Transcript Codon bias domains over bacterial chromosomes

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Codon Bias and
Regulation of Translation
among Bacteria and Phages
Thesis defense of
Marc BAILLY-BECHET
Advisor: Massimo VERGASSOLA
Institut Pasteur, Dept Genomes & Genetics, Unit « In Silico » Genetics
Summary
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Introduction to the bacterial
translation system and the codon bias
Structuration of the bacterial
chromosomes by codon bias domains
Why tRNAs in phages?
Translation processes in
prokariotuc cells
Transfer RNA
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tRNAs are the small RNAs
that link an amino-acid to the
peptide sequence
They have a special
palindromic structure
They are amino acid specific
AND codon « specific »
(wooble)
They differ greatly in number
in the cell (from ~100 to
~5000 for a given amino acid)
Degeneracy of the genetic code
Differential usage of
synonymous codons at the genome scale
Causes of the codon bias
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
Non-selective causes of the codon bias
 Mutation biases (e. g. towards high/low G+C)
 Strand bias on the chromosome (GT bias)
Selective causes of the codon bias:
 Translation efficiency
 Translation accuracy
 Codon-anticodon selection ?
 Codon robustness ?
tRNA concentration
correlates to codon bias
Dong et al. (1996) J. Mol. Biol. 260:649
Codon bias domains
over
bacterial chromosomes
Motivations of the project
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Aim: clustering the genes of an organism
according to their codon bias
Biological interests:
–
–
–
–
Functional analysis of the groups of genes
Role of codon bias in the chromosome structuration
Comparison of the genome organization between species
Inference of some codon bias causes from the
classification
Previous results
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Methods:
correspondance analysis
2 main sub-groups of genes
identified in multiple
organisms:
– Highly expressed
– Horizontal transfer genes
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Methodological difficulties:
– Choice of the number of
groups
– Choice of the distance
Kunst et al. (1997), Nature 390:249
Key idea about the method:
the optimization criteria
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Each group is defined by the probability
distribution of codon usage generated by
the genes it contains
A good classification is one which maximize
the gain of information on these
probability distributions, relative to a
uniform prior distribution
max

groupsamino
acids
*
DKL
Pprior || Ppost 
The clustering algorithm
N
N-1
…….
Threshold
C =40
…….
Key idea about the method:
selection of the number of groups

The good number of groups is the one
maximizing the average stability of genes
attribution inside the groups, relative to the
expected stability in absence of structure
(random case)
b 
s
g
Lg  Cs 
 Lg  C 
s'
s'
S

max  b
s1
s
g C
s
1

S
Number of groups and
clustering significance
Codon usage inside the groups
Qu ickTim e™ a nd a
TIFF (Un compresse d) decompressor
are need ed to see th is picture.
Tests of the algorithm
Gene function is correlated
with codon bias
1.
Highly expressed genes, translation and ribosomal proteins :
COG J (9/22).
2.
Unknown genes, pathogenicity islands and horizontally transfered
genes : COG - (17/19).
3.
Metabolism (synthesis & transport) : COG C (4/6), E (7/4) et
F (7).
4.
Membrane and carbohydrate metabolism genes : COG G (6) et
M (3/3).
5.
B. subtilis only -- Motility genes : COG N (5).
Anabolic genes are grouped
on the lagging strand
Replication and transcription
machineries collisions
Anabolic genes are
usually transcribed
when no replication
occurs
=> being on the
lagging strand is not
counter-selected.
Codon bias domains
Group by group analysis :
influence of the GC%
Group 2
GC=35.8%
Group 4
GC=47%
Acknowledgements (I)
Frank Kunst and all the GMP Team
Why tRNAs in phages?
What’s a phage?
Motivations of the project

Understanding the presence of tRNAs
inside bacteriophages
– Correlation to the host or phage codon bias?
– Differences between lytic and temperate
phages?
– Selection acting on tRNA acquisition and
implications for phage evolution?
Acquisition of tRNA sequences
by bacteriophages
Lysogenic phages are known to insert in microbial
genomes in tRNA sequences
=> Imprecise excision could explain the acquisition of
tRNA sequences
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
Lytic phages cause liberation of the host genetic
material after cell lysis
=> Acquisition of tRNAs sequences in the surrounding
media or neighbour hosts
Datas
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Beginning :
– 200 DNA phage genomes, 23 hosts, 240 tRNAs
Taken out :
– Non sequenced hosts
– Phages genomes without tRNAs
– tRNAs inserted in prophagic regions
– Phages having tRNAs their host do not have
Final dataset :
– 37 phages, 15 hosts, 169 tRNAs
(6 duplicates, 1 triplet)
tRNA distribution in phages
Correlation of host and
phages codon bias
< R > = 0.77  0.27
real data
< R > = 0.38  0.42
phage-random host
=> Codon usage is correlated between the
host and the phage
< R > = 0.83  0.14
real data - Temperate
< R > = 0.61  0.39
real data - Lytic
=> The correlations are higher in temperate
phages
Phage codon frequency distribution
is related to tRNA content
<Nc> =49.9
<Nc> =52.9
First conclusions
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Lytic phages have a codon usage less
similar to the one of their hosts when
compared to temperate phages
Lytic phages have more tRNAs than
temperate ones
Codon usage is more biased in lytic phages
than in temperate ones
Both seem to have tRNAs corresponding to
the codons they use more
Random uptake hypothesis
tRNA content of host matches codon bias
 Codon bias of phage matches the host one’s
=> No need for the phage to have tRNAs !

Random uptake hypothesis: the tRNA content
of a phage should be proportional to its host
tRNA content, and so would be indirectly
correlated to the codon bias of the phage
Statistical tests of the
random uptake hypothesis
1
 f 
N
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
N
 f (k)
k1
1
 f 
N
N
f (k)  f  (k)
k1
Significance for high values of <f>: p = 0.68

– No specific enrichment in tRNAs for the phage high
frequency codons
Significance for high values of <∆f>: p < 0.0007
– Significant enrichment in tRNAs for the codons the
phage uses more than its host
Modelisation of the acquisition
and loss processes
P ,x (n,t)
P ,x (n 1,t)
1 rH  ,x n
Gain
Loss

rH  ,x

P ,x (n  1,t)


(n 1)
P ,x (n,t  dt)


Inference of the parameters
by maximum likelihood
Probability
Likelihood of
the real data,
given the 
model
Maximum

Most probable
P(n)
L(r) 
 P(N )
 , ,x
likelihood
r  0.060
Evolutive processes tested
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Selection based on:
– Frequency of usage of the corresponding codon
in the phage genome (+)
– Frequency of usage of the corresponding codon
in the host genome (-)
– Difference of codon usage frequencies between
phage and host genome (+)

Duplication of tRNA on the phage genome
Master model equation results
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Selection based on the phage frequency of codon
usage is non significant (p=0.15)
Selection based on the rarity of the codon in the
host genome is slightly significant (p=0.018 before
Bonferroni correction)
Selection based on the difference of frequencies
of codon usage between phage and host is highly
significant (p<2.10-7)
The tRNA duplication hypothesis has to be
rejected
Adaptative selection of tRNAs?
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Selection relative to the phage codon usage only
could lead to a static tRNA content, and could be
non-optimal after an host change
Selection relative to the host codon usage only
does not take into account the quick phage
sequence evolution
Selection needs to take both into account to be
adaptative and gives rise to a useful tRNA content
Conclusions
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Translational selection is a strong pressure acting
on phage tRNA content
tRNA content among phages is optimized to
compensate for differences between host and
phage codon usage
This pressure is more important in lytic phages
Acknowledgements (II)
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Massimo Vergassola
Eduardo Rocha
The committee members
Yves Charon
Guillaume Cambray
Aymeric Fouquier d’Herouel
All the family and friends who came today!
Supp. Mat. Part 1
Codons probability distributions
Tests of the algorithm (II)

High CAI genes share the same codon
bias:
– 32/59 in group 1 of B. subtilis
– 33/33 in group 1 of E. coli

Genes in the same operon or pathway
tend to belong to the same group
Transcription and translation
From Miller et al., 1970, Science 169:392
Translation regulation and
synchronization by tRNA recycling
Gene 1
Gene 2
Gene 3
Recycling phenomenon analysis
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On average, tRNA recycling should not
increase translation speed
Recycling could induce a coupling
between close ribosomes, allowing for
protein synthesis synchronization
Synthetases are the limiting factor as
they prevent in most cases a tRNA used
by a ribosome to be re-employed by
another close one
Supp. Mat. Part 2
Phage codon frequency distribution
is related to tRNA content
Master equation model (I)
Random excision
P(n,t  dt)
 (rH)P(n 1,t)  (n 1)P(n 1,t)  (rH  n)P(n,t)
t
n
(rH) rH
lim P(n,t) 
e
t 
n!

Modelisation of the acquisition
and loss processes (II)
P ,x (n,t)
P ,x (n 1,t)
P ,x (n  1,t)
1 rH  ,x
nesf ,x
Gain

rH  ,x



Loss
(n  1)e
sf ,x
P,x (n,t  dt)


Master equation models (II)
Random excision
P(n,t  dt)
 (rH)P(n 1,t)  (n 1)P(n 1,t)  (rH  n)P(n,t)
t
Random excision + selective loss
P(n,t  dt)
 (rH)P(n 1,t)  (n 1)esf P(n 1,t)  (rH  nesf )P(n,t)
t
Random excision + selective loss + random copy
P(n,t  dt)
 (rH  (n 1)c)P(n 1,t)  (n 1)esf P(n 1,t)  (rH  n(esf  c))P(n,t)
t
Selection is significant event
relative to random hosts