Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Sep;31(9):e4397.
doi: 10.1002/pro.4397.

Phenotypic mutations contribute to protein diversity and shape protein evolution

Maria Luisa Romero Romero  1   2 , Cedric Landerer  1   2 , Jonas Poehls  1   2 , Agnes Toth-Petroczy  1   2   3
Affiliations
Review

Phenotypic mutations contribute to protein diversity and shape protein evolution

Maria Luisa Romero Romero et al. Protein Sci. 2022 Sep.

Abstract

Errors in DNA replication generate genetic mutations, while errors in transcription and translation lead to phenotypic mutations. Phenotypic mutations are orders of magnitude more frequent than genetic ones, yet they are less understood. Here, we review the types of phenotypic mutations, their quantifications, and their role in protein evolution and disease. The diversity generated by phenotypic mutation can facilitate adaptive evolution. Indeed, phenotypic mutations, such as ribosomal frameshift and stop codon readthrough, sometimes serve to regulate protein expression and function. Phenotypic mutations have often been linked to fitness decrease and diseases. Thus, understanding the protein heterogeneity and phenotypic diversity caused by phenotypic mutations will advance our understanding of protein evolution and have implications on human health and diseases.

Keywords: frameshifts; protein evolution; transcriptional errors; translational errors.

PubMed Disclaimer

Figures

FIGURE 1
FIGURE 1
Phenotypic mutations contribute to protein diversity. (a) Transcriptional errors, such as nucleotide misincorporation and RNA polymerase slippage, and translational errors, such as amino acid misincorporation, ribosomal frameshift, stop codon readthrough, and premature termination, lead to protein sequence heterogeneity. (b) Phenotypic mutations can generate several transcripts and proteins from a single gene sequence
FIGURE 2
FIGURE 2
Transcription error rates vary across the tree of life. (a) Transcriptional error rates in the form of nucleotide misincorporations are orders of magnitude higher than genomic mutations across the tree of life (Table 1). (b) Increasing effective population size correlates with higher transcriptional error rates. Higher effective population sizes potentially increase the efficacy of selection for local error rate reduction. Effective population size estimates were taken from Sung et al.
FIGURE 3
FIGURE 3
Errors during protein production can be costly, and coping mechanisms are diverse. (a) Most metabolic costs incurred during protein production do not differ between functional and nonfunctional protein variants. Other costs, however, such as opportunity costs may differ at much larger scales. Transcriptional and translational errors are likely deleterious and diminish benefits of the functional protein. In case of strongly deleterious errors, additional cytotoxic costs may occur due to protein misfolding or aggregation. A cell might tolerate additional metabolic or opportunity costs until the error rate causes the cost to exceed the benefit of the remaining functional protein. (b) To minimize the fitness effects of errors in protein production, a cell can either minimize the error rate, modulate protein production level, or increase protein tolerance to errors. Minimizing global error rates are costly as it requires ATP‐dependent proof‐reading. Local reduction of error rates could be achieved by adapting the codon usage. Protein production can either be increased to compensate for the missing erroneous proteins or decreased to minimize cytotoxic costs
FIGURE 4
FIGURE 4
Phenotypic mutations play a role in shaping protein traits and tolerance to genetic mutations. TEM‐1 beta‐lactamase was transcribed with a high‐fidelity RNA polymerase and its error‐prone mutant. Higher transcriptional error rates promoted enhanced TEM‐1 expression levels and stabilized enzyme variants
FIGURE 5
FIGURE 5
Functional innovations by ribosomal frameshifting and stop codon readthrough events. (a) (1) Frameshifting is used to regulate the relative production of proteins in many RNA viruses; for example, the polyproteins Gag and Pol are encoded in one RNA and are separated by a frameshift. (2) Frameshifting is used to regulate protein expression by turning genes on or off; for example, bacterial release factor 2 (RF2) is only produced when an internal stop codon is bypassed via frameshifting. With higher RF2 levels, accurate termination becomes more likely, and production of RF2 is decreased. (3) Phenotypic mutations can regulate modular function. In the copA gene (E. coli), the 5′ part of the mRNA encodes a copper‐binding domain. This domain is used as part of the transporter produced by normal translation and as a soluble chaperone produced by early termination after a frameshift. In the human gene MDH1, stop codon readthrough leads to synthesis of a peroxisomal signal peptide. (b) Phenotypic mutation preceded a genetic solution for dual targeting. In several budding yeasts, there is only one IDP gene; however, it contains a peroxisomal signal peptide (PTS1) in the +1‐frame of the 3′ UTR. In E. gossypii, frameshifting leads to producing a signal peptide and targeting the peroxisome only in a fraction of proteins. Species that underwent Whole‐Genome Duplication have two IDP paralogues: IDP3 bears PTS1 in the 0‐frame and is fully targeted to the peroxisome, while IDP2 has no PTS1, and it localizes to the cytosol
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Woese CR. On the evolution of the genetic code. Proc Natl Acad Sci U S A. 1965;54:1546–1552. - PMC - PubMed
    1. Kunkel TA, Bebenek K. DNA replication fidelity. Annu Rev Biochem. 2000;69:497–529. - PubMed
    1. Milo R, Jorgensen P, Springer M, Weber G. Bionumbers. The database to useful biological numbers; 2007. bionumbers.hms.harvard.edu.
    1. Drummond DA. How infidelity creates a sticky situation. Mol Cell. 2012;48:663–664. - PMC - PubMed
    1. Tawfik DS. Messy biology and the origins of evolutionary innovations. Nat Chem Biol. 2010;6:692–696. - PubMed

Publication types

Substances

LinkOut - more resources