UNIVERSITY OF LJUBLJANA

FACULTY OF PHARMACY

NINA ŠMID

GENOMIC GUIDED ISOLATION OF SECONDARY METABOLITES FROM THE

PLANT PATHOGEN PSEUDOMONAS SYRINGAE PATHOVAR SYRINGAE B728a

GENOMSKO VODENA IZOLACIJA SEKUNDARNIH METABOLITOV

RASTLINSKEGA PATOGENA PSEUDOMONAS SYRINGAE PATOVAR SYRINGAE

B728a

DIPLOMA THESIS

Ljubljana, 2008

2

I performed this diploma thesis at the Institute for Pharmaceutical Biology, University of

Bonn under the supervision of Dr. Harald Groß, Institute for Pharmaceutical Biology,

University of Bonn and Assoc. Prof. Dr. Samo Kreft, Faculty of Pharmacy, University of

Ljubljana. Mass spectroscopic measurements were performed at the Kekulé Institute of

Organic Chemistry and Biochemistry, University of Bonn.

Statement

I state that this diploma thesis is my own work done under supervision of Dr. Harald Groß

and Assoc. Prof. Dr. Samo Kreft.

3

Acknowledgements

I wish to express my sincere gratitude to my supervisor Dr. H. Groß, Institute for

Pharmaceutical Biology, University of Bonn, for giving me the opportunity to work on this

interesting project as an Erasmus student, for introducing me into the world of pharmaceutical

biology, for his guidance, friendly support and assistance during my research and writing and

for his encouragement and motivation.

I also want to give thanks to Assoc. Prof. Dr. Samo Kreft for being my second mentor and

also in his capacity as the former departmental Erasmus coordinator for giving me the

opportunity to apply for the Erasmus exchange program.

I would like to thank Prof. Emer. Dr. Aleš Krbavčič and Assoc. Prof. Dr. Mirjana Gašperlin

for participating in the examination committee.

I would like to thank the entire Erasmus program for organizational and especially financial

support and the University of Bonn for accepting me in the Erasmus exchange program.

Special thanks go to Beate Ponatowski, Pharmaceutical Chemistry, University of Bonn, for

providing me a great mentor.

Some specific tasks involved in this study were partly performed in cooperation with other

research groups. For these works thanks go to:

Prof. Dr. S. E. Lindow, Department of Plant and Microbial Biology, University of California,

Berkeley, USA, for his generous donation of the bacterial strain Pseudomonas syringae pv.

syringae B728a.

The Chamber of Agriculture North Rhine-Westphalia for permission to work with the

bacterial strain Pseudomonas syringae pv. syringae B728a.

E. Eguereva, Institute for Pharmaceutical Biology, University of Bonn, for LC-MS

measurements.

M. Engeser and C. Sondag, Kekulé Institute of Organic Chemistry and Biochemistry,

University of Bonn, for MS measurements.

My thanks go to all my colleagues of the Institute for Pharmaceutical Biology, University of

Bonn, for providing a friendly working environment.

I would like to thank all my friends in Germany and Slovenia who were there when I needed

them, especially my climbing partners Conny and Eymen and neighbour Zdenka for all great

days we spent together during my stay in Bonn.

I thank my family for their support and their belief in me.

Last but not least, I am grateful to my loving Matej for being there for me especially in

difficult times, for all his wise advices and healing hugs.

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TABLE OF CONTENTS

Abstract ...................................................................................................................................... 6

Razširjen povzetek ..................................................................................................................... 7

List of abbreviations ................................................................................................................. 10

1. Genome mining .................................................................................................................... 13

2. Pseudomonas syringae pathovar syringae B728a ................................................................ 16

2.1 Genome features of Pseudomonas syringae pv. syringae B728a ................................... 18

2.2 Secondary metabolites of Pseudomonas syringae pv. syringae B728a .......................... 19

2.2.1 Siderophores ............................................................................................................ 20

2.2.2 N-acyl-homoserine lactones (AHLs) ...................................................................... 22

2.2.3 Compatible solutes .................................................................................................. 23

2.2.4 Phytotoxins .............................................................................................................. 24

2.2.5 Auxins ..................................................................................................................... 28

3. The overall goal of the study ................................................................................................ 29

4. Materials and methods ......................................................................................................... 30

4.1 Materials ......................................................................................................................... 30

4.1.1 Chemicals and solvents ........................................................................................... 30

4.1.2 Bacterial strain......................................................................................................... 30

4.1.3 Media ....................................................................................................................... 31

4.2 Methods .......................................................................................................................... 32

4.2.1 Bioinformatic analysis of the NRPSs and PKSs of the orphan gene clusters ......... 32

4.2.2 Media sterilization ................................................................................................... 32

4.2.3 Cultivation ............................................................................................................... 32

4.2.4 Extraction ................................................................................................................ 33

4.2.5 Chromatography ...................................................................................................... 34

4.2.6 NMR spectroscopy .................................................................................................. 35

4.2.7 Mass spectroscopy................................................................................................... 35

5

5. Results .................................................................................................................................. 37

5.1 Application of the genomic mining method “bioinformatic prediction and screening for

physicochemical properties” to Pss B728a .......................................................................... 37

5.1.1 Bioinformatic prediction of the orphan compounds and deduction of their physico-

chemical properties .......................................................................................................... 37

5.2 Seven-days-screening studies ......................................................................................... 42

5.2.1 Nutrient broth with glycerol (NBgly)...................................................................... 42

5.2.2 King’s B medium (KB) ........................................................................................... 44

5.2.3 Davis minimal broth (DMB) ................................................................................... 45

5.2.4 SRM medium with arbutin and fructose (SRM-AF) ............................................... 46

5.2.5 Modified SRM medium with arbutin and fructose (SRM-HG) .............................. 46

5.3 Large scale studies ......................................................................................................... 50

5.3.1 Nutrient broth with glycerol (NBgly)...................................................................... 50

5.3.2 King’s B medium (KB) ........................................................................................... 50

6. Discussion ............................................................................................................................ 58

7. References ............................................................................................................................ 64

8. Appendix .............................................................................................................................. 68

6

Abstract

Pseudomonas syringae pv. syringae (Pss) B728a is a plant pathogen that causes brown spot

disease on snap bean plants (Phaseolus vulgaris L.) by ice nucleation and production of

phytotoxins. To date Pss B728a is known to produce five compounds: syringopeptins

SP22Phv A and B, syringomycins E and G, and N-3-oxo-hexanoyl-L-homoserine lactone. Due

to its importance as a plant pathogen, its complete genome was recently sequenced. The

analyses of the sequenced genome identified twelve further clustered biosynthetic pathways

coding presumably for an ectoine-analog, syringolin, mangotoxin, achromobactin,

phaseolotoxin, indole-3-acetic acid, a pyoverdin-based metabolite, one polyketide and three

unknown lipopeptides.

In order to realize this tremendous metabolic capacity, this study aimed at the isolation of any

of the corresponding metabolites encoded by the above mentioned orphan gene clusters.

Using a genomic driven isolation strategy, it was possible to prove for the first time the

production of the auxin indole-3-acetic acid and its methyl ester by Pss B728a under special

conditions. Furthermore, media optimization and screening for the deduced physico-chemical

properties led to the localization of the predicted octalipopeptide.

7

Razširjen povzetek

Pred genomsko revolucijo konec 20. stoletja je izolacija novih naravnih spojin potekala

predvsem na podlagi njihovih fizikalno-kemijskih lastnosti ali določanja bioaktivnosti v

ekstraktih, pridobljenih iz naravnih virov. Javna dostopnost zaporedij genomov

najrazličnejših organizmov je v novem stoletju dala priložnost razvoju bioinformacijskih

orodij za odkrivanje naravnih spojin. Z uporabo teh orodij je bilo ugotovljeno, da genomi

vrste mikroorganizmov vsebujejo tako imenovane »sirotne« gruče genov (angl. »orphan«

gene clusters), ki kodirajo sintezo neznanih sekundarnih metabolitov. Iskanje teh gruč genov s

pomočjo bioinformacijskih orodij se običajno imenuje »rudarjenje genoma« (angl. »genome

mining«). Trenutno so za odkrivanje domnevnih sekundarnih metabolitov uveljavljene štiri

strategije: bioinformacijska napoved in izločilno preizkušanje za predvidene fizikalno-

kemijske ali farmakološke lastnosti, inaktivacija genov v kombinaciji s primerjalnim

metaboličnim profiliranjem, genomizotopski pristop in heterologno izražanje. V primeru, da

ne pride do izražanja želene »sirotne« gruče genov, mora biti raziskovalni proces dopolnjen s

tako imenovanim OSMAC (one strain – many compounds) pristopom. Le-ta temelji na

predpostavki, da je bakterijska sinteza sekundarnih metabolitov posledica specifičnega

odgovora na spremenjeno okolje. S spremembo pogojev gojenja in dodajanjem ustreznih

spojin v gojišče se umetno spremeni okolje, kar ima za posledico spremenjen profil

sintetiziranih sekundarnih metabolitov.

Pseudomonas syringae je močno razširjen bakterijski patogen, ki naseljuje široko paleto

rastlinskih vrst. Prvotno je bil izoliran iz okuženega španskega bezga (Syringa vulgaris L.).

Uvrščamo ga med γ proteobakterije, paličaste bakterije s polarnim bičkom. P. syringae je

gensko raznolika vrsta bakterij in je dodatno razdeljena na približno 50 patovarjev glede na

patogenost in spekter gostiteljev.

P. syringae patovar syringae (Pss) sev B728a je znan predvsem kot povzročitelj rjavih lis na

fižolu (Phaseolus vulgaris). Ugodne razmere za njegovo razmnoževanje so relativno visoka

vlažnost in relativno nizke temperature.

P. syringae gostiteljsko rastlino poškoduje na dva načina, s tvorbo listnih lezij ali s

povečanjem občutljivosti rastlin na pozebo. Bakterija se lahko širi s prenosom iz rastline na

rastlino po zraku ali pa se prenese iz listov na plodove in semena, iz katerih nato zraste

okužena rastlina.

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Genom Pss B728a je sestavljen iz enega krožnega kromosoma velikosti 6.093.698 bp,

njegovo zaporedje pa je v celoti določeno.

Interpretacija genoma Pss B728a je pokazala, da ima bakterija velik potencial za sintezo

širokega spektra sekundarnih metabolitov. Glede na njihovo biološko aktivnost jih lahko

razdelimo na siderofore, N-acil homoserin laktone, kompatibilne topljence, fitotoksine in

avksine. Pred predstavljeno nalogo je bilo iz Pss B728a izoliranih in opredeljenih le pet

sekundarnih metabolitov (N-3-oksoheksanoil L-homoserin lakton, SP22PhvA, SP22PhvB,

siringomicin E in siringomicin G). Z uporabo bioinformacijskih orodij je bilo predlaganih

dodatnih dvanajst domnevnih sekundarnih metabolitov.

Namen te naloge je bila izolacija in opredelitev kateregakoli domnevnega sekundarnega

metabolita, kodiranega z identificiranimi gručami genov genoma Pss B728a.

Raziskava je bila osredotočena le na eno od štirih uveljavljenih strategij za odkrivanje spojin

»sirotnih« biosinteznih poti. Pristop bioinformacijske napovedi in izločilnega preizkušanja za

predvidene fizikalno-kemijske ali farmakološke lastnosti smo izbrali zaradi relativno majhnih

stroškov in hitre izvedbe.

V prvem koraku smo, tako z uporabo bioinformacijskih orodij kot tudi s pomočjo objavljenih

podatkov, poskušali predvideti strukturo vsakega domnevnega sekundarnega metabolita.

Glede na ta predvidevanja smo napovedali ali vsaj ocenili fizikalno-kemijske lastnosti

domnevnih spojin, napoved pa je omogočila tudi optimizacijo procesa z izborom ustreznih

topil, kromatografskega materiala in predvsem metod detekcije.

Sledilo je gojenje bakterij za sedemdnevne presejalne študije. Uporabili smo pet različnih

tekočih medijev in spreminjali pogoje gojenja, da bi razširili spekter sintetiziranih

sekundarnih metabolitov. Pogoji za gojenje bakterij so bili izbrani tako, da je bilo verjetno

izražanje večine domnevnih metabolitov. Ker je izražanje sekundarnih metabolitov odvisno

tudi od faze rasti, v kateri je bakterija v določenem času, smo bakterije v vsakem mediju gojili

od enega do sedmih dni, izolacija pa je potekala v enodnevnih časovnih razmakih.

Z organskim topilom smo ekstrahirali le supernatant, ker so sekundarni metaboliti večinoma

zunajcelični produkti.

Presejanje za prisotnost predvidenih sekundarnih metabolitov v ekstraktih smo izvedli z

uporabo LC/MS (tekočinska kromatografija/masna spektroskopija) tehnike, in sicer smo iskali

prisotnost spojin z ustreznimi masami.

Glede na rezultate presejalnih študij smo povečali obseg gojenja bakterij v dveh medijih,

hranilnem bujonu z glicerolom (NBgly) in King B mediju (KB). Gojenje bakterij v velikem

9

obsegu v SRM-HG mediju je zelo obetaven poskus, ki bi ga veljalo narediti v prihodnosti. V

ekstraktu je namreč zelo verjetna prisotnost predvidenih lipopeptidov.

Za izolacijo sekundarnih metabolitov smo uporabili standardno izolacijsko shemo. Ekstrakciji

je sledila vrsta separacijskih procesov, ki smo jih izbrali na osnovi analize podatkov

pridobljenih z 1H NMR in LC/MS.

Tekom študije smo uspeli izolirati indol-3-ocetno kislino in njen metilni ester. Strukturo

izoliranih spojin smo določili in potrdili z uporabo eno in dvodimenzionalne NMR ter masne

in UV spektroskopije.

Indol-3-ocetna kislina (IAA) je najpogostejši avksin, rastlinski hormon, ki pospešuje rast v

višino. Znano je, da poleg rastlin indol-3-ocetno kislino sintetizira tudi vrsta bakterij.

Nekatere med njimi so za rastline koristne, saj spodbujajo rast, medtem ko so druge rastlinski

patogeni. Optimalne količine IAA spodbujajo rast rastlin, posledica povečanih koncentracij pa

je nastanek šišk ali okroglih, rjavih listnih lezij, kot v primeru Pseudomonas syringae pv.

syringae.

Metilni ester indol-3-ocetne kisline je produkt metabolizma IAA z enakim delovanjem kot

prosta kislina. Domneva se, da je skladiščna oblika IAA v višjih rastlinah in je pogost

metabolit bakterijskih rastlinskih patogenov.

Bakterije sintetizirajo IAA iz triptofana po štirih različnih biosinteznih poteh. Pss B728a ima

operon za biosintezo IAA preko intermediata indol-3-acetamida. Operon vsebuje gena za

triptofan monooksigenazo, iaaM, in indol-3-acetamid hidrolazo, iaaH. Poleg tega je možno,

da biosinteza poteka tudi po indol-3-acetaldoksimski poti. Gen Psyr0006 bi lahko kodiral

aldoksim dehidratazo, Psyr0007 pa nitrilazo. Izolacija in opredelitev katerega od potencialnih

intermediatov predstavlja zanimiv izziv za nadaljnje raziskovanje.

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List of abbreviations

δ chemical shift of an atom on the relative δ scale of an NMR spectrum

A adenylation domain

ACN acetonitrile

ACP acyl-carrier protein

AHL acyl-homoserine lactone

API-ESI atmospheric pressure ionization – electrospray ionization

AT acyltransferase domain

bp base pair

C condensation domain

CDCl3 deuterated chloroform

CD3OD deuterated methanol

COSY homonuclear correlation spectroscopy

Cy cyclization domain

d doublet (in connection with NMR data)

dd doublet of doublet (in connection with NMR data)

DEPT distortionless enhancement by polarization transfer

DMB Davis minimal broth

E epimerization domain

EtOAc ethyl acetate

GC content guanine-cytosine content

GS/MS gas chromatography/mass spectroscopy

HPLC high performance liquid chromatography

HMBC Heteronuclear Multiple-Bond Correlation (1H-

13C long range correlation)

HR-EI-MS high-resolution electron impact mass spectra

HSQC Heteronuclear Single-Quantum Correlation (1H-

13C direct correlation)

IAA indole-3-acetic acid

IAAld indole-3-acetaldehyde

IAM indole-3-acetamide

IAN indole-3-acetonitrile

IAOx indole-3-acetaldoxime

i.d. inner diameter

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IPA indole-3-pyruvic acid

J spin-spin coupling constant [Hz]

KB King’s B medium

kb kilo-base pair

KR keto-reductase domain

KS keto-synthase domain

LC liquid chromatography

LC/MS liquid chromatography/mass spectroscopy

Mb mega-base pair

MeOH methanol

MS mass spectroscopy

multi. multiplet (in connection with NMR data)

MWCO molecular weight cutoff

m/z mass-to-charge ratio

NBgly Nutrient broth with glycerol

NMR nuclear magnetic resonance

NRPS non-ribosomal peptide synthetase

ORF open reading frame

OSMAC one strain – many compounds

P. syringae Pseudomonas syringae

PE petroleum ether

PKS polyketide synthase

Pss Pseudomonas syringae pathovar syringae

pv. pathovar

qC quaternary carbon atom

RP-HPLC reversed phase high performance liquid chromatography

rpm rotation per minute

Rt retention time

s singlet (in connection with NMR data)

SP syringopeptin

SPE solid phase extraction

SR syringomycin

SRM-AF SRM medium with arbutin and fructose

SRM-HG modified SRM medium with arbutin and fructose

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T thiolation domain

TAM tryptamine

Te thioesterase domain

TFA trifluoracetic acid

TLC thin layer chromatography

TMS trimethylsilyl

Trp tryptophan

TSIM N-trimethylsilylimidazole

UV/VIS ultraviolet-visible

VLC vacuum liquid chromatography

v/v volume-volume percentage relationship

w/v weight-volume percentage relationship

Abbreviations for amino acids

Ala alanine

Arg arginine

Asp aspartic acid

Cys cysteine

Gln glutamine

Lys lysine

Pro proline

Ser serine

Thr threonine

Val valine

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1. Genome mining

Before the genomic revolution, the isolation of new natural products relied exclusively on the

detection of bioactivity in extracts from natural sources or on physico-chemical properties. At

the end of the 20th

century, the supply of new natural products from this assay-guided

approach appeared to be almost exhausted. With the new century, the power of genomics

generated new methodologies for isolating novel natural products. The large quantity of

publicly accessible DNA sequence data gave bioinformatic tools the opportunity for new

natural products discovery. Using these tools, several microbial genomes have been found to

contain so-called “orphan” gene clusters encoding putative biosynthetic enzymes likely to be

involved in the production of unknown secondary metabolites (1). “Orphan pathways” are

therefore defined as biosynthetic loci for which the corresponding metabolite is unknown.

Through the recently initiated genome sequence projects, it became evident that

microorganisms possess numerous orphan gene clusters related to the secondary metabolite

biosynthesis.

At the beginning of every genomic-guided study stands the discovery of an orphan pathway

and the capability to predict the corresponding structure. The bioinformatic search for the

orphan gene clusters is usually termed “genome mining”, “data mining” or “metabolic

pathway mining” (2). An exact starting point of genome mining for new natural products can

not be defined, however with the turn of the millennium the first publications belonging to

this scientific field could be observed (3, 4).

Once an orphan biosynthesis pathway is analyzed and discovered on the genome level, the

corresponding secondary metabolite has to be tracked down on the metabolome level.

Most of the established methodologies described below, rely on expression of the desired

orphan gene cluster. In case expression is lacking, heterologous expression or the so called

“one strain – many compounds“ (OSMAC) approach has to be integrated into the search

process. The OSMAC concept is based on the assumption that secondary metabolite

production occurs as a specific response to a changed environment. By variation of the culture

conditions (e. g. media composition, temperature, pH, UV-radiation, aeration rate, etc.) or

medium additives (e. g. addition of a cytotoxic compound, enzyme inhibitors, second

microorganism, specific signalling molecules, etc.) a changed environment is artificially

14

mimicked and a shift in the secondary metabolite profile can be observed (5). In this way, the

desired product of an orphan gene cluster might be revealed.

Currently, there are four established strategies to discover the products of orphan biosynthetic

pathways (Fig. 1), which will be presented in the following:

Bioinformatic prediction and screening for deduced physico-chemical or

pharmacological properties

The first step of this method is the in silico prediction of the resulting chemical structure

encoded by an orphan gene cluster as accurately as possible. Based on these predictions,

physico-chemical properties (e.g. a mass range, characteristic UV absorbance spectrum and

polarity) of the putative compound can be defined or at least estimated. Hence, the extraction

and fractionation process can be optimised regarding the selection of solvents,

chromatographic materials and especially the detection method. In cases where the putative

structure shows evident similarity to a pharmacologically active compound family or contains

a known pharmacophore, respectively, even the biological activity can be predicted and used

for its detection. To obtain expression, the OSMAC approach can be applied (6, 7, 8, 9).

Gene inactivation studies combined with comparative metabolic profiling

Essential for this approach is first of all the development of a knock out organism for the gene

cluster of interest. Afterwards comparison of the secondary metabolite spectrum of the

resulting mutant with its wild type identifies the corresponding natural product. Preferably,

LC/MS instrumentation is employed for the comparative metabolite profiling process (10, 11).

The gene inactivation strategy has some useful advantages. At first, the method can be applied

even if the product of the orphan pathway can not be predicted accurately or only fragments

of the orphan gene cluster are known. The second advantage is the automatically provided

proof that the investigated orphan gene cluster is involved in the identified compound (2).

Genomisotopic approach

The genomisotopic (GI) approach was especially developed for orphan biosynthetic pathways

of NRPS or hybrid NRPS/PKS origin. The first step of this approach is the discovery of an

orphan gene cluster. With application of bioinformatic tools the resultant secondary

metabolite or at least a peptide moiety needs to be predicted in the next step. From

bioinformatic analysis, an amino acid that will be specifically incorporated into the final

product of the orphan gene cluster is selected. This specified precursor is subsequently fed to

15

a microorganism in an isotopically labelled form such as 15

N or 15

N–13

C amino acids. Natural

products containing this precursor can then be tracked through the isolation process by 1H–

15N or

15N–

13C selective NMR experiments (12).

Selection of an appropriate amino acid is crucial for the success of this approach. Preferably,

the selected amino acid is only present in the product of the orphan pathway and is not

incorporated into any other component of the metabolome.

Advantage of the genomisotopic approach is, that it locates the products of an orphan

pathway even if bioactivity and the physico-chemical properties can not be accurately

predicted or characteristics are not sufficient (e.g. low mass, weak UV absorption, low or

different bioactivity, physical property that is shared by other produced compounds).

To date, the genomisotopic approach is limited to orphan NRPS and hybrid NRPS/PKS gene

clusters.

Heterologous expression

Heterologous expression is a transfer of an orphan natural product pathway from the original

producer into a different optimised bacterial host system. The strategy is especially needed if

the orphan pathway turned out to be a silent one or when the expression level of a gene is low

in the natural producer. Furthermore, heterologous expression is also desirable for slow

growing microorganisms (2). Despite successful examples in the past (3, 10), this method is

still a challenging task to accomplish. Drawbacks are for example the difficult transfer of

large gene clusters (>30 kb), missing or incomplete expression and low yields. Furthermore,

for some microorganisms well established host systems are not yet developed (2).

Fig. 1: Established genome mining strategies to reveal the products of orphan gene clusters.

mutant

wild type

novel

antibiotics

heterologous expression

host

prediction & screening

UV

MS

source

genomisotopic approach

*

knockout / metabolite profiling

16

2. Pseudomonas syringae pathovar syringae B728a

Pseudomonas syringae is a widespread bacterial pathogen of many plant species and was

initially isolated from a diseased lilac (Syringa vulgaris L.) in 1899. The species designation

is thus linked to the diseased host where the bacterium was first found. P. syringae is a

member of the γ subclass of the Proteobacteria. Under the microscope it could be identified as

a rod-shaped bacterium with polar flagella. The species is a gram-negative strict aerobe, with

few exceptions produces fluorescent pigments, is oxidase and arginine dihydrolase negative

(phenotypes that distinguishes it from most of the other fluorescent pseudomonads), and does

not rot potato (which distinguishes it from Pseudomonas viridiflava) (13). Genome

comparisons indicate that P. syringae is significantly different from other Pseudomonas

species, suggesting that in the adaptation to the phytopathogenic lifestyle its genome must

have undergone fundamental changes without a reduction in size (14). P. syringae is capable

of producing a variety of different symptoms depending on the host species and site of

infection. For example, it causes leaf-spot diseases that defoliate plants such as tomato, bean,

and soybean, trunk cankers, and so-called “blast disease” on fruit, nut, and ornamental species

(15). The species is capable of interacting with a wide range of plants in most regions of the

world (13).

P. syringae is genetically diverse and is now subclassified into approximately 50 pathovars

according to plant pathogenicity and host range. However, such a pathovar system does not

always correspond with DNA homology or physiological and biochemical characteristics. In

some cases strains from different pathovars are more related than strains within the same

pathovar (16).

P. syringae pv. syringae (Pss) strain B728a is known as the causal agent of brown spot

disease on beans (Phaseolus vulgaris) (Fig. 3). It is typical of most strains of this pathovar in

that it exhibits a very pronounced epiphytic (colonization of the surfaces of leaves) phase on

plants. B728a has evolved to exploit at least two distinct habitats: the leaf surface and the

apoplast. After colonization of host plants in a non-pathogenic state Pss B728a can rapidly

take advantage of changing environmental conditions to induce disease in susceptible plants

(15). Generally speaking, net increases in bacterial population sizes are associated with

relatively moist conditions (particularly rain and high relative humidity) and relatively cool

temperatures.

P. syringae has two ways to destroy its host plant, either by lesion formation or by frost

injury. Lesions are caused by expression of a multitude of phytotoxins that lyse the leaf

17

surface. The frost damage is predicated on the production of so called ice nucleation proteins

which are able to nucleate ice at temperatures above the normal freezing point (13). It is

noteworthy that P. syringae is known to be the most ice nucleation active bacterial species

(15). Regularly, the formation of ice occurs on leafs from field-grown plants approximately in

a temperature range from -3 to -8 oC. Ice formation only takes place in the presence of

suitable seed crystals, otherwise water will

be kept in a supercooled condition.

Associations of water molecules

(homogeneous ice nucleation) or other

suitable molecules (heterogeneous ice

nucleation) can serve as seed crystals. In

the case of Pss B728a the ice nucleation is

enabled by outer membrane bound ice

nucleation proteins. Ice proteins

accumulate to form aggregates of various

sizes in association with the outer

membrane of bacterial cells and

subsequently perform heterogenous ice

nucleation (Fig. 2). For the active state,

protein monomers have to be aggregated in

a proper conformation.

Fig. 2: An ice nucleation event occurred in

the test tube on the right due to the large

numbers of ice nucleation-active P.

syringae present on the leaf (Fig. 2 is

reprinted from reference 13).

While for lesions it can be envisioned that they may provide a place for the bacteria to survive

during unfavorable weather conditions, the benefit of frost damage is debatable. Several

mechanisms have been proposed that may provide selective advantage to the bacteria of

causing frost injury, but there is no clear selection for destroying the leaf habitat. The real

function of P. syringae is to live on healthy leaves. Only when conditions become unusually

favorable and population sizes of bacteria become too large both of these events, lesion

formation and frost injury, become highly likely and the entire system crashes. The true

importance of these functions to the bacteria themselves has yet to be discovered.

18

A B C

Fig. 3: (A) Foliar and (B) pod symptoms of bacterial brown spot disease of snap bean

(Phaseolus vulgaris) caused by P. syringae pv. syringae. (C) Symptoms of frost injury to

snap bean plants in the field (Fig. 3 is reprinted from reference 13).

Regarding the transmission and spreading of the bacteria, Pss B728a pursues two strategies

(Fig. 4). If the plants are allowed to mature, bacteria present on pods could move to seeds,

where the bacteria can survive until the seeds are planted and is available to colonize leaves as

the plant emerges. The other way of infection is an epiphytic Pss population which could

serve as inocula that can later invade plants through the air and initiate disease (13).

Fig. 4: Infection process of P. syringae (Fig. 4 is modified from reference 13).

2.1 Genome features of Pseudomonas syringae pv. syringae B728a

Sequencing and annotation of the P. syringae pv. syringae B728a genome was completed in

May, 2005 (Fig. 5). The Pss B728a genome is composed of one circular chromosome of

6,093,698 bp harboring 5,217 genes. The GC content of the genome is 59% (15).

19

2.2 Secondary metabolites of Pseudomonas syringae pv. syringae B728a

Secondary metabolites are a very broad group of organic compounds, with no distinct

boundaries. In contrast to carbohydrates, lipids, proteins or nucleic acids they are not present

in every cell and are not directly involved in the normal growth, development or reproduction

of organisms. Secondary metabolites are often unique to a particular species or group of

organisms and may be produced as defense against other organisms or unfavorable

conditions, for interspecies competition, as signaling molecules, or to facilitate the

reproductive process (17, 18).

Fig. 5: Circular presentation of the Pss B728a overall genome structure and secondary

metabolite genes positions. 1st circle: predicted coding regions on the positive strand. 2

nd

circle: predicted coding regions on the negative strand. 3rd

circle: set of genes with no

orthologs in Pseudomonas syringae pv. tomato strain DC3000. 4th

circle: REP repeat

elements. 5th

and 6th

circles: GC content and skew, respectively. 7th

and 8th

circles: represent

the tRNA (green), rRNA (red), and misc_RNA (blue). Known compounds are listed in black,

putative compounds in colors as follows: siderophores, N-acyl-homoserine lactones,

compatible solutes, phytotoxins, auxin (Fig. 5 is modified from reference 15).

20

The interpretation of the Pss B728a genome revealed, that this bacterium has a great potential

to produce a broad spectrum of secondary metabolites. According to their biological activity

these compounds can be categorized into five groups (Fig. 5).

The colors of genes represented below have the following meaning:

██ - structural genes

██ - accessory genes

██ - regulatory genes

██ - genes encoding for transporters or resistance

██ - hypothetical genes

2.2.1 Siderophores

In order to maintain the necessary supply for their living cells, bacteria need a concentration

of approximately 10-6

mol/l of iron to survive. Iron is hereby available to microorganisms

only in its trivalent form. Due to the low concentration of free Fe(III) in soil or host systems

(10-17

mol/l), bacteria produce a variety of low-molecular-weight complexing agents – called

siderophores. Most siderophores are peptide based and contain either carboxyl, hydroxyl or

N-hydroxy amino side chains, or catechol and thiazoline/oxazoline ring systems as iron-

coordinating functional groups.

Pseudomonads usually produce a high affinity strain-specific siderophore of the pyoverdine-

type and a second smaller siderophore of lesser iron affinity. This applies also for Pss B728a,

where two corresponding gene clusters were identified (18).

Psyr1944-1962

This gene cluster shows the typical homologs of pyoverdine (Fig. 6) biosynthesis and uptake

regions (19).

Psyr1944-1962

21

The resulting putative natural product will show the three distinct structural parts of a typical

pyoverdine (Fig. 6), i.e. a quinoline chromophore, a peptide chain bound to its carboxyl group

via its N-terminus and a dicarboxylic acid or its amide connected amidically to the NH2-group

(20). Usually, a given strain produces two to five pyoverdines, differing only in the small

dicarboxylic acid side chain (18). Based on these facts, the production of at least two

pyoverdins can be predicted for Pss B728a. However, to date none of these pyoverdines have

yet been isolated from Pss B728a.

N+ NHHO

HO NH

R

peptide

O R=CO-CH2-CH2-CONH2

R=CO-CH2-CH2-COOH

R=CO-CH2-CHOH-CONH2

R=CO-CH2-CHOH-COOH

R=CO-CH2-CH2-CO-COOH

Fig. 6: Basic skeleton of a typical pyoverdins-based siderophore.

Psyr2583-2589

Based on a close homology to the biosynthesis genes acsA-acsF coding for the citric acid

based siderophore achromobactin (Fig. 7), Pss B728a might be able to produce

achromobactin as the second iron-chelator. In comparison with the originally identified acs

gene cluster, the sequence of the genes is rearranged (21).

Psyr2583-2589

O

O

OH

COOH

O

HN

HN

O

COOH

O

HN

COOH O

O

COOH

O

O

OH

COOH

O

HN

N

N

COOH

O

HO

COOH

O

HO

COOH

Fig.7: Structure of achromobactin and its cyclized form that prevails in neutral aqueous

solution.

22

2.2.2 N-acyl-homoserine lactones (AHLs)

AHLs are autoinducers, signaling molecules that allow a population of a given bacterial

species to synchronize behaviors that might be insignificant or harmful unless done as

collective answer (15).

Psyr1621-1622

Gene Psyr1621 was identified as the N-AHL ahlI and gene Psyr1622 as its corresponding

regulator ahlR.

Psyr1621-1622

The corresponding metabolite was already proven to be N-3-oxo-hexanoyl-L-homoserine

lactone (Fig. 8) by Cha et al. (22).

Psyr0009

Pss B728a might be capable to produce further AHLs. Psyr0009 possesses a high sequence

identity with the HdtS protein which is known to produce three AHLs including N-(3-OH-7-

cis-tetradecenoyl) homoserine lactone (Fig. 8) (15).

Psyr0009

NH

O

O

O OH

1 3 6

NH

O

O

O OHH

1 3

714

Fig. 8: Structures of N-3-oxo-hexanoyl-L-homoserine lactone (top) and N-(3-OH-7-cis-

tetradecenoyl) homoserine lactone (bottom).

23

2.2.3 Compatible solutes

Many bacteria respond to decreased water availability by accumulating compatible solutes,

which protect enzymes and stabilize membranes. Pseudomonads are known to produce

betaine, (hydroxyl)ectoine and N-acetylglutaminylglutamine amide, mannitol and

glycosylglycerol to achieve enhanced osmotolerance.

Psyr0334

According to this gene which shows a high homology to ectC, Pss B728a might be capable of

producing ectoine (Fig. 9).

Psyr0334

Usually, ectoine is synthesized from L-aspartate-β-semialdehyde via a three-step pathway

with N-acetyl-L-2,4-diaminobutyrate as the last intermediate (Fig. 9).

O

COO-+H3N

H

EctB

Glu

2-oxoglutarate

L-aspartate-ß-semialdehyde

COO-+H3N

+H3N

L-2,4-diaminobutyrate

EctA

CoA

acetyl-CoA

COO-+H3N

NH

H3C

OEctC

COO-NH

HN

H3CH2O

ectoineN-acetyl-L-2,4-diaminobutyrate

Fig. 9: Ectoine biosynthesis.

Since ectA and ectB are absent in the genome of Pss B728a, it is possible that this “orphan”

ectoine synthase is no longer functional (15). However, as demonstrated in other bacteria (e.g.

Halomonas elongata), ectA and ectB are not strictly necessary for ectoine production (M.

Kurz, personal communication), indicating that the first two steps of the biosynthesis can be

taken over by other enzymes. Several genes coding for diaminobutyrate-transaminases or

acyl-transferases are present in the Pss B728a genome, which makes the production of ectoine

again likely (15).

24

2.2.4 Phytotoxins

Pss is known to produce a number of phytotoxins, which contribute significantly to the

virulence of the strain (15).

Pss usually produces two classes of lipodepsipeptides: the syringopeptins (SP) and the

lipodepsinonapeptides (including syringomycins (SR), syringostatin, or pseudomycin). Each

strain secretes a single type of syringopeptins and one or two lipodepsinonapeptides.

Syringopeptins are secreted as pairs of homologues, designated as A and B, which differ in

the length of the lipid moiety, whereby the 3-hydroxylated fatty acid chain contains either 10

(form A) or 12 (form B) carbon atoms.

Psyr2614-2616

This set of genes spans a huge NRPS gene cluster that contains 22 modules, which fit to the

assembly line of syringopeptins.

Psyr2614-2616

The corresponding peptides, dubbed SP22PhvA and B (Fig. 10) were isolated already by

Grgurina et al. (23) in 2002.

Psyr2608-2611

Psyr2608-2611

25

With eight NRPS modules in operon Psyr2608 and a chlorinating enzyme encoded by

Psyr2610, this gene cluster codes for the biosynthesis pathway of syringomycins E and G

(Fig. 11), which were obtained already during isolation studies (23).

N

HN

HN NH

HN

NH

NH

HN

NH

HN

NH

NH

O

NH

HN

HN

HN

HN

HN

NHNH

NH

HN

O

O

O

OO

O O

O

OO

O

O

O

O

O

O

O

OO

O

O

O

O

NH2H2N

OH

HO

HO

R

R=C7H15=SP22PhvAR=C9H19=SP22PhvB

Fig. 10: Structures of syringopeptins SP22PhvA and B. The term Phv derives from Phaseolus

vulgaris.

HN

NH

NH

NH

NH

HN N

H

HN

O

HN

NH

O

O

O

O

O

O

O

O

O

O

O

NH

OH

NH2

HOOH

NH2

NH2

Cl

OH

HO

R

R=C9H19=syringomycin ER=C11H23=syringomycin G

Fig.11: Structures of syringomycins E and G.

26

Psyr1704-1706 (SylA)

Pss strains are also capable of producing a family of peptide derivatives called syringolins,

which represent a new class of proteasome inhibitors. Orthologs of the genes participating in

biosynthesis and export of syringolin A (Fig. 12) were identified in the Pss B728a genome.

During the course of this study this gene cluster was identified in planta as a virulence factor

of Pss B728a and consequently renamed to SylA. However, the metabolite itself has not been

isolated so far from Pss B728a (24).

Psyr1704-1706

COOHNH

NH

O

O

HN

OHN

HN

O

Fig. 12: Structure of syringolin A.

Psyr2549-2555

Another possible and still to be isolated phytotoxin produced by Pss B728a is phaseolotoxin

(Fig. 13) (15).

Psyr2549-2555

N

NH2

H2N

HNHOOC

O

NH

O

NH2

NH

P

H2N

O

HN

SO3H

Fig. 13: Structure of phaseolotoxin.

27

Products of orphan gene clusters

The Pss B728a genome contains a number of “orphan” gene clusters encoding for putative

secondary metabolites of NRPS, PKS, or combined NRPS/PKS origin. For two of them

(Psyr2576-2577 and Psyr3722) we predicted to encode for NRPS origin octalipopeptide and

hexalipopeptide, respectively. Another one (Psyr1792-1794) is a hybrid NRPS/PKS

“orphan” gene cluster and we predicted to encode for synthesis of pentapeptide with

polyketidic elements in between. In addition, a gene cluster for a putative polyketide

(Psyr4311-4314) of PKS origin has been found in genome (15).

Psyr2576-2577 (putative octalipopeptide)

Psyr2576-2577

Psyr3722 (putative hexalipopeptide)

Psyr3722

Psyr1792-1794 (hybrid NRPS/PKS gene cluster)

Psyr1792-1794

Psyr4311-4314 (putative polyketide)

Psyr4311-4314

28

Psyr5011 (mangotoxin)

This gene shows high identity to the mgoA operon which was found to code for mangotoxin

in Pss UMAF0158 (25). To date the structure of mangotoxin remains unknown and

expression in Pss B728a was also not yet observed. The authors who discovered this gene

assume that mangotoxin is a special amino acid similar to the phytotoxin tabtoxin (causal

agent of wildfire of tobacco), also produced by P. syringae.

Psyr5011

2.2.5 Auxins

Although auxins are typical plant metabolites, it has been shown that some pseudomonads of

the genus syringae are able to produce varying amounts of phytohormones, especially indole-

3-acetic acid (IAA) (Fig. 14) (26). In some cases, the production of IAA is known to

contribute to virulence and epiphytic fitness of the considered pseudomonad.

Psyr1536-1537 (+ Psyr0006-0007)

Psyr1536-1537 (+ Psyr0006-0007)

The set of two genes, Psyr1536 (tryptophan monooxygenase) and Psyr1537 (indoleacetamide

hydrolase) together encode for the required enzymes necessary for the production of IAA

from tryptophan. However, since knockout studies showed that alternative IAA biosynthesis

pathways are usually present in pseudomonads, the production of IAA possibly can be also

guided by Psyr0006 (aldoxime dehydratase) and Psyr0007 (nitrilase) (15).

NH

COOH

Fig. 14: Structure of indole acetic acid.

29

In summary, only five secondary metabolites (N-3-oxo-hexanoyl-L-homoserine lactone,

SP22PhvA, SP22PhvB, syringomycin E, syringomycin G) encoded by three gene clusters were

already isolated from Pss B728a and characterized (22, 23). By the means of genome

bioinformatic studies we suggested twelve additional putative secondary metabolites.

3. The overall goal of the study

The overall goal of this study was to isolate and characterize any putative secondary

metabolite encoded by the identified gene clusters in the Pss B728a genome, employing the

´bioinformatic prediction and screening for physico-chemical properties´-method. Special

emphasis was placed upon the search for the products of the compounds deriving from orphan

gene clusters, especially e.g. the putative lipopeptides encoded by Psyr2576-2577 and

Psyr3722.

30

4. Materials and methods

4.1 Materials

4.1.1 Chemicals and solvents

All organic solvents used for extraction and chromatography were research grade and

supplied by Infracor or BASF, except acetonitrile. EtOAc, MeOH and PE were distilled prior

to use. MeOH, if used for LC/MS analysis and acetonitrile for HPLC purposes were obtained

in HPLC grade quality. Water used was deionized and filtered using a Millipore (milli-Q®

academic) system.

Acetonitrile (VWR, 20060.320)

Arbutin, minimum 98% (Sigma, A4256)

Chloroform-d1 99.8% (Deutero, B8949)

D-(+)-Glucose-monohydrate (Merck, 1.08342.1000)

D-(-)-Fructose, minimum 99% (Sigma, F0127)

Glycerol anhydrous (KMF, A3-552.1000)

Hydrochloric acid fuming 37% (Merck, 1.00314.2500)

Iron(III) chloride (Merck, 8.03945.0500)

L-histidine (Fluka, 53319)

Magnesium chloride hexahydrate (Merck, 4.42615.0500)

Magnesium sulfate heptahydrate (Fluka, 63140)

Methanol (Merck, 1.06035.2500)

Methanol-d4 99.8% (Deutero, B9565)

Potassium dihydrogen phosphate (Merck, 4871.1000)

Di-potassium hydrogen phosphate (Merck, 631 A15931)

Silica gel 60 H for TLC (90% <45 µm) (Merck, 1.07736.1000)

Sodium sulfate anhydrous (KMF, 03-020.2500)

Trifluoroacetic acid (Fluka, 91700)

4.1.2 Bacterial strain

Pseudomonas syringae pv. syringae B728a was provided by Steven E. Lindow.

31

4.1.3 Media

Nutrient broth with glycerol (NBgly)

0.5% (w/v) Bacto® peptone (Difco, 0118-17-0)

0.3% (w/v) Meat extract (Roth, 5770.2)

0.2% (v/v) glycerol

King’s B medium (KB)

2.0% (w/v) Bacto® peptone (Difco, 0118-17-0)

0.15% (w/v) sodium sulfate

0.15% (w/v) magnesium chloride

1% (v/v) glycerol

Davis minimal broth (DMB)

1.06% (w/v) DifcoTM

minimal broth (Difco, 275610)

0.2% (v/v) glycerol

SRM medium with arbutin and fructose (SRM-AF)

1.0% (w/v) D-glucose

0.4% (w/v) L-histidine

0.10% (w/v) fructose

0.80 mM MgSO4 x 7H20

0.80 mM KH2PO4

100 µM arbutin

10 µM FeCl3

Modified SRM medium with arbutin and fructose (SRM-HG)

solution 1:solution 2 = 9:1

solution 1:

0.44% (w/v) L-histidine

0.89 mM MgSO4 x 7H20

6.5 mM KH2PO4

5.1 mM K2HPO4

solution 2:

10% (w/v) D-glucose

1,0% (w/v) fructose

1,0 mM arbutin

100 µM FeCl3

32

4.2 Methods

4.2.1 Bioinformatic analysis of the NRPSs and PKSs of the orphan gene clusters

Using web-based bioinformatic tools allowed a subdivision into the catalytic (C)ondendation,

(A)denylation and (T)hiolation or acyl-transferase (AT), keto-synthase (KS), keto-reductase

(KR), acyl-carrier protein (ACP) and thioesterase (Te) domains, respectively. Further analysis

of the A domains led to the prediction of their cognate amino acids and, due to the common

colinearity between the sequence of specific A domains in NRPSs and the sequence of amino

acids in the peptide product, also allowed prediction of the amino acid sequence of the

resulting peptide. Catalytic domains and subsequent specificity prediction of the A domains

present in the open reading frames encoding NRPSs or PKSs, were identified by using the

web-based software NRPS-PKS (http://www.nii.res.in/nrps-pks.html) or the PKS/NRPS

analysis web-site of the TIGR institution (http://www.tigr.org/jravel/nrps/).

4.2.2 Media sterilization

Heat-stable media, buffers and other solutions and glassware were sterilized in a steam

autoclave (H+P Varioklav®) at 121 ºC, and 1.2 bar for 20 min. Millipore filters (cellulose

acetate membrane, 0.2 µm, Renner, ER0609-1) with an MWCO of 0.22 µm were used to

sterilize heat sensitive solutions (e.g. solution 2 of SRM-HG medium) under aseptical

conditions using a clean bench (Heraeus HERA safe).

4.2.3 Cultivation

Maintenance of bacterial stock cultures

The strain was grown in NBgly for three days, mixed with glycerol (1:1) in a 1.5 mL cryo vial

and subsequently frozen and kept at -80 ºC.

Cultivation for screening purpose

Starter cultures of Pss B728a were grown in 12 mL of medium in 50 mL Falcon tubes at room

temperature (20 ºC) and a shaking rate of 400 rpm (IKA KS 125 shaker) for 72 h.

Screening scale cultures were cultivated in 300 mL Erlenmeyer flasks containing 100 mL of

medium and inoculated with 0.5 mL - 1.0 mL of starter culture.

33

Each set of screening scale cultures was composed of a blank Erlenmeyer flask and seven

inoculated Erlenmeyer flasks incubated in darkness for 24 h, 48 h, 72 h, 96 h, 120 h, 144 h, or

168 h, respectively. The shaking rate of the cultures was 130 rpm. KB and DMB screening

scale cultures were cultivated at 28 ºC, SRM-AF and SRM-HG at 18 ºC. For incubation an

Infors HT incubator shaker was applied. One set of SRM-AF screening scale cultures was

static but also cultivated at 18 ºC.

Cultivation for chemical investigations

Large scale cultures were cultivated in 5 L Erlenmeyer flasks containing 1.5 L of medium and

inoculated with 2 to 3 mL of starter culture. NBgly large scale cultures were incubated in

darkness for 48 h at 120 rpm and 25 ºC, KB in darkness for 70 h at 130 rpm and 28 ºC (Infors

HT incubator shaker).

4.2.4 Extraction

Cell and supernatant portions of the cultures were separated by centrifugation using a Heraeus

Contifuge Stratos (8000 rpm, 10-15 min, 15 ºC) for small fermentation broth volumes (8 x 50

mL) or a Hettich Roto Silenta/RP (4000 rpm, 15 min, 20 ºC) cooling centrifuge, respectively

for large fermentation broth volumes (4 x 1 L).

The supernatants obtained after centrifugation were acidified either with TFA or HCl (37%)

to reach pH 1 - 2. A pH paper (Universal indicator paper pH 1-10, Merck, 1.09526.0003) was

used for pH determination. Subsequently, the acidified culture supernatants were extracted

two times with equal volumes of EtOAc. The ethyl acetate extracts were dried in vacuo using

rotary evaporators (Vacuubrand GmbH & Co KG, Wertheim, Germany) operating at less than

40 ºC.

34

4.2.5 Chromatography

Solid phase extraction (SPE)

For application of SPE technique, pre-

packed Discovery®

DSC-18 (1g/6mL

tubes, Supelco, 52606-U) reverse phase

silica cartridges were used (Fig. 15).

Samples applied on columns were

dissolved in MeOH/H2O (50:50). Prior to

sample application the columns were

equilibrated with MeOH/H2O (50:50).

Sample fractionation was performed using

stepwise gradient elution with MeOH/H2O

(50:50) to 100% MeOH. To the top of the

column air pressure was applied to

increase the flow rate. Fractions were dried

in vacuo using a rotary evaporator

operating at less than 40 ºC.

Fig. 15: Solid phase extraction.

Vacuum liquid chromatography (VLC)

VLC was carried out using TLC-grade

silica gel as sorbent. Fritted glass columns

were filled by dry-packing under vacuum

and equilibrated with PE. After sample

application, fractionation was performed

using stepwise gradient elution from PE

containing increasing proportions of

EtOAc followed by ACN and H2O to

produce several subfractions (Fig. 16)

which were dried in vacuo using a rotary

evaporator operating at less than 40 ºC.

Fig. 16: Vacuum liquid chromatography.

35

High performance liquid chromatography (HPLC)

HPLC was carried out using a Merck Hitachi system equipped with a L-6200A pump, a L-

7420 UV/VIS detector and a Knauer interface box, controlled by Knauer Eurochrom

software. Columns used were either:

A: Waters–AtlantisTM

C18 (5 µm, 250 x 4.6 mm)

B: Macherey–Nagel Nucleodur 100-5-C18 EC (5 µm, 250 x 10 mm)

Typical flow rates were 1.0 mL/min (250 x 4.6 mm column) and 2.5 mL/min (250 x 10 mm

column). Injected amounts were depended on the column used and concentration of injected

sample.

4.2.6 NMR spectroscopy

All NMR spectra of extracts and pure compounds were recorded on Bruker Avance 300 DPX

NMR spectrometer operating at 300 MHz (1H) and 75 MHz (

13C). Spectra were processed

using Bruker TopSpin software. They were calibrated to the residual solvent signals with

resonances at δH/C 3.35/49.0 for CD3OD and δH/C 7.26/77.0 for CDCl3.

From DEPT135 experiments the multiplicity of the carbon atoms could be derived.

Additionally, for structure confirmation and assignments the following two-dimensional

NMR techniques were applied:

1H-

1H-COSY

1H-

13C-HSQC

1H-

13C-HMBC.

4.2.7 Mass spectroscopy

LC/MS

LC/MS measurements were obtained employing an Applied Biosystem system consisting of

an Agilent 1100 HPLC system and a MDS Sciex API 2000 mass spectrometer equipped with

an API-ESI source. Due to mass measurement refining, the samples were purified previously

by the integrated HPLC system. Macherey-Nagel Nucleodur 100-5-C18 (5 µm, 125 x 2 mm)

column was used, applying a 2 mM ammonium acetate buffered MeOH/ H2O gradient elution

system, increasing methanol from 10 to 100% over 20 min, holding steady for 10 min at flow

36

rate 0.25 mL/min. Separation was monitored by a photodiode array detector (200-600 nm)

and a mass detector (100-2000 m/z). Masses above 2000 m/z could only be detected by their

doubly charged pseudomolecular ion [M+2H]2+

.

HR-EI-MS

High-resolution electron impact mass spectra (HR-EI-MS) were recorded on a ThermoQuest

Finnigan Mat 95 XL.

GC/MS

GC/MS analyses were performed using a Perkin Elmer AutoSystem XL gas chromatograph

linked to a Perkin Elmer Turbomass mass spectrometer. Chosen parameters were adapted

from Ten and co-workers who proposed a GC/MS analytical procedure for the identification

of indol-based auxins (27). Samples (1 μL of a 1 mg/mL concentrated solution) were injected

by an autosampler into a PE-1 capillary column (30 m x 0.32 mm i.d., 0.25 μm film thickness;

split value 1:19). The temperature program started isotherm for five min at 50 oC and was

increased to 250 oC by 2

oC/min and hold for 15 min. Helium was used as carrier at a constant

flow rate of 2.0 mL/min and the injector, transfer line and source temperature were 250, 180

and 180 oC, respectively. Mass spectra were acquired from 2 to 120 min after injection at an

electron energy of 70 eV and from 35 to 650 atomic mass units at 0.5 s per scan. Samples

were first injected in their free form but gave no signal. Assuming that the indole derivatives

did not possess a sufficient volatility, fractions F5 and F6 were silylated to increase their

lipophilicity and therefore their volatility.

Derivatization. The dry sample (2.4 mg of fraction F5 and 1.9 mg of fraction F6,

respectively) was taken up into a small volume of acetone and transferred to a derivatization

vial and evaporated under a stream of nitrogen. TMS-derivatives were prepared by adding

200 µL TSIM (N-trimethylsilylimidazole) as silylating reagent followed by heating at 80 oC

for 1 h of the sealed vial in the drying oven.

37

5. Results

5.1 Application of the genomic mining method “bioinformatic prediction

and screening for physicochemical properties” to Pss B728a

In order to track down the natural products encoded by the diverse gene clusters, at first their

resulting chemical structure had to be predicted. In a second step their corresponding physical

properties i.e. mass and UV profile has to be determined to identify them by LC/MS

techniques. Hereby, the MS and UV data of the five compounds known to be produced by Pss

B728a were taken from the literature. For gene clusters representing orthologs, their resulting

chemical structure and physico-chemical data were taken as a starting point. In the case of the

presence of orphan gene clusters, based on bioinformatical analyses, tentative structures were

proposed and their corresponding UV and MS data deduced thereof. For the latter value, in

some cases only a more or less narrow mass range can be formulated due to limitations of the

bioinformatic prediction. Table I summarizes the physico-chemical properties of the target

compounds.

5.1.1 Bioinformatic prediction of the orphan compounds and deduction of their physico-

chemical properties

Orphan pyoverdin (Psyr1944-1962)

Gene cluster Psyr1944-1962 showed the characteristic elements of a typical pyoverdin

biosynthesis pathway (28). The open reading frame Psyr1945 codes for the dihydroxy-

quinoline-based chromophore (29) while the seven NRPS module containing cluster

Psyr1957-1960 is responsible for the assembly of the peptidic side chain. Investigation of the

substrate specificity of their corresponding adenylation domains enabled the suggestion of the

resulting peptide backbone (Fig. 17). The small dicarbonic acid fused to the N-terminus of the

molecule could not be predicted bioinformatically but can be empirically encircled to five

dicarbonic acids. Depending on this side chain, the resulting molecule was expected to show a

mass of 1109.4 – 1138.4 m/z or in its ferrated form a range of 1165.4 to 1194.4 m/z.

Intriguingly, an already existing pyoverdin matched exactly the bioinformatic prediction

which was isolated from P. syringae pathovars and P. viridiflava (30).

38

Psyr1957

A TC A TC A TC A TC

Lys Asp1 Thr1 Thr2

A TC A TC A TC

Ser1 Asp2 Ser2

Te

Psyr1959

E E

Psyr1958 Psyr1960Psyr1945

PvsA/PvdL analog encoding the

dihydroxyquinoline-chromophore

N+

NH

ONHHO

HO COOH

O

HN

NH2

OCOOH

NH

HN

O

O

NH

HO

OH

O

NH

OHHN

COOH

OO

HN

COOH

OH

Lys

Asp1Thr1Thr2

Ser1

Asp2

Ser2

Fig. 17: Bioinformatic prediction of the pyoverdin-structure encoded by Psyr1944-1962.

Orphan octalipopeptide (Psyr2576-2577)

Psyr2576-2577 consists of two open reading frames representing eight typical NRPS

modules. Bioinformatic analysis of the substrate-specificity of their adenylation domains led

to the resulting peptide-sequence Leu-Leu-Gln-Leu-Thr-Ile-Leu-Leu (Fig. 18).

A TC A TC A TC A TC

Leu Leu Gln Leu

A TC A TC A TC

Thr Ile Leu

TeA TC

Leu

Psyr2576 Psyr2577

HN

O

NH

HN

O

NH

H2N

O

OH2N

O

OH

HN

O

NH

O

HN

O

COOH

Fig. 18: Bioinformatic prediction of the resulting peptide encoded by Psyr2576-2577.

39

The peptide chain corresponding to the octalipopeptide gene cluster was completely

predictable and would give a mass of 925.6 m/z. Despite the absence of an appropriate fatty

acid synthase or acyl transferase clustered nearby the considered gene cluster, the resulting

peptide was – based on empirical knowledge - also expected to be fused N-terminally to a 3-

hydroxy fatty acid ranging from five to 16 carbons in lenghts because every other peptide ever

obtained from pseudomonads usually bears such a functionalization. Therefore, the resulting

lipopeptide shows a mass range of 1040 to 1180 m/z.

Orphan hexalipopeptide (Psyr3722)

Open reading frame Psyr3722 contains six NRPS modules encoding the sequence Leu-Ser-

Lys-Val-Ala-Ser with a theoretical mass of 603.4 m/z (Fig. 19). Considering the presence of

an N-terminal 3-hydroxy fatty acid ranging from five to 16 carbons, the hexalipopeptide of

interest shows a mass range of 703.4 to 857.6 m/z.

H2N

O

HN

O

OH

NH

O

HN

NH2

O

NH

O

HN COOH

OH

A TC A TC

Leu Ser

A TC A TC A TC

Lys Val Ser

TeA TC

Ala

Psyr3722

Fig. 19: Bioinformatic prediction of the resulting peptide encoded by Psyr3722.

Orphan polyketide (Psyr4311-4314)

The cluster consists of three PKS genes (Fig. 20). Striking is the absence of a thioesterase and

acyltransferases (AT), typical for type I polyketide synthases. Especially the latter fact could

point to either a nonfunctional cluster or to the presence of a trans-AT gene which however

must be clustered elsewhere in the genome. The bioinformatic prediction of the resulting

chemical structure led to a pentaketide with a theoretical mass of 229.1 m/z. Due to

unpredictable post-PKS tailoring reactions, the proposed structure has to be considered as

tentative.

40

Psyr4312

KSKR ACP KS ACP KR

Psyr4313

ACP KS ACP KS

Psyr4314

KS KR ACP KS ACP

Psyr

4311

ACP

OO

O

O

OH

O

OOOHO

OH

OOOHO

Fig. 20: Bioinformatic prediction of the resulting polyketide encoded by Psyr4311-4314.

Orphan hybrid PKS / NRPS gene cluster (Psyr1792-1794)

The mixed gene cluster contains in summary six NRPS modules coding for the sequence Arg-

Pro-Cys-Leu-Ile-Pro, whereby the activated cysteine will get cyclized to a thiazolin ring

system by the cyclization domain ´Cy´ present in ORF Psyr1792 (Fig. 21). The polyketidic

moiety of the hybrid gene cluster adds presumably two carbons to the C-terminal end of the

resulting molecule, yielding a mass of 693.4 m/z.

A T A TC A T KSA TC

Arg Pro Cys Leu

A TTe AC

Ile Pro

Te

Psyr1792

Cy

Psyr1793

KR

Psyr1794

H2N

HN

H2N NH

O

N

S

N

O

NH

O

HN

O

N

HO

Fig. 21: Bioinformatic prediction of the resulting peptide encoded by Psyr1792-1794.

Table I: Physico-chemical properties of natural products produced or presumably produced by Pss B728a.

gene cluster compound name mass M [m/z] UV maxima λmax [nm] polarity localization

Psyr1944-1962 pyoverdine 1109.4 – 1138.4

1165.4 to 1194.4 (ferrated)

free ligand: 373,

ferrated: 403

polar extracellular

Psyr2583-2589 achromobactin 591.2 195 polar extracellular

Psyr1621-1622 N-3-oxo-L-homoserine lactone 213.1 n.a. lipophilic extracelluar

Psyr0009 N-(3-OH-7-cis-tetradecenoyl)

homoserine lactone

325.2 n.a. lipophilic extracellular

Psyr0334 ectoine 142.1 240 polar intracellular

Psyr2614-2616 SP22Phv A 2129.2 or [M+2H]2+

1065.6 ~210 amphiphilic extracellular

SP22Phv B 2157.2 or [M+2H]2+

1079.6 ~210 amphiphilic extracellular

Psyr2608-2611 syringomycin E 1224.6 ~210 amphiphilic extracellular

syringomycin G 1252.6 ~210 amphiphilic extracellular

Psyr1704-1706 syringolin A 493.3 n.a. but detectable at 206 nm medium

polar

extracellular

Psyr2549-2555 phaseolotoxin 531.2 n.a. polar extracellular

Psyr5011 mangotoxin ~188-234 n.a. polar extracellular

Psyr1536-1537 indole-3-acetic acid 175.2 220, 278 medium

polar

extracellular

Psyr2576-2577 orphan octalipopeptide 1039.7 – 1179.9 ~210 amphiphilic extracellular

Psyr3722 orphan hexalipopeptide 703.4 – 857.6 ~210 amphiphilic

Psyr1792-1794 orphan NRPS/PKS compound 693.4 ~210 amphiphilic

Psyr4311-4314 orphan PKS gene cluster 229.1 n.a. lipophilic

n.a. = not available

42

5.2 Seven-days-screening studies

After the translation of the genetical code into actual physico-chemical data, Pss B728a was

investigated for the expression and production of the identified target compounds on the

metabolome level. Since expression of defined secondary metabolites is dependent on the

growth phase in which bacteria are at the certain time (e. g. exponential, stationary, or death

phase), accessible substrates and environmental conditions (e. g. temperature, shaking),

several seven-days-screening studies were initiated, using various culture conditions. Since

most of the predicted metabolites were expected to be secreted, mainly supernatants were

extracted and subsequently screened for the predicted mass ranges and UV profiles of the

target structures employing LC/MS techniques.

5.2.1 Nutrient broth with glycerol (NBgly)

NBgly is considered as a rich medium, providing all necessary precursors (e.g. amino acids,

carbon source) for secondary metabolites production. It is a general purpose medium used for

cultivating a broad variety of microorganisms with non-exacting nutritional requirements and

is therefore a common media for growing pseudomonads. Thus, it was our first choice for

metabolite profiling.

43

Fig. 22: HPLC chromatograms (UV 210-215 nm) of a seven-day-screening study in NBgly

medium.

Metabolite profiling over seven days of Pss B728a using NBgly medium showed the

excretion of a substance at Rt = 16.5 min which was however produced unsteadily and only

prominent on day one and seven (Fig. 22). This peak showed a mass of 463 m/z and UV

maxima at 224 and 280 nm. For the polar compounds, formed on day one to six and eluting

between 3 and 8 min, no significant masses could be detected.

44

5.2.2 King’s B medium (KB)

KB medium was proposed by King (31) in 1954 for the detection and enumeration of P.

fluorescens and other fluorescent bacteria in drinking water. It got established as a rich

standard medium, typical for pseudomonads cultivation. The components potassium sulphate

and magnesium chloride in the medium are known to enhance the formation of pyoverdines in

fluorescent pseudomonads. The experiment was applied in order to see the differences in

profiles of metabolites dependent on media composition.

Fig. 23: HPLC chromatograms (UV 210-215 nm) of a seven-day-screening study in KB

medium.

The metabolite profiling of Pss B728a in KB medium revealed one major peak at a retention

time of 8 min which builds up constantly over time in a linear fashion (Fig. 23). The extracted

mass spectrum at Rt = 8 min shows a pseudomolecular ion [M+H]+ at 176 m/z and UV

maxima at 222 and 280 nm. Database search for these spectral data indicated indole-3-acetic

acid.

45

5.2.3 Davis minimal broth (DMB)

The third typical media for growing pseudomonads is DMB. It contains minimal amounts of

nutrients for bacterial growth. Thus, bacteria may produce some other secondary metabolites

related to adaptation for unfavorable conditions. The screening was therefore applied in order

to observe differences in profiles of metabolites dependent on media composition.

Fig. 24: HPLC chromatograms (UV 210-215 nm) of a seven-day-screening study in DMB

medium.

Metabolite profiling over seven days of Pss B728a showed the excretion of two substances,

eluting at 8 and 12 min. Both compounds are produced unsteadily (Fig. 24). While the peak at

8 min refers to IAA (176 m/z), the peak at Rt = 12 min represents with 213 m/z and an UV

maximum at 211 nm most likely the bacterial signaling compound N-3-oxo-L-homoserine

lactone.

46

5.2.4 SRM medium with arbutin and fructose (SRM-AF)

Several reports described that the expression of virulent genes of P. syringae strains is

strongly temperature dependent. It could be demonstrated for P. syringae pv. glycinea that the

synthesis of toxic exopolysaccharides is maximized at 18 oC, which is why P. syringae strains

are called typical ´cold weather´ pathogens (32). A similar effect can be envisioned regarding

the expression of secondary metabolites.

Furthermore, it has been reported in a number of cases that several genes of P. syringae

strains are induced during colonization of leaf surface, indicating that the bacterium needs to

sense plant surface structures or signal molecules for a gene expression shift (33). Gross and

coworkers were able to prove that the production of the lipopeptides syringomycin and

syringopeptin is coordinated in response to the plant signal molecule arbutin (34, 35). Arbutin

is naturally present in the leaves of some plants and incorporated into an artificial medium it

mimics the natural plant environment.

Since recently also the peptide syringolin, whose biosynthetic pathway is also present in the

Pss B728a genome, was isolated from Pss SM using an arbutin-containing medium without

shaking, a static batch was also considered a promising approach (36).

Therefore, the strategy was to change not only the carbon source (i.e. glucose instead of

glycerol) and the addition of a signaling compound, but also to decrease the cultivation

temperature and to test the different behavior of shaked and static cultures. Hence, two

screening studies were applied with SRM-AF medium, under shaken and static conditions,

respectively.

The experiment revealed that there was no growth of bacteria. A possible explanation is

provided by the observed color change of the medium during the sterilization process from

colorless to red-brown. It can be envisaged that this change in color was due to the

caramelization of sugars which were obviously not sufficiently bioavailable anymore to the

bacteria in that form.

5.2.5 Modified SRM medium with arbutin and fructose (SRM-HG)

In this experiment the medium was buffered with a potassium phosphate system and the

conditions of medium sterilization were changed in order to protect sensitive solutes. The goal

of the study remained the same as in SRM-AF experiment, however, this time, only one set of

screening cultures were grown with shaking.

47

Fig. 25: HPLC chromatograms (UV 210-215 nm) of a seven-day-screening study in SRM-

HG medium.

Beginning with the second cultivation day, the formation of two new compounds can be

observed at LC retention times 27.3 and 29.0 min with m/z 1072 (Fig. 25, 26) and 1086 (Fig.

25, 27), respectively. Intriguingly, these substance peaks were in the expected mass range of

bioinformatically predicted lipopeptides. Closer inspection of the involved gene cluster that

came into consideration to produce compounds in the mass range 1072-1086 m/z pointed to

the gene cluster Psyr2576-2577, which is predicted to code for a putative octalipopeptide. The

other lipopeptide biosynthesis pathway whose products come close to the considered mass

range is Psyr2608-2611, coding for syringomycins E (1225 m/z) and G (1253 m/z). However,

due to their higher mass and the absence of a chlorine isotopic pattern, this compound class

can be excluded. Furthermore, the detected masses did also not match the pseudomolecular

[M+2H]+ ions possibly produced by the syringopeptins SP22Phv A or B.

48

Fig. 26: Extracted ESI-MS spectra at Rt = 27.3 min of the crude extract LC/MS screening of Pss

B728a grown in SRM-HG medium. The upper spectrum shows the pseudomolecular ion in the

positive ([M+H]+ = 1073.2 m/z) and the bottom spectrum in the negative mode ([M-H]

- = 1070.9 m/z).

49

Fig. 27: Extracted ESI-MS spectra at Rt = 29.0 min of the crude extract LC/MS screening of Pss

B728a grown in SRM-HG medium. The upper spectrum shows the pseudomolecular ion in the

positive ([M+H]+ = 1087.2 m/z) and the bottom spectrum in the negative mode ([M-H]

- = 1085.4 m/z).

50

5.3 Large scale studies

Relating to screening scale studies we upscaled the cultivation of bacteria in Nutrient broth

with glycerol (NBgly) and King’s B medium (KB).

Upscaling of the bacteria culture in SRM-HG medium is a very promising experiment to be

done in the future.

5.3.1 Nutrient broth with glycerol (NBgly)

A standard extraction scheme was applied for growing of bacteria in NBgly followed by

extraction and separation procedures. The experiment did not lead to isolation of any

biologically interesting compounds. The overall scheme of experiment is shown on Fig. A1

(Appendix).

5.3.2 King’s B medium (KB)

Pss B728a was cultivated on a 6 L (4 x 1.5 L) scale in King’s B medium. Cell material was

separated from medium by centrifugation. The resultant supernatant was acidified with TFA

and extracted with ethyl acetate. Subsequently, the extract was analyzed directly by LC/MS.

Furthermore, the extract was separated into eleven fractions employing VLC techniques (Fig.

33). The obtained fractions are listed in Table II.

Table II: Fractions and solvent mixtures of VLC.

Fraction Solvent mixture Volume

A 100% PE 300 mL PE = petroleum ether

B EtOAc/PE = 10/90 300 mL EtOAc = ethyl acetate

C EtOAc/PE = 20/80 300 mL ACN = acetonitrile

D EtOAc/PE = 40/60 300 mL H2O = water

E EtOAc/PE = 60/40 300 mL

F EtOAc/PE = 80/20 300 mL

G 100% EtOAc 300 mL

H ACN/EtOAc = 25/ 75 300 mL

I 100% ACN 300 mL

J 100% H2O until yellowish fraction reaches

the frit

K 100% H2O until all yellowish fraction is

washed off the column

51

All fractions were analyzed by 1H NMR, using either chloroform-d1 or methanol-d4 as a

solvent. Fractions A, B and C contained solely lipids and were of no further interest. All other

fractions were analyzed by LC/MS afterwards. Regarding the results, fractions F, G, H and I

were monitored by RP-HPLC using column A and a linear gradient of 40:60 MeOH / H2O

(0.05% TFA) to 100% MeOH over a period of 30 min, followed by isocratic elution at 100%

MeOH for an additional 30 min (UV monitoring at 215 nm). Fraction F was further separated

into six subfractions by RP-HPLC (Fig. 28) using column B and the same mobile phase

conditions and detection wave length as employed previously (Fig. 33).

Fig. 28: HPLC spectrum of fraction F and designation of collected subfractions.

The obtained subfractions F1-F6 were analyzed by LC/MS and 1H NMR. Analysis of the

1H

NMR spectra indicated the presence of indoles in fractions F2, F4, F5 and F6 (Fig. A2, 29,

A3, A4). Since fractions F4 (compound 1) and F2 (compound 2) were obtained pure and in a

sufficient quantity, a complete structure elucidation was performed by NMR.

52

Indole-3-acetic acid methyl ester (1) structure elucidation

1

Table III: NMR spectral data for indole-3-acetic acid methyl ester (1) in CD3OD.

position δC δH, mult., J [Hz] COSY correlations HMBC correlations

2 124.7 CH 7.18, s 3, 3a, 7a, 8

3 108.5 qC

3a 128.6 qC

4 112.3 CH 7.37, d, 7.9 5 3a, 5, 6

5 122.5 CH 7.13, dd, 7.2, 7.9 4, 6 6, 7, 7a

6 119.9 CH 7.04, dd, 7.3, 7.5 5, 7 3a

7 119.4 CH 7.54, d, 8.0 6 3, 5, 7a

7a 138.0 qC

8 31.9 CH2 3.79, s 2, 3, 3a, 9

9 174.8 qC

10 52.4 CH3 3.70, s 9

Fig. 29: 1H NMR spectrum of compound 1.

53

The 1H NMR spectrum (Table III, Fig. 29) of compound 1 showed typical resonances in the

range of 7 - 7.5 ppm and a characteristic splitting pattern (2 doublets, 2 triplets and 1 singlet

resonance), indicative for a mono-substituated indole moiety. The UV spectrum (λmax = 220

and 280 nm) pointed also to an aromatic system (Fig. A7).

Fig. 30: 13

C (bottom) and DEPT135 (top) NMR spectrum of compound 1.

The 13

C NMR spectrum (Table III, Fig. 30) contained a total of eleven resonances (1x CH3,

1x CH2, 5x CH, 4x C, multiplicity determined by DEPT135, see Fig. 30) and supported the

presence of an indole moiety due to characteristic resonances for a 3-substituated indole

skeleton (37). Interpretation of 1H-

1H-COSY (Table III, Fig. 31),

1H-

13C-HSQC (Fig. A5) and

1H-

13C-HMBC (Table III, Fig. 32) confirmed the indole backbone.

This left the remaining signals detected in the 1H and

13C NMR spectra, i.e. a methylene

group (H2-8 at δH 3.79 / δC 31.9), an ester group (C-9 at δC 174.8) and a methoxy group (CH3-

10 at δH 3.70 / δC 52.4) to be assigned. Cross peaks in the HMBC spectrum, this time observed

between H3-10 and C-9 and between H2-8 and C-3 as well as C-9 led to the deduction that

they form a side chain (CH2-CO-OCH3) connected via C-3 to the indole skeleton. The ESI-

MS analysis (Fig. A8) of 1 gave an [M-H]- peak at 188 m/z, consistent with a molecular mass

of indole-3-acetic acid methyl ester (M = 189 m/z).

54

Fig. 31: 1H-

1H-COSY spectrum of compound 1.

55

Fig. 32: 1H-

13C-HMBC spectrum of compound 1.

56

Indole-3-acetic acid (2) structure elucidation

2

Table IV: NMR spectral data for indole-3-acetic acid (2) in CD3OD.

position δC δH, mult., J [Hz]

2 124.6 CH 7.20, s

3 108.9 qC

3a 128.7 qC

4 112.2 CH 7.38, d, 8.0

5 122.4 CH 7.13, ddd, 6.9, 8.5,

1.1

6 119.8 CH 7.04, ddd, 7.0, 7.9,

1.1

7 119.4 CH 7.57, dd, 7.9, 1.1

7a 138.0 CH

8 32.0 CH2 3.77, s

9 176.5 qC

Analysis of the NMR data (Table IV, Fig. A2 and A6) indicated that compound 2 shared

many structural features with indole-3-acetic acid methyl ester (1). The only significant

differences between their NMR features were the absence of C-10 and the upfield shift of the

quaternary carbon C-9 in the 13

C NMR spectra of compound 2, respectively. Differences were

due to the absence of the ester methoxy group. In addition, ESI-MS analysis (Fig. A9)

revealed a mass difference of 14 mass units ([M-H]- = 174 m/z) which proved compound 2 to

be indole-3-acetic acid.

The molecular structure of the compounds obtained in fractions F5 and F6 could not be

determined due to insufficient amount of sample material after repurification. Attempts to

elucidate these indolic compounds by HR-EI-MS and GC/MS methods failed (see appendix

Fig. A10, A11, A12, A13, A14, A15).

57

Fig. 33: Extraction and isolation procedure of large scale Pss B728a cultivation in King’s B

medium (KB).

58

6. Discussion

The aim of this study was the isolation and characterization of any putative secondary

metabolite encoded by the identified gene clusters in the Pss B728a genome.

This study was focused on only one of currently four established strategies to discover the

products of orphan biosynthesis pathways. The approach using bioinformatic prediction and

screening for deduced physico-chemical properties was favored due to relatively low costs, its

feasibility and quick implementation. Moreover, the alternative genomisotopic approach is

more expensive and to date limited to orphan NRPS and hybrid NRPS/PKS gene clusters. In

addition it was not likely to establish heterologous expression or a multitude of gene

inactivation studies combined with comparative metabolic profiling in the given timeframe.

This study began with the analysis of data for orphan gene clusters. It was tried to predict,

either by use of bioinformatic tools or published data, the properties of each putative

secondary metabolite as accurate as possible in order to establish optimal conditions for

bacterial growth and isolation of metabolites.

The cultivation of bacteria was performed under conditions where most of the predicted

metabolites were supposed to be produced. Hereby, it is known that in order to obtain

compounds with special physiological roles i.e. siderophores and ectoine, special conditions

for their production and isolation are required. Iron depletion conditions combined with a

specific isolation scheme should be applied for isolation of siderophores, while for the

production of ectoine high salt concentrations in the medium are needed. As this study was

primarily focused on the isolation of compounds of potential therapeutical use e.g. the

putative lipopeptides, it was taken in account that it was not likely to isolate siderophores and

ectoine under the chosen conditions. During the study few different media were used and

cultivation conditions modified to broaden the spectrum of produced secondary metabolites.

The choice and rationale of the applied media and cultivation conditions are described in

details in the “Results” part.

For the isolation of secondary metabolites a standard isolation scheme was carried out. As

most of metabolites are excreted extracellular only the supernatant was investigated. This

approach reduced the complexity of the obtained extracts and therefore facilitates the isolation

process.

59

During the study two auxins could be isolated, indole-3-acetic acid (IAA) and its methyl ester.

In addition, culture conditions conducive for the production of two predicted lipopeptides

could be established. These finding paved the way for promising experiments in the future,

e.g. to cultivate Pss B728a under corresponding conditions in large scale, to discover the

corresponding lipopeptides and elucidate their structure.

Indole-3-acetic acid (IAA) and methyl ester of indole-3-acetic acid

Indole-3-acetic acid (IAA) is an auxin (gr. auxano, to grow), a plant growing substance which

was discovered in the beginning of the twentieth century (38). It is involved either directly or

indirectly in virtually every aspect of plant development (39). More recently it has been found

that several microorganisms produce IAA identical to that found in plants (40). Although IAA

was the first substance identified as an auxin and is the most common form of

phytohormones, other compounds with auxin activity occur in plants as well. Since now

numerous indolic and structurally related compounds with auxin activity have been isolated

from plants, bacteria and even humans (39).

The ability to synthesize IAA is widespread among a diverse set of microorganisms. Several

of them, including Pseudomonas syringae pv. syringae, are involved in plant pathogenesis,

while other provide benefit by stimulating plant growth. The question is what benefits

bacteria have by producing IAA. Of course, by stimulation of plant growth beneficial

microorganisms can also increase the production of plant metabolites and utilize them for

their own growth. Furthermore, benefits may include inhibition of plant defense enzymes and

acceleration of bacterial invasion, or detoxification of tryptophan analogues, which are

harmful to bacterial cells (40).

IAA and other auxins play an essential role in plants by stimulating cellular expansion and by

increasing transcription of several genes involved in cell division (41). Among the first known

biological effect of auxins is growth inhibition of primary root and stimulation of lateral root

initiation (39). The effect of auxin on a plant depends on the amount of the hormone that is

available to the plant. Optimal levels enhance growth, while supraoptimal levels results in a

disease response. An important consideration in predicting whether bacterial IAA will

stimulate beneficial growth or pathogenesis in a plant is the level of auxin synthesized by the

plant itself. Endogenous levels of IAA may be suboptimal or optimal for growth. Exogenous

addition of auxin produced by bacteria can therefore lead to either optimal or supraoptimal

levels, resulting in the induction of plant growth or pathogenesis, respectively. Supraoptimal

60

levels of IAA lead to induction of gall formation or circular, brown necrotic leaf lesions, as in

the case of Pseudomonas syringae pv. syringae.

Phytohormones such as IAA do not act alone but interact with one another in a variety of

complex ways (40). The so-called hypersensitive response/pathogenicity (hrp) genes and the

genes for syringomycin production have been shown to be involved in pathogenicity of Pss.

IAA may affect pathogenicity indirectly by acting on one or more of these pathways (42).

Plants produce active IAA by releasing it from inactive conjugates and by de novo synthesis.

A number of plant enzymes can liberate IAA from conjugates, and multiple pathways exist

for de novo IAA synthesis in plants.

The majority of IAA in plants is conjugated to sugars, amino acids, peptides or proteins via

ester or amide linkage. Therefore IAA conjugates may serve as reservoirs of inactive IAA that

can be hydrolyzed to provide the plant with active hormone.

By de novo synthesis in both tryptophan-dependent and tryptophan-independent pathways

IAA can be synthesized (43) in chloroplasts as well as in the cytoplasm (38). Tryptophan-

independent biosynthesis most likely directly uses indole to synthesize IAA. It is suggested

that IAA can be synthesized from tryptophan in four different pathways that are distinguished

by the intermediates indole-3-pyruvic acid (IPA), indole-3-acetamide (IAM), tryptamine

(TAM) or indole-3-acetaldoxime (IAOx) (39). It is predicted, that bacteria synthesize IAA

from tryptophan by the same four pathways (40). Therefore, they will be discussed in detail

later. The extensions to which these pathways contribute to IAA biosynthesis in planta

require further investigation (39). It seems that the same plant uses different pathways for

IAA biosynthesis at different developmental stages, in different tissues, or under different

environmental conditions (43).

The level of IAA available to mediate a biological response in a cell is regulated by a number

of factors: biosynthesis, conjugation, transport, and degradation. According to those processes

levels of free IAA generally remain normal while conjugated IAA accumulates (38).

Bacteria use four different tryptophan-dependent IAA biosynthesis pathways, which are

generally named after an intermediate (Fig. 34). None of the pathways is yet defined to the

level of knowing each relevant gene, enzyme, and intermediate (44).

The indole-3-pyruvic acid (IPA) pathway is considered to be a major IAA pathway in

plants, but has also been detected in bacteria. Tryptophan (Trp) is converted to indole-3-

pyruvic acid (IPA) by a transaminase reaction, followed by enzymatic or spontaneous

61

decarboxylation to indole-3-acetaldehyde (IAAld) (40). Direct conversion of Trp to IAAld by

Trp side-chain oxidase has also been detected (41). IAAld is converted to IAA in the final

step by IAAld oxidase.

In the indole-3-acetamide (IAM) pathway Trp monooxygenase catalyzes the synthesis of

indole-3-acetamide (IAM) from Trp. IAM hydrolase then converts IAM to IAA (40).

The tryptamine (TAM) pathway could also convert Trp to IAA. The initial metabolism of

Trp to tryptamine (TAM) is catalyzed by Trp decarboxylase. In the second step a flavin

monooxygenase-like enzyme oxidizes TAM to N-hydoxyl-tryptamine which could be

dehydrogenated to indole-3-acetaldoxime (IAOx) or dehydrogenated and hydrolized to

IAAld. Enzymes that catalyze these conversions have not been identified.

In the indole-3-acetaldoxime (IAOx) pathway Trp is oxidized directly to IAOx by

monooxigenases. IAOx is then converted to indole-3-acetonitrile (IAN) either directly or via

indole-3-methylglucosinolate. The nitrilase converts IAN to IAA (44).

NH

OH

NH2

O

Trp

Trpdecarboxylase

NH

NH2

tryptamine(TAM)

NH

HN

OH

N-hydroxyl-tryptamine

flavin monooxygenase-like enzyme

?

NH

N

OH

indole-3-acetaldoxime(IAOx)

?

NH

O

indole-3-acetaldehyde(IAAld)

IAAld oxidase

NH

OH

O

indole-3-acetic acid(IAA)

NH

OH

O

O

indole-3-pyruvic acid(IPA)

Trptransaminase

IPAdecarboxylase

Trpside-chain

oxidase

NH

C

N

indole-3-acetonitrile(IAN)

nitrilase

?

NH

N

S-glucose

OSO3-

NH

NH2

O

indole-3-acetamide(IAM)

Trpmonooxygenase

IAMhydrolaseindole-3-methylglucosinolate

monooxygenase

Fig. 34: Potential pathways of tryptophan-dependent IAA biosynthesis.

62

The level of the expression of IAA in bacterial cells depends on the biosynthesis pathway, the

location of IAA biosynthesis genes, and the presence of enzymes that convert an active, free

IAA into an inactive, conjugated form. Indole-3-acetamide pathway is constitutive in most of

the microorganisms studied, while the indole-3-acetonitrile and indole-3-pyruvic acid

pathways are usually inducible by intermediates or precursors. The genes involved in

biosynthesis of IAA could be located either on a plasmid or the bacterial chromosome.

Conjugation of free IAA may provide an efficient means by which the bacteria can deliver

continuous concentrations of IAA to host tissues. Furthermore many factors such as

temperature, pH, and the availability of nutrients can affect the synthesis of IAA by bacteria.

It is suggested that a microbe may selectively employ a particular IAA biosynthesis pathway,

of the multiple pathways of which is capable, according to its environment (40).

Pss B728a has an operon for IAA biosynthesis via IAM (indole-3-acetamide) pathway, which

consists of genes for tryptophan monooxygenase, iaaM (Psyr1536) and indole-3-acetamide

hydrolase, iaaH (Psyr1537) (15). These genes were first described, cloned and sequenced

from Pseudomonas syringae pv. savastanoi. The deduced amino acid sequences of iaaM and

iaaH from Pss B728a are more than 90% identical to their respective homologs in

Pseudomonas syringae pv. savastanoi (42).

Pss B728a might also synthesize IAA via IAOx (indole-3-acetaldoxime) pathway. A cluster

found includes a homolog of Rhodococcus globerulus aldoxime dehydratase (Psyr0006),

which could convert indole-3-acetaldoxime into indole-3-acetonitrile, and nitrilase

(Psyr0007), which would catalyze final conversion of indole-3-acetonitrile to IAA (15).

The methyl ester of IAA, which has also been isolated during this study, is a product of IAA

metabolism with the same auxin activity as the free acid (45). It is thought to be a storage

form of IAA in higher plants and is a common metabolite in some phytopathogenic bacteria

(26). The corresponding gene responsible for the methylation is not clustered to the IAA

biosynthesis pathways and must be performed by general acting methyltransferases encoded

elsewhere.

The effort to isolate any of putative IAA precursors and therefore to confirm one of the

predicted pathways regrettably failed due to isolation of the intermediates in insufficient

quantity for spectral analysis. It is proven that pseudomonads produce higher amounts of

indoles when the precursor tryptophan is added to the cultivation medium (26). Since auxin

63

production is inducible in this way, in the future a promising experiment would be the

cultivation of Pss B728a in the presence of tryptophan. With this experimental design the

auxin production will be increased approximately 10 fold which makes the isolation of

biosynthesis intermediates in a higher yield more likely and therefore accessible for structure

elucidation.

64

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68

8. Appendix

Isolation scheme

Fig. A1: Extraction and separation procedure of large scale Pss B728a cultivation in Nutrient

broth with glycerol (NBgly).

69

NMR spectra

Fig. A2: 1H NMR spectrum of compound 2.

Fig. A3: 1H NMR spectrum of fraction F5.

70

Fig. A4: 1H NMR spectrum of fraction F6.

Fig. A5: 1H-

13C-HSQC spectrum of compound 1.

71

Fig. A6: 13

C (bottom) and DEPT135 (top) NMR spectrum of compound 2.

UV spectrum

Fig. A7: UV spectrum of indole-3-acetic acid methyl ester (1).

72

MS spectra

Fig. A8: ESI-MS spectrum of indole-3-acetic acid methyl ester (1).

Fig. A9: ESI-MS spectrum of indole-3-acetic acid (2).

73

Fig. A10: EI-MS spectrum of fraction F5 (underivatized).

Fig. A11: FAB-MS spectrum of fraction F5 (underivatized).

74

Fig. A12: EI-MS spectrum of fraction F6 (underivatized).

Fig. A13: FAB-MS spectrum of fraction F6 (underivatized).

75

TMS- reagent

Fig. A14: GC/MS run of the silylated fraction F5. The peak at 28.2 min corresponds to TMS-

glycerol and fits any of the known indole derivatives.

Fig. A15: GC/MS run of the silylated fraction F6. The peak at 28.2 min corresponds to TMS-

glycerol and fits any of the known indole derivatives.

TMS- reagent

TMS- reagent