The evolution, evolvability and engineering of gene regulatory DNA

Vaishnav, Eeshit Dhaval; Boer, Carl G. de; Molinet, Jennifer; Yassour, Moran; Lin, Fan; Adiconis, Xian; Thompson, Dawn; Levin, Joshua Z.; Cubillos, Francisco A.; Regev, Aviv

doi:10.1038/s41586-022-04506-6

Cited by 183 publications

(300 citation statements)

References 103 publications

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“…Similarly, although the distribution of mutational effects on expression level has been studied in budding yeast, only limited information is available on the effect of mutations – and especially cis -occurring ones – on protein abundance. Previous studies focused on only a small subset of genes [27, 28], did not differentiate between cis and trans mutations [28, 29], assessed too few mutations [29] or were limited to substitutions occurring within a short segment of the promoter sequence [30]. Thus, instead of arbitrarily choosing a σ mut , we performed simulations across a range of standard deviations, from 0.01 to 0.35, to identify the most biologically plausible value.…”

Section: Resultsmentioning

confidence: 99%

“…Random mutations may also have asymmetrical effects on expression level, as recently shown for yeast genes [28]. Recent work additionally predicted that mutations increasing expression are rarer for highly expressed promoters – and vice-versa for lowly expressed ones [30]. Various constraints on the effects of mutations can thus create correlations between transcriptional and translational changes or bias evolution, and we hypothesized that some of them might allow our minimal model to generate realistic signed divergence correlations (Fig 1C; Fig 4C).…”

Section: Resultsmentioning

confidence: 99%

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Evolutionary trade-off and mutational bias could favor transcriptional over translational divergence within paralog pairs

Aubé

Nielly‐Thibault

Landry

2022

Preprint

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How changes in the different steps of protein synthesis – transcription, translation and degradation – contributed to differences of protein abundance among genes is not fully understood. Here, we show that yeast paralogous genes are more divergent in transcription than in translation. We explore two causal mechanisms for this predominance of transcriptional divergence: an evolutionary trade-off between the cost and precision of gene expression and a larger mutational target size for transcription. Performing simulations within a minimal model of post-duplication evolution, we find that both mechanisms are consistent with the observed divergence patterns. We also investigate how additional properties of the effects of mutations on gene expression, such as their asymmetry and correlation across levels of regulation, can shape the evolution of duplicates. Our results highlight how general trade-offs in cellular processes and mutation bias can have far-reaching impacts. If transcriptional divergence is favored between paralogs independently of gene-specific functional constraints, it may have important implications for the evolutionary potential of gene duplication and the contribution of this process to the general evolution of gene expression levels.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Evolutionary trade-off and mutational bias could favor transcriptional over translational divergence within paralog pairs

Aubé

Nielly‐Thibault

Landry

2022

Preprint

View full text Add to dashboard Cite

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“…Similarly, disrupting a drug efflux pump can slow the adaptation of E. coli populations exposed to antibiotics by shifting them onto evolutionary paths where some mutations reduce the effect size of key resistance mutations (Lukačišinová et al, 2020). Nevertheless, both theoretical (Kryazhimskiy et al, 2009(Kryazhimskiy et al, , 2014Perfeito et al, 2014;Vaishnav et al, 2022) and smaller-scale studies in bacteria (Chou et al, 2011;Khan et al, 2011;MacLean et al, 2010;Wang et al, 2016), virus (Levy & Siegal, 2008;MacLean et al, 2010;Rokyta et al, 2011), yeast (Kryazhimskiy et al, 2014;Wei & Zhang, 2019) and multicellular fungi (Schoustra et al, 2016) support a strong role of diminishing return epistasis in adaptation. Our findings, precisely tracking the adaptation of a near complete genome-wide deletion collection, underscores that the power and generality of diminishing indeed are immense.…”

Section: Global Diminishing Return Epistasis Dictates Arsenite Adapta...mentioning

confidence: 99%

Adaptation of the yeast gene knockout collection is near-perfectly predicted by fitness and diminishing return epistasis

Persson

Stenberg

Tamás

et al. 2022

Preprint

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Adaptive evolution of clonally dividing cells and microbes is the ultimate cause of cancer and infectious diseases. The possibility of constraining the adaptation of cell populations, by inhibiting proteins that enhance their evolvability has therefore attracted substantial interest. However, our current understanding of how individual genes influence the speed of adaptation is limited, partly because accurately tracking adaptation for many experimental cell populations in parallel is challenging. Here we use a high throughput artificial laboratory evolution (ALE) platform to track the adaptation of >18.000 cell populations corresponding to single gene deletion strains in the haploid yeast deletion collection. We report that the fitness of gene knockout near-perfectly (R2=0.91) predicts their adaptation dynamics under arsenic exposure, leaving virtually no role for dedicated evolvability functions in the corresponding proteins. We tracked the adaptation of another >23.000 yeast gene knockout populations to a diverse range of selection pressures and generalised the almost perfect (R2=0.72 to 0.98) capacity of initial fitness to predict the rate of adaptation. Finally, we reconstruct mutations in the genes FPS1, ASK10, and ARR3, which together account for almost all arsenic adaptation in wildtype cells, in gene deletions covering a broad fitness range. We show that the predictability of arsenic adaptation can be understood almost entirely as a global epistasis phenomenon where excluding arsenic from cells, through these mutations, is more beneficial in cells with low arsenic fitness regardless of what causes the arsenic defects. The lack of genes with a meaningful effect on the adaptation dynamics of clonally reproducing cell populations diminishes the prospects of developing adjuvant drugs aiming to slow antimicrobial and chemotherapy resistance.

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“…To improve our fundamental understanding of the impact of codon usage, and in an attempt to improve the predictability of optimal codon usage for protein production, we decided to test different machine learning approaches. Very recently, some studies have successfully utilised machine learning methods to predict gene expression based on randomised sequence libraries for non-coding gene regions, such as promoters and 5’ untranslated regions (5’ UTR) in E. coli and Saccharomyces cerevisiae (16, 17).…”

Section: Introductionmentioning

confidence: 99%

Revealing determinants of translation efficiency via whole-gene codon randomisation and machine learning

Nieuwkoop

Terlouw

Ridder

et al. 2022

Preprint

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Codon usage refers to the occurrence of synonymous codons in protein-coding genes. It is known for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synthetic genes for efficient protein production. In this study, we developed a method to generate a large, synonymous codon library of the gene encoding the red fluorescent protein (RFP). After expression in Escherichia coli, 1459 clones of this library were selected of which we measured protein production levels and determined the full coding sequences. Using different machine learning approaches, this data was used to reveal correlations between codon usage and protein production. Interestingly, protein production levels can be relatively accurately predicted (Pearson correlation of 0.762) by a Random Forest model, which only relies on the sequence information for the first 8 codons. This study clearly demonstrated the key role of codons at the start of the coding sequence. As such, it provides not only important fundamental insights on the influence of codon usage on protein production but also relevant clues on optimising the design of efficiently translated synthetic genes.

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The evolution, evolvability and engineering of gene regulatory DNA

Cited by 183 publications

References 103 publications

Evolutionary trade-off and mutational bias could favor transcriptional over translational divergence within paralog pairs

Evolutionary trade-off and mutational bias could favor transcriptional over translational divergence within paralog pairs

Adaptation of the yeast gene knockout collection is near-perfectly predicted by fitness and diminishing return epistasis

Revealing determinants of translation efficiency via whole-gene codon randomisation and machine learning

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