Measuring the selective packaging of RNA molecules by viral coat proteins in cells

Abstract

Some RNA viruses package their genomes with extraordinary selectivity, assembling protein capsids around their own viral RNA while excluding nearly all host RNA. How the assembling proteins distinguish viral RNA from host RNA is not fully understood, but RNA structure is thought to play a key role. To test this idea, we perform in-cellulo packaging experiments using bacteriophage MS2 coat proteins and a variety of RNA molecules in Escherichia coli. In each experiment, plasmid-derived RNA molecules with a specified sequence compete against the cellular transcriptome for packaging by plasmid-derived coat proteins. Following this competition, we quantify the total amount and relative composition of the packaged RNA using electron microscopy, interferometric scattering microscopy, and high-throughput sequencing. By systematically varying the input RNA sequence and measuring changes in packaging outcomes, we are able to directly test competing models of selective packaging. Our results rule out a longstanding model in which selective packaging requires the well-known translational repressor (TR) stem-loop, and instead support more recent models in which selectivity emerges from the collective interactions of multiple coat proteins and multiple stem-loops distributed across the RNA molecule. These findings establish a framework for studying and understanding selective packaging in a range of natural viruses and virus-like particles, and lay the groundwork for engineering synthetic systems that package specific RNA cargoes.

Keywords: RNA packaging; bacteriophage MS2; capsid assembly; packaging signals; plus-strand RNA virus.

Fig. 1.

Overview of the system and the experiment. (A) A structural model of the MS2 capsid contains 180 copies of the coat protein arranged as a 28-nm icosahedral shell (20). (B) A secondary structure model of the packaged MS2 genome contains 15 stem-loop structures (shown in red) that bind tightly to the interior surface of the capsid (15). One of these stem-loops is the famous TR loop (13) (shown by a red arrowhead). A map of the primary structure shows the positions of these stem-loops relative to the viral genes. (C) A schematic of the in-cellulo packaging experiment outlines the key steps of our protocol: 1. We transform Escherichia coli with a plasmid whose insert contains the MS2 coat protein gene flanked on either side by variable untranslated sequences. (Inset) Once transformed, the plasmid insert is transcribed into RNA (i), which is then translated into coat protein (ii). When the cellular concentration of coat protein becomes sufficiently high, capsids assemble and package some of the available pool of RNA (iii). This pool contains a mixture of insert transcripts, plasmid vector-derived transcripts, and cellular transcripts, all of which compete for packaging by the assembling coat proteins. 2. We lyse the cells after 24 h to release the assembled capsids, and then 3. purify the capsids from unpackaged RNA and other cellular debris using nuclease digestion followed by ultracentrifugation. Once purified, we can infer the amount of RNA packaged per particle using a combination of transmission electron microscopy (TEM) and interferometric scattering microscopy (iSCAT). Finally, 4. we extract the packaged RNA from the capsids, and 5. determine its identity using short-read high-throughput RNA sequencing (RNAseq).

Fig. 2.

Varying the insert sequence yields similar capsids with different RNA contents. (A) The pMS2′ insert contains the full MS2 genome (Top), modified with point mutations to prevent the production of viral proteins other than the coat protein (SI Appendix, Fig. S1). Leaky expression of pMS2′ transcripts produced well-formed 28-nm capsids, as observed by negative-stain TEM (Bottom-Left), with an average mass of 3.5 MDa per particle, determined by iSCAT (Bottom-Middle). Approximately 97% (±1% across duplicate experiments) of sequencing reads from the packaged RNA aligned to the pMS2′ insert sequence (Bottom-Right). (B) The pcoat′ insert contains only the MS2 coat protein gene (Top), modified by random codon swaps to scramble the RNA sequence while preserving the amino acid sequence, codon usage, and dinucleotide frequency (SI Appendix, Fig. S5). As with pMS2′, leaky expression of pcoat′ transcripts produced well-formed 28-nm capsids (Bottom-Left) with an average mass of 3.5 MDa (Bottom-Middle). However, only 3% (±1% across duplicate experiments) of sequencing reads aligned to the insert sequence, with 27% aligning to the plasmid vector and 70% to the E. coli (cellular) genome (Bottom-Right). (C) Cryo-EM and single-particle reconstruction showed that the majority of particles produced by both inserts adopt identical capsid structures. The electron density maps of each capsid overlap (Left), and their molecular models are essentially indistinguishable (Right). The pMS2′ map was determined to 2.4-Å resolution, and the pcoat′ map was determined to 2.2-Å resolution.

Fig. 3.

Selectivity depends weakly on RNA length. (A) Inserts of varying lengths were constructed by appending random dinucleotide-preserving shuffled sequences downstream of the coat protein gene. (B) For all lengths tested, TEM confirmed the formation of well-formed 28-nm capsids (Top), while iSCAT measurements showed an average particle mass of 3.5 MDa (Bottom). (C) The fraction of packaged RNAseq reads aligning to the insert, vector, or host genome is plotted as a function of insert length. The results show that the insert packaging fraction increases monotonically with length. However, these fractions remain well below the 97% observed for pMS2′, indicating that RNA length alone is insufficient to achieve high selectivity.

Fig. 4.

Selectivity depends strongly on the presence of special stem-loops. (A, Left) The insert sequence was systematically varied by appending portions of the MS2 sequence downstream of the coat gene and shuffling nucleotides within specific regions, yielding five shuffle types, SI–SV. For detailed descriptions of these shuffle types, see the main text and Dataset S1. (A, Right) Bar plots display the fraction of shuffled nucleotides, the maximum ladder distance (MLD), and the number of packaging signal stem-loops for each shuffle type. MLD, a theoretical measure of RNA molecule size derived from thermodynamic folding models, is displayed as the mean (blue bars) and SD (black bars) calculated across 1,000 predicted equilibrium secondary structures (Materials and Methods and SI Appendix, Fig. S11). (B) Packaging fractions for each insert are shown as a function of length. Shuffle types are indicated by distinct symbols, connected by straight lines to guide the eye. (C) The packaging fraction is shown as a function of the fraction of shuffled nucleotides (Left), MLD (Middle), and the number of stem-loops (Right) for the full-length transcripts of each shuffle type. Best-fit regression lines (gray) are included, along with Pearson correlation coefficients (r) and P-values (p).

Fig. 5.

Selectivity depends on the number of stem-loops. (A) Four additional shuffle types (SI-1, SI-3, SIV+1, and SIV+3) were generated to investigate the roles of the TR loop and its flanking stem-loops (TR-1 and TR+1). For detailed descriptions of these shuffle types, see the main text and Dataset S1. (B) Adding TR and its flanking pair of loops (SIV+1 and SIV+3) to an otherwise random insert increases the packaging fraction of the insert transcripts dramatically, while adding an additional 11 loops (SIII) further increases the packaging fraction only modestly. (C) Removing TR and the flanking loops (SI-1 and SI-3) does not significantly affect selectivity, whereas removing an additional 11 loops (SII) leads to a dramatic drop in selectivity.