4511 14 WS0910 -omics & Quorum sensing - uni-due.de · Metagenomics Molecular community...
Transcript of 4511 14 WS0910 -omics & Quorum sensing - uni-due.de · Metagenomics Molecular community...
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Environmental Microbiology
„-omics & Quorum Sensing“
Bettina Siebers
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Analyses of Microbial Communities
• I. Culture-dependent methods– Enrichment and Isolation
• II. Molecular (Culture-Independent)– (A) Viability and Quantification Using Staining
Techniques– (B) Genetic stains (FISH, chromosome painting, ISRT
fish)– (C) Linking specific genes to specific organisms
using PCR– (D) Environmental Genomics (Metagenomics)
• III. Measuring Microbial Activities in Nature– Radioisotopes (Fish-MAR), microelectrodes, stable
isotopes
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Linking specific genes to specific
organisms using PCR
16S rDNA based methods
Averaging methods (provide an overview of diversity but say nothing about the spatial arrangement in the original sample)
● Clone library
● Denaturing gradient gelelectrophoresis (DGGE)
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MetagenomicsMolecular community analysis
-Biodiversity of a single gene
versus
Metagenomics-All genes in the microbial community
„metagenome“-Aim: Detect as many genes as possible
and determine to which phylogenetic„scaffold“ they belong.
-Done by sequencing overlaps to the genes that include phylogenetic markers
(e.g. 16S rRNA genes)-Much higher costs but more in-depth
-Avoids „selectivity“ of PCR (primers), all genes are sequenced whether they are
amplifiable or not
Great Potential of Metagenomics!-Detects new genes in known organismsand known genes in new organismsm.
-e.g. genes encoding ammoniamonooxygenase „key enzymes of ammonia-
oxidizing Bacteria“ in Archaea (theseArchaea have never been described!)
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Mutations and Evolution• Mutations are important factors of
evolution, because they induce a changein the genome of an organism.
• Environmental conditions decide, which of the spontaneous mutations are positiv ornegativ (= selection).
� Transformation� Transduction � Conjugation
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Universal tree of life
Bacteria Eukarya Archaea
Cyano
ba
cte
ria
Pro
teoba
cte
ria
An
ima
lia
Fu
ng
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Pla
nta
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Cre
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aeo
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ota
Bacteria Eukarya Archaea
Cyano
ba
cte
ria
Pro
teoba
cte
ria
An
ima
lia
Fu
ng
i
Pla
nta
e
Cre
na
rch
aeo
ta
Eu
rya
rchae
ota
Pre-genomics Post-genomics
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Steps in genomic sequencing
I Library making– Small/Large-insert library from genome
II Production sequencing– Generate fragments to be sequenced– Perform sequencing reactions– Determine sequence
III Finishing– Assemble into continuous sequence– Fill gaps
IV Annotation
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Sizes of genomes & numbers of genes�The smallest prokaryotic genomes are the size of the largest viruses, and the largest prokaryotic genomes have more genes than some eukaryotes.
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Thermotoga maritima• Metabolic pathways and transport systems (genome analysis)
Hyperthermophile, Bacteria
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Genome information: What next?
Genome ( Jan 2009)
-Archaea 55 (101)
-Bacteria 776 (2328)
-Eukarya 100 (1015)
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Problems of genome annotation
● Identifying genes and regulatory regions in sequenced genomes is challenging
● Large fraction of genes encoding proteins of unknown function(hypothetical or conserved hypothetical proteins)
● Misannotations „errors“
-annotation procedure
-enzyme families, super- and suprafamilies
Homologs might catalyze different reactions [Gerlt & Babbit, 2000]
● 1,427 characterized enzymes in the protein database (EC #),
encoding genes are unknown
„Functional Genomics“: From gene to function
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Genomics provides a parts list
• Provides list of all parts
• Parts list in itself doesn’t say how the genome works
• Can use to get global picture
– e.g., RNA
expression
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Functional genomics
• Once we know the sequence of genes, we want to know the function
• The genome is the same in all cells of an individual, except for random mutations
• However, in each cell, only a subset of the genes is expressed
– The portion of the genome that is used in
each cell correlates with the cell’s
differentiated state
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„Functional Genomics“-omics= exploration of a special –ome field(-ome = as a whole, totality)Genomics = exploration of the gen-ome, all genes of an organism
-Transcriptomics (transcript)
-Proteomics (protein)
-Metabolomics (metabolit)
-Structural Genomics (protein structure)
-Interactomics (protein interaction)
-Metagenomics (Environmental Genomics, Ecogenomics or Community Genomics)
-Mutagenomics
-XYZ-omics
Metabolome
Proteome
Transcriptome
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Gene expression
The ability of a gene to produce a
biologically active protein (e.g. enzyme).
TTT CTTGTT AAT CAG CAT
AAA GAACAA TTA GTC GTA
5´3´
3´5´DNA
TRANSCRIPTION
TRANSLATION
UUU GUU AAU CAG CAU CUUmRNA
PROTEIN
5´ 3´
Phe Val Asn Gln His LeuH2N- -COOH
Stored information
Intermediary
Functionalunit
Brock
transcriptomics
proteomics
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„Functional Genomics“
Yanai 2002
Classical approaches:Analysis of gene (gene group) function,
involved in certain processes „hypothesis driven“
vs
„Molecular biology in 96-well format“Analysis of genome function
(all genes of an Organism)
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TranscriptomicsRNA Expression Analysis
Determining genomewide RNA expression levels
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Genomewide expression analysis
• Transcriptome: All transcripts (mRNA) in a special cell type under certain environmental/growth conditions or respectively developmental stage
• Goal: to measure mRNA levels of all genes in genome
• RNA levels vary with the following:
– Cell type
– Developmental stage
– External stimuli
• Snapshot of all transcribed genes
• Time and location of expression provide useful information as to gene function
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Spotted-microarray hybridization
• Control and experimental cDNA labeled (reverse transcription)
– One sample labeled with Cy3
– Other sample labeled with Cy5
• Both samples hybridized together to microarray
• Relative intensity determined using confocal laser scanner
• Laser beam excites each spot of DNA
• Amount of fluorescence detected
• Different lasers used for different wavelengths (Cy3/Cy5)
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cDNA Microarray hybridization
• Usually comparative
– Ratio between two samples
• Examples
– CO2 vs. sugar
– Drug treatment vs. no treatment
– log vs. stationary phase
mRNA
cDNA
DNAmicroarray
samples
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Analysis of hybridization
• Results given as
ratios
• Images use colors:
Cy3 = Green
Cy5 = red
Yellow
– Yellow is equal intensity or no change in expression
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Transcriptomics
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Microarray Data Mining
Hierarchical clustering of gene expression
-places genes n clusters with similar expression profile (Eisen et al. 1998)
-clustering implies co-regulation and may imply involvement in similar biological processes
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Proteomics
Using high-throughput methods to identify proteins and to
understand their function
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What is proteomics?
• An organism’s proteome
– A catalog of all proteins
• Expressed throughout life
• Expressed under all conditions
• The goals of proteomics
– To catalog all proteins
– To understand their functions
– To understand how they interact with each
other
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The challenges of proteomics
• Splice variants create an enormous diversity of proteins– ~25,000 genes in humans give rise to 200,000 to 2,000,000
different proteins
– Splice variants may have very diverse functions
• Variable expression profile; Proteins expressed in an organism will vary according to age, health, tissue, and environmental stimuli
• Proteomics requires a broader range of technologies than genomics
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Technologies for proteomics
• 2-D gel electrophoresis– Separates proteins in a mixture on the basis of their
molecular weight and charge
• Mass spectrometry– Reveals identity of proteins
• Protein chips– A wide variety of identification methods
• Yeast two-hybrid method– Determines how proteins interact with each other
• Biochemical genomics– Screens gene products for biochemical activity
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2-D Gel Electrophoresis
Lodish 5e
● First dimension: Isoelectric focussing (IEF)
- Cellular proteins are separated in the
first dimension by their isoelectric point (IEP) „charge“.
- Polyacrylamide gel (strip) with
immobilized pH gradient.
-Proteins will migrate until they reach
the pH where they lose their net
charge (= IEP).
● Second dimension: Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE)- Cellular proteins are separated by their molecular weight (denaturing conditions !).
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2-D Gel Electrophoresis
Lodish 5e
Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE)-Separation of denatured proteins according to their molecular weight
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2-D Gel Electrophoresis
Lodish 5e
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Differential in gel electrophoresis
• Label protein samples from control and experimental tissues– Fluorescent dye #1 for
control
– Fluorescent dye #2 for experimental sample
• Mix protein samples together
• Identify identical proteins from different samples by dye color
withbenzoicacidCy3
withoutbenzoicacidCy5
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Problems associated with 2-D gels
• Poor performance of 2-D gels for the following:
– Very large proteins
– Very small proteins
– Less abundant proteins (e.g. transcription
factors)
– Membrane-bound proteins
• Presumably, the most promising drug targets
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Mass spectrometry
• Measures mass-to-
charge ratio
• Components of mass
spectrometer
– Ion source
– Mass analyzer
– Ion detector
– Data acquisition unitA mass spectrometer
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Identifying proteins with mass spectrometry
• Preparation of protein sample– Extraction from a gel
– Digestion by proteases — e.g., trypsin
• Mass spectrometer measures mass-charge ratio of peptide fragments
• Identified peptides are compared with database
– Software used to generate theoretical peptide mass fingerprint (PMF) for all proteins in database
– Match of experimental readout to database PMF allows researchers to identify the protein
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A mass spectrum
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Stable-isotope protein labeling
• Stable isotopes used to label proteins under different conditions
• Variety of labeling methods– Enzymatic
– Metabolic
– Via chemical reaction
• Relative abundance of labeled and nonlabeled proteins measured in mass spectrum
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• iTRAQ (isobaric tag for relative and absolute quantitation)
• is a non-gel based technique
• identifies and quantifies proteins from different sources in one single experiment
• iTRAQ uses isobaric labels
• Varies only between the mass of ‘Reporter’ and ‘Balance’
Reporter Balance RXN
Isobaric
Reporter Mass Balance Mass
113114115116117118119121
192191190189188187186184
~~~~~~~~
Total
iTRAQ 8-Plex tag
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R BAL C
Reporter Balance Rxn group
R BAL C
R BAL CSAMPLE
Tandem MS Fragmentation
Reporter Ions
iTRAQ methodology
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Limitations of mass spectrometry
• Not very good at identifying minute quantities of protein
• Trouble dealing with phosphorylated proteins
• Does not provide concentrations of proteins
• Improved software eliminating human analysis is necessary for high-throughput projects
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Protein chips
• Thousands of proteins analyzed simultaneously
• Wide variety of assays
– Antibody–antigen
– Enzyme–substrate
– Protein–small molecule
– Protein–nucleic acid
– Protein–protein
– Protein–lipid
Yeast proteins detectedusing antibodies
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Biochemical genomics
• Genome of an organism is already known
• Approach
– Construct plasmids for all ORFs
• Attach ORFs to sequence that will facilitate purification
– Transform cells
– Isolate ORF products
– Test for biochemical activity
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Microfluidics
• Proteomics requires
greater automation
• Microfluidics: a “lab
on a chip”
– Microvalves and pumps allow control of nanoliter amounts
– Can control biochemical reactions
A microfluidics chip
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Microfluidics in action
loading compartmentalization
purgingmixing
500 µm
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Systems Biology
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Hierarchies of organization
• Level 1: genes, RNA,
proteins, metabolites
• Level 2: pathways
• Level 3: functional
modules
• Level 4: networks
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Parts, modules, networks
26" wheels,alloy rims,handcrafted aluminum frame,dual-suspension,downhill front fork,alloy crank,21-speed grip shift system,quick release seat, linear pull brakes,Shimano® derailers,crank and rear sprocket
Parts list Module Network
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Systems biology
• Goal: describe complex cellular systems
• Integrative approach (Bioinformatics, Mathematics, Biologists, Engineers etc.)
• Uses approach of systems engineering “Modeling”– Sees complex processes in terms of circuits
• Inputs
• Outputs
• Dynamics
• Testing the system– Simulations
– Perturbations
• “Communication”
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https://www.im.org/AAIM/Meetings/PastMeetings/2006/APDIM/PlenaryIV-BabyatskyMark-ANewScientificLiteracyforClinicians.pdf
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„Quorum Sensing“
Communication between Bacteria
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Quorum sensing
• Bacteria are able to communicate using signalling molecules released into the environment.
• Bacteria are able to sense the number of bacteria present (cell density) by the level of accumulation of signal molecules. – The more bacteria present the more signal.
• In this way, bacteria are able to regulate their gene expression in response to alterations in cell density
?From the bacterial point of view: Who are my neighbors and what are they going to do?
Modified, J. Czichos
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Quorum sensing
• Quorum sensing enables bacteria to co-ordinate their gene expression.
• Quorum sensing controls various different activities in different bacteria.– Luminescence
– Virulence
– Production of extracellular enzymes
– Plasmid transfer, genetic competence
– Antibiotic synthesis
– Motility
– Sporulation
– Symbiosis
– Biofilm development
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Quorum Sensing
• There are different types of quorum sensing mechanisms:– Peptides or modified peptides are the main
signalling molecule for Gram positive bacteria
– Autoinducer 1 – acylated homoserine lactone signals used by Gram negative bacteria for intraspecies communication.
– Autoinducer 2 – furanone signals used by Gram negative and Gram positive bacteria for interspecies communication.
– Autoinducer 3 – signal controlling virulence in Enterohemorrhagic E. coli.
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• using acylated homoserine lactones (AHL)
• Different gram negative bacteria produce different quorum sensing AI-1 signal molecules.
γ-butryolactone from Streptomyces griseus 3-oxo-C6-HSL from Vibrio fischeri
2-heptyl-3-hydroxy-4-quinolone from Pseudomonas aeruginosa
Autoinducer 1 - Quorum sensing
O
OOH
H
O
O O
HO
ON
H
O
N
H
HO
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Quorum sensing in Vibrio fischeri
• Quorum sensing was first discovered as a form of regulation of bioluminescence in Vibrio fischeri.
– Gram negative, marine, bioluminescent, symbiotic Bacteria
• The lux operon encodes genes involved in bioluminescence. LuxI/LuxR type quorum sensing
• The picture shows colonies glowing because of production of bacterial luciferase.
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The Hawaiian bobtailed squid
● Cephalopoda, Sepiolida, Sepiolidae
- marine (Hawaii)
- seize: 3,5 cm
- life time: 3-10 months
- shallow waters (2-4 cm depths)
- distinct day-neight rhythm
http://www.dal.ca/~ceph/
TCP/Escolopes.html
Euprymna scolopes
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Symbiosis between Vibrio fischeri
and the Hawaiian bobtailed squid
• Vibrio fischeri colonise an internal organ (the light organ) of the squid where they reach high enough density to cause expression of the lux genes, resulting in bioluminescence. Hastings & Nealson, 1977
Symbiosis- Differenciation
- Light organ (1010-1011 V. fisheri cells/ml)
- Marine water (<102 V. fisheri cells/ml)
- Host: Camouflage
(protection against hunters)
- Symbiont: protection, food
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V. fisheri Luminescence
http://www.biology.pl/bakterie_
sw/bakterie_hp.html
Luciferase reaction
FMNH2 + RCHO + O2 → FMN + RCOOH + H2O + Licht (490 nm)
(Oxidatio of long-chain aldehydes (RCHO) and reduced flavin
mononucleotide (FMNH2))
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V. fisheri Luminescence
K. H. Nealson et al. 1970
Cell density
Luminescence
Luciferase
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What is the Signal ?
V. fisheri
„Low cell density“
Culture-supernatant
„High cell density“
http://mcb1.ims.abdn.ac.uk/staff/lag.html
5 h
„Low cell density“
K. H. Nealson et al. 1970
A. Eberhard 1972
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The Signal „Autoinducer“
V. fisheri
N-(3-oxohexanoyl) homoserine-lactone
A. Eberhard et al. 1981
Biosynthesis:
Acyl-ACP + S-Adenosylmethionin → Acyl-Homoserin-Lacton + Methylthioadenosin + ACP
A. Eberhard et al. 1991
J.-G. Cao & E. A. Meighen 1993
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Biosynthesis of AI-1
Eberhard A. et al. 1991
Cao J-G & Meighen E. A.1993
1) Formation of amide linkage
2) Lactonisation, release of methylthioadenosine
3) Formation of the acylated homoserine lactone
ACP = acyl-carrier protein
Biosynthesis:
Acyl-ACP + S-Adenosylmethionin → Acyl-Homoserin-Lacton + Methylthioadenosin + ACP
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LuxI/LuxR-Typ QS in V. fisheri
LuxI
Acyl-homoserine-lactone (AHL) synthase
Synthesis of the „Autoinducer“
LuxR
Transcriptionfactor „activator“
Binds autoinducer and influences transcription
Lux structural genes
luxI C D A B EluxR P O P
Fettsäure Reduktase
Luciferase
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LuxI/LuxR-Typ „QS“
LuxI
AI-1
LuxR
luxI C D A B EluxR
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Quorum sensing in Vibrio fischeri
• At low cell densities the lux operon (luxICBADE) is expressed at a low level and small amounts of 3-oxo-C6-HSL produced diffuse out of the cell.
• The luxCDABE genes are responsible for bioluminescence.
luxRlux
boxluxI C D A B E
LuxR LuxIGenes responsible
for bioluminescence
3-oxo-C6-HSL
inactiveactivator
Low cell density
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Quorum sensing in Vibrio fischeri
• At high cell densities 3-oxo-C6-HSL accumulates in the environment and within the cell.
• Transcription of luxICDABE increases when the complex of LuxR and 3-oxo-C6-HSL (the active activator) binds to the lux box.
• LuxR-autoinducer complex represses transcription of LuxR (negative action compensates for positive action of LuxICDABE promoter)
luxRlux
boxluxI C D A B E
LuxR LuxI
3-oxo-C6-HSL
inactiveactivator
activeactivator
LIGHT
High cell density
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AI-1 quorum sensing systems of other bacteria and the phenotype they control
bacteriumluxR/I homologues
Major AHL phenotype
Aeromonas
hydrophila
AhyR, AhyI C4-HSL Extracellular
protease, biofilm
formation
Agrobacterium
tumefaciens
TraR, TraI 3-oxo-C8-HSL Conjugation
Pseudomonas
aeruginosa
LasR, LasI 3-oxo-C12-HSL Exoenzymes,
biofilm formation, cell-cell spacing,
Twitching motility
Pseudomonas
aurofaciens
PhzR, PhzI C6-HSL Phenazineantibiotic
AHL = Acylated homoserine lactone
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Specificity?● > 70 LuxI/LuxR-type QS systems (Gram-negative Bacteria)
Species-specific communication
● Substrate specificity of LuxI-similar proteins (acyl-residue)
● Binding specificity or LuxR-similar proteins for specific autoinducer
Acylated-homoserine-lactones V. fisheri /LuxI
P. aeruginosa /LasI
P. aeruginosa /RhlI
A. tumefaciens /TraI
V. harveyi /LuxLM
S. Schauder & B. L. Bassler 2001
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LuxI/LuxR Homologs
Pseudomonas aeruginosa
● Opportunistic pathogen
a) LasI/LasR
b) RhlI/RhlR
● Biofilm formation
● Cell-associated and extracellular virulence factors
„Necessary number for effective infection“
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LuxI/LuxR HomologeErwinia carotovora
● Plant-Pathogen (bacterial soft rot (BSR) „Weichfäule“)
CarI/CarR
● Antibiotics
● Exoenzymes
Agrobacterium tumefaciens
● Plant-Pathogen (crown-gall disease, „Wurzelhals Tumore“)
TraI/TraR
● Conjugation
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LuxI/LuxR Homologe
Rhizobium leguminosarum
● Plant-symbiont
CinI/CinR
RhiI/RhiR
BisR
TriR
● Nodulation
● Bacteriocin