Download Level 3 Genes

Genetic Networks
(genetic regulatory networks)
- a group of genes connected through transcription regulators encoded
within the set of genes
Promoter X
gene X
Promoter Y
gene Y
operator X
Genetic Networks
(genetic regulatory networks)
- a group of genes connected through transcription regulators encoded
within the set of genes
gene X
X
gene Y
Genetic Networks
(genetic regulatory networks)
- a group of genes connected through transcription regulators encoded
within the set of genes
gene X
X
X
gene Y
Y
Genetic Networks
(genetic regulatory networks)
By convention we simplify these diagrams as follows:
gene X
X
X
X
gene Y
Y
Y
Genetic Networks
(genetic regulatory networks)
X
Y
Denotes positive
regulation
Y
Denotes negative
regulation
Z
Regulation of flagella gene expression:
A three tiered transcriptional hierarchy
Positive transcriptional regulators
Alternative sigma factors
Anti-sigma factors
Temporal regulation
The Flagella Transcription Hierarchy
1. The Master Regulon
CRP,H-NS,OmpR
other?
FlhCD
The Flagella Transcription Hierarchy
1. The Master Regulon
2. The FlhCD Regulon
CRP,H-NS,OmpR
other?
FlhCD
inside
outside
FlgM
FliA
other?
Basal Body
and Hook
The Flagella Transcription Hierarchy
1. The Master Regulon
2. The FlhCD Regulon
CRP,H-NS,OmpR
other?
Chemotaxis
proteins
Motor
proteins
FlhCD
inside
outside
FlgM
FliA
Basal Body
and Hook
other?
3. The FliA Regulon
Filament
The flhDC promoter integrates inputs from
multiple environmental signals
flhDC
?
CRP - catabolite repression, carbohydrate metabolism
OmpR - osmolarity
IHF - growth state of cell?
HdfR - ?
FliA Regulation by FlgM
FlhDC expression leads to activation of Level 2 genes including the
alternative sigma factor FliA and an anti sigma factor FlgM
FlgM accumulates in the cell
and binds to FliA blocking
its activity (i.e. interaction
with RNA polymerase)
preventing Level 3 gene
expression.
inside
outside
Level 3 Genes
FliA Regulation by FlgM
Other level 2 genes required for Basal body and hook assembly are
made and begin to assemble in the membrane.
Level 3 Genes
inside
outside
Basal Body
and Hook
Assembly
FliA Regulation by FlgM
The Basal body and hook assembly are completed.
Level 3 Genes
inside
outside
Completed Basal Body
and Hook
FliA Regulation by FlgM
The Basal body and hook assembly are completed.
FlgM is exported through
the Basal Body and Hook
Assembly
Level 3 Genes
inside
outside
Completed Basal Body
and Hook
FliA Regulation by FlgM
Level 3 gene expression is initiated.
FlgM is exported through
the Basal Body and Hook
Assembly.
FliA can interact with RNA
polymerase and activate
Level 3 gene expression.
Level 3 Genes
inside
outside
Completed Basal Body
and Hook
FliA Regulation by FlgM
Level 3 gene products are added to the motility machinery including the
flagella filament, motor proteins and chemotaxis signal transduction system.
inside
outside
Filament
The “genetic network diagram” for the fla system
A
B
C
D
E
The “genetic network diagram” for the fla system
Level 1
flhCD
fliL
fliE
fliF
flgA
flgB
flhB
n=6
flgM
fliD
flgK
fliC
meche
mocha
flgM
fliA
Level 2
n=6
Level 3
Using reporter genes to measure gene expression
RNA polymerase
Regulator
Organization of operon on chromosome.
flhD
flhDC promoter
flhC
Using reporter genes to measure gene expression
RNA polymerase
Regulator
Organization of operon on chromosome.
flhD
flhC
flhDC promoter
Clone a copy of the promoter into a reporter plasmid.
Reporter gene
Using reporter genes to measure gene expression
RNA polymerase
Regulator
flhD
flhC
Both the flhDC genes and the reporter
plasmid are regulated in the same way
and thus the level of the reporter
indicates the activity of the promoter.
Reporter gene
Note that the strain still has
a normal copy of the genes.
Gene Expression
in Populations
Gene Expression
in Single Cells
Multi-well plate reader
Video microscopy
- sensitive, fast reading
- high-throughput screening
- liquid cultures
- colonies
- mixed cultures
- “individuality”
- cell cycle regulation
- epigenetic phenomenon
Automation: Both approaches are amenable to high throughput robotics
Gene Expression in Single Cells: Cell to Cell Variability
Michael Elowitz, Rockefeller University
1- Gene Expression Profiling With Real Promoters
Modeling Genetic Networks
- from small defined systems to genome wide Small Defined Networks
High Throughput / High Quality
Expression Profiling
Modeling, Simulation
Fluorescence of flagella reporter strains as a function of time
Fluorescence
relative to max
0.6
0.1
0.01
0
600
Time [min]
The order of flagellar gene expression is the order of assembly
Early
Cluster 1 Class 1 flhDC
Cluster 2
Late
Class 2 fliL
Class 2 fliE
Class 2 fliF
Class 2 flgA
Class 2 flgB
Class 2 flhB
Class 2 fliA
Class 3 fliD
Class 3 flgK
Class 3 fliC
Class 3 meche
Cluster 3Class 3 mocha
Class 3 flgM
Master regulator
Activator of class 3
Simple Mechanism for Temporal Expression Within an Regulon
[protein]
Induction of
positive
regulator
Time
Promoters with
decreasing
affinity for
regulator
Simple Mechanism for Temporal Expression Within an Regulon
[protein]
Using Expression Data to Define and Describe Regulatory Networks
With the flagella regulon, current algorithms can distinguish Level 2 and
Level 3 genes based on subtleties in expression patterns not readily
distinguished by visual inspection.
Using our methods for expression profiling (sensitive, good time resolution)
we have been able to demonstrate more subtle regulation than previously
described.
Different mechanisms can give rise to different patterns- in this case temporal
patterns arise by transcription hierarchies (I.e. Level 1  Level 2  Level 3)
and by differences in binding site affinities within a level.
“You can not infer mechanism from pattern.”
The problem of binding sites:
Aoccdrnig to a rscheearch at an Elingsh uinervtisy, it
deosn't mttaer in waht oredr the ltteers in a wrod are, the
olny iprmoetnt tihng isThat frist and lsat ltteer is at the
rghit pclae. The rset can be a toatl mses and you can sitll
raed it wouthit porbelm. Tihs is bcuseae we do not raed
ervey lteter by it slef but the wrod as a wlohe.
Ceehiro
That'll srecw the splelchekcer
Methods such as the one described here or DNA microarrays can be used
to measure expression of all the genes in a cell simultaneously.
Reverse Engineering challenge – can we use expression data to infer
genetic networks?
C
A
B
M
D
E
F
N
X
Y
W
V
U
Z
O
Engineered Gene Circuits: The Repressilator
A 3-element negative feedback transcriptional loop that should
have sustained oscillatory behavior under the appropriate
conditions:
• strong promoters.
• tight transcription repression with low leakiness.
• comparable protein and mRNA decay rates
Michael Elowitz & Stanislas Leibler
Nature, 2000
Engineered Gene Circuits: The Repressilator
Engineered Gene Circuits: The Repressilator
• Periodic synthesis of GFP (150 minutes, 3x cell cycle time)
• The state of the network is transmitted to the siblings
• Average decorrelation time = 95 +/- 10 minutes
Some lessons learned:
 Even well studied systems still have elements of surprise!
 The best engineered systems do not always live up to there predicted
behavior (we often do not know as much as we think!).
 Predictive ability is limited because of difficult in predicting
quantitative properties.
 The interfacing of modeling with experiments reveals much more
information about biological systems that neither will do alone.