Achieving a golden mean: mechanisms by which coronaviruses ensure synthesis of the correct stoichiometric ratios of viral proteins

Abstract

In retroviruses and the double-stranded RNA totiviruses, the efficiency of programmed -1 ribosomal frameshifting is critical for ensuring the proper ratios of upstream-encoded capsid proteins to downstream-encoded replicase enzymes. The genomic organizations of many other frameshifting viruses, including the coronaviruses, are very different, in that their upstream open reading frames encode nonstructural proteins, the frameshift-dependent downstream open reading frames encode enzymes involved in transcription and replication, and their structural proteins are encoded by subgenomic mRNAs. The biological significance of frameshifting efficiency and how the relative ratios of proteins encoded by the upstream and downstream open reading frames affect virus propagation has not been explored before. Here, three different strategies were employed to test the hypothesis that the -1 PRF signals of coronaviruses have evolved to produce the correct ratios of upstream- to downstream-encoded proteins. Specifically, infectious clones of the severe acute respiratory syndrome (SARS)-associated coronavirus harboring mutations that lower frameshift efficiency decreased infectivity by >4 orders of magnitude. Second, a series of frameshift-promoting mRNA pseudoknot mutants was employed to demonstrate that the frameshift signals of the SARS-associated coronavirus and mouse hepatitis virus have evolved to promote optimal frameshift efficiencies. Finally, we show that a previously described frameshift attenuator element does not actually affect frameshifting per se but rather serves to limit the fraction of ribosomes available for frameshifting. The findings of these analyses all support a "golden mean" model in which viruses use both programmed ribosomal frameshifting and translational attenuation to control the relative ratios of their encoded proteins.

FIG. 1.

Schematic of genomic and subgenomic RNAs. The open reading frames of the SARS-CoV gRNA are shown as open boxes. The position of the frameshift signal where ORF1a and ORF1b overlap is indicated. The 5′ leader sequence and 3′ noncoding region common to the gRNA and all sgRNAs are shown as filled boxes. The positions of the primers gF and gR used to detect gRNA and the primers sgF and sgR used to dedect sgRNA are shown as arrows. Note that the detection of sgRNA requires a leader sequence proximal to the 3′ ORF.

FIG. 2.

Relative abundance of genomic and subgenomic RNA in viral stocks. Plaque-purified virus was used to infect Vero cells. Four days postinfection CPE was observed. Media and detached cells were removed and filtered. RNA was extracted from a 100-μl aliquot using TRIzol. TaqMan analysis was used to determine the total number of genomic and subgenomic RNA molecules compared to a reference RNA transcribed from a SARS replicon (1). The number of copies per ml of viral stock is shown with standard deviations.

FIG. 3.

Changing the MHV −1 PRF-promoting mRNA pseudoknot to the SARS-CoV pseudoknot: structural and functional analysis. (A) The predicted secondary structures of the MHV (left) and SARS-CoV (right) pseudoknots are shown, along with a series of mutants designed to sequentially change the MHV sequence into that of SARS-CoV. S and L denote stem and loop elements, respectively. Circled bases show the sequential mutations made to shorten stem 2 (Δ2) and loop 3 (Δ2Δ3) and to add and move the bulges in stems 2 (Δ2bΔ3) and 3 (Δ2bΔ3b). Frameshifting efficiency (percent) with standard errors is shown below each construct. Color coding indicates the extent of protection from modification by NMIA in the SHAPE reactions as indicated by the bar below. (B) Representative autoradiograms of SHAPE reactions. Dideoxynucleotide sequencing reactions (GUAC) are included for each mutant. The minus sign indicates control samples, and the plus sign represents NMIA-treated samples.

FIG. 4.

Coronavirus sequences upstream of the −1 PRF signal are predicted to fold into highly stable structures. mfold analyses of the predicted secondary structures of sequences 5′ of the slippery sites from six distantly related coronaviruses are shown.

FIG. 5.

Apparent effect of the attenuator sequence on frameshifting is due to its affect on ribosome processivity. (A) pLuci (no-insert readthrough, i.e., RT) was employed as the parent plasmid for all subsequent constructs. The SARS-CoV frameshift signal, including the slippery site (ss) and pseudoknot (Ψk), were cloned between the two luciferase genes (test construct T−). A readthrough plasmid was created by the addition of one nucleotide (n) upstream of the slippery site (control construct C−). Similar constructs containing the upstream attenuator sequence (att) also were made (T+ and C+). The reading frame of the firefly luciferase in each of the constructs is indicated. (B) Apparent frameshifting efficiencies were assayed in Vero cells transfected with each of the indicated constructs. Firefly activity was normalized to Renilla activity, and comparisons were made between the plasmids. The test constructs were compared to the control constructs to determine frameshifting efficiency. (C) Firefly/Renilla luciferase ratios generated by the readthrough constructs containing either the pseudoknot alone (C−) or the pseudoknot and the attenuator sequence (C+) were compared to those generated from pLuci (RT). (D) Full-length luciferase proteins produced using separate transcription and translation reactions were separated by SDS-PAGE. Two different dilutions of T− (i.e., T− and T−2) are shown. An increase in the abundance of smaller proteins from attenuator-containing transcripts is indicated with an asterisk. The expected sizes of the Renilla protein with or without the attenuator at 43 and 37 kDa, respectively, are marked on the gel. The frameshift or readthrough products (Firefly/Renilla) with or without the attenuator are predicted to be 101 and 107 kDa, respectively.