiation on soil disease suppressiveness. (A) Disease index

ranging from 0 (healthy plant) to 3 (dead plant). (B) Suppressive soil (S) and γ-irradiated

(60 kGray, Isotron, The Netherlands) suppressive soil (SG) were cultivated with sugar

beet. Number of infected plants (mean values ±SEM, N=12) were scored 14 days after

germination. Asterisk indicates a statistically significant difference (P<0.05, Student’s t-

test).

0

0.5

1

1.5

2

2.5

S SG

Dis

ease

Index

*

0 1 2 3

B A

7

I - Experimental design

1 Sugar beet grown in the presence of R. solani

(N=4).

2 Sugar beet grown in the absence of R. solani

(N=4).

* Treatments selected

for PhyloChip analysis.

II - PhyloChip analysis

1 Rhizosphere DNA isolation with PowerSoil

® MO BIO kit.

2 16S rDNA amplification; pool of 8 X PCR (temperature gradient for annealing from 52 to 62°C) for each replicate.

3 Fractionate (50 to 200 bp) and end-label with biotin.

4 Hybridize, stain, wash and scan; total of 24 chips (6 treatments; N=4).

5 Data analyses using software Richness > Evenness > Hierarchical clustering (Bray Curtis Similarity) > Primer-E. > Ordination (MDS) > Microbial Communities Dynamics (SIMPER analysis).

6 Selection of the target groups based on three criteria. i. more abundant in suppressive than in conducive soil (S>C);

Pairwise comparison of top ii. more abundant in the 'transplantation soil' than in conducive soil (CS>C);

10% dynamic taxa. iii. more abundant in the suppressive soil when the pathogen is present (Sr>S).

III – Isolation of specific bacterial taxa targeted by PhyloChip analysis

1 Bacterial isolation from General aerobic growth medium (TSA) and suppressive and conducive Pseudomonadaceae semi-selective medium (PSA).

soils (~1000 random isolates).

2 Genetic and phenotypic Screening for antagonistic traits (in vitro tests, PCR) > BOX-PCR > characterization (107 isolates). > 16S rDNA sequencing > in vivo bioassays.

3Coupling culture-based analysis with PhyloChip analysis.

Alignments and BLAST searches in the GreenGenes database using 16S rDNA

sequences of the functional bacterial groups.

IV - Genes and pathways involved in disease suppression

1 Genetic, bioinformatic, and functional analyses

Mutagenesis > Genome library constructions > Sequencing > Signature-sequence-based predictions > in vivo bioassays.

8

Fig. S3. Overall strategy used to decipher the rhizosphere microbiome of sugar beet seedlings

grown in disease suppressive soil. Soils with different levels of disease suppression are

designated as: suppressive soil (S); conducive soil (C); conducive soil amended with 10%

(w/v) suppressive soil (CS); suppressive soil heat treated at 50°C (S50); suppressive soil heat

treated at 80°C (S80); and suppressive soil inoculated with the fungal pathogen Rhizoctonia

solani (Sr). For each replicate of each treatment (N=4), total DNA was isolated and pooled

from three independent extractions using 250 mg of rhizosphere soil.

9

Fig. S4. Composition of bacterial communities in the rhizosphere microbiome of sugar

beet seedlings grown in soils with different levels of disease suppressiveness. The sum of

the microbial abundance of all six soil treatments (N=4) is shown.

Acidobacteria, 2%

Actinobacteria, 9%

Bacteroidetes, 4%

Chloroflexi, 1%

Cyanobacteria, 1%

Firmicutes, 20% Planctomycetes, 2%

Proteobacteria, 39%

Unclassified, 16%

Verrucomicrobia, 2%

All others, 4%

10

Fig. S5. Non-metric multi-dimensional scaling (MDS) ordination of the rhizosphere

microbiomes of sugar beet seedlings grown in soils with different levels of disease

suppressiveness. Based on the relative abundance of 33,346 taxa identified in the sugar

beet rhizosphere microbiome, a resemblance matrix was generated using Bray Curtis

similarity. MDS analysis was performed with Primer-E (version 6.1.13).

▲ suppressive soil (S); ▼ conducive soil (C); ● conducive soil amended with 10% (w/v)

suppressive soil (CS); suppressive soil heat treated at 50°C (S50); ■ suppressive soil

heat treated at 80°C (S80) for 1 hour; and X suppressive soil inoculated with Rhizoctonia

solani (Sr).

2D Stress: 0.06

11

A Proteobacteria B Firmicutes

C Cyanobacteria

Fig. S6. Clustering analysis of the rhizosphere microbiome for (A) Proteobacteria, (B)

Firmicutes, and (C) Cyanobacteria. When separate clustering analyses were performed

for the Proteobacteria or Firmicutes, each of these groups allowed discrimination between

the six soil treatments as was the case in the overall cluster analysis (see Fig. 2B),

reinforcing their association with disease suppressiveness. In contrast, for other phyla

such as the Cyanobacteria (C) dissimilar patterns were found.

S8

0_

1

S8

0_

4

S8

0_

2

S8

0_

3

C_

1

C_

4

C_

2

C_

3

S_

2

S_

1

S_

3

S_

4

CS

_3

CS

_4

CS

_1

CS

_2

Sr_

1

Sr_

3

Sr_

2

Sr_

4

S5

0_

4

S5

0_

3

S5

0_

1

S5

0_

2

100

90

80

70

60

50B

ray C

urt

is S

imila

rity

C C C C S S S S Sr

Sr

Sr

Sr

S5

0

S5

0

CS

CS

CS

CS

S5

0

S5

0

S8

0

S8

0

S8

0

S8

0

100

90

80

70

60

50

Bra

y C

urt

is S

imila

rity

S5

0

S5

0 Sr

Sr

Sr

Sr

S5

0

S5

0

CS

CS

CS

CS

S8

0

S8

0

S8

0

S8

0 S S C C C C S S

100

90

80

70

60

50

Bra

y C

urt

is S

imila

rity

12

Fig. S7. Suppression of Rhizoctonia damping-off disease by selected strains of the γ-

Proteobacteria. (A) Representation of the in vivo bioassay to determine the ability of

antagonistic bacterial isolates to suppress damping-off disease of sugar beet seedlings

caused by Rhizoctonia solani. A mycelial plug of the fungal pathogen is point-inoculated

at 1-cm underneath the soil surface at the edge of the tray (indicated by an arrow). Within

a time period of 2-3 weeks, R. solani progressively infects sugar beet seedlings

positioned in a 20-cm row with a 1-cm distance between the seedlings. The level of

disease suppression is quantified by measuring the disease spread as indicated by

seedlings with damping-off symptoms. (B) Spread of damping-off disease of sugar beet

seedlings in conducive soil that is untreated (Control), treated with Pseudomonas sp.

strain SH-A1 (haplotype A), strain SH-B3 (haplotype B) and strain SH-C52 (haplotype

C), and the nonribosomal peptide synthetase mutant of strain SH-C52 (O33).

Pseudomonas sp. strains representing haplotypes A, B and C, and mutant O33 were

inoculated in soil (105 cells g

-1 soil) one day prior to pathogen inoculation (mean values

±SEM, N=8). An asterisk indicates a statistically significant difference (P<0.05, Dunnett

test) between the treatment and the untreated conducive soil (Control).

0 5 10 15 20

O33

SH-C52

SH-B3

SH-A1

Control

Spread of R. solani (cm)

0 5 10 15 20 q

Distance (cm)

R. solaniinoculation point

A

B

*

13

Fig. S8. Schematic representation of the biosynthetic gene clusters responsible for the

antifungal activity of Pseudomonas sp. strain SH-C52. (A) Genetic organization of the

thaAB (29.7-kb) and thaC1C2D (4.4-kb) gene clusters identified in Pseudomonas sp.

strain SH-C52. Underneath the nonribosomal peptide synthetase (NRPS) genes thaA,

thaB and thaC1, is the module and domain organization of the encoded proteins. The

domains are labeled by: C, condensation; A, adenylation; T, thiolation and TE,

thioesterification. The first module is predicted to be an initiation module as it harbours a

condensation (C)-domain with structural features that are typical for C-domains involved

in N-acylation of the first amino acid of the molecule. Modules 2 through 9 are predicted

to elongate the peptide chain via incorporation of one amino acid per module. Together

these nine catalytic domains are predicted to generate a peptide which is cleaved at the

end of the assembly line by a thioesterase (TE) domain, resulting in the release of a linear

product or a cyclic peptide via an intramolecular cyclization reaction. Based on the

signature sequences in the adenylation (A)-domains, 6 of the amino acid residues in the

peptide moiety could be predicted but 3 could not. Genes thaC1 and thaC2 share

similarities with syrB1 and syrB2, respectively, the latter being involved in chlorination

of the ninth amino acid residue (Thr) of syringomycin, the lipopeptide antibiotic

produced by P. syringae. ThaD has 73% sequence identity to SyrC, an

aminoacyltransferase that shuttles threonyl and chlorothreonyl residues to the syr-syp

biosynthetic assembly line in P. syringae. Based on these in silico analysis, the encoded

compound is predicted to be a chlorinated lipopeptide with nine amino acid residues.

Triangles represent the positions of the single disruptions in the thaAB and thaCD gene

clusters obtained by either random (white triangle) or site-directed mutagenesis (black

triangle). (B) In vitro hyphal growth inhibition of R. solani by Pseudomonas sp. strain

A TCl

C AT C A

T C AT C A

T C A T C AT C A T C A T C TE

TC

module 2 module 4module 7

5 kb

thaC1

thaC2

module 9module 9module 1 module 3 module 5module 6 module 8

thaA

Ser ? Asp/Glu ? ? Thr Thr Asp/Glu Thr

Cl

O33

thaB

thaD

SH-C52

KO26O33

KO25 KO26

A

B

14

SH-C52 and its respective mutants O33 and KO26. The mutant KO25 showed the same

lack of in vitro activity as O33. Disruption of the thaB or thaC2 genes largely eliminated

the antifungal activity of strain SH-C52, which can be observed by the significantly

smaller inhibition zones of these mutants in comparison with the inhibition caused by

parental strain SH-C52.

15

Table S1. Physical and chemical properties of the disease suppressive and conducive

soils obtained from Hoeven, The Netherlands. Both soils were classified as sandy soils

based on analyses performed by BLGG-AgroXpertus (Oosterbeek, The Netherlands).

Suppressive soil Conducive soil

Chemical analysis

pH 5.8 5.6

Organic matter (%) 2.9 2.7

CaCO3 (%) < 0.1 < 0.1

----- mg kg-1

-----

NH4 4.5 10.2

NO3 17.6 1.7

P 3.2 2.5

K 93.0 69.0

Mg 37.0 30.0

Na 20.0 <6.0

Mn <0.25 0.62

----- µg kg-1

-----

Cu 42 38

Co <2.5 7.2

B 109 97

Zn 272 2446

Particle diameter (µm) ----- % -----

0-2 2.5 1.4

2-16 3.4 2.0

16-50 7.5 6.5

50-105 18.9 17.0

105-150 20.7 21.4

150-210 25.5 26.1

210-300 16.3 17.8

300-420 4.1 5.3

420-600 0.8 1.6

600-2000 0.4 0.9

M50 median particles size 159 165

16

Table S2. Fresh weight (N=4) of sugar beet seedlings grown in suppressive soil and

conducive soil in the absence of the pathogen R. solani. No significant differences were

observed between the two treatments (P<0.05, Student’s t-test).

Soil Fresh weight (mg) (±SD)

Suppressive 202 (±49)

Conducive 256 (±38)

17

Table S3. The top 10% most dynamic subset of the rhizosphere microbiome that meets

all of the following criteria: i) more abundant in suppressive than in conducive soil, ii)

more abundant in the ‘transplantation soil’ (conducive soil + 10% suppressive soil) than

in the conducive soil, and iii) more abundant in the suppressive soil when the fungal

pathogen R. solani is present.

Phylum Affiliation

Representative OTU sequence

GenBank accession

Clone / strain

Proteobacteria Pseudomonadaceae EU538127 antecubital fossa skin clone nbt82e01

EU434358 Pseudomonas libanensis strain a110

EU537608 antecubital fossa skin clone nbt74g09

EU434526 Pseudomonas fluorescens strain b339

EU535118 antecubital fossa skin clone nbt171b09

Burkholderiaceae AY550913 Burkholderia sp. FDS-1

AY439198 soil clone MeBr 20

AY321306 Burkholderia tropica LM2-37603

AY326592 Amazon soil clone 141-1

AB299578 Burkholderia sp. 70-VN5-1W

AY439195 soil clone MeBr 1

AY178068 Burkholderia sp. UCT 15

AF408946 Burkholderia sp. Ellin104

AB079372 Burkholderia sp. S-2

Xanthomonadales L76222 Rhodanobacter lindaniclasticus

AY218744 penguin droppings clone KD5-94

Firmicutes Lactobacillaceae EF096273 mouse cecum clone obob1_aaa03h11

18

Table S4. Frequency of antagonistic bacteria isolated from the rhizosphere of sugar beet

plants grown in disease suppressive or conducive soil. Bacteria were isolated on a general

aerobic growth medium (TSA) and on a medium semi-selective for members of the

Pseudomonadaceae. For each soil 200 - 300 isolates were randomly selected and tested

for their ability to inhibit mycelial growth of the fungal pathogen Rhizoctonia solani in

vitro. Isolates that inhibit mycelial growth were classified as antagonistic.

Soil type Replicate Aerobic medium Pseudomonas medium

antagonistic§ % antagonistic

§ %

Suppressive 1 4/96 4.2 39/96 40.6

2 3/96 3.1 42/96 43.8

3 0/96 0.0 23/96 24.0

Total 7/288 2.4 104/288 36.1

Conducive 1 4/76 5.3 0/78 0.0

2 3/38 7.9 1/81 1.2

3 4/75 5.3 2/73 2.8

Total 11/189 5.8 3/232 1.3

§ Number of antagonistic isolates / total number of tested isolates.

19

Table S5. Sequence identities of the 16S rDNA genes of the ten bacterial groups

(haplotypes SH-A to SH-J) from the disease suppressive soil with the 16S rDNA genes

present on the PhyloChip. The data shown represent the best BLAST hits of 16S rDNA

sequences present in the PhyloChip database (GreenGenes).

Haplotype* Hit in the BLAST

search†

GenBank accession

Score Identity

(%) Haplotype

cluster

SH-A Pseudomonas sp. HNR09 EU373356 1352 99.78 I

SH-B Pseudomonas sp. A1Y13 AY512624 1355 99.93 II

SH-C Pseudomonas sp. HNR09 EU373356 1354 99.85 I

SH-D Pseudomonas sp. HNR09 EU373356 1354 99.85 I

SH-E Pseudomonas sp. HNR09 EU373356 1354 99.85 I

SH-F Pseudomonas sp. BIHB 811 DQ885950 1349 99.71 III

SH-G Pseudomonas sp. BIHB 811 DQ885950 1349 99.71 III

SH-H Pseudomonas sp. HNR09 EU373356 1352 99.78 I

SH-I Pseudomonas sp. HNR09 EU373356 1352 99.78 I

SH-J Pseudomonas sp. HNR09 EU373356 1354 99.85 I

* or BOX-PCR group

† greengenes.lbl.gov/cgi-bin/nph-blast_interface.cgi

20

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