Ribosome traffic on messenger RNAs:
stochastic control of gene expression
Ian Stansfield
School of Medical Sciences, Institute of Medical Sciences,
University of Aberdeen
Gene expression
RNA polymerase
DNA
transcription
ribosome
mRNA
translation
N
C
protein
Protein expression ratio
(galactose : ethanol)
Non-linear relation between mRNA and protein abundance
may be explained in part by translation effects
mRNA expression ratio
(galactose : ethanol)
Ideker et al (2001) Science. 292:929-34.
Eukaryote translation
RF
RF
AUG
Cap
UAA
Initiation
Termination
Elongation
mRNA
AAAAA
The ribosome is an accurate and efficient
machine
• Translation elongation
rate;
- 12 - 20 codons per s
• Very low error frequency [1x10-5];
- an intrinsically accurate machine
• 220 000 ribosomes per yeast cell
- translating 15,000 mRNAs
- using 3 million tRNAs
Protein synthesis - a cellular production line
tRNA
+
amino acid
tRNA synthetase
Elongation factor eEF1a
mRNA
ribosome
20 different ‘flavours’ of amino acid
20 different tRNA synthetases
10 amino acids added per ribosome per second
Variations in tRNA abundance cause
stochastic variations in decoding rates
tRNA gene copy number s in bakerХsyeast
U
C
CUU
CUC
CUA
CUG
UCU
UCC
S
UCA
7
L
10 UCG
CCU
1
CCC
L
P
3 CCA
CCG
A
AUU
AUC
AUA
AUG
13
I
1
2
M 11
G
GUU
GUC
GUA
GUG
U
UUU
UUC
UUA
UUG
C
F 11
14
V
2
3
ACU
ACC
T
ACA
ACG
GCU
GCC
A
GCA
GCG
A
11
4
1
UAU
UAC
UAA
UAG
Y
CAU
H
CAC
CAA
10
Q
CAG
5
1
11
6
AAU
AAC
AAA
AAG
8
*
2
11
G
N
K
GAU
D
GAC
GAA
E
GAG
8
9
1
UGU
C
UGC
UGA *
UGG W
CGU
CGG
R
CGA
CGG
AGU
S
AGC
8 AGA R
14 AGG
11
GGU
GGC
G
15 GGA
2 GGG
16
4
6
7
1
2
12
1
16
3
2
Codon bias defines a queuing ‘landscape’ that
can regulate protein productivity
HXT2 wait time window 10 codons
mean wait time
120
100
80
60
40
20
0
0
100
200
300
codons
400
500
600
Mutation in Saccharomyces cerevisiae tRNAGln
confers a pseudohyphal growth phenotype
Wild-type yeast
SUP70 tRNA mutant
• Single-copy tRNAGln
• Two alleles each exhibit ‘pseudohyphal’ cell chain formation under
nutrient replete conditions
Alain Kemp and Alex Schwenger
Murray et al. (1998) Proc Natl Acad Sci USA 95, 8619-8624
tRNAGln sup70-65 pseudohyphal growth in nutrient
replete liquid media
wild-type
tRNAGln
(+ / +)
heterozygote
mutant
tRNAGln (7065 / +)
homozygote
mutant
tRNAGln
(70-65 / 70-65)
sup70-65 tRNA can (inefficiently) mis-read
stop codons: nonsense suppression
AUG
STOP
STOP
Gln
N
N
translation
C
wild-type
Gln
G U C
G U C
C A G
U A G
Gln
Gln
G U C
G U C
C A G
U A G
C
sup70
Protein synthesis - a cellular production line
tRNA
+
amino acid
Gln tRNA synthetase
Elongation factor eEF1a
mRNA
ribosome
SUP70
wild-type tRNA
Protein synthesis - a cellular production line
tRNA
+
amino acid
Gln tRNA synthetase
Elongation factor eEF1a
mRNA
ribosome
sup70-65
mutant tRNA
Modelling ribosomal traffic
Modelling goal: predicting the correspondence
between mRNA and protein abundance
translation
translation
sup70
transcriptome
proteome
Modelling ribosomal traffic
Free
ribosomes
Scanning
ribosomes
Elongating
ribosomes
k1
k2
A
B
C
6
5
4
3
2
1
0
A
B
C
Stochastic Model of Translation
p
p
p
p
mRNA
Codons occupied by one ribosome
Ribosome
The ribosome makes one step ahead with
probability p
The ribosome is not allowed to make one step
ahead because the next codon is occupied.
Romano et al PRL 102, 198104 (2009)
Modelling a ribosome movement on a linear track
analytically
The next codon
is free
The next codon is
occupied, but the
ribosome ahead
also makes one
step forwards
Variations in tRNA abundance define different p value
probabilities of elongation at each codon type
tRNA gene copy number s in bakerХsyeast
U
C
CUU
CUC
CUA
CUG
UCU
UCC
S
UCA
7
L
10 UCG
CCU
1
CCC
L
P
3 CCA
CCG
A
AUU
AUC
AUA
AUG
13
I
1
2
M 11
G
GUU
GUC
GUA
GUG
U
UUU
UUC
UUA
UUG
C
F 11
14
V
2
3
ACU
ACC
T
ACA
ACG
GCU
GCC
A
GCA
GCG
A
11
4
1
UAU
UAC
UAA
UAG
Y
CAU
H
CAC
CAA
10
Q
CAG
5
1
11
6
AAU
AAC
AAA
AAG
8
*
2
11
G
N
K
GAU
D
GAC
GAA
E
GAG
8
9
1
UGU
C
UGC
UGA *
UGG W
CGU
CGG
R
CGA
CGG
AGU
S
AGC
8 AGA R
14 AGG
11
GGU
GGC
G
15 GGA
2 GGG
16
4
6
7
1
2
12
1
16
3
2
Introduction of slow codon clusters
Protein
productivity
All codons equal: p=0.95
1% slow codons regularly spaced
1% slow codons randomly spaced
1% slow codons in 4 clusters
1% slow codons in 1 cluster
ribosome density
• Clusters of slow codons cause protein productivity
to be independent of ribosome density over a given
density range
A more realistic simulation; ‘wide’ ribosomes
and rare codon clusters (and linear mRNA)
No rare codons
Regularly distributed
rare codons
Protein
productivity
Randomly distributed
4 small clusters
1 large cluster
Ribosome density
Codon composition determines a phase transition
response to ribosome density
Responsive to changes
in ribosome availability
Non-responsive to changes
in ribosome availability
Protein
productivity
Ribosome density
• 500 yeast mRNAs modelled
• Two types of yeast mRNA; productivity / initiation
rate coupled (type I) and uncoupled (type II)
• Ribosomal protein mRNAs are all of type I
Phase transitions during ribosome loading onto yeast
mRNAs
Yeast
mRNAs
Artific.
mRNAs
Density versus
Initiation rate
Protein productivity
versus density
The configuration of rare codon clusters defines the
queuing propensity
Density
profiles
Codon
wait
times
Gene A
Gene B
The next stage; incorporating a description of translation
initiation
mRNA
Cap
Initiation
AUG
Stop
80S
Elongation
Luca Ciandrini, Mamen Romano and Ian Stansfield
AAAAA
Summary: A stochastic model for eukaryote translation
• mRNA translation can be modelled using a probability-based model that
accounts for the space ribosomes occupy on mRNAs - exclusion principle
• mRNAs with rare codons exhibit a saturation in the productivity, i.e., if
protein synthesis non-responsive to increased density of ribosomes.
- response of translation to biological noise?
• Ribosomal protein mRNAs exhibit a response to initiation free of
phase transition
• Model correctly predicts the relationship between mRNA length and
ribosome density based on gains/cost optimisation
Contributors and collaborators
University of Aberdeen
Russell Betney
Alain Kemp
Claudia Rato da Silva
Luca Ciandrini
Yvonne Knox
Alex Schwenger
Mamen Romano
Chris Brackley
Marco Thiel
Celso Grebogi
Heather Wallace
Imperial College
J.Krishnan, Eric de Silva
University of Leicester
Declan Bates, Svetlana Amirova
Institute of Medical Sciences,
University of Aberdeen