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structure elucidation. Data generated from MS2 were filtered to obtain the most intensive fragment spectra peaks for each compound of interest. Information about parent ion properties, such as predicted adducts and isotope patterns were added to create files compatible with downstream analysis techniques (<strong>.ms</strong> ITS40 instrument data format or <strong>.mgf</strong> Mascot generic format) (see <strong>Tables S6-16</strong> in the paper associated with this study).</p><p><br></p><p>On the basis of these data, we created fragmentation trees with <a href='https://bio.informatik.uni-jena.de/software/sirius/' rel='noopener noreferrer' target='_blank'><strong>SIRIUS 4.0.1</strong></a><strong>[1][2]</strong> and compared MS/ MS spectra to public databases using the <strong>SIRIUS</strong> included <strong>CSI:FingerID tool[3]</strong> and the <a href='https:// gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp' rel='noopener noreferrer' target='_blank'><strong>Global Natural Products Social Molecular Networking platform</strong></a><strong>[4]</strong> as well as databases <strong>PubChem[5]</strong> and <a href='https://mona.fiehnlab.ucdavis.edu/' rel='noopener noreferrer' target='_blank'><strong>MONA</strong></a>. Identical structures found in different LC methods or ionization modes were grouped under the name of the lower-number crid: 1,168 (365), 1,227 (380) and 1,199 (383).</p><p><br></p><p><strong>Refs:</strong></p><p><strong>[1]</strong> Böcker S, Letzel MC, Lipták Z, Pervukhin A. SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics. 2009 Jan 15;25(2):218-24. doi:10.1093/bioinformatics/btn603. PMID:19015140.</p><p><strong>[2]</strong> Böcker S, Dührkop K. Fragmentation trees reloaded. J Cheminform. 2016 Feb 1;8:5. doi:10.1186/s13321-016-0116-8. PMID:26839597.</p><p><strong>[3]</strong> Dührkop K, Shen H, Meusel M, Rousu J, Böcker S. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc Natl Acad Sci U S A. 2015 Oct 13;112(41):12580-5. doi:10.1073/pnas.1509788112. PMID:26392543.</p><p><strong>[4]</strong> Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, Nguyen DD, Watrous J et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol. 2016 Aug 9;34(8):828-837. doi:10.1038/nbt.3597. PMID:27504778.</p><p><strong>[5]</strong> Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019 Jan 8;47(D1):D1102-D1109. doi:10.1093/nar/gky1033. PMID:30371825.</p>MetaboLightsPublicLiquid Chromatography MS - positive - hilicLiquid Chromatography MS - positive - reverse phaseLiquid Chromatography MS - negative - hilic<p>LC separation was performed with a Thermo Ultimate 3000 UHPLC system (Thermo Scientific) applying 2 different separation methods. Hydrophilic interaction (HILIC)-based separation was carried out using an Aquity UHPLC BEH Amide column (100 x 2.1 mm, 1.7 μm particle sizes; Waters) as described previously<strong>[1]</strong>, at a flow rate of 500 μl/min. The C18 reversed phase (C18RP) separation was achieved using a Kinetex XB-C18 column (particle size 1.7 μm, pore size 100 Å; dimensions 50 x 2.1 mm, Phenomenex). Briefly, solvent A was 0.1% (v/v) formic acid and solvent B was acetonitrile at a flow rate of 400 μl/min. Solvent B was varied as follows: 0 min, 1%; 7.5 min, 95%; 10 min, 95%; subsequently, the column was equilibrated for 3 min at the initial conditions before the next sample analysis.</p><p><br></p><p><strong>Ref:</strong></p><p><strong>[1]</strong> Mülleder M, Bluemlein K, Ralser M. A High-Throughput Method for the Quantitative Determination of Free Amino Acids in Saccharomyces cerevisiae by Hydrophilic Interaction Chromatography-Tandem Mass Spectrometry. Cold Spring Harb Protoc. 2017 Sep 1;2017(9):pdb.prot089094. doi:10.1101/pdb.prot089094. PMID:28864565.</p>A general non-self response as part of plant immunity. 10.1038/s41477-021-00913-1. PMID:34007033Institute of Microbiology ETH ZurichPatrick Kieferblankphyllospheremass spectrometry<p>An approximate volume of 20 μl of ground, frozen plant material was lyophilized for 24 h to dryness and the resulting plant dry weight (PDW) determined. To extract the metabolites, 800 μl of a chilled (4 °C) 80:20 methanol:water solution was added to each sample and homogenized with a QIAGEN TissueLyser II at 20 Hz for 1 min. After homogenization, the samples were heat-treated at 65 °C for 10 min on a heated rotary shaker. The samples were pelleted by centrifugation and the extract (supernatant) transferred into a fresh tube. The finished extract was diluted to a final concentration of 0.5 μg/μl (dry weight per volume) with the buffer solution required for the respective LC method</p><p><br></p><p>In addition, LC method specific blanks were prepared: Acetonitrile:Water (80:20, HILIC) and Water (C18 reverse phase). Prior to each sample a solvent blank was injected.</p>Arabidopsis thalianablankArbidopsis thalianahttps://www.ebi.ac.uk/metabolights/MTBLS2522Shinichi Sunagawa.Patrick Kiefer. ETH Zürich. ladimir-Preolog-Weg 4 8093 Zuirch Switzerland. pkiefer@ethz.ch. 41446325523.Benjamin A Maier.Miriam Bortfeld-Miller.Martin Schäfer.Christine M Vogel.Julia Vorholt. ETH Zurich. Vladimir-Preolog-Weg 4 8093 Zuirch Switzerland. vorholt@micro.biol.ethz.ch. 41446325524.Benjamin Maier. ETH Zürich. ladimir-Preolog-Weg 4 8093 Zuirch Switzerland. bemaier@ethz.ch.Christopher M Field.Barbara Emmenegger.Lucas Hemmerle.Sebastian Pfeilmeier.<p>MS <strong>.raw</strong> data files were converted into <strong>.mzML</strong> data file using <strong>msconvert</strong> from <a href='http://proteowizard.sourceforge.net/tools/msconvert.html' rel='noopener noreferrer' target='_blank'>ProteoWizard</a>.</p><p><br></p><p>Raw files were processed using an untargeted LC-MS data analysis workflow implemented in the Python-based <a href='http://emzed.ethz.ch/' rel='noopener noreferrer' target='_blank'>emzed2 framework</a>. In a first step, individual peak libraries were constructed for each acquisition mode (same LC method and ESI mode). Briefly, after individual subtraction of blank measurements from samples, LC-MS peaks were detected and extracted sample-wise using the <strong>emzed2</strong> implemented <strong>Metabo Feature Finder</strong> from <strong>OpenMS[1]</strong>. Individual peaks of all samples were grouped on the basis of instrument-specific tolerance levels by their m/z and retention time (RT) values into unique consensus peaks and unique peak id (pID) was assigned, where each peak was represented by its mean m/z and RT value. Since compound elemental composition and applied ESI result in a multitude of signals, peak deconvolution was required. To this end, consensus peaks were further grouped by isotopologues (consensus feature IDs, fID) and subsequently, by adducts (adduct IDs, aID) taking into account peak RTs and their specific m/z differences.</p><p><br></p><p>Next, we merged the individual consensus libraries and further grouped adducts measured with the same LC method in positive and negative ESI mode on the basis of RT and specific adduct m/z differences into cross-referring IDs (crid, used as continuous numbering system). To reduce the number of low-quality entries, we removed all features on the crid level that were not detected in at least 4/5 biological replicates in at least one treatment. Remaining crid were used to perform a targeted peak extraction on samples with missing values to update missing at random 'MAR' peaks (peaks missed by feature finding algorithm). In case of complete absence (MNAR), random values were assigned from a Gaussian distribution using mean and standard values determined from sample-specific spectral baseline and noise. Finally, to each crid monoisotopic mass value(s) were assigned and crid were grouped by their masses and to each mass a unique massID was assigned to simplify database searching and possible compound overlaps between applied LC methods. The resulting mass library was used to search publically available databases (KEGG<strong>[2]-[4]</strong>, PubChem<strong>[5]</strong> and PMN<strong>[6]</strong>) to identify compounds with matching monoisotopic mass. Manual curation was used to eliminate unlikely database matches.</p><p><br></p><p>The complete emzed workflow is available at (<a href='https://gitlab.ethz.ch/emzed2-plant-metabolomics-workflows' rel='noopener noreferrer' target='_blank'>https://gitlab.ethz.ch/emzed2-plant-metabolomics-workflows</a>).</p><p><br></p><p>Next, averaged group areas (weighted by individual peak areas) were calculated for each crid and treatment. Before averaging, peak areas were normalized to their individual sample total ion current area. Normalization to biomass was not required since all samples were diluted to the same biomass concentration before analysis. Differential analysis was performed by comparing the weighted average group area of crids in a treatment against those in axenic control plants (Student’s t-test, Benjamini–Hochberg P-value correction) and filtering for significant changes (|log2FC| &gt; 2, FDR &lt; 0.05).</p><p><br></p><p>Correlation analysis:</p><p>Gene expression count data was normalized in <strong>R v.3.5.1</strong> with the libraries <strong>edgeR v.3.24.3[7]</strong> and <strong>limma v.3.38.3[8]</strong>. Spearman correlation values were calculated for each pair of genes between their normalized expression counts across all samples. Spearman was the preferred method as it was observed that many genes showed distinct 'on' or 'off' states that produced misleading Pearson correlation values corresponding to the correlation of only the 2 states. Similarly, Spearman correlation values were calculated between each gene-metabolite pair between the normalized expression count and log2-normalized crid area respectively.</p><p><br></p><p><strong>Refs:</strong></p><p><strong>[1]</strong> Röst HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, Andreotti S et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods. 2016 Aug 30;13(9):741-8. doi:10.1038/nmeth.3959. PMID:27575624.</p><p><strong>[2]</strong> Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000 Jan 1;28(1):27-30. doi:10.1093/nar/28.1.27. PMID:10592173.</p><p><strong>[3]</strong> Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019 Jan 8;47(D1):D590-D595. doi:10.1093/nar/gky962. PMID:30321428.</p><p><strong>[4]</strong> Kanehisa M. KEGG Bioinformatics Resource for Plant Genomics and Metabolomics. Methods Mol Biol. 2016;1374:55-70. doi:10.1007/978-1-4939-3167-5_3. PMID:26519400.</p><p><strong>[5]</strong> Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019 Jan 8;47(D1):D1102-D1109. doi:10.1093/nar/gky1033. PMID:30371825.</p><p><strong>[6]</strong> Schläpfer P, Zhang P, Wang C, Kim T, Banf M, Chae L, Dreher K, Chavali AK, Nilo-Poyanco R, Bernard T, Kahn D, Rhee SY. Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants. Plant Physiol. 2017 Apr;173(4):2041-2059. doi:10.1104/pp.16.01942. Erratum in: Plant Physiol. 2018 Mar;176(3):2583. PMID:28228535.</p><p><strong>[7]</strong> Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010 Jan 1;26(1):139-40. doi:10.1093/bioinformatics/btp616. PMID:19910308.</p><p><strong>[8]</strong> Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 Apr 20;43(7):e47. doi:10.1093/nar/gkv007. PMID:25605792.</p>InfectionDry Weight concentrationReplicateGenotypeDilution factorpkiefer@ethz.ch<p><strong>Plant material and growth conditions:</strong></p><p>The T-DNA insertion lines bbe6 (SALK_054180C), chx16 (SALK_083993C), gstf6 (SALK_065940C), igmt3 (SAIL_679_H07), mlo12 (SAIL_50_C10), at2g43620 (SALK_056680) and prx71 (SALK_123643C) were obtained from the Nottingham Arabidopsis Stock Centre (NASC, http://arabidopsis.info/), the bak1-5 bkk1-1 line from Cyril Zipfel (University of Zurich) and the lines for cyp71a12 (GABI_127H03) and fox1 (GABI_813E08) from E. Sattely (Stanford University).</p><p><br></p><p>Plants for all experiments were grown in climate-controlled growth chambers (CU-41L4, Percival) fitted with full-spectrum lights (Philips Alto II 17 W/841 for gnotobiotic assays, Philips Master TL-D, 18 W/840 for soil assays).</p><p><br></p><p>For gnotobiotic assays, surface-sterilized seeds were placed in 24-well cell-culture plates (Techno Plastic Products AG) on full-strength Murashige and Skoog medium agar (Duchefa; pH 5.8, including vitamins, 3% w/v sucrose). Plants received a 16 h/8 h light/dark cycle for the first 7 d and were then switched to a 9 h/15 h cycle for the remainder of the experiment. All plates were sealed with parafilm for the first 14 d. Growth temperature was maintained at 24 °C during light and 22 °C during dark cycles at a relative humidity of 65%.</p><p><br></p><p>For soil assays, seeds were surface sterilized (70% ethanol, 1 min), placed on soil (Klasmann-Deilmann GmbH, Substrat 1) and vernalized for 4 d at 4 °C in the dark before being placed in a growth chamber. Growth conditions were set to a 11 h/13 h light/dark cycle at 60% relative humidity. After 14 d, seedlings were transferred into gardening pots (9 × 9 × 9.5 cm), with 4 or 5 plants per pot.</p><p><br></p><p><strong>Bacterial strains and growth conditions:</strong></p><p>Bacterial strains were selected from the At-LSPHERE collection20 on the basis of their ability to reach high cell numbers in the phyllosphere. A luxCDABE-tagged, kanamycin- and rifampicin-resistant P. syringae pv. tomato DC3000 (Pst) was provided by Thomas Kroj (UMR BGPI). All At-LSPHERE strains were grown on R2A agar (Sigma-Aldrich) supplemented with 0.5% v/v methanol and allowed to grow for 5 d at 22 °C. Pst was grown on King’s B agar59 for 1-2 d at 22 °C.</p><p><br></p><p><strong>Plant inoculation and infection:</strong></p><p>A sterile plastic loop was used to collect a cell material of each At-LSPHERE strain and cells were re-suspended in 1 ml of 10 mM MgCl2. Tubes containing the resuspended strains were vortexed for 5 min and adjusted to an optical density (OD600) of ~0.02 for inoculation. Axenic, 10-day-old plants were inoculated with 10 μl of bacterial suspension by pipetting. Axenic control plants were mock-inoculated with 10 μl of 10 mM MgCl2.</p><p><br></p><p>Pst for infection was obtained by resuspending cell material in 10 ml of 10 mM MgCl2 and adjusting the concentration to OD600 = 0.1 in 10 mM MgCl2 supplemented with 0.04% v/v Silwet L-77 (Chemtura AgroSolutions).</p><p><br></p><p>For infection of soil-grown plants, the pathogen suspension was applied to 5-week-old plants by spray inoculation (until 'run-off') using a perfume dispenser. Control plants were mock-infected with 10 mM MgCl2 supplemented with 0.04% v/v Silwet L-77.</p><p><br></p><p><strong>Harvest of plant material:</strong></p><p>For each condition, 24 single plants per biological replicate were harvested 9 d after inoculation (plant age, 19 d) by removing the rhizosphere from the phyllosphere using flame-sterilized scalpels and forceps and shock-freezing only the phyllosphere in liquid nitrogen. The 24 plants were pooled, 6 plants each in 5-ml screw cap tubes (Sarstedt AG) containing 2x 5-mm stainless-steel beads (QIAGEN) and stored at -80 °C.</p>Metabolomicsultra-performance liquid chromatography-mass spectrometryPlant Immunitytandem mass spectrometryuntargeted metabolitesBacteriapathogengeneral non-self responseultra-performance liquid chromatography-mass spectrometryPlant Immunitytandem mass spectrometryuntargeted metabolitesBacteriapathogengeneral non-self response<p>For untargeted analysis, LC instrument was connected to an LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific). MS analysis was carried out in positive (HILIC, C18RP) and negative FTMS mode (HILIC) at mass resolution of 30,000 (m/z = 400). Targeted and MS2 mass analysis were carried out with a Thermo QExactive plus instrument (Thermo Fisher Scientific) at mass resolution of 35,000 (m/z = 200). A ramped collision energy of 15, 30 and 45 eV was applied in all MS2 experiments applying high-energy C-trap collision dissociation. For both instruments the same heated electrospray ionization (ESI) probe was used applying following LC method dependent source parameters (first value, HILIC; second value, C18RP): vaporizer temperature (350, 380) °C; aux gas (20, 5); ion spray voltage (+3.5, + 2.75) kV. Ion spray voltage in the negative FTMS mode was set to –3.0 kV. For both LC methods, following parameters were set to identical values: sheath gas, 50; sweep gas, 0; radio frequency level, 50.0; capillary temperature, 275 °C. Lastly, injected sample amount on columns was adapted to instrument sensitivity. A total 2.5 μg plant dry weight (PDW) equivalent was injected when using the LTQ Orbitrap instrument and 0.6 μg PDW when QExactive plus was used.</p>Indole 3-carboxylic acidPlants, like other multicellular lifeforms, are colonized by microorganisms. How plants respond to their microbiota is currently not well understood. We used a phylogenetically diverse set of 39 endogenous bacterial strains from Arabidopsis thaliana leaves to assess host transcriptional and metabolic adaptations to bacterial encounters. We identified a molecular response, which we termed the general non-self response (GNSR) that involves the expression of a core set of 24 genes. The GNSR genes are not only consistently induced by the presence of most strains, they also comprise the most differentially regulated genes across treatments and are predictive of a hierarchical transcriptional reprogramming beyond the GNSR. Using a complementary untargeted metabolomics approach we link the GNSR to the tryptophan-derived secondary metabolism, highlighting the importance of small molecules in plant-microbe interactions. We demonstrate that several of the GNSR genes are required for resistance against the bacterial pathogen Pseudomonas syringae. Our results suggest that the GNSR constitutes a defence adaptation strategy that is consistently elicited by diverse strains from various phyla, contributes to host protection and involves secondary metabolism.A general non-self response as part of plant immunity.Maier Benjamin A BA, Kiefer Patrick P, Field Christopher M CM, Hemmerle Lucas L, Bortfeld-Miller Miriam M, Emmenegger Barbara B, Schäfer Martin M, Pfeilmeier Sebastian S, Sunagawa Shinichi S, Vogel Christine M CM, Vorholt Julia A JAPlant., reactivity, Immune Responses, Plant Immune System, Immune Response, Immune Systems, Immunities, Plant Immune Responses, responsivity, Plant Immunities, Immunity, Systems, System, Responses, Response, Plant Immune Systems, Plant, Immune System, Plant Immune Response, response, Plant ImmunePlant., reactivity, Immune Responses, Plant Immune System, Immune Response, Immune Systems, Immunities, Plant Immune Responses, responsivity, Plant Immunities, Immunity, Systems, System, Responses, Response, Plant Immune Systems, Plant, Immune System, Plant Immune Response, response, Plant Immunesecondary metabolite metabolic process, Microbial Community Composition, host organism, IPP2A2, Materials, triptofano, P62, A., Metabolisms, Pflanze, number, Metabonomic, Gene, Metabonomics, sci, 2-amino-3-(1H-indol-3-yl)propanoic acid, PMS Tryptophan, ratio-Tryptophan, presence, Arabis thaliana, Tryptacin, L Tryptophan ratiopharm, PHAPII, viridiplantae, Human, secondary metabolite metabolism, 5730420M11Rik, ratio Tryptophan, Arabidopsis thalianas, reduced, Metabolism, A. thalianas, responsivity, resistance, thalianas, Phyla <eudicots>, HOW, How, Trofan, tiny, Metabolomic, Sprains, l(3)j5D5, 24B, plantae, Secondary., reactivity, SET, l(3)s2612, Genetic, Htrp, TAF-I, Cresses, PMS-Tryptophan, Microbial Community Structure, ipp2a2, Mouse-ear Cress, stru, hypoplasia, 2pp2a, plants, l(3)S053606, W, L-Tryptophan, CG10293, CG10574, Mouse-ear, DmelCG4299, l(3)j5B5, IGAAD, set, thale-cress, alpha-amino-beta-3-indolepropionic acid, 2PP2A, DmelCG10574, taf-ibeta, Ardeydorm, DmelCG10293, Microbial Community Structures, dSET, dSet, Therapies, Cress, Strains, Mouse ear, Arabidopses, Human Microbiome, Naturruhe, small, phapii, Therapy, 0904/17, L-Tryptophan-ratiopharm, Community Composition, Arbisopsis thaliana, clone 2.39, land plants, Sprain, Arabidopsis thaliana, igaad, StF-IT-1, Trp, A. thaliana, Tryptophan Metabolism Alterations, qkr, Microbial, l(3)S090417, Cistrons, results, group, Secondary Metabolisms, count in organism, I-2PP2A, Phyla <beetles>, SZ1, KH93F, Dm I-2, I2PP2A, Strain, core, Arabidopsis thaliana (thale cress), Genetic Materials, secondary metabolism, L Tryptophan, Microbial Communities, Genetic Material, Tryptan, Ardeytropin, who, Microbiome, thale cress, alpha-Amino-beta-(3-indolyl)-propionic acid, mouse-ear cress, Pseudomonas syringae subsp. syringae, Microbiomes, Levotryptophan, HLA-DR-associated protein II, Microbial Community Compositions, ensemble, DI-2, underdeveloped, Microbial Community, I-2Dm, Optimax, Plant, Community Structure, thaliana, Who/How, CG4299, tryptophane, Mouse-ear Cresses, Treatments, Lyphan, I-2PP1, Phyala, Composition, Secondary, dSET/TAF-Ibeta, higher plants, 2610030F17Rik, Arabidopsis, TAF-IBETA, beta-3-indolylalanine, Therapeutic, Material, qkr[93F], Microbiotas, anon-EST:Liang-2.39, Cistron, Treatment, TAF-Ibeta, response, AA407739, i2pp2a, Strains and Sprains, Human Microbiomes, Communitysecondary metabolite metabolic process, Microbial Community Composition, host organism, IPP2A2, Materials, triptofano, P62, A., Metabolisms, Pflanze, number, Metabonomic, Gene, Metabonomics, sci, 2-amino-3-(1H-indol-3-yl)propanoic acid, PMS Tryptophan, ratio-Tryptophan, presence, Arabis thaliana, Tryptacin, L Tryptophan ratiopharm, PHAPII, viridiplantae, Human, secondary metabolite metabolism, 5730420M11Rik, ratio Tryptophan, Arabidopsis thalianas, reduced, Metabolism, A. thalianas, responsivity, resistance, thalianas, Phyla <eudicots>, HOW, How, Trofan, tiny, Metabolomic, Sprains, l(3)j5D5, 24B, plantae, Secondary., reactivity, SET, l(3)s2612, Genetic, Htrp, TAF-I, Cresses, PMS-Tryptophan, Microbial Community Structure, ipp2a2, Mouse-ear Cress, stru, hypoplasia, 2pp2a, plants, l(3)S053606, W, L-Tryptophan, CG10293, CG10574, Mouse-ear, DmelCG4299, l(3)j5B5, IGAAD, set, thale-cress, alpha-amino-beta-3-indolepropionic acid, 2PP2A, DmelCG10574, taf-ibeta, Ardeydorm, DmelCG10293, Microbial Community Structures, dSET, dSet, Therapies, Cress, Strains, Mouse ear, Arabidopses, Human Microbiome, Naturruhe, small, phapii, Therapy, 0904/17, L-Tryptophan-ratiopharm, Community Composition, Arbisopsis thaliana, clone 2.39, land plants, Sprain, Arabidopsis thaliana, igaad, StF-IT-1, Trp, A. thaliana, Tryptophan Metabolism Alterations, qkr, Microbial, l(3)S090417, Cistrons, results, group, Secondary Metabolisms, count in organism, I-2PP2A, Phyla <beetles>, SZ1, KH93F, Dm I-2, I2PP2A, Strain, core, Arabidopsis thaliana (thale cress), Genetic Materials, secondary metabolism, L Tryptophan, Microbial Communities, Genetic Material, Tryptan, Ardeytropin, who, Microbiome, thale cress, alpha-Amino-beta-(3-indolyl)-propionic acid, mouse-ear cress, Pseudomonas syringae subsp. syringae, Microbiomes, Levotryptophan, HLA-DR-associated protein II, Microbial Community Compositions, ensemble, DI-2, underdeveloped, Microbial Community, I-2Dm, Optimax, Plant, Community Structure, thaliana, Who/How, CG4299, tryptophane, Mouse-ear Cresses, Treatments, Lyphan, I-2PP1, Phyala, Composition, Secondary, dSET/TAF-Ibeta, higher plants, 2610030F17Rik, Arabidopsis, TAF-IBETA, beta-3-indolylalanine, Therapeutic, Material, qkr[93F], Microbiotas, anon-EST:Liang-2.39, Cistron, Treatment, TAF-Ibeta, response, AA407739, i2pp2a, Strains and Sprains, Human Microbiomes, CommunityfalseA general non-self response as part of plant immunityPlants, like other multicellular lifeforms, are colonized by microorganisms. How plants respond to their microbiota is currently not well understood. We used a phylogenetically diverse set of 39 endogenous bacterial strains from Arabidopsis thaliana leaves to assess host transcriptional and metabolic adaptations to bacterial encounters. We identified a molecular response, which we termed the general non-self response (GNSR) that involves the expression of a core set of 24 genes. The GNSR genes are not only consistently induced by the presence of most strains, they also comprise the most differentially regulated genes across treatments and are predictive of a hierarchical transcriptional reprogramming beyond the GNSR. Using a complementary untargeted metabolomics approach we link the GNSR to the tryptophan-derived secondary metabolism, highlighting the importance of small molecules in plant-microbe interactions. We demonstrate that several of the GNSR genes are required for resistance against the bacterial pathogen Pseudomonas syringae. Our results suggest that the GNSR constitutes a defence adaptation strategy that is consistently elicited by diverse strains from various phyla, contributes to host protection and involves secondary metabolism.2021-09-132021-02-25MTBLS2522MTBLC2480934007033CHEBI:24809