Ectomycorrhizal symbiosis prepares its host locally and
systemically for abiotic cue signaling

Maíra de Freitas Pereira, David Cohen, Lucas Auer, Nathalie Aubry, Marie-Béatrice Bogeat-Triboulot, Cyril Buré, Nancy Engle, Yves Jolivet, Annegret Kohler, Ondřej Novák, et al.

► To cite this version:

Maíra de Freitas Pereira, David Cohen, Lucas Auer, Nathalie Aubry, Marie-Béatrice Bogeat-Triboulot, et al.. Ectomycorrhizal symbiosis prepares its host locally and systemically for abiotic cue signaling. The Plant Journal, 2023, 116 (6), pp.1784-1803. 10.1111/tpj.16465. hal-04210267

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# Ectomycorrhizal symbiosis prepares its host locally and systemically for abiotic cue signaling

Maíra de Freitas Pereira<fnref n="1" />, David Cohen<fnref n="1" />, Lucas Auer<fnref n="2" />, Nathalie Aubry<fnref n="1" />, Marie-Béatrice Bogeat-Triboulot<fnref n="1" />, Cyril Bure<fnref n="1" />, Nancy L. Engle<fnref n="3" />, Yves Jolivet<fnref n="1" />, Annegret Kohler<fnref n="2" />, Ondrej Novák<fnref n="4" />, Iva Pavlović<fnref n="4" />, Pierrick Priault<fnref n="1" />, Timothy J. Tschaplinski<fnref n="3" />, Irene Hummel<fnref n="1" />,*,†, Marie-Noëlle Vaultier<fnref n="1" />,*,† and Claire Veneault-Fourrey<fnref n="2" />,*,† ID

Received 8 February 2023; revised 31 August 2023; accepted 5 September 2023.

## SUMMARY

Tree growth and survival are dependent on their ability to perceive signals, integrate them, and trigger timely and fitted molecular and growth responses. While ectomycorrhizal symbiosis is a predominant tree-microbe interaction in forest ecosystems, little is known about how and to what extent it helps trees cope with environmental changes. We hypothesized that the presence of *Laccaria bicolor* influences abiotic cue perception by *Populus trichocarpa* and the ensuing signaling cascade. We submitted ectomycorrhizal or non-ectomycorrhizal *P. trichocarpa* cuttings to short-term cessation of watering or ozone fumigation to focus on signaling networks before the onset of any physiological damage. Poplar gene expression, metabolite levels, and hormone levels were measured in several organs (roots, leaves, mycorrhizas) and integrated into networks. We discriminated the signal responses modified or maintained by ectomycorrhization. Ectomycorrhizas buffered hormonal changes in response to short-term environmental variations systemically prepared the root system for further fungal colonization and alleviated part of the root abscisic acid (ABA) signaling. The presence of ectomycorrhizas in the roots also modified the leaf multi-omics landscape and ozone responses, most likely through rewiring of the molecular drivers of photosynthesis and the calcium signaling pathway. In conclusion, *P. trichocarpa-L. bicolor* symbiosis results in a systemic remodeling of the host's signaling networks in response to abiotic changes. In addition, ectomycorrhizal, hormonal, metabolic, and transcriptomic blueprints are maintained in response to abiotic cues, suggesting that ectomycorrhizas are less responsive than non-mycorrhizal roots to abiotic challenges.

Keywords: mycorrhizas, ozone, soil water deficit, poplar, *Laccaria bicolor*, co-expression modules, metabolome, hormonal profiling.

## INTRODUCTION

Plant growth and survival are dependent on their ability to perceive signals (inner and outer ones, abiotic and biotic ones, local and systemic ones), integrate them *via* rewiring of their signaling pathways, and trigger timely and fitted molecular and growth responses (Aeschbacher et al., 1994; Anderson et al., 2004; Motte et al., 2019; Shinozaki & Yamaguchi-Shinozaki, 1997). Soil water deficit sensing and the downstream signaling involve a complex network, with a key role for abscisic acid (ABA). Soil water

deficit induces an increase in the root ABA concentration that triggers auxin redistribution to stimulate root cell elongation (Xu et al., 2013). In addition, this higher root ABA concentration triggers long-distance transport of ABA, cytokinins, and gibberellic acid from the roots to the leaves, leading to shoot growth inhibition and stomatal closure (see Li et al., 2021 for a review). Tropospheric ozone negatively affects tree growth and triggers a complex regulatory system in leaves (Castagna & Ranieri, 2009; Jolivet et al., 2016; Vainonen & Kangasjärvi, 2015; Vaultier

<footnote n="1">¹Université de Lorraine, AgroParisTech, INRAE, UMR Silva, F-54000 Nancy, France,</footnote>

<footnote n="2">²Université de Lorraine, INRAE, Laboratory of Excellence ARBRE, UMR Interactions Arbres/Microorganismes, F-54000 Nancy, France,</footnote>

<footnote n="3">³Plant Systems Biology Group, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA, and</footnote>

<footnote n="4">⁴Laboratory of Growth Regulators, Faculty of Science of Palacký University & Institute of Experimental Botany of the Czech Academy of Sciences, Šlechtitelů 27, 78371 Olomouc, Czech Republic</footnote>

<footnote>*For correspondence (e-mail irene.hummel@inrae.fr; marie-noelle.vaultier@univ-lorraine.fr; claire.veneault-fourrey@inrae.fr)</footnote>

<footnote>†These authors contributed equally to this work.</footnote>
## 2 Maíra de Freitas Pereira et al.

Jolivet, 2015) as well as physiological changes such as impaired stomatal functioning, reduced photosynthesis, and senescence (Felzer et al., 2007; Renaut et al., 2009) that reduce carbon allocation to roots (Agathokleous et al., 2020). Depending on the ozone sensitiveness of the tree species, ozone inhibits or stimulates root growth (Andersen et al., 1997; Coleman et al., 1996; Grebenc & Kraigher, 2007). In turn, a change in root exudates can modify the diversity of the soil and root microbiota (Agathokleous et al., 2020). Low availability of photosynthesis-derived carbohydrates as well as impaired root development can affect root symbiont functioning and in turn alter tree growth (Andersen, 2003; Cudlín et al., 2007; Grebenc & Kraigher, 2007).

The roots of forest trees are in continuous interaction with soil-borne ectomycorrhizal fungi (Martin et al., 2016; Smith & Read, 2008). Hyphae occupy two ecological niches, that is, the soil and the host roots with which they form typical anatomical structures called ectomycorrhizas (Martin et al., 2016). By colonizing the apoplast of root cortical cells, fungal hyphae form the Hartig net. This interface is the site of a bi-directional nutrient exchange in which ectomycorrhizal fungi trade nutrients and water from surrounding soil to trees for carbohydrates as energy sources. Ectomycorrhizal fungi can improve tree performance by regulating biomass production and nutrient acquisition (Smith & Read, 2008), but the establishment of symbiosis can reduce juvenile tree growth, likely via excessive carbohydrate transfer to the fungus (Kaling et al., 2018). Although the ectomycorrhizal fungus-host interface is restricted to the rhizodermal cells of lateral roots, mycorrhization induces a shift towards defense-related metabolites in leaves that can confer protection against biotic stresses (Dreischhoff et al., 2020; Kaling et al., 2018; Schweiger & Müller, 2015). Since this systemic response primes greater responsiveness to subsequent pathogen attacks (Jung et al., 2012; Kaling et al., 2018; Sanmartín et al., 2020; Song et al., 2019), mycorrhization has been proposed to prime trees against their biotic environment – referred to as mycorrhiza-induced resistance (Dreischhoff et al., 2020). The activation of signaling and stress pathways by mycorrhization also confers the host tolerance to abiotic constraints such as heavy metals (Ma et al., 2014; Shi et al., 2019), salinity (Luo et al., 2009) and high temperature (Pickles et al., 2012). Ectomycorrhizal fungi enhance water uptake by the host plant under dry conditions and mitigate host drought responses (Bogeat-Triboulot et al., 2004; Garbaye, 2000; Lehto & Zwiazek, 2011). For instance, hyphae increase the volume of soil being explored by reaching small soil pores non-accessible to fine roots, and/or ectomycorrhizas increase the root hydraulic capacities (Bogeat-Triboulot et al., 2004; Calvo-Polanco et al., 2019; Xu et al., 2015, 2016). Host aquaporins and hormones (ABA and cytokinins) are also regulated by

mycorrhizal fungi under drought conditions (Calvo-Polanco et al., 2019). Few studies have focused on ectomycorrhizas under ozone stress, and they were mainly focused on changes in fungal community composition (Grebenc & Kraigher, 2007; Winwood et al., 2007). Functional ectomycorrhizas arise from a fine-tuned interplay between hormones and metabolites by regulating the development, immunity, and physiology of the host and fungal cells (Basso & Veneault-Fourrey, 2020; Martin et al., 2016). Therefore, it is plausible that ectomycorrhizas interfere with the tree growth-defense trade-off, as proposed for fungal endophytes (Bastías et al., 2021). As abiotic factors trigger shifts in the tree's hormonal and carbon balances, they likely alter the tree growth-defense trade-off.

Systems biology approaches trace molecular mechanisms and pathways throughout plants (e.g., integrated defense, signal transduction, metabolic changes) to address the complex question of response prioritization (Cramer et al., 2011) and understand how changes in the abiotic environment impair a beneficial interaction, and vice versa. *P. trichocarpa* is very sensitive to ozone and water deficit, which cause rapidly spreading leaf damage and shedding (Cirelli et al., 2016; Street et al., 2006, 2011). This is why we imposed short-term changes in the atmospheric (ozone exposure) or soil (cessation of watering) environment of the *P. trichocarpa*-L. bicolor interaction. These conditions do not impact physiological parameters so we can focus on host signaling (genes, metabolites, and hormones). Our aims were (i) to test whether these abiotic cues modify the ectomycorrhizal molecular (transcripts, metabolites, and hormones) blueprints, and (ii) to identify the local and systemic interference of ectomycorrhizal symbiosis on abiotic cue signaling in the host. We used a systems-biology-based approach to connect *P. trichocarpa* gene co-expression modules and differentially accumulated metabolites and hormones. We addressed the following specific questions: (i) what is the short-term impact of cessation of watering (leading to a soil water content decrease) or of ozone treatment on ectomycorrhizas? and (ii) how do *L. bicolor* ectomycorrhizas interfere with abiotic signaling in *P. trichocarpa* leaves and non-colonized roots? We hypothesized that (i) *P. trichocarpa*-L. bicolor ectomycorrhizas are less responsive than fine roots to abiotic cues, and (ii) *P. trichocarpa*-L. bicolor symbiosis results in a systemic remodeling of the host signaling networks in response to abiotic changes.

## RESULTS

### Experimental design overview

We exposed 3-month-old *P. trichocarpa* cuttings previously inoculated with *L. bicolor* (I) or not (NI) to short-term abiotic treatments, namely ozone exposure (O), cessation of
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Experimental approach. Pre-rooted *Populus trichocarpa* cuttings were inoculated (I) or not (NI) with the ectomycorrhizal fungus *Laccaria bicolor*. Poplar cuttings were distributed across the three treatments: control (C), cessation of watering (43 h without watering; CW) or ozone exposure (200 ppb for 8 h; O). Scale-bar, 10 cm.</figcaption></figure>

(a) Physiological and morphological parameters were assessed in all cuttings. The colonization rate (%), the number of leaves, and plant height (cm) measured on non-inoculated (NI) and inoculated (I) poplar cuttings are given in the left panel, and parameters related to the water status (leaf full-turgor osmotic pressure, leaf relative water content and soil water content) are given in the right panel. ANOVA main effect *P*-values are given; letters indicate statistically significant differences between experimental groups (*P*-value <0.05).

(b) Physiological parameters related to photosynthetic activity and soil and plant respiration were assessed on a subset of non-harvested cuttings (n = 24; four cuttings per condition; Table S1). (c) Leaves and fine roots from non-inoculated and inoculated cuttings, and mycorrhiza (Myc) from inoculated cuttings were harvested for transcriptomics, metabolomics, and hormonomics. The data were used for the meta-transcriptomics analysis to perform quality filtering and co-expression network analysis (WGCNA). Two-way ANOVA was used to detect significantly co-expressed modules, metabolites, and hormone forms (*P*-value <0.05). Cytoscape 3.8.0 was used to visualize data integration into networks, based on planar filtering of Pearson's correlation values.

watering (CW) or non-exposed control (C) (Figure 1). The inoculated cuttings exhibited approximately 13% of ectomycorrhizal root tips, while no ectomycorrhiza was observed on the roots of non-inoculated cuttings (Figure 1a). The roots and ectomycorrhizas from inoculated cuttings had *L. bicolor*-enriched meta-transcriptomes as

compared to non-inoculated ones (Figure S1), confirming the efficiency of *L. bicolor* inoculation. The inoculated cuttings were slightly smaller than the non-inoculated ones, but had the same number of leaves (Figure 1a). Additional physiological investigation revealed no impact of mycorrhization on leaf and root dry weight, pigment content or
4 Maíra de Freitas Pereira et al.

gas exchanges (stomatal conductance) (Figure 1b; Table S1). The cessation of watering significantly reduced the soil water content without affecting the leaf relative water content or the leaf full-turgor osmotic pressure (Figure 1a). The overall homogeneity of the physiological parameters (respiration, net assimilation, and stomatal conductance) was in accordance with the moderate intensity and the short duration of the treatments. Consequently, we were able to investigate signaling pathways in the absence of any interference due to physiological damage or acclimation.

We quantified gene expression as well as the metabolite and hormone levels in mature leaves (L), fine roots (R), and ectomycorrhizal root tips (Myc) in all conditions (Figure 1c). We generated gene co-expression networks for the above-ground and belowground compartments taken separately (Table S2). We screened the most significant modules according to their eigengenes (MEs), their functional enrichment (Table S3: Gene Ontology; Table S4: MapMan Bin,) and their top-30 ranked hubs, that is, the genes exhibiting the highest intramodular connectivity (Table S5; Figures S2 and S3). We highlighted differentially accumulated metabolites and hormones as well as differentially expressed MEs following two-way ANOVA and significant Tukey tests (Tables S6 and S7). We revealed the most significant co-variation patterns between metabolites, hormones, and MEs using Pearson-based correlation matrix (Table S8). Including the expression of the top-30 intramodular hubs as a proxy of transcriptome expression rather than module eigengenes into the correlation matrix generated highly congruent correlation matrices (Table S9). Hub-based correlation matrices were filtered and reduced to planar-filtered networks (Table S9). Based on systems biology approaches, abiotic and biotic responses that occurred at three molecular layers (hubs, metabolites, and hormones) were merged into two synthetic networks capturing the aboveground and belowground processes triggered by abiotic cues and mutualistic interactions.

**Local and systemic impact of *L. bicolor* inoculation on the belowground and aboveground compartments independently of abiotic treatments.**

We first identified poplar transcripts, hormones (active forms and catabolites), and metabolites that differentially accumulated in ectomycorrhizas, uncolonized roots of

inoculated cuttings, and roots of non-inoculated cuttings independently of abiotic treatments (Figure 2a; Table S6) and mapped them into a correlation network (Figure 3). In a second step, we identified modules, metabolites, and hormones systemically modified by the presence of ectomycorrhizas in the leaves of inoculated cuttings (Figures 2b and 4; Table S7).

**Most of the ectomycorrhizal molecular signatures are robust whatever the abiotic environment**

Twenty-two metabolites, three hormone forms, and two-thirds of the poplar transcriptome, represented by 10 co-expression modules (about 18 950 genes), were differentially accumulated in ectomycorrhizas, uncolonized roots of inoculated cuttings and roots of non-inoculated cuttings independently of the abiotic treatments (Figure 2a). As compared to fine roots, ectomycorrhizas accumulated arbutin, hydroquinone, and trehalose - metabolites associated with functional *Populus-Laccaria* ectomycorrhizas (Tschnaplinski et al., 2014). Ectomycorrhizas were significantly depleted in aromatic defense compounds or precursors in lignin biosynthesis compounds (shikimic acid and coniferin), sugars (fructose and glucose), and organic acids of the tricarboxylic acid cycle (citric acid and malic acid) (Tschnaplinski et al., 2014) as well as in two unknown metabolites (10.75 393 303 257 modified sugar and 24.47 648 559 368 flavonoid), two cytokinin transport forms (isopentenyl adenosine, iPR; trans-zeatin riboside, tZR) and 9,10 dihydrojasmonic acid (9,10 dhJA produced after reduction of jasmonic acid (JA)). ME4R and ME7R eigen-genes were significantly higher in ectomycorrhizas than in fine roots (Tables S2-S6). ME4R exhibited functional GO enrichments in 'ribosome' and 'translation' while ME7R was associated with 'protein binding', 'pentatricopeptide repeat-containing protein', and 'protein degradation' (Tables S3a and S4a). ME1R and ME5R had significantly lower eigengenes in ectomycorrhizas than in fine roots. ME1R was enriched in 'DNA binding' and 'regulation of transcription', and ME5R in 'nucleotide binding'.

**Ectomycorrhizas systemically impacts some molecular responses of the whole root system**

In addition to Myc-specific molecular blueprints, ectomycorrhizas differed from the fine roots of non-inoculated

**Figure 2.** Root system (a) and leaf (b) molecular responses of *Populus trichocarpa* following inoculation regardless of the abiotic treatment. Significant differences were revealed by two-way ANOVA and Tukey's HSD test (Tables S6 and S7) and were visualized as heatmaps (R Package pheatmap, version 1.0.12). The mean levels of each metabolite, hormone form or module eigengene (ME) are represented by rows of colored boxes with each column representing one experimental condition. Expression levels range from green (low expression) to brown (high expression). Unknown metabolites were named by retention time (min) first followed by key mass-to charge ratios (m/z) and putative metabolite classes were indicated.

Set 1 represents significant changes in ectomycorrhiza (Myc), non-inoculated (NI) and inoculated (I) fine roots (ectomycorrhizal signatures); Set 2 represents significant changes between non-inoculated fine roots (NI) and ectomycorrhizas (Myc); Set 3 represents significant changes distinguishing inoculated roots (I) from mycorrhizas (Myc) and/or non-inoculated roots (NI).

<footnote>* significant changes that distinguish I roots (fungus-free fine roots of inoculated cuttings) from NI roots (systemic effect of Myc or of the fungal presence). Green, non-inoculated fine roots (NI); yellow, fine roots of inoculated cuttings (I); orange, ectomycorrhizas (Myc).</footnote>
<table><caption>(a) ROOT SYSTEMS</caption><thead><tr><th></th><th>N</th><th>I</th><th>Myc</th><th></th></tr></thead><tbody><tr><td rowspan="10">Set 1</td><td>a</td><td>a</td><td>b</td><td>arbutin</td></tr><tr><td>a</td><td>a</td><td>b</td><td>hydroquinone</td></tr><tr><td>a</td><td>a</td><td>b</td><td>trehalose</td></tr><tr><td>b</td><td>b</td><td>a</td><td>shikimic acid</td></tr><tr><td>b</td><td>b</td><td>a</td><td>coniferin</td></tr><tr><td>b</td><td>b</td><td>a</td><td>fructose</td></tr><tr><td>b</td><td>b</td><td>a</td><td>glucose</td></tr><tr><td>b</td><td>b</td><td>a</td><td>citric acid</td></tr><tr><td>b</td><td>b</td><td>a</td><td>malic acid</td></tr><tr><td>b</td><td>b</td><td>a</td><td>10.75 393 303 257</td></tr><tr><td rowspan="10">Set 2</td><td>b</td><td>b</td><td>a</td><td>24.47 648 559 368 flavonoid</td></tr><tr><td>b</td><td>b</td><td>a</td><td>iPR</td></tr><tr><td>b</td><td>b</td><td>a</td><td>fZR</td></tr><tr><td>b</td><td>b</td><td>a</td><td>9,10 dhJA</td></tr><tr><td>a</td><td>a</td><td>b</td><td>ME4R</td></tr><tr><td>a</td><td>a</td><td>b</td><td>ME7R</td></tr><tr><td>b</td><td>b</td><td>a</td><td>ME1R</td></tr><tr><td>b</td><td>b</td><td>a</td><td>ME5R</td></tr><tr><td>b</td><td>ab</td><td>a</td><td>digalactosylglycerol</td></tr><tr><td>b</td><td>ab</td><td>a</td><td>xylitol</td></tr><tr><td rowspan="10">Set 3</td><td>b</td><td>ab</td><td>a</td><td>epicatechin</td></tr><tr><td>*</td><td>b</td><td>a</td><td>catechin</td></tr><tr><td>*</td><td>b</td><td>a</td><td>ME6R</td></tr><tr><td>*</td><td>a</td><td>ab</td><td>ME17R</td></tr><tr><td>*</td><td>ab</td><td>a</td><td>linoleic acid</td></tr><tr><td>*</td><td>b</td><td>a</td><td>6-hydroxy-2-cyclohexenone(enol)</td></tr><tr><td>*</td><td>ab</td><td>b</td><td>ethyl phosphate</td></tr><tr><td>*</td><td>b</td><td>a</td><td>ME12R</td></tr><tr><td>*</td><td>a</td><td>b</td><td>ME15R</td></tr><tr><td>*</td><td>a</td><td>b</td><td>ME2R</td></tr><tr><td>*</td><td>b</td><td>a</td><td>ME18R</td></tr><tr><td>*</td><td>c</td><td>b</td><td>15.81 368 649 559 flavonoid</td></tr><tr><td>*</td><td>c</td><td>b</td><td>3-O-caffeoylquinic acid</td></tr><tr><td>*</td><td>c</td><td>b</td><td>3-O-caffeoyl-shikimic acid</td></tr><tr><td>*</td><td>c</td><td>b</td><td>quinic acid</td></tr></tbody></table>

<table><caption>(b) MATURE LEAVES</caption><thead><tr><th></th><th>N</th><th>I</th><th></th><th></th></tr></thead><tbody><tr><td rowspan="4">Metabolite</td><td>a</td><td>b</td><td>catechol glucoside</td><td></td></tr><tr><td>a</td><td>b</td><td>salicyltremuloidin</td><td></td></tr><tr><td>b</td><td>a</td><td>a-tocopherol</td><td></td></tr><tr><td>b</td><td>a</td><td>glyceric acid</td><td></td></tr><tr><td rowspan="4">Hormone</td><td>b</td><td>a</td><td>threoно-1,4-lactone</td><td></td></tr><tr><td>a</td><td>b</td><td>fZR</td><td rowspan="4">CKs</td></tr><tr><td>a</td><td>b</td><td>DHZR</td></tr><tr><td>a</td><td>b</td><td>iP7G</td></tr><tr><td>a</td><td>b</td><td>DHZROG</td></tr><tr><td>a</td><td>b</td><td>DHZOG</td><td></td></tr><tr><td>a</td><td>b</td><td>fZOG</td><td></td></tr><tr><td>a</td><td>b</td><td>fZROG</td><td></td></tr><tr><td>b</td><td>a</td><td>iPRMP</td><td></td></tr><tr><td>Modulation</td><td>b</td><td>a</td><td>ME15L</td><td></td></tr></tbody></table>
cuttings by lower amounts of digital acylglycerol, xylitol, epicatechin, and catechin as well as by lower ME6R eigengenes and higher ME17R eigengenes (Figure 2a). ME6R was functionally enriched in 'cell cycle', 'cytoskeleton and motor activity', while ME17R was functionally enriched in 'secondary metabolism' (Tables S3a and S4a). Both the catechin level and ME6R eigengene were significantly lower in ectomycorrhizas and non-colonized fine roots than in the fine roots of non-inoculated cuttings. Ectomycorrhizas also differed from the non-colonized fine roots of inoculated cuttings by lower levels of ethyl phosphate and higher levels of linoleic acid. While ME12R contrasted fine roots according to their inoculation status, the non-colonized fine roots of inoculated cuttings also differed from ectomycorrhizas and the fine roots of non-inoculated cuttings by lower levels of 6-hydroxy-2-cyclohexenone (enol) and higher ME15R eigengenes. ME15R was enriched in 'biotic stress' and 'signaling receptor kinase'. In addition, the non-colonized fine roots of inoculated cuttings differed from ectomycorrhizas and from fine roots of non-inoculated cuttings in terms of

transcript accumulation (ME2R, enriched in 'biotic stimulus signaling' and 'hormone metabolism', and ME18R, enriched in 'protein degradation/cytochrome P450', 'secondary metabolism'), and in the amounts of 15.81368649559-flavonoid, 3-O-caffeoylquinic acid, 3 O-caffeoyl shikimic acid and quinic acid. Such contrasts between the non-colonized fine roots of inoculated cuttings and the fine roots of non-inoculated cuttings suggest a systemic impact of ectomycorrhizal symbiosis on the whole root system.

## Ectomycorrhiza formation structures the co-variation of gene expression, metabolite, and hormone levels in root systems

In the planar network, mycorrhiza-associated metabolites and hormones (increased or depleted) were highly connected to ME1R and ME18R (Figure 3a,b; Table S9). Their metabolite-connected hubs encoded two putative cytochrome P450 (*Potri.007G083500* and *Potri.007G083300*), a transcription factor (*PtSPL19*), a putative DNA chaperone (*PtHsp40-141*), a putative carboxypeptidase (*Potri.002G010800*) and a sugar

<figure><img src="image_4.png" /><figcaption>**Figure 3.** Transcriptomics, metabolomics, and hormonomics correlation network in the *Populus trichocarpa* root system. The significant top-30 genes of the co-expression modules (MEs), metabolites and hormone forms were integrated by MEGENA (n = 34, FDR P-value <0.05, Table S9a). The planar filtered network was visualized using Cytoscape software 3.8.0 (force-directed layout) and represents significant correlations between the top-30 hubs of the modules (rectangles), metabolites (circles) or hormones (triangles). Colors are associated to structuring factors, namely inoculation (orange), abiotic treatment (purple), a combined effect of inoculation and abiotic treatments (green) or an interaction between inoculation and abiotic treatments (gray). (a), (b) and (c), details of the network with annotated genes discussed in this paper.</figcaption></figure>

*Populus trichocarpa* v4.1 gene IDs were used, and their common names are provided according to published references (detailed Table S5).
efflux transporter (*PtSWEET1a*). The presence of various transporters encoding genes in ME1R and ME18R-hubs (*PtNPF5.24*, *PtNPF5.25*, *Potri.019G079551*, *Potri.011G167532*, *PtSWEET1a*, and *PtSWEET1b*) suggests that the nutrient exchange function of ectomycorrhizas remained operational during the initial phase of abiotic changes. ME18R hubs captured the key ectomycorrhizal markers (arbutin, hydroquinone and trehalose), while ME18R and ME1R hubs captured the metabolite set contrasting inoculated vs. non-inoculated root systems (15.81368649559-flavonoid, 3-O-caffeoylquinic acid, 3 O-caffeoyl shikimic acid and quinic acid) (Figure 3a,b). In addition, ME1R hubs mapped in connection with ME2R hubs, generating a hotspot for ectomycorrhizal molecular blueprints (Figure 3b). ME2R hubs included genes putatively involved in JA biosynthesis (allene oxide cyclase *Potri.008G117300*) and JA-signaling with the negative regulators *PtJAZ5* and *PtJAM1.2*, together with a JA-responsive gene in Arabidopsis (*Pt-CYP94.4*). Based on their *Arabidopsis thaliana* best hits, several ME2R hubs were related to plant-pathogen interactions, plant immunity, and the ABA response (e.g., *PtrLEA47*, *Pt-SYP121.1*, *Potri.004G064000*, or *Potri.006G202600*). The ME2R hubs included a putative calmodulin-like protein, a calmodulin-receptor kinase and a calcium-dependent kinase (*Pt-CRCK3.2*, *PtCDPK9*, and *PtCML23-2*) as well as WRKY transcription factors (*PtrWRKY77*, *PtrWRKY73*, and *PtrWRKY31*). Altogether, ME1R, ME2R, and ME18R encompassed the core gene sets coordinating hormonal and metabolite rewiring in ectomycorrhizas by maintaining their functioning under moderate abiotic changes.

Mycorrhiza formation systemically modifies the cytokinin
metabolism as well as membrane- and cell-wall-related
gene expression in leaves

The leaves of inoculated cuttings accumulated tZR,
dihydro-zeatin riboside (DHZR), isopentenyladenine-7-
glucoside (iP7G) and four reversible/irreversible inactivated
glycosylated forms of cytokinin (DHZROG DHZOG, tZOG,
and tZROG), while a cytokinin precursor – isopentenyl
adenosine-5'monophosphate (iPRMP) – was depleted
as compared to the leaves of non-inoculated cuttings
(Figure 2b). They also accumulated catechol glucoside
and salicyltremuloidin, while α-tocopherol, glyceric acid
and threo-1,4-lactone were depleted. These inoculation-
related hormones and metabolites were connected to
metabolites responding to both the inoculation status
and abiotic cues (Figure 5b). The leaf transcriptome reacted
to mycorrhization (ME15L, 253 genes exhibiting lower
expression in inoculated cuttings than in non-inoculated
cuttings) (Figure 2b). This module was enriched in
'carbohydrate metabolism' and 'cell wall remodeling'
(Tables S3 and S4). The ME15L hubs found connected
with glyceric acid on the planar network encoded a
putative heparan-alpha-glucosaminide N-acetyltransferase
(*Potri.005G025200*), a putative MKIAA1688 cytoskeleton

protein (*Potri.019G053300*) and a putative transmembrane
protein (*Potri.018G120200*) (Figure 5a). One-third of the
ME15L hub appeared to include integral components of
membranes or to be involved in transmembrane transport
(v8.1 database, http://aramemnon.uni-koeln.de). It included
putative proton:calcium cation exchangers (*Pop_CAX1.1*,
*Pop_CAX1.2*), a putative plant cyclic nucleotide-gated ion
channel (*PtCNGC2a*), a putative PILS-type auxin transporter
(*Potri.006G112200*), a putative VDAC-type outer membrane
porin (*Potri.012G070300*), and putative TPK/KCO-type
voltage-independent potassium cation channels (*Pop-tri-
TPK06* and *Pop-tri-TPK07*). Our results suggest that the
presence of ectomycorrhizas in roots interfered with leaf
functioning by targeting the cytokinin metabolism and
membrane and cell-wall-related processes.

Additive and interactive impacts of *L. bicolor* inoculation
and abiotic treatments

We studied the molecular changes induced by the *L. bicolor* inoculation – abiotic treatment interaction in the belowground and aboveground compartments as well as on the abiotic responses that retained a mycorrhization signature (Figure 4a–c; Tables S6 and S7).

Abiotic cues and ectomycorrhizas interactively shape the
root hormonal content

Ectomycorrhizas and abiotic treatments interactively
shaped hormone contents (auxin, cytokinin, jasmonate,
ABA and their metabolites) and three metabolite levels
both locally in ectomycorrhizas and systemically in the
non-colonized roots of inoculated cuttings (Figure 4a).
The cessation of watering (CW) increased the ABA levels in
the fine roots of both non-inoculated and inoculated cut-
tings but not in ectomycorrhizas. In response to CW, the
amount of phaseic acid (PA, an ABA catabolite) increased
in the fine roots of inoculated cuttings but decreased in
ectomycorrhizae. In control conditions, ectomycorrhizas
exhibited lower levels of 12-oxophytodienoic acid (cis-
OPDA), jasmonic acid (JA), and JA-isoleucine (JA-Ile) than
the fine roots of non-inoculated cuttings did. The cis-
OPDA, JA, and JA-Ile levels decreased in non-inoculated
roots under CW, while no significant change occurred in
ectomycorrhizas or the non-colonized fine roots of inocu-
lated cuttings. Ozone exposure triggered cis-OPDA accu-
mulation in ectomycorrhizas, as well as JA (but not JA-Ile)
accumulation in the non-colonized roots of inoculated cut-
tings. The level of cis-zeatin riboside (cZR) in the non-
colonized fine roots of inoculated cuttings was higher
under ozone exposure than under the other abiotic condi-
tions. Meanwhile, the iPRMP level decreased in the non-
inoculated roots under CW. The reversible O-glucosyl form
of cZR – cZROG – differentiated ectomycorrhizas from the
control fine roots, but cZROG also decreased in response
to the abiotic treatments in the roots of non-inoculated
<figure><img src="image_5.png" /></figure>
<caption>**Figure 4.** Root system and leaf molecular responses of *Populus trichocarpa* cuttings according to the interactive effect of abiotic treatments and the inoculation status (a, roots; d, leaves), the additive effects of abiotic treatment and inoculation (b, roots), or the abiotic treatment regardless of the inoculation status (c, roots; e, leaves). Significant differences following two-way ANOVA and Tukey's HSD test (Tables S6 and S7) were visualized as heatmaps (R Package pheatmap, version 1.0.12). The mean levels of each metabolite, hormone form, or module eigengene value (ME) are represented by a row of colored boxes, each column representing one experimental condition. Expression levels range from green (low expression) to brown (high expression). Unknown metabolites were named by retention time (min) first followed by key mass-to-charge ratios (m/z) and putative metabolite classes were indicated.</caption>

* significant differences between I roots (fungus-free fine roots of inoculated cuttings) and NI roots (systemic effect of Myc or the fungal presence) under C conditions.

Rose and blue letters, significant responses to ozone (O) exposure or cessation of watering (CW), respectively, compared to the control condition.

Gray, control (C); blue, cessation of watering (CW); pink, ozone (O); green, non-inoculated cuttings (NI); yellow, inoculated cuttings (I); orange, ectomycorrhizas (Myc).

<figure><img src="image_6.png" /><figcaption>**Figure 5.** Transcriptomics, metabolomics, and hormonomics correlation network in *Populus trichocarpa* leaves. The significant top-30 genes of co-expression modules (MEs), metabolites, and hormone forms were integrated by MEGENA (n = 23, FDR P-value <0.05, Table S9b). The planar-filtered network was visualized using Cytoscape software 3.8.0 (force-directed layout) and represents significant correlations between the top 30 hubs of the modules (rectangles), metabolites (circles), or hormones (triangles). Colors are associated with structuring factors, namely inoculation (orange), abiotic treatment (purple), a combined effect of inoculation and abiotic treatments (green), or an interaction between inoculation and abiotic treatments (gray); (a), (b) and (c), details of the network with annotated genes discussed in this paper.</figcaption></figure>

*Populus trichocarpa* v4.1 gene IDs were used. Their common names are provided according to published references (detailed Table S5b).

cuttings, suggesting that the presence of the fungal symbiont decreased the cZROG content. In control conditions, the amounts of the catabolite form of auxin – 2-oxindole-3-acetic acid (oxIAA) – and auxin-amino acid conjugates – IAA-aspartate (IAA-Asp) and IAA-glutamate (IAA-Glu) – were higher in the roots of non-inoculated cuttings than in those of inoculated cuttings and ectomycorrhizas. The abiotic treatments drastically reduced differences in the levels of auxin forms among organs. It is worth noting that the non-inoculated roots displayed significantly reduced

levels of auxins in response to both cessation of watering and ozone exposure as compared to the control condition. Except PA and ABA, all hormones were captured by ectomycorrhiza-related modules on the planar network (Figure 3). ME18R hubs captured JA, JA-Ile, IAA, and iPRMP, while ME1R hubs captured cis-OPDA, cZROG, iPR, tZR, and 9,10 dhJA. ME6R hubs were connected to amino-acid conjugated auxin forms and their catabolic form oxIAA, suggesting a direct involvement of ME6R genes in the regulation of auxin homeostasis in
ectomycorrhizas. ME6R included hubs encoding a putative leucine-rich repeat transmembrane protein kinase (PtLRR-RLK94; *Potri.010G183400*), a putative transcriptional factor repressor of early auxin response genes (*PtrIAA14.1; Potri.008G161200*) and a putative subtilisin-like serine endopeptidase family protein (*Pt-SUB1.2; Potri.014G171600*), the last two standing as good candidates for the integration of auxin stimuli in ectomycorrhizas. Taken together, the hormonal changes following the abiotic treatments appeared buffered in ectomycorrhizas and systemically modified in the non-colonized roots of inoculated cuttings.

## Part of the ABA-mediated root transcriptional responses to cessation of watering retains mycorrhization-dependent expression patterns

Part of the root transcriptome responded to both cessation of watering and mycorrhization (additive effect without any interaction, ME8R, ME13R, ME16R, about 1775 genes; Figure 4b). These three modules captured a specific part of the transcriptional responses to CW. ME13R and ME8R – two ribosome-enriched modules – differentiated mycorrhizas from the fine roots of inoculated cuttings and exhibited lower eigengene values than the control after CW. In contrast, ME16R was a CW-activated module that also discriminated the fine roots from inoculated vs. non-inoculated cuttings. ME16R eigengene was significantly lower in the non-colonized roots of inoculated cuttings than in the fine roots of non-inoculated cuttings. ME16R was enriched for 'lipid metabolism', 'fatty acid synthesis', and 'response to abiotic stress'. ME16R eigengene best captured the variation of the root ABA levels (Table S8a) and three ME16R hubs (a putative serine carboxypeptidase *PtSCPL39*, a putative long-chain acyl-coenzyme A synthetase *PtLACS2.2*, and a putative indole-3-acetic acid-7 *PtIAA14.2*) captured ABA on the planar network (Figure 3c; Table S9a). The ABA-connected ME16R hubs constituted a core set of stress-responsive genes associated with 'lipid metabolism' and 'signaling' and encoding putative lipid transfer proteins (*PtAGP67C*, *PtXYLP16*, *PtXYLP17*, and *PtXYLP19*), putative 3-ketoacyl-cyl-CoA synthase or 3-ketoacylacyl carrier protein synthase (*Pt-I.2*, *Pt-KCS1.1*, and *Potri.008G160000*), putative GDSL-like lipases (*Potri.007G036300* and *Potri.012G068700*), a putative HXXXD-type acyl-transferase (*ABTL1*) and CYP86B1s (*Potri.005G092200* and *Potri.007G072100*), together with a putative laccase (*Pt-GLAC90.1*), a putative ABCG half-transporter (*Potri.014G080200*), a putative xanthine/uracil permease (*Potri.009G086800*), a putative protease (*PtAP5*) and putative hydroxyproline-rich glycoproteins (*PtPAG22*, *PtPRP16*, *PtPRP21*, and *PtPRP31*). It is worth noting that the expression of 11 ME16R hubs was upregulated in poplar roots in response to osmotic stress (*PtAGP67C*, *PtXYLP19*, *Pt-KCS1.1*, *Potri.008G160000*, *Potri.007G036300*, *Potri.012G068700*, *Potri.012G068700*, *ABTL1*, *Potri.005G092200*, *Potri.007G072100*, and *Potri.014G080200*,

Grünhofer et al., 2022). Most of these transcriptional responses also occurred in poplar roots submitted to drought stress (Cohen et al., 2010), together with the upregulation of *PtXYLP16*, *PtXYLP17*, and *PtPRP2* expression. In addition, ME16R hubs mapped in connection with ME11R hubs, and ABA with its catabolic forms, generating a hot-spot of molecular changes following CW (Figure 3c). Our results highlight ME16R and ME11R as master modules of cessation of watering-driven ABA accumulation (Figure 3c). While ME11R discriminated the core set of early soil water-depletion-sensing transcripts (Figure 4c), ME16R included the part of this response alleviated by the presence of *L. bicolor*. These modules emphasized a finely tuned and spatialized control of the root ABA content.

## The three -omics levels of leaf responses to abiotic changes are interactively modulated by mycorrhization

The changes of three modules, nine metabolites, and two hormones were related to an interactive effect of the abiotic treatments and inoculation in poplar leaves (Figure 4d). For example, unknown metabolites (10.56 397 425 471 modified sugar, 10.80 397 425 471 modified sugar, and 13.55 445 217 204 glycoside), gentisic acid-5-O-glucoside and two cytokinin forms (cZR and the transport form isopentenyl adenosine, iPR) accumulated in the leaves of inoculated cuttings under CW as compared to the control, whereas the level of 14.58 168 285 glycoside decreased in the leaves of non-inoculated cuttings under ozone exposure (Figure 4d). The levels of 1,2,4-benzenetriol, catechol, pyrogallic acid, α-salicyloylsalicin, and gentisic acid-5-O-glucoside discriminated the leaves of inoculated vs. non-inoculated cuttings under ozone exposure. The ribosome-enriched ME6L (1429 genes) discriminated the leaves of inoculated cuttings exposed to ozone from those exposed to cessation of watering. ME8L positively contrasted the leaves of inoculated cuttings exposed to ozone and was enriched in 'calcium signaling' and 'signaling receptor kinase'. ME8L hubs encoded putative calmodulin and related proteins involved in signal transduction or calcium binding (e.g., *CaM319*, *CaM1281*, *CaM409*, *PtCML23-1*, *PtCML23-2*, *Potri.011G129100*, or *Potri.014G101700*), a putative SNARE protein (*Potri.001G219200*), and putative stress-associated proteins (e.g., *PtSAP7*, *PtGRF1/2/4b*, and *Pt-ERD15.2*). ME8L suggested that mycorrhization affected ozone signaling in leaves and highlighted the calcium-signaling pathway as the most likely central modulator. The largest module – ME1L; 5130 genes – negatively contrasted the leaves of inoculated cuttings exposed to ozone and was enriched for 'protein binding', 'response to light stimuli', 'photosynthesis', and 'carbohydrate metabolism', and included the following hubs: a putative deoxyxylulose-5-phosphate synthase (*Potri.010G015200*), putative plastid-localized proteins (e.g., *Pt-ALB3.1* and *Potri.016G078600*, a homolog of the Arabidopsis chloroplast signal recognition particle-
GTPase), a putative proton-translocating pyrophosphatase (PtVP1.1), a putative phosphate-transporting ATPase (PtPHT4;3), and a putative oligopeptide transporter (Potri.005G150400). In conclusion, mycorrhization interfered with the leaf response to ozone exposure, most likely through rewiring of the molecular players that drive photosynthesis and the calcium-signaling pathway.

## Poplar responses to abiotic treatments unimpacted by the presence of ectomycorrhizas

Some poplar responses to short-term abiotic changes remained conserved whatever the inoculation status of the cuttings (Figure 4c,e; Tables S6 and S7).

## Some ABA-mediated root transcriptional responses remain conserved whether or not mycorrhizas are formed, and roots transcriptionally respond to ozone exposure of the shoots

Four modules (ME3R, ME11R, ME14R, ME19R; 2834 genes, Figure 4c) were delineated according to the sole response to abiotic treatments. Gene expression differed between abiotic treatments but not between root types. ME19R eigengene specifically discriminated ozone-treated cuttings and was enriched for 'protein binding', 'RNA processing', and 'chromatin structure' (Tables S3a and S4a). The ME14R eigengene differed for each abiotic treatment. Therefore, it was proposed as a generic module for abiotic cue signaling. ME14R was enriched in 'signaling receptor kinases' and included receptor kinases putatively involved in microbial molecule perception (CERK1, PtMD59, PtMD74, PtMD78, PtMD79, PtMIo21, a TIR-NBS-LRR class Potri.001G307300 and an NB-ARC-BED zinc finger Potri.001G405025) (Table S5; Figure S2). ME3R differentiated cessation of watering from ozone-treated cuttings and was a cell-wall-centered module mostly enriched for cellular compartments (plasma membrane, cytoskeleton, and extracellular region). ME11R encompassed genes whose expression was strongly upregulated in response to cessation of watering as compared to the other abiotic conditions. ME11R was enriched for 'response to stress and external stimuli', 'DNA-binding transcription factor activity', and MapMan-tagged for 'abscisic acid' (including ABA metabolism and signal transduction) (Tables S3a and S4a). Strikingly, ME11R eigengene covaried only with ABA levels (Table S8). However, ME16R hubs were best correlated with ABA contents (Figure 3). As previously noted for the ME16R hubs, some ME11R hubs have been reported in poplar literature at least once as transcriptionally upregulated genes in roots submitted to drought or osmotic stress (Cohen et al., 2010; Georgii et al., 2019; Grünhofer et al., 2022; Royer et al., 2016). ME11R hubs encompassed well-known drought-responsive genes, a thioredoxin (Potri.005G244700), a putative homeobox protein (PtATHB5), two NAC transcription factors induced in

response to desiccation (PtNAC105 and PtRD26), a galactinol synthase (GoIS2; Potri.005G006800), a gene encoding a drought-responsive gibberellin 2-oxidase 1 (PtaGA2ox1), a dehydrin (Potri.013G062401), a Late Embryogenesis Abundant protein (Potri.016G046350), together with a biotic-cue-responsive bifunctional inhibitor/lipid-transfer protein (PtAGP68C). We also retrieved the highly ABA-induced protein phosphatase 2C (PtHAB-7) whose expression is highly upregulated under drought (Rigoulot et al., 2019), and a sulfate transporter (PtaSULTR3;1b) whose expression is upregulated in response to drought in roots (Malcheska et al., 2017). Other ME11R hubs were MYBs (Pt2R-MYB117 and Pt2R-MYB147), a putative beta-amylase (Potri.010G062900), and a putative chitinase involved in ethylene/jasmonic acid-mediated signaling (Pt-CHIA5.3). These results collectively indicate that (i) root systems transcriptionally responded to ozone exposure of the shoots, and (ii) at least part of the signaling pathway triggered in the roots by cessation of watering or ozone was the opposite.

## Ozone exposure strongly modulates the leaf transcriptome whereas cessation of watering rewires the leaf metabolome, independently of the inoculation status

The leaves of inoculated and non-inoculated cuttings displayed similar changes under the abiotic treatments (Figure 4e). Eighteen metabolites significantly accumulated under cessation of watering as compared to the control. The accumulation of monopalmitin, monostearin, myo-inositol-1-phosphate, and β-sistosterol suggested changes in membrane lipid composition (notably in chloroplasts) under CW. Six modules revealed ozone-responsive genes. ME2L, ME7L, and ME10L contained ozone-induced genes, while ME3L and ME4L included ozone-repressed genes (Figure 4e). The ozone-activated modules were enriched for 'response to chemicals', 'endogenous stimulus', 'biotic stress', 'signaling receptor kinase' and 'protein and mRNA nucleotide binding' (Tables S3b and S4b). ME2L encompassed putative glutamate receptors, receptor-like kinases (e.g., RLK-LRR-type-III receptor-like protein kinases - PtLRR-RLK58, and PtLRR-RLK57) and kinases (PtWAK82, PtWAK107, PtWAK120, and PtWAK134), or a putative MEKK3 (Potri.002G129100) (Table S5b; Figure S3). This suggests an overlap of the plant defense and ozone responses. ME10L encompassed hubs involved in lipid signaling, for example, a putative phosphatidic acid phosphatase (Potri.006G196200), a putative triacylglycerol lipase (Potri.006G043800), a putative lipoxygenase (PtLOX17), a long-chain acyl-CoA synthetase (Pt-LACS9.3) and a putative acyl-CoA N-acyltransferase (PtPHD40), highlighting the importance of lipid-mediated signaling of ozone perception. The ME7L hubs were mostly involved in chromatin remodeling and mRNA processing, including PHD-finger proteins (PtPHD14, PtPHD19, PtPHD41, and
PtPHD47), putative helicases (Potri.002G160900 and Potri.001G253400), a putative histone-lysine N-methyltransferase (Potri.002G079100), an RNA polymerase (Pt-RPB1.1), and a putative THO2 protein (Potri.013G085000). On the other hand, ME3L hubs were linked to chloroplast and photosynthesis (including a putative Chaperonin-like RbcX protein Potri.011G010300), photorespiration (e.g., Pt-LHCA2*1.1 or Pt-GDCH.3) and signaling (e.g., 14-3-3 protein PtGRF11a, a putative lipoxygenase Potri.003G218100). Some ME4L hubs were related to the mitochondrial respiratory chain (e.g., NADH-ubiquinone oxidoreductase Potri.007G005100, and succinate dehydrogenase Potri.009G001100), protein catabolism (e.g., Potri.009G037800) and vesicle trafficking (PtRabD1b, PtRabF1a, or Pt-RAN1.2). The planar-filtered network highlighted connections between ME10L hubs (PtrWRKY61 and Potri.001G459200) and iPRMP (Figure 5; Table S9b). As expected, a metabolite hotspot was isolated on the planar network (Figure 5b; Table S9b). The metabolite whose accumulation was triggered by the cessation of watering shared no direct connection with any hub and rather few with hormones. Finally, the planar network shed light on ME13L as the closest hub to hormones, and indirectly to metabolites (Figure 5c; Table S9b). ME13L contained genes with altered expression under the two abiotic treatments (Figure 4c) and was enriched for 'transport activity' and 'plasma membrane' (Tables S3b and S4b). The identified transmembrane proteins belonging to ME13L hubs were involved in nutrient/hormone transport or in extracellular cue perception and signaling, for example, the aquaporin NOD26-like intrinsic protein (PtNIP3;3), a putative nucleobase-ascorbate transporter (Potri.008G146400), a putative lysine histidine-like transporter (PtLHT6), a putative anion transporter (PtNPF6.5), a putative transmembrane transporter (Potri.002G029100), a putative PIN-type auxin efflux transporter (PtriPIN11), as well as a putative MLO-type membrane protein involved in cell-to-cell communication (PtMlo22) and putative receptor protein kinases (PtLRR-RLK254, PtLRR-RLK47, PtMD62, PtMD66, PtMD71, and PtMD84) (Figure S3). ME13L hubs (Potri.019G049201, Potri.008G146400, PtMD62, PtMD66, and Pt-HMT3.1) were connected to iPR, cZR, and DHZR (Figure 5c; Table S9b). To conclude, ozone exposure strongly modulated the leaf transcriptome independently of the inoculation status, whereas cessation of watering rewired the leaf metabolism. The molecular controls of the most pivotal leaf functions (photosynthesis, respiration and photorespiration, transmembrane transport, protein binding, and translation) were responsive to ozone independently of the inoculation status.

DISCUSSION

Mycorrhization enhances host productivity and resistance to biotic and abiotic challenges (Calvo-Polanco et al., 2019;

Kaling et al., 2018; Sillo et al., 2022; Usman et al., 2021). According to the concept of mycorrhiza-induced systemic resistance, the mutualistic interaction prepares the host for subsequent challenges by shaping or pre-activating host molecular toolkits involved in stress signaling and the in fine response. In the present study, we consider how the presence of mutualistic interactions within roots modifies/ mitigates host signaling following moderate and short-time disturbances applied at the root (cessation of watering) or shoot (ozone exposure) levels. Populus trichocarpa is considered drought-sensitive compared to other poplar species. Its stress response consists of leaf shedding (Cirelli et al., 2016; Street et al., 2006). Moreover, P. trichocarpa is ozone-sensitive and responds quite early (within 9 h) to acute ozone exposure (Street et al., 2011). We chose the poplar species to study early signaling in response to environmental disturbance because it is a responsive species with a fully sequenced genome (Tuskan et al., 2006). Our study was performed on 3-month-old genetically homogeneous poplar cuttings to avoid different responses due to genetic diversity, and we applied very short-term abiotic changes to focus on signaling networks rather than on physiological acclimation.

In nature, moderate and short abiotic disturbances often precede more severe drought or ozone episodes. Poplar cuttings perceived these moderate environmental changes early and triggered classically reported molecular stress responses (for instance ABA accumulation and activation of stress signaling pathways in water-deprived roots, calcium signaling activation in ozone-treated leaves). P. trichocarpa is known for rapid defoliation in response to stress, slow stomatal closure, and poor stomatal control (Cirelli et al., 2016; Street et al., 2006; Street et al., 2011). Consistently, we did not observe any changes in stomatal conductance or any significant ABA accumulation in the leaves. The integration of multiple omics layers provided an optimal framework for unveiling in an a priori-free manner to what extent the plant is prepared for stress signaling by mycorrhization. By focusing on signaling and short-term abiotic disturbance, we highlighted the following messages.

Ectomycorrhizas are less responsive than uncolonized roots to abiotic constraints

In our study, poplar cuttings sensed moderate environmental changes without being under threat, and mycorrhization emerged as the most structuring parameter for metabolic and hormonal blueprints and for host gene co-expression patterns. These blueprints remained relatively stable in ectomycorrhizas under moderate abiotic disturbance. The topological overlap of whole transcriptome measurements, rather than differential gene expression, revealed core sets of co-expressed poplar genes that exhibited robust mRNA accumulation or depletion in a
specific condition. ME1R, ME2R, and ME18R could drive or
at least be involved in the hormonal homeostasis of ecto-
mycorrhizas. They captured the different forms of jasmo-
nate, auxin, and cytokinin as well as aromatic metabolites
(e.g., phenylpropanoids and flavonoids) and encompassed
molecular players involved in the ethylene and jasmonate
biosynthesis and signaling pathways as well as in the lipid
metabolism. Inhibition of the jasmonate-signaling pathway
allows *in planta* fungal colonization (Daguerre et al., 2020;
Plett et al., 2014). Our work highlighted that the primary
targets of ectomycorrhizal establishment remained acti-
vated in functional ectomycorrhizas even under mild abi-
otic environmental changes. In addition, ABA did not
accumulate in ectomycorrhizas following cessation of
watering, while the ABA concentration doubled in the fine
roots of inoculated and non-inoculated cuttings. Ectomy-
corrhizas were far less responsive than non-colonized roots
to moderate abiotic disturbances, at least for sensing
decreased soil water content. In line with this, Basso
et al. (2020) highlighted decreased jasmonate responsive-
ness of fungus-colonized lateral roots to exogenous hor-
mones. The lower responsiveness of ectomycorrhizas to
abiotic changes and exogenous hormones might be
ascribed to a barrier effect of the fungal mantle or to bio-
logically controlled interference by the fungal partner. This
issue remains to be specifically addressed. Whatever the
biological toolkits at play, ectomycorrhizas appear as a bul-
mark compartment of the cutting.

The ectomycorrhizal signatures systemic throughout
the root system (Figure 1a) included genes related to jas-
monate synthesis or signaling, the calcium-signaling path-
way, or involved in Arabidopsis non-host resistance
(syntaxin and xyloglucan endotransglycolase). Their differ-
ential transcript accumulation suggests that non-colonized
roots perceived the presence of functional ectomycorrhizas
and/or hyphae in their vicinity. Such evidence has been
provided at the transcriptomic level in oak roots inoculated
and colonized (but not mycorrhized) by different ectomy-
corrhizal fungi (Bouffaud et al., 2020). The responsiveness
of the non-colonized fine roots of inoculated cuttings to
biotic cues, abiotic cues, or both was in-between the
responses of non-inoculated roots and ectomycorrhizas
(Figure 4a,b). The distribution of auxin forms and jasmo-
nate forms was complex. The auxin catabolite 2-oxoindole-
3-acetic acid and the two auxin amino-acid conjugates
were depleted in all inoculated root systems (ectomycor-
rizas as well as uncolonized roots) under the control con-
dition, in contrast with a previous study showing that
amino acid-IAA conjugates accumulate during *in vitro*
ectomycorrhiza formation (Vayssières et al., 2015). The abi-
otic treatments drastically reduced differences in the levels
of auxin forms among organs. This is consistent with a
previous study of *P. x canescens* x *Paxillus involitus* inocu-
lated roots displaying lower JA and auxins levels under

osmotic stress than non-inoculated roots did (Luo
et al., 2009). Auxin homeostasis appeared altered by both
mycorrhizas and the abiotic constraints. Whether the same
molecular processes control the auxin level under abiotic
changes and mutualistic association or not, the remodeling
of the auxin profile could finely tune lateral root initiation
at the site of fungal colonization.

Part of the root ABA signaling is alleviated by the
presence of ectomycorrhizas, while some ABA signaling
remains effective

Using systems biology, we pointed out different core sets
of poplar genes connected to the ABA signaling pathway,
with distinct responses to mycorrhization. First, ME11R
genes were responsive to the slight soil water content
change independently of the mycorrhizal status and corre-
sponded to the classical stress or ABA markers (Cohen
et al., 2010; Royer et al., 2016). They likely constituted the
early and/or the most sensitive genes perceiving variation
in the soil water content. The inoculation-driven module
ME2R included genes annotated as drought- and/or
osmotic-stress-responsive (related to the ABA signaling
pathway) as well as genes involved in the response to
biotic stress. As a hormone-related module peaking in
ectomycorrhizas and to a lesser extent in the roots of inoc-
ulated cuttings, and robust to moderate abiotic cues, ME2R
encompassed the most likely candidates for a systemic
effect of symbiosis on the host ABA-signaling pathway. On
the other hand, ME16R captured differential ABA accumu-
lation, which was alleviated in both ectomycorrhizas and
the fungal-free roots of inoculated cuttings. Interestingly,
the ectomycorrhizal (ECM) fungus *Paxillus involitus*
primes poplar roots for increased osmotic stress tolerance
by suppressing signaling pathways negatively regulated
by ABA (Luo et al., 2009). In our conditions, the ABA con-
centration tended to be lower in ECM than in roots,
contrary to a previous study showing an increased ABA
concentration in the root compartment of ectomycorrhizal
Populus cuttings where the drought stress was applied
(Calvo-Polanco et al., 2019). This could be explained by the
fact that the ABA dosage was performed on a mix of ECM
and non-colonized fine roots, or that the drought stress
was more severe. ABA levels thus appeared tightly con-
trolled in roots but dependent on abiotic and biotic
environments.

Poplar aromatic compounds and calcium signaling as
integrators of ectomycorrhizal presence + abiotic cues?

Modulation of the leaf metabolome by mycorrhizas has
been reported in poplar (Luo et al., 2011; Pfabel
et al., 2012; Kaling et al., 2018; Szuba et al., 2020; see
Dreischhoff et al., 2020 for a review). It involves terpenes
and nitrogenous, sulfurous, and phenolic compounds. The
latter is part of plant defense and enhances poplar
resistance to biotic challenges (rust or herbivory; Ullah et al., 2017; Kaling et al., 2018; Maupetit et al., 2018; Ullah et al., 2019) or drought (Tschaplinski et al., 2019). *L. bicolor* also interferes with the oak leaf transcriptome at the level of lignin catabolism (Bouffaud et al., 2020). In the present study, *L. bicolor* interfered with the leaf content in α-salicyloylsalicin, only when inoculated cuttings were fac- ing an abiotic change. *L. bicolor* reduced the ozone- induced accumulation of aromatic compounds in poplar leaves - pyrogallic acid and 1,2,4-benzenetriol- and the sali- cinoid metabolite catechol. Salicinoid synthesis has been depicted as developmentally regulated and not stress- inducible (Fellenberg et al., 2020). Salicinoid did not respond to mycorrhizas in a previous study (Kaling et al., 2018) and thus appear as a versatile poplar com- pound. The systemic impact of *L. bicolor* on the secondary metabolism of the cuttings was especially evident within the root system. We identified ectomycorrhizal signatures within the mRNA and metabolome profiles from the unco- lonized roots of inoculated cuttings. They included a flavo- noid (catechin), intermediates of lignin biosynthesis (quinic acid and derivatives), and one unknown metabolite (15.81 368 649 559 flavonoid). Lignin or lignin-like polymers have structural and defense-related functions. For instance, lig- nin or lignin-like phenolic polymers are induced and rap- idly deposited in cell walls in response to biotic and abiotic stresses (Wuyts et al., 2006; Xie et al., 2018). The link between lignin biosynthesis and growth-defense trade-offs has been extensively reviewed (Ha et al., 2021), and also demonstrated in poplar cuttings (Zhang et al., 2018). We propose that aromatic compounds, in particular those involved in lignin biosynthesis, might be good markers of the integration of abiotic and biotic cues by poplar. The putative role of the identified aromatic metabolites on the growth-defense trade-off should be further investigated using poplar as a woody model plant.

The impact of mycorrhization on the leaf response to ozone was mainly highlighted with ME8L gathering calcium-signaling actors, in accordance with the involve- ment of calcium signaling in ozone perception (Clayton et al., 1999; Evans et al., 2005; Street et al., 2011). Our data suggest that ectomycorrhizal symbiosis could systemically prime the entire response of poplar cuttings to early changes in their abiotic environment through calcium- signaling pathways. Further studies are required to deci- pher how ectomycorrhizal fungi might influence calcium signaling in cuttings and priming against abiotic or biotic stresses. On the other hand, the ozone-treated leaves of inoculated cuttings displayed less expression of photosynthesis-related genes than the control, whereas the leaves of non-inoculated cuttings did not. A link between decreased photosynthesis and chloroplast func- tioning in poplar leaves under acute ozone exposure has been showed (Street et al., 2011). Our current data suggest

that leaf responses to ozone through the photosynthetic function might be negatively impacted by the presence of ectomycorrhizas.

The three -omics layers were less interconnected in leaves than in roots

The leaves of mycorrhizal poplars were clearly differenti-
ated from non-inoculated ones in terms of metabolites and
hormones. Topological overlap measurements highlighted
that mycorrhizal cuttings also exhibited a remodeled leaf
transcriptome and enrichment analysis suggested the
molecular control of photosynthesis, respiration and pho-
torespiration, transmembrane transport, protein binding,
and translation as the most likely targets. The planar net-
works highlighted that the three -omics layers were far less
intertwined in mature leaves than in roots. This peculiar
mapping suggests distinct control mechanisms and
responses for each -omics layer in leaves. We propose
post-transcriptional regulation and long-distance transport
as likely primary drivers of the rewiring of the leaf meta-
bolic and hormonal landscapes. In the literature,
mycorrhiza-induced systemic resistance of leaves is linked
to jasmonate- and salicylate-related signaling, but none
of the studies addressed poplar species (Basso &
Veneault-Fourrey, 2020; Dreischhoff et al., 2020; Pozo &
Azcón-Aguilar, 2007). The jasmonate and salicylic acid con-
centrations were similar in the leaves of mycorrhizal or
non-mycorrhizal *P. trichocarpa* cuttings. On the other hand,
the presence of mature ectomycorrhizas modified the com-
position of cytokinin forms in the leaves, pointing out the
crucial role of this hormone in the systemic fungal
response. Colonization by an arbuscular mycorrhizal fun-
gus reduced the endogenous levels of cytokinin nucleo-
tides in the shoots of pea plants (Goh et al., 2019), but
induced higher cytokinin nucleotide levels in *Medigaco
truncatula* leaves (Adolfsson et al., 2017), as in the present
*P. trichocarpa* – *L. bicolor* symbiosis. Cytokinins have
already been proposed as long-distance root-to-shoot mes-
sengers, and *vice versa* (Davière & Achard, 2017; Kudo
et al., 2010), and their export from ozone-treated shoots to
the roots is considered to be a signal influencing root
development (Collier et al., 2003; Matyssek et al., 2010;
Winwood et al., 2007). The role(s) played by the remodel-
ing of the leaf cytokinin profiles in the presence of a benefi-
cial association in roots and under abiotic cues remain(s)
to be tested.

In conclusion, *P. trichocarpa*-*L. bicolor* ectomycorrhizas are less responsive than fungus-free roots to abiotic cues and especially exhibit a strong hormonal homeostasis. The ABA-mediated root transcriptional response was alleviated by *L. bicolor* inoculation. Moreover, *P. trichocarpa*-*L. bicolor* symbiosis resulted in a systemic remodeling of the host signaling networks in response to abiotic changes. Our study paves the way for further
functional studies on poplar candidate genes to demon-
strate their role in the integration of moderate abiotic dis-
turbances and the presence of beneficial fungi colonizing
roots in both below- and above-ground organs. Further
studies on mature trees with longer-lasting stresses are
required to further document how ectomycorrhizas modify
the root and leaf molecular stress responses. This will be a
prerequisite for predicting the output of fungal associa-
tions on tree performance under stress.

EXPERIMENTAL PROCEDURES

Plant material and abiotic treatments

Cuttings of *Populus trichocarpa* (Washington clone 101-74) were rooted for 2 weeks in distilled water under climate-controlled greenhouse conditions (photoperiod 16 h, photosynthetic photon flux density 450–500 µmol m⁻² sec⁻¹, day temperature 22°C). Random batches of similarly rooted cuttings were transplanted with or without *Laccaria bicolor* inoculum, corresponding to inoculated (I) or non-inoculated (NI) poplars, respectively (Figure 1). Pure *L. bicolor* inoculum was prepared in sterile peat-vermiculite as described in Plett et al. (2011) and mixed in well-dampened atta-pulgite clay (Turf-Pro, Toul, France). For the non-inoculated pots, sterile peat-vermiculite without fungal inoculum mixed with atta-pulgite was used. Each pot was watered with 20 mL of a diluted nutrient solution (0.8 mM KNO₃; 0.8 mM Ca (NO₃)₂, 4 H₂O; 0.3 mM NaH₂PO₄; 0.3 mM MgSO₄, 7 H₂O) and the commercial trace-element solution Kanieltra (CO-FAZ, BP 198-08, Paris, France).

Cuttings were kept under the above conditions for 3 months
to allow roots and symbiosis to develop and then transferred into
phytotron chambers under the same growth conditions for accli-
mation for 2 weeks before the application of the short-term abiotic
treatments. During the acclimation period, all cuttings were culti-
vated under carbon-filtered air and watered to field capacity twice
a day.

Random batches of 48 I and 48 NI cuttings were kept in the initial growth conditions (C), or submitted to ozone exposure (O), or to cessation of watering (CW). O cuttings were exposed to high but realistic ozone peak levels (Du et al., 2018), that is, 200 ppb for 8 h before harvest (from 5 a.m. to 1 p.m. in the daylight time). Ozone was produced from pure O₂ with two ozone generators (OZ500; Fischer, Germany, and CMG3-3; Innovatec II, Germany), and the ozone concentrations injected directly into the chambers were monitored (O341M; Environment S.A., France). For CW cuttings, watering was stopped 43 h before harvest.

Harvest and physiological characterization

At harvest time, the soil water content of each pot was determined from 50 mg of soil dried at 70°C to constant mass. The leaf relative water content was determined on mature leaves as (fresh mass – dry mass)/(turgid mass – dry mass) × 100. Turgid mass was obtained after overnight rehydration at 4°C in the dark with the leaf petiole placed in distilled water. Dry mass was measured after drying at 70°C until constant mass. The leaf full-turgor osmotic pressure was measured on a rehydrated mature leaf whose sap was extracted after freezing–thawing in a syringe, using an osmometer (Wescor 5500).

A random sample of each root system was collected to
measure the mycorrhization rate (Tagu et al., 2001). For molecular
analyses, NI and I cuttings were harvested together, following a

full-random design and keeping the sampling duration and proce-
dure constant for all cuttings. Two mature leaves per cutting (the
9th and 10th leaves from the apex) were immediately flash-frozen.
The root system was rapidly washed, and fine roots and
mycorrhiza-enriched fine roots were sampled under a binocular
microscope and flash-frozen. Leaves (L), roots (R), and ectomycor-
rizas (Myc) were ground individually in liquid nitrogen and
pooled to form four biological replicates (three plants pooled per
replicate and treatment). The biological replicates were paired-
distributed among organs (i.e., L, R, or Myc libraries with the
same number were constructed from the same pool of plants).
The pools were strictly kept for transcriptomics, metabolomics,
and hormonomics analyses.

Additional physiological analyses were performed on a ran-
dom subset of cuttings (four cuttings per condition). Net CO2 concentra-
tion 400 µmol mol⁻¹, light intensity 400 µmol m⁻² sec⁻¹, leaf tem-
perature 25°C, and leaf water vapor pressure deficit 1.2–1.5 kPa.
The root and soil respiration rates were assessed using a closed
photosynthesis system (Li-840, Li-Cor, Lincoln, NE, USA) by incu-
bating the whole root system of each cutting and 50 g of soil,
respectively. Chlorophyll contents (µg cm⁻²) and flavonoid and
anthocyanin indexes were estimated with a Dualex 4 Scientific
optical sensor (Force-A, Orsay, France) on two spots on the adax-
ial side of mature leaves (9th to 13th leaves from the apex). These
plants were carefully measured (height), and harvested for dry
mass determination (leaf, stem, cutting, and root).

RNA extraction, RNA sequencing, and meta-
transcriptomics analysis

Total RNA extraction was performed using the RNeasy Plant mini kit (Qiagen, Courtaboeuf, France), following the manufacturer's instructions and adding 25 mg of polyethylene glycol 8000 per milliliter of RLC buffer. A DNA digestion step with DNase I was also included to remove contaminant DNA. RNA quality was verified using Nanodrop (Thermo Fisher Scientific) and Fragment Analyzer (Agilent, Santa Clara, USA) devices. cDNA sequencing libraries were generated from 1-3 µg of total pooled RNA following the manufacturer's instructions (Illumina Inc., USA), and followed by Illumina HiSeq 3000 mRNA sequencing (RNA-Seq) performed at the Genotoul bioinformatics facilities (Toulouse, France).

A meta-transcriptomics approach was used to evaluate the
diversity of microbial taxa retrieved from our 60 libraries
(Figure S1). Briefly, the procedure consisted of *de novo* assembly
of all reads using Megahit v1.1.3 with default parameters (Li
et al., 2015). Contigs below 500-bp length were discarded, and the
reads of each library were mapped on the remaining contigs using
Bowtie v2.3.0 (Langmead & Salzberg, 2012). Contigs with a low
support (less than three samples with at least 5 counts) were fil-
tered out, and the selected contigs were annotated with a blast-
like approach using DIAMOND v0.9.19 (Buchfink et al., 2015)
against NCBI-NR and JGI-Mycocosm fungal genomes, with a
strong e-value threshold (<10<sup>-50</sup>). *P. trichocarpa* reads counted for
70-75% of all reads, and around 20% of the reads were not
assigned to any sequence in the database (with the defined 10<sup>-50</sup>
e-value threshold). Consistently with the experimental design, *L.
bicolor* was the most represented fungus, especially in the inocu-
lated samples. An unexpectedly high level of fungal reads was
observed in one of the non-inoculated samples (NI roots no. 21),
due to a high proportion of the ectomycorrhizal genus *Thelephora*. This library was clearly artefactual. Therefore, it was discarded in the downstream analyses, together with its paired leaf library (NI leaves no. 21). Similarly, mycorrhiza library no. 1 exhibited a low proportion of *L. bicolor* sequences, not representative of all other mycorrhizal libraries. It was also discarded, along with the corresponding paired libraries.

## Co-expression networks and functional enrichment analysis

RNA sequencing was preprocessed as previously published (Royer et al., 2016), and mapped on the *P. trichocarpa* v4.1 genome sequences (Tuskan et al., 2006; https://phytozome-next.jgi.doe.gov/info, *P. trichocarpa* primary transcripts). A summary of the alignments is given in Table S10, together with the trimming and mapping parameters. We only considered the genes that contained a minimum of 10 reads in each treatment as expressed genes. A total of 28 161 genes were expressed (about 81% of the primary transcripts).

Gene co-expression signed networks were constructed using R package v1.68 WGCNA (Weighted Correlation Network Analysis, Langfelder & Horvath, 2008). Following count normalization using the R package DESeq2 v1.26 (Love et al., 2014), WGCNA functions were run separately on log₂ (normalized count+1)-transformed datasets from leaves (23 samples) and root systems (34 roots and mycorrhizal samples). Soft thresholds of 8 and 18 were used to produce a > 80% model fit to scale-free topology and low mean connectivity for root systems and leaves, respectively. The block-wise Modules function was used to obtain the gene networks complying with the following parameters: maxBlockSize = 10 000, minModuleSize = 50, signed network, deepSplit = 2, and a calculation of coexpression relationships using bi weight mid correlation coefficients (bicor). Modules were summarized by their module eigengene (ME), defined as the first principal component of the expression data of the module. Intramodular connectivity was computed using the 'intramodularConnectivity.fromExpr' function to select the hubs (namely the 30 top-ranked genes, Table S5).

Functional annotation and categorization were achieved using Gene Ontology (GO) and Mapman (Thimm et al., 2004). GO assignment, GO-Slim summarization, and enrichment analysis compared to poplar genome repertoire were performed using Blast2GO (v5.2; Conesa et al., 2005). The 34 699 protein sequences encoded by the *P. trichocarpa* primary transcripts were blasted against *A. thaliana* ('blastp', using default parameters, except *e-value* threshold = 0.001). Only the best hit was imported, linking poplar gene models to the GO identifiers of their closest Arabidopsis homologs. *P. trichocarpa* protein sequences were submitted to the Mercator web application v3.6 (Lohse et al., 2014) using default parameters and assigned to MapMan Bins. Enrichment analysis consisted of a Fisher's exact test combined with false discovery rate correction for multiple tests. A corrected *P*-value of 0.05 identified enriched GO terms (Table S3) and Mapman Bins (Table S4).

## Metabolite profiling

Untargeted metabolite levels were determined from lyophilized leaves, roots, and ectomycorrhizae as described in Tschaplinski et al., 2012. To ensure complete extraction, powdered and freeze-dried material (~20 mg) was twice extracted overnight with 2.5 ml of 80% ethanol. Sorbitol (100 µl (L) or 50 µl (R and Myc) in a 1 mg/ml aqueous solution) was added to the first extract as an internal standard to correct for subsequent differences in derivatization

efficiency and changes in sample volume during heating. The extracts were combined, and 500-µl (L) or 2-ml (R and Myc) aliquots were dried under nitrogen. Metabolites were silylated to produce trimethylsilyl derivatives by adding 500 µl of silylation-grade acetonitrile to the dried extracts followed by 500 µl of N-methyl-N-trimethylsilyltrifluoroacetamide with 1% trimethylchlorosilane. After 2 days, a 1-µl aliquot was injected into an Agilent Technologies (Santa Clara, CA) 7890A/5975C inert XL gas chromatograph/mass spectrometer (MS) configured as previously described (Tschaplinski et al., 2012). The MS was operated in electron impact (70 eV) ionization mode using a scan range of 50-650 Da. Metabolite peaks were quantified by area integration by extracting a characteristic mass-to-charge (m/z) fragment with peaks scaled back to the total ion chromatogram using predetermined scaling factors and normalized to the extracted mass, the recovered internal standard, the analyzed volume, and the injection volume. The peaks were identified using a large in-house user-defined database of ~2600 metabolite signatures of trimethylsilyl-derivatized metabolites and the Wiley Registry 10th Edition combined with NIST 2014 mass spectral database. The latter commercial database was recently upgraded to the Wiley 12th Edition combined with NIST 2020. All unknowns were run through these updated databases, whose combination accurately identified a much larger fraction of the observed metabolites than most analytical laboratory analyses would have done. Unknown metabolites were named by retention time (min) first followed by key mass-to-charge ratios (m/z) and their putative classes were indicated, when appropriate.

## Hormone profiling

Active forms of endogenous hormone levels (cytokinin, auxin, abscisic acid, jasmonic acid and derivates) and their metabolites were determined from 3 mg dry weight of leaf, root, and ectomycorrhizal samples according to the method described by Šimura et al. (2018). Briefly, the hormones were extracted in an aqueous solution of acetonitrile (50% ACN/H₂O, v/v) with the addition of a cocktail of stable isotope-labeled standards (all from Olchemim Ltd, Czech Republic). The extracts were purified using Oasis HLB columns (30 mg/1 ml, Waters) and analytes were eluted using 30% ACN/H₂O (v/v). Eluents were further gently evaporated to dryness under a stream of nitrogen. Separation was performed on an Acquity CR-Class System (Waters, Milford, MA, USA) equipped with an Acquity UPLC® CSH C18 RP column (150 × 2.1 mm, 1.7 µm; Waters), and the effluent was introduced into the electrospray ion source of a triple quadrupole mass spectrometer Xevo™ TQ-S (Waters), operating in multiple reaction monitoring (MRM) mode. The concentrations of all the analytes were calculated as ratios of non-labeled compounds to labeled internal standards or closely eluting stable isotope-labeled tracers (Šimura et al., 2018) using the standard isotope dilution method (Rittenberg & Foster, 1940). Two technical replicates for each of the four biological replicates were performed, and the mean was calculated from all measurements.

## Statistical analyses and data integration

Statistical analyses were performed using R 3.6.1 (http://www.r-project.org/). A two-way ANOVA from the car package (Fox et al., 2012) was used to reveal the metabolites, hormones and MEs impacted by the presence of *L. bicolor*, by the change in environmental conditions, by their joint effect ('inoculation status' + 'abiotic treatment'), or by their interaction ('inoculation status × abiotic treatment'). Normality and homoscedasticity were checked graphically. If needed, data were log
(x + 1)-transformed. Multiple comparisons were done using R package multcomp (Hothorn et al., 2008). Data integration was performed by correlation analysis using MEGENA R package 1.4.1 (Song & Zhang, 2015). First, statistically significant modules (using MEs and normalized expression of 30 intramodular hubs) and differentially accumulated metabolites and hormones were embedded into a Pearson correlation matrix using the calculate.-correlation MEGENA function with default parameters. Then, a correlation map was summarized into a MEGENA-derived planar filtered network using the calculate.PFN function with default parameters. Networks were visualized using Cytoscape 3.8.0 (Shannon et al., 2003).

## ACKNOWLEDGEMENTS

This research work was sponsored by the Plant-Microbe Interfaces Scientific Focus Area (http://pmi.ornl.gov) in the Genomic Science Program (CVF, TJT, NLE, and AK), the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science (CVF, TJT, NLE, and AK), the Laboratory of Excellence ARBRE (grant ANR-11-LABX-0002) (MFP, DC, LA, NA, MBBT, CB, YV, PP, IH, MNV, CVF, and AK). Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy Office of Science (DE-AC05-00OR22725). ON and IP were supported by the Ministry of Education, Youth and Sports of the Czech Republic (European Regional Development Fund-Project 'Plants as a tool for sustainable global development' No. CZ.02.1.01/0.0/0.0/16_019/0000827), and by the Palacky University Olomouc Young Researcher grant (No. JG_2020_002).

## AUTHOR CONTRIBUTIONS

MFP, AK, DC, MBBT, IH, MNV, and CVF developed the experimental design. MFP carried out the experiment, with the support of CB, IH, NA and CVF. MFP, PP, YJ, CB, IH, and MBBT performed the physiological analysis. MFP, IH, MNV, AK, CVF, YJ, and MBBT collected samples. MFP, DC, IH, and AN were involved in sample management. MFP conducted the hormonomics study under the supervision of ON and IP, who are hormonomics specialists. NLE and TJT conducted the metabolomics study. MFP, DC, and IH performed the transcriptomics studies, including WGCNA, DeSeq, and networks analysis. LA and MFP performed the meta-transcriptomics analysis. AK made transcriptome data available. MFP, AK, MBBT, DC, MNV, IH, LA, PP, and CVF performed the data analysis. All authors contributed to the interpretation of results and MFP, IH, MNV, and CVF wrote the manuscript, with the contribution of and editing by all authors.

## CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## DATA AVAILABILITY STATEMENT

The complete expression dataset is available as a series (accession number GSE175506) in the Gene Expression Omnibus database at the National Center for Biotechnology Information. The complete hormonomics and metabolomics data are available in Table S11.

## SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article.

**Figure S1.** Meta-transcriptomics analysis: taxonomy and abundance of fungal genera. A meta-transcriptomics approach was used to evaluate the diversity of the fungal taxa retrieved from the 60 libraries. Fungal genus abundances are displayed according to the plants and the associated treatments (Non-inoculated/Inoculated and Control/Ozone/Cessation of watering), and the sampled organs (Leaves/Fine roots/Mycorrhizas). Abundances are relative to the total filtered mapped read counts.

**Figure S2.** Top-30 hub genes detected in the root and mycorrhizal modules. Heat map showing the log₂ expression values (averaged *per* experimental condition) for each intramodular hub. Top: experimental samples (NI, non-inoculated poplar roots; I, inoculated poplar roots; Myc, their mycorrhizal tips in control conditions (C) or submitted to cessation of watering (CW) or ozone fumigation (O)). Genes are ranked from the top according to their intramodular connectivity. *Populus trichocarpa* v4.1 gene IDs are cited and their common names are provided in brackets (as referred to in previously published references, see Tables S2a-S5a).

**Figure S3.** Top-30 hub genes detected in the leaf modules. Heat map showing the log₂ expression values (averaged *per* experimental condition) for each intramodular hub. Top: experimental samples (NI, mature leaves from non-inoculated poplar cuttings; I, mature leaves from inoculated poplar cuttings in control conditions (C) or submitted to cessation of watering (CW) or ozone fumigation (O)). Genes are ranked from the top according to their intramodular connectivity. *Populus trichocarpa* v4.1 gene IDs are cited and their common names are provided in brackets (as referred to in previously published references, see Tables S2b-S5b).

**Table S1.** Physiological parameters assessed in poplar cuttings before and after abiotic constraints.

**Table S2.** List of expressed poplar genes detected in the root systems (fine roots and Myc) (a) and the leaves (b) of non-inoculated or inoculated poplar cuttings subjected to short-term abiotic cues. Gene IDs and co-expression module classification by WGCNA are provided.

**Table S3.** Functional GO category enrichment for WGCNA co-expression modules delineated in poplar root systems (fine roots and Myc) (a) and leaves (b).

**Table S4.** Mapman bin enrichment for WGCNA co-expression modules delineated in poplar root systems (fine roots and Myc) (a) and leaves (b).

**Table S5.** Top-30 genes ranked based on their intramodular connectivity in each WGCNA co-expression module delineated in the root systems (fine roots and Myc) (a) and the leaves (b) of inoculated or non-inoculated poplar cuttings subjected to abiotic cues. *Populus trichocarpa* v4.1 gene IDs were used, and their common names are provided according to published references.

**Table S6.** Significant changes in hormones and their metabolites (pmol g⁻¹ DW), other metabolites (µg g⁻¹ FW (sorbitol equivalents) and WGCNA module eigengenes (MEs) in the fine roots or mycorrhizas of non-inoculated or inoculated poplar cuttings subjected to short-term abiotic constraints. Two-way ANOVA and Tukey's HSD test were used to highlight significant differences in the experimental dataset.

**Table S7.** Significant changes in hormones and their metabolites (pmol g⁻¹ DW), other metabolites (µg g⁻¹ FW (sorbitol
equivalents) and WGCNA module eigengenes (MEs) in the leaves of non-inoculated or inoculated poplar cuttings subjected to short-term abiotic constraints. Two-way ANOVA and Tukey's HSD test were used to highlight significant differences in the experimental dataset.

**Table S8.** Pearson correlation matrix showing significant MEs, metabolites, and hormones in the root systems (a) and leaves (b).

**Table S9.** MEGENA correlation matrix including the top-30 intra-modular hubs for each significant module, metabolite, and hormone in the root systems (fine roots and Myc) (a) and the leaves (b) of poplar cuttings. These correlations were used to represent networks using Cytoscape (Figures 3 and 5).

**Table S10.** RNA-seq processing. Sample names, replicates, and statistical data of read alignments against the *Populus trichocarpa* genome v4.1 (https://phytozome-next.jgi.doe.gov/info/Ptrichocarpa_v4.1)

**Table S11.** Metabolites (µg g⁻¹ FW (sorbitol equivalent)), hormones and their metabolites (pmol g⁻¹ DW) and MEs detected in the root systems (fine roots and Myc) (a) or leaves (b) of poplar cuttings for all samples. These data were used for the statistical analysis (two-way-ANOVA).

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