PCR assays

The viruses listed in Table S1 were tested for using real-time PCR and found to be negative using specific primers and TaqMan probes on an ABI 7500 thermocycler.

All PCR assays were performed in-house at GOSH (Great Ormond Street Hospital for Children, London) as part of the routine diagnostic service, unless indicated otherwise, in which case they were sent to a different laboratory for testing.

The CSF and urine samples collected during the patient’s last hospitalisation, as well as RNA re-extracted from the brain biopsy, were sent to the Public Health England Virus Reference Laboratory (Colindale) for mumps and mumps vaccine-specific RT-PCR (targeting the SH and HN gene) as well as measles and rubella-specific RT-PCR.

Sequencing

Library preparation—brain biopsy

Total RNA was purified from the frozen brain biopsy and polyA RNA-separated for sequencing library preparation. Samples were sequenced on the Illumina NextSeq500 instrument (Illumina, San Diego, US) using an 81 bp paired-end run. Libraries to be multiplexed in the same run were pooled in equimolar quantities, calculated from qPCR and/or Bioanalyser fragment analysis.

Samples were processed using Illumina’s TruSeq Stranded mRNA LT sample preparation kit (p/n RS-122-2101) according to the manufacturer’s instructions. Deviations from the protocol were as follows: (1) 250 ng total RNA was used as starting material. (2) Fragmentation was carried out for 10 min instead of 8 min. (3) 14 Cycles of PCR were used.

Briefly, mRNA was isolated from total RNA using oligo dT beads to pull down poly-adenylated transcripts. The purified mRNA was fragmented using chemical fragmentation (heat and divalent metal cation) and primed with random hexamers. Strand-specific first-strand cDNA was generated using SuperScript II Reverse Transcriptase (Life Technologies) and actinomycin D. This allows RNA-dependent synthesis while preventing spurious DNA-dependent synthesis. The second cDNA strand was marked by performing synthesis incorporating dUTP.

The cDNA is then “A-tailed” at the 3′ end to prevent self-ligation during the addition of the full-length TruSeq Adaptors (adaptors have a complementary “T” overhang). The adaptors contain sequences that allow the libraries to be amplified by PCR, bind to the flow cell and be uniquely identified by way of a 6 bp index sequence. Finally, a PCR is carried out to amplify only those cDNA fragments that have adaptors bound to both ends.

Library preparation—vaccine

An archived vial from the batch of MMR vaccine used to immunise the child was deep sequenced in a 2 × 251 paired-end sequencing on an Illumina MiSeq as part of a 16-sample pool, generating 1,570,733 read pairs, at the National Institute for Biological Standards and Control (NIBSC).

Lyophilised vaccine was resuspended in 500 µl sterile water for injection and viral RNA extracted using the QIAamp Viral RNA Mini Spin Kit (Qiagen) according to the method of the manufacturer, with the omission of carrier RNA. DNA libraries were prepared by random reverse-transcriptase polymerase chain reaction as described previously [18], followed by fragmentation, adaptor addition and indexing using the Nextera XT library preparation kit (Illumina).

Immunohistochemistry

Formalin-fixed paraffin-embedded sections of brain biopsy were examined by conventional histology. Immunohistochemistry was undertaken using two different antibodies in two independent laboratories.

Immunohistochemistry was undertaken using mumps nucleoprotein (N) antibody (7B10: sc-57921 Santa Cruz) at 1 in 25 using a Leica Bond Max automated staining system with pretreatment HIER (30) ER2. Additional immunohistochemistry was independently undertaken using a different antibody. The monoclonal mumps antibody used [16] recognises the N protein of MuV (N93-51/01) and was used on an automated Leica BondMax immunostainer at a dilution of 1/4000 following HIER2 pre-treatment for 20 min. Detection sites were detected with a polymer-based detection system (Bond, Newcastle upon Tyne, UK, Cat. No. DS9800). Detection of mumps N in the CNS by immunohistochemistry is shown in Figure S2.

Bioinformatics analysis

For the analysis of the brain biopsy sequencing data, we implemented the following steps. We first removed duplicate sequences that can arise from PCR amplification with an in-house script that collapses pairs of reads based on sequence identity using 90 % of the sequence as signature (20 % removed as duplicates). Half of the reads overlapped with their “mates” within pairs and we therefore merged the overlapping reads using PEAR [32], taking into account both sequence match and quality scores. We performed quality control using PrinSeq [25], trimming low-quality ends and removing reads that had average quality less than 15. We subsequently removed human sequences, using a quick aligner (Novoalign version V2.07.13—human reference genome GRCh37) as well as BLASTn [2]. We performed de novo assembly of high-quality contigs using Velvet [31] (kmer = 81). Finally, we annotated the contigs and the unassembled reads against a custom protein reference database using BLASTx. Our custom protein reference database consists of viral, bacterial, human and mouse RefSeq proteins. More specifically, all known viruses in the RefSeq collection are used ftp://ftp.ncbi.nlm.nih.gov/refseq/release/viral/viral.1.protein.faa.gz, as well as all the bacteria of the human microbiome, according to ftp://ftp.ncbi.nih.gov/genomes/HUMAN_MICROBIOM/Bacteria/all.faa.tar.gz. The BLASTx results were the input of metaMix [21].

For the analysis of the vaccine sequencing, we first removed 15 % of the reads as duplicates and merged overlapping reads. We trimmed the reads based on base quality (q = 20) using Trim Galore! (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). We then selected for Jeryl Lynn vaccine strain reads using BLASTn [2] and a Jeryl Lynn nucleotide reference database.

We performed de novo assembly with SPADeS [4] followed by QUAST [12] for both the vaccine and the brain sequencing data. In the latter case, we used the MuV reads as identified by metaMix [21]. Novoalign, Samtools [17] and VarScan2 [15] were used for consensus sequence generation and variant calling. We filtered variants based on quality, depth, frequency and strand bias (quality ≥30, at least 5 reads for the variant site, frequency ≥5 %, p value <0.01). The variants were annotated with SnpEff [7].

We compared the number of non-synonymous changes observed in each of the MuV genes to the number we would expect if the observed missense mutations were randomly distributed across the genome, correcting for the gene length. Significant deviation from the expected number of mutations was tested with the goodness-of-fit two-tailed exact binomial test. Analysis was conducted with the statistical language R (http://www.r-project.org).

We estimated a maximum likelihood phylogenetic tree using RAxML [27] and 64 full MuV genomes from GenBank (accessed on 20 June 2015).