1. Phenotypic variations in a monoclonal bacterial population
Oleg Krichevsky, Itzhak Fishov, Dina Raveh,
Ben-Gurion University, Beer-Sheva
J. Wong, D. Chatenay, M. Poirier, S. Ghozzi, J. Robert
Laboratoire Jean Perrin, FRE 3132 CNRS-UPMC
24 rue Lhomond, Paris

2. Escherichia Coli Bacteria
4 µm
Electronic microscopy
1 colony in phase contrast microscopy

3. Schematic bacterium
Membrane (glycolipide)
Cytoplasme (H2O+ions monovalents et divalents)
Acides nucléiques (ADN, ARN)
protéines (enzymes dont polymérases)
small molecules(ribosome)
Small numbers of molecules (par ex. 1 chromosome, ARNs; protéines).
Dynamic enzymatic reaction: production, transformation,
degradation of the species with time.

4. Bacterium Biochemistry (simplified!)
2) DNA replication
ADNpolymérase, gyrase…
transcription
translation
ADN chromosome
ARNm
Protéine: un gène
1) Central dogma:
ARNpolymérase
ribosome

5. Bacterial culture
Growth by division: 1 bacterium→2 daughter bacteria genetically identical (clone)
Duplication, repartition of the constituants (in particular of the chromosome) t
Division time: 30’à 37°C in nutritive medium(pH~7, protéines, glucides)

6. Population/individual
Culture of a single colony in homogenous medium, obtain a monoclonal population (typically: 1ml de medium grown 12 hours~108 bacteria).
J. Spudich et D. Koshland revealed the individual character of chemotactism. (Nature )
Mutations don’t explain this individuality→ non geneticorigin.
(mutation rate: 10-10/pb/génération)
The authors invoked fluctuations of the small number of particle, of chemical rates to explain those non genetic variability.
This process is more efficient than mutation to allow species adaptation to rapidly fluctutating environnment.

7. Genetic expression network:
ADNARNProtéine (fluorescente)
Example with a negative feedback loop:
Gène
ADN
Promoteur
Taux de transcription kR
Dégradation gR
ARN
Taux de traduction kP
Protéine
Dégradation gP

8. Fluctuations. Network noise. Variability.
Ozbudak et al.: origin of the protein noise expression: transcription/translation Nature genetics 31 (2002).
Elowitz et al.: Intrinsic noise(Fluctuations des éléments du réseau)/extrinsic noise(fluctuations des autres composants de la cellule) Science 297 (2002).
Influence of the regulation mechanism

9. DNA in bacterium 1 chromosome (4 Mpb)
N (1<n<300) plasmid copy number (entre 2 kpb et 100 kpb)

10. résistance antibiotique
Plasmid
Replicon
résistance antibiotique
extrachromosomal DNA fragment
Code for its copy number (replicon sequence: ori, regulation)
Uses the host to replicate
Adds an advantage against otherwise toxic medium (Antibiotic resistance.)
Symbiotic plasmid/bacterium association

11. Without partition system
With partition system

12. <n> s Standard deviation Plasmid copy number (PCN) inE. Coli
PCN=phenotype choice
Measured individual PCN on population scale(~104 individus)
Distribution : variability
Antibiotic resistance: adaptability
<n>
s Standard deviation

13. Direct Visualisation directe of plasmids in bacteria:
G. Scott Gordon, Dmitry Sitnikov, Chris D. WebbOgden Aurelio Teleman, Aaron Straight, Richard Losick, and Schaechter, Schaech Andrew W. Murray, and Andrew Wright, Cell 1997.
Fluorescent protein bounds to the plasmid sequence
Disadvantage: homologous recombinaison

14. Indirect Method Fluorescent protein mOrange coded by the plasmid
[protein] plasmid copy number
Fluorescent intensity bacteriaPCN
Expression copie unique sur le chromosome protéine verte.
Fluorescent gene expression under IPTG inducible tac promoter.

15. Promoter choice Promoter tac fluorescent gene Termination seq Promoter
RNA-polymerase
RNA-polymerase
LacI repressor→no transcription, no gene expression
IPTG LacI repressor titration→transcription, gene expression
Strong induced promoter: minimise expression noise (Elowitz et al.)

16. Measure the fluorescent intensity
Phase contrast
Fluorescent image
Measurement over a population~104, every individual at the same developpment
Low level fluorescence
→Flow cytometry+fluorescent microscopy set up

17. Set up:
cell
optic
detection

18. Soft lithography microchannel
UV exposure
Mask
photosensitive resin
glass
develop, fix
Ready to use channel
Spread PDMS, bake at 90° C
Unmold, fix on a cover glass

19. Field of view: 10µm
Bacterial speed: ~0.1-1 mm/s
Optical differentiel Interferometry profilometry image of the channel (z=2µm)

20. Optical elements detail

22. Time series of fluorescent intensityFO FV
FV = PV + AV
FO = aPO + AO + bPV
Fi: i channel measure of fluorescent intensity
Pi: i protein fluorescent intensity
Ai: i channel autofluorescent intensity
a: normalization constant between green and orange fluorescence
b: leak of green toward orange channel
Rq: a posteriori, no orange to green

23. Bacteria preparation (E. Coli TOP10 strain)
Culture 37°C 12h of a clone picked on a petri dish
Dilution 500X, re culture→DO=0.2
Re dilution 100X, re culture →DO=0.2
Induction 1h 1mM IPTG →protein fluo. production
Bloque chloramphénicol →stop protein production
Wash phosphate buffer, 12h. Protein maturation
Bacteria in exponential phase →reproductibility
No protein production
Fluorescence level
Limit autofluorescence

24. Calibration "Green" bacteria no plasmid
Induced: leak gren→orange, b=0.17
Non induced :autofluorescence

25. 2. "Orange" bacteria, no plasmid (b=0)
Coefficient a=0.58
3. Fluorescent gene in 1 et 2 copies on a plasmid
Linearity between gene copy number and protein expression

26. Study as a function of the replicon (ampicillin resistance)
F: single copy, partition system
R1: low copy number
ColE1: medium copy number, no partition system
R1+:partition system
R1-:without partition system
Hypothesis: On average gene expression does not depend on the copy nor its origin F
R1-
R1+
ColE1
<PV> (a.u.)
27,1
28,5
26,5
25,7
<PO> (a.u.)
27,0
244
173
2167
<n>=<PO>/<PV>=<nP>/<nC>
1,0
7,8
6,5 95
qPCR
0,5
3,2
3,8
23,4
≈constant
Mean plasmid copy number per
chromosome
We take <nC>=1,7 (E. Coli and Salomonella, p.1553, ASM Press, 1996)

27. Variance et variability
Hypothesis on correlation and autoforrelation of fluorescent protein expression [ <PaPb>, (a,b=O,V)]
Poisson F
R1's
ColE1 F
R1-
R1+
ColE1
<nP>
1,71
13,3 11
161 s
0,7
4,2
3,1 40
h (%) 46 34
29,2 25
h=s/<nP>

28. R1- plasmid loss
Bacteria are cultivated without antibiotic for many generations
with
without, 54 générations
without, 99 générations
Diminution de la Population N+ with plasmid diminishes
Population N- without plasmid increases

29. Loss rate We measure N+(54)=60%, N+(99)=16%
We deduce: population + division time est higher than 2 min. compared to population –
Loss rate/bacterium/generation: 0,5%
Boe et Rassmussen, plasmid, 36,p.153 (1996)

30. Numerical simulations
transcription
traduction
ADN chromosome
ARNm
Protein
ARNpolymérase
ribosome
10 réactions biochimiques Rµ:
* X0 -> X1 R0 : free promoter -> RNAP bound promoter
* X1 -> X0 R1 : unbinding of RNAP freeing promoter
* X1 -> X2 + X0 R2 : transcription initiation
* X2 -> X3 R3 : transcription, X3 = mRNA
* X3 -> Ø R4 : mRNA deggradation
* X3 -> X4 R5 : reversible mRNA/ribosome complex formation
* X4 -> X3 R6 : reversible mRNA/ribosome complex dissociation
* X4 -> X5 + X3 R7 : Translation start freeing RBS
* X5 -> X6 R8 : production of protein X6
* X6 -> Ø R9 : protein degradation
Siggia et al., PNAS October 1, 2002 vol. 99 no

31. Stochastic simulations
We have M reactions Rµ (m=1,2,…,M) involving N species.
We define P(t,µ)dt as the probability that the next reaction in [t+t, t+t+dt] is reaction Rµ.
One can show that:
cµdt = average probability, to first order in dt, that a particular combination of
Rµ reactant molecules will react accordingly in the next time interval dt.
hµ = number of distinct molecular reactant combinations for reaction Rµ found to
be present in V at time t.
(Daniel T. Gillespie, JOURNAL OF COMPUTATIONAL PHYSICS 2, (1976))
Example:
X1 + X2 -> X3 h = X1X2
2X -> Y h = X (X-1)/2
Implementation: one has to generate (t,µ) according to P(t,µ)
in order to update at each step the number of reactant molecules implied in
reaction m.

32. Daniel T Gillespie, J. Phys. Chem., 1977, 81 (25), 2340-2361

33. 1 gene which duplicates, binomial repartition
of protein

36. Ages and division time distributions

38. Conclusion
Build up tools in molecular biology, optic and microfluidic to measure variability in bacterial population
Application: plasmid copy number measurement
F: single copy, strictly regulated
R1: partition System1) lowers PCN and 2) lowers variability
ColE1: high pcn but low variability
Plasmid loss rate in absence of partition system
Plasmid metabolic cost: increase in division time

39. Perspectives Thank you for your attention
Synchronisation of bacterial population
Antibiotic concentration effect
Sorting:
Other toxic gene to test variability
Poubelle
Réservoir 2
Thank you for your attention