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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”

DNA RNA PROTEIN

transcription

translation

http://www- rohan.sdsu.edu/~jmahaffy/courses/s00/math121/lectures/func_review_quad/images/transcription.jpg

Miller et al (1970)

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

In vivo / Whole population:

DNA

microarrays

Y. Chen

cDNA

RNA

Real time

PCR

Cells

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

slide5

IPTG, arabinose

Following transcription in real-time

RNA-tagging protein:

MS2-GFP

PLtetO-1

MS2d GFPmut3

aTc

Gene of interest:

Plac/ara

mRFP1

96x MS2-bs

RNA target

RFP protein

Golding et al, Cell (2005)

slide6

Gene-of-interest inactive :

  • No RNA target present
  • Uniform green fluorescence
  • (free MS2-GFP)
  • No RFP present
slide7

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.

slide12

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

Inconsistent

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

Simulation

2-state model reproduces experimental results:

(analytical results, simulations)

Experiment

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

slide15

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).

slide18

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

IR= nPROTEIN* fRFP

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

slope1

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

x(t)

y(t)

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)

http://35.9.122.184/images/07-TourOfTheCell/HTML/source/46.html

* 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

macromolecules:

~300 mg/ml (Zimmerman & Trach 1991)

~20000 ribosomes/cell

~1 mm DNA

http://www.jbc.org/content/vol276/issue14/images/large/bc1411813001.jpeg

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)

slide25

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

F0=0.1

(F* , a*)

al

F0=0.05

*

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

uninfected

infected with phage l

lysogens

lysis

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
slide28

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

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