Presentation is loading. Please wait.

Presentation is loading. Please wait.

Phenotypic variations in a monoclonal bacterial population Oleg Krichevsky, Itzhak Fishov, Dina Raveh, Ben-Gurion University, Beer-Sheva J. Wong, D. Chatenay,

Similar presentations


Presentation on theme: "Phenotypic variations in a monoclonal bacterial population Oleg Krichevsky, Itzhak Fishov, Dina Raveh, Ben-Gurion University, Beer-Sheva J. Wong, D. Chatenay,"— Presentation transcript:

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 1 colony in phase contrast microscopy 4 µm Electronic microscopy

3 Schematic bacterium Cytoplasme (H 2 O+ions monovalents et divalents) Acides nucléiques (ADN, ARN) protéines (enzymes dont polymérases) small molecules(ribosome) Membrane (glycolipide) 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 chromosomeARNm Protéine: un gène 1) Central dogma: ARNpolyméraseribosome

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

6 Population/individual Culture of a single colony in homogenous medium, obtain a monoclonal population (typically: 1ml de medium grown 12 hours~10 8 bacteria). J. Spudich et D. Koshland revealed the individual character of chemotactism. (Nature ) Mutations don’t explain this individuality→ non geneticorigin. (mutation rate: /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) ARN Gène Promoteur ADN Taux de transcription k R Taux de traduction k P Protéine Dégradation  R Dégradation  P Example with a negative feedback loop:

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

10 Plasmid 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 Partition system Without partition systemWith partition system

12 Plasmid copy number (PCN) inE. Coli PCN=phenotype choice Measured individual PCN on population scale(~10 4 individus)  Distribution : variability  Antibiotic resistance: adaptability  Standard deviation

13 G. Scott Gordon, Dmitry Sitnikov, Chris D. WebbOgden Aurelio Teleman, Aaron Straight, Richard Losick, and Schaechter, Schaech Andrew W. Murray, and Andrew Wright, Cell Direct Visualisation directe of plasmids in bacteria: 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 RNA-polymerase Promoter tac fluorescent geneTermination seq LacI repressor→no transcription, no gene expression IPTG LacI repressor titration→transcription, gene expression Strong induced promoter: minimise expression noise (Elowitz et al.) Promoter fluorescent geneTermination seq RNA-polymerase

16 Phase contrastFluorescent image Measure the fluorescent intensity  Measurement over a population~10 4, 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 Mask photosensitive resin glass develop, fix Spread PDMS, bake at 90° C Unmold, fix on a cover glass UV exposure Ready to use channel

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

20 Optical elements detail

21

22 Time series of fluorescent intensity F V = P V + A V F O = aP O + A O +  P V F i : i channel measure of fluorescent intensity P i : i protein fluorescent intensity A i : i channel autofluorescent intensity a: normalization constant between green and orange fluorescence  : leak of green toward orange channel Rq: a posteriori, no orange to green FOFO FVFV

23 Bacteria preparation (E. Coli TOP10 strain) 1.Culture 37°C 12h of a clone picked on a petri dish 2.Dilution 500X, re culture→DO=0.2 3.Re dilution 100X, re culture →DO=0.2 4.Induction 1h 1mM IPTG →protein fluo. production 5.Bloque chloramphénicol →stop protein production 6.Wash phosphate buffer, 12h. Protein maturation  Bacteria in exponential phase →reproductibility  No protein production  Limit autofluorescence  Fluorescence level

24 Calibration 1."Green" bacteria no plasmid Induced: leak gren→orange,  Non induced :autofluorescence

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

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 FR1-R1+ColE1 (a.u.)27,128,526,525,7 (a.u.)27, = / = / 1,07,86,595 qPCR0,53,23,823,4 ≈constant Mean plasmid copy number per chromosome We take =1,7 (E. Coli and Salomonella, p.1553, ASM Press, 1996) Hypothesis: On average gene expression does not depend on the copy nor its origin

27 Variance et variability FR1-R1+ColE1 1,7113,  0,74,23,140  (%) ,225  =  / Hypothesis on correlation and autoforrelation of fluorescent protein expression [, (a,b=O,V)] Poisson F R1's ColE1

28 R1- plasmid loss Bacteria are cultivated without antibiotic for many generations with without, 99 générations without, 54 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 10 réactions biochimiques Rµ: * X0 -> X1R0 : free promoter -> RNAP bound promoter * X1 -> X0R1 : unbinding of RNAP freeing promoter * X1 -> X2 + X0 R2 : transcription initiation * X2 -> X3R3 : transcription, X3 = mRNA * X3 -> ØR4 : mRNA deggradation * X3 -> X4R5 : reversible mRNA/ribosome complex formation * X4 -> X3R6 : reversible mRNA/ribosome complex dissociation * X4 -> X5 + X3R7 : Translation start freeing RBS * X5 -> X6R8 : production of protein X6 * X6 -> ØR9 : protein degradation Siggia et al., PNAS October 1, 2002 vol. 99 no transcription traduction ADN chromosomeARNm Protein ARNpolyméraseribosome Numerical simulations

31  We have M reactions R µ (m=1,2,…,M) involving N species.  We define P( ,µ)d  as the probability that the next reaction in [t+ , t+  +d  ] is reaction R µ.  One can show that: c µ dt = average probability, to first order in dt, that a particular combination of R µ reactant molecules will react accordingly in the next time interval dt. h µ = number of distinct molecular reactant combinations for reaction R µ found to be present in V at time t. ( Daniel T. Gillespie, JOURNAL OF COMPUTATIONAL PHYSICS 2, (1976) )  Example: X 1 + X 2 -> X 3 h = X 1 X 2 2X -> Yh = X (X-1)/2  Implementation: one has to generate ( ,µ) according to P( ,µ) in order to update at each step the number of reactant molecules implied in reaction . Stochastic simulations

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

33 1 gene which duplicates, binomial repartition of protein

34

35

36 Ages and division time distributions

37

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


Download ppt "Phenotypic variations in a monoclonal bacterial population Oleg Krichevsky, Itzhak Fishov, Dina Raveh, Ben-Gurion University, Beer-Sheva J. Wong, D. Chatenay,"

Similar presentations


Ads by Google