Codon Bias and Regulation of Translation among Bacteria

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 n n n Introduction to the bacterial translation system and the codon bias Structuration of the bacterial chromosomes by codon bias domains Why t. RNAs in phages?

Translation processes in prokariotuc cells

Transfer RNA n n t. RNAs 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 § § 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 ?

t. RNA concentration correlates to codon bias Dong et al. (1996) J. Mol. Biol. 260: 649

Codon bias domains over bacterial chromosomes

Motivations of the project n n 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 n n Methods: correspondance analysis 2 main sub-groups of genes identified in multiple organisms: – Highly expressed – Horizontal transfer genes n 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 n n 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

The clustering algorithm N N-1 ……. Threshold C =40 …….

Key idea about the method: selection of the number of groups n 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)

Number of groups and clustering significance

Codon usage inside the groups

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 (7). 4. Membrane and carbohydrate metabolism genes : COG G (6) et M (3/3). 5. B. subtilis only -- Motility genes : COG N (5). F

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 t. RNAs in phages?

What’s a phage?

Motivations of the project n Understanding the presence of t. RNAs inside bacteriophages – Correlation to the host or phage codon bias? – Differences between lytic and temperate phages? – Selection acting on t. RNA acquisition and implications for phage evolution?

Acquisition of t. RNA sequences by bacteriophages Lysogenic phages are known to insert in microbial genomes in t. RNA sequences => Imprecise excision could explain the acquisition of t. RNA sequences n § Lytic phages cause liberation of the host genetic material after cell lysis => Acquisition of t. RNAs sequences in the surrounding media or neighbour hosts

Datas n n n Beginning : – 200 DNA phage genomes, 23 hosts, 240 t. RNAs Taken out : – Non sequenced hosts – Phages genomes without t. RNAs – t. RNAs inserted in prophagic regions – Phages having t. RNAs their host do not have Final dataset : – 37 phages, 15 hosts, 169 t. RNAs (6 duplicates, 1 triplet)

t. RNA 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 t. RNA content <Nc> =49. 9 <Nc> =52. 9

First conclusions n n Lytic phages have a codon usage less similar to the one of their hosts when compared to temperate phages Lytic phages have more t. RNAs than temperate ones Codon usage is more biased in lytic phages than in temperate ones Both seem to have t. RNAs corresponding to the codons they use more

Random uptake hypothesis t. RNA content of host matches codon bias § Codon bias of phage matches the host one’s => No need for the phage to have t. RNAs ! § Random uptake hypothesis: the t. RNA content of a phage should be proportional to its host t. RNA content, and so would be indirectly correlated to the codon bias of the phage

Statistical tests of the random uptake hypothesis n n Significance for high values of <f>: p = 0. 68 – No specific enrichment in t. RNAs for the phage high frequency codons Significance for high values of <∆f>: p < 0. 0007 – Significant enrichment in t. RNAs for the codons the phage uses more than its host

Modelisation of the acquisition and loss processes Gain Loss

Inference of the parameters by maximum likelihood Probability Likelihood of the real data, given the model Maximum Most probable likelihood

Evolutive processes tested n 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 (+) n Duplication of t. RNA on the phage genome

Master model equation results § § 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 t. RNA duplication hypothesis has to be rejected

Adaptative selection of t. RNAs? n n n Selection relative to the phage codon usage only could lead to a static t. RNA 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 t. RNA content

Conclusions n n n Translational selection is a strong pressure acting on phage t. RNA 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) n n n n 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) n High CAI genes share the same codon bias: – 32/59 in group 1 of B. subtilis – 33/33 in group 1 of E. coli n 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 t. RNA recycling Gene 1 Gene 2 Gene 3

Recycling phenomenon analysis n n n On average, t. RNA 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 t. RNA used by a ribosome to be re-employed by another close one

Supp. Mat. Part 2

Phage codon frequency distribution is related to t. RNA content

Master equation model (I) Random excision

Modelisation of the acquisition and loss processes (II) Gain Loss

Master equation models (II) Random excision + selective loss + random copy

Selection is significant event relative to random hosts