Viruses: A frameshift in time

The efficiency with which ribosomes shift reading frames when decoding viral RNA may change over the course of an infection.
  1. Martina M Yordanova
  2. Pavel V Baranov  Is a corresponding author
  1. School of Biochemistry and Cell Biology, University College Cork, Ireland

When an RNA virus infects a cell, ribosomes inside the cell decode the genetic information in the virus’s RNA to produce proteins, which are then used to make more viral particles. A single-stranded RNA molecule consists of a sequence of nucleotides that the ribosome reads three at a time. Each triplet, or codon, codes for either an amino acid (the building blocks that form proteins), or signals for the ribosome to start or stop reading the RNA sequence. Therefore, each nucleotide sequence can therefore be ‘read’ by ribosomes in three different ways, or ‘reading frames’, depending on which nucleotide the ribosome starts reading from. Additionally, an ‘open reading frame’ or ORF is a sequence of nucleotide triplets that code for amino acids located between two stop codons in the same reading frame.

Almost all cellular proteins are encoded in a single reading frame, with only rare exceptions (Baranov et al., 2015). Viruses, however, often break this rule in a process termed ‘programmed ribosomal frameshifting’ (Firth and Brierley, 2012; Atkins et al., 2016). This mechanism occurs at specific locations in the nucleotide sequence called frameshift sites, where a proportion of the ribosomes translating the RNA will shift back or forward one nucleotide and start decoding a different reading frame. Meanwhile, the rest of the ribosomes continue reading the original frame. Thus, the same segment of an RNA molecule can be read to produce two protein molecules with distinct amino acid sequences simultaneously.

It is unclear exactly why viruses employ programmed ribosomal frameshifting. One suggestion is that this mechanism allows for a more compact organization of genetic material. Another is that frameshifting could be used for setting a specific ratio between different viral proteins. Most commonly, ribosomal frameshifting occurs during the synthesis of viral polyproteins, long amino acid chains that are processed into smaller proteins with distinct functions. The advantage of organizing protein synthesis in this way is that only one RNA molecule is needed to encode multiple proteins. However, if all these proteins were synthesized as a part of a single polyprotein, they would occur strictly in a one-to-one ratio after being processed. This would be wasteful, since these proteins are needed in different quantities.

So how could the optimal proportions of these proteins be achieved? The low efficiency frameshifting mechanism solves the problem. Proteins that the virus needs in large quantities are encoded early in the sequence in an open reading frame herein referred to as ORF1A, while proteins that the virus requires in lower quantities are encoded in a different but overlapping downstream reading frame, herein referred to as ORF1B (Figure 1). ORF1A is decoded according to standard rules, producing a shorter version of the viral polyprotein. ORF1B, on the other hand, is only read by the ribosomes that change reading frame at the frameshift site between ORF1A and ORF1B, resulting in a longer polyprotein.

Schematic representation showing how RNA is decoded in the vicinity of the frameshift site between two open reading frames, ORF1A and ORF1B.

Top: most ribosomes (yellow) decoding ORF1A terminate at the stop codon (red arrow), release the protein (not shown) and dissociate from the RNA (gray curve). A small proportion of ribosomes, however, shift frames to decode ORF1B. The ribosome at the frameshift site is outlined with a fuzzy cloud. Center: the density of ribosome footprints (the lines under each of the ribosomes) revealed by ribosome profiling maps to the positions occupied by ribosomes on the RNA molecule. The ratio between the ribosome footprint density at ORF1A and at ORF1B can be used as a measure of frameshifting efficiency. Bottom: schematic of the three possible reading frames in a molecule of RNA, each represented by a bar and denoted by –1, 0, and +1. The clock-like nature of the frameshift site drawing alludes to the temporal regulation of frameshifting as revealed by Cook et al.

This type of frameshifting is sometimes referred to as canonical due to its common occurrence in RNA viruses. It was originally assumed that the ratio of products generated from ORF1A and ORF1B was fixed throughout the virus’s time in the cell (Jacks and Varmus, 1985). Now, in eLife, Ian Brierley, Andrew Firth, Ying Fang and colleagues – including Georgia Cook (University of Cambridge) as first author – report evidence suggesting that this ratio changes over the course of infection (Cook et al., 2022).

The team (who are based at various institutes in the United Kingdom, the United States and China) studied how viral gene expression changes during porcine reproductive and respiratory syndrome virus (PRRSV) infection. To do this, Cook et al. used a technique called ribosome profiling to map which parts of the virus’s RNA sequence were being translated by ribosomes at any given time (Ingolia et al., 2009). These mappings, called ribosome footprints, revealed several new ORFs that encoded previously uncharacterized viral protein products.

Ribosome profiling can also be used to compare how efficiently different proteins are synthesized. For example, in the PRRSV genome the density of footprints mapped to ORF1A is higher than the footprint density at ORF1B. This happens because only a small proportion of the ribosomes reading ORF1A shift reading frame and proceed to ORF1B (Figure 1). By calculating the ratio of footprint densities between the two open reading frames it is possible to estimate frameshifting efficiency.

The PRRSV genome is known to contain two frameshift sites: the canonical site between ORF1A and ORF1B, which is used by many viruses, and a second, rarer frameshift site in ORF1A that results in the production of a shorter polyprotein. The genome of a related virus, called the encephalomyocarditis virus, has been shown to have a similar secondary frameshift site that is stimulated by a viral protein (Napthine et al., 2017). The concentration of this viral protein was found to increase over the course of an infection and cause more ribosomes to shift to the other reading frame. However, by measuring the efficiency of both frameshifting sites in PRRSV, Cook et al. showed that this temporal change is not limited to the protein-stimulated frameshifting, but also occurs in the canonical site between ORF1A and ORF1B.

This finding challenges the current paradigm that regards the canonical frameshifting between ORF1A and ORF1B as a mechanism that enables a fixed ratio between polyprotein products. The temporal change detected in PRRSV suggests that the efficiency of frameshifting may also be altered in other viruses over time. If so, it would be interesting to determine what factors mediate the regulation of the frameshifting event between ORF1A and ORF1B.

An open question that remains is how changes in frameshifting efficiency along the course of an infection relate to the virus’s virulence and transmissibility. It is possible that changes in efficiency are simply due to alterations in the infected cell that make ribosomes more prone to shifting to another reading frame. However, it may be that regulating the efficiency of frameshifting is beneficial for the virus. Alternatively, the antiviral response of the host may induce frameshifting to alter the ratio of viral proteins and negatively impact the virus. Indeed, it has been previously reported that the formation of viral particles can be disrupted by altering frameshifting efficiency (Dulude et al., 2006).

Whatever the case, the search for cellular factors responsible for changes in frameshifting has already begun (Riegger and Caliskan, 2022). The identification of these factors will provide researchers with new targets for modulating frameshifting efficiency in viruses, potentially revealing new ways to fight off viral infections.


Article and author information

Author details

  1. Martina M Yordanova

    Martina M Yordanova is in the School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland

    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9693-3857
  2. Pavel V Baranov

    Pavel V Baranov is in the School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland

    For correspondence
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9017-0270

Publication history

  1. Version of Record published: April 11, 2022 (version 1)


© 2022, Yordanova and Baranov

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.


  • 575
    Page views
  • 81
  • 1

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Martina M Yordanova
  2. Pavel V Baranov
Viruses: A frameshift in time
eLife 11:e78373.

Further reading

    1. Chromosomes and Gene Expression
    Keith Conrad Fernandez, Laura Feeney ... Jayanta Chaudhuri
    Research Article

    During the development of humoral immunity, activated B lymphocytes undergo vigorous proliferative, transcriptional, metabolic, and DNA remodeling activities; hence, their genomes are constantly exposed to an onslaught of genotoxic agents and processes. Branched DNA intermediates generated during replication and recombinational repair must be eliminated to preserve the integrity of these DNA transactions for the faithful duplication and propagation of genomic material. To investigate the role of two structure-selective endonucleases, GEN1 and MUS81, in B cell biology, we established B-cell conditional knockout mouse models and found that targeted deletion of GEN1 and MUS81 in early B-cell precursors abrogates the development and maturation of B-lineage cells while selective loss of the enzymes in mature B cells inhibits the generation of robust germinal centers. Upon activation, these double-null mature B lymphocytes fail to proliferate and survive while exhibiting transcriptional signatures of p53 signaling, apoptosis, and type I interferon response. Metaphase spreads of these endonuclease-deficient cells showed severe and diverse chromosomal abnormalities, including a preponderance of chromosome breaks, consistent with a defect in resolving DNA recombination intermediates. These observations underscore the pivotal roles of GEN1 and MUS81 in safeguarding the genome to ensure the proper development and maintenance of B lymphocytes.

    1. Cancer Biology
    2. Chromosomes and Gene Expression
    Arnaud Carrier, Cécile Desjobert ... Paola B Arimondo
    Research Article Updated

    Aberrant DNA methylation is a well-known feature of tumours and has been associated with metastatic melanoma. However, since melanoma cells are highly heterogeneous, it has been challenging to use affected genes to predict tumour aggressiveness, metastatic evolution, and patients’ outcomes. We hypothesized that common aggressive hypermethylation signatures should emerge early in tumorigenesis and should be shared in aggressive cells, independent of the physiological context under which this trait arises. We compared paired melanoma cell lines with the following properties: (i) each pair comprises one aggressive counterpart and its parental cell line and (ii) the aggressive cell lines were each obtained from different host and their environment (human, rat, and mouse), though starting from the same parent cell line. Next, we developed a multi-step genomic pipeline that combines the DNA methylome profile with a chromosome cluster-oriented analysis. A total of 229 differentially hypermethylated genes was commonly found in the aggressive cell lines. Genome localization analysis revealed hypermethylation peaks and clusters, identifying eight hypermethylated gene promoters for validation in tissues from melanoma patients. Five Cytosine-phosphate-Guanine (CpGs) identified in primary melanoma tissues were transformed into a DNA methylation score that can predict survival (log-rank test, p=0.0008). This strategy is potentially universally applicable to other diseases involving DNA methylation alterations.