The document summarizes research on quantifying and modeling mRNA translation, specifically the relative roles of initiation and elongation. It describes studying ribosome traffic to quantify protein production rates. Experiments show that the relative positioning of codons during elongation can affect translational efficiency. A quantitative model is developed that can predict translation efficiency based on initiation and elongation rates and how depletion of ribosomes affects gene expression. The model is consistent with experimental data on how initiation rate affects mRNA efficiency and ribosome density.

1. A quantitative view on mRNA translation: the
relative role of initiation and elongation
Luca Ciandrini (luca.ciandrini@umontpellier.fr)
University of Montpellier, France
13th June 2016
Quantitative Laws II, Villa del Grumello, Como

2. Translation from nucleotide sequences to functional
proteins
Study the ribosome traffic to quantify the protein
production rate (at the level of translation)
ribosomes
growing
polypeptide
mRNA
5’
3’

3. coding sequence
ribosome
START
elongating
protein
initiation
(recruitment)
tRNA
amino-acid
elongation (usage)
termination
STOP
initiation
(recruitment)
elongation (usage)
termination

4. coding sequence
ribosome
START
elongating
protein
initiation
(recruitment)
tRNA
amino-acid
elongation (usage)
termination
STOP
initiation
(recruitment)
elongation (usage)
termination
How can we predict/
vary the strength of
initiation?
Ribosome
recruitment
Ribosome usage
How depletion of ribosomes affects
gene expression and growth rates?
Codon usage and
bias? Evolution?
Does noise depend
on codon
sequences, etc etc?
Can we tune and
predict, just by
varying the
sequence, “optimal”
protein production
rates?P. Geulich, L. Ciandrini, R.J. Allen and M. C. Romano, Phys Rev E (2012)
Shah et al, Cell (2013)

5. Flow of ribosomes ~ driven lattice gas
Macdonald et al., Biopolymers (1968)
Totally Asymmetric Simple Exclusion Process
coding sequence
ribosome
START
elongating
protein
initiation
(recruitment)
tRNA
amino-acid
elongation (usage)
termination
STOP
initiation
(recruitment)
elongation (usage)
termination

6. The model can predict translation efficiency
depending on initiation and elongation rates
ribosomal current
(mRNA efficiency)
initiation rate
codon dependent
elongation rates
termination rate
L. Ciandrini, I. Stansfield and M. C. Romano, PLoS Comp Bio (2013)
initiation
limited
elongation
limited

7. The model can predict ribosome density
depending on initiation and elongation rates
ribosomal density
initiation rate
codon dependent
elongation rates
termination rate
L. Ciandrini, I. Stansfield and M. C. Romano, PLoS Comp Bio (2013)
initiation
limited ribosomes
queueing

8. 1. How initiation affects mRNA efficiency
L. Dias Fernandes, A. de Moura, L. Ciandrini, to be submitted
J.C. Walter, L. Ciandrini, in preparation

9. Observation: the density of ribosomes depends on
the length of the mRNA
data (yeast) from MacKay et al. (2004)

10. The local concentration of ribosomes determines
translation initiation
↵ = ↵o c = ↵o(c1 + cR)
initiation rate
overall ribosome
concentration
contribution due
to terminating ribosomes
(feedback)
Initiation depends on local concentrations of ribosomes
R
r

11. Contribution due to terminating ribosomes depends
on the end-to-end distance and the production rate
↵ = ↵o c = ↵o(c1 + cR)
initiation rate
R
r
The probability to end up in the
reaction volume is ∝ r/R
J
current of the
driven lattice gas
end-to-end distance
is a constant depending on r,
the affinity mRNA-ribosome,…

12. The mRNA persistence length depends on the
ribosomal density
R ⇠
p
`pL R ⇠
q
`poly
p L
`p= persistence length `poly
p = f` + (1 f)`p
f = fraction of transcript
occupied by ribosomes

13. The initiation rate α is therefore length-dependent
(via “recycling” and finite-size effects)
• Fix a tentative initiation rate
• Simulate the system and extract J and R
• Compute the initiation rate
• Compare to the tentative one
J
We develop a self-consistent method to compute the
initiation rate

14. initiation rate:

15. L. Dias Fernandes, A. De Moura, L. Ciandrini, to be submitted
finite size + local concentration
The theory is consistent with the ribosome density
data
initiation rate:

16. The theory is consistent with the ribosome density
data
finite size + local concentration
initiation rate:
L. Dias Fernandes, A. De Moura, L. Ciandrini, to be submitted

17. Short transcripts are less sensitive to changes in
the ribosomal pool
decreasing concentration
of available ribosomes

18. Short transcripts are less sensitive to changes in
the ribosomal pool
mRNA efficiency short transcript/ mRNA efficiency long transcript

19. 2. How elongation affects mRNA efficiency
AJ Kemp, R Betney, LC, ACM Schwenger, MC Romano,I Stansfield, Molecular Microbiology (2013)
B Gorgoni, L. Ciandrini, M Mc Farland, M C Romano, I Stansfield, submitted
Y. Meriguet, S. Guiziou, J. Bonnet, L. Ciandrini, in preparation

20. sup70-65 mutants exhibits slow translation of the
cognate CAG codon
AJ Kemp, R Betney, L Ciandrini, ACM Schwenger, MC Romano,I Stansfield,
Molecular Microbiology (2013)

21. How do we affect translation if we insert CAA and
CAG repeats at the 5’end?
1.0
1.2
on
B
A
luc
luc
luc
luc
5 x CAA
10 x CAA
5 x CAG
10 x CAG
*** ***
******
0.0
0.00
0.02
0.04
0.06
0.08
0.10
Fold
luciferaseexpressionrate
D
E

22. 5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG0.0
0.2
0.4
0.6
0.8
1.0
1.2
SUP70
wild-type
multicopy
tRNACUG
sup70-65
Fold-changein
luciferaseexpression
B
C
luc
luc
luc
luc
5 x CAA
10 x CAA
5 x CAG
10 x CAG
*** ***
******
*** ***
******
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
0.0 0.5 1.0 1.5 2.0 6 9 12 15
0.00
0.02
0.04
0.06
0.08
Fold concentration of tRNACUG
Gln
relative to wild type
luciferaseexpressi
0 2 4 6 8 10
0.020
0.040
0.060
0.080
Number of CAG codons in tandem repea
Luciferaseexpressionrate
F
E
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
0.0
0.2
0.4
0.6
0.8
1.0
1.2
SUP70
wild-type
multicopy
tRNACUG
sup70-65
Fold-changein
luciferaseexpression
B
C
luc
luc
luc
luc
5 x CAA
10 x CAA
5 x CAG
10 x CAG
*** ***
******
*** ***
******
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
0.0 0.5 1.0 1.5 2.0 6 9 12 15
0.00
0.02
0.04
0.06
0.08
Fold concentration of tRNACUG
Gln
relative to wild type
luciferaseexpressio
0 2 4 6 8 10
0.020
0.040
0.060
0.080
Number of CAG codons in tandem repeats
Luciferaseexpressionrate
F
E
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
0.0
SUP70
wild-type
multicopy
tRNACUG
sup70-65
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Modelprediction:fold-changein
luciferaseexpression
C *** ***
******
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
0.0
SUP70
wild-type
multicopy
tRNACUG
sup70-65
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Modelprediction:fold-changein
luciferaseexpression
C *** ***
******
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
0.0
0.0
0.1
0.1
ribosomaldensity
0.0
Lu
Fmodelexperiments
sup70-65 mutants exhibits slow translation of the
cognate CAG codon

23. The method: we simulated the translation in WT and
sup70-65 mutant to perform an in silico genetic screen
sensitive genesinsensitive
genes

24. The predicted targets of tRNAGln
CUG regulation are
confirmed by experiments
54% relative to WT control 103% relative to WT control
predicted to be sensitive predicted to be insensitive

25. We used constitutively active mutants of the Gcn2
protein kinase to decrease the initiation
Gcn2 reduces (globally) the translation initiation rate via the
phosphorylation of the essential translation initiation factor eIF2-α
We tested our hypothesis on a gene (FAR7)
Also, we observed a significant (almost 2-fold) shift in the
population away from chains and towards single cells
initiation rate
mutant/WT
translationrate
model

26. Conclusions
• Initiation might be the origin of the ribosome density/
mRNA length dependence
• Elongation can be used to regulate gene expression
through the tuning of single-codon elongation rates
• Both initiation and elongation can be limiting steps
of translation

27. Further perspectives
• How to independently study and modulate initiation
and elongation?
…undergoing experiments…

28. Acknowledgements
Thank you
Ian Stansfield
Barbara Gorgoni
IMS, University of Aberdeen
Jean-Charles Walter
L2C, University of Montpellier
soutien UM Défi InPhyNiTi
(Interfaces physiques Numérique et Théorique)
Sarah Guiziou
Jerome Bonnet
CBS, University of Montpellier
Yoann Meriguet
DIMNP, University of Montpellier
Lucas Dias Fernandes
Alessandro de Moura
Maria C Romano
ICSMB, University of Aberdeen

30. The relative positioning of codons (elongation)
affects translational efficiency
Shuffling synonymous codons does not affect the final product
(aa-sequence) and keeps the same codon usage
(codon used in a sequence).
original
synonymous

31. The relative positioning of codons (elongation)
affects translational efficiency

32. The relative positioning of codons (elongation)
affects translational efficiency

33. We are going to design sequences with high, medium
and small translation efficiencies and test them

34. The limited amount of resources constrains
translation efficiency*
*Translation efficiency = overall rate of translation per mRNA
Scott et al., Science 2010
There is evidence that the amount of
ribosomes restrains translation efficiency
(see works by Scott, Klumpp, Hwa; Shah
et al. from Plotkin-Kudla groups).
Competition for translational resources
affects translation

35. The limited amount of resources constrains
translation efficiency*
*Translation efficiency = overall rate of translation per mRNA
Scott et al., Science 2010
There is evidence that the amount of
ribosomes restrains translation efficiency
(see works by Scott, Klumpp, Hwa; Shah
et al. from Plotkin-Kudla groups).
Competition for translational resources
affects translation
Can competition for resources be exploited to regulate gene
expression?

36. Conclusions
• Finite-size effects are relevant in translation
• The three-dimensional conformation of the
polysome might enhance initiation (ribosome
“recycling”)
• The selfish mRNA: is recycling a way to build up a
personal pool of ribosomes?

37. 2. Use strep-tag at the 5‘end to detect the
queue
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG0.0
0.2
0.4
0.6
0.8
1.0
1.2
SUP70
wild-type
multicopy
tRNACUG
sup70-65
Fold-changein
luciferaseexpression
B
C
luc
luc
luc
luc
5 x CAA
10 x CAA
5 x CAG
10 x CAG
*** ***
******
*** ***
******
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
0.0 0.5 1.0 1.5 2.0 6 9 12 15
0.00
0.02
0.04
0.06
0.08
Fold concentration of tRNACUG
Gln
relative to wild type
luciferaseexpressi
0 2 4 6 8 10
0.020
0.040
0.060
0.080
Number of CAG codons in tandem repea
Luciferaseexpressionrate
F
E
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
0.0
0.2
0.4
0.6
0.8
1.0
1.2
SUP70
wild-type
multicopy
tRNACUG
sup70-65
Fold-changein
luciferaseexpression
B
C
luc
luc
luc
luc
5 x CAA
10 x CAA
5 x CAG
10 x CAG
*** ***
******
*** ***
******
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
0.0 0.5 1.0 1.5 2.0 6 9 12 15
0.00
0.02
0.04
0.06
0.08
Fold concentration of tRNACUG
Gln
relative to wild type
luciferaseexpressio
0 2 4 6 8 10
0.020
0.040
0.060
0.080
Number of CAG codons in tandem repeats
Luciferaseexpressionrate
F
E
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
0.0
SUP70
wild-type
multicopy
tRNACUG
sup70-65
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Modelprediction:fold-changein
luciferaseexpression
C *** ***
******
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
0.0
SUP70
wild-type
multicopy
tRNACUG
sup70-65
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Modelprediction:fold-changein
luciferaseexpression
C *** ***
******
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
pSUP70
(CEN)
pSUP70-[2µ] psup70-65
(CEN)
0.0
0.0
0.1
0.1
ribosomaldensity
0.0
Lu
F
A.J. Kemp, R. Betney, L. Ciandrini, A.C.M. Schwenger, M.C. Romano, I. Stansfield, Molecular
Microbiology (2013)
Good agreement with theory

38. 2. Use strep-tag at the 5‘end to detect the
queue
CAG causes ribosomes
queue upstream the
1
1
y
G
sup70-65
G
5
x
C
AA
10
x
C
AA
5
x
C
AG
10
x
C
AG
***
***
2µ] psup70-65
(CEN)
2µ] psup70-65
10 20 30 40 50
0.00
0.05
0.10
0.15
codon number
ribosomaldensity
F
CAG repeats
CAA repeats

39. mRNA-dependent estimates of the initiation rates
density from
experiments
initiation
rates
Integration between model and experimental data
=
=
1 site = 1 codon
mRNA 1D-lattice
ribosome
particle
initiation elongation
termination
α β
γ
γ

40. mRNA-dependent estimates of the initiation rates
Important to discern the interplay between
initiation and elongation
Gene Ontology:
(i) regulatory proteins
(ii) cytoplasmic translation
(iii) constituents of
ribosomes
(iv) respiratory chains -
unannotated
L. Ciandrini, I. Stansfield and M. C. Romano, PLoS Comp Bio

41. : mRNA-dependent estimates of the initiation
rates
Protein production rates
L. Ciandrini, I. Stansfield and M. C. Romano, PLoS Comp Bio
(2013)