# Artificial selection of suppressive or conducive rhizosphere microbiota circumvents the growth-defense trade-off due to a foliar pathogen

Tetiana Kalachova, Barbora Jindřichová, Manuel Blouin, Romana

Pospíchalová, Lenka Burketová, Eric Ruelland, Ruben Puga-Freitas

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► To cite this version:

Tetiana Kalachova, Barbora Jindřichová, Manuel Blouin, Romana Pospíchalová, Lenka Burketová, et al.. Artificial selection of suppressive or conducive rhizosphere microbiota circumvents the growth-defense trade-off due to a foliar pathogen. Plant stress, 2026, 19, pp.101215. 10.1016/j.stress.2025.101215. hal-05444508

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# Artificial selection of suppressive or conducive rhizosphere microbiota circumvents the growth-defense trade-off due to a foliar pathogen

Tetiana Kalachova<sup>a</sup>, Barbora Jindřichová<sup>a</sup>, Manuel Blouin<sup>b</sup>, Romana Pospíchalová<sup>a</sup>, Lenka Burketová<sup>a</sup>, Eric Ruelland<sup>c</sup>, Ruben Puga-Freitas<sup>d, *, ID</sup>

## ARTICLE INFO

**Keywords:**

Arabidopsis thaliana
Directed evolution
Growth-defense trade-off
Plant hormone homeostasis
Pseudomonas syringae
Rhizosphere microbiota community

## ABSTRACT

Plant-pathogen interactions are influenced by physiological responses and rhizospheric microorganisms, which can create disease-suppressive or disease-conducive soils affecting pathogen dynamics. This study used artificial selection to shape soil microbiota conditioned by *Arabidopsis thaliana* to either suppress or promote the foliar pathogen *Pseudomonas syringae* DC3000 (*Pst*). Over successive iterations, plants were inoculated with *Pst*, and soils were selected based on plant symptoms: enhanced resistance (suppressive), increased susceptibility (conducive), or no selection (control). A non-inoculated group (non-conditioned) was also included. Disease symptoms, *Pst* proliferation, and rhizosphere microbiota were monitored each iteration. Selection for suppressive soils reduced disease severity and *Pst* levels, while conducive soils showed the opposite. Each soil type was enriched in distinct bacterial communities. A growth-defense trade-off was evident in control soils but less so in selected soils. Gene expression analysis revealed that plant hormone homeostasis, especially salicylic acid (SA) and jasmonic acid (JA) played key roles with SA linked to local defense and JA to systemic responses. This work highlights artificial selection as a promising strategy to modulate soil microbiota, influencing plant-pathogen interactions and microbial dynamics.

## 1. Introduction

Understanding plant-pathogen interactions is critical for advancing sustainable agriculture and ensuring global food security. These interactions not only influence crop productivity but also affect biodiversity and the stability of agroecosystems (Garrett et al., 2006; Jones and Dangl 2006). With increasing challenges imposed by climate change, the decline of crop diversity, and the emergence of novel pathogens, dissecting the mechanisms underlying plant-microbe-pathogen dynamics has become more urgent than ever (Bebber et al. 2013; Garrett et al. 2021). Traditionally, crop health improvement has relied on plant breeding to enhance resistance traits. However, breeding is often time-consuming and constrained by genetic variability. A key component to sustainable crop production is soil health, which governs nutrient cycling, plant growth, and disease resilience (M. Tahat et al. 2020; Lehmann et al. 2020). Among the many facets of soil health, soil

conditioning, the process by which plants and soil microorganisms modify soil properties via root exudates, microbial activity, and organic matter input, plays a pivotal role in shaping subsequent plant-microbe-pathogen interactions (Berendsen et al. 2012; Philippot et al. 2013). This soil conditioning process can operate as a form of ecological legacy, whereby previous plant generations or communities influence the structure and function of soil microbial assemblages, ultimately shaping plant growth and disease outcomes in subsequent generations (Bever et al. 2010; van der Putten et al. 2013). These legacies can lead to the development of either disease-suppressive soils, rich in antagonistic microbial consortia that enhance plant health, or disease-conducive soils that favor pathogen proliferation and disease development (Mendes et al. 2011; Schlatter et al. 2017). Understanding the mechanisms underlying these divergent outcomes is crucial for harnessing soil microbiota as a natural disease management tool.

Central to these interactions between plants and soil microbiota is

<footnote><sup>a</sup> Institute of Experimental Botany, Academy of Sciences of the Czech Republic, Prague, Czech Republic</footnote>

<footnote><sup>b</sup> UMR Agroécologie Dijon, Université Bourgogne Europe, Institut Agro Dijon, INRAE, 17 rue Sully, Dijon, 21065, France</footnote>

<footnote><sup>c</sup> Université de Technologie de Compiègne, CNRS, GEC Génie Enzymatique et Cellulaire, Compiègne, France</footnote>

<footnote><sup>d</sup> Univ Paris Est Créteil, Sorbonne Université, Université Paris Cité, CNRS, IRD, INRAE, Institut d'Ecologie et des Sciences de l'Environnement de Paris, IEES, F-94000 Créteil, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: ruben.puga-freitas@u-pec.fr (R. Puga-Freitas).</footnote>
the regulation of plant hormone homeostasis, which governs plant growth, stress responses, and microbial recruitment. Key phytohormones such as salicylic acid (SA), jasmonic acid (JA), abscisic acid (ABA), and ethylene (ET) coordinates complex signaling networks that influence root architecture, microbial recruitment and coordinate plant responses to biotic and abiotic stresses (Bari and Jones 2009; Pieterse et al. 2012). SA plays a critical role in systemic acquired resistance (SAR), typically activated against biotrophic pathogens, while JA and ET pathways mediate defense against necrotrophic pathogens and herbivory and play key roles in induced systemic resistance (ISR) elicited by beneficial microbes (Glazebrook 2005; Pieterse et al. 2014). ABA, traditionally associated with abiotic stress responses, is increasingly recognized for its role in modulating plant immunity and influencing rhizosphere microbial dynamics (Wei et al. 2025). Crosstalk among these hormones allows plants to fine-tune immune responses, balancing resistance with resource allocation for growth (Robert-Seilaniantz et al. 2011).

This balance relates directly to the well-documented growth-defense trade-off, where plants respond to biotic stresses at the expense of their growth and development (Huot et al. 2014). The nature and strength of this trade-off depend on environmental conditions, pathogen pressure, and most importantly, interactions with the soil microbiota (Karasov et al. 2017). Beneficial soil microbes can mitigate this trade-off by priming plant defenses through ISR that allows plants to maintain growth while being better prepared for pathogen attack (Zamioudis and Pieterse 2012; Pieterse et al. 2014). Conversely, pathogenic microbes and disease-conducive soil conditions may shift the balance towards increased defense investment, often at the cost of growth (Spooren et al. 2024).

Recruitment of beneficial microbes is central to this dynamic. Plants actively shape their rhizosphere microbiota by releasing specific exudates, including sugars, amino acids, organic acids, and secondary metabolites, which serve as chemical cues to attract beneficial microbes or deter pathogens (Bais et al. 2006; Badri et al. 2009). Additionally, plant immune responses can indirectly modulate rhizosphere community composition by altering root exudation profiles upon pathogen recognition (Lebeis et al. 2015; Korenblum et al. 2020). This selective microbial recruitment helps establish disease-suppressive consortia enriched in keystone taxa such as members of the genera *Pseudomonas*, *Bacillus*, *Streptomyces*, and certain *Actinobacteria*, known for producing antibiotics, siderophores, and enzymes antagonistic to pathogens (Mendes et al. 2011; Cha et al. 2016; Xiong et al. 2017). This sequential process, where pathogen infection triggers changes in root exudation that can recruit beneficial microbes and enhance plant defense, has been proposed as the “Cry for help” hypothesis (Rolfe et al. 2019). Conversely, disease-conducive soils may support microbial communities that promote pathogen survival by outcompeting beneficial microbes or altering nutrient dynamics (Schlatter et al. 2017).

Adding another layer of complexity, these interactions unfold within an evolutionary framework of co-evolution, horizontal gene transfer, and ecological filtering (Stukenbrock and McDonald 2008; Anderson et al. 2010). Pathogens exert strong selective pressures on host, prompting the evolution of plant resistance and tolerance strategies (Anderson et al. 2010). Conversely, plants also evolve specific immune responses to directly suppress pathogens (resistance) or to tolerate infections without limiting pathogen growth (tolerance) (Jones and Dangl 2006; Pagán and García-Arenal 2020). A comprehensive understanding of soil conditioning that views plants as holobionts—integrated units of plant and associated microbiota shaped by ecological and evolutionary processes—allows a more holistic understanding of disease resistance and soil health (Vandenkoornhuyse et al. 2015; Hacquard et al. 2017). The microbiota thus becomes a crucial source of heritable variation and adaptive capacity (Bulgarelli et al. 2013; Trivedi et al. 2020).

Building on this framework, microbiome-based selection strategies, including artificial and controlled natural selection (Conner 2003) offer promising strategies for manipulating plant-soil-microbe interactions to

enhance disease resistance (Swenson et al. 2000b; Kalachova et al. 2023). By selecting plants with phenotypes that favor recruitment of disease-suppressive microbial communities or that modify soil properties inhibiting pathogens, it is possible to engineer soils with enhanced suppressive capacities. While controlled natural selection has been applied in recent studies (Kalachova et al. 2023), artificial selection targeting soil microbiota remains largely unexplored. These approaches align with the emerging paradigm of microbiome engineering and eco-evolutionary plant breeding aimed at integrating soil health and disease resistance traits (Mendes et al. 2013; Busby et al. 2017).

Our study explores the potential of artificial selection, in soil conditioning, to mitigate plant disease incidence in the *Arabidopsis thaliana* and *Pseudomonas syringae* pv. *tomato* DC3000 pathosystem. We hypothesize that (i) artificial selection for disease resistance or susceptibility will drive the development of disease-suppressive or disease-conducive soils, respectively. We further propose that (ii) these soil conditioning effects are mediated by shifts in plant hormone homeostasis and (iii) soil conditioning treatments are associated with changes in rhizospheric microbial community composition.

## 2. Materials and methods

### 2.1. Plant cultivation

*Arabidopsis thaliana* Col-0 seeds were surface-sterilized and stratified at 4 °C for 2 days before sowing. Plants were cultivated in square pots (6 cm side length) filled with a 1:2:7 (v:v:v) mixture of vermicompost, natural soil and pasteurized TS3 Aquasave substrate (pH = 6.0, Recipe-No.: 316; Klasman-Deilmann, Lithuania). Vermicompost, originating from vegetable residues, was used not pasteurized, only in the first step of the experiment, to provide a primary source of rich, diverse and abundant microbial community (Pathma and Sakthivel 2012). The natural soil (cambisol with moor from CEREEP station, Saint-Pierre-Lès-Nemours, France), corresponded to the upper layer (first 20 cm), from which plant material was excluded before air-drying and sieving at 2 mm. Substrate was pasteurized for 72 h at 80 °C in a dry oven. Plants were cultivated in a growth chamber (Snijders, Netherlands) for 5 weeks in total, at 22 °C with daily cycles of 10 h light (100–130 µmol.m⁻².s⁻¹) and 14 h dark and 70 % relative humidity.

### 2.2. Targeted selection for soil conditioning

Before starting the iterative process (iteration 0), 44 mesocosms were made, each composed of a pasteurized substrate mixed with vermicompost in a 200 mL pot. The vermicompost thus provided the initial microbial inoculant. On each mesocosm, two *Arabidopsis thaliana* seeds were sown and after a week of cultivation one plant was removed. The iterative process was started ($i_1$) by randomly dividing this first batch of plants ($i_0$) into three sub-batches (Fig. 1). The three sub-batches consisted of 22, 11 and 11 mesocosms. Plants in the first two sub-batches ($n = 22$ and $n = 11$, respectively) were infiltrated with a *Pst* strain (solid red lightning symbol) while plants in the third sub-batch ($n = 11$) were infiltrated with saline buffer (open red lightning symbol). Seven days after inoculation, disease susceptibility was visually assessed on plants inoculated with *Pst*. On the first sub-batch ($n = 22$), directed and targeted selection was applied. Target of selection was plant resistance to *Pst*, directed by symptom severity: low tolerance (severe symptoms) was targeted for “conducive” soils, while high tolerance (mild symptoms) was targeted for “suppressive” soils. From 24 mesocosms, the four showing either the weakest disease symptoms (for suppressive soils) or the strongest symptoms (for conducive soils) were selected. For the third soil conditioning, defined as our no selection “control”, plants were exposed to *Pst* but no selection was applied, and the last soil conditioning corresponded to “non-conditioned”, with plants never exposed to *Pst* (treated with $MgCl_2$) and no selection applied.

Soil from selected (4 among 24) or non-selected (all) mesocosms
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(caption on next page)
**Fig. 1.** Schematic representation of the experimental set-up for soil conditioning through iterative exposure to *Pst* DC3000. Homogeneous, initial mesocosms (i₀) were prepared. Experiment was conducted for 10 iterations with each iteration comprising the same three steps: (i) plant growing (for 28 days), (ii) plant inoculation to initiate soil conditioning (*Pst* or mock inoculation with saline buffer) and (iii) mesocosm selection (at 7 dp.i). Selected mesocosms were kept according to the excepted soil conditioning and used to prepare the mesocosms of the following iteration. Symbols represent the mesocosms, with their number corresponding to the number of replicates. The shape of the symbols indicates the type of selection (●: directed; ▲: non directed (selection control); and ■: non exposed to *Pst*) with the colors indicating the expected soil conditioning or initial soil. The grey symbols indicate the mesocosms with initial soil before starting the iterative process. Red and green circles correspond to conducive and suppressive soils, respectively, resulting from the directed selection of low or high resistant mesocosms. Yellow triangles and blue squares correspond to the control of soil conditioning (non-directed selection, all mesocosms passed to the next iteration) and to non-conditioned soils, respectively. Color intensity symbolizes mesocosms selection for the next iteration, with light and dark intensity of colors for non-selected and selected mesocosms, respectively. Filled and empty lightning arrows indicate mesocosms with plants infiltrated with *Pst* or a mock solution (saline buffer), respectively.

were retrieved, pooled and homogenized. These pooled soil samples from the parental iteration were used to inoculate a soil microbial community within a pasteurized substratum at a 1:9 (v:v) ratio, to mimic the dynamics of a soil microbial community exposed to successive monocultures. *A. thaliana* seeds were sown on these inoculated soils to pursue the iterative process for ten iterations. Each iteration is denoted iₙ, with n, the iteration number ranging from 1 to 10. Starting at the second iteration, 24 mesocosms were prepared for susceptible and resistant lines, respectively and 12 mesocosms were prepared for control and non-conditioned soils. Then, the iterative process consisted in the four following steps: (i) plant culture (during 28 days); (ii) leaf inoculation with *Pst* (conditioned soils) or a saline buffer (non-conditioned soils); (iii) disease assessment and soil collection, respective to its soil conditioning, (iv) preparation of a new batch of soil to pursue the iterative process.

To assess disease severity and susceptibility to *Pst* in plants growing in non-conditioned soils additional mesocosms (n = 12) were prepared in the last three iterations (i₈ to i₁₀). These mesocosms were excluded from the iterative process.

## 2.3. Inoculation with pathogen and symptom evaluation

The inoculation with *Pseudomonas syringae* pv. *tomato* DC3000 (*Pst*) was performed as in Kalachova et al. (2023). Bacterial pathogen was cultivated overnight on LB medium plates containing 50 µg.mL⁻¹ rifampicin, washed out from the plate and resuspended in 10 mM MgCl₂ up to OD₆₀₀ = 0.0005. Three leaves of similar developmental stage (middle age leaves: 8th-9th-10th leaves, Berens et al., 2019) of 4-week-old plants were syringe-infiltrated with the suspension or mock inoculated (infiltrated with 10 mM MgCl₂).

For disease severity assessment at 7 dp.i., the manifestation of the chlorotic and/or necrotic symptoms on the infected leaves of the living plants was evaluated by 3 to 5 independent observers in accordance with a disease severity scale 0–4, zero being symptomless and four showing distinct chlorotic and necrotic reaction at the infected leaf (Supporting Information Fig. S1). Besides, all plants were then photographed for quantification of the rosette projected leaf area (PLA) using ImageJ software. On the same day, samples were collected for quantitative assessment of *Pst* propagation in tissues and transcriptional response. For the *Pst* quantification, three discs (6 mm in diameter) were sampled on the three originally infected leaves of a given plant and pooled for direct quantification of bacterial load in tissues. The tissues were homogenized in 10 mM MgCl₂, in 2 mL microtubes, with 1 g of 1.3 mm silica beads, using a FastPrep-24 instrument (MP Biomedicals, USA). The resulting homogenates were subjected to ten-fold serial dilutions and plated onto LB medium. Colonies forming units (CFU) were counted after 2 days of incubation at 28 °C.

The remaining material of the infected leaves was flash-frozen in liquid N₂ and used for gene transcription assessment. To investigate transcriptomic response during the early infection stages, we conducted an additional iteration (named 11th), for which inoculated and non-inoculated systemic leaves were sampled at 1 and 2 dp.i.

## 2.4. Plant gene expression

The expression of selected genes was monitored in plants of iteration 10 at 7 dp.i. in the inoculated leaves, and in plants of an additional 11th iteration in the inoculated and non-inoculated systemic leaves, to detect early responses, at 1 and 2 dp.i. Leaf tissue was flash-frozen in liquid nitrogen and used to measure transcript-levels of genes associated with the immunity onset and phytohormonal signaling pathways. Expression of defense-related genes was measured as in Kalachova et al. (2023). Total RNA was isolated from leaf discs using the Spectrum Plant Total RNA kit (Sigma-Aldrich, USA) with a DNase DNA-free kit (Ambion, USA), to remove co-extracted DNA. Subsequently, 1 µg of RNA was converted into cDNA with M-MLV RNase H⁻ Point Mutant reverse transcriptase (Promega Corp., USA) and an anchored oligo dT21 primer (Metabion, Germany). Ten µL reactions mixtures, including 6.25 ng of RNA equivalent, were set-up with a SYBR Green qPCR Kit (ThermoFisher, USA). Gene expression was quantified by qPCR, using a LightCycler 480 SYBR Green I Master kit and LightCycler 480 (Roche, Switzerland). The PCR conditions were: 95 °C for 10 min followed by 45 cycles (95 °C for 10 s, 55 °C for 20 s, and 72 °C for 20 s). Melting curve analysis was conducted to ensure that single products were amplified. Relative gene expression was normalized to the housekeeping gene AtTIP41 using the ΔCt method, and relative expression was calculated using the standard 2^(-ΔΔCt) approach, with non-inoculated plants grown in non-conditioned soil as the reference. Fold-change values were then log2-transformed for visualization and statistical analysis (Rao et al. 2013). Genes of interest were associated with stress-hormone biosynthesis and signalling pathways: *PRI* (pathogenesis-related protein 1) and *ICS1* (isochorismate synthase 1) for SA pathway (Qi et al. 2018); *AOS* (allene oxide synthase), *VSP2* (vegetative storage protein2) and *PDF1.2* (plant defensin 1.2) for JA pathway (Leon-Reyes et al. 2010); *ACS2* (1-aminocyclopropane-1-carboxylic (ACC) synthase 2) and *ERF1* (ethylene response factor 1) for ET pathway (Wang et al. 2002); *ABI1* (abscisic acid insensitive1) and *LEA4-1* (late embryogenesis abundant 4-1) for ABA pathway (Dalal et al. 2009; Cao et al. 2017). A list of all the primers used is available in Supporting Information Table S1.

## 2.5. Soil DNA extraction and sequencing

Environmental DNA was extracted from 500 mg of –20 °C frozen soils as described in Kalachova et al. (2023). Bacteria libraries were constructed by LGC genomics (GmbH, Germany) according to the Illumina protocol “16S metagenomic sequencing library preparation”, using a two-step polymerase chain reaction (PCR) approach with the primers 341F (5'-CCTACGGGNGGCWGCGAG-3') and 785R (5'-GACTACHVGGGTATCTAAKCC-3'; Klindworth et al., 2013). The PCRs included about 1–10 ng of DNA extract (total volume 1µl), 15 pmol of each forward primer and reverse primer in 20 µL volume of 1 x MyTaq buffer containing 1.5 units of MyTaq DNA polymerase (Bioline GmbH, Luckenwalde, Germany) and 2 µl of BioStabII PCR Enhancer (Sigma-Aldrich Co.). For each sample, the forward and reverse primers had the same 10-nt barcode sequence. PCRs were carried out for 30 cycles using the following parameters: 1 min 96 °C pre-denaturation; 96 °C denaturation for 15 s, 55 °C annealing for 30 s, 70 °C extension for 90 s, hold at 8 °C.
DNA concentration of amplicons of interest was assessed by gel electrophoresis. About 20 ng of amplicon DNA of each sample were pooled for up to 48 samples carrying different barcodes. The amplicon pools were purified with one volume of Agencourt AMPure XP beads (Beckman Coulter, Inc., IN, USA) to remove primer dimer and other small mispriming products, followed by an additional purification on MiniElute columns (QIAGEN GmbH, Hilden, Germany). About 100 ng of each purified amplicon DNA pool was used to construct Illumina libraries using the Ovation Rapid DR Multiplex System 1–96 (NuGEN Technologies, Inc., CA, USA). Illumina libraries (Illumina, Inc., CA, USA) were pooled and size selected by preparative gel electrophoresis. Sequencing was done on an Illumina MiSeq using V3 Chemistry.

## 2.6. Amplicon-based metagenomic analyses

The sequence reads (obtained after sequencing) were analyzed using Find Rapidly OTUs with the Galaxy Solution (FROGS) pipeline at the Genotoul platform, following the standard operation procedure (Escudié et al. 2018). Reads were pre-processed to filter out sequences without primers that were too small or of poor quality (ambiguous bases) and to trim the primers sequences. Sequences were clustered using the Swarm algorithm (Mahé et al. 2014). Detected chimera sequences were removed. Clustered Operational taxonomic unit (OTU) were filtered to remove OTU that were present in at least two samples and contained at least 0.00005 % of all sequences. Taxonomy was assigned to OTU using SILVA (release 132) as reference.

## 2.7. Statistical analyses of plant traits and microbial community

All statistical analyses were conducted using the R software v4.4.0 (R Core Team 2024) within RStudio 2025.05.0 + 496. For plant and pathogen-related traits, data representation and statistical modelling were performed using the R packages *ggplot2*, *emmeans*, *FactoMineR*, *factoextra*, *ComplexHeatmap* and *car*. The assumptions of normality and homoscedasticity of residuals were assessed by visual inspection. Variables that violated these assumptions were mathematically transformed (log- or reflect-log-transformed), after which residuals were re-evaluated to ensure compliance with model assumptions. Group differences were assessed using a Student's *t*-test for pairwise comparisons or a Type II ANOVA, followed by Tukey's HSD post hoc test for comparisons involving more than two groups. Statistical significance was defined as $\alpha = 0.05$. All values are reported as mean ± Standard Error of the Mean (SEM).

Bacterial and fungal community diversity analyses were performed with the R package *phyloseq* (McMurdie and Holmes 2013). Bacterial 16S rRNA gene sequences (primers 341F/785R) were rarefied to 12,255 sequences per sample prior to analysis. In the final five experimental iterations (i.e., $i_6$ to $i_{10}$), operational taxonomic units (OTUs) were filtered to identify those specific to soil conditioning treatments. For non-conditioned soil, OTUs were retained only if their abundance exceeded half of the values observed in conditioned soil samples, resulting in a set of 16 specific OTUs. For conditioned soils, from the remaining OTUs, those significantly affected by soil conditioning treatments (p-value < 5 × 10⁻⁷, one-way ANOVA) were retained, resulting in a set of 65 specific OTUs. A correspondence analysis (CA) was subsequently performed on the combined set of 81 OTUs enriched in either conditioned or non-conditioned soils.

## 2.8. Data deposition

Raw data of the 16S amplicon-based metagenomic are available at the Sequence Read Archive (SRA), linked to the NCBI BioProject PRJNA1270752.

# 3. Results

The experimental approach (Fig. 1) combined contrasting selection types and targets to obtain soils with different soil conditioning in response to *Pst*. The rating procedure of disease severity comprising five levels (Supporting Information Fig. S1a) showed a good positive correlation with the effective *Pst* count (out of 545 plants assessed, Pearson correlation *r* = 0.3298, p-value <0.0001). Based on this rating, for directed selection, opposite selection targets were chosen, with plants showing the lower resistance (higher disease score) and the higher resistance (lower disease score) for conducive and suppressive soils respectively. Therefore, plants selected for conducive conditioning had a disease score of 3–4, while the plants in the suppressive conditioning had a disease score of 0–2 (Supporting Information Fig. S1b).

## 3.1. Evolution of plant resistance to Pst during the selection process

At the end of each iteration, plant resistance was measured through the direct quantification of bacteria within leaf tissues (CFU per mm² of leaf area). *Pst* count showed strong variation between iterations (Supporting Information Fig. S2a). Nevertheless, for most of the iterations (except for iterations 5 and 9), *Pst* count was lower in plants growing on suppressive soil as compared to conducive one, while *Pst* count in plants grown on control soils was at an intermediate level (Fig. 2a). On average, a significant difference was observed in CFU numbers, with less CFU in the suppressive than in the conducive soil treatment (small, inserted histogram in Fig. 2a), allowing to validate our first hypothesis that artificial selection for disease resistance or susceptibility will drive the development of disease-suppressive or disease-conducive soils, respectively. In the iterations 8 to 10, on the additional mesocosms prepared from non-conditioned soils and inoculated with *Pst*, we observed a higher disease susceptibility as compared to conditioned soils on the tenth iteration, but not in the eighth and ninth iterations (Supporting Information Fig. S3a), showing an effect of soil conditioning on *Pst* abundance, but a late or unstable one.

The relation between the *Pst* count at a given iteration (parents) and that at the following (offspring) iteration (Fig. 2b) shows a significant (p-value < 0.05) negative linear correlation for all the conditioning types. The regression coefficient of low and high selection treatments as well as the one of no selection control were negative (p-value <0.05, Tukey HSD), indicating that iterations with high *Pst* abundance were followed by iterations with low *Pst* abundance. In other words, microbiota associated with the highest *Pst*-resistant plants were responsible for lowest resistance in the following iteration. As indicated by the value of the slopes this phenomenon was stronger in selection for suppressive soils (−0.32), than in no selection control (−0.26) or in selection for conducive soils (−0.20).

## 3.2. Evolution of plant growth during the selection process

Plant growth was assessed through measurement of the projected leaf area (PLA) at 35 days after sowing, seven days after *Pst* or mock inoculation (Fig. 3). Similarly to *Pst* count, plant growth showed strong variations between iterations (Supporting Information Fig. S2b), likely due uncontrolled variation in cultivation conditions.

From iterations 2 to 10, plants growing in non-conditioned soil showed the higher averaged PLA (30.1 ± 1.3 cm²), then plants growing in suppressive (27.5 ± 0.8 cm²) and conducive (26.5 ± 0.8 cm²) soils, and finally those of the control (23.3 ± 1.1 cm²) (Fig. 3a). The higher PLA of the non-conditioned soil was no longer present when plants from the same treatment were inoculated with *Pst* (Supporting Information Fig. S3b). Values of non-conditioned and suppressive soils were significantly higher than the control (p-value < 0.001 and 0.02 respectively, Tukey HSD).

While looking at parent-offspring traits relationship (Fig. 3b), we observed a negative (-0.18, p-value = 0.002, *t*-test) linear correlation for
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** *Pst* abundance and heritability of *Pst* abundance along the iterative process. (a) CFU expressed as deviations from overall iteration means. Large symbols represent the mean ± s.e.m for initial (grey square, *n* = 22), control (yellow triangle, *n* = 12), conducive (red circle, *n* = 24), and suppressive (green circle, *n* = 24) soils. Small points show individual mesocosm values with the intensity of the colors, light or dark, indicating the non-selected and selected mesocosms for the next iteration, respectively. The small, inserted histogram shows the mean ± s.e.m CFU of all mesocosms from the second to the last iteration for control (yellow, *n* = 102), conducive (red, *n* = 192), and suppressive (green, *n* = 188) soils. Different letters indicate a significant difference (p-value < 0.05, *t*-test). (b) Heritability of *Pst* abundance, as the slope of the best-fit parent-offspring linear regression, for control (yellow), conducive (red), and suppressive (green) soils. The color intensity gradient of the points is related to the iteration number from the first (light intensity) to the last (dark intensity). Solid lines represent the fitted linear regression, and the shaded area shows the 95 % confidence interval.</figcaption></figure>

control soil, a positive (+ 0.10, p-value = 0.03, t-test) for non-
conditioned soil and non-significant (p-value > 0.05, t-test) for sup-
pressive and conducive soils.

3.3. *Effects of artificial selection on gene expression*

When comparing the basal (i.e. in plants non-inoculated with *Pst*) transcript abundance levels, assessed on the last (tenth) (Supporting Information Fig. S4), we observed that the basal transcript level of *ICS1*
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Plant growth and heritability of plant growth along the iterative process. (a) Projected leaf area (PLA) expressed as deviations from overall iteration means. Large symbols represent the mean ± s.e.m for initial (grey square, *n* = 22), control (yellow triangle, *n* = 12), conducive (red circle, *n* = 24), suppressive (green circle, *n* = 24) and non-conditioned (blue square, *n* = 12) soils. Small points show individual mesocosm values with the intensity of the colors, light or dark, indicating the non-selected and selected mesocosms for the next iteration, respectively. The small, inserted histogram shows the mean ± s.e.m PLA of all mesocosms from the second to the last iteration for control (yellow, *n* = 102), conducive (red, *n* = 192), suppressive (green, *n* = 188) and non-conditioned (blue, *n* = 99) soils. Different letters indicate a significant difference (p-value < 0.05, Tukey HSD). (b) Heritability of plant growth, as the slope of the best-fit parent-offspring linear regression, for non-conditioned (blue), control (yellow), conducive (red), and suppressive (green) soils. The color intensity gradient of the points is related to the iteration number from the first (light intensity) to the last (dark intensity). Solid lines represent the fitted linear regression, and the shaded area shows the 95 % confidence interval.</figcaption></figure>
and ACS2, respectively involved in the SA and ethylene pathways, were higher (p-value < 0.05, *t*-test) in conditioned control soil as compared to non-conditioned soils. Conversely, transcript levels of *PR1*, *PDF1.2* and *ABI1*, respectively involved in SA, JA and ABA pathways, were lower (p-value < 0.05, *t*-test) in conditioned control soils. Transcript levels of other markers (*AOS*, *VSP2*, *ERF1* and *LEA4-1*, involved in JA for the first two, ethylene and ABA pathways, respectively) were not significantly different between the control and the non-conditioned soil. At 7 dp.i., plants inoculated with *Pst* and growing in conditioned or non-conditioned soils showed a strong induction of defense-related genes. Transcript levels for *PR1* and *VSP2*, were markedly higher compared to non-inoculated plants and *ERF1* and *LEA4-1* also exhibited substantial higher transcript levels (Supporting Information Fig. S5). Plants grown in non-conditioned soils showed higher transcript levels for *ICS1*, *VSP2*, (p-value < 0.05, TukeyHSD) when compared to the control, for ACS2 when compared to the conducive and for ERF1 when compared to the three conditioned soils.

During the early infection stages (at 1 and 2 dp.i., on an additional 11th iteration), transcript levels of inoculated leaves showed similar patterns (Fig. 4 and Supporting Information Fig. S6), with transcripts of *ICS1*, *PR1* and ACS2 being the most abundant.

Systemic leaves were characterized by lower transcript levels for all the other genes (Supporting Information Fig. S6). Regarding hormone homeostasis related to the infection dynamic, on inoculated leaves, we observed an early transcriptional induction of *ICS1*, *PR1*, and ACS2 as their transcript levels were higher (Supporting Information Fig. S6) compared to 7 dp.i (Supporting Information Fig. S5). Plants growing on suppressive soil showed an even earlier onset of transcriptional activation of *PR1* as compared to the other soils. Conversely, *VSP2* and *LEA4-1* showed a late activation, as their transcript levels started to increase at 2 dp.i. but remained lower compared to 7 dp.i.

### 3.4. Bacterial community dynamics

At the onset of the experiment, the bacterial community composition exhibited rapid shifts between consecutive iterations in the first iterations, as evidenced by changes in beta-diversity (Supporting Information Fig. S7), with no detectable effect of soil conditioning. Bacterial community composition then stabilized starting the fourth iteration with the lower changes in bacterial composition observed over the last five iterations. On the 1575 OTUs initially identified, 65 and 16 OTUs were enriched or depleted during soil conditioning, respectively. Correspondence analysis (CA) based on the abundance of these OTU on the last five iterations (Fig. 5a) showed a clear pattern with non-conditioned, control and conducive soils distributed along the first axis explaining 29.3 % of variance, while suppressive soils were orthogonally distributed along the second axis explaining 23 % of the variance. OTU taxonomic affiliation, however, showed no clear structure at the class taxonomic level related to soil conditioning treatments. The relative abundance of the 65 enriched OTUs was similar (1.25 to 1.43 %) in the first four iterations in all the soils (Fig. 5b), then it increased similarly throughout the iterative process in all conditioned soils, representing up to 9.6 %, 9.1 %, and 10.6 % of the total abundance in control, suppressive, and conducive soils, respectively. On the contrary, the abundance of these OTUs remained stable in non-conditioned soils (< 3.6 %) during all the experiment. While the relative abundance was similar among the conditioned soils, the taxonomic composition diverged between soil conditioning with *Alphaproteobacteria* and *Planctomycetes* being the most abundant classes in control soils, *Alphaproteobacteria* and *Actinobacteria* in suppressive soil and *Bacteroidia* and *Alphaproteobacteria* in conducive soils. Regarding the 16 OTUs depleted by soil conditioning, an opposite trend was observed. The relative abundance of these OTUs gradually increased up to 6.7 % during the experiment in the non-conditioned soils, with *Bacteroidia* and *Bacilli* classes being the most abundant, whereas it remained low (< 1 %) in conditioned soils. The third

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Transcription level of phytohormone related genes in *Arabidopsis thaliana* after *Pseudomonas syringae* pv. *tomato DC3000* infection. Level of transcription at 1 and 2 dpi normalized to that of non-inoculated plants. Gene transcription was measured in infiltrated (Inoculated, *n* = 4) and distal non-inoculated (Systemic, *n* = 4) leaves in plant at the iteration 11. Asterisks indicate significant differences (p-value <0.05, Student *t*-test) with non-inoculated plants.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Bacterial community analysis along the experiment. (a) Correspondence analysis on bacterial diversity from iteration 6 to iteration 10. Only the enriched (n = 65) and depleted (n = 16) OTUs by soil conditioning were considered. First and second axis account for 25 and 13 % of total variance respectively. Each OTU is represented by a diamond symbol and a color that corresponds to its class affiliation. Color correspondence is given in the figure. Ellipses are drawn around samples, respective to their soil conditioning (control: yellow; green: suppressive; red: conducive; blue: non-conditioned) with the light to dark color intensity indicating the iteration, from i6 to i10, respectively (b) Cumulated relative abundance and taxonomic affiliation of the 65 enriched (upper panel) and 16 depleted OTUs (lower panel) at each iteration in response to soil conditioning with from left to right: non conditioned, control, suppressive and conducive soils, respectively. Data rep- resents means across four independent replicates per iteration.</figcaption></figure>
hypothesis was thus validated: soil conditioning was indeed associated with specific changes in the composition of rhizospheric microbiota.

To investigate the relationship between these bacterial communities and physiological processes such as plant defense (with *Pst* abundance as a proxy) and growth (with PLA as a proxy), the 65 OTUs positively correlated with conditioned soil were grouped according to the treatment in which they were most abundant. This classification assigned 19 OTUs to the control (Fig. 6a,b), 21 to suppressive (Fig. 6c,d), and 25 to conducive soils (Fig. 6e,f). These OTUs were then classified within each conditioned soil according to the correlation between their abundance and *Pst* abundance in plant tissues (Fig. 6). For control conditioned soils (Fig. 6a), we observed both negative and positive correlation with *Pst* abundance for 10 and 9 OTUs, respectively, with bacteria from the *Rubinisphaeraceae*, *Caulobacteraceae* and 67–14 families being the more abundant. Moreover, we observed a complex trade-off between *Pst* abundance and plant growth (rho = -0.8, p-value < 0.001, Spearman rank correlation test) indicating that OTUs whose abundance negatively correlated to *Pst* abundance generally showed a positive correlation with PLA (Fig. 6b). Overall, the combined abundance of these OTUs accounted for up to 7.3 % in the control soil while it remained low in the other two conditioned soils (< 1.6 % for suppressive and conducive soil). Surprisingly, for suppressive soil, most of the OTUs (19 out of 21) have their abundance positively correlated with *Pst* level (Fig. 6c), with bacteria from the *Sphingobacteriaceae*, *Nocardioidaceae* and *Frankiaceae* families being the most abundant (Fig. 6d).

OTUs associated to soil suppressiveness were more abundant (accounting for up to 6.6 % of total abundance) as compared to control and conducive soil (<1.7 % of total abundance). As for control soils, a trade-off between defense and growth was also observed with some OTUs abundance being positively correlated with *Pst* amount but also positively or non-correlated with PLA (e.g. 67–14 family) but with more discrepancies (rho = -0.56, p-value = 0.009, Spearman rank correlation test).

Finally, for conducive soils, we observed a similar trend as in control soils with a similar number and abundance of OTUs negatively (14 OTUs) or positively (11 OTUs) correlated to *Pst* abundance (Fig. 6e) with bacteria from the *Sphingobacteriaceae*, *Pirellulaceae* and *Nocardioidaceae* families being the most abundant (Fig. 6f). The abundance of the OTUs associated with conducive soils was higher (accounting for up to 9.3 % of total abundance) than in control (<2.8 % of total abundance) or suppressive soil (<3.4 % of total abundance). However, as in suppressive soils, a trade-off between plant defense and growth was also observed but with some discrepancies (rho = -0.54, p-value = 0.005, Spearman rank correlation test).

Second, for the 16 OTUs depleted by soil conditioning, we investigated the link between their abundance and PLA. For non-conditioned soils, we observed a strong positive correlation with plant growth for 15 out of 16 OTUs. Bacteria from the *Sphingobacteriaceae*, *Paenibacillusaceae* and *Obscuribacteraceae* families being the more abundant. Overall, the combined abundance of these OTUs accounted for up to 7.3 % in the non-conditioned soil while it remained low in conditioned soils (Supporting Information Fig. S8).

## 4. Discussion

Artificial selection on microbial communities has demonstrated potential for modulating complex ecosystem traits such as plant growth (Swenson et al. 2000b), flowering phenology (Mueller et al. 2021), pollutant degradation (Swenson et al. 2000a), and even plant leaf area and green pigmentation (Jacquiod et al. 2022). Moreover, artificial selection has been demonstrated to enhance resistance to pathogenic fungi in animal models such as *Drosophila* (Kraaijeveld and Godfray 1997). Despite these advances, the specific application of artificial selection to plant disease resistance remains largely unexplored. A key challenge in such approaches is the precise and rapid quantification of the trait of interest. In plant-pathogen interactions, plant resistance is often

assessed by pathogen quantification (e.g., bacterial colony counts) or molecular markers of defense activation (Lebeis et al. 2015). Although these methods provide accurate assessments, they are destructive and thus risk to disturb rhizosphere microbiota to be transferred, and they impose significant time delays that hinder the throughput and iterative cycles essential for effective artificial selection. To circumvent this limitation, we developed a visual disease scoring protocol for *Arabidopsis thaliana* resistance against *Pseudomonas syringae* pv. tomato (*Pst*). This method showed good concordance with *Pst* abundance measurements (r<sub>Pearson</sub> = 0.33), allowing a rapid plant phenotyping. However, this visual assessment was maybe not precise enough to choose the best parents and obtain a good heritability of selected trait. Indeed, a model highlighted that the phenotypic variation in a community function is mainly due to nonheritable determinants, such as variation due to pipetting (during the creation of a new generation) or measurement noise (Xie et al. 2019).

### 4.1. Microbial recruitment and soil legacy effects in disease resistance

Our results demonstrated that artificial selection caused significant shifts in bacterial community composition, leading to divergent impacts on *Pst* population dynamics. The slope of the parent-offspring regression can be considered as an estimate of heritability of a microbiota-associated phenotype (Goodnight 2000). Surprisingly, the slope of the parent-offspring regression for *Pst* abundance was negative, whereas it can be expected to positive, under the assumption that variation in community function arises primarily from heritable determinants (Xie et al. 2019). A slope non-significantly different from zero could be attributed to environmental factors that were not maintained constant (Swenson et al. 2000b), loss of between-community variation (Sánchez et al. 2021), fixation of the best performing community (Chang et al. 2020), or fluctuations in PSF strength driven by changes in pathogen abundance between iterations. In plant-soil feedback systems, plants condition the soil microbiota through root exudates and rhizodeposits, selectively enriching microbial taxa that influence plant health, particularly under biotic stress (Bakker et al. 2013; Panke-Buisse et al. 2015). However, the strength and direction of this feedback are highly context-dependent and can vary with pathogen pressure (De Long et al. 2019). In our case, changes in pathogen pressure (absent for the four first weeks, present in the fifth) could lead to inconsistent microbial assembly and community function, thereby decreasing the predictability and heritability of community-level traits. Nevertheless, we did not observe a non-significant slope but a significantly negative one. Such a pattern typically arises when parents and offspring experience negatively correlated environments. In our case, the inoculum for the offspring generation consists not only of microbes but also of abiotic components as parental soil is used at a 1:9 (v/v) ratio to inoculate the pasteurized soil of the offspring generation. Because the best-performing parents grow vigorously, they may have depleted many of the nutrients available in their soil. Therefore, the soil volume used to inoculate the offspring may be relatively nutrient-poor, placing the offspring of the best plants in the worst environment. Given the low proportion of parental soil transferred (1:9), an alternative mechanism is possible: parents may excrete metabolic by-products into the soil that negatively affect plant growth. The accumulation of such toxic compounds would be highest in the fastest-growing plants and lowest in the slowest-growing ones. These two mechanisms (nutrient depletion and toxic by-product accumulation) could therefore create a negative correlation between parental and offspring environments. This kind of abiotic legacy is rarely considered explicitly in artificial selection experiments on microbiota. Disentangling the respective effects of the microbiota and the abiotic environment remains an important challenge for progress in this field. To minimize the transmission of potentially harmful abiotic components to the next generation, one could recommend filtering the microbial community prior to inoculation, thereby reducing abiotic carry-over as much as possible.
Soil conditioning

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

Correlation

<figure><img src="image_12.png" /></figure>

Iteration

<figure><img src="image_13.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Abundance (%)</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Abundance (%)</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Abundance (%)</figcaption></figure>

Class

<figure><img src="image_17.png" /></figure>

(caption on next page)
**Fig. 6.** Relation between OTUs abundance and growth/defense trade-off in the different soil conditioning. The 65 enriched OTU by soil conditioning were grouped according to their maximal abundance either in (a-b) control (19 OTUs), (c-d) suppressive (21 OTUs) or (e-f) conducive (25 OTUs) soils. Scatter plots (a,c,e) show the relation between the relative abundance of positively or negatively correlated OTU for each of these three classes and *Pst* abundance in control (yellow), suppressive (green) and conducive (red) soils. Abundance in non-conditioned soils are given in the rug plot along the Y-axis. Lines, shows the fitted linear regression between cumulated abundance of the OTUs and *Pst* amount. Line type shows the positive (dotted line) or negative (solid line) correlation to *Pst* of OTU in their reference group with the shaded area showing the 95 % confidence interval. Light and dark color intensity of the points is related to the iteration number, with first (i₁ to i₅) and last (i₆ to i₁₀) iterations being represented with light or dark color intensity, respectively. Heatmaps (b, d, f) showing the detailed OTUs composition of the groups, with color indicating the Pearson correlation between OTU abundance and the defense trait (*Pst* abundance; Log10 (CFU mm⁻²)) or plant growth trait (PLA (cm²)), with blue and red being associated with a negative and positive correlation, respectively. The abundance of the OTU, in the last iterations (i₆ to i₁₀), is shown in the boxplot, for the assigned group. The taxonomic family name assigned to the OTU is indicated on the right, the color indicating the taxonomic class according to indications in the figure. Data represents means across four independent replicates per iteration.

## 4.2. Hormone homeostasis links soil conditioning to plant growth-resistance trade-offs

The rhizosphere microbiota is often considered an “extended immune system,” influencing plant immunity (Teixeira et al. 2019). Plants actively shape their microbiota through various mechanisms, such as competition for resources, production of antimicrobial compounds, and activation of immune pathways (Bari and Jones 2009; Pieterse et al. 2014). Consistent with prior findings (Kalachova et al. 2023), salicylic acid (SA) emerged as a principal regulator of local defense responses in leaves inoculated with *Pst*, correlating with disease severity. However, here we observed that systemic acquired resistance (SAR) pathways appeared transient, as SA-related markers declined by 2 days post-inoculation in soil conditioning treatments.

Simultaneously, systemic defense was governed by jasmonic acid (JA)-mediated signaling, evidenced by induction of *PDF1.2*, suggesting activation of induced systemic resistance (ISR) (Zamioudis and Pieterse 2012; Pieterse et al. 2014). This ISR-like priming is generally closely linked to soil microbial communities recruited by *Arabidopsis* during initial pathogen exposure. It can apparently be maintained or amplified via both natural and artificial selection. Conversely, plants grown in non-conditioned soils exhibited delayed SA responses and a greater reliance on ethylene signaling, underscoring how soil microbial composition influences the basal hormone levels and timing of hormonal pathways activation during defense. While our data indicate changes in SA-, JA-, and ethylene-related markers, these observations should be interpreted as indicative rather than conclusive evidence of direct hormonal cross-talk, which would require further validation using transgenic lines or additional functional assays. Nevertheless, the differential activation patterns observed highlight how hormonal signaling networks can be modulated by microbial and environmental context to fine-tune plant immunity (Glazebrook 2005; Berendsen et al. 2012). Previous studies have reported that beneficial microbes can prime plant immune systems and induce resistance (van Hulten et al. 2006; Tiwari et al. 2022), supporting the plausibility of such interactions. The balance between growth and defense in plants is largely mediated by plant hormone signaling pathways, including those involving SA, JA, and ET (Bari and Jones 2009; Huot et al. 2014). The activation of defense mechanisms often comes at the cost of growth, resulting in trade-offs between these two processes. Our results suggest that artificial selection can improve (suppressive) or reduce (conducive) disease resistance through enrichment of specific microbial communities. Interestingly, artificial selection also minimized the typical growth-defense trade-offs while this trade-off was higher in control conditioned soil. Correlation analyses revealed a complex interplay between microbial abundance and plant phenotypes. In suppressive soils, in which *Pst* abundance was reduced, many OTUs were positively correlated with *Pst* levels, a seemingly paradoxical finding that may reflect indirect feedback loops or the presence of commensals with dual roles depending on context (Hacquard et al., 2017), or the fact that beneficial microbes had a different identity depending on the replicate. These observations are consistent with findings reviewed by Ku et al. (2024), who proposed that plant-microbe interactions could fine-tune plant defense responses to optimize the growth-defense balance. While our conclusions are based

on projected leaf area (PLA) as a proxy for growth, we acknowledge that PLA does not resolve biomass allocation between shoots and roots. Future studies incorporating direct measurements of shoot and root dry weight would provide a more mechanistic understanding of how selected microbiomes influence resource allocation (e.g., root-to-shoot ratio) and help validate the hypothesis that artificial selection mitigates growth-defense trade-offs through shifts in plant allocation patterns.

## 4.3. Contrasting microbial taxa in disease-conducive and suppressive soils

Microbial community analyses confirmed that artificial selection influenced rhizospheric microbial assemblages. OTUs enriched during the selection process diverged taxonomically across soil conditioning. Despite similar overall OTU abundance in conditioned soils, the specific OTUs and their joint OTUs varied considerably, reflecting a potential shift in ecological function rather than gross diversity. In line with this interpretation, several families represented among enriched OTUs in conditioned soils have been associated in the literature with nutrient cycling and PGPR-like functions that could indirectly bolster plant vigor and modulate hormone homeostasis, for example *Frankiaceae*, *Burkholderiaceae*, *Sphingomonadaceae*, and *Rhizobiaceae*, which include taxa reported to contribute to nitrogen acquisition or growth promotion (Chukwuneme and Babalola 2025). More broadly, *Micropepsaceae* has been linked to improved nitrogen utilization efficiency and enrichment under high nitrogen inputs (Huang et al. 2022). Such traits provide plausible routes by which selected microbiomes could influence SA/JA/ET signaling thresholds (through changes in resource status or ISR like priming). Previous studies on disease-suppressive soils have shown enrichment in specific microbial taxa, such as *Pseudomonas*, *Bacillus*, *Streptomyces*, *Stenotrophomonas*, *Xanthomonas* and *Burkholderia*, which are known for their antagonistic activity against soil-borne pathogens, production of siderophores, and ISR in plants (Berendsen et al. 2012, 2018; Raaijmakers and Mazzola 2012). On the other hand, specific microbial taxa in disease-conducive soils are less investigated. Surprisingly, we did not observe consistent enrichment with the canonical taxa found in suppressive soils. While artificial selection produced marked differences in microbial community composition and significant effects on host resistance, this suggests that less-characterized taxa, varying taxa according to the different replicates, or emergent community properties may underpin the observed disease suppression. In support of this view, families linked in the literature to negative soil feedback or potential interference with plant immunity, such as *Myxococcaceae* (reported to associate with negative PSF), *Xanthomonadaceae/Xanthomonas* (pathogens equipped with Type III secretion systems and cognate effectors that can suppress or evade plant immunity), and *Gemmatimonadaceae* (often increasing after pathogen infection), were noted as relevant candidates in conducive contexts (White et al. 2009; Jia et al. 2022; Enderle et al. 2025). At the same time, families such as *Burkholderiaceae* also include species with biocontrol activity against fungal pathogens (e.g., *R. solani*), underscoring the context dependence and dual potential of these taxa in shaping disease outcomes.

Moreover, most studies were focused on soilborne pathogens (e.g.,
Rhizoctonia solani, Fusarium oxysporum, or Gaeumannomyces graminis), for which suppression often involves direct microbe-microbe interactions (e.g., antibiotic production or competition; Weller et al., 2002; Raaijmakers and Mazzola, 2012). In contrast, our model involves the foliar pathogen Pseudomonas syringae pv. tomato, which does not directly interact with the rhizosphere. Therefore, suppressive mechanisms at stake in our study likely depend on indirect, plant-mediated mechanisms, such as immune priming or hormonal modulation via the by the root-associated microbiota (Pieterse et al. 2014; Teixeira et al. 2019). In this context, families that we found enriched in our suppressive soils, such as *Micropepsaceae*, which is associated with improved nitrogen utilization, and *Frankiaceae*, which may directly influence host growth through phytohormone production, as well as literature reports of families negatively associated with disease severity (e.g., *Rickettsiaceae* in certain pathosystems) offer testable hypotheses for how selected communities might fine-tune SA/JA/ET responses through shifts in basal hormone levels, activation thresholds, or timing.

## 5. Conclusion

This study expands the current framework of disease-suppressive soils by demonstrating that artificial selection on plant-associated microbiota can enhance foliar pathogen resistance, even in the absence of well-known antagonistic taxa. The selected microbial communities consistently reduced disease severity and influenced the hormonal networks governing plant immunity, including the classical growth-defense trade-off.

However, we did not observe heritability of the selected trait across generations, suggesting that the soil legacy effects were not stably transmitted. This lack of heritability may reflect the dynamic nature of plant-microbe-pathogen interactions, particularly the fluctuating abundance of the pathogen across selection cycles, which likely altered the strength of plant-soil feedback and selective pressures. These findings indicate that microbial suppression of foliar pathogens may be governed less by direct pathogen inhibition, as commonly observed in soilborne pathosystems, and more by host-mediated, hormone-regulated responses shaped by soil microbial communities.

As a proof-of-concept, our study highlights artificial selection of microbiota as a promising but complex strategy to modulate plant health. Future work should focus on improving trait measurement precision, maintaining consistent selective pressures (e.g., pathogen presence), identifying non-canonical microbial contributors to suppression, and characterizing them functionally. Long-term studies should also evaluate the agronomic feasibility and stability of microbiota engineering. Ultimately, integrating artificial selection with multi-omics approaches and ecological modeling could yield robust microbiota-based tools for sustainable crop protection.

## Funding

ER and RPF benefitted from an EC2CO initiative funding from CNRS. In IEB this work was also supported by the project TowArds Next GENeration Crops, reg. no. CZ.02.01.01/00/22_008/0004,581 of the ERDF Programme Johannes Amos Comenius. Mobility exchange between laboratories was supported by the "PHC BARRANDE" programme, funded by the French Ministry for Europe and Foreign Affairs, the French Ministry for Higher Education and Research and the Czech Ministry for Education,Youth and Sports, awarded to LB and RPF.

## CRediT authorship contribution statement

**Tetiana Kalachova:** Writing – review & editing, Writing – original draft, Visualization, Project administration, Investigation, Formal analysis, Data curation, Conceptualization.
**Barbora Jindrichová:** Writing – review & editing, Writing – original draft, Visualization, Investigation, Formal analysis, Data curation.
**Manuel Blouin:** Writing – review & editing.

**Romana Pospíchalová:** Writing – review & editing.
**Lenka Burketová:** Writing – review & editing, Funding acquisition, Conceptualization.
**Eric Ruelland:** Writing – review & editing, Writing – original draft, Project administration, Funding acquisition, Conceptualization.
**Ruben Puga-Freitas:** Writing – review & editing, Writing – original draft, Visualization, Project administration, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Eric Ruelland reports financial support was provided by National Centre for Scientific Research. Lenka Burketova reports financial support was provided by European Regional Development Fund. Ruben Puga Freitas reports financial support was provided by French Ministry for Europe and Foreign Affairs, the French Ministry for Higher Education and Research and the Czech Ministry for Education,Youth and Sports. Lenka Burketova reports financial support was provided by French Ministry for Europe and Foreign Affairs, the French Ministry for Higher Education and Research and the Czech Ministry for Education,Youth and Sports. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

We are grateful to the Genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, https://doi.org/10.15454/1.5572369328961167E12) for providing computing and storage resource. We acknowledge the Imaging Facility of the Institute of Experimental Botany AS CR supported by the MEYS CR (LM2023050 Czech-BioImaging), the Czech Academy of Sciences and IEB AS CR. We also thanks Beatriz Decencière of the CEREEP - Ecotron IDF/UMS CNRS/ENS 3194 for the sandy soil and to Pr. Anne Repellin from iEES-Paris, for English language editing and comments on the manuscript.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.stress.2025.101215.

## Data availability

Raw data of the 16S amplicon-based metagenomic are available at the Sequence Read Archive (SRA), linked to the NCBI BioProject PRJNA1270752. Other data that support the findings of this study are available from the corresponding author upon reasonable request.

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