Structure, Culturability and Adaptation Cues of the Arabidopsis Leaf Microbiota

Research Collection

Doctoral Thesis

Structure, culturability and adaptation cues of the Arabidopsis leaf microbiota

Author(s): Müller, Daniel B.

Publication Date: 2016

Permanent Link: https://doi.org/10.3929/ethz-a-010693815

Rights / License: In Copyright - Non-Commercial Use Permitted

This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use.

ETH Library DISS. ETH NO. 23510

Structure, culturability and adaptation cues of the Arabidopsis leaf microbiota

A thesis submitted to attain the degree of

DOCTOR OF SCIENCES of ETH ZURICH

(Dr. sc. ETH Zurich)

presented by

Daniel Bastian Müller

Dipl. Biol., Goethe University Frankfurt

born July 10, 1984

citizen of Germany

accepted on the recommendation of

Prof. Dr. Julia A. Vorholt Prof. Dr. Rudolf Aebersold Prof. Dr. Martin Ackermann

2016

Contents

Abstract ...... 1

Zusammenfassung ...... 3

1. Introduction ...... 5 1.1 The plant - A huge microbial habitat ...... 6 1.2 Phylogenetic structure of the plant microbiota ...... 8 1.3 Plant microbiota establishment and driving forces ...... 12 1.3.1 Sources of colonizing bacteria ...... 12 1.3.2 Environmental factors ...... 14 1.3.3 Host genetics ...... 14 1.3.4 Microbial interactions ...... 16 1.4 The plant microbiome - Adaptation to life on plants ...... 16 1.5 Importance of the microbiota for host fitness ...... 20 1.6 Scope of this thesis ...... 22

2. Functional overlap of the Arabidopsis leaf and root microbiota ...... 23 2.1 Abstract and Introduction ...... 25 2.2 Results and Discussion ...... 26 2.2.1 Bacterial culture collections from roots and leaves ...... 26 2.2.2 At-RSPHERE and At-LSPHERE culture collections ...... 27 2.2.3 Comparative genome analysis of the culture collections ...... 28 2.2.4 Synthetic community colonization of germ free plants ...... 31 2.2.5 Niche-specific microbiota establishment with SynComs ...... 33 2.3 Conclusion ...... 35 2.4 References ...... 35 2.5 Methods ...... 36 2.6 Extended data figures ...... 44

3. Systems-level proteomics of two ubiquitous leaf commensals reveals complementary adaptive traits for phyllosphere colonization ...... 55 3.1 Summary ...... 57 3.2 Introduction ...... 58 3.3 Experimental procedures ...... 59 3.3.1 Experimental design and statistical rational ...... 59 3.3.2 Bacterial strains and growth conditions ...... 59 3.3.3 Plant growth conditions and inoculation of phyllosphere bacteria ...... 59 3.3.4 Harvest of plants and recovery of phyllosphere bacteria ...... 60 3.3.5 Microscopy ...... 60 3.3.6 Preparation of protein samples for MS ...... 60 3.3.7 SWATH assay library generation ...... 61 3.3.8 SWATH data acquisition ...... 63 3.3.9 SWATH data analysis with OpenSWATH ...... 63 3.3.10 Relative quantification by ANOVA ...... 63 3.3.11 Protein inference by aLFQ ...... 64 3.3.12 Proteome comparison of different conditions ...... 64 3.3.13 Protein comparison with other leaf microbiota members ...... 64 3.3.14 Data availability ...... 65 3.4 Results ...... 65 3.4.1 SWATH assay library construction ...... 65 3.4.2 Overview of SWATH measurements ...... 66 3.4.3 Adaptation of S. melonis Fr1 to the Arabidopsis phyllosphere ...... 66 3.4.4 Adaptation of M. extorquens Fr1 to the Arabidopsis phyllosphere ...... 67 3.4.5 Metabolic specialization of Methylobacterium strains ...... 71 3.4.6 Overlap of regulated proteins between M. extorquens PA1 and S. melonis Fr1 ...... 72 3.5 Discussion ...... 73 3.6 References ...... 76 3.7 Supplementary material ...... 80

4. Bipartite interactions and plant protective abilities of the Arabidopsis leaf microbiota ...... 89 4.1 Summary ...... 91 4.2 Introduction ...... 92 4.3 Methods ...... 93 4.3.1 Pair-wise interaction screen of phyllosphere bacteria ...... 93 4.3.2 Analysis of natural community data ...... 93 4.3.3 Secondary metabolite cluster prediction ...... 93 4.3.4 Plant growth and enumeration of phyllosphere bacteria ...... 94 4.3.5 Whole genome comparison of Sphingomonas isolates ...... 94 4.4 Results ...... 95 4.4.1 Binary interaction network of phyllosphere bacteria ...... 95 4.4.2 Identification of putative gene clusters of natural product biosynthesis ...... 98 4.4.3 Screen for plant colonization efficiency of individual isolates ...... 100 4.4.4 Plant protective abilities of selected leaf microbiota members ...... 101 4.4.5 Whole genome comparison of leaf derived Sphingomonas isolates ...... 102 4.5 Discussion ...... 103 4.6 References ...... 106 4.7 Supplementary material ...... 108

5. Discussion and Outlook ...... 127

6. References ...... 135

7. Acknowledgments ...... 145

In memory of

Frank G. Müller

* May 25, 1952 † May 30, 2007

Abstract

The host microbiota, a phylogenetically diverse community of microorganisms inhabits healthy multicellular organisms, including plants and mammals. Detailed taxonomic analyses revealed that community composition is not random, but exhibits a defined and consistent structure shaped by a variety of cues. Research of the past decades has revealed that the microbiota influences host nutrition and health and consequently, the host and its microbiota should be regarded as co-evolved inter-species consortia. A better understanding of the microbiota and the complex interplay with its host will help to develop and implement effective applications to increase host fitness. Plant microbiota research under natural conditions is an inherently challenging task, owing to phylogenetic complexity and frequently changing environmental conditions. Synthetic communities of known composition represent a valuable tool to conduct experiments under controlled laboratory conditions, however, diverse culture collections mimicking the phylogenetic structure of the natural microbiota are indispensable. This thesis describes the establishment of a bacterial microbiota strain collection, covering the majority of abundant taxa that are reproducibly found on natural Arabidopsis leaves. Re-inoculation of all strains on germ-free plants resulted in leaf community patterns resembling the nature microbiota, while competition with isolates of a corresponding root microbiota culture collection suggests a competitive advantage for leaf colonization and specialization to the cognate habitat. Genome drafts of 206 leaf isolates provided insights into microbial physiology and further revealed significant differences to root- and soil- derived genomes, also reflecting differences in adaptation and organ specialization. Proteomic analysis of two abundant key members of the leaf microbiota underlined different metabolic strategies for phyllosphere colonization and indicated widely distributed and taxa-specific substrates and mechanisms of energy conservation used under in situ conditions. Besides plant host mediated cues and different metabolic capacities of the community members, binary interactions are expected to drive microbiota establishment. Analysis of over 50.000 bipartite interactions revealed that two bacterial orders predominate in production of antibiotics and indicated that chemical warfare is less often targeted against close relatives. Activity of some isolates against known phytopathogenic bacteria on agar plates did not necessarily translate to corresponding phenotypes in planta, but resulted in the identification of seven strains lowering disease severity of Arabidopsis, caused by a common bacterial pathogen. In summary, the At-LSPHERE strain collection with its corresponding genome sequences represents a unique resource for future experimental and bioinformatic analyses of the leaf microbiota and will, in combination with investigations on microbial adaptation on plants, help to move the field of plant microbiota research forward.

Zusammenfassung

Alle höheren Lebewesen, wie auch Pflanzen und Säugetiere, werden von einer Vielzahl an Mikroorganismen, der Mikrobiota, besiedelt. Die detaillierte Analyse der phylogenetischen Zusammensetzung dieser Lebensgemeinschaft von Mikroorganismen zeigte, dass diese nicht zufällig, sondern konstant und reproduzierbar ist und letztlich auf dem Einfluss vieler äusserer Faktoren beruht. Die Forschung der letzten Jahre hat durch eine Fülle an Beispielen eindrucksvoll belegt, dass die Mikrobiota einen weitreichenden Einfluss auf die Ernährung und Gesundheit des beherbergenden Vielzellers hat. Beide Parteien können aus diesem Grund als eine über Millionen von Jahren evolvierte Einheit betrachtet werden. Demnach sollte die Physiologie von Pflanzen und Tieren nicht ungeachtet der dazugehörigen Mikroorganismen untersucht werden, denn ein besseres Verständnis dieses komplexen Wechselspiels kann dazu dienen, Massnahmen zur Verbesserung des Gesundheitszustandes des Wirtsorganismus zu erkennen. Die Komplexität der nativen Mikrobiota, in Kombination mit den kontinuierlich schwankenden Umweltbedingungen, erschwert unweigerlich die Erforschung dieser Gemeinschaft unter natürlichen Bedingungen. Ein Lösungsansatz dieses Problems kann die Verwendung von manuell zusammengestellten, künstlichen bakteriellen Gemeinschaften unter kontrollierten Laborbedingungen, darstellen. Zu diesem Zweck ist eine Stammsammlung, welche die Diversität der nativen Mikrobiota widerspiegelt, unverzichtbar. Diese Arbeit beschreibt die Etablierung einer Stammsammlung, die den Grossteil der Bakterienarten enthält, die unter gewöhnlichen Bedingungen auf Arabidopsis Blättern nachgewiesen werden können. Die Gesamtheit dieser Isolate bildete auf sterilen Laborpflanzen eine bakterielle Gemeinschaft mit grosser Ähnlichkeit zur nativen Pflanzen-Mikrobiota, während Kolonisierung der Pflanze unter kompetitiven Bedingungen mit Wurzelisolaten auf einen Wachstumsvorteil und eine Spezialisierung der Bakterien an ein Leben auf Blättern hindeuten. Sequenzierung der Genome von 206 Blatt-Isolaten ermöglichte tiefe Einblicke in die Physiologie der Bakterien und offenbarte weiter signifikante Unterschiede zu Wurzelisolaten, welche ebenfalls eine Spezialisierung an die Blattoberfläche suggerieren. Eine systematische Analyse der bakteriellen Proteine, die verstärkt während der Kolonisierung der Phyllosphäre produziert werden, verdeutlichte die unterschiedlichen Anpassungen des Stoffwechsels zweier exemplarischer Modellstämme und gibt Hinweise auf weit-verbreitete Substrate, als auch auf spezifischere Mechanismen der Energiekonservierung. Neben Einflüssen der Pflanze und unterschiedlichen metabolischen Fähigkeiten der Bakterien wird angenommen, dass auch die Interaktionen zwischen den einzelnen Mitgliedern der Gemeinschaft für die konstante Struktur der Mikrobiota mitverantwortlich sind. Die Untersuchung von über 50,000 binären Interaktionen offenbarte, dass die Produktion von Antibiotika überwiegend auf zwei bakterielle Ordnungen zurückzuführen ist und sich diese chemischen Waffen selten gegen nahverwandte Organismen richten. Des Weiteren inhibierten einige dieser Stämme auch des Wachstums von bekannten Pflanzen-Pathogenen auf Agarplatten, was nicht zwangsläufig auch zu einer Inhibition

3 des Krankheitserregers auf Pflanzen führte, jedoch konnten sieben Isolate identifiziert werden, welche Arabidopsis-Pflanzen erfolgreich vor Krankheitsymptomen des Erregers schützten. Generell gesehen stellt die etablierte At-LSPHERE Stammsammlung, zusammen mit den dazugehörigen Genomsequenzen eine einzigartige Ressource für weitere experimentelle und bioinformatische Analysen der Pflanzen-Mikrobiota dar und wird, in Kombination mit Analysen der bakteriellen Anpassung an die Blattoberfläche, entscheidend zum Fortschritt des Forschungsfeldes beitragen.

Chapter I

INTRODUCTION

A modified version of this chapter is currently submitted for publication: Daniel B. Müller, Christine Vogel, Yang Bai and Julia A. Vorholt (2016): The plant microbiota: Systems biology insights and perspectives. Annual Reviews of Genetics

5 1 INTRODUCTION

1.1 THE PLANT - A HUGE MICROBIAL HABITAT

Terrestrial plants are colonized by a diverse community of microorganisms above- and belowground which can result in both, beneficial and harmful consequences. Understanding the physiology of microbial plant diseases to prevent yield losses of important crops has been a driving force of early research on plant-microbe interactions. On the other hand, a strong focus was put on symbiotic interactions of microorganisms with belowground plant compartments, due to their obvious role in nutrient uptake and plant growth, including the well-known examples of symbiotic nitrogen- fixing rhizobia within root nodules of legumes and the symbiotic association of arbuscular mycorrhizal fungi with phylogenetic diverse plant species (Parniske, 2008; Udvardi and Poole, 2013). However, over the past few years, the entire plant microbiota has gained considerable interest beyond symbiotic or pathogenic interactions, and has become an important model system to understand microbial community ecology and principles underlying microbiota establishment (Bulgarelli et al., 2013; Vorholt, 2012). The aboveground parts of plants, collectively called the phyllosphere, represent an inherently open and variable habitat that is dominated by leaves, but also includes other compartments like flowers, fruits and in the case of woody plants, heartwood (Vorholt, 2012). From a global perspective, the phyllosphere represents an enormous environment with an estimated leaf surface area of greater 108 km2, which corresponds to as much as 1026 cells colonizing this habitat (Lindow and Brandl, 2003). All aerial plant parts are exposed to the diurnal cycle, so microbial inhabitants have to cope with natural UV radiation and rapidly changing environmental conditions, including wide temperature gradients, differences in humidity and nutrient availability (Fig. 1). The waxy plant cuticle reduces leaching of photoassimilates to the leaf surface, the phylloplane, resulting in an overall oligotrophic environment. At the microscale, differences in cuticle composition and thickness as well as surface structures like hydathodes, stomata or trichomes alter the leaf surface properties, causing substantial spatial heterogeneity and differences in the local carrying capacities (Remus-Emsermann et al., 2012). Consequently, leaf surfaces are colonized unevenly, and bacteria predominantly inhabit leaf veins, the close proximity of trichomes and the grooves between epidermal plant cells, with most cells being arranged in aggregates of thousand or more cells (Monier and Lindow, 2003, 2004). As opposed to the epiphytic lifestyle on the plant surface, a subset of microorganisms enters the endophytic compartment (usually referred to as endophytes) through wounds or natural openings like stomata or hydathodes. The phyllosphere is generally considered to be short-lived and a dynamic habitat, since annual plants complete their life cycle within one year, while perennial and evergreen plants are sheading leaves either seasonally or sequentially throughout the year. In contrast, belowground plant compartments are surrounded by bulk soil, mostly influenced by its edaphic properties and characterized by more constant environmental conditions. Roots penetrating the soil change local oxygen concentrations and release nutrients by a process called rhizodeposition, rendering the root and rhizosphere, the soil immediately

6 Fig. 1: The plant as microbial habitat. Above-ground parts of plants are exposed to the diurnal cycle and changing environmental conditions (A). Leaf colonizing bacteria colonize the leaf surface as well as the apoplastic compartment (B) and typically arrange in aggregates, embedded in extracellular polymeric substances along the veins of epidermal plant cells (C). Similarly, root colonizing bacteria colonize the root surface as well as the root endophytic compartment (D). Generally, a subset of the soil microbiota is enriched in the rhizosphere and on the rhizoplane, and these strains preferentially colonize the axial cell grooves of rhizodermis cells (D). surrounding and firmly attaching to roots, aerobic and nutrient-rich compartments. Rhizodeposites include low molecular carbon sources, polymerized sugars in form of mucilage and secondary metabolites as well as living root boarder and dead root cap cells (Philippot et al., 2013). The amount of carbon released varies greatly by plant species and environmental conditions, but was roughly estimated to account for 10 % of the total photosynthetically assimilated carbon (Jones et al., 2009). Oxygen availability and respiration, in combination with the release of inorganic carbon and uptake of inorganic

7 macro- and micronutrients can substantially alter the pH, therefore changing the biogeochemical properties of the rhizosphere soil (Hinsinger et al., 2009; Philippot et al., 2013). Similarly to the leaf surface, heterogeneity of the root surface causes differences in colonization patterns and the root tip, elongation zones, axial cell grooves and openings at the basis of lateral roots have been demonstrated to be hotspots of microbial colonization (Schmidt and Eickhorst, 2014). Consistent with migration into the apoplast of leaves, a reduced subset of microorganisms is entering the endophytic root compartment. Colonization of the endophytic compartments represents an apparent paradox, but occurs despite a sophisticated innate plant immune system, which is capable of detecting a wide range of evolutionary conserved microbial epitopes, suggesting precise discrimination of pathogens from commensals and symbionts (Bulgarelli et al., 2013; Jones and Dangl, 2006). Overall, the availability of molecular tools, next-generation sequencing technologies and gnotobiotic plant model systems, in combination with the possibility to image the different plant compartments at various scales to link community structure to spatial information makes it a playground for addressing open questions of microbial ecology (Meyer and Leveau, 2012). In this introductory paragraph focus is put on the progress achieved on various aspects within the plant microbiota research field over the past couple of years. While focusing on bacteria, as the most abundant microbial colonizers of plants, several interesting aspects of other plant associated microorganisms are highlighted.

1.2 PHYLOGENETIC STRUCTURE OF THE PLANT MICROBIOTA

Healthy plants are host to a taxonomically diverse community of microorganisms, the microbiota (Bulgarelli et al., 2013; Vorholt, 2012). This microbial community does not represent random assemblages but is rather defined by a conserved phylogenetic structure. Bacteria dominate in abundance, but fungi, oomycetes, algae, protozoa, nematodes and viruses complete the overall community (Bulgarelli et al., 2015; Hacquard et al., 2015; Kemen, 2014; Lindow and Brandl, 2003; Turner et al., 2013b). Archaea are apparently not very abundant on most terrestrial plants, but contribute key metabolic functions to the below ground carbon cycling of anaerobic rice paddies (Conrad, 2009; Hacquard et al., 2015; Vorholt, 2012). Plants can no longer be seen as independent biological entities and more recently, holistic concepts, also including the plant associated microorganisms have been introduced (Vandenkoornhuyse et al., 2015). In total, the huge microbial gene pool of the microbiota represents a reservoir of genes and functions that is available to the host plant. The holobiont concept regards plants and microbes as co-evolved inter-kingdom assemblages, also including the microbiota in possible roles of adapting to new or in buffering suddenly changing environmental conditions, an important aspect with regard to the sessile lifestyle of plants. In combination, the microbiome and the plant genome forms the holobiome, a genomic reflection of the complex network of microbes and plants. Over the past decade, researchers have engaged in unravelling the phylogenetic composition as well as the mechanisms of plant colonization of the distinct host compartments. At first, cultivation- dependent studies analyzed the plant microbiota, relying on prior cultivation of strains under laboratory

8 conditions, therefore under-estimating the overall diversity due to unculturability of taxa living associated with plants under the experimental conditions. More recently, with increasing affordability of next-generation sequencing technologies, detailed cultivation-independent surveys have been conducted by massively parallel amplicon or metagenome sequencing. The increased sensitivity of these methods enabled identification and quantification even of extremely rare taxa, but due to PCR- and sequencing errors, these technologies might ultimately result in an over-estimation of the overall community diversity. Today, cultivation-independent studies have provided deep insights into the community composition of above- and below-ground compartments of various host plants, including the widely used model plant Arabidopsis thaliana (Bodenhausen et al., 2013; Bulgarelli et al., 2012; Delmotte et al., 2009; Horton et al., 2014; Lundberg et al., 2012) and its close relatives (Schlaeppi et al., 2014), several tree species (Kembel et al., 2014; Redford et al., 2010; Shakya et al., 2013), relevant crop plants like barley (Bulgarelli et al., 2015), corn (Peiffer et al., 2013), grapevine (Zarraonaindia et al., 2015), lettuce (Rastogi et al., 2012; Williams and Marco, 2014; Williams et al., 2013), potato (Inceoglu et al., 2011), tomato (Ottesen et al., 2013), rice (Edwards et al., 2015; Knief et al., 2012) , sugarcane (Yeoh et al., 2016) and soybean (Delmotte et al., 2009; Mendes et al., 2014) as well as more specialist plants like salt-excreting Tamarix trees (Finkel et al., 2011). The use of different sampling protocols, primer choices and sequencing pipelines used as well as low-resolution profiling methods make it difficult to re-analyze and directly compare the results, but all studies have conclusively demonstrated that the bacterial plant microbiota is composed of only few dominant Phyla, mainly Actinobacteria, Bactroidetes and Proteobacteria and to a lower extend Firmicutes (Fig. 2). In the following several comprehensive studies that have analyzed more than one plant compartment as well as studies that have set out to determine a core plant microbiota across phylogenetically distinct plants or multiple accessions of one plant species are highlighted. Two ground- breaking studies analyzed bulk soil, the rhizosphere and root compartment of A. thaliana, showing that all three compartments host stable and significantly different communities across multiple environments tested (Bulgarelli et al., 2012; Lundberg et al., 2012). Species richness was highest in bulk soil and reduced in the rhizosphere and root compartment. Taxa of the Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria, predominantly the Betaproteobacteria class, were enriched in the root compartment compared to bulk soil, while Acidobacteria, Verrucomicrobia and Gemmatimonadetes were depleted in abundance. Similar results were obtained on rice plants, confirming that the rhizosphere (weakly), the rhizoplane and the root endosphere host distinct microbial communities from bulk soil (Edwards et al., 2015). The separation of the different compartments was consistent with a selective gradient from the exterior of the root, across the rhizoplane to the interior of the root, with the endosphere compartment being most exclusive, enriching for 394 operational taxonomic units (OTUs) while depleting for 1961 OTUs. Enriched taxa were assigned to Proteobacteria, Bacteroidetes Chloroflexi, Spirocheates and Firmicutes, while taxa of Acidobacteria, Planctomycetes, Gemmatimonadetes, Chloroflexi and

9 Fig. 2: Phylogenetic composition of the plant microbiota. The circular phylogenetic tree is showing all bacterial families detectable on plant roots, leaves and fruits of multiple studies indicated. The circular heatmaps represent relative abundance of each bacterial families on roots (set of innermost circles), leaves (set of middle circles) and fruits (outermost circle) of the indicated studies. Bacterial families are color-coded according to their phylum.

Verrucomicrobia were depleted. Consistently, a weak rhizosphere effect on community composition and enrichment of Actinobacteria, Bacteroidetes and Proteobacteria were also observed in grapevine (Zarraonaindia et al., 2015), while a more pronounced rhizosphere effect was found in barley (Bulgarelli et al., 2015). Additional analysis of the aboveground compartments leaves, fruits and flowers, as part of the grapevine study, also revealed dominance by Acidobacteria, Bacteroidetes, Firmicutes and Proteobacteria. Community composition was distinct for all above- and belowground compartments, with roots and flowers being most dissimilar. In total, aboveground compartments were less diverse than belowground habitats, and the same tendency was observed in A. thaliana (Bodenhausen et al., 2013), tomato (Ottesen et al., 2013) and rice (Knief et al., 2012). The flower community of grapevine was almost exclusively composed of Proteobacteria, mainly the two genera Pseudomonas and Erwinia, while the apple flower microbiome was shown to be more diverse, including taxa of the phyla Deinococcus-Thermus, TM7, Bacteroidetes, Firmicutes and Proteobacteria (Shade et al., 2013). Notably, Methylobacterium, Pseudomonas and Sphingmonas were among the most abundant genera on

10 grapevine leaves, which were previously shown to be high in abundance on A. thaliana, white clover and soybean (Bodenhausen et al., 2013; Delmotte et al., 2009). The phyllosphere communities of all three plants were dominated by Proteobacteria, mostly from the Alphaproteobacteria class, Actinobacteria and Bacteroidetes. Besides the identification of true plant colonizers an emerging focus is put on human pathogens persisting on leafy greens and their implications on foodborne disease outbreaks (Brandl and Sundin, 2013). Human pathogens are apparently not adapted to growth on plants and are not abundant within the host microbiota, but enteropathogenic E. coli and Salmonella spp. were shown to survive for extend periods of time within the phyllosphere (Brandl, 2006; Moyne et al., 2013). Similarly, the rhizosphere has been demonstrated to be a reservoir of human pathogenic bacteria (Berg et al., 2005). Several studies during the past few years have tried to identify a core of shared taxa within the plant microbiota. The core community can be defined at various taxonomic ranks and different levels of complexity, e.g. the core community of one plant compartment, the shared core across all studied compartments of one population, or even across different populations or plant species. The core of common taxa is expected to become less diverse as the ecological context is extended. However, core microbiota members are seemingly competitive in colonizing different plant compartments or plant species under varying environmental conditions and are prime candidates for the analysis of physiological microbiota functions which might be provided to the plant host. Kembel and colleagues profiled the leaf community of 57 diverse tree species in a neotropical forest and identified a core of 104 OTUs that was present on nearly all species analyzed, comprising only 1.4 % of the overall diversity but accounted for 73 % of all sequencing reads (Kembel et al., 2014). In agreement with previous efforts, these abundant taxa were assigned to Alpha-, Beta- and Gammaproteobacteria as well as Actinobacteria and Bacteroidetes. In contrast, no shared OTUs were identified in a study on phyllosphere communities of 56 tree species sampled from the same location, although the overall community structure on higher taxonomic ranks resembled previous reports and was composed of Proteobacteria, Bacteroidetes, Actinobacteria, TM7 and Firmicutes (Redford et al., 2010). Attempts to identify a core set of OTUs enriched in the root endosphere of rice plants grown at several cultivation sites identified 32 commonly shared taxa, while a subset of 11 was also enriched in roots of greenhouse grown individuals (Edwards et al., 2015). Notably, three of the assigned families (Kineosporiaceae, Rhodocyclaceae and Myxococcaceae) were also represented by root enriched OTUs in a study on A. thaliana, were 97 OTUs were identified as root enriched core community across two different soil types and eight Arabidopsis accessions (Lundberg et al., 2012). Similar efforts on four Arabidopsis relatives across natural and greenhouse grown populations identified nine root enriched OTUs of three orders (Actinomycetales, Burkholderiales and Flavobacteriales) and these OTUs constituted up to half of the root microbiota in all samples tested (Schlaeppi et al., 2014). Overall, these studies suggest that there are, depending on experimental setup and depth of analysis, conserved taxa, inhabiting one or more plant organs across multiple host species and environments.

11 1.3 PLANT MICROBIOTA ESTABLISHMENT AND DRIVING FACTORS

Microorganisms colonizing the host plant benefit from plant-derived resources and form taxonomically consistent community patterns as discussed above. In principle, two different, albeit not mutually exclusive, mechanisms might produce such microbiota structures. On the one hand, growing plants provide unoccupied niches to intruding microbial strains capable of exploiting the provided resources, thus resulting in stochastic colonization events. On the other hand, plant-microbe co- evolution might provide the basis for a plant-driven selection process resulting in active recruitment of microbiota members or at least keystone species providing functions to the plant host, which may subsequently contribute to shaping the ultimate community during plant development. It is inherently difficult to disentangle a system that is as complex as the host microbiota and to distinguish between co- evolved interactions and stochastic opportunities (Moran and Sloan, 2015). Generally, the observed consistency of microbial community patterns support the notion of underlying principles and forces driving community formation. In addition, community assembly is a dynamic process reflected by shifts in the community composition over time in response to environmental changes and with plant development. While initial bacterial communities are similar to their respective seed bank, including soil and air, they become increasingly plant-specific and less diverse as plants grow and continue to develop (Chaparro et al., 2014; Copeland et al., 2015; Edwards et al., 2015; Maignien et al., 2014; Shi et al., 2015; Sugiyama et al., 2014). Many factors, comprising environmental conditions, plant-derived primary and secondary metabolites, as well as microbe-microbe and plant-microbe interactions, act on microbial community assembly during all stages of plant growth (Fig. 3). These factors will also determine the overall number of independent colonization events. It is currently unknown whether bacteria establish a founding population early in plant development and then continuously colonize emerging habitats by clonal propagation, or whether, competitive strains and/or changes in plant developmental processes and environmental conditions favor independent colonization events of newly emerging niches. In addition, invasion and strain replacement may occur by direct interaction and competition for already occupied niches.

1.3.1 Sources of colonizing bacteria

Different seed banks may contribute to the colonization and microbiota formation on the host plant. A fraction of the plant microbiota may be acquired vertically from seeds and propagate as endophytes (Hardoim et al., 2012; Johnston-Monje et al., 2014); however, horizontal transmission is likely predominating (Bulgarelli et al., 2013; Vorholt, 2012). Therefore, microbial biogeography (in this context meaning the distribution of strains competent for host colonization) significantly influences the developing community. Soil represents an extremely rich microbial reservoir on Earth (Gans et al., 2005), it is the predominant seed bank of the rhizosphere and root microbiota and one of the main drivers of community

12 Fig. 3: Environmental cues and host-mediated influences on microbiota establishment. formation (Berg and Smalla, 2009; Bulgarelli et al., 2012; Lundberg et al., 2012). Pronounced effects of soil on the rhizosphere microbiota have been reported for Arabidopsis as well as for various crop plants (Bonito et al., 2014; Bulgarelli et al., 2012; Edwards et al., 2015; Inceoglu et al., 2012; Lundberg et al., 2012; Mendes et al., 2014; Peiffer et al., 2013; Zarraonaindia et al., 2015). The microbial diversity declines sequentially from bulk soil to rhizosphere, rhizoplane and roots, which suggests increasingly stronger competition among microorganisms the more the habitat is defined. Alternatively, and not mutually exclusive, the process may be described as a multi-step selection process of the plant, in which growth of some of the microorganisms is preferentially promoted or inhibited (Bulgarelli et al., 2013; Reinhold-Hurek et al., 2015). Aboveground compartments inherently represent more open and fluctuating habitats, and various seed banks (e.g. air, precipitation, plant and animal vectors), apart from soil or plant seeds, might contribute to the establishment of the microbiota. Notably, phyllosphere communities of annual plants are known to establish themselves in reproducible patterns over consecutive years, arguing for local, site-dependent, consistent sources of colonizers (Knief et al., 2010). One of these site-factors is the soil

13 type, since the phyllosphere microbiota is strongly influenced by soil at the beginning of the growth season, but shifts to leaf-specific taxa as the season progresses (Copeland et al., 2015). Soil-derived bacteria may colonize leaves by direct physical contact of soil with plant parts, indirect through rain splash, by bacterial movement along the plant surface or even within plants, as has been demonstrated for endophytic rhizobia in rice plants (Chi et al., 2005). The ability of airborne bacteria to form communities on Arabidopsis leaves as a result of stochastic events and strong selection processes has been shown (Maignien et al., 2014), however, the relative contribution of the inoculum source to the mature leaf microbiota requires further investigation.

1.3.2 Environmental factors

The above-mentioned sources of bacteria act locally at the site of plant growth, and the same holds true for environmental factors that impact microbial community formation. In a number of studies, various other factors have been associated with community changes, including water availability, temperature, UV radiation, and macronutrient distribution (Bogino et al., 2013; Bouasria et al., 2012; Copeland et al., 2015; Kadivar and Stapleton, 2003; Kavamura et al., 2013). Regarding macroelement availability, the effects of low nitrogen or phosphate availability on nitrogen fixing endosymbionts in legume plants or on the association with mycorrhizal fungi have been well described (Oldroyd et al., 2011; Parniske, 2008). Furthermore, nitrogen availability also affected the rhizosphere communities of Medicago (Zancarini et al., 2012) and sugarcane (Yeoh et al., 2016); however, diazotrophs were not specifically enriched (Yeoh et al., 2016). In addition, changes in the composition of the leaf microbiota of maize and soybean in response to nitrogen fertilization have been reported (Ikeda et al., 2011; Manching et al., 2014). Besides abiotic influences, other biotic interactions, e.g. herbivore feeding, may have an impact on plant colonization by microbes (Dematheis et al., 2012; Lee et al., 2012; Yang et al., 2011).

1.3.3 Host genetics

Another factor influencing the plant-associated microbiota is the plant genotype. Differences among plant species are observed for the rhizosphere (Bonito et al., 2014; Oh et al., 2012; Zarraonaindia et al., 2015) and phyllosphere communities (Kembel et al., 2014; Knief et al., 2010), which is not surprising given that distinct plants provide different local habitats to microorganisms with regard to root or leaf architecture, nutrient quality and quantity. Several research groups reported that A. thaliana ecotypes establish rhizosphere (Bulgarelli et al., 2012; Haney et al., 2015; Lundberg et al., 2012) or phyllosphere (Agler et al., 2016; Horton et al., 2014) communities which differ in their composition from each other at a statistically significant level. However, quantitation of differences in microbiota composition between ecotypes or crop cultivars revealed that overall effect of the genotype is relatively small compared to the variation linked to environmental factors (Bulgarelli et al., 2015; Edwards et al.,

14 2015; Peiffer et al., 2013). Edwards and colleagues found that a genotype-dependent effect of rice plants on community structure is stronger in the rhizosphere compartment and less pronounced in the root endosphere. Consistently, only 12 out of 778 detectable bacterial OTUs exhibited a pattern of ecotype- dependent enrichment in the endophytic root compartment of A. thaliana (Lundberg et al., 2012). Interestingly, the root bacterial communities of closely related Arabidopsis species differed more than those of A. thaliana ecotypes; however, host phylogenetic distance alone could not explain interspecies diversity of the root microbiota (Schlaeppi et al., 2014). To exclude site effects and reduce the influence of environmental drivers, a synthetic community approach was employed to investigate host genotype-dependent community development. A SynCom of seven members, representing abundant taxonomic groups of the phyllosphere microbiota, revealed genotype effects on the community composition in a small selection of Arabidopsis accessions (Bodenhausen et al., 2014). In addition, colonization levels were significantly different between the accessions. In another approach, almost 200 Arabidopsis accessions were screened in a hydroponic system to identify accessions with differential abundance of Pseudomonas strains (Haney et al., 2015). In two accessions, rhizosphere communities were strongly reduced in a subset of Pseudomonadaceae compared to ecotype Columbia (Col-0) in natural soil, which was likely attributed to active growth inhibition by a secreted antimicrobial compound (Haney et al., 2015). Complementary to ecotype screening, the community composition of the microbiota of individual plant mutants was analyzed. For example, a mutation of an ABC transporter involved in root exudation (Badri et al., 2009) or artificial modulation of opine secretion (Mondy et al., 2014) caused changes in the rhizosphere microbiota of Arabidopsis. Similarly, silencing of genes of isoflavonoid biosynthesis altered the composition of the rhizosphere bacterial community of soybean (White et al., 2015). These results are consistent with an important role of root exudates on the establishment of rhizosphere and root microbiota. Regarding the phyllosphere, leaf cuticle mutants strongly affected the composition of the phyllosphere bacterial community (Bodenhausen et al., 2014; Reisberg et al., 2013), as can be expected from the cuticle's function as a diffusion barrier. Bacterial colonization of plants occurs despite a sophisticated, innate plant immune system, which is capable of detecting a wide range of evolutionary conserved epitopes; yet fundamental questions on microbial recognition and immune signaling remain unsolved. Phytohormones as well as pattern- and effector-triggered immune signaling pathways (PTI and ETI, respectively) are plausible candidates for fine tuning of the plant-associated microbiota. Using phytohormone mutants of Arabidopsis in combination with natural and synthetic bacterial communities, Lebeis et al. demonstrated that salicylic acid (SA) modulates the root-inhabiting bacterial community (Lebeis et al., 2015). Furthermore, the Arabidopsis myc2 and med25 mutants deficient in jasmonic acid (JA) signaling recruited root bacterial communities that were distinct from those assembled by wild type plants (Carvalhais et al., 2015). In the phyllosphere, induction of SA signaling reduced bacterial diversity, while bacterial diversity increased in plants deficient in JA signaling (Kniskern et al., 2007). In contrast to phytohormones, no

15 clear difference has been reported in the plant microbiota of PTI or ETI pathway mutants, which could be due to the complex interaction between the plant immune system and microbes, or the functional redundancy of plant immune receptors (Cook et al., 2015).

1.3.4 Microbial interactions

The presence of microorganisms per se can have an influence on microbiota establishment. As mentioned above for environmental factors, these effects might either be mediated directly on the level of microbe-microbe interactions, or indirectly through interactions with the host plant. The presence of Rhizoctonia solani, a soil-borne fungal pathogen, caused a significant shift in the composition of a disease-suppressive sugar beet rhizosphere community. Oxalobacteraceae, Burkholderiaceae, Sphingobacteriaceae and Sphingomonadaceae were more abundant in the presence of the pathogen, and stress-related functions were induced in these bacteria (Chapelle et al., 2016). Chapelle et al. proposed a model in which fungal invasion alters rhizosphere structure and, either directly or indirectly, induces stress responses in the community, thus activating antagonistic traits that will ultimately lead to control of the pathogen (Chapelle et al., 2016). Differences in bacterial diversity were also reported in the lettuce rhizosphere and phyllosphere after Rhizoctonia solani infection (Chowdhury et al., 2013; Erlacher et al., 2014), as well as in the phyllosphere of maize infected with the leaf blight disease (Manching et al., 2014). Similarly, powdery mildew infection changed the bacterial community of the cucumber phyllosphere (Suda et al., 2009), while infection of Arabidopsis with Albugo, a leaf oomycete pathogen, strongly reduced the diversity of the phyllosphere microbiota (Agler et al., 2016). Taken together, the current data suggest that a combination of microbe-microbe and host-microbe interactions drives microbiota assembly. More detailed studies will be required to examine the effects of bacterial interactions, including antibiosis as an inducible bacterial trait or bacterial interactions mediated by direct interaction (Ryu, 2015). In this context, it is interesting to note that in the barley rhizosphere genes responsible for the type VI secretion system were enriched, which may suggest that microbe-microbe interactions play an important role in the establishment of the rhizosphere microbiota (Bulgarelli et al., 2015). Analysis of gene enrichment further identified protein candidates involved in microbe-phage interactions, and analysis of nucleotide polymorphisms indicated that a subset of these proteins is under diversifying selection.

1.4 THE PLANT MICROBIOME - ADAPTATION TO LIFE ON PLANTS

The different plant compartments, e.g. leaves or roots, provide a myriad of different ecological niches and are inhabited by distinct microbial communities. The ecological niche encompasses all relationships of an organism or population with its environment, including abiotic and biotic aspects and how it affects its environment. The niche-based community theory assumes that functionally diverse organisms adapt to occupy the various niches available within a certain habitat under competitive

16 conditions, exploiting accessible resource to sustain growth. Traditionally, plant-microbe interactions and microbial adaptation to the plant environment have been analyzed employing binary interactions of selected microorganisms and plants. Over the past decade, large scale omics and metaomics approaches, including genomics, transcriptomics, proteomics, metabolomics and combinations thereof, have complemented our knowledge on the in planta physiology of representative models and have targeted the totality of the microbial community and its genomic potential, the microbiome, within different plant organs. The vast majority of rhizosphere and root colonizers are recruited from the surrounding soil biome, therefore attraction and migration towards roots as well as adhesion to root surfaces are thought to be initial processes during root microbiota establishment. Consistently, chemotaxis and motility have been identified as root or rhizosphere enriched categories in the metagenomes of grapevine (Zarraonaindia et al., 2015), wheat and cucumber (Ofek-Lalzar et al., 2014) and in the metaproteome of rice (Knief et al., 2012). Metabolic cues, including primary and secondary metabolites might direct movement of cells to the root surface. Root exudates, amino and organic acids facilitate movement of pathogenic Ralstonia solanacearum towards its host plant, while citric acid, malic acid and secondary metabolites were demonstrated to chemotactically attract growth-promoting Pseudomonas strains to the root surface (Bulgarelli et al., 2013). On the other hand, members of the genus Flavobacterium (Bacteroidetes), robust plant colonizers and adapted to plant carbohydrate metabolism, possess a unique gliding-motility machinery that is tightly linked to a Bacteroidetes-specific type IX secretion system (T9SS) (Kolton et al., 2014; Kolton et al., 2013). Proteins for the degradation of plant carbohydrates, including cell wall compounds, are predicted to be secreted by the T9SS and the entire complex of gliding motility and secretion is important for migration towards roots and proper root colonization (Kolton et al., 2014). This indicates that individual taxa have evolved specific strategies to successfully move to favorable locations and persist in this environment. On the other hand, transcripts related to motility were underrepresented in a Burkholderia strain in potato plants, indicating that motility might no longer be needed once cells have attached to or entered a plant tissue (Sheibani-Tezerji et al., 2015). Similarly, analysis of the transcriptome of a leaf pathogenic Pseudomonas strain revealed high expression of genes related to motility during epiphytic growth, while expression was strongly reduced when bacteria were colonizing the apoplast (Yu et al., 2013). Quorum sensing and the second messenger cyclic-di-GMP are known to regulate the transition from the motile to the sessile lifestyle, and corresponding genes were abundant in metagenomes of rice root endophytes, highlighting the potential involvement of these regulatory circuits in the colonization process (Sessitsch et al., 2012). Notably, most motility proteins detected in a metaproteomic survey of leaf communities were assigned to Pseudomonas, and motility was previously shown to confer a growth advantage to the model pathogen Pseudomonas syringae (Delmotte et al., 2009; Haefele and Lindow, 1987). Foliar pathogens might have adapted a motile lifestyle, actively searching for nutrients and natural leaf openings to ultimately enter the endophytic compartment, a prerequisite for efficient infection of many pathogens. However, it remains elusive

17 whether phyllosphere commensals actively move across the phylloplane, since motility proteins were assigned to Pseudomonas only. In agreement, transcriptional profiling of Arthrobacter revealed that flagellar biosynthesis genes are not induced on leaves compared to growth on agar plates (Scheublin et al., 2014). Reduced expression of motility proteins might prevent stimulation of the innate immune system though perception of microorganism-associated molecular patterns (MAMPs), e. g. the N- terminal 22 conserved amino acids of flagellin (Flg22). Additionally, adhesion and local replication to from aggregates embedded in extracellular polymeric substances (EPS) improves persistence on leaves and increases resistance to various stresses, including desiccation. Notably, proteins mediating adhesion were enriched in phyllosphere communities of various plants (Delmotte et al., 2009; Knief et al., 2012) as well as in the rhizosphere metagenome of barley (Bulgarelli et al., 2015). Ofek-Lalzar and colleagues analyzed microbial adaptations to the rhizoplane by a combinatorial approach of metagenomics paired with metatranscriptomics and identified among others, lipopolysaccharide (LPS) biosynthesis, plant cell wall polysaccharide-degradation enzymes, sensing and transport of C4 dicarboxylates and different secretion systems as enriched traits compared to surrounding bulk soil (Ofek-Lalzar et al., 2014). Interestingly, the most abundant traits of the metagenome survey were less often enriched in a compartment-specific manner, indicating a core of central pathways and functional capabilities shared between the different compartments. Notably, 65% of the differentially enriched attributes of the metagenome analysis were also detectable in the root metatranscriptome, indicating that these genes are actively transcribed rather than just being present. Gene abundance and expression patterns revealed nutritional differences between the two analyzed plant species cucumber and wheat. Enriched expression of genes related to C4 dicarboxylate utilization characterized the wheat root compartment, while the cucumber metatranscriptome revealed increase abundance of cell wall degrading enzymes, reflecting the differences in plant cell wall architecture (Ofek-Lalzar et al., 2014). Increased abundance of plant cell wall degrading enzymes was also reported in the maize rhizosphere (Li et al., 2014) while sugar uptake was enriched in barley roots compared to respective soil samples (Bulgarelli et al., 2013). Interestingly, approximately 40 % of Arabidopsis root enriched OTUs, were equally abundant on wooden splinters incubated in the same soil type, indicating that these taxa are capable of sustaining growth solely based on lignified cell wall components (Bulgarelli et al., 2012). Conclusively, metabolism of aromatic compounds was enriched in the grapevine rhizosphere (Zarraonaindia et al., 2015). The wood enriched sub-community was dominated by Proteobacteria, and potentially represents saprophytic bacteria involved in general decomposition of organic plant matter. However, the majority of 60 % of Arabidopsis root enriched taxa depends on metabolically active plant cells to support enrichment, potentially depending on rhizodeposites, including sugars, amino and organic acids. This sub-community is dominated by Actinobacteria (Bulgarelli et al., 2012) known to produce an array of biologically active secondary metabolites. Delmotte and coworkers employed community proteogenomics, a combination of metagenome sequencing and metaproteome analysis, to identify proteins abundant on leaf surfaces of naturally grown

18 A. thaliana, clover and soybean plants to infer the physiology of colonizing microbes (Delmotte et al., 2009). Metagenome sequencing significantly improved protein identification and proteins related to one-carbon metabolism, transport processes and stress response were identified as important functional traits on all three plants (Delmotte et al., 2009). Transport proteins comprised TonB receptors of different specificities, mostly assigned to Sphingomonas, beta-barrel porins and ABC transport systems for amino acids, mono- and disaccharides. Complementary metabolomics approaches confirmed that glucose, fructose and sucrose are available on A. thaliana leaves, and sugars, as well as amino acids (e.g. arginine) were depleted upon colonization by heterotrophic epiphytes (Ryffel et al., 2016). Notably, phylloplane metabolite pool sizes varied with the diurnal cycle, indicating that nutrient availability on leaves is temporal and at least partially dependent on the metabolic state of the plant. Methanol is another common substrate available to leaf bacteria as a function of the diurnal cycle and is produced by plants in large quantities as a side product of pectin methylesterases during cell wall remodeling necessary for plant growth (Fall and Benson, 1996). The majority of proteins involved in one-carbon metabolism were assigned to the genus Methylobacterium and confer a competitive advantage to these strains during plant colonization (Fall and Benson, 1996; Sy et al., 2005). Consistently, one-carbon metabolism was identified as enriched category in the phyllosphere and rhizosphere of rice, with methanol converting enzymes predominating leaf-derived proteomes, while proteins involved in anaerobic methanogenesis and aerobic methanotrophy were enriched in the rhizosphere (Knief et al., 2012). Besides adaptations to different available carbon sources, proteins involved in nitrogen, sulfur, phosphate, potassium and iron acquisition were enriched in studies of different host plants (Bulgarelli et al., 2015; Knief et al., 2012; Li et al., 2014; Mendes et al., 2014; Zarraonaindia et al., 2015), indicating that beneficial bacteria with implications on host nutrition might be recruited to the root habitat. In addition to amino acids, nitrate, and ammonia, plant associated microorganisms utilize molecular nitrogen and nitrogenase genes were reported in the metagenome of the rice and soybean rhizosphere (Knief et al., 2012; Mendes et al., 2014), the rice phyllosphere (Knief et al., 2012), as well as in the metatranscriptome of the maize rhizosphere (Li et al., 2014). However, parallel proteomic analysis of the rice samples revealed that nitrogenase genes are present above and below ground, but exclusively produced in the rhizosphere (Knief et al., 2012). This indicates that despite the extensive overlap of encoded functional capabilities within the microbiome of different plant compartments, gene expression patterns might be very different. This tendency is also corroborated by the wheat/cucumber study, where only 2.7 % of analyzed traits differed in abundance between the two metagenomes, however, transcriptional expression revealed that 17 % of all traits are significantly different expressed between the two host plants (Ofek-Lalzar et al., 2014). Other plant microbiome traits found to be enriched in several studies are siderophore production and iron uptake. Bioavailability of iron is not only low in natural soils, but also on plants surfaces itself, to prevent rapid amplification of pathogenic invaders. Besides metabolic capabilities, responses to stress were identified as important functional traits of leaf-colonizing bacteria (Delmotte et al., 2009). The phyllosphere is considered a harsh environment

19 exposed to various stresses, including reactive oxygen species (ROS), UV radiation and desiccation (Vorholt, 2012). Catalase and superoxide dismutase are important enzymes for detoxification of ROS, while pigmentation and photolyase prevent and repair UV induced damage of nucleic acids. EPS formation and secretion of bioactive surfactants can increase water permeability through the plant cuticle and wettability, improving epiphytic fitness during fluctuating humidity (Burch et al., 2014). Gourion et al. analyzed bacterial adaptation to the leaf surface by proteomics in a binary system and identified a response regulator (phyllosphere induced regulator, PhyR), essential for epiphytic colonization of Methylobacterium extorquens (Gourion et al., 2006). This regulator was demonstrated to be an essential part of the conserved PhyR-NepR-σEcfG signaling cascade, the core module of the general stress response in Alphaproteobacteria (Francez-Charlot et al., 2011; Gourion et al., 2008). Environmental stimuli are sensed by activating Pak kinases, signals are integrated and potentially enhanced or fine-tuned, and expression of stress resistance genes is induced by release of the EcfG sigma factor, mediating resistance to solvents, heat, oxidative stress and high osmolarity (Francez-Charlot et al., 2015; Kaczmarczyk et al., 2014, 2015). Proteins of the alphaproteobacterial general stress response, as well as regulators related to stress responses in gammaproteobacteria were identified on A. thaliana, clover and soybean leaves (Delmotte et al., 2009). Root-inhabiting microorganisms are probably less often exposed to frequently changing environmental stresses, however, stress response was identified as significantly enriched functional category in grapevine rhizosphere metagenomes (Zarraonaindia et al., 2015). Besides abiotic stresses, plant microbiota members above and below ground are exposed to biotic stresses and antimicrobial compounds of both, plant and microbial origin. In agreement, drug resistance transporter were induced during phyllosphere colonization of Arthrobacter and genes related to detoxification were shown to be enriched in the metagenome of the barley rhizosphere (Bulgarelli et al., 2015; Scheublin et al., 2014). Additionally, the type III secretion system, mediating the transmission of effector proteins into host cells as well as the type VI secretion system, involved in inhibitory microbe- microbe interactions were overrepresented in the barley rhizosphere, reflecting the exchange of chemical warfare among microbes and the arms race with the plant host. Plant pathogens employ type III secretion systems to inject a diverse arsenal of effector proteins into host cells to subvert the plant innate immune system and ensure successful proliferation within host tissues. The importance of interactions with the plant host was further corroborated by enrichment of the type III and type IV secretion systems in bacteria colonizing the rhizoplane of wheat and cucumber (Ofek-Lalzar et al., 2014).

1.5 IMPORTANCE OF THE MICROBIOTA FOR HOST FITNESS

The plant-associated microbiota can provide benefits to plant growth and health by influencing the nutrient status, by affecting plant-pathogen interactions, and by modifying tolerance to abiotic and biotic stresses (Berendsen et al., 2012; Bulgarelli et al., 2013; Mendes et al., 2013; Vorholt, 2012). The outcome of the respective interactions is context-dependent, and interactions might be beneficial under certain conditions while harmful under others.

20 Diverse members of the plant microbiota affect the plant nutrient status by providing plants with nutrients, increasing nutrient bioavailability, or enhancing nutrient acquisition capacity in soil. Symbiotic associations of legumes with nitrogen-fixing rhizobia (Udvardi and Poole, 2013) and of a large number of taxa with mycorrhizal fungi (Smith and Smith, 2011) are well studied examples of how plants gain access to nitrogen and phosphorous, respectively, under limiting conditions. Besides rhizobia, also other bacteria can fix nitrogen, but the extent to which these diazotrophs contribute to nitrogen nutrition varies depending on the environmental conditions (Urquiaga et al., 2012; Yeoh et al., 2016). Other rhizosphere colonizers can mobilize nutrients that are not readily available to plants, such as phosphorous or iron, through solubilization, mineralization or excretion of siderophores (Bulgarelli et al., 2013; Mendes et al., 2013). In addition, microbial production of plant hormones (e.g. auxin) can alter root architecture and enhance nutrient acquisition by roots (Bailly et al., 2014; Spaepen et al., 2014; Spaepen et al., 2007; Zamioudis et al., 2013). In addition, microbes can directly influence plant nutrient uptake, as observed for the activation of the iron acquisition machinery in Arabidopsis through volatile organic compounds (Zamioudis et al., 2015; Zhang et al., 2009). The microbiota can also increase plant tolerance to abiotic stresses such as flooding, drought, high salinity, extreme temperatures, and heavy metal contamination (Dodd and Perez-Alfocea, 2012; Glick, 2014; Yang et al., 2009). An important enzyme involved in plant growth promotion under stressful conditions, is 1-aminocyclopropane-1-carboxylate (ACC) deaminase, converting the ethylene precursor ACC to α-ketobutyrate and ammonia. ACC deaminase activity reduces ethylene production and prevents ethylene-mediated inhibition of plant growth in response to various stresses (Glick, 2014). The microbiota can also be involved in plant adaptation to changing environmental conditions as shown for adaptation of Brassica rapa to wet or dry conditions (Lau and Lennon, 2012). Apart from adaptation to changing environmental conditions and the influence on host nutrition, microbes can increase host health by protection from diverse biotic stresses (Berendsen et al., 2012; Berg, 2009; Lugtenberg and Kamilova, 2009; Mendes et al., 2013; Turner et al., 2013a). The mechanisms by which plant-associated microbes protect against plant pathogens include competition for niches and nutrients, antibiosis, production of lytic enzymes, inhibition of pathogen virulence, and induction of plant-mediated resistance (Berendsen et al., 2012; Mendes et al., 2013; Pieterse et al., 2014; Raaijmakers and Mazzola, 2012). Phyllosphere-colonizing bacteria also contribute to protection against plant pathogens (Innerebner et al., 2011; Lindow and Leveau, 2002; Raghavendra and Newcombe, 2013; Ritpitakphong et al., 2016; Vorholt, 2012). Innerebner et al. (2011) observed that several plant-derived Sphingomonas isolates protected Arabidopsis against foliar bacterial pathogens, while isolates from air or water did not. The identification of several bacterial mutants showing partial loss of protection suggested that different mechanisms could contribute incrementally to plant protection (Vogel et al., 2012). Recently, it was shown that increased resistance of an Arabidopsis cuticle mutant to the fungal pathogen Botrytis cinerea was conferred by the distinct phyllosphere microbiota harbored by this mutant (Ritpitakphong et al., 2016). A Pseudomonas species isolated from the phyllosphere of the mutant

21 provided protection against B. cinerea in Arabidopsis as well as on apple fruits, indicating that a subset of the microbiota might contribute to plant protection under the experimental conditions. Overall, these comprehensive studies have demonstrated the wide impact of the bacterial microbiota on host physiology and consequently, plant phenotypes and adaptive processes need to be regarded from a holistic perspective, including the plant host and its associated microbiota.

1.6 SCOPE OF THIS THESIS

The bacterial microbiota of various plants is consistently composed of only a few phyla, namely, Actinobacteria, Bacteroidetes, Firmicutes and most dominantly Proteobacteria. However, knowledge beyond phylogenetic composition of the host microbiota, about bacterial adaptation, microbiota establishment as well as strain interactions in the community context are lagging behind. Synthetic communities of representative strains allow for approaches addressing community assembly under controlled conditions in gnotobiotic plant model systems and adaptation of individual strains to the plant environment. A prerequisite for such bottom-up approaches are comprehensive culture collections. Since several previous studies have suggested that a large fraction of the plant microbiota might be cultivable, the goal of this thesis was to establish a diverse bacterial strain collection of the natural Arabidopsis thaliana microbiota as a basis to study community establishment, bacterial interactions and adaptation to plants. While this thesis is focused on the leaf microbiota, the root microbiota and its differences form phyllosphere communities are mentioned at several places. Chapter 2 describes the establishment of bacterial strain collections representing the natural microbiota of A. thaliana plants, demonstrating that more than 50% of the abundant taxa are cultivable under laboratory conditions. Generation of draft genome sequences of the core collections and comparison of genomic contents revealed a large overlap of encoded capabilities, but also specialization to the respective niche. Recolonization experiments under laboratory conditions resulted in community patterns mimicking the natural community, validating its use as a tool to study community establishment, while competition experiments underline specialization of the leaf and root microbiota to its respective plant organ. Subsequently, to gain deeper understanding of microbial adaptation to the leaf habitat, SWATH mass spectrometry was applied as a quantitative proteomics approach to investigate two representative members of the leaf microbiota (chapter 3). In addition, a binary microbe-microbe interaction framework of the leaf microbiota strain collection was generated and the potential of selected members for plant protection against foliar pathogens was evaluated (chapter 4). Finally, general conclusions and future directions are discussed in chapter 5.

22 Chapter II

Functional overlap of the Arabidopsis leaf and root microbiota

Yang Bai*, Daniel B. Müller*, Girish Srinivas*, Ruben Garrido-Oter*, Eva Potthoff, Matthias Rott, Nina Dombrowski, Philipp C. Münch, Stijn Spaepen, Mitja Remus-Emsermann, Bruno Hüttel, Alice C. McHardy, Julia A. Vorholt* & Paul Schulze-Lefert* * These authors contributed equally Nature 528:364-9 (2015), doi: 10.1038/nature16192

AUTHOR CONTRIBUTIONS J.A.V. and P.S.-L. initiated, coordinated and supervised the project. Y.B., M.R., N.D. and S.S. isolated root and soil bacteria strains. Y.B. collected root material and performed culture-independent community profiling. D.B.M., E.P. and M.R.-E. collected environmental leaf material, D.B.M. and E.P. isolated leaf strains and performed culture-independent community profiling. G.S. and R.G.-O. analyzed culture-independent 16S rRNA amplicon sequencing data. Y.B., D.B.M. isolated DNA and prepared samples for genome sequencing. R.G.-O., P.C.M, B.H. and A.C.M. organized the genome sequencing data. R.G.-O. assembled and annotated draft genomes and performed comparative genome analyses. Y.B. and D.B.M. performed recolonization experiments; G.S. and R.G.-O. analyzed the recolonization data. Y.B., D.B.M., R.G.-O., J.A.V. and P.S.-L. wrote the manuscript.

Functional overlap of the Arabidopsis leaf and root microbiota

Yang Bai1*, Daniel B. Müller2*, Girish Srinivas1*, Ruben Garrido-Oter1,3,4*, Eva Potthoff2, Matthias Rott1, Nina Dombrowski1, Philipp C. Münch5,6,7, Stijn Spaepen1, Mitja Remus-Emsermann2, Bruno Hüttel8, Alice C. McHardy4,5, Julia A. Vorholt2* & Paul Schulze-Lefert1,4*

1Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany. 2Institute of Microbiology, ETH Zurich, 8093 Zurich, Switzerland. 3Department of Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany. 4Cluster of Excellence on Plant Sciences (CEPLAS), Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany. 5Computational Biology of Infection Research, Helmholtz Center for Infection Research, 38124 Braunschweig, Germany. 6Max-von-Pettenkofer Institute, Ludwig Maximilian University, German Center for Infection Research (DZIF), partner site LMU Munich, 80336 Munich, Germany. 7German Center for Infection Research (DZIF), partner site Hannover- Braunschweig, 38124 Braunschweig, Germany. 8Max Planck Genome Center, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany.-5 *These authors contributed equally to this work.

Abstract Roots and leaves of healthy plants host taxonomically structured bacterial assemblies, and members of these communities contribute to plant growth and health. We established Arabidopsis leaf- and root- derived microbiota culture collections representing the majority of bacterial species that are reproducibly detectable by culture-independent community sequencing. We found an extensive taxonomic overlap between the leaf and root microbiota. Genome drafts of 400 isolates revealed a large overlap of genome- encoded functional capabilities between leaf- and root-derived bacteria with few significant differences at the level of individual functional categories. Using defined bacterial communities and a gnotobiotic Arabidopsis plant system we show that the isolates form assemblies resembling natural microbiota on their cognate host organs, but are also capable of ectopic leaf or root colonization. While this raises the possibility of reciprocal relocation between root and leaf microbiota members, genome information and recolonization experiments also provide evidence for microbiota specialization to their respective niche.

Introduction Plants and animals harbour abundant and diverse bacterial microbiota1. These taxonomically structured bacterial communities have important functions for the health of their multicellular eukaryotic hosts2–4.

25 The leaf and root microbiota of flowering plants have been extensively studied by culture-independent analyses, which have consistently revealed the co-occurrence of four main bacterial phyla: Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria5–15. Determinants of microbiota composition at lower taxonomic ranks, that is, at genus and species level, are host compartment, environmental factors and host genotype6,7,12,16. Soil harbours an extraordinary rich diversity of bacteria and these define the start inoculum of the Arabidopsis thaliana root microbiota6,7. The inoculum source of the leaf microbiota is thought to be more variable owing to the inherently open nature of the leaf ecosystem, probably involving bacteria transmitted by aerosols, insects, or soil8,9,17. A recent study of the grapevine (Vitis vinifera) microbiota showed that the root-associated bacterial assemblies differed significantly from aboveground communities, but that microbiota of leaves, flowers, and grapes shared a greater proportion of taxa with soil communities than with each other, suggesting that soil may serve as a common bacterial reservoir for belowground and aboveground plant microbiota18. A major limitation of current plant microbiota research is the lack of systematic microbiota culture collections that can be employed in microbiota reconstitution experiments with germ-free plants to address principles underlying community assembly and proposed microbiota functions for plant health under laboratory conditions19.

Bacterial culture collections from roots and leaves We employed three bacterial isolation procedures to establish taxonomically diverse culture collections of the A. thaliana root and leaf microbiota. Bacterial isolates were recovered from pooled or individual roots or leaves of healthy plants using colony picking from agar plates, limiting dilution in liquid media in 96-well microtiter plates, or microbial cell sorting (see Methods). We adopted a two-step barcoded pyrosequencing protocol20 for taxonomic classification of the cultured bacteria by determining ≥550 base pairs (bp) 16S ribosomal RNA (rRNA) gene sequences (Supplementary Fig. 1; Methods). In parallel, parts of the root and leaf material was used for cultivation independent 16S rRNA gene community sequencing to cross-reference Operational Taxonomic Unit (OTU)-defined taxa from the microbiota with individual colony forming units (CFUs) in the culture collections. A total of 5,812 CFUs were recovered from 59 independently pooled A. thaliana root samples of plants mainly grown in Cologne soil, Germany, whereas 2,131 CFUs were retrieved from leaf washes of individual leaves collected from A. thaliana populations at six locations near Tübingen, Germany, or Zurich, Switzerland (Supplementary Data 1). Recovery estimates for root-associated OTUs were calculated using the culture-independent community profiles of the present and two earlier studies6,12 and varied for the top 100 OTUs (70% of sequencing reads) between 54–65% and at ≥0.1% relative abundance (RA) between 52–64% (Methods; Extended Data Fig. 1a–c; Supplementary Data 2). For leaf samples, the culture- independent 16S rRNA gene analyses from individual and pooled leaves (60 samples from six sites) revealed similar community profiles at all tested geographic sites and high leaf-to-leaf consistency (Extended Data Fig. 2). Recovery estimates of the top 100 leaf-associated bacterial OTUs (86% of all

26 sequencing reads) were 54% and at ≥0.1% RA 47% (Extended Data Fig. 1d). The root-derived CFUs correspond to 23 of 38 and the leaf-derived CFUs belong to 28 of 45 detectable bacterial families. Root- and leaf-derived CFUs each represent all four bacterial phyla typically associated with A. thaliana roots and leaves. Thus, most bacterial families that are reproducibly associated with A. thaliana roots and leaves have culturable members.

At-RSPHERE and At-LSPHERE culture collections We selected from the aforementioned culture collections a taxonomically representative core set of bacterial strains after Sanger sequencing of a ≥550 bp fragment of the 16S rRNA gene and additional strain purification (Methods). To increase the intra-species genetic diversity of the culture collections, and because the quantitative contribution of a single isolate to its corresponding OTU cannot be estimated, we included bacterial strains sharing ≥97% 16S rRNA gene sequence identity (widely used for bacterial species definition), but representing independent host colonization events, that is, recovered from different plant roots or leaves. In total we selected 206 root-derived isolates that comprise 28 bacterial families belonging to four phyla (designated At-RSPHERE) and 224 leaf-derived isolates that comprise 29 bacterial families belonging to five phyla (designated At-LSPHERE) (Extended Data Fig. 3a, b; Supplementary Data 1; Methods). Additionally, to represent abundant soil OTUs (≥0.1% RA) we selected 33 bacterial isolates encompassing eight bacterial families belonging to three phyla from unplanted Cologne soil (Extended Data Fig. 3c). Notably, the majority of the At-RSPHERE isolates share ≥97% 16S rRNA gene sequence identity matches with root-associated OTUs reported in four independent studies in which A. thaliana plants had been grown in Cologne soil6,12 or other European6,12 or US soils7 (inner four circles in Fig. 1a; Methods). Similarly, the bulk of At-LSPHERE isolates match leaf-derived OTUs detected in A. thaliana populations at the Tübingen/Zurich locations or US-grown plants (innermost two circles in Fig. 1b). This indicates that representatives of the majority of At- RSPHERE and At-LSPHERE members co-populate the corresponding A. thaliana organs in multiple tested environments, including two continents, Europe and North America. Phylogenetic analysis based on 16S rRNA gene Sanger sequences revealed that 119 out of 206 At-RSPHERE isolates (58%) share ≥97% sequence identity matches with corresponding 16S rRNA gene fragments of At-LSPHERE members (outermost circle in Fig. 1a). Similarly, 108 out of 224 At-LSPHERE isolates (48%) share ≥97% sequence identity matches with At-RSPHERE members (outermost circle in Fig. 1b). This extensive overlap both at the rank of bacterial genera and bacterial families (20 out of 38 detectable families) between leaf- and root-derived bacteria is notable because we collected leaf and root specimen from environments that are geographically widely separated (>500 km) and is consistent with a previous report on leaf and root microbiota overlap in V. vinifera18. This overlap is corroborated by the corresponding culture-independent leaf and root community profiles (Extended Data Fig. 4). As essentially all A. thaliana root associated bacteria are recruited from the surrounding soil biome6,7,12, this

27 raises the possibility that unplanted soil also defines the start inoculum for a substantial proportion of the leaf microbiota with subsequent selection for niche-adapted organisms.

Figure 1 | Taxonomic overlap between At-RSPHERE and At-LSPHERE isolates and their representation in culture-independent microbiota profiling studies. a, b, Phylogenetic trees of At-RSPHERE (a; n = 206 isolates) and At-LSPHERE (b; n = 224 isolates) bacteria. Their taxonomic overlap is shown in the outermost ring (green or brown triangles). a, Representation of At-RSPHERE bacteria in each of four indicated culture- independent profiling studies of the A. thaliana root microbiota; root-associated OTUs with RAs ≥0.1% (dark orange) or ≤0.1% (light orange). b, Representation of At-LSPHERE bacteria in the two indicated culture- independent phyllosphere profiling studies; leaf-associated OTUs with RAs ≥0.1% (dark green) or <0.1% (light green). Taxonomic assignment and phylogenetic tree inference were based on partial 16S rRNA gene Sanger sequences.

Comparative genome analysis of the culture collections To characterize the functional capabilities of the core culture collections we subjected each isolate to whole-genome sequencing and generated a total of 432 high-quality draft genomes (206 from leaf, 194 from root and 32 from soil; Supplementary Data 3). Taxonomic assignment of the whole-genome sequences confirmed that these isolates span a broad taxonomic range, belonging to 35 different bacterial families distributed across five phyla (Supplementary Data 4). Based on the whole-genome taxonomic information, we grouped the isolates into family-level clusters. We found that clusters of genomes are characterized by a relatively large core-genome, with an average of 33.6% of the annotated proteins present in each member and a smaller fraction of singleton genes identified in only one genome per cluster (14.0%). Detailed analysis of phylogenetic diversity of each cluster revealed a substantial overlap between leaf, root and soil isolates (Supplementary Data 5). Many clusters showed no clear separation of isolates based on their ecological niche, suggesting shared core functions. However, other clusters contained isolates of one organ or showed clear separation among them, suggesting niche specialization within some clusters (Supplementary Data 5). We then examined the functional diversity

28 Figure 2 | Analysis of functional diversity between sequenced isolates. a, Principal coordinate analysis (PCoA) plot depicting functional distances between sequenced genomes (n = 432) based on the KEGG Orthology (KO) database annotation. Each point represents a genome. Colours represent the organ of isolation and shapes correspond to their taxonomy. Numbers inside the plot refer to bacterial families listed in b. b, Analysis of functional diversity within bacterial families as measured by pair-wise functional distances between genomes (bottom panel; n = 432). Higher pairwise distances between members of a family indicate a larger degree of functional diversity. Only families with at least five members are shown. The histogram (top panel) was calculated for the entire data set and the y-axis corresponds to the percentage of data points in each bin. Boxplot whiskers extend to the most extreme data point which is no more than 1.5 times the interquartile range from the upper or lower quartiles. between the sequenced isolates in order to determine whether the observed phylogenetic overlap corresponded with functional similarities between leaf and root isolates. Principal coordinates analysis (PCoA) of functional distances (Fig. 2a; Methods) revealed a clear clustering of genomes on the basis of their taxonomy, but only limited separation of genomes on the basis of their ecological compartment. Taken together, both phylogenetic and functional diversification of the genomes is strongly driven by their taxonomic affiliation and weakly by the ecological niche. We examined the functional diversity within each bacterial family (Fig. 2b) in order to identify bacterial taxa with varying degrees of functional versatility. Families belonging to Actinobacteria show a lower functional diversity (average distance 0.37) compared to those belonging to Bacteroidetes, Firmicutes and especially Proteobacteria (0.65 average pair-wise distance), which exhibit a higher degree of within-family functional diversification, even though all familylevel groups have a comparable degree of phylogenetic relatedness. Among these groups, Pseudomonadaceae, Oxalobacteraceae and Methylobacteriaceae members show the highest functional heterogeneity, compared to Microbacteriaceae strains, which we identified as the least functionally diverse family (Fig. 2b). We searched for signatures of niche specialization at individual functional categories using enrichment analysis to identify functional categories over-represented in genomes from root and leaf or soil isolates (Fig. 3; Methods).

29 Figure 3 | Functional analysis of sequenced isolates. a, Phylogeny of family-level clusters of bacterial isolates. the tips of the tree are annotated, from left to right, with the cluster ID, taxonomic classification, followed by the number of sequenced isolates from leaf, root or soil that constitute each cluster. The heat map depicts the average percentage of annotated proteins of each cluster belonging to each functional category. b, Functional enrichment analysis between leaf (n = 206), root (n = 194) and soil (n = 32) genomes. Points and bars correspond to the mean abundance and standard deviation of each functional category. P values were obtained using the non-parametric Mann–Whitney test corrected by the Bonferroni approach. c, Analysis of pan-genome distribution for each cluster of genomes, indicating the percentage of annotated proteins found in only one isolate (singletons), in more than one but not all (shell) or in all genomes within the cluster (core).

Specifically, we found the category ‘carbohydrate metabolism’ to be enriched in the leaf and soil genomes compared to those isolated from roots (Mann–Whitney test, P = 1.29 × 10−7; Fig. 3b). We speculate that this differential enrichment could reflect the availability of simple carbon sources in roots through the process of root exudation (sugars, amino acids, aliphatic acids)21,22, whereas bacteria associated with leaves or unplanted soil might rely on a more diverse repertoire of carbohydrate metabolism genes to access scarce and complex organic carbon, for example, polysaccharides and leaf cuticular waxes. The category ‘xenobiotics biodegradation and catabolism’ is enriched in the root genomes with respect to those isolated from leaves (P = 2.60 × 10−11; Fig. 3b), which is consistent with previous evidence that genes for aromatic compound utilization are expressed in the rhizosphere23. No single taxon is responsible for these significant differences, but this seems to be a general feature across the sequenced bacterial genomes of the respective ecological niche (Extended Data Figs 5 and 6). Interestingly, we observed the same trends of differential abundance of functional categories in V. vinifera root metagenome samples18 compared to their respective unplanted soil controls (Extended Data Fig. 7). Together, these findings indicate a substantial overlap of functional capabilities in the genomes

30 of the Arabidopsis leaf- and root-derived culture collections and differences at the level of individual functional categories that may reflect specialization of the leaf and root microbiota to their respective niche. Additional genomic signatures for niche specific colonization are likely to be hidden in genes for which a functional annotation is currently unavailable (~57%).

Synthetic community colonization of germ-free plants We colonized germ-free A. thaliana plants with synthetic communities (SynComs) consisting of bacterial isolates from our culture collections to assess their potential for host colonization in a gnotobiotic system containing calcined clay as inert soil substitute (Methods). To mimic the taxonomic diversity of leaf and root microbiota in natural environments we employed mainly two SynComs: ‘L’ comprising 218 leaf-derived bacteria and ‘R+S’ consisting of 188 members of which 158 are root- derived and 30 are soil-derived bacteria (Supplementary Data 6). Input SynComs were either inoculated directly before sowing of surface-sterilized seeds in calcined clay and/or spray-inoculated on leaves of three-week-old germ-free plants. For all defined communities we examined three independent SynCom preparations, each tested in three closed containers containing four plants. We employed 16S rRNA gene community profiling with a method validated for defined communities24 to detect potential community shifts between input and output SynComs in samples of seven week-old roots, leaves, or unplanted clay. In this community analysis, ‘indicator OTUs’ either represent a single strain or a known group of isolates (Supplementary Data 6). Upon application of the input R+S SynCom to clay (‘R+S in clay’) and co-cultivation with A. thaliana plants for seven weeks we retrieved reproducible R+ S output communities from clay (without host), root, and leaf compartments (Supplementary Fig. 2). These output SynCom profiles were robust against a 75% reduction in RA of Proteobacteria compared to Actinobacteria, Bacteroidetes and Firmicutes in the input R+S SynCom (input ratios 1:1:1:1 or 1:1:1:0.25, respectively), which was confirmed by PCoA (Fig. 4a). PCoA also revealed distinct output communities in each of the three tested compartments (Fig. 4a; P < 0.001 Extended Data Fig. 8a, b). This indicates that a marked host-independent community change occurred in clay (without host) as well as host-dependent community shifts that are specific for leaves and roots. Next, we tested the ‘L’ SynCom of leaf-derived bacteria by spray inoculation on leaves of three week-old plants. After four weeks of L SynCom co-incubation with plants, output communities were detected in leaves and roots (Supplementary Fig. 3). PCoA revealed that these two output communities were different between each other, but robust against a 75% reduction in RA of input Proteobacteria (Fig. 4b; Supplementary Fig. 3; P < 0.001; Extended Data Fig. 8c, d). The converging output communities despite varying RAs of input SynComs suggest that the communities have reached a steady state. These experiments also reveal that both R+S and L SynCom members not only colonize cognate host organs, but are capable of ectopic colonization of leaves and roots, which might be linked to the extensive species overlap of A. thaliana leaf and root microbiota in natural environments (Fig. 1a, b). Additionally, this provides experimental support for the hypothesis that a subset of leaf-colonizing bacteria originates from unplanted soil and

31 raises the possibility for reciprocal bacterial colonization events between roots and leaves during and/or after the establishment of the respective microbiota, for example, by ascending migration of rhizobacteria from roots to leaves25. Upon leaf spray application of SynComs, a small amount of leaf bacteria is likely to land on the clay surface and thereafter colonize roots, which is not fundamentally different from processes occurring in natural environments, for example, during rain showers and/or leaf dehiscence. A comparison of rank abundance profiles between indicator OTUs for all root- and leaf- derived isolates and corresponding OTUs identified in the environmental root and leaf samples revealed similar trends at phylum, class and family levels (Extended Data Fig. 9). This validates the gnotobiotic plant system as a tool for microbiota reconstitution experiments.

Figure 4 | SynCom colonization of germ-free A. thaliana plants. a, b, Principal coordinate analysis (PCoA) of Bray–Curtis distances of input and output SynCom profiles of RS in clay (a; n = 60) and L spray (b; n = 42) experiments. Each condition was tested with 6 independently prepared SynComs; each preparation was used for 3 independent inoculations. L, leaf-derived strains; RS, root- and soil- derived strains; ER, equal strain ratio; UR, unequal strain ratio.

32 Niche-specific microbiota establishment with SynComs The species overlap between root and leaf microbiota and their corresponding culture collections (Fig. 1a, b; Extended Data Fig. 4) prompted us to test whether R+S and L SynComs equally contribute to root and leaf microbiota establishment. Both SynComs were pooled and inoculated in clay together with surface-sterilized A. thaliana seeds (designated ‘RSL in clay’, Fig. 5a). We also tested whether a preformed root-associated community can interfere with leaf-associated community establishment. After three weeks of co-cultivation, half of the plants grown with the ‘RSL in clay’ SynCom were treated by leaf-spray inoculation with the L SynCom supplemented with 15 root-derived strains (designated ‘RSL in clay & L+15R spray’). Plant organ-specific output communities were determined after a further four weeks of co-incubation. We also inoculated the L SynCom alone in clay and determined output SynComs (designated ‘L in clay’, Fig. 5a). We found significant differences between leaf-associated output communities of the ‘RSL in clay’ and ‘RS in clay’ experiments (Fig. 5b; P < 0.001, Extended Data Fig. 8f; Supplementary Figs 2 and 4) and that the output community on leaves after ‘L in clay’ inoculation is similar to the leaf outputs of ‘RSL in clay’ inoculation (Fig. 5b; P < 0.001, Extended Data Fig. 8f; Supplementary Figs 4 and 5), indicating that in this comparison the leaf-derived SynCom has a stronger influence on leaf microbiota structure than root- and soil-derived bacteria. However, both ‘RSL in clay’ and ‘L in clay’ leaf outputs are significantly different from the leaf output of the ‘L spray’ experiment (Fig. 5b; P < 0.001, Extended Data Fig. 8e; Supplementary Figs 3–5), showing that many leaf-derived isolates do not successfully colonize leaves when only inoculated in the clay environment. For example, of the top 16 genera a total of three are grossly underrepresented in leaf outputs of the ‘RSL in clay’ compared to the ‘RSL in clay & L+15R spray’ experiment (Chryseobacterium, Sphingomonas and Variovorax; Supplementary Fig. 6) and these three genera are abundant in the natural leaf microbiota (Extended Data Fig. 4). Finally, leaf outputs were strikingly similar between ‘RSL in clay & L+15R spray’ and ‘L spray’ only experiments (Fig. 5b; Supplementary Figs 3 and 7), indicating that the L+15R SynCom, leaf spray-inoculated three weeks after RSL application to clay, can displace the RSL leaf output. Collectively, these results support the hypothesis that leaf microbiota establishment benefits from air- and soil-borne inoculations8,17, although we note that our single application of bacteria to leaves does not mimic the continuous exposure of plant leaves to airborne microorganisms in nature. A comparison of the root-associated community outputs of the experiments described above revealed that the ‘RSL in clay’ experiment is more similar to root outputs of the ‘RS in clay’ than ‘L in clay’ experiments (Fig. 5c; P < 0.001 Extended Data Fig. 8g), suggesting that the root- and soil-derived SynCom has a stronger influence on root microbiota structure than the leaf-derived SynCom. In this experiment the fractional contribution of root-specific indicator OTUs increases in the output, but decreases for leaf-specific indicator OTUs, relative to their input, pointing to a potential adaptation of root-derived bacteria for root colonization (Extended Data Fig. 10a; Mann–Whitney; P < 0.05). This is further supported by the observation that in the ‘RSL in clay’ experiment root colonization rates for root-specific indicator OTUs are higher compared to those specific for leaves when applying a 0.1%

33 Figure 5 | SynCom competition supports host-organ-specific community assemblies. a, Pictograms illustrating ‘L spray’, ‘L in clay’, ‘RS in clay’, ‘RSL in clay’, and ‘RSL in clay & L+15R spray’ SynCom experiments. b, c, PCoA of Bray–Curtis distances of leaf (b; n = 69) and root (c; n = 69) outputs of the five experiments illustrated in a. R, root-derived isolates; S, soil- derived isolates; L, leaf-derived isolates. L in clay was tested with 6 independently prepared SynComs; RSL in clay experiment was tested with 3 independently prepared SynComs, each used for 3 independent inoculations. All other experiments were tested with 6 independently prepared SynComs and each preparation was used for 3 independent inoculations.

relative abundance threshold in at least one biological replicate (69% and 33%, respectively). Taken together, this suggests that root-derived bacteria are better adapted to colonize their cognate host niche than leaf-derived bacteria. Further comparisons of the root associated output communities of the ‘L in clay’ and ‘L spray’ experiments (Fig. 5c; Supplementary Figs 3 and 5) revealed similar community composition, indicating convergence of ectopic root-associated community outputs despite different inoculation time points or sites of application. Additional reciprocal transplantation experiments using a ‘R’ (root strains only) SynCom either applied to clay (‘R in clay’) or by spray inoculation (‘R spray’) confirmed the convergence of ectopic community outputs also for root-derived bacteria on leaves (Extended Data Fig. 10 b, c; Supplementary Figs 8 and 9). Convergence of ectopic SynCom outputs is consistent with the hypothesis that a subset of leaf and root colonizing bacteria has the potential to relocate between leaves and roots.

34 Conclusions By employing systematic bacterial isolation approaches, we established expandable culture collections of the A. thaliana leaf- and root-associated microbiota, which capture the majority of the species found reproducibly in their respective natural communities (≥0.1% relative abundance). The sequenced bacterial genomes as well as any future updates are available at http://www.at-sphere.com. These resources together with the remarkable reproducibility of the gnotobiotic reconstitution system enable future studies on bacterial community establishment and functions under laboratory conditions.

References 1. Rosenberg, E. & Xilber-Rosenberg, I. The Hologenome Concept: Human, Animal and Plant Microbiota (Springer, 2013). 2. Spor, A., Koren, O. & Ley, R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nature Rev. Microbiol. 9, 279–290 (2011). 3. Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012). 4. Subramanian, S. et al. Cultivating healthy growth and nutrition through the gut microbiota. Cell 161, 36–48 (2015). 5. Delmotte, N. et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc. Natl Acad. Sci. USA 106,16428–16433 (2009). 6. Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95 (2012). 7. Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012). 8. Vorholt, J. A. Microbial life in the phyllosphere. Nature Rev. Microbiol. 10,828–840 (2012). 9. Bodenhausen, N., Horton, M. W. & Bergelson, J. Bacterial communities associated with the leaves and the roots of Arabidopsis thaliana. PLoS One 8, e56329 (2013). 10. Guttman, D. S., McHardy, A. C. & Schulze-Lefert, P. Microbial genome-enabled insights into plant- microorganism interactions. Nature Rev. Genet. 15, 797–813 (2014). 11. Horton, M. W. et al. Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nat. Commun. 5, 5320 (2014). 12. Schlaeppi, K., Dombrowski, N., Oter, R. G., Ver Loren van Themaat, E. & Schulze-Lefert, P. Quantitative divergence of the bacterial root microbiota in Arabidopsis thaliana relatives. Proc. Natl Acad. Sci. USA 111, 585–592 (2014). 13. Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl Acad. Sci. USA 112, E911–E920 (2015). 14. Hacquard, S. et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 17, 603–616 (2015). 15. Bulgarelli, D. et al. Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 17, 392–403 (2015). 16. Lebeis, S. L. et al. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349, 860–864 (2015). 17. Maignien, L., DeForce, E. A., Chafee, M. E., Eren, A. M. & Simmons, S. L. Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. MBio 5, e00682–e13 (2014). 18. Zarraonaindia, I. et al. The soil microbiome influences grapevine-associated microbiota. MBio 6, e02527–14 (2015). 19. Lebeis, S. L., Rott, M., Dangl, J. L. & Schulze-Lefert, P. Culturing a plant microbiome community at the cross-Rhodes. New Phytol. 196, 341–344 (2012). 20. Goodman, A. L. et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc. Natl Acad. Sci. USA 108, 6252–6257 (2011).

35 21. Faure, D., Vereecke, D. & Leveau, J. J. Molecular communication in the rhizosphere. Plant Soil 321, 279–303 (2009). 22. Bais, H. P., Weir, T. L., Perry, L. G., Gilroy, S. & Vivanco, J. M. The role of root exudates in rhizosphere interactions with plants and other organisms. Annu. Rev. Plant Biol. 57, 233–266 (2006). 23. Ramachandran, V. K., East, A. K., Karunakaran, R., Downie, J. A. & Poole, P. S. Adaptation of Rhizobium leguminosarum to pea, alfalfa and sugar beet rhizospheres investigated by comparative transcriptomics. Genome Biol. 12, R106 (2011). 24. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods 10, 996–998 (2013). 25. Chi, F. et al. Ascending migration of endophytic rhizobia, from roots to leaves, inside rice plants and assessment of benefits to rice growth physiology. Appl. Environ. Microbiol. 71, 7271–7278 (2005).

Supplementary Information is available in the online version of the paper.

Acknowledgements We thank D. Lundberg, S. Lebeis, S. Herrera-Paredes, S. Biswas and J. Dangl for sharing the calcined clay utilization protocol before publication; M. Kisielow of the ETH Zurich Flow Cytometry Core Facility for help with bacterial cell sorting as well as M. Baltisberger, D. Jolic and D. Weigel for their help in finding natural Arabidopsis populations; E. Kemen and M. Agler for sharing the Illumina Mi- Seq protocol for profiling of defined communities before publication and A. Sczyrba for his advice with the genome assembly. This work was supported by funds to P.S.-L. from the Max Planck Society, a European Research Council advanced grant (ROOTMICROBIOTA), the ‘Cluster of Excellence on Plant Sciences’ program funded by the Deutsche Forschungsgemeinschaft, the German Center for Infection Research (DZIF), by funds to J.A.V. from ETH Zurich (ETH Research Grant ETH-41 14-2), a grant from the Swiss National Research Foundation (310030B_152835), and a European Research Council advanced grant (PhyMo).

Author Information Sequencing reads (454 16S rRNA, MiSeq 16S rRNA and WGS HiSeq reads) have been deposited in the European Nucleotide Archive (ENA) under accession numbers PRJEB11545, PRJEB11583 and PRJEB11584, and genome assemblies and annotations corresponding to the leaf, root and soil culture collections have been deposited in the BioProject database under accession numbers PRJNA297956, PRJNA297942 and PRJNA298127. Isolates have been deposited at the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures (https://www.dsmz.de/). The authors declare no competing financial interests. Correspondence should be addressed to J.A.V. ([email protected]) or P.S.- L. ([email protected]).

Methods Sampling of A. thaliana plants and isolation of root-, leaf- and soil-derived bacteria. A. thaliana plants were either harvested from natural populations or grown in different natural soils and used for

36 bacterial isolations by colony picking, limiting dilution or bacterial cell sorting as well as 16S rRNA gene-based community profiling. To obtain a library of representative root colonizing bacteria, A. thaliana plants were grown in different soils (50.958 N, 6.856 E, Cologne, Germany; 52.416 N, 12.968 E, Golm, Germany; 50.982 N, 6.827 E, Widdersdorf, Germany; 47.941 N, 04.012 W, Saint-Evarzec, France; 48.725 N, 3.989 W, Roscoff, France) and harvested before bolting. Briefly, Arabidopsis roots

6 were washed twice in washing buffers (10 mM MgCl2 for limiting dilution and PBS for colony picking ) on a shaking platform for 20 min at 180 rpm and then homogenized twice by Precellys24 tissue lyser (Bertin Technologies) using 3 mM metal beads at 5,600 rpm for 30 s. Homogenates were diluted and used for isolation approaches on several bacterial growth media (Supplementary Data 7). For isolations based on colony picking, diluted cell suspensions were plated on solidified media and incubated, before isolates of plates containing less than 20 colony-forming units (CFUs) were picked after a maximum of two weeks of incubation. For limiting dilution, homogenized roots from each root pool were sedimented for 15 min and the supernatant was empirically diluted, distributed and cultivated in 96-well microtitre plates20. In parallel to the isolation of root-derived bacteria, roots of plants grown in Cologne soil were harvested and used to assess bacterial diversity by culture-independent 16S rRNA gene sequencing. Additionally, soil-derived bacteria were extracted from unplanted Cologne soil by washing soil with PBS buffer, supplemented with 0.02% Silwet L-77 and subjected to bacterial isolation as well as 16S rRNA gene community profiling. For the isolation of representative phyllosphere strains, naturally grown Arabidopsis plants were collected at eight different sites in southern Germany and Switzerland (six main sampling sites used for bacterial isolations and community profiling: 47.4090306 N, 8.470169444 E, Hoengg, Switzerland; 47.474825 N, 8.305008333 E, Baden, Switzerland; 47.4816806 N, 8.217547222 E, Brugg, Switzerland; 48.5560194 N, 9.134944444 E, Farm, Tuebingen, Germany; 48.5989861 N, 9.201655556 E, Haeslach, Germany; 48.602682 N, 9.213247258 E, Haeslach, Germany; and two additional sites only used for bacterial isolation: 47.4074722 N, 8.50825 E, Zurich, Switzerland; 47.4227222 N, 8.548666667 E, Seebach, Switzerland) during spring and autumn of 2013 and used for bacterial isolations as well as 16S rRNA gene profiling. Leaf-colonizing bacteria of individual leaves were washed off by alternating steps of intense mixing and sonication. The suspension was subsequently filtered (CellTrics filters, 10 μM, Partec GmbH, Görlitz, Germany) in order to remove remaining plant or debris particles as well as cell aggregates and applied to cell sorting on a BD FACS Aria III (BD Biosciences) as well as to plating on different media (Supplementary Data 1 and 7). All isolates were subsequently stored in 30% or 40% glycerol at − 80 °C. Culture-independent bacterial 16S rRNA gene profiling of A. thaliana leaf, root and corresponding soil samples. Parts of A. thaliana leaves, roots and corresponding unplanted soil samples used for bacterial isolation were also processed for bacterial 16S rRNA gene community profiling using 454 pyrosequencing. Frozen root and corresponding soil samples were homogenized, DNA was extracted with Lysing Matrix E (MP Biomedicals) at 5,600 rpm for 30 s, and DNA was extracted from all samples using the FastDNA SPIN Kit for soil (MP Biomedicals) according to the

37 manufacturer’s instructions. Lyophilized leaf samples were transferred into 2 ml microcentrifuge tubes containing one metal bead and subsequently homogenized twice for 2 min at 25 Hz using a Retsch tissue lyser (Retsch, Haan, Germany). Homogenized leaf material was resuspended in lysis buffer of the MO BIO PowerSoil DNA isolation Kit (MO BIO Laboratories Inc., Carlsbad, CA, USA), transferred into lysis tubes, provided by the supplier, and DNA extraction was performed following the manufacturer’s protocol. DNA concentrations were measured by PicoGreen dsDNA Assay Kit (Life technologies), and subsequently diluted to 3.5 ng/μl. Bacterial 16S rRNA genes were subsequently amplified6 using primers targeting the variable regions V5-V7 (799F26 and 1193R6, Supplementary Data 7). Each sample was amplified in triplicate by two independent PCR mixtures (a total of 6 replicates per sample plus respective no template controls). PCR products of triplicate were subsequently combined, purified and subjected to 454 sequencing. Obtained sequences were demultiplexed as well as quality and length filtered (average quality score ≥25, minimum length 319 bp with no ambiguous bases and no errors in the barcode sequences allowed)27. High-quality sequences were subsequently processed using the UPARSE24 pipeline and OTUs were taxonomically classified using the Greengenes database28 and the PyNAST29 method. High-throughput identification of leaf-, root- and soil-derived bacterial isolates by 454 pyrosequencing. We adopted a two-step barcoded PCR protocol20 in combination with 454 pyrosequencing to define V5-V8 sequences of bacterial 16S rRNA genes of all leaf, root- and soil- derived bacterial (Supplementary Fig. 1). DNA of isolates was extracted by lysis of 6 μl of bacterial cultures in 10 μl of buffer I containing 25 mM NaOH, 0.2 mM EDTA, pH 12 at 95 °C for 30 min, before the pH value was lowered by addition of 10 μl of buffer II containing 40 mM Tris- HCl at pH 7.5. Position and taxonomy of isolates in 96-well microtitre plates were indexed by a two-step PCR protocol using the degenerate primers 799F and 1392R containing well- and plate-specific barcodes (Supplementary Data 7) to amplify the variable regions V5 to V8. During the first step of PCR amplification, DNA from 1.5 μl of lysed cells was amplified using 2 U DSF-Taq DNA polymerase, 1× complete buffer (both Bioron GmbH), 0.2 mM dNTPs (Life technologies), 0.2 μM of 1 of 96 barcoded forward primer with a 18-bp linker sequence (for example, A1_454_799F1_PCR1_wells; Supplementary Data 7) and 0.2 μM reverse primer (454B_1392R) in a 25 μl reaction. PCR amplification was performed under the following conditions: DNA was initially denaturised at 95 °C for 2 min, followed by 40 cycles of 95 °C for 30 s, 50 °C for 30 s and 72 °C for 45 s, and a final elongation step at 72 °C for 10 min. PCR products of each 96-well microtitre plate were combined and subsequently purified in a two-step procedure using the Agencourt AMPure XP Kit (Beckman Coulter GmbH, Krefeld, Germany) first, then DNA fragments were excised from a 1% agarose gel using the QIAquick Gel Extraction Kit (Qiagen). DNA concentration was measured by Nanodrop and diluted to 1 ng/μl. During the second PCR step, 1 ng of pooled DNA (each pool represents one 96-well microtitre plate) was amplified by 1.25 U PrimeSTAR HS DNA Polymerase, 1× PrimeSTAR Buffer (both TaKaRa Bio S.A.S, Saint-Germain-en-Laye, France), 0.2 mM dNTPs (Thermo Fisher Scientific Inc.), 0.2 μM of 1 of

38 96 barcoded forward primer targeting the 18-bp linker sequence (for example, P1_454_PCR2; Supplementary Data 7) and 0.2 μM reverse primer (454B_1392R) in a 50 μl reaction. The PCR cycling conditions were as follows. First, denaturation at 98 °C for 30 s, followed by 25 cycles of 98 °C for 10 s, 58 °C for 15 s and 72 °C for 30 s, and a final elongation at 72 °C for 5 min. PCR products were purified using the Agencourt AMPure XP Kit (Beckman Coulter GmbH) and QIAquick Gel Extraction Kit (Qiagen) as described for the purification of first step PCR amplicons. DNA concentration was determined by PicoGreen dsDNA Assay Kit (Life technologies) and samples were pooled in equal amounts. The final PCR product libraries were sequenced on the Roche 454 Genome Sequencer GS FLX +. Each sequence contained a plate-barcode, a well-barcode and V5-V8 sequences. The sequences were quality filtered, demultiplexed according to well and plate identifiers27. OTUs were clustered at 97% similarity by UPARSE algorithum24. A nucleotide-based blast (v. 2.2.29) was used to align representative sequences of isolated OTUs to culture-independent OTUs and only hits ≥97% sequence identity covering at least 99% of the length of the sequences were considered. Preparation of A. thaliana leaf (At-LSPHERE), root (At-RSPHERE) and soil bacterial culture collections. Based on representative sequences of OTUs from this as well as previously published culture-independent community analysis, bacterial CFUs in the culture collections with ≥97% 16S rRNA gene identity to root-, leaf- and soil-derived OTUs were purified by three consecutive platings on the respective solidified media before an individual colony was used to inoculate liquid cultures. These liquid cultures were used for validation by Sanger sequencing with both 799F and 1392R primers as well as for the preparation of glycerol stocks for the culture collections and for the extraction of genomic DNA for whole-genome sequencing. A total of 21 leaf-derived strains, previously described as phyllosphere bacteria8,9, were added to the At-LSPHERE collection although these were undetectable in the present culture-independent leaf community profiling. Preparation of bacterial genomic DNA for whole-genome sequencing. To obtain high molecular weight genomic DNA of bacterial isolates in our culture collections, we used a modified DNA precipitation protocol and the Agencourt AMPure XP Kit (Beckman Coulter GmbH). For each bacterial liquid culture, cells were collected by centrifugation at 3,220g for 15 min, the supernatant removed and cells were resuspended in 5 ml SET buffer containing 75 mM NaCl, 25 mM EDTA, 20 mM Tris/HCl at pH 7.5. A total of 20 μl lysozyme solution (50 mg/ml, Sigma) was added before the mixture was incubated for 30 min at 37 °C. Subsequently, 100 μl 20 mg/ml proteinase K (Sigma-Aldrich Chemie GmbH, Taufkirchen, Germany) and 10% SDS (Sigma-Aldrich Chemie GmbH) were added, mixed, and incubated by shaking every 15 min at 55 °C for 1 h. If bacterial cells were insufficiently lysed, remaining cells were collected at 3,220g for 10 min and homogenized using the Precellys24 tissuelyser in combination with lysing matrix E tubes (MP Biomedicals) at 6,300 rpm for 30 s. After cell lysis, 2 ml 5 M NaCl and 5 ml chloroform were added and mixed by inversion for 30 min at room temperature. After centrifugation at 3,220 g for 15 min, 6 ml supernatant were transferred into fresh falcon tubes and 3.6 ml isopropanol were added and gently mixed. After precipitation at 4 °C for 30 min, genomic DNA was

39 collected at 3,220g for 5 min, washed once with 1 ml 70% (v/v) ethanol, dried for 15 min at room temperature and finally dissolved in 250 μl elution buffer (Qiagen). 2 μl 4 mg/ml RNase A (Sigma- Aldrich Chemie GmbH) was added to bacterial genomic DNA solution and incubated over night at 4 °C. The genomic DNA was subsequently purified using the Agencourt AMPure XP Kit (Beckman Coulter GmbH) and analysed by agarose gel (1% (w/v)) electrophoresis. Concentrations were estimated based on loaded Lambda DNA Marker (GeneRuler 1kb Plus, Thermo Scientific) and approximately 1 μg of genomic DNA was transferred into micro TUBE Snap-Cap AFA Fibre vials (Covaris Inc., Woburn, MA, USA). DNA was sheared into 350 bp fragments by two consecutive cycles of 30 s (duty cycle: 10%, intensity: 4, cycle/burst: 200) on a Covaris S2 machine (Covaris, Inc.). The Illumina sequencing libraries were prepared according to the manual of NEBNext Ultra UltraTM DNA Library Prep Kit for Illumina (New England Biolabs, USA). Quality and quantity was assessed at all steps by capillary electrophoresis (Agilent Bioanalyser and Agilent Tapestation). Finally libraries were quantified by fluorometry, immobilized and processed onto a flow cell with a cBot (Illumina Inc., USA) followed by sequencing-by-synthesis with TruSeq v3 chemistry on a HiSeq2500 (Illumina Inc., USA). Genome assembly and annotation. Paired-end Illumina reads were subjected to quality and length trimming using Trimmomatic v. 0.3330 and assembled using two independent methods (A531 and SOAPdenovo32 v. 20.1). In each case, the assembly with the smaller number of scaffolds was selected. Detailed assembly statistics for each sequenced isolate can be found in Supplementary Data 3 and 4. Identification of putative protein-encoding genes and annotation of the genomes were performed using GLIMMER v. 3.0233. Functional annotation of genes was conducted using Prokka v. 1.1134 and the SEED subsystems approach using the RAST server API35. Additionally, annotation of KEGG Orthologue (KO) groups was performed by first generating HMM models for each KO in the database36,37 the HMMER toolkit (v. 3.1b2)38. Next, we employed the HMM models to search all predicted ORFs using the hmmsearch tool, with an E value threshold of 10 × 10−5. Only hits covering at least 70% of the protein sequence were retained and for each gene and the match with the lowest E value was selected. Analyses of phylogenetic diversity within sequenced isolates. Each proteome was searched for the presence of the 31 well-conserved, single-copy, bacterial AMPHORA genes39, designed for the purpose of high-resolution phylogeny reconstruction of genomes. Subsequently, a concatenated alignment of these marker genes was performed using Clustal Omega40 v. 1.2.1. Based on this multiple sequence alignment, a species tree was inferred using FastTree41 v. 2.1, a maximum likelihood tool for phylogeny inference. Whole-genome taxonomic classification of sequenced isolates was conducting using taxator- tk42, a homology/based tool for accurate classification of sequences. Analyses of phylogenetic diversity were performed independently for each cluster based on pairwise tree distances between all isolates (Supplementary Data 5). Analyses of functional diversity between sequenced isolates. Analyses of functional diversity between sequenced isolates were conducted by generating, for each genome in the data set, a profile of presence/absence of each KO group (or phyletic pattern). Subsequently, a distance

40 measure based on the Pearson correlation of each pair of phyletic patterns was calculated, which allowed us to embed each genome as a data point in a metric space. PCoA was performed on this space of functional distances using custom scripts written in R. Pairwise functional distances within each family- level cluster was performed by calculating the average distance between all pairs of genomes belonging to each cluster. Finally, we calculated RAs of each functional category based on the percentage of annotated KO terms assigned to each category. Enrichment tests were performed to identify differentially abundant categories between groups of genomes based on their origin (root versus leaf and root versus soil) using the non-parametric Mann–Whitney Test (MWT). P values were corrected for multiple testing using the Bonferroni method, with a significance threshold α = 0.05. Recolonization experiments of leaf-, root- and soil-derived bacteria on Arabidopsis. Calcined clay16, an inert soil substitute, was washed with water, sterilized twice by autoclaving and heAt- incubated until being completely dehydrated. A. thaliana Col-0 seeds were surface-sterilized with ethanol and stratified overnight at 4 °C. Leaf-, root- and soil-derived bacteria of the culture collections were cultivated in 96-deep-well plates and subsequently pooled (in equal or unequal ratios) in order to prepare synthetic bacterial communities (SynComs) for inoculations below the carrying capacity of

43,44 leaves and roots . To inoculate SynComs into the calcined clay matrix, OD600 was adjusted to 0.5 and 1 ml (~2.75 × 108 cells) was added to 70 ml 0.5× MS media (pH 7; including vitamins, without sucrose), and mixed with 100 g calcined clay in Magenta boxes (~2.75 × 106 cells per gr calcined clay), directly before sowing of surface-sterilized seeds. Plants were grown at 22 °C, 11 h light, and 54% humidity. Alive cell counts (CFUs) of root-associated bacteria by serial dilutions of root homogenates after seven weeks of co-incubation were 1.4 × 108 ± 8.4 × 107 cells per gram root tissue. For leaf spray-inoculation of A. thaliana plants, bacterial SynComs were prepared as described above and adjusted to OD600 0.2, before the solution was diluted tenfold and 170 μl (~1.87 × 106 cells) were sprayed into each magenta box containing four three-week-old plants using a TLC chromatographic reagent sprayer (BS124.000, Biostep GmbH, Jahnsdorf, Germany). The average volume per spraying event was determined by spraying repeatedly into 50 ml tubes and weighing before and after. All plants and corresponding unplanted clay samples were harvested under sterile conditions after a total incubation period of seven weeks. All plants and corresponding unplanted clay samples were harvested under sterile conditions after a total incubation period of seven weeks. During harvest, leaves and roots of individual plants were carefully separated using sterilized tweezers and scissors to avoid crosscontamination and processed separately thereafter. All leaves being obviously contaminated with clay particles or touching the ground were carefully removed and omitted from further processing. Remaining aerial parts of four plants collected from one magenta box were combined and transferred into lysing matrix E tubes (MP Biomedicals), frozen in liquid nitrogen and stored at −80 °C until used for DNA extraction. Roots from one Magenta box were pooled, washed twice in 5 ml PBS at 180 rpm for 20 min, dried on sterilized Whatman glass microfiber filters (GE Healthcare Life Sciences), transferred into lysing matrix E tubes (MP Biomedicals), frozen in liquid nitrogen and stored at −80 °C until further processing. The

41 corresponding unplanted clay samples were washed in 100 ml PBS supplemented with 0.02% Silwet L- 77 at 180 rpm for 10 min, before particles were allowed to settle down for 5 min. The supernatant was collected by centrifugation at 3,220g for 15 min. The pellet was subsequently resuspended in 1 ml water, transferred into lysing matrix E tubes (MP Biomedicals), frozen in liquid nitrogen and stored at −80 °C. To prepare DNA for bacterial 16S rRNA gene-based community analysis, all samples were homogenized twice by Precellys24 tissue lyser (Bertin Technologies), DNA was extracted and concentrations were measured by PicoGreen dsDNA Assay Kit (Life technologies), before bacterial 16S rRNA genes were amplified by degenerate PCR primers (799F and 1193R) targeting the variable regions V5-V7 (Supplementary Data 7). Each sample was amplified in triplicate (plus respective no template control) in 25 μl reaction volume containing 2 U DFS-Taq DNA polymerase, 1× incomplete buffer (both

Bioron GmbH, Ludwigshafen, Germany), 2 mM MgCl2, 0.3% BSA, 0.2 mM dNTPs (Life technologies GmbH, Darmstadt, Germany), 0.3 μM forward and reverse primer and 10 ng of template DNA. After an initial denaturation step at 94 °C for 2 min, the targeted region was amplified by 25 cycles of 94 °C for 30 s, 55 °C for 30 s and 72 °C for 60 s, followed by a final elongation step of 5 min at 72 °C. The three independent PCR reactions were pooled and the remaining primers and nucleotides were removed by addition of 20 U exonuclease I and 5 U Antarctic phosphatase (both New England BioLabs GmbH, Frankfurt, Germany) and incubated for 30 min at 37 °C in the corresponding 1× Antarctic phosphatase buffer. Enzymes were heat-inactivated and the digested mixture was used as template for the 2nd step PCR using the Illumina compatible primers B5-F and 1 of 96 differentially barcoded reverse primers (B5-1 to B5-96, Supplementary Data 7). All samples were amplified in triplicate for 10 cycles using identical conditions of the first-step PCR. Technical replicates of each sample were combined, run on a 1.5% (w/v) agarose gel and the bacterial 16S rRNA gene amplicons were extracted using the QIAquick Gel Extraction Kit (Qiagen) according to the manufacturer’s instructions. DNA concentration was subsequently measured using the PicoGreen dsDNA Assay Kit (Life technologies) and 100 ng of each sample were combined. Final amplicon libraries were cleaned twice using the Agencourt AMPure XP Kit (Beckman Coulter GmbH) and subjected to sequencing on the Illumina MiSeq platform using an MiSeq Reagent kit v3 following the 2 × 350 bp paired-end sequencing protocol (Illumina Inc. USA). Forward and reverse reads were joined, demultiplexed and subjected to quality controls using scripts from the QIIME toolkit27, v. 180 (Phred ≥ 20). The resulting high quality sequences were further clustered at 97% sequence identity together with Sanger sequences of leaf, root and soil isolates using the UPARSE24 pipeline as described above. Taxonomic assignments of representative sequences were performed as explained in the previous sections. OTUs only corresponding to one or more Sanger 16S rRNA gene sequence(s) of purified strains in the At-RSPHERE, At-LSPHERE or soil collection were selected and designated ‘indicator OTUs’. The heat maps were generated using the ggplot2 R package. Accession numbers. Sequencing reads (454 16S rRNA, MiSeq 16S rRNA and WGS HiSeq reads) have been deposited in the European Nucleotide Archive (ENA) under accession numbers PRJEB11545, PRJEB11583 and PRJEB11584. Genome assemblies and annotations corresponding to the leaf, root and

42 soil culture collections have been deposited in the National Center for Biotechnology Information (NCBI) BioProject database under accession numbers PRJNA297956, PRJNA297942 and PRJNA298127, respectively. Code availability. All scripts for computational analysis and corresponding raw data are available at http://www.mpipz.mpg.de/R_scripts. The sequenced bacterial genomes as well as any future updates are available at http://www.At-sphere.com.

References Methods 26. Chelius, M. K. & Triplett, E. W. The diversity of Archaea and Bacteria in association with the roots of Zea mays L. Microb. Ecol. 41, 252–263 (2001). 27. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336 (2010). 28. DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006). 29. Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010). 30. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014). 31. Tritt, A., Eisen, J. A., Facciotti, M. T. & Darling, A. E. An integrated pipeline for de novo assembly of microbial genomes. PLoS One 7, e42304 (2012). 32. Li, R. et al. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 20, 265–272 (2010). 33. Delcher, A. L., Harmon, D., Kasif, S., White, O. & Salzberg, S. L. Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 27, 4636–4641 (1999). 34. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014). 35. Overbeek, R. et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 33, 5691–5702 (2005). 36. Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000). 37. Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014). 38. Eddy, S. R. Accelerated profile HMM searches. PLOS Comput. Biol. 7, e1002195 (2011). 39. Wu, M. & Eisen, J. A. A simple, fast, and accurate method of phylogenomic inference. Genome Biol. 9, R151 (2008). 40. Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539–539 (2011). 41. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximumlikelihood trees for large alignments. PLoS One 5, e9490 (2010). 42. Dröge, J., Gregor, I. & McHardy, A. C. Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods. Bioinformatics 31, 817–824 (2015). 43. Whitman, W. B., Coleman, D. C. & Wiebe, W. J. Prokaryotes: the unseen majority. Proc. Natl Acad. Sci. USA 95, 6578–6583 (1998). 44. Bodenhausen, N., Bortfeld-Miller, M., Ackermann, M. & Vorholt, J. A. A synthetic community approach reveals plant genotypes affecting the phyllosphere microbiota. PLoS Genet. 10, e1004283 (2014).

43 Extended Data Figure 1 | Culture-dependent coverage of A. thaliana root- and leaf-associated OTUs identified in several cultivationindependent studies. a–d, The inner circle depicts taxonomic assignments of top 100 root-associated OTUs (filled dots) for the indicated phyla and families that were identified in the current (a), ref. 6 (b) and ref. 12 (c) studies with Cologne-soil-grown plants, and current leaf (d) study at locations around Tübingen and Zurich. Black squares of the outer ring highlight OTUs sharing ≥ 97% 16S rRNA gene similarity to Arabidopsis root or leaf bacterial culture collection.

44 Extended Data Figure 2 | 16S rRNA gene community profiling of phyllosphere samples from different locations. a–d, The indicated Beta-diversity indices were calculated from leaf samples (n = 60) collected from natural A. thaliana populations growing in the areas around Tübingen and Zurich. The indicated colour code refers to sampling locations, sampling sites, sampling season, and combined or individual leaves of respective plants.

Extended Data Figure 3 | At-RSPHERE, At-LSPHERE and soil bacterial culture collections. a, At- RSPHERE (n = 206 isolates), a culture collection of the A. thaliana root microbiota. b, At-LSPHERE (n = 224 isolates), a culture collection of the A. thaliana leaf microbiota. c, Bacteria isolated from Cologne soil (n = 33 isolates). Numbers inside white circles indicate the number of bacterial isolates sharing ≥ 97% sequence identity, but isolated from independent roots, leaves and soil batches.

Extended Data Figure 4 | Taxonomy overlap between A. thaliana rootand leaf-associated bacterial community from plants grown in natural soils. a, b, Rank abundance plots of top 20 genera (a) and OTUs (b) in root bacterial communities (n = 8) from Cologne with corresponding genera detected in leaf bacterial communities (n = 60) from Zurich and Tübingen. c, d, Rank abundance plots of top 20 genera (c) and OTUs (d) in leaf bacterial communities from Zurich and Tübingen with corresponding genera detected in root bacterial communities from Cologne. Boxplot whiskers extend to the most extreme data point which is no more than 1.5 times the interquartile range from the upper or lower quartiles.

Extended Data Figure 5 | Phylogenetic distribution of ‘carbohydrate metabolism’ genes across sequenced isolates. a, Phylogeny of sequenced leaf (n = 206), root (n = 194) and soil (n = 32) isolates based on the concatenated alignment of the 31 conserved AMPHORA phylogenetic marker genes. The origin of each genome (leaf, root or soil) is shown by different shapes and their taxonomic affiliation (phylum level) is depicted using various colours. Shaded areas correspond to the different clusters of genomes and are annotated with their consensus taxonomy (family level). b, Relative abundance of protein coding genes classified as belonging to the KEGG general term ‘carbohydrate metabolism’, measured as percentage of annotated proteins per genome.

Extended Data Figure 6 | Phylogenetic distribution of ‘xenobiotic biodegradation and metabolism’ genes across sequenced isolates. a, Phylogeny of sequenced leaf (n = 206), root (n = 194) and soil (n = 32) isolates based on the concatenated alignment of the 31 conserved AMPHORA phylogenetic marker genes. The origin of each genome (leaf, root or soil) is shown by different shapes and their taxonomic affiliation (phylum level; class level for Proteobacteria) is depicted using various colours. Shaded areas correspond to the different clusters of genomes and are annotated with their consensus taxonomy (family level). b, Relative abundance of protein coding genes classified as belonging to the KEGG general term ‘xenobiotics biodegradation and metabolism’, measured as percentage of annotated proteins per genome.

Extended Data Figure 7 | V. vinifera metagenome comparison. a, b, Functional enrichment analysis of V. vinifera root and soil shotgun metagenomes (a; n = 47) compared to A. thaliana culture collection root and soil genomes (b; n = 432). Functional category abundances correspond to the percentage of annotated genes in each genome or metagenome sample. Boxplot whiskers extend to the most extreme data point which is no more than 1.5 times the interquartile range from the upper or lower quartiles.

50 Extended Data Figure 8 | Cluster analysis of Bray–Curtis distances between groups of samples in the SynCom colonization of germ-free A. thaliana experiments. a, Comparison of pairwise distances within input samples and between input and output samples of the RS in clay experiments. b, Comparison of pairwise distances between samples within the same cluster and between different clusters of the RS in clay experiments. c, Comparison of pairwise distances between input samples and between input and output samples of the L spray experiments. d, Comparison of pairwise distances within samples within the same cluster and between different clusters of the L spray experiments. e, Comparison of pairwise distances between samples within the same cluster and between different clusters of the leaf output across experiments. f, Comparison of pairwise distances between leaf output samples in the RSL in clay experiments and leaf output samples in the L in clay and RS in clay experiments. g, Comparison of pairwise distances between root output samples in the RSL in clay experiments and root output samples in the L in clay and RS in clay experiments. All comparisons marked with asterisks were subjected to a Student’s t-test (P < 0.001 in each case). L in clay was tested with 6 independently prepared SynComs (n = 6); RSL in clay experiment was tested with 3 independently prepared SynComs, each used for 3 independent inoculations (n = 9). All other experiments were tested with 6 independently prepared SynComs and each preparation was used for 3 independent inoculations (n = 18). L, leaf-derived strains; RS, root- and soil-derived strains.

Extended Data Figure 9 | Similarity of rank abundances of SynCom outputs with corresponding root- and leaf-associated OTUs of plants grown in natural environments. a–c, Rank abundance plots of SynCom root outputs (n = 69) with corresponding root-associated OTUs in natural communities (n = 8) from plants grown in the present study in Cologne soil at the taxonomic ranks of phylum (a), order (b) and family (c). d–f, Rank abundance plots of SynCom leaf outputs (n = 69) with corresponding leaf- associated OTUs in natural communities (n = 60) from plants grown in the present study around Tuebingen or Zurich at the taxonomic ranks of phylum (d), order (e) and family (f). Boxplot whiskers extend to the most extreme data point which is no more than 1.5 times the interquartile range from the upper or lower quartiles.

52 Extended Data Figure 10 | Fractional contribution of At-LSPHERE and At-RPSHERE-specific OTUs and SynCom competition supports host organ-specific community assemblies. a, Fractional contribution of At-LSPHERE and At-RPSHERE specific OTUs in the input, leaf and the root output communities in the ‘RSL in clay’ experiment (n = 9). b, c, PCoA of Bray–Curtis distances of root (b; n = 21) and leaf (c; n = 21) outputs of the ‘R in clay’, ‘RS in clay’, and ‘R spray’ SynCom experiments. R, root-derived isolates; S, soil-derived isolates; L, leaf-derived isolates. RSL in clay experiment was tested with 3 independently prepared SynComs, each used for 3 independent inoculations. All other experiments were tested with 3 independently prepared SynComs and each preparation was used for 3 independent inoculations. Boxplot whiskers extend to the most extreme data point which is no more than 1.5 times the interquartile range from the upper or lower quartiles.

Chapter III

Systems-level proteomics of two ubiquitous leaf commensals reveals complementary adaptive traits

for phyllosphere colonization

Daniel B. Müller, Olga T. Schubert, Hannes Röst, Ruedi Aebersold, Julia A. Vorholt

Mol. Cell. Proteomics (in press), doi: 10.1074/mcp.M116.058164

AUTHOR CONTRIBUTIONS D.B.M. O.T.S., R.A. and J.A.V designed research. D.B.M. grew bacterial strains and plants and prepared samples for mass spectrometric analyses. D.B.M. and O.T.S. fractionated proteins for spectral library generation. O.T.S. measured all samples, D.B.M., O.T.S and H.R analyzed the data. D.B.M. performed gene enrichment analyses and genome comparisons. D.B.M. and O.T.S prepared figures, D.B.M., O.T.S., H.R., R.A. and J.A.V wrote the manuscript.

Systems-level proteomics of two ubiquitous leaf commensals reveals complementary adaptive traits for phyllosphere colonization

Daniel B. Müller1, Olga T. Schubert2, Hannes Röst2, Ruedi Aebersold2,3, Julia A. Vorholt1*

1Department of Biology, Institute of Microbiology, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland 2Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland 3Faculty of Science, University of Zurich, Zurich, Switzerland *To whom all correspondence should be addressed: ETH Zurich, Institute of Microbiology, Vladimir-Prelog-Weg 4, HCI F429, Tel.: +41-44-632-5524, Fax.: +41-44-633-1307; E-mail: [email protected]

SUMMARY Plants are colonized by a diverse community of microorganisms, the plant microbiota, exhibiting a defined and conserved taxonomic structure. Niche separation based on spatial segregation and complementary adaptation strategies likely forms the basis for coexistence of the various microorganisms in the plant environment. To gain insights into organism-specific adaptations on a molecular level, we selected two exemplary community members of the core leaf microbiota and profiled their proteomes upon Arabidopsis phyllosphere colonization. The highly quantitative mass spectrometric technique SWATH MS was used and allowed for the analysis of over two thousand proteins spanning more than three orders of magnitude in abundance for each of the model strains. The data suggest that Sphingomonas melonis utilizes amino acids and hydrocarbon compounds during colonization of leaves while Methylobacterium extorquens relies on methanol metabolism in addition to oxalate metabolism, aerobic anoxygenic photosynthesis and alkanesulfonate utilization. Comparative genomic analyses indicates that utilization of oxalate and alkanesulfonates is widespread among leaf microbiota members whereas, aerobic anoxygenic photosynthesis is almost exclusively found in Methylobacteria. Despite the apparent niche separation between these two strains we also found a relatively small subset of proteins to be co-regulated, indicating common mechanisms, underlying successful leaf colonization. Overall, our results reveal for two ubiquitous phyllosphere commensals species-specific adaptations to the host environment and provide evidence for niche separation within the plant microbiota.

57 INTRODUCTION Higher multicellular organisms live in close association with a remarkable diversity of microorganisms, the microbiota (1-4). The vast majority of community members are non-pathogenic commensal bacteria, but the host microbiota has been associated with beneficial traits ranging from disease susceptibility to host nutrition, inflammatory responses, host immunity and growth promotion (5-7). Rapid advances in next-generation sequencing technologies accelerated the detailed understanding of the phylogenetic composition of the microbiota of various organisms, including the economically highly relevant associations of microbes with terrestrial plants (8-13). The phyllosphere environment comprises the above ground parts of plants and offers an excellent system to study ecological concepts of microbial communities, since gnotobiotic model systems are available and colonization as well as community structure can be linked to spatial information recorded at various scales (14, 15). The predominant component of the phyllosphere are leaves, which represent a harsh, oligotrophic and rapidly changing microbial environment that is exposed to the diurnal cycle and to extreme conditions, including UV radiation, frequent changes in nutrient and water availability as well as wide temperature gradients (13, 16). Nonetheless, a diverse community of microorganisms inhabits the different niches on leaves (10, 12, 17). Cultivation-independent studies revealed that mainly the four phyla Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria constitute the phyllosphere community of different host plants. In particular, the class Alphaproteobacteria predominates in abundance and some of the observed genera, e.g. Sphingomonas and Methylobacterium are conserved between phylogenetically distinct host plants and geographic regions (12, 13, 18, 19). For both genera, beneficial plant-microbe interactions have been reported, either conferring a protection against pathogen infection or an increase in biomass production to the plant (20-23). Unravelling molecular mechanisms of these plant-microbe interactions will be crucial, to develop and implement applications to further improve host fitness. Several studies addressing microbial adaptation to the plant habitat indicated conserved strategies as well as species-specific mechanisms by which the phyllosphere inhabitants adapt to their niches and cope with the harsh conditions they encounter (13, 24-26). However, to explain coexistence and stable community formation of core community members a more profound understanding of their physiology as well as their interactions with the plant host will be required. Earlier studies gave first insights into the physiology of different community members by combining metaproteomics with metagenomic shotgun sequencing of natural phyllosphere communities (10, 12). However, because proteome coverage per species was low in studies on complex natural communities, complementary approaches are required for an in-depth understanding of bacterial fitness traits on leaves. The recently developed SWATH MS (Sequential Windowed Aquisition of all THeoretical fragment ions Mass Spectrometry) technique allows for the reproducible quantification of thousands of proteins in a single run, overcoming technical limitations of previous studies and proteomic strategies and enabling systems level insights into each strain's physiology (27-29). In short, the SWATH MS

58 approach generates a complete map of all detectable fragment ions derived from peptide precursors in a given sample using data independent acquisition (DIA). In a targeted analysis strategy, known m/z and retention time coordinates for each peptide (collected in a spectral library) are then used to extract quantitative signals for the peptides (and by inference, the corresponding proteins) of interest (27, 30, 31). Here, we applied SWATH MS to study proteomic changes of two commensal model strains of the core leaf microbiota, Methylobacterium extorquens PA1 and Sphingomonas melonis Fr1, upon plant colonization.

EXPERIMENTAL PROCEDURES Experimental Design and Statistical Rational For each bacterial strain and growth condition (see Fig. 1A), we generated three biological replicates. The samples on minimal media agar plates were all generated at the same time, while the plant samples were generated and measured at different times due to the large scale of the experiments. We chose 3 replicate experiments for each condition to be able to assess biological variation. The log-normalized transition group intensities (from OpenSWATH output) were approximately normally distributed and were used for all statistical tests. The statistical methods used at each step are described in detail in the corresponding paragraph describing that experimental/analysis procedure.

Bacterial strains and growth conditions Methylobacterium extorquens PA1 (19) and Sphingomonas melonis Fr1 (21) were routinely grown in phosphate buffered minimal media (32) at pH 6.5 supplemented with 25 mM D-glucose and 125 mM methanol as carbon sources at 28°C. For generation of SWATH assay libraries strains were grown in liquid media in baffled flasks until early exponential, exponential and stationary growth phase. In addition, strains were grown on minimal media agar plates containing 1.5% agar at 22°C in plant growth chambers (ATC26, Conviron, Winnipeg, Canada) exposed to the diurnal cycle with a photoperiod of 9 h per day.

Plant growth conditions and inoculation of phyllosphere bacteria Arabidopsis thaliana ecotype Col-0 plants were grown in microboxes (Combiness, Nazareth, Belgium) under gnotobiotic conditions as describes previously (21). Sterilized Lumox Film 25 (Sarstedt, Nümbrecht, Germany) containing 8 holes were applied to the agar surface before sowing of surface sterilized seeds. The film prevents leaves touching the agar surface and therefore cross-contamination. For inoculation of phyllosphere bacteria approximately 1 x 106 CFU were pipetted onto each seed immediately after sowing and microboxes were sealed with covers containing XXL filters for gas exchange. Plants were incubated in standard growth chambers (ATC26, Conviron) at 22°C under long day conditions (16 h photoperiod) for 7 d before conditions were changed to short day conditions (9 h photoperiod) until harvest. All plants grew comparably to un-inoculated control plants, were harvested

59 before flowering and did not show disease symptoms during the time course of the experiment. We did not detect bacterial growth on non-inoculated control plants on minimal or complex media (nutrient broth (NB) without additional NaCl, pH 6.9, Sigma-Aldrich Chemie GmbH, Buchs, Switzerland).

Harvest of plants and recovery of phyllosphere bacteria Plants were harvested after 28 d of incubation, above ground parts were carefully separated from roots using sterilized razor blades and plants of two microboxes were transferred into 50 ml Falcon tubes containing 25 ml ice cold TE-P buffer containing 10 mM Tris-HCl, 1 mM EDTA, 20% Percoll, 0.1% Silwett L-77, 0.02 % Pefabloc SC (Roche Diagnostics, Rotkreuz, Switzerland) at pH 7.5. Phyllosphere bacteria were washed off leaves by three consecutive cycles of intense mixing and sonication before bacterial cells were separated from leaf material by filtration through a nylon mesh of 200 µm pore size (Spectrum Europe B.V., Breda, Netherlands). Cells were subsequently collected by centrifugation at 3220 X g for 10 min, transferred to 2 ml microcentrifuge tubes and washed twice with TE buffer, before cells of one experiment were pooled and stored at -80°C until further processing.

Microscopy Visualization of phyllosphere bacteria on cuticle tape lifts and infrared autofluorescence of anoxygenic photosynthesis was done as described previously (14). In brief, double-sided adhesive tape was glued onto microscopy slides and turgescent Arabidopsis leaves were flattened on the upper sticky layer using sterilized glass rods. Removal of the leaf material by tweezers results in an leaf imprint of the phylloplane which was visualized by phase contrast microscopy using an AxioObserver D1 epifluorescence microscope (Carl Zeiss GmbH, Oberkochen, Germany) connected to a X-Cite 120Q (Lumen Dynamics Group, Mississauga, ON, Canada) light source. Infrared autofluorescence was visualized using a custom filter set consisting of a 320-650 nm excitation filter (BG39, Schott AG, Mainz, Germany), a 650 nm dichroic mirror (5650dcxru, Chroma, Bellows Falls, VT, USA) and a 850 nm longpass emission filter (RG840, Schott AG). All pictures were acquired using an AxioCam Mrm (Carl Zeiss GmbH) and software AxioVision 4.8 (Carl Zeiss GmbH).

Preparation of proteins samples for MS Bacterial cell pellets were dissolved in lysis buffer containing 100 mM ammonium bicarbonate, 8 M urea and 0.1% RapiGest (Waters Corporation, Milford, MA, USA) (33) and bacterial cells were lysed by a combinatory approach of sonication (10 times for 10s, Intensity 50%, Mode 50, Vibra Cell, Sonics & Materials Inc., Danbury, CT, USA) and bead beating (10 times for 30s, CapMix, 3M ESPE AG, Seefeld, Germany) using 0.1 mm silica beads (BioSpec Products, Bartlesville, OK, USA). Protein concentration of lysates was determined using the BCA assay kit according to the manufacturer's instructions (Thermo Fisher Scientific, Reinach, Switzerland). The buffer of all samples was subsequently exchanged using Zeba Spin Desalting Columns (7K MWCO, 5 ml, Thermo Fisher

60 Scientific) and samples were concentrated to a final protein concentration of 1 mg/ml using Amicon Ultra centrifugal filters (0.5 ml, 3K MWCO, Merck Millipore Ltd., Schaffhausen, Switzerland). Protein disulfide bonds were reduced by addition of 5 mM tris(2-carboxylethyl)phosphine (TCEP) and incubation for 30 min at 37°C, before cysteine residues were alkylated by adding 10 mM iodoacetamide (IAA) and incubation for 1 h in the dark at 25°C. Samples were subsequently diluted 1:5 with freshly prepared 50 mM ammonium bicarbonate buffer to achieve urea concentrations below 2 M and sequencing grade modified trypsin (Promega AG, Dübendorf, Switzerland) was added at an enzyme to protein ratio of 1:100. The mixture was incubated over night at 37°C shaking at 300 rpm for digestion (trypsin specifically cleaves C-terminally of lysine and arginine residues, unless followed by a proline). Subsequently, 50% trifluoroacetic acid (TFA) was added to an approximate concentration of 1% to reduce the pH value below 3 and stop the trypsin digest as well as to precipitate RapiGest. The solution was incubated at 37°C shaking at 500 rpm for 30 min before insoluble particles were removed by centrifugation at 20000 g for 10 min. The supernatant was subsequently desalted using Sep-Pak Vac C18 (Waters Corporation) reversed phase columns. At first, columns were activated using buffer A containing 80% acetonitrile (ACN) and 0.1% TFA and equilibrated with buffer B containing 2% ACN and 0.1% TFA. After loading of samples columns were washed five times using buffer B before samples were eluted by gravity flow with buffer C containing 50% ACN and 0.1% TFA. Eluted samples were dried under vacuum and re-solubilized in 2% ACN, 0.1% formic acid (FA) to a final concentration of 0.2-1.0 mg/ml. Three independent biological replicates were generated for each condition.

SWATH assay library generation A SWATH assay library was built for each organism separately. Liquid cultures at different growth stages (early exponential, mid-exponential and stationary phase) were harvested and peptide samples were prepared as described above. To increase library coverage, we then pooled and fractionated the peptide samples of the three different growth stages by isoelectric focussing as described before (33). Several of the resulting 24 fractions were pooled, resulting in 10 fractions. To avoid missing proteins specifically present on plant leaves, we included whole cell lysates obtained from cultures grown on leaves for the generation of the SWATH assay library. We performed the OGE fractionation only on the samples from liquid cultures, where biological material was not limiting and it was readily feasible to obtain the relatively large amounts needed for the fractionation approach. Supplementary Figure 1 shows the contribution of the respective samples to the combined library as well as the fraction of the libraries that contributed to the quantification of proteins by SWATH MS. However, we cannot exclude that the deeper coverage of proteins from liquid culture-derived bacteria compared to plant-derived bacteria in the library, due to OGE fractionation, may slightly favour the identification and quantification of proteins classified as “detectable on plate only” as compared to proteins classified as “detectable on plant only” in the SWATH MS measurements. To each of the samples, iRT peptides (RT-kit WR, Biognosys) for retention time alignment of different LC-MS/MS runs were added (34). Mass spectra

61 were acquired on a TripleTOF 5600 in data/information-dependent acquisition (IDA) mode: The TripleTOF 5600 mass spectrometer (AB Sciex) was coupled to a nanoLC 1Dplus system (Eksigent) and the chromatographic separation of the peptides was performed on a 20-cm emitter (75 μm inner diameter, #PF360-75-10-N-5, New Objective) packed in-house with C18 resin (Magic C18 AQ 3 μm diameter, 200 Å pore size, Michrom BioResources). A linear gradient from 2-35% solvent B (98% ACN/0.1% FA) was run over 120 min at a flow rate of 300 nl/min. The mass spectrometer was operated in IDA mode with a 500 ms survey scan from which up to 20 ions exceeding 250 counts per second were isolated with a quadrupole resolution of 0.7 Da, using an exclusion window of 20 s. Rolling collision energy was used for fragmentation and an MS2 spectrum was recorded after an accumulation time of 150 ms. Raw data files (wiff) were centroided and converted into mzML format using the AB Sciex converter (version 1.2, 111102 beta release) and subsequently converted into mzXML using openMS (version 1.9), followed by indexing with indexmzXML (part of TPP 4.3) (35). The converted data files were searched using the search engines X!Tandem (k-score, TPP 4.6.0) and OMSSA (version 2.1.9), against the corresponding protein sequence database of the 4829 annotated proteins of M. extorquens PA1 (http://www.ncbi.nlm.nih.gov/nuccore/NC_010172.1) or the 3857 annotated proteins of S. melonis Fr1 (IMG database; https://img.jgi.doe.gov/cgi-bin/m/main.cgi; Taxon ID 2517093015). Each protein sequence data base contained the sequences of the 11 iRT peptides and for every target protein a corresponding decoy protein based on the reversed protein sequence. Tryptic or semi-tryptic peptides with up to one missed cleavages were allowed for the database search. The tolerated mass errors were 50 ppm on MS1 level and 0.05 Da on MS2 level. Carbamidomethylation of cysteines was defined as a fixed modification and methionine oxidation as a variable modification. The search results were processed with PeptideProphet (36) and iProphet (37) as part of the TPP 4.6.0 (35). To determine the iProphet cut-off corresponding to a 1% protein FDR, the software tool MAYU (38) was applied. The SWATH assay libraries were constructed from the iProphet results with an iProphet cut-off of 0.952195 for M. extorquens PA1 and 0.96029 for S. melonis Fr1, corresponding to a 1% FDR on protein level. The raw and consensus spectral libraries were built with SpectraST (version 4.0) (39, 40) using the - cICID_QTOF option for high resolution and high mass accuracy. Retention times were converted to iRT units using the retention time information of the spiked-in iRT peptides. The 6 most intense y and b fragment ions of charge state 1, 2 and 3 between 400 and 2000 m/z were extracted from the consensus spectral library using spectrast2tsv.py from msproteomicstools (https://pypi.python.org/pypi/msproteomicstools). Fragment ions falling into the swath window of the precursor were excluded as the resulting signals are often highly interfered. Neutral loss fragment ions were included if they were among the 6 most intense fragment ions: -17 (NH3), -18 (H2O), -64 (typical for oxidized methionines). The library was converted into TraML format using the OpenMS tool ConvertTSVToTraML (development branch of OpenMS 1.10; commit 4caef80). Decoy transition groups were generated based on shuffled sequences (decoys similar to targets were excluded) by the

62 OpenMS tool OpenSwathDecoyGenerator (development branch of OpenMS 1.10; commit 4caef80) and appended to the final SWATH library in TraML format.

SWATH data acquisition The TripleTOF 5600 mass spectrometer was set up as described above, but operated in SWATH mode (27) using the following parameters: For the liquid chromatography, the following two solvents, buffer A (2% acetonitrile and 0.1% formic acid in HPLC-grade water) and buffer B (2% water and 0.1% formic acid in acetonitrile) were used. A linear gradient from 2-35% solvent B, complemented to 100% with corresponding amounts of buffer A, was run over 120 min at a flow rate of 300 nl/min. Acquisition of a 100-ms survey scan was followed by acquisition of 32 fragment ion spectra from 32 precursor isolation windows (swaths) of 26 m/z each. The swaths were overlapping by 1 m/z and thus cover a range of 400- 1200 m/z. The SWATH MS2 spectra were recorded with an accumulation time of 100 ms and cover 100-2000 m/z. The collision energy for each window was determined according to the calculation for a charge 2+ ion centred upon the window with a spread of 15.

SWATH data analysis with OpenSWATH Raw SWATH data files (wiff) were converted into mzXML format using ProteoWizard (version 3.0.3316) (41) without centroiding. For the SWATH data analysis, the assay libraries of both strains were combined. The SWATH data was analysed using OpenSWATH (version 29995b387c238fdc58b195b0390aadcb2b355aa6) (31) with the following parameters: Chromatograms were extracted with 50 ppm around the expected mass of the fragment ions and with an extraction window of +/-5 min around the expected retention time after iRT alignment. The best model to separate true from false positives (per run) was determined by pyprophet (version 0.9.1) with 10 cross-validation runs (42). The pyprophet software re-implements the mProphet algorithm (43), which uses target and decoy signals to compute an optimal discriminant score which is then applied to all peak groups. Next, it estimates the false discovery rate (FDR) based on the Storey-Tibshirani method and computes a q- value for each peak group. The runs were subsequently aligned with the TRIC (TRansfer of Identification Confidence) algorithm which selected a corrected experiment-wide identification q-value cutoff of 0.00159 across the complete experiment to achieve 1% FDR for peptide identification. Next, TRIC used non-linear retention time correction to align all runs and computed a narrow retention time window for each run where the analyte was expected to elute. If a suitable peak group was found inside the window, it was selected for quantification (a manually selected score cut-off of 0.05 was used for quantification) (Röst et al., unpublished).

Relative quantification by ANOVA The data matrix produced by the OpenSWATH pipeline was compared for each pair of conditions separately using the R programming language. First, peptide analytes detected in less than 3 LC-MS/MS

63 for any condition were removed from the data analysis and the filtered data was normalized by its median. An analysis of variance (ANOVA) model was used for pairwise comparison of the conditions and computation of the effect size as well as significance. The resulting p-values were corrected using the Benjamini-Hochberg correction.

Protein inference by aLFQ To obtain a protein intensity value that correlates with its actual abundance, we ran the aLFQ R package (version 1.3.1) (44). Beforehand, the OpenSWATH output was filtered to contain only features below an mScore of 0.01 (1 % FDR). The sum of the 5 most intense transitions per peptide averaged over the 3 most intense peptides per protein was used as the protein intensity for all proteins identified by SWATH MS (29, 45). Also proteins with only 1 or 2 peptides were included.

Proteome comparison of different conditions For the proteome comparison of samples derived from individual strain colonization of plates and plants (three independent biological replicates per condition (n = 3); each plate sample consisted of three individual plates pooled to form one replicates, while plant samples consisted of 30 independent plant growth containers, each containing 8 plants, pooled to form one biological replicate) all proteins of the ANOVA output table with a fold change greater two (log2 fold change ≥ 1 or ≤ -1) and a p-value below 0.05 were considered significantly differentially regulated between the two conditions. To determine which proteins were only detectable in planta, the aLFQ output was filtered for proteins which are detectable in at least two out of three plant derived samples and in no plate derived sample. The same was subsequently done to identify proteins only detectable on plates. Genome annotation files from the Integrated Microbial Genomes (IMG) database of the Joint Genome Institute of the United States Department of Energy (46) were used to obtain KO terms of these proteins. KO terms were subsequently grouped into functional categories based on the KEGG BRITE database (http://www.genome.jp/kegg/brite.html; functional categories are based on hierarchy level B and categories irrelevant for microbial physiology, e.g. Cancers or Neurodegenerative diseases, were excluded). Functional enrichment analysis was performed using custom input files and the Cytoscape plugin BiNGO v 2.44 (47) with all proteins included in the spectral library as reference set of proteins for enrichment analyses.

Protein comparison with other leaf microbiota members We used blastp of the BLAST standalone software (v. 2.2.31+) to identify homologous proteins in the genomes of Methylobacteria of a recently established leaf microbiota strain collection (48). A protein was considered a hit, if the alignment covered at least 70 % of the M. extorquens PA1 query protein sequence with at least 70 % sequence identity. To identify similar proteins in more distantly related strains we downloaded all protein sequences of the assigned KO terms from the UniProt database (on

64 November 2, 2015) and used the HMMER toolkit (www.hmmer.org; v. 3.1b2) to first build Hidden Markov Models based on sequence alignments (Clustal Omega) of each KO term and subsequently query our leaf strain collection database with these models (for the KO term K08928 of the photosynthetic reaction center subunit L, two proteins annotated as subunit M were removed from the downloaded sequence list prior to aligning the sequences). Proteins were considered similar if the alignment covered at least 70% of the model with an e-value threshold of 10e-05.

Data availability The SWATH assay libraries for M. extorquens PA1 and S. melonis FR1 are available through the SWATHAtlas database (www.SWATHAtlas.org). All SWATH MS raw data have been made available through the PeptideAtlas database (http://www.peptideatlas.org/PASS/PASS00686).

RESULTS SWATH assay library construction To reliably quantify the proteome of the two commensal bacteria, M. extorquens PA1 and S. melonis Fr1, under different environmental conditions, we applied the targeted proteomic technique SWATH MS (27). In SWATH MS, identification and quantification of proteins requires knowledge on specific mass spectrometric coordinates for every protein of interest, which are typically compiled in an assay library (27) (Fig. 1A). For every protein, these coordinates consist of a number of representative peptides, their mass-over-charge ratio (m/z) and charge state, the most intense fragment ions formed during fragmentation of the peptide, as well as the normalized chromatographic retention time of the peptide (30). We initially built a comprehensive SWATH assay library for each of the two commensal strains M. extorquens PA1 and S. melonis Fr1 to support the subsequent quantitative proteome measurements. In order to cover the vast majority of proteins produced during colonization of the phyllosphere environment, we performed shotgun mass spectrometry of digested protein extracts of leaf washes as well as extensively fractionated, trypsin-digested protein extracts of liquid cultures grown to early exponential, mid-exponential and stationary growth phase (Fig. 1A). In total, the SWATH assay libraries generated for the two species comprise 3440 (71 % of predicted ORFs) and 2729 (71 %) proteins, for M. extorquens PA1 and S. melonis Fr1 respectively, while 2685 (56 %) and 2132 (55 %) proteins are represented by three or more peptides (Fig. 1B). These are the first assay libraries for core plant microbiota members allowing the comprehensive and accurate quantification of their proteomes by targeted mass spectrometry. Such near-complete SWATH assay libraries covering over 70% proteome coverage are to date only available for few other organisms, i.e., Saccharomyces cerevisiae, Mycobacterium tuberculosis, and Streptococcus pyogenes (28, 29, 31). The two SWATH assay libraries are available for download through the SWATHAtlas database (www.SWATHAtlas.org) as a public resource for future proteomic studies of these two model strains of the plant microbiota.

65 Overview of SWATH measurements To analyze the in planta physiology of M. extorquens and S. melonis under controlled laboratory conditions, we modified a protocol which has been successfully used to examine the in planta proteome of M. extorquens AM1 as well as the metaproteome of complex natural phyllosphere communities (12, 26). In combination with SWATH MS this procedure allowed for the quantification of 2373 proteins (49.1% of predicted ORFs) and 1610 (42.5%) proteins for M. extorquens PA1 and S. melonis Fr1, respectively (Fig. 1C). Estimated protein intensities were spanning more than three orders of magnitude for cells grown on solidified minimal media in agar plates as well as for phyllosphere-derived bacterial proteome samples (Fig. 1D). For M. extorquens we identified 635 candidate proteins being significantly regulated when growing on leaves compared to growth on minimal media (247 up- and 95 downregulated proteins; fold change ≥ |2|, p-value ≤ 0.05) or only detectable in one condition (126 in planta only and 167 not detected in planta) (Fig. 1E). For S. melonis, 545 proteins were significantly regulated when growing on leaves compared to growth on minimal media, with 69 up- and 141 downregulated proteins (fold change ≥ |2|, p-value ≤ 0.05), 240 proteins being only detectable on plants and 95 proteins only detectable on minimal media (Fig. 1E). In summary, we obtained deep proteome coverage for both members of the Arabidopsis microbiota in their natural habitat, the phyllosphere, as well as on minimal media, allowing us to study plant-specific physiology and thus, infer evolved adaptive mechanisms of each of the strains in a comprehensive manner.

Adaptation of S. melonis Fr1 to the Arabidopsis phyllosphere S. melonis is an organo-heterotrophic bacterium that is colonizing the phyllosphere of various host plants. To identify molecular processes representing the adaptation of S. melonis to the leaf compartment we mined the proteome of cells colonizing Arabidopsis plants for induced proteins as compared to growth on minimal media. Colonization of leaves by S. melonis Fr1 led to a significantly higher production of TCA cycle proteins, including isocitrate dehydrogenase (Sphme2DRAFT_3339), three different subunits of the α ketoglutarate dehydrogenase complex (E1 component Sphme2DRAFT_3096, E2 component Sphme2DRAFT_3097, E3 component Sphme2DRAFT_3098), the alpha and beta subunit of the succinyl-CoA synthetase (Sphme2DRAFT_3095, Sphme2DRAFT_0532) as well as a flavoprotein subunit of the succinate dehydrogenase (Sphme2DRAFT_1311) and malate dehydrogenase (Sphme2DRAFT_3093) (Fig. 2A, Tab. 1). Upregulation of these proteins might reflect higher flux through the TCA cycle and utilization of available substrates feeding into this central metabolic cycle compared to the glycolytic growth on minimal media. The most strongly upregulated protein on plants was alanine dehydrogenase (Sphme2DRAFT_2537), converting L-alanine into pyruvate and subsequently feeding into the TCA cycle via oxidative decarboxylation to acetyl-CoA (Fig. 2A, Tab. 1). Besides L-alanine conversion, two proteins associated with degradation of arginine (N- carbamoylputrescine amidase Sphme2DRAFT_1690 and succinylglutamic semialdehyde dehydrogenase Sphme2DRAFT_0117) and one protein involved in valine degradation (methylmalonic

66 acid semialdehyde dehydrogenase Sphme2DRAFT_1054) were among the most strongly upregulated proteins during plant colonization and additionally indicated utilization of available amino acids (Tab. 1). Furthermore, two subunits of the 2-oxoisovalerate dehydrogenase (Sphme2DRAFT_2061, Sphme2DRAFT_2063), responsible for the degradation of branched-chain amino acids, were induced in S. melonis grown on plants. Other differentially regulated proteins involved in acetate utilization (acetate-CoA ligase, Sphme2DRAFT_3183), oxidation of 3-oxoacids (3-oxoacid CoA-transferase subunit B, Sphme2DRAFT_2688) and fatty acid oxidation (long chain fatty acid transport protein, Sphme2DRAFT_3075, 3-hydroxyacyl-CoA dehydrogenase, Sphme2DRAFT_3077, acyl-CoA synthetase Sphme2DRAFT_3079, acyl-CoA dehydrogenases Sphme2DRAFT_0054, Sphme2DRAFT_1323, Sphme2DRAFT_2248 and enoyl-CoA hydratase Sphme2DRAFT_1289, Sphme2DRAFT_2065) indicate that fatty acids, or potentially long chain alkanes, which are part of the cuticle, might serve as additional nutrients during colonization of leaves. In agreement with the utilization of compounds yielding acetyl-CoA, isocitrate lyase (Sphme2DRAFT_2005), the key enzyme of the glyoxylate shunt, was only detectable in planta. Furthermore, dicarboxylates such as malate, succinate or fumarate might be taken up by the Na+/H+-dicarboxylate symporter proteins (Sphme2DRAFT_3255 and Sphme2DRAFT_0570), which were also only detectable during phyllosphere colonization (Fig. 2A, Tab. S1). Other differentially regulated transport proteins comprise iron uptake and efflux pump systems for detoxification and resistance (Tab. S1). Overall, our data indicate that growth of S. melonis on leaves is driven by plant-derived amino acids, small molecules like acetate and fatty acids or other hydrocarbon compounds, catabolized through the TCA cycle.

Adaptation of M. extorquens PA1 to the Arabidopsis phyllosphere M. extorquens PA1 is a facultative methylotroph and capable of utilizing plant derived methanol in the phyllosphere, a byproduct of plant cell wall biosynthesis (22, 49, 50). To identify molecular processes underlining the adaptation of M. extorquens to plant surfaces we looked for significantly induced proteins during growth on leaves as compared to growth on solidified minimal media, as described for S. melonis Fr1 above. This analysis confirmed the catalytic subunit of methanol dehydrogenase MxaF as the most abundant protein under both growth conditions. In addition, we found two other methanol dehydrogenase-like proteins, Mext_1809 (XoxF) and Mext_1339, to be upregulated during plant colonization (Tab. S2, Fig. S2). Furthermore, a large number of proteins involved in formate oxidation (Mext_4581 and Mext_4582, two subunits of the cytoplasmic Fdh1; Mext_0389, Mext_0390; Mext_0391, three subunits of the periplasmic Fdh3) were significantly more produced during plant colonization (Fig. 2B and Tab. S2). Interestingly, most proteins of the serine cycle and the ethylmalonyl- CoA pathway, important for assimilation of one-carbon (C1) compounds (51), were present at a

67 Tab. 1: Most strongly induced proteins during phyllosphere colonization of S. melonis Fr1 and M. extorquens PA1. Only significantly regulated proteins (Benjamini-Hochberg corrected p-value ≤ 0.05) are shown. Gene Locus-Tag Annotation Log2 FC p-value1

Sphingomonas melonis Fr1 2517158111 Sphme2DRAFT_2537 alanine dehydrogenase 5.3 <0.01 2517158262 Sphme2DRAFT_2688 3-oxoacid CoA-transferase, B subunit 3.4 <0.01 2517158089 Sphme2DRAFT_2515 Protease subunit of ATP-dependent Clp proteases 2.6 <0.01 2517158671 Sphme2DRAFT_3097 2-oxoglutarate dehydrogenase complex E2 component 2.1 0.0273 2517158667 Sphme2DRAFT_3093 malate dehydrogenase, NAD-dependent 2.1 <0.01 2517158672 Sphme2DRAFT_3098 dihydrolipoamide dehydrogenase 2.0 <0.01 2517159474 Sphme2DRAFT_3904 hypothetical protein 2.0 <0.01 2517156663 Sphme2DRAFT_1089 D-3-phosphoglycerate dehydrogenase 1.9 <0.01 2517156254 Sphme2DRAFT_0680 Predicted outer membrane lipoprotein 1.9 <0.01 2517158622 Sphme2DRAFT_3048 hypothetical protein 1.9 <0.01 2517158626 Sphme2DRAFT_3052 Predicted sugar kinase 1.9 0.0182 2517157004 Sphme2DRAFT_1430 Sporulation related domain. 1.8 <0.01 2517157264 Sphme2DRAFT_1690 N-carbamoylputrescine amidase 1.7 0.0179 2517157334 Sphme2DRAFT_1760 Multidrug resistance efflux pump 1.6 <0.01 2517157234 Sphme2DRAFT_1660 Predicted periplasmic/secreted protein 1.6 <0.01 2517158635 Sphme2DRAFT_3061 Membrane proteins related to metalloendopeptidases 1.6 <0.01 2517158142 Sphme2DRAFT_2568 Deoxyhypusine synthase 1.6 <0.01 2517156139 Sphme2DRAFT_0565 pyruvate kinase 1.6 <0.01 2517158432 Sphme2DRAFT_2858 NAD-dependent aldehyde dehydrogenases 1.6 <0.01 2517158634 Sphme2DRAFT_3060 tyrosyl-tRNA synthetase 1.6 <0.01 2517156628 Sphme2DRAFT_1054 methylmalonic acid semialdehyde dehydrogenase 1.5 <0.01 2517157977 Sphme2DRAFT_2403 Glutathione S-transferase 1.5 <0.01 2517155766 Sphme2DRAFT_0192 ribosomal protein L21 1.5 <0.01 2517158541 Sphme2DRAFT_2967 Peroxiredoxin 1.5 <0.01 2517156538 Sphme2DRAFT_0964 Glycine cleavage system protein P (pyridoxal-binding), C-terminal domain 1.5 <0.01 2517158824 Sphme2DRAFT_3250 Cysteine synthase 1.5 0.0213 2517156106 Sphme2DRAFT_0532 succinyl-CoA synthetase, beta subunit 1.4 <0.01 2517156919 Sphme2DRAFT_1345 Short-chain alcohol dehydrogenase of unknown specificity 1.4 0.0147 2517158295 Sphme2DRAFT_2721 Glucose/sorbosone dehydrogenases 1.4 0.0106 2517155691 Sphme2DRAFT_0117 succinylglutamic semialdehyde dehydrogenase 1.4 0.0115 2517156060 Sphme2DRAFT_0486 Domain of unknown function (DUF3576). 1.3 <0.01 2517158171 Sphme2DRAFT_2597 hypothetical protein 1.3 <0.01 2517155822 Sphme2DRAFT_0248 RND family efflux transporter, MFP subunit 1.3 <0.01 2517158649 Sphme2DRAFT_3075 Long-chain fatty acid transport protein 1.3 <0.01 2517156431 Sphme2DRAFT_0857 argininosuccinate lyase 1.3 <0.01 2517156535 Sphme2DRAFT_0961 glycine cleavage system T protein 1.3 <0.01 2517156271 Sphme2DRAFT_0697 Beta-lactamase class A 1.3 <0.01 2517158162 Sphme2DRAFT_2588 Ribosomal protein L10 1.3 <0.01 2517158942 Sphme2DRAFT_3368 ATP-binding cassette protein, ChvD family 1.2 <0.01 2517158985 Sphme2DRAFT_3411 Cell shape-determining protein 1.2 <0.01

Methylobacterium extorquens PA1 NC_010172.1_cdsid_YP_001642205.1 Mext_4766 hypothetical protein 4.5 <0.01 NC_010172.1_cdsid_YP_001640200.1 Mext_2738 photosynthetic reaction center cytochrome c subunit (PufC) 4.3 0.022 NC_010172.1_cdsid_YP_001640859.1 Mext_3402 hypothetical protein 4.2 <0.01 NC_010172.1_cdsid_YP_001642131.1 Mext_4692 protease Do 3.9 <0.01 NC_010172.1_cdsid_YP_001641409.1 Mext_3967 hypothetical protein 3.7 <0.01 NC_010172.1_cdsid_YP_001638811.1 Mext_1339 methanol/ethanol family PQQ-dependent dehydrogenase 3.2 <0.01 NC_010172.1_cdsid_YP_001640093.1 Mext_2630 hypothetical protein 3.1 <0.01 NC_010172.1_cdsid_YP_001638682.1 Mext_1209 putative oxalyl-CoA decarboxylase 2.9 0.013 NC_010172.1_cdsid_YP_001640767.1 Mext_3309 molybdopterin binding oxidoreductase 2.9 <0.01 NC_010172.1_cdsid_YP_001641647.1 Mext_4207 triple helix repeat-containing collagen 2.9 <0.01 NC_010172.1_cdsid_YP_001640025.1 Mext_2560 phasin (Gap20) 2.8 <0.01 NC_010172.1_cdsid_YP_001642168.1 Mext_4729 CsbD family protein 2.7 <0.01 NC_010172.1_cdsid_YP_001639679.1 Mext_2213 hypothetical protein 2.7 <0.01 NC_010172.1_cdsid_YP_001637566.1 Mext_0067 hypothetical protein 2.7 <0.01 NC_010172.1_cdsid_YP_001639614.1 Mext_2148 hypothetical protein 2.6 <0.01 NC_010172.1_cdsid_YP_001641262.1 Mext_3817 hypothetical protein 2.6 <0.01 NC_010172.1_cdsid_YP_001641592.1 Mext_4152 hypothetical protein 2.6 <0.01 NC_010172.1_cdsid_YP_001638148.1 Mext_0663 putative DNA-binding protein 2.6 <0.01 NC_010172.1_cdsid_YP_001637886.1 Mext_0393 hypothetical protein 2.6 <0.01 NC_010172.1_cdsid_YP_001638358.1 Mext_0882 PfpI family intracellular peptidase 2.6 <0.01 NC_010172.1_cdsid_YP_001639279.1 Mext_1809 methanol/ethanol family PQQ-dependent dehydrogenase (XoxF) 2.5 <0.01 NC_010172.1_cdsid_YP_001642090.1 Mext_4651 aromatic amino acid beta-eliminating lyase/threonine aldolase 2.5 <0.01 NC_010172.1_cdsid_YP_001639237.1 Mext_1767 hypothetical protein 2.5 <0.01 NC_010172.1_cdsid_YP_001638912.1 Mext_1440 hypothetical protein 2.4 0.025 NC_010172.1_cdsid_YP_001640238.1 Mext_2776 hypothetical protein 2.4 <0.01 NC_010172.1_cdsid_YP_001638796.1 Mext_1324 hypothetical protein 2.4 <0.01 NC_010172.1_cdsid_YP_001637885.1 Mext_0392 hypothetical protein 2.4 <0.01 NC_010172.1_cdsid_YP_001638850.1 Mext_1378 hypothetical protein 2.4 <0.01 NC_010172.1_cdsid_YP_001639962.1 Mext_2496 hypothetical protein 2.4 <0.01 NC_010172.1_cdsid_YP_001640222.1 Mext_2760 NAD(P) transhydrogenase subunit PntAB 2.4 <0.01 NC_010172.1_cdsid_YP_001640865.1 Mext_3408 alkanesulfonate monooxygenase 2.3 <0.01 NC_010172.1_cdsid_YP_001642123.1 Mext_4684 hypothetical protein 2.3 <0.01 NC_010172.1_cdsid_YP_001637681.1 Mext_0184 hypothetical protein 2.3 <0.01 NC_010172.1_cdsid_YP_001639333.1 Mext_1863 hypothetical protein 2.2 <0.01 NC_010172.1_cdsid_YP_001640504.1 Mext_3044 hypothetical protein 2.2 0.023 NC_010172.1_cdsid_YP_001637884.1 Mext_0391 formate dehydrogenase subunit gamma (Fdh3) 2.2 <0.01 NC_010172.1_cdsid_YP_001642264.1 Mext_4825 gluconate 2-dehydrogenase 2.2 0.021 NC_010172.1_cdsid_YP_001640221.1 Mext_2759 NAD(P)(+) transhydrogenase 2.1 <0.01 NC_010172.1_cdsid_YP_001640937.1 Mext_3481 hypothetical protein 2.1 <0.01 NC_010172.1_cdsid_YP_001641211.1 Mext_3766 hypothetical protein 2.1 <0.01 1 Benjamini-Hochberg corrected for multiple testing

68 Fig. 1: Experimental setup and characteristics of SWATH assay libraries and SWATH measurements. Comprehensive SWATH assay libraries were generated for M. extorquens and S. melonis based on various sample types and proteomic changes during growth on the leaf surface was subsequently studied by SWATH MS (A). SWATH assay library coverage of M. extorquens PA1 and S. melonis Fr1 (B). Grey shadings indicate the number of unambiguous peptide identifications per protein. Percentage of quantified and differentially regulated target proteins per strain (C). For (B) and (C), 100% corresponds to all annotated proteins within the genome. Dynamic quantification range of the two sample types analyzed by SWATH MS measurements of both strains (D). The abundance of all identified proteins was estimated using the best flyer approach (see Methods). Overview of differentially regulated proteins of M. extorquens and S. melonis during colonization of leaves or minimal media plates (E). 100 % corresponds to the total number of differentially regulated proteins per strain. significantly lower level during growth on plants, indicating that other multi-carbon compounds are used preferentially for assimilatory processes (Fig. S2, Tab. S2). The detection of enzymes involved in oxalate metabolism suggests that the plant-derived two-carbon (C2) compound might be metabolized by the methylotroph M. extorquens PA1. Formyl-CoA transferase and oxalyl-CoA decarboxylase (Mext_1207 and Mext_1209, respectively), needed for oxalate conversion, were upregulated on leaves and the oxalate formate antiporter (Mext_1212) was only detectable during phyllosphere colonization (Fig. 2B, Tab. S3). Exported formate can subsequently be oxidized by the periplasmic dehydrogenase Fdh3, which was upregulated during plant colonization. Previous characterization of the oxalate metabolism in M. extorquens AM1 revealed that reduction of oxalyl-CoA to glyoxylate is the preferred route of oxalate assimilation and that NAD(P)+ transhydrogenase for redox balance was highly expressed in cells grown on oxalate (52). In the present study, NAD(P)+ transhydrogenase was induced upon plant colonization in M. extorquens PA1, however,

69 the corresponding proteins for assimilatory reduction to glyoxylate were downregulated, indicating that oxalate oxidation is rather used for energy conservation than for carbon assimilation (Tab. S3). Besides oxidation of organic carbon, utilization of sunlight may act as accessory mode of energy conservation for phyllosphere bacteria (53, 54). The M. extorquens PA1 genome encodes for proteins of aerobic anoxygenic photosynthesis and photosynthetic cytochrome PufC (Mext_2738) was among the most upregulated proteins in planta (Tab. 1). In addition, the subunits L and H of the photosynthetic reaction center (Mext_2736 and Mext_4810, respectively) were only detectable in samples from in planta conditions, while two proteins potentially involved in bacteriochlorophyll biosynthesis (HemY domain containing protein and magnesium-protoporphyrin IX monomethyl ester cyclase, Mext_0735 and Mext_4806, respectively) were upregulated during plant colonization. In agreement with this finding, infrared autofluorescence, indicative of anoxygenic photosynthesis was detectable in M. extorquens PA1 cells colonizing the A. thaliana phyllosphere (Fig. 3). Another phyllosphere-specific response of M. extorquens PA1 was the upregulation of genes for alkanesulfonate monooxygenases. In total, the bacterial genome encodes four alkanesulfonate monooxygenases genes including homologs of SsuD and SfnG. Three of these genes are predicted to be part of transcriptional units including genes for uptake of sulfonate substrates and indeed all 16 genes

Fig. 2: Metabolic responses of S. melonis and M. extorquens during growth on plants. Upregulation of TCA cycle proteins during leaf colonization of S. melonis Fr1 (A). Several pathways feeding into this central metabolic cycle indicate which plant derived substrates might be utilized during phyllosphere growth. Pathway and involved proteins of oxalate metabolism by M. extorquens PA1 (B). Arrows are shown in blue (upregulated) and yellow (downregulated) if corresponding proteins are significantly regulated (fold change ≥ |2|, p-value ≤ 0.05). Fold changes indicated below the figures are logarithmized (log2); asterisks indicate a p-value ≤ 0.05.

70 were induced on plants (Tab. S4). Alkanesulfonate monooxygenases are generally involved in sulfur acquisition during sulfur limiting conditions (55). Upregulation of other genes involved in sulfate uptake (sulfate ABC transport permease and sulfate ABC transporter ATPase, Mext_0583 and Mext_0584, respectively) and sulfate assimilation (sulfate adenylyltransferase, Mext_2232 and Mext_2233) further support the notion of sulfur limitation on leaves. Other regulated transport proteins in our data set comprise uptake proteins of unknown specificity (e.g. general substrate transporter Mext_4685) as well as efflux systems to lower the concentration of toxic compounds (e.g. RND family efflux transporter MFP subunit, Mext_2832 among others) (Tab. S1). Notably, many proteins upregulated during plant colonization (Tab. 1) are not yet characterized and might be involved in yet unknown adaptive processes. In conclusion, our results underline the importance of methanol oxidation during plant colonization by M. extorquens and identify anoxygenic photosynthesis and oxalate metabolism as accessory modes of energy conversation during growth on leaves.

Fig. 3: False color images of M. extorquens cells showing infrared autofluorescence indicative of aerobic anoxygenic photosynthesis during leaf colonization. Overlay (A) of pictures acquired from the same field of view in phase contrast (B) and infrared fluorescence channels (C). Scale bar corresponds to 5 µm. Methylobacterium cells grown on minimal medium agar plates in the dark do not show background infrared autofluorescence indicative of anoxygenic photosynthesis (Fig. S3).

Metabolic specialization of Methylobacterium strains Since some traits induced by M. extorquens during plant colonization suggest niche specialization, we analyzed whether anoxygenic photosynthesis, oxalate metabolism and utilization of organic sulfonates are common features of leaf colonizing Methylobacteria. Using BLAST search against genomes of a recently established A. thaliana microbiota strain collection (48) we found that homologs of these proteins are encoded in the genomes of essentially all 31 Methylobacterium strains analyzed (Fig. 4A). The data suggests that the processes we identified in M. extorquens PA1 might be conserved among plant associated Methylobacterium strains. To identify similar proteins in more distantly related strains, we built profile Hidden Markov Models based on the assigned KEGG Orthology (KO) terms and found that genes of oxalate and alkanesulfonate metabolism are widespread among leaf microbiota members, while proteins of aerobic anoxygenic photosynthesis are almost exclusively found in Methylobacteria (Fig. 4B).

71 Fig. 4: Number and distribution of homologs of the identified target proteins in other leaf colonizing Methylobacteria and leaf microbiota members. We identified homologous proteins (Protein Blast; >70% query sequence coverage, >70 % sequence identity) of the target proteins involved in phyllosphere colonization by M. extorquens PA1 in the genomes of 31 leaf colonizing Methylobacteria (Methylobacterium sp. Leaf85 to Methylobacterium sp. Leaf469) of a recently established bacterial phyllosphere strain collection (46) (A). Hidden Markov Models based on assigned KEGG Orthology (KO) terms were used to identify functionally similar proteins in more distantly related leaf microbiota members (B). The color scheme indicates the number of putative homologs identified within the queried genomes of the distinct target strains. Proteins are formyl-CoA transferase (KO term: K07749), oxalyl-CoA decarboxylase (K01577) and the major facilitator transporter (K08177) of the oxalate metabolism, subunits L, M and the cytochrome c subunit of the photosynthesis apparatus (K08928, K08929 and K13992, respectively) as well as the alkanesulfonate monooxygenase homologs SsuD (K04091) and SfnG (K17228).

Overlap of regulated proteins between M. extorquens PA1 and S. melonis Fr1 The analysis of the in planta proteomes of M. extorquens PA1 and S. melonis Fr1 suggest species- specific adaptations, nonetheless common adaptation mechanisms upon plant colonization may also exist. To check if a subset of proteins or protein functions is shared between the two commensals we compared the assigned KO terms of all differentially regulated proteins. In total, genome annotation of M. extorquens PA1 contained KO terms for 263 of all significantly regulated proteins (41 %) with 125 (20 %) and 138 (22 %) showing induced or reduced expression on plants, respectively. On the other hand, 317 S. melonis proteins (58 %) are assigned KO terms and 201 (37 %) of them were induced on leaves, while 116 proteins (21 %) were present at lower levels. We first grouped all KO terms according to their functional category. Subsequent enrichment analysis revealed that the category “amino acid metabolism” is significantly enriched in S. melonis Fr1 on leaves (Fig. 5, p-value = 0.00275, Hypergeometric test, Benjamini-Hochberg FDR correction), underlining our earlier observations

72 Fig. 5: Proteome comparison of M. extorquens PA1 and S. melonis Fr1. KO term assignments of all differentially regulated proteins of M. extorquens PA1 and S. melonis Fr1 and their functional categories based on the KEGG Brite database. Blue and yellow bars represent the percentage of up and down regulated proteins, respectively for each functional category. Asterisks indicate significant enrichments (p ≤ 0.05; Hypergeometric test, Benjamini-Hochberg FDR correction) of up- or downregulated proteins of each functional category. regarding the induced utilization of amino acids. In M. extorquens PA1, proteins without assigned functional category were enriched (Fig. 5, p = 1.24 x 10-4, Hypergeometric test, Benjamini-Hochberg FDR correction) indicating that many potentially important plant adaptive processes are not yet characterized. In total, we found a relatively small subset of 12 KO terms shared among the upregulated proteins and 7 KO terms shared among the downregulated proteins of the two strains (Tab. S5). Notably, one of the proteins induced on leaves in both strains is the alkanesulfonate monooxygenase subunit SsuE (K00299), possibly indicating widespread utilization of sulfonates for sulfur acquisition during plant colonization. Other proteins induced in planta by both commensals include ABC type transporter proteins, a polysaccharide export protein, the phosphoenolpyruvate carboxykinase, phosphoglycerate dehydrogenase, threonine aldolase as well as a number of hypothetical proteins (K09796; Mext_1378, Mext_1379, Sphme2DRAFT_0994) only containing a conserved domain of unknown function (Tab. S5). Overall, the relatively small subset of proteins found to be co-regulated in M. extorquens PA1 and S. melonis Fr1 during growth on plant leaves underlines the apparent niche separation via distinct metabolic capacities and strategies for host colonization.

DISCUSSION Investigating molecular adaptations in members of microbial communities when they colonize their host is crucial to understand stable community patterns observed under environmental conditions and to unravel the molecular basis of host-microbe interactions. Here we extended earlier proteomic analyses of microbial plant community members (10, 56, 57) by applying the massively parallel targeted proteomic technique SWATH MS to reliably quantify the proteomic changes of two ubiquitous plant

73 commensals upon colonization of A. thaliana leaves. Methylobacterium and Sphingomonas are two genera commonly inhabiting the phyllosphere of different host plants and previous studies suggested distinct metabolic capabilities to be important drivers for niche separation (12). In the present study we confirmed the importance of methanol oxidation for energy conservation of M. extorquens during plant colonization including the methanol dehydrogenase-like protein XoxF. Previous studies have shown that XoxF is among the most abundant proteins in natural phyllosphere communities and that a xoxF mutant of M. extorquens is affected in plant colonization under competitive conditions (12, 58). Based on the high proteome coverage achieved with SWATH MS, we found that enzymes of the linear methanol oxidation pathway are induced during growth on plants, however, proteins involved in the metabolic pathways for C1 assimilation were downregulated, indicating that additional carbon compounds might be used preferentially for assimilatory purposes during plant colonization by the facultative methylotroph. In this context, also our observation of upregulation of proteins for oxalate dissimilation is notable (Fig. 2B), indicating utilization of the plant-derived C2 compound by Methylobacterium. Oxalate is produced by a wide range of plants and is involved in various processes, including Calcium storage, detoxification of heavy metals as well as plant protection. Generally, oxalotrophy was reported to be a rare bacterial trait, but it has frequently been found in microbes living in close association with plants (59). Based on assigned KO terms we identified the key enzymes in oxalate conversion in a wide range of phylogenetically distinct leaf microbiota members (Fig. 4B) confirming this tendency. Besides oxalate biosynthesis by the plant host, some phytopathogenic fungi and bacteria are known to produce oxalate as a functionally diverse virulence factor. Therefore, oxalate degradation can be considered a plant protective feature and oxalotrophy was shown to be involved in recruiting beneficial Burkholderia strains to the plant root (59, 60). Methylobacteria and other microbiota members may act as sink of oxalate on leaves and therefore lower the risk of infectious diseases, for example caused by the oxalate-secreting broad host pathogen Botrytis cinerea. Besides proteins related to carbon metabolism, we found a conspicuous overrepresentation of proteins involved in uptake and utilization of sulfonates for sulfur acquisition, e.g. the 16 genes of predicted transcriptional units containing alkanesulfonate monooxygenase genes, were induced upon colonization of the leaf surface. The SsuE subunit of the alkane monooxygenase was not only upregulated in M. extorquens, but also in S. melonis and it was further shown to be induced during epiphytic growth of the plant pathogen Pseudomonas syringae B728a (61, 62). Generally, sulfur metabolism in the phyllosphere is poorly understood, but our findings indicate broader utilization of organic sulfonates by the plant microbiota. S. melonis Fr1 is known to confer a protective effect to the plant host A. thaliana against the foliar pathogen Pseudomonas syringae pv. tomato DC3000. The detailed molecular mechanism of this interaction remains elusive, but several traits might contribute incrementally to plant host protection, including stimulation of plant host immunity and competition for nutrients, especially during the first

74 phase of pathogen invasion into the host-associated microbiota (20, 21). The in planta proteome of S. melonis Fr1 indicates utilization of substrates feeding into the TCA cycle including acetate, fatty acid compounds, dicarboxylates and the amino acid alanine. Alanine dehydrogenase was the most strongly upregulated protein during phyllosphere colonization of S. melonis, being approximately forty times more abundant compared to growth on minimal media. Interestingly, the importance of alanine catabolism for successful in vivo proliferation has been demonstrated for another Pseudomonad, the human pathogen Pseudomonas aeruginosa during infection of the lungs (63). Besides alanine dehydrogenase, enzymes for arginine and valine degradation were among the most upregulated proteins, in line with the recently acquired metabolic footprint by MALDI-TOF imaging of S. melonis, demonstrating arginine utilization on Arabidopsis leaves (64). We furthermore found a set of transport proteins involved in multidrug resistance, efflux of toxic compounds as well as iron uptake induced in S. melonis Fr1 upon plant colonization, which might contribute to its perseverance in the phyllosphere, also with regard to pathogenic strains invading the habitat. The comparison of the differential proteomes of M. extorquens PA1 and S. melonis Fr1 underlines the apparent niche separation via distinct metabolic capacities and strategies for host colonization, as only a relatively small subset of proteins was found to be co-regulated between both strains. Common traits include sulfonate metabolism, phosphate uptake and secretion of polysaccharides. We also found hypothetical proteins containing a conserved domain of unknown function to be co-regulated between the two strains, indicating that other conserved responses might be hidden among proteins for which no functional annotations are yet available (59 % and 42 % of candidate proteins for M. extorquens and S. melonis, respectively). In summary, our study shows that SWATH MS is a suitable tool to study physiological responses of the indigenous microbiota to their natural habitat within the respective host. Our findings indicate that the two ubiquitous commensals M. extorquens and S. melonis have evolved strain-specific and fundamentally different ways to adapt to the plant environment. While S. melonis is utilizing a variety of more general organic substrates like acetate, dicarboxylates, amino acids or potentially hydrocarbon compounds, M. extorquens is highly specified in using C1 and C2 plant compounds as well as anoxygenic photosynthesis to support growth on leaves. Despite the apparent niche separation, a subset of shared proteins between the two strains indicate common adaptive processes to the phyllosphere, and identify organic sulfonates as potentially widespread sulfur source on leaves.

Acknowledgements

The work was supported by ETH Research Grant ETH-41 14-2 and an ERC Advanced grant (PhyMo).

75 References 1. Russell, J. A., Dubilier, N., and Rudgers, J. A. (2014) Nature's microbiome: introduction. Mol. Ecol. 23, 1225-1237 2. Bulgarelli, D., Schlaeppi, K., Spaepen, S., Ver Loren van Themaat, E., and Schulze-Lefert, P. (2013) Structure and functions of the bacterial microbiota of plants. Annu. Rev. Plant Biol. 64, 807-38 3. Turnbaugh, P. J., Ley, R. E., Hamady, M., Fraser-Liggett, C. M., Knight, R., and Gordon, J. I. (2007) The Human Microbiome Project. Nature 449, 804-810 4. Ley, R. E., Hamady, M., Lozupone, C., Turnbaugh, P. J., Ramey, R. R., Bircher, J. S., Schlegel, M. L., Tucker, T. A., Schrenzel, M. D., Knight, R., and Gordon, J. I. (2008) Evolution of mammals and their gut microbes. Science 320, 1647-1651 5. Backhed, F., Fraser, C. M., Ringel, Y., Sanders, M. E., Sartor, R. B., Sherman, P. M., Versalovic, J., Young, V., and Finlay, B. B. (2012) Defining a healthy human gut microbiome: current concepts, future directions, and clinical applications. Cell Host Microbe 12, 611-622 6. Abt, M. C., and Pamer, E. G. (2014) Commensal bacteria mediated defenses against pathogens. Curr. Opinion Immunol. 29, 16-22 7. Lugtenberg, B., and Kamilova, F. (2009) Plant-Growth-Promoting Rhizobacteria. Annu. Rev. Microbiol. 63, 541-556 8. Bulgarelli, D., Rott, M., Schlaeppi, K., Ver Loren van Themaat, E., Ahmadinejad, N., Assenza, F., Rauf, P., Huettel, B., Reinhardt, R., Schmelzer, E., Peplies, J., Gloeckner, F. O., Amann, R., Eickhorst, T., and Schulze-Lefert, P. (2012) Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91-95 9. Lundberg, D. S., Lebeis, S. L., Paredes, S. H., Yourstone, S., Gehring, J., Malfatti, S., Tremblay, J., Engelbrektson, A., Kunin, V., del Rio, T. G., Edgar, R. C., Eickhorst, T., Ley, R. E., Hugenholtz, P., Tringe, S. G., and Dangl, J. L. (2012) Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86-90 10. Knief, C., Delmotte, N., Chaffron, S., Stark, M., Innerebner, G., Wassmann, R., von Mering, C., and Vorholt, J. A. (2012) Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. ISME J 6, 1378-1390 11. Zarraonaindia, I., Owens, S. M., Weisenhorn, P., West, K., Hampton-Marcell, J., Lax, S., Bokulich, N. A., Mills, D. A., Martin, G., Taghavi, S., van der Lelie, D., and Gilbert, J. A. (2015) The soil microbiome influences grapevine-associated microbiota. mBio 6, e02527-14 12. Delmotte, N., Knief, C., Chaffron, S., Innerebner, G., Roschitzki, B., Schlapbach, R., von Mering, C., and Vorholt, J. A. (2009) Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc. Natl. Acad. Sci. USA 106, 16428-16433 13. Vorholt, J. A. (2012) Microbial life in the phyllosphere. Nature Rev. Microbiol. 10, 828-840 14. Remus-Emsermann, M. N., Lücker, S., Müller, D. B., Potthoff, E., Daims, H., and Vorholt, J. A. (2014) Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environ. Microbiol. 16, 2329-2340 15. Meyer, K. M., and Leveau, J. H. J. (2012) Microbiology of the phyllosphere: a playground for testing ecological concepts. Oecologia 168, 621-629 16. Lindow, S. E., and Brandl, M. T. (2003) Microbiology of the phyllosphere. Appl. Environ. Microbiol. 69, 1875-1883 17. Bodenhausen, N., Horton, M. W., and Bergelson, J. (2013) Bacterial communities associated with the leaves and the roots of Arabidopsis thaliana. PloS One 8, e56329 18. Knief, C., Frances, L., Cantet, F., and Vorholt, J. A. (2008) Cultivation-independent characterization of Methylobacterium populations in the plant phyllosphere by automated ribosomal intergenic spacer analysis. Appl. Environ. Microbiol. 74, 2218-2228

76 19. Knief, C., Frances, L., and Vorholt, J. A. (2010) Competitiveness of diverse Methylobacterium strains in the phyllosphere of Arabidopsis thaliana and identification of representative models, including M. extorquens PA1. Microbial Ecol. 60, 440-452 20. Vogel, C., Innerebner, G., Zingg, J., Guder, J., and Vorholt, J. A. (2012) Forward genetic in planta screen for identification of plant-protective traits of Sphingomonas sp. strain Fr1 against Pseudomonas syringae DC3000. Appl. Environ. Microbiol. 78, 5529-5535 21. Innerebner, G., Knief, C., and Vorholt, J. A. (2011) Protection of Arabidopsis thaliana against leaf-pathogenic Pseudomonas syringae by Sphingomonas strains in a controlled model system. Appl. Environ. Microbiol. 77, 3202-3210 22. Abanda-Nkpwatt, D., Musch, M., Tschiersch, J., Boettner, M., and Schwab, W. (2006) Molecular interaction between Methylobacterium extorquens and seedlings: growth promotion, methanol consumption, and localization of the methanol emission site. J. Exp. Botany 57, 4025-4032 23. Koenig, R. L., Morris, R. O., and Polacco, J. C. (2002) tRNA is the source of low-level trans- zeatin production in Methylobacterium spp. J. Bacteriol. 184, 1832-1842 24. Monier, J. M., and Lindow, S. E. (2003) Differential survival of solitary and aggregated bacterial cells promotes aggregate formation on leaf surfaces. Proc. Natl. Acad. Sci USA 100, 15977-15982 25. Jacobs, J. L., Carroll, T. L., and Sundin, G. W. (2005) The role of pigmentation, ultraviolet radiation tolerance, and leaf colonization strategies in the epiphytic survival of phyllosphere bacteria. Microbial Ecol. 49, 104-113 26. Gourion, B., Rossignol, M., and Vorholt, J. A. (2006) A proteomic study of Methylobacterium extorquens reveals a response regulator essential for epiphytic growth. Proc. Natl. Acad. Sci USA 103, 13186-13191 27. Gillet, L. C., Navarro, P., Tate, S., Rost, H., Selevsek, N., Reiter, L., Bonner, R., and Aebersold, R. (2012) Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 28. Selevsek, N., Chang, C. Y., Gillet, L. C., Navarro, P., Bernhardt, O. M., Reiter, L., Cheng, L. Y., Vitek, O., and Aebersold, R. (2015) Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-mass spectrometry. Mol. Cell. Proteomics 14, 739-749 29. Schubert, O. T., Ludwig, C., Kogadeeva, M., Zimmermann, M., Rosenberger, G., Gengenbacher, M., Gillet, L. C., Collins, B. C., Rost, H. L., Kaufmann, S. H., Sauer, U., and Aebersold, R. (2015) Absolute proteome composition and dynamics during dormancy and resuscitation of Mycobacterium tuberculosis. Cell Host Microbe 18, 96-108 30. Schubert, O. T., Gillet, L. C., Collins, B. C., Navarro, P., Rosenberger, G., Wolski, W. E., Lam, H., Amodei, D., Mallick, P., MacLean, B., and Aebersold, R. (2015) Building high-quality assay libraries for targeted analysis of SWATH MS data. Nature Protocols 10, 426-441 31. Röst, H. L., Rosenberger, G., Navarro, P., Gillet, L., Miladinovic, S. M., Schubert, O. T., Wolskit, W., Collins, B. C., Malmstrom, J., Malmstrom, L., and Aebersold, R. (2014) OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol 32, 219-223 32. Peyraud, R., Kiefer, P., Christen, P., Massou, S., Portais, J. C., and Vorholt, J. A. (2009) Demonstration of the ethylmalonyl-CoA pathway by using 13C metabolomics. Proc. Natl. Acad. Sci USA 106, 4846-4851 33. Schubert, O. T., Mouritsen, J., Ludwig, C., Rost, H. L., Rosenberger, G., Arthur, P. K., Claassen, M., Campbell, D. S., Sun, Z., Farrah, T., Gengenbacher, M., Maiolica, A., Kaufmann, S. H. E., Moritz, R. L., and Aebersold, R. (2013) The Mtb proteome library: A resource of assays to quantify the complete proteome of Mycobacterium tuberculosis. Cell Host Microbe 13, 602-612

77 34. Escher, C., Reiter, L., MacLean, B., Ossola, R., Herzog, F., Chilton, J., MacCoss, M. J., and Rinner, O. (2012) Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12, 1111-1121 35. Deutsch, E. W., Mendoza, L., Shteynberg, D., Farrah, T., Lam, H., Tasman, N., Sun, Z., Nilsson, E., Pratt, B., Prazen, B., Eng, J. K., Martin, D. B., Nesvizhskii, A. I., and Aebersold, R. (2010) A guided tour of the trans-proteomic pipeline. Proteomics 10, 1150-1159 36. Keller, A., Nesvizhskii, A. I., Kolker, E., and Aebersold, R. (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383-5392 37. Shteynberg, D., Deutsch, E. W., Lam, H., Eng, J. K., Sun, Z., Tasman, N., Mendoza, L., Moritz, R. L., Aebersold, R., and Nesvizhskii, A. I. (2011) iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol. Cell. Proteomics M111.007690 38. Reiter, L., Claassen, M., Schrimpf, S. P., Jovanovic, M., Schmidt, A., Buhmann, J. M., Hengartner, M. O., and Aebersold, R. (2009) Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol. Cell. Proteomics 8, 2405-2417 39. Lam, H., Deutsch, E. W., Eddes, J. S., Eng, J. K., King, N., Stein, S. E., and Aebersold, R. (2007) Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 7, 655-667 40. Lam, H., Deutsch, E. W., Eddes, J. S., Eng, J. K., Stein, S. E., and Aebersold, R. (2008) Building consensus spectral libraries for peptide identification in proteomics. Nature Meth. 5, 873-875 41. Chambers, M. C., Maclean, B., Burke, R., Amodei, D., Ruderman, D. L., Neumann, S., Gatto, L., Fischer, B., Pratt, B., Egertson, J., Hoff, K., Kessner, D., Tasman, N., Shulman, N., Frewen, B., Baker, T. A., Brusniak, M. Y., Paulse, C., Creasy, D., Flashner, L., Kani, K., Moulding, C., Seymour, S. L., Nuwaysir, L. M., Lefebvre, B., Kuhlmann, F., Roark, J., Rainer, P., Detlev, S., Hemenway, T., Huhmer, A., Langridge, J., Connolly, B., Chadick, T., Holly, K., Eckels, J., Deutsch, E. W., Moritz, R. L., Katz, J. E., Agus, D. B., MacCoss, M., Tabb, D. L., and Mallick, P. (2012) A cross-platform toolkit for mass spectrometry and proteomics. Nature Biotech. 30, 918- 920 42. Teleman, J., Rost, H. L., Rosenberger, G., Schmitt, U., Malmstrom, L., Malmstrom, J., and Levander, F. (2015) DIANA-algorithmic improvements for analysis of data-independent acquisition MS data. Bioinformatics 31, 555-562 43. Reiter, L., Rinner, O., Picotti, P., Huttenhain, R., Beck, M., Brusniak, M. Y., Hengartner, M. O., and Aebersold, R. (2011) mProphet: automated data processing and statistical validation for large- scale SRM experiments. Nature Meth 8, 430-435 44. Rosenberger, G., Ludwig, C., Rost, H. L., Aebersold, R., and Malmstrom, L. (2014) aLFQ: an R- package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data. Bioinformatics 30, 2511-2513 45. Ludwig, C., Claassen, M., Schmidt, A., and Aebersold, R. (2012) Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry. Mol. Cell Proteomics 11, M111.013987 46. Markowitz, V. M., Chen, I. M., Palaniappan, K., Chu, K., Szeto, E., Grechkin, Y., Ratner, A., Jacob, B., Huang, J., Williams, P., Huntemann, M., Anderson, I., Mavromatis, K., Ivanova, N. N., and Kyrpides, N. C. (2012) IMG: the Integrated Microbial Genomes database and comparative analysis system. Nucleic Acids Res. 40, D115-122 47. Maere, S., Heymans, K., and Kuiper, M. (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in biological networks. Bioinformatics 21, 3448- 3449

78 48. Bai, Y., Müller, D. B., Srinivas, G., Garrido-Oter, R., Potthoff, E., Rott, M., Dombrowski, N., Münch, P. C., Spaepen, S., Remus-Emsermann, M., Hüttel, B., McHardy, A. C., Vorholt, J. A., and Schulze-Lefert, P. (2015) Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364-369 49. Sy, A., Timmers, A. C., Knief, C., and Vorholt, J. A. (2005) Methylotrophic metabolism is advantageous for Methylobacterium extorquens during colonization of Medicago truncatula under competitive conditions. Appl. Environ. Microbiol. 71, 7245-7252 50. Fall, R., and Benson, A. A. (1996) Leaf methanol - The simplest natural product from plants. Trends Plant Sci 1, 296-301 51. Ochsner, A. M., Sonntag, F., Buchhaupt, M., Schrader, J., and Vorholt, J. A. (2015) Methylobacterium extorquens: methylotrophy and biotechnological applications. Appl Microbiol Biotech. 99, 517-534 52. Schneider, K., Skovran, E., and Vorholt, J. A. (2012) Oxalyl-Coenzyme A reduction to glyoxylate Is the preferred route of oxalate assimilation in Methylobacterium extorquens AM1. J. Bacteriol. 194, 3144-3155 53. Atamna-Ismaeel, N., Finkel, O., Glaser, F., von Mering, C., Vorholt, J. A., Koblizek, M., Belkin, S., and Beja, O. (2012) Bacterial anoxygenic photosynthesis on plant leaf surfaces. Env Microbiol Rep 4, 209-216 54. Stiefel, P., Zambelli, T., and Vorholt, J. A. (2013) Isolation of optically targeted single bacteria by application of fluidic force microscopy to aerobic anoxygenic phototrophs from the phyllosphere. Appl. Environ. Microbiol. 79, 4895-4905 55. Ellis, H. R. (2011) Mechanism for sulfur acquisition by the alkanesulfonate monooxygenase system. Bioorg. Chem. 39, 178-184 56. Knief, C., Delmotte, N., and Vorholt, J. A. (2011) Bacterial adaptation to life in association with plants - A proteomic perspective from culture to in situ conditions. Proteomics 11, 3086-3105 57. Afroz, A., Zahur, M., Zeeshan, N., and Komatsu, S. (2013) Plant-bacterium interactions analyzed by proteomics. Front. Plant Sci. 4, 21 58. Schmidt, S., Christen, P., Kiefer, P., and Vorholt, J. A. (2010) Functional investigation of methanol dehydrogenase-like protein XoxF in Methylobacterium extorquens AM1. Microbiol. 156, 2575- 2586 59. Kost, T., Stopnisek, N., Agnoli, K., Eberl, L., and Weisskopf, L. (2014) Oxalotrophy, a widespread trait of plant-associated Burkholderia species, is involved in successful root colonization of lupin and maize by Burkholderia phytofirmans. Front. Microbiol. 4, 421 60. Schoonbeek, H. J., Jacquat-Bovet, A. C., Mascher, F., and Metraux, J. P. (2007) Oxalate- degrading bacteria can protect Arabidopsis thaliana and crop plants against Botrytis cinerea. Mol. Plant Microbe Interact. 20, 1535-1544 61. Yu, X., Lund, S. P., Scott, R. A., Greenwald, J. W., Records, A. H., Nettleton, D., Lindow, S. E., Gross, D. C., and Beattie, G. A. (2013) Transcriptional responses of Pseudomonas syringae to growth in epiphytic versus apoplastic leaf sites. Proc. Natl. Acad. Sci. USA 110, E425-434 62. Marco, M. L., Legac, J., and Lindow, S. E. (2005) Pseudomonas syringae genes induced during colonization of leaf surfaces. Environ. Microbiol. 7, 1379-1391 63. Boulette, M. L., Baynham, P. J., Jorth, P. A., Kukavica-Ibrulj, I., Longoria, A., Barrera, K., Levesque, R. C., and Whiteley, M. (2009) Characterization of alanine catabolism in Pseudomonas aeruginosa and its importance for proliferation in vivo. J. Bacteriol. 191, 6329-6334 64. Ryffel, F., Helfrich, E. J., Kiefer, P., Peyriga, L., Portais, J. C., Piel, J., and Vorholt, J. A. (2015) Metabolic footprint of epiphytic bacteria on Arabidopsis thaliana leaves. ISME J. 10, 632-643

79 Tab. S1: Differentially regulated transport proteins of S. melonis Fr1 and M. extorquens PA1 during colonization of the phyllosphere. Locus Tag log2FC p-value1 Annotation KO term

Sphingomonas melonis Fr1 Sphme2DRAFT_1292 ∞ not in planta ABC-type antimicrobial peptide transport system, ATPase component K02003 Sphme2DRAFT_2463 ∞ not in planta TonB-dependent receptor K02014 Sphme2DRAFT_2539 ∞ not in planta K+ transporter K03549 Sphme2DRAFT_2640 ∞ not in planta heavy metal efflux pump (cobalt-zinc-cadmium) K15726 Sphme2DRAFT_3241 ∞ not in planta TonB-dependent receptor no KO term Sphme2DRAFT_3443 ∞ not in planta Na+/H+-dicarboxylate symporters no KO term Sphme2DRAFT_3924 ∞ not in planta Multidrug resistance efflux pump K15727 Sphme2DRAFT_0085 ∞ in planta only Type II secretory pathway, component PulL K02461 Sphme2DRAFT_0092 ∞ in planta only Type II secretory pathway, ATPase PulE/Tfp pilus assembly pathway, ATPase PilB K02454 Sphme2DRAFT_0094 ∞ in planta only hypothetical protein K02452 Sphme2DRAFT_0570 ∞ in planta only Na+/H+-dicarboxylate symporters no KO term Sphme2DRAFT_0861 ∞ in planta only Mg2+ and Co2+ transporters K16074 Sphme2DRAFT_1176 ∞ in planta only Predicted flavoprotein involved in K+ transport no KO term Sphme2DRAFT_1215 ∞ in planta only TonB-dependent receptor no KO term Sphme2DRAFT_1218 ∞ in planta only Outer membrane receptor proteins, mostly Fe transport no KO term Sphme2DRAFT_1440 ∞ in planta only twin arginine-targeting protein translocase TatB K03117 Sphme2DRAFT_1490 ∞ in planta only ABC-type phosphate transport system, periplasmic component K02040 Sphme2DRAFT_1600 ∞ in planta only export-related chaperone CsaA K06878 Sphme2DRAFT_1633 ∞ in planta only Outer membrane receptor proteins, mostly Fe transport K02014 Sphme2DRAFT_1759 ∞ in planta only drug resistance transporter, EmrB/QacA subfamily K03446 Sphme2DRAFT_1905 ∞ in planta only ABC-type multidrug transport system, ATPase component K01990 Sphme2DRAFT_2035 ∞ in planta only Outer membrane receptor proteins, mostly Fe transport K02014 Sphme2DRAFT_2144 ∞ in planta only cation diffusion facilitator family transporter K16264 Sphme2DRAFT_2257 ∞ in planta only autotransporter secretion outer membrane protein TamA K07278 Sphme2DRAFT_2293 ∞ in planta only molybdenum ABC transporter, periplasmic molybdate-binding protein K02020 Sphme2DRAFT_2388 ∞ in planta only Outer membrane receptor proteins, mostly Fe transport K02014 Sphme2DRAFT_2399 ∞ in planta only K+-transporting ATPase, B subunit K01547 Sphme2DRAFT_2554 ∞ in planta only Periplasmic protein involved in polysaccharide export K01991 Sphme2DRAFT_2984 ∞ in planta only ABC-type antimicrobial peptide transport system, ATPase component K09810 Sphme2DRAFT_3177 ∞ in planta only efflux transporter, outer membrane factor (OMF) lipoprotein, NodT family no KO term Sphme2DRAFT_3232 ∞ in planta only Phosphotransferase system, mannose/fructose-specific component IIA K02793 Sphme2DRAFT_3255 ∞ in planta only Na+/H+-dicarboxylate symporters no KO term Sphme2DRAFT_3424 ∞ in planta only Multidrug resistance efflux pump K03543 Sphme2DRAFT_1760 1.6 0.000 Multidrug resistance efflux pump K03543 Sphme2DRAFT_0248 1.3 0.000 RND family efflux transporter, MFP subunit K03585 Sphme2DRAFT_3075 1.3 0.006 Long-chain fatty acid transport protein K06076 Sphme2DRAFT_1931 -1.1 0.023 putative oligopeptide transporter, OPT family no KO term Sphme2DRAFT_1505 -1.2 0.000 preprotein translocase, SecE subunit, bacterial K03073 Sphme2DRAFT_3016 -1.3 0.049 Kef-type K+ transport systems, membrane components K03455 Sphme2DRAFT_1964 -1.5 0.030 amino acid/polyamine/organocation transporter K03294 Sphme2DRAFT_0228 -1.5 0.000 Amino acid transporters K03294 Sphme2DRAFT_2246 -1.6 0.001 glucose/galactose transporter K02429 Sphme2DRAFT_1113 -5.0 0.010 Cation/multidrug efflux pump no KO term

Methylobacterium extorquens PA1 Mext_0396 ∞ not in planta efflux transporter, RND family, MFP subunit no KO term Mext_0672 ∞ not in planta TonB-dependent siderophore receptor K02014 Mext_2137 ∞ not in planta major facilitator superfamily MFS_1 K08177 Mext_2515 ∞ not in planta binding-protein-dependent transport systems inner membrane component K13895 Mext_2624 ∞ not in planta ABC transporter related no KO term Mext_2874 ∞ not in planta ABC transporter related K11085 Mext_4071 ∞ not in planta ABC transporter related K13409 Mext_4199 ∞ not in planta major facilitator superfamily MFS_1 no KO term Mext_4371 ∞ not in planta binding-protein-dependent transport systems inner membrane component K02050 Mext_4496 ∞ not in planta twin-arginine translocation pathway signal K15576 Mext_1172 ∞ in planta only aliphatic sulfonates family ABC transporter, periplsmic ligand-binding protein K15553 Mext_1212 ∞ in planta only major facilitator superfamily MFS_1 K08177 Mext_1299 ∞ in planta only ammonium transporter K03320 Mext_2739 ∞ in planta only type I secretion system ATPase K06148 Mext_2740 ∞ in planta only type I secretion membrane fusion protein, HlyD family K02022 Mext_3265 ∞ in planta only putative exported protein of unknown function no KO term Mext_3338 ∞ in planta only ABC-type nitrate/sulfonate/bicarbonate transport systems components-like protein K02051 Mext_3341 ∞ in planta only aliphatic sulfonates family ABC transporter, periplsmic ligand-binding protein K02051 Mext_3347 ∞ in planta only putative ABC transporter, substrate-binding protein K02051 Mext_3405 ∞ in planta only aliphatic sulfonates family ABC transporter, periplsmic ligand-binding protein K15553 Mext_3406 ∞ in planta only binding-protein-dependent transport systems inner membrane component K15554 Mext_3407 ∞ in planta only ABC transporter related K15555 Mext_3438 ∞ in planta only ABC-2 type transporter K01992 Mext_3494 ∞ in planta only hypothetical protein K01999 Mext_4685 ∞ in planta only General substrate transporter K03761 Mext_2535 2.0 0.000 polysaccharide export protein K01991 Mext_3409 1.9 0.029 aliphatic sulfonates family ABC transporter, periplsmic ligand-binding protein K15553 Mext_4713 1.8 0.000 phosphate ABC transporter, periplasmic phosphate-binding protein K02040 Mext_2832 1.6 0.030 efflux transporter, RND family, MFP subunit no KO term Mext_4223 1.4 0.005 L-lactate transport K03303 Mext_4754 1.3 0.002 secretion protein HlyD family protein no KO term Mext_0584 1.2 0.000 sulfate ABC transporter, ATPase subunit K02045 Mext_4833 1.2 0.000 MscS Mechanosensitive ion channel no KO term Mext_0583 1.2 0.024 sulfate ABC transporter, inner membrane subunit CysW K02047 Mext_0380 1.0 0.000 ABC transporter related K01990 1 Benjamini-Hochberg corrected for multiple testing

80 Tab. S2: Methanol metabolism of M. extorquens PA1 during colonization of the A. thaliana phyllosphere. Locus-Tag Annotation (Gene name) log2 FC p-value1 Nr.

Methanol oxidation Mext_4150 methanol/ethanol family PQQ-dependent dehydrogenase (mxaF) -0.2 <0.01 1 Mext_4147 methanol dehydrogenase subunit beta -0.1 0.726 2 Mext_1809 methanol/ethanol family PQQ-dependent dehydrogenase (xoxF) 2.5 <0.01 3 Mext_1339 methanol/ethanol family PQQ-dependent dehydrogenase 3.2 <0.01 4 Mext_0099 methanol/ethanol family PQQ-dependent dehydrogenase n.d. 5

Formaldehyde oxidation Mext_1809 methanol/ethanol family PQQ-dependent dehydrogenase 2.5 <0.01 6 H4MPT dependent Mext_1834 formaldehyde-activating enzyme -1.7 <0.01 7 Mext_1450 formaldehyde-activating enzyme -0.8 <0.01 8 Mext_1829 methylene-tetrahydromethanopterin dehydrogenase -0.7 <0.01 9 Mext_1831 N(5),N(10)-methenyltetrahydromethanopterin cyclohydrolase -0.8 <0.01 10 Mext_1824 formylmethanofuran dehydrogenase subunit C -0.4 <0.01 11 Mext_1825 formylmethanofuran--tetrahydromethanopterin formyltransferase -0.7 <0.01 12 Mext_1826 formylmethanofuran dehydrogenase subunit A -0.5 <0.01 13 Mext_1827 tungsten-containing formylmethanofuran dehydrogenase subunit B -0.5 <0.01 14 H4FT dependent Mext_1797 methylenetetrahydrofolate dehydrogenase -2.4 <0.01 15 Mext_1798 formiminotransferase-cyclodeaminase -2.6 <0.01 16 Mext_0414 formate--tetrahydrofolate ligase -2.1 <0.01 17

Formate oxidation Mext_4582 formate dehydrogenase subunit alpha (fdh1A) 2.0 <0.01 18 Mext_4581 respiratory-chain NADH dehydrogenase domain-containing protein (fdh1B) 1.8 <0.01 19 Mext_4404 NADH dehydrogenase (ubiquinone) 24 kDa subunit (fdh2C) -0.5 0.026 20 Mext_4405 NADH dehydrogenase (quinone) (fdh2B) -0.3 <0.01 21 Mext_4406 formate dehydrogenase subunit alpha (fdh2A) -0.3 <0.01 22 Mext_4407 hypothetical protein (fdh2D) -0.9 0.011 23 Mext_0389 molybdopterin oxidoreductase (fdh3A) 1.5 <0.01 24 Mext_0390 4Fe-4S ferredoxin (fdh3B) 1.0 <0.01 25 Mext_0391 formate dehydrogenase subunit gamma (fdh3C) 2.2 <0.01 26 Mext_2105 oxidoreductase alpha (molybdopterin) subunit (fdh4A) 0.0 0.95 27 Mext_2104 Hypothetical protein (fdh4B) n.d. 28

Serine cycle Mext_3171 serine hydroxymethyltransferase -0.7 <0.01 29 Mext_1795 serine--glyoxylate transaminase -2.3 <0.01 30 Mext_1796 D-isomer specific 2-hydroxyacid dehydrogenase (hpr) -1.2 <0.01 31 Mext_2747 hydroxypyruvate reductase -1.8 <0.01 32 Mext_0771 hydroxypyruvate reductase n.d. 33 Mext_2784 phosphopyruvate hydratase -0.3 <0.01 34 Mext_1801 phosphoenolpyruvate carboxylase -1.5 <0.01 35 Mext_4533 phosphoenolpyruvate carboxylase -0.3 0.076 36 Mext_1643 malate dehydrogenase -0.6 <0.01 37 Mext_2939 inosine-5'-monophosphate dehydrogenase 0.5 <0.01 38 Mext_1509 malate:quinone oxidoreductase 0.7 0.146 39 Mext_1799 malate--CoA ligase subunit beta (mtkA) -2.7 <0.01 40 Mext_1800 succinyl-CoA synthetase subunit alpha (mtkB) -2.4 <0.01 41 Mext_1802 citrate (pro-3S)-lyase (mclA) -2.0 <0.01 42

Ethylmalonyl-CoA pathway Mext_3469 acetyl-CoA acetyltransferase (phaA) -0.5 <0.01 43 Mext_3470 acetoacetyl-CoA reductase (phaB) -0.9 <0.01 44 Mext_3610 enoyl-CoA hydratase/isomerase -0.6 0.383 45 Mext_4237 enoyl-CoA hydratase/isomerase -0.8 <0.01 46 Mext_4649 enoyl-CoA hydratase -0.6 0.016 47 Mext_3444 dehydratase (croR) -1.3 <0.01 48 Mext_0288 crotonyl-CoA reductase (ccr) -1.0 <0.01 49 Mext_1069 methylmalonyl-CoA epimerase (epi) -0.5 0.025 50 Mext_0290 methylmalonyl-CoA mutase large subunit (ecm) -0.8 <0.01 51 Mext_2228 acyl-CoA dehydrogenase domain-containing protein (msd) -0.6 <0.01 52 Mext_3781 dehydratase (mcd) -2.0 <0.01 53 Mext_1802 citrate (pro-3S)-lyase (mcl) -2.0 <0.01 54 Mext_0282 carboxyl transferase (pccB) -1.3 <0.01 55 Mext_2996 carbamoyl-phosphate synthase L chain ATP-binding (pccA) -1.0 <0.01 56 Mext_4794 methylmalonyl-CoA mutase (mcmA) -1.1 <0.01 57 Mext_2388 methylmalonyl-CoA mutase (mcmB) -1.5 <0.01 58 1 Benjamini-Hochberg corrected for multiple testing

81 Tab. S3: Oxalate metabolism of M. extorquens PA1 during colonization of the A. thaliana phyllosphere.

Locus-Tag Annotation (Gene name) log2 FC p-value1

Oxalate utilzation Mext_1207 formyl-coenzyme A transferase 1.4 <0.01 Mext_1209 putative oxalyl-CoA decarboxylase 2.9 0.013 Mext_1212 major facilitator transporter ∞ only in planta Mext_2958 oxidoreductase alpha (molybdopterin) subunit 1.5 <0.01

Oxalate assimilationOxalatein M. extorquens metabolism AM1 in planta Mext_2139 2-dehydropantoate 2-reductase (panE2) -0.8 <0.01 Mext_2140 AMP-dependent synthetase and ligase (oxs) -1.2 <0.01

Redox balance Mext_2759 NAD(P)(+) transhydrogenase (pntAA) 2.1 <0.01 Mext_2760 NAD(P) transhydrogenase subunit PntAB 2.4 <0.01 Mext_2761 NAD(P)(+) transhydrogenase (pntB) 1.6 <0.01

Periplasmic formate oxidation Mext_0389 molybdopterin oxidoreductase (fdh3A) 1.5 <0.01 Mext_0390 4Fe-4S ferredoxin (fdh3B) 1.0 <0.01 Mext_0391 formate dehydrogenase subunit gamma (fdh3C) 2.2 <0.01 1 Benjamini-Hochberg corrected for multiple testing

82 Tab. S4: Predicted operon structures containing alkanesulfonate monooxygenase genes

Locus-Tag Annotation (Gene name) log2 FC p-value1

Mext_1170 acyl-CoA dehydrogenase domain-containing protein ∞ in planta only Mext_1171 alkanesulfonate monooxygenase ∞ in planta only Mext_1172 aliphatic sulfonate ABC transporter substrate-binding protein ∞ in planta only Mext_1173 cupin ∞ in planta only

Mext_3338 nitrate/sulfonate/bicarbonate ABC transporter periplasmic components-like protein ∞ in planta only Mext_3339 molybdopterin binding oxidoreductase ∞ in planta only Mext_3341 aliphatic sulfonate ABC transporter substrate-binding protein ∞ in planta only Mext_3342 alkanesulfonate monooxygenase ∞ in planta only Mext_3343 Metabolismxanthine permease of alkane sulfonates∞ in planta only Mext_3404 NADPH-dependent FMN reductase ∞ in planta only Mext_3405 aliphatic sulfonate ABC transporter substrate-binding protein ∞ in planta only Mext_3406 binding-protein-dependent transport system inner membrane protein ∞ in planta only Mext_3407 ABC transporter-like protein ∞ in planta only Mext_3408 alkanesulfonate monooxygenase 2.3 <0.01 Mext_3409 aliphatic sulfonate ABC transporter substrate-binding protein 1.9 0.029

Mext_3663 alkanesulfonate monooxygenase ∞ in planta only 1 Benjamini-Hochberg corrected for multiple testing

83 Tab. S5: KEGG orthology terms and functional annotation of co-regulated proteins between M. extorquens PA1 and S. melonis Fr1.

KO term Genes PA1 Genes Fr1 exemplary annotation functional category

Shared up regulated KO terms K01990 Mext_0380 Sphme2DRAFT_1905 ABC transporter related K01610 Mext_1639 Sphme2DRAFT_3227 Phosphoenolpyruvate carboxykinase (ATP) Carbohydrate metabolism, Energy metabolism K00299 Mext_3404 Sphme2DRAFT_0605 NADPH-dependent FMN reductase Energy metabolism, Metabolism of cofactors and vitamins K01620 Mext_4651 Sphme2DRAFT_1332 aromatic amino acid beta-eliminating lyase/threonine aldolase Amino acid metabolism K02334 Mext_2306 Sphme2DRAFT_0732, Sphme2DRAFT_3262 Uracil-DNA glycosylase superfamily K00058 Mext_0660 Sphme2DRAFT_1089 D-3-phosphoglycerate dehydrogenase Amino acid metabolism, Energy metabolism K07305 Mext_1761 Sphme2DRAFT_1326 methionine-R-sulfoxide reductase K01724 Mext_1805 Sphme2DRAFT_0771 transcriptional coactivator/pterin dehydratase Mext_1378, K09796 Mext_1379 Sphme2DRAFT_0994 protein of unknown function DUF461 K01991 Mext_2535 Sphme2DRAFT_2554 polysaccharide export protein K02040 Mext_4713 Sphme2DRAFT_1490 phosphate ABC transporter, periplasmic phosphate-binding protein Membrane transport, Signal transduction, Infectious diseases K00382 Mext_4080 Sphme2DRAFT_3098 FAD-dependent pyridine nucleotide-disulphide oxidoreductase Amino acid metabolism, Carbohydrate metabolism

Shared down regulated KO terms

84 K02014 Mext_0672 Sphme2DRAFT_0191, Sphme2DRAFT_1738, Sphme2DRAFT_2463 TonB-dependent siderophore receptor K06167 Mext_4185 Sphme2DRAFT_3194 beta-lactamase-like protein Metabolism of other amino acids K01447 Mext_0941 Sphme2DRAFT_2582 N-acetylmuramoyl-L-alanine amidase family 2 K01589 Mext_1963 Sphme2DRAFT_0648 phosphoribosylaminoimidazole carboxylase, ATPase subunit Nucleotide metabolism K00940 Mext_2012 Sphme2DRAFT_3212 Nucleoside-diphosphate kinase Nucleotide metabolism K00799 Mext_1924 Sphme2DRAFT_1599 Glutathione S-transferase domain Metabolism of other amino acids, Xenobiotics biodegradation and metabolism K01462 Mext_3949 Sphme2DRAFT_3344 formylmethionine deformylase Fig. S1: Venn diagrams showing the overlap of proteins contained in the SWATH assay libraries obtained by shotgun proteomics (green: OGE-fractionated samples derived from liquid bacterial cultures in three diﬀerent growth stages; red: unfractionated samples derived from bacteria grown on plant leaves) as well as of proteins actually detected by SWATH MS in the unfractionated plate- and plant-derived bacterial samples (yellow: plate; blue: plant). OGE, oﬀ-gel electrophoresis.

A B PA1 plate PA1 OGE Fr1 plate Fr1 OGE SWATH library SWATH library PA1 plant PA1 plant Fr1 plant Fr1 plant SWATH 0 1233 library SWATH 0 1229 library (0%) (35.3%) (0%) (44.7%)

0 314 90 0 117 144 (0%) (9%) (2.6%) (0%) (4.3%) (5.2%)

0 32 0 18 (0%) 152 88 (0.9%) (0%) 32 58 (0.7%) (4.4%) (2.5%) (1.2%) (2.1%)

1420 850 38 (40.7%) 2 43 (30.9%) 2 (1.1%) (0.1%) (1.6%) (0.1%) 101 4 253 1 (2.9%) (0.1%) (9.2%) (0%)

14 4 (0.4%) (0.1%)

85 METHANOL

1-5 hydroxypyruvate

methylene-H4MPT methylene-H4FT 31-33 30 7-8 9 formaldehyde 15 serine glycerate

methenyl-H4MPT methenyl-H4FT 32 6 29 10 16-17 glycine 2 phosphoglycerate

11 formate 17 34 formyl-H4MPT formyl-H4FT 30 18-28 glyoxylate phosphoenolpyruvate 35-36 42 CO2 acetyl-CoA malyl-CoA oxaloacetate citrate 37-39 43-47 40-41 86 hydroxybutyryl-CoA malate isocitrate

48 REGULATION crotonyl-CoA fumarate 2-oxoglutarate up-regulated down-regulated 49 not regulated ethylmalonyl-CoA succinate succinyl-CoA

PATHWAY 50-52 50, 57-58 citric acid cycle ethylmalonyl-CoA pathway mesaconyl-CoA glyoxylate methylmalonyl-CoA H4FT-dependent pathway 53 55-56 H4MPT-dependent pathway 54 serine cycle methylmalyl-CoA propionyl-CoA

Fig. S2: Central carbon metabolism of M. extorquens PA1 during plant colonization as compared to colonization of minimal media agar plates. Pathways and regulation of respective proteins is indicated. Proteins are numbered and listed in Tab. S1 for more details. light microscopy uorescence overlay

10 μm

Fig. S3: Random eld of views of M. extorquens PA1 cells grown on minimal medium agar plates in the dark.

Chapter IV

Bipartite interactions and plant protective abilities of the Arabidopsis leaf microbiota

Daniel B. Müller, Christine Vogel, Florian Ryffel, Markus Kreuzer, Miriam Bortfeld-Miller and Julia A. Vorholt

AUTHOR CONTRIBUTIONS

D.B.M., C.V., F.R. and J.V.A. designed research. D.B.M., F.R. and M.K. performed the binary interaction screen, D.B.M and F.R. analyzed the data. D.B.M. performed AntiSMASH analysis and evaluated the data. D.B.M., C.V. and M.B.M. performed leaf colonization screen and experiments about plant protection of selected isolates, D.B.M. and C.V. analyzed the data. D.B.M. performed whole genome comparisons and generated all figures. D.B.M. and J.A.V. wrote the manuscript.

Bipartite interactions and plant protective abilities of the Arabidopsis leaf microbiota

Daniel B. Müller, Christine Vogel, Florian Ryffel, Markus Kreuzer, Miriam Bortfeld-Miller and Julia A. Vorholt

Institute of Microbiology, D-BIOL, ETH Zurich, Switzerland

SUMMARY

Healthy multicellular organisms are colonized by a diverse community of microorganisms, the microbiota. Elaborate research of the past decade has generated detailed insights into the phylogenetic structure of the plant microbiota and consistently revealed that it is composed of only few phyla and reproducibly establishes over consecutive years. Microbe-microbe interactions are thought to be a main driver of this stable community establishment, however, systematic analyses of bacterial interactions and adaptations in context of microbiota formation are missing. Here we monitored over 50,000 combinations of 224 strains, representing the majority of taxa that are reproducibly associated with Arabidopsis thaliana leaves, showing that Pseudomonadales and Bacillales engage in the most interactions and that antimicrobial activities are less frequently targeted against close relatives. We identified a subset of strains that reduce pathogen load of a bacterial pathogen and show high genomic conservation between protective and non-protective isolates, despite pronounced differences in physiology. Genome mining further identified over 900 gene clusters of natural product biosynthesis and revealed terpenes as the most widespread compound class on leaves, reflecting strain adaptations to their natural environment.

91 INTRODUCTION

Healthy multicellular organisms are colonized by a diverse community of microorganisms, the host microbiota (7; 25; 29). These microbial communities are dominated by bacteria, however, fungi, oomycetes, archaea, and even nematodes and protists complete the overall community structure (15; 19; 26). During the past decade, elaborate research has revealed detailed insights into microbiota functions and demonstrated that these microorganisms influence host physiology and health in many different ways (10; 20). Consequently, the holobiont concept was introduced, regarding host and its microbiota as co-evolved species assemblages, rather than individual organisms (27). Conceptually similar questions in microbial community ecology about microbiota structure, driving forces of its assembly and functions are addressed in a variety of different model systems, however, the microbiota of plants provides an excellent system to study these processes, due to its ease to combined molecular approaches with optical analysis recording spatial information at various scales (22; 24).

Above-ground parts of plants, collectively called the phyllosphere, are dominated by leaves, but also include fruits, flowers and heartwood (19; 29). The phyllosphere is generally considered a harsh environment, exposed to the diurnal cycle and consequently to continuously changing environmental conditions, including wide temperature gradients, fluctuations in water availability as well as exposure to solar radiation. This rapid interplay of fluctuating conditions, changing from hot to cold, sunshine to rain or dry to moist are characteristic aspects of life on leaves, and the extent of fluctuations render this habitat unique. Thus, microbial inhabitants need to constantly cope with changing conditions and adapt their lifestyle accordingly. The vast majority of bacterial cells arrange in aggregates or micro-colonies along the grooves of epidermal plants cells, embedded in a matrix of extracellular polymeric substances, seeking shelter from environmental stresses (19; 29).

In recent years, comprehensive analysis of the plant microbiota of various host species using high- throughput next generation sequencing technologies, have consistently revealed that the bacterial plant microbiota is composed of only few dominant phyla, Actinobacteria, Bacteroidetes, Firmicutes and most dominantly Proteobacteria (7; 13; 15; 23). More recently, Bai and coworkers combined culture- independent microbiota profiling with large-scale bacterial isolation efforts, resulting in successful recovery and cultivation of the majority of species that are reproducibly associated with leaves and roots of natural A. thaliana populations (2). Theses established strain collections of leaf and root isolates, At- LSPHERE and At-RSPHERE, respectively, in combination with acquired draft genome sequences are now available as resource for systematic experimental and in silico analysis of the plant microbiota. Phylogenetic diverse microbiota strain collections will facilitate moving forward towards a systems understanding of microbe-microbe interactions and beneficial microbiota services provided to the plant host. One comprehensive study has analyzed binary interactions of a collection of marine isolates of the Virbionaceae family (12), however, no study has addressed the interaction network of representative strains mimicking the phylogenetic structure of the host microbiota. Here we present a systematic

92 analysis of binary interactions of representative leaf microbiota members on laboratory culture media, combined with in silico genome mining for gene cluster involved in natural product biosynthesis. We further examined the inhibitory potential of leaf microbiota members against known plant pathogens on agar plate and for a selected subset of strains also in planta.

METHODS

Pair-wise interaction screen of phyllosphere bacteria

All strains were routinely grown on R2A agar supplemented with 0.5 % (v/v) methanol or minimal media containing 0.5% (v/v) methanol and 25 mM glucose as carbon sources. Strains tested for sensitivity were re-streaked form cryo stocks and incubated on solid media at room temperature before liquid cultures in 5 ml media in test tubes were inoculated. High-throughput cultivation of inhibitory isolates was done in 96 well plates containing solidified media. To assess binary interactions between the different strains, liquid cultures of strains tested for sensitivity were diluted to a total of 50 ml with melted agar at 45°C, before 25 ml were poured into a square plate to form a uniform layer containing the test strain.The inhibitory interaction partners were resuspendend from 96-well plates, incubated in liquid media for 2h and subsequently 1 µl was applied to the solidified agar layer containing the putatively sensitive strain. Outcomes of the binary interactions were evaluated after two days of incubation at room temperature. Data analysis, statistics and visualization was done in Python, including the NetworkX package and by using the GraPHlAn tool (1).

Analysis of natural community data

The sequence data sets PRJEB7247 (16), SRP018030 (5) and PRJEB11545 (2), originating from three independent studies were downloaded from the NCBI Short Read or European Nucleotide Archive and subsequently analyzed by QIIME v. 1.9.1 (8), using the close reference protocol against the 13_8 release of the GreenGenes database (21). All sequences were clustered into operational taxonomic units (OTUs) at 97% sequence identity against the GreenGenes database. All sequences assigned to Mitochondria or Chloroplast were subsequently removed, the data was normalized and summarized at all taxonomic levels using QIIME.

Secondary metabolite cluster prediction

We employed the antiSMASH (antibiotics & Secondary Metabolite Analysis Shell) standalone toolkit v. 2.0.2 (4) to mine the genomes of all leaf, root and soil isolates for the presence of putative biosynthetic gene cluster of natural product biosynthesis. Fasta files containing nucleotide sequences of all draft

93 genomes were processed using custom batch scripts with the following command line options –smcog –clusterblast –subclusterblast and otherwise default parameters. Subsequent data analysis and statistical tests were performed using custom scripts written in Python, before data was visualized using Graph Pad Prism v. 6.07.

Plant growth and enumeration of phyllosphere bacteria

Arabidopsis thaliana Col-0 seed were surface sterilized as described previously and plants were cultivated in 24-well plates containing Murashige & Skooge (MS) medium supplemented with 3% (w/v) sucrose (28). Bacterial strains were grown on R2A agar supplemented with 0.5 % (v/v) methanol or minimal media containing 0.5% (v/v) methanol as the sole carbon sources before two week old plants were leaf inoculated by pipetting 15 µl of bacterial solution adjusted to OD600 = 0.02 directly onto leaves. Bacterial strains were allowed to colonize plants for additional 14 days, before plants were harvested, roots removed using sterilized tweezers and the phyllosphere compartment transferred to 2ml tubes containing 1.3 ml 100 mM phosphate buffer (pH 7) supplemented with 0.2 % (v/v) Silwett S77. Bacteria were recovered by intense shaking using the Retsch tissue lyser (15 min, 25 Hz) and 5 min of sonication before ten-fold dilution series were spotted on R2A agar for enumeration of leaf colonizing bacteria.

For the analysis of plant protective capabilities, bacteria were inoculated in parallel to sowing of surface sterilized seeds or onto leaves of 11 days old plants as described above. Plants were subsequently infected by pipetting 15 µl of a Pst-lux solution adjusted to OD600 = 0.00003 as described previously (28). Pathogen growth was quantified by luminescence measurements as described (28) three days post infection and disease severity was scored after 9 and 16 dpi according to (30).

Whole genome comparison of Sphingomonas isolates

In order to compare the draft genomes of all genome-sequenced Sphingomonas isolates of the A. thaliana LSPHERE collection we assigned the proteins of all strains to orthologous proteins of the orthoMCL database using web-based orthoMCL v. 2.0. Corresponding tables containing count data of proteins per orthologous group of all strains were generated using custom scripts written in Python. Pairwise Jaccard distances were calculated and non-metrical multidimensional scaling was performed using the R package vegan, before the data was plottet in R.

94 RESULTS

Binary interaction network of phyllosphere bacteria

Microbe-Microbe interactions are expected to be a main driver of bacterial community establishment within their natural habitat (6; 18). To move towards an understanding of the complex network of bacterial interactions that underlie microbiota formation we first screened the inhibitory and antibiotic capabilities of all strains of a recently established strain collection of phyllosphere bacteria from A. thaliana on standard laboratory media in binary interactions. One strain was allowed to grow within the agar, while 96 putative interactors were spotted on top of this layer, before the results of these interactions were evaluated after two days of incubation. Interactions were classified as strong inhibition if the size of the inhibitory halo exceeded 3 mm, while weak inhibitions either showed a smaller or partially turbid halo.

In total, we monitored over 50,000 pairings of 224 diverse strains belonging to the main bacterial phyla inhabiting the phyllosphere of A. thaliana, which revealed 611 inhibitory binary interactions (1.2% of all possible pairings). Overall, 170 strains of the entire collection engaged in binary interactions (Fig. 1A, 1B), 77 strains (34%) inhibited one or multiple strains, with 26 strains showing strong inhibitions. However, most isolates inhibited only one or two other strains. Cluster analysis of all bipartite interactions indicated that interactions are observed across all phylogenetic clades and no subclusters of preferential interactions are formed. The most active strains (>10 inhibitions) form central nodes of the interaction network and isolates assigned to Firmicutes and Proteobacteria, especially the Gammaproteobacteria class, are dominating (Fig. 1A, 1B, 2A). These most potent inhibitors belong to the genera Brevibacillus (Leaf182, 82 inhibitions), Bacillus (Leaf49 and Leaf75, 45 inhibitions) and Exiguobacterium (Leaf196, Leaf187, 26 inhibitions) of the phylum Firmicutes as well as Pseudomonas (Leaf58, Leaf434, Leaf98, Leaf15, 180 inhibitions), Methylophilus (Leaf408 and Leaf421, 36 inhibitions), Novosphingobium (Leaf2, 30 inhibitions), Acinetobacter (Leaf130, 13 inhibitions), Duganella (Leaf126, 12 inhibitions) and Sphingomonas (Leaf11, 10 inhibitions) of the Proteobacteria, in addition to Flavobacterium (Leaf82, 20 inhibitions) and Curtobacterium (Leaf154, 12 inhibtions) of Bacteroidetes and Actinobacteria, respectively and account for 76% of all observed inhibitions.

We observed 95 interactions between strains which originated from the same leaf (1.5% of 6408 possible combinations), but only 9 inhibitory activities between strains belonging to the same genus (0.3% of 3144 possible combinations), significantly less than expected by chance (Fisher test, p-value = 1.13 ∙ 10-8). Furthermore, 131 strains (57%) demonstrated sensitivity against the antibacterial or competitive activity of other phyllosphere isolates, with Sphingomonas Leaf38 showing sensitivity against the most isolates (20 sensitivities), followed by Chryseobacterium Leaf394 (18 sensitivities) and Dyadobacter Leaf189 (16 sensitivities).

95 Fig.1: Bipartite interaction network of phyllosphere bacteria. (A) Strains involved in an increased number of interactions form central nodes of the binary interaction network. All nodes are annotated with isolate ID and are color coded according to their phylum annotation. Node size is proportional to the number of observed interactions. (B) Ordered representations of all strains (network nodes) involved in 5 or more interactions.

The network of bacterial interactions reveals that the majority of sensitivities is based on the inhibitory activity of the two bacterial orders Bacillales and Pseudomonadales (Fig. 2A). Interactions of Bacillales with isolates of the orders Cytophagales, Sphingomonadales, Caulobacterales and Actinomycetales frequently resulted in inhibitions (Fig. 2B), while Pseudomonadales often inhibited Cytophagales, Flavobacteriales, Methylophilales, Xanthomonadales, Sphingomonadales and Sphingobacteriales (Fig. 2C). These trends of inhibitions between bacterial orders raised the question whether these tendencies are also reflected in the abundance of the respective order in natural community samples. To maximize sample number and include various different sampling locations we re-analyzed the data of three independent studies profiling the A. thaliana phyllosphere, however, relative abundance of most bacterial orders revealed only weak correlation with the abundance of Bacillales or Pseudomonadales (Suppl. Fig. S1 and S2, Suppl. Tab. 3). Overall, this established framework of binary interactions provides a valuable basis for further experiments addressing microbe-

96 * *

* *

Fig.2: Bacillales and Pseudomonadales dominate the binary interaction network. (A) Phylogenetic tree of all isolates representing the observed directional interactions. Each tree node is color coded according to phylum annotation, while annotation background color represents order assignment. Directional inhibitory interactions are indicated from red to yellow, the number of sensitivities per strain is summarized in the innermost circle [S], the number of inhibitions caused in the outermost ring [I]. (B) Percent of inhibitions caused by Bacillales or (C) Pseudomonadales isolates against other bacterial orders. Asterisks indicate singnificant over- and under-representation (Fisher exact test, p-value ≤ 0.05), respectively.Bacterial order are Actinomycetales (n=67), Bacillales (n=8), Burkholderiales (n=15), Caulobacterales (n=3), Cytophagales (n=1), Deinococcales (n=1), Enterobacteriales (n=3), Flavobacteriales (n=8), Methylophilales (n=5), Pseudomonadales (n=10), Rhizobiales (n=58), Sphingobacteriales (n=7), Sphingomonadales (n=40), Xanthomonadales (n=3). Full taxonomic assignment of all isolates is listed in Suppl. Tab. 2.

97 microbe interactions in the context of stable community formation (all binary interactions are listed in Suppl. Tab. 4.).

To assess the inhibitory potential of phyllosphere isolates against known bacterial phytopathogens we also included Pseudomonas syringae pv. tomato DC3000 (Pst DC3000), Pseudomonas syringae B728a, Ralstonia solanacearum AW1, Agrobacterium tumefaciens C58 and Xanthmonas campestris pv. campestris (Xcc) in our screening approach and found that several of the top inhibitory strains also show activity against these model pathogens. Pseudomonas isolates Leaf58 showed inhibitory activity against all tested pathogens, except R. solanacearum AW1, while the second highly active Pseudomonas strain Leaf434 inhibited all, but Pst DC3000 and R. solanacearum AW1. Novosphingobium Leaf2 was active against A. tumefaciens C58, Xcc and Pst DC3000, while the strain inhibiting the most commensals, Brevibacillus Leaf182, showed activity against Xcc and R. solanacearum AW1. Leaf strains Duganella Leaf126, Acinetobacter Leaf130 and Burkholderia Leaf177 additionally showed inhibitory activity against P. syringae B728a.

Identification of putative gene clusters of natural product biosynthesis

We next used a functional genomics approach to mine the available genomes of 206 isolates of the core A. thaliana LSPHERE collection for the presence of gene clusters potentially involved in the biosynthesis of natural products, responsible for the inhibitory activity observed within the binary interaction framework. The antiSMASH toolkit employs profile hidden Markov Models (pHMMs) of genes characteristic for distinct chemical classes of secondary metabolites and allows for high- throughput identification and annotation of the biosynthetic gene clusters. In total, 960 secondary metabolite clusters belonging to 25 distinct chemical categories were identified (Fig. 3B). The genomes of isolates belonging to Firmicutes (n=8) harbor on average 7.75 biosynthetic genes cluster, significantly more than isolates belonging to Actinobacteria (average 4.8; n=62) and Proteobacteria (average 4.4; n=122) (Mann-Whitney test, Bonferroni corrected p-value ≤ 0.05, Fig. 3A). The most dominant chemical class are terpenes accounting for 281 mono-functional and 12 hybrid cluster (Fig. 3B), with at least one cluster detected in 189 (92%) of leaf isolates. The high prevalence of genes belonging to terpene biosynthesis might be related to carotenoid synthesis and pigmentation of leaf strains which are continuously exposed to solar radiation within their natural habitat. This tendency prompted us to test whether genomes of strains inhabiting the below-ground root and soil compartment less frequently contain genes for terpene biosynthesis. We thus subjected the genomes of 191 isolates (159 root and 32 soil) to antiSMASH secondary metabolite cluster prediction and detected terpene biosynthesis genes in 150 isolates (78%), confirming that these genes are indeed significantly overrepresented in genomes of leaf isolates (Fisher test, p-value = 2.0∙10-4). We furthermore found, based on the presence and absence of genes involved in carotenoid biosynthesis (according to assigned KEGG Onthology [KO] terms) within the genomes of all leaf, root and soil isolates, that 7 KO terms are overrepresented within genomes

98 Fig.3: The natural product library of the leaf microbiota. (A) Number of secondary metabolite clusters identified per strain of the different phyla comprising the leaf microbiota (Actinobacteria, n=62; Proteobacteria, n=122; Bacteroidetes, n=12; Firmicutes, n=8; Deinococcus-Thermus, n=1). Asterisks indicate significant differences (Mann-Whitney test, Bonferroni-corrected p-value ≤ 0.05). (B) Number of natural product biosynthesis gene cluster identified in the 206 available genomes of the core At-LSPHERE strain collection. of leaf isolates, corroborating this tendency (Fisher test, Bonferroni corrected p-value ≤ 0.05, suppl. Tab. 1).

Gene clusters encoding for natural products with potentially antibiotic effects typically contain biosynthetic genes for bacteroicin, nrps, siderophore, different polyketide synthases (pks) or lantipeptide domains. We found clusters containing at least one of these putatively antibacterial domains in 196 out of the 206 tested leaf isolates, however the number of detected clusters did not correlate with the number of inhibitory interactions observed. Notably, two strains devoid of one of these domains caused two weak inhibitions each, potentially being based on undetected gene clusters or competition for nutrients rather than active antibiosis.

Besides gene clusters which might be involved in inhibitory microbe-microbe interactions, several clusters of homoserine lactone and butyrolactone biosynthesis, important for bacterial communication were detected (Fig. 3B). Acyl-homoserine lactones are characteristic signaling molecules of gram negative quorum sensing systems, while butyrolactones are usually involved in cell to cell communication of gram positive bacteria (3). Consistently, homoserine lactone biosynthesis clusters were exclusively found in isolates belonging to Proteobacteria, while most butyrolactone clusters were found in strains of the phylum Actinobacteria. In consequence, this established database of secondary metabolite cluster will further help to elucidate mechanisms of microbe-microbe interactions and help to identify the compounds that are responsible for the observed inhibitory activity, which might be involved in stable microbiota formation as well as in protection against invasive species on leaves.

99 Screen for plant colonization efficiency of individual isolates

Before analyzing plant protective abilities of individual members of the A. thaliana strain collection we first assessed the plant colonization efficiency of these strains in a high-throughput, 24-well plate based plant model system. Therefore, two week old plants were leaf inoculated with each isolate (four plants on three different plates; n = 12), before colonization of each isolate was evaluated after additional 14 days of incubation by harvesting and counting of colony forming units (CFU) of six representative plants (two plants per plate; n = 6). Overall, the majority of isolates (176 isolates, 77%) colonized plants in this agar-based model system, showing median colonization densities above 106 CFUs/g fresh weight (FW) (Fig. 4). All strains belonging to the Gammaproteobacteria class colonized strains above this threshold, however, plants colonized by Serratia Leaf50, Serratia Leaf51 and Arthrobacter Leaf145 showed severe disease symptoms, indicating that these isolates might be opportunistic plant pathogens. Besides Gammaproteobacteria, strains of the Actinobacteria revealed efficient colonization with 90% of isolates showing densities above the indicated threshold. For all other phylogenetic classes, we observed a larger number of strains showing below threshold colonization densities, especially isolates belonging to Bacteroidetes and Firmicutes. In total we were unable to detect any plant colonization for 26 phylogenetically diverse isolates under the conditions tested.

Fig.4: Plant colonization efficiency of the At-LSPHERE collection. Phylogenetic representation of all isolates, color-coded according to phylum annotation and with unique strain ID. The outer ring of bar plots represents median colonization efficiency of each isolate (log10 CFUs/g FW) and is colored accordingly. Asterisk tree nodes indicate strains that got excluded based on poor growth of pre-cultures prior to plant inoculation, while pentagram tree nodes indicate no growth of inoculum samples after plant inoculation.

100 Plant protective abilities of selected leaf microbiota members

We next selected a subset of strains based on previous results and published literature to test whether these isolates are able to confer a protective advantage to Arabidopsis plants. We employed the screening system published by Vogel and colleagues (28), using a Pst DC3000 derivative (Pst-lux) chromosomally tagged with the luxCDABE operon of Photorhabdus luminescence for rapid, luminescence-based quantification of pathogen growth in planta (14). Based on results of the binary interaction screen on laboratory media and the activity against tested phytopathogens we selected the most active isolates Brevibacillus Leaf182, Pseudomonas Leaf58, Pseudomonas Leaf434 and Novosphingobium Leaf2 for in planta analysis. Based on the hypothesis that plant derived Sphingomonas isolates are capable of plant protection against common pathogens (17), we further selected all isolates belonging to the family Sphingomonadaceae (Sphignomonas, Sphingobium and Novosphingobium; Proteobacteria), despite the rather inefficient colonization patterns observed for some of these isolates during the initial plant colonization screen. We additionally included five Sphingomonas as well as one Novosphingobium reference strains that have been analyzed previously to facilitate cross-comparison of our data (17). Based on the putative involvement of sphingolipids in the stimulation of the immune system (9) we also included all Pedobacter isolates, belonging to the Sphingomonadaceae family of the phylum

Fig.5: Plant protective abilities of selected leaf microbiota members. All strains tested for plant protective capabilities are annotated with their unique strain ID and arranged according to their phylogeny. Heatmaps indicate colonization efficiency (upper), median luminescence as readout for pathogenic Pst-lux growth (middle) and median disease score of plants (lower). To evaluate the effect of inoculation on plant protective outcomes we performed two independent screens, one with seed inoculation of strains at the time of sowing surface sterilized seeds (1) and the second one with inoculation of strains on leaves of 11 day old plants (2).

101 Bacteroidetes. To address differences in time and location of strain application on plant protective outcomes we performed one screen using seed inoculation of strains at the time point of sowing surface sterilized seeds, while the second screen employed leaf inoculation of bacterial isolates on 11 day old plants.

Overall, the majority of Sphingomonas isolates did not efficiently colonize Arabidopsis plants when applied onto surface sterilized seeds, but colonization was improved when strains were directly inoculated onto leaf surfaces of germ-free plants. Despite the insufficient colonization of most isolates, Sphingomonas Leaf257, Sphingomonas Leaf21, Sphingmonas Leaf198 and Sphingomonas Leaf205 colonized plants consistently and conferred protection against the disease caused by Pst-lux, comparable to the protective positive control Sphingomonas melonis Fr1, resulting in reduced pathogen growth and disease severity (Fig. 5). Interestingly, enumeration of isolate Sphingomonas Leaf230 after plant harvest indicated poor colonization under the conditions tested, however, it reproducibly conferred a protective effect to its plant host. In addition, seed inoculation of isolates Sphingomonas Leaf242 as well as Novosphingobium Leaf2 and Novosphingobium Leaf166 resulted in a partially protected plant phenotype, not observed when these strains were applied directly to leaves. Besides isolates belonging to the two Sphingomonadaceae families, both tested Pseudomonas isolates reduced Pst-lux growth efficiently and thus resulted in reduced disease severity. The most potent inhibitor of the binary interaction screen Brevibacillus Leaf182 did not colonize plants sufficiently during this screen and did not result in any plant beneficial outcome. In total, we identified five Sphingomonas and two Pseudomonas isolates that efficiently protect A. thaliana plants against the widely used model pathogen Pst DC3000. Surprisingly, closely related strains based on 16S rRNA gene sequence derived phylogeny possessed strikingly different disease suppressive capabilities.

Whole genome comparison of leaf derived Sphingomonas isolates

The surprising result that closely related Sphingomonas isolates, e. g. Sphingomonas Leaf198 and Sphingomonas Leaf20 share 16S rRNA gene sequence identity >99%, result in different outcomes for the plant when infected with Pst DC3000 prompted us to test whether there are pronounced differences between these strains on a whole genome level. We therefore assigned all proteins of the respective strains to orthologous groups of the orthoMCL database and calculated pairwise Jaccard distances based on presence and absence of these groups for all isolates. Principal coordinate analysis of Jaccard distances subsequently revealed that isolates separate along the first axis in two groups. One group contains protective and non-protective strains, while the second cluster is solely formed of non- protective Sphingomonas isolates, all isolated from plants sampled on different dates in Brugg, Switzerland (Fig. 6). Additionally, Sphingomonas isolates separate along the second axis in three different groups, one cluster containing one protective isolate (Sphingomonas Leaf257), one cluster containing two protective isolates (Sphingomonas Leaf198 and Sphingomonas Leaf230), and a third

102 cluster containing the three protective Sphingomonas melonis reference strains. Interestingly, within the two mixed cluster of protective and non-protective strains, these isolates are arranged very closely together, indicating that these strains show relatively high genome conservation, despite being isolated from geographically distinct locations and their pronounced differences in physiology. For example, only nine orthologous protein groups are shared between the two protective isolates Fig.6: Principal coordinate analysis of Jaccard Sphingomonas Leaf198 and Sphingomonas distances between leaf associated Sphingomonas Leaf230 that are absent from other genomes isolates. Proteins of all genomes were assigned to orthologous protein groups of the orthoMCL database and within this group. Similarly, 168 orthologous data was summarized in a count table containing the number of proteins assigned to the respective orthologous protein groups are unique to isolate group of each strains. Pairwise Jaccard distances based on Sphingomonas Leaf257 within this cluster, presence and absence of all orthologous proteins were calculated and non-metric multidimensional scaling and however, there is no common overlap of plotting was done in R. All strains are annotated according protein groups that are shared between all to their protective abilities and origin of isolation. protective isolates and absent from genomes of non-protective strains. In conclusion, very closely related environmental Sphingomonas isolates reveal striking differences in their physiology and protective services provided to the plant host, despite high conservation of their genomic content. These results indicate that either different modes of action or more likely, complex networks of genes and their regulation rather than the sole presence of immunity confering genes forms the basis for plant protection offered by these Sphingomonas isolates.

DISCUSSION

During the past decade, with onset and progress of next-generation sequencing technologies, a multitude of studies has addressed the phylogenetic structure of the host microbiota in a variety of model systems (15). Consequently, in-depth catalogues of sequencing data were generated, providing detailed knowledge on the phylogenetic composition of the plant microbiota, however, knowledge on bacterial adaptation and microbial interactions of these strains is lagging behind (7; 23). Therefore, future studies need to shift from merely acquiring profiling data to combined efforts of culture-independent analyses paired with culture-depended strain isolations, in order to isolate representative model strains and address their physiology under controlled laboratory conditions. So far, the recently established collection of Arabidopsis leaf and root bacteria provides a unique opportunity to systematically address

103 bacterial interactions between phylogenetically diverse strains that co-exist on natural plants as well as to analyze potentially beneficial services provided to the plant host (2).

By screening more than 50,000 possible combinations of the core At-LSPHERE strain collection, we found that inhibitory interactions are underrepresented between strains belonging to the same genera, indicating that leaf isolates are adapted to rather inhibit distinct phylogenetic groups than closely related strains. If inhibitory microbe-microbe interactions significantly contribute to stable community formation of leaves, then this could lead to preferential co-existence of chemically compatible strains, while co-colonization by strains inhibiting each other is prevented. We did not find an underrepresentation of inhibitory interactions between isolates that originated from the same leaf, indicating that co-existence of these strains is not reflected by higher resistance against the chemical warfare of fellow leaf colonizers. However, even though coexistence on the same plant leaf represents spatial information at the cm scale, the actual distances between the occupied micro-habitats might exceed the impact of cell-cell interactions by orders-of-magnitudes. More detailed spatial analyses of these isolates during leaf colonization will be required to quantify the extent to what these strains actually co-localize and to what extent these inhibitory microbe-microbe interactions could manifest their effect also in planta. In addition, secondary metabolite production might vary depending on growth conditions. We intentionally grew all strains tested for inhibitory interactions on the culture medium used for isolation until stationary growth phase, since this metabolic state seems to mimic life on leafs best, but further media, even if hard to find another substantially different medium that supports growth of all strains, will most likely extend the network of observed interactions. Detailed characterization of the nutrient utilization spectra as well as niche adaptation of these isolates on leaves will reveal which isolates fundamentally overlap in their nutritional strategies and whether interactions with more closely related strains are determined by this overlap. More generally, a model in which chemical warfare dominates as mode of action in relationships with more distantly related strains, while competition for resources and thus niche exclusion forms the basis for interactions with more closely related strains seems reasonable.

Not surprisingly, the most potent inhibiting strains belong to the orders Bacillales and Pseudomonadales, and isolates of both groups are commercially available and applied as biocontrol agents against plant pests (11; 20). Bacillales isolates mostly inhibited Sphingomonadales, Caulobacterales and Actinomycetales, while strains of the order Pseudomonadales preferentially inhibited Flavobacteriales, Methylophilales, Xanthomonadales, Sphingomonadales and Sphingobacteriales. Pearson correlation analysis of relative abundances of theses bacterial orders in natural communities revealed only weak correlations, but multi-partite interactions in these complex communities might obscure the inhibitory tendencies observed during the bipartite assay. Several of these leaf isolates are also capable of inhibiting known plant pathogens when grown on agar plates, and we subsequently identified two Pseudomonas and five Sphingomonas isolates that successfully protect

104 A. thaliana from the disease caused by Pst DC3000. Innerebner and colleagues (17) hypothesized that all plant-derived Sphingomonas isolates might be capable of plant protection against Pst DC3000, however, the majority of isolates tested in this study did not provide this beneficial service, indicating that plant protection is less widespread than previously assumed. Surprisingly, whole genome analysis revealed that very closely related Sphingomonas isolates show high conservation of their genomic content, despite pronounced physiological differences and origins of isolation. We failed to identify genes which are shared between all protective isolates and absent from non-protecting strains, indicating that more complex networks and regulatory circuits rather than immunity inducing and antibiotic biosynthesis genes form the basis for the observed plant beneficial phenotype. We additionally identified two Pseudomonas strains that significantly reduced disease severity of Pst DC3000 on A. thaliana plants. Further analyses, including co-inoculations and plant gene transcriptomics will reveal, how the host responds to the distinct genera, whether the protection is mediated by direct competition or priming of the innate plant immune system and if the mechanisms of plant protection overlap between Sphingomonas and Pseudomonas isolates.

In times of increased need of antimicrobial drugs, it is becoming essential to move beyond traditionally screened habitats for antibiotic producing strains, in order to identify new compounds based on undiscovered chemistry. The leaf environment hast not been systematically analyzed in this regard and we therefore mined the genomes of 206 leaf isolates for the presence of natural product biosynthesis cluster, resulting in the identification of more than 900 gene clusters. Biosynthesis of terpenes accounts for the most widespread secondary metabolite class on leaves, was found to be overrepresented compared to root and soil isolates and is likely to be involved in carotenoid biosynthesis to meet the increased demand for protection against solar radiation. In addition, a multitude of clusters encoding potentially novel natural products have been identified and purification, determination of the molecular structure and characterization of their physiological properties is ongoing.

In summary, our bipartite interaction assay on agar plates revealed that about 1% of possible strain combinations results in inhibitory interactions, with the majority of interactions caused by Bacillales and Pseudomonadales isolates. We detected interactions between isolates originating from the same leaf at expected frequencies showing that co-existence of these strains is not reflected by increased resistance against fellow colonizers, however, antimicrobial compounds were less frequently targeted against closely related strains. Parallel whole genome mining for genes involved in natural product biosynthesis identified gene cluster for a variety of distinct chemical classes and revealed that terpenes are the most abundant secondary metabolite class on leaves, likely to be involved in resistance against solar radiation. A subsequent analysis of plant protection identified a subset of protective Sphingomonas as well as Pseudomonas isolates that reduce plant disease severity. Surprisingly, whole genome analysis of leaf- derived Sphingomonas isolates revealed high conservation of their genomic content, despite substantial differences in physiology and origin of isolation. We were unable to identify shared genes between all

105 protective isolates that are absent from non-protective strains, indicating that plant protection is either caused by different modes of action, or by more complex networks of genes and their regulation rather than the sole presence of immunity inducing genes.

REFERENCES

1. Asnicar F, Weingart G, Tickle TL, Huttenhower C, Segata N. 2015. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3:e1029 2. Bai Y, Müller DB, Srinivas G, Garrido-Oter R, Potthoff E, et al. 2015. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528:364-9 3. Bassler BL, Losick R. 2006. Bacterially speaking. Cell 125:237-46 4. Blin K, Medema MH, Kazempour D, Fischbach MA, Breitling R, et al. 2013. antiSMASH 2.0--a versatile platform for genome mining of secondary metabolite producers. Nucleic acids research 41:W204-12 5. Bodenhausen N, Horton MW, Bergelson J. 2013. Bacterial Communities Associated with the Leaves and the Roots of Arabidopsis thaliana. Plos One 8 6. Bulgarelli D, Garrido-Oter R, Muench PC, Weiman A, Droege J, et al. 2015. Structure and Function of the Bacterial Root Microbiota in Wild and Domesticated Barley. Cell Host & Microbe 17:392-403 7. Bulgarelli D, Schlaeppi K, Spaepen S, van Themaat EVL, Schulze-Lefert P. 2013. Structure and Functions of the Bacterial Microbiota of Plants. In Annu Rev Plant Biol, ed. SS Merchant, 64:807-38. Number of 807-38 pp. 8. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, et al. 2012. Ultra-high- throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME journal 6:1621-4 9. Cinque B, Di Marzio L, Centi C, Di Rocco C, Riccardi C, Grazia Cifone M. 2003. Sphingolipids and the immune system. Pharmacological research 47:421-37 10. Clemente JC, Ursell LK, Parfrey LW, Knight R. 2012. The impact of the gut microbiota on human health: an integrative view. Cell 148:1258-70 11. Compant S, Duffy B, Nowak J, Clement C, Barka EA. 2005. Use of plant growth-promoting bacteria for biocontrol of plant diseases: Principles, mechanisms of action, and future prospects. Applied and environmental microbiology 71:4951-9 12. Cordero OX, Wildschutte H, Kirkup B, Proehl S, Ngo L, et al. 2012. Ecological Populations of Bacteria Act as Socially Cohesive Units of Antibiotic Production and Resistance. Science 337:1228-31 13. Delmotte N, Knief C, Chaffron S, Innerebner G, Roschitzki B, et al. 2009. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc Natl Acad Sci U S A 106:16428-33 14. Fan J, Crooks C, Lamb C. 2008. High-throughput quantitative luminescence assay of the growth in planta of Pseudomonas syringae chromosomally tagged with Photorhabdus luminescens luxCDABE. The Plant journal : for cell and molecular biology 53:393-9 15. Hacquard S, Garrido-Oter R, Gonzalez A, Spaepen S, Ackermann G, et al. 2015. Microbiota and Host Nutrition across Plant and Animal Kingdoms. Cell Host & Microbe 17:603-16 16. Horton MW, Bodenhausen N, Beilsmith K, Meng D, Muegge BD, et al. 2014. Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nature Communications 5

106 17. Innerebner G, Knief C, Vorholt JA. 2011. Protection of Arabidopsis thaliana against leaf- pathogenic Pseudomonas syringae by Sphingomonas strains in a controlled model system. Applied and environmental microbiology 77:3202-10 18. Kemen E. 2014. Microbe-microbe interactions determine oomycete and fungal host colonization. Current Opinion in Plant Biology 20:75-81 19. Lindow SE, Brandl MT. 2003. Microbiology of the phyllosphere. Appl Environ Microbiol 69:1875-83 20. Lugtenberg B, Kamilova F. 2009. Plant-Growth-Promoting Rhizobacteria. Annual review of microbiology 63:541-56 21. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, et al. 2012. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. The ISME journal 6:610-8 22. Meyer KM, Leveau JHJ. 2012. Microbiology of the phyllosphere: a playground for testing ecological concepts. Oecologia 168:621-9 23. Müller DB, Bai Y, Vogel C, Vorholt JA. 2016. The plant microbiota: Systems biology insights and perspectives. Ann. Rev. Genetics (under revision) 24. Remus-Emsermann MNP, Luecker S, Mueller DB, Potthoff E, Daims H, Vorholt JA. 2014. Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environmental Microbiology 16:2329- 40 25. Russell JA, Dubilier N, Rudgers JA. 2014. Nature's microbiome: introduction. Molecular ecology 23:1225-37 26. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. 2007. The Human Microbiome Project. Nature 449:804-10 27. Vandenkoornhuyse P, Quaiser A, Duhamel M, Le Van A, Dufresne A. 2015. The importance of the microbiome of the plant holobiont. New Phytologist 206:1196-206 28. Vogel C, Innerebner G, Zingg J, Guder J, Vorholt JA. 2012. Forward genetic in planta screen for identification of plant-protective traits of Sphingomonas sp. strain Fr1 against Pseudomonas syringae DC3000. Applied and environmental microbiology 78:5529-35 29. Vorholt JA. 2012. Microbial life in the phyllosphere. Nature Reviews Microbiology 10:828-40 30. Whalen MC, Innes RW, Bent AF, Staskawicz BJ. 1991. Identification of Pseudomonas syringae pathogens of Arabidopsis and a bacterial locus determining avirulence on both Arabidopsis and soybean. The Plant cell 3:49-59

107

Fig. S1: Correlation of Bacillales with other bacterial orders on natural A. thaliana leaves. The relative abundance (RA) of Bacillales of the total community (100% corresponds to the total abundance of all detectable OTUs) is plotted against the RA of the indicated bacterial order of the remaining community (RA of all non- Bacillales isolates corresponds to 100%). Samples (n=624) of three independent studies were processed together for this analysis. Statistical data of all correlations are summarized in Suppl. Tab. 3.

108 Fig. S2: Correlation of Pseudomonadales with other bacterial orders on natural A. thaliana leaves. The relative abundance (RA) of Pseudomonadales of the total community (100% corresponds to the total abundance of all detectable OTUs) is plotted against the RA of the indicated bacterial order of the remaining community (RA of all non-Pseudomonadales isolates corresponds to 100%). Samples (n=624) of three independent studies were processed together for this analysis. Statistical data of all correlations are summarized in Suppl. Tab. 3.

109 110 1298 94 53.51 .53935[[65, 9.375 3 8.25 16 31.55 65 19.44 84 K10209 0858 92 82.62 18 .5[[58, 6.25 2 11.86 23 28.16 58 19.21 83 [[40, 3.125 K09845 1 2.06 4 19.42 40 10.42 45 K14598 0844 .53 79 .900[[37, 0 [[108, 9.375 [[29, 3 0 0 18.56 36 0 3.09 52.43 108 6 34.03 1.55 147 3 K10210 17.96 37 14.08 9.95 29 43 7.41 K09844 32 K09836 1768 87 43.71 .600[[64, 0 0 8.76 17 31.07 64 18.75 81 K15746 oprdto root and soil isolates. compared Suppl. Tab.1: Suppl. KO term

present all strains

% all strains KEGG Orthology terms assigned to assigned OrthologyKEGG terms present leaf

% leaf strains

present root

% root strains

present soil

% soil strains 141], 148], 166], 169], 177], 142], 98], carotenoid biosynthesis that a [19, [25, [5, [39, [6, [3, [17, Fisher test matrix 2] .21.73E 9.42 221]] 2] .16.57E 5.81 1.58E 9.10 220]] 223]] 0] .31.01E 3.83 207]] 0] .78.38E 2.37 201]] 8] .39.31E 2.83 187]] 0] .52.42E 3.55 209]]

Leaf vs root effect 6Carotenoid 5.81E‐06 ‐09 6008529Carotenoid biosynthesi 0.048052229 ‐06 Carotenoid 9.93E‐06 ‐09 1Carotenoid 5.34E‐11 ‐15 Carotenoid biosynthesi 0.00037671 ‐08 Carotenoid biosynthesi 0.000904907 ‐07 6Carotenoid 1.39E‐06 ‐10 Fisher test p‐value

Bonferonni corrected p‐value re overrepresented in genomes ofleaf associated bacteria KO Pathway biosynthesi biosynthesi biosynthesi biosynthesi s s s s s s s crtD; crtP; crtZ; crtN; crtC; crtW; cruD; chlorobactene beta‐carotene3hydroxylaseEC:1.14.13.129 diapolycopene carotenoid 1‐hydroxycarotenoid 4,4 beta‐caroteneketolase(CrtWtype) ‐diapophytoene 1,2 ‐hydratase EC:4.2.1.131 oxygenase lauroyltransferase KO desaturaseEC:1.3.8.2 description 3,4 ‐desaturase EC:1.3.99.27 EC:1.14.99.4 4 Suppl. Tab. 2: Phylogenetic annotation of Arabidopsis leaf microbiota strains used in this study. Strain ID Phylum Class Order Family Genus Leaf334 Actinobacteria Actinobacteria Actinomycetales Cellulomonadaceae Cellulomonas Leaf395 Actinobacteria Actinobacteria Actinomycetales Cellulomonadaceae Cellulomonas Leaf380 Actinobacteria Actinobacteria Actinomycetales Geodermatophilaceae Blastococcus Leaf369 Actinobacteria Actinobacteria Actinomycetales Geodermatophilaceae Geodermatophilus Leaf210 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Agreia Leaf244 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Agreia Leaf283 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Agreia Leaf335 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Agreia Leaf222 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Agromyces Leaf172 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Clavibacter Leaf263 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Clavibacter Leaf154 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Leaf183 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Leaf261 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Leaf186 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Leaf254 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Leaf415 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Leaf44 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Leaf8 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Leaf304 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Leaf264 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Leifsonia Leaf325 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Leifsonia Leaf336 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Leifsonia Leaf151 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf159 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf161 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf179 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf203 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf288 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf320 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf347 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf351 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf436 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf441 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Leaf1 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Leaf171 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Leaf314 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Leaf164 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Rathayibacter Leaf185 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Rathayibacter Leaf248 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Rathayibacter Leaf294 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Rathayibacter Leaf296 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Rathayibacter Leaf299 Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Rathayibacter Leaf137 Actinobacteria Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Leaf141 Actinobacteria Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Leaf145 Actinobacteria Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Leaf234 Actinobacteria Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Leaf337 Actinobacteria Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Leaf69 Actinobacteria Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Leaf225 Actinobacteria Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Leaf233 Actinobacteria Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Leaf247 Actinobacteria Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Leaf258 Actinobacteria Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Leaf278 Actinobacteria Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Leaf7 Actinobacteria Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Leaf354 Actinobacteria Actinobacteria Actinomycetales Nocardiaceae Williamsia Leaf245 Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Leaf272 Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Leaf289 Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Leaf291 Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Leaf293 Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Leaf350 Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Leaf446 Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Marmoricola Leaf285 Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Leaf307 Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Nocardioides

111 Suppl. Tab. 2 continued Leaf374 Actinobacteria Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Leaf3 Actinobacteria Actinobacteria Actinomycetales Sanguibacteraceae Sanguibacter Leaf189 Bacteroidetes Cytophagia Cytophagales Cytophagaceae Dyadobacter Leaf180 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Chryseobacterium Leaf201 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Chryseobacterium Leaf313 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Chryseobacterium Leaf394 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Chryseobacterium Leaf404 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Chryseobacterium Leaf405 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Chryseobacterium Leaf359 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Flavobacterium Leaf82 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Flavobacterium Leaf132 Bacteroidetes Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Pedobacter Leaf170 Bacteroidetes Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Pedobacter Leaf176 Bacteroidetes Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Pedobacter Leaf194 Bacteroidetes Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Pedobacter Leaf216 Bacteroidetes Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Pedobacter Leaf250 Bacteroidetes Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Pedobacter Leaf41 Bacteroidetes Sphingobacteriia Sphingobacteriales Sphingobacteriaceae Pedobacter Leaf326 Deinococcus‐Thermus Deinococci Deinococcales Deinococcaceae Deinococcus Leaf13 Firmicutes Bacilli Bacillales Bacillaceae Bacillus Leaf406 Firmicutes Bacilli Bacillales Bacillaceae Bacillus Leaf49 Firmicutes Bacilli Bacillales Bacillaceae Bacillus Leaf75 Firmicutes Bacilli Bacillales Bacillaceae Bacillus Leaf187 Firmicutes Bacilli Bacillales Bacillaceae Exiguobacterium Leaf196 Firmicutes Bacilli Bacillales Bacillaceae Exiguobacterium Leaf182 Firmicutes Bacilli Bacillales Paenibacillaceae Brevibacillus Leaf72 Firmicutes Bacilli Bacillales Paenibacillaceae Paenibacillus Leaf168 Proteobacteria Alphaproteobacteria Caulobacterales Caulobacteraceae Brevundimonas Leaf280 Proteobacteria Alphaproteobacteria Caulobacterales Caulobacteraceae Brevundimonas Leaf363 Proteobacteria Alphaproteobacteria Caulobacterales Caulobacteraceae Brevundimonas Leaf427 Proteobacteria Alphaproteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Leaf443 Proteobacteria Alphaproteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Leaf454 Proteobacteria Alphaproteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Leaf460 Proteobacteria Alphaproteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Leaf324 Proteobacteria Alphaproteobacteria Rhizobiales Aurantimonadaceae Aureimonas Leaf344 Proteobacteria Alphaproteobacteria Rhizobiales Bradyrhizobiaceae Bosea Leaf396 Proteobacteria Alphaproteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Leaf401 Proteobacteria Alphaproteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Leaf420 Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae Devosia Leaf64 Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae Devosia Leaf100 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf102 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf104 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf106 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf108 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf111 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf112 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf113 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf115 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf117 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf118 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf119 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf121 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf122 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf123 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf125 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf361 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf399 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf456 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf465 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf466 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf469 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf85 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf86 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf87 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf88 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf89 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf90 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf91 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium

112 Suppl. Tab. 2 continued Leaf92 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf93 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf94 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf99 Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Leaf155 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf167 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf202 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf262 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf306 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf311 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf321 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf341 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf371 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf383 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf384 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf386 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf391 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf453 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf68 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Rhizobium Leaf166 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Leaf2 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Leaf26 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingobium Leaf10 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf11 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf16 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf17 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf198 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf20 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf205 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf208 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf21 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf22 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf226 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf23 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf230 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf231 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf24 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf242 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf25 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf257 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf28 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf29 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf30 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf32 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf33 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf339 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf34 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf343 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf357 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf37 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf38 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf4 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf407 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf412 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf42 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf5 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf62 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf67 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf9 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Leaf177 Proteobacteria Betaproteobacteria Burkholderiales Burkholderiaceae Burkholderia Leaf160 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Acidovorax Leaf191 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Acidovorax Leaf400 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Acidovorax Leaf73 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Acidovorax Leaf76 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Acidovorax Leaf78 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Acidovorax Leaf84 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Acidovorax Leaf265 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Pseudorhodoferax Leaf274 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Pseudorhodoferax

113 Suppl. Tab. 2 continued Leaf220 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Variovorax Leaf267 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Variovorax Leaf126 Proteobacteria Betaproteobacteria Burkholderiales Oxalobacteraceae Duganella Leaf61 Proteobacteria Betaproteobacteria Burkholderiales Oxalobacteraceae Duganella Leaf139 Proteobacteria Betaproteobacteria Burkholderiales Oxalobacteraceae Massilia Leaf408 Proteobacteria Betaproteobacteria Methylophilales Methylophilaceae Methylophilus Leaf414 Proteobacteria Betaproteobacteria Methylophilales Methylophilaceae Methylophilus Leaf416 Proteobacteria Betaproteobacteria Methylophilales Methylophilaceae Methylophilus Leaf421 Proteobacteria Betaproteobacteria Methylophilales Methylophilaceae Methylophilus Leaf459 Proteobacteria Betaproteobacteria Methylophilales Methylophilaceae Methylophilus Leaf53 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae Erwinia Leaf50 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae Serratia Leaf51 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae Serratia Leaf130 Proteobacteria Gammaproteobacteria Pseudomonadales Moraxellaceae Acinetobacter Leaf127 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Leaf129 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Leaf15 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Leaf434 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Leaf48 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Leaf58 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Leaf59 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Leaf83 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Leaf98 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Leaf70 Proteobacteria Gammaproteobacteria Xanthomonadales Xanthomonadaceae Stenotrophomonas Leaf131 Proteobacteria Gammaproteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas Leaf148 Proteobacteria Gammaproteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas

114 Suppl. Tab. 3: Summary of Pearson correlations of bacterial orders in natural leaf communities.

Inhibiting order Sensitive order Correlation r 95% confidence interval R squared p‐value p‐value summary Significant XY pairs Bacillales Burkholderiales Pearson ‐0.249 ‐0.3212 to ‐0.1739 0.06199 < 0.0001 **** Yes 624 Bacillales Pseudomonadales Pearson ‐0.1668 ‐0.2421 to ‐0.08956 0.02783 < 0.0001 **** Yes 625 Bacillales Flavobacteriales Pearson ‐0.1337 ‐0.2100 to ‐0.05584 0.01789 0.0008 *** Yes 624 Bacillales Xanthomonadales Pearson ‐0.09249 ‐0.1697 to ‐0.01411 0.008555 0.0208 * Yes 624 Bacillales Sphingobacteriales Pearson ‐0.06445 ‐0.1422 to 0.01411 0.004154 0.1077 ns No 624 Bacillales Caulobacterales Pearson 0.003787 ‐0.07472 to 0.08225 1.43E‐05 0.9248 ns No 624 Bacillales Sphingomonadales Pearson 0.005146 ‐0.07337 to 0.08360 2.65E‐05 0.8979 ns No 624 Bacillales Rhizobiales Pearson 0.2072 0.1309 to 0.2812 0.04295 < 0.0001 **** Yes 624 Bacillales Actinomycetales Pearson 0.2138 0.1376 to 0.2874 0.0457 < 0.0001 **** Yes 624 Bacillales Methylophilales Pearson 0.4234 0.3568 to 0.4857 0.1793 < 0.0001 **** Yes 624

Inhibiting order Sensitive order Correlation r 95% confidence interval R squared p‐value p‐value summary Significant XY pairs

115 Pseudomonadales Xanthomonadales Pearson 0.2293 0.1536 to 0.3024 0.05259 < 0.0001 **** Yes 624 Pseudomonadales Flavobacteriales Pearson 0.1161 0.03793 to 0.1928 0.01347 0.0037 ** Yes 624 Pseudomonadales Sphingobacteriales Pearson 0.07924 0.0007530 to 0.1568 0.006279 0.0479 * Yes 624 Pseudomonadales Burkholderiales Pearson 0.02544 ‐0.05316 to 0.1037 0.0006472 0.5259 ns No 624 Pseudomonadales Sphingomonadales Pearson ‐0.02832 ‐0.1066 to 0.05028 0.0008021 0.4801 ns No 624 Pseudomonadales Caulobacterales Pearson ‐0.03316 ‐0.1114 to 0.04545 0.0011 0.4083 ns No 624 Pseudomonadales Rhizobiales Pearson ‐0.15 ‐0.2258 to ‐0.07234 0.02249 0.0002 *** Yes 624 Pseudomonadales Bacillales Pearson ‐0.1552 ‐0.2308 to ‐0.07762 0.02408 < 0.0001 **** Yes 624 Pseudomonadales Actinomycetales Pearson ‐0.1728 ‐0.2479 to ‐0.09558 0.02985 < 0.0001 **** Yes 624 Pseudomonadales Methylophilales Pearson ‐0.176 ‐0.2510 to ‐0.09886 0.03097 < 0.0001 **** Yes 624 Suppl. Tab. 4: All monitored binary interactions plus metadata of both strains involved Inhibiter (A) Sensitive(B) Interaction (A) Phylum (A) Order (A) Family (A) Genus (A) Sampling site (B) Phylum (B) Order (B) Family (B) Genus (B) Sampling site 2 P516 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Phyto Phyto Phyto Xcc ‐ 2 P492 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Phyto Phyto Phyto A_t_C58 ‐ 2 P1376 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Phyto Phyto Phyto DC3000 ‐ 2 459 strong Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 2 443 strong Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Brugg_02.10.2013_L9 2 441 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 2 436 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 2 416 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 2 407 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 2 404 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 2 401 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 2 400 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L9 2 394 strong Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 2 374 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L6 2 369 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Geodermatophilaceae Geodermatophilus Brugg_18.06.2013_only 2 354 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardiaceae Williamsia Brugg_08.10.2013_L6 2 347 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 2 341 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L11 2 335 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Agreia ETH Zurich_16.05.2013_only 2 307 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L11 2 304 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only 2 293 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 2 291 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L7 2 289 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 116 2 258 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Brugg_08.10.2013_L6 2 242 strong Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 2 234 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L6 2 226 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 2 208 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 2 205 strong Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 2 186 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Hoengg_30.04.2013_only 2 182 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 2 180 weak Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 11 441 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 11 407 strong Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 11 404 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 11 401 strong Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 11 400 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L9 11 394 strong Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 11 351 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 11 347 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 11 344 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Proteobacteria Rhizobiales Bradyrhizobiaceae Bosea Brugg_02.10.2013_L9 11 341 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L11 13 369 weak Firmicutes Bacillales Bacillaceae Bacillus Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Geodermatophilaceae Geodermatophilus Brugg_18.06.2013_only 15 89 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 15 88 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L6 15 466 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L7 15 443 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Brugg_02.10.2013_L9 15 41 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 15 407 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 15 404 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 15 401 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 15 400 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L9 15 394 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Suppl. Tab. 4 continued 15 38 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 15 351 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 15 347 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 15 344 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Bradyrhizobiaceae Bosea Brugg_02.10.2013_L9 15 208 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 15 205 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 15 202 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L4 15 198 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 15 180 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 15 176 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_02.10.2013_L4 15 166 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 15 151 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_18.06.2013_only 15 148 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only 15 132 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 15 123 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L7 15 117 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only 15 115 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 15 111 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 16 92 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 16 280 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 Proteobacteria Caulobacterales Caulobacteraceae Brevundimonas Brugg_02.10.2013_L9 21 9 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 23 351 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 28 132 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 30 443 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 Proteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Brugg_02.10.2013_L9 30 407 strong Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 30 404 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 30 401 strong Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 30 400 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L9 30 394 strong Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 117 32 92 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 32 89 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 48 441 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 48 394 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 49 8 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Seebach_17.05.2013_only 49 64 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Hyphomicrobiaceae Devosia Brugg_02.10.2013_L9 49 5 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 49 44 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Hoengg_30.04.2013_only 49 42 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 49 415 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Tuebingen_03.05.2013_only 49 41 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 49 404 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 49 401 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 49 4 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 49 38 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 49 37 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 49 351 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 49 34 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 49 33 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 49 30 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 49 28 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 49 263 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Clavibacter Brugg_08.10.2013_L6 49 26 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingobium Brugg_02.10.2013_L9 49 25 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 49 24 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 49 23 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 49 208 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 49 186 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Hoengg_30.04.2013_only 49 16 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 49 10 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 Suppl. Tab. 4 continued 49 1 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Hoengg_30.04.2013_only 53 394 weak Proteobacteria Enterobacteriales Enterobacteriaceae Erwinia Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 53 126 weak Proteobacteria Enterobacteriales Enterobacteriaceae Erwinia Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 58 P516 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Phyto Phyto Phyto Xcc Phyto_Phyto_Phyto 58 P511 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Phyto Phyto Phyto B728a Phyto_Phyto_Phyto 58 P492 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Phyto Phyto Phyto A_t_C58 Phyto_Phyto_Phyto 58 P1376 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Phyto Phyto Phyto DC3000 Phyto_Phyto_Phyto 58 89 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 58 8 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Seebach_17.05.2013_only 58 67 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 58 5 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 58 443 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Brugg_02.10.2013_L9 58 441 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 58 436 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 58 41 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 58 408 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 58 407 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 58 404 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 58 401 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 58 400 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L9 58 394 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 58 384 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L7 58 38 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 58 374 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L6 58 37 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 58 369 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Geodermatophilaceae Geodermatophilus Brugg_18.06.2013_only 58 354 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardiaceae Williamsia Brugg_08.10.2013_L6 58 351 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 58 347 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 118 58 341 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L11 58 34 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 58 33 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 58 32 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 58 307 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L11 58 304 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only 58 30 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 58 293 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 58 291 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L7 58 29 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 58 289 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 58 28 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 58 258 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Brugg_08.10.2013_L6 58 234 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L6 58 226 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 58 225 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Brugg_08.10.2013_L6 58 208 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 58 205 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 58 20 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 58 198 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 58 189 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 58 186 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Hoengg_30.04.2013_only 58 182 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 58 180 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 58 16 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 58 151 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_18.06.2013_only 58 141 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Hoengg_30.04.2013_only 58 132 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 58 126 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 58 1 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Hoengg_30.04.2013_only Suppl. Tab. 4 continued 59 407 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas ETH Zurich_16.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 59 394 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas ETH Zurich_16.05.2013_only Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 59 38 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas ETH Zurich_16.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 59 34 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas ETH Zurich_16.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 59 1 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas ETH Zurich_16.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Hoengg_30.04.2013_only 61 92 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 61 5 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 61 42 strong Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 61 4 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 61 189 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L6 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 61 16 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 68 111 weak Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 69 38 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Seebach_17.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 69 34 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Seebach_17.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 70 92 weak Proteobacteria Xanthomonadales Xanthomonadaceae Stenotrophomonas Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 70 113 weak Proteobacteria Xanthomonadales Xanthomonadaceae Stenotrophomonas Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 75 8 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Seebach_17.05.2013_only 75 7 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Seebach_17.05.2013_only 75 69 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Seebach_17.05.2013_only 75 44 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Hoengg_30.04.2013_only 75 394 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 75 374 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L6 75 363 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Proteobacteria Caulobacterales Caulobacteraceae Brevundimonas Brugg_02.10.2013_L4 75 351 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 75 347 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 75 337 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_18.06.2013_only 75 335 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Agreia ETH Zurich_16.05.2013_only 75 334 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Cellulomonadaceae Cellulomonas Tuebingen_03.05.2013_only 75 307 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L11 119 75 304 strong Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only 75 285 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_02.10.2013_L9 75 283 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Agreia ETH Zurich_16.05.2013_only 75 141 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Hoengg_30.04.2013_only 75 13 weak Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 Firmicutes Bacillales Bacillaceae Bacillus Tuebingen_03.05.2013_only 76 38 weak Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 78 84 weak Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L7 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_08.10.2013_L7 78 407 weak Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 78 394 weak Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L7 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 82 67 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 82 64 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Rhizobiales Hyphomicrobiaceae Devosia Brugg_02.10.2013_L9 82 62 strong Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 82 407 strong Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 82 38 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 82 34 strong Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 82 33 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 82 30 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 82 28 strong Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 82 226 strong Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 82 205 strong Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 82 198 strong Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 82 189 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 82 16 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 82 148 strong Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only 82 139 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Burkholderiales Oxalobacteraceae Massilia Brugg_08.10.2013_L7 82 132 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 82 131 strong Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only 82 126 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 82 1 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Hoengg_30.04.2013_only 83 226 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only Suppl. Tab. 4 continued 83 208 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 83 189 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_02.10.2013_L9 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 93 242 strong Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 98 92 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 98 89 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 98 69 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Seebach_17.05.2013_only 98 62 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 98 466 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L7 98 443 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Brugg_02.10.2013_L9 98 441 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 98 42 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 98 412 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_18.06.2013_only 98 41 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 98 408 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 98 407 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 98 404 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 98 401 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 98 400 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L9 98 396 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_08.10.2013_L6 98 394 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 98 384 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L7 98 38 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 98 369 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Geodermatophilaceae Geodermatophilus Brugg_18.06.2013_only 98 351 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 98 347 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 98 344 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Bradyrhizobiaceae Bosea Brugg_02.10.2013_L9 98 341 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L11 98 320 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Hoengg_30.04.2013_only 98 250 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_18.06.2013_only 120 98 242 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 98 234 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L6 98 231 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 98 230 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 98 208 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 98 205 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 98 202 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L4 98 198 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 98 189 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 98 186 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Hoengg_30.04.2013_only 98 180 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 98 166 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 98 148 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only 98 132 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 98 126 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 98 123 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L7 98 119 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 98 117 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only 98 115 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 98 111 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 126 P511 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Phyto Phyto Phyto B728a Phyto_Phyto_Phyto 126 92 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 126 89 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 126 441 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 126 41 strong Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 126 400 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L9 126 394 strong Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 126 38 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 126 341 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L11 126 325 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Actinobacteria Actinomycetales Microbacteriaceae Leifsonia Hoengg_30.04.2013_only Suppl. Tab. 4 continued 126 226 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 126 113 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 126 111 weak Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 127 84 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_08.10.2013_L7 127 41 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 127 408 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 127 394 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 127 226 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 127 131 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only 127 111 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 129 113 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 130 P511 weak Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Phyto Phyto Phyto B728a Phyto_Phyto_Phyto 130 41 strong Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 130 408 weak Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 130 394 strong Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 130 38 strong Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 130 325 weak Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Actinobacteria Actinomycetales Microbacteriaceae Leifsonia Hoengg_30.04.2013_only 130 28 strong Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 130 226 weak Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 130 208 weak Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 130 16 weak Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 130 127 weak Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 130 111 weak Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 130 10 weak Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 130 1 strong Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Hoengg_30.04.2013_only 131 38 weak Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 131 34 weak Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 131 30 weak Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 131 234 weak Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L6 121 131 189 weak Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 137 351 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L7 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 137 347 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L7 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 137 30 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 139 182 weak Proteobacteria Burkholderiales Oxalobacteraceae Massilia Brugg_08.10.2013_L7 Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 139 113 weak Proteobacteria Burkholderiales Oxalobacteraceae Massilia Brugg_08.10.2013_L7 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 141 38 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 141 34 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 141 30 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 145 69 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_02.10.2013_L7 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Seebach_17.05.2013_only 145 38 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_02.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 145 30 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_02.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 145 28 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_02.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 145 189 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_02.10.2013_L7 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 148 189 weak Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 148 131 weak Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only 154 89 weak Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 154 84 weak Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_08.10.2013_L7 154 82 weak Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Hoengg_30.04.2013_only 154 8 weak Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Seebach_17.05.2013_only 154 408 weak Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 154 380 strong Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Actinobacteria Actinomycetales Geodermatophilaceae Blastococcus Brugg_02.10.2013_L9 154 369 strong Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Actinobacteria Actinomycetales Geodermatophilaceae Geodermatophilus Brugg_18.06.2013_only 154 347 weak Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 154 132 weak Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 154 131 weak Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only 154 123 weak Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L7 154 118 weak Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.01.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L6 161 4 weak Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Hoengg_30.04.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 167 189 weak Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L9 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 Suppl. Tab. 4 continued 167 113 weak Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 170 189 weak Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L7 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 176 92 weak Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_02.10.2013_L4 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 176 113 weak Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_02.10.2013_L4 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 177 P511 weak Proteobacteria Burkholderiales Burkholderiaceae Burkholderia ETH Zurich_16.05.2013_only Phyto Phyto Phyto B728a Phyto_Phyto_Phyto 177 441 weak Proteobacteria Burkholderiales Burkholderiaceae Burkholderia ETH Zurich_16.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 177 436 weak Proteobacteria Burkholderiales Burkholderiaceae Burkholderia ETH Zurich_16.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 177 404 weak Proteobacteria Burkholderiales Burkholderiaceae Burkholderia ETH Zurich_16.05.2013_only Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 177 394 strong Proteobacteria Burkholderiales Burkholderiaceae Burkholderia ETH Zurich_16.05.2013_only Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 177 361 weak Proteobacteria Burkholderiales Burkholderiaceae Burkholderia ETH Zurich_16.05.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L7 177 341 weak Proteobacteria Burkholderiales Burkholderiaceae Burkholderia ETH Zurich_16.05.2013_only Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L11 177 325 weak Proteobacteria Burkholderiales Burkholderiaceae Burkholderia ETH Zurich_16.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Leifsonia Hoengg_30.04.2013_only 177 208 weak Proteobacteria Burkholderiales Burkholderiaceae Burkholderia ETH Zurich_16.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 182 9 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 182 89 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 182 84 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_08.10.2013_L7 182 8 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Seebach_17.05.2013_only 182 7 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Seebach_17.05.2013_only 182 67 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 182 64 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Hyphomicrobiaceae Devosia Brugg_02.10.2013_L9 182 62 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 182 5 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 182 49 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Firmicutes Bacillales Bacillaceae Bacillus Brugg_02.10.2013_L9 182 466 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L7 182 459 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 182 443 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Brugg_02.10.2013_L9 182 44 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Hoengg_30.04.2013_only 182 42 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 182 41 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_08.10.2013_L6 122 182 407 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 182 401 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 182 400 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L9 182 396 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_08.10.2013_L6 182 391 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L9 182 380 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Geodermatophilaceae Blastococcus Brugg_02.10.2013_L9 182 38 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 182 374 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L6 182 37 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 182 363 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Caulobacterales Caulobacteraceae Brevundimonas Brugg_02.10.2013_L4 182 354 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardiaceae Williamsia Brugg_08.10.2013_L6 182 351 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 182 347 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 182 34 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 182 335 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Agreia ETH Zurich_16.05.2013_only 182 33 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 182 32 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 182 307 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L11 182 304 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only 182 30 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 182 3 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Sanguibacteraceae Sanguibacter Hoengg_30.04.2013_only 182 293 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 182 291 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L7 182 29 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 182 289 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 182 285 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_02.10.2013_L9 182 283 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Agreia ETH Zurich_16.05.2013_only 182 28 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 182 265 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Pseudorhodoferax Brugg_02.10.2013_L9 182 263 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Clavibacter Brugg_08.10.2013_L6 Suppl. Tab. 4 continued 182 258 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Brugg_08.10.2013_L6 182 25 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 182 248 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Rathayibacter Brugg_02.10.2013_L7 182 244 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Agreia Hoengg_30.04.2013_only 182 242 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 182 24 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 182 234 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L6 182 230 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 182 23 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 182 226 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 182 225 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Brugg_08.10.2013_L6 182 220 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Variovorax Brugg_08.10.2013_L6 182 22 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 182 21 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 182 208 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 182 205 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 182 20 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 182 2 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 182 198 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 182 191 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_08.10.2013_L6 182 189 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 182 186 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Hoengg_30.04.2013_only 182 183 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Curtobacterium Brugg_08.10.2013_L7 182 177 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Burkholderiaceae Burkholderia ETH Zurich_16.05.2013_only 182 172 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Clavibacter Brugg_08.10.2013_L7 182 17 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 182 166 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Novosphingobium Brugg_02.10.2013_L9 182 160 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_08.10.2013_L6 182 16 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 123 182 130 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Pseudomonadales Moraxellaceae Acinetobacter Brugg_18.06.2013_only 182 117 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only 182 113 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 182 111 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 182 11 strong Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 182 10 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 182 1 weak Firmicutes Bacillales Paenibacillaceae Brevibacillus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Hoengg_30.04.2013_only 187 38 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 187 363 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L6 Proteobacteria Caulobacterales Caulobacteraceae Brevundimonas Brugg_02.10.2013_L4 187 34 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 187 33 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 187 32 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 187 30 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 187 29 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 187 28 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 187 226 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 187 189 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L6 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 191 38 weak Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 194 416 weak Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Tuebingen_03.05.2013_only Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 194 314 weak Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Brugg_02.10.2013_L4 194 189 weak Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Tuebingen_03.05.2013_only Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 196 9 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L7 196 443 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Brugg_02.10.2013_L9 196 4 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 196 38 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 196 37 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 196 363 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Caulobacterales Caulobacteraceae Brevundimonas Brugg_02.10.2013_L4 196 34 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 196 33 strong Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 196 314 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Brugg_02.10.2013_L4 Suppl. Tab. 4 continued 196 30 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 196 28 strong Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 196 23 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 196 208 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 196 189 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 196 17 strong Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 196 1 weak Firmicutes Bacillales Bacillales Exiguobacterium Brugg_08.10.2013_L7 Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Hoengg_30.04.2013_only 201 17 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L7 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 202 33 strong Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L4 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 202 30 weak Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L4 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 202 17 weak Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L4 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 202 113 weak Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L4 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 202 111 weak Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L4 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 203 113 weak Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 203 111 weak Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 245 374 strong Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L4 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L6 245 307 strong Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L4 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L11 245 285 strong Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L4 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_02.10.2013_L9 250 113 weak Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_18.06.2013_only Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 254 443 weak Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Brugg_02.10.2013_L9 254 401 weak Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Tuebingen_03.05.2013_only Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 262 394 weak Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L7 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 264 335 weak Actinobacteria Actinomycetales Microbacteriaceae Leifsonia Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Agreia ETH Zurich_16.05.2013_only 272 38 weak Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 272 374 weak Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L6 272 307 weak Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L11 272 30 weak Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 272 285 weak Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_02.10.2013_L9 280 151 weak Proteobacteria Caulobacterales Caulobacteraceae Brevundimonas Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_18.06.2013_only 124 285 5 weak Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 285 38 weak Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 285 208 weak Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 289 285 weak Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_02.10.2013_L9 291 285 weak Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L7 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_02.10.2013_L9 293 307 weak Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L11 293 285 weak Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_02.10.2013_L9 304 69 weak Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Seebach_17.05.2013_only 304 380 weak Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Geodermatophilaceae Blastococcus Brugg_02.10.2013_L9 304 351 weak Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 304 347 weak Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 304 299 weak Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Microbacteriaceae Rathayibacter Brugg_02.10.2013_L9 304 293 weak Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 304 291 weak Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L7 304 289 weak Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Nocardioidaceae Aeromicrobium Brugg_02.10.2013_L9 304 234 weak Actinobacteria Actinomycetales Microbacteriaceae Frondihabitans Tuebingen_03.05.2013_only Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L6 306 117 weak Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L4 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only 313 117 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only 313 113 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 337 151 weak Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_18.06.2013_only Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_18.06.2013_only 357 208 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 359 404 strong Bacteroidetes Flavobacteriales Flavobacteriaceae Flavobacterium Brugg_02.10.2013_L9 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 374 208 strong Actinobacteria Actinomycetales Nocardioidaceae Nocardioides Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 394 404 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 394 347 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 394 117 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only 395 347 weak Actinobacteria Actinomycetales Cellulomonadaceae Cellulomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 404 117 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only 404 113 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 405 347 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 Suppl. Tab. 4 continued 405 113 weak Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 407 117 weak Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only 408 443 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Brugg_02.10.2013_L9 408 441 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 408 412 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_18.06.2013_only 408 407 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 408 404 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 408 401 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 408 396 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_08.10.2013_L6 408 394 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 408 384 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L7 408 369 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Geodermatophilaceae Geodermatophilus Brugg_18.06.2013_only 408 363 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Caulobacterales Caulobacteraceae Brevundimonas Brugg_02.10.2013_L4 408 351 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L6 408 347 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 408 344 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Bradyrhizobiaceae Bosea Brugg_02.10.2013_L9 408 320 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Hoengg_30.04.2013_only 408 299 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Rathayibacter Brugg_02.10.2013_L9 408 250 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_18.06.2013_only 408 242 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 408 234 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L6 408 230 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 408 208 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 408 205 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 408 202 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L4 408 198 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 408 189 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 408 180 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 421 250 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 Bacteroidetes Sphingobacteriales Sphingobacteriaceae Pedobacter Brugg_18.06.2013_only 125 421 242 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 421 234 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L6 421 230 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 421 208 weak Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 421 205 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 421 202 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L4 421 198 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 421 189 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 Bacteroidetes Cytophagales Cytophagaceae Dyadobacter Brugg_08.10.2013_L11 421 180 strong Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 434 P516 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Phyto Phyto Phyto Xcc Phyto_Phyto_Phyto 434 P511 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Phyto Phyto Phyto B728a Phyto_Phyto_Phyto 434 P492 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Phyto Phyto Phyto A_t_C58 Phyto_Phyto_Phyto 434 89 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 434 67 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 434 62 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 434 466 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L7 434 459 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 434 443 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Aurantimonadaceae Aurantimonas Brugg_02.10.2013_L9 434 441 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 434 436 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 434 421 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 434 416 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L9 434 414 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_02.10.2013_L7 434 408 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Methylophilales Methylophilaceae Methylophilus Brugg_08.10.2013_L6 434 407 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 434 404 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 434 401 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium Brugg_02.10.2013_L9 434 400 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Acidovorax Brugg_02.10.2013_L9 434 4 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 434 394 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Bacteroidetes Flavobacteriales Flavobacteriaceae Chryseobacterium Brugg_08.10.2013_L6 Suppl. Tab. 4 continued 434 384 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L7 434 38 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 434 37 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 434 369 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Geodermatophilaceae Geodermatophilus Brugg_18.06.2013_only 434 361 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L7 434 347 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_02.10.2013_L9 434 344 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Bradyrhizobiaceae Bosea Brugg_02.10.2013_L9 434 341 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_08.10.2013_L11 434 34 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 434 335 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Agreia ETH Zurich_16.05.2013_only 434 33 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 434 321 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Rhizobiaceae Rhizobium Brugg_02.10.2013_L7 434 320 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Hoengg_30.04.2013_only 434 32 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 434 30 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L4 434 28 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Seebach_17.05.2013_only 434 234 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Micrococcaceae Arthrobacter Brugg_08.10.2013_L6 434 226 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Hoengg_30.04.2013_only 434 225 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Nocardiaceae Rhodococcus Brugg_08.10.2013_L6 434 220 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Comamonadaceae Variovorax Brugg_08.10.2013_L6 434 205 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas ETH Zurich_16.05.2013_only 434 20 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 434 198 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Tuebingen_03.05.2013_only 434 186 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Frigoribacterium Hoengg_30.04.2013_only 434 179 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_08.10.2013_L7 434 17 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 434 16 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L7 434 151 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Microbacterium Brugg_18.06.2013_only 434 148 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only 126 434 131 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Xanthomonadales Xanthomonadaceae Xanthomonas ETH Zurich_16.05.2013_only 434 126 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Burkholderiales Oxalobacteraceae Duganella Brugg_08.10.2013_L7 434 117 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only 434 113 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_08.10.2013_L11 434 111 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_02.10.2013_L9 434 10 weak Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_02.10.2013_L9 434 1 strong Proteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas Brugg_08.10.2013_L6 Actinobacteria Actinomycetales Microbacteriaceae Plantibacter Hoengg_30.04.2013_only 465 62 strong Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L11 465 42 weak Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only Proteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Brugg_08.10.2013_L6 465 172 weak Proteobacteria Rhizobiales Methylobacteriaceae Methylobacterium Brugg_18.06.2013_only Actinobacteria Actinomycetales Microbacteriaceae Clavibacter Brugg_08.10.2013_L7 Chapter V

DISCUSSION AND OUTLOOK

127 The work of thesis was focused on various aspects of the leaf microbiota, including culturability of its members, adaptation of these bacterial strains to the leaf surface, the network of bacterial interactions as well as potentially beneficial services provided to the plant host. The microbiota of healthy organisms represents a complex and phylogenetically diverse community of microorganisms (Hacquard et al., 2015; Vorholt, 2012). Culture-dependent as well as culture-independent research on the phylogenetic structure of the bacterial microbiota of plants has revealed that it is composed of only a few dominant phyla, namely, Actinobacteria, Bacteroidetes, Proteobacteria, and to a lower extend Firmicutes (chapter 1). Apart from bacteria, fungi, oomycetes, protists, nematodes and viruses add to the complexity of the system. Overall, the diversity of the natural microbiota and varying abiotic factors introduce noise, making it difficult to reproduce experiments and comprehensively disentangle the influence of individual drivers on community patterns during the time course of development under environmental conditions (Bulgarelli et al., 2015; Kemen, 2014; Lindow and Brandl, 2003; Turner et al., 2013b; Vorholt, 2012). For the past decades, reductionist approaches of selected model plants and bacteria have been used to unravel the complex interactions of bacteria and plants in binary interactions. On one hand, genes and proteins involved in sophisticated plant immune responses as well as mechanisms of infections by foliar pathogens were identified (Dodds and Rathjen, 2010; Jones and Dangl, 2006), while on the other hand, the molecular basis of symbiotic interactions, e.g. of nitrogen fixing rhizobia with legumes, were discovered (Oldroyd et al., 2011; Udvardi and Poole, 2013). This reductionist research of two interacting species resulted in significant progress in the field of plant microbe interactions. Similarly, one can propose that the use of reductionist microbiota systems of synthetic communities (SynComs) under controlled laboratory conditions will move the field of plant microbiota research forward. The use of controlled plant growth chambers diminishes environmental influences and allows to adjust each parameter individually, enables the implementation of perturbations, like temperature shifts or decreased humidity, and to monitor its effects on microbiota structure. In addition, the use of SynComs of known strain composition streamlines bioinformatic community analysis and circumvents one of the most critical steps, the discrimination of true sequencing reads from technical artifacts, e.g. chimeric reads and sequence errors introduced during PCR amplification. Pioneering work of Bodenhausen and coworkers has successfully employed a reductionist SynCom composed of seven representative strains to identify Arabidopsis mutants harboring distinct phyllosphere communities, indicating that host genetic traits affect the leaf microbiota (Bodenhausen et al., 2014), while Lebeis et al. integrated observations from complex natural communities and a 38 member SynCom to demonstrate the impact of plant immunity on proper root microbiota establishment (Lebeis et al., 2015). However, to systematically address microbiota establishment using synthetic communities, a representative strain collection mimicking the phylogenetic structure of natural communities was needed.

128 Several past studies have indicated that an unexpectedly large fraction of the plant microbiota might be cultivable. Early work of Ercolani on the phyllosphere microbiota of olive trees resulted in the isolation of more than 1700 strains belonging to well-known plant-associated genera (Ercolani, 1991). Consistently, Thompson and coworkers isolated more than 1000 strains belonging to over one hundred species from the phyllosphere of sugar beet (Thompson et al., 1993). More recently, optically targeted isolations of single cells by fluidic force microscopy led to the isolation of 70 strains belonging to 23 genera and the four main phyla known to inhabit the phyllosphere (Stiefel et al., 2013). Original work of Goodman et al. on the human gut microbiota revealed that the majority of sequencing reads of a culture-independent community analysis belong to cultured genus- and species-level taxa of an extensive culture collection of isolates, originating from the same fecal samples, but percent recovery of detectable species was not addressed in this study (Goodman et al., 2011). Having the potentially high cultivability of plant associated bacteria in mind, a parallel approach of combined culture- dependent and culture-independent analyses seemed timely. During the course of this work, we therefore sampled natural Arabidopsis populations at locations around Zurich, Switzerland and Tuebingen, Germany, and subjected them to large-scale isolation approaches in parallel to culture-independent community analysis to assess overall diversity. Cultivation-independent microbiota profiling revealed similar community composition at all sites and demonstrated high leaf-to-leaf consistency between different leaves of one single plant (chapter 2). In addition, comparison to other studies of the Arabidopsis leaf microbiota indicated high conservation of community composition and representation of the dominant phylogenetic groups (Bodenhausen et al., 2013; Delmotte et al., 2009; Horton et al., 2014). We furthermore used fluorescence in situ hybridization to optically address community composition of the same plant populations and record important spatial information of the different bacterial taxa (results are published in (Remus-Emsermann et al., 2014), but were not included as separate chapter into this thesis). Consistently, relative abundance of the different phyla observed during the optical analysis approximately reflected the relative abundance detected during the community profiling experiments. Parallel cultivation-dependent analysis resulted in the isolation of about half of the taxa that are reproducibly detected on A. thaliana leaves during culture- independent surveys (chapter 2). Recovery estimates ranged from 47% to 54% for OTUs ≥0.1% relative abundance and the one hundred most abundant OTUs (accounting for 84% of sequencing reads), respectively. These isolates cover 28 of 45 detectable bacterial families and are distributed across five phyla. Contrary to the hypothesis that only a minor fraction of bacteria in natural habitats is cultivable (Mueller and Ruppel, 2014; Ritz, 2007), our study disproves this assumption at least for the plant microbiota and suggests that there are environments where a substantial proportion of the overall diversity is cultivable under laboratory conditions. We speculate that the surprisingly high cultivability of plant associated bacteria is based on the relatively simple two-dimensional architecture of the plant surface and the presence of rather simple organic carbon sources and nutrients that are provided by the host in a mostly aerobic environment. Complex metabolic networks along oxygen gradients and

129 syntrophic dependencies, commonly found in other diverse habitats like soil or planktonic particles are less likely to occur. The diverse members of the established At-LSPHERE strain collection were subsequently used for proof of principle recolonization experiments of germ-free plants in an adopted calcined clay-based plant model system. Re-inoculation of a SynCom consisting of 218 leaf-derived strains onto Arabidopsis leaves resulted in stable community formation on leaves (and roots) with remarkably high reproducibility (chapter 2). The observed community patterns were robust against a 75% reduction of Proteobacteria against all other phyla, suggesting that community establishment has reached a steady state after the time of incubation. Comparison of relative abundances of OTUs in these experiments with the respective OTUs under natural conditions revealed similar trends at phylum, class and family level, indicating that similar mechanisms act on stable community formation under both conditions. Overall, high resemblance of natural community patterns and technical reproducibility validate the use of the calcined clay-based model system to address microbiota establishment under controlled laboratory conditions. Similarly to leaf inoculation, application of the same SynCom into clay, prior to sowing sterilized seeds also resulted in stable community formation on leaves, demonstrating that leaf isolates can colonize leaves from soil. Detailed community profiling of grapevine leaves, flowers and fruits, in addition to the below-ground compartments root and soil, revealed that above- and below-ground habitats host distinct microbiota and that leaves, fruits and flowers share more taxa with soil than with each other (Zarraonaindia et al., 2015). This finding indicates that soil also serves as bacterial reservoir for above-ground colonization of plants, which is in line with the results of our recolonization experiments. However, several genera typically associated with leaves of Arabidopsis were underrepresented in the reconstituted community, indicating reduced competence of phyllosphere colonization, if these taxa originate from soil. Competition with root- and soil-derived isolates for leaf colonization from clay revealed a stronger influence of the leaf isolates on observed community patterns, indicating higher competitiveness and adaptation of leaf isolates to their cognate host organ. Furthermore, an additional leaf inoculation step of plants which have previously been colonized from clay, resulted in complementation of underrepresented taxa or partial displacement of the initial community. This result suggests that airborne phyllosphere strains can intrude into an immature leaf community from clay. This notion is in-line with a study by Maignien et al. who demonstrated that stable communities are formed on A. thaliana leaves upon exposure of germ-free plants to non-sterile air by stochastic events and subsequent selective processes (Maignien et al., 2014). Although, the single application of the leaf specific SynCom to leaves does not mimic the continuous exposure of environmental plants to surrounding air, it indicates that airborne bacteria can immigrate even on already colonized leaves (chapter 2). This is congruent with the recent observation that the phyllosphere microbiota is strongly influenced by soil at the beginning of the season, but shifts to a leaf-specific signature as the season progresses (Copeland et al., 2015). Overall, the SynCom-derived data presented

130 in chapter 2 corroborate observations on complex communities under environmental conditions, and substantiates a model in which a combination of soil and airborne inoculations drive leaf microbiota establishment. The ecological source of bacteria colonizing the leaf surface is of agricultural importance and detailed understanding will increasing efficiency of already existing and newly discovered biocontrol agents. Whole genome sequencing resulted in the successful generation of genome drafts for 206 isolates and comparison to the At-RSPHERE, consisting of 194 root and 32 soil revealed a large intersection of encoded traits, consistent with the extensive taxonomic overlap. However, enrichment analysis at the level of functional categories, e.g. amino acid metabolism, uncovered significant differences like overrepresentation of 'carbohydrate metabolism' in leaf and soil isolates compared to root-derived strains. The root and rhizosphere compartments are fueled by rhizodeposites including low molecular carbon source and polymerized sugars, while leaf and soil bacteria potentially have to access a broader range of more complex carbon sources, reflecting the higher number of genes assigned to this category. Furthermore, genome mining for the presence of genes involved in natural product biosynthesis (chapter 4) showed that leaf strains preferentially harbor synthesis genes for terpenes, putatively reflecting the increased need of pigmentation and protection against solar radiation within their natural habitat. This finding was corroborated by enrichment of several genes involved in carotenoid biosynthesis, indicating that further adaptive signatures of organ specialization can be found on different levels, e.g. individual pathways, as well as in genes for which no functional information (KEGG orthology) is yet available. Functional analysis of leaf, root and soil derived genomes further indicated pronounced differences in variability within the phylogenetically distinct families. Those belonging to Proteobacteria were found to exhibit high intra-family variability, while Actinobacteria revealed lower within family diversity (chapter 2). Lower diversity of isolates might suggest that these isolates inhabit comparable niches and fulfill similar ecosystem functions. Bulgarelli and colleagues found that 40% of OTUs that are enriched on Arabidopsis roots compared to surrounding bulk soil, are equally abundant on wooden tooth picks incubated in the same soil type and this sub-community was dominated by Actinobacteria (Bulgarelli et al., 2012). This finding implies metabolic adaptation of Actinobacteria and indicates that these strains can support growth solely based on lignified plant material. Actinobacteria might have adapted a saprophytic lifestyle and could play a role in decomposing dead plant cells and cell wall components, potentially also on leaves.

The available genome sequences represent a valuable bioinformatics resource and provide deep insights into the physiology of plant associated bacteria, similarly to metagenomic surveys. However, since gene expression patterns can vary significantly in different habitats (Knief et al., 2012; Ofek-Lalzar et al., 2014), it is important to move from genome-based inference of microbial physiology to functional approaches, when addressing adaptation to the plant environment. Proteomics and transcriptomics have been applied to study the plant microbiota (Gourion et al., 2006; Yu et al., 2013), but previous proteomic

131 efforts have mostly been based on two-dimensional gel electrophoresis prior to analysis, resulting in quantification of relatively few proteins (Afroz et al., 2013; Knief et al., 2011). In addition, metaproteomics of the natural plant microbiota provided insights into the life on leaves, but protein coverage per species is rather low in these complex communities. We therefore applied the highly quantitative SWATH mass spectrometry technique to analyze adaptation of two model strains to the leaf habitat, resulting in quantification of over two thousand proteins spanning more than three orders of magnitudes in abundance (chapter 3). Our results reveal species-specific strategies for successful colonization and underline the apparent metabolic differences of the two model strains. Despite occupation of different niches, a relatively small subset of co-regulated genes indicates common adaptive processes, including the utilization of organic sulfonates as sulfur source on leaves. Comparison of identified target proteins with the At-LSPHERE genome database confirmed that genes for degradation of organic sulfonates are widespread among leaf microbiota members, while anoxygenic photosynthesis as accessory mode of energy conservation represents a species-specific adaptation and is almost exclusively found in Methylobacteria (chapter 3). These comparisons underline the power of combining actual data on bacterial adaptation with bioinformatic approaches to identify taxa-specific as well as more common adaptation strategies to life on plants. Interestingly, both model strains analyzed by SWATH proteomics showed increased abundance of efflux pumps and transporters during leaf colonization, indicating excretion of toxic compounds and potentially antimicrobial substances produced by the plant host. During microbiota establishment, not only adaptation to the leaf habitat and resistance against chemical warfare of the plant, but also microbe- microbe interactions are expected to be an important driver (Bulgarelli et al., 2015). Careful examination of the spatial distribution of strains will be required to test whether they actually co-localize within the different micro-habitats on leaves, and to which extend microbe-microbe interactions could contribute to the observed patterns.

The established At-LSPHERE strain collection provided a unique opportunity to address the antimicrobial potential of strains representing the majority of abundant taxa of an entire habitat on laboratory media (chapter 4). Analysis of more than 50.000 binary interactions indicated, that antimicrobial compounds are less often targeted against close relatives, while bipartite interactions of strains originating from the same leaf were detected at expected frequencies, showing that co-existence on the same leaf in nature is not reflected by increased resistance against fellow colonizers. Most observed strain inhibitions were caused by Bacillales and Pseudomonadales isolates, and not equally distributed, but rather targeted against a subset of other bacterial orders. Meta-analysis of three independent studies profiling the A. thaliana leaf community revealed that these inhibitory tendencies are only weakly conserved within the natural microbiota. This finding indicates that binary interactions are either strongly dependent on growth conditions and differ on plants, that spatial distances on leaves

132 mostly exceed the affected range of antibiotics, that these correlations are obscured by multipartite interactions or a combination of those. A subset of strains further showed inhibition on agar plates against well-known bacterial plant pests and even if this effect did not necessarily translate to plant protection against this pathogen, we also identified a subset of strains capable of reducing disease severity against Pst DC3000. Innerebner et al. hypothesized that all plant-derived Sphingomonas isolates share plant protective abilities (Innerebner et al., 2011), however, of 32 isolates analyzed, we only found 5 strains to be protective, suggesting that plant protection by this genus is less widespread than assumed. We were unable to identify a subset of genes that is present in all protective Sphingomonas isolates and absent from the genomes of non-protective strains, indicating that plant protection is either mediated by different mechanisms or by multiple genes and their regulation, rather than the presence of few immunity conferring genes. Interestingly, we found very closely related Sphingomonas strains with high genome conservation to exhibit pronounced differences in their physiology, indicating that these traits can currently not be predicted in silico. Additionally, we identified two Pseudomonas strains that lowered disease severity of A. thaliana caused by the pathogenic Pseudomonas. Generally, protective and non- protective Sphingomonas isolates as well as protective and pathogenic Pseudomonas strains correlate with the high intra-family variability of Sphingomonadaceae and Pseudomonadaceae observed during the previous whole genome analysis (chapter 2).

The established At-LSPHERE bacterial strain collection, in combination with proteomic analyses addressing bacterial adaptation to the leaf habitat as well as screening approaches to identify members with desired traits and capabilities opens up a multitude of opportunities to study the plant microbiota. By employing the calcined clay model system under controlled conditions, reproducible time-course experiments, abiotic as well as biotic perturbations are within reach. In addition, individual phylogenetic groups can be removed and re-introduced to evaluate the effect on the overall community structure. This approach will identify putative competitors that occupy the niches after removal of given taxa, while re- introduction of those will reveal whether these strains can re-invade into their cognate niches. The diversity of all strains further enables proteomic analyses of strains belonging to the five different phyla, the 28 families and 53 different genera of our strain collection will help to discriminate group-specific from general traits of plant adaptation. In addition, analysis of very closely related strains, e. g. 16S rRNA gene sequence identity ≥ 99%, isolated from different locations or plants will show whether gene expression patterns, adaptation and therefore the occupied niches are as similar as phylogeny suggests. On top, this resource of strains and genomes, representing a major part of the leaf habitat, can be screened and mined for traits of interest (e.g. plant protection as well as the distribution of novel antibiotics or resistance genes). The detailed analysis of plant protective strains, including co- inoculations of beneficial and pathogenic bacteria as well as plant gene transcriptomics will reveal the

133 host responses to the distinct taxa and provide valuable insights whether plant beneficial outcomes are host mediated or based on direct microbe-microbe interactions.

With all these new possibilities opening up, I hope that this resource of phyllosphere isolates and corresponding genome sequences, together with the established protocols of this thesis will help to move this exciting field of plant microbiota research forward.

134 REFERENCES

Afroz, A., Zahur, M., Zeeshan, N., and Komatsu, S. (2013). Plant-bacterium interactions analyzed by proteomics. Front Plant Sci 4. Agler, M.T., Ruhe, J., Kroll, S., Morhenn, C., Kim, S.T., Weigel, D., and Kemen, E.M. (2016). Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol 14, e1002352. Badri, D.V., Quintana, N., El Kassis, E.G., Kim, H.K., Choi, Y.H., Sugiyama, A., Verpoorte, R., Martinoia, E., Manter, D.K., and Vivanco, J.M. (2009). An ABC transporter mutation alters root exudation of phytochemicals that provoke an overhaul of natural soil microbiota. Plant Physiol 151, 2006-2017. Bailly, A., Groenhagen, U., Schulz, S., Geisler, M., Eberl, L., and Weisskopf, L. (2014). The inter- kingdom volatile signal indole promotes root development by interfering with auxin signalling. Plant J 80, 758-771. Berendsen, R.L., Pieterse, C.M.J., and Bakker, P.A.H.M. (2012). The rhizosphere microbiome and plant health. Trends Plant Sci 17, 478-486. Berg, G. (2009). Plant-microbe interactions promoting plant growth and health: perspectives for controlled use of microorganisms in agriculture. Applied microbiology and biotechnology 84, 11-18. Berg, G., Eberl, L., and Hartmann, A. (2005). The rhizosphere as a reservoir for opportunistic human pathogenic bacteria. Environmental microbiology 7, 1673-1685. Berg, G., and Smalla, K. (2009). Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol Ecol 68, 1-13. Bodenhausen, N., Bortfeld-Miller, M., Ackermann, M., and Vorholt, J.A. (2014). A synthetic community approach reveals plant genotypes affecting the phyllosphere microbiota. PLOS Genet 10, e1004283. Bodenhausen, N., Horton, M.W., and Bergelson, J. (2013). Bacterial communities associated with the leaves and the roots of Arabidopsis thaliana. PloS one 8, e56329. Bogino, P., Abod, A., Nievas, F., and Giordano, W. (2013). Water-limiting conditions alter the structure and biofilm-forming ability of bacterial multispecies communities in the alfalfa rhizosphere. PloS one 8, e79614. Bonito, G., Reynolds, H., Robeson, M.S., Nelson, J., Hodkinson, B.P., Tuskan, G., Schadt, C.W., and Vilgalys, R. (2014). Plant host and soil origin influence fungal and bacterial assemblages in the roots of woody plants. Mol Ecol 23, 3356-3370. Bouasria, A., Mustafa, T., De Bello, F., Zinger, L., Lemperiere, G., Geremia, R.A., and Choler, P. (2012). Changes in root-associated microbial communities are determined by species-specific plant growth responses to stress and disturbance. Eur J Soil Biol 52, 59-66. Brandl, M.T. (2006). Fitness of human enteric pathogens on plants and implications for food safety. Annual review of phytopathology 44, 367-392.

135 Brandl, M.T., and Sundin, G.W. (2013). Focus on food safety: human pathogens on plants. Phytopathology 103, 304-305. Bulgarelli, D., Garrido-Oter, R., Muench, P.C., Weiman, A., Droege, J., Pan, Y., McHardy, A.C., and Schulze-Lefert, P. (2015). Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host & Microbe 17, 392-403. Bulgarelli, D., Rott, M., Schlaeppi, K., Ver Loren van Themaat, E., Ahmadinejad, N., Assenza, F., Rauf, P., Huettel, B., Reinhardt, R., Schmelzer, E., et al. (2012). Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91-95. Bulgarelli, D., Schlaeppi, K., Spaepen, S., van Themaat, E.V.L., and Schulze-Lefert, P. (2013). Structure and functions of the bacterial microbiota of plants. Annu Rev Plant Biol 64, 807-838. Burch, A.Y., Zeisler, V., Yokota, K., Schreiber, L., and Lindow, S.E. (2014). The hygroscopic biosurfactant syringafactin produced by Pseudomonas syringae enhances fitness on leaf surfaces during fluctuating humidity. Environmental microbiology 16, 2086-2098. Carvalhais, L.C., Dennis, P.G., Badri, D.V., Kidd, B.N., Vivanco, J.M., and Schenk, P.M. (2015). Linking jasmonic acid signaling, root exudates, and rhizosphere microbiomes. Mol Plant- Microbe Interact 28, 1049-1058. Chaparro, J.M., Badri, D.V., and Vivanco, J.M. (2014). Rhizosphere microbiome assemblage is affected by plant development. ISME J 8, 790-803. Chapelle, E., Mendes, R., Bakker, P.A., and Raaijmakers, J.M. (2016). Fungal invasion of the rhizosphere microbiome. ISME J 10, 265-268. Chi, F., Shen, S.H., Cheng, H.P., Jing, Y.X., Yanni, Y.G., and Dazzo, F.B. (2005). Ascending migration of endophytic rhizobia, from roots to leaves, inside rice plants and assessment of benefits to rice growth physiology. Appl Environ Microbiol 71, 7271-7278. Chowdhury, S.P., Dietel, K., Raendler, M., Schmid, M., Junge, H., Borriss, R., Hartmann, A., and Grosch, R. (2013). Effects of Bacillus amyloliquefaciens FZB42 on lettuce growth and health under pathogen pressure and its impact on the rhizosphere bacterial community. PloS one 8, e68818. Conrad, R. (2009). The global methane cycle: recent advances in understanding the microbial processes involved. Environmental microbiology reports 1, 285-292. Cook, D.E., Mesarich, C.H., and Thomma, B.P. (2015). Understanding plant immunity as a surveillance system to detect invasion. Annu Rev Phytopathol 53, 541-563. Copeland, J.K., Yuan, L., Layeghifard, M., Wang, P.W., and Guttman, D.S. (2015). Seasonal community succession of the phyllosphere microbiome. Mol Plant-Microbe Interact 28, 274- 285. Delmotte, N., Knief, C., Chaffron, S., Innerebner, G., Roschitzki, B., Schlapbach, R., von Mering, C., and Vorholt, J.A. (2009). Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proceedings of the National Academy of Sciences of the United States of America 106, 16428-16433. Dematheis, F., Zimmerling, U., Flocco, C., Kurtz, B., Vidal, S., Kropf, S., and Smalla, K. (2012). Multitrophic interaction in the rhizosphere of maize: root feeding of Western Corn Rootworm larvae alters the microbial community composition. PloS one 7, e37288.

136 Dodd, I.C., and Perez-Alfocea, F. (2012). Microbial amelioration of crop salinity stress. Journal of experimental botany 63, 3415-3428. Dodds, P.N., and Rathjen, J.P. (2010). Plant immunity: towards an integrated view of plant-pathogen interactions. Nature reviews Genetics 11, 539-548. Edwards, J., Johnson, C., Santos-Medellin, C., Lurie, E., Podishetty, N.K., Bhatnagar, S., Eisen, J.A., and Sundaresan, V. (2015). Structure, variation, and assembly of the root-associated microbiomes of rice. Proceedings of the National Academy of Sciences of the United States of America 112, E911-E920. Ercolani, G.L. (1991). Distribution of epiphytic bacteria on olive leaves and the influence of leaf age and sampling time. Microbial Ecol 21, 35-48. Erlacher, A., Cardinale, M., Grosch, R., Grube, M., and Berg, G. (2014). The impact of the pathogen Rhizoctonia solani and its beneficial counterpart Bacillus amyloliquefaciens on the indigenous lettuce microbiome. Front Microbiol 5, 175. Fall, R., and Benson, A.A. (1996). Leaf methanol - The simplest natural product from plants. Trends Plant Sci 1, 296-301. Finkel, O.M., Burch, A.Y., Lindow, S.E., Post, A.F., and Belkin, S. (2011). Geographical location determines the population structure in phyllosphere microbial communities of a salt-excreting desert tree. Applied and environmental microbiology 77, 7647-7655. Francez-Charlot, A., Frunzke, J., and Vorholt, J.A. (2011). The general stress response in Alphaproteobacteria. Bacterial Stress Responses, 2nd Edition, 291-300. Francez-Charlot, A., Kaczmarczyk, A., Fischer, H.M., and Vorholt, J.A. (2015). The general stress response in Alphaproteobacteria. Trends Microbiol 23, 164-171. Gans, J., Wolinsky, M., and Dunbar, J. (2005). Computational improvements reveal great bacterial diversity and high metal toxicity in soil. Science 309, 1387-1390. Glick, B.R. (2014). Bacteria with ACC deaminase can promote plant growth and help to feed the world. Microbiol Res 169, 30-39. Goodman, A.L., Kallstrom, G., Faith, J.J., Reyes, A., Moore, A., Dantas, G., and Gordon, J.I. (2011). Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proceedings of the National Academy of Sciences of the United States of America 108, 6252-6257. Gourion, B., Francez-Charlot, A., and Vorholt, J.A. (2008). PhyR is involved in the general stress response of Methylobacterium extorquens AM1. J Bacteriol 190, 1027-1035. Gourion, B., Rossignol, M., and Vorholt, J.A. (2006). A proteomic study of Methylobacterium extorquens reveals a response regulator essential for epiphytic growth. Proceedings of the National Academy of Sciences of the United States of America 103, 13186-13191. Hacquard, S., Garrido-Oter, R., Gonzalez, A., Spaepen, S., Ackermann, G., Lebeis, S., McHardy, A.C., Dangl, J.L., Knight, R., Ley, R., et al. (2015). Microbiota and host nutrition across plant and animal kingdoms. Cell Host & Microbe 17, 603-616. Haefele, D.M., and Lindow, S.E. (1987). Flagellar motility confers epiphytic fitness advantages upon Pseudomonas syringae. Applied and environmental microbiology 53, 2528-2533.

137 Haney, C.H., Samuel, B.S., Bush, J., and Ausubel, F.M. (2015). Associations with rhizosphere bacteria can confer an adaptive advantage to plants. Nat Plants 1, 15051. Hardoim, P.R., Hardoim, C.C., van Overbeek, L.S., and van Elsas, J.D. (2012). Dynamics of seed-borne rice endophytes on early plant growth stages. PloS one 7, e30438. Hinsinger, P., Bengough, A.G., Vetterlein, D., and Young, I.M. (2009). Rhizosphere: biophysics, biogeochemistry and ecological relevance. Plant Soil 321, 117-152. Horton, M.W., Bodenhausen, N., Beilsmith, K., Meng, D., Muegge, B.D., Subramanian, S., Vetter, M.M., Vilhjalmsson, B.J., Nordborg, M., Gordon, J.I., et al. (2014). Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nature communications 5, 5320. Ikeda, S., Anda, M., Inaba, S., Eda, S., Sato, S., Sasaki, K., Tabata, S., Mitsui, H., Sato, T., Shinano, T., et al. (2011). Autoregulation of nodulation interferes with impacts of nitrogen fertilization levels on the leaf-associated bacterial community in soybeans. Appl Environ Microbiol 77, 1973-1980. Inceoglu, O., Al-Soud, W.A., Salles, J.F., Semenov, A.V., and van Elsas, J.D. (2011). Comparative analysis of bacterial communities in a potato field as determined by pyrosequencing. PloS one 6, e23321. Inceoglu, O., Salles, J.F., and van Elsas, J.D. (2012). Soil and cultivar type shape the bacterial community in the potato rhizosphere. Microb Ecol 63, 460-470. Innerebner, G., Knief, C., and Vorholt, J.A. (2011). Protection of Arabidopsis thaliana against leaf- pathogenic Pseudomonas syringae by Sphingomonas strains in a controlled model system. Appl Environ Microbiol 77, 3202-3210. Johnston-Monje, D., Mousa, W.K., Lazarovits, G., and Raizada, M.N. (2014). Impact of swapping soils on the endophytic bacterial communities of pre-domesticated, ancient and modern maize. BMC Plant Biol 14, 233. Jones, D.L., Nguyen, C., and Finlay, R.D. (2009). Carbon flow in the rhizosphere: carbon trading at the soil-root interface. Plant Soil 321, 5-33. Jones, J.D., and Dangl, J.L. (2006). The plant immune system. Nature 444, 323-329. Kaczmarczyk, A., Hochstrasser, R., Vorholt, J.A., and Francez-Charlot, A. (2014). Complex two- component signaling regulates the general stress response in Alphaproteobacteria. Proceedings of the National Academy of Sciences of the United States of America 111, E5196-5204. Kaczmarczyk, A., Hochstrasser, R., Vorholt, J.A., and Francez-Charlot, A. (2015). Two-tiered histidine kinase pathway involved in heat shock and salt sensing in the general stress response of Sphingomonas melonis Fr1. J Bacteriol 197, 1466-1477. Kadivar, H., and Stapleton, A.E. (2003). Ultraviolet radiation alters maize phyllosphere bacterial diversity. Microb Ecol 45, 353-361. Kavamura, V.N., Taketani, R.G., Lanconi, M.D., Andreote, F.D., Mendes, R., and de Melo, I.S. (2013). Water regime influences bulk soil and rhizosphere of Cereus jamacaru bacterial communities in the Brazilian Caatinga biome. PloS one 8, e73606. Kembel, S.W., O'Connor, T.K., Arnold, H.K., Hubbell, S.P., Wright, S.J., and Green, J.L. (2014). Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest. Proceedings of the National Academy of Sciences of the United States of America 111, 13715-13720.

138 Kemen, E. (2014). Microbe-microbe interactions determine oomycete and fungal host colonization. Current Opinion in Plant Biology 20, 75-81. Knief, C., Delmotte, N., Chaffron, S., Stark, M., Innerebner, G., Wassmann, R., von Mering, C., and Vorholt, J.A. (2012). Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. The ISME journal 6, 1378-1390. Knief, C., Delmotte, N., and Vorholt, J.A. (2011). Bacterial adaptation to life in association with plants - A proteomic perspective from culture to in situ conditions. Proteomics 11, 3086-3105. Knief, C., Ramette, A., Frances, L., Alonso-Blanco, C., and Vorholt, J.A. (2010). Site and plant species are important determinants of the Methylobacterium community composition in the plant phyllosphere. ISME J 4, 719-728. Kniskern, J.M., Traw, M.B., and Bergelson, J. (2007). Salicylic acid and jasmonic acid signaling defense pathways reduce natural bacterial diversity on Arabidopsis thaliana. Mol Plant-Microbe Interact 20, 1512-1522. Kolton, M., Frenkel, O., Elad, Y., and Cytryn, E. (2014). Potential role of Flavobacterial gliding-motility and type IX secretion system complex in root colonization and plant defense. Molecular Plant- Microbe Interactions 27, 1005-1013. Kolton, M., Sela, N., Elad, Y., and Cytryn, E. (2013). Comparative genomic analysis indicates that niche adaptation of terrestrial Flavobacteria Is strongly linked to plant glycan metabolism. PloS one 8. Lau, J.A., and Lennon, J.T. (2012). Rapid responses of soil microorganisms improve plant fitness in novel environments. P Natl Acad Sci USA 109, 14058-14062. Lebeis, S.L., Paredes, S.H., Lundberg, D.S., Breakfield, N., Gehring, J., McDonald, M., Malfatti, S., del Rio, T.G., Jones, C.D., Tringe, S.G., et al. (2015). Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349, 860-864. Lee, B., Lee, S., and Ryu, C.-M. (2012). Foliar aphid feeding recruits rhizosphere bacteria and primes plant immunity against pathogenic and non-pathogenic bacteria in pepper. Ann Bot 110, 281- 290. Li, X., Rui, J., Xiong, J., Li, J., He, Z., Zhou, J., Yannarell, A.C., and Mackie, R.I. (2014). Functional Potential of Soil Microbial Communities in the Maize Rhizosphere. PloS one 9. Lindow, S.E., and Brandl, M.T. (2003). Microbiology of the phyllosphere. Applied and environmental microbiology 69, 1875-1883. Lindow, S.E., and Leveau, J.H. (2002). Phyllosphere microbiology. Current opinion in biotechnology 13, 238-243. Lugtenberg, B., and Kamilova, F. (2009). Plant-growth-promoting rhizobacteria. Annual review of microbiology 63, 541-556. Lundberg, D.S., Lebeis, S.L., Paredes, S.H., Yourstone, S., Gehring, J., Malfatti, S., Tremblay, J., Engelbrektson, A., Kunin, V., del Rio, T.G., et al. (2012). Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86-90. Maignien, L., DeForce, E.A., Chafee, M.E., Eren, A.M., and Simmons, S.L. (2014). Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. mBio 5, e00682-00613.

139 Manching, H.C., Balint-Kurti, P.J., and Stapleton, A.E. (2014). Southern leaf blight disease severity is correlated with decreased maize leaf epiphytic bacterial species richness and the phyllosphere bacterial diversity decline is enhanced by nitrogen fertilization. Front Plant Sci 5, 403. Mendes, L.W., Kuramae, E.E., Navarrete, A.A., van Veen, J.A., and Tsai, S.M. (2014). Taxonomical and functional microbial community selection in soybean rhizosphere. Isme Journal 8, 1577- 1587. Mendes, R., Garbeva, P., and Raaijmakers, J.M. (2013). The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol Rev 37, 634-663. Meyer, K.M., and Leveau, J.H.J. (2012). Microbiology of the phyllosphere: a playground for testing ecological concepts. Oecologia 168, 621-629. Mondy, S., Lenglet, A., Beury-Cirou, A., Libanga, C., Ratet, P., Faure, D., and Dessaux, Y. (2014). An increasing opine carbon bias in artificial exudation systems and genetically modified plant rhizospheres leads to an increasing reshaping of bacterial populations. Mol Ecol 23, 4846-4861. Monier, J.M., and Lindow, S.E. (2003). Differential survival of solitary and aggregated bacterial cells promotes aggregate formation on leaf surfaces. Proceedings of the National Academy of Sciences of the United States of America 100, 15977-15982. Monier, J.M., and Lindow, S.E. (2004). Frequency, size, and localization of bacterial aggregates on bean leaf surfaces. Applied and environmental microbiology 70, 346-355. Moran, N.A., and Sloan, D.B. (2015). The hologenome concept: helpful or hollow? PLoS biology 13, e1002311. Moyne, A.L., Harris, L.J., and Marco, M.L. (2013). Assessments of total and viable Escherichia coli O157:H7 on field and laboratory grown lettuce. PloS one 8, e70643. Mueller, T., and Ruppel, S. (2014). Progress in cultivation-independent phyllosphere microbiology. Fems Microbiology Ecology 87, 2-17. Ofek-Lalzar, M., Sela, N., Goldman-Voronov, M., Green, S.J., Hadar, Y., and Minz, D. (2014). Niche and host-associated functional signatures of the root surface microbiome. Nature communications 5. Oh, Y.M., Kim, M., Lee-Cruz, L., Lai-Hoe, A., Go, R., Ainuddin, N., Rahim, R.A., Shukor, N., and Adams, J.M. (2012). Distinctive bacterial communities in the rhizoplane of four tropical tree species. Microb Ecol 64, 1018-1027. Oldroyd, G.E., Murray, J.D., Poole, P.S., and Downie, J.A. (2011). The rules of engagement in the legume-rhizobial symbiosis. Annual review of genetics 45, 119-144. Ottesen, A.R., Gonzalez Pena, A., White, J.R., Pettengill, J.B., Li, C., Allard, S., Rideout, S., Allard, M., Hill, T., Evans, P., et al. (2013). Baseline survey of the anatomical microbial ecology of an important food plant: Solanum lycopersicum (tomato). BMC microbiology 13, 114. Parniske, M. (2008). Arbuscular mycorrhiza: the mother of plant root endosymbioses. Nat Rev Microbiol 6, 763-775.

140 Peiffer, J.A., Spor, A., Koren, O., Jin, Z., Tringe, S.G., Dangl, J.L., Buckler, E.S., and Ley, R.E. (2013). Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proceedings of the National Academy of Sciences of the United States of America 110, 6548- 6553. Philippot, L., Raaijmakers, J.M., Lemanceau, P., and van der Putten, W.H. (2013). Going back to the roots: the microbial ecology of the rhizosphere. Nat Rev Microbiol 11, 789-799. Pieterse, C.M.J., Zamioudis, C., Berendsen, R.L., Weller, D.M., Van Wees, S.C.M., and Bakker, P.A.H.M. (2014). Induced systemic resistance by beneficial microbes. Annu Rev Phytopathol 52, 347-375. Raaijmakers, J.M., and Mazzola, M. (2012). Diversity and natural functions of antibiotics produced by beneficial and plant pathogenic bacteria. Ann Rev Phytopathol 50, 403-424. Raghavendra, A.K.H., and Newcombe, G. (2013). The contribution of foliar endophytes to quantitative resistance to Melampsora rust. New Phytol 197, 909-918. Rastogi, G., Sbodio, A., Tech, J.J., Suslow, T.V., Coaker, G.L., and Leveau, J.H.J. (2012). Leaf microbiota in an agroecosystem: spatiotemporal variation in bacterial community composition on field-grown lettuce. Isme Journal 6, 1812-1822. Redford, A.J., Bowers, R.M., Knight, R., Linhart, Y., and Fierer, N. (2010). The ecology of the phyllosphere: geographic and phylogenetic variability in the distribution of bacteria on tree leaves. Environmental microbiology 12, 2885-2893. Reinhold-Hurek, B., Buenger, W., Burbano, C.S., Sabale, M., and Hurek, T. (2015). Roots shaping their microbiome: global hotspots for microbial activity. Annu Rev Phytopathol 53, 403-424. Reisberg, E.E., Hildebrandt, U., Riederer, M., and Hentschel, U. (2013). Distinct phyllosphere bacterial communities on Arabidopsis wax mutant leaves. PloS one 8, e78613. Remus-Emsermann, M.N., Lücker, S., Müller, D.B., Potthoff, E., Daims, H., and Vorholt, J.A. (2014). Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environmental microbiology 16, 2329- 2340. Remus-Emsermann, M.N.P., Tecon, R., Kowalchuk, G.A., and Leveau, J.H.J. (2012). Variation in local carrying capacity and the individual fate of bacterial colonizers in the phyllosphere. Isme Journal 6, 756-765. Ritpitakphong, U., Falquet, L., Vimoltust, A., Berger, A., Metraux, J.P., and L'Haridon, F. (2016). The microbiome of the leaf surface of Arabidopsis protects against a fungal pathogen. The New phytologist 210, 1033-1043. Ritz, K. (2007). The Plate Debate: Cultivable communities have no utility in contemporary environmental microbial ecology. Fems Microbiology Ecology 60, 358-362. Ryffel, F., Helfrich, E.J., Kiefer, P., Peyriga, L., Portais, J.C., Piel, J., and Vorholt, J.A. (2016). Metabolic footprint of epiphytic bacteria on Arabidopsis thaliana leaves. The ISME journal 10, 632-643. Ryu, C.M. (2015). Against friend and foe: type 6 effectors in plant-associated bacteria. Journal of microbiology 53, 201-208.

141 Scheublin, T.R., Deusch, S., Moreno-Forero, S.K., Mueller, J.A., van der Meer, J.R., and Leveau, J.H.J. (2014). Transcriptional profiling of Gram-positive Arthrobacter in the phyllosphere: induction of pollutant degradation genes by natural plant phenolic compounds. Environmental microbiology 16, 2212-2225. Schlaeppi, K., Dombrowski, N., Oter, R.G., van Themaat, E.V.L., and Schulze-Lefert, P. (2014). Quantitative divergence of the bacterial root microbiota in Arabidopsis thaliana relatives. Proceedings of the National Academy of Sciences of the United States of America 111, 585- 592. Schmidt, H., and Eickhorst, T. (2014). Detection and quantification of native microbial populations on soil-grown rice roots by catalyzed reporter deposition-fluorescence in situ hybridization. Fems Microbiology Ecology 87, 390-402. Sessitsch, A., Hardoim, P., Doring, J., Weilharter, A., Krause, A., Woyke, T., Mitter, B., Hauberg-Lotte, L., Friedrich, F., Rahalkar, M., et al. (2012). Functional characteristics of an endophyte community colonizing rice roots as revealed by metagenomic analysis. Mol Plant-Microbe Interact 25, 28-36. Shade, A., McManus, P.S., and Handelsman, J. (2013). Unexpected diversity during community succession in the apple flower microbiome. mBio 4. Shakya, M., Gottel, N., Castro, H., Yang, Z.K., Gunter, L., Labbe, J., Muchero, W., Bonito, G., Vilgalys, R., Tuskan, G., et al. (2013). A multifactor analysis of fungal and bacterial community structure in the root microbiome of mature Populus deltoides trees. PloS one 8. Sheibani-Tezerji, R., Rattei, T., Sessitsch, A., Trognitz, F., and Mitter, B. (2015). Transcriptome profiling of the endophyte Burkholderia phytofirmans PsJN indicates sensing of the plant environment and drought stress. mBio 6, e00621-00615. Shi, S., Nuccio, E., Herman, D.J., Rijkers, R., Estera, K., Li, J., da Rocha, U.N., He, Z., Pett-Ridge, J., Brodie, E.L., et al. (2015). Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio 6, e00746-00715. Smith, S.E., and Smith, F.A. (2011). Roles of arbuscular mycorrhizas in plant nutrition and growth: new paradigms from cellular to ecosystem scales. Annu Rev Plant Biol 62, 227-250. Spaepen, S., Bossuyt, S., Engelen, K., Marchal, K., and Vanderleyden, J. (2014). Phenotypical and molecular responses of Arabidopsis thaliana roots as a result of inoculation with the auxin- producing bacterium Azospirillum brasilense. New Phytol 201, 850-861. Spaepen, S., Vanderleyden, J., and Remans, R. (2007). Indole-3-acetic acid in microbial and microorganism-plant signaling. FEMS Microbiol Rev 31, 425-448. Stiefel, P., Zambelli, T., and Vorholt, J.A. (2013). Isolation of optically targeted single bacteria by application of fluidic force microscopy to aerobic anoxygenic phototrophs from the phyllosphere. Applied and environmental microbiology 79, 4895-4905. Suda, W., Nagasaki, A., and Shishido, M. (2009). Powdery mildew-infection changes bacterial community composition in the phyllosphere. Microbes Environ 24, 217-223. Sugiyama, A., Ueda, Y., Zushi, T., Takase, H., and Yazaki, K. (2014). Changes in the bacterial community of soybean rhizospheres during growth in the field. PloS one 9, e100709.

142 Sy, A., Timmers, A.C., Knief, C., and Vorholt, J.A. (2005). Methylotrophic metabolism is advantageous for Methylobacterium extorquens during colonization of Medicago truncatula under competitive conditions. Applied and environmental microbiology 71, 7245-7252. Thompson, I.P., Bailey, M.J., Fenlon, J.S., Fermor, T.R., Lilley, A.K., Lynch, J.M., Mccormack, P.J., Mcquilken, M.P., Purdy, K.J., Rainey, P.B., et al. (1993). Quantitative and qualitative seasonal- changes in the microbial community from the phyllosphere of Sugar-Beet (Beta-Vulgaris). Plant Soil 150, 177-191. Turner, T.R., James, E.K., and Poole, P.S. (2013a). The plant microbiome. Genome Biol 14, 209. Turner, T.R., Ramakrishnan, K., Walshaw, J., Heavens, D., Alston, M., Swarbreck, D., Osbourn, A., Grant, A., and Poole, P.S. (2013b). Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. Isme Journal 7, 2248-2258. Udvardi, M., and Poole, P.S. (2013). Transport and metabolism in Legume-Rhizobia symbioses. Annu Rev Plant Biol 64, 781-805. Urquiaga, S., Xavier, R.P., de Morais, R.F., Batista, R.B., Schultz, N., Leite, J.M., Sa, J.M.E., Barbosa, K.P., de Resende, A.S., Alves, B.J.R., et al. (2012). Evidence from field nitrogen balance and 15 N natural abundance data for the contribution of biological N2 fixation to Brazilian sugarcane varieties. Plant Soil 356, 5-21. Vandenkoornhuyse, P., Quaiser, A., Duhamel, M., Le Van, A., and Dufresne, A. (2015). The importance of the microbiome of the plant holobiont. The New phytologist 206, 1196-1206. Vogel, C., Innerebner, G., Zingg, J., Guder, J., and Vorholt, J.A. (2012). Forward genetic in planta screen for identification of plant-protective traits of Sphingomonas sp. strain Fr1 against Pseudomonas syringae DC3000. Appl Environ Microbiol 78, 5529-5535. Vorholt, J.A. (2012). Microbial life in the phyllosphere. Nat Rev Microbiol 10, 828-840. White, L.J., Jothibasu, K., Reese, R.N., Broezel, V.S., and Subramanian, S. (2015). Spatio temporal influence of isoflavonoids on bacterial diversity in the soybean rhizosphere. Mol Plant-Microbe Interact 28, 22-29. Williams, T.R., and Marco, M.L. (2014). Phyllosphere microbiota composition and microbial community transplantation on lettuce plants grown indoors. mBio 5. Williams, T.R., Moyne, A.L., Harris, L.J., and Marco, M.L. (2013). Season, irrigation, leaf age, and Escherichia coli inoculation influence the bacterial diversity in the lettuce phyllosphere. PloS one 8. Yang, J., Kloepper, J.W., and Ryu, C.M. (2009). Rhizosphere bacteria help plants tolerate abiotic stress. Trends Plant Sci 14, 1-4. Yang, J.W., Yi, H.S., Kim, H., Lee, B., Lee, S., Ghim, S.Y., and Ryu, C.M. (2011). Whitefly infestation of pepper plants elicits defence responses against bacterial pathogens in leaves and roots and changes the below-ground microflora. J Ecol 99, 46-56. Yeoh, Y.K., Paungfoo-Lonhienne, C., Dennis, P.G., Robinson, N., Ragan, M.A., Schmidt, S., and Hugenholtz, P. (2016). The core root microbiome of sugarcanes cultivated under varying nitrogen fertilizer application. Environ Microbiol In press.

143 Yu, X., Lund, S.P., Scott, R.A., Greenwald, J.W., Records, A.H., Nettleton, D., Lindow, S.E., Gross, D.C., and Beattie, G.A. (2013). Transcriptional responses of Pseudomonas syringae to growth in epiphytic versus apoplastic leaf sites. P Natl Acad Sci USA 110, E425-434. Zamioudis, C., Korteland, J., Van Pelt, J.A., van Hamersveld, M., Dombrowski, N., Bai, Y., Hanson, J., Van Verk, M.C., Ling, H.Q., Schulze-Lefert, P., et al. (2015). Rhizobacterial volatiles and photosynthesis-related signals coordinate MYB72 expression in Arabidopsis roots during onset of induced systemic resistance and iron-deficiency responses. Plant J 84, 309-322. Zamioudis, C., Mastranesti, P., Dhonukshe, P., Blilou, I., and Pieterse, C.M.J. (2013). Unraveling root developmental programs initiated by beneficial Pseudomonas spp. bacteria. Plant Physiol 162, 304-318. Zancarini, A., Mougel, C., Voisin, A.S., Prudent, M., Salon, C., and Munier-Jolain, N. (2012). Soil nitrogen availability and plant genotype modify the nutrition strategies of M. truncatula and the associated rhizosphere microbial communities. PloS one 7, e47096. Zarraonaindia, I., Owens, S.M., Weisenhorn, P., West, K., Hampton-Marcell, J., Lax, S., Bokulich, N.A., Mills, D.A., Martin, G., Taghavi, S., et al. (2015). The soil microbiome influences grapevine-associated microbiota. mBio 6. Zhang, H.M., Sun, Y., Xie, X.T., Kim, M.S., Dowd, S.E., and Pare, P.W. (2009). A soil bacterium regulates plant acquisition of iron via deficiency-inducible mechanisms. Plant J 58, 568-577.

144 Acknowledgments

First of all, I would like to thank Prof. Dr. Julia Vorholt for giving me the opportunity to work in her group and be a part of this exciting research field. Thank you for trusting in me, for your constant support over the past few years and for the fun discussions we had. It was much appreciated.

I would also like to thank Dr. Paul Schulze-Lefert and his research group, especially Dr. Yang Bai, for the excellent work on our joint Arabidopsis microbiota project. I have learned a lot, scientifically and personally and I am very thankful to have worked with you.

I furthermore would like to thank Prof. Ruedi Aebersold, Dr. Olga T. Schubert and Dr. Hannes Röst for our fruitful collaboration on bacterial adaptation to the leaf environment. Thank your for excitement about the project, for always answering my questions and introducing me to world of mass spectrometry based proteomics.

I also would like to express my appreciation to my PhD committee members, Prof. Ruedi Aebersold and Prof. Martin Ackermann, for your interest, valuable comments and ideas that contributed to the success of my work.

I am grateful for the assistance of the "Plant-Microbe subgroup" of our lab. In particular I would like to thank the present members Christine Vogel, Dr. Charlotte Carlström and Dr. Miriam Bortfeld-Miller as well as the former members Dr. Florian Ryffel and Dr. Mitja Remus-Emsermann for all the help and fun during the past couple of years. The teamwork was amazing.

Special thanks also to the entire Vorholt and Fischer Labs for the perfect working atmosphere. I really enjoyed being a part of this group. Most especially I would like to thank Dr. Jonas Müller, Dr. Eva Potthoff and Max Mittelviefhaus for always giving me company in the lab, and the endless fun on the mountains.

Finally, I would like to thank my Family, especially my mom, my sister, my grand mom and Eva as well as all my friends here in Zurich and at home for their support and encouragement throughout my studies. I would not have succeeded without you. Danke!

145