Inferring gene function from evolutionary change in signatures of translation efficiency

Background

The genetic code is redundant, meaning that most amino acids can be encoded by more than one codon. Highly expressed genes tend to use optimal codons to increase the accuracy and speed of translation. Thus, codon usage biases provide a signature of the relative expression levels of genes, which can, uniquely, be quantified across the domains of life.

Results

Here we describe a general statistical framework to exploit this phenomenon and to systematically associate genes with environments and phenotypic traits through changes in codon adaptation. By inferring evolutionary signatures of translation efficiency in 911 bacterial and archaeal genomes while controlling for confounding effects of phylogeny and inter-correlated phenotypes, we linked 187 gene families to 24 diverse phenotypic traits. A series of experiments in Escherichia coli revealed that 13 of 15, 19 of 23, and 3 of 6 gene families with changes in codon adaptation in aerotolerant, thermophilic, or halophilic microbes. Respectively, confer specific resistance to, respectively, hydrogen peroxide, heat, and high salinity. Further, we demonstrate experimentally that changes in codon optimality alone are sufficient to enhance stress resistance. Finally, we present evidence that multiple genes with altered codon optimality in aerobes confer oxidative stress resistance by controlling the levels of iron and NAD(P)H.

Conclusions

Taken together, these results provide experimental evidence for a widespread connection between changes in translation efficiency and phenotypic adaptation. As the number of sequenced genomes increases, this novel genomic context method for linking genes to phenotypes based on sequence alone will become increasingly useful.     

Nucleoid-Associated Proteins Affect Mutation Dynamics in E. coli in a Growth Phase-Specific Manner

The binding of proteins can shield DNA from mutagenic processes but also interfere with efficient repair. How the presence of DNA-binding proteins shapes intra-genomic differences in mutability and, ultimately, sequence variation in natural populations, however, remains poorly understood. In this study, we examine sequence evolution in Escherichia coli in relation to the binding of four abundant nucleoid-associated proteins: Fis, H-NS, IhfA, and IhfB. We find that, for a subset of mutations, protein occupancy is associated with both increased and decreased mutability in the underlying sequence depending on when the protein is bound during the bacterial growth cycle. On average, protein-bound DNA exhibits reduced mutability compared to protein-free DNA. However, this net protective effect is weak and can be abolished or even reversed during stages of colony growth where binding coincides – and hence likely interferes with – DNA repair activity. We suggest that the four nucleoid-associated proteins analyzed here have played a minor but significant role in patterning extant sequence variation in E. coli.     

Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity

Background  

There are a number of methods (also called: measures) currently in use that quantify codon usage in genes. These measures are often influenced by other sequence properties, such as length. This can introduce strong methodological bias into measurements; therefore we attempted to develop a method free from such dependencies. One of the common applications of codon usage analyses is to quantitatively predict gene expressivity.     

Proteome sequence features carry signatures of the environmental niche of prokaryotes

Background  

Prokaryotic environmental adaptations occur at different levels within cells to ensure the preservation of genome integrity, proper protein folding and function as well as membrane fluidity. Although specific composition and structure of cellular components suitable for the variety of extreme conditions has already been postulated, a systematic study describing such adaptations has not yet been performed. We therefore explored whether the environmental niche of a prokaryote could be deduced from the sequence of its proteome. Finally, we aimed at finding the precise differences between proteome sequences of prokaryotes from different environments.     

A high-throughput screen to identify inhibitors of ATP homeostasis in non-replicating Mycobacterium tuberculosis

Growing evidence suggests that the presence of a subpopulation of hypoxic non-replicating, phenotypically drug-tolerant mycobacteria is responsible for the prolonged duration of tuberculosis treatment. The discovery of new antitubercular agents active against this subpopulation may help in developing new strategies to shorten the time of tuberculosis therapy. Recently, the maintenance of a low level of bacterial respiration was shown to be a point of metabolic vulnerability in Mycobacterium tuberculosis. Here, we describe the development of a hypoxic model to identify compounds targeting mycobacterial respiratory functions and ATP homeostasis in whole mycobacteria. The model was adapted to 1,536-well plate format and successfully used to screen over 600,000 compounds. Approximately 800 compounds were confirmed to reduce intracellular ATP levels in a dose-dependent manner in Mycobacterium bovis BCG. One hundred and forty non-cytotoxic compounds with activity against hypoxic non-replicating M. tuberculosis were further validated. The resulting collection of compounds that disrupt ATP homeostasis in M. tuberculosis represents a valuable resource to decipher the biology of persistent mycobacteria.     

On Relevance of Codon Usage to Expression of Synthetic and Natural Genes in Escherichia coli

A recent investigation concluded that codon bias did not affect expression of green fluorescent protein (GFP) variants in Escherichia coli, while stability of an mRNA secondary structure near the 5′ end played a dominant role. We demonstrate that combining the two variables using regression trees or support vector regression yields a biologically plausible model with better support in the GFP data set and in other experimental data: codon usage is relevant for protein levels if the 5′ mRNA structures are not strong. Natural E. coli genes had weaker 5′ mRNA structures than the examined set of GFP variants and did not exhibit a correlation between the folding free energy of 5′ mRNA structures and protein expression.IN genomes, natural selection may act on silent sites of codons to make translation of highly expressed genes more efficient, an effect linked primarily to abundances of tRNA isoacceptor molecules (Ikemura 1985; Bulmer 1987; Kanaya et al. 1999). Codon choice may also be linked to formation of secondary structures in mRNA that reduce protein levels, as has been shown with haplotypes of the human COMT gene (Nackley et al. 2006). Kudla et al. (2009) have recently reported an experiment that contributes toward understanding how synonymous codon usage shapes gene expression. They have constructed a library of 154 synthetic variants of a green fluorescent protein (GFP) gene that varied randomly at synonymous sites while retaining the original amino acid sequence. The authors concluded that codon usage (CU) bias did not correlate with protein levels measured as fluorescence of the GFP, but also that the minimum free energy of a mRNA secondary structure in a 42-nucleotide region at [−4,37] that overlaps the start codon (“hairpin stability”) bears a great significance. CU bias was quantified by the widely used codon adaptation index (CAI) method (Sharp and Li 1987), essentially a measure of the distance of a gene''s codon usage to the codon usage of a predefined set of highly expressed genes. The CAI and some of its more recent alternatives, such as measure independent of length and composition (MILC) (Supek and Vlahovicek 2005), have been shown to be a viable surrogate for gene expression in various unicellular organisms. Additionally, in a multiple linear regression of rank fluorescence against a number of sequence-derived attributes, including CAI and the abovementioned hairpin stability, Kudla et al. (2009) did not find CAI to contribute significantly toward the prediction of protein levels, in contrast to the hairpin stability.

Both the codon adaptation index and the 5′ mRNA secondary structures influence protein levels in the Kudla et al. data:

The described statistical analyses, however, failed to address the case in which a nonlinear three-way dependency between hairpin stability, codon usage, and fluorescence might exist; data are visualized in Figure 1, A–C, and in figure 2B in Kudla et al. Such complex patterns in data are readily captured by the support vector machines (SVM) algorithm, reviewed in Noble (2006) and Ben-Hur et al. (2008). We have employed the SVM with a radial basis function kernel to regress fluorescence against both hairpin stability and CAI simultaneously (Figure 1B) and computed the Pearson''s correlation coefficient in cross-validation (here denoted as Q) between true and predicted values of fluorescence (See File S1). A linear model based solely on hairpin stability as employed by Kudla et al. (Figure 1A) can explain Q² = 38.6% of variance in protein levels, while the nonlinear SVM regression that takes CAI into account explains Q² = 52.2% of variance. The difference in Q is statistically significant at P = 10⁻¹⁹⁰ (paired t-test). Note that Kudla et al. utilize the Spearman rank correlation coefficient (ρ) in their article; the hairpin stability would explain ρ² = 44.6% of the variance in expression levels if the requirement for a linear relationship was abandoned in this manner.Open in a separate windowFigure 1.—Regression of protein levels against folding free energy of an mRNA hairpin at nucleotides −4 through 37 (A), against the hairpin free energy and the codon adaptation index (Sharp and Li 1987) (B and C), or against the hairpin free energy and the codon frequencies (D and E). The colors show the measured protein levels, while the background shading reflects the protein levels predicted by the specific model. (A) Predictions by linear regression. (B and E) Predictions by a support vector machine with a radial basis function kernel. (C) Predictions by an M5′ regression tree. (D) A schematic of the M5′ model, where coefficients in the terminal nodes are derived from data where protein levels, all codon frequencies, and hairpin free energies were normalized to [0,1] to facilitate comparison between the influence of codons, the hairpin stability, and the constant in the regression equation. All coefficients ≥0.1 are in boldface type. In the plots (A–C and E), a slight amount of random “jitter” was introduced to the point positions (at most, 3% of the range of each axis) to better visualize overlapping points. In the plot in E, a single outlying point is not shown. See Figure S2 for the same plots without jitter and with the outlier in E included. R² is the squared Pearson''s correlation coefficient between actual and model-predicted protein levels; Q² is similar, but obtained in cross-validation (10-fold, 100 runs), and is a more conservative estimate of regression accuracy.Open in a separate windowFigure 2.—The distributions of RNA folding free energies of a 42-nucleotide window in the mRNA between positions −4 and +37, where the “A” in the “AUG” start codon has index zero. The distributions are shown separately for the 154 gene variants from Kudla et al. (2009) and for the genes from the E. coli K12 genome. The dotted line indicates the 5th percentile of the E. coli values at −10.9 kcal/mol.Compared to the SVM, a more interpretable generalization of the data can be achieved by a different nonlinear regression approach, the M5′ tree (Wang and Witten 1997), which recursively divides the data to reduce the variance of the dependant variable within each partition and then builds separate linear models for the partitions. The resulting regression tree (Figure 1C; supporting information, Figure S1) better explains the correlation between protein levels on one side and hairpin stability and CAI on the other side when compared to a linear model employed by Kudla et al. that regresses protein levels against hairpin stability only [see figure 2B in Kudla et al. (2009) and Figure 1A]; 9.3% more variance is explained by the M5′, P = 10⁻⁹¹ (paired t-test). An interpretation that follows from the general structure of the M5′ tree (Figure S1) is that, at high mRNA hairpin stability, protein levels will generally be quite low and not dependant on CAI; in contrast, with less stable mRNA hairpins, both hairpin stability and CAI play a role in determining protein levels. In the interpretation of the M5′ tree structure, we would place less emphasis on the exact coefficients of the linear models in the leaves because the reliability of these fine-grained features of the M5′ model can strongly depend on the good coverage of all parts of the mRNA–CAI space data points.

The CAI may not be an optimal summary of codon usage for predicting expression of overexpressed genes:

Regarding use of CAI in the present context, it should be noted that CAI''s original purpose was to serve as a proxy for gene expression in conditions of abundance that result in fast growth in the organism''s environmental niche. The CAI or related approaches (Supek and Vlahovicek 2005) may not, however, be an ideal representation of codon usage when examining overexpression of a foreign protein at levels that exceed the natural abundances of the host''s most highly expressed proteins. This was indeed shown to be the case in a recent article by Welch et al. (2009) in which the authors reported an experiment with heterologous expression of variants of two proteins in E. coli: an antibody fragment and a phage DNA polymerase. Welch et al. found that codon frequencies in general, but not CAI specifically, correlated well with protein levels and postulated that for overexpressed proteins optimal codons would correspond to the codons translated efficiently under amino acid starvation (Elf et al. 2003; Dittmar et al. 2005). Analogously to Welch et al., we now apply our regression algorithms not to the CAI, but directly to the codon frequencies that CAI attempts to summarize in the Kudla et al. data (See File S2). An M5′ regression tree trained on the hairpin stability and codon frequencies (Figure 1D) explains 10.6% more variance (P = 10⁻⁸³, paired t-test) in protein levels than an M5′ tree trained on hairpin stability and CAI (Figure 1C, Figure S1). A SVM regression model trained on the hairpin stability and a simple linear combination of selected codon frequencies (Figure 1E) explains 8.8% more variance (P = 10⁻⁸², paired t-test) than the SVM that uses CAI (Figure 1B). An SVM trained on the hairpin stability and the full set of codon frequencies (not shown in Figure 1) explains Q² = 65.0% of variance in the protein abundances, a sizable increase (P ≈ 10⁻²⁶⁰, paired t-test) compared to a linear regression on solely the [−4,37] hairpin stability (Q² = 38.6%) as originally employed by Kudla et al. and also as compared to a set of randomized controls (Q² = 20.1–30.7%; Table S1). Therefore, not relying on a predefined notion of codon optimality—as embodied in the CAI—further strengthens the argument that the correlation of CU and protein levels is far from negligible in this data set.Additionally, we found some correlation between codon frequencies and 5′ mRNA hairpin stability in the Kudla et al. gene variants (Figure S4). The fact that the two factors were not completely independent adds weight to the relevance of CU to protein levels since one could not be certain that even the variance in protein levels explained by 5′ mRNA structures is wholly due to the structures themselves and not to the confounding variables—here, the codon frequencies.The M5′ tree trained on codon frequencies (Figure 1D) follows the same general structure as the M5′ tree trained on the CAI (Figure S1) where the codon frequencies become relevant with mRNA hairpins weaker than −9.75 kcal/mol, while with stronger [−4,37] mRNA hairpins protein levels are generally low. Our interpretation is that the lack of a stable secondary structure that could obstruct translational initiation is a necessary but not a sufficient condition for high protein expression. When the initiation phase is unhindered, the bottleneck would shift to the elongation phase in which codon optimality plays an important role. In the literature, theoretical models of translation may consider either the initiation (Bulmer 1991) or the elongation phase (Xia 1998) as the rate-limiting step of translation under physiological conditions; we are not aware of such analyses describing translation of artificially overexpressed genes.The codons identified as relevant by our M5′ model of the Kudla et al. data are different from, but not inconsistent with, those proposed by Welch et al. (Table S2). We anticipate that the rules for codon optimality for overexpression in an Escherichia coli host will become better defined as more large-scale experiments, such as the two discussed here (Kudla et al. 2009; Welch et al. 2009), are carried out.

The “RNA structure + codon usage” model agrees with independent experimental data and is robust to removal of extreme values:

Our reanalysis of the Kudla et al. data should be viewed in light of the conclusions of Welch et al. (2009) who find that codon usage, but not the 5′ hairpin stability, correlates with protein levels in their data, while noting that their gene variants generally have considerably weaker 5′ mRNA hairpins than the sequences in Kudla et al. Welch et al. reconcile the different outcomes of the two experiments by noting that “inhibition of initiation by especially strong mRNA structure would obscure effects resulting from factors that influence elongation, such as codon usage” (page 9). Here we propose that precisely the same model can be derived solely from the Kudla et al. data. Furthermore, we find that the 154 gene variants from Kudla et al. indeed do have unusually stable 5′ mRNA hairpins (mean free energy = −9.68 kcal/mol) in comparison to natural E. coli genes (mean free energy = −6.15 kcal/mol) (P = 10⁻³⁸ by Mann–Whitney U-test; see Figure 2). The part of the distribution of Kudla et al. gene variants that overlaps with the bulk of the E. coli genes, with 5′ mRNA hairpin free energies lower than ∼ −10 kcal/mol, corresponds to the range where our M5′ model indicates a stronger influence of CU on protein levels (Figure S1, Figure 1D).We investigate to what extent the presence of a group of sequences extreme in their 5′ mRNA hairpin stabilities in the Kudla et al. data set (left peak in Figure 2) influenced the authors'' conclusion that the hairpin stabilities have an overarching influence on protein levels. After removing the sequences below the 5th percentile of the E. coli natural hairpin stabilities (−10.9 kcal/mol), we were left with 109 of the original 154 Kudla et al. sequences. The accuracy of regressing protein levels against mRNA hairpin stability deteriorates greatly (Q² = 18.5%) after removing the 45 sequences, but less so with SVM and M5′ regression that take into account both CU and the hairpin stability (udla et al. basically captured the difference between these extreme cases—in which very strong 5′ mRNA secondary structures blocked expression—and all other sequences. However, to explain the variation in protein levels within the nonextreme set, hairpin stabilities by themselves are not sufficient and need to be complemented with CU.

TABLE 1

Accuracy of the regression of protein levels against 5′ mRNA hairpin stability or against 5′ mRNA hairpin stability and codon frequencies

Data set	Linear regression, hairpin stability only (%)	SVM, hairpin stability + codon frequencies (%)	M5′, hairpin stability + codon frequencies (%)
Full (n = 154)	38.6	65.0	56.7
No strong hairpins (n = 109)	18.5	53.0	40.4

Open in a separate windowThe cross-validation correlation coefficient squared (Q²) is compared with the full Kudla et al. data set (154 proteins) and the reduced data set (109 proteins) where mRNA hairpin folding energies are ≥ −10.9 kcal/mol, the 5th percentile of natural E. coli genes.In addition to measuring protein levels in the 154-sequence data set, Kudla et al. performed an additional experiment where an unstructured 28-codon tag was fused to 5′ ends of 72 (of 154) GFP sequence variants. Adding the tag was found to enhance protein levels, supporting the conclusion of Kudla et al. that 5′ structure of mRNA had a strong influence on protein production. After an analysis of the data, we found (see File S3) that data from this specific experiment are not well suited to serve as a direct verification of our existing M5′ and SVM regression models. Still, we can compare the protein level predictions of our existing SVM model on the same set of sequences before and after adding the unstructured tag. We found that the predicted expression levels have increased for 67 of 72 sequences (Table S3) after adding the tag that fixes 5′ mRNA folding energy at a weak −6.1 kcal/mol, a result consistent with the Kudla et al. experiment. Additionally, we have trained a new SVM regression model only on the tagged 72-sequence set (See File S2) and found that, within this set, SVM regression can again predict GFP levels solely from codon usage (5′ mRNA structure is invariant among these sequences) at Q² = 37.7%. This amount of variance is similar, or even somewhat larger than, the difference in the variance explained by mRNA vs. mRNA+codons (38.6% vs. 65.0%) in the original data. Therefore, codon usage is of similar importance in shaping the protein levels within the tagged 72-sequence set, as it was in the original 154-sequence set.

mRNA 5′ end secondary structure stabilities do not correlate with protein levels for natural E.

coli genes: To further verify our proposed model, we analyzed the relative contributions of mRNA hairpin stabilities and CU on expression levels of natural E. coli genes (See File S2). If the hairpin stabilities were limiting for expression in the range of folding free energies spanned by the E. coli mRNAs, one would expect to see a correlation between the free energy of mRNA 5′ end folding and the abundance of the corresponding protein. We found no such correlation using the folding free energies of the [−4,37] mRNA region (Figure 3) or equal-sized regions centered around the start codon at [−20,21] or on the expected location of a Shine–Dalgarno sequence (Shultzaberger et al. 2001) at [−30,11] (see Figure S3). Unsurprisingly, CAI correlated well with protein levels (Figure 3) in all examined experimental data sets (Lopez-Campistrous et al. 2005; Lu et al. 2007; Ishihama et al. 2008). Therefore, within the boundaries of the mRNA folding free energies spanned by E. coli genes, the CU plays a dominant role in shaping gene expression (or the CU may possibly be shaped by the expression; see Concluding remarks). As for the stronger mRNA hairpins with < −11 kcal/mol, they are present in the Kudla et al. data, but are very rare in the E. coli genome, which could be explained by one of two scenarios: (i) Above a certain threshold, the mRNA hairpin stability may become so detrimental to expression that all the mutants having such hairpins are subject to very strong negative selection and therefore are absent from the genome. And/or (ii) the Kudla et al. data set may not be representative of the genes in the E. coli genome or the mutational processes they undergo; for example, the amino acid sequence of the GFP''s beginning might be unusually conducive to forming RNA hairpins. Unless further analyses prove differently, it seems reasonable to surmise that in natural E. coli genes mRNA secondary structures would shape expression if they were highly stable, consistent with the finding of a universal (albeit not particularly strong) trend toward avoidance of 5′ mRNA structures in genomes (Gu et al. 2010). However, it can also be concluded at this point—and with more confidence—that at lower secondary structure stabilities the CU has an overarching influence on expression. Such a model of expression-related gene sequence determinants in E. coli is fully consistent with our interpretation of the M5′ regression tree that we have derived from the Kudla et al. data.Open in a separate windowFigure 3.—Correlations between the E. coli absolute protein abundances measured in three independent experiments (Lopez-Campistrous et al. 2005; Lu et al. 2007; Ishihama et al. 2008) and the codon adaptation index (CAI) or the free energy of folding of a secondary structure in the mRNA [−4,37] region (in kcal/mol; more negative values denote a more stable RNA secondary structure). “ρ” is the Spearman''s rank correlation coefficient.

Concluding remarks:

We argue that Kudla et al. worked with a set of gene sequences in which strong mRNA secondary structures (that effectively abolished expression) were frequent enough to mask the relevance of codon frequencies on protein levels when examined only with linear regression methods. While mRNA secondary structures can certainly occur when designing synthetic genes, it is highly questionable to what extent Kudla et al.''s conclusion that CU is of little importance for expression would be generally valid for biotechnological applications, especially since we have shown that the influence of CU is nevertheless present even in the Kudla et al. data. What is beyond doubt, however, is that a strong 5′ mRNA secondary structure can be a roadblock in heterologous expression, and therefore the synthetic gene variants harboring such structures should be avoided. The more specific rules regarding the exact location of the hairpin on the gene sequence, the hairpin''s length, or the tolerable levels of folding free energy will have to be established by further experimentation.A recent algorithm for estimating the efficiency of ribosomal binding sites from the mRNA sequence (Salis et al. 2009) explicitly takes into account the folding free energy of RNA secondary structures, along with other factors. When protein overexpression is desired, the conclusions of Welch et al. and (by our reanalysis) the Kudla et al. data indicate that CU should be optimized in addition to the ribosome binding site sequence to ensure that both initiation and elongation phases of translation are free of impediments.On the basis of their results, Kudla et al. also discuss the evolutionary link between the CU of natural genes and the expression levels of proteins for which they code. They propose that selection for translational efficiency acts at a global level in cells; the codons that accelerate elongation would be preferred in a highly expressed gene not because they facilitate production of that particular protein, but to free up ribosomes for the rate-determining initiation phase of translation of the total cellular mRNA pool. Effectively, the flow of causality between CU and expression would be reversed in comparison to the established view. This hypothesis should be critically reevaluated because it depends on the assertion that manipulating a gene''s CU cannot cause protein levels to increase, an assertion poorly supported by the Kudla et al. data.     

REVIGO summarizes and visualizes long lists of gene ontology terms

Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret.REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.     

Ubiquitin signals protein trafficking via interaction with a novel ubiquitin binding domain in the membrane fusion regulator,Vps9p

The conserved vacuolar protein-sorting (Vps) pathway controls the trafficking of proteins to the vacuole/lysosome. Both the internalization of ubiquitylated cargo from the plasma membrane and its sorting at the late endosome via the Vps pathway depend on ubiquitin (Ub) binding motifs present in trafficking regulators. Here we report that Ub controls yet a third step in the Vps pathway. Vps9p, which promotes endosomal and Golgi-derived vesicle fusion, binds directly to Ub via a Cue1p-homologous (CUE) domain. The CUE domain is structurally related to the Ub-associated (UBA) domain. In an assay for vacuolar delivery of a transmembrane receptor fused to Ub, a Ub mutation impairing interaction with Vps9p led to a cytoplasmic block in receptor trafficking. This block resembled that of a receptor fused to wild-type Ub but expressed in a vps9-null background. Strikingly, this trafficking defect caused by a mutant Ub was rescued by deletion of the Vps9p CUE domain, indicating that lack of the CUE domain renders Vps9p independent of Ub for activation in vivo. We thus provide evidence for biochemical and genetic interactions between Ub and a novel Ub binding domain in Vps9p. Ub plays a positive role, whereas the CUE domain plays both positive and negative roles in Vps9p function in trafficking.