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Integration of system phenotypes in microbiome networks to ... · 12/12/2019 · 112 PhONA first...
Transcript of Integration of system phenotypes in microbiome networks to ... · 12/12/2019 · 112 PhONA first...
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Integration of system phenotypes in microbiome networks to identify 1
candidate synthetic communities: a study of the grafted tomato rhizobiome 2
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Ravin Poudel1234#, Ari Jumpponen5, Megan Kennelly4, Cary Rivard6, Lorena Gomez-4
Montano1234, and Karen A. Garrett1234#. 5
1Department of Plant Pathology, University of Florida, Gainesville, FL 32611 6
2Institute for Sustainable Food Systems, University of Florida, Gainesville, FL 32611 7
3Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611 8
4Department of Plant Pathology, Kansas State University, Manhattan, KS 66506 9
5Division of Biology and Ecological Genomics Institute, Kansas State University, Manhattan, KS 10
66506 11
6Department of Horticulture and Natural Resources, Kansas State University, Olathe, KS 66061 12
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#Correspondence: Karen A. Garrett and Ravin Poudel 14
E-mail addresses: [email protected] and [email protected] 15
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Running title: Phenotype-based network for microbial consortia design 17
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ABSTRACT Understanding factors influencing microbial interactions, and designing methods 18
to identify key taxa, are complex challenges for achieving microbiome-based agriculture. Here 19
we use a grafted tomato system to study how grafting and the choice of rootstock influence root-20
associated fungal communities. We then construct a phenotype-OTU network analysis (PhONA) 21
using random forest and network models of the observed sequence-based fungal Operational 22
Taxonomic Units (OTUs) and associated tomato yield data. PhONA provides a framework to 23
select a testable and manageable number of OTUs to support microbiome-based agriculture. We 24
studied three tomato rootstocks (BHN589, RST-04-106 and Maxifort) grafted to a BHN589 25
scion and profiled the associated endosphere and rhizosphere fungal communities by sequencing 26
the fungal Internal Transcribed Spacer (ITS2). The data provided evidence for a rootstock effect 27
(explaining ~2% of the total captured variation, p < 0.01) on the fungal community. Moreover, 28
the most productive rootstock, Maxifort, supported higher fungal species richness than the other 29
rootstocks or controls. Differentially abundant OTUs (DAOTUs) specific to each rootstock were 30
identified for both endosphere and rhizosphere compartments. PhONA integrates the information 31
about individual OTU obtained via differential abundance tests and role analyses. It identified 32
OTUs that were directly important for predicting tomato yield, and other OTUs that were 33
indirectly linked to yield through their links to these OTUs. Fungal OTUs that are directly or 34
indirectly linked with tomato yield represent candidate components of synthetic communities for 35
testing in agricultural systems. 36
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IMPORTANCE The realized benefits of microbiome analyses for plant health and disease 38
management are often limited by the lack of methods to select manageable and testable synthetic 39
microbiomes. We evaluated the composition and diversity of root-associated fungal communities 40
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from grafted tomatoes. We then constructed a phenotype-OTU network analysis (PhONA) using 41
random forest and network models. By incorporating yield data in the network, PhONA 42
identified OTUs that were directly predictive of tomato yield, and other OTUs that were 43
indirectly linked to yield through their links to these OTUs. Follow-up functional studies of taxa 44
associated with effective rootstocks identified using PhONA could support the design of 45
synthetic fungal communities for microbiome-based crop production and disease management. 46
The PhONA framework is flexible for incorporation of other phenotypic data and the underlying 47
models can readily be generalized to accommodate other microbiome or genomics data. 48
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KEYWORDS fungi, phenotypes, microbiome networks, model integration, tomato 50
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Interactions are key to defining system behaviors, structures, and outcomes. In microbial 51
systems, interactions among microbes define their distribution, assemblies, and ecosystem 52
functions. In addition to microbe-microbe interactions, microbes interact with their hosts, and are 53
essential to host health and performance (1-6). In agriculture, plant–microbe interactions 54
improve plant productivity by providing access to nutrients (7-9), infection of plant pathogens (5, 55
10), triggering plant growth promoting factors (11, 12), and enhancing plant resistance (13, 14) 56
and tolerance to abiotic stresses (15-17). Although the importance of microbes and host-microbe 57
interactions to host health and ecological processes is known, interaction-based approaches to 58
manage crop-production remain a scientific frontier. Past attempts to translate information about 59
microbes to design biocontrol agents or biofertilizers have often had limited efficacy and 60
durability (18, 19). Most microbes have been applied as single species, often selected based on 61
pairwise relations of microbes with a pathogen or the host. Interactions among microbes as well 62
as with the host are important, and the net outcome of these complex interactions defines host 63
health and ecosystem functions. Thus, it is critical to understand the ecology of microbes 64
selected for biological applications, and systems approaches centered on host-microbe 65
interaction can help guide the selection of microbes for engineering synthetic communities. 66
Among tools to better understand microbial ecology, network models of microbial 67
communities, and studies of network structures and key groups, have proven popular for 68
generating hypotheses about how to engineer microbial consortia. In such network models of 69
microbiomes, a node represents an OTU, and a link exists between two OTUs if their sequence 70
proportions are significantly associated across samples. When evaluated with other conventional 71
measures of microbial community, such as diversity indices, network models can be used to 72
identify hub taxa that may be key to maintaining microbial assemblages and diversity (20), or to 73
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evaluate changes in community complexity and interactions in response to experimental 74
treatments (21). The use of microbiome networks to describe general community structures and 75
key properties is valuable and often the most practical option when additional information is 76
missing or the goal is to compare across studies with different types of data (22, 23). The utility 77
of network analysis for identifying candidate assemblages for biocontrol can be enhanced by 78
incorporating nodes that represent more types of features (24, 25). For instance, a novel 79
association of host metadata with the microbiome was revealed in an integrated microbiome-80
metadata network (26), where a feature strongly associated with hub microbes can serve as a 81
marker to measure host performance. In agriculture, plant yield or other phenotypic traits can be 82
integrated in microbiome networks, with the potential to identify microbial consortia that are 83
predictive of host phenotypes. Because such models include host phenotypes, they facilitate 84
finding candidate sets of OTUs that may directly or indirectly affect key OTUs or phenotypic 85
traits. Visualization of networks is often valuable for this purpose; however, the real value of 86
phenotype-based network models is their ability to identify candidate taxa or consortia. The 87
hypothesized beneficial sets of OTUs may represent targets for pure culturing efforts or, if 88
cultures exist, the sets can be evaluated in lab or field studies. 89
While phenotype-based network models have the potential to predict key taxa, 90
application of such models should be integrated with findings from other community analyses so 91
that the inference about key taxa is biologically and ecologically meaningful. For example, plant 92
microbiome studies indicate that a small but consistent proportion of variation in microbial 93
communities is often explained by host genotype (27-32), indicating the potential for genotype-94
based modulation of microbial communities in crop-production on a broader scale. These results 95
support the idea of host-specific microbial community selection (33). Many of the microbes 96
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selected may be taxa that are evolutionarily essential for the survival and function of plants (34, 97
35). In addition, the extent of host genotype filtering of microbes differs across the rhizosphere, 98
rhizoplane, and endosphere, and varies from one host species to another (36-38). Results that 99
indicate microbial filtering by different crop hosts, plant compartments, geographic locations, 100
and environmental factors (39, 40)are promising for minimizing the search space, or necessary 101
sample numbers, to identify candidate taxa for synthetic communities. For instance, factors that 102
explain greater variation in microbial community composition, but that are outside the control of 103
management, can be treated as blocks in experimental designs, so that host- or compartment-104
specific effects on the microbial community can be searched to identify candidate taxa. 105
In our current study, building on previously described agricultural experiments in grafted 106
tomato systems (41, 42), we characterized the root-associated fungal (RAF) communities and 107
implemented a new approach to select potential candidate fungal taxa that are predictive of 108
tomato yield and/or that are in significant association with such fungal taxa. The new phenotype-109
OTU network analysis (PhONA) is a network framework to support the selection of candidate 110
taxa and to integrate system traits (such as host yield) in microbe-microbe association networks. 111
PhONA first identifies the OTUs predictive of phenotype using a random forest model and 112
indicates positive or negative association (Pearson's correlation coefficient) of the predicted 113
OTUs with the host phenotype, and then combines the OTUs associated with the phenotype with 114
other OTUs in a network model (Fig. 1). Random forest, a non-parametric ensemble learning 115
method, has often been used in microbiome studies (43, 44) for classification of sample states 116
and is suited for non-linear data types involving complex interactions (45). 117
Phenotype-based selection of microbial consortia is an effective approach to select 118
simplified yet representative microbial taxa and could support the design of microbiome-based 119
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products. Changes in abundance (46), successive selection over multiple generations (3), or 120
analyses of binary host-microbe relationships (47) are some the recent phenotype-based 121
applications to select candidate taxa for biological applications. In contrast to these approaches, 122
first PhONA utilizes a statistical model to select OTUs predictive of host phenotype, then further 123
integrates the predicted OTUs in the network framework. PhONA provides an interaction-based 124
understanding of microbial communities to support microbiome studies. In addition, we 125
contrasted the RAF community’s diversity and interactions among the productive rootstocks and 126
the controls, for endosphere and rhizosphere compartments. Based on our yield data, and 127
previous studies of microbial interactions (21), we expected a greater number of fungal OTUs 128
and a greater number of microbial associations for more productive rootstocks. Moreover, in our 129
previous studies of bacterial communities in the tomato rhizobiome, we observed compartment-130
specific (endosphere vs rhizosphere) effects of grafting and rootstocks on bacterial community 131
composition and diversity (42), and expected similar effects on RAF diversity and composition. 132
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METHODS 134
Experimental Plots, Rootstocks, and Study Sites. We studied grafted tomato plants in 135
high tunnels in an experimental design similar to the one described by Poudel et al. (42). Tomato 136
plants were grafted following a tube-grafting protocol described in Meyer et al. (41). Our study 137
included three rootstocks (BHN589; RST-04-106, and Maxifort) in four treatments: 1) 138
nongrafted BHN589 plants; 2) selfgrafted BHN589 plants (plants grafted to their own rootstock); 139
3) BHN589 grafted to RST-04-106; and 4) BHN589 grafted to Maxifort. We chose BHN589 as 140
scion primarily based on its popularity due to high yield and high-quality fruit with a long shelf 141
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life. For rootstocks, we selected Maxifort because it is a productive and popular rootstock, and 142
RST-04-106 as a new rootstock variety based on tomato breeders’ recommendations. 143
Our study included two sites: Olathe Horticulture Research and Extension Center 144
(OHREC) and Common Harvest Farm, a farm managed by a collaborating farmer. At each study 145
site, the four graft treatments were assigned to four plots per block in a randomized complete 146
block design. Each plot consisted of 5-8 plants, and one middle plant per plot was sampled 147
during the peak growth stage. There were six blocks at OHREC, and four blocks at Common 148
Harvest Farm, such that for each year, a given rootstock was replicated 10 times. The experiment 149
was repeated for two years (2014 and 2015) with a similar design; however, the blocks and 150
rootstocks were randomized in each year. More information about each study site is provided in 151
Table S1. 152
Sample Preparation, DNA Extraction, and Amplicon Generation. To compare the 153
fungal communities, we selected a center plant from each plot and carefully dug it out such that 154
the majority of the roots remained intact. Endosphere and rhizosphere samples were prepared as 155
previously described (42) and the total genomic DNA was extracted using a DNA extraction kit 156
(MoBio UltraClean Soil DNA Isolation Kit; MoBio, Carlsbad, CA, USA) as per manufacturer’s 157
protocol, with slight modification during the homogenization step (42). To recover high genetic 158
diversity, we opted for the two-step PCR approach. The primary PCR amplicons were generated 159
in 50 μL reactions under the following conditions: 1 μM forward and reverse primers, 10 ng 160
template DNA, 200 μM of each dioxynucleotide, 1.5 mM MgCl2, 10 μL 5x Phusion Green HF 161
buffer (Finnzymes,Vantaa, Finland), 24.5 μL molecular biology grade water, and 1 unit (0.5 μL) 162
Phusion Hot Start II DNA Polymerase (Finnzymes, Vantaa, Finland). PCR cycle parameters 163
consisted of a 98° C initial denaturing step for 30 seconds, followed by 30 cycles at 98° C for 10 164
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seconds, 57° C annealing temperature for 30 seconds, and 72° C extension step for 30 seconds, 165
followed by a final extension step at 72° C for 10 minutes. All samples were PCR-amplified in 166
triplicate to minimize stochasticity, pooled, and cleaned using Diffinity RapidTip (Diffinity 167
Genomics, West Chester, PA, USA). In this PCR, we amplified the entire ITS region of fungal 168
rRNA genes using primers ITS1F-CTTGGTCATTTAGAGGAAGTAA and ITS4-169
TCCTCCGCTTATTGATATGC, (e.g. 48). The average amplicon length of the ITS region in 170
fungi is about 600 bp and could not be fully covered with the Illumina MiSeq platform (v.3-171
chemistry) in a single read. Thus, in the following nested PCR, only ITS2 out of the whole ITS 172
region was amplified using fITS7-ITS4 primers (49) incorporating unique Molecular Identifier 173
Tags (MIDs) at the 5' end of the reverse primer (ITS4). For the nested PCR, we used similar 174
reagents and PCR conditions as in the primary PCR, with some modifications: amplification 175
cycles were reduced to ten, total reaction volume was reduced to 25 ul, and 5 ul of cleaned PCR 176
product from the first PCR amplification was used as the DNA template. Similarly, the nested 177
PCR was run in triplicate, pooled by experimental unit, and cleaned with an Agencourt AmPure 178
cleanup kit using a SPRIplate 96-ring magnet (Beckman Coulter, Beverly, MA, USA) as per the 179
manufacturer’s protocol. Then, 200 ng of cleaned, barcoded PCR amplicons were combined per 180
experimental unit, and the final pool was cleaned again using an Agencourt AmPure cleanup kit 181
as above. Illumina MiSeq adaptors were ligated to the library and paired-end sequenced on a 182
MiSeq Personal Sequencing System (Illumina, San Diego, CA, USA) using MiSeq Reagent Kit 183
V3 with 600 cycles. The endosphere and the rhizosphere amplicon libraries were sequenced 184
separately in two runs. Adaptor ligation and sequencing were performed at the Integrated 185
Genomics Facility at Kansas State University. All sequence data generated in this study were 186
deposited in the NCBI Sequence Read Archive depository (BioProject:……). 187
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Bioinformatics and OTU Designation. The sequence library of fastq files was curated 188
using the MOTHUR pipeline (Version 1.33.3; (50)) following steps modified from the MiSeq 189
Standard Operating Protocol (SOP; www.mothur.org/wiki/MiSeq_SOP). Briefly, the forward 190
and the reverse reads were assembled into contigs using the default alignment algorithm. Any 191
sequences shorter than 250 base pairs or containing an ambiguous base call or more than eight 192
homopolymers or missing MIDs were removed from the library. Barcoded sequences were 193
assigned to experimental units, and fasta and group files for the endosphere and rhizosphere 194
libraries were merged and processed together for the remaining steps in the MOTHUR pipeline. 195
The pairwise distance matrix based on the filtered sequences was created and data clustered into 196
OTUs at 97% sequence similarity using the nearest-neighbor joining algorithm. The clustered 197
OTUs were assigned to a putative taxonomic identity using a Bayesian classifier (51) referencing 198
the UNITE plus INSD non-redundant ITS database (52). To minimize the bias resulting from 199
unequal sequence counts per sample, samples were rarified at the sequence frequency of the 200
sample yielding the lowest count (6,777). The final curated OTU database included 1,084,281 201
total sequences representing 16,151 fungal OTUs, including singletons (5376). 202
Statistical analyses. We evaluated the network of associations among fungal OTUs to 203
better understand the community composition and the interactions therein. The observed OTU 204
database was divided into eight subsets, each combination of the four rootstocks and two 205
compartments (endosphere and rhizosphere), such that we constructed eight networks in total. In 206
our network models, a node represents an OTU and a link exists between a pair of OTUs if there 207
is strong evidence (p < 0.01) that their frequencies are correlated across samples. Reducing false 208
associations due to compositional bias in network modeling of microbiome data is important for 209
clearer interpretation (53). Thus, we used a Sparse Correlations for Compositional data (SparCC) 210
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method to evaluate the pairwise associations (53), designed to minimize the compositional bias 211
effect due to normalization. In our analyses, associations were defined in 20 iterations, and the 212
significance of a pairwise association was determined from 100 bootstrapped datasets. Once the 213
matrix defining all the pairwise associations was derived, we selected only those OTUs for 214
which the absolute value of the association was greater than 0.5 (and p < 0.01) in the network 215
analyses for each of the rootstock genotypes. 216
To identify the OTUs associated with tomato yield in each rootstock, a regression-based 217
random forest model was fitted to the observed data. Marketable tomato yield data reported by 218
Meyer et al. (41) was the response variable and fungal OTUs were potential predictors. In order 219
to minimize the influence of rare and transient OTUs, only those OTUs that occurred in more 220
than 20% of the samples were filtered then transformed using Z-scores. The transformed data 221
were used for training the model. OTUs in the resulting model were ranked by their importance 222
based on increased mean square error (%IncMSE) of the model. Random forest uses out-of-bag 223
(OOB) samples (set of samples not used for constructing the tree) to estimate the MSE error for 224
each decision tree. The average MSE across the decision trees is then evaluated with the error 225
due to the permutation of a variable (predictor) in the model, where an increase in error 226
(%IncMSE) represents feature importance. From the list of OTUs ranked by their importance to 227
the model, we selected all the OTUs with %IncMSE > 0 for constructing PhONA. PhONA 228
integrates the results from the random forest model for yield with the OTU-OTU association 229
network. We then plotted the resulting network using the igraph package (54) in R. To evaluate 230
the role of nodes in the network, we placed each node in one of four categories – peripherals, 231
module hubs, network hubs, and connectors – based on the within-module degree and among-232
module connectivity (24, 55). Categorization of individual OTUs based on differential 233
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abundance tests or role analyses were indicated visually as a component of the PhONA, to 234
provide an analysis integrating information from multiple analyses. 235
To evaluate the effects of rootstocks on fungal diversity, Shannon entropy and species 236
richness were evaluated using the vegan package (56) implemented as part of the phyloseq 237
package (57) in R (58). Differences in diversity across the rootstocks were compared using a 238
mixed model ANOVA in the lme4 package in R (59). Study site and sampling year were treated 239
as random factors, blocked by study sites, whereas the rootstock and compartments were treated 240
as fixed factors. Changes in fungal community composition across the samples were estimated 241
based on a Bray-Curtis dissimilarity matrix and visualized in non-metric multidimensional 242
scaling (NMDS) plots. The contribution of factors to the observed variation in fungal 243
composition was estimated in a permutational multivariate analysis of variance (PERMANOVA, 244
using 1000 permutations) using the adonis function in the vegan package (56). To identify OTUs 245
that were sensitive to the rootstock treatments, the observed frequency (proportion) of each OTU 246
was evaluated by fitting a generalized linear model (GLM) with negative binomial distribution, 247
to identify depleted or enriched OTUs (Differentially Abundant OTUs – DAOTUs). Likelihood 248
ratio tests and contrast analyses (between the hybrid rootstock and controls) were performed for 249
the fitted GLM to identify the DAOTUs. We used OTU frequencies from selfgrafts and 250
nongrafts as controls, in comparisons with other rootstocks using contrasts. All tests were 251
adjusted to control the false discovery rate (FDR, p < 0.01) using the Benjamini-Hochberg 252
method (60). A differential abundance test was performed within the controls (selfgraft vs. 253
nongraft) to identify the OTUs responsive to the grafting procedure itself. 254
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RESULTS 255
General Root-Associated Fungal Community in the Grafted Tomato System. Once 256
rare taxa (<10 sequence counts, which accounted for more than 90% of the observed OTUs) 257
were removed, the community consisted of 1586 OTUs and 1,063,017 sequences. Of these 258
sequences, 4.8% remained unclassified at the phylum level (Fig. S1). The classified sequences 259
represented Ascomycota (52.5%), Basidiomycota (25.6%), Zygomycota (11.5%), 260
Chytridiomycota (3.6%), Glomeromycota (1.7%), and Rozellomycota (0.07%) (Fig. 1). At the 261
class level, Pezizomycetes, Agaricomycetes, and Dothideomycetes were the most abundant 262
across all the rootstocks. At the order level, the communities were dominated by Pezizales, 263
Pleosporales, Cantharellales, Mortierellales, and Hypocreales. Analyses at the family level 264
revealed that Pyronemataceae, Mortierellaceae, Ceratobasidiaceae, and Pleosporaceae were the 265
most common taxa in the overall community. At the genus level, Mortierella spp., 266
Thanatephorus spp., and Alternaria spp. were the most abundant genera. 267
Effects of Grafting and Rootstock on α-Diversity. There was strong evidence for an 268
effect of rootstocks on OTU richness (Fdf, 3 = 8.6, p < 0.001) and Shannon entropy (Fdf, 3 = 3.2, p 269
= 0.02) for tomato RAF communities. Mean species richness was higher in both the endosphere 270
(p = 0.01) and rhizosphere (p = 0.001) of one of the hybrid rootstocks, Maxifort, compared to the 271
nongrafted control. Shannon entropy followed trends similar to richness with a higher estimate 272
for Maxifort; however, there was only evidence for higher Shannon entropy in Maxifort for the 273
rhizosphere (p = 0.004), but not for the endosphere (p = 0.6) (Fig. 2). Both species richness and 274
Shannon entropy were higher (p < 0.001) in the rhizosphere than in the endosphere across all the 275
rootstock genotypes (Fig. S2). 276
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Effects of Grafting and Rootstock on RAF Composition. Based on previous studies of 277
the plant genotype effect on the rhizobiome (42), we expected a significant effect of rootstocks 278
on the community compositions. Rootstock explained 2% of the variation in the RAF community 279
composition (PERMANOVA; Fdf, 3 = 1.39, R2 = 0.02, p = 0.02), while compartment, study site, 280
and year explained a greater proportion of the variation than rootstock (Fig. S3 and Table S2). 281
Endosphere-rhizosphere compartments accounted for 8.9% of the variation. Study site and 282
interannual variation explained 8.4% and 5.4% of the total variation, respectively (Table S2). 283
Comparison of DAOTUs. The analysis of differential abundance found effects of 284
rootstock genotype at the individual OTU level. While analyses of alpha diversity indicated 285
higher diversity in the rhizosphere than in the endosphere, we observed the opposite in the 286
analysis of DAOTUs, with nearly twice as many DAOTUs in the endosphere (n = 135) as in the 287
rhizosphere (n = 72) (Figs. 2 and S4). Comparison across rootstocks indicated a greater number 288
of DAOTUs in Maxifort (n = 92) than in RST-04-106 (n = 64) and the selfgraft control (n = 51). 289
Compared to the hybrid rootstocks, the number of depleted taxa was greater in the selfgraft 290
control (n = 27). Among the enriched OTUs in Maxifort, 31 OTUs belonged to Basidiomycota, 291
23 to Ascomycota, and 12 to Glomeromycota, whereas four basidiomycete, three ascomycete, 292
and two zygomycete OTUs were depleted in Maxifort. In the case of RST-04-106, enriched taxa 293
included 21 OTUs in Basidiomycota, 25 OTUs in Ascomycota and four OTUs in 294
Glomeromycota, whereas the depleted OTUs included five in Zygomycota. Between the self- 295
and nongraft controls, seven OTUs in Ascomycota, eight OTUs in Basidiomycota, and five 296
OTUs in Zygomycota were enriched, whereas 10 OTUs in Basidiomycota, six OTUs in 297
Ascomycota, four OTUs in Zygomycota, and four OTUs in Glomeromycota were depleted. 298
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Network Analysis/ General Network Structures. Fungal community complexity, 299
defined in terms of mean node degree and community structures/motifs, varied among the 300
rootstocks in both the endosphere and the rhizosphere, with a greater mean node degree for one 301
of the hybrid rootstocks, Maxifort, compared to both controls and RST-04-106 (Figs. 3, S5, S6, 302
and Table S3). Comparing compartments, complexity was higher in the rhizosphere than in the 303
endosphere (Figs. 3, S5, S6, and Table S3). In addition to the total number of links, the link type 304
(either positive or negative) differed among the rootstocks in both compartments (Table S3), 305
with a higher ratio of negative to positive links in Maxifort in both the endosphere and the 306
rhizosphere compartments. Rhizosphere fungal communities had a higher ratio of negative to 307
positive links as compared to the endosphere, for all rootstocks. Although we observed rootstock 308
specific or compartment specific effects on the node degree and ratio of negative to positive 309
links, the number of modules defined using a simulated annealing (SA) algorithm, were similar 310
in both the endosphere and rhizosphere compartments and across the rootstocks (Table S3). Our 311
analyses of node types divided the observed nodes in the association-network into four 312
categories: peripherals, module hubs, network hubs, and connectors. More taxa in the 313
rhizosphere were identified as key nodes than in the endosphere, and key nodes were more 314
common in the hybrid rootstocks than in the non- and selfgrafted controls (Figs. 4 and 5). 315
Random Forest and PhONA. Using random forest models, we identified the 316
compartment-specific OTUs useful for predicting tomato yield in each rootstock. The number of 317
OTUs with positive %IncMSE (importance) differed between the compartments and among the 318
rootstocks. The number of OTUs with positive %IncMSE was higher in the rhizosphere (54.3 319
17. 1; mean sd) than in the endosphere (28 6.5), with the highest number for Maxifort. This 320
might indicate that rhizosphere OTUs are more important for determining host-specific traits. 321
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For each treatment, the observed PhONA provided a graphical representation that can be used to 322
select candidate taxa based on the network structure, phenotype, and node attributes evaluated in 323
differential abundance tests and role analyses. The rational selection of taxa could even be 324
improved with a priori knowledge about potential biological and ecological functions of the 325
observed OTUs. 326
327
DISCUSSION 328
This study demonstrated the effect of rootstocks on RAF community composition and structure. 329
General diversity-based analyses indicated the effect of rootstocks on RAF community 330
composition. The more productive hybrid rootstock, Maxifort, supported higher fungal richness 331
and Shannon entropy, as well as a greater number of DAOTUs than the controls, consistent with 332
the expectation that higher diversity and a higher number of responsive taxa (DAOTUs) would 333
be associated with a more productive genotype. Also consistent with our expectations, we 334
observed higher microbial diversity and fewer responsive taxa (DAOTUs) in the rhizosphere 335
than in the endosphere. The integrated host phenotype and OTU network in the PhONA 336
indicated potential candidate taxa for each rootstock, and community structures in the 337
endosphere and rhizosphere compartments. The general network analysis found more 338
interactions and complex structures among fungal taxa in Maxifort, consistent with our 339
expectation that increased community complexity would be associated with the more productive 340
rootstock. Community complexity, when defined in terms of mean node degree, also differed 341
between the compartments: the endosphere community was less complex than the rhizosphere 342
community in all the rootstocks. Overall, our study i) showed that rootstocks and grafting are 343
significant drivers of RAF community composition, diversity and structure, and ii) provided 344
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PhONA as an analytical framework to select potential candidate taxa for microbiome-based 345
agriculture, where the taxa were predictive of yield and in significant association with other 346
OTUs in the community. From a practical standpoint, our results indicate the potential for using 347
plant genotypes and agricultural practices to modulate plant-associated microbial communities, 348
and the potential for the PhONA illustrated here to improve identification of candidate taxa to 349
support microbiome-based crop production. 350
In contrast to network models that portray only microbe-microbe interactions, PhONA 351
integrates the results of random forest models of microbe association with phenotypic traits, to 352
support inference about candidate taxa and predictive microbiome analyses. Thus, candidate taxa 353
can be selected not only because they have a direct association with the host response variable(s), 354
but also because they have an indirect association with the host response variable through their 355
associations with other taxa in the community that have direct associations with host traits. For 356
instance, a node that has a positive association with the system phenotype node (in our case, 357
yield) might have negative or positive association with other OTUs. Such OTUs with indirect 358
positive associations with the desired phenotype might also be included in designing a 359
consortium for biofertilizers. Using a PhONA, a rational consortium can be selected based on the 360
phenotype of interest. Applying PhONA for the case of disease or pathogen-based phenotypes 361
could be useful for designing a rational consortium for biocontrol. We also observed some OTUs 362
with direct negative associations with the yield node. For instance, g_Thanatephorus is one of 363
the nodes identified as depleted in a differential abundance test (Fig. 2), and had a negative 364
association with yield for Maxifort (Fig. 3). Some members of g_Thanatephorus (OTU00001) 365
are known plant pathogens. Similarly, the g_Thanatephorus (OTU00012 ) node was observed to 366
be a connector and had a negative association with Maxifort yield (Fig. 3). Effort to control this 367
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particular taxon, as well as the taxa that are have positive associations with g_Thanatephorus, 368
might contribute to maximizing yield. Although we did not observe any disease symptoms in our 369
experiments, OTUs negatively associated with the yield node might represent a case of 370
asymptomatic yield loss. Efforts to explore negatively associated OTUs might provide 371
opportunity to minimize asymptomatic yield loss in crops. The main goal of PhONA is to 372
provide a systems framework to generate hypotheses about the role of microbiome components 373
in host function and performance, and to support the potential for mechanistic/predictive models 374
to understand host-microbiome interactions. In planta experiments with fungal cultures are 375
essential to test the hypotheses generated by these models, to help to differentiate between 376
associations that are based on consistent biological interactions and not simply based on shared 377
(or opposing) niche preferences. It is important to be cautious in attributing biological 378
interactions to the key structures in network attributes, because the links in the PhONA may or 379
may not depict biological interactions. That is, many links may represent correlative 380
relationships rather than causal relationships(24). For instance, the hub node in the network is 381
often regarded as a key node, but the high number of links with the hub node in the association 382
network could be due to shared niches, biological interactions, or a mixture thereof. If the 383
associations are mostly due to shared niches, removing such a hub node will have a more limited 384
effect, whereas removal of a hub node involved in many biological interactions could lead to 385
fragmentation of the microbial community. 386
RAF community composition, diversity, and interactions differed between the 387
endosphere and rhizosphere compartments. Although the endosphere and the rhizosphere are 388
continuous, they are distinct in community composition and diversity. Compartment specificity 389
in community composition and diversity has been reported for other plant species, in both natural 390
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and agricultural settings (27, 61, 62). Usually, bulk soil is considered a source of microbial 391
communities, a subset of which is selected for in the rhizosphere (27, 63), mainly as a function of 392
root exudates and rhizodeposits (38, 64-66). Selection of the rhizosphere microbiome could be 393
specific (e.g., antagonistic to plant pathogens) (67) or more general with less influence of host 394
genotype. In comparison, the endosphere of host plants often supports lower microbial diversity 395
compared to the rhizosphere (62, 63). Host tissues and immune systems act as a biotic filter (2). 396
As a result, the microbiome is more specialized in the endosphere than in the rhizosphere. The 397
RAF compartment specificity may also be an important consideration for microbe-based disease 398
management strategies – especially for the management of pathogens or pests that are 399
compartment specific, such as endoparasites and ectoparasites. 400
RAF community composition and diversity also differed among the rootstock genotypes. 401
Plant genotypes can structure root-associated fungal communities (27, 62, 68). The commercial 402
rootstocks in our study have been bred for defined purposes, specifically to provide resistance 403
against specific soil-borne pathogens and pests. Small variations in the host genotype could alter 404
the physiological and immunological responses in the root systems, thereby selecting genotype-405
specific root-associated fungal communities. For example, root exudates and metabolites could 406
be specific to plant host (69-71), and thus could provide specific control of microbial 407
communities (72, 73). In some cases, the host genotype effect can be directly attributed to root 408
anatomy (74). Effective and efficient root types and architectures are desired agronomic traits to 409
cope with biotic and abiotic stresses (75), and root systems vary among plant species and 410
genotypes (74). Moreover, the control of plant genotypes on microbial communities in the root 411
system is linked to nutrient positive feedbacks between the scion and rootstock. Plant genotypes 412
supporting higher yield and biomass may support higher microbial diversity by excreting a 413
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greater volume of photosynthates as root exudates and metabolites. Although we did not evaluate 414
root exudates, our study is consistent with a role for higher yield and biomass (as for the 415
Maxifort rootstock) associated with higher fungal diversity. Additionally, we observed an effect 416
of rootstocks on the RAF community composition. Collectively, the results support our 417
expectations of rootstock-specific control of the RAF community. 418
While the effects of plant genotype and compartment are evident, our current study of 419
fungi and previous study of bacteria (42) in the same system indicate that the magnitude of the 420
effect may vary between fungi and bacteria. The estimated effect of rootstocks on bacterial 421
communities (3%) was slightly greater than for fungal communities (2%), although the effects of 422
study sites and compartments were far greater in both the bacterial and fungal communities. As 423
in the RAF communities, compartment (endosphere vs. rhizosphere) and study sites played 424
major roles in structuring bacterial community composition (42); however, bacterial 425
communities in the endosphere and the rhizosphere were much more distinct than the fungal 426
endosphere and rhizosphere - possibly a result of their distinct growth forms (predominantly 427
mycelial or unicellular). Compartment and study site effects on the bacterial communities (42) 428
were greater than on the fungal communities in the same experiment: the estimated compartment 429
effect was three-fold higher (about 25-28%) for bacteria than fungi, and the estimated study site 430
effect was two-fold higher (about 17-18%). In another example plant system, Agave species, the 431
bacterial community was most strongly influenced by the plant compartment whereas the fungal 432
community was more strongly influenced by the study site (61). Lack of a strong study-site 433
effect on the fungal community in our study might indicate a lesser influence of edaphic factors 434
on fungal compared to bacterial communities, or the presence of a wider environmental range for 435
optimal fungal growth (76). Despite the differences in the magnitude of the effect for bacteria 436
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versus fungi, in both studies there was strong evidence for the effect of plant genotypes, 437
compartments, and study sites on the microbial community. 438
Our definition of complexity is based on interactions in networks, using a definition 439
similar to that used in other microbiome network analyses (21, 30, 77). However, an increased 440
number of interactions and complex network structures/motifs would tend to be observed 441
whenever there are more nodes in these association networks. The higher number of OTUs 442
associated with Maxifort would tend to result in higher complexity by this measure. Another 443
potential measure of complexity is network density, the proportion of links observed in a 444
network relative to the total number of possible links. In the case of the endosphere community, 445
Maxifort did not have higher density estimates than the other rootstocks, so by that measure of 446
complexity its network was not more complex. Statistical methods comparable to rarefaction, 447
designed to equalize the number of nodes across networks or methods to balance OTU richness 448
for sampling efforts(78), will be a valuable future effort for understanding how network 449
complexity responds to treatments and for making comparisons across studies. In addition, 450
methods to optimize and automate the selection of level of association to maximize the 451
ecosystem functions represent gaps and opportunities for microbiome network analyses. For 452
graphics in the figures in this analysis, we selected a level of association such that an 453
interpretable number of OTUs were depicted. Studies directly applied to identify potential 454
assemblages for agricultural applications could benefit from exploring results for a range of 455
thresholds. 456
The value of information depends on its ability to reduce uncertainty and maximize the 457
utility of predictions. Given the unprecedented complexity of microbial communities, it’s 458
difficult to make a rational selection of candidate taxa for biological applications. A 459
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22
system/community level analytical scheme to incorporate information gained via multiple 460
sources or statistical analyses is imperative. Combining results obtained from different analyses 461
in the PhONA framework supports selection of a rational microbial consortium, enhancing the 462
quality of decision-making. However, careful interpretation is needed, because often two or more 463
OTUs with the same name/taxonomy may have totally different roles in the network. This can be 464
due in part to the low resolving power of the current reference databases. Improvement of 465
reference databases and strain level information will enhance PhONA in the future. 466
Our study indicates the rootstock-genotype specific effect on RAF diversity, composition, 467
and interactions, and also shows how to integrate system phenotypes such as plant yield in a 468
network-based model to support selection of candidate taxa for biological use. However, in 469
sequence-based studies such as ours, the biological and functional significance of the candidate 470
OTUs is commonly unknown. Follow-up experiments with fungal cultures will be necessary to 471
determine the biological roles of the candidate OTUs, and to differentiate causal associations 472
from correlations based on niche preference. Similarly, further development of PhONA to 473
incorporate temporal microbiome data and Bayesian learning and inference methodologies (79, 474
80) has the potential to support causal inference, including understanding of directionality in 475
microbiome networks. PhONA utilizes a random forest model to link OTUs with a system 476
phenotype, although any other models such as generalized linear models or regression could also 477
be used. Given the nature of microbiome data with a high number of features (p) and relatively 478
small number of samples (n), other models to address the n x p problem can readily be applied in 479
PhONA when they improve on random forest models. Additionally, with a larger sample size, 480
more rigorous approaches such as model validation by splitting data into training and test sets 481
could also be used to enhance PhONA. Models to integrate multiomics data to predict the 482
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23
structure of microbial assemblages and provide mechanistic understanding are another domain 483
where an integrated network model like PhONA may offer a new perspective. As lab-based 484
technologies and computational resources become less expensive, and are combined with 485
improved predictive analytics such as PhONA, microbial community analyses can go beyond 486
simple analyses of diversity studies to help make microbiome-based agriculture a reality. 487
488
Acknowledgements 489
We appreciate support from the Ceres Trust, a USDA NCR SARE Research and Education 490
Grant (LNC13-355), the Kansas Agricultural Experiment Station, the National Institute for 491
Mathematical and Biological Synthesis (NIMBioS), and the University of Florida. We thank 492
members of Garrett lab for their comments on the early version of the manuscript. K.A.G., A.J., 493
M.M.K., C.L.R., and R.P. designed the study. R.P. and L.G.M. collected and processed the 494
samples. R.P. analyzed and interpreted the data. R.P., K.A.G., and A.J. wrote the manuscript. 495
R.P., K.A.G., A.J., M.M.K., C.L.R., and L.G.M. revised the manuscript. All authors read and 496
approved the final manuscript. 497
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Uberbacher E, Tuskan GA, Vilgalys R, Doktycz MJ, Schadt CW. 2011. Distinct 676
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diversity accounting for host sharing. Nat Ecol Evol 3:1070-1075. 716
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79. McGeachie MJ, Sordillo JE, Gibson T, Weinstock GM, Liu YY, Gold DR, Weiss ST, 717
Litonjua A. 2016. Longitudinal prediction of the infant gut microbiome with dynamic 718
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microbiome. Curr Opin Biotechnol 51:146-153. 721
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35
FIG 1 A systematic diagram representing the Phenotype-OTU network analysis (PhONA), 722
where an OTU-OTU association network (A) was combined with the nodes selected based on a 723
random forest model (B) to create PhONA (C). 724
725
FIG 2 Enriched and depleted OTUs across tomato rootstock genotypes (nongraft BHN589, 726
selfgraft BHN589, and BHN589 grafted on two hybrid rootstocks (RST-04-106 and Maxifort)) 727
evaluated for the rhizosphere (A) and the endosphere (B), using OTU counts from self-grafts and 728
non-grafts as controls. All the tests were adjusted to control the false discovery rate (FDR, p < 729
0.01) using the Benjamini-Hochberg method. Each point represents an OTU labeled at the genus 730
level and colored based on phylum, and the position along the x axis represents the abundance 731
fold-change contrast with controls (except for the selfgraft vs. nongraft comparison, where the 732
nongraft treatment was used as a control for the contrast). 733
734
FIG 3 Phenotype-OTU network analysis (PhONA) of endosphere fungal taxa for BHN589 735
grafted on Maxifort. Node color indicates the phylum, except that the tomato-color node 736
represents yield associated with the rootstock. Nodes connected to the rootstock yield node with 737
black links are taxa that were predictive of rootstock yield, where dotted and solid lines indicate 738
negative and positive associations (Pearson's correlation coefficient) with the yield node, 739
respectively. Red and blue links represent negative and positive associations, respectively, 740
between OTUs. Nodes are labeled with the lowest available taxonomic information. Square-741
shaped and star-shaped nodes represent DAOTU and key nodes detected in role analyses, 742
respectively. 743
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
36
FIG 4 Partitioning of endosphere fungal OTUs according to their network roles. Nodes were 744
divided into four categories based on within-module degree and among-module connectivity. 745
The blue dashed line represents a threshold value (0.62) for among-module connectivity, and the 746
red dashed line represents a threshold value (2.5) for within-module degree. Nodes were 747
categorized as peripherals, connectors, module hubs, and network hubs. Node color indicates 748
rootstock treatment. 749
750
FIG 5 Partitioning of rhizosphere fungal OTUs according to their network roles. Nodes were 751
divided into four categories based on within-module degree and among-module connectivity. 752
The blue dashed line represents a threshold value (0.62) for among-module connectivity, and the 753
red dashed line represents a threshold value (2.5) for within-module degree. Nodes were 754
categorized as peripherals, connectors, module hubs, and network hubs. Node color indicates 755
rootstock treatment. 756
757
FIG S1 Relative abundance of endosphere and rhizosphere fungi at the phylum level recovered 758
from four tomato rootstock treatments: nongraft BHN589, selfgraft BHN589, and BHN589 759
grafted on two hybrid rootstocks (RST-04-106 and Maxifort). Each individual bar represents a 760
rootstock treatment, and the colored area within the bar represents the relative abundance of the 761
corresponding phylum. 762
763
FIG S2 Comparison of overall fungal diversity (A) and richness (B) associated with tomato 764
rootstock genotypes and controls, evaluated in the endosphere and rhizosphere. The plot is 765
divided by the four tomato rootstock treatments: nongraft BHN589, selfgraft BHN589, and 766
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
37
BHN589 grafted on two hybrid rootstocks (RST-04-106 and Maxifort). Shannon entropy and 767
species richness, measures of community diversity, were both higher for Maxifort (p < 0.005) 768
compared to the self-graft and RST-04-106 in the rhizosphere, while there was not evidence for a 769
difference in Shannon entropy in the endosphere (p = 0.634). Treatment means were separated 770
using the "difflsmeans" function as specified in the lmerTest package in R. Tests for boxplots 771
sharing a letter or letter case type had p > 0.05. 772
773
FIG S3 Non-metric multidimensional scaling (NMDS) ordination plot of samples labeled by 774
tomato rootstock (nongraft BHN589, selfgraft BHN589, and BHN589 grafted on two hybrid 775
rootstocks (RST-04-106 and Maxifort)), compartment (endosphere or rhizosphere), and study 776
site, based on the Bray-Curtis dissimilarity distance matrix of fungal OTUs. Color indicates 777
rootstock treatment, shape indicates study site, and size indicates compartment. Ellipses 778
surrounding the samples indicate the 95% CI of the endosphere and rhizosphere sample 779
centroids. 780
781
FIG S4 Number of DAOTUs in a contrast analysis, evaluated for the endosphere and 782
rhizosphere compartments for four tomato rootstock treatments: nongraft and selfgraft BHN589, 783
and BHN589 grafted on two hybrid rootstocks (Maxifort and RST-04-106). The green color in 784
each bar represents the number of enriched taxa, and the red color represents the number of 785
depleted taxa. The number of differentially changed taxa was greater for the endosphere than for 786
the rhizosphere. Among the contrast pairs, hybrid rootstocks had a greater number of enriched 787
taxa compared to depleted taxa. However, the number of depleted taxa was higher compared to 788
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
38
enriched taxa in the controls. Among the treatments, Maxifort had the highest number of 789
DAOTUs in both compartments. 790
791
FIG S5 Phenotype-OTU network analysis (PhONA) of endosphere fungal taxa for tomato 792
rootstock treatments: (A) nongraft and (B) selfgraft BHN589, and BHN589 grafted on (C) RST-793
04-106. Node color indicates the phylum, except that the tomato-color node represents yield 794
associated with the rootstock. Nodes connected to the rootstock yield node with black links are 795
taxa that were predictive of rootstock yield, where dotted and solid lines indicate negative and 796
positive associations with the yield node, respectively. Red and blue links represent negative and 797
positive associations, respectively, between OTUs. Nodes are labeled with the lowest available 798
taxonomic information. Square-shaped and star-shaped nodes represent DAOTUs and key nodes 799
detected in role analyses, respectively. 800
801
FIG S6 Phenotype-OTU network analysis (PhONA) of rhizosphere fungal taxa for tomato 802
rootstock treatments: (A) nongraft and (B) selfgraft BHN589, and BHN589 grafted on two 803
hybrid rootstocks ((C) Maxifort and (D) RST-04-106). Node color indicates the phylum, except 804
that the tomato-color node represents yield associated with the rootstock. Nodes connected to the 805
rootstock yield node with black links are taxa that were predictive of rootstock yield, where 806
dotted and solid lines indicate negative and positive associations with the yield node, 807
respectively. Red and blue links represent negative and positive associations, respectively, 808
between OTUs. Nodes are labeled with the lowest available taxonomic information. Square-809
shaped and star-shaped nodes represent DAOTUs and key nodes detected in role analyses, 810
respectively. 811
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
39
TABLE S1 Sites included in the study, their soil type, and geographic coordinates. 812
813
TABLE S2 Results of the multivariate permutational analysis of variance (PERMANOVA) for 814
fungal taxon abundance data. Permutation was based on the Bray-Curtis distance matrix 815
generated for root associated fungal communities at the OTU level from four tomato rootstock 816
treatments: nongraft and selfgraft BHN589, and BHN589 grafted on two hybrid rootstocks 817
(Maxifort and RST-04-106) (1000 permutations). P values < 0.05 are in bold. 818
819
TABLE S3 Network attributes and links observed in the fungal association networks for four 820
tomato rootstock treatments: nongraft and selfgraft BHN589, and BHN589 grafted on two hybrid 821
rootstocks (Maxifort and RST-04-106) in each of rhizosphere and endosphere compartments. 822
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
1
1
2 FIG 1 A systematic diagram representing the Phenotype-OTU network analysis (PhONA), 3
where an OTU-OTU association network (A) was combined with the nodes selected based on 4
random forest model (B) to create PhONA (C). 5
6
Phenotype
OTU12
OTU5
OTU9
OTU16
OTU7OTU1
OTU2
OTU4
OTU5
OTU6
OTU7
OTU8OTU9
OTU10
OTU11OTU12
OTU13OTU14
OTU15OTU16
OTU17
OTU18OTU19
Phenotype
OTU12 OTU5
OTU9
OTU16
OTU7
OTU1
OTU4
OTU6
OTU10
OTU13
OTU14
OTU15
OTU17
OTU18
OTU19+ =
(A) OTU-OTU association network based on SparCC
(B) OTUs predictive of host phenotype, based on random forest model
(C) PhONA predicting candidate taxa based on association network (A) and phenotype-predictive OTUs (B)
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
2
(A) 7
8 9
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
3
(B) 10
11 FIG 2 Enriched and depleted OTUs across tomato rootstock genotypes (nongraft BHN589, 12
selfgraft BHN589, and BHN589 grafted on two hybrid rootstocks (RST-04-106 and Maxifort)) 13
evaluated for the rhizosphere (A) and the endosphere (B), using OTU counts from self-grafts and 14
non-grafts as controls. All the tests were adjusted to control the false discovery rate (FDR, p < 15
0.01) using the Benjamini-Hochberg method. Each point represents an OTU labeled at the genus 16
level and colored based on phylum, and the position along the x axis represents the abundance 17
fold change contrast with controls (except for the selfgraft vs. nongraft comparison, where the 18
nongraft treatment was used as a control for the contrast). 19
20
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
4
21
FIG 3 Phenotype-OTU network analysis (PhONA) of endosphere fungal taxa for BHN589 22
grafted on Maxifort. Node color indicates the phylum, except that the tomato-color node 23
represents yield associated with the rootstock. Nodes connected to the rootstock yield node with 24
black links are taxa that were predictive of rootstock yield, where dotted and solid lines indicate 25
negative and positive associations (Pearson's correlation coefficient) with the yield node, 26
respectively. Red and blue links represent negative and positive associations, respectively, 27
between OTUs. Nodes are labeled with the lowest available taxonomic information. Square-28
shaped and star-shaped nodes represent DAOTUs and key nodes detected in role analyses, 29
respectively. 30
Maxifortg__Mortierella
g__Mortierellap__Ascomycota_unclassified
g__Haematonectria
g__Gibberella
g__Rhizophagus
g__Rhizophagus
g__unclassified_Cercozoa
f__Pyronemataceae_unclassified
g__Thanatephorus
g__unclassified_Hypocreales
k__Fungi_unclassified
o__Pleosporales_unclassified
g__unclassified_Ustilaginales
p__Ascomycota_unclassified
g__unclassified_Cercozoa g__Thanatephorus
o__Pezizales_unclassified
f__Pyronemataceae_unclassified
g__Monosporascus g__Mortierella
o__Pezizales_unclassified
g__Alternaria
g__Olpidium
g__Olpidium
g__Monographella
g__Wallemia
g__Mortierella
g__Alternaria
f__Dipodascaceae_unclassified
g__Mortierella
g__Crinipellis
g__unclassified_Cercozoa
g__Guehomyces
g__Podospora
g__Exophiala
g__Acremonium
g__Phoma
g__Rhizophagus
c__Agaricomycetes_unclassified
k__Fungi_unclassified
o__Pleosporales_unclassified
k__Fungi_unclassified
k__Fungi_unclassified
g__unclassified_Hypocreales
g__Hydnomerulius
c__Dothideomycetes_unclassified
g__Occultifur
g__Gibberella
g__Ceratobasidium
p__Ascomycota_unclassified
g__unclassified_Lycoperdaceae
f__Pleosporaceae_unclassified
g__Hydnomerulius
k__Fungi_unclassified
g__unclassified_Orbiliaceae
g__Rhizophagus
o__Pleosporales_unclassified
k__Fungi_unclassified
g__Cryptococcus
f__Pyronemataceae_unclassified
g__Mortierella
g__Conocybe
g__Septoglomusg__Glomus
k__Fungi_unclassified
c__Zygomycota_class_Incertae_sedis_unclassified
g__unclassified_Auriculariales
f__Pyronemataceae_unclassified
g__Conocybeg__Rhizophagus
g__Rhizophagus
g__Coprinopsis
k__Fungi_unclassified
g__Coprinopsis
g__Fusarium
f__Glomeraceae_unclassified
g__Malassezia
g__Rhizophagus
g__Septoglomus
g__unclassified_Cercozoa
g__unclassified_Auriculariales
f__Pyronemataceae_unclassified
g__Glomus
f__Pyronemataceae_unclassified
g__Glomus
g__Glomus
f__Glomeraceae_unclassified
g__Glomus
c__Agaricomycetes_unclassified
k__Fungi_unclassified
g__Rhizophagus
c__Sordariomycetes_unclassified
MaxifortAscomycotaBasidiomycotaCercozoaChytridiomycotaGlomeromycotaUnclassifiedZygomycota
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
5
31
32 33 FIG 4 Partitioning of endosphere fungal OTUs according to their network roles. Nodes were 34
divided into four categories based on within-module degree and among-module connectivity. 35
The blue dashed line represents a threshold value (0.62) for among-module connectivity, and the 36
red dashed line represents a threshold value (2.5) for within-module degree. Nodes were 37
categorized as peripherals, connectors, module hubs, and network hubs. Node color indicates 38
rootstock treatment. 39
40
o__Mortierellales
o__Pezizales
o__Saccharomycetales
o__Olpidiales
o__Pleosporales
o__Chaetothyriales
k__Fungi_unclassified
o__Cantharellales
k__Fungi_unclassifiedo__PezizalesModule hubs Network hubs
Peripherals Connectors-2
-1
0
1
2
3
0.00 0.25 0.50 0.75 1.00Among-module connectivity
With
in-m
odul
e de
gree
RootstocksNongraftSelfgraftRST-04-106Maxifort
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
6
41 42 43
44 45 FIG 5 Partitioning of rhizosphere fungal OTUs according to their network roles. Nodes were 46
divided into four categories based on within-module degree and among-module connectivity. 47
The blue dashed line represents a threshold value (0.62) for among-module connectivity, and the 48
red dashed line represents a threshold value (2.5) for within-module degree. Nodes were 49
categorized as peripherals, connectors, module hubs, and network hubs. Node color indicates 50
rootstock treatment. 51
52
k__Fungi_unclassified
o__Mortierellales
o__Mortierellales
o__Hypocreales
c__Sordariomycetes_unclassified
o__Tremellales
o__Capnodiales
o__Xylariales
o__Ustilaginales
o__Pleosporales
o__Hypocreales
o__Pezizales
k__Fungi_unclassified
o__Wallemiales
o__Tremellales
o__Olpidiales
o__Saccharomycetales
o__Hypocreales
o__Trichosporonales
k__Fungi_unclassified
o__Phallales
k__Fungi_unclassified
o__Hypocrealeso__Pleosporales
o__Saccharomycetales
o__Mortierellales
o__Pezizales
o__Basidiobolales
o__Dothideomycetes_order_Incertae_sedis
k__Fungi_unclassifiedModule hubs Network hubs
Peripherals Connectors-2
0
2
0.00 0.25 0.50 0.75 1.00Among-module connectivity
With
in-m
odul
e de
gree
RootstocksNongraftSelfgraftRST-04-106Maxifort
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
7
53
54 FIG S1 Relative abundance of endosphere and rhizosphere fungi at the phylum level recovered 55
from four tomato rootstock treatments: nongraft BHN589, selfgraft BHN589, and BHN589 56
grafted on two hybrid rootstocks (RST-04-106 and Maxifort). Each individual bar represents a 57
rootstock treatment, and the colored area within the bar represents the relative abundance of the 58
corresponding phylum. 59
60
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
8
(A) 61
62 63
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
9
64
(B) 65
66 FIG S2 Comparison of overall fungal diversity (A) and richness (B) associated with tomato 67
rootstock genotypes and controls, evaluated in the endosphere and rhizosphere. The plot is 68
divided by the four tomato rootstock treatments: nongraft BHN589, selfgraft BHN589, and 69
BHN589 grafted on two hybrid rootstocks (RST-04-106 and Maxifort). Shannon entropy and 70
species richness, measures of community diversity, were both higher for Maxifort (p < 0.005) 71
compared to the self-graft and RST-04-106 in the rhizosphere, while there was not evidence for a 72
difference in Shannon entropy in the endosphere (p = 0.634). Treatment means were separated 73
using the "difflsmeans" function as specified in the lmerTest package in R. Tests for boxplots 74
sharing a letter or letter case type had p > 0.05. 75
76
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
10
77
78 FIG S3 Non-metric multidimensional scaling (NMDS) ordination plot of samples labeled by 79
tomato rootstock (nongraft BHN589, selfgraft BHN589, and BHN589 grafted on two hybrid 80
rootstocks (RST-04-106 and Maxifort)), compartment (endosphere or rhizosphere), and study 81
site, based on the Bray-Curtis dissimilarity distance matrix of fungal OTUs. Color indicates 82
rootstock treatment, shape indicates study site, and size indicates compartment. Ellipses 83
surrounding the samples indicate the 95% CI of the endosphere and rhizosphere sample 84
centroids. 85
86
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
11
87 FIG S4 Number of DAOTUs in a contrast analysis, evaluated for the endosphere and 88
rhizosphere compartments for four tomato rootstock treatments: nongraft and selfgraft BHN589, 89
and BHN589 grafted on two hybrid rootstocks (Maxifort and RST-04-106). The green color in 90
each bar represents the number of enriched taxa, and the red color represents the number of 91
depleted taxa. The number of differentially changed taxa was greater for the endosphere than for 92
the rhizosphere. Among the contrast pairs, hybrid rootstocks had a greater number of enriched 93
taxa compared to depleted taxa. However, the number of depleted taxa was higher compared to 94
enriched taxa in the controls. Among the treatments, Maxifort had the highest number of 95
DAOTUs in both compartments. 96
97
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
12
A)98
99 100
Nongraft
p__Ascomycota_unclassified
g__Gibberella
g__unclassified_Cercozoa
c__Agaricomycetes_unclassified
g__unclassified_Cercozoa
g__Ramicandelaberk__Fungi_unclassified
f__Pleosporaceae_unclassified
p__Ascomycota_unclassified
g__Mortierellag__Phallus
o__Pezizales_unclassified
f__Pyronemataceae_unclassified
o__Pezizales_unclassified
g__Alternariag__Phoma
g__Mortierella
g__Olpidium
g__Haematonectria
g__Thanatephorus
g__Thanatephorus
g__Monographella
g__Mortierella
g__Mortierella
g__Mortierella
g__Alternaria
g__Mortierella
g__Mortierella
g__unclassified_Ceratobasidiaceae
c__Agaricomycetes_unclassified
g__Mortierella
g__Podospora
g__Botryodontia
g__Coprinus
g__unclassified_Ustilaginales
c__Agaricomycetes_unclassified
k__Fungi_unclassifiedg__Rhizophagus
c__Agaricomycetes_unclassified
g__Mortierella
g__Mortierella
k__Fungi_unclassified
k__Fungi_unclassified
g__Fusarium
g__Blastobotrys
p__Ascomycota_unclassified
c__Dothideomycetes_unclassified
g__Mortierella
g__Rhizophagus
g__unclassified_Bionectriaceae
k__Fungi_unclassified
g__unclassified_Orbiliaceae
g__Rhizophagus
f__Pleosporaceae_unclassified
g__Ramicandelaber
k__Fungi_unclassified
f__Pyronemataceae_unclassified
f__Pyronemataceae_unclassified
g__Rhizophagus
g__Rhizophagus
o__Pezizales_unclassified
g__Mortierella
g__Fusarium
g__Rhizophagus
g__Rhizophagus
g__Rhizophagus
f__Pyronemataceae_unclassified
f__Pyronemataceae_unclassified
c__Eurotiomycetes_unclassified
f__Pyronemataceae_unclassified
p__Basidiomycota_unclassified
o__Pleosporales_unclassified
g__Cylindrocarpon
g__Thanatephorus
c__Agaricomycetes_unclassified
f__Pyronemataceae_unclassified
k__Fungi_unclassified
g__Thanatephorus
c__Agaricomycetes_unclassified
k__Fungi_unclassified
NongraftAscomycotaBasidiomycotaCercozoaChytridiomycotaGlomeromycotaUnclassifiedZygomycota
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
13
B)101
102
Selfgraft
g__Gibberella
g__Mortierella
g__Malassezia
g__Olpidium
g__Mortierella
g__Mortierella
g__Fusarium
g__Scedosporium
k__Fungi_unclassifiedg__Thanatephorus
p__Ascomycota_unclassified
p__Ascomycota_unclassified
g__Alternaria
g__Monosporascus
p__Ascomycota_unclassified
g__Thanatephorusp__Basidiomycota_unclassified
g__Acremonium
k__Fungi_unclassified
o__Pezizales_unclassified
f__Pyronemataceae_unclassified
o__Pezizales_unclassified
g__Alternaria
g__Phoma
g__Mortierella
g__Haematonectria
g__Thanatephorus
g__Thanatephorus
g__Monographella
g__Mortierella
p__Ascomycota_unclassified
c__Sordariomycetes_unclassified
p__Ascomycota_unclassified
g__Mortierella
k__Fungi_unclassified
g__Monographella
g__unclassified_Cercozoa
g__Guehomyces
g__Mortierella
g__Davidiella
g__Exophiala
g__Botryodontia
g__unclassified_Ustilaginales
g__Ramicandelaber
c__Agaricomycetes_unclassified
g__unclassified_Cercozoa
k__Fungi_unclassified
g__Gymnoascus
g__Phoma
g__Rhizophagus
g__Fusarium
g__unclassified_Hypocreales
g__Wallemia
g__Spizellomyces
k__Fungi_unclassified
g__unclassified_Orbiliaceaeg__Debaryomyces
g__Preussia
f__Pyronemataceae_unclassified
g__Edenia
g__Mortierella
f__Pyronemataceae_unclassified
f__Pleosporaceae_unclassified
g__Rhizophagus
g__Ceratobasidiumk__Fungi_unclassified
g__Pochonia
g__Mortierella
f__Phaeosphaeriaceae_unclassified
c__Zygomycota_class_Incertae_sedis_unclassifiedg__Rhizophagus
g__Leptospora
k__Fungi_unclassified
f__Stephanosporaceae_unclassified
p__Ascomycota_unclassified
g__Pulvinula
g__unclassified_Pleosporales
k__Fungi_unclassified
p__Glomeromycota_unclassified
g__unclassified_Spizellomycetaceae
g__Rhizophagus
g__Bipolaris
g__Cyphellophora
SelfgraftAscomycotaBasidiomycotaCercozoaChytridiomycotaGlomeromycotaUnclassifiedZygomycota
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
14
C)103
104 105 FIG S5 Phenotype-OTU network analysis (PhONA) of endosphere fungal taxa for tomato 106
rootstock treatments: (A) nongraft and (B) selfgraft BHN589, and BHN589 grafted on (C) RST-107
04-106.Node color indicates the phylum, except that the tomato-color node represents yield 108
associated with the rootstock. Nodes connected to the rootstock yield node with black links are 109
taxa that were predictive of rootstock yield, where dotted and solid lines indicate negative and 110
positive associations with the yield node, respectively. Red and blue links represent negative and 111
positive associations, respectively, between OTUs. Nodes are labeled with the lowest available 112
taxonomic information. Square-shaped and star-shaped nodes represent DAOTUs and key nodes 113
detected in role analyses, respectively. 114
115
RST-04-106
p__Ascomycota_unclassified
g__Alternaria
g__Alternaria g__unclassified_Hypocreales
o__Pezizales_unclassified
p__Ascomycota_unclassified
f__Nectriaceae_unclassified
f__Pyronemataceae_unclassified
g__Phoma
g__Mortierella
g__Olpidium
g__Wallemia
g__Thanatephorus
g__Thanatephorus
g__Olpidium
g__Mortierella
p__Ascomycota_unclassified
g__Gibberella
p__Ascomycota_unclassified
k__Fungi_unclassified
g__Mortierella
g__Monographella
g__unclassified_Cercozoa
c__Agaricomycetes_unclassified
g__Mortierella
g__Exophiala
g__Botryodontia
g__Candida
g__Cordyceps
g__unclassified_Ustilaginales
g__unclassified_Cercozoa
c__Agaricomycetes_unclassified
g__Mortierella
g__Paurocotylis
k__Fungi_unclassified
g__Cryptococcus
g__Septoglomus
p__Basidiomycota_unclassified
g__Coprinopsis
g__unclassified_Orbiliaceae
c__Agaricomycetes_unclassified
g__Mortierella
k__Fungi_unclassified
k__Fungi_unclassified
f__Pyronemataceae_unclassified
g__Septoglomus
c__Zygomycota_class_Incertae_sedis_unclassified
f__Pyronemataceae_unclassified
g__Cyathus
f__Pleosporaceae_unclassified
g__Ceratobasidium
f__Glomeraceae_unclassified
g__Mortierella
g__Malassezia
g__Septoglomus
f__Pyronemataceae_unclassified
k__Fungi_unclassified
f__Pyronemataceae_unclassified
p__Ascomycota_unclassified
g__unclassified_Ceratobasidiaceae
g__Thanatephorus
f__Polyporaceae_unclassified
g__Piriformospora
g__Capronia
g__Rhizophlyctis
k__Fungi_unclassified
g__Mortierella
g__Hyphodontia
RST-04-106AscomycotaBasidiomycotaCercozoaChytridiomycotaGlomeromycotaUnclassifiedZygomycota
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
15
A)116
117 118
Nongraft
g__Mortierella
g__Wallemia
g__Mortierella
g__Alternaria
g__Scedosporium
c__Agaricomycetes_unclassified
k__Fungi_unclassified
g__Blastobotrys
o__Pezizales_unclassified
g__Cryptococcusf__Dipodascaceae_unclassified
g__unclassified_Ustilaginales
k__Fungi_unclassified
g__Lysurus
g__unclassified_Cercozoa
g__Phallus
g__Cryptococcus
g__Mortierella
g__unclassified_Microascales
g__Gibberella
o__Pezizales_unclassified
f__Pyronemataceae_unclassified
g__Phoma
g__Olpidium
g__Thanatephorus
g__Thanatephorus
g__Cryptococcus
g__Olpidium
g__Monographella
g__Mortierella
g__Mortierella
c__Sordariomycetes_unclassified
g__Mortierella
p__Ascomycota_unclassified
o__Capnodiales_unclassified
g__Alternaria
g__Mortierella
g__Wallemia
g__Mortierella
g__unclassified_Ceratobasidiaceae
k__Fungi_unclassified
g__Guehomyces
g__Davidiella
g__Bipolaris
g__Exophiala
g__Botryodontia
g__Coprinus
g__Ramicandelaber
c__Agaricomycetes_unclassified
c__Agaricomycetes_unclassified
k__Fungi_unclassified
g__Acremonium
g__Mortierella
g__Acremonium
c__Agaricomycetes_unclassified
g__Alternaria
g__Ustilago
g__Mortierella
g__unclassified_Hypocreales
g__Melampsora
g__Wallemia
k__Fungi_unclassified
g__Sporobolomyces
c__Dothideomycetes_unclassified
g__Occultifur
g__Trichosporon
g__Mortierella
g__unclassified_Stephanosporaceae
c__Eurotiomycetes_unclassified
g__Gibberella
g__Clonostachys
g__Scedosporium
g__unclassified_Lycoperdaceae
g__Lysurus
f__Pleosporaceae_unclassified
g__unclassified_Bionectriaceae
g__unclassified_Pyronemataceae
g__Pseudogymnoascus
k__Fungi_unclassified
c__Agaricomycetes_unclassified
p__Basidiomycota_unclassified
g__unclassified_Stephanosporaceae
g__Basidiobolus
g__unclassified_Sporidiobolales
g__Rhodosporidium
k__Fungi_unclassifiedg__Rhodotorula
k__Fungi_unclassified
g__unclassified_Bionectriaceae
g__Bullera
g__Mortierella
g__Myrothecium
g__Mortierella
k__Fungi_unclassified
g__Ramicandelaber
g__Occultifur
g__Rhizophagus
f__Pleosporaceae_unclassified
g__Bipolaris
f__Sporidiobolales_family_Incertae_sedis_unclassified
g__Mortierella
g__unclassified_Nectriaceae
g__Fusarium
g__unclassified_Sporidiobolales
g__Lysurus
g__Cryptococcus
g__Cystofilobasidium
g__Parasola
g__Lysurus
k__Fungi_unclassified
g__Septoglomus
g__Lachancea
g__unclassified_Rozellomycota
k__Fungi_unclassified
g__Mortierella
g__Parasola
g__Candida
f__Chionosphaeraceae_unclassified
g__Coprinopsis
o__Pleosporales_unclassified
o__Hypocreales_unclassified
g__Coprinopsis
g__Scedosporium
g__Acremonium
k__Fungi_unclassified
g__Minimedusa
g__Hypoxylon
c__Agaricomycetes_unclassified
p__Ascomycota_unclassifiedg__Mortierella
g__Hyphoderma
g__Hypocrea
g__Occultifur
g__unclassified_Polyporales
k__Fungi_unclassified
g__Funneliformis
c__Sordariomycetes_unclassified
NongraftAscomycotaBasidiomycotaCercozoaChytridiomycotaGlomeromycotaRozellomycotaUnclassifiedZygomycota
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
16
B)119
120 121
Selfgraft
g__unclassified_Cercozoa
g__Guehomyces
f__Pyronemataceae_unclassified
g__Scedosporium
g__Bipolaris
g__Mortierella
g__Rhodosporidium
g__Pseudogymnoascus
g__Fusarium
g__Trichoderma
o__Hypocreales_unclassified
g__Phoma
g__Cryptococcusg__Scedosporium
k__Fungi_unclassified
c__Agaricomycetes_unclassified
k__Fungi_unclassified
o__Pezizales_unclassified
o__Pezizales_unclassified
g__Alternaria
g__Phoma
g__Olpidium
g__Haematonectria
g__Wallemia
g__Thanatephorus
g__Cryptococcus
g__Olpidium
g__Monographella
g__Mortierella
g__Mortierella
c__Sordariomycetes_unclassified c__Sordariomycetes_unclassified
g__Mortierella
g__Cryptococcus
g__Acremonium
p__Ascomycota_unclassified
o__Capnodiales_unclassified
g__Alternariag__Mortierella
f__Dipodascaceae_unclassified
c__Agaricomycetes_unclassified
g__Mortierellag__Davidiella
g__Bipolaris
g__Acremonium
g__Exophiala
g__Botryodontia
g__Metarhizium
g__unclassified_Ustilaginales
g__Ramicandelaber
c__Agaricomycetes_unclassified
g__Acremonium
g__Gymnoascus
p__Ascomycota_unclassified
g__Acremonium
g__Mortierella
g__Acremonium
g__Rhodotorula
g__Mortierella
g__Alternaria
g__Ustilago
g__unclassified_Hypocreales
g__Fusarium
f__Phaeosphaeriaceae_unclassified
g__Rhodotorula
k__Fungi_unclassified
g__Fusarium
g__Blastobotrys
g__Occultifur g__Trichosporon
g__Gibberella
g__Peziza
g__Scedosporium
k__Fungi_unclassified
g__Lysurus
g__unclassified_Bionectriaceaeg__unclassified_Pyronemataceae
g__Mortierella
f__Pleosporaceae_unclassified
k__Fungi_unclassified
g__unclassified_Bionectriaceae
f__Microascaceae_unclassified
g__Myrothecium
g__Botryodontia
k__Fungi_unclassified
g__Occultifur
g__Phallus
g__Bipolaris
g__Ceratobasidium
f__Sporidiobolales_family_Incertae_sedis_unclassified
g__Candida
g__Sporobolomyces
g__Preussiag__unclassified_Cercozoa
g__Stachybotrys
g__unclassified_Gymnoascaceae
k__Fungi_unclassified
g__Umbelopsis
g__Olpidium
g__unclassified_Zygomycota
k__Fungi_unclassified
g__Trichosporon
k__Fungi_unclassified
g__unclassified_Zygomycota
k__Fungi_unclassified
g__unclassified_Pleosporaceae
k__Fungi_unclassified
g__Lysurus
g__Mortierellag__Candida
g__Occultifur
g__Hypocrea
p__Ascomycota_unclassified
g__Mortierella
g__Tomentella
g__Cryptococcus
g__Mortierella
g__Lycoperdon
c__Agaricomycetes_unclassified
SelfgraftAscomycotaBasidiomycotaCercozoaChytridiomycotaUnclassifiedZygomycota
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
17
C)122
123 124
RST-04-106
g__Cryptococcus
g__Olpidium
g__Pseudozyma
g__Mortierella
g__Phallus
g__Sporobolomyces
g__Wallemia
g__Phoma
g__Pseudozyma
g__Clonostachys
k__Fungi_unclassified
g__Mortierella
g__Bipolaris
g__Cryptococcus
g__Mortierella
g__Fusarium
f__Microascaceae_unclassified
g__Cryptococcus
c__Agaricomycetes_unclassified
g__Ramicandelaber
g__Ramicandelaber g__Cryptococcus
k__Fungi_unclassified
c__Sordariomycetes_unclassified
g__Cordyceps
g__Mortierella
o__Pezizales_unclassified
f__Pyronemataceae_unclassified
o__Pezizales_unclassified
g__Alternaria
g__Phoma
g__Olpidium
g__Haematonectria
g__Wallemia
g__Thanatephorus
g__Thanatephorus
g__Monographella
p__Ascomycota_unclassified
c__Sordariomycetes_unclassified
g__Mortierella
g__Acremonium
p__Ascomycota_unclassified
o__Capnodiales_unclassified
g__Alternaria
g__Mortierella
f__Dipodascaceae_unclassified
g__Wallemia
g__Mortierella
g__unclassified_Microascales
g__Guehomyces
g__Davidiella
g__Bipolaris
g__Acremonium
g__Exophiala
g__Botryodontia
g__Candida
g__Metarhizium
g__unclassified_Ustilaginales
c__Agaricomycetes_unclassified
g__Gymnoascus
g__Acremonium
g__Mortierella
g__Phallusg__Acremonium
g__Rhodotorula
g__Mortierella
g__Alternaria
g__unclassified_Hypocreales
g__Cryptococcus
g__Wallemia
g__Rhodotorula
p__Ascomycota_unclassified
g__Occultifur
g__Trichosporon
g__Olpidium
g__unclassified_Stephanosporaceae
c__Eurotiomycetes_unclassified
g__Septoglomus
g__Gibberella
g__Scedosporium
p__Basidiomycota_unclassified
g__Rhizophagus
g__Lysurus
g__Trichosporon
g__Pseudogymnoascus
c__Agaricomycetes_unclassified
g__Mortierella
g__Wallemia
g__Rhizophagus
g__unclassified_Stephanosporaceae
g__Wallemia
g__unclassified_Sporidiobolales
g__Debaryomyces
k__Fungi_unclassified
g__unclassified_Bionectriaceae
g__Wallemia
g__Mortierella
g__Septoglomus
g__Mortierella
f__Nectriaceae_unclassified
g__Myrothecium
k__Fungi_unclassified
g__Stagonosporag__Scedosporium
k__Fungi_unclassified
c__Agaricomycetes_unclassifiedg__unclassified_Sporidiobolales
g__Preussia
g__unclassified_Cercozoa
g__Stachybotrys
g__Mortierella
g__Rhizophagus
g__Lysurus
g__Phallus
g__Wallemia
g__Lysurus
g__Umbelopsisg__Septoglomus
g__Ascobolus
g__Mortierella
c__Agaricomycetes_unclassified
g__Spizellomyces
g__unclassified_Rozellomycota
g__Candida
g__Sterigmatomyces
g__Lysurus
o__Hypocreales_unclassified
g__Entoloma
k__Fungi_unclassified
g__Wallemia
k__Fungi_unclassified
p__Ascomycota_unclassified
g__Rhizophlyctis
g__Capronia
f__Stictidaceae_unclassified
c__Agaricomycetes_unclassified
c__Sordariomycetes_unclassified
g__unclassified_Chaetothyriales
k__Fungi_unclassified
g__Arachnion
g__Wallemia
g__Sporisorium
RST-04-106AscomycotaBasidiomycotaCercozoaChytridiomycotaGlomeromycotaRozellomycotaUnclassifiedZygomycota
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
18
D)125
126 FIG S6 Phenotype-OTU network analysis (PhONA) of rhizosphere fungal taxa for tomato 127
rootstock treatments: (A) nongraft and (B) selfgraft BHN589, and BHN589 grafted on two 128
hybrid rootstocks ((C) Maxifort and (D) RST-04-106). Node color indicates the phylum, except 129
that the tomato-color node represents yield associated with the rootstock. Nodes connected to the 130
rootstock yield node with black links are taxa that were predictive of rootstock yield, where 131
dotted and solid lines indicate negative and positive associations with the yield node, 132
respectively. Red and blue links represent negative and positive associations, respectively, 133
between OTUs. Nodes are labeled with the lowest available taxonomic information. Square-134
shaped and star-shaped nodes represent DAOTUs and key nodes detected in role analyses, 135
respectively. 136
137
Maxifort
f__Nectriaceae_unclassified
g__unclassified_Hypocreales
g__Mortierella
g__Phoma
g__Alternaria
g__Acremonium
g__Scedosporium
k__Fungi_unclassified
p__Basidiomycota_unclassifiedf__Phaeosphaeriaceae_unclassified
g__unclassified_Cercozoa
g__Wallemia
p__Ascomycota_unclassified
g__Mortierella
g__Mortierella
g__Mortierella
f__Pleosporaceae_unclassified
g__unclassified_Rozellomycota
g__Cryptococcus
g__Thanatephorus
g__Stachybotrys
g__unclassified_Nectriaceae
g__Tetracladium
g__Mortierella
g__Scedosporium
g__Septoglomus
g__Cryptococcus
g__Rhodotorula
g__Thanatephorus
g__Galactomyces
g__Phallus
g__Bullera
g__Rhodotorulag__Westerdykella
c__Agaricomycetes_unclassified
g__Candidag__Acremonium
g__Cystofilobasidium g__Mortierella
k__Fungi_unclassified
g__Bipolaris
o__Pezizales_unclassified
f__Pyronemataceae_unclassified
o__Pezizales_unclassified
g__Alternaria
g__Mortierella
g__Olpidium
g__Haematonectria
g__Cryptococcus
g__Fusarium
g__Olpidium
g__Monographella
c__Sordariomycetes_unclassified
g__Mortierella
g__Mortierella
o__Capnodiales_unclassified
g__Mortierella
f__Dipodascaceae_unclassified
k__Fungi_unclassified
g__Mortierella
c__Agaricomycetes_unclassified
g__Guehomyces
g__Davidiella
g__Bipolaris
g__Acremonium
g__Exophialag__Metarhizium
g__unclassified_Ustilaginalesg__Acremonium
k__Fungi_unclassified
g__Coprinellus
p__Ascomycota_unclassified
g__Acremonium
g__Phoma
g__Acremonium
g__Rhodotorula
g__Rhizophagus
o__Pleosporales_unclassified
g__Ustilago
g__Mortierella
g__Mortierella
g__Cryptococcus
g__Fusarium k__Fungi_unclassifiedk__Fungi_unclassified
g__Fusarium
p__Ascomycota_unclassified
g__Hydnomeruliusk__Fungi_unclassified
g__Occultifur
g__Trichosporon
g__unclassified_Stephanosporaceae
c__Eurotiomycetes_unclassified
g__Pseudaleuria
g__Ceratobasidium
g__Wallemia
g__Scedosporium
g__Pseudozyma
g__unclassified_Lycoperdaceae
g__Lysurus
f__Pleosporaceae_unclassified
g__unclassified_Bionectriaceae
g__Trichosporon
g__Pseudogymnoascus
k__Fungi_unclassified
g__Hydnomerulius
k__Fungi_unclassified
g__Mortierella
g__unclassified_Stephanosporaceae
g__Basidiobolus
g__unclassified_Sporidiobolales
g__Rhodosporidium
k__Fungi_unclassified
k__Fungi_unclassified
g__unclassified_Bionectriaceae
f__Microascaceae_unclassified
g__Pseudaleuria
g__Trichoderma
g__Septoglomusg__Mortierella
g__Myrothecium
g__Mortierella
k__Fungi_unclassified
k__Fungi_unclassified
g__Phallus
g__Stagonospora
g__Coprinopsis
g__Sporobolomyces
k__Fungi_unclassified
k__Fungi_unclassified
g__Coprinopsis
c__Agaricomycetes_unclassified
g__Fusarium
g__unclassified_Sporidiobolalesf__Nectriaceae_unclassified
g__Lysurus
g__Cryptococcus
g__Phallus
g__unclassified_Gymnoascaceae
k__Fungi_unclassified
k__Fungi_unclassified
g__Lysurus
g__Umbelopsis
g__Septoglomus
k__Fungi_unclassified
k__Fungi_unclassified
g__unclassified_Rozellomycota
k__Fungi_unclassified
g__Preussia
g__Sporobolomyces
g__Lysurus
g__Malassezia
k__Fungi_unclassified
g__unclassified_Hypocreales
o__Agaricales_unclassified
c__Sordariomycetes_unclassified
g__Coprinopsis
g__Glomus
g__Stagonospora
p__Ascomycota_unclassified
k__Fungi_unclassified
g__Glomus
g__Ganoderma
o__Hypocreales_unclassified
g__Coprinopsis
p__Basidiomycota_unclassified
k__Fungi_unclassified
g__Aureobasidium
f__Pyronemataceae_unclassified
g__Pyrenochaetopsis
g__Wallemia
c__Sordariomycetes_unclassified
k__Fungi_unclassified
g__Acremonium
o__Polyporales_unclassified
f__Hypocreaceae_unclassified
g__Glomusg__Hypocrea
k__Fungi_unclassified
g__Glomus
k__Fungi_unclassified
k__Fungi_unclassified
Maxifort
Ascomycota
Basidiomycota
Cercozoa
Chytridiomycota
Glomeromycota
Rozellomycota
Unclassified
Zygomycota
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
19
138 139 TABLE S1 Sites included in the study, their soil type, and geographic coordinates. 140 141
Study sites Location Latitude Longitude Soil type
Olathe Horticulture Research and Extension Center (OHREC) Johnson County, KS 38.88N 94.99W Chase silt loam
Common Harvest Douglas County, KS 38.96N 95.20W Eudora-Kimo complex
142
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
20
TABLE S2 Results of the multivariate permutational analysis of variance (PERMANOVA) for 143
fungal taxon abundance data. Permutation was based on the Bray-Curtis distance matrix 144
generated for root associated fungal communities at the OTU level from four tomato rootstock 145
treatments: nongraft and selfgraft BHN589, and BHN589 grafted on two hybrid rootstocks 146
(Maxifort and RST-04-106) (1000 permutations). P values < 0.05 are in bold. 147
Factor Sum of Squares % Explained P value
Rootstocks 1.24 2.07 < 0.01
Compartment 5.36 8.92 < 0.001
Study_Site 5.01 8.34 < 0.001
Year 3.23 5.38 < 0.001
Rootstocks:Compartment 0.58 0.97 0.972
Rootstocks:Study_Site 1.39 2.32 < 0.01
Compartment:Study_Site 1.59 2.65 < 0.001
Rootstocks:Year 1.00 1.66 0.111
Compartment:Year 1.00 1.66 < 0.001
Study_Site:Year 1.46 2.43 < 0.001
Rootstocks:Compartment:Study_Site 0.55 0.91 0.998
Rootstocks:Compartment:Year 0.49 0.82 1.000
Rootstocks:Study_Site:Year 1.22 2.03 < 0.01
Compartment:Study_Site:Year 0.60 1.00 < 0.01
Rootstocks:Compartment:Study_Site:Year 0.60 1.00 0.989
Residuals 34.75 57.86
Total 60.07 100.01 148
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint
21
TABLE S3 Network attributes and links observed in the fungal association networks for four tomato rootstock treatments: nongraft 149
and selfgraft BHN589, and BHN589 grafted on two hybrid rootstocks (Maxifort and RST-04-106) in each of rhizosphere and 150
endosphere compartments. 151
152
153
Compartment Rootstocks Node Edge Node Degree Density Modules
(SA) Negative
Edge Positive
Edge Negative/Positive
Edge
Endosphere
Nongraft 605 155 0.26 0.0004 18 5 150 0.03 Selfgraft 600 145 0.24 0.0004 20 9 136 0.07
RST-04-106 584 156 0.27 0.0005 17 10 146 0.07 Maxifort 700 221 0.32 0.0005 20 36 185 0.19
Rhizosphere
Nongraft 950 381 0.40 0.0004 17 82 299 0.27 Selfgraft 995 312 0.31 0.0003 19 67 245 0.27
RST-04-106 991 397 0.40 0.0004 23 58 339 0.17 Maxifort 1175 785 0.67 0.0006 18 289 496 0.58
.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 13, 2019. . https://doi.org/10.1101/2019.12.12.874966doi: bioRxiv preprint