Human Cytomegalovirus RNA2.7 Is Required for Upregulating Multiple Cellular Genes To Promote Cell Motility and Viral Spread Late in Lytic Infection

Analysis of growth kinetics.

The growth kinetics of three viruses generated from a bacterial artificial chromosome (BAC) of HCMV strain Merlin were examined: the parental virus (termed wild type [WT]) and two RNA2.7 deletion mutants (ΔRNA2.7 and ΔTATA) (Fig. 1A). In ΔRNA2.7, the majority of the RNA2.7 coding sequence was deleted, from 50 bp upstream of the TATA box (TATAAA) to 50 bp upstream of the polyadenylation signal (AATAAA) that RNA2.7 shares with an upstream gene, RL5A. In ΔTATA, only the 6 nt of the TATA box were deleted.

A phenotype has been reported for an RNA2.7 deletion mutant of HCMV strain Toledo (29), and the genome sequence of this mutant has been verified (13). The growth kinetics of this mutant were wild type under standard culture conditions but inhibited under conditions of glucose deprivation. This phenotype was attributed to the increased susceptibility of mutant-infected cells to apoptosis and their decreased production of ATP (25). The ability of the Merlin RNA2.7 mutants to grow in human fetal foreskin fibroblast (HFFF2) cells was examined under conditions of single-step and multistep infection (Fig. 1B and C). Neither mutant showed a growth defect in cells under standard culture conditions, but both grew poorly under conditions of glucose deprivation, thus confirming with strain Merlin the earlier findings with strain Toledo (Fig. 1D). Both mutants also exhibited a growth defect in HFFF2 cells synchronized to the G₀ phase of the cell cycle (Fig. 1E).

Transcriptomic analysis.

Since many functions attributed to lncRNAs involve regulation of gene expression, differential expression of viral and cellular transcripts was examined in HFFF2 cells lytically infected with the RNA2.7 mutants in comparison to cells infected with WT. Illumina sequence data were obtained at three time points postinfection (p.i.): 4 h p.i. for the effects of RNA2.7 incorporated into input virions (15, 30) or expressed very early in infection, and 24 and 72 h p.i. for the effects of RNA2.7 expressed during the early and late stages of infection, respectively.

Previous observations that RNA2.7 is expressed with early kinetics and accumulates as infection progresses (18, 19) were confirmed (Fig. 2A). The levels of all other viral transcripts were the same for each virus (Fig. 2B; see also Table S1A in the supplemental material), indicating that the absence of RNA2.7 had no general effect on the proportion of viral RNA (excluding RNA2.7) expressed during the course of infection. These data dealt with the aggregate of viral RNAs and took no account of the behavior of individual RNAs or whether they were sense or antisense (i.e., transcribed in the same direction as a gene or the opposite direction) (22, 31–33). To analyze individual genes, the numbers of reads originating from the sense and antisense strands of each of the 170 protein-coding CDSs and four lncRNA-encoding regions were counted. Comparisons excluded RNA2.7, and differential regulation by RNA2.7 was registered when exhibited by both ΔRNA2.7 and ΔTATA above a defined probability threshold (q < 0.05).

No sense transcripts were differentially expressed by the mutants at 4 h p.i., and only one gene (RL1) was upregulated at 24 h p.i. (Fig. 3A and Table 1, part A; see also Table S1B). In contrast, sense transcripts from 22 genes were differentially expressed at 72 h p.i., with nine upregulated and 13 downregulated (Table 1, part B). Differential expression by ΔRNA2.7 was moderate, ranging from upregulation by 1.66-fold to downregulation by 6.47-fold. RL5A, the 3′-untranslated region (3′ UTR) of which overlaps RNA2.7, was the third most downregulated gene (3.89-fold). Despite the lack of the TATA box, expression of RNA2.7 at 72 h p.i. was observed in ΔTATA at approximately 10% of the level in WT (Fig. 2A). The similarity of the transcription profiles of this region of the genome in WT and ΔTATA provided no evidence for an alternative promoter contributing significantly in the latter (Fig. 2C). No antisense transcripts were differentially regulated at 4 or 24 h p.i. At 72 h p.i., antisense transcripts to three genes in ΔRNA2.7-infected cells were upregulated by ≤1.45-fold, and transcripts antisense to 26 genes were downregulated by ≤2.25-fold (Fig. 3B and Table 1, part C; see also Table S1C). Only two loci were consistently dysregulated in both sense and antisense transcription (RL10 and UL142, both downregulated). There was no overall correlation between the differential expression of sense and antisense transcripts for all viral genes in mutant-infected cells at 72 h p.i. (Spearman’s correlation, P > 0.05; Fig. 3C).

No differentially expressed cellular RNAs were identified at 4 h and 24 h p.i. At 72 h p.i., 1,552 genes were differentially expressed in ΔRNA2.7-infected cells, and 1200 were differentially expressed in ΔTATA-infected cells (see Table S2). The differences in expression level were largely moderate: 99% of the genes dysregulated in ΔRNA2.7-infected cells and ΔTATA-infected cells were differentially expressed by ≤4-fold. In both mutants, most dysregulated genes (74 to 76%) were downregulated. Of the cellular genes differentially regulated by the mutants at 72 h p.i., 931 were common to both ΔRNA2.7 and ΔTATA, with 146 upregulated and 785 downregulated (see Table S2).

Role of cell movement genes.

The functions of the 931 cellular genes dysregulated by the RNA2.7 mutants were investigated by pathway analysis (Table 2). The most prominent category was cell movement, into which 108 genes fell and which scored strongly by gene enrichment (P = 6.83E–10, Fisher exact test) and the concerted up- or downregulation of these genes (z = −5.187). Many genes in other high-scoring categories, such as migration of cells and invasion of cells, were common to the cell movement category (final column in Table 2). Several of these cell-movement-associated (CMA) genes were investigated further.

Of the 108 CMA genes differentially expressed by the RNA2.7 mutants, 99 were downregulated and nine were upregulated (see Table S3A). Ten genes were selected for further analysis that were highly expressed in WT-infected cells and dysregulated (all downregulated) by >1.8-fold in ΔRNA2.7-infected cells and by >1.6-fold in ΔTATA-infected cells (Table 3). These and all subsequent experiments were performed in HFFF2 cells that had been synchronized to the G₀ phase of the cell cycle by serum starvation for 48 h prior to infection. Analysis of transcription of these genes by reverse transcription-PCR (RT-PCR) showed that transcript levels were either maintained or upregulated by infection of cells with WT and markedly decreased during infection with ΔRNA2.7 or ΔTATA (Fig. 4A). Previously published transcriptomic data (34) utilizing nonsynchronized cells showed that all 10 genes were upregulated in WT-infected cells between 24 and 72 h p.i. (Fig. 5A). Two of these genes, encoding Tropomyosin 3 (TPM3) and Syntaxin 3 (STX3), were investigated further. Transcripts accumulated for each between 24 and 120 h p.i. in WT-infected cells but remained at basal levels in cells infected with ΔRNA2.7 or ΔTATA (Fig. 5B), and immunoblotting analysis confirmed decreased protein levels of both proteins in cells infected with ΔRNA2.7 or ΔTATA (Fig. 4B). These experiments validated the downregulation of 10 of the 108 regulated CMA genes at the transcriptional level and two at the protein level. Preliminary evidence that CMA protein levels follow transcript levels more generally was obtained by a single proteomic analysis of WT, ΔRNA2.7 and ΔTATA-infected cells at 72 h p.i., in which 73 of the 108 CMA genes were assessed (see Table S4). Protein and transcript levels correlated strongly (Fig. 4C), although only the members of a subset of CMA proteins were significantly dysregulated by ΔRNA2.7 and ΔTATA (28/73 [38%] and 19/73 [26%], respectively (P < 0.05)).

The results described above implied that cells infected with the RNA2.7 mutants would exhibit lower motility than WT-infected cells at late times in infection. To investigate this, the two RNA2.7 mutations were introduced into the Merlin genome with the green fluorescent protein (GFP) CDS fused to a P2A self-cleaving peptide at the 3′ end of the UL36-coding region, in order to identify infected cells by live-cell microscopy. Human fibroblasts expressing the tetracycline (Tet) repressor (HF-Tet) cells were infected with the parental virus (termed UL36-GFP-WT), UL36-GFP-ΔRNA2.7 or UL36-GFP-ΔTATA, and the speed of movement of infected cells was measured from 72 until 120 h p.i. The results showed that UL36-GFP-WT-infected cells moved 1.6 to 2.2 times faster than UL36-GFP-ΔRNA2.7- or UL36-GFP-ΔTATA-infected cells (Fig. 6A), thus supporting the conclusion that RNA2.7 is involved in promoting the movement of infected cells. Microscopic analysis also revealed that, although cells infected with the RNA2.7 mutants readily developed cytopathic effects, the number of cells that became spherical was lower by 40% in UL36-GFP-ΔRNA2.7-infected cells but not lower in UL36-GFP-ΔTATA-infected cells (Fig. 6B). Moreover, when infected spherical cells that had become detached from the monolayer were removed by replacing the medium, the number of remaining infected spherical cells was lower by 55% in UL36-GFP-WT-infected cells but not lower in mutant-infected cells. These data suggest that, although low-level expression of RNA2.7 by UL36-GFP-ΔTATA (7 to 14% of WT; Fig. 2A and Fig. 5B) was sufficient to promote rounding of infected cells, high-level expression was required for increased detachment. Relevant to this phenotype, 15 of the 931 transcripts dysregulated by the RNA2.7 mutants are associated with focal adhesion formation (see Table S3B). Overall, these results indicate that RNA2.7 may coordinate the regulation of CMA genes and thus enhance infected cell movement late in infection.

During infection, motile infected cells are likely to make multiple contacts with uninfected cells, thus facilitating cell-to-cell transfer of virus. Therefore, we hypothesized that promotion of cell movement may be important for viral spread. To investigate this, HF-Tet cells were infected with UL36-GFP-WT, UL36-GFP-ΔRNA2.7, or UL36-GFP-ΔTATA. Late in infection, isolated infected cells were used to initiate infection in plaque assays on sparse or fully confluent monolayers in the presence of anti-HCMV blocking antibodies, and the number of GFP-positive cells per plaque was monitored. In sparse monolayers, there were 1.9- to 2.0-fold more infected cells in plaques seeded by UL36-GFP-WT-infected cells than in those seeded by cells infected with either mutant, whereas there were no differences in confluent cell monolayers (Fig. 7). This again supports the conclusion that RNA2.7 may promote cell movement, resulting in increased cell-to-cell spread of infectious virus late in infection.

Role of GRIM-19.

The analyses described above implicate RNA2.7 in coordinating the regulation (mostly upregulation) of multiple cellular genes, presumably in order to achieve specific functional goals, such as increasing infected cell movement and viral spread. It is not clear how this is accomplished, but a common mechanism may be involved. One possibility is through interactions with GRIM-19, to which RNA2.7 binds directly (25). To investigate this, three independent shRNA knockdown cell lines (KD1-KD3) against GRIM-19, and a control cell line, were generated from human fibroblasts immortalized by lentiviral transduction of a telomerase gene (HFT cells). The effectiveness of knockdown was confirmed at the transcript (Fig. 8A, left 4 lanes) and protein levels (Fig. 8B), although that of KD1 was marginal. Expression of CMA genes during infection of these cell lines was also examined (Fig. 8A, right 12 lanes), and demonstrated that reduction in GRIM-19 level did not influence the level of transcription of any of the four CMA genes tested (TPM3, STX3, Dystroglycan 1 [DAG1], and Heme-binding protein 1 [HEBP1]). These data indicate that GRIM-19 is unlikely to be involved in the regulation of CMA genes involving RNA2.7.

Role of viral genes.

Attempts were made using a lentivirus-based system to determine whether expression of RNA2.7 in the absence of other HCMV genes is sufficient to upregulate CMA genes. However, even though RNA2.7 was under the control of the powerful HCMV MIEP lacking the IE2-specific repression signal that is commonly used in expression systems, levels of RNA2.7 expression were <4% of those observed during HCMV infection. Efforts to rescue the RNA2.7 phenotype in the context of ΔRNA2.7 and ΔTATA infection were also unsuccessful, probably for the same reason. We also attempted using a recombinant adenovirus system at exceptionally high multiplicity of infection (MOI) to maximize RNA2.7 expression (also driven by the MIEP lacking the IE2-specific repression signal), but this resulted in the development of profound cytopathic effects. Infection with adenovirus under these conditions also prevented subsequent infection with HCMV, thus prohibiting complementation. These two widely used expression systems did not come anywhere near matching the power of the RNA2.7 promoter, and it was therefore not possible to assess the role of RNA2.7 in isolation or in complementing the RNA2.7 mutants.

In an alternative approach to assessing whether regulation of CMA genes depends on the expression of other viral genes late in infection, phosphonoformic acid (PFA) was used to inhibit viral DNA synthesis and hence late gene expression. Cells infected with UL36-GFP-WT and treated with PFA moved 29% more slowly than untreated cells at 48 to 96 h p.i. (Fig. 9A), whereas cells infected with UL36-GFP-ΔRNA2.7 and UL36-GFP-ΔTATA moved 38% more slowly. These results suggest that expression of additional late viral genes is required for the regulation of CMA genes. However, although RNA2.7 is expressed with early kinetics, its level in treated cells was half that in untreated cells at 72 h p.i., raising the perhaps unlikely possibility that this lower level was insufficient to promote RNA2.7-mediated regulation (Fig. 9B).

Role of RNA2.7 subdomains.

Previously published work has shown that the 5′-terminal one-third of RNA2.7 (796 nt) is sufficient to bind to GRIM-19 and protect cells from apoptosis (27). To determine which parts of RNA2.7 are required for upregulation of CMA genes, mutants were generated that lacked either this subdomain or the remaining two-thirds of RNA2.7 (Δ5′ and Δ3′, respectively; Fig. 10A). The deletion in Δ5′ started 50 nt downstream of the transcription initiation site in order to limit promoter-associated effects on expression of the 3′ subdomain of RNA2.7, and the deletion in Δ3′ stopped 50 nt upstream of the polyadenylation signal in order to avoid affecting expression of the upstream gene RL5A, which shares this signal with RNA2.7.

The impact of the deletions on CMA gene expression during infection of HFFF2 cells was assessed. The levels of all four transcripts examined (TPM3, STX3, DAG1, and HEBP1) were reduced in Δ5′-infected cells to levels similar to those in ΔRNA2.7-infected cells. The same observation was made for two of these genes (TPM3 and DAG1) in Δ3′-infected cells, whereas the other two genes (STX3 and HEBP1) were expressed at greater levels than in ΔRNA2.7-infected cells (Fig. 10B). In addition, both UL36-GFP-Δ5′- and UL36-GFP-Δ3′-infected cells moved more slowly than UL36-GFP-WT-infected cells. The motility of UL36-GFP-Δ5′-infected cells and UL36-GFP-Δ3′-infected cells was 43 to 56% lower than that of WT-infected cells, and similar to that of UL36-GFP-ΔRNA2.7-infected cells and UL36-GFP-ΔTATA-infected cells, for which motility was decreased by 42 to 43% (Fig. 10C). In interpreting these results, it is important to note that the level of the residual part of RNA2.7 expressed in Δ3′-infected cells was similar to that of WT (74%), whereas the level in Δ5′-infected cells was only 25% and thus closer to that of ΔTATA (7%) than WT (Fig. 10D and E). As a result, it may be concluded that the 5′-subdomain cannot replace RNA2.7 in regulating CMA genes but that the role of the 3′-subdomain is unresolved.

Role of mRNA stability.

The sequences of the 99 upregulated CMA mRNAs were analyzed for characteristics distinct from those of other cellular mRNAs. The majority (64 mRNAs) have an A+U content of >49.6%, which is greater than the average calculated for all cellular mRNAs (Fig. 11A). Since mRNA instability is associated with higher A+U content (35–41), we investigated whether CMA genes contain motifs associated with lower mRNA stability (35, 42, 43). These motifs comprise A+U-rich elements (ARE motifs 1 to 2E in Table 4) that are based on a pentamer AUUUA core sequence and are known to promote mRNA decay when present in the 3′ UTR (43–45). Other A+U-rich motifs (MEG, H1 to H3, and B1 to B4) and some G+C-rich motifs are associated with lower stability when present in specific regions of an mRNA, conversely, the presence of some G+C-rich motifs (H-1, H-2, B-1, and B-2) correlates with higher mRNA stability in certain instances (35).

The occurrence of instability motifs in the 99 upregulated CMA mRNAs was compared to that in an equal number of randomly selected mRNAs that are not in this category, with this process being repeated 1,000 times. The A+U content of CMA mRNAs was higher than the randomly selected mRNAs in the whole mRNA and the 3′ UTR but lower in the 5′-untranslated region (5′ UTR; Table 4; see also Table S5A). These mRNAs were also enriched in six A+U-rich motifs located in regions of the mRNA associated with faster decay, including the ARE motif in the 3′ UTR. These results suggest that upregulated CMA genes tend to encode less-stable mRNAs and that RNA2.7 may upregulate them by reducing their rate of decay.

To investigate this further, transcription in infected HFFF2 cells was blocked from 72 h p.i. by using actinomycin D to inhibit RNA polymerase II, so that mRNA levels thereafter would be affected by decay but not synthesis. The transcript levels of CMA genes were normalized to that of GAPDH, which encodes a stable transcript having an A+U content of 41 to 45% (depending on transcript variant) that has been used for this purpose in similar studies (46–48). For three of the CMA genes analyzed (STX3, TPM3, and DAG1), the proportion of transcript that persisted in the presence of actinomycin D was consistently higher in cells infected with WT than in cells infected with ΔRNA2.7, ΔTATA, Δ5′, or Δ3′ (Fig. 12A, B, and D). For HEBP1, the same trend was observed with ΔRNA2.7- and Δ5′-infected cells, but there was no significant difference between the persistence of transcripts in WT-infected cells and ΔTATA- or Δ3′-infected cells (Fig. 12C). These results indicate that RNA2.7 may upregulate CMA genes by inhibiting their decay.

To investigate whether the modulation of CMA mRNA stability involving RNA2.7 extends to other cellular mRNAs, the features associated with mRNA stability were collated for the 931 regulated genes (785 upregulated and 146 downregulated). As for the upregulated CMA mRNAs, the majority (69.3%) of all upregulated mRNAs had an A+U content of >49.6% (average, 52.8%; Fig. 11B). Moreover, when the upregulated mRNAs were compared to an equal number of randomly selected mRNAs that are not in this category, with a total of 1,000 comparisons, they had a higher overall A+U content in all comparisons (Table 4; see also Table S5A). These mRNAs were also enriched in motifs associated with lower stability, even more so than the 99 upregulated CMA mRNAs. The general pattern was one of higher A+U content in the CDS and 3′ UTR and a significant enrichment of A+U-rich decay-associated motifs (Table 4; see also Table S5A).

In contrast to the upregulated mRNAs, the majority (75.1%) of the 146 downregulated mRNAs had an A+U content of <49.6% (average, 44.5%; Fig. 11B), and the A+U content was significantly lower in all regions of the mRNA in 1,000 comparisons with other cellular mRNAs (Table 4; see also Table S5A). Also, the downregulated mRNAs were not enriched in A+U-rich decay-associated motifs, but instead were significantly reduced in six of them while having a higher than average occurrence of two stability-associated G+C-rich motifs (Table 4; see also Table S5A). In addition, two G+C-rich motifs associated with mRNA instability were present in the CDSs of downregulated mRNAs at above average frequency, although this may reflect the general G+C richness of these mRNAs. In summary, these findings indicate that nucleotide composition and the presence of motifs affecting RNA stability are prevailing features of the regulated cellular mRNAs, with upregulated transcripts typically comprising less stable A+U-rich mRNAs and downregulated transcripts comprising more stable G+C-rich mRNAs. In addition, although upregulated and downregulated mRNAs possess these opposing features, they share the common characteristic of being relatively G+C-rich in the 5′ UTR compared to other mRNAs.

To investigate whether the modulation of mRNA stability applies to viral genes, the relationship between the A+U content of each viral CDS and the magnitude of its transcriptional dysregulation in RNA2.7 mutant-infected cells were assessed. As for cellular mRNAs, A+U-rich viral transcripts were associated with greater downregulation in the RNA2.7 mutants (Fig. 13A). This correlation was moderate for all viral genes (excluding RNA2.7) aggregated (Fig. 13A), and strong for the 22 differentially expressed genes (Fig. 13C). In contrast, the correlation was weak for all antisense transcripts aggregated (Fig. 13B) and negligible for the 29 differentially expressed antisense transcripts (Fig. 13D). Overall, antisense transcripts in WT-infected cells accounted for only 9.2% of all viral transcripts (even excluding RNA2.7) at 72 h p.i.

Cells and viruses.

Human embryonic kidney cells (293T cells; American Type Culture Collection, ATCC CRL-3216), HFFF2 cells (European Collection of Authenticated Cell Cultures, obtained from Sigma, catalog no. 86031405) and HF-Tet cells (74, 75) were maintained at 37°C in Dulbecco modified Eagle medium supplemented with 10% (vol/vol) fetal bovine serum (FBS), 100 U/ml penicillin, and 100 μg/ml streptomycin and were passaged as monolayers every 3 to 4 days. HFT cells (76) were cultured as monolayers in the same manner with the addition of 50 μg/ml hygromycin B to maintain transduced cells. All cells were tested routinely for the absence of mycoplasma using the MycoAlert mycoplasma detection kit (Lonza).

BACs containing the complete HCMV strain Merlin genome (Merlin-BAC; pAL1111; GenBank accession GU179001.1 [74]) or the Merlin genome with the GFP CDS fused to the 3′-end of the UL36 coding region (UL36-GFP-BAC [34]) were used as parental viruses for constructing RNA2.7 deletion mutants. Two genes (RL13 and UL128) are mutated in Merlin-BAC in order to avoid selecting mutants when virus is reconstituted by transfection into fibroblasts (77). In contrast, viruses reconstituted from UL36-GFP-BAC contain a repaired UL128 and have UL131A under the control of the Tet operator, so that they may be grown stably in repressing HF-Tet cells; the UL128, UL130 and UL131A proteins act as a complex involved in viral entry into nonfibroblast cells, and therefore viruses reconstituted from Merlin-BAC and UL36-GFP-BAC lack this complex. Each deletion mutant was constructed in both Merlin-BAC and UL36-GFP-BAC using BAC recombineering technology (74, 77–79). Briefly, a kanR/rpsL/lacZ selectable cassette flanked by sequences directly adjacent to the region to be deleted was created. This cassette was transfected into Escherichia coli SW102 containing Merlin-BAC or UL36-GFP-BAC, allowing it to replace the sequence to be deleted by homologous recombination. Positive clones were selected using kanamycin. A second round of recombineering was then carried out in which the cassette was removed using negative selection with streptomycin, resulting in markerless deletion of the target sequence.

Parental and mutant viruses were reconstituted by transfecting Merlin-BAC-derived BACs into HFFF2 cells or UL36-GFP-BAC-derived BACs into HF-Tet cells using the basic Nucleofector kit for primary mammalian fibroblasts (VPI-1002; Lonza) and electroporation program T-16. Working stocks of virus were prepared and titrated by plaque assay on the appropriate cell line. The complete genome sequence of each virus was determined by Illumina sequencing as described previously (75) and confirmed as being precisely as designed. The sequence data were also shown to contain no mycoplasma sequence contamination.

The parental virus (termed wild-type, WT) and four RNA2.7 deletion mutants (ΔRNA2.7, ΔTATA, Δ5′, and Δ3′) were generated from Merlin-BAC and its derivative BACs. Similarly, the parental virus (termed UL36-GFP-WT) and the four RNA2.7 deletion mutants (UL36-GFP-ΔRNA2.7, UL36-GFP-ΔTATA, UL36-GFP-Δ5′, and UL36-GFP-Δ3′) were generated from UL36-GFP-BAC and its derivative BACs. The coordinates of the deletions in the Merlin genome (GenBank accession AY446894.2) were as follows: ΔRNA2.7 and UL36-GFP-ΔRNA2.7, 2560 to 5050; ΔTATA and UL36-GFP-ΔTATA, 4995 to 5000; Δ5′ and UL36-GFP-Δ5′, 4179 to 4923; and Δ3′ and UL36-GFP-Δ3′, 2560 to 4178.

Analysis of growth kinetics.

For analysis under standard culture conditions, HFFF2 cells in 6-well plates were infected for 2 h with WT, ΔRNA2.7, or ΔTATA at the specified MOI (quantified in PFU/cell). The medium was then replaced with fresh medium after the cells were washed once with fresh medium. Duplicate 450-μl aliquots of supernatant were harvested every 2 to 3 days and stored at −70°C after pelleting suspended cells and debris by low-speed centrifugation. The pelleted material was added back into the originating well with 1 ml of fresh medium. The stored aliquots of cell-free virus were thawed and titrated on HFFF2 cells. For analysis under conditions of glucose deprivation, HFFF2 cells were incubated in low-glucose (1 g/liter) medium for 48 h prior to infection (MOI of 5), and low-glucose medium was used throughout infection. For analysis under conditions of cell synchronization, HFFF2 cells were synchronized to the G₀ phase of the cell cycle by incubation in medium lacking serum for 48 h prior to infection (MOI of 5), and the cells were then incubated under standard culture conditions.

Transcriptomic analysis.

HFFF2 cells in 12-well plates were incubated for 2 h with WT, ΔRNA2.7, or ΔTATA (MOI of 5). The medium was then replaced with fresh medium after washing the cells once with fresh medium. Total cell RNA was harvested at 4, 24, or 72 h p.i. using TRIzol (Invitrogen) and extracted using a Direct-zol kit (Zymo Research) according to the manufacturer’s instructions, including the on-column DNase digestion step. Three independent experiments were performed. Sequencing libraries were prepared from each RNA sample using 500 ng of RNA and a TruSeq stranded mRNA library preparation kit (Illumina, catalog no. 20020594). The libraries were multiplexed and sequenced using a NextSeq500 with 1 × 75-nt high-output kits (Illumina). Each library generated a read data set of about 40 million single-end reads with >91.2% being high quality (>Q30). After demultiplexing, the reads were filtered for quality (>Q20) and trimmed of adapter sequences using Trim Galore v0.4.2.0 (https://github.com/FelixKrueger/TrimGalore).

To analyze the overall viral transcriptome, Bowtie2 v2.3.5 (80) was used to align the reads to the Merlin genome sequence (GenBank accession AY446894.2) lacking the RNA2.7 sequence (nt 2489 to 4969). These results permitted the proportion of viral reads (excluding RNA2.7) to be determined in comparison with the total number of reads. The trimmed reads were also aligned to the complete Merlin genome sequence, and the difference between the number of aligned reads and that in the alignment lacking the RNA2.7 sequence was taken to be the number of RNA2.7 reads. To analyze the transcript levels of individual viral genes, the reads were aligned to the sequences of each of the 170 Merlin CDSs and the four major lncRNA-encoding regions (22) using Bowtie2. The reads in each alignment were sorted into those originating from sense and antisense transcripts, using an in-house script (SamSplit [81]), and realigned with the relevant sequence. The alignments were visualized using Tablet v1.14.11.07 (82). Reads per kilobase per million mapped reads (RPKM) values were determined as described previously (83), thus normalizing the number of mapped reads to CDS length. RNA2.7 reads were again omitted from all transcriptome calculations to facilitate comparison between the WT and RNA2.7 mutant data. Significant changes in viral gene expression (calculated as the log₂fold change) were determined from the three independent experiments using the R software Limma (q < 0.05) (84).

To analyze the cellular transcriptome, data set quality was confirmed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). The reads were aligned to the human genome sequence (hg19; https://genome.ucsc.edu) using Tophat2 v2.1.1 (85). Genes in RNA2.7 mutant-infected cells that were differentially expressed compared to WT-infected cells were identified using Cufflinks/Cuffdiff v2.2.1 (http://cole-trapnell-lab.github.io/cufflinks/install/) (86). The false discovery rate of differentially expressed transcripts was determined using the Benjamini-Hochberg method, which produced a q value for each gene that was considered statistically significant at <0.05. Pathway analysis was performed using Ingenuity Pathway Analysis software (IPA; Qiagen, 2016 release).

Proteomic analysis.

Confluent HFFF2 cells in 175-cm² flasks were infected for 2 h with WT, ΔRNA2.7, or ΔTATA (MOI of 5). The medium was then replaced with fresh medium after washing the cells once with fresh medium. At 72 h p.i., the cells were washed twice with phosphate-buffered saline (PBS), after which lysis buffer (6 M guanidine and 50 mM HEPES [pH 7]) was added, and the lysed cells were harvested by scraping. The samples were sonicated (30 s on and 30 s off for 10 cycles), and the debris was pelleted by low-speed centrifugation. The cell lysate samples were processed for multistage MS3-based mass spectrometry using isobaric 10-plex tandem-mass tags, and the data were analyzed as described previously (34, 87). A single experiment was conducted, in which 7961 infected cell proteins were identified, including 115 specified by HCMV. Proteins that were differentially expressed in RNA2.7 mutant-infected cells compared to WT-infected cells were identified by calculating Benjamini-Hochberg corrected Significance B values (88), and those with P < 0.05 were considered to be significant.

Transcript quantification by PCR.

Confluent HFFF2 or HF-Tet cells in 12-well plates were serum-starved by incubation in the absence of FBS for 48 h prior to infection, in order to synchronize the cell cycle to the G₀ phase. The cells were then infected for 2 h with WT, ΔRNA2.7, ΔTATA, Δ5′, or Δ3′, or with UL36-GFP-WT, UL36-GFP-ΔRNA2.7, UL36-GFP-ΔTATA, UL36-GFP-Δ5′, or UL36-GFP-Δ3′ (MOI of 5). The medium was then replaced with fresh medium after the cells were washed once with fresh medium. Where appropriate, infected cells were treated with 300 μg/ml PFA (a viral DNA polymerase inhibitor) immediately following the 2-h adsorption period. The infected cells were harvested at 72 h p.i. using TRIzol, and total cellular RNA was isolated using Direct-zol, including the on-column DNase digestion step.

For RT-PCR, reverse transcription was performed using GoScript (Promega) with Oligo-d(T) as the primer, and the resulting cDNA was used as the template for PCRs to amplify specific transcripts along with the GAPDH and G6PD transcripts, which were used to confirm equal loading. Either the Advantage 2 system (Clontech) or the Phusion system (New England Biolabs) was used for PCR (primers are listed in Table S6). For quantitative RT-PCR (RT-qPCR), the one-step RT-qPCR system QuantiTect Virus + ROX Vial kit (Qiagen) was used to quantify the levels of the TPM3, STX3, RNA2.7 and GAPDH transcripts (primers and probes are listed in Table S6), and data were collected using the Applied Biosystems 7500 real-time PCR system (Thermo Fisher). Relative transcript concentrations were determined by comparison to a standard curve and normalized to the level of GAPDH transcript.

Immunoblotting analysis.

Confluent HFFF2 cells in 12-well plates were serum-starved for 48 h and infected for 2 h with WT, ΔRNA2.7, or ΔTATA (MOI of 5). The medium was then replaced with fresh medium after washing the cells once with fresh medium. At 72 h p.i., cell lysates were harvested using Laemmli sample buffer (89). The proteins were resolved on 4 to 20% (wt/vol) precast TGX polyacrylamide gels (Bio-Rad) using the Mini-PROTEAN Tetra system (Bio-Rad), and transferred to polyvinylidene difluoride membranes (Thermo Scientific). The membranes were blocked with PBS containing 5% (vol/vol) FBS on a roller machine for 1 h, and incubated in PBS containing 5% (vol/vol) FBS, 0.1% (vol/vol) Tween 20 and primary antibodies targeting TPM3 (0.4 μg/ml, HPA009066 [Human Protein Atlas]), STX3 (0.4 μg/ml, HPA002191 [Human Protein Atlas]), GRIM-19 (0.4 μg/ml, HPA041213 [Human Protein Atlas]), or actin (1:4,000 dilution, A1978, clone AC-15; Sigma). The target proteins were visualized by incubating with fluorophore-conjugated secondary antibodies against rabbit (1:10,000, catalog no. 35568; Thermo Fisher) or mouse (1:10,000, SA5-35521; Invitrogen) immunoglobulin in PBS containing 5% (vol/vol) FBS and 0.1% (vol/vol) Tween 20. The signal was detected using an Odyssey CLx instrument (Li-Cor).

Live cell fluorescence imaging analysis.

Confluent HF-Tet cells in 12-well plates were serum-starved for 48 h prior to infection for 2 h with UL36-GFP-WT, UL36-GFP-ΔRNA2.7, UL36-GFP-ΔTATA, UL36-GFP-Δ5′, or UL36-GFP-Δ3′ (MOI of 5). The medium was then replaced with fresh medium after washing the cells once with fresh medium. Where appropriate, infected cells were treated with 300 μg/ml PFA immediately after the 2-h viral adsorption period. The medium was then replaced with fresh medium immediately before carrying out automated time-lapse, multiposition, dual-channel microscopy at 48 to 120 h p.i. at ×20 magnification using the Cell Observer live cell imaging system (Zeiss) fitted with the CO₂ module, Temp module S and Incubator XLmulti S1. Images of five random fields of view per well with duplicate wells per condition were obtained every 10 min using both the brightfield and GFP channels. GFP expression was confirmed in all cells and tracked using the Fiji plug-in TrackMate v3.4.2 (90). GFP-expressing cells were identified by the LoG detector; the estimated cell diameter was set at 0.002 cm. Only spots of quality >0.3 were accepted. The Linear Assignment Problem (LAP) algorithm was used to create the cell movement tracks, for which the maximum frame-to-frame linking distance was 0.002 cm, the maximum track distance for gap-closing was 0.003 cm, the maximum gap was 2 frames, and the maximum distance permitted for track segment splitting was 0.002 cm. Only tracks with at least 15 successful detections of the same cells and a quality standard deviation of >0.1 were included in the analysis. An average of 756 tracks per condition were examined in each experiment.

Analysis of infected cells in individual plaques.

Confluent HF-Tet cells in 12-well plates were serum-starved for 48 h prior to infection for 2 h with UL36-GFP-WT, UL36-GFP-ΔRNA2.7, or UL36-GFP-ΔTATA (MOI of 5). The medium was then replaced with fresh medium after washing the cells once with fresh medium. The infected cells were trypsinized at 72 to 96 h p.i. and resuspended in medium. Dispersal into isolated cells was confirmed microscopically, and cell density was determined twice using a hemocytometer. About 60 cells were used to seed plaques in partially (25%) or fully confluent HF-Tet cells in 6-well plates in the presence of 50 μg/ml Cytotect anti-HCMV antibody (Biotest) (91). The number of infected (GFP-expressing) cells in each of the plaques in duplicate wells was counted after 48 h had elapsed, resulting in the assessment of 70 to 100 plaques per virus in each experiment.

Generation of GRIM-19 knockdown cell lines.

Derivatives of the TRC lentiviral cloning vector (92) encoding shRNAs against three different regions of the GRIM-19 gene (TRCN000006492, TRCN0000064929, and TRCN0000236379; Sigma) or a nontargeting control shRNA (SHC002; Sigma) were transfected using Lipofectamine 3000 (Sigma) into 293T cells with the Δ8.9 and pVSV-g plasmids to produce infectious lentiviruses (93). The lentiviruses were used to transduce HFT cells, and successfully transduced cells were selected and maintained by incubation with medium containing 1 or 0.5 μg/ml puromycin.

Analysis of RNA stability motifs and nucleotide composition.

Enrichment of RNA stability motifs in query data sets of human mRNAs was tested using a randomization approach. The query data set was either the 146 downregulated genes, the 785 upregulated genes, or the subset of 99 upregulated CMA genes. Human genome annotation hg19 was used. No genes were excluded from the CMA gene query data set, but five downregulated transcripts and eight upregulated transcripts (see Table S5B) were excluded from the total query data set because the genes lacked protein identities in the human genome database. Nonetheless, nearly all regulated cellular genes (98.6%) were included in the analysis.

The total number of motifs in all sequences was counted in each query data set using a sliding window of a size equal to that of the motif, shifted in 1-nt increments. For genes with multiple transcript variants, the longest variant was used. The mean number of each motif per sequence in a query data set was calculated for the entire mRNA and also for separate regions: the 5′ UTR, the 3′ UTR, and the CDS. This analysis was repeated with 1,000 data sets containing a number of sequences equal to that in the query data set, each randomly sampled from the human genome database (GRCh38.p12 [https://www.ensembl.org/info/data/ftp/index.html]). The mean number of each motif per sequence in each randomly sampled data set was compared with that from the query data set. The difference was considered to be significant if this number was higher or lower in >950/1000 comparisons (i.e., P < 0.05). All A+U analyses were performed in the same manner by determining the mean values for the longest transcript variant.

Data availability.

Transcriptomic sequence read data sets were deposited in the NCBI Sequence Read Archive under the following accession numbers: WT 4 h, SRX8009962, SRX8009951, and SRX8009950; ΔRNA2.7 4 h, SRR12424745, SRR12424744, and SRR12424753; ΔTATA 4 h, SRR12424752, SRR12424751, and SRR12424750; WT 24 h, SRX8009953, SRX8009952, and SRX8009976; ΔRNA2.7 24 h, SRR12424747, SRR12424749, and SRR12424748; ΔTATA 24 h, SRR12424746, SRR12424743, and SRR12424742; WT 72 h, SRX8009963, SRX8009961, and SRX8009960; ΔRNA2.7 72 h, SRR12424759, SRR12424757, and SRR12424758; and ΔTATA 72 h, SRR12424756, SRR12424755, and SRR12424754. Proteomic data sets were deposited in the ProteomeXchange with identifier PXD022277. The Python script for analyzing stability motif enrichment is available in Github (https://github.com/salvocamiolo/stabilityMotifs.git).