Using Direct RNA Nanopore Sequencing to Deconvolute Viral Transcriptomes

Abstract

The genomes of DNA viruses encode deceptively complex transcriptomes evolved to maximize coding potential within the confines of a relatively small genome. Defining the full range of viral RNAs produced during an infection is key to understanding the viral replication cycle and its interactions with the host cell. Traditional short-read (Illumina) sequencing approaches are problematic in this setting due to the difficulty of assigning short reads to individual RNAs in regions of transcript overlap and to the biases introduced by the required recoding and amplification steps. Additionally, different methodologies may be required to analyze the 5' and 3' ends of RNAs, which increases both cost and effort. The advent of long-read nanopore sequencing simplifies this approach by providing a single assay that captures and sequences full length RNAs, either in cDNA or native RNA form. The latter is particularly appealing as it reduces known recoding biases whilst allowing more advanced analyses such as estimation of poly(A) tail length and the detection of RNA modifications including N⁶ -methyladenosine. Using herpes simplex virus (HSV)-infected primary fibroblasts as a template, we provide a step-by-step guide to the production of direct RNA sequencing libraries suitable for sequencing using Oxford Nanopore Technologies platforms and provide a simple computational approach to deriving a high-quality annotation of the HSV transcriptome from the resulting sequencing data. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Productive infection of primary fibroblasts with herpes simplex virus Support Protocol: Cell passage and plating of primary fibroblasts Basic Protocol 2: Preparation and sequencing of dRNA-seq libraries from virus-infected cells Basic Protocol 3: Processing, alignment, and analysis of dRNA-seq datasets.

Keywords: DNA virus; RNA sequencing; annotation; herpesvirus; nanopore; viral transcriptome.

Figure 1. Illustrating the complexity of transcriptional outputs from gene loci from two unrelated DNA viruses.

Alignment of dRNA-Seq data against viral genomes can be used to produce coverage plots (beige, blue-gray) integrated with pileup analyses of sequence read termini (5’ end – red, 3’ end – black). Peak-calling of the pileup data yields putative transcription start site (TSS, red vertical boxes) and cleavage and polyadenylation sites (CPAS, black boxes) that are filtered to remove artefacts occurring at splice junctions (yellow boxes). Full length mRNAs identified by dRNA-seq are depicted in green with untranslated regions (UTRs) shown as narrow horizontal boxes and coding sequences (CDS) shown as broader boxes. Spliced introns are shown as narrow lines. (A) Example of shared TSS. The adenovirus strain 5 (Ad5) E3 locus specifies at least eight distinct polyadenylated RNAs, each encoding a unique protein product (Price, Hayer, Depledge, et al., 2019). They share a common transcription start site (TSS) but differ in their splicing patterns and sites of cleavage and polyadenylation (CPAS). Non-E3 transcripts that overlap in this region are shown in gray. (B) Example of shared CPAS. HSV-1 produces transcripts that differ in their TSS and splicing patterns but that make use of a shared CPAS. This is exemplified in the gene cluster encoding transcripts for UL24, UL25, and UL26. (C) Example of readthrough-derived fusion transcripts. HSV-1 also produces fusion transcripts that are generated following readthrough transcription and splicing. This is exemplified using transcripts spanning the RL2 (ICP0) and clustered UL1/UL2/UL3 loci (Depledge et al., 2019).

Figure 2.. Workflow for direct RNA sequencing of virus-infected cells.

(A) Infected cells are harvested by lysis in the strong denaturant TRIzol and the RNA separated from DNA, proteins and other cellular components by organic extraction and recovered by isopropanol precipitation. (B) The poly(A) fraction of RNA is enriched using oligo dT-coupled magnetic beads. (C) The nanopore RTA adapter is ligated to polyadenylated RNAs and is followed by reverse transcription that produces a complementary cDNA strand that stabilizes the RNA but is itself not sequenced. (D) A further ligation step adds a motor protein (helicase) to the RTA adapter and this (E) allows unwinding of the RNA:cDNA complex at the pore proteins that form the channels embedded in the membrane of the flow cell within the nanopore array sequencer. Traversal of the RNA through a pore disrupts the flow of current and these signal changes allow base calling of the RNA.

Figure 3.. Computational workflow for rapid alignment and analysis of viral dRNA-Seq data.

Following the acquisition of raw sequence data produced by MinKNOW during a nanopore run, Guppy is used to rebasecall data, trim the adapter sequence, reverse the orientation of the sequence read (from 3’->5’ to 5’->3’), and replace uracil bases with thymine bases. Processed sequence reads are aligned against a chosen reference genome sequence using MiniMap2 and (optionally) Illumina data are used in conjunction with FLAIR to perform splice-site correction. At this stage, aligned datasets may be visualized using standard tools such as IGV or GVIZ before subsequent processing to produce transcript abundance counts (using custom scripts) and/or to identify TSS and CPAS (using Homer).

Figure 4.. An HSV-1 specific strategy for generating transcript counts following transcriptome alignment.

Sequence reads (red, green) are shown aligned against three overlapping RNAs (blue). Abundance counts are generated by retaining only reads (green) that map within 50 nucleotides of the TSS (dark blue region) of a given RNA. All remaining reads (red) can be discarded.