Outline
EcoliFunGen: Tackling the Escherichia coli
Unknowneome and a few other things
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Barry L. Wanner
Purdue University
West Lafayette, IN 47907 USA
bl
blwanner@purdue.edu
@ d
d
StoMP
Noisy Bugs: Modelling and Microbiology
E-Science Institute, Edinburgh
15 July 2009
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E. coli knowneome and unknowneome
EcoliFunGen Goals and Approaches
Stochastic gene expression in a two-component system
Development of Resources for functional genomics
Use of Resources to study genetic interactions
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protein localization
Use of Resources to study
Movement of single protein molecules inside living cells
From Functional Genomics to Understanding Membrane
Organization
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EcoliFunGen: Tackling the Escherichia coli Unknowneome
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E. coli K-12 Knowneome and Unknowneome
E. coli pan-genome
51 genomes
ca. 4500 +/- 500 genes
(3,000 gene families) per
genome
core ca. 2100 genes
total >30,000 E. coli genes
total >16,000 gene families
Phylogenetic diversity of E. coli K12
proteins. Histograms of number of
genera, classes and phyla containing
proteins functionally equivalent to
those in the unknowneome (A) and the
“knowneome” (B), as determined via
the CRSH database (Sam Handelman
& John Hunt, unpublished)
Source: Dave Ussery http://www.cbs.dtu.dk/index.shtml
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Goals of the EcoliFunGen Consortium
(Application under Review)
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Creation of a custom-designed function-elucidation database called
EcoliFunGenDB.
Integration of results from all relevant high-throughput (HTP) datasets into this
resource.
Archiving of data from low-throughput (LTP) experiments and community
commentary in this single resource to ensure long-term traceability of
functional inferences.
Creating a set of manually curated functional interaction networks to support
interpretation of HTP datasets, generation of functional hypotheses, and
understanding of E. coli physiology.
Development and critical evaluation of the efficacy of different HTP
experimental pathways for elucidation of biological function.
Reliable and traceable projection of functional inferences from E. coli to other
fully sequenced microbial genomes via the CRSH database of proteins of likely
equivalent function.
Efficient public distribution of all experimental resources produced by the
Consortium, including clones, purified proteins, and antibodies, via a dedicated
service laboratory.
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EcoliFunGen: E. coli Functional Genomics: Tackling the E. coli Unknownoeme
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Why E. coli?
– Best understood model cell, yet nearly one half of its genes have unknown or poorly
understood functions. Case is even worse for others
Approach
– Target selection
• E. coli core
• Human microbiome core
• Conserved in humans
Methods
– Structure
– Computational , e. g., genomic context, virtual screening
– High-throughput experimentation
• Mutant phenotypes
• Protein-protein interactions (Protein Complementation Assay – split GFP)
• Genetic Interactions
• Transcriptomics (RNA-Seq, ChIP-Seq)
– Low-throughput experimentation based on hypotheses generated from highthroughput experimentation
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Signal transduction and stochastic expression in a
seven-component, two-component system
Results from a Systematic Search for Cross Activation of PhoB by
non-partner Histidine Kinases - A Multi-Component Regulatory
Network
PhoQ/
PhoP
EnvZ/OmpR
BasS/
BasR
RstB/
RstA
• Figure removed because model is in manuscript under review
ArcB/
ArcA
KdpD/
KdpE
CusS/
CusR
PhoR/PhoB
YedV/
YedW
QseC/
QseB
BaeS/
BaeR
TorS/
TorR
CpxA/
CpxR
BarA/UvrY
CreC/
CreB
Summary: HK/RR; Tested fourteen; four showed strong activation
(grey ovals), two showed weak cross activation (open ovals), and
eight showed no cross activation (no arrows). J. L. Masella & B. L.
Wanner, unpublished data
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•Stochastic variation in gene expression
Single-cell profiling of PhoB activation by noncognate HKs
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Single-cell profiling of PhoB activation by
noncognate HKs
L. Zhou, G. Gregori, J. M. Blackman, J. P. Robinson, and B. L. Wanner.
Stochastic activation of the response regulator PhoB by noncognate
histidine kinases. In: IMBio, Informations management in der
Biotechnologie e. V., edited by R. Hofestädt, Magdeburg: 2005, p. 11-24.
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Stochastic activation of Pho Regulon in normal cells
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Modeling: Stochastic kinetic model of two component system signalling reveals all-ornone, graded and mixed mode stochastic switching responses
Andrzej M. Kierzek, Lu Zhou, Barry L. Wanner (manuscript under review)
Summary from Modeling
1) Summary statements removed for they are in abstract of manuscript under
review
A Chromosome att80 ::phoAp-gfp in wt background
B pLZ91 (CAS3)::phoAp-gfp in attf80 background
Single cell profiling of PhoB activation by PhoR during Pi starvation
Unpublished data cited in: M. G. Lamarche, B. L. Wanner, S. Crepin, and J. Harel. The
phosphate regulon and bacterial virulence: a regulatory network connecting phosphate
homeostasis and pathogenesis. FEMS Microbiol.Rev. 32 (3):461-473, 2008.
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Phase contrast and fluorescence images of
phoAp-gfp in rhaBp-arcB (sample with cell OD410 of 0.7)
Now under way
Collaborators
Ken Ritchie, Physics, Purdue
Alex Groisman, Physics, UCSD
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•Single cell gene expression
Field 1
Growing E. coli on a “chip”
Chamber size: 5 m by 70 m
Connections: 0.5 m diameter
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Groisman et al., Nature Meth.,
685-689 (2005)
Field 2
Phase contrast (A and C) and fluorescence ( B and D) images of two fields that
were acquired at 100X magnification with a fluorescent microscope.
L. Zhou, G. Gregori, J. P. Robinson, and B. L. Wanner. (unpublished data)
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E. coli Functional Genomics: Development of Resources
In collaboration with Professor Mori and his Groups
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E. coli Functional Genomics: Development and Use of Resources
Yamagata
Nara
Hirotada Mori, NAIST, Ikoma,
ASKA Region, &
Institute Advanced Biosciences,
Keio University, Tsuruoka,
Yamagata Prefecture, Japan
Yamagata
Nara
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E. coli Functional Genomics: Development and Use of Resources
(C) ASKA single gene knockout library (manuscript in preparation)
Construction of chromosomal fusion of target gene with turboGFP
•A.
A Typas,
T
R J.
R.
J Ni
Nichols,
h l D
D. A
A. Si
Siegele,
l M
M. Shales,
Sh l
S R
S.
R. C
Collins,
lli
B
B. Li
Lim, H
H. Braberg,
B b
N Y
N.
Yamamoto,
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R. Takeuchi, B. L. Wanner, H. Mori, J. S. Weissman, N. J. Krogan, and C. A. Gross. Highthroughput, quantitative analyses of genetic interactions in E. coli. Nat.Methods 5 (9):781-787,
2008.
•G. Butland, M. Babu, J. J. az-Mejia, F. Bohdana, S. Phanse, B. Gold, W. Yang, J. Li, A. G.
Gagarinova, O. Pogoutse, H. Mori, B. L. Wanner, H. Lo, J. Wasniewski, C. Christopolous, M. Ali, P.
Venn, A. Safavi-Naini, N. Sourour, S. Caron, J. Y. Choi, L. Laigle, A. Nazarians-Armavil, A.
Deshpande, S. Joe, K. A. Datsenko, N. Yamamoto, B. J. Andrews, C. Boone, H. Ding, B. Sheikh, G.
Moreno-Hagelseib, J. F. Greenblatt, and A. Emili. eSGA: E. coli synthetic genetic array analysis.
Nat.Methods 5 (9):789-795, 2008.
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Concept of Genetic Interactions
Tools for high-throughput Genetic Interactions
Data are
orthogonal to
protein-protien
interactions
1) Structure of Keio collection single-gene deletion mutants
2) Structure of ASKA collection single-gene deletion mutants
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Tools for high-throughput Genetic Interactions
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Construction of double knockouts by conjugation
3) Conversion of ASKA mutant to an Hfr Donor
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♂
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T. J. Silhavy and Z. Gitai. Sex to the rescue. Nat.Methods 5 (9):759-760, 2008.
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High throughput conjugation and quantification
Whole set of gene x 2 (~9,000 colonies)
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E. coli Functional Genomics: Development of Resources
Double knockout analysis of yccE
www.EcoliCommunity.org/GenoBase
-D-glucose
Pentose
phosphate
pathway
Aminosugars
metabolism
Glycolysis
-D-glucose6-P
UDP-N-acetyl-Dglucosamine
Fructose-6-P
D-Arabinose-5-P
Sedoheptulose 7-P
lpcA
gmhB
rfaE
rfaD
Lipopolysaccharide
biosynthesis
KOD2-lipid Ⅳ(A)
Essential steps
lpxL
Synthetic lethal
ADP-L-glycero-Dmannno-heptose
KOD2-lipid A
Syntheti sickness
rfaC
rfaQ
Lipopolysaccharidetransporting ATPase
rfaF
rfaG
rfaP
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E. coli Functional Genomics: Example for Use of Resources
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E. coli Functional Genomics: Use of Resources
www.EcoliCommunity.org/GenoBase
www.EcoliCommunity.org/GenoBase
RecR
Tsr
KdgK
CodB
HupB
Localization pattern of GFP
GFP--fused protein
Cells with GFP signal
3 996
3,996
Whole cells
2,874
Dsicrete dots or spots
555
Cell periphery
526
nucleoids
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Visualizing single molecules inside live E. coli cells
Found PhoR & PhoU to be localized. Initial studies were done with
single-copy Tsr-venus fusions expressed from weak constitutive
promoter. Epifluorescence microscopy, in collaboration with
Professor Ken Ritchie, Physics, Purdue
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Emerging and moving
2.7μm
Pump medium in
2μm
2h_22m_30mw_1m
Medium out
Cell attached
DIC
Excitation filter
F
B
Fluorescent recovery
time after photo
bleaching is 1 minute.
D= 2.4E-9cm^2/s
Emission/Barrier
filer
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Dynamics of single Tsr-Venus protein on the
membrane
2μm
Single molecule detection
1h 35m 60fps DIC
1h_35m_60fps_DIC
2μm
2μm
Fluorescence
Bleached
3h_33m_3m
After bleaching initial fluorescent
laser was turned off and waited for
three minutes and returned on the
laser for catching the emerging
molecule.
Diffusion coefficient: 1.3E-9cm^2/s
Displacement: 1.2μm
All Initial YFP fluorescence were photo
bleached by shortly increasing the laser
power up to the 30 W/mm2 and reduced
to the minimum power 3.2 W/mm2.
Waiting some time until the newly
emerging spot was showing up. During
data acquisition the laser was
continuously turned on. Time resolution
is 16.6 ms (60 fps).
1μm
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Stationary Tsr-Venus
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Technology and Single Molecule Imaging – The Value of a good camera
1.0
(a)
(b)
Y position (µm)
0.5
0.0
-0.5
X position (µm)
-1.0
-1.0
-0.5
0.0
0.5
1.0
(c)
2
Mean squared displacement ((um )
0.044
0.040
0.036
0.032
0.028
0.024
0.020
0.016
0
100
200
300
400
Time (ms)
(a)Time laps (60 fps) video sequence of the Tsr-Venus at the polar or central region. Time resolution is 16.6 ms and the
length of the bar is 3 μm. (b) The typical trajectories of the molecules shown up at the time laps video at left panel and
(c) mean displacement (MSD) vs time graph. These molecules are doing confined motion (compartment size is 380 nm)
within the observation period by confirming graph (c).
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Movement of Single Mobile Tsr-Venus Molecules in an E. coli
membrane when determined with 1 millisecond CCD camera
K. P. Ritchie, D. Oh, Y. Hsieh, and B. L. Wanner (manuscript in preparation)
Monte Carol Simulation
Unpublished data figure removed.
Implication:
E. coli membrane
consists of multiple
domains
as s
Basis:
Protein complexes
Lipid composition
Other
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Acknowledgements
Stochastic gene expression
Lu Zhou & Jennifer Blackman (former
graduate students) in collaboration with G.
Gregori in J. Paul Robinson’s group
Protein localization in E. coli
Yi-Ju Hsieh in collaboration with Hironori
Niki (NIG, Japan) & Hirotada Mori (NAIST,
Japan)
Protein movement inside living cells
Yang Yu & Yi-Ju Hsieh in collaboration with
Dongmyung Oh & Ken Ritchie (Physics)
N
New
E
E. colili R
Resources
Kirill Datsenko (Purdue), Hirotada Mori and
colleagues (NAIST)
Genetic Interactions
Kirill Datsenko (Purdue), Hirotada Mori and
colleagues (NAIST), Carol Gross and
colleagues (UCSF), Jack Greenblatt,
Andrew Emili and colleagues (Toronto)
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Life is like riding a bicycle. To keep
your balance you must keep moving.
Albert Einstein
Support
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NIH
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