Coordination of microbe-host homeostasis via a crosstalk with plant innate immunity


 Asymptomatic plants grown in natural soil are colonized by phylogenetically structured communities of microbes known as the microbiota. Individual microbes can activate microbe-associated molecular pattern (MAMP)-triggered immunity (MTI), which limits pathogen proliferation but curtails plant growth, a phenomenon known as the growth-defense trade-off. We report that in mono-associations, 41% (62/151) of taxonomically diverse root bacterial commensals suppress Arabidopsis thaliana root growth inhibition (RGI) triggered by immune-stimulating MAMPs or damage-associated molecular patterns. Amplicon sequencing of bacteria 16S rRNA genes reveal that immune activation alters the profile of synthetic communities (SynComs) comprised of RGI-non-suppressive strains, while the presence of RGI-suppressive strains attenuates this effect. Root colonization by SynComs with different complexities and RGI-suppressive activities alters the expression of 174 core host genes with functions related to root development and nutrient transport. Further, RGI-suppressive SynComs specifically downregulate a subset of immune-related genes. Pre-colonization with RGI-suppressive SynComs, or mutation of one commensal-downregulated transcription factor, MYB15, render plants more susceptible to opportunistic Pseudomonas pathogens. Our results suggest that RGI-non-suppressive and suppressive root commensals modulate host susceptibility to pathogens by either eliciting or dampening MTI responses, respectively. This interplay buffers the plant immune system against pathogen perturbation and defense-associated growth inhibition, ultimately leading to commensal-host homeostasis.


Introduction 43
Ubiquitous interactions within, and between microbial communities and their plant hosts often 44 shape host phenotypes and drive community diversification, leading to the conceptualization of 45 plants and their associated microbes as discrete ecological units, or holobionts 1 . Analysis of 46 Arabidopsis thaliana grown in different locations has shown that plants accommodate a conserved 47 core microbiota, microbial assemblages that represent a subset of microbes from the surrounding 48 soil seeding inocula 2-5 . While most microbiota members are commensals, a small number provide 49 beneficial services for the host 6,7 or become pathogenic under favorable conditions. Recent studies 50 have shed light on how specialized metabolites 8-11 and abiotic stresses 12 influence host-associated 51 microbiota. However, how microbe-host homeostasis is maintained upon perturbation remains 52 poorly understood. 53 Plants have evolved a sophisticated innate immune system to protect themselves against pathogens. 54 One arm of this system is activated by the extracellular perception of microbe/pathogen-associated 55 molecular patterns (M/PAMPs), e.g. the bacterial flagellin-derived epitope flg22, by cognate host 56 pattern recognition receptors (PRRs). Both pathogenic and beneficial bacteria can carry flg22 epitope 57 variants 13 , resulting in MAMP-triggered immunity (MTI) 14,15 . MTI effectively restricts pathogen 58 proliferation 16 , but, if unrestrained, may result in plant growth penalties, a phenomenon known as 59 the growth-defense trade-off 17 . Pathogens have evolved diverse mechanisms to suppress MTI 18 , but 60 this property is not limited to harmful bacteria, as a previous report showed commensal 61 Alphaproteobacteria from the Arabidopsis root culture collection (At-RSPHERE) 19 can also override 62 flg22-mediated root growth inhibition (RGI) 20 . Similarly, the beneficial rhizobacterium Pseudomonas 63 simiae suppresses more than half of the MAMP-triggered transcriptional responses in mono-64 association with Arabidopsis, possibly through acidification of the rhizosphere 13,21 . However, how 65 plants tolerate a rich diversity of commensals without compromising effective resistance to 66 pathogens is unknown. Here, we used a bottom-up approach to show that phylogenetically diverse 67 root commensals can modulate plant immunity and their combined interactions in community 68 contexts coordinate commensal-host homeostasis under pathogen challenge 22,23 . 69

Results 70
Taxonomically widespread ability of root commensals to interfere with defense-associated growth 71 inhibition 72 To facilitate screening of individual root commensals of the At-RSPHERE culture collection, we took 73 advantage of a flg22-hypersensitive line, pWER::FLS2-GFP 24,25 , in which the flg22 receptor FLS2 is 74 overexpressed but restricted to the root epidermis. This hypersensitivity leads to an enhanced signal-75 to-noise ratio for flg22-mediated RGI (Extended Data Fig. 1a). After three weeks of co-culturing with 76 individual bacterial isolates and flg22, 41% of the strains (62 out of 151) were found to interfere with 77 RGI. RGI-suppressive activity was detected across all four phyla of the microbiota -Actinobacteria, 78 Proteobacteria, Bacteroidetes, and Firmicutes -but was overrepresented among Actinobacteria and 79 Gammaproteobacteria commensals (Fig. 1a). Viable plate counting confirmed that the RGI non-80 suppressive strains still colonize roots in mono-associations (Extended Data Fig. 1b). In contrast, only 81 three strains, Streptomyces strains 107 and 187, and Pseudomonas 401, had detrimental impacts on 82 Arabidopsis in mono-associations, with Pseudomonas 401 most severely compromising plant growth 83 (Extended Data Fig. 1c).   (Table S1) differential in RGI suppressive activity on flg22-92 mediated RGI. Shapes correspond to biological replicates. Different letters indicate statistical significance.

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To examine whether root-derived bacteria were also able to suppress RGI elicited by an endogenous 95 plant-derived danger-associated molecular pattern (DAMP), we treated plants with the DAMP 96 Atpep1, which induces RGI and immune responses 26 . Using Atpep1-treated Col-0 wild type (WT) 97 plants, we found that 12 out of 13 suppressive strains, representing members from diverse taxa, 98 retained the capacity to interfere with RGI, while none of the eight non-suppressive strains elicited 99 this effect (Extended Data Fig. 2a). Thus, phylogenetically diverse root commensals can suppress 100 both DAMP-and MAMP-induced RGI. One isolate, Caulobacter strain 342, suppressed flg22-but not 101 Atpep1-mediated RGI (Extended Data Fig. 2b), suggesting the existence of at least two modes of RGI 102 suppression, one interfering with both MAMP-and DAMP-induced RGI, the other possibly specific to 103 flg22 perception. 104 Although germ-free pWER::FLS2-GFP 24 plants respond to flg22 treatment with enhanced RGI 105 compared to Col-0 on synthetic medium, no growth differences were noted between these two 106 genotypes when grown in natural soil (Extended Data Fig. 2c). Given that root growth in natural soil 107 likely proceeds in the face of chronic exposure to MAMPs and DAMPs as well as colonization by both 108 suppressive and non-suppressive commensals, we speculated that the aforementioned RGI 109 suppression phenotype might act as a dominant community trait. To test this hypothesis, we 110 composed four independent but taxonomically similar 5-member synthetic communities (SynComs) 111 with contrasting capacities for RGI suppression, i.e., non-suppressive SynComs (SynCom NS1 and  112 NS2) and suppressive SynComs (SynCom S1 and S2; Table S1). We observed RGI-suppressive activity  113  in plants inoculated with the suppressive SynComs but not with the non-suppressive SynComs.  114  Furthermore, full RGI suppressive activity was retained when these commensals were combined as  115 10-member SynComs (Fig. 1b). A recent study showed that auxin-mediated RGI could be rescued by 116 Variovorax commensals 27 . However, our four tested SynComs neither induced RGI to a level 117 comparable to flg22 treatment, nor did the presence of Variovorax 434 in SynCom NS1 rescue the 118 flg22-mediated RGI phenotype (Fig. 1b). Therefore, we conclude that RGI is mainly caused by flg22 119 treatment and is widely suppressed by At-RSPHERE members that function dominantly in our setup. 120 We speculated that the co-occurrence of RGI-non-suppressive and suppressive strains might reflect a 121 need for commensal microbes to dampen plant immunity to balance root growth and defense trade-122 offs. Thus, we asked if a single suppressive strain is sufficient to achieve full RGI suppression. We 123 found that the addition of diverse individual suppressive strains to a 5-member non-suppressive 124 SynCom resulted in only partial RGI suppression (Extended Data Fig. 2d). This result suggests that the 125 identity of suppressive commensals, and the input proportion of suppressive to non-suppressive 126 strains affect RGI suppression capacity quantitatively. 127 Previously, commensal Pseudomonas spp. in mono-associations were shown to acidify the growth 128 medium rendering plants insensitive to flg22 21 . To determine if acidification is responsible for RGI 129 suppression by our SynCom, we measured the pH of growth medium of plants co-inoculated with 130 different SynComs and observed average reductions in pH from 5.18 in mock treatment to 4.62 and 131 3.97 in the presence of a SynCom S1 and NS1, respectively. This lack of correlation between RGI 132 suppression and growth medium acidification suggests that this mechanism is unlikely to explain 133 suppression in our community setup. Intriguingly, hrcC, a gene required to form a functional type 134 three secretion system in pathogenic Pseudomonas, is dispensable for RGI suppression mediated by 135 suppressive Pseudomonas strain 569 (Extended Data Fig. 2e-f). Further, the culture filtrate of an 136 Actinobacteria member, Janibacter 101, suppressed both flg22 and Atpep1-induced RGI (Extended 137 Data Fig. 3a-c). Heat treatment and filtration of the culture filtrate showed that the molecule(s) 138 responsible is heat-labile and is retained by a 3kDa filter (Extended Data Fig. 3d). Mass spectrometry 139 analysis revealed that the filtrates of two out of five tested suppressive commensals elicited a 140 significant reduction of intact flg22 peptide (Extended Data Fig. 3e). Thus, the ability of some strains 141 to suppress MTI resembles the activity of pathogenic bacteria 28 and is associated with an ability to 142 modify/degrade flg22 peptide. Together, these data suggest diverse mechanisms in commensals to 143 suppress elicitor-mediated RGI. 144 Activation of immunity shapes root microbiota establishment 145 To determine if plant immunity affects microbiota establishment, we performed reconstitution 146 experiments with gnotobiotic plants grown in an agar matrix. We designed three taxonomically 147 similar SynComs with contrasting RGI suppression capacities for community profiling experiments 148 with strain-specific resolution (total of six SynComs; Table S1). Principal Coordinate Analyses (PCoA) 149 of Bray-Curtis dissimilarities revealed that root-associated bacterial communities were distinct from 150 the corresponding unplanted or planted matrix samples (Extended Data Fig. 4), regardless of the 151 SynCom composition and plant genotypes (Col-0 and pWER::FLS2-GFP). Constrained PCoA revealed 152 that flg22 treatment elicited a consistent community shift in plants inoculated with non-suppressive 153 SynComs, while samples from those inoculated with suppressive SynComs remained together. 154 Consistent with a dominant effect of RGI suppression, roots inoculated with 10-member mixed 155 communities (suppressive plus non-suppressive SynComs) were not affected by flg22 treatment (Fig.  156 2a-c, Extended Data Fig. 5a-c). 157

167
To dissect the contribution of individual strains to the overall community shift, we quantified the 168 relative abundance (RA) of individual strains. The detection of non-suppressive commensals as the 169 most abundant strains in the mixed SynComs suggests that the ability to dominate in a community is 170 not necessarily coupled to RGI suppression ( Fig. 2d- A similar trend was also detected in Col-0 (Extended Data Fig. 5d-f), though the effect was more 176 pronounced in pWER::FLS2-GFP plants, possibly due to enhanced MTI and/or altered root 177 architecture. In addition, we found that flg22 treatment reduced within-sample diversity of non-178 suppressive SynComs, (experiment 1, 2; Extended Data Fig. 5g), suggesting that immune activation 179 can affect the distribution of specific strains in community contexts. 180 Root transcriptomic changes and dampening of immunity by suppressive SynComs 181 Although flg22-mediated RGI is closely associated with immune activation, its role as a bona fide 182 immune output is unclear. Next, we sought to explore how inoculation with suppressive or non-183 suppressive SynComs affected the root transcriptome of plants treated with flg22 grown on an agar 184 matrix (Table S2 and S3). Principal component analyses (PCA) at the transcriptome level revealed 185 distinct expression patterns between Col-0 plants inoculated with live bacteria compared to germ-186 free plants (PC1, 20% variance; Fig. 3a). Interestingly, the transcriptional output of roots inoculated 187 with these two taxonomically similar SynComs were clearly differentiable after two weeks of co-188 cultivation, even in the absence of flg22 treatment (triangles, Fig. 3a). In addition, we observed a 189 separation according to the immune status of the plants, triggered by flg22 exposure, in all samples 190 treated with heat-killed bacteria as well as with the non-suppressive SynCom (PC2, 7% variance; Fig  191  3a). By contrast, MAMP treatment of plants colonized by the suppressive SynCom did not elicit 192 significant changes. Independent transcriptome experiments using pWER::FLS2-GFP plants confirmed 193 these results (Extended Data Fig. 6 and Table S2). 194 Next, we performed k-means clustering of differentially expressed genes (DEGs) involved in flg22 195 response, SynCom response, or both ( Fig. 3b and Table S3). We observed three large clusters (2,221 196 DEGs) that were induced (c4 and c5) or suppressed (c8) by live bacteria independent of flg22 197 treatment ( Fig. 3b-c). Gene ontology (GO) enrichment analyses showed that the SynCom-responsive 198 clusters were primarily enriched in functions related to detoxification, root development, nutrient 199 transport, and response to hypoxia (Extended Data Fig. 7). To determine whether similar GO terms 200 could also be identified in experiments with more complex SynComs, we compared our data with 201 two independent Arabidopsis root transcriptome studies that employed SynComs consisting of both 202 suppressive and non-suppressive commensals (35 members, Teixeira et al., co-submitted manuscript; 203 115 members 29 ). Despite differences in technical setups and SynCom complexities, we identified 174 204 common SynCom-responsive DEGs in the absence of flg22 that were related to the same biological 205 functions mentioned above (Extended Data Fig. 8 and Table S4). 206 Importantly, we found a flg22-inducible cluster (c3), which was significantly upregulated by the non-207 suppressive SynCom but downregulated by the suppressive community ( Fig. 3b-c), in a pattern 208 matching the RGI phenotype of the plant (Fig. 1b) and the bacterial community shifts (Fig. 2). As 209 expected, a portion of defense-related genes were enriched in c3, e.g. PER5, FRK1 and RBOHD (70 210 genes; Fig. 3b). However, additional defense-related DEGs were found outside c3 and were 211 upregulated by flg22 treatment even in the presence of the suppressive SynCom (348 genes; Fig. 3b). 212 Previously characterized examples include regulators of antimicrobial camalexin, e.g., MYB51 30,31 213 (c5); systemic acquired resistance, e.g. FMO1 (c5) and SARD1 (c1) 32,33 ; and endogenous peptides 214 amplifying MTI e.g. PIP1 and PIP2 34 (c5; Fig. 3b). Recent work showed that MAMP responsiveness in 215 germ-free roots was gated by the expression of damage-induced PRRs 35 . However, the sustained 216 expression of FLS2 (c1) in the presence of SynComs indicates that RGI suppression is not due to FLS2 217 downregulation (Fig. 3b). An independent study by Teixeira et al. also identified a cluster of DEGs 218 that was highly induced in axenic Arabidopsis by flg22-treatment but suppressed by the presence of a 219 35-member SynCom consisting of suppressive and non-suppressive root commensals (Extended Data 220 Fig. 9). Remarkably, this cluster showed the largest overlap with our cluster c3 with 58 common DEGs 221 (at least 21 were defense-related) that were downregulated by both SynComs (Extended Data Fig. 9). 222  We further validated our findings by examining the expression of two flg22-inducible defense marker 231 genes 12,24,36 in roots of Arabidopsis by qPCR in the presence of other suppressive SynComs. PER5 and 232 FRK1 remained significantly elevated two weeks after co-inoculation with flg22 and a non-233 suppressive SynCom but not with a suppressive SynCom (Extended Data Fig. 10a). A non-suppressive 234 SynCom alone also significantly induced their expressions, indicating that non-suppressive 235 commensals stimulate specific root immune responses. As expected, a 10-member mixed SynCom 236 did not significantly induce the expression of the two tested marker genes (Extended Data Fig. 10b). 237 To determine if MTI has a direct impact on commensal proliferation independent of any microbe-238 microbe interactions, we focused on transcription factors (TFs) and investigated the contributions of 239 the top three candidates identified in our dataset, WRKY30, MYB15, and WRKY28 (cluster c3; 240 Extended Data Fig. 10c- with Pseudomonas 401 exhibited reduced growth and accumulated pigments in shoots reminiscent 255 of stress-inducible anthocyanins (Extended Data Fig. 1c), which indicates its pathogenic potential in a 256 laboratory environment. Consistent with the fact that 401 was originally isolated from healthy and 257 asymptomatic Arabidopsis roots colonized by a diverse microbial community, the detrimental effect 258 was attenuated when plants were colonized by our SynComs. Interestingly, the attenuation is 259 stronger when plants were co-colonized with the non-suppressive SynCom compared to the 260 suppressive SynCom (Fig. 4a). Recent reports suggest a positive correlation between disease 261 progression in natural Arabidopsis populations and bacterial biomass 40,41 . To determine whether 262 Pseudomonas 401 virulence is related to enhanced plant colonization, we quantified its absolute 263 abundance on pWER::FLS2-GFP pre-colonized with suppressive or non-suppressive SynComs. Plants 264 already colonized by suppressive SynComs harbored significantly higher 401 titers compared to 265 plants pre-colonized with non-suppressive SynComs (Fig. 4b-c). Interestingly, this SynCom-dependent 266 difference appeared to be limited to roots since 401 growth in shoots was similarly restricted by co-267 colonization with either community (Fig. 4b-c). Furthermore, individual SynCom strains did not 268 antagonize 401 in vitro (Extended Data Fig. 10g), suggesting that the underlying growth differences 269 are unlikely to be the result of antibiosis. 270 To determine whether SynComs modulate plant susceptibility to a characterized opportunistic 271 pathogen prevalent in natural A. thaliana populations, we inoculated plants with the opportunistic 272 Pseudomonas leaf pathogen OTU5 (isolate p5.e6) (Fig. S1c). Plants colonized by suppressive SynComs 273 supported higher growth of Pseudomonas OTU5 compared to plants colonized by non-suppressive 274 SynComs, and this SynCom-specific effect was again observed only in roots but not in shoots (Fig. 4b-275 c). Together with the RNA-Seq data, these results suggest that pre-colonization with non-suppressive 276 SynComs activated root immunity and this correlates with reduced growth of the tested 277 opportunistic pathogens, whereas suppressive SynComs failed to provide pathogen protection. In nature, a subset of soil-dwelling bacteria colonizes roots seemingly without influencing host traits, 290 and are thus often considered as commensals. photoassimilates feed up to 20% of root-associated bacteria 45 , which served as sources of organic 306 carbon that limit bacterial growth 46 . We thus speculate that enrichment of these GOs is associated 307 with altered nutrient flux, and reduced oxygen due to microbial respiration in roots. Although our 308 SynComs are taxonomically diverse with predicted varied metabolic repertoires 19,47 , convergence to 309 core transcriptomic outputs indicate integrated responses to a state of "community commensalism". 310 The zigzag model of the plant immune system proposes that effective resistance is the result of 311 quantitative outputs above a certain threshold following MAMP perception 18 . Colonization by 312 suppressive SynComs led to the down-regulation of a subset of flg22-induced genes (Fig. 3, cluster  313 c3), whereas colonization by non-suppressive SynComs alone stimulated them and further 314 upregulated their expression together with flg22. Thus, the responsiveness of these defense-315 associated genes to SynCom colonization differs greatly with respect to the ability of the bacterial 316 community to suppress RGI. However, roots in nature are co-colonized by both groups of 317 commensals and our experiments point to a quantitative output that is dependent on their ratio.  Tübingen, Germany). Bacterial strains were prepared by taking an aliquot from the glycerol stock, 383 followed by incubation on 50% tryptic soy broth (TSB) agar plate (Sigma-Aldrich, USA) at 25 °C from 384 one to four days. Before the start of the experiments, strains were cultured in 50% TSB medium to 385 saturation and subcultured to log phase with fresh medium in a 1:5 ratio. Bacterial culture was 386 pelleted by centrifugation at 8k g for 5 min, followed by two washes with 10mM MgSO4. 387

Screening for RGI suppressive strains in monoassociation 388
After washing, bacteria were diluted with 10mM MgSO4 to a concentration of OD600 about 0. i.e. for a 5-member SynCom, the total bacteria added was OD600=0.0025. The 5-member SynCom is 407 composed of Actinobacteria, Alpha-, Beta-and Gamma-proteobacteria. Bacteriodetes and 408 Firmicutes were not included in these SynCom since no strains with differential ability to suppress 409 RGI were identified in these two phyla. Composition of SynCom used in this manuscript can be found 410 in Table S1. 411

16S amplicon sequencing and community profiling 412
For 16S community profiling, root samples were harvested and libraries were processed according to 413 previously published protocol 4 . Briefly, plants were germinated with the indicated SynCom in the 414 presence or absence of 1 µM flg22 and incubated for 14 days before harvesting. Plants were 415 inoculated with SynCom NS1 and S1 for experiment 1 (Fig 2a,d and Extended Data Fig. 5a,d); SynCom 416 NS3 and S3 for experiment 2 (Fig 2b,e and Extended Data Fig. 5b,e); and SynCom NS4 and S2 for 417 experiment 3 (Fig 2c,f and Extended Data Fig. 5c,f) μl elution buffer and quantified using Quant-iT PicoGreen dsDNA Assay (ThermoFisher). Samples 424 were diluted to 3.5 ng/μl and 3 µL samples were used in a three-step PCR amplification protocol.
Step 1: the V5-V7 region of the bacterial 16S rRNA gene was amplified in triplicate reactions with 426 primers 799F and 1192R in a 25 µL reaction volume containing 2 U DFS- Taq  Step 2: PCR reactions were performed as stated above with the number of cycles reduced to ten 436 using primer pairs 799F and individual reverse barcoded primers. PCR quality and quantity were 437 estimated by loading 5 μl of each reaction on a 1.5% agarose gel. Approximately similar amount of 438 DNA of samples from the same biological replicate were pooled and the mixtures were loaded on a 439 1.5% agarose gel. DNA bands with the correct size were cut out and purified using the QIAquick Gel 440 Extraction Kit (Qiagen). 441 Step 3: Gel-purified DNA were used as templates for the third PCR using forward barcoded primers 442 and p7_pad_R with a total of ten cycles. PCR reactions were loaded on a 1.5% agarose gel and DNA 443 bands with the correct size were cut out and purified using the QIAquick Gel Extraction Kit (Qiagen). 444 Double-barcoded DNA was purified and concentrated by Agencourt AMPure XP beads. Concentration 445 of the purified DNA was determined using Quant-iT PicoGreen dsDNA Assay (ThermoFisher). Paired-446 end Illumina sequencing was performed using 20ng/ul final library in house using the MiSeq 447 sequencer and custom sequencing primers. 448

Amplicon data analysis 449
Forward and reverse sequencing reads were denoised and demultiplexed separately according to the 450 barcode sequence using QIIME 52 with the following parameters: phred=30; bc_err=2. After quality-451 filtering, merging of paired-end reads, amplicon tags were then aligned to a reference set of 452 sequences obtained from the whole-genome assemblies of every strain included in each experiment 453 by using USEARCH (uparse_ref command) 53  The transformant was selected on half TSB plate supplemented with 25ng/µL Gentamycin. The 484 double-crossover deletion mutant was further confirmed by colony PCR and Sanger sequencing. 485

Mass spectrometry 486
For in vitro detection of flg22, 1 µM flg22 was co-incubated with 1ml supernatant for 1 hour at room 487 temperature. Half strength MS medium without sugar was used as a control. 100 µL aliquots sample were mixed with 200 µL UA (8M urea in 100 mM Tris-HCl pH 8.5) and adjusted to 10 mM 489 Dithiothreitol (DTT) using 1M stock. Samples were loaded onto 30 kD spin filters (Vivacon 500, 490 Sartorius) and centrifuged at 14k g for 15 min. The filtrate was collected and loaded onto 2 kD spin 491 filters (Vivacon 500, Sartorius) and centrifuged at 14k g for 30 min, after which 300 µL UA were 492 added and samples were centrifuged again (14k g, 45 min, or until most liquid had passed through 493 the filter). Next, 100 µL 55 mM chloroacetamide were added to the filter and samples were 494 incubated for 30 min in the dark, after which they were centrifuged at 14k g for 20 min. Raw data was directly analyzed on MS1 level using Skyline (https://skyline.ms) 58 against the sequence 517 of the flg22 peptide. LysC specificity was required and a maximum of two missed cleavages allowed. 518 Minimal peptide length was set to seven maximum length to 25 amino acids. Carbamidomethylation 519 of cysteine, oxidation of methionine and protein N-terminal acetylation were set as modifications. 520 Results were filtered for precursor charges of 2, 3. Peaks of the intact flg22 peptide precursor were 521 integrated manually, peak areas were exported for further processing. In vitro halo-of-inhibition assay 536 100 µL washed Pseudomonas strain 401 was inoculated into 50ml warm 25% TSB medium with an 537 initial OD600=0.1. After solidification, 10 µL pre-washed bacterial suspension prepared from 1ml 538 saturated overnight bacterial culture from individual strains was spotted on the 401-preinoculated 539 plates. Any halo-of-inhibition was recorded up to 5 days after incubation at 25 °C. 540

RNA-Seq data analysis 557
Raw Illumina RNA-Seq reads were pre-processed using fastp (v0.19.10) 59 with default settings for 558 paired-end (Col-0 experiment) or single-end reads (pWER::FLS2-GFP experiment). For single-end 559 reads, low quality sequences from the head (8 bases) and tail (2 bases) were trimmed. High quality 560 reads were pseudo-aligned to TAIR 10 Arabidopsis thaliana transcriptome reference (Ensembl) 60 561 using kallisto (v0.46.1) 61 . On average, 6.7 million paired-end and 18.1 million single-end reads per 562 sample were mapped to the reference Arabidopsis transcriptome, respectively. After removal of low 563 abundant transcripts that were absent in at least two replicates under each condition, count data 564 were imported using the tximport 62 package. 565 Differential expression analyses were performed using the DESeq2 63 package. Firstly, raw counts 566 were normalized with respect to the library size (rlog function) and log2 transformed. We tested for 567 sample batch effects by surrogate variable (SV) analysis using the sva 64 package. Significant SVs were 568 automatically detected and integrated into the model for differential analyses. vs. heat-killed bacteria. Transcripts with fold-changes > 1.5 and adjusted p-value for multiple 575 comparisons (Benjamini-Hochberg method) equal to or below 0.05 were considered significant. 576 The log2 scaled counts were normalized by the identified SVs using the limma 65 package 577 (removeBatchEffect function), and transformed as median-centered z-score by transcripts (scaled 578 counts, scale function). Then z-scores were used to conduct k-means clustering for all transcripts. The 579 cluster number (k = 10) was determined by sum of squared error and Akaike information criterion. 580 Next, confirmed transcripts with similar expression patterns were grouped in the same cluster. Significantly changed biological process GO terms (adjusted p-value < 0.05) were visualized in dot 587 plots using the clusterProfiler 70 package. Defense-related genes were extracted based on the GO-588 terms annotation with manual curation. These genes were marked in Table S3 and Table S4. 589 A GO-gene network was built by connecting GO terms with shared differentially changed genes 590 (Jaccard similarity > 0.2), so that GO terms holding close function annotations were gathered. Nodes 591 in the network were colored according to their representation in the k-means clustering analysis, 592 while their size corresponded to the number of genes annotated in the corresponding GO term. GO-593 gene networks were visualized in Cytoscape 71 with a modified configuration from metascape 72 . 594 Differentially expressed genes from another two RNA-Seq data sets Teixeira et al., 2020 and Harbort 595 and Hashimoto et al. 2020 were used to confirm genes involved in SynCom response. The RNA-Seq 596 analyses pipeline was the same as describe above. GO enrichment were conducted based on the 597 common significantly changed genes from this study and published data sets using clusterProfiler 598 package. 599

Quantitative real time PCR 600
Roots from at least five 2-week-old plants were pooled and total RNA was extracted using the 601 RNeasy Plant Mini Kit (Qiagen, Germany) according to the manufacturer's instructions. 200-500ng 602 total RNA was DNase treated followed by 1 st strand cDNA synthesis using oligo dT primers and 603 superscript II reverse transcriptase (Invitrogen). cDNA was diluted 10 times and 5 µL sample was used 604 as a template for quantitative PCR analysis in a 20 µL reaction mixture supplemented with 1X iQ SYBR 605 Green (Bio-Rad, USA), and 0.2 µM primer each. UBQ5 was used for internal normalization. Primers 606 used in this study can be found in Table S5. 607

Statistical analysis 608
Analyses were performed using the R environment. t-test, Dunn's Kruskal-Wallis, Dunnett's test and 609 ANOVA were used to test for statistical significance. A p-value smaller than 0.05 was considered 610 significant. 611

Data and software availability
Raw transcriptome and 16S rRNA amplicon sequencing data from this project were deposited at the 613 EBI Sequence Read Archive under the accession number GSE157128. Mass spectrometry data has 614 been deposited to Panorama Public (https://panoramaweb.org/flg22_RGI.url) and 615 ProteomeExchange (PDX020452). We also make all data and scripts available to the reviewers and 616 the editorial office at https://github.com/YulongNiu/MPIPZ_microbe-host_homeostasis. 617