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Transcription Kinetics in Bacteria. Ido Golding Department of Molecular Biology Princeton University Johan Paulsson Edward C. Cox. Cellular life as a set of discrete events. Transcription (initiation, elongation, termination) Translation RNA & protein degradation

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Transcription kinetics in bacteria

Transcription Kinetics in Bacteria

Ido Golding

Department of Molecular Biology

Princeton University

Johan Paulsson

Edward C. Cox

Cellular life as a set of discrete events
Cellular life as a set of discrete events

  • Transcription (initiation, elongation, termination)

  • Translation

  • RNA & protein degradation

  • Binding of transcription factors

  • DNA replication

  • Homologous recombination

  • Aim:

    To Reliably detect and quantify the kinetics of these processes

    (Usually obscured by population averaging etc.)

  • What does it mean for a gene to be ‘on’ ?

  • Physical nature of the cytoplasm

  • Credo: What is needed in biology ?

Gene expression in bacteria
Gene expression in bacteria

“The central dogma of molecular biology”





Miller et al (1970)

Studying transcription two ends of the spectrum
Studying transcription: Two ends of the spectrum

In vivo / Whole population:



Y. Chen



Real time



In vitro / Single molecule:

S. Zawilski

Optical trapping setup for studying RNA polymerase

J. Shaevitz et al, Nature426: 684 - 687 (2003)

Record of RNA polymerase motion

IPTG, arabinose

Following transcription in real-time

RNA-tagging protein:



MS2d GFPmut3


Gene of interest:



96x MS2-bs

RNA target

RFP protein

Golding et al, Cell (2005)

  • Gene-of-interest inactive :

  • No RNA target present

  • Uniform green fluorescence

  • (free MS2-GFP)

  • No RFP present

  • Gene-of-interest active :

  • RNA target present

  • Localized green fluorescence

  • (bound MS2-GFP)

  • RFP present

What can we measure
What can we measure?

  • Single molecule dynamics of mRNA:

  • Low mRNA levels: each “spot” = 1 mRNA molecule.

  • Chain elongation during transcription

  • Polymer fluctuations

  • Cytoplasmic motion

Spot intensity ~ 70 GFPs

Golding and Cox, PNAS (2004); Golding and Cox, Phys. Rev. Lett. (2006)

What can we measure1

Histogram of

RNA copy number:

1st peak =

inter-peak interval 

50-100 X GFP =

1 transcript

Gene induction kinetics:

Indicators of gene activity

+ additional tests

QPCR: S. Zawilski

Lux: Lutz & Bujard 1997

What can we measure?

2. mRNA & protein numbers:

mRNA number of bound MS2-GFP proteins

 photon flux from localized green fluorescence

Protein  number of mRFP1 proteins

 photon flux from whole-cell red fluorescence

Induction kinetics population average

mRNA per cell

Approach to steady state

Induction kinetics: Population average

Constant rate of production k1 ; first-order elimination with rate constant k2:

dn/dt = k1 – k2n

n(t) = k1 /k2 (1 – e - k2t)

n(t) = mRNA/cell

k1= transcription rate = 0.14 min-1 (fit)

k2= dilution rate = ln 2 / 50 min-1

Average kinetics consistent

with Poisson process.

Induction kinetics population heterogeneity


mRNA copy-number histogram

Induction kinetics: Population heterogeneity

Variance vs mean

Induction kinetics:

Population heterogeneity

Fraction of cells with no mRNA:

P0(t)  e-k1t

k1 (measured)  0.03 min-1

rate of N0 decline << transcription rate!

Probability of zero events

Variance to mean ratio:

Poisson:s2 / n = 1

measured:s2 / n 4


with Poisson process!

Transcription as a 2 state process
Transcription as a “2-state process”

Gene in the OFF state switches ON with a constant probability (k1).

Gene in the ON state either switches OFF (k2),

or makes a transcript with constant probability (ktrans).

Can result in transcriptional bursting (burst size b ~ ktrans / k2)

Golding & Cox, Curr. Biol. (2006).

2 state model reproduces experimental results


2-state model reproduces experimental results:

(analytical results, simulations)


P0(t)declines with ratek1

mRNA number histogram

Measured transcription rate

k1eff = k1 * b

s2/n 1+b

Thattai and van Oudenaarden 2001;Paulsson 2004

RNA kinetics in individual cells

# mRNA vs time

Distribution of on & off times

Distribution of burst sizes

RNA bursts geometrically distributed

On & off times exponentially distributed

Transcriptional bursting in eukaryotes
Transcriptional bursting in eukaryotes

Chubb JR, Trcek T, Shenoy SM, Singer RH.

Curr. Biol. 2006 May 23;16(10):1018-25.

See also: Golding & Cox, Curr. Biol. (2006).

Protein bursting in e coli
Protein bursting in E. coli

Yu J, Xiao J, Ren X, Lao K, Xie XS

Science. 2006 Mar 17;311(5767):1600-3.

Cai L, Friedman N, Xie XS

Nature. 2006 Mar 16;440(7082):358-62.

See also: Golding & Cox, Genome Biology (2006).

Additional findings:

RNA partitioning

DN = difference in RNA numbers between 2 daughter cells.

Binomial statistics - consistent with independent segregation of individual molecules.

Rna translation

1) Protein copy number is proportional to mRNA copy number

2) How many proteins are made from one transcript?

IG = (nRNA*N) * fGFP


IG = green fluorescence level (of spots)

IR = red fluorescence level (of cell)

fGFP = flux from one GFP molecule

fRFP = flux from one RFP molecule

N ~ 50-100, IR / IG = 3.10.2

fRFP /fGFP  31

p = nPROTEIN / nRNA= N * (fGFP /fRFP ) * (IR / IG) 60-110

protein vs mRNA


RNA translation

+ single cells:

protein/RNA correlations

Single molecule dynamics mrna chain elongation
Single molecule dynamics: mRNA chain elongation

  • Measured elongation rate ~ 15 nm/s ~ 25 nt/s

  • Consistent with:

  • bulk measurements (Ryals et al 1982)

  • our fluorescence measurement in single cells (~ 1 transcript/2.5 min)

Golding and Cox, PNAS (2004)

Single molecule dynamics motion in the cytoplasm



Single molecule dynamics: Motion in the cytoplasm

Particle tracking:

constrained motion punctuated by large jumps

Golding & Cox,

Phys. Rev. Lett. (2006)

Single molecule dynamics motion in the cytoplasm1

a=1 (in vitro)

a=0.7 (in vivo)

slope = -(1+a) = -1.77

original trajectories

slope = -1.96

randomized trajectories

Single molecule dynamics: Motion in the cytoplasm

Motion is sub-diffusive:

d2 = a ta , a = 0.70±0.07

(in vitro: a = 1.04 ±0.03)

Similar motion observed in eukaryotic cells

(lipid granules, dextran etc)

Power spectrum of position:

P(f) ~ f -(1+a) , a = 0.77±0.03

Sub-diffusion arising from long-tailed distribution of waiting times:

w(t) ~ t-(1+a)with 0 < a< 1

(Metzler & Klafter 2000)

Interaction with heterogeneous medium

What hinders motion in the cytoplasm
What hinders motion in the cytoplasm?

  • Cytoskeleton?

  • Polymer network spanning the cell.

  • In bacteria: MreB, FtsZ, ParM etc.

  • (Errington 2003)

  • * Motion in actin networks in-vitro

  • is sub-diffusive

  • (Amblard et al 1996, Wong et al 2004)

* However:

MreB & FtsZ mutants exhibit sub-diffusion similar to wild-type

(“Mixed” results in eukaryotes: Weiss et al 2004,

Tolic-Norrelykke et al 2004, Dauty and Verkman 2005)

What hinders motion in the cytoplasm1

(2) Molecular crowding?

Large volume fraction of cell taken by


~300 mg/ml (Zimmerman & Trach 1991)

~20000 ribosomes/cell

~1 mm DNA

What hinders motion in the cytoplasm?

* Sub-diffusion coefficient decreases with growth rate

* Deleting RBS leads to faster motion

* In vitro results (Banks and Fradin 2005)

* Monte-Carlo simulations

(Saxton 1994, Weiss et al 2004)

What hinders motion in the cytoplasm?

Exponent a is insensitive to system parameters:

presence of RBS, length of RNA molecule, growth rate, presence of antibiotics (Cm,Tet), cytoskeletal elements (MreB,FtsZ)…

  • Possible scenario:

    a =al +(1-al)e-f/f0

    (Banks & Fradin 2005)

  • =sub-diffusion exponent

    F0 =threshold density for S.D. = ?

    al =asymptotic value of a ≈ 0.74

    Values for E. coli :

    F* ≈ 0.4 (Zimmerman & Trach 1991)

    a*≈ 0.74 (this study)

    Possible implications…

E. coli :

F* >> F0

a*≈ al


(F* , a*)




obstacle density

Possible consequences how do transcription factors find their target
Possible consequences:How do transcription factors find their target?

  • 3D diffusion is inefficient; 3D+1D is postulated

    (von Hippel & Berg 1989).

  • TFs are often produced close to their target

    (Warren & ten Wolde 2004).

  • Probability of finding target before escaping:

    p ~ (a/r)3-2/a (p→1 for a →2/3)

    where a = target size, r = initial distance

    (Golding & Cox 2006, Halford & Marko 2004).

Current future work


infected with phage l



Current & future work

  • Mechanisms of transcriptional bursting

  • Reporters for other promoters: phage l genetic switch

  • Combine with other cellular markers:

    DNA, RNAP, ribosomes, cytoskeleton…

  • Kinetics of cellular events: phage infection

Thanks to:

D. Peabody, H. Diamant, R. Segev, Y. Zhang,

R. Austin, P. Wolanin, J. Puchalla

& all members of the Cox lab

Johan Paulsson

Ted Cox