FOR FURTHER INFORMATION REFERENCES

1. Gupta VVSR et al. (2011) In: Rainfed farming systems by P. Tow et al., pp. 149-184, Springer Science and Business Media.

2. Garbeva P et al. (2004) Annual Review of Phytopathology. 42, 243-270.

3. Liu K-L. et al. (2012) Applied Environmental Microbiology. 78, 1523-1533.

4. Ophel-Keller K et al. (2012) Australasian Plant Pathology. 37, 243-253.

ACKNOWLEDGEMENTS

Financial support was provided by the CSIRO and Grains RDC. Authors acknowledge the help from Marcus Hicks and Stasia Kroker for lab analysis, Amanda Cook with field sampling at Minnipa and Robin Manley for giving access to the Avon field site.

High-throughput Sequencing of Fungal Communities in Disease Suppressive Soils

Methods Surface soils (0-10 cm) were collected at sowing and in-crop (7wks) in 2010 from field sites located in the Mediterranean cropping region in South Australia (Avon and Minnipa) and characterized as ‘suppressive (SP)’ and ‘non-suppressive (NSP)’ to diseases caused by soilborne necrotrophic pathogens (e.g. Rhizoctonia solani AG8). At each site SP and NSP fields were co-located. DNA from a minimum of 125g of soil sample was used in order to integrate microscale variability in the distribution of soil fungi and 8 field replicates for each treatment were used. Fungal diversity was analysed by high-throughput 454 pyrosequencing using 28S LSU rRNA primers LR3 and LR0R (~600bp3) spanning hypervariable regions D1 and D2 which allows higher classification accuracy to lower taxonomic (genus) level. All sequences were processed through the Ribosomal Database Project (RDP) pyrosequencing pipeline followed by framebot and alignment based on HMM 28S subject to classification on the RDP fungal classifier at 80% confidence. R. solani AG8 levels were measured using taxa specific qRT-PCR (SARDI RDTS4). Wheat roots collected at 7wks were rated (0-5 rating scale) for Rhizoctonia disease incidence. Multivariate analysis of relative abundance data was done using Primer 6 and PERMANOVA+ (Primer-E Ltd.)

Results • Rhizoctonia inoculum levels were generally medium to high disease risk

category (70-250 pg DNA/g soil) in both SP and NSP soils. Disease incidence was higher in NSP fields (2 to 3.5 rating) compared to SP fields (<1.0 rating).

Biological suppression of soilborne diseases reported in southern Australian agricultural fields is a function of the populations, activity and composition of the microorganisms. Although it is an inherent property of all biologically active soils, the level of suppression ability varies with edaphic and environmental variables1. It has been shown that interactions between phyto-pathogenic fungi and soil bacterial and fungal communities can influence the level of soilborne disease incidence1,2. Our aims are to characterize soil fungal communities, in detail, using high-throughput DNA techniques and to identify signatures that represent highly disease suppressive communities.

Gupta, V.V.S.R.1, C.R. Penton2, J.M. Tiedje2, K. Ophel-Keller3, S.M. Neate4, M. Gillings5, P. Harvey1, D.K. Roget1 1CSIRO Ecosystem Sciences, Glen Osmond, SA; 2CME MSU, East Lansing, MI; 3SARDI Glen Osmond; 4DAFF QLD, Toowoomba; 5Macquarie University, Sydney

CSIRO ECOSYSTEM SCIENCES / SUSTAINABLE AGRICULTURE FLAGSHIP

Vadakattu V.S.R. Gupta

Gupta.Vadakattu@csiro.au

http://www.csiro.au/

Conclusions • The 454-pyrosequencing targeting 28S LSU gene yielded >95% of

sequences from the South Australian soils that can be classified up to genera with 54% coverage of RDP fungal classifier database.

• Members of Ascomycota and Basidiomycota contributed 87% of total sequences in all soils.

• Multivariate analysis of relative abundance data indicated significant soil type and suppression based differences in the diversity of fungal communities at Genus, Family and Order levels.

Figure 1: Rarefaction curves for genus-level classification generally indicate the level off for most of the samples suggesting that the large fraction of species diversity within the samples has been covered in both SP and NSP soils.

Table 2: Contributions by various taxonomic units primarily providing the discrimination between fungal communities in suppressive and non-suppressive soils; SIMPER analysis estimates using Primer 6.

• The average sequence length obtained from 28S 454-pyrosequencing after trimming was 506±17bp (79.8±7.4% sequences passed the quality / length / primer filter). A total of 1.1 million sequences were classified, with a min length of 400bp. At 80% bootstrap confidence up to 95.2±3% sequences were classified up to the taxonomic level ‘class’. An average of 13,200 to 30,000 genera-level classified sequences were obtained for each treatment.

Figure 3 : Composition of the 20 most abundant genera accounting for 54.4% and 57.7% of all classified sequences in SP and NSP soils at Avon and Minnipa, respectively. PERMANOVA analysis indicated significant soil type effect (CV=15.3%, P=0.001).

A total of 917 genera with 54% coverage of RDP fungal classifier database were found in all the samples analysed. Shannon index (H’) values were significantly different (P<0.003) between SP (3.98) and NSP (3.84) soils but no significant difference between sites and time of sampling.

Ascomycota

Eukaryota incertae

sedis

Chytridiomycota

Fungi incertae sedis

Basidiomycota

Neocallimastigomycota

Blastocladiomycota

Zygomycota

Glomeromycota Pleosporales

Hypocreales

Sordariales

Xylariales

Eurotiales

Helotiales

Choanoflagellida

Agaricales

Chytridiales

Capnodiales

Spizellomycetales

Pezizales

Cantharellales Tremellales

Eukaryota incertae sedis

Blastocladiales

ChaetothyrialesMonoblepharidales Botryosphaeriales

Zoopagales

Figure 2: Average taxonomic composition, (A) Phyla (B) Order, of the classified genera. The top 20 most abundant Orders accounted for 91% of all genera, whereas the 20 most abundant Families accounted for 62% of all genera.

(A) (B)

Gibberella

Alternaria

PhaeosphaeriaPodospora

Xylaria

Monosiga

Sclerotinia

Schizothecium

Cercophora

Spizellomyces

Chaetomidium

Bionectria

Penicillium

Peethambara

Christiansenia

Zygopleurage

Davidiella

Aspergillus

Pyrenophora

Westerdykella

Avon

Gibberella

Alternaria

Phaeosphaeria

Podospora

XylariaMonosiga

Sclerotinia

Schizothecium

Cercophora

Spizellomyces

Chaetomidium

BionectriaPenicillium

Peethambara

Christiansenia

Zygopleurage

Davidiella

Aspergillus Pyrenophora

Westerdykella

Minnipa

Resemblance: S17 Bray Curtis similarity (+d) - Hellinger transformed relative abundance data

Figure 4: Non-metric MDS analysis showing dissimilarities in fungal communities at Genus (A) and Family (B) levels between suppressive and non-suppressive soils.

2D Stress: 0.17 2D Stress: 0.18

Location-Suppression Avon Non-suppressive

Avon Suppressive

Minnipa Non-suppressive

Minnipa Suppressive

Table 1: Significance of differences (main effects only) at different taxonomic levels – ANOSIM two-way crossed analysis.

Suppressive field Non-Suppressive field

Rhizoctonia disease patch

Brown spear points of diseased crown roots

(A) (B)

Figure 5: Cluster analysis indicates >30% dissimilarity at genus level according to location and suppression but <25% dissimilarity between sampling times within each treatment.

Group Genus Family Order

Location Global R 0.542 0.511 0.294

p< 0.01 0.01 0.1

Suppression Global R 0.751 0.665 0.518

p< 0.01 0.01 0.1

Order Cum.% Family Cum.% Genus Cum.%

Xylariales 4.73 Xylariaceae 3.32 Xylaria 1.71

Sordariales 9.26 Pleosporaceae 5.98 Alternaria 3.12

Agaricales 13.26 Bionectriaceae 8.49 Sclerotinia 4.45

Eurotiales 17.19 Lasiosphaeriaceae 10.88 Spizellomyces 5.78

Spizellomycetales 21.05 Sclerotiniaceae 13.23 Gibberella 7.00

Helotiales 24.87 Spizellomycetaceae 15.49 Podospora 8.10

Hypocreales 28.59 Trichocomaceae 17.59 Peethambara 9.18

Pleosporales 32.01 Nectriaceae 19.61 Schizothecium 10.19

Pezizales 35.02 Phaeosphaeriaceae 21.26 Zygopleurage 11.19

Tremellales 37.98 Christianseniaceae 22.76 Aspergillus 12.12

Cantharellales 40.84 Ceratobasidiaceae 24.25 Anthostomella 13.03

Capnodiales 43.62 Psathyrellaceae 25.71 Christiansenia 13.89

Choanoflagellida 45.90 Davidiellaceae 27.10 Phaeosphaeria 14.72

Pucciniales 47.98 Pezizaceae 28.49 Penicillium 15.54

Russulales 49.99 Codonosigidae 29.72 Bionectria 16.32

Diatrypaceae 30.94 Davidiella 17.09

Bolbitiaceae 32.09 Cercophora 17.81

Raveneliaceae 33.23 Ceratobasidium 18.51

Sporormiaceae 34.34 Monosiga 19.22

Chaetomiaceae 35.45 Parasola 19.91

Ascobolaceae 36.52 Eutypa 20.61

Agaricaceae 37.55 Thanatephorus 21.30

Lobulomycetaceae 38.57 Peziza 21.99

Chytridiaceae 39.55 Sphenospora 22.64

Eukaryota_incertae_sedis 40.51 Westerdykella 23.28

Cantharellales_incertae_sedis 41.45 Coprinus 23.92

Sordariaceae 42.39 Gastrocybe 24.55

Lophiostomataceae 43.32 Ascobolus 25.15

Didymellaceae 44.24 Lobulomyces 25.74

Pleosporales_incertae_sedis 45.12 Coprinopsis 26.32

Neocallimastigaceae 45.98 Pyrenophora 26.90

Pyronemataceae 46.83 Phlyctochytrium 27.46

Blastocladiaceae 47.65 Eupenicillium 28.02

Herpotrichiellaceae 48.46 Phaeodothis 28.57

Hyphodermataceae 49.28 Capsaspora 29.13

Hypocreaceae 50.09 Chaetomidium 29.68

Minimedusa 30.22

Microallomyces 30.76

Average dissimilarity = 24.69 Average dissimilarity = 31.10 Average dissimilarity = 37.35

In cropIn cropIn cropIn crop

SowingSowingSowingSowingSowingSowingSowingSowingSowingSowingSowingSowingSowingSowingSowingIn cropIn cropIn cropIn cropIn cropIn cropIn cropIn cropIn cropIn cropIn cropIn cropIn crop

SowingSowingIn cropIn cropIn cropIn cropIn cropIn cropIn cropIn cropIn cropIn crop

SowingSowingSowingSowingSowingSowing

Sa

mp

ling

time

100 90 80 70 60 50

Similarity (%)