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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.
4
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.
8
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.
10
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
11
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
12
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
13
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