Multifactorial Determinants of Protein Expression in Prokaryotic Open Reading Frames

Allert, Malin; Cox, J. Colin; Hellinga, Homme W.

doi:10.1016/j.jmb.2010.08.010

Cited by 92 publications

(130 citation statements)

References 53 publications

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“…The degree of secondary structure near the 5′-end of mRNAs has been shown to negatively influence the levels of protein expression (17)(18)(19), which is also observed for the GFP sequence variants that we tested (Fig. S6A).…”

Section: Discussionsupporting

confidence: 64%

A selective force favoring increased G+C content in bacterial genes

Raghavan

Kelkar

Ochman

2012

Proc. Natl. Acad. Sci. U.S.A.

119

131

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Bacteria display considerable variation in their overall base compositions, which range from 13% to over 75% G+C. This variation in genomic base compositions has long been considered to be a strictly neutral character, due solely to differences in the mutational process; however, recent sequence comparisons indicate that mutational input alone cannot produce the observed base compositions, implying a role for natural selection. Because bacterial genomes have high gene content, forces that operate on the base composition of individual genes could help shape the overall genomic base composition. To explore this possibility, we tested whether genes that encode the same protein but vary only in their base compositions at synonymous sites have effects on bacterial fitness. Escherichia coli strains harboring G+C-rich versions of genes display higher growth rates, indicating that despite a pervasive mutational bias toward A+T, a selective force, independent of adaptive codon use, is driving genes toward higher G+C contents.bacterial adaptation | genome evolution | mutational patterns

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Section: Discussionsupporting

confidence: 64%

A selective force favoring increased G+C content in bacterial genes

Raghavan

Kelkar

Ochman

2012

Proc. Natl. Acad. Sci. U.S.A.

119

131

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“…However, the effect is limited at the extremes; the difference between the weakest and strongest RBSs (an 87-fold increase in translation efficiency) corresponds to only an ∼4.3-fold increase in mRNA. As another example, many groups have found that secondary structure across the 5′ UTR and initial coding sequence can hinder effective translation (14,(43)(44)(45)(46). In our data, we find that the correlation between secondary structure free energy across the UTR/GFP interface is significant (Fig.…”

Section: Resultssupporting

confidence: 56%

“…We do not examine how expression is altered by a gene's amino acid composition and codon use, which are known to have large effects (26,(43)(44)(45)(46). In follow-up work, we explore the influence of these two factors across a matrix of coding sequences, promoters, and RBSs.…”

Section: Discussionmentioning

confidence: 99%

Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Kosuri

Goodman

Cambray

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

415

453

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The inability to predict heterologous gene expression levels precisely hinders our ability to engineer biological systems. Using well-characterized regulatory elements offers a potential solution only if such elements behave predictably when combined. We synthesized 12,563 combinations of common promoters and ribosome binding sites and simultaneously measured DNA, RNA, and protein levels from the entire library. Using a simple model, we found that RNA and protein expression were within twofold of expected levels 80% and 64% of the time, respectively. The large dataset allowed quantitation of global effects, such as translation rate on mRNA stability and mRNA secondary structure on translation rate. However, the worst 5% of constructs deviated from prediction by 13-fold on average, which could hinder large-scale genetic engineering projects. The ease and scale this of approach indicates that rather than relying on prediction or standardization, we can screen synthetic libraries for desired behavior.next-generation sequencing | synthetic biology | systems biology O rganisms can be engineered to produce chemical, material, fuel, and medical products that are often superior to nonbiological alternatives (1). Biotechnologists have sought to discover, improve, and industrialize such products through the use of recombinant DNA technologies (2, 3). In recent years, these efforts have increased in complexity from expressing a few genes at once to optimizing multicomponent circuits and pathways (4-7). To attain desired systems-level function reliably, careful and time-consuming optimization of individual components is required (8-11).To mitigate this slow trial-and-error optimization, two dominant approaches have taken hold. The first approach seeks to predict expression levels by elucidating the biophysical relationships between sequence and function. For example, several groups have modified promoters (12, 13) and ribosome binding sites (RBSs) (14-16) to see how small sequence changes affect transcription or translation. Such studies are fundamentally challenging due to the vastness of sequence space. In addition, because these approaches mostly look at either transcription or translation individually, they are rarely able to investigate interactions between these processes.The second approach uses combinations of individually characterized elements to attain desired expression without directly considering their DNA sequences (17-25). Current efforts have focused on approaches to limit the number of time-consuming steps required to characterize potential interactions and on identifying existing or engineered elements that act predictably when used in combination (26-28). However, these studies still suggest there are enough idiosyncratic interactions and context effects that it will be necessary to construct and measure many variants of a circuit to achieve desired function (29). For larger circuits, such approaches are necessarily limited in scope due to the difficulty in measuring large numbers of combinations (26, 27...

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“…Figure 2C shows that for linear DNA templates, anti-hIL-23 scFv yields varied between 500 and 950 mg/L, suggesting that single point mutations in the TIR can change the translational efficiency, presumably due to differences in the binding free energy between the ribosomal S1 protein and 5' UTR RNA. As the gene sequence design rules for protein expression are not well understood, 24 this predictable and scalable expression library approach offers the opportunity to rapidly screen DNA synthesized mutants for optimal expression.…”

Section: Resultsmentioning

confidence: 99%