Global Analysis of Mouse Polyomavirus Infection Reveals Dynamic Regulation of Viral and Host Gene Expression and Promiscuous Viral RNA Editing

Murine polyomavirus lytically infects mouse cells, transforms rat cells in culture, and is highly oncogenic in rodents. We have used deep sequencing to follow polyomavirus infection of mouse NIH3T6 cells at various times after infection and analyzed both the viral and cellular transcriptomes. Alignment of sequencing reads to the viral genome illustrated the transcriptional profile of the early-to-late switch with both early-strand and late-strand RNAs being transcribed at all time points. A number of novel insights into viral gene expression emerged from these studies, including the demonstration of widespread RNA editing of viral transcripts at late times in infection. By late times in infection, 359 host genes were seen to be upregulated and 857 were downregulated. Gene ontology analysis indicated transcripts involved in translation, metabolism, RNA processing, DNA methylation, and protein turnover were upregulated while transcripts involved in extracellular adhesion, cytoskeleton, zinc finger binding, SH3 domain, and GTPase activation were downregulated. The levels of a number of long noncoding RNAs were also altered. The long noncoding RNA MALAT1, which is involved in splicing speckles and used as a marker in many late-stage cancers, was noticeably downregulated, while several other abundant noncoding RNAs were strongly upregulated. We discuss these results in light of what is currently known about the polyoma life cycle and its effects on host cell growth and metabolism

Global Analysis of Mouse Polyomavirus Infection Reveals Dynamic Regulation of Viral and Host Gene Expression and Promiscuous Viral RNA Editing

<div><p>Mouse polyomavirus (MPyV) lytically infects mouse cells, transforms rat cells in culture, and is highly oncogenic in rodents. We have used deep sequencing to follow MPyV infection of mouse NIH3T6 cells at various times after infection and analyzed both the viral and cellular transcriptomes. Alignment of sequencing reads to the viral genome illustrated the transcriptional profile of the early-to-late switch with both early-strand and late-strand RNAs being transcribed at all time points. A number of novel insights into viral gene expression emerged from these studies, including the demonstration of widespread RNA editing of viral transcripts at late times in infection. By late times in infection, 359 host genes were seen to be significantly upregulated and 857 were downregulated. Gene ontology analysis indicated transcripts involved in translation, metabolism, RNA processing, DNA methylation, and protein turnover were upregulated while transcripts involved in extracellular adhesion, cytoskeleton, zinc finger binding, SH3 domain, and GTPase activation were downregulated. The levels of a number of long noncoding RNAs were also altered. The long noncoding RNA MALAT1, which is involved in splicing speckles and used as a marker in many late-stage cancers, was noticeably downregulated, while several other abundant noncoding RNAs were strongly upregulated. We discuss these results in light of what is currently known about the MPyV life cycle and its effects on host cell growth and metabolism.</p></div

Gene ontology analysis.

<p>The web based application Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to sort lists of upregulated or downregulated genes by common function and enrichment using the <i>Mus musculus</i> background. A false discovery threshold of 1E-03 was used.</p><p>Gene ontology analysis.</p

Alignment of reads from late leader repeats.

<p>The 100 base reads from time course and aphidicolin samples were realigned to custom reference sequences containing at least one leader to leader splice junction. The reference sequences were 5 variations of a 42-57-42 base splice of late leader exons to each other, the transcription start site, or the 5’UTR of VP1-3. The number of reads aligning to each reference sequence for each condition are shown.</p

Viral RNA splicing events during infection.

<p>Average and standard deviation of aligned reads spanning viral splice junctions during the time course across three biological replicates. Percentages shown are with respect to total early or total late splice junction alignments.</p><p>Viral RNA splicing events during infection.</p

Accumulation of hyper-editing clusters during the time course.

<p>Reads from the time course were aligned with the host mouse genome and discarded. Reads were then aligned to the Py59RA reference genome and discarded. As in reads that did not align to the Py59RA genome were changed to Gs and realigned to a Py59RA reference genome that itself had all As changed to Gs. This allowed for reads that did not originally align to the unaltered Py59RA genome due to hyperediting to be captured. The process was repeated for all combinations of single base mismatch. Clusters of A-G mismatches from the time course consistent with hyperediting by ADAR were mapped to the Py59RA genome.</p

Example of host protein coding transcripts significantly upregulated or downregulated in infected samples compared to mock infection.

<p>Protein coding reads from the time course were aligned to the host genome. <b>(A)</b> Expression of Hist1ha increases compared to mock infection. <b>(B)</b> Col1a2 decreases compared to mock infection.</p

Alignment of time course reads to the Py59RA genome.

<p>NIH 3T6 cells were infected with Py59RA at an MOI of 50 plaque forming units/cell or with a mock infection. Total RNA was harvested at 12, 18, 24, and 36 hours and stranded cDNA libraries were prepared for sequencing on the HiSeq 2000 sequencer. Reads were aligned to the Py59RA genome and visualized on the UCSC genome browser. <b>(A)</b> Time course reads aligned to the Py59RA genome unscaled to show differential expression between early (plus) and late (minus) strands. <b>(B)</b> Time course reads scaled to show changes in early alignment.</p

Number of host genes differentially expressed between mock and Py59RA infected samples.

<p>The number of host transcripts that changed more than 1.5 fold across all three biological replicates was determined using Cuffdiff. Only genes with expression >1 fpkm were counted.</p><p>Number of host genes differentially expressed between mock and Py59RA infected samples.</p

Transfind transcription factor prediction.

<p>The web based application Transfind was used to predict common transcription factor binding sites among the lists of upregulated and downregulated host genes. Predictions of mouse transcription factor binding sites were made with promoter sets of 1000nt from 800nt upstream to 200nt downstream and compared with 1000 genes with the highest predicted factor affinities among 33,837 total mouse genes. Possible factors were represented by the TRANSFAC matrix.</p><p>Transfind transcription factor prediction.</p