Keywords: Translation, Plasticity, RNA, Protein, Error Rates, Ribosome, Aminoacyl-tRNA synthetase, Mass Spectrometry

Transmission of genetic information from DNA to proteins is not as rigid as one may think. Despite their marvelous sophistication, the machines at the core of the central dogma rely on chemical processes and complex interactions, and are not exempt from errors, that organisms can either choose to mitigate, or use to their own advantage. I will present a pair of studies which reflect the two sides of this dichotomy. First, we developed a new methodology which allowed us to detect a large number of amino acid misincorporations within the proteome of Escherichia coli from high precision mass spectrometry data. We show that these errors are mostly the result of a competition between cognate and non cognate tRNAs in the ribosome, and that they respond dynamically to environmental perturbations, such as amino acid starvation and anti ribosome drugs. Furthermore, we demonstrate that cells tend to encode their proteins in a way that minimizes the deleterious effects of translation errors. In a second study, we discovered that mRNA editing is not restricted to eukaryotes, but is present in bacteria. We found that Escherichia coli exploits a tRNA adenosine to inosine editing enzyme to stimulate bacterial drug persistence through the recoding of a toxin's mRNA. In a third study, we monitored the evolution of a large library E. coli cells to measure the effects of synonymous substitutions on protein production and fitness.

Titre : "Erreurs et corrections : plasticité de l'expression génétique chez Escherichia coli"

Résumé : La transmission de l'information génétique est généralement décrite comme un processus déterministe. Malgré leur sophistication, les machineries moléculaires en charge de l'expression génétique fonctionnent grâce à des réaction chimiques stochastiques par nature, et ne sont pas à l'abri d'erreurs, dont un organisme vivant peut choisir de minimiser les effets, ou au contraire de les utiliser à son avantage. Je présente dans ce manuscrit deux études illustrant ces deux possibilités. Dans un premier temps, nous avons développé une nouvelle méthodologie nous permettant de détecter un grand nombre de misincorporation d'acides aminés à travers le proteome d'Escherichia coli, à l'aide de données de spectrométrie de masse. Ces erreurs réagissent de manière dynamique à des perturbations environnementales telles que la privation d'un acide aminé ou la présence d'antibiotiques visant le ribosome. De plus, nous démontrons que la cellule encode ses protéines d'une manière qui minimise leurs effets délétères. Dans une deuxième étude, nous montrons que l'édition d'ARN messagers n'est pas restreinte aux Eukaryotes. Escherichia coli utilise le surplus d'activité d'une enzyme modifiant l'adenosine d'un anticodon d'ARNt en inosine, afin de modifier la sequence codante d'une toxine, accentuant en retour le phénomène de persistance bactérienne. Finalement, nous avons mesuré les fréquences relatives des différents variants d'une banque de séquences afin de determiner les effets de mutation synonymes sur le taux de croissance et l'expression protéique.

Mots clés : Traduction, Plasticité, ARN, Protéine, Taux d'erreurs, Ribosome, Aminoacyl-ARNt synthétase, Spéctrométrie de Masse First and foremost, I would like to thank my PhD supervisor Tzachi Pilpel for his unwavering support. I learned a lot from him as a scientist and a person. It has been fantastic to work with someone so kind-hearted, and with such contagious enthusiasm for the beauty of science and nature. I am very grateful that he granted me such freedom to explore my own interests, and always trusted me, especially when I doubted myself. I hope to carry with me the same open-mindedness and optimism that made him such an exceptional mentor. This collaboration would not have been possible without the original intuition of Ariel Lindner. Not only did he provide invaluable expertise from a scientific viewpoint, I could always count on him during the inexorable hardships of a PhD. I truly appreciate the efforts that he undertook to make sure that my degree stayed on the right tracks, and the time that he devoted to me. I want to thank Tami Geiger for her support with the experimental mass spectrometry, and Jürgen Cox for helping with some of the computational aspects of the project and hosting me in Münich for a week. Both of them demonstrated a great sense of patience towards our naïve understanding of mass spectrometry. This PhD could not have taken place within any other french graduate school than Frontières du Vivant. The flexibility they offered me is unparalleled, and they were unconditionally sympathetic and helpful. I would like to express my gratitude to David Tareste, and especially to Élodie Kaslikowski, for getting me out of trouble more than once.

I want to thank my two reviewers, Rachel Green and Olivier Namy, for the time they will devote to the reading of this manuscript, and Bertrand Cosson for accepting to take part in my jury.

I would like to thank all the past and present members of the Pilpel lab, who gave me a warm welcome from the very beginning and made me feel at home in Israel. I dearly miss already the passionate discussions, the honesty, and the interminable lab meetings. In particular, I would like to thank Orna Dahan and Avia Yehonadav for their help and support with experiments that would otherwise have challenged my limited abilities for wet work. I would also like to thank Dan Bar-Yaakov, who was the main driving force for the RNA editing paper presented in this thesis. It has always been a lot of fun working with him. I hope to see again very soon my friends, the climbers, the rugby players and the music nerds, who made this PhD such an enjoyable and rewarding experience. 6 Finally, I would like to thank warmly both of my parents, who triggered and nourished my curiosity and my appetite for learning from an early age. They always supported me morally (and sometimes materially) along this PhD.

Introduction

Proteins enable most of chemical reactions in cells. They serve as an interface between the information world, stored in an organism's DNA sequence, and its chemical environment. They typically fold into distinct patterns, dictated by their amino acid sequence, which is itself defined almost deterministically by their DNA coding sequence. These folds in turn create a dynamic, three dimensional environment, locally decreasing the energetic barrier of specific chemical reactions, and potentially allowing thermodynamically unfavorable reactions by coupling them to favored ones. By doing so, they open the realm of an out of equilibrium chemistry necessary for the appearance of the order which characterizes living organisms. The regulation of their expression levels, determined by a constant sensing of the environment, offers a formidable way for organisms to navigate through the space of possible metabolisms, and thus to fine-tune their inner-workings to the available resources or challenges they encounter, towards the goal of generating all the necessary building blocks to the replication of the organism.

Proteins are synthesized by polymerization of amino acids using mRNA as a template, through a process called translation. The order in which different amino acids are assembled is crucial, and will eventually determine the 3D conformation of the protein, and its function. Whereas replication and transcription can take advantage of simple base-pairing rules to ensure that the information stored in the DNA in faithfully transmitted over time, translation pairs any of the 64 possible triplets of RNA bases, or codons, to one of the 20 types of amino acids (sense codons) or a translation termination signal (stop codons). This matching relies on a complex machinery. First, free amino acids are linked to small RNA molecules called tRNAs (transfer RNAs) by a set of proteins, the aminoacyl-tRNA synthetases (aaRS). tRNAs share a common core 3D structure, and their identity is defined by a triplet of bases on one of their loops, the anticodon. tRNAs loaded with an amino acid, (aminoacyl-tRNA) are then ready to enter the ribosome, a large molecular machine composed of RNA and proteins. After an initiation phase in which the ribosome positions itself at the beginning of the mRNA's coding sequence and starts a polypeptide chain, it proceeds to elongate this chain by ratcheting along the mRNA three bases at a time, and matching the newly examined codon to an aminoacyl-tRNA with the complementary anticodon. It evaluates the validity of the codon anticodon match by probing the stability of the base pairing between the two RNA segments.

The correspondence table between codons and amino acids, dubbed the genetic code, appears to be near universal, and offers the intriguing property of being error tolerantsingle-letter DNA mutations will lead to either no changes in the encoded amino acid (synonymous mutation) or a substitution to a chemically similar amino acid. Most amino acids are encoded by more than one codon, and bioinformatic studies have shown that, despite being interchangeable in theory, the frequencies of synonymous codons deviate significantly from the expectations from mutational biases alone in an organism-dependent manner, a phenomenon called codon usage bias (CUB). In particular, the intensity of CUB correlates with gene expression levels.

Several hypotheses have been put forward to explain CUB. In higher organisms, it is generally accepted that selection plays less of a role due to typically small effective population sizes. Codons frequencies are therefore best explained by mutational biases, with local preferences of one codon over another deriving from fluctuations of the mutational spectrum. However, in organisms with large effective population sizes, the very small fitness effect of choosing one codon over another can be selected for. The cause of these fitness effects is a topic of debate. Some claim that codons are primarily selected for speed: codons supported by a large number of tRNA genes tend to reduce the ribosome's waiting time, and thus allow for a better use of this costly machinery. It has also been suggested that slower codons are selected in the 5' end of highly expressed genes, in order to space translation initiation events and to prevent the occurrence of downstream molecular "traffic jams". Similarly, the translation of linkers between protein domains appears to be slower, to let these domains fold sequentially. Additionally, RNA structure requirements can constrain the identity of bases in the coding sequence. Another school of thoughts holds the view that some codons are more accurate than others, and are therefore enriched in the coding sequence of highly expressed genes in order to mitigate the deleterious effects of erroneous protein synthesis. The driving force behind this phenomenon is selection against misfolding, as misfolded proteins tend to be dysfunctional, generate spurious protein-protein interaction, and saturate the protein quality control machinery.

Whereas DNA polymerases typically make a mistake every 10 9 to 10 10 bases, proteins are synthesized at a much higher error level, with current estimates ranging between 10 -3 and 10 -4 errors per inserted amino acid. This high error rate implies that a sizeable fraction (~15%) of a population of typical 200 aa long proteins contains at least one mistake. As a result, protein sequences have evolved to be robust to most single amino acid changes, and these constraints limit the choice of codons, in turn funneling the proteins' potential evolutionary paths. A recent trend even suggests that controlled levels of mistranslation can be beneficial to the organism's fitness. Mistranslation selectively affecting a codon or group of codons was shown to help parasitic cells evading their host's immune system, or deal with oxidative stress. In extreme cases of adaptive selection, low abundance mistranslated proteins can be selected for their ability to solve a problem better than the native sequence, thereby indirectly favoring sequences whose mutational neighbors have higher fitness.

In this introduction, I will outline the players and mechanisms of the prokaryotic protein translation, with a particular focus on Escherichia coli, and the various ways these mechanisms can fail and lead to errors. I will describe the evolutionary pressures that shape the evolution of the translation machinery and protein sequences, and review previous attempts to estimate the rates and spectrum of phenotypic errors.

The prokaryotic translation machinery

Here, I will present the main actors of the translation process in Prokaryotes, and review their structural characteristics, functions and involvement in the different stages of translation. I will then briefly present the mechanisms of the three core stages of mRNA translation by the ribosome (initiation, elongation and termination) in the case of faithful translation.

Amino acids

Amino acids are small organic molecules characterized by a carboxylic acid (-COOH) and a primary or secondary amine group (-NH2 or -NH). The general geometry of these molecules is depicted in Figure 1A. The R group, in magenta, is called the residue, and can theoretically be any group of atoms. The α-carbon bearing the residue is chiral, but Life has universally preferred the L geometry represented in the figure to the D stereoisomers. The carboxylic acid end of an amino acid (C-terminus) can react with the amine group of another (N-terminus) and form a peptide bond (Fig. 1B), releasing a water molecule in the process. This condensation reaction offers a natural way to polymerize amino acids. The peptide bond is stable under cellular conditions, and structurally rigid: the 6 atoms within the dashed box all lie on the same plane, thus restricting the number of possible conformations of the resulting peptide. Proteins are usually composed of a combination of the 20 proteinogenic amino acids presented in Fig. 1C. The 20 amino acids are usually grouped by chemical properties: their charge and polarity will affect their ability to form hydrogen bonds with the surrounding water molecules, while their volume and 3D conformation will restrict the flexibility of the peptide chain.

Transfer RNAs

Transfer RNAs, or tRNAs are short (70-80 nt) RNA fragments which serve as adapter molecules during translation. They are characterized by a shared general "cloverleaf" structure (Fig. 2A), which allows them to be non specifically recognized by different players of the translation machinery. The middle loop harbors a 3-nt sequence called the anticodon, which determines the identity of the tRNA. A tRNA bearing a given anticodon will be loaded with the appropriate amino acid by a set of enzymes called aminoacyl-tRNA synthetases (aaRS). Later, it is this anticodon that will allow the ribosome to test whether it is inserting the appropriate amino acid during translation, by assessing the stability of the base pairing between codon and anticodon. tRNAs represent as much as 15% of the RNA molecules of the cell, and it is generally accepted that there relative intra-cellular abundance closely matches the tDNA gene copy number in the organism's genome. They are typically long lived, and can serve many rounds of translation. In E. coli, the 61 sense codons are served by only 39 different tRNA types (Fig. 2C). This implies that, despite all of the 20 amino acids being associated to at least one tRNA type, some codons cannot be translated by a perfectly matching tRNA. Dotted arrows in figure 2C represent the general matching rules in prokaryotes. In addition to these canonical tRNAs, which all serve a similar role as adaptor molecules during translation elongation, E. coli also harbors a distinct class of tRNA for translation initiation, tRNA f-Met , and is able to conditionally insert the noncanonical amino acid Selenocysteine at amber stop codons (UAG) via a suppressor tRNA SelCys .

Premature tRNA transcripts undergo several modifications before serving their role in translation. Figure 2B summarizes the post-transcriptional modifications known to affect tRNAs in Gram-negative bacteria. These modifications stabilize the 3D structure of the molecule, affect its interaction with other players of the translation machinery, or even fine-tune its ability to base pair with cognate and near cognate codons when they occur directly on the anticodon loop.

Aminoacyl-tRNA synthetases (aaRSs)

aaRSs catalyze the specific loading of an amino acid to the 3' end of its cognate tRNA. They use the energy of a phosphate bond to catalyze the aminoacylation reaction. In a first step, the enzyme binds an ATP molecule and its cognate amino acid to form an aa-AMP complex, releasing a phosphate ion in the process. It then recognizes its cognate tRNA molecule, transfers the amino acid to the last tRNA nucleotide (position 76), on its 2' or 3' end, and releases the AMP molecule. aaRSs are divided into two evolutionarily distinct classes that differ by the structure of their catalytic domain. Class I enzymes, responsible for the tRNA aminoacylation of Cys, Ile, Leu, Met, Val, Arg, Gln, Glu, Trp and Tyr, bind the tRNA acceptor helix on the minor groove side, and can load the amino acid on the 2' and 3' -OH groups of the respective tRNA, with a preference for the 2' -OH. Except for TrpRS and TyrRS, which work as dimers, the rest of class I synthetases are monomeric. Conversely, class II synthetases (Gly, His, Pro, Ser, Thr, Asn, Asp, Lys, Ala, Gly and Phe) usually work as monodimers or tetramers, bind the acceptor helix of the tRNA from the major groove site, and generally load the amino acid on the 3' -OH. In E. coli, there is one aaRS gene for each of the 20 amino acids, with the exception of lysine, which is associated to two genes (lysS, constitutively expressed, and lysU, induced during heat shock). The genes are scattered across the genome, and typically expressed at similar relative concentrations 3

The prokaryotic ribosome

The ribosome is a large molecular complex of ribosomal RNAs (rRNA) and a number of ribosomal proteins, which serves as a catalytic hub for the process of translation. It is made of two subunits, named 50S and 30S in prokaryotes after their characteristic sedimentation rate in Svedberg units. The large (50S) subunit is composed of 33 proteins and two rRNA fragments, called the 23S (2904 nt) and 5S (120 nt) rRNAs. The small (30s) subunit is made of a single 16S rRNA (1542 nt) and 21 proteins. The full ribosome (70S) is around 20 nm in diameter, and can be found bound to cytoplasmic mRNAs, where it translates cytosolic proteins, or to the inner membrane via the signalrecognition-particule's receptor, for the translation of inner membrane, periplasmic, outer membrane and secreted proteins. mRNAs are commonly translated by more that one ribosome, forming a complex called a polysome. The 50S subunit contains three cavities capable of accommodating tRNAs: the A-site (Aminoacyl-tRNA binding site) performs the tRNA selection step by probing that the tRNA anticodon matches the codon under scrutiny, the P-site (Peptidyl-tRNA binding site) holds the peptidyl-tRNA attached to the nascent polypeptide, and the E-site (Exit site) hosts the uncharged tRNA after the transfer. The part of the ribosome that catalyzes the addition of the new AA to the nascent peptide chain, called the Peptidyl Transferase Center (PTC), is situated between the A-site and the P-site, and leads to the ribosome exit tunnel, from which the peptide chain will eventually emerge and be released. Despite the ribosome being usually presented as a monolithic complex with fixed stoichiometry of its different components, several lines of evidence have suggested that it actually adapts its composition in response to environmental cues. In particular, it is long known that E. coli ribosomes purified at various growth rates differ slightly in their proteins' ratios [START_REF] Deusser | Heterogeneity of ribosomal populations in Escherichia coli cells grown in different media[END_REF] . Similarly, the seven rRNA operons of E. coli are not perfectly identical in sequence, and are differentially regulated. Finally, rRNA and ribosomal proteins are subjected to post-transcriptional and post-translational modifications in a condition-dependent manner [START_REF] Simsek | An emerging role for the ribosome as a nexus for post-translational modifications[END_REF] . Taken together, these observations suggest that the cell might harness ribosome heterogeneity to fine-tune translation.

Initiation phase

The ribosome, an initiator tRNA f-Met , and three proteins (IF1, IF2 and IF3) are the molecular players of the initiation stage. First, the initiator tRNA f-Met , which is structurally distinct from the elongator tRNA Met , is charged with a methionine by the MetRS. The Met-tRNA f-Met complex is then recognized by a methionyl-tRNA formyltransferase (MTF), which formylates the bound methionine. Initiation Factor 3 (IF3) recognizes an inactive 70S ribosome, and promotes the dissociation of the two subunits. Initiation Factor 1 (IF1) binds to the base of the A-site of the 30S subunit and helps the dissociation. Initiation Factor 2 (IF2), fMet-tRNA f-Met and the mRNA proceed to associate with the 30S subunit in a random order to form the 30S pre-initiation complex. A base pairing interaction between the Shine-Dalgarno sequence of the mRNA and the anti Shine-Dalgarno sequence of the 16S rRNA mediates the recognition of the mRNA by the 30S subunit, and directs it towards the 5' end of the mRNA, usually 8-nt upstream of the AUG codon indicating the start of the coding sequence. The fMet-tRNA f-Met complex is positioned in the P-site, and, following a conformational rearrangement that promotes an interaction between the tRNA and the start codon, IF1 and IF3 are ejected. IF2 facilitates the association of the 30S initiation complex to a free 50S subunit, hydrolyzing a GTP molecule in the process, and leaves the newly formed 70S initiation complex [START_REF] Laursen | Initiation of protein synthesis in bacteria[END_REF] .

Elongation phase

Following initiation, the 70S initiation complex is bound to the mRNA with the start codon facing the tRNA f-Met in the P-site, while the A-site is empty. The ribosome can now start elongating the peptide chain by repeating the following elongation cycle. Elongation Factor Thermo unstable (EF-Tu) first binds a free aa-tRNA and a GTP molecule. These complexes will repeatedly enter the A-site until an aa-tRNA•EF-Tu•GTP molecule whose anticodon matches the codon is found. The ability of the ribosome to discriminate between cognate and non cognate tRNAs relies on differences in free energy between correct and incorrect codon-anticodon matches, and its accuracy is further improved by the addition of an irreversible step (the hydrolysis of the GTP molecule), through a mechanism called kinetic proofreading (KPR), whose role will be discussed in details in a dedicated section. Once a aa-tRNA•EF-Tu•GTP complex has been accepted, GTP is hydrolyzed by EF-Tu, which is itself released, and the remaining aa-tRNA complex is moved to the (PTC). Following release, EF-Tu•GDP transfers its GDP to another elongation factor, EF-Ts, binds a new GTP molecule, and dissociates from EF-Ts, allowing it to bind a new aa-tRNA. The ribosome enters the peptide-bond formation step, in which the amine group of the amino acid in the A-site nucleophilically attacks the ester carbon of the peptidyl-tRNA in the P-site, in a step is catalyzed by the 23S rRNA. Eventually, the peptide chain is transferred from the tRNA in the P-site to the aa-tRNA in the A-site. This transfer enables a translocation step, in which the ribosome will reposition itself by exactly one codon towards the 3' end of the mRNA, and the A-site peptidyl-tRNA moves to the Psite, while the P-site tRNA is transferred to the E-site. First, the two tRNAs move with respect to the 50S subunit: the "head" of the P-site tRNA rotates towards the E-site, the ribosome undergoes a conformational change called ratcheting, which is stabilized by the binding of the elongation factor G GTPase (EF-G) to the 30S subunit A-site. EF-G replaces the A-site peptidyl-tRNA and pushes it towards the P-site, while the tRNA in the P-site is transferred to the E-site. Following GTP hydrolysis, the 30S subunit ratchets, and moves together with the mRNA. EF-G dissociates from the ribosome, which can then proceed to another cycle of elongation, or terminate translation if it reaches a stop codon.

Termination and recycling

After many cycles of elongation, the ribosome should eventually reach one of the stop codons (UAA, UGA and UAG). Two release factors, RF1 and RF2, perform the recognition of the stop codon; both RF1 and RF2 can recognize the UAA stop, but UAG is only read by RF1, and UGA only by RF2. They enter the A-site, and interact with PTC via a conserved GGQ motif, exposing the ester bond between the tRNA and the nascent peptide chain to a nucleophile attack by a water molecule. The glutamine from the GGQ motif stabilizes the deacylated P-site tRNA, thus favoring the reaction. The newly synthesized protein is released, and a third release factor, RF3, binds and destabilizes the ribosome•RF1/RF2 complex. RF3 hydrolyses a GTP molecule, and both RF3 and the tRNA in the E-site dissociate from the ribosome-mRNA complex, which is left with an empty A-site and E-site, and a deacylated tRNA in the P-site. In order to recycle the ribosome, EF-G and the ribosome recycling factor (RRF) bind the remaining complex, and promote the dissociation of the two subunits. The 30S subunit, still bound to the decylated tRNA and the mRNA, finally dissociates from these two molecules thanks to the action of IF3, and a new full cycle of translation can start again.

From folding to degradation: a protein's life cycle.

Before accomplishing its function, a protein must first undergo several steps, which start with folding into a defined 3D structure, but might also include targeting to a specific location within the cell, undergoing post-translational modifications, and assembling into complexes. Proteins are assisted in their folding by a suite of proteins called chaperones. They are eventually diluted by growth, and those that fail to fold properly are preferentially degraded to recycle their amino acids or simply aggregated to mitigate their toxic effects. In this section, I will review our current knowledge of these different steps, as it is important them to understand them to fully comprehend the effects of translation errors.

Chaperone independent folding

Due to its wide use in recombinant protein production, protein folding has been extensively studied in E. coli. It is known since Anfinsen's experiment in 1973 that, at least for small globular proteins, "the three dimensional structure of a native protein in its normal physiological milieu (solvent, pH, ionic strength, presence of other components such as metal ions or prosthetic groups) is the one in which the Gibbs free energy of the whole system is lowest; that is, that the native conformation is determined by the totality of inter-atomic interactions and hence by the amino acid sequence, in a given environment [START_REF] Anfinsen | Principles that govern the folding of protein chains[END_REF] ." Despite the astronomical number of confirmation that can be adopted even by a relatively small peptide chain, folding can happen at very fast time-scales, on the order of milliseconds, an observation dubbed Levinthal's paradox. Levinthal himself noted that the paradox could be easily resolved if "protein folding [was] sped up and guided by the rapid formation of local interactions which then determine the further folding of the peptide; this suggests local amino acid sequences which form stable interactions and serve as nucleation points in the folding process [START_REF] Rooman | What is Paradoxical about Levinthal Paradox?[END_REF] ", i.e. if folding happened sequentially. Proteins typically fold in a way that hides hydrophobic, sticky residues in their core, while their surface harbors by more hydrophilic residues. Unfolded or misfolded proteins tend to expose hydrophobic residues, thereby increasing the risk of disturbing cellular processes through spurious protein-protein interactions and aggregation in the crowded intra-cellular environment. Most natural proteins were measured to exhibit a difference in free energy between folded and unfolded states ΔG on the order of 5-10 kcal/mol [START_REF] Bava | version 4.0: thermodynamic database for proteins and mutants[END_REF] . Assuming thermodynamic equilibrium, the ratio of unfolded to folded proteins is given by the formula

𝑃 𝑢𝑛𝑓𝑜𝑙𝑑𝑒𝑑 𝑃 𝑓𝑜𝑙𝑑𝑒𝑑 = 𝑒 -∆𝐺 𝑘𝑇 ,
where k is Boltzmann's constant (k = 1.986 cal/mol/K). For a typical ΔG = 5 kcal.mol -1 , this ratio is approximately 2.9 × 10 -4 at 37°C, indicating that the folded vastly outnumbers the unfolded (or misfolded) forms.

Because folding happens fast and co-translationally, the nascent peptide chain can start folding as soon as it exits the ribosome tunnel. Proteins are organized into independently folding subunits called domains. Evolutionary evidence revealed that cells slow down translation of the regions between domains called linkers [START_REF] Jacobs | Evidence of evolutionary selection for co-translational folding[END_REF] , thus favoring the formation of stable partial structures during the elongation of the nascent chain.

Chaperone assisted folding in E. coli

Despite the fact that protein folding is a thermodynamically favorable process, it is assisted by the action of the trigger factor protein (TF) in E. coli. TF, present in dimeric form in the cytoplasm, binds monomerically to the large ribosomal subunit, close to the exit tunnel, and interacts with the nascent peptide chain, as it is still bound to the translation ribosome. In vivo, TF preferentially binds ribosomes whose nascent peptide chain reaches at least 100 amino acids in length. It recognizes motifs of 8 amino acids enriched in hydrophobic or basic residues, and aids de novo folding through ATPindependent cycles of binding and release from both the ribosome and the nascent chain, until hydrophobic residue are effectively positioned in the core of the nascent protein and inaccessible to the TF monomer. Approximately 70% of proteins undergo TF-assisted folding, whereas the remaining 30% require the action of additional chaperones (Fig 4A). These proteins include, in E. coli, the Hsp70 chaperone DnaK and its co-chaperones DnaJ and GrpE, and the Hsp60 chaperonin GroEL/GroES system. DnaK assists the folding of ~700 mostly cytosolic proteins. Similarly to TF, it binds 5-7 long stretches of amino acids enriched for hydrophobic residues, and interacts with them through cycles of ATP-dependent binding and release. DnaJ identifies misfolded proteins and transfers them to DnaK, and stimultates DnaK binding through ATP hydrolysis. GrpE releases ADP from DnaK, which upon binding a new molecule of ATP will dissociate from its substrate, thus completing the cycle. Conversely to TF, however, these enzymes can function co-translationally and post-translationally. Approximately 250 different protein substrates can interact with GroEL/GroES, but it is only necessary for the folding of ~85 of them, thanks to redundancy with the other chaperones. The GroEL chaperonin complex serves as a molecular cage for protein folding, which means it can only function post-translationally. It is composed of two rings of 7 subunits, and interacts with its co-chaperonin GroES, which closes the cage as a "molecular lid". Substrates are bound at multiple sites, and concertedly released by the 14 subunits (cf. Fig. 4C). Since only one substrate protein is allowed at once in the cage, the GroEL/GroES system prevents aggregation of the substrate proteins [START_REF] Kerner | Proteome-wide analysis of chaperonin-dependent protein folding in Escherichia coli[END_REF] . Finally, ClpB is a stress-induced chaperone whose function is to process aggregates. It works together with the DnaK, DnaJ and GrpE enzymes: clpB binds to aggregated proteins and addresses them to DnaK for resolubilization after heat shock. For a complete review of the role of chaperones in E. coli's protein folding, see Kim et al. 2013 [START_REF] Kim | Molecular Chaperone Functions in Protein Folding and Proteostasis[END_REF] . In steady state, a protein's expression level is determined by its production rate, 𝛼, its dilution rate 𝛽 𝑑𝑖𝑙 and its degradation rate 𝛽 𝑑𝑒𝑔 14 :

[𝑝𝑟𝑜𝑡] = 𝛼 𝛽 𝑑𝑖𝑙 +𝛽 𝑑𝑒𝑔
The dilution rate is directly determined by the growth rate, and inversely proportional to the cells doubling time. At high growth rate, the half-life of most proteins is greater than the doubling time, which means that degradation does not affect their expression level very much. However, the cell uses degradation to purge misfolded and aggregated proteins, and to perform rapid regulation of protein levels. Distinct proteins carry these two aspects of protein degradation. The Lon protease is in charge of degrading unfolded or misfolded proteins, and requires ATP to unfold and ratchet the misfolded protein through its proteolytic chamber. The ClpAP proteases are in charge of degrading proteins tagged with a degradation signal (degron). They also require ATP to perform degradation. [START_REF] Powers | A Model for Proteostasis in E. coli[END_REF] Aggregation: toxic side product of mitigation strategy?

As mentioned above, misfolded proteins tend to expose hydrophobic residues that "stick" and perturb protein-protein interactions (PPIs). When the chaperone network is overloaded as a result of proteotoxic stress, these misfolded proteins clump together and form aggregates called inclusion bodies. While these aggregates were initially though to be toxic for the cells, it now appears that they serve as damage control strategy. First of all, if the toxicity of unfolded and misfolded proteins stems from its tendency to disturb PPIs, the cost of misfolding is roughly proportional to the surface of contact of these proteins. However, if these proteins aggregate in a roughly spherical shape, for any increase of volume of the aggregate 𝛥𝑉, the associated increase in surface in only proportional to 𝛥𝑉 2/3 , compared to the linear increase expected from an aggregate free situation. Additionally, it serves as a bet-hedging strategy: during cell division, aggregates are asymmetrically transmitted from the mother cell to the daughter cells, and preferentially associate with the cells older poles [START_REF] Lindner | Asymmetric segregation of protein aggregates is associated with cellular aging and rejuvenation[END_REF] . This bet hedging strategy allows a higher growth rate at the population level. Finally, a recent study in S. cerevisiae revealed that aggregation is not necessarily restricted to misfolded protein, but rather that it can be a reversible, adaptive strategy in the response to heat shock by momentarily protecting functional proteins [START_REF] Wallace | Specific, Active Aggregates of Endogenous Proteins Assemble upon Heat Stress[END_REF] .

The proteostasis network functions at the edge of aggregation

A computational model of the proteostasis network, at the systems level, revealed that it balanced energy and chaperone utilization efficiently [START_REF] Powers | A Model for Proteostasis in E. coli[END_REF][START_REF] Santra | Bacterial proteostasis balances energy and chaperone utilization efficiently[END_REF] . The proteostasis network performs sorting of misfolded proteins in a way that resembles the way hospitals sort patients. The network efficiently addresses the sickest proteins to the most ATPexpensive chaperones, and the chaperone concentrations are just high enough to keep the proteome from aggregating. Their protein levels are adjusted to the growth rate, and are therefore higher in fast growing bacteria, when the synthesis rate is high, but also at very low growth rate to prevent degradation of misfolded proteins when they cannot be balanced by protein synthesis.

A study in S. cerevisiae [START_REF] Geiler-Samerotte | Misfolded proteins impose a dosage-dependent fitness cost and trigger a cytosolic unfolded protein response in yeast[END_REF] addressed the direct cost of expression of misfolding-prone proteins by comparing the growth rate of cells expressing a wt-YFP to that of cells carrying destabilized mutants of YFP. When the YFP variants were induced, so that the YFP would represent 0.1% of the total protein content of the cell, the most destabilized mutant suffered from a growth rate reduction of 3.2% compared to the wild type control. This emphasizes the notion that the cost of dealing with misfolding is much higher than the mere synthesis cost of a properly folding protein.

Protein localization

Even though the bacterial cell is much simpler in its organization than its eukaryotic counterpart due to its absence of organelles, it has been estimated that nearly one fifth of E. coli proteins is actively targeted to a defined subcellular localization, which include the inner and outer membranes, the periplasm, and the extra-cellular space (secretion) [START_REF] Tsirigotaki | Protein export through the bacterial Sec pathway[END_REF] . Even cytosolic proteins can be locally restricted to subcellular localization such as the nucleoid (the region of the cytoplasm in which the DNA is stored and condensed), the z-ring (a short-lived structure indicating the middle of the cell before division) or the cell poles. For most proteins, the localization process appears to be driven by diffusion and binding to "anchor proteins", which are actively directed to these subcomponents of the cell. Approximately 96% of the exportome (proteins targeted to one of the membranes, the periplasm, or the extracellular space) requires the action the prokaryotic translocon [START_REF] Tsirigotaki | Protein export through the bacterial Sec pathway[END_REF] . The translocon, embedded in the inner plasma membrane, is a channel composed of proteins SecYEG. Proteins are addressed to the translocon co or post-translationally by the recognition of an n-terminal signal peptide by the SRP and SecA proteins. The signal peptide consists of a stretch of positively charged amino acids, followed by 6-18 hydrophobic amino acids and finally 1-11 polar amino acids containing an cleavage site recognized by the membrane anchored SPase I. Substrates of the Sec translocon can be co-translationally integrated to the membrane, or post-translationally translocated.

Multi-protein assemblies

Most proteins in E. coli carry their function as complexes. These can range in complexity from a simple homo-dimeric form to large hetero complexes. A combination of affinity-purification and mass spectrometry on one side, and cooccurrence of proteins in orthology across species, revealed that the E. coli proteome contains more than 400 complexes [START_REF] Caufield | Protein Complexes in Bacteria[END_REF] . Since complexes only function when all of their components are present, the cell developed strategies to express the various members of a complex at the right stoichiometry. First of all, members of a complex are often transcribed from the same operon, and therefore can be simultaneously regulated. Furthermore, recent ribosome profiling data revealed that the synthesis rate of the different proteins within an operon closely match their stoichiometry in the resulting complex [START_REF] Li | Quantifying Absolute Protein Synthesis Rates Reveals Principles Underlying Allocation of Cellular Resources[END_REF] . Residues facing the interface of a complex are usually more conserved than other surface residues [START_REF] Guharoy | Conserved residue clusters at protein-protein interfaces and their use in binding site identification[END_REF] . In Eukaryotes, dominant negative mutations are often associated with complexes, indicating that a single miscoded protein can inactivate an entire complex [START_REF] Reiner | Exploring the Molecular Etiology of Dominant-Negative Mutations[END_REF] .

A recent study indicated that even monomeric proteins evolve at the edge of multimeric assembly [START_REF] Garcia-Seisdedos | Proteins evolve on the edge of supramolecular self-assembly[END_REF] . Garcia-Seisdedos et al. expressed point mutated E. coli proteins in vitro and heterologously in S. cerevisiae, and observed that in some case they could form up to 1µm long fibrils. Together these results indicate that the oligomerization state of proteins can be easily disturbed, even by single nucleotide mutations.

Mechanisms and rates of phenotypic mutations

Phenotypic mutations are defined as "errors that occur when a DNA coded gene is transcribed to mRNA and subsequently translated to protein [START_REF] Bürger | Why Are Phenotypic Mutation Rates Much Higher Than Genotypic Mutation Rates[END_REF] ". They resemble DNA mutations (insertion, deletion and point mutation), but are not transmittable to the cell's progeny, and typically occur at much higher rates [START_REF] Bürger | Why Are Phenotypic Mutation Rates Much Higher Than Genotypic Mutation Rates[END_REF] . They can be divided into transcription errors (insertion, deletion, point mutation or spurious editing), and translation errors (amino acid substitution, frameshift, readthrough, and premature termination). I will discuss the molecular mechanisms that lead to these errors, and the various ways cells cope with, and even harness them to adapt to the environment.

Transcription errors

Strand specific RNA-seq technologies have shone light on the imprecise nature of transcription initiation and termination [START_REF] Creecy | Quantitative bacterial transcriptomics with RNA-seq[END_REF] . However, the transcription elongation phase of transcription appears to be mostly devoid of errors, and until recently its errors were too rare to be detected by standard RNA sequencing because of machine errors and mutations introduced during reverse-transcription. Traverse & Ochman [START_REF] Traverse | Conserved rates and patterns of transcription errors across bacterial growth states and lifestyles[END_REF] applied the CircSeq method [START_REF] Acevedo | Library preparation for highly accurate population sequencing of RNA viruses[END_REF] to solve this problem and directly measure transcription error rates in E. coli. During CircSeq, mRNAs are first circularized, and then repeatedly reverse transcribed, so that the final cDNA contains tandem repeats of the mRNA's sequence. Errors found across several tandem repeats cannot originate from reverse transcription or sequencing mistakes, and are therefore present at the RNA level, regardless of whether they occurred during transcription of after. They measured the rate of nucleotide substitutions, and found it to be fairly constant across conditions, with an average error rate of 5.10 -5 errors per base, and a tendency to replace C with U (implying a G:U mismatch during transcription). Errors did not localize to the leading or the lagging strand, and were very moderately affected by their local context. In a following article [START_REF] Traverse | Genome-Wide Spectra of Transcription Insertions and Deletions Reveal That Slippage Depends on RNA:DNA Hybrid Complementarity[END_REF] , the same authors studied the rate and spectrum of insertion and deletion transcription errors, and found it to be about an order of magnitude lower than the rate of substitutions, with deletions prevailing over insertions. Surprisingly, these deletions to be more likely to preserve the reading frame, with observed error rates peaking for 3 and 6 nt deletions. However, we cannot exclude that these peaks are the results of degradation of mRNAs containing frame-disturbing deletions, as they are likely to cause premature translation termination and trigger mRNA degradation. Insertions, on the other end, appear to happen mostly within repeating sequences, and usually consist in adding an additional repeat.

In order to polymerize at such high level of accuracy, it has been suggested that RNA polymerases are able to backtrack to correct their mistakes. Specifically, "the polymerase jumps forward and backward along the template DNA with a net movement that is driven in the forward direction by thermodynamically favorable nucleotide addition in the forward-translocated state 31 ". It not only relies on the base pairing ability between the next template DNA base and the incoming RNA base, but also on the base stacking free energy difference between a correctly inserted nucleotide and a mismatch. A mismatch affects the base stacking energy of the preceding and following nucleotide pairs. This standard free energy difference, i.e. the melting energy of the pair of base pairs formed by the two last incorporated nucleotides, serves to discriminate against mismatches in initial selection. A misincorporation also slows down the incorporation of the next nucleotide. The proofreading stems from the ability of the enzyme to cleave the 5' end of the transcript after backtracking. For a detailed review of the mechanisms and determinants of transcriptional accuracy, see Gamba & Jenkin, 2018 [START_REF] Gamba | Transcription fidelity and its roles in the cell[END_REF] .

Despite being relatively rare compared to other phenotypic errors, the effect of transcription errors is amplified by the fact that many copies of a protein can be produced from the same mRNA. This property has been harnessed to engineer bistable switches in E. coli that allow epigenetic inheritance [START_REF] Gordon | Heritable Change Caused by Transient Transcription Errors[END_REF] .

In addition to RNA polymerase errors, other processes can affect RNA sequences posttranscriptionally. 8-oxo-guanine, a derivative of guanine generated when the ribonucleotide pool is exposed to reactive oxygen species, can be created within RNAs, where it affects its base pairing preferences and can induce protein recoding [START_REF] Sekiguchi | Oxidative nucleotide damage: consequences and prevention[END_REF] .

Similarly, adenosine to inosine and cytosine to uracil RNA editing are now believed to be common across higher eukaryotes. Inosine base-pairs with A, C and U, and was shown to induce protein recoding in human, mouse and zebrafish [START_REF] Sie | Conserved recoding RNA editing of vertebrate C1q-related factor C1QL1[END_REF] . In the second chapter of my thesis I will challenge the prevalent notion that RNA editing does not recode bacterial mRNAs.

Frameshifting errors

Translation usually occurs in a defined frame, that the ribosome maintains along the length of the transcript. However, some sequences tend to confuse the ribosome, and induce a slippage towards one of the neighboring frames. In E. coli, +1 and -1 nucleotides frameshifts are the most frequent, and have been harnessed by the cell to regulate the production of key enzymes (programmed frameshift), including an interesting case of self regulation of the frameshifting propensity: the expression of the release factor protein RF2, whose primary role is to terminate translation via its recognition of the UAA and UGA stop codons, is stimulated by a +1 frameshift, resulting in the bypass of a UGA stop codon and the production of a full length, functional protein [START_REF] Craigen | Expression of peptide chain release factor 2 requires high-efficiency frameshift[END_REF] .

Other well-studied cases of programmed frameshift in E. coli include the dnaX frameshifting element, which regulates the relative expression of the τ and 𝛾 subunits of DNA polymerase III by redirecting the ribosome to the -1 frame approximately 50% of the time [START_REF] Flower | The gamma subunit of DNA polymerase III holoenzyme of Escherichia coli is produced by ribosomal frameshifting[END_REF] , or the joint production of a copper transporter and a copper chaperone by the same gene in different frames [START_REF] Meydan | Programmed Ribosomal Frameshifting Generates a Copper Transporter and a Copper Chaperone from the Same Gene[END_REF] . These cases are usually characterized by specific RNA structures, such as slippery homo repeats and pseudo-knots, which have been selected to generate high levels of frameshifting. In order to evaluate the basal frameshift error rates in vivo, Meyerovich et al. [START_REF] Meyerovich | Visualizing high error levels during gene expression in living bacterial cells[END_REF] introduced a plasmid containing a frameshifted GFP in the gram positive bacteria B. subtilis and compared the residual fluorescence to that of a wild type GFP. They found that 2% of the GFP encoding a frameshift at the DNA level were able to revert and produce a functional protein. Since the DNA frameshift was inserted near the beginning of the sequence (10 th codon), it is likely that this figure represent a cumulative rate of frameshifting over the first codons. Assuming that frameshift errors are evenly distributed across the first 10 codons, the resulting figure of 0.2% errors per codon is still astonishingly high compared to other phenotypic errors, especially since early frameshifts are likely to create non functional, truncated proteins.

Until recently, large-scale detection of programmed frameshift relied on the fact that coding sequences show a decreased conservation of the 3 rd codon position (wobble) in evolutionary alignments. Ribosome profiling technologies now generate single nucleotide resolution maps of ribosome density across the transcriptome. The characteristic 3-way periodicity of these profiles reveals the dominant frame in which an mRNA is translated. In their analysis of the translational changes induced by the [PSI+] prion in S. cerevisiae, Baudin-Baillieu et al. [START_REF] Baudin-Baillieu | Genome-wide Translational Changes Induced by the Prion [PSI+][END_REF] took advantage of this feature to identify frameshifts throughout the transcriptome, and showed that they were stimulated by the prion.

Readthrough errors

Stop codon suppression or translation readthrough occurs when the ribosome bypasses a stop codon and interprets it as a sense codon. Like in the frameshift case, one can divide these errors into basal readthrough errors, which happen at any given stop codon, and programmed readthroughs, which are selected for and potentially regulated. In the strict sense, the definition of a readthrough error only applies to cases where a tRNA competes with the release factors, resulting in the ribosome inserting an amino acid in place of the stop codon, and proceeding to translate until it reaches the next stop codon (or the end of the transcript, resulting in the degradation of the mRNA transcript [START_REF] Richards | Quality control of bacterial mRNA decoding and decay[END_REF] . However, cases of frameshifts that bypass a stop codon and add a peptide extension using a non canonical frame share similarities with bona fide readthrough, and can be included in a broader definition of the term. In E. coli, the most archetypal case of programmed readthrough is probably the insertion of selenocysteine at UGA stop codons. A suppressor tRNA bearing the TCA anticodon is first charged with serine by the SerRS, and the selA enzyme converts the Ser-tRNASec to Sec-tRNASec. Insertion of selenocysteine at UGA only happens at sites where the UGA stop codon is associated to a specific RNA structure, the SECIS element [START_REF] Walczak | A novel RNA structural motif in the selenocysteine insertion element of eukaryotic selenoprotein mRNAs[END_REF] .

Ribosome profiling experiments in E. coli revealed that readthrough is a pervasive phenomenon, and estimated that as many as 50 genes showed signs of translating ribosomes in sequences' C-termini [START_REF] Baggett | Global analysis of translation termination in E. coli[END_REF] . This phenomenon was mostly observed at UGA codons, and stimulated by the deletion of RF2. RF2 depletion also disturbed the expression of biosynthetic genes under attenuation control. Fan et al. [START_REF] Fan | Heterogeneity of Stop Codon Readthrough in Single Bacterial Cells and Implications for Population Fitness[END_REF] used a synthetic reporter construct to assess the variability of readthrough efficiency at UGA codons in a population of E. coli cells. The readthrough frequency, around 2% on average, varied substantially from cell to cell, and was correlated with a reduced protein synthesis. High levels of readthrough reduced the lag time necessary to exit stationary phase. The notion that global readthrough can be evolutionarily selected for and adaptive was supported by the finding that it was pervasive and regulated in the fruit fly D. melanogaster [START_REF] Dunn | Ribosome profiling reveals pervasive and regulated stop codon readthrough in Drosophila melanogaster[END_REF] . The C-terminal extensions were added at specific developmental stages, and often contained localization tags, but were not phylogenetically conserved, suggesting that readthrough is an adaptive mechanism to increase the proteome's plasticity, and might perhaps serve as a "stepping stone" for more complex evolutionary processes. Yanagida et al. [START_REF] Yanagida | The Evolutionary Potential of Phenotypic Mutations[END_REF] compared the way different species of yeasts encode their IDP genes (responsible for fatty acids oxidation). They showed that pre whole-genome-duplication (WGD) species encoded IDP with a single gene, which is conditionally addressed to the peroxisome thanks to the addition a localization tag via a regulated +1 frameshift to bypass the canonical stop codon. Post WGD species, on the other hand, simplified this system by differentially expressing two different copies of the gene, with or without the peroxisome tag.

Premature termination

Whenever a ribosome terminates translation before reaching the canonical stop codon of an mRNA transcript, an incomplete protein is formed. Since an incomplete protein is unlikely to perform its function, and will probably misfold, premature termination is considered to be very deleterious, and has been proposed to a major driver of codon usage bias [START_REF] Gilchrist | Measuring and Detecting Molecular Adaptation in Codon Usage Against Nonsense Errors During Protein Translation[END_REF] . It can result from a nonsense transcriptional error, in which a stop codon would appear in the mRNA coding sequence, or from ribosome drop-off, a process stimulated by extensive stalling at a codon due to the lack of cognate aa-tRNAs. Conversely to readthrough, ribosome drop-off is too subtle to be directly measured at the gene level by ribosome profiling, but Sin et al. [START_REF] Sin | Quantitative assessment of ribosome drop-off in E. coli[END_REF] developed a sensitive statistical procedure to quantify the decrease of ribosome density along transcripts globally, in a range of conditions. They estimated the drop off rate to be on the order of 4 × 10 -4 per codon, which implies that for a typical, 300 codon long ORF, only about 90% of the ribosomes would reach the canonical stop codon. Whether the decrease in ribosome density comes from frameshifts leading to out of frame termination or genuine termination events at sense codons, and the fate of these prematurely terminated protein, remains unclear. Zaher & Green 49 revealed an intriguing interplay between amino acid misincorporation and premature termination. They showed that strains deleted for RF3, which was believed to primarily serve in the dissociation of RF1 and RF2 at the end of the translation process, suffered from increased sensitivity to errors in protein synthesis, and that RF3 tended to stimulate premature termination when a the tRNA in the P-site formed a mismatch with its codon. Since frameshifting can, at least transiently, form mismatches in the E and P-sites, RF3 was also shown to mitigate its effects. Finally, premature translation termination by RF3 appears to decrease mRNA stability, but not protein stability.

While ribosomes do not appear to dwell longer on any particular codon type in rich conditions, amino acid starvation has been shown to lift the degeneracy of the genetic code, and to induce ribosomal pausing at codons associated to the amino acid depleted from the medium [START_REF] Subramaniam | An integrated approach reveals regulatory controls on bacterial translation elongation[END_REF] . The severity of the effected on synonymous codons was well predicted by a simple (but counter-intuitive) model of tRNA charging [START_REF] Elf | Selective Charging of tRNA Isoacceptors Explains Patterns of Codon Usage[END_REF] , which showed that tRNAs associated to rare codons were more readily charged than more common isoacceptors during starvation for the associated amino acid. In line with these observations, cassettes expressing YFP in which all codons for a given amino acid were systematically recoded to only one of the codons for this amino acid resulted in measurable differences in fluorescence during starvation for this amino acid [START_REF] Subramaniam | Environmental perturbations lift the degeneracy of the genetic code to regulate protein levels in bacteria[END_REF] . The codon robustness index that they proposed to evaluate the amount of premature termination at a given codon during starvation correlated well with the observed dwelling time of the ribosome, suggesting that premature termination is mostly dictated by competition between aa-tRNAs and release factors.

Single amino acid misincorporations

Until now, our experimental knowledge regarding rates of amino acid misincorporations originates almost exclusively from reporter constructs studies. One of the first reliable estimates of these error rates in vivo came from the ingenious experiment of Edelmann & Gallant [START_REF] Edelmann | Mistranslation in E. coli[END_REF] . They took advantage of the fact E. coli's flagellin does not encode for any cysteine in its sequence, and fed cells with cysteine marked with the radioactive 35S sulfur isotope. After purifying the protein and running it on a SDS polyacrylamide gel, measuring radioactivity levels allowed them to reveal the amount of 35S-Cys inserted per flagellin. They argued that this insertion was likely occurring at CGU/C arginine codons, and deduced that the error rate of misincorporation of cysteine at these codons was on the order of 1.0 x 10 -4 . They also confirmed that this error rate was increased in the presence of small concentrations of streptomycin, and during starvation for arginine, therefore highlighting the role of aa-tRNA competition in determining translation accuracy. Similar tricks were used to estimate specific misincorporation levels in reporter constructs, but were usually limited in scope [START_REF] Toth | Evidence for a unique first position codon-anticodon mismatch in vivo[END_REF][START_REF] Loftfield | The frequency of errors in protein biosynthesis[END_REF][START_REF] Parker | Mistranslation in cells infected with the bacteriophage MS2: direct evidence of Lys for Asn substitution[END_REF][START_REF] Khazaie | The accuracy of Q beta RNA translation. 1. Errors during the synthesis of Q beta proteins by intact Escherichia coli cells[END_REF] .

Kramer & Farabaugh 58 designed a series of firefly luciferases to estimate a wider range of codon-specific error rates. They used the fact that luciferase requires a lysine to be present in position 529 to perform its enzymatic activity, and created luciferase constructs in which codon 529 was systematically mutated to all near-cognate codons and some non-cognate codons. For each of these constructs, the residual luminescence served as a proxy for the rate of misreading of the variable codon by Lys-tRNA UUU . They found error levels to vary widely, and to be mostly determined by competition between cognate and near cognate tRNAs: overexpressing the rare arginine tRNA UCU drastically reduced the ArgLys error levels at cognate AGA and AGG codons. Error rate from the 14 near cognate codons to lysine varied by a factor of 10. The highest error levels were associated to U:U or G:U mismatches, as would be expected from the thermodynamics of base pairing. They also characterized the effects of two aminoglycosides, streptomycin and paromomycin, and two ribosomal mutations. The two drugs increased error levels at near cognate codons, and the rpsD mutation increased the errors at already error prone codons. They were able to measure decreased error levels in the rpsL hyper accurate mutant, with error prone codons reverting to background levels of mistakes.

The first peek at the full translation error spectrum was provided by a mass spectrometry analysis of six recombinant proteins purified from E. coli [START_REF] Zhang | G/U and Certain Wobble Position Mismatches as Possible Main Causes of Amino Acid Misincorporations[END_REF] . They first identified canonical, error free peptide, and then use a blind modification search strategy to identified "modified versions" of these peptides. They excluded PTMs and MS artifacts using a set of ad hoc rules, and retained among the remaining identifications those consistent with amino acid misincorporations. The vast majority of the errors they detected could be rationalized as originating from an mRNA/tRNA mismatch, rather than a transcriptional error or a synthetase error. Furthermore, GmRNA:UtRNA mismatches, but also CmRNA:UtRNA and UmRNA:UtRNA mismatches at the wobble position, were the most frequently observed. They confirmed that the identity of the codons determined its errors by synonymously recoding one of the proteins many times. The error rates they measured from these recoded proteins were well predicted by the nature of the mRNA/tRNA mismatch. As shown by these experiments, misloading errors, i.e. errors in which an aaRS pairs a tRNA to a non-cognate amino acid, are much rarer than ribosomal errors. aaRS tend to be precise, and perform their function with an accuracy in the 10 -4 -10 -5 range [START_REF] Zaher | Fidelity at the molecular level: lessons from protein synthesis[END_REF] . However, several cases of regulated, adaptive mistranslation have been reported, which usually take advantage of the tRNA charging step to generate high levels of a specific subset of translation errors. C. albicans, a pathogenic yeast, is part of a clade that reassigned the CUG codon from leucine to serine. However, it is able to partially revert and insert high levels of leucine at this codon during invasive growth, recoding predominantly proteins expressed at its surface. This processed is believed to promote cell-adherence (leucine is more hydrophobic than serine), and evasion of the host immune response thanks to the increased sequence variability [START_REF] Miranda | Candida albicans CUG mistranslation is a mechanism to create cell surface variation[END_REF] . Another well-characterized case of adaptive mistranslation is the controlled misacylation of methionine onto various non-cognate tRNAs during oxidative stress. This phenomenon was observed in E. coli [START_REF] Jones | Misacylation of specific nonmethionyl tRNAs by a bacterial methionyl-tRNA synthetase[END_REF] , S. cerevisiae [START_REF] Wiltrout | Misacylation of tRNA with methionine in Saccharomyces cerevisiae[END_REF] and H. sapiens [START_REF] Netzer | Innate immune and chemically triggered oxidative stress modifies translational fidelity[END_REF] . In mammalian cells, the levels of mismethionylation shoot from 1% to 10% during ROS exposure. The adaptiveness of this phenomenon stems from the ability of methionine residues to protect proteins against ROS-mediated damage.

Similarly, in E. coli, oxidative stress appears to trigger another type of mistranslation. The editing domain of the threonine aaRS (thrRS) is inactivated by the oxidation of a cysteine residue. The modified enzyme is not able to discriminate against serine, which is then inserted at high levels at threonine codons [START_REF] Ling | Severe oxidative stress induces protein mistranslation through impairment of an aminoacyl-tRNA synthetase editing site[END_REF] . For a complete review on adaptive mistranslation mechanisms, see Pan 2013 [START_REF] Pan | Adaptive Translation as a Mechanism of Stress Response and Adaptation[END_REF] .

How does the cell mitigate the effects of amino acid substitutions?

Since amino acid substitutions tend to be detrimental to fitness, organisms have developed mechanisms to minimize their error rates, and strategies to ensure that the residual errors are well accepted. Here, I will review the various mechanisms that allow the translation machinery to perform at high accuracy, and the ways its components have co-evolved with mRNA sequences to ensure that amino acid substitutions are minimally disruptive to fitness.

Molecular mechanisms of translational accuracy

During translational elongation, the ribosome repeatedly samples aa-tRNAs from the cytosol, with the help of EF-Tu. The process of aa-tRNA selection is blind, and governed by diffusion only. In order to discriminate between cognate and non-cognate tRNAs, the ribosome has to rely exclusively on the difference in free energy (ΔΔG) between correct and incorrect matches in the A-site. Assuming that the selection process relies on thermodynamic equilibrium, an error rate of 10-4 would imply a difference in free energy between cognate and near cognate tRNA to the A-site codon on the order of 10kT, or 0.5 kcal.mol-1 at a temperature of 37°C, but this value is actually higher than the ΔG associated to the perfect binding of a tRNA to its cognate codon. Hopfield [START_REF] Hopfield | Kinetic proofreading: a new mechanism for reducing errors in biosynthetic processes requiring high specificity[END_REF] and Ninio [START_REF] Ninio | Kinetic amplification of enzyme discrimination[END_REF] independently recognized this contradiction, and both proposed that the accuracy of tRNA selection was in fact increased by the addition of an irreversible step, through a mechanism that they respectively termed kinetic proofreading or kinetic amplification. They correctly identified that GTP hydrolysis, which though to be a wasteful reaction, actually provided the necessary boost in accuracy by introducing irreversibility in the selection process (Fig 5A 5C) and used the method of first passage processes to model the speed and accuracy of the ribosome, and their relationship to the different rate constants. They were able to show that the ribosome usually operates in a regime that sacrifices accuracy for speed, i.e. that it could easily achieve higher accuracy at a lower speed, by simply reducing its rate of GTP hydrolysis (Fig 5D ). A linear trade-off between speed and accuracy was observed within an in vitro translation system, in response to variation in the Mg 2+ concentration [START_REF] Johansson | Genetic code translation displays a linear trade-off between efficiency and accuracy of tRNA selection[END_REF] .

Most aaRS also rely on energy consuming proofreading mechanisms to achieve high acylation accuracy [START_REF] Hussain | Mechanistic insights into cognate substrate discrimination during proofreading in translation[END_REF] . They typically discriminate well between cognate and noncognate tRNAs, thanks to information encoded both in the tRNA's anticodon and its backbone structure. Discrimination against non-cognate amino acid relies on a double sieve mechanism: the active site first accepts amino acids chemically similar to the cognate AA, but sterically excludes larger ones. In a second, energy consuming step, the editing site probes the chemical properties of the amino acid in the catalytic site and hydrolyses non cognate amino acids [START_REF] Moras | Proofreading in translation: Dynamics of the double-sieve model[END_REF] . Despite being quite accurate, tRNA acylation is not perfectly error-proof. In particular, it has been suggested that it would be more difficult for the synthetases to exclude small non-cognate amino acids than larger one. This tendency might be at least partially corrected by EF-Tu's binding preferences. LaRiviere et al. [START_REF] Lariviere | Uniform binding of aminoacyl-tRNAs to elongation factor Tu by thermodynamic compensation[END_REF] probed the affinity of EF-Tu to correctly and incorrectly loaded aa-tRNAs, in vitro. They showed that EF-Tu binds correctly charged tRNAs within a limited range of affinities, but binds incorrectly charged tRNAs over a much wider range. The binding strength seems to be determined by a linear combination of two factors, one determined by the tRNA backbone and the other by the amino acid. Among the correct matches, a amino acid which binds EF-Tu with a low affinity is usually associated to a tRNA whose backbone binds the elongation factor with high affinity, and vice versa. Therefore, incorrect matches do not benefit from this compensation effect, possibly leading to very strong or very weak binding to EF-Tu. The authors suggested that the cell might use this mechanism as a safeguard against mistranslation. The genetic code minimizes the effects of amino acid substitutions.

The structure of the genetic code appears is nearly universal, and virtually conserved through all forms of life, with the exception of some minor codon reassignments. Its is characterized by an exceptionally high robustness to point mutations: codons of the same amino acid usually share their first and second positions, and amino acids that share similar chemical properties tend to have similar codons. Although there exist more robust codes, Freeland & Hurst 74 evaluated than only one in a million randomly generated codes (in which the observed sets of codons are randomly reassigned amino acid) would surpass the observed one in terms of robustness to point mutations and translation errors. This result is at first at odds with the notion that the code is near universal, and therefore likely to be poorly evolvable, or as Francis Crick would call it, "frozen". However, it is now believed that the robust properties of the genetic code emerged through the combined effects of co-evolution and selection 75 . First of all, primordial proteins were likely composed of a subset of the current proteogenic amino acids. These primordial amino acids, which were naturally present in the environment, did not require the existence of a complex metabolism. Similarly, primordial translation was likely statistical, and therefore had to be robust to very high error levels. As more complex biosynthetic pathways emerged, the set of codons for an amino acid could be split to encode this amino acid and a newly synthesized one. These two amino acids could, for example, first share an aaRS that would then duplicate and develop an increased specificity towards either one or the other. This process guarantees that amino acids sharing part of their biosynthetic pathways would be encoded by similar codons.

An early genetic code would have likely treated the branched chain amino acids (valine, leucine and isoleucine) interchangeably. Phylogenetic techniques revealed that indeed, there biosynthetic pathways are evolutionarily intertwined and that their aaRS share a common ancestor, and were able to retrace the history of the genetic code's evolution. [START_REF] Caetano-Anollé | Structural Phylogenomics Retrodicts the Origin of the Genetic Code and Uncovers the Evolutionary Impact of Protein Flexibility[END_REF] Organisms balance their pool of tRNAs with the codons they express.

Systems biology often treats the fluxes in the cell like the fluxes of an economy. Processes like translation seem to obey the law of supply and demand: a pool of codons needs to be efficiently translated by a pool of tRNAs, and commonly translated codons should be matched with abundant tRNAs. This necessity of matching the codon pool to the tRNA pool is mostly driven by the pressure to translate proteins both fast and accurately. A common codon that is translated by a rare aa-tRNA will induce ribosome stalling, because the ribosome will have to sample many non cognate aa-tRNA complexes before finding the correct one. The ribosome is a large molecular complex in which the cell invested a lot of resources, and assigning it to an inefficiently translated codon represents an opportunity cost: it could be translating other, more efficiently encoded mRNAs during the same time window. Shah & Gilchrist [START_REF] Shah | Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift[END_REF] formalized this concept and developed an elegant population genetics based model to explain how codon usage bias results form the conflicting forces of mutation, selection for translational speed, and drift. Their model is based on the assumption that the force of selection counteracts the cost of ribosome stalling, and therefore acts proportionally to a gene's protein synthesis level. Wallace et al. [START_REF] Wallace | Estimating Selection on Synonymous Codon Usage from Noisy Experimental Data[END_REF] modified Shah & Gilchrist's model to account for noisy experimental data, yielding higher estimates of the selection coefficients for "good codons". They rightfully noted that selection could not be assumed to work exclusively against ribosomal stalling, because selection against translation errors would lead to a similar signature. The cost associated to translation errors is indeed likely to be proportional to both the expression level of the genes in which they occurred, and to the time the ribosome spends sampling for the correct aa-tRNA. In order to disentangle the effects of speed and accuracy on the codon usage bias of genes, Drummond & Wilke 79 relied on a statistical test that excluded the confounding influence of gene expression. They hypothesized that cells would likely use good, error-proof codons at positions that are crucial for protein folding, and that these positions would be more conserved in evolutionary alignments. They showed that, within genes, conserved positions were indeed encoded with a different set of codons than non-conserved positions, suggesting that selection for translation accuracy was a major determinant of codon usage bias. Whether cells optimize their translation primarily for speed or accuracy remains an open question. Yang et al. [START_REF] Yang | Codon-by-Codon Modulation of Translational Speed and Accuracy Via mRNA Folding[END_REF] were the first to notice that yeast cells seem to control the trade-off between speed and accuracy by the way they encode their mRNAs. They observed that conserved positions correlated to stronger RNA structures 12nt downstream, consistent with the notion that this structure would slow down the ribosome while it probes the conserved codon in its A-site.

The other side of the supply-demand balance is determined by the expression of tRNA genes, the availability of amino acids, and the activity of aaRS genes. This balance is maintained by processes occurring at physiological and evolutionary timescales. On a rapid, physiological timescale, the levels of free amino acids are tightly regulated: their biosynthesis pathways rely on feedback loops such as transcriptional attenuation [START_REF] Naville | Transcription attenuation in bacteria: theme and variations[END_REF] , and the interconnectivity of the metabolism usually allows cells to efficiently reallocate metabolite fluxes to mitigate the effects of amino acid starvation [START_REF] Scott | Emergence of robust growth laws from optimal regulation of ribosome synthesis[END_REF] . Severe amino acid starvation results in the production of ppGpp, a metabolite produced during amino acid induced ribosome stalling. ppGpp in turn activates the stringent response, which redirect resources from high growth rate associated function such as replication, transcription and translation toward amino acid biosynthesis pathways. Impairing the production of ppGpp by the ribosome associated GDPase RelA resulted in a 10-fold reduction of translation accuracy [START_REF] Sørensen | Charging levels of four tRNA species in Escherichia coli Rel(+) and Rel(-) strains during amino acid starvation: a simple model for the effect of ppGpp on translational accuracy[END_REF] . As seen previously [START_REF] Subramaniam | An integrated approach reveals regulatory controls on bacterial translation elongation[END_REF][START_REF] Elf | Selective Charging of tRNA Isoacceptors Explains Patterns of Codon Usage[END_REF][START_REF] Subramaniam | Environmental perturbations lift the degeneracy of the genetic code to regulate protein levels in bacteria[END_REF] , synonymous codons are differentially affected when the cell is starved for the associated amino acid. In order to respond efficiently to this challenge, genes involved in the biosynthesis of the depleted amino acid tend to be preferentially encoded with codons that are robustly translated when the tRNA charging levels are low. [START_REF] Elf | Selective Charging of tRNA Isoacceptors Explains Patterns of Codon Usage[END_REF] On evolutionary timescales, codon usage bias and tDNA gene copy number co-evolve to maintain the balance of supply and demand 2 . Yona et al. revealed that the tRNA pool could rapidly adapt in the laboratory and restore the supply-demand balance after they deleted a rare tRNAArg CCU , by spontaneously mutating the anticodon of one copy of the common tRNAArg UCU to CCU [START_REF] Yona | tRNA genes rapidly change in evolution to meet novel translational demands[END_REF] . Bioinformatic analyses revealed that these anticodon reassignments are in fact common, and can even happen between tRNAs decoding different amino acids [START_REF] Yona | tRNA genes rapidly change in evolution to meet novel translational demands[END_REF][START_REF] Rogers | tRNA anticodon shifts in eukaryotic genomes[END_REF] . This suggests that the tRNA pool is extremely plastic, and can rapidly adapt to changes in translation demand. One can suppose that the high evolvability of the tRNA pool implies that it is indeed optimized for a fast and accurate protein synthesis.

Translation accuracy affects the evolution of protein sequences.

Selection pressures can act on the coding sequences of highly expressed proteins to minimize the impact of their translation errors by choosing appropriate codons, but to what extent does translation accuracy affect the evolution of their primary amino acid sequences? In the context of adaptive selection, where mutations are selected because they confer a significant fitness advantage, Whitehead et al. [START_REF] Whitehead | The look-ahead effect of phenotypic mutations[END_REF] investigated the potential role of phenotypic errors with regard to epistasis. Taking the example of a cysteine bridge in which both cysteines need to be present for the protein to gain activity, they showed that in case of strong selection the intermediate genotypes in which only one cysteine is present could be positively selected because phenotypic errors would lead to a fraction of the protein bearing the two cysteine residues. Bratulic et al. [START_REF] Bratulic | Mistranslation drives the evolution of robustness in TEM-1 βlactamase[END_REF] tested the effects of translation accuracy on the evolution of a plasmid-borne betalactamase. In order to speed up the evolution of sequences, they performed cycles of in vitro PCR mutagenesis, transformation, and plasmid selection based on the fitness advantage they conferred to the host in a medium containing antibiotic. They carried the experiment in parallel a wild type and in an error prone strain. Evolving in an error prone environment conferred sequences a higher folding stability, which was the result of stabilizing non-synonymous mutations on the proteins surface. They did not show sign of synonymous codon selection, and the occurrence of synonymous SNPs at sites where mutations have destabilizing effects was not reduced in error-prone populations. However, despite the large population size, their evolutionary system can hardly be compared to the evolution of natural sequences, as the selection coefficients are very large and the number of generations was limited to about 50. Observing the subtle effects of mistranslation on the evolution of proteins working near optimally would be very difficult in a laboratory setup, and these questions would probably be best addressed through a combination of population genetics and simulation.

Chapter 1: Systematic detection of amino acid substitutions in proteome reveals the mechanistic basis of ribosome errors Introduction

Genetic information propagation along the Central Dogma is subject to errors in DNA replication, RNA transcription and protein translation. DNA replication typically manifests the highest fidelity among these processes, featuring genetic mutation rate on the order of 10 -10 per nucleotide per genome doubling [START_REF] Zhu | Precise estimates of mutation rate and spectrum in yeast[END_REF][START_REF] Lee | Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing[END_REF] 10 -5 to 10 -6 per incorporated nucleotide [START_REF] Traverse | Conserved rates and patterns of transcription errors across bacterial growth states and lifestyles[END_REF] . As for translation, the classical kinetic proof reading theory [START_REF] Hopfield | Kinetic proofreading: a new mechanism for reducing errors in biosynthetic processes requiring high specificity[END_REF] suggested that the error rate per amino acid would have been extremely high (10 -2 ) at chemical equilibrium, and it is only due to the investment of energy in the form of hydrolysis of GTP that it can be reduced to about 10 -4 on average. The two main steps that account for errors in translation are the mischarging -where the wrong amino acid is acylated to a tRNA, or mispairing where a tRNA mispairs with the wrong codon within the ribosome. To reduce errors due to mispairing proofreading is made within the ribosome in a process that consumes energy and that compromises translation speed [START_REF] Wong | An energy-speed-accuracy relation in complex networks for biological discrimination[END_REF]91 . Mischarging of tRNA with the wrong amino acids is also subject to proof reading working at the aminoacyl tRNA synthetase level [START_REF] Schimmel | Mistranslation and its control by tRNA synthetases[END_REF] . Like in any information channel, translation systems must thus face a "trade-off between energy, speed and accuracy [START_REF] Wong | An energy-speed-accuracy relation in complex networks for biological discrimination[END_REF] .

The heavy investment of cells in proofreading the translation process, in energy and in time is a clear indication that too high error rate would be detrimental. Indeed proteins that contain amino acid substitutions tend to misfold and aggregate, promote spurious protein-protein interactions, and they may saturate protein quality control machinery, resulting in proteotoxic stress [START_REF] Drummond | The evolutionary consequences of erroneous protein synthesis[END_REF] . Conversely, some mistakes may be tolerated and a certain level of error might even prove to be advantageous. It has been shown that mistranslation is beneficial in response to environmental stresses as it can help sustain and disseminate cellular phenotypic viability [START_REF] Mohler | Translational fidelity and mistranslation in the cellular response to stress[END_REF] . On an evolutionary time scales too, phenotypic errors might be essential in facilitating adaptation of complex traits when combined with genetic mutations [START_REF] Whitehead | The look-ahead effect of phenotypic mutations[END_REF] [Whitehead, the look ahead effect], and by the purging of deleterious mutations [START_REF] Bratulic | Mistranslation can enhance fitness through purging of deleterious mutations[END_REF] . A computational analysis of codon usage patterns across genomes revealed that a subset of codons are preferred over others at positions crucial for folding in highly expressed proteins, suggesting that evolution indeed favors more accurate codons at these sites [START_REF] Drummond | Mistranslation-Induced Protein Misfolding as a Dominant Constraint on Coding-Sequence Evolution[END_REF] .

Recent development in RNA sequencing technologies quantified the rate of translation errors to reside in the range between 10 -5 and 10 -6 , an order of magnitude or two lower than the rate reported in protein translation [START_REF] Kramer | The frequency of translational misreading errors in E. coli is largely determined by tRNA competition[END_REF] . In contrast, errors in protein translation have remained elusive and difficult to detect. An early effort by Edelmann and Gallant [START_REF] Edelmann | Mistranslation in E. coli[END_REF] , who quantitatively tracked the insertion of radioactively labeled cysteine in E. coli's flagellin, a cysteine free protein, revealed a first global estimate of mistranslation, with misincorporations happening on average every 10,000 amino acids. Since then, the use of fluorescent or luminescent reporter constructs allowed the quantitative tracking of specific types of mistranslation, at defined sites. These methods have highlighted the importance of codon-anticodon recognition and tRNA competition as determinants of these error rates, and were used to characterize the effects of aminoglycoside antibiotics and ribosome ambiguity mutations (ram) [START_REF] Kramer | The frequency of translational misreading errors in E. coli is largely determined by tRNA competition[END_REF][START_REF] Kramer | A comprehensive analysis of translational missense errors in the yeast Saccharomyces cerevisiae[END_REF] . Yet, major questions still remain open. While error rates could be measrured precisely within specific positions of reporter constructs,, the overall error spectrum across the proteome has not yet been characterized. Such measurements would allow the assessment of the relative contribution of mischarging and mispairing.. Further,

Commenté [OD4]:

Can be nice to give here some physiological relevance and refer to disease associated with translational mutations/proteotoxic stress

Commenté [OD5]:

We can also refer here to the paper showing increase met in the proteome in response to oxidative stress

Commenté [EM6]:

This repeats some information from the 1 st paragraph identification of error positions would allow to study the dependency of error on codon identity, and reveal whether specific positions within proteins are more prone to errors.

Mass Spectrometry (MS), which now permits routine, high throughput characterization of canonical proteomes and common post translational modifications (PTMs), was described as an upcoming tool for the study of protein mistranslation for almost a decade [START_REF] Drummond | The evolutionary consequences of erroneous protein synthesis[END_REF] and more recently harnessed to detect various substitutions from several purified recombinant proteins [START_REF] Zhang | G/U and Certain Wobble Position Mismatches as Possible Main Causes of Amino Acid Misincorporations[END_REF] , and to detect and quantify the incorporation of norvaline at leucine positions across the proteome of E. coli mutants [START_REF] Cvetesic | Proteome-wide measurement of non-canonical bacterial mistranslation by quantitative mass spectrometry of protein modifications[END_REF] . Yet, MS has yet to be harnessed for the unbiased study of amino acid substitutions on a proteome wide scale. Such full study was hitherto hindered by the low rate of substitutions compared to other natural and post-processing protein modifications and a much larger search space.

Here, we used Strong Cation-Exchange chromatography (SCX) fractionation and high resolution Liquid Chromatography (LC)-MS-MS to achieve a deep coverage of E. coli's proteome, and assessed the effects of two aminoglycoside antibiotics, streptomycin and paromomycin, on the bacteria's translation error rates and spectrum. In addition, we also assessed the effect of starvation to a particular amino acid on the mistranslation rate of its cognate codons. We have carried out our analysis with MaxQuant [START_REF] Cox | MaxQuant enables high peptide identification rates, individualized p.p.b.range mass accuracies and proteome-wide protein quantification[END_REF] , repurposing its dependent peptide algorithm to identify mass shifts consistent with amino acid substitutions, and stringently filtering out potential artifacts. We then validated these identifications using a set of independent analyses that include a shift in HPLC retention time due to change in hydrophobicity of the encoded amino acid. Performing these experiments and analyses on E. coli in several growth conditions and analyzing similar data in the yeast S. cerevisiae, we could detect over 3500 site-unique substitution events.

This observed set of substitutions could, for the most part, be explained by a single mismatch in the codon-anticodon complex. In particular, G:U mismatches at the 1st and 2nd positions prevail, despite the recent observation that the geometry of the small ribosomal subunit's decoding center prohibits G:U wobble interactions at these positions [START_REF] Rozov | The ribosome prohibits the G•U wobble geometry at the first position of the codon-anticodon helix[END_REF]100 . The increased error rates observed in the presence of aminoglycoside drugs support the conclusion that these mistakes arise in the ribosome due to codonanticodon mispairing. The set of errors that we detected in published MS data of S. cerevisiae shared a strikingly similar pattern of mismatches with E. coli, suggesting that errors are deeply constrained by base pairing chemistry. Furthermore, we show that rapidly evolving amino acid positions are more likely to bear amino acid substitutions. Observed substitutions tended to minimally affect protein energetic stability, and analyzing transcriptome-wide ribosome density data revealed low density at sites of mistakes, indicating at speed-accuracy trade-off. Our experimental observations support the view that organisms do mitigate the effects of translation errors by locally fine-tuning the way they encode proteins. Starving the cells for serine increased errors from this amino acid in a codon dependent manner. Our method offers quantitative estimates of error levels at a much larger scale than previously achieved, and offers a way to systematically study the response of the translation machinery to various stresses and conditions.

Results

A pipeline to confidently identify amino acid substitutions in a proteome

Mass spectrometry allows for large-scale identification of peptides at the proteome level. The task of identification of peptides with amino acid substitutions could thus resemble that of detecting known peptides that underwent post-translational modifications (PTMs). Despite the fact that the detection of common PTMs, such as phosphorylation or acetylation, has become commonplace 101 , detecting amino acid substitutions by specifying a full list of all possible substitutions would result in a dramatic increase in the size of the peptide database. For example, assuming peptides of average length 10, there would be on the order of 200 times more singly-modified than canonical peptides to search for, leading to impractical search times and a considerable loss of statistical power. Blind modification searches 102-104 , i.e. approaches that offer a way to identify (singly) modified peptides without requiring the user to input a list of predefined modifications, take advantage of the fact that modified peptides are usually less abundant than their unmodified counterparts. Therefore, a modified peptide is only likely to be detected if the canonical peptide has already been detected. We used MaxQuant to identify modified peptides with its "dependent peptide search" algorithm. "Dependent Peptides" are defined as peptides that show mass shifts in comparison to the unmodified, genome-encoded "Base Peptides" (Fig. 1B). We then applied a series of filters to the list of dependent peptides, in order to stringently remove known PTMs and artifacts and conservatively retain only amino acid substitutions. The outline of our pipeline is described in Figure 1A. For a detailed description of the pipeline, see Methods.

We generated a deep coverage, high resolution map of the E. coli proteome in rich medium at 37°C, and in addition evaluated the effect of two aminoglycosides antibiotics at sub-lethal concentration, and the effect of starvation to serine on the accuracy of its translation machinery. In total we generated error maps of 9 samples, each in two replicates (see Methods). All together we detect 3596 independent amino acid substitutions (each defined here by a unique position within a specific protein and a unique amino acid substitution) in the E. coli proteome. Similarly we analyze an existing proteome dataset 105 from the yeast S. cerevisiae at a single type, non-treated, condition that yielded 225 substitutions for comparison.

Commenté [OD8]: I think that this section of here we …. That summarizes the main finding is much too long and detailed

Commenté [OD9]:

In order to make it clear I think we should first add a sentence explaining that the common way of identifying peptides in MS relays on compering the observed spectra to the expected ones based on the protein seq of the relevant organism

Commenté [OD10]:

It is strange to discuss first 1B and only then 1A, I suggest to change the order either in the figure or in the text Peptide search performs exhaustive pairing of unidentified spectra to a spectral library derived from the identified spectra. For each pair of (identified, unidentified) spectra of the same charge z, and found in the same fraction, the algorithm first computes the mass difference Δm = munidentified -midentified . It simulates in silico, and sequentially, the addition of a single moiety of mass Δm at any position in the identified peptide, and generates the corresponding theoretical spectrum for the modified peptide. These spectra are then compared to the experimental spectrum using MaxQuant Andromeda's score formula. The pair with the highest score is retained, and the significance of the match is assessed using a target-decoy FDR procedure. C: The observed retention time shift induced by our set of substitutions is accurately predicted by a simple sequence-based retention time model.

Most of the high quality hits are bona fide amino acid substitutions.

Given mass differences detected between base and dependent peptides we must first establish that they represent amino acid substitutions. For that, we took advantage of the fact that many amino acid substitutions change peptide hydrophobicity and they hence result in predictable retention time shifts during liquid chromatography. The retention time of a peptide can be predicted with high accuracy (R 2 > 0.9) approximately as the sum of the hydrophobicity coefficients of its amino acids 106 . Therefore, the predicted HPLC retention time of the substituted amino acid can be computed and compared to the observed retention time recorded for the substituted peptide. We trained a retention time prediction tool on a list of confidently identified peptides, and generated an expectation of the retention time shift induced by the detected substitutions. We compared this expectation to the observed retention time shift for each of the detected substitutions in the MOPS dataset (Fig. 1C). This analysis supports the notion that most of the substitutions detected are genuine amino acid replacements. Note that our sampling strategy allows us to detect substitutions originating from the highly expressed proteins only.

We define a substitution as a combination of a position in a protein, an "origin" amino acid (and its associated codon), and a "destination" amino acid. We then divide all substitutions in two sets: a substitution is classified as a Near Cognate Error (NeCE) if the error-bearing codon of the origin amino acid matches with one nucleotide difference at least to the codons of the destination amino acid, and as Non Cognate Error (NoCE) otherwise. The structure of the genetic code dictates that only a minority of the substitutions would be classified as NeCE. In particular, of all detectable codon to amino acid substitution types 30% are expected to be of the NeCE type. In stark contrast, 88% of the unique substitutions detected by our method with the full E. coli dataset are classified as NeCE. Thus, the great majority of observed substitutions in our data can be rationalized by a similarity between the origin's codon and a codon of the destination amino acid. Such enrichment for NeCE compared to expectation serves as an indication that we inspect genuine amino acid substitutions (see SOM for a formal statistical test) An intriguing possibility is that NeCE substitutions might predominantly represent codon-anticodon mis-pairing events that occur within the A-site of the ribosome, and that NoCE substitutions might occur elsewhere, i.e. in the amino acid charging phase by the relevant aaRS. We attempt below to support the notion that indeed the majority of NeCE events represent mRNA-tRNA mis-pairing events.

Overview of amino acid substitution landscape in E. coli

Substitution matrices are common in biological research, for example decades of research in genomics revealed 4*4 nucleotide substitutions matrices for DNA and RNA

Commenté [OD11]:

The training is done on base peptides correct? it is not very clear

Commenté [OD12]:

Note that up to now MOPS is not mentioned so it is not clear here which data set we are analyzing.

Commenté [OD13]:

I do not think this is a very clear definition. In fact even codons with 3 mismatches will match this definition and those are obviously not near cognates. Also since we are using these definitions through the paper I think an example of a substation and relevant codons can help the reader to better understand polymerases, and 20*20 matrices of substitutions between amino acids in evolution.

Our amino acid substitution data allow us to generate 64*20 codon to amino acid matrices that depict the prevalence of each type of amino acid error in a dataset. Note that in no way these matrices represent real relative probabilities of mistakes as our ability to detect an error depends on the original protein's expression level, which also influences its codon choices. The numbers of unique peptides supporting any codon to amino acid substitution type is show in Fig. 2A; the intensity of the shade is proportional to the logarithm (base 2) of the number of unique genome positions in which substitutions were observed. Because leucine and isoleucine are isomers and thus share the exact same mass, our method is not able to distinguish the two amino acids as destinations of a substitution; thus, we grouped together substitutions towards Ile and Leu. Furthermore, substitution types that transform a codon into its cognate amino acid, involve a stop codon, or substitutions that cannot be detected using our method because they represent a mass shift that corresponds precisely to the mass shift and specificity of a PTM, were grayed out, and discarded from subsequent analyses (see Methods).

An interesting observation we make on this matrix is that the codon that encodes for an amino acid affects its substitution destination. This is nicely illustrated with substitutions from Gly to Asp and Glu. We see that when Gly is encoded by the GGC codon, the frequent substitution destination is the near-cognate Asp (that can be encoded by the near cognate codon GAC), while encoding Gly with GGA often results in substitution of Gly by Glu (presumably due to its near cognate codon GAA). Similar cases in which different codons for the same amino acid tends to show different amino acid substitution pattern can be found in the matric Once we validated the amino acid substitutions we calculated the observed error rate (i.e. the ratio of intensity between the dependent and base peptide) for the all detected substitutions. As example, the SerAGCAsn substitution, was detected in total in 81 peptides across the E. coli proteome of the non-treated samples. Figure 2B shows the error rate estimations in each of these substitutions -each dots in the plot corresponds to one specific SerAGCAsn substitution on a particular genomic position, and the error rate is on the y-axis. Likewise the 10 most frequent substitutions types in the proteome are shown. The majority of the substitutions that are observable in our dataset span the error rate range around 10 -3 , with the most highly abundant substitutions types showing slightly higher error rates. Due to the MS acquisition strategy, positions that feature a low error rate are less likely to be detected, which could lead to an overestimation of the actual error rates. We measure the proteome, and detect translation errors, in three time points along the growth cycle (beginning in exponential growth phase and ending with the stationary phase (Fig S1). An intriguing trend we observed is that error rates seem to consistently decline as cells enter the stationary phase. The actual decline in error rate might be under estimated here, due to the fact that we measure errors from the whole current proteome without restriction to mistakes made at newly synthesized proteins.

A global nucleotide mispairing mechanism for translation errors

We further classified NeCE substitutions based on the location of the mismatch within the codon and the nucleotide types they involved. We define the count density for a given mismatch type as the number of substitutions that can be explained by that type of mismatch divided by the number of substitution types that can be explained by the same mismatch, and report the count density for the two biological repeats in Fig. 4B. This analysis results in three 4*4 "mismatch matrices" that depict the prevalence of mismatching for each nucleotide in the codon with each of the three non-perfectly matching nucleotides in the anticodon Fig 3B . Substitutions that could be caused to multiple mismatches were assigned to the most likely mismatch using an expectationmaximization scheme (see Material and Methods). The most frequently observed substitution type involves G:U mismatches in the first or the second position of the codon. Interestingly, this rule holds only for mismatches where the codon base is G and the anticodon base in U; the fact that the opposite geometry (i.e. errors in which a U is in the codon and a G in the anticodon) seems to be less error prone is surprising at first, but might be explained by the numerous modifications affecting uracil at the tRNA level.

E. coli and S. cerevisiae share similar error profiles

While both characterized by a mostly planktonic lifestyle and high growth rates, E. coli and S. cerevisiae have been diverging from one another for at least 2.7 billion years.

Comparing the error profiles of these two organisms, thus, allows us to look at how strongly these errors are constrained, both by chemical and evolutionary necessities. We reanalyzed a previously published mass spectrometry dataset of strong anion exchange (SAX) and SCX fractionated proteome of S. cerevisiae grown in a single condition, a rich medium (30°C, YPD) 105 using our pipeline.

Commenté [OD20]: I feel that we show in the figure and the legend experimental stuff that we did not explained yet for example in Fig 1B we show the error rates in different growth phase but we never explained that we took samples from different growth phases. In general I feel we need to add in the beginning of the results a much better description of the experiment we did. Since we start by showing the results from MOPS complete I suggest that we will not even mentioned yet the LB + drug or ser starvation. Start only with the MOPS and then explain each additional experiment (LB+drugs and ser starvation in the relevant section).

Commenté [EM21]:

Not true for the two errors involving glycine codons; there rate actually seems to increase.

Commenté [OD22]:

This trend of having less mutations in stat is not seen for all substitutions. In fact the most dramatic change GGC to D shows the opposite trend.

Commenté [OD23]:

???Sorry did not get it ….

Commenté [OD24]:

Which two biological repeats ? we did not say anything about repeats yet….

Commenté [OD25]:

??? Is that the right figure? if so, why to jump to figure 4 before discussing figure 3 ?

Commenté [OD26]: Note that in the upper panel (coli) at the second position U at the codon and G in the anticodon seems to very prevalent almost as the opposite Commenté [OD27]: I suggest to switch the order -first discuss similarity between coli and cerevisiae in term of substitution pattern and only then move to the 4 by 4 matric and discuss comparison between coli and cerevisae also in these matrics.

We were able to detect a total of 225 substitutions in the yeast proteome. Here too the majority of the errors, 143, were classified as NeCE. Comparing the error spectrum between the eukaryote and the prokaryote we observed a high overlap between the set of substitution types seen in the two organisms. This observation reveals a universal error pattern for mistakes that are likely to occur within the ribosome, while most NoCE substitutions likely originate from separate factors unique to each of the species. The most notable difference between the two species is in the most frequently observed substitution of Ala to Cys in yeast, which is not seen in the bacterium. Indeed a recent report 107 reveals the basis for this observation -that eukaryotic, but not prokaryotic Alanyl-tRNA synthetase (AlaRS) have precisely the tendency of mischarging tRNACys with Alanine.

For the yeast data too we computed the 3 4*4 substitution matrices and observed that in similarity to the E. coli matrices they also feature G:U mismatch at the first or second positions (Fig. 4B). Observing such levels of error similarity between such loosely related organisms, exhibiting distinct codon usage biases and a relying on very different translation machineries, hints at the possibility that these errors depend on universal constraints. Whether these constraints are of a purely chemical nature, or the observed substitutions happen to be more tolerable by these organisms remains to be determined.

Commenté [EM28]:

In general, I feel like we could pend a bit more time on the two 4*4 matrices, highlighting the similarities and differences. It is clear that the g:u mismatches at the 1 st and 2 nd position are

The effect of drugs and amino acid starvation on substitution patterns

To gain further insight on error patterns and how they are affected by various perturbations, we either treated E. coli cells with two types of antibiotics that reduce ribosome proof reading capability, or starved them for an amino acid, serine. We applied two aminoglycoside antibiotics, paromomycin and streptomycin, to the bacteria. These two drugs are believed to interfere with the ribosome's proofreading activity 108 , and their effect on translation accuracy was previously measured using a luciferase reporter construct [START_REF] Kramer | The frequency of translational misreading errors in E. coli is largely determined by tRNA competition[END_REF] . We measured the proteome under each of the drug treatments by the MS-MS procedure and re-ran our error detection pipeline. To compare between the error patterns induced by the drugs, we again inspected the 64*20 codon to amino acid matrices (Fig. 4A), the error rate profiles (Fig. 4CZ) and the three 4*4 nucleotide mispairing matrices (Fig. 4C). Comparing the 64*20 matrices between the non-treated and drug treated samples reveals a clear pattern -the drugs increased error rates especially at 3 rd codon wobble positions, while other mismatch positions remained relatively unaffected. This observation is confirmed by the three . Unless I got it wrong, the blue marks denote the 3 position mismatches so in the middle panel they are expected to be more green right ? is that the case ? (not vey dominant in my opinion) can you add some statistics on this claim ?

4*4 matrices. The increased error rate at the 3 rd position can be quantified using MS1 information, as reported in Fig. 4B. We have next starved the bacteria to serine, measured again the proteome by the MS-MS procedure and re-ran our error detection pipeline. The prediction was that upon starvation to this amino acid we should observe elevated level of errors leading from this amino acid to others. Indeed, we observe that the rate of SerAGCAsn steadily increased upon starvation. We quantified further, as cells enter more deeply into the stationary phase, when the effect of starvation is supposed to intensify the rate of the substitution from Ser to Asn increases. This result indicates to a clear mechanism that accounts for mistakes in translation in which a shortage of an amino acid determines its probability to be replaced by others.

Commenté [OD31]:

Actually this matrics shows some differences also in the first position why did you concluded that the 3 position shows most of the changes with and without drug ?

Commenté

Commenté [OD34]

: I think we should add a bit of logic here, why to starve for an amino acid to start with and why did we choose serine in particular.

Commenté [EM35]:

We need to say something about the Elf paper. I suggest something along these lines : "While a model predicts that the TCN serine codons should be affected more strongly by starvation than the AGC and AGT codons, our method was mostly able to fetch substitutions stemming from the two latter codons. This observation does not necessarily contradict the theory, since the AGC and AGT errors are also those most seen in the MOPS complete medium, and it is possible that the TCX codons do suffer more from starvation, but remain at low levels in absolute terms."

Commenté [OD36]:

We should add a short discussion about the patterns we see, way different ser codons are replaced with different amino acids, does it make sence in term of the genetic code mispairing vrs misloading elf predictions etc.

Misincorporations occur at error-tolerant and rapidly translated positions

Drummond & Wilke 79 posited that cells, in order to avoid the fitness loss due to protein misfolding and aggregation, manage their errors by selecting error-proof codons at positions where inserting the correct amino acid is critical to folding or function. They were able to support that theory using computational means, but had to rely on key assumptions. In particular, they used evolutionary conservation as a proxy for sensitivity to phenotypic errors, and they derived the identity of error prone and error proof codons from conservation data. Correspondingly, fast evolving positions within protein are predicted to be less critical for protein folding and function thus correspond to sites where rate of mistranslation is expected to be higher. This assumption that evolutionary conservation correlates with phenotypic error rate was indeed made in several additional recent publications. Yet, the lack of a systematic set of translation error events within a proteome precluded so far the examination of the notion that they occur preferentially in rapidly evolving sites, or in positions that minimally affect protein structure and function. A careful analysis of the classical model of kinetic proofreading revealed an complex trade-off between speed and accuracy during the aa-tRNA selection step by the ribosome: ribosomes are more likely to misincorporate amino acids at sites where they translate rapidly 109 . This trade-off was examplified by mutants that featured modified translation speed 110 , and by in vitro conditions that affect ribosome velocity [START_REF] Johansson | Genetic code translation displays a linear trade-off between efficiency and accuracy of tRNA selection[END_REF] . Yet examination of the theory in natural sites within genes, in which ribosome's speed can now be deduced 111 , was so far impossible to obtain due to lack of ability to measure translation errors genome-wide .

Commenté [EM37]: I just found this, fantastic work!! Tzachi, their idea is very similar to the little trade-off model that I presented to you a while ago. If you remembered, I showed you that slowing down the rate constant of the last irreversible reaction to infinity allowed you to reach the maximal possible accuracy, but that you paid the price in speed, because of the slow down of that particular reaction but also because you ended up going through many more iterations of the proofreading before accepting the product of the reaction. They managed to formalize it very nicely! Commenté [EM38]: Tzachi, they used a really nice construct to measure translation spped, you should have a look at the paper. They took a hyperacurate strain of e.coli and showed that its translation was In order to test if the set of detected substitutions differs from expectations in any way, we first need account for the fact that many local properties of proteins are affected by the protein's expression level, and so is our ability to detect substitutions from that protein.

First, the local property of interest ('score') is recorded at all the positions bearing a substitutions. The average of that set is plotted as a red dashed line. To compare this average to an appropriate control, we devised three strategies to eliminate the potential contributions of protein level, amino acid identity and codon identity on the score. In each of these strategies, we draw 1000 sets of the same size as the set of observed substitutions, and plot the average of each of these sets as a blue dot. In the first strategy, for every bona fide distribution, we draw the score from any position within the same protein. In the second strategy, we draw the score from any position within the same protein that shares the same amino acid as the one bearing the bona fide substitution. Similarly, in the third control, the codon for the sampled position has to be the same as the substituted codon. B. Amino acid conservation: We derived amino acid conservation scores for E. coli proteins, using the COGs database to fetch 50 homologs, MUSCLE to align them, and rate4site to estimate the evolutionary rate at each site. the ribosome density at positions along the E. coli transcriptome. Since ribosome density/speed can only affect errors in cis, this analysis was restricted to NeCE. D. Effect of substitutions on protein stability: for proteins whose 3D structure is known, we evaluated the effect of NeCE on protein stability using FoldX. In control 1, we test if the observed substitutions are on average less destabilising than those stemming from other single-nt mismatches between the codon and the anticodon, at the same position. In control 2 and 3, we test if the observed substitution type observed was less destabilising on average at the observed position than at other positions sharing the same AA, or the same codon.

We decided to test if the mis-incorporations we observed indeed occurred at less conserved, rapidly evolving, positions by comparing the distribution of conservation scores for our observed substitutions to that of carefully selected control positions. Normalized conservation scores were computed for each protein by fetching homologs, aligning their sequences, and running rate4site 112 to determine the evolutionary rate at each site in the protein. We recorded the normalized score for each of the positions for which a substitution was detected. We use standardized rate4site evolutionary rate scores per protein; a high score indicates low conservation of the amino acid position across orthologous proteins. In order to account for the fact that some amino acids tend to be more conserved than other, and that some codons are over-represented at conserved positions, we devised three strategies to generate adequate negative controls (Fig. 5A). In the first and least stringent strategy, for each observed substitution, we sampled a normalized conservation score from any position in the same protein. In the second strategy, the random re-sampling was carried not only within the same protein, but also with the additional constraint that the amino acid identity in the randomly sampled position has to match the same amino acid type observed at the position at which the substitution occurred. Finally, in the most stringent of these negative controls, we performed a random re-sampling within the same protein, at sites sharing the same codon as the observed positions. We generated 1,000 such re-samplings in each of the three types of negative control, and compared the mean of the observed distribution of scores at the observed substitution positions to those of the random control distributions to obtain empirical p-values. The mean rate of evolution at substitution sites is similar to that of random sets of positions generated though the first model, but significantly higher than that of the random generated with the other two (fig 5B). Consistent with the previous prediction [START_REF] Drummond | Mistranslation-Induced Protein Misfolding as a Dominant Constraint on Coding-Sequence Evolution[END_REF] , controlling for the codon reduced the magnitude of the difference between the real error sites and random sites (fig 5B "same codon vs "same AA") , supporting the notion that evolution allows or precludes error-prone codons from sites that are correspondingly tolerant or intolerant to errors. Similarly, to the conservation test, we examined the non-independent possibility that observed amino acids substitutions in the E. coli proteome tend to minimally affect the energetic folding stability of protein in which they occur. To this end, we used FoldX 113 to compute G, i.e. the difference in folding energy between each original, genomeencoded protein and its corresponding substituted version. After obtaining distributions of such scores, we compared these distributions to those obtained upon three random sampling negative control strategies (Fig. 5D). In the first control strategy we analyze the stability difference between the wild-type protein and all the proteins that could be obtained by mutating a single nucleotide of the error-bearing codon ("identity control"). In the other two negative controls, we maintained the identity of the originally observed pair of substituted and substituting residues, but modeled the effect on G upon substituting between these two amino acids, albeit at randomly chosen positions within same proteins sharing the same amino acid, or the same codon ("amino acid position and codon position controls"). In the identity control, we test if codons are preferentially mistranslated to amino acids that are accepted at the position of error, controlling for the established property of the genetic code of allowing substitutions between chemically similar amino acids. The two position controls test if substitutions happen at sites at which they better tolerated, and if the codon identity explains this effect. We find that observed substitutions tend minimally disrupt protein folding, with a mean G of 1.454 kcal/mol. This value is very significantly lower than that obtained under the identity control (mean G ~ 1.9 kcal/mol). Among the possible single nucleotide mismatches that could lead to a mistake, the cell seems to be more permissive to those less disruptive to protein stability. Consistent with the conservation findings, errors are seen preferentially at sites that minimally affect folding, suggesting positional information within genes that allows mistakes to happen where they would be minimally disruptive. Controlling for the codon identity did not explain this effect. We cannot exclude an equally interesting alternative that some substitutions that destabilize protein structure lead to a more rapid degradation, and are thus precluded from being sampled in our method. Lastly, we aimed to test the notion that the ribosome is prone to make an error at positions in which it translates more rapidly. An indirect means to deduce the ribosome speed on each position in each gene is to measure its read density in a ribosome footprint experiment. At steady state flow the product of speed and density should be constant. Hence, region of locally high density in ribosome footprinting indicate a locally low speed of the ribosome. We computed the normalized read density profile of most E. coli protein using ribosome profiling data of bacteria grown on MOPS complete medium (see Material & Methods). We could then ask if error sites feature the expected high speed, i.e. low density. Computing the mean ribosome density among all the error sites, and comparing that mean to the mean of 1000 randomly sampled positions indeed showed a small-effect but statistically significant trend (Fig. 5C) -error sites are less dense, and are hence deduced to be translated more rapidly than matched controls.

Discussion

Here we report on a new method to observe single amino acid misincorporations, which we used to detect over 3500 distinct translation errors across the proteome of E. coli.

Our method takes advantage of the very high accuracy of modern mass spectrometer to generate high confidence identifications. Orbitrap mass spectrometers can be tuned to detect mass differences on the order of thousandth of Daltons, during both the MS1 and MS2 acquisition phases. This accuracy in turn allows us to distinguish peptides and peptide fragments of almost identical masses, but of different atomic and isotopic compositions, and thus greatly improves the performance of database search algorithms. Our method is therefore able to distinguish amino acid substitutions from PTMs of similar masses. Despite the FDR procedure applied at the end our pipeline, we cannot exclude with absolute certainty that some of the substitution types we detect are in fact un-annotated PTMs that cannot be distinguished from amino acid substitutions. However, the retention time shifts in HPLC observed for our set of identifications correlate very well the expected retention time shifts predicted from sequence information alone, an observation that could not be explained by the identification of spurious PTMs.

One cannot guarantee a priori that these substitutions stem from errors in the translation machinery, because non-synonymous errors at the DNA or RNA levels could generate the same mistakes at the protein level. However, our samples originate from clonal populations, which implies that DNA mutations are unlikely to reach a detectable level in the absence of strong adaptive selection, and would be very rarely observed to occur across multiple samples. Since we analyze samples in which the number of cells (~10 [START_REF] Rooman | What is Paradoxical about Levinthal Paradox?[END_REF] ) is greatly superior to the inverse of the observed lower bound of transcription error rates (~10 [START_REF] Simsek | An emerging role for the ribosome as a nexus for post-translational modifications[END_REF] ), and the average number of mRNA per cell for the genes we detect is greater than one, the relative abundance of errors is expected correspond to the transcription error rate at any examined site, and should not fluctuate from sample to sample thanks to the assumption of ergodicity. This estimate is two orders of magnitude lower than the average observed error rates quantified by our method. Even though transcription error rates were shown to be fairly constant over a range of conditions in E. coli, we cannot rule out the possibility that local transcription error rates hotspots could yield peptides detectable by our method.

The set of observed substitutions therefore likely derives from errors within the translation machinery. Two distinct processes have been shown to generate high levels of errors: aaRS can mistakenly load an amino acid to a non-cognate tRNA, and the ribosome can pair a correctly charged aa-tRNA complex to a non-cognate codon. Both processes rely on small energetic differences between correct an incorrect pairings. For the ribosome, the recognition process exploits the difference of free energy between cognate and non-cognate codon-anticodon pairs. Some aaRS also probe the nature of the anticodon of the tRNA before loading, and additionally rely on clues from the tRNA backbone to achieve a high specificity. The amino acid recognition step can be challenging due to similarities between amino acid types, and a subset of these enzymes have to rely on an editing step to achieve higher specificity. Differential binding of EF-Tu to misacylated tRNAs was shown to discriminate against common aaRS mistakes 114 , and thus provides an additional layer of specificity. We argue that most of the substitutions detected in our work stem from errors in the ribosome. Indeed, the overwhelming majority (88%) of the substitutions could be explained by a single codon-anticodon mismatch, a fraction much higher than expected by chance due to the organization of the genetic code (30%). Additionally, treating the cells with aminoglycoside antibiotics known to perturb the accuracy of the ribosome affected the rate and spectrum of errors, increasing in particular the error rates for substitutions involving mismatches at the 3 rd codon position. However, we were able to identify several instances CysAla subtitutions (NoCE) in the S. cerevisiae samples, consistent with a recent report that eukaryotic, but not prokaryotic AlaRS had a tendency to mischarge non-cognate cysteine tRNAs 107 .

Comparing the error spectrum of the E. coli and S. cerevisiae in untreated, rich conditions revealed a large overlap between the set of observed substitution types, and a striking prevalence of Gcodon:Uanticodon mismatches at the first and second positions. Structural analysis of G:U and U:G mismatches within the ribosome revealed that they typically adopted a Watson-Crick G:C like geometry rather than the expected wobble one due to spatial constraints in the decoding center. These errors are therefore believed to originate from rare enolic or anionic states of nucleobases, as proposed by Rozov et al. [START_REF] Rozov | The ribosome prohibits the G•U wobble geometry at the first position of the codon-anticodon helix[END_REF] . The surprising observation that G:U mismatches are typically much more prevalent than the symmetrical U:G conformation could be explained by the abundance of uracil modifications on the anticodons of tRNAs.

The E. coli MOPS dataset allowed us to quantify a large number of substitutions. The mean error rate detected was on the order of 10 -3 , in the higher end of the range of previously reported estimates. Several reasons can be invoked to explain this observation. First, MS detectability is intimately linked to MS1 intensity levels: since the mass spectrometer systematically samples the most intense peptides in each scan, we are bound to preferentially detect and quantify substitutions associated to high error rates. Similarly, a peptide's MS1 intensity depends on its abundance in the sample and on its ability to ionize well. The abundance of the correct peptide is usually much higher than that of the error-bearing one, which means that it will be sampled more often. The quantification depends on the sampling of the lower abundance, error-bearing peptide. Substitutions that increase the peptide's ionization efficiency are therefore bound to increase its detectability, and will result in an inflated error rate. While it is generally accepted that ionization efficiency depends on a peptide's sequence in a very nonlinear fashion, we trained a linear regressor to evaluate the mean effects of amino acid composition on ionization efficiency. median error rate for these codons rose to almost 10 -2 in the stationary phase time point, with some sites reaching an error rate approaching 10 -1 . Other serine codons were also affected, but the scarcity of sampling for these rarer errors precluded a reliable quantification of the process. Theory predicts that the 4-box codons of serine (TCN) should suffer more from serine depletion than the 2-box codons (AGY) because of a differential charging of the tRNA isoacceptors [START_REF] Elf | Selective Charging of tRNA Isoacceptors Explains Patterns of Codon Usage[END_REF] . Our failure to detect a large quantity of errors at TCN sites might be partially explained by the preferential usage of AGY codons in genes over-expressed during serine starvation [START_REF] Elf | Selective Charging of tRNA Isoacceptors Explains Patterns of Codon Usage[END_REF] .

Translation errors have been hypothesized to be a major constraint in protein evolution, and to drive the long known anti-correlation between gene expression and evolutionary rate at the protein level [START_REF] Drummond | Mistranslation-Induced Protein Misfolding as a Dominant Constraint on Coding-Sequence Evolution[END_REF] . According to this theory, the selective pressure to prevent translation errors constrains the synonymous encoding of amino acids critical to protein folding, and organisms must select preferred, error-proof codons at positions where errors are likely to disturb protein stability. These highly constrained sites are characterized by a higher evolutionary conservation, and a slow rate of evolution. Our set of substitutions enabled us to directly test if errors indeed happen preferentially at fast evolving sites. Our analysis carefully controlled for the effects of protein expression level on the detectability of translation errors, the codon usage of proteins, and their evolutionary conservation. It confirmed that indeed, substitutions occur on average at less conserved sites, but also that the choice of codons could not entirely explain this effect, suggesting that other factors might affect translation accuracy in cis. Similarly, simulating the effects of the set of observed substitutions on protein stability revealed that they tended to occur at sites where they minimally affected protein folding. Observed NeCE were also less destabilizing than randomly sampled NeCE at the corresponding sites, suggesting that the spectrum of ribosome errors is even more conservative than the effect of naïve single substitutions at the DNA level. Together, these results confirm that the cells encode their proteins and tune their translation machinery in ways that minimize the deleterious effects of amino acid misincorporations. Since codon identity does not entirely account for the fact that substitutions are preferentially observed at sites where they are tolerated, we tested if the ribosome itself might modulate its accuracy locally. Several lines of evidence indicate that ribosomes optimize both speed and accuracy, and must therefore perform a trade-off between theses two constraints. In particular, decreasing the ribosome's GTP hydrolysis rate should result in an lower processing speed, but a better discrimination between cognate and non cognate aa-tRNAs. 109 We hypothesized that the ribosome might rely on external clues to locally slow down in order to increase its accuracy at critical sites. Our analysis of a published ribosome profiling dataset indeed revealed a subtle but significant shift in ribosome density: the sites at which we observed substitutions were characterized by a lower ribosome density, i.e. a higher speed.

In principle, our method can be adapted and applied to any organism whose genome is sequenced. It provides a way to scan the proteome of organisms in various growth conditions, and can be used to unveil new types of adaptive translation [START_REF] Pan | Adaptive Translation as a Mechanism of Stress Response and Adaptation[END_REF] . In

Commenté [OD56]:

This raise again the need we discussed in the past to normalized the data to our proteome codon usage ….. multicellular organisms, one could extend this analysis to different tissues, and diseases associated to proteostasis defects, such as Alzheimer's 115 . Indeed, the recent report that translation error rates are inversely correlated to the maximum lifespan of rodent species indicates that maintaining high translation accuracy is critical during aging. In model organisms, it can be coupled with genetic manipulations to probe how the different components of the proteostasis network contribute to maintain accurate translation, and how errors affect the physiology and fate of proteins. This, in turn, will provide a new way to study the interplay between translation error rates and protein evolution.

Material and Methods

Strains and growth conditions

To generate the E. coli drugs dataset, MG1655 cells were plated on LB agar and incubated at 30°C overnight. 6 colonies of MG1655 were picked and grown until stationary phase in 3 ml LB, 30°C. All 8 cell cultures were diluted 1/100 and grown aerobically in 100 ml LB supplemented with the relevant antibiotics (see table X) in 500 ml Erlenmeyer flasks at 37°C until they reach mid-log phase (OD ≃ 0.5). For the serine starvation dataset, BW25113 (WT) and JW2880-1 (ΔserA, obtained from the Keio deletion library) cells were plated on LB agar and incubated at 37°C overnight. 2 colonies of each strain were picked and grown in 3 ml of modified MOPS rich defined medium made according to Cluzel et al recipe (SI Appendix) and incubated at 37°C until stationary phase. BW25113 and JW2880-1 cell cultures were diluted 1/1000 and grown aerobically in 220 ml of modified MOPS rich defined medium and MOPS serine starvation medium accordingly in 500 ml Erlenmeyer flasks at 37°C (mediums were made according to Cluzel et al 2012 SI Appendix).

Proteome extraction

We adapted our proteome extraction protocol from Khan et al., 2011 116 . Samples were each split into two 50 ml falcon tubes, centrifuged at 4000 rpm for 5 min, and washed twice with PBS (add 10 ml PBS, vortex, centrifuge for 5 min). Remaining PBS was vacuumed and the pellets were frozen in ethanol-dry ice. Pellets were re-suspended in 1 ml of B-PER bacterial protein extraction buffer (Thermo Fisher Scientific), pooled together, and vortexed vigorously for 1 min. The mixture was centrifuged at 13,000 rpm for 5 min. The supernatant (high solubility fraction) was collected and frozen in an ethanol-dry ice bath. The pellet was re-suspended in 2 ml of 1:10 diluted B-PER reagent. The suspension was centrifuged and washed one more time with 1:10 diluted B-PER reagent. The pellet was re-suspended in 1 ml of Inclusion Body Solubilization Reagent (Thermo Fisher Scientific). The suspension was vortexed for 1 min, shaken for 30 min, and placed in a sonic bath for 10 min at maximum intensity. Cellular debris was removed from the suspension by centrifugation at 13,000 rpm for 15 min. The supernatant was frozen in an ethanol-dry ice bath (low solubility fraction).

SCX fractionation, HPLC and Mass Spectrometry

400µg of protein was taken for in-solution digestion and processed by Filter aided sample preparation (FASP) 117 protocol using 30k Microcon filtration devices (Millipore).

Proteins were subjected to on-filter tryptic digestion for overnight at 37°C and the peptides were fractionated using strong cation exchange (SCX) followed by desalting on C18 StageTips 118 (3M Empore™, St. Paul, MN, USA). Peptides were analyzed by liquid-chromatography using the EASY-nLC1000 HPLC coupled to high-resolution mass spectrometric analysis on the Q-Exactive Plus mass spectrometer (ThermoFisher Scientific, Waltham, MA, USA). Peptides were separated on 50 cm EASY-spray columns (ThermoFisher Scientific) with a 140 min gradient of water and acetonitrile. MS acquisition was performed in a data-dependent mode with selection of the top 10 peptides from each MS spectrum for fragmentation and analysis

Computational methods

Raw files were analyzed with MaxQuant v. 1.5.5.1. The list of parameters is available in the supplementary materials. High and Low solubility fractions were aligned separately. The amino acid substitutions identification procedure relies on the built-in dependent peptide algorithm of MaxQuant.

The Dependent Peptide search

Experimental spectra are first searched using a canonical database search, without any variable modification, and a False Discovery Rate (FDR) of 1% is guaranteed by a target decoy procedure. Identified spectra are turned into a spectral library, and a decoy spectral library is created by reversing the sequences of the identified spectra. For each possible pair consisting of an identified spectrum in the concatenated spectral libraries and an unidentified experimental spectrum of the same charge, and recorded in the same raw file, we apply the following steps : Compute the mass shift Δm by subtracting the mass of the identified (unmodified) spectrum to that of the unidentified (modified) spectrum, Generate modified versions of the theoretical spectrum by adding in silico this mass shift at every position along the peptide, and Evaluate the match between the theoretical spectrum and the experimental spectrum using a formula similar to Andromeda's binomial score.

Finally, for each unidentified peptide, the match with the best score is reported, the nature of the match (target or decoy) is recorded, and a target-decoy procedure 119 is applied to keep the FDR at 1%. Peptides identified using this procedure are called Dependent Peptides (DP), whereas their unmodified counterparts are named Base Peptides (BP). Additionally, the confidence of the mass shift's localization is estimated using a method similar to MaxQuant/Andromeda's PTM Score strategy, which returns the probability that the modification is harbored by any of the peptide's amino acid.

DP identifications filtering

The list of all known modifications was downloaded from www.unimod.org, and those marked as AA substitution, Isotopic label or Chemical derivative were excluded. Entries in this list are characterized by a monoisotopic mass shift, and a site specificity (i.e. they can only occur on a specific amino acid or on peptides' and proteins' termini). We removed from our analysis any DP identification that could be explained by any of the remaining modifications, using the following criteria : the recorded Δm and the known modification's mass shift must not differ by more than 0.01 Da, and the modification must be harbored by a site consistent with the uniprot entry with a probability p ≥ 0.05. Conversely, we computed the list of all possible amino acid substitutions and their associated mass shifts. For every substitution, we only retained DP identifications such that the observed Δm and the AA substitution's mass shift did not differ by more than 0.005 Da, and the mass shift was localized on the substitution's original AA with p ≥ 0.95. Among the remaining DP identifications, those such that the peptide sequence after substitution was a substring of the proteome (allowing Ile-Leu ambiguities), were also removed, to prevent pairing of dependent peptides and base peptides between paralogs.

Finally, the FDR was controlled once again at 1% using the same procedure as above.

Error rate quantification

In order to assess the error rate we quantify and compare pairs of base and dependent peptides across many samples. For each independent substitution, we fetched the quantification profile of the base peptide from MaxQuant's peptides.txt table, and similarly fetch the dependent peptide's quantification profile from the matchedFeatures.txt table. Whenever a peak has been detected and quantified for both the dependent and the base peptide, we estimate the translation error rate as the ratio of their MS1 intensities.

Evolutionary rates computation

For each of the proteins associated to a substitution in the MOPS dataset, we fetched a list of orthologous protein sequences from the COG database 120 , excluding partial matches (membership class = 3). Proteins whose list of orthologs contained less than 50 sequences were excluded from this analysis. For the remaining proteins, we randomly selected 50 sequences from the list, and created evolutionary alignments using MUSCLE 121 . The alignments were then used to compute normalized evolutionary rates per site with the rate4site program 112 . The mean evolutionary rate of sites associated with detected substitutions was compared to that of a 1000 randomly sampled positions, using the strategy described in Fig. 5A Effect of substitutions on protein stability

For each of the proteins associated to a substitution in the MOPS dataset, we attempted to fetch the best 3D structure for its biological assembly in the PDB database to estimate the effect of our substitutions on protein stability using the FoldX software 113 . We excluded membrane proteins, whose stability is poorly modeled by FoldX, and excluded ribosomal protein because the ribosome is too big to be modeled entirely. We restricted our analysis to WT proteins from E. coli, excluding structures determined from orthologs. Among the remaining structures, we selected those with the lowest R-free score. These structures were first "repaired" using the repairPDB command. We then evaluated the effect of a set of amino acid substitutions comprising the detected substitutions and the controls described in Fig. 5D on protein stability (ΔΔG), using the PositionScan command. Finally the mean ΔΔG of our set of substitutions was compared to the mean ΔΔG of 1000 randomly sampled substitutions, using the strategy described in Fig. 5A.

Ribosome density computation

Ribosome profiling data for the MOPS complete experiments were downloaded from Woolstenhulme et al., 2015 122 (GSM1572266, GSM1572267). Reads were aligned using the 3' mapping method described in the corresponding article, and shifted by 12 nt to obtain the density at the A-site. Read counts from both replicates were summed to obtain more robust estimates, and 20 codons were excluded from both the 3' and the 5' ends to avoid known biases. Genes whose read density (i.e. number of reads mapped divided by gene length) was lower than 10 were also excluded. For the remaining positions, we applied the transformation x : log2(x + 1) to stabilize the variance, and standardized the resulting score to obtain the normalized read density (NRD), so that the mean of the NRD per protein is 0 and its standard deviation is 1. The mean NRD of the set of observed substitutions was then compared to that of 1000 randomly sampled substitutions, using the strategy described in Fig. 5A.

Chapter 2: RNA editing in bacteria recodes multiple proteins and regulates an evolutionarily conserved toxin-antitoxin system

As it became clear in the previous chapter, the information transfer from genome to protein sometimes deviates from the expectation specified by the genetic code. Cases of adaptive mistranslation enable cells to conditionally recode their proteins in response to environmental cues, but usually rely on mechanisms generating nonstandard aa-tRNA. These processes affect non specifically all instances of a codon or a set of codons. Could bacterial cells pinpoint their recoding efforts on a specific set of positions, and modulate their proteins activity and specificity in response to internal or external cues? In Eukaryotes, several enzymes were shown to post transcriptionally modify the chemistry of RNA bases, a discovery that spawned the growing field of RNA editing. A large fraction of the research has been devoted to the study the doublestranded RNA-specific adenosine deaminase (ADAR). ADAR targets double stranded RNA, and converts adenosine (A) to inosine (I) within Metazoan cells. I is structurally similar to G, and therefore base pairs with C; it confuses many cellular processes relying on base pairing, including the in vitro reverse transcription step used in RNA sequencing experiments, where it results in a distinct A -> G signature at edited sites. ADAR likely evolved as a defense mechanism against mobile genetic elements, such as LINEs and Alus, which use dsRNA and RNA-DNA hybrids as intermediates. Although mostly directed towards these repetitive elements, RNA editing can occasionally induce non synonymous substitutions in coding sequences, with 19 such cases conserved across primates 123 . In some of these cases, the recoding was shown to induce a change in the protein's activity, and can serve as a switch 124 , in a typical display of molecular exaptation. Despite the large number of studies devoted to RNA editing in Eukaryotes, these mechanisms have not been reported to take place in Prokaryotes. Bacteria lack ADAR, but in E. coli, the tadA gene encodes an evolutionarily related ADAT, which catalyses the editing of adenosine to inosine on the wobble position of an arginine tRNA, and allows it to decode all codons of the CGN arginine 4-box. tadA is essential, but a viable mutant was shown to confer resistance to toxins of the gef family, through a yet unknown mechanism.

We re-analyzed published RNA-seq datasets of E. coli and other Enterobacteria, looking for instances of positions where bases on the RNA differed from the genome encoded sequence, and found several positions which consistently deviated from expectation. In addition to the expected modification on the anticodon of arginine tRNAs, some events occurred on the coding sequences of mRNAs, and are predicted to change the protein sequence. We are the first to report that such mRNA recoding via RNA editing occurs in the bacterial realm. Among these, the adenine of a the tyrosine TAC codon of the hokB gene was consistently modified and read as a G in the RNA seq experiments, suggesting that the codon would be decoded as a cysteine. hokB encode a toxin from the get family, whose activity has been linked to the clinically relevant phenomenon of bacterial persistence. The following chapter, published in Genome Research, describes the first report of RNA editing of bacterial mRNAs, and explores the molecular mechanisms used by E. coli cells to transform this modification into a physiological response. While the lead author, Dan Bar Yaacov, performed all the experiments, I closely collaborated with him to design the experiments, organize and run the computational pipeline, and analyze the results.

Chapter 3: Gene Architectures that Minimize Cost of Gene Expression

Selection acts upon the DNA sequence of open reading frames to ensure a fast, accurate, and energetically inexpensive translation. In particular, a subset of the 61 sense codons is overrepresented among highly expressed genes. These codons are typically decoded by abundant tRNA isoacceptors, suggesting that they are translated fast. However, Tuller et al. noticed that, on average, sequences near the beginning of highly expressed genes were enriched for poorly translated codons. They modelled translation elongation as a stochastic process, in which the average time a ribosome spends decoding a given codon is inversely proportional to the concentration of its cognate aa-tRNA isoacceptor, and proposed that a stretch of poorly translated codons at the beginning of a sequence (the "ramp") could serve as a mechanism to avoid ribosome traffic jams downstream. Ribosomes translating the 5' end of mRNAs mask the ribosome binding site (RBS) and physically prevent re-initiation, which in turn smoothens the expression profile by introducing a quasi-deterministic delay in the system. Since this delay is affected by the concentration of charged isoacceptors in the cytosol, the rate of re-initiation of a given mRNA depends on the physiological state of the cell, opening the possibility that ribosome initiation could be directed preferentially towards a subset of mRNAs depending on the availability of charged tRNAs. This view that initiation rates were affected by elongation and modulated via selection for slower codons was challenged by the experiment of Goodman et al. The authors generated a library of over 14,000 plasmids to test the effects of N-terminal sequences on protein production. They fused a GFP 3' to the first 10 amino acids of 110 natural proteins, and systematically recoded these sequences to investigate the effects of codon rarity and 5' mRNA structure on protein expression, in the context of RBS and promoters of various strength. After transforming in an E. coli host, they measured simultaneously the expression profile of all variants, using a combination of FACS and Next Generation Sequencing. This large scale experiment revealed that translation initiation rates were mostly governed by the nature of the RBS, and the strength of the mRNA structures interacting with it, than they were by codon rarity. These results suggest that, in rich conditions, initiation is mostly limited by the accessibility of the RBS and its affinity to the ribosome. The system operates in a regime were non-linear effects due to sterical exclusion by already translating ribosomes in the mRNA's 5' region ("traffic jams") are rare and have negligible effects on initiation rates. However, this experiment focused on protein production alone, and therefore overlooks an important aspect of translation biology: within cells, natural selection does not simply optimise protein expression levels, but rather favours designs which output the desired amount of proteins while minimising the cost associated to their synthesis, maintenance and degradation.

In the following article, we estimated the fitness costs associated to each of these designs and compared them to the expected benefits, i.e. the protein production levels, to gain a better understanding of how selection balances costs with benefits and constrains the evolution of Nterminal gene architectures. To evaluate the fitness cost of these designs, we grew the pooled library in batch, diluted the cultures daily over the course of 12 days, and monitored the frequency of each design in the population via deep sequencing. My main contribution to this work concerned the mathematical estimation of fitness from read frequency. I also advised Aviv Rotman, Dvir Schirman and Omer Asraf, who were primarily in charge of the data analysis, on many modeling decisions.

Appendix: Prediction of ionization efficiency from amino acid composition

The quantification of error rates that I employed in the first chapter compares the intensity of the peptide bearing the substitution (the dependent peptide) to that of the corresponding unmodified peptide (the base peptide). In principle, it would be more biologically relevant to compare the abundances of the two peptides rather than their intensities. For any given peptide, the intensity I of its peaks in mass spectrometry is directly proportional to its abundance a in the sample. The proportionality coefficient, called ionization efficiency (IE), is defined as IE = I/a, and measures what proportion of the peptide effectively ionizes during electro spray. Therefore, the ratio of intensities IDP/IBP is only a good approximation of the ratio of abundances aDP/aBP inasmuch as their ionization efficiencies stay relatively constant. Unlike other peptide properties such as retention time, ionization efficiency is notoriously difficult to predict from sequence information alone, and is believed to change drastically even between peptides sharing the same amino acid composition. In the next section, we attempt to estimate the average changes in ionization efficiency induced by single amino acid substitution. Because peptide and protein abundances are not directly measurable via mass spectrometry, I devised a statistical procedure to estimate simultaneously protein abundances and peptide ionization efficiency using a simple linear predictor. This predictor, which I trained exclusively on peptides of charge 2+, reveals that one can predict ionization efficiency of peptides based on simple features extracted from their sequence. The value of the different coefficients associated to these features are easily interpretable, and, due to the linear nature of the predictor, the average ionization efficiency difference resulting from a single amino acid substitution can be computed as the difference between the coefficient for the destination and the origin amino acid, in units of log10(fold_change). Most substitutions are predicted to have a moderate effect on ionization efficiency, such that |log10(IEDP/IEBP)| < 0.5. This knowledge can be used to correct constitutive biases in our estimations of error rates. In addition, this technique can serve to give a more accurate representation of the relative protein abundances in a sample, especially when few peptides from this protein were sampled. It is computationally inexpensive, requires little data to train, and partially alleviates the need for spiking in mass spectrometry experiments. It would be interesting to compare the quantification results obtained using this method to those acquired using absolute quantification techniques such as fluorescence microscopy. #o fo ccurences oft hes ubsequence'K P'i np ept ide N t er m Pro 1 if pept ide st art s wit h Pro, 0 ot herwise -2 is R 1i f t he aai n posit ion -2 relat ivetothe N t er m cleavage sit e is 'R', 0 ot herwise -2 is K 1i f t he aai n posit ion -2 relat ivetothe N t er m cleavage sit e is 'K ', 0 ot herwise -1 is R 1i f t he aai n posit ion -1 relat ivetothe N t er m cleavage sit e is 'R', 0 ot herwise -1 is K 1i f t he aai n posit ion -1 relat ivetothe N t er m cleavage sit e is 'K ', 0 ot herwise +1is R 1i f t he aai n posit ion + 1 relat ivetothe C t er m cleavage sit e is 'R', 0 ot herwise +1is K 1i f t he aai n posit ion + 1 relat ivetothe C t er m cleavage sit e is 'K ', 0 ot herwise +1is P 1i f t he aai n posit ion + 1 relat ivetothe C t er m cleavage sit e is 'P', 0 ot herwise inverse len gt h inverse of the pept ide's lengt h length lengt h of the pept ide Tot al

Figure 1 :

 1 Figure 1: structure and properties of L-amino acids. A: General structure of an amino acid. The asymmetric carbon in green is called the α-carbon . The R group, in magenta, is called the residue. B: Formation of the peptide bond. The carboxyl group of amino acid 1 interacts with the amine group of amino acid 2, forming a peptide bond and releasing water. All atoms within the rectangle lie on the same plane. C: Structure and properties of the proteogenic amino acids. All figures adapted from Wikipedia.

Figure 2 .

 2 Figure 2. A: Consensus tRNA secondary structure presented in the "cloverleaf" form with the universal numbering system. B: Modification profile for tRNA sequences from Gram-negative bacteria (69 sequences from 8 species). The pie charts within each position in the cloverleaf correspond to the percentage of all modified nucleosides (modified being drawn in black). In the tables the series of numbers next to the series of symbols indicate the frequency of

Figure 3 :

 3 Figure 3 : overview of the different stages of prokaryotic translation. Figure reproduced from Schmeing & Ramakrishan, 2009 7

Figure 4 .

 4 Figure 4. A: E. coli folding pathways. B: General chaperone mechanism. C: GroEL/GroES refolding mechanism. Figures reproduces from Kim et al. 2013 13

Figure 5 :

 5 Figure 5: kinetic proofreading in the ribosome. A: General scheme of kinetic proofreading (KPR) in the ribosome. The EF-Tu • GTP • tRNA complex bind the A-site reversibly. Because of the difference in affinity to the A-site between correct and incorrect tRNA, the correct complex will be bound to the A-site after equilibration. The GTP hydrolysis forces the system to either reject the tRNA and repeat the cycle, or incorporate the amino acid in the nascent peptide chain. B: State of the art model of tRNA selection in the ribosome. The rate constants were measured for cognate and non-cognate tRNA using FRET. k-2, k3, k5 and k7 depend on the identity of the tRNA, and favor the insertion of cognate over non cognate tRNAs. Reproduced from Wohlgemuth et al., 2011. C: Simplified model of KPR used by Banerjee et al. E : free ribosome. ER/EW: ribosome associated with the right (R) or wrong (W) tRNA. ER*/EW*: activated ribosome • tRNA complexes. PR/PW: incorporation of the R or W tRNA in the peptide chain. D:The rate of GTP hydrolysis governs a trade-off between speed and accuracy in the ribosome. MFPT : mean first passage time. The black curve represents the simulated speed and accuracy of translation elongation as the rate of GTP hydrolysis tends to 0, using parameters measured on the wild type E. coli ribosome. The blue and purple curves are generated using parameters measured on an error prone and hyper accurate ribosome, respectively. The observed value of k2,R (red circle) indicates that the ribosome usually operates in a regime where speed is optimized rather than accuracy. Reproduced fromBanerjee et al., 2017

Figure 1 :

 1 Figure 1: A computational pipeline to confidently identify amino acid substitutions from Mass Spectrometry data. A: Overview of the pipeline. For a detailed description of the different steps, see Material & Methods. B: MaxQuant DependentPeptide search performs exhaustive pairing of unidentified spectra to a spectral library derived from the identified spectra. For each pair of (identified, unidentified) spectra of the same charge z, and found in the same fraction, the algorithm first computes the mass difference Δm = munidentified -midentified . It simulates in silico, and sequentially, the addition of a single moiety of mass Δm at any position in the identified peptide, and generates the corresponding theoretical spectrum for the modified peptide. These spectra are then compared to the experimental spectrum using MaxQuant Andromeda's score formula. The pair with the highest score is retained, and the significance of the match is assessed using a target-decoy FDR procedure. C: The observed retention time shift induced by our set of substitutions is accurately predicted by a simple sequence-based retention time model.

Figure 2 :

 2 Figure2: overview of the substitution profile of E. coli in MOPS complete medium. A : Matrix of substitutions identifications. Each entry in the matrix represents the number of independent substitutions detected for the corresponding (original codon, destination amino acid) pair, in the SC-complete dataset. The logarithmic color bar highlights the dynamic range of detection. Grey squares indicate substitutions from a codon to its cognate amino acid, substitutions from stop codon, substitutions undetectable via our method because they are indistinguishable from one of the PTMs or artifacts in the unimod.org database. Substitutions to Leu and Ile are a priori undistinguishable, and thus grouped together. B : Left panel : For each of the top 10 most frequently detected substitution types, we fetched the quantification profile of the dependent peptide and the base peptide. Each dot represents the ratio of intensities IDP/IBP for each of the samples, when both peaks have been detected and quantified. The black line indicates the medians of the distributions. Right panel: we inferred the most likely mismatch for each of the substitution types, using a procedure described in the Material and Methods. This allows us to guess that the V -> I/L substitutions are likely substitutions from Val to Ile, enabled by a G:U mismatch at the 1 st position.

Figure 3 :

 3 Figure 3 : Comparing the error profiles for E. coli and S. cerevisiae reveals a shared signature of errors. A: the substitutions identifications matrices of S. cerevisiae (green channel, left) and E. coli (red channel, right) are compared and overlaid (middle). The intensity of the color is proportional to the logarithm of the number of independent identification, with one pseudo-count. Values are normalized by the highest entry in the matrix for each of the two organisms. The blue box highlights the recently described property of eukaryotic AlaRS to mischarge tRNA. B: NeCE are classified by the mismatch most likely to generate them. The shade intensity reflects the ratio of independent substitution to number of substitution types associated with the corresponding mismatch. Grey boxes are either correct base-pairings, or mismatches to which no substitutions could be unambiguously mapped. Upeer panel indicates results obtained from E.coli lower panel was generated based on S.cerevisiae data.

  What do you mean by that Commenté [OD30]: I must admit that this does not look very strong from fig 3A (the 64 by 20 matrics)

Figure 4 :

 4 Figure 4 : the error spectrum is affected by external perturbation. A: the substitutions identifications matrices of E. coli in LB (green channel, left), or LB supplemented with paromomycin (red channel, right), are compared and overlaid (middle). The intensity of the color is proportional to the logarithm of the number of independent identification, with one pseudo-count. Values are normalized by the highest entry in the matrix for each of the two organisms. The blue boxes highlight errors involving 3rd position mismatches. B: Quantification of the top 10 most frequent substitutions in the drugs dataset. Errors involving 3rd position mismatches are shaded in light blue. C: NeCE are classified using the same procedure as in Fig. 2B, for the LB samples, with or without paromomycin. D: Effect of serine starvation on errors at serine codons, for the three most frequently detected substitutions affecting serine codons.

Figure 5 :

 5 Figure 5: general properties of substitutions. A. Sampling strategy:In order to test if the set of detected substitutions differs from expectations in any way, we first need account for the fact that many local properties of proteins are affected by the protein's expression level, and so is our ability to detect substitutions from that protein. First, the local property of interest ('score') is recorded at all the positions bearing a substitutions. The average of that set is plotted as a red dashed line. To compare this average to an appropriate control, we devised three strategies to eliminate the potential contributions of protein level, amino acid identity and codon identity on the score. In each of these strategies, we draw 1000 sets of the same size as the set of observed substitutions, and plot the average of each of these sets as a blue dot. In the first strategy, for every bona fide distribution, we draw the score from any position within the same protein. In the second strategy, we draw the score from any position within the same protein that shares the same amino acid as the one bearing the bona fide substitution. Similarly, in the third control, the codon for the sampled position has to be the same as the substituted codon. B. Amino acid conservation: We derived amino acid conservation scores for E. coli proteins, using the COGs database to fetch 50 homologs, MUSCLE to align them, and rate4site to estimate the evolutionary rate at each site. The resulting scores are standardised per proteins, and a high score indicates low conservation. The empirical p-values are computed by dividing the number of blue dots above the red line, divided by 1000. n indicates the number of positions considered in this analysis. C. Ribosome density: Ribosome profiling data from [ref] was processed (see methods) to estimate

  Figure 5: general properties of substitutions. A. Sampling strategy:In order to test if the set of detected substitutions differs from expectations in any way, we first need account for the fact that many local properties of proteins are affected by the protein's expression level, and so is our ability to detect substitutions from that protein. First, the local property of interest ('score') is recorded at all the positions bearing a substitutions. The average of that set is plotted as a red dashed line. To compare this average to an appropriate control, we devised three strategies to eliminate the potential contributions of protein level, amino acid identity and codon identity on the score. In each of these strategies, we draw 1000 sets of the same size as the set of observed substitutions, and plot the average of each of these sets as a blue dot. In the first strategy, for every bona fide distribution, we draw the score from any position within the same protein. In the second strategy, we draw the score from any position within the same protein that shares the same amino acid as the one bearing the bona fide substitution. Similarly, in the third control, the codon for the sampled position has to be the same as the substituted codon. B. Amino acid conservation: We derived amino acid conservation scores for E. coli proteins, using the COGs database to fetch 50 homologs, MUSCLE to align them, and rate4site to estimate the evolutionary rate at each site. The resulting scores are standardised per proteins, and a high score indicates low conservation. The empirical p-values are computed by dividing the number of blue dots above the red line, divided by 1000. n indicates the number of positions considered in this analysis. C. Ribosome density: Ribosome profiling data from [ref] was processed (see methods) to estimate

  Commenté [OD39]: Did not understand this explanation….. Commenté [EM40]: I deleted the portions of the text that compared NeCE and NoCE, since we're now grouping them together in that case. Commenté [OD41]: We should discuss it in the same order as appear in the fig. either change the fig order or the text.

Commenté [OD42] :

 : You should add this names to the figure, currently the violin plots have no legend Commenté [OD43]: Could not understand exactly these two sentences trying to explain the logic behind each of the 3 controls… try to re-write Commenté [OD44]: Need to address the controls Commenté [OD45]: I suggest to start the discussion in a more biology oriented discussion about translation fidelity and only then dive into the mass spec

Figure 1 : 3 Figure 2 :

 132 Figure 1: The predict ed log 10 (IE) was comput ed as P k (w k X i ,k ),a nd t he observed log 10 (IE) was defined as y i -b j .P earson correlat ion coeffici en t = 0.69, σ =0 .58

Figure 3 :

 3 Figure 3: Weightsof the regression coeffici en t s W, fitt ed on t he en t ire dat aset

  . "Phenotypic mutations", i.e. errors in RNA transcription and in protein translation, in which the wrong RNA nucleotide or amino acid are respectively incorporated, occur at considerably higher rate. A recent estimate made in bacteria, is that transcription error rate ranges between Not sure this sentence is very clear, in any case I would not start with it….

	Commenté [OD1]:

Commenté [OD2]: Do we need to explain here what are near cognates? Commenté [OD3]: I would move this above since currently it sounds as if this is correct only to the drugs induced mutations

  Also , although you address in the text the two drugs except from fig 3A you only show one drug. If the other is not informative I suggest to take it out from the paper all together. If it is we should show it's results

	[TP32]: Also check if cerevisiae still
	clusters with WT e coli
	Commenté [OD33]:

  Our model gave satisfactory results (see appendix : Prediction of Ionization efficiency from amino acid composition), and indicated that, except in a few cases, substitutions should not result in a dramatic change in ionization efficiency. It remains difficult to assess to what extent the standard deviation of the error rate for each substitution type reflects biological variability or technical biases. Comparing the median error rates of several substitutions across the different physiological states during bacterial growth revealed that they react dynamically to the changing environment: substitutions rate from valine codons tended to decrease with time, while glycine codons became more error-prone in later stages. The extent of this change might be underestimated due to the fact that we are not specifically quantifying the error rate of newly synthesized protein, but rather quantifying the errors in batch. Starving the cells for serine revealed a striking increase in the error rate of two substitutions involving serine codons, SerAGCAsn and SerAGTAsn. The

	Commenté [EM51]: We don't show differences
	between NeCE and NoCE relating to protein stability
	and amino acid conservation in the main text anymore,
	and in general we have much lower statistical power for
	the NoCE analysis. Do you still want to mention it as
	support for the notion that errors stem from the
	ribosome?
	Commenté [OD52]: Is the data we are showing in fig 3
	is from LB or MOPS ?
	Commenté [EM53]: Shall we talk about the
	discrepancies between the two organisms too? Or insist
	on the universal nature of the constraints, highlighting
	the fact that they probably affect all proteomes very
	similarly?
	Commenté [OD54]: Not clear
	Commenté [EM55]: We predict the log10(ionization
	efficiency) of peptide from sequence alone with a
	pearson R of 0.69

Table 1 :

 1 Feat ures comput ed for t his analysis Name Descript ion Lengt h count AA #o fo ccurences ofA Ai n pept ide count RP #o fo ccurences oft hes ubsequence'RP'i np ept ide count KP

Commenté [OD7]: Didn't we take the results of one of them out ? Ernest  nope, we removed the rpsD mutant

Acknowledgements

Abstract

Translation is limiting the accuracy of information transmission from DNA to proteins. Understanding how cells ensure proper translation of proteins amidst trade-off between accuracy, speed and energy expenditure and whether translation accuracy is modulated across environmental conditions, expression levels or gene locations is largely hindered by lack of a quantitative experimental methods to detect and quantify amino acid misincorporation at the full proteome level. Here we systematically detect and quantify errors in entire proteomes from mass spectrometry data. Following HPLC MS-MS data acquisition, in E. coli and in S. cerevisiae, we identify peptides whose mass deviate from genome-encoded peptide sequence by one amino acid, verifying that the mass shift cannot be explained by a post-translational modification. Our analysis reveled that most substitutions occur between amino acids that share near-cognate codons. Further analyses suggest that the majority of these near-cognate substitutions occur due to codon-to-anticodon mispairing within the ribosome. Patterns of errors due to mispairing were similar in E. coli and yeast, suggesting a universal mechanism that accounts for ribosomal errors. Focusing further on the E. coli, we treated the cells with two drugs that decrease ribosomal proofreading and found that they increase error rate due to mispairing at the wobble codon position. Generally, amino acid substitutions tended to occur in positions that are less evolutionarily conserved, and that minimally affect protein energetic stability, indicating a selective pressure to minimize phenotypic errors when potentially detrimental. Genome wide ribosome density data indicate that mistakes tend to occur in sites where ribosome velocity is relatively high, supporting the notion of a trade-off between speed and accuracy as predicted by proofreading theories. Starving the cells for particular amino acids results in specific patterns of amino acid substitutions reflecting the amino acid deficiency. Together our results reveal a mechanistic basis for ribosome errors in translation.