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# Proteomic analysis reveals how pairing of a Mycorrhizal fungus with plant growth-promoting bacteria modulates growth and defense in wheat

Candida Vannini, Guido Domingo, Valentina Fiorilli, Daniel García Seco, Mara Novero, Milena Marsoni, Florence Wisniewski-dye, Marcella Bracale, Lionel Moulin, Paola Bonfante

► To cite this version:

Candida Vannini, Guido Domingo, Valentina Fiorilli, Daniel García Seco, Mara Novero, et al. Pro-
teomic analysis reveals how pairing of a Mycorrhizal fungus with plant growth-promoting bacteria
modulates growth and defense in wheat. Plant, Cell and Environment, 2021, 44 (6), pp.1946-1960.
10.1111/pce.14039 . hal-03238035

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# Proteomic analysis reveals how pairing of a Mycorrhizal fungus with plant growth-promoting bacteria modulates growth and defense in wheat

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(Article begins on next page)
1 Proteomic analysis reveals how pairing of a Mycorrhizal Fungus with

2 Plant Growth-Promoting Bacteria modulates growth and defense in wheat

3

4 Running head: AMF and PGPB modulate plant growth and defense

5

6 Candida Vannini¹*#; Guido Domingo¹*, Valentina Fiorilli²*; Daniel García

7 Seco³, Mara Novero², Milena Marsoni¹, Florence Wisniewski-Dyé⁴, Marcella

8 Bracale¹, Lionel Moulin³, Paola Bonfante².

9

10 *These authors equally contributed

11 # Corresponding author

12 candida.vannini@uninsubria.it; Orcid ID: https://orcid.org/0000-0002-5196-

13 6142

14

15 Contact information

16 ¹Dipartimento di Biotecnologie e Scienze della Vita, Università degli Studi
dell'Insubria, via J.H Dunant 3, 21100, Varese, Italy.

17 ²DAPartment of Life Sciences and Systems Biology, Università degli Studi di
Torino, Viale P.A. Mattioli 25, 10125, Torino, Italy.

18 ³IRD, CIRAD, University of Montpellier, IPME, Montpellier, France.

19 ⁴Univ Lyon, Université Claude Bernard Lyon 1, CNRS, INRAE, VetAgroSup,

20 UMR Ecologie Microbienne, F-69622, Villeurbanne, France
23

## 24 Funding

This work was supported by the MIC-CERES ("Microbial eco-compatible strategies for improving wheat quality traits and rhizospheric soil sustainability") Project (FC Project ID 2013-1888; AF Project ID 1301-003) jointly supported by the Agropolis Foundation (through the "Investissements d'avenir" program, with the reference number ANR-10-LABX-0001-01) and Fondazione Cariplo.

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## 32 Abstract

Plants rely on their microbiota for improving the nutritional status and environmental stress tolerance. Previous studies mainly focused on bipartite interactions (a plant challenged by a single microbe), while plant responses to multiple microbes have received limited attention. Here, we investigated local and systemic changes induced in wheat by two plant growth-promoting bacteria (PGPB), *Azospirillum brasilense* and *Paraburkholderia graminis*, either alone or together with an arbuscular mycorrhizal fungus (AMF). We conducted phenotypic, proteomic, and biochemical analyses to investigate bipartite (wheat-PGPB) and tripartite (wheat-PGPB-AMF) interactions, also upon a leaf pathogen infection. Results revealed that only AMF and *A. brasilense* promoted plant growth by activating photosynthesis and N assimilation which led to increased glucose and amino acid content. The
bioprotective effect of the PGPB-AMF interactions on infected wheat plants depended on the PGPB-AMF combinations, which caused specific phenotypic and proteomic responses (elicitation of defense related proteins, immune response, and jasmonic acid biosynthesis). In the whole, wheat responses strongly depended on the inoculum composition (single vs. multiple microbes) and the investigated organs (roots vs. leaf). Our findings showed that AMF is the best-performing microbe, suggesting its presence as the crucial one for synthetic microbial community development.

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## Keywords

Funneliformis mosseae, Azospirillum brasilense, Paraburkholderia graminis, Xanthomonas translucens, proteome, pathogens, bi- and tripartite interaction, wheat, growth and defense response.

58

## Acknowledgments

V.F., D.G.S., P.B., L.M., and C.V designed the study; V.F., D.G.S., M.N., and D.G. carried out the majority of experiments; G.D., M.M., and C.V performed the bioinformatic analysis of proteomic data; C.V., V.F., P.B. L.M., F.W.-D., and M.B. interpreted the data and wrote the manuscript.

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Proteomic analysis reveals how pairing of a Mycorrhizal Fungus with

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Plant Growth-Promoting Bacteria modulates growth and defense in wheat

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Abstract

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Plants rely on their microbiota for improving the nutritional status and

72

environmental stress tolerance. Previous studies mainly focused

73

on bipartite interactions (a plant challenged by a single microbe), while plant

74

responses to multiple microbes have received limited attention. Here, we

75

investigated local and systemic changes induced in wheat by two plant growth-

76

promoting bacteria (PGPB), *Azospirillum brasilense* and *Paraburkholderia*

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graminis, either alone or together with an arbuscular mycorrhizal fungus

78

(AMF). We conducted phenotypic, proteomic, and biochemical analyses to

79

investigate bipartite (wheat–PGPB) and tripartite (wheat–PGPB–AMF) interactions, also upon a leaf pathogen infection. Results revealed that only

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AMF and *A. brasilense* promoted plant growth by activating photosynthesis

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and N assimilation which led to increased glucose and amino acid content. The

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bioprotective effect of the PGPB–AMF interactions on infected wheat

83

plants depended on the PGPB-AMF combinations, which caused specific

84

phenotypic and proteomic responses (elicitation of defense related proteins,

85

immune response, and jasmonic acid biosynthesis). In the whole, wheat

86

responses strongly depended on the inoculum composition (single vs. multiple

87

microbes) and the investigated organs (roots vs. leaf). Our findings showed that

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AMF is the best-performing microbe, suggesting its presence as the crucial one
for synthetic microbial community development.

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Keywords

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*Funneliformis mosseae*, *Azospirillum brasilense*, *Paraburkholderia graminis*, *Xanthomonas translucens*, proteome, pathogens, bi- and tripartite interaction, wheat, growth and defense response.

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INTRODUCTION

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Like humans, animals, and fungi, plants live among a variety of microbial species, which together comprise the plant microbiota (Schlaeppi & Bulgarelli, 2015). Plant root-associated microbes have received increasing attention, starting from their taxonomic description (Bulgarelli et al., 2012; Lundberg et al., 2012) to their role in plant health (Müller, Vogel, Bai, & Vorholt, 2016). Data generated by multiple omics approaches demonstrate that the plant microbiota does not represent a random assembly of microbes living in the soil.

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On the contrary, plant microbiota composition is determined by an active host plant-driven selection process, which depends on the plant genotype, environmental conditions, and microbial interactions (Durán et al., 2018; Hacquard et al., 2015; Saad, Eida, & Hirt, 2020; Thiergart et al., 2019; Uroz, Courty, & Oger, 2019). The complexity of the microbial community structure parallels the many beneficial functions currently assigned to the plant

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microbiota: stimulation of plant growth through phytohormone production, improvement of the plant nutrient status through the increased uptake of nutrients such as inorganic phosphate (Pi) and nitrogen (N) and increased availability of nutrients such as iron, greater tolerance to abiotic stress (e.g., drought) and biotic stress, and increased activation of plant innate immunity (Hacquard, Spaepen, Garrido-Oter, & Schulze-Lefert, 2017). Many of these benefits have been traditionally associated with the so-called plant growth-promoting bacteria (PGPB), as well as with the root symbionts, such as arbuscular mycorrhizal (AM) fungi and N-fixing bacteria (Lugtenberg, Caradus, & Johnson, 2016). Comparison of the data generated by culture-independent approaches with the knowledge obtained from the investigation of controlled binary interactions of bacterial and fungal isolates with their host plant has led to the creation of the so-called synthetic communities (SynComs) (Herrera Paredes et al., 2018; Tsolakidou et al., 2019). Simultaneous inoculation of the host plant with several different beneficial microbes allows the investigation of plant responses under controlled and reproducible conditions. These new tools therefore form the basis of the so-called microbial revolution, defined as the microbe-driven increase in crop productivity, leading to higher sustainability (Baez-Rogelio, Morales-García, Quintero-Hernández, & Muñoz-Rojas, 2017).

Together with rice and corn, wheat is one of the most important crops worldwide (Fernie & Yan, 2019). Recently, many studies have been conducted
on wheat-associated microbes, providing a detailed list of bacteria and fungi associated with wheat plants under natural conditions (Kuźniar et al., 2020; Mahoney, Yin, & Hulbert, 2017; Naylor, DeGraaf, Purdom, & Coleman-Derr, 2017; Pagé, Tremblay, Masson, & Greer, 2019) or describing the core microbiome of wheat (Simonin et al., 2020), thus revealing the ecological rules that regulate microbial assembly (Hassani, Özkurt, Seybold, Dagan, & Stukenbrock, 2019). Ecological studies suggest that higher soil microbial diversity results in a greater resilience of the plant population (van der Heijden et al., 1998). However, the assumption that a mixture of beneficial microbes automatically provides greater plant protection is an oversimplification (Rosier, Bishnoi, Lakshmanan, Sherrier, & Bais, 2016). In this context, the responses of wheat to its microbiota are still unknown.

In this study, to disentangle the inherent complexity of plant-microbiota interactions (Vorholt, Vogel, Carlström, & Müller, 2017), we followed a reductionist approach, where we selected two PGPB species, *Azospirillum brasilense* and *Paraburkholderia graminis*, which are associated with wheat plants under natural conditions, as well as an arbuscular mycorrhizal fungus (AMF), *Funneliformis mosseae*. We hypothesized that targeted inoculation of wheat plants with the AMF and one of the two PGPB could provide an experimentally tractable system for evaluating the outcome of the interaction between beneficial microbes. Previously, we demonstrated that inoculation with *F. mosseae* improved plant growth and enhanced bioprotection in wheat
155 (Fiorilli et al., 2018). The current study aimed to investigate the long-term local
156 and systemic effects of *P. graminis* or *A. brasilense* on the wheat proteome in
157 non-mycorrhizal and mycorrhizal plants. We compared the proteomic changes
158 in wheat triggered by co-inoculation of PGPB and *F. mosseae* with those
159 elicited by single inoculations. We also investigated the bioprotective effects
160 of bipartite (wheat–PGPB) and tripartite (wheat–PGPB–AMF) interactions on
161 wheat plants against the leaf pathogen, *Xanthomonas translucens*. While *A.*
162 *brasilense* drastically altered the bioprotective effect of the AMF, *P. graminis*
163 did not affect AMF-induced pathogen resistance. Overall, proteomic changes
164 revealed the molecular mechanisms underlying the tripartite interaction and
165 showed that the beneficial effects of the AMF on plants are differentially
166 modulated by the plant-associated PGPB.

167

168 MATERIALS AND METHODS

169 Bacterial strains, mycorrhizal fungus and wheat genotype

170 Two plant growth promoting bacteria (PGPB), *Azospirillum brasilense* Sp245
171 (obtained from UMR Ecologie Microbienne, Villeurbanne) and
172 *Paraburkolderia graminis* C4D1M (type strain of the species, LMG collection,
173 Ghent, Belgium), one wheat pathogen, *Xanthomonas translucens* CFBP2054
174 (obtained from CFBP collection), and one mycorrhizal fungus, *Funneliformis*
175 *mosseae* (BEG.12, MycAgro Lab, France) were used in our experiments.
176 In detail, *A. brasilense* strain Sp245 was isolated from wheat roots and was
177 shown to stimulate root development and increase plant dry mass (Kapulnik,
178 Okon, & Henis, 1985), while the strain C4D1M of *P. graminis* was isolated
179 from senescent corn roots and found to positively interact with different species
180 of wheat (L. Moulin, personal communication).

181 Gfp-tagged derivatives were also included for cytology analyses: *A. brasilense*
182 Sp245 eGFP carrying the pMP2444 plasmid eGFP, GmR (Wisniewski-Dyé et
183 al., 2011) and *P. graminis* C4D1M eGFP, constructed by triparental mating
184 (using a Tn7 eGFP construct described in (Norris, Kang, Wilcox, & Hoang,
185 2010) with a single insertion of the Tn7 upstream of the *glmS* gene). *A.*
186 *brasilense* was cultivated at 28°C on LBA medium (Luria Broth low salt, agar)
187 and *P. graminis* in YMA medium (yeast extract, 3 g; mannitol, 10 g; KH2PO4,
188 0.5 g; MgSO4, 0.2 g; NaCl, 0.1 g; agar, 18 g; distilled water, 1 L; pH 6.8) and
189 grown overnight in the same broth medium for inoculation. Strains were stored
190 at -80°C in 20% glycerol. *X. translucens* CFBP 2054 was grown at 28 °C on
191 Peptone sucrose agar (PSA) medium, retrieved from Petri dish with sterile
192 water to reach OD 0.5 for leaf clipping and infiltration assays.

193 The *Triticum aestivum* cv Chinese Spring was used for all experiments (seeds
194 obtained from Valeria Terzi, CREA, Italy).

195

196 Plant material and plant inoculations
The methodologies have already been described in previous articles on the wheat response to *Xanthomonas* (Garcia-Seco et al., 2017a) and to mycorrhizal fungi (Fiorilli et al., 2018). Twelve combinations were studied: 1). Control plants (C), 2) *A. brasilense*-inoculated plants (Az), 3) *P. graminis*-inoculated plants (P), 4) *F. mosseae*-inoculated plants (M), 5) *C+Xanthomonas translucens* (X), 6) Az+X, 7) P+X, 8) M+X, 9) Az+M, 10) P+M, 11) Az+M+X, 12) P+M+X. An overview of the experiment is given in Figure S1.

Seeds were disinfected by immersing for 40 min in a sodium hypochlorite solution and washed with sodium thiosulfate (Hurek et al., 1994) and pregerminated. The seedlings were transferred to pots containing a mix of sterile quartz sand + either the *F. mosseae* carrier inoculum substrate (the substrate without the fungus) for control and PGPB conditions, or the *F. mosseae* inoculum (30% v/v) for mycorrhizal and mycorrhizal+PGPB conditions. PGPB were inoculated directly after seedling transfer to pots, with 1 ml per plant at OD 1 from an overnight broth culture washed once and diluted with water.

For each inoculated condition, 10 pots containing 1 plant were used for phenotyping of root and fresh weight at 50 dpi, 5 pots were used for proteomic, and 5 pots for leaf-clipping assays with *Xanthomonas translucens*.

All plants were maintained under glasshouse conditions under cycles of 12 h of light at 21 °C and 50% relative humidity (RH) and 12 h of dark at 21 °C and 50% RH, watered twice a week with water, and once with a modified Long-
219 Ashton solution containing a low phosphorous concentration (32 µM
220 Na₂HPO₄*12H₂O).

221 Spikes weight were measured separately in the mature plants at the end of their
222 natural cycle. The spikes were threshed and 1000-kernel weights were
223 determined.

224 For *Xanthomonas* infections, two types of inoculation were performed. A
225 phenotyping leaf-clipping assay with scissors soaked in a 0.5 OD *X.
226 translucens* culture was performed at 46 dpi on a first set of plants for
227 phenotyping the length of the symptoms at 4-day post-clipping (dpc; starting
228 point of the lesions) and 26 dpc. A second set of plants dedicated to proteomic
229 analyses was infiltrated at 49 dpi with a 0.5 OD *X. translucens* culture using a
230 microneedle, as described in Garcia-Seco et al. (Garcia-Seco et al., 2017b), and
231 sampled the following day.

232

233 **Evaluation of wheat roots microbial colonization**

234 The mycorrhizal and mycorrhizal+PGPB roots were stained with 0.1% cotton
235 blue and the level of mycorrhizal colonization was assessed as previously
236 described (Trouvelot, Kough, & Gianinazzi-Pearson, 1986).

237 For Colony Forming Unit (CFU) counting from plant roots, root fragments
238 were weighted then pulverized with a FastPrep™ in tubes containing a ceramic
239 bead in 500 µL of sterile water, centrifuged at 1000 rpm for 30 s and drops of
240 20 µL of serial dilutions were plated on bacterial media and counted 24 h later.
241

242 Proteomic analysis and data processing

243 Plant samples (root and leaves) were sampled at 50 dpi and pulverized with
244 liquid nitrogen. The used protocol for total protein extraction was based on
245 SDS and phenol extraction (Wu, Xiong, Wang, Scali, & Cresti, 2014). Then,
246 samples were digested and analysed by Liquid Chromatography-Mass
247 Spectrometry (LC-MS/MS) as described previously (Garcia-Seco et al.,
248 2017b). Mass spectrometer raw files were analysed by MaxQuant (version
249 1.6.2.3, default parameters) against UniProt T. aestivum (Version 2017-1,
250 150,716 entries), Uniprot *Rhizophagus irregularis* (Version 2015-10, 29,847
251 entries), Uniprot *P. graminis* (Version 2015-10, 6,732 entries) and Uniprot *A.
252 brasilense* (Version 2015-10, 7,636 entries). On January 2019, the UniProt T.
253 aestivum database has been updated. Therefore, we obtained the updated
254 protein IDs by BLAST search of our dataset against the Uniprot *T. aestivum*
255 2019 database (143,020 entries). Unknown proteins were annotated by BLAST
256 search against the Uniprot viridiplantae database (Version 2019-01, 6,913,939
257 entries), taking the first hit with a valid annotation.

258 All MS proteomic data have been deposited in the ProteomeXchange
259 Consortium via PRIDE partner repository with the Username:
260 reviewer04430@ebi.ac.uk and Password: 2vlyEEVZ.
261 MaxQuant output files were processed as described earlier (Vannini et al., 2019). Only proteins detected in at least two of the three biological replicates (75%) sharing the same treatment and tissue were considered.

264 To compare the differences among analytical groups we performed an ANOVA based multiple samples coupled with Tukey test using the *R* package LIMMA. Only proteins with false discovery rate (FDR) below 0.01 were considered Differentially Abundant Proteins (DAPs) within the various comparisons. In order to produce a reliable and robust dataset, all proteins which gave one nonzero and two zero outcomes (two-time imputation) in at least one of the samples in each comparison were considered unreliable and therefore eliminated.

272 In order to use bioinformatic tools available only for *A. thaliana*, a local BLAST of *T. aestivum* proteins against the TAIR10 database (version 2012-05-07) was performed.

275 The enrichment analysis was performed using the Gene Ontology Resource (http://geneontology.org), running PANTHER algorithm with *A. thaliana* as background and FDR < 0.05 or using the AgriGO Singular Enrichment Analysis (SEA) compare tool (http://bioinfo.cau.edu.cn/agriGO/analysis.php?method=compare), with *A. thaliana* TAIR10_2017 protein database as background, default parameters and a FDR threshold of 0.05 (Du, Zhou, Ling, Zhang, & Su, 2010).
282

283 **Amino acid analysis**

284 For the amino acids (AAs) extraction, 0.1 g of lyophilized samples were re-suspended in 10 mL of 0.1% (v/v) formic acid in water/methanol (50:50). 10
285 µL of 10 mM deuterated internal standards (L-Phenyl-d5-alanine and L-alanine
286 $^{15}$N Met), were added. Free AAs were quantified by Liquid Chromatography
287 Tandem Mass Spectrometry (LC-MS/MS) as described previously (Fiorilli et
288 al., 2018).
289

290

291 **Total glucose and nitrogen content**

292 Soluble sugars were extracted as described by (Shi, Wang, Yang, Li, & Miao,
293 2016) with minor modifications. Briefly, 0.2 g of leaves material were boiled
294 (80°C) in ethanol 80% for 30 min. After centrifugation (13000g x 5 min) the
295 supernatant was recovered. The extraction was repeated twice and all
296 supernatants were collected. Sucrose in solution was hydrolysed with HCl (2%
297 of HCl concentrated V/V) for 5 min at 90°C. After acid neutralization by KOH
298 (5% of 5N KOH V/V) total glucose was estimated by the dinitrosalicylic (DNS)
299 method (Miller, 1959).
300 Wheat root N content was determined by CHNS elemental analyzer Thermo
301 Fisher Scientific following the manufacturer's specifications. About 2-3 mg of
302 sample for each replicate were weighed and placed in a tin capsule containing
303 9.5 to 10.5 mg of vanadium pentoxide. The N₂ product by sample combustion
304 was quantitatively determined through a separation with a gas chromatograph
305 (GC) followed by a quantification using a thermal conductivity detector. Three
306 tests were prepared for each sample.

307

308 **Statistics**

309 Phenotyping data of plant weight, CFU and lesion length were analyzed,
310 depending on normality of data, by ANOVA followed by Tukey post-hoc test
311 or Kruskal-Wallis test followed by Mann-Whitney pairwise comparisons, in R
312 Environment (rstatix) and figures produced by ggplot.

313 Data from quantification of amino acid, total glucose and nitrogen were
314 subjected to statistical analysis by ANOVA and Tukey post-hoc test.

315

316 **RESULTS**

317 **PGPB and AMF impact plant growth**

318 Wheat plants were inoculated with *A. brasilense* Sp245, *P. graminis* C4D1M,
319 *A. brasilense* Sp245 plus *F. mosseae*, or *P. graminis* C4D1M plus *F. mosseae*
320 (hereafter referred to as Az, P, AzM, and PM plants, respectively) and grown
321 under controlled conditions (Figure S1). The root and shoot biomass of these
plants was determined at 50 days post-inoculation (dpi) and compared with that
of mock-inoculated control (C) and *F. mosseae* only-inoculated (M) plants. In
isolation, *A. brasilense* Sp245 exerted a strong positive effect on the growth of
roots and shoots, whereas *P. graminis* C4D1M did not induce statistically
significant growth of these organs (Figure 1). Monitoring these plants until
seed production revealed that *P. graminis* significantly increased the seed
yield, doubling the spike weight (Figure S2).

To determine whether the positive impact of the two PGPB on plant
growth was associated with an efficient colonization process, bacteria on the
root surface were counted at different time points. *A. brasilense* exhibited the
greatest colonizing potential, with the bacterial count remaining constant
across different time points. Colonization by *P. graminis* decreased with time
to 1 × 10² colony forming units (CFU) at 21 dpi (Figure S3). The success of
AMF colonization was evaluated at 50 dpi by calculating the total length of
colonized roots (F%) and total number of arbuscules (A%) in plants inoculated
with AMF alone or AMF plus PGPB (*A. brasilense* or *P. graminis*).
Colonization by the AMF resulted in abundant arbuscules in all plants. No
differences were detected in F% and A% among plants inoculated with AMF
alone or together with PGPB, indicating that the presence or absence of PGPB
does not affect mycorrhizal colonization (Figure S3).

The shoot weight of AzM and PM plants was significantly higher than
that of Az and P plants (Figure 1B) but comparable with that of M plants,
344 indicating that PGPB did not lead to any additional yield benefit compared with
345 the mycorrhizal condition.

346

347 **AMF alone or in combination with a PGPB triggers different responses to**
348 **X. translucens** infection

349 Inoculated wheat plants were assessed for protective effect to *X. translucens*
350 leaf infection by leaf-clipping plants with the pathogen at 46 dpi and recording
351 leaf symptoms at 4 and 26 days post-leaf clipping (dpc). Pathogen-infected
352 plants were identified as AzX, PX, MX, AzMX, and PMX and positive control
353 plants as CX. Disease symptoms were evident in CX plants at 4 and 26 dpc
354 (Figure 2). Lesion length in MX plants was significantly less than that in CX
355 plants both at 4 and 26 dpc, consistent with our previous results (Fiorilli et al.,
356 2018).

357 Lesion length appeared extended in AzX and CX plants at both time
358 points. AzMX plants showed reduced symptoms in comparison with AzX
359 plants at 4 dpc but showed extended lesions compared with CX and AzX plants
360 at 26 dpc. This result indicates that the *F. mosseae*-induced bioprotection in
361 wheat is abrogated between 4 and 26 dpc in the presence of *A. brasilense*.

362 At 26 dpc, a significant reduction in symptoms was observed only in MX
363 and PMX plants when compared with CX plants, whereas lesions were
364 significantly increased in AzX and AzMX plants compared with PX, MX, and
365 PMX plants. These results indicate that inoculation with *F. mosseae* alone
366 (Fiorilli et al., 2018) or in combination with *P. graminis* increased protection
367 against *X. translucens*. Overall, this experiment showed that *A. brasilense*
368 inoculation alone did not protect wheat plants against the pathogen, and rather
369 undermined the positive effect exerted by *F. mosseae*.

370

371 **Quantitative overview of proteomics analysis**

372 We conducted proteomic analysis of the roots (R) of Az, P, M, AzM, and PM
373 plants (hereafter referred to as RAz, RP, RM, RAzM, and RPM samples,
374 respectively) as well as the leaves of these plants (hereafter referred to as LAz,
375 LP, LM, LAzM, and LPM samples, respectively). We also performed
376 proteomic analysis of the leaves of these plants following infection with *X.
377 translucens* (hereafter referred to as LAzX, LPX, LMX, LAzMX, and LPMX,
378 respectively). Each treatment was analyzed in triplicate.

379 A total of 3,846 and 3,883 wheat proteins were identified and quantified
380 in root and leaf samples, respectively. Samples were clustered by condition
381 according to their protein expression patterns (Figure S4). Replicates within
382 each analytical group clustered together, confirming the high reproducibility of
383 biological replicates. Protein abundance was compared between samples, and
384 differentially abundant proteins (DAPs) were identified using the following
385 thresholds: false discovery rate (FDR) < 0.01 and log2fold-change (log2FC) >
386 0.5 (Tables S1–S5 and S7–S15).
387 Functional characterization of DAPs was conducted with Gene Ontology
388 (GO) enrichment analysis to determine the main biological processes
389 stimulated by microbial inoculations.

390 In root samples, approximately 7%, 0.6%, and 0.6% of all identified
391 proteins were assigned to AMF, *A. brasilense*, and *P. graminis* proteomes,
392 respectively, confirming the presence of all three root-associated microbes at
393 harvest.

394

395 Wheat response to single inoculation: an overview

396 A large number of significant DAPs (*P* < 0.01) were identified; 639 DAPs (386
397 in leaves and 253 in roots) in the C vs. Az comparison, and 1,085 DAPs (424
398 in leaves and 661 in roots) in the C vs. P comparison (Tables S1–S4). In leaves,
399 approximately 50% of the DAPs were common between the C vs. Az and C vs.
400 P comparisons (Figure S5A). By contrast, in roots, proteomic expression was
401 highly specific, mirroring the stronger impact on the colonized niche; only 12%
402 of the DAPs were common between the C vs. Az and C vs. P comparisons
403 (Figure S5B).

404 To decipher the molecular mechanisms involved in *A. brasilense*-
405 induced plant growth promotion, we performed GO enrichment analysis of
406 LAz vs LC, LP vs LC, Raz vs RC and RP vs RC (Figure 3). In leaf samples,
407 “photosynthesis light harvesting” and “photosynthetic electron transport
408 chain” were the two most enriched GO terms. These data were consistent with
the higher total glucose content of LAz samples compared with LC, LP, and LM samples (Figure 4A). Our experiments, therefore, confirmed that *A. brasilense* exhibits a greater ability to drive an increase in the glucose content of shoots than other beneficial microorganisms (*F. mosseae* and *P. graminis*). Moreover, the LAz sample showed a higher abundance of sucrose transporter 2D (SUT2D) than the LC sample; SUT2D shows high similarity to rice SUT2 (OsSUT2), which is involved in sucrose mobilization to sink cells (Eom et al., 2011).

The "amino acid metabolism" GO term was highly enriched in enzymes involved in the synthesis of aspartate, proline, and branched-chain amino acids. The increased content of amino acids both in root and leaf samples validated the proteomics data (Table S6). The presence of *A. brasilense* on wheat roots also increased the abundance of the phosphate transporter Pht1-10, which is induced by the AMF (Fiorilli et al., 2018) and enhances Pi uptake. Interestingly, the abundance of Pht1-10 was lower in the RP sample vs RC (Figure S6). Moreover, Pht1-10 was among the four proteins whose abundance showed opposite trends between RAz and RP samples (Table S5). Plants assimilate and metabolize ammonium ($NH_4^+$) provided by diazotrophs, including *Azospirillum* spp. (Carvalho, Balsemão-Pires, Saraiva, Ferreira, & Hemerly, 2014). In LAz samples, we observed an increase in the abundance of ferredoxin-glutamate dehydrogenase, 2-oxoglutarate (2-OxG)/malate translocator, and isocitrate dehydrogenase (ICDH), and a decrease in the
431 abundance of ferredoxin-nitrite reductase; both these trends are indicative of a
432 higher NH₄⁺ assimilation rate in the leaves of Az plants. The increase in
433 photosynthesis could contribute to enhanced tolerance to NH₄⁺ toxicity by
434 increasing NH₄⁺ assimilation (Setién et al., 2013). Higher concentrations of
435 free amino acids and N in RAz samples support these proteomic results (Table
436 S6, Figure 4B).

437 In roots and leaves, inoculation with *A. brasilense* stimulated the
438 mitochondrial electron transport for ATP synthesis, even if the proteins
439 involved were different (Tables S1 and S2).

440 Proteomic data showed that *P. graminis* inoculation also had a substantial
441 impact on primary metabolism (glycolysis, tricarboxylic acid cycle, and
442 aerobic respiration) in roots and leaves (Figures 3, Figure S6, Tables S3–S5).
443 In the LP sample, DAPs related to photosynthesis did not showed a clear
444 pattern (Table S4).

445 Similar to Az plants, *P. graminis*-inoculated plants showed significantly
446 higher concentrations of N and almost all amino acids than C plants,
447 particularly in the roots (Figure 4B, Table S6). In P plants, the more efficient
448 N uptake could be due to the increased abundance of the high-affinity nitrate
449 transporter NAR2 and its activator NRT2. The abundance of the wheat ortholog
450 of rice ammonium-inducible transporter 1-2 (OsAMT1-2) was also increased
451 in RP sample (Figure S6, Table S5).
Overall, most of these proteomic changes affecting respiration, photosynthesis, N assimilation, and mineral nutrition mirror the differential growth response of wheat upon PGPB inoculation, as illustrated in Figure 1, with a significant systemic effect of only *A. brasilense* on plant growth.

## AMF plays a dominant role in plant roots upon binary association with PGPB

We previously showed that *F. mosseae* elicits a significant proteomic change in wheat roots and leaves during colonization (Fiorilli et al., 2018). Consistent with this observation, AzM and PM samples showed high numbers of DAPs; RAzM vs. RC and RP vs. RC comparisons revealed 709 and 1055 DAPs, respectively (Tables S7 and S8), whereas LAzM vs. LC and LPM vs. LC comparisons revealed 504 and 808 DAPs, respectively (Tables S9 and S10, Figure S7).

Venn diagrams showed a specific contribution by *F. mosseae* in the roots of co-inoculated plants, mainly AzM plants (Figure S7B and S7D). These data are consistent with our previous proteomic data showing that *F. mosseae* has a stronger local but a weaker systemic impact on wheat (Fiorilli et al., 2018). In roots, inoculation with *F. mosseae* alone or together with *A. brasilense* or *P. graminis* led to an increased abundance of 196 proteins, several of which have been previously shown to increase in abundance during mycorrhization (Fiorilli et al., 2018). In particular, we found increased levels of key enzymes
involved in glycolysis and the pentose phosphate pathway (glyceraldehyde-3-phosphate dehydrogenase, pyruvate kinase, and 6-phosphogluconate dehydrogenase), fatty acid biosynthesis and metabolism (plastid acetyl-CoA carboxylase and 3-oxoacyl-[acyl-carrier-protein] reductase, GDSL esterase/lipase), and mineral nutrition (OsAMT3;1 homolog). We also detected significant up-regulation of some proteins involved in plant defense, including one acidic endochitinase protein, cysteine-rich receptor-like protein kinase 25 (CRK25), and some Germin-like proteins (GLPs).

Venn diagrams also showed that most DAPs were exclusively expressed in AzM and PM plants. This trend was mainly detected in leaves (Figure S7A and S7C). In fact, GO enrichment analysis showed that oxidative phosphorylation, response to oxidative stress, photosynthesis, and response to abiotic stimulus were among the up-regulated biological processes in the LAzM vs. LPM comparison. Ribosome biogenesis, translation, and gene expression were down-regulated processes (Table S11, Figure S8).

Altogether, our results revealed that organ-specific proteomic changes in AMF-inoculated wheat plants (Fiorilli et al., 2018) are mostly maintained upon binary inoculation. This suggests that the AMF plays a dominant role in the root protein profile.

**Defense proteins are induced locally and systemically during bipartite and tripartite interactions**
Some rhizosphere-associated beneficial bacteria trigger a plant immunization phenomenon, called induced systemic resistance (ISR), thus priming the plant immune system (C. M. Pieterse et al., 2014). To verify whether *A. brasilense* and *P. graminis* possess the tools to elicit immune system priming in wheat, we first examined the enriched GO terms involved in plant-microbe interactions. Whereas the "response to wounding" was one of the up-regulated GO enriched categories in RAz samples, the DAPs enriched in the category "response to bacterium" showed a decrease, in LAz samples (Figure 3). In particular, we found reduced abundance of two chitinases, two pathogenesis-related (PR) proteins, and one protein (A0A3B6BXY2) highly similar to the Arabidopsis heat stable 1 (HS1) protein, which exhibits antibacterial activity (Park et al., 2007). This decrease in the abundance of defense-related proteins in leaves could at least partly explain the susceptibility of Az plants to *X. translucens* infection in leaf-clipping tests (Figure 2). The sucrose transporter SUT2D specifically induced by *Azospirillum* (Table S2) in wheat leaves could also play a role in the susceptibility of Az plants to pathogen infection. *Xanthomonas* TAL effectors usually target SWEET family sugar transporters to sustain pathogen growth (Verdier et al., 2012).

In RP samples harvested at 50 dpi, one of the enriched GO terms was "defense response/incompatible interaction" with the induction of several defense proteins (Figure 3, Table S5). Among them, the A0A3B6DGK2 protein, which is similar to RPM1-interacting protein 4 (RIN4), a major
regulator of plant defense that plays important roles in both pathogen-
associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-
triggered immunity (ETI) (Ray, Macoy, Kim, Lee, & Kim, 2019). Additionally,
lipoxygenase 1 (LOX1), 12-oxophytodienoate reductase 1 (OPR1), OPR3, and
allene oxide cyclase 3 (AOC3) point to up-regulation of the biosynthesis of
jasmonic acid (JA), a plant hormone that plays a key role in the biotic stress
response and overall plant immunity (C. M. J. Pieterse et al., 1998). Moreover,
RP samples showed an increased abundance of a protein (A0A3B6KT24) that
is similar to the respiratory burst oxidase homolog protein D (RbohD) and is
involved in the generation of reactive oxygen species (ROS) during
incompatible plant-pathogen interactions (Torres, Dangl, & Jones, 2002), a
CERK homolog (A0A3B6RF20), and proteins required for lignin biosynthesis,
which are activated in tomato plants associated with native microbiota (M.
Chialva et al., 2018). Lastly, *P. graminis*-inoculated plants showed an
increased abundance of some proteins involved in isoprene metabolism,
suggesting the up-regulation of pathways involved in plant defense. These data
support the hypothesis that similar to other PGPB (C. M. J. Pieterse et al., 1998)
and unlike *A. brasilense*, *P. graminis* elicits an immune response in the roots
but does not elicit strong ISR at 50 dpi in the leaves against *X. translucens*
infection (Figure 2).

To describe the systemic bioprotective effect of AMF and PGPB co-
inoculation in wheat, we performed LAzM vs. LAz and LPM vs. LP
comparisons (Tables S12 and S13). Our analysis showed an increase in several DAPs, putatively involved in the biotic stress response, in both LAzM and LPM samples (Figure 5A). Among these DAPs, we found proteins either involved in JA biosynthesis, such as a phospholipase D, three LOXs, and an AOC, or induced by JA, such as a dirigent protein, PR4 (wheat protein) (Desmond et al., 2005), and OsMPK1 (Singh & Jwa, 2013). Moreover, we observed an increase in the abundance of other proteins involved in signaling of the plant immune response, such as the homolog of brassinosteroid (BR)-signaling kinase 1 (BSK1) (Shi et al., 2013) and a calcium-transporting ATPase, whose homolog (ACA8) is required for limiting the growth of virulent bacteria in Arabidopsis (Frei dit Frey et al., 2012). Another protein, whose expression was highly induced in LPM and LAzM samples, was manganese superoxide dismutase 1 (Mn-SOD1), which belongs to the polyphyletic family of enzymes and protect cells from reactive superoxide radical-induced damage, thus conferring increased stress tolerance (S.-Wang et al., 2017).

Overall, this analysis revealed some unexpected features: *A. brasilense* alone down-regulates plant defense (which is consistent with the observed disease susceptibility phenotype shown in Figure 2), while single inoculations of *P. graminis* and *F. mosseae* trigger a similar number of proteins involved in the plant immune response in an organ-dependent way. Co-inoculation of wheat plants with the AMF and *A. brasilense* or *P. graminis* elicits the plant immune response not only in LPM but also in LAzM samples, at least in the
short term, suggesting that both microbial pairs (AMF-A. brasilense and AMF-P. graminis) induce a priming response at least at the proteome level (Figure 5A).

**Microbial pairs modulate wheat response to X. translucens by inducing different proteomic changes**

Leaf inoculation of M, Az, P, and C plants with X. translucens revealed that pathogen susceptibility detected in the leaves of LAzX plants was alleviated by the presence of mycorrhizal colonization at 4 dpc (based on the LAzMX vs. LAzX comparison) (Figure 2A). To decipher the main proteins responsible for the reduction of symptoms at 4 dpc, we analyzed the DAPs identified in the LAzMX vs. LAzX and LAzMX vs. LMX comparisons (Tables S14 and S15).

The up-regulated proteins included those involved in JA biosynthesis and response. Several LOXs and two lipases including phospholipase A1-II and phospholipase D, which generate fatty acid substrates for JA biosynthesis (Browse, 2009), were highly induced in the LAzMX vs. LAzX and LAzMX vs. LMX comparisons (Ishiguro, Kawai-Oda, Ueda, Nishida, & Okada, 2001; Lee & Park, 2019; C. Wang et al., 2000; Wasternack & Hause, 2013) (Figure 5A, Tables S14 and S15). In addition, two allene oxide synthase (AOS) enzymes, which catalyze the first step in the JA biosynthesis pathway, and two AOCs, which are committed for the second step in this pathway (Schaller & Stintzi, 2009), were highly induced in LAzMX samples (Figure 5A) compared
with LAzX and LMX samples. We also found that two JA-induced dirigent-like proteins, which act downstream of the JA biosynthesis pathway, were induced in LAzMX samples. This increase of proteins involved in JA biosynthesis was also observed in LPMX samples (Figure 5A).

Our results showed that *A. brasilense*-AMF and *P. graminis*-AMF interactions amplified JA signaling during pathogen attack. In addition, proteins involved in biotic stress, which were induced during *A. brasilense*-AMF and *P. graminis*-AMF interactions in LAzM and LPM samples, respectively (Figure 5A), were also recruited during *X. translucens* infection, as testified by their higher abundance in LAzMX and LPMX samples, respectively.

These proteomic data correlate with the reduced lesion length observed at the early time point (4 dpc) in AzMX plants with respect to AzX plants. However, at 26 dpc, a significant reduction in lesion size resulted only in MX and PMX plants compared with CX plants (Figure 2B). These data suggest that when co-inoculated with *A. brasilense*, the bioprotective effect exerted of *F. mosseae* is transient and probably related to prompt induction of the JA response.

Further analysis is needed to clarify the molecular changes during the later stage of pathogen attack under different conditions. However, the proteomic profile of LAzMX samples was very different from that of LPMX samples (799 DAPs; Table S16). Among the most abundant proteins identified
in the LAzMX vs. LPMX comparison, we found proteins involved in the response to abiotic stimulus and oxidative stress, while those implicated in translation, ribosome biogenesis, gene expression were down-regulated. A similar pattern was already observed in the LAzM vs. LPM comparison (Figure S8).

Overall, these data highlight the intricate network of processes that regulate wheat-PGPB-AMF-pathogen interactions (as observed in LAzMX and LPMX samples). However, elicitation of defense priming in the proteome of LAzM and LPM samples does not necessarily lead to better performance once the plant is under pathogen attack.

## DISCUSSION

Wheat, one of the earliest food crops to be domesticated, is currently the second most widely cultivated crop in the world and one of the most important grain sources for humans. Given the increasing relevance of plant microbiota, many researches have described wheat-associated microbiota by considering the effects in different organs as well as in grain production. The results of this study illustrate how proteomic changes in wheat plants depend on the inoculum composition (single or multiple microbes) and the organ under study, and lead to differential growth effects and pathogen resistance. All analyses revealed that the AMF was the crucial driver of plant growth and defense priming under our growth conditions (low P). However, the overall changes induced by the
628 AMF-PGPB consortium can interfere with the final mycorrhizal-induced
629 resistance (MIR) outcome (Figure 6).

630

631 **Effect of beneficial microbes on wheat growth is organ- and microbial**
632 **identity-dependent**

633 In addition to their N-fixing ability, *Azospirillum* spp. exhibit a remarkable
634 capacity to benefit a wide range of plant species by activating multiple
635 mechanisms (Fukami, Cerezini, & Hungria, 2018); however, the available
636 omics data are limited to the effects of *A. brasilense* inoculation on roots
637 (Drogue et al., 2014; Spaepen, Bossuyt, Engelen, Marchal, & Vanderleyden,
638 2014). In this study, we showed that the higher root and shoot biomass of plants
639 colonized by *A. brasilense* is supported by the sustained activation of the main
640 metabolic processes (respiration, photosynthesis, and N assimilation), while
641 the roots act as a strong sink for nutrients, such as hexoses and amino acids.
642 These results are consistent with the findings of Zeffa and colleagues, who
643 showed that *A. brasilense* promotes plant growth in maize by enhancing the
644 plant photosynthetic potential or by increasing the N use efficiency (Zeffa et
645 al., 2019) On the other hand, *P. graminis* did not efficiently increase the root
646 and shoot biomass of wheat plants but increased the spike biomass.

647 Wheat actively responds to *P. graminis* inoculation by eliciting many
648 metabolic processes, which involve a higher number of DAPs compared with
649 those induced by *A. brasilense*. Some of these processes were, however,
common to the two bacterial species (e.g., processes involved in ROS scavenging) as well described for many other PGPB (Fukami et al., 2018).

Wheat responds well to AMF, particularly *F. mosseae* (Fiorilli et al., 2018). The wheat–PGPB–AMF tripartite interaction led to intensive proteomic changes where nutrient transporters and many enzymes involved in primary and secondary metabolism, protein biosynthesis, and ROS homeostasis were elicited.

Overall, plant growth experiments, nutrient quantification, and proteomic analyses demonstrated that the AMF plays a leading role in tripartite interactions, particularly in the root, while PGBP (at least *Azospirillum*) affects systemic growth, as evident from the leaf proteome.

661

## The bioprotective effect of the AMF is modulated by the nature of the co-inoculated PGPB

PGPB are considered essential components of the plant microbiota because of their ability to improve plant growth via multiple mechanisms, including plant health protection (Berendsen, Pieterse, & Bakker, 2012; Lugtenberg et al., 2016). *Azospirillum* is not a typical biocontrol agent, despite studies showing its ability to increase pathogen resistance in plants (Bashan & de-Bashan, 2002; Kusajima et al., 2018; Tortora, Díaz-Ricci, & Pedraza, 2012; Yasuda, Isawa, Shinozaki, Minamisawa, & Nakashita, 2009). On the other hand, some *Paraburkholderia* taxa, such as *P. phytofirmans*, induce resistance against a
broad range of plant pathogens by inducing plant-mediated responses in aerial organs (Miotto-Vilanova et al., 2016). Proteomic analysis of wheat plants inoculated with a single microbe showed that proteins involved in plant defense were down-regulated in LAz samples. Moreover, according to the “pathogen starvation” model, which links plant resistance with soluble sugars (Bezrutczyk et al., 2018), the high sugar and amino acid contents of LAz leaves coupled with an enhanced abundance of sugar transporters could guarantee a nutrient-rich niche for the pathogen. Under these conditions, the plant could not activate any defense mechanisms, notwithstanding a light improvement in the presence of the AMF at 4 dpc.

*P. graminis* induced diverse proteomic changes in roots characterized by an increase in the abundance of proteins involved in microbe-associated molecular pattern (MAMP) perception, PTI and ETI regulation (RIN4), ROS production and detoxification, lignin biosynthesis, and isoprene metabolism. These findings suggest that *P. graminis* elicits an immunomodulatory response; however, this does not lead to ISR.

The protein profiles clearly indicate the capacity of mycorrhizal plants, associated with PGPB, to increase the number of defense-related proteins in leaves in the absence of the pathogen, and an augmented capacity to express these proteins upon pathogen infection (Figure 5A). The up-regulation of JA biosynthesis proteins was a key finding because this hormone is considered the first regulator of the plant immune response (Hickman et al., 2017; C. M. J.
Pieterse et al., 1998). Several studies reported that AM symbiosis protects plants against pathogens, suggesting that JA defense mechanisms play a key role in MIR (Jung, Martinez-Medina, Lopez-Raez, & Pozo, 2012).

AMF are a crucial component of the plant microbiota (Bonfante, Venice, & Lanfranco, 2019) and the first inducers of plant immunity. A previous study showed that co-inoculation of wheat with an AMF and *Pseudomonas* spp. (PGPB) leads to synergistic effects, priming the host immunity through chitosan-induced callose deposition (Pérez-de-Luque et al., 2017). A comparable result has been described in tomato plants grown in native soil containing multiple bacteria and AMF; MAMPs released by various microbes enhance the plant immunity, thus activating PTI markers. When challenged by pathogenic *Fusarium* spp., the tomato plants were strongly protected because of the activation of specific antifungal proteins (Chialva, Zhou, Spadaro, & Bonfante, 2018).

According to a previously proposed hypothesis (Cameron, Neal, van Wees, & Ton, 2013), JA could act as a long distance signaling molecule that in mycorrhizal wheat, and also in the presence of both PGPB species, activates the systemic priming of plant defense. However, in our system, lesion length was reduced only at the early time point (4 dpc) in AzMX plants in comparison with AzX plants. At 26 dpc, a significant reduction in lesion length was observed only in MX and PMX plants in comparison with CX plants (Figure 2B). We speculate that additional determinants induced by the AMF-PGPB
interactions interfere with cellular processes, leading to MIR. Proteomic data
showed that in LAzMX samples, the abundance of proteins involved in
ribosome biogenesis and gene expression decreased compared with their
abundance in LMX and LBMX samples (Figure 5B). Ribosomal genes are
highly responsive to stress and signaling molecules, indicating that the encoded
proteins play roles in stress amelioration, besides house-keeping. The
instantaneous up-regulation of ribosomal genes in response to stress might
function as a prompt defense response (Moin et al., 2016). In addition, a
reduction in HMGA subfamily transcription factor and H2B and H1 histones
could affect the transcription of defense-related genes (Isaac, Hartney, Druffel,
& Hadwiger, 2009). Finally, a reduction in the abundance of two proteins
involved in stomata regulation, H1.3 and ubiquitin-specific protease 24
(Rutowicz et al., 2015; Zhao et al., 2016), and some proteins involved in
cuticular wax production, could promote leaf pathogen invasion in AzMX
plants.

731

732 CONCLUSION

733 Plant-associated microbiota hold great promise for the development of
734 sustainable crop systems, and this can be guaranteed by the use of SynComs
735 (Kong, Hart, & Liu, 2018). However, results obtained from on-field microbiota
736 census and those obtained using reductionist approaches, mostly through
737 laboratory-based experiments, have not yet been fully integrated (Fitzpatrick
738 et al., 2020). Our results suggest that beneficial microbes have different impacts
739 on plants, at least in wheat, and the plant growth-promoting effects of beneficial
740 microbes are not always accompanied by enhanced pathogen resistance, as
741 shown by *A. brasilense* inoculation (Figure 6). On the other hand, a bacterium
742 that does not show strong growth-promoting effect, such as *P. graminis*, may
743 be more effective against pathogen attack, if associated with an AMF (Figure
744 6). Our data highlight the crucial role of AM fungi, which are often absent in
745 SynComs, as well as the potential contrasting effects of different AMF-PGPB
746 consortia on plant defense. In a wider context, these findings suggest that
747 SynCom efficiency should be validated by checking the outcome of the
748 interaction under different conditions (microbe-microbe interactions;
749 nutritional status, plant life cycle and biotic stress) before their exploitation for
750 crop growth.

751

752 ACKNOWLEDGMENTS

753 This work was supported by the MIC-CERES (“Microbial eco-compatible
754 strategies for improving wheat quality traits and rhizospheric soil
755 sustainability”) Project (FC Project ID 2013–1888; AF Project ID 1301–003)
756 jointly supported by the Agropolis Foundation (through the “Investissements
757 d’avenir” program, with the reference number ANR-10-LABX-0001-01) and
758 Fondazione Cariplo. The authors thank the Functional Genomics Center Zurich
759 (FGCZ) for providing highly valuable technical support.
760

761 AUTHOR CONTRIBUTIONS

762 V.F., D.G.S., P.B., L.M., and C.V designed the study; V.F., D.G.S., M.N., and

763 D.G. carried out the majority of experiments; G.D., M.M., and C.V performed

764 the bioinformatic analysis of proteomic data; C.V., V.F., P.B. L.M., F.W.-D.,

765 and M.B. interpreted the data and wrote the manuscript.

766

767 CONFLICT OF INTEREST

768 The authors have no conflict of interest to declare.

769

770 SUPPORTING INFORMATION

771 Table S1. Differentially abundant proteins (DAPs) identified by comparative

772 proteomic analysis of the roots (R) of *Azospirillum brasilense*-inoculated (Az)

773 and mock-inoculated (control; C) wheat plants (RAz vs. RC comparison).

774 Table S2. DAPs identified by comparative proteomic analysis of the leaves (L)

775 of Az and C plants (LAz vs. LC comparison).

776 Table S3. DAPs identified by comparing the leaf proteome of

777 *Paraburkholderia graminis*-inoculated (P) and C plants (RP vs. RC

778 comparison).

779 Table S4. DAPs identified by comparing the leaf proteome of P and C plants

780 (LP vs. LC comparison).
781 Table S5. Root-specific DAPs identified by comparing the root proteome of
782 Az or P plants with that of C plants (RAz vs. RC and RP vs. RC comparisons,
783 respectively).

784 Table S6. Amino acid content (ratio).

785 Table S7. DAPs identified by comparing the root proteome of wheat plants co-
786 inoculated with the arbuscular mycorrhizal fungus (AMF), *Funneliformis
787 mosseae*, and *A. brasilense* (AzM) with that of C plants (RAzM vs. RC
788 comparison).

789 Table S8. DAPs identified by comparing the root proteome of wheat plants co-
790 inoculated with the AMF and *P. graminis* (PM) with that of C plants (RPM vs.
791 RC comparison).

792 Table S9. DAPs identified by comparing the leaf proteomes of AzM and C
793 plants (LAzM vs. LC comparison).

794 Table S10. DAPs identified by comparing the leaf proteomes of PM and C
795 plants (LPM vs. LC comparison).

796 Table S11. DAPs identified by comparing the leaf proteomes of AzM and PM
797 plants (LAzM vs. LPM comparison).

798 Table S12. DAPs identified by comparing the leaf proteomes of Az plants
799 treated with or without the AMF (LAzM vs. LAz comparison).

800 Table S13. DAPs identified by comparing the leaf proteomes of P plants treated
801 with or without the AMF (LPM vs. LP comparison).
Table S14. DAPs identified by comparing the leaf proteomes of Az plants infected with or without the leaf pathogen, *Xanthomonas translucens* (LAzMX vs. LAzX comparison).

Table S15. DAPs identified by comparing the leaf proteomes of AzM plants and AMF only-inoculated (M) plants infected with *X. translucens* (LAzMX vs. LMX comparison).

Table S16. DAPs identified by comparing the leaf proteomes of AzMX plants and PM plants infected with *X. translucens* (LAzMX vs. LPMX comparison).

Figure S1. Overview of the experimental set up.

Figure S2. Spike fresh weight.

Figure S3. Evaluation of microbial root colonization.

Figure S4. Hierarchical clustering analysis of protein intensities.

Figure S5. Overlap of differentially abundant proteins (DAPs) between Az vs. C and P vs. C comparisons.

Figure S6. Heat map of DAPs belonging to “carbohydrate metabolism”, “photosynthesis”, “ion transport”, and “defense” GO terms in LAz vs. LC, LP vs. LC, RAz vs. RC, and RP vs. RC comparisons.

Figure S7. Venn diagrams of DAPs identified by comparing the proteome of mock-inoculated control (C) wheat plants with that of plants inoculated with only plant-growth promoting bacteria (PGPB; single inoculation) or co-inoculated with PGPB and arbuscular mycorrhizal fungus (AMF) (double inoculation).
Figure S8. Schematic representation of AgriGO SEA COMPARE function of up/down-regulated in LAzM vs. LPM and LAzMX vs. LPMX comparisons.

826

## FIGURE LEGENDS

**Figure 1.** Effect of arbuscular mycorrhizal (AM) symbiosis on the biomass of different organs of wheat plants. (A) Fresh weight of roots (RFW; grams). (B) Fresh weight of shoots (SFW; grams). Plants were either mock-inoculated (control; C) or inoculated with different microbial combinations: *Azospirillum brasilense* only (Az), *Paraburkholderia graminis* only (P), *Funneliformis mosseae* only (M), *A. brasilense* plus *F. mosseae* (AzM), and *P. graminis* plus *F. mosseae* (PM). Wheat plants were harvested at 50 days post-inoculation (dpi). Data represented as mean ± standard deviation (SD; n ≥ 6) were subjected to a one-way analysis of variance (ANOVA). Asterisks indicate significant differences (*P* < 0.05; Tukey's test). Different lowercase letters indicate significant differences.

839

**Figure 2.** Phenotypic evaluation of disease symptoms caused by the bacterial pathogen *Xanthomonas translucens*. Lesion length (mm) was assessed on leaves of C, Az, P, M, AzM, and PM plants at 4 (A) and 26 (B) days post-leaf clipping (dpc). Data at 4 dpc (not normally distributed) were analysed using the Kruskal-Wallis test. Asterisks indicate significant differences at the 5%
level using Mann-Whitney pairwise comparisons. Data at 26 dpc were subjected to one-way analysis of variance (ANOVA). Asterisks indicate significant differences (*P* < 0.05; Tukey's test). Different lowercase letters indicate significant differences.

**Figure 3.** Gene Ontology (GO) enrichment analysis of DAPs identified by comparing Az or P vs. C roots (RAz vs. RC and RP vs. PC) and leaves (LAz vs. LC and LP vs. LC). Enriched GO terms were selected using the following thresholds: false discovery rate (FDR) ≤ 0.05 and fold enrichment > 3.5. Red and blue indicate the enrichment of GO biological process terms for up-and down-regulated DAPs, respectively.

**Figure 4.** Total glucose and nitrogen (N) contents of wheat leaves and roots, respectively. (A, B) Total glucose content of leaves (A) and N content of roots (B) of C, Az, P, M, AzM, and PM wheat plants harvested at 50 dpi. Data represented as mean ± SD (n ≥ 3) were subjected to a one-way ANOVA. Different lowercase letters indicate significant differences (*P* < 0.05; Tukey's test).

**Figure 5.** Heat map of the main DAPs involved in plant defense (A) and in cell wall production, epigenetic regulation, translation (B) found in wheat leaves inoculated with PGPB and/or AM fungus (AMF) and treated with or without
X. translucens. Log2fold-change (Log2FC) values indicate the changes in protein abundance with respect to the control. Red and blue indicate maximum and minimum values, respectively. Asterisks indicate significant differences (ANOVA FDR < 0.01; Tukey's test).

**Figure 6.** (A) Scheme showing the molecular and phenotypic responses of non-mycorrhizal wheat colonized by *A. brasilence* (left side) and *P. graminis* (right side). (B) Scheme showing the molecular and phenotypic responses of mycorrhizal (*F. mosseae*) wheat alone (center) or colonized by *A. brasilence* (left side) and *P. graminis* (right side). The green boxes include the effects on the leaves while the brown ones include the effects on the roots.

**Figure S1.** Overview of the experimental set up. Twelve treatments of two different plant growth-promoting bacteria (PGPB) and one arbuscular mycorrhizal fungus (AMF) were tested. Surface-sterilized wheat seeds were pre-germinated for 4 days, transplanted into pots (see Experimental procedures) at t0, and inoculated with PGPR 1 day after. The AMF was present in the substrate at t0 (M treatment). The leaf-clipping assay was performed by infecting plants with *Xanthomonas translucens* on day 46, and the length of lesions was monitored at 4 and 26 days post-clipping (dpc; corresponding to day 50 and 72, respectively). To perform proteomics analysis, leaves were
888 infiltrated with a micro needle on day 49, and the infiltrated zone was sampled
889 on day 50 and frozen in liquid nitrogen.

890

891 **Figure S2.** Spike fresh weight of control, Az and P plants evaluated at the end
892 of wheat natural life cycle. Data (means ± SD, n ≥ 6) were subjected to one-
893 way analysis of variance (ANOVA). The asterisks indicated significant
894 differences at the 5% level using Tukey's test.

895

896 **Figure S3.**

897 Evaluation of microbial colonization of wheat plants. (A) Quantification of
898 bacterial population (colony forming units [CFU]) per gram of wheat roots at
899 7, 14, and 21 dpi. (B, C) Frequency of mycorrhizal hyphae (F%) (B) and
900 arbuscule abundance (A%) (C) in plant samples stained with trypan blue. One
901 hundred, 1-cm root fragments were analyzed for each sample. Data represent
902 mean ± standard error (SE) of two biological replicates per treatment. Az,
903 *Azospirillum brasilense* Sp245; P, *Paraburkholderia graminis* C4D1M; M,
904 *Funelliformis mosseae*; AzM, *A. brasilense* Sp245 plus *F. mosseae*; PM, *P.
905 graminis* C4D1M plus *F. mosseae*.

906

907 **Figure S4.** Hierarchical clustering analysis of protein intensities in different
908 organs of wheat plants. (A) Leaf dataset; (B) root dataset.

909
**Figure S5.** Overlap of differentially abundant proteins (DAPs) between Az vs. C and P vs. C comparisons. (A, B) Venn Diagrams of DAPs in leaves (A) and roots (B). Red and blue values indicate up- and down-regulated proteins, respectively. (C, D) Heat map of DAPs regulated in response to both plant growth-promoting bacteria (PGPB) in leaves (C) and roots (D). Red and blue indicate maximum and minimum values, respectively.

**Figure S6.** Heat map of DAPs belonging to “carbohydrate metabolism”, “photosynthesis”, “ion transport”, and “defense” GO terms in LAz vs. LC, LP vs. LC, RAz vs. RC, and RP vs. RC comparisons. Log₂fold-change (Log₂FC) values are shown. Red and blue indicate maximum and minimum values, respectively.

**Figure S7.** Venn diagrams of differentially abundant proteins (DAPs) found in all single and double inoculations vs. control (C) plants. DAPs exclusively and commonly regulated by *A. brasilense* and AMF upon single and double inoculations in leaves (A) and roots (B). DAPs exclusively and commonly regulated by *P. graminis* and AMF upon single and double inoculations in leaves (C) and roots (D).

**Figure S8.** Schematic representation of AgriGO SEA COMPARE function of up/down-regulated in LAzM vs LPM and LAzMX vs LPMX comparisons. The
932 colored blocks represent the level of regulation of each term, where the degree

933 of color saturation (yellow-to-red) of the corresponding box, was determined

934 by the adjusted P-value of the GO term (red = more significant).

935

936 DATA AVAILABILITY STATEMENT

937 Data available on request from the authors

938

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